Sel’skokhozyaistvennaya Biologiya [Agricultural Biology], 2018, Vol. 53, ¹ 6, p. 1262-1273.
(how to cite this article)

doi: 10.15389/agrobiology.2018.6.1262eng

UDC 636.4:637.074

Acknowledgements:
Supported financially by the Ministry of Science and Higher Education of the Russian Federation (grant No. 14.604.21.0182, identification No. RFMEFI60417X0182)

 

LIPID COMPOSITION OF MUSCLE AND FAT TISSUES OF DUROC PIGS
(Sus scrofa domesticus Erxleben, 1777) — FEATURES AND CORRELATIONS

À.S. Pavlova1, À.À. Vanyushkina1, Å.À. Ushina1, À.N. Egorova1,2,
D.À. Petrova1, À.À. Belous3, N.À. Anikanov1, P.V. Mazin1

1Center of Life Sciences, Skolkovo Institute of Science and Technology, 3, ul. Nobelya, Moscow 143026 Russia, e-mail A.Pavlova@skoltech.ru (✉ corresponding author), A.Vanyushkina@skoltech.ru, E.Yushina@skoltech.ru, A.Egorova@skoltech.ru, D.Petrova@skoltech.ru (✉ corresponding author), N.Anikanov@skoltech.ru, P.Mazin@skoltech.ru;
2Moscow Institute of Physics and Technology (State University), 9, Institutskii per., Dolgoprudny, Moscow Province, 141700 Russia;
3Ernst Federal Science Center for Animal Husbandry,60, pos. Dubrovitsy, Podolsk District, Moscow Province, 142132 Russia, e-mail belousa663@gmail.com

ORCID:
Pavlova À.S. orcid.org/0000-0003-0726-8395
Petrova D.À. orcid.org/0000-0001-6803-1717
Vanyushkina À.À. orcid.org/0000-0002-2538-689X
Belous À.À. orcid.org/0000-0001-7533-4281
Ushina Å.À. orcid.org/0000-0001-7774-4145
Anikanov N.À. orcid.org/0000-0001-7774-4145
Egorova À.N. orcid.org/0000-0002-0405-9590
Mazin P.V. orcid.org/0000-0001-9268-3352
The authors declare no conflict of interests

Received September 17, 2018

 

A key direction in animal genetics and animal breeding is currently the study of metabolic characteristics of animals in combination with their genotyping, which leads to the development of the new specific markers for prediction the individual phenotypic characteristics of animals based on correlation studies. This would further make possible to create new methods for animal phenotyping using lipid analysis techniques to assess the complex effect of environmental and genetic factors (D.P. Lo Fiego et al., 2002; D.P. Lo Fiego et al., 2005; R. Rossi et al., 2002). Data obtained as a result of the analysis of the genetic features of the animal and its metabolic characteristics make it possible to create predictive models for accurate phenotyping of animals and their offspring. In this work we have for the first time performed the comparative non-targeted mass-spectrometry study of the lipid composition of muscle and adipose tissue in Russian Duroc pigs using positive ion registration mode. The aim of the work was to carry out lipidomic analysis of the pig muscle and adipose tissue as an input for predictive models for animal phenotyping. The study was carried out on the samples of adipose and muscle tissue collected post-mortem from 150-180 day-old Duroc boars (n = 9). Samples were taken from three regions of the longest back muscle, three regions of the biceps femoris, and two regions of subcutaneous dorsal fat (72 samples in total). Analysis of the lipid compounds was performed by liquid chromatography coupled with high-precision time-of-flight mass spectrometry, preceded by the methyl tert-butyl ether and methanol extraction of lipids. Type of ionization used was electrospray. A total of 844 mass spectrometry peaks satisfied the quality criteria and were used for the statistical analysis. Peaks were annotated using the LIPID MAPS database search (http://www.lipidmaps.org), with an accuracy of 10 ppm. Statistical analysis shows significant differences in the Pearson correlation for adipose and muscular tissue samples compared or the same tissue samples compared. Correlation coefficients between lipid patterns of adipose and muscular tissue samples are lower (from 0.48 to 0.86, r = 0.69 on average with 95 % confidence interval from 0.61 to 0.79). Correlation coefficients between lipid patterns in two samples of muscle or adipose tissue are higher (from 0.73 to 0.99, r = 0.93 on average with 95 % confidence interval from 0.86 to 0.97). Unpaired t-test shows differences at p-value < 0.01 Data clustering confirms the difference between muscle samples and subcutaneous fat samples. The main classes of lipids detected in the samples were triglycerides (TAG), diglycerides (DAG), phosphatidylcholines (PC), phosphatidylethanolamines (PE), phosphatidylserines (PS), phosphatidic acids (PA), phosphatidylinositols (PI), and lysophosphatidylcholines (LPC). We have found that adipose tissue samples are enriched in triacylglycerols, while muscle tissue samples are enriched in phospholipids. To summarize, we have identified the main lipid types present in different regions of muscle and adipose tissue of pigs, and revealed the similarities and differences in the lipid composition between the two analyzed tissue types, as well as between two different types of muscles (biceps femoris and longisimus dorsi), and also between muscle and fat tissues. Considering the results obtained in this work we may conclude that liquid chromatography coupled with high-precision time-of-flight mass spectrometry efficiently produces accurate and reproducible lipidomes data. These data may be used in animal breeding, in the search for new genetic markers associated with economically important traits and in breeding programs to evaluate the traits determined by lipid composition.

