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Dinoflagellates

Dinoflagellates are a diverse group of eukaryotic, unicellular marine organisms that play a crucial role in aquatic ecosystems.
They are characterized by their unique cell structure, including two flagella and a distinctive nucleus.
Dinoflagellates are known for their bioluminescence, symbiotic relationships with coral reefs, and the ability to produce potent toxins that can lead to harmful algal blooms.
Understanding the biology and ecology of dinoflagellates is essential for managing marine environments and human health.
Reserach in this field is rapidly evolving, and PubCompare.ai's innovative AI-driven protocol comparison features can help scienctists optimize their dinoflagellate studies and ensure reproducibility.

Most cited protocols related to «Dinoflagellates»

1
Transcriptome Annotation and Assembly Evaluation
Cited 10 times
Transcriptomes were annotated using the dammit pipeline (version v1.0.dev0) [49 ], which relies on the following databases as evidence: Pfam-A (version 28.0) [50 (link)], Rfam (version 12.1) [51 (link)], and OrthoDB (version 8) [52 (link)]. In the case where there were multiple database hits, one gene name per contig was selected by choosing the name of the lowest e-value match (<1e-05).
All assemblies were evaluated using metrics generated by the Transrate program (v1.0.3) [53 (link)]. Trimmed reads were used to calculate a Transrate score for each assembly, which represents the geometric mean of all contig scores multiplied by the proportion of input reads providing positive support for the assembly [50]. Comparative metrics were calculated using Transrate for each MMETSP sample between DIB and the NCGR assemblies using the Conditional Reciprocal Best Basic Local Alignment Search Tool hits (CRBB) algorithm [54 (link)]. A forward comparison was made with the NCGR assembly used as the reference and each DIB re-assembly as the query. Reverse comparative metrics were calculated with each DIB re-assembly as the reference and the NCGR assembly as the query. Transrate scores were calculated for each assembly using the Trimmomatic quality-trimmed reads prior to digital normalization.
Benchmarking Universal Single-Copy Orthologs (BUSCO) software (version 3) was used with a database of 215 orthologous genes specific to protistans and 303 genes specific to eukaryota with open reading frames (ORFs) in the assemblies. BUSCO scores are frequently used as one measure of assembly completeness [55 (link)].
To assess the occurrences of fixed-length words in the assemblies, unique 25-mers were measured in each assembly using the HyperLogLog (HLL) estimator of cardinality built into the khmer software package [56 (link)]. We used the HLL function to digest each assembly and count the number of distinct fixed-length substrings of DNA (k-mers).
Unique gene names were compared from a random subset of 296 samples using the dammit annotation pipeline [49 ]. If a gene name was annotated in NCGR but not in DIB, this was considered a gene uniquely annotated in NCGR. Unique gene names were normalized to the total number of annotated genes in each assembly.
A Tukey’s honest significant different post-hoc range test of multiple pairwise comparisons was used in conjunction with an analysis of variance to measure differences between distributions of data from the top eight most-represented phyla (Bacillariophyta, Dinophyta, Ochrophyta, Haptophyta, Ciliophora, Chlorophyta, Cryptophyta, and Others) using the agricolae package version 1.2-8 in R version 3.4.2 (2017-09-28). Margins sharing a letter in the group label are not significantly different at the 5% level (refer to Fig. 8). Averages are reported ± standard deviation.
Johnson L.K., Alexander H, & Brown C.T. (2018). Re-assembly, quality evaluation, and annotation of 678 microbial eukaryotic reference transcriptomes. GigaScience, 8(4), giy158.
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Publication 2018
Chlorophyta Ciliophora Cryptophyta Diatoms Dinoflagellates Eukaryota Genes GPER protein, human Haptophyta Open Reading Frames Transcriptome
2
Symbiodinium Taxonomic Diversity Assessment
Cited 8 times
To assess which of the three primer pairs performed the best, each primer pair was assessed for specificity (i.e. preferential amplification of Symbiodinium DNA) and sensitivity (i.e. ability to amplify Symbiodinium from Symbiodinium-rare environments). Additionally, potential within-Symbiodinium taxonomic biases were assessed by comparing the proportions of sequences for each primer pair that belonged to each of the nine major lineages of Symbiodinium, clades A–I (Pochon & Gates, 2010 (link)). Our assumption was that if no or only small clade biases exist, the proportion of sequences from each clade should be similar between primers. Consequently, a large deviation in clade proportions by any one of the primer pairs was regarded as indicative of taxonomic bias.
