Body site, host species, and seasonal drivers of zoonotic bacterial distribution in Korean insectivorous bat microbiomes

Body site, host species, and seasonal drivers of zoonotic bacterial distribution in Korean insectivorous bat microbiomes | 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 Body site, host species, and seasonal drivers of zoonotic bacterial distribution in Korean insectivorous bat microbiomes Tatsuya Unno, Geon Choi, Hanbit Hwang, Nyeong-Jin Cheon, Sang Eun Jeong, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9148344/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Bats harbor diverse zoonotic bacteria, yet how pathogen carriage varies across body sites, host species, and seasons --- and whether individual bats maintain infections chronically --- remains largely unknown. We surveyed 2,747 oral, skin, and stool samples from eight insectivorous bat species across four seasons (2021–2024) in South Korea using 16S rRNA gene sequencing. Ten zoonotic genera were detected in 63.7% of samples. Feces harbored 10-fold higher pathogen loads than oral samples, dominated by Helicobacter and Chlamydia , while skin carried high levels of vector-borne Rickettsia and Bartonella . Three species posed distinct risks: Myotis macrodactylus stool contained Helicobacter at 14.4% relative abundance, Pipistrellus abramus --- a synanthropic species known to be house-dwelling --- carried extreme Rickettsia on skin (9.37%), and Miniopterus fuliginosus harbored the broadest pathogen diversity. Pathogen abundance peaked in summer and dropped sharply in winter, though Yersinia persisted during hibernation. Longitudinal tracking of 80 recaptured bats over 2–3 years showed that pathogen carriage was predominantly transient, indicating environmental acquisition each season rather than chronic colonization. Predicted metabolic profiles shifted between active and hibernation periods, with hibernation microbiomes enriched in lipid catabolism consistent with host torpor physiology. These results identify stool and skin as the primary transmission routes, pinpoint high-risk species and seasons, and demonstrate that population-level pathogen prevalence reflects ongoing environmental exposure --- findings directly relevant to wildlife disease surveillance and bat conservation management. Bat microbiome Zoonotic bacteria 16S rRNA Body site Seasonal dynamics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Bats (order Chiroptera) harbor a disproportionately high diversity of zoonotic pathogens relative to other mammalian orders. Beyond well-documented viral reservoirs, bats carry diverse bacterial pathogens including Leptospira [ 1 ], Bartonella [ 2 ], Salmonella [ 3 ], Yersinia [ 4 ], Campylobacter [ 5 ], Pasteurella [ 6 ], Brucella [ 7 ], Rickettsia [ 8 ], Coxiella [ 9 ], and Chlamydia [ 10 ]. Many of these bacteria pose direct zoonotic risks through fecal contamination, ectoparasite vectors, or direct contact. However, the ecological factors governing pathogen distribution across bat body sites, host species, and seasons remain poorly characterized. The commensal microbiome plays a critical role in pathogen colonization, persistence, and transmission through competitive exclusion, immune modulation, and metabolic interactions [ 11 , 12 ]. Body site is a fundamental determinant of microbiome structure: skin microbiomes interact directly with the external environment and ectoparasites, oral microbiomes occupy a transitional niche, and stool microbiomes reflect the gut community shaped by diet and host physiology [ 12 ]. Each body site may therefore present different opportunities for pathogen acquisition, maintenance, and transmission. The eight insectivorous bat species examined in this study span a range of ecological traits relevant to zoonotic risk. Rhinolophus ferrumequinum (greater horseshoe bat) is a large, obligate cave-dwelling species that forms dense hibernation colonies of hundreds to thousands of individuals in Korean limestone caves [ 13 ], maximizing opportunities for fecal–oral and ectoparasite transmission. In contrast, Pipistrellus abramus (Japanese pipistrelle) is a synanthropic species that roosts in building crevices, bridges, and roof spaces [ 14 ], placing it in direct proximity to human populations. Among the four Myotis species, ecological variation is also substantial: M. macrodactylus (eastern long-fingered bat) forages over water and hibernates in caves [ 15 ], while M. bombinus (Far Eastern myotis) and the smaller M. aurascens and M. petax occupy cave and rock-crevice habitats. Miniopterus fuliginosus (Eastern bent-wing bat), a cave bat of the family Miniopteridae, forms large maternity colonies of up to 15,000 individuals and is one of the most abundant cave bats in East Asia [ 16 ]. Murina hilgendorfi (Hilgendorf's tube-nosed bat) is a cave-dwelling species [ 13 ]. Seasonal variation is particularly relevant for temperate-zone bats that undergo hibernation. The transition between active and torpid states alters host metabolism, immune function, ectoparasite activity, and environmental exposure --- all of which may influence pathogen dynamics [ 17 ]. Understanding these seasonal patterns is essential for targeted surveillance strategies. This study examines the distribution of zoonotic bacteria across three body sites (oral, skin, stool) in these eight species in South Korea using 16S rRNA gene amplicon sequencing. Specifically, we aim to: (1) characterize the microbiome structure across body sites and bat species; (2) determine the distribution and prevalence of zoonotic bacterial genera across body sites, species, and seasons; (3) identify which body sites and species pose the greatest zoonotic risk; and (4) determine seasonal patterns in pathogen abundance relevant to surveillance timing. MATERIALS AND METHODS Sample collection Oral, skin, and stool samples were collected from bats across 9 regions and 20 field sites in South Korea between 2021 and 2024. Samples were collected across four seasons: winter (December–February), spring (March–May), summer (June–August), and fall (September–November). A total of 2,825 samples were initially collected from eight insectivorous bat species (Table 1 ). Table 1 Sample summary after quality filtering Species Oral Skin Stool Total Miniopterus fuliginosus 275 421 271 967 Rhinolophus ferrumequinum 365 360 51 776 Myotis macrodactylus 170 138 64 372 Myotis bombinus 163 125 66 354 Pipistrellus abramus 45 35 29 109 Myotis petax 36 28 --- 64 Myotis aurascens 33 23 --- 56 Murina hilgendorfi 28 21 --- 49 Total 1,115 1,151 481 2,747 This study was approved by the Research Planning Review Committee of the National Institute of Ecology (NIEIACUC-2021-001). We declare that we adhered to the Wildlife Protection and Management Act of Korea as well as the Institutional Research Ethics Regulations and Guidelines. All handling and sampling permissions were obtained from the seven corresponding local governments on each sampling year. DNA extraction and sequencing DNA was extracted from oral, skin, and stool swabs. The V3–4 region of the 16S rRNA gene was amplified and sequenced on the Illumina MiSeq platform. Library preparation and sequencing were performed at CJ Bioscience (Seoul, South Korea). Bioinformatic processing Raw paired-end reads were trimmed of primer sequences using cutadapt and processed through the DADA2 pipeline in R [ 18 ]: quality filtering (maxN = 0, maxEE = c(2,2), truncQ = 2), error rate learning, sample inference, paired-read merging, and chimera removal using the consensus method, yielding 265,331 unique ASVs. Quality filtering comprised: (1) exclusion of species with insufficient samples (n < 12; 41 samples removed); (2) exclusion of stool samples from species with very low stool representation ( M. aurascens , Mu. hilgendorfi , M. petax ; 15 samples); (3) read depth filtering (< 10,000 reads; 24 samples); (4) rarefaction to 10,000 reads; and (5) removal of rare ASVs (present in ≤ 2 samples with ≤ 10 total reads; 157,254 ASVs removed, 1.9% of reads). The final dataset comprised 2,747 samples (1,115 oral, 1,151 skin, 481 stool) with 59,215 ASVs. Taxonomic classification ASVs were classified against the SILVA database (v138) using the naive Bayesian classifier in DADA2 [ 18 ]. For validation, all ASVs were searched against the NCBI 16S ribosomal RNA type strain database (September 2025; 27,354 sequences) using BLASTn (v2.16.0) with ≥ 80% identity and up to five hits per query. BLAST results were used for supplementary validation of zoonotic genera, not reclassification. Statistical analyses Alpha-diversity (Chao1, Shannon) and beta-diversity (Bray-Curtis dissimilarity, NMDS, PERMANOVA via adonis2() in vegan [ 19 ]) were calculated. For longitudinal analysis, 80 individually marked bats recaptured across 2–3 years (301 samples) were used to compare within-individual vs between-individual Bray-Curtis dissimilarity (Mann-Whitney U test) and to assess pathogen persistence (detection above 0.1% relative abundance in all sampling years). Zoonotic genera ( Bartonella , Brucella , Campylobacter , Chlamydia , Coxiella , Helicobacter , Leptospira , Pasteurella , Rickettsia , Salmonella , Yersinia ) were identified from SILVA assignments. Relative abundances were compared using ANOVA with Tukey's HSD. Indicator species analysis was performed using multipatt() in the indicspecies R package. Metabolic function was predicted using PICRUSt2 [ 20 ] with MetaCyc [ 21 ] pathway abundances compared across body sites and between active and hibernation periods using linear models with Benjamini-Hochberg FDR correction (q < 0.05). AI-assisted tools (Claude, Anthropic) were used for data analysis scripting and manuscript drafting; all results were independently verified by the authors. RESULTS Microbiome characterization The final dataset comprised 2,747 samples from eight bat species (Table 1 ). Proteobacteria dominated across all sample types (59.7%), followed by Firmicutes (20.0%), Actinobacteriota (9.7%), Bacteroidota (3.7%), and Cyanobacteria (3.0%). Skin microbiomes had the highest richness (Chao1 = 271.9) and diversity (Shannon = 3.69), followed by stool (Chao1 = 127.3, Shannon = 2.65) and oral (Chao1 = 88.0, Shannon = 2.09) (all p < 0.001; Fig. 1 ). PERMANOVA confirmed that both species identity and season significantly structured communities within each sample type (all p = 0.001). Species effects were strongest in oral (R 2 = 0.295, F = 66.2), moderate in skin (R 2 = 0.097, F = 17.5), and weakest in stool (R 2 = 0.057, F = 7.2). Season explained a smaller but consistent proportion of variation across all body sites (oral R 2 = 0.051, F = 19.9; skin R 2 = 0.054, F = 21.7; stool R 2 = 0.034, F = 5.5). NMDS ordination (stress: oral = 0.271, skin = 0.263, stool = 0.345) visualized species-specific clustering, with 95% confidence ellipses showing clear separation among species in oral communities and greater overlap in skin and stool (Fig. 2 ). Zoonotic pathogen prevalence and body site distribution Ten of eleven targeted zoonotic genera were detected; Pasteurella was absent. At least one zoonotic genus was found in 63.7% of all samples. The most prevalent were Bartonella (29.1%), Yersinia (28.1%), Rickettsia (22.3%), and Helicobacter (14.5%) (Table 2 ). Total pathogen abundance differed significantly across sample types (F = 129.9, p = 1.27 × 10 − 54 ), with stool harboring the highest load (6.91%), followed by skin (1.67%) and oral (0.69%). Helicobacter and Chlamydia were strongly enriched in stool, while Rickettsia and Bartonella were most abundant on skin, consistent with ectoparasite-mediated acquisition. Remarkably, individual stool samples reached extreme pathogen dominance: Yersinia comprised 97.5% of total reads (9,731/9,982) in a M. macrodactylus summer stool sample, and Helicobacter reached 92.7% (9,235/9,959) in another M. macrodactylus stool sample from the same season, indicating near-complete microbiome displacement by a single pathogen