Healthy Fecal Microbiota Transplant May Prevent Worsening Depression in Fawn-Hooded Rats

To test if the neurobehavioral characteristics of the FH recipients upon SD- and FH-FMT were differentially altered, three anxiety-like and depressive-like behavioral tests, including the forced swim test (FST), open-field test (OFT), and sucrose preference test (SPT), were conducted on the two groups of FMT-processed FH rats and the control SD and FH rats. Significant differences were observed among the four groups of rats for FST (one-way ANOVA, adjusted P = 0.004), OFT (one-way ANOVA, adjusted P = 0.00012), and SPT (one-way ANOVA, adjusted P = 0.031). As shown in Fig. 1A to C, compared with the healthy SD rats, FH rats displayed a significant increase in the immobility time during FST (two-sample unpaired t test, adjusted P = 0.020), a significant decrease in the central to total movement ratios during OFT (two-sample unpaired t test, adjusted P = 0.025), and a nonsignificant decrease in sucrose preference index during SPT (two-sample unpaired t test, adjusted P = 0.338). Interestingly, FH recipients receiving FH fecal microbiota (FH-FH rats), compared with FH rats, displayed further higher differences with SD rats on the FST scores (two-sample unpaired t test, adjusted P = 0.007; Fig. 1A) and OFT scores (two-sample unpaired t test, adjusted P = 0.0005; Fig. 1B), indicating that the ‘depressive’ microbiota transplantation to FH rats could lead to the aggravation of depressive-like behaviors. Similar to the trends of differences between FH and SD rats, FH-FH rats had significantly higher FST scores (two-sample unpaired t test, adjusted P = 0.025; Fig. 1A), insignificantly lower OFT scores (two-sample unpaired t test, adjusted P = 0.066, Fig. 1B), and significantly lower SPT scores (two-sample unpaired t test, adjusted P = 0.047, Fig. 1C) than FH recipients receiving SD fecal microbiota (FH-SD rats). Except for that in OFT, FH-SD rats had similar levels of FST scores (two-sample unpaired t test, adjusted P = 0.701; Fig. 1A) and SPT scores (two-sample unpaired t test, adjusted P = 0.184; Fig. 1C) with SD rats. Altogether, these results indicated that the ‘healthy’ microbiota transplantation to FH rats could prevent the development of FH-SD rats’ depressive symptoms.

Similarly, to determine if the depression-related neurotransmitter concentrations altered in FH-FH and FH-SD rats, we measured two major neurotransmitters (i.e., serotonin and norepinephrine) in all four groups of rats. Owing to the genetic dysfunctional nature of the serotoninergic system in the FH rats, the serotonin concentrations in serum of FH rats (unpaired Wilcoxon test, adjusted P = 8.22 × 10⁻⁵), FH-FH rats (unpaired Wilcoxon test, adjusted P = 6.51 × 10⁻⁵) and FH-SD rats (Wilcoxon test, adjusted P = 6.51 × 10⁻⁵) were significantly lower than those in SD rats’ serum (Fig. 1D). Nevertheless, the contents of hippocampal serotonin were significantly different between the four groups of rats (one-way ANOVA, adjusted P = 1.2 × 10⁻⁶). As shown in Fig. 1E, FH rats had insignificantly lower content of hippocampal serotonin than SD rats (two-sample unpaired t test, adjusted P = 0.180). The contents of hippocampal serotonin in FH-FH rats were significantly lower than those in SD rats (two-sample unpaired t test, adjusted P = 0.0008), suggesting that the hippocampal serotonin metabolism in FH-FH rats was abnormal. However, the reduction was inferred to be remedied by the ‘healthy’ fecal microbiota, because the contents of hippocampal serotonin in FH-SD rats were observed to be significantly higher than those in FH-FH rats (two-sample unpaired t test, adjusted P = 0.0002) and FH rats (two-sample unpaired t test, adjusted P = 0.011). Similarly, the variations of hippocampal norepinephrine contents between the four groups were also significant (one-way ANOVA, adjusted P = 0.0002), and their pairwise comparisons were shown in Fig. 1F. It was observed that the contents of hippocampal norepinephrine in SD rats were significantly higher than those in FH rats (two-sample unpaired t test, adjusted P = 0.014) and FH-FH rats (two-sample unpaired t test, adjusted P = 0.006). The contents in FH-SD rats were also significantly higher than those in FH rats (two-sample unpaired t test, adjusted P = 0.005) and FH-FH rats (two-sample unpaired t test, adjusted P = 0.004), while they were similar to those in SD rats (two-sample unpaired t test, adjusted P = 0.574). One plausible inference of these observations could point out that the transplantation of SD fecal microbiota in FH rats influenced their neuromodulation in the hippocampus through the enteric nervous system (ENS), but not the neurotransmitter metabolism in serum through the intestinal barrier. Interestingly, the more obvious differences between FH-SD and FH-FH rats than those between SD and FH rats for the hippocampal serotonin suggested some unknown biological effect of FMT on the neurotransmitter pool in the hippocampus.

