Gyrosigma Classification Essay

1. Introduction

Worldwide every year, fisheries, aquaculture, human health and tourism are threatened by harmful algal blooms (HABs) in marine, brackish, as well as continental waters. Although most phytoplankton species are benign, about 2% of them can cause harm through the production of toxins or by an excessive accumulated biomass, which can affect co-occurring organisms and alter food-web dynamics [1,2]. In addition to the ecological and economic damages, public health is also at risk: the consumption of shellfish that have fed on toxic phytoplankton and accumulated toxins, and exposure to the aerosols of HAB toxins can cause illness or even mortality. Depending on the species, it can take only a few toxic cells per liter to poison shellfish and make them unsuitable for human consumption [3]. Monitoring of microalgae is therefore required by all countries with a marine coastline. HAB monitoring programs are currently based on cells identified and counted by light microscopy and on the mouse bioassay for detecting biotoxins. The mouse bioassay for the detection of phytoplankton toxins in shellfish has recently been banned by the European Commission (July 2011), and there is a mandatory replacement by chemical methods (Liquid Chromatography-Mass Spectrometry, LC-MS) in the next three years.

However, the effectiveness of monitoring programs using light microscopic identification is limited by the fact that it is time consuming and that morphology, as determined by light microscopy, may be insufficient to give definitive species and toxin attribution. Thus, there is a need to implement molecular methods to ensure a fast and reliable species identification. Monitoring for toxic species using molecular techniques advances the state of knowledge for detection of harmful species because more samples can be analyzed in a shorter time period and with greater accuracy. Within the actual context of the dramatic decreasing number of taxonomic experts of phytoplankton [4], which are, notwithstanding, essential for other ecological studies, such techniques offer important advances. This is of particular interest for potentially toxic algae, because the difficulty in determining their exact identification by using light microscopy can have disastrous consequences for human health. Microarrays offer a near real-time ecosystem analysis and offer broader ecological interpretation of how key species, such as toxic algae, can extend their geographical distribution with climate change or can become invasive after introduction from remote areas [5]. Microarrays offer the most expeditious method to have high sample throughput with highly accurate species detection, in a universal approach [6,7,8]. In the FP7 EU project, MIDTAL (Microarrays for the Detection of Toxic Algae), an earlier protocol for detection of toxigenic microalgae by Gescher et al. [9] was optimized. Microarrays (as phylochips) detect multiple species simultaneously using species-specific probes that have been applied primarily for the detection of bacteria [10,11,12,13]. At present, 140 probes for various toxic algal species at various taxonomic levels are spotted onto the current generation of the MIDTAL microarray. As part of the MIDTAL project, the primary goal was to be able to infer cell numbers from the molecular signal to provide an early warning system for toxic algae. Because the MIDTAL microarray is a universal array that can be used globally, it offers a real possibility of detecting invasive species, especially in view of global warming where warm temperate species are moving northward, e.g., Gamberiodiscus.

In this study, we show the effectiveness of using microarrays for the detection of toxic algae and its combination with toxin detection. We compare these results with light microscopy data from a regular French monitoring network of toxic phytoplankton. The microarray used in this study represents the third generation array developed within the EU-MIDTAL project. In generation one, probes (18–22 nt) developed for Fluorescent in situ Hybridization (FISH) were used directly; in generation two, these FISH probes, and any newly designed probes, were lengthened to 25 or more nt; in generation three, an additional poly-T spacer to lift the probes farther above the surface was tested and optimized (Figure 1). At each generation, minor changes in the hybridization protocol were made and a final optimized protocol can be found in Lewis et al. [14].

Figure 1. Scheme of the development of the MIDTAL microarray. The scheme pictures the different microarray generations with its different probes, tests and enhancements of protocols (RNA and hybridization). (* Higher temperature during 3rd washing step).

Figure 1. Scheme of the development of the MIDTAL microarray. The scheme pictures the different microarray generations with its different probes, tests and enhancements of protocols (RNA and hybridization). (* Higher temperature during 3rd washing step).

