Samples were collected monthly (June to October) from a Microcystis spp.-dominated bloom in Lake Tai, China in 2014 at several stations across the northern parts of the lake as described previously (Krausfeldt et al., 2017 (link); Stough et al., 2017 (link)). Biomass was collected by filtering between 25 and 180 mL (volume dependent on sample biomass density) of lake water through 0.2-μm nominal pore-size SterivexTM filters (EMD Millipore Corporation, Burlington, MA, United States) and immediately treated with ∼2 mL of RNAlater (Qiagen) for preservation. Samples were collected in a manner consistent with the separation of bacteria and phytoplankton from dissolved materials: free virus particles generally pass through these filters. Environmental parameters and nutrient data were collected with each sample and have been reported elsewhere (Tang et al., 2018 (link)). Total RNA was extracted, quality checked, ribosomally reduced, and sequenced on the IlluminaTM HiSeq platform at HudsonAlpha Institute Genomic Services Laboratory (Huntsville, AL, United States) as previously described (Krausfeldt et al., 2017 (link); Stough et al., 2017 (link); Tang et al., 2018 (link)). Raw sequence data was retrieved from the HudsonAlpha servers and primarily handled in the CLC Genomics Workbench v. 10.1.1 suite (QIAGEN, Hilden, Germany). Quality scoring was used to remove reads with a score <0.3. Trimmed reads from all 35 samples were combined into a single file and assembled using MEGAHIT to reduce redundancy that can be seen within different contig fragments from identical genes (Li et al., 2015 (link)).
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Microcystis
Microcystis
Microcystis is a genus of cyanobacteria known for its ability to produce harmful algal blooms and toxins.
These freshwater microorganisms can pose a significant threat to aquatic ecosystems and human health.
Researchers studying Microcystis face the challenge of identifying the most effective protocols and products from the vast array of scientific literature.
PubCompare.ai's AI-driven protocol comparison tool can optimize Microcystis research by enhancing reproducibility and accuracy, helping researchers easily locate and identify the best protocols and products.
This innovative platform leverages the power of artificial intelligence to assist researchers in taking their Microcystis studies to new heights and delivering more reliable, impactful findings.
These freshwater microorganisms can pose a significant threat to aquatic ecosystems and human health.
Researchers studying Microcystis face the challenge of identifying the most effective protocols and products from the vast array of scientific literature.
PubCompare.ai's AI-driven protocol comparison tool can optimize Microcystis research by enhancing reproducibility and accuracy, helping researchers easily locate and identify the best protocols and products.
This innovative platform leverages the power of artificial intelligence to assist researchers in taking their Microcystis studies to new heights and delivering more reliable, impactful findings.
Most cited protocols related to «Microcystis»
Bacteria
Biologic Preservation
Genes
Genome
Microcystis
Nutrients
Phytoplankton
Virion
Vision
Six grab samples were collected from Kranji Reservoir at 10-20 cm beneath a visible algal surface scum on 14 and 15 January 2010 at 1700, 2000, 0600, 0800, 1300 and 1500 hours followed by filtration onto a 0.22-μm Sterivex membrane and preservation in RNAlater (Invitrogen, Carlsbad, CA, USA; Wang, 2011 ). Environmental metadata for temperature, nutrient concentrations and dissolved microcystin toxin concentrations were measured for each sample. Detailed methods for sample processing and analysis are provided as an online supplement.
