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Gut Microbiota and Liver Fibrosis: One Potential Biomarker for Predicting Liver Fibrosis

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Gut Microbiota and Liver Fibrosis: One Potential Biomarker for Predicting Liver Fibrosis Hindawi BioMed Research International Volume 2020, Article ID 3905130, 15 pages https://doi.org/10.1155/2020/3905130 Research Article Gut Microbiota and Liver Fibrosis: One Potential Biomarker for Predicting Liver Fibrosis Zhiming Li ,1 Ming Ni ,1 Haiyang Yu ,1 Lili Wang ,2 Xiaoming Zhou ,1 Tao Chen ,1 Guangzhen Liu ,1 and Yuanxiang Gao 1 1Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China 266000 2Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, China 266000 Correspondence should be addressed to Yuanxiang Gao; [email protected] Received 14 February 2020; Accepted 19 May 2020; Published 20 June 2020 Academic Editor: Kim Bridle Copyright © 2020 Zhiming Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Purpose. To investigate the relationship between gut microbiota and liver fibrosis and establish a microbiota biomarker for detecting and staging liver fibrosis. Methods. 131 Wistar rats were used in our study, and liver fibrosis was induced by carbon tetrachloride. Stool samples were collected within 72 hours after the last administration. The V4 regions of 16S rRNA gene were amplified. The sequencing data was processed using the Quantitative Insights Into Microbial Ecology (QIIME version 1.9). The diversity, principal coordinate analysis (PCoA), nonmetric multidimensional scaling (NMDS), and linear discriminant analysis (LDA) effect size (LEfSe) were performed. Random-Forest classification was performed for discriminating the samples from different groups. Microbial function was assessed using the PICRUST. Results. The Simpson in the control group was lower than that in the liver fibrosis group (p =0:048) and differed significantly among different fibrosis stages (p =0:047). The Chao1 index in the control group was higher than that in the liver fibrosis group (p <0:001). NMDS analysis showed a marked difference between the control and liver fibrosis groups (p <0:001). PCoA analysis indicated the different community composition between the control and liver fibrosis groups with variances of PC1 13.76% and PC2 5.89% and between different liver fibrosis stages with variances of PC1 10.51% and PC2 7.78%. LEfSe analysis showed alteration of gut microbiota in the liver fibrosis group. Biomarkers obtained from Random-Forest classification showed excellent diagnostic accuracy in prediction of liver fibrosis with AUROCs of 0.99. The AUROCs were 0.77~0.84 in prediction of stage F4. There were six increased and 17 decreased metabolic functions in the liver fibrosis group and 6 metabolic functions significantly differed among four liver fibrosis stages. Conclusion. Gut microbiota is a potential biomarker for detecting and staging liver fibrosis with high diagnostic accuracies. 1. Introduction cirrhosis [3]. A high prevalence of 40.8% for small intestinal bacterial overgrowth was found in patients with CLD [4] The crosstalk between gut and liver is deduced by the gut- and even 50.5% for patients with decompensated cirrhosis. liver axis [1]. They are connected by bile acids and portal However, these studies focused on advanced liver disease, venous system. A number of liver diseases including non- i.e., liver cirrhosis. alcoholic fatty liver disease (NAFLD), liver cirrhosis and Liver fibrosis is characterized by excessive deposition cancer have a strong link with gut microbiota [2]. Chronic of extracellular matrix (ECM). The activation of hepatic liver diseases (CLD) can lead to impaired gastrointestinal stellate cells (HSCs) is crucial for liver fibrogenesis. The motility, spontaneous bacterial peritonitis, portal hyper- dysfunction of the intestinal barrier in CLD patients tension, and intestinal bacteria overgrowth. Recently, one resulted in the increased translocation of intestinal bacteria gut microbiome analysis study validated that the compo- and their components [5]. Due to continuous exposure of sition of gut bacterial species altered in patients with liver lipopolysaccharide (LPS) from gram-negative bacteria of 2 BioMed Research International F0 F1 F2 F3 F4 H&E (a) (b) ((c)c) (d) ((e)e) M-T (f) (g) (h) (i) (j) Figure 1: H&E and Masson’s trichrome staining in liver fibrosis. Liver fibrosis stages: F0: no fibrosis (a, f); F1: fibrous portal expansion (b, g); F2: few bridges or septa (c, h); F3: numerous bridges or septa (d, i); F4: cirrhosis (e, j). M-T: Masson trichrome staining. the gut microbiota, transforming growth factor- (TGF-) β bacterial composition, and the composition and perfor- signaling in HSCs was activated by Toll-like receptor mance of NOPB-based reagent were described in detail (TLR) 4-mediated innate immunity [6]. Therefore, gut by Han et al. [7]. Total DNA was extracted from stool microbita in CLD patients promotes liver