Hallegraeff, G. M. Ocean climate change, phytoplankton community responses, and harmful algal blooms: A formidable predictive challenge 1. J. Phycol. 46, 220–235 (2010).
Google Scholar
Itakura, S. & Imai, I. Economic impacts of harmful algal blooms on fisheries and aquaculture in western Japan—An overview of interannual variability and interspecies comparison. PICES Sci. Rep. 47, 17 (2014).
Haigh, N. & Esenkulova, S. Economic losses to the British Columbia salmon aquaculture industry due to harmful algal blooms, 2009–2012. PICES Sci. Rep. 47, 2 (2014).
Sharma, N. K. et al. (eds) Cyanobacteria: An Economic Perspective 245–256 (Wiley, 2014).
O’Neil, J. M., Davis, T. W., Burford, M. A. & Gobler, C. J. The rise of harmful cyanobacteria blooms: The potential roles of eutrophication and climate change. Harmful Algae 14, 313–334. https://doi.org/10.1016/j.hal.2011.10.027 (2012).
Google Scholar
Paerl, H. W. & Huisman, J. Climate change: A catalyst for global expansion of harmful cyanobacterial blooms. Environ. Microbiol. Rep. 1, 27–37. https://doi.org/10.1111/j.1758-2229.2008.00004.x (2009).
Google Scholar
Hallegraeff, G. M. et al. Perceived global increase in algal blooms is attributable to intensified monitoring and emerging bloom impacts. Commun. Earth Environ. 2, 117. https://doi.org/10.1038/s43247-021-00178-8 (2021).
Google Scholar
Hennon, G. M. M. & Dyhrman, S. T. Progress and promise of omics for predicting the impacts of climate change on harmful algal blooms. Harmful Algae 91, 101587. https://doi.org/10.1016/j.hal.2019.03.005 (2020).
Google Scholar
Kudela, R., Berdalet, E. & Urban, E. Harmful Algal Blooms: A Scientific Summary for Policy Makers (2015).
Lezcano, M., Velázquez, D., Quesada, A. & El-Shehawy, R. Diversity and temporal shifts of the bacterial community associated with a toxic cyanobacterial bloom: An interplay between microcystin producers and degraders. Water Res. 125, 52–61. https://doi.org/10.1016/j.watres.2017.08.025 (2017).
Google Scholar
Scherer, P. I. et al. Temporal dynamics of the microbial community composition with a focus on toxic cyanobacteria and toxin presence during harmful algal blooms in two South German Lakes. Front. Microbiol. 8, 02387. https://doi.org/10.3389/fmicb.2017.02387 (2017).
Google Scholar
Woodhouse, J. N. et al. Microbial communities reflect temporal changes in cyanobacterial composition in a shallow ephemeral freshwater lake. ISME J. 10, 1337–1351. https://doi.org/10.1038/ismej.2015.218 (2016).
Google Scholar
Beaver, J. R. et al. Land use patterns, ecoregion, and microcystin relationships in U.S. lakes and reservoirs: A preliminary evaluation. Harmful Algae 36, 57–62. https://doi.org/10.1016/j.hal.2014.03.005 (2014).
Google Scholar
Loftin, K. A. et al. Cyanotoxins in inland lakes of the United States: Occurrence and potential recreational health risks in the EPA National Lakes Assessment 2007. Harmful Algae 56, 77–90. https://doi.org/10.1016/j.hal.2016.04.001 (2016).
Google Scholar
Casero, M. C., Velázquez, D., Medina-Cobo, M., Quesada, A. & Cirés, S. Unmasking the identity of toxigenic cyanobacteria driving a multi-toxin bloom by high-throughput sequencing of cyanotoxins genes and 16S rRNA metabarcoding. Sci. Total Environ. 665, 367–378. https://doi.org/10.1016/j.scitotenv.2019.02.083 (2019).
Google Scholar
Chaffin, J. D., Sigler, V. & Bridgeman, T. B. Connecting the blooms: Tracking and establishing the origin of the record-breaking Lake Erie Microcystis bloom of 2011 using DGGE. Aquat. Microb. Ecol. 73, 29–39 (2014).
Google Scholar
Stanley, E. H. & Jones, J. B. (eds) Stream Ecosystems in a Changing Environment 321–348 (Elsevier, 2016).
Google Scholar
Giblin, S. M. & Gerrish, G. A. Environmental factors controlling phytoplankton dynamics in a large floodplain river with emphasis on cyanobacteria. River Res. Appl. 36, 1137–1150. https://doi.org/10.1002/rra.3658 (2020).
