Abstract

Background

   Lactobacillus reuteri DSM 17,938 exhibits antidepressant and anxiolytic
   potential. The purpose of this study is to validate the effects of L.
   reuteri DSM 17,938 and preliminarily explore its underlying
   antidepressant and anxiolytic mechanisms, thereby providing a general
   direction for researching the targets of its antidepressant and
   anxiolytic effects.

Methods

   The depressive mouse model induced by lipopolysaccharide (LPS) was
   intervened with L. reuteri DSM 17,938 (5 × 10^9 cfu/ml), and behavioral
   experiments were conducted to evaluate the therapeutic effects of the
   probiotic on depression. Moreover, the antidepressant and anxiolytic
   mechanism of probiotics was investigated through fecal metagenomics and
   fecal non-targeted metabolomics, as well as non-targeted metabolomics
   of the hippocampus and prefrontal cortex.

Results

   In the behavioral experiments, L. reuteri DSM 17,938 significantly
   reversed the phenomena of reduced total moving distance, decreased
   center zone stay time and increased peripheral zone stay time in the
   open field test of LPS-induced depressed mice, and significantly
   reduced the immobility time of mice in the forced swimming test. L.
   reuteri DSM 17,938 restored gut microbial richness and ameliorated
   intestinal metabolic pathways in a depression mouse model, with
   lipopolysaccharide biosynthesis and ATP-binding cassette transporter
   (ABC) transporter metabolic pathways being significantly enriched.
   Untargeted metabolomics of the hippocampus and prefrontal cortex
   revealed that LPS intervention primarily induced dysregulation of amino
   acid metabolism-related pathways in these brain regions. In contrast,
   L. reuteri DSM 17,938 administration restored neural homeostasis, as
   evidenced by KEGG functional enrichment analysis identifying activated
   amino acid metabolism and unsaturated fatty acid metabolism pathways.

Conclusion

   These findings collectively suggest that L. reuteri DSM 17,938 exerts
   antidepressant and anxiolytic effects by modulating gut microbiota
   composition to improve intestinal metabolism and subsequently
   rectifying amino acid and unsaturated fatty acid metabolic pathways in
   the hippocampus and prefrontal cortex. This study elucidate the
   gut-brain axis mechanisms underlying its antidepressant and anxiolytic
   effect and highlight its potential as a novel probiotic-based strategy
   for mood disorders.

Supplementary Information

   The online version contains supplementary material available at
   10.1186/s13099-025-00739-8.

   Keywords: Depression, LPS, Lactobacillus reuteri DSM 17938,
   Metagenomics, Metabolomics

Introduction

   Major Depressive Disorder (MDD), as a prevalent mental disorder, has
   caused severe social harm and imposed a substantial economic burden
   worldwide, affecting approximately 5.3% of the global population
   [[40]1–[41]3]. A 2021 study published in The Lancet revealed that the
   COVID-19 pandemic led to a staggering global increase of approximately
   53.2 million additional cases of MDD in 2020, representing a 27.6%
   surge in prevalence, which has triggered widespread societal concern
   [[42]4]. The pathogenesis of MDD involves a complex interplay of
   genetic predisposition, socioenvironmental factors, and psychological
   states, reflecting its heterogeneous and multifactorial nature [[43]5,
   [44]6]. Multiple hypotheses have been proposed to elucidate the
   pathogenesis of MDD, including but not limited to the monoaminergic
   neurotransmitter hypothesis, neuroinflammation and immune dysregulation
   theory, neuroplasticity-associated signaling pathway alterations, as
   well as structural and functional brain remodeling, collectively
   highlighting the multidimensional complexity of this disorder [[45]7].
   Despite remaining the cornerstone of MDD management (e.g., selective
   serotonin reuptake inhibitors, serotonin-norepinephrine reuptake
   inhibitors, tricyclic antidepressants), current pharmacological
   interventions demonstrate suboptimal efficacy [[46]8–[47]10]. Notably,
   20%-60% of patients exhibit treatment resistance to initial
   antidepressant monotherapy [[48]11–[49]14], while at least one-third
   experience diminishing therapeutic returns during continued treatment.
   This progressive attenuation of drug responsiveness underscores the
   substantial risk of MDD relapsing even with sustained antidepressant
   regimens, highlighting the critical need for alternative therapeutic
   strategies [[50]15]. Consequently, elucidating the pathogenesis of MDD
   and developing novel therapeutic strategies have emerged as a pivotal
   research priority, driven by the unmet clinical needs in achieving
   sustained remission.

   A growing body of research suggests that disruption of gut microbiota
   plays a pivotal role in the development of depression [[51]16, [52]17].
   Fecal microbiota transplantation (FMT) from patients with MDD has been
   shown to induce depression-like behaviors in germ-free mice,
   highlighting the causal relationship between gut dysbiosis and MDD
   pathogenesis [[53]18]. A meta-analysis incorporating 16 clinical
   studies with 1,003 participants demonstrated that compared to controls,
   MDD patients exhibited reduced abundance of Veillonellaceae,
   Prevotellaceae, and Sutterellaceae families, long with Coprococcus,
   Faecalibacterium, Ruminococcus, Bifidobacterium, and Escherichia
   genera, while showing increased abundance of Actinomycetaceae family
   and Paraprevotella genus [[54]19], further elucidating the specific
   microbial taxa and alterations closely associated with MDD
   pathogenesis. Our research team identified a key cross-species
   microbial signature in MDD by integrating gut microbiota alterations
   from both clinical patients and LPS-induced depression animal models.
   Their findings revealed that disrupted microbial
   homeostasis-characterized by enriched pro-inflammatory bacteria and
   depleted anti-inflammatory butyrate-producing taxa—serves as a
   fundamental pathophysiological feature of MDD [[55]20], suggesting gut
   microbiota’s critical involvement in disease pathogenesis through
   inflammatory modulation. Mounting evidence indicates that gut
   microbiota interacts with the central nervous system through vagus
   nerve modulation, immune system interactions, neuroendocrine pathways,
   and microbial metabolites. Gut microbial perturbations may compromise
   intestinal barrier integrity, allowing pro-inflammatory mediators and
   toxins to enter systemic circulation, thereby triggering
   neuroinflammation and neurotransmitter dysregulation that elevates MDD
   risk [[56]21, [57]22]. These findings collectively highlight the gut
   microbiome’s critical role in MDD pathophysiology, underscoring its
   potential as a therapeutic target for depressive disorders.

