Gut microbiome community structure correlates with different behavioral phenotypes in the Belyaev farm-fox experiment

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Delmont, Alex L. Mitchell, Robert Finn, Guojie Zhang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4697888/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Mar, 2026 Read the published version in Communications Biology → Version 1 posted You are reading this latest preprint version Abstract Domestication represents one of the largest biological shifts of life on Earth, and for many animal species, behavioral selection is thought to facilitate early stages of the process. The gut microbiome of animals can respond to environmental changes and have diverse and powerful effects on host behavior. As such, we hypothesize that selection for tame behavior during early domestication, may have indirectly selected on certain gut microbiota that contribute to the behavioral plasticity necessary to adapt to the new social environment. Here, we explore the gut microbiome of foxes from the tame and aggressive strains of the “Russian-Farm-Fox-Experiment”. Microbiota profiles revealed a significant depletion of bacteria in the tame fox population that have been associated with aggressive and fear-related behaviors in other mammals. Our metagenomic survey allowed for the reconstruction of microbial pathways enriched in the gut of tame foxes, such as glutamate degradation, which converged with host genetic and physiological signals, revealing a potential role of functional host-microbiota interactions that could influence behaviors associated with domestication. Overall, by characterizing how compositional and functional potential of the gut microbiota and host behaviors co-vary during early animal domestication, we provide further insight into our mechanistic understanding of this adaptive, eco-evolutionary process. Biological sciences/Microbiology/Microbial genetics/Bacterial genetics Biological sciences/Genetics/Behavioural genetics Biological sciences/Ecology/Microbial ecology Biological sciences/Genetics/Animal breeding Biological sciences/Ecology/Behavioural ecology Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Understanding the evolutionary mechanisms involved in the process of domestication provides crucial insights into how wild animals have adapted to new social environments over time. Behavioral shifts are thought to facilitate the early stages of animal domestication 1 , 2 with a selection for tameness as the single most consistent trait across domesticated species 3 , 4 . Tameness denotes an attenuation of the flight-or-fight response, or in other words, a reduced fear and long-term stress response towards humans, which is often a prerequisite to successful breeding in captivity 5 . Rapid adaptation via behavioral plasticity is traditionally viewed as being primarily encoded by the genome, however, ample evidence shows that the gut microbiome of animals can respond to environmental changes and may play a crucial role on their adaptive capacity 6 , 7 . Further, mounting evidence has demonstrated how the gut microbiome influences host behavior, cognition, brain development and physiology 8 – 10 , which may have profound implications for host ecology and evolution, including adaptation via behavioral selection 11 – 13 . The complex bidirectional communication between animals and their gut microbiota, called the microbiota-gut-brain-axis, can be mediated through a variety of mechanisms and it is becoming increasingly clear that gut derived metabolites are important to these interactions 14 – 18 . The biochemical communication pathways can occur through various direct and indirect mechanisms, which include transport of gut-derived metabolites from circulation into the brain, initiation of immune and vagus nerve activation, stimulation of enterochromaffin cells in the gut, and even epigenetic alterations 10 , 14 – 17 , 19 . Experimental studies have also implicated microbiome-derived signals in regulating fear and aggressive behavior in animals, as well as, expression of genes in the brain involved in such behaviors 20 – 25 , both of which have interesting implications for domestication. However, the eco-evolutionary relevance of the microbiome-gut-brain axis outside of laboratory animals remains largely unexplored. The Russian Farm Fox Experiment is one of the most extensive experimental domestication studies. Initiated in 1959 at the Institute of Cytology and Genetics (ICG) (Novosibirsk, Russia), Dimitry K. Belyaev and Lyudmila Trut selectively bred captive populations of the silver fox ( Vulpes vulpes ) that were originally derived from fur farms in eastern Canada 26 , 27 , to understand the evolutionary processes of adapting to a new social environment 1 , 2 , 5 , 28 . After decades of selecting on foxes solely for behavior, specific strains of fox with markedly different behavioral phenotypes were gradually established. Foxes bred for tame phenotypes, herein referred to as the “tame” strain, displayed a reduced-fear and positive response towards humans, whereas aggressive phenotypes, herein referred to as the “aggressive” strain, exhibited aggression towards humans along with avoidance behavior 2 , 5 , 28 , 29 . A genetic basis has been extensively documented for both behavioral phenotypes of these foxes 5 , 30 – 33 . However, it has recently been demonstrated that both host genetics and the microbiome can interdependently regulate certain behaviors in mice 14 . Taken together, we hypothesize that selection for behavioral traits in the fox domestication experiment has inadvertently led to selection on specific bacterial gut microbiota community structures that may potentially contribute to shaping fox behaviors - something we have previously demonstrated in a similar red jungle fowl domestication experiment 12 . In this study we characterized the fecal microbiome community (as a proxy for gut) of foxes from the tame and aggressive strains of the Russian Farm Fox Experiment, in order to test the hypothesis that the gut microbial community compositions, and their neuroactive potentials, differ between fox behavioral phenotypes. All foxes were housed in the same or similar facilities and fed identical diets, allowing us to assess the differences between the gut microbiota of the two behavioral phenotypes without introducing the confounding variables of environment, and especially diet, both of which can strongly influence the gut community composition 34 . Results and discussion In order to profile the gut microbiome of the two fox behavioral strains, we generated both targeted bacterial 16S rRNA amplicon and shotgun metagenomic data from 123 fecal samples that represented two collection years (2015: tame (n = 10); aggressive (n = 10); 2017: tame (n = 51); aggressive (n = 52)). Because rare microbial species are typically not well assembled in metagenomic surveys because of low coverage 35 , 16S amplicon data was used as a starting point to characterize the microbiota’s broad taxonomic composition in order to study links with behavioral phenotypes. Complementary metagenomic data was incorporated to 1) recover and compare novel bacterial species between the behavioral selections lines, through the reconstruction of metagenome assembled genomes (MAGs), and 2) provide insights into the potential microbiome functions relevant to the microbiota-gut-brain-axis. Due to the strong batch effects (namely sample storage treatment known to significantly impact microbial taxonomic profiles 36 , 37 ), between the two collections years (Suppl. Figure 1), the 2015 dataset was removed from the 16S analysis and additionally removed from the shotgun metagenomic analysis after we generated the MAG reference catalogue. Gut microbial diversity reduced in tame foxes To create initial community composition profiles of the gut microbiota of the two silver fox behavioral strains, a 16S rRNA gene amplicon survey was performed on 2017 fecal samples from aggressive (n = 52) and tame (n = 50) individuals. A total of 1,525 amplicon sequence variants (ASVs) were identified after quality filtering. Diversity analysis at the genus level identified a lower Shannon diversity estimate in the microbiota of the tame, compared to aggressive, fox populations (Fig. 1 A; Shannon tame = 2.45 ± 0.006; Shannon aggr = 2.52 ± 0.003; p-value < 0.0001). Marked reductions in the gut microbial diversity of domesticated animals have been identified in a number of species, and are typically associated with anthropogenic factors, such as shifts in environment and diet, during the domestication process 38 – 41 . Interestingly, increased gut microbial diversity is also associated with aggressive and fearful behavior in mammals 42 – 47 . As the foxes from the two selection lines were housed in similar controlled environments, the lower diversity estimates identified in the tame gut microbiota are unlikely due to shifts in environmental factors. Alternatively, it is possible that the depletion or loss of certain gut microbes during the early stages of the domestication process may initiate the behavioral shifts necessary to adapt to the new social environment, and knowledge of individual microbial species responsible for these phenotypes may be valuable in understanding their biological role during domestication. Tame foxes are depleted in bacteria associated with fearful and aggressive behavior To investigate whether certain microbes were associated with the different behavioral phenotypes, we modelled relative abundance of 16S sequence data at different taxonomic levels. At the phylum level, we observed that the gut microbiota of tame foxes were significantly depleted of Tenericutes in comparison to aggressive foxes (Wald test, t-value = -4.785, p-value = 1.53e-05), mainly due to the reduction of the bacterial family Anaeroplasmataceae (Wald test, t-value = -4.785, p-value = 7.88e-05). This finding is interesting for several reasons. Firstly, in other mammals such as hamsters and mice, Tenericutes, and more specifically Anaeroplasmataceae , have been reported to play a role in social behavior, and consistently and positively correlate with aggression 44 – 46 , 48 . Secondly, not only do some members of Tenericutes show heritability in multiple human populations 49 , but Tenericutes are also less capable of recovering after perturbation in the gut 44 . Together we hypothesize that it may be possible to select against at least some bacteria during early domestication, that remain depleted or lost throughout the process. When considering the data at the order level, we noted a depletion of bacteria from the order Desulfovibrionales in tame foxes (Wald test, t-value = -2.938, p-value = 0.04). Maternal stress and perturbations in the gut microbiota of Siberian hamsters produced offspring that were not only enriched in Desulfovibrionales but also displayed increased levels of aggression when treated with stress 50 . Cusick and co-workers 50 went on to suggest that both maternal microbiome and response to stress interact in ways that impact the behavior and gut microbiota of their offspring, both of which would have interesting implications in the eco-evolutionary processes of domestication. Modelled relative abundance at the genus level revealed three additional taxa depleted in the tame fox strain, specifically Ruminococcaceae (UCG-014) , Anaeroplasma and Lachnospiraceae (UCG-010) (Fig. 1 B-C), all three of which have intriguing links to behavior in other mammals. In particular, Ruminococcaceae and Lachnospiraceae not only positively associate with aggressive behaviors in mammals, including dogs, mice and hamsters 21 , 42 , 46 – 48 , but also exhibit lower abundance in some captive and domestic animal populations of gaur and yaks 38 , 40 . Further, Lachnospiraceae are positively associated with brain reactivity to fear in humans, particularly in the prefrontal cortex 51 , which is a brain region involved in memory, learning and regulating fear, and shown to be modulated by changes in gut microbiota 8 , 52 , 53 . In addition to Lachnospiraceae , Bacteroides similarly associate with brain reactivity to fear 51 . In this regard, when we explored our data at the ASVs level, we see they are also depleted in the tame strain (Fig. 2 A). Interestingly, Bacteroides are observed in high abundance in the gut microbiota of wild red foxes from the grasslands in China 54 . Additional analysis at the ASV level revealed 53 ASVs to be differentially abundant between the aggressive and tame behavioral strains, and an additional 23 ASVs were discriminant to either selection line (Fig. 2 ; Suppl. Table 1). Most of these differences occurred in abundant and highly prevalent taxa (Fig. 2 B). In addition to Bacteriodes , we detected a significant depletion of Alloprevotella, Prevotellaceae and Blautia ASVs (Fig. 2 A; Suppl. Table 1), all of which have previously been associated with aggression in dogs, hamsters, mice and voles 42 , 46 – 48 , 55 . Taken together, these data suggest that the gut microbiota in tame foxes are depleted of bacteria not only associated with aggressive and fearful behaviors, but also in taxa found in abundance in their wild counterparts. Furthermore, similar patterns in the gut microbiome of other domesticated animals have been observed suggesting a role of the gut microbiome in domestic behaviors. Genome-resolved metagenomics characterize the neuroactive potential of the fox gut microbiota To identify novel gut bacterial species, and assess the functional potential of the gut microbiota found in the aggressive and tame fox strains, we next carried out shotgun metagenomic sequencing. Initially, we generated a reference catalogue of metagenome-assembled genomes (MAGs) using a dataset of 123 samples that represented two collection years (2015 + 2017) (tame (n = 61); aggressive (n = 62)). Briefly, the metagenomic data yielded 15.8 billion high-quality short-reads, of which ca. 30–70% (per sample) mapped to the fox genome (Suppl. Table 2a). After removing reads corresponding to the fox genome, we performed a large metagenomic co-assembly (7.09 billion reads), which produced 105,106 contigs > 2500 nt. Subsequent manual binning within the anvi’o 56 framework using differential coverage across all samples resulted in 237 non-redundant MAGs of which 50% of the reads mapped back to (Fig. 3 A; Suppl. Figure 2; Suppl. Table 2b). Four samples had a sequencing depth of less than 10 million single-end reads (< 1 Gb) after quality control, and were removed from downstream analyses (as per recent recommendations 35 ). At the phylum level, MAGs were affiliated to Firmicutes (n = 145), followed by Bacteroidetes (n = 27), Proteobacteria (n = 25), Actinobacteria (n = 17), Tenericutes (n = 17), Spirochaetes (n = 3), Cyanobacteria (n = 1), Fusobacteria (n = 1) and Deferribacteres (n = 1) (Fig. 3 A). In addition, all MAGs were affiliated to known bacterial orders, and 43% of them could also be assigned to a known species (average nucleotide identity > 95%). Complete taxonomic assignments using the Genome Taxonomy Database Toolkit (GTDB-Tk) 57 are found in Suppl. Table 2b. Modelled microbial abundance revealed 22 MAGs to be significantly differentially abundant between the aggressive and tame fox strains, and an additional 3 MAGs were discriminant to either strain (Fig. 3 ). Although overlap between shotgun metagenomic and 16S data exist at broad taxonomic levels (Bowers et al., 2022; Gehrig et al., 2022), comparison of community structures across the two sequencing strategies do not necessarily merge perfectly as a consequence of differences in taxonomic databases (Lesker et al., 2020; Odom et al., 2023; Tessler et al., 2017), detection limits (Durazzi et al., 2021), sequencing depths (Durazzi et al., 2021; Tessler et al., 2017) and PCR biases (Bowers et al., 2022; Sze & Schloss, 2019). Nonetheless, corroborating 16S sequence data, there was an enrichment of Anaeroplasmataceae , Helicobacter C sp. and several Bacteroidaceae MAGs, namely Paraprevotella sp., Prevotellamassilia sp . and Prevotellamassilia sp.000437675 , in the aggressive fox strain, among six additional enriched MAGs (Fig. 2 A; Fig. 3 B). Conversely, the tame fox strain was enriched in the MAGs Collinsella sp., Fusobacterium sp.900015295 and Helicobacter bilis , similarly identified at higher taxonomic resolution in the 16S data (Fig. 2 A) in addition to eight other MAGs (Fig. 3 B). In order to describe the neuroactive potential of gut microbiota in relation to gut–brain interactions in the foxes, we applied a previously described module-based framework 58 . This framework identifies microbial pathways that metabolize molecules with the potential to interact with the host nervous system. We found 35 out of the 56 annotated gut–brain modules (GBMs) known to produce or degrade neuroactive compounds, spread widely across the phylogenic range of MAGs (Fig. 3 A). We subsequently compared the microbial neuroactive potential of the gut microbiota between tame and aggressive fox strains by assessing the detection of GBMs in the 22 MAGs that were significantly enriched in one of the behavioral phenotypic groups (Fig. 3 B). We detected six GBMs associated with the tame fox strain, three of which were identified in less than 5% of all MAGs (Fig. 3 B; Suppl. Figure 3). Two of the GBMs enriched in the tame population were associated with short chain fatty acids (SCFA), namely butyrate synthesis II and isovaleric acid synthesis I, and an additional three GBMs were from the glutamate-derived pathway, glutamate degradation II, GABA synthesis III and g-Hydroxybutyric acid (GHB) degradation (Fig. 3 B). The final GBM was associated with the estrogen hormone signaling pathway, 17-beta-estradiol degradation, and was present in five out of the eleven MAGs enriched in the tame selection line (Fig. 3 B). Interestingly, circulating levels of estradiol have been linked to variation in social behaviors, including a positive correlation with aggression in multiple animal species, namely sparrows, cichlids and mice 59 – 63 . It is therefore possible that the gut bacteria in the tame fox strain have the potential to reduce estradiol in circulation and in turn decrease aggressive behaviors important in the early domestication process. We further applied the GBM framework to the cleaned shotgun data (7.09 billion reads), prior to assembly, for all fox samples, in order to increase the potential detection of GBMs associated to behavioral phenotypes. Most GBMs (n = 31) were present in over 75% of all fox samples and one was rare, namely nitric oxide degradation II and exclusively found in the tame fox selection line (Fig. 4 ). Modelled differential abundance of GBMs per metagenomic sample revealed three significant enrichments associated with the aggressive behavioral group; S-adenosylmethionine (SAM) synthesis (Wald test, t-value = -4.23, p-value = 3.9e-04), acetate synthesis I (Wald test, t-value = -4.34, p-value = 3.8e-04) and acetate synthesis III (Wald test, t-value = -3.15, p-value = 0.01) (Fig. 4 B). Gut microbiota have the functional potential to produce neuroactive metabolites that influence the serotonergic system relevant to domestic behaviors Serotonin (5-HT) has been implicated as one of the main neurotransmitters involved in animal aggression and plays an inhibitory role across a wide range of species 64 – 68 . Similarly, some domesticated animals have higher levels of brain 5-HT, including the Belyaev foxes as previously demonstrated, or peripheral 5-HT, that correlates with reduced aggressive and fear-related phenotypes 69 – 74 . Although the majority of 5-HT is produced in the gut (~ 90%), it is generally believed it cannot directly affect levels in the brain, because 5-HT cannot pass the blood–brain barrier (BBB) 75 . However, peripheral levels of 5-HT can alter brain functionality and behavior 76 . Furthermore, germ-free rodents have altered 5-HT concentrations and turnover in the brain, altered levels of circulating 5-HT and its precursor L-tryptophan, and decreased cecal and fecal 5-HT 77 – 81 . Gut bacteria can also modulate serotoninergic gene expression profiles in the brain 82 , 83 . Together, this suggests a role for the gut microbiota in modulating 5-HT signaling pathways in the central nervous systems, however the mechanistic link between the gut microbiota and 5-HT production in the brain is not yet fully defined. Here, we identified that foxes from the tame strain were significantly enriched in Enterococcus faecalis , Roseburia sp900548205 and Clostridiales MAGs, all taxa known to increase peripheral host serotonin levels by either directly producing it, promoting host serotonin biosynthesis, or upregulating the expression of serotonin transport genes in the gut 58 , 75 , 84 – 88 (Fig. 3 B; Wald test, t-value = 0.9, p-value = 0.007, Wald test, t-value = 2.86, p-value = 0.008 and order-level Clostridiales : t-value = 3.7, p-value = 0.001). Interestingly, Clostridiales were also enriched in red jungle fowl gut microbiota selected for low fear of humans 12 , and positively associated with impaired fear memory in mice 89 , which further suggest a role for this taxa in early domestication. The SCFAs butyrate and acetate can also influence the host serotonin system. Experimental studies have revealed that butyrate induces 5-HT colonic secretion from the gut, whereas acetate has been shown to do the opposite 75 , 90 , and the potential for butyrate and acetate synthesis were enriched in either the tame or aggressive fox strain gut microbiota respectively (Fig. 3 B, Fig. 4 ). Our 16S taxonomic data further support this pattern, where acetate producing bacteria 91 were significantly enriched in the aggressive fox strain, namely Bacteroides spp. , Prevotella spp. , Ruminococcus spp. and Blautia spp . (Fig. 2 ). Intriguingly, butyrate is implicated in neuronal plasticity, fear memory formation, and increased fear extinction 19 , 92 – 94 , whereas acetate has been proposed to induce impairments in learning and coping with stress 95 . Further, SCFAs, such as butyrate and acetate, can cross the BBB and are known to be strong epigenetic modulators 96 and epigenetic mechanisms may play an integral role in both the microbiota-gut-brain-axis 20 – 25 , 97 and domestication 98 – 104 . We detected the nitric oxide (NO) degradation II GBM exclusively in the tame fox metagenomic data, although in low abundance (Fig. 4 B). Nitric oxide appears to play an important role in normal brain 5-HT functioning and has been implicated in both fear-related and aggressive behavior in mammals 105 – 109 . Together these data suggest that fox gut microbiota have the potential to influence the host serotonin system, however, how this translates into behavior remains to be defined. Nonetheless, the taxonomic and functional potential of the gut microbiota enriched in tame foxes indicate the capacity to influence fear memory formation and promote fear extinction learning, both of which would be relevant to overcoming a fear response toward humans during domestication. Convergence of host and microbial selection signals on glutamate signaling pathway Extensive studies have demonstrated that genes coding for different types of glutamate receptors in the host are associated with domestication in not only dogs, ducks, rabbits and chickens 110 – 114 , but also on the Belyaev foxes, where genomic regions, gene expression and allele frequencies involved in glutamatergic signaling differentiate between the tame and aggressive strains 32 , 33 . Mounting evidence has now shown that the gut microbiota can influence the genetic composition and functional connectivity of certain regions in the brain of the host 8 , 52 , 53 , 115 and, further, alter gene expression of glutamatergic receptors in the brain 116 , 117 . In light of these findings, it is interesting that GBMs associated with glutamate degradation, GABA synthesis and g-Hydroxybutyric acid (GHB) degradation, all from the glutamate-derived pathway, were enriched in the MAGs from the gut of the tame fox strains (Fig. 3 B). We additionally detected the potential for acetate synthesis in the aggressive population and gut-derived acetate can cross the blood brain barrier and influence GABAergic and glutamatergic neurotransmission in the brain 118 . Glutamate is the main excitatory neurotransmitter in the brain and plays an important role in fear conditioning, synaptic plasticity, learning, and memory 119 , 120 . GABA, on the other hand, is an inhibitory neurotransmitter that counteracts glutamate, and GABA signaling has been implicated in fear extinction learning 121 , 122 . Further, increased levels of glutamate in the brain can trigger aggression in mice 123 , 124 whereas GABA is mainly associated with an inhibitory role in aggression 125 – 131 . GHB has also been implicated in aggressive behavior in animals and can increase levels of glutamate in the brain 132 – 134 . Moreover, the potential for glutamate synthesis has been identified in the gut microbiota of aggressive mice and red jungle fowl selected for high fear towards humans 12 , 46 . As with 5-HT, glutamate and GABA cannot pass the blood brain barrier 135 , however, certain gut bacteria have been shown to increase GABA and glutamate in the brain 136 and additionally promote consistent changes in GABA receptors in the brain accompanied by behavioral shifts in the host 23 . Further, GABA producing bacteria in the gut can alter mood and fear-related behavior in studies modelling depression 137 . As such, these findings suggest a role for glutamate and GABA signaling in the behavioral shifts shared among animals during domestication, with both a host genomic and gut microbial component to its regulation. Conclusion Here we identified shifts in the fecal (as a proxy for gut) microbiota between behavioral phenotypes of the Belyaev foxes that have been linked to aggressive and fear-related behaviors, behaviors that are consistently reduced in domestic animals. Although our approach does not allow for interpretation of causality nor directionally of the microbiota-gut-brain axis interactions, such correlative findings set the stage for generating mechanistic hypotheses for further exploration. While the depletion of certain gut microbiota, such as Ruminococcaceae , Anaeroplasmataceae , and Lachnospiraceae during the early stages of the domestication process may initiate the behavioral shifts necessary to adapt to the new social environment, the enrichment of others, such as Enterococcus faecalis and Clostridiales may be equally important. Our metagenomic survey also allowed for the reconstruction of several microbial pathways enriched in the gut of tame foxes, such as glutamate degradation and GABA synthesis, which converged with host genetic and physiological signals, revealing a potential role of functional host-microbiota interactions that could influence behaviors associated with domestication. In future studies, the coupling of metagenomics, metatranscriptomics and metabolomics would provide opportunities to validate which bioactive metabolites are being produced, and together with antibiotic treatment and/or fecal transplant experiments, may establish a causative role of the functional pathways by which bacteria affect behavior and to what extent. Further, longitudinal