Hinesehlers1733
The feed conversion ratio (FCR) is an important productive trait that greatly affects profits in the pig industry. Elucidating the genetic mechanisms underpinning FCR may promote more efficient improvement of FCR through artificial selection. In this study, we integrated a genome-wide association study (GWAS) with transcriptome analyses of different tissues in Yorkshire pigs (YY) with the aim of identifying key genes and signalling pathways associated with FCR.
A total of 61 significant single nucleotide polymorphisms (SNPs) were detected by GWAS in YY. All of these SNPs were located on porcine chromosome (SSC) 5, and the covered region was considered a quantitative trait locus (QTL) region for FCR. Some genes distributed around these significant SNPs were considered as candidates for regulating FCR, including TPH2, FAR2, IRAK3, YARS2, GRIP1, FRS2, CNOT2 and TRHDE. According to transcriptome analyses in the hypothalamus, TPH2 exhibits the potential to regulate intestinal motility through serotonergic synapse and oxytocin signalling pathways. In addition, GRIP1 may be involved in glutamatergic and GABAergic signalling pathways, which regulate FCR by affecting appetite in pigs. Moreover, GRIP1, FRS2, CNOT2, and TRHDE may regulate metabolism in various tissues through a thyroid hormone signalling pathway.
Based on the results from GWAS and transcriptome analyses, the TPH2, GRIP1, FRS2, TRHDE, and CNOT2 genes were considered candidate genes for regulating FCR in Yorkshire pigs. These findings improve the understanding of the genetic mechanisms of FCR and may help optimize the design of breeding schemes.
Based on the results from GWAS and transcriptome analyses, the TPH2, GRIP1, FRS2, TRHDE, and CNOT2 genes were considered candidate genes for regulating FCR in Yorkshire pigs. These findings improve the understanding of the genetic mechanisms of FCR and may help optimize the design of breeding schemes.
Most Distylium species are endangered. Distylium species mostly display homoplasy in their flowers and fruits, and are classified primarily based on leaf morphology. However, leaf size, shape, and serration vary tremendously making it difficult to use those characters to identify most species and a significant challenge to address the taxonomy of Distylium. To infer robust relationships and develop variable markers to identify Distylium species, we sequenced most of the Distylium species chloroplast genomes.
The Distylium chloroplast genome size was 159,041-159,127 bp and encoded 80 protein-coding, 30 transfer RNAs, and 4 ribosomal RNA genes. There was a conserved gene order and a typical quadripartite structure. Phylogenomic analysis based on whole chloroplast genome sequences yielded a highly resolved phylogenetic tree and formed a monophyletic group containing four Distylium clades. A dating analysis suggested that Distylium originated in the Oligocene (34.39 Ma) and diversified within approximately 1 Ma. The evidence shows that Distylium is a rapidly radiating group. Four highly variable markers, matK-trnK, ndhC-trnV, ycf1, and trnT-trnL, and 74 polymorphic simple sequence repeats were discovered in the Distylium plastomes.
The plastome sequences had sufficient polymorphic information to resolve phylogenetic relationships and identify Distylium species accurately.
The plastome sequences had sufficient polymorphic information to resolve phylogenetic relationships and identify Distylium species accurately.
Single-cell RNA sequencing (scRNA-seq) is the most widely used technique to obtain gene expression profiles from complex tissues. PF-05221304 inhibitor Cell subsets and developmental states are often identified via differential gene expression patterns. Most of the single-cell tools utilized highly variable genes to annotate cell subsets and states. However, we have discovered that a group of genes, which sensitively respond to environmental stimuli with high coefficients of variation (CV), might impose overwhelming influences on the cell type annotation.
In this research, we developed a method, based on the CV-rank and Shannon entropy, to identify these noise genes, and termed them as "sensitive genes". To validate the reliability of our methods, we applied our tools in 11 single-cell data sets from different human tissues. The results showed that most of the sensitive genes were enriched pathways related to cellular stress response. Furthermore, we noticed that the unsupervised result was closer to the ground-truth cell labebels. We hope our method would provide new insights into the reduction of data noise in scRNA-seq data analysis and contribute to the development of better scRNA-seq unsupervised clustering algorithms in the future.
Mutations in an enzyme target are one of the most common mechanisms whereby antibiotic resistance arises. Identification of the resistance mutations in bacteria is essential for understanding the structural basis of antibiotic resistance and design of new drugs. However, the traditionally used experimental approaches to identify resistance mutations were usually labor-intensive and costly.
We present a machine learning (ML)-based classifier for predicting rifampicin (Rif) resistance mutations in bacterial RNA Polymerase subunit β (RpoB). A total of 186 mutations were gathered from the literature for developing the classifier, using 80% of the data as the training set and the rest as the test set. The features of the mutated RpoB and their binding energies with Rif were calculated through computational methods, and used as the mutation attributes for modeling. Classifiers based on five ML algorithms, i.e. decision tree, k nearest neighbors, naïve Bayes, probabilistic neural network and support vector machine, were first built, and a majority consensus (MC) approach was then used to obtain a new classifier based on the classifications of the five individual ML algorithms. The MC classifier comprehensively improved the predictive performance, with accuracy, F-measure and AUC of 0.78, 0.83 and 0.81for training set whilst 0.84, 0.87 and 0.83 for test set, respectively.
The MC classifier provides an alternative methodology for rapid identification of resistance mutations in bacteria, which may help with early detection of antibiotic resistance and new drug discovery.
The MC classifier provides an alternative methodology for rapid identification of resistance mutations in bacteria, which may help with early detection of antibiotic resistance and new drug discovery.
