DOI QR코드

DOI QR Code

Identifying long non-coding RNAs and characterizing their functional roles in swine mammary gland from colostrogenesis to lactogenesis

  • Shi, Lijun (Institute of Animal Science, Chinese Academy of Agricultural Sciences) ;
  • Zhang, Longchao (Institute of Animal Science, Chinese Academy of Agricultural Sciences) ;
  • Wang, Ligang (Institute of Animal Science, Chinese Academy of Agricultural Sciences) ;
  • Liu, Xin (Institute of Animal Science, Chinese Academy of Agricultural Sciences) ;
  • Gao, Hongmei (Institute of Animal Science, Chinese Academy of Agricultural Sciences) ;
  • Hou, Xinhua (Institute of Animal Science, Chinese Academy of Agricultural Sciences) ;
  • Zhao, Fuping (Institute of Animal Science, Chinese Academy of Agricultural Sciences) ;
  • Yan, Hua (Institute of Animal Science, Chinese Academy of Agricultural Sciences) ;
  • Cai, Wentao (Institute of Animal Science, Chinese Academy of Agricultural Sciences) ;
  • Wang, Lixian (Institute of Animal Science, Chinese Academy of Agricultural Sciences)
  • Received : 2021.07.06
  • Accepted : 2021.09.11
  • Published : 2022.06.01

Abstract

Objective: This study was conducted to identify the functional long non-coding RNAs (lncRNAs) for swine lactation by RNA-seq data of mammary gland. Methods: According to the RNA-seq data of swine mammary gland, we screened lncRNAs, performed differential expression analysis, and confirmed the functional lncRNAs for swine lactation by validation of genome wide association study (GWAS) signals, functional annotation and weighted gene co-expression network analysis (WGCNA). Results: We totally identified 286 differentially expressed (DE) lncRNAs in mammary gland at different stages from 14 days prior to (-) parturition to day 1 after (+) parturition, and the expressions of most of lncRNAs were strongly changed from day -2 to day +1. Further, the GWAS signals of sow milk ability trait were significantly enriched in DE lncRNAs. Functional annotation revealed that these DE lncRNAs were mainly involved in mammary gland and lactation developing, milk composition metabolism and colostrum function. By performing weighted WGCNA, we identified 7 out of 12 lncRNA-mRNA modules that were highly associated with the mammary gland at day -14, day -2, and day +1, in which, 35 lncRNAs and 319 mRNAs were involved. Conclusion: This study suggested that 18 lncRNAs and their 20 target genes were promising candidates for swine parturition and colostrum occurrence processes. Our research provided new insights into lncRNA profiles and their regulating mechanisms from colostrogenesis to lactogenesis in swine.

Keywords

Acknowledgement

We acknowledge financial support by the Chinese Academy of Agricultural Sciences.

