Evidence map›Paper›PMID 27551921›Full record

ArticlePloS one2016

Irreversibility of T-Cell Specification: Insights from Computational Modelling of a Minimal Network Architecture.

Erica Manesso, Hao Yuan Kueh, George Freedman, Ellen V Rothenberg, Carsten Peterson

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.9field-weighted citation impact, top 23% of its field
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

5 citing papers in PubMed, 10 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Kinetic models of hematopoietic differentiation.Wiley interdisciplinary reviews. Systems biology and medicine · 2019
    Review
  5. 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

5 authors at 2 institutions in 2 countries.

Erica ManessoComputational Biology and Biological Physics, Department of Astronomy and Theoretical Physics, Lund University, SE-223 62 Lund, Sweden.
Hao Yuan KuehDivision of Biology, California Institute of Technology, Pasadena, CA 91125, United States of America.
George FreedmanDivision of Biology, California Institute of Technology, Pasadena, CA 91125, United States of America.
Ellen V RothenbergDivision of Biology, California Institute of Technology, Pasadena, CA 91125, United States of America.
Carsten PetersonComputational Biology and Biological Physics, Department of Astronomy and Theoretical Physics, Lund University, SE-223 62 Lund, Sweden.ORCID http://orcid.org/0000-0001-7362-2191
California Institute of Technology · USLund University · SE

Funding

Kinetic modeling and single-cell molecular mechanisms of early T-cell commitmentR01HL119102 · NHLBI · CALIFORNIA INSTITUTE OF TECHNOLOGY · PI PETERSON, CARSTEN, ROTHENBERG, ELLEN V. · 2014 to 2018
$3.0M
Key nodes in the T-cell developmental gene regulatory networkR01AI095943 · NIAID · CALIFORNIA INSTITUTE OF TECHNOLOGY · PI ROTHENBERG, ELLEN V. · 2011 to 2015
$2.0M
Single-cell analysis of immune cell fate decision makingR00HL119638 · NHLBI · UNIVERSITY OF WASHINGTON · PI KUEH, HAO YUAN · 2016 to 2018
$747k
Single cell analysis of hematopoietic cell fate determinationK99HL119638 · NHLBI · CALIFORNIA INSTITUTE OF TECHNOLOGY · PI KUEH, HAO YUAN · 2014 to 2015
$180k
NHLBI NIH HHS K99 HL119638NHLBI NIH HHS R00 HL119638NHLBI NIH HHS R01 HL119102NIAID NIH HHS R01 AI095943
6 · The paper itself

Abstract

BACKGROUND/

objectivesA cascade of gene activations under the control of Notch signalling is required during T-cell specification, when T-cell precursors gradually lose the potential to undertake other fates and become fully committed to the T-cell lineage. We elucidate how the gene/protein dynamics for a core transcriptional module governs this important process by computational means.

methodsWe first assembled existing knowledge about transcription factors known to be important for T-cell specification to form a minimal core module consisting of TCF-1, GATA-3, BCL11B, and PU.1 aiming at dynamical modeling. Model architecture was based on published experimental measurements of the effects on each factor when each of the others is perturbed. While several studies provided gene expression measurements at different stages of T-cell development, pure time series are not available, thus precluding a straightforward study of the dynamical interactions among these genes. We therefore translate stage dependent data into time series. A feed-forward motif with multiple positive feed-backs can account for the observed delay between BCL11B versus TCF-1 and GATA-3 activation by Notch signalling. With a novel computational approach, all 32 possible interactions among Notch signalling, TCF-1, and GATA-3 are explored by translating combinatorial logic expressions into differential equations for BCL11B production rate.

resultsOur analysis reveals that only 3 of 32 possible configurations, where GATA-3 works as a dimer, are able to explain not only the time delay, but very importantly, also give rise to irreversibility. The winning models explain the data within the 95% confidence region and are consistent with regard to decay rates.

conclusionsThis first generation model for early T-cell specification has relatively few players. Yet it explains the gradual transition into a committed state with no return. Encoding logics in a rate equation setting allows determination of binding properties beyond what is possible in a Boolean network.

Indexed as

Models, BiologicalAnimalsCell DifferentiationCell LineageComputational BiologyGene Regulatory NetworksReceptors, NotchSignal TransductionT-LymphocytesReceptors, Notch

Identifiers

PMID27551921
PMCPMC4995000
OpenAlexW2509318452

What OpenQuestion holds

Textmetadata
LicenceCC0
Read underepoch 390

Registered trials

None linked

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.