Here, we report the draft genomes of two Microcystis aeruginosa strains, i.e., M. aeruginosa BLCC-F108, which was isolated from a toxic bloom in eutrophic waters in Lake Okeechobee (Florida, USA), and M. aeruginosa BLCC-F158, which was isolated from mesotrophic waters in Lake Tohopekaliga (Florida, USA). Genomic analyses show disparate toxin potentials for these two strains.
ABSTRACT
Here, we report the draft genomes of two Microcystis aeruginosa strains, i.e., M. aeruginosa BLCC-F108, which was isolated from a toxic bloom in eutrophic waters in Lake Okeechobee (Florida, USA), and M. aeruginosa BLCC-F158, which was isolated from mesotrophic waters in Lake Tohopekaliga (Florida, USA). Genomic analyses show disparate toxin potentials for these two strains.
ANNOUNCEMENT
Microcystis aeruginosa (Kützing) Kützing is a cosmopolitan, freshwater, bloom-forming cyanobacterium that is notorious for its role in cyanobacterial harmful algal blooms (cyanoHABs). The occurrence of cyanoHABs can cause discoloration of the water and surface scums and is driven primarily by increases in nutrients (e.g., nitrogen and phosphorus) from point and nonpoint sources and internal cycling (1). Microcystis aeruginosa is also capable of producing toxic bioactive compounds that can be concentrated in water, sediments, animals, and plants, representing both environmental and public health threats (1). Because cyanoHABs are increasing in frequency, intensity, and duration globally (2), it is essential to understand their genomic diversity.
Microcystis aeruginosa BLCC-F108 was isolated from a subsurface grab sample from a toxic bloom in Lake Okeechobee, Florida, in April 2019, and M. aeruginosa BLCC-F158 was isolated from a subsurface grab sample from Lake Tohopekaliga, Florida, in January 2020. For isolations, samples were spread onto BG11 agar plates (3) and grown under a 12:12 light/dark cycle. Individual colonies were picked, grown in liquid BG11 medium, and visually checked by light microscopy for contamination to ensure unicyanobacterial cultures. Because it is difficult to achieve axenic cyanobacterial cultures and heterotrophic bacteria are well-known inhabitants of the Microcystis mucilaginous sheath (4), only unicyanobacterial cultures were achieved. An enzyme-linked immunosorbent assay (ELISA) kit (Eurofins Abraxis, Warminster, PA, USA) was used, following the manufacturer’s protocols, to evaluate microcystin production for the two strains. M. aeruginosa BLCC-F108 was confirmed to be a microcystin producer, while M. aeruginosa BLCC-F158 did not produce microcystins.
DNA was extracted using a DNeasy plant minikit (Qiagen, Germantown, MD, USA). For genome sequencing, libraries were prepared using the Illumina TruSeq library construction kit, and 2 × 150-nucleotide paired-end reads were generated with an Illumina HiSeq instrument. Default parameters were used for all software unless otherwise noted. Trimming was performed using fastp v0.20.1 (5), and quality was checked using FastQC v0.11.9 (6). Raw reads were de novo assembled using SPAdes v3.14.1 (7) with meta parameters. Contigs were then binned using MaxBin2 v2.2.4 (8), the bin corresponding to cyanobacteria was extracted, and the completeness and contamination were assessed using CheckM v1.0.18 (9) before annotation with the Prokaryotic Genome Annotation Pipeline (PGAP) v4.12 (10). The presence of secondary metabolite biosynthetic gene clusters (BCGs) was assessed using antiSMASH v5.1.2 (11). The draft genome size of M. aeruginosa BLCC-F108 is 5,037,850 bp, and the draft genome size of M. aeruginosa BLCC-F158 is 5,168,077 bp. Complete results can be found in Table 1.
TABLE 1.
Genome data of two Microcystis aeruginosa strains
| Feature | Data for: |
|
|---|---|---|
| Microcystis aeruginosa BLCC-F108 | Microcystis aeruginosa BLCC-F158 | |
| Assembly size (bp) | 5,037,850 | 5,168,077 |
| No. of reads | 9,909,010 | 8,564,402 |
| No. of contigs | 487 | 512 |
| N50 (bp) | 17,855 | 27,581 |
| G+C content (%) | 42.60 | 42.80 |
| No. of coding sequences | 4,428 | 4,699 |
| Completeness (%) | 99.23 | 99.89 |
| Contamination (%) | 0.51 | 0.62 |
| No. of tRNAs | 41 | 42 |
| Coverage (×) | 125 | 235 |
| SRA accession no. | SRR12598970 | SRR12599144 |
| GenBank accession no. | JACEGB000000000 | JACEGC000000000 |
Several BCGs were identified on the basis of identity to known BCGs within the antiSMASH database. M. aeruginosa BLCC-F108 was found to have a 100% match to a microcystin gene cluster, a 91% match to a piricyclamide gene cluster, a 100% match to a micropeptin K139 gene cluster, and a 78% match to an aeruginosin 98-A gene cluster. M. aeruginosa BLCC-F158 was found to have a 100% match to toxic compound anabaenopeptin, micropeptin K139, and microviridin J gene clusters, as well as an 80% match to a microviridin B gene cluster.
