Funding from the Appalachian State University Research Council (URC) grant and CAPES Print Travel fellowship that supported the visit of FAO to Universidade de S&#227;o Paulo, Ribeir&#227;o Preto - S&#227;o Paulo, Brazil, are gratefully acknowledged. This study was financed in part by the Coordena&#231;&#227;o de Aperfei&#231;oamento de Pessoal de N&#237;vel Superior - Brasil (CAPES) - Finance Code 001. ECPDM is grateful for the CAPES Print Travel grant that supported her visit to Appalachian State University. ECPDM is a research fellow 2 from the Conselho Nacional de Desenvolvimento Cient&#237;fico e Tecnol&#243;gico, Brasil (CNPq).

1. Experimental wine production
<ol>
	<li>Obtain Traminnette grapes from Dynamis Estate wine in Jonesville, North Carolina vineyard, destem, and crush to release must into two separate 600 L open-top fermenters and leave on skins for about 4 days with the lids on.</li>
	<li>Punch the caps down once a day to keep skins wet and limit volatile acid (VA) production. 
	NOTE: The idea is that a lot of the aromatic precursors (monoterpenes) are situated in the skins and leave the juice in contact with the skin to increase the aromatic potential of the wine.</li>
	<li>After 4 days, pitch Saccharomyces cerevisiae var cerevisiae QA23 (Lallemand). This yeast was selected as it is a strong fermenter and is associated with enhancing the varietal character in the wine from the grapes.</li>
	<li>In one tank, Traminette R, add 20 g/hL Fermaid O when the brix is at 17.7 and 20 g/hL Fermaid O and 12.5 g/hL Fermaid K at 13.1 g/hL to achieve fermentation security, while the other tank Traminette L, add 40 g/hL Stimula Sauvignon blanc when the brix is at 17.1. Also add 20 g/hL Fermaid O when the brix is at 13.4 to enhance the varietal character of the wine.</li>
	<li>Take duplicate samples of must (day 1), yeast added (day 4), fermented must (day 15), and finished wine samples (day 30) and keep at -20 &#176;C before DNA extraction and metagenomic analysis.</li>
</ol>
2. DNA extraction for metagenomics
<ol>
	<li>Keep all duplicate samples obtained above on ice until the first centrifugation.</li>
	<li>Transfer 20 mL of the fermented sample into a sterile 50 mL screw cap tube.</li>
	<li>Add 10 mL of cold water and homogenize (vortex). Centrifuge for 1 min at 800 x g at 4 &#176;C and transfer the supernatant to a new 50 mL tube. 
	NOTE: This step will remove the solids of the sample.</li>
	<li>Repeat step 2.3 two times and pool the supernatants in the same tube.</li>
	<li>Centrifuge supernatant at 3,000 x g at 4 &#176;C for 20 min to pellet the cells. Discard the supernatant.</li>
	<li>Resuspend the pellet in 1 mL of PBS, transfer to a 2 mL screw-cap tube, and centrifuge at 14,000 x g for 2 min. Perform two more washes using PBS.</li>
	<li>At this step, freeze the pellets at -20&#176;C or follow step 2.8. It is important that all the samples are treated in the same way.</li>
	<li>Add 978 &#181;L of sodium phosphate buffer. Prepare sodium phosphate buffer, add 3.1 g of NaH2PO4&#183;H2O and 10.9 g of Na2HPO4 (anhydrous) to distilled H2O to make a volume of 1 L, and adjust pH to 7.4.</li>
	<li>Add 122 &#181;L of DNA buffer (DNA spin kit) and vortex.</li>
	<li>Let it stand in the refrigerator (4 &#176;C) for 45 min-1 h. Vortex the samples every 15 min. This step can be left overnight (o/n) or until ready to continue using the DNA spin kit.</li>
	<li>Transfer 1 mL of sample into a Lysis solution 1 tube (DNA spin kit) and tighten the cap (write the sample number on the side of the tube and not on the cap; the kit reagents will remove anything written on the cap).</li>
	<li>Homogenize the sample in a bead-beating grinder (6.5 m/s, CY: 24 x 2) for 60 s three times. If the bead-beating grinder has the device to put ice, put 50 g of dry ice under the tubes. If not, keep the samples on wet ice for 5 min between each homogenization step.</li>
	<li>Centrifuge Lysing Matrix E tubes (DNA spin kit) for 1 min at 16800 x g(or max speed).</li>
	<li>Transfer the supernatant into a clean microtube. Thereafter, add 250 &#181;L of protein precipitation solution (PPS) reagent (DNA spin kit) and mix by shaking the tube by hand 10 times.</li>
	<li>Centrifuge for 5 min at 16800 x g to pellet the precipitate.</li>
	<li>Transfer the supernatant into a sterile 15 mL tube. Add 1 mL of binding matrix suspension (DNA spin kit) to the supernatant. 
	NOTE: Resuspend the binding matrix suspension before use until it is homogeneous.</li>