Briefly, RNA was extracted from filters and prepared for sequencing using a series of kits according to the manufacturers' protocols. The Trizol reagent (Invitrogen) was used for RNA extraction, followed by depletion of tRNA, rRNA and eukaryotic mRNA using Ambion kits MegaClear, MicrobeEnrich and MicrobeExpress, respectively, followed by treatment with mRNA-only (Epicentre, Madison, WI, USA). RNA was amplified using MessageAmpII (Ambion, Austin, TX, USA), followed by double-stranded cDNA synthesis (Superscript, Invitrogen) and multiplexing for sequencing in a single lane of an Illumina GAII using Illumina (San Diego, CA, USA) adaptors ligated to custom barcodes. Sequence data have been submitted to the GenBank databases under accession PRJNA238448. Quantitative PCR (qPCR) was used to quantify cDNA transcripts using published primers and conditions (Nonneman and Zimba, 2002 ; Furukawa et al., 2006 (link); Shao et al., 2009 (link)). Ribosomal RNA transcripts were identified by blastn (<1e-20, at >60% sequence identity and >50 bp alignment) to the SILVA database and classified in MG-RAST. Blastx against NR (bit-score>40) was used to taxonomically and functionally classify mRNA in MEGAN using the KEGG hierarchy. Expression of Microcystis genes was measured by recruiting reads to a M. aeruginosa pan-genome (>90 bp alignment; 90% identity) constructed from two publicly available genomes followed by calculation of RPKM (reads per kilobase per million mapped reds) in CLC Genomics Workbench to generate transcript profiles. Sample transcript profiles were log2 transformed, normalized and compared by Principal Component Analysis and Hierarchical Clustering in Multiexperiment Viewer (Saeed et al., 2006). Differentially expressed genes were identified through the Student's t-test and with a multiple comparison test Significance Analysis of Microarrays (SAM) implemented in Multiexperiment Viewer. The differential distribution of KEGG pathways in Microcystis among different samples and among the four most abundant bacterial phyla was determined using a non-parametric Gene Set Enrichment Analysis (GSEA). Evidence for community polyketide and non-ribosomal peptide synthase (PKS and NRPS, respectively) expression was obtained by identifying ketosynthase (KS) and condensation (C) domains with the online tool NaPDoS (Ziemert et al., 2012 (link)).
Briefly, RNA was extracted from filters and prepared for sequencing using a series of kits according to the manufacturers' protocols. The Trizol reagent (Invitrogen) was used for RNA extraction, followed by depletion of tRNA, rRNA and eukaryotic mRNA using Ambion kits MegaClear, MicrobeEnrich and MicrobeExpress, respectively, followed by treatment with mRNA-only (Epicentre, Madison, WI, USA). RNA was amplified using MessageAmpII (Ambion, Austin, TX, USA), followed by double-stranded cDNA synthesis (Superscript, Invitrogen) and multiplexing for sequencing in a single lane of an Illumina GAII using Illumina (San Diego, CA, USA) adaptors ligated to custom barcodes. Sequence data have been submitted to the GenBank databases under accession PRJNA238448. Quantitative PCR (qPCR) was used to quantify cDNA transcripts using published primers and conditions (Nonneman and Zimba, 2002 ; Furukawa et al., 2006 (link); Shao et al., 2009 (link)). Ribosomal RNA transcripts were identified by blastn (<1e-20, at >60% sequence identity and >50 bp alignment) to the SILVA database and classified in MG-RAST. Blastx against NR (bit-score>40) was used to taxonomically and functionally classify mRNA in MEGAN using the KEGG hierarchy. Expression of Microcystis genes was measured by recruiting reads to a M. aeruginosa pan-genome (>90 bp alignment; 90% identity) constructed from two publicly available genomes followed by calculation of RPKM (reads per kilobase per million mapped reds) in CLC Genomics Workbench to generate transcript profiles. Sample transcript profiles were log2 transformed, normalized and compared by Principal Component Analysis and Hierarchical Clustering in Multiexperiment Viewer (Saeed et al., 2006). Differentially expressed genes were identified through the Student's t-test and with a multiple comparison test Significance Analysis of Microarrays (SAM) implemented in Multiexperiment Viewer. The differential distribution of KEGG pathways in Microcystis among different samples and among the four most abundant bacterial phyla was determined using a non-parametric Gene Set Enrichment Analysis (GSEA). Evidence for community polyketide and non-ribosomal peptide synthase (PKS and NRPS, respectively) expression was obtained by identifying ketosynthase (KS) and condensation (C) domains with the online tool NaPDoS (Ziemert et al., 2012 (link)).
Anabolism
austin
Bacteria
Biologic Preservation
Dietary Supplements
DNA, Complementary
Eukaryota
Filtration
Gene Expression
Genes
Genome
Microarray Analysis
microcystin
Microcystis
non-ribosomal peptide synthase
Nutrients
Oligonucleotide Primers
Polyketides
Radioallergosorbent Test
Ribosomal RNA
RNA, Messenger
Tissue, Membrane
Toxins, Biological
Transfer RNA
trizol
The sequences of the ten M. aeruginosa genomes were obtained by combining several approaches. First, a single read library was constructed for each of the ten M. aeruginosa strains and sequenced with the GSflex version (250 nt length), until 16 to 25-fold coverage was obtained. Then, 3 to 8-fold coverage 454 Titanium reads (average 450 nt length), obtained from mate-paired libraries with 2–3 kb or 6–8 kb insert sizes, were added. In agreement with other de novo sequencing projects, the percentage of 454 reads used for assembly was approximately 97% and the percentage of Illumina reads used for error correction around 93%. The assembly was performed using Newbler Software (v2.3, Roche). All sequences, which were not included in scaffolds after the assembly process and which displayed a ≥500 bp length, were considered to be scaffolds (see Table S1 ). In order to improve the quality of the sequences, Illumina reads (51 nt length) were mapped onto all the scaffolds, using SOAP (http://soap.genomics.org.cn ) as described by Aury et al.[21] (link).