Google Scholar
Graham, J. L., Ziegler, A. C., Loving, B. L. & Loftin, K. A. Fate and Transport of Cyanobacteria and Associated Toxins and Taste-and-Odor Compounds from Upstream Reservoir Releases in the Kansas River, Kansas, September and October 2011 65 (US Geological Survey, 2012).
Knowlton, M. F. & Jones, J. R. Seston, light, nutrients and chlorophyll in the lower Missouri River, 1994–1998. J. Freshw. Ecol. 15, 283–297. https://doi.org/10.1080/02705060.2000.9663747 (2000).
Google Scholar
Otten, T. G., Crosswell, J. R., Mackey, S. & Dreher, T. W. Application of molecular tools for microbial source tracking and public health risk assessment of a Microcystis bloom traversing 300 km of the Klamath River. Harmful Algae 46, 71–81 (2015).
Google Scholar
Preece, E. P., Hardy, F. J., Moore, B. C. & Bryan, M. A review of microcystin detections in Estuarine and Marine waters: Environmental implications and human health risk. Harmful Algae 61, 31–45. https://doi.org/10.1016/j.hal.2016.11.006 (2017).
Google Scholar
Reinl, K. L., Sterner, R. W., Lafrancois, B. M. & Brovold, S. Fluvial seeding of cyanobacterial blooms in oligotrophic Lake Superior. Harmful Algae 100, 101941. https://doi.org/10.1016/j.hal.2020.101941 (2020).
Google Scholar
Bridgeman, T. B. et al. From River to Lake: Phosphorus partitioning and algal community compositional changes in Western Lake Erie. J. Great Lakes Res. 38, 90–97 (2012).
Google Scholar
Brown, B. L. et al. Metagenomic analysis of planktonic microbial consortia from a non-tidal urban-impacted segment of James River. Stand Genomic Sci. 10, 65. https://doi.org/10.1186/s40793-015-0062-5 (2015).
Google Scholar
Hamner, S. et al. Metagenomic profiling of microbial pathogens in the Little Bighorn River, Montana. Int. J. Environ. Res. Public Health 16, 071097. https://doi.org/10.3390/ijerph16071097 (2019).
Google Scholar
Staley, C. et al. Application of Illumina next-generation sequencing to characterize the bacterial community of the Upper Mississippi River. J. Appl. Microbiol. 115, 1147–1158. https://doi.org/10.1111/jam.12323 (2013).
Google Scholar
Winter, C., Hein, T., Kavka, G., Mach, R. L. & Farnleitner, A. H. Longitudinal changes in the bacterial community composition of the Danube River: A whole-river approach. Appl. Environ. Microbiol. 73, 421–431. https://doi.org/10.1128/aem.01849-06 (2007).
Google Scholar
Jackson, C. R., Millar, J. J., Payne, J. T., Ochs, C. A. & Wommack, K. E. Free-living and particle-associated bacterioplankton in large rivers of the Mississippi River basin demonstrate biogeographic patterns. Appl. Environ. Microbiol. 80, 7186–7195. https://doi.org/10.1128/AEM.01844-14 (2014).
Google Scholar
Payne, J. T., Jackson, C. R., Millar, J. J. & Ochs, C. A. Timescales of variation in diversity and production of bacterioplankton assemblages in the Lower Mississippi River. PLoS ONE 15, e0230945. https://doi.org/10.1371/journal.pone.0230945 (2020).
Google Scholar
Payne, J. T., Millar, J. J., Jackson, C. R. & Ochs, C. A. Patterns of variation in diversity of the Mississippi river microbiome over 1,300 kilometers. PLoS ONE 12, e0174890. https://doi.org/10.1371/journal.pone.0174890 (2017).
Google Scholar
Read, D. S. et al. Catchment-scale biogeography of riverine bacterioplankton. ISME J. 9, 516–526. https://doi.org/10.1038/ismej.2014.166 (2015).
Google Scholar
Reddington, K. et al. Metagenomic analysis of planktonic riverine microbial consortia using nanopore sequencing reveals insight into river microbe taxonomy and function. GigaScience 9, 53. https://doi.org/10.1093/gigascience/giaa053 (2020).
Google Scholar
Staley, C. et al. Core functional traits of bacterial communities in the Upper Mississippi River show limited variation in response to land cover. Front. Microbiol. 5, 414 (2014).
