14-3-3σ Regulates β-Catenin-Mediated Mouse Embryonic Stem Cell Proliferation by Sequestering GSK-3β

[[abstract]]Background: Pluripotent embryonic stem cells are considered to be an unlimited cell source for tissue regeneration and cell-based therapy. Investigating the molecular mechanism underlying the regulation of embryonic stem cell expansion is thus important. 14-3-3 proteins are implicated in controlling cell division, signaling transduction and survival by interacting with various regulatory proteins. However, the function of 14-3-3 in embryonic stem cell proliferation remains unclear. Methodology and Principal Findings: In this study, we show that all seven 14-3-3 isoforms were detected in mouse embryonic stem cells. Retinoid acid suppressed selectively the expression of 14-3-3σ isoform. Knockdown of 14-3-3σ with siRNA reduced embryonic stem cell proliferation, while only 14-3-3σ transfection increased cell growth and partially rescued retinoid acid-induced growth arrest. Since the growth-enhancing action of 14-3-3σ was abrogated by β-catenin knockdown, we investigated the influence of 14-3-3σ overexpression on β-catenin/GSK-3β. 14-3-3σ bound GSK-3β and increased GSK-3β phosphorylation in a PI-3K/Akt-dependent manner. It disrupted β-catenin binding by the multiprotein destruction complex. 14-3-3σ overexpression attenuated β-catenin phosphorylation and rescued the decline of β-catenin induced by retinoid acid. Furthermore, 14-3-3σ enhanced Wnt3a-induced β-catenin level and GSK-3β phosphorylation. DKK, an inhibitor of Wnt signaling, abolished Wnt3a-induced effect but did not interfere GSK-3β/14-3-3σ binding. Significance:Our findings show for the first time that 14-3-3σ plays an important role in regulating mouse embryonic stem cell proliferation by binding and sequestering phosphorylated GSK-3β and enhancing Wnt-signaled GSK-3β inactivation. 14-3-3σ is a novel target for embryonic stem cell expansion

Differential and shared genetic effects on kidney function between diabetic and non-diabetic individuals

A large-scale GWAS provides insight on diabetes-dependent genetic effects on the glomerular filtration rate, a common metric to monitor kidney health in disease.Reduced glomerular filtration rate (GFR) can progress to kidney failure. Risk factors include genetics and diabetes mellitus (DM), but little is known about their interaction. We conducted genome-wide association meta-analyses for estimated GFR based on serum creatinine (eGFR), separately for individuals with or without DM (nDM = 178,691, nnoDM = 1,296,113). Our genome-wide searches identified (i) seven eGFR loci with significant DM/noDM-difference, (ii) four additional novel loci with suggestive difference and (iii) 28 further novel loci (including CUBN) by allowing for potential difference. GWAS on eGFR among DM individuals identified 2 known and 27 potentially responsible loci for diabetic kidney disease. Gene prioritization highlighted 18 genes that may inform reno-protective drug development. We highlight the existence of DM-only and noDM-only effects, which can inform about the target group, if respective genes are advanced as drug targets. Largely shared effects suggest that most drug interventions to alter eGFR should be effective in DM and noDM.</p

A saturated map of common genetic variants associated with human height

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes(1). Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel(2)) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.A large genome-wide association study of more than 5 million individuals reveals that 12,111 single-nucleotide polymorphisms account for nearly all the heritability of height attributable to common genetic variants

A saturated map of common genetic variants associated with human height.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries