Drugs modulating stochastic gene expression affect the erythroid differentiation process


Autoři: Anissa Guillemin aff001;  Ronan Duchesne aff001;  Fabien Crauste aff002;  Sandrine Gonin-Giraud aff001;  Olivier Gandrillon aff001
Působiště autorů: Laboratoire de biologie et modélisation de la cellule. LBMC - Ecole Normale Supérieure - Lyon, Université Claude Bernard Lyon 1, Centre National de la Recherche Scientifique: UMR5239, Institut National de la Santé et de la Recherche Médicale: U1210 - Ecol aff001;  Laboratoire de biologie et modélisation de la cellule. LBMC - Ecole Normale Supérieure - Lyon, Université Claude Bernard Lyon 1, Centre National de la Recherche Scientifique: UMR5239, Institut National de la Santé et de la Recherche Médicale: U1210 - Ecol aff001;  Inria Dracula, Villeurbanne, France aff002;  Univ. Bordeaux, CNRS, Bordeaux INP, IMB, UMR 5251, F-33400, Talence, France aff003
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225166

Souhrn

To better understand the mechanisms behind cells decision-making to differentiate, we assessed the influence of stochastic gene expression (SGE) modulation on the erythroid differentiation process. It has been suggested that stochastic gene expression has a role in cell fate decision-making which is revealed by single-cell analyses but studies dedicated to demonstrate the consistency of this link are still lacking. Recent observations showed that SGE significantly increased during differentiation and a few showed that an increase of the level of SGE is accompanied by an increase in the differentiation process. However, a consistent relation in both increasing and decreasing directions has never been shown in the same cellular system. Such demonstration would require to be able to experimentally manipulate simultaneously the level of SGE and cell differentiation in order to observe if cell behavior matches with the current theory. We identified three drugs that modulate SGE in primary erythroid progenitor cells. Both Artemisinin and Indomethacin decreased SGE and reduced the amount of differentiated cells. On the contrary, a third component called MB-3 simultaneously increased the level of SGE and the amount of differentiated cells. We then used a dynamical modelling approach which confirmed that differentiation rates were indeed affected by the drug treatment. Using single-cell analysis and modeling tools, we provide experimental evidence that, in a physiologically relevant cellular system, SGE is linked to differentiation.

Klíčová slova:

Artemisinin – Cell differentiation – Drug discovery – Drug interactions – Drug therapy – Entropy – Gene expression – Kullback Leibler divergence


Zdroje

1. Benzer S. Induced synthesis of enzymes in bacteria analyzed at the cellular level. Biochim Biophys Acta. 1953;11(3):383–95. doi: 10.1016/0006-3002(53)90057-2 13093744

2. Balaban NQ. Persistence: mechanisms for triggering and enhancing phenotypic variability. Curr Opin Genet Dev. 2011;21(6):768–75. doi: 10.1016/j.gde.2011.10.001 22051606

3. 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

4. Chubb JR. Symmetry breaking in development and stochastic gene expression. Wiley Interdiscip Rev Dev Biol. 2017. doi: 10.1002/wdev.284 28719044

5. Keegstra JM, Kamino K, Anquez F, Lazova MD, Emonet T, Shimizu TS. Phenotypic diversity and temporal variability in a bacterial signaling network revealed by single-cell FRET. Elife. 2017;6. doi: 10.7554/eLife.27455 29231170

6. Elowitz MB, Levine AJ, Siggia ED, Swain PS. Stochastic gene expression in a single cell. Science. 2002;297(5584):1183–1186. doi: 10.1126/science.1070919 12183631

7. Raser JM, O’Shea EK. Control of stochasticity in eukaryotic gene expression. Science. 2004;304(5678):1811–4. doi: 10.1126/science.1098641 15166317

8. Becskei A, Kaufmann BB, van Oudenaarden A. Contributions of low molecule number and chromosomal positioning to stochastic gene expression. Nat Genet. 2005;37(9):937–44. doi: 10.1038/ng1616 16086016

