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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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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<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 &#38; 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&#174; 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&#8212; 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 &#956;L</td>
		</tr>
		<tr>
			<td>SterivexTM GP 0.22&#160; &#956;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/&#956;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&#160; 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/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/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/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/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/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/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/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/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/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/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/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/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/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 ...

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JoVE Journal

Environment

5.0K Views.  Hiroshima University. 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. 

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

A Small Volume Procedure for Viral Concentration from Water

Small-scale concentration of viruses (sample volumes 1-10 L, here simulated with spiked 100 ml water samples) is an efficient, cost-effective way to identify optimal parameters for virus concentration. Viruses can be concentrated from water using filtration (electropositive, electronegative, glass wool or size exclusion), followed by secondary concentration with beef extract to release viruses from filter surfaces, and finally tertiary concentration resulting in a 5-30 ml volume virus concentrate. In order to identify optimal concentration procedures, two different electropositive filters were evaluated (a glass/cellulose filter [1MDS] and a nano-alumina/glass filter [NanoCeram]), as well as different secondary concentration techniques; the celite technique where three different celite particle sizes were evaluated (fine, medium and large) followed by comparing this technique with that of the established organic flocculation method. Various elution additives were also evaluated for their ability to enhance the release of adenovirus (AdV) particles from filter surfaces. Fine particle celite recovered similar levels of AdV40 and 41 to that of the established organic flocculation method when viral spikes were added during secondary concentration. The glass/cellulose filter recovered higher levels of both, AdV40 and 41, compared to that of a nano-alumina/glass fiber filter. Although not statistically significant, the addition of 0.1% sodium polyphosphate amended beef extract eluant recovered 10% more AdV particles compared to unamended beef extract.

An approach was developed for identifying optimal viral concentration conditions for small volume water samples using spikes of human adenovirus. The techniques described here are used to identify concentration parameters for other viral targets, and applied to large-scale viral concentration experimentation.

Small-scale concentration of viruses (sample volumes 1-10 L, here simulated with spiked 100 ml water samples) is an efficient, cost-effective way to ...

Use of Chironomidae (Diptera) Surface-Floating Pupal Exuviae as a Rapid Bioassessment Protocol for Water Bodies

Rapid bioassessment protocols using benthic macroinvertebrate assemblages have been successfully used to assess human impacts on water quality. Unfortunately, traditional benthic larval sampling methods, such as the dip-net, can be time-consuming and expensive. An alternative protocol involves collection of Chironomidae surface-floating pupal exuviae (SFPE). Chironomidae is a species-rich family of flies (Diptera) whose immature stages typically occur in aquatic habitats. Adult chironomids emerge from the water, leaving their pupal skins, or exuviae, floating on the water&#8217;s surface. Exuviae often accumulate along banks or behind obstructions by action of the wind or water current, where they can be collected to assess chironomid diversity and richness. Chironomids can be used as important biological indicators, since some species are more tolerant to pollution than others. Therefore, the relative abundance and species composition of collected SFPE reflect changes in water quality. Here, methods associated with field collection, laboratory processing, slide mounting, and identification of chironomid SFPE are described in detail. Advantages of the SFPE method include minimal disturbance at a sampling area, efficient and economical sample collection and laboratory processing, ease of identification, applicability in nearly all aquatic environments, and a potentially more sensitive measure of ecosystem stress. Limitations include the inability to determine larval microhabitat use and inability to identify pupal exuviae to species if they have not been associated with adult males.

Use of Chironomidae Diptera Surface-Floating Pupal Exuviae as a Rapid Bioassessment Protocol for Water Bodies

Rapid bioassessment protocols using benthic macroinvertebrates are often used to monitor and assess water quality. An efficient protocol involves collections of Chironomidae surface-floating pupal exuviae (SFPE). Here, techniques for field collection, laboratory processing, slide mounting, and identification of Chironomidae SFPE are described.

Rapid bioassessment protocols using benthic macroinvertebrate assemblages have been successfully used to assess human impacts on water quality. ...

