Home Liver Diseases Distinct signatures of gut microbiome and metabolites associated with significant fibrosis in non-obese NAFLD

Distinct signatures of gut microbiome and metabolites associated with significant fibrosis in non-obese NAFLD

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Distinct signatures of gut microbiome and metabolites associated with significant fibrosis in non-obese NAFLD

Baseline characteristics of human subjects

This study included 171 subjects with biopsy-proven NAFLD (NAFL, n = 88; NASH, n = 83) and 31 no-NAFLD controls. All of the study subjects were classified into two groups (non-obese, BMI < 25; obese, BMI ≥ 25), and each group was divided into three subgroups according to the histological spectra of NAFLD or fibrosis severity. Supplementary Tables 1 and 2 present the detailed characteristics of each group, including clinical, metabolic, biochemical, and histological profiles. Subjects with NASH or significant fibrosis (F2–4) had higher levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), and insulin resistance in both the obese and non-obese groups. Subjects with significant fibrosis had higher NAFLD activity scores and presented more severe liver histology in terms of the histological classification of NAFLD (Supplementary Table 3 and Supplementary Fig. 1). More detailed baseline characteristics in each fibrosis stage, including well-known NAFLD-associated genetic variants, such as PNPLA3, TM6SF2, MBOAT7-TMC4, and SREBF-2, are shown in Supplementary Table 4.

Microbial community changes according to fibrosis severity are different in non-obese and obese NAFLD subjects

We analyzed data from 16S rRNA gene amplicon sequencing and compared the microbial diversity according to the histological spectra of NAFLD or fibrosis severity (Fig. 1). Alpha diversity based on the Shannon metric was plotted, and beta diversity based on the Bray–Curtis distance was plotted for comparison. No significant alterations in diversity among groups stratified by the histological spectra of NAFLD or fibrosis severity were observed (Fig. 1a). We then classified the subjects into two groups according to BMI status. In the non-obese group, a significant decrease in microbial diversity between F1 and F0 (P = 0.0074) as well as between F2–4 and F0 (P = 0.0084) was observed (Fig. 1b). Moreover, apparent clustering between F0 and F2–4 was observed (P = 0.038). In the obese group, no significant differences in diversity were found among groups stratified by the histological spectra of NAFLD or fibrosis severity (Fig. 1c). These results indicate that fibrosis severity rather than necroinflammatory activity is more likely associated with the changes of gut microbiome and underlying BMI status may also be an important factor responsible for alterations of the gut microbiome.

Fig. 1: Comparison of the diversity of gut microbial communities in all, non-obese, and obese subjects.

The alpha and beta diversity of a all (n = 202), b non-obese subjects (n = 64) (**P = 0.0074, F0 vs F1; **P = 0.0084, F0 vs F2–4), and c obese subjects (n = 138) divided by the histological spectra of NAFLD or fibrosis severity. Alpha diversity was based on the Shannon index with 12,000 rarefied sequences per sample. The box plots indicate the median, 25th to 75th percentiles (boxes), and 10th to 90th percentiles (whiskers). Statistical analysis was performed using a two-sided nonparametric Mann–Whitney test or a nonparametric Kruskal–Wallis test with Dunn’s multiple comparison test. NMDS plots were generated using relative OTU abundance data according to the Bray–Curtis distance, and statistical significance was measured using adonis analysis (panel b; *P = 0.038, F0 vs F2–4).

Alterations of fibrosis-related microbiota are more prominent in non-obese NAFLD rather than obese NAFLD

Differences in the specific microbial taxa by fibrosis severity in non-obese and obese subjects were compared using univariate and multivariate analyses (Fig. 2). In univariate analysis, the gradual enrichment of Veillonellaceae, as well as Enterobacteriaceae, was observed according to fibrosis severity in non-obese subjects (Fig. 2a). In obese subjects, Rikenellaceae was gradually enriched. In contrast, the abundance of Ruminococcaceae significantly decreased as fibrosis became more severe, which was observed only in non-obese subjects. At the genus level, Faecalibacterium, Ruminococcus (Ruminococcaceae), Coprococcus, and Lachnospira (Lachnospiraceae) were significantly depleted in the significant fibrosis group (Fig. 2b), while the abundances of Enterobacteriaceae_Other (Enterobacteriaceae) and Citrobacter gradually increased according to fibrosis severity. These alterations were also observed only in non-obese subjects. These results could be also found in the correlation plots (Supplementary Fig. 2). Enterobacteriaceae and Veillonellaceae had positive correlations (P = 1.09 × 10−4, P = 2.44 × 10−3, respectively), but Ruminococcaceae showed an inverse correlation (P = 4.41 × 10−4) with fibrosis severity.