   Probiotics, as live microorganisms, have emerged as a potential
   therapeutic strategy for MDD through restoration of gut microbiota
   composition [[58]23, [59]24]. Clinical studies demonstrate that a
   12-week intervention with Lacticaseibacillus paracasei Strain Shirota
   significantly alleviated depressive symptoms in both MDD and bipolar
   disorder patients, with concurrent improvements in sleep quality and
   reduction of sleep disturbances [[60]25]. Systematic reviews and
   meta-analyses of randomized controlled trials (RCTs) indicate that
   probiotic formulations substantially improved depressive symptoms in
   MDD patients compared to placebo controls [[61]26], underscoring their
   clinical relevance for managing mood disorders. Our previous
   investigations revealed markedly reduced relative abundance of multiple
   Lactobacillus species, including Lactobacillus reuteri, in LPS-induced
   depression animal models [[62]20]. Moreover, Lactobacillus reuteri DSM
   17,938 has been shown to exert protective effects on intestinal barrier
   integrity, attenuate inflammatory/immune responses, and modulate
   gastrointestinal function in gut disorder models [[63]27–[64]30],
   suggesting its potential as a microbial target for probiotic-based MDD
   interventions.

   This study established an lipopolysaccharide (LPS)-induced depressive
   mouse model, with daily intragastric administration of L. reuteri DSM
   17,938 during the modeling phase to evaluate its therapeutic efficacy
   on depression-like behaviors. Through metagenomic profiling of cecal
   contents combined with untargeted metabolomics analyses of hippocampal
   and prefrontal cortex tissues, we systematically investigated gut
   microbial remodeling and host metabolic reprogramming. The integrated
   analysis of microbial-metabolite interactions and neurochemical
   alterations aims to elucidate the underlying mechanisms of L. reuteri
   DSM 17,938’s antidepressant effects, particularly focusing on its
   regulatory roles in the gut-brain axis.

Materials and methods

Animals

   This study utilized specific pathogen-free (SPF) adult male C57BL/6J
   mice (7-week-old, 18–22 g) obtained from the Experimental Animal Center
   of Chongqing Medical University. Given that sex differences in
   neuroinflammation and depressive-like behaviors have been widely
   reported, and to avoid the confounding effects of hormonal fluctuations
   in females, we exclusively utilized male mice in this study [[65]31,
   [66]32]. Animals were maintained under controlled conditions: ambient
   temperature 20–22 °C, humidity 40–55%, 12-hour light/dark cycle (lights
   on at 08:00, off at 20:00), housed 4 per cage with ad libitum access to
   food and water. Prior to experimentation, mice underwent a 7-day
   acclimatization period to minimize environmental stress. All
   experimental protocols were approved by the Ethical Committee of
   Chongqing Medical University with the approval number: CY2024-008-01,
   conducted in strict compliance with China’s Regulations on the
   Administration of Laboratory Animals and U.S. Public Health Service
   (PHS) policy (NIH Guide for the Care and Use of Laboratory Animals, 8th
   edition) and Animal Welfare Act standards (9 CFR, Part 3).

Experimental procedure

   The LPS-induced depressive mouse model was established with
   modifications based on published methodology [[67]33]. As illustrated
   in Figs. [68]1A and 7-week-old healthy male C57BL/6J mice were randomly
   assigned to four groups: control (CON, n = 9), lipopolysaccharide (LPS,
   n = 8), fluoxetine (FLU, n = 9), and probiotic (PRO, n = 9). LPS (from
   E. coli O111:B4) was procured from Sigma-Aldrich (USA), fluoxetine
   hydrochloride from MedChemExpress (MCE), and L. reuteri DSM 17,938
   lyophilized powder from BNCC (BaNa Culture Collection), with probiotic
   culturing protocols following established methods [[69]34]. Daily
   between 09:00–10:00, CON and LPS groups received intraperitoneal (i.p.)
   injections of saline (0.9% NaCl) or LPS (0.5 mg/kg in saline)
   respectively for 10 consecutive days. Body weight measured before each
   administration determined dosing volumes. Concurrently, FLU group
   received daily fluoxetine (20 mg/kg, i.p.) [[70]35, [71]36], while PRO
   group was administered viable L. reuteri DSM 17,938 (5 × 10^9 CFU/mL)
   via oral gavage, with dosage validated by previous studies
   [[72]37–[73]39]. Behavioral assessments commenced immediately
   post-intervention to evaluate depression-related phenotypes.

Fig. 1.

   [74]Fig. 1
   [75]Open in a new tab

   L. reuteri DSM 17,938 rescues depressive-like and Anxiety-like
   behaviors in LPS-mice. (A) The modeling protocol comprised three
   distinct phases: 7-day acclimatization phase, 10-day LPS administration
   phase, and terminal behavioral testing phase. During the intervention
   period (Days 1–10), mice received daily probiotic L. reuteri DSM 17,938
   via oral gavage (9:00–9:30 AM), concurrent with intraperitoneal (i.p.)
   injections of LPS (0.5 mg/kg) and/or fluoxetine (20 mg/kg) at
   synchronized timepoints, maintaining strict temporal consistency across
   treatment groups. (B-D) Total movement distance, peripheral zone
   duration, and center zone duration in the open field test (OFT). (E)
   Immobility time in the forced swim test (FST). (F) Immobility time in
   the tail suspension test (TST). (G) Sucrose preference in the sucrose
   preference test (SPT). Data are presented as means ± SEM. n = 9 mice in
   CON, FLU, PRO group, n = 8 mice in LPS group. *P < 0.05, **P < 0.01 by
   One-way ANOVA followed by non-parametric factorial Kruskal-Wallis
   sum-rank test

Behavioral testing

   Behavioral assessments commenced the following day post-intervention
   with sequentially conducted tests: Open field test (OFT), Tail
   suspension test (TST), Forced swim test (FST) and Sucrose preference
   test (SPT). To ensure physiological validity, only one behavioral test
   was performed daily to minimize stress and fatigue. Locomotor
   trajectories were tracked and quantified using the automated
   video-tracking system (EthoVision XT 15.0, Noldus Information
   Technology, Netherlands). Behavioral datasets were analyzed with IBM
   SPSS Statistics 26.0 for statistical analysis and processed for
   graphical visualization using GraphPad Prism 10.0.1, with all
   experimental procedures conducted under standardized lighting and
   noise-controlled conditions.

Open field test (OFT)

   OFT was conducted in a acrylic arena (44 × 44 × 44 cm). At trial
   initiation, each mouse was gently placed at the arena’s geometric
   center. Behavioral recording commenced immediately using an overhead
   camera for 5 min 40 s, with the initial 30 s designated as
   acclimatization period. Quantitative parameters including total
   locomotion distance, central zone dwell time (defined as 22 × 22 cm
   central quadrant), and peripheral zone occupancy were analyzed during
   the subsequent 5-min observation window. Inter-trial sanitation
   protocols involved thorough decontamination of the arena surfaces with
   75% ethanol to eliminate olfactory cues between subjects.

