Temporal analysis of the bovine lymph node transcriptome during cattle tick (Rhipicephalus microplus) infestation

Livestock production is a fundamental source of revenue and nutrition, wherein cattle-farming constitutes one of the major agricultural industries. Vectors and vector-borne diseases constitute one of the major factors that decrease the livelihood of all farming communities, more so in resource-poor communities and developing countries. Understanding the immunological responses during tick infestation in cattle is instrumental in the development of novel and improved tick control strategies, such as vaccines. In this study, gene expression patterns were compared within the lymph nodes of three cattle breeds at different life stages of the cattle tick, Rhipicephalus microplus. For Bonsmara (5/8Bos taurus indicus × 3/8B. t. taurus) cattle specifically, some 183 genes were found to be differentially expressed within the lymph nodes during larval and adult tick feeding, relative to uninfested cattle. Overall, the data provides evidence for a transcriptional regulatory network that is activated during immature tick infestation, but is down-regulated towards basal transcriptional levels when adult ticks are feeding. Specific processes in the lymph nodes of Bonsmara cattle were found to be differentially regulated on a transcriptional level. These include: (1) Leukocyte recruitment to the lymph node via chemokines and chemotaxis, (2) Trans-endothelial and intranodal movement on the reticular network, (3) Active regulation of cellular transcription and translation in the lymph node (including leukocyte associated cellular regulatory networks) and (4) Chemokine receptors regulating the movement of cells out of the lymph node. This work provides a first transcriptome analysis of bovine lymph node responses in tick-infested cattle. Findings show a dynamic immune response to tick infestation for the Bonsmara cattle breed, and that suppression of the maturation of the cattle hosts' immunity is especially evident during the larval feeding stages.

Transformation of animal genomics by next-generation sequencing technologies: a decade of challenges and their impact on genetic architecture

For more than a quarter of a century, sequencing technologies from Sanger's method to next-generation high-throughput techniques have provided fascinating opportunities in the life sciences. The continuing upward trajectory of sequencing technologies will improve livestock research and expedite the development of various new genomic and technological studies with farm animals. The use of high-throughput technologies in livestock research has increased interest in metagenomics, epigenetics, genome-wide association studies, and identification of single nucleotide polymorphisms and copy number variations. Such studies are beginning to provide revolutionary insights into biological and evolutionary processes. Farm animals, such as cattle, swine, and horses, have played a dual role as economically and agriculturally important animals as well as biomedical research models. The first part of this study explores the current state of sequencing methods, many of which are already used in animal genomic studies, and the second part summarizes the state of cattle, swine, horse, and chicken genome sequencing and illustrates its achievements during the last few years. Finally, we describe several high-throughput sequencing approaches for the improved detection of known, unknown, and emerging infectious agents, leading to better diagnosis of infectious diseases. The insights from viral metagenomics and the advancement of next-generation sequencing will strongly support specific and efficient vaccine development and provide strategies for controlling infectious disease transmission among animal populations and/or between animals and humans. However, prospective sequencing technologies will require further research and in-field testing before reaching the marketplace.

Large-scale gene co-expression network as a source of functional annotation for cattle genes

Background

Genome sequencing and subsequent gene annotation of genomes has led to the elucidation of many genes, but in vertebrates the actual number of protein coding genes are very consistent across species (~20,000). Seven years after sequencing the cattle genome, there are still genes that have limited annotation and the function of many genes are still not understood, or partly understood at best. Based on the assumption that genes with similar patterns of expression across a vast array of tissues and experimental conditions are likely to encode proteins with related functions or participate within a given pathway, we constructed a genome-wide Cattle Gene Co-expression Network (CGCN) using 72 microarray datasets that contained a total of 1470 Affymetrix Genechip Bovine Genome Arrays that were retrieved from either NCBI GEO or EBI ArrayExpress.

