Europe as a secondary distribution hub in the worldwide invasion of the potato cyst nematode Globodera rostochiensis.

The potato cyst nematode Globodera rostochiensis originates from the Andean Mountain region in South America and has unintentionally been introduced to all inhabited continents. Several studies have examined the population genetic structure of this pest in various countries by using microsatellite markers. However, merging microsatellite data produced from different laboratories is challenging and can introduce uncertainty when interpreting the results. To overcome this challenge and to explore invasion routes of this pest, we have genotyped 22 G. rostochiensis populations from all continents. Within populations, the highest genetic diversity was observed in the South American populations, the European populations showed an intermediate level of genetic diversity and the remaining populations were the less diverse. This confirmed pre-existing knowledge such as a first introduction event from South America to Europe, but the less diverse populations could originate either from South America or from Europe. At the continental scale, STRUCTURE genetic clustering output indicated that North America and Asia have experienced at least two introduction events. Comparing different evolutionary scenarios, the Approximate Bayesian Computation analysis showed that Europe served as a secondary distribution centre for the invasion of G. rostochiensis into all other continents (North America, Africa, Asia and Oceania).

Species-specific microRNA discovery and target prediction in the soybean cyst nematode.

The soybean cyst nematode (SCN) is a devastating pathogen for economic and food security considerations. Although the SCN genome has recently been sequenced, the presence of any miRNA has not been systematically explored and reported. This paper describes the development of a species-specific SCN miRNA discovery pipeline and its application to the SCN genome. Experiments on well-documented model nematodes (Caenorhabditis elegans and Pristionchus pacificus) are used to tune the pipeline's hyperparameters and confirm its recall and precision. Application to the SCN genome identifies 3342 high-confidence putative SCN miRNA. Prediction specificity within SCN is confirmed by applying the pipeline to RNA hairpins from known exonic regions of the SCN genome (i.e., sequences known to not be miRNA). Prediction recall is confirmed by building a positive control set of SCN miRNA, based on a limited deep sequencing experiment. Interestingly, a number of novel miRNA are predicted to be encoded within the intronic regions of effector genes, known to be involved in SCN parasitism, suggesting that these miRNA may also be involved in the infection process or virulence. Beyond miRNA discovery, gene targets within SCN are predicted for all high-confidence novel miRNA using a miRNA:mRNA target prediction system. Lastly, cross-kingdom miRNA targeting is investigated, where putative soybean mRNA targets are identified for novel SCN miRNA. All predicted miRNA and gene targets are made available in appendix and through a Borealis DataVerse open repository ( https://borealisdata.ca/dataset.xhtml?persistentId=doi:10.5683/SP3/30DEXA ).

Characterization of microRNAs in the cyst nematode Heterodera glycines identifies possible candidates involved in cross-kingdom interactions with its host Glycine max.

The soybean cyst nematode (SCN - Heterodera glycines) is one of the most damaging pests to the cultivated soybean worldwide. Using a wide array of stylet-secreted effector proteins, this nematode can restructure its host cells into a complex and highly active feeding structure called the syncytium. Tight regulation of these proteins is thought to be essential to the successful formation of this syncytium. To date, multiple mechanisms have been proposed to regulate the expression of these proteins including through post-transcriptional regulation. MicroRNAs (miRNAs) are a class of small, roughly 22-nucleotide-long, non-coding RNA shown to regulate gene expression through its interaction with the 3' untranslated region of genes. These same small RNAs have also been hypothesized to be able to cross over kingdom barriers and regulate genes in other species in a process called cross-kingdom interactions. In this study, we characterized the miRNome of the SCN via sequencing of small-RNAs isolated from whole nematodes and exosomes representing all developmental stages. We identified 121 miRNA loci encoding 96 distinct miRNA families including multiple lineage- and species-specific candidates. Using a combination of plant- and animal-specific miRNA target predictors, we generated a unique repertoire of miRNA:mRNA interacting partners in the nematode and its host plant leading to the identification of a set of nine probable cross-kingdom miRNA candidates.

