Next-generation sequencing has been used to strengthen knowledge about taxonomic diversity and ecology of fungi within food ecosystems. However, primer amplification and identification bias could edge our understanding into the fungal ecology. The aim of this study is to compare the performance of two primer pairs over two nuclear ribosomal RNA (rRNA) regions of the fungal kingdom, namely the ITS2 and 26S regions. Fermented cocoa beans were employed as biological material and the fungal ecology during fermentation was studied using amplicon-based sequencing tools, making use of a manually curated 26S database constructed in this study, and validated with SILVA's database. To explore potential biases introduced by PCR amplification of fungal communities, a mock community of known composition was prepared and tested. The relative abundances observed for ITS2 suggest that species with longer amplification fragments are underestimated and concurrently species that render shorter amplification fragments are overestimated. However, this correlation between amplicon length and estimation is not valid for all the species analysed. Variability in the amplification lengths contributed to the preferential amplification phenomenon. DNA extracted from twenty fermented cocoa bean samples were used to assess the performance of the two target regions. Overall, the metataxonomic data set recovered similar taxonomic composition and provided consistent results in OTU richness among biological samples. However, 26S region provided higher alpha diversity index and greater fungal rRNA taxonomic depth and robustness results compared with ITS2. Based on the results of this study we suggest the use of the 26S region for targeting fungi. Furthermore, this study showed the efficacy of the manually curated reference database optimized for annotation of mycobiota by using the 26S as a gene target.
Metataxonomic comparison between internal transcribed spacer and 26S ribosomal large subunit (LSU) rDNA gene
Mota-Gutierrez, Jatziri;Ferrocino, Ilario;Rantsiou, Kalliopi;Cocolin, Luca
Last
2019-01-01
Abstract
Next-generation sequencing has been used to strengthen knowledge about taxonomic diversity and ecology of fungi within food ecosystems. However, primer amplification and identification bias could edge our understanding into the fungal ecology. The aim of this study is to compare the performance of two primer pairs over two nuclear ribosomal RNA (rRNA) regions of the fungal kingdom, namely the ITS2 and 26S regions. Fermented cocoa beans were employed as biological material and the fungal ecology during fermentation was studied using amplicon-based sequencing tools, making use of a manually curated 26S database constructed in this study, and validated with SILVA's database. To explore potential biases introduced by PCR amplification of fungal communities, a mock community of known composition was prepared and tested. The relative abundances observed for ITS2 suggest that species with longer amplification fragments are underestimated and concurrently species that render shorter amplification fragments are overestimated. However, this correlation between amplicon length and estimation is not valid for all the species analysed. Variability in the amplification lengths contributed to the preferential amplification phenomenon. DNA extracted from twenty fermented cocoa bean samples were used to assess the performance of the two target regions. Overall, the metataxonomic data set recovered similar taxonomic composition and provided consistent results in OTU richness among biological samples. However, 26S region provided higher alpha diversity index and greater fungal rRNA taxonomic depth and robustness results compared with ITS2. Based on the results of this study we suggest the use of the 26S region for targeting fungi. Furthermore, this study showed the efficacy of the manually curated reference database optimized for annotation of mycobiota by using the 26S as a gene target.File | Dimensione | Formato | |
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Manuscript_IJFM_clear.pdf
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IJFM, 2019, 290, 132–140.pdf
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