Comparing whole-genome shotgun sequencing and DNA metabarcoding approaches for species identification and quantification of pollen species mixtures

Monitoring the Land and Sea: Enhancing Efficiency Through CRISPR-Cas Driven Depletion and Enrichment of Environmental DNA

Characterizing biodiversity using environmental DNA (eDNA) represents a paradigm shift in our capacity for biomonitoring complex environments, both aquatic and terrestrial. However, eDNA biomonitoring is limited by biases toward certain species and the low taxonomic resolution of current metabarcoding approaches. Shotgun metagenomics of eDNA enables the collection of whole ecosystem data by sequencing all molecules present, allowing characterization and identification. Clustered regularly interspaced short palindromic repeats (CRISPR) and the CRISPR-associated proteins (Cas)-based methods have the potential to improve the efficiency of eDNA metagenomic sequencing of low-abundant target organisms and simplify data analysis by enrichment of target species or nontarget DNA depletion before sequencing. Implementation of CRISPR-Cas in eDNA has been limited due to a lack of interest and support in the past. This perspective synthesizes current approaches of CRISPR-Cas to study underrepresented taxa and advocate for further application and optimization of depletion and enrichment methods of eDNA using CRISPR-Cas, holding promise for eDNA biomonitoring.

Metagenomic approaches and opportunities in arid soil research

Arid soils present unique challenges and opportunities for studying microbial diversity and bioactive potential due to the extreme environmental conditions they bear. This review article investigates soil metagenomics as an emerging tool to explore complex microbial dynamics and unexplored bioactive potential in harsh environments. Utilizing advanced metagenomic techniques, diverse microbial populations that grow under extreme conditions such as high temperatures, salinity, high pH levels, and exposure to metals and radiation can be studied. The use of extremophiles to discover novel natural products and biocatalysts emphasizes the role of functional metagenomics in identifying enzymes and secondary metabolites for industrial and pharmaceutical purposes. Metagenomic sequencing uncovers a complex network of microbial diversity, offering significant potential for discovering new bioactive compounds. Functional metagenomics, connecting taxonomic diversity to genetic capabilities, provides a pathway to identify microbes' mechanisms to synthesize valuable secondary metabolites and other bioactive substances. Contrary to the common perception of desert soil as barren land, the metagenomic analysis reveals a rich diversity of life forms adept at extreme survival. It provides valuable findings into their resilience and potential applications in biotechnology. Moreover, the challenges associated with metagenomics in arid soils, such as low microbial biomass, high DNA degradation rates, and DNA extraction inhibitors and strategies to overcome these issues, outline the latest advancements in extraction methods, high-throughput sequencing, and bioinformatics. The importance of metagenomics for investigating diverse environments opens the way for future research to develop sustainable solutions in agriculture, industry, and medicine. Extensive studies are necessary to utilize the full potential of these powerful microbial communities. This research will significantly improve our understanding of microbial ecology and biotechnology in arid environments.

Unveiling the diet of two generalist stink bugs, Halyomorpha halys and Pentatoma rufipes (Hemiptera: Pentatomidae), through metabarcoding of the ITS2 region from gut content

BACKGROUND

The use of DNA metabarcoding has become an increasingly popular technique to infer feeding relationships in polyphagous herbivores and predators. Understanding host plant preference of native and invasive herbivore insects can be helpful in establishing effective integrated pest management (IPM) strategies. The invasive Halyomorpha halys and native Pentatoma rufipes are piercing-sucking stink bug pests that are known to cause economic damage in commercial fruit orchards.

RESULTS

In this study, we performed molecular gut content analysis (MGCA) on field-collected specimens of these two herbivorous pentatomids using next-generation amplicon sequencing (NGAS) of the internal transcribed spacer 2 (ITS2) barcode region. Additionally, a laboratory experiment was set up where H. halys was switched from a mixed diet to a monotypic diet, allowing us to determine the detectability of the initial diet in a time series of ≤3 days after the diet switch. We detected 68 unique plant species from 54 genera in the diet of two stink bug species, with fewer genera found per sample and a smaller diet breadth for P. rufipes than for H. halys. Both stink bug species generally prefer deciduous trees over gymnosperms and herbaceous plants. Landscape type significantly impacted the observed genera in the diet of both stink bug species, whereas season only had a significant effect on the diet of H. halys.