2. Experimental Section

2.1. Field Sampling

In 2011, water samples from the sub-surface (1 m depth) were collected at Arcachon Bay in France (Figure 2) between July and October for microarray analysis (Table 1). The sampling site termed Tès (1°10'00 W, 44°40'00 N), is directly located in front of the town of Arcachon inside the bay. Data of toxic, harmful, and other phytoplankton abundances is provided by IFREMER (Ifremer/Quadrige2/Rephy DATA) from the paired station named Teychan (1.5 km from Tès). Cell counts were done as previously described by Medlin and Schmidt [15] and Kegel et al. [16].

Table 1. Information about field samples taken at Arcachon Bay like sample name, sample date, filtered volume, total extracted RNA and degree of labeling (DoL).

Sample nameSample dateVolume filtered (L)Total RNA extracted (µg)DoL

Figure 2. Sampling sites in Arcachon Bay (France): the station Tès (Teychan).

Figure 2. Sampling sites in Arcachon Bay (France): the station Tès (Teychan).

For the microarray analysis, a minimum of three liters (Table 1) were filtered onto 3 µm nitrocellulose filters (47 mm) in triplicate. For each sampling date, the first and second replicated filter was transferred into cryogenic vials containing 1 mL of TRI Reagent (Sigma-Aldrich). Those samples were snap frozen and stored at –80 °C until further process for RNA extraction. Toxicity was measured by one of the Partners (Queens University Belfast, UK) with a newly developed multiplex optical Surface Plasmon Resonance biosensor (Multi SPR) in parallel with the enzyme-linked immunosorbent assay (ELISA) [17]. The target toxins are domoic acid (DA) for amnesic shellfish poisoning (ASP), okadaic acid (OA) and dinophysistoxins (DTXs) for diarrhetic shellfish poisoning (DSP) and saxitoxin (STX) for paralytic shellfish poisoning (PSP) toxin analogs. Therefore, the third replicated filter was transferred into cryogenic vials without TRI Reagent and sent frozen to Queens University Belfast who was responsible for the toxin measurements.

2.3. RNA Labeling and Fragmentation

The PlatinumBright Infrared Labeling Kit from KREATECH (Amsterdam, Netherlands) was used to label 1.5 µg RNA of field sample using 2 µL ULS dye and 2 µL 10× labeling solution in a total volume of 20 µL. Samples were labeled by incubation for 30 min at 85 °C. After incubation, samples were placed on ice and spun down and then purified with KREApure columns (KREATECH) according to the manufacturer’s instructions. Concentration and incorporation of the dye was measured by a NanoVue (GE Healthcare). The DoL (degree of labeling) was calculated and was between 1.9 and 2.2% (Table 1). RNA was fragmented by adding 1/10 volume of RNA fragmentation buffer (100 mM ZnCl2 in 100 mM Tris-HCL, pH 7.0) and an incubation of 15 min at 70 °C. The reaction was stopped with the addition of 1/10 volume of 0.5 M EDTA (pH 8.0) and the samples were placed on ice. The RNA was fragmented to reduce the effect of the secondary structure on the accessibility of the probe. Despite this fragmentation, we still have heterogeneous probe sensitivity, which reflects the influence of the secondary structure and we can only partially overcome this by fragmenting the RNA to remove the strongest secondary structure formations.

2.4. Microarray Design

Probe design was done with the open software package ARB [18]. All oligonucleotides including the positive and negative controls were synthesized by Thermo Fisher Scientific (Ulm, Germany) with a C6 aminolink at the 5' end of the molecule. The probes had a length between 18 and 25 nt and a 15 nT-long poly (dT) tail following the NH2 link at the 5' end. Table 2 shows a list of the probes and their targets. The complete hierarchy for each probe can be found in the GPR-Analyzer which is available online at The probe sequences are patent pending and a commercial kit will soon be available from Kreatech containing the array and all reagents for hybridization. Epoxy-coated slides (Genetix or Schott) of MIDTAL version 3.2 were printed using a pin printer VersArray ChipWriter Pro (Bio-Rad Laboratories GmbH, Munich, Germany) and split pins (Point Technologies, Inc., CO) as described by Kegel et al. [16]. One array contained 136 different probes and 4–8 replicates, as well as three negative (NEGATIVE1_dT, NEGATIVE2_dT, NEGATIVE3_dT), one positive control (TBP = TATA-box binding protein), Poly-T-Cy5 (spotting control), and two internal controls (DunGS02_25_dT and DunGS05_25_dT for Dunaliella tertiolecta) (MIDTAL ver3.2). After spotting, slides were incubated for 30 min at 37 °C and then stored at −20 °C.