	<li>Invert the tubes by hand for 2 min, then let the tubes stand in a rack for 3 min (to allow settling of silica matrix).</li>
	<li>Carefully remove 1 mL of the supernatant. Resuspend the matrix in the remaining supernatant.</li>
	<li>Transfer 600 &#181;L of the mixture into a spin filter tube (DNA spin kit) and centrifuge for 1 min at 14500 x g.</li>
	<li>Add the remaining mixture and centrifuge.</li>
	<li>Decant flow-through and add 500 &#181;L of wash solution (DNA spin kit) into the spin filter tube and centrifuge for 1 min at 14500 x g(ensure to add EtOH to wash solution; see the product handbook). Decant the flow-through and repeat the wash two more times (3 washes in total).</li>
	<li>Decant the flow-through and centrifuge for an additional 2 min at 14,500 x g to dry the matrix of residual wash solution.</li>
	<li>Remove the spin filter and place it in a fresh catch tube (DNA spin kit). Air dry the spin filter for 5 min at room temperature (RT).</li>
	<li>Warm the DNAse-free water (DNA spin kit) at 55 &#176;C for 5 min. Add 50 &#181;L of DNAse-free water and gently stir the matrix on the filter membrane with a pipette tip for efficient elution of the DNA. Let samples stand at RT for 1 min, then centrifuge for 1 min at 14500 x g to elute DNA.</li>
	<li>Place the samples on ice. Load the sample mixture (2 &#181;L of sample + 2 &#181;L of water + 1 &#181;L of loading buffer) into a gel. Measure the DNA concentration.</li>
	<li>Store the samples at -20 &#176;C.</li>
	<li>Use a spectrophotometer to quantify the DNA extracted. 
	NOTE: A 1 &#181;L volume of DNA was dropped and quantified, and the quality of DNA was noted at an A260/A280 ratio.</li>
</ol>
3. DNA electrophoresis
<ol>
	<li>Prepare 1% agarose gel in 0.5 Tris/Borate/EDTA (TBE) buffer (20 mL for a mini gel, 70-100 mL for a medium gel). Weigh agarose+buffer, boil in microwave to melt agarose, reweigh, and add dH2O to the original weight. Cool the agarose solution to 55-60 &#176;C and pour into the gel former with comb assembly. Leave the gel to solidify.</li>
	<li>Add 1 &#181;L of 10x gel loading buffer to the sample (10 &#181;L) and load onto the gel next to a molecular weight marker. Run from negative (black) to positive (red) at 100-115 V (large gel) or 90 V (mini gel) until the blue dye is near the bottom.</li>
	<li>Stain gel for 30 min in 1 &#181;L/mL ethidium bromide or SYBR green (nitrile gloves and goggles needed), rinse in water, and view under an ultraviolet (UV) light source. From the duplicate samples, select the highest yield for further processing.</li>
</ol>
4. High throughput sequencing
<ol>
	<li>Amplify the V4 hypervariable region of the 16S rRNA gene using specific primers 515F (5&#39; -GTGYCAGCMGCCGCGGTAA-3&#39;) and 806R (5&#39;-GGACTACNVGGGTWTCTAAT-3&#39;)22. Set the polymerase chain reaction (PCR) conditions at 94 &#176;C for 2 min, 30 cycles of 94 &#176;C for 20 s, 58 &#176;C for 20 s, 65 &#176;C for 1 min, and 65 &#176;C for 10 min (final extension).</li>
	<li>Carry out PCR reactions with a high-fidelity PCR master mix (Table of Materials).</li>
	<li>Generate libraries with DNA library preparation kit and then sequence using paired-end sequencing on a sequencing platform. 
	NOTE: Here, the libraries generated with NEBNext Ultra II DNA Library Prep Kit were sequenced using paired-end Illumina sequencing (2 &#215; 250 bp) on the novaseq6000 platform.</li>
	<li>Follow the sequencing steps:
	<ol>
		<li>Step 1: Shear the DNA with an enzyme, usually by a method that selects for a general size.
		<ol>
			<li>Add 3.5 &#181;L of sequencing buffer and 1.5 &#181;L of enzyme mix to 25 &#181;L of DNA (ca. 1 &#181;g,) mix 10 times with a pipette and spin quickly.</li>
			<li>Place in a thermocycler with the following conditions: preheat lid at 75&#176;C, (1) 30 min for 20 &#176;C (2) 30 min for 65 &#176;C (3) hold at 4&#176;C.</li>
		</ol>
		</li>
		<li>Step 2: Add adaptors via ligation
		<ol>
			<li>Add 15 &#181;L of ligation master mix, 1.25 &#181;L of adapter, 0.5 &#181;L of ligation enhancer, and 30 &#181;L of end prep DNA mix with a pipette.</li>
			<li>Incubate at 20 &#176;C for 15 min in a thermocycler and then add 1.5 &#181;L uracil-specific excision reagent (USER) enzyme to the ligation mix to obtain a total of 48.26 &#181;L. Incubate 37 &#176;C for 15 min.</li>
			<li>Add 43.5 &#181;L of magnetic beads to the adapter ligation DNA, mix 20 times with a pipette, and incubate for 5 min at RT. Separate the beads with a magnetic rack, then incubate for 5-10 min, and discard the supernatant.</li>
