Impact of Antibiotic Resistance Genes in Gut Microbiome of Patients With Cirrhosis

Abstract

Background and aims: Cirrhosis is associated with changes in intestinal microbiota that can lead to hepatic encephalopathy (HE) and infections, especially with antibiotic-resistant organisms. However, the impact of gut microbial antibiotic resistance gene (ARG) burden on clinical outcomes is unclear. The aims of the study were to determine the impact of ARGs in cirrhosis-related gut metagenome on outcomes and disease progression, study the effect of rifaximin on ARG burden, and compare ARGs in cirrhosis with chronic kidney disease (CKD) and diabetes.

Methods: In outpatients with cirrhosis who underwent metagenomics, we evaluated change in ARG abundances with progression and their multivariable impact on 90-day hospitalizations and deaths over 1 year. We also studied ARGs pre- and 8 weeks post-rifaximin in patients with compensated cirrhosis in an open-label trial. Finally, ARGs from CKD and diabetes studies were compared with cirrhosis on machine learning.

Results: A total of 163 patients with cirrhosis (43 compensated, 20 ascites-only, 30 HE-only, 70 both) and 40 controls were included. ARG abundances were higher in cirrhosis versus controls and worsened with advancing cirrhosis severity; 44 patients were hospitalized and 14 died. ARG abundances were associated with hospitalizations and mortality while controlling for cirrhosis complications, medications, and demographics. Rifaximin trial: ARG abundance patterns were minimally affected in 19 patients post-rifaximin. CKD/diabetes comparison: ARG abundance patterns in cirrhosis are distinguishable on machine learning and include more gram-positive ARGs.

Conclusions: Cirrhosis is associated with high gut microbial ARG gene burden compared with controls, which worsens with disease progression and may be different from CKD and diabetes. ARGs are not affected by rifaximin and are associated with hospitalizations and death.

Keywords: Ascites; Hepatic Encephalopathy; Infections; Machine Learning; Rifaximin.

Figure 1.

Comparison of healthy controls and cirrhosis. For all comparisons, purple is higher in cirrhosis, orange is higher in controls. (A) Volcano plot of Kruskal-Wallis comparison of bacterial species. (B) Volcano plot of DESeq2 lineage of ARO terms. (C) Volcano plot of DESeq2 lineage of resistomes and variants. (D) Volcano plot of DESeq2 lineage of AMR gene families.

Figure 2.

Comparison of microbial and ARG changes before and after rifaximin. (A) Volcano plot of DESeq2 lineage of bacterial species compared between pre- (orange) and post-rifaximin (purple). (B) Volcano plot of Kruskal-Wallis comparison of resistomes that changed between pre- (orange) and post-rifaximin (purple) showing higher Klebsiella oxytoca pre, which was not found post-rifaximin. (C) Volcano plot of Kruskal-Wallis comparison of ARO terms that changed between pre- (orange) and post-rifaximin (purple) showing no significant change between the time-points. (D) Volcano plot of Kruskal-Wallis comparison of AMR gene families that changed between pre- (orange) and post-rifaximin (purple) showed reduction in baseline AMR gene expressions after rifaximin.

Figure 3.

Principal coordinate analysis of cirrhosis compared to other chronic diseases. (A-C) Comparison of cirrhosis (purple) with T2D (orange, Qin et al) showing clear separation between the groups on resistome, AMR gene family, and ARO term abundances. (D-F) Comparison of cirrhosis (purple) with CKD on dialysis (orange, Wang et al) showing clear separation between the groups on resistome, AMR gene family, and ARO term abundances.