Alterations of the gut microbiota associated with the occurrence and progression of viral hepatitis

Abstract

Background:

Gut microbiota is the largest population of microorganisms and is closely related to health. Many studies have explored changes in gut microbiota in viral hepatitis. However, the correlation between gut microbiota and the occurrence and progression of viral hepatitis has not been fully clarified.

Methods:

PubMed and BioProject databases were searched for studies about viral hepatitis disease and 16S rRNA gene sequencing of gut microbiota up to January 2023. With bioinformatics analyses, we explored changes in microbial diversity of viral hepatitis, screened out crucial bacteria and microbial functions related to viral hepatitis, and identified the potential microbial markers for predicting risks for the occurrence and progression of viral hepatitis based on ROC analysis.

Results:

Of the 1389 records identified, 13 studies met the inclusion criteria, with 950 individuals including 656 patient samples (HBV, n = 546; HCV, n = 86; HEV, n = 24) and 294 healthy controls. Gut microbial diversity is significantly decreased as the infection and progression of viral hepatitis. Alpha diversity and microbiota including Butyricimonas, Escherichia-Shigella, Lactobacillus, and Veillonella were identified as the potential microbial markers for predicting the risk of development of viral hepatitis (AUC>0.7). Microbial functions including tryptophan metabolism, fatty acid biosynthesis, lipopolysaccharide biosynthesis, and lipid metabolism related to the microbial community increased significantly as the development of viral hepatitis.

Conclusions:

This study demonstrated comprehensively the gut microbiota characteristics in viral hepatitis, screened out crucial microbial functions related to viral hepatitis, and identified the potential microbial markers for predicting the risk of viral hepatitis.

1 Introduction

Viral hepatitis is a significant healthcare burden worldwide, resulting in around 300 million infections and more than 1.3 million deaths each year (Policy, 2017). It is caused by five types of viruses: hepatitis A virus (HAV), hepatitis B virus (HBV), hepatitis C virus (HCV), hepatitis D virus (HDV), and hepatitis E virus (HEV). HBV and HCV are responsible for about 90% of the mortality from viral hepatitis (Jefferies et al., 2018), and long-term infection could lead to other dangerous consequences such as liver cirrhosis (LC), hepatocellular carcinoma (HCC), and even death (Winer et al., 2017; Datfar et al., 2021; Su et al., 2022b). HDV is an incomplete virus that requires HBV for its replication and globally affects around 5% of people with HBV infection(WHO). HAV and HEV are the common infectious etiologies of acute hepatitis throughout the world (Lin et al., 2017; Aslan and Balaban, 2020). Given the great threat of viral hepatitis to public health, it is meaningful to explore how to reverse hepatitis virus infection and to prevent the development of end-stage liver disease.

Researchers have proved that the immune system is involved in one way or another in patients with viral hepatitis (Barathan et al., 2018; Su et al., 2023). It could control the viral infection; however, the long-lasting inflammation could lead to hepatic damage (Hou et al., 2018). Thus, the immune responses are critical for determining the outcome of hepatitis virus infection (Su et al., 2022a; Chi et al., 2023). The gut microbiota is the largest population of microorganisms in the human body. It resides in the intestine and is made up of trillions of bacteria, fungi, and other microbes. It is critical for the host defense and immune homeostasis in humans (Rinninella et al., 2019; Xue et al., 2020). Alterations in the gut microbiota are associated with immune homeostasis disturbances (Yang and Cong, 2021). Recent studies have raised the possibility that gut microbial dysbiosis is involved in the occurrence and progression of viral hepatitis (Chen et al., 2022; Ali et al., 2023).

