Donor Microbiota Metabolic Capacity Determines Engraftment Dynamics and Modulates Gut–Brain Signaling in Recipient Mice

Donor Microbiota Metabolic Capacity Determines Engraftment Dynamics and Modulates Gut–Brain Signaling in Recipient Mice | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Donor Microbiota Metabolic Capacity Determines Engraftment Dynamics and Modulates Gut–Brain Signaling in Recipient Mice Aitak Farzi, Marija Durdevic, Frederike Fellendorf, Patrick Schimmel, and 16 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9474472/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract This study performs patient-to-mouse fecal microbiota transplantation (FMT) as an experimental platform to investigate gut-brain axis alterations with potential relevance to psychiatric disorders, integrating metabolic modeling with measured metabolites and multi-layer molecular profiling. Microbial communities of a stool sample associated with bipolar disorder (BD) displayed a reduced ecological diversity and diminished metabolic potential, particularly within glutamate, aspartate, and GABA biosynthetic pathways. Upon transplantation with BD patient microbiota, recipient mice displayed a markedly altered microbiome characterized by loss of Akkermansia and expansion of Alloprevotella, alongside disruptions in amino acid and carbohydrate metabolism not evident in mice colonized by a healthy donor microbiota. Metabolic microbiome alterations in BD-recipient mice were also correlated to reduced glutathione levels in gut tissue, likely indicating increased oxidative stress, and decreased mRNA expression of key enteroendocrine hormones, including peptide YY and glucagon. Brain metabolomic profiling of BD-recipient mice revealed significant depletion of glycine, choline, and methionine levels connected to anxiety-like phenotypes in elevated plus-maze and light-dark box behavioral tests. Akkermansia abundance positively correlated with physical activity and exploratory behavior, highlighting an important role of this taxon in gut-brain signaling. Collectively, these findings identify distinct microbial, metabolic, and neurobehavioral signatures transmittable from humans to mice via FMT and demonstrate that differences in donor microbiome diversity and metabolic capacity shape engraftment dynamics in recipient mice, which contribute to differences in gut-brain signaling. Health sciences/Diseases/Psychiatric disorders/Bipolar disorder Biological sciences/Neuroscience Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Bipolar disorder (BD) represents a psychiatric condition, affecting approximately 0.4-2.4% of the population [1] and among the leading cause of global disability [2]. Beyond its profound psychiatric manifestations - characterized by episodic cycles of depression, mania, and hypomania - individuals with BD face an increased suicide risk [3]. Depressive and manic symptoms can also coexist as mixed affective states and are classified as more severe forms of BD with higher rates of comorbid conditions and suicide rates [4]. The etiopathogenesis of BD involves complex interactions between genetic predisposition, epigenetic modifications, metabolic disturbances, and environmental factors that act synergistically to establish disease vulnerability [5]. Recent research has highlighted the potential role of the gut microbiota in modulating central nervous system (CNS) function through the "gut-brain axis" (GBA), a bidirectional signaling circuit that links microbial activity with neurological, immunological, and metabolic processes. The intestinal microbiota produces various metabolites such as short-chain fatty acids (SCFAs) which influence neuromodulatory processes through multiple layers, modulate intestinal barrier permeability, and stimulate enteroendocrine L-cells to release glucagon-like peptide 1 (GLP-1) and peptide YY (PYY), which, among others, contribute to appetite regulation and metabolic homeostasis [5, 6, 7]. Aberrations in the gut microbiota, termed dysbiosis, have been found in multiple psychiatric disorders, including major depressive disorder [8, 9], anxiety disorders [11], schizophrenia [12], and attention-deficit/hyperactivity disorder [13]. Emerging evidence demonstrates that individuals with BD exhibit a dysbiotic gut microbiota characterized by a reduced microbial diversity and altered community composition, implicating these changes as potential contributors to BD pathogenesis [14]. For instance, a depletion of beneficial taxa such as Akkermansia muciniphila [15] and SCFA-producing bacteria, alongside enrichment of potentially pathogenic taxa, resulting in impaired production of neuroprotective metabolites and intestinal barrier defects, has been described in BD [16]. Dysbiosis often coincides with elevated circulating pro-inflammatory cytokines [16, 17, 18], systemic oxidative stress [19, 20], and increased blood-brain barrier permeability [22], hallmark features in BD pathophysiology. The mechanistic pathways linking dysbiosis to CNS dysfunction appear to operate through metabolic changes, such as impaired SCFA production [23], reduced GABA synthesis [24], and dysregulated tryptophan metabolism [24] by the microbial ecosystem and immunological stimulation [25]. Notably, microbiota from individuals with BD have been shown to alter the expression of disease-relevant genes implicated in BD pathogenesis [26], suggesting that dysbiotic microbiota directly contribute to molecular alterations in the CNS. Fecal microbiota transplantation (FMT), a procedure that transfers gut microbiota from one individual to another, has emerged as a powerful translational tool to investigate the mechanistic role of the microbiome in CNS disorders [27]. Furthermore, FMT from healthy donors has been suggested to ameliorate psychiatric symptoms [28]. While previous FMT studies have demonstrated that dysbiotic microbiota from psychiatric patients exert pathogenic effects, detailed characterization of microbial community dynamics, transplantation efficacy, and metabolic potential, is often lacking. In this study, we used FMT to analyze the effect of these factors on GBA communication using a dysbiotic microbiome associated with BD. Materials and Methods Ethical Approval All experiments were approved by the ethical committee at the Federal Ministry of Education, Science and Research of the Republic of Austria (permit BMBWF-66.010/0047-V/3b/2019). The study was conducted according to the guidelines of the Declaration of Helsinki and received approval from the Institutional Review Board of the Medical University of Graz (protocol number 31-120 ex 18/19; approval date: March 27, 2019). Written informed consent was obtained from both donors. Donor selection The study included two female donors: one diagnosed with BD during a mixed affective episode and one healthy control matched for age and body weight. Clinical assessment of symptom severity was performed using the Young Mania Rating Scale (YMRS) for manic symptoms [29] and the Hamilton Depression Rating Scale (HAM-D) for depressive symptoms [30]. The BD donor exhibited a YMRS score of 14 and a HAM-D score of 20, while the control donor scored 0 on both scales. The YMRS is a validated questionnaire used to measure current (hypo)manic symptoms. The HAM-D measures severity of depressive symptoms rated by an experienced professional. Further information can be found in the Supplementary Information. Donor Fecal Samples Collection and Processing Fecal samples were collected in plastic containers equipped with an anaerobic atmosphere generator (AnaeroGen™ 2.5L, Thermo Fisher Scientific). Between 60 and 100 grams of fecal material were processed within six hours post-collection inside an anaerobic chamber (Whitley A85 Workstation, Don Whitley Scientific). The samples were suspended in 150 mL of anaerobic, reduced, sterile phosphate-buffered saline (PBS), homogenized, and passed through a mesh to remove solid debris. Glycerol was added to a final concentration of 10%, and the resulting suspension was aliquoted into 4 mL anaerobic tubes (Anaerobic Hungate Culture Tubes, Chemglass Life Sciences). Prepared samples were stored at −70°C until use for fecal transfer. Mice Fourteen female C57BL/6J mice, 20–30 weeks of age, were group under controlled environmental conditions. The animals were maintained at a constant ambient temperature of 22°C with a 12h:12h light-dark cycle (lights on at 07:00, lights off at 17:00). Water and standard chow were provided ad libitum for the duration of the study. Fecal microbiota transplantation (FMT) All mice were pretreated with antibiotics in their drinking water (0.5 mg/mL neomycin, 1 mg/mL meropenem, and 0.3 mg/mL vancomycin) for one week to deplete the existing gut microbiota and facilitate engraftment of the donor microbiota [31]. One day prior to FMT, mice were fasted to reduce fecal blockage during the procedure and were provided with regular tap water. While under isoflurane anesthesia, one group received fecal material from a donor with BD, and another group received stool from a healthy control donor (Supplementary Fig. 1). Approximately 0.2 mL of the human stool suspension was administered rectally to each mouse, given the advantages of rectal versus oral administration [32]. To further support microbiota colonization, a few drops of the stool suspension were also placed in the cages. Tissue Extraction and Processing Tissue extraction was performed as previously described [33]. More detailed information can be found in the Supplementary Information. RNA Extraction and RT-qPCR Total RNA was isolated from colon and amygdala tissues using the RNeasy Tissue Mini Kit and the RNeasy Lipid Tissue Mini Kit (Qiagen), respectively. One microgram of RNA from each sample was reverse-transcribed into cDNA using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA). Quantitative real-time PCR was performed on the CFX384 Touch Real-Time PCR Detection System (Bio-Rad) using TaqMan® Gene Expression Assays outlined in the Supplementary Information. Relative quantification was performed using the 2^−ΔΔCt method, with the mean expression level of the control group serving as the calibrator [34]. Sample Preparation for NMR Spectroscopy and Data Processing Fecal, colon, plasma, and amygdala samples collected 13 days after FMT were processed for nuclear magnetic resonance (NMR) spectroscopy as previously described [35] and described in the Supplementary Information. Behavioral Tests The open field test (OFT), elevated plus maze (EPM), and light/dark box tests (LDT) were used to assess locomotion, exploration and anxiety-like behaviour. Details of the behavioural tests are described in Supplementary Information (Supplementary Fig. 1). Bacterial DNA Extraction and 16S rRNA Gene Sequencing Bacterial DNA was extracted from fecal samples of both human donors and recipient mice using the QIAamp DNA Mini Kit (Qiagen, Hilden, Germany), following the manufacturer’s protocol. PCR amplification and sequencing were carried out by Novogene Europe (Cambridge, UK) and are described in the Supplementary Information. 16S rRNA Gene Sequencing Data Processing The 16S rRNA sequencing data were processed using QIIME2 (version 2023.2) on a local Galaxy instance ( https://galaxy.medunigraz.at ) [39]. Initial steps included quality filtering, denoising, and chimera removal using the DADA2 denoise-pyro plugin. To ensure high-quality amplicon sequence variants (ASVs), all reads were trimmed by 15 bases from the 5' end and truncated to 220 bases at the 3' end. Taxonomic assignment of ASVs was carried out using a Naïve Bayes classifier trained on the SILVA ribosomal RNA database (version 138) [40]. Phylogenetic tree construction was performed using the align-to-tree-mafft-fasttree pipeline. Information on further analyses can be found in the Supplementary Information. ASV-based source tracking was performed by directly comparing amplicon sequence variants (ASVs) detected in donor samples with those detected in mouse samples. ASVs identical between donor and mouse were classified as donor-derived, whereas ASVs detected exclusively in mouse samples were classified as mouse-specific. Metabolic Modeling Microbial interactions and metabolic production within the community were modeled using MICOM (v0.37), a tool that applies cooperative trade-off flux balance analysis (ctFBA) to infer metabolic interactions among taxa [43]. MICOM has been shown to accurately predict microbial growth rates and metabolite production. For modeling human microbiome data, the AGORA database (v201), which contains genome-scale metabolic models for human gut bacteria, was leveraged [44]. Mouse microbiome data were modeled using the McMurGut database, which includes mouse-specific gut bacterial models, paired with a standard mouse chow diet as the medium [45]. All simulations were performed at the genus level. To ensure biologically realistic outputs, a trade-off value was determined first in order to balance community-wide growth versus individual optimization. The resulting growth rates were confirmed to fall within expected biological ranges. To assess ecological relationships, in silico knockouts from MICOM were performed for each genera individually. The impact of each knockout on the growth rates of other community members was evaluated: an increase in another genera's growth upon