studies of individuals across generations could provide further insights as to (i) when, and how quickly compositional changes occur within the microbiota of animals during early domestication, (ii) how these changes may reflect causal links to the behavioral shifts detected throughout the process, and even (iii) which host genetic mechanisms are driving the gut microbiome community trends. With regards to this latter point, we propose two hypotheses that may warrant future exploration. Firstly, previous characterization of the genomic differences found between the Belyaev fox behavioral strains reported not only significant enrichment for GO terms linked to the nervous system, but also immune responses, specifically “cytokine activity” and “interleukin-1 receptor binding” 32 . Given that differentiation in the host immune system can lead to differentiation in the gut microbiome 138 , we hypothesize that this intriguing observation may suggest that selection for behavioral phenotypic differences in the earliest stages of domestication may include, or even predominantly focus on, immune system differences, and that these in turn could shape the gut microbiome, and hence behavior. Our second hypothesis draws both on the recent observations from fish 139 and humans 140 , that epigenetic changes in host genomes can directly shape the gut microbiome, and that rapid epigenetic divergence has been reported in the early stages of chickens subjected to behavioral selection 98 , 99 , 102 – 104 . As such, an alternate (or complementary) hypothesis, is that epigenetic changes in the fox genomes may be involved in shaping their gut microbiomes. Although this is yet to be tested, that fact that gut microbiomes are also well known to shape their host epigenomes 141 , this process could even involve some degree of reciprocal feedback. Ultimately however, although intriguing, we of course acknowledge that any role for the microbiota in the evolution of domestic behaviors in animals does not displace other contributing factors, but rather adds an additional layer to our understanding of how such behaviors arise. Understanding the eco-evolutionary mechanisms involved in the process of domestication provides crucial insights into how wild animals may adapt to human encounters over time. In this process there is likely a bidirectional relationship between microbiota and host factors, including behavior, that further interact with the environment. Methods Animals and sample collection Fecal samples were obtained from 61 tame and 62 aggressive silver foxes ( Vulpes vulpes ) maintained at the experimental farm of the Institute of Cytology and Genetics (ICG) (Novosibirsk, Russia). Foxes were approximately 5 months old at the time of collection. Each fox was housed individually in a cage with a wire net floor. The ground under the fox cage was covered with a piece of tissue at ~ 7 am and collected in the morning before feeding (~ 7–9 am) to ensure that only fresh fecal samples were obtained. No contact with urine was allowed. All foxes were raised in standard conditions and underwent identical treatment with minimally necessary human interaction until behavioral testing was performed 142 . Selection for the tame strain began in 1959 at the ICG, and was developed through selection of conventional farm-bred foxes from across the former Soviet Union due to their less aggressive and fearful behavior towards humans. The aggressive strain was developed by selecting conventional farm-bred foxes for an aggressive response towards humans, beginning in the late 1960s at the ICG. The farm-bred foxes originated from foxes from eastern Canada where fox farm breeding began in the second part of the nineteenth century 26 . A description of the selective breeding program was previously described 5 , 28 , 142 . Fecal samples were collected and either frozen at -20°C in the summer of 2015 (tame: n = 10, aggressive: n = 10) or preserved in RNAlater stabilization solution in the summer of 2017 (tame: n = 51, aggressive: n = 52) and stored at -20°C. DNA extraction Prior to DNA extraction, RNAlater was removed with centrifugation (13,000 g for 10 minutes) and the pellet was washed twice with 1 mL of PBS. DNA was extracted from approximately 100 mg of fecal sample using the DNeasy PowerSoil Kit DNA (Qiagen, Venlo, NL) following the manufacturer’s protocol with several modifications. Samples were incubated for 10 min at 65 ºC after adding Solution C1 and bead-beaten for 10 mins at 30 Hz using a TissueLyser II (Qiagen, Hilden, Germany). Purified DNA was incubated in Solution C6 for 15 min at 37 ºC before the final elution spin. Four negative controls (i.e. all reagents except sample continued in the workflow from extraction to sequencing as any other extracts) were included in order to check for potential reagent contamination. Bacterial community composition from 16S rRNA amplicon sequencing A dual indexed PCR approach was used to target the V3-V4 variable region of the bacterial 16S rRNA gene (~ 465 bp) for all fecal samples (n = 103) using the primer pair Bact-341F (5’-CCTAYGGG RBGCASCAG-3’) and Bact-806R (5’-GGACTACNNGGGTATCTAAT-3’) with Illumina Nextera overhang adapters (Illumina Inc., San Diego, CA, USA) 143 – 146 . PCR was performed in triplicates and pooled prior to indexing PCR for each individual in order to reduce PCR bias. Pooled libraries were sequenced on an Illumina MiSeq platform using 250PE. Full methodological details can be found in Supplementary material. Illumina adapters and primer sequences were removed from the 16S metabarcoding sequence data using cutadapt v.2.6 147 and subsequently analyzed using the program DADA2 v.1.12.1 148 and R v.3.6.1 149 to infer amplicon sequence variants (ASVs). Complete code was modified from 150 . Briefly, forward and reverse reads were trimmed to 230 bp. The entire dataset was used to define an error rate at each base pair, and all sequences were denoised using the pooled approach to increase the likelihood of resolving rare sequence variants. Forward and reverse reads were merged, and any pair without perfect overlap and < 400bp was removed prior to chimeric sequence filtering. Each ASV was annotated with the RDP Bayesian classifier 151 against the SILVA database 152 to produce a 16S amplicon taxa table. All subsequent analyses were done in R v.3.6.3 unless otherwise stated 153 . ASV data was pre-processed with the phyloseq package v.1.30.0 154 , and potential contaminants were assessed with the decontam package v 1.6.0 155 . Twelve putative contaminants were removed from the ASV table. Only samples with > 10,000 reads and ASVs present in a minimum of 5% of all samples were included in downstream 16S data analysis. Metagenomic shotgun sequencing Shotgun metagenome sequence data were prepared on all DNA extracts (n = 103) using the BEST single-tube library preparation protocol 156 as optimized to be BGISEQ-500 compatible 157 . Briefly, genomic DNA was fragmented to 350 bp using a M220 Focused Ultrasonicator (Covaris, Woburn, MA). Sheared DNA was converted into BGISEQ-500 libraries following four steps: blunt end-repair, adapter ligation (20 µM BGI 2.0 adapters), fill-in reaction and SPRI magnetic bead purification (Sigma-Aldrich). Indexing PCR cycle number for all metagenomic libraries (7–11 cycles) were determined through qPCR library quantification. Libraries were pooled equimolar over 6 lanes in 100bp or 150bp paired-end mode on the BGISeq-500 platform aiming for a minimum of 50 million reads per sample. Assembly and genome-resolved metagenomics Prior to sequence assembly, all paired-end reads were demultiplexed and quality filtered. AdapterRemoval v.2.3.1 158 was used to trim unidentified bases and adapter sequences from the ends of the read and PCR duplicates were removed with seqkit v.0.8.0 159 . Host and human reads were removed using bwa-mem algorithm v.0.7.15 160 against the human (RefSeq: GCF_000001405.26) and fox (RefSeq: GCF_000002315.4) reference genomes. Quality filtered metagenomic reads were then co-assembled using MEGAHIT v.1.1.1 with k-mer sizes: 77,87,97,107,127,137,147,157,167 and default parameters 161 . Contigs less than 2500 nt were removed from the resulting assembly output and corresponding header names were simplifying using anvi’o v.6.2 162 . Metagenomic reads were mapped to the assembled contigs using bwa-mem algorithm v.0.7.15 with default parameters 160 and Samtools v.1.9 163 was used to sort and index the output SAM files into BAM files. BAM files were used to generate a contig depth of coverage table with jgi_summarize_bam_contig_depths (MetaBAT2 v.2.12.1) 164 . We then applied the automatic binning algorithm in CONCOCT 165 on this coverage table to generate 10 large contig clusters to maximize explained patterns while minimizing fragmentation error, as performed elsewhere 166 , 167 . Subsequently, a manual binning and curation was performed for each CONCOCT cluster following the genome-resolved metagenomic workflow implemented in anvi’o v.6.2 162 . Briefly, anvi’o was used to generate a contigs database that identified open reading frames using Prodigal v.2.6.3 168 and single-copy core genes using HMMER v.3.2.1 169 against the collection of built-in HMM profiles for Bacteria and Archaea. Gene-level taxonomy was classified using Kaiju v.1.5.0 170 , with NCBI’s non-redundant protein database, including fungi and microbial eukaryotes, and genes were further annotated with functions using the NCBI’s Clusters of Orthologous Groups (COG) 171 . Anvi’o was then used to profile each metagenomic BAM file to estimate the coverage and detection statistics of contigs in the contigs database, and combined mapping profiles into a merged profile database for all individuals. In addition, we imported an anvi'o collection corresponding to the 10 CONCOCT clusters. Finally, each CONCOCT cluster was manually binned and further refined using the anvi’o interactive interface 172 taking into account sequence composition, differential coverage, GC-content, and taxonomic signal of the considered contigs. MAGs with completeness > 50% and redundancy < 10% were retained for downstream analyses 173 (Genomic features of the MAGs can be found in Suppl.Table 2b). The taxonomy of MAGs was inferred using the Genome Taxonomy Database Toolkit (GTDB-Tk) 174 version 95. However, we used NCBI taxonomy from the GTDB output to describe the phylum of MAGs in the results and discussion sections, in order to be in line with the literature. MAGs were considered to be detected in a given sample when > 50% of their length was covered by reads to minimize non-specific read recruitments 167 . The number of recruited reads below this cut-off was set to 0 before determining vertical coverage, the number of bases covering each genome divided by its length. Gut-brain module (GBM) detection The fox shotgun metagenomic data was translated into neuroactive potential using a previously described module-based reconstruction framework 58 . Briefly, we searched for the presence of 56 gut-brain modules (GBMs), each corresponding to a process of synthesis or degradation of a neuroactive compound by the gut microbiota, in each of the fox MAGs (n = 204). As module structure follows the Kyoto Encyclopedia of Genes and Genomes (KEGG) database syntax, gene calls for each MAG were exported from the contig database within anvi’o and functionally annotated with KEGG identifiers using GhostKoala 175 . GBM coverage was calculated as the number of pathway steps for which at least one of the orthologous groups is found in a genome, divided by the total number of steps constituting the GBM using Omixer-RPM v.0.3.2 ( https://github.com/raeslab/omixer-rpm ). GBM presence in microbial MAGs was defined with a detection threshold of at least 66% coverage, to provide tolerance to miss-annotations and missing data in incomplete genomes 58 . GBM detection was visualized with corrplot v.0.84 176 in the 28 differentially abundant fox MAGs to identify over/under-represented metabolic GBMs between the two behavioral selection lines. Differential abundance estimates Diversity estimates and hypothesis testing of Shannon diversity were performed with the breakaway package v.4.6.16 177,178 and DivNet package v.0.3.5 179 for the 16S dataset. These packages use sophisticated models to account for sequencing depth and rare taxa in high-dimensional data and incorporate taxon interactions when estimating α-diversity. Diversity estimates with uncertainties were used to support hypothesis testing between selection lines 180 . Expected relative abundance of microbial taxa was modeled directly from read counts for 16S and shotgun sequence data at different taxonomic levels (phylum, class, order, family, genus, and ASVs) using a beta-binomial model controlling for collection year and controlling for the effect of selection and collection year on dispersion. 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Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat Commun 7 , 11257 (2016). Tatusov, R. L. et al. The COG database: an updated version includes eukaryotes. BMC Bioinformatics 4 , 41 (2003). Delmont, T. O. & Eren, A. M. Identifying contamination with advanced visualization and analysis practices: metagenomic approaches for eukaryotic genome assemblies. PeerJ 4 , e1839 (2016). Delmont, T. O. et al. Reconstructing rare soil microbial genomes using in situ enrichments and metagenomics. Front Microbiol 6 , 358 (2015). Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36 , 1925–1927 (2020). Kanehisa, M., Sato, Y. & Morishima, K. BlastKOALA and GhostKOALA: KEGG Tools for Functional Characterization of Genome and Metagenome Sequences. J Mol Biol 428 , 726–731 (2016). Wei, T. & Simko, V. R package ‘corrplot’: Visualization of a Correlation Matrix. Preprint at (2017). Willis, A. & Bunge, J. Estimating diversity via frequency ratios. Biometrics 71 , 1042–1049 (2015). Willis, A., Bunge, J. & Whitman, T. Improved detection of changes in species richness in high diversity microbial communities. J R Stat Soc Ser C Appl Stat 66 , 963–977 (2017). Willis, A. D. & Martin, B. D. Estimating diversity in networked ecological communities. Biostatistics (2020) doi:10.1093/biostatistics/kxaa015. Willis, A. D. Rigorous Statistical Methods for Rigorous Microbiome Science. mSystems 4 , e00117-19 (2019). Martin, B. D., Witten, D. & Willis, A. D. Modeling microbial abundances and dysbiosis with beta-binomial regression. Ann Appl Stat 14 , 94–115 (2020). Benjamini, Y. & Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B (Methodological) 57 , 289–300 (1995). Wickham, H. Ggplot2: Elegant Graphics for Data Analysis . (Springer-Verlag New York, 2016). Additional Declarations There is NO Competing Interest. Supplementary Files Suppl.Table1.FOX.xlsx Suppl.Table2.FOX.xlsx SupportingInfoPuetzFOXmanuscript.docx Cite Share Download PDF Status: Published Journal Publication published 27 Mar, 2026 Read the published version in Communications Biology → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4697888","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":328062369,"identity":"c07abeeb-f5da-4bd2-843b-2b448d035826","order_by":0,"name":"Lara C Puetz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYBACPhTeByBmYyeghQ3BZGZgnAESYSZFCzMPhCaghf3sww8MNQx5Bsf7D3+2+bVNng9o24ePOXi08KQbSzAcYyg2OHOYwTi377ZhG9A2yZnb8DksjUGCsYEhccONZIbk3J7bjEAtbMy8+LTwP2P+AdZy/zHDYcue2/aEtUiksUFtYWZsZvhxO5EILc/YLBKOSRRLnkk2ZuxtuJ3cBtSK1y/8/GnMNz7U2OTxHT/4+MOPP7dt57c3H/zwEY8WMEhgkEgAMxjbwGQDAfUwXWDwhyjFo2AUjIJRMMIAAB54R4i3hzxRAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-0323-799X","institution":"University of Copenhagen","correspondingAuthor":true,"prefix":"","firstName":"Lara","middleName":"C","lastName":"Puetz","suffix":""},{"id":328062370,"identity":"3d1cf01f-995b-4c26-9d53-4adf32f9d9ce","order_by":1,"name":"Tom O. Delmont","email":"","orcid":"https://orcid.org/0000-0001-7053-7848","institution":"Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay","correspondingAuthor":false,"prefix":"","firstName":"Tom","middleName":"O.","lastName":"Delmont","suffix":""},{"id":328062371,"identity":"46055d86-8036-4125-b3ee-ca7804567a34","order_by":2,"name":"Alex L. Mitchell","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Alex","middleName":"L.","lastName":"Mitchell","suffix":""},{"id":328062372,"identity":"843cf18d-84ca-4ecf-9dfc-98384160fbc6","order_by":3,"name":"Robert Finn","email":"","orcid":"https://orcid.org/0000-0001-8626-2148","institution":"EMBL-EBI","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Finn","suffix":""},{"id":328062373,"identity":"5deb4ce3-32b5-4e40-8252-9de90b66ca90","order_by":4,"name":"Guojie Zhang","email":"","orcid":"https://orcid.org/0000-0001-6860-1521","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Guojie","middleName":"","lastName":"Zhang","suffix":""},{"id":328062374,"identity":"bc4732d0-9f11-4163-b68e-3ecd565e6d0c","order_by":5,"name":"Darya V. Shepeleva","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Darya","middleName":"V.","lastName":"Shepeleva","suffix":""},{"id":328062375,"identity":"8313a9d4-2001-4e26-afb0-afd54530198f","order_by":6,"name":"Anastasiya V. Kharlamova","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Anastasiya","middleName":"V.","lastName":"Kharlamova","suffix":""},{"id":328062376,"identity":"cd112056-bf49-40a9-9586-70b700512e7b","order_by":7,"name":"Anna Kukekova","email":"","orcid":"","institution":"University of Illinois","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Kukekova","suffix":""},{"id":328062377,"identity":"a975c5d8-6bbe-4ac4-80ad-4f47b9dc6286","order_by":8,"name":"Lyudmila N. Trut","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lyudmila","middleName":"N.","lastName":"Trut","suffix":""},{"id":328062378,"identity":"2c99f64e-8fde-4280-86a5-fbc6953088c8","order_by":9,"name":"M Thomas P Gilbert","email":"","orcid":"https://orcid.org/0000-0002-5805-7195","institution":"University of Copenhagen","correspondingAuthor":false,"prefix":"","firstName":"M","middleName":"Thomas P","lastName":"Gilbert","suffix":""}],"badges":[],"createdAt":"2024-07-06 18:05:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4697888/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4697888/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s42003-026-09717-5","type":"published","date":"2026-03-27T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62248102,"identity":"17725d46-2c0e-4e74-bd33-0808b3c36f9f","added_by":"auto","created_at":"2024-08-12 05:27:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":198565,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiversity differences of fecal microbiota between aggressive (n=52) and tame (n=50) fox strains from the bacterial 16S rRNA gene amplicon survey. A) \u003c/strong\u003eDiversity estimates with uncertainties of Shannon diversity in fox fecal samples collected in 2015 (unpublished pilot study data) and 2017. Differential relative abundance of the genera \u003cstrong\u003eB)\u003c/strong\u003e \u003cem\u003eRuminococcaceae (UCG-014)\u003c/em\u003e, \u003cstrong\u003eC)\u003c/strong\u003e \u003cem\u003eAnaeroplasma\u003c/em\u003e and\u003cem\u003e \u003c/em\u003e\u003cstrong\u003eD)\u003c/strong\u003e\u003cem\u003e Lachnospiraceae (UCG-010)\u003c/em\u003e. Microbial community composition was modelled at the genus level for the 16S by fitting the beta-binomial regression model implemented in the ‘corncob’ package in R. Differentially abundant taxa were considered significant using the parametric Wald test with a controlled false discovery rate (p-value cut-off \u0026lt;0.05) ** p≤ 0.01, *** p≤ 0.001.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4697888/v1/656d76e8d6303852b8de51cd.png"},{"id":62247734,"identity":"cecf0a24-4ee6-4ea9-80cd-f98c88c87974","added_by":"auto","created_at":"2024-08-12 05:19:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":276867,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential abundance of bacterial 16S ASVs between aggressive and tame foxes (n=102). A)\u003c/strong\u003eDifferences in the abundance of ASVs, grouped by genus and phylum level taxonomic classifications, between aggressive and tame populations estimated with corncob using the Wald test with a controlled false discovery rate. B) Prevalence plots of 1,525 bacterial 16S rRNA amplicon sequence variants (ASVs) found in fox fecal samples at the phylum level. Each point represents the total counts of a unique ASV corresponding to the fraction of individuals it was detected In. ASVs that were differentially abundant (n=53) or discriminant (n=23) in either the aggressive (*) or tame population are highlighted.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4697888/v1/aab8944e2261fc0c1f80ea29.png"},{"id":62247739,"identity":"ae24f01b-bc9f-4b59-a65b-4f65fa95cd79","added_by":"auto","created_at":"2024-08-12 05:19:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":800288,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGut-brain module (GBM) distribution in fox MAGs A)\u003c/strong\u003e Maximum-likelihood phylogenetic tree comprising 237 gut-associated MAGs identified in 123 silver fox fecal samples, rooted at the level of Bacteroidetes. The innermost circular layer represents the percent completion of each genome followed by the associated phylum in the second layer. The following middle layers represent the 35 gut-brain modules (GBMs) detected in the collections of MAGs and categorized by functional association. Six MAGs were differentially abundant or exclusive to a behavioral phenotypic group and highlighted on the tree based on the enrichment in either the aggressive (yellow) or the tame (teal) populations. \u003cstrong\u003eB)\u003c/strong\u003e Detection of GBMs (minimum 66% coverage) in the 22 differentially occurring fox MAGs highlighted in A) Pathways and functions were annotated for each GBM along the bottom x-axis. GBMs exclusively present in MAGs enriched in the tame population are highlighted in teal whereas none were exclusively present in the aggressive population.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4697888/v1/aa87a4afa98a9b40af1c8a65.png"},{"id":62247735,"identity":"69d1f5d8-6c6b-4704-8d0a-19d279ffacd7","added_by":"auto","created_at":"2024-08-12 05:19:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":88095,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGut-brain module (GBM) distribution in fox metagenomes A) \u003c/strong\u003eHeatmap of the log\u003csub\u003e10\u003c/sub\u003e frequency of 35 GBMs detected in 102 fecal metagenomes of silver fox selected for tame and aggressive behaviors towards humans. Clustering of GBMs and metagenomes is based on GBM frequency (Euclidean distance and ward linkage), and the data were visualized using anvi’o. \u003cstrong\u003eB)\u003c/strong\u003e Differential abundance of GBMs between aggressive and tame fox metagenomes (n=102).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4697888/v1/d18b49cdbaffe202b7c02f7f.png"},{"id":105618232,"identity":"6e6712eb-4cea-4b47-8603-67814fe9051d","added_by":"auto","created_at":"2026-03-28 07:11:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2873938,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4697888/v1/e38c344c-2943-4a66-a610-6ca21c54d2c9.pdf"},{"id":62247740,"identity":"83cf222b-1aba-4acf-b3f7-903876ce9d44","added_by":"auto","created_at":"2024-08-12 05:19:04","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":710495,"visible":true,"origin":"","legend":"","description":"","filename":"Suppl.Table1.FOX.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4697888/v1/b975ab6c7e7a9c42224146cb.xlsx"},{"id":62248658,"identity":"47e9fe48-ed64-4f09-9b08-c52e2cd6bcd4","added_by":"auto","created_at":"2024-08-12 05:35:03","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":100598,"visible":true,"origin":"","legend":"","description":"","filename":"Suppl.Table2.FOX.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4697888/v1/9740980790951d44f1a2cbc6.xlsx"},{"id":62248100,"identity":"baa33e70-016e-4772-9a83-f62a48177877","added_by":"auto","created_at":"2024-08-12 05:27:03","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":388433,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupportingInfoPuetzFOXmanuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-4697888/v1/352f7670edc5b5c52f8cd493.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Gut microbiome community structure correlates with different behavioral phenotypes in the Belyaev farm-fox experiment","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUnderstanding the evolutionary mechanisms involved in the process of domestication provides crucial insights into how wild animals have adapted to new social environments over time. Behavioral shifts are thought to facilitate the early stages of animal domestication \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e with a selection for tameness as the single most consistent trait across domesticated species \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Tameness denotes an attenuation of the flight-or-fight response, or in other words, a reduced fear and long-term stress response towards humans, which is often a prerequisite to successful breeding in captivity \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Rapid adaptation via behavioral plasticity is traditionally viewed as being primarily encoded by the genome, however, ample evidence shows that the gut microbiome of animals can respond to environmental changes and may play a crucial role on their adaptive capacity \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Further, mounting evidence has demonstrated how the gut microbiome influences host behavior, cognition, brain development and physiology \u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, which may have profound implications for host ecology and evolution, including adaptation via behavioral selection \u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe complex bidirectional communication between animals and their gut microbiota, called the microbiota-gut-brain-axis, can be mediated through a variety of mechanisms and it is becoming increasingly clear that gut derived metabolites are important to these interactions \u003csup\u003e\u003cspan additionalcitationids=\"CR15 CR16 CR17\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The biochemical communication pathways can occur through various direct and indirect mechanisms, which include transport of gut-derived metabolites from circulation into the brain, initiation of immune and vagus nerve activation, stimulation of enterochromaffin cells in the gut, and even epigenetic alterations \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Experimental studies have also implicated microbiome-derived signals in regulating fear and aggressive behavior in animals, as well as, expression of genes in the brain involved in such behaviors \u003csup\u003e\u003cspan additionalcitationids=\"CR21 CR22 CR23 CR24\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, both of which have interesting implications for domestication. However, the eco-evolutionary relevance of the microbiome-gut-brain axis outside of laboratory animals remains largely unexplored.