Chromosome-wide co-fluctuation of stochastic gene expression in mammalian cells

English version

Autoři: Mengyi Sun ^aff001; Jianzhi Zhang ^aff001
Působiště autorů: Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, United States of America ^aff001
Vyšlo v časopise: Chromosome-wide co-fluctuation of stochastic gene expression in mammalian cells. PLoS Genet 15(9): e32767. doi:10.1371/journal.pgen.1008389
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pgen.1008389

Souhrn

Gene expression is subject to stochastic noise, but to what extent and by which means such stochastic variations are coordinated among different genes are unclear. We hypothesize that neighboring genes on the same chromosome co-fluctuate in expression because of their common chromatin dynamics, and verify it at the genomic scale using allele-specific single-cell RNA-sequencing data of mouse cells. Unexpectedly, the co-fluctuation extends to genes that are over 60 million bases apart. We provide evidence that this long-range effect arises in part from chromatin co-accessibilities of linked loci attributable to three-dimensional proximity, which is much closer intra-chromosomally than inter-chromosomally. We further show that genes encoding components of the same protein complex tend to be chromosomally linked, likely resulting from natural selection for intracellular among-component dosage balance. These findings have implications for both the evolution of genome organization and optimal design of synthetic genomes in the face of gene expression noise.

Klíčová slova:

Biology and life sciences – Biochemistry – Proteins – Protein complexes – DNA-binding proteins – Histones – Genetics – Gene expression – Heredity – Genetic mapping – Linkage analysis – Epigenetics – Genomics – Animal genomics – Mammalian genomics – Genetic loci – Cell biology – Chromosome biology – Chromatin – Chromosomes – Autosomes – Chromosome pairs

Zdroje

1. Elowitz MB, Levine AJ, Siggia ED, Swain PS. Stochastic gene expression in a single cell. Science. 2002;297(5584):1183–6. doi: 10.1126/science.1070919 12183631

2. Raser JM, O'shea EK. Noise in gene expression: origins, consequences, and control. Science. 2005;309(5743):2010–3. doi: 10.1126/science.1105891 16179466

3. Blake WJ, Kærn M, Cantor CR, Collins JJ. Noise in eukaryotic gene expression. Nature. 2003;422(6932):633–7. doi: 10.1038/nature01546 12687005

4. Batada NN, Hurst LD. Evolution of chromosome organization driven by selection for reduced gene expression noise. Nat Genet. 2007;39(8):945–9. doi: 10.1038/ng2071 17660811

5. Lehner B. Selection to minimise noise in living systems and its implications for the evolution of gene expression. Mol Syst Biol. 2008;4:170. doi: 10.1038/msb.2008.11 18319722

6. Wang Z, Zhang J. Impact of gene expression noise on organismal fitness and the efficacy of natural selection. Proc Natl Acad Sci U S A. 2011;108(16):E67–76. doi: 10.1073/pnas.1100059108 21464323

7. Bahar R, Hartmann CH, Rodriguez KA, Denny AD, Busuttil RA, Dolle ME, et al. Increased cell-to-cell variation in gene expression in ageing mouse heart. Nature. 2006;441(7096):1011–4. doi: 10.1038/nature04844 16791200

8. Kemkemer R, Schrank S, Vogel W, Gruler H, Kaufmann D. Increased noise as an effect of haploinsufficiency of the tumor-suppressor gene neurofibromatosis type 1 in vitro. Proc Natl Acad Sci U S A. 2002;99(21):13783–8. doi: 10.1073/pnas.212386999 12368469

9. Veening J-W, Smits WK, Kuipers OP. Bistability, epigenetics, and bet-hedging in bacteria. Annu Rev Microbiol. 2008;62:193–210. doi: 10.1146/annurev.micro.62.081307.163002 18537474

10. Zhang Z, Qian W, Zhang J. Positive selection for elevated gene expression noise in yeast. Mol Syst Biol. 2009;5:299. doi: 10.1038/msb.2009.58 19690568