Results

The total of 16,607 probe sets, which represented 11,397 genes, with unique Entrez ID were consolidated into 32 co-expression modules that contained between 29 and 2569 probe sets. All of the identified modules showed strong functional enrichment for gene ontology (GO) terms and Reactome pathways. For example, modules with important biological functions such as response to virus, response to bacteria, energy metabolism, cell signaling and cell cycle have been identified. Moreover, gene co-expression networks using “guilt-by-association” principle have been used to predict the potential function of 132 genes with no functional annotation. Four unknown Hub genes were identified in modules highly enriched for GO terms related to leukocyte activation (LOC509513), RNA processing (LOC100848208), nucleic acid metabolic process (LOC100850151) and organic-acid metabolic process (MGC137211). Such highly connected genes should be investigated more closely as they likely to have key regulatory roles.

Conclusions

We have demonstrated that the CGCN and its corresponding regulons provides rich information for experimental biologists to design experiments, interpret experimental results, and develop novel hypothesis on gene function in this poorly annotated genome. The network is publicly accessible at http://www.animalgenome.org/cgi-bin/host/reecylab/d.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3176-2) contains supplementary material, which is available to authorized users.

Single Nucleotide Polymorphism Discovery in Bovine Pituitary Gland Using RNA-Seq Technology

Examination of bovine pituitary gland transcriptome by strand-specific RNA-seq allows detection of putative single nucleotide polymorphisms (SNPs) within potential candidate genes (CGs) or QTLs regions as well as to understand the genomics variations that contribute to economic trait. Here we report a breed-specific model to successfully perform the detection of SNPs in the pituitary gland of young growing bulls representing Polish Holstein-Friesian (HF), Polish Red, and Hereford breeds at three developmental ages viz., six months, nine months, and twelve months. A total of 18 bovine pituitary gland polyA transcriptome libraries were prepared and sequenced using the Illumina NextSeq 500 platform. Sequenced FastQ databases of all 18 young bulls were submitted to NCBI-SRA database with NCBI-SRA accession numbers SRS1296732. For the investigated young bulls, a total of 113,882,3098 raw paired-end reads with a length of 156 bases were obtained, resulting in an approximately 63 million paired-end reads per library. Breed-wise, a total of 515.38, 215.39, and 408.04 million paired-end reads were obtained for Polish HF, Polish Red, and Hereford breeds, respectively. Burrows-Wheeler Aligner (BWA) read alignments showed 93.04%, 94.39%, and 83.46% of the mapped sequencing reads were properly paired to the Polish HF, Polish Red, and Hereford breeds, respectively. Constructed breed-specific SNP-db of three cattle breeds yielded at 13,775,885 SNPs. On an average 765,326 breed-specific SNPs per young bull were identified. Using two stringent filtering parameters, i.e., a minimum 10 SNP reads per base with an accuracy ≥ 90% and a minimum 10 SNP reads per base with an accuracy = 100%, SNP-db records were trimmed to construct a highly reliable SNP-db. This resulted in a reduction of 95,7% and 96,4% cut-off mark of constructed raw SNP-db. Finally, SNP discoveries using RNA-Seq data were validated by KASP^™ SNP genotyping assay. The comprehensive QTLs/CGs analysis of 76 QTLs/CGs with RNA-seq data identified KCNIP4, CCSER1, DPP6, MAP3K5 and GHR CGs with highest SNPs hit loci in all three breeds and developmental ages. However, CAST CG with more than 100 SNPs hits were observed only in Polish HF and Hereford breeds.These findings are important for identification and construction of novel tissue specific SNP-db and breed specific SNP-db dataset by screening of putative SNPs according to QTL db and candidate genes for bovine growth and reproduction traits, one can develop genomic selection strategies for growth and reproductive traits.

A genome-wide association study of immune response traits in Canadian Holstein cattle

Background

Breeding for enhanced immune response (IR) has been suggested as a tool to improve inherent animal health. Dairy cows with superior antibody-mediated (AMIR) and cell-mediated immune responses (CMIR) have been demonstrated to have a lower occurrence of many diseases including mastitis. Adaptive immune response traits are heritable, and it is, therefore, possible to breed for improved IR, decreasing the occurrence of disease. The objective of this study was to perform genome-wide association studies to determine differences in genetic profiles among Holstein cows classified as High or Low for AMIR and CMIR. From a total of 680 cows with immune response phenotypes, 163 cows for AMIR (81 High and 82 Low) and 140 for CMIR (75 High and 65 Low) were selectively genotyped using the Illumina Bovine SNP50 BeadChip. Results were validated using an unrelated population of 164 Holstein bulls IR phenotyped for AMIR and 146 for CMIR.

