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Ileal microbial microbiome and its secondary bile acids modulate susceptibility to nonalcoholic steatohepatitis in dairy goats

Microbiome volume 12, Article number: 247 (2024) Cite this article

Abstract

Background

Liver damage from nonalcoholic steatohepatitis (NASH) presents a significant challenge to the health and productivity of ruminants. However, the regulatory mechanisms behind variations in NASH susceptibility remain unclear. The gut‒liver axis, particularly the enterohepatic circulation of bile acids (BAs), plays a crucial role in regulating the liver diseases. Since the ileum is the primary site for BAs reabsorption and return to the liver, we analysed the ileal metagenome and metabolome, liver and serum metabolome, and liver single-nuclei transcriptome of NASH-resistant and susceptible goats together with a mice validation model to explore how ileal microbial BAs metabolism affects liver metabolism and immunity, uncovering the key mechanisms behind varied NASH pathogenesis in dairy goats.

Results

In NASH goats, increased total cholesterol (TC), triglyceride (TG), and primary BAs and decreased secondary BAs in the liver and serum promoted hepatic fat accumulation. Increased ileal Escherichia coli, Erysipelotrichaceae bacterium and Streptococcus pneumoniae as well as proinflammatory compounds damaged ileal histological morphology, and increased ileal permeability contributes to liver inflammation. In NASH-tolerance (NASH-T) goats, increased ursodeoxycholic acid (UDCA), isodeoxycholic acid (isoDCA) and isolithocholic acid (isoLCA) in the liver, serum and ileal contents were attributed to ileal secondary BAs-producing bacteria (Clostridium, Bifidobacterium and Lactobacillus) and key microbial genes encoding enzymes. Meanwhile, decreased T-helper 17 (TH17) cells and increased regulatory T (Treg) cells proportion were identified in both liver and ileum of NASH-T goats. To further validate whether these key BAs affected the progression of NASH by regulating the proliferation of TH17 and Treg cells, the oral administration of bacterial UDCA, isoDCA and isoLCA to a high-fat diet-induced NASH mouse model confirmed the amelioration of NASH through the TH17 cell differentiation/IL-17 signalling/PPAR signalling pathway by these bacterial secondary BAs.

Conclusion

This study revealed the roles of ileal microbiome and its secondary BAs in resilience and susceptibility to NASH by affecting the hepatic Treg and TH17 cells proportion in dairy goats. Bacterial UDCA, isoDCA and isoLCA were demonstrated to alleviate NASH and could be novel postbiotics to modulate and improve the liver health in ruminants.

Video Abstract

Background

In ruminants, long-term feeding of a high-concentrate diet (HCD) is a common practice to meet the energy requirements for animal growth and production (milk and meat), which can cause a series of health issues, including liver metabolic dysfunction. To date, numerous studies in ruminants have focused mainly on liver ketosis and fatty liver caused by a negative energy balance in dairy cows [1,2,3]. Several studies have also reported that long-term consumption of a HCD by dairy goats and cows can result in lipometabolic disturbances and inflammatory responses in the liver [4, 5], suggesting that nonalcoholic steatohepatitis (NASH, also known as the metabolic dysfunction-associated steatohepatitis) characterized by hepatic lipid accumulation, inflammation, and liver damage are critical health issues under HCD feeding. Although liver functions and health in ruminants are highly recognized, the modes of action involved in resilience and susceptibility to NASH induced by HCD in dairy goats have not been well defined, preventing effective strategies to improve overall health and productivity.

In general, the individual variations in susceptibility to metabolic diseases, including NAFLD/NASH, could be attributed to several factors, such as host genetics, diet, life style, and host immune function differences [6,7,8]. The liver is anatomically and physiologically linked to the gut through the biliary tract, portal vein and systemic circulation, which is called the gut‒liver axis [7]. Liver-originated substances can enter the gut through the biliary tract, and the liver also receives gut-derived metabolites through the portal vein [9]. It has been demonstrated the increased abundance of Escherichia coli and Bacteroides vulgatus in the gut, and bacteria-derived metabolites such as trimethylamine (TMA), plasma phenylacetic acid (PAA) or ethanol can promote the progress of NAFLD [7]. Recent research further highlighted the gut microbiota‒immune interaction in NAFLD/NASH pathogenesis [10]. Since 70% of the blood in the liver originates from the portal vein, gut bacteria-derived products during gut dysbiosis can recruit and activate immune cells in the gut and liver through pathogen-associated molecular patterns and produce inflammatory cytokines, which is a key mechanism triggering and amplifying hepatic inflammation in NAFLD/NASH [10, 11]. These findings suggest that altered gut microbiota and metabolites during gut dysbiosis may cause gut and liver immune homeostasis imbalance and further induce inflammation-mediated liver injury; however, these mechanisms have not been confirmed in ruminants.

We speculated that gut microbial dysbiosis-induced metabolic disturbances could increase susceptibility to liver damage in dairy goats. Specifically, the altered gut microbiome (the small intestine microbiota and its metabolites) caused by HCD could affect individualized resilience and pathogenesis in response to NASH. In this study, we investigated the mechanisms underlying the difference in susceptibility to NASH in dairy goats fed a high-concentrate diet (HCD) by comparing liver and serum metabolomes, ileal metagenomes and metabolomes, as well as liver tissue and single-nuclei transcriptomes. Additionally, a mouse model was used to validate the potential effects of the identified bacterial metabolites on susceptibility to NASH, with the aim of developing targeted strategies to prevent liver damage under HCD feeding in intensive production systems, which are crucial for sustaining livestock performance.

Results

Individual differences in NASH susceptibility in dairy goats fed HCD

The overall study design is shown in Additional file 2: Fig. S1. The concentrations of serum alanine transaminase (ALT), aspartate transaminase (AST), total cholesterol (TC) and triglyceride (TG) in the low-concentrate diet (LCD) group did not significantly change with time. Compared with those in the LCD group, the serum ALT concentrations in the HCD group were significantly higher from the 4th week (P = 0.006) to 10th week (P < 0.001). The serum AST (P = 0.014), TC (P = 0.002) and TG (P = 0.002) concentrations were significantly increased in the HCD group from 6th week to 10th week (P < 0.001). After the 10th week, the above indicators did not change significantly in the HCD group (Fig. 1A). Compared with those of the LCD group, the livers of the 10 HCD-fed goats presented significant fibrosis (P = 0.009), inflammatory cell accumulation (P < 0.001) and lipid droplet accumulation (P = 0.004), and these goats composed the NASH group. The livers of the other five dairy goats in the HCD group did not show fibrosis (P = 0.269) or inflammatory cellular infiltration (P = 0.157), although the area of lipid droplets showed increased tendency compared with LCD group (P = 0.073), which was defined as the NASH-tolerant (NASH-T) group (Fig. 1B, C).

Fig. 1
figure 1

Phenotypes of NASH and NASH-T in dairy goats fed HCD. A The serum ALT, AST, TC and TG levels of goats in the LCD and HCD groups were continuously monitored on the last day of every 2 weeks (n = 15). B Representative Sirius red, H&E and Oil red O-stained sections of livers from Con, NASH and NASH-T goats (scale bar, 10 μm). In the HCD group, 10 dairy goats developed NASH, which was referred to as the NASH group. The other five goats from the HCD group did not present with NASH symptoms and were included in the NASH-tolerant (NASH-T) group. The LCD group served as the control group. C Proportion of fibrous tissue area, number of inflammatory cells and lipid droplet area in the livers of the Con, NASH and NASH-T groups. D The concentrations of IL-1β, IL-4, IL-6, IL-10, LPS, TNF-α, TC, TG, ALT and AST in the liver and serum of the Con, NASH and NASH-T groups (n = 5). Data of the serum ALT, AST, TC and TG (A) were analysed using repeated measures via a general linear model, followed by Tukey’s honestly significant difference (HSD) test. The proinflammatory cytokines are expressed as the mean ± SEM, and one-way ANOVA was performed, followed by Tukey’s HSD test. *P < 0.05, **P < 0.01, ***P < 0.001 indicate significance. Con, control group; HCD, high-concentrate diet; LCD, low-concentrate diet; NASH, nonalcoholic steatohepatitis; NASH-T, nonalcoholic steatohepatitis tolerance; ALT, alanine transaminase; AST, aspartate transaminase; TC, total cholesterol; TG, triglyceride; FDR, false discovery rate

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In the liver and serum, the concentrations of IL-1β, IL-6, TNF-α, LPS, TC, TG, ALT and AST were significantly higher in the NASH group than those in the Con and NASH-T groups (P < 0.05), whereas the concentrations of IL-4 and IL-10 were significantly lower in the NASH group than those in the Con and NASH-T groups (P < 0.05) (Fig. 1D).

