Alterations in fecal bacteriome virome interplay and microbiota-derived dysfunction in patients with schizophrenia

doi:10.21203/rs.3.rs-4711218/v1

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Alterations in fecal bacteriome virome interplay and microbiota-derived dysfunction in patients with schizophrenia

https://doi.org/10.21203/rs.3.rs-4711218/v1

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Rising studies have consistently reported gut bacteriome alterations in schizophrenia (SCZ). However, little is known about the role of the gut virome on shaping the gut bacteriome in SCZ. Here in, we sequenced the fecal virome, bacteriome, and host peripheral metabolome in 49 SCZ patients and 49 health controls (HCs). We compared gut bacterial community composition and specific abundant bacteria in SCZ patients and HCs. Specific gut viruses and host peripheral metabolites co-occurring with differential bacteria were identified using Multiple co-inertia analysis (MCIA). Additionally, we construct a latent serial mediation model (SMM) to investigate the effect of the gut virome on SCZ through the bacteriome and host metabolic profile.

SCZ patients exhibited decreased gut bacterial β-diversity compared to HCs, with eight differentially abundant bacteria, including coprobacillaceae, enterococcaceae etc. Gut viruses including Suoliviridae, Rountreeviridae, Intestiviridae etc. co-occur with these SCZ-related bacteria. We found that the viral-bacterial transkingdom correlations observed in HCs were dramatically lost in SCZ. The altered correlations profile observed in SCZ may impact microbiota-derived peripheral metabolites enriched in the bile acids pathway, eicosanoids pathway, and others, contributing to host immunity dysfunction and inflammation. The SMM model suggested potential causal chains between gut viruses and SCZ, indicating that the gut virome's effect on SCZ is significantly mediated by the bacteriome and metabolites.

In conclusion, these findings provide a comprehensive prospective on the role of gut microbiota in the pathogenesis of SCZ. They reveal that SCZ patients harbor an abnormal bacteriome-virome ecology, shedding light on the potential development of microbial therapeutics.

Schizophrenia

Gut Microbiota

Gut virome

Metabolic dysfunction

Inflammation

Schizophrenia (SCZ) is a serious psychiatric disorder affecting approximately 0.7% of the population worldwide (1). While the precise etiology of SCZ remains elusive, it is well established that both genetic and environmental factors contribute to the pathogenesis. The gut microbiota, mostly obtained from the external environmental after birth, provides crucial signals for the development and function of the host brain and behavior (2–4), making it an excellent tool for studying complex brain diseases, such as SCZ. Recent evidences have revealed associations between gut bacteria and the onset and development of SCZ, including altered community composition, changes in the abundance of specific gut bacterial taxa, and modifications in bacteria neuroactive metabolites pathways (5–7). Furthermore, fecal microbiota transplantation in rodent models has implied a causal relationship between specific gut bacteria and SCZ-like behavior (6). However, focusing solely on the gut bacteria overlooks the plasticity of the gut bacteria community and the derived metabolites that can profoundly impact the host's physiology. To gain a comprehensive understanding of the role of gut microbiota in SCZ, it is valuable to adopt a holistic approach that explores not only the gut microbiota but also the gut virome, which can shape the gut bacteria community, and microbial-derived metabolites.

Gut virome has recently been found to be associated with psychiatric disease (8). Moreover, previous studies have indicated the intriguing role of the gut viruses (Caudovirales, Siphoviridae and Macteriophages) in influencing human cognitive function(9). Bacteriophages dominate the human virome, constituting over 90% of the total viral entities within the gut virome(10). As natural predators of bacteria, bacteriophages shape the gut bacteria community through a 'predator-prey' dynamic and they play a role in regulating gut bacterial metabolism and vitality (11, 12). Considering the crucial role of gut virome in shaping the gut bacteria community and functionality, its potential significance may have been underestimated in studies investigating the relationship between gut microbiota and SCZ. However, to our knowledge, no research has been reported on this aspect to date.

Microbial-derived metabolites serve as key mediators through which the gut microbiota communicates with the host immune system and the brain. Therefore, characterizing the relatively holistic microbial community and host peripheral metabolite profile of SCZ patients spontaneously holds remarkable value. In this study, to explore SCZ-specific gut bacteria and co-occurring gut viruses and metabolites, we identified gut bacteria that exhibit differential abundance between SCZ patients and healthy control (HCs) and conducted Multiple Co-Inertia Analysis (MCIA) for dimension reduction, and selecting metabolites and viruses that are closely related with those differential gut bacteria. Compared with other dimensionality reduction approaches, MCIA offers a nuanced understanding of multi-omics data, making it an effective and insightful approach for integrative analysis (13, 14). Subsequently, we compared the virial-bacterial transkingdom correlation profile between SCZ and HCs and conducted enrichment analysis to evaluate the biological function of selecting metabolites. Furthermore, utilizing the differential bacteria, gut viruses and metabolites identified by MCIA, we developed serial mediation analysis models to investigate the potential causal relationship between gut microbiota (virome/ bacteria), metabolites and SCZ.

