Effects of Different Sources of Fat in the Diet of Pigs on the Liver Transcriptome Estimated by RNA-Seq

Open access


In this study, we have attempted to analyse the impact of dietary fats on the liver transcriptome in pigs. Four nutritional groups were created. The animals’ diets differed among groups in terms of the presence of corn dried distillers’ grains with solubles (DDGS) (group I - no DDGS, groups II, III, IV - 20% DDGS) as well as the type of fat (rapeseed oil - groups I and II, beef tallow - group III, coconut oil - group IV) used. Using the RNA-Seq method we identified 39 differentially expressed genes (DEGs) as a result of Cuffdiff analysis of the differences among all groups. Analysis of these genes with Panther Gene Classification System revealed that among identified DEGs, genes responsible for lipid and fatty acids metabolism are overrepresented as well as the genes engaged in oxidoreductase and catalytic activity. The article presents for the first time the RNAseq analysis of the liver transcriptome in pigs fed with different sources of fats. The results may be useful for the elaboration of new therapies for cardiovascular diseases in humans as well as for the preparation of new nutrition strategies in animals.

Agyekum A.K., Woyengo T.A., Słomiński B.A., Yin Y.L., Nyachoti C.M. (2014). Effects of formulating growing pig diet with increasing levels of wheat-corn distillers dried grains with solubles on digestible nutrient basis on growth performance and nutrient digestibility. J. Anim. Physiol. Anim. Nutr. (Berl)., 98: 651-658.

Benjamini Y., Hochberg Y. (1995). Controlling the false discovery rate:apractical and powerful approach to multiple testing. J. Roy. Stat. Soc. B, 57: 289-300.

Chomczyński P., Sacchi N. (1987). Single-step method of RNAisolation by acid guanidinium thiocyanate-phenol-chloroform extraction. Anal. Biochem., 162: 156-159.

Cromwell G.L., Azain M.J., Adeola O., Baidoo S.K., Carter S.D., Crenshaw T.D., Kim W.S., Mahan D.C., Miller P.S., Shannon M.C. (2011). Corn distillers dried grains with solubles in diets for growing-finishing pigs: Acooperative study. J. Anim. Sci., 89: 2801-2811.

Culnan D.M., Cooney R.N., Stanley B., Lynch C.J. (2009). Apolipoprotein A-IV,aputative satiety/antiatherogenic factor, rises after gastric bypass. Obesity (Silver Spring), 17: 46-52.

Deluca D.S., Levin J.Z., Sivachenko A., Fennell T., Nazaire M.D., Williams C., Reich M., Winckler W., Getz G. (2012). RNA-Se QC: RNA-seq metrics for quality control and process optimization. Bioinformatics, 28: 1530-1532.

Dodt M., Roehr J.T., Ahmed R., Dieterich C. (2012). FLEXBAR-Flexible Barcode and Adapter Processing for Next-Generation Sequencing Platforms. Biology (Basel), 1: 895-905.

Döring F., Lüersen K., Schmelzer C., Hennig S., Lang I.S., Görs S., Rehfeldt C., Otten W., Metges C.C. (2013). Influence of maternal low protein diet during pregnancy on hepatic gene expression signature in juvenile female porcine offspring. Mol. Nutr. Food Res., 57: 277-290.

Esteve - Codina A., Kofler R., Palmieri N., Bussotti G., Notredame C., Pérez- - Enciso M. (2011). Exploring the gonad transcriptome of two extreme male pigs with RNA-seq. BMC Genomics, 12: 552.

Feranil A.B., Duazo P.L., Kuzawa C.W., Adair L.S. (2011). Coconut oil is associated with a beneficial lipid profile in pre-menopausal women in the Philippines. Asia Pac. J. Clin. Nutr., 20: 190-195.

