Application of next generation semiconductor based sequencing to detect the botanical composition of monofloral, polyfloral and honeydew honey

Analysis of Free Amino Acid Composition and Honey Plant Species in Seven Honey Species in China

Honey is well-known as a food product that is rich in active ingredients and is very popular among consumers. Free amino acids (FAAs) are one of the important nutritional components of honey, which can be used not only as a nutritional indicator of honey but also as an indicator of plant source identification. In this study, the contents of 20 FAAs in seven types of honey from 11 provinces in China were examined for the first time. The 20 FAAs were analyzed by ultra-performance liquid chromatography-mass spectrometry/mass spectrometry (UPLC-MS/MS). By analyzing 93 honey samples from seven types of honey, the FAAs were found to range from 394.4 mg/kg (linden honey) to 1771.7 mg/kg (chaste honey). Proline ranged from 274.55 to 572.48 mg/kg, and methionine was only present in some of the linden honey, chaste honey, acacia honey, and rape honey. Evaluated by amino acid principal component analysis, multifloral grassland honey had the highest overall evaluation score, acacia and jujube honey were the most similar, while chaste honey was the least similar to the other types of honey. In addition, DNA was extracted from 174 Xinjiang grassland honey samples and different plant leaves for PCR and sequencing to identify the species of nectar plants. As a result, 12 families and 25 species of honey plants were identified. The results confirmed the diversity of FAAs in dissimilar types and sources of honey. This study provides a reference for expanding honey quality standards and verifying the authenticity of honey.

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).

Rapid analysis technologies with chemometrics for food authenticity field: A review

In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management. The common analytical methods used to determine food authenticity present challenges, such as complicated analysis processes and time-consuming procedures, necessitating the development of rapid, efficient analysis technology for food authentication. Spectroscopic techniques, ambient ionization mass spectrometry (AIMS), electronic sensors, and DNA-based technology have gradually been applied for food authentication due to advantages such as rapid analysis and simple operation. This paper summarizes the current research on rapid food authenticity analysis technology from three perspectives, including breeds or species determination, quality fraud detection, and geographical origin identification, and introduces chemometrics method adapted to rapid analysis techniques. It aims to promote the development of rapid analysis technology in the food authenticity field.

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.

Authentication of incense (Pittosporum undulatum Vent.) honey from the Azores (Mel dos Açores) by a novel real-time PCR approach

'Mel dos Açores' is a unique nectar honey produced from the exceptional and diverse flora of the Azores archipelago, categorised as incense honey ('mel de incenso') or multifloral honey ('mel multiflora'). Incense honey should contain over 30 % of pollen grains of Pittosporum undulatum Vent. In this work, a real-time PCR method targeting the ITS region was proposed for the first time to detect P. undulatum in the honey from the Azores. The approach exhibited high analytical performance, achieving a quantification limit of 0.01 pg of incense DNA. The method was successfully applied to 22 honey samples, from which incense was detected in all 9 monofloral incense honeys and in 5 out of 10 multifloral samples from the Azores. Generally, the quantitative results for incense DNA were in good agreement with the melissopalynological data. Therefore, a simple, cost-effective and reliable tool was herein proposed to authenticate and valorise the Azores honey.

Exploring Consumers' Preferences and Attitudes to Honey: Generation Approach in Slovakia

Honey is popular among consumers for its composition and healing properties. The aim of the paper is to study the differences in honey preferences across various age generations in Slovakia. The study is based on primary data obtained by conducting an online questionnaire survey on a sample of 1850 Slovak consumers of honey in 2022. Multiple correspondence analyses and non-parametric tests were applied to study the differences in preferences across selected age cohorts (Generation Z, Generation Y, Generation X and Silver Generation). The results show that Silver Generation tends to consume honey due to its nutritional values and prefers to consume monofloral honey of a dark colour, while Generation Z does not use honey in cosmetics or consume it due to its nutritional values and are inclined to prefer polyfloral honey. The utilisation of honey in cosmetics was associated mostly with Generation X. Younger consumers (Generation Z and Generation Y) have a very low awareness of creamed honey and honey with additions in comparison to Silver Generation or Generation X. In addition, the results reveal that propolis, royal jelly and bee pollen were the most attractive additions for honey across all age cohorts in Slovakia, while spirulina and chilli were the least attractive additions.