Both coding sequence prediction and automatic annotation were performed by using the Microscope platform, a web-based framework for the systematic and efficient revision of microbial genome annotation (http://www.genoscope.cns.fr/agc/microscope ) [22] . Expert validations were carried out for specific genes. This platform was also used for the comparative genome analyses performed on the twelve Microcystis genomes and on those of other cyanobacterial species.
The overall characteristics of the ten M. aeruginosa genomes are reported inTable S1 . Despite high genome coverage values, from 27 to 121 scaffolds were obtained per strain at the end of the assembly. The number of scaffolds was positively correlated to the size of the genomes (Spearman test, P<0.05).
Both coding sequence prediction and automatic annotation were performed by using the Microscope platform, a web-based framework for the systematic and efficient revision of microbial genome annotation (
The overall characteristics of the ten M. aeruginosa genomes are reported in
Comparative Genomic Hybridization
Cyanobacteria
DNA Library
Genes
Genome
Genome, Microbial
Microcystis
Microscopy
Open Reading Frames
Strains
Titanium
Thirty-three species of cyanobacteria, including Prochlorococcus, Synechococcus, Synechocystis, Gloeobacter, Cyanothece, Microcystis, Trichodesmium, Acaryochloris, Anabaena and Nostoc were used in this analysis. Since sequences of 36 species had not been fully released, they were not considered in our comparisons. All of the 33 genome sequences (as of Nov. 2008) were accessed from IMG in FASTA format [41 ].
In order to identify genes that may encode metacaspases, proven metacaspases from marine diatom Thalassiosira pseudonana (Protein id: 270038, 2505, 268857, 270007, 38187 in Thalassiosira pseudonana "finished chromosomes" database v3.0 [11 (link),42 ]) were used to construct a query protein set. BLASTp (protein-protein BLAST) [22 (link),43 (link),44 (link)] was conducted locally to search all proteins from each of the 33 cyanobacteria. Proteins found by this method that fit the criteria for a genuine metacaspase were added to the query set for another round of BLASTp searches. A threshold e-value of 1e-10 was set in the first two rounds, which changed into 2e-20 subsequently. The procedure was continued until no new proteins were found.
Proteins identified by BLASTp were aligned using Clustal X (Version 1.83) program [45 (link)] with a gap opening penalty of 10, a gap extension penalty of 0.2, and Gonnet as the weight matrix. The alignment was examined by inspection of peptidase C14, caspase catalytic subunit P20 domain (COG 4249, KOG1546 in the NCBI Conserved Domain Database [23 (link),24 ]). A protein was accepted as a metacaspase if it was possible to recognize P20 domain and if the most conserved His and Cys residues known to participate in the function of metacaspases [18 (link)] were present. However, minor alterations of the conserved His and Cys residues were tolerated. Specifically, putative MCA genes encoding Tyr in place of His and Ser/Asn/Gln/Gly instead of Cys were taken into account as well. Structure analyses of the obtained metacaspases were performed using the SMART (S imple M odular A rchitecture R esearch T ool) [25 ,26 (link)] and the CDD (C onserved D omains D atabase) [23 (link),24 ], relying on hidden Markov models and Reverse Position-Specific BLAST separately. Sequences of the P20 domain (about 300 aa in length) used for phylogenetic tree construction were obtained from the SMART database [25 ,26 (link)]. Trees based on metacaspase P20 domain and cyanobacterial 16s rRNA were constructed using NJ methods of the MEGA package (Version 4.0) [46 (link)], and the reliability of each branch was tested by 1000 bootstrap replications. In phylogenetic analysis of MCA, putative metacaspase of Gamma proteobacterium and human caspase-3 were used as outgroups to root the tree.