			<li>Wash the pellet with 100 &#181;L of 80% ethanol, dry for 30 s, and wash again. Elute beads and pellet in 8.5 &#181;L of 10 mM Tris HCl/0.1x Tris-EDTA(TE)/HPLC H20, mix with a pipette and incubate at RT for 2 min.</li>
			<li>Incubate in the magnetic rack and remove 7.5 &#181;L of eluted DNA as supernatant. Place the elute in a new tube and conduct a PCR step.</li>
			<li>First, add 12.5 &#181;L of master mix, 2.5 &#181;L of index primer i7 primer, and 2.5 &#181;L of universal PCR primer/i5 primer to 7.5 &#181;L adapter-ligated DNA to obtain 25 &#181;L of PCR reaction mix. Use the following PCR condition: preheat the lid to 103 &#176;C. 98 &#176;C for 30 s, 98 &#176;C for 10 s, 65 &#176;C for 75 s, 65 &#176;C for 5 min and hold at 4 &#176;C after 10 cycles.</li>
			<li>Clean the 25 &#181;L amplified DNA. Add 22.5 &#181;L of magnetic beads and mix 20 times. Incubate at RT for 5 min and remove 50 &#181;L of the supernatant. Wash the beads with 100 &#181;L of 80% ethanol, and remove all ethanol by drying the beads gently for 30 s.</li>
			<li>Resuspend the dark brown beads in 16 &#181;L of water, mix 20 times with a pipette, and place in the magnetic rack for 30-60 s. Transfer 15 &#181;L to a new tube.</li>
			<li>Determine the DNA concentration using a fluorometer. Mix 90 &#181;L of buffer + 1 &#181;L dye + 2 &#181;L of DNA library sample, and read the concentration. Dilute the DNA to 2 nM, load 27-30 &#181;L to the flow cell, and place it in the sequencer.</li>
		</ol>
		</li>
		<li>Step 3: Sequence the adaptors, hybridize the libraries generated above to the matrix of the patterned flow cell, and capture DNA per the manufacturer&#39;s instructions. Use it as the template for the second synthesis.
		<ol>
			<li>Then, amplify the second strand into a clonal cluster. Linearize the cluster and block the active sites. Add sequencing primer to provide a site for sequencing by synthesis in the sequence per the manuacturer&#39;s protocol. 
			NOTE: Chemically, the modified nucleotides bind to the DNA template strand using natural complementarity. Each nucleotide has a fluorescent tag and a reversible terminator that blocks the inclusion of the next base. Therefore, the presence of a fluorescent signal indicates which nucleotide is inserted, and the terminator is cleaved for the next base to bind.</li>
		</ol>
		</li>
		<li>Step 4: Amplify the single-bound strand to give clusters of sequences that are sequenced in tandem by synthesis. 
		NOTE: The clusters give a brighter signal than a single strand through bidirectional scanning and two-channel sequencing chemistry. The sequencing software automatically transfers base call (*.cbcl) files to the specified output folder location for data analysis.</li>
	</ol>
	</li>
</ol>
5. Bioinformatics
<ol>
	<li>Generate the sequence data as FASTQ files. Analyze to include the following parts.
	<ol>
		<li>Part I:
		<ol>
			<li>Save the FASTQ files obtained from the sequencer, click to open a spreadsheet file, and create mapping/metadata files.</li>
			<li>Open the Nephele website (https://nephele.niaid.nih.gov/),&#160;upload the FASTQ files, read QC, filtering, and trimming in QIIME222. Deposit the raw sequences as sequence read archive (SRA) and GenBank in NCBI BioProject (https://submit.ncbi.nlm.nih.gov/subs/bioproject/)</li>
		</ol>
		</li>
		<li>Part II: Go to the Nephele website (https://nephele.niaid.nih.gov/),&#160;do the demultiplexed paired-end reads, filter substitution, and chimera errors, and merge using DADA223. Use the Naive Bayes classifier trained on the Silva version 132 99% OTU database to perform bacterial taxonomic assignment at 97% similarity24.</li>
		<li>Part III: Open the MicrobiomeDB website, click to upload files, and generate OTU alpha-diversity, beta-diversity, relative abundance, and rarefaction curves (https://microbiomedb.org/mbio/app).</li>
		<li>Part IV: Open a spreadsheet and transfer data to make heatmaps, Venn diagrams, and linear discriminant analysis effect size (https://microbiomedb.org/mbio/app).</li>
	</ol>
	</li>
</ol>
6. Statistical analysis
<ol>
	<li>Open QIIME222, and determine the significant differences in alpha diversity between groups using the alpha-group-significance script. This will also perform the Kruskal-Wallis test.</li>
	<li>Determine the differences in beta diversity between groups by using PERMANOVA, including a pairwise test25.</li>
	<li>Obtain the significant differences in the bacterial community structure amongst the groups using MicrobiomeDB. A p-value &#8804;0.05 was considered statistically significant.</li>
</ol>

