Acidic proteomes are linked to microbial alkaline preference in African lakes

IF 11.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL Water Research Pub Date : 2024-09-03 DOI:10.1016/j.watres.2024.122393
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Abstract

Microbial amino acid composition (AA) reflects adaptive strategies of cellular and molecular regulations such as a high proportion of acidic AAs, including glutamic and aspartic acids in alkaliphiles. It remains understudied how microbial AA content is linked to their pH adaptation especially in natural environments. Here we examined prokaryotic communities and their AA composition of genes with metagenomics for 39 water and sediments of East African lakes along a gradient of pH spanning from 7.2 to 10.1. We found that Shannon diversity declined with the increasing pH and that species abundance were either positively or negatively associated with pH, indicating their distinct habitat preference in lakes. Microbial communities showed higher acidic proteomes in alkaline than neutral lakes. Species acidic proteomes were also positively correlated with their pH preference, which was consistent across major bacterial lineages. These results suggest selective pressure associated with high pH likely shape microbial amino acid composition both at the species and community levels. Comparative genome analyses further revealed that alkaliphilic microbes contained more functional genes with higher acidic AAs when compared to those in neutral conditions. These traits included genes encoding diverse classes of cation transmembrane transporters, antiporters, and compatible solute transporters, which are involved in cytoplasmic pH homeostasis and osmotic stress defense under high pH conditions. Our results provide the field evidence for the strong relationship between prokaryotic AA composition and their habitat preference and highlight amino acid optimization as strategies for environmental adaptation.

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酸性蛋白质组与非洲湖泊中微生物偏好碱性有关
微生物的氨基酸组成(AA)反映了细胞和分子调控的适应性策略,如嗜碱性微生物中谷氨酸和天冬氨酸等酸性 AA 的比例较高。微生物的 AA 含量如何与其酸碱度适应性(尤其是在自然环境中)相关联,这方面的研究仍然不足。在这里,我们利用元基因组学研究了东非湖泊中 39 个水体和沉积物中的原核生物群落及其基因中的 AA 组成,这些水体和沉积物的 pH 值梯度从 7.2 到 10.1。我们发现,香农多样性随着pH值的升高而降低,物种丰度与pH值呈正相关或负相关,这表明它们在湖泊中具有独特的生境偏好。在碱性湖泊中,微生物群落的酸性蛋白质组高于中性湖泊。物种的酸性蛋白质组与它们的酸碱度偏好也呈正相关,这在主要细菌系中是一致的。这些结果表明,与高 pH 值相关的选择性压力可能会在物种和群落水平上影响微生物的氨基酸组成。基因组比较分析进一步显示,与中性条件下的微生物相比,嗜碱性微生物含有更多酸性氨基酸含量更高的功能基因。这些特征包括编码不同种类阳离子跨膜转运体、反转运体和相容性溶质转运体的基因,这些基因参与了高pH条件下的细胞质pH平衡和渗透压力防御。我们的研究结果为原核生物 AA 组成与其生境偏好之间的密切关系提供了实地证据,并强调了氨基酸优化作为环境适应策略的重要性。
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来源期刊
Water Research
Water Research 环境科学-工程:环境
CiteScore
20.80
自引率
9.40%
发文量
1307
审稿时长
38 days
期刊介绍: Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include: •Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management; •Urban hydrology including sewer systems, stormwater management, and green infrastructure; •Drinking water treatment and distribution; •Potable and non-potable water reuse; •Sanitation, public health, and risk assessment; •Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions; •Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment; •Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution; •Environmental restoration, linked to surface water, groundwater and groundwater remediation; •Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts; •Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle; •Socio-economic, policy, and regulations studies.
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