Printing of MIDTAL slides version 3.3 was done by Scienion AG using a sciFlexarrayer S11 and epoxy-coated slides from Genetix. One array contained eight replicates of 140 different probes including the seven controls stated above. After printing, the slides were transferred to a 75% humidity chamber, kept there overnight at RT, and stored afterwards in a sealed aluminum bag refilled with argon at 4 °C.

Table 2. Summary of probes designed or modified from published FISH probes and used to form the third generation of the MIDTAL microarray, including the targeted species, and whether it was made from the 18S or 28S rRNA gene. Probe sequences are not provided because the microarray is patent pending and will soon be commercially available from Kreatech, Amsterdam, The Netherlands. A complete taxonomic ordering of the probes can be seen in the GPR-Analyzer program and the MIDTAL hierarchy file that comes with that program.

Probe NameTargeted TaxonGene
DunGS02_25_dTDunaliella spp.

1. Introduction

Estuaries can be defined as partially enclosed bodies of coastal water with a free connection to the open sea, within which seawater is diluted by a freshwater system [1]. These areas contain varying amounts of salinity and nutrients dependent upon tidal changes and upstream runoff. Within the coastal waters of the U.S. alone, nutrient eutrophication is often the primary cause of numerous diverse coastal problems. Disturbances include red tides, fish kills, marine mammal deaths, shellfish poisoning, hypoxia, and anoxia [2,3]. Often times, these events are preceded by changes in algal species composition. Thus, the importance for studying primary producers from these dynamic ecosystems may provide answers for prevention of large-scale disturbance within those ecologically important habitats.

Intertidal zones are often referred to as littoral zones, and are characterized as the foreshore portion that is exposed to the atmosphere during low tide events and covered at high tide. Algal communities inhabiting those areas consist of a mixed flora containing freshwater, brackish, and marine species. Mud samples found within littoral zones contain both epipelic and epipsammic diatoms [4]. These primary producers serve as a food source for many groups of surface sediments feeders [5] and also act as agents of biogenic stabilization through the fast synthesis of a mucilaginous matrix during low flow [6,7]. Mud samples within estuaries reflect algal distribution patterns related to gradients of temperature, desiccation, salinity, and organic pollution [8]. It is likely that the distribution of aquatic species along so many variables is not disjunctive, but rather represents a continuum within the habitat. Therefore, species composition of primary producers within mud samples is a result of complex interactions varying between environmental factors and inter- and intra-specific competitive interactions [9].

Algae are a heterogeneous, photoautotrophic group of organisms ubiquitously found in aquatic habitats, such as oceans, seas, freshwater lakes, ponds, and streams. As primary producers, algae conduct up to 40% of the global photosynthesis [10], contributing to a significant portion of atmospheric oxygen [11]. In addition to their ecological importance as primary producers and chemical modulators of aquatic ecosystems, the use and applications of various algal species for environmental studies have increased in recent years. Prokaryotic presence in marine sediments has been addressed [12], but very few studies look at the eukaryotic primary producer in sediment [13]. Diatoms, which exist in almost all aquatic environments, play important roles as useful bioindicators for monitoring water quality [14,15]. Despite displaying an incredible biodiversity and ecological importance, the diversity of algae has not been fully explored [16]. Among the estimated 10 million algal species present in nature, only 100,000 of them have been identified, and there is a great deal of work that remains to be done for a more comprehensive understanding of their diversity. Although the microscopy-based study of algal diversity has been the conventional and primary method of taxonomic studies, this method has various limitations [17]. The methodology is time-consuming, and identification requires expertise in taxonomy; more importantly, the resolution allowed by light microscopy is limited in differentiating species of extremely small size, resulting in underestimation of the actual species diversity [18]. In addition, the microscopic approach suffers from setbacks related to the scarcity of morphologically important markers in some species, adding to the problem of underrepresentation. Microscopic identification of live and dead diatoms at the time of collection allows for the examination of total species available in a community and physiologically active species. However, even when documented as live, cultivating some of the algal species in cultures is known to be difficult [19]. Even if successful and widely used, the culture-dependent approach can result in bias towards species that thrive well in culture conditions, and leave unaccounted those that are resistant to cultivation by standard culture methods [20]. Additionally, morphological changes in response to environmental conditions can potentially mislead the microscopic identification. Thus, the microscopy and culture-dependent approach alone may not reflect the true diversity in the environment [21], which calls for evaluation of current identification and processing approaches in algal assessment as a complement to the conventional microscopic approach. The ultimate goal of this research is to better understand the high algal diversity in estuarine environments.