Large-scale data mining pipeline for identifying novel soybean genes involved in resistance against the soybean cyst nematode.

The soybean cyst nematode (SCN) [Heterodera glycines Ichinohe] is a devastating pathogen of soybean [Glycine max (L.) Merr.] that is rapidly becoming a global economic issue. Two loci conferring SCN resistance have been identified in soybean, Rhg1 and Rhg4; however, they offer declining protection. Therefore, it is imperative that we identify additional mechanisms for SCN resistance. In this paper, we develop a bioinformatics pipeline to identify protein-protein interactions related to SCN resistance by data mining massive-scale datasets. The pipeline combines two leading sequence-based protein-protein interaction predictors, the Protein-protein Interaction Prediction Engine (PIPE), PIPE4, and Scoring PRotein INTeractions (SPRINT) to predict high-confidence interactomes. First, we predicted the top soy interacting protein partners of the Rhg1 and Rhg4 proteins. Both PIPE4 and SPRINT overlap in their predictions with 58 soybean interacting partners, 19 of which had GO terms related to defense. Beginning with the top predicted interactors of Rhg1 and Rhg4, we implement a "guilt by association" in silico proteome-wide approach to identify novel soybean genes that may be involved in SCN resistance. This pipeline identified 1,082 candidate genes whose local interactomes overlap significantly with the Rhg1 and Rhg4 interactomes. Using GO enrichment tools, we highlighted many important genes including five genes with GO terms related to response to the nematode (GO:0009624), namely, Glyma.18G029000, Glyma.11G228300, Glyma.08G120500, Glyma.17G152300, and Glyma.08G265700. This study is the first of its kind to predict interacting partners of known resistance proteins Rhg1 and Rhg4, forming an analysis pipeline that enables researchers to focus their search on high-confidence targets to identify novel SCN resistance genes in soybean.

Single Nematode Transcriptomic Analysis, Using Long-Read Technology, Reveals Two Novel Virulence Gene Candidates in the Soybean Cyst Nematode, Heterodera glycines.

The soybean cyst nematode (Heterodera glycines, SCN), is the most damaging disease of soybean in North America. While management of this pest using resistant soybean is generally still effective, prolonged exposure to cultivars derived from the same source of resistance (PI 88788) has led to the emergence of virulence. Currently, the underlying mechanisms responsible for resistance breakdown remain unknown. In this study, we combined a single nematode transcriptomic profiling approach with long-read sequencing to reannotate the SCN genome. This resulted in the annotation of 1932 novel transcripts and 281 novel gene features. Using a transcript-level quantification approach, we identified eight novel effector candidates overexpressed in PI 88788 virulent nematodes in the late infection stage. Among these were the novel gene Hg-CPZ-1 and a pioneer effector transcript generated through the alternative splicing of the non-effector gene Hetgly21698. While our results demonstrate that alternative splicing in effectors does occur, we found limited evidence of direct involvement in the breakdown of resistance. However, our analysis highlighted a distinct pattern of effector upregulation in response to PI 88788 resistance indicative of a possible adaptation process by SCN to host resistance.

Soil microbial gene expression in an agricultural ecosystem varies with time and neonicotinoid seed treatments.

Neonicotinoids, a class of systemic insecticides, have been widely used for decades against various insect pests. Previous studies have reported non-target effects of neonicotinoids on some beneficial macro- and micro-organisms. Considering the crucial role the soil microbiota plays in sustaining soil fertility, it is critical to understand how neonicotinoid exposure affects the microbial taxonomic composition and gene expression. However, most studies to date have evaluated soil microbial taxonomic compositions or assessed microbial functions based on soil biochemical analysis. In this study, we have applied a metatranscriptomic approach to quantify the variability in soil microbial gene expression in a 2 year soybean/corn crop rotation in Quebec, Canada. We identified weak and temporally inconsistent effects of neonicotinoid application on soil microbial gene expression, as well as a strong temporal variation in soil microbial gene expression among months and years. Neonicotinoid seed treatment altered the expression of a small number of microbial genes, including genes associated with heat shock proteins, regulatory functions, metabolic processes and DNA repair. These changes in gene expression varied during the growing season and between years. Overall, the composition of soil microbial expressed genes seems to be more resilient and less affected by neonicotinoid application than soil microbial taxonomic composition. Our study is among the first to document the effects of neonicotinoid seed treatment on microbial gene expression and highlights the strong temporal variability of soil microbial gene expression and its responses to neonicotinoid seed treatments.

