97 research outputs found

    ARHGDIA mutations cause nephrotic syndrome via defective RHO GTPase signaling

    Get PDF
    Nephrotic syndrome (NS) is divided into steroid-sensitive (SSNS) and -resistant (SRNS) variants. SRNS causes end-stage kidney disease, which cannot be cured. While the disease mechanisms of NS are not well understood, genetic mapping studies suggest a multitude of unknown single-gene causes. We combined homozygosity mapping with whole-exome resequencing and identified an ARHGDIA mutation that causes SRNS. We demonstrated that ARHGDIA is in a complex with RHO GTPases and is prominently expressed in podocytes of rat glomeruli. ARHGDIA mutations (R120X and G173V) from individuals with SRNS abrogated interaction with RHO GTPases and increased active GTP-bound RAC1 and CDC42, but not RHOA, indicating that RAC1 and CDC42 are more relevant to the pathogenesis of this SRNS variant than RHOA. Moreover, the mutations enhanced migration of cultured human podocytes; however, enhanced migration was reversed by treatment with RAC1 inhibitors. The nephrotic phenotype was recapitulated in arhgdia-deficient zebrafish. RAC1 inhibitors were partially effective in ameliorating arhgdia-associated defects. These findings identify a single-gene cause of NS and reveal that RHO GTPase signaling is a pathogenic mediator of SRNS.ope

    Hormone-like (endocrine) Fgfs: their evolutionary history and roles in development, metabolism, and disease

    Get PDF
    Fibroblast growth factors (Fgfs) are proteins with diverse functions in development, repair, and metabolism. The human Fgf gene family with 22 members can be classified into three groups, canonical, intracellular, and hormone-like Fgf genes. In contrast to canonical and intracellular Fgfs identified in invertebrates and vertebrates, hormone-like Fgfs, Fgf15/19, Fgf21, and Fgf23, are vertebrate-specific. The ancestral gene of hormone-like Fgfs was generated from the ancestral gene of canonical Fgfs by gene duplication early in vertebrate evolution. Later, Fgf15/19, Fgf21, and Fgf23 were generated from the ancestral gene by genome duplication events. Canonical Fgfs act as autocrine/paracrine factors in an Fgf receptor (Fgfr)-dependent manner. In contrast, hormone-like Fgfs act as endocrine factors in an Fgfr-dependent manner. Canonical Fgfs have a heparin-binding site necessary for the stable binding of Fgfrs and local signaling. In contrast, hormone-like Fgfs acquired endocrine functions by reducing their heparin-binding affinity during their evolution. Fgf15/19 and Fgf23 require βKlotho and αKlotho as cofactors, respectively. However, Fgf21 might physiologically require neither. Hormone-like Fgfs play roles in metabolism at postnatal stages, although they also play roles in development at embryonic stages. Fgf15/19 regulates bile acid metabolism in the liver. Fgf21 regulates lipid metabolism in the white adipose tissue. Fgf23 regulates serum phosphate and active vitamin D levels. Fgf23 signaling disorders caused by hereditary diseases or tumors result in metabolic disorders. In addition, serum Fgf19 or Fgf21 levels are significantly increased by metabolic disorders. Hormone-like Fgfs are newly emerging and quite unique in their evolution and function

    Future perspectives in melanoma research: meeting report from the "Melanoma Bridge", Napoli, December 5th-8th 2013

    Get PDF

    Time-varying spectral analysis in exercise and sport science

    No full text
    We discuss the application of time-frequency analysis to biomechanical-type signals, and in particular to signals that would be encountered in the study of rotation rates of bicycle pedaling. We simulate a number of such signals and study how well they are represented by various time-frequency methods. We show that time-frequency representations track very well the instantaneous frequency even when there are very fast changes. In addition, we do a correlation analysis between time-series whose instantaneous frequency is changing and show that the traditional correlation coefficient is insufficient to characterize the correlations. We instead show that the correlation coefficient should be evaluated directly from the instantaneous frequencies of the time series, which can be easily estimated from their time-frequency distributions
    corecore