Profiling the Bacterial Community in Grapes Using Amplicon Metagenomics

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O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Metagenomics+methods+for+the+study+of+plant-associated+microbial+communities:+A+review.">Metagenomics methods for the study of plant-associated microbial communities: A review.</a> Journal of Microbiological Methods. 170, 105860 (2020).</li><li>van Hijum, S. A., Vaughan, E. E., Vogel, R. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Application+of+state-of-art+sequencing+technologies+to+indigenous+food+fermentations.">Application of state-of-art sequencing technologies to indigenous food fermentations.</a> Current Opinion in Biotechnology. 24, 178-186 (2013).</li><li>V&#237;quez-R, L., Fleischer, R., Wilhelm, K., Tschapka, M., Sommer, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Jumping+the+green+wall:+The+use+of+PNA-DNA+clamps+to+enhance+microbiome+sampling+depth+in+wildlife+microbiome+research.">Jumping the green wall: The use of PNA-DNA clamps to enhance microbiome sampling depth in wildlife microbiome research.</a> Ecology and Evolution. 10 (20), 11779-11786 (2020).</li><li>Bukin, Y. S., Galachyants, P., Yu, I. V., Morozov, S. V., Bukin, A. S., Zakharenko, T. I., Zemskaya, <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+effect+of+16S+rRNA+region+choice+on+bacterial+community+metabarcoding+results.">The effect of 16S rRNA region choice on bacterial community metabarcoding results.</a> Scientific Data. 6, 190007 (2019).</li></ol>

The authors have no conflicts of interest to declare.

The protocol of metagenomics starts from the sampling of the grape must, and when yeast was added to the must, the fermenting wine and final wine samples. This was followed by duplicate DNA extraction that was successfully extracted from these samples. The quantities obtained varied in concentration from 15 ng/ &#181;L to 87 ng/ &#181;L. This shows that the DNA extraction protocol is effective for metagenomic studies of wine. Although the quality of the DNA at A260/A280 varies, this may be attributed to different paramet...

Traminette grape is characterized by production of superior wine quality, in addition to appreciable yield and partial resistance to several fungal infections1,2,3. The natural fermentation of grapes relies on associated microorganisms, wine production environment, and fermentation vessels4,5. Oftentimes, many wineries rely on wild yeast and bacteria for fermentation, production of alcohol, esters, aroma, and flavor development6.
The goal of this study is to examine the bacterial composition of grapes and monitor their dynamics during fermentation. Although, the modern use of starter cultures such as Saccharomyces cerevisiae for primary fermentation, where alcohol is produced, is common to different wine styles7. In addition, secondary fermentation, where malic acid is decarboxylated by Oenococcus oeni to lactic acid, improves the organoleptic and taste profile of the wine and reduces the acidity of wine8,9. With the recent advances in the use of culture-independent methods, it is now possible to determine different microbes associated with the wine grape and the species that are transferred to must and participate in the fermentation at different times up to the final product10.
The roles and dynamics of wild bacteria from different grapes transferred to the must during wine fermentation are poorly understood. The taxonomy of many of these bacteria is not even known, or their phenotypic properties are uncharacterized. This makes their application in coculture fermentation still poorly underutilized. However, microbiological culture-based analysis has been used to determine the bacterial population associated with grapes and wine10. It is widely known that selective culture plating is tedious, prone to contamination, has a low reproducibility, and output can be doubtful; it also misses bacterial species whose growth requirements are unknown. Previous studies indicate that culture-independent, 16S rRNA gene-based methodologies offer a more dependable and cost-effective approach to characterizing complex microbial communities11. For example, sequencing the hypervariable regions of the 16S rRNA gene has been successfully employed to study bacteria in grapes leaves, berries, and wine12,13,14. Studies have shown that the use of either 16S rRNA metabarcoding or whole metagenomic sequencing is suitable for microbiome studies15. There is emerging information about the possible linkage of bacterial diversity to their metabolic attributes during wine production, which could help in the determination of oenological properties and terroir16.
The need to maximize the advantages of the metagenomic tools using next-generation sequencing (NGS) to study the grape and wine microbial ecology has been emphasized16,17. Also, the use of culture-independent methods based on high throughput sequencing to profile microbial diversity of the food and fermentation ecosystem has become very relevant and valuable to many laboratories and is recommended for industrial use18,19. It provides an advantage of detection and taxonomic profiling of the present microbial populations and the contribution of environmental microbes, their relative abundance, and alpha and beta diversity20. The sequencing of the variable region of the 16S region has become an important gene of choice and has been used during different microbial ecological studies.
While many studies focus on fungi, especially yeast, during wine fermentation21, this study reported the 16S amplicon sequencing and bioinformatic tools used to study the bacteria during Traminette grapes fermentation for wine production.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Agarose gel&#160;</td>
			<td>Promega, Madison, WI USA</td>
			<td>V3121</td>
			<td>Electrophoresis&#160;</td>
		</tr>
		<tr>
			<td>FastPrep DNA spinKit for soil&#160;</td>
			<td>MP Biomedicals, Solon, OH USA</td>
			<td>116560-200</td>
			<td>DNA extraction&#160;</td>
		</tr>
		<tr>
			<td>FastQC software</td>
			<td>Babraham Institute, United Kingdom</td>
			<td></td>
			<td>Bioinformatics&#160;</td>
		</tr>
		<tr>
			<td>Fermaid O</td>
			<td>Scott Laboratory, Petaluma, CA USA&#160;</td>
			<td></td>
			<td>Fermentation&#160;</td>
		</tr>
		<tr>
			<td>High-Fidelity PCR Master Mix&#160;</td>
			<td>New England Biolabs, USA</td>
			<td>F630S</td>
			<td>Polymerase chain reaction for sequencing&#160;</td>
		</tr>
		<tr>
			<td>NEBNext Ultra</td>
			<td>New England Biolabs, USA</td>
			<td>NEB #E7103</td>
			<td>DNA Library Prep</td>
		</tr>
		<tr>
			<td>NEBNext Ultra II DNA Library Prep Kit&#160;</td>
			<td>Illumina, San Diego, CA&#160; USA</td>
			<td></td>
			<td>DNA sequencing&#160;</td>
		</tr>
		<tr>
			<td>NovaSeq Control Software (NVCS)</td>
			<td>Illumina, San Diego, CA&#160; USA</td>
			<td></td>
			<td>DNA sequencing&#160;</td>
		</tr>
		<tr>
			<td>Novaseq6000 platform&#160;</td>
			<td>Illumina, San Diego, CA&#160; USA</td>
			<td></td>
			<td>DNA sequencing&#160;</td>
		</tr>
		<tr>
			<td>QuiBit</td>
			<td>Thermoscientific, Waltham, MA, USA</td>
			<td></td>
			<td>DNA quantification&#160;</td>
		</tr>
		<tr>
			<td>Quickdrop spectrophotometer&#160;</td>
			<td>Molecular device, San Jose, CA, USA</td>
			<td></td>
			<td>DNA quantification&#160;</td>
		</tr>
		<tr>
			<td>Sodium Phosphate&#160;</td>
			<td>Sigma Aldrich</td>
			<td>342483</td>
			<td>DNA extraction buffer</td>
		</tr>
		<tr>
			<td>Stimula Sauvignon Blanc</td>
			<td>Scott Laboratory, Petaluma, CA USA&#160;</td>
			<td></td>
			<td>Fermentation&#160;</td>
		</tr>
	</tbody>
</table>