Flicek P., Amode M.R., Barrell D., Beal K., Brent S., Carvalho - Silva D., Clapham P., Coates G., Fairley S., Fitzgerald S., Gil L., Gordon L., Hendrix M., Hourlier T., Johnson N., K ähäri A.K., Keefe D., Keenan S., Kinsella R., Komorowska M., Kościelny G., Kulesha E., Larsson P., Longden I., Mc Laren W., Muffato M., Overduin B., Pignatelli M., Pritchard B., Riat H.S., Ritchie G.R., Ruffier M., Schuster M., Sobral D., Tang Y.A., Taylor K., Trevanion S., Vandrovcova J., White S., Wilson M., Wilder S.P., Aken B.L., Birney E., Cunningham F., Dun- ham I., Durbin R., Fernández - Suarez X.M., Harrow J., Herrero J., Hub- bard T.J., Parker A., Proctor G., Spudich G., Vogel J., Yates A., Zadissa A., Searle S.M. (2012). Ensembl 2012, Nucleic Acids Research, 40: D84-D90.

Gunawan A., Sahadevan S., Cinar M.U., Neuhoff C., Große- Brinkhaus C., Frieden L., Tesfaye D., Tholen E., Looft C., Wondim D.S., Hölker M., Schellan - der K., Uddin M.J. (2013). Identification of the novel candidate genes and variants in boar liver tissues with divergent skatole levels using RNAdeep sequencing. PLo S One, 8:e72298.

Jiménez- Chillarón J.C., Díaz R., Martínez D., Pentinat T., Ramón- Krauel M., Ribó S., Plösch T. (2012). The role of nutrition on epigenetic modifications and their implications on health. Review. Biochimie, 94: 2242-2263.

Jun H., Daiwen C., Bing Y. (2010). Metabolic and transcriptomic responses of weaned pigs induced by different dietary amylose and amylopectin ratio. PLo S One, 5: e15110.

Jung W.Y., Kwon S.G., Son M., Cho E.S., Lee Y., Kim J.H., Kim B.W., Parkda H., Hwang J.H., Kim T.W., Park H.C., Park B.Y., Choi J.S., Cho K.K., Chung K.H., Song Y.M., Kim I.S., Jin S.K., Kim D.H., Lee S.W., Lee K.W., Bang W.Y., Kim C.W. (2012). RNA-Seq approach for genetic improvement of meat quality in pig and evolutionary insight into the substrate specificity of animal carbonyl reductases. PLo S One, 7: e42198.

Keller J., Ringseis R., Priebe S., Guthke R., Kluge H., Eder K. (2011). Dietary L-carnitine alters gene expression in skeletal muscle of piglets. Mol. Nutr. Food Res., 55: 419-429.

Kim S., Sohn I., Ahn J.I., Lee K.H., Lee Y.S., Lee Y.S. (2004). Hepatic gene expression profiles inalong-term high-fat diet-induced obesity mouse model. Gene, 340: 99-109.

Kirpich I.A., Gobejishvili L.N., Bon Homme M., Waigel S., Cave M., Arteel G., Barve S.S., Mc Clain C.J., Deaciuc I.V. (2011). Integrated hepatic transcriptome and proteome analysis of mice with high-fat diet-induced nonalcoholic fatty liver disease. J. Nutr. Biochem., 22: 38-45.

Langmead B., Salzberg S. (2012). Fast gapped-read alignment with Bowtie 2. Nature Methods, 9: 357‒359.

Li F., Duan Y., Li Y., Tang Y., Geng M., Oladele O.A., Kim S.W., Yin Y. (2015). Effects of dietary n-6:n-3 PUFAratio on fatty acid composition, free amino acid profile and gene expression of transporters in finishing pigs. Br. J. Nutr., 113: 739-748.

Linneen S.K., De Rouchy J.M., Dritz S.S., Goodband R.D., Tokach M.D., Nels- sen J.L. (2008). Effects of dried distillers grains with solubles on growing and finishing pig performance inacommercial environment. J. Anim. Sci., 86: 1579-1587.

Luo W., Cheng D., Chen S., Wang L., Li Y., Ma X., Song X., Liu X., Li W., Liang J., Yan H., Zhao K., Wang C., Wang L., Zhang L. (2012). Genome-wide association analysis of meat quality traits inaporcine Large White × Minzhu intercross population. Int. J. Biol. Sci., 8: 580-595.

