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><tbody><tr><td>Agarose gel&amp;nbsp;</td><td>Promega, Madison, WI USA</td><td>V3121</td><td>Electrophoresis&amp;nbsp;</td></tr><tr><td>FastPrep DNA spinKit for soil&amp;nbsp;</td><td>MP Biomedicals, Solon, OH USA</td><td>116560-200</td><td>DNA extraction&amp;nbsp;</td></tr><tr><td>FastQC software</td><td>Babraham Institute, United Kingdom</td><td></td><td>Bioinformatics&amp;nbsp;</td></tr><tr><td>Fermaid O</td><td>Scott Laboratory, Petaluma, CA USA&amp;nbsp;</td><td></td><td>Fermentation&amp;nbsp;</td></tr><tr><td>High-Fidelity PCR Master Mix&amp;nbsp;</td><td>New England Biolabs, USA</td><td>F630S</td><td>Polymerase chain reaction for sequencing&amp;nbsp;</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&amp;nbsp;</td><td>Illumina, San Diego, CA&amp;nbsp; USA</td><td></td><td>DNA sequencing&amp;nbsp;</td></tr><tr><td>NovaSeq Control Software (NVCS)</td><td>Illumina, San Diego, CA&amp;nbsp; USA</td><td></td><td>DNA sequencing&amp;nbsp;</td></tr><tr><td>Novaseq6000 platform&amp;nbsp;</td><td>Illumina, San Diego, CA&amp;nbsp; USA</td><td></td><td>DNA sequencing&amp;nbsp;</td></tr><tr><td>QuiBit</td><td>Thermoscientific, Waltham, MA, USA</td><td></td><td>DNA quantification&amp;nbsp;</td></tr><tr><td>Quickdrop spectrophotometer&amp;nbsp;</td><td>Molecular device, San Jose, CA, USA</td><td></td><td>DNA quantification&amp;nbsp;</td></tr><tr><td>Sodium Phosphate&amp;nbsp;</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&amp;nbsp;</td><td></td><td>Fermentation&amp;nbsp;</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...

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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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Research

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Biology

642 Views.  Appalachian State University. Het onderzoeksbereik richt zich op het begrijpen van het fermentatiemicrobioom. Dit is om vragen te beantwoorden zoals hoe divers microben in natuurlijke fermentatie zijn, en welk gen codeert voor metaboliet is nodig voor smaak en veiligheid. Een van de nieuwste ontwikkelingen in ons vakgebied is het begrijpen hoe de dynamiek van het microbioom correleert met het metabolische profiel dat wordt waargenomen in gefermenteerde voedingsmiddelen en dranken.De sequentie van de volgende genera...

Video: Auteur in de schijnwerpers: onderzoek naar het fermentatiemicrobioom door middel van sequencing van de volgende generatie

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

Terroir refers to the combination of environmental factors that affect the characteristics of crops such as grapevine (Vitis vinifera) according to particular habitats and management practices. This article shows how certain terroir signatures can be detected in the berry metabolome and transcriptome of the grapevine cultivar Corvina using multivariate statistical analysis. The method first requires an appropriate sampling plan. In this case study, a specific clone of the Corvina cultivar was selected to minimize genetic differences, and samples were collected from seven vineyards representing three different macro-zones during three different growing seasons. An untargeted LC-MS metabolomics approach is recommended due to its high sensitivity, accompanied by efficient data processing using MZmine software and a metabolite identification strategy based on fragmentation tree analysis. Comprehensive transcriptome analysis can be achieved using microarrays containing probes covering ~99% of all predicted grapevine genes, allowing the simultaneous analysis of all differentially expressed genes in the context of different terroirs. Finally, multivariate data analysis based on projection methods can be used to overcome the strong vintage-specific effect, allowing the metabolomics and transcriptomics data to be integrated and analyzed in detail to identify informative correlations.