profiling the bacterial community of fermenting traminette grapes during wine production using metagenomic amplicon sequencing

Advances in sequencing technology and the relatively easy access to the use of bioinformatics tools to profile microbial community structures have facilitated a better understanding of both culturable and non-culturable microbes in grapes and wine. During industrial fermentation, microbes, known and unknown, are often responsible for product development and off-flavor. Therefore, profiling the bacteria from grape to wine can enable an easy understanding of in situ microbial dynamics. In this study, the bacteria of Traminette grapes must undergoing fermentation, and the final wine were subjected to DNA extraction that yielded 15 ng/&#181;L to 87 ng/&#181;L. The 16S amplicon of the hypervariable region of the V4 region was sequenced, relatively abundant bacteria consisting of phyla Proteobacteria, Actinobacteriota, Firmicutes, Bacteroidota, Fusobacteriota and followed by the Verrucomicrobiota, Halobacterota, Desulfobacterota, Myxococcota, and Acidobacteriota. A Venn diagram analysis of the shared unique operational taxonomic units (OTU) revealed that 15 bacteria phyla were common to both grape must, fermenting stage, and final wine. Phyla that were not previously reported were detected using the 16S amplicon sequencing, as well as genera such as Enterobacteriaceae and Lactobacillaceae. Variation in the organic nutrient use in wine and its impact on bacteria was tested; Traminette R tank containing Fermaid O and Traminette L stimulated with Stimula Sauvignon blanc + Fermaid O. Alpha diversity using the Kruskal-Wallis test determined the degree of evenness. The beta diversity indicated a shift in the bacteria at the fermentation stage for the two treatments, and the final wine bacteria looked similar. The study confirmed that 16S amplicon sequencing can be used to monitor bacteria changes during wine production to support quality and better utilization of grape bacteria during wine production.

The quantity and quality of DNA extracted from grapes must, fermenting wine, and final wine were first determined; the quantity value ranges from 15-87 ng/&#181;L (Table 1).
Sequencing and bioinformatics 
The Illumina high throughput sequencer generated a FASTQ file that was imported to the Nephele and viewed on QIIME 2 platform26. Firstly, FastQC software was used to check for the sequence quality. Then, it was tr...

Watch this Scientific Journal Video about Profiling the Bacterial Community of Fermenting Traminette Grapes during Wine Production using Metagenomic Amplicon Sequencing at JoVE.com

Author Spotlight: Exploring the Fermentation Microbiome Through Next-Generation Sequencing

Here, we present a protocol to describe amplicon metagenomic for determining the bacterial community of Traminette grapes, fermenting grapes, and final wine.

Profiling the Bacterial Community of Fermenting Traminette Grapes during Wine Production using Metagenomic Amplicon Sequencing

Advances in sequencing technology and the relatively easy access to the use of bioinformatics tools to profile microbial community structures have ...

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612 Views.  Appalachian State University. Here, we present a protocol to describe amplicon metagenomic for determining the bacterial community of Traminette grapes, fermenting grapes, and final wine.

Video: Author Spotlight: Exploring the Fermentation Microbiome Through Next-Generation Sequencing

Investigating the Microbial Community in the Termite Hindgut - Interview

Jared Leadbetter explains why the termite-gut microbial community is an excellent system for studying the complex interactions between microbes.     The symbiotic relationship existing between the host insect and lignocellulose-degrading gut microbes is explained, as well as the industrial uses of these microbes for degrading plant biomass and generating biofuels.

Jared Leadbetter explains why the termite-gut microbial community is an excellent system for studying the complex interactions between microbes. The symbiotic relationship existing between the host insect and lignocellulose-degrading gut microbes is explained, as well as the industrial uses of these microbes for degrading plant biomass and generating biofuels.

