bioRxivMicrobiologyPreprint
Prediction of the intestinal resistome by a novel 3D-based method
Etienne Ruppé, Amine Ghozlane, Julien Tap, Nicolas Pons, Anne-Sophie Alvarez, Nicolas Maziers, Trinidad Cuesta, Sara Hernando-Amado, Jose Luís Martínez, Teresa M. CoqueiD, Fernando BaqueroiD, Val F. LanzaiD, Luis Maiz, Tiphaine Goulenok, Victoire de Lastours, Nawal Amor, Bruno Fantin, Ingrid Wieder, Antoine AndremontiD, Willem van SchaikiD, Malbert Rogers, Xinglin Zhang, Rob J.L. Willems, Alexandre G. De Brevern, Jean-Michel Batto, , S. Dusko Ehrlich
Abstract
The intestinal microbiota is considered to be a major reservoir of antibiotic resistance determinants (ARDs) that could potentially be transferred to bacterial pathogens. Yet, this question remains hypothetical because of the difficulty to identify ARDs from intestinal bacteria. Here, we developed and validated a new annotation method (called pairwise comparative modelling, PCM) based on homology modelling in order to characterize the Human resistome. We were able to predict 6,095 ARDs in a 3.9 million protein catalogue from the Human intestinal microbiota. We found that predicted ARDs (pdARDs) were distantly related to known ARDs (mean amino-acid identity 29.8%). Among 3,651 pdARDs that were identified in metagenomic species, 3,489 (95.6%) were assumed to be located on the bacterial chromosome. Furthermore, genes associated with mobility were found in the neighbourhood of only 7.9% (482/6,095) of pdARDs. According to the composition of their resistome, we were able to cluster subjects from the MetaHIT cohort (n=663) into 6 \"resistotypes\". Eventually, we found that the relative abundance of pdARDs was positively associated with gene richness, but not when subjects were exposed to antibiotics. Altogether, our results support that most ARDs in the intestinal microbiota should be considered as intrinsic genes of commensal microbiota with a low risk of transfer to bacterial pathogens.
- organism: human
- assay: metagenomics
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
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