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Syanobakteereista EKOGENOMIIKKAA ja TAXONOMIAA 14.11.2017.

ORIGINAL RESEARCH article

Front. Microbiol., 14 November 2017
Nuline M. Walter et al. Sec. Aquatic Microbiology
Volume 8 - 2017 | https://doi.org/10.3389/fmicb.2017.02132
  • 1Laboratory of Microbiology, Institute of Biology, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
  • 2Radboud Institute for Molecular Life Sciences, Centre for Molecular and Biomolecular Informatics, Radboud University Medical Centre, Nijmegen, Netherlands
  • 3Theoretical Biology and Bioinformatics, Utrecht University, Utrecht, Netherlands
  • 4Laboratory of Microbiology, Ghent University, Ghent, Belgium
  • 5Center of Technology - CT2, SAGE-COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

Cyanobacteria are major contributors to global biogeochemical cycles. The genetic diversity among Cyanobacteria enables them to thrive across many habitats, although only a few studies have analyzed the association of phylogenomic clades to specific environmental niches. In this study, we adopted an ecogenomics strategy with the aim to delineate ecological niche preferences of Cyanobacteria and integrate them to the genomic taxonomy of these bacteria. First, an appropriate phylogenomic framework was established using a set of genomic taxonomy signatures (including a tree based on conserved gene sequences, genome-to-genome distance, and average amino acid identity) to analyse ninety-nine publicly available cyanobacterial genomes. Next, the relative abundances of these genomes were determined throughout diverse global marine and freshwater ecosystems, using metagenomic data sets. The whole-genome-based taxonomy of the ninety-nine genomes allowed us to identify 57 (of which 28 are new genera) and 87 (of which 32 are new species) different cyanobacterial genera and species, respectively. The ecogenomic analysis allowed the distinction of three major ecological groups of Cyanobacteria (named as i. Low Temperature; ii. Low Temperature Copiotroph; and iii. High Temperature Oligotroph) that were coherently linked to the genomic taxonomy. This work establishes a new taxonomic framework for Cyanobacteria in the light of genomic taxonomy and ecogenomic approaches.

Introduction

Earth is home to nearly one trillion (1012) microbial species that have evolved over ~4 billion years (Locey and Lennon, 2016). Cyanobacteria emerged ~3 billion years ago, ushering Earth's transition from anoxygenic to oxygenic conditions through photosynthesis (Schirrmeister et al., 2011a). Throughout their evolution, Cyanobacteria became one of the most diverse and widely distributed Prokaryotes, occupying many niches within terrestrial, planktonic, and benthic habitats. Their long history evolved in a broad heterogeneity comprising unicellular and multicellular, photosynthetic and non-photosynthetic (i.e., Melainabacteria) (Schirrmeister et al., 2011a; Di Rienzi et al., 2013; Soo et al., 2014), free-living, symbiotic, toxic and predatory organisms (Soo et al., 2015), with genomes sizes ranging from 1 to 10 Mb (Shih et al., 2013). Here we consider Cyanobacteria phylum as consisting only of oxygenic phototrophs.

Cyanobacteria (also known as the Cyanophyceae, Cyanophyta, cyanoprokaryota, blue-green algae or blue-green bacteria) share similar metabolic features with eukaryotic algae and have been named according to the Botanical Code (Kauff and Büdel, 2010). The inclusion of Cyanobacteria in taxonomic schemes of Bacteria was only proposed in 1978 by Stanier et al. (1978), and through time the bacterial taxonomic names have come into conflict with the botanical nomenclature (Oren, 2004; Oren and Garrity, 2014). More than two decades passed before a Note to General Consideration 5 (1999) was published for Cyanobacteria to be included under the rules of the International Committee on Systematic Bacteriology (ICSB)/International Committee on Systematic of Prokaryotes (ICSP) (Tindall, 1999; De Vos and Trüper, 2000; Labeda, 2000). Taxa nomenclature within this group has long been a topic of discussion, but currently there is no consensus (Hoffmann et al., 2005; Oren and Tindall, 2005; Oren et al., 2009; Oren and Ventura, 2017). As a result, more than 50 genera of Cyanobacteria have been described since 2000, and many of them remain unrecognized in the List of Prokaryotic Names with Standing in Nomenclature, LPSN, http://www.bacterio.net (Parte, 2014) or in databases (e.g., NCBI).

The Cyanobacteria form a challenging group for the microbiologists. Their traditional taxonomy based on morphologic traits does not reflect the results of phylogenetic analyses (Rippka et al., 1979; Boone and Castenholz, 2001; Gugger and Hoffmann, 2004; Schirrmeister et al., 2011b; Hugenholtz et al., 2016). The predominance of morphology assembled unrelated Cyanobacteria into polyphyletic species and genera and higher taxonomic categories which require revisions in the future (Komárek et al., 2014). The polyphyly is an indicative of the taxonomic mislabeling of many taxa. The 16S rRNA gene sequences were useful in charting and characterizing microbial communities (Kozlov et al., 2016) but this molecule lack sensitivity for evolutionary changes that occur in ecological dynamics, where microbial diversity is organized by physicochemical parameters (Choudoir et al., 2012; Becraft et al., 2015). Hence, the processes that shape cyanobacterial communities over space and time are less known. A recent study proposed that there should be 170 genera of Cyanobacteria based on 16S rRNA sequences only (Kozlov et al., 2016). Farrant et al. (2016) delineated 121 Prochlorococcus and 15 Synechococcus ecologically significant taxonomic units (ESTUs) in the global ocean using single-copy petB sequences (encoding cytochrome b6) and environmental cues.

High Throughput Sequencing (HTS) have revolutionized the practice of microbial systematics, providing an informative, reproducible, and portable tool to delineate species, reconstruct their evolutionary history, and infer ecogenomic features (Gevers et al., 2005; Konstantinidis and Tiedje, 2005a,b; Garrity and Oren, 2012; Gribaldo and Brochier-Armanet, 2012; Shih et al., 2013; Sutcliffe et al., 2013; Hugenholtz et al., 2016). This approach allows both cultured (Al-saari et al., 2015; Appolinario et al., 2016) and uncultured microorganisms (Iverson et al., 2012; Brown et al., 2015; Hugerth et al., 2015) to be studied. The latter is especially important because the cyanobacterial cultivation in laboratory is another hurdle in the study of this group of bacteria.

Recommendations that nomenclature should agree with and reflect genomic information were stated during the pre-genomic era (Wayne et al., 1987), due nothing describes an organism better than its genome. Sequence-based methods to delimit prokaryotic species have emerged to define and to improve cut-offs criteria during the genomic era (Gevers et al., 2005; Konstantinidis and Tiedje, 2005a,b; Konstantinidis et al., 2006; Goris et al., 2007; Richter and Rossello-Mora, 2009; Auch et al., 2010a; Thompson et al., 2013a,b; Varghese et al., 2015), demonstrating a greater discriminatory power. Inexorable advances in methodologies will incorporate genomics into the taxonomy and systematics of the prokaryotes, boosting the credibility of taxonomy in the current post-genomic era (Coenye et al., 2005; Chun and Rainey, 2014). Up-to-date, while several groups have been analyzed through a genomic-wide view (Gupta et al., 2015; Adeolu et al., 2016; Hahnke et al., 2016; Ahn et al., 2017; Amin et al., 2017; Waite et al., 2017), many others have faced hurdles, such as Cyanobacteria. However, a genomic taxonomy approach has successfully been applied to elucidate the taxonomic structure of the two cyanobacterial genera, Prochlorococcus and Synechococcus (Thompson et al., 2013a; Coutinho et al., 2016a,b). As genomic taxonomy postulates numeric, non-subjective, cut-offs for taxa delimitation, strains were considered to belong to the same species when share at least 98.8% 16S rRNA gene sequence similarity, 95% of AAI, and 70% GGD (Konstantinidis and Tiedje, 2005a; Thompson et al., 2013a,b), while species from the same genus form monophyletic branches (Yarza et al., 2008; Qin et al., 2014). It is in agreement with the concept of species as a discrete, monophyletic and genomically homogeneous population of organisms that can be discriminated from other related populations by means of diagnostic properties (Rossello-Mora and Amann, 2001; Stackenbrandt et al., 2002). The availability of whole-genomes opened the doors for an in-depth knowledge in microbial diversity and ecology, where the entire genomic pool may be applied to understanding the forces that govern community structure. The use of ecogenomic analysis postulates a reliable and scalable approach to delineate species and genera in order to reconstruct their evolution and to draw a global picture of possible ecological determinants (Di Rienzi et al., 2013; Soo et al., 2014; Spang et al., 2015; Thompson et al., 2015; Anantharaman et al., 2016; Garrity, 2016; Hug et al., 2016; Hugenholtz et al., 2016). Our hypothesis is that a phylogenomic framework will reflect ecologic groups found in nature.

