Combined genomic, machine learning and biochemical approaches for the characterization of lipopeptides from Mycobacterium avium subsp. paratuberculosis. - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement Access content directly
Conference Papers Year : 2023

Combined genomic, machine learning and biochemical approaches for the characterization of lipopeptides from Mycobacterium avium subsp. paratuberculosis.

Franck Biet

Abstract

Introduction and objectives: Mycobacterium avium subsp. paratuberculosis (Map), the agent of paratuberculosis or Johne's disease, is responsible for considerable economic losses in the dairy industry worldwide. This animal pathogenic mycobacterium is a Nontuberculous Mycobacteria (NTM) of the Mycobacterium avium complex (MAC). Genomic studies initiated in 2016, and the recent complete genomes obtained, have confirmed the clonal nature of this subspecies, which is divided into three distinct genetic lineages designated CII (for Bovine strains), SI and SIII (for Ovine strains). Our previous studies have established that, unlike other mycobacteria of the avium complex, Map does not produce glycopeptidolipids on the surface of its cell wall, but rather sugar-free lipopeptide antigens. Molecular and genetic characterization of these antigens, which are synthesized by Non-Ribosomal Peptide Synthases (NRPS), has shown that they are unique to Map and may differ between genetic lineages. In this study, we have characterized previously unidentified antigens. Materials and methods: Genomic sequencing of new Map strains was carried out in Illumina and PacBio to obtain complete genomes of these strains. Genomic analyses using machine learning approaches were used to identify new NRPS and make metabolite predictions. Chemical synthesis produced predicted antigens to guide formal identification and characterization of native antigens by mass spectrometry (MALDI-TOF, HPLC-MS) and NMR analyses. Results, discussion and conclusion: Genomic and biochemical analyses have enabled us to characterize a new lipopeptide antigen in Map. Our results add to our knowledge of the lipopeptides produced in Map, and open up new prospects for the development of serological diagnostics based on synthetic molecules, in the same way as current diagnostics using a cell extract that is not specific to Map and difficult to produce.
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Dates and versions

hal-04210353 , version 1 (18-09-2023)

Identifiers

  • HAL Id : hal-04210353 , version 1

Cite

Franck Biet, Sylvie Bay, John Bannantine. Combined genomic, machine learning and biochemical approaches for the characterization of lipopeptides from Mycobacterium avium subsp. paratuberculosis.. 18. Congrès national de la SFM - Microbes 2023, Société Française de Microbiologie, Oct 2023, Rennes, France. ⟨hal-04210353⟩
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