genus. Table 2 Zoonotic pathogen prevalence, relative abundance, and distribution across body sites Genus ASVs Prevalence (%) Mean abund. (%) Max abund. (%) Oral (%) Skin (%) Stool (%) Yersinia 115 28.1 0.67 97.5 0.54 0.46 1.47 Helicobacter 48 14.5 0.49 92.7 0.01 0.04 2.66 Bartonella 97 29.1 0.40 40.5 0.04 0.52 0.92 Rickettsia 293 22.3 0.39 34.6 0.08 0.58 0.63 Chlamydia 17 10.7 0.24 53.6 0.02 0.06 1.22 Others a 36 < 3.0 < 0.01 --- --- --- --- Total 606 63.7 2.19 --- 0.69 1.67 6.91 a Coxiella (3.0%), Salmonella (1.4%), Leptospira (0.4%), Brucella (0.3%), Campylobacter (0.2%). Body site values are mean relative abundance (%). Species-specific pathogen profiles and seasonal dynamics Pathogen burden differed significantly across species (F = 7.82, p = 2.07 × 10 − 9 ). M. macrodactylus (3.70%) and P. abramus (3.49%) carried the highest loads. M. macrodactylus stool contained Helicobacter at 14.4% mean relative abundance (81.2% prevalence), representing the highest single-genus pathogen load observed. P. abramus carried extreme Rickettsia on skin (9.37%, 51.4% prevalence); as a synanthropic species roosting in human structures, this poses particular concern for ectoparasite-mediated spillover. Mi. fuliginosus harbored the most diverse pathogen profile, with substantial levels of five genera including Bartonella (0.96%) and Chlamydia (2.09% in stool). Pathogen abundance also showed significant seasonal variation (F = 11.28, p = 2.36 × 10 − 7 ; Fig. 3 ). Summer had the highest mean abundance (3.26%), followed by spring (2.02%), fall (1.80%), and winter (0.92%). Most genera peaked in summer, consistent with increased ectoparasite activity. However, Yersinia showed a distinct pattern: highest in spring (1.08%) and winter (0.87%), with 33.2% winter prevalence, suggesting persistence during hibernation when host immune function is suppressed. Longitudinal microbiome stability and pathogen persistence Eighty individuals (39 R. ferrumequinum , 24 Mi. fuliginosus , 5 M. bombinus , 5 P. abramus , 3 M. macrodactylus , 2 M. aurascens , 1 Mu. hilgendorfi , 1 M. petax ) were recaptured across 2–3 years (2021–2024), yielding 301 samples for longitudinal comparison. Within-individual Bray-Curtis dissimilarity was significantly lower than between-individual dissimilarity for oral microbiomes (0.654 ± 0.234 vs 0.751 ± 0.204, Mann-Whitney U, p = 5.5 × 10 − 4 ), indicating that individual bats maintain a recognizable oral microbial signature across years (Fig. 5 A). No significant difference was observed for skin (p = 0.80) or stool (p = 0.33), suggesting these communities are more strongly shaped by environmental exposure than host identity. Pathogen carriage was predominantly transient: across all body sites, very few recaptured individuals carried the same zoonotic genus above 0.1% relative abundance in every sampling year (Fig. 5 B). In oral samples, only 3 of 52 multi-year individuals (5.8%) persistently carried Yersinia ; all other pathogen-site combinations showed ≤ 2.3% persistence. In stool, Helicobacter was persistent in 2 of 5 multi-year individuals (40%), though the small sample limits interpretation (stool not shown in Fig. 5 B due to limited sample size). The predominantly transient carriage pattern suggests that bats acquire zoonotic bacteria through environmental or vector-mediated exposure rather than maintaining them as stable colonizers. Predicted metabolic function PICRUSt2 predicted 586 MetaCyc pathways across all samples. Body site was the strongest determinant of predicted metabolic function, with 559 of 578 testable pathways differing significantly across oral, skin, and stool communities (linear model controlling for species and activity mode, q < 0.05; Fig. 4 ). Of the 559 significant pathways, 133 were most abundant in oral, 283 in skin, and 143 in stool microbiomes. Oral microbiomes were enriched in de novo biosynthetic pathways including purine and pyrimidine biosynthesis, NAD biosynthesis, S-adenosyl-L-methionine (SAM) cycle, and asparagine/aspartate biosynthesis. Skin microbiomes were enriched in gluconeogenesis, cobalamin biosynthesis, glyoxylate cycle, and acyl-CoA thioesterase pathways. Stool microbiomes were enriched in glycolysis/Entner-Doudoroff pathways, NAD salvage, nucleotide degradation (guanosine nucleotides degradation), and ubiquinol biosynthesis. Notably, skin and stool shared similar metabolic profiles --- for stool-enriched pathways, the mean abundance difference between stool and skin was typically < 6%, whereas both differed substantially from oral communities. This metabolic similarity between skin and stool microbiomes likely reflects physical contact between bats and their feces during roosting, as bats hang inverted in dense clusters where ventral skin surfaces are regularly exposed to fecal material. Comparing active and hibernation periods within each body site (controlling for species), 328 pathways differed significantly in oral (182 hibernation-enriched, 146 active-enriched), 308 in skin (150 hibernation-enriched, 158 active-enriched), and 197 in stool (56 hibernation-enriched, 141 active-enriched) (Table 3 ; Additional file 1). In oral microbiomes, the most strongly hibernation-enriched pathway was fatty acid β-oxidation I (coef = + 1.28 × 10 − 3 , q = 1.88 × 10 − 13 ), followed by sucrose degradation II and ergothioneine biosynthesis, consistent with a metabolic shift toward lipid catabolism and antioxidant production during torpor. In skin, chitin derivatives degradation was most strongly hibernation-enriched (coef = + 1.31 × 10 − 3 , q = 1.02 × 10 − 13 ), along with guanosine nucleotide degradation and protocatechuate degradation. In stool, hibernation-enriched pathways included tRNA charging and glycolysis IV, though fewer pathways were affected overall (56 vs 182 in oral). Active-period microbiomes across all body sites were enriched in biosynthetic pathways including peptidoglycan biosynthesis, L-lysine biosynthesis, and unsaturated fatty acid biosynthesis (cis-vaccenate, gondoate), reflecting higher microbial growth rates during the foraging season. Table 3 Top 5 differentially abundant metabolic pathways between hibernation and active periods per body site Body site Enriched in Pathway Coefficient (×10 − 3 ) q-value Oral Hibernation Fatty acid β-oxidation I + 1.28 1.88 × 10 − 13 Oral Hibernation Sucrose degradation II + 1.27 7.55 × 10 − 13 Oral Hibernation Ergothioneine biosynthesis II + 1.20 3.92 × 10 − 16 Oral Hibernation Stearate biosynthesis I + 1.16 2.36 × 10 − 17 Oral Hibernation Fatty acid β-oxidation II + 1.14 1.09 × 10 − 9 Oral Active Peptidoglycan biosynthesis V −1.56 3.99 × 10 − 30 Oral Active L-lysine biosynthesis II −1.52 1.44 × 10 − 31 Oral Active Glucose-1-phosphate metabolism −1.29 6.34 × 10 − 14 Oral Active Peptidoglycan biosynthesis IV −1.19 4.44 × 10 − 22 Oral Active cis-vaccenate biosynthesis −1.09 3.87 × 10 − 10 Skin Hibernation Chitin derivatives degradation + 1.31 1.02 × 10 − 13 Skin Hibernation Guanosine nucleotides degradation II + 0.97 2.97 × 10 − 32 Skin Hibernation Protocatechuate degradation + 0.96 3.11 × 10 − 12 Skin Hibernation Inositol degradation + 0.90 4.15 × 10 − 19 Skin Hibernation Phytol degradation + 0.84 1.87 × 10 − 3 Skin Active Petroselinate biosynthesis −1.17 2.11 × 10 − 19 Skin Active Cardiolipin biosynthesis II −0.94 7.88 × 10 − 19 Skin Active Superpathway of dTDP-glucose biosynthesis −0.92 8.16 × 10 − 33 Skin Active cis-vaccenate biosynthesis −0.91 8.99 × 10 − 17 Skin Active Gondoate biosynthesis −0.91 1.47 × 10 − 14 Stool Hibernation tRNA charging + 8.35 7.21 × 10 − 5 Stool Hibernation Glucose-1-phosphate metabolism + 6.96 1.33 × 10 − 4 Stool Hibernation Inositol degradation + 5.88 2.06 × 10 − 4 Stool Hibernation Glycolysis IV + 5.61 1.09 × 10 − 6 Stool Hibernation D-glucuronate degradation I + 5.46 1.33 × 10 − 4 Stool Active Lipid A biosynthesis −1.91 2.09 × 10 − 3 Stool Active Ethanolamine utilization −1.47 1.05 × 10 − 2 Stool Active Polyprenol biosynthesis −1.41 5.17 × 10 − 5 Stool Active Pentose phosphate pathway −1.31 1.00 × 10 − 6 Stool Active C4 photosynthetic carbon assimilation −1.25 2.48 × 10 − 8 DISCUSSION Our findings reveal a clear hierarchy of pathogen burden across body sites: stool (6.91%) > skin (1.67%) > oral (0.69%), with a 10-fold difference between stool and oral samples. Stool contained the highest overall load, with Helicobacter , Yersinia , Chlamydia , Bartonella , and Rickettsia all elevated. Bat feces accumulating in caves and roosting sites represent a significant contamination source. The dominance of Helicobacter in stool --- previously underreported in bat microbiome studies --- is particularly notable, as Helicobacter species include gastric pathogens of humans and animals. Rickettsia and Bartonella were most abundant on skin, consistent with ectoparasite-mediated acquisition. The extreme Rickettsia load on P. abramus skin (9.37%) suggests that bat skin serves as a maintenance site for vector-borne pathogens, with ectoparasites rather than ingestion as the primary acquisition route. Three species emerged with distinct high-risk profiles. M. macrodactylus carried the highest burden (3.70%), driven by Helicobacter in stool (14.4%, 81.2% prevalence), possibly involving novel bat-adapted lineages supported by low BLAST identity (97.5–98.8%). P. abramus , a synanthropic species roosting in human buildings, carried the highest Rickettsia (9.37% on skin), increasing ectoparasite-mediated spillover risk. Mi. fuliginosus had the most diverse pathogen profile across all body sites. Total pathogen abundance peaked in summer (3.26%) and was lowest in winter (0.92%), correlating with ectoparasite activity, bat foraging, and increased human-bat contact. Notably, Yersinia persisted through winter (33.2% prevalence), suggesting proliferation during hibernation when host immunity is suppressed. The strong species-specific oral microbiome signatures (R 2 = 0.295) suggest host phylogeny shapes microbial communities, consistent with the observation that fecal microbiomes are distinguishable by host species [ 22 ]. Yet stool diversity did not differ among species (p = 0.78) despite dramatic differences in pathogen burden (18.3% in M. macrodactylus vs. 1.1% in P. abramus ), indicating pathogen load is independent of community diversity. Longitudinal sampling of 80 recaptured individuals revealed that oral microbiomes maintain individual-specific signatures across years, consistent with the strong host-species effect observed in cross-sectional analysis. This pattern mirrors findings in humans, where oral and skin microbiomes retain individual-specific signatures over months to years while environmentally exposed sites are more variable [ 23 ]. In contrast, skin and stool communities showed no within-individual stability, suggesting these niches are shaped primarily by environmental exposure. Critically, pathogen carriage was predominantly transient rather than persistent, with most individuals losing or acquiring zoonotic genera between sampling years. This pattern argues against bats serving as chronic reservoirs for these bacteria at the individual level and instead supports environmental or vector-mediated acquisition during each active season. The transient nature of carriage also implies that population-level prevalence reflects ongoing exposure pressure rather than a fixed subset of persistently colonized individuals, which has implications for surveillance strategies: sampling should target periods and conditions of peak exposure (summer, high ectoparasite load) rather than attempting to identify persistently infected individuals. PICRUSt2 analysis revealed that body site was the dominant driver of predicted metabolic function, with oral microbiomes enriched in de novo biosynthetic pathways and skin/stool enriched in salvage and degradation pathways. The oral enrichment of purine, pyrimidine, NAD, and one-carbon (SAM cycle) biosynthesis suggests a nutrient-limited niche where bacteria must synthesize