A Genome-Wide Association Study for Clinical Mastitis in First Parity US Holstein Cows Using Single-Step Approach and Genomic Matrix Re-Weighting Procedure

Clinical mastitis (CM) is one of the health disorders with large impacts on dairy farming profitability and animal welfare. The objective of this study was to perform a genome-wide association study (GWAS) for CM in first-lactation Holstein. Producer-recorded mastitis event information for 103,585 first-lactation cows were used, together with genotype information on 1,361 bulls from the Illumina BovineSNP50 BeadChip. Single-step genomic-BLUP methodology was used to incorporate genomic data into a threshold-liability model. Association analysis confirmed that CM follows a highly polygenic mode of inheritance. However, 10-adjacent-SNP windows showed that regions on chromosomes 2, 14 and 20 have impacts on genetic variation for CM. Some of the genes located on chromosome 14 (LY6K, LY6D, LYNX1, LYPD2, SLURP1, PSCA) are part of the lymphocyte-antigen-6 complex (LY6) known for its neutrophil regulation function linked to the major histocompatibility complex. Other genes on chromosome 2 were also involved in regulating immune response (IFIH1, LY75, and DPP4), or are themselves regulated in the presence of specific pathogens (ITGB6, NR4A2). Other genes annotated on chromosome 20 are involved in mammary gland metabolism (GHR, OXCT1), antibody production and phagocytosis of bacterial cells (C6, C7, C9, C1QTNF3), tumor suppression (DAB2), involution of mammary epithelium (OSMR) and cytokine regulation (PRLR). DAVID enrichment analysis revealed 5 KEGG pathways. The JAK-STAT signaling pathway (cell proliferation and apoptosis) and the ‘Cytokine-cytokine receptor interaction’ (cytokine and interleukines response to infectious agents) are co-regulated and linked to the ‘ABC transporters’ pathway also found here. Gene network analysis performed using GeneMania revealed a co-expression network where 665 interactions existed among 145 of the genes reported above. Clinical mastitis is a complex trait and the different genes regulating immune response are known to be pathogen-specific. Despite the lack of information in this study, candidate QTL for CM were identified in the US Holstein population.     

The Evolution and Origin of Animal Toll-Like Receptor Signaling Pathway Revealed by Network-Level Molecular Evolutionary Analyses

Genes carry out their biological functions through pathways in complex networks consisting of many interacting molecules. Studies on the effect of network architecture on the evolution of individual proteins will provide valuable information for understanding the origin and evolution as well as functional conservation of signaling pathways. However, the relationship between the network architecture and the individual protein sequence evolution is yet little known. In current study, we carried out network-level molecular evolution analysis on TLR (Toll-like receptor ) signaling pathway, which plays an important role in innate immunity in insects and mammals, and we found that: 1) The selection constraint of genes was negatively correlated with its position along TLR signaling pathway; 2) all genes in TLR signaling pathway were highly conserved and underwent strong purifying selection; 3) the distribution of selective pressure along the pathway was driven by differential nonsynonymous substitution levels; 4) The TLR signaling pathway might present in a common ancestor of sponges and eumetazoa, and evolve via the TLR, IKK, IκB and NF-κB genes underwent duplication events as well as adaptor molecular enlargement, and gene structure and conservation motif of NF-κB genes shifted in their evolutionary history. Our results will improve our understanding on the evolutionary history of animal TLR signaling pathway as well as the relationship between the network architecture and the sequences evolution of individual protein.     

Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action

A drug exerts its effects typically through a signal transduction cascade, which is non-linear and involves intertwined networks of multiple signaling pathways. Construction of such a signaling pathway network (SPNetwork) can enable identification of novel drug targets and deep understanding of drug action. However, it is challenging to synopsize critical components of these interwoven pathways into one network. To tackle this issue, we developed a novel computational framework, the Drug-specific Signaling Pathway Network (DSPathNet). The DSPathNet amalgamates the prior drug knowledge and drug-induced gene expression via random walk algorithms. Using the drug metformin, we illustrated this framework and obtained one metformin-specific SPNetwork containing 477 nodes and 1,366 edges. To evaluate this network, we performed the gene set enrichment analysis using the disease genes of type 2 diabetes (T2D) and cancer, one T2D genome-wide association study (GWAS) dataset, three cancer GWAS datasets, and one GWAS dataset of cancer patients with T2D on metformin. The results showed that the metformin network was significantly enriched with disease genes for both T2D and cancer, and that the network also included genes that may be associated with metformin-associated cancer survival. Furthermore, from the metformin SPNetwork and common genes to T2D and cancer, we generated a subnetwork to highlight the molecule crosstalk between T2D and cancer. The follow-up network analyses and literature mining revealed that seven genes (CDKN1A, ESR1, MAX, MYC, PPARGC1A, SP1, and STK11) and one novel MYC-centered pathway with CDKN1A, SP1, and STK11 might play important roles in metformin’s antidiabetic and anticancer effects. Some results are supported by previous studies. In summary, our study 1) develops a novel framework to construct drug-specific signal transduction networks; 2) provides insights into the molecular mode of metformin; 3) serves a model for exploring signaling pathways to facilitate understanding of drug action, disease pathogenesis, and identification of drug targets.     

A Forward-Genetic Screen and Dynamic Analysis of Lambda Phage Host-Dependencies Reveals an Extensive Interaction Network and a New Anti-Viral Strategy

Latently infecting viruses are an important class of virus that plays a key role in viral evolution and human health. Here we report a genome-scale forward-genetics screen for host-dependencies of the latently-infecting bacteriophage lambda. This screen identified 57 Escherichia coli (E. coli) genes—over half of which have not been previously associated with infection—that when knocked out inhibited lambda phage''s ability to replicate. Our results demonstrate a highly integrated network between lambda and its host, in striking contrast to the results from a similar screen using the lytic-only infecting T7 virus. We then measured the growth of E. coli under normal and infected conditions, using wild-type and knockout strains deficient in one of the identified host genes, and found that genes from the same pathway often exhibited similar growth dynamics. This observation, combined with further computational and experimental analysis, led us to identify a previously unannotated gene, yneJ, as a novel regulator of lamB gene expression. A surprising result of this work was the identification of two highly conserved pathways involved in tRNA thiolation—one pathway is required for efficient lambda replication, while the other has anti-viral properties inhibiting lambda replication. Based on our data, it appears that 2-thiouridine modification of tRNA^Glu, tRNA^Gln, and tRNA^Lys is particularly important for the efficient production of infectious lambda phage particles.     

Genome Assembly Anchored QTL Map of Bovine Chromosome 14

Bovine chromosome 14 (BTA14) has been widely explored for quantitative trait loci (QTL) and genes related to economically important traits in both dairy and beef cattle. We reviewed more than 40 investigations and anchored 126 QTL to the current genome assembly (Btau 4_0). Using this anchored QTL map, we observed that, in dairy cattle, the region spanning 0 – 10 Mb on BTA14 has the highest density QTL map with a total of 56 QTL, mainly for milk production traits. It is very likely that both somatic cell score (SCS) and clinical mastitis share some common QTL in two regions: 61.48 Mb - 73.84 Mb and 7.86 Mb – 39.55 Mb, respectively. As well, both ovulation rate and twinning rate might share a common QTL region from 34.16 Mb to 65.38 Mb. However, there are no common QTL locations in three pregnancy related phenotypes: non-return rate, pregnancy rate and daughter pregnancy rate. In beef cattle, the majority of QTL are located in a broad region of 15 Mb – 45 Mb on the chromosome. Functional genes, such as CRH, CYP11B1, DGAT1, FABP4 and TG, as potential candidates for some of these QTL, were also reviewed. Therefore, our review provides a standardized QTL map anchored within the current genome assembly, which would enhance the process of selecting positional and physiological candidate genes for many important traits in cattle.     

