4.3.2 Develop sampling methods for annually estimating reproductive success within each recovery unit. While it is extremely valuable to monitor clutch hatching success and chick fledging success at each site as a measure of habitat quality, it is critical to determine the number of young fledged per male for each recovery unit to measure the potential for population stability and growth. Measuring the number of young fledged per male requires intensive monitoring, and at sites with large numbers of birds, some method of identifying individual males. Extensive color banding of adults and their young, enabling determination of young fledged per male, has been undertaken in large portions of coastal Oregon, the shoreline of Monterey Bay, and coastal San Diego County for the past several years. These efforts should continue. Since there are insufficient color band combinations to monitor all individuals in every recovery unit, sampling procedures should be developed to color band adequate samples of males, and if necessary their chicks, in the other recovery units to obtain estimates of the number of young fledged per male. Color banding for measuring reproductive success should be integrated with banding for estimating population size.
4.3.3 Develop methods to monitor western snowy plover survival rates within each recovery unit. Extensive color banding of adult plovers and their young in coastal Oregon, the shoreline of Monterey Bay, and coastal San Diego County has enabled survival rates of adults and young to be calculated for several years (see Population Status and Trends and Survival sections). These efforts should continue. Information on survival rates of birds from other recovery units can be derived from birds banded for monitoring reproductive success or estimating population size.
4.4 Conduct studies on western snowy plover habitat use and availability.
4.4.1 Identify western snowy plover brood habitat and map brood home ranges. Brood movements should be mapped and distances 202
quantified to identify how large an area must be protected for broods. Determine home ranges of western snowy plovers through radio telemetry studies. Traditionally used brood habitat should be identified and protected through actions 2 and 3.
4.4.2 Identify components of high-quality western snowy plover brood rearing habitat. The elements of high-quality brood habitat should be determined to facilitate creation and enhancement of suitable characteristics at other breeding locations.
4.4.3 Quantify wintering habitat needs of western snowy plovers along the Pacific coast. The amount of habitat needed to support wintering western snowy plovers along the Pacific coast should be determined. This effort should include estimating the numbers of western snowy plovers that can be supported at wintering locations listed in Appendix B and identifying important site characteristics.
This action will require consideration of wintering habitat quality along the Pacific coast of the United States and Mexico, and quantifying the combined interior and coastal populations.
4.4.4 Identify any important migration stop-over areas used by migrating but not by breeding or wintering western snowy plovers. Additional information on western snowy plover migration patterns is needed because migration involves expenditure of energy that may affect survival or productivity.
Although monitoring and protection of breeding and wintering locations are currently higher priorities than protection of migration sites, further investigations of, and protective measures for, migration sites should be undertaken when feasible. Threats and management needs of identified migration stop-over habitat should be evaluated and included in management monitoring, and protection tasks (see action 1.6).
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4.5 Develop and implement a research program to determine causes of adult western snowy plover mortality, including investigation of possible causes, magnitude, and frequency of catastrophic mortality.
Determine causes of mortality and the stage in the annual cycle (e.g., post- breeding, migration, winter, pre-breeding, breeding) at which mortality occurs for each sex and age class. This assessment can be done through intensive, bi-weekly monitoring to determine relative health and potential for disease. Monitoring could include fat content and weight related to the season.
4.6 Improve techniques for banding western snowy plovers. Improve the technique for banding birds to reduce injuries. Because western snowy plover injuries are usually associated with Federal metal bands but not with plastic bands, removal of U.S. Fish and Wildlife Service lettering from the inside of the metal band should be investigated. Eliminating use of the U.S. Fish and Wildlife Service metal band also should be considered. Experimentation with new techniques must be conducted cautiously and may need to include pre-testing on nonlisted surrogate species.
4.6.1 Compile information regarding number and types of banding injuries to western snowy plovers to determine extent and causes of banding injuries. Several banding injuries to western snowy plovers have been reported. However, there is currently no consistent reporting of injuries to determine the extent or types of injuries. Working groups should compile information on banding injuries to use in determining the type and extent of the problem and in developing a course of action. Information collected should include number of injuries, type of injury (abrasion, foot loss, broken leg, etc.), probable cause of injuries (foreign object lodged between band and leg, wearing of band, etc.), effect of injuries on behavior (breeding, foraging, predator avoidance), type of bands (plastic or metal) associated with injuries, whether metal bands had writing on the inside or other rough areas likely to cause abrasion or lodging of foreign object.
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4.6.2 Review compiled information and determine and implement a appropriate course of action to minimize banding injuries. The information complied in step 4.6.1 should be reviewed to determine the appropriate course of action to minimize banding injuries. Review may reveal that banding injuries are rare or have little impact on breeding success or survival, in which case no changes to banding procedures may be necessary. However, extensive numbers of injuries or impacts on breeding success and survival may require actions such as changing the location of metal bands from the tarsus to tibiotarsus, discontinuing use of metal bands, or using different band types. All decisions regarding changes to banding procedures should consider effects of such changes to the type, quantity, and quality of data that may be gathered from banding efforts, and whether such changes will affect the ability to determine population trends, monitor success of management actions, or otherwise affect recovery efforts. For example, discontinuing use of metal bands may affect the ability to gather information on survival, longevity, and dispersal useful in analyzing population viability.
4.7 Identify effects of oil spills on western snowy plovers. Research should be conducted on the direct and indirect effects of oil spills on western snowy plovers, including, but not limited to: (1) how oil spills affect the plover’s prey base; (2) chronic effects of oiling; (3) transmission of oil on partially-oiled birds from the breast to the egg; (4) at what stage oiled plovers need to be captured or re-captured; (5) preferable methods to remove oil from soiled birds; and (6) impacts to plovers during oil clean- up and remediation activities.
4.8 Monitor levels of environmental contaminants in western snowy plovers. When abandoned eggs and/or dead chicks that are not needed for law enforcement investigations become available, they should be collected for potential contaminants assessment. Egg removal and salvage of dead chicks should only be done by individuals possessing proper Federal and State authorizations. Chemical analysis of salvaged specimens should be 205
coordinated through our Division of Environmental Contaminants. All salvaged eggs should be analyzed for organochlorine pesticides, total polychlorinated biphenyls (PCB’s), selenium, mercury, and boron.
All sampling should be opportunistic, based on availability of eggs that are known to be abandoned. Eggs should never be removed from the beach as long as there is any realistic chance that they might hatch. In the case of unhatched eggs from a partially hatched clutch, eggs should not be collected until at least 36 hours after the known hatch date of the other eggs. Full clutches should not be collected unless it is known that 35 or more days have elapsed since the last egg was laid. When this opportunistic sampling of failed eggs indicates potential problems with contaminants, follow up studies should be carried out (see action 4.9).
4.9 Design and conduct contaminants studies if monitoring of contaminants in action 4.8 indicates potential contaminants effects.
When opportunistic sampling of failed eggs (action 4.8) indicates potential problems with contaminants, additional studies should be carried out to evaluate the extent of contamination in western snowy plover diets, its effects on nest success and egg hatchability, and its effects on various life stages of snowy plovers (eggs vs. adults). Thresholds when management action is required should be identified. When the target threshold is exceeded research should be conducted to identify the source.
4.10 Identify, prioritize, and carry out needed investigations of the effects of human recreation on western snowy plovers. Many studies on the effects of recreational activities on western snowy plovers have already been conducted. To avoid duplicating previous or ongoing efforts, recovery unit working groups should evaluate and prioritize additional study needs to determine the effects of human recreation on western snowy plover. Western snowy plover should be monitored for effects from recreational activities such as off-road vehicle riding, horseback riding, walking, jogging, fishing, aircraft, ultralight aircraft, and kite- flying.
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4.11 Revise the population viability analysis (Appendix D), if needed, when sufficient additional information on demographic characteristics (survival rates, reproductive success) is available from each recovery unit and information is obtained on the probability and magnitude of catastrophic mortality events. As new information on population numbers, survival rates, and reproductive success are acquired from monitoring (actions 1.1 and 1.2), monitoring techniques are improved (action 4.3), and mortality sources and rates of mortality are determined (action 4.5), the population viability analysis should be reviewed and revised if additional information differs significantly from that used to construct the original analysis.
5 Undertake public information and education programs. Expanded efforts are needed to increase public awareness of the needs of western snowy plovers, other rare beach species, and the beach and dune ecosystem. Public outreach efforts should be a major focus of each of the working groups for the six recovery units. Appendix C, Table C-1 identifies 84 locations where public information and education is either currently occurring or is recommended to achieve management goals.
5.1 Develop and implement public information and education programs.
Millions of beach recreationists come in contact with western snowy plover nesting and wintering areas each year. Disregard to signs, symbolic fencing, and leash laws by beach users can directly affect the productivity and health of western snowy plovers on those beaches.
Public information and education efforts play a key role in obtaining compliance of beach recreationists with plover protection measures that, in turn, affect the birds' recovery. Central messages to the beach-going public include: (1) respect areas fenced or posted for protection of plovers and other rare beach species; (2) do not approach or linger near western snowy plovers or their nests; (3) if pets are permitted on beaches used by plovers, keep the pets leashed; (4) don't leave or bury trash or food scraps on beaches, as garbage attracts predators that may prey upon plover eggs or chicks; and (5) do not build wood structures that can be used as predator perches.
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Because of the importance of information and education for the western snowy plover recovery effort, as part of this recovery plan, we developed an Information and Education Plan for the Western Snowy Plover, Pacific coast population (Appendix K).
5.2 Inform Federal, State, and local resource/regulatory agencies and local planning departments of threats to breeding and wintering western snowy plovers and their habitats. Periodic meetings and/or workshops should be held to inform Federal, State, and local resource management and regulatory agencies, and city and county planning departments about threats, research, and management needs for plovers.
A network of public agency staff from each of the six recovery unit working groups should develop a coordinated approach to present this information to these agencies periodically, or as needed.
5.3 Develop and maintain updated information and education materials on western snowy plovers. Members of the six recovery unit working groups should develop new western snowy plover information and education materials for target audiences to stimulate public interest and awareness. In addition, all materials should be kept reasonably current regarding the status of the species and protection efforts. These materials should also explain the need for conservation of the beach and dune ecosystem and the plight of other rare beach-dwelling species.
Videos detailing needed western snowy plover recovery actions by location and recovery unit should be developed, and might be efficiently produced in conjunction with updated public service advertisements.
5.4 Alert landowners and beach-goers about access restrictions within western snowy plover habitats. Land managers should begin providing informational and educational outreach at least 2 weeks prior to the onset of the nesting season to provide beach-goers and interested landowners with advance notice of impending restrictions on publicly- owned western snowy plover breeding habitats. This outreach is particularly important for the first year of restrictions. If necessary, 208
follow-up publicity that includes information on citations issued to violators should be implemented to help reinforce the message.
5.5 Provide trained personnel to facilitate protective measures, provide public education, and respond to emergency situations. Biologists, docents, volunteers, and other personnel should be trained to patrol western snowy plover nesting areas to monitor birds, distribute educational materials, respond to emergency situations, and ensure that beach-goers stay out of fenced areas and adhere to other plover protection measures. Biologists engaged in monitoring, management, or research activities should also advance the public’s understanding of plover management needs.
5.6 Develop protocols for handling sick, displaced, injured, oiled, and dead birds or salvaged eggs. Land managers within each recovery unit should develop protocols for all trained personnel identifying who should be contacted when injured, dead, oiled, or displaced birds are found, and who is permitted to handle these birds. Federal and State salvage permits are necessary for the disposal of dead birds and the transportation of injured birds. Federal and State endangered species permits are necessary for wildlife rehabilitators to accept and care for injured and sick birds. Coordination with biologists that are monitoring and banding western snowy plovers is essential for capture and release of injured/rehabilitated birds. Live chicks that are found should not be moved or taken for rehabilitation as these chicks are often not abandoned, even though plover adults may not be obvious at the time the chicks are seen. Protocols should also be developed on how to collect and preserve salvaged eggs used for contaminants analysis.
5.7 Establish a distribution system and repository for information and education materials. Land managers must distribute information and education materials to target audiences. To reach the large population of potential beach-goers within a few hours’ drive of many major metropolitan areas, broad-scale information and education mechanisms should be implemented, including distribution by mass media such as 209
newspapers, radio and television announcements, and internet web sites.
Land managers should also focus their information and education efforts on user groups at beach parking lot entry stations and kiosks, visitor centers, marinas, beach-front housing developments, equestrian and angler access points, and locations providing off-road vehicle permits.
Public outreach efforts should be directed to groups within the geographical location of the managed beaches (e.g., to private and commercial equestrian users) and to groups outside of the area who use the beaches on a regular or seasonal basis (e.g., to off-road vehicle associations from out-of-state or inland locations). Land managers, with the help of docents and volunteers, should coordinate with local school teachers to develop and present environmental education lesson plans and participatory activities for elementary and middle school groups.
We will act as a central repository for current and new information and education materials received; upon request, we will make these materials available to recovery unit working groups and the general public. We will also maintain information on western snowy plovers at our website (http://www.fws.gov/arcata). Major distributional efforts should also continue by Federal, State, and local agencies, and private conservation organizations.
5.8 Establish a reporting and distribution system for annual monitoring data and management techniques. Our Arcata Fish and Wildlife Office should coordinate and produce an annual report of submitted breeding and wintering monitoring data and distribute it to recovery unit working groups. This report should describe results of monitoring throughout the western snowy plover population’s range. A distribution system should also be established for sharing information on predator management techniques, nest protection, etc. among working groups.
6 Review progress towards recovery and revise recovery efforts as appropriate. Communication, evaluation, and coordination play a major role in western snowy plover recovery efforts. Land managers within each of the six recovery unit working groups should review the effectiveness of their 210
management activities in coordination with other members of their working group, and revise management measures as appropriate. They should also provide results of annual population monitoring and the effectiveness of management activities to their working group and to our Arcata Fish and Wildlife Office.
6.1 Develop and implement a tracking process for the completion of recovery actions and the achievement of delisting criteria. A tracking process should be developed to track the completion of recovery actions and progress toward delisting. Utilizing information from specific actions, the recovery criteria such as the implementation of management activities can be tracked. Information from the tracking process can be used in outreach and in helping identify when the western snowy plover can be delisted.
6.2 Review progress toward recovery annually within each recovery unit working group and revise site-specific recovery efforts as appropriate to meet recovery goals. Communication, evaluation, and coordination play a major role in western snowy plover recovery efforts.
Land managers within each of the six recovery unit working groups should review the effectiveness of their management activities in coordination with other members of their working group, and revise management measures as appropriate. They should also provide results of annual population monitoring and the effectiveness of management activities to their working group and to our Arcata Fish and Wildlife Office.
Additionally, the working groups in conjunction with land managers should review success in meeting management goal breeding numbers recommended in Appendix B, and develop recommendations for any necessary revisions to those numbers based on site-specific conditions.
Ongoing and needed management activities recommended in Appendix C also should be evaluated and revised according to site specific conditions. Revisions to management goals and management activities should be provided to our Arcata Fish and Wildlife Office. 211
6.3 Assess the applicability, value, and success of this recovery plan to the recovery of the western snowy plover every 5 years until the recovery criteria are achieved. Rather than revising the entire recovery plan, it is proposed that minor revisions, clarifications, and prioritization changes be made through an addendum, to be produced and distributed every 5 years. This addendum would address data gaps identified in this version of the recovery plan including recommended management prescriptions, specific habitat management
recommendations, management goal breeding numbers, directed surveys; and necessary changes discussed in previous recovery actions.
It would provide a summary of the recovery actions implemented to date, and it would be a forum to solicit comments from the Recovery Team, stakeholders, and others interested parties on any proposed major changes. Major changes, elimination, or addition of recovery actions may initiate a revision.
6.4 Prepare a delisting package for the Pacific coast population of the western snowy plover. If actions 6.1 through 6.3 indicate recovery criteria have been met, actions to ameliorate or eliminate threats have been implemented and determined to be effective, and analyses of threats demonstrate that threats identified during and since the listing process have been ameliorated or eliminated, prepare a delisting package.
6.5 Prepare and implement a post-delisting monitoring plan. If delisting is warranted, prepare a post-delisting monitoring plan. Section 4 of the Endangered Species Act requires, in cooperation with the States, monitoring for a minimum of five years all species that have been recovered (i.e., delisted).
7 Dedicate sufficient U.S. Fish and Wildlife Service staff for coordination of western snowy plover recovery implementation. Our Arcata Fish and Wildlife Office holds lead responsibility for coordinating implementation of western snowy plover recovery. We should assure that the Arcata Fish and Wildlife Office has sufficient staff to handle the primary responsibility of 212
implementing the western snowy plover recovery plan. Duties should include coordination and distribution of monitoring information and educational materials; transmission of copies of annual population monitoring results to our field offices that are responsible for western snowy plover issues; compilation and distribution of annual population status updates to all working groups; coordination with our other field offices in CNO and Region 1 regarding western snowy plover conservation actions, consultations, habitat conservation plans, and permits; facilitating coordination among the working groups created for the six recovery units; and fund raising to support recovery implementation actions.
8 Establish an international conservation program with the government of Mexico to protect western snowy plovers and their breeding and wintering locations in Mexico. Meeting the recovery goals outlined in this recovery plan is dependent only on actions recommended for implementation along the Pacific coast of the United States. However, other actions are identified for Mexico to complement conservation efforts in the United States. Efforts should be made to establish an international conservation program between the U.S. Fish and Wildlife Service and Mexico’s National Institute of Ecology, Ministry of Environment, Natural Resources and Fisheries.
Programs to facilitate implementation of this conservation program should include Partners in Flight, North American Waterfowl Management Plan, and the Borderlands Initiative.
8.1 Develop a joint effort between the United States and Mexico to protect western snowy plover populations and their habitat. Joint efforts should be implemented to determine important habitat in Mexico and protect these breeding and wintering locations from human disturbance.
8.2 Encourage research and monitoring of breeding and wintering western snowy plovers in Baja California, Mexico, by universities and authorities of Mexico. Joint efforts should be made to develop and implement a long-term monitoring program for western snowy plover populations of Mexico. They should include developing methods for 213
consistent monitoring, coordination of banding and color-marking with banders from the United States, assessment of the population status of breeding and wintering birds, and assessment of environmental impacts that may adversely affect plover populations.
8.3 Encourage development and implementation of public information and conservation education in Mexico for western snowy plovers.
Public information and educational efforts should be coordinated and implemented by the United States and Mexico. They should include development of bilingual pamphlets for distribution to anglers, tourists, and local communities, and construction and placement of bilingual signs alerting them of the presence of nesting western snowy plovers. 9 Coordinate with other survey, assessment, and recovery efforts for the western snowy plover throughout North America. Western snowy plovers range through much of North America, and many individuals of the Pacific Coast population of western snowy plovers may overwinter in areas that overlap with other populations. Participation and coordination with other groups working on survey, assessment, and recovery efforts may yield valuable information on the distribution, status, and management needs for the Pacific Coast population of the western snowy plover. This coordination effort should be included in establishment of an international conservation program with Mexico.
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IV. IMPLEMENTATION SCHEDULE
The following Implementation Schedule outlines actions needed, responsible parties, and estimated costs to recover the United States portion of the Pacific coast population of the western snowy plover. Considering the recovery criteria, results of the population viability analysis (Appendix D), and fulfillment of the recommendations contained in the recovery plan, recovery of the western snowy plover could occur in approximately 40 years. This time estimate assumes dedicated, proactive efforts toward improvements in western snowy plover management in the near-term, and subsequent management at a maintenance level commensurate with fulfillment of the recovery criteria.
The total cost of implementing actions outlined in this recovery plan over 40 years is $149,946,000. However, this figure represents only a portion of the overall costs because the cost of many actions cannot be estimated at this time. For example, costs associated with intensive protection and management on Federal and State lands (Action 3.3) should be determined by members of each of the six recovery unit working groups because they are most familiar with their site- specific needs and constraints. Costs of many actions were estimated based on current management recommendations provided in Appendix C. However, coastal ecosystems are dynamic and necessary management actions may vary with time, as site conditions change. Improvements over time in methods for predator control, control of nonnative vegetation, and monitoring are also expected and may affect actual costs.
It should be recognized that expenditure of funds for recovery of the western snowy plover will provide far-reaching benefits beyond those gained for a single species. Allocation of these funds will also benefit many other sensitive fish and wildlife species, the coastal beach-dune ecosystem, public appreciation for natural habitats, and aesthetics. These estimated costs do not reflect a cost/benefit analysis that incorporates other values or economic effects with implementation of the recommendations contained in this recovery plan.
We believe that protection and management costs could be substantially reduced by selecting protection strategies that are more restrictive of other beach uses. 216
While we believe that it is neither feasible nor desirable to completely eliminate beach recreation in most western snowy plover habitat, we also recognize that management strategies that protect western snowy plovers on beaches where public use is also maintained require a continuing commitment of person-power, and are inherently expensive.
The Implementation Schedule lists and ranks actions that should be undertaken within the next 5 years. This schedule will be reviewed routinely until the recovery objective is met, and priorities and actions will be subject to revision. 217
Key to Acronyms used in the
Implementation Schedule
Definition of action priorities:
Priority 1 - An action that must be taken to prevent extinction or prevent the species from declining irreversibly in the foreseeable future.
Priority 2 - An action that must be taken to prevent a significant decline in species population or habitat quality, or some other significant negative impact short of extinction.
Priority 3 - All other actions necessary to provide for full recovery of the species.
Definition of action durations and costs:
Annual - An action that will be implemented each year.
Continual - An action that will be implemented on a routine basis once begun.
Ongoing - An action that is currently being implemented and will continue until action is no longer necessary.
As needed - An action that will be implemented on an “as needed” basis.
Unknown - Either action duration or associated costs are not known at this time.
To Be Determined (TBD) - Costs to be determined at a later date. 218
Responsible parties*:
ARMY U.S. Army
BLM U.S. Bureau of Land Management
CCC California State Coastal Commission
CDFG California Department of Fish and Game
CDPR California Department of Parks and Recreation
CE U.S. Army Corps of Engineers
CI Cities
CO Counties
CON California Coastal Conservancy
EBRPD East Bay Regional Park District
ES U.S. Fish and Wildlife Service, Division of Ecological Services (includes Endangered Species and Contaminants)
FAA U.S. Department of Transportation, Federal Aviation Administration
HARD Hayward Area Recreation and Park District
IA U.S. Fish and Wildlife Service, Office of International Affairs LE U.S. Fish and Wildlife Service, Division of Law Enforcement LMAO Land Management Agencies and Organizations and other Cooperators.
(This category includes Federal and local land management agencies listed above, private organizations and individuals that own and manage snowy plover breeding and wintering habitat, and private conservation groups that provide on-site protection of lands owned by others.)
MPOSD Mid-Peninsula Open Space District
MPRPD Monterey Peninsula Regional Park District
NASA National Aeronautics and Space Administration-Ames Research Center
NAVY U.S. Navy
NMFS National Marine Fisheries Service
NPS National Park Service
ODFW Oregon Department of Fish and Wildlife
ODLCD Oregon Department of Land Conservation and Development OPRD Oregon Parks and Recreation Department
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P Private landowners (except HARD, MPOSD, and TNC)
PA U.S. Fish and Wildlife Service, Public Affairs
PGH Port of Grays Harbor
PO Port of Oakland
PRBO Point Reyes Bird Observatory Conservation Science
PSL Port of San Luis Harbor District
RSCH Research institutions and agencies
RW U.S. Fish and Wildlife Service, Division of Refuges and Wildlife (includes Realty)
SDRPJPA San Dieguito River Park Joint Powers Authority
TNC The Nature Conservancy
TPL Trust for Public Land
USAF U.S. Air Force
USCG U.S. Coast Guard
USFS U.S. Forest Service
USFWS U.S. Fish and Wildlife Service
BBL U.S. Geological Survey, Bird Banding Laboratory
BRD U.S. Geological Survey, Biological Resources Division USMC U.S. Marine Corps
WDFW Washington Department of Fish and Wildlife
WDNR Washington Department of Natural Resources
WS U.S. Department of Agriculture, Wildlife Services Branch WSPRC Washington State Parks and Recreation Commission
* All responsible parties listed for actions in Implementation Schedule are considered lead agencies for those actions.
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IMPLEMENTATION SCHEDULE
Western Snowy Plover Pacific Coast Population Recovery Plan Cost Estimate (in $1,000 units)
Priority
No.
Action
Description
Action
Number
Action
Duration
Responsible
Parties
Total
Costs FY1 FY2 FY3 FY4 FY5 Comments/Notes
1 Annually monitor
abundance, population
size and distribution at
breeding and wintering
locations.
1.1 annual LMAO, CO, CI,
RSCH
2,194 54.9 54.9 54.9 54.9 54.9 Assumes 157 window survey days, with 2 biologists per location at.
Action needed to determine
fulfillment of recovery criteria.
1 Develop and implement a
program to monitor
productivity and annual
survival.
1.2 annual LMAO, CO, CI.
RSCH
TBD Action needed to determine
fulfillment of recovery criteria.
Depends partly on completion of
4.3.2 and 4.3.3.
1 Develop and implement a
program to monitor
habitat condition and
threats at all breeding and
wintering sites.
1.3 annual LMAO, RSCH 1,125 60 27 27 27 27 Assumes initial cost for development of standardized
monitoring program and subsequent
monitoring for 155 sites.
3 Develop and implement
training and certification
programs for western
snowy plover survey
coordinators and
observers.
1.4 continual ES, LMAO,
RSCH
363.5 32 8.5 8.5 8.5 8.5 Assumes initial cost to develop program and subsequent
implementation.
Cost Estimate (in $1,000 units)
Priority
No.
Action
Description
Action
Number
Action
Duration
Responsible
Parties
Total
Costs FY1 FY2 FY3 FY4 FY5 Comments/Notes
221
3 Improve submittal
system for monitoring
data to ensure consistent
reporting.
1.5 continual ES, LMAO, BBL,
PRBO
346 32 8 8 8 8 Assumes initial cost to develop
submittal and reporting system and
subsequent annual review.
3 Assess and evaluate new
breeding wintering and
migration areas for
threats and management
needs and update lists as
data become available.
1.6 continual ES, LMAO,
PRBO
TBD Depends on results of annual
surveys and monitoring.
3 Coordinate monitoring of
snowy plovers and
California least terns to
minimize disturbances.
1.7 annual ES, RW, NAVY,
USMC, USAF,
CDFG, CDPR,
WS, BRD
1,020 25.5 25.5 25.5 25.5 25.5 Coordinate at biannual pre- and post-season California least tern
monitoring meeting. Assumes 2
meetings at 2 days per meeting with
9 agency staff attending.
3 Develop a post-delisting
monitoring plan.
1.8 TBD ES, LMAO, CO,
CI, RSCH
TBD
Cost Estimate (in $1,000 units)
Priority
No.
Action
Description
Action
Number
Action
Duration
Responsible
Parties
Total
Costs FY1 FY2 FY3 FY4 FY5 Comments/Notes
222
1 Develop a prioritized list
of wintering and
breeding sites where
natural coastal processes
need protection and/or
enhancement.
2.1.1 2 yrs ES, LMAO,
CO, CI, RSCH
59.65 59.65 Assumes time to evaluate sites and
development of the prioritized list.
1 Identify and implement
mechanisms to protect,
enhance or restore
natural coastal processes.
2.1.2 continual ES, LMAO,
CO, CI, RSCH
TBD Incorporate into ongoing
management in action 3. Costs will
depend on mechanisms identified
and carried out.
1 Develop and implement
prioritized removal and
control for introduced
beachgrass and other
non-native vegetation.
2.2.1.1 continual CE, LMAO, CO,
CI
TBD App C identifies 86 sites. Costs
range for mechanical, manual and/or
chemical control: $1,000 to
$87,000/hectare ($400 to $35,000
per acre).
2 Replace exotic dune
plants with native dune
vegetation where it is
likely to improve habitat.
2.2.1.2 continual CE, LMAO, CO,
CI
TBD Estimated cost of planting native
vegetation: $30,000 per hectare
($12,000 per acre). Number of sites
to be determined.
Cost Estimate (in $1,000 units)
Priority
No.
Action
Description
Action
Number
Action
Duration
Responsible
Parties
Total
Costs FY1 FY2 FY3 FY4 FY5 Comments/Notes
223
3 Evaluate breeding and
wintering sites to
determine whether
dredged materials may be
used to enhance or create
nesting habitat.
2.2.2.1 2 yrs CE, ES, LMAO,
CO, CI
110 55 55 Assumes cost to evaluate each site.
3 Develop and implement
plans to use dredged
materials may be used to
enhance or create nesting
habitat.
2.2.2.2 ongoing CE, ES, LMAO,
CO, CI
TBD Costs will depend on completion of
acts on 2.2.2.1.
3 Identify sites where
beach nourishment may
be effective in creating
and enhancing habitat.
2.2.3.1 2yrs CE, ES, LMAO,
CO, CI
110 55 55 Assumes cost to evaluate each site.
3 Develop and implement
beach nourishment plans
for site identified in
action 2.2.3.1.
2.2.3.2 ongoing CE, ES, LMAO,
CO, CI
TBD Cost dependent on number of sites
identified in 2.2.3.1 and outcome of
4.1.1. Cost Estimate (in $1,000 units)
Priority
No.
Action
Description
Action
Number
Action
Duration
Responsible
Parties
Total
Costs FY1 FY2 FY3 FY4 FY5 Comments/Notes
224
1 Create, manage, and
enhance coastal ponds
and playas for breeding
habitat.
2.2.4 ongoing ES, RW, CE,
CDFG, NASA,
HARD, LMAO
TBD App C identifies 15 sites. Costs
dependent on type and area of
restoration.
1 Seasonally close areas
used by breeding snowy
plovers.
2.3.1.1.1 annual LMAO, CO,
CON, CI
559.2 13.98 13.98 13.98 13.98 13.98 App C identifies 81 sites. Assumes cost to close these sites.
1 Fence areas used by
breeding snowy plovers
2.3.1.1.2 annual LMAO, CO,
CON, CI
14,840 371 371 371 371 371 App C identifies 64 sites. Cost assumes 1 kilometer fencing
required per site at a cost of $5,900
per kilometer.
1 Post signs in areas used
by breeding snowy
plovers
2.3.1.1.3 annual LMAO, CO,
CON, CI
202 5 5 5 5 5 App C identifies 65 sites. Cost
dependent on number of signs
needed at each site, but assumes cost
for installation and a minimum of 4
signs at $20 per sign.
1 Evaluate effects of
existing and planned
access at all breeding and
wintering locations and
any new locations
identified.
2.3.1.2.1 1 year LMAO, CO, CI 455 455 Appendix C identifies 81 sites.
Assumes cost to conduct use survey
for the identified sites.
1 Develop and implement
plans to minimize
adverse access effects.
2.3.1.2.2 continual LMAO, CO, CI TBD Costs depend on outcome of
2.3.1.2.1. Cost Estimate (in $1,000 units)
Priority
No.
Action
Description
Action
Number
Action
Duration
Responsible
Parties
Total
Costs FY1 FY2 FY3 FY4 FY5 Comments/Notes
225
3 Implement and enforce
pet restrictions.
2.3.2 continual LMAO, CO, CI 39,406 985 985 985 985 985 Appendix C identifies 120 sites Assumes staff time to implement
and enforce restrictions at the
identified sites.
1 Annually review
recreational activities and
develop and implement
plans to prevent
disturbance from
disruptive recreational
activities at breeding and
wintering sites
2.3.3 annual LMAO, CO, CI 21,948 549 549 549 549 549 Assumes staff cost to develop and implement plans at each site
annually.
3 Prevent driftwood
removal through posting
of signs
2.3.4 continual LMAO, CO, CI 1,805 50 45 45 45 45 Appendix C identifies 26 sites. Cost dependent on number of signs
needed at each site, but assumes cost
for installation and a minimum of 4
signs at $20 per sign.
1 Prevent disturbance,
mortality, and habitat
degradation by
prohibiting or restricting
off-road vehicles and
beach-raking machines.
2.3.5 continual LMAO, CO, CI 18,760 469 469 469 469 469 Appendix C identifies 101 sites.
Assumes staff time for monitoring
on weekends.
3 Implement restrictions on
horseback riding through
annual coordination.
2.3.6 annual LMAO, CO, CI 1,033.7 25.8 25.8 25.8 25.8 25.8 Appendix C identifies 72 sites.
Assumes staff time to implement
restrictions.
Cost Estimate (in $1,000 units)
Priority
No.
Action
Description
Action
Number
Action
Duration
Responsible
Parties
Total
Costs FY1 FY2 FY3 FY4 FY5 Comments/Notes
226
3 Implement and enforce
restrictions on livestock
through annual
coordination.
2.3.7 annual LMAO, CO, CI 255 6.3 6.3 6.3 6.3 6.3 Appendix C identifies 18 sites.
Assumes staff time to implement
restrictions.
1 Determine enforcement
needs and provide
sufficient wardens,
agents or officers to
enforce protective
measures in breeding and
wintering habitat.
2.3.8.1 continual LE, LMAO,
CO, CI
TBD Cost will depend on identified
enforcement needs.
3 Develop and implement
training programs for
enforcement personnel to
improve enforcement of
regulations and minimize
effects of enforcement.
2.3.8.2 continual LE, LMAO, CO,
CI
320 8 8 8 8 8 Annual training cost estimate $8,000
per year.
2 Develop and implement a
program to annually
coordinate with local
airports, aircraft
operations regarding
minimum altitude
requirementss.
2.3.9 annual LMAO, CO,
CI, FAA, LE
339.8 8.5 8.5 8.5 8.5 8.5 Assumes staff costs per recovery unit to compile list and notify
aircraft operations and facilities.
3 Implement and enforce
anti-littering regulations.
2.4.1.1 annual LMAO, CO, CI TBD Incorporate into ongoing management and Action 3.
Cost Estimate (in $1,000 units)
Priority
No.
Action
Description
Action
Number
Action
Duration
Responsible
Parties
Total
Costs FY1 FY2 FY3 FY4 FY5 Comments/Notes
227
3 Evaluate the effects of
current litter and garbage
management on
predation at breeding and
wintering sites.
2.4.1.2 2 yrs LMAO, CO, CI 110 55 55 Assumes evaluation time per site. 3 Develop and implement
garbage and litter
management plans where
litter and garbage
contribute to predation.
2.4.1.3 continual LMAO, CO, CI TBD Costs will depend on 2.4.1.2 and plans developed.
3 Annually identify and
remove predator perches
and unnatural habitats
attractive to predators.
2.4.2 continual LMAO, CO, CI 375.2 9.4 9.4 9.4 9.4 9.4 Assumes staff time to complete action each year.
1 Erect predator exclosures
to reduce egg predation
and improve
productivity.
2.4.3 annual LMAO, CO, CI 18,266 456 456 456 456 456 App C identifies 53 sites. Assumes cost per unit installation.
1 Evaluate the need for
predator removal and
implement where
warranted and feasible.
2.4.4 as
needed
LMAO, CO, CI,
WS, CDFG
TBD App C identifies 61 sites for
additional predator control. Costs
dependent on assessment of needs
and feasability.
3 Remove bird and
mammal carcasses in
nesting areas.
2.4.5 as
needed
LMAO, CO, CI TBD
Cost Estimate (in $1,000 units)
Priority
No.
Action
Description
Action
Number
Action
Duration
Responsible
Parties
Total
Costs FY1 FY2 FY3 FY4 FY5 Comments/Notes
228
1 U.S. Fish and Wildlife
Service biologists should
participate in Area
Committees responsible
for maintaining the Area
Contingency Plans for
the Pacific Coast to
facilitate the updating of
spill response plans to
include protection of
western snowy plovers.
2.5.1 annual ES 5,154 128.9 128.9 128.9 128.9 128.9 Assumes staff time from the six ES office responsible for coastlines of
CA, OR, and WA.
1 Assign monitors to
beaches that are
inhabited by western
snowy plovers to protect
western snowy plovers
from injury during spill
responses.
2.5.2 as
needed
ES, USCG,
LMAO, CO, CI
1,984 49.6 49.6 49.6 49.6 49.6 Assumes cost of two weeks of monitoring for five incidents per
year.
2 Compensate the loss of
plover breeding and
wintering habitat
associated with recovery
efforts for other sensitive
species.
2.6 ongoing ES, RW,
CE, LMAO
TBD Costs dependent on effectiveness of
minimizing habitat loss.
3 Investigate feasibility and
methods for discouraging
pinniped use of nesting
areas.
2.7.1 5 yrs ES, NMFS,
NAVY, LMAO
320 64 64 64 64 64 Assumes staff time to investigate.
Cost Estimate (in $1,000 units)
Priority
No.
Action
Description
Action
Number
Action
Duration
Responsible
Parties
Total
Costs FY1 FY2 FY3 FY4 FY5 Comments/Notes
229
3 Identify areas where
pinniped use is
negatively affecting
nesting and implement
any appropriate methods.
2.7.2 TBD ES, NMFS,
NAVY, LMAO
TBD Costs dependent on number of sites
identified and methods determined
in 2.7.1.
1 Establish and maintain
snowy plover working
groups for each of the six
recovery units.
3.1 continual ES, LMAO,
CO, C I, P
3,650 96 96 91 91 91 Essential mechanism to advance
plover recovery. Includes biannual
meeting costs and staff costs to
establish new working groups.
2 Develop and implement
regional participation
plans for each of the six
recovery units.
3.2 1 yr for
develop-
ment,
continual
thereafter
ES, LMAO 193 193 Assumes staff cost to develop and
implement participation plans.
3 Develop and implement
management plans for
Federal lands.
3.3.1 ongoing RW, ARMY,
BLM, CE,
NASA, NAVY,
NPS, USAF,
USMC, USFS
TBD Implementation cost dependent on
content of plans developed.
Cost Estimate (in $1,000 units)
Priority
No.
Action
Description
Action
Number
Action
Duration
Responsible
Parties
Total
Costs FY1 FY2 FY3 FY4 FY5 Comments/Notes
230
3 Develop and implement
management plans and
Habitat Conservation
Plans on State wildlife
areas, State ecological
reserves, and State
beaches.
3.3.2 5 years CDFG, CDPR,
ODFW, OPRD,
WDFW, WDNR,
WSPRC
966 193 193 193 193 193 Assumes cost for each recovery unit to assist in development.
Implementation cost to be
determined.
3 Develop and implement
Habitat Conservation
Plans or other
management plans for
sites owned by local
governments or private
landowners.
3.4 5 years ES, LMAO, CO,
CI, P, EBRPD,
HARD, MPOSD,
MPRPD, PGH,
PO, SL, TNC,
SDRPJPA
966 193 193 193 193 193 Assumes cost for each recovery unit to assist in development.
Implementation cost to be
determined.
2 Provide technical
assistance to local
governments in
developing and
implementing local land
use protection measures
through periodic
workshops.
3.5 10 years ES, CCC, CDFG,
CDPR, CON,
ODFW, ODLCD,
OPRD, WDNR,
WDFW, WSPRC,
CO, CI
TBD Estimated at 2 workshops per
recovery unit at a cost of $
(Patty Carol in RO)
Cost Estimate (in $1,000 units)
Priority
No.
Action
Description
Action
Number
Action
Duration
Responsible
Parties
Total
Costs FY1 FY2 FY3 FY4 FY5 Comments/Notes
231
3 Develop and implement
cooperative programs
and partnerships with the
California State Coastal
Commission, the Oregon
Department of Land
Conservation and
Development, the
Washington State Parks
and Recreation
Commission, the Oregon
Parks and Recreation
Department, the
California Department of
Parks and Recreation,
and the Oregon
Department of Fish and
Wildlife.
3.6 continual ES, CCC,
ODLCD, ODFW,
OPRD, CDPR,
WSPRC
TBD Costs may vary from year to year
based on identified program needs.
3 Obtain long-term
agreements with private
landowners.
3.7 12 years ES, CDFG, P
CDPR, ODFW,
WDFW, WSPRC,
LMAO
2,319 193 193 193 193 193 Assumes staff time to facilitate 6 agreements per year per recovery
unit. Appendix C identifies 72 sites.
3 Identify and protect
habitat available for
acquisition.
3.8 ongoing CON, ES, RW,
LMAO
TBD
Cost Estimate (in $1,000 units)
Priority
No.
Action
Description
Action
Number
Action
Duration
Responsible
Parties
Total
Costs FY1 FY2 FY3 FY4 FY5 Comments/Notes
232
3 Ensure that any section
10(a)(1)(B) and section
7(a)(2) permits contribute
to Pacific coast western
snowy plover
conservation.
3.9 ongoing ES,
Federal agencies
1,288 32`32 32 32 32 32 Assumes staff time for annual
evaluation.
3 Ensure that section 7
consultations contribute
to Pacific coast western
snowy plover
conservation.
3.10 ongoing ES,
Federal agencies
1,288 32`32 32 32 32 32 Assumes staff time for annual
evaluation.
2 Evaluate effectiveness of
habitat restoration by
removal of introduced
beachgrass and identify
additional studies
necessary.
4.1.1 continual CON, ES,
LMAO, RSCH
TBD Depends on the number and location
of sites as well as the temporal
duration of the restoration project.
3 Evaluate the impacts and
potential benefits of past
and ongoing beach
nourishment activities
and identify and carry out
any additional studies
necessary.
4.1.2 ongoing ES, LMAO,
RSCH, CE, CI,
CO
TBD
2 Develop higher-
efficiency nest
enclosures.
4.2.1 ongoing ES, LMAO,
RSCH
20 10 5 3 2 0 Compare new exclosures with
current ones to determine effects on
snowy plovers.
Cost Estimate (in $1,000 units)
Priority
No.
Action
Description
Action
Number
Action
Duration
Responsible
Parties
Total
Costs FY1 FY2 FY3 FY4 FY5 Comments/Notes
233
2 Develop California least
tern enclosures that
prevent harm to snowy
plovers.
4.2.2 as
needed
ES, USMC,
CDFG, CDPR,
LMAO, RSCH
TBD Costs specific to sites with
California least tern enclosures.
Estimated cost for materials
(fencing/posts): $7 per linear foot
($23 per meter).
3 Identify, prioritize and
carry out investigations
on control of predators.
4.2.3 as
needed
ES, RW,
LMAO, WS,
CDFG, RSCH,
CO, CI, P
TBD Cost dependent on number and types
of studies identified.
3 Investigate predator
management at the
landscape level.
4.2.4 as
needed
ES, RW, LMAO,
WS, RSCH, CO,
CI, P
TBD Costs dependent on number and
types of studies identified.
3 Investigate techniques for
identifying nest
predators.
4.2.5 continual LMAO, RSCH TBD
2 Improve methods of
monitoring population
size.
4.3.1 ongoing ES, LMAO,
RSCH
TBD Dependent on costs of intensive
monitoring of some sites.
2 Develop sampling
methods for annually
estimating reproductive
success.
4.3.2 2 years ES, RSCH 64 64 Assumes time to compile and review data and develop methodology.
3 Develop methods to
monitor plover survival
rates.
4.3.3 ongoing ES, LMAO,
RSCH
TBD
Cost Estimate (in $1,000 units)
Priority
No.
Action
Description
Action
Number
Action
Duration
Responsible
Parties
Total
Costs FY1 FY2 FY3 FY4 FY5 Comments/Notes
234
3 Identify brood habitat
and map brood home
ranges.
4.4.1 ongoing
continual
ES, LMAO,
RSCH, CO, CI, P
TBD Costs dependent on study design.
May include radio telemetry.
3 Identify components of
high-quality brood
rearing habitat
4.4.2 1 year ES, LMAO,
RSCH, CO, CI, P
131 131 Assumes study at 6 geographically
representative sites for duration of
breeding season.
3 Quantify wintering
habitat needs along the
Pacific coast.
4.4.3 5 years ES, RSCH, BRD,
PRBO
75 75 Assumes study at 6 geographically
representative sites during winter
months.
3 Identify important
migration stop-over
habitat.
4.4.4 ongoing ES, LMAO TBD
3 Develop and implement a
research program to
determine causes of adult
mortality.
4.5 ongoing LMAO, RSCH TBD Costs dependent on study design. 3 Compile information
regarding number and
types of banding injuries
to plovers.
4.6.1 1 year ES, RSCH,
PRBO, BRD,
BBL
32 32 Assumes staff time to develop,
distribute and compile information
requests.
3 Review compiled
information (see 4.6.1)
and determine and
implement an appropriate
course of action.
4.6.2 1 year ES, RSCH,
PRBO, BRD,
BBL
32 Assumes staff time to review
compiled information, distribution
and coordination with other
responsible parties.
Cost Estimate (in $1,000 units)
Priority
No.
Action
Description
Action
Number
Action
Duration
Responsible
Parties
Total
Costs FY1 FY2 FY3 FY4 FY5 Comments/Notes
235
3 Identify effects of oil
spills on snowy plovers.
4.7 as
needed
ES, RSCH,
BRD, LMAO
TBD Typical range of cost for study is
estimated between $25,000 -
$100,000.
3 Monitor levels of
environmental
contaminants in snowy
plovers.
4.8 as
needed
ES, RSCH,
BRD, LMAO
TBD Depends on number and type of
samples. Cost estimate $700 per
sample, but may vary depending on
type of contaminant.
3 Design and conduct
contaminants studies if
monitoring of
contaminants in action
4.8 indicates potential
contaminants effects.
4.9 as
needed
LMAO, ES,
RSCH, BRD
TBD Depends on number of sites and
samples analyzed. Cost estimates for
studies range from $25,000 to
$50,000 per site.
3 Identify, prioritize and
carry out studies on the
effects of human
recreation on western
snowy plovers.
4.10 ongoing LMAO, ES,
RSCH, PRBO,
BRD
TBD Costs dependent on research needs
identified.
3 Revise the population
viability analysis when
sufficient additional
information is available
4.11 1 year ES, RSCH,
PRBO, BRD
25 Assumes cost to conduct modeling.
2 Develop and implement
public information and
education programs.
5.1 ongoing ES, PA,
LMAO
TBD TBD TBD TBD TBD TBD Depends on individual recovery unit strategies. See Appendix K
(Information & Education Plan) for
estimates of component expenses.
Cost Estimate (in $1,000 units)
Priority
No.
Action
Description
Action
Number
Action
Duration
Responsible
Parties
Total
Costs FY1 FY2 FY3 FY4 FY5 Comments/Notes
236
3 Inform Federal, State and
local planning agencies
and local planning
departments of threats to
breeding and wintering
snowy plovers and their
habitats.
5.2 continual ES, LMAO, CCC,
CDFG, CDPR,
ODFW, ODLCD,
OPRD, WDFW,
WDNR, WSPRC,
CO/CI
TBD
3 Develop and maintain
updated information and
education materials on
snowy plovers.
5.3 ongoing ES, PA, LMAO,
CO, CI
TBD Incorporate into ongoing
management and Action 3.1 through
3.10. See Appendix K
3 Alert landowners and
beach-goers about access
restrictions within snowy
plover habitats.
5.4 ongoing ES,
PA,
LMAO,
CO, CI
TBD Incorporate into ongoing
management and Action 3.1 through
3.10. See Appendix K
3 Provide trained personnel
to facilitate protective
measures, provide public
education, and respond to
emergency situations.
5.5 continual LMAO, CO, CI TBD Need to secure funds for volunteer coordinator and staff to train
volunteers. Incorporate into Action
3.1 through 3.10. See Appendix K.
3 Develop protocols for
handling sick, displaced,
injured, oiled, and dead
birds or salvaged eggs.
