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.
The kidney eliminates drugs and metabolic waste products. This project aims to build a computational model to understand this process throughout life. It is anticipated that such a model will set the stage for one that can make predictions about how drugs will be eliminated.
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|Nagle, Megha A; Wu, Wei; Eraly, Satish A et al. (2013) Organic anion transport pathways in antiviral handling in choroid plexus in Oat1 (Slc22a6) and Oat3 (Slc22a8) deficient tissue. Neurosci Lett 534:133-8|
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|Wikoff, William R; Nagle, Megha A; Kouznetsova, Valentina L et al. (2011) Untargeted metabolomics identifies enterobiome metabolites and putative uremic toxins as substrates of organic anion transporter 1 (Oat1). J Proteome Res 10:2842-51|
|Nagle, Megha A; Truong, David M; Dnyanmote, Ankur V et al. (2011) Analysis of three-dimensional systems for developing and mature kidneys clarifies the role of OAT1 and OAT3 in antiviral handling. J Biol Chem 286:243-51|
|Wu, Wei; Dnyanmote, Ankur V; Nigam, Sanjay K (2011) Remote communication through solute carriers and ATP binding cassette drug transporter pathways: an update on the remote sensing and signaling hypothesis. Mol Pharmacol 79:795-805|
|Kouznetsova, Valentina L; Tsigelny, Igor F; Nagle, Megha A et al. (2011) Elucidation of common pharmacophores from analysis of targeted metabolites transported by the multispecific drug transporter-Organic anion transporter1 (Oat1). Bioorg Med Chem 19:3320-40|
|Ahn, Sun-Young; Jamshidi, Neema; Mo, Monica L et al. (2011) Linkage of organic anion transporter-1 to metabolic pathways through integrated ""omics""-driven network and functional analysis. J Biol Chem 286:31522-31|
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