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Abstract

Gut microbiome in critically ill patients shows profound dysbiosis. The most vulnerable is the subgroup of chronically critically ill (CCI) patients - those suffering from long-term dependence on support systems in intensive care units. It is important to investigate their microbiome as a potential reservoir of opportunistic taxa causing co-infections and a morbidity factor. We explored dynamics of microbiome composition in the CCI patients by combining "shotgun" metagenomics with chromosome conformation capture (Hi-C). Stool samples were collected at 2 time points from 2 patients with severe brain injury with different outcomes within a 1-2-week interval. The metagenome-assembled genomes (MAGs) were reconstructed based on the Hi-C data using a novel hicSPAdes method (along with the bin3c method for comparison), as well as independently of the Hi-C using MetaBAT2. The resistomes of the samples were derived using a novel assembly graph-based approach. Links of bacteria to antibiotic resistance genes, plasmids and viruses were analyzed using Hi-C-based networks. The gut community structure was enriched in opportunistic microorganisms. The binning using hicSPAdes was superior to the conventional WGS-based binning as well as to the bin3c in terms of the number, completeness and contamination of the reconstructed MAGs. Using Klebsiella pneumoniae as an example, we showed how chromosome conformation capture can aid comparative genomic analysis of clinically important pathogens. Diverse associations of resistome with antimicrobial therapy from the level of assembly graphs to gene content were discovered. Analysis of Hi-C networks suggested multiple "host-plasmid" and "host-phage" links. Hi-C metagenomics is a promising technique for investigating clinical microbiome samples. It provides a community composition profile with increased details on bacterial gene content and mobile genetic elements compared to conventional metagenomics. The ability of Hi-C binning to encompass the MAG's plasmid content facilitates metagenomic evaluation of virulence and drug resistance dynamics in clinically relevant opportunistic pathogens. These findings will help to identify the targets for developing cost-effective and rapid tests for assessing microbiome-related health risks.