Results

A generalized quasi likelihood score method was used to determine single nucleotide polymorphisms (SNP) and chromosomal regions associated with immune response. After applying a 5% chromosomal false discovery rate, 186 SNPs were significantly associated with AMIR. The majority (93%) of significant markers were on chromosome 23, with a similar peak found in the bull population. For CMIR, 21 SNP markers remained significant. Candidate genes within 250,000 base pairs of significant SNPs were identified to determine biological pathways associated with AMIR and CMIR. Various pathways were identified, including the antigen processing and presentation pathway, important in host defense. Candidate genes included those within the bovine Major Histocompatability Complex such as BoLA-DQ, BoLA-DR and the non-classical BoLA-NC1 for AMIR and BoLA-DQ for CMIR, the complement system including C2 and C4 for AMIR and C1q for CMIR, and cytokines including IL-17A, IL17F for AMIR and IL-17RA for CMIR and tumor necrosis factor for both AMIR and CMIR. Additional genes associated with CMIR included galectins 1, 2 and 3, BCL2 and β-defensin.

Conclusions

The significant genetic variation associated with AMIR and CMIR in this study may imply feasibility to include immune response in genomic breeding indices as an approach to improve inherent animal health.

The GENOTEND chip: a new tool to analyse gene expression in muscles of beef cattle for beef quality prediction

Background

Previous research programmes have described muscle biochemical traits and gene expression levels associated with beef tenderness. One of our results concerning the DNAJA1 gene (an Hsp40) was patented. This study aims to confirm the relationships previously identified between two gene families (heat shock proteins and energy metabolism) and beef quality.

Results

We developed an Agilent chip with specific probes for bovine muscular genes. More than 3000 genes involved in muscle biology or meat quality were selected from genetic, proteomic or transcriptomic studies, or from scientific publications. As far as possible, several probes were used for each gene (e.g. 17 probes for DNAJA1). RNA from Longissimus thoracis muscle samples was hybridised on the chips. Muscles samples were from four groups of Charolais cattle: two groups of young bulls and two groups of steers slaughtered in two different years. Principal component analysis, simple correlation of gene expression levels with tenderness scores, and then multiple regression analysis provided the means to detect the genes within two families (heat shock proteins and energy metabolism) which were the most associated with beef tenderness. For the 25 Charolais young bulls slaughtered in year 1, expression levels of DNAJA1 and other genes of the HSP family were related to the initial or overall beef tenderness. Similarly, expression levels of genes involved in fat or energy metabolism were related with the initial or overall beef tenderness but in the year 1 and year 2 groups of young bulls only. Generally, the genes individually correlated with tenderness are not consistent across genders and years indicating the strong influence of rearing conditions on muscle characteristics related to beef quality. However, a group of HSP genes, which explained about 40% of the variability in tenderness in the group of 25 young bulls slaughtered in year 1 (considered as the reference group), was validated in the groups of 30 Charolais young bulls slaughtered in year 2, and in the 21 Charolais steers slaughtered in year 1, but not in the group of 19 steers slaughtered in year 2 which differ from the reference group by two factors (gender and year). When the first three groups of animals were analysed together, this subset of genes explained a 4-fold higher proportion of the variability in tenderness than muscle biochemical traits.

Conclusion

This study underlined the relevance of the GENOTEND chip to identify markers of beef quality, mainly by confirming previous results and by detecting other genes of the heat shock family as potential markers of beef quality. However, it was not always possible to extrapolate the relevance of these markers to all animal groups which differ by several factors (such as gender or environmental conditions of production) from the initial population of reference in which these markers were identified.