Cited by 39Pluripotent Stem Cells ResearchGene Regulatory Network AnalysisPeroxisome Proliferator-Activated Receptors

A transient disruption of fibroblastic transcriptional regulatory network facilitates trans-differentiation

Open access
Tomaru, Yasuhiro, Hasegawa, Ryota, Suzuki, Takahiro, Sato, Taiji, Kubosaki, Atsutaka, Suzuki, Masanori, Kawaji, Hideya, Forrest, Alistair R.R. +4 more·2014·Nucleic Acids Research

Abstract

Transcriptional Regulatory Networks (TRNs) coordinate multiple transcription factors (TFs) in concert to maintain tissue homeostasis and cellular function. The re-establishment of target cell TRNs has been previously implicated in direct trans-differentiation studies where the newly introduced TFs switch on a set of key regulatory factors to induce de novo expression and function. However, the extent to which TRNs in starting cell types, such as dermal fibroblasts, protect cells from undergoing cellular reprogramming remains largely unexplored. In order to identify TFs specific to maintaining the fibroblast state, we performed systematic knockdown of 18 fibroblast-enriched TFs and analyzed differential mRNA expression against the same 18 genes, building a Matrix-RNAi. The resulting expression matrix revealed seven highly interconnected TFs. Interestingly, suppressing four out of seven TFs generated lipid droplets and induced PPARG and CEBPA expression in the presence of adipocyte-inducing medium only, while negative control knockdown cells maintained fibroblastic character in the same induction regime. Global gene expression analyses further revealed that the knockdown-induced adipocytes expressed genes associated with lipid metabolism and significantly suppressed fibroblast genes. Overall, this study reveals the critical role of the TRN in protecting cells against aberrant reprogramming, and demonstrates the vulnerability of donor cell's TRNs, offering a novel strategy to induce transgene-free trans-differentiations.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/nar/gku567

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • PRRX1 and TWIST1 were identified as key members of fibroblast transcriptional regulatory networks and are involved in a positive feedback loop that leads to fibroblast activation.

    “They were both identified as key members of fibroblast transcriptional regulatory networks and involved in a positive feedback loop that leads to fibroblast activation16,17.”

    From Integrative genomic analyses identify candidate causal genes for calcific aortic valve stenosis involving tissue-specific regulation (2024) · cites this paper as [16]

    In Results, 'Building a fibroblastic TRN model by Matrix-RNAi' (page unavailable), PRRX1 is identified as an influential network member, but the interconnected TWIST factor is TWIST2, not TWIST1. Table 1 in Results lists TWIST1 among 18 fibroblast-enriched factors, but the paper does not establish a PRRX1–TWIST1 positive feedback loop leading to fibroblast activation. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 31

