Characterization of Taxonomic and Functional Dynamics Associated with Harmful Algal Bloom Formation in Recreational Water Ecosystems

Harmful algal bloom (HAB) formation leads to the eutrophication of water ecosystems and may render recreational lakes unsuitable for human use. We evaluated the applicability and comparison of metabarcoding, metagenomics, qPCR, and ELISA-based methods for cyanobacteria/cyanotoxin detection in bloom and non-bloom sites for the Great Lakes region. DNA sequencing-based methods robustly identified differences between bloom and non-bloom samples (e.g., the relative prominence of Anabaena and Planktothrix). Shotgun sequencing strategies also identified the enrichment of metabolic genes typical of cyanobacteria in bloom samples, though toxin genes were not detected, suggesting deeper sequencing or PCR methods may be needed to detect low-abundance toxin genes. PCR and ELISA indicated microcystin levels and microcystin gene copies were significantly more abundant in bloom sites. However, not all bloom samples were positive for microcystin, possibly due to bloom development by non-toxin-producing species. Additionally, microcystin levels were significantly correlated (positively) with microcystin gene copy number but not with total cyanobacterial 16S gene copies. In summary, next-generation sequencing-based methods can identify specific taxonomic and functional targets, which can be used for absolute quantification methods (qPCR and ELISA) to augment conventional water monitoring strategies.

Sporadic diurnal fluctuations of cyanobacterial populations in oligotrophic temperate systems can prevent accurate characterization of change and risk in aquatic systems

Cyanobacteria increasingly threaten recreational water use and drinking water resources globally. They require dynamic monitoring to account for variability in their distribution arising from diel cycles associated with oscillatory vertical migration. While this has been discussed in marine and eutrophic freshwater contexts, reports of diurnal vertical migration of cyanobacteria in oligotrophic freshwater lakes are scant. Typical monitoring protocols do not reflect these dynamics and frequently focus only on surface water sampling approaches, and either ignore sampling time or recommend large midday timeframes (e.g., 10AM-3PM), thereby preventing accurate characterization of cyanobacterial community dynamics. To evaluate the impact of diurnal migrations and water column stratification on cyanobacterial abundance and composition, communities were characterized in a shallow well-mixed lake interconnected to a thermally stratified lake in the Turkey Lakes Watershed (Ontario, Canada) using amplicon sequencing of the 16S rRNA gene across a multi-time point sampling series in 2018 and 2022. This work showed that cyanobacteria are present in oligotrophic lakes and their community structure varies (i) diurnally, (ii) across the depth of the water column, (iii) interannually within the same lake and (iv) between different lakes that are closely interconnected within the same watershed. It underscored the need for integrating multi-timepoint, multi-depth discrete sampling guidance into lake and reservoir monitoring programs to describe cyanobacteria community dynamics and signal change to inform risk management associated with the potential for cyanotoxin production. Ignoring variability in cyanobacterial community dynamics (such as that reported herein) and reducing sample numbers can lead to a false sense of security and missed opportunities to identify and mitigate changes in trophic status and associated risks such as toxin or taste and odor production, especially in sensitive, oligotrophic systems.

Binning Metagenomic Contigs Using Unsupervised Clustering and Reference Databases

Metagenomics can directly extract the genetic material of all microorganisms from the environment, and obtain metagenomic samples with a large number of unknown DNA sequences. Binning of metagenomic contigs is a hot topic in metagenomics research. There are two key challenges for the current unsupervised metagenomic clustering algorithms. First, unsupervised metagenomic clustering methods rarely use reference databases, causing a certain waste of resources. Second, unsupervised metagenomic clustering methods are restricted by the characteristics of the sequences and the clustering algorithms, and the binning effect is limited. Therefore, a new binning method for metagenomic contigs using unsupervised clustering methods and reference databases is proposed to address these challenges, to make full use of the advantages of unsupervised clustering methods and reference databases constructed by scientists to improve the overall binning effect. This method uses the integrated SVM classification model to further bin the unsupervised clustering parts that do not perform well. Our proposed method was tested on simulated datasets and a real dataset and compared with other state-of-the-art metagenomic clustering methods including CONCOCT, Metabin2.0, Autometa, and MetaBAT. The results show that our method can achieve higher precision rate and improve the binning effect.

Using FlowCam and molecular techniques to assess the diversity of Cyanobacteria species in water used for food production

Globally, the occurrence of cyanobacteria in water currently remains an important subject as they produce cyanotoxins that pose threat to human health. Studies on the contamination of maize meals during mill grinding processes using cyanobacteria-contaminated water have not been conducted. The present study aimed to assess the diversity of cyanobacteria in the samples (process water, uncooked maize meal, and cooked maize meal (porridge)). Polymerized Chain Reaction (PCR) and Advanced digital flow cytometry (FlowCAM) were used to detect and identify cyanobacterial species available in these samples. 16S Primers (forward and reverse) tailed with Universal Sequences were used for amplification and sequencing of full-length 16S rRNA genes from cyanobacteria found in all samples. Cyanobacterial species from order Nostocales, Pseudanabaenales, Oscillatoriales Chroococcales, Synechococcales, and unclassified cyanobacterial order, some of which have the potential to produce cyanotoxins were amplified and identified in process water, raw maize meal and porridge samples using PCR. Images of the genus Microcystis, Phormidium, and Leptolyngbya were captured in process water samples using FlowCAM. These findings show the presence of cyanobacteria species in process water used for maize meal and the absence in cooked maize meal. The presence of cyanobacteria in process water is likely another route of human exposure to cyanotoxins.