Host-microbiota association in a migratory species: Constitutive humoral immunity in the partially migratory bat Leptonycteris yerbabuenae is linked to the gut microbiota in a sex-specific manner

Host-microbiota association in a migratory species: Constitutive humoral immunity in the partially migratory bat Leptonycteris yerbabuenae is linked to the gut microbiota in a sex-specific manner | 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 Host-microbiota association in a migratory species: Constitutive humoral immunity in the partially migratory bat Leptonycteris yerbabuenae is linked to the gut microbiota in a sex-specific manner David Alfonso Rivera-Ruiz, José Juan Flores-Martínez, Carlos Rosales, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9440532/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract The immune system and gut microbiota are interconnected components that play a key role in host health. The nature of this relationship in wildlife is under-explored, especially in migratory animals, which are exposed to diverse microorganisms that can impact their immune system, microbiota and health. This study explored the relationship between constitutive humoral immunity (bacterial killing-ability, BKA, and total immunoglobulin G concentration, tIgG) and the diversity and composition of gut microbiota (16S rRNA gene amplicons) in the lesser long-nosed bat Leptonycteris yerbabuenae. Males of this bat form resident populations whereas some females migrate to complete their reproductive cycle. Each sex harbored a distinctive fecal microbiota, yet no significant relationships were found between BKA and microbiota diversity, but tIgG was negatively correlated with Shannon's and Simpson's inverse indices in females and positively with the Shannon's index in males. Humoral immunity in both sexes was significantly related to fecal bacterial genera known to harbor immunostimulatory species, species linked to intestinal mucosa integrity, and infection-associated species. Other free-living and unclassified bacterial genera were associated with immunity in a sex-specific manner, highlighting the importance of novel and uncommon bacteria for immune activity in wildlife. These findings suggest that gut microbiota composition, and to a lesser extent diversity, are linked to constitutive humoral immunity in L. yerbabuenae. The distinct relationship exhibited by each sex suggests that migration and other sex-associated traits may be crucial to understanding the natural variation of immunity in wildlife. Immune system gut microbiota bats migration Leptonycteris yerbabuenae bacterial killing ability immunoglobulins Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Vertebrate gut microbiota maximizes digestion, restrains the growth of pathogens and is involved in multiple physiological processes [ 1 , 2 ]. These benefits require a stable microbiota confined to the intestinal lumen [ 3 ]. Otherwise, imbalanced microbiota (dysbiosis) can be the cause or consequence of various diseases [ 3 ]. To maintain a healthy microbiota, the immune system confines the microbiota to the intestinal lumen while maintaining constant communication with it [ 4 , 5 ]. The relationship between immunity and microbiota is bidirectional: the immune system shapes microbiota, and the microbiota drives the development and activity of multiple immunological components [ 4 ]. This host-microbiota interplay mediated by the immune system is key to host health[ 4 , 6 ] and can be shaped by sex, energy, nutrition, and infection [ 7 – 10 ]. Hence, effective communication between the immune system and the gut microbiota is essential for the host's fitness. Despite the growing evidence linking gut microbiota with immune function, most studies have focused on humans and laboratory animals. This limits our understanding of how this interaction operates under natural conditions. As with humans [ 11 , 12 ], immune function in wild animals is affected by environmental and physiological constraints [ 9 , 13 ]. The wide range of factors affecting the immune system results in significant immunological variability at the individual level [ 9 ]. Similarly, the gut microbiota of wildlife is dynamic. Microbial communities adapt to the life history traits of their hosts and their biological context [ 14 , 15 ]. The interaction between immunity and microbiota in wildlife remains an underexplored topic, especially in migratory species, which face environmental and physiological constraints that shape their gut microbiota and immune system [ 16 – 20 ]. These changes may affect the susceptibility of migratory animals to infection, as gut microbiota may prevent the invasion of enteric pathogens encountered during migration [ 21 ], while the physiological cost of migration may compromise immune function [ 18 – 20 ]. Therefore, understanding the relationship between immune system and gut microbiota in natural conditions is crucial for comprehending the host-microbiota relationship in animals [ 22 , 23 ] and the health of their populations [ 24 – 27 ]. Although the interplay between the immune system and microbiota in wild animal has not been widely explored, previous studies suggest that they are linked. The limited evidence available indicates that natural variation in the immune system is not associated with the diversity of gut microbiota, but that it is linked to the presence or abundance of specific bacterial groups. For instance, cellular immune response in the barn swallow ( Hirundo rustica ) [ 28 ] and plasma bacterial killing-ability (BKA) in the common vampire bat ( Desmodus rotundus ) [ 29 ] are not associated with gut bacterial diversity but with specific bacterial groups. Similarly, bacterial composition appears to modulate BKA in the Eurasian teal ( Anas crecca ) [ 30 ] and total immunoglobulin G concentration (tIgG) in D. rotundus [ 31 ]. Although these studies highlight the importance that specific gut bacteria have on the immune system activity in wildlife, they skewed their analysis to specific bacterial groups. In the Eurasian teal, the analysis focused on the phylum level, while in vampire bats, Ingala et al 2019 [ 31 ] focused on the core microbiota. This analysis strategy limits the taxonomic resolution of the results and excludes low-abundant bacteria that could be key to immune system activity. Therefore, a more comprehensive and specific analysis is warranted to gain a better understanding of the relationship between immunity and microbiota in wildlife. Bats are ideal organisms for studying the relationship between immunity and microbiota because they harbor a wide variety of microorganisms found in the environment and in other animal species [ 32 ]. This makes it possible to evaluate the relationship between immunity and microbiota across a wide range of microorganisms that are present in multiple ecosystems and wildlife species. The relationship between immunity and microbiota in bats has been widely studied with respect to pathogenic viruses [ 33 , 34 ]. Although our knowledge about bats and their associated bacteria is limited [ 35 ], pioneer studies show that infection can alter their microbiota [ 36 – 38 ], and that some bacteria inhabiting bats can negatively affect bats themselves [ 39 – 45 ] as well as other mammalian species [ 35 , 46 , 47 ]. Recent studies have emphasized the importance of the gut microbiome in understanding the unique immune system of bats [ 48 , 49 ], and a handful of experimental studies support this idea [ 50 – 52 ]. However, the relationship between immunity and gut microbiota in natural environments has been scarcely studied in bats [ 29 , 31 , 37 ], and to our knowledge it has not been investigated in migratory species. To investigate the relationship between the gut microbiota and immunity in wildlife, we used the lesser long-nosed bat ( Leptonycteris yerbabuenae ) as a model species. Individuals of populations of this species mate in west-central Mexico in fall-winter and then most pregnant females migrate in spring to northern Mexico and southern USA to have their young [ 53 – 55 ]. We characterized gut microbiota by amplifying the 16S rRNA gene from fecal samples and examined how it was related to BKA and tIgG. Previous studies on this species have shown that high inter-individual variability in BKA and tIgG is related to demographic variables, but it might be also modulated by inter-individual differences in gut microbiota [ 56 – 59 ]. Consistent with previous research in bats [ 29 , 31 ] and birds [ 28 ], we did not expect that BKA and tIgG would be related to microbiota diversity, but rather to its composition. We expected that humoral immunity in L. yerbabuenae would be associated with bacterial genera (1) that harbor species with known immunostimulatory properties under healthy conditions, and (2) that harbor species associated with infection, including intrinsically pathogenic bacteria, pathobionts and other taxa that proliferate during dysbiosis. The gut microbiota of female L. yerbabuenae changes in response to reproductive activity, migration and dietary changes [ 57 – 59 ]. In contrast, adult male are residents and do not undergo the physiological and environmental changes that females experience throughout their life cycle [ 53 – 55 ]. Therefore, we assessed the relationship between immunity and microbiota separately for each sex. Methods Study sites and sampling of bats. The study was conducted between 2019 and 2024. Fecal samples of L. yerbabuenae were collected in west-central Mexico (Don Panchito Island, Chamela, Jalisco, 19°32'08.4´´N, 105°05'18.8´´W; La Fábrica cave, Coquimatlán, Colima, 19º09´05.8´´N, 103º50´06.9´´O), and in northwestern Mexico (Mariana cave, Carbó, Sonora, 29º35´25.9´´N, 110º48´8.9´´W). Only adult male samples (n = 38) were collected in west-central Mexico. In contrast, adult females (N = 24) were captured in West central of Mexico (Chamela, non-reproductive state; n = 8), and in northwestern Mexico (pregnant and lactating; n = 16). This sex-biased capture is consistent with demographic patterns of L. yerbabuenae in these regions [ 53 – 55 ]. Sampling was conducted following the guidelines of the American Society of Mammalogists [ 60 ]. Bats were captured between 22:00 and 06:00 hours. Each bat was placed in a clean cotton bag until processing. We recorded the sex, age category (adult or young), reproductive condition, length forearm (Mitutoyo CD-6, Mexico; ±0.01 mm) and body mass (Ohaus, Nueva Jersey, USA, ± 0.1 g) of individuals. Adult and young individuals were distinguished by examining multiple phenotypical traits: the classical epiphyseal–diaphyseal fusion of the 4th metacarpal–phalangeal joint [ 61 , 62 ], hair color (young had short grayish hair) and body mass index. Reproductive males of L. yerbabuenae were identified by scrotal testicles and a dorsal patch on the scapular region [ 63 ]. Females were classified as pregnant by palpation of their abdomen and lactating if they presented hairless nipples that released milk after being pressed. Finally, each bat was placed in plastic containers lined with sterile paper bags for fecal collection and a sterile net on the top that allowed bat to perch. The containers were checked approximately every 10 minutes to collect feces, which were placed in cryotubes and immediately immersed in liquid nitrogen until stored at -70°C in the laboratory. Immunological assays We quantified tIgG and used previously published (56) and unpublished data on BKA to evaluate the relationship between immunity and microbiota in L. yerbabuenae . The methodology used to perform BKA is described in Rivera et al 2023 [ 56 ]. As a measure of adaptive humoral immunity, we quantified tIgG by enzyme-linked immunosorbent assay in 96-well plates. Each sample was worked in triplicate and plasma (2µl) was diluted to a factor of 1/20,000 and incubated for 18 hours at 4°C. Following this incubation, 2 washes (Wash solution: 200 µl of 0.05% PBS-Tween-20) were performed to remove excess sample. The bottom of the plate was blocked with 50 µl of bovine serum albumin (BSA, diluted 1% in PBS 1X), incubated for 2 hours at room temperature and then washed twice. To detect plasma tIgG, a volume of 50 µL of peroxidase-coupled anti-IgG antibody (Goat anti-Bat IgG (H + L) Secondary Antibody [HRP], NB7238, NOVUS BIOLOGICAL) was added at a 1/10000 dilution and allowed to react for 2 hours at room temperature. After incubation, two washes were performed and 50 µL of SIGMA FAST-OPD (p9187) developer solution were added to each well. The colorimetric reaction stopped after 5 minutes with 100 µl of H 2 SO 4 and the absorbance of the plate was recorded at 490 nm. Antibody concentration for each sample was determined from the average optical density (OD) of three replicates, as color intensity is directly proportional to antibody concentration. Gut microbiota analysis In line with previous studies on L. yerbabuenae [ 57 , 58 ], we extracted DNA from bat feces using a DNeasy Blood & Tissue Kit (Qiagen, Valencia, CA), following the manufacturer's instructions with some modifications. The cell lysis was done by incubating the sample with ATL buffer and proteinase K, 3µL of lysozyme (3 mg/mL; Sigma-Aldrich) and incubation at 37°C for 30 minutes at 500 rpm to enhance bacterial cell wall breakdown. After completing the DNA extraction process, DNA was precipitated with 1/10 volume of sodium acetate (3M) and 300 µL of absolute alcohol at -20℃. After cleanup, DNA was diluted in molecular grade water (30 µL) and stored at -20°C. Sequencing and amplification of 16S rRNA gene The amplification of the V4 region of the 16S rRNA gene was performed according to Caporaso et al. [ 64 ] and Carrillo et al. [ 65 ] using the Earth Microbiome Project primers 515F/806R. The PCR reaction (25 µL) contained 2.5 µL of Takara ExTaq PCR buffer 10X, 2 µL of Takara dNTPs, 0.7µL of bovine serum albumin (BSA, 20 mg ml-1), 0.125 µL of Takara Ex Taq DNA Polymerase (5 U µl-1) (TaKaRa, Shiga, Japan), 1 µL of primers and nuclease-free water. The PCR mix was amplified in a thermocycler (Eppendorf, Germany) with the following parameters: initial denaturation step at 94°C for 3 minutes, followed by 35 cycles of 94°C for 45 seconds, 50°C for 60 seconds, and 72°C for 90 seconds, and a final extension at 72°C for 10 minutes and a cooling temperature of 4°C until freezing. This process was performed in triplicate per sample and confirmed by agarose gel electrophoresis at 1% and 80V for 30 minutes to verify the presence of amplicons. The PCR amplicons were purified with magnetic beads (SeraMag Beads) and the DNA concentration of the 16S rRNA gene fragments was quantified with a Qubit (InVitroGen) to determine an optimal concentration (20 µg/µL per sample) for sequencing on Illumina MiSeq platform (Yale Center for Genome Analysis, CT, USA). Bioinformatics processing of sequences The raw sequences were demultiplexed and denoised with QIIME2 software (version 2024.10). The removal of chimeras and artifacts was done with the DADA2 algorithm [ 66 ]. According to quality parameters, forward sequences were truncated to a length of 225 bp and reverse sequences were truncated to 205 bp. Taxonomic clustering of the filtered sequences in Amplicon Sequence Variants (ASVs) was performed using the QIIME2 command " qiime feature-classifier classify-consensus-vsearch " from the SILVA V4 database (SSU release 138.1 515806). Only bacterial sequences with an assigned phylum were retained. Bacterial sequences without an assigned phylum or sequences identified as chloroplasts and mitochondria were removed. Sequences were aligned using the MAFFT algorithm [ 67 , 68 ] and then filtered using the “ qiime alignment mask ” command to remove non-conserved and highly scattered regions within the alignment [ 69 ]. Finally, the phylogeny of the sequences was constructed using the FastTree algorithm [ 70 ], and the artifacts generated in QIIME2 were exported to the R platform (version 2.2) for further bioinformatics processing. All sequences included in this study have been deposited in NCBI BioProject PRJNA1404331. Statistical analysis The artifacts generated in QIIME2 were imported into the R platform using the qiime2R package to create a phyloseq file [ 71 ]. Subsequently, the decontam package identified seventeen sequences based on those detected in the negative control (reagents of the extraction and the amplification steps) [ 72 ]. The ASVs abundance table filtered by the decontam package was exported back to the QIIME2 platform to re-generate the artifacts necessary to construct the phyloseq file in the R platform. All samples were included in the analysis of general microbiota composition ( N = 24 females and 38 males). All female samples were included to test the relationship between immunity and microbiota; however, fewer samples were used for males ( n = 38; of which 32 were used to BKA and 30 for tIgG), due to missing immunological data for some individuals. Location and reproductive status were controlled in the analysis of immunity and microbiota because they affect the microbiota of L. yerbabuenae (Fig. 1 , Supplementary Fig. 1–6, Supplementary Table 1–3; Gaona et al.