  1. [1]Suzuki H. Forrest A.R. van Nimwegen E. Daub C.O. Balwierz P.J. Irvine K.M. Lassmann T. Ravasi T. Hasegawa Y. de Hoon M.J. The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line Nat. Genet. 2009 41 553 562 19377474 PMC6711855
  2. [2]Macneil L.T. Walhout A.J. Gene regulatory networks and the role of robustness and stochasticity in the control of gene expression Genome Res. 2011 21 999 PMC3083081 21324878
  3. [3]Jopling C. Boue S. Izpisua Belmonte J.C. Dedifferentiation, transdifferentiation and reprogramming: three routes to regeneration Nat. Rev. Mol. Cell Biol. 2011 12 79 89 21252997
  4. [4]Takahashi K. Yamanaka S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors Cell 2006 126 663 676 16904174
  5. [5]Takahashi K. Tanabe K. Ohnuki M. Narita M. Ichisaka T. Tomoda K. Yamanaka S. Induction of pluripotent stem cells from adult human fibroblasts by defined factors Cell 2007 131 861 872 18035408
  6. [6]Caiazzo M. Dell'Anno M.T. Dvoretskova E. Lazarevic D. Taverna S. Leo D. Sotnikova T.D. Menegon A. Roncaglia P. Colciago G. Direct generation of functional dopaminergic neurons from mouse and human fibroblasts Nature 2011 476 224 227 21725324
  7. [7]Marro S. Pang Z.P. Yang N. Tsai M.C. Qu K. Chang H.Y. Sudhof T.C. Wernig M. Direct lineage conversion of terminally differentiated hepatocytes to functional neurons Cell Stem Cell 2011 9 374 382 21962918 PMC3218088
  8. [9]Ieda M. Fu J.D. Delgado-Olguin P. Vedantham V. Hayashi Y. Bruneau B.G. Srivastava D. Direct reprogramming of fibroblasts into functional cardiomyocytes by defined factors Cell 2010 142 375 386 20691899 PMC2919844
  9. [11]Hasegawa R. Tomaru Y. de Hoon M. Suzuki H. Hayashizaki Y. Shin J.W. Identification of ZNF395 as a novel modulator of adipogenesis Exp. Cell Res. 2013 319 68 76 23142027
  10. [13]Ohi Y. Qin H. Hong C. Blouin L. Polo J.M. Guo T. Qi Z. Downey S.L. Manos P.D. Rossi D.J. Incomplete DNA methylation underlies a transcriptional memory of somatic cells in human iPS cells Nat. Cell Biol. 2011 13 541 549 21499256 PMC3987913
  11. [14]Fantom Consortium and the RIKEN PMI and CLST The A promoter-level mammalian expression atlas Nature 2014 507 462 470 24670764 PMC4529748
  12. [15]Tomaru Y. Simon C. Forrest A.R. Miura H. Kubosaki A. Hayashizaki Y. Suzuki M. Regulatory interdependence of myeloid transcription factors revealed by Matrix RNAi analysis Genome Biol. 2009 10 R121 19883503 PMC2810662
  13. [16]Tomaru Y. Nakanishi M. Miura H. Kimura Y. Ohkawa H. Ohta Y. Hayashizaki Y. Suzuki M. Identification of an inter-transcription factor regulatory network in human hepatoma cells by Matrix RNAi Nucleic Acids Res. 2009 37 1049 1060 19129217 PMC2651797
  14. [17]Blais A. Dynlacht B.D. Constructing transcriptional regulatory networks Genes Dev. 2005 19 1499 1511 15998805
  15. [18]Severin J. Lizio M. Harshbarger J. Kawaji H. Daub C.O. Hayashizaki Y. Consortium F. Bertin N. Forrest A.R. Interactive visualization and analysis of large-scale sequencing datasets using ZENBU Nat. Biotechnol. 2014 32 217 219 24727769
  16. [19]Yu H. Gerstein M. Genomic analysis of the hierarchical structure of regulatory networks Proc. Natl. Acad Sci. U.S.A. 2006 103 14724 14731 17003135 PMC1595419
  17. [20]Kouno T. de Hoon M. Mar J.C. Tomaru Y. Kawano M. Carninci P. Suzuki H. Hayashizaki Y. Shin J.W. Temporal dynamics and transcriptional control using single-cell gene expression analysis Genome Biol. 2013 14 R118 24156252 PMC4015031
  18. [21]Kim M. Kim I. Kim S.H. Jung M. Han S. Lee J. Nam J.S. Lee S.K. Bang S. Cryopreserved human adipogenic-differentiated pre-adipocytes: a potential new source for adipose tissue regeneration Cytotherapy 2007 9 468 476 17786608
  19. [23]Janderova L. McNeil M. Murrell A.N. Mynatt R.L. Smith S.R. Human mesenchymal stem cells as an in vitro model for human adipogenesis Obes. Res. 2003 11 65 74 12529487
  20. [24]Li J. Huang N.F. Zou J. Laurent T.J. Lee J.C. Okogbaa J. Cooke J.P. Ding S. Conversion of human fibroblasts to functional endothelial cells by defined factors Arterioscler. Thromb. Vasc. Biol. 2013 33 1366 1375 PMC3898631 23520160
  21. [25]Sun N. Longaker M.T. Wu J.C. Human iPS cell-based therapy: considerations before clinical applications Cell Cycle 2010 9 880 885 20160515 PMC3638036
  22. [26]Okita K. Nakagawa M. Hyenjong H. Ichisaka T. Yamanaka S. Generation of mouse induced pluripotent stem cells without viral vectors Science 2008 322 949 953 18845712
  23. [27]Montini E. Cesana D. Schmidt M. Sanvito F. Ponzoni M. Bartholomae C. Sergi Sergi L. Benedicenti F. Ambrosi A. Di Serio C. Hematopoietic stem cell gene transfer in a tumor-prone mouse model uncovers low genotoxicity of lentiviral vector integration Nat. Biotechnol. 2006 24 687 696 16732270
  24. [28]Tanaka T. Tomaru Y. Nomura Y. Miura H. Suzuki M. Hayashizaki Y. Comprehensive search for HNF-1beta-regulated genes in mouse hepatoma cells perturbed by transcription regulatory factor-targeted RNAi Nucleic Acids Res. 2004 32 2740 2750 15148361 PMC419602
  25. [29]Musacchio T. Torchilin V.P. siRNA delivery: from basics to therapeutic applications Front. Biosci. 2013 18 58 79 23276909
  26. [30]Zuo Y. Qiang L. Farmer S.R. Activation of CCAAT/enhancer-binding protein (C/EBP) alpha expression by C/EBP beta during adipogenesis requires a peroxisome proliferator-activated receptor-gamma-associated repression of HDAC1 at the C/ebp alpha gene promoter J. Biol. Chem. 2006 281 7960 7967 16431920
  27. [31]Yamanouchi K. Ban A. Shibata S. Hosoyama T. Murakami Y. Nishihara M. Both PPARgamma and C/EBPalpha are sufficient to induce transdifferentiation of goat fetal myoblasts into adipocytes J. Reprod. Dev. 2007 53 563 572 17272924
  28. [32]Tontonoz P. Hu E. Spiegelman B.M. Stimulation of adipogenesis in fibroblasts by PPAR gamma 2, a lipid-activated transcription factor Cell 1994 79 1147 1156 8001151
  29. [33]Fajas L. Fruchart J.C. Auwerx J. Transcriptional control of adipogenesis Curr. Opin. Cell Biol. 1998 10 165 173 9561840
  30. [34]Sugii S. Evans R.M. Epigenetic codes of PPARgamma in metabolic disease FEBS Lett. 2011 585 2121 2128 21605560 PMC3129683
  31. [35]Fan Q. Tang T. Zhang X. Dai K. The role of CCAAT/enhancer binding protein (C/EBP)-alpha in osteogenesis of C3H10T1/2 cells induced by BMP-2 J. Cell. Mol. Med. 2009 13 2489 2505 19120697 PMC6529959

