Please wait a minute...
Frontiers of Agricultural Science and Engineering

ISSN 2095-7505

ISSN 2095-977X(Online)

CN 10-1204/S

Postal Subscription Code 80-906

Front. Agr. Sci. Eng.    2015, Vol. 2 Issue (3) : 237-241    https://doi.org/10.15302/J-FASE-2015057
RESEARCH ARTICLE
Identification of genome regions and important variations associated with expression level of MYH genes and its correlation with meat quality traits in pigs
Ye HOU1,2,Lu JING1,2,Yunxia ZHAO1,2,Sheng WANG1,2,An LIU1,2,Shuhong ZHAO1,2,Xinyun LI1,2,*()
1. Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education of China, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
2. The Cooperative Innovation Center for Sustainable Pig Production, Wuhan 430070, China
 Download: PDF(156 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

Meat quality is one of the most important economic traits in pig breeding. It has been reported that the composition of type II muscle fibers is correlated with meat quality in pigs. Type II muscle fibers contain three isoforms, IIa, IIb and IIx, which contain specific myofibrillar proteins MYH2, MYH4 and MYH1, respectively. In this study, the expression levels of MYH1, MYH2, and MYH4 genes in the Longissimus thoracis (LT) muscle were measured in 114 Yorkshire pigs. Further, the correlations between the expression level of MYH genes and the meat quality traits of intramuscular fat, drip loss, water-holding capacity and postmortem pH values were analyzed. The results showed that the expression level of MYH2 was positively correlated with the intramuscular fat (R= 0.20, P<0.05). The expression levels of MHY1 and MYH4 were negatively correlated with the pH at 24 h post mortem (pH24h ; R= -0.28, P<0.01; R= -0.25, P<0.01, respectively). Besides, the 60K SNP chip was used for genotyping the individuals. Genome wide association analysis indicated that 15 SNPs were significantly associated with the expression levels of these three MYH genes. The results indicated the expression levels of MYH1, MYH2 and MYH4 genes could be useful markers for improvement of meat quality in pigs.

