Article Figures & Data
Figures
- FIG 1
Gut microbiome alterations in patients with cirrhosis or HCC-cirrhosis and in controls and cirrhosis etiology distribution in study groups. (A) Box plot of α-diversity (observed features) displaying a significant decrease in bacterial richness in cirrhotic patients and patients with HCC-cirrhosis compared to healthy controls (P values = 0.014 and 0.028, correspondingly). (B) Box plot of β-diversity (unweighted UniFrac distance) displaying a significant difference in bacterial composition in cirrhotic patients and patients with HCC-cirrhosis compared to healthy controls (P values = 0.004 and 0.016, correspondingly). (C and D) Box plot of α-diversity (observed OTU indices) (C) and β-diversity (unweighted UniFrac distance matrix) (D) of cirrhosis etiologies (in cirrhosis without HCC), displaying a significant decrease in bacterial richness (P value = 0.04) and altered bacterial composition (P value = 0.03) in cirrhotic patients with NAFLD compared to HCV-cirrhosis patients. (E and F) Box plot of α-diversity (observed OTU indices) (E) and β-diversity (unweighted UniFrac distance matrix) (F) of HCC-cirrhosis etiologies (in the HCC-cirrhosis group), showing that there were no significant differences in bacterial richness (P value = 0.11) or composition (P value = 0.07) in HCC patients with NAFLD-cirrhosis compared to HCV-cirrhosis.
- FIG 2
Differentially abundant taxa in patients with HCC-cirrhosis compared to the controls. (A) LDA scores computed for differentially abundant taxa in the fecal microbiomes of patients with HCC-cirrhosis (green) and healthy controls (red). Length indicates effect size associated with a taxon. P = 0.05 for the Kruskal-Wallis test; LDA score > 2. (B) A graphical representation of the classification accuracy of a machine-learning random forest model in receiver operating characteristic (ROC) curves, displayed here as ROC curves for each class (AUC of 0.9) and average ROCs and AUCs, including “microaveraging” of 0.87 (to calculate metrics globally by averaging across each sample) and “macroaveraging” of 0.94 (to give equal weight to the classification of each sample). (C) Confusion matrix displaying the classification results, with overall accuracy of 82%, baseline accuracy of 0.545, and an accuracy ratio of 1.5. (D) Important features are represented in an abundance heat map, consisting of log10 frequencies of the most important taxa in each sample and group (HCC-cirrhosis and healthy controls). These are the features that maximize model accuracy, as determined using recursive feature elimination.
- FIG 3
Differentially abundant taxa in patients with HCC-cirrhosis compared to cirrhotic patients without HCC. (A) LDA scores computed for differentially abundant taxa in the fecal microbiomes of patients with liver cirrhosis (red) and patients with HCC-cirrhosis (green). Length indicates effect size associated with a taxon. P = 0.05 for the Kruskal-Wallis test; LDA score > 2. (B) Taxonomic cladogram from LEfSe showing differences in fecal taxa of cirrhosis patients compared to HCC-cirrhosis patients. There were differences in the relative abundances of Alphaproteobacteria (P value = 0.039), Clostridium (P value = 0.024), CF231 (P value = 0.010), Verrucomicrobia (P value = 0.036), and Akkermansia muciniphila (P value = 0.039).
- FIG 4
Gut microbiome alterations in overweight HCC-cirrhosis patients. (A) Box plot of α-diversity (Shannon’s index) displaying a significant decrease in diversity in patients with HCC-cirrhosis that were overweight (BMI > 25) compared to counterparts that were not overweight (P value = 0.024). (B) Box plot of β-diversity displaying a significant difference in bacterial composition in overweight patients with HCC-cirrhosis compared to counterparts that were not overweight (P value = 0.033). (C) LDA scores computed for differentially abundant taxa in the fecal microbiomes of overweight patients with HCC-cirrhosis. Length indicates effect size associated with a taxon. P = 0.05 for the Kruskal-Wallis test; LDA score > 2.
- FIG 5
Fatty liver in HCC-cirrhosis and significant association with the relative abundance of Akkermansia. (A) Box plot of α-diversity (Faith’s phylogenetic diversity [PD]) displaying a significant decrease in diversity in patients with HCC-cirrhosis that had a fatty liver compared to counterparts without a fatty liver (P value = 0.025). (B) Box plot of β-diversity (unweighted UniFrac distance matrix) displaying a significant difference in bacterial composition in patients with HCC-cirrhosis that had a fatty liver compared to counterparts without a fatty liver (P value = 0.008). (C) LDA scores computed for differentially abundant taxa in the fecal microbiomes of patients with HCC-cirrhosis that had a fatty liver. Length indicates effect size associated with a taxon. P = 0.05 for the Kruskal-Wallis test; LDA score > 2.
Tables
- TABLE 1
Demographic and clinical characterization of study groupsa
↵a One-way analysis of variance was used to evaluate the difference among the three groups. Continuous variables were compared using the Wilcoxon-Mann-Whitney rank sum test for comparisons between patient groups. Fisher’s exact test was used to compare categorical variables. NA, not applicable.