In order to identify genes that may encode metacaspases, proven metacaspases from marine diatom Thalassiosira pseudonana (Protein id: 270038, 2505, 268857, 270007, 38187 in Thalassiosira pseudonana "finished chromosomes" database v3.0 [11 (link),42 ]) were used to construct a query protein set. BLASTp (protein-protein BLAST) [22 (link),43 (link),44 (link)] was conducted locally to search all proteins from each of the 33 cyanobacteria. Proteins found by this method that fit the criteria for a genuine metacaspase were added to the query set for another round of BLASTp searches. A threshold e-value of 1e-10 was set in the first two rounds, which changed into 2e-20 subsequently. The procedure was continued until no new proteins were found.
Proteins identified by BLASTp were aligned using Clustal X (Version 1.83) program [45 (link)] with a gap opening penalty of 10, a gap extension penalty of 0.2, and Gonnet as the weight matrix. The alignment was examined by inspection of peptidase C14, caspase catalytic subunit P20 domain (COG 4249, KOG1546 in the NCBI Conserved Domain Database [23 (link),24 ]). A protein was accepted as a metacaspase if it was possible to recognize P20 domain and if the most conserved His and Cys residues known to participate in the function of metacaspases [18 (link)] were present. However, minor alterations of the conserved His and Cys residues were tolerated. Specifically, putative MCA genes encoding Tyr in place of His and Ser/Asn/Gln/Gly instead of Cys were taken into account as well. Structure analyses of the obtained metacaspases were performed using the SMART (
Anabaena
CASP3 protein, human
Caspase
Catalytic Domain
Chromosomes
Cyanobacteria
Cyanothece
cysteinylglycine
Diatoms
DNA Replication
Gamma Rays
Genes
Genome
glutaminyl-glycine
Homo sapiens
Marines
Microcystis
Nostoc
Peptide Hydrolases
Plant Roots
Prochlorococcus
Proteins
Protein Subunits
RNA, Ribosomal, 16S
Staphylococcal Protein A
Synechococcus
Synechocystis
Trees
Trichodesmium
Protocol full text hidden due to copyright restrictions
Open the protocol to access the free full text link
Aphanizomenon
Cells
Chlorophyll
chlorophyll a'
Cyanobacteria
Discrimination, Psychology
Eukaryota
Hypersensitivity
Microcystis
Phycocyanin
Phytoplankton
Pigmentation
Satellite Cell, Muscle
SKAP2 protein, human
Vision
Wind
Most recents protocols related to «Microcystis»
Samples were collected in sterile one-liter Nalgene bottles in the summer algal bloom of 2018 from surface water from Cibolo Creek (CC) and Leon Creek (LC) in San Antonio, Texas. Both samples were collected and characterized in terms of water quality parameters and filtered with 20 µm filters. As previously reported, the 20 µm mesh net retains most of the Microcystis colonies, which contain also the Microcystis-attached heterotrophic bacteria, whereas the <20 µm size fraction mainly contains free-living bacteria [38 (link),39 (link)].
Two experimental designs were established in the laboratory setting: First, the filters were transferred to glass tubes with BG11 media (described inSection 2.2 ) to isolate the algal material and grown in the laboratory until they reached the same conditions of the algal sample already present in the lab, Microcystis aeruginosa PCC7806. Second, the filtrate was also transferred to glass tubes, as it was interesting to compare the algal bloom behavior of heterotrophic bacteria with the PCC7806 sample in the laboratory. In both systems, 23 mL of BG11 media was used.
Once the cultured environmental algal samples reached the same optical density (OD) values as M. aeruginosa PCC7806, the environmental algal samples were mixed in a 1:1 ratio with PCC7806 in BG11 media. In total, there were three glass tubes. One tube contained the PCC7806 by itself (2 mL of the strain in BG11 media). Another tube contained a mixture of 1 mL of PCC7806 with 1 mL of algal sample from CC, and the last tube contained a mixture of 1 mL of PCC7806 with 1 mL of algal sample from LC.
For the mixture of heterotrophic bacteria, algal samples were added in a 1:1 ratio and set up in duplicate (untreated and treated samples). The bacterial growth monitoring was conducted by measuring OD in a BioTek EPOCH microplate spectrophotometer at 600 nm. The room temperature was . After the end of the experiment, samples were characterized in terms of water quality parameters following the standard methods for alkalinity, hardness, nitrate, nitrite, chlorine, phosphorous, and chemical oxygen demand (COD), as well as microcystin content, using a Beacon Microcystin Plate Kit. Samples were taken directly to the laboratory, and water quality and growth experiments were performed immediately to minimize variability.
