Investigation of candidate genes for metabolic disorders expressed in liver and pituitary gland by comparing the RNA-seq data of Polish-HF and Polish-Red cattle
DOI:
https://doi.org/10.12775/TRVS.2018.004Keywords
RNA-seq, liver, pituitary gland, cattle, breeds, ketosis, SOD, GPx, antioxidants, bioinformaticsAbstract
Background: Metabolic disorder is a major health problem in dairy cattle, particularly to high milk producing dairy cattle. It is worthily emphasized that metabolic diseases have a very complex etiology and pathogenesis, and the impact of these diseases on hepatic and pituitary gland gene expression and organism oxidative balance is not fully described. The presented study was aimed to determine and predict the hepatic and pituitary gland expression of potential candidate genes in context to maintenance of oxidative balance, negative nitrogen balance, as well as ketosis in Polish HF and Polish Red cattle.
Methods: Based on the RNA-seq experimental data, we investigated the candidate genes (SOD1, SOD2, SOD3, GPx2, GPx3, GPx5, GPx6, GPx7, GPx8, BDH1, FN1, ACSL3, HMGCL, HMGCS2, BDH2, ACSL6, ACAT2, IDH3B, ACAT1, HMGCS1, ACSL4, ACSL1, PC, CPT1A, OXCT1 and ACSL5 respectively) expressions in liver and pituitary gland tissues of Polish HF and Polish Red cattle. The RNA-seq experimental design comprised of young bulls aged between 6 to 12 months were investigated. For each breed, six liver and six pituitary gland tissues were sequenced using Next-seq 500 illumina platform. The RNA-seq expression data were normalized by the reads per kilobase of exon per million reads mapped (RPKM) method.
Results: By comparing the RNA-seq data of liver and pituitary gland tissues, the investigated candidate genes were highly expressed in the hepatic tissues than to pituitary gland in investigated cattle breeds. However, by comparing the Polish HF and Polish Red cattle breeds, results revealed a similar trend of gene expression profiling of all investigated candidate genes for both metabolic tissues. In case of hepatic gene expression profiling, the SOD1, FN1, HMGCL, HMGCS2, ACAT2, ACAT1, HMGCS1, ACSL1 and ACSL5 were highly expressed (FPKM values of >40), followed by SOD2, GPX3, IDH3B, PC and BDH2 as moderately expressed (FPKM values: >10 to <40), and averagely expressed SOD3, GPX5, GPX6, GPX7, GPX2, GPX8, BDH1, ACSL3, ACSL6, ACSL4, CPT1A and OXCT1 respectively, in Polish HF and Polish Red breeds. In case of pituitary gland gene expression profiling, the SOD1 and GPx3 were highly expressed (FPKM values of >40), followed by SOD2, GPX8, IDH3B, ACAT1, ACSL4 and PC as moderately expressed (FPKM values: >10 to <40), and averagely expressed SOD3, GPX3,GPX5, GPX6, GPX7, GPX2, BDH1, BDH2, ACSL3, ACSL6, CPT1A, OXCT1, FN1, HMGCL, HMGCS2, ACAT2, ACAT1, HMGCS1, ACSL1 and ACSL5 respectively, in Polish HF and Polish Red breeds.
Conclusions: Based on this presented results on hepatic and pituitary gland gene expression, a further research plan is an essential pre-requisite to validate the identified candidate genes. Study indicated the understanding the genetic factors that predispose metabolic disorders in cattle would benefit the dairy industry as a whole by providing producers, breeding services, and veterinarians a tool to forecast a cow’s susceptibility to metabolic disorders.
References
Kroezen V, Schenkel FS, Miglior F, Baes CF, Squires EJ. Candidate gene association analyses for ketosis resistance in Holsteins. J Dairy Sci. 2018;101:5240–5249.
Biswal S, Nayak DC, Sardar KK Prevalence of ketosis in dairy cows in milk shed areas of Odisha state, India. Vet World. 2016;9:1242–1247.
Raboisson D., Mounié M., Khenifard E., Maigné E The economic impact of subclinical ketosis at the farm level: Tacklingthe challenge of over-estimation due to multiple interactions. Prev Vet Med. 2015;122: 417–425.
Herdt T.H. Ruminant adaptation to negative energy balance. Influences on the etiology of ketosis and fatty liver. Vet Clin North Am Food Anim Pract. 2000;16:215–230.
Bernabucci U., Ronchi B., Lacetera N., Nardone A.: Influence of Body Condition Score on Relationships Between Metabolic Status and Oxidative Stress in Periparturient Dairy Cows. J Dairy Sci. 2005;88:2017–2026.
Sordillo L.M., Aitken S.L Impact of oxidative stress on the health and immune function of dairy cattle. Vet Immunol Immunop. 2009;128:104–109.
Raboisson D., Mounié M., Maigné E. Diseases, reproductive performance, and changes in milk production associated with subclinical ketosis in dairy cows: A meta-analysis and review. J Dairy Sci. 2014;97:7547–7563.
Contreras GA, O’Boyle NJ, Herdt TH, et al. Lipomobilization in periparturient dairy cows influences the composition of plasma nonesterified fatty acids and leukocyte phospholipid fatty acids. J Dairy Sci. 2010;93:2508–2516.
Dechow C.D, Rogers G.W, Sander-Nielsen U., et al.: Correlations among body condition scores from various sources, dairy form, and cow health from the United States and Denmark. J Dairy Sci. 2004;87:3526–3533.
