Wildlife Heritage A ccount Project Proposal Form · ILDLIFE HERITAGE TRUST ACCOUNT PROJECT PROPOSAL FORM. Rev 10/28/15 Page 2. recruitment as well as little expression of disease

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BOARD OF WILDLIFE COMMISSIONERS

WILDLIFE HERITAGE TRUST ACCOUNT PROJECT PROPOSAL FORM Rev 10/28/15 Page 1

Wildlife Heritage Account Project Proposal Form

APPLICANT INFORMATION

Person/Organization/Agency: Nevada Department of Wildlife (NDOW)

Date: January 26, 2018

Name: Peregrine Wolff Title: Wildlife Health Specialist

Address: 6980 Sierra Center Parkway City: Reno

State: NV Zip 89511 Phone: 805-857-5809

Cell: 805-857-5809 Fax:

Email: [email protected]

Other:

PROJECT INFORMATION

Project Title: Gene Transcription Analysis to Link Habitat and Environment to Immunity and Health of Bighorn Sheep in Nevada

State Fiscal Year(s) Wildlife Heritage Account Funds are Needed: 2019

Project Location: Statewide bighorn sheep herds where we have collected samples for gene transcription analysis (and # of samples collected) are:

Desert Bighorn Herds: Garfield Hills (7) Lone Mountain (27) Stillwater Range (12) Desatoya Mountains (8)

California Bighorn Herds: Double H (10) Santa Rosa’s (9) Granites (4)

Is a Project Map Attached? Yes ☐ No ☒ (a map must include the project title, map scale, date map was created, and a north arrow)

Purpose of the Project:

Respiratory disease is a key factor impacting the success of the ongoing conservation and recovery of wild sheep populations. Decades of research has identified the primary pathogens involved in the bighorn sheep pneumonia complex; however we don’t fully understand the wide variability in herd response following infection. The response of populations infected with these primary pathogens has ranged from a loss of 100% of the herd to a loss of a portion of the herd followed by years to decades of poor lamb

http://ndow.org/index.shtm

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recruitment as well as little expression of disease and no impact on lamb survival. The prevailing thought is that this variation is caused by variable pathogen virulence, or intrinsic or external factors that impact individual or herd immunity. Gene-based analytical techniques provide an innovative opportunity to improve our understanding of the intrinsic and extrinsic factors that may be impacting the susceptibility and response to infection with these respiratory pathogens. With this technology we are looking at how the sheep are reacting to their environment and exposure to pathogens. We propose to apply a bighorn-specific transcript panel to Nevada bighorn sheep sampled from multiple populations with differing herd performance, exposure, and response to pathogen spill-over events. If patterns of gene expression within different herds or metapopulations are identified, this will illuminate specific factors that influence bighorn sheep susceptibility, or response, to disease. This will potentially allow managers to identify mitigation and management actions to either prevent infection or improve herd recovery if infected.

Detailed Description of Project:

Respiratory disease is the key impediment threatening the conservation and recovery of bighorn sheep populations. Over 14,000 adult bighorn sheep have perished in 175 die-offs from the 1970s through 2014 in 17 of 20 jurisdictions they inhabit. Over 75 herds have experienced three or more years of poor lamb recruitment after disease events. Mycoplasma ovipneumoniae (M. ovi) is a primary causative agent driving epidemic respiratory disease (i.e., pneumonia) in bighorn sheep. Additional bacteria, including Pasteurellaceae, as well as respiratory viruses, and other factors (e.g., paranasal sinus tumors) likely contribute to the severity of disease in individuals or herds. Initial spillover of M. ovi may occur via contact with domestic sheep or goats, and if established, can subsequently be circulated within and between populations of wild sheep or mountain goats. Following pathogen spillover, several herd outcomes have been documented that ranged from little to no impact on health and recruitment to all-age pneumonia die-offs, followed by years of pneumonia deaths in lambs. Questions of importance to desert bighorn sheep managers include better definition of the factors that contribute to variation in herd response to respiratory disease, and what management actions might improve post-disease herd survival and recovery.

