Richmondbrady2465

Collagen-enriched cuticle forms the outermost layer of skin in nematode Caenorhabditis elegans. The nematode's genome encodes 177 collagens, but little is known about their role in maintaining the structure or barrier function of the cuticle. In this study, we found six permeability determining (PD) collagens. Loss of any of these PD collagens-DPY-2, DPY-3, DPY-7, DPY-8, DPY-9, and DPY-10-led to enhanced susceptibility of nematodes to paraquat (PQ) and antihelminthic drugs- levamisole and ivermectin. Upon exposure to PQ, PD collagen mutants accumulated more PQ and incurred more damage and death despite the robust activation of antioxidant machinery. We find that BLMP-1, a zinc finger transcription factor, maintains the barrier function of the cuticle by regulating the expression of PD collagens. We show that the permeability barrier maintained by PD collagens acts in parallel to FOXO transcription factor DAF-16 to enhance survival of insulin-like receptor mutant, daf-2. In all, this study shows that PD collagens regulate cuticle permeability by maintaining the structure of C. elegans cuticle and thus provide protection against exogenous toxins.The protein molecular chaperone Hsp90 (Heat shock protein, 90 kilodalton) plays multiple roles in the biogenesis and regulation of client proteins impacting myriad aspects of cellular physiology. Amino acid alterations located throughout Saccharomyces cerevisiae Hsp90 have been shown to result in reduced client activity and temperature-sensitive growth defects. Although some Hsp90 mutants have been shown to affect activity of particular clients more than others, the mechanistic basis of client-specific effects is unknown. We found that Hsp90 mutants that disrupt the early step of Hsp70 and Sti1 interaction, or show reduced ability to adopt the ATP-bound closed conformation characterized by Sba1 and Cpr6 interaction, similarly disrupt activity of three diverse clients, Utp21, Ssl2, and v-src. In contrast, mutants that appear to alter other steps in the folding pathway had more limited effects on client activity. Protein expression profiling provided additional evidence that mutants that alter similar steps in the folding cycle cause similar in vivo consequences. Our characterization of these mutants provides new insight into how Hsp90 and cochaperones identify and interact with diverse clients, information essential for designing pharmaceutical approaches to selectively inhibit Hsp90 function.The gram-negative bacterium Coxiella burnetii is the causative agent of Query (Q) fever in humans and coxiellosis in livestock. Host genetics are associated with C. burnetii pathogenesis both in humans and animals; however, it remains unknown if specific genes are associated with severity of infection. We employed the Drosophila Genetics Reference Panel to perform a genome-wide association study to identify host genetic variants that affect host survival to C. burnetii infection. The genome-wide association study identified 64 unique variants (P  less then  10-5) associated with 25 candidate genes. We examined the role each candidate gene contributes to host survival during C. burnetii infection using flies carrying a null mutation or RNAi knockdown of each candidate. We validated 15 of the 25 candidate genes using at least one method. This is the first report establishing involvement of many of these genes or their homologs with C. selleck burnetii susceptibility in any system. Among the validated genes, FER and tara play roles in the JAK/STAT, JNK, and decapentaplegic/TGF-β signaling pathways which are components of known innate immune responses to C. burnetii infection. CG42673 and DIP-ε play roles in bacterial infection and synaptic signaling but have no previous association with C. burnetii pathogenesis. Furthermore, since the mammalian ortholog of CG13404 (PLGRKT) is an important regulator of macrophage function, CG13404 could play a role in host susceptibility to C. burnetii through hemocyte regulation. These insights provide a foundation for further investigation regarding the genetics of C. burnetii susceptibility across a wide variety of hosts.We propose a novel Bayesian approach that robustifies genomic modeling by leveraging expert knowledge (EK) through prior distributions. The central component is the hierarchical decomposition of phenotypic variation into additive and nonadditive genetic variation, which leads to an intuitive model parameterization that can be visualized as a tree. The edges of the tree represent ratios of variances, for example broad-sense heritability, which are quantities for which EK is natural to exist. Penalized complexity priors are defined for all edges of the tree in a bottom-up procedure that respects the model structure and incorporates EK through all levels. We investigate models with different sources of variation and compare the performance of different priors implementing varying amounts of EK in the context of plant breeding. A simulation study shows that the proposed priors implementing EK improve the robustness of genomic modeling and the selection of the genetically best individuals in a breeding program. We observe this improvement in both variety selection on genetic values and parent selection on additive values; the variety selection benefited the most. In a real case study, EK increases phenotype prediction accuracy for cases in which the standard maximum likelihood approach did not find optimal estimates for the variance components. Finally, we discuss the importance of EK priors for genomic modeling and breeding, and point to future research areas of easy-to-use and parsimonious priors in genomic modeling.We propose an extended Gaussian mixture model for the distribution of causal effects of common single nucleotide polymorphisms (SNPs) for human complex phenotypes that depends on linkage disequilibrium (LD) and heterozygosity (H), while also allowing for independent components for small and large effects. Using a precise methodology showing how genome-wide association studies (GWASs) summary statistics (z-scores) arise through LD with underlying causal SNPs, we applied the model to GWAS of multiple human phenotypes. Our findings indicated that causal effects are distributed with dependence on total LD and H, whereby SNPs with lower total LD and H are more likely to be causal with larger effects; this dependence is consistent with models of the influence of negative pressure from natural selection. Compared with the basic Gaussian mixture model it is built on, the extended model-primarily through quantification of selection pressure-reproduces with greater accuracy the empirical distributions of z-scores, thus providing better estimates of genetic quantities, such as polygenicity and heritability, that arise from the distribution of causal effects.