Multi-class secondary metabolites in cyanobacterial blooms from a tropical water body: Distribution patterns and real-time prediction
•16 cyanobacterial metabolites were detected in a freshwater lake in Singapore.•MC-RR and CYN occurred the most frequently, both intra- and extracellularly.•MCs decreased with increases in CYN over time.•The rapid prediction of MCs and CYN was achieved by RF using easily accessible WQIs.•Chlorophyll...
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| Veröffentlicht in: | Water research (Oxford) 2022-04, Vol.212, p.118129-118129, Article 118129 |
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| creator | You, Luhua Tong, Xuneng Te, Shu Harn Tran, Ngoc Han bte Sukarji, Nur Hanisah He, Yiliang Gin, Karina Yew-Hoong |
| description | •16 cyanobacterial metabolites were detected in a freshwater lake in Singapore.•MC-RR and CYN occurred the most frequently, both intra- and extracellularly.•MCs decreased with increases in CYN over time.•The rapid prediction of MCs and CYN was achieved by RF using easily accessible WQIs.•Chlorophyll-a and chloride were the primary predictors for MCs and CYN, respectively.
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 in |
| doi_str_mv | 10.1016/j.watres.2022.118129 |
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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.
[Display omitted]</description><identifier>ISSN: 0043-1354</identifier><identifier>EISSN: 1879-2448</identifier><identifier>DOI: 10.1016/j.watres.2022.118129</identifier><identifier>PMID: 35121419</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Anabaenopeptin ; Anatoxin ; Chlorophyll A ; Cyanobacteria ; Cyanobacteria Toxins ; Cylindrospermospin ; Environmental drivers ; Eutrophication ; Lakes - analysis ; Microcystin ; Microcystins ; Random forest model ; Tropical Climate</subject><ispartof>Water research (Oxford), 2022-04, Vol.212, p.118129-118129, Article 118129</ispartof><rights>2022 Elsevier Ltd</rights><rights>Copyright © 2022 Elsevier Ltd. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c362t-4f8abb41ba9c8beb113077619ca9f1d08134e5ca6046ab9f575295c87a0e7f143</citedby><cites>FETCH-LOGICAL-c362t-4f8abb41ba9c8beb113077619ca9f1d08134e5ca6046ab9f575295c87a0e7f143</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.watres.2022.118129$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35121419$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>You, Luhua</creatorcontrib><creatorcontrib>Tong, Xuneng</creatorcontrib><creatorcontrib>Te, Shu Harn</creatorcontrib><creatorcontrib>Tran, Ngoc Han</creatorcontrib><creatorcontrib>bte Sukarji, Nur Hanisah</creatorcontrib><creatorcontrib>He, Yiliang</creatorcontrib><creatorcontrib>Gin, Karina Yew-Hoong</creatorcontrib><title>Multi-class secondary metabolites in cyanobacterial blooms from a tropical water body: Distribution patterns and real-time prediction</title><title>Water research (Oxford)</title><addtitle>Water Res</addtitle><description>•16 cyanobacterial metabolites were detected in a freshwater lake in Singapore.•MC-RR and CYN occurred the most frequently, both intra- and extracellularly.•MCs decreased with increases in CYN over time.•The rapid prediction of MCs and CYN was achieved by RF using easily accessible WQIs.•Chlorophyll-a and chloride were the primary predictors for MCs and CYN, respectively.
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.