knockout was interpreted as competition, while a decrease was interpreted as cooperation. Differentially abundant metabolites were identified using the mp_diff_analysis function from the MicrobiotaProcess R package. These metabolites were further analyzed with the web-based analysis platform MetaboAnalyst (v6.0) to evaluate their involvement in metabolic pathways and to compare metabolic activity between groups [46]. For donor-derived metabolites, differential abundance was evaluated using log 2 fold change, with values exceeding |log 2 FC| > 2 deemed substantially altered, consistent with the approach used for microbial assessment. Final visualizations, including microbe-metabolite interaction networks, were generated in R using the ggplot2 and visNetwork packages [47]. Metabolic Analysis To identify differentially abundant metabolites between the two groups of mice, we employed linear modeling using the MaAsLin2 tool [48]. The default settings of MaAsLin2 were used, except that no additional normalization was applied. Metabolic values were log-transformed, and the general linear model option was selected to assess group differences. Reported regression coefficients correspond to log 2 fold change, reflecting the magnitude and direction of group differences. Multiple hypothesis testing was addressed using the Benjamini-Hochberg procedure, and corrected P values were reported as false discovery rates (FDR). Significant metabolites were subsequently analyzed using the web-based platform MetaboAnalyst (v6.0) to evaluate their involvement in relevant metabolic pathways and to compare metabolic activity profiles between the groups. Statistical Analysis of qPCR Genes and Behavioral Tests Differences between mouse groups were evaluated using either Student’s t-test or the Mann–Whitney U test, as appropriate. To account for multiple comparisons, p-values were adjusted using the BH method to control the FDR. Statistical significance was defined as an adjusted p-value < 0.05. Gene expression data from qPCR were log 2 -transformed prior to analysis, whereas behavioral data were analyzed using raw values. All correlation analyses were conducted in R using Spearman's rank correlation as implemented in the cor() function from the stats package [49] and described in the Supplementary Information. Results BD-associated Dysbiotic Gut Microbiota Exhibits Impaired Metabolic Potential The healthy donor fecal microbiota was markedly more diverse, comprising 602 amplicon sequence variants (ASVs), whereas the BD donor microbiota only showed 225 ASVs (Supplementary Fig. 2a-b top). Several high abundant genera in the control donor (relative abundance > 0.5%) such as Methanobrevibacter , Christensenellaceae R-7 group, Clostridia vadin BB60 group, Oscillospiraceae (UCG-002 and UCG-003), Ruminococcaceae CAG-352, and Subdoligranulum , were completely absent in the BD donor (Supplementary Fig. 2c). Conversely, Ruminococcus gnavus , a presumed pathobiont of the gastrointestinal (GI) tract, was only detected in the BD donor (Supplementary Fig. 2c) [51]. Strikingly, only 64 genera were shared between the two donor microbiotas (Supplementary Fig. 2b bottom). Among these, 19 were significantly depleted (e.g. Bifidobacterium , unc. Oscillospiraceae , unc. Christensenellaceae ) and 13 enriched in the BD donor (|log 2 FC| ≥ 2; e.g. Odoribacter , Parabacteroides , Blautia ) (Supplementary Fig. 2c; Supplementary Table 1). Although these findings highlight only differences between two individuals, they underscore the reported microbial community shifts associated with BD [14]. The depleted microbiota of the BD donor also translated into an altered metabolic capacity, which was modeled using flux balance analysis with MICOM inferred from microbial taxonomic patterns [43]. Of the 186 predicted metabolites, 147 were shared between both donors, 38 were exclusively predicted in the control donor microbiome, while only one unique metabolite (Folate; KEGG id:C00504) was predicted for the BD donor microbiome (Supplementary Fig. 3a; Supplementary Fig. 4a; Supplementary Table 2). Although the majority of the 38 metabolites unique to the healthy control donor exhibited relatively low production rates (<1 mmol·h -1 ·gDW -1 , millimoles per hour per gram of dry weight) as inferred from MICOM analysis, they represented several important metabolite classes, including specific organic acids, vitamins and cofactors (e.g., pyridoxal 5'-phosphate, biotin), amines and neurotransmitter-related compounds (e.g., tryptamine, trimethylamine), as well as certain bile acids (Supplementary Fig. 4a-b). Out of the 147 shared metabolites, 42 metabolites with a production rate exceeding 1 mmol·h -1 ·gDW -1 were enriched in the control (Supplementary Fig. 3b; Supplementary Table 2). Amongst these were important TCA cycle intermediates, amino acids or their precursors, as well as short-chain fatty acids (SCFAs), including the well-known neurotransmitter 4-aminobutanoate (= γ-aminobutyric acid; GABA). Metabolomics modeling revealed a significantly greater breadth of the healthy donor microbiome in many biosynthesis and metabolic pathways, which were depleted in the BD microbiome (Supplementary Fig. 3c). Together, these data indicate that the BD donor microbiota is characterized by reduced microbial diversity and metabolic capacity. Dysbiosis in BD Donor Impairs Bacterial Growth and Engraftment in Mice A diminished microbial metabolic disrupts microbial-metabolic networks that normally support microbiome resilience [52]. Our MICOM-based analysis revealed growth impairment of several bacterial taxa of the BD donor microbiota (Fig. 1a, left panel; Supplementary Fig. 5a, left panel; Supplementary Table 4). Notably, highly abundant genera (rel. ab. > 1%) including Bacteroides , Bifidobacterium , Alistipes , Barnesiella , Faecalibacterium , Methanobrevibacter , Akkermansia , showed reduced growth potential. The reduced growth potential was observed even for genera with comparable abundance levels in both donors (Fig. 1a; Supplementary Table 4). Although comparable proportions of donor ASVs (43% from control donor and 30% from BD donor; Fig. 1c) and genera (69% from control donor and 76% from BD donor; Supplementary Fig. 5c) successfully engrafted in the murine gut after FMT, the composition of the transferred communities differed between the two groups. This also translated into different bacterial growth rates in the newly established mouse communities after FMT (Fig. 1b, left; Supplementary Fig. 5b, left), which were strongly correlated with the relative abundances of bacterial taxa (Fig. 1b; Supplementary Fig. 5d). Notably, Akkermansia , a mucosa-associated bacterium implicated in gut-brain axis signaling [52, 53, 54], displayed a lower growth rate in mice colonized with BD microbiota compared to control microbiota (FDR = 0.029; Fig. 1b, left; Supplementary Table 4). To further explore the interdependence of bacterial taxa within the microbial community, we performed in silico genus-level knockouts. By systematically removing individual genera of the community and assessing their impact on the growth rates of others, we uncovered a network of cooperative interactions that supported or inhibited the growth of the key genus Akkermansia (denoted by blue or red arrows in Supplementary Fig. 5e-f). Mice receiving the control donor microbiota developed a functionally cohesive microbial ecosystem, whereas those colonized with the BD microbiota exhibited a different and less cooperative community structure, highlighting the influence of donor-specific microbial configurations for engraftment in the mouse host. Distinct Microbial Communities and Predicted Metabolite Production in BD-Recipient Mice Although microbial richness in mice after FMT showed no statistically significant difference between groups (Wilcoxon rank-sum test=0.53; Figs. 1d-e; Supplementary Table 5), principal coordinate analysis (PCoA; weighted UniFrac) demonstrated significant different microbial community structures (adonis, p<0.05; Fig. 1f; Supplementary Fig. 6a; Supplementary Table 6). To identify bacterial taxa driving compositional differences in recipient mice after FMT, we performed differential abundance analysis across all taxonomic levels (full results in Supplementary Table 7). At the phylum level, Verrucomicrobiota were significantly enriched in the control-microbiome recipient mice, whereas Bacteroidota predominated in the BD-recipient group (denoted as BD in all graphs and tables; Supplementary Table 6). Ten ASVs affiliated with Muribaculaceae and two with Alloprevotella were significantly enriched in the BD-recipient group. In contrast, the control group showed higher relative abundance of Akkermansia muciniphila , Lachnospiraceae NK4A136 , Parabacteroides distasonis, and Bacteroides vulgatus ASVs , amongst others (Supplementary Fig. 6b; Supplementary Table 7). Consistent with these findings, genus-level differential abundance analysis revealed significant enrichment of Alloprevotella and Muribaculaceae in the BD-recipient group, while Akkermansia was more prevalent in control-microbiome recipient mice (Fig. 1g; Supplementary Table 7). ASV-based source-tracking analyses showed that Akkermansia primarily originated from the donor in both groups, while Muribaculaceae displayed a mixed origin from both mouse and donor (Fig. 1g, Supplementary Fig. 6b). Interestingly, although Muribaculaceae are not prevalent members of the human microbiome [56], donor-derived Muribaculaceae were enriched in the BD-microbiome recipient mice, where half of Muribaculaceae were donor-derived, whereas only about one-quarter were donor-derived in the control group (Fig. 1g, Supplementary Fig. 6b). Notably, three significantly different and highly abundant Muribaculaceae ASVs of the BD donor were effectively transferred into recipient mice (Supplementary Fig. 6b). Correlation analysis further revealed a significant negative association between Akkermansia and Muribaculaceae, as well as Alloprevotella (Supplementary Figs. 6c-d) indicating altered co-occurrence patterns in Co-microbiome versus BD-microbiome recipient mice. In silico simulations of microbial metabolite production using the MICOM framework revealed that although overall metabolite production rates were comparable between groups, with 112 metabolites shared between them (Supplementary Fig. 7a), 16 metabolites exhibited significant differences in predicted abundance (Fig. 2a; Supplementary Table 8). These included several amino acids, bile acids, and tricarboxylic acid (TCA) cycle intermediates (Fig. 2a). Specifically, the control-microbiome recipient group showed higher predicted levels of L-glutamate, L-alanine, L-glutamine, and L-aspartate, whereas L-threonine and glycine were enriched in BD-recipient mice. Metabolic network analysis further revealed distinct roles of specific bacterial taxa in metabolite synthesis and consumption (Figs. 2b-c, Supplementary Interactive Figs. 1-2). In the control group, Muribaculaceae were identified as the primary producer of L-alanine, L-aspartate and L-malate, while Bacteroides utilized L-alanine and contributed to pyruvate synthesis. Additionally, Bacteroides , along with other bacteria, supported L-glutamate production, which was then utilized by Akkermansia to synthesize L-glutamine through glutamine synthetase activity (EC:6.3.1.2) (Fig. 2b, Supplementary Fig. 7b; Supplementary Interactive Fig. 1). These interactions confirmed a cooperative metabolic network between Muribaculaceae and Akkermansia in the control group (Supplementary Interactive Fig. 1). In contrast, the BD-associated microbiota displayed a distinct metabolic architecture, characterized by a cooperative network predominantly established between Muribaculaceae and Alloprevotella (Fig. 2c; Supplementary Interactive Fig. 2). Notably, Alloprevotella emerged as a key consumer of glycine and acetaldehyde while serving as a producer of L-threonine (Fig. 2c; Supplementary Interactive Fig. 2). Pathway analysis showed significant metabolic differences between groups strongly impacting amino acid metabolism. Alanine, aspartate and glutamate metabolism was enriched in control-microbiome recipient mice (FDR 0.5) (Supplementary Fig. 7c), whereas glycine, serine and threonine metabolism was enriched in BD-recipient mice (Supplementary Fig. 7d). Stool Metabolite Profiling Indicates Altered Amino Acid And Carbohydrate Metabolism in BD-Recipient Mice Among the 50 stool metabolites assessed, valeric acid, sucrose, and fructose were elevated in the BD-recipient group (Fig. 3a-b; Supplementary Table 9), whereas capric acid, glutamic acid, and aspartic acid were decreased in the BD-recipient group (Fig. 3a-c). Valeric acid, a microbial fermentation product and SCFA, showed positive correlations with fructose and sucrose (Fig. 3d; Supplementary Interactive Fig. 3), while it was negatively correlated with glutamine and glutamate. Valeric acid was also positively correlated with Muribaculaceae and negatively with Akkermansia (Fig. 3d; Supplementary Interactive Fig. 3). Pathway analysis identified galactose, starch and sucrose metabolism as the most enriched pathways in BD-recipient mice (Fig. 3e). Signatures of Increased Oxidative Stress and Altered Enteroendocrine Hormone Production in the Colon of BD-Recipient Mice Among the 35 colon metabolites measured, glutathione