Harmful algal blooms and liver diseases: focusing on the areas near the four major rivers in South Korea

The dependence in microcystin removal with powdered activated carbon on variant properties, carbon properties, and dissolved organic matter

Powdered activated carbon (PAC) is commonly used by water treatment plants to remove harmful cyanotoxins such as microcystins (MCs) produced during seasonal harmful algal blooms. MC removal by PAC depends upon the properties of the PAC, the properties of the MC variant, and the presence and properties of dissolved organic matter (DOM). To identify which of these factors has the greatest impact on the removal of MC by PAC, we evaluated the removal of four different MC variants (MC-LR, MC-LA, MC-RR and desmethylated MC-RR) by three different PAC types (wood-based, coal-blend and coal-based). The role of DOM properties was evaluated using DOM isolated from two different sources, a terrestrial source (Suwannee River Fulvic Acid, SRFA) and a microbial source (Grand Lake St Marys DOM, GLSM). The results of adsorption experiments conducted over a period of 72 h demonstrated the wood-based PAC, which had the highest surface area and mesopore volume of the PAC tested, had the highest adsorption rate and capacity for all four MC variants. Of the variants studied, neutrally charged MC-RR was adsorbed more rapidly and to a greater extent on all of the PAC types than were the other variants. Although MC-LA and MC-LR had the greatest hydrophobicity, their negative charges resulted in their being adsorbed the least. As expected, DOM inhibited microcystin adsorption to PAC. The degree of inhibition, however, did not significantly vary for the two DOM types evaluated, indicating the properties of the DOM on MC adsorption to PAC was less important than the PAC properties or MC variant properties. Overall, PAC properties were a more important factor in MC removal than were the MC properties or DOM conditions.

Beyond cyanotoxins: increased Legionella, antibiotic resistance genes in western Lake Erie water and disinfection-byproducts in their finished water

Background

Western Lake Erie is suffering from harmful cyanobacterial blooms, primarily toxic Microcystis spp., affecting the ecosystem, water safety, and the regional economy. Continued bloom occurrence has raised concerns about public health implications. However, there has been no investigation regarding the potential increase of Legionella and antibiotic resistance genes in source water, and disinfection byproducts in municipal treated drinking water caused by these bloom events.

Methods

Over 2 years, source water (total n = 118) and finished water (total n = 118) samples were collected from drinking water plants situated in western Lake Erie (bloom site) and central Lake Erie (control site). Bloom-related parameters were determined, such as microcystin (MC), toxic Microcystis, total organic carbon, N, and P. Disinfection byproducts (DBPs) [total trihalomethanes (THMs) and haloacetic acids (HAAs)] were assessed in finished water. Genetic markers for Legionella, antibiotic resistance genes, and mobile genetic elements were quantified in source and finished waters.

Results

Significantly higher levels of MC-producing Microcystis were observed in the western Lake Erie site compared to the control site. Analysis of DBPs revealed significantly elevated THMs concentrations at the bloom site, while HAAs concentrations remained similar between the two sites. Legionella spp. levels were significantly higher in the bloom site, showing a significant relationship with total cyanobacteria. Abundance of ARGs (tetQ and sul1) and mobile genetic elements (MGEs) were also significantly higher at the bloom site.

Discussion

Although overall abundance decreased in finished water, relative abundance of ARGs and MGE among total bacteria increased after treatment, particularly at the bloom site. The findings underscore the need for ongoing efforts to mitigate bloom frequency and intensity in the lake. Moreover, optimizing water treatment processes during bloom episodes is crucial to maintain water quality. The associations observed between bloom conditions, ARGs, and Legionella, necessitate future investigations into the potential enhancement of antibiotic-resistant bacteria and Legionella spp. due to blooms, both in lake environments and drinking water distribution systems.

Self-optimization of training dataset improves forecasting of cyanobacterial bloom by machine learning