Whole-genome resequencing reveals selection signatures associated with milk production traits in African Kenana dairy zebu cattle

Milk production and composition are the most economically important traits affecting profitability in dairy cattle. In this study, we aimed at detecting signatures of positive selection in Kenana, known as one of the high milk production African indigenous zebu cattle, using next-generation sequencing data. To detect genomic signatures of positive selection, we applied three methods based on population comparison, fixation index (F_ST), cross population composite likelihood ratio (XP-CLR) and nucleotide diversity (Pi). Further analysis showed that several candidate genes such as CSN3, IGFBP-2, RORA, ABCG2, B4GALT1 and GHR are positively selected for milk production traits in Kenana cattle. The candidate genes and enriched pathways identified in this study may provide a basis for future genome-wide association studies and investigations into genomic targets of selection in dairy cattle.     

Identification of splice variants,expression analysis and single nucleotide polymorphisms of the PRMT2 gene in dairy cattle

Protein arginine N-methyltransferase 2 (PRMT2), also named HRMT1L1, belongs to the Bovine Protein arginine N-methyltransferase (PRMT) genes which are involved in the immune response. To explore the variability of the PRMT2 gene and resistance to mastitis in cows, splice variant (SV), and single nucleotide polymorphisms (SNPs) were identified in this study. A SV (PRMT2-SV) lacking exon 7 (98-bp) of the PRMT2 gene was found in healthy and mastitis-infected mammary gland tissues. Two of four SNPs were significantly associated with bovine milk yield and protein content. Further, we estimated the relative expression of PRMT2-SV in the mammary gland tissue of dairy cattle by using quantitative real-time polymerase chain reaction. The result showed that expression of the PRMT2-SV mRNA was significantly upregulated 4.02-fold (p < 0.05) in infected mammary tissues (n = 5) compared to healthy tissues (n = 5). Our findings reveal that PRMT2-SV may play an important role in mastitis resistance in dairy cattle. The SNPs may be used as a possible candidate SNPs for marker-assisted selection and management in Chinese Holstein cattle.     

Lactic Acid Bacteria Isolated from Bovine Mammary Microbiota: Potential Allies against Bovine Mastitis

Bovine mastitis is a costly disease in dairy cattle worldwide. As of yet, the control of bovine mastitis is mostly based on prevention by thorough hygienic procedures during milking. Additional strategies include vaccination and utilization of antibiotics. Despite these measures, mastitis is not fully under control, thus prompting the need for alternative strategies. The goal of this study was to isolate autochthonous lactic acid bacteria (LAB) from bovine mammary microbiota that exhibit beneficial properties that could be used for mastitis prevention and/or treatment. Sampling of the teat canal led to the isolation of 165 isolates, among which a selection of ten non-redundant LAB strains belonging to the genera Lactobacillus and Lactococcus were further characterized with regard to several properties: surface properties (hydrophobicity, autoaggregation); inhibition potential of three main mastitis pathogens, Staphylococcus aureus, Escherichia coli and Streptococcus uberis; colonization capacities of bovine mammary epithelial cells (bMEC); and immunomodulation properties. Three strains, Lactobacillus brevis 1595 and 1597 and Lactobacillus plantarum 1610, showed high colonization capacities and a medium surface hydrophobicity. These strains are good candidates to compete with pathogens for mammary gland colonization. Moreover, nine strains exhibited anti-inflammatory properties, as illustrated by the lower IL-8 secretion by E. coli-stimulated bMEC in the presence of these LAB. Full genome sequencing of five candidate strains allowed to check for undesirable genetic elements such as antibiotic resistance genes and to identify potential bacterial determinants involved in the beneficial properties. This large screening of beneficial properties while checking for undesirable genetic markers allowed the selection of promising candidate LAB strains from bovine mammary microbiota for the prevention and/or treatment of bovine mastitis.     