The specific PCR conditions used for each primer pair are given in Table 1 with PCR reagents as detailed above. The sequences returned from each PCR were annotated using blastn and the NCBI ‘nt’ database according to their closest match to one of the following categories: Symbiodinium, dinoflagellate, stony coral, Hydrozoan, uncultured eukaryote, other, or ‘no match’. To do this, the .fasta file produced from the initial QC from each sample was run against NCBI’s ‘nt’ database with the max_target_seqs argument set to 1 and an output format string of ‘6 qseqid sseqid staxids sscinames sblastnames evalue’. For sequences to be categorised as Symbiodinium, an e-value >50 was required (representing approximately a 100% coverage match at 80–85% nt identity) in addition to the closest match being of Symbiodinium origin. Additionally, closest match subject sequences were screened for two sequences in particular (JN406302 and JN406301), which are mis-annotated as Symbiodinium (highly divergent from any other Symbiodinium sequences) in the ‘nt’ database. Thus, query sequences matching these sequences were annotated as ‘Unclutured eukaryote.’ Notably, before controlling for this, 58% of all ‘Symbiodinium’ sequences amplified by the ITSintfor2 and ITS-DINO primer pairs (0% for the SymVar primer pair) in the SRF-OO samples matched these sequences as their closest match (Fig. S2).
blastn was also used to associate Symbiodinium sequences to one of the nine clades. To do this, a .fasta file was created for each sample that contained all sequences that had previously been categorised as Symbiodinium. This file was used as an input for blastn with the max_target_seqs argument set to 1 and an output format string of ‘6 qseqid sseqid evalue pident gcovs’. A custom BLAST database was used that contained a single representative sequence for each of the nine clades (Data S1).
Hume B.C., Ziegler M., Poulain J., Pochon X., Romac S., Boissin E., de Vargas C., Planes S., Wincker P, & Voolstra C.R. (2018). An improved primer set and amplification protocol with increased specificity and sensitivity targeting the Symbiodinium ITS2 region. PeerJ, 6, e4816.
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Publication 2018
Base Sequence Calculi Coral Dinoflagellates Eukaryota Hydrozoa Hypersensitivity Oligonucleotide Primers
3
Phylogenetic Analysis of Apicomplexans and Dinoflagellates
Cited 8 times
Three apicomplexans and three dinoflagellates were used as outgroups (Additional file 1: Table S2)44 (link). Maximum likelihood (ML) analyses were carried out using RAxML-HPC2 v7.6.6114 (link) on CIPRES Science Gateway115 . The DNA partition was analyzed with GTR + gamma. ProtTest 2.4116 (link) selected the MtArt + I + G + F amino acid replacement matrix as the best-fitting model for alpha-tubulin amino acid sequences. The alpha-tubulin amino acid partition was run under the MtArt + gamma model as this was the best-fitting model available in RAxML. Support for the best-scoring ML tree came from 1000 bootstrap replicates. Bayesian inference (BI) analysis was performed with MrBayes 3.2.2117 (link) on CIPRES Science Gateway using the GTR + I + G model for the DNA partition as selected by MrModeltest v.2.2118 and using mixed model for the alpha-tubulin amino acid partition. Markov chain Monte Carlo (MCMC) simulations were run with two sets of four chains for 4,000,000 generations with a sample frequency of 100 generations. The first 10,000 trees were discarded as burn-in. All remaining trees were used to calculate posterior probabilities using a majority rule consensus. Systematic schemes are mainly based on Lynn5 and Adl et al.44 (link), except for some revisions made in the present study.
The approximately unbiased (AU) test119 (link) was used to test the monophyly of the focal group against competing phylogenetic hypotheses (Table 1). Constrained ML trees enforcing the monophyly of the respective focal groups were generated based on SSU rDNA data. For all constraints, internal relationships within the constrained groups and among the remaining taxa were unspecified. The site-wise likelihoods for the resulting constrained topologies and the non-constrained ML topology were calculated using PAUP120 and were then analyzed in CONSEL121 (link) with standard parameters to obtain p-values.