essential building blocks de novo, whereas stool microbiomes rely on host diet-derived substrates via glycolysis and nucleotide salvage. Strikingly, skin and stool microbiomes shared highly similar metabolic profiles despite occupying anatomically distinct niches. Given that bat skin microbiomes are strongly shaped by the local environment [ 24 ], this convergence may reflect the roosting behavior of cave bats: hanging inverted in dense clusters, bats' ventral skin surfaces are regularly exposed to fecal material, which could facilitate microbial exchange between skin and gut communities. The seasonal metabolic shifts observed between active and hibernation periods parallel the well-documented physiological transition from carbohydrate oxidation to lipid catabolism during torpor [ 25 , 26 ] (Additional file 1). Additional file 1 Figure S1 . Active vs hibernation differences in predicted metabolic pathways within oral (A), skin (B), and stool (C) microbiomes (linear model controlling for species). Bars show the hibernation vs active coefficient (×10 − 3 ); blue = hibernation-enriched, red = active-enriched. Top 10 pathways in each direction are shown. All displayed pathways are significant at q < 0.05. (TIFF) The enrichment of fatty acid β-oxidation I and II in oral microbiomes during hibernation is consistent with this host metabolic shift, suggesting that the changed oral environment during torpor selects for bacteria with lipid-catabolizing capacity. The concurrent enrichment of predicted ergothioneine biosynthesis is also of interest, given that hibernating bats face oxidative stress during periodic arousal episodes [ 27 ], though whether microbially produced ergothioneine reaches functionally relevant levels remains unknown. In skin microbiomes, the hibernation-associated enrichment of chitin degradation pathways could relate to ectoparasite-derived chitinous material accumulating on skin during torpor when grooming ceases, though other sources of environmental chitin (e.g., fungal cell walls) cannot be excluded. The active-period enrichment of biosynthetic pathways (peptidoglycan, amino acids, unsaturated fatty acids) across all body sites is consistent with higher microbial growth rates supported by increased nutrient availability from host foraging. Notably, stool showed the fewest hibernation-associated changes (56 pathways vs 182 in oral), consistent with the cessation of food intake during torpor reducing substrate input to the gut. White-nose syndrome has been shown to restructure bat skin microbiomes during hibernation [ 28 ], and our findings suggest that the metabolic functions of these microbiomes are tightly linked to the host's seasonal physiology. Whether disruptions to hibernation --- from disease, cave disturbance, or climate change --- also alter microbiome metabolic function warrants further investigation. This study has several limitations. The 16S V3–4 region provides genus-level resolution at best. BLAST validation against the NCBI 16S type strain database (58,095 of 59,215 ASVs returned hits at ≥ 80% identity) confirmed reliable genus assignments for Bartonella (97.8–100% identity), Rickettsia (89.4–99.8%), Helicobacter (97.5–98.8%), and Chlamydia (85.0–95.3%; low identity suggesting novel bat-associated lineages). However, substantial ambiguity was found within Enterobacteriaceae: SILVA-assigned Yersinia ASVs frequently matched Serratia and Rahnella at equal or higher identity (97–100%), and Salmonella ASVs were indistinguishable from Citrobacter and Enterobacter (99.3–99.8%). Overall, 7.6% of ASVs showed ambiguous genus assignments, and Yersinia and Salmonella should be interpreted as Yersiniaceae and Enterobacteriaceae sensu lato, respectively. Detection of Rickettsia or Helicobacter does not confirm pathogenic species, as both genera include commensal or endosymbiotic members. PICRUSt2 predictions are based on reference genomes and may not fully represent the metabolic potential of bat-associated bacteria [ 29 ]. Additionally, the detection of chlorophyll a biosynthesis among skin-enriched pathways likely reflects environmental cyanobacteria or other phototrophs from cave surfaces rather than true skin commensals, and some predicted skin metabolic functions may similarly reflect transient environmental microbes rather than resident community members. Regional comparisons were not possible due to uneven sampling, and some species had small sample sizes limiting seasonal comparisons. In conclusion, this survey of 2,747 samples reveals stool as the primary pathogen reservoir, skin as a vector-borne pathogen hotspot, and summer as the peak risk season. M. macrodactylus , P. abramus , and Mi. fuliginosus pose the highest zoonotic risk with distinct pathogen profiles. Longitudinal data indicate that pathogen carriage is transient rather than persistent, implying that population-level prevalence reflects ongoing environmental exposure rather than chronic colonization. These findings support targeted surveillance focusing on stool and skin from high-risk species during summer, particularly near human settlements. Metagenomic approaches are needed for species-level pathogen identification. Declarations Availability of data and materials The dataset(s) supporting the conclusions of this article are available in the NCBI Sequence Read Archive (SRA) repository, under BioProject PRJNA1436137 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1436137). Ethics approval This study was approved by the Research Planning Review Committee of the National Institute of Ecology (NIEIACUC-2021-001). All handling and sampling permissions were obtained from the seven corresponding local governments on each sampling year. Competing interests The authors declare that they have no competing interests. Authors' contributions SEJ contributed to research planning, experimental design, and data analysis. TWL, JS, JYK, YSC, USA, and LK contributed to field investigations and data interpretation. KK and SP conducted primary field surveys and were responsible for sample acquisition and DNA extraction. TU, GC, HH, and NJC conducted bioinformatic and statistical analyses. TU wrote the manuscript. JJ and DH validated the bioinformatic analysis results and reviewed the manuscript. SSK was the Principal Investigator, responsible for conceptualization, overall research execution, and project administration. All authors read and approved the final manuscript. Funding This work was supported by a grant from the National Institute of Ecology (NIE), funded by the Ministry of Climate, Energy and Environment (MCEE) of the Republic of Korea (NIE-B-2024-38). Acknowledgements We thank CJ Bioscience (Seoul, South Korea) for library preparation and sequencing services. AI-assisted tools (Claude, Anthropic) were used for data analysis scripting and manuscript drafting; all results were independently verified by the authors. References Vashi NA, Reddy P, Wayne DB, et al. Bat-associated leptospirosis. J Gen Intern Med. 2010;25:162–4. Kamani J, Baneth G, Mitchell M, et al. Bartonella species in bats (Chiroptera) and bat flies (Nycteribiidae) from Nigeria, West Africa. Vector Borne Zoonotic Dis. 2014;14:625–32. Islam A, Mikolon A, Mikoleit M, et al. Isolation of Salmonella Virchow from a fruit bat (Pteropus giganteus). EcoHealth. 2013;10:348–51. Muhldorfer K, Speck S, Kurth A, et al. Yersinia species isolated from bats, Germany. Emerg Infect Dis. 2010;16:578–80. Hazeleger WC, Jacobs-Reitsma WF, Lina PHC, et al. Wild, insectivorous bats might be carriers of Campylobacter spp. PLoS ONE. 2018;13:e0190647. Blehert DS, Maluping RP, Green DE, et al. Acute pasteurellosis in wild big brown bats (Eptesicus fuscus). J Wildl Dis. 2014;50:136–9. Mazwi KD, Lekota KE, Glover BA, et al. Whole genome sequence analysis of Brucella spp. from human, livestock, and wildlife in South Africa. J Microbiol. 2024;62:759–73. Matei IA, Panciera L, Pagani P, et al. Rickettsia spp. in bats of Romania: high prevalence of Rickettsia monacensis in two insectivorous bat species. Parasit Vectors. 2021;14:107. Silva-Ramos CR, Faccini-Martinez AA, Hidalgo M, et al. First molecular evidence of Coxiella burnetii in bats from Colombia. Res Vet Sci. 2022;150:33–5. Fritschi J, Hoerr V, Gurtner C, et al. Prevalence and phylogeny of Chlamydiae and hemotropic mycoplasma species in captive and free-living bats. BMC Microbiol. 2020;20:182. Ingala MR, Simmons NB, Dunbar M, et al. You are more than what you eat: potentially adaptive enrichment of microbiome functions across bat dietary niches. Anim Microbiome. 2021;3:82. Federici L, Masulli M, De Laurenzi V, et al. An overview of bats microbiota and its implication in transmissible diseases. Front Microbiol. 2022;13:1012189. Kim SS, Choi YS, Yoo JC. The thermal preference and the selection of hibernacula in seven cave-dwelling bats. Korean J Ecol Environ. 2014;47:258–72. Voigt CC, Phelps KL, Aguirre LF, et al. Bats and buildings: the conservation of synanthropic bats. In: Voigt CC, Kingston T, editors. Bats in the Anthropocene: Conservation of Bats in a Changing World. Cham: Springer; 2016. pp. 427–62. Guo DY, Luo B, Zhang KK, et al. Social vocalizations of big-footed myotis (Myotis macrodactylus) during foraging. Integr Zool. 2019;14:446–59. Kimprasit T, Nunome M, Iida K, et al. Dispersal history of Miniopterus fuliginosus bats and their associated viruses in east Asia. PLoS ONE. 2021;16:e0244006. Alpizar P, Rojas TN, Martel C, et al. Agricultural fast food: bats feeding in banana monocultures are heavier but have less diverse gut microbiota. Front Ecol Evol. 2021;9:746783. Callahan BJ, McMurdie PJ, Rosen MJ, et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3. Oksanen J, Simpson GL, Blanchet FG et al. vegan: Community Ecology Package. R package version 2.6-4. 2022. Douglas GM, Maffei VJ, Zaneveld JR, et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol. 2020;38:685–8. Caspi R, Billington R, Keseler IM, et al. The MetaCyc database of metabolic pathways and enzymes --- a 2019 update. Nucleic Acids Res. 2020;48:D445–53. Song H, Unno T. A comprehensive database of human and livestock fecal microbiome for community-wide microbial source tracking: a case study in South Korea. Appl Biol Chem. 2024;67:58. Oh J, Byrd AL, Park M, et al. Temporal stability of the human skin microbiome. Cell. 2016;165:854–66. Avena CV, Parfrey LW, Leff JW, et al. Deconstructing the bat skin microbiome: influences of the host and the environment. Front Microbiol. 2016;7:1753. Xiao GH, Liu S, Xiao YH, et al. Seasonal changes in gut microbiota diversity and composition in the greater horseshoe bat. Front Microbiol. 2019;10:2247. Sun H, Wang J, Xing Y, et al. Gut transcriptomic changes during hibernation in the greater horseshoe bat (Rhinolophus ferrumequinum). Front Zool. 2020;17:21. Yin Q, Ge H, Liao CC, et al. Antioxidant defenses in the brains of bats during hibernation. PLoS ONE. 2016;11:e0152135. Ange-Stark M, Parise KL, Cheng TL, et al. White-nose syndrome restructures bat skin microbiomes. Microbiol Spectr. 2023;11:e0271523. Jung S. Advances in functional analysis of the microbiome: integrating metabolic modeling, metabolite prediction, and pathway inference with next-generation sequencing data. J Microbiol. 