Keywords pig      IMF      pH      MYH1      MYH2      MYH4     
Corresponding Author(s): Xinyun LI   
Just Accepted Date: 04 June 2015   Online First Date: 06 July 2015    Issue Date: 10 November 2015
 Cite this article:   
Ye HOU,Lu JING,Yunxia ZHAO, et al. Identification of genome regions and important variations associated with expression level of MYH genes and its correlation with meat quality traits in pigs[J]. Front. Agr. Sci. Eng. , 2015, 2(3): 237-241.
 URL:  
https://academic.hep.com.cn/fase/EN/10.15302/J-FASE-2015057
https://academic.hep.com.cn/fase/EN/Y2015/V2/I3/237
Gene Primer symbol Primer sequence 5′−3′ Product length/bp Tm/°C
MYH1 MYH1-F AGAAGATCAACTGAGTGAACT 149 58
MYH1-R AGAGCTGAGAAACTAACGTG
MYH2 MYH2-F GCTGAGCGAGCTGAAATCC 137 60
MYH2-R ACTGAGACACCAGAGCTTCT
MYH4 MYH4-F ACCTCTCCAAGTTCCGCAAG 172 59
MYH4-R TGTGCATTTCTTTGGTCACTTT
Tab.1  List of oligonucleotide primers of MYH genes
Fig.1  The expression level of MYH1, MYH2 and MYH4 genes in LT muscle in 114 Yorkshire pigs
MYH1 MYH2 MYH4 WHC PH45min PH24h DLS IMF
MYH1 1 0.61 0.96 −0.16 0.11 −0.28** −0.17 0.01
MYH2 1 0.52 0.09 0.16 0.17 0.14 0.20*
MYH4 1 −0.12 0.08 −0.25** −0.18 <0.01
WHC 1 0.04 0.12 0.23 −0.13
pH45min 1 0.18 0.03 −0.11
pH24h 1 0.24 −0.09
DLS 1 −0.29
IMF 1
Tab.2  Correlation coefficients for a matrix of expression levels of MYH genes and meat quality traits
Gene SNP Chr. Position Gene Variant P-value (×10−4)
MYH1 MARC0059864 10 75940391 ENSSSCG00000011153 Downstream gene 4.88
ASGA0071803 15 156955591 ENSSSCG00000016397 Intron 5.18
DIAS0001219 11 9547194 NSSSCG00000009348 Intergenic 9.25
RIF1
ALGA0059421 10 61014593 Intergenic 9.55
ASGA0048857 10 69757171 ITIH2 Upstream gene 9.68
MYH2 ASGA0014535 3 52293293 MRPS9 Intergenic 4.93
ALGA0109569 3 51843143 TGFBRAP1 Intron 6.42
MARC0059864 10 75940391 ENSSSCG00000026093 Downstream gene 8.25
ALGA0090727 16 54349200 ENSSSCG00000016979 Intergenic 8.45
MYH4 ASGA0010629 2 86507283 POC5 Intergenic 2.85
SV2C
H3GA0011751 4 8230320 ENSSSCG00000027885 Intron 6.47
WISP1 Intergenic
ASGA0101845 2 88201093 TBCA Intron 6.94
MARC0059864 10 75940391 LARP4B Downstream gene 8.73
ALGA0084260 15 21658450 ENSSSCG00000024217 Intergenic 9.54
ENSSSCG00000023479
ASGA0071803 15 156955591 ENSSSCG00000016397 Intron 9.77
RIF1 Intergenic
Tab.3  SNPs and genes related to the expression levels of MYH genes
1 Schiaffino  S, Reggiani  C. Myosin isoforms in mammalian skeletal muscle. Journal of Applied Physiology, 1994, 77(2): 493–501
pmid: 8002492
2 Hämäläinen  N, Pette  D. Patterns of myosin isoforms in mammalian skeletal muscle fibres. Microscopy Research and Technique, 1995, 30(5): 381–389
https://doi.org/10.1002/jemt.1070300505 pmid: 7787237
3 Lefaucheur  L, Lebret  B, Ecolan  P, Louveau  I, Damon  M, Prunier  A, Billon  Y, Sellier  P, Gilbert  H. Muscle characteristics and meat quality traits are affected by divergent selection on residual feed intake in pigs. Journal of Animal Science, 2011, 89(4): 996–1010
https://doi.org/10.2527/jas.2010-3493 pmid: 21148787
4 Kim  G D, Jeong  J Y, Jung  E Y, Yang  H S, Lim  H T, Joo  S T. The influence of fiber size distribution of type IIB on carcass traits and meat quality in pigs. Meat Science, 2013, 94(2): 267–273
https://doi.org/10.1016/j.meatsci.2013.02.001 pmid: 23523735
5 Ryu  Y C, Kim  B C. Comparison of histochemical characteristics in various pork groups categorized by postmortem metabolic rate and pork quality. Journal of Animal Science, 2006, 84(4): 894–901
pmid: 16543567
6 Binas  B, Danneberg  H, McWhir  J, Mullins  L, Clark  A J. Requirement for the heart-type fatty acid binding protein in cardiac fatty acid utilization. The FASEB Journal, 1999, 13(8): 805–812
7 Han  X, Jiang  T, Yang  H, Zhang  Q, Wang  W, Fan  B, Liu  B. Investigation of four porcine candidate genes (H-FABP, MYOD1, UCP3 and MASTR) for meat quality traits in Large White pigs. Molecular Biology Reports, 2012, 39(6): 6599–6605