9. 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

10. Swain PS, Elowitz MB, Siggia ED. Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc Natl Acad Sci U S A. 2002;99(20):12795–800. doi: 10.1073/pnas.162041399 12237400

11. Symmons O, Raj A. What’s Luck Got to Do with It: Single Cells, Multiple Fates, and Biological Nondeterminism. Mol Cell. 2016;62(5):788–802. doi: 10.1016/j.molcel.2016.05.023 27259209

12. Dar RD, Weiss R. Perspective: Engineering noise in biological systems towards predictive stochastic design. APL Bioengineering. 2018;2(2):020901. doi: 10.1063/1.5025033 31069294

13. Moris N, Edri S, Seyres D, Kulkarni R, Domingues AF, Balayo T, et al. Histone Acetyltransferase KAT2A Stabilizes Pluripotency with Control of Transcriptional Heterogeneity. Stem Cells. 2018. doi: 10.1002/stem.2919 30270482

14. Arias AM, Hayward P. Filtering transcriptional noise during development: concepts and mechanisms. Nat Rev Genet. 2006;7(1):34–44. doi: 10.1038/nrg1750 16369570

15. Viney M, Reece SE. Adaptive noise. Proc Biol Sci. 2013;280(1767):20131104. rspb.2013.1104 [pii] doi: 10.1098/rspb.2013.1104 23902900

16. Bertaux F, Stoma S, Drasdo D, Batt G. Modeling dynamics of cell-to-cell variability in TRAIL-induced apoptosis explains fractional killing and predicts reversible resistance. PLoS Comput Biol. 2014;10(10):e1003893. doi: 10.1371/journal.pcbi.1003893 25340343

17. Weinberger LS, Burnett JC, Toettcher JE, Arkin AP, Schaffer DV. Stochastic gene expression in a lentiviral positive-feedback loop: HIV-1 Tat fluctuations drive phenotypic diversity. Cell. 2005;122(2):169–82. doi: 10.1016/j.cell.2005.06.006 16051143

18. Weinberger L, Dar R, Simpson M. Transient-mediated fate determination in a transcriptional circuit of HIV. Nature Genetics. 2008;40(4):466–470. doi: 10.1038/ng.116 18344999

19. Wong VC, Bass VL, Bullock ME, Chavali AK, Lee REC, Mothes W, et al. NF-kappaB-Chromatin Interactions Drive Diverse Phenotypes by Modulating Transcriptional Noise. Cell Rep. 2018;22(3):585–599. doi: 10.1016/j.celrep.2017.12.080 29346759

20. Maamar H, Raj A, Dubnau D. Noise in gene expression determines cell fate in Bacillus subtilis. Science. 2007;317(5837):526–9. doi: 10.1126/science.1140818 17569828

21. Cagatay T, Turcotte M, Elowitz MB, Garcia-Ojalvo J, Suel GM. Architecture-dependent noise discriminates functionally analogous differentiation circuits. Cell. 2009;139(3):512–22. S0092-8674(09)01033-2 [pii] doi: 10.1016/j.cell.2009.07.046 19853288

22. Losick R, Desplan C. Stochasticity and cell fate. Science. 2008;320(5872):65–8. 320/5872/65 [pii] doi: 10.1126/science.1147888 18388284

23. Wernet MF, Mazzoni EO, Çelik A, Duncan DM, Duncan I, Desplan C. Stochastic spineless expression creates the retinal mosaic for colour vision. Nature. 2006;440(7081):174–180. doi: 10.1038/nature04615 16525464

24. Johnston RJ, Desplan C. Interchromosomal Communication Coordinates Intrinsically Stochastic Expression Between Alleles. Science. 2014;343(6171):661–665. doi: 10.1126/science.1243039 24503853