To determine if FMT influenced immune responses in the rats, cytokines in both the serum and hippocampus of the control and FMT-processed rats were quantified. It was found that the contents of serum cytokines IL-2, IL-4, IL-6, IL-10, IL-17A, and interferon (IFN)-γ were significantly different between the four groups of rats (Kruskal-Wallis test, adjusted P < 0.05), while the levels of serum IL-1β and TNF-α were not very different among the four groups (Kruskal-Wallis test, adjusted P ≥ 0.05). As shown in Fig. S1 (left panel), compared with those in FH rats, the contents of serum IL-2, IL-6, and IL-17A were significantly lower in FH-FH rats, while the levels of serum IL-4, IL-10, and IFN-γ in FH-FH rats were significantly higher in FH-FH rats (Wilcoxon test, adjusted P < 0.05). The increasing or reduction of serum cytokines in FH-FH rats was not suppressed by SD-FMT processing, because the contents of these serum cytokines in FH-FH and FH-SD rats had no significant differences (Wilcoxon test, adjusted P ≥ 0.05). However, considering that, all measured serum cytokines had similar contents between FH and SD rats in Fig. S1 (Wilcoxon test, adjusted P > 0.05), it was inferred that serum cytokine metabolism and the depression of FH rats had no causal links. For the hippocampal cytokines, the levels of hippocampal cytokines IL-1β (two-sample unpaired t test, adjusted P_IL-1β = 0.047) and TNF-α (Wilcoxon test, adjusted P_TNF-α = 0.012) were significantly lower in SD rats than those in FH rats, respectively, and they also showed trends toward lower levels in FH-SD rats compared to FH-FH rats (two-sample unpaired t test, adjusted P_IL-1β = 0.16; Wilcoxon test, adjusted P_TNF-α = 0.021; Fig. S1 right panel). Interestingly, hippocampal IL-17A contents in FH-FH rats were significantly higher than that in FH (two-sample unpaired t test, P = 0.027) and FH-SD rats (two-sample unpaired t test, P = 0.003), suggesting that FH microbiome transplantation could induce IL-17A accumulation in FH rat hippocampus while SD microbiota could not. All the above results indicated that the hippocampal cytokine metabolism should be associated with the mental disorder, and SD fecal microbiota transplantation could ameliorate the dysfunction of hippocampal cytokine metabolism in FH recipients.

To determine if the gut microbiota in FH-SD rats had been altered, species-level beta-diversity based on 16S rRNA amplicon sequencing data were analyzed using DEICODE. As shown in Fig. 2A, FH and FH-FH rats had significantly different beta diversity compared to FH-SD and SD rats (permutational analyses of multivariate dispersions [PERMDISP]: F = 0.230, P = 0.818, n = 999 permutations; permutational analysis of variance [PERMANOVA]: F = 8.689, P = 0.001, n = 999 permutations). The SD microbiota was significantly differentiated from the FH microbiota by the proportion of Roseburia sp. CAG 380 and Dialister sp. CAG: 357. Likewise, the FH microbiota was characterized by increased proportions of Bifidobacterium pseudolongum and Candidatus Gastranaerophilus phascolarctosicola. The log ratio of DEICODE-feature loadings of these four species were employed further to examine the proportion of SD:FH-associated species. A significantly greater log ratio of Roseburia sp. CAG 380 and Dialister sp. CAG: 357 (in the numerator) to Bifidobacterium pseudolongum and Candidatus Gastranaerophilus phascolarctosicola (in the denominator) between FH and SD rats, as well as between FH-FH and FH-SD rats, were observed (Wilcoxon test, P < 0.05; Fig. S2). This suggested a successful transfer of the SD gut microbiota to the FH recipients. To further identify differentially proportional taxa and account for the compositional data, Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC) was applied. As shown in Fig. 2B, there were eight species with significantly different proportions both between the two control groups (i.e., SD versus FH) and between the two FMT-processed groups (i.e., FH-FH versus FH-SD) (effect sizes with Bonferroni, adjusted P < 0.05). These differentially proportional species were Akkermansia muciniphila, Akkermanisia muciniphila CAG:154, Bifidobacterium adolescentis, Dialister sp. CAG357, Firmicutes bacterium CAG:41, Ruminococcus sp. CAG:108, Sutterella wadsworthensis CAG:135, Veillonella sp. ACP1 and their proportions were significantly lower in SD and FH-SD rats than in FH and FH-FH rats. Interestingly, Dialister sp. CAG357 was the sole pairwise differentially proportional species that could determine the species-level beta-diversity differentiation between SD and FH-SD gut microbiota and the FH and FH-FH gut microbiota (Fig. 2A). Therefore, it was considered that the SD fecal microbiota transplantation was successful in shifting the microbiota of the recipient FH rats toward the SD-characteristic microbiota, and the significant decrease of Dialister sp. CAG357 might play a key role in the gut microbiota reassembly in FH-SD rats.

