Name: a standardized procedure for monitoring harmful algal blooms in chile by metabarcoding analysis
Uploaded: 2021-08-26T15:00:00.000+00:00
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Description: Watch this Scientific Journal Video about A Standardized Procedure for Monitoring Harmful Algal Blooms in Chile by Metabarcoding Analysis at JoVE.com

This study was supported by the grant (JPMJSA1705) for a study on Science and Technology Research Partnership for Sustainable Development-Monitoring Algae in Chile (SATREPS-MACH). We thank Dr. Sandra Rios (Universidad de Los Lagos) for allowing us to use a film clip. We thank Neal Andrew Holland for his diligent proofreading of the manuscript. We express our sincere appreciation to the laboratory group members at CREAN-IFOP at Puerto Montt, Chile, for advising us on phytoplankton identification.

1. Sample collection and pretreatment
<ol>
	<li>Collect approximately 3 L of water sample from the target spot.</li>
	<li>Filter 1 L of water sample for 16S rRNA analysis through a tandem filtration (1 &#181;m and 0.2 &#181;m pore-size membrane) to separate free-living and attached bacteria.</li>
	<li>Filter another 1 L of water sample for 18S-rRNA analysis (phytoplankton detection) through a single filtration with a 0.2 &#181;m membrane. 
	NOTE: Filtration of the water sample must be completed within 12 h of sampling.</li>
	<li>Cut filtered membrane in half with sterile surgical scissors and wrap it with aluminum foil. Store at -20 &#176;C for upto 1 month or proceed to the next step.</li>
	<li>Extract DNA with Chelex method as described9. Store at -20 &#176;C for upto 1 month.</li>
</ol>
2. Microscope analysis
<ol>
	<li>Transfer 1 mL of the water sample by a pipette onto a 1 mL grid-slide.</li>
	<li>Observe the sample under a microscope.</li>
	<li>Record names and quantity of phytoplankton species.</li>
</ol>
3. 16S rRNA and 18S rRNA metabarcoding analysis
NOTE: This process consists of seven sections: preparation, first PCR amplicon generation, first amplicon cleanup, indexation by second PCR, second PCR amplicon verification and cleanup, DNA concentration adjustment, and DNA denaturation and sequencing. The entire process takes a minimum of 5 days (40 h) by an experienced lab personnel. See the Table of Materials for product numbers and manufactures.
<ol>
	<li>Preparation
	<ol>
		<li>Clean pipettes and laminar hood cabinet with 70% ethanol followed by UV exposure for 30 min frequently. Sterilize materials to be used.</li>
		<li>Thaw DNA samples at room temperature, centrifuge at 100 x g for 2 min, and transfer 100 &#181;L of each sample supernatant to 8-tube strips.</li>
	</ol>
	</li>
	<li>First PCR amplicon generation 
	NOTE: Perform the following procedures in a laminar hood cabinet. Always dilute primers from 1 &#181;M stock to the target concentration with PCR grade water to avoid primer contamination.
	<ol>
		<li>Prepare first PCR master mix in a sterile 1.5 mL tube for reactions (Table 1, Table 2).</li>
		<li>Aliquot 22.5 &#181;L of the master mix in an 8-tube strip and add 2.5 &#181;L of DNA sample. Use 2.5 &#181;L of PCR grade water for negative control.</li>
		<li>Run the first PCR cycle (Table 3).</li>
		<li>Prepare 100 mL of 2% Agarose-TBE gel containing 10 &#181;L of 1x nucleic acid gel stain.</li>
		<li>Load a mixture of 1.5 &#181;L of 1x DNA loading dye and 4 &#181;L of PCR product on the agarose gel. Also, load 100 bp DNA ladder on the gel.</li>
		<li>Perform electrophoresis at 100 V for 30 min.</li>
		<li>Ensure that there is a band at 500-600 bp range under a UV light image capture. Primer-dimer band is round 80 bp. 
		NOTE: Marine water samples contain PCR inhibitors. Missing amplicons can sometimes be solved by diluting samples 1:100 or 1:1000 with PCR grade water.</li>
		<li>Store first PCR products at -20 &#176;C until the next step. 
		CAUTION: Do not exceed one week of storage.</li>
	</ol>
	</li>
	<li>First PCR amplicon cleanup 
	NOTE: This section can be performed outside a laminar hood cabinet.
	<ol>
		<li>Use magnetic beads DNA cleanup system to remove PCR reaction residues, including primer-dimer products.</li>
		<li>Transfer 20 &#181;L of each cleaned first PCR product to a new 96 well plate. Seal the plate with a micro-seal film. Store at -20 &#176;C until the next step proceeds. 
		CAUTION: Do not exceed more than one week of storage.</li>
	</ol>
	</li>
	<li>Indexation by second PCR 
	NOTE: In this section, purified first PCR products will be amplified with various index primer combinations.
	<ol>
		<li>Dilute all Index 1 and Index 2 primers (Table 4) to 1 &#181;M with PCR grade water in 8 tube PCR strips placed in a laminar hood cabinet. 
		NOTE: The steps henceforward in this section can be performed outside of a laminar hood cabinet, as index primers are specific to the first PCR reactions overhang adapter.</li>
		<li>Position Index 1 primer in a horizontal row Index 2 primers in a vertical row (Table 5).</li>
		<li>In a new 96-well plate, add 12.5 &#181;L of hot-start-ready formulation to each well.</li>
		<li>Add 2.5 &#181;L of each index primer (1 &#181;M) to each well as in Table 4 using a multichannel pipette.</li>
		<li>Add 7.5 &#181;L of purified first PCR product.</li>
		<li>Mix gently by pipetting up and down 10 times. Cover the plate with a micro-seal film.</li>
		<li>Run the second PCR cycle (Table 3).</li>
		<li>Keep the plate at -20 &#176;C. 
		CAUTION: Do not exceed one week of storage.</li>
	</ol>
	</li>
	<li>Second PCR amplicon verification and cleanup
	<ol>
		<li>Use a fragment analyzer and associated reagent. Vortex and spin down reagent before use.</li>