11. Turing AM. The chemical basis of morphogenesis. Philos Trans R Soc Lond B Biol Sci. 1952;237(641):37–72.

12. Chang HH, Hemberg M, Barahona M, Ingber DE, Huang S. Transcriptome-wide noise controls lineage choice in mammalian progenitor cells. Nature. 2008;453(7194):544. doi: 10.1038/nature06965 18497826

13. Huang S. Non-genetic heterogeneity of cells in development: more than just noise. Development. 2009;136(23):3853–62. doi: 10.1242/dev.035139 19906852

14. Taniguchi Y, Choi PJ, Li GW, Chen H, Babu M, Hearn J, et al. Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science. 2010;329(5991):533–8. doi: 10.1126/science.1188308 20671182

15. Newman JR, Ghaemmaghami S, Ihmels J, Breslow DK, Noble M, DeRisi JL, et al. Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise. Nature. 2006;441(7095):840–6. doi: 10.1038/nature04785 16699522

16. Stewart-Ornstein J, Weissman JS, El-Samad H. Cellular noise regulons underlie fluctuations in Saccharomyces cerevisiae. Mol Cell. 2012;45(4):483–93. doi: 10.1016/j.molcel.2011.11.035 22365828

17. Stewart-Ornstein J, Nelson C, DeRisi J, Weissman JS, El-Samad H. Msn2 coordinates a stoichiometric gene expression program. Curr Biol. 2013;23(23):2336–45. doi: 10.1016/j.cub.2013.09.043 24210615

18. Raj A, van Oudenaarden A. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell. 2008;135(2):216–26. doi: 10.1016/j.cell.2008.09.050 18957198

19. Sanchez A, Choubey S, Kondev J. Regulation of noise in gene expression. Annu Rev Biophys. 2013;42:469–91. doi: 10.1146/annurev-biophys-083012-130401 23527780

20. Brown CR, Mao C, Falkovskaia E, Jurica MS, Boeger H. Linking stochastic fluctuations in chromatin structure and gene expression. PLoS Biol. 2013;11(8):e1001621. doi: 10.1371/journal.pbio.1001621 23940458

21. Raj A, Peskin CS, Tranchina D, Vargas DY, Tyagi S. Stochastic mRNA synthesis in mammalian cells. PLoS Biol. 2006;4(10):e309. doi: 10.1371/journal.pbio.0040309 17048983

22. Becskei A, Kaufmann BB, van Oudenaarden A. Contributions of low molecule number and chromosomal positioning to stochastic gene expression. Nat Genet. 2005;37(9):937. doi: 10.1038/ng1616 16086016

23. Reinius B, Mold JE, Ramskold D, Deng Q, Johnsson P, Michaelsson J, et al. Analysis of allelic expression patterns in clonal somatic cells by single-cell RNA-seq. Nat Genet. 2016;48(11):1430–5. doi: 10.1038/ng.3678 27668657

24. Picelli S, Faridani OR, Björklund ÅK, Winberg G, Sagasser S, Sandberg R. Full-length RNA-seq from single cells using Smart-seq2. Nat Protoc. 2014;9(1):171. doi: 10.1038/nprot.2014.006 24385147

25. Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161(5):1202–14. doi: 10.1016/j.cell.2015.05.002 26000488

26. Hashimshony T, Senderovich N, Avital G, Klochendler A, de Leeuw Y, Anavy L, et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol. 2016;17(1):77.

