Metagenomics Approaches for the Detection and Surveillance of Emerging and Recurrent Plant Pathogens

Metagenomics approaches in unveiling the dynamics of Plant Growth-Promoting Microorganisms (PGPM) vis-à-vis Phytophthora sp. suppression in various crop ecological systems

Phytophthora species are destructive pathogens causing yield losses in different ecological systems, such as potato, black pepper, pepper, avocado, citrus, and tobacco. The diversity of plant growth-promoting microorganisms (PGPM) plays a crucial role in disease suppression. Knowledge of metagenomics approaches is essential for assessing the dynamics of PGPM and Phytophthora species across various ecosystems, facilitating effective management strategies for better crop protection. This review discusses the dynamic interplay between PGPM and Phytophthora sp. using metagenomics approaches that sheds light on the potential of PGPM strains tailored to specific crop ecosystems to bolster pathogen suppressiveness.

Mitigating Emerging and Reemerging Diseases of Fruit and Vegetable Crops in a Changing Climate

Fruit and vegetable crops are important sources of nutrition and income globally. Producing these high-value crops requires significant investment of often scarce resources, and, therefore, the risks associated with climate change and accompanying disease pressures are especially important. Climate change influences the occurrence and pressure of plant diseases, enabling new pathogens to emerge and old enemies to reemerge. Specific environmental changes attributed to climate change, particularly temperature fluctuations and intense rainfall events, greatly alter fruit and vegetable disease incidence and severity. In turn, fruit and vegetable microbiomes, and subsequently overall plant health, are also affected by climate change. Changing disease pressures cause growers and researchers to reassess disease management and climate change adaptation strategies. Approaches such as climate smart integrated pest management, smart sprayer technology, protected culture cultivation, advanced diagnostics, and new soilborne disease management strategies are providing new tools for specialty crops growers. Researchers and educators need to work closely with growers to establish fruit and vegetable production systems that are resilient and responsive to changing climates. This review explores the effects of climate change on specialty food crops, pathogens, insect vectors, and pathosystems, as well as adaptations needed to ensure optimal plant health and environmental and economic sustainability.

PhytoPipe: a phytosanitary pipeline for plant pathogen detection and diagnosis using RNA-seq data

BACKGROUND

Detection of exotic plant pathogens and preventing their entry and establishment are critical for the protection of agricultural systems while securing the global trading of agricultural commodities. High-throughput sequencing (HTS) has been applied successfully for plant pathogen discovery, leading to its current application in routine pathogen detection. However, the analysis of massive amounts of HTS data has become one of the major challenges for the use of HTS more broadly as a rapid diagnostics tool. Several bioinformatics pipelines have been developed to handle HTS data with a focus on plant virus and viroid detection. However, there is a need for an integrative tool that can simultaneously detect a wider range of other plant pathogens in HTS data, such as bacteria (including phytoplasmas), fungi, and oomycetes, and this tool should also be capable of generating a comprehensive report on the phytosanitary status of the diagnosed specimen.

RESULTS

We have developed an open-source bioinformatics pipeline called PhytoPipe (Phytosanitary Pipeline) to provide the plant pathology diagnostician community with a user-friendly tool that integrates analysis and visualization of HTS RNA-seq data. PhytoPipe includes quality control of reads, read classification, assembly-based annotation, and reference-based mapping. The final product of the analysis is a comprehensive report for easy interpretation of not only viruses and viroids but also bacteria (including phytoplasma), fungi, and oomycetes. PhytoPipe is implemented in Snakemake workflow with Python 3 and bash scripts in a Linux environment. The source code for PhytoPipe is freely available and distributed under a BSD-3 license.

CONCLUSIONS

PhytoPipe provides an integrative bioinformatics pipeline that can be used for the analysis of HTS RNA-seq data. PhytoPipe is easily installed on a Linux or Mac system and can be conveniently used with a Docker image, which includes all dependent packages and software related to analyses. It is publicly available on GitHub at https://github.com/healthyPlant/PhytoPipe and on Docker Hub at https://hub.docker.com/r/healthyplant/phytopipe .

High diversity of fungal ecological groups in Andean-Patagonian Nothofagus forests

Native Andean-Patagonian Nothofagus forests harbour a unique diversity of microorganisms with diverse ecological roles. Although ectomycorrhizal associations constitute an important fragment of the biota associated with these forests, the factors affecting such communities are largely unknown. We investigated the biodiversity, relative abundance, and composition of ectomycorrhizal fungal communities in relation to two host tree species and ages and the soil properties in six monospecific and mixed evergreen-deciduous Nothofagus forests. We used the internal transcribed spacer (ITS2) region by sequencing 9,600 ectomycorrhizae (ECM) root tips for the identification of fungi. In total, 1,125 fungal taxa at the genus level distributed over 131 orders were identified. The phyla Ascomycota (34.5%) and Basidiomycota (62.1%) were the most abundant, whereas Mucoromycota (3.1%), Chytridiomycota, Cryptomycota, Olpidiomycota, and Zoopagomycota occurred less frequently. The highest taxon diversity was found in old trees, whereas young trees often exhibited a lower diversity of the associated fungi. The fungal taxa were grouped into seven broad ecological categories, of which saprotrophic associations were most common, followed by pathotrophic, pathotrophic-saprotrophic-symbiotrophic, pathotrophic-saprotrophic, and symbiotrophic associations. We did not detect significant differences in the number of taxa in each category between young and old N. dombeyi and N. obliqua. Overall, the scale of the Illumina sequencing approach allowed us to detect a fungal taxa diversity that would not be possible to find through surveys of fruiting bodies alone and that have never been observed in Nothofagus forests before. Our findings suggest the impact of the proximity between sites, the similarity of the soil conditions, and anthropogenic use of the forests on the belowground fungal community's diversity and composition. Furthermore, there were differences between above- and belowground occurrences of the edible mushrooms B. loyo and Ramaria spp. However, future research, including on EMC tips found beneath fairy rings could provide significantly better correlations with the occurrence of aboveground fruiting body.

