A general strategy for cellular reprogramming: The importance of transcription factor cross-repression

Stem Cells

Volumn 31, Issue 10, 2013, Pages 2127-2135

  Del Sol, Isaac Crespo Antonio ^a  

^a UNIVERSITY OF LUXEMBOURG   (Luxembourg)

Author keywords

Cellular reprogramming; Cross antagonistic motif; Cross repression; Dedifferentiation; Differentiation; Network stability; Positive circuit; Retroactivity; Transdifferentiation

Indexed keywords

HEPATOCYTE NUCLEAR FACTOR 3BETA; HEPATOCYTE NUCLEAR FACTOR 4; TRANSCRIPTION FACTOR; TRANSCRIPTION FACTOR GATA 1;

ARTICLE; BONE MARROW CELL; CELL TRANSDIFFERENTIATION; CROSS REPRESSION; ERYTHROID CELL; FIBROBLAST; GENE IDENTIFICATION; GENE REPRESSION; LIVER CELL; LYMPHOCYTE DIFFERENTIATION; NUCLEAR REPROGRAMMING; PHENOTYPE; PREDICTION; TH1 CELL; TH2 CELL;

CELLULAR REPROGRAMMING; CROSS-ANTAGONISTIC MOTIF; CROSS-REPRESSION; DEDIFFERENTIATION; DIFFERENTIATION; NETWORK STABILITY; POSITIVE CIRCUIT; RETROACTIVITY; TRANSDIFFERENTIATION;

ANIMALS; CELL DIFFERENTIATION; COMPUTER SIMULATION; ERYTHROID CELLS; FIBROBLASTS; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORKS; HEPATOCYTES; HUMANS; MODELS, BIOLOGICAL; MYELOID CELLS; NUCLEAR REPROGRAMMING; TH1 CELLS; TH2 CELLS; TRANSCRIPTION FACTORS;

EID: 84887942772     PISSN: 10665099     EISSN: None     Source Type: Journal    
DOI: 10.1002/stem.1473     Document Type: Article

References (55)