This article describes the application of untargeted metabolomics, transcriptomics and multivariate statistical analysis to grape berry transcripts and metabolites in order to gain insight into the terroir concept, i.e., the impact of the environment on berry quality traits.

Terroir refers to the combination of environmental factors that affect the characteristics of crops such as grapevine (Vitis vinifera) according to ...

Genetics

Metagenomic Analysis of Silage

Metagenomics is defined as the direct analysis of deoxyribonucleic acid (DNA) purified from environmental samples and enables taxonomic identification of the microbial communities present within them. Two main metagenomic approaches exist; sequencing the 16S rRNA gene coding region, which exhibits sufficient variation between taxa for identification, and shotgun sequencing, in which genomes of the organisms that are present in the sample are analyzed and ascribed to "operational taxonomic units"; species, genera or families depending on the extent of sequencing coverage. 
In this study, shotgun sequencing was used to analyze the microbial community present in cattle silage and, coupled with a range of bioinformatics tools to quality check and filter the DNA sequence reads, perform taxonomic classification of the microbial populations present within the sampled silage, and achieve functional annotation of the sequences. These methods were employed to identify potentially harmful bacteria that existed within the silage, an indication of silage spoilage. If spoiled silage is not remediated, then upon ingestion it could be potentially fatal to the livestock.

Metagenomics was used to investigate the microbiome of silage cattle feed. Analysis was performed by shotgun sequencing. This approach was used to characterize the composition of the microbial community within the cattle feed.

Metagenomics is defined as the direct analysis of deoxyribonucleic acid (DNA) purified from environmental samples and enables taxonomic identification of ...

Exploring the Root Microbiome: Extracting Bacterial Community Data from the Soil, Rhizosphere, and Root Endosphere

The intimate interaction between plant host and associated microorganisms is crucial in determining plant fitness, and can foster improved tolerance to abiotic stresses and diseases. As the plant microbiome can be highly complex, low-cost, high-throughput methods such as amplicon-based sequencing of the 16S rRNA gene are often preferred for characterizing its microbial composition and diversity. However, the selection of appropriate methodology when conducting such experiments is critical for reducing biases that can make analysis and comparisons between samples and studies difficult. This protocol describes in detail a standardized methodology for the collection and extraction of DNA from soil, rhizosphere, and root samples. Additionally, we highlight a well-established 16S rRNA amplicon sequencing pipeline that allows for the exploration of the composition of bacterial communities in these samples, and can easily be adapted for other marker genes. This pipeline has been validated for a variety of plant species, including sorghum, maize, wheat, strawberry, and agave, and can help overcome issues associated with the contamination from plant organelles.

Here, we describe a protocol to obtain amplicon sequence data of soil, rhizosphere, and root endosphere microbiomes. This information can be used to investigate the composition and diversity of plant-associated microbial communities, and is suitable for the use with a wide range of plant species.

The intimate interaction between plant host and associated microorganisms is crucial in determining plant fitness, and can foster improved tolerance to ...

Evaluating the Impact of Hydraulic Fracturing on Streams using Microbial Molecular Signatures

Hydraulic fracturing (HF), commonly called &#34;fracking&#34;, uses a mixture of high-pressure water, sand, and chemicals to fracture rocks, releasing oil and gas. This process revolutionized the U.S. energy industry, as it gives access to resources that were previously unobtainable and now produces two-thirds of the total natural gas in the United States. Although fracking has had a positive impact on the U.S. economy, several studies have highlighted its detrimental environmental effects. Of particular concern is the effect of fracking on headwater streams, which are especially important due to their disproportionately large impact on the health of the entire watershed. The bacteria within those streams can be used as indicators of stream health, as the bacteria present and their abundance in a disturbed stream would be expected to differ from those in an otherwise comparable but undisturbed stream. Therefore, this protocol aims to use the bacterial community to determine if streams have been impacted by fracking. To this end, sediment, and water samples, from streams near fracking (potentially impacted) and upstream or in a different watershed of fracking activity (unimpacted) must be collected. Those samples are then subjected to nucleic acid extraction, library preparation, and sequencing to investigate microbial community composition. Correlational analysis and machine learning models can subsequently be employed to identify which features are explanative of variation in the community, as well as identification of predictive biomarkers for fracking&#39;s impact. These methods can reveal a variety of differences in the microbial communities among headwater streams, based on the proximity to fracking, and serve as a foundation for future investigations on the environmental impact of fracking activities.