5.6 1 with
periodic
review
LMAO,
CO, CI
32.2 32.2 Assumes staff time to develop
protocol.
Cost Estimate (in $1,000 units)
Priority
No.
Action
Description
Action
Number
Action
Duration
Responsible
Parties
Total
Costs FY1 FY2 FY3 FY4 FY5 Comments/Notes
237
3 Establish a distribution
system and repository for
information and
education materials.
5.7 continual ES, LMAO,
CO, CI
TBD Incorporate into ongoing
management and Action 3.1 through
3.10 and 7. See Appendix K.
3 Establish a reporting and
distribution system for
annual monitoring data.
5.8 annual ES 644 16 16 16 16 16 Assumes time spent collecting and compiling data.
2 Develop and implement a
tracking process for the
completion of recovery
actions and the
achievement of delisting
criteria.
6.1 continual ES, RW, ARMY,
BLM, CE,
NASA, NAVY,
NPS, USAF,
USFS, USMC,
CDFG, CDPR,
ODFW, OPRD,
WDFW, WDNR,
WSPRC, LMAO
688 64 16 16 16 16 Assumes staff time to develop and
implement tracking process.
3 Review progress toward
recovery annually.
6.2 annual ES, LMAO 566 14 14 14 14 14 Assumes staff time to compile and review data.
3 Assess the applicability,
value and success of this
plan to the recovery of
the western snowy plover
every 5 years.
6.3 every 5
years
258 32.2 Assumes staff time to review every
5 years.
3 Prepare a delisting
package for the Pacific
coast population of the
western snowy plover.
6.4 6 months ES 64 64 Assumes staff time to prepare
delisting package.
Cost Estimate (in $1,000 units)
Priority
No.
Action
Description
Action
Number
Action
Duration
Responsible
Parties
Total
Costs FY1 FY2 FY3 FY4 FY5 Comments/Notes
238
3 Prepare and implement a
post-delisting monitoring
plan.
6.5 6 months ES 64 64 Assumes staff time to prepare and implement post-delisting monitoring
plan.
1 Dedicate sufficient U.S.
Fish and Wildlife Service
staff for coordination of
western snowy plover
recovery implementation.
7 continual ES 5,152 128.8 128.8 128.8 128.8 128.8 Assumes staff time to coordinate recovery implementation
3 Develop a joint United
States and Mexico effort
to protect snowy plover
populations and their
habitat.
8.1 continual ES, IA TBD
3 Encourage research and
monitoring of breeding
and wintering snowy
plovers in Baja
California, Mexico by
universities and
authorities of Mexico.
8.2 continual ES, IA, RSCH,
BRD
TBD
3 Encourage development
and implementation of
public information and
conservation education in
Mexico.
8.3 continual ES, IA, PA TBD
Cost Estimate (in $1,000 units)
Priority
No.
Action
Description
Action
Number
Action
Duration
Responsible
Parties
Total
Costs FY1 FY2 FY3 FY4 FY5 Comments/Notes
239
3 Coordinate with other
survey, assessment, and
recovery efforts for the
western snowy plover
throughout North
America.
9 continual ES, IA, RSCH,
BRD
TBD
Total Cost of Recovery through 2046: $149,946,000 plus additional costs that cannot be estimated at this time. 240
241
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243
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Cairns, W.E. and I.A. MacLaren. 1980. Status of the piping plover on the east coast of North America. American Birds 34(2):206-208.
California Coastal Commission. 1995. Regional cumulative assessment project.
ReCAP pilot project, findings and recommendations: Monterey Bay Region. 162 pp. plus appendices.
California Department of Fish and Game. Undated [1994]. Managing non-native species in California: The red fox. The Resources Agency, California Department of Fish and Game, Sacramento, CA. 8 pp.
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Wastewater disposal and earthquake swarm activity
at the southern end of the Central Valley, California
T. H. W. Goebel1,2, S. M. Hosseini 3, F. Cappa 4,5, E. Hauksson 1, J. P . Ampuero1, F. Aminzadeh 3, and J. B. Saleeby 1
1DivisionofGeologicalandPlanetarySciences,CaliforniaInstituteofTechnology,Pasadena,California,USA, 2Nowat DepartmentofEarthandPlanetarySciences,UniversityofCalifornia,SantaCruz,California,USA, 3Departmentof ChemicalEngineeringandMaterialSciences,UniversityofSouthernCalifornia,LosAngeles,California,USA, 4Geoazur Laboratory,UniversityofNiceSophiaAntipolis,Nice,France, 5InstitutUniversitairedeFrance,Paris,France AbstractFractureandfaultzonescanchannelfluidflowandtransmitinjection-inducedporepressure changesoverlargedistances( >km),atwhichseismicityisrarelysuspectedtobehumaninduced.Weuse seismicityanalysisandhydrogeologicalmodelstoexaminetheroleofseismicallyactivefaultsininducing earthquakes. We analyze a potentially injection-induced earthquake swarm with three events aboveM4 near the White Wolf fault (WWF). The swarm deviates from classic main aftershock behavior, exhibiting uncharacteristically low Gutenberg-Richterb of 0.6, and systematic migration patterns. Some smaller eventsoccurredsoutheastoftheWWFinanareaofseveraldisposalwells,oneofwhichbecameactivejust 5 months before the main swarm activity. Hydrogeological modeling revealed that wastewater disposal likelycontributedtoseismicityvialocalizedpressureincreasealongaseismicallyactivefault.Ourresults suggest that induced seismicity may remain undetected in California without detailed analysis of local geologicsetting,seismicity,andfluiddiffusion.
1. Introduction
Fluidinjectionintohydrocarbonand geothermalreservoirscanchangethelocalstressfieldpotentiallycausing earthquakes at several kilometers distance, both immediately and months to years after peak injection [e.g., Hsieh and Bredehoeft,1981; Bourouis and Bernard,2007; Keranen et al.,2013; Kim,2013; Martínez-Garzón et al.,2014; Schoenball et al.,2014; Rubinstein et al.,2014].Injection-inducedfaultslipis,inadditiontoporoelas- ticstresschanges,commonlyattributedtoanincreaseinporepressure,whichreducesthefrictionalresistance on fault surfaces [e.g.,Healy et al., 1968;Hsieh and Bredehoeft, 1981;Ellsworth, 2013;Keranen et al., 2013]. In recentyears,inducedearthquakeshavebeenobservedintheproximityofmanyhigh-volumewastewaterdis- posal(WD)wellsinthecentralU.S.[e.g., Horton,2012; Frohlich and Brunt,2013; Keranen et al.,2013; Kim,2013; Skoumal et al.,2014].Manyoftheidentifiedinjectionwellsresponsibleforinducingseismicityaresuspected toinjectatdepthclosetotheupperbasementsurface,wherecriticallystressedfaultsslipmoreeasilywhen exposedtochangingpressuresandearthquakerupturescangrowtolargersizesthaninsedimentarybasins [e.g., Das and Scholz,1983; Horton,2012; Keranen et al.,2013; Kim,2013; Ellsworth,2013].
While many previous studies focused on regions within the central and eastern U.S., where induced event detection is facilitated by low background seismicity rates, detecting induced events in California hydrocar- bonbasinsreceivedlessattention,despiteextensiveinjectionoperationsandseismicallyactivefaults.Upto now, few injection-induced earthquakes outside of geothermal reservoirs have been observed in California [Kanamori and Hauksson, 1992; Goebel et al., 2015]. Consequently, the most acute anthropogenically con- trolledseismichazardstemsfromfluid-injectionactivityclosetoactivefaults.Suchinjectionactivitycanlead toearthquakesupto
Mw5.7orlargerasobservedinOK[ Keranen et al.,2013].
In California, annual fluid-injection volumes exceed those in OK where induced seismicity is suggested to bewidespread.Theaveragenumberofactiveinjectionwellsbetween2010and2013of ∼9900exceedsthe ∼8600wellsinOK[ CA Department of Conservation,2012].ThewellsinCaliforniainjectonaverageatdepthof ∼1.5kmwhichisabout0.5kmdeeperthaninOK[ Goebel,2015].Morerecentlyandcoincidentwithcontinu- ouslyincreasingoilpricesbetween2001and2014(except2009and2010),Californiaexperiencedasystematic increase in injection volumes in connection with more extensive well-stimulation operations. This increase
RESEARCH LETTER
10.1002/2015GL066948
Key Points:
• Anomalousswarmactivityassociated
withWhiteWolffault
• Inducedseismicitylikelycausedby
localizedpressure increasealonga
seismicallyactivefault
• Inducedseismicitymaybemasked
by natural earthquake activity in
California
Supporting Information:
• SupportingInformationS1
• TablesS1–S8
•M o v i eS 1
•M o v i eS 2
• FiguresS1–S22
Correspondence to:
T.H.W.Goebel,
[email protected]
Citation:
Goebel, T. H. W., S. M. Hosseini,
F. Cappa, E. Hauksson, J. P. Ampuero,
F. Aminzadeh, and J. B. Saleeby (2016),
Wastewater disposal and earthquake
swarm activity at the southern end
of the Central Valley, California,
Geophys. Res. Lett., 43, 1092–1099,
doi:10.1002/2015GL066948.
Received8NOV2015
Accepted16JAN2016
Acceptedarticleonline22JAN2016
Publishedonline4FEB2016
©2016. AmericanGeophysicalUnion.
AllRightsReserved.
GOEBELETAL. EARTHQUAKESWARMSANDFLUIDINJECTION 1092
Geophysical Research Letters 10.1002/2015GL066948
ininjectionactivitytogetherwiththelackofavailableinjectionsitesawayfromactivefaultsrequiresamore detailedassessmentofpossibleseismogenicconsequencesoffluidinjectioninCalifornia.
This study is structured as follows: we first provide an overview of the geologic setting, as well as injection and seismic activity at the southern end of the Central Valley. This is followed by a detailed assessment of theseismicallyactivefaultswithinthestudyregion.Wetheninvestigateapossiblecorrelationbetweenfluid injectionandseismicity,includingvariationsinfrequency-magnitudedistributionwiththeonsetofinjection rateincrease.Lastly,wecreateadetailedhydrogeologicalmodelthatincorporateslocalgeologysuchasactive faultstructuresandexamineinjection-inducedpressurechanges.
2. Swarm Activity, Wastewater Disposal Operations, and Geological Setting This study concentrates on a potentially injection-induced earthquake swarm in 2005, which is associated withtheWhiteWolffault(WWF)andoccurredatthesouthernendoftheCentralValley,CA.Theswarm,which is referred to as White Wolf swarm in the following, deviates from standard main shock-aftershock patterns.
Itiscomprisedofa
ML4.5eventon22September,followedbytwo ML4.7( Mw4.6)and ML4.3eventsthesame dayaswellassomesmallermagnitude“foreshocks.”TheWhiteWolfswarmissuspectedtobeconnectedto fluid-injectionactivitybasedonastatisticalassessmentofinjectionandseismicityratechanges[ Goebel et al., 2015]. The statistical assessment showed that an abrupt increase in injection rates in 2005 was followed by a large increase in seismicity rates, which exceeded the 95% confidence interval of previous rate variations since 1980. In other words, the area did not experience a comparable rate increase within a 10 km radius of the well prior to the start of injection in 2005. Moreover, the strong correlation between rapid injection rate changes and the subsequent seismicity sequence had a
≈3% probability of coinciding by chance based on
testswithrandomlydeterminedonsetsofinjectionratechanges[ Goebel et al.,2015].
TheWhiteWolfswarmoccurredatthesouthernendofKernCounty,thelargestoil-producing( >75%ofthe state’s oil production) and fluid-injecting (>80% of all injection wells) county in California [CA Department of Conservation, 2012]. The region experienced one major earthquake since 1950, i.e., the 1952,Mw7.3 Kern county event, and hosts many seismically active faults including the White Wolf, Wheeler Ridge, and Pleito faults(Figure1a).
Fluid injection rates at the southern end of the Central Valley increased rapidly from∼20,000 to more than 100,000m3/mobetween2001and2010(Figure1b).Themajorityofwastewater,i.e.,80to95%,wasinjected within the Tejon Oil Field involving only three closely spaced wells. These wells started injecting in 2001 (WD01),2004(WD04),and2005(WD05),andinjectionfluidsweregenerallycontainedbelow950m.Effective welldepthsarereportedbetween
∼1200and ∼1500m[ CA Department of Conservation,2012].Theinjection wellstargeteda25–30mthin,highlypermeable(0.2–1.0 ⋅10−12 m2)stratigraphiczonewithintheMonterey formation [CA Department of Conservation, 2012]. This injection zone is composed of turbiditic sand lenses withmaximumlateralextentsof1to2km(FigureS2inthesupportinginformation).Moreover,thewellbores include a significant, horizontally drilled portion with 520 to 580 m long perforation zones that maximized injectionrates.
InjectionintowellWD05occurredatconsistentlyhighratesof ∼57,000m3/mo,starting5monthsbeforethe White Wolf swarm in September 2005. During these five months, the wellbore accommodated more than 75%ofthetotalinjectionactivityoftheentirestudyarea.WD05islocatedinanareaofcloselyspaced,north- west striking, Early Miocene, buried, normal faults that show evidence of local Holocene reactivation. Based on geological mapping, seismicity, and well-log data, we identified a seismically active normal fault located betweentheWWFandinjectionsiteWD05(Figure1a,seesupportinginformationTextS1andFiguresS1–S3 fordetailsoffaultidentification).Thisfaultisreferredtoas“Tejonfault”inthefollowingtext.Bothseismicity and well-log data suggest that the Tejon fault is shallow close to the injection site and deepens toward the northwestbelowtheWheelerRidgefaultbeforeintersectingwiththeWWF(Figure1c).Thehorizontallyelon- gatedperforationzoneofwellWD05,whichextendsdirectlyeastfromthewellhead,increasedtheprobability ofintersectingtheTejonfaultwiththeborehole.
3. Seismicity Analysis and Hydrogeological Modeling
TheWhiteWolfswarmshowsevidenceofsystematiceventmigrationbetweeninjectionsitesandthe Mw4.6 hypocenter(Figures1cand2).Thissystematic migrationisbestseeninrelocatedcatalogsthatincludenewly GOEBELETAL. EARTHQUAKESWARMSANDFLUIDINJECTION 1093
Geophysical Research Letters 10.1002/2015GL066948
Figure 1. Seismicity and injection activity within the study area centered at the Tejon Oil Field. (a) Fault traces (black and green lines), epicenters (dots),injection wells (blue triangles), and oilfield locations (blue lines). Seismicity is colored according to days after injection into WD05, gray dots show background seismicity.
Earthquake locations are from the standard Southern California Seismic Network (SCSN) catalog. (b) Injection (blue) and seismicity rates (gray) between 1980 and 2014. (c) Seismicity cross section within a 5 km zone between c and c’ in Figure 1a. Seismicity up to 300 days after the start of injection is shown in colored dots (see colorbar in Figure 1a), background events by gray dots, and Tejon fault seismicity prior to injection by black dots. The gray rectangle qualitatively highlights a zone of high seismic activity that coincides with the Tejon fault. Earthquake locations are based on a high-quality waveform relocated catalog [Shearer et al., 2005].
detectedeventsusingatemplate-matchingmethodandmayeasilyremainundetectedinstandardseismicity catalogs (Figure 2). Within the first 2 months of wastewater disposal in WD05, we observed some shallow seismicity at 4 km depth beneath the injection site and on the Tejon fault to the northwest, while little to noseismicactivityoccurredtothesouthandsoutheast.Withinthefollowingmonths,theareanorthwestof well WD05 in direction of the WWF became progressively active. Much of this seismicity was concentrated justupdipoftheintersectionpointbetweentheTejonandWhiteWolffaultbetween ∼70and150daysafter
injection(seeanimationsinthesupportinginformation).
Figure 2. Seismicity migration along the Tejon fault relative to the start of injection in WD05. We show both the SCSN (gray dots) and relocated earthquake catalogs (black circles). The latter includes new event detections using a template-matching approach. Epicentral distances are reported relative to well-head location, error bars show average, absolute location uncertainties. The dashed curve highlights the expected trend for a square-root dependence of distance on time characteristic for a diffusive process.
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Figure 3. Fluid injection rates in well WD05, seismic activity, and frequency-magnitude distributions (FMDs) of the likely induced White Wolf swarm. (a) Injection rates (blue), cumulative number of earthquakes (black), and event magnitudes (circles) within a 10 km radius of injection in well WD05. (b) FMD before (green), during (red), and after (orange) peak injection rates as well as 95% confidence interval for a FMD with b = 1.
In the two years prior to injection into WD05, seismicity rates within a 10 km radius of the well were largely constant (Figure 3a). The start of injection also marked the onset of seismicity rate increase which peaked about 150 days later. The occurrence of the three
ML >4 events was closely followed by a∼30% decrease in
injectionratesinwellWD05andinjectionrateskeptdecreasingovertheensuing4years.
In addition to seismicity rate changes, the frequency-magnitude distribution (FMD) strongly deviated from expectedbehaviorofGutenberg-Richter-typeFMDswith bvaluesclosetounity(Figure3b).Duringtheperiod of peak injection in WD05 thebvalue within a 10 km radius was 0.6. This value was significantly lower than before (b=1) and after (b=0.9) peak injection activity. Moreover, we observed a long-term decrease in b value coincident with a rate increase in cumulative injection rates in WD01, WD04, and WD05 (Figure S4).
UsingMonteCarlosimulationsofearthquakemagnitudesbasedontheobserved avalue,magnitudeofcom- pletenessof1.5,wecomputethe95%confidenceintervalofFMDswith b=1.TheobservedFMDexceedsthe upper confidence bound, indicating a significantly larger proportion of large-magnitude events during the White Wolf swarm which is also confirmed by the corresponding moment magnitudes for events aboveM4 (see supporting information Text S1 for details on moment magnitude computations). Decreasing and low b values may be characteristic for gradual fault activation processes, observed in laboratory experiments [Goebel et al., 2013] and during fluid injection [e.g.,Maxwell et al., 2009; Skoumal et al., 2014; Huang and Beroza,2015].
Tofurthertestifthe2005WhiteWolfswarmwasconnectedtowastewaterdisposal,wemodelpermeability structure and pressure diffusion close to the injection site. Pressure diffusion was likely influenced by high permeabilityalongtheseismicallyactivepartoftheTejonfaultlocatedN-WofwellWD05.Slipalongthisfault may have been pivotal in maintaining elevated permeability down to the depth of the White Wolf swarm.
The existence of a zone with higher permeability than the surrounding lithology is supported by several observationssuchasfollows:
1.The asymmetric seismicity distribution concentrated N-W of well WD05 with little to no seismicity to the southandeastoftheinjectionsites.Mostoftheseeventsoccurredatdepthsbetweenthe Mw4.6hypocenter
andtheinjectiondepth(FigureS5).
2.The presence of background seismic activity on the upper portion of the Tejon fault revealing that part of thefaultwasseismicallyactivepriortoinjection.
3.ThepresenceofseveralmappedfaultsandfracturezoneslocatedN-WoftheinjectionsitebetweenWD05 andWWF(FigureS3).
Our3-Dnumericaldiffusionmodelincludesthreeprincipalstratigraphiczones,inadditiontotheTejonfault which is implemented as a vertical zone of elevated permeability (Figure 4a). These three zones are (1) the injectionzone,i.e.,a20–30mthinturbiditicsandlensintheMontereyformationwithalateralextentofup to
∼1.5 km, labeled as “Transition zone” in the industry data [CA Department of Conservation, 2012]; (2) the crystalline(gneissic)basementcomplex,and(3)theMontereyformation.Permeabilityishighwithinthesand GOEBELETAL. EARTHQUAKESWARMSANDFLUIDINJECTION 1095
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Figure 4. Hydrogeological modeling and predicted pore pressure increase caused by injection in well WD05. (a) Schematic representation of model setup including the high-permeability zone between injection site andMw4.6 event. The hydrogeological model is based on geologic mapping and industry data described in Text S1 in the supporting information. (b) Model-dependent pore pressure change ( ΔPp) at distance of theMw4.6 event (∼11 km) for
different values of fault zone width and permeability. (c) Observed hypocenter migration along the Tejon fault zone (black circles). The theoretical position of a 0.01 MPa pressure front is shown for a 500 m wide fault zone and three different permeability values (150, 200, and 250 mD). The Mw4.6 event is shown by a red star. The inset shows
distribution of pore pressures at each hypocenter fork=200mD andw=500m, vertical lines are mean and standard deviation.
lenses (i.e.∼1 D) and very low (∼10−4 mD) above injection depth which is one of the requirements for the selection of an injection site. Similarly, permeability is low (∼10−4 mD) within the basement and Monterey formationsoutsideoftheinjectionzone(permeabilitiesarereportedinmillidarcy,1mD ≈10−15 m2).
Our hydrogeological model is based on the most complete available data sets within the upper∼2–3km of sedimentary basins and includes seismicity records, geologic mapping results, and industry data (i.e., well logs, stratigraphic columns, and interpreted reservoir structure, see supporting information Text S1).
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Belowthesedepths,fewgeophysicaldataareavailableexceptfortheseismicityrecord.Toaccountforlarger uncertainty at these depths, we chose a coarse modeling scale which primarily resolves the influence of large-scale structural heterogeneity such as large fault zones. More detailed information about the model setupcanbefoundinthesupportinginformationTextS1.
Ourmodelingresultsshowthatthinfaultzoneswithhigherpermeabilityleadtofluid-pressureincreasesuffi- cienttotriggerearthquakesatdistancessimilartothe
Mw4.6hypocenter.Weperformedadetailedsensitivity
analysisofpressurechangesresultingfromvaryingfaultzonewidthandpermeability.Permeabilitywasvaried over 7 orders of magnitude between 10−3 to 103 mD to account for generally large uncertainties in perme- ability measurements [Manga et al., 2012], and fault width was varied between 100 and 1000 m (Figure 4b andsupportinginformationTableS1).Themodeledpressurechangesexperiencedbyafaultat ∼11kmdis- tance span 4 orders of magnitude depending on the initial conditions, with significantly stronger effects of permeability changes compared to variations in fault zone width (Figure 4b). For fault zone permeability above
∼300 mD and fault width below∼800 m, we observe a pressure increase at theMw4.6 hypocenter of at least 0.01 MPa (Figure 4b), which is sufficient to induce seismicity on faults favorably oriented to slip [Keranen et al., 2014;Hornbach et al., 2015]. Fault zones with lower permeability may result in similar magni- tudepressurechangesiftheyaresufficientlynarrow.Theheredescribedseismogenicconsequencesofthin, high-permeabilitypressurechannelsareinagreementwithpreviousstudiesofinducedseismicityinColorado andArkansas[ Hsieh and Bredehoeft,1981; Zhang et al.,2013].
The migration pattern of seismic events relative to injection into WD05 can be used to constrain a plausible range of permeability values by correlating seismicity and modeled pressure front location along the Tejon fault(Figure4c).Theshapeoftheporepressurefrontinadistance-timeplotdependsontheparticularreser- voir geometry and permeability values with a commonly observed square-root dependence of distance on time for simple radial symmetric models (Figure 2) [Talwani and Acree, 1984;Shapiro et al., 1997]. Using the morecomplex3-Dreservoirandfaultgeometriesinourmodel,wefindthatfaultzonepermeabilitybetween 150and250mDandawidthsmallerthan500mbestagreeswiththeobservedseismicityforachangeinfluid pressureof0.01MPa.Thisiswithinthegeneralrangeoffaultzonepermeabilityvaluesinferredfromseismicity migration [e.g.,Ingebritsen and Manning, 2010;Manga et al., 2012]. Much of the seismicity occurred close to the arrival of the initial pressure pulse of
∼0.01 MPa, indicating that faults within the area may have been criticallystressed,i.e.,stresslevelswerewithinanarrowrangebelowtheshearstrength(Figure4cinset).
4. Discussion
The identification of human-induced earthquakes in tectonically active regions such as California is gener- allycomplicatedbythemanynaturallyoccurringseismicitysequences.Toaddressthesechallenges,weused detailedstatisticalanalysismethods[ Goebel et al.,2015]aswellasgeologicalanddiffusionmodelstoevalu- ateapotentiallyinducedoriginofanearthquakesequenceclosetotheWWFin2005.Thissequencedeviates from commonly observed tectonic sequences in the area by showing significantly elevated seismicity rates abovethebackgroundassociatedwitharapidincreaseininjectionrates.Moreover,theseismicitysequence showedevidencefordeepmigrationwithinthecrystallinebasementbetweeninjectionwellsandthenearby WhiteWolffaultsuggestingthatwastewaterdisposallikelycontributedtotriggeringtheearthquakeswarm.
The recorded largest magnitude event (
Mmax=4.6) of the sequence and the cumulative injection volume (Vtot ∼1.8 ⋅ 106 m3) fall well within the trend ofVtot and Mmax reported byMcGarr [2014] and are similar to observations ofVtot and Mmax in Timpson, Texas, and Painesville, Ohio [McGarr, 2014]. This analysis assumes thatinjectioninallthreewells(WD01,WD04,andWD05)contributedtotheinducedseismicitysequence.
Our results highlight that injection related earthquake triggering processes may involve multiple mecha- nisms.AplausiblemechanismforthetriggeringoftheWhiteWolfswarmisthediffusionofpressuresfromthe ∼1.5kmdeepinjectionsitealongthenorthweststrikingTejonfaultintotheintersectionzonewiththeWWF.
This pressure channeling effect may have been further intensified if the WWF acted as flow barrier, thereby trappingthepressurefrontwithinthedamagezoneoftheTejonfaultresultinginmorerapidpressureincrease at the intersection between the two faults. Other triggering mechanisms may have included stress transfer at the front of the pressurized zone as well as injection-induced aseismic slip, which progressively became moreseismogenicatlargerdepthwithinthebasementcomplex.Shallowaseismicslipiswelldocumentedin high-resolution,controlledinjectionexperimentsandmayhideearlyfaultactivationprocesses[ Cornet et al., 1997; Guglielmi et al., 2015]. A potentially injection-induced origin of the White Wolf swarm is intrinsically GOEBELETAL. EARTHQUAKESWARMSANDFLUIDINJECTION 1097
Geophysical Research Letters 10.1002/2015GL066948
connectedtothespecificgeologicsettingthataccommodatedpressurediffusiontoseismogenicdepthatthe southernendoftheCentralValley.Moredetailedassessmentsofthegeologicsettingclosetoinjectionwells arerequiredtoexplainthelackoflarge-scaleinjection-inducedearthquakeactivityinCaliforniahydrocarbon basins[ Goebel,2015].
Casesofrelativelydeepinducedseismicityfarfrominjectionsiteshavebeenreportedinseveralotherregions such as Oklahoma, Colorado, and Arkansas, where induced earthquakes occurred at 8 km depth and 7 to 35kmdistancefromtheinjectionwell[ Hsieh and Bredehoeft,1981; Horton,2012; Keranen et al.,2014].Inaddi- tiontotheselargedistances,thereareseveralotherfactorsthatcancomplicateinducedseismicitydetection inCaliforniahydrocarbonbasins:(1)Faultsmaychannelinducedpressurechangesandleadtolocalizedpore pressure increase; (2) background seismicity rates are generally high so that small-rate variations cannot be detected; (3) small-magnitude earthquakes close to injection sites can easily be missed because of sparse station coverage within hydrocarbon basins; and (4) transient aseismic and seismic slip processes lead to a dynamic increase in permeability and progressive fault activation [Cornet et al., 1997;Bourouis and Bernard, 2007; Guglielmi et al., 2015;W e ie ta l ., 2015]. All of these factors can potentially complicate mitigation strate- giessuchastrafficlightsystemswhichrelyonasystematicseismicityrateincreaseandeventmigrationfrom thewell.
Based on our empirical results, injection-induced earthquakes are expected to contribute marginally to the overallseismicityinCalifornia[seealso Goebel,2015; Goebel et al.,2015].Thisisinlinewithphysicalmodelsof crustalstrengthdistributionswhichsuggestalimittotheamountofstrainenergythatisavailableforshallow earthquake ruptures [e.g.,Sibson, 1974]. Moreover, the frictional properties of shallow, sedimentary faults inhibitrupturegrowthanddiminishtheseismogenicimpactofsurficialpressureperturbation[e.g., Das and Scholz,1983].However,consideringthenumerousactivefaultsinCalifornia,theseismogenicconsequences ofevenafewinducedcasescanbedevastating.
5. Conclusion
Wastewaterinjection-inducedearthquakesarerareinCaliforniacomparedtowidespreadtectonicseismicity.
Nevertheless, the proximity of high-rate injectors and large active faults can cause noticeable earthquakes undercertaingeologicconditions.Ourresultssuggestaconnectionbetweenwastewaterdisposalandseis- micity with events up to
Mw4.6 at the southern end of the Central Valley. Wastewater injection within this regionshouldbemonitoredcarefullybecauseofthepresenceofhigh-permeabilityfaultstructuresthatcon- necttheinjectionsitewiththenearbyWWF.TherelativelyshallowcrystallinebasementsouthoftheWWFmay increasetheprobabilityofinducingearthquakes,iffluidsmigratebeyondtheintendedgeologicformations.
Thepresentexampleshowsthatinjection-inducedearthquakesmayremainunidentifiedintectonicallyactive regionsifonlystandardseismicitycatalogswithcomparablyhighmagnitudesofcompletenessareanalyzed.
Wepresentapathwaytomorereliableidentificationofpossiblyinducedearthquakesbyextendingtheseis- micityrecordstolowermagnitudesandbyanalyzingwaveformrelocatedcatalogsaswellashydrogeological models. Such a detailed analysis of the available data may help recognize regions with increased induced seismicitypotentialandpreventinjection-inducedseismicityinCaliforniainthefuture.
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Acknowledgments
The authors thank Semechah Lui,
Preston Jordan, Bill Foxall, Emily
Brodsky, and members of the Induced
Seismicity Consortium at USC for
helpful comments and discussions.
The manuscript benefitted from
comments by Art McGarr and Michael
Wysession. The well and operational
data were supplied by the California
Department of Conservation (www.
doggr.com), and the seismicity data
by the Southern California Earthquake
Center (doi: 10.7909/C3WD3xH1) and
Advanced National Seismic Systems.
This study was funded by NEHRP/USGS
grants G14AP00075 and G15AP00095
to Caltech, by the Southern California
Earthquake Center (SCEC) under
contribution numbers 12017 and
15168 and the Induced Seismicity
Consortium (ISC) at USC.
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GOEBELETAL. EARTHQUAKESWARMSANDFLUIDINJECTION 1099
Upstream oil and gas production and ambient air pollution in California David J.X. Gonzalez a,b,⁎, Christina K. Francis c,G a r yM .S h a wd,M a r kR .C u l l e ne,1, Michael Baiocchi f,M a r s h a l lB u r k eg
a Department of Environmental Science, Policy and Management and School of Public Health, University of California, Berkeley, Berkeley, CA 94720, USA b Emmett Interdisciplinary Program in Environment and Resources, Stanford University, Stanford, CA, USA c Program in Environmental Science and Studies, Johns Hopkins University, Baltimore, MD, USA d Department of Pediatrics, Stanford University, Stanford, CA, USA e Founding Director of the Stanford Center for Population Health Sciences, USA f Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA g Department of Earth System Science, School of Earth, Energy and Environmental Sciences, Stanford University, Stanford, CA, USA
HIGHLIGHTS
• Oil and gas wells have been linked to ad-
verse health, but mechanisms not well
understood.
• Applied a quasi-experimental design
with daily air pollution and oil produc-
tion data
• We leveraged wind direction as source
of exogenous variation for exposure to
wells.
• Upstream oil and gas production emit-
ted air pollutants at concentrations that
may be harmful.
• Evaluated proximity as an appropriate
indicator of air pollution exposure from
wells
GRAPHICAL ABSTRACT
abstractarticle info
Article history:
Received 12 April 2021
Received in revised form 31 August 2021
Accepted 7 September 2021
Available online 11 September 2021
Editor: Lidia Morawska
Background:Prior studies have found that residential proximity to upstream oil and gas production is associated with increased risk of adverse health outcomes. Emissions of ambient air pollutants from oil and gas wells in the preproduction and production stages have been proposed as conferring risk of adverse health effects, but the ex- tent of air pollutant emissions and resulting nearby pollution concentrations from wells is not clear.
Objectives: We examined the effects of upstream oil and gas preproduction (count of drilling sites) and produc- tion (total volume of oil and gas) activities on concentrations of five ambient air pollutants in California.
Methods: We obtained data on approximately 1 million daily observations from 314 monitors in the EPA Air Quality System, 2006-2019, including daily concentrations of five routinely monitored ambient air pollutants:
PM
2.5,C O ,N O2,O 3, and VOCs. We obtained data on preproduction and production operations from Enverus and the California Geographic Energy Management Division (CalGEM) for all wells in the state. For each monitor and each day, we assessed exposure to upwind preproduction wells and total oil and gas production volume within 10 km. We used a panel regression approach in the analysis and fita d j u s t e dfixed effects linear regression models for each pollutant, controlling for geographic, seasonal, temporal, and meteorological factors.
Keywords:
Oil and gas
Exposure assessment
Ambient air pollution
Particulate matter
Science of the Total Environment 806 (2022) 150298
⁎ Corresponding author at: 108 Hilgard Hall, University of California, Berkeley, Berkeley, CA 94720, USA.
E-mail address: [email protected](D.J.X. Gonzalez).
1 Retired.
https://doi.org/10.1016/j.scitotenv.2021.150298
0048-9697/© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Contents lists available at ScienceDirect
Science of the Total Environment
journal homepage: www.elsevier.com/locate/scitotenv
Results:We observed higher concentrations of PM2.5 and CO at monitors within 3 km of preproduction wells, NO2 at monitors at 1-2 km, and O3 at 2-4 km from the wells. Monitors with proximity to increased production volume observed higher concentrations of PM2.5,N O2, and VOCs within 1 km and higher O3 concentrations at 1-2 km. Re- sults were robust to sensitivity analyses.
Conclusion: Adjusting for geographic, meteorological, seasonal, and time-trending factors, we observed higher concentrations of ambient air pollutants at air quality monitors in proximity to preproduction wells within 4 km and producing wells within 2 km.
© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).
1. Introduction
Recent studies have found that residing in proximity to oil and gas wells is associated with adverse cardiovascular, psychological, perinatal, and other health outcomes (Casey et al., 2015, 2018; Currie et al., 2017; Denham et al., 2021 ; McKenzie et al., 2014, 2018, 2019 ; Tang et al., 2020; Whitworth et al., 2017 ; Willis et al., 2021 ). Studies in California have found higher risk of preterm birth and low birthweight with expo- sure to upstream oil production, as well as impaired lung function and higher asthma prevalence (Gonzalez et al., 2020; Johnston et al., 2021; Shamasunder et al., 2018 ; Tran et al., 2020 ). Several possible mecha- nisms have been hypothesized for the observed associations between proximity to wells and adverse health outcomes, including emissions of ambient air contaminants during various stages of upstream oil and gas production ( Adgate et al., 2014 ; Allshouse et al., 2019 ; Gonzalez et al., 2020; Johnston et al., 2019; McKenzie et al., 2012). There is a po- tential for widespread risk of exposure to air pollutant emissions from upstream oil and gas development, with an estimated 17.6 million U.S. residents, including 2.1 million Californians, living within 1.6 km (1 mile) of at least one active well ( Czolowski et al., 2017).
Despite widespread potential exposure to wells and reported health risks, the effects of upstream oil and gas production on ambient air qual- ity are still not well understood (Johnston et al., 2019). Under the Clean Air Act and its amendments, local regulatory agencies are responsible for maintaining networks of in situ air pollution monitors ( Grainger et al., 2017 ). Agencies routinely monitor criteria air pollutants, which are statutorily regulated under the Clean Air Act and which include fine particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5), carbon monoxide (CO), nitrogen dioxide (NO 2), and ozone (O3). Other hazardous pollutants are also routinely monitored, including non-methane volatile organic compounds (VOCs) such as ac- etaldehyde, benzene, ethylbenzene formaldehyde, n-hexane, toluene and xylene. In prior studies, such as in situ monitoring campaigns con- ducted in California, Colorado, and Texas, investigators have reported elevated concentrations of PM 2.5,C O ,N O 2,O 3, and VOCs near wells (Allshouse et al., 2019 ; Arbelaez and Baizel, 2015 ; Garcia-Gonzales et al., 2019a ; Schade and Roest, 2016, 2018 ). Sources of PM 2.5 emissions associated with upstream oil and gas production may include combustion of diesel fuel from on-site equipment and heavy trucks, dust from construction sites and unpaved roads, and secondary formation in the atmosphere ( Adgate et al., 2014 ); emissions of CO and NO2 may also be associated with fossil fuel combustion in vehicles and off-road equipment ( Holloway et al., 2000 ; Jackson et al., 2014 ); O3 may be formed as a secondary pollutant in photochemical reactions involving nitrous oxides (such as NO 2) and VOCs in the presence of sunlight (Mauzerall et al., 2005; Rodriguez et al., 2009).
Studies have found elevated concentrations of harmful pollutants near oil and gas wells ( Garcia-Gonzales et al., 2019b ). However, prior studies have been geographically and temporally constrained and often do not mirror methods applied by population health researchers.
In particular, exposure characterization is often spatial in nature, whereas population health researchers often seek to exploit temporal variation to isolate the role of exposure to oil and gas wells from expo- sure to other spatially correlated activities that may affect pollution and health (Currie et al., 2017; Willis et al., 2021). Additionally, the unique geological conditions of California may constrain external validity of air quality studies that investigate oil and gas production-related emis- sions in other settings (Garcia-Gonzales et al., 2019a). Population health studies investigating exposure to upstream oil and gas production typ- ically use proximity to wells as the indicator of exposure without di- rectly measuring concentrations of air pollutant emissions or other potential hazards, such as noise and water pollution ( Casey et al., 2015; Currie et al., 2017 ; Gonzalez et al., 2020 ; McKenzie et al., 2014 ; Rasmussen et al., 2016; Tang et al., 2020; Tran et al., 2020; Willis et al., 2021). Improved understanding of pollutants emitted during upstream oil and gas production, including the classes of pollutants emitted (or secondarily produced) and the distances to which they are transported could help population health scientists more accurately parameterize exposure assessments and determine which aspects of exposure to pro- duction activities may adversely affect human health.
In our prior study (Gonzalez et al., 2020), we found that proximity to wells was associated with higher preterm birth risk, but we were not able to measure speci fic chemical pollutants parents were potentially exposed to during their pregnancy, or to separate proximity to wells from other activities that may also affect preterm birth risk. Our objec- tives in the current study were to examine how upstream oil prepro- duction and production activities affected ambient air quality in California from 2006 to 2019, with the aim of informing population health studies of exposure to upstream oil and gas production. We investigated whether marginal changes in preproduction and produc- tion activities resulted in increased concentrations of PM
2.5, CO, NO 2,
O3, and VOCs. Where we observed marginal increases in pollutant concentrations with proximity to wells, we also aimed to determine the distance at which elevated concentrations decay to background levels. To address these objectives, we applied a quasi-experimental de- sign using a panel of publicly available air quality monitoring data.
2. Methods
2.1. Study design
We constructed a panel dataset with repeated daily measures of ambient air pollutant concentrations as well as upstream oil and gas production across California from January 1, 2006, to December 31, 2019. We made use of geospatial and temporal variation in oil and gas extraction activities, including well preproduction (defined as the inter- val between spudding, or initiation of drilling, and completion) and pro- duction (total monthly volume of oil and gas produced), and leveraged daily variation in wind direction as a source of exogenous variation. The type and magnitude of emissions may vary by stage due to differences in activities related to preproduction and production, and the intensity of well pad activity varies within each stage ( Allshouse et al., 2017 ).
For each monitor, we assessed daily exposure to upwind wells in pre- production and production during the study period. In the current study, we did not assess exposure of any human populations; rather, we assessed exposure of air quality monitors as a surrogate receptor.
Then we used a fixed effects regression approach to assess the effect of exposure to preproduction and producing wells on the concentra- tions of each pollutant, accounting for geographic, seasonal, and time- trending, and meteorological factors.
D.J.X. Gonzalez, C.K. Francis, G.M. Shaw et al. Science of the Total Environment 806 (2022) 150298 2 Fig. 1. (a) A map of the study region, showing air basins, air quality monitor locations, and 10 km buffers around wells in preproduction (orange) and production (purple), as well as the overlap (red). (b) Count of wells spudded and completed by month across California, including recompletions of previously drilled wells. (c) Total oil and gas production by month for all wells in California, reported as million barrels of oil equivalent (BOE).
D.J.X. Gonzalez, C.K. Francis, G.M. Shaw et al. Science of the Total Environment 806 (2022) 150298 3 2.2. Data
We obtained air quality data from the U.S. Environmental Protection Agency (EPA) Air Quality System (AQS). This dataset comprised daily measurements of seven air pollutants, with daily mean concentrations of PM 2.5 (μgm -3) as well as daily max concentrations of CO (ppm), NO2 (ppb), O3 (ppb), and non-methane VOCs (ppb C). In all analyses, the unit of observation was the pollutant concentration at each monitor for each day, or the monitor-day. We included data for all 314 AQS mon- itors in California that were operating during the study period and that monitored for the five pollutants of interest ( Fig. 1). Missing air pollu- tion data were omitted from the analyses; we did not impute missing air pollution data. Due to the sparse monitoring of VOCs compared to other pollutants, we included data on VOC measurements for 1999- 2005; we excluded pre-2006 measurements for other pollutants because data for wild fire smoke plumes, described below, were not available before 2006. Air qualtity monitors detected and measured non-methane VOC concentrations via the EPA Method TO-3 for ethylbenzene, n-hexane, toluene, benzene, and ethylene using cryo- genic preconcentration techniques, gas chromatography, andflame ion- ization detection. Xylene concentrations were estimated using preconcentration techniques, gas chromatography, and Saturn 2000 ion mass spectrometry. Acetaldehyde and formaldehyde concentrations were measured using 2,4-dinitrophenylhydrazine (DNPH) silica gel cartridges, an O3 scrubber, and ultraviolet absorption spectroscopy.
Data on the oil and gas wells, including development dates and monthly production volume, was obtained from the California Geologic Energy Management Division (CalGEM) and Enverus, a private data ag- gregation service. The analytic dataset included 38,157 wells that were in the preproduction and 90,697 wells in production in California during the study period (Table S1). We defined the preproduction stage of the well as starting with the reported spud date (when drilling begins) and ending with the completion date. We assessed monitors as exposed to proximate preproduction wells on days when the well was between the dates of spudding and completion. Preproduction wells were in- cluded in the study if the preproduction interval (spudding to comple- tion) occurred during the study period. For wells with missing data for spud date, we assumed that the preproduction interval began 30 days before completion; for wells missing completion date, we assumed the preproduction stage ended 30 days after spudding. Wells missing both spud and completion dates were assumed to have been drilled out- side the study period; since the record dates to the late 19th century, we expected there to be missingness in these variables for wells drilled prior to 1999. Wells in the production stage were included for all sites with any reported oil or gas production during the study period. Be- cause oil and gas are frequently produced from the same wells, we used a combined metric of oil and gas production reported as barrels of oil equivalent (BOE). The dataset comprised 8,064,549 well-month observations of a total of approximately 3.8 billion BOE.
We obtained meteorological data from the North American Regional Reanalysis (NARR), a product developed by the National Centers for En- vironmental Prediction. This dataset included modeled daily mean wind direction and speed, reported as vectors ( u and v), as well as observa- tions of mean daily surface temperature (°C) and total daily precipita- tion (mm). There were no missing estimates for these meteorological variables. We also obtained administrative shape files for air basins across the state from the California Air Resources Board (CARB). We used data from the 2010 decennial census to determine whether mon- itors were located in urban areas (with 50,00 or more residents) or urban clusters (with 2500-50,000 residents) compared with rural areas, which comprise all other areas. To control for potential effects of wildfire smoke on daily concentrations ambient air pollutants, we used data on the daily location of wildfire smoke plumes from the Haz- ard Mapping System of the National Oceanic and Atmospheric Adminis- tration (NOAA), which assessed the number of overhead smoke plumes at the zip code level ( Schroeder et al., 2008).
2.3. Exposure assessment
We constructed a panel dataset where, for each monitor and each day with a pollutant observation, we summed (a) the number of up- wind wells in preproduction and (b) the total volume of upwind oil and gas production (BOE) in 1 km increments out to 10 km ( Fig. 2 ).
We determined the wind direction for each monitor and day from the u and v vector components from the NARR wind product. The resultant of the u and v vector components conveys wind direction and speed (magnitude). Preproduction and production wells that intersected the upwind quadrant on each day for each monitor comprised the primary exposure variables; wells outside the quadrant were excluded in the primary analyses.
As sensitivity analyses, we also assessed exposure to wells in the downwind quadrant as a placebo exposure. Additionally, we assessed exposure to all preproduction wells and production volume in 1 km an- nuli (or rings) radiating out from the monitor, i.e., without taking wind into account.
The receptor in our exposure assessment was the air quality moni- tor; this study did not consider any human receptors or health out- comes. Our aim was to use air monitors as a proxy for the residential receptors typically targeted in population health studies that assess ex- posure to oil and gas wells.
2.4. Identification strategy
We leveraged daily variation in wind direction as a plausibly exoge- nous source of variation, uncorrelated with well preproduction and pro- duction activities as well as other sources of pollution. This strategy allowed us to, by design, isolate the marginal contributions of additional preproduction wells and production volume to ambient air pollutant concentrations.
2.5. Statistical analyses
We used adjusted fixed effects linear regression models to assess how marginal changes in (a) the count of wells in preproduction or (b) the volume of oil and gas production affects concentrations of each observed pollutant (PM
2.5, CO, NO 2,O 3, and VOCs). For each
combination of pollutants and well stage (preproduction or production), we fit the following model:
Y
md ¼ Umda þ Dmda þ Omda þ Cmd þ γmd þ δby þ emd,
where Y is the observed daily concentration of the pollutant at monitor m on day d; U is a vector of either the (a) upwind count preproduction wells or (b) upwind sum oil and gas production on day d in annulus a (0-1 km, 1-2 km, … 9-10 km) radiating from monitor m; D is similar to U but for downwind wells; O is also similar to U, but were wells in the two quadrants orthogonal to the upwind quadrant (i.e., lateral wells); C is a vector of covariates (day of week, precipitation in mm, temperature in °C, wind speed in ms-1, and the count of overhead smoke p l u m e s )a tm o n i t o rm on day d; γ is a fixed effect for monitor by month, n; δ is a fixed effect for air basin, b,b yy e a r ,y; and e is an error term representing unmodeled sources of variation in pollution at monitor m on day d.W e fit additional models with polynomial terms for each ex- posure bin to examine whether the response was nonlinear.
We compared the point estimates for upwind wells with downwind placebos. As sensitivity analyses, we also modi fied the fixed effects in the model, using monitor-by-year and air basin-by-month-by-year fixed effects in the model. Additionally, we fit models as described above in the primary analysis but using exposure assessment data that did not take wind into account (i.e., the sum of all preproduction wells or production volume within each annulus). Finally, as an additional sensitivity analysis for co-exposure to wild fire smoke, we fit models D.J.X. Gonzalez, C.K. Francis, G.M. Shaw et al. Science of the Total Environment 806 (2022) 150298 4 for PM2.5 where monitor-day observations that had >0 smoke plumes overhead were omitted.