Hepatitis virus infection alters the diversity of the gut microbiome. One study showed increased diversity of microbiota in the treatment-naive HCV group (Sultan et al., 2021), while another study showed the opposite result, with lower alpha diversity among HCV-infected patients (Aly et al., 2016). Other researchers have found significant changes in the gut microbiome among HBV-induced disease groups when compared with healthy individuals. For example, the abundance of Veilonella, Streptococcus, and Enterococcus increased significantly in the HBV-related acute-on-chronic liver failure (HBV-ACLF) group, and Bacteroides, Lachnospiracea incertae sedis, and Clostridium cluster XIVa were enriched in patients with HBV-related HCC patients (HBV-HCC) (Yang et al., 2018; Huang et al., 2020; Yao et al., 2021). Nevertheless, it is not completely clear whether there is a correlation between the microbiome change in different types of viral hepatitis. How the gut microbiome is altered during the process from hepatitis virus infection to chronic hepatic disorders needs to be studied in greater detail. Besides, how alterations in the gut microbiota affect the development of viral hepatitis is not well understood.

Therefore, we conducted this study using available data of 16S ribosomal RNA (rRNA) gene amplicon sequencing of gut microbiota-related viral hepatitis disease studies. We reanalyzed these data by using rigorous bioinformatics methods to elucidate shifts in the microbial community structure and diversity along with the development of viral hepatitis and to reveal crucial microbial taxonomy and functions related to viral hepatitis, aiming to provide recommendations for delaying disease progression during hepatic exacerbation.

2 Materials and methods

2.1 Search strategy and selection criteria

The PubMed (https://pubmed.ncbi.nlm.nih.gov/) and BioProject (https://www.ncbi.nlm.nih.gov/bioproject) databases were searched for articles and data about viral hepatitis and the gut microbiota published up to January 2023. The search strategy was developed based on keywords, medical subject headings (MeSH) terms, and synonyms (Table S1). Studies were included according to the following criteria: (1) it included a viral hepatitis or its related hepatic disease group and a control group that can be distinguished, (2) it used stool or rectal swab samples used for sequencing, and (3) it employed 16S ribosomal RNA (16S rRNA) gene sequencing and the data available. Raw 16S rRNA gene sequence and metadata information were downloaded from publicly available databases or obtained from the authors. Animal experiments or in vitro studies, reviews, meta-analyses, comments, letters, poster abstracts, and studies with less than three individuals (either case or control groups) were excluded.

2.2 Processing of raw data

The Sequence Read Archive (SRA) files were downloaded from the National Center for Biotechnology Information (NCBI) SRA database and converted to FASTQ format by the SRA Toolkit (https://github.com/ncbi/sratoolkit, Version 3.0.0). These files were then imported into Quantitative Insights in Microbial Ecology (QIIME) version 2 for microbiome bioinformatics analysis(Estaki et al., 2020). The QIIME2 DADA2 plug-in, which takes input dereplicated amplicon sequencing reads as an input, removed the primers and low-quality reads from the sequences, and finally returned the inferred composition of the samples. This information was used to implement sequence quality control for single-end and paired-end 16S rRNA gene reads(Callahan et al., 2016; Estaki et al., 2020). The outputs of the DADA2 pipeline were representative sequences, some statistics on the procedure, and a feature table of amplicon sequence variants (ASVs) with ≥ 97% identity matches to the expected sequences in the extreme dataset(Nearing et al., 2018).

2.3 Filtering data

After generating an ASV table and representative sequences, a q2-feature-table plug-in in QIIME2 was used to filter the low-quality features to improve the quality of the downstream statistical analysis. The samples containing the minimum count of sequences that might be caused by sequencing errors or a low level of the microbiome leading to low uptake of deoxyribonucleic acid (DNA) were filtered. Meanwhile, the low abundance features were filtered based on the interquartile range (IQR). Then the generated filtered sequences were classified with the q2-feature-classifier plug-in based on the SILVA database (https://www.arb-silva.de/, version 138) released in the QIIME2 package(Bokulich et al., 2018).