To determine if the genetic functional potential of the recipient FH rat microbiome was altered by the donor SD rat microbiome upon FMT, MetaCyc database-mapped enzymatic reactions and pathways for the metagenomic data of the four groups were analyzed using DEICODE beta-diversity. As shown in Fig. 3A, FH and FH-FH rats had significantly different beta-diversities compared to FH-SD and SD rats in terms of enzymatic reactions (PERMDISP: F = 1.420, P = 0.179, n = 999 permutations; PERMANOVA: F = 7.155, P = 0.001, n = 999 permutations). It was found that the group of SD and FH-SD microbiomes was characterized by the gene encoding 1-deoxy-d-xylulose 5-phosphate reductoisomerase, while the clustering of FH and FH-FH microbiomes was characterized by genes encoding citrate hydro-lyase, d-threo-isocitrate hydro-lyase, sucrose phosphorylase, acetolactate synthase, butyryl-CoA dehydrogenase, isoamylase, and phosphoglucomutase. Similar to the taxonomic beta-diversity analysis, the functional beta-diversity in FH-SD rats was more similar to SD rats than to FH and FH-FH rats (Fig. S3A). The metabolic pathways that defined the SD and FH-SD microbiomes were quinate degradation I and II, gallate biosynthesis, urea cycle, and carbamoyl-phosphate synthesis, while the pathways that characterized the FH and FH-FH microbiomes were l-citrulline biosynthesis, l-citrulline degradation, and l-proline biosynthesis II (from arginine). Quinate was one of several aromatic compounds that can be metabolized by microorganisms to the central intermediate protocatechuate and then be further metabolized via the β-ketoadipate pathway to acetyl-CoA and succinyl-CoA. According to a Spearman’s rank correlation analysis between the robust-Aitchison principle component analysis (RPCA) generated distance-ordination matrices of functional genes at the reaction level (along the X-axis) and pathway level (along the y-axis), it was demonstrated that there was a significant association between the pathways and enzyme reactions with ρ = 0.8741 (over 999 permutations, P = 0.001; Fig. S3B). By applying ANCOM-BC, the pairwise differentially proportional enzyme-encoding genes and pathways in both the control groups (i.e., SD versus FH) and FMT-processed groups (i.e., FH-FH versus FH-SD) were identified. Nine metabolic pathways were pairwise differentially proportional, including glycolipid biosynthesis, chondroitin sulfate degradation, cytidine monophosphate (CMP)-legionaminate biosynthesis, dermatan sulfate degradation, pyruvate fermentation-acetoin I, pyruvate fermentation-acetoin, starch degradation II, and zwittermicin A biosynthesis (Fig. S3C). As shown in Fig. 3B, there were 29 pairwise differentially abundant enzyme genes, all of which were significantly less abundant in SD or FH-SD microbiomes than those in FH or FH-FH microbiomes, respectively (effect sizes with Bonferroni, adjusted P < 0.05). Most of the pairwise differentially abundant genes were associated with carbon metabolism. Among them, the gene encoding acetolactate synthase, which could catalyze the conversion between pyruvate and 2-acetolactate and was involved in valine and isoleucine biosynthesis and then pantothenate and CoA biosynthesis, was the only one associated with the beta-diversity differentiation between SD/FH-SD and FH/FH-FH (Fig. 3A). Hence, SD-FMT significantly changed the microbiome profile of FH rats, resulting in a significant reduction in the proportion of genes encoding acetolactate synthase.