27. Raj A, Van Den Bogaard P, Rifkin SA, Van Oudenaarden A, Tyagi S. Imaging individual mRNA molecules using multiple singly labeled probes. Nat Methods. 2008;5(10):877–9. doi: 10.1038/nmeth.1253 18806792

28. Yan C, Wu S, Pocetti C, Bai L. Regulation of cell-to-cell variability in divergent gene expression. Nat Commun. 2016;7:11099. doi: 10.1038/ncomms11099 27010670

29. Nasheuer HP, Smith R, Bauerschmidt C, Grosse F, Weisshart K. Initiation of eukaryotic DNA replication: regulation and mechanisms. Prog Nucleic Acid Res Mol Biol. 2002;72:41–94. 12206458

30. Dekker J, Mirny L. The 3D genome as moderator of chromosomal communication. Cell. 2016;164(6):1110–21. doi: 10.1016/j.cell.2016.02.007 26967279

31. Belton J-M, McCord RP, Gibcus JH, Naumova N, Zhan Y, Dekker J. Hi–C: a comprehensive technique to capture the conformation of genomes. Methods. 2012;58(3):268–76. doi: 10.1016/j.ymeth.2012.05.001 22652625

32. Dekker J, Marti-Renom MA, Mirny LA. Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data. Nat Rev Genet. 2013;14(6):390. doi: 10.1038/nrg3454 23657480

33. Giorgetti L, Lajoie BR, Carter AC, Attia M, Zhan Y, Xu J, et al. Structural organization of the inactive X chromosome in the mouse. Nature. 2016;535(7613):575–9. doi: 10.1038/nature18589 27437574

34. Phillips R, Theriot J, Kondev J, Garcia H. Physical biology of the cell: Garland Science; 2012.

35. Buenrostro JD, Wu B, Chang HY, Greenleaf WJ. ATAC‐seq: a method for assaying chromatin accessibility genome‐wide. Curr Protoc Mol Biol. 2015;109(1):21.29.1–9

36. Bahar Halpern K, Tanami S, Landen S, Chapal M, Szlak L, Hutzler A, et al. Bursty gene expression in the intact mammalian liver. Mol Cell. 2015;58(1):147–56. doi: 10.1016/j.molcel.2015.01.027 25728770

37. Dar RD, Razooky BS, Singh A, Trimeloni TV, McCollum JM, Cox CD, et al. Transcriptional burst frequency and burst size are equally modulated across the human genome. Proc Natl Acad Sci USA. 2012;109(43):17454–9. doi: 10.1073/pnas.1213530109 23064634

38. Suter DM, Molina N, Gatfield D, Schneider K, Schibler U, Naef F. Mammalian genes are transcribed with widely different bursting kinetics. Science. 2011;332(6028):472–4. doi: 10.1126/science.1198817 21415320

39. Xu J, Carter AC, Gendrel A-V, Attia M, Loftus J, Greenleaf WJ, et al. Landscape of monoallelic DNA accessibility in mouse embryonic stem cells and neural progenitor cells. Nat Genet. 2017;49(3):377–86. doi: 10.1038/ng.3769 28112738

40. Buenrostro JD, Wu B, Litzenburger UM, Ruff D, Gonzales ML, Snyder MP, et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature. 2015;523(7561):486–90. doi: 10.1038/nature14590 26083756

41. Dixon JR, Selvaraj S, Yue F, Kim A, Li Y, Shen Y, et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature. 2012;485(7398):376–80. doi: 10.1038/nature11082 22495300

42. Hahn S, Kim D. Physical origin of the contact frequency in chromosome conformation capture data. Biophys J. 2013;105(8):1786–95. doi: 10.1016/j.bpj.2013.08.043 24138854

43. Mahmutovic A, Fange D, Berg OG, Elf J. Lost in presumption: stochastic reactions in spatial models. Nat Methods. 2012;9(12):1163–6. doi: 10.1038/nmeth.2253 23223170

44. Hager GL, McNally JG, Misteli T. Transcription dynamics. Mol Cell. 2009;35(6):741–53. doi: 10.1016/j.molcel.2009.09.005 19782025

45. Lever MA, Th'ng JP, Sun X, Hendzel MJ. Rapid exchange of histone H1.1 on chromatin in living human cells. Nature. 2000;408(6814):873–6. doi: 10.1038/35048603 11130728