Investigations into disease dynamics using standard and widely accepted diagnostics may not be sensitive enough to provide the clues to defining factors that influence variations in herd responses. Proven molecular techniques such as gene-based analyses (i.e., gene transcription) provide an innovative opportunity to improve our understanding of disease susceptibility in desert bighorn by evaluating transcript levels for multiple genes that respond to environmental stressors (i.e., pathogens, trauma, contaminants, or environmental disturbances). Cells of the immune system respond to stimuli by communicating with each other via differential expression of cell surface proteins (receptors and ligands) and secretion of soluble messengers. While many of these genes can be transcriptionally-induced by multiple and varied intrinsic or extrinsic environmental factors, the literature supports the hypothesis that stressors could be differentiated based upon the “sum” of unique genes that are up- or down-regulated. Leukocyte gene transcripts encoding cell-surface and secreted messengers are under tight transcriptional control and exhibit short half-lives, and thus can serve as sensitive and real-time measures of immunologic perturbations. Gene transcription in particular, uses a quantitative polymerase chain reaction (qPCR) assay to target genes that change in response to bacteria, viral, inflammatory thermal stress, nutritional stress, environmental toxicants, and overall cellular function and metabolic conditions. This helps researchers and managers to identify when and why individuals are physiologically responding to their environment. These techniques are proven in human medical science, but have only recently been applied to wildlife. For example, disease susceptibility investigated in sea otter (Enhydra lutris) populations was found to be increased in populations that had gene transcript profiles indicative of hydrocarbon exposure or food resource limitation. A corroborating study of polar bear (Ursus maritimus) transcriptomics revealed the synergistic effects of nutritional deficiency and contaminant exposure on disease dynamics; and gene


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transcript analysis of Mojave desert tortoise (Gopherus agassizii) identified increased susceptibility to disease associated with dietary insufficiencies.

Historically, large-scale investigations into populations and ecosystems have been driven by catastrophic events (i.e., unusual mortality events). These investigations relied heavily on routine clinical diagnostics and pathology from sick or deceased animals. Here, we advocate a different approach, i.e., a model of baseline and long-term monitoring of gene transcription patterns to assess populations for subtle yet significant changes in their physiology. Taking a proactive approach to monitoring populations will augment considerably our ability to understand risk factors impacting individual herds and subsequently identifying management actions to mitigate these risks. Proactive longitudinal studies on wildlife species at risk, such a bighorn sheep, could provide baseline data upon which apparent perturbations in real time could be assessed. Recently we developed a gene transcription panel specific for bighorn sheep representative of multiple physiological pathways that included immune function, nutrition, and detoxification. The results of pilot analyses of bighorn sheep blood samples to validate the panel showed separation of transcript profiles in statistical space, which indicated unique transcript profiles for each population analyzed (pilot populations included the Muddy Mountains, River Mountains, Bare Mountain and Pintwater Range). Stressor-specific analyses of gene transcription profiles can inform management of actions that may mitigate stressor impacts and improve bighorn sheep recovery.

How Does this Project Meet the Objectives of the Wildlife Heritage Program? (See NRS 501.3575)

Nevada has the most successful bighorn sheep restoration program in the West. However a number of our herds have also been impacted by disease events following pathogen spill-over; with some suffering catastrophic die-offs, followed by years of little to no lamb recruitment and others display little population impact following infection. This gene transcription technology and assessment of herds will allow managers to gain knowledge about how the sheep themselves are responding to their environment and greatly compliment the knowledge of herd health that we have gained form physical exams, disease testing and herd demography profiles. By adding the information gained from these gene transcription profiles wildlife professionals can improve the management of individual herds in relation to their immediate environment and pathogen profiles, allowing improved health in all of Nevada’s bighorn herds.

Legal Description of the Property on Which the Proposed Project is to be Located (must include the property address, access roads, township, range and section):

The project will assess samples taken from many different locations - see the list of mountain ranges in the project location section on page 1.

Does this Project Have Additional Funding Sources Other than Your Wildlife Heritage Account

Request? Yes ☒ No ☐

Does this Project Involve Habitat Restoration and Improvement of a Long-term or Permanent Nature?