Data-driven model (DDM) prediction of aquatic ecological responses, such as cyanobacterial harmful algal blooms (CyanoHABs), is critically influenced by the choice of training dataset. However, a systematic method to choose the optimal training dataset considering data history has not yet been developed. Providing a comprehensive procedure with self-based optimal training dataset-selecting algorithm would self-improve the DDM performance. In this study, a novel algorithm was developed to self-generate possible training dataset candidates from the available input and output variable data and self-choose the optimal training dataset that maximizes CyanoHAB forecasting performance. Nine years of meteorological and water quality data (input) and CyanoHAB data (output) from a site on the Nakdong River, South Korea, were acquired and pretreated via an automated process. An artificial neural network (ANN) was chosen from among the DDM candidates by first-cut training and validation using the entire collected dataset. Optimal training datasets for the ANN were self-selected from among the possible self-generated training datasets by systematically simulating the performance in response to 46 periods and 40 sizes (number of data elements) of the generated training datasets. The best-performing models were screened to identify the candidate models. The best performance corresponded to 6-7 years of training data (∼18 % lower error) for forecasting 1-28 d ahead (1-28 d of forecasting lead time (FLT)). After the hyperparameters of the screened model candidates were fine-tuned, the best-performing model (7 years of data with 14 d FLT) was self-determined by comparing the forecasts with unseen CyanoHAB events. The self-determined model could reasonably predict CyanoHABs occurring in Korean waters (cyanobacteria cells/mL ≥ 1000). Thus, our proposed method of self-optimizing the training dataset effectively improved the predictive accuracy and operational efficiency of the DDM prediction of CyanoHAB.

Relationship between freshwater harmful algal blooms and neurodegenerative disease incidence rates in South Korea

BACKGROUND

Due to anthropogenic activities and global warming, the severity and distribution of harmful algal blooms (HABs) have been increasing steadily worldwide, including in South Korea (S. Korea). Previous studies reported that exposure to HABs could increase the risk of HAB-related diseases. However, very few studies examined the linkage between HABs and disease occurrence, particularly in S. Korea. The objective of this study was to evaluate the potential impact of HABs on neurodegenerative diseases (NDs), including Alzheimer's disease, Parkinson's disease, and motor neuron disease, at a population level.

METHODS

Thirteen-year data (2005-2017) for chlorophyll-a (chl-a) concentrations as a bloom-related parameter, annual numbers of NDs, and population information were collected. First, the entire area of S. Korea was divided into a grid of 1 km, and the population number in each 1-km grid was collected using the Statistical Geographic Information Service Plus system. Cross-sectional time series data were analyzed with two statistical models, a generalized linear mixed model and a generalized linear model.

RESULTS

The results show a general trend of increasing chl-a concentration and NDs year by year. We observed positive correlations between HAB intensity and the incidence rate of NDs. Particularly, HABs seem to have the most long-term carry-over effect on Parkinson's disease. Another key finding was that a 5-km radius from the HAB location was the boundary that showed the most significant associations with three NDs.

CONCLUSIONS

This study provides statistical evidence that supports the potential risk of NDs from the exposure to HAB. Thus, it is recommended to monitor a broad spectrum of cyanotoxins, including neurotoxins, in bloom-affected regions in S. Korea and epidemiological studies in the future.

Promotion effect on liver tumor progression of microcystin-LR at environmentally relevant levels in female krasV12 transgenic zebrafish

Microcystin-LR (MC-LR) is a kind of natural toxin which exists widely in aquatic environments and has been reported to be hepatotoxic and carcinogenic. At present, the promoting mechanism of MC-LR on hepatocellular carcinoma (HCC) remains largely unexplored. In this study, the hepatocellular promoting effect of MC-LR was described in Kras^V12 transgenic zebrafish, a doxycycline (DOX) inducible HCC model. Our results showed that MC-LR could aggravate the progression of HCC at an environmentally relevant concentration (3 μg/L), which was accompanied by the decreased activity and down-regulated transcription level of serine/threonine phosphatase 2A (PP2A). Using TMT labeling quantitative phosphoproteomics, we found that the 1049 phosphopeptides were significantly changed (508 up-regulated and 541 down-regulated) in liver from combined exposure to DOX and 3 μg/L MC-LR group compared to the DOX group. Enriched pathways by KEGG analysis suggested that differentially phosphorylated proteins were mainly related to Wnt signaling pathway. Furthermore, the mRNA expression and protein abundance of β-Catenin in Wnt signaling pathway were significantly up-regulated following exposure to MC-LR. In short, our results suggested that MC-LR significantly inhibited the activity of PP2A, which in turn activated Wnt signaling, eventually resulting in progression of liver tumor in transgenic zebrafish.