		<li>Allow the sample buffer and DNA screen tape to equilibrate at room temp for 30 min. Then, place the DNA screen tape in a fragment analyzer.</li>
		<li>Mix 2 &#181;L of sample buffer and 3 &#181;L of second PCR amplicon in new 8 tube strips. Insert the 8 tube strips into the fragment analyzer. Press Run to start. 
		CAUTION: The formation of air bubbles must be avoided.</li>
		<li>Ensure that second PCR amplicons are approximately 613 bp (600 - 630 bp) for both 16S and 18S rRNA genes.</li>
		<li>Purify second PCR products using a magnetic beads DNA cleanup system.</li>
	</ol>
	</li>
	<li>DNA concentration adjustment
	<ol>
		<li>Measure DNA concentration in the purified second PCR products using a nucleic acid quantification spectrophotometer.</li>
		<li>Calculate target gene concentration in nM, accounting for the average final library size as 613 bp for both 16S and 18S rRNA genes: 
		<img alt="Equation 1" src="/files/ftp_upload/62967/62967eq01v3.jpg" /></li>
		<li>Dilute each purified second PCR product with sterile PCR water to 4 nM in a new 0.2 mL 96 well plate. 
		NOTE: The plate can be stored at -20 &#176;C here. Otherwise, the rest of procedures must be performed without halting.</li>
		<li>Aliquot 3 &#181;L of each 4 nM second PCR product and mix all in a new sterile 1.5 mL tube as a pooled library. Keep the tube at 4 &#176;C or on ice bath at all times.</li>
		<li>Measure the concentration of the pooled library for confirmation using a nucleic acid quantification spectrophotometer. Adjust the concentration to 4 nM if it is higher than 4 nM. 
		NOTE: Over-concentrated DNA produces overestimated read numbers, hampering analysis.</li>
	</ol>
	</li>
	<li>DNA denaturation and sequencing 
	NOTE: This section uses Illumina MiSeq system with specific reagents. See product numbers in Table of Materials. 
	NOTE: Strictly follow time. Thaw all reagents except cartridges on the day of sequencing.
	<ol>
		<li>A day before sequencing, remove a pre-filled ready-to-use reagent cartridge from -20 &#176;C and store at 4 &#176;C for thawing.</li>
		<li>Set a heat block suitable for 1.5 mL centrifuge tubes to 96 &#176;C.</li>
		<li>Place the hybridization buffer on ice.</li>
		<li>Dilute molecular grade NaOH from 1 N to 0.2 N in a new tube with PCR grade water.</li>
		<li>Dilute a ready-to-use control library with TE buffer (pH 8.0) from 10 nM stock to 4 nM in a new tube (i.e., 2 &#181;L of control library + 3 &#181;L of TE buffer).</li>
		<li>Mix 16 &#181;L of 4 nM pooled sample library with 4 &#181;L of 4 nM control library in a new tube labeled &#34;1&#34;.</li>
		<li>Mix 10 &#181;L of sample in tube &#34;1&#34; with 10 &#181;L of 0.2 N NaOH in a new tube labeled &#34;2&#34;.</li>
		<li>Vortex the tube 2 for 5 s, spin down briefly, and incubate at room temperature for 5 min.</li>
		<li>Add 980 &#181;L of hybridization buffer to tube 2.</li>
		<li>In a new tube labeled &#34;3&#34;, mix 260 &#181;L the sample from tube 2 with 390 &#181;L of hybridization buffer. Mix by inverting the tube.</li>
		<li>Incubate tube 3 at 96 &#176;C for 2 min and immediately place it on ice for a maximum 2 min.</li>
		<li>Remove the cartridge from 4 &#176;C refrigerator.</li>
		<li>Set up the sample-sheet for sequencing with each corresponding index 1 and index 2 adapters as in Table 6.</li>
		<li>Remove the flowcell from the MiSeq v3 kit. Gently clean the flowcell with sterile molecular grade PCR water. 
		NOTE: Do not pour water on the capillary dots of the flowcell. Gently wipe off water from the flowcell with non-fibrous paper.</li>
		<li>Load the full volume of tube 3 (650 &#181;L) into the cartridge.</li>
		<li>In instrument operation software, select sequencing and follow the instructions. Insert flowcell, incorporation buffer, and cartridge. Load sample-sheet. Press Run to start the reaction. 
		NOTE: This sequence run will take 3-5 days. The data will be automatically uploaded to Basespace platform. Raw data will be stored in two folders in computer: Analysis folder containing fastq files and Output containing bcl files and jpeg pictures.</li>
	</ol>
	</li>
</ol>
4.Taxonomic assignment to sequencing data
<ol>
	<li>FASTQ file processing 
	NOTE: DADA2 package23 of R24 is one of recommended databases to process FASTQ files. The guideline is available at [https://github.com/mickeykawai/exec_dada2]. This guideline was prepared by adopting the recent version of DADA2 pipeline tutorial [https://benjjneb.github.io/dada2/tutorial.html].
	<ol>
		<li>Process raw FASTQ paired-end sequences for trimming, quality filtering, dereplication and counting of unique sequences, sample inference, merging into contigs and removing chimeric sequences.</li>
		<li>Use following DADA2 specified parameters for both 16S rRNA and 18S rRNA gene fragments; trimLefts=0, 0, truncLens=[Check the sequence quality and set it to an appropriate length]. 
		NOTE: Default values for the other parameters are maxN=0, maxEE=2,2, trancQ=2.</li>
	</ol>
	</li>
	<li>Taxonomy assignment 
	NOTE: SILVA rRNA database is recommended for taxonomy assignment for DADA2 [silva_nr_v132_train_set.fa.gz is set as a default].
	<ol>
		<li>Remove chloroplast and mitochondrial OTUs for 16S rRNA gene fragment analysis. Remove singletons from 16S rRNA and 18S rRNA analysis.</li>
		<li>Rarify all samples to even sequencing depth based on the sample having the lowest sequencing depth.</li>
		<li>Record the number of reads, OTUs assigned and at what level, filtered reads, and the number of singletons excluded.</li>
	</ol>
	</li>
	<li>Statistical analysis
	<ol>
		<li>Perform statistical analysis on microbial data using R packages of phyloseq25 and vegan26.</li>
	</ol>
	</li>
</ol>