Participants

The study recruited participants from West China Hospital of Sichuan University between 2021 and 2022. All participants were interviewed by at least two trained psychiatrists using the Structured Clinical Interview of the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (SCID-5). We recruit SCZ patients who fulfilled the DSM-5 criteria for SCZ (experienced their first episode) and had not been taking any psychiatric medicines for more than 30 days. The inclusion and exclusion criteria for SCZ patients and HCs are described in Supplementary Methods.

Ethical principles

This study was approved by the Institutional Ethics Committee of West China Hospital, Sichuan University. After the study procedure had been fully explained, written informed consent was obtained from all participants, and in the case of participants under 18 years old, from both the participants themselves and their guardians.

Fecal sample collection and gut microbiome sequencing

Fecal samples were collected using a sterile collection container. To avoid contamination from urine and the container, approximately 1-2ml of feces was sampled from the middle portion of the feces. The sample was transferred to cryogenic viats (Corning, sterile, internal thread) using a sterile swab, and immediately placed in liquid nitrogen for rapid freezing and stored. The entire process was completed within two hours. Gut microbiota sequencing analyses were conducted on 95 participants (The quantity of fecal samples from 1 patient and 2 HCs is insufficient for simultaneous sequencing of the bacteriome and virome). The sequencing, data quality and assembly steps are described in the Supplementary Methods.

Metabolomics data acquisition and preprocessing

Peripheral blood was collected in EDTA tubes on the same day they were enrolled in this study. Metabolites extraction from samples of 92 participants (3 patients’ sample experienced hemolysis or refused blood sample collection), UHPLC‒MS/MS, and qualitative and relative quantitative approach are described in the Supplementary Methods. The quality control samples were tightly clustered on principal component analysis plot, indicating that the data quality is satisfactory and suitable for subsequent analysis (Supplementary Fig. 1). After the pre-processing steps, we got 1086 metabolites and annotated them by using KEGG, HMDB, and LIPID Maps database. The identified IDs from each database were converted into HMDB ID prepared for enrichment analysis, ultimately, 329 metabolites successfully corresponded to their HMDB ID.

Statistical analysis

Demographic characteristics.

Demographic characteristics including age, gender and body mass index (BMI) of different groups (SCZ patients and HCs) were compared using the independent t-test or chi-square test as appropriate. BMI was calculated as weight divided by height squared (kg/m²). All the analyses above were calculated in R v4.2.2.

The gut bacteriome diversity comparison

The gut bacteriome read counts data were used to calculate alpha and beta diversity. Differences in alpha diversity metrics between the two groups were evaluated using the non-parametric Wilcoxon rank-sum test, depending on whether the data satisfied the assumption of normality. For beta diversity, permutational multivariate analysis of variance (PERMANOVA) was applied to compare the microbial overall community compositions between SCZ patients and HCs based on the UniFrac distance metric. Multidimensional scaling (MDS) was employed visualizing the dissimilarities of beta diversity patterns between SCZ and HCs.

The gut microbiota data pre-processing

Read counts data were normalized using the Trimmed mean of M-values (TMM) method. We then calculated relative abundance by using normalized data for filter step, that is, only the taxa of bacteria with relative abundance higher than 0.01% and the taxa of virus higher than 0.001%, and prevalence higher than 5% across samples were considered for differential testing. Filtered data then underwent log2 transformation. The pre-processed data was used for further analyses.

Identifying differential abundance of gut bacteria

Differential abundance analyses were performed to identify microbial taxa exhibiting significantly different abundances between SCZ patients and HCs.

MaAslin2. Microbiome Multivariate Association with Linear Models (MaAsLin2) is a multivariable statistical framework designed to identify associations between clinical metadata and high-dimensional multi-omics data. It was employed to detect differential abundant microbial taxa between SCZ and HC while adjusting for potential confounders including age, gender, BMI and medication usage days.