P-TarPmiR accurately predicts plant-specific miRNA targets.

microRNAs (miRNAs) are small non-coding ribonucleic acids that post-transcriptionally regulate gene expression through the targeting of messenger RNA (mRNAs). Most miRNA target predictors have focused on animal species and prediction performance drops substantially when applied to plant species. Several rule-based miRNA target predictors have been developed in plant species, but they often fail to discover new miRNA targets with non-canonical miRNA-mRNA binding. Here, the recently published TarDB database of plant miRNA-mRNA data is leveraged to retrain the TarPmiR miRNA target predictor for application on plant species. Rigorous experiment design across four plant test species demonstrates that animal-trained predictors fail to sustain performance on plant species, and that the use of plant-specific training data improves accuracy depending on the quantity of plant training data used. Surprisingly, our results indicate that the complete exclusion of animal training data leads to the most accurate plant-specific miRNA target predictor indicating that animal-based data may detract from miRNA target prediction in plants. Our final plant-specific miRNA prediction method, dubbed P-TarPmiR, is freely available for use at http://ptarpmir.cu-bic.ca . The final P-TarPmiR method is used to predict targets for all miRNA within the soybean genome. Those ranked predictions, together with GO term enrichment, are shared with the research community.

The SoyaGen Project: Putting Genomics to Work for Soybean Breeders.

The SoyaGen project was a collaborative endeavor involving Canadian soybean researchers and breeders from academia and the private sector as well as international collaborators. Its aims were to develop genomics-derived solutions to real-world challenges faced by breeders. Based on the needs expressed by the stakeholders, the research efforts were focused on maximizing realized yield through optimization of maturity and improved disease resistance. The main deliverables related to molecular breeding in soybean will be reviewed here. These include: (1) SNP datasets capturing the genetic diversity within cultivated soybean (both within a worldwide collection of > 1,000 soybean accessions and a subset of 102 short-season accessions (MG0 and earlier) directly relevant to this group); (2) SNP markers for selecting favorable alleles at key maturity genes as well as loci associated with increased resistance to key pathogens and pests (Phytophthora sojae, Heterodera glycines, Sclerotinia sclerotiorum); (3) diagnostic tools to facilitate the identification and mapping of specific pathotypes of P. sojae; and (4) a genomic prediction approach to identify the most promising combinations of parents. As a result of this fruitful collaboration, breeders have gained new tools and approaches to implement molecular, genomics-informed breeding strategies. We believe these tools and approaches are broadly applicable to soybean breeding efforts around the world.

Population genetic structure of the carrot weevil (Listronotus oregonensis) in North America.

Population genetic studies of insect pests enhance our ability to anticipate problems in agroecosystems, such as pest outbreaks, insecticide resistance, or expansions of the host range. This study focuses on geographic distance and host plant selection as potential determinants of genetic differentiation of the carrot weevil Listronotus oregonensis, a major pest of several apiaceous crops in North America. To undertake genetic studies on this species, we assembled the first complete genome sequence for L. oregonensis. Then, we used both haplotype discrimination with mitochondrial DNA (mtDNA) and a genotyping-by-sequencing (GBS) approach to characterize the genetic population structure. A total of 220 individuals were sampled from 17 localities in the provinces of Québec, Ontario, Nova Scotia (Canada), and the state of Ohio (USA). Our results showed significant genetic differences between distant populations across North America, indicating that geographic distance represents an important factor of differentiation for the carrot weevil. Furthermore, the GBS analysis revealed more different clusters than COI analysis between Québec and Nova Scotia populations, suggesting a recent differentiation in the latter province. In contrast, we found no clear evidence of population structure associated with the four cultivated apiaceous plants tested (carrot, parsley, celery, and celeriac) using populations from Québec. This first characterization of the genetic structure of the carrot weevil contributes to a better understanding of the gene flow of the species and helps to adapt local pest management measures to better control this agricultural pest.