[ 57 ] and Viquez et al.[ 59 ]). Therefore, the relationship between immunity and microbiota was addressed independently in each sex. Rarefaction is currently the most effective tool for controlling differences in library size [ 73 , 74 ]. Alpha and beta diversity metrics were calculated from the rarefied data at a minimum depth of 3477 reads for females and 6765 reads for males, as both cohorts exhibited differences in minimum library size. The minimum rarefaction threshold for both cohorts satisfactorily captured the diversity of the samples (Supplementary Fig. 7). Rarefaction of the data was performed using the rarefy_even_depth function of the metagMisc package [ 75 ]. To visualize the general and predominant characteristics of the fecal microbiota of L. yerbabuenae , samples were grouped according to their relative abundance at the phylum and family level by reproductive status for females and locality for males. Only taxonomic groups present in more than 3% of the readings were described and the remaining taxa were grouped in the category 'Others '. Alpha diversity metrics Several alpha diversity indices were used to obtain a complete picture of the diversity of samples [ 76 ]. We calculated the number of ASVs observed [ 77 ], the Faith's phylogenetic diversity index (Faith's PD [ 78 ]), the Shannon's index and the inverse Simpson's index to provide a measure of alpha diversity [ 79 ]. Richness, Shannon's index and Simpson's inverse index were calculated using the estimate_richness function of the phyloseq package [ 71 ], while the Faith's PD index was calculated using the estimate_pd function of the btools package [ 80 ]. Generalized linear models and general linear models were built to assess the relationship of alpha diversity with BKA and tIgG, respectively. We used the glmmTMB package for BKA models [ 81 ] and the stats package for tIgG [ 82 ]. The fitdistrplus package was used to find the probability function that best fitted the immunological variables [ 83 ], using a beta distribution for BKA and a normal distribution for tIgG. The significance of the models was determined by deviance analysis using the ANOVA function of the car package [ 84 ]. We used the confint and coef function from the stats package to calculate confidence intervals and determine the direction of the relationship between immunity and alpha diversity metrics, respectively [ 82 ]. In accordance with the principle of independence of explanatory variables [ 85 ], reproductive status was not used as a covariate for Shannon's index, inverse Simpson's index and Faith's PD in the female model (Supplementary Fig. 1 and Table 1 ), and locality in all male alpha diversity metrics (Supplementary Fig. 2). Outliers that could significantly affect the models for BKA and tIgG were identified by means of Cook's distances. We removed one female for the inverse Simpson index and two males for the Shannon index in the model of tIgG. Finally, all models were validated by residual analysis using the DHARMa package [ 86 ]. Beta diversity metrics To measure beta diversity, the immunological ranges were divided into low and high values based on the median. The cut-off point for BKA was 81.5%, so individuals with low and high BKA had a percentage below or above 81.5%, respectively. For tIgG, low and high ranges were defined according to absorbance values below and above 0.83 OD units. Dissimilarity matrices were calculated using phylogenetically unweighted UniFrac (absence and presence of ASVs) and weighted UniFrac (richness and abundance of ASVs) distances [ 87 , 88 ]. From these distances, the centroids and ellipses of groups with low or high immunological activity were calculated and plotted together with the individual values of each sample using the multidimensional scaling (MDS) method. A permutational multivariate analysis of variance (PERMANOVA [ 89 ]) was used to assess whether unweighted and weighted UniFrac distances differed between individuals with low and high BKA and tIgG using the adonis2 function of the vegan package [ 90 ]. Reproductive status was used as a covariant in the female model and locality in the male model. Finally, we used the betadisper function from the vegan package to calculate the multivariate dispersion of the immunological groups and test whether the PERMANOVA results were associated with unequal group variances. Differential abundance analysis We conducted an Analysis of Compositions of Microbiomes with bias correction 2 (ANCOM-BC2 [ 91 ]) on the non-rarefied sequences to identify the ASVs that differed between individuals with low and high immunological activity. Sensitivity analysis for the pseudo-count addition was used to reduce false positives. We considered taxa that were identified as structural zeros to identify taxa that were exclusively present in individuals with low or high immunological activity. We focused on the most dominant sequences within each group (low and high BKA and tIgG), establishing an arbitrary threshold of relative abundance greater than 1%. The remaining sequences were grouped in the "Others < 1%" category. For ASVs present in both groups (low and high BKA and tIgG), we filtered out ASVs that were not present in at least 17% of samples. A minimum sequencing threshold of 1000 sequences was set, which included all female and male samples (minimum library size of 3477 and 6765 reads, respectively). We controlled for reproductive status in females and for locality in males. All ASVs classified at genus level (or higher taxonomic ranks if genus was not possible) with p-values < 0.05 were reported in the results, but we only considered those ASVs that remained significant after adjustment for multiple comparisons (Benjamini & Hochberg method) and sensitivity filter. Results Overall composition of the fecal microbiota of L. yerbabuenae Sixty-nine fecal samples were successfully amplified, from which 62 adult samples were retained, presenting a total of 6,780,469 reads and representing 5,117 amplicon sequence variants (ASVs). The fecal microbiota composition of L. yerbabuenae was dominated by phyla Proteobacteria and Firmicutes, whose relative abundance varied across different reproductive groups in females and males at different localities (Fig. 1 ). These differences in the fecal microbiota composition of L. yerbabuenae were accentuated at lower taxonomic levels. At family level, Enterobacteriaceae, Clostridiaceae, Mycoplasmataceae and Enterococcaceae represented a major component of the fecal microbiota in both males and females. Other taxonomic groups were more characteristic of certain demographic groups, such as Cellvibrionaceae, Erwinaceae, Gemellaceae, Lactobacillaceae, Moraxellaceae, Pasteurellaceae, Xanthobacteraceae, and Yersiniaceae that were identified as major components of the fecal microbiota of females but were absent in males. In contrast, no bacterial families were exclusive of males. Association between BKA and fecal microbiota diversity in L. yerbabuenae There was no significant association between the alpha and beta diversity of the fecal microbiota and BKA in both sexes (Supplementary Fig. 8–10 and supplementary Table 4–6). In contrast, the ANCOMBC2 analysis identified many ASVs as structural zeros associated with high and low BKA. In females, 911 and 1,074 ASVs were represented exclusively individuals with low and high BKA, respectively (Supplementary material 2). Among males, 2,529 ASVs were unique to individuals with low BKA, while 533 ASVs were unique to those with high BKA (Supplementary material 3). We focused on the most abundant ASVs (relative abundance greater than 1%): 24 ASVs in females and 29 ASVs in males (Fig. 2 ). The results of the differential abundance analysis between ASVs that were present in both immunological groups (low and high BKA) in females showed twenty-eight ASVs. However, none of these retained their significance after adjustment for multiple comparisons and sensitivity filters (Supplementary Fig. 11). The p and q -values ( p -corrected values) of ASVs identified as differentially abundant are reported (Supplementary Table 7). In males, 50 ASVs were differentially abundant between low and high BKA individuals. Thirty-four ASVs passed the sensitivity filter and retained their significance after adjusting for multiple comparisons (Fig. 3 ). The p and q -values of ASVs are reported (Supplementary Table 8). Relationship of tIgG to alpha diversity indices in L. yerbabuenae The tIgG of females was not related to the observed number of ASVs or the Faith's PD index (Table 1 and Supplementary Fig. 12). In contrast, tIgG showed a weak and negative correlation with the Shannon's index and the Simpson's inverse index in females according to the estimated regression coefficients (Table 1 ; Fig. 4 A and B). Regarding males, tIgG was not related to the observed number of ASVs, the Simpson's inverse index and the Faith's PD index (Table 1 ; Fig. 4 D and Supplementary Fig. 13). However, the Shannon's index was significantly related to tIgG, but this association was marginally positive (Table 1 ; Fig. 4 C). Table 1 Generalized linear model results for total immunoglobulin G (tIgG) as a function fecal microbiota alpha diversity in female and male of Leptonycteris yerbabuenae. Alpha diversity metrics β SE 95% CI [LL, UL] F d.f p n Female Observed number of ASVs -0.001 0.001 [-0.004, 0.001] 1.31 22 0.264 24 Shannon index -0.117 0.051 [-0.224, -0.011] 5.24 22 0.031 24 Inverse Simpson's index -0.042 0.016 [-0.077, -0.006] 6.20 21 0.021 23 Faith's PD index -0.013 0.012 [-0.039, 0.012] 1.10 22 0.304 24 Male Observed number of ASVs 3x10 − 4 3x10 − 4 [-3x10 − 4 , 0.001] 1.10 28 0.301 30 Shannon index 0.088 0.042 [0.001, 0.175] 4.41 26 0.045 28 Inverse Simpson's index 0.018 0.010 [-0.001, 0.039] 3.51 28 0.071 30 Faith's PD index 0.004 0.004 [-0.004, 0.013] 1.24 28 0.274 30 Note. β = estimated regression coefficients, SE = standard error, CI = confidence interval, = d.f = degrees of freedom. The relationship between beta diversity metrics and tIgG. tIgG was not related to any beta diversity metrics considered (Unweighted and weighted distances) in females and males with low and high tIgG concentrations (Supplementary Fig. 14 and Table 9). Association between tIgG and individual ASVs of the fecal microbiota The ANCOMBC2 differential abundance analysis showed that low and high tIgG individuals differed in several ASVs. ANCOMBC2 structural zero analysis identified 820 and 1,151 ASVs exclusive to females with low and high tIgG, respectively (Supplementary Material 4). In males, 702 ASVs were exclusive to individuals with low tIgG, and 2,332 ASVs were exclusive to those with high tIgG (Supplementary Material 5). From these ASVs, we selected the most abundant: 24 in females and 26 in males (Fig. 5 ). Among the ASVs shared by females with low and high tIgG, 35 ASVs were differentially abundant between low and high tIgG females, of which 21 ASVs passed the sensitivity filter and remained significant after adjustment for multiple comparisons (Fig. 6 ). The p and q -values of ASVs identified as differentially abundant are reported (Supplementary Table 10). In males, 28 ASVs were identified as differentially abundant between males with low and high tIgG. However, only eight ASVs passed the sensitivity filter and the multiple comparison adjustment (Fig. 7 ). The p and q -values of ASVs identified as differentially abundant are reported (Supplementary Table 11). Discussion The partially migratory bat L. yerbabuenae was the biological model to explore the correlation between constitutive humoral immunity (plasma BKA and tIgG) and fecal microbiota in wildlife migratory species. In general, humoral immunity was not associated with the diversity of fecal microbiota, but its alpha diversity was related to tIgG in opposite directions within each sex. Similarly, we found compelling evidence that bacterial composition was related to humoral immunity in a sex-specific manner. ASVs belonging to bacterial genera with species that have immunostimulatory and mucosa-strengthening properties, as well as genera with species associated with dysbiosis and pathogenicity, were related to humoral immunity in both females and males. However, some bacterial genera and ASVs were specific to each sex. Unclassified and free-living bacterial genera were also associated with immunity in a sex-specific manner. These results suggest that the fecal microbiota of L. yerbabuenae , and likely that of other wildlife species too, is associated with constitutive humoral immunity. While these associations do not establish causality, they are consistent with the hypothesis that fecal microbiota may influence humoral immune function. Differences in the way microbiota associated with humoral immunity in females and males probably originate from the microgenderome, microbiome and hormone characteristics of each sex [ 7 , 8 ]. Furthermore, the stability and functionality of the microbiome in each sex might be also influenced by the contrasting movement strategies of females (migratory) and males (residents). The hypothetical mechanisms that could explain the association between immunity and microbiota in L. yerbabuenae are described Fig. 8 . Alpha and Beta diversity metrics were not related to BKA Alpha and beta diversity metrics of fecal microbiota were not related to BKA in L. yerbabuenae . This finding aligns with our hypothesis that general diversity metrics may obscure the relationship between BKA and the gut microbiota because they encompass all gut bacteria. The relationship between BKA and microbiota is likely restricted to specific microbial groups as has been observed in previous studies on bats [ 29 ]. Alpha but not beta diversity metrics exhibits a sex-biased relationship with tIgG None of the beta diversity metrics were related to tIgG. Conversely, tIgG was negatively associated with the Shannon and inverse Simpson indices in females. Females with reduced fecal ASV diversity and higher tIgG were likely experiencing dysbiosis, which is characterized by a reduction in beneficial microorganisms and an expansion of dysbiotic and pathogenic microorganism [ 3 ]. The low bacterial diversity of these females with high tIgG may be an indicative that they have just completed the migratory movements [ 16 ]. A dysbiotic microbiota could increase intestinal permeability and allow luminal components to translocate into the bloodstream [ 92 ], stimulating systemic IgG production [ 93 – 95 ]. Consistent with this idea, females with high tIgG had a microbiota enriched with genera that include pathogenic species (Supplementary Table 14). However, future studies are needed to confirm the signatures of dysbiosis in L. yerbabuenae. In contrast, males with greater fecal microbiota ASV diversity had higher