Google Scholar
Staley, C. et al. Species sorting and seasonal dynamics primarily shape bacterial communities in the Upper Mississippi River. Sci. Total Environ. 505, 435–445. https://doi.org/10.1016/j.scitotenv.2014.10.012 (2015).
Google Scholar
Van Rossum, T. et al. Year-long metagenomic study of river microbiomes across land use and water quality. Front. Microbiol. 6, 1405 (2015).
Google Scholar
Kim, K. H. et al. Application of metagenome analysis to characterize the molecular diversity and saxitoxin-producing potentials of a cyanobacterial community: A case study in the North Han River, Korea. Appl. Biol. Chem. 61, 153–161. https://doi.org/10.1007/s13765-017-0342-4 (2018).
Google Scholar
Graham, J. L. et al. Cyanotoxin occurrence in large rivers of the United States. Inland Waters 10, 109–117. https://doi.org/10.1080/20442041.2019.1700749 (2020).
Google Scholar
Zuellig, R. E., Graham, J. L., Stelzer, E. A., Loftin, K. A. & Rosen, B. H. Cyanobacteria, Cyanotoxin Synthetase Gene, and Cyanotoxin Occurrence Among Selected Large River Sites of the Conterminous United States, 2017–18 22 (US Geological Survey, 2021).
Kramer, B. J. et al. Nitrogen limitation, toxin synthesis potential, and toxicity of cyanobacterial populations in Lake Okeechobee and the St. Lucie River Estuary, Florida, during the 2016 state of emergency event. PLoS ONE 13, e0196278 (2018).
Google Scholar
Bouma-Gregson, K. et al. Impacts of microbial assemblage and environmental conditions on the distribution of anatoxin-a producing cyanobacteria within a river network. ISME J. 13, 1618–1634. https://doi.org/10.1038/s41396-019-0374-3 (2019).
Google Scholar
Tillett, D. et al. Structural organization of microcystin biosynthesis in Microcystis aeruginosa PCC7806: An integrated peptide–polyketide synthetase system. Chem. Biol. 7, 753–764 (2000).
Google Scholar
Dittmann, E., Fewer, D. P. & Neilan, B. A. Cyanobacterial toxins: Biosynthetic routes and evolutionary roots. FEMS Microbiol. Rev. 37, 23–43. https://doi.org/10.1111/j.1574-6976.2012.12000.x (2013).
Google Scholar
Jungblut, A. D. & Neilan, B. A. Molecular identification and evolution of the cyclic peptide hepatotoxins, microcystin and nodularin, synthetase genes in three orders of cyanobacteria. Arch. Microbiol. 185, 107–114. https://doi.org/10.1007/s00203-005-0073-5 (2006).
Google Scholar
Meriluoto, J. et al. (eds) Handbook of Cyanobacterial Monitoring and Cyanotoxin Analysis 501–525 (Wiley, 2017).
Google Scholar
Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639–2643. https://doi.org/10.1038/ismej.2017.119 (2017).
Google Scholar
Graham, J. L., Dubrovsky, N. M., Loftin, K. A., Rosen, B. H. & Stelzer, E. A. Cyanotoxin, Chlorophyll-a, and Cyanobacterial Toxin Genetic Data for Samples Collected at Twelve Large River Sites Throughout the United States, June Through October 2019 (U.S. Geological Survey, 2022).
Dodds, W. K. & Smith, V. H. Nitrogen, phosphorus, and eutrophication in streams. Inland Waters 6, 155–164. https://doi.org/10.5268/IW-6.2.909 (2016).
Google Scholar
Debroas, D. et al. Overview of freshwater microbial eukaryotes diversity: A first analysis of publicly available metabarcoding data. FEMS Microbiol. Ecol. 93, 23. https://doi.org/10.1093/femsec/fix023 (2017).
Google Scholar
Henson, M. W. et al. Nutrient dynamics and stream order influence microbial community patterns along a 2914 kilometer transect of the Mississippi River. Limnol. Oceanogr. 63, 1837–1855. https://doi.org/10.1002/lno.10811 (2018).
Google Scholar
Ghai, R. et al. Metagenomics of the water column in the pristine upper course of the Amazon river. PLoS ONE 6, e23785. https://doi.org/10.1371/journal.pone.0023785 (2011).
Google Scholar
Liao, J. et al. Cyanobacteria in lakes on Yungui Plateau, China are assembled via niche processes driven by water physicochemical property, lake morphology and watershed land-use. Sci. Rep. 6, 36357. https://doi.org/10.1038/srep36357 (2016).