Towards DNA-Based Methods Analysis for Honey: An Update

Honey is a natural product widely consumed all over the world due to its relationship with healthy benefits. Additionally, environmental and ethical issues have a higher weight in the consumer's choice to buy honey as a natural product. Following the high demand of this product, several approaches have been suggested and developed aiming at the assessment of honey's quality and authenticity. Target approaches, such as pollen analysis, phenolic compounds, sugars, volatile compounds, organic acids, proteins, amino acids, minerals, and trace elements, showed an efficacy, particularly concerning the honey origin. However, a special highlight is given to DNA markers, due to their useful applicability in environmental and biodiversity studies, besides the geographical, botanical, and entomological origins. Different DNA target genes were already explored for addressing diverse sources of honey DNA, with DNA metabarcoding attaining a relevant importance. This review aims to describe the latest advances on DNA-based methods applied in honey related studies, identifying the research needs for the development of new and additional required methodologies, and to select the most adequate tools for future research projects.

Metabarcoding of eDNA for tracking the floral and geographical origins of bee honey

Authentic honey products have a high commercial value and are often falsified via adulteration. Metabarcoding of environmental DNA (eDNA) from bacterial, floral, and entomological sources has recently been proposed as a useful tool for identifying and authenticating floral and geographical origins of bee honey. In this study, eDNA metabarcoding was applied to reveal the bacterial, plant, and honey bee DNA signatures of 48 commercial honey products from six different geographical origins. Bacterial DNA composition in commercial honey showed different relative abundance of Paenibacillus and Bacillus in geographically different samples, and high abundance of Methylobacterium in chestnut honey implying potential use of bacterial DNA composition for honey authentication. Using the chloroplast trnL (UAA) as a DNA marker, floral origins of commercial honey were investigated. Based on floral DNA signatures, 12 monofloral honey samples were identified among the 45 samples tested. Targeted amplicon sequencing of cytochrome oxidase I (COI) gene from entomological DNA identified three different Apis mellifera sequence variants, specific to geographic origin of honey, suggesting that COI can be implemented as a DNA marker to trace the origin of honey. Therefore, the current study demonstrated the potential of eDNA based metabarcoding as a robust tool for evaluating commercial bee honey by exploring their floral and geographical origins.

Microbiome analysis of raw honey reveals important factors influencing the bacterial and fungal communities

Raw honeys contain diverse microbial communities. Previous studies have focused on isolating bacteria and fungi that are culturable, while missing a large proportion of the microbial community due to culture-based constraints. This study utilized next-generation sequencing (NGS) to analyze the composition of microorganisms in raw honey; these data can reveal environmental and physicochemical variables that are associated with different microbial communities. To examine the microbial composition (bacteria and fungi) of raw honey and analyze its association with physicochemical properties, four types of honey (monofloral, wildflower, manuka, and feral; n total = 36) were analyzed via amplicon metagenomics. The analyzed honey samples had relatively similar bacterial communities but more distinct and diverse fungal communities. Honey type was determined as a significant factor influencing alpha and beta diversity metrics of bacterial and fungal communities. For the bacterial communities, titratable acidity (TA) was associated with community richness and diversity. For the fungal communities, Brix, TA, and color were associated with community richness, while water activity and color were associated with community diversity. Additionally, important bacterial and fungal amplicon sequence variants (ASVs) that influenced the overall community were identified. Results from this study provide important insights into the microbial communities associated with different types of raw honey, which could improve our understanding of microbial dynamics in beehives, improve honey production, and prevent honeybee disease.

A genotyping by sequencing approach can disclose Apis mellifera population genomic information contained in honey environmental DNA

Awareness has been raised over the last years on the genetic integrity of autochthonous honey bee subspecies. Genomic tools available in Apis mellifera can make it possible to measure this information by targeting individual honey bee DNA. Honey contains DNA traces from all organisms that contributed or were involved in its production steps, including the honey bees of the colony. In this study, we designed and tested a genotyping by sequencing (GBS) assay to analyse single nucleotide polymorphisms (SNPs) of A. mellifera nuclear genome using environmental DNA extracted from honey. A total of 121 SNPs (97 SNPs informative for honey bee subspecies identification and 24 SNPs associated with relevant traits of the colonies) were used in the assay to genotype honey DNA, which derives from thousands of honey bees. Results were integrated with information derived from previous studies and whole genome resequencing datasets. This GBS method is highly reliable in estimating honey bee SNP allele frequencies of the whole colony from which the honey derived. This assay can be used to identify the honey bee subspecies of the colony that produced the honey and, in turn, to authenticate the entomological origin of the honey.