Keywords: lipidome, animal phenotyping, Sus scrofa domesticus, pigs, Duroc boars, high-performance liquid chromatography, mass spectrometry, muscle tissue, adipose tissue.

 

Full article (Rus)

Full article (Eng)

 

REFERENCES

  1. VanRaden P.M. Efficient methods to compute genomic predictions. J. Dairy Sci., 2008, 91(11): 4414-4423 CrossRef
  2. Sermyagin A.A., Gladyr’ E.A., Kharitonov S.N., Ermilov A.N., Strekozov N.I., Brem G., Zinovieva N.A. Genome-wide association study for milk production and reproduction traits in Russian Holstein cattle population. Sel'skokhozyaistvennaya Biologiya [Agricultural Biology], 2016, 51(2): 182-193 CrossRef
  3. Koshiba S., Motoike I., Kojima K., Hasegawa T., Shirota M., Saito T., Saigusa D., Danjoh I., Katsuoka F, Ogishima S., Kawai Y., Yamaguchi-Kabata Y., Sakurai M., Hirano S., Nakata J., Motohashi H., Hozawa A., Kuriyama S., Minegishi N., Nagasaki M., Takai-Igarashi T., Fuse N., Kiyomoto H., Sugawara J., Suzuki Y., Kure S., Yaegashi N., Tanabe O., Kinoshita K., Yasuda J., Kawai Y. The structural origin of metabolic quantitative diversity. Sci. Rep., 2016, 6: Article number 31463 CrossRef
  4. Saito K., Matsuda F. Metabolomics for functional genomics, systems biology, and biotechnology. Annu. Rev. Plant Biol., 2010, 61(1): 463-489 CrossRef
  5. Zabolotnaya A.A., Bekenev V.A. Zhivotnovodstvo Rossii, 2013, spetsvypusk 2: 29-31 (in Russ.)
  6. Wood J.D., Enser M., Fisher A.V., Nute G.R., Sheard P.R., Richardson R.I., Hughes S.I., Whittington F.M. Fat deposition, fatty acid composition and meat quality: a review. Meat Science, 2008, 78(4): 343-358 CrossRef
  7. Lo Fiego D.P., Santoro P., Macchioni P., De Leonibus E. Influence of genetic type, live weight at slaughter and carcass fatness on fatty acid composition of subcutaneous adipose tissue of raw ham in the heavy pig. Meat Science, 2005, 69(1): 107-114 CrossRef
  8. Cameron N.D., Cameron N.D., Enser M., Nute G.R., Whittington F.M., Penman J.C., Fisken A.C., Perry A.M., Wood J.D. Genotype with nutrition interaction on fatty acid composition of intramuscular fat and the relationship with flavour of pig meat. Meat Science, 2000, 55(2): 187-195 CrossRef
  9. Hogberg A., Pickova J., Babol J., Andersson K., Dutta P.C. Muscle lipids, vitamins E and A and lipid oxidation as affected by diet and RN genotype in female and castrated male Hampshire crossbreed pigs. Meat Science, 2002, 60(4): 411-420 CrossRef
  10. Smith S.J., Iverson S.J., Bowen W.D. Fatty acid signatures and classification trees: new tools for investigating the foraging ecology of seals. Can. J. Fish. Aquat. Sci., 1997, 56(4): 1377-1386 CrossRef
  11. Joensen H., Grahl-Nielsen O. The redfish species Sebastes viviparus, Sebastes marinus and Sebastes mentella have different composition of their tissue fatty acids. Comp. Biochem. Physiol. B Biochem. Mol. Biol., 2001, 129(1): 73-85 CrossRef
  12. Gallardo E., Narvaez-Rivas M., Pablos F., Jurado J. M., Leon-Camacho M. Subcutaneous fat triacylglycerols profile from Iberian pigs as a tool to differentiate between intensive and extensive fattening systems. J. Agric. Food Chem., 2012, 60(7): 1645-1651 CrossRef
  13. Perona J.S., Ruiz-Gutierrez V. Quantitative lipid composition of Iberian pig muscle and adipose tissue by HPLC. Journal of Liquid Chromatography & Related Technologies, 2005, 28(15): 2445-2457 CrossRef
