916
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

A comprehensive study on the longissius dorsi muscle of Ashdan yaks under different feeding regimes based on transcriptomic and metabolomic analyses

, , , , , , , , , & show all

References

  • Wang H, Chai Z, Hu D, et al. A global analysis of CNVs in diverse yak populations using whole-genome resequencing. BMC Genomics. 2019;20(1):61.
  • Qi XB, Zhang Q, He YX, et al. The transcriptomic landscape of yaks reveals molecular pathways for high altitude adaptation. Genome Biol Evol. 2019;11(1):72–85.
  • Wu JG. The distributions of Chinese yak breeds in response to climate change over the past 50 years. Anim Sci J. 2016;87(7):947–958.
  • Ruan CM, Wang J, Yang YX, et al. Proteomic analysis or Tianzhu White Yak (Bos grunniens) testis at different sexual developmental stages. Anim Sci J. 2019;90(3):333–343.
  • Bao Q, Zhang XL, Bao PJ, et al. Using weighted gene co-expression network analysis (WGCNA) to identify the hub genes related to hypoxic adaptation in yak (Bos grunniens). Genes Genomics. 2021;43(10):1231–1246.
  • Shi FY, Wang HC, Degen AA, et al. Rumen parameters of yaks (Bos grunniens) and indigenous cattle (Bos taurus) grazing on the Qinghai‐Tibetan Plateau. J Anim Physiol Anim Nutr (Berl). 2019;103(4):969–976.
  • Fei G. Genome-wide selection and association analysis of early growth traits in Ashtan yak (Bos grunniens). Chin Acad Agric Sci. 2021;10:8–10.
  • Wu XY, Zhou XL, Ding XJ, et al. The selection of reference genes for quantitative real-time PCR in the Ashidan yak mammary gland during lactation and dry period. Animals (Basel). 2019;9(11):943.
  • Huang C, Ge F, Ren WW, et al. Copy number variation of the HPGDS gene in the Ashidan yak and its associations with growth traits. Gene. 2021;772:145382.
  • Xu Y, Shi T, Cai HF, et al. Associations of MYH3 gene copy number variations with transcriptional expression and growth traits in Chinese cattle. Gene. 2014;535(2):106–111.
  • Jian H, Li XJ, Coleman K, et al. SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. Nat Methods. 2021;18(11):1342–1351.
  • Oliveira PSND, Coutinho LL, Tizioto PC, et al. An integrative transcriptome analysis indicates regulatory mRNA-miRNA networks for residual feed intake in Nelore cattle. Sci Rep. 2018;8(1):17072.
  • Chen ID, Rathi VK, DeAndrade DS, Jay PK. Association of genes with physiological functions by comparative analysis of pooled expression microarray data. Physiol Genom. 2013;45(2):69–78.
  • Han Y, Li LZ, Kastury NL, Thomas CT, Lam MY, Lau E. Transcriptome features of striated muscle aging and predictability of protein level changes. Mol Omics. 2021;17(5):796–808.
  • Capomaccio S, Vitulo N, Verini-Supplizi A, et al. RNA sequencing of the exercise transcriptome in equine athletes. PLoS One. 2013;8(12):e83504.
  • Salilew-Wondim D, Tesfaye D, Hoelker M, Schellander K. Embryo transcriptome response to environmental factors: Implication for its survival under suboptimal conditions. Anim Reprod Sci. 2014;149(1-2):30–38.
  • Hatzirodos NH, Irving-Rodgers HF, Hummitzsch K, Harland ML, Morris SE, Rodgers RJ. Transcriptome profiling of granulosa cells of bovine ovarian follicles during growth from small to large antral sizes. BMC Genomics. 2014;15:24.
  • Fan X, Fu Y, Zhou X, et al. Single-cell transcriptome analysis reveals cell lineage specification in temporal-spatial patterns in human cortical development. Sci Adv. 2020;6(34):eaaz2978.
  • Olivera-Martinez I, Schurch N, Li RA, et al. Major transcriptome re-organisation and abrupt changes in signalling, cell cycle and chromatin regulation at neural differentiation in vivo. Development. 2014;141(16):3266–3276.
  • Amberg A, Riefke B, Schlotterbeck G, et al. NMR and MS methods for metabolomics. Drug Saf Eval. 2017;1641:229–258.
  • Berk M, Ebbels T, Montana G. A statistical framework for biomarker discovery in metabolomic time course data. Bioinformatics. 2011;27(14):1979–1985.
  • Serena MD, Bonafè M, Giudetti MA. Urinary metabolic biomarkers in cancer patients: an overview. Methods Mol Biol. 2021;2292:203–212.
  • Goldansaz SA, Guo AC, Sajed T, Steele MA, Plastow GS, Wishart DS. Livestock metabolomics and the livestock metabolome: a systematic review. PLoS One. 2017;12(5):e0177675.
  • Lindeque JZ, Van Rensburg PJJ, Louw R, et al. Obesity and metabolomics: metallothioneins protect against high-fat diet-induced consequences in metallothionein knockout mice. OMICS. 2015;19(2):92–103.