Food inauthenticity: Authority activities, guidance for food operators, and mitigation tools

Historically, food fraud was a major public health concern which helped drive the development of early food regulations in many markets including the US and EU market. In the past 10 years, the integrity of food chains with respect to food fraud has again been questioned due to high profile food fraud cases. We provide an overview of the resulting numerous authoritative activities underway within different regions to counter food fraud, and we describe the guidance available to the industry to understand how to assess the vulnerability of their businesses and implement appropriate mitigation. We describe how such controls should be an extension of those already in place to manage wider aspects of food authenticity, and we provide an overview of relevant analytical tools available to food operators and authorities to protect supply chains. Practical Application: Practical Application of the provided information by the food industry in selecting resources (guidance document, analytical methods etc.).

Identification of Seasonal Honey Based on Quantitative Detection of Typical Pollen DNA

Monofloral honey is produced from the nectar of a single predominant botanical species in a particular season and has certain unique properties. Valuable monofloral honey produced in a particular season with unique properties is often targeted for adulteration. Herein, a method for the identification of monofloral honey and determination of its production season was developed. Major nectar plants, including Prunus sp., Robinia pseudoacacia, Castanea sp., and Kalopanax sp., were selected to evaluate the honey produced between April and July in South Korea. Results showed that the highest amount of DNA from each plant was detected in the corresponding flowering season. The pollens tended to accumulate in the honeycomb after the flowering season. The accumulations result in an increase in the diversity of pollen detected in honey. Additionally, DNA quantity of each plant decreased in the samples as the number of plant DNA types increased from May to July. Moreover, the authenticity of the commercial monofloral honey samples showed only cherry blossom honey was found authentic, which exhibited the expected high amount of Prunus sp. DNA. This molecular tool is expected to be useful in verifying the origin of monofloral honey and its production season.

Honey Environmental DNA Can Be Used to Detect and Monitor Honey Bee Pests: Development of Methods Useful to Identify Aethina tumida and Galleria mellonella Infestations

Environmental DNA (eDNA) contained in honey derives from the organisms that directly and indirectly have been involved in the production process of this matrix and that have played a role in the hive ecosystems where the honey has been produced. In this study we set up PCR-based assays to detect the presence of DNA traces left in the honey by two damaging honey bee pests: the small hive beetle (Aethina tumida) and the greater wax moth (Galleria mellonella). DNA was extracted from 82 honey samples produced in Italy and amplified using two specific primer pairs that target the mitochondrial gene cytochrome oxidase I (COI) of A. tumida and two specific primer pairs that target the same gene in G. mellonella. The limit of detection was tested using sequential dilutions of the pest DNA. Only one honey sample produced in Calabria was positive for A. tumida whereas about 66% of all samples were positively amplified for G. mellonella. The use of honey eDNA could be important to establish early and effective measures to contain at the local (e.g., apiary) or regional scales these two damaging pests and, particularly for the small hive beetle, to prevent its widespread diffusion.

Tracing the origin of honey products based on metagenomics and machine learning

The adulteration of honey is common. Recently, High Throughput Sequencing (HTS)-based metabarcoding method has been applied successfully to pollen/honey identification to determine floral composition that, in turn, can be used to identify the geographical origins of honeys. However, the lack of local references materials posed a serious challenge for HTS-based pollen identification methods. Here, we sampled 28 honey samples from various geographic origins without prior knowledge of local floral information and applied a machine learning method to determine geographical origins. The machine learning method uses a resilient backpropagation algorithm to train a neural network. The results showed that biological components in honey provided characteristic traits that enabled accurate geographic tracing for nearly all honey samples, confidently discriminating honeys to their geographic origin with >99% success rates, including those separated by as little as 39 km.

Molecular Detection of Nosema spp. in Honey in Bulgaria

Environmental DNA (eDNA) analysis is related to screening genetic material of various organisms in environmental samples. Honey represents a natural source of exogenous DNA, which allows for the detection of different honey bee pathogens and parasites. In the present study, we extracted DNA from 20 honey samples from different regions in Bulgaria and tested for the presence of DNA of the ectoparasitic mite Varroa destructor, as well as Nosema apis and Nosema ceranae. Only Nosema ceranae was detected, showing up in 30% of all samples, which confirms the widespread prevalence of this pathogen. All positive samples were found in plain regions of the country, while this pathogen was not detected in mountainous parts. None of the samples gave positive amplifications for the Nosema apis and Varroa mite. The obtained results from this study confirm previous observations that eDNA contained in honey is a potent source for effective biomonitoring of actual diseases in the honey bee.