46. Fyodorov DV, Zhou B-R, Skoultchi AI, Bai Y. Emerging roles of linker histones in regulating chromatin structure and function. Nat Rev Mol Cell Biol. 2018;19(3):192–206. doi: 10.1038/nrm.2017.94 29018282

47. Bernas T, Brutkowski W, Zarębski M, Dobrucki J. Spatial heterogeneity of dynamics of H1 linker histone. Eur Biophys J. 2014;43(6–7):287–300. doi: 10.1007/s00249-014-0962-0 24830851

48. Veitia RA. A generalized model of gene dosage and dominant negative effects in macromolecular complexes. FASEB J. 2010;24(4):994–1002. doi: 10.1096/fj.09-146969 20007508

49. Milo R, Jorgensen P, Moran U, Weber G, Springer M. BioNumbers—the database of key numbers in molecular and cell biology. Nucleic Acids Res. 2009;38(suppl_1):D750–3.

50. Sigal A, Milo R, Cohen A, Geva-Zatorsky N, Klein Y, Liron Y, et al. Variability and memory of protein levels in human cells. Nature. 2006;444(7119):643–6. doi: 10.1038/nature05316 17122776

51. Budnik B, Levy E, Harmange G, Slavov N. Mass-spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation. bioRxiv. 2018:102681.

52. Aken BL, Achuthan P, Akanni W, Amode MR, Bernsdorff F, Bhai J, et al. Ensembl 2017. Nucleic Acids Res. 2016;45(D1):D635–42. doi: 10.1093/nar/gkw1104 27899575

53. Ibn-Salem J, Muro EM, Andrade-Navarro MA. Co-regulation of paralog genes in the three-dimensional chromatin architecture. Nucleic Acids Res. 2016;45(1):81–91. doi: 10.1093/nar/gkw813 27634932

54. Cherry JL. Expression level, evolutionary rate, and the cost of expression. Genome Biol Evol. 2010;2:757–69. doi: 10.1093/gbe/evq059 20884723

55. Gout JF, Kahn D, Duret L. The relationship among gene expression, the evolution of gene dosage, and the rate of protein evolution. PLoS Genet. 2010;6(5):e1000944. doi: 10.1371/journal.pgen.1000944 20485561

56. Söllner JF, Leparc G, Hildebrandt T, Klein H, Thomas L, Stupka E, et al. An RNA-Seq atlas of gene expression in mouse and rat normal tissues. Scientific Data. 2017;4:170185. doi: 10.1038/sdata.2017.185 29231921

57. Vosnakis N, Koch M, Scheer E, Kessler P, Mély Y, Didier P, et al. Coactivators and general transcription factors have two distinct dynamic populations dependent on transcription. EMBO J. 2017:e201696035.

58. Marinov GK, Williams BA, McCue K, Schroth GP, Gertz J, Myers RM, et al. From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing. Genome Res. 2014;24(3):496–510. doi: 10.1101/gr.161034.113 24299736

59. Phifer-Rixey M, Bonhomme F, Boursot P, Churchill GA, Piálek J, Tucker PK, et al. Adaptive evolution and effective population size in wild house mice. Mol Biol Evol. 2012;29(10):2949–55. doi: 10.1093/molbev/mss105 22490822

60. Xu H, Liu J-J, Liu Z, Li Y, Jin Y-S, Zhang J. Synchronization of stochastic expressions drives the clustering of functionally related genes. Sci Adv. 2019; in press.