Janovick-Guretzky N. A., Dann H. M., Carlson D. B. , Murphy M. R., Loor J. J., Drackley J. K.: Housekeeping Gene Expression in Bovine Liver is Affected by Physiological State, Feed Intake, and Dietary Treatment J Dairy Sci. 2007;90:2246–2252.
Shi X., Li D., Deng Q., Peng Z., Zhao C., Li X., Wang Z., Li X., Liu G. Acetoacetic acid induces oxidative stress to inhibit the assembly of very low density lipoprotein in bovine hepatocytes J Dairy Res. 2016;83:442-446.
Berge A.C., Vertenten G. A field study to determine the prevalence, dairy herd management systems, and fresh cow clinical conditions associated with ketosis in western European dairy herds. J Dairy Sci. 2014;97:2145–2154
Pryce J. E., Parker Gaddis K. L., Koeck A. et al. Invited review: Opportunities for genetic improvement of metabolic diseases. J Dairy Sci. 2016;99:6855–6873.
Pareek CS, Błaszczyk P, Dziuba P, Czarnik U, Fraser L, Sobiech P, et al. Single nucleotide polymorphism discovery in bovine liver using RNA-seq technology. PLoS ONE 2017;12:e0172687.
van Dorland, H.A., Graber, M., Kohler, S., Steiner, A., Bruckmaier, R.M. Comparison of hepatic adaptation in extreme metabolic phenotypes observed in early lactation dairy cows on-farm. J Anim Physio Anim Nut. 2013;98:693-703.
van Dorland, H.A., Richter, S., Morel, I., Doherr, M.G., Castro, N., Bruckmaier, R.M. Variation in hepatic regulation of metabolism during the dry period and in early lactation in dairy cows. . J Dairy Sci. 2009;92:1924-1940.
Cánovas, A., Rincón, G., Islas-Trejo, A., Jimenez-Flores, R., Laubscher, A., Medrano, F.. RNA sequencing to study gene expression and single nucleotide polymorphism variation associated with citrate content in cow milk. . J Dairy Sci. 2013;96:2637- 2648.
Khan, M.J., Hosseini, A., Burrell, S., Rocco, S.M., McNamara, J.P., Loor, J.J. Change in subcutaneous adipose tissue metabolism and gene network expression during the transition period in dairy cows, including differences due to sire genetic merit. . J Dairy Sci. 2013;96:2171-2182.
Weber, C., Hametner, C., Tuchscherer, A., Losand, B., Kanitz, E., Otten, W., Sauerwein, H., Bruckmaier, R.M., Becker, F., Kanitz, W., Hammon, H.M.. Hepatic gene expression involved in glucose and lipid metabolism in transition cows: Effects of fat mobilization during early lactation in relation to milk performance and metabolic changes. . J Dairy Sci. 2013a;96:5670-5681.
Weber, C., Hametner, C., Tuchscherer, A., Losand, B., Kanitz, E., Otten, W., Singh, S.P., Bruckmaier, R.M., Becker, F., Kanitz, W., Hammon, H.M. Variation in fat mobilization during early lactation differently affects feed intake, body condition, and lipid and glucose metabolism in high-yielding dairy cows. . J Dairy Sci. 2013b;96:165-180.
Clempson, A.M., Pollott, G.E., Brickell, J.S., Wathes, D.C. Associations between bovine IGFBP2 polymorphisms with fertility, milk production, and metabolic status in UK dairy cows. Anim Biotech. 2012;23:101-113.
Tetens, J., Seidenspinner, T., Buttchereit, N., Thaller, G. Whole-genome association study for energy balance and fat/protein ratio in German Holstein bull dams. Anim Genet. 2012;44:1-8.
Buitenhuis, A.J., Sundekilde, U.K., Poulsen, N.A., Bertram, H.C., Larsen, L.B., Sorensen, P. Estimation of genetic parameters and detection of quantitative trait loci for metabolites in Danish Holstein milk. J Dairy Sci. 2013;96:3285-3295.
Parker Gaddis, K.L., Cole, J.B., Clay, J.S., Maltecca, C. Genomic selection for producer-recorded health event data in US dairy cattle . J Dairy Sci. 2014;97:3190–3199.
Parker Gaddis K.L., Megonigal J.H., Clay J.S., Wolfe C.W. Genome-wide association study for ketosis in US Jerseys using producer-recorded data. . J Dairy Sci. 2018;101:413–424.
Mahmoudi, A., Zargaran, A., Amini, H.R., Assadi, A., Hokmabad, R.V., Eghbalsaied, S. A SNP in the 3’-untranslated region of AMPKγ1 may associate with serum ketone body production of Holstein dairy cows. Gene. 2015;574:48–52.
Tetens, J., Heuer, C., Heyer, I., Klein, M.S., Gronwald, W., Junge, W., Oefner, P.J., Thaller, G., Krattenmacher, N. Polymorphisms within the APOBR gene are highly associated with milk levels of prognostic ketosis biomarkers in dairy cows. Physio Genom. 2015;47:129–137.
Chomczynski P, Sacchi N. Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction. Anal Biochem. 1987;162:156–159.
Martin MC. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnetjournal, 2011. North America.17 (http://journal.embnet.org/index.php/embnetjournal/article/view/200/479).
Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–1760.
Anders S, Pyl PT, Huber W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;1:166–169.
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.
Downloads
Published
How to Cite
Issue
Section
License
Title, logo and layout of TR in VS are reserved trademarks of TR in VR.
Stats
Number of views and downloads: 434
Number of citations: 0