Metabolic profiling revealed altered bile acids (BAs) metabolites in the liver, serum and ileal contents of NASH and NASH-T goats

NASH is linked to metabolic dysregulation; given the significant metabolic differences between NASH-susceptible and tolerant goats, we further investigated the metabolic changes in the liver, serum and ileum. Unsupervised PCA and supervised PLS-DA of the identified metabolites in the liver (Additional file 2: Fig. S2A; Fig. S3A, B, C, D), serum (Additional file 2: Fig. S2D; Fig. S3E, F, G, H) and ileal contents (Additional file 2: Fig. S2G; Fig. S3I, J, K, L) revealed obvious differences in the metabolome profiles among the three groups. Metabolite cluster analysis showed that most of arachidonic acid metabolites enriched in the liver, serum and ileum of NASH goats, while more BAs derivatives and secondary BAs metabolites were clustered in those of Con and NASH-T goats (Additional file 2: Fig. S2B, E, H). In the liver, serum and ileal contents, KEGG pathway enrichment analysis revealed that the differentially abundant metabolites between the NASH vs. Con and NASH vs. NASH-T groups were significantly enriched in the bile secretion and primary BA biosynthesis pathways and arachidonic acid metabolism. In the ileal contents, the differentially abundant metabolites between the NASH vs. Con and NASH vs. NASH-T groups were also significantly enriched in the secondary BAs biosynthesis pathway (Additional file 2: Fig. S2C, F and I; Additional file 1: Tables S1, 2, 3). Moreover, primary BAs in the liver, including GCA, TCA, TCDCA and CDCA, were significantly enriched in the NASH and NASH-T groups compared with those in the Con group. In the serum and ileum, the relative abundances of GCA, CA, TCA and CDCA were significantly higher in the NASH group and NASH-T group than those in the Con group. However, the abundance of secondary BAs and their derivatives in the liver, serum and ileum was significantly lower in the NASH group than those in the Con and NASH-T groups (Additional file 1: Tables S4, 5, 6).

Further targeted BAs profiling revealed that the concentrations of three secondary BAs were significantly higher in the liver, serum and ileum of the NASH-T group than those of the Con and NASH groups: ursodeoxycholic acid (UDCA), isodeoxycholic acid (isoDCA) and isolithocholic acid (isoLCA) (Fig. 2; Additional file 1: Tables S7, 8, 9).

Fig. 2
figure 2

Profiles of BAs in the serum, liver and ileum contents of Con, NASH and NASH-T goats. Targeted BAs metabolomic profiling of the A liver, B serum and C ileum contents. The data are expressed as the mean ± SEM, and one-way ANOVA was performed, followed by Tukey’s HSD test (E). *P < 0.05, **P < 0.01, ***P < 0.001 indicate significance. BAs, bile acids; Con, control group; NASH, nonalcoholic steatohepatitis group; NASH-T, nonalcoholic steatohepatitis tolerance group

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The ileal microbiome and histological morphology differed between NASH and NASH-T dairy goats

Primary BAs metabolites produced by the liver are converted into secondary BAs by gut microbiota and reabsorbed in the ileum. Therefore, we further investigated the changes in ileal microbiota and tissue morphology. A total of 30 phyla, 328 genera and 1107 bacterial species were identified, of which 9 phyla, 26 genera and 24 species were considered the dominant bacterial taxa (the relative abundance of each taxon > 1.0%, Additional file 2: Fig. S4 A, B, C). The PCoA plot revealed separate microbiomes among the three groups (ANOSIM R = 0.374, P = 0.015) (Fig. 3A), and ileal bacterial richness (P = 0.044) and α-diversity (P = 0.048) were significantly lower in the NASH group than in the Con and NASH-T groups (Fig. 3B).

Fig. 3
figure 3

Different ileum bacteria and altered secondary BAs biosynthesis functions among the Con, NASH and NASH-T groups. A The bacterial β-diversity and B α-diversity in the ileum contents of the Con, NASH and NASH-T groups. C LEfSE analysis revealed differential ileum bacteria among the Con, NASH and NASH-T groups from the phylum to the species level. D Co-occurrence network analysis of bacteria in the ileum of dairy goats in the Con, NASH and NASH-T groups. The node size indicates the abundance of a species. E Altered genes in the secondary BAs biosynthesis pathway. F Sankey diagram showing the bacteria whose genes encode the key enzymes in the secondary BAs biosynthesis pathway. BAs, bile acids; Con, control group; NASH, nonalcoholic steatohepatitis group; NASH-T, nonalcoholic steatohepatitis tolerance group

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LEfSe analysis revealed that Clostridium cuniculi, Clostridium perfringens, Clostridium scindens, Clostridium botulinum, Ruminococcus sp., Bifidobacterium pseudolongum and Lactobacillus fermentum were predominant in the NASH-T group (Fig. 3C; Additional file 1: Tables S10, 11, 12). Spearman correlation analysis revealed that ileum UDCA, isoDCA and isoLCA were positively associated with Clostridium, Bifidobacterium, Bacteroides, Eubacterium and Ruminococcus (Additional file 1: Table S13). Functional enrichment analysis of the ileal microbiome revealed that in the secondary BAs biosynthesis pathway, the relative abundances of bile salt hydrolase (BSH), 7α-hydroxysteroid dehydrogenase (7α-HSDH) and 3α-hydroxycholanate dehydrogenase (NADP +) (BaiA) were significantly higher in the Con and NASH-T groups than those in the NASH group. In addition, the relative abundances of 7β-hydroxysteroid dehydrogenase (7β-HSDH) and 3β-hydroxy-delta5-steroid dehydrogenase (3β-HSDH) were significantly higher in the NASH-T group than in the NASH and Con groups (Fig. 3E; Additional file 1: Tables S14, 15, 16). Microbial KEGG functional annotation revealed that the BSH gene was associated with most Clostridium, Lactobacillus and Bifidobacterium genera in the NASH-T group, as well as Bacteroides and Eubacterium genera in the Con group. The BaiA gene was carried in C. scindens and C. perfringens. The 7β-HSDH gene was found in the genomes of C. scindens and Bacteroides fragilis. The 3β-HSDH gene was detected in the genomes of Lachnospiraceae bacterium and C. scindens (Fig. 3F).

