Distinct host-pathogen and microbiome responses of aoudad (Ammotragus lervia) and bighorn sheep (Ovis canadensis) following exposure to Mycoplasma ovipneumoniae

Distinct host-pathogen and microbiome responses of aoudad (Ammotragus lervia) and bighorn sheep (Ovis canadensis) following exposure to Mycoplasma ovipneumoniae | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Distinct host-pathogen and microbiome responses of aoudad (Ammotragus lervia) and bighorn sheep (Ovis canadensis) following exposure to Mycoplasma ovipneumoniae Logan F. Thomas, Christopher Panaretos, Matthew A. Scott, Robert Valeris-Chacin, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7383688/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Apr, 2026 Read the published version in BMC Veterinary Research → Version 1 posted 15 You are reading this latest preprint version Abstract Background: Pathogens can shape their host communities over various timescales. The potential role of host-pathogen coevolution in driving contemporary shifts in disease ecology is becoming increasingly important as host species emerge and persist outside their native ranges. In North America, Mycoplasma ovipneumoniae can cause fatal pneumonia epizootics in native bighorn sheep ( Ovis canadensis ), whereas introduced free-ranging sympatric aoudad ( Ammotragus lervia ) typically act as asymptomatic reservoirs. To elucidate the role of host–pathogen coevolution in shaping these observed patterns of host impacts, we integrated findings on microbiome composition and host transcriptomic responses in aoudad and bighorn sheep following controlled exposure to M. ovipneumoniae , with or without leukotoxigenic Pasteurellaceae. Results: Aoudad maintained significantly higher microbial richness (Chao1) and evenness (Shannon index) across tonsillar swabs and lower respiratory tract samples, whereas bighorn sheep experienced microbiome perturbations and enhanced growth of some opportunistic taxa. Exposure to M. ovipneumoniae reduced the relative abundance of key commensal genera (e.g., Bibersteinia , Mannheimia , Pasteurella , Roseomonas ) and enriched Mycoplasma in both hosts, but bacterial community destabilization was more pronounced in bighorn sheep. Transcriptome profiling revealed that bighorn sheep upregulated pro-inflammatory and oxidative-stress pathways—including interleukin-1, interleukin-12, and NF-κB signaling—alongside reactive oxygen species generation. In contrast, aoudad exhibited comparatively muted inflammatory signatures, enhanced expression of molecular chaperones, antigen-processing machinery, and integrin-mediated regulatory genes (notably CD46, ILK, and NFKBIZ). Network analysis identified distinct hub genes likely underpinning effective pathogen clearance and mucosal resilience in aoudad versus immunopathology in bighorn sheep. Conclusions: Our integrated microbiome and transcriptomic data underscore the importance if coevolutionary history in driving host-specific responses to shared respiratory pathogens. Aoudad display microbiome stability and balanced immunoregulation, whereas bighorn sheep suffer dysbiosis and excessive inflammation, potentially increasing mortality risk. Incorporating evolutionary and ecological context into managing disease interfaces requires a direct understanding of host-pathogen interactions, as well as how these interactions create observed pathobiological and epidemiological patterns commonly targeted for disease management interventions. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Background Pathogens shape ecological systems in many ways, and their effects are appreciated across multiple biologically significant timescales. Specifically, pathogens can influence fitness and life history traits, speciation processes and species distributions, and ecological community dynamics 1 – 5 . These aspects are often targets for conservationists and wildlife managers, so understanding the evolutionary, current, and projected future roles of pathogens in explaining the behavior of wildlife systems is crucial. At the individual and species levels, great variation in responses to infection exist 6 . For example, grey squirrels’ ( Sciurus carolinensis ) reduced susceptibility to a parapoxvirus compared to the closely related red squirrels ( Sciurus vulgaris ) led to the partial ecological replacement of the latter after the introduction of grey squirrels into native red squirrel habitat 7 . Parasites exhibit impacts on populations that are relatively short at an ecological scale, but these impacts compound and generate spatiotemporally specific evolutionary consequences 8 , 9 . Specifically, environmental variation at an ecological scale can drive variation in infection probability for a given host, as well as host responses to infection 6 , 8 . The anthropogenic movement of animals and their pathogens plus modification of habitat and landscape features that govern pathogen exposure all serve to uniquely alter host-pathogen relationships 10 , 11 . These anthropogenic changes frequently occur in a shorter timeframe than the evolutionary patterns governing host-pathogen coevolution in the absence of human disturbance. Consequently, altering host-pathogen systems can cause acute and/or persistent issues for wildlife conservation and management. Significant biodiversity loss of North American bats and amphibians has been thoroughly demonstrated after the recent introduction of Geomyces destructans from Europe and Batrachochytrium dendrobatidis from Asia, respectively 12 – 14 . Though not as acute or pronounced, the introduction of nonindigenous Artiodactyls and their pathogens to North America has also significantly impacted native species. Examples include introduction of exotic lice from introduced fallow deer ( Dama dama ) causing hair-loss syndrome in mule deer ( Odocoileus hemionus ) in Pacific Northwestern United States, Brucella abortus spillover from domestic cattle ( Bos taurus ) to Rocky Mountain elk ( Cervus canadensis ) in Wyoming, and recent die-offs of Wyoming pronghorn ( Antilocapra americana ) due to Mycoplasma bovis maintained by domestic cattle. While these examples demonstrate relatively recent disease issues stemming from nonindigenous hosts and pathogens, fatal diseases of desert bighorn sheep ( Ovis canadensis ; BHS) following contacts with domestic sheep ( Ovis aries ; DS) have long hindered wild populations. Evidence of BHS mortality associated with novel/introduced pathogen(s) from DS contact dates as early as 1942 15 . However, the coincidence of large decreases in BHS populations with widespread expansion of domestic sheep across the western United States in the late 1890s may suggests fatal pathogens spilled over earlier 16 , 17 . Up to 60% of DS harbor the bacterial agent Mycoplasma ovipneumoniae with little or no disease 18 . Moreover, this agent has been implicated in fatal pneumonia epizootics in BHS across the United States and Canada 18 – 20 . Physical separation of DS and BHS has successfully mitigated pneumonia epizootics in BHS, and is a primary tool used by wildlife managers to benefit BHS health 21 . Indeed, M. ovipneumoniae multi-locus strain typing efforts have repeatedly implicated DS as the pathogen source for BHS dying of respiratory disease 18 , 22 . At the population level, a single DS flock harbors a high diversity of M. ovipneumoniae strains whereas a BHS population during a pneumonia epizootic typically harbors one distinct strain 18 . Clinically, exposure of BHS to M. ovipneumoniae results in a more dramatic decrease in upper respiratory bacterial clearance than is seen in DS 23 , 24 . Given the pathobiology of the agent, the defective clearance was likely secondary to ciliostasis and/ or loss of cilia on tracheoepithelial cells 25 , 26 . These clinical and epidemiological findings together suggest that DS coevolved with M. ovipneumoniae while BHS did not. Another coevolutionary difference between these host species is reflected in functional characteristics of the Pasteurellaceae species each host harbors in the upper respiratory tract. Domestic sheep harbor leukotoxigenic Pasteurellaceae species in the upper respiratory tract more frequently than do BHS 27 . Further, BHS neutrophils are significantly more susceptible to Pasteurellaceae -derived leukotoxin-A than DS neutrophils 28 . Leukotoxigenic Pasteurellacea and M. ovipneumoniae have synergistic pathological effects on the BHS respiratory tract, making disease more severe than with M. ovipneumoniae alone 24 . This highlights the importance of native microbiota and subsequent microbiome shifts observed in hosts with varying coevolution histories before and after exposure to pathogens. The DS-BHS respiratory disease system illustrates the importance of the relationship between host-pathogen coevolution and the characteristic host responses and microbiome patterns that stem from these relationships. Nonindigenous Artiodactyl introductions to North America are not limited to domestic commercial hoofstock. The exotic hoofstock industry is comprised of 1.5-2 million animals across 125 different species in the United States 29 . Animal husbandry practices for exotic hoofstock vary drastically, and many species exist as free-ranging feral populations. One of these species is the aoudad ( Ammotragus lervia ; AD). Aoudad are gregarious Caprinae native to northern Africa but have been extensively introduced outside of their native range to Spain, Italy, Greece, and the United States including Oregon, New Mexico, Texas, Arizona, California and Utah (Cassinello 1998). The largest North American populations occur in Texas where at least 20,000 individuals occupy the landscape 30 . Recent work has experimentally demonstrated that AD can function as a reservoir, maintaining the pathogen within its populations, as well as to be a transmission source of M. ovipneumoniae for other species (such as BHS) with varying clinical responses, raising concerns about the estimated 8,000 feral aoudad occupying habitat crucial to BHS in Texas 31 . The evolutionary history of AD is complex 32 , raising questions about their degree of coevolution with and subsequent pathological response to M. ovipneumoniae and leukotoxigenc Pasteurellaceae. As part of an effort to evaluate transmission propensity of M. ovipneumoniae and leukotoxigenic Pasteurellaceae from AD to BHS, we initiated a post-hoc exploratory multi-omics analysis. Specifically, we described the bacterial microbiome and transcriptomic signals of AD and BHS exposed to M. ovipneumoniae in the presence and absence of leukotoxigenic Pasteurellaceae . Our descriptions of microbiome and transcriptomic responses to respiratory pathogens in nonindigenous AD and native BHS seek to provide crucial information to assess and predict individual and population level outcomes of interspecific contacts. Further, our efforts illustrate the importance of characterizing features of differential host-pathogen coevolution in facilitating wildlife conservation, as well as global human and animal health. We present molecular implications of an applied eco-pathological study system, which may extend to applications in other disease systems, especially those involving species introductions. Materials and Methods Experimental design This study, approved under Texas A&M AgriLife Animal Care and Use Committee permit 2020-038A, involved capturing 20 BHS (11 females, 9 males, aged 0–4 years) from the Elephant Mountain Wildlife Management Area (30°02’03.2" N 103°33’57.6” W) and relocating them to Mason Mountain Wildlife Management Area (30°49’18.8"N 99°13’20.0"W) on study day 0. Bighorn sheep were either unexposed (control BHS, n = 6) or allowed contacts with AD experimentally inoculated with either DS-origin M. ovipneumoniae alone (movi BHS, n = 6; movi AD, n = 6) or DS-origin M. ovipneumoniae plus DS-origin leukotoxigenic Pasteurellaceae (wash BHS, n = 7; wash AD, n = 6). Aoudad (8 females, 4 males, aged 0–2 years) were captured in a semi-free ranging setting and brought to the study site on day 40 (39-day BHS acclimatization period). Each animal (experimental units, 20 BHS and 12 AD) was randomly allocated to their respective species-specific treatment group upon initial capture. Neither age nor sex were considered when allocating animals to treatment groups. Randomization was achieved by having an unbiased Texas Parks and Wildlife Department employee attain and distribute animals from the trailer to treatment-specific facility pens. However, because all animals were fit with ear tags upon capture, and were retained throughout the experiment, research personnel were privy to animal allocation immediately. Authors were aware of individuals’ treatment group memberships prior to sample processing and data analyses. Prior to analysis, one movi BHS was excluded from the study due to mortality occurring before experimental contacts with AD (day 39). Sample sizes were selected based on a balance of facility capacity, spatial requirements for animal welfare, and consideration of the native population from which the BHS were captured. Additionally, the present study was post-hoc to investigating experimental transmission of M. ovipneumoniae from AD to BHS. Thus, our power analysis was conducted based upon detecting differences in time-to-clinical score onset between movi BHS and wash BHS. Specifically, we utilized a fixed-design two arm sample size calculation for time-to-event data using nSurvival in the gsDesign package in program R 33 . Daily health assessments determined that euthanasia via intravenous sodium pentobarbital (390 mg/mL) administration (86 mg/kg) of animals in stage III plane II anesthesia was necessary for scores exceeding 5 for five consecutive days 34 . Specifically, we observed animals at least once daily to observe the presence/absence of: coughing, nasal discharge, ear paresis, lethargy, anorexia, head shaking, and/or nose licking. Animals had continuous access to natural forage, mineral licks, commercial sheep and goat pellets, and water. By day 155, all 20 BHS were removed via mortality or euthanasia. Seven AD were euthanized on day 155, one on day 168, and two on day 211. Tonsil swabs were collected from BHS on days 0 (n = 19) and 39 (n = 19) and from AD on days 40 (n = 12) and 72 (n = 11). Lung samples were collected from BHS (n = 19) and all AD (n = 12) upon their respective euthanasia and/or mortality dates. Animal restraint and handling Bighorn sheep were initially captured (day 0) using a helicopter net gun, restrained with leg hobbles and blindfolds, and administered intramuscular azaperone (15 mg) immediately and sustained-release haloperidol (0.2–0.7 mg/kg; ZooPharm, Laramie, Wyoming 82070, USA) 30–45 minutes after capture to minimize stress 35 . Additionally, 50 mg of eprinomectin (LongRange, Boehringer Ingelheim Animal Health, Duluth, GA 30096, USA) was administered subcutaneously at initial capture to mitigate gastrointestinal helminth loads. The same sedation protocol was followed for AD, but initial capture involved physical restraint with blindfolds and leg hobbles only (day 40). Thereafter (days 39, 40, 72, euthanasia) animals were chemically immobilized at via remote dart projector for animal handling and sampling with a nalbuphine, azaperone, and medetomidine compounded at 40, 10, and 10 mg/ mL, respectively 36 . Inoculum preparation and administration We utilized nasal washings from eight M. ovipneumoniae -positive DS, as determined by quantitative polymerase chain reaction (qPCR) results from previously collected nasal swabs, to create our inoculum 37 . Nasal washings consisted of sterile phosphate-buffered saline washed through the nares of DS until at least 50 mL were obtained. The inoculum for wash AD was unaltered whereas movi AD inoculum was exposed to 123.5 µg/mL of ceftiofur sodium (1g Naxcel powder; Zoetis Parsippany, NJ 07054, USA) for two hours at 37 Celsius immediately prior to inoculation to eliminate non-target bacterial overgrowth. Aoudad were inoculated via instillation syringes for the corresponding AD group (movi AD; wash AD); 5 mL administered into the nares, 10 mL administered into the pharynx, and 2 mL administered into each conjunctival sac. Sample collection and handling Sterile rayon-flocked nasal and tonsil swabs were collected in 2 mL of tryptic soy broth and frozen immediately at -80 Celsius. Retropharyngeal lymph nodes, tonsils, tracheobronchial lymph nodes, cranioventral and dorsocaudal lung sections, and trachea were aseptically collected and immediately frozen at -80 Celsius in tryptic soy broth with bovine serum albumin (ThermoFisher Scientific, 339 Waltham, MA 02451, USA). Lung tissue samples were biased towards margins of grossly apparent pathological change. All respiratory tissue and nasal swab samples were submitted to the Washington Animal Disease Diagnostic lab (WADDL; Pullman, WA, USA) for the detection of M. ovipneumoniae by qPCR. Tonsillar swabs targeting the detection of Pasteurellaceae were subjected to aerobic and anaerobic culture at the Texas Veterinary Medical Diagnostic Laboratory (College Station, TX, USA). Respiratory tissues, however, were submitted to WADDL for aerobic and anaerobic culture for the detection of Pasteurellaceae. Microbiome Analyses DNA Extraction In a biosafety cabinet, a portion of a swab or a piece of tissue was cut with sterilized scissors and placed within a PowerBead Pro tube (Qiagen, Hilden, Germany). DNA was extracted using the PowerSoil Pro kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions, with mechanical homogenization performed by a Mini-Beadbeater-24 (BioSpec, Bartlesville, Oklahoma, USA) using a regime of 5 cycles of 1 minute of beating per cycle. The extraction was performed in a QiaCube Connect (Qiagen, Hilden, Germany). Sample DNA concentration was measured using the Qubit dsDNA HS Assay Kit (Thermo Fisher, Waltham, Massachusetts, USA) on the Qubit Flex Fluorometer (Thermo Fisher, Waltham, Massachusetts, USA). Full Length 16S rRNA gene Sequencing Amplification of the full length bacterial 16S ribosomal gene was performed using the 16S Barcoding Kit 1–24 (SQK-16S024, Oxford Nanopore Technologies, Oxford, United Kingdom) with the following DNA input and PCR cycles: for tonsillar swabs, 10 ng of DNA was used as input for the PCR reaction, and the thermocycler was run for 25 cycles; for tissue pieces, 200 ng of DNA was used as input for the PCR reaction, and the thermocycler was run for 30 cycles. Barcoded samples were pooled and sequenced for 24 hours on a MinION sequencer (Oxford Nanopore Technologies, Oxford, United Kingdom) using an R9.4.1 flow cell and the Fast Model (MinKNOW version 22.10.7, Guppy version 6.3.9). After the sequencing run, the FAST5 files were base called again with the High Accuracy Model (standalone Guppy version 6.5.7) on a dedicated workstation for downstream analysis. Bioinformatics Processing for full Length 16S rRNA gene Reads were classified with Centrifuge (version 1.0.4) using the indexes for Bacteria and Archaea (updated 4/5/2018, https://ccb.jhu.edu/software/centrifuge/manual.shtml ). The rest of the bioinformatics processing was performed using R version 4.3.1. A count table was generated from the Centrifuge output via Pavian ( https://github.com/fbreitwieser/pavian ) 38 .The count table and the metadata were incorporated in a phyloseq object (phyloseq version 1.46.0). Samples with less than 5,000 reads were not included in the analysis. Reads classified as Archaea were removed (no reads were classified as Eukarya, chloroplasts, or mitochondria). Additionally, bacterial genera present in less than 3 samples or with maximum abundance < 100 reads were removed. Rarefaction curves by species and sample type were created with the ampvis2 package version 2.8.9 ( https://kasperskytte.github.io/ampvis2/index.html ). Microbiome Analysis All microbiome analyses were performed using R version 4.3.1. Unconditional linear regression models regressing the Shannon and Chao1 indices on species, treatment groups, sex, age (as a continuous variable), and the presence of relevant respiratory bacteria ( M. ovipneumoniae , M. haemolytica , P. multocida , B. trehalosi , and T. pyogenes ) were built separately for tonsillar swabs and lower respiratory tract (LRT) tissue samples. PERMANOVA models on three dissimilarity distances (Bray-Curtis, Jaccard, and Aitchison) using species, treatment groups, sex, age (as a continuous variable), and the presence of relevant respiratory bacteria ( M. ovipneumoniae , M. haemolytica , P. multocida , B. trehalosi , and T. pyogenes ) as main effects (one PERMANOVA model per dissimilarity distance and covariate) were run using the adonis2 function from the vegan package (2.6–10). The multivariate homogeneity of the group-level dispersion was tested using betadisper (vegan package) in those instances where a main effect was statistically significant in PERMANOVA. Quantile-quantile plots were visually inspected and a variance inflation factor threshold of < 5 was used to assess data appropriateness. ALDEx2 (1.34.0) was used to detect differentially abundant genera between species, treatment groups, sex, age (as a continuous variable), and the presence of relevant respiratory bacteria ( M. ovipneumoniae , M. haemolytica , P. multocida , B. trehalosi , and T. pyogenes ). Specifically, Wilcoxon rank sum tests were used for sex, species, and bacterial presence/absence while Kruskal-Wallis tests were utilized for treatment and age. Benjamini-Hochberg correction was employed to control false discovery rates, a priori set at 0.05. Histograms of diversity indices were visually inspected across comparators to ensure their distributions did not differ. Transcriptomic Analyses Tissue RNA isolation, sequencing, and bioinformatic processing Approximately 20 mg of frozen lung (canioventral and dorsocaudal) and tracheobronchiole lymph node tissue were sterilely dissected and independently utilized for total RNA extraction via the RNeasy Plus Mini Kit (Qiagen). Samples were first aseptically placed into PowerBead Pro Tubes (Qiagen), followed by the pipetting of 350 µL of Buffer RLT Plus containing 14.3 M β-mercaptoethanol at a volumetric ratio of 100:1; all samples were handled and maintained at approximately 4°C until tissue homogenization. Samples were homogenized via a bead-beating centrifuge, where lysed supernatant from each sample was pipetted into sterile microcentrifuge tubes and automatically processed via a QIAcube Connect device (Qiagen, Germantown, MD) according to manufacturer protocol. Following isolation, RNA quantity (total yield = 396.0–17,085.0 ng), purity (mean 260/280 nm = 2.08 ± 0.04, mean 260/230 nm = 1.92 ± 0.23), and integrity (mean RIN = 6.2 ± 0.7) were measured via a Qubit Flex Fluorometer (ThermoFisher Scientific, Waltham, MA), NanoDrop Eight Spectrophotometer (ThermoFisher Scientific, Waltham, MA), and TapeStation 4200 System (Agilent Technologies, Santa Clara, CA), respectively. Isolated RNA was prepared for high-throughput sequencing at Texas A&M Institute for Genome Sciences & Society (TIGSS; College Station, TX) via the Stranded mRNA Library Kit (Illumina, San Diego, CA), following manufacturer’s instruction. Specifically, 150 base pair paired-end sequencing (2×150) was performed on one flow cell lane of a NovaSeq 6000 S4 v1.7 + instrument (S4 reagent kit, v1.5; Illumina, San Diego, CA) at the North Texas Genome Center (NTGC, Arlington, TX); sequencing resulted in a mean of 28.6 M ± 8.4 paired-end reads per sample. Following sample demultiplexing via bcl2fastq2 v2.20, raw sequenced reads were quality assessed with FastQC v0.11.9 39 . Reads were subsequently trimmed for ambiguous base calling, retained Illumina adaptors, and minimum read lengths with Trimmomatic v0.39 40 (mean retainment: 96.54% ± 0.94) using the following parameters: “ILLUMINACLIP:TruSeq3.fa:2:30:10:2:TRUE”, “SLIDINGWINDOW:4:20”, “MINLEN:28”, “LEADING:3”, and “TRAILING:3”. Following quality assessment and trimming, retained trimmed reads were mapped and indexed to the sheep reference genome assembly ARS-UI_Ramb_v3.0 ( Ovis aries ) with HISAT2 v2.2.1 41 Notably, the AD and BHS genomes were not utilized for this study due to incomplete chromosome assembly, deficient gene-level annotation records, and contaminated sequence segments at the time of bioinformatic processing (October 2023). Mean overall alignment rate was 74.41% ± 12.30; no differences in were seen in concordant nor overall alignment rates between AD and BHS samples (Wilcoxon signed-rank test; p > 0.05). Prior to transcript assembly, Sequence Alignment Map (.sam) files generated from HISAT2 were converted to Binary Alignment Map (.bam) files via Samtools v1.14, utilizing default parameters 42 . Transcript assembly and relative gene-level expression estimation was performed via StringTie2 v2.2.0, with default parameters 43 , 44 . Following merged Gene Transfer Format (.gtf) file generation of expression estimates for each sample, post-processing for the appending of ambiguous gene-level identifications (“MSTRG” tags) was performed with a custom Perl script ( https://gist.github.com/gpertea/b83f1b32435e166afa92a2d388527f4b ). All raw sequencing data and curated metadata produced by this study are available at the National Center for Biotechnology Information Gene Expression Omnibus (NCBI-GEO) under the accession number GSE295667. Gene-level data processing and analysis Gene-level count matrices produced from each sample were managed and analyzed in R v4.2.1. Raw count data were pre-processed with the filterByExpr toolkit 45 and count-per-million (CPM) filtering, specifically removing any genes having a minimum total count of less than 100 and those which failed to possess a row sum of greater than 1.0 in at least eight samples; filtering resulted in 15,024 genes for downstream analysis. All filtered libraries were normalized with the Relative Log Expression (RLE) method 46 , 47 , then converted into log2-counts-per-million values (log2CPM) for weighted correlation network analysis. Weighted correlation network analysis was performed with the Bioconductor package WGCNA v1.72-1 48,49 . Metadata from all AD and BHS samples were aligned to each respective sample library. To evaluate potential outlier samples, canonical Euclidean distance-based network adjacency matrices were estimated and used to identify outliers based on standardized connectivity 50 , 51 . Samples with a standardized connectivity >-5.0 were considered outliers and to be removed; no samples were considered outliers for this study (S1 Fig). An adjacency matrix was constructed from calculated signed biweight midcorrelation coefficients between all genes across all samples. Soft thresholding allowed for the calculation of the power parameter (β) required to exponentially raise the adjacency matrix, targeting a scale-free topology fitting index (R 2 ) of > 80%; β = 24 was selected for this study (S2 Fig). Gene-level co-expression modules were constructed with the automatic, one-step “blockwiseModules” function within WGCNA, utilizing the following parameters: power = 24, corType = “bicor,” TOMType = “signed,” networkType = “signed,” maxBlockSize = 15024, minModuleSize = 20, mergeCutHeight = 0.25, and pamRespectsDendro = FALSE; all other parameters were set to default. Constructed co-expression modules were assigned a color by the WGCNA R package, with all genes not assembling into a specific module placed in the “grey” module. Module-trait correlations were identified with independent Kendall’s Tau correlation matrices between module eigengenes (MEs). Modules were considered significantly correlated with each trait having a p-value ≤ 0.05 and |R| ≥ 0.4. Color scaling was performed with the Bioconductor package viridis v0.6.4 ( 10.5281/zenodo.4679423 ) to allow ease of visual interpretation for individuals with color blindness. Functional enrichment analyses of trait-correlated gene expression modules Functional enrichment analysis of genes found within modules which demonstrated significant correlation with metadata traits was performed with g:Profiler ve110_eg57_p18_4b54a898 (accessed December 1, 2023) 52 . Due to the lack of annotation in modeling sheep-specific genomic mechanism and pathways, we elected to utilize orthogonal annotations from human ( Homo sapiens ) databases 53 . Enrichment analyses performed with unordered gene lists, utilizing only annotated genes, selecting the biological pathways from KEGG, Reactome, and WikiPathways as the data source background, and applying the g:SCS multiple test correction technique with an adjusted p-value cutoff of 0.05 54–56 . Hub gene detection from bacteria detection-related modules From the resulting module-trait correlation analysis, we selected those modules which displayed significant associations with the metadata concerning bacterial isolation for hub gene analysis (i.e., those genes within each module which may possess greater biological significance with respect to bacterial isolation/detection) 57 . Pearson correlation coefficients were calculated between individual gene expression values and module genes (kME) and between individual gene expression values and the bacteria isolation metadata component for which each module selected was significantly associated with (GS) for each gene; any gene possessing kME and GS values > 0.8 and > 0.5, respectively, was considered a hub gene within a module. Protein-protein interaction network analysis of hub genes Hub genes identified from modules with significant correlation with bacterial isolations and/or qPCR identification of M. ovipneumoniae were used for network construction of known and predicted protein-protein interactions with the Search Tool for the Retrieval of Interacting Genes (STRING) database v12.0 58 , utilizing DS annotations. Protein-protein interactions of gene products were predicted via the physical subnetwork setting, which display edges (i.e., associations between gene products) only if there is evidence of their specific binding or forming of a physical complex 58 , 59 . Here, three independent analyses were performed. First, we combined all hub genes identified in modules with significant correlation to P. multocida isolation (n = 1,056), selected a minimum interaction score of 0.900 (highest confidence), removed nodes (i.e., gene products) which were disconnected from the network, and employed a k-means clustering algorithm empirically set at k = 8 based on the distance matrix acquired from combined interaction scores. Next, we used hub genes identified in the module with significant correlation to M. haemolytica isolation (brown; n = 231), selected a minimum interaction score of 0.400 (medium confidence), removed nodes (i.e., gene products) which were disconnected from the network, and employed a k-means clustering algorithm empirically set at k = 6 based on the distance matrix acquired from combined interaction scores. Lastly, we used hub genes from the purple module which shared hub genes identified from P. multocida isolation and M. ovipneumonaie qPCR identification (n = 65), utilizing the same parameters as the aforementioned M. haemolytica -based analysis except for lowering the number of clusters to k = 3. Results Microbiome Fifty-seven tonsillar swabs and 29 LRT tissue samples were successfully sequenced. Three hundred and forty-two bacterial genera were detected in both types of samples. Library size ranged from 9,406 to 268,637 with a median of 135,965 reads in tonsillar swab samples, and from 12,731 to 369,115 with a median of 125,883, in the LRT tissue samples. Rarefaction curves and the distribution of the 20 most abundant bacterial genera per sample type and host species are shown in Figures S1 -S4. Alpha Diversity Richness, as measured by Chao1 index, was not significantly associated with any of the available metadata variables in tonsillar swabs (Supplementary Table 1). Shannon index (capturing diversity in the microbiome composition of each sample) was significantly higher in wash than in movi treatment groups in tonsillar swabs (Supplementary Table 2, Fig. 1 E). Shannon index was significantly positively associated with M. ovipneumoniae and M. haemolytica detection in tonsillar swabs (Supplementary Table 1; Figs. 1 I and 1 M, respectively). In LRT tissue samples, the Chao1 index was significantly higher in AD than in BHS (Supplementary Table 2, Fig. 2 A). Shannon index followed the same trends as Chao1 in LRT tissue samples with the addition of being higher when T. pyogenes was not present (Supplementary Table 2, Figs. 2 A, 2 E, 2 I, and 2 M). Beta Diversity Species, age, M. ovipneumoniae serological status and qPCR status, and B. trehalosi detection were associated with the differences in microbiome composition in tonsillar samples in at least one of the dissimilarity measures evaluated (Supplementary Table 3, Figs. 1 B, 1 D, 1 J- 1 L). These variables explained a moderate to low percentage of the variance in the dissimilarity distances utilized in this study (from 4–33%). However, there was evidence of significant differences in the dispersion for species (Fig. 1 D), and M. ovipneumoniae qPCR status (Fig. 1 J-L ;P < 0.05) in at least one of the dissimilarity distances. Species, age, M. ovipneumoniae qPCR status, and M. haemolytica prevalence were associated with the differences in microbiome composition in LRT tissue samples in at least one of the dissimilarity measures evaluated (Supplementary Table 4, Figs. 2 B- 2 D, 2 J, 2 K, 2 N, and 2 O). These variables explained a moderate to low percentage of the variance in the dissimilarity distances utilized in this study (from 5 to 15%). However, there was evidence of significant differences in the dispersion of the dissimilarity distances in all of these variables (P < 0.05), except for age, as the test of multivariate homogeneity of groups dispersions does not allow for a continuous predictor. Bacterial Differential Abundance In tonsillar swabs, several bacterial genera were differentially abundant by host species (n = 36), age (n = 36), M. ovipneumoniae seroprevalence (n = 132), and M. ovipneumoniae qPCR status (n = 136). All differentially abundant bacterial genera are listed in Supplementary Dataset 1. Providencia and Roseomonas were more relatively abundant in BHS than in AD (BH-corrected P < 0.01) while Moraxella exhibited the inverse pattern (BH-corrected P = 0.005, Fig. 3 ). Mycoplasma relative abundance increased with age (BH-corrected P < 0.05), whereas the relative abundance of Roseomonas and Providencia decreased as age increased (BH-corrected P < 0.001; Fig. 4 ). Animals seropositive for anti- M. ovipneumoniae antibodies showed significantly lower relative abundance of Bibersteinia , Providencia , Mannheimia , Pasteurella , Histophilus , and Roseomonas (BH-corrected P < 0.05) compared to seronegative animals. Interestingly, Mycoplasma relative abundance significantly increased in M. ovipneumoniae seropositive animals (BH-corrected P < 0.05; Fig. 5 ). Similar findings were observed for M. ovipneumoniae PCR status, with the exception that the increase in relative abundance for Mycoplasma did not reach statistical significance (BH-corrected P = 0.1065307; Fig. 6 ). No differential abundant genera associated with sex, treatments, B. trehalosi , M. haemolytica , nor T. pyogenes prevalence were observed in tonsilla swabs. Additionally, no bacterial genera were detected in LRT tissue samples to be differentially abundant depending on any of the available variables (species, sex, age, treatment groups, and the presence of M. ovipneumoniae , B. trehalosi , M. haemolytica , and T. pyogenes in LRT tissue samples). Host Transcriptomics Co-expression module identification Filtered genes (n = 15,024) were utilized for module placement via WGCNA one-step network construction. Network construction resulted in 23 color-coded modules containing 14,924 genes; 100 genes were not conserved into any co-expression module and were subsequently placed into the “grey” module (Fig. 7 ; Supplementary Dataset 2). Across the 23 assigned modules, the turquoise module possessed the largest number of co-expressed genes (n = 6,562) and the darkturquoise module possessed the smallest number of co-expressed genes (n = 33); the average size of each module was 659 ± 1,296 genes. The complete list of genes and module assignments is found in Table S5. Module-trait correlations Independent Kendall’s Tau correlation coefficients were generated within WGCNA for identifying and visualizing module-trait relationships (Fig. 8 ; Supplementary Dataset 3). Regarding host species (AD, coded “0”; BHS, coded “1”), three modules possessed significant correlations: midnightblue (τ = -0.72, P = 0.001), brown (τ = 0.68, P = 0.003), and lightyellow (τ = 0.59, P = 0.01). Regarding treatment group (Movi or wash), one module possessed a significant correlation: purple (τ = 0.48, P = 0.05). For Movi PCR values (positive or negative), one module possessed a significant correlation: purple (τ = 0.50, P = 0.04). Regarding bacterial isolation results, several modules possessed significant correlations. Concerning Pasteurella multocida (Pm), four modules possessed significant negative correlations: purple (τ = -0.57, P = 0.02), brown (τ = -0.52, P = 0.03), black (τ = -0.57, P = 0.02), and green (τ = -0.59, P = 0.01). Concerning Bibersteinia trehalosi (Btreh), one module was found to have positive correlation: grey60 (τ = 0.48, P = 0.05). Regarding Mannheimia haemolytica (Mh), one module was identified with significant positive correlation: brown (τ = 0.62, P = 0.008). No significant module-trait correlations were identified for tissue type nor Trueperella pyogenes isolation. Regarding host species (AD, coded “0”; BHS, coded “1”), three modules possessed significant correlations: midnightblue (τ = -0.72, P = 0.001), brown (τ = 0.68, P = 0.003), and lightyellow (τ = 0.59, P = 0.01). Regarding treatment group (movi or wash), one module possessed a significant correlation: purple (τ = 0.48, P = 0.05). For movi PCR values (positive or negative), one module possessed a significant correlation: purple (τ = 0.50, P = 0.04). Regarding bacterial isolation results, several modules possessed significant correlations. Concerning Pasteurella multocida (Pm), four modules possessed significant negative correlations: purple (τ = -0.57, P = 0.02), brown (τ = -0.52, P = 0.03), black (τ = -0.57, P = 0.02), and green (τ = -0.59, P = 0.01). Concerning Bibersteinia trehalosi (Btreh), one module was found to have positive correlation: grey60 (τ = 0.48, P = 0.05). Regarding Mannheimia haemolytica (Mh), one module was identified with significant positive correlation: brown (τ = 0.62, P = 0.008). No significant module-trait correlations were identified for tissue type nor Trueperella pyogenes isolation. Functional enrichment analyses The significantly enriched pathways for the seven aforementioned modules are found in Table S2 . The black module enriched for two pathways, both related to metabolism. The brown module enriched for 191 pathways, primarily related to ribosomal processing of RNA, cellular metabolism, antigen processing and cross-presentation, neutrophil degranulation, cell response to stress and external stimuli, interleukin-1 and interleukin-12 signaling, B-cell NF-κB activation and downstream signaling, and response to infectious disease. The green module enriched for 38 pathways, primarily related to cellular chaperoning, RNA processing and metabolism, CLEC7A signaling, and NF-κB activation. The grey60 module enriched for two pathways related to viral infection and translation events. The lightyellow module enriched for 19 pathways, primarily related to oxidative phosphorylation, ATP synthesis, and cellular activity involving neurons. The midnightblue module enriched for eight pathways, primarily related to antigen processing and presentation, heat shock protein response, and attenuation of heat shock transcriptional response. The purple module enriched for 127 pathways, primarily related to tumor necrosis factor α, mitogen-activated protein kinase, and NF-κB signaling, multiple toll-like receptor cascades, interleukin-1, 4, 10, 17, and 18 signaling, integrin-mediated cellular adhesion, T-cell receptor activity and signaling, inflammatory response, and transforming growth factor β receptor signaling. Bacterial hub genes and predicted protein-protein interactions The WGCNA-based hub gene analyses resulted in the identification of 1,373 total hub genes as follows: MMblack-Pm: 234, MMbrown-Mh: 231, MMbrown-Pm: 332, MMgreen-Pm: 354, MMgrey60-Btreh: 21, MMpurple-Pm: 136, MMpurple-Movi: 65 (S6 Table). As the correlations with Pm and associated co-expression modules were all independently in the same direction (negative), these hub genes were combined for protein-protein interaction networking (n = 1,065). From the Pm-associated hub genes, 186 gene products were identified to have predicted physical interactions (Fig. 9 ). The physical interaction network demonstrated high interconnectivity between the eight distinct clusters, with a median cluster size of 16 gene products. From the Mh-associated hub genes from the brown module, 64 gene products were identified to have predicted physical interactions (Fig. 10 ). The physical interaction network demonstrated high interconnectivity between the six distinct clusters, with a median cluster size of 9 gene products. Of particular interest, the hub genes identified within MMpurple-Pm and MMpurple-Movi analyses shared complete overlap (n = 65). Here, 13 gene products were identified to have predicted physical interactions (Fig. 11 ). The physical interaction network demonstrated low interconnectivity between the three distinct clusters, with only one gene product, ACTN4 (Cluster 3), demonstrating unique identity compared to the other 12 gene products. All gene products and their interconnectivity scores within each network analysis are found in Table S7. Discussion Natural and anthropogenic changes in the communities of host species, as well as the introduction and movement of pathogens, represent significant global biotic changes 5 . Here, we use the introduction of AD to the southwestern United States as a case study to elucidate potential outcomes of exposure to shared pathogens, and the potential reliance of these outcomes on differential host-pathogen co-evolutionary histories. Significant clinical differences exist between DS and BHS when exposed to M. ovipneumoniae and leukotoxigenic Pasteurellaceae 60 , 61 . These host species genetically and biogeographically diverged from one another between 2.52 and 5.63 million years ago 62 , 63 , allowing much opportunity for different host-pathogen co-evolution patterns to occur. Bighorn sheep and AD were even further evolutionarily and biogeographically separated from one another. Assessing both respiratory microbiome and host transcriptomic profiles from these two host species allowed us to assess whether and how they differ when exposed to pathogens lethal to BHS. Our focus is to extend findings from this host-pathogen system to novel, emerging, and existing host-pathogen systems perturbed by anthropogenic activities. Characterizing functionally distinct processes for the microbiomes and transcriptomes for host species with (co-evolved) and without (naïve) coevolution with a given agent presents a unique opportunity to describe how other altered host-pathogen systems may behave. When combined with clinical data from known exposures of either the naïve or co-evolved host species to an agent of interest, these predictions could be improved. The applied nature of this host-pathogen system has implications for BHS health and epidemiological dynamics for M. ovipenumoniae in Texas where BHS and AD co-occur. Together, our microbiome and transcriptomic findings highlight a complex web of host-pathogen-microbiome interactions shaped by evolutionary history and species-specific immunometabolic and microbiome architectures. Clear differences in microbial richness and composition between AD and BHS across respiratory tract compartments, particularly in response to Mycoplasma ovipneumoniae and Pasteurellaceae detection, reflect divergent microbial dynamics likely stemming from distinct coevolutionary trajectories. BHS appeared more susceptible to microbiome destabilization and pathogen-associated shifts, consistent with prior evidence of impaired mucociliary clearance and heightened susceptibility to leukotoxigenic bacterial toxins. Conversely, AD exhibited microbiome responses suggestive of resilience or tolerance, potentially indicative of greater immunological or ecological compatibility with M. ovipneumoniae and perhaps some species/variants of Pasteurellaceae . The association of M. ovipneumoniae with reduced abundance of multiple key bacterial genera and the interplay with M. haemolytica and B. trehalosi support the hypothesis that microbial synergy, not just single-pathogen effects, contributes to disease progression and respiratory microbiome disruption in this system. Transcriptomic profiling reinforced and expanded these findings by identifying immunometabolic and neuroimmune signatures associated with pathogen detection and species identity. Notably, expression profiles in BHS revealed enrichment of pro-inflammatory pathways (e.g., IL-1, IL-12, NF-κB activation), alongside metabolic activity indicative of oxidative stress and potential immunopathology. In contrast, AD tissues exhibited a comparatively tempered pro-inflammatory response with stronger representation of stress-regulated and antigen processing pathways. These host-specific molecular signatures support the observed epidemiological outcomes in which BHS suffer acute, often fatal pneumonia following exposure to pathogens that AD can carry asymptomatically. Such patterns reinforce the role of differential host-pathogen coevolution in governing disease susceptibility and highlight the potential consequences of pathogen spillover from nonindigenous hosts to immunologically naïve wildlife species. Microbiome Associations with diversity and differential abundance in the upper and lower respiratory tracts Bacterial diversity of the upper respiratory tract (tonsillar swabs) was significantly impacted by the treatment group (movi vs wash groups) and detection of M. ovipneumoniae and M. haemolytica . The increasing microbiome diversity associated with M. haemolytica detection may underly the treatment group effect given the inocula preparation techniques 64 . Considering the documented synergistic role of M. haemolytica and M. ovipneumoniae in clinical disease progression 65 , 66 , this pattern is not surprising. The importance of this synergism is highlighted by similar bacterial diversity values in control and movi BHS. Notably, bacterial diversity and composition were both impacted by M. ovipneumoniae detection, whose mucociliary disruption may have allowed normally excluded bacterial species to persist in the upper respiratory tract 67 , 68 . Other studies have found that the effects of B. trehalosi on the microbiome depends on the presence 69 or absence 70 of M. haemolytica . Thus, it is reasonable to consider that the bacterial compositional changes observed with B. trehalosi occurred were, at least in part, related to the interactions between M. ovipneumoniae and M. haemolytica stemming from the two inocula used in this study. The relative abundance of approximately 40% of the bacterial genera shifted in M. ovipneumoniae qPCR positive tonsillar swabs, including pivotal respiratory bacterial genera ( Bibersteinia , Providencia , Mannheimia , Pasteurella , Histophilus , and Roseomonas ). This observation coupled with the finding of higher evenness (that is, less bacterial genera dominating the microbiome composition) in those swabs, suggests profound changes in the upper respiratory microbiome when M. ovipneumoniae is present. Interestingly, the relative abundance of Mannheimia significantly decreased when M. ovipneumoniae was detected in opposition to the expected synergism. This finding warrants further research to elucidate the nuances of the interactions between these two bacteria. Furthermore, our study was unable to obtain sufficient sequencing reads to detect statistically significantly differentially abundant bacterial genera in LRT samples, which is central to make inference on the role of M. ovipneumoniae in LRT colonization and subsequent microbiome shifts 71 . Recent work further illustrates the increasingly recognized importance of integrated microbiome shifts across organ systems, and their role in pathogenesis, specifically in DS exposed to M. ovipneumoniae 72 . Host species is a major determinant of the establishment and maintenance of a particular microbiome 73 . Therefore, it is expected that microbiome shifts, and the potential role of pivotal respiratory bacteria vary between different host species. Previous efforts to describe microbiome shifts important to BHS disease progression identified Providencia, Rosemonas , and Moraxella as important determinants of disease outcome in BHS 74 . Bighorn sheep in the present study demonstrated higher abundances of Providencia and Roseomonas compared to AD in the tonsillar swabs, similar to the previous work with free-ranging individuals 74 . In the LRT, microbiome diversity and composition were strongly impacted by host species, with AD exhibiting higher diversity in the LRT compared to BHS. Additionally, detection of pivotal respiratory bacteria ( M. ovipneumoniae and M. haemolytica) were associated with differences in bacterial composition. Just as in the upper respiratory tract, the interactions between M. ovipneumoniae and M. haemolytica seem to play a key role (maybe a keystone role?) driving microbiome responses in the LRT. Taken together, there is evidence to suggest that AD and BHS naturally or experimentally exposed to M. ovipneumoniae and other pivotal respiratory bacteria have distinct changes in the microbial populations in the respiratory environment, possibly stemming from distinct pathobiological responses associated with host-pathogen coevolution. Host Transcriptomics Metabolic pathway expression associated with Pasteurella multocida in both host species Genes related to metabolic pathways were negatively correlated with the presence of Pasteurella multocida in the lungs of both AD and BHS While the enriched pathways were relatively non-specific, studies evaluating the lung cells of both mice and goat lung cells have demonstrated that Pasteurella multocida induces a regulatory response related to cellular metabolic processes and may disrupt key signaling pathways that would promote host cell apoptosis during infection 75 – 77 . Our study seemingly corroborates these findings, as these two modules describe a similar relationship with cellular metabolism and NF-κB activation with the likelihood of isolating Pasteurella multocida from AD and BHS. Possible in vivo bacterial inhibition of Mannheimia haemolytica and Pasteurella multocida mediated by cellular metabolic patterns of bighorn sheep Interestingly, the relationship of gene expression from this module suggests an inverse relationship in Mannheimia haemolytica and Pasteurella multocida isolation specifically within BHS. Boukahil and Czuprynski in 2018 identified a similar pattern from in vitro biofilm production on bovine bronchial epithelial cells, specifically where these two bacteria were capable of inhibiting each other’s biofilm development when in close proximity to each other on the epithelial surface 78 . Notably, this study from 2018 did not establish a definitive antagonistic between Mannheimia haemolytica and Pasteurella multocida , nor a mechanism by which they inhibit each other. Here, our study describes 139 genes within the brown module which are hub genes of both Mannheimia haemolytica and Pasteurella multocida , many of which are related to carbohydrate metabolism, mitochondrial translation activation, and G-protein chaperoning, thus serving as a possible mechanistic explanation to this phenomenon. Overlapping hub genes differentially associated with Pasteurella multocida and Mycoplasma ovipneumoniae With respect to hub gene analyses, the further assessment of overlapping genes between modules, and the results of predictive protein-protein interactions, particular interest was given to those genes found within the purple module as it relates to Pasteurella multocida (negatively associated) and Mycoplasma ovipneumoniae (positively associated) detection. Of particular interest, ABL1 , ACTN4 , ILK , CD46 , COL4A1 , COL4A2 , and NFKBIZ gene products have been shown to be directly involved in integrin regulation and signaling 79 – 84 . Further, CD46 has been shown to be a key receptor in downregulating complement activation, interacts with several β1 integrins, and may serve as a key binding receptor for bacterial pathogens 84 – 87 . Regarding infectious respiratory disease, integrins may serve as mediators of immune cell proliferation and molecular targets exploited by bacteria for cellular adhesion and immunological evasion 88 . Mycoplasma hyopneumoniae has been shown to interact with integrin β1-fibronectin to enter host cells and evade the immune system 89 . Similarly, Pasteurella multocida can produce a fibronectin-binding protein which interacts with immobilized fibronectin and type I collagen of host cells to promote colonization and invasion of host tissues 90 . Studies have demonstrated the paradoxical nature of integrin promotion and expression in pulmonary disease, as integrin-dependent mechanisms facilitate neutrophil migration and enhance macrophage-mediated clearance of bacteria, but persistent or dysregulated integrin activation may exacerbate tissue damage through sustained inflammation and bacterial infiltration or evasion 91 – 96 . Host species differences in immunological and neuroimmune-axis gene expression profiles Our work revealed three distinct gene expression modules associated with species-level differences between AD and BHS, offering insights into potential mechanisms underlying susceptibility to infectious bacterial diseases. Three modules – midnightblue, brown, and lightyellow – exhibited significant correlations with species identity, highlighting divergent immunometabolic profiles that may influence host-pathogen interactions. Gene expression for pathways related to antigen processing and presentation, heat shock protein (HSP) responses, and attenuation of HSP transcriptional activity were depressed in BHS compared to AD. HSPs are critical in modulating immune responses during pathogen-induced stress and inflammation 97 , 98 Importantly, HSP70 and HSP90 proteins aid the host by protecting secreting intracellular proteins from proteolysis, support antigen presentation and immunoglobulin biosynthesis, and aid in cellular stabilization during infectious processes and/or heat stress 99 – 102 . In contrast, BHS had enriched immune pathways including interleukin-1 and interleukin-12 signaling, neutrophil degranulation, and B-cell NF-κB activation; these pathways are critical for initiating and sustaining inflammatory responses and adaptive immunity 103 – 106 . Bighorn sheep, which appear more susceptible to Mycoplasma -associated pneumonia and Pasteurella -related pleuropneumonia than AD, may mount a heightened but potentially dysregulated inflammatory response that exacerbates tissue damage during infection 107 – 109 . Similar immune and inflammatory profiles have been observed in domestic livestock species during the clinical onset of respiratory disease 110 – 113 . Bighorn sheep expression patterns were enriched for oxidative phosphorylation, ATP synthesis, and neuronally associated cellular activity. These findings suggest increased metabolic activity and possibly heightened neuroimmune regulation in BHS tissues. Enhanced mitochondrial activity has been associated with both immune activation and pathogenic oxidative stress 114 – 118 . In the context of infectious respiratory disease in livestock, excessive reactive oxygen species (ROS) production, a byproduct of elevated oxidative phosphorylation, can damage pulmonary tissue 119 – 121 . Moreover, the bidirectional relationship between peripheral neuronal activity and immune cell activation, termed the neuroimmune axis, has been implemented in airway diseases and cellular stress 122 – 125 . Here, it may be that BHS exhibit distinct tissue-level responses involving stress or inflammation-induced neurogenic pathways compared to AD. Transcriptomic implications for species-level disease susceptibility These interspecies differences in co-expressed immune and metabolic modules may suggest that BHS rely on a more acute, inflammatory-based immune activation strategy, potentially leading to immunopathologies, while AD may benefit from a more stress-tolerant antigen processing system. This could partially explain observed epidemiological patterns where BHS populations suffer high mortality from bacterial pneumonia, especially following Mycoplasma spp. and Pasteurella spp. co-infections, whereas AD appear less impacted under similar pathogen exposure scenarios 107 – 109 . Further validation in tissue-specific expression contexts and functional assays is warranted, but these transcriptional signatures provide a valuable framework for understanding disease susceptibility differences between wild and nonindigenous sheep species and inform conservation and disease management strategies. Limitations and considerations As with many studies focused on free-ranging wildlife in captive settings, our study was met with challenges stemming from low sample sizes and lack of knowledge on pre-study pathogen exposure patterns in our study animals. These limitations introduce the possibility