Evidence map›Paper›PMID 42679820›Full record

ArticleCell2026

Ratiometric transcriptional activation by protein degradation.

Melissa A Gray, Katelyn L Randal, Jennifer A Co, Michelle T Tang, Athena Z Xue, Sophia W Chen, Hlib Razumkov, Qusay Q Omran, David E Solow-Cordero, Jeonghye Yu and 5 more

Abstract read
PubMed Publisher
In one paragraph

Article in Cell, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors.

Melissa A GrayDepartment of Chemistry, Stanford University, Stanford, CA 94305, USA.
Katelyn L RandalDepartment of Chemistry, Stanford University, Stanford, CA 94305, USA.
Jennifer A CoDepartment of Chemistry, Stanford University, Stanford, CA 94305, USA.
Michelle T TangDepartment of Chemical and Systems Biology, Stanford School of Medicine, Stanford, CA, USA.
Athena Z XueDepartment of Chemistry, Stanford University, Stanford, CA 94305, USA.
Sophia W ChenDepartment of Chemistry, Stanford University, Stanford, CA 94305, USA.
Hlib RazumkovDepartment of Chemistry, Stanford University, Stanford, CA 94305, USA; Department of Chemical and Systems Biology, Stanford School of Medicine, Stanford, CA, USA.
Qusay Q OmranDepartment of Genetics, Stanford University, Stanford, CA 94305, USA; Stanford Medical Scientist Training Program, Stanford University, Stanford, CA 94305, USA.
David E Solow-CorderoSarafan ChEM-H, Stanford University, High-Throughput Screening @ the Nucleus, Stanford, CA 94305, USA.
Jeonghye YuDepartment of Chemistry, Stanford University, Stanford, CA 94305, USA.
Stephanie A RobinsonDepartment of Chemistry, Stanford University, Stanford, CA 94305, USA.
Cara A StarnbachDepartment of Chemistry, Stanford University, Stanford, CA 94305, USA.
Nathanael S GrayDepartment of Chemical and Systems Biology, Stanford School of Medicine, Stanford, CA, USA; Stanford Cancer Institute, Stanford, CA, USA; Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA.
Steven M CorselloDepartment of Chemical and Systems Biology, Stanford School of Medicine, Stanford, CA, USA; Stanford Cancer Institute, Stanford, CA, USA; Division of Oncology, Department of Medicine, Stanford School of Medicine, Stanford, CA, USA.
Steven M BanikDepartment of Chemistry, Stanford University, Stanford, CA 94305, USA; Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA. Electronic address: sbanik@stanford.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cells can respond to alterations in the abundances of specific proteins through transcriptional outputs. Synthetic approaches inspired by native post-transcriptional circuits that convert protein abundance changes into programmable gene expression would be transformative. Here, we discover and describe design principles that effectively convert protein degradation into transcriptional outputs in live cells. We define ratiometric transcriptional activation, where control over the ratio between a transcription factor and a protein of interest fused to its inhibitor enables detection of abundance changes with high sensitivity at scale. We show that ratiometric transcriptional activation can be implemented in single cells using triply orthogonal circuits or in multicellular pools, operating independently of the mechanism of protein downregulation and enabling simultaneous detection of multiple protein downregulation events through outputs such as cell survival, fluorescent protein expression, or barcode sequencing. These circuits can be applied to oncogenic targets and enable discovery of new molecular glue degraders.

Indexed as

amplificationanti-CRISPRCRISPRgene circuitshigh-throughputmolecular gluemultiplexed circuitsPROTACproteostasis detectionsynthetic biologysynthetic circuitstargeted protein degradation

Identifiers

PMID42679820

What OpenQuestion holds

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Registered trials

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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.