Prioritizing single-nucleotide polymorphisms and variants associated with clinical mastitis

Adjustment of p-value expression to ontology using machine learning for genetic prediction, prioritization, interaction, and its validation in glomerular disease

The results of gene expression analysis based on p-value can be extracted and sorted by their absolute statistical significance and then applied to multiple similarity scores of their gene ontology (GO) terms to promote the combination and adjustment of these scores as essential predictive tasks for understanding biological/clinical pathways. The latter allows the possibility to assess whether certain aspects of gene function may be associated with other varieties of genes, to evaluate regulation, and to link them into networks that prioritize candidate genes for classification by applying machine learning techniques. We then detect significant genetic interactions based on our algorithm to validate the results. Finally, based on specifically selected tissues according to their normalized gene expression and frequencies of occurrence from their different biological and clinical inputs, a reported classification of genes under the subject category has validated the abstract (glomerular diseases) as a case study.

Emerging Roles of Noncoding RNAs in Bovine Mastitis Diseases

Non-coding RNAs (ncRNAs) are an abundant class of RNA with varying nucleotide lengths. They have been shown to have great potential in eutherians/human disease diagnosis and treatments and are now gaining more importance for the improvement of diseases in livestock. To date, thousands of ncRNAs have been discovered in the bovine genome and the continuous advancement in deep sequencing technologies and various bioinformatics tools has enabled the elucidation of their roles in bovine health. Among farm animals' diseases, mastitis, a common inflammatory disease in cattle, has caused devastating economic losses to dairy farmers over the last few decades. Here, we summarize the biology of bovine mastitis and comprehensively discuss the roles of ncRNAs in different types of mastitis infection. Based on our findings and relevant literature, we highlighted various evidence of ncRNA roles in mastitis. Different approaches (in vivo versus in vitro) for exploring ncRNA roles in mastitis are emphasized. More particularly, the potential applications of emerging genome editing technologies, as well as integrated omics platforms for ncRNA studies and implications for mastitis are presented.

SNP prioritization in targeted sequencing data associated with humoral immune responses in chicken

Our study aimed to identify single nucleotide polymorphisms (SNPs) with a significant impact on the innate immunity represented by antibody response against lipopolysaccharide (LPS) and lipoteichoid acid (LTA) and the adaptive immune response represented toward keyhole limpet hemocyanin (KLH) using the SNP prioritization method. Data set consisted of 288 F2 experimental individuals, created by crossing Green-legged Partridgelike and White Leghorn. The analyzed SNPs were located within 24 short genomic regions of GGA1, GGA2, GGA3, GGA4, GGA9, GGA10, GGA14, GGA18, and GGZ, pre-targeted based on literature references and database information. For the specific antibody response toward KLH at d 0 the most highly prioritized SNP for additive and dominance effects were located on GGA2 in the 3’UTR of MYD88. For the response at d 7, the most highly prioritized SNP pointed at the 3’UTR of MYD88, but potential causal additive variants were located within ADIPOQ and one in PROCR. The highest priority for additive and dominance effects in the antibody response toward lipoteichoic acid at d 0 was attributed to the same SNP, located on GGA2 in the 3’UTR region of MYD88. Two SNPs among the top-10 for additive effect were located in the exon of NOCT. SNPs selected for their additive effect on antibody response toward lipopolysaccharide at d 0 marked 3 genes – NOCT, MYD88, and SNX8, while SNPs selected for their dominance effect marked – NOCT, ADIPOQ, and MYD88. The top-10 variants identified in our study were located in different functional parts of the genome. In the context of causality three groups can be distinguished: variants located in exons of protein coding genes (ADIPOQ, NOCT, PROCR, SNX8), variants within exons of non-coding transcripts, and variants located in genes’ UTR regions. Variants from the first group influence protein structure and variants from both latter groups’ exhibit regulatory roles on DNA (UTR) or RNA (lncRNA).

Genome-Wide DNA Methylation Analysis of Mammary Gland Tissues From Chinese Holstein Cows With Staphylococcus aureus Induced Mastitis

Staphylococcus aureus intramammary infection is one of the most common causes of chronic mastitis in dairy cows, whose development may be associated with epigenetic changes in the expression of important host defense genes. This study aimed to construct a genome-wide DNA methylation profile of the mammary gland of Chinese Holstein cows (n = 3) following experimentally induced S. aureus mastitis, and to explore the potential gene regulatory mechanisms affected by DNA methylation during S. aureus mastitis. DNA was extracted from S. aureus-positive (n = 3) and S. aureus-negative (n = 3) mammary gland quarters and subjected to methylation-dependent restriction-site associated DNA sequencing (Methyl-RAD Seq). Results showed that CmCGG/CmCWGG DNA methylation sites were unevenly distributed and concentrated on chromosomes 5, 11, and 19, and within intergenic regions and intron regions of genes. Compared with healthy control quarters, 9,181 significantly differentially methylated (DM) CmCGG sites and 1,790 DM CmCWGG sites were found in the S. aureus-positive quarters (P < 0.05, |log2FC| > 1). Furthermore, 363 CmCGG differently methylated genes (DMGs) and 301 CmCWGG DMGs (adjusted P < 0.05, |log2FC| > 1) were identified. Gene ontology and KEGG enrichment analysis indicated that CmCGG DMGs are involved in immune response pathways, while the CmCWGG DMGs were mainly enriched in gene ontology terms related to metabolism. The mRNAs of 526 differentially methylated CmCGG genes and 124 differentially methylated CmCWGG genes were also significantly differentially expressed (RNA-Seq data) in the same samples, herein denoted differentially methylated and expressed genes (DMEGs) (P < 0.05). Functional enrichment analysis of DMEGs revealed roles related to biological processes, especially the regulation of immune response to diseases. CmCGG DMEGs like IL6R, TNF, BTK, IL1R2, and TNFSF8 enriched in several immune-related GO terms and pathways indicated their important roles in host immune response and their potential as candidate genes for S. aureus mastitis. These results suggest potential regulatory roles for DNA methylation in bovine mammary gland processes during S. aureus mastitis and serves as a reference for future epigenetic regulation and mechanistic studies.