A system-level model reveals that transcriptional stochasticity is required for hematopoietic stem cell differentiation

HSCs differentiation has been difficult to study experimentally due to the high number of components and interactions involved, as well as the impact of diverse physiological conditions. From a 200-node network, that was grounded on experimental data, we derived a 21-node regulatory network by colla...

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Published inNPJ systems biology and applications Vol. 10; no. 1; pp. 145 - 21
Main Authors Herrera, Joel, Bensussen, Antonio, García-Gómez, Mónica L., Garay-Arroyo, Adriana, Álvarez-Buylla, Elena R.
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 05.12.2024
Nature Publishing Group
Nature Portfolio
Subjects
631/136/532
631/449
Adipocytes
Animals
Bioinformatics
Biology
Biomedical and Life Sciences
Bone marrow
Boolean
Cell culture
Cell cycle
Cell differentiation
Cell Differentiation - genetics
Cell Differentiation - physiology
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Dendritic cells
Gene expression
Gene Regulatory Networks - genetics
Hematopoietic stem cells
Hematopoietic Stem Cells - cytology
Hematopoietic Stem Cells - metabolism
Humans
Hypoxia
Life Sciences
Metabolism
Metabolites
Models, Biological
Neutrophils
Phosphorylation
Physiology
Reactive oxygen species
Reactive Oxygen Species - metabolism
Signal Transduction - genetics
Signal Transduction - physiology
Stem cells
Stochastic Processes
Stochasticity
Systems Biology
Systems Biology - methods
Transcription factors
Transcription Factors - genetics
Transcription Factors - metabolism
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Abstract HSCs differentiation has been difficult to study experimentally due to the high number of components and interactions involved, as well as the impact of diverse physiological conditions. From a 200-node network, that was grounded on experimental data, we derived a 21-node regulatory network by collapsing linear pathways and retaining the functional feedback loops. This regulatory network core integrates key nodes and interactions underlying HSCs differentiation, including transcription factors, metabolic, and redox signaling pathways. We used Boolean, continuous, and stochastic dynamic models to simulate the hypoxic conditions of the HSCs niche, as well as the patterns and temporal sequences of HSCs transitions and differentiation. Our findings indicate that HSCs differentiation is a plastic process in which cell fates can transdifferentiate among themselves. Additionally, we found that cell heterogeneity is fundamental for HSCs differentiation. Lastly, we found that oxygen activates ROS production, inhibiting quiescence and promoting growth and differentiation pathways of HSCs.
AbstractList HSCs differentiation has been difficult to study experimentally due to the high number of components and interactions involved, as well as the impact of diverse physiological conditions. From a 200-node network, that was grounded on experimental data, we derived a 21-node regulatory network by collapsing linear pathways and retaining the functional feedback loops. This regulatory network core integrates key nodes and interactions underlying HSCs differentiation, including transcription factors, metabolic, and redox signaling pathways. We used Boolean, continuous, and stochastic dynamic models to simulate the hypoxic conditions of the HSCs niche, as well as the patterns and temporal sequences of HSCs transitions and differentiation. Our findings indicate that HSCs differentiation is a plastic process in which cell fates can transdifferentiate among themselves. Additionally, we found that cell heterogeneity is fundamental for HSCs differentiation. Lastly, we found that oxygen activates ROS production, inhibiting quiescence and promoting growth and differentiation pathways of HSCs.