2.4 Data analysis

Files of the filtered feature table, phylogenetic tree, and taxonomic classifications were imported into RStudio 4.2.1. The vegan package was utilized to normalize the feature table to scale based on each sample’s library size that transformed the feature table into a relative feature table, aiming to remove technical bias caused by variations in sample collection, library preparation, or sequencing manifesting as uneven sampling depth and sparsity, which could not reflect the true difference in the underlying biology(Weiss et al., 2017). Then, the microeco package was used to calculate the alpha diversity indices including richness (Observed Species, Chao1, and ACE), diversity (Shannon, Simpson, Invsimpson, and Fisher), and phylogenetic diversity (PD) to evaluate the overall structure of the gut microbiota. Beta diversity was analyzed with the vegan package, based on principal coordinates (PCoA) and the non-metric multidimensional scaling (NMDS) analyses. Objects that are ordinated closer together have smaller dissimilarity values than those ordinated further farther apart. Next, the permutational multivariate analysis of variance (PERMANOVA) test and analysis of similarities (ANOSIM) based on the Bray-Curtis distance were performed to evaluate the similarities between the groups. Besides, based on the distribution of ASVs, the UpSet plot was used to show the ASV intersections among different groups through the UpSet package.

The SpiecEasi package was employed to identify the different genus and species through the linear discriminant analysis effect size (LEfSe) method (Linear Discriminant Analysis [LDA] score ≥2). The Kyoto Encyclopedia of Genes and Genomes (KEGG) functional pathways related to the microbial community were predicted with the Tax4Fun2 package based on 16S rRNA gene sequencing data classified with the SILVA database. Finally, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was calculated to evaluate the prediction effectiveness of the alpha diversity indices and differential microbial taxonomy.

The forest plots of the comparisons of the alpha diversity between the case and control groups were generated in Review Manager 5.3. A fixed-effects or random-effects model was selected according to the deviance information criterion (DIC). The standardized mean difference (SMD) and the corresponding 95% credible interval (CI) were used to evaluate the results. The beta diversity and LEfSe analyses were visualized by R 4.1.1. Graphs of the differential KEGG functional pathways were generated in GraphPad Prism 8.0.2. ROC curve analysis was conducted in GraphPad Prism 8.0.2. A model with an AUC score of > 0.7 would be considered acceptable. All tests were two-sided with a P-value of 0.05 set as the threshold for significance.

3 Results

3.1 Study characteristics

Of the 1,317 studies found in PubMed and the 73 records found in BioProject, 13 records related to HBV, HCV, and HEV were eventually included for subsequent analysis. Note that all the records concerning HAV and HDV were excluded (Figure 1). Among the 12 included studies from PubMed, 1 was about HEV infection(Wu et al., 2020), 4 were about HCV infection(Aly et al., 2016; Taylor et al., 2020; Sultan et al., 2021; Ali et al., 2023), and 7 were HBV-related liver disorders (Wang et al., 2017; Liu et al., 2018; Liu et al., 2019; Ni et al., 2019; Chen et al., 2020; Zheng et al., 2020; Li et al., 2022). The remaining unpublished data was from BioProject and concerned HBV (BioProject accession number PRJEB32568). These studies were from China, the United States, and Egypt, and contained a total of 950 individual samples (HBV, n = 546; HCV, n = 86; HEV, n = 24; healthy control [HC], n = 294). More comprehensive details of the included studies are presented in Table 1.