One alternative approach for taxonomic studies is molecular systematics based on nucleotide sequence comparisons. For nucleotide sequence-based taxonomic studies, unlike the cultivation-dependent approach, a live cell is not required, only a DNA sequence. Cloning and sequencing highly conserved genes, such as the Small Subunit (SSU) ribosomal DNA (rDNA), obtained from environmental samples can be used to objectively classify microbes and their diversity in the ecosystem [22]. This goal is accomplished by comparing the sequences with reference sequences of known species. Molecular approaches have been successful thus far, despite the limited availability of known algal-specific nucleotide sequences in databases. Studies that employed molecular tools for taxonomic purposes detected a number of new groups that could be assigned to existing phyla [23]. Furthermore, the work by Viprey et al. [24] showed that great diversity existed within the already established taxa.

In this study, we present a combined approach, in which molecular protocols were incorporated in assessing algal diversity from environmental samples. The objective of this research was to develop a methodology for better taxonomic identification of algal communities with precise analyses of the percentage of diatoms within the natural algal communities from the southeastern United States. The mud samples taken from the Savannah River Estuary, Georgia, which is known to be rich in algal biodiversity [16], were split into two aliquots. The mud samples were subsequently used for total genomic DNA extraction and for a morphological assessment in parallel; then, the results from the molecular and morphological identifications were compared. Diatoms from the same field samples were also cultured in the laboratory to provide positive control DNA sequences in the molecular community assessment. As anthropogenic changes continue to occur, such as the deepening and widening of the Savannah River Port, algal biodiversity will be altered in all related habitats, including mudflats, and we will be able to evaluate the direction and extent of these changes as we have established the baseline diversity with the current study.

2. Experimental Section

2.1. Study Sites and Sampling

Five mud samples were taken from the Savannah River Estuary in December 2011 (Savannah River United States Geological Survey (USGS) site 2198920, Lat 32°09′57″, Long 81°09′14″). The sampling methods followed the standard protocols of the American Public Health Association [25]. At the time of collection the following physical and chemical field data was collected: Dissolved oxygen 7.8 mg/L, Total nitrogen 0.57 mg/L, Total phosphorus 0.22 mg/L, pH 7.7, conductivity 14,500 µS/cm, turbidity 50 NTU, and temperature air 8.2/water 13.7 °C. Two of the samples were used for initial whole algal community analyses, performed immediately upon returning to the laboratory. In addition, within 24 h of collection, observed viable algae were isolated and placed in medium for growth. The mudflats had a combination of freshwater, marine and brackish taxa [26]; therefore, there were 3 possible culturing conditions for each isolate, based on algal preference. Freshwater algae were grown in Bolds medium. Marine algae were grown in filtered water from the location and salt-water medium (Guillard’s F/2 Marine Water Enrichment Solution, Sigma Aldrich G0154, St. Louis, MO, USA). A 1:1 ratio of Bolds media (Sigma Aldrich, B5282) and salt-water medium was used to grow brackish taxa. Subsequently, the algal cultures from the Savannah River were started in liquid media and agar plates. That was required to provide suitable artificial conditions for planktonic or epipelic/epipsammic habitat preference of the live algal individuals. Isolated algal species were designated either freshwater (if appeared in the NAWQA list for the last 20 years in both rivers and lakes) or marine unless the original description designated it as being brackish. Specific data from Underwood [9] was used to confirm our classification for common species. Live field samples and cultures in different stages of development were maintained in a controlled environmental chamber (Percival Scientific, Inc. Model I-366LL, Perry, IA 50220, USA). The cultures in the incubator were kept at 18 °C and under a 14 h of light and 10 h of dark cycle. Full light in the chamber was 96–100 µmol·m−2·µmol·s−1. Humidity was 90% ± 5% and was controlled with a microprocessor.