Reference-Free Plant Disease Detection Using Machine Learning and Long-Read Metagenomic Sequencing

Surveillance for early disease detection is crucial to reduce the threat of plant diseases to food security. Metagenomic sequencing and taxonomic classification have recently been used to detect and identify plant pathogens. However, for an emerging pathogen, its genome may not be similar enough to any public genome to permit reference-based tools to identify infected samples. Also, in the case of point-of care diagnosis in the field, database access may be limited. Therefore, here we explore reference-free detection of plant pathogens using metagenomic sequencing and machine learning (ML). We used long-read metagenomes from healthy and infected plants as our model system and constructed k-mer frequency tables to test eight different ML models. The accuracy in classifying individual reads as coming from a healthy or infected metagenome were compared. Of all models, random forest (RF) had the best combination of short run-time and high accuracy (over 0.90) using tomato metagenomes. We further evaluated the RF model with a different tomato sample infected with the same pathogen or a different pathogen and a grapevine sample infected with a grapevine pathogen and achieved similar performances. ML models can thus learn features to successfully perform reference-free detection of plant diseases whereby a model trained with one pathogen-host system can also be used to detect different pathogens on different hosts. Potential and challenges of applying ML to metagenomics in plant disease detection are discussed. IMPORTANCE Climate change may lead to the emergence of novel plant diseases caused by yet unknown pathogens. Surveillance for emerging plant diseases is crucial to reduce their threat to food security. However, conventional genomic based methods require knowledge of existing plant pathogens and cannot be applied to detecting newly emerged pathogens. In this work, we explored reference-free, meta-genomic sequencing-based disease detection using machine learning. By sequencing the genomes of all microbial species extracted from an infected plant sample, we were able to train machine learning models to accurately classify individual sequencing reads as coming from a healthy or an infected plant sample. This method has the potential to be integrated into a generic pipeline for a meta-genomic based plant disease surveillance approach but also has limitations that still need to be overcome.

Metagenomics: An Effective Approach for Exploring Microbial Diversity and Functions

Various fields have been identified in the "omics" era, such as genomics, proteomics, transcriptomics, metabolomics, phenomics, and metagenomics. Among these, metagenomics has enabled a significant increase in discoveries related to the microbial world. Newly discovered microbiomes in different ecologies provide meaningful information on the diversity and functions of microorganisms on the Earth. Therefore, the results of metagenomic studies have enabled new microbe-based applications in human health, agriculture, and the food industry, among others. This review summarizes the fundamental procedures on recent advances in bioinformatic tools. It also explores up-to-date applications of metagenomics in human health, food study, plant research, environmental sciences, and other fields. Finally, metagenomics is a powerful tool for studying the microbial world, and it still has numerous applications that are currently hidden and awaiting discovery. Therefore, this review also discusses the future perspectives of metagenomics.

Area Wide Monitoring of Plant and Honey Bee (Apis mellifera) Viruses in Blueberry (Vaccinium corymbosum) Agroecosystems Facilitated by Honey Bee Pollination

Healthy agroecosystems are dependent on a complex web of factors and inter-species interactions. Flowers are hubs for pathogen transmission, including the horizontal or vertical transmission of plant-viruses and the horizontal transmission of bee-viruses. Pollination by the European honey bee (Apis mellifera) is critical for industrial fruit production, but bees can also vector viruses and other pathogens between individuals. Here, we utilized commercial honey bee pollination services in blueberry (Vaccinium corymbosum) farms for a metagenomics-based bee and plant virus monitoring system. Following RNA sequencing, viruses were identified by mapping reads to a reference sequence database through the bioinformatics portal Virtool. In total, 29 unique plant viral species were found at two blueberry farms in British Columbia (BC). Nine viruses were identified at one site in Ontario (ON), five of which were not identified in BC. Ilarviruses blueberry shock virus (BlShV) and prune dwarf virus (PDV) were the most frequently detected viruses in BC but absent in ON, while nepoviruses tomato ringspot virus and tobacco ringspot virus were common in ON but absent in BC. BlShV coat protein (CP) nucleotide sequences were nearly identical in all samples, while PDV CP sequences were more diverse, suggesting multiple strains of PDV circulating at this site. Ten bee-infecting viruses were identified, with black queen cell virus frequently detected in ON and BC. Area-wide bee-mediated pathogen monitoring can provide new insights into the diversity of viruses present in, and the health of, bee-pollination ecosystems. This approach can be limited by a short sampling season, biased towards pollen-transmitted viruses, and the plant material collected by bees can be very diverse. This can obscure the origin of some viruses, but bee-mediated virus monitoring can be an effective preliminary monitoring approach.

Current and emerging trends in techniques for plant pathogen detection

Plant pathogenic microorganisms cause substantial yield losses in several economically important crops, resulting in economic and social adversity. The spread of such plant pathogens and the emergence of new diseases is facilitated by human practices such as monoculture farming and global trade. Therefore, the early detection and identification of pathogens is of utmost importance to reduce the associated agricultural losses. In this review, techniques that are currently available to detect plant pathogens are discussed, including culture-based, PCR-based, sequencing-based, and immunology-based techniques. Their working principles are explained, followed by an overview of the main advantages and disadvantages, and examples of their use in plant pathogen detection. In addition to the more conventional and commonly used techniques, we also point to some recent evolutions in the field of plant pathogen detection. The potential use of point-of-care devices, including biosensors, have gained in popularity. These devices can provide fast analysis, are easy to use, and most importantly can be used for on-site diagnosis, allowing the farmers to take rapid disease management decisions.