McCabe M., Waters S., Morris D., Kenny D., Lynn D., Creevey C. (2012). RNA-seq analysis of differential gene expression in liver from lactating dairy cows divergent in negative energy balance. BMC Genomics, 13: 193.

Michas G., Micha R., Zampelas A. (2014). Dietary fats and cardiovascular disease: putting together the pieces ofacomplicated puzzle. Atherosclerosis, 234: 320-328.

Müller M., Kersten S. (2003). Nutrigenomics: goals and strategies. Review. Nat. Rev. Genet., 4: 315‒322.

Nevin K.G., Rajamohan T. (2004). Beneficial effects of virgin coconut oil on lipid parameters and in vitro LDLoxidation. Clin. Biochem., 37: 830-835.

Nevin K.G., Rajamohan T. (2006). Virgin coconut oil supplemented diet increases the antioxidant status in rats. Food Chem., 99: 260-266.

Partridge C.G., Fawcett G.L., Wang B., Semenkovich C.F., Cheverud J.M. (2014). The effect of dietary fat intake on hepatic gene expression in LG/J AND SM/Jmice. BMC Genomics, 15: 99.

Pfaffl M.W. (2001). Anew mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res., 29: e45.

Ramayo - Caldas Y., Mach N., Esteve - Codina A., Corominas J., Castelló A., Ball- ester M., Estellé J., Ibáñez - Escriche N., Fernández A.I., Pérez- Enciso M., Folch J.M. (2012). Liver transcriptome profile in pigs with extreme phenotypes of intramuscular fatty acid composition. BMC Genomics, 13: 547.

Ropka-Molik K., Żukowski K., Eckert R., Gurgul A., Piórkowska K., Oczkowicz M. (2014). Comprehensive analysis of the whole transcriptomes from two different pig breeds using RNA-Seq method. Anim. Genet., 45: 674-684.

Samborski A., Graf A., Krebs S., Kessler B., Bauersachs S. (2013). Deep sequencing of the porcine endometrial transcriptome on day 14 of pregnancy. Biol. Reprod., 88: 84.

Świątkiewicz M., Oczkowicz M., Ropka- Molik K., Hanczakowska E. (2016). Effect of dietary fatty acids composition on adipose tissue quality and expression of genes related to lipid metabolism in porcine livers. Anim. Feed Sci. Technol., 216: 204-215.

Tan B., Yin Y., Liu Z., Tang W., Xu H., Kong X., Li X., Yao K., Gu W., Smith S.B., Wu G. (2011). Dietary L-arginine supplementation differentially regulates expression of lipid-metabolic genes in porcine adipose tissue and skeletal muscle. J. Nutr. Biochem., 22: 441-445.

Tholen E., Looft C., Wondim D.S., Hölker M., Schellander K., Uddin M.J. (2013). Identification of the novel candidate genes and variants in boar liver tissues with divergent skatole levels using RNAdeep sequencing. PLo S One, 8: e72298.

Trapnell C., Pachter L., Salzberg S.L. (2009). Top Hat: discovering splice junctions with RNA-Seq. Bioinformatics, 25: 1105-1111.

Trapnell C., Williams B.A., Pertea G., Mortazavi A., Kwan G.,van Baren M.J., Salzberg S.L., Wold B.J., Pachter L. (2010). Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol., 28: 511-515.

Tyra M., Żak G. (2012). Analysis of relationships between fattening and slaughter performance of pigs and the level of intramuscular fat (IMF) in longissimus dorsi muscle. Ann. Anim. Sci., 12: 169-178.

Annals of Animal Science

The Journal of National Research Institute of Animal Production

Journal Information

IMPACT FACTOR 2017: 1.018
5-year IMPACT FACTOR: 0.959

CiteScore 2017: 1.01

SCImago Journal Rank (SJR) 2017: 0.413
Source Normalized Impact per Paper (SNIP) 2017: 0.822

Cited By


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 281 281 24
PDF Downloads 119 119 14