Ecological River Health Assessment Using Multi-Metric Models in an Asian Temperate Region with Land Use/Land Cover as the Primary Factor Regulating Nutrients, Organic Matter, and Fish Composition

This study was performed to determine the ecological health of a temperate river over nine years (2011−2019); it also analyzed the trophic structure and linkage of nutrients (nitrogen [N] and phosphorus [P]), sestonic chlorophyll-a (CHL-a), and the top trophic fish in the Asian monsoon region. Water chemistry, trophic indicators, and tolerance guilds were primarily influenced by land use and land cover (LULC); the magnitude of variation was also related to geographic elevation, artificial physical barriers (weirs), and point sources. Levels of nutrients, organic matter, and CHL-a largely influenced by the intensity of the monsoon seasonality for a particular LULC and stream order. Mann−Kendall tests based on a long-term annual dataset showed that annual organic matter and CHL-a increased over time because of longer hydraulic residence time after weir construction. The results of empirical nutrient models suggested that P was the key determinant for algal growth (CHL-a); the strong P-limitation was supported by N:P ratios > 17 in ambient waters. Linear regression models and canonical correspondence analysis (CCA) were used to determine the influences of LULC and water quality on the trophic/tolerance linkages, fish community compositions and structures, and river health. Tolerant species had a positive functional relationship with nutrient enrichment through total phosphorus (TP) (R2 = 0.55, p < 0.05) and total nitrogen (TN) (R2 = 0.57, p < 0.05), organic pollution in terms of biological oxygen demand (BOD) (R2 = 0.41, p < 0.05) and chemical oxygen demand (COD) (R2 = 0.49, p < 0.05), and algal growth (R2 = 0.47, p < 0.05); sensitive species exhibited the opposite pattern. The degradation of river health, based on the multi-metric index of biotic integrity (IBI) model, was evident in the downriver region (“fair−poor” condition) and was supported by the quantitative fish community index (QFCI) model. The outcomes suggested that the degradation and variation of ecological river health, trophic linkages of water chemistry (N, P)-algal biomass-fish, were largely controlled by the land use pattern and construction of physical barriers in relation to the Asian monsoon.

A Classification-Based Machine Learning Approach to the Prediction of Cyanobacterial Blooms in Chilgok Weir, South Korea

Cyanobacterial blooms appear by complex causes such as water quality, climate, and hydrological factors. This study aims to present the machine learning models to predict occurrences of these complicated cyanobacterial blooms efficiently and effectively. The dataset was classified into groups consisting of two, three, or four classes based on cyanobacterial cell density after a week, which was used as the target variable. We developed 96 machine learning models for Chilgok weir using four classification algorithms: k-Nearest Neighbor, Decision Tree, Logistic Regression, and Support Vector Machine. In the modeling methodology, we first selected input features by applying ANOVA (Analysis of Variance) and solving a multi-collinearity problem as a process of feature selection, which is a method of removing irrelevant features to a target variable. Next, we adopted an oversampling method to resolve the problem of having an imbalanced dataset. Consequently, the best performance was achieved for models using datasets divided into two classes, with an accuracy of 80% or more. Comparatively, we confirmed low accuracy of approximately 60% for models using datasets divided into three classes. Moreover, while we produced models with overall high accuracy when using logCyano (logarithm of cyanobacterial cell density) as a feature, several models in combination with air temperature and NO3-N (nitrate nitrogen) using two classes also demonstrated more than 80% accuracy. It can be concluded that it is possible to develop very accurate classification-based machine learning models with two features related to cyanobacterial blooms. This proved that we could make efficient and effective models with a low number of inputs.

Improving Biosensors by the Use of Different Nanomaterials: Case Study with Microcystins as Target Analytes

The eutrophication of lakes and rivers without adequate rainfall leads to excessive growth of cyanobacterial harmful algal blooms (CyanoHABs) that produce toxicants, green tides, and unpleasant odors. The rapid growth of CyanoHABs owing to global warming, climate change, and the development of rainforests and dams without considering the environmental concern towards lakes and rivers is a serious issue. Humans and livestock consuming the toxicant-contaminated water that originated from CyanoHABs suffer severe health problems. Among the various toxicants produced by CyanoHABs, microcystins (MCs) are the most harmful. Excess accumulation of MC within living organisms can result in liver failure and hepatocirrhosis, eventually leading to death. Therefore, it is essential to precisely detect MCs in water samples. To date, the liquid chromatography-mass spectrometry (LC-MS) and enzyme-linked immunosorbent assay (ELISA) have been the standard methods for the detection of MC and provide precise results with high reliability. However, these methods require heavy instruments and complicated operation steps that could hamper the portability and field-readiness of the detection system. Therefore, in order for this goal to be achieved, the biosensor has been attracted to a powerful alternative for MC detection. Thus far, several types of MC biosensor have been proposed to detect MC in freshwater sample. The introduction of material is a useful option in order to improve the biosensor performance and construct new types of biosensors. Introducing nanomaterials to the biosensor interface provides new phenomena or enhances the sensitivity. In recent times, different types of nanomaterials, such as metallic, carbon-based, and transition metal dichalcogenide-based nanomaterials, have been developed and used to fabricate biosensors for MC detection. This study reviews the recent advancements in different nanomaterial-based MC biosensors.

