DOI QR코드

DOI QR Code

Multi-tissue observation of the long non-coding RNA effects on sexually biased gene expression in cattle

  • Yoon, Joon (Department of Natural Science, Interdisciplinary Program in Bioinformatics, Seoul National University) ;
  • Kim, Heebal (Department of Natural Science, Interdisciplinary Program in Bioinformatics, Seoul National University)
  • 투고 : 2018.07.06
  • 심사 : 2018.11.14
  • 발행 : 2019.07.01

초록

Objective: Recent studies have implied that gene expression has high tissue-specificity, and therefore it is essential to investigate gene expression in a variety of tissues when performing the transcriptomic analysis. In addition, the gradual increase of long non-coding RNA (lncRNA) annotation database has increased the importance and proportion of mapped reads accordingly. Methods: We employed simple statistical models to detect the sexually biased/dimorphic genes and their conjugate lncRNAs in 40 RNA-seq samples across two factors: sex and tissue. We employed two quantification pipeline: mRNA annotation only and mRNA+lncRNA annotation. Results: As a result, the tissue-specific sexually dimorphic genes are affected by the addition of lncRNA annotation at a non-negligible level. In addition, many lncRNAs are expressed in a more tissue-specific fashion and with greater variation between tissues compared to protein-coding genes. Due to the genic region lncRNAs, the differentially expressed gene list changes, which results in certain sexually biased genes to become ambiguous across the tissues. Conclusion: In a past study, it has been reported that tissue-specific patterns can be seen throughout the differentially expressed genes between sexes in cattle. Using the same dataset, this study used a more recent reference, and the addition of conjugate lncRNA information, which revealed alterations of differentially expressed gene lists that result in an apparent distinction in the downstream analysis and interpretation. We firmly believe such misquantification of genic lncRNAs can be vital in both future and past studies.

키워드

참고문헌

  1. 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
  2. Zhang Y, Yang L, Chen L-L. Life without A tail: new formats of long noncoding RNAs. Int J Biochem Cell Biol 2014;54:338-49. https://doi.org/10.1016/j.biocel.2013.10.009
  3. Gomez E, Caamano JN, Corrales FJ, et al. Embryonic sex induces differential expression of proteins in bovine uterine fluid. J Proteome Res 2013;12:1199-210. https://doi.org/10.1021/pr300845e
  4. Chitwood JL, Rincon G, Kaiser GG, Medrano JF, Ross PJ. RNAseq analysis of single bovine blastocysts. BMC Genomics 2013;14:350. https://doi.org/10.1186/1471-2164-14-350
  5. Mwai O, Hanotte O, Kwon Y-J, Cho S. African indigenous cattle: unique genetic resources in a rapidly changing world. Asian-Australas J Anim Sci. 2015;28:911-21. https://doi.org/10.5713/ajas.15.0002R
  6. Splan RK, Cundiff LV, Van Vleck LD. Genetic parameters for sex-specific traits in beef cattle. J Anim Sci 1998;76:2272-8. https://doi.org/10.2527/1998.7692272x
  7. Gill JL, Bishop SC, McCorquodale C, Williams JL, Wiener P. Association of selected SNP with carcass and taste panel assessed meat quality traits in a commercial population of Aberdeen Angus-sired beef cattle. Genet Sel Evol 2009;41:36. https://doi.org/10.1186/1297-9686-41-36
  8. Yang X, Schadt EE, Wang S, et al. Tissue-specific expression and regulation of sexually dimorphic genes in mice. Genome Res 2006;16:995-1004. https://doi.org/10.1101/gr.5217506
  9. Handa RJ, Burgess LH, Kerr JE, O'Keefe JA. Gonadal steroid hormone receptors and sex differences in the hypothalamo-pituitary-adrenal axis. Horm Behav 1994;28:464-76. https://doi.org/10.1006/hbeh.1994.1044
  10. Rhodes ME, Rubin RT. Functional sex differences ('sexual diergism') of central nervous system cholinergic systems, vasopressin, and hypothalamic-pituitary-adrenal axis activity in mammals: a selective review. Brain Res Rev 1999;30:135-52. https://doi.org/10.1016/s0165-0173(99)00011-9
  11. Nishida Y, Yoshioka M, St-Amand J. Sexually dimorphic gene expression in the hypothalamus, pituitary gland, and cortex. Genomics 2005;85:679-87. https://doi.org/10.1016/j.ygeno.2005.02.013
  12. Sanchez-Cardenas C, Fontanaud P, He Z, et al. Pituitary growth hormone network responses are sexually dimorphic and regulated by gonadal steroids in adulthood. Proc Natl Acad Sci USA 2010;107:21878-83. https://doi.org/10.1073/pnas.1010849107
  13. Seo M, Caetano-Anolles K, Rodriguez-Zas S, et al. Comprehensive identification of sexually dimorphic genes in diverse cattle tissues using RNA-seq. BMC Genomics 2016;17:81. https://doi.org/10.1186/s12864-016-2400-4
  14. Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat Methods 2015;12:357-60. https://doi.org/10.1038/nmeth.3317
  15. Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 2014;30:923-30. https://doi.org/10.1093/bioinformatics/btt656
  16. Li A, Zhang J, Zhou Z, Wang L, Liu Y, Liu Y. ALDB: a domesticanimal long noncoding RNA database. PLOS ONE 2015;10:e0124003. https://doi.org/10.1371/journal.pone.0124003
  17. Iwakiri J, Terai G, Hamada M. Computational prediction of lncRNA-mRNA interactions by integrating tissue specificity in human transcriptome. Biol Direct 2017;12:15. https://doi.org/10.1186/s13062-017-0183-4
  18. Nelder JAaB RJ, Kotz CBR S, Balakrishnan N, Vidakovic B, Johnson NL, editors. Generalized linear models. Encyclopedia of Statistical Sciences; 2006.
  19. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009;4:44-57. https://doi.org/10.1038/nprot.2008.211
  20. Immonen E, Sayadi A, Bayram H, Arnqvist G. Mating changes sexually dimorphic gene expression in the seed beetle Callosobruchus maculatus. Genome Biol Evol 2017;9:677-99. https://doi.org/10.1093/gbe/evx029
  21. Muret K, Klopp C, Wucher V, et al. Long noncoding RNA repertoire in chicken liver and adipose tissue. Genet Sel Evol 2017;49:6. https://doi.org/10.1186/s12711-016-0275-0