Systems Biology of Anionic Drug and Metabolite Handling

说明(由申请人提供)：许多常用的阴离子药物(如。抗生素、利尿剂、血管紧张素转换酶抑制剂、非甾体抗炎药)以及与疾病相关的代谢物都是有机阴离子，通过肾脏近端小管的运输而排出。参与这些药物和代谢物运输的限速基因是Oat1/SLC22a6和OAT3/SLC22a8。Oat1/SLC22a6(NKT)和几个相关的SLC22基因家族成员最早是由Pi‘s小组发现的，而Oat1/SLC22a6和Oat3/SLC22a8基因缺失的效应最近也由Pi’s小组发表。相关SLC22转运蛋白的突变/多态似乎与遗传性代谢性疾病和复杂的代谢表型有关。随着发育、成熟和衰老，这些基因的表达以及它们的功能都会发生显著变化；这被认为在一生中药物和代谢物处理的变化中发挥了作用。对药物和代谢物消除的全面理解只有在从转运体、细胞、小管到器官的多个层面上进行分析时才能出现。多年来，PI的团队已经在燕麦表达和非表达条件下的这些分析水平上开发了丰富的转录组、代谢组和流动组/生理数据集。这里认为，如果对有机阴离子药物/代谢物在多个水平上模拟来自单个实验室的丰富的“组学”和生理数据，就可以获得连贯的“系统”图景。我们的目标是与加州大学圣迭戈分校(SA1)的几位顶尖系统生物学家合作进行单级和多尺度建模。提供的初步数据显示了PI与这些系统生物学家的合作程度；在某些情况下，论文已经或即将共同撰写。我们还旨在研究上述肾脏药物/代谢物处理模型(在多个水平上)，在已知燕麦基因表达发生重大变化的不同生命时期的动态环境中(SA2)。实验和最初的粗粒度建模将与原型有机阴离子的持续湿实验室研究并列执行，这些研究将用于进一步限制每个级别的建模。这反过来将推动进一步的实验，这将有助于改进模型。最终目标是为在复杂代谢性疾病表型的临床背景下具有预测能力的模型奠定基础(例如。高尿酸血症)和药物基因组学。

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.