tIgG, suggesting that males with higher ASV diversity were enriched with bacteria that stimulate IgG production [ 93 – 95 ]. Overall, these results suggest that alpha diversity of fecal microbiota is related to tIgG in a sex-specific manner in L. yerbabuenae , supporting the hypothesis that the microgenderome contributes to sex-based immune differences [ 7 , 8 ]. Furthermore, we cannot rule out the possibility that these results were influenced by the migratory status of some females [ 16 ]. Bacteria with immunostimulatory properties are increased in individuals with high BKA and tIgG. BKA was associated with multiple ASVs that were either exclusive to or upregulated in individuals with high BKA (Supplementary Table 12). Given that BKA is associated with complement system [ 96 ] and lysozyme activity [ 31 , 97 ], one possible scenario is that these ASVs stimulate BKA through these immunological components. For instance, administration of lactic acid bacteria to fish can enhance complement and lysozyme activity [ 98 – 100 ]. BKA stimulation might occur due to the activation of intestinal immune cells that enhance other systemic immune components, or to the translocation of bacterial components into the blood, which enhances systemic humoral immunity [ 101 – 103 ]. Accordingly, lactic acid bacteria, such as Enterococcus [ 104 , 105 ] in females, and Fructobacillus [ 106 , 107 ] and Weissella [ 108 , 109 ] in males, were associated with high BKA. Upregulated ASVs belonging to Exiguobacterium and Lactococcus in males with high BKA also support this scenario. The Exiguobacterium genus could enhance BKA under healthy conditions because certain probiotic species, such as E. acetylicum G1-33 , can boost the immune gene activity of the liver, a key organ for producing some complement molecules [ 110 ]. On the other hand, some Lactococcus species enhance complement and lysozyme activity when administered as probiotics to Nile tilapia [ 111 ]. Several ASVs were either exclusive to or upregulated in individuals with high tIgG (Supplementary Table 14). These ASVs may enhance systemic tIgG production if they are involved in producing short-chain fatty acids (SCFAs), which can enter the bloodstream and stimulate IgG production [ 112 , 113 ]. Accordingly, we detected ASVs that might produce SCFAs that were associated with high tIgG in females ( Actinomyces , Bacillus and Paeniclostridium ) and males ( Streptococcus ) [ 115 – 118 ]. In line with our findings, the genera Actinomyces and Streptococcus were correlated to increased levels of SCFAs in Jamaican fruit bats ( Artibeus jamaicensis ) [ 114 ]. Furthermore, certain Bacillus species have been demonstrated to elevate systemic immunoglobulin levels in mice [ 115 ], reinforcing the idea that these SCFA-producing bacteria may enhance systemic IgG production. High BKA and tIgG are linked to potentially pathogenic bacteria and/or bacteria associated with dysbiosis. High BKA and tIgG could also result from infection. Most bacterial ASVs associated with high BKA and tIgG belonged to bacterial genera with known dysbiotic and pathogenic species for bats [ 36 – 45 ] and other animals (Supplementary Tables 12 and 14). Several of these ASVs were sex-associated, suggesting that the gut microbiota of females and males harbored different dysbiotic and/or pathogenic bacteria with the potential to increase humoral immunity. In few cases, the presence of these ASVs was shared by both sexes. These ASVs belonging to bacterial genera associated with infection could increase BKA (through complement and lysozyme activity [ 116 – 119 ]) and tIgG [ 93 – 95 ] if they disrupt the intestinal mucosa and promote a microbial translocation into the bloodstream that stimulates a systemic immune response [ 120 , 121 ]. Another possibility is that an extra intestinal infection would increase BKA [ 122 , 123 ] (likely through complement and lysozyme [ 116 – 119 ]) and tIgG [ 124 – 130 ], while also disrupting the gut microbiota [ 3 ]. A dysbiotic microbiota allows pathogens and opportunistic pathobionts to proliferate [ 3 ]. These scenarios might explain why some bacterial genera containing species associated with infection were linked to high BKA and tIgG. The most notable ASVs associated with high BKA and tIgG (Supplementary Tables 12 and 14) belonged to Chlamydia (females), Escherichia-Shigella (males for BKA and both sexes for IgG ), Gemella (males), Mycoplasma ( females for BKA and males for IgG), Paeniclostridium (females), Providencia (males), Streptococcus (both sexes for BKA and males for IgG), and Ureaplasma (both sexes). These bacterial genera harbor species associated with infection and were detected in individuals with high BKA and tIgG. Some genera are worth highlighting due to the extent of their association. The Escherichia-Shigella genus stands out because this group of enterobacteria was represented by four different ASVs in individuals with both high BKA and tIgG. The genera Clostridium sensu stricto 1 , Streptococcus , and Ureaplasma are notable in relation to BKA for several reasons. These genera were represented by multiple ASVs and were exclusive to and upregulated in individuals with high BKA in both sexes. Disease-associated bacterial ASVs are linked to low BKA and tIgG. Only two infection-associated ASVs were linked with low BKA in females, whereas 11 such ASVs were associated in males and two ASVs ( Acinetobacter ) were present in both sexes (Supplementary Table 13). This suggest that lower BKA in females was weakly associated with their gut microbiota composition, whereas lower BKA in males was strongly associated with it. In contrast (Supplementary Table 15), low tIgG values were linked to slightly more infection-associated ASVs in females (10) than in males (7) with three genera shared by both sexes. Furthermore, of the ASVs associated with low BKA and tIgG, only one ( Fructobacillus in males) has not been associated with infection. Most ASVs associated with low BKA and tIgG belonged to bacterial genera that could proliferate if the host faces a disease or poor healthy conditions (Supplementary Tables 13 and 15). Consistent with this idea, individuals with low BKA and/or tIgG might be experiencing an energy trade-off [ 19 ] or an infection [ 30 ]. A low BKA during malnutrition or caloric deficit may result from reduced complement molecule production (Reviewed [ 131 ]), whereas tIgG may decrease during certain infections [ 132 ] or when infection and malnutrition occur concurrently [ 133 ]. Similarly, gut microbiota can be disrupted by infection, caloric deficit or malnutrition [ 3 , 10 ]. Therefore, elevated ASVs in individuals with low BKA and tIgG may result from disease or poor health conditions. The bacterial genera Acinetobacter, Anaerococcus, Clostridium sensu stricto 1, Corynebacterium, Helicobacter, Mycoplasma, Pannonibacter and Staphylococcus are noteworthy within these ASVs because they were associated with low levels of both BKA and tIgG. Healthy bacterial groups could strengthen the intestinal mucosa and lower BKA and tIgG Another possible scenario is that some of the ASVs associated with low BKA and tIgG could strengthen the intestinal mucosa. A fortified mucosa decreases the translocation of microbial cells and molecules from intestinal lumen that could stimulate BKA and tIgG. Congruent with this hypothesis, a small number of bacterial genera associated with low BKA and tIgG (Supplementary Tables 13 and 15) have been associated with a fortified intestinal mucosa. The genera Clostridium sensu stricto 1 [ 134 ] and Corynebacterium [ 135 – 138 ] were the most notable bacteria that include species associated with a strong intestinal mucosa because these genera were associated with low BKA in males and with low tIgG in females and males. Other bacterial groups that could strengthen the intestinal mucosa are Paraclostridium [ 139 ] (low BKA in males), Fructobacillus [ 106 , 107 ] (low tIgG in males), Enterococcus [ 105 ] and Streptococcus [ 140 ] (low tIgG in females). Therefore, if individuals with low BKA and tIgG values are not ill, it is likely that accompanying ASVs might favor a strong intestinal barrier. Unclassified and free-living bacterial genera associated with the immune system of bats Many ASVs associated with BKA and tIgG could only be classified at the order or family level (Supplementary Table 16–19). We refer to these as unclassified ASVs. These ASVs were associated with humoral immunity in females or in males. Notably, unclassified ASVs associated with high BKA were only present in males. The presence of unclassified ASVs underscores the incomplete taxonomic characterization of fecal microbiota in bats. It is difficult to establish a relationship between the biology of these ASVs with the immune system because these taxonomic groups include many microorganisms with different lifestyles. However, it is important to highlight Enterobacteriaceae for the following reasons. First, several enterobacterial ASVs were associated with high and low BKA and tIgG in both sexes. Second, these enterobacterial ASVs could be important for the health of L. yerbabuenae because this family is associated with death and disease in bats [ 39 – 45 ], but they can also establish a healthy bond with their hosts [ 141 ]. This is a noteworthy issue given that, similarly to L. yerbabuenae [ 58 , 59 , 142 ], Enterobacteriaceae is present in multiple bat species and can dominate their gut microbiota [ 46 , 143 , 144 ] .Therefore, it is unlikely that most gut enterobacteria in bats are associated with disease. Future studies should address this question to determine which enterobacteria are pathogenic, commensal or beneficial for bats, and how they interact with their immune system. Multiple environmental or free-living bacterial genera were associated with BKA and tIgG (Supplementary Table 16–19). Of these, the photosynthetic bacterium Blastochloris sp. [ 145 ] and the rare opportunistic pathogen Pannonibacter sp. [ 146 – 148 ] were associated with the humoral immunity of both sexes. Only one ASV ( Salinisphaera sp. ) was exclusively associated with humoral immunity in male, whereas most were associated with the humoral immunity of females. Some of these ASVs linked to females are associated with plant and insect microbiota, suggesting that they likely originate from their diet [ 53 , 54 , 149 , 150 ]. These ASVs can colonize animal bodies and affect key aspects of host health. Some examples are illustrated by the Asaia genus in nectarivorous insects [ 151 – 153 ], the genus Fimbriiglobus in fishes [ 154 , 155 ], the genus Rhodopseudomonas in aquatic animals [ 156 – 159 ], and the rare opportunistic pathogen Cyanobium PCC-6307 in shrimps [ 160 , 161 ]. Therefore, we cannot discard their importance for L. yerbabuenae and other animal species in which these genera are found. Other bacterial genera associated with humoral immunity are commonly found in aquatic environments or caves (Supplementary Table 16–19). It is likely that these microorganisms originate from the sea near Don Panchito Island and caves where L. yerbabuenae inhabit [ 162 ]. The presence of these bacterial groups is not surprising, as several microorganisms from the environment and other animal species can be detected in bat microbiota [ 32 ]. As we collected the feces released by the bats immediately during the measurement and sampling process, it is unlikely that these bacteria originate from environmental contamination. Hence, they should be transient or natural inhabitants of the gut microbiota of L. yerbabuenae . Conclusion The relationship between fecal microbiota and constitutive humoral immunity varied with sex in L. yerbabuenae . Immunity was weakly linked to bacterial diversity, but it showed a strong association with bacterial composition. These sex-biased patterns likely arose from divergent fecal microbiota profiles, as females harbor a larger number of bacterial families than males. Consistently, previous work in captive Artibeus jamaicensis have shown that sex-based differences in fecal microbiota composition correlate with contrasting intestinal metabolomes [ 114 ]. These metabolic variations include key immunoregulatory metabolites, supporting our hypothesis that gut microbiota divergences between females and males L. yerbabuenae influence their immune function. The primary drivers of these results likely stem from the migratory behavior of female versus the residency of males, or other sex-associated biological traits. Most ASVs associated with humoral immunity in both sexes belong to bacterial genera with known immunostimulatory and intestinal mucosa-strengthening species, and genera with species associated with infection. Collectively, these findings demonstrate that the fecal microbiota of L. yerbabuenae is linked to humoral immunity, particularly with bacteria that may enhance immune function and mucosal integrity, as well as with bacteria associated with infection or poor health. Future mechanistic studies should investigate whether and how bacterial inhabiting the gut influences the systemic immunity of wildlife. Furthermore, the association with potentially pathogenic genera is significant, as it suggests that L. yerbabuenae harbors bacteria that might be important for human health and other animals. Metagenomic approaches will help characterize the functions of gut microbiota and aid in identifying these bacteria at the genome-scale to corroborate their beneficial or pathogenic potential. This approach will also help resolve the identity of several immunity-associated ASVs that currently remain unclassified beyond the order or family level. Finally, future studies should isolate bat gut bacteria and use them as microbial targets or immunological challenges to confirm their importance to the immune system of L. yerbabuenae . Declarations Acknowledgments This article is part of the requirements for D.A.R.-R. to obtain a PhD degree in the Posgrado en Ciencias Biológicas-UNAM We would like to thank Rodrigo A. Medellín L. and the personnel at the Chamela Biological Station in Jalisco for their logistic support, and to Natalia S. Herrera, León A. Pizano and Omar Calva during field work. We would also like to thank Gastón Contreras Jiménez (Laboratorio de Microscopía y Microdisección Laser, Instituto de Ecología, UNAM) and Arit de Leon-Lorenzana (Investigadora por Mexico SECIHTI, Universidad Intercultural Maya de Quintana Roo) for their technical assistance with tIgG determination and DNA library preparation, respectively. Data Availability All sequences included in this study have been deposited in NCBI BioProject PRJNA1404331. All other datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Funding Funding was provided by research grants from Dirección General de Asuntos del Personal Académico (DGAPA; IN204219, IN203822) and from Secretaría de Ciencia, Humanidades, Tecnología e Innovación (SECIHTI; CBF2023-2024-192) to L. Gerardo Herrera M. Additional funding was also obtained from DGAPA to Luisa I. Falcon (BV200421). David A. Rivera-Ruiz was supported by a student grant from SECIHTI. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions Conceptualization: David Alfonso Rivera-Ruiz and L. Gerardo Herrera M.; Methodology: David Alfonso Rivera-Ruiz, Marco Tulio Solano de la Cruz, Luisa I. Falcón, Osiris Gaona and L. Gerardo Herrera M.; Formal Analysis: David Alfonso Rivera-Ruiz; Field work: David Alfonso Rivera-Ruiz, José Juan Flores-Martínez and L. Gerardo Herrera M.; Data Curation: David Alfonso Rivera-Ruiz; Original Draft Preparation: David Alfonso Rivera-Ruiz and L. Gerardo Herrera M.; Review and Editing:Marco Tulio Solano de la Cruz, Luisa I. Falcón, Carlos Rosales, Osiris Gaona and José Juan Flores-Martínez; , Supervision: David Alfonso Rivera-Ruiz and L. Gerardo Herrera M.; Project Administration: L. Gerardo Herrera M. Ethics Approval The protocol of the study was approved by the Ethics Committee in Research and Teaching of the Institute of Biology, National Autonomous University of Mexico. The study was conducted under permits from Dirección General de Vida Silvestre (13222/18, 8053/19, and 12346/23). Artificial Intelligence (AI)-assisted technologies We used AI to generate the graphical abstract. References Khan IM, Nassar N, Chang H et al (2024) The microbiota: a key regulator of health, productivity, and reproductive success in mammals. Front Microbiol 15:1480811. https://doi.org/10.3389/fmicb.2024.1480811 Fontaine SS, Kohl KD (2020) Optimal integration between host physiology and functions of the gut microbiome. 