  14. Narvaez-Rivas M., Pablos F., Jurado J. M., Leon-Camacho M. Authentication of fattening diet of Iberian pigs according to their volatile compounds profile from raw subcutaneous fat. Anal. Bioanal. Chem.,2011, 399(6): 2115-2122 CrossRef
  15. Rezzi S., Ramadan Z., Martin F.P.J., Fay L.B., Van Bladeren P., Lindon J.C., Nicholson J.K., Kochhar S. Human metabolic phenotypes link directly to specific dietary preferences in healthy individuals. J. Proteome Res., 2007, 6(11): 4469-4477 CrossRef
  16. Wilson I.D. Metabolic phenotyping by liquid chromatography-mass spectrometry to study human health and disease. Anal. Chem., 2015, 87(5): 2519-2519 CrossRef
  17. Clausen M.R., Christensen K.L., Hedemann M.S., Liu Y., Purup S., Schmidt M., Callesen H., Stagsted J., Bertram H.C. Metabolomic phenotyping of a cloned pig model. BMC Physiol., 2011, 11(1): 14 CrossRef
  18. Christensen K.L., Hedemann M.S., Jorgensen H., Stagsted J., Knudsen K.E. Liquid chromatography-mass spectrometry based metabolomics study of cloned versus normal pigs fed either restricted or ad libitum high-energy diets. J. Proteome Res., 2012, 11(7): 3573-3580 CrossRef
  19. Rainville P.D., Theodoridis G., Plumb R.S., Wilson I.D. Advances in liquid chromatography coupled to mass spectrometry for metabolic phenotyping. TrAC Trends in Analytical Chemistry, 2014, 61: 181-191 CrossRef
  20. Lo Fiego D.P., Macchioni P., Minelli G., Santoro P. Lipid composition of covering and intramuscular fat in pigs at different slaughter age. Italian Journal of Animal Science, 2010, 9(2): 200-205 CrossRef
  21. Rossi R., Corino C. Influence of long-term nutrition with different dietary fats on fatty acid composition of heavy pigs backfat. Italian Journal of Animal Science, 2002, 1(1): 7-16 CrossRef
  22. Libiseller G., Dvorzak M., Kleb U., Gander E., Eisenberg T., Madeo F., Neumann S., Trausinger G., Sinner F., Pieber T., Magnes C. IPO: a tool for automated optimization of XCMS parameters. BMC Bioinformatics, 2015, 16(1): 118 CrossRef
  23. Smith C., Want E., O'Maille G., Abagyan R., Siuzdak G. XCMS: Processing mass-spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem., 2006, 78(3): 779-787 CrossRef
  24. Benjamini Y., Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), 1995, 57(1): 289-300 CrossRef
  25. Hotelling H. Relations between two sets of variates. Biometrika, 1936, 28(3/4): 321-377 CrossRef
  26. Bozek K., Khrameeva E., Reznick J., Omerbaši? D., Bennett N., Lewin G., Azpurua J., Gorbunova V., Seluanov A., Regnard P., Wanert F., Marchal J., Pifferi F., Aujard F., Liu Z., Shi P., Pääbo S., Schroeder F., Willmitzer L., Giavalisco P., Khaitovich P. Lipidome determinants of maximal lifespan in mammals. Scientific Reports, 2017, 7(1): 5 CrossRef
  27. Cameron N., Enser M. Fatty acid composition of lipid in Longissimus dorsi muscle of Duroc and British Landrace pigs and its relationship with eating quality. Meat Science, 1991, 29(4): 295-307 CrossRef
  28. Cajka T., Fiehn O. Comprehensive analysis of lipids in biological systems by liquid chromatography-mass spectrometry. TrAC Trends in Analytical Chemistry, 2014, 61: 192-206 CrossRef
Hummel J., Segu S., Li Y., Irgang S., Jueppner J., Giavalisco P. Ultra performance liquid chromatography and high resolution mass-spectrometry for the analysis of plant lipids. Front. Plant Sci., 2011, 2: 54 CrossRef

back