Jared Leadbetter explains why the termite-gut microbial community is an excellent system for studying the complex interactions between microbes.     The ...

Layers of Symbiosis - Visualizing the Termite Hindgut Microbial Community

Jared Leadbetter takes us for a nature walk through the diversity of life resident in the termite hindgut - a microenvironment containing 250 different species found nowhere else on Earth.  Jared reveals that the symbiosis exhibited by this system is multi-layered and involves not only a relationship between the termite and its gut inhabitants, but also involves a complex web of symbiosis among the gut microbes themselves.

Jared Leadbetter takes us for a nature walk through the diversity of life resident in the termite hindgut - a microenvironment containing 250 different species found nowhere else on Earth. Jared reveals that the symbiosis exhibited by this system is multi-layered and involves not only a relationship between the termite and its gut inhabitants, but also involves a complex web of symbiosis among the gut microbes themselves.

Jared Leadbetter takes us for a nature walk through the diversity of life resident in the termite hindgut - a microenvironment containing 250 different ...

Large-Scale Screens of Metagenomic Libraries

Metagenomic libraries archive large fragments of contiguous genomic sequences from microorganisms without requiring prior cultivation. Generating a streamlined procedure for creating and screening metagenomic libraries is therefore useful for efficient high-throughput investigations into the genetic and metabolic properties of uncultured microbial assemblages. Here, key protocols are presented on video, which we propose is the most useful format for accurately describing a long process that alternately depends on robotic instrumentation and (human) manual interventions. First, we employed robotics to spot library clones onto high-density macroarray membranes, each of which can contain duplicate colonies from twenty-four 384-well library plates. Automation is essential for this procedure not only for accuracy and speed, but also due to the miniaturization of scale required to fit the large number of library clones into highly dense spatial arrangements. Once generated, we next demonstrated how the macroarray membranes can be screened for genes of interest using modified versions of standard protocols for probe labeling, membrane hybridization, and signal detection. We complemented the visual demonstration of these procedures with detailed written descriptions of the steps involved and the materials required, all of which are available online alongside the video.

Metagenomic libraries archive large fragments of contiguous genomic sequences from microorganisms without requiring prior cultivation.  Generating a ...

Glycan Profiling of Plant Cell Wall Polymers using Microarrays

Plant cell walls are complex matrixes of heterogeneous glycans which play an important role in the physiology and development of plants and provide the raw materials for human societies (e.g. wood, paper, textile and biofuel industries)1,2. However, understanding the biosynthesis and function of these components remains challenging.
Cell wall glycans are chemically and conformationally diverse due to the complexity of their building blocks, the glycosyl residues. These form linkages at multiple positions and differ in ring structure, isomeric or anomeric configuration, and in addition, are substituted with an array of non-sugar residues. Glycan composition varies in different cell and/or tissue types or even sub-domains of a single cell wall3. Furthermore, their composition is also modified during development1, or in response to environmental cues4.
In excess of 2,000 genes have Plant cell walls are complex matrixes of heterogeneous glycans been predicted to be involved in cell wall glycan biosynthesis and modification in Arabidopsis5. However, relatively few of the biosynthetic genes have been functionally characterized 4,5. Reverse genetics approaches are difficult because the genes are often differentially expressed, often at low levels, between cell types6. Also, mutant studies are often hindered by gene redundancy or compensatory mechanisms to ensure appropriate cell wall function is maintained7. Thus novel approaches are needed to rapidly characterise the diverse range of glycan structures and to facilitate functional genomics approaches to understanding cell wall biosynthesis and modification.
Monoclonal antibodies (mAbs)8,9 have emerged as an important tool for determining glycan structure and distribution in plants. These recognise distinct epitopes present within major classes of plant cell wall glycans, including pectins, xyloglucans, xylans, mannans, glucans and arabinogalactans. Recently their use has been extended to large-scale screening experiments to determine the relative abundance of glycans in a broad range of plant and tissue types simultaneously9,10,11.
Here we present a microarray-based glycan screening method called Comprehensive Microarray Polymer Profiling (CoMPP) (Figures 1 &amp; 2)10,11 that enables multiple samples (100 sec) to be screened using a miniaturised microarray platform with reduced reagent and sample volumes. The spot signals on the microarray can be formally quantified to give semi-quantitative data about glycan epitope occurrence. This approach is well suited to tracking glycan changes in complex biological systems12 and providing a global overview of cell wall composition particularly when prior knowledge of this is unavailable.

A technique called Comprehensive Microarray Polymer Profiling (CoMPP) for the characterisation of plant cell wall glycans is described. This method combines the specificity of monoclonal antibodies directed to defined glycan-epitopes with a miniature microarray analytical platform allowing screening of glycan occurrence in a broad range of biological contexts.

Plant cell walls are complex matrixes of heterogeneous glycans which play an important role in the physiology and development of plants and provide the ...