\u003c/p\u003e \u003cp\u003eThe Russian Farm Fox Experiment is one of the most extensive experimental domestication studies. Initiated in 1959 at the Institute of Cytology and Genetics (ICG) (Novosibirsk, Russia), Dimitry K. Belyaev and Lyudmila Trut selectively bred captive populations of the silver fox (\u003cem\u003eVulpes vulpes\u003c/em\u003e) that were originally derived from fur farms in eastern Canada \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, to understand the evolutionary processes of adapting to a new social environment \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. After decades of selecting on foxes solely for behavior, specific strains of fox with markedly different behavioral phenotypes were gradually established. Foxes bred for tame phenotypes, herein referred to as the \u0026ldquo;tame\u0026rdquo; strain, displayed a reduced-fear and positive response towards humans, whereas aggressive phenotypes, herein referred to as the \u0026ldquo;aggressive\u0026rdquo; strain, exhibited aggression towards humans along with avoidance behavior \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. A genetic basis has been extensively documented for both behavioral phenotypes of these foxes \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan additionalcitationids=\"CR31 CR32\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. However, it has recently been demonstrated that both host genetics and the microbiome can interdependently regulate certain behaviors in mice \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Taken together, we hypothesize that selection for behavioral traits in the fox domestication experiment has inadvertently led to selection on specific bacterial gut microbiota community structures that may potentially contribute to shaping fox behaviors - something we have previously demonstrated in a similar red jungle fowl domestication experiment \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study we characterized the fecal microbiome community (as a proxy for gut) of foxes from the tame and aggressive strains of the Russian Farm Fox Experiment, in order to test the hypothesis that the gut microbial community compositions, and their neuroactive potentials, differ between fox behavioral phenotypes. All foxes were housed in the same or similar facilities and fed identical diets, allowing us to assess the differences between the gut microbiota of the two behavioral phenotypes without introducing the confounding variables of environment, and especially diet, both of which can strongly influence the gut community composition \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cp\u003eIn order to profile the gut microbiome of the two fox behavioral strains, we generated both targeted bacterial 16S rRNA amplicon and shotgun metagenomic data from 123 fecal samples that represented two collection years (2015: tame (n\u0026thinsp;=\u0026thinsp;10); aggressive (n\u0026thinsp;=\u0026thinsp;10); 2017: tame (n\u0026thinsp;=\u0026thinsp;51); aggressive (n\u0026thinsp;=\u0026thinsp;52)). Because rare microbial species are typically not well assembled in metagenomic surveys because of low coverage \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, 16S amplicon data was used as a starting point to characterize the microbiota\u0026rsquo;s broad taxonomic composition in order to study links with behavioral phenotypes. Complementary metagenomic data was incorporated to 1) recover and compare novel bacterial species between the behavioral selections lines, through the reconstruction of metagenome assembled genomes (MAGs), and 2) provide insights into the potential microbiome functions relevant to the microbiota-gut-brain-axis.\u003c/p\u003e \u003cp\u003eDue to the strong batch effects (namely sample storage treatment known to significantly impact microbial taxonomic profiles \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e), between the two collections years (Suppl. Figure\u0026nbsp;1), the 2015 dataset was removed from the 16S analysis and additionally removed from the shotgun metagenomic analysis after we generated the MAG reference catalogue.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGut microbial diversity reduced in tame foxes\u003c/h2\u003e \u003cp\u003eTo create initial community composition profiles of the gut microbiota of the two silver fox behavioral strains, a 16S rRNA gene amplicon survey was performed on 2017 fecal samples from aggressive (n\u0026thinsp;=\u0026thinsp;52) and tame (n\u0026thinsp;=\u0026thinsp;50) individuals. A total of 1,525 amplicon sequence variants (ASVs) were identified after quality filtering. Diversity analysis at the genus level identified a lower Shannon diversity estimate in the microbiota of the tame, compared to aggressive, fox populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA; Shannon\u003csub\u003etame\u003c/sub\u003e= 2.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006; Shannon\u003csub\u003eaggr\u003c/sub\u003e = 2.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003; p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Marked reductions in the gut microbial diversity of domesticated animals have been identified in a number of species, and are typically associated with anthropogenic factors, such as shifts in environment and diet, during the domestication process \u003csup\u003e\u003cspan additionalcitationids=\"CR39 CR40\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Interestingly, increased gut microbial diversity is also associated with aggressive and fearful behavior in mammals \u003csup\u003e\u003cspan additionalcitationids=\"CR43 CR44 CR45 CR46\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. As the foxes from the two selection lines were housed in similar controlled environments, the lower diversity estimates identified in the tame gut microbiota are unlikely due to shifts in environmental factors. Alternatively, it is possible that the depletion or loss of certain gut microbes during the early stages of the domestication process may initiate the behavioral shifts necessary to adapt to the new social environment, and knowledge of individual microbial species responsible for these phenotypes may be valuable in understanding their biological role during domestication.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eTame foxes are depleted in bacteria associated with fearful and aggressive behavior\u003c/h2\u003e \u003cp\u003eTo investigate whether certain microbes were associated with the different behavioral phenotypes, we modelled relative abundance of 16S sequence data at different taxonomic levels. At the phylum level, we observed that the gut microbiota of tame foxes were significantly depleted of Tenericutes in comparison to aggressive foxes (Wald test, t-value = -4.785, p-value\u0026thinsp;=\u0026thinsp;1.53e-05), mainly due to the reduction of the bacterial family \u003cem\u003eAnaeroplasmataceae\u003c/em\u003e (Wald test, t-value = -4.785, p-value\u0026thinsp;=\u0026thinsp;7.88e-05). This finding is interesting for several reasons. Firstly, in other mammals such as hamsters and mice, Tenericutes, and more specifically \u003cem\u003eAnaeroplasmataceae\u003c/em\u003e, have been reported to play a role in social behavior, and consistently and positively correlate with aggression \u003csup\u003e\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Secondly, not only do some members of Tenericutes show heritability in multiple human populations \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, but Tenericutes are also less capable of recovering after perturbation in the gut \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Together we hypothesize that it may be possible to select against at least some bacteria during early domestication, that remain depleted or lost throughout the process.\u003c/p\u003e \u003cp\u003eWhen considering the data at the order level, we noted a depletion of bacteria from the order \u003cem\u003eDesulfovibrionales\u003c/em\u003e in tame foxes (Wald test, t-value = -2.938, p-value\u0026thinsp;=\u0026thinsp;0.04). Maternal stress and perturbations in the gut microbiota of Siberian hamsters produced offspring that were not only enriched in \u003cem\u003eDesulfovibrionales\u003c/em\u003e but also displayed increased levels of aggression when treated with stress \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Cusick and co-workers \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e went on to suggest that both maternal microbiome and response to stress interact in ways that impact the behavior and gut microbiota of their offspring, both of which would have interesting implications in the eco-evolutionary processes of domestication.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eModelled relative abundance at the genus level revealed three additional taxa depleted in the tame fox strain, specifically \u003cem\u003eRuminococcaceae (UCG-014)\u003c/em\u003e, \u003cem\u003eAnaeroplasma\u003c/em\u003e and \u003cem\u003eLachnospiraceae (UCG-010)\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-C), all three of which have intriguing links to behavior in other mammals. In particular, \u003cem\u003eRuminococcaceae\u003c/em\u003e and \u003cem\u003eLachnospiraceae\u003c/em\u003e not only positively associate with aggressive behaviors in mammals, including dogs, mice and hamsters \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, but also exhibit lower abundance in some captive and domestic animal populations of gaur and yaks \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Further, \u003cem\u003eLachnospiraceae\u003c/em\u003e are positively associated with brain reactivity to fear in humans, particularly in the prefrontal cortex \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, which is a brain region involved in memory, learning and regulating fear, and shown to be modulated by changes in gut microbiota \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition to \u003cem\u003eLachnospiraceae\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e similarly associate with brain reactivity to fear \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. In this regard, when we explored our data at the ASVs level, we see they are also depleted in the tame strain (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Interestingly, \u003cem\u003eBacteroides\u003c/em\u003e are observed in high abundance in the gut microbiota of wild red foxes from the grasslands in China \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Additional analysis at the ASV level revealed 53 ASVs to be differentially abundant between the aggressive and tame behavioral strains, and an additional 23 ASVs were discriminant to either selection line (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Suppl. Table\u0026nbsp;1). Most of these differences occurred in abundant and highly prevalent taxa (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). In addition to \u003cem\u003eBacteriodes\u003c/em\u003e, we detected a significant depletion of \u003cem\u003eAlloprevotella, Prevotellaceae and Blautia ASVs\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA; Suppl. Table\u0026nbsp;1), all of which have previously been associated with aggression in dogs, hamsters, mice and voles \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Taken together, these data suggest that the gut microbiota in tame foxes are depleted of bacteria not only associated with aggressive and fearful behaviors, but also in taxa found in abundance in their wild counterparts. Furthermore, similar patterns in the gut microbiome of other domesticated animals have been observed suggesting a role of the gut microbiome in domestic behaviors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGenome-resolved metagenomics characterize the neuroactive potential of the fox gut microbiota\u003c/h2\u003e \u003cp\u003eTo identify novel gut bacterial species, and assess the functional potential of the gut microbiota found in the aggressive and tame fox strains, we next carried out shotgun metagenomic sequencing. Initially, we generated a reference catalogue of metagenome-assembled genomes (MAGs) using a dataset of 123 samples that represented two collection years (2015\u0026thinsp;+\u0026thinsp;2017) (tame (n\u0026thinsp;=\u0026thinsp;61); aggressive (n\u0026thinsp;=\u0026thinsp;62)). Briefly, the metagenomic data yielded 15.8\u0026nbsp;billion high-quality short-reads, of which ca. 30\u0026ndash;70% (per sample) mapped to the fox genome (Suppl. Table\u0026nbsp;2a). After removing reads corresponding to the fox genome, we performed a large metagenomic co-assembly (7.09\u0026nbsp;billion reads), which produced 105,106 contigs\u0026thinsp;\u0026gt;\u0026thinsp;2500 nt. Subsequent manual binning within the anvi\u0026rsquo;o \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e framework using differential coverage across all samples resulted in 237 non-redundant MAGs of which 50% of the reads mapped back to (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA; Suppl. Figure\u0026nbsp;2; Suppl. Table\u0026nbsp;2b). Four samples had a sequencing depth of less than 10\u0026nbsp;million single-end reads (\u0026lt;\u0026thinsp;1 Gb) after quality control, and were removed from downstream analyses (as per recent recommendations \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e). At the phylum level, MAGs were affiliated to \u003cem\u003eFirmicutes\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;145), followed by \u003cem\u003eBacteroidetes\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;27), \u003cem\u003eProteobacteria\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;25), \u003cem\u003eActinobacteria\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;17), \u003cem\u003eTenericutes\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;17), \u003cem\u003eSpirochaetes\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;3), \u003cem\u003eCyanobacteria\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;1), \u003cem\u003eFusobacteria\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;1) and \u003cem\u003eDeferribacteres\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In addition, all MAGs were affiliated to known bacterial orders, and 43% of them could also be assigned to a known species (average nucleotide identity\u0026thinsp;\u0026gt;\u0026thinsp;95%). Complete taxonomic assignments using the Genome Taxonomy Database Toolkit (GTDB-Tk)\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e are found in Suppl. Table\u0026nbsp;2b.