In total we fit 27 models, and, as the primary analysis, we focused on the adjusted fixed effects regression models for exposure to preproduc- tion wells and production volume. In particular, we were interested in the point estimates for exposure to upwind wells and production within 5 km of the monitor.
All data preparation and analyses were conducted using R v. 4.0 ( R Core Team, 2020).
3. Results
3.1. Descriptive statistics
The analytic dataset comprised 1,058,230 daily observations of the five pollutants from 314 monitors across California collected from 2006 to 2019, with additional observations for VOCs from 1999 to 2005 (Table 1). Most (208) monitors were located in urban areas and approximately half (158) were in the four air basins with the majority of oil and gas wells (96.4%) and production (87.2%): Sacramento Valley, San Joaquin Valley, South Central Coast, and South Coast (Table S1). Not all monitors collected data for all pollutants. The majority (79.5%) of monitor-days included observations for O 3, with 43% of monitor-days including data for NO2 and PM2.5. Some 31% of monitor-days included CO observations and 8.9% included observations of VOCs. Among the 94,349 monitor-days with an observation for VOCs, 39.3% were in the San Joaquin Valley and 12.8% were in the South Coast basin, both basins where most oil and gas wells were concentrated. For each pollutant, there were more observations at monitors more than 10 km from wells than monitors near wells. More observations were collected in the later years of the study period compared to earlier in the study pe- riod. The number of monitors in operation throughout the study period was relatively consistent from year to year; the minimum number of monitors in operation was 223 in 2006 and the maximum was 245 in both 2012 and 2014, with a median of 239 (Fig. S4). The number of monitors that assessed PM2.5 concentrations increased throughout the study period. Concentrations of pollutants at monitors within 10 km of wells were similar to the concentrations at monitors further away (Table 1).
Wells in all production stages were concentrated in the San Joaquin Valley, which includes Kern County, with substantial production in the South Coast air basin, which includes Los Angeles County (Table S1).
Among the 314 monitors included in the analytic dataset, 79 (25.2%) were within 10 km of at least one oil or gas well, 33 (10.5%) were within 3 km, and 11 (3.5%) were within 1 km. Of the monitor-days included in the analysis, 46,477 (4.4%) were exposed to at least one preproduction or production well within 1 km, 115,648 (10.9%) were within 3 km, and 239,764 (22.7%) were within 10 km. For monitor-days with data for PM2.5 and VOCs, there were no preproduction wells within 1 km.
Among exposed monitor-days, the median number of preproduc- tion wells within each upwind 1-km bin was between 1 and 4, with a maximum of 41 (Table S2). For producing wells, median upwind expo- sure spanned 7.2 to 166.9 BOE, with a right-skew and a maximum of 24,166.1 BOE. There was both seasonal and geographic variation in wind direction: in the San Joaquin Valley, the wind predominantly orig- inated in the northwest; in the South Coast basin, wind predominantly came from the southwest (Fig. S1). Exposure to preproduction wells was correlated with exposure to production volume for all annuli be- yond 1 km. Across producing wells, daily production volume was right-skewed, with a median of 7.3 BOE per day and mean (± SD) of
17.1 (± 50.6) BOE per day. Exposure to preproduction wells was highly correlated for adjacent annuli and moderately correlated with further annuli; we observed a similar trend for production volume (Table S3).
Exposure to preproduction wells was moderately correlated with expo- sure to production volume at distances greater than 1 km from wells.
3.2. Primary analyses
In the primary analysis, we observed increased concentrations of PM2.5,C O ,N O2,a n dO 3 with proximity to preproduction wells (Fig. 3).
For PM2.5, we observed an increase of 2.35 μgm -3 (95% CI: 0.81, 3.89) for each additional upwind preproduction well site within 2 km of the monitor, and 0.97 μgm -3 (0.52, 1.41) for an additional well between 2 and 3 km from the monitor. For CO, we observed an increase of
0.09 ppm (-0.0004, 0.18) with an additional upwind well within 2 km and 0.02 (0.004, 0.032) for a well at 2-3 km. Concentrations of NO 2 increased 2.27 with well at 0-1 km, 2.91 (0.99, 4.84) for a well at 1- 2 km, and 0.65 (0.31, 0.99) for a well at 2-3 km upwind. For O 3, there
Fig. 2. A visualization of the exposure assessment method at a monitor located in Bakers field, California, using sample data from July 1, 2009, when the wind was blowing from the northwest (arrow). For each monitor-day, we assessed exposure to (a) the count of wells in preproduction and (b) the total volume of oil and gas produce d upwind (darker shaded area) of the monitor. As a placebo test, we assessed exposure to wells downwind (lighter shade) of the monitor.
D.J.X. Gonzalez, C.K. Francis, G.M. Shaw et al. Science of the Total Environment 806 (2022) 150298 5 were no signi ficant changes for an additional well within 2 km, an increase of 0.31. (0.20, 0.42) with an additional well at 2-3 km, and an increase of 0.14 (0.05, 0.23) with a well at 3-4 km. There were no in- creases in concentration with upwind exposure to VOCs, though nota- bly there was no exposure to preproduction wells within 1 km. Across all pollutants, we did not observe any substantial increased concentra- tions beyond 4 km. In the placebo test, with exposure assessed to down- wind wells, we did not observe any substantial increases in pollutant concentrations.
We observed increased concentrations of PM 2.5 ,N O 2,O 3,a n d VOCs with higher exposure to upwind production ( Fig. 4 ). We estimated the marginal effect of exposure to an additional 100 BOE of daily total oil and gas volume within each 1-km annulus. This de- gree of exposure roughly corresponds with median upwind produc- tion volume within each annulus among exposed monitor-days (Table S2) and is comparable to cutoffs used in recent population health work ( Tran et al., 2020 ). For each additional 100 BOE of total oil and gas production within 1 km, we observed an increase of
1.93 μgm
-3 (95% CI: 1.08, 2.78) in the concentration of PM 2.5 .F o r NO2, we observed an increase of 0.62 ppb (0.37, 0.86) with an additional 100 BOE within 1 km. The concentration of O 3,i n c r e a s e d by 0.11 ppb (0.08, 0.14) with for each 100 additional BOE at 1- 2 km. There was an increase in VOC concentrations of 0.04 (0.01,
0.07) ppb C for an additional 100 BOE of production within 1 km.
We did not observe any substantial changes in CO concentrations with upwind exposure to production volume. In the downwind pla- cebo tests, we observed an increase in PM 2.5 concentrations for exposure to increased production within 1 km, a small increase in NO2 concentrations at 1-2 km, and an increase in O 3 at 3-4 km.
3.3. Sensitivity analyses
We performed several sensitivity analyses. Fitting models that in- cluded exposure variables for both preproduction and production did not substantially change the results; point estimates and confidence in- tervals were similar in models with exposure variables for both prepro- duction and production compared to models examining each exposure separately (Fig. S4). In models with polynomial term for exposure we did not see evidence of non-linear responses to upwind exposure.
Changing model specification in the primary analysis for preproduction wells (Table S4) or for production volume did not qualitatively change findings (Table S5). In a sensitivity analysis, we fit the model as de- scribed above but omitted the 35,422 monitor-days with smoke plumes overhead, comprising 7.8% of the PM 2.5 analytic dataset. The results were similar to the smoke-adjusted results for exposure to wells in both the preproduction and production stages (Fig. S3).
4. Discussion
We observed higher concentrations of ambient air pollutants at air monitors exposed to wells in both the preproduction and production stages. Concentrations of PM 2.5 were substantially higher on days when a well was in preproduction within 3 km of the monitor, and also when production volume increased within 1 km of the monitor.
Notably, we observed increases in PM 2.5 within 1 km of producing wells with and without considering wind direction. There are several possible explanations for this result: it may be attributable to high volume of producing wells near monitors in San Joaquin Valley orthogonal to the upwind direction, imperfect data on wind direction, Table 1
Descriptive statistics of the air monitors, pollutant concentrations, and meteorological factors during the study period, 2006-2019. The unit of observation is the monitor-day; some mon- itors observe multiple pollutants. VOCs in the dataset comprise non-methane volatile organic compounds. ≤ 10 km to
wells
>1 0 k mt o
wells
All
Monitors, n (column %) 79 (25.2) 235 (74.8) 314 (100)
Urban 57 (72.2) 151 (64.3) 208 (66.2)
Rural 22 (27.8) 84 (35.7) 106 (33.8)
Sacramento Valley 16 (20.2) 26 (11.1) 42 (26.6)
San Joaquin Valley 18 (22.8) 24 (10.2) 42 (26.6)
South Central Coast 15 (19.0) 14 (6.0) 29 (18.4)
South Coast 15 (19.0) 30 (12.8) 45 (28.5)
PM
2.5 43 (54.4) 155 (66.0) 198 (63.1)
CO 34 (43.0) 76 (32.3) 110 (35.0)
NO2 45 (57.0) 94 (40.0) 139 (44.3)
O3 65 (82.3) 172 (73.2) 237 (75.5)
VOCs 24 (30.4) 24 (10.2) 48 (15.3)
Observations, n (column %) 307,095 (29.0) 751,135 (71.0) 1,058,230 (100) Urban 214,011 (69.7) 507,287 (67.5) 721,298 (68.2)
Rural 93,084 (30.3) 243,848 (32.5) 336,932 (31.8)
PM
2.5 137,657 (44.8) 317,065 (42.2) 454,722 (43.0)
CO 98,165 (32.0) 229,646 (30.6) 327,811 (31.0)
NO
2 157,567 (51.3) 297,197 (39.6) 454,764 (43.0)
O3 252,572 (82.2) 588,448 (78.3) 841,020 (79.5)
VOCsa 44,992 (14.7) 49,357 (6.6) 94,349 (8.9)
2006–2009 77,013 (25.1) 200,404 (26.7) 277,417 (26.2)
2010–2014 104,839 (34.1) 264,066 (35.2) 368,905 (34.9)
2015–2019 107,248 (34.9) 268,876 (35.8) 376,124 (35.5)
Smoke plume overhead 21,780 (7.1) 54,299 (7.2) 76,079 (7.2) Pollutant concentrations, daily mean ± SD
PM2.5 (μg/m3) 10.6 ± 9.5 9.9 ± 9.0 10.1 ± 9.1
CO (ppm) 0.5 ± 0.4 0.5 ± 0.4 0.5 ± 0.4
NO
2 (ppb) 21.4 ± 14.6 22.1 ± 14.5 21.9 ± 14.5
O3 (ppm) 0.04 ± 0.01 0.04 ± 0.02 0.04 ± 0.02
VOCs (ppb C) 120 ± 166 104 ± 142 112 ± 155
Meteorological factors, daily mean ± SD
Precipitation (mm) 0.9 ± 4.0 1.2 ± 5.1 1.1 ± 4.8
Temperature (°C) 18.6 ± 7.8 17.2 ± 9.1 17.6 ± 8.8
Wind speed (m/s) 3.0 ± 2.1 3.2 ± 2.0 3.1 ± 2.0
a The data for VOCs includes observations for 1999-2019.
D.J.X. Gonzalez, C.K. Francis, G.M. Shaw et al. Science of the Total Environment 806 (2022) 150298 6 or shifts in wind direction during the day that were not adequately captured when we integrated wind direction over the course of a 24 h period. In addition to elevated PM 2.5 levels, concentrations of O 3 increased when production activity increased between 1 and 4 km upwind of the monitor, but not for activity within 1 km of the monitor. This result may be attributable to secondary formation from primary pollutants emitted from during preproduction and production. Ground-level O
3 may be secondarily formed from
photochemical reactions involving CO, NOx, and VOCs, all of which we also observed were emitted from wells ( Real et al., 2007 ; Rodriguez et al., 2009). We observed increased CO concentrations on days when preproduction wells were drilled within 3 km of the monitor.
Concentrations of NO 2 were higher on days when there was a preproduction well within 2 km or increased production volume within 1 km. For VOCs, we found higher concentrations when production volume increased within 1 km of the monitor. In the current study, VOCs comprised non-methane organic compounds in- cluding acetaldehyde, benzene, ethylene, and formaldehyde.
In models that considered both preproduction wells and production volume, we observed similar estimates to the models where we consid- ered preproduction and production separately, as shown in Fig. S4. Pre- production activity near monitors was correlated with production volume, though this may not be apparent based on the correlation ma- trix in Table S3, which shows low correlation between preproduction Fig. 3. Point estimates (95% CIs) for the marginal effect of one additional preproduction well upwind (left column) and downwind (right column) of the monitor. The bar plots show the number of monitor-days with exposure at least one preproduction well within each distance bin.
D.J.X. Gonzalez, C.K. Francis, G.M. Shaw et al. Science of the Total Environment 806 (2022) 150298 7 wells and production volume. However, among all monitor-days with a preproduction well within 1 km of the monitor, there was also >0 BOE of production volume.
In this study, we conducted a quasi-experimental analysis that relied on the existing network of air quality monitors. The siting of air quality monitors is delegated to local authorities and prior studies have found evidence of bias in where monitors are sited, which should be consid- ered when interpreting the results from the current study ( Grainger et al., 2017 ; Grainger and Schreiber, 2019 ). For example, in counties just marginally in attainment for National Ambient Air Quality Stan- dards (NAAQS), regulators had an incentive to place new monitors far from pollution sources, whereas in areas already in non-attainment, the regulators were incentivized to place monitors close to polluting sources (Grainger et al., 2017 ). This could lead to biased estimates of emissions from oil and gas wells, as monitors may be sited away from the most intensively producing oil fields. There is also evidence that monitors are less likely to be located in communities with racially and socioeconomically marginalized populations, which could lead to underestimation of oil and gas-related emissions if oil production in ex- cluded areas was more intensive and polluting (Grainger and Schreiber, 2019). In the current study, the majority of oil and gas production was concentrated in Kern and Los Angeles Counties, both of which were in Fig. 4. Point estimates (95% CIs) for the marginal effect of 100 additional barrels of oil equivalent (BOE) of daily production volume, for wells upwind (leftcolumn) and downwind (right column) of the monitor. The bar plots show the number of monitor-days with exposure at least 1 BOE of daily production volume within each distance bin. Note that more monitor-days had exposure to production volume than preproduction wells.
D.J.X. Gonzalez, C.K. Francis, G.M. Shaw et al. Science of the Total Environment 806 (2022) 150298 8 non-attainment for PM2.5 throughout the study period (Environmental Protection Agency, 2021).
Findings from the current study indicate both primary emission and secondary formation of pollutants from upstream oil and gas production activities. However, identifying speci fic processes that resulted in ob- served pollutant emissions was outside the scope of the study.
4.1. Comparison to prior studies
Using proximity as a metric of exposure to upstream oil and gas pro- duction appears to adequately capture exposures to chemical contami- nants. Proximity-based methods, such as inverse distance weighting or estimating production activity within 1 km of receptors, have been used in prior population health studies to estimate acute or chronic exposure to wells. Thefive pollutants we examined in this study represent a subset of potential hazards associated with exposure to oil and gas wells, which may include other air pollutants as well as water and noise pollution (Adgate et al., 2014; Jackson et al., 2014). Recent studies from California have reported fugitive methane from idle and unplugged wells, as well as urban oil and gas infrastructure, which may correlate with emissions of benzene, toluene, ethylene, xylene, and other air toxics ( Lebel et al., 2020; Okorn et al., 2021). To differentiate risks conferred by air pollutants, population health researchers could utilize variations in wind direction.
Prior field studies have also found emissions of pollutants from up- stream oil and gas facilities. A 2018 study in Texas found high concen- trations of nitrous oxides and saturated hydrocarbons associated with oil and gas production in the Eagle Ford Shale ( Schade and Roest, 2018). Another recent study in Colorado, which combined in situ mon- itoring and cancer risk assessment, found higher exposure to benzene and other non-methane hydrocarbons (toluene, ethylbenzene, and xy- lene) and elevated risk of cancer and other adverse health outcomes with close proximity to oil and gas facilities (McKenzie et al., 2018). No- tably, the dataset in the current study did not include toluene, ethylben- zene, and xylene. Garcia-Gonzales et al. (2019a) found higher concentrations of VOCs downwind of a well site in Los Angeles. A study in Pennsylvania found that exposure metrics used in prior epide- miological studies were poorly correlated with observed pollutant con- centrations ( Wendt Hess et al., 2019 ). However, this study assessed exposure to wells at distances greater than 10 km, where we would not expect to detect increases in pollution, and the authors did not ac- count for meteorological factors that may affect pollutant concentra- tions (Buonocore et al., 2020).
In prior studies, Tran et al. (2020) and Gonzalez et al. (2020) used differing proximity metrics to assess exposure to upstream oil and gas production and adverse birth outcomes in California. For their analysis of production volume and adverse birth outcomes, Tran et al. used a similar exposure assessment method to the one we employed in the current study, assessing “high” exposure to births with >100 BOE within 1 km of the residence. In the current analysis, we modeled expo- sure to production volume continuously rather than categorically. We found substantial increases in concentrations of PM
2.5,N O 2,a n dO 3
with exposure to an additional 100 BOE within 1 km, indicating that the metrics employed by Tran et al. likely were effective in capturing as- pects of air pollution near active wells. Gonzalez et al. used inverse distance-squared weighting (IDW), a different approach that relies on the assumption that both density and proximity of wells confers risk of air pollution exposures. Notably, Gonzalez et al. (2020) conducted an exploratory analysis of the association between proximity to oil and gas wells, assessed using an IDW index, and concentrations of four pollutants (NO
2,O 3,P M 10,a n dP M 2.5). For that supplemental
analysis, Gonzalez et al. also used data from EPA Air Quality System for mean monthly concentrations of air pollutants and fit fixed effects linear regression models estimating the effect of “high” exposure to wells (the highest tertile of the IDW index). These authors observed substantially higher concentrations of PM 10 and PM 2.5, lower concentrations of NO 2, and no substantial changes for O 3; for all pollutants, effects. This indicates that the IDW method may be less effec- tive as an exposure metric for the air pollutants investigated in this study than the methods employed in the current study. Additionally, the approaches in both Tran et al. (2020) and Gonzalez et al. (2020) may not adequately capture exposure to secondary pollutants such as O3, which in the current study had higher concentrations several km downwind of wells.
4.2. Limitations and strengths
The current study had several limitations. We relied on daily changes in wind direction as a source of exogenous variation. On days with vari- able wind direction, estimating mean wind direction integrated over the course of the day could lead to exposure misclassification if, for example, wind blew from multiple directionsduring the course of a 24-h period.
Data for many pollutants that may be emitted during upstream oil and gas production operations are not routinely monitored and reported in the EPA Air Quality System. Consequently, the results of the current study likely reflect only a subset of pollutants potentially emitted from upstream oil and gas production. Population health studies referring to our estimates of chemical contaminant exposure should consider the pos- sibility of co-exposures to additional pollutants emitted during oil and gas production. We also did not have suf ficient data to investigate specific VOC constituents, which may be associated with particular health end- points of interest. Additionally, there were relatively few monitor-days with exposure to preproduction wells within 1 km. None of the monitors that measure concentrations of PM2.5 and VOCs were within 1 km of a preproduction well. We found evidence that drilling sites up within 1 to 3 km of air monitors increased PM2.5 concentrations, and concentrations of PM2.5 within 1 km of preproduction wells may be similar to or higher than our estimates for wells at 1-3 km. We did not expect to observe changes in VOC concentrations further than 1 km, as prior work has re- ported decay of VOCs within 100-200 m from well sites ( Garcia- Gonzales et al., 2019a; Zielinska et al., 2014). Because of this, we were un- able to make any inferences about the effect of preproduction activities on concentrations of VOCs.
In the primary analyses, we adjusted for exposure to wildfire smoke plumes to account for potential contributions of smoke to the pollutants of interest. Exposure was assessed as the number of overhead plumes for each monitor-day, but this method may not accurately indicate smoke conditions at ground level. A sensitivity analysis for PM 2.5 omitting smoke days from the analysis yielded similar results to the smoke-adjusted models, suggesting that our statistical adjustment for smoke plumes was sufficient.
For the analyses of wells in the production stage, data on total oil and gas production volume were available at the monthly level. Because of this constraint, in the exposure assessment we assumed that production occurred evenly throughout the month. This could lead to exposure misclassification if production was concentrated in certain days of the month. Future researchers building on these findings should consider obtaining daily production volume data, if possible. Finally, we were not able to differentiate between drilling or production methods (i.e., conventional vs. unconventional methods, such as hydraulic frac- turing), so we were not able to determine whether certain unconven- tional methods resulted in higher emissions.
Strengths of this study include the large panel dataset, comprising over 1 million daily observations from high quality air monitors with broad geographic and temporal variation. We were able to control for unobserved potential confounders through the study design, using wind as a plausibly exogenous source of variation uncorrelated to both upstream oil production and other sources of pollution. The mon- itor fixed effect accounts for average differences between monitoring lo- cations, such as from pollution sources unrelated to oil and gas.
Leveraging temporal variation from oil production activities and daily changes in wind direction accounts for other nearby pollution sources that are not both spatially collocated and temporally correlated with D.J.X. Gonzalez, C.K. Francis, G.M. Shaw et al. Science of the Total Environment 806 (2022) 150298 9 oil and gas production. Based on this analytic approach, we think there is unlikely to be residual confounding. Additionally, we conducted sev- eral tests to validate the robustness of the results.
5. Conclusion
We conducted a quasi-experimental study to examine whether up- stream oil and gas production results in emissions of ambient air pollut- ants. Adjusting for geographic, meteorological, seasonal, and time- trending factors, and leveraging daily changes in wind direction as an exogenous source of variation, we observed that proximity to oil and gas wells in both preproduction and production increased concentra- tions of PM 2.5, CO, NO 2,O 3, and VOCs at distances up to 4 km downwind of wells. These findings indicate that proximity to wells is an appropriate metric for air pollution-related exposures in population health studies. Notably, increases in PM
2.5 concentrations near wells
could be a mediating factor for previously reported increases in risk of adverse birth outcomes with proximity to wells in California ( Bekkar et al., 2020 ; Gonzalez et al., 2020 ; Tran et al., 2020 ). Further research on hazards associated with upstream oil and gas production would improve understanding of potential health and environmental risks.
Acute emissions of particular pollutants may be associated with specific steps of oil and gas preproduction or production, and more work is needed to determine if this is the case and, if so, which processes produce high emissions. Mitigating exposure to oil and gas wells would likely reduce exposure to ambient air pollutants.
Funding
This work was supported by the Ford Foundation Predoctoral Fel- lowship, the Vice Provost for Graduate Education at Stanford University, and the Stanford School of Earth, Energy, and Environmental Sciences.
The funding sources did not have any involvement in the design, con- duct, or writing of this study.
CRediT authorship contribution statement
David J.X. Gonzalez: Conceptualization, Data curation, Methodol- ogy, Formal analysis, Visualization, Writing – original draft, Writing – review & editing.Christina K. Francis:Data curation, Writing– original draft. Gary M. Shaw: Writing – review & editing.Mark R. Cullen:Meth- odology, Writing – review & editing. Michael Baiocchi: Methodology, Writing – review & editing. Marshall Burke: Conceptualization, Meth- odology, Supervision, Writing– review & editing.
Data availability
Data and code used in this analysis are available at https://github. com/djxgonzalez/cal-drilling-air-quality.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to in flu- ence the work reported in this paper.
Acknowledgements
We would like to thank Sam Heft-Neal and Anne Driscoll for assis- tance with the smoke plume data.
Appendix A. Supplementary data
Supplementary data to this article can be found online athttps://doi. org/10.1016/j.scitotenv.2021.150298.
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D.J.X. Gonzalez, C.K. Francis, G.M. Shaw et al. Science of the Total Environment 806 (2022) 150298 11 AN ABSTRACT OF THE DISSERTATION OF
Britton C. Goodale for the degree of Doctor of Philosophy in Toxicology presented on August 12, 2013.
Title: Developmental Toxicity of Polycyclic Aromatic Hydrocarbons: Defining Mechanisms with Systems-based Transcriptional Profiling
Abstract approved:___________________________________________________________________________________ Robert L. Tanguay
Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous in the environment as components of fossil fuels an d by -products of combustion. Defining toxicity mechanisms for this large family of multi-ring structures and substituted derivatives is a substantial challenge. Several PAHs, such as benzo(a)pyrene (BaP), are mutagenic, toxic to wildlife, and classified as probable carcinogens to humans. PAHs are present in the environment both in the gaseous phase as well as associated with particulates, and exposures occur via complex mixtures; combustion emissions contain PAHs along with many other contaminants. Cardiac dysfunction and adverse birth outcomes associated with exposure to airborne PAHs suggest that this family of compounds may have non -mutagenic biological activities that affect human health. Some PAHs exert toxic effects via binding the aryl hydrocarbon rec eptor (AHR), a ligand -activated transcription factor that mediates transcription of many downstream target genes, including cytochrome P450 metabolizing enzymes. Unlike planar halogenated hydrocarbons, such as 2,3,7,8 -tetrachlorodibenzo-p-dioxin (TCDD), PA Hs are readily metabolized by CYP1A, CYP1B1 and other enzymes, which create reactive intermediates and/or facilitate excretion. Mechanisms of PAH toxicity therefore include canonical AHR signaling, induction of oxidative stress, and other lesser -understood activities that do not require the AHR. We employed zebrafish as a model to rapidly assess developmental toxicity, global transcriptional responses and AHR activation in embryos exposed to parent and oxygenated PAHs (OPAHs). Using comparative analysis of mRNA expression profiles from microarrays with embryos exposed to benz(a)anthracene (BAA), dibenzothiophene (DBT) and pyrene (PYR), we identified expression biomarkers and disrupted biological processes that precede developmental abnormalities. These transcriptional responses were associated with PAH body burdens in the embryos detected by GC -MS. We found that uptake data w ere essential for discerning molecular pathways from dose -related differences, and identified two primary toxicity profiles. While BAA disrupted transcripts involved in vasculogenesis, DBT and PYR misregulated ion homeostasis and muscle-related genes. NfKB signaling was predicted to be involved in both responses, but canonical AHR signaling was only activated by BAA. In order to study the role of the AHR in mediating toxicity of PAHs, we developed an AHR2 mutant zebrafish line, which has a mutation in the transactivation domain of AHR2. We used AHR agonists TCDD and leflunomide as toxicological probes to characterize AHR activity in the mut ant line, and determined that the mutants were functionally null. Finally, we used AHR2 deficient zebrafish embryos to investigate mechanisms by which two four-ring OPAHs induced developmental effects. 1,9 benz-10-anthrone (BEZO) and benz(a)anthracene-7,12-dione (7,12-B[a]AQ) both caused malformations in developing embryos, but they differentially induced CYP1A expression. Despite this difference, the toxicity produced from both compounds was AHR2 -dependent. We used mRNA -seq to compare the transcriptional profiles of BEZO and 7,12 -B[a]AQ, and identified transcriptional networks that will be investigated further to determine how ligands differentially modulate AHR activity. We also discovered novel transcripts that are potentially important mediators of AHR t oxic effects.
Comparison across all five parent and OPAHs highlighted clusters of genes that, surprisingly, were similarly expressed in response to the OPAHs, DBT and PYR. These commonly-regulated transcripts may be important to consider when investigating toxicity of PAH mixtures. Together, these studies show that PAHs act via different transcriptional mechanisms, but can be categorized based on transcriptional profiles and differential AHR activation. The clusters of transcripts identified may be involved in common pathways; further investigation of transcription factors and coactivators that interact with mixexpressed genes is a promising area of research for elucidating diverse functions of the AHR.
©Copyright by Britton C. Goodale
August 12, 2013
All Rights Reserved
Developmental Toxicity of Polycyclic Aromatic Hydrocarbons: Defining Mechanisms with Systems-based Transcriptional Profiling
by
Britton C. Goodale
A DISSERTATION
submitted to
Oregon State University
in partial fulfillment of
the requirements for the
degree of
Doctor of Philosophy
Presented August 12, 2013
Commencement June 2014
Doctor of Philosophy dissertation of Britton C. Goodale presented on August 12, 2013.
APPROVED:
_________________________________________________________________________________________________________ Major Professor, representing Toxicology
_________________________________________________________________________________________________________ Head of the Department of Environmental and Molecular Toxicology
_________________________________________________________________________________________________________ Dean of the Graduate School
I understand that my dissertation will become part of the permanent collection of Oregon State University libraries. My signature below authorizes release of my dissertation to any reader upon request.
_______________________________________________________________________________________________________ Britton C. Goodale, Author
ACKNOWLEDGEMENTS
The completion of this dissertation has by no means been a solitary effort, and I am grateful to the many people who made it possible. I would like to express my sincere gratitude to my graduate mentor, Robert Tanguay, for giving me the opportunity to join th e lab and pursue what were, five years ago, fairly specific interests in toxicology. Thank you for sharing your endless excitement about new ideas, and for broadening my perspective and enthusiasm for this field of research more than I could have imagined. None of the work presented here would have been possible without the support I had during my time at OSU, and I am appreciative of the excellent resources that the Tanguay lab has provided. I would like to thank the many members of the Tanguay lab who hav e helped me over the years: Jane La Du, for her humor and for always being there to help in a pinch; Kate Saili, Tamara Tal, Lisa Truong, Galen Miller and Sumitra Sengupta for providing training in the lab, and for their mentorship and friendship; Andrea K necht, Mike Simonich, and Annika Swanson for their help and patience with the OPAH studies; Leah Wehmas, Derik Haggard, Siba Das and Cory Gerlach for editing, experiment assistance, and helping me through this final year with excellent company in the lab; Sean Bugel for sharing the office with the window and the challenge assay, providing thoughtful answers to my many questions, and for the excellent discussions; Jill Franzosa for encouragement and advice, both in the lab and on the climbing wall; and Marga ret Corvi for helping me keep perspective, making the OPAH body burden studies possible, and being an inspiration to work with. All of the studies in this dissertation were truly made possible by the SARL staff, and I am thankful to Carrie Barton and Cari Buchner for their dedication to the zebrafish colony, particularly the AHR2 mutants (they will some day express their gratitude, too, I’m sure). Thank you to Greg Gonnerman, Chapell Miller for screening help, and to all of the other staff who have kept SARL running.
I would like to thank others at OSU for their support; I am grateful to my committee members Daniel Sudakin, Michael Freitag, Kim Anderson , Barbara Taylor and Jeffrey Greenwood for their wisdom and guidance of this project. The Anderson lab welcomed me to OSU, and I can’t imagine m y time here without them. Thank you to the many folks who helped me with chemistry questions, compound requests, advised me on environmental relevance for my studies, and gave me good excuses to go to campus. I thank the EMT department for making my gradua te work possible, especially department head Craig Marcus for helping me accomplish many goals, and all of the EMT office staff for making things run smoothly.
I am especially grateful to my friends who, from near and far, supported me through every step of graduate school. Thank you for being my family when I arrived, exploring the west coast with me, and making Oregon home. Since graduate work feels, much of the time, like a process of many failures, successes in other arenas were an essential part of li fe the past five years. Thank you to everyone who buoyed my sanity with triumphant climbs, bikes, and foraging adventures. I am so thankful to Erik Endrulat, for his support through this endeavor, and for gently reminding me to think about balance. I was u nable to conduct a controlled study on the topic , but nevertheless attribute my perplexing decrease in flu susceptibility the past two years to the good food and abundance of laughter I’ve enjoyed with him. Most of all, I am forever grateful to my family, for their love and support through this entire journey, especially when I chose to move across the country. To my sister, Jane:
thank you for your incredible insight, and for your gift of finding the humor in whatever situation I presented you with. Thank you to my Dad, Steve, for teaching me to be a problem solver, and continually giving me new things to think about. To my Mom, Anne : thank you for your words of advice, even when I didn’t want to hear it, cheering for me always, and the steadfast assurance that I wasn’t doing this on my own.
CONTRIBUTION OF AUTHORS
Many people at Oregon State University and Pacific Northwest National Lab contributed to the studies presented in this thesis. In Chapter 2, Susan Tilton statistically analyzed the microarray data, conducted transcription factor enrichment/pathway analysis, provided guidance and training for other bioinformatics techniques employed, and contributed intellectually to the manuscript. Margaret Corvi contributed experiment planning guidance and assisted with all aspects of sample preparation for the PAH body burden studies. Glenn Wilson conducted GC-MS analysis of PAH body burden samples. Derek Janszen statistically analyzed the PAH developmental toxicity data. Kim Anderson and Katrina Waters b oth contributed training and intellectual guidance for the manuscript.
In Chapter 3, Jane La Du conducted outcrossing and screening for ahr2hu3335 carriers, and assisted with characterization and AHR knockdown studies. William Bisson conducted the in sili co AHR docking studies. Derek Janszen carried out statistical analysis of developmental toxicity data. Katrina Waters provided statistical expertise and contributed intellectually to the studies.
In Chapter 4, Susan Tilton provided bioinformatic support an d conducted transcription factor network prediction and enrichment analysis for biological functions associated with RNA-seq transcripts. Christopher Sullivan processed RNA -seq data, and provided bioinformatic core support for mapping, assembly, and statistical analysis of RNA-seq data.
TABLE OF CONTENTS
Page
Chapter 1 - Introduction ....................................................................................................................... 2 Human exposure to PAHs ..................................................................................................................... 3 Emerging concerns: cardiac function and effects during development ............................ 5 Mechanisms of PAH toxicity: activation of the aryl hydrocarbon receptor ..................... 6 Effects of PAH exposure on fish development ............................................................................. 8 Discovering endogenous functions of the AHR ......................................................................... 10 The zebrafish model ............................................................................................................................. 11 Substituted PAHs: adding complexity to toxicity evaluation ............................................... 12 Categorizing compounds, predicting PAH toxicity .................................................................. 13 Summary and study objectives ........................................................................................................ 14 References ................................................................................................................................................. 16 Chapter 2 - Structurally distinct polycyclic aromatic hydrocarbons induce differential transcriptional responses in developing zebrafish ................................................................. 25 Abstract ...................................................................................................................................................... 26 Introduction ............................................................................................................................................. 27 Methods ..................................................................................................................................................... 29 Results and discussion ......................................................................................................................... 34 Acknowledgements ............................................................................................................................... 49 References ................................................................................................................................................. 50 Chapter 3 - AHR2 mutant reveals functional diversity of aryl hydrocarbon receptors in zebrafish ............................................................................................................................................. 72 Abstract ...................................................................................................................................................... 73 Introduction ............................................................................................................................................. 74 Results ........................................................................................................................................................ 76 Discussion ................................................................................................................................................. 80 Materials and Methods ........................................................................................................................ 85 Acknowledgments ................................................................................................................................. 89 References ................................................................................................................................................. 89 Chapter 4 - Ahr-dependent developmental toxicity and differential transcriptional profiles induced by 4-ring oxygenated PAHs ........................................................................... 104 Abstract ................................................................................................................................................... 105 Introduction .......................................................................................................................................... 106 Materials and Methods ..................................................................................................................... 107 Results ..................................................................................................................................................... 112 Discussion .............................................................................................................................................. 122 Acknowledgements ............................................................................................................................ 127 References .............................................................................................................................................. 128 Chapter 5 - Discussion ...................................................................................................................... 168 References .............................................................................................................................................. 171 Chapter 6 - Future Directions ........................................................................................................ 172 Bibliography......................................................................................................................................... 174 Appendices ........................................................................................................................................... 187
LIST OF FIGURES
Figure Page Figure 1-1 Schematic diagram of the aryl hydrocarbon receptor pathway .................................. 22 Figure 1-2 Zebrafish early development ...................................................................................................... 23 Figure 1-3 Polycyclic aromatic hydrocarbon structures ....................................................................... 24 Figure 2-1 PAHs induce abnormalities in developing zebrafish ........................................................ 55 Figure 2-2 Body burdens of PAH detected in embryos .......................................................................... 56 Figure 2-3 Differentially expressed transcripts in PAH-exposed embryos ................................... 57 Figure 2-4 Direct comparison of PYR and DBT expression values ................................................... 58 Figure 2-5 Comparison of AHR, RELA and JUN regulatory networks .............................................. 59 Figure 3-1 Schematic diagram of predicted AHR2 protein in ahr2hu3335 zebrafish .................... 94 Figure 3-2 Fin and skeletal abnormalities in ahr2hu3335 zebrafish .................................................... 95 Figure 3-3 ahr2hu3335 embryos are resistant to TCDD developmental abnormalities ............... 96 Figure 3-4 ahr2hu3335 embryos are resistant to TCDD-induced CYP induction ............................ 97 Figure 3-5 Molecular docking of TCDD and Leflunomide in zebrafish AHR isoforms .............. 98 Figure 3-6 CYP1A protein expression patterns ........................................................................................ 99 Figure 4-1 Structures of BEZO, 7,12-B[a]AQ and BAA ........................................................................ 132 Figure 4-2 Developmental toxicity of 7,12-B[a]AQ and BEZO at 120 hpf ................................... 133 Figure 4-3 Cyp1 expression and morphology in OPAH-exposed embryos at 48 hpf ............. 135 Figure 4-4 Heatmap of transcripts significantly induced by BAA exposure at 48 hpf. .......... 138 Figure 4-5 Comparison of transcripts induced by BEZO and 7,12-B[a]AQ exposure ............ 139 Figure 4-6 qRT-PCR analysis of OPAH targets in control and AHR2 morphants ..................... 142 Figure 4-7 Biological processes affected by BEZO and 7,12-B[a]AQ exposure ......................... 144 Figure 4-S1 Comparative expression across 5 PAH structures ....................................................... 149
LIST OF TABLES
Table Page Table 2-2 Comparison of malformations induced by PAH treatments ........................................... 61 Table 2-3 Biological functions affected by PAH exposure at 24 hpf ................................................. 62 Table 2-4 Biological functions affected by PAH exposure at 48 hpf ................................................. 64 Table 2-4 (Continued) .......................................................................................................................................... 65 Table 2-4 (Continued) .......................................................................................................................................... 66 Table 2-S1 Primer sequences used for qRT-PCR ...................................................................................... 67 Table 2-S2 Percent recovery by GC-MS for PAH body burden studies ............................................ 68 Table 2-S4 Gene expression values detected by microarray and qRT-PCR .................................. 69 Table 2-S5 Predicted transcription factors for PAH responses .......................................................... 70 Table 3-1 Primer sequences for PCR experiments ............................................................................... 100 Table 3-2 Concentration responses for developmental effects ....................................................... 101 Table 3-3 Predicted binding energy values for zebrafish AHR2, AHR1B and AHR1A ........... 102 Table 3-4 Summary of zebrafish AHR ligand binding, activity and expression ........................ 103 Table 4-1 Cross-platform comparison of expression data for genes differentially induced by PAHs ......................................................................................................................................................................... 147 Table 4-2 Biological processes misregulated by OPAHs .................................................................... 148 Table 4-S1 Primers used for qRT-PCR analysis ..................................................................................... 150 Table 4-S2 Sequencing mapping .................................................................................................................. 151 Table 4-S3 7,12-B[a]AQ significantly misexpressed transcripts .................................................... 152 Table 4-S4 BEZO significantly misexpressed transcripts .................................................................. 160
DEDICATION
This dissertation is dedicated to my grandparents, Edith and John Artz, Walter Goodale, and Garrett and Pearl Morrell, whose love and support of all educational and creative endeavors have been incredible gifts to me.
Developmental toxicity of polycyclic aromatic hydrocarbons: defining mechanisms with systems-based transcriptional profiling
2 Chapter 1 - Introduction
Combustion of fuels for transportation, heating and industrial activities produces volatile and fine particulate matter emissions that decrease air quality and contribute to environmental contamination, particularly in ur ban areas. Polycyclic aromatic hydrocarbons (PAHs), a group of chemicals comprised of multiple fused benzene rings, are formed from these combustion (pyrogenic) processes and are components of fossil fuels (petrogenic sources). Environmental samples contai n a diversity of parent PAHs, which differ in the number and arrangement of rings, as well as substituted (a lkyl-, nitro-, amino-, and oxy -) structures (Ciganek et al. 2004) . PAHs are contaminants of extant concern because of their carcinogenic properties and ubiquity both in high -population areas and hazardous waste sites (Schoeny 1993; Collins et al. 1998). Recently, PAH exposure has been associated with non-cancer health effects such as immune system deficiency, cardiovascular disease, low birth weight, neural tube defects and learning deficits in children (Burstyn et al. 2005; Choi et al. 2006; Hertz-Picciotto et al. 2008; Perera et al. 2009; Lee et al. 2011; Ren et al. 2011) . Both pyrogenic and petrogenic PAHs are ubiquitous in the natural world, but anthropogenic activities such as automobile combustion, fossil fuel burning, oil refining and coal tar seal coating have contributed to increasing concentrations in local environments (Mahler et al. 2005; Polidori et al. 2010; Van Metre and Mahler 2010) . Humans and wildlife are inveterately exposed to this family of compounds; however, certain environmental conditions, occupational settings, residential combustion practices and dietary habits are associated with increased disease risk that compelled the inclu sion of 16 PAHs in the EPA list of priority pollutants (EPA 2012).
PAH-containing mixtures such as soot, coal tar mixtures and tobacco smoke have long been known carcinogens in humans (Bostrom et al. 2002). Animal studies have provided data on the ability of individual PAH structures to induce tumors, as well as shed light on the mechanisms by which PAHs induce toxicity. Carcinogenic mechanisms of Benzo(a)pyrene (BaP), a commonly detected PAH classified as a probable human carcinogen, have been well-studied in a variety of animal and cell models. In order to estimate cancer risk for environmental mixtures, which contain multiple PAH structures, the US EPA employed a Toxic Equivalency Factor (TEF) approach with available data to rank seven PAHs for
3 potency a s carcinogens in comparison to BaP (Schoeny 1993) . Since then, potency equivalency factors and risk assessments have been published for a wider range of PAHs, and the EPA has provided a draft document on the development of a relative potency factor (RPF) a pproach for PAH mixtures (Collins et al. 1998; Bostrom et al. 2002; EPA 2010) .
Assessment of health risk from PAH exposure is driven primarily by carcinogenicity data for parent PAHs, as data is limited and uncertainty surrounds the toxicity endpoints of n on- carcinogenic PAHs (Jennings 2012)
Determining the contribution of individual PAHs to the increased risk of morbidity such as heart attack, asthma and low birth weight remains a daunting challenge, as the mechanisms by which PAHs may cause these effects are not defined. Though PAHs are not new to the human exposure paradigm, the recent epidemiological associations of PAH exposure with multiple diseases, as well as increased ability to detect a broad spectrum of substituted PAHs in environmental samples, i s cause to consider them emerging contaminants of concern to human health. The goal of this work is to investigate biological activity and group PAHs based on proposed molecular mechanisms, providing an important step towards understanding the mechanisms b y which this diverse family of chemicals affects the health of humans and wildlife.
Human exposure to PAHs
Exposure to PAHs in the general population occurs primarily via inhalation of aerosols or fine particulate matter from smoke (combustion emissions as well as cigarette), and through ingestion of smoked or grilled meats, fish and charred foods. Dermal exposures can also occur from exposure to petroleum products, and accidental ingestion of PAHs through house dust is a concern for small children (Ramesh et al. 2004) . Cigarette smoke is a primary source of PAHs for individuals who smoke or are exposed regularly to second-hand smoke. Several studies have shown that for non -smoking individuals, the largest contributor to PAH exposures is the diet (Menzie et al. 1992) . This is true particularly for the higher molecular weight (and more car cinogenic) PAHs, which are less volatile and associate with particulate matter. Deposition of PAH particles on crops also contributes PAHs to the diet and may be of concern for agricultural sites located near major industrial areas or roadways.
4 In urban environments, a irborne carcinogenic PAHs are predicted to increase lung cancer risk. The low molecular weight PAHs partition into the volatile fraction of air, while higher molecular weight PAHs associated with small particulate s can travel deep into the lungs (Ramirez et al. 2011) . PAHs are lipophilic so upon exposure, they are readily absorbed by organisms. However, t hey can also be metabolized, which complicates measurem ent of exposure, as well as their toxic effects. PAH metabolites are detected in urine, where 1 - hydroxypyrene (1-OHP) is a commonly measured biomarker of PAH exposure (Hansen et al. 2008). High 1-OHP levels are detected in coke oven and aluminum smelter w orkers, as well as residents living near industries with high PAH emissions, such as coal fired power plants (Hu et al. 2011) . Increased levels of PAH metabolites are observed in children in polluted areas. For instance, 6-7 year old children who attended an elementary school near a heavily trafficked road in Guangzhou, China had higher levels of PAH metabolites in their urine than children who attended a school farther from large roadways (Fan et al. 2012).
Reactive PAH metabolites form adducts with DNA, R NA and protein. Adduct formation is part of the toxic mechanism of many PAHs, leading to DNA damage and mutations, but can also be used as a measure of exposure (Baird et al. 2005) . Human PAH exposures can be monitored by detection of PAH -DNA adducts in wh ite blood cells and other tissues. These biomarkers of PAH exposure have been associated with various cancers and other health endpoints such as reduced fetal growth (Kriek et al. 1998; Tang et al. 2006) . Many studies estimate PAH exposure by monitoring PAHs in air, either from point sources or personal air monitors. While these studies do not determine internal PAH dose, they provide exposure information for a more complete spectrum of PAHs. Background ranges of PAHs are reported at 0.02 -1.2 ng/m 3 in rural areas and 0.15 -19.3 ng/m 3 in urban air; average total exposure in the U.S. has been estimated at 3 mg/day (Mumtaz and George 1995) . Much higher PAH concentrations occur in major cities, occupational settings such as petroleum and dye industries, and in ho mes where low-efficiency fuels are used indoors . A number of studies associate increased cancer risk with these exposures. PAHs associated with small particulate matter measured at a school in Delhi, India were predicted to cause an Incremental Lifetime Ca ncer Risk of 3.18 x 10 -6, which is higher than the acceptable risk level of 10 -6 (Jyethi et al. 2013) . A study of air control measures implemented during the Beijing Olympics found that controlling emissions could substantially decrease risk of
5 excess cancer cases (Jia et al. 2011) . Few studies have directly predicted effects of reduced emissions on other health endpoints. Research suggests, however, that reducing PAH exposure may have many other positive implications, such as reduced inflammatory and vascular disease.
Emerging concerns: cardiac function and effects during development Multiple studies have shown increased risk of cardiac dysfunction with exposure to fine (pm 2.5) particulate matter, which contains PAHs along with many other contaminants. A smaller set of studies have specifically investigated relationships between PAHs and cardiac function. Occupational exposure to PAHs in asphalt workers is associated with an increased risk of fatal ischemic heart disease (Burstyn et al. 2005) . Higher 1 -OHP levels were associated with decreased heart rate variability in boilermakers and coke oven workers, suggesting an acute effect of PAH exposure on cardiac autonomic function (Lee et al. 2011; Li et al. 2012) . In myocardial infarction survivors, an associa tion was observed between exposure to particulate matter and symptoms of cardiovascular disease (Kraus et al. 2011).
Analysis of National Health and Nutrition Examination Survey data also identified a higher prevalence of peripheral arterial disease in sub jects with greater than average fluorene and phenanthrene metabolites (Xu et al. 2013). These smaller PAHs are generally present in the gas phase of emissions, and can be more prevalent than the larger PAHs (Bostrom et al. 2002). Reducing exposure to part iculate air pollution has been shown to improve cardiovascular health of patients with heart disease (Langrish et al. 2011).
