Tissue-Specificity of Gene Expression Diverges Slowly between Orthologs, and Rapidly between Paralogs

PLoS Computational Biology

Volumn 12, Issue 12, 2016, Pages

  Kryuchkova Mostacci, Nadezda ^a,b   Robinson Rechavi, Marc ^a,b  

^a UNIVERSITY OF LAUSANNE   (Switzerland)

^b SWISS INSTITUTE OF BIOINFORMATICS   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

CONSERVATION; CORRELATION METHODS; GENE EXPRESSION; GENE ONTOLOGY; MAMMALS;

FUNCTIONAL SIMILARITY; GENE ONTOLOGY TERMS; GENES EXPRESSION; LOW CONSERVATION; ORTHOLOGOUS GENES; ORTHOLOGS; PARALOGS; TERM ANALYSIS; TISSUE SPECIFICITY; TISSUE SPECIFICS;

TISSUE;

GENE EXPRESSION; HUMAN VERSUS ANIMAL COMPARISON; MODEL; NONHUMAN; ORTHOLOGY; PARALOGY; PRIMATE; SPECIES; TISSUE SPECIFICITY; ANIMAL; ANTIBODY SPECIFICITY; ANURA; BIOLOGICAL MODEL; BIOLOGY; GENETICS; HUMAN; MOLECULAR EVOLUTION; MOUSE;

ANIMALS; ANURA; COMPUTATIONAL BIOLOGY; EVOLUTION, MOLECULAR; GENE EXPRESSION; HUMANS; MICE; MODELS, GENETIC; ORGAN SPECIFICITY;

EID: 85007518240     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1005274     Document Type: Article

References (48)