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eModelled microbial abundance revealed 22 MAGs to be significantly differentially abundant between the aggressive and tame fox strains, and an additional 3 MAGs were discriminant to either strain (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Although overlap between shotgun metagenomic and 16S data exist at broad taxonomic levels (Bowers et al., 2022; Gehrig et al., 2022), comparison of community structures across the two sequencing strategies do not necessarily merge perfectly as a consequence of differences in taxonomic databases (Lesker et al., 2020; Odom et al., 2023; Tessler et al., 2017), detection limits (Durazzi et al., 2021), sequencing depths (Durazzi et al., 2021; Tessler et al., 2017) and PCR biases (Bowers et al., 2022; Sze \u0026amp; Schloss, 2019). Nonetheless, corroborating 16S sequence data, there was an enrichment of \u003cem\u003eAnaeroplasmataceae\u003c/em\u003e, \u003cem\u003eHelicobacter C sp.\u003c/em\u003e and several \u003cem\u003eBacteroidaceae\u003c/em\u003e MAGs, namely \u003cem\u003eParaprevotella sp., Prevotellamassilia sp\u003c/em\u003e. and \u003cem\u003ePrevotellamassilia sp.000437675\u003c/em\u003e, in the aggressive fox strain, among six additional enriched MAGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Conversely, the tame fox strain was enriched in the MAGs \u003cem\u003eCollinsella sp., Fusobacterium sp.900015295\u003c/em\u003e and \u003cem\u003eHelicobacter bilis\u003c/em\u003e, similarly identified at higher taxonomic resolution in the 16S data (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) in addition to eight other MAGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eIn order to describe the neuroactive potential of gut microbiota in relation to gut\u0026ndash;brain interactions in the foxes, we applied a previously described module-based framework \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. This framework identifies microbial pathways that metabolize molecules with the potential to interact with the host nervous system. We found 35 out of the 56 annotated gut\u0026ndash;brain modules (GBMs) known to produce or degrade neuroactive compounds, spread widely across the phylogenic range of MAGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). We subsequently compared the microbial neuroactive potential of the gut microbiota between tame and aggressive fox strains by assessing the detection of GBMs in the 22 MAGs that were significantly enriched in one of the behavioral phenotypic groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). We detected six GBMs associated with the tame fox strain, three of which were identified in less than 5% of all MAGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB; Suppl. Figure\u0026nbsp;3). Two of the GBMs enriched in the tame population were associated with short chain fatty acids (SCFA), namely butyrate synthesis II and isovaleric acid synthesis I, and an additional three GBMs were from the glutamate-derived pathway, glutamate degradation II, GABA synthesis III and g-Hydroxybutyric acid (GHB) degradation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The final GBM was associated with the estrogen hormone signaling pathway, 17-beta-estradiol degradation, and was present in five out of the eleven MAGs enriched in the tame selection line (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Interestingly, circulating levels of estradiol have been linked to variation in social behaviors, including a positive correlation with aggression in multiple animal species, namely sparrows, cichlids and mice \u003csup\u003e\u003cspan additionalcitationids=\"CR60 CR61 CR62\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. It is therefore possible that the gut bacteria in the tame fox strain have the potential to reduce estradiol in circulation and in turn decrease aggressive behaviors important in the early domestication process.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe further applied the GBM framework to the cleaned shotgun data (7.09\u0026nbsp;billion reads), prior to assembly, for all fox samples, in order to increase the potential detection of GBMs associated to behavioral phenotypes. Most GBMs (n\u0026thinsp;=\u0026thinsp;31) were present in over 75% of all fox samples and one was rare, namely nitric oxide degradation II and exclusively found in the tame fox selection line (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Modelled differential abundance of GBMs per metagenomic sample revealed three significant enrichments associated with the aggressive behavioral group; S-adenosylmethionine (SAM) synthesis (Wald test, t-value = -4.23, p-value\u0026thinsp;=\u0026thinsp;3.9e-04), acetate synthesis I (Wald test, t-value = -4.34, p-value\u0026thinsp;=\u0026thinsp;3.8e-04) and acetate synthesis III (Wald test, t-value = -3.15, p-value\u0026thinsp;=\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003cb\u003eGut microbiota have the functional potential to produce neuroactive metabolites that influence the serotonergic system relevant to domestic behaviors\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSerotonin (5-HT) has been implicated as one of the main neurotransmitters involved in animal aggression and plays an inhibitory role across a wide range of species \u003csup\u003e\u003cspan additionalcitationids=\"CR65 CR66 CR67\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Similarly, some domesticated animals have higher levels of brain 5-HT, including the Belyaev foxes as previously demonstrated, or peripheral 5-HT, that correlates with reduced aggressive and fear-related phenotypes \u003csup\u003e\u003cspan additionalcitationids=\"CR70 CR71 CR72 CR73\" citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. Although the majority of 5-HT is produced in the gut (~\u0026thinsp;90%), it is generally believed it cannot directly affect levels in the brain, because 5-HT cannot pass the blood\u0026ndash;brain barrier (BBB) \u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. However, peripheral levels of 5-HT can alter brain functionality and behavior \u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. Furthermore, germ-free rodents have altered 5-HT concentrations and turnover in the brain, altered levels of circulating 5-HT and its precursor L-tryptophan, and decreased cecal and fecal 5-HT \u003csup\u003e\u003cspan additionalcitationids=\"CR78 CR79 CR80\" citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. Gut bacteria can also modulate serotoninergic gene expression profiles in the brain \u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e,\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. Together, this suggests a role for the gut microbiota in modulating 5-HT signaling pathways in the central nervous systems, however the mechanistic link between the gut microbiota and 5-HT production in the brain is not yet fully defined.\u003c/p\u003e \u003cp\u003eHere, we identified that foxes from the tame strain were significantly enriched in \u003cem\u003eEnterococcus faecalis\u003c/em\u003e, \u003cem\u003eRoseburia sp900548205\u003c/em\u003e and \u003cem\u003eClostridiales\u003c/em\u003e MAGs, all taxa known to increase peripheral host serotonin levels by either directly producing it, promoting host serotonin biosynthesis, or upregulating the expression of serotonin transport genes in the gut \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e,\u003cspan additionalcitationids=\"CR85 CR86 CR87\" citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB; Wald test, t-value\u0026thinsp;=\u0026thinsp;0.9, p-value\u0026thinsp;=\u0026thinsp;0.007, Wald test, t-value\u0026thinsp;=\u0026thinsp;2.86, p-value\u0026thinsp;=\u0026thinsp;0.008 and order-level \u003cem\u003eClostridiales\u003c/em\u003e: t-value\u0026thinsp;=\u0026thinsp;3.7, p-value\u0026thinsp;=\u0026thinsp;0.001). Interestingly, \u003cem\u003eClostridiales\u003c/em\u003e were also enriched in red jungle fowl gut microbiota selected for low fear of humans \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, and positively associated with impaired fear memory in mice \u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e, which further suggest a role for this taxa in early domestication. The SCFAs butyrate and acetate can also influence the host serotonin system. Experimental studies have revealed that butyrate induces 5-HT colonic secretion from the gut, whereas acetate has been shown to do the opposite \u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e,\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e, and the potential for butyrate and acetate synthesis were enriched in either the tame or aggressive fox strain gut microbiota respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Our 16S taxonomic data further support this pattern, where acetate producing bacteria \u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e were significantly enriched in the aggressive fox strain, namely \u003cem\u003eBacteroides spp.\u003c/em\u003e, \u003cem\u003ePrevotella spp.\u003c/em\u003e, \u003cem\u003eRuminococcus spp.\u003c/em\u003e and \u003cem\u003eBlautia spp\u003c/em\u003e. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Intriguingly, butyrate is implicated in neuronal plasticity, fear memory formation, and increased fear extinction \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan additionalcitationids=\"CR93\" citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e, whereas acetate has been proposed to induce impairments in learning and coping with stress \u003csup\u003e\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e. Further, SCFAs, such as butyrate and acetate, can cross the BBB and are known to be strong epigenetic modulators \u003csup\u003e\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e and epigenetic mechanisms may play an integral role in both the microbiota-gut-brain-axis \u003csup\u003e\u003cspan additionalcitationids=\"CR21 CR22 CR23 CR24\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e and domestication \u003csup\u003e\u003cspan additionalcitationids=\"CR99 CR100 CR101 CR102 CR103\" citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe detected the nitric oxide (NO) degradation II GBM exclusively in the tame fox metagenomic data, although in low abundance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Nitric oxide appears to play an important role in normal brain 5-HT functioning and has been implicated in both fear-related and aggressive behavior in mammals \u003csup\u003e\u003cspan additionalcitationids=\"CR106 CR107 CR108\" citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e\u003c/sup\u003e. Together these data suggest that fox gut microbiota have the potential to influence the host serotonin system, however, how this translates into behavior remains to be defined. Nonetheless, the taxonomic and functional potential of the gut microbiota enriched in tame foxes indicate the capacity to influence fear memory formation and promote fear extinction learning, both of which would be relevant to overcoming a fear response toward humans during domestication.