Genes of the RNASE5 pathway contain SNP associated with milk production traits in dairy cattle

Background

Identification of the processes and mutations responsible for the large genetic variation in milk production among dairy cattle has proved challenging. One approach is to identify a biological process potentially involved in milk production and to determine the genetic influence of all the genes included in the process or pathway. Angiogenin encoded by angiogenin, ribonuclease, RNase A family 5 (RNASE5) is relatively abundant in milk, and has been shown to regulate protein synthesis and act as a growth factor in epithelial cells in vitro. However, little is known about the role of angiogenin in the mammary gland or if the polymorphisms present in the bovine RNASE5 gene are associated with lactation and milk production traits in dairy cattle. Given the high economic value of increased protein in milk, we have tested the hypothesis that RNASE5 or genes in the RNASE5 pathway are associated with milk production traits. First, we constructed a “RNASE5 pathway” based on upstream and downstream interacting genes reported in the literature. We then tested SNP in close proximity to the genes of this pathway for association with milk production traits in a large dairy cattle dataset.

Results

The constructed RNASE5 pathway consisted of 11 genes. Association analysis between SNP in 1 Mb regions surrounding these genes and milk production traits revealed that more SNP than expected by chance were associated with milk protein percent (P < 0.05 significance). There was no significant association with other traits such as milk fat content or fertility.

Conclusions

These results support a role for the RNASE5 pathway in milk production, specifically milk protein percent, and indicate that polymorphisms in or near these genes explain a proportion of the variation for this trait. This method provides a novel way of understanding the underlying biology of lactation with implications for milk production and can be applied to any pathway or gene set to test whether they are responsible for the variation of complex traits.     

Integrated Enrichment Analysis of Variants and Pathways in Genome-Wide Association Studies Indicates Central Role for IL-2 Signaling Genes in Type 1 Diabetes,and Cytokine Signaling Genes in Crohn's Disease

Pathway analyses of genome-wide association studies aggregate information over sets of related genes, such as genes in common pathways, to identify gene sets that are enriched for variants associated with disease. We develop a model-based approach to pathway analysis, and apply this approach to data from the Wellcome Trust Case Control Consortium (WTCCC) studies. Our method offers several benefits over existing approaches. First, our method not only interrogates pathways for enrichment of disease associations, but also estimates the level of enrichment, which yields a coherent way to promote variants in enriched pathways, enhancing discovery of genes underlying disease. Second, our approach allows for multiple enriched pathways, a feature that leads to novel findings in two diseases where the major histocompatibility complex (MHC) is a major determinant of disease susceptibility. Third, by modeling disease as the combined effect of multiple markers, our method automatically accounts for linkage disequilibrium among variants. Interrogation of pathways from eight pathway databases yields strong support for enriched pathways, indicating links between Crohn''s disease (CD) and cytokine-driven networks that modulate immune responses; between rheumatoid arthritis (RA) and “Measles” pathway genes involved in immune responses triggered by measles infection; and between type 1 diabetes (T1D) and IL2-mediated signaling genes. Prioritizing variants in these enriched pathways yields many additional putative disease associations compared to analyses without enrichment. For CD and RA, 7 of 8 additional non-MHC associations are corroborated by other studies, providing validation for our approach. For T1D, prioritization of IL-2 signaling genes yields strong evidence for 7 additional non-MHC candidate disease loci, as well as suggestive evidence for several more. Of the 7 strongest associations, 4 are validated by other studies, and 3 (near IL-2 signaling genes RAF1, MAPK14, and FYN) constitute novel putative T1D loci for further study.     

Pathway Network Analyses for Autism Reveal Multisystem Involvement,Major Overlaps with Other Diseases and Convergence upon MAPK and Calcium Signaling

