Insights into the interaction mechanisms between Microcystin-degrading bacteria and Microcystis aeruginosa

IF 11.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL Water Research Pub Date : 2024-08-14 DOI:10.1016/j.watres.2024.122241
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Abstract

Interactions between bacteria and cyanobacteria influence the occurrence and development of harmful cyanobacterial blooms (HCBs). Bloom-forming cyanobacteria and cyanotoxin-degrading bacteria are essential in HCBs, nonetheless, their interactions and the underlying mechanisms remain unclear. To address this gap, a typical microcystin-LR (MC-LR)-degrading bacterium and a toxic Microcystis aeruginosa strain were co-cultivated to investigate their interactions. The cyanobacterial growth was enhanced by 24.8 %-44.3 % in the presence of the bacterium in the first 7 days, and the cyanobacterium enhanced the bacterial growth by 59.2 %-117.5 % throughout the growth phases, suggesting a mutualistic relationship between them. The presence of the bacterium increased cyanobacterial intracellular MC-LR content on days 4, 8, and 10 while reducing the extracellular MC-LR concentration, revealing the dual roles of the bacterium in enhancing cyanotoxin production and degrading cyanotoxins. The bacterium alleviated the oxidative stress, which may be crucial in promoting cyanobacterial growth. Critical functional genes related to cyanobacterial photosynthesis and MC-LR synthesis, and bacterial MC-LR degradation were up-regulated in the presence of the bacterium and cyanobacterium, respectively. Moreover, extracellular polymeric substances (EPS) were produced at the cell interface, implying EPS play a role in cyanobacterial-bacterial interactions. This study is the first to unveil the interaction mechanisms between cyanotoxin-degrading bacteria and bloom-forming cyanobacteria, shedding light on the dynamics of HCBs.

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微囊藻毒素降解菌与铜绿微囊藻之间相互作用机制的启示
细菌与蓝藻之间的相互作用影响着有害蓝藻水华(HCB)的发生和发展。藻华形成蓝藻和蓝藻毒素降解细菌在有害蓝藻藻华中至关重要,但它们之间的相互作用及其内在机制仍不清楚。为了填补这一空白,我们共同培养了一种典型的微囊藻毒素-LR(MC-LR)降解菌和一种有毒菌株,以研究它们之间的相互作用。在有该细菌存在的前 7 天,蓝藻的生长速度提高了 24.8%-44.3%,而在整个生长阶段,蓝藻的生长速度提高了 59.2%-117.5%,这表明它们之间存在互作关系。在第 4、8 和 10 天,该细菌的存在增加了蓝藻细胞内 MC-LR 的含量,同时降低了细胞外 MC-LR 的浓度,这表明该细菌具有促进蓝藻毒素产生和降解蓝藻毒素的双重作用。该细菌减轻了氧化应激,这可能是促进蓝藻生长的关键。与蓝藻光合作用和 MC-LR 合成以及细菌 MC-LR 降解相关的关键功能基因在该细菌和蓝藻存在时分别上调。此外,细胞界面还产生了胞外高分子物质(EPS),这意味着 EPS 在蓝藻与细菌的相互作用中发挥作用。该研究首次揭示了蓝藻毒素降解菌与形成藻华的蓝藻之间的相互作用机制,揭示了六氯苯的动态变化。
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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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