Biomarker discovery, disease classification, and similarity query processing on high-throughput MS/MS data of inborn errors of metabolism.

In newborn errors of metabolism, biomarkers are urgently needed for disease screening, diagnosis, and monitoring of therapeutic interventions. This article describes a 2-step approach to discover metabolic markers, which involves (1) the identification of marker candidates and (2) the prioritization of them based on expert knowledge of disease metabolism. For step 1, the authors developed a new algorithm, the biomarker identifier (BMI), to identify markers from quantified diseased versus normal tandem mass spectrometry data sets. BMI produces a ranked list of marker candidates and discards irrelevant metabolites based on a quality measure, taking into account the discriminatory performance, discriminatory space, and variance of metabolites' concentrations at the state of disease. To determine the ability of identified markers to classify subjects, the authors compared the discriminatory performance of several machine-learning paradigms and described a retrieval technique that searches and classifies abnormal metabolic profiles from a screening database. Seven inborn errors of metabolism-- phenylketonuria (PKU), glutaric acidemia type I (GA-I), 3-methylcrotonylglycinemia deficiency (3-MCCD), methylmalonic acidemia (MMA), propionic acidemia (PA), medium-chain acylCoAdehydrogenase deficiency (MCADD), and 3-OH long-chain acyl CoA dehydrogenase deficiency (LCHADD)-were investigated. All primarily prioritized marker candidates could be confirmed by literature. Some novel secondary candidates were identified (i.e., C16:1 and C4DC for PKU, C4DC for GA-I, and C18:1 forMCADD), which require further validation to confirm their biochemical role during health and disease.