While it is difficult to discern effects of PAHs from other co -occurring contaminants in combustion-related exposures, epidemiological studies collectively suggest that PAHs affect cardiac function and increase risk of cardiac -related injury. A number of studies in animals have supported these associations. BaP exposure increased atherosclerosis and disrupted gene expression in the a ortas of mice, as well as altered blood pressure patterns in rats (Jules et al. 2012; Kerley-Hamilton et al. 2012). In utero exposure to BaP also caused cardiac dysfunction later in life in rats (Jules et al. 2012) . PAH exposure similarly affects cardiac function in fish, and developmental exposure to PAHs causes cardiac defects in developing zebrafish (Incardona et al. 2004) . While epidemiological studies suggest that smaller PAHs (2-3 rings) are associated with cardiac toxicity, few studies in mammalian m odels have investigated the effects of these individual compounds on heart development. Studies in
6 zebrafish, however, have identified structure -related differences in the mechanisms by which these compounds affect the heart (Incardona et al. 2011).
The ef fects of PAH exposure on vascular function and inflammation may have increased impact during embryonic development. Adverse pregnancy outcomes, such as low birth weight, are associated with living near major roadways and in other areas with high vehicle emissions (Wilhelm et al. 2012) . Oxidative stress is believed to play a role in this toxicity, and was supported by a study that showed dietary vitamin C reduced risk of reduced fetal growth associated with BaP exposure (Duarte-Salles et al. 2012) PAH exposure in rodents decreases vascularization in the placenta, and studies in fish embryos demonstrate PAHs disrupt molecular pathways important for proper heart formation (Rennie et al. 2011) .
While neural tube defects, asthma, and learning deficits are also as sociated with PAH exposure in epidemiological studies, the mechanisms by which PAHs may interfere with developmental processes, and the individual PAHs responsible, remain to be elucidated (Perera et al. 2009; Ren et al. 2011).
Mechanisms of PAH toxicity: activation of the aryl hydrocarbon receptor PAHs exhibit varied non -genotoxic activity, which can not only contribute to their carcinogenicity but also mediate multiple other toxic effects. Some PAHs, including BaP, can cause toxicity by binding the aryl h ydrocarbon receptor (AHR), a ligand -activated member of the basic helix -loop-helix Per -ARNT-Sim (bHLH -PAS) family of transcription factors.
Ligand binding induces dimerization with the Ah receptor nuclear translocator (ARNT), translocation to the nucleus, and alter ation of gene transcription, including cytochrome P450 phase 1 ( CYP1A, CYP1B1 ) and phase 2 ( UGT1A6, ALDH3A1 ) metabolizing enzymes (Figure 1) (Nebert et al. 2000; Sartor et al. 2009) . While parent PAHs are generally unreactive, metabolism by CYP1A a nd other metabolizing enzymes forms more reactive metabolites such as PAH epoxides and radical cation intermediates (Cavalieri and Rogan 1995). These reactive compounds can cause cellular damage by forming DNA and protein adducts. Additionally, t hey can a ctivate redox -responsive genes containing antioxidant response elements (AREs), including phase II metabolizing enzymes (Bock 2012). Further metabolism by hydroxylases forms hydroxy -PAHs, and glucoronidases and sulfotransferases conjugate these oxygenated PAHs, facilitating their excretion. Because of this complex process involving multiple intermediates, metabolism induced by AHR
7 activation can increase toxicity or allow for excretion, depending on the PAH structure. In the case of BaP, carcinogenicity in mice is dependent on a functional AHR (Shimizu et al. 2000). The AHR pathway and cytochrome P450 enzymes are conserved among vertebrates.
Differences exist, however, in affinity of compounds for AHR s between species, as well as within populations, which a ffects their carcinogenicity and potency as toxicants (Hahn 2002; Wirgin et al. 2011) . The work presented i n this thesis focuses on non -carcinogenic mechanisms. However, the large body of research on mechanisms by which BaP interacts with the AHR to cause cancer has contributed greatly to our understanding of AHR function.
While the molecular signaling pathways by which BaP induces its diverse array of toxicological effects are not fully elucidated, BaP toxicity is known to be mediated by AHR activation in many species. Beyond this, the ability of multiple CYP enzymes, which vary between tissue types, individuals and species, to metabolize PAHs creates a complex array of toxicological profiles.
In addition to toxicological effects caused via AHR induction a nd the formation of reactive PAH intermediates, sustained activation of the AHR leads to a number of other adverse effects. These have been well-characterized in studies with halogenated hydrocarbons, such as 2,3,7,8 -tetrachlorodibenzo–p-dioxin (TCDD) and polychlorinated biphenyls (PCBs), which have high affinity for the AHR but are not readily metabolized. AHR activation during development causes teratogenic effects in rodents and fish, which include malformations in the heart and jaw. AHR activation addit ionally causes a wide range of neurologic, immune and reproductive effects (reviewed in (White and Birnbaum 2009) ). A TEF approach has been employed by the World Health Organization (WHO) to rank the potency of halogenated hydrocarbons for assessment of he alth risk to humans and wildlife (Van den Berg et al. 2006) . In contrast to the TEFs for PAHs, which are based on carcinogenic potency, TEFs for dioxin -like compounds are determined from a variety of AHR -mediated endpoints, including chronic toxicity, enzyme induction, tumor promotion and lethality, and generally correlate with ligand affinity for the receptor.
Because AHR activation by dioxin -like compounds leads to transcriptional activation of CYP1A, the enzyme has been widely used as a biomarker of AHR activation. This tool has been particularly useful for investigating exposure to dioxin -like compounds in wild
8 populations (Hahn 2002; Sarkar et al. 2006; Jensen et al. 2010) . As interactions of the AHR such as ligand and DNA binding have been characteriz ed, many other assays have also been developed to identify AHR -activating compounds. Several assays screen for AHR activators using dioxin responsive elements (DREs) in promoters driving reporter genes such as luciferase or GFP. The chemical activated luci ferase gene expression (CALUX) assay is widely used to screen compounds for AHR activation activity (Murk et al. 1996) . Several in silico models of the AHR ligand binding pocket have also been created to predict binding and screen for alternative AHR ligan ds. For example, a chemical library screen and an in silico AHR molecular docking study identified leflunomide, a rheumatoid arthritis drug, as an AHR agonist, which was confirmed in human, mouse and zebrafish (O'Donnell et al. 2010). PAHs that have high a ffinity for the AHR induce AHR -mediated toxicological effects similar to those caused by exposure to dioxin -like compounds. Because of their structural diversity and aforementioned metabolism, however, PAHs have a wide range of additional biological activities that complicate the interpretation of CYP1A activity as a biomarker for exposure and toxic effects. Some PAHs, such as fluoranthene, inhibit CYP1A, and can lead to synergistic toxicity in fish embryos when combined with other PAHs (Billiard et al. 200 6; Timme-Laragy et al. 2007) . Other conditions, such as hypoxia, can also inhibit CYP1A induction (Fleming and Di Giulio 2011) . Finally, CYP1A induction can be elevated by PAHs (such as chrysene) in the absence of other signs of toxicity (Incardona et al. 2006). Because of these complex interactions, AHR affinity on its own is not a sufficient predictor of PAH toxicity.
Effects of PAH exposure on fish development
PAH exposure causes developmental abnormalities in fish embryos, including pericardial and yol k sac edema, disrupted cardiac function, craniofacial and spinal malformations, anemia and reduced growth, which have been described in many studies addressing toxicity of PAH mixtures to wild fish populations (Barron et al. 2004) . Many of these effects ar e similar to those described for planar halogenated compounds (PCBs). Fish are exposed to high levels of PAHs from events such as oil spills, from runoff, and from sites contaminated by industrial activity. Research on fish populations exposed to PAHs has contributed to our understanding of the A HR, its crosstalk with other signaling pathways and the complex mechanisms of PAH toxicity. While dioxin-like compounds are toxic to marine life, there are
9 several examples of fish populations adapted to live in hea vily contaminated sites (Nacci et al. 2009; Bugel et al. 2010) . These phenotypes provide fascinating information about interactions between pathways that facilitate adaptation to the external environment; many of the adaptive mechanisms have yet to be full y determined. A study of Atlantic tomcod in the Hudson River found that populations resistant to contaminants had a deletion in AHR2 that rendered it non -functional (Wirgin et al. 2011) . Atlantic killifish from a heavily contaminated wood treatment facilit y site on the Elizabeth River, VA are exposed to high concentrations of PAHs and exhibit higher levels of DNA damage compared to fish from a reference site with low PAH levels (Wills et al. 2010; Jung et al. 2011) . Their embryos are resistant to developmen tal defects induced by PAHs and PCBs, and their adaptive phenotype highlights the complexity of AHR regulation. PCB 126, a dioxin -like compound that is not readily metabolized, does not induce Cyp1a expression in these embryos, while PAHs Ba P and benzo(k)f luoranthene (BkF) induce Cyp1a (though at lower levels than reference site embryos). This suggests that ligands can interact differently with the A HR and/or other transcription factors to induce Cyp1a expression both in the presence and absence of overt toxicity.
Research on the effects of crude and weathered oil in various fish species has also contributed to our understanding of biological effects of PAHs. In adult fish, narcosis has been described as a toxicological endpoint of exposure to low molecular weight PAHs, and is characterized by loss of balance, lethargy and decreased respiration which is reversible but can result in death during prolonged exposure to high concentrations (Vanwezel and Opperhuizen 1995) . In fish embryos, however, three ring PA Hs disrupt heart function, causing arrhythmia and eventual heart failure (Incardona et al. 2004) . Studies from a number of labs have demonstrated that PAHs induce malformations in embryos via different mechanisms, depending on their structures. 3 -ring PAHs induce cardiac toxicity with a suggested mechanism of ion channel disruption (Incardona et al. 2004), Many 4 and 5-ring PAHs bind the aryl hydrocarbon receptor, causing cardiac toxicity along with other effects (Incardona et al. 2006) . Because of the diff erent mechanisms, predicting toxicity of PAH mixtures remains a challenge. A need for studies that clarify the biological effects of low molecular weight PAHs has been recognized in order to better understand the effect of PAH contamination in aquatic ecos ystems (Hylland 2006) . Characterizing the different
10 mechanisms of PAH developmental toxicity is an important first step in predicting toxicity of mixtures, both in aquatic systems and human populations.
Discovering endogenous functions of the AHR
As previously discussed, the AHR pathway has been well -studied and plays a central role in the toxicological mechanisms of many PAHs. Despite over 30 years since its discovery as the receptor responsible for mediating TCDD toxicity, characterization of endogenous functions of the receptor remains a rapidly developing area of research. PAS (Per -ARNT-Sim) family transcription factors, of which the AHR is a member, are involved in a variety of physiological processes including circadian rhythms and oxygen sensing (McIntosh et al. 2010). The AHR dimerization partner required for transcriptional activation, ARNT (also known as Hif1b), is a member of the Hypoxia Inducible Factor (HIF) family of proteins. AHR binding partners such hsp90 and p300 also interact with other transcription factors (Beischlag et al. 2008) . Many studies have demonstrated AHR crosstalk with other transcriptional regulators such as NFkB, ER and GCR via direct or indirect mechanisms (Puga et al. 2009) . A handful of studies have also shown AHR dimeri zation with other proteins, such as klf6, and activation of other downstream genes via an alternative recognition sequence (Wilson et al. 2013) . This propensity to interact with other transcription factors supports the notion that AHR -mediated developmental toxicity may be, at least in part, caused by disruption of endogenous functions mediated in concert with other interacting proteins. This is supported by studies that have demonstrated that Cyp1a and other metabolic genes highly induced by AHR are not responsible for the toxic effects of TCDD (Antkiewicz et al. 2006). The means by which AHR activation leads to downregulation of genes, in particular, is not well -defined. A study of transcriptional binding of the A HR with and without exogenous ligand identi fied a large number of genes involved in developmental and vascular processes that were bound by the A HR in the absence of TCDD and BaP. Upon ligand binding, the targets shifted to genes involved in xenobiotic metabolism (Sartor et al. 2009) . Along the sam e lines, recent studies have identified roles for the AHR in a myriad of processes, including progenitor cell expansion and differentiation (Smith et al. 2013) . It would appear that many functions of the AHR in normal development have yet to be discovered.
11 AHR knockout mouse strains developed by three different groups illustrate the importance of the AHR in normal liver development and immune function, and continue to expand understanding of the receptor’s role in both toxicological responses and normal phy siology (Fernandez-Salguero et al. 1995; Schmidt et al. 1996; Lahvis et al. 2005) . Because their development can be observed non -invasively, fish have provided much insight into mechanisms of AHR-mediated toxicity during development. Three AHR isoforms hav e been identified in zebrafish: AHR1A, AHR1B, and AHR2 (Tanguay et al. 1999; Andreasen et al. 2002; Hahn 2002; Karchner et al. 2005) . Numerous studies with known AHR ligands, however, have identified AHR2 as the primary mediator of early life stage toxicol ogical effects in zebrafish (Prasch et al. 2003; Teraoka et al. 2003; Antkiewicz et al. 2006) . Other genes, including foxq1a and sox9b have been uncovered as mediators of TCDD -induced effects on jaw and heart development (Xiong et al. 2008; Planchart and M attingly 2010; Hofsteen et al. 2013) . The ability to transiently knock down genes in zebrafish has enabled study of the roles of these genes during development. Knockdown of AHR2 has also highlighted crosstalk between the AHR pathway and NRF2, which provid es protection against oxidative stress induced by PAHs (Timme-Laragy et al. 2009) . While AHR2 knockdown is able to rescue TCDD -induced toxicity, it does not completely prevent Ahr2 activity, and is a transient effect. An AHR2 knockout zebrafish would therefore greatly expand capability to investigate biological functions of the receptor during development and throughout the lifespan.
The zebrafish model
The zebrafish is an excellent system in which to pursue mechanisms of toxicity during development, which is rapid. By 5 days post -fertilization all organ systems are functional (Figure 2)(Kimmel et al. 1995; Saili 2012) . Development external to the mother allows for non-invasive observation and imaging over the course of development. Additionally, environmental factors such as chemical exposure can be meticulously controlled. A fully sequenced genome with ever -increasing annotation allows for investigation of genetic targets of interest. Genes can be specifically targeted with antisense oligos (morpholinos) to transiently knock down expression during development. Many transgenic zebrafish lines are available for studies. The small size of zebrafish makes them adaptable to development in 96 well plates and amenable for large scale genetic screens. Forward and r everse genetic
12 screens have been employed to identify thousands of mutants with specific mutations and phenotypes. More recently, zinc finger and TALEN technologies have enabled the creation of targeted knockouts in zebrafish. Chemical screens can be condu cted in zebrafish with amounts of chemical comparable to cell-culture stud ies, allowing for rapid screening of large numbers of chemicals in vivo. Recent development of high-throughput technology has enabled much -needed investigation of compounds that are detected in environmental samples but lack toxicity data.
Substituted PAHs: adding complexity to toxicity evaluation PAHs in the environment exist not only as parent PAH structures, but as substituted derivatives such as oxygenated PAHs (OPAHs). Because substitutions occur via both biotic and abiotic processes, it is expected that concentrations of substituted PAHs may be higher than parent PAHs under certain conditions. As advances in detection methods allow for quantification of a wider range of compoun ds that constitute exposure paradigms, there is an accompanying need for toxicity data in order to assess health risks. The large number of substituted PAH structures poses a significant challenge; identifying groups of structures that behave similarly wou ld help the prioritization of studies to determine toxicological mechanisms of these compounds, and eventually improve predictive capability for modeling toxicity of PAHs and mixtures. Our laboratory conducted a toxicity screen for developmental toxicity a nd Ahr activation of 38 oxygenated PAHs (OPAHs) in zebrafish embryos (Knecht et al. 2013) . Embryos were statically exposed from 6 to 120 hours post fertilization (hpf) to 0.8 – 500 uM concentrations of OPAH in embryo media, then screen ed for malformations. For each compound, embryos exposed to a concentration that caused ~80% malformations but not mortality were examined for Cyp1a expression with immunohistochemistry. We found that OPAHs induced toxicity at a wide range of concentrations. Some observations could be made based on structure; several quinones (1,4-naphthoquione, phenanthrene-quinone, 1,2-napthoquinone) had steep dose -response curves and caused mortality at concentrations < 4 μM. Cyp1a expression was not observed at these concentrations (Knecht et al. 2013) . A substantial group of compounds caused malformations such as pericardial edema and yolk sac edema at concentrations < 20 μM.
Some of these, such as benzo(a)fluorenone, phenanthrene -1,4-dione, and benz(a)anthracene-7,12-dione, induced Cyp1a expression in the vasculature. Many,
13 including 1,9-benz-10-anthrone and 9-hydroxybenzo(a)pyrene, induced malformations but no Cyp1a expression. A large number of PAHs did not induce malformations below 20 μM.
Of these, some induced Cyp1a expression that was specifically expressed in the liver at higher concentrations. We also observed interesting patterns of Cyp1a expression in the lateral line of zebrafish exposed to oxygenated napthalenes. The differential Cyp1a expression patterns observed with OPAHs that were similarly toxic suggested differential involvement of the aryl hydrocarbon receptor.
Categorizing compounds, predicting PAH toxicity
In the studies presented her e, I used whole genome microarrays to identify transcriptional profiles of developmental toxicity induced by three parent PAHs and two OPAHs in zebrafish. Differential proposed mechanisms of dibenzothiophene (DBT), pyrene (PYR) and benz(a)anthracene (BAA) – induced developmental toxicity in zebrafish embryos are discussed in Chapter 2. I examined PAH body burdens following exposures and found large differences in the amount of PAH in embryos. Measuring uptake was import ant for discerning dose -dependent differences from biological mechanisms. Expanding the investigation of different PAH structures, I chose two 4 -ring OPAHs, benz(a)anthracene - 7,12-dione (7,12-B[a]AQ) and 1,9-benz-10-anthrone (BEZO) from the OPAH toxicity s creen conducted in our laboratory to further investigate transcriptional profiles. 7,12-B[a]AQ is an oxygenated derivative of parent PAH BAA, while BEZO is a mono -oxygenated PAH with a different ring arrangement. In Chapter 4, I investigated the role of AHR2 in mediating the toxicity of these two compounds.
A handful of other studies have compared transcriptional profiles across several PAH structures in other model systems, with a similar goal of identifying biomarkers of exposure and increasing toxicity prediction capability for this class of compounds. A study of gene expression changes induced by 3-4 ring PAHs in leukemia (THP-1) cells identified groups of PAHs that induced similar gene expression profiles. Predictor genes were identified, which included calcium binding proteins, transcription factors, immune response and genes with oxidoreductase activity (Wan et al. 2008) In a similar study in liver hepatoma cells (HepG2) gene expression profiles were determined from cells exposed to 2 -5 ring PAHs (Song et al. 2012). Transcriptional signatures were compared between known carcinogens and non - carcinogens. Interestingly, clustering did not predict known carcinogeni city; however,
14 known carcinogens induced genes involved in oxidative stress, while non -carcinogenic PAHs such as fluoranthene did not. An interesting study of human lymphocytes identified a small number of genes that were significantly differentially expressed in PAH -exposed coke oven workers (Wu et al. 2011) . These genes were involved in metal io n binding and transport, and included myosin XVB and solute carrier family 25 member 34. These studies have begun to identify biomarkers and propose mechanisms for diverse PAH structures, including the non-genotoxic PAHs. However, the majority of these studies have been carried out in cell lines, and none have explored PAH toxicity in developing embryos. Our in vivo approach provides a unique set of data that can be used to group PAHs based on a large set of genes that are important during early development . The availability of an annotated genome and high homology of genes between vertebrates allows us to compare biomarkers and eventually validate across species. Global transcriptional analysis of PAH exposure in multiple systems will create a powerful dat aset from which to identify biological mechanisms associated with structural differences. The overarching objective of the studies presented here is to employ transcriptional profiling techniques to identify potential differential mechanisms by which PAHs induce developmental toxicity, and to further characterize the role of the aryl hydrocarbon receptor in biological pathways that are disrupted by PAH exposure.
Summary and study objectives
We hypothesized that PAHs induce developmental effects in embryos v ia distinct mechanisms that could be identified by comparative investigation of global changes in transcription that occur following chemical exposure. We compared the transcriptional profiles of 5 PAH structures that induce different malformation profiles in developing zebrafish (Figure 3). In Chapter 2, we used a whole genome mRNA microarray to compare parent PAHs which differentially activate the aryl hydrocarbon receptor, DBT, PYR and BAA.
Using concentrations that induced malformations but not mortali ty, we identified genes that were differentially regulated over time and in response to the three PAH structures.
PAH body burdens were analyzed at both time points, which was important for discerning dose-related differences from those that represented un ique molecular mechanisms. By analyzing functional roles of misregulated genes and their predicted regulatory transcription factors, we showed that the BAA response (A HR activated) could be
15 distinguished from regulatory networks disrupted by DBT and PYR ex posure (A HR not activated).
In chapter 3, we developed a zebrafish line with a mutation in ahr2 to enable deeper mechanistic investigation of the role of the AHR in PAH-induced toxicity. We characterized AHR activity in the mutant line using TCDD and lefl unomide as toxicological probes to investigate function, ligand binding and C yp1a induction patterns of paralogues AHR2, AHR1A and AHR1B. In these studies, I determined that ahr2hu3335 zebrafish are functionally null. We then further explored function of t he other zebrafish AHR paralogues, and demonstrated differential ligand binding and C yp1a expression patterns mediated by the three receptors.
Because the ahr2hu3335 line was developed from a founder identified in a screen of a mutant library generated by random mutagenesis (TILLING, Targeting Induced Local Lesions In Genomes), the line required multiple generations of outcrosses to reduce background mutations. Embryo production and quality was variable over the first three outcrosses, and limited our ability to conduct reliable studies of PAH -induced toxicity in the ahr2hu3335 line.
We investigated BAA, DBT and PYR -induced toxicity in ahr2hu3335 and ahr2+ zebrafish, and confirmed the differential involvement of the AHR in mediating toxicological effects (Appendix 1). Background malformations in these experiments were higher than normal, however, so we utilized an AHR2 morpholino to knock down expression in the remaining studies in this dissertation.
While BAA induced toxicity dependent on the AHR, DBT and PYR caused toxicity via other mechanisms. We conducted preliminary characterization of other phenotypes associated with these PAHs, with the goal of identifying more sensitive endpoints to assess for toxic effects. We observed a unique hyperactive phenotyp e in PYR -exposed embryos, and investigated this response over a wider concentration range. We also investigated whether localized inflammation could be visualized in the transgenic mpx:gfp zebrafish line, which expresses green fluorescent protein (driven b y myeloid-specific peroxidase promoter) in neutrophils. Preliminary data from these studies are presented in Appendix 2.
16 In Chapter 4, We explored the role of the AHR in mediating toxicity induced by 4 -ring OPAHs BEZO and 7,12 -B[a]AQ. We showed that despi te very different C yp1a expression profiles, both BEZO and 7,12 -B[a]AQ induced toxicity via AHR2. We used RNA -seq to compare transcriptional profiles induced by the OPAHs at 48 hpf, and identified potential novel targets of the AHR as well as intriguing me chanistic differences by which the AHR may interact with other transcription factors to differentially regulated target genes . We additionally compared sets of transcripts across both platforms (microarray and RNA -seq), and identified patterns of expression across all 5 PAH structures.
Together these studies show that PAHs act through multiple mechanisms that differentially involve the AHR to induce developmental toxicity. We identified clusters of transcripts involved in mechanisms, which can be further p ursued to unravel molecular targets of PAHs, as well as be utilized as biomarkers to begin to predict effects of additional PAH structures. These studies demonstrate the power of transcriptomics approaches for comparing toxicity pathways of structurally -related compounds, identifying biomarkers of toxic effects, and generating hypothes es to further mechanistic understanding of a large family of compounds
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22 Figure 1-1 Schematic diagram of the aryl hydrocarbon receptor pathway Upon entering the cell, some PAHs are bound by the aryl hydrocarbon receptor (AHR), which resides in the cytosol, and in its unliganded state is bound by 90 kDa heat-shock protein (HSP90), co-chaperone p23, and aryl hydrocarbon interacting protein (AIP). Upon ligand binding, the AHR translocates to the nucleus and dimerizes with the aryl hydrocarbon receptor nuclear translocator (ARNT). Together with other interacting proteins, such as CREB binding protein (CBP/P300), the AHR/ARNT heterodimer binds to aryl hydrocarbon receptor response elements (AHREs) in the genome and activates transcription of many genes. Genes directly activated by the AHR include phase I and II metabolizing enzymes such as cytochrome p4501A and 1B1 (CYP1A and CYP1B1). These enzymes metabolize PAHs to more reactive metabolites, which can be further metabolized and excreted, but can also cause toxicity by interacting with DNA and proteins. The battery of genes induced by the AHR includes the AHR repressor as well as other targets which may mediate PAH-induced toxicity. Binding of AHR to ARNT and localization to AHRE in response to ligand activation may additionally disrupt endogenous pathways, leading to toxic effects.
23 Figure 1-2 Zebrafish early development
Stages of zebrafish development from fertilization to 120 hours post fertilization (hpf) (adapted from Saili 2012).
24 Figure 1-3 Polycyclic aromatic hydrocarbon structures Compounds investigated in this dissertation include parent PAHs dibenzothiophene, pyrene, and benz(a)anthracene, and OPAHs 1,9-Benz-10-anthrone and benz(a)anthracene- 7,12-dione.
25 Chapter 2 - Structurally distinct polycyclic aromatic hydrocarbons induce differential transcriptional responses in developing zebrafish
Britton C. Goodale1, Susan C. Tilton2, Margaret M. Corvi1, Glenn R. Wilson1, Derek B. Janszen2, Kim A. Anderson1, Katrina M. Waters2 and Robert L. Tanguay1*
1.Department of Environmental and Molecular Toxicology, the Environmental Health Sciences Center, Oregon State University, Corvallis, OR,
2.Computational Biology and Bioinformatics, Pacific Northwest National Laboratory
Toxicology and Applied Pharmacology
Reprinted with permission
Elsevier B.V. © 2013 All rights reserved.
26 Abstract
Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous in the environment as components of fossil fuels and by -products of combustion. These multi -ring chemicals differentially activate the aryl hydrocarbon receptor (AHR) in a structurally d ependent manner, and induce toxicity via both AHR -dependent and -independent mechanisms. PAH exposure is known to induce developmental malformations in zebrafish embryos, and recent studies have shown cardiac toxicity induced by compounds with low AHR affin ity. Unraveling the potentially diverse molecular mechanisms of PAH toxicity is essential for understanding the hazard posed by complex PAH mixtures present in the environment. We analyzed transcriptional responses to PAH exposure in zebrafish embryos expo sed to benz(a)anthracene (BAA), dibenzothiophene (DBT) and pyrene (PYR) at concentrations that induced developmental malformations by 120 h post -fertilization (hpf). Whole genome microarray analysis of mRNA expression at 24 and 48 hpf identified genes that were differentially regulated over time and in response to the three PAH structures. PAH body burdens were analyzed at both time points using GC -MS, and demonstrated differences in PAH uptake into the embryos. This was important for discerning dose -related differences from those that represented unique molecular mechanisms. While BAA misregulated the least number of transcripts, it caused strong induction of cyp1a and other genes known to be downstream of the AHR, which were not induced by the other two PA Hs. Analysis of functional roles of misregulated genes and their predicted regulatory transcription factors also distinguished the BAA response from regulatory networks disrupted by DBT and PYR exposure. These results indicate that systems approaches can be used to classify the toxicity of PAHs based on the networks perturbed following exposure, and may provide a path for unraveling the toxicity of complex PAH mixtures.
Keywords: AHR; microarray; dibenzothiophene; pyrene; benz(a)anthracene; systems toxicology
27 Introduction
Polycyclic aromatic hydrocarbons (PAHs) are a diverse class of chemical s composed of multiple fused benzene rings, which originate from both petrogenic and pyrogenic sources and are ubiquitous in the environment. Many PAHs are biologically active, cause toxicity in a variety of organisms, and can adversely affect human health . Increasing PAH concentrations in the environment , particularly in urban areas, ha s been attributed to anthropogenic activities such as fossil fuel burning, automobile exhaust, oil refining and coal tar seal coating (Van Metre and Mahler 2005; Polidori et al. 2010; Van Metre and Mahler 2010). PAHs are present in the ultrafine particulate fraction as well as the gas phase of ambient air , and are considered carcinogenic components of cigarette smoke, vehicle exhaust, wood smoke and other emissions (Bostrom et al. 2002; Ramirez et al. 2011) . The primary routes of human exposu re are inhalation and ingestion. PAHs associated with ultrafine particulate matter can accumulate in the bronchial epithelium, while volatile PAHs are readily absorbed through the a lveolar epithelium (Ramirez et al. 2011) . For non - smoking individuals, ingestion via foods and unintentional consumption of hous ehold dust (of particular concern for young c hildren) is a primary contributor to PAH exposure (Menzie et al. 1992; Ramesh et al. 2004) . Seven non -substituted PAHs are considered possible carcinogens (group 2B) by the US EPA , and 16 PAHs are listed as priority pollutants because of their prevalence in urban and suburban air (EPA 2012) . PAH- containing coal tar mixtures are known to be carcinogenic in humans (International Agency for Research on Cancer) (Collins et al. 1998) . Human exposure to PAHs almost always occurs within complex mixtures, which may contain multiple PAHs and often include other chemicals such as halogenated hydrocarbons and metals . Because of complex exposure patterns, it is difficult to associate health effects in human populations with individual PAHs.
While the bulk of research o n PAHs has focused on mutagenic and carcinogenic properties, exposure to PAH mixtures and ultrafine particulate matter is associated with a n array of other health effects in humans, including immune system deficiency, cardiovascular disease and impaired development (Burstyn et al. 2005; Choi et al. 2006; Hertz-Picciotto et al. 2008; Lee et al. 2011; Ren et al. 2011) . Activation of the aryl hydrocarbon receptor (AHR) and generation of reactive oxygen species (ROS) are key modes of action initiated by some PAHs, but the full extent of the molecular responses that result from exposure to this
28 diverse set of compounds has not been characterized. A number of PAHs, including benzo(a)pyrene (BaP) and dimethylbenz(a)anthracene (DMBA) , bind the AHR and induce expression of phase I and II metabolizing genes, such as CYP1A1, GSTA1, NQO1 and UGT1A6, along with many other downstream transcripts (Guengerich 2000; Nebert et al. 2000) .
Activation of the AHR pathway and metabolism of PAHs can result in a protective effect against PAH toxicity . In many cases, however, AHR activation and metabolism by CYP enzymes increase PAH reactivity and toxicity , which is consequential to the PAH , route of exposure, and exposure concentration (Nebert et al. 2004; Shi et al. 2010) . The low molecular weight PAHs (2 -3 rings) are generally poor AHR ligands and less potent carcinogens, but are often detected at higher levels in environmental samp les and human urine than their higher molecular weight counterparts (Durant et al. 1996; Naumova et al . 2002; Ciganek et al. 2004; Hecht et al. 2010).
Several studies have associated PAH exposure during pregnancy with adverse birth outcomes such as reduced fetal growth and neural tube defects (Choi et al. 2006; Ren et al. 2011). In rodents, exposure to BaP and DMBA induces abnormal vasculature in the placenta and interferes with fetal growth (Detmar et al. 2008; Rennie et al. 2011) . Developmental exposure to BaP also impairs cardiac function later in life (Jules et al. 2012).
In zebrafish embryos , BaP -induced cardiac toxicity is mediated by the aryl hydrocarbon receptor (AHR2) (Incardona et al. 2011) . However, other PAH structures induce cardiac toxicity and developmental effects via distinct mechanisms that are not AHR -dependent (Incardona et al. 2005) . Analyses of global mRNA transcriptional responses to individual PAH exposures demonstrate that structurally-distinct PAHs induce unique gene expression patterns in both human macrophage leukemia ( THP-1) cells and circulating leukocytes of rats (Wan et al. 20 08; Jung et al. 2011) . Little is known, however, about the toxicity pathways and molecular signatures of these diverse exposures during embryonic development.
We used whole genome mRNA microarrays to investigate transcriptional responses that lead to deve lopmental toxicity of three distinct PAHs in developing zebrafish.
Dibenzothiophene (DBT), pyrene (PYR) and benz(a)anthracene (BAA) all induce developmental abnormalities by 5 days post fertilization, but have different proposed
29 toxicity mechanisms. DBT (3 rings) induces cardiac toxicity that is independent of the AHR (Incardona et al. 2004). BAA (4 rings) induces Cyp1a expression and developmental toxicity via activation of AHR2, while PYR (4 rings) toxicity was shown to be metabolism-dependent (Incardona et al. 2006) . We determined PAH body burden and corresponding transcriptional profiles in PAH -exposed zebrafish embryos at 24 and 48 hours post - fertilization, before toxicity could be visibly identified. We found that DBT, PYR and BAA induce mRNA expressio n profiles that differentially implicate AHR activity, and highlight multiple pathways that can be disrupted by exposure to PAHs over the course of vertebrate development.
Methods
Zebrafish lines and embryos:
Adult wild type 5D zebrafish were housed at the Sinnhuber Aquatic Research Laboratory on a recirculating system maintained at 28±1°C with a 14 h light/10 h dark schedule. Embryos were collected from group spawns of adult zebrafish as described previously (Reimers et al. 2006) and all experiments were conducted with fertilized embryos according to Oregon State University Institutional Animal Care and Use Protocols.
Chemical Exposures and Developmental Toxicity Assessment:
Dibenzothiophene (>99%), pyrene (99%) and 1,2-benzanthracene (99%) were purchased from Sigma -Aldrich and dissolved in DMSO (J.T. Baker) at 50 mM, 50 mM and 25 mM concentrations, respectively. Embryos were cleaned, developmentally staged, and batch - exposed in glass vials at 6 h post fertilization (hpf) (chorions intact ) to PAHs or vehicl e control with 1% final DMSO concentration in E2 embryo medium (Kimmel et al. 1995) . For all experiments, e xposures were conducted on a rocker and embryos were protected from light until the experimental time points. For devel opmental toxicity experiments, PAH solutions were removed at 48 hpf and embryos were rinsed 4x and incubated in fresh embryo medi um until 120 hpf , when they were assessed visually for malformations as previously described (Truong et al. 2011) . Preliminary range -finding studies were conducted with each PAH and all further developmental toxicity assessments were conducted at 25 μM with 20 embryos per vial in 2 ml exposure solution. Microarray and body burden exposures were conducted with 40 embryos per vial in 4 ml solution.
30 Analysis of Developmental Toxicity Endpoints:
Embryos were anesthetized with tricaine methanesulfonate an d visually assessed at 120 hpf for yolk sac, axis, trunk, somite, fin, cardiac, eye, snout, jaw, otic vesicle, brain and pigment malformations. Mortality and the percentage of embryos with each malformation were calculated for each treatment group with the vial (20 embryos) as the experimental unit. The experiment was repeated 3 times . A generalized linear model (binomial distribution, logit link) one -way ANOVA was performed for the 8 endpoints which were observed in at least 3 embryos across all treatment groups. If the overall p-value indicated differences among the treatment percentages, individual comparisons were conducted using Tukey’s all pairwise post hoc test in R version 2.12.
Detection of PAH body burden in zebrafish embryos:
Embryos were exposed to 0, 1, 5, 10 and 25 μM PAH (1% DMSO) solutions in glass vials as described previously , with 40 embryos per vial in 4 m l exposure solution. As with all exposures in this study, embryos were exposed at 6 hpf with chorions intact and incubated at 28˚C on a rocker. Control embryo s hatched just before 48 hpf; exposure to 10 and 25 μM PAH delayed hatching by 3 -4 h, but all treatment groups hatched on their own by 72 hpf.
Because several exposure concentrations are above solubility for PAHs in embryo medium, PAH precipitate accumulated on the outside of the chorion. In order to measure the amount of PAH internalized by the embryos, chorions were removed immediately following exposure and before analysis as described below. For each biological replicate, 2 vials were combined after exposure.
For analysis at 24 hpf, embryos were rinsed with fish water and transferred to a clean glass petri dish. They were incubated in 82 μg/ml pronase (Sigma-Aldrich) at room temperature, gently agitated for 3 min, then rinsed thoroughly using an automated dechorionating system as previously described (Mandrell et al. 2012) . Following rinsing, embryos were placed in a 28˚C incubator for 20 min, after which >95% of chorions were removed by gentle agitation of the dish.
At 48 hpf, the majority of embryos had hatched and did not require batch dechorionation.
They were chilled on ice to reduce activity , PAH solution was removed, and embryos wer e transferred to a clean glass petri dish with cold fish water. Chorions were removed from any
31 remaining embryos with forceps, and embryos were gently agitated and rinsed 4x with fish water. Immediately following dechorionation, 50 embryos from each treatm ent group were loaded into microcentrifuge tubes with approximately 80 mg 1 mm glass beads and placed on ice for at least 10 min. Embryos were homogenized in 500 μl ethyl acetate with a bullet blender (Next Advance, Averill Park, NY). Samples were then vor texed and incubated 15 min before centrifuging for 5 min at 16,000 RCF. 400 μl of supernatant was stored in amber vials at 4˚C until analysis.
Percent PAH recovery for this method was calculated from 4 replicates of unexposed 24 and 48 hpf embryo samples l oaded into microcentrifuge tubes as above and spiked with 12.5 μl PAH stock in DMSO. Samples were processed identically to experimental samples. Zebrafish extracts were analyzed using an Agilent 5975B Gas Chromatograph -Mass Spectrometer (GC-MS) with a DB -5MS column (30 m x 0.25 mm x 0.25 μm) in electron impact mode (70 eV) using selective ion monitoring (SIM). The GC parameters were as follows: injection port maintained at 300 °C, 1.0 m l min-1 helium flow, 70 °C initial temperature, 1 min hold, 10 °C min-1 ramp to 300 °C, 4 min hold, and 10 °C min -1 ramp to 310 °C, 4 min hold. The MS temperatures were operated at 150, 230 and 280 °C for the quadrupole, source and transfer line respectively. Standards for BAA, DBT and PYR (>97% purity) were purchased from AccuStandard (New Haven, CT). Isotopically labeled chrysene -D12 and acenaphthylene-D8 were purchased from C/D/N incorporated (Quebec, Canada). A nine point calibration curve (10 pg/ μl to 10 ng/μl) was conducted to determine relative response ratios of PAHs to deuterated surrogate standards; chrysene -D12 was used as the deuterated surrogate for PYR (r 2 = 0.9992) and BAA (r 2 = 0.9982), acenaphthylene-D8 was used for DBT (r 2 =
0.9991).
Calibration verification standards for target analytes and surrogates were analyzed at least every 22 samples and reported values within ± 20% of the true value were considered to meet our data quality objectives (DQO) . Only results from samples run betwee n two calibration verifications that met the DQO were accepted; the majority were within ±10% of the true value . PAH s in all laboratory blanks (solvent -exposed embryos) w ere below detection except for 5 samples in which DBT and BAA were detected. This poss ible contamination was <10% of the levels detected in our lowest exposure sample groups, and
32 deemed negligible. Body burden ( μmol/g embryo ) was calculated using average embryo weights of 0.4 mg at 24 hpf and 0.3 mg at 48 hpf. Pairwise comparisons were cond ucted between PAH-exposed samples and time -matched controls, as well between 24 and 48 hpf at each exposure concentration with Mann -Whitney Rank Sum tests using SigmaPlot software.
Microarray analysis of mRNA expression:
Embryos batch-exposed in groups of 40 to 25 μM DBT, PYR, BAA or 1% DMSO control were homogenized in TRI Reagent (Molecular Research Center, Cincinnati, OH) at 24 and 48 hpf for RNA isolation. Four independent biological replicates were prepared for each treatment.
Total RNA was isolated with phenol -chloroform extraction, and RNA was quantified and quality confirmed with a Nano Drop ND -1000 UV -Vis spectrophotometer and Agilent Bioanalyzer 2100 . Microarray analysis was performed by the University of Wisconsin McArdle Laboratory of Cancer Research Microa rray Facility. Briefly, cDNA was synthesized from 1.2 μg of total RNA from each sample and labeled with cy3 (experimental samples) or cy5 (pooled control sample) according to the Agilent protocol with minor modifications.
Equal amounts of cy3 and cy5 labeled samples were mixed, fragmented, and hybridized to Agilent Zebrafish V2 array chips. Slides were scanned immediately with an Agilent microarray scanner (Agilent Technologies, Santa Clara, CA). Microarray files were submitted to the NCBI Gene Expression Omnibus, accession number GSE44130 http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE44130.
Microarray analysis:
Raw intensity data were processed by Agilent Feature Extraction software using Lowess normalization. Quality control analysis was performed on preprocessed data in GeneSpring v.11 ( Silicon Genetics, Redwood City, CA) software using feature intensity distributions from box-whisker plots to determine interquartile range span and median intensity value across the experiment. The intra -group versus between -group comparisons were made using correlation matrix plots, followed with principle components analysis to determine potential outliers. One bi ological replicate from the DBT 48 hpf treatment group was removed as an outlier, resulting in an N=3 for that treatment group . Normalized data were transformed to time -specific controls and analyzed by one -way ANOVA for unequal
33 variances (Welch’s ANOVA) with Tukey’s post hoc test and 5% false discovery rate calculation (Benjamini and Hochberg 1995). Values are reported as fold change (log2) with associated Benjamini -Hochberg adjusted p value in each treatment group compared to time-matched control . Correlation analysis between treatment groups was performed by linear regression of log2 fold change values, using the union of significant genes from both groups. Based on the significant correlation between DBT and PYR treatments at both time points, these datasets were further filtered to identify the subset of genes that were significantly different between them (p<0.05, 1.5 -fold change) . Genes that did not meet these criteria were considered similar between the DBT and PYR treatments for functional and transcription factor analysis.
Bioinformatic analysis:
Unsupervised hierarc hical clustering of microarray data was performed using Euclidean distance metric and centroid linkage clustering to group gene expression patterns by similarity. The clustering algorithms, heat map visualizations and centroid calculations were performed with Multi-Experiment Viewer (Saeed et al. 2003) software based on log2 expression ratio values. For downstream bioinformatic analysis, zebrafish identifiers on the Agilent platform were converted to human ortholog s using Bioinformatics Resource Manager v. 2.3 (Tilton et al. 2012) . Genes that did not have human orthologs were still included in the bioinformatic analysis using their zebrafish identifier. Both Meta Core (GeneGO) and DAVID software recognize mixed identifiers (Entrez Gene ID) from human and zebrafish. Significant targets from the microarray and genes of interest are referred to by zebrafish gene identifiers, where zebrafish -derived information was available in the literature. Functional annotation and network information, however, were primarily derived from other species, and data for many genes of interest were only available in the mammalian literature; we present this information with human gene identifiers throughout the results and discussion. Functional enrichment was determined using the D AVID functional annotation tool (Huang da et al. 2009) , which utilizes the Fisher Exact test to measure gene enrichment in biological process Gene Ontology (GO) category terms for significant genes compared to background, which consisted of all genes on th e Agilent platform. GO biological process categories from levels 3, 4, and 5 were included for enrichment calculation . Since the DAVID functional annotation tool clusters GO terms by
34 similarity to reduce redundancy, the biological processes are presented in the results with a representative process from each significant cluster (p<0.05) that represented at least 1% of genes from the exposure group. To identify major transcriptional regulators of gene expression by PAHs, the Statistical Interactome tool was used in MetaCore to measure the interconnectedness of genes in the experimental dataset relative to all known interactions in the background dataset . Statistical significance of over -connected interactions was calculated using a hypergeometric distribution , where the p value represents the probability of a particular mapping arising by chance for experimental data compared to the background (Nikolsky et al. 2009) . Networks were constructed in MetaCore for experimental data using an algorithm that identifies the shortest path to directly connect nodes in the dataset to transcription factors . Network visualizations were generated in Cytoscape (Shannon et al. 2003).
Quantitative RT-PCR
Validation of gene expression changes identified in the microarray analysis was conducted for a group of transcripts selected to represent differential regulation patterns by the three PAHs at 24 and 48 hpf. Gene-specific primers (MWG Operon) for qRT-PCR amplification are listed in Table S1 . Sub aliquots of 10 μg total RNA from the microarray analysis w ere reverse transcribed using Superscript III (Invitrogen) according to manufacturer instructions. All qRT -PCR assays were performed in 20 µl reactions consisting of 10 µl Power SYBR Green PCR master mix (Applied Biosystems), 0.4 μl each primer, 9.2 μl H2O and 50 ng equivalen ts of cDNA. Amplification (StepOne Plus, Applied Biosystems) was performed with cycling parameters as follows: 95°C for 10 min; 40 cycles of 95°C for 15 s, 60°C for 1 min; 95°C for 15 sec an d 60°C for 1 min. A melt curve was performed at 3° increments to assess for multiple products. Relative fold change values in PAH -treated samples compared to vehicle controls were calculated for genes of interest, normalized to β- actin, by the method descr ibed by Pfaffl (Pfaffl 2001) . Three independent biological replicates were assessed and statistically analyzed by one -way ANOVA with Tukey’s post - hoc test using SigmaPlot software.
Results and discussion
35 Dibenzothiophene, pyrene, and benz(a)anthracene induce developmental toxicity in zebrafish embryos.
Exposure to DBT , PYR or BAA caused a significant increase in the incidence of abnormal embryos compared to the vehicle control exposure at 120 hpf. All three compounds induced pericardial edema, snout and jaw malformations (Fig. 1). PYR and BAA exposures caused significant increases in yolk sac edema, while DBT did not. DBT, however, induced distinct axis malformations (Fig . 1B) which were not present in BAA - or PYR-exposed embryos (Tables 1, 2). Interestingly , the 25 μM concentration of all three PAHs induced malformations in >80% of embryos by 120 hpf, while mortality was < 10%, not significantly different from control (Table 1). The phenotypes induced by these PAHs suggested different underlying pathologies . BAA ind uced more severe edema , while necrotic tissue was observed in PYR-exposed embryos, particularly in the anterior yolk sac, liver and digestive tract (Fig. 1C); these embryos did not survive more than a couple hours past 120 hpf . This was observed previously by Incardona et al, who demonstrated that DBT, PYR and BAA induced malformations in zebrafish that were differentially dependent on activation of the AHR and metabolism by Cyp1a (Incardona et al. 2004; Incardona et al. 2005) . The differential proposed mec hanisms of these PAHs presented an ideal opportunity to investigate the diversity of molecular pathways that lead to developmental effects of PAH exposure. The objective of this study was not to mimic environmental exposures, but rather to identify molecul ar pathways that precede morphological changes induced by different PAH structures. Based on our developmental toxicity data, 25 μM was identified as an appropriate concentration for microarray analysis of early gene expression changes elicited by DBT, PYR and BAA exposure.
Zebrafish exposed to dibenzothiophene, pyrene and benz(a)anthracene in embryo medium accumulate differential PAH body burdens.
