Natural contaminants in bee pollen: DNA metabarcoding as a tool to identify floral sources of pyrrolizidine alkaloids and fungal diversity

Quantitative Analysis of Pyrrolizidine Alkaloids in Food Matrices and Plant-Derived Samples Using UHPLC-MS/MS

Pyrrolizidine alkaloids (PAs) are a class of nitrogen-containing basic organic compounds that are frequently detected in foods and herbal medicines. Owing to their potential hepatotoxic, genotoxic, and carcinogenic properties, PAs have become a significant focus for monitoring global food safety. In this study, an ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method was developed for the detection and analysis of three foods (tea, honey, and milk) susceptible to PA contamination. This optimized method effectively separated and detected three types of PAs, namely, three pairs of isomers and two pairs of chiral compounds. The limits of detection (LODs) and limits of quantification (LOQs) were determined to be 0.015-0.75 and 0.05-2.5 µg/kg, respectively, with the relative standard deviations (RSDs) of both the interday and intraday precisions remaining below 15%. The average PA recoveries from the honey, milk, and tea matrices fell within the ranges of 64.5-103.4, 65.2-112.2, and 67.6-107.6%, respectively. This method was also applied to 77 samples collected from 33 prefecture-level cities across 16 provinces and included 40 tea, 6 milk, 8 honey, 14 spice, and 9 herbal medicine samples. At least one PA was detected in twenty-three of the samples, with herbal medicines exhibiting the highest total PA content. The obtained results indicate that the developed method demonstrated good repeatability and stability in the detection and quantitative analyses of PAs in food- and plant-derived samples. This method is therefore expected to provide reliable technical support for food safety risk monitoring.

Determination of mycobiota and aflatoxin contamination in commercial bee pollen from eight provinces and one autonomous region of China

The co-occurrence of fungi and mycotoxins in various foods has been frequently reported in many countries, posing a serious threat to the health and safety of consumers. In this study, the mycobiota in five types of commercial bee pollen samples from China were first revealed by DNA metabarcoding. Meanwhile, the content of total aflatoxins in each sample was investigated by high-performance liquid chromatography with fluorescence detection. The results demonstrated that Cladosporium (0.16 %-89.29 %) was the most prevalent genus in bee pollen, followed by Metschnikowia (0-81.12 %), unclassified genus in the phylum Ascomycota (0-81.13 %), Kodamaea (0-73.57 %), and Penicillium (0-36.13 %). Meanwhile, none of the assayed aflatoxins were determined in the 18 batches of bee pollen samples. In addition, the fungal diversity, community composition, and trophic mode varied significantly among five groups. This study provides comprehensive information for better understanding the fungal communities and aflatoxin residues in bee pollen from different floral origins in China.

How reliable is metabarcoding for pollen identification? An evaluation of different taxonomic assignment strategies by cross-validation

Metabarcoding is a powerful tool, increasingly used in many disciplines of environmental sciences. However, to assign a taxon to a DNA sequence, bioinformaticians need to choose between different strategies or parameter values and these choices sometimes seem rather arbitrary. In this work, we present a case study on ITS2 and rbcL databases used to identify pollen collected by bees in Belgium. We blasted a random sample of sequences from the reference database against the remainder of the database using different strategies and compared the known taxonomy with the predicted one. This in silico cross-validation (CV) approach proved to be an easy yet powerful way to (1) assess the relative accuracy of taxonomic predictions, (2) define rules to discard dubious taxonomic assignments and (3) provide a more objective basis to choose the best strategy. We obtained the best results with the best blast hit (best bit score) rather than by selecting the majority taxon from the top 10 hits. The predictions were further improved by favouring the most frequent taxon among those with tied best bit scores. We obtained better results with databases containing the full sequences available on NCBI rather than restricting the sequences to the region amplified by the primers chosen in our study. Leaked CV showed that when the true sequence is present in the database, blast might still struggle to match the right taxon at the species level, particularly with rbcL. Classical 10-fold CV-where the true sequence is removed from the database-offers a different yet more realistic view of the true error rates. Taxonomic predictions with this approach worked well up to the genus level, particularly for ITS2 (5-7% of errors). Using a database containing only the local flora of Belgium did not improve the predictions up to the genus level for local species and made them worse for foreign species. At the species level, using a database containing exclusively local species improved the predictions for local species by ∼12% but the error rate remained rather high: 25% for ITS2 and 42% for rbcL. Foreign species performed worse even when using a world database (59-79% of errors). We used classification trees and GLMs to model the % of errors vs. identity and consensus scores and determine appropriate thresholds below which the taxonomic assignment should be discarded. This resulted in a significant reduction in prediction errors, but at the cost of a much higher proportion of unassigned sequences. Despite this stringent filtering, at least 1/5 sequences deemed suitable for species-level identification ultimately proved to be misidentified. An examination of the variability in prediction accuracy between plant families showed that rbcL outperformed ITS2 for only two of the 27 families examined, and that the % correct species-level assignments were much better for some families (e.g. 95% for Sapindaceae) than for others (e.g. 35% for Salicaceae).

A preliminary metabarcoding analysis of Portuguese raw honeys

The microbial diversity in Portuguese raw honeys remains largely uncharacterized, constituting a serious knowledge gap in one of the country's most important resources. This work provides an initial investigation with amplicon metabarcoding analysis of two Lavandula spp. from different geographical regions of Portugal and one Eucalyptus spp. honey. The results obtained allowed to identify that each honey harbors diverse microbiomes with taxa that can potentially affect bee and human health, cause spoilage, and highlight bad bee-hive management practices. We verified that prokaryotes had a tendency towards a more marked core bacterial and a relative homogenous taxa distribution, and that the botanical origin of honey is likely to have a stronger impact on the fungal community. Thus, the results obtained in this work provide important information that can be helpful to improve this critical Portuguese product and industry.