2025;63:e2411006. Additional Declarations No competing interests reported. Supplementary Files figS1picrust2mode.pdf Additional file 1: Figure S1. Active vs hibernation differences in predicted metabolic pathways within oral (A), skin (B), and stool (C) microbiomes (linear model controlling for species). Bars show the hibernation vs active coefficient (×10 −3 ); blue = hibernation-enriched, red = active-enriched. Top 10 pathways in each direction are shown. All displayed pathways are significant at q < 0.05. (TIFF) Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9148344","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":610845462,"identity":"216df9be-0e0f-4bb4-9037-c41ec02e7ac8","order_by":0,"name":"Tatsuya Unno","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYNCCCoYEKCsBjyoo4GFgBpJnSNbC2EaKFnv284c/886zyzOXSGD88IMhLZ+wLTzJbNK825KLLWckMEv2MORYNhB2WDIbM++2A4kbbiQwSAMDwoCwLfyPmT/zzgFrYf5NnBaJZAZp3gawFjagLTlEaLnx2ExyzrHkYoMzD9ssewzSCGth7098/OFNjV2ewfHkwzd+VCQT1oIEGBsYGEjSMApGwSgYBaMAJwAA/Lk09/7ce2cAAAAASUVORK5CYII=","orcid":"","institution":"Chungbuk National University","correspondingAuthor":true,"prefix":"","firstName":"Tatsuya","middleName":"","lastName":"Unno","suffix":""},{"id":610845463,"identity":"f955fe4f-5238-4c1d-8b9d-0f75321305eb","order_by":1,"name":"Geon Choi","email":"","orcid":"","institution":"Chungbuk National University","correspondingAuthor":false,"prefix":"","firstName":"Geon","middleName":"","lastName":"Choi","suffix":""},{"id":610845464,"identity":"a4e1bb1e-fee6-4af0-93cd-95f98dafbedf","order_by":2,"name":"Hanbit Hwang","email":"","orcid":"","institution":"Chungbuk 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Ecology","correspondingAuthor":false,"prefix":"","firstName":"Taek-Woo","middleName":"","lastName":"Lee","suffix":""},{"id":610845468,"identity":"29dda8f4-370c-45e0-82c5-904c261e04ef","order_by":6,"name":"Kihyun Kim","email":"","orcid":"","institution":"3National Institute of Ecology","correspondingAuthor":false,"prefix":"","firstName":"Kihyun","middleName":"","lastName":"Kim","suffix":""},{"id":610845469,"identity":"ffc131a0-4060-4a09-849d-3e40da2bf525","order_by":7,"name":"Soyeon Park","email":"","orcid":"","institution":"3National Institute of Ecology","correspondingAuthor":false,"prefix":"","firstName":"Soyeon","middleName":"","lastName":"Park","suffix":""},{"id":610845470,"identity":"36dd7eb9-0518-44ac-a73f-e71262a9038d","order_by":8,"name":"Jangwon Seo","email":"","orcid":"","institution":"Chungbuk National University","correspondingAuthor":false,"prefix":"","firstName":"Jangwon","middleName":"","lastName":"Seo","suffix":""},{"id":610845471,"identity":"cc8e29b4-1989-4ec5-bd63-4901dd648ad4","order_by":9,"name":"Jae-Yeon Kang","email":"","orcid":"","institution":"3National Institute of Ecology","correspondingAuthor":false,"prefix":"","firstName":"Jae-Yeon","middleName":"","lastName":"Kang","suffix":""},{"id":610845472,"identity":"504a5efc-b6bb-4cc5-ba04-d16281391506","order_by":10,"name":"Yu-Seong Choi","email":"","orcid":"","institution":"National Insitute of Biological Resources","correspondingAuthor":false,"prefix":"","firstName":"Yu-Seong","middleName":"","lastName":"Choi","suffix":""},{"id":610845473,"identity":"3ba2453e-ce30-410e-ae28-c13f85918385","order_by":11,"name":"Ung-San Ahn","email":"","orcid":"","institution":"World Heritage Office","correspondingAuthor":false,"prefix":"","firstName":"Ung-San","middleName":"","lastName":"Ahn","suffix":""},{"id":610845474,"identity":"1295cf69-fce9-4949-9d3d-2d2495dab2f7","order_by":12,"name":"Lyoun Kim","email":"","orcid":"","institution":"Cave Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Lyoun","middleName":"","lastName":"Kim","suffix":""},{"id":610845475,"identity":"109ef083-ea58-4381-a83f-1643ccba0ca5","order_by":13,"name":"Jeonghwan Jang","email":"","orcid":"","institution":"Jeonbuk National University","correspondingAuthor":false,"prefix":"","firstName":"Jeonghwan","middleName":"","lastName":"Jang","suffix":""},{"id":610845476,"identity":"54e70a6a-9e84-4ea2-95f4-8fe4c72c3753","order_by":14,"name":"Dukki Han","email":"","orcid":"","institution":"Kangwon National University","correspondingAuthor":false,"prefix":"","firstName":"Dukki","middleName":"","lastName":"Han","suffix":""},{"id":610845477,"identity":"05b8d8e0-7631-4e5a-93a8-257a24e45f0e","order_by":15,"name":"Sun-Sook Kim","email":"","orcid":"","institution":"SEAM-BAT","correspondingAuthor":false,"prefix":"","firstName":"Sun-Sook","middleName":"","lastName":"Kim","suffix":""}],"badges":[],"createdAt":"2026-03-17 11:39:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9148344/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9148344/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105421645,"identity":"868951a9-d5d7-4b6c-959c-48a01ed2bdba","added_by":"auto","created_at":"2026-03-25 21:33:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":21912,"visible":true,"origin":"","legend":"\u003cp\u003eAlpha-diversity across sample types. Boxplots of Chao1 richness and Shannon diversity index for oral, skin, and stool microbiomes. Brackets indicate pairwise significance (***p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9148344/v1/c5b600b8224941649d5c584d.png"},{"id":105421648,"identity":"98dca3c3-248d-432e-84a6-84d8c23e8e15","added_by":"auto","created_at":"2026-03-25 21:33:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":322550,"visible":true,"origin":"","legend":"\u003cp\u003eNMDS ordination of bat microbiome communities based on Bray-Curtis dissimilarity for oral (A), skin (B), and stool (C) microbiomes. Points are coloured by bat species and shaped by season (circle = spring, square = summer, triangle = fall, diamond = winter); ellipses represent 95% confidence intervals per species. Stress values and sample sizes are shown for each panel.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9148344/v1/08c0d1f2cac8230e4e4f4e33.png"},{"id":105421649,"identity":"11a62921-e30d-42a1-8ec4-2b1d6eb54697","added_by":"auto","created_at":"2026-03-25 21:33:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28993,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal variation in zoonotic pathogen abundance. Grouped bar chart showing mean relative abundance (%) of the five most prevalent zoonotic genera across seasons. Error bars represent standard error of the mean.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9148344/v1/032636b95683eb441f27f39d.png"},{"id":105421647,"identity":"adbb70ea-5148-4084-8218-6faf84642a1c","added_by":"auto","created_at":"2026-03-25 21:33:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":77594,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted metabolic pathways differing across body sites (PICRUSt2). Top 7 pathways enriched in each body site (21 total; linear model controlling for species and activity mode); grouped bars show mean relative abundance (%) for oral (red), skin (blue), and stool (green). All displayed pathways are significant at q \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9148344/v1/f809e41c64e6338a2b532d30.png"},{"id":105421650,"identity":"2d286baf-02ee-4db8-bed6-fd3a315d8256","added_by":"auto","created_at":"2026-03-25 21:33:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":43230,"visible":true,"origin":"","legend":"\u003cp\u003eLongitudinal microbiome stability and pathogen persistence in recaptured bats (n = 80 individuals across 2--3 years). (A) Bray-Curtis dissimilarity between samples from the same individual across years (within, green) versus different individuals of the same species and body site (between, red). Significance tested by Mann-Whitney U (***p \u0026lt; 0.001, ns = not significant). (B) Proportion of recaptured individuals showing persistent (detected in all years), transient (some years), or absent pathogen carriage above 0.1% relative abundance, for oral (O) and skin (S) samples.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9148344/v1/bc6c0cc6735bdc52b56cb681.png"},{"id":106208496,"identity":"52962233-71a4-4ad5-96e7-eb5846d8c0f6","added_by":"auto","created_at":"2026-04-06 06:25:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1490811,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9148344/v1/3e5fe2b1-1a68-4fc5-96da-52522cbb3506.pdf"},{"id":105421646,"identity":"06e74ba3-dc42-40d5-a53e-f7c25f483a74","added_by":"auto","created_at":"2026-03-25 21:33:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":30457,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 1\u003c/strong\u003e: Figure S1. Active vs hibernation differences in predicted metabolic pathways within oral (A), skin (B), and stool (C) microbiomes (linear model controlling for species). Bars show the hibernation vs active coefficient (×10\u003csup\u003e−3\u003c/sup\u003e); blue = hibernation-enriched, red = active-enriched. Top 10 pathways in each direction are shown. All displayed pathways are significant at q \u0026lt; 0.05. (TIFF)\u003c/p\u003e","description":"","filename":"figS1picrust2mode.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9148344/v1/8940a89486be6742458aab05.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Body site, host species, and seasonal drivers of zoonotic bacterial distribution in Korean insectivorous bat microbiomes","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eBats (order Chiroptera) harbor a disproportionately high diversity of zoonotic pathogens relative to other mammalian orders. Beyond well-documented viral reservoirs, bats carry diverse bacterial pathogens including \u003cem\u003eLeptospira\u003c/em\u003e [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], \u003cem\u003eBartonella\u003c/em\u003e [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], \u003cem\u003eSalmonella\u003c/em\u003e [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], \u003cem\u003eYersinia\u003c/em\u003e [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], \u003cem\u003eCampylobacter\u003c/em\u003e [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], \u003cem\u003ePasteurella\u003c/em\u003e [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], \u003cem\u003eBrucella\u003c/em\u003e [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], \u003cem\u003eRickettsia\u003c/em\u003e [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], \u003cem\u003eCoxiella\u003c/em\u003e [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and \u003cem\u003eChlamydia\u003c/em\u003e [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Many of these bacteria pose direct zoonotic risks through fecal contamination, ectoparasite vectors, or direct contact. However, the ecological factors governing pathogen distribution across bat body sites, host species, and seasons remain poorly characterized.\u003c/p\u003e \u003cp\u003eThe commensal microbiome plays a critical role in pathogen colonization, persistence, and transmission through competitive exclusion, immune modulation, and metabolic interactions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Body site is a fundamental determinant of microbiome structure: skin microbiomes interact directly with the external environment and ectoparasites, oral microbiomes occupy a transitional niche, and stool microbiomes reflect the gut community shaped by diet and host physiology [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Each body site may therefore present different opportunities for pathogen acquisition, maintenance, and transmission.\u003c/p\u003e \u003cp\u003eThe eight insectivorous bat species examined in this study span a range of ecological traits relevant to zoonotic risk. \u003cem\u003eRhinolophus ferrumequinum\u003c/em\u003e (greater horseshoe bat) is a large, obligate cave-dwelling species that forms dense hibernation colonies of hundreds to thousands of individuals in Korean limestone caves [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], maximizing opportunities for fecal\u0026ndash;oral and ectoparasite transmission. In contrast, \u003cem\u003ePipistrellus abramus\u003c/em\u003e (Japanese pipistrelle) is a synanthropic species that roosts in building crevices, bridges, and roof spaces [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], placing it in direct proximity to human populations. Among the four \u003cem\u003eMyotis\u003c/em\u003e species, ecological variation is also substantial: \u003cem\u003eM. macrodactylus\u003c/em\u003e (eastern long-fingered bat) forages over water and hibernates in caves [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], while \u003cem\u003eM. bombinus\u003c/em\u003e (Far Eastern myotis) and the smaller \u003cem\u003eM. aurascens\u003c/em\u003e and \u003cem\u003eM. petax\u003c/em\u003e occupy cave and rock-crevice habitats. \u003cem\u003eMiniopterus fuliginosus\u003c/em\u003e (Eastern bent-wing bat), a cave bat of the family Miniopteridae, forms large maternity colonies of up to 15,000 individuals and is one of the most abundant cave bats in East Asia [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. \u003cem\u003eMurina hilgendorfi\u003c/em\u003e (Hilgendorf's tube-nosed bat) is a cave-dwelling species [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeasonal variation is particularly relevant for temperate-zone bats that undergo hibernation. The transition between active and torpid states alters host metabolism, immune function, ectoparasite activity, and environmental exposure --- all of which may influence pathogen dynamics [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Understanding these seasonal patterns is essential for targeted surveillance strategies.