https://doi.org/10.1007/s11033-012-1490-6 pmid: 22311016
8 Zhao  S, Wang  J, Song  X, Zhang  X, Ge  C, Gao  S. Impact of dietary protein on lipid metabolism-related gene expression in porcine adipose tissue. Nutrition & metabolism, 2010, 7(1): 6
https://doi.org/10.1186/1743-7075-7-6 pmid: 20205889
9 Pang  W J, Yu  T Y, Bai  L, Yang  Y J, Yang  G S. Tissue expression of porcine FoxO1 and its negative regulation during primary preadipocyte differentiation. Molecular Biology Reports, 2009, 36(1): 165–176
https://doi.org/10.1007/s11033-007-9163-6 pmid: 18293098
10 Fujii  J, Otsu  K, Zorzato  F, de Leon  S, Khanna  V K, Weiler  J E, O’Brien  P J, MacLennan  D H. Identification of a mutation in porcine ryanodine receptor associated with malignant hyperthermia. Science, 1991, 253(5018): 448–451
https://doi.org/10.1126/science.1862346 pmid: 1862346
11 Milan  D, Jeon  J T, Looft  C, Amarger  V, Robic  A, Thelander  M, Rogel-Gaillard  C, Paul  S, Iannuccelli  N, Rask  L, Ronne  H, Lundström  K, Reinsch  N, Gellin  J, Kalm  E, Roy  P L, Chardon  P, Andersson  L. A mutation in PRKAG3 associated with excess glycogen content in pig skeletal muscle. Science, 2000, 288(5469): 1248–1251
https://doi.org/10.1126/science.288.5469.1248 pmid: 10818001
12 Hu  Z L, Park  C A, Wu  X L, Reecy  J M. Animal QTLdb: an improved database tool for livestock animal QTL/association data dissemination in the post-genome era. Nucleic Acids Research, 2013, 41(D1): D871–D879
https://doi.org/10.1093/nar/gks1150 pmid: 23180796
13 Reyer  H, Ponsuksili  S, Wimmers  K, Murani  E. Association of N-terminal domain polymorphisms of the porcine glucocorticoid receptor with carcass composition and meat quality traits. Animal Genetics, 2014, 45(1): 125–129
https://doi.org/10.1111/age.12083 pmid: 23980817
14 Zambonelli  P, Davoli  R, Bigi  M, Braglia  S, De Paolis  L F, Buttazzoni  L, Gallo  M, Russo  V. SNPs detection in DHPS-WDR83 overlapping genes mapping on porcine chromosome 2 in a QTL region for meat pH. BMC Genetics, 2013, 14(1): 99
https://doi.org/10.1186/1471-2156-14-99 pmid: 24103193
15 Dong  Q, Liu  H, Li  X, Wei  W, Zhao  S, Cao  J. A genome-wide association study of five meat quality traits in Yorkshire pigs. Frontiers of Agricultural Science and Engineering, 2014, 1(2): 137–143
https://doi.org/10.15302/J-FASE-2014014
16 Wimmers  K, Ngu  N T, Jennen  D G, Tesfaye  D, Murani  E, Schellander  K, Ponsuksili  S. Relationship between myosin heavy chain isoform expression and muscling in several diverse pig breeds. Journal of Animal Science, 2008, 86(4): 795–803
https://doi.org/10.2527/jas.2006-521 pmid: 18156349
17 Lipka  A E, Tian  F, Wang  Q, Peiffer  J, Li  M, Bradbury  P J, Gore  M A, Buckler  E S, Zhang  Z. GAPIT: genome association and prediction integrated tool. Bioinformatics, 2012, 28(18): 2397–2399
https://doi.org/10.1093/bioinformatics/bts444 pmid: 22796960
18 Hu  H, Wang  J, Zhu  R, Guo  J, Wu  Y. Effect of myosin heavy chain composition of muscles on meat quality in Laiwu pigs and Duroc. Science in China Series C: Life sciences, 2008, 51(2): 127–132
19 Ryu  Y C, Choi  Y M, Lee  S H, Shin  H G, Choe  J H, Kim  J M, Hong  K C, Kim  B C. Comparing the histochemical characteristics and meat quality traits of different pig breeds. Meat Science, 2008, 80(2): 363–369
https://doi.org/10.1016/j.meatsci.2007.12.020 pmid: 22063341
20 Ando  K, Fukuhara  S, Moriya  T, Obara  Y, Nakahata  N, Mochizuki  N. Rap1 potentiates endothelial cell junctions by spatially controlling myosin II activity and actin organization. The Journal of Cell Biology, 2013, 202(6): 901–916
https://doi.org/10.1083/jcb.201301115 pmid: 24019534
21 Buonomo  S B, Wu  Y, Ferguson  D, de Lange  T. Mammalian Rif1 contributes to replication stress survival and homology-directed repair. The Journal of Cell Biology, 2009, 187(3): 385–398