To test this hypothesis, we first established a phylogenomic framework, using genomic signatures (i.e., a tree based on conserved gene sequences, average amino acid identity, and genome-to-genome distance), with the circumscription of species and genera. We then classified the genomes in three major groups according to their ecological traits as inferred through metagenomics and environmental metadata. Finally, we correlated the three disclosed ecogenomic groups (i. Low Temperature; ii. Low Temperature Copiotroph; and iii. High Temperature Oligotroph) with the circumscribed species and genera. We observed that the taxonomic delineation of species and genera is coherent with the ecogenomic groups.

Materials and Methods

Genome Election

Cyanobacterial genomes publicly available in January 2016 were retrieved from RefSeq (NCBI Reference Sequence Database), GenBank and GEBA (Genomic Encyclopedia of Bacteria and Archaea) databases. Genome completeness was assessed with CheckM (Parks et al., 2015), and the genomes that were at least 90% complete and assembled in < 500 contigs were used for further analyses. Ninety-nine genomes were selected based on that criterion, and they are listed in Table 1 (additional information on Table S2).

s://www.frontiersin.org/articles/10.3389/fmicb.2017.02132/full 

Annotation and Genomic Taxonomy

All genomes were annotated using Prokka version 1.11 (Seemann, 2014), with default settings, in order to avoid any possible bias. Genomic taxonomy of the ninety-nine cyanobacterial genomes was performed according to Thompson et al. (2013a) and Coutinho et al. (2016a) and are briefly described here. Average Amino acid Identity (AAI) and Genome-to-Genome Distance (GGD) were calculated as described previously (Konstantinidis and Tiedje, 2005a; Auch et al., 2010a,b; Meier-Kolthoff et al., 2013). GGD were calculated using the Genome-to-Genome Distance Calculator tool, version 2.1 under recommended settings (Meier-Kolthoff et al., 2013; http://ggdc.dsmz.de/), whereas AAI values were carried out through GenTaxo as previously described (Coutinho et al., 2016a). The species cut-offs delimitation were ≥95% AAI and ≥70% GGD, and ≥70% AAI for genus delimitation.

The Manhattan distances were calculated based on the percentage AAI values of every genome (genome-genome matrix) and was used as the input for making the hierarchical clustering using the hclust() function in R (R Development Core Team, 2011). This distance is able to indicate how far/close the genomes are located from each other. The heatmap was produced by heatmap.2 {gplots} package in R, with background color of each panel mapping to percentage AAI values.

Phylogenetic Analysis

To establish the phylogenetic structure of the phylum Cyanobacteria, phylogenetic trees were constructed using the 16S rRNA gene sequences and the concatenated alignments of a set of conserved genes, most of which encode ribosomal proteins.

Ribosomal RNA Sequences

The small subunit ribosomal RNA (16S rRNA) sequences from all cyanobacterial strains for which whole genome sequence data are publicly available (exception see below, thus N = 97), as well as 16S rRNA gene sequences from additional type-strains available (N = 14) were all analyzed. The sequences were retrieved from the ARB SILVA database (Pruesse et al., 2007; Quast et al., 2013). Whenever sequences were not available, they were retrieved directly from the genomes using RNammer 1.2 Server (Lagesen et al., 2007). Sequences were aligned through MUSCLE v. 3.8 (Edgar, 2004), with default settings, and Gblocks 0.91b (Castresana, 2000; Talavera and Castresana, 2007) was used for alignment curation. Using MEGA 6 (Tamura et al., 2013), best-fitting nucleic acid substitution models were calculated through the MLModelTest feature. Models were ranked based on their Bayesian Information Criterion (BIC) scores as described by Tamura et al. (2013). The model with the lowest BIC score was selected and used for further phylogenetic analysis. The phylogenetic inference was obtained using the Maximum Likelihood method based on the Kimura 2 parameter method with the Gamma distributed rate variation (K2+G) as the nucleotide substitution model, which was estimated from the data. The support branches of tree topology were checked by 1,000 bootstrap replicates. The 16S rRNA gene alignments were used to estimate the degree of genetic distance between strains through the Tajima-Nei method (Tajima and Nei, 1984).

Gloeobacter violaceus PCC 7421 was set as the outgroup in both trees. Trees were visualized with FigTree, version 1.4.2 (Rambaut, 2015). Due to incomplete or partial sequences, Synechococcus sp. CB0101 was omitted from these analyses. Planktothrix mougeotii NIVA-CYA 405 as well as Planktothrix prolifica NIVA-CYA 540 were not included in the phylogenetic analyses because 16S rRNA sequences are not currently available for these strains (and not retrievable from their genomes).

The type-strains or the type-species of each taxa were included in the 16S phylogenetic tree to confirm the phylogenetic relatedness of the cyanobacterial genomes. Designations of type strain or type species were not available for Chaemaesiphon minutus PCC6605, Pleurocapsa sp. PCC7319, Rivularia sp. PCC7116, Synechocystis sp. PCC7509, Trichodesmium erythraeum IMS01, Xenococcus sp. PCC7305, cyanobacterium ESFC-1, and cyanobacterium JSC-12. Geitlerinema sp. PCC7105 is the reference strain for marine species of Geitlerinema, and PCC73106 is the reference strain for Gloeocapsa (Sarma, 2012).

Conserved Marker Genes

A tree was generated using 31 conserved gene sequences previously validated as phylogenetic markers for (cyano) bacteria (Wu and Eisen, 2008, and recently used by Shih et al., 2013 and Komárek et al., 2014). The sequences of these proteins were mined using the AutoMated Phylogenomic infeRence Application—AMPHORA2 tool (Wu and Scott, 2012), through default settings for the Bacteria option, and with a cut-off value of 1.e−10. Individual alignments were performed for each of the 31 gene sets through MUSCLE v. 3.8 with default settings (Edgar, 2004). All alignments were then concatenated.  

 A Maximum Likelihood tree was constructed using RaxML v. 7 (Stamatakis, 2006) and the Dayhoff+G likelihood model. One thousand bootstrap replications were calculated to evaluate the relative support of the branches. Trees were visualized with FigTree, version 1.4.2 (Rambaut, 2015).

Abundance of Cyanobacterial Genomes Across Aquatic Environments and Ecological Correlations

Marine and freshwater metagenomes were retrieved to determine the abundance of ninety-nine cyanobacterial genomes across the Earth. A set of 191 marine metagenomes from the Tara Ocean project were retrieved for analysis along with their associated metadata (Sunagawa et al., 2015). Sample-associated environmental data were inferred across multiple depths at global scale of Tara's metagenomics sampling: (i) surface water layer (5 m, s.d. = 0); and (ii) subsurface layer, including deep chlorophyll maximum zone (71 m, s.d. = 41 m) and mesopelagic zone (600 m, s.d. = 220 m) (Sunagawa et al., 2015). Eight freshwater metagenomes were retrieved for analysis from the Caatinga biome microbial community project along with their associated metadata (Lopes et al., 2016).

