Evidence map›Paper›PMID 32898473›Full record

ArticleCell systems2020

Optogenetic Control Reveals Differential Promoter Interpretation of Transcription Factor Nuclear Translocation Dynamics.

Susan Y Chen, Lindsey C Osimiri, Michael Chevalier, Lukasz J Bugaj, Taylor H Nguyen, R A Greenstein, Andrew H Ng, Jacob Stewart-Ornstein, Lauren T Neves, Hana El-Samad

Open access · bronzeAbstract read
In one paragraph

Article in Cell systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
3.3field-weighted citation impact, top 7% 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

32 citing papers in PubMed, 62 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Characterizing Optogenetic Tools for Use in Synthetic Gene Circuits.Methods in molecular biology (Clifton, N.J.) · 2026
    Article
  5. Hydraulic Activation of the AsLOV2 Photoreceptor.bioRxiv : the preprint server for biology · 2025
    Article
  6. Review
  7. Review
  8. Article
  9. Review
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Maintaining Transcriptional Specificity Through Mitosis.Annual review of genomics and human genetics · 2022
    Review
  18. Article
  19. Review
  20. Quantifying information of intracellular signaling: progress with machine learning.Reports on progress in physics. Physical Society (Great Britain) · 2022
    Review
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

10 authors at 5 institutions in 1 country.

Susan Y ChenDepartment of Biochemistry and Biophysics, California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158, USA.
Lindsey C OsimiriDepartment of Biochemistry and Biophysics, California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158, USA; The UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA 94143, USA.
Michael ChevalierDepartment of Biochemistry and Biophysics, California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158, USA.
Lukasz J BugajDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
Taylor H NguyenDepartment of Biochemistry and Biophysics, California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158, USA.
R A GreensteinDepartment of Microbiology and Immunology, George Williams Hooper Foundation, Tetrad Graduate Program, University of California, San Francisco, San Francisco, CA 94143, USA.
Andrew H NgDepartment of Biochemistry and Biophysics, California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158, USA; The UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA 94143, USA; Cell Design Institute, University of California, San Francisco, San Francisco, CA 94158, USA.
Jacob Stewart-OrnsteinDepartment of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA.
Lauren T NevesDepartment of Biochemistry and Biophysics, California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158, USA.
Hana El-SamadDepartment of Biochemistry and Biophysics, California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA; Cell Design Institute, University of California, San Francisco, San Francisco, CA 94158, USA. Electronic address: hana.el-samad@ucsf.edu.
QB3 · USUniversity of California, San Francisco · USUniversity of California, Berkeley · USUniversity of Pennsylvania · USUniversity of Pittsburgh · US

Funding

Unraveling the quantitative dynamics of cAMP-PKA signaling in yeastR01GM119033 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI EL-SAMAD, HANA · 2017 to 2020
$1.2M
NIGMS NIH HHS R01 GM119033
6 · The paper itself

Abstract

Gene expression is thought to be affected not only by the concentration of transcription factors (TFs) but also the dynamics of their nuclear translocation. Testing this hypothesis requires direct control of TF dynamics. Here, we engineer CLASP, an optogenetic tool for rapid and tunable translocation of a TF of interest. Using CLASP fused to Crz1, we observe that, for the same integrated concentration of nuclear TF over time, changing input dynamics changes target gene expression: pulsatile inputs yield higher expression than continuous inputs, or vice versa, depending on the target gene. Computational modeling reveals that a dose-response saturating at low TF input can yield higher gene expression for pulsatile versus continuous input, and that multi-state promoter activation can yield the opposite behavior. Our integrated tool development and modeling approach characterize promoter responses to Crz1 nuclear translocation dynamics, extracting quantitative features that may help explain the differential expression of target genes.

Indexed as

Active Transport, Cell NucleusCell NucleusDNA-Binding ProteinsGene ExpressionOptogeneticsPromoter Regions, GeneticProtein TransportSaccharomyces cerevisiaeSaccharomyces cerevisiae ProteinsTranscription FactorsCRZ1 protein, S cerevisiaeDNA-Binding ProteinsSaccharomyces cerevisiae ProteinsTranscription Factorsdynamic decodingnucleo-cytoplasmic pulsingoptogeneticspromoter interpretationtranscription factorstranslocation dynamics

Identifiers

PMID32898473
PMCPMC7648432
OpenAlexW3083371607

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.