1. Graf T, Enver T. Forcing cells to change lineages. Nature 2009;462: 587-594.
2. Cantor AB, Orkin SH Hematopoietic development: A balancing act. Curr Opin Genet Dev 2001;11:513-519.
3. Graf T. Differentiation plasticity of hematopoietic cells. Blood 2002; 99:3089-3101.
4. Orkin SH, Zon LI Hematopoiesis: An evolving paradigm for stem cell biology. Cell 2008;132:631-644.
5. Arinobu Y. Reciprocal activation of GATA-1 and PU. 1 marks initial specification of hematopoietic stem cells into myeloerythroid and myelolymphoid lineages. Cell Stem Cell 2007;1:416-427.
6. Iwasaki H, Akashi K. Myeloid lineage commitment from the hemato-poietic stem cell. Immunity 2007;26:726-740.
7. Zhou L. TGF-[bgr]-induced Foxp3 inhibits TH17 cell differentiation by antagonizing ROR[ggr]t function. Nature 2008;453:236-240.
8. Laslo P. Multilineage transcriptional priming and determination of alternate hematopoietic cell fates. Cell 2006;126:755-766.
9. Frontelo P. Novel role for EKLF in megakaryocyte lineage commitment. Blood 2007;110:3871-3880.
10. Hwang ES, Szabo SJ, Schwartzberg PL et al. T helper cell fate specified by kinase-mediated interaction of T-bet with GATA-3. Science 2005;307:430-433.
11. Heins N. Glial cells generate neurons: The role of the transcription factor Pax6. Nature Neurosci 2002;5:308-315.
12. Kajimura S. Regulation of the brown and white fat gene programs through a PRDM16/CtBP transcriptional complex. Genes Dev 2008; 22:1397-1409.
13. Niwa H. Interaction between Oct3/4 and Cdx2 determines trophecto-derm differentiation. Cell 2005;123:917-929.
14. Ralston A, Rossant J. Genetic regulation of stem cell origins in the mouse embryo. Clin Genet 2005;68:106-112.
15. Waddington CH. The Strategy of the Genes (Allen & Unwin, 1957).
16. Kauffman S. Metabolic stability and epigenesis in randomly constructed genetic nets. J Theor Biol 1969;22:437-467.
17. Kauffman S. The origins of order: Self organization and selection in evolution. Macmillan Publishers Limited, 1993.
18. Szabo SJ, Kim ST, Costa GL et al. A novel transcription factor, T-bet, directs Th1 lineage commitment. Cell 2000;100:655-669.
19. Heyworth C, Pearson S, May G et al. Transcription factor-mediated lineage switching reveals plasticity in primary committed progenitor cells. Embo J 2002;21:3770-3781.
20. Thomas R, Thieffry D, Kaufman M. Dynamical behavior of biological regulatory networks. 1. Biological role of feedback loops and practical use of the concept of the loop-characteristic state. Bull Math Biol 1995;57:247-276.
21. Lukk M et al. A global map of human gene expression. Nat Biotech-nol 2010;28:322-324.
22. Muller F-J et al. A bioinformatic assay for pluripotency in human cells. Nat Methods 2011;8:315-317.
23. Dudley JT, Tibshirani R, Deshpande T et al. Disease signatures are robust across tissues and experiments. Mol Syst Biol 2009;5:307.
24. Ding S, Wang W. Recipes and mechanisms of cellular reprogramming: A case study on budding yeast Saccharomyces cerevisiae. BMC Syst Biol 2011;5:50.
25. Chang R, Shoemaker R, Wang W. Systematic search for recipes to generate induced pluripotent stem cells. PLoS Comput Biol 2011;7:e1002300.
26. Chickarmane V, Peterson C. A computational model for understanding stem cell, trophectoderm and endoderm lineage determination. PLoS One 2008;3:e3478.
27. Flottmann M, Scharp T, Klipp E. A stochastic model of epigenetic dynamics in somatic cell reprogramming. Front Physiol 2012;3:216.
28. MacArthur BD, Please CP, Oreffo RO Stochasticity and the molecular mechanisms of induced pluripotency. PLoS One 2008;3:e3086.
29. Wang J, Xu L, Wang E., Huang S. The potential landscape of genetic circuits imposes the arrow of time in stem cell differentiation. Biophys J 2010;99:29-39.
30. Del Vecchio D., Ninfa AJ, Sontag ED Modular cell biology: Retroac-tivity and insulation. Mol Syst Biol 2008;4:161.
31. Chalancon G, Ravarani CN, Balaji S et al. Interplay between gene expression noise and regulatory network architecture. Trends Genet 2012;28:221-232.
32. Lee HJ, Takemoto N, Kurata H et al. GATA-3 induces T helper cell type 2 (Th2) cytokine expression and chromatin remodeling in committed Th1 cells. J Exp Med 2000;192:105-115.
33. Hwang ES, Szabo SJ, Schwartzberg PL et al. T helper cell fate specified by kinase-mediated interaction of T-bet with GATA-3. Science 2005;307:430-433.
34. Sekiya S, Suzuki A. Direct conversion of mouse fibroblasts to hepatocyte-like cells by defined factors. Nature 2011;475:390-393.
35. Mendoza L. A network model for the control of the differentiation process in Th cells. Biosystems 2006;84:101-114.
36. Krumsiek J, Marr C, Schroeder T et al. Hierarchical differentiation of myeloid progenitors is encoded in the transcription factor network. PLoS One 2011;6:e22649.
37. Dore LC, Crispino JD Transcription factor networks in erythroid cell and megakaryocyte development. Blood 2011;118:231-239.
38. Huang P, He Z, Ji S et al. Induction of functional hepatocyte-like cells from mouse fibroblasts by defined factors. Nature 2011;475: 386-389.
39. Crespo I, Krishna A, Le Bechec A et al. Predicting missing expression values in gene regulatory networks using a discrete logic modeling optimization guided by network stable states. Nucleic Acids Res 2013; 41:e8.
40. Wang J, Lu M, Qiu C et al. TransmiR: A transcription factor-micro-RNA regulation database. Nucleic Acids Res 2010;38:D119-D122.
41. Hsu S-D, Lin F.M., Wu WY et al. miRTarBase: A database curates experimentally validated microRNA-target interactions. Nucleic Acids Res 2010;39:D163-D169.
42. Murphy KM, Reiner SL The lineage decisions of helper T cells. Nat Rev Immunol 2002;2:933-944.
43. Jopling C, Boue S, Izpisua Belmonte JC. Dedifferentiation, transdifferentiation and reprogramming: Three routes to regeneration. Nat Rev Mol Cell Biol 2011;12:79-89.
44. Kyrmizi I, Hatzis P, Katrakili N et al. Plasticity and expanding complexity of the hepatic transcription factor network during liver development. Genes Dev 2006;20:2293-2305.
45. Zaret KS. Genetic programming of liver and pancreas progenitors: Lessons for stem-cell differentiation. Nat Rev Genet 2008;9:329-340.
46. Schrem H, Klempnauer J, Borlak J. Liver-enriched transcription factors in liver function and development. Part I: The hepatocyte nuclear factor network and liver-specific gene expression. Pharmacol Rev 2002;54:129-158.
47. Schrem H, Klempnauer J, Borlak J. Liver-enriched transcription factors in liver function and development. Part II: The C/EBPs and D site-binding protein in cell cycle control, carcinogenesis, circadian gene regulation, liver regeneration, apoptosis, and liver-specific gene regulation. Pharmacol Rev 2004;56:291-330.
48. Novichkova S, Egorov S, Daraselia N. MedScan, a natural language processing engine for MEDLINE abstracts. Bioinformatics 2003;19: 1699-1706.
49. Daraselia, N. et al. Extracting human protein interactions from MED-LINE using a full-sentence parser. Bioinformatics 2004;20:604-611.
50. Armananzas R, Inza I, Santana R et al. A review of estimation of distribution algorithms in bioinformatics. BioData Min 2008;1:6.
51. Jothi R, Balaji S, Wuster A et al. Genomic analysis reveals a tight link between transcription factor dynamics and regulatory network architecture. Mol Syst Biol 2009;5:294.
52. Johnson DB. Finding all the elementary circuits of a directed graph. SIAM J Comput 1975;4:77-84.
53. Garg A, Di Cara A, Xenarios I et al. Synchronous versus asynchronous modeling of gene regulatory networks. Bioinformatics 2008;24: 1917-1925.
54. Garg A, Xenarios I, Mendoza L et al. An Efficient Method for Dynamic Analysis of GeneRegulatory Networks and in silico Gene Perturbation Experiments. In: Speed T, Huang H, ed. Lecture Notes in Computer Science, Vol 4453. Springer Berlin/Heidelberg, 2007: 62-76.
55. Mussel C, Hopfensitz M, Kestler HA BoolNet - An R package for generation, reconstruction and analysis of Boolean networks. Bioinfor-matics 2010;26:1378-1380.