Analytical approaches to metabolomics and applications to systems biology

Abstract

Phenotypic expression of renal diseases encompasses a complex interaction between genetic, environmental, and local tissue factors. The level of complexity requires integrated understanding of perturbations in the network of genes, proteins, and metabolites. Metabolomics attempts to systematically identify and quantitate metabolites from biological samples. The small molecules represent the end result of complexity of biological processes in a given cell, tissue, or organ, and thus form attractive candidates to understand disease phenotypes. Metabolites represent a diverse group of low-molecular-weight structures including lipids, amino acids, peptides, nucleic acids, and organic acids, which makes comprehensive analysis a difficult analytical challenge. The recent rapid development of a variety of analytical platforms based on mass spectrometry and nuclear magnetic resonance have enabled separation, characterization, detection, and quantification of such chemically diverse structures. Continued development of bioinformatics and analytical strategies will accelerate widespread use and integration of metabolomics into systems biology. Here, we will discuss analytical and bioinformatic techniques and highlight recent studies that use metabolomics in understanding pathophysiology of disease processes.

Figure 1. Systems biology of renal disease

The relationship between genome, transcriptome, proteome and metabolome is depicted. The complexity of the dataset increases from genome to trancriptome to proteome. Integration of these datasets provides comprehensive understanding of pathophysiology of renal disease.

Figure 2. Workflow for mass spectrometry based metabolomic analysis

Biological samples are processed and subjected to either gas (GC) or liquid (LC) chromatography separation. The eluent is ionized by one of several modes of ionization such as EI, CI, ESI, APCI and MALDI. Subsequently, the resultant mass spectra are derived from the mass analyzers and further processed by data and statistical analysis and the metabolite of interest is identified. EI, electron impact ionization; CI, chemical ionization; ESI, electrospray ionization; APCI, atmospheric pressure chemical ionization; MALDI, matrix assisted laser desorption ionization; TOF, time of flight; FTMS, Fourier transform mass spectrometry, MS/MS, tandem mass spectrometry; PC, principal component.

Figure 3. Metabolomic profiling of prostate cancer

Z score plots for 626 metabolites in localized prostate cancer and metastatic samples normalized to the mean of the benign prostate samples (Reproduced from37).