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A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Caught in Comparisons: Japanese Salmon in an Uneven World. , (2013).</li><li>Sakai, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Discovery+of+an+unrecorded+population+of+Yamato+salamander+(Hynobius+vandenburghi)+by+GIS+and+eDNA+analysis.">Discovery of an unrecorded population of Yamato salamander (Hynobius vandenburghi) by GIS and eDNA analysis.</a> Environmental DNA. 1 (3), 281-289 (2019).</li></ol>

The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

This protocol successfully performed 18S rRNA and 16S rRNA gene metabarcoding analysis to identify algal and bacterial species in seawater samples for monitoring Chilean HABs. The dominant algae and some minor algae detected by this protocol were consistent with those obtained by microscopy, confirming the protocol&#39;s reliability. The highlight of the protocol is that the analysis detected Alexandrium spp. and Pseudochattonella spp., the two most problematic HAB causing species in southern Chile, from the seawater of Metri even at a low abundance. Especially, Pseudochattonella spp. are challenging species to identify under a microscope because the cells are small and easily ruptured under light or use of fixatives27,28. Metabarcoding can provide algal species information that is scarcely present in seawater samples that microscopy cannot identify. The protocol also detected over 30 bacterial species. Although how these bacteria are related to algal growth is unknown at this point, massive data collection of algal-bacterial species in the time-series analysis will possibly provide such important discoveries. Thus, adding metabarcoding analysis to the conventional Chilean HAB monitoring programs will undoubtedly strengthen the current HAB monitoring efficiency. In fact, this visual protocol for metabarcoding analysis can be beneficial not only for Chilean water&#39;s algal-bacterial monitoring but also for the coast monitoring programs in other HAB-affected areas worldwide.
Although this protocol provided the above-stated advantages, the drawbacks of the method should be discussed. As seen in the visual protocol, the metabarcoding method is time-consuming and complex, requiring expensive machinery and reagents. The laboratory personnel must be specially trained, or it will waste material, labor, and time. In addition, algal detection by metabarcoding must be paired with microscopy to verify that the same dominant species are identified from the two orthogonal methods29. Microscopy is a non-invasive tool, for the most part, to identify algal species, meaning that it is harder to make mistakes when the algal species have unique and apparent shapes. Of course, human error can occur if the species have very similar shapes to one another. On the other hand, since metabarcoding analysis requires multiple steps, it naturally increases errors. It could be a sample mixed up, wrong reagent addition, or missing some procedures. Therefore, it is critical to compare the metabarcoding results with those obtained by microscopy. Lastly, the protocol is applicable only qualitatively, and the results must be calculated for relative abundance. As Lamb et al. stated in 2019, the current metabarcoding as the quantitative performance is still limited, and additional research is required before metabarcoding can be confidently utilized for quantitative applications30.
This protocol was optimized for 140 - 170 samples per run from the 16S Metagenomic Sequencing Library Preparation manual issued by Illumina Inc. The optimized protocol was tested many times and further modified to issue this final version. Therefore, it is highly recommended to follow each step precisely. The most critical part of the protocol is that extra care is required to avoid sample contamination. The pipettes and laminar flow hood must be cleaned with 70% alcohol and UV exposure at all times, and sterilizable materials should be autoclaved. The primers and reagents should be diluted directly from the stock at every new run, or reuse of reagents and exposure to several dilutions can be a sample contamination cause. The protocol specifies when the operation can be done outside of the laminar flow hood. Unless otherwise, samples should be treated in a cleaned laminar flow hood. When dealing with multiple 8-tube strips simultaneously, it is recommended to work on the first 8-tube strip and cap it before moving to the next 8-tube strip. Leaving the lid open for a long time can cause sample contamination as 16S and 18S rRNA genes are highly ubiquitous in surroundings. The stated time, such as mixing time and incubation time, should be followed as exactly described because the best duration for each step was selected based on the multiple test runs. Excess or insufficient time can, for example, reduce sample yield. The process should be halted only at the part that protocol says it can stop. It is crucial to have a negative control as it confirms that the positive outcomes are not artificial false positives. Lastly, it is highly recommended to plan the days because it requires at least five days to process all the steps, and some parts cannot be stopped until the next halt sign.
The limitation of taxonomic assignment for sequenced data are briefly noted here. Newly discovered nucleotide sequences for bacterial and algal species are updated in the databank daily. While well-studied algal and bacterial species are registered reliably, there are also many sequences for unknown species updated in the databank. It indicates that the resolution of registered nucleotide sequences is not always enough for species identification but only for genus identification. Thus, microscopic species identification must be orthogonally performed. Establishing an open-access pipeline for this work has a significant advantage in dealing with a large set of sequenced data at once and efficiently investigating when an error occurs. Additionally, the output of DADA2 is in a text or csv file, which makes it easy to further statistical analysis. On the other hand, a large server is required to perform taxonomic assignments, especially when a dataset is large. To set up the pipeline, engineers are needed to set up the pipeline, and the professionals must understand parameters, operation, Linux, and bioinformatics.
Aside from the scope as a HAB monitoring tool, the use of this protocol can be expanded for investigative research purposes. For example, there are ongoing debates between the Chilean government and local fishermen along with environment peace associations regarding the increased number of local HAB31,32. The government claims that it is due to a natural phenomenon such as global warming and El Ni&#241;o, while the later parties claim that salmon aquaculture is the cause. Salmon is not an indigenous species of Chile and was not in Chilean water a half-century ago. The Chilean government at that time aimed to grow the economy and create jobs for poor areas by developing a salmon business33. With the intervention from overseas for capital profit, Chile made great success in pen-style aquaculture development, and the number of salmon has increased remarkably in the last several decades31,32,33. Subsequently, a large amount of food for salmon has been thrown into the sea, leading local people to suspect that salmon aquaculture is the reason for the frequent local HABs31,32. The truth is unknown now, but it must be understood eventually so that strategies to protect the local marine environment, economy, and human health from HABs can be devised. The molecular-based analysis on the local algal-bacterial relationship may contribute to a step forward clarification for such a subject. For example, the technique can search the algal and bacterial species that were not present in the target oceanic area before but have increased dramatically in recent years, which is somewhat similar to the study done by Sakai et al. for the discovery of an unrecorded population of Yamato salamander (Hynobius vandenburghi) by GIS and eDNA analysis34. Therefore, beyond the HAB monitoring tool, this metabarcoding protocol has the potential usage for other prospects.

Many marine phytoplankton species are known to produce endogenous toxins, and when these species accumulate in sufficient numbers, they are harmful to the marine environment. Such Harmful Algal Blooms (HABs) are observed today on the coasts of most continents of the world1. Toxic phytoplankton first accumulate in bivalve tissues, leading to illness and death in higher trophic levels of organisms, including humans, upon digestion. Subsequently, these events bring severe implications to the local economy, socioeconomics, and public health2. The damage to the global economy caused by HABs is estimated to be millions to billions of dollars each year3. Chile is one of many countries suffering from frequent HABs.
Chile is a country of long land, stretching over 4,300 km north and south. The elongated western land faces the Pacific Ocean, which naturally increases the chances of Chile experiencing HABs. Especially in southern Chile, there are many world-famous salmon aquacultures, and every time a HAB occurs in the region, the algal-toxins result in massive farmed salmon falling ill and die4,5,6. In Chile, the year when the HAB hit the economy hardest was 2016, with an estimated annual loss of US $ 800 million7,8. The causative toxic algal species vary by year and location. For the 2016 case, a complex of Alexandrium catanella and Pseudochattonella verruclosa caused a widespread HAB in most of the southern Chilean coast7,8. The most recent HAB in Chile occurred with the causative algal species of Heterosigma Akashiwo in March 2021 at Camanchaca, located in southern Chile, where the area has a large salmon farm.
Chile has been conducting coastal monitoring for many years using two main methods; observing seawater with microscopes to identify toxic algae species regularly and measuring toxin levels in shellfish by biochemical assays9. Early detection of toxic algae and toxin levels in shellfish do not prevent HABs; however, these analyses can trigger immediate countermeasures and reduce damages to local communities. To further strengthen the effectiveness of this strategy, a molecular-based analytical method was recently added to our conventional Chilean HAB monitoring program to detect algae and related bacterial communities in seawater samples. Specifically, a Massive Parallel Sequencing method using metabarcoding that targets 16S rRNA and 18S rRNA genes was chosen. Although this technique requires complicated procedures and expensive machinery and reagents, it is an advanced technology that can detect thousands of algae and bacteria genera/species present in a seawater sample at once.
The causes of HAB are speculated to be various, such as temperature and season, but it is impossible to generalize them. This is because HAB species and frequencies depend on the region and spatiotemporal conditions, involving natural phenomena such as geographic uniqueness, upwelling nutrient mixing, and element runoff from land due to errosion2,10,11,12. Additionally, artificial factors such as eutrophication influence local HABs12,13. Due to the complex multifactor, it is not easy to make an accurate HAB prediction. In recent years, there is a view that specific bacterial populations may be related to the development of HABs as one of the factors, and research to support this hypothesis has been increasingly evident14,15,16,17,18. Molecular biology techniques are generally used to study bacterial assemblage; however, such a standardized method has not yet been established in Chilean HAB monitoring9. To study algal-bacterial association with HABs, it is imperative to concurrently perform metabarcoding analysis for the current Chilean coastal monitoring programs. Thus, this protocol visually introduces our Chilean HAB monitoring program, focusing on a stepwise procedure for analyzing algae and bacteria species detection in seawater samples using metabarcoding analysis.
The full protocol describing our Chilean HAB monitoring program is available in Yarimizu et al.9. It includes the procedures of seawater sampling, microscopic algal species detection, algal-bacterial gene detection, pigment analysis, meteorological data collection, and physical and chemical water property assays. The stepwise protocol of 16S rRNA and 18S rRNA metabarcoding analysis for algal and bacterial species detection is available as a preprint19. This protocol demonstrates particularly metabarcoding analysis steps since it is the most complicated part and the highlight of our HAB monitoring program. This protocol also includes an introduction of the program and microscopy detection of algal species. When analyzing algal species by metabarcoding, it is crucial to simultaneously perform microscopy to verify the results from the two methods. This protocol does not include how to use software for taxonomy assignment, but database recommendation is briefly stated at the end of the following section.