and L-alanine were decreased in BD-recipient mice (Figs. 4a-b); however, these changes did not retain statistical significance following p-value adjustments. Glutathione was negatively correlated with three modeled metabolites that were significantly enriched in BD-recipient mice, namely glycine, L-threonine, and acetaldehyde (Fig. 4c; Supplementary Interactive Fig. 3). In contrast, none of the 45 plasma metabolites measured showed significant changes (Fig. 4d), which might be attributed to a rapid kinetic turnover and metabolization via the portal system. We next examined colonic mRNA expression of tight junction proteins, immune-related genes, and enteroendocrine hormones (Pyy, Gcg). While peptide YY (Pyy) and proglucagon (Gcg) were significantly downregulated in the BD-recipient group (Fig. 4e), tight junction and immune-related genes showed no significant differences (Supplementary Figs. 8a-b). Altered Brain Metabolome in BD-Recipient Mice Related to Oxidative Stress and Neurotransmitter Metabolism Metabolomic profiling of brain tissue revealed differences in 10 out of 50 measured metabolites. Glycine, choline, glycerol, inosine, methionine, acetic acid, pyroglutamic acid, and phosphorylcholine were found to be depleted in the BD-recipient mice (Figs. 5a-b). Conversely, adenosine monophosphate (AMP) and inosinic acid were enriched (Fig. 5c). Network analysis revealed robust correlations between 18 brain metabolites and 2 stool-derived metabolites. Furthermore, glycine showed a strong positive correlation with Alistipes and microbial-derived L-malate (Fig. 5d; Supplementary Interactive Fig. 3). Pathway analysis of the depleted brain metabolites highlighted alterations in one-carbon metabolism and glutathione metabolism, as well as changes in glycine, serine and threonine metabolism (Fig. 5e), which play critical roles in regulating oxidative stress and maintaining neurotransmitter biosynthesis [61]. In addition to these metabolic changes, the central expression of pro-inflammatory genes Il1b and Ccl2 exhibited a trend toward reduced expression in BD mice (Supplementary Fig. 9b), while gene expression of glycine receptors, immune-related genes, and tight junction genes in the brain revealed no significant differences (Supplementary Fig. 9a-d). Distinct Microbial Ecosystems Shape Divergent Behavioral Outcomes in Recipient Mice Finally, we assessed the behavior of recipient mice after FMT (Fig. 6; Supplementary Fig. 10). In the EPM, the BD-recipient group exhibited less entries and cumulative duration in the open arms (Fig. 6a), indicating increased anxiety-like behavior. Conversely, in the LDT, BD-recipient mice spent more time rearing in the light compartment (Fig. 6b) and showed increased inactivity in the light compartment (Fig. 6c), further supporting altered exploratory behaviors. Microbial correlations with behavioral parameters revealed that in the control group, Akkermansia abundance was positively correlated with total locomotion distance (Spearman's rho = 1, p < 0.001, adjusted p < 0.1) and rearing counts in the light compartment (Spearman's rho = 0.95, p < 0.001, adjusted p < 0.1) (Fig. 6d). Although no statistically significant differences were observed in the OFT (Supplementary Fig. 10c), further analyses revealed that in the control group, Akkermansia abundance was positively associated with total distance moved and mean velocity (Fig. 6e). Conversely, Bacteroides abundance was negatively associated with cumulative duration spent in the center zone of the OFT arena (Fig. 6e). Discussion By combining FMTs with multi-layer profiling and constraint-based metabolic modeling, we demonstrate that microbiota alterations observed in a BD donor with current symptoms of a mixed episode led to distinct functional interactions among microbial taxa in recipient mice and induce coordinated metabolic and behavioral phenotypes. We show that fecal microbiome dysbiosis, characterized by reduced microbial diversity and predicted growth impairment of bacterial taxa, impairs microbiome transplantation efficiency and cooperative microbial networks, which translate into measurable changes in different layers of the GBA of recipient mice. The impaired cooperative community structure in recipient mice led to lower Akkermansia muciniphila abundance and lower fecal L-Glutamate and L-Aspartate levels that ultimately promoted anxiety-like behaviors and reduced central choline and glycine levels. These findings highlight that functional interactions among microbial taxa strongly affect transplantation dynamics and gut-brain signaling in response to FMT. Our study confirms that the microbiota in BD exhibits lower microbial richness and a distinct community composition [14, 50, 62]. Importantly, these compositional differences translate into a reduced metabolic capacity and disturbed growth potential of certain taxa, as revealed by MICOM modeling. This shows that dysbiosis in BD reflects a broader loss of functional redundancy and cooperative metabolic interactions. Upon transplantation, donor-derived bacteria engrafted in recipient mice; however, BD-derived microbiota reconstituted a distinct community structure compared with control FMT recipients, despite achieving a similar α-diversity. This dissociation between richness and composition underscores a key ecological principle: dysbiotic communities can re-establish aberrant configurations through self-reinforcing ecological dynamics [63, 64]. Notably, BD-recipient mice showed preferential enrichment of Alloprevotella and Muribaculaceae and reduced engraftment of Akkermansia , a keystone taxon implicated in gut barrier integrity, SCFA production, and microbiota–brain signaling [65, 66]. These findings indicate that a dysbiotic microbiome imposes constraints on ecosystem assembly that limit colonization by beneficial taxa, thereby shaping downstream metabolic outcomes. Our findings are in line with the argument that microbial transfer during FMT is not proportional, as donor taxa often do not retain their relative abundances following transplantation [67]. Instead, the recipient microbiome reflects selective engraftment dynamics that should be considered when interpreting FMT studies. Metabolic modeling revealed that these compositional shifts profoundly altered community-level metabolic organization. As an example, in controls, metabolic interactions among Akkermansia , Bacteroides , and Muribaculaceae supported the production of amino acids such as glutamate, glutamine, alanine and aspartate potentially sustaining production of neurotransmitter precursors critical for glutamatergic and GABAergic signaling among others. In contrast, BD microbiota signified by Muribaculaceae and Alloprevotella interactions, showed reduced contributions to glutamine synthesis and a shift towards biosynthesis of glycine, serine and threonine, a pattern commonly associated with oxidative stress responses and nutrient limitation [68], [69]. This metabolic alteration is intriguing given the central role of the glutamate-glutamine-GABA cycle in mood regulation, cognition, and behavioral control [70], [71]. The loss of microbial support for this axis might be a plausible link between dysbiosis and the emergence of anxiety-like behaviors observed in BD-colonized mice. Consistent with modeling predictions, measured stool metabolites in BD-recipient mice revealed accumulation of simple sugars and valeric acid, a microbial fermentation product, alongside depletion of glutamic and aspartic acid, with enrichment in pathways related to starch, sucrose, and galactose metabolism. These findings together, suggest inefficient carbohydrate fermentation and impaired amino-acid utilization, a metabolic phenotype that aligns with reduced SCFA production and altered energy harvest reported in psychiatric disorders [72], [73], [74]. In the colon, metabolic signatures pointed toward increased oxidative stress and altered enteroendocrine signaling, including the observed trends toward reduced glutathione and alanine levels in BD-microbiome recipient mice. Network analysis revealed robust negative correlations between glutathione and metabolites enriched in BD mice (e.g. glycine, threonine, acetaldehyde), likely because of a redox imbalance and increased oxidative stress [75]. Oxidative stress has long been implicated in BD pathophysiology [76] and, and our findings suggest that gut microbial metabolism may contribute to this process by impairing antioxidant capacity at the mucosal interface. Intriguingly, the most pronounced effects of BD microbiota were observed in the brain metabolome, wherein eight of ten significantly altered metabolites were depleted. These included glycine, choline, methionine, glycerol, inosine, acetic acid, pyroglutamic acid, and phosphorylcholine, metabolites central to one-carbon metabolism, antioxidant defense, membrane biosynthesis, and methylation reactions. Concurrent elevation of AMP and inosinic acid further implicates disturbed purine metabolism and altered energy homeostasis, processes repeatedly associated with BD and mood disorders more broadly [74, 75]. The coordinated depletion of choline, methionine, and glycine is particularly notable, as these metabolites serve as key methyl donors supporting DNA and histone methylation, folate biosynthesis, and mitochondrial function. Disruption of one-carbon metabolism has been linked to altered neurodevelopment, impaired synaptic plasticity, and aberrant stress responses [79]. Our findings suggest that microbiota-driven depletion of methyl-donating and antioxidant metabolites may compromise the brain’s epigenetic and metabolic adaptability, providing a mechanistic bridge between gut dysbiosis and neurobiological vulnerability in BD. Interestingly, reductions in cortical choline-containing compounds are reported in anxiety disorders and have been linked to chronically elevated arousal in anxiety disorders and an increased neurometabolic demand for choline compounds [80]. In our behavioral tests, BD-colonized mice exhibited increased anxiety-like behavior and altered exploratory patterns. Importantly, microbial-behavior correlations revealed that Akkermansia abundance was strongly positively associated with locomotion and exploratory behavior in control mice, an association absent in BD mice due to Akkermansia depletion. Akkermansia is known to produce acetate and SCFAs, reinforce barrier integrity, and support glutamatergic metabolism [55], [66], functions consistent with anxiolytic effects observed in prior studies [15], [81]. Furthermore, our findings are in line with a recent study that demonstrated anxiolytic effects of Akkermansia through increased circulating choline and central glycine levels [82]. A key conceptual advance of this study is the demonstration that differences in engraftment efficiency of the donor microbiome and subsequent establishment of a cooperative network impact FMT outcome and need to be assessed also in studies that investigate gut-brain signaling. Another interesting observation was that despite pronounced alterations in stool, colon, and brain metabolites, plasma metabolites remained largely unchanged, likely reflecting rapid metabolic turnover. This highlights the value of constraint-based modeling (MICOM) in inferring metabolic capacity in addition to direct measurements. Together, these results support a model in which dysbiosis perpetuates itself through altered ecological interactions, impaired metabolic cooperation, and depletion of neuroactive and antioxidant metabolites, ultimately contributing to behavioral dysfunction. Therapeutically, this suggests that effective microbiota-targeted interventions may need to restore metabolic networks and ecosystem balance, rather than focusing on single taxa alone. Akkermansia emerges as a promising candidate, but our data caution that successful intervention will likely require re-establishing the cooperative microbial environment that supports its growth and function. Limitations This study used a single donor pair in order to minimize inter-donor variability and to isolate mechanistic properties of donor microbiomes. This design, however, precludes population-level generalization. While future studies incorporating larger donor cohorts, longitudinal designs, and targeted manipulation of specific metabolic pathways will be essential to establish causality and identify BD-specific dysbiotic signatures, this study aimed to investigate the trajectory of a single defined biological input and its downstream cascade of engraftment and establishment of a cooperative microbial network, followed by its output of metabolic and behavioral effects. Declarations Data and code availability Datasets and scripts used in this work are accessible through the GitHub repository: https://github.com/marijazmf/BP_paper_analysis . Microbiome raw data is accessible via project ID: PRJEB90027. Acknowledgments A.F. and T.M. are grateful to the Austrian Science Fund (FWF) for excellence cluster 10.55776/COE14. For open access purposes, the authors have applied a CC BY public copyright license to any author-accepted manuscript version arising from this submission. We thank the Land Styria and the Medical University of Graz for financial support. This research was further funded by Stadt Graz, Austria, and a Fulbright-Austrian Marshall Plan Foundation Award to G.B.T. The research of GG was supported by the Austrian Science Fund (FWF) grants [doi.org/10.55776/COE7 and DK-MOLIN W1241]. The research of T.M. was supported by the Austrian Science Fund (FWF) for Grants DOI 10.55776/P28854, 10.55776/I3792, 10.55776/DOC130, and 10.55776/W1226, the Austrian Research Promotion Agency (FFG) grants 864690 and 870454; the Integrative Metabolism Research Center Graz; the Austrian Infrastructure Program 2016/2017; the Styrian Government (Zukunftsfonds, doc.fund program); the City of Graz; and BioTechMed-Graz (flagship project). This project was funded in part by the FFG and the European Union (EFRE) under grant 912192. Author contributions M.D. performed bioinformatics data analysis and visualization, prepared the figures, and drafted the manuscript. F.T.F. performed patient recruitment and characterization. P.S. assisted in writing the manuscript. T.K. provided the mouse model database and diet for metabolic modeling. G.B.