Cookbeasley5433

Egg production is an important economic trait in laying chickens as higher yields bring higher profits. Small yellow follicle (SYFL) development is a key determinant of chicken reproductive performance; however, the majority of SYFLs are not selected during the process of chicken reproduction and thus, atresia occurs. Although there have been numerous omic studies focused on egg production, the molecular mechanisms involved are still not well-understood. In this study, we used high-throughput technology to analyze the differences between the SYFL mRNA transcriptomes of high- (H) and low-egg-yielding (L) Taihang layer hens, with the aim of identifying the potential candidate genes involved in controlling the rate of egg production. We constructed six cDNA libraries, three from H and three from L Taihang hens and then performed high-throughput sequencing. Comparison of the H and L groups showed 415 differentially expressed genes (DEGs). In the high-yield group, 226 were upregulated and 189 were downregulated. Dnd low egg-yielding chickens. Luciferase assays showed that miR-302a-3p targets the 3'UTR of DRD1, and overexpression of miR-302a-3p significantly inhibits the expression of DRD1 in chicken GCs (p less then 0.01). Functional experiments revealed that by targeting DRD1, miR-302a-3p acts as an inhibitor of GC proliferation. Taken together, we concluded that miR-302a-3p affects chicken GC proliferation by targeting DRD1. Our data expanded the knowledge base of genes whose functions are important in egg production and the molecular mechanisms of high-yield egg production in chicken small yellow follicles.Short read 16 S rRNA amplicon sequencing is a common technique used in microbiome research. However, inaccuracies in estimated bacterial community composition can occur due to amplification bias of the targeted hypervariable region. A potential solution is to sequence and assess multiple hypervariable regions in tandem, yet there is currently no consensus as to the appropriate method for analyzing this data. Additionally, there are many sequence analysis resources for data produced from the Illumina platform, but fewer open-source options available for data from the Ion Torrent platform. Herein, we present an analysis pipeline using open-source analysis platforms that integrates data from multiple hypervariable regions and is compatible with data produced from the Ion Torrent platform. We used the ThermoFisher Ion 16 S Metagenomics Kit and a mock community of twenty bacterial strains to assess taxonomic classification of six amplicons from separate hypervariable regions (V2, V3, V4, V6-7, V8, V9) using our analysis pipeline. We report that different amplicons have different specificities for taxonomic classification, which also has implications for global level analyses such as alpha and beta diversity. Finally, we utilize a generalized linear modeling approach to statistically integrate the results from multiple hypervariable regions and apply this methodology to data from a representative clinical cohort. We conclude that examining sequencing results across multiple hypervariable regions provides more taxonomic information than sequencing across a single region. The data across multiple hypervariable regions can be combined using generalized linear models to enhance the statistical evaluation of overall differences in community structure and relatedness among sample groups.Background Expression of the long noncoding RNA (lncRNA) HOXA11-AS significantly increased in keloids by unclarified molecular regulation mechanisms. Methods Using successfully primary cultured keloid-derived fibroblasts from central region of chronic keloid tissues (sample 0), small interfering RNAs were designed and transfected into two keloid fibroblast samples (samples 1 and 2) to knockdown HOXA11-AS. One nonspecific transfection control (sample 3) and one blank control (sample 4) were used to remove nonspecific overlap from the studied group. The lncRNAs, messenger RNAs (mRNAs), and microRNAs (miRNAs) of five samples were sequenced to identify differentially expressed (DE) profiles in HOXA11-AS-knockdown keloid fibroblasts in samples 1 and 2 (by intersection), which facilitated removal of overlap with the nonspecific controls (samples 3 and 4, by union). Using stepwise bioinformatic analysis, a HOXA11-AS-interacted competing endogenous network (ceRNA) was screened based on three DE profiles. Results Keloous miRNAs (hsa-miRNA-19a-3p, hsa-miR-141-3p, and hsa-miR-140-5p). Conclusion An interactive network of HOXA11-AS-three miRNAs-NIPAL3 was predicted in keloid fibroblasts by integrative bioinformatic analysis and in vitro validation.Phthalates are a diverse group of chemicals used in consumer products. Because they are so widespread, exposure to these compounds is nearly unavoidable. Recently, growing scientific consensus has suggested that phthalates produce health effects in developing infants and children. These effects may be mediated through mechanisms related to the epigenome, the constellation of mitotically heritable chemical marks and small compounds that guide transcription and translation. The present study examined the relationship between prenatal, first-trimester exposure of seven phthalates and epigenetics in two pregnancy cohorts (n = 262) to investigate sex-specific alterations in infant blood DNA methylation at birth (cord blood or neonatal blood spots). Prenatal exposure to several phthalates was suggestive of association with altered DNA methylation at 4 loci in males (all related to ΣDEHP) and 4 loci in females (1 related to ΣDiNP; 2 related to BBzP; and 1 related to MCPP) at a cutoff of q less then 0.2. Additionally, a subset of dyads (n = 79) was used to interrogate the relationships between two compounds increasingly used as substitutions for common phthalates (ΣDINCH and ΣDEHTP) and cord blood