However, Erysipelotrichaceae bacterium, E. coli, Streptococcus pneumoniae and Streptococcus pylori were enriched in the NASH group (Fig. 3C; Additional file 1: Tables S10, 11, 12). Cooccurrence network and Spearman correlation analysis revealed that S. pneumoniae, Streptococcus pyogenes, Erysipelotrichaceae_bacterium and E. coli were negatively correlated with C. cuniculi and C. botulinum and B. longum and B. pseudolongum (Fig. 3D) while positively correlated with proinflammatory metabolites, including 20-trihydroxy-LTB4, 13,14-dihydro-PGF2α, PGA1 and 6-Keto-PTGF1α (Additional file 1: Table S13). These proinflammatory metabolites may further lead to ileum damage. The ileum crypt depth (CD) (P = 0.027; P = 0.043) and ileum wall thickness (P = 0.044; P = 0.048) were increased, while the ileum villus height (VH) (P = 0.030; P = 0.045) and VH:CD (P = 0.042; P = 0.064) were reduced in the NASH group compared with those in the Con and NASH-T groups (Additional file 2: Fig. S5A and B). The concentration of IL-1β (P = 0.029; P = 0.063), IL-6 (P = 0.032; P = 0.044), TNF-α (P = 0.009; P = 0.035) and LPS (P = 0.008; P = 0.033) was increased, while the IL-4 (P = 0.007; P = 0.02) and IL-10 (P < 0.001; P = 0.006) were decreased in the ileum of NASH goats compared with those in the Con and NASH-T goats (Additional file 2: Fig. S5C). The relative expression levels of claudin-1 and occludin proteins in the ileum of NASH goats were significantly decreased compared with those in the Con (P = 0.007; P = 0.026) and NASH-T goats (P < 0.001; P = 0.004). The relative expression level of ZO-1 was lower in the NASH and NASH-T groups compared with Con group (P = 0.005; P = 0.009) (Additional file 2: Fig. S5D).

Hepatic gene expression related to bile acid metabolism and T-cell differentiation differed between NASH and NASH-tolerant dairy goats

Given that secondary BAs returning from the ileum to the liver may modulate hepatic immunity and influence the progression of NASH, we further analyzed the transcriptome and single-nuclei transcriptome of the liver tissue. The liver tissue transcriptome profiles of the NASH group were distinct from those of the Con and NASH-T groups (Additional file 2: Fig. S6A). KEGG pathway enrichment analysis revealed that the DEGs between the NASH vs. Con group and the NASH vs. NASH-T group were significantly enriched in functions related to primary BAs biosynthesis, bile secretion, the IL-17 signalling pathway, arachidonic acid metabolism, and the PPAR signalling pathway (Additional file 2: Fig. S6B and Additional file 1: Tables S17, 18, 19). Further gene set enrichment analysis revealed that bile secretion, primary BAs biosynthesis, the IL-17 signalling pathway and the PPAR signalling pathway were significantly upregulated in the NASH groups compared with both the Con and NASH-T groups (Additional file 1: Table S20). Further RT‒qPCR of the DEGs verified expression of genes HMGCR, SULT1C2, UGT8 and ABCC2 involved in the bile secretion pathway were significantly upregulated in the NASH and NASH-T groups compared to the Con group. In the primary BAs biosynthesis pathway, CYP8B1 was significantly upregulated in the NASH and NASH-T groups compared to the Con group, and expression of BAAT in the NASH group was significantly higher than that in the Con group. For the genes involved in IL-17 signalling pathway, the expressions of IL-17A, Act1, FOSB and IL-1β in the NASH group were significantly higher than those in the Con and NASH-T groups. For the genes involved in PPAR signalling pathway, the expressions of MMP-1 and FABP1 in the NASH group were significantly lower than those in the Con and NASH-T groups (Additional file 2: Fig. S6C and Additional file 1: Table S21).

Hepatic single-nuclei sequencing identified a total of 27 cell clusters (Additional file 2: Fig. S7), and their respective numbers of cells are shown in Additional file 2: Table S22. Seven cell types were identified on the basis of the marker genes (Additional file 2: Fig. S8) in the liver, including hepatocytes (clusters 1, 2, 4, 5, 7, 8, 20 and 21), macrophages (clusters 0, 12 and 13), T cells (clusters 3, 9, 14, 17 and 23), endothelial cells (clusters 6 and 18), natural killer (NK) cells (clusters 11, 25 and 24), hepatic stellate cells (HSCs) (cluster 10) and B cells (clusters 15, 16 and 26) (Fig. 4A). The proportion of endothelial cells in the NASH group was lower, while the proportions of hepatocytes, HSCs, B cells, T cells, NK cells and macrophages were higher in the NASH group than those in Con and NASH-T groups. In particular, the proportion of T cells in the NASH group (44.86%) was significantly higher than that in the Con (24.20%) (P = 0.037) and NASH-T groups (30.94%) (P = 0.042) (Fig. 4B). Notably, among the maker genes of T cells, hepatic CCR9-expressing T cells are indeed of gut origin. Further analysis of T-cell subsets on the basis of marker genes (Additional file 2: Fig. S9) identified eight T-cell subtypes, including NKT cells, TH2 cells, γδT cells, TH17 cells, naïve CD4+ T cells, naïve CD8+ T cells, follicular T-helper (Tfh) cells and Treg cells (Fig. 4C). The percentage of TH17 cells in the NASH group (45.37%) was significantly higher than that in the Con (24.00%) (P = 0.012) and NASH-T groups (30.63%) (P = 0.034), whereas the percentage of Treg cells in the NASH group (18.47%) was significantly lower than that in the Con (42.14%) (P < 0.001) and NASH-T groups (39.39%) (P = 0.005) (Fig. 4D). Immunofluorescence assays further verified the changes of hepatic TH17 and Treg cells in the NASH and NASH-T goats (Fig. 5).

Fig. 4
figure 4

Single-nuclei RNA sequencing showed differences in liver cell types among Con, NASH and NASH-T goats. A UMAP plot visualization of seven liver cell types. B Differences in the proportions of liver cell types among the Con, NASH and NASH-T groups. C UMAP plot visualization of eight T-cell subsets. D Differences in the proportions of liver T-cell subsets among the Con, NASH and NASH-T groups. KEGG pathway enrichment of differentially expressed genes among the Con, NASH and NASH-T groups in E TH17 cells and F Treg cells. Con, control group; NASH, nonalcoholic steatohepatitis group; NASH-T, nonalcoholic steatohepatitis tolerance group; Con, control group; NASH, nonalcoholic steatohepatitis group; NASH-T, nonalcoholic steatohepatitis tolerance group

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Fig. 5
figure 5

Immunofluorescence analysis of the marker genes of TH17 and Treg cells. A, C, E Immunofluorescence identification of marker genes of TH17 cells (IL-17A and IL-23R) and Treg cells (Foxp3, CTLA-4, CD127 and IL-2RA). B, D, F Differences in the antibody-positive areas among the Con, NASH and NASH-T groups. Con, control group; NASH, nonalcoholic steatohepatitis group; NASH-T, nonalcoholic steatohepatitis tolerance group

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KEGG pathway enrichment analysis revealed that IL-23R, IL-17A, FOSB and JUN genes in TH17 cells were significantly upregulated in the NASH group compared with those in the Con and NASH-T groups (Fig. 4E and Additional file 1: Tables S23, 24, 25), whereas Foxp3 and IL-2RA genes in Treg cells were downregulated in the NASH group compared with those in the Con and NASH-T groups (Fig. 4F and Additional file 1: Tables S26, 27, 28). Moreover, the IL-17 signalling pathway and PPAR signalling pathway were also enriched in TH17 cells. In the IL-17 signalling pathway, the levels of IL-17A, TNF-α, Act1, TRAF6, ERK, FOSB and JUN genes were significantly higher in the NASH group than those in Con and NASH-T groups. With respect to the PPAR signalling pathway, FABP1 and MMP-1 were significantly increased in the NASH group compared with those in the Con and NASH-T groups (Fig. 4E and Additional file 1: Tables S23, 24, 25).

