Decoding the microbiome and resistome of advanced chronic liver disease through long-read metagenomics

Background Patients with advanced chronic liver disease (ACLD) and underlying cirrhosis frequently require repeated courses of antimicrobial therapy, with both the frequency and spectrum of antimicrobial exposure increasing alongside disease progression. In this population, impaired immune function and increased intestinal barrier dysfunction contribute to a heightened susceptibility of multidrug-resistant bacterial infections. Methods To comprehensively characterise the gastrointestinal microbiome and antimicrobial resistance gene (ARG) landscape across the clinical spectrum of ACLD, and to identify microbial and resistome signatures associated with disease severity, we employed long-read metagenomic sequencing (Oxford Nanopore Technologies) to profile the gastrointestinal microbiome and resistome in patients with acute-on-chronic liver failure (ACLF), decompensated cirrhosis (DC), and stable cirrhosis (SC), compared to healthy controls (HC). Results ACLF patients showed marked enrichment of Enterococcus faecium , with six of 28 samples showing >95% relative abundance, highlighting its potential as a bacterial biomarker of advanced cirrhosis. We reconstructed 28 high-quality metagenome-assembled genomes (MAGs) of E. faecium from cirrhosis patients, 17 from ACLF cases. E. faecium dominance was associated with substantially reduced microbial diversity and a marked depletion of key commensal taxa, including Blautia, Akkermansia, Faecalibacterium, and Bifidobacterium . Consistent with these compositional shifts, resistome analysis revealed significant enrichment of clinically relevant antimicrobial resistance genes (ARGs) in DC and ACLF, including determinants conferring resistance to aminoglycosides, beta-lactams, and glycopeptides, consistent with prior antimicrobial exposure. Conclusion Long-read metagenomics enables high-resolution characterisation of microbial and resistome dynamics across ACLD severities. By capturing taxonomic shifts, functional potential, and ARG enrichment, this approach provides valuable insights into microbiome trajectories linked with disease severity, informing mechanistic research and potential clinical interventions.