Tail suspension test (TST)

   Mice were secured 2 cm from the tail tip using medical-grade adhesive
   tape and suspended vertically in a plexiglass chamber with the snout
   positioned 30 cm above the testing platform. Behavioral sessions were
   video-recorded for 5 min 30 s. Quantitative analyses focused on the
   initial 5-min interval using EthoVision XT 15.0 (Noldus Information
   Technology, Netherlands): struggling duration and immobility time were
   algorithmically quantified.

Forced swim test (FST)

   A glass cylinder was filled with clean water (24 ± 2 °C) to a height of
   30 cm. Mice were gently placed into the cylinder, and their behavior
   was recorded for 5 min and 30 s. After recording, mice were carefully
   removed, dried with absorbent bedding in their home cages, and returned
   to their original housing. The cylinder was cleaned and refilled with
   fresh water between trials. Behavioral analysis using EthoVision XT
   software focused on the first 5 min, quantifying struggling duration
   (vigorous movement) and immobility time (passive floating).

Sucrose preference test (SPT)

   Mice were single-housed with 12-hour food and water deprivation, then
   provided with two bottles: 5% sucrose solution and plain water. Fluid
   consumption was measured over 12 h. Bottle positions were alternated
   every 6 h to eliminate side bias. Total sucrose and water intake were
   recorded post-test. Food remained restricted during testing. The
   sucrose preference rate was calculated according to the formula of
   water consumption/(sugar water consumption + water consumption) × 100%.

Sample collection

   Following behavioral testing, mice were anesthetized with isoflurane
   and subjected to transcardial perfusion with ice-cold
   phosphate-buffered saline (PBS). Cecal contents were aseptically
   collected for metagenomic sequencing and untargeted metabolomics.
   Hippocampal and prefrontal cortex tissues were dissected for untargeted
   metabolomic profiling. All samples were snap-frozen in liquid nitrogen
   and stored at -80 °C until further processing.

Metagenome sequencing and diversity analysis

   Metagenomic analysis was performed using the Majorbio Cloud Platform
   [[76]40, [77]41]. The workflow included: DNA Extraction: Total DNA was
   manually extracted from cecal contents using the FastPure Feces DNA
   Isolation Kit (YH-feces, Shanghai Major Yuhua). Library Preparation:
   DNA integrity was verified by 1% agarose gel electrophoresis. DNA was
   fragmented to ~ 350 bp using Covaris M220 (Genes Company, China).
   Samples with aberrant band patterns were excluded. Libraries were
   constructed using NEXTFLEX Rapid DNA-Seq (Bioo Scientific, USA):
   adapter ligation, magnetic bead purification to remove self-ligated
   adapters, Polymerase Chain Reaction (PCR) amplification for template
   enrichment, and final library recovery. Sequencing: Libraries were
   sequenced on the Illumina NovaSeq™ X Plus platform (Illumina, USA) by
   Shanghai Majorbio Biomedical Technology Co., Ltd. Bridge PCR: Library
   fragments hybridized to flow cell primers. Amplification generated DNA
   clusters, followed by linearization into single strands. Illumina
   Sequencing: Modified DNA polymerase and fluorescently labeled dNTPs
   were added. Each cycle incorporated one nucleotide, detected via laser
   scanning. Fluorescent signals and 3′-end blocking groups were
   chemically cleaved for subsequent cycles. Alpha Diversity: Assessed
   using Chao1, Shannon, and Pielou_e indices. Beta Diversity: Principal
   Coordinate Analysis (PCoA) (ANOSIM) based on Bray-Curtis distances.
   Genus-level Principal Component Analysis (PCA) with ANOSIM (999
   permutations) and abundance normalization. LEfSe: Linear discriminant
   analysis (LDA) identified differentially abundant taxa, validated by
   non-parametric factorial Kruskal-Wallis sum-rank test.

LC-MS/MS analysis and data analysis

   The LC-MS/MS analysis of sample was conducted on a Thermo UHPLC-Q
   Exactive system equipped with an ACQUITYHSS T3 column (100 mm × 2.1 mm
   i.d., 1.8 μm; Waters, USA) at Majorbio Bio-Pharm Technology Co. Ltd.
   (Shanghai, China). The mobile phases consisted of 0.1% formic acid in
   water: acetonitrile (95:5, v/v) (solvent A) and 0.1% formic acid
   inacetonitrile: isopropanol: water (47.5:47.5, v/v) (solvent B). The
   flow rate was 0.40 mL/min and the column temperature was 40℃. MS
   conditions: The ultra-performance liquid chromatography (UPLC) system
   was interfaced with a Thermo UHPLC-Q Exactive hybrid
   quadrupole-Orbitrap mass spectrometer featuring a dual-polarity
   electrospray ionization (ESI) source. Operational parameters were
   established as follows: Ionization source: 1. Temperature: 400 °C; 2.
   Sheath gas: 40 arbitrary units (arb); 3. Auxiliary gas: 10 arb; 4.
   Polarity-specific voltages: Negative ion mode: -2800 V, Positive ion
   mode: +3500 V; Collision energy: Stepped normalization at 20, 40, and
   60 V for tandem MS; Mass resolution: Full scan: 70,000 FWHM, MS/MS:
   17,500 FWHM; Data collection employed a Data-Dependent Acquisition
   (DDA) strategy across a m/z range of 70–1050.

   Raw LC/MS data preprocessing was conducted using Progenesis QI software
   (Waters Corporation, Milford, USA), generating a three-dimensional
   CSV-formatted dataset containing: (1) sample identifiers, (2)
   metabolite nomenclature, and (3) spectral response intensities. The
   initial dataset underwent rigorous curation to eliminate analytical
   artifacts, including internal standard signals, spurious peaks
   (instrumental noise, column bleeding artifacts, and derivatization
   byproducts), followed by redundancy reduction and peak alignment.
   Metabolite annotation was achieved through cross-referencing with three
   established databases: HMDB ([78]http://www.hmdb.ca/), Metlin
   ([79]https://metlin.scripps.edu/), and the Majorbio Database.
   Subsequent analyses were executed via the Majorbio Cloud Platform
   (cloud.majorbio.com). Features detected in ≥ 80% of samples within any
   experimental group were retained. Post-filtering procedures included:
   (1) Imputation of minimal quantifiable values for metabolites below
   detection thresholds; (2) Sum normalization of metabolic features; (3)
   Response intensity standardization using sum normalization to mitigate
   technical variability from sample preparation and instrumental drift.
   Quality control (QC) measures involved discarding variables
   demonstrating > 30% relative standard deviation (RSD) in QC samples.
   The final preprocessed matrix was log10-transformed prior to downstream
   analytical procedures.