CONCLUSION

This study provides further insights into the dietary composition of two polyphagous pentatomid pests and illustrates that metabarcoding can deliver a relevant species-level resolution of host plant preference. © 2024 Society of Chemical Industry.

Exploring microbial diversity in the rhizosphere: a comprehensive review of metagenomic approaches and their applications

The rhizosphere, the soil region influenced by plant roots, represents a dynamic microenvironment where intricate interactions between plants and microorganisms shape soil health, nutrient cycling, and plant growth. Soil microorganisms are integral players in the transformation of materials, the dynamics of energy flows, and the intricate cycles of biogeochemistry. Considerable research has been dedicated to investigating the abundance, diversity, and intricacies of interactions among different microbes, as well as the relationships between plants and microbes present in the rhizosphere. Metagenomics, a powerful suite of techniques, has emerged as a transformative tool for dissecting the genetic repertoire of complex microbial communities inhabiting the rhizosphere. The review systematically navigates through various metagenomic approaches, ranging from shotgun metagenomics, enabling unbiased analysis of entire microbial genomes, to targeted sequencing of the 16S rRNA gene for taxonomic profiling. Each approach's strengths and limitations are critically evaluated, providing researchers with a nuanced understanding of their applicability in different research contexts. A central focus of the review lies in the practical applications of rhizosphere metagenomics in various fields including agriculture. By decoding the genomic content of rhizospheric microbes, researchers gain insights into their functional roles in nutrient acquisition, disease suppression, and overall plant health. The review also addresses the broader implications of metagenomic studies in advancing our understanding of microbial diversity and community dynamics in the rhizosphere. It serves as a comprehensive guide for researchers, agronomists, and policymakers, offering a roadmap for harnessing metagenomic approaches to unlock the full potential of the rhizosphere microbiome in promoting sustainable agriculture.

Diverse patterns of correspondence between protist metabarcodes and protist metagenome-assembled genomes

Two common approaches to study the composition of environmental protist communities are metabarcoding and metagenomics. Raw metabarcoding data are usually processed into Operational Taxonomic Units (OTUs) or amplicon sequence variants (ASVs) through clustering or denoising approaches, respectively. Analogous approaches are used to assemble metagenomic reads into metagenome-assembled genomes (MAGs). Understanding the correspondence between the data produced by these two approaches can help to integrate information between the datasets and to explain how metabarcoding OTUs and MAGs are related with the underlying biological entities they are hypothesised to represent. MAGs do not contain the commonly used barcoding loci, therefore sequence homology approaches cannot be used to match OTUs and MAGs. We made an attempt to match V9 metabarcoding OTUs from the 18S rRNA gene (V9 OTUs) and MAGs from the Tara Oceans expedition based on the correspondence of their relative abundances across the same set of samples. We evaluated several metrics for detecting correspondence between features in these two datasets and developed controls to filter artefacts of data structure and processing. After selecting the best-performing metrics, ranking the V9 OTU/MAG matches by their proportionality/correlation coefficients and applying a set of selection criteria, we identified candidate matches between V9 OTUs and MAGs. In some cases, V9 OTUs and MAGs could be matched with a one-to-one correspondence, implying that they likely represent the same underlying biological entity. More generally, matches we observed could be classified into 4 scenarios: one V9 OTU matches many MAGs; many V9 OTUs match many MAGs; many V9 OTUs match one MAG; one V9 OTU matches one MAG. Notably, we found some instances in which different OTU-MAG matches from the same taxonomic group were not classified in the same scenario, with all four scenarios possible even within the same taxonomic group, illustrating that factors beyond taxonomic lineage influence the relationship between OTUs and MAGs. Overall, each scenario produces a different interpretation of V9 OTUs, MAGs and how they compare in terms of the genomic and ecological diversity they represent.

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).

