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 co-expressed 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.

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 on genome resources and genotyping techniques useful for nematologists who are thinking about initiating population genomic or genome sequencing projects. This review also intends to foster the development of population genomics in plant-parasitic nematodes through the highlighting of 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, the pathogenicity and the evolutionary potential of plant-parasitic nematodes.

AbstractmicroRNAs (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.

There is a global need for identifying viral pathogens, as well as for providing certified clean plant materials, in order to limit the spread of viral diseases. A key component of management programs for viral-like diseases is having a diagnostic tool that is quick, reliable, inexpensive, and easy to use. We have developed and validated a dsRNA-based nanopore sequencing protocol as a reliable method for detecting viruses and viroids in grapevines. We compared our method, which we term direct-cDNA sequencing from dsRNA (dsRNAcD), to direct RNA sequencing from rRNA-depleted total RNA (rdTotalRNA), and found that it provided more viral reads from infected samples. Indeed, dsRNAcD was able to detect all of the viruses and viroids detected using Illumina MiSeq sequencing (dsRNA-MiSeq). Furthermore, dsRNAcD sequencing was also able to detect low-abundance viruses that rdTotalRNA sequencing failed to detect. Additionally, rdTotalRNA sequencing resulted in a false-positive viroid identification due to the misannotation of a host-driven read. Two taxonomic classification workflows, DIAMOND & MEGAN (DIA & MEG) and Centrifuge & Recentrifuge (Cent & Rec), were also evaluated for quick and accurate read classification. Although the results from both workflows were similar, we identified pros and cons for both workflows. Our study shows that dsRNAcD sequencing and the proposed data analysis workflows are suitable for consistent detection of viruses and viroids, particularly in grapevines where mixed viral infections are common.