Two experimental designs were established in the laboratory setting: First, the filters were transferred to glass tubes with BG11 media (described in
Once the cultured environmental algal samples reached the same optical density (OD) values as M. aeruginosa PCC7806, the environmental algal samples were mixed in a 1:1 ratio with PCC7806 in BG11 media. In total, there were three glass tubes. One tube contained the PCC7806 by itself (2 mL of the strain in BG11 media). Another tube contained a mixture of 1 mL of PCC7806 with 1 mL of algal sample from CC, and the last tube contained a mixture of 1 mL of PCC7806 with 1 mL of algal sample from LC.
For the mixture of heterotrophic bacteria, algal samples were added in a 1:1 ratio and set up in duplicate (untreated and treated samples). The bacterial growth monitoring was conducted by measuring OD in a BioTek EPOCH microplate spectrophotometer at 600 nm. The room temperature was . After the end of the experiment, samples were characterized in terms of water quality parameters following the standard methods for alkalinity, hardness, nitrate, nitrite, chlorine, phosphorous, and chemical oxygen demand (COD), as well as microcystin content, using a Beacon Microcystin Plate Kit. Samples were taken directly to the laboratory, and water quality and growth experiments were performed immediately to minimize variability.
Algal Bloom
Alkalies
Bacteria
Chemical Oxygen Demand
Chlorine
EPOCH protocol
Heterotrophy
microcystin
Microcystis
Microcystis aeruginosa
ML 23
Nitrates
Nitrites
Phosphorus
Sterility, Reproductive
Strains
Vision
The bacterial strain Microcystin aeruginosa PC7806 isolated from Lake Erie was used as a standard for all experiments. The algae were grown in BG11 media, as mentioned in Section 2.1 , which consisted of 1.5 g of sodium nitrate, 0.04 g dipotassium phosphate, 0.075 g magnesium sulfate heptahydrate, 0.036 g calcium chloride dihydrate, 0.006 g citric acid, 0.006 g ammonium iron (III) citrate, 0.001 g EDTA (disodium salt), 0.02 g sodium carbonate, 1.0 mL Trace Metal mix, and distilled water to a volume of 1 L. The method is based on the ATCC Medium 616 for Blue Green Algae. The Trace Metal Mix (1 L) was prepared, which consisted of 2.86 g of boric acid, 1.81 g manganese chloride tetrahydrate, 0.222 g zinc sulfate heptahydrate, 0.39 g sodium molybdate dihydrate, 0.079 g copper sulfate pentahydrate, and 0.0494 g cobalt (II) nitrate hexahydrate. The pH was adjusted to 7.1 with 1 M sodium hydroxide.
The preparation of the media and inoculation of algal samples consisted of inoculating 2 mL of M. aeruginosa PCC7806 in the stationary phase (optical density OD > 2) to a volume of 23 mL of BG11 media. Microcystis growth was monitored on a regular basis by the measurement of absorbance at 600 nm. Light exposure was kept constant for 12 h for 31 days.
Light was provided by a heat lamp with a 60 W bulb. All samples were grown on a VWR advanced digital shaker, which was set to 100 rpm. The incubation period for the samples was a total of 31 days with no addition of carbon source of media other than at the beginning of the experiment. Samples from the beginning and end of experiment were analyzed in terms of pH, conductivity, and phosphate levels according to standard methods. Samples of 2 mL were collected on a regular basis. Samples were taken on days 4, 5, 11, 15, 20, 26, 30, and 31. These samples were used for RNA extraction, reverse transcription, and q-PCR targeting the genes 16S rRNA (Eubacteria), mcyB, and luxS. A sample of 200 µL was also taken for microcystin test using ELISA kit for Microcystin.
The preparation of the media and inoculation of algal samples consisted of inoculating 2 mL of M. aeruginosa PCC7806 in the stationary phase (optical density OD > 2) to a volume of 23 mL of BG11 media. Microcystis growth was monitored on a regular basis by the measurement of absorbance at 600 nm. Light exposure was kept constant for 12 h for 31 days.
Light was provided by a heat lamp with a 60 W bulb. All samples were grown on a VWR advanced digital shaker, which was set to 100 rpm. The incubation period for the samples was a total of 31 days with no addition of carbon source of media other than at the beginning of the experiment. Samples from the beginning and end of experiment were analyzed in terms of pH, conductivity, and phosphate levels according to standard methods. Samples of 2 mL were collected on a regular basis. Samples were taken on days 4, 5, 11, 15, 20, 26, 30, and 31. These samples were used for RNA extraction, reverse transcription, and q-PCR targeting the genes 16S rRNA (Eubacteria), mcyB, and luxS. A sample of 200 µL was also taken for microcystin test using ELISA kit for Microcystin.