Metagenome reads were mapped to a database containing the ninety-nine analyzed cyanobacterial genomes through Bowtie2 (Langmead and Salzberg, 2012) using -very-sensitive-local and -a options. Abundance of genomes across samples was calculated based on the number of mapped reads as described by Iverson et al. (2012). Metagenomes were compared based on the relative abundances of the ninety-nine analyzed genomes within them using non-metric multidimensional scaling (NMDS).

Spearman correlation coefficients (R, or Spearman's rho) were calculated for the abundance of each genome and the levels of measured environmental parameters across samples. Next, a dissimilarity matrix of Manhattan distances was calculated based on the Spearman correlation values of every genome. All correlations were used by this analysis regardless of the corrected p-value, as non-significant correlations are still ecologically informative as they indicate weak associations between microorganisms and environmental parameters. Finally, this dissimilarity matrix was used as input for hierarchical clustering using the complete linkage method within the hclust() function in R. The resulting dendrogram was visually inspected to define groups (i.e., ecogenomic groups) of organisms with similar correlation patterns which were named based on the main correlated feature.

The classification reassessment was made integrating the results of genomic taxonomy, phylogenomic analysis and ecogenomic signals through an accurately comparison.

Results

Phylogenomic Framework Reconstruction

The tree based on conserved marker genes (Figure 1) revealed the topology with the presence of well-defined nodes in general with bootstrap support values greater than 50% over 1,000 replicates. The phylogenomic tree (Figure 1) gave a higher resolution than the 16S rRNA phylogenetic analysis (Figure S1 and Table S1), in the means that strains were better discriminated in the conserved marker genes tree (e.g., Parasynechococcus group, Figure 1 and Figure S1). The species assignations were considered correct when organisms located on the same phylogenetic branch as the corresponding type strains or type species presented the 16S rRNA sequence similarity higher than 98.8%, such as Crinalium epipsammum SAG22.89T (Figure S1) and Crinalium epipsammum PCC9333 (Figure S1 and Figure 1).

Figure

Figure 1. Phylogenomic tree of the Cyanobacteria phylum with the proposed new names. Tree construction was performed using 100 genomes (ninety-nine used in this study plus the outgroup), based on a set of conserved marker genes. The numbers at the nodes indicate bootstrap values as percentages greater than 50%. Bootstrap tests were conducted with 1,000 replicates. The unit of measure for the scale bars is the number of nucleotide substitutions per site. The Gloeobacter violaceus PCC 7421 sequence was designated as outgroup. Capital letters indicate environmental source: F, freshwater; M, marine; P, peat bog (sphagnum); S, soil; T, thermal; and §, other habitat. New names are highlighted in red. Overwritten T indicates type strain or type species. Ecogenomic groups are depicted in different colors as indicated in the legend: Low Temperature group; Low Temperature Copiotroph group; and High Temperature Oligotroph group. Cases depicted in the Results section are in bold.

 

Genomic Diversity of Cyanobacteria

In total, we found 57 branches corresponding to genera based on the AAI and GGD analyses (Figure 2). The genus and species cut-off delimitation were ≥70% and ≥95% AAI similarity respectively. Thirty-three new genera and 87 species (of which 28 are new species) were circumscribed. From a total of ninety-nine genomes used in this study, 69 were previously classified to the species level, whereas the remaining 30 had incomplete taxonomic classification (i.e., only sp. or unclassified). In total, 13 genera (from a total of 33) and 38 species (from a total of 69) were taxonomically reclassified and/or re-named. Thus, we found that 71 of all analyzed genomes required reassignment at one or more ranks to reconcile existing taxonomic classifications with our new genomic taxonomy (Figure 2 and Figure S1).

 Figure 2. Heatmap displaying the AAI levels between cyanobacterial genomes. The intraspecies limit is assumed as ≥95%, whereas genera delimitation is assumed as ≥70% (dashed lines) AAI. Clustering the genomes by AAI similarity was done using a hierarchical clustering method in R (hclust), based on Manhattan distances. The AAI values are associated with the respective thermal color scale located at the bottom left corner of the figure. The proposed new genera and species names were adopted in this figure.

 

Over the next section, we highlight four specific cases to exemplify cyanobacterial taxonomic issues that were resolved through our genome-driven approach (see Figure S2). These cases illustrate how the use of genomic taxonomy in Cyanobacteria provides relevant information (Data Sheet 1, Formal description of new genera and species).

Case I. Oscillatoria group. Analysis of the five genomes of Oscillatoria distinguished four genera, based on the genomic signatures (i.e., GGD, AAI, 16S, and conserved marker genes tree): 
(i) Oscillatoria acuminata PCC 6304 type strain formed a separate group;
 (ii) Oscillatoria sp. PCC 10802 formed a separate divergent group, corresponding to a new genus named Somacatellium (S. hydroxylic PCC 10802T); 
(iii) Oscillatoria nigroviridis strain PCC 7112 (closest related with Microcoleus vaginatus FGP-2 type strain) belongs to the genus Microcoleus (M. nigroviridis PCC 7112T); and
 (iv) Oscillatoria strains PCC 6407 and PCC 6506 formed a new genus named Toxinema (T. oscillati PCC 6407T and T. oscillati PCC 6506).
 
Case II. Leptolyngbya group. The five Leptolyngbya strains were polyphyletic, forming different phylogenetic branches. 
Thus, (i) Leptolyngbya boryana PCC 6306T type strain forms a separate group with cyanobacterium JSC-12, while the rest of the Leptolyngbya strains cluster apart;
 (ii) strain PCC 7376 forms a new genus named Enugrolinea (E. bermudensis PCC 7376T); 
(iii) strain PCC 7375 forms a new genus named Adonisia (A. splendidus PCC 7375T); 
(iv) strain PCC 7104 forms a new genus named Allonema (A. longislandicus PCC 7104T); and 
(v) strain PCC 6406 forms a new genus named Euryforis (E. eilemai PCC 6406T).
 
Case III. Arthrospira group. Examination of the four Arthrospira strains indicated that 
i) A. platensis C1 should be considered a new species, named A. sesilensis (A. sesilensis C1T);
 (ii) strain PCC 8005 belongs to a new species, named A. nitrilium (A. nitrilium PCC 8005T); and 
(iii) the type strain of Arthrospira platensis (PCC 7345) formed a tight cluster along with NIES-39 and Paraca.
 
Case IV. Synechococcus group. The nine Synechococcus strains split in 
(i) S. elongatus PCC 6301T type strain forms a separate group with S. elongatus PCC 7942;
 (ii) strain PCC 6312 forms a new genus named Stenotopis (S. californii PCC 6312T);
 (iii) strain PCC 7335 belongs to a new genus named Coccusdissimilis (C. mexicanus PCC 7335T);
 (iv) strains JA23Ba213 and JA33Ab formed a new genus named Leptococcus (L. springii JA23Ba213T and L. yellowstonii JA33AbT)
(v) strain PCC 7336 formed a new genus named Eurycoccus (E. berkleyi PCC 7336T);
 (vi) strain PCC 7502 belonged to a new genus named Leptovivax (L. bogii PCC 7502T); and
 (vii) Synechococcus euryhalinus PCC 7002 represents a new genus named Enugrolinea (E. euryhalinus PCC 7002T).

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Members of the Low Temperature group were characterized by positive correlations with the concentration of nitrogen (N)  and phosphorus (P)  sources; weak positive correlations with minimum generation time, silicate and depth; and by negative correlations with temperature, microbial cell abundance, oxygen availability, and salinity (Figures 3, 4). 

 Meanwhile, members of the Low Temperature Copiotroph group were characterized by strong positive correlations with the concentration of nitrogen (N) and phosphorus(P); positive correlations (stronger than those presented by Low Temperature group) with minimum generation time, silicate and depth; and by negative correlations (also stronger than those presented by Low Temperature group) with temperature, microbial cell abundance (in particular with autotroph cell density), oxygen availability, and salinity (Figures 3, 4).