LC-MS Analysis of Human Platelets as a Platform for Studying Mitochondrial Metabolism

Perturbed mitochondrial metabolism has received renewed interest as playing a causative role in a range of diseases. Probing alterations to metabolic pathways requires a model in which external factors can be well controlled, allowing for reproducible and meaningful results. Many studies employ transformed cellular models for these purposes; however, metabolic reprogramming that occurs in many cancer cell lines may introduce confounding variables. For this reason primary cells are desirable, though attaining adequate biomass for metabolic studies can be challenging. Here we show that human platelets can be utilized as a platform to carry out metabolic studies in combination with liquid chromatography-tandem mass spectrometry analysis. This approach is amenable to relative quantification and isotopic labeling to probe the activity of specific metabolic pathways. Availability of platelets from individual donors or from blood banks makes this model system applicable to clinical studies and feasible to scale up. Here we utilize isolated platelets to confirm previously identified compensatory metabolic shifts in response to the complex I inhibitor rotenone. More specifically, a decrease in glycolysis is accompanied by an increase in fatty acid oxidation to maintain acetyl-CoA levels. Our results show that platelets can be used as an easily accessible and medically relevant model to probe the effects of xenobiotics on cellular metabolism.

Here we show isolated human platelets can be used as an accessible ex vivo model to study metabolic adaptations in response to the complex I inhibitor rotenone. This approach employs isotopic tracing and relative quantification by liquid chromatography-mass spectrometry and can be applied to a variety of study designs.

Perturbed mitochondrial metabolism has received renewed interest as playing a causative role in a range of diseases. Probing alterations to metabolic ...

Characterization and Application of Passive Samplers for Monitoring of Pesticides in Water

Five different water passive samplers were calibrated under laboratory conditions for measurement of 124 legacy and current used pesticides. This study provides a protocol for the passive sampler preparation, calibration, extraction method and instrumental analysis. Sampling rates (RS) and passive sampler-water partition coefficients (KPW) were calculated for silicone rubber, polar organic chemical integrative sampler POCIS-A, POCIS-B, SDB-RPS and C18 disk. The uptake of the selected compounds depended on their physicochemical properties, i.e., silicone rubber showed a better uptake for more hydrophobic compounds (log octanol-water partition coefficient (KOW) &#62; 5.3), whereas POCIS-A, POCIS-B and SDB-RPS disk were more suitable for hydrophilic compounds (log KOW &#60; 0.70).

A protocol about the characterization and application of five different passive sampling devices is presented.

Five different water passive samplers were calibrated under laboratory conditions for measurement of 124 legacy and current used pesticides. This study ...

Experimental Protocol for Biodiesel Production with Isolation of Alkenones as Coproducts from Commercial Isochrysis Algal Biomass

The need to replace petroleum fuels with alternatives from renewable and more environmentally sustainable sources is of growing importance. Biomass-derived biofuels have gained considerable attention in this regard, however first generation biofuels from edible crops like corn ethanol or soybean biodiesel have generally fallen out of favor. There is thus great interest in the development of methods for the production of liquid fuels from domestic and superior non-edible sources. Here we describe a detailed procedure for the production of a purified biodiesel from the marine microalgae Isochrysis. Additionally, a unique suite of lipids known as polyunsaturated long-chain alkenones are isolated in parallel as potentially valuable coproducts to offset the cost of biodiesel production. Multi-kilogram quantities of Isochrysis are purchased from two commercial sources, one as a wet paste (80% water) that is first dried prior to processing, and the other a dry milled powder (95% dry). Lipids are extracted with hexanes in a Soxhlet apparatus to produce an algal oil (&#34;hexane algal oil&#34;) containing both traditional fats (i.e., triglycerides, 46-60% w/w) and alkenones (16-25% w/w). Saponification of the triglycerides in the algal oil allows for separation of the resulting free fatty acids (FFAs) from alkenone-containing neutral lipids. FFAs are then converted to biodiesel (i.e., fatty acid methyl esters, FAMEs) by acid-catalyzed esterification while alkenones are isolated and purified from the neutral lipids by crystallization. We demonstrate that biodiesel from both commercial Isochrysis biomasses have similar but not identical FAME profiles, characterized by elevated polyunsaturated fatty acid contents (approximately 40% w/w). Yields of biodiesel were consistently higher when starting from the Isochrysis wet paste (12% w/w vs. 7% w/w), which can be traced to lower amounts of hexane algal oil obtained from the powdered Isochrysis product.