\u003c/p\u003e \u003cp\u003eThis study examines the distribution of zoonotic bacteria across three body sites (oral, skin, stool) in these eight species in South Korea using 16S rRNA gene amplicon sequencing. Specifically, we aim to: (1) characterize the microbiome structure across body sites and bat species; (2) determine the distribution and prevalence of zoonotic bacterial genera across body sites, species, and seasons; (3) identify which body sites and species pose the greatest zoonotic risk; and (4) determine seasonal patterns in pathogen abundance relevant to surveillance timing.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample collection\u003c/h2\u003e \u003cp\u003eOral, skin, and stool samples were collected from bats across 9 regions and 20 field sites in South Korea between 2021 and 2024. Samples were collected across four seasons: winter (December\u0026ndash;February), spring (March\u0026ndash;May), summer (June\u0026ndash;August), and fall (September\u0026ndash;November). A total of 2,825 samples were initially collected from eight insectivorous bat species (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample summary after quality filtering\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOral\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSkin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStool\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMiniopterus fuliginosus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e967\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRhinolophus ferrumequinum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMyotis macrodactylus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMyotis bombinus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e354\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePipistrellus abramus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMyotis petax\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMyotis aurascens\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMurina hilgendorfi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1,115\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1,151\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e481\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2,747\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis study was approved by the Research Planning Review Committee of the National Institute of Ecology (NIEIACUC-2021-001). We declare that we adhered to the Wildlife Protection and Management Act of Korea as well as the Institutional Research Ethics Regulations and Guidelines. All handling and sampling permissions were obtained from the seven corresponding local governments on each sampling year.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDNA extraction and sequencing\u003c/h3\u003e\n\u003cp\u003eDNA was extracted from oral, skin, and stool swabs. The V3\u0026ndash;4 region of the 16S rRNA gene was amplified and sequenced on the Illumina MiSeq platform. Library preparation and sequencing were performed at CJ Bioscience (Seoul, South Korea).\u003c/p\u003e\n\u003ch3\u003eBioinformatic processing\u003c/h3\u003e\n\u003cp\u003eRaw paired-end reads were trimmed of primer sequences using cutadapt and processed through the DADA2 pipeline in R [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]: quality filtering (maxN\u0026thinsp;=\u0026thinsp;0, maxEE\u0026thinsp;=\u0026thinsp;c(2,2), truncQ\u0026thinsp;=\u0026thinsp;2), error rate learning, sample inference, paired-read merging, and chimera removal using the consensus method, yielding 265,331 unique ASVs.\u003c/p\u003e \u003cp\u003eQuality filtering comprised: (1) exclusion of species with insufficient samples (n\u0026thinsp;\u0026lt;\u0026thinsp;12; 41 samples removed); (2) exclusion of stool samples from species with very low stool representation (\u003cem\u003eM. aurascens\u003c/em\u003e, \u003cem\u003eMu. hilgendorfi\u003c/em\u003e, \u003cem\u003eM. petax\u003c/em\u003e; 15 samples); (3) read depth filtering (\u0026lt;\u0026thinsp;10,000 reads; 24 samples); (4) rarefaction to 10,000 reads; and (5) removal of rare ASVs (present in \u0026le;\u0026thinsp;2 samples with \u0026le;\u0026thinsp;10 total reads; 157,254 ASVs removed, 1.9% of reads). The final dataset comprised 2,747 samples (1,115 oral, 1,151 skin, 481 stool) with 59,215 ASVs.\u003c/p\u003e\n\u003ch3\u003eTaxonomic classification\u003c/h3\u003e\n\u003cp\u003eASVs were classified against the SILVA database (v138) using the naive Bayesian classifier in DADA2 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. For validation, all ASVs were searched against the NCBI 16S ribosomal RNA type strain database (September 2025; 27,354 sequences) using BLASTn (v2.16.0) with \u0026ge;\u0026thinsp;80% identity and up to five hits per query. BLAST results were used for supplementary validation of zoonotic genera, not reclassification.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eAlpha-diversity (Chao1, Shannon) and beta-diversity (Bray-Curtis dissimilarity, NMDS, PERMANOVA via adonis2() in vegan [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]) were calculated. For longitudinal analysis, 80 individually marked bats recaptured across 2\u0026ndash;3 years (301 samples) were used to compare within-individual vs between-individual Bray-Curtis dissimilarity (Mann-Whitney U test) and to assess pathogen persistence (detection above 0.1% relative abundance in all sampling years). Zoonotic genera (\u003cem\u003eBartonella\u003c/em\u003e, \u003cem\u003eBrucella\u003c/em\u003e, \u003cem\u003eCampylobacter\u003c/em\u003e, \u003cem\u003eChlamydia\u003c/em\u003e, \u003cem\u003eCoxiella\u003c/em\u003e, \u003cem\u003eHelicobacter\u003c/em\u003e, \u003cem\u003eLeptospira\u003c/em\u003e, \u003cem\u003ePasteurella\u003c/em\u003e, \u003cem\u003eRickettsia\u003c/em\u003e, \u003cem\u003eSalmonella\u003c/em\u003e, \u003cem\u003eYersinia\u003c/em\u003e) were identified from SILVA assignments. Relative abundances were compared using ANOVA with Tukey's HSD. Indicator species analysis was performed using multipatt() in the indicspecies R package. Metabolic function was predicted using PICRUSt2 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] with MetaCyc [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] pathway abundances compared across body sites and between active and hibernation periods using linear models with Benjamini-Hochberg FDR correction (q\u0026thinsp;\u0026lt;\u0026thinsp;0.05). AI-assisted tools (Claude, Anthropic) were used for data analysis scripting and manuscript drafting; all results were independently verified by the authors.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMicrobiome characterization\u003c/h2\u003e \u003cp\u003eThe final dataset comprised 2,747 samples from eight bat species (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Proteobacteria dominated across all sample types (59.7%), followed by Firmicutes (20.0%), Actinobacteriota (9.7%), Bacteroidota (3.7%), and Cyanobacteria (3.0%).\u003c/p\u003e \u003cp\u003eSkin microbiomes had the highest richness (Chao1\u0026thinsp;=\u0026thinsp;271.9) and diversity (Shannon\u0026thinsp;=\u0026thinsp;3.69), followed by stool (Chao1\u0026thinsp;=\u0026thinsp;127.3, Shannon\u0026thinsp;=\u0026thinsp;2.65) and oral (Chao1\u0026thinsp;=\u0026thinsp;88.0, Shannon\u0026thinsp;=\u0026thinsp;2.09) (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePERMANOVA confirmed that both species identity and season significantly structured communities within each sample type (all p\u0026thinsp;=\u0026thinsp;0.001). Species effects were strongest in oral (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.295, F\u0026thinsp;=\u0026thinsp;66.2), moderate in skin (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.097, F\u0026thinsp;=\u0026thinsp;17.5), and weakest in stool (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.057, F\u0026thinsp;=\u0026thinsp;7.2). Season explained a smaller but consistent proportion of variation across all body sites (oral R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.051, F\u0026thinsp;=\u0026thinsp;19.9; skin R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.054, F\u0026thinsp;=\u0026thinsp;21.7; stool R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.034, F\u0026thinsp;=\u0026thinsp;5.5). NMDS ordination (stress: oral\u0026thinsp;=\u0026thinsp;0.271, skin\u0026thinsp;=\u0026thinsp;0.263, stool\u0026thinsp;=\u0026thinsp;0.345) visualized species-specific clustering, with 95% confidence ellipses showing clear separation among species in oral communities and greater overlap in skin and stool (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eZoonotic pathogen prevalence and body site distribution\u003c/h3\u003e\n\u003cp\u003eTen of eleven targeted zoonotic genera were detected; \u003cem\u003ePasteurella\u003c/em\u003e was absent. At least one zoonotic genus was found in 63.7% of all samples. The most prevalent were \u003cem\u003eBartonella\u003c/em\u003e (29.1%), \u003cem\u003eYersinia\u003c/em\u003e (28.1%), \u003cem\u003eRickettsia\u003c/em\u003e (22.3%), and \u003cem\u003eHelicobacter\u003c/em\u003e (14.5%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTotal pathogen abundance differed significantly across sample types (F\u0026thinsp;=\u0026thinsp;129.9, p\u0026thinsp;=\u0026thinsp;1.27 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;54\u003c/sup\u003e), with stool harboring the highest load (6.91%), followed by skin (1.67%) and oral (0.69%). \u003cem\u003eHelicobacter\u003c/em\u003e and \u003cem\u003eChlamydia\u003c/em\u003e were strongly enriched in stool, while \u003cem\u003eRickettsia\u003c/em\u003e and \u003cem\u003eBartonella\u003c/em\u003e were most abundant on skin, consistent with ectoparasite-mediated acquisition. Remarkably, individual stool samples reached extreme pathogen dominance: \u003cem\u003eYersinia\u003c/em\u003e comprised 97.5% of total reads (9,731/9,982) in a \u003cem\u003eM. macrodactylus\u003c/em\u003e summer stool sample, and \u003cem\u003eHelicobacter\u003c/em\u003e reached 92.7% (9,235/9,959) in another \u003cem\u003eM. macrodactylus\u003c/em\u003e stool sample from the same season, indicating near-complete microbiome displacement by a single pathogen genus.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eZoonotic pathogen prevalence, relative abundance, and distribution across body sites\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASVs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrevalence (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean abund. (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMax abund. (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOral (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSkin (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStool (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYersinia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHelicobacter\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBartonella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRickettsia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eChlamydia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e606\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e63.