Oxidative stress responses in brackish water flea exposed to microcystin-LR and algal bloom waters from Nakdong River, Republic of Korea

Microcystis blooms and the impact of their toxins, particularly microcystin (MC), in coastal ecosystems is an emerging threat, but the species-specific effects of MC and the potential for bioconcentration are not fully understood. We exposed the brackish water flea, Diaphanosoma celebensis, to MC-LR, which showed antioxidant responses measured at the molecular to enzyme levels but no acute toxicity. We extended our experimental investigation to measure the released MC and its uptake by D. celebensis exposed to river water. In a short-term exposure (48 h) experiment, D. celebensis exposed to water from an algal bloom (approximately 2 μg L^-1 MC) assimilated more than 50 pg MC per individual. The significant increase of MCs suggests the potential for the species to accumulate MCs. The dose-dependent increase in the antioxidant response observed in the mRNA levels also showed that D. celebensis exposed to diluted algal bloom waters were affected by toxins from cyanobacteria.

Microcystis toxin-mediated tumor promotion and toxicity lead to shifts in mouse gut microbiome

Microcystins (MCs) are the most prevalent cyanotoxins reported in freshwater. While numerous studies have examined the toxicological impacts of MCs on mammalian systems, very few have examined the chronic impacts of MCs on the gut microbiome of exposed organisms. Our understanding of the relationship of MCs, especially lysed toxic cyanobacteria, and the gut microbiota is very limited. The objective of this study was to determine the impacts of MC-LR and Microcystis lysate ingestion on the gut microbiome in a hepatocellular carcinoma mouse model, simulating a high-risk population and exposure at an environmentally relevant MC level. Mice were assigned to 4 groups (MC-LR; Microcystis lysate; Negative control; Positive (liver carcinogen) control). Fecal samples were collected every 8 weeks. Bacterial community and colony counts were analyzed. The abundance of Firmicutes in the positive control and lysate group was higher than the negative control and MC group. Exposure to MC-LR or lysate was associated with significantly decreased bacterial diversity. A distinct separation of the three groups (MC-LR/lysate/carcinogen) from the negative was much more apparent in their gut microbiome as the exposure time increased. The MC-LR and lysate groups showed gut microbiome structure responding to lipid metabolism disturbance and high stress. Bacterial colony count was significantly lower in all the treated groups than the negative control. Our study highlights that chronic exposure to MC-LR and Microcystis lysate negatively impacts gut microbiome succession and altered the bacterial community structure into the one similar to the carcinogen group, which may indicate that the change favors progression of hepatocellular carcinoma. In a future study, more in-depth investigation is warranted to better understand the liver-gut nexus in promoting liver cancer among those exposed to MC and toxic cyanobacteria.

Characterization of the gut microbiota of Nicaraguan children in a water insecure context

OBJECTIVES

The gut microbiota varies across human populations. The first years of life are a critical period in its development. While delivery mode and diet contribute to observed variation, the additional contribution of specific environmental factors remains poorly understood. One factor is waterborne enteric pathogen exposure. In this pilot study, we explore the relationship between household water security and the gut microbiota of children.

METHODS

From Nicaraguan households (n = 39), we collected drinking water samples, as well as fecal samples from children aged one month to 5.99 years (n = 53). We tested water samples for total coliforms (CFU/mL) and the presence of common enteric pathogens. Composition and diversity of the gut microbiota were characterized by 16S rRNA sequencing. Households were classified as having drinking water that was "low" (<29 CFU/mL) or "high" (≥29 CFU/mL) in coliforms. We used permutational analyses of variance and Mann-Whitney U-tests to identify differences in the composition and diversity of the gut microbiota of children living in these two home types.

RESULTS

Insecure access led households to store drinking water and 85% tested positive for coliforms. High concentrations of Salmonella and Campylobacter were found in water and fecal samples. Controlling for age, the gut microbiota of children from high coliform homes were compositionally different and less diverse than those from low coliform homes.

CONCLUSIONS

Results indicate that research exploring the ways water insecurity affects human biology should consider the gut microbiome and that investigations of inter-population variation in the gut microbial community of children should consider pathogen exposure and infection.