Background: Inborn errors of metabolism (IEM) are increasingly viewed as complex diseases as they often present as a spectrum of disease phenotypes with a clear disconnect between the severity of mutation at the primary affected locus and the phenotype. The lack of genotype and phenotype correlation greatly impacts the ability to predict a patient’s disease course. It also illustrates the existence of a fundamental gap in our knowledge of disease pathophysiology. The era of one-gene one-disease is being abandoned, and the contribution of modifying factors considered. However, identifying the modifying factors is not trivial, as rare diseases have rare data.

Hypothesis: We hypothesize that by embracing the concept of IEM as complex diseases that 1. datasets and 2. network approaches generated in populations with common disorders can be used to study disease modifying biology in IEM thereby overcoming the rare disease rare data drawback. Our strategy is based on observing that differentially expressed genes from IEM experimental models highlight highly connected subnetworks in the molecular networks established in common disease populations.

Aim:  Our lab’s aim is to use our innovative data-driven multi-scale computational approach to derive and then wet-lab validate novel candidate modifying genes and their associated biology related to the screenable inborn errors of fatty acid oxidation (FAO) the lysosomal storage disorder, Gaucher disease (GD). This is a key collaboration between myself and a faculty expert in IEM, Dr Sander Houten, also of the Department of Genetics and Genomics Sciences at ICAHN.

Impact: Combined these two aims will break new ground for FAO and GD and rare diseases in general  by overcoming inherent limitations of rare data through combining novel methodologies with existing data. We furthermore perform validations and these methods which could point the IEM research field into multiple new directions. These novel insights are needed to propel the IEM field into the next generation of understanding.

Background: Inflammatory bowel disease (IBD) involves chronic inflammation of all or part of your digestive tract and primarily includes ulcerative colitis and Crohn's disease. IBD can be debilitating with severe diarrhea, pain, fatigue and weight loss and is sometimes associated with life-threatening complications.  Currently there is an unmet therapeutic need with many patients either non-responsive or refractory to current treatment options. MSSM from the clinic, to preclinical models to computational modelers have extensive expertise in IBD. Because of this expertise Janssen pharmaceuticals in 2013 entered into a multi-million dollar collaboration with MSSM clinicians, preclinical scientists and computational biologists to generate new patient information, understand preclinical IBD mouse models and computational interpret all the data as predictive networks of IBD.

Hypothesis: The underlying hypothesis we are testing is that the through multi-scale modeling of IBD patient derived molecular insights that we can better understand the disease pathology and predict candidate novel genes and biology for potential therapeutic development. I have been involved in various aspects of this collaboration using my integrative biologist skill set to help generate data for and interpret the networks.

Impact: Data driven models have proven effective in understanding other complex diseases (Diabetes, obesity CVD) thus the application of these models to IBD is a much needed complementary approach to interpreting a very complex disease process.

For a complete list of Dr. Argmann's publications http://www.ncbi.nlm.nih.gov/pubmed/?term=argmann+C

Identifying novel biological insights on complex human diseases requires spanning both the biological and computational worlds. Our biological data mining strategies aim to do this by helping to ask the right computational questions in order to get the right biological answers.

One major part of our group’s research is to facilitate generation of large scale datasets which can be integrated into causal predictive molecular networks which we then interpret for the pathophysiology of underlying complex biological questions.  Our network insights are refined into candidate genes and pathways and formulated into testable hypotheses, some of which we perform experimental validation of ourselves.

Ultimately we aim to use this knowledge to predict novel therapeutic candidates for diseases of interest. We have applied these methods in multiple disciplines covering complex diseases (metabolic syndrome, IBD and cancer), complex traits (aging) as well as inborn errors of metabolism (Gaucher’s disease and Mitochondrial disorders).

Our system biology approaches are highly collaborative projects as they require various expertise from clinicians in the clinic, to experimentalist  in the wet lab to the computational analyst in the dry lab.  Three of my group’s main projects are summarized:

Background: Adult human beta cells have proven remarkably resistant to therapeutic replication and expansion. This has significantly limited therapeutic options for Type 1 and 2 diabetics, both types which result entirely or in part from a deficiency of normal insulin-producing pancreatic beta cells. 

Hypothesis/Aims: This knowledge gap has prompted an extensive search for strategies to regenerate or replace lost or dysfunctional beta cells.  An intriguing, unexplored proliferative model is insulinoma, rare tumors of the adult beta cell. With this rationale, Dr Andrew Stewart (Director of the Diabetes and Obesity Institute at MSSM) initiated a collaboration with myself,  Dr Bojan Losic (MSSM GGS faculty) and Dr Eric Schadt (Director of the Icahn Institute for Multi-Scale Biology and GGS department) in order to perform the first comprehensive genomic and transcriptomic characterization of human insulinomas. We are then integrating these molecular landscapes with those of normal beta cell transcriptomes in order to reveal underlying biological processes in insulinomas including proliferation mechanisms.  Several predicted cell cycle gene candidates are being experimentally confirmed.

Impact: Overall our integrative approach hopes to uncover the key hallmarks and drivers of human insulinoma which distinguish them from normal beta cells and may serve as therapeutic targets for the diabetes field.