FIG S1
Weighted phylogenetic β-diversity analysis. Box plots of phylogenetic β-diversity measured by weighted UniFrac distance matrix, displaying no significant differences between study groups (cirrhosis compared to controls P value = 0.243, HCC-cirrhosis compared to controls P value = 0.263, cirrhosis compared to HCC-cirrhosis P value = 0.117). Download FIG S1, TIF file, 1.1 MB.
Copyright © 2020 Lapidot et al.
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FIG S2
Gut microbial composition in association with cirrhosis severity. (A) Box plot of β-diversity (unweighted UniFrac distance matrix) displaying a significant difference in bacterial composition according to cirrhosis severity in compensated cirrhosis and decompensated cirrhosis (P value = 0.024). (B) LDA scores computed for differentially abundant taxa in the fecal microbiomes of patients with compensated cirrhosis (red) and decompensated cirrhosis (green). Length indicates effect size associated with a taxon (P = 0.05 for the Kruskal-Wallis test; LDA score > 2). (C) Taxonomic cladogram from LEfSe showing differences in fecal taxa of cirrhotic patients according to disease severity. Dot size is proportional to the abundance of the taxon. Download FIG S2, TIF file, 2.0 MB.
Copyright © 2020 Lapidot et al.
This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.
FIG S3
Gut microbial composition in association with diuretics in cirrhosis. (A) Box plot of α-diversity (observed features) displaying a significant decrease in diversity in cirrhotic patients that received diuretics compared to counterparts that did not receive diuretics (P value = 0.003). (B) Box plot of β-diversity (unweighted UniFrac distance matrix) displaying a significant difference in bacterial composition in cirrhotic patients that received diuretics compared to counterparts that did not receive diuretics (P value = 0.006). (C) Taxonomic cladogram from LEfSe showing differences in fecal taxa of cirrhotic patients (compensated and decompensated cirrhosis). Patients with cirrhosis receiving diuretics exhibited marked alterations in the abundances of various bacteria, including an increased relative abundance of Bacilli (P value = 0.031). Dot size is proportional to the abundance of the taxon. (D) Taxonomic cladogram showing alterations in fecal microbiome of patients with decompensated cirrhosis receiving diuretics. Download FIG S3, TIF file, 2.0 MB.
Copyright © 2020 Lapidot et al.
This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.
FIG S4
High-protein-diet consumption in patients with cirrhosis is associated with microbial alterations. (A) Box plot of α-diversity (observed features) displaying a significant decrease in diversity in cirrhotic patients that consumed a high-protein diet compared to counterparts that did not consume a high-protein diet (P value = 0.003). (B) Box plot of β-diversity (unweighted UniFrac distance matrix) displaying a significant difference in bacterial composition in cirrhotic patients who reported consumption of a high-protein diet compared to counterparts who did not report consumption of a high-protein diet (P value = 0.009). (C) Taxonomic cladogram from LEfSe showing differences in fecal taxa of cirrhotic patients according to disease severity. Dot size is proportional to the abundance of the taxon. (D) LDA scores computed for differentially abundant taxa in the fecal microbiomes of patients with compensated cirrhosis (red) and decompensated cirrhosis (green). Length indicates effect size associated with a taxon (P = 0.05 for the Kruskal-Wallis test; LDA score > 2). Download FIG S4, TIF file, 2.0 MB.
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TABLE S1
Study questionnaire. Download Table S1, DOCX file, 2.0 MB.
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TABLE S2
Significantly altered bacteria in patients with cirrhosis compared to healthy controls. Download Table S2, DOCX file, 0.01 MB.
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TABLE S3
Correlation analysis of food frequency questionnaires and gut bacteria in cirrhosis group. Spearman correlation analysis was performed for comparisons between parameters of food frequency questionnaires and fecal bacteria at the genus level in the HCC-cirrhosis group. In this table, the strongest correlations that corresponded to a P value of >0.05 are presented. These correlations did not pass the multiple-comparison correction test (false-discovery-rate [q] value < 0.05). Download Table S3, DOCX file, 0.01 MB.
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TABLE S4
Significantly altered bacterial composition in patients with HCC-cirrhosis compared to healthy volunteers. Download Table S4, DOCX file, 0.01 MB.
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TABLE S5
Feature importance analysis for random forest classifier in comparisons between HCC and healthy controls. Download Table S5, DOCX file, 0.01 MB.
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TABLE S6
Correlation analysis of food frequency questionnaires and gut bacteria in the HCC-cirrhosis group: Spearman correlation analysis between parameters of food frequency questionnaires and fecal bacteria at the genus level in the HCC-cirrhosis group. In this table, the strongest correlations that received a P value of >0.05 are presented. Two of these correlations passed the multiple-comparison correction test (q value < 0.05): consumption of artificial sweeteners was significantly correlated with Verrucomicrobia genus Akkermansia muciniphila and consumption of products containing large amounts of sugar with Synergistetes genus Cloacibacillus. Download Table S6, DOCX file, 0.01 MB.
Copyright © 2020 Lapidot et al.
This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.
