Testing the effect of pollen exine rupture on metabarcoding with Illumina sequencing

The impact of extraction method and pollen concentration on community composition for pollen metabarcoding

Premise

Plants and pollinators closely interact with each other to form complex networks of species interactions. Metabarcoding of pollen collections has recently been proposed as an advantageous method for the construction of such networks, but the extent to which diversity and community analyses depend on the extraction method and pollen concentration used remains unclear.

Methods

In this study, we used a dilution series of two pollen mixtures (a mock community and pooled natural pollen loads from bumblebees) to assess the effect of mechanical homogenization and two DNA extraction kits (spin column DNA extraction kit and magnetic bead DNA extraction kit) on the detected pollen richness and community composition.

Results

All species were successfully detected using the three methods, even in the most dilute samples. However, the extraction method had a significant effect on the detected pollen richness and community composition, with simple mechanical homogenization introducing an extraction bias.

Discussion

Our findings suggest that all three methods are effective for detecting plant species in the pollen loads on insects, even in cases of very low pollen loads. However, our results also indicate that extraction methods can have a profound impact on the ability to correctly assess the community composition of the pollen loads on insects. The choice of extraction methodology should therefore be carefully considered to ensure reliable and unbiased results in pollen diversity and community analyses.

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).

Conservation and diversity of the pollen microbiome of Pan-American maize using PacBio and MiSeq

Pollen is a vector for diversification, fitness-selection, and transmission of plant genetic material. The extent to which the pollen microbiome may contribute to host diversification is largely unknown, because pollen microbiome diversity within a plant species has not been reported, and studies have been limited to conventional short-read 16S rRNA gene sequencing (e.g., V4-MiSeq) which suffers from poor taxonomic resolution. Here we report the pollen microbiomes of 16 primitive and traditional accessions of maize (corn) selected by indigenous peoples across the Americas, along with the modern U.S. inbred B73. The maize pollen microbiome has not previously been reported. The pollen microbiomes were identified using full-length (FL) 16S rRNA gene PacBio SMRT sequencing compared to V4-MiSeq. The Pan-American maize pollen microbiome encompasses 765 taxa spanning 39 genera and 46 species, including known plant growth promoters, insect-obligates, plant pathogens, nitrogen-fixers and biocontrol agents. Eleven genera and 13 species composed the core microbiome. Of 765 taxa, 63% belonged to only four genera: 28% were Pantoea, 15% were Lactococcus, 11% were Pseudomonas, and 10% were Erwinia. Interestingly, of the 215 Pantoea taxa, 180 belonged to a single species, P. ananatis. Surprisingly, the diversity within P. ananatis ranged nearly 10-fold amongst the maize accessions analyzed (those with ≥3 replicates), despite being grown in a common field. The highest diversity within P. ananatis occurred in accessions that originated near the center of diversity of domesticated maize, with reduced diversity associated with the north-south migration of maize. This sub-species diversity was revealed by FL-PacBio but missed by V4-MiSeq. V4-MiSeq also mis-identified some dominant genera captured by FL-PacBio. The study, though limited to a single season and common field, provides initial evidence that pollen microbiomes reflect evolutionary and migratory relationships of their host plants.

A primer on pollen assignment by nanopore-based DNA sequencing

The possibility to identify plants based on the taxonomic information coming from their pollen grains offers many applications within various biological disciplines. In the past and depending on the application or research in question, pollen origin was analyzed by microscopy, usually preceded by chemical treatment methods. This procedure for identification of pollen grains is both time-consuming and requires expert knowledge of morphological features. Additionally, these microscopically recognizable features usually have a low resolution at species-level. Since a few decades, DNA has been used for the identification of pollen taxa, as sequencing technologies evolved both in their handling and affordability. We discuss advantages and challenges of pollen DNA analyses compared to traditional methods. With readers with little experience in this field in mind, we present a hands-on primer for genetic pollen analysis by nanopore sequencing. As our lab mainly works with pollen collected within agroecological research projects, we focus on pollen collected by pollinating insects. We briefly consider sample collection, storage and processing in the laboratory as well as bioinformatic aspects. Currently, pollen metabarcoding is mostly conducted with next-generation sequencing methods that generate short sequence reads (<1 kb). Increasingly, however, pollen DNA analysis is carried out using the long-read generating (several kb), low-budget and mobile MinION nanopore sequencing platform by Oxford Nanopore Technologies. Therefore, we are focusing on aspects for palynology with the MinION DNA sequencing device.