We used established databases in standard ways to systematically characterize gene ontologies, pathways and functional linkages in the large set of genes now associated with autism spectrum disorders (ASDs). These conditions are particularly challenging—they lack clear pathognomonic biological markers, they involve great heterogeneity across multiple levels (genes, systemic biological and brain characteristics, and nuances of behavioral manifestations)—and yet everyone with this diagnosis meets the same defining behavioral criteria. Using the human gene list from Simons Foundation Autism Research Initiative (SFARI) we performed gene set enrichment analysis with the Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Database, and then derived a pathway network from pathway-pathway functional interactions again in reference to KEGG. Through identifying the GO (Gene Ontology) groups in which SFARI genes were enriched, mapping the coherence between pathways and GO groups, and ranking the relative strengths of representation of pathway network components, we 1) identified 10 disease-associated and 30 function-associated pathways 2) revealed calcium signaling pathway and neuroactive ligand-receptor interaction as the most enriched, statistically significant pathways from the enrichment analysis, 3) showed calcium signaling pathways and MAPK signaling pathway to be interactive hubs with other pathways and also to be involved with pervasively present biological processes, 4) found convergent indications that the process “calcium-PRC (protein kinase C)-Ras-Raf-MAPK/ERK” is likely a major contributor to ASD pathophysiology, and 5) noted that perturbations associated with KEGG’s category of environmental information processing were common. These findings support the idea that ASD-associated genes may contribute not only to core features of ASD themselves but also to vulnerability to other chronic and systemic problems potentially including cancer, metabolic conditions and heart diseases. ASDs may thus arise, or emerge, from underlying vulnerabilities related to pleiotropic genes associated with pervasively important molecular mechanisms, vulnerability to environmental input and multiple systemic co-morbidities.     

Massively Parallel Signature Sequencing and Bioinformatics Analysis Identifies Up-Regulation of TGFBI and SOX4 in Human Glioblastoma

Background

A comprehensive network-based understanding of molecular pathways abnormally altered in glioblastoma multiforme (GBM) is essential for developing effective therapeutic approaches for this deadly disease.

Methodology/Principal Findings

Applying a next generation sequencing technology, massively parallel signature sequencing (MPSS), we identified a total of 4535 genes that are differentially expressed between normal brain and GBM tissue. The expression changes of three up-regulated genes, CHI3L1, CHI3L2, and FOXM1, and two down-regulated genes, neurogranin and L1CAM, were confirmed by quantitative PCR. Pathway analysis revealed that TGF- β pathway related genes were significantly up-regulated in GBM tumor samples. An integrative pathway analysis of the TGF β signaling network identified two alternative TGF−β signaling pathways mediated by SOX4 (sex determining region Y-box 4) and TGFBI (Transforming growth factor beta induced). Quantitative RT-PCR and immunohistochemistry staining demonstrated that SOX4 and TGFBI expression is elevated in GBM tissues compared with normal brain tissues at both the RNA and protein levels. In vitro functional studies confirmed that TGFBI and SOX4 expression is increased by TGF- β stimulation and decreased by a specific inhibitor of TGF- β receptor 1 kinase.

Conclusions/Significance

Our MPSS database for GBM and normal brain tissues provides a useful resource for the scientific community. The identification of non-SMAD mediated TGF−β signaling pathways acting through SOX4 and TGFBI (GENE ID:7045) in GBM indicates that these alternative pathways should be considered, in addition to the canonical SMAD mediated pathway, in the development of new therapeutic strategies targeting TGF−β signaling in GBM. Finally, the construction of an extended TGF- β signaling network with overlaid gene expression changes between GBM and normal brain extends our understanding of the biology of GBM.     

Whole-genome analysis of Klebsiella pneumoniae from bovine mastitis milk in the U.S.

Dairy cattle mastitis has long been one of the most common and costly diseases in the dairy industry worldwide, due to its significant impact on milk production and animal welfare. Among all mastitis causing bacterial pathogens, Klebsiella pneumoniae causes the largest milk loss. To better understand the genomic features of this population, 180 K. pneumoniae strains isolated from dairy cattle mastitis milk in 11 U.S. states were sequenced. The phylogenetic analysis classified all mastitis-causing K. pneumoniae into two major phylogroups, with exclusive predominance in phylogroup KpI. Analysis of more than 61 sequence types, 51 capsular types and 12 lipopolysaccharide O-antigen types revealed great genomic diversity of this K. pneumoniae population. Approximately 100 gene units in accessory genomes were detected with significantly higher prevalence in bovine mastitis strains, compared to human-sourced or dairy environmental strains. The most notable genes were identified associated with ferric citrate uptake, lactose fermentation and resistance to heavy metals. The acquired antimicrobial resistance genes were identified in sporadic mastitis strains. This comprehensive genomic epidemiological study provides insights for a better understanding of the virulence of mastitis-causing K. pneumoniae strains and may lead to the development of novel diagnostic tools and preventive strategies.