Changes in the proportion of TH17 and Treg cells in the ileum

Due to parts of T cells that identified in the liver probably originated in the gut, further analysis of changes in the proportion of TH17 and Treg cells in the ileum using flow cytometry showed increased proportion of TH17 cell (P < 0.001; P = 0.004) and decreased Treg cell (P < 0.001; P = 0.008) in the ileum of NASH goats compared with those in Con and NASH-T groups. The proportion of TH17 cell in the ileum between NASH-T and Con groups did not show significant difference, while the proportion of Treg cell in the ileum of NASH-T was lower than that in the Con groups (Additional file 2: Fig. S10).

Alleviation effects of supplementation with secondary BAs on NASH through modulating the differentiation of TH17 and Treg cells in the liver in a mouse model

A mouse high-fat diet-induced NASH model was used to verify whether bacteria-derived UDCA, isoDCA and isoLCA could mitigate NASH through affecting the differentiation of TH17 and Treg cells (Fig. 6A). When the mice developed NASH, their serum ALT (P = 0.002), AST (P = 0.006), TC (P = 0.004) and TG (P = 0.004) levels increased, and the accumulation of inflammatory cells (P < 0.001), lipid droplets (P < 0.001) and fibrosis (P < 0.001) in their liver increased. Oral administration of UDCA, isoDCA and isoLCA to NASH model mice decreased these serum parameters (Fig. 6B) and relieved these symptoms (Fig. 6C, D, E). Oral administration of UDCA, isoDCA and isoLCA to NASH mice also inhibited TH17 cell differentiation (Fig. 6F) and facilitated the proliferation of Treg cells in their liver and ileum. However, isoLCA did not significantly promote Treg cells (Fig. 6G, Additional file 2: Fig. S11). We further validated the expression of 16 proteins involved in the IL-17 signalling pathway, TH17 cell differentiation and the PPAR signalling pathway on the basis of the liver transcriptome and single-nuclei transcriptome profiling of dairy goats. Compared with the NASH group, the UDCA, isoDCA and isoLCA groups presented decreased expression levels of IL-17A, Act1, TRAF6, ERK, JUN, FOSB, IL-6, TNF-α, S100A9, IL-23R, RORγt, MMP-1, PPARγ and FABP1 but increased expression levels of IL-2R and Foxp3 (Fig. 6H, I and Additional file 1: Table S29).

Fig. 6
figure 6

Effects of oral administration of isoDCA, isoLCA and UDCA on NASH amelioration. A A mouse model of NASH was induced via a high-fat diet. After the NASH model was successfully established, UDCA, isoDCA and isoLCA were orally administered to the mice. B Concentrations of serum ALT, AST, TC and TG in the different groups. Representative C H&E, D Oil red O and (E) Sirius red-stained sections of livers from the Con, NASH, isoDCA, isoLCA and UDCA consumption groups are shown (scale bar, 50 μm). Effects of isoDCA, isoLCA and UDCA supplementation on the differentiation of F TH17 and G Treg cells in the liver. H Representative Western blots of the IL-17A, Act1, TRAF6, ERK, JNK, FOSB, IL-6, TNF-α, S100A9, IL-23R, RORγt, MMP1, PPARγ, FABP1, IL-2R and Foxp3 signatures in the livers of mice in the Con, NASH, isoDCA, isoLCA and UDCA consumption groups. I The relative expression levels of IL-17A, Act1, TRAF6, ERK, JNK, FOSB, IL-6, TNF-α, S100A9, IL-23R, RORγt, MMP1, PPARγ, FABP1, IL-2R and Foxp3 in the different groups were determined (n = 3). The data are expressed as the mean ± SEM, and one-way ANOVA was performed, followed by Tukey’s HSD test. ALT, alanine transaminase; AST, aspartate transaminase; TC, total cholesterol; TG, triglyceride; H&E, haematoxylin‒eosin staining; NIC, number of inflammatory; LDEA, lipid droplet expression area; FTEA, fibrous tissue expression area; UDCA, ursodeoxycholic acid; isoDCA, isodeoxycholic acid; isoLCA and isolithocholic acid; Con, control group; NASH, nonalcoholic steatohepatitis group; NASH-T, nonalcoholic steatohepatitis tolerance group

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Discussion

Compared with liver metabolic dysfunctions known to occur in other ruminants, the current study is the first to evaluate the underlying mechanisms of individualized susceptibility to NASH in dairy goats fed HCD. In both mice and humans, genetic variation is thought to be the primary determining factor of susceptibility to NASH [12]. Different from human/mouse studies, the current study revealed that the ileal bacterial secondary BAs could modulate susceptibility to NASH in dairy goats by affecting hepatic Treg and TH17 cells proportion and BAs metabolism through gut‒liver axis, which provides a new perspective for understanding the mechanisms behind the varied resilience and susceptibility to NASH in dairy goats. Moreover, the gut-original T cells were identified in the liver and the altered proportion of TH17 and Treg cells in the ileum of NASH and NASH-T goats, showing the migration of immune cells from the gut to the liver, which might provide new evidence for the gut‒liver axis in ruminants and its role in the regulation of liver health in dairy goats. In addition, we identified several ileal bacteria that carried genes encoding key enzymes involved in secondary BAs (UDCA, isoDCA and isoLCA) synthesis in NASH-T goats, which have not been reported before and might provide potential evidence for microbial secondary BAs metabolites in ruminants.

The present study revealed that NASH pathogenesis in dairy goats was related to the hepatic accumulation of TC, TG and primary BAs, and the proinflammatory compounds enter into the liver from gut through increased intestinal permeability caused by the HCD-induced ileal microbiome dysbiosis. The upregulation of genes involved in TC and primary bile acid synthesis in NASH goats may explain their accumulation in the liver and serum [13,14,15,16]. However, unlike previous studies, the present study also detected high levels of arachidonic acid metabolites in the liver, serum and ileum of NASH goats. Arachidonic acid metabolites, such as prostaglandin, thromboxane A2, leukotriene-B4 and leukotriene E3, have been recognized as proinflammatory mediators [17]. Thus, increases in these compounds might participate in the liver inflammatory development. However, further studies are needed to investigate the role of arachidonic acid metabolites in NAFLD/NASH pathogenesis. Furthermore, we identified more E. coli, Erysipelotrichaceae bacterium, S. pneumoniae and Streptococcus pyogenes in the ileum, as well as high concentrations of LPS and proinflammatory cytokines in the ileum, serum and liver of NASH goats. These bacteria have been shown to mediate inflammatory responses through the production of LPS or the secretion of proinflammatory cytokines such as IL-1β, IL-6 and TNF-α [18,19,20,21], which may be related to the impaired intestinal barrier we observed, allowing proinflammatory compounds to enter the liver. The “leaky gut” (impaired intestinal barrier) induced by microbiome dysbiosis has been recognized as one of the NAFLD/NASH pathogenesis in human studies [7, 9]. In ruminants, the liver damage caused by dysbiotic ruminal bacteria and their derivatives (such as LPS) has been shown to occur under long-term HCD feeding [22, 23]. However, compared to the rumen with its multi-layered squamous epithelium, bacteria and their metabolites more easily translocate from the small intestine with its simple columnar epithelium to the liver and cause liver injury [24, 25]. Thus, the increased abundance of the opportunistic pathogens in the ileum and the elevated levels of LPS, and proinflammatory cytokines in the ileum, serum and liver, suggest that proinflammatory compounds derived from the dysbiotic ileal microbiome induced by a HCD could lead to liver damage in NASH goats.

Compared with NASH goats, NASH-T goats presented mild lipid droplet accumulation but no significant inflammatory response or fibrosis in the liver when they were fed the same HCD. Our results suggest that this effect might be attributed to secondary BAs metabolism and the altered proportion of TH17 and Treg cells in the liver and gut. The primary BAs derived from the liver are released into the small intestine and further metabolized and modified by gut microbes into secondary BAs and return back to the liver, which is called enterohepatic circulation of BAs [26]. The enterohepatic circulation of secondary BAs formed by small intestine microbial biotransformation plays a vital role in NAFLD/NASH pathogenesis in humans [27,28,29], which has not been well defined in ruminant liver health. Gut microbes could accelerate the conversion of cholesterol to BAs to promote cholesterol clearance and thereby lower cholesterol levels in the liver [30]. Thus, the higher concentrations of secondary BAs, especially UDCA, isoDCA and isoLCA, in the ileum of NASH-T goats may explain the lower level of TC in their liver and serum.

