Is a lower-toxicity strain of Microcystis aeruginosa really less toxic?

Some well-known hazards of blooming cyanobacteria are caused by toxic metabolites such as microcystins (MCs), though many other bioactive chemicals of unknown toxicity are present in their exudates. It is also unclear whether toxicity of cyanobacterial cells depends on growth phases in the life cycle. In this study, we compared toxicity to Daphnia magna of Microcystis aeruginosa - a common cyanobacterial species - exudates (MaE) from two MC-producing strains over both exponential growth and stationary phases in acute and chronic experiments. Specifically, we assessed mitochondrial dysfunction, oxidative stress and lipid peroxidation, and filtering activity and heartbeat rate of Daphnia exposed to MaE. All MaE treatments induced common characteristics of Microcystis toxicity including disorder in the mitochondrial membrane and aberrant heart rate. MaE from cells at stationary growth phase were more toxic than those at exponential phase. Surprisingly, the MC-lower strain had higher toxicity than MC-higher one. Microcystis at different stage of blooms may differentially affect waterfleas owing to variable MaE-induced physiological dysfunction, abundance and grazing rate. Our study suggested that Microcystis strains with lower microcystin-producing ability might release other detrimental chemicals and should not be ignored in harmful bloom monitoring.

Phytosphingosine inhibits cell proliferation by damaging DNA in human cell lines.

Harmful cyanobacterial blooms have caused numerous biosecurity incidents owing to the production of hazardous secondary metabolites such as microcystin. Additionally, cyanobacteria also release many other components that have not been explored. We identified compounds of a toxic mixture exudated from a dominant, blooming species, Microcystis aeruginosa, and found that phytosphingosine (PHS) was one of the bioactive components. Since PHS exhibited toxicity and is deemed a hazardous substance by the European Chemicals Agency, we hypothesized that PHS is a potentially toxic compound in M. aeruginosa exudates. However, the mechanisms of PHS ecotoxicity remain unclear. We assessed the cytotoxicity of PHS using an in vitro cell model in eight human cell lines and observed that the nasopharyngeal carcinoma cell line CNE2 was the most sensitive. We exposed CNE2 cells to 0-25 µmol/L PHS for 24 hr to explore its toxicity and mechanism. PHS exposure resulted in abnormal nuclear morphology, micronuclei, and DNA damage. Moreover, PHS significantly inhibited cell proliferation and arrested cell cycle at S phase. The results of Western blot suggested that PHS increased the expression of DNA damage-related proteins (ATM, p-P53 and P21) and decreased the expression of S phase-related proteins (CDK2, CyclinA2 and CyclinE1), indicating the toxicological mechanism of PHS on CNE2 cells. These data provide evidence that PHS has genetic toxicity and inhibits cell proliferation by damaging DNA. Our study provides evidence that PHS inhibits cell proliferation by damaging DNA. While additional work is required, we propose that PHS been considered as a potentially toxic component in MaE in addition to other well-characterized secondary compounds.

Phytosphingosine-induced cell apoptosis via a mitochondrially mediated pathway.

Cyanobacterial blooms, usually dominated by Microcystis aeruginosa, pose a serious threat to global freshwater ecosystems owing to their production and release of various harmful secondary metabolites. Detection of the chemicals in M. aeruginosa exudates using metabolomics technology revealed that phytosphingosine (PHS) was one of the most abundant compounds. However, its specific toxicological mechanism remained unclear. CNE-2 cells were selected to illustrate the cytotoxic mechanism of PHS, and it was determined to cause excessive production of reactive oxygen species and subsequently damage the mitochondrial structure. Mitochondrial membrane rupture led to matrix mitochondrial membrane potential disintegration, which induced Ca2+ overload and interrupted ATP synthesis. Furthermore, rupture of the mitochondrial membrane induced the opening of the permeability transition pore, which caused the release of proapoptotic factors into the cytoplasm and the expression of apoptosis-related proteins Bax, Bcl-2, cytochrome-c and cleaved caspase-3 in CNE-2 cells. These events, in turn, activated the mitochondrially mediated intrinsic apoptotic pathway. A mitochondrial repair mechanism, namely, PINK1/Parkin-mediated mitophagy, was then blocked, which further promoted apoptosis. Our findings suggest that more attention should be paid to the ecotoxicity of PHS, which is already listed as a contaminant of emerging concern.

Using machine-learning algorithms to identify patients at high risk of upper gastrointestinal lesions for endoscopy.

BACKGROUND AND AIM: Endoscopic screening for early detection of upper gastrointestinal (UGI) lesions is important. However, population-based endoscopic screening is difficult to implement in populous countries. By identifying high-risk individuals from the general population, the screening targets can be narrowed to individuals who are in most need of an endoscopy. This study was designed to develop an artificial intelligence (AI)-based model to predict patient risk of UGI lesions to identify high-risk individuals for endoscopy. METHODS: A total of 620 patients (from 5300 participants) were equally allocated into 10 parts for 10-fold cross validation experiments. The machine-learning predictive models for UGI lesion risk were constructed using random forest, logistic regression, decision tree, and support vector machine (SVM) algorithms. A total of 48 variables covering lifestyles, social-economic status, clinical symptoms, serological results, and pathological data were used in the model construction. RESULTS: The accuracies of the four models were between 79.3% and 93.4% in the training set and between 77.2% and 91.2% in the testing dataset (logistics regression: 77.2%; decision tree: 87.3%; random forest: 88.2%; SVM: 91.2%;). The AUCs of four models showed impressive predictive ability. Comparing the four models with the different algorithms, the SVM model featured the best sensitivity and specificity in all datasets tested. CONCLUSIONS: Machine-learning algorithms can accurately and reliably predict the risk of UGI lesions based on readily available parameters. The predictive models have the potential to be used clinically for identifying patients with high risk of UGI lesions and stratifying patients for necessary endoscopic screening.