Gut microbiome alterations in patients with stage 4 hepatitis C

Abstract

Background: Hepatitis C virus (HCV) causes debilitating liver diseases, which may progress to cirrhosis and cancer, and claims 500,000 annual lives worldwide. While HCV epidemiology, pathophysiology, and therapy are being deeply studied, rare attention is given to reciprocal interactions between HCV infection , HCV-induced chronic liver diseases, and the human gut microbiome. As Egypt has the world's highest prevalence of HCV infections, we launched this study to monitor differences in the gut microbial community composition of Egyptian HCV patients that may affect, or result from, the patients' liver state.

Results: To this end, we analyzed stool samples from six stage 4-HCV patients and eight healthy individuals by high-throughput 16S rRNA gene sequencing using Illumina MiSeq. Overall, the alpha-diversity of the healthy persons' gut microbiomes was higher than those of the HCV patients. Whereas members of phylum Bacteroidetes were more abundant in HCV patients, healthy individuals had higher abundance of Firmicutes, Proteobacteria, and Actinobacteria. Genus-level analysis showed differential abundance of Prevotella and Faecalibacterium (higher in HCV patients) vs. Ruminococcus and Clostridium (healthy group), indicating that the higher abundance of Bacteroidetes in HCV patients is most likely due to Prevotella overabundance. The probiotic genus, Bifidobacterium, was only observed in the microbiotas of healthy individuals.

Conclusions: To the best of our knowledge, this study provides a first overview of major phyla and genera differentiating stage 4-HCV patients from healthy individuals and suggests possible microbiome remodeling in chronic hepatitis C, possibly shaped by bacterial translocation as well as the liver's impaired role in digestion and protein synthesis. Future studies will investigate the microbiome composition and functional capabilities in more patients while tracing some potential biomarker taxa (e.g., Prevotella, Faecalibacterium vs. Bifidobacterium).

Keywords: Gastro-intestinal tract; High-throughput sequencing; Infectious diseases; Liver disease; Microbiome; Next-generation sequencing; Virology.

Fig. 1

Alpha diversity estimation in patients vs. control group (Chao and Shannon indexes)

Fig. 2

Core microbiomes of analyzed samples. a Main taxa of the core microbiome of all fecal samples and their relative distribution in healthy controls (blue) compared to patients with HCV (orange). Left panel average relative abundance of 16S sequence reads representing core taxa (in percent). Right panel actual values of the average proportions of 16S sequence reads representing core taxa per group. b Venn diagram representing the core OTUs (genus level) in each analyzed group and their intersection

Fig. 3

Boxplots representing the average proportion of each 16S sequence read attributed to each taxon between the two groups (Blue healthy control samples; Red patient samples). a On the phylum level, b on the genus level—major taxa; c on the genus level—minor taxa; d on the species level—selected taxa

Fig. 4

Principal coordinate analysis representing the beta diversity estimated by the weighted UNIFRAC method [30]. Each sphere represents one sample (Blue healthy control, H1–H8; Red patients with HCV, P1–P8). The three principal coordinates (PC1 through PC3) explain 55.68, 10.28 and 9.72 %, respectively

Fig. 5

LEfSe classification analysis

Fig. 6

Suggested gut-liver axis model for chronic HCV infection. HCV starts to invade hepatocytes with massive destruction of the hepatocyte architecture and structure (1), which leads to their loss of functions, such as bile salt production and protein synthesis (2). Consequently, such damage leads to disruption of the gut homeostasis and environmental changes (3), which can affect and change the microbiota abundance and composition (possible dysbiosis, 4). The overabundance of microbes, such as Prevotella, may produce signaling microbial metabolites that may influence and initiate the inflammatory mediators leading to liver inflammation and cirrhosis (5). This model is based on an integration of the findings of this study with literature [, , –38, 42, 43, 45]