GC-MS analysis and data analysis

   Chromatographic separation and mass spectrometric detection were
   conducted on an Agilent 8890GC-5977BMSD system employing a DB-5
   capillary column (30 m × 0.25 mm, 0.25 μm film thickness). Helium
   served as the mobile phase at a constant flow rate of 0.9 mL/min.
   Thermal regulation parameters were configured as: Critical zones:
   Injection port: 250 °C; Transfer line: 280 °C; Ionization source:
   250 °C. Gradient program: (1) Initial hold at 60 °C for 1 min; (2) Ramp
   to 280 °C (8 °C/min); (3) Secondary ramp to 300 °C (20 °C/min); (4)
   Final isothermal phase: 6 min at 300 °C. A 5.9-min solvent delay was
   implemented to minimize interference. Raw spectral data were analyzed
   using MS-DIAL 4.8 software, with metabolite identification achieved
   through spectral matching against three reference repositories: NIST
   2017 Mass Spectral Library, HMDB ([80]https://hmdb.ca/), PubChem
   ([81]https://pubchem.ncbi.nlm.nih.gov/). Compounds demonstrating RSD
   values < 30% in QC samples were retained for subsequent analysis.
   Orthogonal partial least squares-discriminant analysis (OPLS-DA) was
   conducted using MetaboAnalyst 6.0 to discriminate intergroup metabolic
   profiles. Significantly altered metabolites were subjected to KEGG
   functional enrichment analysis, and the screened functional pathways
   were selected for downstream biological interpretation.

Statistical analysis

   Data were statistically analyzed using IBM SPSS Statistics 25.
   Normality was assessed via the Shapiro-Wilk test. Normally distributed
   data were analyzed by one-way ANOVA, while non-normally distributed
   data were subjected to the Kruskal-Wallis rank-sum test. Multiple
   comparisons were performed using LSD post hoc tests. Results are
   expressed as mean ± standard deviation (Mean ± SD). Statistical
   significance was defined at p < 0.05. Graphical representations were
   generated with GraphPad Prism 8. KEGG pathway enrichment significance
   was assessed using the Wilcoxon rank-sum testc test, followed by FDR
   correction (q-value < 0.05) or Bonferroni correction for multiple
   testing. Pathways with q-value < 0.05 p_adjust < 0.05 were considered
   statistically significantly enriched.

Results

L. reuteri DSM 17,938 alleviates anxiety-like and depression-like behaviors
in the LPS-induced depressive mouse model

   In the OFT, LPS intervention significantly reduced the total movement
   distance (Fig. [82]1B, p < 0.01) and center zone duration (Fig. [83]1C,
   p < 0.05) of mice in the open field, while increasing the peripheral
   zone duration (Fig. [84]1D, p < 0.05). Probiotic intervention, however,
   increased the total movement distance and center zone duration of mice
   in the open field and decreased their peripheral zone duration. These
   OFT results suggest that L. reuteri DSM 17,938 significantly
   ameliorates anxiety-like behaviors in the LPS-induced depressive mouse
   model. In the FST, LPS prolonged the immobility time of mice
   (Fig. [85]1E, p < 0.05), indicating “despair-like” depression-like
   behaviors, whereas probiotic intervention reversed these
   depression-like behaviors in the FST, demonstrating the therapeutic
   potential of L. reuteri DSM 17,938 against LPS-induced depression-like
   behaviors. In the SPT, LPS decreased the sucrose preference of mice
   (Fig. [86]1F, p < 0.05), suggesting “anhedonia-like” behaviors induced
   by LPS intervention. Although both fluoxetine and probiotic
   intervention groups showed an upward trend in sucrose preference, the
   differences were not statistically significant, indicating that neither
   fluoxetine nor the probiotic improved the “anhedonia-like” phenotype.
   In the TST, no significant differences in immobility time were observed
   among the groups (Fig. [87]1G, p > 0.05).

Structural analysis of gut microbiota via metagenomics

Gut microbiota diversity and composition analysis

   To clarify the impact of probiotics on the α-diversity of gut
   microbiota in the LPS-induced depression mouse model, the richness and
   diversity of microbial composition were evaluated using the Chao and
   Shannon indices. Compared with the CON group, the Chao index of the LPS
   group was significantly reduced, while those of the FLU and PRO groups
   were markedly increased (Fig. [88]2A, p < 0.01). However, no
   significant differences were observed in the Shannon and Pielou_e
   indices among groups (Fig. [89]2B and C, P > 0.05). These α-diversity
   results suggest that fluoxetine and L. reuteri DSM 17,938 reversed the
   LPS-induced decline in gut microbial richness, though no significant
   alterations in microbial diversity or evenness were detected. For
   β-diversity, PCA revealed significant separation between the CON/PRO
   groups and the LPS group (ANOSIM: R = 0.362, P = 0.001; Fig. [90]2D).
   PCoA based on Bray-Curtis distances further demonstrated distinct
   clustering of the PRO group compared to other groups (ANOSIM:
   R = 0.444, P = 0.001; Fig. [91]2E), indicating that L. reuteri DSM
   17,938 intervention substantially altered gut microbial structure.

Fig. 2.

   [92]Fig. 2
   [93]Open in a new tab

   L. reuteri DSM 17,938 significantly altered gut microbiota composition.
   (A–C) Intergroup comparison of α-diversity indices. (D) Principal
   Coordinate Analysis (PCoA). (E) Principal Component Analysis (PCA).
   (F–H) LEfSe-based Linear Discriminant Analysis (LDA score > 3,
   P < 0.05) across groups. *P < 0.05, **P < 0.01, ***P < 0.005,
   ****P < 0.001 by One-way ANOVA followed by Scheffe test as post-hoc
   test, with FDR correction for multiple testing

