A Model for the Epigenetic Switch Linking Inflammation to Cell Transformation: Deterministic and Stochastic Approaches
Open accessAbstract
Recently, a molecular pathway linking inflammation to cell transformation has been discovered. This molecular pathway rests on a positive inflammatory feedback loop between NF-κB, Lin28, Let-7 microRNA and IL6, which leads to an epigenetic switch allowing cell transformation. A transient activation of an inflammatory signal, mediated by the oncoprotein Src, activates NF-κB, which elicits the expression of Lin28. Lin28 decreases the expression of Let-7 microRNA, which results in higher level of IL6 than achieved directly by NF-κB. In turn, IL6 can promote NF-κB activation. Finally, IL6 also elicits the synthesis of STAT3, which is a crucial activator for cell transformation. Here, we propose a computational model to account for the dynamical behavior of this positive inflammatory feedback loop. By means of a deterministic model, we show that an irreversible bistable switch between a transformed and a non-transformed state of the cell is at the core of the dynamical behavior of the positive feedback loop linking inflammation to cell transformation. The model indicates that inhibitors (tumor suppressors) or activators (oncogenes) of this positive feedback loop regulate the occurrence of the epigenetic switch by modulating the threshold of inflammatory signal (Src) needed to promote cell transformation. Both stochastic simulations and deterministic simulations of a heterogeneous cell population suggest that random fluctuations (due to molecular noise or cell-to-cell variability) are able to trigger cell transformation. Moreover, the model predicts that oncogenes/tumor suppressors respectively decrease/increase the robustness of the non-transformed state of the cell towards random fluctuations. Finally, the model accounts for the potential effect of competing endogenous RNAs, ceRNAs, on the dynamics of the epigenetic switch. Depending on their microRNA targets, the model predicts that ceRNAs could act as oncogenes or tumor suppressors by regulating the occurrence of cell transformation.
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At the molecular level, IL-6 increases the activity of DNMT1 with activating effects on JAK/STAT3, NF-κB and PI3K/AKT/mTOR signaling pathways, affecting regulation of autophagy-related functions required to maintain cellular homeostasis under stress such as a prenatal insult.
“At the molecular level, IL-6 increases the activity of DNA methyltransferases 1 (DNMT1) with activating effects on the JAK/STAT3, NF-κB and PI3K/AKT/mTOR signaling pathways [144–148] affecting the regulation of autophagy-related functions, that are required to maintain cellular homeostasis under stress [149], such as a prenatal insult.”
From The ASD Living Biology: from cell proliferation to clinical phenotype (2019) · cites this paper as [146]
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- Authors
- Gérard, C., Gonze, D., Lemaigre, F., & Novák, B.
- Affiliations
- University of Oxford (GB)
- Université Libre de Bruxelles (BE)
- de Duve Institute (BE)
- Corresponding author
- Gérard, Claude
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-01-30
- Volume
- 10
- Issue
- 1
- Pages
- e1003455
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC3907303
- OpenAlex
- W2009987946
- Cited by
- 20
- References
- 67
- Citations by year
- 2024: 2 · 2023: 1 · 2022: 1 · 2021: 4 · 2019: 3 · 2018: 1 · 2017: 2 · 2016: 2 · 2015: 3 · 2014: 1
- FWCI
- 1.09
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DIAL (Catholic University of Leuven) (submittedVersion, other-oa)
- Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- European Commission (241548)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › NF-κB Signaling Pathways
- Physical Sciences › Mathematics › Modeling and Simulation › Mathematical Biology Tumor Growth
- Keywords
- epigenetic switch, NF-κB, Lin28, let-7 microRNA, interleukin-6, STAT3, positive feedback loop, bistability, cell-to-cell variability, tumor suppressor, oncogenes, competing endogenous RNA, microRNA regulation
- MeSH terms
- Cell Transformation, Neoplastic, Computer Simulation, Humans, Inflammation, Kinetics, Models, Biological, Oncogenes, Stochastic Processes, Genes, Tumor Suppressor, NF-kappa B, RNA-Binding Proteins, MicroRNAs, Epigenesis, Genetic, STAT3 Transcription Factor, PTEN Phosphohydrolase
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 ›
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