25. Kupiec JJ. A Darwinian theory for the origin of cellular differentiation. Mol Gen Genet. 1997;255(2):201–8. doi: 10.1007/s004380050490 9236778

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

27. Moris N, Pina C, Arias AM. Transition states and cell fate decisions in epigenetic landscapes. Nature Reviews Genetics. 2016;17(11):693–703. doi: 10.1038/nrg.2016.98 27616569

28. Braun E. The unforeseen challenge: from genotype-to-phenotype in cell populations. Rep Prog Phys. 2015;78(3):036602. doi: 10.1088/0034-4885/78/3/036602 25719211

29. Xiong W, F JE Jr. A positive-feedback-based bistable ‘memory module’ that governs a cell fate decision. Letters to nature. 2003;426:7. doi: 10.1038/nature02089

30. Ferrell J. Bistability, Bifurcations, and Waddington’s Epigenetic Landscape. Current Biology. 2012;22(11):R458–R466. doi: 10.1016/j.cub.2012.03.045 22677291

31. Richard A, Boullu L, Herbach U, Bonnafoux A, Morin V, Vallin E, et al. Single-Cell-Based Analysis Highlights a Surge in Cell-to-Cell Molecular Variability Preceding Irreversible Commitment in a Differentiation Process. PLoS Biol. 2016;14(12):e1002585. doi: 10.1371/journal.pbio.1002585 28027290

32. Stumpf PS, Smith RCG, Lenz M, Schuppert A, Müller FJ, Babtie A, et al. Stem Cell Differentiation as a Non-Markov Stochastic Process. Cell Systems. 2017;5:268–282. doi: 10.1016/j.cels.2017.08.009 28957659

33. Mojtahedi M, Skupin A, Zhou J, Castaño IG, Leong-Quong RYY, Chang H, et al. Cell fate-decision as high-dimensional critical state transition. BioRvix. 2016; http://dx.doi.org/10.1101/041541.

34. Semrau S, Goldmann JE, Soumillon M, Mikkelsen TS, Jaenisch R, van Oudenaarden A. Dynamics of lineage commitment revealed by single-cell transcriptomics of differentiating embryonic stem cells. Nat Commun. 2017;8(1):1096. doi: 10.1038/s41467-017-01076-4 29061959

35. Gandrillon O, Schmidt U, Beug H, Samarut J. TGF-beta cooperates with TGF-alpha to induce the self-renewal of normal erythrocytic progenitors: evidence for an autocrine mechanism. Embo J. 1999;18(10):2764–2781. doi: 10.1093/emboj/18.10.2764 10329623

36. Bossone K, Ellis J, Holaska J. Inhibiting histone acetyltransferase activity rescues differentiation of emerin-null myogenic progenitors. bioRxiv. 2018.

37. Dar RD, Hosmane NN, Arkin MR, Siliciano RF, Weinberger LS. Screening for noise in gene expression identifies drug synergies. Science. 2014;344(6190):1392–6. doi: 10.1126/science.1250220 24903562

38. Megaridis MR, Lu Y, Tevonian EN, Junger KM, Moy JM, Bohn-Wippert K, et al. Fine-tuning of noise in gene expression with nucleosome remodeling. APL Bioengineering. 2018;2(2):026106. doi: 10.1063/1.5021183 31069303

39. Duchesne R, Guillemin A, Crauste F, Gandrillon O. Calibration, Selection and Identifiability Analysis of a Mathematical Model of the in vitro Erythropoiesis in Normal and Perturbed Contexts. In Silico Biology. 2019; p. 1–15.

40. Nocedal J, Wright S. Numerical Optimization. 2nd ed. Springer series in operations research. New York: Springer; 2006.

41. Nash S. Newton-Type Minimization Via the Lanczos Method. SIAM Journal on Numerical Analysis. 1984;21(4):770–788. doi: 10.1137/0721052

42. E J, T O, P P, et al. SciPy: Open source scientific tools for Python; 2001–. Available from: http://www.scipy.org/.

43. Burnham K, Anderson D. Model selection and multimodel inference: a practical information-theoretic approach. New York: Springer; 2010.

