high-throughput sequencing and bioinformatic analysis in grape must and wine for microbial diversity

Profiling the Bacterial Community in Grapes Using Amplicon Metagenomics

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.

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

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

Research scope focuses on the understanding of the fermentation microbiome. This is to answer questions such as how diverse microbes in natural fermentation are, and what gene coding for metabolite is necessary for flavor and safety. One of the latest advancement in our field involves understanding how the dynamics of the microbiome correlate with the metabolic profile observed in fermented foods and beverages.The next generation sequence stands out as a pivotal technology that is currently driving advancements in our research field

High-Throughput Sequencing and Bioinformatic Analysis in Grape Must and Wine for Microbial Diversity

To begin, use specific primers 515f and 806r to amplify the V4 hypervariable region of the 16S rNA gene. Perform PCR using the given conditions. Next, add 3.5 microliters of sequencing buffer and 1.5 microliters of enzyme mix to the tube.After mixing and spinning the sample, set up a thermocycler reaction. Now, add the adapter ligation mixture into the tube and incubate at 20 degrees Celsius for 15 minutes in a thermocycler. Add 1.5 microliters of uracil specific extension reagent enzyme to the ligation mix, and incubate at 37 degrees Celsius for 15 minutes.Now, add 43.5 microliters of magnetic beads to the adapter ligation DNA and mix 20 times. After five minutes, place the tube in a magnetic rack for five to 10 minutes for bead separation and discard the supernatant. Wash the pellet with 100 microliters of 80%ethanol and dry the beads for 30 seconds.To elute the beads in pellet, add 8.5 microliters of 10 millimolar Tris HCl into the tube and incubate for two minutes. Place the tube in a magnetic rack and remove 7.5 microliters of eluded DNA as supernatant into a new tube. Add PCR reagents to the adapter ligated DNA containing tube to set up a 25 microliter reaction.To clean amplified DNA, add 22.5 microliters of magnetic beads into the tube and mix 20 times. After five minutes, remove 50 microliters of supernatant and wash the beads with 100 microliters of 80%ethanol. Resuspend he dark brown beads in 16 microliters of water and mix 20 times.Place the tube in the magnetic rack for 30 to 60 seconds and transfer 15 microliters to a new tube. Mix 90 microliters of buffer, one microliter of dye, and two microliters of DNA library sample and read the DNA concentration on a fluorimeter. After diluting the DNA to two nanomolar, load 27 to 30 microliters of the flow cell and place it in the sequencer.Save the FASTQ files obtained from the sequencer. Click to open a spreadsheet file and create a mapping or metadata file. Open the Nephele website, upload the FASTQ files, and read QC End QIIME2, Perform Filtering and Trimming.Next, using DADA2, execute demultiplexed paired-end reads, filter substitution, chimera errors, and merging. Use the Naive Bayes classifier trained on the Silva version 132 99%operational taxonomic unit, or OTU database, to perform bacterial taxonomic assignment at 97%similarity. Open the MicrobiomeDB website.Click to upload the files, and generate OTU alpha diversity, beta diversity, relative abundance, and rarefaction curves. Finally, open a spreadsheet and transfer data to make heat maps, Venn diagrams, and linear discriminant analysis effect size. A heat map based on the relative abundance of bacteria at different stages of winemaking displayed the dominance of phyla such as Proteobacteria, Actinobacteriota, Firmicutes, Bacteroidota, and Fusobacteriota.At the genus level, important genera like Enterobacteriaceae and Lactobacillaceae were identified. A Venn diagram analysis revealed 15 shared unique OTUs from the grape must to the final wine. The results of the amplicon sequencing based on the V4 variable region also indicated the alpha diversity in the two traminette R and L.Beta diversity analysis showed a shift in bacteria during fermentation, but the bacterial composition in the final wine was similar to that in the must and yeast added stages.