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Falcón","email":"","orcid":"","institution":"National Autonomous University of Mexico","correspondingAuthor":false,"prefix":"","firstName":"Luisa","middleName":"I.","lastName":"Falcón","suffix":""},{"id":628387135,"identity":"03ca601d-dbf7-4a12-beb3-33f75ce25ae0","order_by":4,"name":"Osiris Gaona","email":"","orcid":"","institution":"National Autonomous University of Mexico","correspondingAuthor":false,"prefix":"","firstName":"Osiris","middleName":"","lastName":"Gaona","suffix":""},{"id":628387136,"identity":"ba044543-0dd0-4df5-b8d4-c2ebb25af895","order_by":5,"name":"Marco Tulio Solano de la Cruz","email":"","orcid":"","institution":"National Autonomous University of Mexico","correspondingAuthor":false,"prefix":"","firstName":"Marco","middleName":"Tulio Solano de la","lastName":"Cruz","suffix":""},{"id":628387137,"identity":"e226adaa-2c51-4298-bb6d-578f652d96f1","order_by":6,"name":"L. Gerardo Herrera M.","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYDACCSjNz8xDqhbJZpK1GBwgVot8dPOxzxUV9+SNj/MeYObdYRfNIHbGAK8WwzvHkmeeOVNsuO0wXwIz75nk3AbptAT8WmbkGDM2tiUwbjvMY8DM28YM1JJ8gICW/M+Mjf8S7Dc3g7XUA7UkNuD3i0QOM2NjQ0LiBmawlsOEbTGQSDNmbDiWkDwD6JeDc9uO57YR8ov8jOTHjA01Cbb9/WcPPnjbVp3bL52DP8QMkB0BZrPhVQ+ypYGQilEwCkbBKBgFAC7DQhO5Pg6NAAAAAElFTkSuQmCC","orcid":"","institution":"National Autonomous University of Mexico","correspondingAuthor":true,"prefix":"","firstName":"L.","middleName":"Gerardo Herrera","lastName":"M.","suffix":""}],"badges":[],"createdAt":"2026-04-16 16:23:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9440532/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9440532/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107772638,"identity":"7b675eb2-049b-46f0-aa17-8c6b5c4f63bb","added_by":"auto","created_at":"2026-04-25 05:15:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":401168,"visible":true,"origin":"","legend":"\u003cp\u003eComposition of fecal microbiota in adults of Leptonycteris yerbabuenae in the Pacific region of Mexico. The relative abundance of the main phyla and families present in different reproductive stages of females are shown in panels A and B, respectively. Panels C and D report the relative abundance of phyla and families in adult males from Coquimatlán and Chamela, respectively.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9440532/v1/31339fdad436584a43e44157.png"},{"id":107772639,"identity":"dc1b1b25-972f-4ec9-ba0f-17ee93f2d061","added_by":"auto","created_at":"2026-04-25 05:15:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":518078,"visible":true,"origin":"","legend":"\u003cp\u003eThe ANCOMBC2 results show ASVs identified as structural zeros in low and high plasma bacterial killing-ability (BKA) individuals of Leptonycteris yerbabuenae. The y-axis shows the relative abundance of ASVs unique to low and high BKA groups in females (A and B) and males (C and D). The x-axis consists of a single category that distinguishes the immune range to which the ASVs belong. The \"Others \u0026lt;1%\" category represents low-abundance sequences that did not reach a minimum relative abundance threshold of 1%. Panels A and B depict the unique ASVs of females with low (panel A) and high (panel B) BKA, respectively. Panels C and D depict males.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9440532/v1/34d0249da0601b1d6742ef6f.png"},{"id":107772641,"identity":"dab8959a-be45-4a44-9fa8-13c7e3ff020b","added_by":"auto","created_at":"2026-04-25 05:15:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":619512,"visible":true,"origin":"","legend":"\u003cp\u003eANCOMBC2 differential abundance analysis showing the fecal ASVs that differ between adult males of Leptonycteris yerbabuenae with low and high plasma bacterial killing-ability (BKA). The taxonomic classification of fecal ASVs is shown in the Y-axis and the comparison between low (reference group) and high BKA males is shown in the X-axis. The color of each box indicates the change of each ASV (blue indicates a decrease and red an increase) relative to the reference group. The numerical values within each box indicate the magnitude of the log change relative to the reference group. The Benjamini \u0026amp; Hochberg (BH) method was used to correct for multiple testing. ASVs in black were identified as significant (p \u0026lt; 0.05) without using the multiple testing correction method, while differentially significant ASVs after multiple testing correction are indicated in blue. ASVs marked with an asterisk were significant after applying the ANCOM-BC2 sensitivity filter.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9440532/v1/94e711264471b904a9276219.png"},{"id":107772643,"identity":"8fc1c6da-73f8-4a90-bd4c-01a74675e852","added_by":"auto","created_at":"2026-04-25 05:15:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":309874,"visible":true,"origin":"","legend":"\u003cp\u003eTotal immunoglobulin G concentration (tIgG) as a function of fecal microbiota alpha diversity in L. yerbabuenae. Each point represents one individual and their associated tIgG value was calculated[AH1] [DR2] \u0026nbsp;through optical density (OD). Shannon's index and Simpson's inverse index are plotted with a regression line and their respective 95% confidence intervals (gray area). tIgG in relation to A) Shannon's index and B) Simpson's inverse index in females, and C) and D) in males.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9440532/v1/b989765fd2c11771a94b4c6e.png"},{"id":107869143,"identity":"ea6be791-a225-405d-a06c-22f71aa22680","added_by":"auto","created_at":"2026-04-27 07:36:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":500250,"visible":true,"origin":"","legend":"\u003cp\u003eThe ANCOMBC2 results showing ASVs identified as structural zeros in high and low total immunoglobulin G concentration (tIgG) in Leptonycteris yerbabuenae. The y-axis shows the relative abundance of ASVs unique to low and high tIgG. The x-axis consists of a single category that distinguishes the immune range to which the ASVs belong. The \"Others \u0026lt;1%\" category represents low-abundance sequences that did not reach a minimum relative abundance threshold of 1%. Panels A and B depict the unique ASVs of females with low (panel A) and high (panel B) tIgG, respectively. Panels C and D depict males.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9440532/v1/6357760556a4849815174ec4.png"},{"id":107870543,"identity":"3689a1d7-e235-49ad-9c93-95aa9f2fcd68","added_by":"auto","created_at":"2026-04-27 07:39:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":469388,"visible":true,"origin":"","legend":"\u003cp\u003eANCOMBC2 differential abundance analysis showing the fecal ASVs that differ between adult females of Leptonycteris yerbabuenae with low and high total immunoglobulin G concentration (tIgG). The taxonomic classification of fecal ASVs is indicated in the rows and the comparison between low (reference group) and high tIgG females is represented in the column. The color of each box indicates the change of each ASV (blue indicates a reduction and red an increase) relative to the reference group. The numerical values within each box indicate the magnitude of the logarithmic change relative to the reference group. The Benjamini \u0026amp; Hochberg (BH) method was used as a multiple testing correction. ASVs in black were identified as significant (p \u0026lt; 0.05) without using the multiple testing correction method, while differentially significant ASVs after multiple testing correction are indicated in blue. ASVs marked with an asterisk were significant after applying the ANCOM-BC2 sensitivity filter (SS filter).\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-9440532/v1/d86d6aea54f4b70f0d5c0aac.png"},{"id":107869090,"identity":"9a825d94-e81d-441a-bcdb-4779298b3dbc","added_by":"auto","created_at":"2026-04-27 07:36:01","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":414647,"visible":true,"origin":"","legend":"\u003cp\u003eANCOMBC2 differential abundance analysis showing the fecal ASVs that differ between individuals with low and high total immunoglobulin G concentration (tIgG) in adult males of Leptonycteris yerbabuenae. The taxonomic classification of the fecal ASVs is shown in the rows and the comparison between low (reference group) and high BKA males is shown in the columns. The color of each box indicates the change of each ASV (blue indicates a decrease and red an increase) relative to the reference group. The numerical values within each box indicate the magnitude of the log change relative to the reference group. The Benjamini \u0026amp; Hochberg (BH) method was used to correct for multiple testing. ASVs in black were identified as significant (p \u0026lt; 0.05) without using the multiple testing correction method, while differentially significant ASVs after multiple testing correction are indicated in blue. ASVs marked with an asterisk were significant after applying the ANCOM-BC2 sensitivity filter (SS filter).\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-9440532/v1/48fa77333842a98dbeb0dad6.png"},{"id":107869720,"identity":"ab3f4bd9-5429-4897-abf9-ae4be3bd65e3","added_by":"auto","created_at":"2026-04-27 07:37:57","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":365632,"visible":true,"origin":"","legend":"\u003cp\u003eHypothesized mechanisms to explain the relationship between immunity and microbiota in Leptonycteris yerbabuenae. A) graphical scheme showing our results, in which the fecal microbiota (mainly composition) is linked to humoral immunity (Bacterial killing-ability of plasma, BKA; Total immunoglobulin G, tIgG) in a sex specific-manner. B) Flowchart showing the possible mechanisms that could explain the relationship between immunity and microbiota. In general, the diagram suggests that differences in the microgenderome influence the relationship between immunity and the gut microbiota. Based on the biological properties of bacterial genera associated with immune activity, it is hypothesized that some bats may have healthy or dysbiotic microbiota.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-9440532/v1/6645ef2d144b34e841bceb54.png"},{"id":107872415,"identity":"5af52849-2979-4c7d-bd71-11f3e990e8a4","added_by":"auto","created_at":"2026-04-27 07:56:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4201746,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9440532/v1/9fd01b86-19d6-431e-aacb-d8586af93c2a.pdf"},{"id":107869870,"identity":"2f5a37cb-4fd6-4f28-8efa-b67a6e946ea3","added_by":"auto","created_at":"2026-04-27 07:38:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3650112,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9440532/v1/a08dce282e0e5de35fa63ebe.docx"},{"id":107869733,"identity":"5924717b-261b-459b-afad-01371f682582","added_by":"auto","created_at":"2026-04-27 07:38:00","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":80166,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial21.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9440532/v1/698e845a69da2d03c887a430.xlsx"},{"id":107869135,"identity":"d8a7c122-2107-4347-8b15-151aa45e33b5","added_by":"auto","created_at":"2026-04-27 07:36:12","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":117765,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9440532/v1/6355a258c7637d396dd699a8.xlsx"},{"id":107869024,"identity":"4ab019f4-1566-4c01-b282-bd3d65c7450f","added_by":"auto","created_at":"2026-04-27 07:35:46","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":79914,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9440532/v1/7fb3b0aa7904a71d89310a69.xlsx"},{"id":107772647,"identity":"9ecb4468-a794-4f57-bed8-1a9a2df24fd4","added_by":"auto","created_at":"2026-04-25 05:15:12","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":115926,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9440532/v1/9f209678b5a33aa6825b9c7b.xlsx"},{"id":107772649,"identity":"9e932c94-a636-4099-8cc4-add4f3106880","added_by":"auto","created_at":"2026-04-25 05:15:12","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":2997086,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9440532/v1/38d916c330c217b8a6c7eb75.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eHost-microbiota association in a migratory species: Constitutive humoral immunity in the partially migratory bat Leptonycteris yerbabuenae is linked to the gut microbiota in a sex-specific manner\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eVertebrate gut microbiota maximizes digestion, restrains the growth of pathogens and is involved in multiple physiological processes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These benefits require a stable microbiota confined to the intestinal lumen [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Otherwise, imbalanced microbiota (dysbiosis) can be the cause or consequence of various diseases [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. To maintain a healthy microbiota, the immune system confines the microbiota to the intestinal lumen while maintaining constant communication with it [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The relationship between immunity and microbiota is bidirectional: the immune system shapes microbiota, and the microbiota drives the development and activity of multiple immunological components [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This host-microbiota interplay mediated by the immune system is key to host health[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and can be shaped by sex, energy, nutrition, and infection [\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Hence, effective communication between the immune system and the gut microbiota is essential for the host's fitness.