Type down paper [Application of Genomics in Medicine]

Fields of metabolomics . Perhaps the two most commonly talked about topics with regard to metabolomics is how the emerging technology can better address responses to cancer and cancer treatment (cancer metabolomics) and how metabolites can better direct personalized recommendations with regard to nutrition. (Nishiumi, 2014) In the case of cancer, amino acids are frequently identified as metabolites of interest given that tumor cells actively store them for carcinogenic processes like angiogenesis and cellular proliferation. In nutritional metabolomics, differences in metabolites not only reflect overall activity of metabolic processes but also dietary intake. (German, 2003)

Exactly which metabolic profile should serve as the reference point seems unclear, however. It is ultimately the hope of clinician that metabolite presences and activity can be used as a common diagnostic and prognostic tool for healthcare providers seeking a “steady-state” physiological readout of biological and cellular mechanisms. By extension, having such a readout would enable one to understand homeostasis on a person-to-person basis, as well as environmental and chemical modulations that my affect the body, a specific aim of ongoing research, according to the Metabolomics Society.

In a foundational 2006 paper, Vladimir Shulaev outlines the three principle methods of metabolomic analysis: targeted analysis, metabolomic fingerprinting, and metabolite profiling. In targeted analysis (the method most commonly used), both the location and structure of the metabolite(s) of interest are known beforehand. The most quantitative approach to metabolomics, targeted analysis merely gives a reading of concentration in a given sample. The major drawback (aside from needing to understand the metabolite of interest beforehand) are that purified standards of the metabolite of interest must also be readily available (and this is not feasible for most known metabolites). However, targeted analysis can be tailored to either trace amounts of metabolites or large and numerous metabolites. Nevertheless, the ability to perform any type of “global profiling” is nevertheless impossible thus far. (Shulaev, 2006)

Metabolomic fingerprinting, on the other hand, is both insufficient at identify individual, key metabolites, and very-well suited to identify systems-wide metabolic activity. This method requires some form of spectroscopic method (NMR or mass spec, for example) but without use of a separation method like electrophoresis or chromatography. While this method has been useful for identification of novel biomarkers, (Harrigan, 2012) it is relatively useless in identifying the mechanisms of metabolic pathways since individual metabolites are not readily identifiable. (Shulaev, 2006)

Metabolite profiling is the most well-established metabolomic method available in the biomedical sciences. Profiling is used to quantify a metabolite set in a sample. In medicine, a BMP (basic metabolic panel) is a test used to measure blood glucose as well as acids, bases, and key electrolytes. Moreover, recent research has focused on using metabolite profiling as a means of monitoring mutations that would normally go unnoticed. The rationale, as Shulaev explains it, is that even if a mutation were in place, physiological or biochemical mechanisms could counteract it. By using metabolite profiling, metabolomics could essentially identify mutations with no previously understood phenotype. (Shulaev, 2006)

Technologies. The most common way to carry out metabolic analysis usually uses mass-spectrometry (either gas or liquid, and also abbreviated MS) in order to identify the compound. Because individual ions and metabolites require distinct conditions required for ionization in the gas or liquid states, vast molecules can be characterized on the basis of the breakdown when included with known sets of control ions. A major hurdle of this technology is the identification of large numbers of metabolites at once. (Jones, 2014) Databases such as Model SEED, MetaCyc, and KEGG are frequently used to catalog and compare profiles of different metabolites.

Possible outcomes . Despite the inherent assumptions that metabolic processes are more or less generalizable by identification of key metabolites, the individual metabolic function of individual patients can be better studied and a “nutritional phenotype” can be generated. In cancer patients, the metabolic process of tumors can be readily surveyed as can the metabolic processes of patients whose bodies are con tending with tumor invasiveness.

Conclusion.

The field of metabolomics owes much of its foundation to the underlying theory of metabolic control and compensation. While large “metabolic fluxes” are relatively easy to observe, they are less to observe by the activity of individual enzymes, and more easily identified by the composition and concentration of metabolites. With ever-expanding technologies that target metabolic pathways, our cumulative understanding of the field deepens continuously. The main obstacle that lies in the way of accessible metabolomic applications to public health is research. While research in the field has seen healthy growth in recent years, there are few benchmarks that have been set for using metabolomic data in healthcare. Part of this is undoubtedly due to the individual variation in metabolic processes that changes from person-to-person. Nevertheless, finding a means by which to standardize this unique type of data must occur before we can successfully tailor diets, medication, and exercise recommendations, one the basis of metabolic profiles.

References.

German, J. B., Roberts, M. A., & Watkins, S. M. (2003). Genomics and metabolomics as markers for the interaction of diet and health: lessons from lipids. The Journal of nutrition, 133(6), 2078S-2083S.

Harrigan, G. G., & Goodacre, R. (Eds.). (2012). Metabolic profiling: its role in biomarker discovery and gene function analysis. Springer Science & Business Media.

Hirayama, A.; Kami, K.; Sugimoto, M.; Sugawara, M.; Toki, N.; Onozuka, H.; Kinoshita, T.; Saito, N.; Ochiai, A.; Tomita, M.; et al. Quantitative metabolome profiling of colon and stomach cancer microenvironment by capillary electrophoresis time-of-flight mass spectrometry. Cancer Res. 2009, 69, 4918–4925.