Multi-class secondary metabolites in cyanobacterial blooms from a tropical water body: Distribution patterns and real-time prediction

Luhua You; Xuneng Tong; Shu Harn Te; Ngoc Han Tran; Nur Hanisah Bte Sukarji; Yiliang He; Karina Yew-Hoong Gin

doi:10.1016/j.watres.2022.118129

Multi-class secondary metabolites in cyanobacterial blooms from a tropical water body: Distribution patterns and real-time prediction

Water Res. 2022 Apr 1:212:118129. doi: 10.1016/j.watres.2022.118129. Epub 2022 Jan 29.

Authors

Luhua You¹, Xuneng Tong², Shu Harn Te¹, Ngoc Han Tran¹, Nur Hanisah Bte Sukarji¹, Yiliang He³, Karina Yew-Hoong Gin⁴

Affiliations

¹ E2S2-CREATE, NUS Environmental Research Institute, National University of Singapore, 1 Create Way, Create Tower, #15-02, 138602, Singapore.
² Department of Civil and Environmental Engineering, National University of Singapore, 1 Engineering Drive 2, 117576, Singapore.
³ School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
⁴ E2S2-CREATE, NUS Environmental Research Institute, National University of Singapore, 1 Create Way, Create Tower, #15-02, 138602, Singapore; Department of Civil and Environmental Engineering, National University of Singapore, 1 Engineering Drive 2, 117576, Singapore. Electronic address: ceeginyh@nus.edu.sg.

PMID: 35121419
DOI: 10.1016/j.watres.2022.118129

Abstract

Cyanobacterial blooms that produce toxins occur in freshwaters worldwide and yet, the occurrence and distribution patterns of many cyanobacterial secondary metabolites particularly in tropical regions are still not fully understood. Moreover, predictive models for these metabolites by using easily accessible water quality indicators are rarely discussed. In this study, we investigated the co-occurrence and spatiotemporal trends of 18 well-known and less-studied cyanobacterial metabolites (including [D-Asp3] microcystin-LR (DM-LR), [D-Asp3] microcystin-RR (DM-RR), microcystin-HilR (MC-HilR), microcystin-HtyR (MC-HtyR), microcystin-LA (MC-LA), microcystin-LF (MC-LF), microcystin-LR (MC-LR), microcystin-LW (MC-LW), microcystin-LY (MC-LY), microcystin-RR (MC-RR) and microcystin-WR (MC-WR), Anatoxin-a (ATX-a), homoanatoxin-a (HATX-a), cylindrospermospin (CYN), nodularin (NOD), anabaenopeptin A (AptA) and anabaenopeptin B (AptB)) in a tropical freshwater lake often plagued with blooms. Random forest (RF) models were developed to predict MCs and CYN and assess the relative importance of 22 potential predictors that determined their concentrations. The results showed that 11 MCs, CYN, ATX-a, HATX-a, AptA and AptB were found at least once in the studied water body, with MC-RR and CYN being the most frequently occurring, intracellularly and extracellularly. AptA and AptB were detected for the first time in tropical freshwaters at low concentrations. The metabolite profiles were highly variable at both temporal and spatial scales, in line with spatially different phytoplankton assemblages. Notably, MCs decreased with the increase of CYN, possibly revealing interspecific competition of cyanobacteria. The rapid RF prediction models for MCs and CYN were successfully developed using 4 identified drivers (i.e., chlorophyll-a, total carbon, rainfall and ammonium for MCs prediction; and chloride, total carbon, rainfall and nitrate for CYN prediction). The established models can help to better understand the potential relationships between cyanotoxins and environmental variables as well as provide useful information for making policy decisions.

Keywords: Anabaenopeptin; Anatoxin; Cylindrospermospin; Environmental drivers; Microcystin; Random forest model.

MeSH terms

Chlorophyll A
Cyanobacteria Toxins
Cyanobacteria*
Eutrophication*
Lakes / analysis
Microcystins*
Tropical Climate

Substances

Cyanobacteria Toxins
Microcystins
Chlorophyll A