Rapid assessment of phytoplankton assemblages using Next Generation Sequencing – Barcode of Life database: a widely applicable toolkit to monitor biodiversity and harmful algal blooms (HABs)

Abstract

19Harmful algal blooms have important implications for the health, functioning and services of aquatic 20 ecosystems. Our ability to detect and monitor these events is often challenged by the lack of rapid and 21 cost-effective methods to identify bloom-forming organisms and their potential for toxin production, 22 samples, which match commercial kit performance and offer a cost-efficient solution for large scale 34 ecological assessments of harmful blooms while giving benefits of reproducibility and increased 35 accessibility. Our bioinformatics pipeline was designed to handle low taxonomic resolution for 36 problematic genera of cyanobacteria such as the Anabaena-Aphanizomenon-Dolichospermum species 37 complex, two clusters of Anabaena (I and II), Planktothrix and Microcystis. This newly developed HAB-38 ID toolset was further validated by applying it to assess cyanobacterial and algal composition in field 39 samples from waterbodies with recurrent HABs events. 40 41 construction of a reference BOLD database for algal and cyanobacterial 16S ribosomal RNA, using 93 isolates from the Great Lakes and other regions; 2) optimization of Polymerase Chain Reaction (PCR) 94 primers and DNA extraction protocols; 3) validation of the NGS method on mock communities generated 95 from known cyanobacterial and algal cultures; 4) application of the HAB-ID tool to environmental 96 samples and comparison of its performance with traditional microscopy. 97 98 Fig 1. Project phasesfrom initial optimization to high-throughput analysis of environmental 99 samples 100 101 Material and methods 102 Ethics statement 103 Environmental samples were collected from permanent Environment and Climate Change 104 Canada (ECCC) survey stations in Lakes Erie, St. Clair and Winnipeg. No specific permissions 105were required for the sampling stations/activities because the sampling stations were not 106 privately owned or protected. This study did not involve endangered or protected species.

Spatio-temporal connectivity of the aquatic microbiome associated with cyanobacterial blooms along a Great Lake riverine-lacustrine continuum

“…Cyanobacteria were further observed to be more abundant during summer and fall than in winter and spring. Here, the different cyanobacterial genera were classified using the BOLD database constructed with species representing a diverse array of cyanobacteria ( Ivanova et al, 2019 ). Taxonomic assignment remained problematic for sequences corresponding to the genera Anabaena , Aphanizomenon , and Dolichospermum , which were then classified as the Anabaena-Aphanizomenon-Dolichospermum complex (AAD complex).…”

“…Chimeras were removed with the ‘removeBimeraDenovo’ command and taxonomy was assigned with the Silva database version 128 released in 2016 ( Yilmaz et al, 2014 ). Additionally, a Barcode of Life Data System (BOLD) reference database that was developed using sequences of cyanobacterial and algal cultures, was used to classify sequences belonging to the Cyanobacteria phylum ( Ivanova et al, 2019 ). Sequences corresponding to Archaea, Chloroplasts, and Eukaryotes were removed from the ASV table and identical sequences that only varied in length were collapsed with the ‘collapseNoMismatch’ command in dada2.…”

“…A phylogenetic analysis of the 15 most abundant cyanobacterial ASVs was performed with sequences from the BOLD database ( Ivanova et al, 2019 ) and from GenBank release 239 ( Benson et al, 2018 ). Two Escherichia coli 16S sequences were used as outgroup (accession number HG917881 and HF584705).…”

Rapid on-site detection of harmful algal blooms: real-time cyanobacteria identification using Oxford Nanopore sequencing

“…Nevertheless, the use of genomic tools is of particular interest in HABs monitoring since recent fast throughput genomics-based technology development indicates its application in the field at acceptable costs. Examples include metabarcoding time-series of cyanobacterial dynamics (Tromas et al, 2017), real-time DNA barcoding in rainforests (Pomerantz et al, 2018), workflows for tracking species diversity in the field (Maestri et al, 2019), metagenomics analysis of planktonic riverine microbial consortia (Reddington et al, 2020), metabarcoding analyses of algal and cyanobacteria assemblages to monitor biodiversity and ecosystems health (Ivanova et al, 2022;MacKeigan et al, 2022). Hence, there is a rational basis for monitoring cHABs and assess public health risks based upon the current fast-throughput genomics-based techniques (Te et al, 2015;Pérez-Carrascal et al, 2021;Urban et al, 2021;Yuan and Yoon, 2022).…”