Figure 1

3.2 Hepatitis virus infection and progression significantly reduce gut microbial diversity

As estimated by the Observed Species, Chao1, Shannon, Simpson, Fisher, and PD indexes, the alpha diversity was reduced significantly in HBV-infected patients (P < 0.05; Figure 2). As HBV infection progressed, there was a significant trend for downregulation in alpha diversity according to the Observed Species, Chao1, ACE, Shannon, Simpson, Invsimpson, and Fisher indexes (P < 0.05; Figure 3). The pooled estimate showed significant decreases in chronic hepatitis B (CHB) (SMD = -0.28; 95%CI, -0.45 to -0.11; P < 0.05; Figure 4) and HCV (SMD = -0.25; 95%CI, -0.39 to -0.10; P < 0.05; Figure 5), compared with the HC group. There was a numerical but nonsignificant trend for downregulation in patients with HBV-related LC (HBV-LC) and HBV-HCC compared with the HC group (Figures S1, S2). The comparisons of the alpha diversity between groups that could not be analyzed comprehensively were shown in the form of boxplots. Compared with HCV-infected patients, the alpha diversity showed significant decreases in the (HCV-LC) group (P < 0.05; Figure 6). Meanwhile, the results of boxplots showing the changes in alpha diversity between HEV-infected individuals and patients with HEV-related acute liver failure (HEV-ALF) displayed a nonsignificant upward trend among the patients with HEV-ALF (Figure S3).

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

The PCoA and NMDS analyses were conducted with PERMANOVA and ANOSIM, respectively, to evaluate the similarities in the gut microbiome composition among the groups in all the included studies. The PERMANOVA and ANOSIM results for the datasets of PRJEB32568 and PRJNA838083 (Li et al., 2022) reporting four separate groups (HC, CHB, HBV-LC, and HBV-HCC) indicated that as HBV infection progressed, there were significant differences in species composition between the groups (P < 0.01; Figures 7, 8). The PERMANOVA results for the PRJNA558158 (Chen et al., 2020) and PRJNA540574 (Zheng et al., 2020) datasets showed the same trend (P < 0.01; Figure 7). Based on the ASV level, there were differences between the groups— according to the UpSet plots showing the ASV intersections between groups—including the total abundance of ASVs, the number of shared ASVs, and the number of unique ASVs for each patient group (Figure S4).

Figure 7

Figure 8

3.3 Crucial microbiota and microbial functions associated with viral hepatitis

LEfSe analysis was used to identify the dominant microbiota in each group. Based on the LDA section, the abundance of 19 genera, including Alloprevotella, Butyricimonas, and Colidextribacter, was significantly up-regulated in HBV-infected patients, while Bacteroides, Parabacteroides, and Sutterella were down-regulated, compared with those in the HC group (P < 0.05; Figure 9). Among the HCV-infected patients, 10 taxa including Desulfovibrio, Eubacterium eligens, and Prevotalla were increased significantly, while 11 genera including Barnesiella, Colidextribacter, and Dorea were decreased significantly, compared with those in the HC group (P < 0.05; Figure 10). More details of LEfSe analysis were shown in Tables S2-S4.

Figure 9

Figure 10

Based on 16S rRNA gene sequences, the KEGG profile was constructed to predict the microbial community function. The dominant microbial functions related to viral hepatitis were summarized in Figure 11. The results showed that 88 microbial functions including tryptophan metabolism, fatty acid biosynthesis, and lipopolysaccharide (LPS) biosynthesis were remarkably increased in HBV-related liver disorders (Figure 11A), while 14 microbial functions including lipid metabolism and thiamine metabolism were significantly enriched with HCV infection and progression (Figure 11B).

Figure 11

3.4 Alpha diversity and Butyricimonas, Veillonella, Escherichia-Shigella, and Lactobacillus may serve as potential gut microbial markers to predict the risk for viral hepatitis

ROC curve analysis was conducted to evaluate the potential to use the gut microbiota as a non-invasive marker to predict the risk for viral hepatitis. Those with an AUC score > 0.7 are considered to have a high risk. In the model of HBV/HCV infection and progression, all alpha diversity metrics reached AUC scores of > 0.7 (P < 0.05; Figure 12). The Observed Species, Fisher, and InvSimpson indexes reached AUC values of 0.824, 0.841, and 0.943, respectively, in the model predicting the risk for the occurrence of HBV-LC, HBV-HCC, and HCV (Figures 12B, C, E), and the Observed Species and Shannon indexes reached AUC values of 0.752 and 0.843, respectively, for predicting the progression of HBV and HCV (Figures 12D, F).