   LEfSe analysis with a LDA threshold > 3 (P < 0.05) was applied to
   identify taxa differentially abundant from phylum to species levels. As
   shown in Figs. [94]2F and 37 differentially enriched taxa were
   identified between the CON and LPS groups (Supplementary Table [95]1).
   The CON group exhibited enrichment of 18 taxa, primarily from
   Firmicutes (Clostridiaceae, Clostridium, Clostridium sp. CAG_557),
   Bacteroidetes (Bacteroides acidifaciens, Lepagella, Muribaculum,
   Lepagella muris, Muribaculum intestinale, Helicobacter sp. UBA3407),
   and Spirochaetes (Spirochaetia, Brachyspirales, Brachyspiraceae,
   Brachyspira). The LPS group showed enrichment of 19 taxa, predominantly
   Firmicutes (Hungatella, Enterocloster, Thomasclavelia, Enterocloster
   aldenensis, Clostridium innocuum, Erysipelotrichia,
   Erysipelotrichales), Proteobacteria (Enterobacteriaceae, Escherichia,
   Klebsiella, Escherichia coli, Klebsiella oxytoca), and Desulfobacterota
   (Desulfovibrionia, Desulfovibrionales, Desulfovibrionaceae,
   Desulfovibrio). In contrast, the FLU group displayed minimal
   enrichment, with only Phocaeicola vulgatus at the species level
   (Fig. [96]2G; Supplementary Table [97]2). Notably, 97 differentially
   abundant taxa were identified between the LPS and PRO groups
   (Supplementary Table [98]3). The LPS group harbored 62 enriched taxa,
   mainly Firmicutes (10 taxa), Bacteroidetes (39 taxa), Proteobacteria (6
   taxa), and Verrucomicrobia (7 taxa). The PRO group exhibited 35
   enriched taxa, dominated by Actinobacteria (19 taxa), Firmicutes (10
   taxa), and Desulfobacterota (6 taxa) (Fig. [99]2H). These findings
   indicate that LPS intervention disrupted gut microbial composition,
   while fluoxetine exerted limited effects on microbiota restructuring.
   In contrast, L. reuteri DSM 17,938 significantly reshaped microbial
   community structure, as evidenced by the substantial taxonomic
   differences between the PRO and LPS groups.

KEGG functional enrichment analysis of differentially abundant species

   Gene sets were constructed using differentially abundant species
   between groups, followed by KEGG functional enrichment analysis to
   elucidate the biological significance of each gene set and identify
   functional pathways associated with global microbial functions.
   Compared to the CON group, the LPS group exhibited alterations in 124
   functional pathways (Supplementary Table [100]4). The top-ranked
   enriched pathways in the LPS group included ATP-binding cassette
   transporter (ABC) transporters, phosphotransferase system (PTS),
   microbial metabolism in diverse environments, and two-component system
   metabolism (Fig. [101]3A). Conversely, pathways enriched in the CON
   group relative to the LPS group comprised acarbose and validamycin
   biosynthesis, biosynthesis of cofactors, teichoic acid biosynthesis,
   mismatch repair, oxidative phosphorylation and so on, suggesting
   LPS-induced perturbations in these metabolic functions. Between the FLU
   and LPS groups, 31 differential metabolic pathways were identified
   (Supplementary Table [102]5). The FLU group showed enrichment in LPS
   biosynthesis, lysosome, glycosaminoglycan degradation, citrate cycle
   (TCA cycle), ubiquinone and other terpenoid-quinone biosynthesis, while
   the LPS group exhibited enrichment in terpenoid backbone biosynthesis,
   valine/leucine/isoleucine biosynthesis, ribosome, and aminoacyl-tRNA
   biosynthesis (Fig. [103]3B). A total of 47 divergent pathways were
   observed between the LPS and PRO groups (Supplementary Table [104]6).
   Key pathways enriched in the LPS group included amino sugar and
   nucleotide sugar metabolism, O-antigen nucleotide sugar biosynthesis,
   biofilm formation - pseudomonas aeruginosa, other glycan degradation,
   glycosaminoglycan degradation, pentose and glucuronate
   interconversions, biosynthesis of nucleotide sugars, lysosome, and
   lipopolysaccharide biosynthesis. In contrast, the PRO group displayed
   enrichment in ribosome, Staphylococcus aureus infection,
   photosynthesis, sulfur metabolism, aminoacyl-tRNA biosynthesis,
   peptidoglycan biosynthesis, sulfur relay system, PTS, diabetic
   cardiomyopathy, teichoic acid biosynthesis, and purine metabolism
   (Fig. [105]3C).

Fig. 3.

   [106]Fig. 3
   [107]Open in a new tab

   KEGG Functional Enrichment Analysis of Intergroup Differentially
   Abundant Taxa. (A) CON vs. LPS groups; (B) FLU vs. LPS groups; (C) PRO
   vs. LPS groups. The Wilcoxon rank-sum test was applied for intergroup
   comparisons, with FDR correction for multiple testing

Untargeted metabolomics (LC-MS/MS) of cecal content

OPLS-DA analysis of differential metabolites

   To effectively distinguish intergroup differential metabolites,
   orthogonal partial least squares-discriminant analysis (OPLS-DA) was
   applied for further screening. Supervised OPLS-DA efficiently filters
   out classification-unrelated factors, thereby enhancing the resolution
   and validity of the analytical model. OPLS-DA results demonstrated
   significant metabolic differences among groups (Fig. [108]4A–C).

Fig. 4.

   [109]Fig. 4
   [110]Open in a new tab

   OPLS-DA score plot and permutation test histogram of gut differential
   metabolites. (A) CON vs. LPS groups; (B) FLU vs. LPS groups; (C) PRO
   vs. LPS groups. All differences were tested for significance by
   two-tailed Welch’s test (Unpaired)