  • Forcisi S, Moritz F, Kanawati B, Tziotis D, Lehmann R, Schmitt-Kopplin P. Liquid chromatography–mass spectrometry in metabolomics research: mass analyzers in ultra high pressure liquid chromatography coupling. J Chromatogr A. 2013;1292:51–65.
  • Cheng J, Pan Y, Yang S, et al. Integration of transcriptomics and non-targeted metabolomics reveals the underlying mechanism of follicular atresia in Chinese buffalo. J Steroid Biochem Mol Biol. 2021;212:105944.
  • Carrillo JA, He YH, Li YK, et al. Integrated metabolomic and transcriptome analyses reveal finishing forage affects metabolic pathways related to beef quality and animal welfare. Sci Rep. 2016;6(1):25948.
  • Li W, Guan JW, Qiu LX, Sun YT, Du M. Study on the molecular mechanism of regulating tenderness of Longissimus Dorsi muscle of donkey based on transcriptomics and metabolomics. Acta Vett Zootech Sin. 2022;53:743–754.
  • Zhan HW, Xiong YC, Wang ZC, et al. Integrative analysis of transcriptomic and metabolomic profiles reveal the complex molecular regulatory network of meat quality in Enshi black pigs. Meat Sci. 2022;183:108642.
  • Liu JQ, Li J, Chen WT, et al. Comprehensive evaluation of the metabolic effects of porcine CRTC3 overexpression on subcutaneous adipocytes with metabolomic and transcriptomic analyses. J Anim Sci Biotechnol. 2021;12(1):19.
  • Sun HZ, Zhu Z, Zhou M, Wang J, Dugan MR, Guan LL. Gene co-expression and alternative splicing analysis of key metabolic tissues to unravel the regulatory signatures of fatty acid composition in cattle. RNA Biol. 2021;18(6):854–862.
  • Xi B, Luo J, Gao YQ, et al. Transcriptome–metabolome analysis of fatty acid of Bamei pork and Gansu Black pork in China. Bioprocess Biosyst Eng. 2021;44(5):995–1002.
  • Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;18(1):83.
  • Li QF, Zhao XB, Liu HL, Li N, Xie Z. A review of the research on taxonomic status in yak (Poephagus). Acta Zootaxonom Sin. 2006;31:520–524.
  • Xu SY, Zhao LL, Xiao SJ, Gao TX. Whole genome resequencing data for three rockfish species of Sebastes. Sci Data. 2019;6(1):97.
  • Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–2120.
  • Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat Methods. 2015;12(4):357–360.
  • Trapnell C, William BA, Pertea G, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010;28(5):511–515.
  • Roberts A, Trapnell C, Donaghey J, Rinn JL, Pachter L. Improving RNA-Seq expression estimates by correcting for fragment bias. Genome Biol. 2011;12(3):R22.
  • Anders S, Pyl PT, Huber W. HTSeq-a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31(2):166–169.
  • Anders S, Huber W. Differential expression of RNA-Seq data at the gene level-the DESeq package. Biol Comput Sci. 2012;12:f1000research.
  • Pertea M, Pertea GM, Antonescu CM, Chang TC, Mendell JT, Salzberg SL. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol. 2015;33(3):290–295.
  • Florea L, Song L, Salzberg SL. Thousands of exon skipping events differentiate among splicing patterns in sixteen human tissues. F1000Res. 2013;2:188.
  • Li H, Handsaker B, Wysoker A, et al. The Sequence Alignment/Map (SAM) format and SAMtools. Bioinformatics. 2009;25(16):2078–2079.
  • Li H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics. 2011;27(21):2987–2993.
  • Hao LZ, Yang X, Huang YY, et al. Using mineral elements to authenticate the geographical origin of Yak meat. Kafkas Üniv Vet Fakült Derg. 2019;25:93–98.
  • Liu XC, Zhang S, Sun BZ, et al. Progress in understanding quality characteristics of yak meat. Meat Res/Roulei Yanjiu. 2020;34:78–83.
  • Guan JQ, Long K, Ma J, et al. Comparative analysis of the microRNA transcriptome between yak and cattle provides insight into high-altitude adaptation. PeerJ. 2017;5:e3959.
  • Onopiuk A, Półtorak A, Sun DW, Wierzbicka A. Effects of selected myofibrillar protein activities on beef tenderization process based on electrophoretic analysis. J Food Process Eng. 2018;41(1):e12596.
  • Kemp CM, Sensky PL, Bardsley RG, Buttery PJ, Parr T. Tenderness–An enzymatic view. Meat Sci. 2010;84(2):248–256.
  • Zhang P, Shang P, Zhang B, et al. Comparison of slaughtering performance and meat quality of Tibetan pigs under indoor feeding and grazing conditions. Chin J Anim Sci. 2019;55:107–109.
  • Liu YX, Ma XM, Xiong L, et al. Effects of intensive fattening with total mixed rations on carcass characteristics, meat quality, and meat chemical composition of yak and mechanism based on serum and transcriptomic profiles. Front Vet Sci. 2020;7:599418.
