Evidence map›Paper›PMID 23813012›Full record

ArticleBioinformatics (Oxford, England)2013

Hard-wired heterogeneity in blood stem cells revealed using a dynamic regulatory network model.

Nicola Bonzanni, Abhishek Garg, K Anton Feenstra, Judith Schütte, Sarah Kinston, Diego Miranda-Saavedra, Jaap Heringa, Ioannis Xenarios, Berthold Göttgens

Open access · bronzeAbstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed
5.1field-weighted citation impact, top 4% 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

48 citing papers in PubMed, 93 citations in OpenAlex.

  1. Article
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  7. A novel Boolean network inference strategy to model early hematopoiesis aging.Computational and structural biotechnology journal · 2023
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  8. Review
  9. Review
  10. Boolean modelling in plant biology.Quantitative plant biology · 2022
    Review
  11. Article
  12. Statistical mechanics meets single-cell biology.Nature reviews. Genetics · 2021
    Review
  13. Article
  14. Article
  15. Review
  16. Dynamic Modeling of Transcriptional Gene Regulatory Networks.Methods in molecular biology (Clifton, N.J.) · 2021
    Article
  17. Inhibition of 3-phosphoinositide-dependent protein kinase 1 (PDK1) can revert cellular senescence in human dermal fibroblasts.Proceedings of the National Academy of Sciences of the United States of America · 2020
    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

9 authors at 1 institution in 1 country.

Nicola BonzanniIBIVU Centre for Integrative Bioinformatics, VU University Amsterdam, AIMMS Amsterdam Institute for Molecules Medicines and Systems, VU University Amsterdam, De Boelelaan 1081, NKI-AVL The Netherlands.
Abhishek Garg
K Anton Feenstra
Judith Schütte
Sarah Kinston
Diego Miranda-Saavedra
Jaap Heringa
Ioannis Xenarios
Berthold Göttgens
University of Cambridge · GB

Funding

Cancer Research UK 12765Medical Research Council MC_PC_12009National Centre for the Replacement, Refinement and Reduction of Animals in Research G0900729/1Wellcome Trust 079249Wellcome Trust 100140
6 · The paper itself

Abstract

motivationCombinatorial interactions of transcription factors with cis-regulatory elements control the dynamic progression through successive cellular states and thus underpin all metazoan development. The construction of network models of cis-regulatory elements, therefore, has the potential to generate fundamental insights into cellular fate and differentiation. Haematopoiesis has long served as a model system to study mammalian differentiation, yet modelling based on experimentally informed cis-regulatory interactions has so far been restricted to pairs of interacting factors. Here, we have generated a Boolean network model based on detailed cis-regulatory functional data connecting 11 haematopoietic stem/progenitor cell (HSPC) regulator genes.

resultsDespite its apparent simplicity, the model exhibits surprisingly complex behaviour that we charted using strongly connected components and shortest-path analysis in its Boolean state space. This analysis of our model predicts that HSPCs display heterogeneous expression patterns and possess many intermediate states that can act as 'stepping stones' for the HSPC to achieve a final differentiated state. Importantly, an external perturbation or 'trigger' is required to exit the stem cell state, with distinct triggers characterizing maturation into the various different lineages. By focusing on intermediate states occurring during erythrocyte differentiation, from our model we predicted a novel negative regulation of Fli1 by Gata1, which we confirmed experimentally thus validating our model. In conclusion, we demonstrate that an advanced mammalian regulatory network model based on experimentally validated cis-regulatory interactions has allowed us to make novel, experimentally testable hypotheses about transcriptional mechanisms that control differentiation of mammalian stem cells. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

Gene Regulatory NetworksModels, GeneticAnimalsCell LineErythrocytesGenes, RegulatorHematopoiesisHematopoietic Stem CellsMiceTranscription FactorsTranscription Factors

Identifiers

PMID23813012
PMCPMC3694641
OpenAlexW2039053938

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.