Lactobacillus reuteri DSM 17,938 ameliorates LPS-induced depression-like and anxiety-like behaviors by modulating gut microbiota and brain metabolic function
- Open Access
- 01.12.2025
- Research
Erschienen in:
Abstract
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 [1‐3]. 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 [4]. The pathogenesis of MDD involves a complex interplay of genetic predisposition, socioenvironmental factors, and psychological states, reflecting its heterogeneous and multifactorial nature [5, 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 [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 [8‐10]. Notably, 20%-60% of patients exhibit treatment resistance to initial antidepressant monotherapy [11‐14], 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 [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 [16, 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 [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 [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 [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 [21, 22]. These findings collectively highlight the gut microbiome’s critical role in MDD pathophysiology, underscoring its potential as a therapeutic target for depressive disorders.
Anzeige
Probiotics, as live microorganisms, have emerged as a potential therapeutic strategy for MDD through restoration of gut microbiota composition [23, 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 [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 [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 [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 [27‐30], 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 [31, 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 [33]. As illustrated in Figs. 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 [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.) [35, 36], while PRO group was administered viable L. reuteri DSM 17,938 (5 × 109 CFU/mL) via oral gavage, with dosage validated by previous studies [37‐39]. Behavioral assessments commenced immediately post-intervention to evaluate depression-related phenotypes.
Anzeige
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 [40, 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.
Anzeige
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 (http://www.hmdb.ca/), Metlin (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 (https://hmdb.ca/), PubChem (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. 1B, p < 0.01) and center zone duration (Fig. 1C, p < 0.05) of mice in the open field, while increasing the peripheral zone duration (Fig. 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. 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. 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. 1G, p > 0.05).
Anzeige
Fig. 1
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
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. 2A, p < 0.01). However, no significant differences were observed in the Shannon and Pielou_e indices among groups (Fig. 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. 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. 2E), indicating that L. reuteri DSM 17,938 intervention substantially altered gut microbial structure.
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. 2F and 37 differentially enriched taxa were identified between the CON and LPS groups (Supplementary Table 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. 2G; Supplementary Table 2). Notably, 97 differentially abundant taxa were identified between the LPS and PRO groups (Supplementary Table 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. 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.
Fig. 2
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
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 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. 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 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. 3B). A total of 47 divergent pathways were observed between the LPS and PRO groups (Supplementary Table 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. 3C).
Fig. 3
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. 4A–C).
Fig. 4
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. 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 7). Top-ranked pathways in the bubble plot (Fig. 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 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. 5C). KEGG enrichment revealed 58 pathways (Supplementary Table 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. 5D). No overlap was observed with FLU-associated species-derived pathways (Supplementary Table 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. 5E). KEGG analysis identified 26 enriched pathways (Supplementary Table 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. 5F). Two pathways overlapped with species-derived functional enrichments (Supplementary Table 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
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. 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 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. 6D) (Supplementary Table 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 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. 6E) (Supplementary Table 13). 40 differential metabolites were screened between the PRO and LPS groups (Supplementary Table 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. 6F) (Supplementary Table 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
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. 7A, B, C). A total of 73 differential metabolites were identified between CON and LPS groups (Supplementary Table 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. 7B) (Supplementary Table 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. 7D), with 46 differential metabolites identified (Supplementary Table 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. 7E) (Supplementary Table 19). Fifteen differential metabolites were screened between LPS and PRO groups (Supplementary Table 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. 7F) (Supplementary Table 21). These results indicate L. reuteri DSM 17,938 may ameliorate depressive-like and anxiety-like behaviors by modulating these prefrontal metabolic pathways.
Anzeige
Fig. 7
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 [42‐45]. 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 [46, 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 [20, 48‐50]. 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 [21, 25, 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 [52‐54], while conflicting findings have also been documented [55, 56]. Additionally, research indicates significantly reduced Firmicutes abundance in the gut microbiota of anxiety disorder patients [57, 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 [59‐62]. Neuroinflammation plays a pivotal role in the pathogenesis of clinical depression [63‐65]. 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 [66‐68]. Genetic inheritance is a significant factor in MDD pathogenesis [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 [69‐71]. ABC transporters encoded by these genes participate in substance transport across the blood-brain barrier and mitochondria, potentially influencing blood-brain barrier permeability [72‐75]. 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 [76‐79]. Additionally, mitochondrial ABC transporters are primarily involved in reactive oxygen species (ROS) metabolism, which is closely linked to MDD pathogenesis [80, 81]. Preclinical studies further validate the efficacy of ROS-targeted therapies in treating MDD [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 [83, 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 [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.
Anzeige
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 [86, 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 [88‐90]. Glycine, serine, and threonine act as energy sources and nutrients, exerting antioxidant and anti-aging effects through modulation of relevant metabolic axes [91‐93]. 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 [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 [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.
Acknowledgements
We would like to thank all the staff at the NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases.
Anzeige
Declarations
Ethics approval and consent to participate
All experimental protocols were conducted in compliance with guidelines established by the Ethical Committee of Chongqing Medical University (ECCMU, Chongqing, China) regulations. At the end of the experiment, mice were humanely euthanized.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