Molecular Epidemiology of Ralstonia pseudosolanacearum Phylotype I Strains in the Southwest Indian Ocean Region and Their Relatedness to African Strains

The increasing requirement for developing tools enabling fine strain traceability responsible for epidemics is tightly linked with the need to understand factors shaping pathogen populations and their environmental interactions. Bacterial wilt caused by the Ralstonia solanacearum species complex (RSSC) is one of the most important plant diseases in tropical and subtropical regions. Sadly, little, outdated, or no information on its epidemiology is reported in the literature, although alarming outbreaks are regularly reported as disasters. A large set of phylotype I isolates (n = 2,608) was retrieved from diseased plants in fields across the Southwest Indian Ocean (SWIO) and Africa. This collection enabled further assessment of the epidemiological discriminating power of the previously published RS1-MLVA14 scheme. Thirteen markers were validated and characterized as not equally informative. Most had little infra-sequevar polymorphism, and their performance depended on the sequevar. Strong correlation was found with a previous multilocus sequence typing scheme. However, 2 to 3% of sequevars were not correctly assigned through endoglucanase gene sequence. Discriminant analysis of principal components (DAPC) revealed four groups with strong phylogenetic relatedness to sequevars 31, 33, and 18. Phylotype I-31 isolates were highly prevalent in the SWIO and Africa, but their dissemination pathways remain unclear. Tanzania and Mauritius showed the greatest diversity of RSSC strains, as the four DAPC groups were retrieved. Mauritius was the sole territory harboring a vast phylogenetic diversity and all DAPC groups. More research is still needed to understand the high prevalence of phylotype I-31 at such a large geographic scale.

Benchmarking and Validation of a Bioinformatics Workflow for Meat Species Identification Using 16S rDNA Metabarcoding

DNA-metabarcoding is becoming more widely used for routine authentication of meat-based food and feed products. Several methods validating species identification methods through amplicon sequencing have already been published. These use a variety of barcodes and analysis workflows, however, no methodical comparison of available algorithms and parameter optimization are published hitherto for meat-based products' authenticity. Additionally, many published methods use very small subsets of the available reference sequences, thereby limiting the potential of the analysis and leading to over-optimistic performance estimates. We here predict and compare the ability of published barcodes to distinguish taxa in the BLAST NT database. We then use a dataset of 79 reference samples, spanning 32 taxa, to benchmark and optimize a metabarcoding analysis workflow for 16S rDNA Illumina sequencing. Furthermore, we provide recommendations as to the parameter choices, sequencing depth, and thresholds that should be used to analyze meat metabarcoding sequencing experiments. The analysis workflow is publicly available, and includes ready-to-use tools for validation and benchmarking.

Managing the Transition to Widespread Metagenomic Monitoring: Policy Considerations for Future Biosurveillance

The technological possibilities and future public health importance of metagenomic sequencing have received extensive attention, but there has been little discussion about the policy and regulatory issues that need to be addressed if metagenomic sequencing is adopted as a key technology for biosurveillance. In this article, we introduce metagenomic monitoring as a possible path to eventually replacing current infectious disease monitoring models. Many key enablers are technological, whereas others are not. We therefore highlight key policy challenges and implementation questions that need to be addressed for "widespread metagenomic monitoring" to be possible. Policymakers must address pitfalls like fragmentation of the technological base, private capture of benefits, privacy concerns, the usefulness of the system during nonpandemic times, and how the future systems will enable better response. If these challenges are addressed, the technological and public health promise of metagenomic sequencing can be realized.

ITS Metabarcoding Reveals the Effects of Oregano Essential Oil on Fusarium oxysporum and Other Fungal Species in Soil Samples

The destructive effects of Fusarium wilts are known to affect the production of many crops. The control of Fusarium oxysporum and other soilborne pathogens was mainly based on soil fumigation (methyl bromide), which has long been prohibited and, nowadays, is based on a limited number of available fungicides due to legislation restrictions on residue tolerances and environmental impacts. Alternatively, natural and environmentally safe compounds, such as essential oils, are being investigated for their efficacy in the control of soilborne diseases. The great fungicidal ability of the oregano essential oil components (carvacrol and thymol) has been reported to inhibit the germination and the mycelial development of several fungal species, including F. oxysporum. The aim of our study was to demonstrate how the metabarcoding approach can provide valuable information about the positive or negative impacts of two different doses of oregano essential oil on Fusarium oxysporum and other fungal species which were present in the studied soil samples through the amplification of the ITS1 and ITS2 regions, which were analyzed on a MiSeq platform. A higher dose of oregano essential oil decreased the abundance of F. oxysporum, along with other fungal species, but also had negative effects on Trichoderma evansii and Mortierella chlamydospora, species with possible fungicidal properties. Soil properties, essential oil properties, the fungal composition, and interactions between fungal species should be considered as factors influencing the effectiveness of essential oils as biological control agents for soilborne pathogens.

Analysis and Interpretation of metagenomics data: an approach

Advances in next-generation sequencing technologies have accelerated the momentum of metagenomic studies, which is increasing yearly. The metagenomics field is one of the versatile applications in microbiology, where any interaction in the environment involving microorganisms can be the topic of study. Due to this versatility, the number of applications of this omics technology reached its horizons. Agriculture is a crucial sector involving crop plants and microorganisms interacting together. Hence, studying these interactions through the lenses of metagenomics would completely disclose a new meaning to crop health and development. The rhizosphere is an essential reservoir of the microbial community for agricultural soil. Hence, we focus on the R&D of metagenomic studies on the rhizosphere of crops such as rice, wheat, legumes, chickpea, and sorghum. These recent developments are impossible without the continuous advancement seen in the next-generation sequencing platforms; thus, a brief introduction and analysis of the available sequencing platforms are presented here to have a clear picture of the workflow. Concluding the topic is the discussion about different pipelines applied to analyze data produced by sequencing techniques and have a significant role in interpreting the outcome of a particular experiment. A plethora of different software and tools are incorporated in the automated pipelines or individually available to perform manual metagenomic analysis. Here we describe 8-10 advanced, efficient pipelines used for analysis that explain their respective workflows to simplify the whole analysis process.