KEGG pathway enrichment analysis of differential metabolites

   Following OPLS-DA filtering to remove non-relevant variations,
   screening criteria for significant differential metabolites were
   defined as P-value < 0.05, VIP_pred_OPLS_DA > 1, and Fold Change = 1.
   Comparative analysis revealed 297 differential metabolites between the
   LPS and CON groups (Fig. [111]5A), with 128 upregulated and 169
   downregulated metabolites post-LPS intervention. KEGG pathway
   enrichment analysis of these metabolites identified 26 significantly
   enriched pathways (Supplementary Table [112]7). Top-ranked pathways in
   the bubble plot (Fig. [113]5B) included: choline metabolism in cancer
   (p = 0.00003719), glycerophospholipid metabolism (p = 0.0003958),
   arginine and proline metabolism (p = 0.0008781), fat digestion and
   absorption (p = 0.002901), retrograde endocannabinoid signaling
   (p = 0.00621), and thermogenesis (p = 0.009043). Notably, there are 11
   pathways overlapped with those enriched by differential species-derived
   gene sets (Supplementary Table [114]4): glycerophospholipid metabolism,
   thermogenesis, biosynthesis of siderophore group nonribosomal peptides,
   autophagy-yeast, bile secretion, Kaposi sarcoma-associated herpesvirus
   infection, autophagy-animal, axon regeneration, lipopolysaccharide
   biosynthesis, Fc-γ R-mediated phagocytosis, and choline metabolism in
   cancer, suggesting LPS-induced gut microbiota alterations may drive
   these metabolic shifts, potentially contributing to MDD pathogenesis.
   The FLU vs. LPS comparison identified 175 differential metabolites (49
   upregulated, 126 downregulated; Fig. [115]5C). KEGG enrichment revealed
   58 pathways (Supplementary Table [116]8), with the top 30 ranked by
   p-value including human cytomegalovirus infection (p = 0.0004668),
   African trypanosomiasis (p = 0.0008651), serotonergic synapse
   (p = 0.001676), C-type lectin receptor signaling pathway
   (p = 0.001681), choline metabolism in cancer (p = 0.001681), oxytocin
   signaling pathway (p = 0.00201), steroid hormone biosynthesis
   (p = 0.002279), amoebiasis (p = 0.002367), phenylalanine metabolism
   (p = 0.002617), and regulation of lipolysis in adipocytes
   (p = 0.002752) (Fig. [117]5D). No overlap was observed with
   FLU-associated species-derived pathways (Supplementary Table [118]5),
   indicating minimal microbiota-mediated metabolic remodeling in the FLU
   group. Between PRO and LPS groups, 156 differential metabolites were
   detected (47 upregulated, 109 downregulated; Fig. [119]5E). KEGG
   analysis identified 26 enriched pathways (Supplementary Table [120]9),
   with top pathways including carbon fixation in photosynthetic organisms
   (p = 0.000216), pyrimidine metabolism (p = 0.00439), D-amino acid
   metabolism (p = 0.005423), arginine and proline metabolism
   (p = 0.005423), biosynthesis of plant secondary metabolites
   (p = 0.0061), caprolactam degradation (p = 0.007989), cutin/suberin/wax
   biosynthesis (p = 0.0086), cell cycle-yeast (p = 0.01044), β-alanine
   metabolism (p = 0.01196), and renal cell carcinoma (p = 0.01562)
   (Fig. [121]5F). Two pathways overlapped with species-derived functional
   enrichments (Supplementary Table [122]6): lipopolysaccharide
   biosynthesis (p = 0.03211) and ABC transporters (p = 0.03475),
   suggesting L. reuteri DSM 17,938 alleviates MDD via
   microbiota-modulated metabolic reprogramming, with LPS biosynthesis and
   ABC transporters as key bridging mechanisms.

Fig. 5.

   [123]Fig. 5
   [124]Open in a new tab

   Volcano Plots of Gut Differential Metabolites and KEGG Pathway
   Enrichment Bubble Plots. (A–B) CON vs. LPS groups; (C–D) FLU vs. LPS
   groups; (E–F) PRO vs. LPS groups. Bonferroni correction for multiple
   comparisons

Untargeted metabolomics analysis of the hippocampus by GC-MS

   The screening criteria for differential metabolites were set as
   follows: VIP > 1 and fold change (FC) set at 1.5-fold. OPLS-DA revealed
   significant separation between groups (Fig. [125]6A, B, C). Based on
   these criteria, 15 differential metabolites were identified between the
   CON and LPS groups, primarily including galactonic acid, serine,
   isoleucine, valine, etc. (Supplementary Table [126]10). KEGG functional
   enrichment analysis identified four significantly altered metabolic
   pathways: Phosphonate and phosphonate metabolism (p = 0.0155), valine,
   leucine and isoleucine biosynthesis (p = 0.0207), D-amino acid
   metabolism (p = 0.0385), and butanoate metabolism (p = 0.0385)
   (Fig. [127]6D) (Supplementary Table [128]11). These findings suggest
   that LPS may induce depression-like and anxiety-like behaviors in
   experimental mice by altering phosphate/phosphonate metabolism,
   butanoate metabolism, and other amino acid metabolic pathways. 35
   differential metabolites were identified between the FLU and LPS
   groups, mainly comprising serine, dehydroascorbic acid, palmitic acid,
   docosahexaenoic acid, L-threonine, etc. (Supplementary Table [129]12).
   KEGG pathway enrichment revealed significant involvement in three
   metabolic pathways: valine, leucine and isoleucine biosynthesis
   (p = 0.000652), glycine, serine and threonine metabolism (p = 0.0115),
   and Biosynthesis of unsaturated fatty acids (p = 0.0136) (Fig. [130]6E)
   (Supplementary Table [131]13). 40 differential metabolites were
   screened between the PRO and LPS groups (Supplementary Table [132]14).
   KEGG analysis demonstrated significant enrichment in five metabolic
   pathways: glycine, serine and threonine metabolism (p = 0.000974),
   valine, leucine and isoleucine biosynthesis (p = 0.00104), glycerolipid
   metabolism (p = 0.00435), pentose phosphate pathway (p = 0.00894), and
   glyoxylate and dicarboxylate metabolism (p = 0.016) (Fig. [133]6F)
   (Supplementary Table [134]15). This indicates that L. reuteri DSM
   17,938 ameliorates depression-like and anxiety-like behaviors by
   modulating these hippocampal metabolic pathways. Notably, the valine,
   leucine and isoleucine biosynthesis pathway was consistently enriched
   across all comparison groups, suggesting its particularly crucial role
   in these metabolic alterations.

Fig. 6.

   [135]Fig. 6
   [136]Open in a new tab

   Hippocampal differential metabolites OPLS-DA and KEGG functional
   pathway enrichment analysis. (A, D) CON vs. LPS groups; (B, E) FLU vs.
   LPS groups; (C, F) PRO vs. LPS groups. FDR correction for multiple
   comparisons