Ammonium
Bacteria
boric acid
Calcium Chloride Dihydrate
Carbon
Citrates
Citric Acid
Cobalt
Cyanobacteria
Edetic Acid
Electric Conductivity
Enzyme-Linked Immunosorbent Assay
Eubacterium
Fingers
Genes
Heptahydrate Magnesium Sulfate
Iron
Light
manganese chloride, tetrahydrate
Metals
microcystin
Microcystis
ML 23
Nitrates
Phosphates
Plant Bulb
potassium phosphate, dibasic
Reverse Transcription
RNA, Ribosomal, 16S
sodium carbonate
Sodium Chloride
Sodium Hydroxide
sodium molybdate(VI)
sodium nitrate
Strains
Sulfate, Copper
Vaccination
Vision
Zinc Sulfate, Heptahydrate
The 20 species of cyanobacteria studied were provided by Profª Dr. Célia Leite Sant'anna, from the Institute of Environmental Research, Secretary of the Environment, São Paulo, Brazil. All strains (Table 2 ) are identified as CCIBt (Culture Collection of the Botanic Institute) and their respective code. The proposed cyanobacteria strains were all isolated from Brazilian environments and chosen because of the lack of information on their physiology. Thus, we included species from different habitats, such as Leptolyngbya sp. isolated from the Pantanal (MS, Brazil), a tropical marsh area, and Gloeothece sp. from the soil of the Atlantic Jungle (Brazil). In addition, here we include species described for the first time, such as the genus Inacoccus described by Gama, J. Rigonato, M. F. Fiore & C. L. Sant’Anna [47 (link)], and species with extensive characterization in the literature, such as those of the genus Nostoc.
2 ). The culture media were sterilized in an autoclave (20 min, 121 °C, 1 bar; AV Phoenix Luferco, Brazil) and, for all cultures the initial inoculum was obtained from exponentially growing cells with ~ 0.05 µg mL−1 of chlorophyll a in vivo determined in a fluorometer (Turner Designs, Trilogy, USA). The total duration of the cultures was 6 days (144 h).
The cyanobacteria were grown under controlled temperature (24 ± 1 °C), pH, light intensity, and CO2 concentration. Cultivations (2L) were carried out in a tubular borosilicate glass photobioreactor (CPBR) with 3L capacity. Illumination was performed from inside the CPBR by light emitting diodes (LED). The photosynthetic active radiation (PAR) was determined daily in the CPBR and kept constant throughout the cultivation; the photoperiod was 12 h light:12 h dark. Each species was cultivated at its respective saturating irradiance (Table2 ), previously determined from rapid light curves using a PhytoPAM II equipment (Heinz Walz, Germany). The cultivation was mixed by air bubbling using commercial pumps (Big Air, AZ30) with porous stones at the end of the pipe. The initial pH of the medium was adjusted to 7.4. After the start of cultivation, the pH was kept adjusted by the automatic insertion of CO2 (25% v/v) in a mixture with argon (75% v/v). The CO2 insertion system was activated when the culture pH reached 8.4 and turned off at pH 7.8.