Targeted Whole Genome Sequencing (TWG-Seq) of Cucumber Green Mottle Mosaic Virus Using Tiled Amplicon Multiplex PCR and Nanopore Sequencing

Rapid and reliable detection tools are essential for disease surveillance and outbreak management, and genomic data is essential to determining pathogen origin and monitoring of transmission pathways. Low virus copy number and poor RNA quality can present challenges for genomic sequencing of plant viruses, but this can be overcome by enrichment of target nucleic acid. A targeted whole genome sequencing (TWG-Seq) approach for the detection of cucumber green mottle mosaic virus (CGMMV) has been developed where overlapping amplicons generated using two multiplex RT-PCR assays are then sequenced using the Oxford Nanopore MinION. Near complete coding region sequences were assembled with ≥100× coverage for infected leaf tissue dilution samples with RT-qPCR cycle quantification (Cq) values from 11.8 to 38 and in seed dilution samples with Cq values 13.8 to 27. Consensus sequences assembled using this approach showed greater than 99% nucleotide similarity when compared to genomes produced using metagenomic sequencing. CGMMV could be confidently detected in historical seed isolates with degraded RNA. Whilst limited access to, and costs associated with second-generation sequencing platforms can influence diagnostic outputs, the portable Nanopore technology offers an affordable high throughput sequencing alternative when combined with TWG-Seq for low copy or degraded samples.

A Genome-Wide Association study in Arabidopsis thaliana to decipher the adaptive genetics of quantitative disease resistance in a native heterogeneous environment

Pathogens are often the main selective agents acting in plant communities, thereby influencing the distribution of polymorphism at loci affecting resistance within and among natural plant populations. In addition, the outcome of plant-pathogen interactions can be drastically affected by abiotic and biotic factors at different spatial and temporal grains. The characterization of the adaptive genetic architecture of disease resistance in native heterogeneous environments is however still missing. In this study, we conducted an in situ Genome-Wide Association study in the spatially heterogeneous native habitat of a highly genetically polymorphic local mapping population of Arabidopsis thaliana, to unravel the adaptive genetic architecture of quantitative disease resistance. Disease resistance largely differed among three native soils and was affected by the presence of the grass Poa annua. The observation of strong crossing reactions norms among the 195 A. thaliana genotypes for disease resistance among micro-habitats, combined with a negative fecundity-disease resistance relationship in each micro-habitat, suggest that alternative local genotypes of A. thaliana are favored under contrasting environmental conditions at the scale of few meters. A complex genetic architecture was detected for disease resistance and fecundity. However, only few QTLs were common between these two traits. Heterogeneous selection in this local population should therefore promote the maintenance of polymorphism at only few candidate resistance genes.

Prospects of advanced metagenomics and meta-omics in the investigation of phytomicrobiome to forecast beneficial and pathogenic response

Microorganisms dwell in diverse plant niches as non-axenic biotic components that are beneficial as well pathogenic for the host. They improve nutrients-uptake, stress tolerance, phytohormone synthesis, and strengthening the defense system through phyllosphere, rhizosphere, and endosphere. The negative consequences of the microbial communities are largely in the form of diseases characterized by certain symptoms such as gall, cankers, rots etc. Uncultivable and unspecified nature of different phytomicrobiomes communities is a challenge in the management of plant disease, a leading cause for the loss of the plant products. Metagenomics has opened a new gateway for the exploration of microorganisms that are hitherto unknown, enables investigation of the functional aspect of microbial gene products through metatranscriptomics and metabolomics. Metagenomics offers advantages of characterizing previously unknown microorganisms from extreme environments like hot springs, glaciers, deep seas, animal gut etc. besides bioprospecting gene products such as Taq polymerase, bor encoded indolotryptoline, hydrolases, and polyketides. This review provides a detailed account of the phytomicrobiome networks and highlights the importance and limitations of metagenomics and other meta-omics approaches for the understanding of plant microbial diversity with special focus on the disease control and its management.

New opportunities in plant microbiome engineering for increasing agricultural sustainability under stressful conditions

Plant microbiome (or phytomicrobiome) engineering (PME) is an anticipated untapped alternative strategy that could be exploited for plant growth, health and productivity under different environmental conditions. It has been proven that the phytomicrobiome has crucial contributions to plant health, pathogen control and tolerance under drastic environmental (a)biotic constraints. Consistent with plant health and safety, in this article we address the fundamental role of plant microbiome and its insights in plant health and productivity. We also explore the potential of plant microbiome under environmental restrictions and the proposition of improving microbial functions that can be supportive for better plant growth and production. Understanding the crucial role of plant associated microbial communities, we propose how the associated microbial actions could be enhanced to improve plant growth-promoting mechanisms, with a particular emphasis on plant beneficial fungi. Additionally, we suggest the possible plant strategies to adapt to a harsh environment by manipulating plant microbiomes. However, our current understanding of the microbiome is still in its infancy, and the major perturbations, such as anthropocentric actions, are not fully understood. Therefore, this work highlights the importance of manipulating the beneficial plant microbiome to create more sustainable agriculture, particularly under different environmental stressors.

