Genome sequences of lower Great Lakes Microcystis sp. reveal strain-specific genes that are present and expressed in western Lake Erie blooms

Abstract

Blooms of the potentially toxic cyanobacterium Microcystis are increasing worldwide. In the Laurentian Great Lakes they pose major socioeconomic, ecological, and human health threats, particularly in western Lake Erie. However, the interpretation of "omics" data is constrained by the highly variable genome of Microcystis and the small number of reference genome sequences from strains isolated from the Great Lakes. To address this, we sequenced two Microcystis isolates from Lake Erie (Microcystis aeruginosa LE3 and M. wesenbergii LE013-01) and one from upstream Lake St. Clair (M. cf aeruginosa LSC13-02), and compared these data to the genomes of seventeen Microcystis spp. from across the globe as well as one metagenome and seven metatranscriptomes from a 2014 Lake Erie Microcystis bloom. For the publically available strains analyzed, the core genome is ~1900 genes, representing ~11% of total genes in the pan-genome and ~45% of each strain's genome. The flexible genome content was related to Microcystis subclades defined by phylogenetic analysis of both housekeeping genes and total core genes. To our knowledge this is the first evidence that the flexible genome is linked to the core genome of the Microcystis species complex. The majority of strain-specific genes were present and expressed in bloom communities in Lake Erie. Roughly 8% of these genes from the lower Great Lakes are involved in genome plasticity (rapid gain, loss, or rearrangement of genes) and resistance to foreign genetic elements (such as CRISPR-Cas systems). Intriguingly, strain-specific genes from Microcystis cultured from around the world were also present and expressed in the Lake Erie blooms, suggesting that the Microcystis pangenome is truly global. The presence and expression of flexible genes, including strain-specific genes, suggests that strain-level genomic diversity may be important in maintaining Microcystis abundance during bloom events.

Fig 1

Cladograms of twenty Microcystis strains based on A- phylogenetic analysis using RAxML inference on concatenated alignments of six housekeeping genes (ftsZ, glnA, gltX, pgi, recA, and tpi), ribosomal protein S3, global nitrogen regulator (ntcA), and phycocyanin subunit B (cpcB) genes and B- parsimony search of presence/absence of flexible genes within each genome. SC = Subclade as assigned by Humbert et al. (2013) [31], red lines indicate where phylogenetic assignments differ between trees A and B, *strains which are toxic based on the presence of the mcy gene operon.

Fig 2. Organization and repeat structure of CRISPR-Cas arrays identified in lower Great Lakes Microcystis strains.

Genes colored with vertical stripes indicate genes strain-specific to that particular strain of Microcystis. CRISPR-Cas categories, subtypes, and repeat types were based on architecture described by Yang et al. (2015) [36].

Fig 3. Rank abundance of the coverage of strain-specific genes from LGL Microcystis isolates in metagenomic and metatranscriptomic data from multiple bloom stages of the 2014 Lake Erie Microcystis bloom.

Coverage is presented as the ratio of strain-specific genes compared to average coverage for marker genes used in the phylogenetic analysis. (A, B, C) Metagenomic data, (D, E, F) Metatranscriptomic data with genes ranked by metagenomic abundance. (A, D) strain LE3; (B, E) strain LSC13-02; (C, F) strain LE013-01. Closed circles have an annotated function, open circles are hypothetical proteins.

Fig 4

Relative abundance of strain-specific genes in (A) metagenomes and (B) metatranscriptomes from multiple bloom stages of the 2014 Lake Erie Microcystis bloom, annotated by COG category. Relative abundance was determined by normalizing the metagenomic and metatranscriptomic strain-specific gene read coverage by the read coverage of phylogenetic marker genes. COG categories are: C- Energy production and conversion, E- Amino acid transport and metabolism, G- Carbohydrate transport and metabolism, H- Coenzyme transport and metabolism, J- Translation, ribosomal structure, and biogenesis, K- Transcription, L- Replication, recombination, and repair, M- Cell wall/membrane/envelope biogenesis, O- Posttranslational modification, protein turnover, chaperones, Q- Secondary metabolites biosynthesis, transport, and catabolism, R- General function prediction only, S- Function unknown, T- Signal transduction mechanisms, V- Defense mechanisms, X- Mobilome: prophages, transposons.