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

IF 13.8 1区 生物学 Q1 MICROBIOLOGY Microbiome Pub Date : 2024-06-25 DOI:10.1186/s40168-024-01817-w
Ziheng Wu, Minchun Li, Liping Qu, Chuanlun Zhang, Wei Xie
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Abstract

Background: Microbial 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.

Results: Here, 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+ transporter related to the "salt-in" strategy, was ranked as the most important feature. The relative abundance of COG0168 was observed to increase with rising salinity across metagenomes, the stenohaline strains, and the dominant Actinobacteriota and Proteobacteria phyla.

Conclusions: We 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+ transporters for microorganisms thriving under osmotic stress in the short residence-time estuary. Video Abstract.

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微生物适应典型短停留时间河口盐度梯度的元基因组学见解。
背景:微生物对盐度的适应是微生物学领域的一项经典研究。研究表明,微生物可以通过无机离子吸收的 "盐入 "策略或依赖相容性溶质的 "盐出 "策略来承受盐度胁迫。虽然这些见解大多基于实验室培养的分离物,但探索微生物在自然盐度梯度中的适应机制对于深入了解河口生态系统中微生物的适应性至关重要:在此,我们对从典型的亚热带短停留时间河口采集的过滤地表水样本进行了元基因组分析,并按盐度将其分为低、中、高盐度元基因组。我们的研究结果通过分类和同源群(COG)功能注释揭示了盐度驱动的微生物群落组成和功能变化。通过元基因组分选,重建了 127 个细菌和古细菌元基因组组装基因组(MAGs)。根据一个盐度类别中的平均相对丰度明显超过其他两个类别中的平均相对丰度一个数量级的情况,这些 MAGs 被划分为低盐度、中盐度或高盐度的石炭酸特异性基因组。不符合这一标准的鱼类被归类为极海洋性鱼类,这表明它们对盐度的耐受范围更广。应用基于机器学习的特征选择方法 Boruta 算法,我们从盐碱性细菌 MAGs 中发现了重要的基因组特征。在总共获得的 12,162 个 COGs 中,有 40 个被确定为重要特征,其中 "无机离子转运和代谢 "COGs 类别最为突出。此外,8 个 COG 与微生物渗透调节有关,其中 4 个与 "盐入 "策略有关,3 个与 "盐出 "策略有关,1 个与水通道活性调节有关。COG0168 被注释为与 "盐进 "策略有关的 Trk 型 K+ 转运体,被列为最重要的特征。在元基因组、盐碱菌株、优势放线菌门和变形菌门中,我们观察到 COG0168 的相对丰度随着盐度的升高而增加:我们的研究结果表明,盐度对栖息在河口生态系统中的微生物群落的分类和功能特征都有影响。我们的研究结果揭示了河口微生物群落所采用的多种盐度适应策略,并强调了由 Trk 型 K+ 转运体介导的 "盐入 "策略对于在短停留时间河口渗透压胁迫下茁壮成长的微生物所起的关键作用。视频摘要。
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来源期刊
Microbiome
Microbiome MICROBIOLOGY-
CiteScore
21.90
自引率
2.60%
发文量
198
审稿时长
4 weeks
期刊介绍: Microbiome is a journal that focuses on studies of microbiomes in humans, animals, plants, and the environment. It covers both natural and manipulated microbiomes, such as those in agriculture. The journal is interested in research that uses meta-omics approaches or novel bioinformatics tools and emphasizes the community/host interaction and structure-function relationship within the microbiome. Studies that go beyond descriptive omics surveys and include experimental or theoretical approaches will be considered for publication. The journal also encourages research that establishes cause and effect relationships and supports proposed microbiome functions. However, studies of individual microbial isolates/species without exploring their impact on the host or the complex microbiome structures and functions will not be considered for publication. Microbiome is indexed in BIOSIS, Current Contents, DOAJ, Embase, MEDLINE, PubMed, PubMed Central, and Science Citations Index Expanded.
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