<table><tbody><tr><td>1.5 mL single tube</td><td>ThermoFisher</td><td>Q32856</td><td>sterile</td></tr><tr><td>1.5 mL tubes</td><td>ThermoFisher</td><td>2150N</td><td>sterile</td></tr><tr><td>8-strip 0.2 mL PCR tubes and flat cap</td><td>ThermoFisher</td><td>AB0451 &amp;amp; 4323032</td><td>sterile</td></tr><tr><td>96-well 0.2 mL PCR plate</td><td>ThermoFisher</td><td>N8010560</td><td></td></tr><tr><td>AccuRuler 100 bp DNA Ladder</td><td>MaestroGen</td><td>02001-500</td><td></td></tr><tr><td>Chelex 100 Chelating Resin</td><td>Bio-Rad</td><td>1432832</td><td></td></tr><tr><td>Chemical Duty Vacuum Pressure Pump</td><td>MilliporeSigma</td><td>WP6122050</td><td></td></tr><tr><td>D1000 Reagents</td><td>Agilent Technologies</td><td>5067-5583</td><td></td></tr><tr><td>D1000 sample buffer</td><td>Agilent Technologies</td><td>5067-5602</td><td></td></tr><tr><td>DNA loading dye (Blue/Orange Loading Dye 6X)</td><td>Promega</td><td>G1881</td><td></td></tr><tr><td>Ethanol Molecular Biology Grade</td><td>E7023</td><td>Merck KGaA</td><td></td></tr><tr><td>Filtration device</td><td>ThermoFisher</td><td>09-740-36H, 09-740-23E</td><td></td></tr><tr><td>Fluorescent DNA concentration measurement kit (Qubit dsDNA HS Assay kit)</td><td>Thermo Fisher Scientific</td><td>Q32851</td><td></td></tr><tr><td>Fluorometer (Qubit4 Fluorometer)</td><td>ThermoFisher</td><td>Q33238</td><td></td></tr><tr><td>Fragment analysis verification matrix (D1000 ScreenTape)</td><td>Agilent Technologies</td><td>5067-5582</td><td></td></tr><tr><td>Fragment Analyzer (Agilent Tapestation 4150)</td><td>Agilent</td><td>G2992AA</td><td></td></tr><tr><td>GelRed Nucleic Acid Gel Stain</td><td>Biotium</td><td>41002</td><td></td></tr><tr><td>Generic bench vortexer (Vortex Genie 2)</td><td>Scientific Industries</td><td>SI-0236</td><td></td></tr><tr><td>Heat block</td><td></td><td></td><td></td></tr><tr><td>High performance sequencing-grade DNA polymerase (KAPA HiFi Hotstart ReadyMix), 2X</td><td>Roche</td><td>KK2602</td><td></td></tr><tr><td>HT1 Hybridization buffer</td><td>Illumina</td><td>20015892</td><td></td></tr><tr><td>Illumina Nextera XT v2 Index Kit set A, B, C and D</td><td>Illumina</td><td>FC-131-2001, -2002, -2003, -2004</td><td></td></tr><tr><td>Laminar hood cabinet</td><td>Biobase Co.</td><td>Biobase PCR-800 PCR Cabinet</td><td></td></tr><tr><td>Loading tips (Specific for Agilent TapeStation systems)</td><td>Agilent Technologies</td><td>5067-5598</td><td></td></tr><tr><td>Magnetic beads DNA cleanup system (ProNex&amp;reg; Size-Selective Purification System)</td><td>Promega</td><td>NG2002</td><td></td></tr><tr><td>Magnetic stand for 96 wells (Magnetic Stand-96)</td><td>ThermoFisher</td><td>AM10027</td><td></td></tr><tr><td>Micro-seal film for 96-well plate</td><td>ThermoFisher</td><td>4311971</td><td>sterile</td></tr><tr><td>MiSeq Reagent Kit v3</td><td>Illumina</td><td>MS-102-3003</td><td>includes pre-filled, ready-to-use reagent cartridges.</td></tr><tr><td>MiSeq system</td><td>Illumina</td><td>SY-410-1003</td><td>includes pre-installed software</td></tr><tr><td>Molecular grade nuclease-free water</td><td>Integrated DNA Technologies</td><td>11-05-01-04</td><td></td></tr><tr><td>Molecular grade sodium hydroxide (NaOH) 1M</td><td>Merck KGaA</td><td>1091371000</td><td></td></tr><tr><td>Multichannel pipettes</td><td>Not specified</td><td>Not specified</td><td></td></tr><tr><td>Multi-Purpose Agarose</td><td>Cleaver Scientific</td><td>CSL-AG500</td><td></td></tr><tr><td>Ow D2 Wide-Gel Electrophoresis System</td><td>ThermoFisher</td><td>D2</td><td></td></tr><tr><td>Ow EC-105 Compact Power Supply</td><td>ThermoFisher</td><td>105ECA-115</td><td></td></tr><tr><td>Pellet Pestle&amp;mdash; Cordless Motor</td><td>ThermoFisher</td><td>K749540-0000</td><td></td></tr><tr><td>PhiX control Kit v3</td><td>Illumina</td><td>FC-110-300</td><td>ready-to-use control library for Illumina sequencing</td></tr><tr><td>Pipette tips</td><td>Not specified</td><td>Not specified</td><td>sterile</td></tr><tr><td>Pipettes</td><td>Not specified</td><td>Not specified</td><td>2, 10, 20, 100, 1000 &amp;mu;L</td></tr><tr><td>SterivexTM GP 0.22&amp;nbsp; &amp;mu;m filter unit</td><td>MilliporeSigma</td><td>SVGP01050</td><td></td></tr><tr><td>Tabletop centrifuge</td><td>Eppendorf 5427R Centrifuge</td><td>Eppendorf AG</td><td></td></tr><tr><td>TBE buffer</td><td>ThermoFisher</td><td>B52</td><td></td></tr><tr><td>TE buffer (pH 8.0)</td><td>ThermoFisher</td><td>AM9849</td><td></td></tr><tr><td>Terr PCR Direct FFPE Kit</td><td>Takara Bio USA</td><td>639284</td><td>includes 2X Terra PCR Direct Buffer</td></tr><tr><td>Terra PCR Direct Polymerase Mix, 1.25 U/&amp;mu;L</td><td>Takara Bio USA</td><td>639271</td><td>High performance Inhibitor-tolerant DNA polymerase</td></tr><tr><td>ThermalCycler (MiniAMP Plus Thermal Cycler)</td><td>ThermoFisher</td><td>A37835</td><td></td></tr><tr><td>TransIlluminator and image capture system</td><td>Analytik Jena, AG</td><td>GelDoc-ItTS2 Imager</td><td></td></tr><tr><td>Whatman 1.0&amp;nbsp; m pore-sized membrane</td><td>MilliporeSigma</td><td>WHA111110</td><td></td></tr></tbody></table>

