Evidence map›Paper›PMID 35701838›Full record

ArticleMicrobiome2022

Growth rate determines prokaryote-provirus network modulated by temperature and host genetic traits.

Zhenghua Liu, Qingyun Yan, Chengying Jiang, Juan Li, Huahua Jian, Lu Fan, Rui Zhang, Xiang Xiao, Delong Meng, Xueduan Liu and 2 more

Abstract readVideo-Audio Media
PubMed Full text at PMC Publisher
In one paragraph

Article in Microbiome, 2022. 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

12 authors.

Zhenghua LiuKey Laboratory of Biometallurgy of Ministry of Education, School of Minerals Processing and Bioengineering, Central South University, Changsha, 410006, China.
Qingyun YanEnvironmental Microbiomics Research Center, School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China.
Chengying JiangState Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China.
Juan LiCollege of Agronomy, Hunan Agricultural University, Changsha, 410125, China.
Huahua JianState Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
Lu FanDepartment of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
Rui ZhangState Key Laboratory of Marine Environmental Science, The Institute of Marine Microbes and Ecospheres, Xiamen University, Xiamen, 361102, China.
Xiang XiaoState Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
Delong MengKey Laboratory of Biometallurgy of Ministry of Education, School of Minerals Processing and Bioengineering, Central South University, Changsha, 410006, China.
Xueduan LiuKey Laboratory of Biometallurgy of Ministry of Education, School of Minerals Processing and Bioengineering, Central South University, Changsha, 410006, China.
Jianjun WangState Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China. jjwang@niglas.ac.cn.
Huaqun YinKey Laboratory of Biometallurgy of Ministry of Education, School of Minerals Processing and Bioengineering, Central South University, Changsha, 410006, China. yinhuaqun_cs@sina.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundProkaryote-virus interactions play key roles in driving biogeochemical cycles. However, little is known about the drivers shaping their interaction network structures, especially from the host features. Here, we compiled 7656 species-level genomes in 39 prokaryotic phyla across environments globally and explored how their interaction specialization is constrained by host life history traits, such as growth rate.

resultsWe first reported that host growth rate indicated by the reverse of minimal doubling time was negatively related to interaction specialization for host in host-provirus network across various ecosystems and taxonomy groups. Such a negative linear growth rate-specialization relationship (GrSR) was dependent on host optimal growth temperature (OGT), and stronger toward the two gradient ends of OGT. For instance, prokaryotic species with an OGT ≥ 40 °C showed a stronger GrSR (Pearson's r = -0.525, P < 0.001). Significant GrSRs were observed with the presences of host genes in promoting the infection cycle at stages of adsorption, establishment, and viral release, but nonsignificant with the presence of immune systems, such as restriction-modification systems and CRISPR-Cas systems. Moreover, GrSR strength was increased with the presence of temperature-dependent lytic switches, which was also confirmed by mathematical modeling.

conclusionsTogether, our results advance our understanding of the interactions between prokaryotes and proviruses and highlight the importance of host growth rate in interaction specialization during lysogenization. Video Abstract.

Indexed as

ProvirusesVirusesEcosystemProkaryotic CellsTemperatureGenetic traitsGrowth rateHost-virus interactionInfection cycleSpecializationTemperature

Identifiers

PMID35701838
PMCPMC9195381

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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.