 Finally, members of High Temperature Oligotroph group were characterized by negative correlations with the concentration of nitrogen and phosphorus and positive correlations with temperature and autotroph cell abundance (Figures 3, 4).

As suggested by correlation analyses (Figures 4C,D), NMDS revealed the Low Temperature Copiotroph group to be more abundant in cold and eutrophic waters, while the High Temperature Oligotroph group exhibited the opposite pattern and was more abundant in warm and oligotrophic environments (Figures 4A,B). In turn, Low Temperature was more abundant at intermediate conditions between these polar opposites and was shown to be more abundant in samples with higher cell densities and NO2 concentrations.

We also investigated the abundance of the ecogenomic groups in freshwater environments. Unfortunately, there is no currently available large-scale dataset of freshwater metagenomes with associated metadata comparable to the Tara Oceans dataset. To define freshwater ecogenomic groups we chose to extrapolate the classification obtained from the analyses of the marine dataset. In freshwater metagenomes, the Low Temperature Copiotroph was the dominant group in all the analyzed samples (Figure S4A). NMDS of freshwater samples suggested that Low Temperature group displayed a preference for higher pH and DOC, nitrite and total nitrogen concentrations whereas the High Temperature Oligotroph group has a preference for habitats with higher concentrations of POC, phosphorus, ammonia and nitrate (Figures S4B,C).

Discussion

The use of HTS technologies and environmental surveys have allowed studies that link phylogenomics and ecogenomics of Cyanobacteria. High-throughput genome sequence technologies are causing a revolution in microbial diversity studies. Recent studies have obtained dozens of new metagenome-assembled genomes from complex environmental samples (Brown et al., 2015; Hugerth et al., 2015; Almstrand et al., 2016; Haroon et al., 2016; Pinto et al., 2016). The abundance of these genomes across different environments can now be inferred from metagenomics, including their metabolic and ecological potential. It is clear that a new system is required to allow for precise taxonomic identification of these new genomes.

WGS as the Basic Unit for Cyanobacteria Genomic Taxonomy (CGT)

Comparative genomic studies allow for identification of sequence groups with high genotypic similarity based on variation in protein coding genes distributed across the genomes. Analyses of environmental metagenomes and microbiomes have shown that microbial communities consist of genotypic clusters of closely related organisms (Farrant et al., 2016). These groups display cohesive environmental associations and dynamics that differentiate them from other groups co-existing in the same environment. In light of new concepts, restlessness is mounting with the inability to define the microbial species itself. Evolution studies on closely related bacteria show rapid and highly variable gene fluxes in evolving microbial genomes, suggesting that extensive gene loss and horizontal gene transfer leading to innovation are the dominant evolutionary processes (Batut et al., 2014; Puigbò et al., 2014). CGT will solve the often-observed issue that even closely related genomes contain high gene content variation, that gives phenotypic variation. CGT is completely adjusting to the genomics era, addressing the needs of its users in microbial ecology and clinical microbiology, in a new paradigm of open access (Beiko, 2015). CGT will provide a predictive operational framework for reliable automated and openly available identification and classification (Thompson et al., 2015).

Proposals for Cyanobacterial Taxonomy

A main gap exists and is growing each day between the formal taxonomy of Cyanobacteria and the forest of acronyms and numbers in the different databases. Indeed, the nameless operational taxonomic units (OTUs), strains, isolates and WGS sequences (Beiko, 2015; Kozlov et al., 2016) form the great majority of data in private and public databases. There is a need to re-examine the Cyanobacteria prokaryote species, taking into account all recently developed concepts, e.g., the gene flow unit, OTU, ESTU and Candidate taxonomic unit (CTU) in the context of a pragmatic genome-based taxonomic scheme. The type species or strain can be a culture, DNA or a WGS. The CGT system should maintain all of the existing information, integrating it with new data on DNA, genomes, isolates/strains, cultured and uncultured, “Candidatus” cases and reconstructed genomes from metagenomes (Brown et al., 2015; Hugerth et al., 2015). The international initiatives of GEBA are currently working on determining the WGS of all type strains of known microbial species to shorten this gap (more than eleven thousand genomes).

We strongly recommended that the modern taxonomy should be based on WGS. The enormous amount of unique gene sequences (e.g., 16S rRNA gene) databases should be always compared to the available genome-based phylogeny. Studies focusing on one specific taxa/group cannot be disregarded the phylogenetic analysis for the whole major taxa. It will avoid the inclusion of the previously erroneous taxa on the analysis. Furthermore, the anxiety to give a new name should be reconsidered. Proposes of new taxa where the phylogenetic relationship was not firmly established are frequently found (e.g., Rajaniemi et al., 2005).

Ecogenomics and the Delineation of the Ecological Niches of Cyanobacteria

Correlation analysis allowed us to characterize how the abundance of the analyzed genomes is associated with environmental parameters at both marine and freshwater habitats. These associations shed light on ecological interactions taking place within aquatic habitats that are responsible for delineating the ecological niches of Cyanobacteria. Our results showed that taxonomic affiliation and niche occupancy are coherently linked, i.e., closely related species of the same genus often shared correlation patterns, and consequently were assigned to the same ecogenomic group.

The identification of specific features responsible for defining niche occupancy among these organisms depends on extensive experimental data focusing on both physiological and morphological features, which is outside of our scope. Nevertheless, we speculate that some features are likely playing a role in this process:

(1) Transcriptional patterns: The way in which Cyanobacteria regulate gene expression in response to changing environmental conditions is likely to play a role in defining which habitats are better suitable for growth of different species.

(2) Nutrient uptake and utilization: Throughout the aquatic environment a myriad of gradients of nutrient abundance are formed (Stocker and Seymour, 2012). The cyanobacterial capacity for uptake and utilization of limiting nutrients (e.g., P, N and Fe) is associated with their ecological niches occupancy (Thompson et al., 2013a; Coutinho et al., 2016b; Farrant et al., 2016). Considering that significant associations were detected between the abundance of the analyzed genomes and the nutrients sources (phosphorus and nitrogen), we assume that the diversity and efficiency of their nutrient transporters plays a major role in defining the cyanobacterial affiliation to the proposed ecogenomic groups.

(3) Photosynthetic machinery and efficiency: Cyanobacteria are remarkably diverse when considering their photosynthetic physiology. Species differ with regard their preferred light intensities and wavelengths which affects their photosynthetic efficiency (Moore et al., 1998; Ting et al., 2002). They also can be differentiated regarding their carboxysomes, sub-cellular structures where carbon fixation takes place (Yeates et al., 2008). To our knowledge, no study has consistently compared the photosynthetic yields of all the strains analyzed here, therefore we cannot determine if the proposed ecogenomic groups differ regarding this parameter. Nevertheless, distinctions regarding their requirements for efficient photosynthesis are likely linked to their patterns of niche occupancy.

Ecogenomics, Global Changes, and Cyanobacterial Communities

Over the past two centuries, human development has affected aquatic ecosystems due to nutrient over-enrichment (eutrophication), hydrologic alterations, global warming and ocean acidification. Temperature is one of the most important factors determining the taxonomic composition of marine microbial communities (Sunagawa et al., 2015). Our data shows that temperature is central for regulating the composition and functioning of cyanobacterial communities. Global warming can affect growth rates and bloom potentials of many taxa within this phylum (Fu et al., 2007; Paerl and Huisman, 2008; Flombaum et al., 2013; Pittera et al., 2014). Niche based models predict an increase in the absolute levels of organisms formerly classified as Prochlorococcus and Synechococcus due to global warming (Flombaum et al., 2013). Consequently, the functioning of the biogeochemical cycles in which these organisms are involved will also be affected (Fu et al., 2007). Nevertheless, much less is known regarding how global warming could affect communities of Cyanobacteria aside from these two groups of organisms.