\u003c/p\u003e \u003cp\u003eDespite the growing evidence linking gut microbiota with immune function, most studies have focused on humans and laboratory animals. This limits our understanding of how this interaction operates under natural conditions. As with humans [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], immune function in wild animals is affected by environmental and physiological constraints [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The wide range of factors affecting the immune system results in significant immunological variability at the individual level [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Similarly, the gut microbiota of wildlife is dynamic. Microbial communities adapt to the life history traits of their hosts and their biological context [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The interaction between immunity and microbiota in wildlife remains an underexplored topic, especially in migratory species, which face environmental and physiological constraints that shape their gut microbiota and immune system [\u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These changes may affect the susceptibility of migratory animals to infection, as gut microbiota may prevent the invasion of enteric pathogens encountered during migration [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], while the physiological cost of migration may compromise immune function [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Therefore, understanding the relationship between immune system and gut microbiota in natural conditions is crucial for comprehending the host-microbiota relationship in animals [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and the health of their populations [\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough the interplay between the immune system and microbiota in wild animal has not been widely explored, previous studies suggest that they are linked. The limited evidence available indicates that natural variation in the immune system is not associated with the diversity of gut microbiota, but that it is linked to the presence or abundance of specific bacterial groups. For instance, cellular immune response in the barn swallow (\u003cem\u003eHirundo rustica\u003c/em\u003e) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and plasma bacterial killing-ability (BKA) in the common vampire bat (\u003cem\u003eDesmodus rotundus\u003c/em\u003e) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] are not associated with gut bacterial diversity but with specific bacterial groups. Similarly, bacterial composition appears to modulate BKA in the Eurasian teal (\u003cem\u003eAnas crecca\u003c/em\u003e) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and total immunoglobulin G concentration (tIgG) in \u003cem\u003eD. rotundus\u003c/em\u003e [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Although these studies highlight the importance that specific gut bacteria have on the immune system activity in wildlife, they skewed their analysis to specific bacterial groups. In the Eurasian teal, the analysis focused on the phylum level, while in vampire bats, Ingala et al 2019 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] focused on the core microbiota. This analysis strategy limits the taxonomic resolution of the results and excludes low-abundant bacteria that could be key to immune system activity. Therefore, a more comprehensive and specific analysis is warranted to gain a better understanding of the relationship between immunity and microbiota in wildlife.\u003c/p\u003e \u003cp\u003eBats are ideal organisms for studying the relationship between immunity and microbiota because they harbor a wide variety of microorganisms found in the environment and in other animal species [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This makes it possible to evaluate the relationship between immunity and microbiota across a wide range of microorganisms that are present in multiple ecosystems and wildlife species. The relationship between immunity and microbiota in bats has been widely studied with respect to pathogenic viruses [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Although our knowledge about bats and their associated bacteria is limited [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], pioneer studies show that infection can alter their microbiota [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], and that some bacteria inhabiting bats can negatively affect bats themselves [\u003cspan additionalcitationids=\"CR40 CR41 CR42 CR43 CR44\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] as well as other mammalian species [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Recent studies have emphasized the importance of the gut microbiome in understanding the unique immune system of bats [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], and a handful of experimental studies support this idea [\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. However, the relationship between immunity and gut microbiota in natural environments has been scarcely studied in bats [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], and to our knowledge it has not been investigated in migratory species.\u003c/p\u003e \u003cp\u003eTo investigate the relationship between the gut microbiota and immunity in wildlife, we used the lesser long-nosed bat (\u003cem\u003eLeptonycteris yerbabuenae\u003c/em\u003e) as a model species. Individuals of populations of this species mate in west-central Mexico in fall-winter and then most pregnant females migrate in spring to northern Mexico and southern USA to have their young [\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. We characterized gut microbiota by amplifying the 16S rRNA gene from fecal samples and examined how it was related to BKA and tIgG. Previous studies on this species have shown that high inter-individual variability in BKA and tIgG is related to demographic variables, but it might be also modulated by inter-individual differences in gut microbiota [\u003cspan additionalcitationids=\"CR57 CR58\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Consistent with previous research in bats [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and birds [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], we did not expect that BKA and tIgG would be related to microbiota diversity, but rather to its composition. We expected that humoral immunity in \u003cem\u003eL. yerbabuenae\u003c/em\u003e would be associated with bacterial genera (1) that harbor species with known immunostimulatory properties under healthy conditions, and (2) that harbor species associated with infection, including intrinsically pathogenic bacteria, pathobionts and other taxa that proliferate during dysbiosis. The gut microbiota of female \u003cem\u003eL. yerbabuenae\u003c/em\u003e changes in response to reproductive activity, migration and dietary changes [\u003cspan additionalcitationids=\"CR58\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. In contrast, adult male are residents and do not undergo the physiological and environmental changes that females experience throughout their life cycle [\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Therefore, we assessed the relationship between immunity and microbiota separately for each sex.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cem\u003eStudy sites and sampling of bats.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe study was conducted between 2019 and 2024. Fecal samples of \u003cem\u003eL. yerbabuenae\u003c/em\u003e were collected in west-central Mexico (Don Panchito Island, Chamela, Jalisco, 19\u0026deg;32'08.4\u0026acute;\u0026acute;N, 105\u0026deg;05'18.8\u0026acute;\u0026acute;W; La F\u0026aacute;brica cave, Coquimatl\u0026aacute;n, Colima, 19\u0026ordm;09\u0026acute;05.8\u0026acute;\u0026acute;N, 103\u0026ordm;50\u0026acute;06.9\u0026acute;\u0026acute;O), and in northwestern Mexico (Mariana cave, Carb\u0026oacute;, Sonora, 29\u0026ordm;35\u0026acute;25.9\u0026acute;\u0026acute;N, 110\u0026ordm;48\u0026acute;8.9\u0026acute;\u0026acute;W). Only adult male samples (n\u0026thinsp;=\u0026thinsp;38) were collected in west-central Mexico. In contrast, adult females (N\u0026thinsp;=\u0026thinsp;24) were captured in West central of Mexico (Chamela, non-reproductive state; n\u0026thinsp;=\u0026thinsp;8), and in northwestern Mexico (pregnant and lactating; n\u0026thinsp;=\u0026thinsp;16). This sex-biased capture is consistent with demographic patterns of \u003cem\u003eL. yerbabuenae\u003c/em\u003e in these regions [\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSampling was conducted following the guidelines of the American Society of Mammalogists [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Bats were captured between 22:00 and 06:00 hours. Each bat was placed in a clean cotton bag until processing. We recorded the sex, age category (adult or young), reproductive condition, length forearm (Mitutoyo CD-6, Mexico; \u0026plusmn;0.01 mm) and body mass (Ohaus, Nueva Jersey, USA, \u0026plusmn; 0.1 g) of individuals. Adult and young individuals were distinguished by examining multiple phenotypical traits: the classical epiphyseal\u0026ndash;diaphyseal fusion of the 4th metacarpal\u0026ndash;phalangeal joint [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], hair color (young had short grayish hair) and body mass index. Reproductive males of \u003cem\u003eL. yerbabuenae\u003c/em\u003e were identified by scrotal testicles and a dorsal patch on the scapular region [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Females were classified as pregnant by palpation of their abdomen and lactating if they presented hairless nipples that released milk after being pressed. Finally, each bat was placed in plastic containers lined with sterile paper bags for fecal collection and a sterile net on the top that allowed bat to perch. The containers were checked approximately every 10 minutes to collect feces, which were placed in cryotubes and immediately immersed in liquid nitrogen until stored at -70\u0026deg;C in the laboratory.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eImmunological assays\u003c/h2\u003e \u003cp\u003eWe quantified tIgG and used previously published (56) and unpublished data on BKA to evaluate the relationship between immunity and microbiota in \u003cem\u003eL. yerbabuenae\u003c/em\u003e. The methodology used to perform BKA is described in Rivera et al 2023 [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. As a measure of adaptive humoral immunity, we quantified tIgG by enzyme-linked immunosorbent assay in 96-well plates. Each sample was worked in triplicate and plasma (2\u0026micro;l) was diluted to a factor of 1/20,000 and incubated for 18 hours at 4\u0026deg;C. Following this incubation, 2 washes (Wash solution: 200 \u0026micro;l of 0.05% PBS-Tween-20) were performed to remove excess sample. The bottom of the plate was blocked with 50 \u0026micro;l of bovine serum albumin (BSA, diluted 1% in PBS 1X), incubated for 2 hours at room temperature and then washed twice. To detect plasma tIgG, a volume of 50 \u0026micro;L of peroxidase-coupled anti-IgG antibody (Goat anti-Bat IgG (H\u0026thinsp;+\u0026thinsp;L) Secondary Antibody [HRP], NB7238, NOVUS BIOLOGICAL) was added at a 1/10000 dilution and allowed to react for 2 hours at room temperature. After incubation, two washes were performed and 50 \u0026micro;L of SIGMA FAST-OPD (p9187) developer solution were added to each well. The colorimetric reaction stopped after 5 minutes with 100 \u0026micro;l of H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e and the absorbance of the plate was recorded at 490 nm. Antibody concentration for each sample was determined from the average optical density (OD) of three replicates, as color intensity is directly proportional to antibody concentration.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGut microbiota analysis\u003c/h3\u003e\n\u003cp\u003eIn line with previous studies on \u003cem\u003eL. yerbabuenae\u003c/em\u003e [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], we extracted DNA from bat feces using a DNeasy Blood \u0026amp; Tissue Kit (Qiagen, Valencia, CA), following the manufacturer's instructions with some modifications. The cell lysis was done by incubating the sample with ATL buffer and proteinase K, 3\u0026micro;L of lysozyme (3 mg/mL; Sigma-Aldrich) and incubation at 37\u0026deg;C for 30 minutes at 500 rpm to enhance bacterial cell wall breakdown. After completing the DNA extraction process, DNA was precipitated with 1/10 volume of sodium acetate (3M) and 300 \u0026micro;L of absolute alcohol at -20℃. After cleanup, DNA was diluted in molecular grade water (30 \u0026micro;L) and stored at -20\u0026deg;C.\u003c/p\u003e\n\u003ch3\u003eSequencing and amplification of 16S rRNA gene\u003c/h3\u003e\n\u003cp\u003eThe amplification of the V4 region of the 16S rRNA gene was performed according to Caporaso et al. [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] and Carrillo et al. [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] using the Earth Microbiome Project primers 515F/806R. The PCR reaction (25 \u0026micro;L) contained 2.5 \u0026micro;L of Takara ExTaq PCR buffer 10X, 2 \u0026micro;L of Takara dNTPs, 0.7\u0026micro;L of bovine serum albumin (BSA, 20 mg ml-1), 0.125 \u0026micro;L of Takara Ex Taq DNA Polymerase (5 U \u0026micro;l-1) (TaKaRa, Shiga, Japan), 1 \u0026micro;L of primers and nuclease-free water. The PCR mix was amplified in a thermocycler (Eppendorf, Germany) with the following parameters: initial denaturation step at 94\u0026deg;C for 3 minutes, followed by 35 cycles of 94\u0026deg;C for 45 seconds, 50\u0026deg;C for 60 seconds, and 72\u0026deg;C for 90 seconds, and a final extension at 72\u0026deg;C for 10 minutes and a cooling temperature of 4\u0026deg;C until freezing. This process was performed in triplicate per sample and confirmed by agarose gel electrophoresis at 1% and 80V for 30 minutes to verify the presence of amplicons. The PCR amplicons were purified with magnetic beads (SeraMag Beads) and the DNA concentration of the 16S rRNA gene fragments was quantified with a Qubit (InVitroGen) to determine an optimal concentration (20 \u0026micro;g/\u0026micro;L per sample) for sequencing on Illumina MiSeq platform (Yale Center for Genome Analysis, CT, USA).\u003c/p\u003e\n\u003ch3\u003eBioinformatics processing of sequences\u003c/h3\u003e\n\u003cp\u003eThe raw sequences were demultiplexed and denoised with QIIME2 software (version 2024.10). The removal of chimeras and artifacts was done with the DADA2 algorithm [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. According to quality parameters, forward sequences were truncated to a length of 225 bp and reverse sequences were truncated to 205 bp. Taxonomic clustering of the filtered sequences in Amplicon Sequence Variants (ASVs) was performed using the QIIME2 command \"\u003cem\u003eqiime feature-classifier classify-consensus-vsearch\u003c/em\u003e\" from the SILVA V4 database (SSU release 138.1 515806). Only bacterial sequences with an assigned phylum were retained. Bacterial sequences without an assigned phylum or sequences identified as chloroplasts and mitochondria were removed. Sequences were aligned using the MAFFT algorithm [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] and then filtered using the \u0026ldquo;\u003cem\u003eqiime alignment mask\u003c/em\u003e\u0026rdquo; command to remove non-conserved and highly scattered regions within the alignment [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Finally, the phylogeny of the sequences was constructed using the FastTree algorithm [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e], and the artifacts generated in QIIME2 were exported to the R platform (version 2.2) for further bioinformatics processing. All sequences included in this study have been deposited in NCBI BioProject PRJNA1404331.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe artifacts generated in QIIME2 were imported into the R platform using the qiime2R package to create a phyloseq file [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Subsequently, the decontam package identified seventeen sequences based on those detected in the negative control (reagents of the extraction and the amplification steps) [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. The ASVs abundance table filtered by the decontam package was exported back to the QIIME2 platform to re-generate the artifacts necessary to construct the phyloseq file in the R platform.\u003c/p\u003e \u003cp\u003eAll samples were included in the analysis of general microbiota composition (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24 females and 38 males). All female samples were included to test the relationship between immunity and microbiota; however, fewer samples were used for males (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;38; of which 32 were used to BKA and 30 for tIgG), due to missing immunological data for some individuals. Location and reproductive status were controlled in the analysis of immunity and microbiota because they affect the microbiota of \u003cem\u003eL. yerbabuenae\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary Fig.\u0026nbsp;1\u0026ndash;6, Supplementary Table\u0026nbsp;1\u0026ndash;3; Gaona et al.[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] and Viquez et al.