The involvement of gut bacteria in the biosynthesis of secondary BAs has been demonstrated in previous studies [27,28,29]. In the current study, the high abundances of C. cuniculi, C. perfringens, C. scindens, C. botulinum, Ruminococcus sp., B. pseudolongum and L. fermentum in the ileum of NASH-T goats were found to be participating in the production of UDCA, isoDCA and isoLCA. We further identified key genes encoding key enzymes that mediate the production of UDCA, isoDCA and isoLCA by these bacteria, including BSH, 7α-HSDH, BaiA, 7β-HSDH and 3β-HSDH. Recent studies have revealed the biosynthetic processes of UDCA, isoDCA and isoDCA, including BSH-mediated deconjugation, microbial Bai gene-mediated 7α/β-dehydroxylation and hydroxysteroid dehydrogenase-mediated oxidation and differential isomerization in humans and mice [31,32,33,34,35]. Additionally, numerous studies have identified key gut microbes that possess genes encoding enzymes that catalyse the production of secondary BAs [16, 28, 36, 37]. Thus, high levels of UDCA, isoDCA and isoLCA in the ileum of NASH-T goats might be produced by the high abundances of the Clostridium, Bifidobacterium, Lactobacillus and Ruminococcus genera, as reported previously. The present study further identified several ileum bacteria that carry genes encoding key enzymes involved in secondary BAs metabolism in NASH-T goats, which have not been previously reported, including C. scindens and C. perfringens, which carry the BaiA gene; C. scindens and B. fragilis, which carry the 7β-HSDH gene; and Lachnospiraceae bacterium and C. scindens, which carry the 3β-HSDH gene. These new findings might provide potential evidence for microbial secondary BAs metabolites in ruminants. The ability of NASH-T goats to harbour a high abundance of BA-producing bacteria, which might be helpful for rearing NASH-T dairy goats in future production practices, is important.

Hepatic immune cells play an integral role in modulating the pathogenesis of NAFLD/NASH. In recent years, the implementation of single-cell or single-nuclei RNA sequencing has tremendously advanced our understanding of the complex heterogeneity of various liver immune cell subsets in NAFLD/NASH [38]. An increased proportion of macrophages, HSCs and T cells in the livers of individuals with NASH has been observed in humans and mice and is associated with liver inflammation and fibrosis [39, 40]. Similarly, elevated proportions of macrophages, HSCs and T cells were observed in the livers of NASH goats, which could account for the accumulation of inflammatory cytokines and collagenous fibres. Notably, recent evidence has shown that hepatic T cells are most likely derived from the peripheral circulation or the gut [41, 42]. Hepatic T cells can be identified on the basis of specific surface marker expression [41]. In the present study, the expression of CD4, CD3E, CD44, CD69 and CCR9 was detected in hepatic T cells. CD44 and CD69 expression is strongly initiated after hepatic T-cell activation [41,42,43]. In addition, hepatic CCR9-expressing T cells are indeed of gut origin [41]. These findings suggest that the hepatic T cells we identified in goats mostly migrated from the systemic circulation and gut, which might provide new evidence for the role of the gut‒liver axis in the regulation of NASH pathogenesis in dairy goats.

We further found the significant difference in the proportions of Treg cells and decreased TH17 cells in both the liver and gut between NASH-T and NASH goats. TH17 cells were identified through detection of the expression of the surface markers IL-23R and IL-17A [44]. Moreover, TH17 cells can activate the IL-17 signalling pathway and further trigger inflammation by secreting IL-17A, which can induce the expression of TNF-α, IL-6 and MMP-1 via Act1/TRAF6/p38MAPK-dependent AP-1 (an activating transcription factor composed of c-FOS and c-JUN) activation [45, 46]. In the present study, the upregulated genes (IL-17A, Act1, JUN, FOSB, TNF-α, IL-6 and MMP-1) enriched in the IL-17 signalling pathway in liver tissue and hepatic TH17 cells suggested an activated inflammatory reaction in the liver when goats developed NASH. Moreover, MMP-1 produced by the activated IL-17 signalling pathway also participates in the PPAR signalling pathway, in which activated PPARγ promotes the expression of FABP1 and MMP-1, resulting in fatty acids being stored as TG in adipocytes [47]. Thus, the upregulated PPAR signalling pathway and genes (PPARγ, FABP1 and MMP1) enriched in this pathway could explain the high concentration of TG and lipid droplet accumulation in the livers of NASH goats consuming a HCD. In contrast, Treg cells exhibit upregulated expression of transcription factors (Foxp3) and cell surface markers (IL-2RA, CTLA-4 and CD127) and can secrete anti-inflammatory cytokines (IL-10 and IL-4) to mediate immune tolerance [48, 49]. The production of IL-17A can be inhibited by Foxp3 induced by IL-2R through the TH17 cell differentiation pathway [49]. In the present study, increased Treg cells and decreased TH17 cells, as well as high concentrations of IL-4 and IL-10, might play a vital anti-inflammatory role in the liver and gut of NASH-T goats.

Notably, the differentiation of TH17 and Treg cells could be controlled by bacterial BA metabolites [44, 50, 51]. Although some studies have reported the roles of UDCA, isoDCA and isoLCA in different diseases through the regulation of TH17 and Treg cell differentiation [51,52,53], more interestingly, we simultaneously observed a decrease in TH17 cells and an increase in Treg cells in the liver and ileum, in both NASH-T goats and NASH mice supplemented with these three BAs. This further supports the migration of gut microbial secondary BAs and immune cells along the gut‒liver axis. Our results revealed that the oral administration of UDCA, isoDCA and isoLCA could alleviate NASH symptoms by inhibiting TH17 cell differentiation while facilitating Treg cell differentiation in the livers of NASH mice by affecting the expression of genes involved in the TH17 cell differentiation/IL-17 signalling/PPAR signalling pathway, which verified our observations in dairy goats and revealed the potential key mechanisms behind the varied resilience and susceptibility to NASH in goats. Thus, owing to the antagonistic effects on the function and differentiation of TH17 and Treg cells, a higher proportion of Treg cells and a lower proportion of TH17 cells in the livers of NASH-T goats suggest an enhanced ability to maintain immune tolerance in individuals who are more resilient to NASH when consuming a HCD. In addition, TH17 and Treg cells can also control the development of liver fibrogenesis by activating and inhibiting the activation of HSCs, respectively [54, 55], which might explain the phenotypic differences in liver fibrosis between NASH and NASH-T goats. In the current study, we further demonstrated the alleviated effect of the three secondary BAs metabolites on NASH using mouse models, which might enhance the reliability and universality of these findings. Although mice have been widely used as validation models in humans and in some ruminant studies, such as cows [56, 57], the variation in the animal biology between the two species warrants future study using direct fed microbes or postbiotics-based feeding trials.

Conclusion

This study revealed ileal secondary BAs (UDCA, isoDCA and isoLCA) inhibited TH17 cell differentiation and promoted Treg cell proliferation by regulating the expression of genes involved in the TH17 cell differentiation/IL-17/PPAR signalling pathway, thereby preventing NAFL from progressing to NASH (Fig. 7). The identification of gut-original T cells in the liver further provides new evidence for gut‒liver axis in ruminants of its role in modulating liver metabolic dysfunctions. Nonetheless, the gut‒liver immune cell communication needs further research in a larger population to validate the findings concerning the tolerance of NASH in dairy goats. Regardless, the findings from current study provide new insights into NASH pathogenesis via the gut‒liver axis and provide a theoretical basis for the production of beneficial BA metabolites to maintain liver immune homeostasis under HCD feeding. Bacteria and their UDCA, isoDCA and isoLCA that were demonstrated to alleviate NASH could be novel probiotics and postbiotics to modulate the small intestinal microbiome and improve the liver health in ruminants under intensive production system.