Species of cyanobacteria investigated in the prospection
| Species | Strain code | Growing medium | Ik |
|---|---|---|---|
| Gloeothece sp. (C.Nägeli) | CCIBt3513 | ASM-1 | 57 (4.26) |
| Aphanizamenon sp. (Morren ex Bornet & Flahault) | CCIBt3148 | ASM-1 | 293 (5.34) |
| Geitlerinema unigranulatum (J.Komárek & M.T.P.Azevedo) | CCIBt3231 | BG11 | 148 (26.62) |
| Nostoc sp. (Vaucher ex Bornet & Flahault) | CCIBt3248 | BG11 | 145 (2.46) |
| Inacoccus carmineus (W.A.Gama, J.Rigonata, M.F.Fiore & C.L.Sant'Anna) | CCIBt3411 | ASM-1 | 196 (9.12) |
| Não identificado | CCIBt3540 | ASM-1 | 79 (3.05) |
| Não identificado | CCIBt3157 | BG11 | 241 (4.32) |
| Aphanocapsa holsatica (G.Cronberg & Komárek) | CCIBt3053 | BG11 | 247 (4.30) |
| Microcystis novacekii (Komárek Compère) | CCIBt3189 | ASM-1 | 399 (5.48) |
| Trichormus variabilis (Komárek & Anagnostidis) | CCIBt3122 | BG11 | 227 (2.77) |
| Nostoc sp. (Vaucher ex Bornet & Flahault) | CCIBt3206 | BG11 | 146 (0.55) |
| Synechococcus sp. (Nägeli) | CCIBt3050 | BG11 | 238 (10.41) |
| Microcystis sp. (Kützing) | CCIBt3078 | ASM-1 | 283 (2.56) |
| Geitlerinema sp. (Anagnostidis) | CCIBt3241 | BG11 | 259 (1.80) |
| Sphaerocavum brasiliense (De Azevedo & C.L.Sant' Anna) | CCIBt3179 | BG11 | 340 (4.63) |
| Phormidium sp. (Kützing ex Gomont) | CCIBt3280 | BG11 | 242 (15.47) |
| Microcystis aeruginosa (Kützing) | CCIBt3174 | BG11 | 135 (1.72) |
| Rhabdoderma sp. (Schmidle & Lauterborn) | CCIBt3165 | BG11 | 275 (0.99) |
| Nostoc sp. (Vaucher ex Bornet & Flahault) | CCIBt3249 | BG11 | 187 (4.91) |
| Leptolyngbya sp. (Anagnostidis & Komárek) | CCIBt1046 | BG11 | 117 (16.21) |
Each species has its own identification and respective saturating irradiance values (Ik). Numbers in parentheses correspond to the standard deviation of the mean (n = 3)
The cyanobacteria were grown under controlled temperature (24 ± 1 °C), pH, light intensity, and CO2 concentration. Cultivations (2L) were carried out in a tubular borosilicate glass photobioreactor (CPBR) with 3L capacity. Illumination was performed from inside the CPBR by light emitting diodes (LED). The photosynthetic active radiation (PAR) was determined daily in the CPBR and kept constant throughout the cultivation; the photoperiod was 12 h light:12 h dark. Each species was cultivated at its respective saturating irradiance (Table
Anabaena variabilis
Argon
Calculi
Cells
Chlorophyll A
Cyanobacteria
Geitlerinema unigranulatum
Light
Lighting
Marshes
Microcystis
Microcystis aeruginosa
Microcystis novacekii
Nostoc
Phormidium
Photobioreactors
Photosynthesis
physiology
Plants
Radiation
Sphaerocavum brasiliense
Strains
Synechococcus
Microcystis and Cyanobacteria targets were identified and measured using quantitative polymerase chain reaction (qPCR). Power SYBR PCR Kit (Thermo Fisher), was used to quantify these targets on a QuantStudio 5 and 6 System (Thermo Fisher Scientific, Waltham, WA). PCR reactions were set up by adding 2 μL of DNA to the following master mix: 10 μL of 2× SYBR Green, 5 μL of nuclease-free H2O, 2 µL of 1 ng/μL Bovine Serum Albumin fraction V, 0.5 μL of a 10 μM forward primer, and 0.5 μL of a 10 μM reverse primer. The primers used for Cyanobacteria 16S rRNA were CYAN108F and CYAN377R72 (link) while the primers used for 16s rRNA in Microcystis were MIC209F and MIC409R73 (link). The following thermal programs were applied following default conditions: 95 °C for 10 min, followed by 40 cycles of 95 °C for 15 s. The annealing temperature varied between the two targets, cyanobacteria 16s rRNA primers were annealed at 56 °C for 30 s followed by an extension step at 72 °C for 30 s and Microcystis specific primers were annealed at 64 °C for 1 min. A melting curve stage analysis was done for 15 s at 95 °C with dissociation from 60 to 95 °C. Each sample was run as a series containing one undiluted sample, followed by a tenfold dilution to check for inhibition. A no-template negative control lacking spiked DNA was also included. For each target, tenfold dilutions series were generated from corresponding plasmids of varying concentrations. Undiluted plasmid concentrations for Cyanobacteria were 4.78 × 107 ng/μL while Microcystis was 6.42 × 107 ng/μL respectively. See Table S5 for primer details. Final gene copy numbers per sample were adjusted for initial raw filtration volume of collected sample.