Gao F., Warren A., Zhang Q., Gong J., Miao M., Sun P., Xu D., Huang J., Yi Z, & Song W. (2016). The All-Data-Based Evolutionary Hypothesis of Ciliated Protists with a Revised Classification of the Phylum Ciliophora (Eukaryota, Alveolata). Scientific Reports, 6, 24874.
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Publication 2016
alpha-Tubulin Amino Acids Amino Acid Sequence CBX4 protein, human Dinoflagellates Gamma Rays Recombinant DNA Trees
4
Metagenomic Sampling and Fractionation of Seawater
Cited 8 times
A sample of approximately 150 l of seawater was collected at one site per atoll (Figure 1). The water for the metagenomes was collected from below the boundary layer (in crevices and against the benthos) to avoid confounding problems with the water column. The sampling was conducted at the same time of day to help minimize diurnal effects. The water was collected from over ∼20 m2 of reef using a modified bilge pump connected to low density polyethylene (LDPE) collapsible bags (19 l; Cole-Parmer, Vernon Hills, IL; Figure S1). The containers were transported to the surface and the research vessel within two hours of collection, thereby reducing potential in situ community changes. To remove potential sources of DNA contamination, containers, bilge pumps, and tubing were washed once with 10% bleach, three times with freshwater, and once with 100 kDa filtered seawater prior to sampling.
Two size fractions were prepared for the metagenomic analysis from the seawater samples: 1) A large fraction containing mostly microbes, some small eukaryotes (such as dinoflagellates and protists), and a few VLPs, and 2) a small fraction containing mostly VLPs and some small microbes. To obtain these fractions the seawater was processed through a series of filters. The large eukaryotes were removed by filtering the entire sample through 100 µm Nitex, into a barrel lined with a clean, high-density polyethylene bag. The filtrate was then concentrated to ∼500 ml on a 100 kDa tangential flow filter (TFF), which captured the unicellular eukaryotes, microbes and VLPs (i.e., the water was removed). During the filtration, pressures were kept below 0.6 bar (10 psi) to ensure that the viruses were not destroyed. The concentrated sample was then passed through 0.45 µm Sterivex filters (Millipore, Inc) using a 50 ml syringe. In this step, the large metagenomic fraction consisting of microbial cells was caught on the filter (microbiomes) and the filtrate was the small metagenomic fraction (viromes). All filtrations were performed on the research vessel, and the samples were stored for further processing in the laboratory at SDSU. The Sterivex filters were frozen at −80°C. The 0.45 µm filtrates (i.e., the virome) were extracted with chloroform to kill any residual cells (10% vol:vol; most viruses are resistant to chloroform) and stored at 4°C.
The DNA for the microbiomes was isolated from the Sterivex filters by removing the filter membranes and performing DNA extractions using a bead-beating protocol (MoBio, Carlsbad CA). The DNA obtained was amplified with Genomiphi (GE Healthcare Life Sciences, Inc, Piscataway, NJ) in six to eight 18-hour reactions [23] –[28] . The reactions were pooled and purified using silica columns (Qiagen Inc, Valencia, CA). The DNA was then precipitated with ethanol and re-suspended in water at a concentration of approximately 300 ng µl−1.
The viruses in the small metagenomic fractions (i.e., 0.45 µm filtrate treated with chloroform) were purified using cesium chloride (CsCl) step gradients to remove free DNA and any cellular material [29] (link), [30] . Viral DNA was isolated using CTAB/phenol:chloroform extractions and amplified in six to eight 18-hour Genomiphi reactions. These reactions were pooled and purified using silica columns (Qiagen Inc, Valencia, CA). The DNA was then precipitated with ethanol and re-suspended in water at a concentration of approximately 300 ng µl−1.
Both the virome and microbiome DNAs were sequenced at 454 Life Sciences (Branford, CT) using their parallel pyrosequencing approach.
Dinsdale E.A., Pantos O., Smriga S., Edwards R.A., Angly F., Wegley L., Hatay M., Hall D., Brown E., Haynes M., Krause L., Sala E., Sandin S.A., Thurber R.V., Willis B.L., Azam F., Knowlton N, & Rohwer F. (2008). Microbial Ecology of Four Coral Atolls in the Northern Line Islands. PLoS ONE, 3(2), e1584.