Yes ☐ No ☒


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Please Describe in Detail the Reason Why You Need Wildlife Heritage Account Funding to Fund this Project:

The acquisition of Heritage funding will allow us to complete gene transcription testing for the 7 of the 10 herds which were sampled in 2017-18. The USGS is securing funding to test samples from the remaining 3 3 herds. (Muddy Mountains, Bare Mountain, Nevada Test and Training Range (NTTR)) which are in, or immediately adjacent to, Clark County). Their funding will also support sampling and testing of these southern Nevada herds during FY 2019. In 2016, 11 herds were sampled across Nevada and USGS has funded the gene transcription testing for these herds. Once all testing is complete we will have 21 herds tested in NV. These herds include desert and California bighorns as well as herds that display the full gamut of pathogen profiles and herd responses. Preliminary results indicate that this technology will allow us to have a greater understanding of the intrinsic and extrinsic environmental factors that are impacting our bighorn sheep herds and potentially help us determine appropriate management actions for each herd. By getting the samples run and the results analyzed, we can: 1) determine if there are herds whose management should be reviewed in light of their gene transcription profiles, 2) identify herds we want to investigate further during the 2018-19 capture season and 3) discuss incorporating this testing regimen into our annual federal aid grant funding.

Project Duration: one year ☒ two years ☐ three years ☐ more ☐

Estimated Start Date: July 1, 2018 Estimated End Date: June 30, 2019


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PROJECT FUNDING

The funding breakdown below should only be for the upcoming fiscal year. While projects may be extended beyond the first fiscal year, such an extension must be due to unusual circumstances and approved by the Wildlife Commission (see NAC 501.340). Double click on the table to activate the embedded spreadsheet.

$ 84,500.00

$ 9,100.00

$ 89,598.00

$ 25,625.00

$ 124,323.00

$ 3,500.00

$ 3,500.00

$ 212,323.00 4. Total Project Funding

c. Materials

d.

e.

f.

g.

h. Total Donations/In-kind Services (lines a – g)

d.

e. Total Other Cash Funding Sources (lines a – d)

3. Donations or In-kind Services for this Project

a. Volunteer Time

b. Equipment

1. Wildlife Heritage Account Cash Amount Requested

2. Other Cash Funding Sources for this Project

a. USGS Center Base Funding

b. FY18 USGS Cyclical Funding

c. USFWS Wildlife Health Project


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PROJECT COSTS The cost breakdown below should only be for the first fiscal year of the project. While projects may be extended beyond the first fiscal year, such an extension must be due to unusual circumstances and approved by the Wildlife Commission (see NAC 501.340). Double click on the table to activate the embedded spreadsheet.

Heritage Costs All Other Costs1. Land Acquisition

2. Personnel (NDOW employee costs can't be included) $ 3,500.00

3. Travel (NDOW travel costs can't be included)

a. Per diem

b. Mileage

c. Total Travel Costs (lines a & b) $ - $ -

4. Equipment Items

a.

b.

c.

d.

e. Total Equipment Costs (line a – d) $ - $ -

5. Materials

a.

b.

c.

d. $ -

e. Total Material Costs (lines a – d) $ - $ -

6. Miscellaneous Costs

a. Gene transcription $1097.40/sample $ 84,500.00 $ 124,323.00

b.

c.

d.

e. Total Miscellaneous Costs (lines a – d) $ 84,500.00 $ 124,323.00

7. Total Heritage Costs Only $ 84,500.00

(add lines 1, 2, 3c, 4e, 5e, 6e)

$ 127,823.00

(add lines 1, 2, 3c, 4e, 5e, 6e)

9. Total Project Costs $ 212,323.00

(add lines 7 & 8)

(bote: tota l project funding from previous table must match tota l project costs )

8. Total All Other Costs


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Are there going to be any Ongoing Costs for this Project? Yes ☐ No ☒

If there are Ongoing Costs Associated with this Project, is there an Anticipated Funding Source for

These Costs? Yes ☐ No ☐

Do You Anticipate Needing Additional Wildlife Heritage Account Funds Beyond the Upcoming Fiscal Year? If So, Please Describe What You Think Your Funding Requirements will be and for What Purposes (As noted above, extensions beyond the first fiscal year must be due to unusual circumstances and approved by the Wildlife Commission.): No

How Will You Give Credit to the Wildlife Heritage Account and Other Funding Sources?