Semi-automated sequence curation for reliable reference datasets in ITS2 vascular plant DNA (meta-)barcoding

One of the most critical steps for accurate taxonomic identification in DNA (meta)-barcoding is to have an accurate DNA reference sequence dataset for the marker of choice. Therefore, developing such a dataset has been a long-term ambition, especially in the Viridiplantae kingdom. Typically, reference datasets are constructed with sequences downloaded from general public databases, which can carry taxonomic and other relevant errors. Herein, we constructed a curated (i) global dataset, (ii) European crop dataset, and (iii) 27 datasets for the EU countries for the ITS2 barcoding marker of vascular plants. To that end, we first developed a pipeline script that entails (i) an automated curation stage comprising five filters, (ii) manual taxonomic correction for misclassified taxa, and (iii) manual addition of newly sequenced species. The pipeline allows easy updating of the curated datasets. With this approach, 13% of the sequences, corresponding to 7% of species originally imported from GenBank, were discarded. Further, 259 sequences were manually added to the curated global dataset, which now comprises 307,977 sequences of 111,382 plant species.

Plants, pollinators and their interactions under global ecological change: The role of pollen DNA metabarcoding

Anthropogenic activities are triggering global changes in the environment, causing entire communities of plants, pollinators and their interactions to restructure, and ultimately leading to species declines. To understand the mechanisms behind community shifts and declines, as well as monitoring and managing impacts, a global effort must be made to characterize plant-pollinator communities in detail, across different habitat types, latitudes, elevations, and levels and types of disturbances. Generating data of this scale will only be feasible with rapid, high-throughput methods. Pollen DNA metabarcoding provides advantages in throughput, efficiency and taxonomic resolution over traditional methods, such as microscopic pollen identification and visual observation of plant-pollinator interactions. This makes it ideal for understanding complex ecological networks and their responses to change. Pollen DNA metabarcoding is currently being applied to assess plant-pollinator interactions, survey ecosystem change and model the spatiotemporal distribution of allergenic pollen. Where samples are available from past collections, pollen DNA metabarcoding has been used to compare contemporary and past ecosystems. New avenues of research are possible with the expansion of pollen DNA metabarcoding to intraspecific identification, analysis of DNA in ancient pollen samples, and increased use of museum and herbarium specimens. Ongoing developments in sequencing technologies can accelerate progress towards these goals. Global ecological change is happening rapidly, and we anticipate that high-throughput methods such as pollen DNA metabarcoding are critical for understanding the evolutionary and ecological processes that support biodiversity, and predicting and responding to the impacts of change.

Towards exhaustive community ecology via DNA metabarcoding

Exhaustive biodiversity data, covering all the taxa in an environment, would be fundamental to understand how global changes influence organisms living at different trophic levels, and to evaluate impacts on interspecific interactions. Molecular approaches such as DNA metabarcoding are boosting our ability to perform biodiversity inventories. Nevertheless, even though a few studies have recently attempted exhaustive reconstructions of communities, holistic assessments remain rare. The majority of metabarcoding studies published in the last years used just one or two markers and analysed a limited number of taxonomic groups. Here, we provide an overview of emerging approaches that can allow all-taxa biological inventories. Exhaustive biodiversity assessments can be attempted by combining a large number of specific primers, by exploiting the power of universal primers, or by combining specific and universal primers to obtain good information on key taxa while limiting the overlooked biodiversity. Multiplexes of primers, shotgun sequencing and capture enrichment may provide a better coverage of biodiversity compared to standard metabarcoding, but still require major methodological advances. Here, we identify the strengths and limitations of different approaches, and suggest new development lines that might improve broad scale biodiversity analyses in the near future. More holistic reconstructions of ecological communities can greatly increase the value of metabarcoding studies, improving understanding of the consequences of ongoing environmental changes on the multiple components of biodiversity.