Fig. 2: Phylogenetic comparisons of the gut microbiome in non-obese and obese subjects.
figure2

a The 13 family- and b 14 genus-level taxa with top abundances are depicted for clarity. Statistical significance was measured using a nonparametric Kruskal–Wallis test with Dunn’s multiple comparison test. The P values are as follows: panel (a): **P = 0.0013, **P = 0.0054, **P = 0.0016, and *P = 0.04; panel (b): *P = 0.0217, **P = 0.0013, *P = 0.0209, *P = 0.0234, and ***P = 0.0014. Multivariate associations between specific gut-microbiome components and fibrosis severity stratified by obesity status (cf). Arcsine-root transformed abundances of bacteria were regressed against c age, sex, and BMI, d age, sex, and diabetes, e age, sex, and BMI (without cirrhotic subjects), and f age, sex, and BMI (without no-NAFLD subjects). Statistical analyses for multivariate associations were performed using MaAsLin with adjustments for multiple comparisons (q value). The P and q values are as follows: panel (c): **P = 0.0057, ***P < 0.001, and ##q = 0.0297, *P = 0.0313, **P = 0.0012, and #q = 0.0972; panel (d): from left, **P = 0.0020, **P < 0.0100, (F0 vs F1), and **P = 0.0013 (F0 vs F2–4); panel (e): **P = 0.0071, ***P = 0.0001, and ##q = 0.0339, *P = 0.0191, and **P = 0.0015; panel (f): *P = 0.0136, *P = 0.0292, and **P = 0.0023. The box plots indicate the median, 25th to 75th percentiles (boxes), and 10th to 90th percentile (whiskers). Colors inside the box represent fibrosis severity. ad n = 64 for non-obese and n = 138 for obese; e n = 60 for non-obese and n = 125 for obese; f n = 43 for non-obese and n = 129 for obese. *P < 0.05, **P < 0.01, ***P < 0.001, #q < 0.10, ##q < 0.05. Source data are provided as a Source Data file.

For multivariate analysis, we adjusted for age, sex, and BMI using MaAsLin17. In the phylum Firmicutes, Veillonellaceae showed a steeper increase in relative abundance in non-obese subjects than in obese subjects (non-obese, P = 0.0002, q = 0.0297), while the abundance of Ruminococcaceae was inversely correlated with fibrosis severity in non-obese subjects (P = 0.0012, q = 0.0972) (Fig. 2c). Ruminococcus, a representative genus of Ruminococcaceae, also showed a significant inverse association with fibrosis severity only in non-obese subjects (P = 0.0011, q = 0.152) (Supplementary Fig. 3). In addition, Veillonellaceae and Enterobacteriaceae showed a significant, positive association with serum free fatty acid (FFA) levels in non-obese subjects (q = 0.178, q = 0.118, respectively) but not in obese subjects (Supplementary Fig. 4). Adipose tissue insulin resistance (adipo-IR) and glycosylated hemoglobin (HbA1c) were also positively correlated with the abundance of Veillonellaceae in non-obese subjects (adipo-IR, q = 0.142; HbA1c, q = 0.157). In contrast, serum FFA levels were inversely correlated with the abundance of Ruminococcus in all subjects (q = 0.0838) and in non-obese subjects (q = 0.144) but not in obese subjects (q = 1.00).

