Gut microbiota and inflammation in chronic kidney disease and their roles in the development of cardiovascular disease

The health and proper functioning of the cardiovascular and renal systems largely depend on crosstalk in the gut-kidney-heart/vessel triangle. Recent evidence suggests that the gut microbiota has an integral function in this crosstalk. Mounting evidence indicates that the development of chronic kidney and cardiovascular diseases follows chronic inflammatory processes that are affected by the gut microbiota via various immune, metabolic, endocrine, and neurologic pathways. Additionally, deterioration of the function of the cardiovascular and renal systems has been reported to disrupt the original gut microbiota composition, further contributing to the advancement of chronic cardiovascular and renal diseases. Considering the interaction between the gut microbiota and the renal and cardiovascular systems, we can infer that interventions for the gut microbiota through diet and possibly some medications can prevent/stop the vicious cycle between the gut microbiota and the cardiovascular/renal systems, leading to a decrease in chronic cardiovascular and renal diseases

Membrane protein structure, function, and dynamics: a perspective from experiments and theory

Membrane proteins mediate processes that are fundamental for the flourishing of biological cells. Membrane-embedded transporters move ions and larger solutes across membranes; receptors mediate communication between the cell and its environment and membrane-embedded enzymes catalyze chemical reactions. Understanding these mechanisms of action requires knowledge of how the proteins couple to their fluid, hydrated lipid membrane environment. We present here current studies in computational and experimental membrane protein biophysics, and show how they address outstanding challenges in understanding the complex environmental effects on the structure, function, and dynamics of membrane proteins.JTD, IA, and MR used the computational resources of the Modeling Facility of the Department of Chemistry, University of California Irvine funded by NSF Grant CHE-0840513 for this work. A-NB was supported in part by the Marie Curie International Reintegration Award IRG-26920.TWA was supported by ARC DP120103548, NSF MCB1052477, DE Shaw Anton (PSCA00061P; NRBSC, through NIH RC2GM093307), VLSCI (VR0200), and NCI (dd7). BA and SV acknowledge the support by ERC advanced Grant No. 268888. ZC and PG would like to acknowledge Reference Framework (NSRF) 2011–2013, National Action ‘‘Cooperation,’’ under grant entitled ‘‘Magnetic Nanoparticles for targeted MRI therapy (NANOTHER),’’ with code ‘‘11RYM-1-1799.’’ The program is cofunded by the European Regional Development Fund and national resources. Part of the calculations presented herein were performed using resources of the LinkSCEEM-2 project, funded by the EC under FP7 through Capacities Research Infrastructure, INFRA-2010-1.2.3 Virtual Research Communities, Combination of Collaborative Project and Coordination and Support Actions (CPCSA) under Grant agreement no. RI-261600. GB was supported in part by NSF grant MCB1330728 from the National Science Foundation and Grant PO1GM55876-14A1 from the National Institutes of Health. LD received funding from EU FP7 (PIOF-GA-2012-329534). LD, and MLK used the computational resources of Temple University, supported by the National Science Foundation through major research instrumentation grant number CNS-09-58854. JS acknowledges support from the Instituto de Salud Carlos III FEDER (CP12/03139