Journal
SCIENCE SIGNALING
Volume 7, Issue 311, Pages -Publisher
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/scisignal.2004621
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Funding
- NIH [T-R01 DK087650, GM54508, DK78897, P30NS046593]
- Systems Biology Center [GM-071558]
- Natural Sciences and Engineering Research Council of Canada [BP-342902]
- NIH from the National Center for Research Resources [P41RR013186]
- NIH National Cancer Institute shared resources [5R24 CA095823-04]
- National Science Foundation Major Research Instrumentation [DBI-9724504]
- NIH shared instrumentation [1 S10 RR0 9145-01]
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Podocytes are kidney cells with specialized morphology that is required for glomerular filtration. Diseases, such as diabetes, or drug exposure that causes disruption of the podocyte foot process morphology results in kidney pathophysiology. Proteomic analysis of glomeruli isolated from rats with puromycin-induced kidney disease and control rats indicated that protein kinase A (PKA), which is activated by adenosine 3',5'-monophosphate (cAMP), is a key regulator of podocyte morphology and function. In podocytes, cAMP signaling activates cAMP response element-binding protein (CREB) to enhance expression of the gene encoding a differentiation marker, synaptopodin, a protein that associates with actin and promotes its bundling. We constructed and experimentally verified a beta-adrenergic receptor-driven network with multiple feedback and feedforward motifs that controls CREB activity. To determine how the motifs interacted to regulate gene expression, we mapped multicompartment dynamical models, including information about protein subcellular localization, onto the network topology using Petri net formalisms. These computational analyses indicated that the juxtaposition of multiple feedback and feedforward motifs enabled the prolonged CREB activation necessary for synaptopodin expression and actin bundling. Drug-induced modulation of these motifs in diseased rats led to recovery of normal morphology and physiological function in vivo. Thus, analysis of regulatory motifs using network dynamics can provide insights into pathophysiology that enable predictions for drug intervention strategies to treat kidney disease.
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