Intestinal Fungal Dysbiosis and Systemic Immune Response to Fungi in Patients With Alcoholic Hepatitis

Abstract

Chronic alcohol consumption causes increased intestinal permeability and changes in the intestinal microbiota composition, which contribute to the development and progression of alcohol-related liver disease. In this setting, little is known about commensal fungi in the gut. We studied the intestinal mycobiota in a cohort of patients with alcoholic hepatitis, patients with alcohol use disorder, and nonalcoholic controls using fungal-specific internal transcribed spacer amplicon sequencing of fecal samples. We further measured serum anti-Saccharomyces cerevisiae antibodies (ASCA) as a systemic immune response to fungal products or fungi. Candida was the most abundant genus in the fecal mycobiota of the two alcohol groups, whereas genus Penicillium dominated the mycobiome of nonalcoholic controls. We observed a lower diversity in the alcohol groups compared with controls. Antibiotic or steroid treatment was not associated with a lower diversity. Patients with alcoholic hepatitis had significantly higher ASCA levels compared to patients with alcohol use disorder and to nonalcoholic controls. Within the alcoholic hepatitis cohort, patients with levels of at least 34 IU/mL had a significantly lower 90-day survival (59%) compared with those with ASCA levels less than 34 IU/mL (80%) with an adjusted hazard ratio of 3.13 (95% CI, 1.11-8.82; P = 0.031). Conclusion: Patients with alcohol-associated liver disease have a lower fungal diversity with an overgrowth of Candida compared with controls. Higher serum ASCA was associated with increased mortality in patients with alcoholic hepatitis. Intestinal fungi may serve as a therapeutic target to improve survival, and ASCA may be useful to predict the outcome in patients with alcoholic hepatitis.

Trial registration: ClinicalTrials.gov NCT02075918.

Figure 1.. Patients with alcohol-associated liver disease have a low fungal diversity.

The fungal diversity and richness was calculated in 11 non-alcoholic controls, 15 patients with alcohol use disorder and 59 alcoholic hepatitis patients. (A) Shannon-Index. (B) Simpson-Index. (C) Chao-Richness. Kruskal-Wallis test for nonparametric data and Dunn post-hoc test. *p<0.05 p>0.01, **p<0.01 p>0.001. Ctrl, non-alcoholic controls; AUD, alcohol use disorder, AH, alcoholic hepatitis.

Figure 2.. The fungal composition is altered in patients with alcohol-associated liver disease.

ITS sequencing of fecal samples from 11 non-alcoholic controls, 15 patients with alcohol use disorder and 59 alcoholic hepatitis patients. (A-B) The graphs demonstrate the mean relative abundance of sequence reads in each genus for each group. 0-1 corresponds to 0-100% abundance. A total of 81 different genera were detected. Shown are only genera that cover in total at least 95% abundance of all genera. In (B), the standard error of the mean is shown as error bar. *p<0.05, Kruskal-Wallis test for nonparametric data and Dunn post-hoc test. (C) Principal component analysis (PC) was used to show ß-diversity between the groups based on the abundance of 81 fungi at the genus level. The axes represent the two most discriminating axes using the euclidean distance metric. 56.5% of the variances are explained by PC1 and 9.2% by PC2. Ctrl, non-alcoholic controls; AUD, alcohol use disorder, AH, alcoholic hepatitis.

Figure 3.. Intestinal fungi correlate with clinical parameters in alcoholic hepatitis.

Heat map representing color-coded spearman’s correlations of clinical parameters. Red color indicates positive- and blue color negative correlation. All variables are coded from low to high- i.e. red color in antibiotics means antibiotic use is positively correlated with the respective fungi. *p<0.05 p>0.01, **p<0.01 p>0.001. Fibrosis stage, 0 no fibrosis, 1 portal fibrosis, 2 expansive periportal fibrosis, 3 bridging fibrosis, 4 cirrhosis. Lobular fibrosis, 0 no fibrosis, 1 zone 3 (centrilobular) fibrosis, 2 zone 2+3 (midzonal) fibrosis, 3 panlobular fibrosis. Pericellular fibrosis, 0 absent, 1 present. Steatosis, 1 mild < 33%, 2 moderate < 33–66%, 3 marked > 66%. Mallory bodies, 0 absent, 1 present. Bilirubinostasis, 0 no, 1 hepato-canalicular, 2 cholangiolar, 3 both. Ballooning, 0 occasional hepatocellular, 1 marked hepatocellular, 2 none present. Megamitochondria, 0 absent, 1 present. PMN infiltration, 0 no, 1 mild, 2 severe. Inflammation, 0 no, 1 mild, 2 severe. PMN, polymorphonuclear infiltration; INR, international normalized ratio; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, gamma-glutamyl-transferase; AP, alkaline phosphatase; BMI, body mass index. MELD, Model for End-stage Liver Disease, MELDNa, Sodium Model for End-stage Liver Disease.

Figure 4.. ASCA are increased in alcoholic hepatitis and associated with increased mortality.

(A) Logarithmic ASCA levels were compared in 23 non-alcoholic controls, 40 patients with alcohol use disorder and 163 alcoholic hepatitis patients. Kruskal-Wallis test for nonparametric data and Dunn post-hoc test. *p<0.05 p>0.01, **p<0.01 p>0.001, ****p<0.001. (B) Logarithmic zonulin levels were compared and correlated (Spearman’s correlation) with ASCA levels (C) in 23 non-alcoholic controls, 40 patients with alcohol use disorder and 156 alcoholic hepatitis patients. (D) Kaplan-Meier curve of 180-day mortality for patients with alcoholic hepatitis. Patients were grouped according to their serum levels of ASCA. Patients that were lost to follow-up were censored at the time they were last seen alive. The number of patients at risk is indicated as well as the number of censored patients in brackets. At day-180, all remaining patients were censored, even though they had a longer survival, resulting in the high number of censored patients. (E) Receiver operating curves with area under the curve and Delong’s p value for the comparison of ASCA-MELD and MELD-score. (F) Longitudinal analysis of clinical parameter and serum marker of 24 alcoholic hepatitis patients. Measurement at day zero (D0, after admission) and around ninety-day follow-up visit (D90, follow-up measurement around ninety days after day zero, 18 of the 24 samples were collected at day-90, 6 had a longer follow up (between 90 and 154 days)). Paired t-test. Ctrl, non-alcoholic controls; AUD, alcohol use disorder; AH, alcoholic hepatitis. MELD; ASCA, anti–Saccharomyces cerevisiae antibodies; Model for End-stage Liver Disease.