The internal body burden of PAH in embryos was measured after exposures as described for the microarray analysis. T o allow for structure -toxicity comparisons between the three compounds, as well as to relate gene expression data to other model systems, DBT, PYR and BAA were detected by GC-MS in zebrafish embryos exposed to a range of concentrations (1- 25 μM). PAH recovery averaged between 80 and 125% (Table S 2). Measured PAH values were therefore reported as detected in experimental samples, unadjusted for recovery. The
36 amount of PAH detected in embryos revealed stark diffe rences between the three PAH structures. At all concentrations and time points, DBT body burden in embryos was the highest, averaging 3.4 and 5.3 μmol/g at 24 and 48 hpf, respectively, following exposure to 25 μM DBT (Fig. 2A). DBT had the highest solubili ty in water, and uptake did not plateau in the range of concentrations tested here. PAH body burden of embryos exposed to 25 μM PYR averaged 1.0 and 2.9 μmol/g at 24 hpf and 48 hpf, respectively, and uptake appeared to reach a plateau, likely because of low compound solubility in embryo medium (Fig. 2B). BAA body burden was markedly lower than the other two PAHs at all exposure concentrations.
An apparent saturation was reached at 0.10 μmol/g embryo at 24 hpf , an order of magnitude lower than PYR (Fig. 2C). Water solubility of BAA was the lowest of these PAHs, and the high concentrations employed in this study were above solubility with 1% DMSO in embryo medium. At 48 hpf, BAA concentrations averaged 0.19 μmol/g in embryos from the 25 μM exposure group.
Studies of early-life exposure to PAHs showed that the bioconcentration factor (BCF) in fish embryos correlated with the octanol water partition coefficient (K ow) (Mathew et al. 2008) .
The log Kow values of DBT, PYR and BAA are 4.38, 4.88 and 5.79, respectively (Hansch 1995).
BAA would therefore be predicted to have the highest BCF of the PAHs in our study. BCFs have primarily been calculated for larvae (post -hatch), however, and the short duration exposures employed in our study did not allow steady -states to be achieved. Steady -state PAH concentrations were similarly not attained in zebrafish eggs in a study reported by Petersen and colleagues (Petersen and Kristensen 1998) . Metabolism could also explain differences in parent PAH concentration, but is expecte d to be low during the developmental stages chosen for gene expression analysis in this study (Petersen and Kristensen 1998) . Metabolism increases upon hatching in Atlantic killifish embryos, and zebrafish exhibit greater inducibility of cyp1a starting at 48 hpf, the approximate time of hatching in our laboratory (Binder and Stegeman 1984; Andreasen et al. 2002) . While metabolism could potentially explain the small decrease in BAA between 24 and 48 hpf at the lower concentrations, it is unlikely to explain the large difference in body burdens observed between PAH structures. Differences between PAHs in this study appear to be driven by their solubility in embryo medium, rather than their BCFs or metabolism. The 25 μM exposures for the microarray study represent an acute exposure intended to identify
37 mRNA expression changes tha t precede appearance of morphological abnormalities. While total dose and maximum exposure calculations were beyond the scope of this study, the measurement of parent PAH in the embryos at the time of gene expression analysis provided important information for mechanistic comparison between the PAHs. mRNA expression profiles induced by PAH exposure are different at 24 and 48 h post fertilization.
Pairwise analysis of variance across all exposure groups identified significant expression changes in 1079 trans cripts compared to time -matched controls (Table S3) . Entrez or Ensdart IDs were identified for 935 of these in the Ensembl zebrafish genome assembly (Zv9). Unsupervised bidirectional clustering of all experimental groups indica ted a strong developmental time point effect, and revealed unique gene expression patterns in response to the three PAHs. At 24 hpf, DBT and PYR exposure groups clustered closely, while BAA induced a strikingly different expression pattern (Fig s. 3A, D). DBT and PYR exposure groups also clustered at 48 hpf, but with distinct separation from the 24 hpf samples and with a notably larger group of down -regulated transcripts. The expression profile induced by BAA at 48 hpf clustered more closely with 24 hpf BAA than with the other 48 hpf PAH samples (Fig. 3A). At 24 hpf, DBT, PYR and BAA exposures induced significant changes in 357, 67 and 38 transcripts, respectively. As reflected in the heatmap, more transcripts were differentially expressed at 48 hpf, but relative quantities of differentially expressed transcripts were maintained; DBT induced changes in 656 , PYR in 191 and BAA in 107 transcripts (Figs. 3B-D). Fifteen genes that were significantly differentially regulated by at least one of the PAHs were selected for qRT -PCR validation of t he differential regulatory patterns observed in the array. PAH - and time -dependent expression changes were confirmed for the majority of genes examined ( Table S4). Fold change values were similar between the microarray and qRT -PCR for most genes, demonstra ting good reliability of the microarray for identifying meaningful changes in gene expression induced by PAH exposure. mstnb, which was identified as a significantly decreased transcript at 24 hpf, was not decreased in BAA -exposed samples analyzed by qRT -PCR. Upon further investigation, however, we identified 5 probes on the Agilent array that target mstnb, only one of which identified a significant expression difference (p value 0.04). This suggested nonspecificity of that probe for mstnb, or potentially differential splicing. As others have reported previously,
38 we observed lower correlation between the microarray and qRT -PCR for down -regulated transcripts; qRT -PCR identified fewer changes that met statistical significance (p < 0.05), but trends in regulation were consistent (Morey et al. 2006).
For each PAH, we compared transcripts significantly differentially expressed at 24 hpf with transcripts that were significant at 48 hpf. These experimental time points encompass a period of rapid development in zebr afish, during which fin morphogenesis begins, the circulatory system forms, tactile sensitivity and swimming behavior are initiated, and pigment develops (Kimmel et al. 1995) . Developmental progression is reflected in the stronger influence of time point than PAH structure in the bidirectional clustering (Fig. 3A).
While 95 transcripts were mis expressed by DBT at both 24 and 48 hpf, they represented only 27% of the 24 hpf significant gene set, and did not include the most highly misexpressed transcripts fr om either time point. The most differentially expressed transcripts across both time points were acana, ankrd1b and hspb11 (Fig. 3B). ankrd1b and hspb11 are both involved in myogenesis; hspb11 is specifically expressed in muscle pioneers, up-regulated by intracellular calcium, and involved in muscle fiber organization in developing zebrafish (Kluver et al. 2011; Kojic et al. 2011). Similar to DBT, the expression of only a few transcripts were significant impacted by PYR at both 24 and 48 hpf. The two genes differentially expressed >2 fold were tnfb, a member of the tumor necrosis factor family of proinflammatory cytokines , and zgc:153258 (Fig. 3C). These robust responses , conserved over time , represent potential biomarkers of exposure to the individual PAHs .
The transcripts with the largest fold changes, however, were not consistent over time, which suggested that separate analysis at each time point could provide better insight into mechanisms driving responses to DBT and PYR exposure.
The BAA transcriptional profile is consistent from 24 to 48 hpf, and distinct from DBT and PYR-induced changes.
In contrast to DBT and PYR, 45% of transcripts differentially expressed by BAA at 24 hpf were also significant at 48 hpf, and those with the largest fold changes were conserved between time points (Fig . 3D). The most highly misexpressed genes at 24 hpf were cyp1a, cyp1b1, cyp1c1, cyp1c2, ahrra and foxq1l. All of these genes were elevated and, along with sult6b1 and ctgfb, remained elevated at 48 hpf. The cyp1 genes and aryl-hydrocarbon
39 receptor repressor (ahrra) are well -known targets of AHR, while sult6b1 is a recently identified sulfotransferase that could potentially be involved in BAA metabolism. Together, these genes represent a consistent signature of the transcri ptional response to BAA exposure in zebrafish embryos from 24 to 48 hpf.
We conducted between-PAH comparisons separately at 24 and 48 hpf to identify significant transcripts unique to each PAH exposure . Though BAA exposure affected the smallest number of transcripts, they were highly induced and formed a distinct cluster (Fig. 3D) that overlapped minimally with the DBT and PYR transcriptional profiles. Only 7 of the significant genes in the 24 hpf BAA exposure group were similarly differentially expressed in response to DBT or PYR. At 48 hpf, the BAA expression pattern remained distinct, where only 27 of the 107 differentially expressed transcripts were similarly regulated by another PAH. In additions, we observed no correlation between the transcriptional r esponse induced by BAA and either of the other PAHs at 24 or 48 hpf (r 2 < 0.2), using linear regression analysis of all significant transcripts. The entire set of significant BAA transcripts was therefore used for analysis of pathways and biological functi ons disrupted by BAA exposure at 24 and 48 hpf (discussed below).
DBT and PYR induce a similar dose-dependent transcriptional profile.
Common patterns in gene expression between DBT and PYR exposure groups are apparent in the heatmap in Figure 3A at both time points; however, t he magnitude of the PYR - induced transcriptional respon se is visibly lower. This trend was reflected in the linear regression analysis of PYR vs. DBT log 2 expression values , where a strong positive correlation was observed at 24 hpf (Fig. 4A, union of DBT and PYR significant transcripts, r2 = 0.77, p < 0.001). The regression slope, however, demonstrate d that DBT -induced expression changes were on average 1.6 fold greater than PYR -induced changes in these transcripts. This apparent dose -effect suggest ed that solubility and uptake were the primary drivers of differential transcriptional response s between these compounds, rather than unique molecular targets . The dose-effect is supported by the body burden data; DBT body burdens were 3.4 times higher than PYR body burdens in the 25 μM exposure cohorts at 24 hpf . This trend persisted with a similar correlation at 48 hpf (r2 = 0.647, p< 0.001), wherein DBT on average induced 1.75-fold greater expression changes than PYR (Fig. 4B)
40 and DBT body burdens were 1.8 times greater than PYR. We found that comparing the PAHs following ANOVA analysis exaggerated transcriptional profile differences, because many PYR -induced changes did not reach statistical significance ( p< 0.05 compared to control). Because of the significant correlation in responses, we employed a direct statistical comparison of DBT and PYR to better define the conserved transcriptional response, as well as identify transcripts with meaningful expression differences between the two groups.
Direct pairwise comparison of DBT and P YR log2 FC values at 24 hpf identified 343 similarly expressed transcripts, and only 42 that were significantly different (p< 0.05). At 48 hpf, 139 were significantly different, while 572 transcripts were similar between the two PAHs. Because of the overwh elming conservation of response , functional analysis was performed using the set of similarly expressed genes at each time point to identify biological processes disrupted by DBT and PYR exposure. We focused further mechanistic analysis on the DBT-PYR vs. the BAA responses.
Disruption of ion transport, muscle function, and metabolism by DBT and PYR at 24 hpf Of the 343 transcripts representing the conserved DBT and PYR response s at 24 hpf , approximately 70% were under-expressed compared to control . 308 had sufficient annotation, which translated to 256 unique DAVID IDs . Fatty acid biosynthesis, ion transport, skeletal muscle contraction, steroid biosynthesis and oxoacid metabolism were the most enriched of the 12 significant biological processes identified b y DAVID functional analysis, which together depict wide disruption o f molecular signaling by 24 hpf (Table 4).
We used the MetaCore Statistical Interactome tool to identify major transcription factors predicted to regulate significant genes in this dataset . JUN, RELA, SP1, PPARA, RXRA, ESR1, ESR2, and NR3C1 (glucocorticoid receptor) were predicted to regulate the largest sets of differentially expressed transcripts (Table S5). Transcripts were up- and down-regulated in approximately equal numbers, and there was considerable overlap of the predicted targets of these transcription factors. The extensive molecular responses to these PAHs highlighted a complex network of regulatory processes involv ed in normal embryo development that were unlikely to be mediated through one primary transcription factor but rather were responsive to chemical -induced perturbations such as oxidative stress, inflammation, altered metabolism and disruption of ion balance and cardiac function.
41 The ion transport biological process contained the largest number of misregulated transcripts at 24 hpf (Table 3) and is discussed further below as a common biological response to all PAH exposures in this study. I on balance is important for muscle development and function, which was also signific antly affected by DBT and PYR at 24 hpf.
Transcripts in the skeletal muscle contraction and muscle cell development biological processes were primarily under-expressed, and included myoglobin, which is also required for angiogenesis in zebrafish (Table 3)(Vlecken et al. 2009) . Members of the ion transport cluster may interact with these transcripts or themselves be important for muscle and cardiac function in the context of zebrafish development.
Genes involved in fatty acid biosynthesis, steroid biosynthe sis and oxoacid metaboli sm were also primarily underexpressed (Table 3). A notable exception was cholesterol 25- hydroxylase (ch25h), which encodes a cholesterol metabolizing enzyme involved in the inflammatory response , and was elevated > 4 fold by both DBT and PYR (Park and Scott 2010). The functions of these genes and their roles during development have yet to be characterized in zebrafish, but together they implicate disruption of metabolic processes.
Transcription factors RELA and JUN are predicted to regulate renin-angiotensin system- related genes misexpressed in DBT and PYR exposed embryos.
In contrast to the previously -discussed biological processes , the majority of genes associated with negative r egulation of cell proliferation (Table 3) have known roles in zebrafish development, and many are predicted downstream targets of the NF-κB family member RELA (Table S5; bdnf, tnfrsf9a, zgc:114127, agt, msxe, tnfb) as well as JUN (Table S5; cx43, smad3b, tnfrsf9a, agt, tnfb). These processes are likely mediate d through multiple interacting transcription factors. Of the 15 transcription factors that were significant at 24 hpf, RELA and JUN were predicted to regulate the most highly induced genes in the DBT - PYR dataset, agt and tnfb, as well as many other genes i nvolved in the significant biological processes. The inflammatory cytokine tnfb is one of two TNF homologs in zebrafish, both of which are highly induced in larvae in response to LPS stimulation (Wiens and Glenney 2011). Angiotensinogen (AGT) is the precursor of angiotensin (ANG II), a potent regulator of blood pressure and water homeostasis in the renin-angiotensin signaling (RAS) network (Wu et al. 2011) . Transcription of AGT is induced by glucocorticoids through the
42 glucocorticoid receptor, as well as by TNF and other inflammatory cytokines via activation of NF-κB (Brasier and Li 1996). ANG II induces AGT transcription in a positive feedback loop involving NF-κB, and also activates JUN via JNK signaling in cardiac myocytes and vascular smooth muscle cells (Brasier and Li 1996; Brasier et al. 2000) . As an initi ator of tissue inflammation, TNF activates NF -κB, induces inflammatory and anti -apoptotic gene expression in a cell -type dependent manner, and also activates JUN via JNK (Tian et al. 2005). In this study, DBT and PYR exposure led to increased expression of tnfb and complement component 7, along with macrophage-related genes mpeg1 and mst1, all of which are involved in innate inflammatory response. Both Tnf and Agt are expressed in the myocardium of rats following ischemia, remodel ATP -dependent calcium chan nels, and have been implicated in atherosclerosis and hypertension (Isidoro Tavares et al. 2009) . In developing rat embryos, activation of angiotensin receptors with exogenous ANG II disrupts cardiac looping (Price et al. 1997) . These two genes, in combina tion with the significant enrichment o f other targets of RELA, JUN , and the glucocorticoid receptor, suggest that inflammatory response and RAS signaling may play a role in the cascade of effects observed in response to DBT and PYR exposure. Cardiovascular functions of t he RAS system are conserved in teleosts (Le Mevel et al. 2008) , and t hough not yet explored in embryonic zebrafish, the RAS system has been identified as important for fetal cardiovascular response, body fluid balance, and neuroendocrine reg ulation, and may be involved in fetal programming of hypertension later in life (Mao et al. 2009).
We created a map of key predicted transcription factors, including RELA and JUN, and their downstream targets that were significantly misregulated in the th ree PAH exposure groups at 24 or 48 hpf (Fig. 5). A substantial number of transcripts, including TNF and AGT, are predicted to be regulated by both transcription factors, but RELA is predicted to regulate the largest number of genes that were induced by DBT/PYR exposure at 24 or 48 hpf. While RELA was also identified as a significant transcriptional regulator of BAA genes (discussed below), the DBT -PYR and BAA exposure networks overlapped with only 10 RELA targets (Fig. 5, purple). Fig. 5 therefore highlights the distinct nature of RELA regulatory roles in the toxicity pathways of different PAH structures.
Developmental processes in DBT and PYR-exposed embryos are widely misregulated at 48 hpf.
43 By 48 hpf, 572 transcripts were differentially expressed in DBT and PYR embryos compared to controls, 478 of which were annotated. DAVID functional analysis identified 21 biological processes that were sig nificantly affected by DBT and PYR exposure; oxoacid metabolic process was the most enriched functional cluster, but was composed of different genes than at 24 hpf (Table 4) . Many of the most significant processes misregulated at 48 hpf were directly related to embryonic development , and the r egionalization, neurogenesis and central nervous system development function s together highlight widespread disruption of nervous system development (Table 4). Thirty-five transcription factors were predicted to regulate significantly enriched groups of genes within this dataset ; JUN , PPAR A, R ELA, RXRAa and SP1 were significant at both 24 and 48 hpf (Table S5). NR3C1 and ER, which were significant at 24 hpf, were no longer enriched at 48 hpf, whereas CREB1, P53, YY1 and TBP became significant with the largest numbers of misregulated downstream targets. The breadth of expression changes at 48 hpf is not restricted to a singular toxicity pathway, but rather encompasses substantial network perturbations consistent with aberrant embryonic development. These and the many other mis expressed transcripts may result from a cascade of processes downstream of the genes disrupted at 24 hpf, but also reflect the vast molecular changes that occur in a normally developing zebrafish between these two time points.
Biological functions of BAA-misregulated genes are consistent with AHR-dependent toxicity.
Human or mouse homologues were available for 29 of the 38 transcripts significantly misregulated by BAA at 24 hpf, which translated to 19 unique DAVID IDs. Several genes, including cyp1a, were represented by multiple probes within this significant tran script set.
Functional analysis of genes misregulated by BAA at 24 hpf identified two biological processes, hormone metabolism and tissue development, that were significantly enriched within this dataset (Table 4). Metabolic process genes were up -regulated, and included well-known biomarkers of AHR activation such as cyp1a, as well as si:dkey-94e7.2, a predicted homolog of retinol dehydrogenase 11 ( RDH11). Expression of genes involved in tissue development was also primarily increased (Table 3), likely via AHR signaling. A foxq1 homolog was induced by BAA, and based on the probe sequence we identified the transcript as foxq1b, an AHR-dependent TCDD-inducible gene expressed in zebrafish jaw primordium (Planchart and Mattingly 2010) . Tissue development genes ptn and ctgfb (Table 3) are not
44 known to be directly regulated by the AHR, but may be important mediators of AHR - dependent developmental toxicity; ctgfb was also induced in developing jaw s of zebrafish exposed to TCDD (Xiong et al. 2008).
Transcription fac tor prediction identified AHR as significant at 24 hpf, along with its dimerization partner, ARNT, and C/EBPδ (Table S5). The large fold changes in a relatively small number of significant transcripts suggest that BAA interacts with one primary transcription f actor at 24 hpf, and the transcriptional profile supports previous demonstration of AHR-dependent toxicity induced by BAA (Incardona et al. 2006).
BAA transcriptional response indicates oxidative and metabolic stress at 48 hpf The BAA transcriptional res ponse expanded to 107 mis expressed transcripts at 48 hpf, 99 of which were sufficiently annotated. Though the cyp1 genes remained the most strongly elevated, they were joined by ahrra, wfikkn1, and cathepsin L.1 (ctsl.1), which was elevated 4-fold. Ctsl.1 encodes a widely expressed protease important for blood pressure regulation, and was recently identified as dioxin -responsive (Mbewe-Campbell et al. 2012) . DAVID functional annotation clustering of the 70 unique targets identified eight significantly enriched biological functions (Table 4). Genes involved in h ormone metabolism again formed a significant cluster, which included two phase 2 metabolizing enzymes, ugt1b5 and ugt1b7, along with the cyp1 transcripts. Transcripts associated with cation transport, in contrast, were not known AHR targets, and are discussed further within the ion transport response common to all three PAHs. The cellular homeostasis transcript group was composed of antioxidant-related genes (gsr, prdx1, and zgc:92066, a homolog of FTMT), and transcripts involved in blood pressure regulation and chemokine signaling that are not known to be direct targets of AHR (Table 4).
Vascular development genes are misexpressed in BAA-exposed embryos.
Genes involved in vasculature developme nt were over-represented among transcripts affected by BAA at 48 hpf. They included chemokine receptor cxcr4a, which had increased expression, and its ligand, cxcl12b, which was under-expressed. Interestingly, this expression pattern was also observed in a microar ray analysis of TCDD -induced transcriptional changes in zebrafish jaw, suggesting misregulat ed chemokine signaling may indeed be involved in AHR-mediated toxicity in the developing embryo (Xiong et al. 2008) .
45 cxcr4a is required for arterial -venous network formation and is expressed in response to low blood flow and in unperfused blood vessels in developing zebrafish (Bussmann et al. 2011). Other vasculature development genes included connexin 39.4 (cx39.4), connective tissue growth factor b (ctgfb), c-fos induced growth factor (figf, previously vegf-d) and TCDD- inducible poly(ADP-ribose) polymerase (tiparp) (Table 4) . atp2a2a (reduced expression) is not annotated as a vascular development gene, but is required for heart looping in zebrafish (Ebert et al. 200 5). Together these transcriptional changes convey disruption of vascular development and circulatory system function. This is in agreement with blood pressure misregulation and endothelial dysfunction in rats developmentally exposed to benzo(a)pyrene, another PAH known to induce AHR signaling (Jules et al. 2012).
RELA is a significant transcription factor in the BAA regulatory network Transcription factor analysis at 48 hpf predicted involvement of multiple transcription factors (Table S5) . AHR was interes tingly no longer significant, though its dimerization partner ARNT was predicted to regulate a significantly enriched cluster of genes. SP1, TP53, CREB1 and RELA were upstream of the largest number of genes misregulated by BAA at 48 hpf (Table S5) . RELA in teracts directly with AHR and is an important regulator of inflammatory immune and oxidative stress responses (Tian et al. 1999). The RELA and AHR regulatory networks are displayed in Figure 5, which shows that the AHR regulates a set of genes that were di stinct to the BAA exposures and were not predicted to be under direct regulation by RELA. The large number of genes downstream of RELA, however, which includes some of the most highly misexpressed genes such as CTGF and CTSL.1, suggests RELA may play an important role in the BAA toxicity pathway (Figure 5).
Differential affinities for the AHR result in few transcripts common to PAH exposure The few genes similarly misregulated by all three PAHs represent potential general biomarkers of PAH exposure . At 24 hpf, only 5 transcripts were similarly affected by all 3 PAHs. The most highly elevated probes (approximately 3-fold for all PAHs), A_15_P477220 and A_15_P247256, both target ESTs that are not yet annotated in the zebrafish V9 genome.
Decreased transcripts at 24 hpf included slco5a1, a solute carrier organic transporter family member, and a non -specific probe. By 48 hpf, 23 transcripts were similarly expressed in response to all three PAHs. T he most elevated genes across all three PAHs were cyp1a,
46 cyp1b1, wfikkn1, LOC794658 (similar to chrm3), s100z and cxcr4a. The largest decreases were observed in g0s2, kif20a, cfdl, and two uncharacterized genes, zgc:171318 and zgc:153311. Though the molecular toxicity pathways of BAA and DBT/PYR are, on the whole, very different, these genes highlight some commonalities.
The most commonly used biomarker of AHR activation, cyp1a, was elevated by all three PAHs at 48 hpf. However, DBT and PYR only induced 1. 2 and 2.1 fold changes, respectively, whereas BAA induced cyp1a 34.5-fold. The minimal and delayed cyp1a induction suggests that it occurs via metabolites or very weak AHR activation by DBT and PYR. Barron and colleagues reported the pot ency of BAA as an AHR agonist as 519 times greater than PYR, and though DBT was not analyzed, 3-ring PAHs included in the study were less potent than PYR or were inactive in assay systems (Barron et al. 2004) . Though Cyp1a protein expression is induced by PYR exposure in zebrafish embryos, Incardona et al. reported a markedly different exp ression pattern than was observed with BAA, and suggested Cyp1a metabolism and hepatic toxicity were drivers of the developmental effects (Incardona et al. 2006). DBT, in contrast, has been reported to induce developmental toxicity via disruption of early cardiac function, as well as act as a Cyp1a inhibitor (Incardona et al. 2004; Wassenberg et al. 2005) . In light of these proposed different mechanisms of action, the overlap of transcripts misregulated by DBT and PYR at both 24 and 48 hpf in this study is striking. Indeed, the different malformations observed in DBT and PYR -treated embryos , despite similar molecular response profiles, may be a result of metabolic processes that are more active after the 48 hpf time point employed in this study. The marked e ffects of DBT exposure on axis formation, which were not observed in response to PYR, could also be explained by the dramatic differences in PAH body burden at these equivalent exposure concentrations. Signaling that directs axis formation occurs early in development; the uptake of DBT was relatively rapid, whereas the lower solubility and uptake of PYR potentially did not achieve a threshold concentration to induce such effects.
Disruption of ion transport and calcium signaling is common across all PAHs Ion transport and homeostatic processes were misregulated by all three PAHs in this study, though the significant transcripts in the DBT -PYR response are largely different from those affected by BAA. All three exposures induced differential expression of genes involved in
47 calcium homeostasis, suggesting that calcium signaling plays a role in PAH-induced developmental toxicity, as has been shown previously in dioxin-exposed zebrafish at 48 hpf (Alexeyenko et al. 2010) . In a separate study of TCDD effects on h eart development, transcriptional changes related to calcium homeostasis preced ed the development of cardiac malformations in zebrafish, suggesting that they may be causal for malformations rather than simply a result of reduced blood flow (Carney et al. 2006). Though early calcium influx is a well-known response to several AHR ligands, the dependence of this response on AHR binding and the consequence within the developmental context are unknown.
PAHs have previously been shown to increase intracellular ca lcium through protein tyrosine kinases, inhibiting SERCA activity, and activating RYR receptors, though the intensity and duration of the response is dependent on PAH and cell type (Archuleta et al. 1993; Krieger et al. 1995; Gao et al. 2005) . All three PA Hs in our study increased transcription of calcium binding protein s100z. The S100 family of EF-hand calcium binding proteins regulates a diverse range of cellular functions in a calcium -dependent manner, and is associated with many pathological conditions including inflammation, atherosclerosis, diabetes, and neurodegeneration (Hermann et al. 2012) . Future investigation of the dependence of these transcriptional changes on AHR signaling will provide insight into whether they represent a common mechanism, o r are induced via different molecular responses to the PAHs in our study
Differential AHR activation results in distinct RELA regulatory responses to PAH exposures RELA was a predicted transcriptional regulator of both the BAA and DBT -PYR toxicological responses. Despite this, there was little overlap in the transcriptional networks (Figure 5).
Differential AHR activation c an explain the AHR gene battery that was uniquely induced by BAA at 24 hpf. However, a large portion of the RELA network expressed in re sponse to DBT and PYR was not affected by BAA exposure (Figure 5). This difference could potentially be explained by dose. BAA is the least soluble in water, and body burden was an order of magnitude lower that the other PAHs. We therefore cannot exclude t he possibility that BAA would activate the DBT -PYR transcriptional network at an eq uivalent internal concentration. Body burdens of DBT and PYR -exposed embryos are within the range reported to induce toxicity through mechanisms classified under “nonpolar n arcosis”, such
48 as interference with lipid fluidity and membrane function (Vanwezel and Opperhuizen 1995). The general pattern of narcotic response, including lost sense of balance, response to stimuli, and reduced ventilation frequency, is not applicable t o the early developmental stages of embryos analyzed in this study. However, DBT concentrations in embryos averaged 3.4 μmol/g at 24 hpf, and PYR reached 2.9 μmol/g by 48 hpf following exposure to 25 μM waterborne concentrations. Toxicity from nonpolar narcosis has been reported to occur at 2-8 μmol/g body weight, depending on the compound and organism (Vanwezel and Opperhuizen 1995). The data presented here characterize the extensive molecular response to these relatively high internal concentrations, and our analysis identified RELA as a significant mediator in the DBT -PYR transcriptional network. Interestingly, though BAA toxicity was induced by a lower body burden concentration of 0.12 μmol/g at 48 hpf, network analysis also identified RELA as a signific ant regulator of BAA -induced transcriptional changes. This suggests that RELA involvement in PAH toxicity is modulated by both AHR activation and PAH concentration. Future studies with multiple PAHs would be useful for identifying whether transcriptional n etworks identified here are differentially induced by diverse PAH structures.
Transcriptional responses to PAH exposures are conserved across species We compared the profiles of genes differentially regulated by three PAHs in the developing zebrafish embry o and identified disrupted biological processes that overlap notably with studies in other model systems. The genes differentially regulated by BAA were consistent with previous reports of AHR activation by this PAH, and many of them were identified in array studies with other known AHR ligands in zebrafish. All three PAHs misregulated genes important in vasculature development and cardiac function. This has been observed in BaP -exposed rats, as well as in previous studies of fish exposed to a number of PAH s (Incardona et al. 2009; Incardona et al. 2011; Huang et al. 2012; Jules et al. 2012) . Oxidative stress was a component of the toxic response, as has also been reported previously, and we observed differential regulation of immune -related genes, particula rly by DBT and PYR.
Though fewer studies have examined PAHs that are not strong AHR agonists, PAHs that do not induce CYP1A have similarly been observed to induce inflammatory cytokines in cells in culture (Suresh et al. 2009; Ovrevik et al. 2010) . PAHs ar e known immunotoxicants in fish, with well -established effects on lymphocytes (Krieger et al. 1994; Reynaud and Deschaux
49 2006). The gene expression changes observed in this study, however, primarily represent innate immune responses, as the adaptive immune system is not mature until weeks 4 -6 of development (Meeker and Trede 2008) . We therefore would not expect to see substantial overlap between the genes observed in this study and others conducted with tissues from adult organisms. Nevertheless, calcium bi nding and immune response were identified as important differentially expressed gene clusters in human macrophage leukemia cells exposed to diverse PAHs in vitro, and metal ion binding and transport were the most significant biological process es associated with occupational PAH exposure in peripheral blood of coke-oven workers (Wan et al. 2008; Wu et al. 2011) . Chronic PAH exposure in coke-oven workers has also been associated with altered immunological parameters, including increased TNFα in serum, as well increased markers of lipid peroxidation and oxidative stress (Jeng et al. 2011) . Increased malondialdehyde and decreased reduced glutathione were similarly observed in bronchial asthma patients, and correlated with blood phenanthrene levels, providing fur ther evidence of PAH -induced oxidative stress in human populations (Suresh et al. 2009).
We identified multiple potential biomarkers of individual PAHs over time, as well as genes commonly misregulated by PAHs with differential AHR affinity. Many of the s ignificant biological processes disrupted in this study , such as ion homeostasis, have been observed previously in other models, and provide insight into fundamental molecular pathways that are sensitive to PAH exposure and conserved between organ systems and species. Further investigation of these pathways in response to more structurally diverse PAHs in the environment will be invaluable to understanding the hazard potential of PAH exposure during development.
Acknowledgements
We are grateful to Cari Buch ner, Carrie Barton and the staff at the Sinnhuber Aquatic Research Laboratory for fish husbandry expertise. Bradley Stewart processed the microarrays at the University of Wisconsin McArdle Laboratory of Cancer Research Microarray Facility. We would also like to thank Tanguay laboratory members, in particular Jane La Du, Michael Simonich and Sean Bugel, for their feedback and critical review of the manuscript. This work was supported by the NIEHS Superfund Research Program P42 ES016465, Core Center Grant P30 ES000210 and the NIEHS Training Grant T32 ES007060.
50 Pacific Northwest National Laboratory is a multi -program national laboratory operated by Battelle Memorial Institute for the DOE under contract number DE-AC05-76RLO1830.
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55 Figure 2-1 PAHs induce abnormalities in developing zebrafish Representative images of 120 hpf larvae after exposure to (A) 1% DMSO control, (B) 25 μM DBT, (C) 25 μM PYR, and (D) 25 μM BAA from 6 to 48 hpf.
56 Figure 2-2 Body burdens of PAH detected in embryos
PAH was detected in embryos exposed to (A) DBT, (B) PYR, and (C) BAA from 6 to 24 (solid lines) or 48 (dashed lines) hpf. *Significantly different than time-matched DMSO control (Mann-Whitney rank sum test, p < 0.05). aSignificant difference between 48 and 24 hpf samples at the same exposure concentration (Mann-Whitney rank sum test, p < 0.05).
57 Figure 2-3 Differentially expressed transcripts in PAH-exposed embryos A) Bidirectional hierarchical clustering heatmap of log2 fold change (FC) values of all 1079 genes significantly differentially expressed compared to control (One-way ANOVA with 5% FDR, adjusted p value < 0.05). Comparison of significant genes between 24 and 48 hpf for (B) DBT, (C) PYR and (D) BAA exposure groups is shown by Venn diagram. Heatmap enlargements show transcripts differentially expressed (adjusted p value < 0.05, >2-FC) at both time points for each PAH.
58 Figure 2-4 Direct comparison of PYR and DBT expression values Comparison of gene expression between PYR and DBT treatment groups at 24 and 48 hpf. Linear regressions of log2 FC values for the union of transcripts significantly (p<0.05) misregulated by DBT or PYR compared to control (n = 712). Linear associations at (A) 24 hpf and (B) 48 hpf were both significant (p < 0.001).
59 Figure 2-5 Comparison of AHR, RELA and JUN regulatory networks Networks of transcripts under regulatory control of AHR, RELA and JUN, that were misregulated in response to BAA (red), or DBT/PYR (blue) exposure. Transcripts that overlapped between the two PAH networks are represented in purple. Significant transcripts (p<0.05) from both the 24 and 48 hpf time points were combined to create the regulatory network.
60 Table 2-1 Developmental abnormalities in PAH-exposed embryos Mean percentage of embryos (95 percentile) with malformations observed at 120 hpf following exposure to 25 μM BAA, DBT, PYR or DMSO control from 6 to 48 hpf. * Significantly different than DMSO Control, p < 0.05, one-way ANOVA with Dunnett's post hoc test.
Effect
Treatment Mortality Axis Eye Jaw Pericardial Edema Snout Yolk Sac Any Malformation Control 4 (1, 13) 3.1 (1, 10) 4.2 (1, 16) 7.3 (2, 21) 4.2 (1, 13) 4.2 (1, 15) 4.2 (1, 15) 11 (5, 22) DBT 3.3 (0, 58) 44.8 (9, 87)* 15.5 (1, 78) 75.9 (21, 97)* 55.2 (12, 92)* 19 (2, 76)* 15.5 (1, 75) 83.3 (39, 97)* PYR 5 (0, 57) 12.3 (1, 61) 5.3 (0, 72) 98.3 (33, 100)* 68.4 (19, 95)* 22.8 (2, 79)* 36.8 (4, 88)* 98.3 (33, 100)* BAA 1.7 (0, 67) 8.5 (1, 55) 10.2 (0, 74) 81.4 (25, 98)* 74.6 (23, 97)* 32.2 (4, 85)* 54.2 (9, 94)* 85 (41, 98)*
60 61
Table 2-2 Comparison of malformations induced by PAH treatments P values of Tukey’s all pairwise post hoc test are displayed for each comparison, malformations that were significantly differe nt between treatment groups are shaded (p < 0.05).
Effect
Test Mortality Axis Eye Jaw
Pericardial
Edema Snout Yolk Sac
Any
Malformation
DBT v Control 8.53E-01 4.30E-06 1.41E-01 2.70E-06 2.50E-06 4.66E-02 1.01E-01 7.20E-09 PYR v Control 7.09E-01 6.02E-02 6.53E-01 1.50E-04 3.10E-08 2.22E-02 1.87E-03 2.10E-05 BAA v Control 9.47E-01 1.83E-01 3.31E-01 8.20E-07 4.60E-09 3.44E-03 3.10E-05 6.10E-09 DBT v PYR 9.31E-01 1.91E-03 4.84E-01 1.75E-01 4.63E-01 9.27E-01 1.67E-01 1.85E-01 BAA v DBT 8.93E-01 5.10E-04 8.34E-01 8.83E-01 1.80E-01 4.59E-01 8.26E-03 9.80E-01 BAA v PYR 7.23E-01 8.07E-01 7.93E-01 2.58E-01 8.22E-01 6.87E-01 3.78E-01 2.21E-01
61 62
Table 2-3 Biological functions affected by PAH exposure at 24 hpf Significantly enriched biological functions identified by DAVID analysis of all transcripts differentially regulated (adjusted p value < 0.05) by BAA exposure or by DBT and PYR at 24 hpf. E score: overall cluster enrichment score, %: percentage of total gene list involved in functio nal cluster, p-values determined by modified Fischer’s Exact test (EASE score).
Biological Process GO Term Downregulated genes Upregulated genes E Score
% P Value
BAA hormone metabolic
process
GO:0042445 cyp1a, cyp1b1, cyp1c1, cyp1c2,
si:dkey-94e7.2
2.06 15.79 5.12E-03
tissue development GO:0009888 mstnb foxq1l, ptn, ctgfb 1.21 21.05 2.77E-02 DBT and PYR
fatty acid biosynthetic
process
GO:0006633 elovl6, fads2, ptgds, si:ch73-131e21.5, tpi1b ch25h, elovl7a 2.67 3.02 6.10E-04 ion transport GO:0006811 atp2a1l, cpt1b, gabra1, grin1b, KCNAB1, kcnip1b, kcnip3, LOC100004247, rhbg,
sfxn4, si:ch211-195b13.1, si:ch211-221p4.4,
slc24a5, zgc:101827, zgc:113361,
zgc:158296
LOC571584, si:ch211-244h7.4,
slc22a18, slc31a1, tmem38b,
zgc:162356, zgc:162495
2.32 8.30 7.86E-03
skeletal muscle contraction GO:0003009 homer1, mb, si:rp71-17i16.4, tnni2b.2 2.18 1.51 1.10E-03 steroid biosynthetic
process
GO:0006694 cyp17a1, hmgcs1, hsd17b7, lss, nsdhl, rdh8l ch25h, dhcr7 2.14 3.02 9.43E-04 oxoacid metabolic process GO:0043436 acsf3, cpt1b, elovl6, fabp11b, fads2, ghra, hibadhb, mdh1b, ptgds, rbp1a, rnpep,
si:ch73-131e21.5, tpi1b, tyrp1b,
zgc:113076, zgc:154046
ch25h, elovl7a, mthfd1 1.93 7.17 1.27E-02
intermediate filament
organization
GO:0045109 dnajb6b, krt1-19d, krt23, nefm 1.80 1.13 6.71E-03 negative regulation of cell
proliferation
GO:0008285 bdnf, cd9a, cx43, smad3b, tnfrsf9a, wfdc1,
zgc:114127, zgc:158296
agt, msxe, notch2, tbx16, tnfb 1.77 4.91 1.67E-02
muscle cell development GO:0055001 homer1, LOC796577, myoz1a, zgc:158296 myog 1.71 1.89 1.89E-02 sterol biosynthetic process GO:0016126 hmgcs1, lss, nsdhl ch25h, dhcr7 1.56 1.89 5.49E-03
62 63
Table 2-3 (Continued)
Biological Process GO Term Downregulated genes Upregulated genes E Score
% P Value
DBT and PYR cellular amide metabolic
process
GO:0043603 ghra, hibadhb, mdh1b, tpi1b, zgc:113076 1.41 1.89 2.64E-02 monosaccharide catabolic
process
GO:0046365 hibadhb, mdh1b, pfkma, tpi1b, zgc:162337 1.28 1.89 3.16E-02 regulation of erythrocyte
differentiation
GO:0045646 inhbaa, mafba, spi1l 1.09 1.13 4.65E-02
63 64
Table 2-4 Biological functions affected by PAH exposure at 48 hpf Significantly enriched biological functions identified by DAVID analysis of all transcripts differentially regulated (adjusted p value < 0.05) in BAA exposed embryos, and common transcripts disrupted by DBT and PYR at 48 hpf. E score: overall cluster enrichment score, %: percentage of total gene list involved in functional cluster, p-values determined by modified Fischer’s Exact test (EASE score).
Biological Process GO Term Downregulated Genes Upregulated Genes E
Score % P Value
BAA
cellular homeostasis GO:0019725 edn2, cxcl12b, atp2a2a slc30a1, cxcr4a, zgc:92066, gsr, prdx1, ccl1, ccr9a
2.75 14.29 4.50E-04
chemotaxis GO:0006935 cxcl12b, edn2 ccr9a, cxcr4a, ccl1 2.50 7.14 2.18E-03 hormone met. process GO:0042445 lrata ugt1b5, ugt1b7, cyp1b1, cyp1c2, cyp1a, cyp1c1
2.16 5.71 1.26E-02
tetrapyrrole met.
process
GO:0033013 - zgc:77234, ugt1b5, ugt1b7, cyp1a 1.92 4.29 1.25E-02 vasculature
development
GO:0001944 cx39.4, cxcl12b ctgfb, cxcr4a,figf, tiparp 1.89 8.57 1.01E-02 H2O2 met. process GO:0042743 - cyp1a, prdx1 1.59 4.29 5.65E-03 cation transport GO:0006812 armc1l, atp2a2a, cx39.4, slc5a1 cdkn1bl, slc30a1, zgc:92066 1.32 10.00 3.83E-02 organ development GO:0048513 atp2a2a, col7a1, cx39.4, cxcl12b, edn2, prl, slc5a1 cdkn1bl, ctgfb, cxcr4a, cyp1a, figf, foxq1, foxf2a, tiparp, ved
1.29 21.43 4.12E-02
DBT and PYR
oxoacid metabolic
process
GO:0043436 acaa1, acadl, adipor1a, adipor1b, agxt2l1, agxta, agxtb, amt, cpt1b, elovl4b, glsa, got2a, hadha,
hadhb, idh3b, LOC565975, lta4h, mdh1b, padi2,
rbp2b, rnpep, sc5dl, sgpl1, zgc:113076,
zgc:136850, zgc:154046
aspg, cbsb, cyp1a, cyp26c1, gldc, npc1,
phgdh, ppat, psat1, slc1a3a
4.72 8.35 2.66E-05
embryonic
development ending
in birth or egg
hatching
GO:0009792 ift52, ric8a, zfpm2b capn2b, cebpb, col4a3bp, cyp1a, eng1b, evx1, foxa, gas1b, gata2a, hoxa2b, hoxa4a,
hoxb2a, hoxc1a, hoxc6b, msxe, nkx2.7, pax1a,
phgdh, si:ch211-204c21.1, tcap, tgfbr1a
3.61 5.90 1.01E-04
regionalization GO:0003002 egr2b, ift52, neurod cyp26c1, egr2a, eng1b, evx1, foxa, gas1b, hhex, hoxa2b, hoxa4a, hoxb2a, hoxc4a,
hoxc6b, pax1a, tcap, tgfbr1a
3.38 4.18 2.75E-04
64 65
Table 2-4 (Continued)
Biological Process GO Term Downregulated Genes Upregulated Genes E
Score % P Value
DBT and PYR
neurogenesis GO:0022008 bdnf, clic5, crx, egr2b, gnao1b, LOC799290, mbp, mbpa, neurod, otx5, rab3aa, rnd1, spon2b, vcanb
ascl1a, btg4, cebpb, cxcr4a, egr2a, eng1b,
epha2, evx1, foxa, gas1b, gata2a, gdf7,
her15.1, hoxa2b, mag, nr2f6b, phgdh,
slc1a3a, tgfbr1a, unc5b, uts1
2.77 7.62 3.27E-03
embryonic organ
development
GO:0048568 clic5, neurod, zfpm2b cebpb, gas1b, gata2a, hoxa2b, hoxa4a, hoxb2a, hoxc4a, myca, otop1, tcap, tgfbr1a
2.43 3.44 2.40E-03
positive regulation of
macromolecule
metabolic process
GO:0010604 crx, egr2b, fkbp1ab, ift52, klf2a, LOC570917, maf, neurod, npas4, otx5, psmd4b, psmd7, psmd8,
rxrga, tnni2b.2, zfpm2b, zgc:110116
ascl1a, cask, cebpb, cebpg, egr2a, evx1, foxa,
gata2a, gdf7, her15.1, hhex, hoxa2b,
im:7162084, irf11, myca, pth1a, sox19a,
tgfbr1a, tnfb, uts1, vgll2b, zgc:158781
2.35 9.34 2.19E-03
negative regulation of
cell communication
GO:0010648 gnai2, hcrt, rgs11, zgc:136569, bcl6ab cyp26c1, dkk1b, gas1b, hhex, im:7162084, npc1, onecut1, rgs4
1.99 3.44 1.01E-02
cellular component
morphogenesis
GO:0032989 bbs7, bdnf, clic5, cryaa, egr2b, rab3aa, spon2b, vcanb
bcl6ab, col4a3bp, cxcr4a, egr2a, gas1b, gdf7,
hoxa2b, LOC796577, onecut1, sfrp5,
si:ch211-204c21.1, slc1a3a, tcap, unc5b
1.93 5.16 9.16E-03
central nervous
system development
GO:0007417 egr2b, faim2, gnao1b, LOC799290, mbpa, mbpa, neurod, sepp1a, sh3gl2
ascl1a, cxcr4a, cyp26c1, dkk1b, egr2a,
eng1b, evx1, foxa, gas1b, gata2a, hhex,
hoxa2b, hoxb2a, msxe, nkx2.7, phgdh
1.87 5.41 1.27E-02
hormone met. process GO:0042445 lrata, rbp2b crhbp, cyp1a, cyp1b1, cyp26c1, scarb1, 1.82 1.97 1.51E-02 organ morphogenesis GO:0009887 agc1, bbs7, clic5, crx, cryaa, cryaa, fkbp1ab, ift52, neurod, otx5, sgpl1, six6a
cmlc1, gas1b, hhex, hoxa2b, hoxa4a, hoxb2a,
hoxc4a, im:7162084, msxe, myca, otop1,
tcap, tgfbr1a, tnfb, zgc:158781
1.79 6.39 1.62E-02
fatty acid oxidation GO:0019395 acaa1, adipor1a, adipor1b, cpt1b, hadha, hadhb, zgc:154046
1.66 1.47 7.21E-03
65 66
Table 2-4 (Continued)
Biological Process GO Term Downregulated Genes Upregulated Genes E
Score % P Value
DBT and PYR
muscle tissue dev. GO:0060537 fkbp1ab, LOC100536295, rxrga, zfpm2b cmlc1, LOC796577, nkx2.7, tcap, vgll2b 1.65 2.21 2.45E-02 reg. of transmission of
nerve impulse
GO:0051969 bdnf, cspg5b, egr2b, gnai2, hcrt, rab3aa,
zgc:136569
egr2a, slc1a3a, tnfb, uts1 1.58 2.46 2.56E-02
pos. reg. of cellular
process
GO:0048522 bdnf, crx, egr2b, fkbp1ab, gnai2, hcrt, ift52, klf2a, LOC556700, LOC570917, maf, mfge8a, neurod,
npas4, otx5, pnp4b, psmd4b, psmd7, psmd8, rxrga,
si:ch211-135f11.1, tnni2b.2, trim35, zfpm2b,
zgc:100906, zgc:110116, zgc:110680
ascl1a, bcl6ab, cask, cebpb, cebpg, cyp1a, egf,
egr2a, evx1, flt4, foxa, GAS1 (3 of 3), gas1b,
gata2a, gdf7, her15.1, hhex, hoxa2b,
im:7162084, irf11, LOC794658, myca, ncs1a,
nkx2.7, onecut1, plk2b, pth1a, scarb1,
si:dkey-24p1.4, slc1a3a, sox19a, sst1.1,
tgfbr1a, tnfb, uts1, vgll2b, zgc:154093,
zgc:158781, zgc:85939
1.57 15.5 2.29E-02
oxidoreduction
coenzyme met.
process
GO:0006733 coq3, idh3b, idh3g, itgb1bp3, mdh1b, pgls, pnp4b, zgc:113076
1.55 1.72 3.73E-03
pos. reg. of catalytic
activity
GO:0043085 gadd45bb, gnai2, gnao1b, gng13b, hcrt, psmd4b, psmd7, psmd8, ptplad1, rgn, si:ch211-135f11.1,
zgc:110116
cmlc1, cxcr4a, egf, LOC794658, myca, npr3,
pth1a, scarb1, slc11a2, tgfbr1a, tnfb
1.52 5.9 2.22E-02
pyridine nucleotide
metabolic process
GO:0019362 idh3b, itgb1bp3, mdh1b, pgls, pnp4b, zgc:113076 1.44 1.47 9.14E-03 cellular amino acid
metabolic process
GO:0006520 agxt2l1, agxta, agxtb, amt, glsa, got2a, padi2 aspg, cbsb, gldc, phgdh, ppat, psat1, slc1a3a 1.43 3.19 2.33E-02 cell morph. involved
in differentiation
GO:0000904 bdnf, clic5, cryaa, egr2b, rab3aa, spon2b, vcanb cxcr4a, egr2a, gas1b, gdf7, hoxa2b, si:ch211- 204c21.1, slc1a3a, unc5b
1.38 3.44 3.54E-02
ear development GO:0043583 bdnf, clic5 gas1b, her15.1, hoxa2b, myca, otop1, tcap 1.35 1.97 2.53E-02 66 67
Table 2-S1 Primer sequences used for qRT-PCR
Ensembl Transcript ID
Zebrafish Gene
Symbol Forward primer Reverse Primer
ENSDART00000010918 agt TGACGGACACACAGTTTTAC GTTGCTTCAGGTTGAAATGC ENSDART00000105896 atp2a1l AGCAGTTCATTCGTTACCTG AGAACAACCAGCCAGAAATC ENSDART00000077511 ccr9a GCATGTTGGTATTTGAAGCC CTGTGTCCGACATAACAGAG ENSDART00000066439 ch25h ACCACAAATACACATCCACC TCATTCAAAGTGCAGTGTCC ENSDART00000017756 ctsl.1 GGACTCCTACCCCTATGAAG ATAACCAACAGCCAGAACAC ENSDART00000038200 cyp1a TGCCGATTTCATCCCTTTCC AGAGCCGTGCTGATAGTGTC ENSDART00000099870 cyp1b1 CTGCATTGATTTCCGAGACGTG CACACTCCGTGTTGACAGC ENSDART00000019953 cyp1c1 AGTGGCACAGTCTACTTTGAGAG TCGTCCATCAGCACTCAG ENSDART00000016487 cyp1c2 GTGGTGGAGCACAGACTAAG TTCAGTATGAGCCTCAGTCAAAC ENSDART00000103784 edn2 CCAGGATCAGCTAGAGAGAG ATTTCACTGGTGTGGAAGAG ENSDART00000109464 g0s2 ATAACCACCGACAAACAAGG AGCATGTCAAAGTCTGGTTC ENSDART00000100386 mstnb AAGAGGACGATGAACATGC GATCGTATTCGGTGTCTTCC ENSDART00000025669 slc16a9b TCCCTGTCACCAAGAACTAC TGAAGTAAAACGCCAGATCG ENSDART00000130131 sult6b1 GTGGGTTTAACTGGATGGTG GAGACCACTGTGTCTTTCG ENSDART00000017569 tnfb GTCCTACAGCACCATTTACC ATTCAGTGCACAACTCTCAC 68 Table 2-S2 Percent recovery by GC-MS for PAH body burden studies Percent recovery GC-MS method of PAH detection in zebrafish embryos, calculated from laboratory control samples spiked with BAA, DBT or PYR in DMSO.