Using RNA-mediated Interference Feeding Strategy to Screen for Genes Involved in Body Size Regulation in the Nematode C. elegans

Double-strand RNA-mediated interference (RNAi) is an effective strategy to knock down target gene expression1-3. It has been applied to many model systems including plants, invertebrates and vertebrates. There are various methods to achieve RNAi in vivo4,5. For example, the target gene may be transformed into an RNAi vector, and then either permanently or transiently transformed into cell lines or primary cells to achieve gene knockdown effects; alternatively synthesized double-strand oligonucleotides from specific target genes (RNAi oligos) may be transiently transformed into cell lines or primary cells to silence target genes; or synthesized double-strand RNA molecules may be microinjected into an organism. Since the nematode C. elegans uses bacteria as a food source, feeding the animals with bacteria expressing double-strand RNA against target genes provides a viable strategy6. Here we present an RNAi feeding method to score body size phenotype. Body size in C. elegans is regulated primarily by the TGF- &beta; - like ligand DBL-1, so this assay is appropriate for identification of TGF-&beta; signaling components7. We used different strains including two RNAi hypersensitive strains to repeat the RNAi feeding experiments. Our results showed that rrf-3 strain gave us the best expected RNAi phenotype. The method is easy to perform, reproducible, and easily quantified. Furthermore, our protocol minimizes the use of specialized equipment, so it is suitable for smaller laboratories or those at predominantly undergraduate institutions.

Using RNA-mediated Interference Feeding Strategy to Screen for Genes Involved in Body Size Regulation in the Nematode C. elegans

We demonstrate how to use the RNAi feeding technique to knock down target genes and score body size phenotype in C. elegans. This method could be used for a large scale screen to identify potential genetic components of interest, such as those involved in body size regulation by DBL-1/TGF-&beta; signaling.

Double-strand RNA-mediated interference (RNAi) is an effective strategy to knock down target gene expression1-3. It has been applied to many model systems ...

Analysis of Translation Initiation During Stress Conditions by Polysome Profiling

Precise control of mRNA translation is fundamental for eukaryotic cell homeostasis, particularly in response to physiological and pathological stress. Alterations of this program can lead to the growth of damaged cells, a hallmark of cancer development, or to premature cell death such as seen in neurodegenerative diseases. Much of what is known concerning the molecular basis for translational control has been obtained from polysome analysis using a density gradient fractionation system. This technique relies on ultracentrifugation of cytoplasmic extracts on a linear sucrose gradient. Once the spin is completed, the system allows fractionation and quantification of centrifuged zones corresponding to different translating ribosomes populations, thus resulting in a polysome profile. Changes in the polysome profile are indicative of changes or defects in translation initiation that occur in response to various types of stress. This technique also allows to assess the role of specific proteins on translation initiation, and to measure translational activity of specific mRNAs. Here we describe our protocol to perform polysome profiles in order to assess translation initiation of eukaryotic cells and tissues under either normal or stress growth conditions.

Here, we describe a method to analyze changes in the initiation of mRNA translation of eukaryotic cells in response to stress conditions. This method is based on the velocity separation on sucrose gradients of translating ribosomes from non-translating ribosomes.

Precise control of mRNA translation is fundamental for eukaryotic cell homeostasis, particularly in response to physiological and pathological stress. ...

Efficient Nucleic Acid Extraction and 16S rRNA Gene Sequencing for Bacterial Community Characterization

There is a growing appreciation for the role of microbial communities as critical modulators of human health and disease. High throughput sequencing technologies have allowed for the rapid and efficient characterization of bacterial communities using 16S rRNA gene sequencing from a variety of sources. Although readily available tools for 16S rRNA sequence analysis have standardized computational workflows, sample processing for DNA extraction remains a continued source of variability across studies. Here we describe an efficient, robust, and cost effective method for extracting nucleic acid from swabs. We also delineate downstream methods for 16S rRNA gene sequencing, including generation of sequencing libraries, data quality control, and sequence analysis. The workflow can accommodate multiple samples types, including stool and swabs collected from a variety of anatomical locations and host species. Additionally, recovered DNA and RNA can be separated and used for other applications, including whole genome sequencing or RNA-seq. The method described allows for a common processing approach for multiple sample types and accommodates downstream analysis of genomic, metagenomic and transcriptional information.

We describe an efficient, robust, and cost effective method for extracting nucleic acid from swabs for characterization of bacterial communities using 16S rRNA gene amplicon sequencing. The method allows for a common processing approach for multiple sample types and accommodates a number of downstream analytic processes.

There is a growing appreciation for the role of microbial communities as critical modulators of human health and disease. High throughput sequencing ...

Cellular Redox Profiling Using High-content Microscopy

Reactive oxygen species (ROS) regulate essential cellular processes including gene expression, migration, differentiation and proliferation. However, excessive ROS levels induce a state of oxidative stress, which is accompanied by irreversible oxidative damage to DNA, lipids and proteins. Thus, quantification of ROS provides a direct proxy for cellular health condition. Since mitochondria are among the major cellular sources and targets of ROS, joint analysis of mitochondrial function and ROS production in the same cells is crucial for better understanding the interconnection in pathophysiological conditions. Therefore, a high-content microscopy-based strategy was developed for simultaneous quantification of intracellular ROS levels, mitochondrial membrane potential (&#916;&#936;m) and mitochondrial morphology. It is based on automated widefield fluorescence microscopy and image analysis of living adherent cells, grown in multi-well plates, and stained with the cell-permeable fluorescent reporter molecules CM-H2DCFDA (ROS) and TMRM (&#916;&#936;m and mitochondrial morphology). In contrast with fluorimetry or flow-cytometry, this strategy allows quantification of subcellular parameters at the level of the individual cell with high spatiotemporal resolution, both before and after experimental stimulation. Importantly, the image-based nature of the method allows extracting morphological parameters in addition to signal intensities. The combined feature set is used for explorative and statistical multivariate data analysis to detect differences between subpopulations, cell types and/or treatments. Here, a detailed description of the assay is provided, along with an example experiment that proves its potential for unambiguous discrimination between cellular states after chemical perturbation.