2.2. Morphological Assessment

The taxonomy of the algal community was enumerated in three field mud samples, following the Standard Methods [25], by assessing the whole community analyses in a Palmer Maloney counting chamber [27] (colonial forms can be observed as the depth of the chamber is 0.4 mm) or on flat slides at 400× (algae flattened in a single layer, higher resolution achieved). A minimum of 300 live natural algal units (single cells, filaments or colonies) and cell numbers within units (including diatoms) were identified and enumerated to the lowest taxonomic level. Then, the remainder of the subsample in the Palmer Maloney chamber and 2–5 additional subsamples were scanned to observe new algal species to determine the species richness until no new live taxa were observed on 2 consecutive transects. In the total community assessment, all algae analyzed were assumed to be physiologically active due to visible, healthy looking chloroplasts.

The taxonomy of the diatom community was specifically assessed on samples treated with a strong acid to increase species level identification accuracy. For the samples in which whole community DNA was isolated and to increase certainty in the diatom species identification, 10 mL of the remnant preserved samples was digested with nitric acid to remove all organic matter. A permanent diatom slide was prepared with the acid-cleaned material mounted in Naphrax resin for increased clarity (RI 1.74, The Biology Shop, Hazelbrook, New South Wales, Australia). For the diatom counts, 600 diatom valves were identified and counted to determine the species’ relative abundances. Then, the whole surface of the cover slip was scanned, and all diatom species were recorded. The identification was based on the current literature [28,29,30,31,32,33]. Permanent slides were archived and deposited as part of the diatom slide collection of the Georgia College and State University (GCSU) Natural History Museum.

2.3. Culturing of Isolates from Environmental Samples

Single viable cells from the composite field samples were isolated one at a time. Viable algal cells were taken with a glass tip and placed in a vial with a liquid medium or in a petri dish with agar medium. In the process we potentially took more than one species as algal cell size ranged from 12 to 140 μm. Algal isolates grew under artificial conditions as described above with the goal to increase number of cells within populations and increase yield in DNA isolation. Growth was monitored every third day. After observing increase in abundance, cultures grown in liquid or in agar medium were combined for DNA extractions. Thus, we created subsets of the natural community with known membership and expected higher matching with morphology.

2.4. Genomic DNA Isolation

Environmental DNA isolation was attempted for the total community from the mud sample-sediment, composite liquid cultures, and composite agar culture. Total genomic DNA was extracted from the algal culture and sediment samples using the MO BIO Power Soil DNA Isolation kit (MoBio PowerSoil DNA kit; Mo Bio Laboratories, Carlsbad, CA, USA), according to the manufacturer’s instruction. Briefly, 500 μL of samples was added to the Power Bead and lysis buffer-containing tubes provided in the kit, followed by the addition of Solutions C1 through C5. The DNA was eluted off of the column using the C6 elution buffer. The DNA concentration and quality was then determined using a Nanodrop ND-100 Spectrophotometer (Wilmington, DE, USA). The extracted genomic DNA samples were stored at −20 °C prior to the continuation of the cloning experiments.

2.5. PCR Amplification Using 18S rDNA Primers

The eukaryotic 18S rDNA universal primers published in previous studies were used to amplify 18S rDNA genes, using the total genomic DNA as template [34]. The sequences of the forward and reverse primers used were 1F (5′-CTG GTT GAT CCT GCC AG-3′) and 1520R (5′-CYG CAG GTT CAC CTA C-3′), respectively. PCR amplification was performed using a Phusion® High-Fidelity PCR Kit (New England Biolabs Inc., Bio Laboratories, Carlsbad, CA, USA), according to the manufacturer’s instruction. The 18S rDNA was amplified under the following conditions: The PCR reaction mixture contained 0.5 μM of each of the forward (F) and reverse (R) primers, 10 μL of GC buffer at a final concentration of 1×, 200 μM of dTNPs, 5–10 μL of template DNA for each sample, and 1.0 unit of DNA polymerase, and it was brought to a final reaction volume of 50 μL with ddH2O. The touch down PCR reaction parameters were set as follows: after the initial denaturation at 98 °C for 30 s, the first 10 cycles were performed by denaturation at 98 °C for 10 s, and the annealing temperature started at 62.9 °C and was lowered by 1 °C for each cycle until it reached 53.9 °C, followed by an extension at 72 °C for 30 s. The remaining 25 cycles were run by denaturation at 98 °C for 10 s, annealing at 53.9 °C, and a 30 s extension at 72 °C, followed by the final extension of 5 min at 72 °C.