Figure 12

Among the crucial microbiota screened out based on the LDA section, 10 genera including Butyricimonas, Veillonella, Escherichia-Shigella, and Lactobacillus had a high potential to predict the risk of HBV progression (AUC > 0.7; Figure 13C). Among them, the AUC score of the particular model of Butyricimonas was 0.775 to predict the risk of HBV infection (Figure 13A). The model of Butyricimonas, Veillonella, and Escherichia-Shigella had AUC scores of 0.917, 0.797, and 0.794, respectively, to predict HBV-LC (Figure 13B). Besides, Veillonella and Lactobacillus reached AUC scores of 0.857 and 0.745, respectively, between HCV-infected patients and HC (Figure 13D), while genera including Clostridia_UCG-014, Dorea, Monoglobus, and Ruminococcus decreased in the HCV-infected group, with AUC values of 0.900, 0.857, 0.886, and 0.857, respectively, compared with the HC group (Figure 14).

Figure 13

Figure 14

4 Discussion

In recent years, an increasing number of studies have explored the correlation between gut microbiota perturbations and the occurrence and progression of viral hepatitis. For example, a reduction in gut microbial diversity has been linked to the severity of the disease and poor health (Wilmanski et al., 2019). Changes in the abundance of some gut bacteria can lead to the secretion of anti-inflammatory factors, thus accelerating disease progression (Peng et al., 2022). Moreover, gut microbiome-derived metabolites such as LPS and bile acids could interact with liver immune cells leading to pathological effects (Milosevic et al., 2021). However, the existing research could not identify consistent microbial taxa that respond to the disease (Wang et al., 2017; Chen et al., 2020). Few studies have systematically assessed the association between the gut microbiome and viral hepatitis, and the implication of the gut microbiota on the progression of viral hepatitis remains unclear. Therefore, we comprehensively assessed gut microbiota perturbations across a spectrum of the occurrence and progression of viral hepatitis to evaluate the reproducibility and specificity of potential gut microbial biomarkers.

Alpha diversity summarizes the structure of an ecological community regarding its richness, evenness, or both (Willis, 2019). It is a validated marker of gastrointestinal health and metabolic disorders (Plassais et al., 2021). We identified showed significant decreases in gut microbial alpha diversity after HBV/HCV infection as well as lower diversity was detected as the disease progressed, as has been observed in previous studies (Aly et al., 2016; Chen et al., 2020). This result followes the general assumption that higher microbial diversity is more beneficial to host health (Nikolova et al., 2021). Meanwhile, the AUC values of the diversity indexes were high, indicating that the change in the gut microbial alpha diversity could be a potential indicator to predict the risk for HBV/HCV infection and progression. An nonsignificant trend for upregulation in the patients with HEV-ALF versus the HEV-infected individuals demonstrated that there might be compensatory modulation in the early stage of disease progression to reestablish gut homeostasis. However, it is unclear what occurs during HAV and HDV infection due to the absence of datasets. Regarding beta diversity, we noted consistent significant dissimilarities among communities at different HBV progression stages in two datasets according to both PCoA and NMDS analyses, indicating that the gut microbiome composition was significantly alteredas HBV infection progressed. However, there were insignificant differences in beta diversity between HBV/HCV-infected patients and HC. Thus the suitability of beta diversity as a biomarker needs to be further confirmed.

