A couple of weeks ago I attended the NIH Eastern Regional Comprehensive Metabolomics Resource Core (RCMRC) Symposium at the Research Triangle Institute, in Research Triangle Park, North Carolina. This was an all-day event focused on the fascinating field of metabolomics.
Metabolomics is the study of metabolism via the analysis of low molecular weight compounds present in organs, tissues, cells or body fluids. Such molecules are considered as metabolites; that is, products of the biochemical pathways that constitute the processes of life. The suffix –omics refers to a branch of biology that studies a biological system in its entirety; for example, genomics is the study of the entire genome, proteomics is the study of all of the proteins in an organism. Indeed, some have even attempted to extend the use of -omics outside of biology. Metabolomics attempts to study metabolism as a whole by analyzing metabolic profiles as characterized by all of the metabolites present in a particular system. It is thus one of the so-called “big data” sciences because of the massive amounts of data generated in some metabolomics studies.
The concept behind metabolomics is simple. Take a biological sample from an organism, get as much of it into solution as you can, then identify as many of the small molecules that it contains as possible via some automated analytical method. What exactly is a small molecule? There is some discussion around that point, but it is generally agreed to refer to chemicals with a molecular weight of 1 kilodalton or less. This creates a metabolic snapshot of the sample in a particular state, at a particular moment. If you change the state in some identifiable way, for example, by exposing the organism to a toxic substance, and create another snapshot, you can then examine the differences between the two states. From these differences you can infer the effect of the exposure on the metabolism of the organism in its entirety.
Of course, the process really isn’t simple. To account for biological variability, multiple samples from the same state will have to be analyzed. Difficulties will arise in solubilizing the sample. Different analytical methods will identify different subsets of metabolites. The number of metabolites identified will be large, at least hundreds and possibly thousands of substances from each sample, so a computer will be necessary to characterize the differences.
The most common analytical method to analyze samples for metabolomics study is an old one, nuclear magnetic resonance (NMR) spectroscopy. This method, developed in the 1940s and 50s, takes advantage of the fact that a magnet spinning in a magnetic field (like an atomic nucleus) emits electromagnetic radiation at a frequency specific to the strength of the magnetic field. NMR patterns for very many chemicals have been identified and cataloged in the ensuing years, making the method ideal for analysis by computers.
Metabolomics studies have proved important in many scientific disciplines. For example, in medicine, specific disease states, such as cancer, can be identified by the changes they cause in the metabolic profile of body fluids. This allows reproducible, sometimes early, diagnosis, and early initiation of treatment which could result in a greater chance of success. Metabolomics profiles can be used to differentiate groups of people according to their diets, or to indicate exposure to an environmental toxicant. These findings can be correlated with the prevalence of particular conditions (e.g., obesity, diabetes), to help understand the effects of diet or chemical exposure on these conditions. Metabolomics studies can also help identify effective new drugs, by determining which compounds cause specific changes in metabolic profiles indicative of disease states. Metabolomics has also been used to detect product adulteration in botanicals such as ginseng and herbal extracts, by analysis of the small molecule profiles of the products.
Metabolomics is another example of the technological advances made possible by interfacing computers with standard analytical techniques. It allows scientists to investigate a biological processes in its entirety rather than focusing on a small part, then trying to infer what the whole is like, as in the story of the blind men and the elephant. It will contribute greatly to our understanding of life, as well as to human health and well-being.