BIOL 2360 – Biochemistry IIA
What is metabolomics?
Metabolomics combines strategies to identify and quantify cellular metabolites using analytical methods such as gas chromatography, liquid chromatography and nuclear magnetic resonance spectroscopy. These analytical approaches are done to analyze different cell products such as those from gene expression (transcripts), proteins, and metabolites. All of these so-called ’omics approaches including genomics, transcriptomics, proteomics and metabolomics are considered important tools to be applied and utilized to understand the biology of an organism and its response to environmental stimuli or genetic perturbation.
There are four approaches in metabolomics: target analysis, metabolite profiling, metabolomics, and metabolic fingerprinting. Target analysis includes the determination and quantification of a small set of known metabolites (targets) using one particular analytical technique for the compounds of interest. Metabolite profiling aims at the analysis of a larger set of compounds, both identified and unknown with respect to their chemical nature. Metabolomics employs complementary analytical methodologies for example, LC-MS/MS, GC-MS, and/or NMR, in order to determine and quantify as many metabolites as possible, either identified or unknown compounds. Metabolic fingerprinting is where mass profile of the sample of interest is generated and then compared in a large sample population to screen for differences between the samples. When signals that can significantly discriminate between samples are detected, the metabolites are identified and the biological relevance of that compound can be revealed thus reducing the analysis time.
Metabolomics can be used for a large range of applications, including phenotyping of genetically modified plants and substantial equivalence testing, determination of gene function and monitoring responses to biotic and abiotic stress. Metabolomics decreases the gap between genotype and phenotype by providing a more comprehensive view of how cells function, as well as identifying novel or striking changes in specific metabolites. It can provide new hypotheses and new targets for biotechnology.
Metabolic profiling or metabolomicics is referred to as being either targeted or non-targeted:
TARGETED: In the targeted approach, specific metabolites of known identity are profiled. In the case of targeted MS, this involves the addition of multiple stable isotope-labelled standards to the biological sample before the extraction and derivatization steps to control for differences in analyte loss during sample processing and to compensate for ionization-suppression effects. Targeted methods provide an excellent survey of metabolic fuel selection and a profile of energy-yielding metabolic pathways, including elements of mitochondrial metabolism.
NON-TARGETED: In non-targeted profiling it involves the use of NMR or MS for simultaneous measurement of as many metabolites as possible in a biological specimen. These approaches are generally used to compare two biological or clinical states and to report on differences between the two states based on peak areas of raw spectral data.
How has metabolomics contributed to the understanding of disease mechanisms and how has it contributed to the improvement or creating strategies for treatment of these diseases?
- Human diabetes and insulin resistance
Targeted mass spectrometry (MS) based metabolic profiling has been increasingly applied to studies of human conditions. The profiling of an obese person was done in a research conducted that revealed that branch chain fatty acid catabolism correlates with insulin resistance. The results from this research showed that metabolomics can provide a more detailed picture of metabolic status of normal and pre-diabetic subjects, which can be used for further development and could contribute to more exact sub-classification of different forms of diabetes, leading to the development of more effective drugs.
- Human cardiovascular disease (CVD)
In a study conducted using MS-based metabolic profiling the application of metabolomics was done to determine metabolic lesions in heart failure and myocardial infarction (MI). The growing number of metabolomics studies in the area of heart failure may ultimately facilitate optimal design of perioperative treatment regimens based on the particular form of cardiovascular disease and the metabolic status of the heart. Comprehensive metabolic profiling, or “metabolomics” is increasingly being applied to CVD, leading to recent discoveries with both form and function implications.
Metabolomics profiling of coronary artery disease (CAD): Targeted MS/MS-based methods were used to profile 45 plasma acylcarnitines and 15 amino acids in a larger study of CAD. With the use of principal components analysis for data reduction, two principal components analysis–derived metabolite factors were found to be associated with CAD: one is composed of branched-chain amino acids (BCAAs) and their associated metabolites and one is composed of urea cycle metabolites, including arginine and citrulline. These metabolite clusters discriminated individuals with CAD from those without CAD in both discovery and validation data sets.
Myocardial infraction: identified certain proteins such as troponin I, troponin T, C-reactive protein, and B-type natriuretic peptide as diagnostic markers for CVD events and heart failure.
Another recent study has identified a fascinating link between the diet, gut microflora, host metabolism, and metabolomic biomarkers of risk for incident CVD events. The study used a non-targeted LC-MS–based metabolomics approach to profile stable patients who subsequently experienced MI, stroke, or death over the ensuing 3-year period compared with age- and sex-matched control subjects who did not experience events.
Integration of Genetics and Metabolomics for the Identification of Novel Disease Pathways:
A potential avenue for translating metabolomics-derived biomarkers to disease mechanisms is the integration of metabolomics with other “omics” methods. Human genome-wide association studies have mapped loci associated with polygenic disorders like CVD and diabetes mellitus, but they account for only a small fraction of these diseases and have made limited contributions to knowledge-based therapeutic interventions. This results in part because both conditions are actually a family of diseases in which genetic variability, environmental factors and resultant perturbations in metabolic control within multiple tissues and organs combine to disrupt homeostasis and tissue functions.