Lindgrenbanke4555

The up-regulated genes were significantly enriched in Gene Ontology terms and pathways involved in cell proliferation and differentiation. In chicken follicular theca cells, Krüppel-like factor 4 (KLF4), ATPase phospholipid transporting 8A1 (ATP8A1), and Complexin-1 (CPLX1) were significantly up-regulated when the expression of gga-miR-135a-5p was inhibited. In addition, KLF4, ATP8A1, and CPLX1 confirmed as targets of gga-miR-135a-5p by using a dual-luciferase assay in vitro The results suggest that gga-mir-135a-5p may involve in proliferation and differentiation in chicken ovarian follicular theca cells by targeting KLF4, ATP8A1, and CPLX1.An integrative analysis focused on multi-tissue transcriptomics has not been done for asthma. Tissue-specific DEGs remain undetected in many multi-tissue analyses, which influences identification of disease-relevant pathways and potential drug candidates. Transcriptome data from 609 cases and 196 controls, generated using airway epithelium, bronchial, nasal, airway macrophages, distal lung fibroblasts, proximal lung fibroblasts, CD4+ lymphocytes, CD8+ lymphocytes from whole blood and induced sputum samples, were retrieved from Gene Expression Omnibus (GEO). Differentially regulated asthma-relevant genes identified from each sample type were used to identify (a) tissue-specific and tissue-shared asthma pathways, (b) their connection to GWAS-identified disease genes to identify candidate tissue for functional studies, (c) to select surrogate sample for invasive tissues, and finally (d) to identify potential drug candidates via connectivity map analysis. We found that inter-tissue similarity in gene expression wal biopsies. These genes were also associated with asthma in the GWAS catalog. Support Vector Machine showed that DEGs based on macrophages and epithelial cells have the highest and lowest discriminatory accuracy, respectively. Drug (entinostat, BMS-345541) and genetic perturbagens (KLF6, BCL10, INFB1 and BAMBI) negatively connected to disease at multi-tissue level could potentially repurposed for treating asthma. Collectively, our study indicates that the DEGs, perturbagens and disease are connected differentially depending on tissue/cell types. While most of the existing literature describes asthma transcriptome data from individual sample types, the present work demonstrates the utility of multi-tissue transcriptome data. Future studies should focus on collecting transcriptomic data from multiple tissues, age and race groups, genetic background, disease subtypes and on the availability of better-annotated data in the public domain.Plant breeding leads to the genetic improvement of target traits by selecting a small number of genotypes from among typically large numbers of candidate genotypes after careful evaluation. In this study, we first investigated how mutations at conserved nucleotide sites normally viewed as deleterious, such as nonsynonymous sites, accumulated in a wheat, Triticum aestivum, breeding lineage. By comparing a 150 year old ancestral and modern cultivar, we found recent nucleotide polymorphisms altered amino acids and occurred within conserved genes at frequencies expected in the absence of purifying selection. Mutations that are deleterious in other contexts likely had very small or no effects on target traits within the breeding lineage. Second, we investigated if breeders selected alleles with favorable effects on some traits and unfavorable effects on others and used different alleles to compensate for the latter. An analysis of a segregating population derived from the ancestral and modern parents provided one example of this phenomenon. The recent cultivar contains the Rht-B1b green revolution semi-dwarfing allele and compensatory alleles that reduce its negative effects. However, improvements in traits other than plant height were due to pleiotropic loci with favorable effects on traits and to favorable loci with no detectable pleiotropic effects. selleck inhibitor Wheat breeding appears to tolerate mutations at conserved nucleotide sites and to only select for alleles with both favorable and unfavorable effects on traits in exceptional situations.Ewen's sampling formula is a foundational theoretical result that connects probability and number theory with molecular genetics and molecular evolution; it was the analytical result required for testing the neutral theory of evolution, and has since been directly or indirectly utilized in a number of population genetics statistics. Ewen's sampling formula, in turn, is deeply connected to Stirling numbers of the first kind. Here, we explore the cumulative distribution function of these Stirling numbers, which enables a single direct estimate of the sum, using representations in terms of the incomplete beta function. This estimator enables an improved method for calculating an asymptotic estimate for one useful statistic, Fu's [Formula see text] By reducing the calculation from a sum of terms involving Stirling numbers to a single estimate, we simultaneously improve accuracy and dramatically increase speed.North Carolina's health care systems face dramatic risks from climate change that are here now and will last for decades, including disease outcomes and increased health risks that will disproportionately affect disadvantaged populations. At the local level, health care practitioners have to become advocates for climate mitigation; longer-term responses must involve society at all levels.The built environment is an important contributor to both climate change and public health. Transportation, land use, and buildings are three factors significantly impacting environmental and human health in urban areas. Health and built environment experts should actively collaborate to both cool cities and increase positive health outcomes.Emerging and endemic vector-borne diseases remain significant causes of morbidity and economic burden in North Carolina. Effective policies must promote climate change resilience through public health preparedness at local and regional scales to proactively address the diverse environmental, climatic, and demographic factors amplifying vector-borne disease risk.