Proteomics is the science of detecting, identifying, and quantifying the products of gene translation and represents another approach to uncovering variation that underlies the pathogenesis of rare diseases. A single gene can generate an array of protein species based upon alternative translational start and stop sites and splicing. The derived proteins can be further diversified in relative abundance, structure, and function by posttranslational modifications including phosphorylation, glycosylation, acetylation, and tagging for degradation. Proteomics analyses can detect primary perturbations that cause disease (e.g., congenital disorders of glycosylation), pathogenetic or compensatory pathway activation (e.g., the activation of kinases through quantitative analysis of substrates for phosphorylation), and candidate proteins for validation as biomarkers to aid in diagnosis, prognostication, or therapeutic trials (e.g., newborn screening by tandem mass spectrometry or detection of increased circulating levels of cardiac muscle-specific enzymes after myocardial infarction) (see, e.g., Duncan and Hunsucker, 2005; Haffner and Maher, 2006; Suzuki et al., 2009; Van Eyk, 2010). One challenge is that proteomic analysis requires expensive equipment (e.g., mass spectrometry) and data analysis tools, which means that this technique is usually centralized in special laboratories.
Metabolomics involves the study of the small-molecule metabolites found in an organism. As in proteomics, mass spectrometry can be used to detect abnormal metabolic products, to diagnose rare diseases, and to understand alterations in relevant biological pathways. An example is the elucidation of a series of synthetic enzyme deficiencies that result in the production of abnormal bile acids leading to serious liver, neurologic, connective tissue, and nutritional disorders (Heubi et al., 2007).
With the aid of translational bioinformatics (Schadt et al., 2005a; Vodovotz et al., 2008), the construction of molecular networks and pathways relevant to specific rare disorders is increasingly possible. Bioinformatic analyses of data from gene expression arrays, proteomics studies, and clinical observations on patients with rare diseases can define signatures of fundamental disease mechanisms (Dudley et al., 2009; Patel et al., 2010; Suthram et al., 2010). Integration of this information with signatures of drug activities or therapeutic responses could intuitively promote discovery regarding the etiology, pathogenesis, and treatment of unclassified or poorly understood disorders (Schadt et al., 2005b). For example, if two diseases show overlapping or identical signatures, established treatments for one might benefit the other. Drugs that show signatures that oppose those seen for certain diseases emerge as candidate therapies. Bioinformatic methods can screen known chemical compounds for structural characteristics that