In addition to three variables (age, sex, and BMI), the presence of DM is also known to affect general changes in the microbial community18. We found that Enterobacteriaceae (P = 0.0002, q = 0.039) and Faecalibacterium (P = 0.0029, q = 0.12) were associated with the presence of DM in all subjects (Supplementary Fig. 5a). The depletion of Lachnospira (P = 4.36 × 10−4, q = 0.316) (Supplementary Fig. 5b) in the non-obese, as well as the enrichment of Klebsiella (P = 0.0026, q = 0.207) (Supplementary Fig. 5c), which belongs to Enterobacteriaceae, in the obese, was observed in subjects with DM. However, the associations of Veillonellaceae and Ruminococcaceae with fibrosis severity remained significant even after adjustment for DM (Fig. 2d) and the use of metformin (Supplementary Fig. 6). The anti-diabetic medications prescribed in each subgroup are listed in Supplementary Table 5. The oral hypoglycemic agents include metformin, linagliptin, glimepiride, etc., and some patients received combined treatment. To exclude the confounding effect of cirrhosis, we also performed sensitivity analysis only for study subjects without cirrhosis. Nonetheless, the significant associations of Veillonellaceae and Ruminococcaceae with fibrosis severity were not affected by cirrhosis (Fig. 2e). In addition, we conducted the same analysis with only NAFLD patients except for healthy control subjects. Similarly, Veillonellaceae and Ruminococcaceae were significantly associated with fibrosis severity notwithstanding the smaller population (Fig. 2f). To determine whether these notable microbiome changes in non-obese subjects could be attributed to the host gene effect, we adjusted for genetic variants of PNPL3, TM6SF2, MBOAT7-TMC4, and SREBF-2 in multivariate analysis. The associations between two identified taxa and fibrosis severity in non-obese subjects remained significant even after adjustment for host genetic variants (Supplementary Fig. 7). Taken together, these findings indicate that enrichment of specific taxa according to fibrosis severity might be more prominent in the non-obese group than in the obese group.

Non-obese and obese NAFLD subjects have different stool metabolites levels according to fibrosis severity

We next assessed the stool metabolites that are mainly associated with the gut microbiome using Q-TOF and GC-FID systems. The composition of the bile acid pool varied between non-obese and obese subjects (Fig. 3a and Supplementary Fig. 8). The levels of unconjugated and conjugated bile acids noticeably increased in non-obese subjects with fibrosis, and the total stool bile acid levels were higher (unconjugated bile acids, 2.3 times; conjugated bile acids, 3.6 times) in non-obese subjects with significant fibrosis (F2–4) than in those without fibrosis (F0) (Fig. 3b). In obese subjects, total conjugated bile acid levels decreased with increasing fibrosis severity. Specifically, cholic acid (CA), chenodeoxycholic acid (CDCA), ursodeoxycholic acid (UDCA), glycochenodeoxycholic acid (GCDCA), and glycoursodeoxycholic acid (GUDCA) levels significantly increased in non-obese subjects with worsening fibrosis severity (Fig. 3c). Lithocholic acid (LCA) and deoxycholic acid (DCA) levels were elevated in obese subjects with significant fibrosis, but only LCA reached statistical significance (Fig. 3c). Among three SCFAs, stool propionate levels gradually increased as fibrosis became more severe in non-obese subjects (non-obese; P = 0.0032, obese; P = 0.7979) (Fig. 3d).

Fig. 3: Comparison of stool metabolites levels stratified by fibrosis severity and obesity status.
figure3