References

  1. Kim S. Mammary gland growth and nutrient mobilization in lactating sows: a dynamic model to describe nutrient flow. Champaign, IL, USA: University of Illinois; 1999.
  2. Boyd RD, Kensinger RS. Metabolic precursors for milk synthesis. Wageningen, The Netherlands: Wageningen Press; 1998.
  3. Quesnel H, Farmer C, Devillers N. Colostrum intake: Influence on piglet performance and factors of variation. Livest Sci 2012;146:105-14. https://doi.org/10.1016/j.livsci.2012.03.010
  4. Vadmand CN, Krogh U, Hansen CF, Theil PK. Impact of sow and litter characteristics on colostrum yield, time for onset of lactation, and milk yield of sows. J Anim Sci 2015;93:2488-500. https://doi.org/10.2527/jas.2014-8659
  5. Hurley WL. Review: Mammary gland development in swine: embryo to early lactation. Animal 2019;13:S11-9. https://doi.org/10.1017/S1751731119000521
  6. Balzani A, Cordell HJ, Sutcliffe E, Edwards SA. Heritability of udder morphology and colostrum quality traits in swine. J Anim Sci 2016;94:3636-44. https://doi.org/10.2527/jas.2016-0458
  7. Palombo V, Loor JJ, D'Andrea M, et al. Transcriptional profiling of swine mammary gland during the transition from colostrogenesis to lactogenesis using RNA sequencing. BMC Genomics 2018;19:322. https://doi.org/10.1186/s12864-018-4719-5
  8. Jin Y, Zhang K, Huang W, et al. Identification of functional lncRNAs in pseudorabies virus type II infected cells. Vet Microbiol 2020;242:108564. https://doi.org/10.1016/j.vetmic.2019.108564
  9. Liang G, Yang Y, Li H, et al. LncRNAnet: a comprehensive Sus scrofa lncRNA database. Anim Genet 2018;49:632-5. https://doi.org/10.1111/age.12720
  10. St Laurent G, Wahlestedt C, Kapranov P. The Landscape of long noncoding RNA classification. Trends Genet 2015;31:239-51. https://doi.org/10.1016/j.tig.2015.03.007
  11. Miao Z, Wang S, Zhang J, et al. Identification and comparison of long non-conding RNA in Jinhua and Landrace pigs. Biochem Biophys Res Commun 2018;506:765-71. https://doi.org/10.1016/j.bbrc.2018.06.028
  12. Huang W, Zhang X, Li A, Xie L, Miao X. Genome-wide analysis of mRNAs and lncRNAs of intramuscular fat related to lipid metabolism in two pig breeds. Cell Physiol Biochem 2018;50:2406-22. https://doi.org/10.1159/000495101
  13. Wang J, Ren QL, Hua LS, et al. Comprehensive analysis of differentially expressed mRNA, lncRNA and circRNA and their ceRNA networks in the longissimus dorsi muscle of two different pig breeds. Int J Mol Sci 2019;20:1107. https://doi.org/10.3390/ijms20051107
  14. Li S, Chen C, Chai M, et al. Identification and analysis of lncRNAs by whole-transcriptome sequencing in porcine preadipocytes induced by BMP2. Cytogenet Genome Res 2019;158:133-44. https://doi.org/10.1159/000501182
  15. Wang Y, Hu T, Wu L, Liu X, Xue S, Lei M. Identification of non-coding and coding RNAs in porcine endometrium. Genomics 2017;109:43-50. https://doi.org/10.1016/j.ygeno.2016.11.007
  16. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014;30:2114-20. https://doi.org/10.1093/bioinformatics/btu170
  17. 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;33:290-5. https://doi.org/10.1038/nbt.3122
  18. Guttman M, Garber M, Levin JZ, et al. Correction: Corrigendum: Ab initio reconstruction of cell type-specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs (2010;28:503-10). Nat Biotechnol 2010;28:756. https://doi.org/10.1038/nbt0710-756b
  19. Ahmad A, Dey L. A k-mean clustering algorithm for mixed numeric and categorical data. Data Knowl Eng 2007;63:503-27. https://doi.org/10.1016/j.datak.2007.03.016
  20. Cai WT, Li C, Li JY, Song JZ, Zhang SL. Integrated small RNA sequencing, transcriptome and GWAS data reveal microRNA regulation in response to milk protein traits in Chinese Holstein cattle. Front Genet 2021;12:726706. https://doi.org/10.3389/fgene.2021.726706
  21. Guil S, Esteller M. Cis-acting noncoding RNAs: friends and foes. Nat Struct Mol Biol 2012;19:1068-75. https://doi.org/10.1038/nsmb.2428
  22. Derrien T, Johnson R, Bussotti G, et al. The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression. Genome Res 2012;22:1775-89. https://doi.org/10.1101/gr.132159.111
  23. Xie C, Mao X, Huang J, et al. KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res 2011;39(Suppl_2):W316-22. https://doi.org/10.1093/nar/gkr483
  24. Le K, Guo H, Zhang Q, et al. Gene and lncRNA co-expression network analysis reveals novel ceRNA network for triplenegative breast cancer. Sci Rep 2019;9:15122. https://doi.org/10.1038/s41598-019-51626-7
  25. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008;9:559. https://doi.org/10.1186/1471-2105-9-559
  26. Kensinger RS, Collier RJ, Bazer FW, Ducsay CA, Becker HN. Nucleic acid, metabolic and histological changes in gilt mammary tissue during pregnancy and lactogenesis. J Anim Sci 1982;54:1297-308. https://doi.org/10.2527/jas1982.5461297x
  27. Wang L, Cho KB, Li Y, Tao G, Xie Z, Guo B. Long noncoding RNA (lncRNA)-mediated competing endogenous RNA networks provide novel potential biomarkers and therapeutic targets for colorectal cancer. Int J Mol Sci 2019;20:5758. https://doi.org/10.3390/ijms20225758
  28. Li X, Wang H, Yao B, Xu W, Chen J, Zhou X. lncRNA H19/miR-675 axis regulates cardiomyocyte apoptosis by targeting VDAC1 in diabetic cardiomyopathy. Sci Rep 2016;6:36340. https://doi.org/10.1038/srep36340
  29. Nguyen DA, Neville MC. Tight junction regulation in the mammary gland. J Mammary Gland Biol Neoplasia 1998;3:233-46. https://doi.org/10.1023/a:1018707309361
  30. Kessler EC, Wall SK, Hernandez LL, Gross JJ, Bruckmaier RM. Short communication: Mammary gland tight junction permeability after parturition is greater in dairy cows with elevated circulating serotonin concentrations. J Dairy Sci 2019;102:1768-74. https://doi.org/10.3168/jds.2018-15543
  31. Arrowsmith S, Wray S. Oxytocin: its mechanism of action and receptor signalling in the myometrium. J Neuroendocrinol 2014;26:356-69. https://doi.org/10.1111/jne.12154
  32. Gimpl G, Fahrenholz F. The oxytocin receptor system: structure, function, and regulation. Physiol Rev 2001;81:629-83. https://doi.org/10.1152/physrev.2001.81.2.629
  33. Hennighausen L, Robinson GW. Signaling pathways in mammary gland development. Dev Cell 2001;1:467-75. https://doi.org/10.1016/s1534-5807(01)00064-8
  34. Wan YH, Saghatelian A, Chong LW, Zhang CL, Cravatt BF, Evans RM. Maternal PPAR gamma protects nursing neonates by suppressing the production of inflammatory milk. Gene Dev 2007;21:1895-908. https://doi.org/10.1101/gad.1567207
  35. Quesnel H, Farmer C. Review: nutritional and endocrine control of colostrogenesis in swine. Animal 2019;13:S26-34. https://doi.org/10.1017/S1751731118003555