Prefrontal untargeted metabolomics (GC-MS) analysis

   Untargeted metabolomics analysis of prefrontal cortex tissues in
   experimental mice was performed with differential metabolite screening
   criteria set as VIP > 1 and FC ≥ 1.5. OPLS-DA revealed significant
   intergroup separations (Fig. [137]7A, B, C). A total of 73 differential
   metabolites were identified between CON and LPS groups (Supplementary
   Table [138]16). KEGG pathway enrichment analysis of these metabolites
   highlighted nine significantly enriched pathways, including the most
   prominent: valine, leucine and isoleucine biosynthesis (p = 0.000136),
   glycine, serine and threonine metabolism (p = 0.000978), phenylalanine,
   tyrosine and tryptophan biosynthesis (p = 0.00115), glycerolipid
   metabolism (p = 0.00126), and phenylalanine metabolism (p = 0.00519)
   (Fig. [139]7B) (Supplementary Table [140]17). These findings suggest
   LPS may induce MDD-related behavioral changes in experimental animals
   through prefrontal metabolic pathway alterations. OPLS-DA demonstrated
   clear separation between FLU and LPS groups (Fig. [141]7D), with 46
   differential metabolites identified (Supplementary Table [142]18). KEGG
   enrichment analysis revealed six significantly enriched pathways:
   lipoic acid metabolism (p = 0.0131), glyoxylate and dicarboxylate
   metabolism (p = 0.016), glycine, serine and threonine metabolism
   (p = 0.018), unsaturated fatty acid biosynthesis (p = 0.0213), primary
   bile acid biosynthesis (p = 0.0338), and fatty acid biosynthesis
   (p = 0.0351) (Fig. [143]7E) (Supplementary Table [144]19). Fifteen
   differential metabolites were screened between LPS and PRO groups
   (Supplementary Table [145]20). KEGG analysis identified five
   significantly enriched pathways: alanine, aspartate and glutamate
   metabolism (p = 0.00187), unsaturated fatty acid biosynthesis
   (p = 0.0031), arginine biosynthesis (p = 0.0359), nicotinate and
   nicotinamide metabolism (p = 0.0385), and histidine metabolism
   (p = 0.041) (Fig. [146]7F) (Supplementary Table [147]21). These results
   indicate L. reuteri DSM 17,938 may ameliorate depressive-like and
   anxiety-like behaviors by modulating these prefrontal metabolic
   pathways.

Fig. 7.

   [148]Fig. 7
   [149]Open in a new tab

   Prefrontal Differential Metabolites OPLS-DA and KEGG Functional Pathway
   Enrichment Analysis. (A, D) CON vs. LPS groups; (B, E) FLU vs. LPS
   groups; (C, F) PRO vs. LPS groups. FDR correction for multiple
   comparisons

Discussion and conclusions

   MDD remains a critical public health challenge worldwide. Research on
   the pathogenesis and therapeutic approaches for MDD has progressively
   advanced in recent years [[150]42–[151]45]. The bidirectional
   relationship between gut microbiota and MDD pathogenesis has been
   explored and increasingly validated, with multiple studies confirming
   the reliability and efficacy of MDD treatment through modulation of gut
   microbiota [[152]46, [153]47]. Our preliminary research identified
   reduced abundance of various Lactobacillus species, including L.
   reuteri, in the gut of depressive mouse models. Accumulating evidence
   suggests that specific strains of L. reuteri exhibit promising
   therapeutic effects against MDD, indicating that particular strains of
   L. reuteri may serve as key targets for probiotic interventions via the
   microbiota-gut-brain axis [[154]20, [155]48–[156]50]. In this study, L.
   reuteri DSM 17,938 significantly alleviated LPS-induced depressive-like
   and anxiety-like behaviors. Metagenomic analysis revealed that L.
   reuteri DSM 17,938 restored gut microbial richness in depressive mouse
   models, with the lipopolysaccharide biosynthesis and ABC transporter
   pathways identified as potential targets through which L. reuteri DSM
   17,938 improves host metabolism by modulating intestinal metabolic
   functions. Additionally, key amino acid metabolism pathways and
   unsaturated fatty acid metabolism pathways were implicated in the
   antidepressant and anxiolytic effects of L. reuteri DSM 17,938.
   Notably, the pentose phosphate pathway emerged as a critical metabolic
   route through which L. reuteri DSM 17,938 ameliorates MDD via the
   gut-brain axis.

   Disruption of gut microbiota is closely associated with the
   pathogenesis of MDD, and probiotics exert antidepressant and anxiolytic
   effects by ameliorating gut microbiota dysbiosis [[157]21, [158]25,
   [159]51]. Our results demonstrate that intragastric administration of
   L. reuteri DSM 17,938 reversed the LPS-induced reduction in gut
   microbial richness and induced significant alterations in the gut
   microbial composition of LPS-treated mice, though it did not restore
   species composition to the pre-LPS intervention state. Furthermore,
   post-probiotic intervention gut microbial species predominantly
   clustered within the phyla Firmicutes, Bacteroidetes, Pseudomonas, and
   Verrucomicrobia. Some studies have reported decreased Bacteroidetes
   abundance and increased Actinobacteria abundance in the gut of MDD
   patients, with the latter positively correlating with depressive
   symptoms [[160]52–[161]54], while conflicting findings have also been
   documented [[162]55, [163]56]. Additionally, research indicates
   significantly reduced Firmicutes abundance in the gut microbiota of
   anxiety disorder patients [[164]57, [165]58]. As mentioned above,
   studies investigating gut microbiota dysregulation in MDD patients
   remain inconsistent, and the bidirectional relationship between gut
   microbiota dysbiosis and MDD pathogenesis requires further
   investigation.

   LPS, a major component of the cell wall of Gram-negative bacteria, acts
   as a potent inducer of immune activation, participates in peripheral
   inflammatory responses, and influences cognitive function in the brain
   [[166]59–[167]62]. Neuroinflammation plays a pivotal role in the
   pathogenesis of clinical depression [[168]63–[169]65]. Patients with
   depression often exhibit elevated levels of inflammatory cytokines, and
   persistent inflammatory exposure increases the risk of MDD in
   susceptible populations. Conversely, inhibition of pro-inflammatory
   cytokines and their signaling pathways correlates with symptom
   improvement in MDD [[170]66–[171]68]. Genetic inheritance is a
   significant factor in MDD pathogenesis [[172]3]. Studies have
   identified the ABC gene family as closely associated with MDD and other
   psychiatric disorders, with extensive involvement in the genetic
   effects of MDD [[173]69–[174]71]. ABC transporters encoded by these
   genes participate in substance transport across the blood-brain barrier
   and mitochondria, potentially influencing blood-brain barrier
   permeability [[175]72–[176]75]. During MDD progression, stress-induced
   blood-brain barrier disruption allows peripheral immune components to
   infiltrate the central nervous system, disrupting brain homeostasis and
   contributing to MDD development [[177]76–[178]79]. Additionally,
   mitochondrial ABC transporters are primarily involved in reactive
   oxygen species (ROS) metabolism, which is closely linked to MDD
   pathogenesis [[179]80, [180]81]. Preclinical studies further validate
   the efficacy of ROS-targeted therapies in treating MDD [[181]82]. In
   summary, while the precise mechanisms remain unclear, a recent study
   associating ABC family gene variants in MDD patients and healthy
   controls with clinical symptoms and cognitive function revealed that
   ABC family genes influence MDD severity through cognitive pathways
   [[182]83, [183]84]. Here, by analyzing metagenomic enrichment of
   metabolic functions in gut differential species after L. reuteri DSM
   17,938 intervention in LPS-treated mice and comparing these results
   with functional enrichment of gut differential metabolites, we
   identified the lipopolysaccharide biosynthesis and ABC transporter
   pathways as enriched in both analyses. This suggests that L. reuteri
   DSM 17,938 may regulate intestinal microbial metabolic pathways by
   altering gut microbiota composition. Previous studies have shown that
   probiotic treatments modulate gut microbial metabolic pathways and host
   metabolic function [[184]85], aligning with our findings. We
   hypothesize that L. reuteri DSM 17,938 ameliorates peripheral
   inflammation in LPS-treated mice by regulating gut lipopolysaccharide
   biosynthesis. Furthermore, L. reuteri DSM 17,938 may protect
   blood-brain barrier integrity by modulating ABC transporter activity,
   preventing peripheral inflammatory factors from entering the brain,
   improving oxidative stress, and ultimately reversing depressive-like
   and anxiety-like behaviors in LPS-treated mice.