Searching for the Fusarium spp. Which Are Responsible for Trichothecene Contamination in Oats Using Metataxonomy to Compare the Distribution of Toxigenic Species in Fields from Spain and the UK

The contamination of oats with Fusarium toxins poses a high risk for food safety. Among them, trichothecenes are the most frequently reported in European oats, especially in northern countries. The environmental conditions related to the climate change scenario might favour a distribution shift in Fusarium species and the presence of these toxins in Southern European countries. In this paper, we present an ambitious work to determine the species responsible for trichothecene contamination in Spanish oats and to compare the results in the United Kingdom (UK) using a metataxonomic approach applied to both oat grains and soil samples collected from both countries. Regarding T-2 and HT-2 toxin producers, F. langsethiae was detected in 38% and 25% of the oat samples from the UK and Spain, respectively, and to the best of our knowledge, this is the first report of the detection of this fungus in oats from Spain. The relevant type B trichothecene producer, F. poae, was the most frequently detected Fusarium species in oats from both origins. Other important trichothecene producers, such as the Fusarium tricinctum species complex or Fusarium cerealis, were also frequently detected in oat fields. Many Fusarium toxins, including T-2 and HT-2 toxins, deoxynivalenol, or nivalenol, were detected in oat samples. The results obtained in this work revealed a clear change in the distribution of trichothecene producers and the necessity to establish the potential of these species to colonize oats and their ability to produce mycotoxins.

New-Generation Sequencing Technology in Diagnosis of Fungal Plant Pathogens: A Dream Comes True?

The fast and continued progress of high-throughput sequencing (HTS) and the drastic reduction of its costs have boosted new and unpredictable developments in the field of plant pathology. The cost of whole-genome sequencing, which, until few years ago, was prohibitive for many projects, is now so affordable that a new branch, phylogenomics, is being developed. Fungal taxonomy is being deeply influenced by genome comparison, too. It is now easier to discover new genes as potential targets for an accurate diagnosis of new or emerging pathogens, notably those of quarantine concern. Similarly, with the development of metabarcoding and metagenomics techniques, it is now possible to unravel complex diseases or answer crucial questions, such as "What's in my soil?", to a good approximation, including fungi, bacteria, nematodes, etc. The new technologies allow to redraw the approach for disease control strategies considering the pathogens within their environment and deciphering the complex interactions between microorganisms and the cultivated crops. This kind of analysis usually generates big data that need sophisticated bioinformatic tools (machine learning, artificial intelligence) for their management. Herein, examples of the use of new technologies for research in fungal diversity and diagnosis of some fungal pathogens are reported.

Bioinformatics approaches and applications in plant biotechnology

BACKGROUND

In recent years, major advance in molecular biology and genomic technologies have led to an exponential growth in biological information. As the deluge of genomic information, there is a parallel growth in the demands of tools in the storage and management of data, and the development of software for analysis, visualization, modelling, and prediction of large data set.

MAIN BODY

Particularly in plant biotechnology, the amount of information has multiplied exponentially with a large number of databases available from many individual plant species. Efficient bioinformatics tools and methodologies are also developed to allow rapid genome sequence and the study of plant genome in the 'omics' approach. This review focuses on the various bioinformatic applications in plant biotechnology, and their advantages in improving the outcome in agriculture. The challenges or limitations faced in plant biotechnology in the aspect of bioinformatics approach that explained the low progression in plant genomics than in animal genomics are also reviewed and assessed.

CONCLUSION

There is a critical need for effective bioinformatic tools, which are able to provide longer reads with unbiased coverage in order to overcome the complexity of the plant's genome. The advancement in bioinformatics is not only beneficial to the field of plant biotechnology and agriculture sectors, but will also contribute enormously to the future of humanity.

Oralbiotica/Oralbiotics: The Impact of Oral Microbiota on Dental Health and Demineralization: A Systematic Review of the Literature

The oral microbiota plays a vital role in the human microbiome and oral health. Imbalances between microbes and their hosts can lead to oral and systemic disorders such as diabetes or cardiovascular disease. The purpose of this review is to investigate the literature evidence of oral microbiota dysbiosis on oral health and discuss current knowledge and emerging mechanisms governing oral polymicrobial synergy and dysbiosis; both have enhanced our understanding of pathogenic mechanisms and aided the design of innovative therapeutic approaches as ORALBIOTICA for oral diseases such as demineralization. PubMed, Web of Science, Google Scholar, Scopus, Cochrane Library, EMBEDDED, Dentistry & Oral Sciences Source via EBSCO, APA PsycINFO, APA PsyArticles, and DRUGS@FDA were searched for publications that matched our topic from January 2017 to 22 April 2022, with an English language constraint using the following Boolean keywords: ("microbio*" and "demineralization*") AND ("oral microbiota" and "demineralization"). Twenty-two studies were included for qualitative analysis. As seen by the studies included in this review, the balance of the microbiota is unstable and influenced by oral hygiene, the presence of orthodontic devices in the oral cavity and poor eating habits that can modify its composition and behavior in both positive and negative ways, increasing the development of demineralization, caries processes, and periodontal disease. Under conditions of dysbiosis, favored by an acidic environment, the reproduction of specific bacterial strains increases, favoring cariogenic ones such as Bifidobacterium dentium, Bifidobacterium longum, and S. mutans, than S. salivarius and A. viscosus, and increasing of Firmicutes strains to the disadvantage of Bacteroidetes. Microbial balance can be restored by using probiotics and prebiotics to manage and treat oral diseases, as evidenced by mouthwashes or dietary modifications that can influence microbiota balance and prevent or slow disease progression.