Assessing the lysis of diverse pollen from bulk environmental samples for DNA metabarcoding

Pollen is ubiquitous year-round in bulk environmental samples and can provide useful information on previous and current plant communities. Characterization of pollen has traditionally been completed based on morphology, requiring significant time and expertise. DNA metabarcoding is a promising approach for characterizing pollen from bulk environmental samples, but accuracy hinges on successful lysis of pollen grains to free template DNA. In this study, we assessed the lysis of morphologically and taxonomically diverse pollen from one of the most common bulk environmental sample types for DNA metabarcoding, surface soil. To achieve this, a four species artificial pollen mixture was spiked into surface soils collected from Colorado, North Carolina, and Pennsylvania, and subsequently subjected to DNA extraction using both the PowerSoil and PowerSoil Pro Kits (Qiagen) with a heated incubation (either 65 °C or 90 °C). Amplification and Illumina sequencing of the internal transcribed spacer subunit 2 (ITS2) was completed in duplicate for each sample (total n, 76), and the resulting sequencing reads taxonomically identified using GenBank. The PowerSoil Pro Kit statistically outperformed the PowerSoil Kit for total DNA yield. When using either kit, incubation temperature (65 °C or 90 °C) used had no impact on the recovery of DNA, plant amplicon sequence variants (ASVs), or total plant ITS2 reads. This study highlighted that lysis of pollen in bulk environmental samples is feasible using commercially available kits, and downstream DNA metabarcoding can be used to accurately characterize pollen DNA from such sample types.

The potential of metabarcoding plant components of Malaise trap samples to enhance knowledge of plant-insect interactions

The worldwide rapid declines in insect and plant abundance and diversity that have occurred in the past decades have gained public attention and demand for political actions to counteract these declines are growing. Rapid large-scale biomonitoring can aid in observing these changes and provide information for decisions for land management and species protection. Malaise traps have long been used for insect sampling and when insects are captured in these traps, they carry traces of plants they have visited on the body surface or as digested food material in the gut contents. Metabarcoding offers a promising method for identifying these plant traces, providing insight into the plants with which insects are directly interacting at a given time. To test the efficacy of DNA metabarcoding with these sample types, 79 samples from 21 sites across Germany were analysed with the ITS2 barcode. This study, to our knowledge, is the first examination of metabarcoding plant DNA traces from Malaise trap samples. Here, we report on the feasibility of sequencing these sample types, analysis of the resulting taxa, the usage of cultivated plants by insects near nature conservancy areas and the detection of rare and neophyte species. Due to the frequency of contamination and false positive reads, isolation and PCR negative controls should be used in every reaction. Metabarcoding has advantages in efficiency and resolution over microscopic identification of pollen and is the only possible identification method for the other plant traces from Malaise traps and could provide a broad utility for future studies of plant-insect interactions.

Assessing pollen nutrient content: a unifying approach for the study of bee nutritional ecology

Poor nutrition and landscape changes are regularly cited as key factors causing the decline of wild and managed bee populations. However, what constitutes 'poor nutrition' for bees currently is inadequately defined. Bees collect and eat pollen: it is their only solid food source and it provides a broad suite of required macro- and micronutrients. Bees are also generalist foragers and thus the different pollen types they collect and eat can be highly nutritionally variable. Therefore, characterizing the multidimensional nutrient content of different pollen types is needed to fully understand pollen as a nutritional resource. Unfortunately, the use of different analytical approaches to assess pollen nutrient content has complicated between-studies comparisons and blurred our understanding of pollen nutrient content. In the current study, we start by reviewing the common methods used to estimate protein and lipids found in pollen. Next, using monofloral Brassica and Rosa pollen, we experimentally reveal biases in results using these methods. Finally, we use our collective data to propose a unifying approach for analysing pollen nutrient content. This will help researchers better study and understand the nutritional ecology-including foraging behaviour, nutrient regulation and health-of bees and other pollen feeders. This article is part of the theme issue 'Natural processes influencing pollinator health: from chemistry to landscapes'.

Using DNA Metabarcoding to Identify Floral Visitation by Pollinators

The identification of floral visitation by pollinators provides an opportunity to improve our understanding of the fine-scale ecological interactions between plants and pollinators, contributing to biodiversity conservation and promoting ecosystem health. In this review, we outline the various methods which can be used to identify floral visitation, including plant-focused and insect-focused methods. We reviewed the literature covering the ways in which DNA metabarcoding has been used to answer ecological questions relating to plant use by pollinators and discuss the findings of this research. We present detailed methodological considerations for each step of the metabarcoding workflow, from sampling through to amplification, and finally bioinformatic analysis. Detailed guidance is provided to researchers for utilisation of these techniques, emphasising the importance of standardisation of methods and improving the reliability of results. Future opportunities and directions of using molecular methods to analyse plant–pollinator interactions are then discussed.

