Viral diversity and auxiliary metabolic genes in rare earth element mine drainage in South China

IF 11.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL
Chang Hu , Jia-nan Lu , Ziwu Chen , Li Tian , Yalin Yin , Gengbo Jiang , Ying-heng Fei , Ye-Tao Tang , Shizhong Wang , Chao Jin , Rongliang Qiu , Yuanqing Chao
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Abstract

In extreme environments, viruses play a crucial role in regulating the structure and metabolic activities of microbial communities, thereby impacting the overall biogeochemical cycles. Previous research found that rare earth element acid mine drainage (REE-AMD) harbors a wide array of microbial species. However, our understanding of the viruses that infect these microorganisms remains limited. In this study, we utilized metagenomic analysis to explore the viral diversity, interactions between viruses and their hosts, as well as the viruses encoded auxiliary metabolic genes (AMGs) within REE-AMD. The results demonstrated that viral communities showed increased diversity with REEs pollution. Furthermore, AMGs exhibited habitat and host specificity. Viruses in water samples contaminated with REEs tended to encode AMGs related to cellular metabolic processes and stress responses to protect their hosts. In contrast, viruses in sediment samples were more likely to encode AMGs associated with nutrient competition, thereby expanding the ecological niches of hosts and viruses. Viruses would carry more AMGs from the dominant prokaryotes. Additionally, under REEs stress, viruses encode a greater number of carbon- and sulfur-related AMGs, influencing the carbon and sulfur cycles of microorganisms in REE-AMD. Overall, our study provides a first systematic characterization of the viral community in REE-AMD, which is crucial for understanding the intricate interactions among viruses, their hosts, and the surrounding environment.

Abstract Image

Abstract Image

华南稀土矿水中病毒多样性及辅助代谢基因
在极端环境中,病毒在调节微生物群落的结构和代谢活动方面起着至关重要的作用,从而影响整个生物地球化学循环。以前的研究发现,稀土元素酸性矿井排水(REE-AMD)中蕴藏着大量微生物物种。然而,我们对感染这些微生物的病毒的了解仍然有限。在这项研究中,我们利用元基因组分析探索了 REE-AMD 中的病毒多样性、病毒与其宿主之间的相互作用以及病毒编码的辅助代谢基因(AMGs)。结果表明,病毒群落的多样性随着 REEs 污染的增加而增加。此外,AMGs 还具有栖息地和宿主特异性。受 REEs 污染的水样中的病毒倾向于编码与细胞代谢过程和应激反应有关的 AMGs,以保护其宿主。相比之下,沉积物样本中的病毒更有可能编码与营养竞争相关的AMGs,从而扩大宿主和病毒的生态位。病毒将携带更多来自优势原核生物的 AMGs。此外,在REEs胁迫下,病毒会编码更多与碳和硫有关的AMGs,从而影响REE-AMD中微生物的碳和硫循环。总之,我们的研究首次系统地描述了REE-AMD中的病毒群落,这对于了解病毒、宿主和周围环境之间错综复杂的相互作用至关重要。
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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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