-T. and S.G. performed experiments. K.C.K.I. assisted in data analysis. S.M., J.W.-S., S.A.B., M.L., and N.D. assisted in patient recruitment and characterization. H.H. and T.M. performed NMR-based metabolomics analyses. C.H. and C.M.-E. supervised stool processing and fecal microbiota transplantation (FMT) methodology. A.F. designed and supervised the study and revised the manuscript. E.Z.R. supervised the clinical study and patient recruitment. G.G. supervised the study and assisted in writing the manuscript. All authors reviewed and approved the final manuscript. Declaration of interests The authors declare no competing interests. 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Additional Declarations The authors have declared there is NO conflict of interest to disclose Supplementary Files Table2.xlsx Table 2 Table3.xlsx Table 3 Table9.xlsx Table 9 Table8.xlsx Table 8 Table10.xlsx Table 10 Table7.xlsx Table 7 Table1.xlsx Table 1 Table4.xlsx Table 4 SupplementaryMethods.docx Supplementary Methods Table5.xlsx Table 5 Table6.xlsx Table 6 supfig.1.pdf supfig.10.pdf supfig.2.pdf supfig.3.pdf supfig.4.pdf supfig.5.pdf supfig.6.pdf supfig.7.pdf supfig.8.pdf supfig.9.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewer # 1 agreed at journal 03 May, 2026 Reviewers invited by journal 27 Apr, 2026 Editor assigned by journal 21 Apr, 2026 Submission checks completed at journal 21 Apr, 2026 First submitted to journal 20 Apr, 2026 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. 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Farzi","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-9606-3871","institution":"Medical University of Graz","correspondingAuthor":true,"prefix":"","firstName":"Aitak","middleName":"","lastName":"Farzi","suffix":""},{"id":630470215,"identity":"94c84032-3332-44e8-a418-e01faf8c6da0","order_by":1,"name":"Marija Durdevic","email":"","orcid":"","institution":"Medical University of Graz","correspondingAuthor":false,"prefix":"","firstName":"Marija","middleName":"","lastName":"Durdevic","suffix":""},{"id":630470216,"identity":"1bb3ff44-1a69-434a-9357-3d2e609b8f42","order_by":2,"name":"Frederike Fellendorf","email":"","orcid":"https://orcid.org/0000-0001-7215-3848","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Frederike","middleName":"","lastName":"Fellendorf","suffix":""},{"id":630470217,"identity":"b56180b1-bdd3-4433-a7e3-0c0ab7700461","order_by":3,"name":"Patrick Schimmel","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Patrick","middleName":"","lastName":"Schimmel","suffix":""},{"id":630470218,"identity":"780e205e-805b-4a32-a06f-3f4990f8d2f8","order_by":4,"name":"Torben 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Moissl-Eichinger","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Christine","middleName":"","lastName":"Moissl-Eichinger","suffix":""},{"id":630470232,"identity":"6f419f83-337a-4030-bbd9-c8f8107a358f","order_by":18,"name":"Gregor Gorkiewicz","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Gregor","middleName":"","lastName":"Gorkiewicz","suffix":""},{"id":630470233,"identity":"4bc60467-ea8a-4d5d-b118-0e15e4c60a4c","order_by":19,"name":"Eva Reininghaus","email":"","orcid":"https://orcid.org/0000-0001-5964-4087","institution":"Medical University of Graz","correspondingAuthor":false,"prefix":"","firstName":"Eva","middleName":"","lastName":"Reininghaus","suffix":""}],"badges":[],"createdAt":"2026-04-20 15:52:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9474472/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9474472/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108939964,"identity":"74345f1e-a97c-406b-b5f9-860a5ce8c7c4","added_by":"auto","created_at":"2026-05-11 05:10:52","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":475163,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferences in gut microbiota composition, growth rate, and diversity between control (CO) and bipolar disorder (BD) groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA) Predicted bacterial growth rates (left) and arcsine square-root–transformed relative abundances (right) of high abundant genera (rel. ab. \u0026gt; 1%) in CO-Donor and BD-Donor communities. Points represent growth rate estimates, bars represent transformed relative abundance, colored by donor groups.\u003c/p\u003e\n\u003cp\u003eB) Same analyses as in A), shown for mice recipient communities stratified by host group (CO vs BD). Growth rates are displayed as points (left), and arcsine square-root–transformed relative abundances as horizontal bars (right). Asterisks indicate statistically significant differences in growth rate potential of highlighted \u003cem\u003eAkkermansia\u003c/em\u003e. Results are expressed as mean values with corresponding standard deviations (SD).\u003c/p\u003e\n\u003cp\u003eC) Venn diagram showing overlap and uniqueness of amplicon sequence variants (ASVs) between CO-Donor, BD-Donor, CO-, and BD-recipient mice groups. Highlighted percentages indicate the fraction of donor ASVs shared with corresponding recipient groups.\u003c/p\u003e\n\u003cp\u003eD) Rarefaction curves showing the number of observed ASVs as a function of sequencing depth for each group, illustrating no significant differences in richness between recipient groups.\u003c/p\u003e\n\u003cp\u003eE) Violin plots of observed ASVs comparing alpha diversity between CO- and BD-recipient groups; median and interquartile ranges are indicated, with the reported p-value showing no statistical significance in alpha diversity between groups.\u003c/p\u003e\n\u003cp\u003eF) Principal coordinates analysis (PCoA) based on weighted Unifrac beta diversity distance, colored by mice group (CO vs BD). Ellipses represent group dispersion, and point size reflects phylogenetic diversity (PD). Statistical differences in microbial community composition were assessed using PERMANOVA (Adonis); results are shown within the panel.\u003c/p\u003e\n\u003cp\u003eG) Arcsine square-root–transformed relative abundances of four different genera partitioned into ASVs unique to recipients (light) or shared with donors proportions (dark). Asterisks indicate statistically significant differences between groups (FDR \u0026lt; 0.05). Prevotellaceae UCG-001 was no longer statistically significant after p-value adjustment (p \u0026lt; 0.05, FDR = 0.06)\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9474472/v1/aecec3bf1801d42ec89f34f8.jpg"},{"id":108940160,"identity":"22755dc6-870e-4579-bb6c-f5e059763d2c","added_by":"auto","created_at":"2026-05-11 05:11:32","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":486432,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMICOM-predicted differences in metabolite production and distinct microbial metabolic production networks underlying functional divergence between CO- and BD-recipient mice microbiota\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA) Differentially abundant metabolites predicted by MICOM, shown as log\u003csub\u003e2\u003c/sub\u003e net production rates (mmol·h⁻¹·gDW⁻¹) for the CO- and BD-recipient groups (left panel) and corresponding linear discriminative score (log\u003csub\u003e10\u003c/sub\u003e(LDA), right panel). Metabolites are grouped by biochemical class (amino acids, bile acids, TCA cycle intermediates, and others). Results are expressed as mean values with corresponding standard deviations (SD).\u003c/p\u003e\n\u003cp\u003eB) Metabolic production network inferred from MICOM analysis for the CO-recipient group. Circles represent microbial genera, while squares represent produced metabolites, with edges indicating predicted production (soft blue-green) or consumption relationships (dusty rose). Only genera with a relative abundance \u0026gt;1% and metabolites with a net production rate \u0026gt;2 mmol·h⁻¹·gDW⁻¹ are shown. The network displays only statistically significant genera and metabolites, along with their corresponding production or consumption relationships.\u003c/p\u003e\n\u003cp\u003eC) Metabolic production network of the BD-recipient mice, visualized as in B), showing group-specific significant microbe–metabolite interactions.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9474472/v1/58ed0fa765f9d05306eaa1bc.jpg"},{"id":108940106,"identity":"f00c21c2-ccca-48cf-a698-f722589746ca","added_by":"auto","created_at":"2026-05-11 05:11:18","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":394518,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional metabolic divergence between CO- and BD-recipient microbiota revealed by stool metabolomics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA) Volcano plot of NMR measured stool metabolites showing differential abundance between CO- and BD-recipient mice. Metabolites are colored according to significance groups. D-glucose did not remain statistically significant after multiple-testing correction (p \u0026lt; 0.05, q = 0.26). Statistical significance thresholds were set at p \u0026lt; 0.05 and q \u0026lt; 0.2.\u003c/p\u003e\n\u003cp\u003eB) - C) Boxplots showing distribution of representative BD- and CO-enriched stool metabolites quantified by NMR.\u003c/p\u003e\n\u003cp\u003eD) Correlation-based metabolic networks integrating stool metabolites, MICOM-predicted metabolites, and bacterial genera. Nodes represent experimentally measured stool metabolites, MICOM-modeled metabolites, and bacterial taxa, while edges denote significant correlations. Edge thickness reflects correlation strength and edge color indicates positive or negative associations.\u003c/p\u003e\n\u003cp\u003eE) Pathway impact analysis highlighting metabolic pathways significantly affected by BD-enriched stool metabolites, with bubble size representing pathway impact and color indicating false discovery rate (FDR).\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9474472/v1/f3339ab16d030f0f5b1c5291.jpg"},{"id":108939965,"identity":"82d4be2f-ca24-444b-9313-9d9b4018abe2","added_by":"auto","created_at":"2026-05-11 05:10:52","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":252245,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eColon metabolite alterations and host endocrine gene expression associated with CO- and BD-recipient microbiota\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA) Volcano plot of NMR measured colon metabolites comparing CO- and BD-recipient mice. Glutathione (p \u0026lt; 0.05, q = 0.3) and L-alanine (p \u0026lt; 0.05, q = 0.6) are highlighted as nominally significant metabolites; however, both failed to retain significance after multiple-testing correction. Statistical significance thresholds were set at p \u0026lt; 0.05 and q \u0026lt; 0.2.\u003c/p\u003e\n\u003cp\u003eB) Boxplots showing the distribution of glutathione and L-alanine levels in colon samples from CO and BD recipients.\u003c/p\u003e\n\u003cp\u003eC) Correlation network illustrating significant associations between colon-measured glutathione and MICOM-predicted metabolites. Nodes represent experimentally measured colon metabolites and MICOM-modeled metabolites; edges indicate significant correlations, with edge color denoting correlation direction and thickness proportional to correlation strength.\u003c/p\u003e\n\u003cp\u003eD) Volcano plot of NMR measured plasma metabolites, showing no metabolites reaching statistical significance between groups.\u003c/p\u003e\n\u003cp\u003eE) Boxplots of qPCR-measured endocrine genes (Pyy and Gcg) in colon tissue, both of which are significantly reduced in BD-recipient mice.\u003c/p\u003e\n\u003cp\u003eF) Correlation network depicting positive associations between endocrine gene expression levels.\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9474472/v1/826440f5c44dbe0960de0411.jpg"},{"id":108940107,"identity":"a36b239c-b52c-41e0-9673-da6cf7a655ac","added_by":"auto","created_at":"2026-05-11 05:11:18","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":445427,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBrain metabolomic alterations and associated microbial–metabolic networks in CO- and BD-recipient mice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA) Volcano plot of NMR measured brain metabolites comparing CO- and BD-recipient mice. Metabolites are colored by significance group, with selected differentially abundant metabolites labeled. Statistical significance thresholds were set at p \u0026lt; 0.05 and q \u0026lt; 0.2.