Here, we present a protocol to investigate the impacts of hydraulic fracturing on nearby streams by analyzing their water and sediment microbial communities.

Hydraulic fracturing (HF), commonly called &#34;fracking&#34;, uses a mixture of high-pressure water, sand, and chemicals to fracture rocks, releasing oil ...

Environment

Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources

Studies in the field of microbiology rely on the implementation of a wide range of methodologies. In particular, the development of appropriate methods substantially contributes to providing extensive knowledge of the metabolism of microorganisms growing in chemically defined media containing unique nitrogen and carbon sources. In contrast, the management through metabolism of multiple nutrient sources, despite their broad presence in natural or industrial environments, remains virtually unexplored. This situation is mainly due to the lack of suitable methodologies, which hinders investigations.
We report an experimental strategy to quantitatively and comprehensively explore how metabolism operates when a nutrient is provided as a mixture of different molecules, i.e., a complex resource. Here, we describe its application for assessing the partitioning of multiple nitrogen sources through the yeast metabolic network. The workflow combines information obtained during stable isotope tracer experiments using selected 13C- or 15N-labeled substrates. It first consists of parallel and reproducible fermentations in the same medium, which includes a mixture of N-containing molecules; however,a selected nitrogen source is labeled each time. A combination of analytical procedures (HPLC, GC-MS) is implemented to assess the labeling patterns of targeted compounds and to quantify the consumption and recovery of substrates in other metabolites. An integrated analysis of the complete dataset provides an overview of the fate of consumed substrates within cells. This approach requires an accurate protocol for the collection of samples&#8211;facilitated by a robot-assisted system for online monitoring of fermentations&#8211;and the achievement of numerous time-consuming analyses. Despite these constraints, it allowed understanding, for the first time, the partitioning of multiple nitrogen sources throughout the yeast metabolic network. We elucidated the redistribution of nitrogen from more abundant sources toward other N-compounds and determined the metabolic origins of volatile molecules and proteinogenic amino acids.

This protocol describes an experimental procedure to quantitatively and comprehensively investigate the metabolism of multiple nutrient sources. This workflow, based on a combination of isotopic tracer experiments and an analytical procedure, allows the fate of consumed nutrients and the metabolic origin of molecules synthetized by microorganisms to be determined.

Studies in the field of microbiology rely on the implementation of a wide range of methodologies. In particular, the development of appropriate methods ...

Bioengineering

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

One of the major questions in microbial ecology is &#8220;who is there?&#8221; This question can be answered using various tools, but one of the long-lasting gold standards is to sequence 16S ribosomal RNA (rRNA) gene amplicons generated by domain-level PCR reactions amplifying from genomic DNA. Traditionally, this was performed by cloning and Sanger (capillary electrophoresis) sequencing of PCR amplicons. The advent of next-generation sequencing has tremendously simplified and increased the sequencing depth for 16S rRNA gene sequencing. The introduction of benchtop sequencers now allows small labs to perform their 16S rRNA sequencing in-house in a matter of days. Here, an approach for 16S rRNA gene amplicon sequencing using a benchtop next-generation sequencer is detailed. The environmental DNA is first amplified by PCR using primers that contain sequencing adapters and barcodes. They are then coupled to spherical particles via emulsion PCR. The particles are loaded on a disposable chip and the chip is inserted in the sequencing machine after which the sequencing is performed. The sequences are retrieved in fastq format, filtered and the barcodes are used to establish the sample membership of the reads. The filtered and binned reads are then further analyzed using publically available tools. An example analysis where the reads were classified with a taxonomy-finding algorithm within the software package Mothur is given. The method outlined here is simple, inexpensive and straightforward and should help smaller labs to take advantage from the ongoing genomic revolution.