Application of Next Generation Semiconductor-Based Sequencing for the Identification of Apis mellifera Complementary Sex Determiner (csd) Alleles from Honey DNA

Simple Summary

Honey contains traces of the DNA of the honey bees that produced it. This environmental DNA can therefore be used to investigate the genome of the honey bees. In this study, we used a next generation sequencing technology to analyze the variability of a key gene of Apis mellifera L., the complementary sex determiner (csd) gene, using honey environmental DNA as a source of honey bee DNA. This gene determines the sex of the bees. Two different alleles at this locus are needed to produce females whereas males have only one copy of this gene as they are haploid. In case two identical alleles are present in a diploid individual, the larvae are not vital and are discarded by the workers. Therefore, there is an advantage in maintaining a large csd diversity in honey bee populations. In light of the recent decline in honey bee populations, it is important to monitor the allele variability at this gene. The applied methodology provided a new strategy to disclose the genetic diversity at the csd gene at the population-wide level and identify most, if not all, csd alleles present in the colonies in a single analysis.

Abstract

The complementary sex determiner (csd) gene plays an essential role in the sex determination of Apis mellifera L. Females develop only if fertilized eggs have functional heterozygous genotypes at this gene whereas males, being haploids, are hemizygous. Two identical csd alleles produce non vital males. In light of the recent decline in honey bee populations, it is therefore important to monitor the allele variability at this gene. In this study, we tested the application of next generation semiconductor-based sequencing technology (Ion Torrent) coupled with environmental honey DNA as a source of honey bee genome information to retrieve massive sequencing data for the analysis of variability at the hypervariable region (HVR) of the csd gene. DNA was extracted from 12 honey samples collected from honeycombs directly retrieved from 12 different colonies. A specifically designed bioinformatic pipeline, applied to analyze a total of about 1.5 million reads, identified a total of 160 different csd alleles, 55% of which were novel. The average number of alleles per sample was compatible with the number of expected patrilines per colony, according to the mating behavior of the queens. Allele diversity at the csd could also provide information useful to reconstruct the history of the honey.

Identification of pollen taxa by different microscopy techniques

Melissopalynology is an important analytical method to identify botanical origin of honey. Pollen grain recognition is commonly performed by visual inspection by a trained person. An alternative method for visual inspection is automated pollen analysis based on the image analysis technique. Image analysis transfers visual information to mathematical descriptions. In this work, the suitability of three microscopic techniques for automatic analysis of pollen grains was studied. 2D and 3D morphological characteristics, textural and colour features, and extended depth of focus characteristics were used for the pollen discrimination. In this study, 7 botanical taxa and a total of 2482 pollen grains were evaluated. The highest correct classification rate of 93.05% was achieved using the phase contrast microscopy, followed by the dark field microscopy reaching 91.02%, and finally by the light field microscopy reaching 88.88%. The most significant discriminant characteristics were morphological (2D and 3D) and colour characteristics. Our results confirm the potential of using automatic pollen analysis to discriminate pollen taxa in honey. This work provides the basis for further research where the taxa dataset will be increased, and new descriptors will be studied.

Molecular Approaches to Agri-Food Traceability and Authentication: An Updated Review

In the last decades, the demand for molecular tools for authenticating and tracing agri-food products has significantly increased. Food safety and quality have gained an increased interest for consumers, producers, and retailers, therefore, the availability of analytical methods for the determination of food authenticity and the detection of major adulterations takes on a fundamental role. Among the different molecular approaches, some techniques such as the molecular markers-based methods are well established, while some innovative approaches such as isothermal amplification-based methods and DNA metabarcoding have only recently found application in the agri-food sector. In this review, we provide an overview of the most widely used molecular techniques for fresh and processed agri-food authentication and traceability, showing their recent advances and applications and discussing their main advantages and limitations. The application of these techniques to agri-food traceability and authentication can contribute a great deal to the reassurance of consumers in terms of transparency and food safety and may allow producers and retailers to adequately promote their products.