Data availability.
The whole-genome shotgun projects for Microcystis aeruginosa BLCC-F108 and M. aeruginosa BLCC-F158 have been deposited in DDBJ/ENA/GenBank under the accession numbers JACEGB000000000 and JACEGC000000000, respectively. The versions described in this paper are the first versions, JACEGB010000000 and JACEGC010000000, respectively. The GenBank BioProject, BioSample, and SRA accession numbers for M. aeruginosa BLCC-F108 are PRJNA647122, SAMN15575897, and SRR12598970, respectively, and those for M. aeruginosa BLCC-F158 are PRJNA647120, SAMN15576007, and SRR12599144, respectively.
ACKNOWLEDGMENTS
This work was partially supported by the U.S. Department of Agriculture, National Institute of Food and Agriculture, Hatch project FLA-FTL-005697, the University of Florida Institute of Food and Agricultural Sciences Early Career Seed Fund program, the Florida Fish and Wildlife Conservation Commission, and the Florida Sea Grant College Program of the U.S. Department of Commerce National Oceanic and Atmospheric Administration (NOAA) (grant NA 18OAR4170085).
The views expressed are those of the authors and do not necessarily reflect the views of these organizations.
REFERENCES
- 1.Harke M, Steffen M, Gobler C, Otten T, Wilhelm S, Wood S, Paerl H. 2016. A review of the global ecology, genomics, and biogeography of the toxic cyanobacterium, Microcystis spp. Harmful Algae 54:4–20. doi: 10.1016/j.hal.2015.12.007. [DOI] [PubMed] [Google Scholar]
- 2.Paerl H, Paul VJ. 2012. Climate change: links to global expansion of harmful cyanobacteria. Water Res 46:1349–1363. doi: 10.1016/j.watres.2011.08.002. [DOI] [PubMed] [Google Scholar]
- 3.Stanier RY, Deruelles J, Rippka R, Herdman M, Waterbury JB. 1979. Generic assignments, strain histories and properties of pure cultures of cyanobacteria. Microbiology 111:1–61. doi: 10.1099/00221287-111-1-1. [DOI] [Google Scholar]
- 4.Cai H, Jiang H, Krumholz LR, Yang Z. 2014. Bacterial community composition of size-fractioned aggregates within the phycosphere of cyanobacterial blooms in a eutrophic freshwater lake. PLoS One 9:e102879. doi: 10.1371/journal.pone.0102879. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Chen S, Zhou Y, Chen Y, Gu J. 2018. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34:i884–i890. doi: 10.1093/bioinformatics/bty560. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Andrews S. 2010. FastQC: a quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc.
- 7.Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, Lesin VM, Nikolenko SI, Pham S, Prjibelski AD, Pyshkin AV, Sirotkin AV, Vyahhi N, Tesler G, Alekseyev MA, Pevzner PA. 2012. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 19:455–477. doi: 10.1089/cmb.2012.0021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Wu Y, Simmons BA, Singer SW. 2016. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32:605–607. doi: 10.1093/bioinformatics/btv638. [DOI] [PubMed] [Google Scholar]
- 9.Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. 2015. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res 25:1043–1055. doi: 10.1101/gr.186072.114. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Tatusova T, DiCuccio M, Badretdin A, Chetvernin V, Nawrocki EP, Zaslavsky L, Lomsadze A, Pruitt KD, Borodovsky M, Ostell J. 2016. NCBI Prokaryotic Genome Annotation Pipeline. Nucleic Acids Res 44:6614–6624. doi: 10.1093/nar/gkw569. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Blin K, Shaw S, Steinke K, Villebro R, Ziemert N, Lee SY, Medema MH, Weber T. 2019. antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline. Nucleic Acids Res 47:81–87. doi: 10.1093/nar/gkz310. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
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Data Availability Statement
The whole-genome shotgun projects for Microcystis aeruginosa BLCC-F108 and M. aeruginosa BLCC-F158 have been deposited in DDBJ/ENA/GenBank under the accession numbers JACEGB000000000 and JACEGC000000000, respectively. The versions described in this paper are the first versions, JACEGB010000000 and JACEGC010000000, respectively. The GenBank BioProject, BioSample, and SRA accession numbers for M. aeruginosa BLCC-F108 are PRJNA647122, SAMN15575897, and SRR12598970, respectively, and those for M. aeruginosa BLCC-F158 are PRJNA647120, SAMN15576007, and SRR12599144, respectively.