The project’s funding sources will be acknowledged in any reports, presentations or peer reviewed publications that are generated from this research. Please see the attached, condensed version of the completion report for the Office of Federal Research that was prepared for the pilot project that was funded by USGS and USFWS in 2017.

Authorizing Signature:

Review Date: 4-2-18


Condensed version of the Office of Federal Research Report prepared for the pilot trial in 2017

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Development and utility of a gene transcription panel for desert bighorn sheep (Ovis

canadensis nelsoni)

Lizabeth Bowen Kathy Longshore Peregrine Wolff Sarah Bullock ·Shannon Waters A.

Keith Miles

Lizabeth Bowen · Kathy Longshore · Shannon Waters · A. Keith Miles

U.S. Geological Survey, Western Ecological Research Center, Sacramento, CA, 95826, USA

Peregrine Wolff

Nevada Department of Wildlife, 6980 Sierra Center Pkwy. Suite 120, Reno, NV, 89511, USA

Sarah Bullock

Desert National Wildlife Refuge, 16001 Corn Creek Road, Las Vegas, NV, 89124, USA

Traditional approaches to bighorn respiratory disease have focused mainly on the role

that pathogens and other factors play in the respiratory disease complex. Unknown are the

effects that genetic and environmental variation have on animal immunity and infections because

current health evaluations and diagnostics for desert bighorn sheep provide limited information

on the overall health of the animal and almost no information on the potential contributing risk-

factors inherent in the habitat. This lack of diagnostic information makes it difficult to identify

specific environmental conditions and stressors potentially linked to variable herd responses

from the spillover of such pathogens as M. ovi in desert bighorn sheep herds in Nevada. When

classical diagnostic methods fail to explain morbidity and mortality events, the use of novel

techniques such as gene transcription profile analysis may help identify factors contributing to

excessive mortality. Wildlife health is a reflection of environment and thus an important

indicator of ecological health.

Emerging laboratory techniques such as gene-based health diagnostics (i.e., gene

transcription) provide an innovative opportunity to improve our understanding of health in desert

bighorn by evaluating transcript levels for multiple genes that respond to environmental stressors

(i.e., pathogens, trauma, contaminants, or environmental stresses like drought). Gene-based

techniques have the ability to measure the physiological response of individuals to disease as

well as the influence of external factors on the health of an individual and collectively, the

population. Gene transcription in particular, uses a quantitative polymerase chain reaction

(qPCR) assay to target genes that change in response to bacteria, viral, inflammatory thermal

stress, nutritional stress, environmental toxicants, and overall cellular function and metabolic

conditions, which in turn can identify when and why individuals are physiologically responding

to their environment. However, at the time of disease state, an organism is clearly at the latter

stages of a succession of physiological, biochemical, and immunological events triggered by one

or more causative incidents; delineating these events in chronological order may not be possible.


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These techniques are used in human medical science, but have only recently been applied to

wildlife, including the American mink (Bowen and others, 2007), sea otter (Miles and others,

2012; Bowen and others, 2015a.), polar bear (Bowen and others, 2015b), and the Mojave Desert

tortoise (Bowen and others, 2015c).

We developed a panel of gene-transcript assays for desert bighorn sheep, a first step in

facilitating an understanding of how environmental conditions and stressors are linked to the

recent disease outbreaks across southern Nevada, including the DNWR (Table 1). We can now

apply the gene transcription panel to multiple bighorn sheep throughout their range to 1) provide

a comprehensive analysis of transcript panels at population levels; 2) associate transcript profiles

with herd responses to pathogen introduction and disease outcomes through use of non-metric

multidimensional scaling (NMDS); 3) identify outliers in each population and as a whole and

associate these with individual health assessments; and 4) associate transcript profiles with

specific environmental conditions. Accomplishing these objectives can inform potential

management actions that may lessen the impact of the disease and improve desert bighorn

recovery.