Characterizing microbial community has been a longstanding goal in environmental microbiology. Next-generation sequencing methods now allow for the characterization of microbial communities at an unprecedented depth with minimal cost and labor. We detail here our approach to sequence bacterial 16S ribosomal RNA genes using a benchtop sequencer.

One of the major questions in microbial ecology is &#8220;who is there?&#8221; This question can be answered using various tools, but one of the ...

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 ...

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

Classification and nomenclature of genes in a family can significantly contribute to the description of the diversity of encoded proteins and to the prediction of family functions based on several features, such as the presence of sequence motifs or of particular sites for post-translational modification and the expression profile of family members in different conditions. This work describes a detailed protocol for gene family characterization. Here, the procedure is applied to the characterization of the Arabidopsis T&#243;xicos in Levadura (ATL) E3 ubiquitin ligase family in grapevine. The methods include the genome-wide identification of family members, the characterization of gene localization, structure, and duplication, the analysis of conserved protein motifs, the prediction of protein localization and phosphorylation sites as well as gene expression profiling across the family in different datasets. Such procedure, which could be extended to further analyses depending on experimental purposes, could be applied to any gene family in any plant species for which genomic data are available, and it provides valuable information to identify interesting candidates for functional studies, giving insights into the molecular mechanisms of plant adaptation to their environment.

This article describes the procedure for the identification and characterization of a gene family in grapevine applied to the family of Arabidopsis T&#243;xicos in Levadura (ATL) E3 ubiquitin ligases.

Classification and nomenclature of genes in a family can significantly contribute to the description of the diversity of encoded proteins and to the ...

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. ...

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

The human gut is colonized by trillions of bacteria that support physiologic functions such as food metabolism, energy harvesting, and regulation of the immune system. Perturbation of the healthy gut microbiome has been suggested to play a role in the development of inflammatory diseases, including multiple sclerosis (MS). Environmental and genetic factors can influence the composition of the microbiome; therefore, identification of microbial communities linked with a disease phenotype has become the first step towards defining the microbiome&#8217;s role in health and disease. Use of 16S rRNA metagenomic sequencing for profiling bacterial community has helped in advancing microbiome research. Despite its wide use, there is no uniform protocol for 16S rRNA-based taxonomic profiling analysis. Another limitation is the low resolution of taxonomic assignment due to technical difficulties such as smaller sequencing reads, as well as use of only forward (R1) reads in the final analysis due to low quality of reverse (R2) reads. There is need for a simplified method with high resolution to characterize bacterial diversity in a given biospecimen. Advancements in sequencing technology with the ability to sequence longer reads at high resolution have helped to overcome some of these challenges. Present sequencing technology combined with a publicly available metagenomic analysis pipeline such as R-based Divisive Amplicon Denoising Algorithm-2 (DADA2) has helped advance microbial profiling at high resolution, as DADA2 can assign sequence at the genus and species levels. Described here is a guide for performing bacterial profiling using two-step amplification of the V3-V4 region of the 16S rRNA gene, followed by analysis using freely available analysis tools (i.e., DADA2, Phyloseq, and METAGENassist). It is believed that this simple and complete workflow will serve as an excellent tool for researchers interested in performing microbiome profiling studies.

Described here is a simplified standard operating procedure for microbiome profiling using 16S rRNA metagenomic sequencing and analysis using freely available tools. This protocol will help researchers who are new to the microbiome field as well as those requiring updates on methods to achieve bacterial profiling at a higher resolution.

The human gut is colonized by trillions of bacteria that support physiologic functions such as food metabolism, energy harvesting, and regulation of the ...

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

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