An experimentally validated network of nine haematopoietic transcription factors reveals mechanisms of cell state stability

Schütte, Judith; Wang, Huange; Antoniou, Stella; Jarratt, Andrew; Wilson, Nicola K.; Riepsaame, Joey; Calero-Nieto, Fernando J.; Moignard, Victoria; Basilico, Silvia; Kinston, Sarah; Hannah, Rebecca; Chan, Mun Chiang; Nürnberg, Sylvia; Ouwehand, Willem H.; Bonzanni, Nicola; Bruijn, Marella Ftr de; Göttgens, Berthold

doi:10.7554/elife.11469

Cited by 65 publications

(82 citation statements)

References 56 publications

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“…Therefore, both ChIP-Seq and Luciferase assays served to validate the regulatory relationships proposed in silico between Gata2 and Cbfa2t3h, and between Gata2 and Nfe2. Several of the Boolean rules for the MEP predicted network model have been previously reported [growth factor independent 1B (Gfi1b) being regulated via the transcription factors T-cell acute lymphocytic leukemia protein 1 (Tal1)/Ets/Gata or friend leukemia integration 1 (Fli1) being regulated via Ets factors], thereby reiterating the utility of the proposed Boolean network model in this study (27,30).…”

Section: Resultssupporting

confidence: 73%

“…We interrogated existing Chromatin Immunoprecipitation Sequencing (ChIP-Seq) data for Gata2 in 416B cells (27) and generated new ChIP-Seq data for Gata2 in HoxB8-FL cells to investigate binding of Gata2 to Cbfa2t3h and Nfe2 (Fig. 5C).…”

Section: Resultsmentioning

confidence: 99%

“…Transcription factor networks have been successfully modeled using methods such as Bayesian networks (27), which are computationally efficient and allow network perturbations to be simulated. However, Bayesian network topology is limited to acyclic graphs and therefore cannot capture feedback between transcription factors in the network.…”

Section: Discussionmentioning

confidence: 99%

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Reconstructing blood stem cell regulatory network models from single-cell molecular profiles

Hamey

Nestorowa

Kinston

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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Adult blood contains a mixture of mature cell types, each with specialized functions. Single hematopoietic stem cells (HSCs) have been functionally shown to generate all mature cell types for the lifetime of the organism. Differentiation of HSCs toward alternative lineages must be balanced at the population level by the fate decisions made by individual cells. Transcription factors play a key role in regulating these decisions and operate within organized regulatory programs that can be modeled as transcriptional regulatory networks. As dysregulation of single HSC fate decisions is linked to fatal malignancies such as leukemia, it is important to understand how these decisions are controlled on a cell-bycell basis. Here we developed and applied a network inference method, exploiting the ability to infer dynamic information from single-cell snapshot expression data based on expression profiles of 48 genes in 2,167 blood stem and progenitor cells. This approach allowed us to infer transcriptional regulatory network models that recapitulated differentiation of HSCs into progenitor cell types, focusing on trajectories toward megakaryocyteerythrocyte progenitors and lymphoid-primed multipotent progenitors. By comparing these two models, we identified and subsequently experimentally validated a difference in the regulation of nuclear factor, erythroid 2 (Nfe2) and core-binding factor, runt domain, alpha subunit 2, translocated to, 3 homolog (Cbfa2t3h) by the transcription factor Gata2. Our approach confirms known aspects of hematopoiesis, provides hypotheses about regulation of HSC differentiation, and is widely applicable to other hierarchical biological systems to uncover regulatory relationships.gene regulatory networks | hematopoiesis | single cell | Boolean network | stem progenitor cells

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Section: Resultssupporting

confidence: 73%

Section: Resultsmentioning

confidence: 99%

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Reconstructing blood stem cell regulatory network models from single-cell molecular profiles

Hamey

Nestorowa

Kinston

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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“…Dynamic analysis can reveal nontrivial properties, including transient phenomena, and can be used to identify key regulatory factors or interactions involved in the control of cell-fate commitment (8,9). Furthermore, genome-wide approaches such as ChIP-sequencing (ChIP-seq) can unveil novel regulations to be further incorporated in a gene-network model (10). Here, we combined a logical multilevel formalism, capturing the main qualitative aspects of the dynamics of a regulatory network in the absence of quantitative kinetic data (11), with a meta-analysis of all available ChIP-seq datasets for a selection of TFs, revealing tens of previously unknown regulations.…”

mentioning

confidence: 99%

Logical modeling of lymphoid and myeloid cell specification and transdifferentiation

Collombet

Oevelen

Sardina

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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101

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Blood cells are derived from a common set of hematopoietic stem cells, which differentiate into more specific progenitors of the myeloid and lymphoid lineages, ultimately leading to differentiated cells. This developmental process is controlled by a complex regulatory network involving cytokines and their receptors, transcription factors, and chromatin remodelers. Using public data and data from our own molecular genetic experiments (quantitative PCR, Western blot, EMSA) or genome-wide assays (RNA-sequencing, ChIP-sequencing), we have assembled a comprehensive regulatory network encompassing the main transcription factors and signaling components involved in myeloid and lymphoid development. Focusing on B-cell and macrophage development, we defined a qualitative dynamical model recapitulating cytokine-induced differentiation of common progenitors, the effect of various reported gene knockdowns, and the reprogramming of pre-B cells into macrophages induced by the ectopic expression of specific transcription factors. The resulting network model can be used as a template for the integration of new hematopoietic differentiation and transdifferentiation data to foster our understanding of lymphoid/myeloid cell-fate decisions.

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“…Besides, the regression coefficient of a phenotype on one another was chosen uniformly from [0. 5,1], and standard deviation of was randomly drawn from [0.1, 0.5].…”

Section: Synthetic Phenotypic and Qtl Datamentioning

confidence: 99%

Using probabilistic graphical models to reconstruct biological networks and linkage maps

Wang¹

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An experimentally validated network of nine haematopoietic transcription factors reveals mechanisms of cell state stability

Cited by 65 publications

References 56 publications

Reconstructing blood stem cell regulatory network models from single-cell molecular profiles

Reconstructing blood stem cell regulatory network models from single-cell molecular profiles

Logical modeling of lymphoid and myeloid cell specification and transdifferentiation

Using probabilistic graphical models to reconstruct biological networks and linkage maps

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