Evidence map›Paper›PMID 28874819›Full record

ArticleScientific reports2017

Selection for energy efficiency drives strand-biased gene distribution in prokaryotes.

Na Gao, Guanting Lu, Martin J Lercher, Wei-Hua Chen

Abstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

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  6. PeerJ · 2022
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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.

Na GaoKey Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology (HUST), 430074, Wuhan, Hubei, China.
Guanting LuDepartment of Blood Transfusion, Tangdu Hospital, the Fourth Military Medical University, No 1, Xinsi Road, Chanba District, 710000, Xi'an, China.
Martin J LercherInstitute for Computer Science and Cluster of Excellence on Plant Sciences CEPLAS, Heinrich Heine University, 40225, Düsseldorf, Germany.ORCID 0000-0003-3940-1621
Wei-Hua ChenKey Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology (HUST), 430074, Wuhan, Hubei, China. weihuachen@hust.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lagging-strand genes accumulate more deleterious mutations. Genes are thus preferably located on the leading strand, an observation known as strand-biased gene distribution (SGD). Despite of this mechanistic understanding, a satisfactory quantitative model is still lacking. Replication-transcription-collisions induce stalling of the replication machinery, expose DNA to various attacks, and are followed by error-prone repairs. We found that mutational biases in non-transcribed regions can explain ~71% of the variations in SGDs in 1,552 genomes, supporting the mutagenesis origin of SGD. Mutational biases introduce energetically cheaper nucleotides on the lagging strand, and result in more expensive protein products; consistently, the cost difference between the two strands explains ~50% of the variance in SGDs. Protein costs decrease with increasing gene expression. At similar expression levels, protein products of leading-strand genes are generally cheaper than lagging-strand genes; however, highly-expressed lagging genes are still cheaper than lowly-expressed leading genes. Selection for energy efficiency thus drives some genes to the leading strand, especially those highly expressed and essential, but certainly not all genes. Stronger mutational biases are often associated with low-GC genomes; as low-GC genes encode expensive proteins, low-GC genomes thus tend to have stronger SGDs to alleviate the stronger pressure on efficient energy usage.

Indexed as

Genome, BacterialSelection, GeneticBacillus subtilisEnergy MetabolismEscherichia coliGC Rich SequenceGene Expression Regulation, BacterialMutation RateMycoplasma pneumoniae

Identifiers

PMID28874819
PMCPMC5585166

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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.