Gene Co-expression Networks from RNA Sequencing of Dairy Cattle Identifies Genes and Pathways Affecting Feed Efficiency

Overview

Journal BMC Bioinformatics

Publisher Biomed Central

Specialty Biology

Date 2018 Dec 19

PMID 30558534

Citations 24

Authors

S M Salleh

G Mazzoni

P Lovendahl

H N Kadarmideen

Affiliations

Soon will be listed here.

Abstract

Background: Selection for feed efficiency is crucial for overall profitability and sustainability in dairy cattle production. Key regulator genes and genetic markers derived from co-expression networks underlying feed efficiency could be included in the genomic selection of the best cows. The present study identified co-expression networks associated with high and low feed efficiency and their regulator genes in Danish Holstein and Jersey cows. RNA-sequencing data from Holstein and Jersey cows with high and low residual feed intake (RFI) and treated with two diets (low and high concentrate) were used. Approximately 26 million and 25 million pair reads were mapped to bovine reference genome for Jersey and Holstein breed, respectively. Subsequently, the gene count expressions data were analysed using a Weighted Gene Co-expression Network Analysis (WGCNA) approach. Functional enrichment analysis from Ingenuity® Pathway Analysis (IPA®), ClueGO application and STRING of these modules was performed to identify relevant biological pathways and regulatory genes.

Results: WGCNA identified two groups of co-expressed genes (modules) significantly associated with RFI and one module significantly associated with diet. In Holstein cows, the salmon module with module trait relationship (MTR) = 0.7 and the top upstream regulators ATP7B were involved in cholesterol biosynthesis, steroid biosynthesis, lipid biosynthesis and fatty acid metabolism. The magenta module has been significantly associated (MTR = 0.51) with the treatment diet involved in the triglyceride homeostasis. In Jersey cows, the lightsteelblue1 (MTR = - 0.57) module controlled by IFNG and IL10RA was involved in the positive regulation of interferon-gamma production, lymphocyte differentiation, natural killer cell-mediated cytotoxicity and primary immunodeficiency.

Conclusion: The present study provides new information on the biological functions in liver that are potentially involved in controlling feed efficiency. The hub genes and upstream regulators (ATP7b, IFNG and IL10RA) involved in these functions are potential candidate genes for the development of new biomarkers. However, the hub genes, upstream regulators and pathways involved in the co-expressed networks were different in both breeds. Hence, additional studies are required to investigate and confirm these findings prior to their use as candidate genes.

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References

Veerkamp R, Koenen E, de Jong G . Genetic correlations among body condition score, yield, and fertility in first-parity cows estimated by random regression models. J Dairy Sci. 2001; 84(10):2327-35. DOI: 10.3168/jds.S0022-0302(01)74681-4. View

Arthur P, Archer J, Johnston D, Herd R, Richardson E, Parnell P . Genetic and phenotypic variance and covariance components for feed intake, feed efficiency, and other postweaning traits in Angus cattle. J Anim Sci. 2002; 79(11):2805-11. DOI: 10.2527/2001.79112805x. View

Brem R, Yvert G, Clinton R, Kruglyak L . Genetic dissection of transcriptional regulation in budding yeast. Science. 2002; 296(5568):752-5. DOI: 10.1126/science.1069516. View

Parhami F, Mody N, Gharavi N, Ballard A, Tintut Y, Demer L . Role of the cholesterol biosynthetic pathway in osteoblastic differentiation of marrow stromal cells. J Bone Miner Res. 2002; 17(11):1997-2003. DOI: 10.1359/jbmr.2002.17.11.1997. View

Schroder K, Hertzog P, Ravasi T, Hume D . Interferon-gamma: an overview of signals, mechanisms and functions. J Leukoc Biol. 2003; 75(2):163-89. DOI: 10.1189/jlb.0603252. View

van Eerden E, van den Brand H, Parmentier H, de Jong M, Kemp B . Phenotypic selection for residual feed intake and its effect on humoral immune responses in growing layer hens. Poult Sci. 2004; 83(9):1602-9. DOI: 10.1093/ps/83.9.1602. View

Li J, Burmeister M . Genetical genomics: combining genetics with gene expression analysis. Hum Mol Genet. 2005; 14 Spec No. 2:R163-9. DOI: 10.1093/hmg/ddi267. View

Zhang B, Horvath S . A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 2006; 4:Article17. DOI: 10.2202/1544-6115.1128. View

Huster D, Purnat T, Burkhead J, Ralle M, Fiehn O, Stuckert F . High copper selectively alters lipid metabolism and cell cycle machinery in the mouse model of Wilson disease. J Biol Chem. 2007; 282(11):8343-55. DOI: 10.1074/jbc.M607496200. View

10.

Llewellyn S, Fitzpatrick R, Kenny D, Murphy J, Scaramuzzi R, Wathes D . Effect of negative energy balance on the insulin-like growth factor system in pre-recruitment ovarian follicles of post partum dairy cows. Reproduction. 2007; 133(3):627-39. DOI: 10.1530/REP-06-0122. View

11.

Langfelder P, Zhang B, Horvath S . Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics. 2007; 24(5):719-20. DOI: 10.1093/bioinformatics/btm563. View

12.

Schadt E, Molony C, Chudin E, Hao K, Yang X, Lum P . Mapping the genetic architecture of gene expression in human liver. PLoS Biol. 2008; 6(5):e107. PMC: 2365981. DOI: 10.1371/journal.pbio.0060107. View

13.

Wang Z, Gerstein M, Snyder M . RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2008; 10(1):57-63. PMC: 2949280. DOI: 10.1038/nrg2484. View

14.

Herd R, Arthur P . Physiological basis for residual feed intake. J Anim Sci. 2008; 87(14 Suppl):E64-71. DOI: 10.2527/jas.2008-1345. View

15.

Ametaj B, Koenig K, Dunn S, Yang W, Zebeli Q, Beauchemin K . Backgrounding and finishing diets are associated with inflammatory responses in feedlot steers. J Anim Sci. 2008; 87(4):1314-20. DOI: 10.2527/jas.2008-1196. View

16.

Langfelder P, Horvath S . WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008; 9:559. PMC: 2631488. DOI: 10.1186/1471-2105-9-559. View

17.

Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A . ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics. 2009; 25(8):1091-3. PMC: 2666812. DOI: 10.1093/bioinformatics/btp101. View

18.

Graugnard D, Piantoni P, Bionaz M, Berger L, Faulkner D, Loor J . Adipogenic and energy metabolism gene networks in longissimus lumborum during rapid post-weaning growth in Angus and Angus x Simmental cattle fed high-starch or low-starch diets. BMC Genomics. 2009; 10:142. PMC: 2676302. DOI: 10.1186/1471-2164-10-142. View

19.

Schaefer E, Gleason J, Dansinger M . Dietary fructose and glucose differentially affect lipid and glucose homeostasis. J Nutr. 2009; 139(6):1257S-1262S. PMC: 2682989. DOI: 10.3945/jn.108.098186. View

20.

Lkhagvadorj S, Qu L, Cai W, Couture O, Barb C, Hausman G . Gene expression profiling of the short-term adaptive response to acute caloric restriction in liver and adipose tissues of pigs differing in feed efficiency. Am J Physiol Regul Integr Comp Physiol. 2009; 298(2):R494-507. DOI: 10.1152/ajpregu.00632.2009. View