Serum and hippocampal metabolomics were performed for the four groups of rats. Comparative metabolomics analysis demonstrated that serum metabolomic profiles were relatively conserved between all four rat groups (Fig. 4A and B), but hippocampal metabolite profiles were considerably different between control rats and FMT-processed rats (Fig. 4C and D). Similarly, DEICODE-generated biplots of serum metabolomics (Fig. S4A) showed differentiation of SD and FH-SD rats to FH and FH-FH rats (PERMDISP: F = 0.929, P = 0.382, n = 999 permutations and PERMANOVA: F = 4.907, P = 0.001, n = 999 permutations), while hippocampus metabolic diversity of SD rats showed distinction with those of the FMT-processed and control FH rats (PERMDISP: F = 1.113, P = 0.276, n = 999 permutations; PERMANOVA: F = 12.617, P = 0.001, n = 999 permutations) even though the hippocampal metabolomes of FH-SD rats had the trend toward those of SD rats (Fig. S4B). These results indicated that the transplantation of SD gut microbiota to FH rats had a greater effect on the recipients’ serum metabolism than hippocampal metabolism. Among the significantly differentially abundant metabolites in control and FMT-processed rats, arachidonic acid (C20:4) in serum was the sole metabolite that was significantly pairwise differentially abundant in FH and FH-FH rats, compared to SD and FH-SD rats. The abundance of serum C20:4 in FH-SD rats was significantly lower than that in FH-FH rats (Wilcoxon test, P = 0.0007) and was similar to that in the SD donor (P = 0.243). This indicated that the transplantation of SD gut microbiota reduced the serum concentration of C20:4 in FH rats.

Meanwhile, by doing the correlational analyses on the serum and hippocampal metabolomes, respectively, the associative nature of metabolites among themselves was identified. Interestingly, it was found that several carbon-rich fatty acids (i.e., palmitic acid (C16:0), heptadecanoic acid (C17:0), stearic acid (C18:0), and linoleic acid (C18:2)) and nitrogen-rich metabolites (e.g., 2-aminoethanol, glucopyranose, glycine, and uracil) had strongly inverse relationships in rat serum (Fig. S5A), while numerous nitrogen-rich metabolites (e.g., serine, ornithine, asparagine, and glutamine) had highly negative relationships with several brain osmolyte compounds or their precursors (i.e., N-acetylaspartic acid [NAA], inositol, and pyroglutamic acid) in rat hippocampus (Fig. S5B). Considering that the level of C20:4, a derivative of C16:0, C17:0, C18:0, and C18:2 in lipid metabolism, was pairwise significantly reduced in the serum of SD and FH-SD rats as shown in Fig. 4A and B, the contents of nitrogen-rich metabolites in serum of SD and FH-SD rats were expected to be higher in comparison with those in FH and FH-FH rats regardless of the significance, which would result in the increase of amino acids and decrease of osmolytes in the hippocampus through the blood-brain barrier (BBB). This inference was supported by the hippocampal metabolomes of the four groups of rats, where the log ratio of serine to NAA was significantly higher in FH-SD and SD rats than that in FH rats (Wilcoxon test, P < 0.05; Fig. S6).

To examine the co-occurrences between metabolomes in host tissues and specific bacterial species in gut microbiota, we employed MMvec, which uses neural networks to infer the nature of interactions across omics data sets. The heatmap reflecting the conditional probabilities between the serum metabolomes and DEICODE-associated and ANCOM-BC-associated bacteria taxa along principal component 1 (PC1) (Fig. S7A) suggested a strong likelihood of co-occurrence for all pairings with positive and higher conditional probabilities. The model showed a higher predictive accuracy of Q² = 0.35 than the absolute null or baseline model (where no formula was used) on the cross-validation samples (Fig. S7B). Nevertheless, the hippocampal metabolomes did not show a good prediction of co-occurrences with the DEICODE-associated and ANCOM-BC-associated microbes (Q² ≈ 0), hence no visualized data were shown here. This confirmed the expectation that the intestinal microbiome more strongly influenced serum metabolites than hippocampal metabolites.

In conclusion, we demonstrated that gut microbiota transplantation from ‘healthy’ SD rats to FH rats could suppress the aggravation of depression symptoms in the recipients through the gut microbe-induced modulation of host immune and metabolic activity. This study took a first step toward understanding the potential of FMT as a therapy for depression.