Spike
(nmoles)
Average Detected
(nmoles)
Percent
Recovery
Standard
Deviation
BAA 24 hpf
5 4.5 89.1 3.7
25 22.1 88.6 5.6
50 47.2 94.4 3.8
125 118.6 94.8 4.9
DBT 24 hpf
5 5.1 101.4 2.5
25 25.0 100.1 1.7
50 49.9 99.9 3.8
125 134.5 107.6 2.6
PYR 24 hpf
5 5.5 109.7 4.0
25 29.1 116.4 10.3
50 61.6 123.1 5.2
125 156.7 125.4 10.6
BAA 48 hpf
5 4.6 91.1 1.1
25 20.1 80.3 1.5
50 44.1 88.2 3.0
125 111.3 89.0 2.5
DBT 48 hpf
5 5.3 105.7 3.1
25 26.0 103.8 3.3
50 51.2 102.4 4.7
125 136.17 108.94 2.94
PYR 48 hpf
5 5.1 102.3 6.1
25 25.8 103.4 3.7
50 56.0 111.9 4.2
125 144.7 115.8 5.2
69 Table 2-S4 Gene expression values detected by microarray and qRT-PCR Mean log2 fold change and B-H adjusted p-values of differentially regulated transcripts from the microarray, compared with log2 fold change (mean ± SD) detected with qRT-PCR. aSignificantly different from vehicle control (One-way ANOVA with Tukey’s post hoc test and 5% FDR, p<
0.05) bSignificantly different from vehicle control (One-way ANOVA with Tukey’s post hoc test, p< 0.05).
24 hpf 48 hpf
Zebrafish Gene
Symbol
Human
Gene
Symbol
DBT
Array
log2 FC
DBT QPCR
log2 FC
PYR
Array
log2 FC
PYR QPCR
log2 FC
BAA
Array
log2 FC
BAA QPCR
log2 FC
DBT
Array
log2 FC
DBT QPCR
log2 FC
PYR
Array
log2 FC
PYR QPCR
log2 FC
BAA
Array
log2 FC
BAA QPCR
log2 FC
agt AGT 2.6ᵃ 2.7 ± 0.7ᵇ 1.5 2.1 ± 1.3 -0.0 0.1 ± 0.9 --- --- --- --- --- --- atp2a1l ATP2A1 -1.1ᵃ -1.8 ± 0.7 -0.6 -0.9 ± 0.8 -0.1 0.1 ± 0.9 --- --- --- --- --- --- ccr9a CCR9 0.04 0.4 ± 1.0 0.2 0.8 ± 0.5 0.7 1.7 ± 0.5 -0.2 0.2 ± 0.5 -0.1 -0.2 ± 0.5 1.8ᵃ 1.3 ± 0.3ᵃ ch25h CH25H 2.2 2.7 ± 1.3ᵇ 2.6ᵃ 3.0 ± 1.1ᵇ 0.1 1.0 ± 0.5 --- --- --- --- --- --- ctsl.1 CTSL1 --- --- --- --- --- --- 0.5 -0.2 ± 0.4 -0.1 -0.4 ± 0.1 2.1ᵃ 1.7 ± 0.2ᵇ cyp1a CYP1A1 0.6 -0.0 ± 0.5 0.7 0.5 ± 0.5 4.1ᵃ 4.8 ± 0.4ᵇ 0.3 0.8 ± 0.2 1.1 1.4 ± 0.5ᵇ 5.1ᵃ 6.6 ± 0.3ᵇ cyp1b1 CYP1B1 0.2 -0.8 ± 0.2 0.3 -0.5 ± 0.5 2.8ᵃ 2.4 ± 0.3ᵇ -0.2 0.6 ± 0.3 0.0 0.1 ± 0.6 2.5ᵃ 2.6 ± 0.3ᵇ cyp1c1
-0.8 -0.3 ± 0.4 0.5 -0.1 ± 0.5 2.5ᵃ 2.2 ± 0.4ᵇ 0.2 -0.2 ± 0.2 0.7 -0.2 ± 0.3 4.7ᵃ 4.1 ± 0.4ᵇ cyp1c2 -0.2 -0.3 ± 0.2 0.3 0 ± 0.2 1.7ᵃ 1.6 ± 0.3ᵇ -0.4 -0.2 ± 0.3 -0.2 -0.3 ± 0.3 2.5ᵃ 3.1 ± 0.3ᵇ g0s2 G0S2 -1.3ᵃ -1.6 ± 0.4ᵇ -0.8 -0.5 ± 0.3 -0.2 0.1 ± 0.2 -0.2 -0.4 ± 0.5 -1.0ᵃ -0.8 ± 0.4 -1.1ᵃ -0.6 ± 0.2 mstnb MSTN -1.8ᵃ -0.7 ± 0.1 -0.6 -0.2 ± 0.3 -1.5ᵃ -0.3 ± 0.2 --- --- --- --- --- --- si:ch211-202b2.2 EDN2 --- --- --- --- --- --- -0.0 -0.0 ± 0.1 -0.3 0.0 ± 0.3 -0.9ᵃ -0.8 ± 0.2ᵇ slc16a9b SLC16A9 3.0ᵃ 3.3 ± 0.3ᵇ 2.6ᵃ 2.9 ± 0.4ᵇ 0.2 0.3 ± 0.8 --- --- --- --- --- --- sult6b1 SULT6B1 0.1 0.1 ± 0.1 -0.2 0.2 ± 0.3 1.0ᵃ 1.0 ± 0.2 0.0 -0.3 ± 0.3 0.0 -0.3 ± 0.2 1.4ᵃ 1.0 ± 0.2ᵇ tnfb TNF 2.7ᵃ 2.9 ± 1.0ᵇ 3.2ᵃ 3.2 ± 0.6ᵇ 0.6 0.0 ± 0.2 1.0 0.8 ± 0.2 2.5ᵃ 2.3 ± 0.7ᵇ 0.1 -0.2 ± 0.2
69 70
Table 2-S5 Predicted transcription factors for PAH responses Transcription factors identified as significantly over-connected (p<0.05) to genes differentially expressed (p<0.05) in response to PAH exposure at 24 and 48 hpf. Significance was calculated by hypergeometric distribution in MetaCore. Actual: Number of genes in the dataset that interact with the transcription factor, Expected: Number of genes in the dataset predicted to interact with the transcription factor based on total number of interactions on the Agilent platform calculated as mean value for hypergeometic distribution, Ratio: Connectivity ratio (Actual/Expected), p-value: Probability to have the given value of Actual or higher (FDR adjusted p -value <0.05)
24 hpf 48 hpf
Network
Object
Name
BAA
Actual
BAA
Expected
BAA
Ratio
BAA
p-value
DBT-
PYR
Actual
DBT-PYR
Expected
DBT-
PYR
Ratio
DBT-
PYR p-
value
BAA
Actual
BAA
Expected
BAA
Ratio
BAA
p-value
DBT-
PYR
Actual
DBT-PYR
Expected
DBT-
PYR
Ratio
DBT-
PYR p-
value
AHR 4.00 0.51 7.78 1.53E-03 TFAP2A 10.00 2.65 3.77 2.81E-04 ARNT 4.00 0.13 30.03 8.38E-06 5.00 0.69 7.29 6.08E-04 CEBPD 3.00 0.19 15.68 8.74E-04 6.00 0.95 6.31 3.66E-04 JUN 19.00 9.07 2.09 1.95E-03 25.00 13.87 1.80 3.29E-03 CREB1 14.00 5.14 2.73 4.69E-04 55.00 30.53 1.80 1.31E-05 CUX1 6.00 0.60 9.97 2.95E-05 EGR2 9.00 3.10 2.90 3.81E-03 EN1 4.00 0.33 12.21 2.58E-04 ERG 8.00 1.38 5.81 5.67E-05 ESR1 37.00 19.46 1.90 1.12E-04 ESR2 11.00 2.89 3.81 1.51E-04 FOXO1 15.00 3.91 3.83 8.20E-06 FOSL2 6.00 1.13 5.32 7.97E-04
NR3C1 26.00 12.96 2.01 5.73E-04 10.00 3.09 3.24 9.31E-04 GLI1 7.00 1.50 4.66 6.80E-04 HBP1 3.00 0.19 15.97 5.68E-04
HES1 6.00 1.19 5.05 1.16E-03 HOXB1 3.00 0.19 15.97 5.68E-04 IRF4 6.00 1.44 4.17 2.95E-03
JUND 12.00 3.82 3.14 4.38E-04
KLF4 14.00 5.54 2.53 1.35E-03
KLF5 6.00 0.81 7.37 1.23E-04
NR1H3 9.00 2.69 3.34 1.45E-03
MYEF2 2.00 0.06 31.94 9.78E-04
70 71
Table 2-S5 (Continued)
24 hpf 48 hpf
Network
Object
Name
BAA
Actual
BAA
Expected
BAA
Ratio
BAA
p-value
DBT-
PYR
Actual
DBT-PYR
Expected
DBT-
PYR
Ratio
DBT-
PYR p-
value
BAA
Actual
BAA
Expected
BAA
Ratio
BAA
p-value
DBT-
PYR
Actual
DBT-PYR
Expected
DBT-
PYR
Ratio
DBT-
PYR p-
value
NFATC4 4.00 0.45 8.88 9.40E-04 NFE2L2 7.00 1.19 5.89 1.77E-04 REST 20.00 9.99 2.00 2.55E-03 NR4A2 5.00 0.76 6.60 8.84E-04 OTX2 6.00 1.38 4.36 2.34E-03 TP53 42.00 27.11 1.55 3.05E-03
PITX2 8.00 1.66 4.82 2.22E-04
PPARA 13.00 2.50 5.20 1.25E-06 13.00 3.82 3.40 1.14E-04 PKNOX1 3.00 0.28 10.65 2.22E-03
PGR 17.00 6.07 2.80 1.28E-04
RARA 10.00 3.69 2.71 3.90E-03
RELA 24.00 11.39 2.11 4.65E-04 16.00 3.81 4.20 7.49E-07 35.00 17.41 2.01 6.65E-05 RORA 10.00 2.46 4.07 1.76E-04
RXRA 10.00 2.68 3.73 3.59E-04 11.00 4.10 2.68 2.71E-03 SF1 9.00 2.38 3.78 5.89E-04
SIX1 5.00 0.53 9.39 1.60E-04
SIX4 3.00 0.20 14.65 9.16E-04
SOX5 11.00 3.29 3.35 4.39E-04
SOX6 5.00 0.72 6.94 6.19E-04
SP1 74.00 48.31 1.53 5.79E-05 24.00 11.95 2.01 3.48E-04 112.00 73.86 1.52 1.38E-06 SP3 9.00 1.97 4.56 1.45E-04
STAT5A 13.00 4.10 3.17 2.34E-04
TBP 27.00 15.03 1.80 2.40E-03
TCF7L2 17.00 6.67 2.55 3.88E-04
TWIST2 2.00 0.06 31.94 9.78E-04
UBTF 2.00 0.06 31.94 9.78E-04
YY1 30.00 15.56 1.93 4.52E-04
71 72
Chapter 3 - AHR2 mutant reveals functional diversity of aryl hydrocarbon receptors in zebrafish
Britton C. Goodale1, Jane K. La Du1, William H. Bisson2, Derek B. Janszen3, Katrina M. Waters3 and Robert L. Tanguay1*
1. Department of Environmental and Molecular Toxicology, the Environmental Health Sciences Center, Oregon State University, Corvallis, OR, USA
2. Pharmaceutical Biochemistry Group, School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland
3. Computational Biology and Bioinformatics Group, Pacific Northwest National Laboratory, Richland, WA, USA
Published in PLOS ONE
7(1): e29346. doi:10.1371/journal.pone.0029346
Open access article distributed under the terms of the Creative Commons Attribution License
73 Abstract
The aryl hydrocarbon receptor (AHR) is well known for mediating the toxic effects of TCDD and has been a subject of intense research for over 30 years. Current investigations continue to uncover its endogenous and regulatory roles in a wide variety of cellular and molecular signaling processes. A zebrafish line with a mutation in ahr2 (ahr2hu3335), encoding the AHR paralogue responsible for medi ating TCDD toxicity in zebrafish, was developed via Targeting Induced Local Lesions IN Genomes (TILLING) and predicted to express a non -functional AHR2 protein. We characterized AHR activity in the mutant line using TCDD and leflunomide as toxicological pr obes to investigate function, ligand binding and CYP1A induction patterns of paralogues AHR2, AHR1A and AHR1B. By evaluating TCDD-induced developmental toxicity, mRNA expression changes and CYP1A protein in the AHR2 mutant line, we determined that ahr2hu3335 zebrafish are functionally null. In silico modeling predicted differential binding of TCDD and leflunomide to the AHR paralogues.
AHR1A is considered a non -functional pseudogene as it does not bind TCCD or mediate in vivo TCDD toxicity. Homology modelin g, however, predicted a ligand binding conformation of AHR1A with leflunomide. AHR1A -dependent CYP1A immunohistochemical expression in the liver provided in vivo confirmation of the in silico docking studies. The ahr2hu3335 functional knockout line expands the experimental power of zebrafish to unravel the role of the AHR during development, as well as highlights potential activity of the other AHR paralogues in ligand-specific toxicological responses.
74 Introduction
The aryl hydrocarbon receptor (AHR), whi le best known for its role as an environmental sensor and mediator of 2,3,7,8 -tetrachlorodibenzo-p-dioxin (TCDD) toxicity, has captured attention in recent years with a growing body of research elucidating its endogenous functions. As a member of the bHLH -per-ARNT-sim(PAS) family of proteins, the AHR is a transcriptional regulator containing two evolutionarily -conserved domains: a basic helix - loop-helix (bHLH) domain, which enables binding to aromatic hydrocarbon -responsive elements (AHREs), and a PAS doma in, consisting of two 51 - amino acid imperfect repeats (PAS-A and PAS-B), responsible for dimerization, ligand binding and interaction with other proteins (Fukunaga et al. 1995; Fukunaga and Hankinson 1996) . Originally discovered for its role in modulating TCDD sensitivity in mice, the AHR binds a wide variety of ligand structures, including polycyclic and halogenated aromatic hydrocarbons (PAH and HAHs).
Ligand binding induces disassociation from a cytoplasmic protein complex and translocation to the nucle us where the AHR heterodimerizes with the aryl hydrocarbon nuclear translocator (ARNT) (Nebert et al. 1975; Schmidt and Bradfield 1996; Denison and Nagy 2003). The AHR-ARNT heterodimer, along with other transcriptional enhancers, binds to AHREs and activat es transcription of CYP1A, as well as NQO1, ALHD3A1, UGT1A6 and many other genes involved in metabolism, oxidative stress response and cell signaling (Nebert et al. 2000; Sartor et al. 2009) . The role of the AHR in mediating toxicity of environmental contaminant exposure has been extensively studied (reviewed in (Gu et al. 2000; Nebert et al. 2004; Kerkvliet 2009) ), and mechanism of action in immune, reproductive, developmental and other toxicological responses remain active areas of investigation. The dive rsity of physiological systems impacted by AHR activation and its crosstalk with other regulatory pathways support the notion that endogenous functions for the receptor likely preceded its role as an environmental sensor (Puga et al. 2009).
TCDD binding ac tivity of the AHR is conserved among vertebrates. Substitutions in critical residues produce variation in ligand affinity, which underlies differences in TCDD sensitivity between species, inbred mouse strains, and wild fish populations (Nebert et al. 1975; Ema et al. 1994; Hahn 2002; Wirgin et al.) . Structural comparisons of receptors provide information necessary for risk assessment extrapolation between species, as well as insight into receptor evolution (Hahn et al. 2006) . In addition, in silico modeling of the 75 AHR has emerged as a powerful screening tool for potential AHR ligands (Bisson et al. 2009; Murray et al.).
Developing fish embryos are extremely sensitive to AHR -mediated planar hydrocarbon toxicity and hold a number of experimental advantages inc luding development external to the mother, ease of observation, and genetic tractability. As such, zebrafish are a valuable model for investigation of developmental signaling processes in the context of xenobiotic exposures (Billiard et al. 2002; Carney et al. 2006) . In teleosts, genome -wide duplication events have resulted in co -orthologs for many mammalian genes. While some gene duplicates have become non -functional, others have been evolutionarily conserved via the partitioning of functions between paral ogues (Postlethwait et al. 2004) . Three AHR isoforms have been identified in zebrafish: AHR1A, AHR1B, and AHR2 (Tanguay et al. 1999; Andreasen et al. 2002; Hahn 2002; Karchner et al. 2005) . Numerous studies with known AHR ligands, however, have identified AHR2 as the primary mediator of early life stage toxicological effects in zebrafish (Prasch et al. 2003; Teraoka et al. 2003; Antkiewicz et al. 2006). Antisense oligonucleotide (morpholino) knockdown of AHR2 affords almost complete resistance to TCDD -induced developmental toxicity, and prevents the inhibitory effects of AHR ligands on epimorphic regeneration (Prasch et al. 2003; Mathew et al. 2006) .
Toxicity of many other HAHs and PAHs is also primarily dependent on AHR2. While AHR1B does bind TCDD, it is l ess sensitive to activation by TCDD than AHR2 (Karchner et al. 2005; Antkiewicz et al. 2006; Billiard et al. 2006) . In contrast, AHR1A does not bind TCDD and is deficient in transactivation activity (Andreasen et al. 2002; Karchner et al. 2005) . Beyond functioning as xenobiotic sensors, the zebrafish AHR paralogues are proposed to serve endogenous functions that have yet to be elucidated.
Recent studies have highlighted endogenous roles for the AHR in a complex array of immune system, cell cycle regulatory, reproductive and developmental processes (Peterson et al. 1993; Hernandez -Ochoa et al. 2009; Kerkvliet 2009; Matsumura et al. 2009; Singh et al. 2009) . AHR knockout mouse strains developed by three different groups illustrate the importance of the AHR in normal liver development and immune function, and continue to expand understanding of the receptor’s role in both toxicological responses and normal physiology (Fernandez-Salguero et al. 1995; Schmidt et al. 1996; Lahvis et al. 2005) . A 76 functional zebrafi sh AHR2 knockout line will allow for investigation of the biological functions of the receptor throughout the zebrafish lifespan, and will eliminate the concern of incomplete knockdown that can occur with morpholinos. Complete loss of AHR2 activity in a zebrafish line will also enable functional analysis of the other two receptors, which has to date been experimentally difficult. As the primary mediator of TCDD toxicity, we proposed AHR2 as a target of great value to the zebrafish community for Targeting In duced Local Lesions IN Genomes (TILLING). Here we describe characterization of AHR function in the first TILLING -indentified AHR2 mutant zebrafish. We report loss of AHR2 function in a mutant AHR2 line, and present evidence of ligand - and tissue -specific a ctivation and function of AHR1A and AHR1B.
Results
Generation of a functionally null AHR2 zebrafish line
The ahr2hu3335 line was established, upon request, by the Hubrecht institute from a TILLING- identified founder with a TTG to TAG point mutation in resi due Leu534, resulting in a premature stop codon in the transactivation domain of AHR2 (Figure 1). While the bHLH and PAS domains are predicted to remain intact in the truncated protein, the transactivation domain of zebrafish AHR2 is required for transcrip tional activation (Andreasen et al. 2002) . In addition, the premature stop codon location is > 55 nucleotides upstream of an exon -exon boundary, likely rendering the mutant AHR2 mRNA a target of nonsense-mediated mRNA decay, which will be further discussed below (Wittkopp et al. 2009).
ahr2 hu3335 zebrafish survived to adulthood with no consistently observed abnormalities during development. Jaw, gill and fin malformations were observed in adult fish, but did not appear to cause significant morbidity or mo rtality (Figure 2). The fins of ahr2hu3335 adult zebrafish are damaged compared to their ahr2+ clutch mates, a characteristic which persisted in the offspring of wild -type 5D-outcrossed ahr2hu3335/+ zebrafish (Figure 2A, B).
Visible jaw malformations in ahr2hu3335 adults prompted us to investigate bone structure using non-destructive microCt scanning. MicroCt imaging revealed structural differences in the neurocrania of an ahr2hu3335 and an aged -matched wild-type strain 5D adult zebrafish, including a stri king extension of the ethmoid and mandibular regions (Figure 2C, D) 77 (Cubbage and Mabee 1996) . Further, the dentary, maxilla and premaxilla of the ahr2hu3335 zebrafish had notably different structure, creating an extended mandible. Other bones, such as the orbitals and supraorbitals, appeared smaller in the ahr2hu3335 zebrafish, which may be an artifact of scanning reduced bone thickness compared to the wild type (Cubbage and Mabee 1996).
In comparison to their ahr+ and ahr+/hu3335 siblings, spawning activi ty of ahr2hu3335 homozygous crosses was less robust and egg fertilization rates were low (50 -75%). As is discussed further in regard to developmental toxicity assays, pericardial edema and jaw malformations occurred with higher incidence in some of the ahr2hu3335 clutches. Sporadic spawning activity of ahr2hu3335 homozygous crosses and successful in vitro fertilization demonstrated that the ahr2hu3335 mutation does not prevent reproductive function in this line. Irregular spawning, however, suggests deficits in reproductive physiology or behavior. ahr2hu3335 embryos are resistant to TCDD-induced developmental toxicity To assess AHR2 function in the ahr2hu3335 strain, we compared developmental toxicity of TCDD in the ahr2hu3335 mutants to ahr2+ embryos. Expo sure to 0.1, 1 or 10 nM TCDD resulted in a concentration -dependent increase in axis malformations and pericardial edema observed at 120 hpf in the ahr2+ embryos (Figure 3A, C). Of the fifteen endpoints evaluated, TCDD concentration was significantly correl ated with increases in yolk sac and pericardial edemas, and axis, eye, snout, jaw and trunk malformations (Table 2). Mortality, touch response, fin, pigment, brain, circulatory, somite and otic malformations were not significant responses in either fish li ne. ahr2hu3335 embryos were resistant to TCDD - dependent malformations, and the responses of ahr2+ and ahr2hu3335 embryos to TCDD exposure were significantly different from each other (Table 2). Background pericardial edema and jaw malformations were observ ed in ahr2hu3335 embryos but were not TCDD - dependent.
mRNA expression indicates the ahr2hu3335 mutation abrogates AHR2 function We evaluated mRNA expression to further assess AHR2 function, and observed a 16 -fold difference in AHR2 transcript abundance bet ween ahr2+ and ahr2hu3335 embryos (Figure 4A). This supports the hypothesis that AHR2 mRNA is degraded in the ahr2hu3335 line. We next examined AHR2-dependent gene expression to determine whether the point mutation 78 perturbs expression of downstream transcr iptional targets. Expression of CYP1A, CYP1B1, CYP1C1, CYP1C2, AHR1A and AHR1B transcripts were not significantly different between ahr2hu3335 and ahr2+ embryos (Figure 4A).
To further confirm the lack of AHR2 functionality, we investigated mRNA expression changes in response to TCDD, which induces AHR2 -dependent expression of a number of mRNAs at 48 hpf (Jonsson et al. 2007) . Developmental TCDD exposure induced robust expression of CYP1A, CYP1C1 and CYP1C2 mRNA at 48 hpf in ahr2+ embryos relative to vehicle-treated controls (Figure 4B). As expected in the absence of a functional AHR2, mRNA expression was not significantly elevated in ahr2hu3335 embryos exposed to TCDD.
AHR1A predicted to bind leflunomide but not TCDD
We recently reported a homology model th at has been used to predict binding affinity of potential ligands to the human, mouse and zebrafish AHRs (O'Donnell et al.) . In order to investigate differential function of the zebrafish AHR paralogues, we tested TCDD and a known AHR ligand with a non -classical structure, leflunomide, in a series of molecular docking studies. Sequence alignment of the mouse and zebrafish AHR -PASB domains produced identities of 65.1% (zfAHR1A), 78.5% (zfAHR1B) and 70.5 % (zfAHR2). High similarity between the three isoforms at the primary and predicted tertiary structural levels was also noted, with 74.3% (AHR2/1B) and 69.9% (AHR1B/1A) identity. TCDD and leflunomide were docked into zebrafish AHR1A -, AHR1B -, and AHR2 -LBD homology models. TCDD docked in AHR2 and AHR1B with pre dicted binding energies of -3.97 kcal/mol and -4.86 kcal/mol, respectively, but was unable to dock in AHR1A (Table 3, Figure 5A-B). Leflunomide was also able dock in AHR2 and AHR1B, with predicted binding energies of -2.13 kcal/mol and -1.97 kcal/mol, res pectively (Table 3). Interestingly, in contrast to TCDD, leflunomide docked into AHR1A, but in a unique orientation (Bisson et al. 2009) (Table 3, Figure 5E).
AHR1A possesses specific residues that play potential roles in TCDD insensitivity (Karchner et al. 2005). Key residues characterized in the mouse AHR -LBD influencing TCDD binding are conserved in zebrafish AHR2 and AHR1B, which are both TCDD sensitive (Pandini et al. 2007; Bisson et al. 2009; Pandini et al. 2009) . In AHR1A, residues His296, Ala386 an d Gln388 have been substituted with Tyr296, Thr386 and His388 (Karchner et al. 2005). The 79 side chains of these residues cause both decreased volume and altered polarity of the AHR1A binding pocket, in comparison to AHR2, AHR1B, as well as mouse and human A HRs (26). TCDD docking is consequently not possible in AHR1A, which has been confirmed both in vitro and in vivo (Andreasen et al. 2002; Karchner et al. 2005) . Homology modeling predicted that leflunomide, however, is able to dock in AHR1A with a unique orientation not found in human, mouse, or zebrafish AHR1B and AHR2 isoforms (O'Donnell et al.) . As shown previously, leflunomide docks in AHR2 and AHR1B with a hydrogen bond (HB) interaction between the nitrogen atom of the isoxazole ring of the ligand and t he OH of the side chain of Thr294 ( (O'Donnell et al.) , Figure 5C, D). Here we employed the homology model to examine AHR1A interaction with leflunomide for the first time, and discovered that the leflunomide docking position is flipped and a double HB inte raction between the nitrogen and oxygen of the isoxazole ring of the ligand and the side chain of Thr354 is formed (Figure 5E). A binding energy of -2.19 kcal/mol was predicted which is in the range calculated for the other two isoforms (Table 3). Based on these data, we predicted that leflunomide would be a functional AHR1A ligand.
CYP1A protein induction patterns are ligand- and AHR isoform-dependent We used immunohistochemical analysis of CYP1A protein expression as a biomarker of AHR activation to inves tigate in vivo AHR ligand binding patterns in TCDD and leflunomide - exposed larvae. Exposure to 1 nM TCDD from 6 -24 hpf induces AHR2 -dependent CYP1A expression in a number of tissues, including the heart, liver and enteric tract, with the predominant expression in the vascular endothelium of larvae (Figure 6A) (Andreasen et al. 2002). We focused our evaluation of AHR function on the most robust CYP1A induction patterns, which were observed in vasculature and liver (Andreasen et al. 2002; Carney et al. 2004). As predicted by the qRT -PCR results, CYP1A protein expression in TCDD -exposed ahr2hu3335 larvae was limited to faint vascular expression, just above background, in all embryos examined (Figure 6B). Exposure to 10 nM TCDD, which induced severe malformations and robust CYP1A expression in wild-type embryos, did not notably increase CYP1A expression in ahr2hu3335 larvae (data not shown).
To confirm the predicted binding of leflunomide to all three zebrafish AHRs in vivo , we examined CYP1A induction in ahr2hu3335 larvae exposed to 10 µM leflunomide from 48 -72 80 hpf. In comparison to wild type larvae, vascular CYP1A expression was drastically reduced in leflunomide -exposed ahr2hu3335 larvae (Figure 6C, D). In contrast to TCDD exposure, however, AHR2 -independent C YP1A expression was observed in the developing livers of leflunomide-exposed ahr2hu3335 larvae (Figure 6D). This expression pattern persisted in larvae exposed until 120 hpf, with vascular expression remaining low and liver expression increasing, likely due to growth that occurs from 72-120 hpf (data not shown).
Based on molecular docking studies (Figure 5), we hypothesized that leflunomide induced CYP1A in ahr2hu3335 larvae via activation of the other AHR homologs, and utilized splice - blocking morpholinos to transiently knock down AHR1A and AHR1B separately and in combination. We conducted immunohistochemical analysis of CYP1A expression at 72 hpf to capture the window of morpholino efficacy, which was confirmed with PCR using primers flanking the target si tes (Supplemental Figure 1). As the liver is small at 72 hpf, we employed double-staining with a hepatocyte nuclear factor 4α (HNF4α) antibody to confirm the presence of hepatocytes (Dong et al. 2007) (data not shown). CYP1A expression in AHR1B morpholino -injected ahr2hu3335 larvae persisted in the liver (Figure 6E), but was notably absent in the vasculature. In contrast, injection of the AHR1A morpholino in ahr2hu3335 embryos blocked leflunomide -induced expression of CYP1A in the liver, while faint vascular expression remained. When co -injected, the AHR1A and AHR1B morpholinos blocked all CYP1A expression in leflunomide -exposed ahr2hu3335 larvae (Figure 6F). When expression of all 3 AHR isoforms was eliminated, CYP1A expression in leflunomide -exposed embryos was indistinguishable from vehicle-exposed controls (Figure 6G).
Discussion
ahr2hu3335 zebrafish, homozygous for a point mutation in ahr2, survive to adulthood and are functional AHR2 knockouts by all measures tested. The premature stop codon in residue 534 is predicted to result in a non -functional protein due to its truncated transactivation domain. Though we cannot exclude the possibility that some biological activity of a potential cryptic protein remains, we saw no evidence to support its presence. A nalysis of ahr2hu3335 mRNA levels suggests that the mutant AHR2 transcript is at least partially degraded and the truncated protein may be present only at very low levels, if at all. 81 The ahr2hu3335 adult zebrafish exhibit notable fin and skeletal differen ces compared to wild type. We also observed a higher background of developmental abnormalities in ahr2hu3335 larvae. These phenotypes may not necessarily be due to the mutation; reduced spawning and small clutch sizes of ahr2hu3335 zebrafish limited the se lection of embryos for experiments, whereas large wild type clutches allow for precise selection of high -quality embryos. Studies in both AHR -deficient and AHR ligand -treated mice provide strong evidence of an endogenous role of the receptor in female repr oductive physiology.
Deficiencies in maintaining pregnancy and surviving lactation have been reported in AHR knockout mice (Abbott et al. 1999) , and disruption of AHR function alters ovarian development, folliculogenesis, steroid hormone synthesis, ovulati on and possibly reproductive senescence (Hernandez-Ochoa et al. 2009) . In keeping with AHR knockout mouse models, ahr2hu3335 zebrafish are capable of producing viable embryos, but exhibit decreased reproductive success. It is important to note, however, th at other ENU -induced mutations throughout the genome of this fish line could be responsible for observed phenotypic abnormalities. Zebrafish TILLING mutants require multiple outcrosses to reduce undesired mutations to background levels. Because outcrosses of the ahr2hu3335 line were in progress at the time of this study, it is premature to attribute all phenotypic abnormalities observed in ahr2hu3335 homozygotes to the mutation in ahr2. Decreased reproductive capacity of homozygous mutants, as well as fin a nd jaw abnormalities may represent interesting models of endogenous AHR function and certainly warrant further investigation if they persist in the mutant line following further outcrosses.
In the present study, we used TCDD as a tool to investigate AHR2 f unction in the ahr2hu3335 line. We found that ahr2hu3335 embryos were resistant to TCDD -induced developmental toxicity at concentrations that cause severe malformations in ahr2+ embryos. ahr2hu3335 embryos treated with 10nM TCDD showed few signs of morbidi ty at 120 hpf. Transient AHR2 knockdown delays, but does not prevent, TCDD -induced mortality (Prasch et al. 2003). Therefore it would be interesting to examine longer -term effects of TCDD exposure in future experiments with the ahr2hu3335 line.
The most w ell-known biomarker of AHR activation is the induction of CYP1A expression.
Among the suite of cytochrome P450 metabolizing enzymes in zebrafish, CYP1A, CYP1B1, 82 CYP1C1 and CYP1C2 are elevated in response to AHR agonist exposure (Jonsson et al. 2007). In ag reement with our developmental toxicity data, no elevation in CYP1A, CYP1C1 or CYP1C2 expression was observed in TCDD -exposed ahr2hu3335 embryos. Taken together, these data support the concept that AHR2 is not functional in this line. The notable, but statistically insignificant, increase in CYP1A expression following TCDD treatment in ahr2hu3335 embryos is likely due to TCDD activation of AHR1B, as further discussed below.
While the dependence of CYP1A activation by TCDD on AHR2 is well -established, studies with PAHs in zebrafish embryos have revealed diverse CYP1A expression patterns dependent on other AHR isoforms (Incardona et al. 2005; Incardona et al. 2006). This study represents the first time that an in silico -based modeling approach was utilized to investigate ligand binding by all three receptors. Molecular docking with TCDD predicted that both AHR1B and AHR2, but not AHR1A, would bind TCDD due to substitutions in the binding pocket. In contrast to TCDD, in silico modeling with leflunomide predicts favorable binding energies for all three zebrafish AHR isoforms. Interestingly, leflunomide docked into AHR1A with a different predicted conformation than in the other two receptors, but with equivalent affinity. This finding is particularly intriguing, as AHR1A is incapable of binding classical AHR ligands (Karchner et al. 2005) , is deficient in transactivation activity (Andreasen et al. 2002), and therefore was once considered non-functional.
We confirmed the AHR modeling results in vivo using CYP1A prote in expression as a biomarker of AHR activation. In keeping with our mRNA expression and in silico modeling studies, TCDD-exposed ahr2hu3335 larvae were largely devoid of CYP1A protein expression observed in TCDD -exposed ahr2+ larvae. Leflunomide also induc es strong vascular CYP1A protein expression in ahr2+ larvae, but unlike with TCDD, the ahr2hu3335 embryos exhibited striking leflunomide -induced CYP1A expression in the liver. This finding is in agreement with the modeling results. To tease apart AHR isofo rm-dependence of the residual CYP1A expression, we transiently knocked down the receptors individually and in combination in ahr2hu3335 larvae. We found AHR1B -dependent vascular induction and AHR1A -dependent liver induction of CYP1A expression. Knockdown o f AHR1A and AHR1B in combination prevented all CYP1A induction. Taken together, these data suggest that, contrary to 83 previous observations with TCDD, all three AHR isoforms are involved in leflunomide - induced CYP1A expression in zebrafish larvae.
These data demonstrate that there are concrete differences in ligand binding activity of the zebrafish AHRs, and that AHR1A is not a pseudogene as previously proposed, but rather has affinity for different ligand structures. While residual CYP1A expression has bee n observed in TCDD -treated AHR2 -morphants, it was faint and vascular in nature, attributable to incomplete knockdown (Prasch et al. 2003) . Our immunohistochemical results with the ahr2hu3335 line suggest that mild vascular expression of CYP1A is induced vi a AHR1B, and can be effectively knocked down to background with morpholino injection. AHR1A - dependent CYP1A expression is seemingly incongruous with previous investigation of AHR1A function in vitro, but the lack of a known AHR1A ligand limited previous ef forts. The AHR1A-dependent CYP1A expression pattern we observed here is consistent with the reported AHR1A mRNA expression in the liver (Andreasen et al. 2002).
Putative AHR1A ligands could be identified with further in silico modeling; work by Incardona and colleagues also offers clues with several PAHs that induce CYP1A expression independently of AHR2 (Incardona et al. 2005; Incardona et al. 2006; Incardona et al.) .
Pyrene induced liver expression of CYP1A in an AHR1A-dependent manner (Incardona et al. 2006), and more recently retene -induced CYP1A expression was shown to be incompletely dependent on AHR2 (Scott et al.). Here, we offer further evidence that AHR1A is a functional receptor in vivo , though the transactivation requirements for this receptor r emain to be elucidated. In vitro data with AHR chimera proteins suggest that transactivation requirements of AHR1A differ from those of AHR2 (Andreasen et al. 2002).
The presence of three apparently functional aryl hydrocarbon receptors in zebrafish raises several interesting questions: How do these receptors differ? What functions have led to their evolutionary conservation? And to what extent do the AHR1 receptors need to be considered in toxicological studies in zebrafish? While the presence of multiple AHRs certainly complicates study of receptor function in fish, subfunction partitioning among isoforms presents a unique opportunity to unravel the many physiological functions of the AHR that are conserved among vertebrates (Postlethwait et al. 2004) . As summarized in Table 4, the studies presented here add to a body of research demonstrating significant 84 differences in receptor expression, ligand binding, and mRNA induction activity. With respect to transcript localization, AHR2 is widely distributed throu gh most organs investigated in adult zebrafish, while AHR1A is mainly expressed in the liver, and to a lesser extent in the heart, kidney and swim bladder (Andreasen et al. 2002) . AHR1B expression has yet to be fully characterized, but our CYP1A IHC result s suggest that the isoform is widely distributed, but is expressed at much lower levels than AHR2. The subfunction partitioning of these receptors is not strictly locational. Overlapping expression of AHR2 and AHR1A has been previously described, and we al so noted overlap in AHR2 - and AHR1- dependent CYP1A expression patterns (Andreasen et al. 2002; Karchner et al. 2005) . A cell or tissue-level analysis may reveal more subtle localization differences, as has been implied in differential PAH -induced CYP1A pat terns in endocardial and myocardial tissue (Incardona et al. 2006; Incardona et al.) . Little is yet known about the endogenous function of these receptors and their downstream transcriptional targets. If expression of AHR1A and AHR1B is limited, it may be difficult to detect significant changes in their transcriptional targets in whole embryo homogenate. As we have shown here, however, the ahr2hu3335 line will ease the study of the other two receptors by removing the overpowering transcriptional changes ind uced through AHR2. The three receptors together present an intriguing opportunity to unravel multiple regulatory functions that may be conserved in the mammalian AHR.
This is the first report of CYP1A induction dependent on all three of the zebrafish AHRs.
However, toxicity mediated through the AHR1 receptors has not, as of yet, been documented. Pyrene liver toxicity was reduced with AHR1A knockdown, but AHR2 knockdown prevented the majority of the chemical’s developmental effects (Incardona et al. 2006). In the case of TCDD and other similarly-structured HAHs, the small binding pocket of AHR1A prevents it from having a role in ligand -induced toxicological effects. AHR1A and AHR1B receptors may hold little importance in toxicological studies with these compo unds.
Indeed the studies presented here support the large body of previous research indicating that TCDD-induced early life stage toxicity is mediated through AHR2. Though some CYP1A and other downstream target induction may occur via AHR1B, any developmen tal abnormalities caused by this pathway are more subtle than those investigated to date. The possibility remains, however, that AHR1B may play a role in later life stage impacts of 85 TCDD. These data warrant further investigation of the AHR isoforms with st ructurally diverse, less -well studied compounds. Ultimately, further bioinformatic and modeling efforts with zebrafish and mammalian AHRs could help determine the best model for human AHR activity, taking into account both ligand binding and receptor expre ssion characteristics.
This was the first time that all three AHR isoforms were knocked down in developing zebrafish. Our findings suggest that, consistent with mammalian literature, AHR function is not required to complete development (Schmidt et al. 1996 ; Gonzalez and Fernandez - Salguero 1998). Without full histological evaluations of the AHR1Amo/AHR1Bmo/ahr2hu3335 larvae at 120hpf, we cannot exclude non -lethal malformations, particularly hepatic abnormalities, which have been reported in AHR knockout mice (Schmidt et al. 1996; Lahvis et al. 2005) . It may not be possible to fully answer the question of whether the AHR paralogues are required for hepatic development in zebrafish with the tools employed here, as the liver undergoes significant development aft er 72hpf, when morpholino efficacy is in decline. We therefore present the ahr2hu3335 line as a valuable resource available to the zebrafish research community, and suggest that development of both AHR1A and AHR1B (already requested by the research communi ty) mutant lines would further extend the power of this model for investigating both the endogenous and ligand -mediated roles of the AHR in developing vertebrates.
Materials and Methods
Zebrafish lines and embryos
Adult zebrafish were housed according to Institutional Animal Care and Use Committee protocols at Oregon State University on a recirculating system with water temperature of 28±1°C and a 14 h light/10 h dark schedule. Zebrafish embryos carrying a point mutation in ahr2 (ahr2hu3335 strain) were requested and generously provided by the Hubrecht Institute.
The ahr2hu3335 line was identified from a library of N-ethyl-N-nitrosourea (ENU) - mutagenized zebrafish using the TILLING method as previously described (Wienholds et al. 2003). Offspring of hetero zygous ahr2hu3335 carriers were raised to adulthood at the Sinnhuber Aquatic Research Laboratory, and genotyped for the ahr2hu3335 point mutation withDNA isolated from fin clips (Wienholds et al. 2003) . PCR amplification was performed 86 with genomic DNA and ahr2 gene-specific primers (Table 1), the product was purified using a QIAquick PCR purification kit (Qiagen) and sequenced with an ABI 3730 capillary sequencer at the Center for Genome Research and Biocomputing at Oregon State University.
Homozygous carriers of the T -> A point mutation in residue 534 (Figure 1) were identified to create an ahr2hu3335 population. Because the TILLING method relies on random mutagenesis, mutant lines of interest carry other mutations throughout the genome. F1 fish are predic ted to carry 3 000-6000 mutations and multiple outcrosses are necessary to reduce off -target mutations (Moens et al. 2008) . ahr2hu3335 carriers were outcrossed to thewild type 5D ( ahr2+/+) line, and homozygous mutants were identified from an incross of their progeny. The ahr2hu3335 mutant line has been maintained with subsequent outcrosses on the wild type 5D background, which was also used for all ahr2+ control experiments in our laboratory.
All developmental toxicity experiments were conducted with fertil ized embryos obtained from group spawns of adult zebrafish as described previously (Reimers et al. 2006) .
Embryos used in experiments are defined as homozygous ( ahr2hu3335), heterozygous (ahr2hu3335/+) or wild-type (ahr2+) for the point mutation in AHR2.
MicroCt imaging
Micro computed tomography (µCT) was used for nondestructive three-dimensional imaging of zebrafish heads. The fish were scanned using a Scanco µCT40 scanner (Scanco Medical AG, Basserdorf, Switzerland) at 45 kVp, 177 mA, and a voxel size of 12 x 12 x 12 mm. The heads were imaged at threshold settings of 140 (scale 0 – 1000).
Homology modeling, molecular docking and binding energy calculations Molecular Modeling of zebrafish AHR2, AHR1B and AHR1A isoforms was conducted as described previously (17). Briefly, the homology models of mouse, human, rat and zebrafish AHR-LBD (ligand binding domains) were built using the NMR resolved structure of the PAS domain of human hypoxia-inducible-factor 2α as the 3D-template. Models were then refined in the i nternal coordinates with Molsoft ICM v3.5 -1p. Molecular docking of TCDD and Leflunomide ligands and binding energy calculation were performed as reported (Bisson et al. 2009).
87 Chemical exposures and developmental toxicity assessment TCDD (99.2% purity in DMSO, Cambridge Isotope Laboratories) and leflunomide (Sigma - Aldrich) were dissolved in DMSO. All exposures were conducted in E2 embryo medium with staged embryos (Kimmel et al. 1995) . Embryos were batch exposed to 0.1, 1, 10 nM TCDD or 0.1% DMSO vehicle c ontrol in 2 mL embryo medium in glass vials from 6 -24 hours post fertilization (hpf). Embryos were then rinsed 4X with embryo media and transferred to plastic dishes to develop until the indicated experimental time points. Embryo homogenate for mRNA expression analysis was collected at 48 hpf, and developmental toxicity of TCDD exposure was assessed by visually inspecting embryos at 120 hpf for malformations as previously described (Truong et al.) with three biological replicates. Developmental toxicity assay data were analyzed by fitting a 2 parameter logistic regression model to the concentration-response data for each malformation. Significance of the TCDD concentration-response curve was calculated for each fish line. Differential responses were assessed with a t -test to compare the parameters from the ahr2+ model to those from the ahr2hu3335 model. No adjustment for multiplicity was made. R software v12.0 (2010) was used for these analyses.