a standardized procedure for monitoring harmful algal blooms in chile by metabarcoding analysis

Harmful algae blooms (HABs) monitoring has been implemented worldwide, and Chile, a country famous for its fisheries and aquaculture, has intensively used microscopic and toxin analyses for decades for this purpose. Molecular biological methods, such as high-throughput DNA sequencing and bacterial assemblage-based approaches, are just beginning to be introduced in Chilean HAB monitoring, and the procedures have not yet been standardized. Here, 16S rRNA and 18S rRNA metabarcoding analyses for monitoring Chilean HABs are introduced stepwise. According to a recent hypothesis, algal-bacterial mutualistic association plays a critical synergetic or antagonistic relationship accounting for bloom initiation, maintenance, and regression. Thus, monitoring HAB from algal-bacterial perspectives may provide a broader understanding of HAB mechanisms and the basis for early warning. Metabarcoding analysis is one of the best suited molecular-based tools for this purpose because it can detect massive algal-bacterial taxonomic information in a sample. The visual procedures of sampling to metabarcoding analysis herein provide specific instructions, aiming to reduce errors and collection of reliable data.

This protocol uses 18S rRNA gene metabarcoding analysis to identify algal species in seawater samples. A representative result is shown in Figure 1, in which the seawater collected from Metri, Puerto Montt, Chile (&#8722;41.597; &#8722;72.7056) on February 19, 2019, was analyzed with this protocol. The result showed a total of 13,750 reads with over 30 algal species in the seawater sample. The dominant alga in this sample was Navicula spp. with a relative abundance of 70.77%. Also, sufficient abundance was observed for Micromonas (6.40%), Chaetoceros spp. (4.44%), Scripsiella spp. (2.44%), and Prorocentrum spp. (1.28%). Pseudochattonella spp., one of the highest toxic algal causatives of Chilean HAB, was detected with 0.52% from this seawater sample.
To verify the data reliability, the algal species identified by 18S rRNA gene analysis were compared to those obtained by microscopy in the same seawater sample (Figure 2). Consistent with 18S rRNA gene analysis, the microscopy showed that the dominant species was Navicula spp. with a relative abundance of 74.1% and Prorocentrum spp. (0.60%) as a minor species. Conversely, Heterocapsa spp. (9.04%) documented from microscopic observation was not identified by 18S rRNA gene analysis in this sample. There was 12.6% of small around unidentifiable phytoplankton cells recorded by microscopy. This could be Micromonas, according to the 18S rRNA results.
The protocol uses 16S rRNA gene metabarcoding analysis to identify bacterial species in seawater samples. A representative result is shown in Figure 3, in which the same seawater used for 18S rRNA gene analysis was analyzed with 16S rRNA gene analysis. The result showed a total of 31,758 reads with over 30 bacterial species in the seawater sample. It should be outlined that this seawater sample was passed through tandem filter membranes (1 &#181;m and 0.2 &#181;m pore sizes) to separate free-living bacteria from attached bacteria. Then, cells captured by each filter membrane were treated for DNA extraction, followed by the 16S rRNA gene analysis. The representative result in Figure 3 shows bacteria species identified from 0.2 &#181;m pore size membrane, which are defined as free-living bacteria. The dominant free-living bacteria was Amylibacter spp. with a relative abundance of 20.02%, followed by Clade Ia (13.53%) and Aligiphilus spp. (7.06%). The rest of the bacterial species detected from this seawater sample were relatively equally distributed. The same analysis can be done for cells captured by 1 &#181;m pore-sized membrane as attach-bacteria species detection.
When these metabarcoding assays are performed at scheduled time-points over a certain study period, the results can be summarized as time-series analysis. One way to do it is to plot the relative abundance of particular algal and bacterial species as a function of time to find a unique growth pattern. Figure 4 shows a representative time-series plot of Alexandrium and Pseudochattonella in Metri, Chile. Another way to summarize a time-series metabarcoding analysis is to plot all identified algal and bacterial as a function of time, representing the population change of certain groups of organisms. Figure 5 and Figure 6 summarizes the relative abundance of all the bacterial genus and order, respectively, which are detected from the seawater of Metri over five months.
Table 1: First PCR master mix content: The table shows master mix content per reaction for 16S rRNA and 18S rRNA analyses. The primer sequences are listed in Table 3. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62967/Table 1.xlsx" target="_blank">Please click here to download this Table.</a>
Table 2: Primer sequences: The primers for first PCR are listed for 16S rRNA and 18S rRNA analyses. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62967/Table 2.xlsx" target="_blank">Please click here to download this Table.</a>
Table 3: PCR cycles: The thermal cycles for first PCR and second PCR are listed. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62967/Table 3.xlsx" target="_blank">Please click here to download this Table.</a>
Table 4: Index sequences: Index 1 (i7) and Index 2 (i5) primers to be used for second PCR are listed. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62967/Table 4.xlsx" target="_blank">Please click here to download this Table.</a>
Table 5: Example of Index 1 and Index 2 positioning: To reduce error, index primers must be placed in the position first, and aliquot of each is transferred to a 96-well plate using multichannel pipette. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62967/Table 5.xlsx" target="_blank">Please click here to download this Table.</a>
Table 6: Example of sample-sheet: Prior to sequencing, a sample-sheet must be created corresponding to index 1 and index 2 adapters. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62967/Table 6.xlsx" target="_blank">Please click here to download this Table.</a>
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/62967/62967fig01.jpg" /> 
Figure 1: Representative result of 18S rRNA metabarcoding analysis: Algal species present in a seawater sample collected from Metri, Los Lagos, Chile on February 19, 2019, were identified by 18S rRNA metabarcoding assay. The sequences assigned &#34;unknown&#34; were eliminated from, and the relative abundance of each identified species was plotted. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62967/62967fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/62967/62967fig02.jpg" /> 
Figure 2: Representative result of microscopic analysis: The algal species were identified from a water sample from Metri, Los Lagos, Chile on February 19, 2019, by microscopy. The quantity of each species was counted manually and plotted. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62967/62967fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/62967/62967fig03.jpg" /> 
Figure 3: Representative result of 16S rRNA metabarcoding analysis: Bacterial species present in a water sample from Metri, Los Lagos, Chile on February 19, 2019, were identified by 16S rRNA metabarcoding assay. The relative abundance of each identified species was plotted. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62967/62967fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/62967/62967fig04.jpg" /> 
Figure 4: Representative time-series plot of Alexandrium spp. and Pseudochattonella spp. obtained from 18S rRNA metabarcodingin Metri, Chile: Figure reprinted from Yarimizu et al9. The two toxic algal species, Alexandrium and Pseudochattonella were selectively monitored by 18S rRNA analysis over time, and the relative abundance were plotted as a function of time-point. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62967/62967fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/62967/62967fig05.jpg" /> 
Figure 5: Representative genus time-series plot obtained from 16S rRNA and 18S rRNA metabarcoding: All bacterial and eukaryotic genus identified in the water of Metri, Chile, monitored over five months. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62967/62967fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/62967/62967fig06.jpg" /> 
Figure 6: Representative order time-series plot obtained from 16S rRNA and 18S rRNA metabarcoding: All bacterial and eukaryotic&#160;orders identified in the water of Metri, Chile, monitored over five months. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62967/62967fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Table S1 and Table S2:&#160; 
Table S1: Raw data for Figure 5 and Figure 6.&#160;Table S2: Result of QC check on raw data of Figure 5 and Figure 6. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62967/Tables S.xlsx" target="_blank">Please click here to download these Tables.</a>