  • Skřivanová V, Tůmová E, Englmaierová M, Chodova D, Skrivan M. Do rearing system and free-range stocking density affect meat quality of chickens fed feed mixture with rapeseed oil? Czech J Anim Sci. 2017;62(4):141–149.
  • Rahman MH, Hossain MM, Rahman SME, Amin MR, Oh DH. Evaluation of physicochemical deterioration and lipid oxidation of beef muscle affected by freeze-thaw cycles. Korean J Food Sci Anim Resour. 2015;35(6):772–782.
  • Cassens RG, Carpenter CE, Eddinger TJ. An analysis of microstructural factors which influence the use of muscle as a food. Food Struct. 1984;3:2.
  • Humada MJ, Sañudo C, Serrano E. Chemical composition, vitamin E content, lipid oxidation, colour and cooking losses in meat from Tudanca bulls finished on semi-extensive or intensive systems and slaughtered at 12 or 14 months. Meat Sci. 2014;96(2 Pt A):908–915.
  • Rhee MS, Ryu YC, Imm JY, Kim BC. Combination of low voltage electrical stimulation and early postmortem temperature conditioning on degradation of myofibrillar proteins in Korean native cattle (Hanwoo). Meat Sci. 2000;55(4):391–396.
  • Lomiwes D, Farouk MM, Frost DA, Dobbie PM, Young OA. Small heat shock proteins and toughness in intermediate pHu beef. Meat Sci. 2013;95(3):472–479.
  • Latinkić BV, Cooper B, Towers N, Sparrow D, Kotecha S, Mohun TJ. Distinct enhancers regulate skeletal and cardiac muscle-specific expression programs of the cardiac α-actin gene in Xenopus embryos. Dev Biol. 2002;245(1):57–70.
  • Sibut V, Hennequet-Antier C, Bihan-Duval L, Marthey S, Duclos MJ, Berri C. Identification of differentially expressed genes in chickens differing in muscle glycogen content and meat quality. BMC Genomics. 2011;12(1):112.
  • Venturini GC, Stafuzza NB, Cardoso DF, et al. Association between ACTA1 candidate gene and performance, organs and carcass traits in broilers. Poult Sci. 2015;94(12):2863–2869.
  • Shin SC, Chung ER. Identification of differentially expressed genes between high and low marbling score grades of the longissimus lumborum muscle in Hanwoo (Korean cattle). Meat Sci. 2016;121:114–118.
  • Xie P, Guo SB, Fan YN, Zhang H, Gu DF, Li HH. Atrogin-1/MAFbx enhances simulated ischemia/reperfusion-induced apoptosis in cardiomyocytes through degradation of MAPK phosphatase-1 and sustained JNK activation. J Biol Chem. 2009;284(9):5488–5496.
  • Wang AL, Zhang Y, Li MJ, Lan SY, Wang JQ, Chen h SNP identification in FBXO32 gene and their associations with growth traits in cattle. Gene. 2013;515(1):181–186.
  • Chen B, Xu JG, He XM, et al. A genome-wide mRNA screen and functional analysis reveal FOXO3 as a candidate gene for chicken growth. PLoS One. 2015;10(9):e0137087.
  • Cleveland BM, Evenhuis JP. Molecular characterization of atrogin-1/F-box protein-32 (FBXO32) and F-box protein-25 (FBXO25) in rainbow trout (Oncorhynchus mykiss): Expression across tissues in response to feed deprivation. Comp Biochem Physiol B Biochem Mol Biol. 2010;157(3):248–257.
  • Ying W, Tokach MD, DeRouchey JM, et al. Effects of dietary L-carnitine and dried distillers grains with solubles on growth, carcass characteristics, and loin and fat quality of growing-finishing pigs. J Anim Sci. 2013;91(7):3211–3219.
  • James BW, Owen KQ, Lawrence TE, et al. Interactive effects between paylean (ractopamine⋅ HCl) and dietary L-carnitine on finishing pig growth performance and carcass characteristics. Kansas Agric Exp Stat Res Rep. 2002;10:106–110.
  • Ma XY, Lin YC, Jiang ZY, et al. Dietary arginine supplementation enhances antioxidative capacity and improves meat quality of finishing pigs. Amino Acids. 2010;38(1):95–102.
  • Ma XY, Jiang ZY, Lin YC, Zheng CT, Zhou GL. Dietary supplementation with carnosine improves antioxidant capacity and meat quality of finishing pigs. J Anim Physiol Anim Nutr (Berl). 2010;94(6):e286–e295.
  • Dransfield E, Sosnicki AA. Relationship between muscle growth and poultry meat quality. Poult Sci. 1999;78(5):743–746.
  • D'Astous-Pagé J, Gariépy C, Blouin R, et al. Carnosine content in the porcine longissimus thoracis muscle and its association with meat quality attributes and carnosine-related gene expression. Meat Sci. 2017;124:84–94.
  • Cong J, Zhang L, Li J, Wang S, Gao F, Zhou G. Effects of dietary supplementation with carnosine on meat quality and antioxidant capacity in broiler chickens. Br Poult Sci. 2017;58(1):69–75.