While exometabolomics can help us understand what microbes consume from and release into the environment, and how they live in communities, the combination of multiple data types is a useful approach to garner insight into biological processes. It can improve functional inferences that typically are generated via a single type of dataset. As most organisms we work with here at the JGI have had their complete genomes sequenced, we have the ability to link metabolomics data with respective transporters, COGs, and biosynthetic clusters from genomic data; and with gene expression from transcriptomic data.
There have been several reports of the integration of metabolomic data with genomics and transcriptomics. Guo et al. integrated transcriptomic associates and cell wall metabolism to correlate co-expressed gene modules with distinct cell wall characteristics in rice plants . Cho et al. similarly integrated metabolomics and transcriptomics in B. subtilis and R. rubrum to reveal metabolic pathways which were interconnected with methionine salvage . Kleesen et al. used a combination of transcriptomics and metabolomics to identify the metabolic response of Chlamydomonas to rapamycin treatment . We strive to extend these integrations to many types of environmental samples, to help understand plant-microbe interactions, microbe-microbe interactions, and to build our understanding of microbial sequence to function.
Linking biosynthetic clusters to secondary metabolites:
Biosynthetic Clusters (BCs) are gene clusters; sets of two or more genes found within an organism’s DNA, which are known to encode for specific secondary metabolites (natural products). While IMG-ABC provides information on the biosynthetic clusters found in each genome available in IMG, the secondary metabolites produced by these clusters are commonly lacking. Reverse-phase chromatography (C18) can be used in combination with MS/MS to detect and identify secondary metabolites being produced by organisms. These putatively identified molecules can then be compared and linked to their respective BCs. In 2013, our research group reported that many metabolites identified in cyanobacteria could be verified by the biosynthetic pathways present in their respective genomes . More recent work from our group has looked at matching as many secondary metabolites with their respective biosynthetic clusters as possible, as a method to verify the putative identification of such compounds. Many compounds putatively identified by LC-MS/MS do not have commercially available standards, so having the supporting biosynthetic cluster could potentially be used as a surrogate for chemical standards.
An example of a biosynthetic cluster for the production a hopanoid. Molecules can be measured by LC-MS/MS from bacterial culture, putatively identified, and then validated by the presence of the correct BC in the respective genome.