To isolate the PCR product of the predicted size from the background smear, the PCR product was first resolved on a 1% agarose gel, followed by excising the band size of approximately 2 KB, then extracting it from the agarose using QIAquick Gel Extraction Kit (Qiagen, Valencia, CA, USA), according to the manufacturer’s instruction. DNA was then eluted from the column by the addition of 32 μL of EB buffer. The PCR products obtained from the genomic DNA extracts of sediment and cultured samples displayed a size of approximately 1700 bp. DNA purity was checked at every stage, starting from the gel extraction of PCR fragments to sequencing. The DNA purity, based on the A260/A280 ratio, was within the range of 1.6–1.8.

2.6. 18S rDNA Cloning and Plasmid DNA Isolation

The PCR-amplified 18S rDNA products were subsequently cloned into the pCR®4Blunt-Topo® vector of the Zero Blunt® TOPO® PCR Cloning Kit (Invitrogen, Carlsbad, CA, USA) following the manufacturer’s instruction. Then, the cloning reaction mixture was transformed into One Shot® Chemically Competent E. coli cells provided with the kit (Invitrogen), following the manufacturer’s protocol. Positive transformants were picked and cultured overnight in LB liquid medium containing kanamycin (50 μg/mL). The isolation of plasmid DNA was conducted using a QIAprep® Spin Miniprep Kit (Qiagen). Plasmids were checked for inserts by restriction analysis with EcoRI (New England Bio-Labs, Ipswich, MA, USA) digestion overnight at 37 °C, followed by the separation of DNA fragments on 1% agarose gels containing ethidium bromide.

2.7. Restriction Fragment Length Polymorphism (RFLP) Analyses of 18S rDNA

Plasmids positive for PCR products were further analyzed and compared by Restriction Fragment Polymorphism (RFLP) analysis to reduce the redundancy of clones for distinct identity for sequencing. Briefly, 4 µL of isolated plasmid DNA from each bacterial colony was digested with the restriction enzymes, AluI and MspI (New England BioLabs) overnight at 37 °C. The restricted DNA fragments were resolved on 2% (w/v) agarose gels in 0.5× TBE. The restriction profile was captured using a PhotoDoc-It Imaging System (Ultra-Violet Products Ltd., Upland, CA, USA) and manually screened to avoid redundancy. Only distinct 18S rDNA inserts were selected for sequencing. The effectiveness of AluI and MspI restriction digests in discriminating 18S rDNA sequences from very closely relates species was tested by comparing the hypothetical RFLP patterns using the same enzymes via the Geneious version R7 ( [35]).

2.8. DNA Sequencing and Analyses

Non-redundant clones were completely sequenced using the T3 primer at Sequetech (Mountain View, CA, USA), and the sequences were analyzed using Orientation Checker [36] to identify and correct their orientation. The resulting processed sequences were subsequently tested by Pintail [37] to detect low quality sequences and chimeric gene artifacts.

The sequences obtained from this study were used as queries to identify the nearest identifiable matches from existing eukaryotic sequences in the GenBank and EMBL databases using the BLAST search engine. The top five closest eukaryotic sequences retrieved from the EMBL and NCBI sequence databases for each distinct sequence were subsequently aligned with the corresponding query sequences using ClustalW and ClustalX version 2 [38], which is the Multiple Sequence Alignment tool of the EMBL [39]. The remainder of the 18S rDNA clone sequences were searched by BLAST for sequence similarities and aligned with reference sequences of the highest similarity scores obtained from GenBank and SILVA [40] using ClustalW [41]. Finally, the sequence with the highest similarity, as well as the closest phylogenetic relationship to the query sequence determined by the alignment tool, was selected as the closest match.

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