In a departure from the "top-of-the-funnel"-directed projects of the EFI Microbiology Core, untargeted metabolomics experiments were coupled to transcriptomics (RNA-seq) to uncover previously unknown connections between methionine salvage and homocysteine catabolism, as well as methionine salvage and isoprenoid biosynthesis in Rhodospirillum rubrum. The novel analytical strategy and bioinformatics toolset demonstrated successfully in this investigation could prove useful in fitting metabolic pathways into highly connected networks, potentially providing a more comprehensive understanding of the metabolic capabilities of specific organisms.
While recent advances in metabolomic measurement technologies have been dramatic, extracting biological insight from complex metabolite profiles remains a challenge. We present an analytical strategy that uses data obtained from high resolution liquid chromatography–mass spectrometry and a bioinformatics toolset for detecting actively changing metabolic pathways upon external perturbation. We begin with untargeted metabolite profiling to nominate altered metabolites and identify pathway candidates, followed by validation of those pathways with transcriptomics. Using the model organisms Rhodospirillum rubrum and Bacillus subtilis, our results reveal metabolic pathways that are interconnected with methionine salvage. The rubrum-type methionine salvage pathway is interconnected with the active methyl cycle in which re-methylation, a key reaction for recycling methionine from homocysteine, is unexpectedly suppressed; instead, homocysteine is catabolized by the trans-sulfuration pathway. Notably, the non-mevalonate pathway is repressed, whereas the rubrum-type methionine salvage pathway contributes to isoprenoid biosynthesis upon 5′-methylthioadenosine feeding. In this process, glutathione functions as a coenzyme in vivo when 1-methylthio-d-xylulose 5-phosphate (MTXu 5-P) methylsulfurylase catalyzes dethiomethylation of MTXu 5-P. These results clearly show that our analytical approach enables unexpected metabolic pathways to be uncovered.
Figure 1: An overview of our analytical strategy. Specific changes in bacterial growth conditions and genetic knockout yield high resolution LC–MS data from which active pathways are detected by computational analysis and experimental validation, employing metabolite profiling, nominating altered metabolites, modeling molecular formulas, evaluating pathway activities and validating detected active pathways with qRT-PCR and RNAseq. Black, blue and red arrows indicate chronology of events in the workflow.
Figure 3: Performance evaluation of putative peak annotation of a R. rubrum at 20 min, b B. subtilis at 2 min. The performance of the putative peak annotation was indirectly evaluated by introducing a search HRPP, calculated by dividing the total number of hits in the KEGG database by the total number of input peaks.
Reprinted with permission from Metabolomics. Copyright © 2014 Metabolomics.