Distinct signatures of gut microbiota and metabolites in primary biliary cholangitis with poor biochemical response after ursodeoxycholic acid treatment

Abstract

Background: About 1/3 of primary biliary cholangitis (PBC) patients suffered from poor response worldwide. And these patients present intestinal disturbances. We aimed to identify signatures of microbiota and metabolites in PBC patients with poor response, comparing to patients with response.

Methods: This study enrolled 25 subjects (14 PBC patients with response and 11 PBC patients with poor response). Metatranscriptomics and metabolomics analysis were carried out on their fecal.

Results: PBC patients with poor response had significant differences in the composition of bacteria, characterized by decreased Gemmiger etc. and increased Ruminococcus etc. The differential microbiota functions characterized by decreased abundance of elongation factor Tu and elongation factor G base on the KO database, as well as decreased abundance of Replicase large subunit etc. based on the SWISS-PROT database. PBC with poor response also had significant differences in 17 kinds of bacterial metabolites, characterized by decreased level of metabolites vital in bile acids metabolism pathway (L-Cysteine etc.) and the all-trans-Retinoic acid, a kind of immune related metabolite. The altered microbiota was associated with the differential expressed metabolites and clinical liver function indicators. 1 bacterial genera, 2 bacterial species and 9 metabolites simultaneously discriminated PBC with poor response from PBC with response with high accuracy.

Conclusion: PBC patients with poor response exhibit unique changes in microbiota and metabolite. Gut microbiota and metabolite-based algorithms could be used as additional tools for differential prediction of PBC with poor prognosis.

Fig. 1

Diagram of the study design

Fig. 2

Fecal microbiome variations in PBC with poor response (the response n = 14 versus the none response n = 11 n represent biological replicates). A Alpha diversity comparison of two groups at the species level; B Alpha diversity comparison of two groups at the species level; C Beta diversity comparison of two groups; D The PCoA and NMDS overall bacterial community structure of the two groups at the genus level; E The PCoA and NMDS overall bacterial community structure of the two groups at the species level. *P < 0.05

Fig. 3

Gut microbiota signatures in PBC with poor response (the response n = 14 versus the none response n = 11, n represent biological replicates). A The composition of microbiota in the two group at the genus level; B The composition of microbiota in the two group at the species level; C Boxplots show the relative abundance of taxa exclusively altered in PBC with poor response at the genus level; D Boxplots show the relative abundance of taxa exclusively altered in PBC with poor response at the species level; E Stampplot show the relative abundance of taxa exclusively altered in PBC with poor response at the genus level; F Stampplot show the relative abundance of taxa exclusively altered in PBC with poor response at the species level

Fig. 4

Microbiota function in PBC with poor response (the response n = 14 versus the none response n = 11, n represent biological replicates). A The overall microbiota transcript function of the two groups based on the KO database; B The overall microbiota function community structure of the two groups based on the KO database; C The significantly different microbiota function between the group based on the KO database; D Spearman analysis showed the relationship between differential microbiota and microbiota function based on the KO database; E The overall microbiota transcript function of the two groups based on the swissprot database; F The overall microbiota function community structure of the two groups based on the swissprot database; G The significantly different microbiota function between the group based on the swissprot database; H. Spearman analysis showed the relationship between differential microbiota and microbiota function based on the swissprot database. * < 0.05

Fig. 5

The fecal metabolite in PBC with poor response (the response n = 14 versus the none response n = 11, n represent biological replicates). A The number of differential metabolites in the poor response group compared to the response group; B The bubble chart of metabolites pathway between the two group. The X-axis Rich Factor is the number of differential metabolites annotated in this Pathway divided by all identified metabolites annotated in this Pathway. The higher the value is, the higher the ratio of differential metabolites annotated in this Pathway is. The dot size represents the number of differential metabolites annotated in this Pathway; C BAs metabolism related pathway enrichment analysis network plot. The circles represent metabolic pathway, and the triangles represent metabolites, and rhombus represent the class of the metabolites. Red indicates up-regulation and yellow indicates down-regulation; D The rank sum test showed the primary BAs and secondary BAs in the two groups; E The rank sum test showed the BAs pool in the two groups; F Immune related pathway enrichment analysis network plot. The circles represent metabolic pathway, and the triangles represent metabolites, and rhombus represent the class of the metabolites. Yellow indicates down-regulation

Fig. 6

The spearman analysis showed the relationship between microbiota and metabolites (A), microbiota and BAs pools (B). * < 0.05; n = 25, n represent biological replicates

Fig. 7

The spearman analysis showed the relationship between clinic indictors and microbiota (A), clinic indictors and metabolites (B). * < 0.05; n = 25, n represent biological replicates

Fig. 8

Disease classification based on the ROC plot. X-axis represents 1-specificity, y-axis represent sensitivity. The area under the curve is the AUC value. A higher AUC value indicates a more suitable metabolite as a biomarker