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The complex landscape of haematopoietic lineage commitments is encoded in the coarse-grained endogenous network
Haematopoietic lineage commitments are presented by a canonical roadmap in which haematopoietic stem cells or multipotent progenitors (MPPs) bifurcate into progenitors of more restricted lineages and ultimately mature to terminally differentiated cells. Although transcription factors playing signifi...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8564612/ https://www.ncbi.nlm.nih.gov/pubmed/34737882 http://dx.doi.org/10.1098/rsos.211289 |
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author | Wang, Mengyao Wang, Junqiang Zhang, Xingxing Yuan, Ruoshi |
author_facet | Wang, Mengyao Wang, Junqiang Zhang, Xingxing Yuan, Ruoshi |
author_sort | Wang, Mengyao |
collection | PubMed |
description | Haematopoietic lineage commitments are presented by a canonical roadmap in which haematopoietic stem cells or multipotent progenitors (MPPs) bifurcate into progenitors of more restricted lineages and ultimately mature to terminally differentiated cells. Although transcription factors playing significant roles in cell-fate commitments have been extensively studied, integrating such knowledge into the dynamic models to understand the underlying biological mechanism remains challenging. The hypothesis and modelling approach of the endogenous network has been developed previously and tested in various biological processes and is used in the present study of haematopoietic lineage commitments. The endogenous network is constructed based on the key transcription factors and their interactions that determine haematopoietic cell-fate decisions at each lineage branchpoint. We demonstrate that the process of haematopoietic lineage commitments can be reproduced from the landscape which orchestrates robust states of network dynamics and their transitions. Furthermore, some non-trivial characteristics are unveiled in the dynamical model. Our model also predicted previously under-represented regulatory interactions and heterogeneous MPP states by which distinct differentiation routes are intermediated. Moreover, network perturbations resulting in state transitions indicate the effects of ectopic gene expression on cellular reprogrammes. This study provides a predictive model to integrate experimental data and uncover the possible regulatory mechanism of haematopoietic lineage commitments. |
format | Online Article Text |
id | pubmed-8564612 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-85646122021-11-03 The complex landscape of haematopoietic lineage commitments is encoded in the coarse-grained endogenous network Wang, Mengyao Wang, Junqiang Zhang, Xingxing Yuan, Ruoshi R Soc Open Sci Physics and Biophysics Haematopoietic lineage commitments are presented by a canonical roadmap in which haematopoietic stem cells or multipotent progenitors (MPPs) bifurcate into progenitors of more restricted lineages and ultimately mature to terminally differentiated cells. Although transcription factors playing significant roles in cell-fate commitments have been extensively studied, integrating such knowledge into the dynamic models to understand the underlying biological mechanism remains challenging. The hypothesis and modelling approach of the endogenous network has been developed previously and tested in various biological processes and is used in the present study of haematopoietic lineage commitments. The endogenous network is constructed based on the key transcription factors and their interactions that determine haematopoietic cell-fate decisions at each lineage branchpoint. We demonstrate that the process of haematopoietic lineage commitments can be reproduced from the landscape which orchestrates robust states of network dynamics and their transitions. Furthermore, some non-trivial characteristics are unveiled in the dynamical model. Our model also predicted previously under-represented regulatory interactions and heterogeneous MPP states by which distinct differentiation routes are intermediated. Moreover, network perturbations resulting in state transitions indicate the effects of ectopic gene expression on cellular reprogrammes. This study provides a predictive model to integrate experimental data and uncover the possible regulatory mechanism of haematopoietic lineage commitments. The Royal Society 2021-11-03 /pmc/articles/PMC8564612/ /pubmed/34737882 http://dx.doi.org/10.1098/rsos.211289 Text en © 2021 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Physics and Biophysics Wang, Mengyao Wang, Junqiang Zhang, Xingxing Yuan, Ruoshi The complex landscape of haematopoietic lineage commitments is encoded in the coarse-grained endogenous network |
title | The complex landscape of haematopoietic lineage commitments is encoded in the coarse-grained endogenous network |
title_full | The complex landscape of haematopoietic lineage commitments is encoded in the coarse-grained endogenous network |
title_fullStr | The complex landscape of haematopoietic lineage commitments is encoded in the coarse-grained endogenous network |
title_full_unstemmed | The complex landscape of haematopoietic lineage commitments is encoded in the coarse-grained endogenous network |
title_short | The complex landscape of haematopoietic lineage commitments is encoded in the coarse-grained endogenous network |
title_sort | complex landscape of haematopoietic lineage commitments is encoded in the coarse-grained endogenous network |
topic | Physics and Biophysics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8564612/ https://www.ncbi.nlm.nih.gov/pubmed/34737882 http://dx.doi.org/10.1098/rsos.211289 |
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