61. Liu Y, Beyer A, Aebersold R. On the dependency of cellular protein levels on mRNA abundance. Cell. 2016;165(3):535–50. doi: 10.1016/j.cell.2016.03.014 27104977

62. Kustatscher G, Grabowski P, Rappsilber J. Pervasive coexpression of spatially proximal genes is buffered at the protein level. Mol Syst Biol. 2017;13(8):937. doi: 10.15252/msb.20177548 28835372

63. Eden E, Geva-Zatorsky N, Issaeva I, Cohen A, Dekel E, Danon T, et al. Proteome half-life dynamics in living human cells. Science. 2011;331(6018):764–8. doi: 10.1126/science.1199784 21233346

64. Popovic D, Koch B, Kueblbeck M, Ellenberg J, Pelkmans L. Multivariate control of transcript to protein variability in single mammalian cells. Cell Syst. 2018;7(4):398–411. doi: 10.1016/j.cels.2018.09.001 30342881

65. Gedeon T, Bokes P. Delayed protein synthesis reduces the correlation between mRNA and protein fluctuations. Biophys J. 2012;103(3):377–85. doi: 10.1016/j.bpj.2012.06.025 22947853

66. Teichmann SA, Veitia RA. Genes encoding subunits of stable complexes are clustered on the yeast chromosomes. Genetics. 2004;167(4):2121–5. doi: 10.1534/genetics.103.024505 15342545

67. Franks A, Airoldi E, Slavov N. Post-transcriptional regulation across human tissues. PLoS Comput Biol. 2017;13(5):e1005535. doi: 10.1371/journal.pcbi.1005535 28481885

68. Hurst LD, Pál C, Lercher MJ. The evolutionary dynamics of eukaryotic gene order. Nat Rev Genet. 2004;5(4):299–310. doi: 10.1038/nrg1319 15131653

69. Chen X, Zhang J. The genomic landscape of position effects on protein expression level and noise in yeast. Cell Syst. 2016;2(5):347–54. doi: 10.1016/j.cels.2016.03.009 27185547

70. Ori A, Iskar M, Buczak K, Kastritis P, Parca L, Andrés-Pons A, et al. Spatiotemporal variation of mammalian protein complex stoichiometries. Genome Biol. 2016;17(1):47.

71. Slavov N, Semrau S, Airoldi E, Budnik B, van Oudenaarden A. Differential stoichiometry among core ribosomal proteins. Cell Rep. 2015;13(5):865–73. doi: 10.1016/j.celrep.2015.09.056 26565899

72. Levesque MJ, Raj A. Single-chromosome transcriptional profiling reveals chromosomal gene expression regulation. Nat Methods. 2013;10(3):246–8. doi: 10.1038/nmeth.2372 23416756

73. Fukuoka Y, Inaoka H, Kohane IS. Inter-species differences of co-expression of neighboring genes in eukaryotic genomes. BMC Genomics. 2004;5(1):4. doi: 10.1186/1471-2164-5-4 14718066

74. Singer GA, Lloyd AT, Huminiecki LB, Wolfe KH. Clusters of co-expressed genes in mammalian genomes are conserved by natural selection. Mol Biol Evol. 2004;22(3):767–75. doi: 10.1093/molbev/msi062 15574806

75. Sémon M, Duret L. Evolutionary origin and maintenance of coexpressed gene clusters in mammals. Mol Biol Evol. 2006;23(9):1715–23. doi: 10.1093/molbev/msl034 16757654

76. Lercher MJ, Hurst LD. Co-expressed yeast genes cluster over a long range but are not regularly spaced. J Mol Biol. 2006;359(3):825–31. doi: 10.1016/j.jmb.2006.03.051 16631793

77. Spellman PT, Rubin GM. Evidence for large domains of similarly expressed genes in the Drosophila genome. J Biol. 2002;1(1):5. 12144710

78. Ghanbarian AT, Hurst LD. Neighboring genes show correlated evolution in gene expression. Mol Biol Evol. 2015;32(7):1748–66. doi: 10.1093/molbev/msv053 25743543

79. Liao B-Y, Zhang J. Coexpression of linked genes in mammalian genomes is generally disadvantageous. Mol Biol Evol. 2008;25(8):1555–65. doi: 10.1093/molbev/msn101 18440951