that observed patterns may have been impacted not only by species, sex, and treatment group, but also by inherent interindividual variations and differences in pathogen exposure histories. Due to animal facility and animal procurement considerations, our design did not control for confounders, particularly acclimatization period differences between groups (BHS, 40 days; AD days). Additionally, the pathogenesis of M. ovipneumoniae -induced respiratory disease and subsequent transmission dynamics in both BHS and AD can be influenced by a variety of factors, including strain composition 109 , 126 . This further limited our ability to ascertain the precise pathological stage of each individual relative to our microbiome and host transcriptomic findings. Finally, failure to collect nasal and tonsillar swab samples from each species and treatment group may further limit the generalizability of our results. A true multi-omics approach requires paired swab and tissue samples across the study period, which was not feasible given the animal handling requirements for these species in captivity. Overall, we were limited by commonplace issues associated with wildlife research that warrant interpretive caution. Conclusions Our results provided evidence of microbiome and transcriptome differences between AD and BHS in regard to host-pathogen relationships. Here, our findings are linked to divergent clinical disease outcomes and have potential implications for BHS conservation in regions of cohabitation. Moreover, these findings illustrate the need to integrate ecological, evolutionary, and molecular perspectives when managing disease risks in disturbed host-pathogen systems, particularly those involving introduced species like AD. With numerous mammalian introductions globally altering host-pathogen dynamics, understanding coevolutionary relationships becomes critical for predicting disease emergence and transmission. Future work should prioritize integrating microbiome and gene expression profiling from free-ranging individuals with ongoing epidemiological surveillance to better assess and manage potentially emerging disease threats at both a population and ecosystem level. Declarations Ethics approval and consent to participate All work was authorized under the A&M AgriLife Animal Care and Use Committee permit 2020-038A. Reporting of methods, results, and study limitations complies with the ARRIVE 2.0 Essential 10 guidelines. Availability of data and materials The datasets generated and/or analyzed during the current study are available as supplementary data files and/or data repository. All raw transcriptomic sequencing data and curated metadata produced by this study are available at the National Center for Biotechnology Information Gene Expression Omnibus (NCBI-GEO) under the accession number GSE295667 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE295667). All raw microbiome sequencing data and curated metadata produced by this study are available at the National Center for Biotechnology Information Sequence Read Archive (SRA) under the accession number PRJNA1306317 (https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA1306317). Competing interests The authors declare that they have no competing interests. Funding This study was funded by the Foundation for North American Wild Sheep, Grant-In-Aid Project Number 1920-71 (Walter E. Cook.) and the Texas Parks and Wildlife Department’s Wildlife Research Program through Federal Aid in Wildlife Restoration Act (Pittman-Robertson) Grant Program (“Evaluating the risk of Mycoplasma transmission from Aoudad to Bighorn Sheep”). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author contributions WC was the senior project lead, procured funding, managed and conceptualized the project, and directed the scope of the study and prepared the manuscript; CP created the bioinformatic pipeline used to assess the microbiome data, and performed statistical analyses related to microbiome efforts; MS conceptualized and led the transcriptome efforts, performed all transcriptome statistical analyses, and prepared the manuscript; LT collected samples, conducted animal husbandry and handling, designed and conceptualized the study, procured funding, provided input on statistical analyses with coauthor assistance, guided the overall scope of the manuscript in its preparation, and managed tasks among coauthors; RV-C conceptualized and led the microbiome efforts, finalized all microbiome statistical analyses, and prepared the manuscript. Consent to Publish declaration: Not applicable References Ricklefs, R. E. 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Dots represent bacterial genera. The red dot represents a statistically significantly differentially abundant bacterial genus (Benjamini-Hochberg adjusted P value\u0026lt;0.05, horizontal dashed line). The median difference between BHS and AD samples in ALDEx2 is represented on the x-axis, with positive values related to bacterial genera more abundant in BHS samples.\u003c/p\u003e","description":"","filename":"image3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7383688/v1/d687f02c7465cf143d01a033.jpg"},{"id":90861610,"identity":"68563e80-7dc2-40b0-b67b-ef25b1658fdf","added_by":"auto","created_at":"2025-09-09 06:17:18","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":76572,"visible":true,"origin":"","legend":"\u003cp\u003eA volcano plot of differentially abundant bacteria in tonsillar swabs of aoudad (AD) and bighorn sheep (BHS) according to age. Dots represent bacterial genera. The red dot represents a statistically significantly differentially abundant bacterial genus (Benjamini-Hochberg adjusted P value\u0026lt;0.05, horizontal dashed line). The expected difference on the centered log ratio (of the Monte Carlo iterations of the Dirichlet distribution for each sample in ALDEx2) as host age increases in one day is represented on the x-axis, with positive values related to bacterial genera more abundant in older animals.\u003c/p\u003e","description":"","filename":"image4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7383688/v1/23873630014075b180007170.jpg"},{"id":90860698,"identity":"d7030945-1ac1-46ce-88c4-57f1e8c7d1ee","added_by":"auto","created_at":"2025-09-09 06:09:17","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":98407,"visible":true,"origin":"","legend":"\u003cp\u003eA volcano plot of differentially abundant bacteria in tonsillar swabs of aoudad (AD) and bighorn sheep (BHS) according to \u003cem\u003eMycoplasma ovipneumoniae\u003c/em\u003e seroprevalence. Dots represent bacterial genera. The red dot represents a statistically significantly differentially abundant bacterial genus (Benjamini-Hochberg adjusted P value\u0026lt;0.05, horizontal dashed line). The median difference between samples from \u003cem\u003eM. ovipneumoniae \u003c/em\u003eseropositive and negative animals in ALDEx2 is represented on the x-axis, with positive values related to bacterial genera more abundant in samples from \u003cem\u003eM. ovipneumoniae \u003c/em\u003eseropositive animals.\u003c/p\u003e","description":"","filename":"image5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7383688/v1/c477ea620b967e38faa35b56.jpg"},{"id":90861597,"identity":"3a5c1d45-d287-4a5e-82c5-8acb457fc9f8","added_by":"auto","created_at":"2025-09-09 06:17:17","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":102653,"visible":true,"origin":"","legend":"\u003cp\u003eA volcano plot of differentially abundant bacteria in tonsillar swabs of aoudad (AD) and bighorn sheep (BHS) according to \u003cem\u003eM. ovipneumoniae \u003c/em\u003ePCR status. Dots represent bacterial genera. The red dot represents a statistically significantly differentially abundant bacterial genus (Benjamini-Hochberg adjusted P value\u0026lt;0.05, horizontal dashed line). The median difference between \u003cem\u003eM. ovipneumoniae \u003c/em\u003ePCR positive and negative samples in ALDEx2 is represented on the x-axis, with positive values related to bacterial genera more abundant in \u003cem\u003eM. ovipneumoniae \u003c/em\u003ePCR positive samples.\u003c/p\u003e","description":"","filename":"image6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7383688/v1/2fc66a7e5c28324695a9bc6d.jpg"},{"id":90861600,"identity":"c7581055-97cc-4131-8ba2-8733cf71a465","added_by":"auto","created_at":"2025-09-09 06:17:17","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":208311,"visible":true,"origin":"","legend":"\u003cp\u003eCluster dendrogram of 15,024 filtered genes generated through dissimilarity metrics (1- Topological Overlap Matrix) and hierarchical clustering. The x-axis represents the retained genes and their associated module placement (Module colors) and the y-axis represents the co-expressional distance (Height) within each module by their correlation value.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7383688/v1/21e87b18d3032a31d7df06d4.png"},{"id":90861598,"identity":"964e3c7f-c630-4129-87a3-037d31e8df99","added_by":"auto","created_at":"2025-09-09 06:17:17","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":577320,"visible":true,"origin":"","legend":"\u003cp\u003eKendall’s Tau module-trait correlation heatmap. Correlation coefficients between each gene expression module and metadata trait of interest were calculated between the first principal component of each module and each specified trait (Tissue (lung = 0, lymph node = 1), Species (AD = 0, BHS = 1), Movi PCR (\u003cem\u003eMycoplasma ovipneumoniae \u003c/em\u003ePCR results; negative = 0, positive = 1), Pm (\u003cem\u003ePasteurella multocida \u003c/em\u003eculture results; negative = 0, positive = 1), Btreh (\u003cem\u003eP Bibersteinia trehalosi \u003c/em\u003eculture results; negative = 0, positive = 1), Mh (\u003cem\u003eMannheimia haemolytica \u003c/em\u003eculture results; negative = 0, positive = 1), Tpyo (\u003cem\u003eTrueperella pyogenes \u003c/em\u003eculture results; negative = 0, positive = 1)). Scaling of yellow and purple colors indicate a strong positive negative correlation, respectively.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7383688/v1/e8c1bf1bf0b27c79164ffd8b.png"},{"id":90862367,"identity":"2d7024cd-ad67-409e-8135-37c3bf1ef62f","added_by":"auto","created_at":"2025-09-09 06:25:18","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":566962,"visible":true,"origin":"","legend":"\u003cp\u003eProtein-protein interaction network of interconnected hub genes associated with \u003cem\u003ePasteurella multocida \u003c/em\u003eculture results from the lungs and respiratory lymph nodes. Hub genes identified from the black, brown, green, and purple modules (n\u003csup\u003etotal\u003c/sup\u003e= 1,065) were combined to generate an overlapping protein-protein interaction network. Edges (lines) between each node (gene product) represent a predicted interaction between two products (minimum interaction score of 0.900). Colors within each node represent the predicted k-means cluster based on interaction scores.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-7383688/v1/af81c7d62a9d9871755459a2.png"},{"id":90860707,"identity":"83890ac6-0f1e-4480-98d1-6f24683cbdd5","added_by":"auto","created_at":"2025-09-09 06:09:17","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":263514,"visible":true,"origin":"","legend":"\u003cp\u003eProtein-protein interaction network of interconnected \u003cem\u003eMannheimia haemolytica\u003c/em\u003e-associated hub genes found in the brown module (n=64). Edges (lines) between each node (gene product) represent a predicted interaction between two products (minimum interaction score of 0.400). Colors within each node represent the predicted k-means cluster based on interaction scores.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-7383688/v1/203c90d77004fedb038592ca.png"},{"id":90860700,"identity":"e08390d6-6b1b-4a4a-8e92-1e899d4faf24","added_by":"auto","created_at":"2025-09-09 06:09:17","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":126857,"visible":true,"origin":"","legend":"\u003cp\u003eProtein-protein interaction network of interconnected \u003cem\u003eMycoplasma ovipneumoniae \u003c/em\u003eand \u003cem\u003ePasteurella multocida\u003c/em\u003e-associated hub genes found in the purple module (n=13). Edges (lines) between each node (gene product) represent a predicted interaction between two products (minimum interaction score of 0.400). Colors within each node represent the predicted k-means cluster based on interaction scores.\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-7383688/v1/4c2f291bcc4f8c7e83e8e2db.png"},{"id":108804614,"identity":"8e7f45e7-2d13-47f3-9656-7a100da60193","added_by":"auto","created_at":"2026-05-08 15:22:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3783523,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7383688/v1/0aecb525-e2d9-42d3-95e3-9f9aacefddfd.pdf"},{"id":90860703,"identity":"113ac9cf-e7b7-4fdc-9727-57e228c849ba","added_by":"auto","created_at":"2025-09-09 06:09:17","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1412562,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7383688/v1/5635f201ef114dcbf97327b6.docx"},{"id":90860705,"identity":"eb547078-179d-4347-8743-ee08192cf2aa","added_by":"auto","created_at":"2025-09-09 06:09:17","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":27108,"visible":true,"origin":"","legend":"","description":"","filename":"ARRIVE2.0Checklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-7383688/v1/3a8ee2308b6672721b21a775.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Distinct host-pathogen and microbiome responses of aoudad (Ammotragus lervia) and bighorn sheep (Ovis canadensis) following exposure to Mycoplasma ovipneumoniae","fulltext":[{"header":"Background","content":"\u003cp\u003ePathogens shape ecological systems in many ways, and their effects are appreciated across multiple biologically significant timescales. Specifically, pathogens can influence fitness and life history traits, speciation processes and species distributions, and ecological community dynamics \u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. These aspects are often targets for conservationists and wildlife managers, so understanding the evolutionary, current, and projected future roles of pathogens in explaining the behavior of wildlife systems is crucial. At the individual and species levels, great variation in responses to infection exist \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. For example, grey squirrels\u0026rsquo; (\u003cem\u003eSciurus carolinensis\u003c/em\u003e) reduced susceptibility to a parapoxvirus compared to the closely related red squirrels (\u003cem\u003eSciurus vulgaris\u003c/em\u003e) led to the partial ecological replacement of the latter after the introduction of grey squirrels into native red squirrel habitat \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Parasites exhibit impacts on populations that are relatively short at an ecological scale, but these impacts compound and generate spatiotemporally specific evolutionary consequences \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Specifically, environmental variation at an ecological scale can drive variation in infection probability for a given host, as well as host responses to infection \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The anthropogenic movement of animals and their pathogens plus modification of habitat and landscape features that govern pathogen exposure all serve to uniquely alter host-pathogen relationships \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. These anthropogenic changes frequently occur in a shorter timeframe than the evolutionary patterns governing host-pathogen coevolution in the absence of human disturbance. Consequently, altering host-pathogen systems can cause acute and/or persistent issues for wildlife conservation and management. Significant biodiversity loss of North American bats and amphibians has been thoroughly demonstrated after the recent introduction of \u003cem\u003eGeomyces destructans\u003c/em\u003e from Europe and \u003cem\u003eBatrachochytrium dendrobatidis\u003c/em\u003e from Asia, respectively \u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Though not as acute or pronounced, the introduction of nonindigenous Artiodactyls and their pathogens to North America has also significantly impacted native species. Examples include introduction of exotic lice from introduced fallow deer (\u003cem\u003eDama dama\u003c/em\u003e) causing hair-loss syndrome in mule deer (\u003cem\u003eOdocoileus hemionus\u003c/em\u003e) in Pacific Northwestern United States, \u003cem\u003eBrucella abortus\u003c/em\u003e spillover from domestic cattle (\u003cem\u003eBos taurus\u003c/em\u003e) to Rocky Mountain elk (\u003cem\u003eCervus canadensis\u003c/em\u003e) in Wyoming, and recent die-offs of Wyoming pronghorn (\u003cem\u003eAntilocapra americana\u003c/em\u003e) due to \u003cem\u003eMycoplasma bovis\u003c/em\u003e maintained by domestic cattle.\u003c/p\u003e\u003cp\u003eWhile these examples demonstrate relatively recent disease issues stemming from nonindigenous hosts and pathogens, fatal diseases of desert bighorn sheep (\u003cem\u003eOvis canadensis\u003c/em\u003e; BHS) following contacts with domestic sheep (\u003cem\u003eOvis aries\u003c/em\u003e; DS) have long hindered wild populations. Evidence of BHS mortality associated with novel/introduced pathogen(s) from DS contact dates as early as 1942 \u003csup\u003e15\u003c/sup\u003e. However, the coincidence of large decreases in BHS populations with widespread expansion of domestic sheep across the western United States in the late 1890s may suggests fatal pathogens spilled over earlier \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Up to 60% of DS harbor the bacterial agent \u003cem\u003eMycoplasma ovipneumoniae\u003c/em\u003e with little or no disease \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Moreover, this agent has been implicated in fatal pneumonia epizootics in BHS across the United States and Canada \u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Physical separation of DS and BHS has successfully mitigated pneumonia epizootics in BHS, and is a primary tool used by wildlife managers to benefit BHS health \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Indeed, \u003cem\u003eM. ovipneumoniae\u003c/em\u003e multi-locus strain typing efforts have repeatedly implicated DS as the pathogen source for BHS dying of respiratory disease \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. At the population level, a single DS flock harbors a high diversity of \u003cem\u003eM. ovipneumoniae\u003c/em\u003e strains whereas a BHS population during a pneumonia epizootic typically harbors one distinct strain\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Clinically, exposure of BHS to \u003cem\u003eM. ovipneumoniae\u003c/em\u003e results in a more dramatic decrease in upper respiratory bacterial clearance than is seen in DS \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Given the pathobiology of the agent, the defective clearance was likely secondary to ciliostasis and/ or loss of cilia on tracheoepithelial cells \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. These clinical and epidemiological findings together suggest that DS coevolved with \u003cem\u003eM. ovipneumoniae\u003c/em\u003e while BHS did not. Another coevolutionary difference between these host species is reflected in functional characteristics of the \u003cem\u003ePasteurellaceae\u003c/em\u003e species each host harbors in the upper respiratory tract. Domestic sheep harbor leukotoxigenic \u003cem\u003ePasteurellaceae\u003c/em\u003e species in the upper respiratory tract more frequently than do BHS\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Further, BHS neutrophils are significantly more susceptible to \u003cem\u003ePasteurellaceae\u003c/em\u003e-derived leukotoxin-A than DS neutrophils \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Leukotoxigenic \u003cem\u003ePasteurellacea\u003c/em\u003e and \u003cem\u003eM. ovipneumoniae\u003c/em\u003e have synergistic pathological effects on the BHS respiratory tract, making disease more severe than with \u003cem\u003eM. ovipneumoniae\u003c/em\u003e alone \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. This highlights the importance of native microbiota and subsequent microbiome shifts observed in hosts with varying coevolution histories before and after exposure to pathogens. The DS-BHS respiratory disease system illustrates the importance of the relationship between host-pathogen coevolution and the characteristic host responses and microbiome patterns that stem from these relationships.