Evidence map›Paper›PMID 29792159›Full record

ArticleBMC genomics2018

On the possibility of death of new genes - evidence from the deletion of de novo microRNAs.

Guang-An Lu, Yixin Zhao, Zhongqi Liufu, Chung-I Wu

Abstract read
In one paragraph

Article in BMC genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Death of new microRNA genes inGenome research · 2018
    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.

Guang-An LuState Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510275, Guangdong, China.
Yixin ZhaoState Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510275, Guangdong, China.
Zhongqi LiufuState Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510275, Guangdong, China.
Chung-I WuState Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510275, Guangdong, China. ciwu@uchicago.edu.

Funding

985 Project 33000-18821105National Basic Research Program (973 Program) of China 2014CB542006National Natural Science Foundation of China 31730046
6 · The paper itself

Abstract

backgroundNew genes are constantly formed, sometimes from non-genic sequences, creating what is referred to as de novo genes. Since the total number of genes remains relatively steady, gene deaths likely balance out new births. In metazoan genomes, microRNAs (miRs) genes, small and non-coding, account for the bulk of functional de novo genes and are particularly suited to the investigation of gene death.

resultsIn this study, we discover a Drosophila-specific de novo miRNA (mir-977) that may be facing impending death. Strikingly, after this testis-specific gene is deleted from D. melanogaster, most components of male fitness increase, rather than decrease as had been expected. These components include male viability, fertility and males' ability to repress female re-mating. Given that mir-977 has a negative fitness effect in D. melanogaster, this de novo gene with an adaptive history for over 60 Myrs may be facing elimination. In some other species where mir-977 is not found, gene death may have already happened.

conclusionThe surprising result suggests that de novo genes, constantly rising and falling during evolution, may often be transiently adaptive and then purged from the genome.

Indexed as

Evolution, MolecularGene DeletionAnimalsDrosophila melanogasterFertilityGenomicsMaleMeiosisMicroRNAsMicroRNAsde novo geneDrosophilaGene deathmicroRNANew gene

Identifiers

PMID29792159
PMCPMC5966946

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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.