fibrosis. Because samples by using the PowerMax (stool/soil) DNA isolation liver fibrogenesis is an ongoing process, changes of gut kit (MoBio Laboratories, Carlsbad, CA, United States). ° microbiota associated with liver fibrosis stages are still Then, DNA samples were stored at -20 C for further anal- unrevealed in detail. ysis. Extracted DNA concentrations were assessed with To investigate the relationship between gut microbiota absorption ratios of DNA at 260/280 nm and 260/230 nm and liver fibrosis, we used 16S rRNA Amplicon Pyrose- by using a NanoDrop 2000c spectrophotometer (Thermo quencing to disclose the alteration of microbiota composi- Scientific, USA). tion in liver fibrosis rats. The purpose of the study was to establish a microbiota biomarker for detecting and staging 2.3. 16S rRNA Gene Sequencing. The V4 regions of 16S liver fibrosis. rRNA gene were amplified with the forward primer 515F (5′–GTGCCAGCMGCCGCGGTAA–3′) and the reverse 2. Materials and Methods primer 806R (5′–GGACTACHVGGGTWTCTAAT–3′). PCR amplification was performed in a mixture containing 2.1. Induced Liver Fibrosis Rat Model. 131 specific pathogen- 25 μl of Phusion High-Fidelity PCR Master Mix, 3 μlof free Wistar rats (male, 200 ± 50 g) were used in our study DMSO, 3 μl of each forward and reverse primer, 10 μlof and were housed in our institutional animal laboratory DNA Template, and 6 μl of ddH O. Thermal cycling reac- ° 2 ° with a room temperature of 25 C and room humidity of tion was as the following: initial denaturation at 98 C for ° 50%. Rats of the liver fibrosis group were subcutaneously 30 s, followed by 30 cycles of denaturation at 98 C for 15 s, ° ° administrated twice a week with 0.3 ml/100 g mixed solu- annealing at 58 C for 15 s, and extension at 72 C for 15 s, ° tion, which was composed of carbon tetrachloride (CCl4) and final extension at 72 C for 1 min. PCR products were and olive oil at a ratio of 1 : 1. Rats of the control group purified with AMPure XP Beads (Beckman Coulter, India- were treated with saline. napolis, IN). The amplicons were normalized, pooled, and sequenced on the Illlumina HiSeq 4000 sequencer (2 × 150 2.2. Stool Sample Collection and DNA Extraction. All stool bp; Guhe Information and Technology Co., Ltd., Hangzhou, samples of both liver fibrosis and control groups were China). collected within 72 hours after the last CCl4 or saline administration and stored in a stool container with a novel 2.4. Sequence Analysis and Statistical Analysis. The sequenc- chemical stabilizer, i.e., N-octylpyridinium bromide- ing data was processed using the Quantitative Insights Into (NOPB-) based reagent. It allows stool sample transporta- Microbial Ecology (QIIME version 1.9) [8]. The low-quality tion and storage at room temperature with preservation of sequence reads were removed following the criteria: (1) reads BioMed Research International 3 p = 0.465 p = 0.048 7 6 0.8 5 4 0.6 Simpson Shannon 3 0.4 2 1 0.2 G1 G2 G1 G2 (a) (b) p < 0.001 p = 0.051 2000 7 6 1500 5 Chao 4 1000 Shannon 3 500 2 G1 G2 F1 F2 F3 F4 Groups (c) (d) p p = 0.047 = 0.396 2000 0.8 1500 0.6 Chao Simpson 1000 0.4 500 0.2 F1 F2 F3 F4 F1 F2 F3 F4 (e) (f) Figure 2: Differences of alpha diversity between the control and liver fibrosis groups and between different liver fibrosis stages. Between the control and liver fibrosis group, no difference of Shannon was found (p =0:465) (a). The Simpson in the control group was lower than that in the liver fibrosis group (p =0:048) (b). The Chao1 of the alpha diversity index in the control group was higher than that in the liver fibrosis group (p <0:001) (c). Between different liver fibrosis stages, there were no differences of Shannon and Chao1 indices (d, f). Significant difference of Simpson was found with p =0:047 (e). G1: control group; G2: liver fibrosis group; F1~F4: liver fibrosis stages. with a length of <150 bp, (2) reads with an average Phred The R package (3.2.0) was applied to analyze the distribu- score of <20, (3) reads containing ambiguous bases, and (4) tion of sequence length in all samples. OTU tables were used reads containing mononucleotide repeats of >8 bp. High- to record the abundance of each OTU of samples. Taxon quality reads were clustered into 16S rRNA Operational Tax- abundance at the levels of phylum, class, order, family, genus, onomic Units (OTUs) with ≥97% sequence homology. The and species was calculated and statistically compared among taxonomic classification of each OTU was performed by groups using R stats package. Based on the OTU table in VSEARCH searching the representative sequence set against QIIME, alpha diversities including Chao1, Simpson, and the SILVA reference database [9]. Shannon were calculated. The significant differences of alpha 4 BioMed Research International diversity metrics were performed using the R package NMDS “Vegan.” The Venn diagram generated by R package was used to visualize the shared and unique OTUs among groups.
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