Honey Quality Control: Review of Methodologies for Determining Entomological Origin

Honey is a widely consumed natural product, and its entomological origin can significantly influence its market value. Therefore, traceability of the entomological origin of honey should also be considered in honey quality control protocols. Although several methods exist, such as physicochemical characterization and bioactivity profiling of honey of different entomological origins, the most promising three methods for entomological authentication of honey include protein-based identification, chemical profiling, and a DNA-based method. All of these methods can be applied for reliable identification of the entomological origin of honey. However, as the honey is a complex matrix, the inconsistency of the results obtained by these methods is a pragmatic challenge, and therefore, the use of each method in all the cases is questionable. Most of these methodologies can be used for authentication of newly harvested honey and it is worth understanding the possibility of using these methods for authentication of relatively old samples. Most probably, using DNA-based methods targeting small fragments of DNA can provide the best result in old samples, however, the species-specific primers targeting short fragments are limited and not available for all species. Therefore, using universal primers in combination with a DNA metabarcoding approach can be a good solution that requires further investigation. This present article describes the applications of different methods, their pros, and their cons to identify honey based on entomological origin.

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.

Deciphering the community structure and the functional potential of a hypersaline marsh microbial mat community

Microbial mats are vertically stratified communities of microorganisms characterized by pronounced physiochemical gradients allowing for high species diversity and a wide range of metabolic capabilities. High Throughput Sequencing has the potential to reveal the biodiversity and function of such ecosystems in the cycling of elements. The present study combines 16S rRNA amplicon sequencing and shotgun metagenomics on a hypersaline marsh in Tristomo bay (Karpathos, Greece). Samples were collected in July 2018 and November 2019 from microbial mats, deeper sediment, aggregates observed in the water overlying the sediment, as well as sediment samples with no apparent layering. Metagenomic samples' coassembly and binning revealed 250 bacterial and 39 archaeal metagenome-assembled genomes, with completeness estimates higher than 70% and contamination less than 5%. All MAGs had KEGG Orthology terms related to osmoadaptation, with the 'salt in' strategy ones being prominent. Halobacteria and Bacteroidetes were the most abundant taxa in the mats. Photosynthesis was most likely performed by purple sulphur and nonsulphur bacteria. All samples had the capacity for sulphate reduction, dissimilatory arsenic reduction, and conversion of pyruvate to oxaloacetate. Overall, both sequencing methodologies resulted in similar taxonomic compositions and revealed that the formation of the microbial mat in this marsh exhibits seasonal variation.

Characterizing a subtropical hypereutrophic lake: From physicochemical variables to shotgun metagenomic data

Lake Cajititlán is a subtropical and endorheic lake, which is heavily impacted by nutrient pollution. Agricultural runoff and poorly treated wastewater have entered this reservoir at alarming rates during past rainy seasons, causing the cultural eutrophication of this body of water and resulting in several massive fish kill events. In this study, shotgun metagenomic sequencing was used to examine the taxonomic and functional structure of microbial communities in Lake Cajititlán during the rainy season. Several water quality features and their interactions with microbial communities were also assessed to identify the major factors affecting the water quality and biota, specifically fish species. According to current water quality regulations, most of the physicochemical variables analyzed (dissolved oxygen, pH, Secchi disk, NH4+, NO3−, blue-green algae, total phosphorus, and chlorophyll-a) were outside of the permissible limits. Planktothrix agardhii and Microcystis aeruginosa were the most abundant phytoplankton species, and the dominant bacterial genera were Pseudomonas, Streptomyces, and Flavobacterium, with Pseudomonas fluorescens, Stenotrophomonas maltophilia, and Aeromonas veronii representing the most abundant bacterial species. All of these microorganisms have been reported to be potentially harmful to fish, and the latter three (P. fluorescens, S. maltophilia, A. veronii) also contain genes associated with pathogenicity in fish mortality (fur, luxS, aer, act, aha, exu, lip, ser). Genetic evidence from the microbial communities analyzed herein reveals that anthropogenic sources of nutrients in the lake altered genes involved in nitrogen, phosphorus, sulfur, and carbon metabolism, mainly at the beginning of the rainy season. These findings suggest that abiotic factors influence the structure of the microbial communities, along with the major biogeochemical cycles of Lake Cajititlán, resulting in temporal variations and an excess of microorganisms that can thrive in high-nutrient and low-oxygen environments. After reviewing the literature, this appears to be the first study that focuses on characterizing the water quality of a subtropical hypereutrophic lake through associations between physicochemical variables and shotgun metagenomic data. In addition, there are few studies that have coupled the metabolism of aquatic ecosystems with nutrient cycles.

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.