\u003c/p\u003e\n\u003cp\u003eB) Distribution of selected CO-enriched brain metabolites shown as boxplots for CO-recipient mice.\u003c/p\u003e\n\u003cp\u003eC) Distribution of the same metabolites in BD-recipient mice, highlighting group-specific differences.\u003c/p\u003e\n\u003cp\u003eD) Correlation network integrating brain measured metabolites, stool measured metabolites, MICOM-predicted metabolites, and bacterial genera. Nodes represent metabolite classes and taxa as indicated, while edges denote significant correlations. Edge thickness reflects correlation strength and edge color indicates positive or negative associations.\u003c/p\u003e\n\u003cp\u003eE) Pathway impact analysis of CO-enriched brain metabolites, with bubble size corresponding to pathway impact and color indicating false discovery rate (FDR).\u003c/p\u003e","description":"","filename":"fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9474472/v1/44ce8f880aa832f608661615.jpg"},{"id":108940133,"identity":"4ba72851-4048-42df-b2ce-3dac382fed7e","added_by":"auto","created_at":"2026-05-11 05:11:26","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":575486,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBehavioral alterations and microbiota-behavior associations in CO- and BD-recipient mice across elevated plus maze (EPM), light-dark transition (LDT), and open field tests (OFT)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA) EPM performance of CO- and BD-recipient mice shown as boxplots with individual data points, including total distance moved, time spent in the central zone, frequency of entries into the open arms, and time spent in the open arms (seconds and percent of total test time); asterisks indicate statistically significant group differences. All of nose-point, center-point and tail-base measurements are plotted. Center-point or nose-point only are plotted in Supplementary figure 7a-b.\u003c/p\u003e\n\u003cp\u003eB-C) LDT outcomes are presented as boxplots showing active (A), inactive (I), and total (A+I) time spent in the light and dark compartments (B). Additional panels depict rearing behavior (C), quantified as rearing distance (cm) and rearing duration (s) in both compartments. Asterisks indicate statistically significant differences between CO and BD groups. Locomotion and speed measurements are shown in Supplementary figure 7c-d.\u003c/p\u003e\n\u003cp\u003eD-E) Spearman correlation matrix between the relative abundances of bacterial genera and LDT (D) or OFT (E) behavioral parameters in CO group only. Circle size and color indicating correlation strength and direction. Asterisks denote significant correlations.\u003c/p\u003e","description":"","filename":"fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9474472/v1/55f88ea1da5df6fbf86ad367.jpg"},{"id":108979758,"identity":"318c2f93-3d82-44bb-9e49-151366f121ae","added_by":"auto","created_at":"2026-05-11 12:01:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3069342,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9474472/v1/3a01383c-1ec9-467a-8e4e-3776a0526109.pdf"},{"id":108939929,"identity":"aa594bb3-f6ad-478a-868a-d981b310f19b","added_by":"auto","created_at":"2026-05-11 05:10:41","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20407,"visible":true,"origin":"","legend":"Table 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05:11:22","extension":"pdf","order_by":45,"title":"","display":"","copyAsset":false,"role":"supplement","size":567027,"visible":true,"origin":"","legend":"","description":"","filename":"supfig.9.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9474472/v1/7870bbb86d4ccf431ff3daf2.pdf"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Donor Microbiota Metabolic Capacity Determines Engraftment Dynamics and Modulates Gut–Brain Signaling in Recipient Mice","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBipolar disorder (BD) represents a psychiatric condition, affecting approximately 0.4-2.4% of the population [1] and among the leading cause of global disability [2]. Beyond its profound psychiatric manifestations - characterized by episodic cycles of depression, mania, and hypomania - individuals with BD face an increased suicide risk [3]. Depressive and manic symptoms can also coexist as mixed affective states and are classified as more severe forms of BD with higher rates of comorbid conditions and suicide rates [4]. The etiopathogenesis of BD involves complex interactions between genetic predisposition, epigenetic modifications, metabolic disturbances, and environmental factors that act synergistically to establish disease vulnerability [5].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRecent research has highlighted the potential role of the gut microbiota in modulating central nervous system (CNS) function through the \u0026quot;gut-brain axis\u0026quot; (GBA), a bidirectional signaling circuit that links microbial activity with neurological, immunological, and metabolic processes. The intestinal microbiota produces various metabolites such as short-chain fatty acids (SCFAs) which influence neuromodulatory processes through multiple layers, modulate intestinal barrier permeability, and stimulate enteroendocrine L-cells to release glucagon-like peptide 1 (GLP-1) and peptide YY (PYY), which, among others, contribute to appetite regulation and metabolic homeostasis [5, 6, 7]. Aberrations in the gut microbiota, termed dysbiosis, have been found in multiple psychiatric disorders, including major depressive disorder [8, 9], anxiety disorders [11], schizophrenia [12], and attention-deficit/hyperactivity disorder [13].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEmerging evidence demonstrates that individuals with BD exhibit a dysbiotic gut microbiota characterized by a reduced microbial diversity and altered community composition, implicating these changes as potential contributors to BD pathogenesis [14]. For instance, a depletion of beneficial taxa such as \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e [15] and SCFA-producing bacteria, alongside enrichment of potentially pathogenic taxa, resulting in impaired production of neuroprotective metabolites and intestinal barrier defects, has been described in BD [16].\u003c/p\u003e\n\u003cp\u003eDysbiosis often coincides with elevated circulating pro-inflammatory cytokines [16, 17, 18], systemic oxidative stress [19, 20], and increased blood-brain barrier permeability [22], hallmark features in BD pathophysiology. The mechanistic pathways linking dysbiosis to CNS dysfunction appear to operate through metabolic changes, such as impaired SCFA production [23], reduced GABA synthesis [24], and dysregulated tryptophan metabolism [24] by the microbial ecosystem and immunological stimulation [25]. Notably, microbiota from individuals with BD have been shown to alter the expression of disease-relevant genes implicated in BD pathogenesis [26], suggesting that dysbiotic microbiota directly contribute to molecular alterations in the CNS.\u003c/p\u003e\n\u003cp\u003eFecal microbiota transplantation (FMT), a procedure that transfers gut microbiota from one individual to another, has emerged as a powerful translational tool to investigate the mechanistic role of the microbiome in CNS disorders [27]. Furthermore, FMT from healthy donors has been suggested to ameliorate psychiatric symptoms [28]. While previous FMT studies have demonstrated that dysbiotic microbiota from psychiatric patients exert pathogenic effects, detailed characterization of microbial community dynamics, transplantation efficacy, and metabolic potential, is often lacking. In this study, we used FMT to analyze the effect of these factors on GBA communication using a dysbiotic microbiome associated with BD.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003ch3\u003eEthical Approval\u003c/h3\u003e\n\u003cp\u003eAll experiments were approved by the ethical committee at the Federal Ministry of Education, Science and Research of the Republic of Austria (permit BMBWF-66.010/0047-V/3b/2019). The study was conducted according to the guidelines of the Declaration of Helsinki and received approval from the Institutional Review Board of the Medical University of Graz (protocol number 31-120 ex 18/19; approval date: March 27, 2019). Written informed consent was obtained from both donors.\u003c/p\u003e\n\u003ch3\u003eDonor selection\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eThe study included two female donors: one diagnosed with BD during a mixed affective episode and one healthy control matched for age and body weight. Clinical assessment of symptom severity was performed using the Young Mania Rating Scale (YMRS) for manic symptoms [29] and the Hamilton Depression Rating Scale (HAM-D) for depressive symptoms [30]. The BD donor exhibited a YMRS score of 14 and a HAM-D score of 20, while the control donor scored 0 on both scales. The YMRS is a validated questionnaire used to measure current (hypo)manic symptoms. The HAM-D measures severity of depressive symptoms rated by an experienced professional. Further information can be found in the Supplementary Information.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eDonor Fecal Samples Collection and Processing\u003c/h3\u003e\n\u003cp\u003eFecal samples were collected in plastic containers equipped with an anaerobic atmosphere generator (AnaeroGen\u0026trade; 2.5L, Thermo Fisher Scientific). Between 60 and 100 grams of fecal material were processed within six hours post-collection inside an anaerobic chamber (Whitley A85 Workstation, Don Whitley Scientific). The samples were suspended in 150 mL of anaerobic, reduced, sterile phosphate-buffered saline (PBS), homogenized, and passed through a mesh to remove solid debris. Glycerol was added to a final concentration of 10%, and the resulting suspension was aliquoted\u0026nbsp;into 4 mL anaerobic tubes (Anaerobic Hungate Culture Tubes, Chemglass Life Sciences). Prepared samples were stored at \u0026minus;70\u0026deg;C until use for fecal transfer.\u003c/p\u003e\n\u003ch3\u003eMice\u003c/h3\u003e\n\u003cp\u003eFourteen female C57BL/6J mice, 20\u0026ndash;30 weeks of age, were group under controlled environmental conditions. The animals were maintained at a constant ambient temperature of 22\u0026deg;C with a 12h:12h light-dark cycle (lights on at 07:00, lights off at 17:00). Water and standard chow were provided ad libitum for the duration of the study.\u003c/p\u003e\n\u003ch3\u003eFecal microbiota transplantation (FMT)\u003c/h3\u003e\n\u003cp\u003eAll mice were pretreated with antibiotics in their drinking water (0.5 mg/mL neomycin, 1 mg/mL meropenem, and 0.3 mg/mL vancomycin) for one week to deplete the existing gut microbiota and facilitate engraftment of the donor microbiota [31]. One day prior to FMT, mice were fasted to reduce fecal blockage during the procedure and were provided with regular tap water.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile under isoflurane anesthesia, one group received fecal material from a donor with BD, and another group received stool from a healthy control donor (Supplementary Fig. 1). Approximately 0.2 mL of the human stool suspension was administered rectally to each mouse, given the advantages of rectal versus oral administration [32]. To further support microbiota colonization, a few drops of the stool suspension were also placed in the cages.\u003c/p\u003e\n\u003ch3\u003eTissue Extraction and Processing\u003c/h3\u003e\n\u003cp\u003eTissue extraction was performed as previously described [33]. More detailed information can be found in the Supplementary Information.\u003c/p\u003e\n\u003ch3\u003eRNA Extraction and RT-qPCR\u003c/h3\u003e\n\u003cp\u003eTotal RNA was isolated from colon and amygdala tissues using the RNeasy Tissue Mini Kit and the RNeasy Lipid Tissue Mini Kit (Qiagen), respectively. One microgram of RNA from each sample was reverse-transcribed into cDNA using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA). Quantitative real-time PCR was performed on the CFX384 Touch Real-Time PCR Detection System (Bio-Rad) using TaqMan\u0026reg; Gene Expression Assays outlined in the Supplementary Information. Relative quantification was performed using the 2^\u0026minus;\u0026Delta;\u0026Delta;Ct method, with the mean expression level of the control group serving as the calibrator\u0026nbsp;[34].\u003c/p\u003e\n\u003ch3\u003eSample Preparation for NMR Spectroscopy and Data Processing\u003c/h3\u003e\n\u003cp\u003eFecal, colon, plasma, and amygdala \u0026nbsp;samples collected 13 days after FMT were processed for nuclear magnetic resonance (NMR) spectroscopy as previously described [35] and described in the Supplementary Information.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eBehavioral Tests\u003c/h3\u003e\n\u003cp\u003eThe open field test (OFT), elevated plus maze (EPM), and light/dark box tests (LDT) were used to assess locomotion, exploration and anxiety-like behaviour. Details of the behavioural tests are described in Supplementary Information (Supplementary Fig. 1).\u003c/p\u003e\n\u003ch3\u003eBacterial DNA Extraction and 16S rRNA Gene Sequencing\u003c/h3\u003e\n\u003cp\u003eBacterial DNA was extracted from fecal samples of both human donors and recipient mice using the QIAamp DNA Mini Kit (Qiagen, Hilden, Germany), following the manufacturer\u0026rsquo;s protocol. PCR amplification and sequencing were carried out by Novogene Europe (Cambridge, UK) and are described in the Supplementary Information.