This paper presents a high-content microscopy workflow for simultaneous quantification of intracellular ROS levels, as well as mitochondrial membrane potential and morphology &#8211; jointly referred to as mitochondrial morphofunction &#8211; in living adherent cells using the cell-permeant fluorescent reporter molecules 5-(and-6)-chloromethyl-2&#39;,7&#39;-dichlorodihydrofluorescein diacetate, acetyl ester (CM-H2DCFDA) and tetramethylrhodamine methylester (TMRM).

Reactive oxygen species (ROS) regulate essential cellular processes including gene expression, migration, differentiation and proliferation. However, ...

Guided Protocol for Fecal Microbial Characterization by 16S rRNA-Amplicon Sequencing

The human intestinal microbiome plays a central role in protecting cells from injury, in processing energy and nutrients, and in promoting immunity. Deviations from what is considered a healthy microbiota composition (dysbiosis) may impair vital functions leading to pathologic conditions. Recent and ongoing research efforts have been directed toward the characterization of associations between microbial composition and human health and disease.
Advances in high-throughput sequencing technologies enable characterization of the gut microbial composition. These methods include 16S rRNA-amplicon sequencing and shotgun sequencing. 16S rRNA-amplicon sequencing is used to profile taxonomical composition, while shotgun sequencing provides additional information about gene predictions and functional annotation. An advantage in using a targeted sequencing method of the 16S rRNA gene variable region is its substantially lower cost compared to shotgun sequencing. Sequence differences in the 16S rRNA gene are used as a microbial fingerprint to identify and quantify different taxa within an individual sample.
Major international efforts have enlisted standards for 16S rRNA-amplicon sequencing. However, several studies report a common source of variation caused by batch effect. To minimize this effect, uniformed protocols for sample collection, processing, and sequencing must be implemented. This protocol proposes the integration of broadly used protocols starting from fecal sample collection to data analyses. This protocol includes a column-free, direct-PCR approach that enables simultaneous handling and DNA extraction of large numbers of fecal samples, along with PCR amplification of the V4 region. In addition, the protocol describes the analysis pipeline and provides a script using the latest version of QIIME (QIIME 2 version 2017.7.0 and DADA2). This step-by-step protocol is aimed to guide those interested in initiating the use of 16S rRNA-amplicon sequencing in a robust, reproductive, easy to use, detailed way.

This manuscript describes a detailed standardized protocol of high-throughput 16S rRNA-amplicon sequencing. The protocol introduces an integrated, uniformed, feasible, and inexpensive protocol starting from fecal sample collection through data analyses. This protocol enables analysis of large numbers of samples with rigorous standards and several controls.

The human intestinal microbiome plays a central role in protecting cells from injury, in processing energy and nutrients, and in promoting immunity. ...

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

In recent decades, vector-borne diseases have re-emerged and expanded at alarming rates, causing considerable morbidity and mortality worldwide. Effective and widely available vaccines are lacking for a majority of these diseases, necessitating the development of novel disease mitigation strategies. To this end, a promising avenue of disease control involves targeting the vector microbiome, the community of microbes inhabiting the vector. The vector microbiome plays a pivotal role in pathogen dynamics, and manipulations of the microbiome have led to reduced vector abundance or pathogen transmission for a handful of vector-borne diseases. However, translating these findings into disease control applications requires a thorough understanding of vector microbial ecology, historically limited by insufficient technology in this field. The advent of next-generation sequencing approaches has enabled rapid, highly parallel sequencing of diverse microbial communities. Targeting the highly-conserved 16S rRNA gene has facilitated characterizations of microbes present within vectors under varying ecological and experimental conditions. This technique involves amplification of the 16S rRNA gene, sample barcoding via PCR, loading samples onto a flow cell for sequencing, and bioinformatics approaches to match sequence data with phylogenetic information. Species or genus-level identification for a high number of replicates can typically be achieved through this approach, thus circumventing challenges of low detection, resolution, and output from traditional culturing, microscopy, or histological staining techniques. Therefore, this method is well-suited for characterizing vector microbes under diverse conditions but cannot currently provide information on microbial function, location within the vector, or response to antibiotic treatment. Overall, 16S next-generation sequencing is a powerful technique for better understanding the identity and role of vector microbes in disease dynamics.

Here we present a next-generation sequencing protocol for 16S rRNA sequencing which enables identification and characterization of microbial communities within vectors. This method involves DNA extraction, amplification and barcoding of samples through PCR, sequencing on a flow-cell, and bioinformatics to match sequence data to phylogenetic information.

In recent decades, vector-borne diseases have re-emerged and expanded at alarming rates, causing considerable morbidity and mortality worldwide. Effective ...