With increasing number of novel bacteria being isolated from the human gut ecosystem, there is a greater need to study their role in the gut ecosystem and their effect on the host health. In the present study, we carried out in silico genome-wide analysis of two novel Megasphaera sp. isolates NM10 (DSM25563) and BL7 (DSM25562), isolated from feces of two healthy individuals and validated the key features by in vitro studies. The analysis revealed the general metabolic potential, adaptive features and the potential effects of these isolates on the host. The comparative genome analysis of the two human gut isolates NM10 and BL7 with ruminal isolate Megasphaera elsdenii (DSM20460) highlighted the differential adaptive features for their survival in human gut. The key findings include features like bile resistance, presence of various sensory and regulatory systems, stress response systems, membrane transporters and resistance to antibiotics. Comparison of the “glycobiome” based on the genomes of the ruminal isolate with the human gut isolates NM10 and BL revealed the presence of diverse and unique sets of Carbohydrate-Active enzymes (CAZymes) amongst these isolates, with a higher collection of CAZymes in the human gut isolates. This could be attributed to the difference in host diet and thereby the environment, consequently suggesting host specific adaptation in these isolates. In silico analysis of metabolic potential predicted the ability of these isolates to produce important metabolites like short chain fatty acids (butyrate, acetate, formate, and caproate), vitamins and essential amino acids, which was further validated by in vitro experiments. The ability of these isolates to produce important metabolites advocates for a potential healthy influence on the host. Further in vivo studies including transcriptomic and proteomic analysis will be required for better understanding the role and impact of these Megasphaera sp. isolates NM10 and BL7 on the human host.

Over the past 75 years, human pathogens have acquired antibiotic resistance genes (ARGs), often from environmental bacteria. Integrons play a major role in the acquisition of antibiotic resistance genes. We therefore hypothesized that focused exploration of integron gene cassettes from microbial communities could be an efficient way to find novel mobile resistance genes. DNA from polluted Indian river sediments were amplified using three sets of primers targeting class 1 integrons and sequenced by long- and short-read technologies to maintain both accuracy and context. Up to 89% of identified open reading frames encode known resistance genes, or variations thereof (> 1000). We identified putative novel ARGs to aminoglycosides, beta-lactams, trimethoprim, rifampicin, and chloramphenicol, including several novel OXA variants, providing reduced susceptibility to carbapenems. One dihydropteroate synthase gene, with less than 34% amino acid identity to the three known mobile sulfonamide resistance genes (sul1–3), provided complete resistance when expressed in Escherichia coli. The mobilized gene, here named sul4, is the first mobile sulfonamide resistance gene discovered since 2003. Analyses of adjacent DNA suggest that sul4 has been decontextualized from a set of chromosomal genes involved in folate synthesis in its original host, likely within the phylum Chloroflexi. The presence of an insertion sequence common region element could provide mobility to the entire integron. Screening of 6489 metagenomic datasets revealed that sul4 is already widespread in seven countries across Asia and Europe. Our findings show that exploring integrons from environmental communities with a history of antibiotic exposure can provide an efficient way to find novel, mobile resistance genes. The mobilization of a fourth sulfonamide resistance gene is likely to provide expanded opportunities for sulfonamide resistance to spread, with potential impacts on both human and animal health.

Environmental and commensal bacteria maintain a diverse and largely unknown collection of antibiotic resistance genes (ARGs) that, over time, may be mobilized and transferred to pathogens. Metagenomics enables cultivation-independent characterization of bacterial communities but the resulting data is noisy and highly fragmented, severely hampering the identification of previously undescribed ARGs. We have therefore developed fARGene, a method for identification and reconstruction of ARGs directly from shotgun metagenomic data. fARGene uses optimized gene models and can therefore with high accuracy identify previously uncharacterized resistance genes, even if their sequence similarity to known ARGs is low. By performing the analysis directly on the metagenomic fragments, fARGene also circumvents the need for a high-quality assembly. To demonstrate the applicability of fARGene, we reconstructed β-lactamases from five billion metagenomic reads, resulting in 221 ARGs, of which 58 were previously not reported. Based on 38 ARGs reconstructed by fARGene, experimental verification showed that 81% provided a resistance phenotype in Escherichia coli. Compared to other methods for detecting ARGs in metagenomic data, fARGene has superior sensitivity and the ability to reconstruct previously unknown genes directly from the sequence reads. We conclude that fARGene provides an efficient and reliable way to explore the unknown resistome in bacterial communities. The method is applicable to any type of ARGs and is freely available via GitHub under the MIT license.