Cited by

Sorted by citations

Authors
Tomaru, Y., Hasegawa, R., Suzuki, T., Sato, T., Kubosaki, A., Suzuki, M., Kawaji, H., Forrest, A. R. R., Hayashizaki, Y., FANTOM Consortium, Shin, J. W., & Suzuki, H.
Affiliations
  • RIKEN (JP)
  • Chugai Pharmaceutical Co., Ltd. (Japan) (JP)
Corresponding author
  • Shin, Jay W.
  • Suzuki, Harukazu
Venue
Nucleic Acids Research
Published
2014-07-10
Volume
42
Issue
14
Pages
8905-8913
ISSN
  • 0305-1048 (ISSN-L)
  • 1362-4962
OpenAlex
W2105553482
Cited by
39
References
4
Citations by year
2025: 2 · 2024: 2 · 2023: 3 · 2022: 5 · 2021: 4 · 2020: 4 · 2019: 5 · 2018: 3 · 2017: 4 · 2016: 4 · 2015: 2 · 2014: 1
FWCI
1.50
Type
journal article
Language
en
Open access
Locations
  • Nucleic Acids Research (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • UWA Profiles and Research Repository (University of Western Australia) (submittedVersion, cc-by)
  • UWA Profiles and Research Repository (UWA) (submittedVersion, cc-by)
Funders
  • Ministry of Education, Culture, Sports, Science and Technology
  • Japan Society for the Promotion of Science (22710201)
  • RIKEN
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Pluripotent Stem Cells Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Peroxisome Proliferator-Activated Receptors
Keywords
transcriptional regulatory networks, transdifferentiation, dermal fibroblasts, transcription factor knockdown, cellular reprogramming, adipogenesis, lipid droplets, PPARγ, CEBPA
MeSH terms
Adult, Cells, Cultured, Fibroblasts, Humans, Infant, Newborn, Transcription Factors, Adipocytes, RNA Interference, Gene Regulatory Networks, Cell Transdifferentiation, Transcriptome

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review