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.69\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e6.91\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e \u003cem\u003eCoxiella\u003c/em\u003e (3.0%), \u003cem\u003eSalmonella\u003c/em\u003e (1.4%), \u003cem\u003eLeptospira\u003c/em\u003e (0.4%), \u003cem\u003eBrucella\u003c/em\u003e (0.3%), \u003cem\u003eCampylobacter\u003c/em\u003e (0.2%). Body site values are mean relative abundance (%).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSpecies-specific pathogen profiles and seasonal dynamics\u003c/h2\u003e \u003cp\u003ePathogen burden differed significantly across species (F\u0026thinsp;=\u0026thinsp;7.82, p\u0026thinsp;=\u0026thinsp;2.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e). \u003cem\u003eM. macrodactylus\u003c/em\u003e (3.70%) and \u003cem\u003eP. abramus\u003c/em\u003e (3.49%) carried the highest loads. \u003cem\u003eM. macrodactylus\u003c/em\u003e stool contained \u003cem\u003eHelicobacter\u003c/em\u003e at 14.4% mean relative abundance (81.2% prevalence), representing the highest single-genus pathogen load observed. \u003cem\u003eP. abramus\u003c/em\u003e carried extreme \u003cem\u003eRickettsia\u003c/em\u003e on skin (9.37%, 51.4% prevalence); as a synanthropic species roosting in human structures, this poses particular concern for ectoparasite-mediated spillover. \u003cem\u003eMi. fuliginosus\u003c/em\u003e harbored the most diverse pathogen profile, with substantial levels of five genera including \u003cem\u003eBartonella\u003c/em\u003e (0.96%) and \u003cem\u003eChlamydia\u003c/em\u003e (2.09% in stool).\u003c/p\u003e \u003cp\u003ePathogen abundance also showed significant seasonal variation (F\u0026thinsp;=\u0026thinsp;11.28, p\u0026thinsp;=\u0026thinsp;2.36 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Summer had the highest mean abundance (3.26%), followed by spring (2.02%), fall (1.80%), and winter (0.92%). Most genera peaked in summer, consistent with increased ectoparasite activity. However, \u003cem\u003eYersinia\u003c/em\u003e showed a distinct pattern: highest in spring (1.08%) and winter (0.87%), with 33.2% winter prevalence, suggesting persistence during hibernation when host immune function is suppressed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLongitudinal microbiome stability and pathogen persistence\u003c/h2\u003e \u003cp\u003eEighty individuals (39 \u003cem\u003eR. ferrumequinum\u003c/em\u003e, 24 \u003cem\u003eMi. fuliginosus\u003c/em\u003e, 5 \u003cem\u003eM. bombinus\u003c/em\u003e, 5 \u003cem\u003eP. abramus\u003c/em\u003e, 3 \u003cem\u003eM. macrodactylus\u003c/em\u003e, 2 \u003cem\u003eM. aurascens\u003c/em\u003e, 1 \u003cem\u003eMu. hilgendorfi\u003c/em\u003e, 1 \u003cem\u003eM. petax\u003c/em\u003e) were recaptured across 2\u0026ndash;3 years (2021\u0026ndash;2024), yielding 301 samples for longitudinal comparison. Within-individual Bray-Curtis dissimilarity was significantly lower than between-individual dissimilarity for oral microbiomes (0.654\u0026thinsp;\u0026plusmn;\u0026thinsp;0.234 vs 0.751\u0026thinsp;\u0026plusmn;\u0026thinsp;0.204, Mann-Whitney U, p\u0026thinsp;=\u0026thinsp;5.5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), indicating that individual bats maintain a recognizable oral microbial signature across years (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). No significant difference was observed for skin (p\u0026thinsp;=\u0026thinsp;0.80) or stool (p\u0026thinsp;=\u0026thinsp;0.33), suggesting these communities are more strongly shaped by environmental exposure than host identity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePathogen carriage was predominantly transient: across all body sites, very few recaptured individuals carried the same zoonotic genus above 0.1% relative abundance in every sampling year (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). In oral samples, only 3 of 52 multi-year individuals (5.8%) persistently carried \u003cem\u003eYersinia\u003c/em\u003e; all other pathogen-site combinations showed\u0026thinsp;\u0026le;\u0026thinsp;2.3% persistence. In stool, \u003cem\u003eHelicobacter\u003c/em\u003e was persistent in 2 of 5 multi-year individuals (40%), though the small sample limits interpretation (stool not shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eB due to limited sample size). The predominantly transient carriage pattern suggests that bats acquire zoonotic bacteria through environmental or vector-mediated exposure rather than maintaining them as stable colonizers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePredicted metabolic function\u003c/h2\u003e \u003cp\u003ePICRUSt2 predicted 586 MetaCyc pathways across all samples. Body site was the strongest determinant of predicted metabolic function, with 559 of 578 testable pathways differing significantly across oral, skin, and stool communities (linear model controlling for species and activity mode, q\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Of the 559 significant pathways, 133 were most abundant in oral, 283 in skin, and 143 in stool microbiomes. Oral microbiomes were enriched in de novo biosynthetic pathways including purine and pyrimidine biosynthesis, NAD biosynthesis, S-adenosyl-L-methionine (SAM) cycle, and asparagine/aspartate biosynthesis. Skin microbiomes were enriched in gluconeogenesis, cobalamin biosynthesis, glyoxylate cycle, and acyl-CoA thioesterase pathways. Stool microbiomes were enriched in glycolysis/Entner-Doudoroff pathways, NAD salvage, nucleotide degradation (guanosine nucleotides degradation), and ubiquinol biosynthesis. Notably, skin and stool shared similar metabolic profiles --- for stool-enriched pathways, the mean abundance difference between stool and skin was typically\u0026thinsp;\u0026lt;\u0026thinsp;6%, whereas both differed substantially from oral communities. This metabolic similarity between skin and stool microbiomes likely reflects physical contact between bats and their feces during roosting, as bats hang inverted in dense clusters where ventral skin surfaces are regularly exposed to fecal material.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eComparing active and hibernation periods within each body site (controlling for species), 328 pathways differed significantly in oral (182 hibernation-enriched, 146 active-enriched), 308 in skin (150 hibernation-enriched, 158 active-enriched), and 197 in stool (56 hibernation-enriched, 141 active-enriched) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Additional file 1). In oral microbiomes, the most strongly hibernation-enriched pathway was fatty acid β-oxidation I (coef\u0026thinsp;=\u0026thinsp;+\u0026thinsp;1.28 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, q\u0026thinsp;=\u0026thinsp;1.88 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e), followed by sucrose degradation II and ergothioneine biosynthesis, consistent with a metabolic shift toward lipid catabolism and antioxidant production during torpor. In skin, chitin derivatives degradation was most strongly hibernation-enriched (coef\u0026thinsp;=\u0026thinsp;+\u0026thinsp;1.31 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, q\u0026thinsp;=\u0026thinsp;1.02 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e), along with guanosine nucleotide degradation and protocatechuate degradation. In stool, hibernation-enriched pathways included tRNA charging and glycolysis IV, though fewer pathways were affected overall (56 vs 182 in oral). Active-period microbiomes across all body sites were enriched in biosynthetic pathways including peptidoglycan biosynthesis, L-lysine biosynthesis, and unsaturated fatty acid biosynthesis (cis-vaccenate, gondoate), reflecting higher microbial growth rates during the foraging season.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 5 differentially abundant metabolic pathways between hibernation and active periods per body site\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody site\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnriched in\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoefficient (\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eq-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHibernation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFatty acid β-oxidation I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e1.88 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHibernation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSucrose degradation II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e7.55 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHibernation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eErgothioneine biosynthesis II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e3.92 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHibernation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStearate biosynthesis I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e2.36 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;17\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHibernation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFatty acid β-oxidation II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e1.09 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeptidoglycan biosynthesis V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e3.99 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;30\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL-lysine biosynthesis II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e1.44 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;31\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlucose-1-phosphate metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e6.34 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;14\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeptidoglycan biosynthesis IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e4.44 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;22\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecis-vaccenate biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e3.87 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHibernation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChitin derivatives degradation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e1.02 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHibernation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGuanosine nucleotides degradation II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e2.97 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;32\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHibernation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtocatechuate degradation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e3.11 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;12\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHibernation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInositol degradation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e4.15 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;19\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHibernation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhytol degradation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e1.87 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePetroselinate biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e2.11 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;19\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCardiolipin biosynthesis II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e7.88 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;19\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSuperpathway of dTDP-glucose biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e8.16 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;33\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecis-vaccenate biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e8.99 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;17\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGondoate biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e1.47 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;14\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHibernation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etRNA charging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;8.