Campylobacter Species, Microbiological Source Tracking and Risk Assessment of Bacterial pathogens

Campylobacter species continue to remain critical pathogens of public health interest. They are responsible for approximately 500 million cases of gastroenteritis per year worldwide. Infection occurs through the consumption of contaminated food and water. Microbial risk assessment and source tracking are crucial epidemiological strategies to monitor the outbreak of campylobacteriosis effectively. Various methods have been proposed for microbial source tracking and risk assessment, most of which rely on conventional microbiological techniques such as detecting fecal indicator organisms and other novel microbial source tracking methods, including library-dependent microbial source tracking and library-independent source tracking approaches. However, both the traditional and novel methods have their setbacks. For example, while the conventional techniques are associated with a poor correlation between indicator organism and pathogen presence, on the other hand, it is impractical to interpret qPCR-generated markers to establish the exact human health risks even though it can give information regarding the potential source and relative human risk. Therefore, this article provides up-to-date information on campylobacteriosis, various approaches for source attribution, and risk assessment of bacterial pathogens, including next-generation sequencing approaches such as shotgun metagenomics, which effectively answer the questions of potential pathogens are there and in what quantities.

Investigating plant disease outbreaks with long-read metagenomics: sensitive detection and highly resolved phylogenetic reconstruction applied to Xylella fastidiosa

Early disease detection is a prerequisite for enacting effective interventions for disease control. Strains of the bacterial plant pathogen Xylella fastidiosa have recurrently spread to new crops in new countries causing devastating outbreaks. So far, investigation of outbreak strains and highly resolved phylogenetic reconstruction have required whole-genome sequencing of pure bacterial cultures, which are challenging to obtain due to the fastidious nature of X. fastidiosa. Here, we show that culture-independent metagenomic sequencing, using the Oxford Nanopore Technologies MinION long-read sequencer, can sensitively and specifically detect the causative agent of Pierce’s disease of grapevine, X. fastidiosa subspecies fastidiosa. Using a DNA sample from a grapevine in Virginia, USA, it was possible to obtain a metagenome-assembled genome (MAG) of sufficient quality for phylogenetic reconstruction with SNP resolution. The analysis placed the MAG in a clade with isolates from Georgia, USA, suggesting introduction of X. fastidiosa subspecies fastidiosa to Virginia from the south-eastern USA. This proof of concept study, thus, revealed that metagenomic sequencing can replace culture-dependent genome sequencing for reconstructing transmission routes of bacterial plant pathogens.

Best practices in metabarcoding of fungi: From experimental design to results

The development of high-throughput sequencing (HTS) technologies has greatly improved our capacity to identify fungi and unveil their ecological roles across a variety of ecosystems. Here we provide an overview of current best practices in metabarcoding analysis of fungal communities, from experimental design through molecular and computational analyses. By reanalysing published data sets, we demonstrate that operational taxonomic units (OTUs) outperform amplified sequence variants (ASVs) in recovering fungal diversity, a finding that is particularly evident for long markers. Additionally, analysis of the full-length ITS region allows more accurate taxonomic placement of fungi and other eukaryotes compared to the ITS2 subregion. Finally, we show that specific methods for compositional data analyses provide more reliable estimates of shifts in community structure. We conclude that metabarcoding analyses of fungi are especially promising for integrating fungi into the full microbiome and broader ecosystem functioning context, recovery of novel fungal lineages and ancient organisms as well as barcoding of old specimens including type material.

Microbial Community and Function-Based Synthetic Bioinoculants: A Perspective for Sustainable Agriculture

Interactions among the plant microbiome and its host are dynamic, both spatially and temporally, leading to beneficial or pathogenic relationships in the rhizosphere, phyllosphere, and endosphere. These interactions range from cellular to molecular and genomic levels, exemplified by many complementing and coevolutionary relationships. The host plants acquire many metabolic and developmental traits such as alteration in their exudation pattern, acquisition of systemic tolerance, and coordination of signaling metabolites to interact with the microbial partners including bacteria, fungi, archaea, protists, and viruses. The microbiome responds by gaining or losing its traits to various molecular signals from the host plants and the environment. Such adaptive traits in the host and microbial partners make way for their coexistence, living together on, around, or inside the plants. The beneficial plant microbiome interactions have been exploited using traditional culturable approaches by isolating microbes with target functions, clearly contributing toward the host plants' growth, fitness, and stress resilience. The new knowledge gained on the unculturable members of the plant microbiome using metagenome research has clearly indicated the predominance of particular phyla/genera with presumptive functions. Practically, the culturable approach gives beneficial microbes in hand for direct use, whereas the unculturable approach gives the perfect theoretical information about the taxonomy and metabolic potential of well-colonized major microbial groups associated with the plants. To capitalize on such beneficial, endemic, and functionally diverse microbiome, the strategic approach of concomitant use of culture-dependent and culture-independent techniques would help in designing novel "biologicals" for various crops. The designed biologicals (or bioinoculants) should ensure the community's persistence due to their genomic and functional abilities. Here, we discuss the current paradigm on plant-microbiome-induced adaptive functions for the host and the strategies for synthesizing novel bioinoculants based on functions or phylum predominance of microbial communities using culturable and unculturable approaches. The effective crop-specific inclusive microbial community bioinoculants may lead to reduction in the cost of cultivation and improvement in soil and plant health for sustainable agriculture.

Microbe-Mediated Thermotolerance in Plants and Pertinent Mechanisms- A Meta-Analysis and Review

Microbial symbionts can mediate plant stress responses by enhancing thermal tolerance, but less attention has been paid to measuring these effects across plant-microbe studies. We performed a meta-analysis of published studies as well as discussed with relevant literature to determine how the symbionts influence plant responses under non-stressed versus thermal-stressed conditions. As compared to non-inoculated plants, inoculated plants had significantly higher biomass and photosynthesis under heat stress conditions. A significantly decreased accumulation of malondialdehyde (MDA) and hydrogen peroxide (H2O2) indicated a lower oxidation level in the colonized plants, which was also correlated with the higher activity of catalase, peroxidase, glutathione reductase enzymes due to microbial colonization under heat stress. However, the activity of superoxide dismutase, ascorbate oxidase, ascorbate peroxidase, and proline were variable. Our meta-analysis revealed that microbial colonization influenced plant growth and physiology, but their effects were more noticeable when their host plants were exposed to high-temperature stress than when they grew under ambient temperature conditions. We discussed the mechanisms of microbial conferred plant thermotolerance, including at the molecular level based on the available literature. Further, we highlighted and proposed future directions toward exploring the effects of symbionts on the heat tolerances of plants for their implications in sustainable agricultural production.