Fig. 7
figure 7

Mechanism by which secondary BAs regulate liver TH17/Treg cell differentiation to maintain NASH tolerance. ① Long-term HCD feeding induces the expression of HMGCR to promote TC synthesis in the liver. More primary BAs are synthesized in large quantities from TC. ② In the livers of NASH goats, increased expression levels of SULT1C2, UGT8, ABCC2 and BAAT promote bile secretion, which results in the accumulation of primary BAs in the liver, serum and intestinal tract. ③ In the ileum contents of NASH goats, potential pathogenic microorganisms, such as Erysipelotrichaceae, Escherichia and Streptococcus, produce more proinflammatory cytokines, which enter the liver through the circulation and cause liver inflammation. ④ In NASH-T goats, liver-derived primary BAs are secreted into the intestinal tract. ⑤ More BAs-producing bacteria from the Bifidobacterium, Clostridium and Lactobacillus genera are enriched in the ileum contents of NASH-T goats, which produce a large number of secondary BAs, especially UDCA, isoLCA and isoDCA catalysed by BSH, BaiA, 7β-HSDH, 7α-HSDH and 3β-HSDH. ⑥ The production of secondary BAs accelerates cholesterol clearance. ⑦, ⑧ Moreover, the return of secondary BAs to the liver through the portal vein inhibits TH17 cell differentiation and promotes Treg cell proliferation in the liver by regulating the expression of genes involved in the TH17 cell differentiation/IL-17/PPAR signalling pathway, thereby preventing NASH occurrence. HCD, high-concentrate diet; TC, total triglycerides; BAs, bile acids; HMGCR, 3-hydroxy-3-methylglutaryl-CoA reductase; BAAT, bile acid-CoA amino acid N-acyltransferase; CYP8B1, cytochrome P450 family 8 subfamily B member 1; SULT1C2, sulfotransferase family 1C member 2, transcript variant X3; ABCC2, ATP-binding cassette subfamily C member 2. BSH, bile salt hydrolase; 7α-HSDH, 7α-hydroxysteroid dehydrogenase; BaiA, 3α-hydroxycholanate dehydrogenase (NADP +); 7β-HSDH, 7β-hydroxysteroid dehydrogenase (NADP +); 3β-HSDH, 3β-hydroxy-delta5-steroid dehydrogenase; NASH, nonalcoholic steatohepatitis; NASH-T, nonalcoholic steatohepatitis tolerance; NAFL, nonalcoholic fatty liver

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Methods

Dairy goat feeding trial

The dairy goats were selected and purchased from a well-managed large-scale commercial dairy goat farm in the suburbs of Shaanxi, China (34°42′N, 108°57′E). The dairy goat trial was conducted at the animal experimental centre of Northwest A&F University (Shaanxi, China). A total of 30 healthy and multiparous dairy goats with similar body weights (54 ± 2.4 kg), parity (1.03 ± 0.220) and age (3.58 ± 0.066 years) were randomly divided into two groups: those fed a LCD (ratio of concentrate to forage = 3:7) or an HCD (ratio of concentrate to forage = 7:3). The feeding trial was conducted for 10 weeks, including a 1-week pre-feeding period and a 9-week experimental period (Additional file 2: Fig. S1). Each dairy goat was raised in a single cage (1.0 m × 2.0 m) and fed a 1.2-kg diet (on a dry matter basis) a day at 08:00 and 17:00, with free access to water. The ingredients and nutritional components of the diet are shown in Additional file 1: Table S30.

Sample collection

Blood samples were collected into three 5-mL pro-coagulation tubes (Becton, Dickinson and Company, Franklin Lakes, USA) from the jugular vein at 3-h postfeeding on the last day of every two experimental weeks. The serum samples obtained after centrifugation were divided into four aliquots, two of which were stored at − 20 °C for the determination of serum biochemical parameters, including ALT, AST, TC, triglyceride (TG) and proinflammatory cytokines and LPS concentrations. The other two samples were stored at − 80 °C for nontarget metabolome and targeted BAs metabolome analysis.

When the goats were slaughtered at the end of the trial, the liver tissues and ileum contents were collected from each dairy goat. The liver tissues were divided into eight aliquots for nontarget metabolome analysis, targeted BAs metabolome analysis, transcriptome analysis, single-nuclei RNA sequencing, haematoxylin and eosin (H&E), Oil red O and Sirius red staining and measurement of proinflammatory cytokine and LPS concentrations. Ileal tissue was used to perform H&E staining and measure the concentrations of proinflammatory cytokines and LPS. The ileum contents were divided into three aliquots for metagenome, nontarget metabolome and targeted BAs metabolome analyses. All of the samples were snap frozen in liquid nitrogen and stored at − 80 °C until processing.

Assessment of ileum histological morphology and liver tissue inflammatory cell infiltration, hepatic lipid accumulation and fibrosis using H&E, Oil red O, and Sirius red staining

The fresh ileum and liver tissues collected after slaughter were fixed with 10% neutral formaldehyde, dehydrated and embedded in paraffin. After being stained with haematoxylin for 10–20 min and eosin for 3–5 min, the ileum and liver tissue slices were sealed with neutral gum. For liver Oil red O staining, fresh liver tissue was fixed with formaldehyde and then stained with Oil red O dye solution for 10 min, after which the liver tissue sections were sealed with glycerine. Liver Sirius red staining was performed to assess liver collagen fibres. After fixation, paraffin embedding and slicing, the liver tissues were stained with Sirius red composite dye for 5–10 min and sealed with glycerine.

A digital trinocular camera microscope (BA210) (Motic, China) was used to acquire images for the above analysis. The ileum tissue images were imported into Motic Images Advanced 3.2 to measure the VH, CD and ileum wall thickness. An Image-Pro Plus 6.0 image analysis system (Media Cybernetics, USA) was used to determine the area of fat droplet expression and the percentages of type I collagen and type III collagen in the collected images. The number of inflammatory cells and the level of hepatic lipid accumulation and collagen fibres were analysed via one-way ANOVA combined with the LSD multiple comparison test via SPSS Statistics (version 22.0, Chicago, USA), ***P < 0.001, **0.001 < P < 0.01, *0.01 ≤ P ≤ 0.05.

Immunofluorescence analysis

Specific antibodies against TH17 and Treg cells were detected via immunofluorescence to identify different cell types in liver tissue. The detailed procedures for the pre-treatment treatment of liver tissue are shown in Additional file 3: M1. After fixation and serum sealing, the liver tissue sections were incubated with primary antibodies against IL-17A and Foxp3 (HUABIO, Hangzhou, China) and CTLA-4, CD127, IL-23R and IL-2RA (Affinity Biosciences, OH, USA), and the samples were incubated at 4 °C overnight. Next, secondary antibody and DAPI (Servicebio, Hubei, China) were added successively, and the slides were sealed with anti-fluorescence attenuation tablets. Digital slide scanning (VS200, Olympus, Japan) was used for image collection. A Halo data analysis system (version 3.2.1851.439) was used to calculate the percentage of positive tissue.

