SMRT-AgRenSeq-d in potato (Solanum tuberosum) as a method to identify candidates for the nematode resistance Gpa5.

Potato is the third most important food crop in the world. Diverse pathogens threaten sustainable crop production but can be controlled, in many cases, through the deployment of disease resistance genes belonging to the family of nucleotide-binding, leucine-rich-repeat (NLR) genes. To identify effective disease resistance genes in established varieties, we have successfully established SMRT-AgRenSeq in tetraploid potatoes and have further enhanced the methodology by including dRenSeq in an approach that we term SMR-AgRenSeq-d. The inclusion of dRenSeq enables the filtering of candidates after the association analysis by establishing a presence/absence matrix across resistant and susceptible varieties that is translated into an F1 score. Using a SMRT-RenSeq-based sequence representation of the NLRome from the cultivar Innovator, SMRT-AgRenSeq-d analyses reliably identified the late blight resistance benchmark genes Rpi-R1, Rpi-R2-like, Rpi-R3a, and Rpi-R3b in a panel of 117 varieties with variable phenotype penetrations. All benchmark genes were identified with an F1 score of 1, which indicates absolute linkage in the panel. This method also identified nine strong candidates for Gpa5 that controls the potato cyst nematode (PCN) species Globodera pallida (pathotypes Pa2/3). Assuming that NLRs are involved in controlling many types of resistances, SMRT-AgRenSeq-d can readily be applied to diverse crops and pathogen systems.

HISS: Snakemake-based workflows for performing SMRT-RenSeq assembly, AgRenSeq and dRenSeq for the discovery of novel plant disease resistance genes.

BACKGROUND: In the ten years since the initial publication of the RenSeq protocol, the method has proved to be a powerful tool for studying disease resistance in plants and providing target genes for breeding programmes. Since the initial publication of the methodology, it has continued to be developed as new technologies have become available and the increased availability of computing power has made new bioinformatic approaches possible. Most recently, this has included the development of a k-mer based association genetics approach, the use of PacBio HiFi data, and graphical genotyping with diagnostic RenSeq. However, there is not yet a unified workflow available and researchers must instead configure approaches from various sources themselves. This makes reproducibility and version control a challenge and limits the ability to perform these analyses to those with bioinformatics expertise. RESULTS: Here we present HISS, consisting of three workflows which take a user from raw RenSeq reads to the identification of candidates for disease resistance genes. These workflows conduct the assembly of enriched HiFi reads from an accession with the resistance phenotype of interest. A panel of accessions both possessing and lacking the resistance are then used in an association genetics approach (AgRenSeq) to identify contigs positively associated with the resistance phenotype. Candidate genes are then identified on these contigs and assessed for their presence or absence in the panel with a graphical genotyping approach that uses dRenSeq. These workflows are implemented via Snakemake, a python-based workflow manager. Software dependencies are either shipped with the release or handled with conda. All code is freely available and is distributed under the GNU GPL-3.0 license. CONCLUSIONS: HISS provides a user-friendly, portable, and easily customised approach for identifying novel disease resistance genes in plants. It is easily installed with all dependencies handled internally or shipped with the release and represents a significant improvement in the ease of use of these bioinformatics analyses.

Rust expression browser: an open source database for simultaneous analysis of host and pathogen gene expression profiles with expVIP.

BACKGROUND: Transcriptomics is being increasingly applied to generate new insight into the interactions between plants and their pathogens. For the wheat yellow (stripe) rust pathogen (Puccinia striiformis f. sp. tritici, Pst) RNA-based sequencing (RNA-Seq) has proved particularly valuable, overcoming the barriers associated with its obligate biotrophic nature. This includes the application of RNA-Seq approaches to study Pst and wheat gene expression dynamics over time and the Pst population composition through the use of a novel RNA-Seq based surveillance approach called "field pathogenomics". As a dual RNA-Seq approach, the field pathogenomics technique also provides gene expression data from the host, giving new insight into host responses. However, this has created a wealth of data for interrogation. RESULTS: Here, we used the field pathogenomics approach to generate 538 new RNA-Seq datasets from Pst-infected field wheat samples, doubling the amount of transcriptomics data available for this important pathosystem. We then analysed these datasets alongside 66 RNA-Seq datasets from four Pst infection time-courses and 420 Pst-infected plant field and laboratory samples that were publicly available. A database of gene expression values for Pst and wheat was generated for each of these 1024 RNA-Seq datasets and incorporated into the development of the rust expression browser ( http://www.rust-expression.com ). This enables for the first time simultaneous 'point-and-click' access to gene expression profiles for Pst and its wheat host and represents the largest database of processed RNA-Seq datasets available for any of the three Puccinia wheat rust pathogens. We also demonstrated the utility of the browser through investigation of expression of putative Pst virulence genes over time and examined the host plants response to Pst infection. CONCLUSIONS: The rust expression browser offers immense value to the wider community, facilitating data sharing and transparency and the underlying database can be continually expanded as more datasets become publicly available.

Genomic Investigation of the Strawberry Pathogen Phytophthora fragariae Indicates Pathogenicity Is Associated With Transcriptional Variation in Three Key Races.

The oomycete Phytophthora fragariae is a highly destructive pathogen of cultivated strawberry (Fragaria × ananassa), causing the root rotting disease, "red core". The host-pathogen interaction has a well described gene-for-gene resistance relationship, but to date neither candidate avirulence nor resistance genes have been identified. We sequenced a set of American, Canadian, and United Kingdom isolates of known race type, along with three representatives of the closely related pathogen of the raspberry (Rubus idaeus), P. rubi, and found a clear population structure, with a high degree of nucleotide divergence seen between some race types and abundant private variation associated with race types 4 and 5. In contrast, between isolates defined as United Kingdom races 1, 2, and 3 (UK1-2-3) there was no evidence of gene loss or gain; or the presence of insertions/deletions (INDELs) or Single Nucleotide Polymorphisms (SNPs) within or in proximity to putative pathogenicity genes could be found associated with race variation. Transcriptomic analysis of representative UK1-2-3 isolates revealed abundant expression variation in key effector family genes associated with pathogen race; however, further long read sequencing did not reveal any long range polymorphisms to be associated with avirulence to race UK2 or UK3 resistance, suggesting either control in trans or other stable forms of epigenetic modification modulating gene expression. This work reveals the combined power of population resequencing to uncover race structure in pathosystems and in planta transcriptomic analysis to identify candidate avirulence genes. This work has implications for the identification of putative avirulence genes in the absence of associated expression data and points toward the need for detailed molecular characterisation of mechanisms of effector regulation and silencing in oomycete plant pathogens.