Oral exposure to environmental cyanobacteria toxins: Implications for cancer risk

Hernandez, Brenda Y.; Zhu, Xiaolei; Sotto, Patrick P; Paulino, Yvette C.

doi:10.1016/j.envint.2021.106381

Cited by 21 publications

(6 citation statements)

References 41 publications

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“…Cyanotoxins are grouped, according to the physiological systems, organs, tissues, or cells that are primarily affected, in neurotoxins, hepatotoxins, cytotoxins, irritants, and gastrointestinal toxins. Many cyanotoxins are also tumor promoters, with carcinogenic activity, and are the causative agents of serious health threats for humans [68].…”

Section: Heavy Metalsmentioning

confidence: 99%

Cyanobacteria, Cyanotoxins, and Neurodegenerative Diseases: Dangerous Liaisons

Sini

Dang

Fais

et al. 2021

IJMS

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The prevalence of neurodegenerative disease (ND) is increasing, partly owing to extensions in lifespan, with a larger percentage of members living to an older age, but the ND aetiology and pathogenesis are not fully understood, and effective treatments are still lacking. Neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis are generally thought to progress as a consequence of genetic susceptibility and environmental influences. Up to now, several environmental triggers have been associated with NDs, and recent studies suggest that some cyanotoxins, produced by cyanobacteria and acting through a variety of molecular mechanisms, are highly neurotoxic, although their roles in neuropathy and particularly in NDs are still controversial. In this review, we summarize the most relevant and recent evidence that points at cyanotoxins as environmental triggers in NDs development.

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Section: Heavy Metalsmentioning

confidence: 99%

Cyanobacteria, Cyanotoxins, and Neurodegenerative Diseases: Dangerous Liaisons

Sini

Dang

Fais

et al. 2021

IJMS

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“…Observations of MICX are often left-censored because its concentrations are typically low in natural aquatic environments. ,, MICX is an emerging pollutant and an important human health factor in aquatic ecosystems . It can lead to severe liver damage and promote the risk of cancer. , There is an urgent need to predict MICX and understand its drivers for management purposes . We included MICX and 23 relevant physical-chemical variables as predictors, covering thousands of lakes across the United States to build the two-stage model we proposed, and compared it with the baseline one-stage model.…”

Section: Introductionmentioning

confidence: 99%

“…44 It can lead to severe liver damage and promote the risk of cancer. 45,46 There is an urgent need to predict MICX and understand its drivers for management purposes. 47 We included MICX and 23 relevant physical-chemical variables as predictors, covering thousands of lakes across the United States to build the two-stage model we proposed, and compared it with the baseline one-stage model.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Interpretable Machine Learning Framework for Accurate Prediction of Trace Pollutants: With an Application to Microcystin

Wu,

Liang,

Qiu

2023

ACS EST Water

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Trace pollutants are widely observed in aquatic ecosystems and can significantly impact human health and the environment. Accurate prediction of trace pollutants and understanding their response to environmental drivers are key to environmental management, yet these tasks remain challenging. An important reason for this challenge is that monitoring data for trace pollutants are often left-censored, leading to biased estimation and inaccurate response-driver relationships. Here we propose a novel two-stage interpretable machine learning framework applicable to left-censored trace pollutant data. The two stages include (1) a classifier to predict the presence of the pollutant and (2) a regressor to predict the pollutant concentration if present. The two stages were followed by a model interpretation to understand the contribution of drivers to the presence and concentration of the pollutant accordingly. We take the prediction of microcystin (MICX) in lakes across the United States as a case study. Applying this framework to MICX consistently improved prediction accuracy, including prediction of its occurrence and concentration regardless of the algorithms and performance metrics used. The best-performing algorithm using the two-stage framework, compared with the baseline model, improves classification performance by 48% to 290% and the regression performance by 11% to 33%, depending on the metric used to evaluate the performance. The interpretable machine learning model also successfully revealed the impacts of the most important drivers on the presence of MICX and its concentration. Our results showed the advantages of this framework, including its interpretability to understand the driver-response relationship, ability to handle nonlinearity, better prediction performance, differentiation between the underlying processes, and potential to be generalized to other pollutants. As such, we anticipate that the framework we propose will be a starting point for using state-of-the-art interpretable machine learning models for predicting trace pollutants.

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“…During the overgrowth of HCBs, toxin-producing cyanobacteria release various microcystins (MCs) into the water body, with MC-LR, MC-RR, and MC-YR being the most toxic and widely distributed MC variants (Hoeger et al, 2007;Atencio et al, 2008;Zeller et al, 2012;Okogwu et al, 2014;He et al, 2018;Tao et al, 2018;Zhang et al, 2021). MCs are potent liver toxins and tumor promoters (Fujiki and Suganuma, 1994;Zhang et al, 2012;Tian et al, 2013;Wang et al, 2014Wang et al, , 2022Hernandez et al, 2021). These cyanotoxins are extremely stable and are not effectively removed by conventional methods.…”

Section: Introductionmentioning

confidence: 99%

Structural insight into the substrate-binding mode and catalytic mechanism for MlrC enzyme of Sphingomonas sp. ACM-3962 in linearized microcystin biodegradation

Guo

Li²,

Jiang³

et al. 2023

Front. Microbiol.

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Removing microcystins (MCs) safely and effectively has become an urgent global problem because of their extremely hazardous to the environment and public health. Microcystinases derived from indigenous microorganisms have received widespread attention due to their specific MC biodegradation function. However, linearized MCs are also very toxic and need to be removed from the water environment. How MlrC binds to linearized MCs and how it catalyzes the degradation process based on the actual three-dimensional structure have not been determined. In this study, the binding mode of MlrC with linearized MCs was explored using a combination of molecular docking and site-directed mutagenesis methods. A series of key substrate binding residues, including E70, W59, F67, F96, S392 and so on, were identified. Sodium dodecane sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) was used to analyze samples of these variants. The activity of MlrC variants were measured using high performance liquid chromatography (HPLC). We used fluorescence spectroscopy experiments to research the relationship between MlrC enzyme (E), zinc ion (M), and substrate (S). The results showed that MlrC enzyme, zinc ion and substrate formed E-M-S intermediates during the catalytic process. The substrate-binding cavity was made up of N and C-terminal domains and the substrate-binding site mainly included N41, E70, D341, S392, Q468, S485, R492, W59, F67, and F96. The E70 residue involved in both substrate catalysis and substrate binding. In conclusion, a possible catalytic mechanism of the MlrC enzyme was further proposed based on the experimental results and a literature survey. These findings provided new insights into the molecular mechanisms of the MlrC enzyme to degrade linearized MCs, and laid a theoretical foundation for further biodegradation studies of MCs.

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Oral exposure to environmental cyanobacteria toxins: Implications for cancer risk

Cited by 21 publications

References 41 publications

Cyanobacteria, Cyanotoxins, and Neurodegenerative Diseases: Dangerous Liaisons

Cyanobacteria, Cyanotoxins, and Neurodegenerative Diseases: Dangerous Liaisons

A Two-Stage Interpretable Machine Learning Framework for Accurate Prediction of Trace Pollutants: With an Application to Microcystin

Structural insight into the substrate-binding mode and catalytic mechanism for MlrC enzyme of Sphingomonas sp. ACM-3962 in linearized microcystin biodegradation

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