Experimental Protocol for Biodiesel Production with Isolation of Alkenones as Coproducts from Commercial Isochrysis Algal Biomass

Detailed methods are presented for the production of biodiesel along with the co-isolation of alkenones as valuable coproducts from commercial Isochrysis microalgae.

The need to replace petroleum fuels with alternatives from renewable and more environmentally sustainable sources is of growing importance. ...

Modification and Application of a Leaf Blower-vac for Field Sampling of Arthropods

Rice fields host a large diversity of arthropods, but investigating their population dynamics and interactions is challenging. Here we describe the modification and application of a leaf blower-vac for suction sampling of arthropod populations in rice. When used in combination with an enclosure, application of this sampling device provides absolute estimates of the populations of arthropods as numbers per standardized sampling area. The sampling efficiency depends critically on the sampling duration. In a mature rice crop, a two-minute sampling in an enclosure of 0.13 m2 yields more than 90% of the arthropod population. The device also allows sampling of arthropods dwelling on the water surface or the soil in rice paddies, but it is not suitable for sampling fast flying insects, such as predatory Odonata or larger hymenopterous parasitoids. The modified blower-vac is simple to construct, and cheaper and easier to handle than traditional suction sampling devices, such as D-vac. The low cost makes the modified blower-vac also accessible to researchers in developing countries.

Assessment of arthropod abundance in crops is critical for investigating population dynamics and species interactions. Here we describe the modification and application of a leaf blower-vac for suction sampling of arthropods in rice.

Rice fields host a large diversity of arthropods, but investigating their population dynamics and interactions is challenging. Here we describe the ...

Comparison of Scale in a Photosynthetic Reactor System for Algal Remediation of Wastewater

An experimental methodology is presented to compare the performance of two different sized reactors designed for wastewater treatment. In this study, ammonia removal, nitrogen removal and algal growth are compared over an 8-week period in paired sets of small (100 L) and large (1,000 L) reactors designed for algal remediation of landfill wastewater. Contents of the small and large scale reactors were mixed before the beginning of each weekly testing interval to maintain equivalent initial conditions across the two scales. System characteristics, including surface area to volume ratio, retention time, biomass density, and wastewater feed concentrations, can be adjusted to better equalize conditions occurring at both scales. During the short 8-week representative time period, starting ammonia and total nitrogen concentrations ranged from 3.1-14 mg NH3-N/L, and 8.1-20.1 mg N/L, respectively. The performance of the treatment system was evaluated based on its ability to remove ammonia and total nitrogen and to produce algal biomass. Mean &#177; standard deviation of ammonia removal, total nitrogen removal and biomass growth rates were 0.95&#177;0.3 mg NH3-N/L/day, 0.89&#177;0.3 mg N/L/day, and 0.02&#177;0.03 g biomass/L/day, respectively. All vessels showed a positive relationship between the initial ammonia concentration and ammonia removal rate (R2=0.76). Comparison of process efficiencies and production values measured in reactors of different scale may be useful in determining if lab-scale experimental data is appropriate for prediction of commercial-scale production values.

An experimental methodology is presented to compare the performance of small (100 L) and large (1,000 L) scale reactors designed for algae remediation of landfill wastewater. System characteristics, including surface area to volume ratio, retention time, biomass density, and wastewater feed concentrations, can be adjusted based on application.

An experimental methodology is presented to compare the performance of two different sized reactors designed for wastewater treatment. In this study, ...

Isolation of Mandibular Gland Reservoir Contents from Bornean 'Exploding Ants' (Formicidae) for Volatilome Analysis by GC-MS and MetaboliteDetector

The aim of this manuscript is to present a protocol describing the metabolomic analysis of Bornean &#39;exploding ants&#39; belonging to the Colobopsis cylindrica (COCY) group. For this purpose, the model species C. explodens is used. Ants belonging to the minor worker caste possess distinctive hypertrophied mandibular glands (MGs). In territorial combat, they use the viscous contents of their enlarged mandibular gland reservoirs (MGRs) to kill rival arthropods in characteristic suicidal &#39;explosions&#39; by voluntary rupture of the gastral integument (autothysis). We show the dissection of worker ants of this species for the isolation of the gastral portion of the wax-like MGR contents as well as listing the necessary steps required for solvent-extraction of the therein contained volatile compounds with subsequent gas chromatography-mass spectrometry (GC-MS) analysis and putative identification of metabolites contained in the extract. The dissection procedure is performed under cooled conditions and without the use of any dissection buffer solution to minimize the changes in the chemical composition of the MGR contents. After solvent-based extraction of volatile metabolites contained therein, the necessary steps for analyzing the samples via liquid-injection-GC-MS are presented. Lastly, data processing and putative metabolite identification with the use of the open-source software MetaboliteDetector is shown. With this approach, the profiling and identification of volatile metabolites in MGRs of ants belonging to the COCY group via GC-MS and the MetaboliteDetector software become possible.

