Dipeptidyl peptidase IV inhibitor protects against renal interstitial fibrosis in a mouse model of ureteral obstruction.

Dipeptidyl peptidase IV (DPPIV) is an exopeptidase that modulates the function of several substrates, among which insulin-releasing incretin hormones are the most well known. DPPIV also modulate substrates involved in inflammation, cell migration, and cell differentiation. Although DPPIV is highly expressed in proximal renal tubular cells, the role of DPPIV inhibition in renal disease is not fully understood. For this reason, we investigated the effects of LC15-0444, a DPPIV inhibitor, on renal function in a mouse model of renal fibrosis. Eight-week-old C57/BL6 mice were subjected to unilateral ureteral obstruction (UUO) and were treated with LC15-0444 (a DPPIV inhibitor) at a dose of 150 mg/kg per day in food or vehicle for 14 days. DPPIV activity was significantly increased in obstructed kidneys, and reduced after treatment with LC15-0444. Administration of LC15-0444 resulted in a significant decrease in albuminuria, urinary excretion of 8-isoprostane, and renal fibrosis. DPPIV inhibition also substantially decreased the synthesis of several proinflammatory and profibrotic molecules, as well as the infiltration of macrophages. UUO significantly increased, and LC15-0444 markedly suppressed, levels of phosphorylated Smad2/3, TGFß1, toll-like receptor 4, high-mobility group box-1, NADPH oxidase 4, and NF-κB. These results suggest that activation of DPPIV in the kidney has a role in the progression of renal disease and that targeted therapy inhibiting DPPIV may prove to be a useful new approach in the management of progressive renal disease, independent of mechanisms mediated by glucagon-like peptide-1.

A deep-learning strategy to identify cell types across species from high-density extracellular recordings.

High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but don't reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals, revealing the computational roles of neurons with distinct functional, molecular, and anatomical properties. We combine optogenetic activation and pharmacology using the cerebellum as a testbed to generate a curated ground-truth library of electrophysiological properties for Purkinje cells, molecular layer interneurons, Golgi cells, and mossy fibers. We train a semi-supervised deep-learning classifier that predicts cell types with greater than 95% accuracy based on waveform, discharge statistics, and layer of the recorded neuron. The classifier's predictions agree with expert classification on recordings using different probes, in different laboratories, from functionally distinct cerebellar regions, and across animal species. Our classifier extends the power of modern dynamical systems analyses by revealing the unique contributions of simultaneously-recorded cell types during behavior.