Watch this Scientific Journal Video about A Standardized Procedure for Monitoring Harmful Algal Blooms in Chile by Metabarcoding Analysis at JoVE.com

A Standardized Procedure for Monitoring Harmful Algal Blooms in Chile by Metabarcoding Analysis

This protocol introduces steps of metabarcoding analysis, targeting 16S rRNA and 18S rRNA genes, for monitoring harmful algal blooms and their associated microbiome in seawater samples. It is a powerful molecular-based tool but requires several procedures, which are visually explained here step-by-step.

Harmful algae blooms (HABs) monitoring has been implemented worldwide, and Chile, a country famous for its fisheries and aquaculture, has intensively used ...

a-standardized-procedure-for-monitoring-harmful-algal-blooms-chile

Nanyang Technological University

Research

JoVE Journal

Environment

5.1K Views.  Hiroshima University. Dit protocol geeft inzicht in algenbloei en hoe toxische algensoorten effectief kunnen worden gemonitord. De procedure helpt een vroege waarschuwing te geven voor algenbloei om de gemeenschap te beschermen tegen bijbehorende schade. Metabarcoding-analyse kan veel soorten tegelijk in een monster detecteren, maar het vereist gecompliceerde procedures.Dit protocol legt elke stap visueel uit om menselijke fouten tijdens de analyse te minimaliseren. De microscopieprocedures zullen worden ge...

Video: A Standardized Procedure for Monitoring Harmful Algal Blooms in Chile by Metabarcoding Analysis

Unraveling the Unseen Players in the Ocean - A Field Guide to Water Chemistry and Marine Microbiology

Here we introduce a series of thoroughly tested and well standardized research protocols adapted for use in remote marine environments. The sampling protocols include the assessment of resources available to the microbial community (dissolved organic carbon, particulate organic matter, inorganic nutrients), and a comprehensive description of the viral and bacterial communities (via direct viral and microbial counts, enumeration of autofluorescent microbes, and construction of viral and microbial metagenomes). We use a combination of methods, which represent a dispersed field of scientific disciplines comprising already established protocols and some of the most recent techniques developed. Especially metagenomic sequencing techniques used for viral and bacterial community characterization, have been established only in recent years, and are thus still subjected to constant improvement. This has led to a variety of sampling and sample processing procedures currently in use. The set of methods presented here provides an up to date approach to collect and process environmental samples. Parameters addressed with these protocols yield the minimum on information essential to characterize and understand the underlying mechanisms of viral and microbial community dynamics. It gives easy to follow guidelines to conduct comprehensive surveys and discusses critical steps and potential caveats pertinent to each technique.

Here, we present a comprehensive protocol to assess the organic and inorganic nutrient availability and the abundance and structure of microbial and viral communities in remote marine environments.

Here we introduce a series of thoroughly tested and well standardized research protocols adapted for use in remote marine environments. The sampling ...

An Aquatic Microbial Metaproteomics Workflow: From Cells to Tryptic Peptides Suitable for Tandem Mass Spectrometry-based Analysis

Meta-omic technologies such as metagenomics, metatranscriptomics and metaproteomics can aid in the understanding of microbial community structure and metabolism. Although powerful, metagenomics alone can only elucidate functional potential. On the other hand, metaproteomics enables the description of the expressed in situ metabolism and function of a community. Here we describe a protocol for cell lysis, protein and DNA isolation, as well as peptide digestion and extraction from marine microbial cells collected on a cartridge filter unit (such as the Sterivex filter unit) and preserved in an RNA stabilization solution (like RNAlater). In mass spectrometry-based proteomics studies, the identification of peptides and proteins is performed by comparing peptide tandem mass spectra to a database of translated nucleotide sequences. Including the metagenome of a sample in the search database increases the number of peptides and proteins that can be identified from the mass spectra. Hence, in this protocol DNA is isolated from the same filter, which can be used subsequently for metagenomic analysis.

This protocol is for the extraction and concentration of protein and DNA from microbial biomass collected from seawater, followed by the generation of tryptic peptides suitable for tandem mass spectrometry-based proteomic analysis.

Meta-omic technologies such as metagenomics, metatranscriptomics and metaproteomics can aid in the understanding of microbial community structure and ...

