https://doi.org/10.4081/ijfs.2025.13171

16S rRNA metabarcoding applied to the microbiome of insect products (novel food): a comparative analysis of three reference databases

Vol. 14 No. 1 (2025)
Submitted: 27 September 2024
Accepted: 29 November 2024
Published: 16 January 2025
16S rRNA metabarcoding, NGS, food microbiome, genomic reference database
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Authors

Gabriele Spatola
g.spatola2@studenti.unipi.it
https://orcid.org/0009-0004-0413-2614
Department of Veterinary Sciences, University of Pisa, Italy.
Alice Giusti
Department of Veterinary Sciences, University of Pisa, Italy.
Laura Gasperetti
Experimental Zooprophylactic Institute of Lazio and Tuscany, Pisa, Italy.
Roberta Nuvoloni
Department of Veterinary Sciences, University of Pisa, Italy.
Alessandra Dalmasso
https://orcid.org/0000-0002-9991-9046
Department of Veterinary Sciences, University of Turin, Grugliasco (TO), Italy.
Francesco Chiesa
https://orcid.org/0000-0001-6781-682X
Department of Veterinary Sciences, University of Turin, Grugliasco (TO), Italy.
Andrea Armani
https://orcid.org/0000-0002-9614-2032
Department of Veterinary Sciences, University of Pisa, Italy.

The 16S rRNA metabarcoding, based on Next-Generation Sequencing (NGS), is used to assess microbial biodiversity in various matrices, including food. The process involves a “dry-lab” phase where NGS data are processed through bioinformatic pipelines, which finally rely on taxonomic unit assignment against reference databases to assign them at order, genus, and species levels. Today, several public genomic reference databases are available for the taxonomic assignment of the 16S rRNA sequences. In this study, 42 insect-based food products were chosen as food models to find out how reference database choice could affect the microbiome results in food matrices. At the same time, this study aims to evaluate the most suitable reference database to assess the microbial composition of these still poorly investigated products. The V3-V4 region was sequenced by Illumina technology, and the R package “DADA2” was used for the bioinformatic analysis. After a bibliographic search, three public databases (SILVA, RDP, NCBI RefSeq) were compared based on amplicon sequence variant (ASV) assignment percentages at different taxonomic levels and diversity indices. SILVA assigned a significantly higher percentage of ASVs to the family and genus levels compared to RefSeq and RDP. However, no significant differences were noted in microbial composition between the databases according to α and β diversity results. A total of 121 genera were identified, with 56.2% detected by all three databases, though some taxa were identified only by one or two. The study highlights the importance of using updated reference databases for accurate microbiome characterization, contributing to the optimization of metabarcoding data analysis in food microbiota studies, including novel foods.