[Display omitted]</description><subject>Anabaenopeptin</subject><subject>Anatoxin</subject><subject>Chlorophyll A</subject><subject>Cyanobacteria</subject><subject>Cyanobacteria Toxins</subject><subject>Cylindrospermospin</subject><subject>Environmental drivers</subject><subject>Eutrophication</subject><subject>Lakes - analysis</subject><subject>Microcystin</subject><subject>Microcystins</subject><subject>Random forest model</subject><subject>Tropical Climate</subject><issn>0043-1354</issn><issn>1879-2448</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kcuO1DAQRS0EYpqBP0DISzZpXI7zMAskNMAM0iA2sLbKTkVyK4mD7YD6A_hv3MrAklVJVade9zL2EsQRBLRvTsdfmCOloxRSHgF6kPoRO0Df6Uoq1T9mByFUXUHdqCv2LKWTEIWs9VN2VTcgQYE-sN9ftin7yk2YEk_kwjJgPPOZMtow-UyJ-4W7My7BossUPU7cTiHMiY8xzBx5jmH1rqTLPRS5DcP5Lf_gU47ebtmHha-YS2VJHJeBR8Kpyn4mvkYavLsQz9mTEadELx7iNfv-6eO3m7vq_uvt55v395WrW5krNfZorQKL2vWWLEAtuq4F7VCPMIgeakWNw1aoFq0em66RunF9h4K6EVR9zV7vc9cYfmyUspl9cjRNuFDYkpGtbIu2sm4KqnbUxZBSpNGs0c9FGwPCXAwwJ7MbYC4GmN2A0vbqYcNmZxr-Nf1VvADvdoDKnz89RZOcp8UVKSK5bIbg_7_hD4yAm9Y</recordid><startdate>20220401</startdate><enddate>20220401</enddate><creator>You, Luhua</creator><creator>Tong, Xuneng</creator><creator>Te, Shu Harn</creator><creator>Tran, Ngoc Han</creator><creator>bte Sukarji, Nur Hanisah</creator><creator>He, Yiliang</creator><creator>Gin, Karina Yew-Hoong</creator><general>Elsevier Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20220401</creationdate><title>Multi-class secondary metabolites in cyanobacterial blooms from a tropical water body: Distribution patterns and real-time prediction</title><author>You, Luhua ; Tong, Xuneng ; Te, Shu Harn ; Tran, Ngoc Han ; bte Sukarji, Nur Hanisah ; He, Yiliang ; Gin, Karina Yew-Hoong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c362t-4f8abb41ba9c8beb113077619ca9f1d08134e5ca6046ab9f575295c87a0e7f143</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Anabaenopeptin</topic><topic>Anatoxin</topic><topic>Chlorophyll A</topic><topic>Cyanobacteria</topic><topic>Cyanobacteria Toxins</topic><topic>Cylindrospermospin</topic><topic>Environmental drivers</topic><topic>Eutrophication</topic><topic>Lakes - analysis</topic><topic>Microcystin</topic><topic>Microcystins</topic><topic>Random forest model</topic><topic>Tropical Climate</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>You, Luhua</creatorcontrib><creatorcontrib>Tong, Xuneng</creatorcontrib><creatorcontrib>Te, Shu Harn</creatorcontrib><creatorcontrib>Tran, Ngoc Han</creatorcontrib><creatorcontrib>bte Sukarji, Nur Hanisah</creatorcontrib><creatorcontrib>He, Yiliang</creatorcontrib><creatorcontrib>Gin, Karina Yew-Hoong</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Water research (Oxford)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>You, Luhua</au><au>Tong, Xuneng</au><au>Te, Shu Harn</au><au>Tran, Ngoc Han</au><au>bte Sukarji, Nur Hanisah</au><au>He, Yiliang</au><au>Gin, Karina Yew-Hoong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multi-class secondary metabolites in cyanobacterial blooms from a tropical water body: Distribution patterns and real-time prediction</atitle><jtitle>Water research (Oxford)</jtitle><addtitle>Water Res</addtitle><date>2022-04-01</date><risdate>2022</risdate><volume>212</volume><spage>118129</spage><epage>118129</epage><pages>118129-118129</pages><artnum>118129</artnum><issn>0043-1354</issn><eissn>1879-2448</eissn><abstract>•16 cyanobacterial metabolites were detected in a freshwater lake in Singapore.•MC-RR and CYN occurred the most frequently, both intra- and extracellularly.•MCs decreased with increases in CYN over time.•The rapid prediction of MCs and CYN was achieved by RF using easily accessible WQIs.•Chlorophyll-a and chloride were the primary predictors for MCs and CYN, respectively.
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.
[Display omitted]</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>35121419</pmid><doi>10.1016/j.watres.2022.118129</doi><tpages>1</tpages></addata></record> |
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| subjects | Anabaenopeptin Anatoxin Chlorophyll A Cyanobacteria Cyanobacteria Toxins Cylindrospermospin Environmental drivers Eutrophication Lakes - analysis Microcystin Microcystins Random forest model Tropical Climate |
| title | Multi-class secondary metabolites in cyanobacterial blooms from a tropical water body: Distribution patterns and real-time prediction |
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