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Publication 2008
Blood Vessel Cells cesium chloride Cetrimonium Bromide Chloroform Dinoflagellates DNA DNA, Viral DNA Contamination Ethanol Eukaryota Filtration Freezing Metagenome Microbiome Phenol Polyethylene, High-Density Polyethylene, Low-Density Silicon Dioxide Syringes Tissue, Membrane Umbilical Artery, Single Virome Virus
5
Identifying Symbiodinium Clade-Specific Genes
Cited 6 times
Eight expressed sequence tag (EST) libraries were obtained from the National Center for Biotechnology Information (NCBI) database (GenBank) in May 2007. The libraries represented Symbiodinium clade A (2,163 sequences); Symbiodinium clade C (5,156 sequences); Amphidinium carterae (3,383 sequences); Alexandrium tamarense (10,885 sequences); Heterocapsa triquetra (6,807 sequences); Karenia brevis (6,986 sequences); Karlodinium micrum (16,532 sequences) and Lingulodinium polyedrum (3,639 sequences). Each of these libraries differed widely. For example, the Symbiodinium clade A library was generated from cells that have been in cultures for over 25 years [37] , whereas the clade C library encompasses Symbiodinium cDNAs isolated from the staghorn coral Acropora aspera exposed to a variety of stresses, including elevated temperature, ammonium supplementation, and seawater with different inorganic carbon concentrations [38] . The other dinoflagellate EST libraries were obtained from cultures grown and harvested under a variety of conditions, including isolation during different phases of growth or time points in the daily cycle [39] (link)–[43] .
Using the two Symbiodinium datasets as queries, a Perl script [44] (link) linking the BLASTn output files from the BLAST v2.2.15 package (http://www.ncbi.nlm.nih.gov/) was used to retrieve homologous sequences from the six non-Symbiodinium dinoflagellate target libraries with an e-value threshold of 10−25. This relatively stringent cutoff was defined to restrict the integration of paralogous genes and limit the inclusion of short sequence fragments (<200 bp). Sequence identity of each homologous group of sequences was assessed at the protein-level using BLASTx. Eighty-four sequence alignments containing all homologous sequences retrieved in the BLAST analyses were created in the BioEdit v5.0.9 sequence alignment software [69] using ClustalW [70] (link), then checked and manually edited. Because individual EST alignments contain sequences from either a single Symbiodinium clade (A or C) or both clades plus other dinoflagellates (see Table S1), candidate genes suitable for downstream characterization were selected using the following criterion: genes were shortlisted for gene characterization based on the presence of conserved regions that would allow for forward and reverse primer design. To facilitate work on all clades of Symbiodinium, alignments containing contigs from both Symbiodinium libraries (A and C) were prioritized. Symbiodinium clades A and C represent the most ancestral and derived Symbiodinium lineages, respectively, so primers targeting these very divergent clades would most likely also allow Symbiodinium from all other clades (B, D, E, F, G, H and I) to be recovered. Non-Symbiodinium sequences were also included in these alignments, because they provided information on how variable a given candidate gene was between dinoflagellate groups, while also allowing for the design of ‘Symbiodinium-specific’ primers in variable regions or ‘dinoflagellate-specific’ primers in conserved regions. In a single case where no Symbiodinium clade A contig was available for comparison with clade C (e.g. calmodulin gene; Table 1, Table S1), the non-Symbiodinium dinoflagellate contigs were used in the primer design. Finally, gene alignments were sorted again to identify those that allowed for design of primers yielding amplicons of between 150 bp and 1000 bp in length. Forward and reverse Symbiodinium-specific primers were designed across the conserved regions of the candidate genes using MacVector v11.0.2 (MacVector Inc., NC, USA), minimizing self/duplex hybridization and internal secondary structure problems.
Pochon X., Putnam H.M., Burki F, & Gates R.D. (2012). Identifying and Characterizing Alternative Molecular Markers for the Symbiotic and Free-Living Dinoflagellate Genus Symbiodinium. PLoS ONE, 7(1), e29816.