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e7.21 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHibernation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlucose-1-phosphate metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;6.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e1.33 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHibernation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInositol degradation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;5.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e2.06 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHibernation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlycolysis IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;5.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e1.09 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHibernation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eD-glucuronate degradation I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;5.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e1.33 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLipid A biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e2.09 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEthanolamine utilization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e1.05 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePolyprenol biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e5.17 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePentose phosphate pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e1.00 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC4 photosynthetic carbon assimilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e2.48 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur findings reveal a clear hierarchy of pathogen burden across body sites: stool (6.91%) \u0026gt; skin (1.67%) \u0026gt; oral (0.69%), with a 10-fold difference between stool and oral samples. Stool contained the highest overall load, with \u003cem\u003eHelicobacter\u003c/em\u003e, \u003cem\u003eYersinia\u003c/em\u003e, \u003cem\u003eChlamydia\u003c/em\u003e, \u003cem\u003eBartonella\u003c/em\u003e, and \u003cem\u003eRickettsia\u003c/em\u003e all elevated. Bat feces accumulating in caves and roosting sites represent a significant contamination source. The dominance of \u003cem\u003eHelicobacter\u003c/em\u003e in stool --- previously underreported in bat microbiome studies --- is particularly notable, as \u003cem\u003eHelicobacter\u003c/em\u003e species include gastric pathogens of humans and animals.\u003c/p\u003e \u003cp\u003e \u003cem\u003eRickettsia\u003c/em\u003e and \u003cem\u003eBartonella\u003c/em\u003e were most abundant on skin, consistent with ectoparasite-mediated acquisition. The extreme \u003cem\u003eRickettsia\u003c/em\u003e load on \u003cem\u003eP. abramus\u003c/em\u003e skin (9.37%) suggests that bat skin serves as a maintenance site for vector-borne pathogens, with ectoparasites rather than ingestion as the primary acquisition route.\u003c/p\u003e \u003cp\u003eThree species emerged with distinct high-risk profiles. \u003cem\u003eM. macrodactylus\u003c/em\u003e carried the highest burden (3.70%), driven by \u003cem\u003eHelicobacter\u003c/em\u003e in stool (14.4%, 81.2% prevalence), possibly involving novel bat-adapted lineages supported by low BLAST identity (97.5\u0026ndash;98.8%). \u003cem\u003eP. abramus\u003c/em\u003e, a synanthropic species roosting in human buildings, carried the highest \u003cem\u003eRickettsia\u003c/em\u003e (9.37% on skin), increasing ectoparasite-mediated spillover risk. \u003cem\u003eMi. fuliginosus\u003c/em\u003e had the most diverse pathogen profile across all body sites.\u003c/p\u003e \u003cp\u003eTotal pathogen abundance peaked in summer (3.26%) and was lowest in winter (0.92%), correlating with ectoparasite activity, bat foraging, and increased human-bat contact. Notably, \u003cem\u003eYersinia\u003c/em\u003e persisted through winter (33.2% prevalence), suggesting proliferation during hibernation when host immunity is suppressed.\u003c/p\u003e \u003cp\u003eThe strong species-specific oral microbiome signatures (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.295) suggest host phylogeny shapes microbial communities, consistent with the observation that fecal microbiomes are distinguishable by host species [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Yet stool diversity did not differ among species (p\u0026thinsp;=\u0026thinsp;0.78) despite dramatic differences in pathogen burden (18.3% in \u003cem\u003eM. macrodactylus\u003c/em\u003e vs. 1.1% in \u003cem\u003eP. abramus\u003c/em\u003e), indicating pathogen load is independent of community diversity.\u003c/p\u003e \u003cp\u003eLongitudinal sampling of 80 recaptured individuals revealed that oral microbiomes maintain individual-specific signatures across years, consistent with the strong host-species effect observed in cross-sectional analysis. This pattern mirrors findings in humans, where oral and skin microbiomes retain individual-specific signatures over months to years while environmentally exposed sites are more variable [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In contrast, skin and stool communities showed no within-individual stability, suggesting these niches are shaped primarily by environmental exposure. Critically, pathogen carriage was predominantly transient rather than persistent, with most individuals losing or acquiring zoonotic genera between sampling years. This pattern argues against bats serving as chronic reservoirs for these bacteria at the individual level and instead supports environmental or vector-mediated acquisition during each active season. The transient nature of carriage also implies that population-level prevalence reflects ongoing exposure pressure rather than a fixed subset of persistently colonized individuals, which has implications for surveillance strategies: sampling should target periods and conditions of peak exposure (summer, high ectoparasite load) rather than attempting to identify persistently infected individuals.\u003c/p\u003e \u003cp\u003ePICRUSt2 analysis revealed that body site was the dominant driver of predicted metabolic function, with oral microbiomes enriched in de novo biosynthetic pathways and skin/stool enriched in salvage and degradation pathways. The oral enrichment of purine, pyrimidine, NAD, and one-carbon (SAM cycle) biosynthesis suggests a nutrient-limited niche where bacteria must synthesize essential building blocks de novo, whereas stool microbiomes rely on host diet-derived substrates via glycolysis and nucleotide salvage. Strikingly, skin and stool microbiomes shared highly similar metabolic profiles despite occupying anatomically distinct niches. Given that bat skin microbiomes are strongly shaped by the local environment [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], this convergence may reflect the roosting behavior of cave bats: hanging inverted in dense clusters, bats' ventral skin surfaces are regularly exposed to fecal material, which could facilitate microbial exchange between skin and gut communities.\u003c/p\u003e \u003cp\u003eThe seasonal metabolic shifts observed between active and hibernation periods parallel the well-documented physiological transition from carbohydrate oxidation to lipid catabolism during torpor [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] (Additional file 1).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAdditional file 1\u003c/strong\u003e \u003cp\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Active vs hibernation differences in predicted metabolic pathways within oral (A), skin (B), and stool (C) microbiomes (linear model controlling for species). Bars show the hibernation vs active coefficient (\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e); blue\u0026thinsp;=\u0026thinsp;hibernation-enriched, red\u0026thinsp;=\u0026thinsp;active-enriched. Top 10 pathways in each direction are shown. All displayed pathways are significant at q\u0026thinsp;\u0026lt;\u0026thinsp;0.05. (TIFF)\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe enrichment of fatty acid β-oxidation I and II in oral microbiomes during hibernation is consistent with this host metabolic shift, suggesting that the changed oral environment during torpor selects for bacteria with lipid-catabolizing capacity. The concurrent enrichment of predicted ergothioneine biosynthesis is also of interest, given that hibernating bats face oxidative stress during periodic arousal episodes [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], though whether microbially produced ergothioneine reaches functionally relevant levels remains unknown. In skin microbiomes, the hibernation-associated enrichment of chitin degradation pathways could relate to ectoparasite-derived chitinous material accumulating on skin during torpor when grooming ceases, though other sources of environmental chitin (e.g., fungal cell walls) cannot be excluded. The active-period enrichment of biosynthetic pathways (peptidoglycan, amino acids, unsaturated fatty acids) across all body sites is consistent with higher microbial growth rates supported by increased nutrient availability from host foraging. Notably, stool showed the fewest hibernation-associated changes (56 pathways vs 182 in oral), consistent with the cessation of food intake during torpor reducing substrate input to the gut.\u003c/p\u003e \u003cp\u003eWhite-nose syndrome has been shown to restructure bat skin microbiomes during hibernation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and our findings suggest that the metabolic functions of these microbiomes are tightly linked to the host's seasonal physiology. Whether disruptions to hibernation --- from disease, cave disturbance, or climate change --- also alter microbiome metabolic function warrants further investigation.\u003c/p\u003e \u003cp\u003eThis study has several limitations. The 16S V3\u0026ndash;4 region provides genus-level resolution at best. BLAST validation against the NCBI 16S type strain database (58,095 of 59,215 ASVs returned hits at \u0026ge;\u0026thinsp;80% identity) confirmed reliable genus assignments for \u003cem\u003eBartonella\u003c/em\u003e (97.8\u0026ndash;100% identity), \u003cem\u003eRickettsia\u003c/em\u003e (89.4\u0026ndash;99.8%), \u003cem\u003eHelicobacter\u003c/em\u003e (97.5\u0026ndash;98.8%), and \u003cem\u003eChlamydia\u003c/em\u003e (85.0\u0026ndash;95.3%; low identity suggesting novel bat-associated lineages). However, substantial ambiguity was found within Enterobacteriaceae: SILVA-assigned \u003cem\u003eYersinia\u003c/em\u003e ASVs frequently matched \u003cem\u003eSerratia\u003c/em\u003e and \u003cem\u003eRahnella\u003c/em\u003e at equal or higher identity (97\u0026ndash;100%), and \u003cem\u003eSalmonella\u003c/em\u003e ASVs were indistinguishable from \u003cem\u003eCitrobacter\u003c/em\u003e and \u003cem\u003eEnterobacter\u003c/em\u003e (99.3\u0026ndash;99.8%). Overall, 7.6% of ASVs showed ambiguous genus assignments, and \u003cem\u003eYersinia\u003c/em\u003e and \u003cem\u003eSalmonella\u003c/em\u003e should be interpreted as Yersiniaceae and Enterobacteriaceae sensu lato, respectively. Detection of \u003cem\u003eRickettsia\u003c/em\u003e or \u003cem\u003eHelicobacter\u003c/em\u003e does not confirm pathogenic species, as both genera include commensal or endosymbiotic members. PICRUSt2 predictions are based on reference genomes and may not fully represent the metabolic potential of bat-associated bacteria [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, the detection of chlorophyll a biosynthesis among skin-enriched pathways likely reflects environmental cyanobacteria or other phototrophs from cave surfaces rather than true skin commensals, and some predicted skin metabolic functions may similarly reflect transient environmental microbes rather than resident community members. Regional comparisons were not possible due to uneven sampling, and some species had small sample sizes limiting seasonal comparisons.