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eConvergence of host and microbial selection signals on glutamate signaling pathway\u003c/h2\u003e \u003cp\u003eExtensive studies have demonstrated that genes coding for different types of glutamate receptors in the host are associated with domestication in not only dogs, ducks, rabbits and chickens \u003csup\u003e\u003cspan additionalcitationids=\"CR111 CR112 CR113\" citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e\u003c/sup\u003e, but also on the Belyaev foxes, where genomic regions, gene expression and allele frequencies involved in glutamatergic signaling differentiate between the tame and aggressive strains \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Mounting evidence has now shown that the gut microbiota can influence the genetic composition and functional connectivity of certain regions in the brain of the host \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e\u003c/sup\u003e and, further, alter gene expression of glutamatergic receptors in the brain \u003csup\u003e\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e,\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e\u003c/sup\u003e. In light of these findings, it is interesting that GBMs associated with glutamate degradation, GABA synthesis and g-Hydroxybutyric acid (GHB) degradation, all from the glutamate-derived pathway, were enriched in the MAGs from the gut of the tame fox strains (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). We additionally detected the potential for acetate synthesis in the aggressive population and gut-derived acetate can cross the blood brain barrier and influence GABAergic and glutamatergic neurotransmission in the brain \u003csup\u003e\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGlutamate is the main excitatory neurotransmitter in the brain and plays an important role in fear conditioning, synaptic plasticity, learning, and memory \u003csup\u003e\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e,\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e\u003c/sup\u003e. GABA, on the other hand, is an inhibitory neurotransmitter that counteracts glutamate, and GABA signaling has been implicated in fear extinction learning \u003csup\u003e\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e,\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e\u003c/sup\u003e. Further, increased levels of glutamate in the brain can trigger aggression in mice \u003csup\u003e\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e,\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e\u003c/sup\u003e whereas GABA is mainly associated with an inhibitory role in aggression \u003csup\u003e\u003cspan additionalcitationids=\"CR126 CR127 CR128 CR129 CR130\" citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e\u003c/sup\u003e. GHB has also been implicated in aggressive behavior in animals and can increase levels of glutamate in the brain \u003csup\u003e\u003cspan additionalcitationids=\"CR133\" citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e134\u003c/span\u003e\u003c/sup\u003e. Moreover, the potential for glutamate synthesis has been identified in the gut microbiota of aggressive mice and red jungle fowl selected for high fear towards humans \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. As with 5-HT, glutamate and GABA cannot pass the blood brain barrier \u003csup\u003e\u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e135\u003c/span\u003e\u003c/sup\u003e, however, certain gut bacteria have been shown to increase GABA and glutamate in the brain \u003csup\u003e\u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e136\u003c/span\u003e\u003c/sup\u003e and additionally promote consistent changes in GABA receptors in the brain accompanied by behavioral shifts in the host \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Further, GABA producing bacteria in the gut can alter mood and fear-related behavior in studies modelling depression \u003csup\u003e\u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e137\u003c/span\u003e\u003c/sup\u003e. As such, these findings suggest a role for glutamate and GABA signaling in the behavioral shifts shared among animals during domestication, with both a host genomic and gut microbial component to its regulation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eHere we identified shifts in the fecal (as a proxy for gut) microbiota between behavioral phenotypes of the Belyaev foxes that have been linked to aggressive and fear-related behaviors, behaviors that are consistently reduced in domestic animals. Although our approach does not allow for interpretation of causality nor directionally of the microbiota-gut-brain axis interactions, such correlative findings set the stage for generating mechanistic hypotheses for further exploration. While the depletion of certain gut microbiota, such as \u003cem\u003eRuminococcaceae\u003c/em\u003e, \u003cem\u003eAnaeroplasmataceae\u003c/em\u003e, and \u003cem\u003eLachnospiraceae\u003c/em\u003e during the early stages of the domestication process may initiate the behavioral shifts necessary to adapt to the new social environment, the enrichment of others, such as \u003cem\u003eEnterococcus faecalis\u003c/em\u003e and \u003cem\u003eClostridiales\u003c/em\u003e may be equally important. Our metagenomic survey also allowed for the reconstruction of several microbial pathways enriched in the gut of tame foxes, such as glutamate degradation and GABA synthesis, which converged with host genetic and physiological signals, revealing a potential role of functional host-microbiota interactions that could influence behaviors associated with domestication. In future studies, the coupling of metagenomics, metatranscriptomics and metabolomics would provide opportunities to validate which bioactive metabolites are being produced, and together with antibiotic treatment and/or fecal transplant experiments, may establish a causative role of the functional pathways by which bacteria affect behavior and to what extent. Further, longitudinal studies of individuals across generations could provide further insights as to (i) when, and how quickly compositional changes occur within the microbiota of animals during early domestication, (ii) how these changes may reflect causal links to the behavioral shifts detected throughout the process, and even (iii) which host genetic mechanisms are driving the gut microbiome community trends. With regards to this latter point, we propose two hypotheses that may warrant future exploration. Firstly, previous characterization of the genomic differences found between the Belyaev fox behavioral strains reported not only significant enrichment for GO terms linked to the nervous system, but also immune responses, specifically \u0026ldquo;cytokine activity\u0026rdquo; and \u0026ldquo;interleukin-1 receptor binding\u0026rdquo; \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Given that differentiation in the host immune system can lead to differentiation in the gut microbiome \u003csup\u003e\u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e138\u003c/span\u003e\u003c/sup\u003e, we hypothesize that this intriguing observation may suggest that selection for behavioral phenotypic differences in the earliest stages of domestication may include, or even predominantly focus on, immune system differences, and that these in turn could shape the gut microbiome, and hence behavior. Our second hypothesis draws both on the recent observations from fish \u003csup\u003e\u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e139\u003c/span\u003e\u003c/sup\u003e and humans \u003csup\u003e\u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e140\u003c/span\u003e\u003c/sup\u003e, that epigenetic changes in host genomes can directly shape the gut microbiome, and that rapid epigenetic divergence has been reported in the early stages of chickens subjected to behavioral selection \u003csup\u003e\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e,\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e,\u003cspan additionalcitationids=\"CR103\" citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e\u003c/sup\u003e. As such, an alternate (or complementary) hypothesis, is that epigenetic changes in the fox genomes may be involved in shaping their gut microbiomes. Although this is yet to be tested, that fact that gut microbiomes are also well known to shape their host epigenomes \u003csup\u003e\u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e141\u003c/span\u003e\u003c/sup\u003e, this process could even involve some degree of reciprocal feedback.\u003c/p\u003e \u003cp\u003eUltimately however, although intriguing, we of course acknowledge that any role for the microbiota in the evolution of domestic behaviors in animals does not displace other contributing factors, but rather adds an additional layer to our understanding of how such behaviors arise. Understanding the eco-evolutionary mechanisms involved in the process of domestication provides crucial insights into how wild animals may adapt to human encounters over time. In this process there is likely a bidirectional relationship between microbiota and host factors, including behavior, that further interact with the environment.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAnimals and sample collection\u003c/h2\u003e \u003cp\u003eFecal samples were obtained from 61 tame and 62 aggressive silver foxes (\u003cem\u003eVulpes vulpes\u003c/em\u003e) maintained at the experimental farm of the Institute of Cytology and Genetics (ICG) (Novosibirsk, Russia). Foxes were approximately 5 months old at the time of collection. Each fox was housed individually in a cage with a wire net floor. The ground under the fox cage was covered with a piece of tissue at ~\u0026thinsp;7 am and collected in the morning before feeding (~\u0026thinsp;7\u0026ndash;9 am) to ensure that only fresh fecal samples were obtained. No contact with urine was allowed. All foxes were raised in standard conditions and underwent identical treatment with minimally necessary human interaction until behavioral testing was performed \u003csup\u003e\u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e142\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSelection for the tame strain began in 1959 at the ICG, and was developed through selection of conventional farm-bred foxes from across the former Soviet Union due to their less aggressive and fearful behavior towards humans. The aggressive strain was developed by selecting conventional farm-bred foxes for an aggressive response towards humans, beginning in the late 1960s at the ICG. The farm-bred foxes originated from foxes from eastern Canada where fox farm breeding began in the second part of the nineteenth century \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. A description of the selective breeding program was previously described \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e142\u003c/span\u003e\u003c/sup\u003e. Fecal samples were collected and either frozen at -20\u0026deg;C in the summer of 2015 (tame: n\u0026thinsp;=\u0026thinsp;10, aggressive: n\u0026thinsp;=\u0026thinsp;10) or preserved in RNAlater stabilization solution in the summer of 2017 (tame: n\u0026thinsp;=\u0026thinsp;51, aggressive: n\u0026thinsp;=\u0026thinsp;52) and stored at -20\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction\u003c/h2\u003e \u003cp\u003ePrior to DNA extraction, RNAlater was removed with centrifugation (13,000 g for 10 minutes) and the pellet was washed twice with 1 mL of PBS. DNA was extracted from approximately 100 mg of fecal sample using the DNeasy PowerSoil Kit DNA (Qiagen, Venlo, NL) following the manufacturer\u0026rsquo;s protocol with several modifications. Samples were incubated for 10 min at 65 \u0026ordm;C after adding Solution C1 and bead-beaten for 10 mins at 30 Hz using a TissueLyser II (Qiagen, Hilden, Germany). Purified DNA was incubated in Solution C6 for 15 min at 37 \u0026ordm;C before the final elution spin. Four negative controls (i.e. all reagents except sample continued in the workflow from extraction to sequencing as any other extracts) were included in order to check for potential reagent contamination.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBacterial community composition from 16S rRNA amplicon sequencing\u003c/h2\u003e \u003cp\u003eA dual indexed PCR approach was used to target the V3-V4 variable region of the bacterial 16S rRNA gene (~\u0026thinsp;465 bp) for all fecal samples (n\u0026thinsp;=\u0026thinsp;103) using the primer pair Bact-341F (5\u0026rsquo;-CCTAYGGG RBGCASCAG-3\u0026rsquo;) and Bact-806R (5\u0026rsquo;-GGACTACNNGGGTATCTAAT-3\u0026rsquo;) with Illumina Nextera overhang adapters (Illumina Inc., San Diego, CA, USA) \u003csup\u003e\u003cspan additionalcitationids=\"CR144 CR145\" citationid=\"CR143\" class=\"CitationRef\"\u003e143\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e146\u003c/span\u003e\u003c/sup\u003e. PCR was performed in triplicates and pooled prior to indexing PCR for each individual in order to reduce PCR bias. Pooled libraries were sequenced on an Illumina MiSeq platform using 250PE. Full methodological details can be found in Supplementary material.\u003c/p\u003e \u003cp\u003eIllumina adapters and primer sequences were removed from the 16S metabarcoding sequence data using cutadapt v.2.6 \u003csup\u003e147\u003c/sup\u003e and subsequently analyzed using the program DADA2 v.1.12.1 \u003csup\u003e148\u003c/sup\u003e and R v.3.6.1 \u003csup\u003e149\u003c/sup\u003e to infer amplicon sequence variants (ASVs). Complete code was modified from \u003csup\u003e\u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e150\u003c/span\u003e\u003c/sup\u003e. Briefly, forward and reverse reads were trimmed to 230 bp. The entire dataset was used to define an error rate at each base pair, and all sequences were denoised using the pooled approach to increase the likelihood of resolving rare sequence variants. Forward and reverse reads were merged, and any pair without perfect overlap and \u0026lt;\u0026thinsp;400bp was removed prior to chimeric sequence filtering. Each ASV was annotated with the RDP Bayesian classifier \u003csup\u003e\u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e151\u003c/span\u003e\u003c/sup\u003e against the SILVA database \u003csup\u003e\u003cspan citationid=\"CR152\" class=\"CitationRef\"\u003e152\u003c/span\u003e\u003c/sup\u003e to produce a 16S amplicon taxa table. All subsequent analyses were done in R v.3.6.3 unless otherwise stated \u003csup\u003e\u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e153\u003c/span\u003e\u003c/sup\u003e. ASV data was pre-processed with the phyloseq package v.1.30.0 \u003csup\u003e154\u003c/sup\u003e, and potential contaminants were assessed with the decontam package v 1.6.0 \u003csup\u003e155\u003c/sup\u003e. Twelve putative contaminants were removed from the ASV table. Only samples with \u0026gt;\u0026thinsp;10,000 reads and ASVs present in a minimum of 5% of all samples were included in downstream 16S data analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMetagenomic shotgun sequencing\u003c/h2\u003e \u003cp\u003eShotgun metagenome sequence data were prepared on all DNA extracts (n\u0026thinsp;=\u0026thinsp;103) using the BEST single-tube library preparation protocol \u003csup\u003e\u003cspan citationid=\"CR156\" class=\"CitationRef\"\u003e156\u003c/span\u003e\u003c/sup\u003e as optimized to be BGISEQ-500 compatible \u003csup\u003e\u003cspan citationid=\"CR157\" class=\"CitationRef\"\u003e157\u003c/span\u003e\u003c/sup\u003e. Briefly, genomic DNA was fragmented to 350 bp using a M220 Focused Ultrasonicator (Covaris, Woburn, MA). Sheared DNA was converted into BGISEQ-500 libraries following four steps: blunt end-repair, adapter ligation (20 \u0026micro;M BGI 2.0 adapters), fill-in reaction and SPRI magnetic bead purification (Sigma-Aldrich). Indexing PCR cycle number for all metagenomic libraries (7\u0026ndash;11 cycles) were determined through qPCR library quantification. Libraries were pooled equimolar over 6 lanes in 100bp or 150bp paired-end mode on the BGISeq-500 platform aiming for a minimum of 50\u0026nbsp;million reads per sample.