Quantification of Heavy Metals and Other Inorganic Contaminants on the Productivity of Microalgae

Increasing demand for renewable fuels has researchers investigating the feasibility of alternative feedstocks, such as microalgae. Inherent advantages include high potential yield, use of non-arable land and integration with waste streams. The nutrient requirements of a large-scale microalgae production system will require the coupling of cultivation systems with industrial waste resources, such as carbon dioxide from flue gas and nutrients from wastewater. Inorganic contaminants present in these wastes can potentially lead to bioaccumulation in microalgal biomass negatively impact productivity and limiting end use. This study focuses on the experimental evaluation of the impact and the fate of 14 inorganic contaminants (As, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb, Sb, Se, Sn, V and Zn) on Nannochloropsis salina growth. Microalgae were cultivated in photobioreactors illuminated at 984 &#181;mol m-2 sec-1 and maintained at pH 7 in a growth media polluted with inorganic contaminants at levels expected based on the composition found in commercial coal flue gas systems. Contaminants present in the biomass and the medium at the end of a 7 day growth period were analytically quantified through cold vapor atomic absorption spectrometry for Hg and through inductively coupled plasma mass spectrometry for As, Cd, Co, Cr, Cu, Mn, Ni, Pb, Sb, Se, Sn, V and Zn. Results show N. salina is a sensitive strain to the multi-metal environment with a statistical decrease in biomass yieldwith the introduction of these contaminants. The techniques presented here are adequate for quantifying algal growth and determining the fate of inorganic contaminants.

Integration of microalgal cultivation with industrial flue gas will ultimately introduce heavy metals and other inorganic compounds into the growth media. This study presents a procedure used to determine the end fate and impact of heavy metals and inorganic contaminants on the growth of Nannochloropsis salina grown in photobioreactors.

Increasing demand for renewable fuels has researchers investigating the feasibility of alternative feedstocks, such as microalgae. Inherent advantages ...

Experimental Protocol for Detecting Cyanobacteria in Liquid and Solid Samples with an Antibody Microarray Chip

Global warming and eutrophication make some aquatic ecosystems behave as true bioreactors that trigger rapid and massive cyanobacterial growth; this has relevant health and economic consequences. Many cyanobacterial strains are toxin producers, and only a few cells are necessary to induce irreparable damage to the environment. Therefore, water-body authorities and administrations require rapid and efficient early-warning systems providing reliable data to support their preventive or curative decisions. This manuscript reports an experimental protocol for the in-field detection of toxin-producing cyanobacterial strains by using an antibody microarray chip with 17 antibodies (Abs) with taxonomic resolution (CYANOCHIP). Here, a multiplex fluorescent sandwich microarray immunoassay (FSMI) for the simultaneous monitoring of 17 cyanobacterial strains frequently found blooming in freshwater ecosystems, some of them toxin producers, is described. A microarray with multiple identical replicates (up to 24) of the CYANOCHIP was printed onto a single microscope slide to simultaneously test a similar number of samples. Liquid samples can be tested either by direct incubation with the antibodies (Abs) or after cell concentration by filtration through a 1- to 3-&#956;m filter. Solid samples, such as sediments and ground rocks, are first homogenized and dispersed by a hand-held ultrasonicator in an incubation buffer. They are then filtered (5 - 20 &#956;m) to remove the coarse material, and the filtrate is incubated with Abs. Immunoreactions are revealed by a final incubation with a mixture of the 17 fluorescence-labeled Abs and are read by a portable fluorescence detector. The whole process takes around 3 h, most of it corresponding to two 1-h periods of incubation. The output is an image, where bright spots correspond to the positive detection of cyanobacterial markers.

Experimental Protocol for Detecting Cyanobacteria in Liquid and Solid Samples with an Antibody Microarray Chip

The presence of cyanobacterial toxins in fresh water reservoirs for human consumption is a major concern for water management authorities. To evaluate the risk of water contamination, this article describes an protocol for the in-field detection of cyanobacterial strains in liquid and solid samples by using an antibody microarray chip.

Global warming and eutrophication make some aquatic ecosystems behave as true bioreactors that trigger rapid and massive cyanobacterial growth; this has ...

A Multi-Omics Extraction Method for the In-Depth Analysis of Synchronized Cultures of the Green Alga Chlamydomonas reinhardtii

Microalgae have been the focus of research for their applications in the production of high value compounds, food and fuel. Moreover, they are valuable photosynthetic models facilitating the understanding of the basic cellular processes. System wide studies enable comprehensive and in-depth understanding of molecular functions of the organisms. However, multiple independent samples and protocols are required for proteomics, lipidomics and metabolomics studies introducing higher error and variability. A robust high throughput extraction method for the simultaneous extraction of chlorophyll, lipids, metabolites, proteins and starch from a single sample of the green alga Chlamydomonas reinhardtii is presented here. The illustrated experimental setup is for Chlamydomonas cultures synchronized using 12 h/12 h light/dark conditions. Samples were collected over a 24 h cell cycle to demonstrate that the metabolites, lipids and starch data obtained using various analytical platforms are well conformed. Furthermore, protein samples collected using the same extraction protocol were used to conduct detailed proteomics analysis to evaluate their quality and reproducibility. Based on the data, it can be inferred that the illustrated method provides a robust and reproducible approach to advance understanding of various biochemical pathways and their functions with greater confidence for both basic and applied research.

A Multi-Omics Extraction Method for the In-Depth Analysis of Synchronized Cultures of the Green Alga Chlamydomonas reinhardtii

System-wide analysis of multiple biomolecules is crucial to gain functional and mechanistic insights into biological processes. Hereby, an extensive protocol is described for high throughput extraction of lipids, metabolites, proteins and starch from a single sample harvested from synchronized Chlamydomonas culture.

Microalgae have been the focus of research for their applications in the production of high value compounds, food and fuel. Moreover, they are valuable ...

Developmental Biology

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Fast detection of cyanobacteria and cyanotoxins is achieved using a Fast Detection Strategy (FDS). Only 24 h are needed to unravel the presence of cyanobacteria and related cyanotoxins in water samples and in an organic matrix, such as bivalve extracts. FDS combines remote/proximal sensing techniques with analytical/bioinformatics analyses. Sampling spots are chosen through multi-disciplinary, multi-scale, and multi-parametric monitoring in a three-dimensional physical space, including remote sensing. Microscopic observation and taxonomic analysis of the samples are performed in the laboratory setting, which allows for the identification of cyanobacterial species. Samples are then extracted with organic solvents and processed with LC-MS/MS. Data obtained by MS/MS are analyzed using a bioinformatic approach using the online platform Global Natural Products Social (GNPS) to create a network of molecules. These networks are analyzed to detect and identify toxins, comparing data of the fragmentation spectra obtained by mass spectrometry with the GNPS library. This allows for the detection of known toxins and unknown analogues that appear related in the same molecular network.

A fast-multidisciplinarystrategy for early detection of cyanobacterial blooms and associated cyanotoxins is described here. It allows for the detection of cyanobacteria and related cyanotoxins in water samples and in organic matrices, such as bivalve samples, in 24 h.