Distribution of the Main Apis mellifera Mitochondrial DNA Lineages in Italy Assessed Using an Environmental DNA Approach

Growing interest has been emerging on the need to monitor the genetic integrity of the European Apis mellifera subspecies that could be threatened by the human-mediated dispersion of non-native populations and lines. Mitochondrial DNA (mtDNA) lineages can provide useful information for this purpose. In this study, we took advantage of the environmental DNA (eDNA) contained in the honey, which can be analyzed to detect the main groups of mitotypes of the honey bees that produced it. In this study, we applied this eDNA to produce a distribution map all over the Italian peninsula and the two major islands (Sicily and Sardinia) of the following three honey bee mtDNA lineages: A, C and M. A total of 607 georeferenced honey samples, produced in all Italian regions, was analyzed to detect these lineages. The A lineage was widespread in Sicily, as expected, considering that A. m. siciliana carries the African lineage. Surprisingly, this lineage was also reported in about 14% of all other samples produced in almost all continental regions, and in Sardinia. The applied method obtained an updated distribution map of honey bee mtDNA lineages that could be useful to design policies for the conservation of Italian honey bee genetic resources.

Pollen DNA metabarcoding identifies regional provenance and high plant diversity in Australian honey

Accurate identification of the botanical components of honey can be used to establish its geographical provenance, while also providing insights into honeybee (Apis mellifera L.) diet and foraging preferences. DNA metabarcoding has been demonstrated as a robust method to identify plant species from pollen and pollen-based products, including honey. We investigated the use of pollen metabarcoding to identify the floral sources and local foraging preferences of honeybees using 15 honey samples from six bioregions from eastern and western Australia. We used two plant metabarcoding markers, ITS2 and the trnL P6 loop. Both markers combined identified a total of 55 plant families, 67 genera, and 43 species. The trnL P6 loop marker provided significantly higher detection of taxa, detecting an average of 15.6 taxa per sample, compared to 4.6 with ITS2. Most honeys were dominated by Eucalyptus and other Myrtaceae species, with a few honeys dominated by Macadamia (Proteaceae) and Fabaceae. Metabarcoding detected the nominal primary source provided by beekeepers among the top five most abundant taxa for 85% of samples. We found that eastern and western honeys could be clearly differentiated by their floral composition, and clustered into bioregions with the trnL marker. Comparison with previous results obtained from melissopalynology shows that metabarcoding can detect similar numbers of plant families and genera, but provides significantly higher resolution at species level. Our results show that pollen DNA metabarcoding is a powerful and robust method for detecting honey provenance and examining the diet of honeybees. This is particularly relevant for hives foraging on the unique and diverse flora of the Australian continent, with the potential to be used as a novel monitoring tool for honeybee floral resources.

Analysis of honey environmental DNA indicates that the honey bee (Apis mellifera L.) trypanosome parasite Lotmaria passim is widespread in the apiaries of the North of Italy

Lotmaria passim is a trypanosomatid that infects honey bees. In this study, we established an axenic culture of L. passim from Italian isolates and then used its DNA as a control in subsequent analyses that investigated environmental DNA (eDNA) to detect this trypasonosomatid. The source of eDNA was honey, which has been already demonstrated to be useful to detect honey bee parasites. DNA from a total of 164 honey samples collected in the North of Italy was amplified with three L. passim specific PCR primers and 78% of the analysed samples gave positive results. These results indicated a high prevalence rate of this trypanosomatid in the North of Italy, where it might be considered another threat to honey bee health.

Genomic Profiling: The Strengths and Limitations of Chloroplast Genome-Based Plant Variety Authentication

Genomic profiling is a suitable tool for variety authentication and has applications in both operational quality and regulatory raw material control. It can be used to differentiate species or varieties and to identify admixtures as well as field contaminants. To establish a molecular profile, reliable and very accurate sequence data are required. As a result of the influence of the pollinator plant, nuclear genome-based authentication is in most cases not suitable for a direct application on the fruit. Sequences must be used that come exclusively from the localized mother plant. Parts of the fruit of maternal origin, e.g., components derived from the blossom, are suitable as a basis for this. Alternatively, DNA from cell organelles that are maternally inherited, such as mitochondria or chloroplasts, can be used. The latter will be discussed in this review in closer detail. Although individual gene segments on the chloroplast genome are already used for species differentiation in barcoding studies on plants, little is known about the usefulness of the entire chloroplast genome for intraspecies differentiation in general and for differentiation between modern varieties in particular. Results from the literature as well as from our own work suggest that chloroplast genome sequences are indeed very well-suited for the differentiation of old varieties. On the other hand, they are less or not suitable for the genetic differentiation of modern cultivars, because they are often too closely related.