Biochemical parameters in the serum and liver and the concentrations of inflammatory cytokines and LPS in the ileum, liver and serum

The concentrations of ALT, AST, TC and TG in the serum and liver were measured using an automatic blood analyser (Celltac α MEK-6450, Nihon Kohden). The serum ALT, AST, TC and TG levels were analysed using repeated measures via the general linear model of SPSS Statistics Version 22 (IBM, Chicago, USA). Time was set as the within-subject factor, whereas diet was set as the between-subject factor. Linear contrasts were used to examine the responses to different levels of concentrate. Linear and quadratic contrasts were also used to examine the responses to time. The concentrations of IL-1β, IL-4, IL-6, IL-10, TNF-α and LPS in the serum, liver and ileum were detected using the respective ELISA kits (COIBO BIO, Shanghai, China). Differences in the concentrations of inflammatory cytokines and LPS in the ileum, liver and serum were analysed via one-way ANOVA combined with the LSD multiple comparison test using SPSS Statistics (version 22.0, Chicago, USA), ***P < 0.001, **0.001 < P < 0.01,*0.01 ≤ P ≤ 0.05.

Metagenomic analysis of ileum contents

Total DNA was extracted from the ileum contents via a Mag-Bind® Soil DNA Kit (Omega Biotek, USA), and the DNA concentration and purity were measured with a NanoDrop 2000 (Thermo Fisher, MA, USA). The DNA extract was fragmented to an average size of approximately 400 bp using Covaris M220 (Gene Company Limited, China) for paired-end library construction. Paired-end (150 bp) sequencing was performed on an Illumina NovaSeq sequencing platform (Illumina, San Diego, USA) using NovaSeq 6000 S4 Reagent Kit v1.5 (300 cycles).

After QC and host DNA removal, MEGAHIT (https://github.com/voutcn/megahit, version1.1.2) was used to assemble the sequence data, and contigs ≥300 bp in length were retained for downstream analysis. CD-HIT (http://www.bioinformatics.org/cd-hit/, version 4.6.1) was used to cluster the predicted gene sequences at 90% identity and 90% coverage [58]. SOAPaligner (http://soap.genomics.org.cn/, version 2.21) was used to map the high-quality reads to the nonredundant gene set at 95% identity. During the alignment, the number of reads assigned to each gene was calculated, allowing us to estimate gene abundance in each sample. After calculating gene abundance, Diamond (http://www.diamondsearch.org/index.php, version 0.8.35) was used to align the amino acid sequences of the nonredundant (NR) gene catalogue (using BLASTP with an e-value of 1e−5). Taxonomic annotation was conducted based on alignment results, utilizing the taxonomic information from the NR database. This process enabled the assignment of gene abundance to specific taxonomic groups. The read counts for each gene were grouped according to their assigned taxonomic classification. The total number of reads for each taxonomic group was determined by summing the read counts of all genes classified within that group. Principal coordinate analysis (PCoA) was performed based on the Bray-Curtis distance algorithm, and ANOSIM was used to analyse the differences in species composition among groups. The Wilcoxon rank-sum test was used to analyse the species and functional differences between any two groups, and the P-value was corrected using the false discovery rate (FDR). LEfSe analysis (http://huttenhower.sph.harvard.edu/galaxy/root?tool_id =lefse_upload) combined with linear discriminant analysis (LDA) was used to identify the most representative species in the multigroup difference comparison.

Nontargeted metabolome analysis of liver tissue, serum and ileal contents

The metabolome profiles were assessed using LC–MS/MS with a UHPLC-Q Exactive HF‒X system (Thermo Fisher Scientific, MA, USA). The sample pretreatment and chromatographic conditions followed those of Wang et al. [59]. The metabolomics processing software Progenesis QI (Waters Corporation, Milford, USA) was used for LC–MS raw data analysis. The MS and MS/MS data were mapped to the HMDB metabolic public database (http://www.hmdb.ca/) and METLIN (https://metlin.scripps.edu/). Principal component analysis (PCA) and least partial square discriminant analysis (PLS-DA) were performed using the ropls package (version 1.6.2) in R package. Hierarchical cluster analysis (HCA) of the metabolites was performed using SciPy (version 1.0.0). The metabolic pathway annotations were performed using KEGG database (https://www.kegg.jp/kegg/pathway.html). The Python software package SciPy Stats was used for pathway enrichment analysis, and the biological pathways most relevant to the experimental treatment were identified via Fisher’s exact test. The significantly differentially abundant metabolites were determined on the basis of the variable weight value (VIP) (VIP > 1) obtained by the OPLS-DA model and Student’s t-test with corrected P-values with FDR (FDR-adjusted P < 0.05).

Liver, ileum and serum-targeted bile acid profiling

The methods used for the preparation of the BAs standard mixture and sample pre-treatment are shown in Additional file 3: M2. Qualitative and quantitative detection of BAs was performed via LC-ESI-MS/MS (UHPLC Qtrap, Thermo Fisher, MA, USA) following the conditions described in Gómez et al. [60]. The ion fragments were identified using the SCIEX quantitative software OS. The bile acid concentration in the liver and ileum samples (ng/mg) was calculated as follows: (solution concentration (ng/mL) × constant volume (mL) × dilution ratio)/sample weight (mg). The BA concentration in the serum sample (ng/mg) was calculated as (solution concentration (ng/mL) × dilution ratio)/sample weight (mg).

Liver transcriptome profiling and data processing

RNA was extracted from liver tissue using MJZol total RNA extraction kit (Majorbio, Shanghai, China) according to the manufacturer’s instructions. Library construction was performed using an Illumina® Stranded mRNA Prep Kit (Illumina, San Diego, USA) according to Illumina’s library construction protocol, and RNA-seq was conducted using an Illumina NovaSeq 6000 sequencer (San Diego, USA) (2 × 150 bp paired end). The raw sequencing data were subjected to QC using fastp (https://github.com/OpenGene/fastp). The clean data were subsequently mapped to the goat (Capra_hircus) reference genome (version GCF_0017044 15.1) to obtain the mapped data for subsequent transcript assembly and expression calculations using HISAT2 (http://ccb.jhu.edu/software/hisat2/index.shtml).

The quantification of gene expression, KEGG pathway enrichment analysis, and gene set enrichment analysis (GSEA) were performed using RSEM (http://deweylab.biostat.wisc.edu/rsem/), KOBAS (http://kobas.cbi.pku.edu.cn/download.php), and GSEA (http://software.broadinstitute.org/gsea/index.jsp). The genes that were differentially expressed between groups were identified using DESeq2 (http://bioconductor.org/packages/stats/bioc/DESeq2/). The criteria for significantly differentially expressed genes were log2FC | ≥1 and FDR-adjusted P < 0.05. The DEGs were verified using RT-qPCR (Additional file 3: M3)

Liver single-nuclei RNA sequencing and data processing

The pre-treatment of liver tissue is shown in Additional file 3: M4. A final concentration of 1000 nuclei/μL was used for analysis on a 10 × Genomics Chromium™ system. The qualified cDNA was subsequently used to construct the next-generation sequencing library, which was sequenced using the Illumina NovaSeq 6000 platform (Illumina, San Diego, USA) (PE150), and the sequencing volume was ≥ 20-k reads/cell. The raw data were subjected to quality control (QC) using Cell Ranger (version 7.0.0, https://support.10xgenomics.com/single-cell-gene-expression/software/ overview/elcome).

The reads were subsequently mapped to the goat (Capra_hircus) reference genome (https://www. ncbi.nlm.nih.gov/genome/?term=txid9925[orgn], version GCF_0017044 15.1), to obtain high-quality cell number, gene number and genome information. A nonlinear dimensionality reduction algorithm, uniform manifold approximation and projection (UMAP), was used for data dimensionality reduction, and the clusters obtained by sequencing were displayed. The differentially expressed genes were identified using a differential analysis algorithm to identify marker genes. The cell types were annotated using SingleR (version 1.8.1, https://www.bioconductor.org/packages/release/bioc/html/SingleR.html) to identify the major categories of liver cells. T-cell subclasses were identified on the basis of the known marker genes of cell types. KEGG functional enrichment analysis was subsequently conducted with the KEGG database (version 2021.09, http://www.genome.jp/kegg/).

