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Beyond glycemic control, SGLT2 inhibitors (SGLT2i) have protective effects on cardiorenal function. Renoprotection has been suggested to involve inhibition of NHE3 leading to reduced ATP-dependent tubular workload and mitochondrial oxygen consumption. NHE3 activity is also important for regulation of endosomal pH, but the effects of SGLT2i on endocytosis are unknown. We used a highly differentiated cell culture model of proximal tubule (PT) cells to determine the direct effects of SGLT2i on Na⁺-dependent fluid transport and endocytic uptake in this nephron segment. Strikingly, canagliflozin but not empagliflozin reduced fluid transport across cell monolayers, and dramatically inhibited endocytic uptake of albumin. These effects were independent of glucose and occurred at clinically relevant concentrations of drug. Canagliflozin acutely inhibited surface NHE3 activity, consistent with a direct effect, but did not affect endosomal pH or NHE3 phosphorylation. Additionally, canagliflozin rapidly and selectively inhibited mitochondrial complex I activity. Inhibition of mitochondrial complex I by metformin recapitulated the effects of canagliflozin on endocytosis and fluid transport, whereas modulation of downstream effectors AMPK and mTOR did not. Mice given a single dose of canagliflozin excreted twice as much urine over 24 h compared with empagliflozin-treated mice despite similar water intake. We conclude that canagliflozin selectively suppresses Na⁺-dependent fluid transport and albumin uptake in PT cells via direct inhibition of NHE3 and of mitochondrial function upstream of the AMPK/mTOR axis. These additional targets of canagliflozin contribute significantly to reduced PT Na⁺-dependent fluid transport in vivo. 

Abstract Advances in quantitative biology data collection and analysis across scales (molecular, cellular, organismal, and ecological) have transformed how we understand, categorize, and predict complex biological systems. This surge of quantitative data creates an opportunity to apply, develop, and evaluate mathematical models of biological systems and explore novel methods of analysis. Simultaneously, thanks to increased computational power, mathematicians, engineers and physical scientists have developed sophisticated models of biological systems at different scales. Novel modeling schemes can offer deeper understanding of principles in biology, but there is still a disconnect between modeling and experimental biology that limits our ability to fully realize the integration of mathematical modeling and biology. In this work, we explore the urgent need to expand the use of existing mathematical models across biological scales, develop models that are robust to biological heterogeneity, harness feedback loops within the iterative modeling process, and nurture a cultural shift towards interdisciplinary and cross-field interactions. Better integration of biological experimentation and robust mathematical modeling will transform our ability to understand and predict complex biological systems.