Thirty male Fawn-hooded (FH) rats and 10 healthy male Sprague–Dawley (SD) rats at the age of 3 weeks old and of nonspecific pathogen-free (SPF) grade were purchased from the Department of Laboratory Animal Science at Beijing University Health Science Center (PUHSC). All the rats (n = 40) were prehoused in cages (i.e., n = 5 rats per cage) for 3 days in the animal experimental room at PUHSC under SPF conditions for acclimatization. They were housed at 22 ± 1°C with a humidity of 70% on 12 h-day:12 h-night cycles (lights on 7:00 to 19:00) and were given a standard laboratory chow diet and water ad libitum.

From a total of 30 FH rats and 10 SD rats, 10 FH rats and 10 SD rats were used as donors for the fecal microbiota transplantation (FMT), respectively. Fecal pellet samples were collected a day before the FMT experiment and processed as described by Zheng et al. (12). Briefly, the fecal samples were mixed with sterile phosphate-buffered saline (PBS, pH 7.0) at a weight-to-volume ratio of 1:6 under anaerobic conditions, and then centrifuged at 800 × g for 2 min to discard the fecal debris. The microbiota in the supernatants was stored overnight at 4°C for the FMT on the following day. The remaining 20 FH rats were divided into two equal-sized groups based on equivalent average body weight. The fecal extracts from the FH and SD donor rats were administered to the first group named ‘FH-FH’ and the second group named ‘FH-SD’ rats, respectively. Each inoculation volume was 0.5 mL performed at a total of eight times around a single-day interval. In parallel to these experimental groups, an additional control group of 10 FH rats and 10 SD rats were treated with PBS under the same incubation conditions.

Animal model rats were examined for their neurobehaviors using the following commonly conducted behavioral tests: forced swim test (FST), open-field test (OFT), and sucrose preference test (SPT). Each test was performed post-FMT in the animal experimental room at PUHSC on three separate days with prior acclimation for 1 h. The details of the testing operations are as follows:

The experiment was carried out according to Ge et al. (47). The rats were placed individually in a PVC-made translucent cylinder (50 cm in height × 20 cm in diameter) filled with 30 cm water (22.5 ± 0.5°C), in which the rats could not support themselves touching the bottom with their bodies. The testing paradigm included two sections: an initial 15 min pretest for acclimation, and then a 5-min test 24 h later. A video camera was held near the cylinder to record the duration of the rat's immobile state in the second section. Immobility was defined such that all motions of the rat limbs stroke was absent except for movements required to keep the rat’s head above water.

All the rats were individually tested in an open-field apparatus consisting of a gray square base (100 × 100 cm²) with gray walls (40 cm in height), made up of polyvinyl chloride (PVC). Each rat was gently placed in the center of the chamber, and its spontaneous activity was recorded for 10 min using the video-computerized tracking system, which was set at 100 cm above the chamber. For recording purposes, the base area was equally arranged into 5 × 5 squares, and the value of the total square amounts in which the experimental rat set foot during the 10-min testing was used as an index of locomotor activity, while the proportion of central squares (inner 36% of the base area) to the total squares in which the experimental rat set foot during the 10-min testing was construed as an index of anxiety-like behavior. The chamber was cleaned up before the next rat was placed in.

Two no-drip pet water feeding bottles containing 1.0% (wt/vol) sucrose were hung on different sides of the experimental cage. Before conducting the experiment, rats were mono housed in the experimental cage with the provision of two water bottles for 48 h to overcome neophobia, followed by exposure to a 6-h period of water and food deprivation. Next, the solutions in the two bottles were each replaced with 80 mL of 1% sucrose and plain water, respectively. Each experimental rat was provided with open access to the two bottles hanging on each side of the cage for 30 min. Later, the sucrose and plain water bottles were switched, and the open access was provided for another 30 min, to eliminate side-preference artifacts. Each of the bottles was weighed before and after use. The sucrose preference index of each rat was computed according to the following formula I_SP = ×100%, where I_SP was the sucrose preference index of each rat, ΔM_s was the weight difference of the sucrose bottle before and after SPT, and ΔM_w was the weight difference of the water bottle before and after SPT.