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Publication 2012
Alexandrium tamarense Ammonium Calmodulin Carbon Cells Coral Crossbreeding Dinoflagellates DNA, Complementary DNA Library Expressed Sequence Tags FCER2 protein, human Fever Genes Homologous Sequences isolation Oligonucleotide Primers Proteins Sequence Alignment Triquetral Bone

Most recents protocols related to «Dinoflagellates»

1
Symbiodiniaceae Dinoflagellate Culture Isolation
Two dinoflagellate culture isolates from the family Symbiodiniaceae: B. psygmophilum and E. voratum were obtained from the Marine Symbiosis and Coral Reef Biology laboratory at Victoria University of Wellington (New Zealand). The culture isolates had different cell sizes with the mean girdle diameter of B. psygmophilum 6.69 ± 0.83 µm and E. voratum 9.29 ± 0.77 µm. To start the cultures, approximately 100 cells mL −1of each isolate was used. The cultures were grown in f/2 growth medium enriched with nutrients (Guillard, 1975 ). They were maintained in sterile plastic flatbottomed vessels (70 mL Labserv, ThermoFisher Scientific, NZ) with a 12:12 light: dark cycle under 100 µmol m−2 s−1 photosynthetically active radiation (PAR) at 25 °C. All cultures were harvested during their late exponential phase prior to the freezing experiments.
Kihika J.K., Wood S.A., Rhodes L., Smith K.F., Butler J, & Ryan K.G. (2023). Assessment of the recovery and photosynthetic efficiency of Breviolum psygmophilum and Effrenium voratum (Symbiodiniaceae) following cryopreservation. PeerJ, 11, e14885.
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Publication 2023
Blood Vessel Cells Coral Reefs Culture Media Dinoflagellates Electromagnetic Radiation Light Marines Nutrients Sterility, Reproductive Symbiosis
2
Seasonal Phytoplankton Dynamics in Coastal Lagoons
Data analysis was performed using R statistical software version 4.0.2 (RStudio Team 2016 ). Mean seasonal values were calculated for each parameter measured in triplicate: salinity, temperature, turbidity and dissolved oxygen concentration measured in situ, and Fv/Fm measured at laboratory. Abundances by classes of microphytoplankton (Bacillariophyceae or Diatoms, Dinophyceae or Dinoflagellates, Cyanophyceae, Cryptophyceae, Chlorophyta and Others) or groups of pico- and nano-phytoplankton (PC-cyan, PE-cyan, PEUK and NANO) were considered by year and season. Microphytoplankton community was also described through the calculation of diversity indexes (species richness, Shannon’s Diversity Index), based on single taxonomic entities identified during counting and considered as taxonomic units. A list of all species or taxonomic units identified can be found in Table S1 (Supplementary).
All data were previously log10(x+1) transformed to normalise data, reduce effects of extreme values and meet ANOVA conditions.
Spatial and seasonal differences within lagoons were assessed through analysis of variance (ANOVA) tests for each variable of interest. When significant (p < 0.05), differences were further investigated through post-hoc Tukey’s test. For some variables, since normality was not achieved despite transformation, a Kruskal-Wallis (K-W) non-parametric test was applied and eventual significant differences were further tested through Dunn’s post-hoc test. Then, a global analysis was performed, with visualisation through Non-metric Multidimensional Scaling (NMDS) of abiotic parameters (water temperature (°C), salinity, turbidity (FNU), dissolved oxygen concentration (%), silicates (µM), DIP (µM), NO3− (µM), NO2− (µM) and NH4+ (µM)) and biotic variables (Chl a (µg L−1), Fv/Fm, Diatoms (cell L−1), Dinoflagellates (cell L−1), Cyanophyceae (cell L−1), Cryptophyceae (cell L−1), Chlorophyceae (cell L−1), Others (cell L−1), PE-cyan (cell L−1), PC-cyan (cell L−1), PEUK (cell L−1), NANO (cell L−1), species richness and Shannon’s Diversity Index) separately. To test potential effects of season, lagoon and stations, datasets were investigated through the permutational multivariate analysis of variance (PERMANOVA) test and significant differences (p < 0.05) were afterwards detailed through the pairwise post-hoc test.
Finally, relationships between biological and environmental variables were analysed through Spearman’s ranks order correlation for each lagoon.