44. Paulsson J. Models of stochastic gene expression. Phys Life Rev. 2005;2:157–175. doi: 10.1016/j.plrev.2005.03.003

45. Eling N, Morgan MD, Marioni JC. Challenges in measuring and understanding biological noise. Nature Reviews Genetics. 2019. doi: 10.1038/s41576-019-0130-6

46. Hansen MMK, Desai RV, Simpson ML, Weinberger LS. Cytoplasmic Amplification of Transcriptional Noise Generates Substantial Cell-to-Cell Variability. Cell Syst. 2018;7(4):384–397 e6. doi: 10.1016/j.cels.2018.08.002 30243562

47. Wang K, Phillips CA, Saxton AM, Langston MA. EntropyExplorer: an R package for computing and comparing differential Shannon entropy, differential coefficient of variation and differential expression. BMC Research Notes. 2015;8(1):832. doi: 10.1186/s13104-015-1786-4 26714840

48. Eisenberg DTA, Kuzawa CW, Hayes MG. Improving qPCR telomere length assays: Controlling for well position effects increases statistical power: IMPROVING qPCR TELOMERE LENGTH ASSAYS. American Journal of Human Biology. 2015;27(4):570–575. doi: 10.1002/ajhb.22690 25757675

49. Dixit PD. Quantifying extrinsic noise in gene expression using the maximum entropy framework. Biophys J. 2013;104(12):2743–50. S0006-3495(13)00560-2 [pii] doi: 10.1016/j.bpj.2013.05.010 23790383

50. MacArthur BD, Lemischka IR. Statistical mechanics of pluripotency. Cell. 2013;154(3):484–9. S0092-8674(13)00895-7 [pii] doi: 10.1016/j.cell.2013.07.024 23911316

51. Teschendorff AE, Enver T. Single-cell entropy for accurate estimation of differentiation potency from a cell’s transcriptome. Nature Communications. 2017;8(1):15599. doi: 10.1038/ncomms15599 28569836

52. Herbach U, Bonnaffoux A, Espinasse T, Gandrillon O. Inferring gene regulatory networks from single-cell data: a mechanistic approach. BMC Systems Biology. 2017;11(1):105. doi: 10.1186/s12918-017-0487-0 29157246

53. Ganguli A, Choudhury D, Datta S, Bhattacharya S, Chakrabarti G. Inhibition of autophagy by chloroquine potentiates synergistically anti-cancer property of artemisinin by promoting ROS dependent apoptosis. Biochimie. 2014;107 Pt B:338–49. doi: 10.1016/j.biochi.2014.10.001 25308836

54. Hart FD, Boardman PL. Indomethacin: A New Non-Steroid Anti-Inflammatory Agent. Br Med J. 1963;2(5363):965–70. doi: 10.1136/bmj.2.5363.965 14056924

55. Wang J, Mi JQ, Debernardi A, Vitte AL, Emadali A, Meyer JA, et al. A six gene expression signature defines aggressive subtypes and predicts outcome in childhood and adult acute lymphoblastic leukemia. Oncotarget. 2015;6(18):16527–42. doi: 10.18632/oncotarget.4113 26001296

56. Heller LE, Roepe PD. Artemisinin-Based Antimalarial Drug Therapy: Molecular Pharmacology and Evolving Resistance. Tropical Medicine and Infectious Disease. 2019;4(2):89. doi: 10.3390/tropicalmed4020089

57. Yan M, Li D, Zhao G, Li J, Niu F, Li B, et al. Genetic polymorphisms of pharmacogenomic VIP variants in the Yi population from China. Gene. 2018;648:54–62. doi: 10.1016/j.gene.2018.01.040 29337087