1. Studer RA, Robinson-Rechavi M, How confident can we be that orthologs are similar, but paralogs differ?Trends Genet.2009;25:210–6. doi: 10.1016/j.tig.2009.03.00419368988
2. Gabaldón T, Koonin E V, Functional and evolutionary implications of gene orthology. Nat. Rev. Genet. Nature Publishing Group; 2013;14:360–6. doi: 10.1038/nrg345623552219
3. Studer R, Penel S, Duret L, Robinson-Rechavi M, Pervasive positive selection on duplicated and nonduplicated vertebrate protein coding genes. Genome Res.2008;18:1393–402. doi: 10.1101/gr.076992.10818562677
4. Studer RA, Robinson-Rechavi M, Large-scale analysis of orthologs and paralogs under covarion-like and constant-but-different models of amino acid evolution. Mol. Biol. Evol.2010;27:2618–27. doi: 10.1093/molbev/msq14920551039
5. Nehrt NL, Clark WT, Radivojac P, Hahn MW, Testing the ortholog conjecture with comparative functional genomic data from mammals. PLoS Comput. Biol.2011;7:e1002073. doi: 10.1371/journal.pcbi.100207321695233
6. Thomas PD, Wood V, Mungall CJ, Lewis SE, Blake JA, On the use of gene ontology annotations to assess functional similarity among orthologs and paralogs: A short report. PLoS Comput. Biol.2012;8:1–7.
7. Chen X, Zhang J, The ortholog conjecture is untestable by the current gene ontology but is supported by RNA sequencing data. PLoS Comput. Biol.2012;8:e1002784. doi: 10.1371/journal.pcbi.100278423209392
8. Altenhoff AM, Studer RA, Robinson-Rechavi M, Dessimoz C, Resolving the ortholog conjecture: orthologs tend to be weakly, but significantly, more similar in function than paralogs. PLoS Comput. Biol.2012;8:e1002514. doi: 10.1371/journal.pcbi.100251422615551
9. Yanai I, Graur D, Ophir R, Incongruent expression profiles between human and mouse orthologous genes suggest widespread neutral evolution of transcription control. OMICS. 2004;8:15–24. doi: 10.1089/15362310477354746215107234
10. Rogozin IB, Managadze D, Shabalina SA, Koonin E V, Gene family level comparative analysis of gene expression n mammals validates the ortholog conjecture. Genome Biol. Evol.2014;6:754–62. doi: 10.1093/gbe/evu05124610837
11. Wu Y-C, Bansal MS, Rasmussen MD, Herrero J, Kellis M. Phylogenetic identification and functional characterization of orthologs and paralogs across human, mouse, fly, and worm. bioRxiv. 2014;
12. Liao B-Y, Zhang J, Evolutionary conservation of expression profiles between human and mouse orthologous genes. Mol. Biol. Evol.2006;23:530–40. doi: 10.1093/molbev/msj05416280543
13. Brawand D, Soumillon M, Necsulea A, Julien P, Csárdi G, Harrigan P, et al. The evolution of gene expression levels in mammalian organs. Nature. 2011;478:343–8. doi: 10.1038/nature1053222012392
14. Gu Z, Rifkin SA, White KP, Li W-H, Duplicate genes increase gene expression diversity within and between species. Nat. Genet.2004;36:577–9. doi: 10.1038/ng135515122255
15. Huminiecki L, Wolfe KH, Divergence of spatial gene expression profiles following species-specific gene duplications in human and mouse. Genome Res.2004;14:1870–9. doi: 10.1101/gr.270520415466287
16. Chung W- Y, Albert R, Albert I, Nekrutenko A, Makova KD, Rapid and asymmetric divergence of duplicate genes in the human gene coexpression network. BMC Bioinformatics. 2006;7:1–14.
17. Assis R, Bachtrog D, Rapid divergence and diversification of mammalian duplicate gene functions. BMC Evol. Biol. BMC Evolutionary Biology; 2015;15:1–7.
18. Huerta-Cepas J, Dopazo J, Huynen MA, Gabaldón T, Evidence for short-time divergence and long-time conservation of tissue-specific expression after gene duplication. Brief. Bioinform.2011;12:442–8. doi: 10.1093/bib/bbr02221515902
19. Piasecka B, Robinson-Rechavi M, Bergmann S, Correcting for the bias due to expression specificity improves the estimation of constrained evolution of expression between mouse and human. Bioinformatics. 2012;28:1865–72. doi: 10.1093/bioinformatics/bts26622576178
20. Gilad Y, Mizrahi-Man O, A reanalysis of mouse ENCODE comparative gene expression data. F1000Research. 2015;4:121. doi: 10.12688/f1000research.6536.126236466
21. Kryuchkova-Mostacci N, Robinson-Rechavi M, A benchmark of gene expression tissue-specificity metrics. Brief. Bioinform.2016;1–10.
22. Fagerberg L, Hallstrom BM, Oksvold P, Kampf C, Djureinovic D, Odeberg J, et al. Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics. Mol. Cell. Proteomics.2014;13:397–406. doi: 10.1074/mcp.M113.03560024309898