a Composition of bile acid profiles in different clinical settings. Stacked plots are generated using the average abundances of the 13 bile acids. b The stacked bars represent (left) unconjugated bile acids levels and (right) conjugated bile acids levels, which are stratified by fibrosis severity and obesity status. c Box plots represent the concentrations of the stool bile acids, which are stratified by fibrosis severity and obesity status; cholic acid (CA), ***P = 0.0007, *P = 0.0260, n = 57 for non-obese, and n = 111 for obese; chenodeoxycholic acid (CDCA), ***P < 0.0001, *P = 0.0164, n = 54 for non-obese, and n = 96 for obese; ursodeoxycholic acid (UDCA), *P = 0.0107, **P = 0.0012, n = 56 for non-obese, and n = 95 for obese; glycochenodeoxycholic acid (GCDCA), **P = 0.0023, n = 52 for non-obese, and n = 110 for obese; lithocholic acid (LCA), **P = 0.0075, *P = 0.0265, **P = 0.0045, n = 59 for non-obese, and n = 122; deoxycholic acid (DCA), *P = 0.0119, *P = 0.0128, n = 60 for non-obese, and n = 122 for obese; glycoursodeoxycholic acid (GUDCA), **P = 0.0064, *P = 0.0115, n = 47 for non-obese, and n = 99 for obese. d Box plots represent the most abundant fecal short-chain fatty acids (SCFAs) levels (acetate, propionate, and butyrate), which are stratified by fibrosis severity and obesity status; propionate, *P = 0.0114, *P = 0.0182, n = 51 for non-obese, and n = 100 for obese; butyrate, n = 43 for non-obese, and n = 98 for obese; acetate, *P = 0.0440, n = 42 for non-obese, and n = 89 for obese. The box plots indicate the median, 25th to 75th percentiles (boxes), and minimum to maximum values (whiskers). Outliers were removed by the ROUT method (Q = 1%) and data were analyzed using a nonparametric Kruskal–Wallis test with Dunn’s multiple comparison test. *P < 0.05, **P < 0.01, ***P < 0.001. Taurolithocholic acid (TLCA), taurodeoxycholic acid (TDCA), taurochenodeoxycholic acid (TUDCA), taurocholic acid (TCA), glycodeoxycholic acid (GDCA), glycocholic acid (GCA).

Non-obese NAFLD and obese NAFLD show different patterns of bacterial taxa-metabolites networks

To understand the interaction among gut microbiota in non-obese and obese subjects, the co-occurrence of taxa was measured and depicted with relative abundances in correlation with fibrosis severity (Supplementary Fig. 9). As expected, Veillonellaceae and Enterobacteriaceae were inversely correlated with Ruminococcaceae in non-obese subjects. However, strong interactions among these bacteria were not observed in the obese and all of the subjects, implying its specific role in the progression of fibrosis in non-obese subjects.

To explore the key drivers responsible for this observation, we analyzed the co-occurrence analysis of taxa and metabolites as illustrated in Fig. 4. The strong interactions among bile acids were observed in both non-obese and obese subjects (Fig. 4a and b). Interestingly, primary bile acids had an inverse relationship with Ruminococcaceae, which are known as indicators of healthy intestines, in both non-obese and obese subjects. Veillonellaceae showed positive interactions with primary bile acids and UDCA, as well as propionate. Bile acids usually retain the potential to modulate the growth of susceptible bacteria or enrich relatively resistant bacteria independent of obesity status. Nevertheless, the relationship of gut bacterial taxa and stool metabolites with severe fibrosis was more noticeable in non-obese NAFLD subjects than in obese NAFLD subjects.

Fig. 4: Network profiles between microbial taxa and stool metabolite components in non-obese and obese subjects.
figure4

Co-occurrence coefficients among family-level microbiome components and stool metabolites were calculated by SparCC, and networks (P < 0.05) are depicted using Cytoscape. a Non-obese and b obese. The solid line (orange) and dotted line (gray) indicate positive and negative correlations, respectively. The shape of the node denotes the components used in this study (ellipse: microbiome, diamond: stool bile acids, and round rectangle: SCFAs) and the color indicates the degree of correlation with fibrosis severity. The P-value for each coefficient was obtained by bootstrapping the dataset 500 times and applying SparCC to each of those 500 datasets. Source data are provided as a Source Data file.

Microbiome–metabolite combination reflects significant fibrosis in non-obese subjects with NAFLD