Murine validation trial

The method used to establish the murine NASH model is shown in Additional file 3: M5. After the NASH model was successfully established, the mice were randomly divided into four groups: the NASH group, the isoDCA group, the isoLCA group and the UDCA group. UDCA, isoDCA and isoLCA were administered orally at a dose of 200 μL 50 mg/kg per day for 8 weeks. After oral administration of BA on the last day of the 8th week, the mice were fasted for 24 h and weighed. Blood samples were collected for ALT, AST, TC and TG assays. Liver tissues were collected for histopathological analysis (the methods for H&E staining, Oil red O staining and Sirius red staining were the same as those used for goat examination) and flow cytometry.

Western blot

The tissues were lysed with RIPA lysis buffer (Biosharp, Anhui, China) at 4 °C for 30 min and centrifuged at 4 °C and 12,000 rpm for 10 min. The supernatant was removed, and the protein concentration was determined using a BCA protein assay kit (Beyotime, Shanghai, China). The target proteins were separated via 10% SDS-PAGE (Yena, Shanghai, China) and subsequently transferred to 0.2-μm PVDF membranes, which were placed in 5% skim milk diluted with TBST buffer for 2 h of incubation. The PVDF membranes were incubated with primary antibodies against claudin-1 (1:1000, AB307792), occludin (1:100–1:1000, AB222691), ZO-1 (1:500–1:3000, AB96587), Foxp3 (1:1000, AB215206), RORrt (1:1000, 4102), IL-2RA (1:200, AB231441), IL-6 (1:1000, AB229381), TNF-α (1:50 (2 µg/ml), AB1793), S100A9 (1:1000, AB242945), IL-17A (1:2000–1:8000, AB79056), IL-23R (1:500–1:2000, AB175072), ACT1 (1:500–1:3000, AB137395), TRAF6 (1:500–1:3000, AB137452), FOSB (1:500–1:1000, AB11959), JUN (1:1000–1:10,000, AB124956), ERK (1:1000, AB201015), FABP1 (1:1000–1:10,000, AB171739), PPARγ (1:1000, AB178866), MMP-1 (1:500–1:3000, AB137332) and β-actin (1:50,000, AB8226) overnight at 4 °C. All of the antibodies used were purchased from Abcam, UK. For the Western blot assay, experiments were performed in triplicate.

Flow cytometry

After the goats’ ileum tissue and mouse liver and ileum tissues were digested with trypsin (1 mL per 30 mg), the cells were collected and passed through 70-μm and 40-μm cell strainers. Then, 0.5 μL of anti-mouse CD4-FITC was added to 100 μL of cell suspension at a concentration of 5 × 106 cells/mL and incubated on ice in the dark for 30 min. After termination of the antibody incubation, 0.5 mL of fixation buffer and 0.5 mL of permeabilization wash buffer were added to the fixed and heavy suspension cells, respectively. The cells were subsequently incubated with 1.25 μL of anti-mouse IL-17A-PE for 40 min to detect T-helper 17 (TH17) cells. To determine the proportion of regulatory T (Treg) cells, 0.5 μL of anti-mouse CD4-FITC and 0.5 μL of anti-mouse CD25-APC were added to 100 μL of the cell suspension. After termination of the antibody incubation, 0.5 mL of fixation buffer and 0.5 mL of permeabilization wash buffer were added to the fixed and heavy suspension cells, respectively. The cells were incubated with 2.5 μL of anti-mouse Foxp3-PE-Cy7 for 40 min to detect Treg cells. Flow cytometry (LSRII, Biosciences, USA) was used to detect proteins in the samples. The percentages of TH17 and Treg cells were analysed using FlowJo software (Biosciences, USA). Flow cytometry experiments were conducted with three replicates.

Data availability

The raw sequencing data from the metagenome, transcriptome and single-nuclei RNA sequencing were deposited into the NCBI Sequence Read Archive (SRA) under accession numbers PRJNA1054354, PRJNA1053813, and PRJNA1053848, respectively.

Abbreviations

NAFLD:

Nonalcoholic fatty liver disease

NASH:

Nonalcoholic steatohepatitis

NASH-T:

Nonalcoholic steatohepatitis tolerance

HCD:

High-concentrate diet

LCD:

Low-concentrate diet

ALT:

Alanine transaminase

AST:

Aspartate transaminase

TC:

Total cholesterol

TG:

Triglyceride

H&E:

Haematoxylin‒eosin

UDCA:

Ursodeoxycholic acid

isoDCA:

Isodeoxycholic acid

isoLCA:

Isolithocholic acid

BAs:

Bile acids

VH:

Villus height

CD:

Crypt depth

NIC:

Number of inflammatory

LDEA:

Lipid droplet expression area

FTEA:

Fibrous tissue expression area

BSH:

Bile salt hydrolase

7α-HSDH:

7α-Hydroxysteroid dehydrogenase

BaiA:

3α-Hydroxycholanate dehydrogenase (NADP +)

7β-HSDH:

7β-Hydroxysteroid dehydrogenase (NADP +)

3β-HSDH:

3β-Hydroxy-delta5-steroid dehydrogenase

FDR:

False discovery rate

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Acknowledgements

We thank the members of the Innovative Research Team of Animal Nutrition & Healthy Feeding of Northwest A&F University. Further, we also thank the High Performance Computing Platform of Northwest A&F University.

Funding

This project was funded by the National Natural Science Foundation of China (Grant No. 32302775), China Postdoctoral Science Foundation (Grant No. 2022M722616) and Shaanxi Province Sanqin Talent Introduction Plan Regional Young Talent Project.

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Author notes

  1. Yue Wang, Xiaodong Chen, and Sharon A. Huws are co-first authors.

Authors and Affiliations

  1. College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China

    Yue Wang, Xiaodong Chen, Guanghao Xu, Jing Li, Jianrong Ren, Jingyi Xu, Junhu Yao & Shengru Wu

  2. Faculty of Land and Food Systems, the University of British Columbia, Vancouver, BC, V6T 1Z4, Canada

    Yue Wang & Le Luo Guan

  3. College of Animal Science and Technology, Ningxia University, Yinchuan, China

    Xiaodong Chen

  4. Institute of Global Food Security, School of Biological Sciences, Queen’s University Belfast, 19 Chlorine Gardens, Belfast, Northern Ireland, BT9 5DL, UK

    Sharon A. Huws

  5. Key Laboratory of Livestock Biology, Northwest A&F University, Yangling, Shaanxi, China

    Yue Wang, Xiaodong Chen, Guanghao Xu, Jing Li, Jianrong Ren, Jingyi Xu, Junhu Yao & Shengru Wu

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Contributions

Y.W. wrote the original draft. Y. W. and S. W conceptualization. X. C., S. W., S. A. H. and Y. W. performed the experimental design and sample collection. Y. W., X. C., J. R., G. X., J.X. conducted the sample detection and analysis. Y. W., S. W., J. Y., L. G. and S. A. H. reviewed and revised the manuscript. L.G., J. Y., S. W. and Y. W. performed project administration, supervision, validation. All authors reviewed the manuscript.

Corresponding authors

Correspondence to Le Luo Guan, Junhu Yao or Shengru Wu.

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All the experimental designs and protocols used in the present study were approved by the Institutional Animal Care and Use Committee (IACUC) of Northwest A&F University (Shaanxi, China, approval number: NWAFU-DK-2022020) and were in accordance with the recommendations of the university’s guidelines for animal research.

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Wang, Y., Chen, X., Huws, S.A. et al. Ileal microbial microbiome and its secondary bile acids modulate susceptibility to nonalcoholic steatohepatitis in dairy goats. Microbiome 12, 247 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40168-024-01964-0

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Microbiome

ISSN: 2049-2618

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