https://doi.org/10.1083/jcb.200902039 pmid: 19948482
22 Koc  E C, Burkhart  W, Blackburn  K, Koc  H, Moseley  A, Spremulli  L L. Identification of four proteins from the small subunit of the mammalian mitochondrial ribosome using a proteomics approach. Protein Science, 2001, 10(3): 471–481
23 Chakkalakal  J V, Nishimune  H, Ruas  J L, Spiegelman  B M, Sanes  J R. Retrograde influence of muscle fibers on their innervation revealed by a novel marker for slow motoneurons. Development, 2010, 137(20): 3489–3499
https://doi.org/10.1242/dev.053348 pmid: 20843861
[1] Zhaogui YAN, Shengyu LIU, Junlian ZHANG, Guan HUANG, Lijun DUAN, Yaomei YE. Optimizing hairy root production from explants of Phyllanthus hainanensis, a shrub used for traditional herbal medicine[J]. Front. Agr. Sci. Eng. , 2020, 7(4): 513-522.
[2] Zhong WEI, Ville-Petri FRIMAN, Thomas POMMIER, Stefan GEISEN, Alexandre JOUSSET, Qirong SHEN. Rhizosphere immunity: targeting the underground for sustainable plant health management[J]. Front. Agr. Sci. Eng. , 2020, 7(3): 317-328.
[3] Lin FU, Wu XIONG, Francisco DINI-ANDREOTE, Beibei WANG, Chengyuan TAO, Yunze RUAN, Zongzhuan SHEN, Rong LI, Qirong SHEN. Changes in bulk soil affect the disease-suppressive rhizosphere microbiome against Fusarium wilt disease[J]. Front. Agr. Sci. Eng. , 2020, 7(3): 307-316.
[4] Li XUE, Ertao WANG. Arbuscular mycorrhizal associations and the major regulators[J]. Front. Agr. Sci. Eng. , 2020, 7(3): 296-306.
[5] Ruigao SONG, Yu WANG, Yanfang WANG, Jianguo ZHAO. Base editing in pigs for precision breeding[J]. Front. Agr. Sci. Eng. , 2020, 7(2): 161-170.
[6] Chris PROUDFOOT, Gus MCFARLANE, Bruce WHITELAW, Simon LILLICO. Livestock breeding for the 21st century: the promise of the editing revolution[J]. Front. Agr. Sci. Eng. , 2020, 7(2): 129-135.
[7] Nicolas MUNIER-JOLAIN, Martin LECHENET. Methodological considerations for redesigning sustainable cropping systems: the value of data-mining large and detailed farm data sets at the cropping system level[J]. Front. Agr. Sci. Eng. , 2020, 7(1): 21-27.
[8] Dongdong LI, Meng WANG, Xianyan KUANG, Wenxin LIU. Genetic study and molecular breeding for high phosphorus use efficiency in maize[J]. Front. Agr. Sci. Eng. , 2019, 6(4): 366-379.
[9] Uwe LUDEWIG, Lixing YUAN, Günter NEUMANN. Improving the efficiency and effectiveness of global phosphorus use: focus on root and rhizosphere levels in the agronomic system[J]. Front. Agr. Sci. Eng. , 2019, 6(4): 357-365.
[10] Rui WANG, Weiming SHI, Yilin LI. Phosphorus supply and management in vegetable production systems in China[J]. Front. Agr. Sci. Eng. , 2019, 6(4): 348-356.
[11] Gu FENG, Jingping GAI, Xionghan FENG, Haigang LI, Lin ZHANG, Keke YI, Jialong LV, Yiyong ZHU, Li TANG, Yilin LI. Strategies for improving fertilizer phosphorus use efficiency in Chinese cropping systems[J]. Front. Agr. Sci. Eng. , 2019, 6(4): 341-347.
[12] Martin BLACKWELL, Tegan DARCH, Richard HASLAM. Phosphorus use efficiency and fertilizers: future opportunities for improvements[J]. Front. Agr. Sci. Eng. , 2019, 6(4): 332-340.
[13] Torsten MÜLLER, Fusuo ZHANG. Adaptation of Chinese and German maize-based food-feed-energy systems to limited phosphate resources—a new Sino-German international research training group[J]. Front. Agr. Sci. Eng. , 2019, 6(4): 313-320.
[14] Guohua LI, Qian LIU, Haigang LI, Fusuo ZHANG. Comparison of analytical procedures for measuring phosphorus content of animal manures in China[J]. Front. Agr. Sci. Eng. , 2019, 6(4): 431-440.
[15] Qing XUE, Xinyue HE, Saskia D. SACHS, Gero C. BECKER, Tao ZHANG, Andrea KRUSE. The current phosphate recycling situation in China and Germany: a comparative review[J]. Front. Agr. Sci. Eng. , 2019, 6(4): 403-418.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed