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ASSISTANT PROFESSOR | Genetics and Genomic Sciences


Education PhD, University of Western Ontario Biography Dr. Argmann has a doctorate from the faculty of Science at the University of Western Ontario, Canada where she showed that PPARγ and LXR activation could dramatically reduce macrophage foam cell formation, a key event in atherosclerosis. During her postdoctoral studies at the Institut de Génétique et de Biologie Moléculaire et Cellulaire in Strasbourg, France she contributed to the development of high-throughput mouse metabolic phenotyping protocols and demonstrated that resveratrol, a compound found in red wine, improves mitochondrial function and protects against metabolic disease in vivo. She then started as a research scientist in Dr Schadt’s genetics group at Rosetta Inpharmatics where she contributed to the designing of large-scale genetic mouse crosses to address novel facets of metabolic disease. She was involved in integrating DNA variation, gene expression, and clinical data collected, in order to uncover core networks associated with metabolic disease processes, which in turn were used to identify novel therapeutic targets for the Diabetes and Obesity franchise. In 2010, during her postdoctoral position in Dr. Aerts’s lab at the Academic Medical Center of the University of Amsterdam, she developed her current line of research which she continues now as an assistant professor at Mount Sinai in the Institute for Genomics and Multiscale Biology. Her work involves extending the genetics of gene expression strategy to include intermediate phenotypes, such as sphingolipidomics data, to address the role of these lipids in metabolic disease-related pathophysiologies. In addition, she is using these ‘complex disease’ strategies to identify candidate modifier genes of inborn errors of metabolism, such as Gaucher’s disease severity. The ultimate goal is to develop a general network medicine approach for inborn errors of metabolism. For a complete list of Dr. Argmann's publications[Author]&cauthor=true&cauthor_uid=22355651 click here Research Aim of the biological data mining group Helping people extract novel biological insights from very complex and large data sets requires spanning both the biological and computational worlds. Our biological data mining group aims to do this by helping to ask the right computational questions in order to get the right biological answers. To accomplish this we entertain many collaborations and are imbedded in the Icahn Institute for Multi-scale Biology. Selected research interests 1. Network medicine for inborn errors of metabolism: Despite their seemingly monogenetic nature, many inborn errors of metabolism, such as Gaucher’s disease, have a remarkably heterogeneous clinical presentation making the disease course and severity difficult to predict. In fact, from a contemporary perspective there is no clear distinction between simple Mendelian disorders and complex diseases such that collectively these disorders represent a continuum of diminishing effects from a single gene influenced by modifier genes to increasingly shared influence by multiple genes. This realization highlights the need for an unbiased approach to finding candidate modifier genes for seemingly ‘monogenetic’ diseases and reveals the possibility of applying an experimental model system ‘designed’ for complex diseases to inborn errors of metabolism. Our preliminary research is demonstrating that experimental model systems successfully utilized to advance our understanding of complex disease (e.g. genetics of gene expression data in complex genetic reference populations of mice) are equally useful in advancing our understanding of inborn errors of metabolism, in particular by revealing the molecular networks underlying Gaucher’s disease severity. 2. Network medicine for Complex metabolic diseases: Genetics of gene expression strategies have been very successful in bringing novel insight into complex disease pathophysiology. However, metabolites are more likely to serve as the most proximal reporters of a phenotype and yield critical biological insights. Since individual metabolyte profiles or metabotypes in analogy with genotype, have been shown to be determined by genetics and environment, we propose to extend the genetics of gene expression strategy to include intermediate phenotypes obtained using lipidomic and metabolomic platforms. Through statistical integration of these multi-scale layers we aim to obtain a detailed picture pathophysiology in complex disease.


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