A Broad Review of Soybean Research on the Ongoing Race to Overcome Soybean Cyst Nematode.

Plant pathogens greatly impact food security of the ever-growing human population. Breeding resistant crops is one of the most sustainable strategies to overcome the negative effects of these biotic stressors. In order to efficiently breed for resistant plants, the specific plant-pathogen interactions should be understood. Soybean is a short-day legume that is a staple in human food and animal feed due to its high nutritional content. Soybean cyst nematode (SCN) is a major soybean stressor infecting soybean worldwide including in China, Brazil, Argentina, USA and Canada. There are many Quantitative Trait Loci (QTLs) conferring resistance to SCN that have been identified; however, only two are widely used: rhg1 and Rhg4. Overuse of cultivars containing these QTLs/genes can lead to SCN resistance breakdown, necessitating the use of additional strategies. In this manuscript, a literature review is conducted on research related to soybean resistance to SCN. The main goal is to provide a current understanding of the mechanisms of SCN resistance and list the areas of research that could be further explored.

Draft Genome of the Insect-Parasitic Nematode Bradynema Listronoti.

Bradynema listronoti is an insect-parasitic nematode known to infect the carrot weevil, Listronotus oregonensis. We present the first sequence for this species and for any Allantonematidae, produced with a combination of short and long reads. The draft genome of B. listronoti is 80.6 Mb in size, assembled in 152 scaffolds.

Genomic Profiling of Virulence in the Soybean Cyst Nematode Using Single-Nematode Sequencing.

Soybean cyst nematode (SCN) is one of the most important diseases in soybean. Currently, the main management strategy relies on planting resistant cultivars. However, the overuse of a single resistance source has led to the selection of virulent SCN populations, although the mechanisms by which the nematode overcomes the resistance genes remain unknown. In this study, we used a nematode-adapted single-cell RNA-seq approach to identify SCN genes potentially involved in resistance breakdown in Peking and PI 88788 parental soybean lines. We established for the first time the full transcriptome of single SCN individuals allowing us to identify a list of putative virulence genes against both major SCN resistance sources. Our analysis identified 48 differentially expressed putative effectors (secreted proteins required for infection) alongside 40 effectors showing evidence of novel structural variants, and 11 effector genes containing phenotype-specific sequence polymorphisms. Additionally, a differential expression analysis revealed an interesting phenomenon of coexpressed gene regions with some containing putative effectors. The selection of virulent SCN individuals on Peking resulted in a profoundly altered transcriptome, especially for genes known to be involved in parasitism. Several sequence polymorphisms were also specific to these virulent nematodes and could potentially play a role in the acquisition of nematode virulence. On the other hand, the transcriptome of virulent individuals on PI 88788 was very similar to avirulent ones with the exception of a few genes, which suggest a distinct virulence strategy to Peking.

Recent Advances in Population Genomics of Plant-Parasitic Nematodes.

Plant-parasitic nematodes are a costly burden of crop production. Ubiquitous in nature, phytoparasitic nematodes are associated with nearly every important agricultural crop and represent a significant constraint on global food security. Population genetics is a key discipline in plant nematology to understand aspects of the life strategies of these parasites, in particular their modes of reproduction, geographic origins, evolutionary histories, and dispersion abilities. Advances in high-throughput sequencing technologies have enabled a recent but active effort in genomic analyses of plant-parasitic nematodes. Such genomic approaches applied to multiple populations are providing new insights into the molecular and evolutionary processes that underpin the establishment of these nematodes and into a better understanding of the genetic and mechanistic basis of their pathogenicity and adaptation to their host plants. In this review, we attempt to update information about genome resources and genotyping techniques useful for nematologists who are thinking about initiating population genomics or genome sequencing projects. This review is intended also to foster the development of population genomics in plant-parasitic nematodes through highlighting recent publications that illustrate the potential for this approach to identify novel molecular markers or genes of interest and improve our knowledge of the genome variability, pathogenicity, and evolutionary potential of plant-parasitic nematodes.