Community Structure and Functional Annotations of the Skin Microbiome in Healthy and Diseased Catfish, Heteropneustes fossilis

The skin mucosa of fish serves as a primary barrier against pathogens. In lesion sites in diseased fish, the mucosal barrier is expected to be compromised, with a substantial presence of potential pathogens. An understanding of the skin microbiome and its functional repertoire would provide important insights into host-microbe interactions, which has important implications for prophylactic measures in aquaculture. This study revealed the skin microbiomes and their functional annotations from healthy and diseased stinging catfish (Heteropneustes fossilis) based on 16S rRNA metagenomics. The OTUs consisted of four major phyla, Proteobacteria, Bacteroidota, Actinobacteriota and Firmicutes. Among members of the predominant phyla, Proteobacteria were rich in healthy fishes, but Bacteroidota and Firmicutes were significantly differentiated in healthy and diseased fish. The diversified microbiome was high in the skin of healthy fishes and did not significantly differ from that of the diseased groups. At the genus level, Pseudomonas showed the highest abundance in healthy fish but was nearly absent in diseased fish, whereas Flavobacterium showed the highest abundance in diseased fish. Linear discriminant analysis identified two phyla (Bacteroidota, Firmicutes) and two genera (Flavobacterium, Allorhizobium) that were consistently identified in diseased fishes. Functional prediction analysis specified that the genes related to physiological functions such as metabolism, immune and digestive systems and environmental adaptations could be highly expressed in diseased fishes. The present study indicates that the compositions, richness and functions of the bacterial community could influence the health status of cultured stinging catfish. Aquaculture-associated pathogenic bacteria may be identified, and preventive measures can be taken for the surveillance of fish health.

Soil Nitrogen Treatment Alters Microbiome Networks Across Farm Niches

Agriculture is fundamental for food production, and microbiomes support agriculture through multiple essential ecosystem services. Despite the importance of individual (i.e., niche specific) agricultural microbiomes, microbiome interactions across niches are not well-understood. To observe the linkages between nearby agricultural microbiomes, multiple approaches (16S, 18S, and ITS) were used to inspect a broad coverage of niche microbiomes. Here we examined agricultural microbiome responses to 3 different nitrogen treatments (0, 150, and 300 kg/ha/yr) in soil and tracked linked responses in other neighbouring farm niches (rumen, faecal, white clover leaf, white clover root, rye grass leaf, and rye grass root). Nitrogen treatment had little impact on microbiome structure or composition across niches, but drastically reduced the microbiome network connectivity in soil. Networks of 16S microbiomes were the most sensitive to nitrogen treatment across amplicons, where ITS microbiome networks were the least responsive. Nitrogen enrichment in soil altered soil and the neighbouring microbiome networks, supporting our hypotheses that nitrogen treatment in soil altered microbiomes in soil and in nearby niches. This suggested that agricultural microbiomes across farm niches are ecologically interactive. Therefore, knock-on effects on neighbouring niches should be considered when management is applied to a single agricultural niche.

Real-Time On-Site Diagnosis of Quarantine Pathogens in Plant Tissues by Nanopore-Based Sequencing

Rapid and sensitive assays for the identification of plant pathogens are necessary for the effective management of crop diseases. The main limitation of current diagnostic testing is the inability to combine broad and sensitive pathogen detection with the identification of key strains, pathovars, and subspecies. Such discrimination is necessary for quarantine pathogens, whose management is strictly dependent on genotype identification. To address these needs, we have established and evaluated a novel all-in-one diagnostic assay based on nanopore sequencing for the detection and simultaneous characterization of quarantine pathogens, using Xylella fastidiosa as a case study. The assay proved to be at least as sensitive as standard diagnostic tests and the quantitative results agreed closely with qPCR-based analysis. The same sequencing results also allowed discrimination between subspecies when present either individually or in combination. Pathogen detection and typing were achieved within 13 min of sequencing owing to the use of an internal control that allowed to stop sequencing when sufficient data had accumulated. These advantages, combined with the use of portable equipment, will facilitate the development of next-generation diagnostic assays for the efficient monitoring of other plant pathogens.

Metagenomic sequencing for detection and identification of the boxwood blight pathogen Calonectria pseudonaviculata

Pathogen detection and identification are key elements in outbreak control of human, animal, and plant diseases. Since many fungal plant pathogens cause similar symptoms, are difficult to distinguish morphologically, and grow slowly in culture, culture-independent, sequence-based diagnostic methods are desirable. Whole genome metagenomic sequencing has emerged as a promising technique because it can potentially detect any pathogen without culturing and without the need for pathogen-specific probes. However, efficient DNA extraction protocols, computational tools, and sequence databases are required. Here we applied metagenomic sequencing with the Oxford Nanopore Technologies MinION to the detection of the fungus Calonectria pseudonaviculata, the causal agent of boxwood (Buxus spp.) blight disease. Two DNA extraction protocols, several DNA purification kits, and various computational tools were tested. All DNA extraction methods and purification kits provided sufficient quantity and quality of DNA. Several bioinformatics tools for taxonomic identification were found suitable to assign sequencing reads to the pathogen with an extremely low false positive rate. Over 9% of total reads were identified as C. pseudonaviculata in a severely diseased sample and identification at strain-level resolution was approached as the number of sequencing reads was increased. We discuss how metagenomic sequencing could be implemented in routine plant disease diagnostics.