[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]). Therefore, the relationship between immunity and microbiota was addressed independently in each sex. Rarefaction is currently the most effective tool for controlling differences in library size [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Alpha and beta diversity metrics were calculated from the rarefied data at a minimum depth of 3477 reads for females and 6765 reads for males, as both cohorts exhibited differences in minimum library size. The minimum rarefaction threshold for both cohorts satisfactorily captured the diversity of the samples (Supplementary Fig.\u0026nbsp;7). Rarefaction of the data was performed using the rarefy_even_depth function of the metagMisc package [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. To visualize the general and predominant characteristics of the fecal microbiota of \u003cem\u003eL. yerbabuenae\u003c/em\u003e, samples were grouped according to their relative abundance at the phylum and family level by reproductive status for females and locality for males. Only taxonomic groups present in more than 3% of the readings were described and the remaining taxa were grouped in the category \u003cem\u003e'Others\u003c/em\u003e'.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAlpha diversity metrics\u003c/h2\u003e \u003cp\u003eSeveral alpha diversity indices were used to obtain a complete picture of the diversity of samples [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. We calculated the number of ASVs observed [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e], the Faith's phylogenetic diversity index (Faith's PD [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]), the Shannon's index and the inverse Simpson's index to provide a measure of alpha diversity [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Richness, Shannon's index and Simpson's inverse index were calculated using the estimate_richness function of the phyloseq package [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e], while the Faith's PD index was calculated using the estimate_pd function of the btools package [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. Generalized linear models and general linear models were built to assess the relationship of alpha diversity with BKA and tIgG, respectively. We used the glmmTMB package for BKA models [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e] and the stats package for tIgG [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. The fitdistrplus package was used to find the probability function that best fitted the immunological variables [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e], using a beta distribution for BKA and a normal distribution for tIgG. The significance of the models was determined by deviance analysis using the ANOVA function of the car package [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. We used the confint and coef function from the stats package to calculate confidence intervals and determine the direction of the relationship between immunity and alpha diversity metrics, respectively [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. In accordance with the principle of independence of explanatory variables [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e], reproductive status was not used as a covariate for Shannon's index, inverse Simpson's index and Faith's PD in the female model (Supplementary Fig.\u0026nbsp;1 and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and locality in all male alpha diversity metrics (Supplementary Fig.\u0026nbsp;2). Outliers that could significantly affect the models for BKA and tIgG were identified by means of Cook's distances. We removed one female for the inverse Simpson index and two males for the Shannon index in the model of tIgG. Finally, all models were validated by residual analysis using the DHARMa package [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBeta diversity metrics\u003c/h3\u003e\n\u003cp\u003eTo measure beta diversity, the immunological ranges were divided into low and high values based on the median. The cut-off point for BKA was 81.5%, so individuals with low and high BKA had a percentage below or above 81.5%, respectively. For tIgG, low and high ranges were defined according to absorbance values below and above 0.83 OD units. Dissimilarity matrices were calculated using phylogenetically unweighted UniFrac (absence and presence of ASVs) and weighted UniFrac (richness and abundance of ASVs) distances [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. From these distances, the centroids and ellipses of groups with low or high immunological activity were calculated and plotted together with the individual values of each sample using the multidimensional scaling (MDS) method. A permutational multivariate analysis of variance (PERMANOVA [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]) was used to assess whether unweighted and weighted UniFrac distances differed between individuals with low and high BKA and tIgG using the adonis2 function of the vegan package [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. Reproductive status was used as a covariant in the female model and locality in the male model. Finally, we used the betadisper function from the vegan package to calculate the multivariate dispersion of the immunological groups and test whether the PERMANOVA results were associated with unequal group variances.\u003c/p\u003e\n\u003ch3\u003eDifferential abundance analysis\u003c/h3\u003e\n\u003cp\u003eWe conducted an Analysis of Compositions of Microbiomes with bias correction 2 (ANCOM-BC2 [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]) on the non-rarefied sequences to identify the ASVs that differed between individuals with low and high immunological activity. Sensitivity analysis for the pseudo-count addition was used to reduce false positives. We considered taxa that were identified as structural zeros to identify taxa that were exclusively present in individuals with low or high immunological activity. We focused on the most dominant sequences within each group (low and high BKA and tIgG), establishing an arbitrary threshold of relative abundance greater than 1%. The remaining sequences were grouped in the \"Others\u0026thinsp;\u0026lt;\u0026thinsp;1%\" category. For ASVs present in both groups (low and high BKA and tIgG), we filtered out ASVs that were not present in at least 17% of samples. A minimum sequencing threshold of 1000 sequences was set, which included all female and male samples (minimum library size of 3477 and 6765 reads, respectively). We controlled for reproductive status in females and for locality in males. All ASVs classified at genus level (or higher taxonomic ranks if genus was not possible) with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were reported in the results, but we only considered those ASVs that remained significant after adjustment for multiple comparisons (Benjamini \u0026amp; Hochberg method) and sensitivity filter.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eOverall composition of the fecal microbiota of L. yerbabuenae\u003c/h2\u003e \u003cp\u003eSixty-nine fecal samples were successfully amplified, from which 62 adult samples were retained, presenting a total of 6,780,469 reads and representing 5,117 amplicon sequence variants (ASVs). The fecal microbiota composition of \u003cem\u003eL. yerbabuenae\u003c/em\u003e was dominated by phyla Proteobacteria and Firmicutes, whose relative abundance varied across different reproductive groups in females and males at different localities (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These differences in the fecal microbiota composition of \u003cem\u003eL. yerbabuenae\u003c/em\u003e were accentuated at lower taxonomic levels. At family level, Enterobacteriaceae, Clostridiaceae, Mycoplasmataceae and Enterococcaceae represented a major component of the fecal microbiota in both males and females. Other taxonomic groups were more characteristic of certain demographic groups, such as Cellvibrionaceae, Erwinaceae, Gemellaceae, Lactobacillaceae, Moraxellaceae, Pasteurellaceae, Xanthobacteraceae, and Yersiniaceae that were identified as major components of the fecal microbiota of females but were absent in males. In contrast, no bacterial families were exclusive of males.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between BKA and fecal microbiota diversity in L. yerbabuenae\u003c/h2\u003e \u003cp\u003eThere was no significant association between the alpha and beta diversity of the fecal microbiota and BKA in both sexes (Supplementary Fig.\u0026nbsp;8\u0026ndash;10 and supplementary Table\u0026nbsp;4\u0026ndash;6). In contrast, the ANCOMBC2 analysis identified many ASVs as structural zeros associated with high and low BKA. In females, 911 and 1,074 ASVs were represented exclusively individuals with low and high BKA, respectively (Supplementary material 2). Among males, 2,529 ASVs were unique to individuals with low BKA, while 533 ASVs were unique to those with high BKA (Supplementary material 3). We focused on the most abundant ASVs (relative abundance greater than 1%): 24 ASVs in females and 29 ASVs in males (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The results of the differential abundance analysis between ASVs that were present in both immunological groups (low and high BKA) in females showed twenty-eight ASVs. However, none of these retained their significance after adjustment for multiple comparisons and sensitivity filters (Supplementary Fig.\u0026nbsp;11). The \u003cem\u003ep\u003c/em\u003e and \u003cem\u003eq\u003c/em\u003e-values (\u003cem\u003ep\u003c/em\u003e-corrected values) of ASVs identified as differentially abundant are reported (Supplementary Table\u0026nbsp;7). In males, 50 ASVs were differentially abundant between low and high BKA individuals. Thirty-four ASVs passed the sensitivity filter and retained their significance after adjusting for multiple comparisons (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The \u003cem\u003ep\u003c/em\u003e and \u003cem\u003eq\u003c/em\u003e-values of ASVs are reported (Supplementary Table\u0026nbsp;8).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRelationship of tIgG to alpha diversity indices in L. yerbabuenae\u003c/h2\u003e \u003cp\u003eThe tIgG of females was not related to the observed number of ASVs or the Faith's PD index (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplementary Fig.\u0026nbsp;12). In contrast, tIgG showed a weak and negative correlation with the Shannon's index and the Simpson's inverse index in females according to the estimated regression coefficients (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and B). Regarding males, tIgG was not related to the observed number of ASVs, the Simpson's inverse index and the Faith's PD index (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD and Supplementary Fig.\u0026nbsp;13). However, the Shannon's index was significantly related to tIgG, but this association was marginally positive (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\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\u003eGeneralized linear model results for total immunoglobulin G (tIgG) as a function fecal microbiota alpha diversity in female and male of Leptonycteris yerbabuenae.\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=\"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=\"left\" 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\u003eAlpha diversity metrics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI [LL, UL]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ed.f\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFemale\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObserved number of ASVs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[-0.004, 0.001]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShannon index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[-0.224, -0.011]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInverse Simpson's index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[-0.077, -0.006]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFaith's PD index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[-0.039, 0.012]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObserved number of ASVs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3x10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3x10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[-3x10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e, 0.001]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShannon index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[0.001, 0.175]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInverse Simpson's index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[-0.001, 0.039]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFaith's PD index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[-0.004, 0.013]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eNote. β\u0026thinsp;=\u0026thinsp;estimated regression coefficients, SE\u0026thinsp;=\u0026thinsp;standard error, CI\u0026thinsp;=\u0026thinsp;confidence interval, = d.f\u0026thinsp;=\u0026thinsp;degrees of freedom.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eThe relationship between beta diversity metrics and tIgG.\u003c/em\u003e \u003c/p\u003e \u003cp\u003etIgG was not related to any beta diversity metrics considered (Unweighted and weighted distances) in females and males with low and high tIgG concentrations (Supplementary Fig.\u0026nbsp;14 and Table\u0026nbsp;9).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between tIgG and individual ASVs of the fecal microbiota\u003c/h2\u003e \u003cp\u003eThe ANCOMBC2 differential abundance analysis showed that low and high tIgG individuals differed in several ASVs. ANCOMBC2 structural zero analysis identified 820 and 1,151 ASVs exclusive to females with low and high tIgG, respectively (Supplementary Material 4). In males, 702 ASVs were exclusive to individuals with low tIgG, and 2,332 ASVs were exclusive to those with high tIgG (Supplementary Material 5). From these ASVs, we selected the most abundant: 24 in females and 26 in males (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Among the ASVs shared by females with low and high tIgG, 35 ASVs were differentially abundant between low and high tIgG females, of which 21 ASVs passed the sensitivity filter and remained significant after adjustment for multiple comparisons (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The \u003cem\u003ep\u003c/em\u003e and \u003cem\u003eq\u003c/em\u003e-values of ASVs identified as differentially abundant are reported (Supplementary Table\u0026nbsp;10). In males, 28 ASVs were identified as differentially abundant between males with low and high tIgG. However, only eight ASVs passed the sensitivity filter and the multiple comparison adjustment (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The \u003cem\u003ep\u003c/em\u003e and \u003cem\u003eq\u003c/em\u003e-values of ASVs identified as differentially abundant are reported (Supplementary Table\u0026nbsp;11).