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssembly and genome-resolved metagenomics\u003c/h2\u003e \u003cp\u003ePrior to sequence assembly, all paired-end reads were demultiplexed and quality filtered. AdapterRemoval v.2.3.1 \u003csup\u003e158\u003c/sup\u003e was used to trim unidentified bases and adapter sequences from the ends of the read and PCR duplicates were removed with seqkit v.0.8.0 \u003csup\u003e159\u003c/sup\u003e. Host and human reads were removed using bwa-mem algorithm v.0.7.15 \u003csup\u003e160\u003c/sup\u003e against the human (RefSeq: GCF_000001405.26) and fox (RefSeq: GCF_000002315.4) reference genomes. Quality filtered metagenomic reads were then co-assembled using MEGAHIT v.1.1.1 with k-mer sizes: 77,87,97,107,127,137,147,157,167 and default parameters \u003csup\u003e\u003cspan citationid=\"CR161\" class=\"CitationRef\"\u003e161\u003c/span\u003e\u003c/sup\u003e. Contigs less than 2500 nt were removed from the resulting assembly output and corresponding header names were simplifying using anvi\u0026rsquo;o v.6.2 \u003csup\u003e162\u003c/sup\u003e. Metagenomic reads were mapped to the assembled contigs using bwa-mem algorithm v.0.7.15 with default parameters \u003csup\u003e\u003cspan citationid=\"CR160\" class=\"CitationRef\"\u003e160\u003c/span\u003e\u003c/sup\u003e and Samtools v.1.9 \u003csup\u003e163\u003c/sup\u003e was used to sort and index the output SAM files into BAM files.\u003c/p\u003e \u003cp\u003eBAM files were used to generate a contig depth of coverage table with jgi_summarize_bam_contig_depths (MetaBAT2 v.2.12.1) \u003csup\u003e\u003cspan citationid=\"CR164\" class=\"CitationRef\"\u003e164\u003c/span\u003e\u003c/sup\u003e. We then applied the automatic binning algorithm in CONCOCT \u003csup\u003e\u003cspan citationid=\"CR165\" class=\"CitationRef\"\u003e165\u003c/span\u003e\u003c/sup\u003e on this coverage table to generate 10 large contig clusters to maximize explained patterns while minimizing fragmentation error, as performed elsewhere \u003csup\u003e\u003cspan citationid=\"CR166\" class=\"CitationRef\"\u003e166\u003c/span\u003e,\u003cspan citationid=\"CR167\" class=\"CitationRef\"\u003e167\u003c/span\u003e\u003c/sup\u003e. Subsequently, a manual binning and curation was performed for each CONCOCT cluster following the genome-resolved metagenomic workflow implemented in anvi\u0026rsquo;o v.6.2 \u003csup\u003e162\u003c/sup\u003e. Briefly, anvi\u0026rsquo;o was used to generate a contigs database that identified open reading frames using Prodigal v.2.6.3 \u003csup\u003e168\u003c/sup\u003e and single-copy core genes using HMMER v.3.2.1 \u003csup\u003e169\u003c/sup\u003e against the collection of built-in HMM profiles for Bacteria and Archaea. Gene-level taxonomy was classified using Kaiju v.1.5.0 \u003csup\u003e170\u003c/sup\u003e, with NCBI\u0026rsquo;s non-redundant protein database, including fungi and microbial eukaryotes, and genes were further annotated with functions using the NCBI\u0026rsquo;s Clusters of Orthologous Groups (COG) \u003csup\u003e\u003cspan citationid=\"CR171\" class=\"CitationRef\"\u003e171\u003c/span\u003e\u003c/sup\u003e. Anvi\u0026rsquo;o was then used to profile each metagenomic BAM file to estimate the coverage and detection statistics of contigs in the contigs database, and combined mapping profiles into a merged profile database for all individuals. In addition, we imported an anvi'o collection corresponding to the 10 CONCOCT clusters. Finally, each CONCOCT cluster was manually binned and further refined using the anvi\u0026rsquo;o interactive interface \u003csup\u003e\u003cspan citationid=\"CR172\" class=\"CitationRef\"\u003e172\u003c/span\u003e\u003c/sup\u003e taking into account sequence composition, differential coverage, GC-content, and taxonomic signal of the considered contigs. MAGs with completeness\u0026thinsp;\u0026gt;\u0026thinsp;50% and redundancy\u0026thinsp;\u0026lt;\u0026thinsp;10% were retained for downstream analyses \u003csup\u003e\u003cspan citationid=\"CR173\" class=\"CitationRef\"\u003e173\u003c/span\u003e\u003c/sup\u003e (Genomic features of the MAGs can be found in Suppl.Table\u0026nbsp;2b).\u003c/p\u003e \u003cp\u003eThe taxonomy of MAGs was inferred using the Genome Taxonomy Database Toolkit (GTDB-Tk) \u003csup\u003e\u003cspan citationid=\"CR174\" class=\"CitationRef\"\u003e174\u003c/span\u003e\u003c/sup\u003e version 95. However, we used NCBI taxonomy from the GTDB output to describe the phylum of MAGs in the \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003eresults and discussion\u003c/span\u003e sections, in order to be in line with the literature.\u003c/p\u003e \u003cp\u003eMAGs were considered to be detected in a given sample when \u0026gt;\u0026thinsp;50% of their length was covered by reads to minimize non-specific read recruitments \u003csup\u003e\u003cspan citationid=\"CR167\" class=\"CitationRef\"\u003e167\u003c/span\u003e\u003c/sup\u003e. The number of recruited reads below this cut-off was set to 0 before determining vertical coverage, the number of bases covering each genome divided by its length.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGut-brain module (GBM) detection\u003c/h2\u003e \u003cp\u003eThe fox shotgun metagenomic data was translated into neuroactive potential using a previously described module-based reconstruction framework \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Briefly, we searched for the presence of 56 gut-brain modules (GBMs), each corresponding to a process of synthesis or degradation of a neuroactive compound by the gut microbiota, in each of the fox MAGs (n\u0026thinsp;=\u0026thinsp;204). As module structure follows the Kyoto Encyclopedia of Genes and Genomes (KEGG) database syntax, gene calls for each MAG were exported from the contig database within anvi\u0026rsquo;o and functionally annotated with KEGG identifiers using GhostKoala \u003csup\u003e\u003cspan citationid=\"CR175\" class=\"CitationRef\"\u003e175\u003c/span\u003e\u003c/sup\u003e. GBM coverage was calculated as the number of pathway steps for which at least one of the orthologous groups is found in a genome, divided by the total number of steps constituting the GBM using Omixer-RPM v.0.3.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/raeslab/omixer-rpm\u003c/span\u003e\u003cspan address=\"https://github.com/raeslab/omixer-rpm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). GBM presence in microbial MAGs was defined with a detection threshold of at least 66% coverage, to provide tolerance to miss-annotations and missing data in incomplete genomes \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. GBM detection was visualized with corrplot v.0.84 \u003csup\u003e176\u003c/sup\u003e in the 28 differentially abundant fox MAGs to identify over/under-represented metabolic GBMs between the two behavioral selection lines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDifferential abundance estimates\u003c/h2\u003e \u003cp\u003eDiversity estimates and hypothesis testing of Shannon diversity were performed with the breakaway package v.4.6.16 \u003csup\u003e177,178\u003c/sup\u003e and DivNet package v.0.3.5 \u003csup\u003e179\u003c/sup\u003e for the 16S dataset. These packages use sophisticated models to account for sequencing depth and rare taxa in high-dimensional data and incorporate taxon interactions when estimating α-diversity. Diversity estimates with uncertainties were used to support hypothesis testing between selection lines \u003csup\u003e\u003cspan citationid=\"CR180\" class=\"CitationRef\"\u003e180\u003c/span\u003e\u003c/sup\u003e. Expected relative abundance of microbial taxa was modeled directly from read counts for 16S and shotgun sequence data at different taxonomic levels (phylum, class, order, family, genus, and ASVs) using a beta-binomial model controlling for collection year and controlling for the effect of selection and collection year on dispersion. The model was fit using corncob v.0.1.0 \u003csup\u003e181\u003c/sup\u003e, an r-based package designed specifically for marker gene compositional data, which uses sophisticated models to account for sequencing depth and rare taxa in high-dimensional data and estimates abundance with uncertainties to support hypothesis testing between selection lines. The Wald test was used to test for differential taxon abundances between selection lines with a controlled false discovery rate (p-value cutoff\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003csup\u003e\u003cspan citationid=\"CR182\" class=\"CitationRef\"\u003e182\u003c/span\u003e\u003c/sup\u003e. Graphical representations were performed in R using the package ggplot2 v.3.2.1.9000 \u003csup\u003e183\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sequence data that support the findings of this study will be available in European Nucleotide Archive (ENA) at https://www.ebi.ac.uk/ena/, study accession number PRJEB29232.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBelyaev, D. K. Destabilizing selection as a factor in domestication. \u003cem\u003eJournal of Heredity\u003c/em\u003e \u003cstrong\u003e70\u003c/strong\u003e, 301\u0026ndash;308 (1979).\u003c/li\u003e\n\u003cli\u003eTrut, L. N. Early Canid Domestication: The Farm-Fox Experiment. \u003cem\u003eAm Sci\u003c/em\u003e \u003cstrong\u003e87\u003c/strong\u003e, (1999).\u003c/li\u003e\n\u003cli\u003eWilkins, A. S., Wrangham, R. W. \u0026amp; Fitch, W. T. 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(Springer-Verlag New York, 2016).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4697888/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4697888/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDomestication represents one of the largest biological shifts of life on Earth, and for many animal species, behavioral selection is thought to facilitate early stages of the process. The gut microbiome of animals can respond to environmental changes and have diverse and powerful effects on host behavior. As such, we hypothesize that selection for tame behavior during early domestication, may have indirectly selected on certain gut microbiota that contribute to the behavioral plasticity necessary to adapt to the new social environment. Here, we explore the gut microbiome of foxes from the tame and aggressive strains of the \u0026ldquo;Russian-Farm-Fox-Experiment\u0026rdquo;. Microbiota profiles revealed a significant depletion of bacteria in the tame fox population that have been associated with aggressive and fear-related behaviors in other mammals. Our metagenomic survey allowed for the reconstruction of microbial pathways enriched in the gut of tame foxes, such as glutamate degradation, which converged with host genetic and physiological signals, revealing a potential role of functional host-microbiota interactions that could influence behaviors associated with domestication. Overall, by characterizing how compositional and functional potential of the gut microbiota and host behaviors co-vary during early animal domestication, we provide further insight into our mechanistic understanding of this adaptive, eco-evolutionary process.\u003c/p\u003e","manuscriptTitle":"Gut microbiome community structure correlates with different behavioral phenotypes in the Belyaev farm-fox experiment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-12 05:18:59","doi":"10.21203/rs.3.rs-4697888/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-biology","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsbio","sideBox":"Learn more about [Communications Biology](http://www.nature.com/commsbio/)","snPcode":"","submissionUrl":"","title":"Communications Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"36b82685-bd96-458b-ba25-dd840bb34eae","owner":[],"postedDate":"August 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":34720983,"name":"Biological sciences/Microbiology/Microbial genetics/Bacterial genetics"},{"id":34720984,"name":"Biological sciences/Genetics/Behavioural genetics"},{"id":34720985,"name":"Biological sciences/Ecology/Microbial ecology"},{"id":34720986,"name":"Biological sciences/Genetics/Animal breeding"},{"id":34720987,"name":"Biological sciences/Ecology/Behavioural ecology"}],"tags":[],"updatedAt":"2026-03-28T07:10:50+00:00","versionOfRecord":{"articleIdentity":"rs-4697888","link":"https://doi.org/10.1038/s42003-026-09717-5","journal":{"identity":"communications-biology","isVorOnly":false,"title":"Communications Biology"},"publishedOn":"2026-03-27 04:00:00","publishedOnDateReadable":"March 27th, 2026"},"versionCreatedAt":"2024-08-12 05:18:59","video":"","vorDoi":"10.1038/s42003-026-09717-5","vorDoiUrl":"https://doi.org/10.1038/s42003-026-09717-5","workflowStages":[]},"version":"v1","identity":"rs-4697888","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4697888","identity":"rs-4697888","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}