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Whole Blood Transcriptional Profiling Comparison Between Different Milk Yield of Chinese Holstein Cows Using RNA-Seq Data

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Whole Blood Transcriptional Profiling Comparison Between Different Milk Yield of Chinese Holstein Cows Using RNA-Seq Data The Author(s) BMC Genomics 2016, 17(Suppl 7):512 DOI 10.1186/s12864-016-2901-1 RESEARCH Open Access Whole blood transcriptional profiling comparison between different milk yield of Chinese Holstein cows using RNA-seq data Xue Bai1, Zhuqing Zheng1,2, Bin Liu1,2,3, Xiaoyang Ji1,2, Yongsheng Bai4* and Wenguang Zhang1,5* From The International Conference on Intelligent Biology and Medicine (ICIBM) 2015 Indianapolis, IN, USA. 13-15 November 2015 Abstract Background: The objective of this research was to investigate the variation of gene expression in the blood transcriptome profile of Chinese Holstein cows associated to the milk yield traits. Results: We used RNA-seq to generate the bovine transcriptome from the blood of 23 lactating Chinese Holstein cows with extremely high and low milk yield. A total of 100 differentially expressed genes (DEGs) (p < 0.05, FDR < 0. 05) were revealed between the high and low groups. Gene ontology (GO) analysis demonstrated that the 100 DEGs were enriched in specific biological processes with regard to defense response, immune response, inflammatory response, icosanoid metabolic process, and fatty acid metabolic process (p < 0.05). The KEGG pathway analysis with 100 DEGs revealed that the most statistically-significant metabolic pathway was related with Toll-like receptor signaling pathway (p < 0.05). The expression level of four selected DEGs was analyzed by qRT-PCR, and the results indicated that the expression patterns were consistent with the deep sequencing results by RNA-Seq. Furthermore, alternative splicing analysis of 100 DEGs demonstrated that there were different splicing pattern between high and low yielders. The alternative 3’ splicing site was the major splicing pattern detected in high yielders. However, in low yielders the major type was exon skipping. Conclusion: This study provides a non-invasive method to identify the DEGs in cattle blood using RNA-seq for milk yield. The revealed 100 DEGs between Holstein cows with extremely high and low milk yield, and immunological pathway are likely involved in milk yield trait. Finally, this study allowed us to explore associations between immune traits and production traits related to milk production. Keywords: Whole blood, Immune response, Milk yield, Differentially expressed genes, Dairy cattle Background technologies, a large number of candidate genes and Milk yield and milk composition of lactating cows are SNPs associated with milk performance traits have been the most important economic traits in dairy cattle. In identified, such as DGAT1 and GHR genes [1–6]. Previ- the past century, genetic selection on milk yield has im- ous studies described the associations between DGAT1 proved milk production in cattle. With the development K232A polymorphism and milk production traits [7, 8]. of quantitative trait loci (QTLs), genome-wide associ- Blott et al. identified a significant SNP (BFGL-NGS- ation studies (GWAS) and RNA sequencing (RNA-seq) 118998) inside the GHR gene that has an important role related to milk traits [9]. * Correspondence: [email protected]; [email protected] Mammary gland is an important organ to synthesize 4Department of Biology, Indiana State University, Terre Haute, IN 47809, U.S.A. and secrete milk. The mammary epithelial cell has a re- 1College of Animal Science, Inner Mongolia Agricultural University, Hohhot markable ability to convert blood circulating nutrients 010018, China into milk components [10]. Thus, almost all the studies Full list of author information is available at the end of the article © 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. The Author(s) BMC Genomics 2016, 17(Suppl 7):512 Page 174 of 325 related to milk performance traits are based on mam- sample). Nearly 68 % of the reads were mapped to the bo- mary gland. For example, Cui et al. collected mammary vine genome UMD3.1.66 and approximately 62 % of the gland samples from four lactating cows after slaughter. reads in every individual were uniquely mapped to the bo- They used RNA-seq to generate the bovine mammary vine genome. The alignment information for each sample transcriptome with high and low milk fat and protein is presented in Table 1. Of these, 16,314 and 16,151 percentage [11]. Finucane et al. compared bovine mam- expressed transcripts were revealed in high yielders and mary expression