Systems Biology of Anionic Drug and Metabolite Handling

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DESCRIPTION (provided by applicant): Many commonly prescribed anionic drugs (eg. antibiotics, diuretics, ACE inhibitors, NSAIDs) as well as disease-associated metabolites are organic anions that are excreted as a result of transport by the proximal tubule of the kidney. The rate limiting genes involved in transport of these drugs and metabolites are Oat1/SLC22a6 and Oat3/SLC22a8. Oat1/SLC22a6 (NKT) and several related SLC22 gene family members were first identified by the PI's group, and the effects of genetic deletion of Oat1/SLC22a6 and Oat3/SLC22a8 have recently been published by the PI's group. Mutations/polymorphisms in related SLC22 transporters appear to be associated with both inherited metabolic disease and complex metabolic phenotypes. The expression of these genes and, thus their functionality, changes markedly through development, maturity and aging; this is presumed to play a role in alterations in drug and metabolite handling throughout life. A comprehensive understanding of drug and metabolite elimination can only emerge when the process is analyzed at multiple levels-from the transporter, to the cell, to the tubule, to the organ. Over the years, the PI's group has developed a rich data set of transcriptomic, metabolomic and fluxomic/physiological data at these levels of analysis in Oat-expressing and non-expressing conditions. It is argued here that a coherent "systems" picture can be achieved if this rich set of "omics" and physiological data from a single lab is modeled at many levels for organic anionic drugs/metabolites. We aim to perform single level and multiscale modeling in collaboration with several premier systems biologists here at UCSD (SA1). Preliminary data is presented showing the extent of the PI's collaborations with these systems biologists; in some cases, papers have been or will soon be co-authored. We also aim to study the aforementioned models of renal drug/metabolite handling (at multiple levels) in a dynamic setting during different periods of life when Oat gene expression is known to undergo large changes (SA2). Experiments and initial coarse-grained modeling will be performed side-by-side with continued wet lab studies of a prototypical organic anion, which will be used to further constrain modeling at each level. This will, in turn, drive further experimentation, that will help refine the models. The ultimate goal is to set the stage for a model with predictive power in the clinical contexts of complex metabolic disease phenotypes (eg. hyperuricemia) and pharmacogenomics.