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.

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.

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 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.

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.