Evidence map›Paper›PMID 38918820›Full record

ArticleMicrobiome2024

Metagenomic insights into microbial adaptation to the salinity gradient of a typical short residence-time estuary.

Ziheng Wu, Minchun Li, Liping Qu, Chuanlun Zhang, Wei Xie

Abstract read
In one paragraph

Article in Microbiome, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

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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

22 citing papers in PubMed.

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  16. Nanopore sequencing in veterinary medicine: from concepts to clinical applications.Frontiers in cellular and infection microbiology · 2025
    Review
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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

5 authors.

Ziheng WuSchool of Marine Sciences, Sun Yat-Sen University and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China.
Minchun LiSchool of Marine Sciences, Sun Yat-Sen University and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China.
Liping QuSchool of Marine Sciences, Sun Yat-Sen University and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China.
Chuanlun ZhangDepartment of Ocean Science and Engineering, Shenzhen Key Laboratory of Marine Archaea Geo-Omics, Southern University of Science and Technology, Shenzhen, 518055, China.
Wei XieSchool of Marine Sciences, Sun Yat-Sen University and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China. xiewei9@mail.sysu.edu.cn.

Funding

Guangdong Basic and Applied Basic Research Foundation 2021B1515120080National Key Basic Research Program of China 2022YFC2805505National Natural Science Foundation of China 92051117Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) SML2023SP218
6 · The paper itself

Abstract

backgroundMicrobial adaptation to salinity has been a classic inquiry in the field of microbiology. It has been demonstrated that microorganisms can endure salinity stress via either the "salt-in" strategy, involving inorganic ion uptake, or the "salt-out" strategy, relying on compatible solutes. While these insights are mostly based on laboratory-cultured isolates, exploring the adaptive mechanisms of microorganisms within natural salinity gradient is crucial for gaining a deeper understanding of microbial adaptation in the estuarine ecosystem.

resultsHere, we conducted metagenomic analyses on filtered surface water samples collected from a typical subtropical short residence-time estuary and categorized them by salinity into low-, intermediate-, and high-salinity metagenomes. Our findings highlighted salinity-driven variations in microbial community composition and function, as revealed through taxonomic and Clusters of Orthologous Group (COG) functional annotations. Through metagenomic binning, 127 bacterial and archaeal metagenome-assembled genomes (MAGs) were reconstructed. These MAGs were categorized as stenohaline-specific to low-, intermediate-, or high-salinity-based on the average relative abundance in one salinity category significantly exceeding those in the other two categories by an order of magnitude. Those that did not meet this criterion were classified as euryhaline, indicating a broader range of salinity tolerance. Applying the Boruta algorithm, a machine learning-based feature selection method, we discerned important genomic features from the stenohaline bacterial MAGs. Of the total 12,162 COGs obtained, 40 were identified as important features, with the "inorganic ion transport and metabolism" COG category emerging as the most prominent. Furthermore, eight COGs were implicated in microbial osmoregulation, of which four were related to the "salt-in" strategy, three to the "salt-out" strategy, and one to the regulation of water channel activity. COG0168, annotated as the Trk-type K

conclusionsWe demonstrated that salinity exerts influences on both the taxonomic and functional profiles of the microbial communities inhabiting the estuarine ecosystem. Our findings shed light on diverse salinity adaptation strategies employed by the estuarine microbial communities, highlighting the crucial role of the "salt-in" strategy mediated by Trk-type K

Indexed as

ArchaeaBacteriaEstuariesMetagenomeMetagenomicsSalinityAdaptation, PhysiologicalMicrobiotaSeawaterWater MicrobiologyMetagenomicsPearl river estuarySalinity adaptabilitySalt-in strategy

Identifiers

PMID38918820
PMCPMC11200988

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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.