Multiplatform Metabolomics Characterization Reveals Novel Metabolites and Phospholipid Compositional Rules of <i>Haemophilus influenzae</i> Rd KW20
<i>Haemophilus influenzae</i> is a gram-negative bacterium of relevant clinical interest. <i>H. influenzae</i> Rd KW20 was the first organism to be sequenced and for which a genome-scale metabolic model (GEM) was developed. However, current <i>H. influenzae</i> GE...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
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| Series: | International Journal of Molecular Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1422-0067/24/13/11150 |
| Summary: | <i>Haemophilus influenzae</i> is a gram-negative bacterium of relevant clinical interest. <i>H. influenzae</i> Rd KW20 was the first organism to be sequenced and for which a genome-scale metabolic model (GEM) was developed. However, current <i>H. influenzae</i> GEMs are unable to capture several aspects of metabolome nature related to metabolite pools. To directly and comprehensively characterize the endometabolome of <i>H. influenzae</i> Rd KW20, we performed a multiplatform MS-based metabolomics approach combining LC-MS, GC-MS and CE-MS. We obtained direct evidence of 15–20% of the endometabolome present in current <i>H. influenzae</i> GEMs and showed that polar metabolite pools are interconnected through correlating metabolite islands. Notably, we obtained high-quality evidence of 18 metabolites not previously included in <i>H. influenzae</i> GEMs, including the antimicrobial metabolite cyclo(Leu-Pro). Additionally, we comprehensively characterized and evaluated the quantitative composition of the phospholipidome of <i>H. influenzae</i>, revealing that the fatty acyl chain composition is largely independent of the lipid class, as well as that the probability distribution of phospholipids is mostly related to the conditional probability distribution of individual acyl chains. This finding enabled us to provide a rationale for the observed phospholipid profiles and estimate the abundance of low-level species, permitting the expansion of the phospholipidome characterization through predictive probabilistic modelling. |
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| ISSN: | 1661-6596 1422-0067 |