58. Murakami A. Non-specific protein modifications may be novel mechanism underlying bioactive phytochemicals. Journal of Clinical Biochemistry and Nutrition. 2018;62(2):115–123. doi: 10.3164/jcbn.17-113 29610550

59. Raue A, Kreutz C, Maiwald T, Bachmann J, Schilling M, Klingmüller U, et al. Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood. Bioinformatics. 2009;25(15):1923–1929. doi: 10.1093/bioinformatics/btp358 19505944

60. Wu Y, Ma S, Xia Y, Lu Y, Xiao S, Cao Y, et al. Loss of GCN5 leads to increased neuronal apoptosis by upregulating E2F1- and Egr-1-dependent BH3-only protein Bim. Cell Death Dis. 2017;8(1):e2570. doi: 10.1038/cddis.2016.465 28125090

61. Kubota Y, Nomura K, Katoh Y, Yamashita R, Kaneko K, Furuyama K. Novel Mechanisms for Heme-dependent Degradation of ALAS1 Protein as a Component of Negative Feedback Regulation of Heme Biosynthesis. Journal of Biological Chemistry. 2016;291(39):20516–20529. doi: 10.1074/jbc.M116.719161 27496948

62. Liu CG, Sleat DE, Donnelly RJ, Lobel P. Structural Organization and Sequence of CLN2, the Defective Gene in Classical Late Infantile Neuronal Ceroid Lipofuscinosis. Genomics. 1998;50(2):206–212. https://doi.org/10.1006/geno.1998.5328. 9653647

63. Vu TM, Ishizu AN, Foo JC, Toh XR, Zhang F, Whee DM, et al. Mfsd2b is essential for the sphingosine-1-phosphate export in erythrocytes and platelets. Nature. 2017;550:524. doi: 10.1038/nature24053 29045386

64. Ishii M, Egen JG, Klauschen F, Meier-Schellersheim M, Saeki Y, Vacher J, et al. Sphingosine-1-phosphate mobilizes osteoclast precursors and regulates bone homeostasis. Nature. 2009;458:524. doi: 10.1038/nature07713 19204730

65. Slentz-Kesler K, Moore JT, Lombard M, Zhang J, Hollingsworth R, Weiner MP. Identification of the Human Mnk2 Gene (MKNK2) through Protein Interaction with Estrogen Receptor β. Genomics. 2000;69(1):63–71. https://doi.org/10.1006/geno.2000.6299. 11013076

66. Waddington CH. The strategy of the genes. London, UK: Allen and Unwin. 1957.

67. Paulsson J. Summing up the noise in gene networks. Nature. 2004;427(6973):415–8. doi: 10.1038/nature02257 14749823

68. Singer ZS, Yong J, Tischler J, Hackett JA, Altinok A, Surani MA, et al. Dynamic heterogeneity and DNA methylation in embryonic stem cells. Mol Cell. 2014;55(2):319–31. doi: 10.1016/j.molcel.2014.06.029 25038413

69. Tzelepis K, Koike-Yusa H, De Braekeleer E, Li Y, Metzakopian E, Dovey OM, et al. A CRISPR Dropout Screen Identifies Genetic Vulnerabilities and Therapeutic Targets in Acute Myeloid Leukemia. Cell Rep. 2016;17(4):1193–1205. doi: 10.1016/j.celrep.2016.09.079 27760321

70. Gupta PB, Fillmore CM, Jiang G, Shapira SD, Tao K, Kuperwasser C, et al. Stochastic state transitions give rise to phenotypic equilibrium in populations of cancer cells. Cell. 2011;146(4):633–44. doi: 10.1016/j.cell.2011.07.026 21854987

71. Brock A, Krause S, Ingber DE. Control of cancer formation by intrinsic genetic noise and microenvironmental cues. Nat Rev Cancer. 2015;15(8):499–509. doi: 10.1038/nrc3959 26156637


Článek vyšel v časopise

PLOS One


2019 Číslo 11