| Grant number: | 17/50116-6 |
| Support Opportunities: | Regular Research Grants |
| Start date: | April 01, 2018 |
| End date: | March 31, 2020 |
| Field of knowledge: | Biological Sciences - Biochemistry - Biochemistry of Microorganisms |
| Agreement: | BBSRC, UKRI |
| Principal Investigator: | Rafael Silva Rocha |
| Grantee: | Rafael Silva Rocha |
| Principal researcher abroad: | Martin Welch |
| Institution abroad: | University of Cambridge, England |
| Host Institution: | Faculdade de Medicina de Ribeirão Preto (FMRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil |
Abstract
We will examine how wild-type PA01 catabolises propionate (in comparison with succinate, a control substrate). This will be done by integrating data obtained from a combination of transcriptomic (RNA-Seq), metabolomic (1N NMR, GC-MS/MS and LC-MS) and proteomic (iTRAQ) analyses. The Welch team has expertise in RNA-Seq, metabolomic and proteomic analyses, whereas the Silva-Rocha team has expertise in data integration, genome scale metabolic analyses, and regulatory network analysis, making this overall a very well-matched collaboration capable of adding significant extra value to the project. Specifically, we will use the RNA-Seq (dataset obtained in Brazil and UK) and proteomic (UK) analyses to identify which pathways are expressed during growth of wild-type cells in minimal medium containing propionate or succinate as a sole carbon source. In parallel, a genome-scale metabolic model (Brazil) will be constructed based on PseudoCyc and KEGG annotations. The transcriptomic/proteomic data will then be used to confirm pathway expression and to guide "hole filling". Candidates for hole filling will be tested by confirming the growth phenotype of the corresponding mutant on SCFAs (the UK lab hosts a copy of the two-allele comprehensive PA mutant bank). To further verify the model, we will use metabolomic profiling (UK) to confirm the presence of key predicted metabolites. This way, we aim to generate a robust model that correctly predicts the overall architecture and dynamics of PA SCFA metabolism. It is critical to note here that the analysis of SCFA metabolism has been neglected in previous PA metabolic models. (AU)
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