Ligorini V., Crayol E., Huneau F., Garel E., Malet N., Garrido M., Simon L., Cecchi P, & Pasqualini V. (2023). Small Mediterranean coastal Lagoons Under Threat: Hydro-ecological Disturbances and Local Anthropogenic Pressures (Size Matters). Estuaries and Coasts.
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Publication 2023
Biopharmaceuticals Chlorophyceae Chlorophyta Cryptophyta Cyanobacteria Diatoms Dinoflagellates L Cells Oxygen Phytoplankton Salinity Silicates
3
Coral Holobiont Chlorophyll and Protein Analysis
These three parameters were assessed from nubbins used for the photosynthetic measurements. Freeze-dried samples were re-suspended in 10 mL of filtered seawater and homogenized with a Potter tissue grinder. Freeze-drying the samples did not impact the measurement of these three parameters [80 (link)]. From each nubbin, a 5 mL sub-sample was used for the determination of the total chlorophyll a and c2 concentration. It was first centrifuged at 3000× g for 10 min at 4 °C to pellet the dinoflagellates. The pellet was then re-suspended in 5 mL of 100% acetone to extract chlorophyll a and c2 at 4°C in the dark for 24 h. Finally, the extract was centrifuged at 15 °C, 530 g for 15 min before reading the absorbance at 630, 663 and 750 nm with a spectrofluorometer (UVmc2 Safas, Xenius, Monaco). Chlorophyll a and c2 concentrations were calculated according to the equations of Jeffrey and Humphrey [81 (link)].
The protein content of the coral holobiont was quantified in a 500-μL subsample of each nubbin. The subsample was incubated for 5 h at 60 °C in 0.5 M sodium hydroxide (1:1). It was then centrifuged for 1 min at 1000× g and the supernatant was distributed in a 96-well microplate in triplicate. The bicinchoninic acid (BCA) assay kit solution (Interchim) was then added to each well according to Smith et al. [82 (link)] before incubating the microplate for 30 min at 60 °C. Finally, the absorbance was read at 562 nm with a spectrofluorometer (UVmc2 SAFAS, Xenius, Monaco). The standard curve was conducted with known concentrations of bovine serum albumin (BSA). Finally, a 100 μL subsample from each nubbin was used to estimate the dinoflagellate density with a Z1 Coulter Particle Counter (Beckman Coulter, Pasadena, CA, USA). Each sample was counted in triplicate, and each measurement was performed twice.
Blanckaert A.C., Biscéré T., Grover R, & Ferrier-Pagès C. (2023). Species-Specific Response of Corals to Imbalanced Ratios of Inorganic Nutrients. International Journal of Molecular Sciences, 24(4), 3119.
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Publication 2023
Acetone bicinchoninic acid Biological Assay Chlorophyll A Coral Dinoflagellates Freezing Photosynthesis Proteins Serum Albumin, Bovine Sodium Hydroxide Tissues
4
Measuring Coral Photosynthesis and Respiration
Rates of respiration (R) and net photosynthesis (Pn) were measured on the same six nubbins as in 4.2. Nubbins were transferred into a 60 mL closed transparent plexiglass chamber filled with 0.45 μm filtered seawater, maintained at 25 °C and stirred. Chambers were equipped with an oxygen sensor (Polymere Optical Fiber, PreSens, Regensburg, Germany) connected by an optical fiber to an Oxy-4 (Channel fiber-optic oxygen meter, PreSens, Regensburg, Germany). The oxygen concentration was recorded with the Oxy4v2-30fb software, in the dark for R and at 200 µmol photons m−2 s−1 for Pn. Calibrations were conducted at 0% O2 with nitrogen-saturated seawater and at 100% O2 with air-saturated seawater. Incubations were stopped when a variation of at least 10% in the dissolved oxygen concentration was reached. The gross photosynthesis (Pg) rate was obtained by adding Pn and the absolute value of R. This calculated Pg is likely to be an underestimation of the actual gross photosynthesis rate [79 (link)]. Nubbins were used for the nutrient uptake rate measurements and then frozen at −80 °C for later analysis of tissue parameters.