Google Scholar
Monchamp, M.-E. et al. Homogenization of lake cyanobacterial communities over a century of climate change and eutrophication. Nat. Ecol. Evol. 2, 317–324. https://doi.org/10.1038/s41559-017-0407-0 (2018).
Google Scholar
Pessi, I. S., Maalouf, P. D. C., LaughinghouseBaurain, H. D. D. & Wilmotte, A. On the use of high-throughput sequencing for the study of cyanobacterial diversity in Antarctic aquatic mats. J. Phycol. 52, 356–368. https://doi.org/10.1111/jpy.12399 (2016).
Google Scholar
Tanvir, R. U., Hu, Z., Zhang, Y. & Lu, J. Cyanobacterial community succession and associated cyanotoxin production in hypereutrophic and eutrophic freshwaters. Environ. Pollut. 290, 118056. https://doi.org/10.1016/j.envpol.2021.118056 (2021).
Google Scholar
Chételat, J., Pick, F. R. & Hamilton, P. B. Potamoplankton size structure and taxonomic composition: Influence of river size and nutrient concentrations. Limnol. Oceanogr. 51, 681–689 (2006).
Google Scholar
Heiskary, S. & Markus, H. Establishing relationships among nutrient concentrations, phytoplankton abundance, and biochemical oxygen demand in Minnesota, USA, rivers. Lake Reserv. Manag. 17, 251–262 (2001).
Google Scholar
Smith, V. H. Eutrophication of freshwater and coastal marine ecosystems a global problem. Environ. Sci. Pollut. Res. 10, 126–139 (2003).
Google Scholar
Verspagen, J. M. et al. Rising CO2 levels will intensify phytoplankton blooms in eutrophic and hypertrophic lakes. PLoS ONE 9, e104325 (2014).
Google Scholar
Zepernick, B. N. et al. Elevated pH conditions associated with Microcystis spp. blooms decrease viability of the cultured diatom Fragilaria crotonensis and natural diatoms in Lake Erie. Front. Microbiol. 12, 598736. https://doi.org/10.3389/fmicb.2021.598736 (2021).
Google Scholar
Urban, L. et al. Freshwater monitoring by nanopore sequencing. Elife 10, 61504. https://doi.org/10.7554/eLife.61504 (2021).
Google Scholar
Lee, C. J. & Henderson, R. J. Tracking Water-Quality in U.S. Streams and Rivers: U.S. Geological Survey National Water Quality Network. https://nrtwq.usgs.gov/nwqn (2020).
Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data (2010).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Google Scholar
Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257. https://doi.org/10.1186/s13059-019-1891-0 (2019).
Google Scholar
Lu, J. B. F., Thielen, P. & Salzberg, S. L. Bracken: Estimating species abundance in metagenomics data. PeerJ Comput. Sci. 3, 104. https://doi.org/10.7717/peerj-cs.104 (2017).
Google Scholar
Bagley, M. et al. High-throughput environmental DNA analysis informs a biological assessment of an urban stream. Ecol. Ind. 104, 378–389. https://doi.org/10.1016/j.ecolind.2019.04.088 (2019).
Google Scholar
Magoč, T. & Salzberg, S. L. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963. https://doi.org/10.1093/bioinformatics/btr507 (2011).
Google Scholar
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857. https://doi.org/10.1038/s41587-019-0209-9 (2019).
Google Scholar
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583. https://doi.org/10.1038/nmeth.3869 (2016).
Google Scholar
Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549. https://doi.org/10.1093/molbev/msy096 (2018).
Google Scholar
Nübel, U., Garcia-Pichel, F. & Muyzer, G. PCR primers to amplify 16S rRNA genes from cyanobacteria. Appl. Environ. Microbiol. 63, 3327–3332. https://doi.org/10.1128/aem.63.8.3327-3332.1997 (1997).
Google Scholar
Neilan, B. A. et al. rRNA sequences and evolutionary relationships among toxic and nontoxic cyanobacteria of the genus Microcystis. Int. J. Syst. Bacteriol. 47, 693–697. https://doi.org/10.1099/00207713-47-3-693 (1997).
Google Scholar
Team R Core. R: A Language and Environment for Statistical Computing (2013).
McMurdie, P. J. & Holmes, S. Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).
Google Scholar
Oksanen, J. et al. Vegan: Community Ecology Package. R Package Version 2.5-2 (2018).
Wickham, H. ggplot2-Elegant Graphics for Data Analysis (Springer, 2016).
Google Scholar
U.S. Geological Survey. National Water Information System Database. https://doi.org/10.5066/F7P55KJN (2022).
Source: Ecology - nature.com