Pollen Monitoring by Optical Microscopy and DNA Metabarcoding: Comparative Study and New Insights

Environmental samples collected in Brindisi (Italy) by a Hirst-type trap and in Lecce (Italy) by a PM10 sampler were analysed by optical microscopy and DNA-metabarcoding, respectively, to identify airborne pollen and perform an exploratory study, highlighting the benefits and limits of both sampling/detection systems. The Hirst-type trap/optical-microscopy system allowed detecting pollen on average over the full bloom season, since whole pollen grains, whose diameter vary within 10–100 μm, are required for morphological detection with optical microscopy. Conversely, pollen fragments with an aerodynamic diameter ≤10 μm were collected in Lecce by the PM10 sampler. Pollen grains and fragments are spread worldwide by wind/atmospheric turbulences and can age in the atmosphere, but aerial dispersal, aging, and long-range transport of pollen fragments are favoured over those of whole pollen grains because of their smaller size. Twenty-four Streptophyta families were detected in Lecce throughout the sampling year, but only nine out of them were in common with the 21 pollen families identified in Brindisi. Meteorological parameters and advection patterns were rather similar at both study sites, being only 37 km apart in a beeline, but their impact on the sample taxonomic structure was different, likely for the different pollen sampling/detection systems used in the two monitoring areas.

Preservation methods of honey bee-collected pollen are not a source of bias in ITS2 metabarcoding

Pollen metabarcoding is emerging as a powerful tool for ecological research and offers unprecedented scale in citizen science projects for environmental monitoring via honey bees. Biases in metabarcoding can be introduced at any stage of sample processing and preservation is at the forefront of the pipeline. While in metabarcoding studies pollen has been preserved at - 20 °C (FRZ), this is not the best method for citizen scientists. Herein, we compared this method with ethanol (EtOH), silica gel (SG) and room temperature (RT) for preservation of pollen collected from hives in Austria and Denmark. After ~ 4 months of storage, DNAs were extracted with a food kit, and their quality and concentration measured. Most DNA extracts exhibited 260/280 absorbance ratios close to the optimal 1.8, with RT samples from Austria performing slightly worse than FRZ and SG samples (P < 0.027). Statistical differences were also detected for DNA concentration, with EtOH samples producing lower yields than RT and FRZ samples in both countries and SG in Austria (P < 0.042). Yet, qualitative and quantitative assessments of floral composition obtained using high-throughput sequencing with the ITS2 barcode gave non-significant effects of preservation methods on richness, relative abundance and Shannon diversity, in both countries. While freezing and ethanol are commonly employed for archiving tissue for molecular applications, desiccation is cheaper and easier to use regarding both storage and transportation. Since SG is less dependent on ambient humidity and less prone to contamination than RT, we recommend SG for preserving pollen for metabarcoding. SG is straightforward for laymen to use and hence robust for widespread application in citizen science studies.

Plant biodiversity assessment through pollen DNA metabarcoding in Natura 2000 habitats (Italian Alps)

Monitoring biodiversity is of increasing importance in natural ecosystems. Metabarcoding can be used as a powerful molecular tool to complement traditional biodiversity monitoring, as total environmental DNA can be analyzed from complex samples containing DNA of different origin. The aim of this research was to demonstrate the potential of pollen DNA metabarcoding using the chloroplast trnL partial gene sequencing to characterize plant biodiversity. Collecting airborne biological particles with gravimetric Tauber traps in four Natura 2000 habitats within the Natural Park of Paneveggio Pale di San Martino (Italian Alps), at three-time intervals in 1 year, metabarcoding identified 68 taxa belonging to 32 local plant families. Metabarcoding could identify with finer taxonomic resolution almost all non-rare families found by conventional light microscopy concurrently applied. However, compared to microscopy quantitative results, Poaceae, Betulaceae, and Oleaceae were found to contribute to a lesser extent to the plant biodiversity and Pinaceae were more represented. Temporal changes detected by metabarcoding matched the features of each pollen season, as defined by aerobiological studies running in parallel, and spatial heterogeneity was revealed between sites. Our results showcase that pollen metabarcoding is a promising approach in detecting plant species composition which could provide support to continuous monitoring required in Natura 2000 habitats for biodiversity conservation.

Harnessing the Power of Metabarcoding in the Ecological Interpretation of Plant-Pollinator DNA Data: Strategies and Consequences of Filtering Approaches

Although DNA metabarcoding of pollen mixtures has been increasingly used in the field of pollination biology, methodological and interpretation issues arise due to its high sensitivity. Filtering or maintaining false positives, contaminants, and rare taxa or molecular features could lead to different ecological results. Here, we reviewed how this choice has been addressed in 43 studies featuring pollen DNA metabarcoding, which highlighted a very high heterogeneity of filtering methods. We assessed how these strategies shaped pollen assemblage composition, species richness, and interaction networks. To do so, we compared four processing methods: unfiltering, filtering with a proportional 1% of sample reads, a fixed threshold of 100 reads, and the ROC approach (Receiver Operator Characteristic). The results indicated that filtering impacted species composition and reduced species richness, with ROC emerging as a conservative approach. Moreover, in contrast to unfiltered networks, filtering decreased network Connectance and Entropy, and it increased Modularity and Connectivity, indicating that using cut-off thresholds better describes interactions. Overall, unfiltering might compromise reliable ecological interpretations, unless a study targets rare species. We discuss the suitability of each filtering type, plead for justifying filtering strategies on biological or methodological bases and for developing shared approaches to make future studies more comparable.