Genome Resources of Two Pathotypes of the Potato Cyst Nematode Globodera rostochiensis from New York.

The potato cyst nematode Globodera rostochiensis is a regulated pest posing a serious threat to potato production worldwide. Although the endemic pathotype (Ro1) of G. rostochiensis has been confined to New York State for several decades as a result of quarantine regulations and management with resistant potato cultivars, a virulent pathotype, Ro2, has emerged, for which control measures are scarce. The ability to detect Ro2 early in fields is necessary to sustain the success of G. rostochiensis quarantine in the United States. Here, we report the comparative analysis of whole-genome sequences of multiple single-cyst-derived Ro1 and Ro2 lines, propagated from original field populations. The identified discriminant variants are good targets for developing molecular diagnostic tools for differentiating G. rostochiensis pathotypes in New York.

The Genomic Impact of Selection for Virulence against Resistance in the Potato Cyst Nematode, Globodera pallida.

Although the use of natural resistance is the most effective management approach against the potato cyst nematode (PCN) Globodera pallida, the existence of pathotypes with different virulence characteristics constitutes a constraint towards this goal. Two resistance sources, GpaV (from Solanum vernei) and H3 from S. tuberosum ssp. andigena CPC2802 (from the Commonwealth Potato Collection) are widely used in potato breeding programmes in European potato industry. However, the use of resistant cultivars may drive strong selection towards virulence, which allows the increase in frequency of virulent alleles in the population and therefore, the emergence of highly virulent nematode lineages. This study aimed to identify Avirulence (Avr) genes in G. pallida populations selected for virulence on the above resistance sources, and the genomic impact of selection processes on the nematode. The selection drive in the populations was found to be specific to their genetic background. At the genomic level, 11 genes were found that represent candidate Avr genes. Most of the variant calls determining selection were associated with H3-selected populations, while many of them seem to be organised in genomic islands facilitating selection evolution. These phenotypic and genomic findings combined with histological studies performed revealed potential mechanisms underlying selection in G. pallida.

Prevalence of the root lesion nematode virus (RLNV1) in populations of Pratylenchus penetrans from North America.

Root lesion nematode virus 1 (RLNV1) was discovered in the migratory endoparasitic nematode species Pratylenchus penetrans. It was found in a P. penetrans population collected from soil samples in Beltsville, Maryland, USA. In this study, the distribution of the RLNV1 in 31 geographically distinct P. penetrans populations obtained from different crops was examined. The results demonstrate that RLNV1 is widespread in North American populations of P. penetrans and exhibits low genetic variability in the helicase and RNA-dependent RNA polymerase regions of the genome.Root lesion nematode virus 1 (RLNV1) was discovered in the migratory endoparasitic nematode species Pratylenchus penetrans. It was found in a P. penetrans population collected from soil samples in Beltsville, Maryland, USA. In this study, the distribution of the RLNV1 in 31 geographically distinct P. penetrans populations obtained from different crops was examined. The results demonstrate that RLNV1 is widespread in North American populations of P. penetrans and exhibits low genetic variability in the helicase and RNA-dependent RNA polymerase regions of the genome.

PIPE4: Fast PPI Predictor for Comprehensive Inter- and Cross-Species Interactomes.