Isolation of Mandibular Gland Reservoir Contents from Bornean &#039;Exploding Ants&#039; Formicidae for Volatilome Analysis by GC-MS and MetaboliteDetector

Minor workers of the ant species Colobopsis explodens are dissected to isolate the wax-like content stored in their hypertrophied mandibular gland reservoirs for subsequent solvent-extraction and analysis by gas chromatography-mass spectrometry. The&#160;annotation and identification of volatile constituents using the open-source software MetaboliteDetector is also described.

The aim of this manuscript is to present a protocol describing the metabolomic analysis of Bornean &#39;exploding ants&#39; belonging to the Colobopsis ...

The Plant Infection Test: Spray and Wound-Mediated Inoculation with the Plant Pathogen Magnaporthe Grisea

Plants possess a powerful system to defend themselves against potential threats by pathogenic fungi. For agriculturally important plants, however, current measures to combat such pathogens have proved too conservative and, thus, not sufficiently effective, and they can potentially pose environmental risks. Therefore, it is extremely necessary to identify host-resistance factors to assist in controlling plant diseases naturally through the identification of resistant germplasm, the isolation and characterization of resistance genes, and the molecular breeding of resistant cultivars. In this regard, there is need to establish an accurate, rapid, and large-scale inoculation method to breed and develop plant resistance genes. The rice blast fungal pathogen Magnaporthe grisea causes severe disease symptoms and yield losses. Recently, M. grisea has emerged as a model organism for studying the mechanisms of plant-fungal pathogen interactions. Hence, we report the development of a plant virulence test method that is specific for M. grisea. This method provides for both spray inoculation with a conidial suspension and wounding inoculation with mycelium cubes or droplets of conidial suspension. The key step of the wounding inoculation method for detached rice leaves is to make wounds on plant leaves, which avoids any interference caused by host penetration resistance. This spray/wounding protocol contributes to the rapid, accurate, and large-scale screening of the pathotypes of M. grisea isolates. This integrated and systematic plant infection method will serve as an excellent starting point for gaining a broad perspective of issues in plant pathology.

The Plant Infection Test: Spray and Wound-Mediated Inoculation with the Plant Pathogen Magnaporthe Grisea

Here, we present a protocol to test plant virulence with the plant pathogen Magnaporthe grisea. This report will contribute to the large-scale screening of the pathotypes of fungi isolates and serve as an excellent starting point for understanding the resistant mechanisms of plants during molecular breeding.

Plants possess a powerful system to defend themselves against potential threats by pathogenic fungi. For agriculturally important plants, however, current ...

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

Applying toxicity testing of chemicals in higher order organisms, such as mice or rats, is time-consuming and expensive, due to their long lifespan and maintenance issues. On the contrary, the nematode Caenorhabditis elegans (C. elegans) has advantages to make it an ideal choice for toxicity testing: a short lifespan, easy cultivation, and efficient reproduction. Here, we describe a protocol for the automatic phenotypic profiling of C. elegans in a 384-well plate. The nematode worms are cultured in a 384-well plate with liquid medium and chemical treatment, and videos are taken of each well to quantify the chemical influence on 33 worm features. Experimental results demonstrate that the quantified phenotype features can classify and predict the acute toxicity for different chemical compounds and establish a priority list for further traditional chemical toxicity assessment tests in a rodent model.

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

A quantitative method has been developed to identify and predict the acute toxicity of chemicals by automatically analyzing the phenotypic profiling of Caenorhabditis elegans. This protocol describes how to treat worms with chemicals in a 384-well plate, capture videos, and quantify toxicological related phenotypes.

Applying toxicity testing of chemicals in higher order organisms, such as mice or rats, is time-consuming and expensive, due to their long lifespan and ...