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e16S rRNA Gene Sequencing Data Processing\u003c/h3\u003e\n\u003cp\u003eThe 16S rRNA sequencing data were processed using QIIME2 (version 2023.2) on a local Galaxy instance (\u003cu\u003ehttps://galaxy.medunigraz.at\u003c/u\u003e) [39]. Initial steps included quality filtering, denoising, and chimera removal using the DADA2 denoise-pyro plugin. To ensure high-quality amplicon sequence variants (ASVs), all reads were trimmed by 15 bases from the 5\u0026apos; end and truncated to 220 bases at the 3\u0026apos; end. Taxonomic assignment of ASVs was carried out using a Na\u0026iuml;ve Bayes classifier trained on the SILVA ribosomal RNA database (version 138) [40]. Phylogenetic tree construction was performed using the align-to-tree-mafft-fasttree pipeline. Information on further analyses can be found in the Supplementary Information.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eASV-based source tracking was performed by directly comparing amplicon sequence variants (ASVs) detected in donor samples with those detected in mouse samples. ASVs identical between donor and mouse were classified as donor-derived, whereas ASVs detected exclusively in mouse samples were classified as mouse-specific.\u003c/p\u003e\n\u003ch3\u003eMetabolic Modeling\u003c/h3\u003e\n\u003cp\u003eMicrobial interactions and metabolic production within the community were modeled using MICOM (v0.37), a tool that applies cooperative trade-off flux balance analysis (ctFBA) to infer metabolic interactions among taxa [43]. MICOM has been shown to accurately predict microbial growth rates and metabolite production. For modeling human microbiome data, the AGORA database (v201), which contains genome-scale metabolic models for human gut bacteria, was leveraged [44]. Mouse microbiome data were modeled using the McMurGut database, which includes mouse-specific gut bacterial models, paired with a standard mouse chow diet as the medium [45]. All simulations were performed at the genus level. To ensure biologically realistic outputs, a trade-off value was determined first in order to balance community-wide growth versus individual optimization. The resulting growth rates were confirmed to fall within expected biological ranges.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo assess ecological relationships, in silico knockouts from MICOM were performed for each genera individually. The impact of each knockout on the growth rates of other community members was evaluated: an increase in another genera\u0026apos;s growth upon knockout was interpreted as competition, while a decrease was interpreted as cooperation.\u003c/p\u003e\n\u003cp\u003eDifferentially abundant metabolites were identified using the mp_diff_analysis function from the MicrobiotaProcess R package. These metabolites were further analyzed with the web-based analysis platform MetaboAnalyst (v6.0) to evaluate their involvement in metabolic pathways and to compare metabolic activity between groups [46]. For donor-derived metabolites, differential abundance was evaluated using log\u003csub\u003e2\u003c/sub\u003e fold change, with values exceeding |log\u003csub\u003e2\u003c/sub\u003eFC| \u0026gt; 2 deemed substantially altered, consistent with the approach used for microbial assessment. Final visualizations, including microbe-metabolite interaction networks, were generated in R using the ggplot2 and visNetwork packages [47].\u003c/p\u003e\n\u003ch3\u003eMetabolic Analysis\u003c/h3\u003e\n\u003cp\u003eTo identify differentially abundant metabolites between the two groups of mice, we employed linear modeling using the MaAsLin2 tool [48]. The default settings of MaAsLin2 were used, except that no additional normalization was applied. Metabolic values were log-transformed, and the general linear model option was selected to assess group differences. Reported regression coefficients correspond to log\u003csub\u003e2\u003c/sub\u003e fold change, reflecting the magnitude and direction of group differences. Multiple hypothesis testing was addressed using the Benjamini-Hochberg procedure, and corrected P values were reported as false discovery rates (FDR). Significant metabolites were subsequently analyzed using the web-based platform MetaboAnalyst (v6.0) to evaluate their involvement in relevant metabolic pathways and to compare metabolic activity profiles between the groups.\u003c/p\u003e\n\u003ch3\u003eStatistical Analysis of qPCR Genes and Behavioral Tests\u003c/h3\u003e\n\u003cp\u003eDifferences between mouse groups were evaluated using either Student\u0026rsquo;s t-test or the Mann\u0026ndash;Whitney U test, as appropriate. To account for multiple comparisons, p-values were adjusted using the BH method to control the FDR. Statistical significance was defined as an adjusted p-value \u0026lt; 0.05. Gene expression data from qPCR were log\u003csub\u003e2\u003c/sub\u003e-transformed prior to analysis, whereas behavioral data were analyzed using raw values. All correlation analyses were conducted in R using Spearman\u0026apos;s rank correlation as implemented in the cor() function from the stats package [49] and described in the Supplementary Information.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003eBD-associated Dysbiotic Gut Microbiota Exhibits Impaired Metabolic Potential\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eThe healthy donor fecal microbiota was markedly more diverse, comprising 602 amplicon sequence variants (ASVs), whereas the BD donor microbiota only showed 225 ASVs (Supplementary Fig. 2a-b top). Several high abundant genera in the control donor (relative abundance \u0026gt; 0.5%) such as \u003cem\u003eMethanobrevibacter\u003c/em\u003e, \u003cem\u003eChristensenellaceae\u0026nbsp;\u003c/em\u003eR-7 group, \u003cem\u003eClostridia vadin\u0026nbsp;\u003c/em\u003eBB60 group, \u003cem\u003eOscillospiraceae\u0026nbsp;\u003c/em\u003e(UCG-002 and UCG-003), \u003cem\u003eRuminococcaceae\u0026nbsp;\u003c/em\u003eCAG-352, and \u003cem\u003eSubdoligranulum\u003c/em\u003e, were completely absent in the BD donor (Supplementary Fig. 2c). Conversely, \u003cem\u003eRuminococcus gnavus\u003c/em\u003e, a presumed pathobiont of the gastrointestinal (GI) tract, was only detected in the BD donor (Supplementary Fig. 2c) [51]. Strikingly, only 64 genera were shared between the two donor microbiotas (Supplementary Fig. 2b bottom). Among these, 19 were significantly depleted (e.g. \u003cem\u003eBifidobacterium\u003c/em\u003e, unc. \u003cem\u003eOscillospiraceae\u003c/em\u003e, unc. \u003cem\u003eChristensenellaceae\u003c/em\u003e) and 13 enriched in the BD donor (|log\u003csub\u003e2\u003c/sub\u003eFC| \u0026ge; 2; e.g.\u0026nbsp;\u003cem\u003eOdoribacter\u003c/em\u003e, \u003cem\u003eParabacteroides\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e) (Supplementary Fig. 2c; Supplementary Table 1). Although these findings highlight only differences between two individuals, they underscore the reported microbial community shifts associated with BD [14].\u003c/p\u003e\n\u003cp\u003eThe depleted microbiota of the BD donor also translated into an altered metabolic capacity, which was modeled using flux balance analysis with MICOM inferred from microbial taxonomic patterns [43]. Of the 186 predicted metabolites, 147 were shared between both donors, 38 were exclusively predicted in the control donor microbiome, while only one unique metabolite (Folate; KEGG id:C00504) was predicted for the BD donor microbiome (Supplementary Fig. 3a; Supplementary Fig. 4a; Supplementary Table 2). Although the majority of the 38 metabolites unique to the healthy control donor exhibited relatively low production rates (\u0026lt;1 mmol\u0026middot;h\u003csup\u003e-1\u003c/sup\u003e\u0026middot;gDW\u003csup\u003e-1\u003c/sup\u003e, millimoles per hour per gram of dry weight) as inferred from MICOM analysis, they represented several important metabolite classes, including specific organic acids, vitamins and cofactors (e.g., pyridoxal 5\u0026apos;-phosphate, biotin), amines and neurotransmitter-related compounds (e.g., tryptamine, trimethylamine), as well as certain bile acids (Supplementary Fig. 4a-b).\u003c/p\u003e\n\u003cp\u003eOut of the 147 shared metabolites, 42 metabolites with a production rate exceeding 1 mmol\u0026middot;h\u003csup\u003e-1\u003c/sup\u003e\u0026middot;gDW\u003csup\u003e-1\u003c/sup\u003e were enriched in the control (Supplementary Fig. 3b; Supplementary Table 2). Amongst these were important TCA cycle intermediates, amino acids or their precursors, as well as short-chain fatty acids (SCFAs), including the well-known neurotransmitter 4-aminobutanoate (= \u0026gamma;-aminobutyric acid; GABA). Metabolomics modeling revealed a significantly greater breadth of the healthy donor microbiome in many biosynthesis and metabolic pathways, which were depleted in the BD microbiome (Supplementary Fig. 3c). Together, these data indicate that the BD donor microbiota is characterized by reduced microbial diversity and metabolic capacity.\u003c/p\u003e\n\u003ch3\u003eDysbiosis in BD Donor Impairs Bacterial Growth and Engraftment in Mice\u003c/h3\u003e\n\u003cp\u003eA diminished microbial metabolic disrupts microbial-metabolic networks that normally support microbiome resilience [52]. Our MICOM-based analysis revealed growth impairment of several bacterial taxa of the BD donor microbiota (Fig. 1a, left panel; Supplementary Fig. 5a, left panel; Supplementary Table 4). Notably, highly abundant genera (rel. ab. \u0026gt; 1%) including \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eBifidobacterium\u003c/em\u003e, \u003cem\u003eAlistipes\u003c/em\u003e, \u003cem\u003eBarnesiella\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eMethanobrevibacter\u003c/em\u003e, \u003cem\u003eAkkermansia\u003c/em\u003e, showed reduced growth potential. The reduced growth potential was observed even for genera with comparable abundance levels in both donors (Fig. 1a; Supplementary Table 4). Although comparable proportions of donor ASVs (43% from control donor and 30% from BD donor; Fig. 1c) and genera (69% from control donor and 76% from BD donor; Supplementary Fig. 5c) successfully engrafted in the murine gut after FMT, the composition of the transferred communities differed between the two groups. This also translated into different bacterial growth rates in the newly established mouse communities after FMT (Fig. 1b, left; Supplementary Fig. 5b, left), which were strongly correlated with the relative abundances of bacterial taxa (Fig. 1b; Supplementary Fig. 5d). Notably, \u003cem\u003eAkkermansia\u003c/em\u003e, a mucosa-associated bacterium implicated in gut-brain axis signaling [52, 53, 54], displayed a lower growth rate in mice colonized with BD microbiota compared to control microbiota (FDR = 0.029; Fig. 1b, left; Supplementary Table 4).\u003c/p\u003e\n\u003cp\u003eTo further explore the interdependence of bacterial taxa within the microbial community, we performed in silico genus-level knockouts. By systematically removing individual genera of the community and assessing their impact on the growth rates of others, we uncovered a network of cooperative interactions that supported or inhibited the growth of the key genus \u003cem\u003eAkkermansia\u0026nbsp;\u003c/em\u003e(denoted by blue or red arrows in Supplementary Fig. 5e-f). Mice receiving the control donor microbiota developed a functionally cohesive microbial ecosystem, whereas those colonized with the BD microbiota exhibited a different and less cooperative community structure, highlighting the influence of donor-specific microbial configurations for engraftment in the mouse host.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eDistinct Microbial Communities and Predicted Metabolite Production in BD-Recipient Mice\u003c/h3\u003e\n\u003cp\u003eAlthough microbial richness in mice after FMT showed no statistically significant difference between groups (Wilcoxon rank-sum test=0.53; Figs. 1d-e; Supplementary Table 5), principal coordinate analysis (PCoA; weighted UniFrac) demonstrated significant different microbial community structures (adonis, p\u0026lt;0.05; Fig. 1f; Supplementary Fig. 6a; Supplementary Table 6). To identify bacterial taxa driving compositional differences in recipient mice after FMT, we performed differential abundance analysis across all taxonomic levels (full results in Supplementary Table 7). At the phylum level, Verrucomicrobiota were significantly enriched in the control-microbiome recipient mice, whereas Bacteroidota predominated in the BD-recipient group (denoted as BD in all graphs and tables; Supplementary Table 6). Ten ASVs affiliated with \u003cem\u003eMuribaculaceae\u0026nbsp;\u003c/em\u003eand two with \u003cem\u003eAlloprevotella\u0026nbsp;\u003c/em\u003ewere significantly enriched in the BD-recipient group. In contrast, the control group showed higher relative abundance of \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e, \u003cem\u003eLachnospiraceae NK4A136\u003c/em\u003e, \u003cem\u003eParabacteroides distasonis,\u003c/em\u003e and \u003cem\u003eBacteroides vulgatus\u0026nbsp;\u003c/em\u003eASVs\u003cem\u003e,\u003c/em\u003e amongst others (Supplementary Fig. 6b; Supplementary Table 7). Consistent with these findings, genus-level differential abundance analysis revealed significant enrichment of \u003cem\u003eAlloprevotella\u0026nbsp;\u003c/em\u003eand \u003cem\u003eMuribaculaceae\u0026nbsp;\u003c/em\u003ein the BD-recipient group, while \u003cem\u003eAkkermansia\u0026nbsp;\u003c/em\u003ewas more prevalent in control-microbiome recipient mice (Fig. 1g; Supplementary Table 7).