   To investigate the effects of L. reuteri DSM 17,938 on host metabolism,
   we performed untargeted metabolomics analyses on the hippocampus and
   prefrontal cortex of mice. The results revealed that LPS intervention
   primarily induced dysregulation of amino acid metabolism-related
   pathways in the brain. For instance, in the hippocampus of the LPS
   group, serine and isoleucine levels decreased, while valine levels
   increased. In the prefrontal cortex, lysine and phenylalanine levels
   were elevated, consistent with trends observed in previous studies of
   depressive animal models [[185]86, [186]87]. These findings suggest
   that these differential metabolites may serve as critical biomarkers
   for depression, and their alterations could lead to dysregulation of
   related metabolic pathways, contributing to MDD pathogenesis.
   Additionally, in the PRO group, amino acid- and vitamin-related
   metabolic pathways—such as alanine, aspartate, and glutamate
   metabolism; arginine biosynthesis; nicotinate and nicotinamide
   metabolism; and histidine metabolism—were enriched in the prefrontal
   cortex. Valine, leucine, and isoleucine, classified as branched-chain
   amino acids (BCAAs), are nutritionally essential amino acids with
   protein anabolic properties. They play vital physiological roles in
   regulating metabolism and signaling, protein and neurotransmitter
   synthesis, and improving disease prognosis [[187]88–[188]90]. Glycine,
   serine, and threonine act as energy sources and nutrients, exerting
   antioxidant and anti-aging effects through modulation of relevant
   metabolic axes [[189]91–[190]93]. In the brain, polyunsaturated fatty
   acids regulate the structure and function of neurons, glial cells, and
   endothelial cells. Studies demonstrate their critical roles in neuronal
   survival, neurogenesis, synaptic function, and modulation of
   neuroinflammation [[191]94]. In summary, our results indicate that L.
   reuteri DSM 17,938 significantly improves specific amino acid
   metabolism pathways and unsaturated fatty acid metabolism, suggesting
   these pathways may harbor key targets for antidepressant and anxiolytic
   effects. The probiotic L. reuteri DSM 17,938 likely exerts its
   antidepressant and anxiolytic activity by modulating these amino acid
   metabolism pathways in the hippocampus and prefrontal cortex, as well
   as unsaturated fatty acid biosynthesis pathways. Furthermore,
   dysregulated glucose metabolism has been implicated in depression, and
   the pentose phosphate pathway—which generates nucleotides and
   NADPH—plays a crucial role in maintaining normal neural cell function
   [[192]95]. Here, the pentose phosphate pathway was enriched in both the
   gut and hippocampus of L. reuteri DSM 17,938-treated mice. Combined
   with our findings, this suggests that L. reuteri DSM 17,938 may improve
   cerebral pentose phosphate metabolism by modulating its intestinal
   counterpart, contributing to its antidepressant and anxiolytic effects.

   However, this study has several limitations. (1) We only explored the
   therapeutic effects of L. reuteri DSM 17,938 on the LPS-induced
   depressive mouse model. Whether L. reuteri DSM 17,938 exhibits
   significant antidepressant and anxiolytic effects in other depression
   models, such as chronic unpredictable mild stress (CUMS) or chronic
   social defeat stress (CSDS), remains unknown. Further research is
   required to determine if L. reuteri DSM 17,938 has broad-spectrum
   antidepressant and anxiolytic properties. (2) While this study
   preliminarily validated the anti-MDD effects of L. reuteri DSM 17,938
   and delineated its potential mechanisms—specifically targeting the
   lipopolysaccharide biosynthesis, ABC transporter pathways, and select
   amino acid and unsaturated fatty acid metabolism pathways—the precise
   mechanisms by which L. reuteri DSM 17,938 regulates gut LPS
   biosynthesis to influence peripheral inflammation, as well as the
   specific effects of ABC transporter pathways on blood-brain barrier and
   mitochondrial function post-probiotic intervention, warrant further
   investigation. Furthermore, although we identified LPS biosynthesis and
   ABC transporters as key gut pathways potentially linked to brain
   metabolic changes (e.g., amino acid and fatty acid pathways), we did
   not measure intermediary signaling factors such as plasma cytokines,
   vagus nerve activity, or BBB integrity, which are crucial for
   confirming the direct signaling mechanisms connecting gut alterations
   to brain metabolism. To address these gaps, future studies should
   supplementally examine peripheral inflammation, blood-brain barrier
   integrity, mitochondrial function, and oxidative stress following
   probiotic intervention.

   In this study, we confirmed that L. reuteri DSM 17,938 exerts
   antidepressant and anxiolytic effects. L. reuteri DSM 17,938 restored
   gut microbial richness in depressive mouse models, and its
   intervention-induced alterations in gut microbial composition modulated
   the lipopolysaccharide biosynthesis and ABC transporter pathways. These
   findings suggest that L. reuteri DSM 17,938 may alleviate MDD by
   improving peripheral inflammation, blood-brain barrier function, and
   mitochondrial oxidative stress. Additionally, key amino acid metabolism
   pathways and unsaturated fatty acid metabolism pathways in the brain
   represent critical targets for L. reuteri DSM 17,938’s anti-MDD
   effects. The pentose phosphate pathway is further implicated as a key
   metabolic route through which L. reuteri DSM 17,938 ameliorates
   depression via the gut-brain axis.

Supplementary Information

   Below is the link to the electronic supplementary material.
   [193]Supplementary Material 1^ (157.4KB, docx)

Acknowledgements