The efficiency of the photosystem II of the dinoflagellate (Fv/Fm) was also measured on the same six nubbins. The nubbins were first placed in darkness for 10 min. Then, using an optical fiber connected to a Dual Pam/F (Fiber version, Heinz Waltz, Germany), a saturated pulse of photosynthetically active radiation (PAR) was sent and the Fv/Fm recorded. Nubbins were not sacrificed after this measurement and used for the different analyses.
Blanckaert A.C., Biscéré T., Grover R, & Ferrier-Pagès C. (2023). Species-Specific Response of Corals to Imbalanced Ratios of Inorganic Nutrients. International Journal of Molecular Sciences, 24(4), 3119.
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Publication 2023
Darkness Dinoflagellates Fibrosis Freezing M-200 Nitrogen Nutrients Oxygen Photosynthesis Photosystem II Plexiglas Pulse Rate Radiation Respiratory Rate Tissues
5
Phylogenetic Analysis of IntracellularBinding Proteins
To determine how the phylogenetic relationships between IBPs varied depending on the domain architecture, environment and taxonomic assignments, we produced gene trees of the most environmentally abundant gene architectures, as well as gene trees of IBPs across all domain architectures. The alignments of the amino acid sequences of HMMER hits to the DUF3494 domain were produced using muscle (v2.0.4) [51 (link)], and low quality columns of the alignment were removed using TrimAl (v1.2) [52 (link)]. The trees were generated with FastTree (v2.1.1) [53 (link)], using the default parameters, and visualised using interactive tree of life (IToL; v6.6) [54 (link)]. We repeated this method for IBPs within MAGs. Gene trees with fewer than 60 leaves, or with multi-copy DUF3494 domain architectures, were rooted at their midpoint. For the remaining trees, we rooted the trees using an outgroup of 130 IBPs from the dinoflagellate Polarella glacialis [55 (link)] (accessions in Supplementary Table S4).
Winder J.C., Boulton W., Salamov A., Eggers S.L., Metfies K., Moulton V, & Mock T. (2023). Genetic and Structural Diversity of Prokaryotic Ice-Binding Proteins from the Central Arctic Ocean. Genes, 14(2), 363.
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Publication 2023
Amino Acids Dinoflagellates Genes MAG protein, human Muscle Tissue O,O-diisopropyl-S-benzylthiophosphate Sequence Alignment Trees

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More about "Dinoflagellates"

Dinoflagellates are a diverse group of eukaryotic, unicellular marine organisms that play a crucial role in aquatic ecosystems.
These microscopic plankton, also known as dinophytes or peridineans, are characterized by their unique cell structure, including two flagella and a distinctive nucleus.
They are a key component of phytoplankton, the base of the marine food web.
Dinoflagellates are renowned for their bioluminescence, a phenomenon where they emit a stunning blue-green glow when disturbed.
This remarkable ability is thought to serve as a defense mechanism, deterring predators or attracting prey.
Additionally, many dinoflagellate species form symbiotic relationships with coral reefs, providing essential nutrients and contributing to the vibrant colors of these underwater ecosystems.
One of the most notable features of dinoflagellates is their ability to produce potent toxins, which can lead to harmful algal blooms (HABs).
These toxin-producing species, often referred to as 'red tides,' can have devastating impacts on marine life and human health, causing seafood poisoning and other related illnesses.
Understanding the biology and ecology of dinoflagellates is crucial for managing and mitigating the effects of these harmful events.
Researchers in the field of dinoflagellate studies utilize a variety of techniques and equipment to investigate these fascinating organisms.
Some commonly used tools include the Axiovert 25 and CKX41 microscopes for detailed observation, the Innova 42 shaker for culturing, the Anti-human TSR antibody for immunohistochemical analysis, and the Sc-14013 antibody for molecular studies.
Additionally, dyes like H2DCFDA are employed to assess cellular processes, while the CKX53 microscope and DNeasy PowerSoil Kit are used for genomic analysis.
The Mini-Beadbeater-1 is a valuable tool for cell disruption and DNA extraction, and Tween 60 is a commonly used detergent for various experimental protocols.
PubCompare.ai's innovative AI-driven protocol comparison features can greatly enhance dinoflagellate research by helping scientists optimize their studies and ensure reproducibility.
By leveraging this cutting-edge technology, researchers can easily locate the best protocols from literature, pre-prints, and patents, while benefiting from intelligent comparisons that improve accuracy and efficiency.