Evidence map›Paper›PMID 26090929›Full record

ArticlePLoS computational biology2015

A Minimal Regulatory Network of Extrinsic and Intrinsic Factors Recovers Observed Patterns of CD4+ T Cell Differentiation and Plasticity.

Mariana Esther Martinez-Sanchez, Luis Mendoza, Carlos Villarreal, Elena R Alvarez-Buylla

Abstract read
In one paragraph

Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

35 citing papers in PubMed.

  1. Article
  2. Automated model refinement using perturbation-observation pairs.NPJ systems biology and applications · 2025
    Article
  3. Article
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  6. Article
  7. Hybrid lineages of CD4Frontiers in immunology · 2024
    Review
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  9. Article
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  11. Article
  12. Review
  13. Article
  14. Review
  15. Identification of dynamic driver sets controlling phenotypical landscapes.Computational and structural biotechnology journal · 2022
    Article
  16. Article
  17. CD2Annals of translational medicine · 2021
    Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Mariana Esther Martinez-SanchezDepartamento de Ecología Funcional, Instituto de Ecología, Universidad Nacional Autónoma de México, Coyoacán, México Distrito Federal, México; Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Coyoacán, México Distrito Federal, México.
Luis MendozaDepartamento de Biología Molecular y Biotecnología, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, México Distrito Federal, México.
Carlos VillarrealCentro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Coyoacán, México Distrito Federal, México; Departamento de Física Teórica, Instituto de Física, Universidad Nacional Autónoma de México, México Distrito Federal, México.
Elena R Alvarez-BuyllaDepartamento de Ecología Funcional, Instituto de Ecología, Universidad Nacional Autónoma de México, Coyoacán, México Distrito Federal, México; Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Coyoacán, México Distrito Federal, México.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

CD4+ T cells orchestrate the adaptive immune response in vertebrates. While both experimental and modeling work has been conducted to understand the molecular genetic mechanisms involved in CD4+ T cell responses and fate attainment, the dynamic role of intrinsic (produced by CD4+ T lymphocytes) versus extrinsic (produced by other cells) components remains unclear, and the mechanistic and dynamic understanding of the plastic responses of these cells remains incomplete. In this work, we studied a regulatory network for the core transcription factors involved in CD4+ T cell-fate attainment. We first show that this core is not sufficient to recover common CD4+ T phenotypes. We thus postulate a minimal Boolean regulatory network model derived from a larger and more comprehensive network that is based on experimental data. The minimal network integrates transcriptional regulation, signaling pathways and the micro-environment. This network model recovers reported configurations of most of the characterized cell types (Th0, Th1, Th2, Th17, Tfh, Th9, iTreg, and Foxp3-independent T regulatory cells). This transcriptional-signaling regulatory network is robust and recovers mutant configurations that have been reported experimentally. Additionally, this model recovers many of the plasticity patterns documented for different T CD4+ cell types, as summarized in a cell-fate map. We tested the effects of various micro-environments and transient perturbations on such transitions among CD4+ T cell types. Interestingly, most cell-fate transitions were induced by transient activations, with the opposite behavior associated with transient inhibitions. Finally, we used a novel methodology was used to establish that T-bet, TGF-β and suppressors of cytokine signaling proteins are keys to recovering observed CD4+ T cell plastic responses. In conclusion, the observed CD4+ T cell-types and transition patterns emerge from the feedback between the intrinsic or intracellular regulatory core and the micro-environment. We discuss the broader use of this approach for other plastic systems and possible therapeutic interventions.

Indexed as

Models, ImmunologicalCD4-Positive T-LymphocytesCell DifferentiationCell PlasticityCellular MicroenvironmentComputational BiologyCytokinesSignal TransductionCytokines

Identifiers

PMID26090929
PMCPMC4475012

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.