\u003c/p\u003e\u003cp\u003eNonindigenous Artiodactyl introductions to North America are not limited to domestic commercial hoofstock. The exotic hoofstock industry is comprised of 1.5-2\u0026nbsp;million animals across 125 different species in the United States \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Animal husbandry practices for exotic hoofstock vary drastically, and many species exist as free-ranging feral populations. One of these species is the aoudad (\u003cem\u003eAmmotragus lervia\u003c/em\u003e; AD). Aoudad are gregarious \u003cem\u003eCaprinae\u003c/em\u003e native to northern Africa but have been extensively introduced outside of their native range to Spain, Italy, Greece, and the United States including Oregon, New Mexico, Texas, Arizona, California and Utah (Cassinello 1998). The largest North American populations occur in Texas where at least 20,000 individuals occupy the landscape \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Recent work has experimentally demonstrated that AD can function as a reservoir, maintaining the pathogen within its populations, as well as to be a transmission source of \u003cem\u003eM. ovipneumoniae\u003c/em\u003e for other species (such as BHS) with varying clinical responses, raising concerns about the estimated 8,000 feral aoudad occupying habitat crucial to BHS in Texas \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The evolutionary history of AD is complex \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, raising questions about their degree of coevolution with and subsequent pathological response to \u003cem\u003eM. ovipneumoniae\u003c/em\u003e and leukotoxigenc \u003cem\u003ePasteurellaceae.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAs part of an effort to evaluate transmission propensity of \u003cem\u003eM. ovipneumoniae\u003c/em\u003e and leukotoxigenic \u003cem\u003ePasteurellaceae\u003c/em\u003e from AD to BHS, we initiated a post-hoc exploratory multi-omics analysis. Specifically, we described the bacterial microbiome and transcriptomic signals of AD and BHS exposed to \u003cem\u003eM. ovipneumoniae\u003c/em\u003e in the presence and absence of leukotoxigenic \u003cem\u003ePasteurellaceae\u003c/em\u003e. Our descriptions of microbiome and transcriptomic responses to respiratory pathogens in nonindigenous AD and native BHS seek to provide crucial information to assess and predict individual and population level outcomes of interspecific contacts. Further, our efforts illustrate the importance of characterizing features of differential host-pathogen coevolution in facilitating wildlife conservation, as well as global human and animal health. We present molecular implications of an applied eco-pathological study system, which may extend to applications in other disease systems, especially those involving species introductions.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eExperimental design\u003c/h2\u003e\u003cp\u003eThis study, approved under Texas A\u0026amp;M AgriLife Animal Care and Use Committee permit 2020-038A, involved capturing 20 BHS (11 females, 9 males, aged 0\u0026ndash;4 years) from the Elephant Mountain Wildlife Management Area (30\u0026deg;02\u0026rsquo;03.2\" N 103\u0026deg;33\u0026rsquo;57.6\u0026rdquo; W) and relocating them to Mason Mountain Wildlife Management Area (30\u0026deg;49\u0026rsquo;18.8\"N 99\u0026deg;13\u0026rsquo;20.0\"W) on study day 0. Bighorn sheep were either unexposed (control BHS, n\u0026thinsp;=\u0026thinsp;6) or allowed contacts with AD experimentally inoculated with either DS-origin \u003cem\u003eM. ovipneumoniae\u003c/em\u003e alone (movi BHS, n\u0026thinsp;=\u0026thinsp;6; movi AD, n\u0026thinsp;=\u0026thinsp;6) or DS-origin \u003cem\u003eM. ovipneumoniae\u003c/em\u003e plus DS-origin leukotoxigenic \u003cem\u003ePasteurellaceae\u003c/em\u003e (wash BHS, n\u0026thinsp;=\u0026thinsp;7; wash AD, n\u0026thinsp;=\u0026thinsp;6). Aoudad (8 females, 4 males, aged 0\u0026ndash;2 years) were captured in a semi-free ranging setting and brought to the study site on day 40 (39-day BHS acclimatization period).\u003c/p\u003e\u003cp\u003eEach animal (experimental units, 20 BHS and 12 AD) was randomly allocated to their respective species-specific treatment group upon initial capture. Neither age nor sex were considered when allocating animals to treatment groups. Randomization was achieved by having an unbiased Texas Parks and Wildlife Department employee attain and distribute animals from the trailer to treatment-specific facility pens. However, because all animals were fit with ear tags upon capture, and were retained throughout the experiment, research personnel were privy to animal allocation immediately. Authors were aware of individuals\u0026rsquo; treatment group memberships prior to sample processing and data analyses. Prior to analysis, one movi BHS was excluded from the study due to mortality occurring before experimental contacts with AD (day 39).\u003c/p\u003e\u003cp\u003eSample sizes were selected based on a balance of facility capacity, spatial requirements for animal welfare, and consideration of the native population from which the BHS were captured. Additionally, the present study was post-hoc to investigating experimental transmission of \u003cem\u003eM. ovipneumoniae\u003c/em\u003e from AD to BHS. Thus, our power analysis was conducted based upon detecting differences in time-to-clinical score onset between movi BHS and wash BHS. Specifically, we utilized a fixed-design two arm sample size calculation for time-to-event data using nSurvival in the gsDesign package in program R \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDaily health assessments determined that euthanasia via intravenous sodium pentobarbital (390 mg/mL) administration (86 mg/kg) of animals in stage III plane II anesthesia was necessary for scores exceeding 5 for five consecutive days \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Specifically, we observed animals at least once daily to observe the presence/absence of: coughing, nasal discharge, ear paresis, lethargy, anorexia, head shaking, and/or nose licking. Animals had continuous access to natural forage, mineral licks, commercial sheep and goat pellets, and water. By day 155, all 20 BHS were removed via mortality or euthanasia. Seven AD were euthanized on day 155, one on day 168, and two on day 211. Tonsil swabs were collected from BHS on days 0 (n\u0026thinsp;=\u0026thinsp;19) and 39 (n\u0026thinsp;=\u0026thinsp;19) and from AD on days 40 (n\u0026thinsp;=\u0026thinsp;12) and 72 (n\u0026thinsp;=\u0026thinsp;11). Lung samples were collected from BHS (n\u0026thinsp;=\u0026thinsp;19) and all AD (n\u0026thinsp;=\u0026thinsp;12) upon their respective euthanasia and/or mortality dates.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAnimal restraint and handling\u003c/h3\u003e\n\u003cp\u003eBighorn sheep were initially captured (day 0) using a helicopter net gun, restrained with leg hobbles and blindfolds, and administered intramuscular azaperone (15 mg) immediately and sustained-release haloperidol (0.2\u0026ndash;0.7 mg/kg; ZooPharm, Laramie, Wyoming 82070, USA) 30\u0026ndash;45 minutes after capture to minimize stress \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Additionally, 50 mg of eprinomectin (LongRange, Boehringer Ingelheim Animal Health, Duluth, GA 30096, USA) was administered subcutaneously at initial capture to mitigate gastrointestinal helminth loads. The same sedation protocol was followed for AD, but initial capture involved physical restraint with blindfolds and leg hobbles only (day 40). Thereafter (days 39, 40, 72, euthanasia) animals were chemically immobilized at via remote dart projector for animal handling and sampling with a nalbuphine, azaperone, and medetomidine compounded at 40, 10, and 10 mg/ mL, respectively \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eInoculum preparation and administration\u003c/h3\u003e\n\u003cp\u003eWe utilized nasal washings from eight \u003cem\u003eM. ovipneumoniae\u003c/em\u003e-positive DS, as determined by quantitative polymerase chain reaction (qPCR) results from previously collected nasal swabs, to create our inoculum \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Nasal washings consisted of sterile phosphate-buffered saline washed through the nares of DS until at least 50 mL were obtained. The inoculum for wash AD was unaltered whereas movi AD inoculum was exposed to 123.5 \u0026micro;g/mL of ceftiofur sodium (1g Naxcel powder; Zoetis Parsippany, NJ 07054, USA) for two hours at 37 Celsius immediately prior to inoculation to eliminate non-target bacterial overgrowth. Aoudad were inoculated via instillation syringes for the corresponding AD group (movi AD; wash AD); 5 mL administered into the nares, 10 mL administered into the pharynx, and 2 mL administered into each conjunctival sac.\u003c/p\u003e\n\u003ch3\u003eSample collection and handling\u003c/h3\u003e\n\u003cp\u003eSterile rayon-flocked nasal and tonsil swabs were collected in 2 mL of tryptic soy broth and frozen immediately at -80 Celsius. Retropharyngeal lymph nodes, tonsils, tracheobronchial lymph nodes, cranioventral and dorsocaudal lung sections, and trachea were aseptically collected and immediately frozen at -80 Celsius in tryptic soy broth with bovine serum albumin (ThermoFisher Scientific, 339 Waltham, MA 02451, USA). Lung tissue samples were biased towards margins of grossly apparent pathological change. All respiratory tissue and nasal swab samples were submitted to the Washington Animal Disease Diagnostic lab (WADDL; Pullman, WA, USA) for the detection of \u003cem\u003eM. ovipneumoniae\u003c/em\u003e by qPCR. Tonsillar swabs targeting the detection of \u003cem\u003ePasteurellaceae\u003c/em\u003e were subjected to aerobic and anaerobic culture at the Texas Veterinary Medical Diagnostic Laboratory (College Station, TX, USA). Respiratory tissues, however, were submitted to WADDL for aerobic and anaerobic culture for the detection of \u003cem\u003ePasteurellaceae.\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003eMicrobiome Analyses\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eDNA Extraction\u003c/h2\u003e\u003cp\u003eIn a biosafety cabinet, a portion of a swab or a piece of tissue was cut with sterilized scissors and placed within a PowerBead Pro tube (Qiagen, Hilden, Germany). DNA was extracted using the PowerSoil Pro kit (Qiagen, Hilden, Germany) according to the manufacturer\u0026rsquo;s instructions, with mechanical homogenization performed by a Mini-Beadbeater-24 (BioSpec, Bartlesville, Oklahoma, USA) using a regime of 5 cycles of 1 minute of beating per cycle. The extraction was performed in a QiaCube Connect (Qiagen, Hilden, Germany). Sample DNA concentration was measured using the Qubit dsDNA HS Assay Kit (Thermo Fisher, Waltham, Massachusetts, USA) on the Qubit Flex Fluorometer (Thermo Fisher, Waltham, Massachusetts, USA).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eFull Length 16S rRNA gene Sequencing\u003c/h3\u003e\n\u003cp\u003eAmplification of the full length bacterial 16S ribosomal gene was performed using the 16S Barcoding Kit 1\u0026ndash;24 (SQK-16S024, Oxford Nanopore Technologies, Oxford, United Kingdom) with the following DNA input and PCR cycles: for tonsillar swabs, 10 ng of DNA was used as input for the PCR reaction, and the thermocycler was run for 25 cycles; for tissue pieces, 200 ng of DNA was used as input for the PCR reaction, and the thermocycler was run for 30 cycles. Barcoded samples were pooled and sequenced for 24 hours on a MinION sequencer (Oxford Nanopore Technologies, Oxford, United Kingdom) using an R9.4.1 flow cell and the Fast Model (MinKNOW version 22.10.7, Guppy version 6.3.9). After the sequencing run, the FAST5 files were base called again with the High Accuracy Model (standalone Guppy version 6.5.7) on a dedicated workstation for downstream analysis.\u003c/p\u003e\n\u003ch3\u003eBioinformatics Processing for full Length 16S rRNA gene\u003c/h3\u003e\n\u003cp\u003eReads were classified with Centrifuge (version 1.0.4) using the indexes for Bacteria and Archaea (updated 4/5/2018, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ccb.jhu.edu/software/centrifuge/manual.shtml\u003c/span\u003e\u003cspan address=\"https://ccb.jhu.edu/software/centrifuge/manual.shtml\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The rest of the bioinformatics processing was performed using R version 4.3.1. A count table was generated from the Centrifuge output via Pavian (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/fbreitwieser/pavian\u003c/span\u003e\u003cspan address=\"https://github.com/fbreitwieser/pavian\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.The count table and the metadata were incorporated in a phyloseq object (phyloseq version 1.46.0). Samples with less than 5,000 reads were not included in the analysis. Reads classified as Archaea were removed (no reads were classified as Eukarya, chloroplasts, or mitochondria). Additionally, bacterial genera present in less than 3 samples or with maximum abundance\u0026thinsp;\u0026lt;\u0026thinsp;100 reads were removed. Rarefaction curves by species and sample type were created with the ampvis2 package version 2.8.9 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://kasperskytte.github.io/ampvis2/index.html\u003c/span\u003e\u003cspan address=\"https://kasperskytte.github.io/ampvis2/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eMicrobiome Analysis\u003c/h2\u003e\u003cp\u003eAll microbiome analyses were performed using R version 4.3.1. Unconditional linear regression models regressing the Shannon and Chao1 indices on species, treatment groups, sex, age (as a continuous variable), and the presence of relevant respiratory bacteria (\u003cem\u003eM. ovipneumoniae\u003c/em\u003e, \u003cem\u003eM. haemolytica\u003c/em\u003e, \u003cem\u003eP. multocida\u003c/em\u003e, \u003cem\u003eB. trehalosi\u003c/em\u003e, and \u003cem\u003eT. pyogenes\u003c/em\u003e) were built separately for tonsillar swabs and lower respiratory tract (LRT) tissue samples.\u003c/p\u003e\u003cp\u003ePERMANOVA models on three dissimilarity distances (Bray-Curtis, Jaccard, and Aitchison) using species, treatment groups, sex, age (as a continuous variable), and the presence of relevant respiratory bacteria (\u003cem\u003eM. ovipneumoniae\u003c/em\u003e, \u003cem\u003eM. haemolytica\u003c/em\u003e, \u003cem\u003eP. multocida\u003c/em\u003e, \u003cem\u003eB. trehalosi\u003c/em\u003e, and \u003cem\u003eT. pyogenes\u003c/em\u003e) as main effects (one PERMANOVA model per dissimilarity distance and covariate) were run using the adonis2 function from the vegan package (2.6\u0026ndash;10). The multivariate homogeneity of the group-level dispersion was tested using betadisper (vegan package) in those instances where a main effect was statistically significant in PERMANOVA. Quantile-quantile plots were visually inspected and a variance inflation factor threshold of \u0026lt;\u0026thinsp;5 was used to assess data appropriateness.\u003c/p\u003e\u003cp\u003eALDEx2 (1.34.0) was used to detect differentially abundant genera between species, treatment groups, sex, age (as a continuous variable), and the presence of relevant respiratory bacteria (\u003cem\u003eM. ovipneumoniae\u003c/em\u003e, \u003cem\u003eM. haemolytica\u003c/em\u003e, \u003cem\u003eP. multocida\u003c/em\u003e, \u003cem\u003eB. trehalosi\u003c/em\u003e, and \u003cem\u003eT. pyogenes\u003c/em\u003e). Specifically, Wilcoxon rank sum tests were used for sex, species, and bacterial presence/absence while Kruskal-Wallis tests were utilized for treatment and age. Benjamini-Hochberg correction was employed to control false discovery rates, \u003cem\u003ea priori\u003c/em\u003e set at 0.05. Histograms of diversity indices were visually inspected across comparators to ensure their distributions did not differ.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eTranscriptomic Analyses\u003c/h2\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003eTissue RNA isolation, sequencing, and bioinformatic processing\u003c/h2\u003e\u003cp\u003eApproximately 20 mg of frozen lung (canioventral and dorsocaudal) and tracheobronchiole lymph node tissue were sterilely dissected and independently utilized for total RNA extraction via the RNeasy Plus Mini Kit (Qiagen). Samples were first aseptically placed into PowerBead Pro Tubes (Qiagen), followed by the pipetting of 350 \u0026micro;L of Buffer RLT Plus containing 14.3 M β-mercaptoethanol at a volumetric ratio of 100:1; all samples were handled and maintained at approximately 4\u0026deg;C until tissue homogenization. Samples were homogenized via a bead-beating centrifuge, where lysed supernatant from each sample was pipetted into sterile microcentrifuge tubes and automatically processed via a QIAcube Connect device (Qiagen, Germantown, MD) according to manufacturer protocol. Following isolation, RNA quantity (total yield\u0026thinsp;=\u0026thinsp;396.0\u0026ndash;17,085.0 ng), purity (mean 260/280 nm\u0026thinsp;=\u0026thinsp;2.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04, mean 260/230 nm\u0026thinsp;=\u0026thinsp;1.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23), and integrity (mean RIN\u0026thinsp;=\u0026thinsp;6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7) were measured via a Qubit Flex Fluorometer (ThermoFisher Scientific, Waltham, MA), NanoDrop Eight Spectrophotometer (ThermoFisher Scientific, Waltham, MA), and TapeStation 4200 System (Agilent Technologies, Santa Clara, CA), respectively. Isolated RNA was prepared for high-throughput sequencing at Texas A\u0026amp;M Institute for Genome Sciences \u0026amp; Society (TIGSS; College Station, TX) via the Stranded mRNA Library Kit (Illumina, San Diego, CA), following manufacturer\u0026rsquo;s instruction. Specifically, 150 base pair paired-end sequencing (2\u0026times;150) was performed on one flow cell lane of a NovaSeq 6000 S4 v1.7\u0026thinsp;+\u0026thinsp;instrument (S4 reagent kit, v1.5; Illumina, San Diego, CA) at the North Texas Genome Center (NTGC, Arlington, TX); sequencing resulted in a mean of 28.6 M\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4 paired-end reads per sample.