To assess the utility of the gut microbiome for indicating significant fibrosis, the areas under the receiver-operating characteristic curve (AUCs) for diagnosing significant fibrosis were compared (Fig. 5). Veillonellaceae and Ruminococcaceae were selected as the most representative and significant fibrosis-related bacterial taxa. The combined bacterial marker to diagnose significant fibrosis yielded an AUC of 0.765 in non-obese subjects (0.559 in all subjects; 0.544 in obese subjects). In addition, we selected four stool metabolites (CA, CDCA, UDCA, and propionate) as fibrosis-related metabolites, and the combination of the four metabolites diagnosed significant fibrosis with an AUC of 0.758 in non-obese subjects (0.574 in all subjects; 0.520 in obese subjects). The addition of stool metabolites to these bacterial taxa significantly enhanced the diagnostic power with an improved AUC of 0.939 (0.584 in all subjects; 0.520 in obese subjects). The diagnostic power of this microbiome–metabolite combination was higher in non-obese subjects than that of FIB-4, which is widely used as a non-invasive fibrosis test in NAFLD. Considering the broad spectrum of pathological changes between healthy controls and cirrhotic subjects, we also assessed the AUCs restricted to study subjects without cirrhosis (Supplementary Fig. 10a) or without healthy controls (Supplementary Fig. 10b). Nevertheless, the AUCs using six combined markers sustained high diagnostic power in non-obese subjects (0.838 and 0.810 in the populations without cirrhotic subjects and healthy controls, respectively).

Fig. 5: Receiver-operating characteristic (ROC) curves for the diagnosis of significant fibrosis in all, non-obese, and obese subjects.
figure5

ROC curves using the combination of two bacteria (Veillonellaceae and Ruminococcaceae) and four stool metabolites (CA, CDCA, UDCA, and propionate) were plotted for the diagnosis of significant fibrosis in a all, b non-obese subjects, and c obese subjects, and the areas under the ROC curves (AUCs) were calculated. FIB-4, fibrosis 4 index.

The Western NAFLD cohort similarly shows different microbiome patterns between non-obese and obese subjects

To validate our results using an external independent cohort, we used public datasets. Caussy et al. reported a gut-microbiome-based biomarker for diagnosing NAFLD-related cirrhosis using a well-characterized NAFLD twin cohort14. A total of 168 subjects were divided into non-obese and obese groups according to BMI ((ge)30, obese; <30, non-obese), and each group was divided based on the presence of advanced fibrosis. Baseline characteristics of the validation cohort are described in Supplementary Table 6. Non-obese subjects with advanced fibrosis were enriched with Veillonellaceae (P = 0.0120), which was not observed in obese subjects (P = 0.8818) (Fig. 6a). In addition, Ruminococcaceae showed a tendency to decrease only in non-obese (P = 0.1350; obese, P = 0.9944) subjects with advanced fibrosis. The combination of two fibrosis-related bacteria diagnosed advanced fibrosis with an AUC of 0.721 in non-obese subjects (0.578 in obese subjects) (Fig. 6b). Because the Western NAFLD twin cohort lacked metabolites data, the diagnostic power of the Western NAFLD twin cohort seemed to be lower than that of our Korean NAFLD cohort using the taxa-metabolite combination. These results indicate that changes in Veillonellaceae/Ruminococcaceae according to fibrosis severity in non-obese subjects are replicated in a Western cohort with the potential of the microbiome-based marker to diagnose fibrosis in non-obese subjects with NAFLD.

Fig. 6: Validation of gut-microbiome differences between non-obese and obese subjects using a Western NAFLD cohort.
figure6

a Abundance of Veillonellaceae and Ruminococcaceae stratified by obesity status and advanced fibrosis (*P = 0.0120, n = 107 for non-obese, and n = 61 for obese). b ROC curves using the combination of two selected bacteria (Veillonellaceae and Ruminococcaceae) were plotted for the diagnosis of advanced fibrosis in non-obese and obese subjects, and the areas under the ROC curve (AUCs) were calculated. The box plots indicate the median, 25th to 75th percentiles (boxes), and 10th to 90th percentiles (whiskers). Statistical analysis was performed using a two-sided Mann–Whitney test. *P < 0.05. ns, not significant.