The ecogenomic groups identified and their associations with environmental parameters shed light into the potential changes that communities of Cyanobacteria will undergo following global climate changes. Our results indicate that an increase in temperature will lead to decreases in the relative abundances of Low Temperature and Low Temperature Copiotroph groups, while that of High Temperature Oligotroph group increases, especially those of species Eurycolium neptunis, E. ponticus, E. chisholmi, and E. nereus. One major impact of this alteration is a possible effect on the degree of nitrogen fixation mediated by Cyanobacteria, as none of the species assigned to the High Temperature Oligotroph group are known to fix nitrogen (Latysheva et al., 2012). In fact, our data shows that higher temperatures are associated with lower relative abundances of nitrogen fixating Cyanobacteria of the genera Trichodesmium and Anabaena (Zehr, 2011). Both beneficial and deleterious effects of the ocean warming and associated phenomena (e.g., acidification) on the rates of growth and N2 fixation have been reported (Hutchins et al., 2007; Shi et al., 2012; Fu et al., 2014), and recent laboratory and field experiments (Hong et al., 2017) showed that the acidification inhibit growth and N2 fixation in T. erythraeum IMS101T due a decrease in cytosolic pH resulting biochemical cost of proton pumping across membranes. Rising temperatures might shift cyanobacterial community composition toward a state were diazotrophs are relatively less abundant. Because nitrogen is often a limiting nutrient to marine primary productivity (Tyrrell, 1999; Moore et al., 2013), alterations in the oceanic levels of nitrogen fixation could affect not only non-diazotrophic Cyanobacteria but also heterotrophic microbes as well as the higher tropic levels that are sustained by microorganisms.

Furthermore, our findings suggest that changes in temperature can affect the contributions of Cyanobacteria to the global carbon pump (Flombaum et al., 2013; Biller et al., 2015). For example, the five strongest positive correlations with temperature between the High Temperature Oligotroph group involve the high-light adapted members of the Eurycolium genus (i.e., strains MIT9312T, MIT9301T, MIT9215, MIT9202T, and AS9601T). These are high-light adapted strains that display lower photosynthetic efficiency than their low-light adapted counterparts (Moore et al., 1998; Moore and Chisholm, 1999). Our results suggest that the relative abundance of high-light adapted strains would increase induced by the rising temperatures. In turn, these changes could affect the efficiency of carbon fixation in the ocean, a change that could also be influenced by the alterations in nitrogen fixation mentioned above.

Conclusions

The present study proposes a first attempt toward integrating taxonomy and ecogenomics, offering a compelling new perspective for the development of Cyanobacteria studies. Our results show that closely related genomes often share a niche and can be assigned to the same ecogenomic group. End-users of Cyanobacteria taxonomy may benefit from a more reproducible and portable taxonomic scheme. Future studies are needed to expand the evolutionary and physiological basis for the cyanobacterial niche occupancy, integrating other important ecological variables such as phage susceptibility, light utilization strategies, horizontal gene transfer, and inter-species interactions.

Author Contributions

All authors contributed to the writing of the manuscript. JW, FC, BD, JS, FT, and CT designed and planned the study. JW and FC performed the bioinformatics analyses, analyzed the results, and compiled the data. All authors approved the final version of the manuscript.

Funding

This work was supported by the National Counsel of Technological and Scientific Development (CNPq), Coordination for the Improvement of Higher Education Personnel (CAPES), and Rio de Janeiro Research Foundation (FAPERJ).

Sitaatti blogiini 30.6. 2023 

Syanobakteereista Tieteellistä tutkimusta ja innovatiivisia suunnitteluja niitten hyödynnyksestä energiataloudessa

 LÄHDE 1.  Yllättävä löytö  syanobakteerien asettamisesta alkuvirkoihinsa näin miljardien vuosien jälkeen ihmiskunnalle  edullisena resurssina. Fylogeneettistä  tietoa  ja LÄHDE 2.  käyttösuunnitteluja

LÄHDE 1.  ORIGINAL RESEARCH article
Front. Microbiol., 14 November 2017
Sec. Aquatic Microbiology
Volume 8 - 2017 | https://doi.org/10.3389/fmicb.2017.02132
This article is part of the Research Topic

Cyanobacterial Blooms in a Changing World

. 2019 Sep;35(5):e2835.
doi: 10.1002/btpr.2835. Epub 2019 May 22.
Cyanobacteria as an eco-friendly resource for biofuel production: A critical review
Affiliations

Abstract

The rapid increase in worldwide population coupled with the increasing demand for fossil fuels has led to an increased urgency to develop sustainable sources of energy and chemicals from renewable resources. Using microorganisms to produce high-value chemicals and next-generation biofuels is one sustainable option and is the focus of much current research. Cyanobacteria are ideal platform organisms for chemical and biofuel production because they can be genetically engineered to produce a broad range of products directly from CO2, H2O, and sunlight, and require minimal nutrient inputs. The purpose of this review is to provide an overview on advances that have been or could be made to improve strains of cyanobacteria for industrial purposes. First, the benefits of using cyanobacteria as a platform for chemical and biofuel production are discussed. Next, an overview of cyanobacterial strain improvements by genetic engineering is provided. Finally, mutagenesis techniques to improve the industrial potential of cyanobacteria are described. Along with providing an overview on various areas of research that are currently being investigated to improve the industrial potential of cyanobacteria, this review aims to elucidate potential targets for future research involving cyanobacteria as an industrial microorganism. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1357–1371, 2016

Alkeellinen levämuoto on CYANOPHYCEAE

 https://www.biologydiscussion.com/algae/cyanophyceae-characteristics-occurrence-and-classification/46739

 

Affinities of Cyanophyceae:

The members of Cyanophyceae show some relationship with both bacteria and Rhodo­phyceae.

Similarities of Cyanophyceae with Bacteria:

1. Cell structure is prokaryotic in both the group, having unorganised nucleus and devoid of membrane bound organelle.

2. The capsule of bacteria (if present) and mucilaginous sheath of blue green algal cells are made up of fine fibrils.

3. Cell wall composed of mucopeptide (murein).

4. Oscillatoria (blue green alga) shows simi­larity with Beggiatoa (sulphur bacterium), both in shape and movement.

5. Both are sensitive to antibiotics.

6. Both the groups show similarity in many metabolic processes like nitrogen and sulphur metabolism.

7. Absence of sexual reproduction.

8. Genetic recombination has been reported in Anacystis nidulans, a member of Cyano­phyceae, showing similarity with bacteria.

ADVERTISEMENTS:

Similarities of Cyanophyceae with Rhodophyceae (Red Algae):

1. Both the groups resemble in the absence of motile cells.

2. The Cyanophycean pigments, c-phycocyanin (blue) and c-phycoerythrin (red) are chemically similar to the Rhodophycean pigment r-phycocyanin and r-phycoerythrin.

3. Stignema and some other members of Cyanophyceae have pit connections, and show relationship by having similar struc­tures as found in the members of Rhodophyceae.

Economic Importance of Cyanophyceae:

The Cyanophycean members show both beneficial and harmful activities.

Beneficial Activities:

1. Nostoc commune is boiled and used as soup in China.

2. Few species of Nostoc, Anabaena, Scyto­nema form a thick substratum over the soil resulting a reclamation of land.

3. About twenty two (22) filamentous members of Cyanophyceae like Nostoc, Anabaena, Aulosira, Anabinopsis, Calothrix, Scytonema etc. can fix atmospheric nitrogen and form nitrogenous compounds. These compounds are further absorbed by the plant for their metabolic activity and increase yield.

All the above members have heterocyst. But certain non-heterocystous members like Plectonema boryanum are able to fix atmospheric nitrogen in anaerobic condition.

Harmful Activities:

1. Some members of Cyanophyceae cause damage of building plasters, stones etc. It can be avoided by spraying CuSO4 and sodium arcenate.

2. Some members like Microcystis, Anabaena, form water blooms and can grow well in O2 deficient water. Continuous respiration by submerged plants and animals during night time (when photosynthesis does not take place) causes the depletion of O2 to almost zero level. At that condition mortality of both animals and other submerged plants takes place due to suffocation.