ANCOM-BC. Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC) enables differential abundance analysis for microbial absolute abundances while accounting for the composition of microbiome data. ANCOM-BC was used to discern taxa that demonstrates differential abundance between SCZ and HC groups while adjusting for the influence of confounding variables same as MaAslin2.

The differential bacterial were identified by combining the bacteria showed significant differences between the two generated by MaAsLin2 analysis and ANCOM-BC.

Investigating the potential diagnostic value of differential gut bacteria

To assess the potential value of identified gut bacteria as diagnostic markers for SCZ, we utilized Extreme Gradient Boosting (XGBoost) analysis with the disease diagnosis as an outcome and validated through 1000 times 5-fold internal cross-validation. The importance of significant differential bacteria was interpreted using metrics such as gain, cover, and frequency. Predictive power was quantified by the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (CI). Analyses were conducted using the 'xgboost' R package.

Transkingdom viral-bacterial correlation pattern comparison in SCZ and HCs

We conducted gut viral-bacterial transkingdom partial correlations in SCZ and HCs group, respectively. The confounders include gender, BMI and age. The analysis was performed by using 'ppcor' package (version 1.1) in R, and p value less than 0.05 was considered as statistically significant. We utilized the chi-square test to compare the quantities of significant and non-significant correlations between two groups, as well as the numbers of significant positive and negative correlations in each group.

Identifying co-occurring gut viruses and metabolites with differential gut bacteria.

We conducted MCIA synthetic analysis to detect metabolites and gut virus which have robust relationships with selected differential gut bacteria using 'omicade4' package (version 1.40.0) in R. The RV coefficient was calculated to characterize the overall correlation between three omics data (the range is from 0 to 1). Then, the percentage of variance accounted by the first three of principal components (PC) of MCIA on each taxonomic levels (family, genus, and species) were calculated, and we selected top features with greatest weights (both on positive and negative side) on each PC, which representing the robust signals defining major trends.

Metabolites enrichment analysis.

To further evaluate the biological functions of these metabolites, we conducted over representation analysis (ORA) using MetaboAnalyst 6.0 (https://www.metaboanalyst.ca/) platform. We uploaded HMDB ID of metabolites to mitigate errors and omissions stemming from mismatch. The enrichment ratio is calculated as the number of hits within a particular metabolic pathway divided by the expected number of hits. The metabolic pathway was considered as statistically significant enrichment when p value < 0.05 (adjusted by FDR).

Exploring potential causality relationships between gut microbiota, microbial-derived metabolites and SCZ/HCs

Given that the potential modulating relationships between gut virus, bacteria and SCZ are not clearly, we developed 4 bi-direct mediation analyses testing the effect of gut virus and bacteria on SCZ (Model 1: gut virus → gut bacteria → SCZ and Model 2: gut bacteria → gut virus→ SCZ), and whether the effect of gut microbiota on SCZ disease mediated or suppressed by metabolites (Model 3: gut virus → metabolites → SCZ and Model 4: gut bacteria → metabolites → SCZ). Gut viruses, bacteria and metabolites were characterized by latent variables. The observed variables of latent gut bacteria variable were differential bacteria identified by MaAsLin2 and ANCOM-BC analysis. Co-occurring gut viruses identified by MCIA analysis comprised the latent gut virus variable. And the latent metabolites variable was comprised by hit metabolites that are significant in metabolic pathways, which represent the metabolic pattern associated with gut bacteria. Based on the results of mediation analyses, we further developed serial mediation model (SMM) analysis to test a potential serial causal chain of gut virus → gut bacteria → metabolites → SCZ (Model 5). Two mediators of model 5 produced three indirect paths, which is SMM1: gut virus → gut bacteria → SCZ; SMM2: gut virus → metabolites → SCZ; SMM3: gut virus → gut bacteria → metabolites → SCZ. The latent variables construction was the same as the above mediation models.

In model 1 to model 5, we considered gender, age and BMI as covariates. All involved continuous variables were centered and scaled before running the analyses, and standardized coefficients (beta) were reported for the regression paths, total, direct and indirect effect. Mediation effect was considered as significant if the p value was less than 0.05 and the accelerated CIs (lower limit, LL to upper limit, UL) do not include 0. Model fit indices were reported using Comparative Fit Index (CFI), Goodness of fit (GOF) index and a root mean square error of approximation (RMSEA). The mediation analysis and SMM analysis were performed using lavaan package (version: 0.6–17) in R software.