profiles before and after parturition low yielders, respectively (Additional file 1: Table S1). using microarray [12]. However, there are still limita- tions in analysis of milk performance traits using bovine DEGs and splice events between high and low yielders mammary tissue, such as sampling difficultly, high cost To provide a better understanding of the biological of sampling and tremendous damage to the lactating mechanism of milk yield, it is essential to identify the cows, which resulted in small sample size. DEGs between high and low milk yield cows. Based on Milk is composed of fat, protein, lactose, minerals, vi- the Cuffdiff analysis, a total of 100 DEGs (p<0.05, FDR< tamins and water, and all these nutrients derive from 0.05) were examined between the high and low yielders blood [10, 13–15]. Some data shows that synthesizing (Fig. 1). All the DEGs were located in chromosomes ran- 1L milk requires 400-500L blood flow though the mam- domly, but there were no DEGs identified in chromo- mary gland. So plenty of blood is essential for synthesiz- some 14, 20, 27, and 28. The expression level of 100 ing milk. Previous studies associated with milk traits DEGs was from 2 to 1063 FPKM in high group, and 0.4 were only focused on blood physiological and biochem- to 1794 FPKM in low group. In addition, 43 of the 100 ical indexes, and there are very few studies associating DEGs were highly expressed in the high yielders; gene expression with milk traits in cattle blood. A gene whereas, the other 57 DEGs showed lower expression in expression profile from blood provides new opportun- low yielders. The expression level of 100 DEGs is shown ities to clarify the basic mechanisms of complex traits in in Fig. 2 and the detail information is presented in cattle milk. Besides, whole blood is a complex mixture Additional file 2: Table S2. of cells and can accurately reflect the physiological con- Splice events are thought to contribute to phenotypic dition and health level of cows. Many studies have used complexity during the mammalian evolution [20]. In blood to diagnose disease in dairy cattle, such as mastitis total, we obtained 44,572 and 36,467 splice events in [16, 17]. Most importantly, blood is easier to sample in high and low yielders, respectively, compared to the an- comparison with other tissues and involves limited notated bovine genome UMD3.1.66. Of these, 214 (in handling of animals. Furthermore, the lactation process high yielders) and 202 (in low yielders) differentially requires multiple tissues and organs to complete, such expressed splice events were identified. Further analysis as mammary gland, liver and adipose tissue. Hence, showed that the 214 and 202 splice events involved 59 blood has the potential ability to represent milk per- DEGs and 57 DEGs, respectively. Major splicing events formance traits more directly and comprehensively. such as, exon skipping (ES), intron retention (IR), alter- Sandri et al. (2015) analyzed the gene expression profile native 5’ splicing site (A5SS), alternative 3’ splicing site in the blood related to the gene merit for milk product- (A3SS) and mutually exclusive exon (MXE) were de- ive traits using microarray [18]. Marcel et al. analyzed tected in our studied bovine blood transcriptomes. The porcine peripheral blood mononuclear cells transcription A3SS was the major splicing pattern observed in high profile of pigs with divergent humoral immune response group; but in low group, the major type was ES (Fig. 3). and lean growth performance [19]. To complete under- This suggests that high milk yield cows are more in- stand the impact of blood transcriptome profiles on milk clined to take the A3SS pattern. In addition, more splice yield, comprehensive cataloguing of gene expression sites were found in chromosome 26 in both groups. change within high yielders and low yielders is required. The aim of this study is to compare gene expression pro- Functional classification of DEGs files in bovine whole blood of high and low milk yield The DAVID tool [21] was used to annotate the function cows, and to investigate potential molecular biomarkers in of the 100 DEGs with the particular categories focusing the blood transcriptome that relate to the productive po- on the gene ontology (GO) and Kyoto Encyclopedia of tential of lactating cows using RNA-seq techniques. Genes and Genomes (KEGG) pathway. A total of 55 clusters (p<0.05) were significantly annotated with GO Results terms within three major function groups: biological Analysis of expressed transcripts between high and low process (BP), cellular component (CC), and molecular yielders function (MF).
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