\u003c/p\u003e\u003cp\u003eFollowing sample demultiplexing via bcl2fastq2 v2.20, raw sequenced reads were quality assessed with FastQC v0.11.9 \u003csup\u003e39\u003c/sup\u003e. Reads were subsequently trimmed for ambiguous base calling, retained Illumina adaptors, and minimum read lengths with Trimmomatic v0.39 \u003csup\u003e40\u003c/sup\u003e (mean retainment: 96.54% \u0026plusmn; 0.94) using the following parameters: \u0026ldquo;ILLUMINACLIP:TruSeq3.fa:2:30:10:2:TRUE\u0026rdquo;, \u0026ldquo;SLIDINGWINDOW:4:20\u0026rdquo;, \u0026ldquo;MINLEN:28\u0026rdquo;, \u0026ldquo;LEADING:3\u0026rdquo;, and \u0026ldquo;TRAILING:3\u0026rdquo;. Following quality assessment and trimming, retained trimmed reads were mapped and indexed to the sheep reference genome assembly ARS-UI_Ramb_v3.0 (\u003cem\u003eOvis aries\u003c/em\u003e) with HISAT2 v2.2.1 \u003csup\u003e41\u003c/sup\u003e Notably, the AD and BHS genomes were not utilized for this study due to incomplete chromosome assembly, deficient gene-level annotation records, and contaminated sequence segments at the time of bioinformatic processing (October 2023). Mean overall alignment rate was 74.41% \u0026plusmn; 12.30; no differences in were seen in concordant nor overall alignment rates between AD and BHS samples (Wilcoxon signed-rank test; p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Prior to transcript assembly, Sequence Alignment Map (.sam) files generated from HISAT2 were converted to Binary Alignment Map (.bam) files via Samtools v1.14, utilizing default parameters \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Transcript assembly and relative gene-level expression estimation was performed via StringTie2 v2.2.0, with default parameters \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Following merged Gene Transfer Format (.gtf) file generation of expression estimates for each sample, post-processing for the appending of ambiguous gene-level identifications (\u0026ldquo;MSTRG\u0026rdquo; tags) was performed with a custom Perl script (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gist.github.com/gpertea/b83f1b32435e166afa92a2d388527f4b\u003c/span\u003e\u003cspan address=\"https://gist.github.com/gpertea/b83f1b32435e166afa92a2d388527f4b\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). All raw sequencing data and curated metadata produced by this study are available at the National Center for Biotechnology Information Gene Expression Omnibus (NCBI-GEO) under the accession number GSE295667.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eGene-level data processing and analysis\u003c/h2\u003e\u003cp\u003eGene-level count matrices produced from each sample were managed and analyzed in R v4.2.1. Raw count data were pre-processed with the filterByExpr toolkit \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e and count-per-million (CPM) filtering, specifically removing any genes having a minimum total count of less than 100 and those which failed to possess a row sum of greater than 1.0 in at least eight samples; filtering resulted in 15,024 genes for downstream analysis. All filtered libraries were normalized with the Relative Log Expression (RLE) method \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, then converted into log2-counts-per-million values (log2CPM) for weighted correlation network analysis. Weighted correlation network analysis was performed with the Bioconductor package WGCNA v1.72-1 \u003csup\u003e48,49\u003c/sup\u003e. Metadata from all AD and BHS samples were aligned to each respective sample library. To evaluate potential outlier samples, canonical Euclidean distance-based network adjacency matrices were estimated and used to identify outliers based on standardized connectivity \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Samples with a standardized connectivity \u0026gt;-5.0 were considered outliers and to be removed; no samples were considered outliers for this study (S1 Fig). An adjacency matrix was constructed from calculated signed biweight midcorrelation coefficients between all genes across all samples. Soft thresholding allowed for the calculation of the power parameter (β) required to exponentially raise the adjacency matrix, targeting a scale-free topology fitting index (R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) of \u0026gt;\u0026thinsp;80%; β\u0026thinsp;=\u0026thinsp;24 was selected for this study (S2 Fig). Gene-level co-expression modules were constructed with the automatic, one-step \u0026ldquo;blockwiseModules\u0026rdquo; function within WGCNA, utilizing the following parameters: power\u0026thinsp;=\u0026thinsp;24, corType = \u0026ldquo;bicor,\u0026rdquo; TOMType = \u0026ldquo;signed,\u0026rdquo; networkType = \u0026ldquo;signed,\u0026rdquo; maxBlockSize\u0026thinsp;=\u0026thinsp;15024, minModuleSize\u0026thinsp;=\u0026thinsp;20, mergeCutHeight\u0026thinsp;=\u0026thinsp;0.25, and pamRespectsDendro\u0026thinsp;=\u0026thinsp;FALSE; all other parameters were set to default. Constructed co-expression modules were assigned a color by the WGCNA R package, with all genes not assembling into a specific module placed in the \u0026ldquo;grey\u0026rdquo; module. Module-trait correlations were identified with independent Kendall\u0026rsquo;s Tau correlation matrices between module eigengenes (MEs). Modules were considered significantly correlated with each trait having a p-value\u0026thinsp;\u0026le;\u0026thinsp;0.05 and |R| \u0026ge; 0.4. Color scaling was performed with the Bioconductor package viridis v0.6.4 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5281/zenodo.4679423\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.4679423\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to allow ease of visual interpretation for individuals with color blindness.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eFunctional enrichment analyses of trait-correlated gene expression modules\u003c/h2\u003e\u003cp\u003eFunctional enrichment analysis of genes found within modules which demonstrated significant correlation with metadata traits was performed with g:Profiler ve110_eg57_p18_4b54a898 (accessed December 1, 2023) \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Due to the lack of annotation in modeling sheep-specific genomic mechanism and pathways, we elected to utilize orthogonal annotations from human (\u003cem\u003eHomo sapiens\u003c/em\u003e) databases \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Enrichment analyses performed with unordered gene lists, utilizing only annotated genes, selecting the biological pathways from KEGG, Reactome, and WikiPathways as the data source background, and applying the g:SCS multiple test correction technique with an adjusted p-value cutoff of 0.05 \u003csup\u003e54\u0026ndash;56\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eHub gene detection from bacteria detection-related modules\u003c/h2\u003e\u003cp\u003eFrom the resulting module-trait correlation analysis, we selected those modules which displayed significant associations with the metadata concerning bacterial isolation for hub gene analysis (i.e., those genes within each module which may possess greater biological significance with respect to bacterial isolation/detection) \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Pearson correlation coefficients were calculated between individual gene expression values and module genes (kME) and between individual gene expression values and the bacteria isolation metadata component for which each module selected was significantly associated with (GS) for each gene; any gene possessing kME and GS values\u0026thinsp;\u0026gt;\u0026thinsp;0.8 and \u0026gt;\u0026thinsp;0.5, respectively, was considered a hub gene within a module.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eProtein-protein interaction network analysis of hub genes\u003c/h2\u003e\u003cp\u003eHub genes identified from modules with significant correlation with bacterial isolations and/or qPCR identification of \u003cem\u003eM. ovipneumoniae\u003c/em\u003e were used for network construction of known and predicted protein-protein interactions with the Search Tool for the Retrieval of Interacting Genes (STRING) database v12.0 \u003csup\u003e58\u003c/sup\u003e, utilizing DS annotations. Protein-protein interactions of gene products were predicted via the physical subnetwork setting, which display edges (i.e., associations between gene products) only if there is evidence of their specific binding or forming of a physical complex \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. Here, three independent analyses were performed. First, we combined all hub genes identified in modules with significant correlation to \u003cem\u003eP. multocida\u003c/em\u003e isolation (n\u0026thinsp;=\u0026thinsp;1,056), selected a minimum interaction score of 0.900 (highest confidence), removed nodes (i.e., gene products) which were disconnected from the network, and employed a k-means clustering algorithm empirically set at k\u0026thinsp;=\u0026thinsp;8 based on the distance matrix acquired from combined interaction scores. Next, we used hub genes identified in the module with significant correlation to \u003cem\u003eM. haemolytica\u003c/em\u003e isolation (brown; n\u0026thinsp;=\u0026thinsp;231), selected a minimum interaction score of 0.400 (medium confidence), removed nodes (i.e., gene products) which were disconnected from the network, and employed a k-means clustering algorithm empirically set at k\u0026thinsp;=\u0026thinsp;6 based on the distance matrix acquired from combined interaction scores. Lastly, we used hub genes from the purple module which shared hub genes identified from \u003cem\u003eP. multocida\u003c/em\u003e isolation and \u003cem\u003eM. ovipneumonaie\u003c/em\u003e qPCR identification (n\u0026thinsp;=\u0026thinsp;65), utilizing the same parameters as the aforementioned \u003cem\u003eM. haemolytica\u003c/em\u003e -based analysis except for lowering the number of clusters to k\u0026thinsp;=\u0026thinsp;3.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eMicrobiome\u003c/h2\u003e\u003cp\u003eFifty-seven tonsillar swabs and 29 LRT tissue samples were successfully sequenced. Three hundred and forty-two bacterial genera were detected in both types of samples. Library size ranged from 9,406 to 268,637 with a median of 135,965 reads in tonsillar swab samples, and from 12,731 to 369,115 with a median of 125,883, in the LRT tissue samples. Rarefaction curves and the distribution of the 20 most abundant bacterial genera per sample type and host species are shown in Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-S4.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eAlpha Diversity\u003c/h2\u003e\u003cp\u003eRichness, as measured by Chao1 index, was not significantly associated with any of the available metadata variables in tonsillar swabs (Supplementary Table\u0026nbsp;1). Shannon index (capturing diversity in the microbiome composition of each sample) was significantly higher in wash than in movi treatment groups in tonsillar swabs (Supplementary Table\u0026nbsp;2, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Shannon index was significantly positively associated with \u003cem\u003eM. ovipneumoniae\u003c/em\u003e and \u003cem\u003eM. haemolytica\u003c/em\u003e detection in tonsillar swabs (Supplementary Table\u0026nbsp;1; Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eI and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eM, respectively). In LRT tissue samples, the Chao1 index was significantly higher in AD than in BHS (Supplementary Table\u0026nbsp;2, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Shannon index followed the same trends as Chao1 in LRT tissue samples with the addition of being higher when \u003cem\u003eT. pyogenes\u003c/em\u003e was not present (Supplementary Table\u0026nbsp;2, Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI, and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eM).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eBeta Diversity\u003c/h2\u003e\u003cp\u003eSpecies, age, \u003cem\u003eM. ovipneumoniae\u003c/em\u003e serological status and qPCR status, and \u003cem\u003eB. trehalosi\u003c/em\u003e detection were associated with the differences in microbiome composition in tonsillar samples in at least one of the dissimilarity measures evaluated (Supplementary Table\u0026nbsp;3, Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eJ-\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eL). These variables explained a moderate to low percentage of the variance in the dissimilarity distances utilized in this study (from 4\u0026ndash;33%). However, there was evidence of significant differences in the dispersion for species (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), and \u003cem\u003eM. ovipneumoniae\u003c/em\u003e qPCR status (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eJ-L ;P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in at least one of the dissimilarity distances.\u003c/p\u003e\u003cp\u003eSpecies, age, \u003cem\u003eM. ovipneumoniae\u003c/em\u003e qPCR status, and \u003cem\u003eM. haemolytica\u003c/em\u003e prevalence were associated with the differences in microbiome composition in LRT tissue samples in at least one of the dissimilarity measures evaluated (Supplementary Table\u0026nbsp;4, Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eK, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eN, and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eO). These variables explained a moderate to low percentage of the variance in the dissimilarity distances utilized in this study (from 5 to 15%). However, there was evidence of significant differences in the dispersion of the dissimilarity distances in all of these variables (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), except for age, as the test of multivariate homogeneity of groups dispersions does not allow for a continuous predictor.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eBacterial Differential Abundance\u003c/h2\u003e\u003cp\u003eIn tonsillar swabs, several bacterial genera were differentially abundant by host species (n\u0026thinsp;=\u0026thinsp;36), age (n\u0026thinsp;=\u0026thinsp;36), \u003cem\u003eM. ovipneumoniae\u003c/em\u003e seroprevalence (n\u0026thinsp;=\u0026thinsp;132), and \u003cem\u003eM. ovipneumoniae\u003c/em\u003e qPCR status (n\u0026thinsp;=\u0026thinsp;136). All differentially abundant bacterial genera are listed in Supplementary Dataset 1. \u003cem\u003eProvidencia\u003c/em\u003e and \u003cem\u003eRoseomonas\u003c/em\u003e were more relatively abundant in BHS than in AD (BH-corrected P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) while \u003cem\u003eMoraxella\u003c/em\u003e exhibited the inverse pattern (BH-corrected P\u0026thinsp;=\u0026thinsp;0.005, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). \u003cem\u003eMycoplasma\u003c/em\u003e relative abundance increased with age (BH-corrected P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas the relative abundance of \u003cem\u003eRoseomonas\u003c/em\u003e and \u003cem\u003eProvidencia\u003c/em\u003e decreased as age increased (BH-corrected P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAnimals seropositive for anti-\u003cem\u003eM. ovipneumoniae\u003c/em\u003e antibodies showed significantly lower relative abundance of \u003cem\u003eBibersteinia\u003c/em\u003e, \u003cem\u003eProvidencia\u003c/em\u003e, \u003cem\u003eMannheimia\u003c/em\u003e, \u003cem\u003ePasteurella\u003c/em\u003e, \u003cem\u003eHistophilus\u003c/em\u003e, and \u003cem\u003eRoseomonas\u003c/em\u003e (BH-corrected P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared to seronegative animals. Interestingly, \u003cem\u003eMycoplasma\u003c/em\u003e relative abundance significantly increased in \u003cem\u003eM. ovipneumoniae\u003c/em\u003e seropositive animals (BH-corrected P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Similar findings were observed for \u003cem\u003eM. ovipneumoniae\u003c/em\u003e PCR status, with the exception that the increase in relative abundance for \u003cem\u003eMycoplasma\u003c/em\u003e did not reach statistical significance (BH-corrected P\u0026thinsp;=\u0026thinsp;0.1065307; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNo differential abundant genera associated with sex, treatments, \u003cem\u003eB. trehalosi\u003c/em\u003e, \u003cem\u003eM. haemolytica\u003c/em\u003e, nor T. \u003cem\u003epyogenes\u003c/em\u003e prevalence were observed in tonsilla swabs. Additionally, no bacterial genera were detected in LRT tissue samples to be differentially abundant depending on any of the available variables (species, sex, age, treatment groups, and the presence of \u003cem\u003eM. ovipneumoniae\u003c/em\u003e, \u003cem\u003eB. trehalosi\u003c/em\u003e, \u003cem\u003eM. haemolytica\u003c/em\u003e, and T. \u003cem\u003epyogenes\u003c/em\u003e in LRT tissue samples).\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eHost Transcriptomics\u003c/h2\u003e\u003cdiv id=\"Sec24\" class=\"Section4\"\u003e\u003ch2\u003eCo-expression module identification\u003c/h2\u003e\u003cp\u003eFiltered genes (n\u0026thinsp;=\u0026thinsp;15,024) were utilized for module placement via WGCNA one-step network construction. Network construction resulted in 23 color-coded modules containing 14,924 genes; 100 genes were not conserved into any co-expression module and were subsequently placed into the \u0026ldquo;grey\u0026rdquo; module (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e; Supplementary Dataset 2). Across the 23 assigned modules, the turquoise module possessed the largest number of co-expressed genes (n\u0026thinsp;=\u0026thinsp;6,562) and the darkturquoise module possessed the smallest number of co-expressed genes (n\u0026thinsp;=\u0026thinsp;33); the average size of each module was 659\u0026thinsp;\u0026plusmn;\u0026thinsp;1,296 genes. The complete list of genes and module assignments is found in Table S5.