Disease Severity and Proton Pump Inhibitor Use Impact Strongest on Faecal Microbiome Composition in Liver Cirrhosis

Vanessa Stadlbauer; Irina Komarova; Ingeborg Klymiuk; Marija Durdevic; Alexander Reisinger; Andreas Blesl; Florian Rainer; Angela Horvath


Liver International. 2020;40(4):866-877. 

In This Article

Materials and Methods


We included faecal 16S microbiome sequencing data from cirrhotic patients recruited at the outpatient clinic of the Department of Gastroenterology and Hepatology, University Hospital of Graz, Austria who were screened for an intervention study[10] between July 2012 and September 2013 in this post-hoc analysis. All patients gave written informed consent. Diagnosis of cirrhosis was based on liver histopathological examinations, or a combination of clinical, radiological and/or labouratory features. Patients with a Child-Pugh score of 12 or higher, alcohol consumption within 2 weeks prior to inclusion, active infection at screening, gastrointestinal haemorrhage within 2 weeks prior to inclusion, immuno-modulating drugs, hepatic encephalopathy stage two or higher, renal failure (creatinine over 1.7 mg/dL), other severe diseases unrelated to cirrhosis, malignancy or pregnancy were excluded. Stool and blood samples analysed in this study were taken before patients received any study medication. The study protocol was approved by the institutional review board (ethics committee) in Graz (23-096 ex 10/11), registered at clinicaltrials.gov (NCT01607528) and performed according to the declaration of Helsinki. The following characteristics were assessed as possible microbiome shaping factors: Age, sex, smoking status, aetiology of cirrhosis (alcohol, hepatitis C and other aetiologies; other aetiologies contained cholestatic liver disease, non-alcoholic steatohepatitis, hepatitis B, hemochromatosis and Wilsons disease), severity of liver disease (Child-Pugh grade and Model for End-stage Liver Disease (MELD) score and the individual labouratory parameters albumin, bilirubin, hemoglobin, uric acid, creatinine, prothrombin time (international normalized ratio) as well as complications (presence of ascites, hepatic encephalopathy), nutritional status (Subjective Global Assessment (SGA)),[24] comorbidities,[25] drug intake (number of different drug classes and the following individual drug classes: proton pump inhibitors, beta blocker, other antihypertensives, diuretics, lactulose, antidiabetics, antidepressants, silymarin), intestinal permeability (lactulose/mannitol ratio, sucrose recovery, zonulin in stool, diamino-oxidase (DAO) in serum), intestinal inflammation (calprotectin in stool) and systemic inflammation (C-reactive protein (CRP), interleukin-6, interleukin-8, interleukin-10, tumour necrosis factor (TNF)-alpha, soluble CD 163 (sCD163), soluble Mannose receptor (sMR), each in plasma); neutrophil resting, priming and full burst; biomarkers of bacterial translocation (lipopolysaccharide [LPS], lipopolysaccharide binding protein [LBP], soluble CD14 (sCD14)).

Total DNA Isolation, 16S Library Preparation, Sequencing and Analysis

Total DNA was isolated from frozen stool samples using MagnaPure LC DNA Isolation Kit III (Bacteria, Fungi) (Roche, Mannheim, Germany) according to manufacturer's instructions including mechanic and enzymatic lysis as described in Klymiuk et al 2017.[26] For 16S rDNA sequencing hypervariable regions V1-V2 were amplified in a target-specific PCR (primers: 27F-AGAGTTTGATCCTGGCTCAG; R357-CTGCTGCCTYCCGTA) and amplification products were sequenced after indexing and purification on an Illumina MiSeq desktop sequencer (Illumina, Eindhoven, the Netherlands) according to published procedures.[26,27]

Statistical Analysis

For microbiome analysis, demultipexed FASTQ files were processed using Qiime2 tools implemented in Galaxy (https://galaxy.medunigraz.at). Denoising (removing primers, quality filtering, correcting errors in marginal sequences, removing chimeric sequences, removing singletons, joining paired-end reads and dereplication) were done with DADA2.[28] Taxonomy assignement was based on Silva 132 database release at 99% OTU level and trained using a Naïve Bayes classifier. After pre-processing, an average of 59 369 reads per sample could be reached. All analyses, except diversity analysis, were done on an unrarefied feature table. For normalization Hellinger transformation was used. Rare taxa with a relative abundance of less than 0.01% across all samples were filtered. Chloroplast and cyanobacteria filtering was performed to remove contaminants. Alpha diversity analysis was performed using Chao1 on a rarefied feature table (sequencing depth 14 086). Beta diversity was analysed with Redundancy Analysis (RDA)[29] based on Bray Curtis dissimilarity. Differentially abundant taxa were identified with Analysis of Composition of Microbiomes (ANCOM).[30] As machine learning methods to select genera associated with the explanatory variables, LDA-effect size (LEfSe)[31] was used for categorical variables and Least Absolute Shrinkage and Selection Operator (LASSO) Regularized Regression[32] for continuous variables. Network analysis was based on Spearman's rho associations between taxa and converting the pairwise correlations into dissimilarities to ordinate nodes in a two dimensional PCoA plot. Nearest Neighbour Propensity Score matching was performed without replacement based on logistic regression, using R 3.6.1[33] package "MatchIt."[34,35] The web-based software Calypso version 7.14 (http://cgenome.net/calypso/) was used for analyses of microbiome data.[36] For non-microbiome analyses SPSS V25.0.0.1 (IBM, Armonk, NY, USA) was used (descriptive statistics, group comparisions, Spearman Rho correlation, Collinearity analysis) Visualization was performed in R 3.6.1[33] package "ggplot2".[37] Sequencing data have been made publically available at the NCBI Sequence Read Archive (accession number PRJNA390475). A formal samples size calculation was not performed.

Labouratory Measurements

Albumin, bilirubin, hemoglobin, uric acid, creatinine, prothrombin time and CRP were measured in the routine labouratory. LPS was detected in serum with an adapted protocol using HEK-Blue hTLR4 reporter cells (Invivogen, Toulouse, France) as published previously.[38] All other assays were handled according to manufacturers' instructions. Enzyme linked immunosorbent assay were used to measure calprotectin, zonulin and serum DAO (Immundiagnostic AG, Bensheim, Germany), as well as LBP and sCD14 (Hycult biotechnology, Uden, the Netherlands). sCD163 and sMR in plasma samples were measured by an in-house sandwich ELISA using a BEP-2000 ELISA-analyser (Dade Behring) as previously described.[10,39,40]

Cytokines (interleukin-6, interleukin-8, interleukin-10, TNF-alpha) were measured with ProcartaPlex (eBioscience, Vienna, Austria). Neutrophil oxidative burst was measured by flow cytometry using a commercially available kit (Glycotope, Heidelberg, Germany).