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Crossref
Scopus
Google Scholar
Europe PMC
Abellan-Schneyder I, Matchado MS, Reitmeier S, Sommer A, Sewald Z, Baumbach J, List M, Neuhaus K, 2021. Primer, pipelines, parameters: issues in 16S rRNA gene sequencing. mSphere 6:e01202-20. DOI: https://doi.org/10.1128/mSphere.01202-20
Almeida A, Mitchell AL, Tarkowska A, Finn RD, 2018. Benchmarking taxonomic assignments based on 16S rRNA gene profiling of the microbiota from commonly sampled environments. Gigascience 7:giy054. DOI: https://doi.org/10.1093/gigascience/giy054
Balvočiūtė M, Huson DH, 2017. SILVA, RDP, Greengenes, NCBI and OTT—how do these taxonomies compare? BMC genomics 18:114. DOI: https://doi.org/10.1186/s12864-017-3501-4
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP, 2016. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods 13:581-3. DOI: https://doi.org/10.1038/nmeth.3869
Campos PM, Darwish N, Shao J, Proszkowiec-Weglarz M, 2022. Research Note: Choice of microbiota database affects data analysis and interpretation in chicken cecal microbiota. Poult Sci 101:101971. DOI: https://doi.org/10.1016/j.psj.2022.101971
Catozzi C, Ceciliani F, Lecchi C, Talenti A, Vecchio D, De Carlo E, Grassi C, Sanchez A, Francino O, Cuscó A, 2020. Milk microbiota profiling on water buffalo with full-length 16S rRNA using nanopore sequencing. JDS 103:2693-700. DOI: https://doi.org/10.3168/jds.2019-17359
Ceccarani C, Severgnini M, 2023. A comparison between Greengenes, SILVA, RDP, and NCBI reference databases in four published microbiota datasets. bioRxiv 2023. doi: 10.1101/2023.04.12.535864. DOI: https://doi.org/10.1101/2023.04.12.535864
Ercolini D, 2013. High-throughput sequencing and metagenomics: moving forward in the culture-independent analysis of food microbial ecology. Appl Environ Microbiol 79:3148-55. DOI: https://doi.org/10.1128/AEM.00256-13
Frigerio J, Agostinetto G, Galimberti A, De Mattia F, Labra M, Bruno A, 2020. Tasting the differences: microbiota analysis of different insect-based novel food. Food Res Int 137:109426. DOI: https://doi.org/10.1016/j.foodres.2020.109426
Giusti A, Spatola G, Mancini S, Nuvoloni R, Armani A, 2024. Novel foods, old issues: Metabarcoding revealed mislabeling in insect-based products sold by e-commerce on the EU market. Food Res Int 184:114268. DOI: https://doi.org/10.1016/j.foodres.2024.114268
Glöckner FO, Yilmaz P, Quast C, Gerken J, Beccati A, Ciuprina A, Bruns G, Yarza P, Peplies J, Westram R, Ludwig W, 2017. 25 years of serving the community with ribosomal RNA gene reference databases and tools. J Biotech 261:169-76. DOI: https://doi.org/10.1016/j.jbiotec.2017.06.1198
Hakimzadeh A, Abdala Asbun A, Albanese D, Bernard M, Buchner D, Callahan B, Caporaso JG, Curd E, Djemiel C, Durling MB, Elbrecht V, Gold Z, Gweon HS, Hajibabaei M, Hildebrand F, Mikryukov V, Normandeau E, Özkurt E, Palmer JM, Pascal G, Porter TM, Straub D, Vasar M, Větrovský T, Anslan S, 2024. A pile of pipelines: an overview of the bioinformatics software for metabarcoding data analyses, Mol Ecol Resour 24:13847. DOI: https://doi.org/10.1111/1755-0998.13847
Handelsman J, 2004. Metagenomics: application of genomics to uncultured microorganisms. Microbiol Mol Biol Rev 68:669-85. DOI: https://doi.org/10.1128/MMBR.68.4.669-685.2004
Jagadeesan B, Gerner-Smidt P, Allard MW, Leuillet S, Winkler A, Xiao Y, Chaffron S, Van Der Vossen J, Tang S, Katase M, McClure P, Kimura B, Chai LC, Chapman J, Grant K, 2019. The use of next generation sequencing for improving food safety: translation into practice. Food Microbiol 79:96-115. DOI: https://doi.org/10.1016/j.fm.2018.11.005
Katiraei S, Anvar Y, Hoving L, Berbée JF, van Harmelen V, Willems van Dijk K, 2022. Evaluation of full-length versus V4-region 16S rRNA sequencing for phylogenetic analysis of mouse intestinal microbiota after a dietary intervention. Curr. Microbiol 79:276. DOI: https://doi.org/10.1007/s00284-022-02956-9
Kergourlay G, Taminiau B, Daube G, Vergès MCC, 2015. Metagenomic insights into the dynamics of microbial communities in food. Int J Food Microbiol 213:31-9. DOI: https://doi.org/10.1016/j.ijfoodmicro.2015.09.010
Kers JG, Saccenti E, 2022. The power of microbiome studies: some considerations on which alpha and beta metrics to use and how to report results. Front Microbiol 12:796025. DOI: https://doi.org/10.3389/fmicb.2021.796025
Kim BR, Shin J, Guevarra RB, Lee JH, Kim DW, Seol KH, Lee JH, Kim HB, Isaacson RE, 2017. Deciphering diversity indices for a better understanding of microbial communities. JMB 27:2089-93. DOI: https://doi.org/10.4014/jmb.1709.09027
Lan Y, Wang Q, Cole JR, Rosen GL, 2012. Using the RDP classifier to predict taxonomic novelty and reduce the search space for finding novel organisms. PLoS One 7:32491. DOI: https://doi.org/10.1371/journal.pone.0032491
Ramakodi MP, 2022. Influence of 16S rRNA reference databases in amplicon-based environmental microbiome research. Biotechnol Lett 44:523-33. DOI: https://doi.org/10.1007/s10529-022-03233-2
Regueira‐Iglesias A, Balsa‐Castro C, Blanco‐Pintos T, Tomás I, 2023. Critical review of 16S rRNA gene sequencing workflow in microbiome studies: from primer selection to advanced data analysis. Mol Oral Microbiol 38:347-99. DOI: https://doi.org/10.1111/omi.12434
Sierra MA, Li Q, Pushalkar S, Paul B, Sandoval TA, Kamer AR, Corby P, Guo Y, Ruff RR, Alekseyenko AV, Li X, Saxena D, 2020. The influences of bioinformatics tools and reference databases in analyzing the human oral microbial community. Genes 11:878. DOI: https://doi.org/10.3390/genes11080878
Smith PE, Waters SM, Gómez Expósito R, Smidt H, Carberry CA, McCabe MS, 2020. Synthetic sequencing standards: a guide to database choice for rumen microbiota amplicon sequencing analysis. Front Microbiol 11:606825. DOI: https://doi.org/10.3389/fmicb.2020.606825
Wang Q, Garrity GM, Tiedje JM, Cole JR, 2007. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbial 73:5261-7. DOI: https://doi.org/10.1128/AEM.00062-07
Yap M, Ercolini D, Álvarez-Ordóñez A, O’Toole PW, O’Sullivan O, Cotter PD, 2022. Next-generation food research: use of meta-omic approaches for characterizing microbial communities along the food chain. Annu Rev Food Sci Technol 13:361-84. DOI: https://doi.org/10.1146/annurev-food-052720-010751

How to Cite

1.
Spatola G, Giusti A, Gasperetti L, Nuvoloni R, Dalmasso A, Chiesa F, Armani A. 16S rRNA metabarcoding applied to the microbiome of insect products (novel food): a comparative analysis of three reference databases. Ital J Food Safety [Internet]. 2025 Jan. 16 [cited 2025 Jun. 24];14(1). Available from: https://www.pagepressjournals.org/ijfs/article/view/13171
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