Demographic characteristics of study subjects

We recruited a total of 98 participants (49 SCZ patients and 49 HCs). The demographic characteristics are presented in Table 1. The two groups were matched for gender and there were no significant differences in age and BMI. No SCZ patients took anti-psychotic drugs for more than 30 days; among 48 patients who underwent gut virome and bacteriome sequencing, 21 patients were drug-naïve, while 16 patients took drugs anti-psychotic drugs for less than 5 days (see Supplementary Fig. 2).

Table 1

Demographic characteristics
	Overall (n = 98)	SCZ (n = 49)	HC (n = 49)	p-value
Age ^a	25.54 ± 8.08	24.96 ± 7.65	26.12 ± 8.52	0.479
Gender(Female (%))^b	59 (60.2)	29 (59.2)	30 (61.2)	1
Educational attainment (years) ^a	13.66 ± 3.60	12.54 ± 3.75	14.78 ± 3.10	0.002^**
BMI ^a	21.50 ± 3.15	21.12 ± 3.36	21.89 ± 2.91	0.228
No smoking(%)^b	88 (89.8)	40 (81.6)	48 (98)	0.019^*
Notes: BMI was calculated as weight divided by height squared (kg/m²). Age, educational attainment and BMI data are presented as the mean ± standard deviation.
^a The p-value was obtained by independent t-test
^b The p-value was obtained by chi-square test
BMI, Body mass index; HC, healthy control group; SCZ, Patients with schizophrenia group; p < 0.05; p < 0.01; **p < 0.001

Diversity differences of the gut microbiota in SCZ and HC

For alpha diversity, we calculated bacterial diversity at different taxonomic levels, including Shannon index, Simpson index, Chao1 index and abundance-based coverage estimator (ACE) index shown in Fig. 1A and Supplementary Table 1. No significant differences were observed in gut bacterial alpha diversity between SCZ and HCs. For beta diversity, the results generated by PERMANOVA showed significant separation of SCZ and HCs (R² = 0.015, p = 0.049 on family level; R² = 0.017, p = 0.017 on genus level; R² = 0.017, p = 0.011 on species level). The results were shown in Fig. 1B.

Gut microbiota data pre-processing

After data pre-processing, on the levels of family, genus and species, there were 50, 130 and 647 bacterial entities remained. For viral entities, on the family, genus and species level, there were 34, 199 and 139 viral entities remained.

Differential gut bacteria

MaAsLin2 analysis and ANCOM-BC were used to identify bacterial signatures associated with schizophrenia participants. Combining the bacteria showed significant differences between SCZ and HC group generated by MaAsLin2 analysis and ANCOM-BC, at levels of family, genus and species, 8, 14 and 50 bacteria were identified, respectively (Fig. 2).

The diagnostic value of identified differential bacteria

At family level, the XGBoost model achieved an area under the receiver operating characteristic curve (AUC) of 0.664 (95% CI [0.661–0.666]) in distinguishing SCZ cases from HCs on the test set. Turicibacteraceae and Rikenellaceae emerged as the most important contributors to predictive performance. Modeling at the genus level yielded an AUC of 0.592, and 0.763 at species level. The details and ranked differential bacteria features based on their importance are shown in Supplementary Fig. 3.

Transkingdom partial correlation pattern differed in SCZ and HCs

We calculated the overall gut viral-bacterial transkingdom correlations on family level (34 viruses and 50 bacteria). There are 153 significant correlations between gut virome and bacteria in SCZ group, and 132 significant correlations in HC group. The significant correlated number was not differed between two groups (χ² = 1.689, p = 0.194). Among all significant correlations, there are 88 positive correlations in SCZ group and 56 positive correlations in HC group. The imbalanced ratio between positive and negative correlations in SCZ and HCs showed significant difference (χ² = 6.457, p = 0.011). The increased number of correlations in SCZ patients was primarily driven by Mimiviridae, Forsetiviridae, Duneviridae and Assiduviridae, and the decreased number of correlations in SCZ patients was primarily driven by Rountreeviridae and Intestiviridae (see Fig. 3).

Identified gut viruses and peripheral metabolites co-occurring with differential bacteria

We selected gut virus and metabolites that most associated with differential bacteria on family level using MCIA analysis. The RV coefficient value suggested a weak to medium relevance between gut virome, gut bacteriome and metabolome, where the values range is from 0.16 to 0.26 (depictured in Fig. 4A). The sum of the first three represents over 50% variance of the family level (PC1: 22.74%; PC2: 19%; PC3:10.86%).