Cyanobacteria
Filtration
Microcystis
Oligonucleotide Primers
Plasmids
Psychological Inhibition
RNA, Ribosomal, 16S
Serum Albumin, Bovine
Specimen Collection
SYBR Green I
Technique, Dilution
Analysis of the final dataset was performed in R v3.5.374 using the packages phyloseq75 (link), vegan76 , and ggplot277 . Various alpha diversity estimates (Chao1, Shannon Index, Simpson Index, InvSimpson Index) and between-Sample distances (Bray–Curtis) were computed. Distance matrices were then used to cluster samples using non-metric multidimensional scaling (NMDS). Kruskal–Wallis and post-hoc Pairwise Wilcoxon Rank Sum Tests were used to test for differences in alpha diversity. PERMANOVAs using the adonis function on the Bray–Curtis distance matrix with 999 permutations were used to test for statistically significant differences in microbiota composition and diversity between sample groups. Prior to these analyses, data were also checked for overdispersion using the betadisper function in the Vegan package76 . ANOVAs were used to test for the impact of environmental variables on Cyanobacteria in our CCA plot. To both (i) compare environmental variables to fluctuations in potential cyanotoxin producing species and (ii) understand the relationship between metagenomics and qPCR-derived quantification of cyanobacteria and Microcystis species, we calculated correlation coefficients for the various datasets. When multiple comparisons were made, p values were adjusted for multiple testing using the Benjamini and Hochberg false discovery rate controlling procedure. Where appropriate, data were log transformed and for all tests assumptions of normality and homoscedasticity were validated visually (with Q–Q plots) and statistically (using Levene’s test for equality of variance) to determine appropriate tests.
Adonis
Cyanobacteria
Impacts, Environmental
Microbial Community
Microcystis
neuro-oncological ventral antigen 2, human
Vegan
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More about "Microcystis"
Microcystis is a genus of cyanobacteria, also known as blue-green algae, that are renowned for their ability to produce harmful algal blooms and potent toxins.
These freshwater microorganisms can pose a significant threat to aquatic ecosystems and human health, making them a crucial focus for researchers and environmental scientists.
Studying Microcystis presents various challenges, as researchers must navigate through a vast array of scientific literature to identify the most effective protocols and products.
PubCompare.ai, an innovative AI-driven platform, aims to optimize Microcystis research by enhancing reproducibility and accuracy.
This tool leverages the power of artificial intelligence to assist researchers in easily locating and identifying the best protocols and products, whether they are published in peer-reviewed journals, preprints, or patents.
The Concentrator Plus system can be utilized to concentrate and purify samples containing Microcystis cells or toxins, while the Prominence system and SPSS v20 software can be employed for data analysis.
The Luna C18 column is a common choice for separating and analyzing Microcystis-related compounds using high-performance liquid chromatography (HPLC) techniques.
Additionally, flow cytometry, such as the CytoFLEX system, can be employed to quantify and characterize Microcystis populations.
Trifluoroacetic acid (TFA) is a commonly used solvent in the analysis of Microcystis toxins, and Digoxin has been studied for its potential to inhibit the toxic effects of Microcystis.
The compound S-4800 has also been investigated for its ability to suppress Microcystis blooms.
By leveraging the power of AI and the wealth of available scientific resources, researchers can take their Microcystis studies to new heights, delivering more reliable and impactful findings that contribute to our understanding and mitigation of these harmful cyanobacteria.
These freshwater microorganisms can pose a significant threat to aquatic ecosystems and human health, making them a crucial focus for researchers and environmental scientists.
Studying Microcystis presents various challenges, as researchers must navigate through a vast array of scientific literature to identify the most effective protocols and products.
PubCompare.ai, an innovative AI-driven platform, aims to optimize Microcystis research by enhancing reproducibility and accuracy.
This tool leverages the power of artificial intelligence to assist researchers in easily locating and identifying the best protocols and products, whether they are published in peer-reviewed journals, preprints, or patents.
The Concentrator Plus system can be utilized to concentrate and purify samples containing Microcystis cells or toxins, while the Prominence system and SPSS v20 software can be employed for data analysis.
The Luna C18 column is a common choice for separating and analyzing Microcystis-related compounds using high-performance liquid chromatography (HPLC) techniques.
Additionally, flow cytometry, such as the CytoFLEX system, can be employed to quantify and characterize Microcystis populations.
Trifluoroacetic acid (TFA) is a commonly used solvent in the analysis of Microcystis toxins, and Digoxin has been studied for its potential to inhibit the toxic effects of Microcystis.
The compound S-4800 has also been investigated for its ability to suppress Microcystis blooms.
By leveraging the power of AI and the wealth of available scientific resources, researchers can take their Microcystis studies to new heights, delivering more reliable and impactful findings that contribute to our understanding and mitigation of these harmful cyanobacteria.