One-week post-FMT, the control, and experimental rats were sacrificed to obtain the hippocampal and serum samples. The hippocampal tissue from each rat was divided into two aliquots, one for sample preparation and quantification of neurotransmitters and immune cytokines, and the other one for sample preparation and quantification of untargeted metabolomics.

Sample preparation for neurotransmitters and immune cytokines was processed as follows: the hippocampal tissue was weighed and homogenized in the lysis buffer (composition consists of 10 mM HCl, 1 mM EDTA, 4 mM Na₂S₂O₅) to a ratio of 6 mL lysis buffer to 1 mg tissue sample. The mixed homogenate was then centrifuged at 18,000 × g and 4°C for 10 min. The supernatant was filtered using a 0.2 μm Millipore filter (Millipore, MA, United States), and stored at −80°C until further use.

Sample preparation for metabolomics was processed as follows: The hippocampal tissue was desalinated using methanol, i.e., 20 mg of the hippocampus tissue was mixed with 800 mL methanol-water (4:1 vol/vol) solution containing 5 μg/mL myristic acid-1,2-¹³C₂, which was used as the internal standard for the metabolomics. This mixture was further milled using the rotor beater mill (Retsch, Haan, Germany) three times, incubated at 4°C for 60 min, and centrifuged at 20,000 × g and 4°C for 10 min. The supernatant was filtered using a 0.2 μm Millipore filter and stored at −80°C until further derivatization and analysis. One hundred twenty microliters of the desalinated hippocampal extract were added to an MS-certified glass vial equipped with a 200 μL insert (Freeze-Dryer, Boyikang, Beijing, China) for 4 h of lyophilization. This lyophilized material was derivatized using a method analogous to Moros et al. (48), where the lyophilized samples were mixed with 30 μL methoxyamine (10 mg/mL) in pyridine for 16 h at room temperature for the methoxyamination followed by the trimethylsilylation with 30 μL of N-methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA) with 1% trimethylchlorosilane (TMCS). Subsequently, 30 μL of methyl myristate in n-pentadecane was added as an injection external standard.

For serum metabolomics, 50 μg of fresh serum sample was extracted with 200 μL of a cold methanol-water solution containing the internal standard myristic acid-1,2-¹³C₂. Later, the mixture was centrifuged, lyophilized, and derivatized in a similar method to that used for hippocampal samples (as described above).

Absolute quantification of serotonin in the hippocampal-tissue- and serum extracts was determined using the serotonin-specific enzyme-linked immunosorbent assay (ELISA) kit DEE5900 and DEE8900 (Demeditec Diagnostics GmbH, Kiel, Germany), respectively. The concentrations of noradrenaline in the hippocampal-tissue-extracts were determined with the noradrenaline-sensitive ELISA kit BCU39-K01 (Eagle Biosciences Inc., Amherst, NH, United States). All the samples were run in three biological replicates on each ELISA microplate.

Metabolite profiling of the derivatized samples was performed on a GCMS-QP2010 (SHIMADZU, Kyoto, Japan). A 0.5 μL of the derivatized sample was injected into an RTx-5MS column (30 m × 0.25 mm × 0.25 μm, Restek Corp., PA, USA) with helium as carrier gas at a constant flow of 1.5 mL/min. The inlet temperature was set to be 250°C. The initial oven temperature was held at 80°C, ramped to 300°C by 20°C per min, and then held at 300°C for 3 min. Electron impact was used as an ionization source with the ionization energy of 70 eV at 200°C. The transfer line temperature was set to be 220°C. Mass spectra were recorded at 50 to 700 m/z for 4.5 to 18 min. Metabolites were identified based on the mass spectrum in comparison to the standard NIST library 2.0 (National Institute of Standards and Technology, 2008) and Wiley 9 (Wiley-VCH Verlag GmbH & Co. KgaA, Weinheim, Germany), with a threshold of match > 80 (with a maximum match equal to 100). Relative metabolite abundances were calculated from peak areas (unique mass) of identified metabolites using GCMS LabSolution software and followed by calibration using the peak area of the internal standard (myristic acid-1,2-¹³C₂) and the external standard (methyl myristate) to minimize the instrumental errors.

To detect the immune cytokines in rat hippocampus and serum, an AimPlex multiplex immunoassay kit (Beijing Quantobio Biotechnology Co. Ltd., Beijing, China) was used according to the manufacturer’s instructions. This assay combined the techniques in ELISA and high-throughput flow cytometry and could detect several proteins from very small samples quickly. Here, eight inflammatory cytokines were measured, including IL-4, IL-10, TNF-α, IL-1β, IL-2, IL-6, IL-17A, and IFN-γ.

