Sarah H Elsea
Baylor College of Medicine, USA
Title: A multi-omics precision medicine approach to diagnosis of inborn errors of metabolism
Biography
Biography: Sarah H Elsea
Abstract
Metabolomics is the study of the distinctive chemical fingerprint produced by specific cellular processes. Untargeted mass spectrometry-based metabolomic profiling for small molecules in body fluids is an emerging technique used to produce and analyze this chemical fingerprint. This technology holds the promise of providing new insights into human disease states and serving as a primary diagnostic tool for novel and previously characterized inborn errors of metabolism (IEM), as well as for the identification of biomarkers of disease and treatment. Clinical metabolomic profiling allows for parallel screening of hundreds of metabolites in a single biological specimen. On average, ~900 small molecules are detected in a given plasma sample with a core group of ~350 analytes found in all specimens tested to date. The analytes detected encompass numerous classes of small molecule biomarkers including acylcarnitine’s, amino acids, bile acids, carbohydrates, lipids, and nucleotides. In addition, metabolomic data in many cases affords a much richer view of a patient's metabolic disturbance by identifying: (1) elevated metabolites located far upstream of the genetic defect, (2) treatment related compounds, including commonly tested therapeutic drug monitoring analytes, and (3) spectrally unique analytes that are not yet associated with a biochemical phenotype. In our clinical experience, the integration of whole exome sequencing data with the metabolomics profile has improved the interpretation of genetic variants, including ruling out the diagnosis of IEMs, as well as supporting a specific diagnosis, and for the identification of new disease and/or treatment biomarkers. For undifferentiated clinical phenotypes such as intellectual disability, hypotonia, autism, or seizures, many different tests involving different sample types are often needed for diagnosis. This can lead to prohibitive costs and ongoing diagnostic odysseys. Data will be presented on genomic and metabolomic profiling of previously non-diagnostic cases which pointed to genetic disorders such as aromatic amino acid decarboxylase deficiency, GABA transaminase deficiency, adenylosuccinate lyase deficiency, and peroxisome biogenesisdisorders, illustrating the powerful synergy of genomic and metabolomic analysis in determining the pathogenicity of variants of uncertain significance. Ultimately, a clinical systems biology approach to the integration clinical data with genomic, transcriptomic, epigenomic, proteomic, and metabolomics data will provide a comprehensive precision medicine approach to improve understanding of natural biological variation and to improve diagnosis and management of disease.