The need for larger-scale and increasingly complex protein-protein interaction (PPI) prediction tasks demands that state-of-the-art predictors be highly efficient and adapted to inter- and cross-species predictions. Furthermore, the ability to generate comprehensive interactomes has enabled the appraisal of each PPI in the context of all predictions leading to further improvements in classification performance in the face of extreme class imbalance using the Reciprocal Perspective (RP) framework. We here describe the PIPE4 algorithm. Adaptation of the PIPE3/MP-PIPE sequence preprocessing step led to upwards of 50x speedup and the new Similarity Weighted Score appropriately normalizes for window frequency when applied to any inter- and cross-species prediction schemas. Comprehensive interactomes for three prediction schemas are generated: (1) cross-species predictions, where Arabidopsis thaliana is used as a proxy to predict the comprehensive Glycine max interactome, (2) inter-species predictions between Homo sapiens-HIV1, and (3) a combined schema involving both cross- and inter-species predictions, where both Arabidopsis thaliana and Caenorhabditis elegans are used as proxy species to predict the interactome between Glycine max (the soybean legume) and Heterodera glycines (the soybean cyst nematode). Comparing PIPE4 with the state-of-the-art resulted in improved performance, indicative that it should be the method of choice for complex PPI prediction schemas.

Neonicotinoid Seed Treatments Have Significant Non-target Effects on Phyllosphere and Soil Bacterial Communities.

The phyllosphere and soil are dynamic habitats for microbial communities. Non-pathogenic microbiota, including leaf and soil beneficial bacteria, plays a crucial role in plant growth and health, as well as in soil fertility and organic matter production. In sustainable agriculture, it is important to understand the composition of these bacterial communities, their changes in response to disturbances, and their resilience to agricultural practices. Widespread pesticide application may have had non-target impacts on these beneficial microorganisms. Neonicotinoids are a family of systemic insecticides being vastly used to control soil and foliar pests in recent decades. A few studies have demonstrated the long-term and non-target effects of neonicotinoids on agroecosystem microbiota, but the generality of these findings remains unclear. In this study, we used 16S rRNA gene amplicon sequencing to characterize the effects of neonicotinoid seed treatment on soil and phyllosphere bacterial community diversity, composition and temporal dynamics in a 3-year soybean/corn rotation in Quebec, Canada. We found that habitat, host species and time are stronger drivers of variation in bacterial composition than neonicotinoid application. They, respectively, explained 37.3, 3.2, and 2.9% of the community variation. However, neonicotinoids did have an impact on bacterial community structure, especially on the taxonomic composition of soil communities (2.6%) and over time (2.4%). They also caused a decrease in soil alpha diversity in the middle of the growing season. While the neonicotinoid treatment favored some bacterial genera known as neonicotinoid biodegraders, there was a decline in the relative abundance of some potentially beneficial soil bacteria in response to the pesticide application. Some of these bacteria, such as the plant growth-promoting rhizobacteria and the bacteria involved in the nitrogen cycle, are vital for plant growth and improve soil fertility. Overall, our results indicate that neonicotinoids have non-target effects on phyllosphere and soil bacterial communities in a soybean-corn agroecosystem. Exploring the interactions among bacteria and other organisms, as well as the bacterial functional responses to the pesticide treatment, may enhance our understanding of these non-target effects and help us adapt agricultural practices to control these impacts.

Transcriptome analysis of Globodera pallida from the susceptible host Solanum tuberosum or the resistant plant Solanum sisymbriifolium.

A transcriptome analysis of G. pallida juveniles collected from S. tuberosum or S. sisymbriifolium 24 h post infestation was performed to provide insights into the parasitic process of this nematode. A total of 41 G. pallida genes were found to be significantly differentially expressed when parasitizing the two plant species. Among this set, 12 were overexpressed when G. pallida was parasitizing S. tuberosum and 29 were overexpressed when parasitizing S. sisymbriifolium. Out of the 12 genes, three code for secretory proteins; one is homologous to effector gene Rbp-4, the second is an uncharacterized protein with a signal peptide sequence, and the third is an ortholog of a Globodera rostochiensis effector belonging to the 1106 effector family. Other overexpressed genes from G. pallida when parasitizing S. tuberosum were either unknown, associated with a stress or defense response, or associated with sex differentiation. Effector genes namely Eng-1, Cathepsin S-like cysteine protease, cellulase, and two unknown genes with secretory characteristics were over expressed when G. pallida was parasitizing S. sisymbriifolium relative to expression from S. tuberosum. Our findings provide insight into gene regulation of G. pallida while infecting either the trap crop S. sisymbriifolium or the susceptible host, S. tuberosum.