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eModule-trait correlations\u003c/h2\u003e\u003cp\u003eIndependent Kendall\u0026rsquo;s Tau correlation coefficients were generated within WGCNA for identifying and visualizing module-trait relationships (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e; Supplementary Dataset 3). Regarding host species (AD, coded \u0026ldquo;0\u0026rdquo;; BHS, coded \u0026ldquo;1\u0026rdquo;), three modules possessed significant correlations: midnightblue (τ = -0.72, P\u0026thinsp;=\u0026thinsp;0.001), brown (τ\u0026thinsp;=\u0026thinsp;0.68, P\u0026thinsp;=\u0026thinsp;0.003), and lightyellow (τ\u0026thinsp;=\u0026thinsp;0.59, P\u0026thinsp;=\u0026thinsp;0.01). Regarding treatment group (Movi or wash), one module possessed a significant correlation: purple (τ\u0026thinsp;=\u0026thinsp;0.48, P\u0026thinsp;=\u0026thinsp;0.05). For Movi PCR values (positive or negative), one module possessed a significant correlation: purple (τ\u0026thinsp;=\u0026thinsp;0.50, P\u0026thinsp;=\u0026thinsp;0.04). Regarding bacterial isolation results, several modules possessed significant correlations. Concerning \u003cem\u003ePasteurella multocida\u003c/em\u003e (Pm), four modules possessed significant negative correlations: purple (τ = -0.57, P\u0026thinsp;=\u0026thinsp;0.02), brown (τ = -0.52, P\u0026thinsp;=\u0026thinsp;0.03), black (τ = -0.57, P\u0026thinsp;=\u0026thinsp;0.02), and green (τ = -0.59, P\u0026thinsp;=\u0026thinsp;0.01). Concerning \u003cem\u003eBibersteinia trehalosi\u003c/em\u003e (Btreh), one module was found to have positive correlation: grey60 (τ\u0026thinsp;=\u0026thinsp;0.48, P\u0026thinsp;=\u0026thinsp;0.05). Regarding \u003cem\u003eMannheimia haemolytica\u003c/em\u003e (Mh), one module was identified with significant positive correlation: brown (τ\u0026thinsp;=\u0026thinsp;0.62, P\u0026thinsp;=\u0026thinsp;0.008). No significant module-trait correlations were identified for tissue type nor \u003cem\u003eTrueperella pyogenes\u003c/em\u003e isolation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eRegarding host species (AD, coded \u0026ldquo;0\u0026rdquo;; BHS, coded \u0026ldquo;1\u0026rdquo;), three modules possessed significant correlations: midnightblue (τ = -0.72, P\u0026thinsp;=\u0026thinsp;0.001), brown (τ\u0026thinsp;=\u0026thinsp;0.68, P\u0026thinsp;=\u0026thinsp;0.003), and lightyellow (τ\u0026thinsp;=\u0026thinsp;0.59, P\u0026thinsp;=\u0026thinsp;0.01). Regarding treatment group (movi or wash), one module possessed a significant correlation: purple (τ\u0026thinsp;=\u0026thinsp;0.48, P\u0026thinsp;=\u0026thinsp;0.05). For movi PCR values (positive or negative), one module possessed a significant correlation: purple (τ\u0026thinsp;=\u0026thinsp;0.50, P\u0026thinsp;=\u0026thinsp;0.04). Regarding bacterial isolation results, several modules possessed significant correlations. Concerning Pasteurella multocida (Pm), four modules possessed significant negative correlations: purple (τ = -0.57, P\u0026thinsp;=\u0026thinsp;0.02), brown (τ = -0.52, P\u0026thinsp;=\u0026thinsp;0.03), black (τ = -0.57, P\u0026thinsp;=\u0026thinsp;0.02), and green (τ = -0.59, P\u0026thinsp;=\u0026thinsp;0.01). Concerning Bibersteinia trehalosi (Btreh), one module was found to have positive correlation: grey60 (τ\u0026thinsp;=\u0026thinsp;0.48, P\u0026thinsp;=\u0026thinsp;0.05). Regarding Mannheimia haemolytica (Mh), one module was identified with significant positive correlation: brown (τ\u0026thinsp;=\u0026thinsp;0.62, P\u0026thinsp;=\u0026thinsp;0.008). No significant module-trait correlations were identified for tissue type nor Trueperella pyogenes isolation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003eFunctional enrichment analyses\u003c/h2\u003e\u003cp\u003eThe significantly enriched pathways for the seven aforementioned modules are found in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. The black module enriched for two pathways, both related to metabolism. The brown module enriched for 191 pathways, primarily related to ribosomal processing of RNA, cellular metabolism, antigen processing and cross-presentation, neutrophil degranulation, cell response to stress and external stimuli, interleukin-1 and interleukin-12 signaling, B-cell NF-κB activation and downstream signaling, and response to infectious disease. The green module enriched for 38 pathways, primarily related to cellular chaperoning, RNA processing and metabolism, CLEC7A signaling, and NF-κB activation. The grey60 module enriched for two pathways related to viral infection and translation events. The lightyellow module enriched for 19 pathways, primarily related to oxidative phosphorylation, ATP synthesis, and cellular activity involving neurons. The midnightblue module enriched for eight pathways, primarily related to antigen processing and presentation, heat shock protein response, and attenuation of heat shock transcriptional response. The purple module enriched for 127 pathways, primarily related to tumor necrosis factor α, mitogen-activated protein kinase, and NF-κB signaling, multiple toll-like receptor cascades, interleukin-1, 4, 10, 17, and 18 signaling, integrin-mediated cellular adhesion, T-cell receptor activity and signaling, inflammatory response, and transforming growth factor β receptor signaling.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003ch2\u003eBacterial hub genes and predicted protein-protein interactions\u003c/h2\u003e\u003cp\u003eThe WGCNA-based hub gene analyses resulted in the identification of 1,373 total hub genes as follows: MMblack-Pm: 234, MMbrown-Mh: 231, MMbrown-Pm: 332, MMgreen-Pm: 354, MMgrey60-Btreh: 21, MMpurple-Pm: 136, MMpurple-Movi: 65 (S6 Table). As the correlations with Pm and associated co-expression modules were all independently in the same direction (negative), these hub genes were combined for protein-protein interaction networking (n\u0026thinsp;=\u0026thinsp;1,065). From the Pm-associated hub genes, 186 gene products were identified to have predicted physical interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The physical interaction network demonstrated high interconnectivity between the eight distinct clusters, with a median cluster size of 16 gene products. From the Mh-associated hub genes from the brown module, 64 gene products were identified to have predicted physical interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). The physical interaction network demonstrated high interconnectivity between the six distinct clusters, with a median cluster size of 9 gene products. Of particular interest, the hub genes identified within MMpurple-Pm and MMpurple-Movi analyses shared complete overlap (n\u0026thinsp;=\u0026thinsp;65). Here, 13 gene products were identified to have predicted physical interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). The physical interaction network demonstrated low interconnectivity between the three distinct clusters, with only one gene product, \u003cem\u003eACTN4\u003c/em\u003e (Cluster 3), demonstrating unique identity compared to the other 12 gene products. All gene products and their interconnectivity scores within each network analysis are found in Table S7.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eNatural and anthropogenic changes in the communities of host species, as well as the introduction and movement of pathogens, represent significant global biotic changes \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Here, we use the introduction of AD to the southwestern United States as a case study to elucidate potential outcomes of exposure to shared pathogens, and the potential reliance of these outcomes on differential host-pathogen co-evolutionary histories. Significant clinical differences exist between DS and BHS when exposed to \u003cem\u003eM. ovipneumoniae\u003c/em\u003e and leukotoxigenic \u003cem\u003ePasteurellaceae\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. These host species genetically and biogeographically diverged from one another between 2.52 and 5.63\u0026nbsp;million years ago \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e, allowing much opportunity for different host-pathogen co-evolution patterns to occur. Bighorn sheep and AD were even further evolutionarily and biogeographically separated from one another. Assessing both respiratory microbiome and host transcriptomic profiles from these two host species allowed us to assess whether and how they differ when exposed to pathogens lethal to BHS. Our focus is to extend findings from this host-pathogen system to novel, emerging, and existing host-pathogen systems perturbed by anthropogenic activities. Characterizing functionally distinct processes for the microbiomes and transcriptomes for host species with (co-evolved) and without (na\u0026iuml;ve) coevolution with a given agent presents a unique opportunity to describe how other altered host-pathogen systems may behave. When combined with clinical data from known exposures of either the na\u0026iuml;ve or co-evolved host species to an agent of interest, these predictions could be improved. The applied nature of this host-pathogen system has implications for BHS health and epidemiological dynamics for \u003cem\u003eM. ovipenumoniae\u003c/em\u003e in Texas where BHS and AD co-occur.\u003c/p\u003e\u003cp\u003eTogether, our microbiome and transcriptomic findings highlight a complex web of host-pathogen-microbiome interactions shaped by evolutionary history and species-specific immunometabolic and microbiome architectures. Clear differences in microbial richness and composition between AD and BHS across respiratory tract compartments, particularly in response to \u003cem\u003eMycoplasma ovipneumoniae\u003c/em\u003e and \u003cem\u003ePasteurellaceae\u003c/em\u003e detection, reflect divergent microbial dynamics likely stemming from distinct coevolutionary trajectories. BHS appeared more susceptible to microbiome destabilization and pathogen-associated shifts, consistent with prior evidence of impaired mucociliary clearance and heightened susceptibility to leukotoxigenic bacterial toxins. Conversely, AD exhibited microbiome responses suggestive of resilience or tolerance, potentially indicative of greater immunological or ecological compatibility \u003cem\u003ewith M. ovipneumoniae\u003c/em\u003e and perhaps some species/variants of \u003cem\u003ePasteurellaceae\u003c/em\u003e. The association of \u003cem\u003eM. ovipneumoniae\u003c/em\u003e with reduced abundance of multiple key bacterial genera and the interplay with \u003cem\u003eM. haemolytica\u003c/em\u003e and \u003cem\u003eB. trehalosi\u003c/em\u003e support the hypothesis that microbial synergy, not just single-pathogen effects, contributes to disease progression and respiratory microbiome disruption in this system.\u003c/p\u003e\u003cp\u003eTranscriptomic profiling reinforced and expanded these findings by identifying immunometabolic and neuroimmune signatures associated with pathogen detection and species identity. Notably, expression profiles in BHS revealed enrichment of pro-inflammatory pathways (e.g., IL-1, IL-12, NF-κB activation), alongside metabolic activity indicative of oxidative stress and potential immunopathology. In contrast, AD tissues exhibited a comparatively tempered pro-inflammatory response with stronger representation of stress-regulated and antigen processing pathways. These host-specific molecular signatures support the observed epidemiological outcomes in which BHS suffer acute, often fatal pneumonia following exposure to pathogens that AD can carry asymptomatically. Such patterns reinforce the role of differential host-pathogen coevolution in governing disease susceptibility and highlight the potential consequences of pathogen spillover from nonindigenous hosts to immunologically na\u0026iuml;ve wildlife species.\u003c/p\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003eMicrobiome\u003c/h2\u003e\u003cdiv id=\"Sec30\" class=\"Section3\"\u003e\u003ch2\u003eAssociations with diversity and differential abundance in the upper and lower respiratory tracts\u003c/h2\u003e\u003cp\u003eBacterial diversity of the upper respiratory tract (tonsillar swabs) was significantly impacted by the treatment group (movi vs wash groups) and detection of \u003cem\u003eM. ovipneumoniae and M. haemolytica\u003c/em\u003e. The increasing microbiome diversity associated with \u003cem\u003eM. haemolytica\u003c/em\u003e detection may underly the treatment group effect given the inocula preparation techniques\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. Considering the documented synergistic role of \u003cem\u003eM. haemolytica\u003c/em\u003e and \u003cem\u003eM. ovipneumoniae\u003c/em\u003e in clinical disease progression\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e, this pattern is not surprising. The importance of this synergism is highlighted by similar bacterial diversity values in control and movi BHS. Notably, bacterial diversity and composition were both impacted by \u003cem\u003eM. ovipneumoniae\u003c/em\u003e detection, whose mucociliary disruption may have allowed normally excluded bacterial species to persist in the upper respiratory tract \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Other studies have found that the effects of \u003cem\u003eB. trehalosi\u003c/em\u003e on the microbiome depends on the presence\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e or absence\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e of \u003cem\u003eM. haemolytica\u003c/em\u003e. Thus, it is reasonable to consider that the bacterial compositional changes observed with \u003cem\u003eB. trehalosi\u003c/em\u003e occurred were, at least in part, related to the interactions between \u003cem\u003eM. ovipneumoniae\u003c/em\u003e and \u003cem\u003eM. haemolytica\u003c/em\u003e stemming from the two inocula used in this study.\u003c/p\u003e\u003cp\u003eThe relative abundance of approximately 40% of the bacterial genera shifted in \u003cem\u003eM. ovipneumoniae\u003c/em\u003e qPCR positive tonsillar swabs, including pivotal respiratory bacterial genera (\u003cem\u003eBibersteinia\u003c/em\u003e, \u003cem\u003eProvidencia\u003c/em\u003e, \u003cem\u003eMannheimia\u003c/em\u003e, \u003cem\u003ePasteurella\u003c/em\u003e, \u003cem\u003eHistophilus\u003c/em\u003e, and \u003cem\u003eRoseomonas\u003c/em\u003e). This observation coupled with the finding of higher evenness (that is, less bacterial genera dominating the microbiome composition) in those swabs, suggests profound changes in the upper respiratory microbiome when \u003cem\u003eM. ovipneumoniae\u003c/em\u003e is present. Interestingly, the relative abundance of \u003cem\u003eMannheimia\u003c/em\u003e significantly decreased when \u003cem\u003eM. ovipneumoniae\u003c/em\u003e was detected in opposition to the expected synergism. This finding warrants further research to elucidate the nuances of the interactions between these two bacteria. Furthermore, our study was unable to obtain sufficient sequencing reads to detect statistically significantly differentially abundant bacterial genera in LRT samples, which is central to make inference on the role of \u003cem\u003eM. ovipneumoniae\u003c/em\u003e in LRT colonization and subsequent microbiome shifts\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. Recent work further illustrates the increasingly recognized importance of integrated microbiome shifts across organ systems, and their role in pathogenesis, specifically in DS exposed to \u003cem\u003eM. ovipneumoniae\u003c/em\u003e \u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHost species is a major determinant of the establishment and maintenance of a particular microbiome\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. Therefore, it is expected that microbiome shifts, and the potential role of pivotal respiratory bacteria vary between different host species. Previous efforts to describe microbiome shifts important to BHS disease progression identified \u003cem\u003eProvidencia, Rosemonas\u003c/em\u003e, and \u003cem\u003eMoraxella\u003c/em\u003e as important determinants of disease outcome in BHS \u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. Bighorn sheep in the present study demonstrated higher abundances of \u003cem\u003eProvidencia\u003c/em\u003e and \u003cem\u003eRoseomonas\u003c/em\u003e compared to AD in the tonsillar swabs, similar to the previous work with free-ranging individuals \u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn the LRT, microbiome diversity and composition were strongly impacted by host species, with AD exhibiting higher diversity in the LRT compared to BHS. Additionally, detection of pivotal respiratory bacteria (\u003cem\u003eM. ovipneumoniae\u003c/em\u003e and \u003cem\u003eM. haemolytica)\u003c/em\u003e were associated with differences in bacterial composition. Just as in the upper respiratory tract, the interactions between \u003cem\u003eM. ovipneumoniae\u003c/em\u003e and \u003cem\u003eM. haemolytica\u003c/em\u003e seem to play a key role (maybe a keystone role?) driving microbiome responses in the LRT. Taken together, there is evidence to suggest that AD and BHS naturally or experimentally exposed to \u003cem\u003eM. ovipneumoniae\u003c/em\u003e and other pivotal respiratory bacteria have distinct changes in the microbial populations in the respiratory environment, possibly stemming from distinct pathobiological responses associated with host-pathogen coevolution.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\u003ch2\u003eHost Transcriptomics\u003c/h2\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMetabolic pathway expression associated with\u003c/span\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003ePasteurella multocida\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ein both host species\u003c/span\u003e\u003c/p\u003e\u003cp\u003eGenes related to metabolic pathways were negatively correlated with the presence of \u003cem\u003ePasteurella multocida\u003c/em\u003e in the lungs of both AD and BHS While the enriched pathways were relatively non-specific, studies evaluating the lung cells of both mice and goat lung cells have demonstrated that \u003cem\u003ePasteurella multocida\u003c/em\u003e induces a regulatory response related to cellular metabolic processes and may disrupt key signaling pathways that would promote host cell apoptosis during infection \u003csup\u003e\u003cspan additionalcitationids=\"CR76\" citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. Our study seemingly corroborates these findings, as these two modules describe a similar relationship with cellular metabolism and NF-κB activation with the likelihood of isolating \u003cem\u003ePasteurella multocida\u003c/em\u003e from AD and BHS.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePossible in vivo bacterial inhibition of\u003c/span\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eMannheimia haemolytica\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eand\u003c/span\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003ePasteurella multocida\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003emediated by cellular metabolic patterns of bighorn sheep\u003c/span\u003e\u003c/p\u003e\u003cp\u003eInterestingly, the relationship of gene expression from this module suggests an inverse relationship in \u003cem\u003eMannheimia haemolytica\u003c/em\u003e and \u003cem\u003ePasteurella multocida\u003c/em\u003e isolation specifically within BHS. Boukahil and Czuprynski in 2018 identified a similar pattern from \u003cem\u003ein vitro\u003c/em\u003e biofilm production on bovine bronchial epithelial cells, specifically where these two bacteria were capable of inhibiting each other\u0026rsquo;s biofilm development when in close proximity to each other on the epithelial surface \u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. Notably, this study from 2018 did not establish a definitive antagonistic between \u003cem\u003eMannheimia haemolytica\u003c/em\u003e and \u003cem\u003ePasteurella multocida\u003c/em\u003e, nor a mechanism by which they inhibit each other. Here, our study describes 139 genes within the brown module which are hub genes of both \u003cem\u003eMannheimia haemolytica\u003c/em\u003e and \u003cem\u003ePasteurella multocida\u003c/em\u003e, many of which are related to carbohydrate metabolism, mitochondrial translation activation, and G-protein chaperoning, thus serving as a possible mechanistic explanation to this phenomenon.