Discovery metabolomics involves the comparison of the metabolome between control and test conditions to find differences in the metabolite profiles. The discovery process comprises three main steps: profiling, identification, and analysis.
Profiling consists of the untargeted acquisition of data under conditions of high analytical reproducibility. Identification involves the use of existing reference standards to annotate detected metabolites. Analysis, which is carried out with statistical software, is matter of sifting through the resulting data.
While this three-step process may seem clear enough, discovery metabolomics is far from straightforward. Indeed, it presents the scientific community with a number of complications. For example, scientists need to find ways of standardizing separation techniques, determining the biological relevance of discovered fragments, and handling the sheer volume of potential metabolites. If discovery metabolomics is to achieve its full potential, scientists will need to stay current, sharing and incorporating the latest technological innovations, tasks that may be accomplished more easily with the benefit of social networking.
Another way to stay current is to read articles such as this one. It explores some of the more interesting applications now emerging in metabolomics. If these applications are any indication, metabolomics is poised to dramatically expand its impact on research and development.
Discovery (nontargeted) metabolomics faces a significant challenge—identifying unknown metabolites in complex biological mixtures. The most common approach consists of searching available databases using exact mass and MS/MS spectral data. “Unlike protein fragments that can be easily differentiated by mass/charge ratio and MS/MS data, metabolite structures are often too similar to each other to be identified on the basis of typical mass spec data alone,” says David F. Grant, Ph.D., associate professor of pharmaceutical sciences at the University of Connecticut.
The identification of unknown metabolites is further complicated by not having all possible metabolites in well-annotated reference databases such as the Human Metabolome Database (HMDB). Another database, PubChem, contains millions of candidate chemical structures, but only a small proportion of them have biochemical significance.
Dr. Grant’s lab is working on three complementary approaches to allow identification of endogenous mammalian metabolites during the initial discovery phase. The first approach expands existing metabolite databases by including structures derived in silico. The team used the assumption that common Phase I drug metabolizing enzymes, such as cytochrome P450, metabolize endogenous compounds. Using well-known biotransformation rules, they computationally generated over 400,000 anticipated metabolites not previously found in any other existing database.
Dr. Grant’s second approach is to add additional filters to narrow down the possible candidate metabolites identified from their In Vivo/In Silico Metabolites Database (IIMDB) and PubChem. Possible matches are evaluated with MolFind software, which relies on HPLC/MS data. Besides mass/charge ratios and MS/MS spectral data, HPLC/MS parameters include retention time, drift time, and ECOM50. The initial set of structures is sequentially sorted by comparing experimentally derived features with computationally predicted features.
The last approach, which is followed after the last filter, involves passing the remaining candidate compound set through a bioinformatics tool called BioSM. This software was “trained” on known mammalian endogenous biochemical structures to identify biochemical structures in chemical structure space. BioSM predicts whether a given structure is consistent with biochemical scaffolds, and therefore could represent a real human metabolite.
“Our software tools lay an important foundation for future metabolite discovery,” continues Dr. Grant. “We hope the research community will continue to standardize discovery technologies and protocols to take full advantage of our computational models.”
Metabolon develops commercial applications of metabolomics technologies. Its comprehensive platform covers the continuum from discovery to diagnostics.
“Metabolon’s discovery approach solves many of the key challenges in metabolomics analysis. Our platform automatically identifies known and novel metabolites in a biological sample while simultaneously ensuring the noise is effectively removed,” says Steve Watkins, Ph.D., the company’s chief technology officer. “One of the key innovations is proprietary software. It accounts for all ion features in a biological sample and compares these features to an authenticated chemical library of thousands of biochemicals.”
After global profiling reveals involvement of certain biochemical pathways, Metabolon offers focused quantitative assays for each intermediate in the pathway of interest. The comparative metabolomics study of nonalcoholic fatty liver disease and nonalcoholic steatohepatitis (NASH) provided multiple insights into lipid metabolism presented in these diseases. While the two diseases are physiologically similar, their global metabolic profiles revealed key differences.
In particular, peroxisomal dysfunction was found only in the pathogenesis of NASH. Because biochemical pathways are well understood, the analysis of metabolic perturbations may serve as a basis for hypothesis-driven studies of the specific biochemical nodes.
To do that, Metabolon offers precise targeted assays for the critical compounds. “Targeted assays are always developed under custom specification,” continues Dr. Watkins. “We also pursue development of our own CLIA diagnostic assays.” Two of Metabolon’s diagnostic assays are currently marketed via its clinical lab partners. Quantose™ provides a quantitative measure of glucose metabolism as a predictive risk assessment tool for those at risk of developing type 2 diabetes.
Dr. Watkins points out that one of the most exciting opportunities for metabolomics is to provide insights into molecular mechanisms of dysregulations defined by a “static” biomarker, such as a nucleotide polymorphism.
Genome-wide association studies (GWASs) identify disease-specific genomic loci, but this is not sufficient to understand the function of the underlying biological processes. Linking genotypes to metabolic signature can shed light on how a genome works. Associations of genomic loci with metabolic traits could be used to gain novel information about possible metabolic changes associated with biological processes underlying that association.
To support new discoveries in this space, Metabolon began collaborating with Human Longevity. Metabolon’s metabolic profiles will supplement whole-genome sequencing and help to make inroads in research on aging and age-related diseases.