HSCs differentiation has been difficult to study experimentally due to the high number of components and interactions involved, as well as the impact of diverse physiological conditions. From a 200-node network, that was grounded on experimental data, we derived a 21-node regulatory network by collapsing linear pathways and retaining the functional feedback loops. This regulatory network core integrates key nodes and interactions underlying HSCs differentiation, including transcription factors, metabolic, and redox signaling pathways. We used Boolean, continuous, and stochastic dynamic models to simulate the hypoxic conditions of the HSCs niche, as well as the patterns and temporal sequences of HSCs transitions and differentiation. Our findings indicate that HSCs differentiation is a plastic process in which cell fates can transdifferentiate among themselves. Additionally, we found that cell heterogeneity is fundamental for HSCs differentiation. Lastly, we found that oxygen activates ROS production, inhibiting quiescence and promoting growth and differentiation pathways of HSCs.HSCs differentiation has been difficult to study experimentally due to the high number of components and interactions involved, as well as the impact of diverse physiological conditions. From a 200-node network, that was grounded on experimental data, we derived a 21-node regulatory network by collapsing linear pathways and retaining the functional feedback loops. This regulatory network core integrates key nodes and interactions underlying HSCs differentiation, including transcription factors, metabolic, and redox signaling pathways. We used Boolean, continuous, and stochastic dynamic models to simulate the hypoxic conditions of the HSCs niche, as well as the patterns and temporal sequences of HSCs transitions and differentiation. Our findings indicate that HSCs differentiation is a plastic process in which cell fates can transdifferentiate among themselves. Additionally, we found that cell heterogeneity is fundamental for HSCs differentiation. Lastly, we found that oxygen activates ROS production, inhibiting quiescence and promoting growth and differentiation pathways of HSCs.
Abstract HSCs differentiation has been difficult to study experimentally due to the high number of components and interactions involved, as well as the impact of diverse physiological conditions. From a 200-node network, that was grounded on experimental data, we derived a 21-node regulatory network by collapsing linear pathways and retaining the functional feedback loops. This regulatory network core integrates key nodes and interactions underlying HSCs differentiation, including transcription factors, metabolic, and redox signaling pathways. We used Boolean, continuous, and stochastic dynamic models to simulate the hypoxic conditions of the HSCs niche, as well as the patterns and temporal sequences of HSCs transitions and differentiation. Our findings indicate that HSCs differentiation is a plastic process in which cell fates can transdifferentiate among themselves. Additionally, we found that cell heterogeneity is fundamental for HSCs differentiation. Lastly, we found that oxygen activates ROS production, inhibiting quiescence and promoting growth and differentiation pathways of HSCs.
ArticleNumber 145
Author Bensussen, Antonio
García-Gómez, Mónica L.
Garay-Arroyo, Adriana
Herrera, Joel
Álvarez-Buylla, Elena R.
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  givenname: Mónica L.
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  fullname: García-Gómez, Mónica L.
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  givenname: Adriana
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  email: eabuylla@gmail.com
  organization: Instituto de Ecología, Universidad Nacional Autónoma de México
BackLink https://www.ncbi.nlm.nih.gov/pubmed/39639033$$D View this record in MEDLINE/PubMed
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Snippet HSCs differentiation has been difficult to study experimentally due to the high number of components and interactions involved, as well as the impact of...
Abstract HSCs differentiation has been difficult to study experimentally due to the high number of components and interactions involved, as well as the impact...
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SubjectTerms 631/136/532
631/449
Adipocytes
Animals
Bioinformatics
Biology
Biomedical and Life Sciences
Bone marrow
Boolean
Cell culture
Cell cycle
Cell differentiation
Cell Differentiation - genetics
Cell Differentiation - physiology
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Dendritic cells
Gene expression
Gene Regulatory Networks - genetics
Hematopoietic stem cells
Hematopoietic Stem Cells - cytology
Hematopoietic Stem Cells - metabolism
Humans
Hypoxia
Life Sciences
Metabolism
Metabolites
Models, Biological
Neutrophils
Phosphorylation
Physiology
Reactive oxygen species
Reactive Oxygen Species - metabolism
Signal Transduction - genetics
Signal Transduction - physiology
Stem cells
Stochastic Processes
Stochasticity
Systems Biology
Systems Biology - methods
Transcription factors
Transcription Factors - genetics
Transcription Factors - metabolism
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Title A system-level model reveals that transcriptional stochasticity is required for hematopoietic stem cell differentiation
URI https://link.springer.com/article/10.1038/s41540-024-00469-8
https://www.ncbi.nlm.nih.gov/pubmed/39639033
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