Computational Analysis of HTS Data and Its Application in Plant Pathology

High-throughput sequencing is a basic tool of biological research, and it is extensively used in plant pathology projects. Here, we describe how to handle data coming from a variety of sequencing experiments, focusing on the analysis of Illumina reads. We describe how to perform genome assembly and annotation with DNA reads, correctly analyze RNA-seq data to discover differentially expressed genes, handle amplicon sequencing data from microbial communities, and utilize small RNA sequencing data to predict miRNA sequences and their putative targets.

Two Decades of Desiccation Biology: A Systematic Review of the Best Studied Angiosperm Resurrection Plants

Resurrection plants have an extraordinary ability to survive extreme water loss but still revive full metabolic activity when rehydrated. These plants are useful models to understand the complex biology of vegetative desiccation tolerance. Despite extensive studies of resurrection plants, many details underlying the mechanisms of desiccation tolerance remain unexplored. To summarize the progress in resurrection plant research and identify unexplored questions, we conducted a systematic review of 15 model angiosperm resurrection plants. This systematic review provides an overview of publication trends on resurrection plants, the geographical distribution of species and studies, and the methodology used. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses protocol we surveyed all publications on resurrection plants from 2000 and 2020. This yielded 185 empirical articles that matched our selection criteria. The most investigated plants were Craterostigma plantagineum (17.5%), Haberlea rhodopensis (13.7%), Xerophyta viscosa (reclassified as X. schlechteri) (11.9%), Myrothamnus flabellifolia (8.5%), and Boea hygrometrica (8.1%), with all other species accounting for less than 8% of publications. The majority of studies have been conducted in South Africa, Bulgaria, Germany, and China, but there are contributions from across the globe. Most studies were led by researchers working within the native range of the focal species, but some international and collaborative studies were also identified. The number of annual publications fluctuated, with a large but temporary increase in 2008. Many studies have employed physiological and transcriptomic methodologies to investigate the leaves of resurrection plants, but there was a paucity of studies on roots and only one metagenomic study was recovered. Based on these findings we suggest that future research focuses on resurrection plant roots and microbiome interactions to explore microbial communities associated with these plants, and their role in vegetative desiccation tolerance.

Integrating High throughput Sequencing into Survey Design Reveals Turnip Yellows Virus and Soybean Dwarf Virus in Pea (Pisum Sativum) in the United Kingdom

There is only limited knowledge of the presence and incidence of viruses in peas within the United Kingdom, therefore high-throughput sequencing (HTS) in combination with a bulk sampling strategy and targeted testing was used to determine the virome in cultivated pea crops. Bulks of 120 leaves collected from twenty fields from around the UK were initially tested by HTS, and presence and incidence of virus was then determined using specific real-time reverse-transcription PCR assays by testing smaller mixed-bulk size samples. This study presents the first finding of turnip yellows virus (TuYV) in peas in the UK and the first finding of soybean dwarf virus (SbDV) in the UK. While TuYV was not previously known to be present in UK peas, it was found in 13 of the 20 sites tested and was present at incidences up to 100%. Pea enation mosaic virus-1, pea enation mosaic virus-2, pea seed-borne mosaic virus, bean yellow mosaic virus, pea enation mosaic virus satellite RNA and turnip yellows virus associated RNA were also identified by HTS. Additionally, a subset of bulked samples were re-sequenced at greater depth to ascertain whether the relatively low depth of sequencing had missed any infections. In each case the same viruses were identified as had been identified using the lower sequencing depth. Sequencing of an isolate of pea seed-borne mosaic virus from 2007 also revealed the presence of TuYV and SbDV, showing that both viruses have been present in the UK for at least a decade, and represents the earliest whole genome of SbDV from Europe. This study demonstrates the potential of HTS to be used as a surveillance tool, or for crop-specific field survey, using a bulk sampling strategy combined with HTS and targeted diagnostics to indicate both presence and incidence of viruses in a crop.

Suitability of the MinION long read sequencer for semi-targeted detection of foodborne pathogens

Foodborne pathogens are still a significant source of morbidity and mortality worldwide. In addition to this the current methodologies to track these microorganisms cannot cope with the current intensive production systems, thus novel methods are of outmost importance. DNA-based methods have already demonstrated suitable to address this issue, but most of them are targeted methods such as real-time PCR (qPCR), meaning that one will only find what is looking for, thus taking the risk of missing relevant pathogens in a given sample. To overcome this limitation we have developed an easy-to-implement methodology which enables the detection of several pathogens simultaneously by using long-read Next Generation Sequencing (NGS) with MinION. The method was named "semi-targeted" due to the combination of a non-targeted detection method, NGS, with the usage of selective media in order to partially eliminate non-pathogenic interfering bacteria. To this end, we included an enrichment step for the recovery of different pathogens, namely Salmonella Enteritidis and Typhimurium, Listeria monocytogenes and Escherichia coli O157:H7, after DNA extraction and library preparation, the samples were analyzed with MinION implementing the low-cost Flongle Flow Cells. The methodology was successfully evaluated in spiked milk samples with an excellent agreement with the results obtained by qPCR and culture-based methods. The method can provide accurate results after only 2 h of sequencing. Sample multiplexing, along with the lower cost of the Flongle Flow Cells and the reduced price of the MinION platform, make the assay cost-effective that is of importance for the food industry. Starting the method with a classical microbiological approach, the enrichment, the method is easy to implement in testing laboratories, it provides flexibility in terms of potential pathogens to be detected, and the positive results can be easily confirmed following culture-based, or other type, of confirmation procedures.