Evidence map›Paper›PMID 35798838›Full record

ArticleNature ecology & evolution2022

Low protein expression enhances phenotypic evolvability by intensifying selection on folding stability.

Shraddha Karve, Pouria Dasmeh, Jia Zheng, Andreas Wagner

Open access · bronzeAbstract read
In one paragraph

Article in Nature ecology & evolution, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.3field-weighted citation impact, top 18% 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

5 citing papers in PubMed, 8 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. 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

4 authors at 2 institutions in 4 countries.

Shraddha KarveDepartment of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0001-6103-2450
Pouria DasmehDepartment of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland.
Jia ZhengDepartment of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland.
Andreas WagnerDepartment of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland. andreas.wagner@ieu.uzh.ch.ORCID http://orcid.org/0000-0003-4299-3840
SIB Swiss Institute of Bioinformatics · CHUniversity of Zurich · CH

Funding

Swiss National Science Foundation 172887
6 · The paper itself

Abstract

Protein abundance affects the evolution of protein genotypes, but we do not know how it affects the evolution of protein phenotypes. Here we investigate the role of protein abundance in the evolvability of green fluorescent protein (GFP) towards the novel phenotype of cyan fluorescence. We evolve GFP in E. coli through multiple cycles of mutation and selection and show that low GFP expression facilitates the evolution of cyan fluorescence. A computational model whose predictions we test experimentally helps explain why: lowly expressed proteins are under stronger selection for proper folding, which facilitates their evolvability on short evolutionary time scales. The reason is that high fluorescence can be achieved by either few proteins that fold well or by many proteins that fold less well. In other words, we observe a synergy between a protein's scarcity and its stability. Because many proteins meet the essential requirements for this scarcity-stability synergy, it may be a widespread mechanism by which low expression helps proteins evolve new phenotypes and functions.

Indexed as

Escherichia coliProteinsGenotypeMutationPhenotypeProteins

Identifiers

PMID35798838
PMCPMC7613228
OpenAlexW4284671541

What OpenQuestion holds

Textmetadata
LicenceTDM
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.