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Positive Feedback Between PU.1 and the Cell Cycle Controls Myeloid Differentiation
Open accessAbstract
Regulatory gene circuits with positive-feedback loops control stem cell differentiation, but several mechanisms can contribute to positive feedback. Here, we dissect feedback mechanisms through which the transcription factor PU.1 controls lymphoid and myeloid differentiation. Quantitative live-cell imaging revealed that developing B cells decrease PU.1 levels by reducing PU.1 transcription, whereas developing macrophages increase PU.1 levels by lengthening their cell cycles, which causes stable PU.1 accumulation. Exogenous PU.1 expression in progenitors increases endogenous PU.1 levels by inducing cell cycle lengthening, implying positive feedback between a regulatory factor and the cell cycle. Mathematical modeling showed that this cell cycle-coupled feedback architecture effectively stabilizes a slow-dividing differentiated state. These results show that cell cycle duration functions as an integral part of a positive autoregulatory circuit to control cell fate.
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“Volumes of evidences indicate that specific cellular levels of PU.1 are critical for distinct cell fates, where highest PU.1 amounts drive macrophage development and lower concentrations are required for granulocytic/B-cell fate adoption (4,5).”
From FBW7 Inhibits Myeloid Differentiation in Acute Myeloid Leukemia via GSK3-Dependent Ubiquitination of PU.1 (2021) · cites this paper as [5]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“although the RNA transcript levels are modest in absolute terms, the impact of PU.1 on the cells can be magnified by the high stability of PU.1 protein (82).”
From Mechanisms of Action of Hematopoietic Transcription Factor PU.1 in Initiation of T-Cell Development (2019) · cites this paper as [82]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The target genes stimulated by PU.1 include both pro-proliferative and G1-prolonging cell cycle effectors, whereas some important proliferative genes are repressed when PU.1 levels are high (82, 83).”
From Mechanisms of Action of Hematopoietic Transcription Factor PU.1 in Initiation of T-Cell Development (2019) · cites this paper as [82]
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In the citing paper
“Interestingly, PU.1 can also restrict proliferation in hematopoietic stem and progenitor cells (HSPCs), likely a mechanism to promote PU.1 accumulation during myeloid differentiation (Fukuchi et al., 2008; Kueh et al., 2013).”
From PU.1 enforces quiescence and limits hematopoietic stem cell expansion during inflammatory stress (2021) · cites this paper as Kueh et al., 2013
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“PU.1 similarly represses cell cycle genes in T cell progenitors”
From PU.1 is required to restrain myelopoiesis during chronic inflammatory stress (2023) · cites this paper as Kueh et al., 2013
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“and thus cell cycle restriction may play a similar role in lymphoid development (Kueh et al., 2013; Staber et al., 2013; Champhekar et al., 2015).”
From PU.1 is required to restrain myelopoiesis during chronic inflammatory stress (2023) · cites this paper as Kueh et al., 2013
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“cellular levels of PU.1 direct different cell fates from these hematopoietic progenitors where the highest amounts of PU.1 are required for macrophage and lower concentrations for granulocyte or B-cell differentiation (4,5).”
From FBW7 Inhibits Myeloid Differentiation in Acute Myeloid Leukemia via GSK3-Dependent Ubiquitination of PU.1 (2021) · cites this paper as [5]
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“One problem is that the long half-life of PU.1 protein (82) can mask some loss effects at time points <2 days after deletion, while development of the pro-T cells can proceed to new stages if time windows are extended further.”
From Mechanisms of Action of Hematopoietic Transcription Factor PU.1 in Initiation of T-Cell Development (2019) · cites this paper as [82]
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“Ironically, because PU.1 protein can persist longer than a cell cycle (82), the very slowdown of cell division caused by deletion of Spi1 (see above) can also interfere with the dilution needed to complete the clearance of the PU.1 protein.”
From Mechanisms of Action of Hematopoietic Transcription Factor PU.1 in Initiation of T-Cell Development (2019) · cites this paper as [82]
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“The connection between cell cycle inhibition and myeloid differentiation likely centers on cell cycle lengthening as a mechanism to promote PU.1 accumulation and myeloid differentiation in hematopoietic progenitors (Kueh et al., 2013).”
From PU.1 enforces quiescence and limits hematopoietic stem cell expansion during inflammatory stress (2021) · cites this paper as Kueh et al., 2013
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“Previous work has shown that PU.1 restricts the cell cycle activity of myeloid-committed progenitors (Kueh et al., 2013), thereby facilitating homeostatic myeloid differentiation via accumulation of sufficient myeloid lineage determinants prior to cell division.”
From PU.1 is required to restrain myelopoiesis during chronic inflammatory stress (2023) · cites this paper as Kueh et al., 2013
In the citing paper
“Hematopoietic lineage specification is mediated by the activation of lineage-specific transcription regulators, such as PU.1 and GATA-1 (5, 52–54).”
From Histone deacetylase 1 activates PU.1 gene transcription through regulating TAF9 deacetylation and transcription factor IID assembly (2017) · cites this paper as [54]
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Metadata
- Authors
- Kueh, H. Y., Champhekar, A., Nutt, S. L., Elowitz, M. B., & Rothenberg, E. V.
- Affiliations
- California Institute of Technology (US)
- Walter and Eliza Hall Institute of Medical Research (AU)
- Corresponding author
- Kueh, Hao Yuan
- Rothenberg, Ellen V.
- Venue
- Science
- Published
- 2013-08-09
- Volume
- 341
- Issue
- 6146
- Pages
- 670-673
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC3913367
- OpenAlex
- W2083385452
- Cited by
- 273
- References
- 46
- Citations by year
- 2026: 13 · 2025: 18 · 2024: 16 · 2023: 17 · 2022: 14 · 2021: 21 · 2020: 26 · 2019: 19 · 2018: 20 · 2017: 20 · 2016: 29 · 2015: 28 · 2014: 23 · 2013: 8
- FWCI
- 13.06
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- National Heart, Lung, and Blood Institute (R33HL089123)
- National Cancer Institute (R01CA090233, RC2CA148278)
- National Institute of Allergy and Infectious Diseases (R01AI083514)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Developmental Biology and Gene Regulation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- PU.1, positive feedback loop, cell cycle, myeloid differentiation, lymphoid differentiation, B cell development, macrophage development, transcriptional regulation, cell fate determination, mathematical modeling, live-cell imaging
- MeSH terms
- Proto-Oncogene Protein Spi-1, Animals, Cell Cycle, Cell Differentiation, Cells, Cultured, Gene Expression Regulation, Macrophages, Mice, Inbred Strains, Proto-Oncogene Proteins, Trans-Activators, Myeloid Cells, Feedback, Physiological, Mice, Gene Regulatory Networks, Precursor Cells, B-Lymphoid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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