\u003c/p\u003e \u003cp\u003eIn conclusion, this survey of 2,747 samples reveals stool as the primary pathogen reservoir, skin as a vector-borne pathogen hotspot, and summer as the peak risk season. \u003cem\u003eM. macrodactylus\u003c/em\u003e, \u003cem\u003eP. abramus\u003c/em\u003e, and \u003cem\u003eMi. fuliginosus\u003c/em\u003e pose the highest zoonotic risk with distinct pathogen profiles. Longitudinal data indicate that pathogen carriage is transient rather than persistent, implying that population-level prevalence reflects ongoing environmental exposure rather than chronic colonization. These findings support targeted surveillance focusing on stool and skin from high-risk species during summer, particularly near human settlements. Metagenomic approaches are needed for species-level pathogen identification.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset(s) supporting the conclusions of this article are available in the NCBI Sequence Read Archive (SRA) repository, under BioProject PRJNA1436137 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1436137).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Research Planning Review Committee of the National Institute of Ecology (NIEIACUC-2021-001). All handling and sampling permissions were obtained from the seven corresponding local governments on each sampling year.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSEJ contributed to research planning, experimental design, and data analysis. TWL, JS, JYK, YSC, USA, and LK contributed to field investigations and data interpretation. KK and SP conducted primary field surveys and were responsible for sample acquisition and DNA extraction. TU, GC, HH, and NJC conducted bioinformatic and statistical analyses. TU wrote the manuscript. JJ and DH validated the bioinformatic analysis results and reviewed the manuscript. SSK was the Principal Investigator, responsible for conceptualization, overall research execution, and project administration. All authors read and approved the final manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by a grant from the National Institute of Ecology (NIE), funded by the Ministry of Climate, Energy and Environment (MCEE) of the Republic of Korea (NIE-B-2024-38).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank CJ Bioscience (Seoul, South Korea) for library preparation and sequencing services. AI-assisted tools (Claude, Anthropic) were used for data analysis scripting and manuscript drafting; all results were independently verified by the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVashi NA, Reddy P, Wayne DB, et al. Bat-associated leptospirosis. J Gen Intern Med. 2010;25:162\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKamani J, Baneth G, Mitchell M, et al. Bartonella species in bats (Chiroptera) and bat flies (Nycteribiidae) from Nigeria, West Africa. Vector Borne Zoonotic Dis. 2014;14:625\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIslam A, Mikolon A, Mikoleit M, et al. Isolation of Salmonella Virchow from a fruit bat (Pteropus giganteus). EcoHealth. 2013;10:348\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuhldorfer K, Speck S, Kurth A, et al. Yersinia species isolated from bats, Germany. Emerg Infect Dis. 2010;16:578\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHazeleger WC, Jacobs-Reitsma WF, Lina PHC, et al. Wild, insectivorous bats might be carriers of Campylobacter spp. PLoS ONE. 2018;13:e0190647.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlehert DS, Maluping RP, Green DE, et al. Acute pasteurellosis in wild big brown bats (Eptesicus fuscus). J Wildl Dis. 2014;50:136\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMazwi KD, Lekota KE, Glover BA, et al. Whole genome sequence analysis of Brucella spp. from human, livestock, and wildlife in South Africa. J Microbiol. 2024;62:759\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatei IA, Panciera L, Pagani P, et al. Rickettsia spp. in bats of Romania: high prevalence of Rickettsia monacensis in two insectivorous bat species. Parasit Vectors. 2021;14:107.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilva-Ramos CR, Faccini-Martinez AA, Hidalgo M, et al. First molecular evidence of Coxiella burnetii in bats from Colombia. Res Vet Sci. 2022;150:33\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFritschi J, Hoerr V, Gurtner C, et al. Prevalence and phylogeny of Chlamydiae and hemotropic mycoplasma species in captive and free-living bats. BMC Microbiol. 2020;20:182.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIngala MR, Simmons NB, Dunbar M, et al. You are more than what you eat: potentially adaptive enrichment of microbiome functions across bat dietary niches. Anim Microbiome. 2021;3:82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFederici L, Masulli M, De Laurenzi V, et al. An overview of bats microbiota and its implication in transmissible diseases. Front Microbiol. 2022;13:1012189.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim SS, Choi YS, Yoo JC. The thermal preference and the selection of hibernacula in seven cave-dwelling bats. Korean J Ecol Environ. 2014;47:258\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVoigt CC, Phelps KL, Aguirre LF, et al. Bats and buildings: the conservation of synanthropic bats. In: Voigt CC, Kingston T, editors. Bats in the Anthropocene: Conservation of Bats in a Changing World. Cham: Springer; 2016. pp. 427\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo DY, Luo B, Zhang KK, et al. Social vocalizations of big-footed myotis (Myotis macrodactylus) during foraging. Integr Zool. 2019;14:446\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKimprasit T, Nunome M, Iida K, et al. Dispersal history of Miniopterus fuliginosus bats and their associated viruses in east Asia. PLoS ONE. 2021;16:e0244006.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlpizar P, Rojas TN, Martel C, et al. Agricultural fast food: bats feeding in banana monocultures are heavier but have less diverse gut microbiota. Front Ecol Evol. 2021;9:746783.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCallahan BJ, McMurdie PJ, Rosen MJ, et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOksanen J, Simpson GL, Blanchet FG et al. vegan: Community Ecology Package. R package version 2.6-4. 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDouglas GM, Maffei VJ, Zaneveld JR, et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol. 2020;38:685\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaspi R, Billington R, Keseler IM, et al. The MetaCyc database of metabolic pathways and enzymes --- a 2019 update. Nucleic Acids Res. 2020;48:D445\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong H, Unno T. A comprehensive database of human and livestock fecal microbiome for community-wide microbial source tracking: a case study in South Korea. Appl Biol Chem. 2024;67:58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOh J, Byrd AL, Park M, et al. Temporal stability of the human skin microbiome. Cell. 2016;165:854\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAvena CV, Parfrey LW, Leff JW, et al. Deconstructing the bat skin microbiome: influences of the host and the environment. Front Microbiol. 2016;7:1753.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao GH, Liu S, Xiao YH, et al. Seasonal changes in gut microbiota diversity and composition in the greater horseshoe bat. Front Microbiol. 2019;10:2247.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun H, Wang J, Xing Y, et al. Gut transcriptomic changes during hibernation in the greater horseshoe bat (Rhinolophus ferrumequinum). Front Zool. 2020;17:21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin Q, Ge H, Liao CC, et al. Antioxidant defenses in the brains of bats during hibernation. PLoS ONE. 2016;11:e0152135.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnge-Stark M, Parise KL, Cheng TL, et al. White-nose syndrome restructures bat skin microbiomes. Microbiol Spectr. 2023;11:e0271523.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJung S. Advances in functional analysis of the microbiome: integrating metabolic modeling, metabolite prediction, and pathway inference with next-generation sequencing data. J Microbiol. 2025;63:e2411006.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bat microbiome, Zoonotic bacteria, 16S rRNA, Body site, Seasonal dynamics","lastPublishedDoi":"10.21203/rs.3.rs-9148344/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9148344/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBats harbor diverse zoonotic bacteria, yet how pathogen carriage varies across body sites, host species, and seasons --- and whether individual bats maintain infections chronically --- remains largely unknown. We surveyed 2,747 oral, skin, and stool samples from eight insectivorous bat species across four seasons (2021\u0026ndash;2024) in South Korea using 16S rRNA gene sequencing. Ten zoonotic genera were detected in 63.7% of samples. Feces harbored 10-fold higher pathogen loads than oral samples, dominated by \u003cem\u003eHelicobacter\u003c/em\u003e and \u003cem\u003eChlamydia\u003c/em\u003e, while skin carried high levels of vector-borne \u003cem\u003eRickettsia\u003c/em\u003e and \u003cem\u003eBartonella\u003c/em\u003e. Three species posed distinct risks: \u003cem\u003eMyotis macrodactylus\u003c/em\u003e stool contained \u003cem\u003eHelicobacter\u003c/em\u003e at 14.4% relative abundance, \u003cem\u003ePipistrellus abramus\u003c/em\u003e --- a synanthropic species known to be house-dwelling --- carried extreme \u003cem\u003eRickettsia\u003c/em\u003e on skin (9.37%), and \u003cem\u003eMiniopterus fuliginosus\u003c/em\u003e harbored the broadest pathogen diversity. Pathogen abundance peaked in summer and dropped sharply in winter, though \u003cem\u003eYersinia\u003c/em\u003e persisted during hibernation. Longitudinal tracking of 80 recaptured bats over 2\u0026ndash;3 years showed that pathogen carriage was predominantly transient, indicating environmental acquisition each season rather than chronic colonization. Predicted metabolic profiles shifted between active and hibernation periods, with hibernation microbiomes enriched in lipid catabolism consistent with host torpor physiology. These results identify stool and skin as the primary transmission routes, pinpoint high-risk species and seasons, and demonstrate that population-level pathogen prevalence reflects ongoing environmental exposure --- findings directly relevant to wildlife disease surveillance and bat conservation management.\u003c/p\u003e","manuscriptTitle":"Body site, host species, and seasonal drivers of zoonotic bacterial distribution in Korean insectivorous bat microbiomes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-25 21:33:07","doi":"10.21203/rs.3.rs-9148344/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d6b43c33-1c92-4381-871d-a8e221fbd57a","owner":[],"postedDate":"March 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-06T06:25:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-25 21:33:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9148344","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9148344","identity":"rs-9148344","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}