80. Naumova N, Imakaev M, Fudenberg G, Zhan Y, Lajoie BR, Mirny LA, et al. Organization of the mitotic chromosome. Science. 2013;342(6161):948–53. doi: 10.1126/science.1236083 24200812

81. Elf J, Barkefors I. Single-molecule kinetics in living cells. Annu Rev Biochem. 2018; 88:635–59. doi: 10.1146/annurev-biochem-013118-110801 30359080

82. Veitia RA. Gene dosage balance in cellular pathways: implications for dominance and gene duplicability. Genetics. 2004;168(1):569–74. doi: 10.1534/genetics.104.029785 15454568

83. McGary KL, Slot JC, Rokas A. Physical linkage of metabolic genes in fungi is an adaptation against the accumulation of toxic intermediate compounds. Proc Natl Acad Sci USA. 2013;110(28):11481–6. doi: 10.1073/pnas.1304461110 23798424

84. Carrera J, Covert MW. Why build whole-cell models? Trends Cell Biol. 2015;25(12):719–22. doi: 10.1016/j.tcb.2015.09.004 26471224

85. Rustici G, Mata J, Kivinen K, Lió P, Penkett CJ, Burns G, et al. Periodic gene expression program of the fission yeast cell cycle. Nat Genet. 2004;36(8):809–17. doi: 10.1038/ng1377 15195092

86. Esumi S, Kakazu N, Taguchi Y, Hirayama T, Sasaki A, Hirabayashi T, et al. Monoallelic yet combinatorial expression of variable exons of the protocadherin-alpha gene cluster in single neurons. Nat Genet. 2005;37(2):171–6. doi: 10.1038/ng1500 15640798

87. Wada T, Wallerich S, Becskei A. Stochastic Gene Choice during Cellular Differentiation. Cell Rep. 2018;24(13):3503–12. doi: 10.1016/j.celrep.2018.08.074 30257211

88. Guo Y, Monahan K, Wu H, Gertz J, Varley KE, Li W, et al. CTCF/cohesin-mediated DNA looping is required for protocadherin alpha promoter choice. Proc Natl Acad Sci U S A. 2012;109(51):21081–6. doi: 10.1073/pnas.1219280110 23204437

89. Raj A, Rifkin SA, Andersen E, Van Oudenaarden A. Variability in gene expression underlies incomplete penetrance. Nature. 2010;463(7283):913–8. doi: 10.1038/nature08781 20164922

90. Baker M. Synthetic genomes: The next step for the synthetic genome. Nature. 2011;473(7347):403–8. doi: 10.1038/473403a 21593873

91. Ruepp A, Waegele B, Lechner M, Brauner B, Dunger-Kaltenbach I, Fobo G, et al. CORUM: the comprehensive resource of mammalian protein complexes—2009. Nucleic Acids Res. 2009;38(suppl_1):D497–501.

92. Geman S, Geman D. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. Readings in Computer Vision: Elsevier; 1987. p. 564–84.

93. Gilks WR. Markov chain monte carlo. Encyclopedia of Biostatistics. 2005.

94. Hwang B, Lee JH, Bang D. Single-cell RNA sequencing technologies and bioinformatics pipelines. Exp Mol Med. 2018;50(8):96. doi: 10.1038/s12276-018-0071-8 30089861

95. Battich N, Stoeger T, Pelkmans L. Control of transcript variability in single mammalian cells. Cell. 2015;163(7):1596–610. doi: 10.1016/j.cell.2015.11.018 26687353

96. Medrzycki M, Zhang Y, Cao K, Fan Y. Expression analysis of mammalian linker-histone subtypes. Journal of visualized experiments. JoVE. 2012;61:3577.

97. Bult CJ, Eppig JT, Kadin JA, Richardson JE, Blake JA, Group MGD. The Mouse Genome Database (MGD): mouse biology and model systems. Nucleic Acids Res. 2008;36(suppl_1):D724–8.