Figure 4B is MCIA projection plot, which showed the first two PCs of MCIA of gut bacteria, gut virome, and metabolomics datasets. The SCZ and HCs separate along horizontal and vertical axis (PC1 and PC2), which explains the largest variance of MCIA. For metabolites, the top 50 metabolites (both on positive and negative side) with greatest weights on PC1 to PC3 were extracted. After removed duplicate metabolites, 202 metabolites were selected among 1086 metabolites on family level. The full list of those selected metabolites was provided in Supplementary Table 2.

For gut virome, the top 3 viruses (both on positive and negative side) with greatest weights on PC1 to PC3 were extracted. After the duplicate virus removed, 12 viruses were selected among 34 gut virus on family level. We calculated transkingdom partial correlations (taking BMI, age and gender as confounders) between the 8 differential bacteria and their co-occurring 12 gut viruses. There are 8 significant correlations in SCZ group, and 22 significant correlations in HC group. The significant association number significant differed in SCZ and HC groups (χ² = 7.74, p = 0.005), which was primarily driven by viruses Rountreeviridae, Intestiviridae and Suoliviridae (depictured in Fig. 4C).

Enrichment analysis of selected metabolites

Figure 4D delineates the bio-functions of those bacteria associated metabolites characterized by enrichment analysis. After FDR correction, purines and purine derivatives, fatty acids and conjugates, bile acids and derivatives, eicosanoids, benzoic acids and derivatives pathways are significantly enriched. The details including name of hit metabolites, enrichment ratio and p-value are described in Supplementary Table 3.

Effect of gut microbiota and metabolites on SCZ

Bi-direct mediation analyses test the effect of gut virus and bacteria on SCZ (Model 1 and Model 2), and whether the effect of those gut microbiota on SCZ disease mediate or suppressed by metabolites (Model 3 and Model 4). Results indicated that the total effect of gut virus on SCZ (Model 1) was not significant, and the total effect not significant mediated by gut bacteria but suppressed by metabolites (Model 3: β_indirect = 0.35, LLCI = 0.239, ULCI = 1.710, p = 0.009). The total effect of gut bacteria on SCZ (Model 2) was significant, while the effect was not significant mediate by gut virus but by metabolites (Model 4: β_indirect = 0.153, LLCI = 0.078, ULCI = 0.967, p = 0.021). See Fig. 5. The model fit indices indicated the model 1 to model 4 were fit well and described in Supplementary Table 4.

In model5, after removing observed variables that not significant in latent variables, the latent virus variable (X) was established with 7 gut virus on family level (Schitoviridae, Pachyviridae, Pithoviridae, Intestiviridae, Suoliviridae, Rountreeviridae and Demerecviridae), latent bacteria variable (M1) was established with 6 differential gut bacteria on family level (Coprobacillaceae, Enterococcaceae, Erysipelotrichaceae, Morganellaceae, Peptostreptococcaceae and Rikenellaceae), and latent metabolic variable (M2) comprised by 12 metabolities (Glycocholic acid, Taurodeoxycholic acid, Taurochenodeoxycholic acid, Prostaglandin H₂, Hippuric acid, Syringic acid, Hypoxanthine, Caffeine, Paraxanthine, Theobromine, 1-Methylxanthine and 1-7-Dimethyluric acid). The coefficients of each observed variable to latent variable and coefficients of each regression path are described in Supplementary Table 5. The results showed that the total effect of virus on SCZ was not significant. However, after controlling the effect of all indirect effect, the direct path showed effect of selected virus on SCZ developed was significant (β_dir = -0.616, LLCI = -2.690, ULCI = -0.312, p = 0.013), suggesting the relationship between gut virus and SCZ significantly suppressed by gut bacteria and metabolites. The suppressed effect primarily driven by indirect effect of path gut virus → metabolites → SCZ (β_SSM2 = 0.582, LLCI = 0.347, ULCI = 2.941, p = 0.009).

The coefficients of indirect path involving two mediators (SMM3) showing the effects of reduction selected virus to SCZ developed through the increase in the levels of two mediators (Model5: a₁=-0.523; d₁₂ = 0.635; b₂ = 0.882; β_SMM3 = -0.293, LLCI = -1.414, ULCI= -0.014, p = 0.046). Interestingly, the results of Model2 (β_dir = 0.359, LLCI =, ULCI =, p =) and Model5 (b₁ = − 0.161, LLCI = -2.032, ULCI = 0.857, p = 0.425) suggested that after controlling the effect of virus, the direct effect between gut bacteria and SCZ was not significant (See Fig. 5E). The fit index of SMM suggesting this model fits well (GOF = 0.804, CFI = 0.825 and RMSEA = 0.086).