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe partially migratory bat \u003cem\u003eL. yerbabuenae\u003c/em\u003e was the biological model to explore the correlation between constitutive humoral immunity (plasma BKA and tIgG) and fecal microbiota in wildlife migratory species. In general, humoral immunity was not associated with the diversity of fecal microbiota, but its alpha diversity was related to tIgG in opposite directions within each sex. Similarly, we found compelling evidence that bacterial composition was related to humoral immunity in a sex-specific manner. ASVs belonging to bacterial genera with species that have immunostimulatory and mucosa-strengthening properties, as well as genera with species associated with dysbiosis and pathogenicity, were related to humoral immunity in both females and males. However, some bacterial genera and ASVs were specific to each sex. Unclassified and free-living bacterial genera were also associated with immunity in a sex-specific manner. These results suggest that the fecal microbiota of \u003cem\u003eL. yerbabuenae\u003c/em\u003e, and likely that of other wildlife species too, is associated with constitutive humoral immunity. While these associations do not establish causality, they are consistent with the hypothesis that fecal microbiota may influence humoral immune function. Differences in the way microbiota associated with humoral immunity in females and males probably originate from the microgenderome, microbiome and hormone characteristics of each sex [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Furthermore, the stability and functionality of the microbiome in each sex might be also influenced by the contrasting movement strategies of females (migratory) and males (residents). The hypothetical mechanisms that could explain the association between immunity and microbiota in \u003cem\u003eL. yerbabuenae\u003c/em\u003e are described Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAlpha and Beta diversity metrics were not related to BKA\u003c/h2\u003e \u003cp\u003eAlpha and beta diversity metrics of fecal microbiota were not related to BKA in \u003cem\u003eL. yerbabuenae\u003c/em\u003e. This finding aligns with our hypothesis that general diversity metrics may obscure the relationship between BKA and the gut microbiota because they encompass all gut bacteria. The relationship between BKA and microbiota is likely restricted to specific microbial groups as has been observed in previous studies on bats [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eAlpha but not beta diversity metrics exhibits a sex-biased relationship with tIgG\u003c/h2\u003e \u003cp\u003eNone of the beta diversity metrics were related to tIgG. Conversely, tIgG was negatively associated with the Shannon and inverse Simpson indices in females. Females with reduced fecal ASV diversity and higher tIgG were likely experiencing dysbiosis, which is characterized by a reduction in beneficial microorganisms and an expansion of dysbiotic and pathogenic microorganism [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The low bacterial diversity of these females with high tIgG may be an indicative that they have just completed the migratory movements [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A dysbiotic microbiota could increase intestinal permeability and allow luminal components to translocate into the bloodstream [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e], stimulating systemic IgG production [\u003cspan additionalcitationids=\"CR94\" citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]. Consistent with this idea, females with high tIgG had a microbiota enriched with genera that include pathogenic species (Supplementary Table\u0026nbsp;14). However, future studies are needed to confirm the signatures of dysbiosis in \u003cem\u003eL. yerbabuenae.\u003c/em\u003e In contrast, males with greater fecal microbiota ASV diversity had higher tIgG, suggesting that males with higher ASV diversity were enriched with bacteria that stimulate IgG production [\u003cspan additionalcitationids=\"CR94\" citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]. Overall, these results suggest that alpha diversity of fecal microbiota is related to tIgG in a sex-specific manner in \u003cem\u003eL. yerbabuenae\u003c/em\u003e, supporting the hypothesis that the microgenderome contributes to sex-based immune differences [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Furthermore, we cannot rule out the possibility that these results were influenced by the migratory status of some females [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eBacteria with immunostimulatory properties are increased in individuals with high BKA and tIgG.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eBKA was associated with multiple ASVs that were either exclusive to or upregulated in individuals with high BKA (Supplementary Table\u0026nbsp;12). Given that BKA is associated with complement system [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e] and lysozyme activity [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e], one possible scenario is that these ASVs stimulate BKA through these immunological components. For instance, administration of lactic acid bacteria to fish can enhance complement and lysozyme activity [\u003cspan additionalcitationids=\"CR99\" citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]. BKA stimulation might occur due to the activation of intestinal immune cells that enhance other systemic immune components, or to the translocation of bacterial components into the blood, which enhances systemic humoral immunity [\u003cspan additionalcitationids=\"CR102\" citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e]. Accordingly, lactic acid bacteria, such as \u003cem\u003eEnterococcus\u003c/em\u003e [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e, \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e] in females, and \u003cem\u003eFructobacillus\u003c/em\u003e [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e] and \u003cem\u003eWeissella\u003c/em\u003e [\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e, \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e] in males, were associated with high BKA. Upregulated ASVs belonging to \u003cem\u003eExiguobacterium\u003c/em\u003e and \u003cem\u003eLactococcus\u003c/em\u003e in males with high BKA also support this scenario. The \u003cem\u003eExiguobacterium\u003c/em\u003e genus could enhance BKA under healthy conditions because certain probiotic species, such as \u003cem\u003eE. acetylicum G1-33\u003c/em\u003e, can boost the immune gene activity of the liver, a key organ for producing some complement molecules [\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e]. On the other hand, some \u003cem\u003eLactococcus\u003c/em\u003e species enhance complement and lysozyme activity when administered as probiotics to Nile tilapia [\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral ASVs were either exclusive to or upregulated in individuals with high tIgG (Supplementary Table\u0026nbsp;14). These ASVs may enhance systemic tIgG production if they are involved in producing short-chain fatty acids (SCFAs), which can enter the bloodstream and stimulate IgG production [\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e, \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e]. Accordingly, we detected ASVs that might produce SCFAs that were associated with high tIgG in females (\u003cem\u003eActinomyces\u003c/em\u003e, \u003cem\u003eBacillus\u003c/em\u003e and \u003cem\u003ePaeniclostridium\u003c/em\u003e) and males (\u003cem\u003eStreptococcus\u003c/em\u003e) [\u003cspan additionalcitationids=\"CR116 CR117\" citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e]. In line with our findings, the genera \u003cem\u003eActinomyces\u003c/em\u003e and \u003cem\u003eStreptococcus\u003c/em\u003e were correlated to increased levels of SCFAs in Jamaican fruit bats (\u003cem\u003eArtibeus jamaicensis\u003c/em\u003e) [\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e]. Furthermore, certain \u003cem\u003eBacillus\u003c/em\u003e species have been demonstrated to elevate systemic immunoglobulin levels in mice [\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e], reinforcing the idea that these SCFA-producing bacteria may enhance systemic IgG production.\u003c/p\u003e \u003cp\u003e \u003cem\u003eHigh BKA and tIgG are linked to potentially pathogenic bacteria and/or bacteria associated with dysbiosis.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eHigh BKA and tIgG could also result from infection. Most bacterial ASVs associated with high BKA and tIgG belonged to bacterial genera with known dysbiotic and pathogenic species for bats [\u003cspan additionalcitationids=\"CR37 CR38 CR39 CR40 CR41 CR42 CR43 CR44\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] and other animals (Supplementary Tables\u0026nbsp;12 and 14). Several of these ASVs were sex-associated, suggesting that the gut microbiota of females and males harbored different dysbiotic and/or pathogenic bacteria with the potential to increase humoral immunity. In few cases, the presence of these ASVs was shared by both sexes. These ASVs belonging to bacterial genera associated with infection could increase BKA (through complement and lysozyme activity [\u003cspan additionalcitationids=\"CR117 CR118\" citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e]) and tIgG [\u003cspan additionalcitationids=\"CR94\" citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e] if they disrupt the intestinal mucosa and promote a microbial translocation into the bloodstream that stimulates a systemic immune response [\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e, \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e]. Another possibility is that an extra intestinal infection would increase BKA [\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e, \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e] (likely through complement and lysozyme [\u003cspan additionalcitationids=\"CR117 CR118\" citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e]) and tIgG [\u003cspan additionalcitationids=\"CR125 CR126 CR127 CR128 CR129\" citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e], while also disrupting the gut microbiota [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. A dysbiotic microbiota allows pathogens and opportunistic pathobionts to proliferate [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These scenarios might explain why some bacterial genera containing species associated with infection were linked to high BKA and tIgG.\u003c/p\u003e \u003cp\u003eThe most notable ASVs associated with high BKA and tIgG (Supplementary Tables\u0026nbsp;12 and 14) belonged to \u003cem\u003eChlamydia\u003c/em\u003e (females), \u003cem\u003eEscherichia-Shigella\u003c/em\u003e (males for BKA and both sexes for IgG\u003cem\u003e), Gemella\u003c/em\u003e (males), \u003cem\u003eMycoplasma (\u003c/em\u003efemales for BKA and males for IgG), \u003cem\u003ePaeniclostridium\u003c/em\u003e (females), \u003cem\u003eProvidencia\u003c/em\u003e (males), \u003cem\u003eStreptococcus\u003c/em\u003e (both sexes for BKA and males for IgG), and \u003cem\u003eUreaplasma\u003c/em\u003e (both sexes). These bacterial genera harbor species associated with infection and were detected in individuals with high BKA and tIgG. Some genera are worth highlighting due to the extent of their association. The \u003cem\u003eEscherichia-Shigella\u003c/em\u003e genus stands out because this group of enterobacteria was represented by four different ASVs in individuals with both high BKA and tIgG. The genera \u003cem\u003eClostridium sensu stricto 1\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, and \u003cem\u003eUreaplasma\u003c/em\u003e are notable in relation to BKA for several reasons. These genera were represented by multiple ASVs and were exclusive to and upregulated in individuals with high BKA in both sexes.\u003c/p\u003e \u003cp\u003e \u003cem\u003eDisease-associated bacterial ASVs are linked to low BKA and tIgG.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eOnly two infection-associated ASVs were linked with low BKA in females, whereas 11 such ASVs were associated in males and two ASVs (\u003cem\u003eAcinetobacter\u003c/em\u003e) were present in both sexes (Supplementary Table\u0026nbsp;13). This suggest that lower BKA in females was weakly associated with their gut microbiota composition, whereas lower BKA in males was strongly associated with it. In contrast (Supplementary Table\u0026nbsp;15), low tIgG values were linked to slightly more infection-associated ASVs in females (10) than in males (7) with three genera shared by both sexes. Furthermore, of the ASVs associated with low BKA and tIgG, only one (\u003cem\u003eFructobacillus\u003c/em\u003e in males) has not been associated with infection. Most ASVs associated with low BKA and tIgG belonged to bacterial genera that could proliferate if the host faces a disease or poor healthy conditions (Supplementary Tables\u0026nbsp;13 and 15). Consistent with this idea, individuals with low BKA and/or tIgG might be experiencing an energy trade-off [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] or an infection [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. A low BKA during malnutrition or caloric deficit may result from reduced complement molecule production (Reviewed [\u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e]), whereas tIgG may decrease during certain infections [\u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e] or when infection and malnutrition occur concurrently [\u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e]. Similarly, gut microbiota can be disrupted by infection, caloric deficit or malnutrition [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, elevated ASVs in individuals with low BKA and tIgG may result from disease or poor health conditions. The bacterial genera \u003cem\u003eAcinetobacter, Anaerococcus, Clostridium sensu stricto 1, Corynebacterium, Helicobacter, Mycoplasma, Pannonibacter\u003c/em\u003e and \u003cem\u003eStaphylococcus\u003c/em\u003e are noteworthy within these ASVs because they were associated with low levels of both BKA and tIgG.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eHealthy bacterial groups could strengthen the intestinal mucosa and lower BKA and tIgG\u003c/h2\u003e \u003cp\u003eAnother possible scenario is that some of the ASVs associated with low BKA and tIgG could strengthen the intestinal mucosa. A fortified mucosa decreases the translocation of microbial cells and molecules from intestinal lumen that could stimulate BKA and tIgG. Congruent with this hypothesis, a small number of bacterial genera associated with low BKA and tIgG (Supplementary Tables\u0026nbsp;13 and 15) have been associated with a fortified intestinal mucosa. The genera \u003cem\u003eClostridium sensu stricto 1\u003c/em\u003e [\u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e134\u003c/span\u003e] and \u003cem\u003eCorynebacterium\u003c/em\u003e [\u003cspan additionalcitationids=\"CR136 CR137\" citationid=\"CR135\" class=\"CitationRef\"\u003e135\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e138\u003c/span\u003e] were the most notable bacteria that include species associated with a strong intestinal mucosa because these genera were associated with low BKA in males and with low tIgG in females and males. Other bacterial groups that could strengthen the intestinal mucosa are \u003cem\u003eParaclostridium\u003c/em\u003e [\u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e139\u003c/span\u003e] (low BKA in males), \u003cem\u003eFructobacillus\u003c/em\u003e [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e] (low tIgG in males), \u003cem\u003eEnterococcus\u003c/em\u003e [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e] and \u003cem\u003eStreptococcus\u003c/em\u003e [\u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e140\u003c/span\u003e] (low tIgG in females). Therefore, if individuals with low BKA and tIgG values are not ill, it is likely that accompanying ASVs might favor a strong intestinal barrier.