For leflunomide exposures, embryos were transferred into indivi dual wells of a 96 -well plate and exposed to 10 µM leflunomide or 0.1% DMSO control in 100 µl embryo medium from 48-72 hpf, when they were humanely euthanized and fixed for immunohistochemistry analysis.
Total RNA isolation and reverse transcription
For qR T-PCR studies, 20 embryos per treatment group were homogenized in TRIzol (Invitrogen) and stored at -80°C until use. Total RNA was isolated via phenol/guanidine isothiocyanate/chloroform separation. For morpholino splice -blocking confirmation, 15 embryos were homogenized in RNAzol (Molecular Research Center) for total RNA isolation.
RNA was quantified using a SynergyMx microplate reader (Biotek) with the Gen5 Take3 module to calculate 260/280 O.D. ratios. Superscript III First -Strand Synthesis (Invitrogen) was used with oligo(dT) primer to reverse transcribe cDNA from total RNA.
Quantitative RT-PCR
88 Relative abundance of AHR1A, AHR1B, AHR2, CYP1A, CYP1B1, CYP1C1 and CYP1C2 mRNA transcripts were assessed in whole embryo homogenate. Gene -specific primers (MWG Operon) are listed in Table 1. All qRT -PCR assays were performed in 20 µl reactions consisting of 10 µl Power SYBR Green PCR master mix (Applied Biosystems), 0.4 ul each primer, 9.2 ul H 2O and 50 ng equivalents of cDNA. Amplification (Step One Plus, Applie d Biosystems) was performed with cycling parameters as follows: 95°C for 10 min; 40 cycles of 95°C for 15 s, 60°C for 1 min; 95°C for 15 sec and 60°C for 1 min. A melt curve was performed at 3° increments to assess for multiple products. qRT-PCR analysis w as performed with StepOne Software v2.1 (Applied Biosystems) using the ΔΔCt method with genes of interest normalized to β -actin (Livak and Schmittgen 2001).
Three independent biological replicates were assessed and statistically analyzed by comparing ahr2hu3335 to ahr2+ or TCDD -treated to control with a Student’s t -test using Graphpad Prism 5.01 software (Graphpad Software Inc. La Jolla,CA).
Morpholino injection
Splice-blocking morpholinos designed against AHR1A and AHR1B were purchased from Gene Tools (Phi lomath, OR). The AHR1A splice -blocking morpholino (AHR1Amo, 5’ CTTTTGAAGTGACTTTTGGCCCGCA 3’) was described previously (Incardona et al. 2006) and was tagged on the 3’ end with fluorescein. We designed a morpholino to target the exon7/intron7 boundary of AH R1B (AHR1Bmo, 5’ ACACAGTCGTCCATGATTACTTTGC 3’). A standard control morpholino from Gene Tools (cmo, 5’ CCTCTTACCTCAGTTACAATTTATA 3’) was used as a negative control. ahr2hu3335 embryos were injected at the 1 -2 cell stage with approximately 2 nl of 1.5 mM mo rpholino dissolved in ultrapure water with 0.5% phenol red. For AHR1Amo +AHR1Bmo co -injections, the final concentration of each morpholino was 0.83 mM. Embryos were allowed to develop in fish water and screened for successful morpholino incorporation with fluorescein visualization at 24 hpf. mRNA mis - splice was confirmed with PCR primers flanking the target sites at 24 and 72 hpf (AHR1A and AHR1B-mo primers Table 1).
Immunohistochemistry (IHC)
Wild-type strain 5D and ahr2hu3335 embryos treated with 1nM TCDD (or 0.1% DMSO control) from 6-24 hpf were fixed at 120 hpf in 4% paraformaldehyde (J.T. Baker) overnight 89 at 4°C. Leflunomide treated embryos (48 -72 hpf ) were fixed at 72 hpf to capture the window of morpholino efficacy. Mouse α fish CYP1A monoclonal (1:500 dilution, Biosense laboratories) and goat α human HNF4α polyclonal (1:100 dilution, Santa Cruz Biotechnology) primary antibodies were used. Secondary antibodies consisted of Alexafluor® 546 rabbit α mouse IgG (H+L) (1:1000) and Alexafluor® 488 donkey α goat IgG (H+L) (1:1000) (Molecular Probes, Eugene, OR). Immunohistochemistry was performed as previously described (Mathew et al. 2006) . Briefly, whole f ixed embryos were permeabilized with 0.005% trypsin on ice for 10 min, washed 3X with PBST and post -fixed in 4% paraformaldehyde for 10 min. Samples were blocked for 1h in 10% normal goat serum (single labeling) or BlockAid (double labeling) (Invitrogen). Samples were incubated with primary antibodies overnight at 4°C, followed by 4 30min washes in PBST and incubation with secondary antibody overnight at 4°C. At least 8 embryos per treatment group were imaged by epi -fluorescence microscopy using a Zeiss Ax iovert 200M microscope with 5X and 10X objectives.
Acknowledgments
This work was supported by the NIEHS Environmental Health Sciences SRP grant Project #3 P42 ES016465, Core Center Grant P30 ES00210 and the NIEHS Training Grant T32 ES7060 to RLT. We gratef ully acknowledge Edwin Cuppen at the Hubrecht Institute for the ahr2hu3335 line and the European Commission Consortium Integrated Project “ ZF-MODELS- Zebrafish Models for Human Development and Disease”, Contract No. LSHG -CT-2003- 503496. We would like to tha nk the Skeletal Biology Laboratory at OSU for the microCt analysis, and the Center for Genome Research and Biocomputing at OSU for sequencing support. We are grateful to Cari Buchner, Carrie Barton, Brittany McCauslin, Jessika Dobson and the staff at the S innhuber Aquatic Research Laboratory for fish husbandry expertise and to members of the Tanguay laboratory for their helpful feedback and critical review of the manuscript.
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94 Figure 3-1 Schematic diagram of predicted AHR2 protein in ahr2hu3335 zebrafish The ahr2hu3335 zebrafish line has a T -> A point mutation in residue 534, resulting in a premature stop codon in the transactivation domain of the protein. The predicted truncated protein contains the ligand binding, DNA binding and ARNT binding domains, but lacks the transactivation domain previously shown to be essential for a functional AHR2 protein (Hahn 1998; Andreasen et al. 2002).
NLS: nuclear localization signal, NES1: nuclear export signal 1, NES2: nuclear export signal 2.
95 Figure 3-2 Fin and skeletal abnormalities in ahr2hu3335 zebrafish A-B) Brightfield and (C-D) microCt imaging of adult ahr2+ and ahr2hu3335zebrafish. Notable differences were observed in the dentate (d), premaxilla (pm), maxilla (mx), supraorb ital (so), infraorbital 3(inf) and operculum (op).
96 Figure 3-3 ahr2hu3335 embryos are resistant to TCDD developmental abnormalities A) Percent of embryos with axis malformations and B) percent incidence pericardial edema at 120 hpf in embryos treated with 0, 0.1, 1 or 10 nM TCDD from 6-24 hpf. Vehicle control groups (c, 0.1% DMSO) are displayed at 10-4 for graphing purposes. Data represent three independent experiments with 20 embryos per treatment group. C) Representative image of ahr2+ and (D) ahr2hu3335 embryos developmentally exposed to 10 nM TCDD at 120 hpf.
97 Figure 3-4 ahr2hu3335 embryos are resistant to TCDD-induced CYP induction A) Comparative analysis of AHR1A, AHR1B, CYP1A, CYP1B, CYP1C1 and CYP1C2 mRNA expression in wild-type 5D and ahr2hu3335 mutant embryos at 48hpf . ΔΔCt values were calculated by comparing sample ΔCt values (normalized to β-actin) to the mean ahr2+ ΔCt for each gene. Data were analyzed by paired student’s t-test, * p < .05. B) Developmental exposure (6-24 hpf) to 1nM TCDD induced significant CYP1A, CYP1C1 and CYP1C2 expression at 48 hpf in ahr2+ embryos. Data is shown normalized to vehicle-treated controls and was analyzed with paired student’s t-test, *p < .05, ** p < .01. C) Developmental exposure to 1nM TCDD did not induce significant mRNA expression changes in ahr2hu3335 embryos. While CYP1A was elevated, the difference was not significant (paired student’s t - test treated vs. vehicle control.
98 Figure 3-5 Molecular docking of TCDD and Leflunomide in zebrafish AHR isoforms A) TCDD docking orientation in zebrafish AHR2- and B) AHR1B-LBD homology model binding pocket (ICM v3.5-1n, Molsoft). C) Leflunomide docking orientation into AHR2-, D) AHR1B- and E) AHR1A homology model binding pockets. The residues are displayed as sticks and colored by atom type with the carbon atoms in green. The protein backbone is displayed as ribbon and colored by secondary structure. The ligand is displayed as sticks and colored by atom
99 Figure 3-6 CYP1A protein expression patterns
CYP1A expression at 120 hpf in (A) ahr2+ and (B) ahr2hu3335 larvae following exposure to 1 nM TCDD from 6-24 hpf. C) Leflunomide-induced CYP1A expression at 72 hpf in wild-type and (D) ahr2hu3335 mutants. E) Leflunomide-induced CYP1A expression in AHR1B-morphant ahr2hu3335 larvae and F) larvae co-injected with AHR1A and AHR1B of morpholinos. (G) DMSO control. TCDD-exposed embryos were IHC processed side-by-side and imaged at 120 hpf using the same exposure settings and a single focal plane. Leflunomide-exposed embryos and DMSO control were processed side-by- side and imaged at 72 hpf using the same exposure times; images were created from a z -stack of 10
15.4uM slices centered on the liver.
100
Table 3-1 Primer sequences for PCR experiments
mo- morpholino mis-splice detection mut- mutant point mutation detection
Target Forward Primer (5’- 3’) Reverse Primer (5’- 3’)
AHR1A CGCAAAAGGAGGAAACCTGTC CCTGTAGCAAAAATTCCCCCT
AHR1B GGTTTGTCGTCAAACAACAGTAACCACG CCACCAACACAAAGCCATTAAGAGCCTG AHR1B-mo CTTTGTGTGTCGTTTCCGATGCC GCACAGTAGAGCATATCAGCTGC AHR2 TGGACTAGATCAGACAACCC GAAGAGGGAGAGTCATTGTG
AHR2-mut TATTGCTAGGCAGAGAGCAC GATGTCTTCTGTGATGATTTCG
CYP1A TGCCGATTTCATCCCTTTCC AGAGCCGTGCTGATAGTGTC
CYP1B1 CTGCATTGATTTCCGAGACGTG CACACTCCGTGTTGACAGC
CYP1C1 AGTGGCACAGTCTACTTTGAGAG TCGTCCATCAGCACTCAG
CYP1C2 GTGGTGGAGCACAGACTAAG TTCAGTATGAGCCTCAGTCAAAC
β-ACTIN AAGCAGGAGTACGATGAGTC TGGAGTCCTCAGATGCATTG
101
Table 3-2 Concentration responses for developmental effects Effect p-value of ahr2+ TCDD
concentration-response
p-value of ahr2hu3335
TCDD concentration-
response
p- value of ahr2+
ahr2hu3335 differential
response
yolk sac edema < 0.0001 0.7181 0.0004
axis < 0.0001 0.2754 0.0006
eye < 0.0001 1.0000 0.0005
snout < 0.0001 0.6706 0.0004
jaw < 0.0001 0.8632 0.0011
pericardial edema < 0.0001 0.0848 0.0002
102
Table 3-3 Predicted binding energy values for zebrafish AHR2, AHR1B and AHR1A (kcal/mol), ND – unable to dock
AHR2 AHR1B AHR1A
TCDD -3.97 -4.86 ND
Leflunomide -2.13 -1.97 -2.19
103
Table 3-4 Summary of zebrafish AHR ligand binding, activity and expression Receptor Receptor mRNA
expression in
adult zebrafish
In vitro TCDD
binding and
activity
Homology model
predicted ligand binding
Dominant receptor-
dependent CYP1A protein
induction pattern (larval)
TCDD Leflunomide
AHR1A heart,
swimbladder,
liver,
kidney(Andreasen
et al. 2002)
N(Andreasen et
al. 2002;
Karchner et al.
2005)
N Y liver
AHR1B NA Y(Karchner et
al. 2005)
Y Y vasculature
AHR2 brain, heart,
muscle,
swimbladder,
liver, gill, skin, eye,
kidney,
fin(Andreasen et
al. 2002)
Y(Andreasen et
al. 2002;
Karchner et al.
2005)
Y Y liver, vasculature
104
Chapter 4 - Ahr-dependent developmental toxicity and differential transcriptional profiles induced by 4-ring oxygenated PAHs
Goodale, B.C.1, Tilton, S.C.2, Sullivan, C.M.1,3,Waters, K.M2. and Tanguay, R.L1.
1.Department of Environmental and Molecular Toxicology, the Environmental Health Sciences Center, Oregon State University, Corvallis, OR,
2.Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, WA
3. Center for Genome Research and Biocomputing, Oregon State University, Corvallis, OR
Formatted for submission to Toxicological Sciences
105
Abstract
Polycyclic aromati c hydrocarbons (PAHs) are common air pollutants and combustion byproducts that exhibit diverse mutagenic, carcinogenic, proinflammatory and teratogenic properties. Unsubstituted (parent) PAHs are defined by three or more fused benzene rings; oxygen-substituted PAHs (OPAHs) are also formed during combustion, as well as via phototoxidation and biological degradation of parent PAHs. Despite their prevalence both in contaminated industrial sites and in urban air, their mechanisms of action in biological systems are relatively understudied. Like parent PAHs, OPAHs exhibit structure-dependent mutagenic activities and differential activation of the aryl hydrocarbon receptor (AHR) . In the canonical AHR signaling pathway, the AHR translocates to the nucleus upon lig and binding, dimerizes with the aryl hydrocarbon receptor nuclear translocator (ARNT), and binds to DNA response elements to activate transcription of a suite of downstream genes that include cytochrome p450 phase 1 metabolizing enzymes ( CYP1A, CYP1B1). In a screen of diverse OPAHs for developmental toxicity in zebrafish, 4 -ring OPAHs benzanthrone (BEZO) and benz(a)anthracene -7,12-dione (7,12 -B[a]AQ) induced similar morphological aberrations and markers of oxidative stress, but only 7,12 -B[a]AQ significant ly induced cyp1a expression. We investigated the role of the AHR in mediating the toxicity of BEZO and 7,12-B[a]AQ, and found that knockdown of ahr2 rescued developmental effects caused by both compounds. Using comparative RNA -seq, we show that BEZO induce s expression of xenobiotic metabolizing genes directly regulated by AHR with distinctively lower potency than 7,12-B[a]AQ. The much larger majority of significantly -induced transcripts, including genes that regulated redox -homeostasis, were affected simila rly by these two OPAHs.
Biological functions and transcription factors predicted to regulate the genes significantly misexpressed by BEZO and 7,12 -B[a]AQ suggest that the A HR interacts differentially with other transcription factors and coactivators to med iate the developmental toxicity caused by these compounds.
106
Introduction
Polycyclic aromatic hydrocarbons (PAHs) are major components of combustion emissions and contaminants at hazardous waste sites . Automobiles, wood burning, coal-based energy production and other combustion processes produce both parent (unsubstituted) PAHs, consisting of multiple fused carbon rings, as well as a variety of substituted derivatives.
PAHs are associated with both the gaseous and ultrafine particulate fractions of urban ai r, can accumulate in the lungs when inhaled, and are considered major carcinogenic components of combustion emissions (Bostrom et al. 2002; Ramirez et al. 2011) .
Oxygenated, nitrated, and methylated PAHs also form from parent PAHs through photo - oxidation (via ozone and hydroxyl radical ) reactions as well as biotic metabolism in the environment (Yu 2002; Lundstedt et al. 2007) . The EPA has identified 16 PAHs on its priority pollutant list, based on their prevalence at Superfund sites and potential health effects (EPA 2012) . Parent PAHs are routinely measured in order to estimate total PAH contamination levels and potential hazard, but degradation products, such as oxygenated PAHs (OPAHs) are less frequently accounted for. As detection methods have improved an d standards are more widely available, recent studies showed that OPAHs are present in PAH- contaminated environmental samples, and it is expected that they may be present at higher concentrations than parent PAHs (Lundstedt et al. 2006; Layshock et al. 2010; Walgraeve et al. 2010). Despite this, little data is available about their toxicity. Like a number of parent PAHs, some OPAHs are mutagenic (Durant et al. 1996; Gurbani et al. 2013) . Based on the varied and structure -dependent toxicological effects of u nsubstituted PAHS, which include developmental toxicity mediated by the aryl hydrocarbon receptor (AHR), as well as cardiac toxicity and immune effects, substituted PAH structures are likely to also induce non-cancer toxic effects. A screen of OPAH toxicit y in developing zebrafish demonstrated that OPAHs cause developmental effects at a wide range of waterborne concentrations, and different structural elements likely explain their differential induction of a variety of morphological abnormalities (Knecht et al. 2013) . Like unsubstituted PAHs, OPAHs differentially induce expression of known targets of the aryl hydrocarbon receptor, such as cyp1a. They also induce expression of genes involved in redox homeostasis, suggesting that oxidative stress also plays a role in their toxicity. Because of their ubiquity, and potential greater prevalence in some environmental situations than parent PAHs, there is a need to understand 107
mechanisms by which OPAHs cause toxicity, and how these mechanisms are related to, or distinct from, those identified for parent PAHs.
Here we compare the transcriptional signatures and proposed toxicological mechanisms of two structurally-related OPAHs, 1,9 benz-10-anthrone (BEZO) and benz(a)anthracene-7,12- dione (7,12 -B[a]AQ) during embryonic development. These 4 -ring OPAHs, which are detected in environmental samples, differ in their ring arrangement and oxygenation pattern. BEZO is detected in air samples associated with high traffic emissions, but is also an important intermediate used in pr oduction of dyes (Nielsen et al. 1999; Wei et al. 2012) .
Exposure has been associated with hepatic and dermal lesions in workers, as well as ascorbic acid depletion in animal models (Singh et al. 2003) . 7,12 -B[a]AQ is also detected in air and at industri al waste sites, and was among the most abundant OPAHs detected in urban air in Beijing (Wang et al. 2011; Wei et al. 2012) . 7,12-B[a]AQ can be formed from benz(a)anthracene (BAA), an EPA priority pollutant PAH which is mutagentic and induces developmental toxicity via the A HR (Incardona et al. 2006) . Both compounds were identified in a zebrafish toxicity screen to disrupt normal development, while differentially inducing cyp1a expression, suggesting distinct modes of action. We investigated the role of Ahr2 in mediating developmental toxicity. We used whole genome mRNA sequencing to investigate global differences in transcription induced by these compounds. Despite their differential cyp1a activation and early morphology profiles, we found that developmenta l toxicity of both BEZO and 7,12 -B[a]AQ was dependent on ahr2. Transcriptional profiling with RNA -seq showed largely similar gene expression between the compounds, with cellular redox homeostasis genes playing a large role in the toxicological response. Th e large difference in cyp1a expression, coupled with more subtle differences in the expression of interacting transcription factors, highlights the ability of the AHR to mediate toxicity via alternate pathways in a ligand -dependent manner. Understanding th e multitude of AHR interactions is important for assessing and predicting health risk posed by OPAHs.
Comparative transcriptional profiling additionally sheds light on conserved mechanisms and biomarkers that may be more appropriate than cyp1a for inferring exposure and A HR activation by this class of emerging contaminants.
Materials and Methods
Fish Husbandry
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All experiments were conducted wit h wild -type 5D zebrafish . Adult zebrafish were maintained at the Sinnhuber Aquatic Research Laboratory on a recircu lating system with a water temperature of 28 ± 1˚C, and a 14 hr light: 10 hr dark photoperiod. All experiments were conducted with embryos collected in the morning from multiple adult zebrafish set up for group spawning as described previously (Reimers et al. 2006) . Adult care and reproductive techniques were conducted according to Institutional Animal Care and Use Committee protocols at Oregon State University.
Chemicals
Analytical grade (> 98% purity) 1,9 -benz-10-anthrone (BEZO) was purchased from Fluka, and benz(a)anthracene -7,12-dione (7,12 -B[a]AQ) w as purchased from Sigma -Aldrich.
Compounds were dissolved to 10 mM in dimethyl sulfoxide (DMSO). Stocks were sonicated in a bath sonicator for 15 min before each use. For embryo exposures, BEZO and 7,12 - B[a]AQ stocks in 100% DMSO were dissolved in embryo media to a final concentration of 1% DMSO.
Developmental toxicity
Embryos were cleaned, developmentally staged and batch -exposed in glass vials at 6 hours post fertilization ( hpf) to 5, 7.5 and 10 μM concentr ations of OPAH or 1% DMSO vehicle control, 20 em bryos per vial in 2 mL exposure solution (Kimmel et al. 1995) . Vials were protected from light and incubated on a rocker at 28˚C for the duration of the exposure.
Embryos were collected at 48 hpf for RNA and immunohistochemical analysis. For developmental toxicity evaluations, embryos remained in solution until 72 hpf, when they were rinsed 3 times, transferred to individual wells of a 96 -well plate, and incubated in fresh embryo media until evaluation at 120 hpf. Embryos were then euthanized with MS- 222 (tricaine methanesulfonate) and evaluated for yolk sac, axis, trunk, somite, fin, cardiac, eye, snout, jaw, otic vesicle, brain and pigment malformations. Mortality and the percentage of embryos with each malfo rmation were calculated for each treatment group with the vial (20 embryos) as the experimental unit. Representative larvae were imaged at 48 and 120 hpf with a Nikon Coolpix 5000 digital camera . Experiments were repeated in triplicate, and percent inciden ce data across replicates was analyzed for significance using Sigma Plot 109
software. Data were analyzed for morpholino and treatment effects by two -way ANOVA with Tukey’s all pairwise post hoc test for each endpoint.
Morpholino injection
Embryos were injected at the single cell stage with a fluorescein-tagged translation- blocking morpholinos targeting ahr2 (ahr2-MO, 5’ TGTACCGATACCCGCCGACATGGTT 3’), or a standard nonsense control (control-MO, 5’ cctcttacctcagttacaatttata 3’ ) purchased from Gene Tools (Philomat h, OR) at a concentration of 0.75mM. Injection volume was ~2nL.
Fertilized, normally developing embryos were screened for morpholino incorporation at 4 hpf by fluorescence microscropy. Three independent replicates were conducted.
RNA isolation
Groups of 20 embryos were homogenized at 48 hpf in RNAzol (Molecular Research Center, Cincinnati, OH) using a bullet blender with 0.5 mM zirconium oxide beads (Next Advance, Averill Park, NY). Samples were stored at -80˚C until RNA isolation via phenol guanidine extraction. RNA was quantified and quality assessed using a SynergyMx microplate reader with the Gen5 Take3 module to calculate O.D. 260/280 ratios. Quality of RNA samples for sequencing was additionally assessed with an Agilent Bioanalyzer 2100 . cDNA was synthesized from 1 μg (confirmation with RNA from sequencing samples) or 2 μg of RNA using the ABI high capacity cDNA synthesis kit. cDNA was diluted and 50 ng equivalents of RNA were used for QPCR reactions. 3 -4 biological replicates were collected per treatment group.
Quantitative RT-PCR
Gene-specific primers (MWG Operon) for qRT -PCR amplification are listed in Table S1. All qRT-PCR assays were performed in 20 µl reactions consisting of 10 µl Power SYBR Green PCR master mix (Applied Biosystems), 0.4 μl each primer, 9.2 μl H 2O and 50 ng equivalents of cDNA. Amplification (Step One Plus, Applied Biosystems) was performed with cycling parameters as follows: 95°C for 10 min; 40 cycles of 95°C for 15 s, 60°C for 1 min; 95°C for 15 sec and 60°C for 1 min. A melt curve was performed at 3° increments to assess for multiple products. Relative fold change values in PAH -treated samples compared to vehicle controls were calculated for genes of interest, normalized to β -actin, by the method 110
described by Pfaffl (Pfaffl 2001). Four biological replicates were assessed and statistically analyzed by Two-way ANOVA with Tukey’s post-hoc test using Sigmaplot software.
Immunohistochemistry
Whole embryos were fixed at 48 hpf in 4% paraformaldehyde at 4˚C overnight. Mouse α fish CYP1A monoclonal (1:500 dilution, Biosense laboratories) primary antibody and Alexafluor® 594 goat α mouse IgG (H+L) (1:1000 dilution, Molecular Probes, Eugene, OR) secondary antibody were used for immunofluores cent labeling of Cyp1a as described by Svoboda et al (Svoboda et al. 2001) . Briefly, embryos were washed in phosphate -buffered saline containing 0.1% Tween-20 (PBST), permeabilized by a 1 h incubation in distilled H2O followed by 20 min in cold acetone and 30 min incubation in 1 mg/ml collagenase. Embryos were blocked for 1h in PBST with 10% normal goat serum, incubated with 1˚ antibody in 10% NGS overnight at 4˚C, washed in PBST, and incubated in 2˚ antibody 4 h at RT. Embryos were imaged by epi -fluorescence microscopy using a Zeiss Axiovert 200M microscope with 5X objective. Merged images were created from z -stacks of 5 20uM slices captured under identical exposure conditions. Two overlapping frames were spliced to capture the entire length of each larva.
Paired-end mRNA sequencing
mRNA was isolated from total RNA samples, fractionated and libraries prepared with custom barcodes for sequencing at the University of Oregon Genomics Core Facility. 50 bp paired-end sequencing was conducted with an Il lumina Hi Seq 2000 sequencer; sequence (fastq) files were transferred to the Oregon State University Center for Genome Research and Biocomputing for analysis. Sequences were filtered based on Illumina quality scores and analyzed for quality using FastQC analytical sof tware (Babraham Bioinformatics) .
Reads were trimmed to exclude low quality sequences at the end of reads.
Sequence mapping, transcriptome assembly and statistical analysis Paired-end sequence reads were aligned with TopHat version 2.0.7 (Trapnell et al. 2009) to Danio rerio genome assembly Zv9.70 using the following parameters: 50 bp minimum intron length, 10000 bp maximum intron length, 200 mate pair inner distance, 150 bp mate pair inner distance stdev, --no-mixed option (only paired alignments). Transcripts were 111
assembled using Cufflinks, and merged with Cuffmerge using Zv9.70 as a guide to create a GTF file with all transcripts assembled from our data (Trapnell et al. 2012) . Differentially expressed transcripts, first including novel transcripts from our dataset, were identified with Cuffdiff by comparing BEZO and 7,12 B[a]AQ exposed samples to the 1% DMSO control. Upper quartile normalization and bias correction using Zv9.70 were performed, and an FDR of 0.01 was applied. Differentially expressed genes with an adjusted p value of <0.05 were considered significant. We conducted a second statistical analysis in Cuff diff, using only the Zv9 .70 transcriptome (no novel transcripts), to identify significant gene lists with better annotation for analysis of biol ogical functions for each BEZO and 7,12 -B[a]AQ, using an FDR of 0.05. Log 2 fold change (log 2FC) values were calculated by comparing each sample FPKM value to the mean control FPKM value. For comparisons with microarray PAH data, log 2FC values based on fluo rescence intensity values were used , microarray data is available in the NCBI GEO omnibus Accession: GSE44130 (Goodale et al. 2013). Hierarchical clustering and heatmap visualizations were conducted with Multiexperiment Viewer (MeV) (Saeed et al. 2003).
Pathway analysis
Go Rilla (Gene Ontology enRIchment anaLysis and visualization) tool was used to identify enriched gene ontology ( GO) terms from clusters of genes in the BEZO -7,12-B[a]AQ heatmap (Eden et al. 2009). Official gene symbols were used, with the background of official gene symbols for all transcripts identified in the dataset. GO Rilla recognized 15,768 zebrafish gene symbols provided from our combined GTF file. GO terms with p values <0.001 were considered significant.
The Bioinformatics Resour ce Manager v 2.3 (BRM) was used to identify Entrez IDs and human homologs of significant genes identified in our statistical comparison using the Zv9 transcriptome (Tilton et al. 2012) . We used Metacore GeneGo software to identify enriched biological processes, GO terms, and predicted transcription factors from the BEZO and 7,12- B[a]AQ significant gene lists ( p < 0.05). Statistical significance of over -connected interactions was calculated using a hypergeometric distribution, where the P value represents the probability of a particular mapping arising by chance for experimental data compared to the background (Nikolsky et al. 2009) . Networks were constructed in 112
MetaCore for experimental data using an algorithm that identifies the shortest path to directly connect nodes in the dataset to transcription factors. Network visualizations were generated in Cytoscape (Shannon et al. 2003). MetaCore processes, GO terms, and predicted transcription factors were filtered to include only those associated with at least 10 genes in the da taset. For MetaCore processes, a statistical cutoff of P < 0.005 was used. GO terms with associated P values < 0.001 were considered significant, and the 20 most significant GO terms for each OPAH were reported. Transcription factors with st atistical overconnectedness P values < 0.001 were considered significant.
Results
AHR2 morphants are resistant to BEZO and 7,12-B[a]AQ induced developmental toxicity We investigated the role of AHR2 in developmental toxicity of 7,12 -B[a]AQ and BEZO by exposing control and ahr2 morpholino-injected (morphant) zebrafish embryos to 0, 5, 7.5 and 10 μM concentrations of OPAH and observing morphological changes at 120 hpf. Both 7,12-B[a]AQ and BEZO induced a concentration -dependent increase in the percentage of control -injected embryos with pericardial edema (Figure 2A and B, dark circles, F and G).
Interestingly, neither 7,12 -B[a]AQ nor BEZO caused a significant increase in pericardial edema in ahr2 morphants (Figure 2A and B, light circles, I and J). BEZO and 7 ,12-B[a]AQ- exposed control morphants also had significant jaw, eye and axis malformations, which were not significantly increased in ahr2 morphants (two way ANOVA, P < 0.05). While a slight increase in embryos exhibiting at least one malformation was obser ved in ahr2 morphants exposed to 7,12-B[a]AQ or BEZO, the increases were not significant. As observed previously, 7,12 -B[a]AQ and BEZO induced similar malformation profiles, which also included significant increases in jaw, yolk sac edema, and to a lesser extent curved body axis (Figure 2F, G). The compounds induced a similar concentration response, with the 7.5 μM exposure eliciting a response in nearly 100% of embryos.
BEZO and 7,12-B[a]AQ induce distinct malformations and cytochrome P450 phase 1 metabolizing enzyme expression at 48 hpf
To further compare the developmental toxicity of thes e 4-ring OPAHs, we focused on the earlier 48 hpf developmental time point, when robust Cyp1a expression can be visualized by immunohistochemistry following ligand exposure, but the liver and its metabolic activity 113
are not yet functional. We examined cyp1a and cyp1b1 mRNA expression in control and ahr2 morphant embryos expo sed to 7.5 μM 7,12 -B[a]AQ or BEZO, compared to vehicle control. As is expected for an Ahr2 agonist, 7,12 B[a]AQ exposure induced a robust, 197-fold average increase in cyp1a expression in control -injected embryos (Figure 3A). The ahr2 morpholino caused a 6 4% reduction, with cyp1a induced 61 -fold in ahr2 morphants exposed to 7,12 B[a]AQ. Cyp1b1 expression was also induced by 7,12-B[a]AQ exposure, with an average 17 -fold change in control -injected embryos (Figure 3A). cyp1b1 induction was 96% prevented with t he ahr2 morpholino; basal expression was significantly (albeit only
1.8 fold) higher in ahr2 morphants compared to control morphants, and was not significantly induced by 7,12 B[a]AQ exposure. 7,12 -B[a]AQ induced malformations, including pooling of blood v entral to the developing heart, slight curved body axis and reduced size, which were apparent by 48 hpf (Figure 3E, compared to 3C vehicle control).
Cyp1a protein, visualized by whole -mount immunohistochemistry, was robustly expressed throughout the trunk and brain vasculature, as well as the developing heart (Figure 3K, compared to 3I control). The ahr2 morpholino completely prevented all of the morphological effects observed (Figure 3F, compared to 3C,D controls). Cyp1a protein expression was largely prevented, with some remaining vascular expression in the eye, brain, and trunk (Figure 3L). This expression is in agreement with the 61 -fold change in cyp1a mRNA in the ahr2 morphants. The incomplete block of cyp1a expression by morpholino is in line with pr evious studies, and may be due to incomplete mo rpholino efficacy or induction via one of the other zebrafish AHR isoforms. It is important to note, however, that it does not correspond with any observable malformations at 48 or 120 hpf.
In contrast to the strong cyp1a induction observed with 7,12 -B[a]AQ, exposure to 7.5 μM BEZO induced a 4.5 -fold increase in cyp1a in control morphants (Figure 3B) . While statistically significant, this cyp1a induction is only ~2% of the 7,12 -B[a]AQ response.
Knockdown of ahr2 resulted in a 42% rescue of the cyp1a induction by BEZO. Cyp1b1 was induced 1.8 -fold by BEZO exposure. This expression level was similar to that in ahr2 morphants, which showed no difference in cyp1b1 expression between treatment groups.
While cyp1a and cyp1b1 induction was minimal, BEZO induced distinct physiological abnormalities by 48hpf. Severe edema was consistently observed surrounding the developing heart (Figure 3G). Compared to the 120 hpf time point, the BEZO and 7,12 114
B[a]AQ phenotypes could be readily distinguished at 48 hpf, where BEZO -exposed embryos had more pronounced pericardial edema, but did not exhibit the axis and shorter trunk length induced by 7,12 -B[a]AQ (Figure 3G compared to 3E). BEZO -induced malformations were not accompanied with any Cyp1a protein expression detectable by whole -mount immunohistochemistry (Figure 3M). This was consistent with the very low cyp1a mRNA induction. In agreement with the 120 hpf malformation data, however, knockdown of AHR2 completely rescued the BEZO-induced developmental abnormalities at 48 hpf (Figure 3H).
Comparison of mRNA expression profiles with mRNA-seq
The large difference in cyp1a mRNA expression at an EC100 concentration for malformations suggested differences in transcriptional regulation betw een BEZO and B[a]AQ. We used whole genome mRNA expression profiling (mRNA -seq) to identify global mRNA expression changes induced by these two OPAHs at 48 hpf. Paired -end 50bp RNA - seq was conducted on embryos exposed to 10 μM 7,12 -B[a]AQ, 10 μM BEZO or 1% DMSO control from 6 -48 hpf. Three biological replicates of pooled RNA from 20 embryos were sequenced for each exposure group. An average of 48.2 ± 7.9(s tdev) million paired sequence reads passing Illumina QC were obtained per sample. Alignment to Sanger zebrafish genome build Zv9.70 resulted in 36.2 ± 6.1 million mapped reads per sample (75%). Of these, an average of 28.8 ± 5.3 million paired reads per sample (~80%) mapped uniquely to one location in the genome (Table S2) . Transcripts were assembled with cufflinks and merged with the Zv9.70 transcriptome to obtain a combined transcript file with 32,432 Ensembl genes and 13,478 novel transcripts. Comparison of each treatment with the DMSO control using CuffDiff identified 964 differentially expressed transc ripts in the 7,12-B[a]AQ group and 696 in the BEZO group ( p <0.05 with a FDR of 0.01). The union of significant transcripts was 1351.
Are transcripts misexpressed in embryos exposed to the parent PAH BAA similarly affected by OPAHs?
We previously identifi ed transcripts misexpressed in response to three parent PAHs, dibenzothiophene (DBT), pyrene (PYR), and benz(a)anthracene (BAA) using the Agilent mRNA microarray platform (Goodale et al. 2013) . BAA is a previously -identified AHR agonist, while the other t wo PAHs do not induce AHR -dependent toxicity. We hypothesized 115
that the genes significantly misexpre ssed by BAA would respond similarly to 7,12 -B[a]AQ, because of their identical ring structure s (Figure 1). We examined the expression of these genes across 7,12-B[a]AQ and BEZO, in comparison to the parent PAHs, in order to better refine a set of genes distinct to PAHs that induce toxicity via the AHR . We also sought to discern any differences between the three putative AHR ligands, and to identify misexpressed transcripts common to all PAH exposures. Ensembl IDs and corresponding transcripts were identified in the RNA -seq dataset for 57 transcripts that were significantly misexpressed by BAA at 48 hpf in the microarray study (Goodale et al. 2013) . Of these transcripts, 32 were significant for 7,12 -B[a]AQ and 17 for BEZO. Caution is necessary for comparison across studies on different platforms; the magnitude of fold change values detected by microarray vs. RNA-seq have been shown to be different, and the platf orms can differ in their sensitivity (van Delft et al. 2012) . Comparison of significant gene lists in particular is problematic because of the different background sets and statistical methods employed. We have found heatmaps useful for visualizing patter ns in expression across PAHs and identifying clusters of similarly expressed transcripts for further analysis. A heatmap of the BAA significant transcripts was created using log2FC values for each sample compared to the average control (1% DMSO) value on i ts platform ( Fluorescence intensity for microarray, FPKM value for RNA -seq). Bidirectional hierarchical clustering was conducted in the MultiExperiment Viewer to identify clusters of differentially expressed transcripts (Figure 4). Replicates of each PAH t reatment group clustered together; no platform-based clustering was observed among the controls. 7,12-B[a]AQ samples were the most distant from controls, and clustered closely with BAA. As expected for compounds lacking AHR activity, DBT and PYR clustered most closely with the control samples. The BEZO cluster grouped between the BAA -7,12-B[a]AQ cluster and the other PAHs, with less robust expression than 7,12-B[a]AQ and BAA, but also notably different than PYR and DBT.
Expression patterns on the whole were similar across BEZO, BAA and 7,12 -B[a]AQ, but differed in magnitude, suggesting that differences between the PAHs for this set of transcripts lie in dose, potency, or platform, rather than mechanism . No directional differences were observed that would sug gest distinct regulatory mechanisms between these PAHs. Several distinct clusters of genes were identified by the bidirectional hierarchical clustering. The genes in the top custer , cyp1a, cyp1c1, wfikkn1, ahrrrb, cyp1c2 116
and cyp1b1were the most highly indu ced by BAA and 7,1 2-B[a]AQ, with mean FC values reaching 230 for cyp1a. They were also induced by BEZO, but to a lower extent (4 -fold or less), in agreement with our previous data for cyp1a. A large group of genes below the top cluster were only mildly induced by BAA, and were more variable for the rest of the PAHs. A small cluster of genes in the middle of the heatmap , s100z, cxcr4a, slc1a4 and psat1, were consistently induced by all 5 PAH exposures. Cxr4a and s100z were previously identified in the microa rray study among the most highly elevated transcripts across the three parent PAH structures (Goodale et al. 2013) . The cluster below this consisted of transcripts that were robustly elevated by BEZO, B[a]AQ and BAA, such as ctsl.1, sult6b1 and gsr, but were not increased in DBT and PYR samples. Finally, the bottom cluster of the heatmap consisted of transcripts generally repressed by all 5 PAHs. This transcript set had subtle differences in expression levels between the AHR -activating vs AHR -independent PA Hs. In order to investigate whether log2FC values in the heatmap accurately portrayed expression trends across compounds that were tested on different platforms, we validated expression of genes from multiple clusters with qRT-PCR across all 5 PAHs (Table 1). Log2FC values of cyp1a, ctsl.1, sult6b1, and cxcr4a were very consistent between q -RT-PCR and both genome -wide platforms. Expression patterns of ctgfb and s100z, which were not as highly induced by any of the PAHs, were consistent in the qRT -PCR, but statistical significance was more variable across platforms (Table 1). Statistical analysis methods employed (ANOVA across treatment groups vs. pairwise with control, multiple testing corrections) were different between qRT - PCR, microarray, and RNA -seq. Sig nificant gene list comparisons may therefore result in erroneous conclusions with this data. Our qRT -PCR analysis supports the idea, however, that clustering based on gene expression values may be a useful way to identify meaningful differences between PAH s using data from these two platforms. Based on the clustering analysis presented here with the AHR -related transcript list , we might predict BEZO to be an AHR agonist with lower potency than BAA and 7,12-B[a]AQ.
Novel BEZO and 7,12-B[a]AQ-induced transcripts identified with RNA-seq mRNA-seq data provides a rich resource to compare expression across the entire genome, including novel transcripts not yet annotated in the current assembly. In order to identify transcriptional differences between BEZO and 7,1 2-B[a]AQ that might explain morphological and apparent potency differences for inducing A HR-related targets, we used 117
Cuffdiff to compare each OPAH treatment group to the 1% DMSO control. In our primary analysis, we employed a transcript set that included all transcripts in the zebrafish genome (Zv9) as well as novel transcripts identified in our dataset.
Comparison of the significant gene lists (p <0.05, 0.01 FDR) revealed 309 transcripts that were significantly misexpressed in response to both PAHs. 387 transcripts were unique to BEZO, and 655 were unique to BaAQ. 366 of the significant transcripts had a fold change > 2 (Figure 5A). Of these, 125 transcripts were unique to BEZO, while 171 were unique to 7,12- B[a]AQ. 70 transcripts were similarly expresse d between the two OPAHs, and the numbers of over - vs under -expressed transcripts were nearly equivalent . 10 of the transcripts similarly expressed in the BEZO and 7,12 -B[a]AQ groups were not annotated in Zv9 (novel to our dataset ). BEZO and 7,12 -B[a]AQ uni quely induced changes in 22 and 33 novel transcripts, respectively. Annotation of the zebrafish genome has undergone rapid improvement in recent years; experimentally identified transcripts from RNA -seq studies, however, have yet to be fully incorporated into the transcriptome. While “novel” transcripts may result from sequencing errors, repeats, or incorrect assembly, they may also represent as-of-yet unidentified genes that could be involved in mechanisms of toxicity, which are not yet fully elucidated fo r the A HR. We examined novel transcripts that had the largest fold changes between treatment groups using both Gbrowse, to examine coverage across our samples, as well as with the Ensembl genome browser to view predicted transcripts from publicly available RNA-seq data. Many of our “novel” transcripts corresponded with regions of high transcriptional coverage in other datasets, but were not annotated as transcripts in publicly available databases . Novel transcript XLOC_030523 was one of the most highly induced transcripts in the 7,12-B[a]AQ treatment groups ( 9.7 fold, p < 0.05). The genomic context of this predicted transcript (Chromosome 3: 63102008-63105155) is displayed in Figure 5B (top). The pooled RNA -seq alignment track shows a long region of moderate coverage upstream of sox9b on the -1 strand, and a predicted transcript based on this RNA - seq data (5 dpf exon track). There are no Ensembl/Havana annotated transcripts within this region. The Gbrowse view (Figure 5B, bottom) shows the predicted transcrip t from our dataset, along with neighboring transcripts that were likely predicted as separate because of the low coverage. The control samples all had very low coverage of this region, while the 7,12-B[a]AQ samples had much higher coverage . This supports t he Cufflinks -Cuffdiff 118
identification of this as an overexpressed transcript in the 7,12 -B[a]AQ -exposed embryos.
Several of the neighboring predicted transcripts were also significantly increased by 7,12- B[a]AQ, but not by BEZO. By visualizing our data al ongside a set of recently -identified long intergenic non-coding RNAs (lincRNAs) we discovered that several of our predicted “novel” transcripts are part of a leader -like lincRNA (Chew et al. 2013) . Its location near sox9b is particularly intriguing, given the important role of sox9b in mediating AHR -dependent effects of TCDD on tissue regeneration, jaw and heart development (Andreasen et al. 2006; Xiong et al. 2008; Hofsteen et al. 2013) . The mechanism by which AHR activation results in decreased sox9b expression is not known. In our dataset sox9b was decreased by 7,12- B[a]AQ, though not significantly, perhaps because RNA was from whole embryo rather than specific tissue types. Novel transcripts identified within this dataset will require additional validation. Examination of the genomic context, as demonstrated in Figure 5B, suggested that many are not sequencing artifacts but rather have not yet been annotated in Zv9, and should be included in our comparison of BEZO and 7,12-B[a]AQ.
We performed hierarchic al clustering on the union of BEZO and 7,12 -B[a]AQ significantly misexpressed transcripts with FC >2, and identified clusters of transcripts with differ ent expression patterns (Figure 5C). Considering now the entire set of misexpressed transcripts, rather than the AHR agonist -associated transcripts discussed earlier, a set of genes underexpressed in BEZO and 7,12 -B[a]AQ-exposed embryos was notable. Few genes were identified in the microarray as down-regulated by BAA. This could potentially reflect a difference in the platforms used; we nevertheless compared expression of the BEZO and 7,12-B[a]AQ significant transcripts from Figure 5C across all 5 PAHs (Figure S3). The genes down-regulated by the OPAHs were generally not affected by BAA. Many, however, were similarly misregulated by DBT and PYR. There was also a notable group of transcripts induced by the OPAHs, including gstp1, which were not strongly affected by the parent PAHs. Differences between the OPAHs remained subtle, however. No oppositely expressed gene clusters were apparent between BEZO and 7,12-B[a]AQ.
We used GO rilla Gene Ontology enrichment analysis to investiga te biological processes associated with differentially expressed gene clusters (Figure 5C). Because significant transcripts were iden tified from our merged transcriptome, many did not have sufficient 119
annotation for ontology analysis (novel transcripts and transcript s lacking official gene names); biological processes were only identified for 5 of the clusters. The significant (P <0.001) GO process for cluster 12, which contained the previously -discussed cyp1 genes, was Response to Chemical Stimulus (GO:0042221, Figure 5C bottom) . The only significant term identified for the large group of transcripts overexpressed in both BEZO and 7,12 - B[a]AQ was Oxidation -reduction Process (GO:0055114 ). Cluster 8, expressed more highly in BEZO -exposed embryos, was enriched for Cell-cycle Arrest (GO:0007050).
Phototransduction (GO:0007602) was significant for the very small but prominent cluster 7, which was underexpressed in BEZO -exposed embryos and contained opn1lw2, opn1sw1 (opsin isoforms) and crygm2d11 (gamma D crystallin). Another group of genes in cluster 5, containing rs1 (retinoschisin) and arr3a (retinal arrestin) were enriched for Visual Perception (GO:0007601). No significant biological processes were identified for the remaining clusters of misexpressed transcripts.
Ahr2 knockdown prevents induction of transcripts unique to BEZO and 7,12-B[a]AQ toxicity profiles
Knockdown of AHR2 prevented ind uction of morphological defects by BEZO and 7,12 - B[a]AQ. AHR binding for these compounds, however, has not been determined. It is feasible that some transcriptional changes observed are not dependent on AHR2, particularly if AHR-dependent toxicity is mediated via an intermediate (endogenous ligand or metabolite) following an upstream interaction of the OPAH with another target. We selected a set of differentially expressed transcripts from the RNA -seq dataset and investigated their expression in ahr2 morphants with qRT-PCR. We focused on lesser-known transcripts since AHR2-dependence of known AHR targets (Cyp1 enzymes) was previously established. wfikkn1, a top BAA target identified in the microarray analysis, was induced 5.5 fold by 7,12-B[a]AQ (Figure 6A), but not by BEZO. No significant induction was observed in the ahr2 morphants. Glutathione S -transferase pi 2 (gstp2), by contrast, is a redox responsive gene and was significantly induced by both 7,12 -B[a]AQ (4.8 fold) and BEZO (3.1 fold).