This study represents the first comprehensive and in-depth analysis to explore the role of the gut microbiota (involving gut virome and bacteriome) and the related peripheral metabolome modulation effect in SCZ. We found gut microbiome composing, characterized by β-diversity index and relative abundance of specific gut bacteria significant differed between SCZ and HCs. We further identified specific gut viruses and peripheral metabolites that correlated with differential gut bacteria. Based on these relationships, we found there are potential causal chains between the identified gut viruses and SCZ, suggesting that gut viruses may lead to the development of SCZ through regulating specific gut bacteria and peripheral metabolites. Interestingly, while previous studies have shown that gut bacteria often have a significant effect on SCZ through metabolites, which we also confirmed in this study, we found that the direct effect between gut bacteria and SCZ was no longer significant after controlling the effect of gut viruses. These results indicated a possibility that gut bacteria are not directed associated with SCZ, but as a key mediator in the “gut-brain” axis. The negative regulation effect between gut virus and gut bacteria in SCZ needs more exploration.

A recent systematic review have suggested that gut bacteria were associated with SCZ and reported relatively consistent alterations in overall community composition of gut bacteria (β-diversity) between SCZ and HCs, with higher Prevotella, lower Bacteroides, Haemophilus, and Streptococcus were more pronounced in SCZ (15). In our study, we confirmed the altered microbiome composing characterized by β-diversity index, increased Prevotella and decreased Bacteroides in SCZ. Moreover, the identified differential gut bacteria across all taxonomic showed predictive diagnostic value. These results are consistent with previous studies and support the role of gut bacteria in the pathogenesis of SCZ.

The overall transkingdom correlation analysis between gut viruses and bacteria showed that SCZ patients exhibit a comprehensive increased correlation profile with a significant imbalanced positive and negative correlation ratio. Mimiviridae, one of the drivers of this increased correlation pattern in SCZ patients, has been reported to be associated with obesity and diabetes (16). Although the BMI indices of SCZ patients were in the normal range and did not differ from HCs in this study, a well-established study reported that glucose dysregulation is an inherent risk of SCZ at disease onset (17). Increased Mimiviridae-gut bacteria correlation may indicate a potential mechanism of metabolic dysfunction in SCZ. When it comes to the relationship between differential bacteria and their co-occurring gut virus, the significant transkingdom correlations observed in HCs dramatically lost in SCZ patient population, especially driven by Suoliviridae, Rountreeviridae and Intestiviridae. These results may have revealed a SCZ-specific viral-bacterial correlation profile. The presence of depletion of virial-bacterial correlation profile in SCZ in combination with the significant negative effect of core virial component on SCZ-specific bacteria component further suggests a causal role for these microbiotas in developing SCZ. We noticed that Suoliviridae and Intestiviridae are under Crassvirales Order, which were considered to infect bacteria of the Bacteroidota phylum (including Rikenellaceae Family) (18). However, our understanding of how gut virus shape the gut bacteria community still limited, and the majority of gut viruses cannot be linked to any particular hosts (19). Further research is needed to explore the biological regulatory relationship between these gut viruses and bacteria.

Recent studies have highlighted the significance of gut microbiota and microbial-derived metabolites in modulating the human immune system. These include: 1) influencing the integrity of the intestinal barrier, allowing traffic across it to enter the host peripheral circulation, (20); 2) binding to surface and intracellular receptors on immune cells and intestinal epithelial cells, thereby activating or inhibiting immune responses(21); 3) gut virus, mainly bacteriophages, enter the peripheral circulation and interact with immune cells, thereby triggering host immune responses and regulating T cell and B cell activity and cytokine release (22); and 4) microbial metabolites, such as tryptamine, bile acids et.al, serve as signaling molecules to maintain host immune homeostasis(21, 23). Moreover, microbial-derived metabolites are also involved in the processes of glutamate and GABA synthesis and degradation (5), the endocannabinoid system (24), and microglia homeostasis regulation (25). These physiological pathways have all been implicated in the pathogenesis of SCZ.

In this study, we observed a stable gut bacteria-related core metabolic component made up of 12 metabolites. The direct effect coefficient between the core metabolic component and SCZ was the highest, showing the most important role of metabolites in contributed to SCZ developed among the serial causal chain of gut virus → gut bacteria → metabolites → SCZ. These metabolites enrich pathways are considered as important compounds involved in immune and inflammation regulation. Previous studies demonstrated that the chronic low-grade inflammation state characterized by high white blood counts and cytokines underlying SCZ (26–29). And SCZ patients are beneficial from adjunctive anti-inflammatory drugs treatment (30), but the pathogenesis is still not clearly. According to the results in this study, we considered gut microbiota species may be acting as inflammation-modulatory and underlying the chronic low-grade inflammation state, which is believed to be associated with SCZ.