We developed real-time PCR assays for 14 genes of interest and two reference genes.

These have been validated on desert bighorn sheep samples randomly selected from populations

experiencing differing extrinsic and intrinsic pressures; these included the Muddy and River

Mountains, Pintwater Range, and Bare Mountain. Genes of interest represent immunological

and physiological systems critical to responses to intrinsic and extrinsic stressors, including

inflammation, cell signaling, detoxification, antiviral, antibacterial, apoptosis, and general stress.

Geometric mean transcript values differed among populations, and between age classes

and sexes. The fixed effects models identified significant effects (sex, age class, and location) on

gene transcript levels. Bighorn sheep location (population) accounted for the majority of

significant effects on gene transcription, influencing Gata, HSP70, IL1b, MX1, Tbet, TGFb, and

IL-10. Sheep age class significantly influenced transcription of AHR, IL1b, Tbet, and TNFa,

while sex influenced only IL1b.

When analyzed without apriori structure (i.e. location), bighorn sheep separated into four

well-defined groups and two outlier groups as depicted by NMDS (Figure 2) and confirmed by

cluster analysis (SIMPROF). Cluster groups contained sheep from at least two populations (Fig.

2). Separation of clusters was driven by transcript patterns of five genes: IL10, IFNg, CD69,

HSP70, MX1 (Table 6; Principal components analysis, R 2.8.1, R Development Core Team,

2012).

In light of the difficulties inherent in understanding patterns of bighorn respiratory

disease, we have used novel molecular tools to examine a small number of individuals from four

unique populations. Historically, large scale investigations into populations and ecosystems have

been driven by catastrophic events (i.e., unusual mortality events).We propose the approach of

using a model of baseline and long-term monitoring of sensitive molecular parameters (e.g.,

patterns of gene transcription profiles) to continually assess populations for subtle yet significant


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changes. In lieu of a traditional pathogen-focused study, we focused on a host immune function

analysis, where both intrinsic and extrinsic factors are taken into consideration.

Our results indicated that population (sheep location) was the primary driver behind

patterns of gene transcription, suggesting unique environmental (including pathogen presence)

circumstances exist for each population.

Muddy Mountains

The Muddy Mountain population is the least disturbed of the four populations. This

population is M. ovi free and has the least amount of anthropogenic disturbance; as such the

Muddy Mountain population can be defined as a ’reference‘ population.

River Mountains

In general, the River Mountain population transcript pattern was indicative of a relative

lack of immunosuppressive (low TGFb) and anti-inflammatory (low IL10) responses. The River

Mountain population had relatively low levels of MyD88 transcripts. The MyD88-dependent

TLR pathway may play a crucial role in sheep airway epithelial cells in response to M. ovi

infection (Xue and others, 2015).

There was evidence of two strains of M. ovi in this population. First detected in a ewe

captured in Hemenway Park during springof 2013. Presence of a second, more virulent strain of

M. ovi, was confirmed in spring 2015. Since 2015, results of aerial surveys and disease

surveillance portray a herd in decline, possibly due to bacterial pneumonia (Nevada Department

of Wildlife, 2017). This population is exposed to human activity at the wildland/urban interface.

Bare Mountains

Transcript patterns in the Bare Mountains were indicative of an increased anti-viral

response as evidenced by higher MX1 and Tbet levels. This herd was exposed to M. ovi in 2014.

During fall 2015 diagnostic tests revealed active (PCR) M. ovi infection and definitive prior

exposure (ELISA). Ten of the 12 sheep sampled had poor body condition (BCS <2.5). A severe

Psoroptic mite infestation was noted in two sheep. In early November 2016, respiratory disease

surveillance continued and eight bighorn sheep were captured, sampled and released on Bare

Mountain. Lab diagnostic test results from this capture were similar to results obtained from fall

2015 and portrayed a herd still coping with M. ovi infection (Nevada Department of Wildlife,

2017). This herd has also consistently tested positive for both BRSV and PI3 with fairly high

titers Notable is that the Bare Mountains are overpopulated with feral burros.