Fast detection of cyanobacteria and cyanotoxins is achieved using a Fast Detection Strategy (FDS). Only 24 h are needed to unravel the presence of ...

Chemistry

Enhancing Chlorophyll Production in Microalgae via Growth Factor Optimization

Evaluation of the Effect of Growth Factors on Chlorophylls a and b Production from Microalgae

Microalgae contain two main groups of pigments: chlorophylls and carotenoids. Chlorophyll is a green pigment that absorbs light energy and transforms it into chemical energy to facilitate the synthesis of organic compounds. This pigment serves as a valuable primary source for biotechnological input products in the food, pharmaceutical, and cosmetic industries due to its high antioxidant properties and coloring capabilities. The objective of this research was to evaluate the effect of growth factors (CO2 concentration, light color, and light intensity) through a Taguchi L4 experimental design on cell growth and the cellular content of chlorophyll a and b in Chlorella sorokiniana, followed by validation of the method using Haematococcus pluvialis microalgae as an additional study model. Cell growth was quantified using the optical density spectrophotometric technique at a wavelength of 550 nm. For the quantification of chlorophylls, a cell extract was obtained using a 90% pure acetone solution, and subsequently, the concentrations of chlorophylls a and b were quantified using spectrophotometric techniques at wavelengths of 647 nm and 664 nm, according to the method described by Jeffrey and Humphrey. The experimental results indicated that controlling conditions of low CO2 addition, purple light, and low light intensity increases both cell growth and the concentration of chlorophylls a and b within the cells. The implementation of this chlorophyll quantification method allows for quick, simple, and precise determination of chlorophyll content, as the wavelengths used are at the absorbance peaks of both types of chlorophylls, making this technique easily reproducible for any microalgae under study.

Author Spotlight: Optimizing Growth Factors for Production of Biotechnologically Relevant Secondary Metabolites

The present study highlights the advantages of employing the method developed by Jeffrey and Humphrey for extracting and quantifying fat-soluble pigments from microalgae. This method serves as a valuable tool for assessing the influence of growth factors on chlorophyll production and cellular content in these organisms.

Microalgae contain two main groups of pigments: chlorophylls and carotenoids. Chlorophyll is a green pigment that absorbs light energy and transforms it ...

Biochemistry

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Metabolic models are reconstructed based on an organism&#39;s available genome annotation and provide predictive tools to study metabolic processes at a systems-level. Genome-scale metabolic models may include gaps as well as reactions that are unverified experimentally. Reconstructed models of newly isolated microalgal species will result in weaknesses due to these gaps, as there is usually sparse biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology is an effective, high-throughput method that functionally determines cellular metabolic activities&#160;in response to a wide array of entry metabolites. Combining the high throughput phenotypic assays with metabolic modeling can allow existing metabolic network models to be rapidly reconstructed or optimized by providing biochemical evidence to support and expand genomic evidence. This work will show the use of PM assays for the study of microalgae by using the green microalgal model species Chlamydomonas reinhardtii as an example. Experimental evidence for over 254 reactions obtained by PM was used in this study to expand and refine a genome-scale C. reinhardtii metabolic network model, iRC1080, by approximately 25 percent. The protocol created here can be used as a basis for functionally profiling the metabolism of other microalgae, including known microalgae mutants and new isolates.

This protocol demonstrates the use of a phenotype microarray (PM) technology platform to define metabolic requirements of Chlamydomonas reinhardtii, a green microalga, and refine an existing metabolic network model.

Metabolic models are reconstructed based on an organism&#39;s available genome annotation and provide predictive tools to study metabolic processes at a ...

Biology

Autofluorescence Imaging to Evaluate Red Algae Physiology

Red algae (Rhodophyta) contain phycobiliproteins and colonize habitats with dim light, however some (e.g., some Chroothece species) can also develop in full sunshine. Most rhodophytes are red, however some can appear bluish, depending on the proportion of blue and red biliproteins (phycocyanin and phycoerythrin). Different phycobiliproteins can capture light at diverse wavelengths and transmit it to chlorophyll a, which makes photosynthesis under very different light conditions possible. These pigments respond to habitat changes in light, and their autofluorescence can help to study biological processes. Using Chroothece mobilis as a model organism and the spectral lambda scan mode in a confocal microscope, the adaptation of photosynthetic pigments to different monochromatic lights was studied at the cellular level to guess the species' optimal growth conditions. The results showed that, even when the studied strain was isolated from a cave, it adapted to both dim and medium light intensities. The presented method is especially useful for studying photosynthetic organisms that do not grow or grow very slowly under laboratory conditions, which is usually the case for those living in extreme habitats.

The present protocol describes step-by-step autofluorescence imaging and evaluation of phycobiliprotein changes in red algae based on spectral analysis. This is a label-free and non-destructive method to evaluate cellular adaptation to extreme habitats, when only scarce material is available and cells grow slowly, or not at all, under laboratory conditions.

Red algae (Rhodophyta) contain phycobiliproteins and colonize habitats with dim light, however some (e.g., some Chroothece species) can also develop in ...

Metabolite Profiling of Hard Coral Samples Using GC-MS

Gas Chromatography-Mass Spectrometry-Based Targeted Metabolomics of Hard Coral Samples

Gas chromatography-mass spectrometry (GC-MS)-based approaches have proven to be powerful for elucidating the metabolic basis of the cnidarian-dinoflagellate symbiosis and how coral responds to stress (i.e., during temperature-induced bleaching). Steady-state metabolite profiling of the coral holobiont, which comprises the cnidarian host and its associated microbes (Symbiodiniaceae and other protists, bacteria, archaea, fungi, and viruses), has been successfully applied under ambient and stress conditions to characterize the holistic metabolic status of the coral.
However, to answer questions surrounding the symbiotic interactions, it is necessary to analyze the metabolite profiles of the coral host and its algal symbionts independently, which can only be achieved by physical separation and isolation of the tissues, followed by independent extraction and analysis. While the application of metabolomics is relatively new to the coral field, the sustained efforts of research groups have resulted in the development of robust methods for analyzing metabolites in corals, including the separation of the coral host tissue and algal symbionts.
This paper presents a step-by-step guide for holobiont separation and the extraction of metabolites for GC-MS analysis, including key optimization steps for consideration. We demonstrate how, once analyzed independently, the combined metabolite profile of the two fractions (coral and Symbiodiniaceae) is similar to the profile of the whole (holobiont), but by separating the tissues, we can also obtain key information about the metabolism of and interactions between the two partners that cannot be obtained from the whole alone.

Author Spotlight: Separation of Coral Host Tissues and Algal Symbionts and Analyzing Their Metabolites 

Here, we present the extraction and preparation of polar and semi-polar metabolites from a coral holobiont, as well as separated coral host tissue and Symbiodiniaceae cell fractions, for gas chromatography-mass spectrometry analysis.

A Standardized Procedure for Monitoring Harmful Algal Blooms in Chile by Metabarcoding Analysis

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