Among the 12 metabolites, glycocholic acid, taurodeoxycholic acid (TDCA), and taurochenodeoxycholic acid (TCDCA) are hit metabolites of bile acids (BAs) pathway. There are mutually regulatory relationships existing between the BAs pool and gut microbiome composition in humans. Primary BAs undergo modification by gut microbiota, leading to the formation of secondary BAs, to utility their physiological function. Apart from BAs well-known role in aiding the digestion and nutrition absorption, BAs also exhibit bacteriostatic properties, suppressing bacterial overgrowth in the intestine and biliary tree (31). This mutually regulation function renders the composition of BAs pool highly sensitive to alterations of gut microbiota, especially Bacteroides (32). Abnormal peripheral BAs profiles have been previously reported in SCZ patients. The concentrations of certain BAs were significantly decreased in SCZ patients, along with impaired BAs deconjugation potentials (33). In our study, although not showing significant differences between groups, the detected BAs exhibited similar changing trends (mean relative peak areas of TDCA and TCDCA lower in SCZ group). Moreover, the physiological function of peripheral circulating BAs is interesting, they could be acting as signaling molecules that activate anti-inflammatory response, including suppress macrophages phagocytosis and lipopolysaccharide-stimulated pro-inflammatory cytokines release, such as IL-1, IL-6 and TNF-α(31, 34). In this study, the lower BAs concentrations related to anti-inflammation signaling dysfunction maybe underlying the chronic low-grade inflammation state in SCZ, but further investigation is needed. Notably, it is interesting to observe that the BAs receptors agonist and TCDCA supplements could reduce inflammation response and be exploited for treatment for Inflammatory Bowel Disease (IBD), an autoimmune disease. (32, 35). Interventions targeting for BAs metabolism through gut microbiota may also hold therapeutic potential for SCZ treatment.

Eicosanoids are lipid signaling molecules derived from fatty acids (FAs). Prostaglandins (PGs) is one of the primary subfamilies members of eicosanoids and relates with mounting or inhibiting inflammation and immune response (36). PGs are generated through cyclooxygenases (COX) enzymes, which are considered as prominent therapeutic targets for SCZ. The mixed COX inhibitor, aspirin, was used as adjuvant therapy alongside regular antipsychotic treatment, and reduced psychotic symptoms in SCZ (37). Additionally, the COX-2 inhibitor celecoxib has been demonstrated had therapeutic effects in schizophrenia, particularly in early disease stages (30, 38, 39). Thus, the eicosanoids metabolic pathway is believed to be a promising therapeutic target in SCZ.

In this study, we found four metabolites significantly enriched in the eicosanoids pathway, with significantly higher levels of PGH₂ in the SCZ group compared to the HC group. The elevated levels of PGH₂ suggested an inflammatory state in SCZ patients. PGH₂ was also one of the observed variables when performing serial mediation analysis. Peripheral PGs may be modulating by specific gut microenvironment (40–42), and affecting by dietary factors (such as the omega-6/omega-3 PUFA consumption) (43, 44). An imbalanced intake of omega-3/omega-6 PUFAs could lead to alteration of gut microbe composition, particularly a significant increase of the Firmicutes-to-Bacteroidetes ratio (45, 46). These results may also explain the role of fatty acids and conjugates metabolic pathway dysfunction in this study.

However, to our knowledge, there is no direct evidence supporting the notion that gut bacteria could impact peripheral PGs. Given that, the significant effect of gut virus on metabolites raised our interest. Previous studies have reported the gut virus induced immune and inflammatory response (47), thus, the raised PGH₂ level in SCZ may be a reaction to altered gut virus but need further evidence. Above all, the gut microbiota may contribute to dysregulation of eicosanoids metabolism, regulating inflammation response in patients with SCZ. Except for the COX inhibitor, aspirin and celecoxib, our study extended another potential therapeutic strategy that targets for eicosanoids metabolic pathway.