Again no signif icant induction was observed in ahr2 morphants (Figure 6B). Arginase 2 (arg2) was one of the few genes significantly induced by BEZO but unchanged by 7,12 - B[a]AQ (Figure 5C cluster 8). This differential expression was confirmed by qRT -PCR in the control morphants, and AHR2 knockdown prevented arg2 induction by BEZO (Figure 6C). 120
We also confirmed induction of insulin-like growth factor binding protein 1a (igfbp1a), one of the transcripts most highly induced by BEZO, and plac-8 onzin related like 4 (ponzr4), one of the transcripts most reduced by OPAH exposure in the RNA -seq dataset (Figure 6D,E).
Comparative expression levels detected by qRT -PCR between BEZO and 7,12 -B[a]AQ were consistent with those observed with RNA -seq, and AHR2 knockdown prevented misexpression of both of these genes (Figure 6D,E striped bars). Finally, we investigated p53 expression, as a master regulator that was induced by BEZO in the RNA -seq dataset. A very slight (1.2 fold) induction was detected by qRT -PCR, with no significant diffe rence between 7,12-B[a]AQ and BEZO. ahr2 morphants had slightly higher p53 expression than control morphants, which was not affected by OPAH exposure (Figure 6F).
Predicted transcription factors and biological processes affected by BEZO and 7,12-B[a]AQ To better understand biological processes affected by exposure to BEZO and 7,12 -B[a]AQ during development, we analyzed the entire sets of genes significantly affected by each OPAH for statistically enriched biological processes. For this analysis, we used si gnificant genes identified by CuffDiff analysis across transcripts annotated in the zebrafish (Zv9) transcriptome (novel transcripts were not included). Significant transcripts for 7,12-B[a]AQ and BEZO exposures are listed in Tables S3 and S4, respectively . When compared to the previous analysis, which included novel transcripts, we observed good overlap in the significant gene lists ; 337(93%) and 317(83%) of genes significant for 7,12 -BaAQ and BEZO, respectively, were previously identified as significant. Entrez IDs (human homolog preferred) were identified for 587 of the 600 significant genes. Metacore GeneGo software was used to identify significant biological processes, gene ontology terms, and transcriptional regulators associated with the significant g ene lists. The most si gnificant process invoked by both OPAHs was Hypoxia and Oxidative Stress Response (Figure 7A). As suggested by the heatmap clustering (Figure 5C), Visual Perception was highly affected by BEZO, but not 7,12 -B[a]AQ. Immune -Related Processes Involving the Complement and Kallikrein-Kinin Systems, as well as Inflammation (Phagocytosis) and Cell Cycle were affected by 7,12 -B[a]AQ. The significant gene ontology terms agreed with these processes, and provide more insight into affected molecul ar mechanisms. Metabolic processes and response to chemical stimulus were the most significantly enriched GO terms, and involved over 150 genes (Table 2). The majority of the most enriched GO terms for each OPAH were 121
significant for both ( p < 0.001 ). Notab ly, Tissue Development and Oxidation-reduction Processes were less significant in BEZO, while Visual Perception and Sensory Perception of Light Stimulus were not significant for 7,12 -B[a]AQ. The decreased expression of eye - related genes ( opsins, crystallins, visual system homeobox 1 , retina and anterior neural fold homeobox 1) was also reflected in the transcription factors (TFs) predicted to be important in the BEZO response. NR2E3 nuclear receptor subfamily 2, group E, member 3 (also known as PNR, photore ceptor-specific nuclear receptor) and Maf (v-maf avian musculaponeurotic fibrosarcoma oncogene homolog) were among the most significant, and are both important in eye development. Other top predicted TFs for BEZO were C/EBPbeta and SP1. Among the most sign ificant TFs predicted for 7,12 -B[a]AQ were C/EBPbeta, ATF-2, RELA and c -Jun (Table 2). Both OPAHs were predicted to regulate networks involving HIF1A, C/EBPalpha, c-Myc and NRF2. Many of the se TFs interact, and tightly coordinate responses to oxidative stress/redox homeostasis as well as many other cellular functions . The enriched biological processes and transcription factors highlight the prominent role of oxidative stress and hypoxia-related signaling for both of these oxygenated PAHs, while other processes, such as visual perception and AHR-mediated metabolic /chemical response processes, represent potential structure-dependent differences in toxicological mechanisms.
While AHR was significant ( P < 4.26E-04 and 2.49E -03 for 7,12 -B[a]AQ and BEZO, respectively), it was not as significant as other TFs. The number of genes predicted with high confidence to interact with AHR was also much lower. Because we experimentally determined that both BEZO and 7,12 -B[a]AQ-induced developmental toxicity depended on AHR2, we were interested to identify known interactors of AHR that were misexpressed in BEZO and 7,12 -B[a]AQ-exposed embryos. Significantly misexpressed genes that have been shown to interact with the AHR (including both high and low confidence interactions) ar e displayed in Figure 6B. BEZO (blue) and 7,12 -B[a]AQ (yellow) significant genes had some overlap, which included overexpressed TFs NFE2L1 (homolog of antioxidant regulator NRF, which shares highest similarity with nrf2b in zebrafish) and CITED2 (Cbp/p300-interacting transactivator), among others (Timme-Laragy et al. 2012) . Though a large group of AHR - interacting genes were unique to 7,12 -B[a]AQ (Figure 7B, yellow), the only TF was FOXQ1 (Forkead box Q1) which mediates TCDD -induced jaw malformations (Planchart and Mattingly 2010). AHR-interacting genes unique to BEZO included TFs P53, C/EBPbeta, and 122
CXCR4. While these were induced by both OPAHs, they were induced more strongly (and significantly) by BEZO, and could potentially contribute to the severe develo pmental toxicity of BEZO despite its weak induction of canonical AHR target genes.
Discussion
We found that BEZO and 7,12 -B[a]AQ caused morphological abnormalities in developing zebrafish, including disrupted heart development, craniofacial abnormalities, eye defects and edema, at similar waterborne exposure concentrations. Their disparate induction of cyp1a mRNA and protein expression, which was identified previously and confirmed in this study, led us to predict differential involvement of the A HR in the toxicity mechanisms of these environmentally relevant OPAHs (Knecht et al. 2013) . Despite the practically negligible induction of cyp1a and lack of cyp1b1 induction by BEZO, the BEZO -induced toxicological endpoints evaluated in this study were dependent on AHR2. Cyp1a expression and metabolic activity are both widely used as biomarkers of AHR activation, particularly for environmental monitoring studies. For dioxin -like compounds, Cyp1a has been demonstrated to correlate well with ligand affinity for the re ceptor as well as A HR- associated toxicological effects in a plethora of organisms (Safe 1998; Billiard et al. 2002) .
AHR activation and Cyp1a expression are also associated with toxicity of many PAHs, such as benzo(a)pyrene. However, the structural diversi ty of the parent compounds, not to mention their substituted derivatives and propensity for metabolism, complicates prediction and interpretation of A HR interactions for this chemical class. This has been previously shown in zebrafish, where 4 - and 5 -ring PAH structures d ifferentially interact with AHR s, resulting in tissue -specific Cyp1a expression patterns as well as a range of morphological effects (Incardona et al. 2006; Incardona et al. 2011; Knecht et al. 2013) .
Fluoranthene and dibenzothiophene inhibit Cyp1a activity, though the mechanism by which these PAHs act as inhibitors remains unknown (Willett et al. 2001; Wassenberg et al. 2005).
Studies with AHR agonists and alternative AHR ligands, both in cell culture and animal models, have recently highli ghted how ligands can differentially mediate a multitude of AHR-dependent biological processes (Patel et al. 2009; Murray et al. 2011; Narayanan et al. 2012). Adding to the complexity are reports of AHR -mediated responses in the absence of a xenobiotic ligand, including both cyp1a induction in hyperoxia, as well as decreased cyp1a expression resulting from oxidative stress (Barker et al. 1994; Couroucli et al. 2002) In this 123
context, an environmentally -relevant OPAH that induces A HR-dependent developmental toxicity in the absence of strong Cyp1a expression should perhaps not be entirely unexpected.
Because of its use in dye manufacture and associated reports of dermal lesions and decreased liver function in exposed workers, a number of studies have investigat ed BEZO toxicity in rodents. Exposure to high concentrations of BEZO causes decreased ascorbic acid associated with liver, kidney, and testis histopathological changes in guinea pigs, which could be attenuated with ascorbic acid supplementation (Das et al. 1994). In agreement with our study, Singh et al. observed decreased cytochrome P -450 phase 1 enzymes and ethoxyresorufin-O-deethylase (EROD) activity in guinea pigs exposed to BEZO (2003). Both in that study and others, BEZO caused a decrease in glutathio ne (GSH), and an increase in cytochrome P -450 phase 2 enzymes including glutathione peroxidase and glutathione reductase (Dwivedi et al. 2001). To our knowledge, involvement of the AHR in these effects awaits investigation.
Using mRNA sequencing, we compar ed the global transcriptional profile of BEZO with that of Cyp1a -inducer 7,12 -B[a]AQ, as well as parent PAHs previously investigated via microarray. Because of the 60 -fold difference in cyp1a expression at concentrations that induce comparable toxicity, we anticipated that other genes associated with AHR activation would be differentially regulated by BEZO and 7,12 -B[a]AQ, helping to elucidate alternate toxicity mechanisms. We previously identified a set of transcripts differentially expressed in zebrafish embryos exposed to BAA, the unsubstituted parent PAH of 7,12 -B[a]AQ, which induces AHR2-dependent morphological abnormalities in zebrafish, and toxicity in a variety of model systems (Incardona et al. 2006; Jennings 2012) . Few of the BAA-induced transcripts were similarly regulated by other parent PAHs that exert AHR -independent toxic effects . We identified differential regulation of this specific set of AHR-interacting transcripts by examining their fold change values in BEZO and 7,12 -B[a]AQ exposed embryos (compared to controls) . In addition to cyp1a, cyp1c1, cyp1c2, cyp1b1, ahrrb and wfikkn1 were more highly expressed in 7,12 -B[a]AQ than BEZO. With the exception of wfikkn1, these transcripts are all known targets of the A HR (Baba et al. 2001; Jonsson et a l. 2007). Other transcripts associated with A HR activation, such as oxidative stress and phase 124
2 metabolizing enzymes gsr, prdx1, and sult6b1, were induced at comparable levels by BEZO, 7,12 -B[a]AQ and BAA. None of the BAA transcripts were oppositely expr essed in response to BEZO vs. 7,12 -B[a]AQ and BAA. While expression of the phase 1 metabolizing enzymes implies a potency , dose, or metabolism difference between these 4 -ring PAHs, the consistency of expression for the rest of the transcripts across all three compounds perhaps suggests that the majority of transcriptional responses are not directly mediated by A HR, but rather by the network of transcription factors, such as NRF2, NFkB subunit RELA, CEBPB that are known to interact with A HR (Tian et al. 1999 ; Vogel et al. 2004; Timme - Laragy et al. 2009).
Examination of all identified transcripts that were significantly misexpressed in response to BEZO or 7,12 -B[a]AQ supported the notion that these and multiple other TFs mediate the web of transcriptional chan ges that result from exposure to OPAHs. Many interacting and tightly regulated mechanisms coordinate to respond to stimuli such as xenobiotic exposure, hypoxia, UV irradiation and endogenous (hormone) signaling. Crosstalk between the A HR and other transcription factors has been widely reported. As a member of the PAS family of transcription factors, the AHR interacts with other PAS proteins and shares the requirement of ARNT for dimerization and transcriptional activation with H IF1A (Gu et al. 2000) . AHR crosstalk via other binding partners and coactivators, including p300 (CREB binding protein), HSP90, and the AHR repressor ( AHRR) have been reported (Beischlag et al. 2008; Evans et al. 2008) . We identified 366 transcripts that were significantly differentia lly expressed 2 fold or greater in response to OPAH exposure. Despite the fact that only 19% of these were significant in both OPAH exposure groups, we did not identify any clusters of transcripts with strong evidence of opposite (down vs. up) regulation b etween BEZO and 7,12-B[a]AQ. Rather, we observed clusters with more subtle differences in the degree of regulation. Phase 1 enzymes, as discussed previously, were more robustly induced by 7,12 - B[a]AQ. BEZO, on the other hand, induced striking decreased exp ression of a cluster of genes involved in photoreception, which were relatively unaffected by 7,12 -B[a]AQ. The largest clusters of significant transcripts in the BEZO/7,12 -B[a]AQ comparison (Figure 5C) had similar levels of expression, however. The observa tion that relatively few of these transcripts were similarly disrupted in embryos exposed to the parent PAH BAA (Figure S1) suggests that perhaps the bulk of the transcriptional response to OPAHs is not mediated by 125
direct ligand activation of the A HR. These more reactive PAHs likely interact with cells in a multitude of other ways, causing oxidative stress, DNA and/or protein damage. It is important to keep in mind that a global transcriptional analysis does not discern between adaptive and harmful responses; indeed many of the pathways induced by these PAHs, such as NRF2-mediated antioxidant activity, likely protect the embryo from damage rather than mediate toxic effects (Van Tiem and Di Giulio 2011; Garner and Di Giulio 2012).
By comparing the BEZO and 7, 12-B[a]AQ-induced transcripts across all PAHs, we identified clusters of transcripts that are differentially expressed in response to diverse PAH structures. We found that many of the transcripts misexpressed in response to the OPAHs followed similar expre ssion patterns in DBT and PYR -exposed embryos. Inflammatory signaling via NFkB and CEBPB was significant for both OPAHs as well as DBT and PYR in the microarray analysis (Goodale et al. 2013) ; these pathways may be similarly involved in responding to PAH e xposure via a mechanism that can be activated by a broad range of structures. We compared transcript expression across all 5 PAHs at concentrations that induced malformations, and were able to identify genes more consistently expressed than the cyp1 transcripts, which may be useful to predict AHR -independent biological effects for mixtures containing multiple PAHs. Of all PAHs investigated, BAA induced expression of the smallest number of significant genes, a pattern which is supported in the heatmap (Figur e S1). We attributed some of this difference to uptake, which was much lower for BAA than the other two parent PAHs. The toxicity of BAA demonstrated, for the parent PAHs, that a PAH with affinity for the AHR can induce toxicity at a much lower body burden than PAHs that do not induce AHR activity. Without body burden data we cannot definitively discern uptake vs. mechanistic differences between the OPAHs. Based on the similar log Kows of BEZO and 7,12 -B[a]AQ (4.81 and 4.4, respectively ), however, we would not predict large uptake differences (Meylan and Howard 1995) The similarity of expression among transcripts (in exception of the canonical AHR targets), as well as transcriptional clusters that were more strongly misexpressed by BEZO than 7,12 -B[a]AQ, ad ditionally suggests that the difference between these compounds is more complex than uptake. Further exploration of differential AHR -mediated pathways will be necessary to identify true mechanistic differences.
126
The relative insignificance of A HR in the pr edicted biological functions and transcription factors for BEZO and 7,12 -B[a]AQ raises the question of why A HR2 knockdown offers pronounced protection against these compounds. For 7,12-B[a]AQ and parent PAH BAA, we might predict that following the initial binding to the AHR, a cascade of effects mediated by both the AHR response complex and metabolites leads to the wide array of transcriptional changes. For BEZO, however, the mechanism is less clear; is BEZO binding the AHR directly? If so, why isn’t Cyp1a robustly expressed? We investigated ahr2 dependence of transcripts that were highly misexpressed by BEZO, but are not known to be associated with AHR. If BEZO was affecting other pathways, and perhaps activating the AHR via a crosstalk mechanism, we would expect some transcripts to be induced in the absence of AHR2. In the small set of transcripts investigated here, however, we saw no evidence of AHR2- independent regulation. Rather, we identified arg2, and igfbp1a, AHR2-dependent transcripts induced more strongly by BEZO than 7,12-B[a]AQ.
The subtle differences in gene expression that correspond with distinct morphologies at 48 hpf in our study suggest differential interactions of the AHR with other TFs or co-activators.
Regulation of TFs often occurs post -transcriptionally, so we would not necessarily expect large changes in TF transcripts themselves. A predicted network from the distinct cluster of genes associated with visual perception in BEZO -exposed embryos predicted the involvement of p53, which has been previously demonstrated to be involved in disrupted eye development in zebrafish (Kim et al. 2013) . P53 was significantly induced 1.5 fold by BEZO, but only 1.1 fold (not significantly) by 7,12 -B[a]AQ in the RNA -seq analysis. We saw no difference, how ever, between BEZO and 7,12 -B[a]AQ with qRT -PCR, and expression of p53 was actually increased slightly in ahr2 mutants. Interestingly, arg2, which is induced by hypoxia and involved in endothelial dysfunction/reduced NO signaling, has also recently been implicated in retinopathy (Durante 2013). Arg2 deficient mice are resistant to retinal degeneration induced in oxygen induced retinopathy (Narayanan et al. 2011) . While the mechanism of AHR2-dependent arg2 induction remains unknown, it may be a mediator of eye-specific effects observed with BEZO. Another transcription factor, CCAAT/enhancer binding protein beta (CEBPB), was also significantly induced by BEZO 1.7 fold, but not by 7,12-B[a]AQ. CEBPB is a mediator of the acute phase response, and AHR activation by TCDD 127
was shown to decrease C/EBPB presence on the promoter of acute phase gene Saa3 (Patel et al. 2009).
Further studies are needed to elucidate the involvement of other predicted transcription factors and interacting proteins, such as ARNT and CEBPB , in the toxicological effects mediated by these OPAHs. Several selective AHR modulators (SAhRMs), which bind to the AHR and repress inflammatory signaling via a mechanism that does not involve binding to AHR response elements, have been identified in cell c ulture (Patel et al. 2009) . SAhRMs do not induce canonical AHR signaling such as CYP1A expression, and it is hypothesized that repressive activity occurs via AHR interaction with coactivators /repressors (Narayanan et al. 2012). We observed reductions in acute phase response genes (c3, c4, cfdl) in response to OPAH exposure, which suggests that these alternate mechanisms could also explain some of the ahr2-dependent gene regulation observed in our study. There is evidence of cell -type specific activities of the AHR,however. Interactions observed in a particular cell line may therefore not translate to the same transcriptional relationships in the whole animal context.Further investigation will be necessary to elucidate non -canonical AHR2 signaling pathways i n the developing embryo. Comparative transcriptional profiling of PAHs demonstrates that PAHs likely mediate toxicity via a suite of mechanisms, including both canonical and non -canonical AHR signaling, depending on structure. We have identified clusters o f transcripts associated with structures that induced AHR -dependent and AHR - independent toxicity; further elucidation of the complex web of AHR interactions will catalyze predictive capability for this diverse group of environmentally pertinent chemicals.
Acknowledgements
The authors would like to thank Cari Buchner, Carrie Barton and the staff at the Sinnhuber Aquatic Research Laboratory for their excellent fish husbandry and expertise. Jason Carriere processed the RNA samples and conducted mRNA sequencing at the University of Oregon Genomics Core Facility, Eugene, OR. We would also like to thank Tanguay laboratory members, in particular Lisa Truong, Derik Haggard and Jill Franzosa for their advice with RNA-seq analysis and critical review of the manuscript . We are grateful to OSU Superfund Research Program Core D/Anderson Laboratory for their analytical support, advice, and providing OPAHs. This work was supported by the NIEHS Superfund Research Program P42 ES016465, Core Center Grant P30 ES0 00210 and the NIEHS Training Grant T32 ES007060. 128
Pacific Northwest National Laboratory is a multi -program national laboratory operated by Battelle Memorial Institute for the DOE under contract number DE-AC05-76RLO1830.
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132
Figure 4-1 Structures of BEZO, 7,12-B[a]AQ and BAA
BEZO and 7,12-B[a]AQ, both oxygenated 4-ring PAHs, are compared with 4-ring parent PAH BAA.
133
Figure 4-2 Developmental toxicity of 7,12-B[a]AQ and BEZO at 120 hpf (A) Exposure from 6-72 hpf to 5, 7.5 and 10 μM 7,12-B[a]AQ caused concentration-dependent increases in the percent of control morpholino(MO)-injected larvae with pericardial edema (dark circles). 7,12-B[a]AQ exposure did not cause significant pericardial edema above control levels in ahr2-MO injected embryos (light circles) (B) BEZO exposure induced pericardial edema at 5, 7.5 and 10 uM in control-MO injected embryos (dark circles), ahr2 morphants showed no significant increase (light circles). The percentage of embryos with at least 1 malformation was significantly increased by both 7,12-B[a]AQ(C) and BEZO (D) in control-MO injected embryos (dark circles), while ahr2 morphants had no significant increase in the percentage of malformed embryos (light circles) . Data represent 3 independent replicates analyzed by one-way ANOVA with Tukey’s pairwise posthoc test. a significantly different than control bsignificantly different than control and 5 uM, p < 0.05. (E, F, G) Representative images of control-MO injected embryos exposed to 1% DMSO vehicle control, 10 μM 7,12-B[a]AQ, and 10 μM BEZO, respectively. Severe malformations including pericardial edema, yolk sac edema, craniofacial malformations and eye defects can be observed in F and G. ( H, I, J) Representative images of ahr2-MO injected embryos exposed to 1% DMSO vehicle control, 10 μM 7,12-B[a]AQ, and 10 μM BEZO, respectively show rescue of morphological abnormalities.
134
Figure 4-2
135
Figure 4-3 Cyp1 expression and morphology in OPAH-exposed embryos at 48 hpf A) Exposure to 7.5 μM 7,12-B[a]AQ induced differential expression of cyp1a and cyp1b1 detected by qRT-PCR in control morpholino injected (c-MO) and ahr2-MO injected embryos compared to DMSO controls. B) Exposure to 7.5 μM BEZO induced differential expressi on of cyp1a and cyp1b1 in c-MO and ahr2-MO injected embryos compared to DMSO controls, but with much smaller fold change values than 7,12-B[a]AQ. Note different scale compare to Figure A. Data represent 4 biological replicates analyzed by two-way ANOVA with Tukey’s posthoc test. Treatment groups not sharing a letter are statistically different (p < 0.05).
C, D) Representative light microscope images of 48 hpf control-MO and ahr2-MO injected embryos exposed to 1% DMSO vehicle control. E, F) 48 hpf control-MO and ahr2-MO injected embryos exposed to 7.5 μM 7,12-B[a]AQ. Pooling of blood is apparent in E (arrow). G, H) 48 hpf control -MO and ahr2-MO injected embryos exposed to 7.5 μM BEZO. Pericardial edema is apparent in G (arrow).
I, J) Immofluorescent labeling of Cyp1a protein in 48 hpf control-MO and ahr2-MO injected embryos exposed to 1% DMSO vehicle control. K, L) Cyp1a protein expression in 48 hpf control -MO and ahr2- MO injected embryos exposed to 7.5 μM 7,12-B[a]AQ, c-MO injected embryos showed strong fluorescence throughout the vasculature (K). BEZO exposed embryos showed no Cyp1a protein expression (M, N).
136
Figure 4-3
137
Figure 4-3 (Continued)
138
Figure 4-4 Heatmap of transcripts significantly induced by BAA exposure at 48 hpf.
Log2FC values were cluster using birectional hierarchical clustering. Individual samples are normalized to respective platform mean control value. A= array, fluorescence intensity values.
S=mRNA-seq FPKM values). Insufficient data is represented in gray.
139
Figure 4-5 Comparison of transcripts induced by BEZO and 7,12-B[a]AQ exposure A) Venn comparison of significant gene lists identified in mRNA -seq analysis of BEZO and 7,12- B[a]AQ with fold change >2 compared to control. B) novel transcript underexpressed in both BEZO and 7,12-B[a]AQ exposed samples occurs in a region with high transcriptional coverage but no annotation in Zv9 (top). Visualization of individual sample transc ript reads across the predicted transcripts shows consistency within treatments (bottom). C) Bidirectional hierarchical clustering of all 366 transcripts significantly induced > 2 fold by BEZO or 7,12-B[a]AQ. Biological functions that were significantly enriched in clusters of transcripts are noted.
140
Figure 4-5
141
Figure 4-5 (Continued)
142
Figure 4-6 qRT-PCR analysis of OPAH targets in control and AHR2 morphants Exposure to 7.5 μM 7,12-B[a]AQ or BEZO induced differential expression of wfikkn1 (A), gstp2 (B), arg2 (C), igfbp1a (D), ponzr4 (E) and p53 (F) detected by qRT-PCR in c-MO and ahr2-MO injected embryos compared to DMSO controls. Data represent 4 biological replicates analyzed by two -way ANOVA with Tukey’s posthoc test. Significantly different induction is designated with different letters (p < 0.05).
143
Figure 4-6
144
Figure 4-7 Biological processes affected by BEZO and 7,12-B[a]AQ exposure A)MetaCore processes significantly over-represented among transcripts misexpressed by BEZO and 7,12-B[a]AQ. B) Predicted interactions between the AHR and transcripts differentially expressed in BEZO (blue) and 7,12-B[a]AQ (yellow) exposed embryos. Interactions were predicted using the Metacore statistical interactome tool. Color of circles represents expression of transcript compared to control (red = increased, blue = decreased). Symbols designating network object classes
145
Figure 4-7
146
Figure 4-7 (Continued)
147
Table 4-1 Cross-platform comparison of expression data for genes differentially induced by PAHs Log2 fold change values of PAH-treated samples compared to 1% DMSO control detected by microarray fl uorescence intensity (DBT, PYR and BAA, previous study) or RNA-seq FPKM values (BEZO and 7,12-B[a]AQ, this study) compared to qRT-PCR log2FC values. *Significantly different than control, one-way ANOVA with Tukey’s all pairwise posthoc test, p < 0.05 † At least one probe significantly different than control, one-way ANOVA with Tukey’s all pairwise posthoc test, 0.05 FDR, adjusted p value < 0.05. ‡ Significantly different than control, Cuff diff pairwise comparison, 0.01 FDR, adjusted p value < 0.05)
DBT PYR BEZO BAA 7,12-B[a]AQ
Gene Microarray QPCR Microarray QPCR RNA-seq QPCR Microarray QPCR RNA-seq QPCR cyp1a 0.7 ± 0.21 0.81 ± 0.23 1.24 ± 0.54‡ 1.43 ± 0.47* 1.84 ± 0.29† 1.61 ± 0.15* 4.56 ± 0.14‡ 6.63 ± 0.32* 7.85 ± 0.25† 7.24 ± 0.09* ctsl.1 0.46 ± 0.5 -0.17 ± 0.35 -0.08 ± 0.49 -0.39 ± 0.09 1.53 ± 0.88† 1.63 ± 0.16* 2.08 ± 0.2‡ 1.68 ± 0.16* 2.51 ± 0.31† 2.32 ± 0.23* sult6b1 0.03 ± 0.1 -0.27 ± 0.26 0.04 ± 0.33 -0.28 ± 0.18 0.8 ± 0.42† 0.87 ± 0.33* 1.42 ± 0.2‡ 1.03 ± 0.24* 1.94 ± 0.5† 2.42 ± 0.26* cxcr4a 1.61 ± 0.33‡ 1.19 ± 0.13* 0.63 ± 0.36‡ 0.28 ± 0.33 1.14 ± 0.42† 0.83 ± 0.22* 0.5 ± 0.11‡ 0.21 ± 0.38 0.77 ± 0.46† 0.74 ± 0.09* ctgfb -0.05 ± 0.13 -0.73 ± 0.23 0.25 ± 0.61 -0.56 ± 0.21 -0.13 ± 0.67 -0.02 ± 0.06 1.37 ± 0.46‡ 1.23 ± 0.17* 0.56 ± 0.48 1.25 ± 0.07* s100z 0.96 ± 0.27‡ 0.33 ± 0.17 1.17 ± 0.25‡ 0.51 ± 0.16 0.34 ± 0.22 0.91 ± 0.08* 0.52 ± 0.12‡ 0.06 ± 0.25 1.89 ± 0.36† 2 ± 0.12*
147
148
Table 4-2 Biological processes misregulated by OPAHs
Gene ontology terms over-represented in BEZO and 7,12-B[a]AQ significant gene lists. The 20 most significant GO terms for each OPAH are listed (Metacore GeneGO, p < 0.001, shading according to degree of significance).
GO term BEZO 7,12-BaAQ
# of
genes/total
in process
single-organism metabolic process 6.22E-13 1.76E-20 184/2713 small molecule metabolic process 1.40E-13 3.96E-16 156/2169 response to chemical stimulus 5.70E-09 6.98E-16 164/2599 response to organic substance 6.54E-09 4.83E-14 129/1889 response to lipid 5.69E-08 2.08E-13 67/718
organ development 1.24E-12 2.40E-11 155/2420
organic acid metabolic process 1.25E-06 1.70E-12 75/839 response to hormone stimulus 6.90E-08 1.78E-12 73/840
response to organic cyclic compound 8.57E-09 4.81E-12 68/754 developmental process 9.87E-10 9.06E-12 219/4138
tissue development 4.37E-04 3.26E-11 81/1261
metabolic process 7.02E-06 4.38E-11 332/7643
response to endogenous stimulus 1.34E-08 6.01E-11 89/1187 system development 8.77E-11 2.04E-10 189/3339
oxidation-reduction process 4.98E-05 8.80E-11 67/846
response to oxygen-containing compound 4.57E-09 1.17E-10 73/881 visual perception 1.18E-10 7.67E-02 26/203
sensory perception of light stimulus 1.30E-10 7.84E-02 26/204 multicellular organismal development 5.17E-10 5.07E-10 205/3781 anatomical structure development 1.14E-09 1.13E-09 199/3744
149
Figure 4-S1 Comparative expression across 5 PAH structures Bidirectional hierarchically clustered heatmap of log2FC values of transcripts signific antly induced by BEZO or 7,12-B[a]AQ exposure, for which probes were identified on the Agilent zebrafish microarray platform. Individual samples are normalized to respective platform mean control value.
A= array, fluorescence intensity values. S=mRNA-seq FPKM values.
150
Table 4-S1 Primers used for qRT-PCR analysis
Gene Ensembl ID Forward Primer (5’- 3’) Reverse Primer (5’- 3’) arg2 ENSDARG00000039269 AACGGCGGACTGACCTAC CCAGAGCGGATGCAACTA β-actin ENSDARG00000037746 AAGCAGGAGTACGATGAGTC TGGAGTCCTCAGATGCATTG cyp1a ENSDARG00000026039 TGCCGATTTCATCCCTTTCC AGAGCCGTGCTGATAGTGTC cyp1b1 ENSDARG00000068934 CTGCATTGATTTCCGAGACGTG CACACTCCGTGTTGACAGC ctgfb ENSDARG00000070586 TGTAACCAATGACAATGAGC CATCCAGACAACTCGAAACG ctsl.1 ENSDARG00000003902 GGACTCCTACCCCTATGAAG ATAACCAACAGCCAGAACAC cxcr4a ENSDARG00000057633 ACACGGTAAACTTGTACAGC ATGTGACAAACGAGTCCTG gstp2 ENSDARG00000057338 TCTGGACTCTTTCCCGTCTCTCAA ATTCACTGTTTGCCGTTGCCGT igfbpa ENSDARG00000014947 AAGCGGTGTGCACCGAGAGC CCCGGTCACGAACACGGTGG ponzr4 ENSDARG00000087440 GCTGTATTCCTCCAATCACG CCTTGCCTTCATCTCTCGTC p53 ENSDARG00000035559 CTCTCCCACCAACATCCACT ACGTCCACCACCATTTGAAC s100z ENSDARG00000038729 GATCACCGTCTTCCACAAC GTCCTTCTGAGACATGAGG wfikkn1 ENSDARG00000044671 TATGCACACACAGTCAACAC GGACTCATTTACCTGTCGAG
151
Table 4-S2 Sequencing mapping
Summary of total counts and mapping statistics of reads that passed Illumina CASAVA QC for RNA - seq samples in this study. Reads were mapped to genome assembly Zv9.
Sample
Name
Total
paired
reads
(Illumina
QC ok)
Reads
mapped to
genome
(Zv9)
%
reads
mapped
Reads
mapped
uniquely
% reads
uniquely
mapped
Reads
mapped
to two
locations
Reads
mapped
to 3 or
more
locations
Total hits
(only
paired
accepted)
DMSO_A 41,403,566 31,136,272 75.2 25,206,504 81 1,681,278 4,248,490 40,196,648 DMSO_B 59,745,304 45,224,497 75.7 37,320,636 82.5 2,876,464 5,027,397 59,347,598 DMSO_C 59,035,420 44,516,465 75.4 36,303,716 81.6 2,424,866 5,787,883 59,634,276 BEZO_A 51,837,910 38,800,129 74.8 31,274,174 80.6 2,056,482 5,469,473 51,499,242 BEZO_B 46,499,422 35,095,720 75.5 25,391,954 72.4 1,827,628 7,876,138 46,369,830 BEZO_C 41,818,250 31,305,119 74.9 25,532,988 81.6 1,664,374 4,107,757 39,621,128 BaAQ_A 49,416,290 37,181,558 75.2 29,973,554 80.6 1,953,912 5,254,092 50,418,194 BaAQ_B 36,241,038 26,888,497 74.2 21,517,188 80 1,372,508 3,998,801 35,143,112 BaAQ_C 47,733,496 35,477,524 74.3 27,090,316 76.4 1,971,892 6,415,316 51,354,196
152
Table 4-S3 7,12-B[a]AQ significantly misexpressed transcripts Zebrafish genome assembly Zv9 transcripts identified by Cuffdiff as significantly differentially expressed in 7,12-B[a]AQ-exposed embryos compared to control (1% FDR, q < 0.05). ). Log 2 fold change values represent mean BEZO-exposed compared to control.
Gene ID Gene Log2(fold_change) q_value
ENSDARG00000026039 cyp1a 7.81 0.00E+00
ENSDARG00000058980 cyp1c1 4.81 0.00E+00
ENSDARG00000018298 cyp1c2 4.41 0.00E+00
ENSDARG00000059387 fgf7 3.82 4.35E-03
ENSDARG00000068934 cyp1b1 3.57 0.00E+00
ENSDARG00000076534 si:ch211-14a17.10 3.11 1.29E-02
ENSDARG00000057338 gstp2 2.69 1.32E-02
ENSDARG00000003902 ctsl.1 2.44 1.22E-05
ENSDARG00000015355 fosl1a 2.31 4.96E-03
ENSDARG00000005039 gstp1 2.27 0.00E+00
ENSDARG00000007344 tcap 2.25 0.00E+00
ENSDARG00000022139 ocstamp 2.06 7.57E-03
ENSDARG00000074971 DHRS13 (2 of 5) 2.00 1.93E-07
ENSDARG00000061481 CABZ01041812.1 1.96 0.00E+00
ENSDARG00000058734 prdx1 1.92 0.00E+00
ENSDARG00000086826 sult6b1 1.92 0.00E+00
ENSDARG00000061634 zgc:158614 1.90 1.87E-13
ENSDARG00000055974 TPMT (1 of 2) 1.86 2.88E-08
ENSDARG00000038729 s100z 1.84 5.98E-03
ENSDARG00000016713 dhrs13l1 1.83 5.80E-06
ENSDARG00000031683 fos 1.80 2.23E-10
ENSDARG00000086047 CABZ01067657.1 1.75 0.00E+00
ENSDARG00000019236 gsr 1.74 8.45E-11
ENSDARG00000075014 sqstm1 1.73 2.90E-02
ENSDARG00000073695 mamdc2b 1.70 1.40E-03
ENSDARG00000021149 cbr1l 1.69 0.00E+00
ENSDARG00000056638 pir 1.68 1.85E-11
ENSDARG00000044685 nr0b2a 1.59 9.65E-12
ENSDARG00000091715 CR926130.2 1.58 1.61E-12
ENSDARG00000071567 TSTD1 1.58 3.77E-08
ENSDARG00000067652 im:7150988 1.57 0.00E+00
ENSDARG00000089936 sepw2b 1.53 3.96E-08
ENSDARG00000089697 si:dkey-3d18.4 1.52 3.59E-03
ENSDARG00000035422 cyr61l1 1.48 1.71E-02
ENSDARG00000033285 gsto2 1.47 1.14E-07
ENSDARG00000002204 hspb11 1.46 2.23E-10
ENSDARG00000030896 foxq1a 1.44 3.00E-02
ENSDARG00000005713 ethe1 1.41 3.30E-07
ENSDARG00000034852 nt5c2l1 1.39 8.31E-04
ENSDARG00000089507 ugt1b4 1.34 2.43E-11
ENSDARG00000089586 AL844567.2 1.33 2.45E-03
ENSDARG00000063223 arl14 1.32 4.60E-02
ENSDARG00000044935 hpdb 1.29 7.05E-07
ENSDARG00000075524 CYLC2 1.29 5.53E-11
ENSDARG00000056795 serpine1 1.28 6.52E-03
153
Gene ID Gene Log2(fold_change) q_value
ENSDARG00000014646 AOC2 1.25 1.93E-07
ENSDARG00000090375 ALPK3 (2 of 2) 1.24 1.22E-03
ENSDARG00000002405 si:ch211-225b11.1 1.22 4.78E-04
ENSDARG00000053136 b2m 1.22 1.68E-02
ENSDARG00000070000 txnipb 1.18 1.49E-08
ENSDARG00000043442 MAL (2 of 3) 1.18 5.20E-06
ENSDARG00000007823 atf3 1.16 5.80E-06
ENSDARG00000079938 zgc:173594 1.16 4.00E-03
ENSDARG00000061081 arpp21 1.15 1.04E-04
ENSDARG00000025122 abhd4 1.15 2.02E-02
ENSDARG00000006220 ugt1ab 1.14 3.76E-02
ENSDARG00000073786 cmbl 1.13 3.07E-05
ENSDARG00000094719 si:dkeyp-1h4.9 1.12 2.89E-04
ENSDARG00000058005 hgd 1.09 9.84E-04
ENSDARG00000018566 CU302436.1 1.09 5.51E-11
ENSDARG00000022165 MGST1 (1 of 2) 1.08 4.73E-02
ENSDARG00000055643 cyb5a 1.08 9.57E-07
ENSDARG00000067701 myoz3a 1.07 1.54E-03
ENSDARG00000071005 ppp1r3ca 1.06 2.34E-05
ENSDARG00000014947 igfbp1a 1.06 8.85E-03
ENSDARG00000078674 hspb9 1.05 2.07E-08
ENSDARG00000023217 CREM 1.04 4.35E-02
ENSDARG00000028386 htatip2 1.02 8.05E-03
ENSDARG00000091116 pkhd1l1 1.01 8.62E-07
ENSDARG00000032496 zgc:91887 1.00 2.93E-04
ENSDARG00000063297 abcb6 0.99 1.24E-02
ENSDARG00000039232 DUSP8 (2 of 2) 0.94 1.84E-03
ENSDARG00000031776 zgc:92066 0.94 2.09E-05
ENSDARG00000061120 slc43a2b 0.93 4.75E-04
ENSDARG00000092379 si:dkeyp-51b9.3 0.92 2.81E-02
ENSDARG00000093494 si:ch211-217k17.9 0.92 3.25E-06
ENSDARG00000093584 zgc:193505 0.91 3.97E-05
ENSDARG00000028957 maff 0.90 1.53E-05
ENSDARG00000070020 cyp2aa9 0.90 9.84E-04
ENSDARG00000026611 socs3b 0.86 1.54E-03
ENSDARG00000055510 ypel3 0.86 3.59E-03
ENSDARG00000016132 keap1a 0.85 3.98E-03
ENSDARG00000090014 CAPN2 (1 of 4) 0.85 6.87E-03
ENSDARG00000061841 tiparp 0.84 2.07E-02
ENSDARG00000017034 sqrdl 0.84 7.79E-03
ENSDARG00000074634 keap1b 0.82 5.94E-03
ENSDARG00000093521 B3GNT3 (2 of 4) 0.82 3.77E-02
ENSDARG00000087093 si:ch211-157c3.4 0.82 4.33E-02
ENSDARG00000060315 si:dkey-193b15.6 0.81 5.36E-03
ENSDARG00000095434 si:ch211-217k17.8 0.79 7.76E-03
ENSDARG00000033364 zgc:158387 0.79 8.80E-06
ENSDARG00000089046 - 0.79 2.93E-02
ENSDARG00000055045 casp3b 0.77 3.57E-02
ENSDARG00000036457 cacng6a 0.76 1.33E-02
154
Gene ID Gene Log2(fold_change) q_value
ENSDARG00000060113 znf395a 0.75 1.99E-03
ENSDARG00000042310 blvrb 0.75 5.20E-03
ENSDARG00000094300 si:ch211-160e1.5 0.75 4.91E-04
ENSDARG00000030722 xirp1 0.75 2.05E-03
ENSDARG00000026655 tspo 0.74 6.60E-03
ENSDARG00000006982 msxd 0.73 2.91E-02
ENSDARG00000077549 AIFM2 0.73 5.72E-03
ENSDARG00000089920 MLIP 0.73 7.09E-05
ENSDARG00000040190 qdpra 0.73 2.90E-02
ENSDARG00000030905 cited2 0.72 3.67E-03
ENSDARG00000091996 si:ch211-117m20.5 0.72 3.46E-04
ENSDARG00000094557 nupr1 0.70 1.19E-02
ENSDARG00000059914 SDR42E2 0.70 3.45E-02
ENSDARG00000069559 si:ch211-239f4.7 0.69 1.64E-03
ENSDARG00000021787 abcb5 0.69 9.22E-04
ENSDARG00000028106 glrx 0.69 2.61E-02
ENSDARG00000038147 hbbe3 0.67 5.46E-03
ENSDARG00000079634 FILIP1 (1 of 2) 0.66 3.56E-02
ENSDARG00000019646 twist3 0.64 4.35E-02
ENSDARG00000035810 rgcc 0.64 5.36E-03
ENSDARG00000079119 CR848759.2 0.64 4.17E-02
ENSDARG00000013968 psap 0.59 4.32E-03
ENSDARG00000021059 alas1 0.59 5.20E-03
ENSDARG00000044212 CR385063.1 0.59 5.87E-03
ENSDARG00000089358 CABZ01035189.1 0.59 1.63E-02
ENSDARG00000011245 esrp1 0.58 1.06E-02
ENSDARG00000021833 ahr2 0.57 4.57E-03
ENSDARG00000021366 fbp1a 0.57 1.66E-02
ENSDARG00000070355 FBXO2 0.56 6.89E-03
ENSDARG00000025338 hagh 0.56 4.13E-02
ENSDARG00000070047 rgs4 0.54 2.46E-02
ENSDARG00000071601 pvalb9 0.54 2.19E-02
ENSDARG00000036107 txnipa 0.54 1.16E-02
ENSDARG00000015343 pgd 0.53 2.22E-02
ENSDARG00000038643 alas2 0.53 2.46E-02
ENSDARG00000044125 txn 0.52 3.05E-02
ENSDARG00000035519 histh1l 0.51 2.46E-02
ENSDARG00000007216 abce1 -0.46 4.73E-02
ENSDARG00000006008 dct -0.46 4.73E-02
ENSDARG00000039578 pa2g4a -0.48 3.13E-02
ENSDARG00000005122 atp2a2b -0.49 3.29E-02
ENSDARG00000014179 pfkma -0.50 2.91E-02
ENSDARG00000030913 zgc:152873 -0.51 4.67E-02
ENSDARG00000061375 sgpl1 -0.51 3.55E-02
ENSDARG00000000212 KRT23 (1 of 15) -0.51 1.89E-02
ENSDARG00000060594 hadhab -0.51 2.44E-02
ENSDARG00000068076 psmd7 -0.51 2.02E-02
ENSDARG00000007697 fabp7a -0.51 2.64E-02
ENSDARG00000030972 dnaja1l -0.52 4.73E-02
155
Gene ID Gene Log2(fold_change) q_value
ENSDARG00000010571 ezh2 -0.53 4.03E-02
ENSDARG00000017568 HNRNPAB (2 of 2) -0.53 3.55E-02
ENSDARG00000056248 wu:fb15e04 -0.53 1.39E-02
ENSDARG00000012234 psme3 -0.53 4.33E-02
ENSDARG00000054155 pcna -0.53 1.10E-02
ENSDARG00000056314 a2ml -0.53 1.16E-02
ENSDARG00000037284 ptges3a -0.54 3.73E-02
ENSDARG00000059357 SARNP -0.54 1.80E-02
ENSDARG00000063631 ch1073-291c23.1 -0.54 4.91E-02
ENSDARG00000036371 acta1a -0.54 2.19E-02
ENSDARG00000063509 lrrc58b -0.55 4.76E-02
ENSDARG00000095268 si:dkey-261h17.1 -0.55 1.21E-02
ENSDARG00000033760 pmelb -0.55 1.44E-02
ENSDARG00000037057 gcdh -0.55 2.90E-02
ENSDARG00000037116 cxcl12a -0.55 1.70E-02
ENSDARG00000071353 AL929007.1 -0.55 7.82E-03
ENSDARG00000007354 pdia3 -0.55 1.63E-02
ENSDARG00000076790 si:ch211-55g3.6 -0.56 2.91E-02
ENSDARG00000006314 itgav -0.56 1.15E-02
ENSDARG00000090495 pla2g15 -0.56 3.25E-02
ENSDARG00000026829 cotl1 -0.57 5.84E-03
ENSDARG00000012729 hcls1 -0.57 1.73E-02
ENSDARG00000054259 nat10 -0.57 4.02E-02
ENSDARG00000058292 sephs1 -0.57 4.59E-02
ENSDARG00000012694 c3a -0.57 2.01E-02
ENSDARG00000060041 LIG1 -0.57 6.44E-03
ENSDARG00000037997 tubb5 -0.57 2.64E-02
ENSDARG00000016235 rbp1a -0.57 1.50E-02
ENSDARG00000012066 dcn -0.57 2.01E-02
ENSDARG00000070651 PRKCD (1 of 2) -0.57 3.36E-02
ENSDARG00000017261 gdpd1 -0.58 3.48E-02
ENSDARG00000056600 papss2b -0.58 1.10E-02
ENSDARG00000009342 txndc5 -0.58 3.98E-03
ENSDARG00000045367 tuba1 -0.58 1.74E-02
ENSDARG00000070050 sfrp2 -0.58 3.41E-02
ENSDARG00000037845 col9a3 -0.58 4.13E-03
ENSDARG00000021720 COL4A6 -0.59 8.75E-03
ENSDARG00000001057 bysl -0.59 3.12E-02
ENSDARG00000008732 zgc:66479 -0.59 3.59E-03
ENSDARG00000045297 phb2 -0.60 1.80E-02
ENSDARG00000088514 and1 -0.60 2.79E-03
ENSDARG00000061124 srpr -0.60 7.79E-03
ENSDARG00000091792 BX088649.1 -0.60 3.45E-02
ENSDARG00000040041 mcm4 -0.60 5.44E-03
ENSDARG00000087055 BX842614.2 -0.61 1.10E-02
ENSDARG00000036700 si:ch211-114n24.6 -0.62 1.14E-03
ENSDARG00000053493 aldh1a2 -0.63 5.44E-03
ENSDARG00000054807 sec13 -0.63 8.60E-03
ENSDARG00000038056 FGFBP2 (1 of 2) -0.63 1.87E-03
156