Pintwater Range

Transcript profiles from the Pintwater population were indicative of exposure to a

hydrocarbon or dioxin-like substance (AHR), inflammation (TNFa), and stress (HSP70). Studies

in sea otters exposed to hydrocarbons (crude oil) demonstrate an increase in AHR transcription

for approximately two weeks post exposure and subsequent decline to baseline levels of

transcription two weeks following peak transcription (Bowen, unpublished data). Interestingly,

transcripts were increased for an anti-microbial transcription factor (controls transcription of


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IFNg) (Tbet), but decreased considerably for another anti-microbial indicator (IFNg). An

inhibitor may be blocking increased transcription of IFNg, but when exposed to Mycoplasma,

IFNg deficient mice developed severe immunopathology (Bodhankar and others, 2010). During

November 2016, blood samples were obtained from 14 ewes and 12 rams. Lab diagnostic tests

revealed no active (PCR) M. ovi infection among these sheep. However, results of ELISA

testing found definitive prior exposure among 14 sheep. All animals were tested for three

respiratory viruses, bovine respiratory syncytial virus (BRSV), parainfluenza #3 PI3 and bovine

virus diarrhea virus (BVDV). All adult animals had titers to both BRSV and PI3. The 4 lambs

were negative for PI3 and 3 of the 4were negative for BRSV. This likely means that these

respiratory viruses were not actively circulating within the herd at the time of sampling as then

you might expect that lambs would also have titers, as by November they would have likely lost

their protective maternal antibody. All sheep were negative for BVDV as expected.

Cluster analysis

Individuals within a wildlife population comprise a range of physiological states. As

such, clusters designated by NMDS analysis (figure 1) included individuals across populations,

indicating some similar physiologic responses. Cluster 3 comprised a majority of sheep from the

Muddy Mountains, the designated reference population (based on historic lack of M. ovi). Thus,

cluster 3 should represent animals whose physiologic responses are similar to those in the

reference population; indeed, cluster 3 is comprised of sheep from all populations, a reflection of

the natural occurrence of relatively “physiologically normal” individuals from the populations

sampled. Cluster 1 is comprised of sheep from the River Mountains and Pintwater Range with

transcript profiles representing increased (relative to other clusters) anti-viral and inflammatory

responses, and decreased anti-inflammatory responses. The latter has been linked with the

ability of Mycobacterium to evade immune responses (Redford, 2011). Cluster 2 comprised

sheep mostly from the Pintwater Range, with an additional two from the Muddy Mountains and

one from the Bare Mountains. These sheep were characterized by high levels of HSP70, which

has been implicated in exposure to a number of stressors including thermal stress (Iwama and

others,1999; Tsan and Gao, 2004). Cluster 4 comprised sheep mostly from the Pintwater Range.

This cluster characterized the most divergent transcript profiles among the clusters, with multiple

gene implications of physiological response to hydrocarbons or dioxin-like substances and virus.

Whether analyzed by population or by cluster grouping, it is important to note that

continued transcription of genes responsible for immunologic function, including detoxification,

can be physiologically costly (Graham and others, 2010). Perhaps the largest cost is the

reallocation of nutrients and energy from one portion of an individual's resource budget to other

metabolic functions. A subsequent reduction of fitness, evidenced by decreased reproductive

capability, increased susceptibility to disease, or disadvantageous behavioral changes, may

follow physiological mitigation of stressors (Graham and others, 2010; Martin and others, 2010).


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Figure 1. Multivariate, nonparametric, multi-dimensional scaling (NMDS) of gene transcription

profiles of bighorn sheep sampled from four different populations (Muddy Mountains, River

Mountains, Bare Mountains, Pintwater Range). Significant clusters are identified; SIMPROF, R

2.8.1, R Development Core Team, 201

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Wildlife Heritage A ccount Project Proposal Form · ILDLIFE HERITAGE TRUST ACCOUNT PROJECT PROPOSAL FORM. Rev 10/28/15 Page 2. recruitment as well as little expression of disease