Limitation and future directions

While this study provides novel insights into the role of gut microbiota in SCZ, several limitations should be acknowledged. First, the cross-sectional design of the study precludes the establishment of causal relationships between gut microbiota, metabolites, and SCZ. Longitudinal studies are needed to elucidate temporal dynamics and causal interactions among these factors. Second, the process of qualitative and quantitative analysis from large-scale untargeted metabolomics spectra may result in the loss of a significant amount of information. For instance, recent research has extended BAs library in untargeted metabolic spectra and highlighting the abundance of modified BAs in brain (48). The current data analysis workflow may underestimate the actual impact of the discovered metabolites. Third, the relatively small sample size may limit the generalizability of the findings, and larger cohorts are required to validate these results. Fourth, although we controlled for potential confounders such as age, sex, BMI and medication use days, other factors, such as diet and smoking, may have influenced the gut microbiota composition and metabolite profiles. Future studies should consider these factors and employ more comprehensive data collection and analysis strategies.

Despite the limitations present in our study, our findings lay pivotal groundwork for further exploration into the gut-brain axis in SCZ. Prospective studies that delve into the gut microbiota and metabolite profiles of individuals at heightened risk for SCZ could pave the way for pinpointing early biomarkers and potential therapeutic avenues. Furthermore, interventional research, including strategies such as supplementation with probiotics or prebiotics, fecal microbiota transplantation, or targeted modulation of metabolites, could shed light on the gut microbiota's direct involvement in SCZ, thereby uncovering innovative treatment methodologies. The integration of multi-omics data, encompassing metagenomics, metatranscriptomics, and metabolomics, promises a more in-depth comprehension of the functional interplays between the gut microbiota, the host's metabolism, and brain functionality in SCZ.

In sum, our research uncovers significant deviations in the gut microbiota composition, the interactions between viral and bacterial entities, and the peripheral metabolite profiles in patients with SCZ. We have identified potential causative links between specific gut viruses, bacteria, metabolites, and SCZ, underlining the intricate interconnections within the gut-brain axis. The predominance of differential bacteria-related metabolites in immune and inflammatory pathways implies a potential role of the gut microbiota in fostering the chronic, low-grade inflammation observed in SCZ patients. Our study offers fresh perspectives on the involvement of gut microbiota in the pathogenesis of SCZ and establishes a solid foundation for forthcoming investigations aimed at microbiota-focused diagnostic and therapeutic strategies for this challenging condition.

Acknowledgments

This work was financially supported by the National Nature Science Foundation of China (Grant NO. 82171499 to QW, Grant NO.82071524 to ML and Grant NO. 82301683 to ST), Chinese National Programs for Brain Science and Brian-like Intelligence Technology, China Depression Cohort Study (STI2030-Major Projects-2021ZD0200700 to QW), Science and Technology Project of Sichuan Province (Grant NO. 2023YFS0030 to QW), Post Doctor Research Project, West China Hospital, Sichuan University (2023HXBH084 to MX), Natural Science Foundation of Sichuan Province (Grant no. 2024NSFSC1560 to ST, Grant no. 2022NSFSC1591 to ZZ, and Grant no. 2023NSFSC0126 to JC). This work was supported by Biomedical Big Data Intelligent Computing Center of Oujiang Lab.

QW and ST conceived the study. ST and YW performed the data analysis. QW, ST and YW interpreted the results and prepared the manuscript. LX, YH, YT, SL, YL, QM, YY, MD, MX, JC, ZZ, QL, JZ, MZ, MW, YC and ML contributed to data collection from participants.

Disclosures

All authors declare that they have no conflict of interest.

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The authors have declared there is NO conflict of interest to disclose

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Supplementary Table 2. Peripheral metabolites co-occurring with differential gut bacteria

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Alterations in fecal bacteriome virome interplay and microbiota-derived dysfunction in patients with schizophrenia

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Abstract

Figures

Introduction

Methods

Participants

Ethical principles

Fecal sample collection and gut microbiome sequencing

Metabolomics data acquisition and preprocessing

Statistical analysis

The gut bacteriome diversity comparison

The gut microbiota data pre-processing

Identifying differential abundance of gut bacteria

Investigating the potential diagnostic value of differential gut bacteria

Transkingdom viral-bacterial correlation pattern comparison in SCZ and HCs

Exploring potential causality relationships between gut microbiota, microbial-derived metabolites and SCZ/HCs

Results

Demographic characteristics of study subjects

Diversity differences of the gut microbiota in SCZ and HC

Gut microbiota data pre-processing

Differential gut bacteria

The diagnostic value of identified differential bacteria

Transkingdom partial correlation pattern differed in SCZ and HCs

Identified gut viruses and peripheral metabolites co-occurring with differential bacteria

Enrichment analysis of selected metabolites

Effect of gut microbiota and metabolites on SCZ

Discussion

Limitation and future directions

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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