LEfSe analysis showed that compared with the HC, 19 genera were dominant in HBV-infected individuals and 10 genera were dominant in HCV-infected individuals, suggesting that the changes in the abundance of crucial taxa led to significant alterations in the gut microbiome composition in viral hepatitis. Among these dominant genera, Prevotella could lead to a reduction in short-chain acids (SCFAs) (Iljazovic et al., 2021), which play a crucial role in delaying the progression of HBV-related HCC (Mcbrearty et al., 2021). Lactobacillus, Escherichia-Shigella, and Veillonella could contribute to the production of pro-inflammatory factors such as LPS and tumor necrosis factor (TNF) (Liu et al., 2022, Mata Forsberg et al., 2019). These processes may lead to the development of viral hepatitis by affecting the immune responses in vivo (Ji et al., 2019; Radzikowska et al., 2019; Joo et al., 2021). However, we were unable to identify the dominant microbiota related to HAV, HDV, and HEV infection due to the lack of a dataset or insufficient information provided for comprehensive analysis. At the same time, among HCV-infected individuals, we identified four decreased genera Clostridia_UCG-014, Dorea, Monoglobus, and Ruminococcus as probiotics with great potential for the prevention and treatment of HCV. Besides, we found that 10 genera, including Butyricimonas, Escherichia-Shigella, Lactobacillus, and Veillonella, could have great potential to distinguish HBV- or HCV-infected individuals from HC and to predict the risk for the development of HBV infection. The upregulation of the crucial bacteria could influence the production of gut metabolites, including tryptophan, LPS, fatty acids, and lipids (Aujoulat et al., 2014; Huang et al., 2019; Mao et al., 2020). Changes in metabolites could be associated with some microbial functions that contribute to the pathogenesis and progression of the disease (Ren et al., 2020).

In this study, functions including tryptophan metabolism, LPS biosynthesis, fatty acid biosynthesis, and lipid metabolism were significantly enriched in the HBV- or HCV-infected group. Tryptophan is an essential amino acid that possesses diverse metabolic, neurological, and immunological roles, and it is involved in viral infections including HBV (Mehraj and Routy, 2015; Fiore and Murray, 2021). A high level of LPS can promote the secretion of inflammatory cytokines including TNF and interleukin-6 (IL-6), stimulating immune cells and finally may lead to the progression of viral hepatitis (Feng et al., 2020). Fatty acids are known to play diverse roles in immune cells. It can activate the inflammatory cell signaling pathways via cell surface or intracellular receptors (Calder, 2011). Abnormal fatty acid levels in the liver can result in synergistic induction of HBV-related proteins and liver inflammatory factors, which might affect HBV progression (Cho et al., 2014). Lipid metabolism is intimately connected to every step of the HCV life cycle, and HCV enhances its replication by modulating lipid metabolism in the host cell (Popescu et al., 2014). All of these findings suggest that changes in specific taxa can alter the production of metabolites, which may contribute to the development of viral hepatitis through diverse microbial functions. However, few studies have been able to clarify the implication of alteration in the gut microbiome and microbial functions in viral hepatitis, which is of great significance in understanding the occurrence and progression of viral hepatitis.

This study has several limitations. First, we failed to collect all of the data from gut microbiome—related viral hepatitis studies due to the non-availability of data or inadequate information about the detailed characteristics such as grouping or disease stages. Additionally, the existing evidence limits our analysis: For example, no studies have evaluated the relationship between the gut microbiota and HAV or HDV. Finally, the authors of the included studies applied inconsistent variable regions and instruments for 16S rRNA gene sequencing, which may have generated potential bias during analysis.

5 Conclusions

In conclusion, our study demonstrates that the occurrence and progression of viral hepatitis are accompanied by a significant decrease in gut microbial diversity. The decreased gut microbial alpha diversity as well as the increased abundance of genera including Butyricimonas, Escherichia-Shigella, Veillonella, and Lactobacillus have the greatest potential to serve as biomarkers to predict the risk for viral hepatitis. Meanwhile, Clostridia_UCG-014, Dorea, Monoglobus, and Ruminococcus have potential value as probiotics for the prevention and treatment of viral hepatitis. Crucial microbial functions including tryptophan metabolism, fatty acid biosynthesis, LPS biosynthesis, and lipid metabolism—related to the significantly upregulated microbial community as viral hepatitis progresses—play diverse roles in the activation of immune cells and inflammatory responses. These processes provide a valuable direction to confirm the association between the gut microbiota and the occurrence and progression of viral hepatitis.

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