DNA methylation. ΣDINCH, but not ΣDEHTP, was suggestive of association with DNA methylation (q less then 0.2). Together, these results demonstrate that prenatal exposure to both classically used phthalate metabolites and their newer alternatives is associated with sex-specific infant DNA methylation. Research and regulatory actions regarding this chemical class should consider the developmental health effects of these compounds and aim to avoid regrettable substitution scenarios in the present and future.Patients with inflammatory bowel disease (IBD), including ulcerative colitis and Crohn's disease, are at higher risk to develop colorectal cancer (CRC). However, the underlying mechanisms of this predisposition remain elusive. find more We performed in-depth comparative computational analyses to gain new insights, including weighted gene co-expression network analysis (WGCNA) and gene ontology and pathway enrichment analyses, using gene expression datasets from IBD and CRC patients. When individually comparing IBD and CRC to normal control samples, we identified clusters of highly correlated genes, differentially expressed genes, and module-trait associations specific for each disease. When comparing IBD to CRC, we identified common hub genes and commonly enriched pathways. Most notably, IBD and CRC share significantly increased expression of five genes (MMP10, LCN2, REG1A, REG3A, and DUOX2), enriched inflammatory and neutrophil activation pathways and, most notably, highly significant enrichment of IL-4 and IL-13 signaling. Thus, our work expands our knowledge about the intricate relationship between IBD and CRC development and provides new rationales for developing novel therapeutic strategies.Background Classification and annotation of enzyme proteins are fundamental for enzyme research on biological metabolism. Enzyme Commission (EC) numbers provide a standard for hierarchical enzyme class prediction, on which several computational methods have been proposed. However, most of these methods are dependent on prior distribution information and none explicitly quantifies amino-acid-level relations and possible contribution of sub-sequences. Methods In this study, we propose a double-scale attention enzyme class prediction model named DAttProt with high reusability and interpretability. DAttProt encodes sequence by self-supervised Transformer encoders in pre-training and gathers local features by multi-scale convolutions in fine-tuning. Specially, a probabilistic double-scale attention weight matrix is designed to aggregate multi-scale features and positional prediction scores. Finally, a full connection linear classifier conducts a final inference through the aggregated features and prediction scores. Results On DEEPre and ECPred datasets, DAttProt performs as competitive with the compared methods on level 0 and outperforms them on deeper task levels, reaching 0.788 accuracy on level 2 of DEEPre and 0.967 macro-F 1 on level 1 of ECPred. Moreover, through case study, we demonstrate that the double-scale attention matrix learns to discover and focus on the positions and scales of bio-functional sub-sequences in the protein. Conclusion Our DAttProt provides an effective and interpretable method for enzyme class prediction. It can predict enzyme protein classes accurately and furthermore discover enzymatic functional sub-sequences such as protein motifs from both positional and spatial scales.High NUE (nitrogen use efficiency) has great practical significance for sustainable crop production. Wheat is one of the main cultivated crops worldwide for human food and nutrition. However, wheat grain productivity is dependent upon cultivars with high NUE in addition to the application of nitrogen fertilizers. In order to understand the molecular mechanisms exhibiting a high NUE response, a comparative transcriptomics study was carried out through RNA-seq analysis to investigate the gene expression that regulates NUE, in root and shoot tissue of N-efficient (PBW677) and N-inefficient (703) cultivars under optimum and nitrogen (N) stress. Differentially expressed gene analysis revealed a total of 2,406 differentially expressed genes (DEGs) present in both the contrasting cultivars under N stress. The efficient genotype PBW677 had considerably more abundant DEGs with 1,653 (903 roots +750 shoots) compared to inefficient cultivar PBW703 with 753 (96 roots +657 shoots). Gene ontology enrichment and pathway anaort 13 potential candidate genes which showed alternate gene expression in the two contrasting cultivars under study. These genes could serve as potential targets for future breeding programs.Asthma is among the most common chronic diseases worldwide, creating a substantial healthcare burden. In late-onset asthma, there are wide global differences in asthma prevalence and low genetic heritability. It has been suggested as evidence for genetic susceptibility to asthma triggered by exposure to multiple environmental factors. Very few genome-wide interaction studies have identified gene-environment (G×E) interaction loci for asthma in adults. We evaluated genetic loci for late-onset asthma showing G×E interactions with multiple environmental factors, including alcohol intake, body mass index, insomnia, physical activity, mental status, sedentary behavior, and socioeconomic status. In gene-by-single environment interactions, we found no genome-wide significant single-nucleotide polymorphisms. However, in the gene-by-multi-environment interaction study, we identified three novel and genome-wide significant single-nucleotide polymorphisms rs117996675, rs345749, and rs17704680. Bayes factor analysis suggested that for rs117996675 and rs17704680, body mass index is the most relevant environmental factor; for rs345749, insomnia and alcohol intake frequency are the most relevant factors in the G×E interactions of late-onset asthma.