Proteomics and systems biology: current and future applications in the nutritional sciences

Abstract

In the last decade, advances in genomics, proteomics, and metabolomics have yielded large-scale datasets that have driven an interest in global analyses, with the objective of understanding biological systems as a whole. Systems biology integrates computational modeling and experimental biology to predict and characterize the dynamic properties of biological systems, which are viewed as complex signaling networks. Whereas the systems analysis of disease-perturbed networks holds promise for identification of drug targets for therapy, equally the identified critical network nodes may be targeted through nutritional intervention in either a preventative or therapeutic fashion. As such, in the context of the nutritional sciences, it is envisioned that systems analysis of normal and nutrient-perturbed signaling networks in combination with knowledge of underlying genetic polymorphisms will lead to a future in which the health of individuals will be improved through predictive and preventative nutrition. Although high-throughput transcriptomic microarray data were initially most readily available and amenable to systems analysis, recent technological and methodological advances in MS have contributed to a linear increase in proteomic investigations. It is now commonplace for combined proteomic technologies to generate complex, multi-faceted datasets, and these will be the keystone of future systems biology research. This review will define systems biology, outline current proteomic methodologies, highlight successful applications of proteomics in nutrition research, and discuss the challenges for future applications of systems biology approaches in the nutritional sciences.

Figure 1

Growth in proteomic and systems biology publications in last decade. (A) The number of proteomics publications has risen rapidly in the last decade. The percentage of proteomics publications related to nutritional sciences research has remained constant at 2–3% of the total. (B) The number of systems biology publications has risen rapidly in the last decade. The percentage of these related to nutritional sciences research has remained constant at 3–4% of the total. These data were generated by performing a Pubmed [All Fields] search for either “proteomics”/“systems biology,” or [“proteomics”/”systems biology” AND (nutrition OR obesity OR diabetes OR “cardiovascular disease”)] with the requisite publication dates.

Figure 2

Typical MS-based workflow for quantitative proteomics. Depending on the approach, labeling can be at one of several points in the experiment as indicated by the dashed arrow or, alternatively, a label-free route can be followed. Total protein is isolated in vivo or in vitro from the system under study. Metabolic labeling (e.g. SILAC) labels proteins in vivo; alternatively, following protein isolation, labeling may be by chemical derivatization or done enzymatically prior to protein separation. After protein separation, in a bottoms-up proteomic strategy, proteins are digested to peptides, which again may be labeled (e.g. isotope-coded affinity tags, iTRAQ, or tandem mass tags approaches). The complexity of the sample may be reduced by fractionation (HPLC), often in several dimensions prior to MS or MS/MS analysis. The final, and often most time-consuming step, is data analysis.