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- Campbell, Christopher, et al.
(författare)
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Accumulation of succinyl coenzyme a perturbs the methicillin-resistant staphylococcus aureus (Mrsa) succinylome and is associated with increased susceptibility to beta-lactam antibiotics
- 2021
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Ingår i: mBio. - : American Society for Microbiology. - 2161-2129 .- 2150-7511. ; 12:3
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Tidskriftsartikel (refereegranskat)abstract
- Penicillin binding protein 2a (PBP2a)-dependent resistance to β-lactam antibiotics in methicillin-resistant Staphylococcus aureus (MRSA) is regulated by the activity of the tricarboxylic acid (TCA) cycle via a poorly understood mechanism. We report that mutations in sucC and sucD, but not other TCA cycle enzymes, negatively impact β-lactam resistance without changing PBP2a expression. Increased intracellular levels of succinyl coenzyme A (succinyl-CoA) in the sucC mutant significantly perturbed lysine succinylation in the MRSA proteome. Suppressor mutations in sucA or sucB, responsible for succinyl-CoA biosynthesis, reversed sucC mutant phenotypes. The major autolysin (Atl) was the most succinylated protein in the proteome, and increased Atl succinylation in the sucC mutant was associated with loss of autolytic activity. Although PBP2a and PBP2 were also among the most succinylated proteins in the MRSA proteome, peptidoglycan architecture and cross-linking were unchanged in the sucC mutant. These data reveal that perturbation of the MRSA succinylome impacts two interconnected cell wall phenotypes, leading to repression of autolytic activity and increased susceptibility to β-lactam antibiotics.
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| 2. |
- Kyle, Jennifer E., et al.
(författare)
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Interpreting the lipidome : bioinformatic approaches to embrace the complexity
- 2021
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Ingår i: Metabolomics. - : Springer-Verlag New York. - 1573-3882 .- 1573-3890. ; 17:6
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Forskningsöversikt (refereegranskat)abstract
- BACKGROUND: Improvements in mass spectrometry (MS) technologies coupled with bioinformatics developments have allowed considerable advancement in the measurement and interpretation of lipidomics data in recent years. Since research areas employing lipidomics are rapidly increasing, there is a great need for bioinformatic tools that capture and utilize the complexity of the data. Currently, the diversity and complexity within the lipidome is often concealed by summing over or averaging individual lipids up to (sub)class-based descriptors, losing valuable information about biological function and interactions with other distinct lipids molecules, proteins and/or metabolites.AIM OF REVIEW: To address this gap in knowledge, novel bioinformatics methods are needed to improve identification, quantification, integration and interpretation of lipidomics data. The purpose of this mini-review is to summarize exemplary methods to explore the complexity of the lipidome.KEY SCIENTIFIC CONCEPTS OF REVIEW: Here we describe six approaches that capture three core focus areas for lipidomics: (1) lipidome annotation including a resolvable database identifier, (2) interpretation via pathway- and enrichment-based methods, and (3) understanding complex interactions to emphasize specific steps in the analytical process and highlight challenges in analyses associated with the complexity of lipidome data.
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