3. Blue green algae contaminate the water of reservoirs. They develop a foul odour in water and make it unhygienic for human being and cause several diseases.

Different diseases like gastric troubles may appear by drinking the water contaminated with Microcystis and Anabaena.


Sinilevätilanne mainitaan jälleen lehdistössä 30.6.2023 "Sinilevä" on gram-negatiivisia syanobakteereja


Vår Föda lehti kirjoitti artikkelin sinilevistä eli syanobakteereista 1997. Tämä lehti kuului dietetiikan opintojen lähdekirjallisuuteen 1998-2001 aikaan kun luin dietetiikkaa.


Alkuosa artikkelia tässä suomennettuna.

Otsikko: Cyanobakterier - blågröna alger

LÄHDE: Ulla Beckman Sundh. Vår Föda 1/1997 (päivityksiä 990415 ja tammikuussa 2002 Toxikologiska enheten, Livsmedelsverket

Hakusana ”Cyanobakterer”, Vår Föda ”

Suomennosta keskiviikko, 27. kesäkuuta 2007 21:46


CYANOBAKTEERIT eli sinilevät

Sinilevät” ovat organismeja, joita löytää ennen kaikkea makeasta ja murtovedestä. Ne ovat yksisoluisia mikro-organismeja, joita esiintyy vapaana vesissä. Joissain lajeissa muodostuu soluista ketjuja tai kolonioita, mutta nekään eivät ole silminnähtäviä vaan mikroskooppisia, ellei nyt sitten leväkasvu ole massiivista. Toimintaa tarkastellen syanobakteerit ovat levien kaltaisia, mutta niistä puuttuu kuten bakteereistakin solutuma ja siinä suhteessa ne kuuluvat bakteereihin. Sentakia näitä nimitetäänkin nykyisin mieluiten syanobakteereiksi kuin siniviheriöiksi leviksi. Useimmat syanobakteerit ovat siniviheriöitä.


Cyanobakteerien väri Väriä niihin tulee niiden sisältämästä KLOROFYLLISTÄ ja fotosynteettisistä pigmenteistä, FYKOBILIINEISTÄ. Erään luokan FYKOBILIINEJÄ sanotaan FYKOSYANIINEIKSI. FYKOSYANIINI antaa yhdessä KLOROFYLLIN kanssa sinivihervää väriä, mikä on syanobakteereille tyypillinen. Mutta on myös sellaisia syanobakteereita, joiden sisältämä pigmentti on nimeltään FYKOERYTRIINI ja se on punainen fykobiliini. Nämä syanobakteerit saavat punaisen tai ruskean värin.


Fotosynteesi hyödyntää sekä hiilidioksidia (CO2) että typpeä (N2) ja vapauttaa (O2) happikaasua

Syanobakteerit fotosyntetisoivat eli ne pystyvät muuttamaan auringon energiaa kemialliseksi energiaksi ja siinä prosessissa ne vapauttavat happikaasua. CO2 eli hiilidioksidi on pääasiallisin hiilimolekyylin lähde näille fotosyntetisoiville syanobakteereille, mutta jotkut lajit pystyvät hyödyntämään myös orgaanista hiiltä. Eräät lajit ovat typpeä fiksaavia (Nitrogenum, N), mikä merkitsee sitä, että ne pystyvät yhteyttämään ( assimiloimaan) fotosynteesissä myös typpikaasua (N2).


Maapallon alkuhappi muodostui syanobakteerien avulla.

Nykyisten syanobakteerien kaltaisten mikro-organismien FOSSIILIT, joita kallioista löytyy, ovat 2.5 miljardia ( eli 2500 miljoonaa) vuotta vanhoja elioitä ja aikoinaan ne ovat olleet hallitsevin elämänmuoto maan historian ikiaikaisten jaksojen aikana. Uskottavinta on, että syanobakteerin tehtävänä on ollut kehkeyttää maapallon happiatmosfäärin alku noin 2000 miljoonaa vuotta siten (2 miljardia vuotta sitten).


Sinileväkukintaa

Sinikukintaa tapahtuu, kun syanobakteerien kasvuolosuhteet ovat niille suotuisat. Silloin voi nähdä liemimäistä kasvustoa ja ohikelluvia hajoavia levämassan riekaleina. Leväkukintaa suosii tyvenet tuulettomat melko lämpimät vedet ( 15- 30 Celsius-asteiset) ja hyvä fosforin (P), typen(N) tai ammoniakin (NH3) saatavuus. Yllämainitut elementit ovat elollisten organismien tärkeitä ravinteita. Niinpa lannoitteitten iso huuhtoutuminen järviin ja rannikoille edistää syanobakteerien kukintaa. Leväkasvun ei välttämättä tarvitse näkyä pintaan, mutta koska organismilla on tarvetta auringonvalosta, niin sitä havaitsee useinkin vesipintojen tienoossa varsinkin veden ollessa niin sameaa ja orgaanisia aineita sisältävää, ettei aurinko pääse tunkeutumaan säteineen syvempään. Syanobakteereilla on kellukkeena eräänlainen ilmakupla, jolla ne voivat nousta ja laskeutua asettuen optimaalisiin kasvuolosuhteisiin. Tuulet ja virtaukset voivat pakata syanobakteereita rantojen lähelle.


Ruotsin luonnonsuojeluvirasto tapaa raportoida järvien paikallisten fosforipitoisuuksien jakaumista ja antaa ennusteita mahdollisista olosuhteista levä- tai syanobakteerikasvuston ilmenemisiin. Raportisa on myös dokumentoiduista toksisten syanobbakteerien esiintymisistä. Vaikka toksisia leväkukintoja voi esiintyä ravintoköyhissäkin järvissä, niin suurin osa ongelmajärviä on E-Ruotsin tasanko- ja maanviljelysseutujen järvet.

Kts. Suomi: https://www.syke.fi/fi-FI/Ajankohtaista/Levatilannekatsaukset


Myrkylliset sinileväkukinnat

Moni syanobakteerilaji tuottaa toksiineja ja niitä taas on useita. Kauan on pidetty toksiineja syanobakteerien toissijaisina aineenvaihdunnallisina välituotteina, koska energiaa vaativat prosessit tapahtuvat useassa vaiheessa. Toksiinien merkitystä syanobakteereille on pohdittu, eikä sitä ole varmuudella määritelty, mutta arvellaan syanobakteerien saavan niistä etuja. Jo niitten synteesitie on komplisoitunut, joten senkin takia niitä ei varmaan vain sattumoisin kehity, vaan niistä on syanobakteerille jokin hyöty. Kasvikunnan puolelta tiedetään, että sekundäärimetaboliitit saattavat toimia kemiallisena kommunikaattorina tai kemiallisena taistelumuotona. Ne voivat toimia myös pakkassuojana tai ravintovarastona. Eräs mahdollisuus on sekin, että ne antavat kasville mahdollisuutta sopeutua päivänvaloon tai yöhön tai eri vuodenaikoihin. Ne voivat myös suojautua niitä syöviltä eläimiltä, itikoilta tai mikro-organismeilta.


Syanobakteeeritoksiinit

Näitä muodostuu syanobakteerisolun sisällä ja niitä joutuu ympäröivään veteen organismin kuollessa tai rikkoutuessa. Voi olla, että solu alkaa vuotaa toksiineja ikääntyessään tai muista syistä, mitkä muuntavat soluseinämän läpäisevyyttä. Luonnossa tällainen toksiini hajoaa muutamassa päivässä tai viikossa, mutta steriileissä olosuhteissa sitä voi varastoitua useita vuosia. Jos ihminen tai eläin saa kokonaisia syanobakteereja kehoonsa, toksiineja vapautuu ruoansulatuskanavassa.


Tunnettuja, toksiineja tuottavia syanobakteerilajeja, Cyanophyceae spp.