\u003c/p\u003e\n\u003cp\u003eASV-based source-tracking analyses showed that \u003cem\u003eAkkermansia\u0026nbsp;\u003c/em\u003eprimarily originated from the donor in both groups, while \u003cem\u003eMuribaculaceae\u0026nbsp;\u003c/em\u003edisplayed a mixed origin from both mouse and donor (Fig. 1g, Supplementary Fig. 6b). Interestingly, although Muribaculaceae are not prevalent members of the human microbiome [56], donor-derived \u003cem\u003eMuribaculaceae\u0026nbsp;\u003c/em\u003ewere enriched in the BD-microbiome recipient mice, where half of \u003cem\u003eMuribaculaceae\u003c/em\u003e were donor-derived, whereas only about one-quarter were donor-derived in the control group (Fig. 1g, Supplementary Fig. 6b). Notably, three significantly different and highly abundant \u003cem\u003eMuribaculaceae\u0026nbsp;\u003c/em\u003eASVs of the BD donor were effectively transferred into recipient mice (Supplementary Fig. 6b). Correlation analysis further revealed a significant negative association between \u003cem\u003eAkkermansia\u0026nbsp;\u003c/em\u003eand \u003cem\u003eMuribaculaceae,\u0026nbsp;\u003c/em\u003eas well as \u003cem\u003eAlloprevotella\u0026nbsp;\u003c/em\u003e(Supplementary Figs. 6c-d) indicating altered co-occurrence patterns in Co-microbiome versus BD-microbiome recipient mice.\u003c/p\u003e\n\u003cp\u003eIn silico simulations of microbial metabolite production using the MICOM framework revealed that although overall metabolite production rates were comparable between groups, with 112 metabolites shared between them (Supplementary Fig. 7a), 16 metabolites exhibited significant differences in predicted abundance (Fig. 2a; Supplementary Table 8). These included several amino acids, bile acids, and tricarboxylic acid (TCA) cycle intermediates (Fig. 2a). Specifically, the control-microbiome recipient group showed higher predicted levels of L-glutamate, L-alanine, L-glutamine, and L-aspartate, whereas L-threonine and glycine were enriched in BD-recipient mice. Metabolic network analysis further revealed distinct roles of specific bacterial taxa in metabolite synthesis and consumption (Figs. 2b-c, Supplementary Interactive Figs. 1-2). In the control group, \u003cem\u003eMuribaculaceae\u0026nbsp;\u003c/em\u003ewere identified as the primary producer of L-alanine, L-aspartate and L-malate, while \u003cem\u003eBacteroides\u0026nbsp;\u003c/em\u003eutilized L-alanine and contributed to pyruvate synthesis. Additionally, \u003cem\u003eBacteroides\u003c/em\u003e, along with other bacteria, supported L-glutamate production, which was then utilized by \u003cem\u003eAkkermansia\u0026nbsp;\u003c/em\u003eto synthesize L-glutamine through glutamine synthetase activity (EC:6.3.1.2) (Fig. 2b, Supplementary Fig. 7b; Supplementary Interactive Fig. 1). These interactions confirmed a cooperative metabolic network between \u003cem\u003eMuribaculaceae\u0026nbsp;\u003c/em\u003eand \u003cem\u003eAkkermansia\u0026nbsp;\u003c/em\u003ein the control group (Supplementary Interactive Fig. 1). In contrast, the BD-associated microbiota displayed a distinct metabolic architecture, characterized by a cooperative network predominantly established between \u003cem\u003eMuribaculaceae\u0026nbsp;\u003c/em\u003eand \u003cem\u003eAlloprevotella\u0026nbsp;\u003c/em\u003e(Fig. 2c; Supplementary Interactive Fig. 2). Notably, \u003cem\u003eAlloprevotella\u0026nbsp;\u003c/em\u003eemerged as a key consumer of glycine and acetaldehyde while serving as a producer of L-threonine (Fig. 2c; Supplementary Interactive Fig. 2).\u003c/p\u003e\n\u003cp\u003ePathway analysis showed significant metabolic differences between groups strongly impacting amino acid metabolism. Alanine, aspartate and glutamate metabolism was enriched in control-microbiome recipient mice (FDR \u0026lt; 0.001; pathway impact \u0026gt; 0.5) (Supplementary Fig. 7c), whereas glycine, serine and threonine metabolism was enriched in BD-recipient mice (Supplementary Fig. 7d).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eStool Metabolite Profiling Indicates Altered Amino Acid And Carbohydrate Metabolism in BD-Recipient Mice\u003c/h3\u003e\n\u003cp\u003eAmong the 50 stool metabolites assessed, valeric acid, sucrose, and fructose were elevated in the BD-recipient group (Fig. 3a-b; Supplementary Table 9), whereas capric acid, glutamic acid, and aspartic acid were decreased in the BD-recipient group (Fig. 3a-c). Valeric acid, a microbial fermentation product and SCFA, showed positive correlations with fructose and sucrose (Fig. 3d; Supplementary Interactive Fig. 3), while it was negatively correlated with glutamine and glutamate. Valeric acid was also positively correlated with \u003cem\u003eMuribaculaceae\u0026nbsp;\u003c/em\u003eand negatively with \u003cem\u003eAkkermansia\u0026nbsp;\u003c/em\u003e(Fig. 3d; Supplementary Interactive Fig. 3). Pathway analysis identified galactose, starch and sucrose metabolism as the most enriched pathways in BD-recipient mice (Fig. 3e).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eSignatures of Increased Oxidative Stress and Altered Enteroendocrine Hormone Production in the Colon of BD-Recipient Mice\u003c/h3\u003e\n\u003cp\u003eAmong the 35 colon metabolites measured, glutathione and L-alanine were decreased in BD-recipient mice (Figs. 4a-b); however, these changes did not retain statistical significance following p-value adjustments. Glutathione was negatively correlated with three modeled metabolites that were significantly enriched in BD-recipient mice, namely glycine, L-threonine, and acetaldehyde (Fig. 4c; Supplementary Interactive Fig. 3). In contrast, none of the 45 plasma metabolites measured showed significant changes (Fig. 4d), which might be attributed to a rapid kinetic turnover and metabolization via the portal system.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe next examined colonic mRNA expression of tight junction proteins, immune-related genes, and enteroendocrine hormones (Pyy, Gcg). While peptide YY (Pyy) and proglucagon (Gcg) were significantly downregulated in the BD-recipient group (Fig. 4e), tight junction and immune-related genes showed no significant differences (Supplementary Figs. 8a-b).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eAltered Brain Metabolome in BD-Recipient Mice Related to Oxidative Stress and Neurotransmitter Metabolism\u003c/h3\u003e\n\u003cp\u003eMetabolomic profiling of brain tissue revealed differences in 10 out of 50 measured metabolites. Glycine, choline, glycerol, inosine, methionine, acetic acid, pyroglutamic acid, and phosphorylcholine were found to be depleted in the BD-recipient mice (Figs. 5a-b). Conversely, adenosine monophosphate (AMP) and inosinic acid were enriched (Fig. 5c). Network analysis revealed robust correlations between 18 brain metabolites and 2 stool-derived metabolites. Furthermore, glycine showed a strong positive correlation with Alistipes and microbial-derived L-malate (Fig. 5d; Supplementary Interactive Fig. 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePathway analysis of the depleted brain metabolites highlighted alterations in one-carbon metabolism and glutathione metabolism, as well as changes in glycine, serine and threonine metabolism (Fig. 5e), which play critical roles in regulating oxidative stress and maintaining neurotransmitter biosynthesis [61].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition to these metabolic changes, the central expression of pro-inflammatory genes Il1b and Ccl2 exhibited a trend toward reduced expression in BD mice (Supplementary Fig. 9b), while gene expression of glycine receptors, immune-related genes, and tight junction genes in the brain revealed no significant differences (Supplementary Fig. 9a-d).\u003c/p\u003e\n\u003ch3\u003eDistinct Microbial Ecosystems Shape Divergent Behavioral Outcomes in Recipient Mice\u003c/h3\u003e\n\u003cp\u003eFinally, we assessed the behavior of recipient mice after FMT (Fig. 6; Supplementary Fig. 10). In the EPM, the BD-recipient group exhibited less entries and cumulative duration in the open arms (Fig. 6a), indicating increased anxiety-like behavior. Conversely, in the LDT, BD-recipient mice spent more time rearing in the light compartment \u0026nbsp;(Fig. 6b) and showed increased inactivity in the light compartment (Fig. 6c), further supporting altered exploratory behaviors.\u003c/p\u003e\n\u003cp\u003eMicrobial correlations with behavioral parameters revealed that in the control group, \u003cem\u003eAkkermansia\u0026nbsp;\u003c/em\u003eabundance was positively correlated with total locomotion distance (Spearman\u0026apos;s rho = 1, p \u0026lt; 0.001, adjusted p \u0026lt; 0.1) and rearing counts in the light compartment (Spearman\u0026apos;s rho = 0.95, p \u0026lt; 0.001, adjusted p \u0026lt; 0.1) (Fig. 6d). Although no statistically significant differences were observed in the OFT (Supplementary Fig. 10c), further analyses revealed that in the control group, \u003cem\u003eAkkermansia\u0026nbsp;\u003c/em\u003eabundance was positively associated with total distance moved and mean velocity (Fig. 6e). Conversely, \u003cem\u003eBacteroides\u0026nbsp;\u003c/em\u003eabundance was negatively associated with cumulative duration spent in the center zone of the OFT arena (Fig. 6e).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBy combining FMTs with multi-layer profiling and constraint-based metabolic modeling, we demonstrate that microbiota alterations observed in a BD donor with current symptoms of a mixed episode led to distinct functional interactions among microbial taxa in recipient mice and induce coordinated metabolic and behavioral phenotypes. We show that fecal microbiome dysbiosis, characterized by reduced microbial diversity and predicted growth impairment of bacterial taxa, impairs microbiome transplantation efficiency and cooperative microbial networks, which translate into measurable changes in different layers of the GBA of recipient mice. The impaired cooperative community structure in recipient mice led to lower \u003cem\u003eAkkermansia muciniphila\u0026nbsp;\u003c/em\u003eabundance and lower fecal L-Glutamate and L-Aspartate levels that ultimately promoted anxiety-like behaviors and reduced central choline and glycine levels. These findings highlight that functional interactions among microbial taxa strongly affect transplantation dynamics and gut-brain signaling in response to FMT.\u003c/p\u003e\n\u003cp\u003eOur study confirms that the microbiota in BD exhibits lower microbial richness and a distinct community composition [14, 50, 62]. Importantly, these compositional differences translate into a reduced metabolic capacity and disturbed growth potential of certain taxa, as revealed by MICOM modeling. This shows that dysbiosis in BD reflects a broader loss of functional redundancy and cooperative metabolic interactions.\u003c/p\u003e\n\u003cp\u003eUpon transplantation, donor-derived bacteria engrafted in recipient mice; however, BD-derived microbiota reconstituted a distinct community structure compared with control FMT recipients, despite achieving a similar \u0026alpha;-diversity. This dissociation between richness and composition underscores a key ecological principle: dysbiotic communities can re-establish aberrant configurations through self-reinforcing ecological dynamics [63, 64]. Notably, BD-recipient mice showed preferential enrichment of \u003cem\u003eAlloprevotella\u0026nbsp;\u003c/em\u003eand \u003cem\u003eMuribaculaceae\u0026nbsp;\u003c/em\u003eand reduced engraftment of \u003cem\u003eAkkermansia\u003c/em\u003e, a keystone taxon implicated in gut barrier integrity, SCFA production, and microbiota\u0026ndash;brain signaling [65, 66]. These findings indicate that a dysbiotic microbiome imposes constraints on ecosystem assembly that limit colonization by beneficial taxa, thereby shaping downstream metabolic outcomes. Our findings are in line with the argument that microbial transfer during FMT is not proportional, as donor taxa often do not retain their relative abundances following transplantation [67]. Instead, the recipient microbiome reflects selective engraftment dynamics that should be considered when interpreting FMT studies.