Employing DNA metabarcoding to determine the geographical origin of honey

Unfavourable climatic conditions force Iranian beekeepers to translocate over large distances in the course of the year. However, irrespective of the main place of production, the honey is always labeled with the name of the beekeepers' hometown, which leads consequently to mislabeled products. The present study investigates the capability of DNA metabarcoding to locate the geographical origin of honey. The molecular markers (ITS2 and rbcL) allowed identification of 926 plant species in studied samples. A comprehensive review of floristic reference books specified 34 key species that could be used to successfully determine the geographical origin in 91.4% of samples. These key species were usually present in honey with tiny amounts and thus, conventional palynology might not be able to detect them. The present investigation indicates that although ITS2 is able to detect more species than rbcL, utilizing a combination of both markers provides more robust evidence of geographical origin.

Honey as a Source of Environmental DNA for the Detection and Monitoring of Honey Bee Pathogens and Parasites

Environmental DNA (eDNA) has been proposed as a powerful tool to detect and monitor cryptic, elusive, or invasive organisms. We recently demonstrated that honey constitutes an easily accessible source of eDNA. In this study, we extracted DNA from 102 honey samples (74 from Italy and 28 from 17 other countries of all continents) and tested the presence of DNA of nine honey bee pathogens and parasites (Paenibacillus larvae, Melissococcus plutonius, Nosema apis, Nosema ceranae, Ascosphaera apis,Lotmaria passim, Acarapis woodi, Varroa destructor, and Tropilaelaps spp.) using qualitative PCR assays. All honey samples contained DNA from V. destructor, confirming the widespread diffusion of this mite. None of the samples gave positive amplifications for N. apis, A. woodi, and Tropilaelaps spp. M. plutonius was detected in 87% of the samples, whereas the other pathogens were detected in 43% to 57% of all samples. The frequency of Italian samples positive for P. larvae was significantly lower (49%) than in all other countries (79%). The co-occurrence of positive samples for L. passim and A. apis with N. ceranae was significant. This study demonstrated that honey eDNA can be useful to establish monitoring tools to evaluate the sanitary status of honey bee populations.

Shotgun sequencing of honey DNA can describe honey bee derived environmental signatures and the honey bee hologenome complexity

Honey bees are large-scale monitoring tools due to their extensive environmental exploration. In their activities and from the hive ecosystem complex, they get in close contact with many organisms whose traces can be transferred into the honey, which can represent an interesting reservoir of environmental DNA (eDNA) signatures and information useful to analyse the honey bee hologenome complexity. In this study, we tested a deep shotgun sequencing approach of honey DNA coupled with a specifically adapted bioinformatic pipeline. This methodology was applied to a few honey samples pointing out DNA sequences from 191 organisms spanning different kingdoms or phyla (viruses, bacteria, plants, fungi, protozoans, arthropods, mammals). Bacteria included the largest number of species. These multi-kingdom signatures listed common hive and honey bee gut microorganisms, honey bee pathogens, parasites and pests, which resembled a complex interplay that might provide a general picture of the honey bee pathosphere. Based on the Apis mellifera filamentous virus genome diversity (the most abundant detected DNA source) we obtained information that could define the origin of the honey at the apiary level. Mining Apis mellifera sequences made it possible to identify the honey bee subspecies both at the mitochondrial and nuclear genome levels.

The hidden 'plant side' of insect novel foods: A DNA-based assessment

In the context of novel foods, a category for which the market demand is increasing worldwide, the consumption of edible insects and related insect-based products is expected to grow in the next years. Insects represent an important source of energy for the human diet but there is a lack of scientific knowledge about their processing to ensure safe food items to the consumer. In this study we adopted a combined DNA-based approach to verify the identity of the declared species in five categories of commercial insect-based products (mt COI DNA barcoding) and to characterize plant declared ingredients or contaminants (nu ITS2 DNA metabarcoding) with particular attention to putative elements of allergenic concern belonging, for example to the insect rearing substrate. Moreover, the same approach has been used to assess its sensitivity to cases of contamination and counterfeits to insect flour with low cost (and potentially allergenic) vegetable flours like wheat and soybean. Results show the success of insect DNA barcoding authentication even for highly processed products. Furthermore, the DNA metabarcoding analysis revealed a high efficacy as a screening method to identify both plant ingredients and vegetal traces belonging to insect farming or possible adulteration events, also acting as an early warning strategy for the occurrence of allergens of human concern. This approach could support the development of new risk assessment procedures for novel foods by regulatory authorities to ensure their quality, safety, and acceptance which will become more required in order to face the challenge of feeding the world population in the next decades.