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eOverlapping hub genes differentially associated with\u003c/span\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003ePasteurella multocida\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eand\u003c/span\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eMycoplasma ovipneumoniae\u003c/span\u003e\u003c/p\u003e\u003cp\u003eWith respect to hub gene analyses, the further assessment of overlapping genes between modules, and the results of predictive protein-protein interactions, particular interest was given to those genes found within the purple module as it relates to \u003cem\u003ePasteurella multocida\u003c/em\u003e (negatively associated) and \u003cem\u003eMycoplasma ovipneumoniae\u003c/em\u003e (positively associated) detection. Of particular interest, \u003cem\u003eABL1\u003c/em\u003e, \u003cem\u003eACTN4\u003c/em\u003e, \u003cem\u003eILK\u003c/em\u003e, \u003cem\u003eCD46\u003c/em\u003e, \u003cem\u003eCOL4A1\u003c/em\u003e, \u003cem\u003eCOL4A2\u003c/em\u003e, and \u003cem\u003eNFKBIZ\u003c/em\u003e gene products have been shown to be directly involved in integrin regulation and signaling \u003csup\u003e\u003cspan additionalcitationids=\"CR80 CR81 CR82 CR83\" citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e. Further, CD46 has been shown to be a key receptor in downregulating complement activation, interacts with several β1 integrins, and may serve as a key binding receptor for bacterial pathogens \u003csup\u003e\u003cspan additionalcitationids=\"CR85 CR86\" citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e. Regarding infectious respiratory disease, integrins may serve as mediators of immune cell proliferation and molecular targets exploited by bacteria for cellular adhesion and immunological evasion \u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eMycoplasma hyopneumoniae\u003c/em\u003e has been shown to interact with integrin β1-fibronectin to enter host cells and evade the immune system \u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e. Similarly, \u003cem\u003ePasteurella multocida\u003c/em\u003e can produce a fibronectin-binding protein which interacts with immobilized fibronectin and type I collagen of host cells to promote colonization and invasion of host tissues \u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e. Studies have demonstrated the paradoxical nature of integrin promotion and expression in pulmonary disease, as integrin-dependent mechanisms facilitate neutrophil migration and enhance macrophage-mediated clearance of bacteria, but persistent or dysregulated integrin activation may exacerbate tissue damage through sustained inflammation and bacterial infiltration or evasion \u003csup\u003e\u003cspan additionalcitationids=\"CR92 CR93 CR94 CR95\" citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e\u003ch2\u003eHost species differences in immunological and neuroimmune-axis gene expression profiles\u003c/h2\u003e\u003cp\u003eOur work revealed three distinct gene expression modules associated with species-level differences between AD and BHS, offering insights into potential mechanisms underlying susceptibility to infectious bacterial diseases. Three modules \u0026ndash; midnightblue, brown, and lightyellow \u0026ndash; exhibited significant correlations with species identity, highlighting divergent immunometabolic profiles that may influence host-pathogen interactions. Gene expression for pathways related to antigen processing and presentation, heat shock protein (HSP) responses, and attenuation of HSP transcriptional activity were depressed in BHS compared to AD. HSPs are critical in modulating immune responses during pathogen-induced stress and inflammation \u003csup\u003e\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e,\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e\u003c/sup\u003e Importantly, HSP70 and HSP90 proteins aid the host by protecting secreting intracellular proteins from proteolysis, support antigen presentation and immunoglobulin biosynthesis, and aid in cellular stabilization during infectious processes and/or heat stress \u003csup\u003e\u003cspan additionalcitationids=\"CR100 CR101\" citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e\u003c/sup\u003e. In contrast, BHS had enriched immune pathways including interleukin-1 and interleukin-12 signaling, neutrophil degranulation, and B-cell NF-κB activation; these pathways are critical for initiating and sustaining inflammatory responses and adaptive immunity \u003csup\u003e\u003cspan additionalcitationids=\"CR104 CR105\" citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e\u003c/sup\u003e. Bighorn sheep, which appear more susceptible to \u003cem\u003eMycoplasma\u003c/em\u003e-associated pneumonia and \u003cem\u003ePasteurella\u003c/em\u003e-related pleuropneumonia than AD, may mount a heightened but potentially dysregulated inflammatory response that exacerbates tissue damage during infection\u003csup\u003e\u003cspan additionalcitationids=\"CR108\" citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e\u003c/sup\u003e. Similar immune and inflammatory profiles have been observed in domestic livestock species during the clinical onset of respiratory disease\u003csup\u003e\u003cspan additionalcitationids=\"CR111 CR112\" citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBighorn sheep expression patterns were enriched for oxidative phosphorylation, ATP synthesis, and neuronally associated cellular activity. These findings suggest increased metabolic activity and possibly heightened neuroimmune regulation in BHS tissues. Enhanced mitochondrial activity has been associated with both immune activation and pathogenic oxidative stress\u003csup\u003e\u003cspan additionalcitationids=\"CR115 CR116 CR117\" citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e\u003c/sup\u003e. In the context of infectious respiratory disease in livestock, excessive reactive oxygen species (ROS) production, a byproduct of elevated oxidative phosphorylation, can damage pulmonary tissue \u003csup\u003e\u003cspan additionalcitationids=\"CR120\" citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e\u003c/sup\u003e. Moreover, the bidirectional relationship between peripheral neuronal activity and immune cell activation, termed the neuroimmune axis, has been implemented in airway diseases and cellular stress \u003csup\u003e\u003cspan additionalcitationids=\"CR123 CR124\" citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e\u003c/sup\u003e. Here, it may be that BHS exhibit distinct tissue-level responses involving stress or inflammation-induced neurogenic pathways compared to AD.\u003c/p\u003e\u003cdiv id=\"Sec33\" class=\"Section3\"\u003e\u003ch2\u003eTranscriptomic implications for species-level disease susceptibility\u003c/h2\u003e\u003cp\u003eThese interspecies differences in co-expressed immune and metabolic modules may suggest that BHS rely on a more acute, inflammatory-based immune activation strategy, potentially leading to immunopathologies, while AD may benefit from a more stress-tolerant antigen processing system. This could partially explain observed epidemiological patterns where BHS populations suffer high mortality from bacterial pneumonia, especially following \u003cem\u003eMycoplasma\u003c/em\u003e spp. and \u003cem\u003ePasteurella\u003c/em\u003e spp. co-infections, whereas AD appear less impacted under similar pathogen exposure scenarios\u003csup\u003e\u003cspan additionalcitationids=\"CR108\" citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e\u003c/sup\u003e. Further validation in tissue-specific expression contexts and functional assays is warranted, but these transcriptional signatures provide a valuable framework for understanding disease susceptibility differences between wild and nonindigenous sheep species and inform conservation and disease management strategies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec34\" class=\"Section3\"\u003e\u003ch2\u003eLimitations and considerations\u003c/h2\u003e\u003cp\u003eAs with many studies focused on free-ranging wildlife in captive settings, our study was met with challenges stemming from low sample sizes and lack of knowledge on pre-study pathogen exposure patterns in our study animals. These limitations introduce the possibility that observed patterns may have been impacted not only by species, sex, and treatment group, but also by inherent interindividual variations and differences in pathogen exposure histories. Due to animal facility and animal procurement considerations, our design did not control for confounders, particularly acclimatization period differences between groups (BHS, 40 days; AD days). Additionally, the pathogenesis of \u003cem\u003eM. ovipneumoniae\u003c/em\u003e-induced respiratory disease and subsequent transmission dynamics in both BHS and AD can be influenced by a variety of factors, including strain composition \u003csup\u003e\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e,\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e\u003c/sup\u003e. This further limited our ability to ascertain the precise pathological stage of each individual relative to our microbiome and host transcriptomic findings. Finally, failure to collect nasal and tonsillar swab samples from each species and treatment group may further limit the generalizability of our results. A true multi-omics approach requires paired swab and tissue samples across the study period, which was not feasible given the animal handling requirements for these species in captivity. Overall, we were limited by commonplace issues associated with wildlife research that warrant interpretive caution.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur results provided evidence of microbiome and transcriptome differences between AD and BHS in regard to host-pathogen relationships. Here, our findings are linked to divergent clinical disease outcomes and have potential implications for BHS conservation in regions of cohabitation. Moreover, these findings illustrate the need to integrate ecological, evolutionary, and molecular perspectives when managing disease risks in disturbed host-pathogen systems, particularly those involving introduced species like AD. With numerous mammalian introductions globally altering host-pathogen dynamics, understanding coevolutionary relationships becomes critical for predicting disease emergence and transmission. Future work should prioritize integrating microbiome and gene expression profiling from free-ranging individuals with ongoing epidemiological surveillance to better assess and manage potentially emerging disease threats at both a population and ecosystem level.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll work was authorized under the A\u0026amp;M AgriLife Animal Care and Use Committee permit 2020-038A. Reporting of methods, results, and study limitations complies with the ARRIVE 2.0 Essential 10 guidelines.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available as supplementary data files and/or data repository. All raw transcriptomic sequencing data and curated metadata produced by this study are available at the National Center for Biotechnology Information Gene Expression Omnibus (NCBI-GEO) under the accession number GSE295667 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE295667). All raw microbiome sequencing data and curated metadata produced by this study are available at the National Center for Biotechnology Information Sequence Read Archive (SRA) under the accession number PRJNA1306317 (https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA1306317).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Foundation for North American Wild Sheep, Grant-In-Aid Project Number 1920-71 (Walter E. Cook.) and the Texas Parks and Wildlife Department\u0026rsquo;s Wildlife Research Program through Federal Aid in Wildlife Restoration Act (Pittman-Robertson) Grant Program (\u0026ldquo;Evaluating the risk of Mycoplasma transmission from Aoudad to Bighorn Sheep\u0026rdquo;). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWC was the senior project lead, procured funding, managed and conceptualized the project, and directed the scope of the study and prepared the manuscript; CP created the bioinformatic pipeline used to assess the microbiome data, and performed statistical analyses related to microbiome efforts; MS conceptualized and led the transcriptome efforts, performed all transcriptome statistical analyses, and prepared the manuscript; LT collected samples, conducted animal husbandry and handling, designed and conceptualized the study, procured funding, provided input on statistical analyses with coauthor assistance, guided the overall scope of the manuscript in its preparation, and managed tasks among coauthors; RV-C conceptualized and led the microbiome efforts, finalized all microbiome statistical analyses, and prepared the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent to Publish declaration:\u003c/em\u003e\u003c/strong\u003e Not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRicklefs, R. 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Evidence for strain-specific immunity to pneumonia in bighorn sheep. \u003cem\u003eJournal of Wildlife Management\u003c/em\u003e \u003cstrong\u003e81\u003c/strong\u003e, 133\u0026ndash;143 (2017).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7383688/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7383688/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003ePathogens can shape their host communities over various timescales. The potential role of host-pathogen coevolution in driving contemporary shifts in disease ecology is becoming increasingly important as host species emerge and persist outside their native ranges. In North America, \u003cem\u003eMycoplasma ovipneumoniae\u003c/em\u003e can cause fatal pneumonia epizootics in native bighorn sheep (\u003cem\u003eOvis canadensis\u003c/em\u003e), whereas introduced free-ranging sympatric aoudad (\u003cem\u003eAmmotragus lervia\u003c/em\u003e) typically act as asymptomatic reservoirs. To elucidate the role of host\u0026ndash;pathogen coevolution in shaping these observed patterns of host impacts, we integrated findings on microbiome composition and host transcriptomic responses in aoudad and bighorn sheep following controlled exposure to \u003cem\u003eM. ovipneumoniae\u003c/em\u003e, with or without leukotoxigenic Pasteurellaceae.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eAoudad maintained significantly higher microbial richness (Chao1) and evenness (Shannon index) across tonsillar swabs and lower respiratory tract samples, whereas bighorn sheep experienced microbiome perturbations and enhanced growth of some opportunistic taxa. Exposure to \u003cem\u003eM. ovipneumoniae\u003c/em\u003e reduced the relative abundance of key commensal genera (e.g., \u003cem\u003eBibersteinia\u003c/em\u003e, \u003cem\u003eMannheimia\u003c/em\u003e, \u003cem\u003ePasteurella\u003c/em\u003e, \u003cem\u003eRoseomonas\u003c/em\u003e) and enriched \u003cem\u003eMycoplasma\u003c/em\u003e in both hosts, but bacterial community destabilization was more pronounced in bighorn sheep. Transcriptome profiling revealed that bighorn sheep upregulated pro-inflammatory and oxidative-stress pathways\u0026mdash;including interleukin-1, interleukin-12, and NF-κB signaling\u0026mdash;alongside reactive oxygen species generation. In contrast, aoudad exhibited comparatively muted inflammatory signatures, enhanced expression of molecular chaperones, antigen-processing machinery, and integrin-mediated regulatory genes (notably CD46, ILK, and NFKBIZ). Network analysis identified distinct hub genes likely underpinning effective pathogen clearance and mucosal resilience in aoudad versus immunopathology in bighorn sheep.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e\u003cp\u003eOur integrated microbiome and transcriptomic data underscore the importance if coevolutionary history in driving host-specific responses to shared respiratory pathogens. Aoudad display microbiome stability and balanced immunoregulation, whereas bighorn sheep suffer dysbiosis and excessive inflammation, potentially increasing mortality risk. Incorporating evolutionary and ecological context into managing disease interfaces requires a direct understanding of host-pathogen interactions, as well as how these interactions create observed pathobiological and epidemiological patterns commonly targeted for disease management interventions.\u003c/p\u003e","manuscriptTitle":"Distinct host-pathogen and microbiome responses of aoudad (Ammotragus lervia) and bighorn sheep (Ovis canadensis) following exposure to Mycoplasma ovipneumoniae","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 06:09:12","doi":"10.21203/rs.3.rs-7383688/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-30T06:08:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-26T16:20:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-23T11:32:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-17T19:10:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-11T19:57:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"148313530240191128748727155414481354254","date":"2025-09-04T14:51:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"175577375339159649963719644004750721627","date":"2025-09-02T15:55:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"124978718923172499031687969287019738239","date":"2025-09-01T21:48:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"195269405371214052587507780974455095718","date":"2025-09-01T07:45:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"24334880999460049344023564263800713129","date":"2025-09-01T05:57:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-01T04:28:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-01T04:23:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-21T04:06:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-20T16:33:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Veterinary Research","date":"2025-08-20T16:28:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"58df2e33-5359-48ed-9272-526e9dd24dba","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T10:00:10+00:00","versionOfRecord":{"articleIdentity":"rs-7383688","link":"https://doi.org/10.1186/s12917-026-05375-1","journal":{"identity":"bmc-veterinary-research","isVorOnly":false,"title":"BMC Veterinary Research"},"publishedOn":"2026-04-27 15:57:54","publishedOnDateReadable":"April 27th, 2026"},"versionCreatedAt":"2025-09-09 06:09:12","video":"","vorDoi":"10.1186/s12917-026-05375-1","vorDoiUrl":"https://doi.org/10.1186/s12917-026-05375-1","workflowStages":[]},"version":"v1","identity":"rs-7383688","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7383688","identity":"rs-7383688","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}