\u003c/p\u003e\n\u003cp\u003eMetabolic modeling revealed that these compositional shifts profoundly altered community-level metabolic organization. As an example, in controls, metabolic interactions among \u003cem\u003eAkkermansia\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, and \u003cem\u003eMuribaculaceae\u0026nbsp;\u003c/em\u003esupported the production of amino acids such as glutamate, glutamine, alanine and aspartate potentially sustaining production of neurotransmitter precursors critical for glutamatergic and GABAergic signaling among others. In contrast, BD microbiota signified by \u003cem\u003eMuribaculaceae\u0026nbsp;\u003c/em\u003eand \u003cem\u003eAlloprevotella\u0026nbsp;\u003c/em\u003einteractions, showed reduced contributions to glutamine synthesis and a shift towards biosynthesis of glycine, serine and threonine, a pattern commonly associated with oxidative stress responses and nutrient limitation [68], [69]. This metabolic alteration is intriguing given the central role of the glutamate-glutamine-GABA cycle in mood regulation, cognition, and behavioral control [70], [71]. The loss of microbial support for this axis might be a plausible link between dysbiosis and the emergence of anxiety-like behaviors observed in BD-colonized mice. Consistent with modeling predictions, measured stool metabolites in BD-recipient mice revealed accumulation of simple sugars and valeric acid, a microbial fermentation product, alongside depletion of glutamic and aspartic acid, with enrichment in pathways related to starch, sucrose, and galactose metabolism. These findings together, suggest inefficient carbohydrate fermentation and impaired amino-acid utilization, a metabolic phenotype that aligns with reduced SCFA production and altered energy harvest reported in psychiatric disorders [72], [73], [74].\u003c/p\u003e\n\u003cp\u003eIn the colon, metabolic signatures pointed toward increased oxidative stress and altered enteroendocrine signaling, including the observed trends toward reduced glutathione and alanine levels in BD-microbiome recipient mice. Network analysis revealed robust negative correlations between glutathione and metabolites enriched in BD mice (e.g. glycine, threonine, acetaldehyde), likely because of a redox imbalance and increased oxidative stress [75]. Oxidative stress has long been implicated in BD pathophysiology [76] and, and our findings suggest that gut microbial metabolism may contribute to this process by impairing antioxidant capacity at the mucosal interface.\u003c/p\u003e\n\u003cp\u003eIntriguingly, the most pronounced effects of BD microbiota were observed in the brain metabolome, wherein eight of ten significantly altered metabolites were depleted. These included glycine, choline, methionine, glycerol, inosine, acetic acid, pyroglutamic acid, and phosphorylcholine, metabolites central to one-carbon metabolism, antioxidant defense, membrane biosynthesis, and methylation reactions. Concurrent elevation of AMP and inosinic acid further implicates disturbed purine metabolism and altered energy homeostasis, processes repeatedly associated with BD and mood disorders more broadly [74, 75].\u003c/p\u003e\n\u003cp\u003eThe coordinated depletion of choline, methionine, and glycine is particularly notable, as these metabolites serve as key methyl donors supporting DNA and histone methylation, folate biosynthesis, and mitochondrial function. Disruption of one-carbon metabolism has been linked to altered neurodevelopment, impaired synaptic plasticity, and aberrant stress responses [79]. Our findings suggest that microbiota-driven depletion of methyl-donating and antioxidant metabolites may compromise the brain\u0026rsquo;s epigenetic and metabolic adaptability, providing a mechanistic bridge between gut dysbiosis and neurobiological vulnerability in BD. Interestingly, reductions in cortical choline-containing compounds are reported in anxiety disorders and have been linked to chronically elevated arousal in anxiety disorders and an increased neurometabolic demand for choline compounds [80].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn our behavioral tests, BD-colonized mice exhibited increased anxiety-like behavior and altered exploratory patterns. Importantly, microbial-behavior correlations revealed that \u003cem\u003eAkkermansia\u0026nbsp;\u003c/em\u003eabundance was strongly positively associated with locomotion and exploratory behavior in control mice, an association absent in BD mice due to \u003cem\u003eAkkermansia\u0026nbsp;\u003c/em\u003edepletion. \u003cem\u003eAkkermansia\u0026nbsp;\u003c/em\u003eis known to produce acetate and SCFAs, reinforce barrier integrity, and support glutamatergic metabolism [55], [66], functions consistent with anxiolytic effects observed in prior studies [15], [81]. Furthermore, our findings are in line with a recent study that demonstrated anxiolytic effects of \u003cem\u003eAkkermansia\u003c/em\u003e through increased circulating choline and central glycine levels [82].\u003c/p\u003e\n\u003cp\u003eA key conceptual advance of this study is the demonstration that differences in engraftment efficiency of the donor microbiome and subsequent establishment of a cooperative network impact FMT outcome and need to be assessed also in studies that investigate gut-brain signaling. Another interesting observation was that despite pronounced alterations in stool, colon, and brain metabolites, plasma metabolites remained largely unchanged, likely reflecting rapid metabolic turnover. This highlights the value of constraint-based modeling (MICOM) in inferring metabolic capacity in addition to direct measurements.\u003c/p\u003e\n\u003cp\u003eTogether, these results support a model in which dysbiosis perpetuates itself through altered ecological interactions, impaired metabolic cooperation, and depletion of neuroactive and antioxidant metabolites, ultimately contributing to behavioral dysfunction. Therapeutically, this suggests that effective microbiota-targeted interventions may need to restore metabolic networks and ecosystem balance, rather than focusing on single taxa alone. \u003cem\u003eAkkermansia\u0026nbsp;\u003c/em\u003eemerges as a promising candidate, but our data caution that successful intervention will likely require re-establishing the cooperative microbial environment that supports its growth and function.\u003c/p\u003e\n\u003ch2\u003eLimitations\u003c/h2\u003e\n\u003cp\u003eThis study used a single donor pair in order to minimize inter-donor variability and to isolate mechanistic properties of donor microbiomes. This design, however, precludes population-level generalization. While future studies incorporating larger donor cohorts, longitudinal designs, and targeted manipulation of specific metabolic pathways will be essential to establish causality and identify BD-specific dysbiotic signatures, this study aimed to investigate the trajectory of a single defined biological input and its downstream cascade of engraftment and establishment of a cooperative microbial network, followed by its output of metabolic and behavioral effects.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData and code availability\u003c/p\u003e\n\u003cp\u003eDatasets and scripts used in this work are accessible through the GitHub repository: \u003cu\u003ehttps://github.com/marijazmf/BP_paper_analysis\u003c/u\u003e. Microbiome raw data is accessible via project ID:\u0026nbsp;PRJEB90027.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eA.F. and T.M. are grateful to the Austrian Science Fund (FWF) for excellence cluster 10.55776/COE14. For open access purposes, the authors have applied a CC BY public copyright license to any author-accepted manuscript version arising from this submission.\u003c/p\u003e\n\u003cp\u003eWe thank the Land Styria and the Medical University of Graz for financial support. This research was further funded by Stadt Graz, Austria, and a Fulbright-Austrian Marshall Plan Foundation Award to G.B.T. The research of GG was supported by the Austrian Science Fund (FWF) grants [doi.org/10.55776/COE7 and DK-MOLIN W1241]. The research of T.M. was supported by the Austrian Science Fund (FWF) for Grants DOI 10.55776/P28854, 10.55776/I3792, 10.55776/DOC130, and 10.55776/W1226, the Austrian Research Promotion Agency (FFG) grants 864690 and 870454; the Integrative Metabolism Research Center Graz; the Austrian Infrastructure Program 2016/2017; the Styrian Government (Zukunftsfonds, doc.fund program); the City of Graz; and BioTechMed-Graz (flagship project). This project was funded in part by the FFG and the European Union (EFRE) under grant 912192.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eM.D.\u003c/strong\u003e performed bioinformatics data analysis and visualization, prepared the figures, and drafted the manuscript. \u003cstrong\u003eF.T.F.\u003c/strong\u003e performed patient recruitment and characterization. \u003cstrong\u003eP.S.\u003c/strong\u003e assisted in writing the manuscript. \u003cstrong\u003eT.K.\u003c/strong\u003e provided the mouse model database and diet for metabolic modeling. \u003cstrong\u003eG.B.-T.\u003c/strong\u003e and \u003cstrong\u003eS.G.\u003c/strong\u003e performed experiments. \u003cstrong\u003eK.C.K.I.\u003c/strong\u003e assisted in data analysis. \u003cstrong\u003eS.M., J.W.-S., S.A.B., M.L.,\u003c/strong\u003e and \u003cstrong\u003eN.D.\u003c/strong\u003e assisted in patient recruitment and characterization. \u003cstrong\u003eH.H.\u003c/strong\u003e and \u003cstrong\u003eT.M.\u003c/strong\u003e performed NMR-based metabolomics analyses. \u003cstrong\u003eC.H.\u003c/strong\u003e and \u003cstrong\u003eC.M.-E.\u003c/strong\u003e supervised stool processing and fecal microbiota transplantation (FMT) methodology. \u003cstrong\u003eA.F.\u003c/strong\u003e designed and supervised the study and revised the manuscript. \u003cstrong\u003eE.Z.R.\u003c/strong\u003e supervised the clinical study and patient recruitment. \u003cstrong\u003eG.G.\u003c/strong\u003e supervised the study and assisted in writing the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eDeclaration of interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eDeclaration of generative AI and AI-assisted technologies in the writing process\u003c/p\u003e\n\u003cp\u003eArtificial intelligence (ChatGPT, OpenAI) was used solely to enhance the language and readability of the manuscript; no graphs or results were generated by the tool.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eK. R. Merikangas \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Prevalence and Correlates of Bipolar Spectrum Disorder in the World Mental Health Survey Initiative,\u0026rdquo; \u003cem\u003eArch. Gen. Psychiatry\u003c/em\u003e, vol. 68, no. 3, p. 241, Mar. 2011, doi: 10.1001/archgenpsychiatry.2011.12. \u003c/li\u003e\n\u003cli\u003eD. Vigo, G. Thornicroft, and R. 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A.\u003c/em\u003e, vol. 121, no. 1, p. e2308706120, Jan. 2024, doi: 10.1073/pnas.2308706120.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9474472/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9474472/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study performs patient-to-mouse fecal microbiota transplantation (FMT) as an experimental platform to investigate gut-brain axis alterations with potential relevance to psychiatric disorders, integrating metabolic modeling with measured metabolites and multi-layer molecular profiling. Microbial communities of a stool sample associated with bipolar disorder (BD) displayed a reduced ecological diversity and diminished metabolic potential, particularly within glutamate, aspartate, and GABA biosynthetic pathways. Upon transplantation with BD patient microbiota, recipient mice displayed a markedly altered microbiome characterized by loss of Akkermansia and expansion of Alloprevotella, alongside disruptions in amino acid and carbohydrate metabolism not evident in mice colonized by a healthy donor microbiota. Metabolic microbiome alterations in BD-recipient mice were also correlated to reduced glutathione levels in gut tissue, likely indicating increased oxidative stress, and decreased mRNA expression of key enteroendocrine hormones, including peptide YY and glucagon. Brain metabolomic profiling of BD-recipient mice revealed significant depletion of glycine, choline, and methionine levels connected to anxiety-like phenotypes in elevated plus-maze and light-dark box behavioral tests. Akkermansia abundance positively correlated with physical activity and exploratory behavior, highlighting an important role of this taxon in gut-brain signaling. Collectively, these findings identify distinct microbial, metabolic, and neurobehavioral signatures transmittable from humans to mice via FMT and demonstrate that differences in donor microbiome diversity and metabolic capacity shape engraftment dynamics in recipient mice, which contribute to differences in gut-brain signaling.","manuscriptTitle":"Donor Microbiota Metabolic Capacity Determines Engraftment Dynamics and Modulates Gut–Brain Signaling in Recipient Mice","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 05:08:37","doi":"10.21203/rs.3.rs-9474472/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-05-04T00:56:44+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2026-04-27T15:32:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-21T11:34:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-21T08:02:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Translational Psychiatry","date":"2026-04-20T15:49:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d6a75ac7-3016-435f-b0af-3af42f7196cc","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-05-04T00:56:44+00:00","index":1,"fulltext":"This content is not available."}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67104388,"name":"Health sciences/Diseases/Psychiatric disorders/Bipolar disorder"},{"id":67104389,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-05-11T05:08:38+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 05:08:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9474472","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9474472","identity":"rs-9474472","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}