23. Farrell CM, O’Leary NA, Harte RA, Loveland JE, Wilming LG, Wallin C, et al. Current status and new features of the Consensus Coding Sequence database. Nucleic Acids Res.2014;42:D865–72. doi: 10.1093/nar/gkt105924217909
24. The ENCODE Project ConsortiumA user’s guide to the encyclopedia of DNA elements (ENCODE). PLoS Biol.2011;9:e1001046. doi: 10.1371/journal.pbio.100104621526222
25. Lan X, Pritchard JK, Coregulation of tandem duplicate genes slows evolution of subfunctionalization in mammals. Science. 2016;352:1009–13. doi: 10.1126/science.aad841127199432
26. Gout J-F, Lynch M, Maintenance and loss of duplicated genes by dosage subfunctionalization. Mol. Biol. Evol.2015;32:2141–8. doi: 10.1093/molbev/msv09525908670
27. Julien P, Brawand D, Soumillon M, Necsulea A, Liechti A, Schütz F, et al. Mechanisms and evolutionary patterns of mammalian and avian dosage compensation. PLoS Biol.2012;10:e1001328. doi: 10.1371/journal.pbio.100132822615540
28. Davis JC, Petrov D a. Do disparate mechanisms of duplication add similar genes to the genome?Trends Genet.2005;21:548–51. doi: 10.1016/j.tig.2005.07.00816098632
29. Pelechano V, Wei W, Jakob P, Steinmetz LM, Genome-wide identification of transcript start and end sites by transcript isoform sequencing. Nat. Protoc.2014;9:1740–59. doi: 10.1038/nprot.2014.12124967623
30. Ezkurdia I, Rodriguez JM, Carrillo-de Santa Pau E, Vázquez J, Valencia A, Tress ML, Most highly expressed protein-coding genes have a single dominant isoform. J. Proteome Res.2015;14:1880–7. doi: 10.1021/pr501286b25732134
31. Tress ML, Abascal F, Valencia A, Alternative splicing may not be the key to proteome complexity. Trends Biochem. Sci. Elsevier Ltd; 2016;0:1–13.
32. Bastian F, Parmentier G, Roux J, Moretti S, Lauder V, Robinson-Rechavi M, Bgee: integrating and comparing heterogeneous transcriptome data among species. Data Integr. Life Sci.Springer Berlin Heidelberg; 2008. p. 124–31.
33. Vilella AJ, Severin J, Ureta-Vidal A, Heng L, Durbin R, Birney E, EnsemblCompara GeneTrees: Complete, duplication-aware phylogenetic trees in vertebrates. Genome Res.2009;19:327–35. doi: 10.1101/gr.073585.10719029536
34. Hedges SB, Dudley J, Kumar S, TimeTree: A public knowledge-base of divergence times among organisms. Bioinformatics. 2006;22:2971–2. doi: 10.1093/bioinformatics/btl50517021158
35. Kryuchkova-Mostacci N, Robinson-Rechavi M, Tissue-specific evolution of protein coding genes in human and mouse. PLoS One. 2015;10:e0131673. doi: 10.1371/journal.pone.013167326121354
36. Li JJ, Huang H, Bickel PJ, Brenner SE, Comparison of D. melanogaster and C. elegans developmental stages, tissues, and cells by modENCODE RNA-seq data. Genome Res.2014;24:1086–101. doi: 10.1101/gr.170100.11324985912
37. Necsulea A, Kaessmann H, Evolutionary dynamics of coding and non-coding transcriptomes. Nat. Rev. Genet. Nature Publishing Group; 2014;15:734–48. doi: 10.1038/nrg380225297727
38. Merkin J, Russell C, Chen P, Burge CB, Evolutionary dynamics of gene and isoform regulation in mammalian tissues. Science. 2012;338:1593–9. doi: 10.1126/science.122818623258891
39. Keane TM, Goodstadt L, Danecek P, White M a, Wong K, Yalcin B, et al. Mouse genomic variation and its effect on phenotypes and gene regulation. Nature. 2011;477:289–94. doi: 10.1038/nature1041321921910
40. Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. Nature Publishing Group; 2012;7:562–78. doi: 10.1038/nprot.2012.01622383036
41. Flicek P, Ahmed I, Amode MR, Barrell D, Beal K, Brent S, et al. Ensembl 2013. Nucleic Acids Res.2013;41:D48–55. doi: 10.1093/nar/gks123623203987
42. R Core TeamR: A language and environment for statistical computing [Internet]. Vienna, Austria; 2015. p. R Foundation for Statistical Computing, Vienna.
43. Sarcar D, Lattice: Multivariate data visualization with R [Internet]. New York: Springer; 2008.
44. Wickham H, The Split-Apply-Combine strategy for data analysis. J. Stat. Softw.2011;40:1–29.
45. Warnes G, Bolker B, Bonebakker L, Gentleman R, Huber W, Liaw A, et al. Gplots: Various R programming tools for plotting data [Internet]. 2016.
46. Storey J, Tibshirani R, Statistical significance for genomewide studies. Proc Natl Acad Sci U S A. 2003;2003.
47. Storey JD. Qvalue: Q-value estimation for false discovery rate control [Internet]. 2015.
48. Yanai I, Benjamin H, Shmoish M, Chalifa-Caspi V, Shklar M, Ophir R, et al. Genome-wide midrange transcription profiles reveal expression level relationships in human tissue specification. Bioinformatics. 2005;21:650–9. doi: 10.1093/bioinformatics/bti04215388519