A next generation sequencing approach for targeted Varroa destructor (Acari: Varroidae) mitochondrial DNA analysis based on honey derived environmental DNA

Honey contains DNA from many different organisms that are part of hive micro-environmental niches and honey bee pathospheres. In this study, we recovered and sequenced mite mitochondrial DNA (mtDNA) from honey from different locations around the world (Europe, Asia, Africa, North and South America). DNA extracted from 17 honey samples was amplified with eight primer pairs targeting three mite mtDNA genes, obtaining 88 amplicons that were sequenced with an Ion Torrent sequencing platform. A bioinformatic pipeline compared produced reads with Varroa spp. mtDNA sequence entries available in GenBank and assigned them to different mitotypes. In all honey samples, the highest percentage of reads was attributed to the K1 lineage, including a few variants derived from it, in addition to J1 reads observed in the two South American samples and C1-1 reads obtained from the Chinese honey. This study opens new possibilities to analyse mite lineages and variants and monitor their geographical and temporal distribution, simplifying surveillance against this damaging honey bee parasite.

Shotgun metagenomics of honey DNA: Evaluation of a methodological approach to describe a multi-kingdom honey bee derived environmental DNA signature

Honey bees are considered large-scale monitoring tools due to their environmental exploration and foraging activities. Traces of these activities can be recovered in the honey that also may reflect the hive ecological micro-conditions in which it has been produced. This study applied a next generation sequencing platform (Ion Torrent) for shotgun metagenomic analysis of honey environmental DNA (eDNA). The study tested a methodological framework to interpret DNA sequence information useful to describe the complex ecosystems of the honey bee colony superorganism, its pathosphere and the heterogeneity of the agroecological environments and environmental sources that left DNA marks in the honey. Analysis of two honeys reported sequence reads from five main organism groups (kingdoms or phyla): arthropods (that mainly included reads from Apis mellifera, several other members of the Hymenotpera, in addition to members of the Diptera, Coleoptera and Lepidoptera, as well as aphids and mites), plants (that clearly confirmed the botanical origin of the two honeys, i.e. orange tree blossom and eucalyptus tree blossom honeys), fungi and bacteria (including common hive and honey bee gut microorganisms, honey bee pathogens and plant pathogens), and viruses (which accounted for the largest number of reads in both honeys, mainly assigned to Apis mellifera filamentous virus). The shotgun metagenomic approach that was used in this study can be applied in large scale experiments that might have multiple objectives according to the multi-kingdom derived eDNA that is contained in the honey.

Entomological signatures in honey: an environmental DNA metabarcoding approach can disclose information on plant-sucking insects in agricultural and forest landscapes

Honeydew produced from the excretion of plant-sucking insects (order Hemiptera) is a carbohydrate-rich material that is foraged by honey bees to integrate their diets. In this study, we used DNA extracted from honey as a source of environmental DNA to disclose its entomological signature determined by honeydew producing Hemiptera that was recovered not only from honeydew honey but also from blossom honey. We designed PCR primers that amplified a fragment of mitochondrial cytochrome c oxidase subunit 1 (COI) gene of Hemiptera species using DNA isolated from unifloral, polyfloral and honeydew honeys. Ion Torrent next generation sequencing metabarcoding data analysis assigned Hemiptera species using a customized bioinformatic pipeline. The forest honeydew honeys reported the presence of high abundance of Cinara pectinatae DNA, confirming their silver fir forest origin. In all other honeys, most of the sequenced reads were from the planthopper Metcalfa pruinosa for which it was possible to evaluate the frequency of different mitotypes. Aphids of other species were identified from honeys of different geographical and botanical origins. This unique entomological signature derived by environmental DNA contained in honey opens new applications for honey authentication and to disclose and monitor the ecology of plant-sucking insects in agricultural and forest landscapes.