One-week post-FMT, fecal samples were collected from the control and experimental rats and were processed for 16S rRNA amplicon sequencing and shotgun metagenomic sequencing. DNA extraction for amplicon-based sequencing was processed using the QIAamp Fast DNA Stool Minikit (Qiagen, Duesseldorf, Germany) to extract the microbial genomic DNA. The V3-V4 region of the 16S rRNA gene was PCR-amplified from the DNA samples using the universal primer 338F (5′-ACTCCTACGGGAGGCAGCA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) for Illumina HiSeq paired-end sequencing. DNA extraction for shotgun-based sequencing was processed using the NEBNext Ultra™ DNA Library Prep kit (New England BioLabs, MA, USA) to build the sequencing library from a total amount of 1 μg DNA per sample. The generated library was sequenced on the Illumina HiSeq2500 platform.

Demultiplexed sequencing data were quality filtered by trimming to 150 bases and were denoised using Deblur (49) through Qiita (50) using the default to generate amplicon sequencing variants (ASVs). The deblurred sequence fragments were inserted into the Greengenes Database (v.13_8) phylogenetic tree using SATé-enabled phylogenetic placement (51). The final feature table obtained from Qiita is composed of 37 samples and 7,467 features. A rarefied data table of 5,000 reads per sample was employed for performing downstream data analyses using Qiime2 (52).

Quality control-filtered paired-end sequencing reads were then concatenated, converted to Fasta format, and processed by the SHOGUN align function (53) and associated Web of Life phylogenetic database (54). SHOGUN-aligned files were then utilized by Woltka (https://github.com/qiyunzhu/woltka↗) for gOTU table generation and functional pathway characterization on a per-sample basis. This tool maps sequencing reads to microbial genes based on their associated genomic coordinates to compute microbial functional units (e.g., MetaCyc pathways, protein, enzyme, reaction, and pathway information) (55). In doing so, it avoids microbial functional profiling based on the presence or absence of predefined marker genes. The gOTU table was filtered to remove microbial features per sample with less than 0.001% of relative abundance, leaving 1,543 out of 5,842 gOTUs (with the rank-none parameter) and leaving 894 out of 3,325 gOTUs (with the rank-free parameter) found across all samples.

To test the normality of the data distribution, the Shapiro-Wilk test was applied. Accordingly, either parametric tests such as the two-sample t test and one-way ANOVA or nonparametric tests such as the Wilcoxon test and the Kruskal-Wallis test were used on the pairwise comparison groups and multigroup comparisons, respectively. To check the variances between the groups, Bartlett’s test was applied. The feature abundance table generated from Qiita (taxonomic assignments) and Woltka (functional assignments) was used as input for beta-diversity RPCA (using DEICODE) to calculate between-group beta diversity in QIIME2 (56). The beta diversity significance within and among groups was examined by the QIIME2 diversity plugin with PERMDISP and PERMANOVA tests. The resulting PCoA and the biplots were visualized using the QIIME2 plugin Emperor (57). Distance matrices used for between-group differences were tested using PERMANOVA and permuted t tests in QIIME2. The feature loadings in the biplot axis with the most difference in groups were visualized using Qurro (58). The abundance of the highest- and lowest-ranked features were used to compute log ratios in different rat groups. ANCOM-BC was used to calculate the pairwise differential species (59). To estimate the conditional probability of a metabolite abundance given the presence of a single microbe, a log-transformed conditional probability matrix from each cross-omics feature pair, i.e., metagenomics (based on the species-level metagenomic data) and metabolomics was built using a neural network algorithm MMvec (60). The GC-MS metabolomics data were filtered for central carbon metabolites in the hippocampal tissue and serum extract. A post hoc method Benjamini-Hochberg test was applied to control false discovery rate (FDR) during multigroup comparisons and adjusted P values were reported wherever applicable.

The raw SHOTGUN sequencing data were deposited in the National Microbiology Data Center (NMDC) under the accession number NMDC10017888↗, and the website is as follows: https://nmdc.cn/resource/genomics/sra/detail/NMDC40013589↗.

The raw SHOTGUN sequencing data were deposited in the National Microbiology Data Center (NMDC) under the accession number NMDC10017888↗, and the website is as follows: https://nmdc.cn/resource/genomics/sra/detail/NMDC40013589↗.