Evidence map›Paper›PMID 28211871›Full record

ArticleCell death and differentiation2017

Expression level is a key determinant of E2F1-mediated cell fate.

Igor Shats, Michael Deng, Adam Davidovich, Carolyn Zhang, Jungeun S Kwon, Dinesh Manandhar, Raluca Gordân, Guang Yao, Lingchong You

Open access · bronzeAbstract read
In one paragraph

Article in Cell death and differentiation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
2.4field-weighted citation impact, top 10% 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

33 citing papers in PubMed, 63 citations in OpenAlex.

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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 3 institutions in 1 country.

Igor ShatsDepartment of Biomedical Engineering, Duke University, Durham, NC, USA.
Michael DengDepartment of Biomedical Engineering, Duke University, Durham, NC, USA.
Adam DavidovichDepartment of Biomedical Engineering, Duke University, Durham, NC, USA.
Carolyn ZhangDepartment of Biomedical Engineering, Duke University, Durham, NC, USA.
Jungeun S KwonDepartment of Molecular and Cellular Biology, University of Arizona, Tucson, AZ, USA.
Dinesh ManandharDepartment of Biostatistics and Bioinformatics, Center for Genomic and Computational Biology, Duke University, Durham, NC, USA.
Raluca GordânDepartment of Biostatistics and Bioinformatics, Center for Genomic and Computational Biology, Duke University, Durham, NC, USA.
Guang YaoDepartment of Molecular and Cellular Biology, University of Arizona, Tucson, AZ, USA.
Lingchong YouDepartment of Biomedical Engineering, Duke University, Durham, NC, USA.
Duke University · USUniversity of Arizona · USDuke University Hospital · US

Funding

New methods for quantitative modeling of protein-DNA interactionsR01GM117106 · NIGMS · DUKE UNIVERSITY · PI GORDAN, RALUCA · 2015 to 2019
$1.8M
Temporal E2F Dynamics and Cell-Fate Decisions in Single Mammalian CellsR01GM106107 · NIGMS · DUKE UNIVERSITY · PI YOU, LINGCHONG · 2014 to 2017
$1.2M
NIGMS NIH HHS R01 GM106107NIGMS NIH HHS R01 GM117106
6 · The paper itself

Abstract

The Rb/E2F network has a critical role in regulating cell cycle progression and cell fate decisions. It is dysfunctional in virtually all human cancers, because of genetic lesions that cause overexpression of activators, inactivation of repressors, or both. Paradoxically, the downstream target of this network, E2F1, is rarely strongly overexpressed in cancer. E2F1 can induce both proliferation and apoptosis but the factors governing these critical cell fate decisions remain unclear. Previous studies have focused on qualitative mechanisms such as differential cofactors, posttranslational modification or state of other signaling pathways as modifiers of the cell fate decisions downstream of E2F1 activation. In contrast, the importance of the expression levels of E2F1 itself in dictating the downstream phenotypes has not been rigorously studied, partly due to the limited resolution of traditional population-level measurements. Here, through single-cell quantitative analysis, we demonstrate that E2F1 expression levels have a critical role in determining the fate of individual cells. Low levels of exogenous E2F1 promote proliferation, moderate levels induce G1, G2 and mitotic cell cycle arrest, and very high levels promote apoptosis. These multiple anti-proliferative mechanisms result in a strong selection pressure leading to rapid elimination of E2F1-overexpressing cells from the population. RNA-sequencing and RT-PCR revealed that low levels of E2F1 are sufficient to induce numerous cell cycle-promoting genes, intermediate levels induce growth arrest genes (i.e., p18, p19 and p27), whereas higher levels are necessary to induce key apoptotic E2F1 targets APAF1, PUMA, HRK and BIM. Finally, treatment of a lung cancer cell line with a proteasome inhibitor, MLN2238, resulted in an E2F1-dependent mitotic arrest and apoptosis, confirming the role of endogenous E2F1 levels in these phenotypes. The strong anti-proliferative activity of moderately overexpressed E2F1 in multiple cancer types suggests that targeting E2F1 for upregulation may represent an attractive therapeutic strategy in cancer.

Indexed as

ApoptosisApoptosis Regulatory ProteinsApoptotic Protease-Activating Factor 1Bcl-2-Like Protein 11Boron CompoundsCell Cycle CheckpointsCell Line, TumorE2F1 Transcription FactorGlycineHCT116 CellsHistonesHumansProto-Oncogene ProteinsTamoxifenTime-Lapse ImagingAPAF1 protein, humanApoptosis Regulatory ProteinsApoptotic Protease-Activating Factor 1BBC3 protein, humanBcl-2-Like Protein 11Boron CompoundsE2F1 Transcription FactorGlycineHistonesMLN2238Proto-Oncogene ProteinsTamoxifen

Identifiers

PMID28211871
PMCPMC5384025
OpenAlexW2589001620

What OpenQuestion holds

Textmetadata
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.