Complete Characterization of Polyacyltrehaloses from Mycobacterium tuberculosis H37Rv Biofilm Cultures by Multiple-Stage Linear Ion-Trap Mass Spectrometry Reveals a New Tetraacyltrehalose Family.
Georgiana E PurdyFong-Fu HsuPublished in: Biochemistry (2021)
Polyacylated trehaloses in Mycobacterium tuberculosis play important roles in pathogenesis and structural roles in the cell envelope, promoting the intracellular survival of the bacterium, and are potential targets for drug development. Herein, we describe a linear ion-trap multiple-stage mass spectrometric approach (LIT MSn) with high-resolution mass spectrometry to the structural characterization of a glycolipid family that includes a 2,3-diacyltrehalose, 2,3,6-triacyltrehalose, 2,3,6,2',4'-petaacyltrehalose, and a novel 2,3,6,2'-tetraacyltrehalose (TetraAT) subfamily isolated from biofilm cultures of M. tuberculosis H37Rv. The LIT MSn spectra (n = 2, 3, or 4) provide structural information to unveil the location of the palmitoyl/stearoyl and one to four multiple methyl-branched fatty acyl substituents attached to the trehalose backbone, leading to the identification of hundreds of glycolipid species with many isomeric structures. We identified a new TetraAT subfamily whose structure has not been previously defined. We also developed a strategy for defining the structures of the multiple methyl-branched fatty acid substituents, leading to the identification of mycosanoic acid, mycolipenic acid, mycolipodienoic acid, mycolipanolic acid, and a new cyclopropyl-containing acid. The observation of the new TetraAT family, and the realization of the structural similarity between the various subfamilies, may have significant implications in the biosynthetic pathways of this glycolipid family.
Keyphrases
- mycobacterium tuberculosis
- fatty acid
- mass spectrometry
- pulmonary tuberculosis
- liquid chromatography
- high resolution mass spectrometry
- pseudomonas aeruginosa
- staphylococcus aureus
- emergency department
- candida albicans
- stem cells
- single cell
- healthcare
- gas chromatography
- human immunodeficiency virus
- neural network
- drug induced