Evidence map›Paper›PMID 41705696›Full record

ArticleeLife2026

Ribosome demand links transcriptional bursts to protein expression noise.

Sampriti Pal, Upasana Ray, Riddhiman Dhar

Abstract read
In one paragraph

Article in eLife, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

3 authors.

Sampriti PalDepartment of Bioscience and Biotechnology, IIT Kharagpur, Kharagpur, India.
Upasana RayDepartment of Bioscience and Biotechnology, IIT Kharagpur, Kharagpur, India.
Riddhiman DharDepartment of Bioscience and Biotechnology, IIT Kharagpur, Kharagpur, India.ORCID https://orcid.org/0000-0003-4642-0492

Funding

Indian Institute of Technology Kharagpur ISIRDScience and Engineering Research Board ECR/2017/002328
6 · The paper itself

Abstract

Stochastic variation in protein expression generates phenotypic heterogeneity in a cell population and has an important role in antibiotic persistence, mutation penetrance, tumor growth, and therapy resistance. Studies investigating molecular origins of noise have predominantly focused on the transcription process. However, the noise generated in the transcription process is further modulated by translation. This influences the expression noise at the protein level which eventually determines the extent of phenotypic heterogeneity in a cell population. Studies across different organisms have revealed a positive association between translational efficiency and protein noise. However, the molecular basis of this association has remained unknown. In this work, through stochastic modeling of translation in single mRNA molecules and empirical measurements of protein noise, we show that ribosome demand associated with high translational efficiency in a gene drives the correlation between translational efficiency and protein noise. We also show that this correlation is present only in genes with bursty transcription. Thus, our work reveals the molecular basis of how coding sequence of genes, along with their promoters, can regulate noise. These findings have important implications for investigating protein noise and phenotypic heterogeneity across biological systems.

Indexed as

Protein BiosynthesisRibosomesSaccharomyces cerevisiaeTranscription, GeneticRNA, MessengerStochastic ProcessesRNA, Messengercomputational biologyprotein expression noiseribosome demandS. cerevisiaesystems biologytranscriptional burststranslational burststranslational efficiency

Identifiers

PMID41705696
PMCPMC12916104

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.