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eUnclassified and free-living bacterial genera associated with the immune system of bats\u003c/h2\u003e \u003cp\u003eMany ASVs associated with BKA and tIgG could only be classified at the order or family level (Supplementary Table\u0026nbsp;16\u0026ndash;19). We refer to these as unclassified ASVs. These ASVs were associated with humoral immunity in females or in males. Notably, unclassified ASVs associated with high BKA were only present in males. The presence of unclassified ASVs underscores the incomplete taxonomic characterization of fecal microbiota in bats. It is difficult to establish a relationship between the biology of these ASVs with the immune system because these taxonomic groups include many microorganisms with different lifestyles. However, it is important to highlight Enterobacteriaceae for the following reasons. First, several enterobacterial ASVs were associated with high and low BKA and tIgG in both sexes. Second, these enterobacterial ASVs could be important for the health of \u003cem\u003eL. yerbabuenae\u003c/em\u003e because this family is associated with death and disease in bats [\u003cspan additionalcitationids=\"CR40 CR41 CR42 CR43 CR44\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], but they can also establish a healthy bond with their hosts [\u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e141\u003c/span\u003e]. This is a noteworthy issue given that, similarly to \u003cem\u003eL. yerbabuenae\u003c/em\u003e [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e142\u003c/span\u003e], Enterobacteriaceae is present in multiple bat species and can dominate their gut microbiota [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e143\u003c/span\u003e, \u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e144\u003c/span\u003e] .Therefore, it is unlikely that most gut enterobacteria in bats are associated with disease. Future studies should address this question to determine which enterobacteria are pathogenic, commensal or beneficial for bats, and how they interact with their immune system.\u003c/p\u003e \u003cp\u003eMultiple environmental or free-living bacterial genera were associated with BKA and tIgG (Supplementary Table\u0026nbsp;16\u0026ndash;19). Of these, the photosynthetic bacterium \u003cem\u003eBlastochloris sp.\u003c/em\u003e [\u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e145\u003c/span\u003e] and the rare opportunistic pathogen \u003cem\u003ePannonibacter\u003c/em\u003e sp. [\u003cspan additionalcitationids=\"CR147\" citationid=\"CR146\" class=\"CitationRef\"\u003e146\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR148\" class=\"CitationRef\"\u003e148\u003c/span\u003e] were associated with the humoral immunity of both sexes. Only one ASV (\u003cem\u003eSalinisphaera sp.\u003c/em\u003e) was exclusively associated with humoral immunity in male, whereas most were associated with the humoral immunity of females. Some of these ASVs linked to females are associated with plant and insect microbiota, suggesting that they likely originate from their diet [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR149\" class=\"CitationRef\"\u003e149\u003c/span\u003e, \u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e150\u003c/span\u003e]. These ASVs can colonize animal bodies and affect key aspects of host health. Some examples are illustrated by the \u003cem\u003eAsaia\u003c/em\u003e genus in nectarivorous insects [\u003cspan additionalcitationids=\"CR152\" citationid=\"CR151\" class=\"CitationRef\"\u003e151\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e153\u003c/span\u003e], the genus \u003cem\u003eFimbriiglobus\u003c/em\u003e in fishes [\u003cspan citationid=\"CR154\" class=\"CitationRef\"\u003e154\u003c/span\u003e, \u003cspan citationid=\"CR155\" class=\"CitationRef\"\u003e155\u003c/span\u003e], the genus \u003cem\u003eRhodopseudomonas\u003c/em\u003e in aquatic animals [\u003cspan additionalcitationids=\"CR157 CR158\" citationid=\"CR156\" class=\"CitationRef\"\u003e156\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR159\" class=\"CitationRef\"\u003e159\u003c/span\u003e], and the rare opportunistic pathogen \u003cem\u003eCyanobium PCC-6307\u003c/em\u003e in shrimps [\u003cspan citationid=\"CR160\" class=\"CitationRef\"\u003e160\u003c/span\u003e, \u003cspan citationid=\"CR161\" class=\"CitationRef\"\u003e161\u003c/span\u003e]. Therefore, we cannot discard their importance for \u003cem\u003eL. yerbabuenae\u003c/em\u003e and other animal species in which these genera are found. Other bacterial genera associated with humoral immunity are commonly found in aquatic environments or caves (Supplementary Table\u0026nbsp;16\u0026ndash;19). It is likely that these microorganisms originate from the sea near Don Panchito Island and caves where \u003cem\u003eL. yerbabuenae\u003c/em\u003e inhabit [\u003cspan citationid=\"CR162\" class=\"CitationRef\"\u003e162\u003c/span\u003e]. The presence of these bacterial groups is not surprising, as several microorganisms from the environment and other animal species can be detected in bat microbiota [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. As we collected the feces released by the bats immediately during the measurement and sampling process, it is unlikely that these bacteria originate from environmental contamination. Hence, they should be transient or natural inhabitants of the gut microbiota of \u003cem\u003eL. yerbabuenae\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe relationship between fecal microbiota and constitutive humoral immunity varied with sex in \u003cem\u003eL. yerbabuenae\u003c/em\u003e. Immunity was weakly linked to bacterial diversity, but it showed a strong association with bacterial composition. These sex-biased patterns likely arose from divergent fecal microbiota profiles, as females harbor a larger number of bacterial families than males. Consistently, previous work in captive \u003cem\u003eArtibeus jamaicensis\u003c/em\u003e have shown that sex-based differences in fecal microbiota composition correlate with contrasting intestinal metabolomes [\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e]. These metabolic variations include key immunoregulatory metabolites, supporting our hypothesis that gut microbiota divergences between females and males \u003cem\u003eL. yerbabuenae\u003c/em\u003e influence their immune function. The primary drivers of these results likely stem from the migratory behavior of female versus the residency of males, or other sex-associated biological traits.\u003c/p\u003e \u003cp\u003eMost ASVs associated with humoral immunity in both sexes belong to bacterial genera with known immunostimulatory and intestinal mucosa-strengthening species, and genera with species associated with infection. Collectively, these findings demonstrate that the fecal microbiota of \u003cem\u003eL. yerbabuenae\u003c/em\u003e is linked to humoral immunity, particularly with bacteria that may enhance immune function and mucosal integrity, as well as with bacteria associated with infection or poor health. Future mechanistic studies should investigate whether and how bacterial inhabiting the gut influences the systemic immunity of wildlife. Furthermore, the association with potentially pathogenic genera is significant, as it suggests that \u003cem\u003eL. yerbabuenae\u003c/em\u003e harbors bacteria that might be important for human health and other animals. Metagenomic approaches will help characterize the functions of gut microbiota and aid in identifying these bacteria at the genome-scale to corroborate their beneficial or pathogenic potential. This approach will also help resolve the identity of several immunity-associated ASVs that currently remain unclassified beyond the order or family level. Finally, future studies should isolate bat gut bacteria and use them as microbial targets or immunological challenges to confirm their importance to the immune system of \u003cem\u003eL. yerbabuenae\u003c/em\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article is part of the requirements for D.A.R.-R. to obtain a PhD degree in the Posgrado en Ciencias Biol\u0026oacute;gicas-UNAM We would like to thank Rodrigo A. Medell\u0026iacute;n L. and the personnel at the Chamela Biological Station in Jalisco for their logistic support, and to Natalia S. Herrera, Le\u0026oacute;n A. Pizano and Omar Calva during field work. We would also like to thank Gast\u0026oacute;n Contreras Jim\u0026eacute;nez (Laboratorio de Microscop\u0026iacute;a y Microdisecci\u0026oacute;n Laser, Instituto de Ecolog\u0026iacute;a, UNAM) and Arit de Leon-Lorenzana (Investigadora por Mexico SECIHTI, Universidad Intercultural Maya de Quintana Roo) for their technical assistance with tIgG determination and DNA library preparation, respectively.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll sequences included in this study have been deposited in NCBI BioProject PRJNA1404331. All other\u0026nbsp;\u003cem\u003edatasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding was provided by research grants from Direcci\u0026oacute;n General de Asuntos del Personal Acad\u0026eacute;mico (DGAPA; IN204219, IN203822) and from\u0026nbsp;Secretar\u0026iacute;a de Ciencia, Humanidades, Tecnolog\u0026iacute;a e Innovaci\u0026oacute;n (SECIHTI; CBF2023-2024-192)\u0026nbsp;to L. Gerardo Herrera M. Additional funding was also obtained from DGAPA to Luisa I. Falcon (BV200421). David A. Rivera-Ruiz was supported by a student grant from SECIHTI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization:\u0026nbsp;David Alfonso Rivera-Ruiz\u003csup\u003e\u0026nbsp;\u003c/sup\u003eand L. Gerardo Herrera M.; Methodology:\u0026nbsp;David Alfonso Rivera-Ruiz, Marco Tulio Solano de la Cruz, Luisa I. Falc\u0026oacute;n, Osiris Gaona\u003csup\u003e\u0026nbsp;\u003c/sup\u003eand\u0026nbsp;L. Gerardo Herrera M.; Formal Analysis:\u0026nbsp;David Alfonso Rivera-Ruiz; Field work:\u0026nbsp;David Alfonso Rivera-Ruiz, Jos\u0026eacute; Juan Flores-Mart\u0026iacute;nez \u003csup\u003e\u0026nbsp;\u003c/sup\u003eand L. Gerardo Herrera M.; Data Curation:\u0026nbsp;David Alfonso Rivera-Ruiz; \u0026nbsp;Original Draft Preparation:\u0026nbsp;David Alfonso Rivera-Ruiz\u003csup\u003e\u0026nbsp;\u003c/sup\u003eand L. Gerardo Herrera M.; Review and Editing:Marco Tulio Solano de la Cruz, Luisa I. Falc\u0026oacute;n, Carlos Rosales, Osiris Gaona and Jos\u0026eacute; Juan Flores-Mart\u0026iacute;nez;\u003csup\u003e\u0026nbsp;,\u003c/sup\u003eSupervision:\u0026nbsp;David Alfonso Rivera-Ruiz\u003csup\u003e\u0026nbsp;\u003c/sup\u003eand L. Gerardo Herrera M.; Project Administration:\u0026nbsp;L. Gerardo Herrera M.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe protocol of the study was\u0026nbsp;approved by the Ethics Committee in Research and Teaching of the Institute of Biology, National Autonomous University of Mexico. The study was conducted under permits from Direcci\u0026oacute;n General de Vida Silvestre (13222/18,\u0026nbsp;8053/19, and 12346/23).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eArtificial Intelligence (AI)-assisted technologies\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used AI to generate the graphical abstract.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKhan IM, Nassar N, Chang H et al (2024) The microbiota: a key regulator of health, productivity, and reproductive success in mammals. 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J Mammal gyaf080:1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jmammal/gyaf080\u003c/span\u003e\u003cspan address=\"10.1093/jmammal/gyaf080\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"microbial-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meco","sideBox":"Learn more about [Microbial Ecology](https://www.springer.com/journal/248)","snPcode":"248","submissionUrl":"https://submission.nature.com/new-submission/248/3","title":"Microbial Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Immune system, gut microbiota, bats, migration, Leptonycteris yerbabuenae, bacterial killing ability, immunoglobulins","lastPublishedDoi":"10.21203/rs.3.rs-9440532/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9440532/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe immune system and gut microbiota are interconnected components that play a key role in host health. The nature of this relationship in wildlife is under-explored, especially in migratory animals, which are exposed to diverse microorganisms that can impact their immune system, microbiota and health. This study explored the relationship between constitutive humoral immunity (bacterial killing-ability, BKA, and total immunoglobulin G concentration, tIgG) and the diversity and composition of gut microbiota (16S rRNA gene amplicons) in the lesser long-nosed bat Leptonycteris yerbabuenae. Males of this bat form resident populations whereas some females migrate to complete their reproductive cycle. Each sex harbored a distinctive fecal microbiota, yet no significant relationships were found between BKA and microbiota diversity, but tIgG was negatively correlated with Shannon's and Simpson's inverse indices in females and positively with the Shannon's index in males. Humoral immunity in both sexes was significantly related to fecal bacterial genera known to harbor immunostimulatory species, species linked to intestinal mucosa integrity, and infection-associated species. Other free-living and unclassified bacterial genera were associated with immunity in a sex-specific manner, highlighting the importance of novel and uncommon bacteria for immune activity in wildlife. These findings suggest that gut microbiota composition, and to a lesser extent diversity, are linked to constitutive humoral immunity in L. yerbabuenae. The distinct relationship exhibited by each sex suggests that migration and other sex-associated traits may be crucial to understanding the natural variation of immunity in wildlife.\u003c/p\u003e","manuscriptTitle":"Host-microbiota association in a migratory species: Constitutive humoral immunity in the partially migratory bat Leptonycteris yerbabuenae is linked to the gut microbiota in a sex-specific manner","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-25 05:15:06","doi":"10.21203/rs.3.rs-9440532/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"185096617346682308379334741952033226651","date":"2026-05-11T17:52:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"137046680374903546448186254763226893491","date":"2026-05-11T15:17:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253732241567899501164763965977567530914","date":"2026-05-10T04:05:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61355567473782294643946180379371914292","date":"2026-05-09T22:45:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T12:15:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"21787825801844365657153799886345904686","date":"2026-04-20T11:11:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-17T15:46:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-16T23:56:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-16T23:56:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Microbial Ecology","date":"2026-04-16T16:18:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"microbial-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meco","sideBox":"Learn more about [Microbial Ecology](https://www.springer.com/journal/248)","snPcode":"248","submissionUrl":"https://submission.nature.com/new-submission/248/3","title":"Microbial Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8e03b3b5-4cbb-46ea-bd8d-b64c15188983","owner":[],"postedDate":"April 25th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"185096617346682308379334741952033226651","date":"2026-05-11T17:52:11+00:00","index":80,"fulltext":""},{"type":"reviewerAgreed","content":"137046680374903546448186254763226893491","date":"2026-05-11T15:17:32+00:00","index":78,"fulltext":""},{"type":"reviewerAgreed","content":"253732241567899501164763965977567530914","date":"2026-05-10T04:05:25+00:00","index":68,"fulltext":""},{"type":"reviewerAgreed","content":"61355567473782294643946180379371914292","date":"2026-05-09T22:45:28+00:00","index":67,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T12:15:27+00:00","index":38,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-25T05:15:06+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-25 05:15:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9440532","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9440532","identity":"rs-9440532","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}