WeChat Mini Program
Old Version Features

Metagenomic Insights into Microbial Adaptation to the Salinity Gradient of a Typical Short Residence-Time Estuary

MICROBIOME(2024)

School of Marine Sciences | Shenzhen Key Laboratory of Marine Archaea Geo-Omics

Cited 5|Views11
Abstract
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. 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. 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.
More
Translated text
Key words
Salinity adaptability,Salt-in strategy,Metagenomics,Pearl river estuary
PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Related Papers
S Altschul, T Madden, A Schaffer, Jh Zhang, Z Zhang, W Miller,D Lipman
1998

被引用88986 | 浏览

1999

被引用881 | 浏览

Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper

要点】:本文通过对典型短居留时间热带港湾的表层水进行宏基因组分析,揭示了微生物群落随盐度梯度的适应性变化,强调了“盐-in”策略在微生物适应渗透压应激中的关键作用。

方法】:研究采用宏基因组学方法,对低、中、高盐度分类的表层水样本进行了微生物群落组成和功能分类注释,通过宏基因组组装和分类,识别了与盐度适应性相关的关键基因组特征。

实验】:研究者收集了热带短居留时间港湾的过滤表层水样本,并利用Boruta算法筛选出重要的基因组特征,共重构了127个细菌和古菌宏基因组组装基因组(MAGs),实验结果揭示了微生物群落盐度适应性的多样性。