Leväkukinnoista noin puolet tuottavat toksiineja. Ei tiedetä tarkasti, mistä syystä toiset leväkukinnot tuottavat toksiineja ja toiset eivät. Jopa sama laji voi tietyssä järvessä tuottaa toksiineja, mutta toisessa järvessä ei. Leväkukinnon toksisuuskin voi vaihdella päivästä toiseen, jopa tunnista toisen. Ruotsissa todetut toksiset leväkukinnot ovat peräisin seuraavista lajeista:



ANABAENA ( (filamentous)

A.flos-aquae, A. farciminiformis,

APHANIZOMENON

Aph. flos-aquae, Aph. grazile

MICROCYSTIS ( non-filamentous type)

M. aeruginosa, M. wesenbergii, M. viridis

OSCILLATORIA (filamentous type)

O. agardhii , O. sancta.

Lisätietoa:

http://www.biologydiscussion.com/algae/cyanophyceae-characteristics-occurrence-and-classification/46739


Syanobakteerien toksiinityypeistä

  • LPS

Lipopolysakkariditoksiini (LPS) soluseinämistä lie syy mahasuolitaudin puhkeamisiin, kontaktiallergiaoireisiin ja muihin immunogeenisiin reaktioihin. Tällainen altistus voi tulla levävesissä uimisesta tai syanobaktereja sisältävän veden juomisesta. LPS esiintyy usein Gramnegatiivisten bakteereitten soluseinämän kalvossa ja näihin kuuluu myös syanobakteerit. LPS muodostaa kompleksin valkuaisaineitten ja fosfolipidien kanssa. Levien LPS antaa tosin yleensä heikomman reaktion kuin eräät gramnegatiiviset bakteerit kuten Salmonella. On niukkaa dokumentaatiota (tieto 20 vuoden takaa) syanobakteerien lipopolysakkarideista (LPS) ja tarvittaisiin lisätutkimuksia sen kemiallisesta rakenteesta, stabiliteetista ja vaikutuksista, jotta voidaan arvioida, minkä asteista vaikutusta syanobakteerien LPS aiheuttaa ihmisissä ja eläimissä.


  • Hermomyrkyt anatoksiinit ja saxitoksiinit

Anatoksiini on alkaloidi, sekundäärinen amini, jota muodostaa leväsuvut Anabaena, Oscillatoria, Aphanizomenon ja Cylindrospermum.

Homoanatoksiini on rakenteeltaan paljon edellisen kaltainen ja sitä muodostaa Oscillaria formosa.

Anatoksiini-a ja homoanatoksiini-a vaikuttavat solutasossa samaan tapaan kuin kehon oma asetyylikoliini (AK). (Asetyylikoliini mm vapautuu hermopäätteistä lihassoluihin, mistä lihas supistuu. Sitten asetyylikoliiniesteraasi (ACE) hajoittaa asetyylikoliinin (AK) ja lihas pääsee relaksoitumaan, kunnes uusi impulssi tulee- normaalisti). Anatoksiini-a ja anatoksiini-b vaikuttavat myös samalla tavalla kuin AK siten, että lihas supistuu, mutta entsyymi ACE ei pysty hajoittamaan niitä ja tämä seikka johtaa lihaksen ylistimuloitumiseen. Tuloksena on lihanykäyksiä, kunnes lihas uupuu ja lamaantuu. Jos tämä kohtaa hengityslihaksistoa, voi myrkytyksen saanut ihminen kuolla tukehtumisen.

Anatoksiini-a (S) on sellainen alkaloidi, joka on myös fosfaattiesteri ja sitä muodostaa Anabaena-suku. Myrkytyksessä aine antaa saman oireen kuin anatoksiini-a ja homoanatoksiini-a, mutta johtuen siitä, että se estää asetyylikoliiniesteraasia, jolloin kehon oma AK aines ei hajoa, vaan jää jäljelle aiheuttaen lihasten ylistimuloitumista.

Saxitoksiinit ovat alkaloideja, joita muodostavat suvut Anabaena ja Aphanizomenon. Saxitoksiini ja neosaxitoksiini blokeeraavat Na+kanavia hermoissa, mikä aiheuttaa, että hermoimpulssit eivät pääse kulkeutumaan edelleen. Kun mitään hermoimpulssia ei kulje, ei pääse asetyylikoliinia (AK) erittymään hermon toisesta päädystä, eikä lihassolut saa impulsseja supistumiseen, mikä johtaa velttohalvaukseen (paralyysiin) . Tällaisesta kuten muista hermotoksiineista voi myrkytyksen saanut henkilö tukehtua, jos hengitylihaksisto altistuu.


(Huom: Myös meren DINOFLAGELLAATAT voivat tuottaa SAXITOKSIINEJA ja aiheuttaa kotiloperäisiä myrkytyksiä, joita kutsutaan nimellä PSP paralyyttinen simpukka- ja kuoriaiseläinmyrkytys, paralytic shellfish poisoning ( marine toxins)


  • Maksamyrkylliset mikrokystiinit ja nodulariinit

Globaalisesti ottaen nämä ovat syanobakteerien tavallisimmat toksiinit. Maksatoksiinit ovat rinkulan muotoisia peptideitä . Mikrokystiinit käsittävät 7 aminohapon heptapeptidejä.

Nodulariinit ovat 5 aminohapon pentapeptidejä. Näihin peptideihin kuuluvat aminohappokokoomukset voivat vaihdella ja nykyään tunnetaan yli 50 mikrokystiiniä ja vajaat kymmenen nodulariinia. Käytetään kirjain merkintää rakenteeseen osallistuvista aminohapoista. Esim. mikrokystiini-LR sisältää erityisissä asemissaan leusiinia (L) ja arginiinia (R). Mikrokystiinejä muodostaa suvut Microcystis, Anabaena ja Oscillatoria.

Nodulariineja muodostaa Nodularia spumigena. Toksiineille on yhteistä, että niissä on erityinen aminohappo ADDA ( (2S, 3S, 8S, 9S)-3-Amino-9-methoxy-2,6,8-trimethyl-10-phenylDeca-4,6-Dienoic Acid). Mikrokystiinit ja nodulariinit ovat stabiileja ja hyvin vesiliukoisia.


Maksatoksiinien kuljetus kehossa tapahtuu samaan tapaan kuin sappisuolojen, mistä todennäköisesti johtuu, että vauriot ovat juuri maksassa. Toksiinien vesiliukoisuus vaikuttaa, että niiden on vaikea mennä solunseinän läpi, mutta kulkeutuessaan kalvon läpi ne käyttävät samaa systeemiä, mikä kuljettaa ravintoaineita ja solusubstanssejakin. Toksiinit kiinnittyvät vahvasti proteiinifosfataaseihin. Tälle toksisuudelle välttämätön alue molekyylissä on edellä mainittu Adda-alue. Toksiinit tuhoavat maksasolujen tukirunkoa rakentavia mikrofilamentteja tällä proteiinifosfataasi-interaktiollaan ja niin solut alkavat kutistua ja menettää toimintakykyään. Maksasolut voivat myös luhistua ja niin maksakapillaarit alkavat liukua erilleen ja verta pääsee tihkumaan maksakudokseen. Verenmenetystä voi tulla niin paljon, että kehittyy verenkierrollinen kollapsi ja henkilö voi menehtyä sisäisiin verenvuotoihin.


Maksamyrkyllisyys (hepatotoksisuus) ja mahdollinen tumorigeenisyys

On näyttöä siitäkin, että mikrokystiinit ovat akuutin toksisuuden lisäksi ajan mittaan tuumorinedistäjiä. Taustamekanismina on soluproteiinien lisääntynyt fosforyloituminen, koska proteiinifosfataasit 1A ja 2A estyvät mikrokystiinistä. Proteiinien fosforylaatioasteen muuttuminen voi vaikuttaa geeniteitse solukasvuun. Myös nodulariinit estävät proteiinifosfataasia. Ei tiedetä vielä, onka tällä ihmiselle merkitystä altistuksen pitkittyessä kuukausiin ja vuosiin.


