Microcystin-LR (MC-LR) and microcystin-RR (MC-RR) produced by harmful cyanobacterial blooms (HCBs) pose substantial threats to the ecosystem and public health due to their potential hepatotoxicity. Degradation of microcystins (MCs) by indigenous bacteria represents a promising method for removing MCs from fresh water without harming the aquatic environment, but only a few microcystin (MC)-degrading bacteria have been isolated and had their mechanisms reported. This study aimed to isolate indigenous bacteria from Lake Taihu, and investigate the capability and mechanism of MC degradation by these bacteria. During a Microcystis bloom, an indigenous MC-degrading bacterium designated MC-LTH2 was successfully isolated from Lake Taihu, and identified as Stenotrophomonas acidaminiphila based on phylogenetic analysis. In the presence of MC-LR together with MC-RR, the strain MC-LTH2 was capable of totally degrading both simultaneously in 8 days, at rates of 3.0 mg/(L?d) and 5.6 mg/(L?d), respectively. The degradation rates of MCs were dependent on temperature, pH, and initial MC concentration. Adda (3-amino-9-methoxy-2, 6, 8-trimethyl-10-phenyldeca-4, 6-dienoic acid) was detected as an intermediate degradation product of MCs using high performance liquid chromatography coupled with time-of-flight mass spectrometry (HPLC-TOF-MS). To the best of our knowledge, this is the first report of Stenotrophomonas acidaminiphila capable of degrading two MC analogues and other compounds containing Adda residue completely under various conditions, although the mlrA gene in the strain was not detected. These results indicate the Stenotrophomonas acidaminiphila strain MC-LTH2 possesses a significant potential to be used in bioremediation of water bodies contaminated by MC-LR and MC-RR, and is potentially involved in the degradation of MCs during the disappearance of the HCBs in Lake Taihu.
Microcystin-LR (MC-LR) and microcystin-RR (MC-RR) are the two most common microcystins (MCs) present in fresh water posing a direct threat to public health because of their hepatotoxicity. A novel MC-degrading bacterium designated MC-LTH1 capable of degrading MC-LR and -RR was isolated, and the degradation rates and mechanisms of MC-LR and -RR for this bacterium were investigated. The bacterium was identified as Bordetella sp. and shown to possess a homologous mlrA gene responsible for degrading MCs. To the best of our knowledge, this is the first report of mlrA gene detection in Bordetella species. MC-LR and -RR were completely degraded separately at rates of 0.31 mg/(L h) and 0.17 mg/(L h). However, the degradation rates of MC-LR and -RR decreased surprisingly to 0.27 mg/(L h) and 0.12 mg/(L h), respectively, when both of them were simultaneously present. Degradation products were identified by high performance liquid chromatography coupled with time-of-flight mass spectrometry. Adda (m/z 332.2215, C20H29NO3) commonly known as a final product of MC degradation by isolated bacteria was detected as an intermediate in this study. Linearized MC-LR (m/z 1013.5638, C49H76N10O13), linearized MC-RR (m/z 1056.4970, C49H77N13O13), and tetrapeptide (m/z 615.3394, C32H46N4O8) were also detected as intermediates. These results indicate that the bacterial strain MC-LTH1 is quite efficient for the detoxification of MC-LR and MC-RR, and possesses significant bioremediation potential.
Related JoVE Video
Journal of Visualized Experiments
What is Visualize?
JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.
How does it work?
We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.
Video X seems to be unrelated to Abstract Y...
In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.