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bioRxiv · 10.1101/099481

Taxonomic identification of airborne pollen from complex environmental samples by DNA metabarcoding: a methodological study for optimizing protocols

Leontidou, C.·Vernesi, C.·De Groeve, J.·Cristofolini, F.·Vokou, D.·Cristofori, A.
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Abstract

Metabarcoding is a promising DNA-based method for identifying airborne pollen from environmental samples with advantages over microscopic methods. This method requires several preparatory steps of the samples, with the extraction protocol being of fundamental importance to obtain an optimal DNA yield. Currently, there is no consensus in sample preparation and DNA extraction, especially for gravimetric pollen samplers. Therefore, the aim of this study was to develop protocols to process environmental samples for pollen DNA extraction and further metabarcoding analysis, and to assess the efficacy of these protocols for the taxonomic assignment of airborne pollen, collected by gravimetric (Tauber trap) and volumetric samplers (Burkard spore trap). Protocols were tested across an increasing complexity of samples, from single-species pure pollen to environmental samples. A short fragment (about 150 base pair) of chloroplast DNA was amplified by universal primers for plants (trnL). After PCR amplification, amplicons were Sanger-sequenced and taxonomic assignment was accomplished by comparison to a custom-made reference database including chloroplast DNA sequences of 46 plant families, including most of the anemophilous taxa occurring in the study area (Trentino, Italy, Eastern Italian Alps). Using as a benchmark the classical morphological pollen analysis, it emerged that DNA metabarcoding is applicable efficiently across a complexity of samples, provided that sample preparation, DNA extraction and amplification protocols are specifically optimized.

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Leontidou, C., Vernesi, C., De Groeve, J., Cristofolini, F., Vokou, D., Cristofori, A.. 2017-01-10. Taxonomic identification of airborne pollen from complex environmental samples by DNA metabarcoding: a methodological study for optimizing protocols. https://doi.org/10.1101/099481

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