Evidence map›Paper›PMID 39868080›Full record

ArticlebioRxiv : the preprint server for biology2025

A Narrow Range of Transcript-error Rates Across the Tree of Life.

Weiyi Li, Stephan Baehr, Michelle Marasco, Lauren Reyes, Danielle Brister, Craig S Pikaard, Jean-Francois Gout, Marc Vermulst, Michael Lynch

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. 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

9 authors.

Weiyi LiDepartment of Genetics, Stanford University School of Medicine, Stanford University, Stanford, CA, 94305.ORCID 0000-0002-1168-7093
Stephan BaehrBiodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ 85287.
Michelle MarascoDepartment of Biology, Howard Hughes Medical Institute, Indiana University, Bloomington, IN 47405, USA.
Lauren ReyesBiodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ 85287.
Danielle BristerBiodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ 85287.
Craig S PikaardDepartment of Biology, Howard Hughes Medical Institute, Indiana University, Bloomington, IN 47405, USA.ORCID 0000-0001-8204-7459
Jean-Francois GoutMississippi State University, Department of Biological Sciences, Mississippi State, MS 39762.
Marc VermulstLeonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, 90089.
Michael LynchBiodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ 85287.

Funding

Causes and Population-genetic Consequences of Molecular VariationR35GM122566 · NIGMS · TRUSTEES OF INDIANA UNIVERSITY · PI Michael R LYNCH · 2017 to 2026
$7.7M
Roles of RNA Polymerases IV and V in siRNA-mediated gene silencingR01GM077590 · NIGMS · WASHINGTON UNIVERSITY · PI PIKAARD, CRAIG STUART · 2006 to 2020
$3.5M
Understanding the role of transcription errors in aging and diseaseR01AG054641 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI VERMULST, MARC · 2017 to 2021
$1.7M
NIA NIH HHS R01 AG054641NIGMS NIH HHS R01 GM077590NIGMS NIH HHS R35 GM122566
6 · The paper itself

Abstract

The expression of genomically-encoded information is not error-free. Transcript-error rates are dramatically higher than DNA-level mutation rates, and despite their transient nature, the steady-state load of such errors must impose some burden on cellular performance. However, a broad perspective on the degree to which transcript-error rates are constrained by natural selection and diverge among lineages remains to be developed. Here, we present a genome-wide analysis of transcript-error rates across the Tree of Life using a modified rolling-circle sequencing method, revealing that the range in error rates is remarkably narrow across diverse species. Transcript errors tend to be randomly distributed, with little evidence supporting local control of error rates associated with gene-expression levels. A majority of transcript errors result in missense errors if translated, and as with a fraction of nonsense transcript errors, these are underrepresented relative to random expectations, suggesting the existence of mechanisms for purging some such errors. To quantitatively understand how natural selection and random genetic drift might shape transcript-error rates across species, we present a model based on cell biology and population genetics, incorporating information on cell volume, proteome size, average degree of exposure of individual errors, and effective population size. However, while this model provides a framework for understanding the evolution of this highly conserved trait, as currently structured it explains only 20% of the variation in the data, suggesting a need for further theoretical work in this area.

Identifiers

PMID39868080
PMCPMC11761650

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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.