Mahdollisia oireita altistuksesta

Järvien ja niiden ympäristän fauna vaikuttuu ja vapaa-ajantoiminta kärsii, jos alkaa toksinen leväkukinta. Varhaisimpia tietoja syanobakteerien aiheuttamasta karjankadosta on merkattu muistiin Australiasta 1878. Viime vuosina on julkaistu tietoja koirien ja karjaeläinten kuolemantapauksista, kun ne ovat juoneet toksiinipitoista vettä tai syöneet rantojen levämassaa. Eri puolilta maailmaa on tullut myös tietoja ihmisten ilm. syanobakteerien aiheuttamista sairastumisista.

Syanobakteerien  hermo- ja maksatoksiinit ovat potentiellisti tappavia, mutta koirien jo kuollessa toksiinilla saastuneesta vedestä ihmiset harvemmin saavat vaarallisia myrkytyksiä. Usein nimittäin toksiinipitoinen vesi sisältää haju- ja makuaineksia, jotka inhottavat ihmistä, mutta koiraa saattaa samainen haju viehättää. Syanobakteerialtistukseen joutuneet ihmiset saavat erilaisia oirekuvia. Suoraa todistettua yhteyttä altistuksen ja oireen kesken ei voi vetää, mutta useissa tapauksissa ovat viitteet hyvinkin vahvoja.

Seuraavia yhteyksiä on kuvattu: Uimisen jälkeen kutiavaa ihottumaa, heinänuhaa, ripulia, oksennuksia, kuumetta, mahasuolitautia, nivelvaivaa, lihaskipua, päänsärkyä, keuhkotulehduksia, silmätulehduksia.

Juomavedesta tullutta mahasuolitulehdusta, kuumetta, oksentelua, ripulia, mahakipuja, pahoinvointia, heikkouden tunnetta, maksatulehdusta.

Dialyysin yhteydessä kuumetta, oksennuksia, lihaskipuja, verenpaineenputoamista, maksatulehdusta, kuolemantapauksia.

On olemassa myös julkaistu oirekuvauksia, joissa on katsottu syanobakteerit syyksi.


Talteen asian akutisoituessa tänä kesänä: 2023, kesäkuun 30. päivä

Sinilevätilanne 2018 Suomessa

http://www.ymparisto.fi/levatilanne

Levätilanne

Itämeri pintalevä viikko 31
Sinilevälautat Suomen merialueilla viikolla 31. Kuvassa vaaleansinisillä alueilla levä on pääasiassa ylempään vesipatsaaseen sekoittuneena, keltaisilla alueilla on todennäköistä pintalevää. Punaiset alueet ovat pintalevälautta-alueita. © ESRI World Light Grey Base, SYKEn satelliittitulkintatuote 30.7. -1 .8.2018 (Iso kuva, png 3,55 MB)
 
Sinilevätilanne Suomen merialueilla helpottunut, järvillä edelleen keskimääräistä enemmän sinilevää

Sinilevien määrä Suomen merialueilla on vähentynyt viime viikosta. Sinilevää on silti edelleen etenkin Suomenlahdella sekoittuneena veteen sekä pintaesiintymänä Selkämeren eteläosassa ja Ahvenanmaan eteläpuolella. Järvillä runsaat sinilevähavainnot ovat vähentyneet viime viikosta, mutta sinilevätilanne on edelleen keskimääräistä huonompi. Uusia pintakukintoja voi vielä esiintyä, jos tyyni ja helteinen sää jatkuu. Jokainen voi lähettää sinilevähavaintoja Järvi-meriwiki-palveluun Havaintolähetti-sovelluksen avulla ja tallentaa sinne levähavaintoja. Kansalaishavainnot tukevat valtakunnallista levätilanteen arviointia. Myös havainnot siitä, että sinilevää ei ole, ovat tärkeitä. Lisätietoa: Valtakunnallinen leväkatsaus 2.8.2018

HUOM: Uimarantojen levätilanteesta tietoa kuntien verkkosivuilta.

Seuraa levätilannetta

Tiedotteita ja katsauksia

  • Sinilevätilanne Suomen merialueilla helpottunut, järvillä edelleen keskimääräistä enemmän sinilevää (valtakunnallinen leväkatsaus 2.8. SYKE)
  • Sinileväkukinnot vähentyneet Kaakkois-Suomessa (tiedote 2.8. Kaakkois-Suomen ELY-keskus)
  • Sinilevähavainnot Pirkanmaalla hieman laskussa  (tiedote 2.8. Pirkanmaan ELY-keskus)
    Tiedotteet 2018
    Sinilevähavainnot Pirkanmaalla hieman laskussa (Pirkanmaa)
    Pirkanmaan järvien sinilevähavainnot ovat vähentyneet hieman viime viikon tilanteesta. Runsaasti sinilevää havaittiin viikolla 31 Kangasalan Kirkkojärvessä, Nokian Tottijärvessä ja Pyhäjärvessä Vesilahden kirkonkylän rannassa. Vähän sinilevää on havaittu Pyhäjärvessä kuudella eri havaintopaikalla, Lempoisten uimalassa (Lempäälä), Mäyriänrannassa (Lempäälä), Halkoniemen uimarannassa (Nokia), Maatialanlahdella (Nokia), Lehtiniemessä (Nokia) ja Kranstolpan uimarannassa (Pirkkala). Vähäisiä sinilevähavaintoja on myös tehty Sastamalan HouhajärvessäJärviwikin leväbarometrin mukaan sinileviä on ajankohtaan nähden hieman normaalia enemmän.
    Lisäksi yleisöhavaintoja vähäisistä sinileväesiintymistä on viikon aikana kirjattu Järviwikiin Pyhäjärven Reipin uimarannasta, Pyhäjärven Turrin uimarannasta ja Lempäälän Mäyhäjärvestä.

    Rusehtavat suopursuruostesienilautat yleistyvät järvissä loppukesällä
    Heinä-elokuun vaihteesta lähtien järvien pinnoille kerääntyy usein rusehtavia keräymiä ja lauttoja, joiden aiheuttajana ovat kuusen suopursuruostesienen itiöt. Itiöt ovat ihmisille vaarattomia, mutta runsaasti itiöitä sisältävät lautat saattavat ärsyttää ihoa tai silmiä. Suopursuruostesientä esiintyy eniten idän ja pohjoisen järvissä sekä suopursun levinneisyysalueella.
    Itiölauttoja esiintyy eniten voimakkaiden sateiden jälkeen. Ne keräävät herkästi pinnalleen muita roskia ja saattavat varsinkin lämpimässä tuoksahtaa epämiellyttävälle. Lautat hajoavat yleensä muutaman päivän kuluessa.

    Järvien lämpötilat uimakelpoisia
    Tampereen uimarantojen lämpötila on pääosin 18-23 oC. Tampereen uimarantojen karttapohjaisesta sovelluksesta saa tietoa uimarantojen uimavesien lämpötiloista ja sinilevätilanteesta. Lämpötila- ja leväpalveluun pääsee sivulta www.tampere.fi/uimarannat.
    Lisätietoja:
    Pirkanmaan ELY-keskus:
    ylitarkastaja A.P.


  • Poikkeuksellisen kuuma heinäkuu vaikutti sinileväesiintymiin odotettua vähemmän (tiedote 2.8. Pohjois-Savon ELY-keskus)
  • Sinileväinen vesi on riski ihmisille ja eläimille (tiedote, 19.7., THL, Evira, Myrkytystietokeskus,SYKE)
  • Sirpa Lehtinen: Miksi sinilevät ovat uutinen vuodesta toiseen? (blogi 18.7. SYKE)
  • Itämeren ravinnekuorma edelleen liian suuri, myös ilmaston lämpeneminen lisää sinileväkukintojen riskiä (tiedote 17.7. SYKE)