Species of Megamonas and Ruminococcus are identified by metagenome sequencing

To identify fibrosis-related species consisting of Veillonellaceae and Ruminococcaceae, metagenome analysis of stool samples collected from 38 non-obese subjects (F0, n = 25; F2–3, n = 13) was further conducted. Consistent with the results of 16S rRNA gene amplicon sequencing, Veillonellaceae and Ruminococcaceae were the most significant families related to fibrosis in both univariate and multivariate analyses of metagenome data (Fig. 7a, b). At the genus and species levels, Ruminococcus bromii, Megamonas hypermegale, and M. funiformis were the most significant taxa related to fibrosis severity in non-obese subjects. We also assessed the abundances of microbial genes related to primary and secondary bile acid metabolism (Fig. 7c). The abundances of genes encoding bile salt hydrolase (bsh) and 7α-hydroxy-3-oxochol-4-en-24-oyl-CoA dehydrogenase (baiCD) were significantly downregulated in non-obese subjects with significant fibrosis, which might be associated with increasing amounts of total conjugated bile acids and unconjugated primary bile acids. Major contributors to bsh were species belonging to Ruminococcaceae, Lachnospiraceae, and Eubacteriaceae (Supplementary Fig. 11).

Fig. 7: Identification of fibrosis-related bacteria in non-obese subjects using metagenomic shotgun analysis.
figure7

Stool metagenome analysis of 38 non-obese subjects was conducted (F0, n = 25; F2–3, n = 13). a Abundances of fibrosis-related taxa are depicted for clarity. Taxa enriched in subjects with fibrosis stage 0 (top) and fibrosis stage of 2 or 3 (bottom). Statistical analysis was performed using a two-sided nonparametric Mann–Whitney test. The P values are as follows: (top) ***P = 0.0009, *P = 0.0113, *P = 0.0124, **P = 0.0024, *P = 0.0124, **P = 0.0051, **P = 0.0049, **P = 0.0070, *P = 0.0451, **P = 0.0041, **P = 0.0011, ***P < 0.0001, and **P = 0.0022; (bottom) *P = 0.0294, *P = 0.0346, *P = 0.0136, *P = 0.0163, *P = 0.0411, *P = 0.0164, **P = 0.0050, *P = 0.0112, and *P = 0.0336. b Multivariate associations between specific gut-microbiome components and fibrosis severity. Arcsine-root transformed abundances of bacteria were regressed against age, sex, and BMI. Only significant coefficients (P < 0.05) are depicted, and the color inside the box represents the enriched fibrosis group. c Heatmap showing the abundances of microbial genes related to bile acid metabolism pathway (left). The box plots indicate the median, 25th to 75th percentiles (boxes), and 10th to 90th percentiles (whiskers) (right) (*P = 0.0124 and *P = 0.0237). d Network profiles between microbial taxa and stool metabolite components. Coefficients among genus, species-level bacteria, and stool metabolite components were calculated by SparCC. Coefficients (P < 0.05) are depicted using Cytoscape. The P-value for each coefficient was obtained by bootstrapping the dataset 500 times and applying SparCC to each of those 500 datasets. The solid line (orange) and dotted line (gray) indicate positive and negative correlations, respectively. The shape of the node denotes the components used in this study (ellipse: microbiome, diamond: stool bile acids, and round rectangle: SCFAs) and the color indicates the degree of correlation with fibrosis severity. *P < 0.05, **P < 0.01, ***P < 0.001. Source data are provided as a Source data file.

The interaction between fibrosis-related bacterial species and stool metabolites was also revealed by network analysis based on the metagenome data of non-obese NAFLD subjects (Fig. 7d). The distinct co-occurrence pattern and strong correlation between key bacteria and metabolites were reconstructed; R. bromii, Faecalibacterium prausnitzii, and Roseburia intestinalis were inversely correlated with fibrosis severity and primary bile acid level, and Megamonas spp. showed a significant, positive correlation with primary bile species and UDCA, along with increasing fibrosis severity. However, Ruminococcus gnavus, a member of Lachnospiraceae family, was positively associated with primary bile acids and Megamonas species.

Administration of Ruminococcus faecis alleviates liver damage in NAFLD mouse models

To identify the protective or worsening effect on liver damage, we administered four representative species-level bacteria belonging to Ruminococcaceae and Veillonellaceae–Ruminococcus faecis (R. faecis), R. bromii, Megamonas funiformis (M. funiformis), and Veillonella parvula (V. parvula) to C57BL/6 mice fed methionine- and choline-deficient (MCD) diets for 5 weeks (Fig. 8a). We demonstrated that R. faecis feeding decreased serum ALT and AST levels compared to sham feeding (Fig. 8b). No worsening effect was found in mice given M. funiformis and V. parvula. An alleviating effect of R. faecis on fibrosis was shown with H&E and Sirius red staining (Fig. 8c), and the histological severity of NAFLD induced by an MCD diet was significantly regressed in mice fed R. faecis (Fig. 8d, e). The mRNA expression of fibrogenic genes (Col1a1, Timp1, and a-SMA) was also downregulated in mice treated with R. faecis compared to untreated control mice fed an MCD diet (Fig. 8f). In parallel with the changes in biochemical and histological liver injury markers, the cecal levels of secondary bile acids (DCA and LCA) were also decreased by an MCD diet and increased by R. faecis treatment (Fig. 8g). To confirm the alleviating effect of R. faecis on liver damage in other mouse NAFLD models, we used a choline-deficient, L-amino acid-defined, high-fat diet (CDAHFD) model which prevents body weight loss in mice and shows no insulin resistance, and a genetic leptin-deficient (db/db) model, which develops spontaneously diabetes and fatty liver with insulin resistance. In both models, R. faecis administration decreased ALT and AST levels (Supplementary Fig. 12). However, the liver ratio against body weight decreased only in db/db mice. Nevertheless, the fasting insulin levels in serum and insulin resistance measured by ipGTT in db/db mice were not affected by R. faecis treatment.

Fig. 8: Effect of fibrosis-associated bacteria on liver damage induced by an MCD diet.
figure8

Mice were acclimated for 1 week on a standard chow diet. Then, they were treated with streptomycin (1 g/L) in drinking water for colonization of four fibrosis-related bacteria. Following 5 weeks, the mice were given daily 200 μL of either bacteria (109 CFU/mouse in PBS) or sham in an MCD diet. a Scheme of the animal experiment. b Effects of the MCD diet and bacteria on serum ALT and AST levels (ALT, ***P = 0.0002 and ***P = 0.0003; AST, ***P = 0.0047 and *P = 0.0281) n = 8 for normal chow, MCD, R. faecis, R. bromii, and M. funifomis group) and n = 13 for V. parvula group. c Representative images of Ruminococcus faecis-treated liver tissues stained with H&E and Sirius red. Scale bar indicates 200 μm. d Comparison of histological NAFLD activity scores calculated on H&E stained liver tissues (***P < 0.0001 and ***P = 0.0006; n = 12 for all groups). e Comparison of collagen proportionate areas measured on Sirius red-stained liver tissues (***P = 0.0002 and ***P = 0.0002; n = 8 for all groups). f Relative fibrogenic mRNA expression of liver harvested from Ruminococcus faecis-treated mice (**P = 0.0016, **P = 0.0016, *P = 0.0293, **P = 0.0047, and *P = 0.0356; n = 5–6 for normal chow and MCD + Ruminococcus faecis, n = 8 for MCD). g Comparison of secondary bile acids levels measured in the cecum of Ruminococcus faecis-treated mice (***P = 0.0003, ***P = 0.0006, and *P = 0.0104; n = 7–8 for normal chow and MCD, n = 8 for MCD + Ruminococcus faecis). The bar graphs indicate the means with SDs. Statistical analysis was performed using a nonparametric Kruskal–Wallis test with Dunn’s multiple comparison test or a two-sided Mann–Whitney test. *P < 0.05, **P < 0.01, ***P < 0.001.

In search of the relationship between administered taxa and changes in bile acid or SCFA composition, we further analyzed the compositions of bile acid and SCFA (Fig. 8g, Supplementary Fig. 13, and Supplementary Table 7). We observed that the cecal levels of LCA and DCA were decreased by an MCD diet and were normalized by R. faecis treatment. However, an increase in the cecal levels of SCFAs was not observed by V. parvula treatment, rather a slight decrease in the level of propionate was observed in the cecum. These results indicated that intestinal bacteria could affect the regression of NAFLD in an insulin-independent manner, supporting our human-associated data found in non-obese NAFLD subjects.

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