In-vitro anti-fungal assay and association analysis reveal a role for the Pinus monticola PR10 gene (PmPR10-3.1) in quantitative disease resistance to white pine blister rust

Pathogenesis-related (PR) proteins play important roles in plant defense response. However, functional investigation of PR10 genes is still limited and their physiological roles have not been conclusively characterized in biological processes of conifer trees. Here we identified multiple novel members in the western white pine (Pinus monticola) PmPR10 family by bioinformactic mining available transcriptomic data. Phylogenetic analysis of protein sequences revealed four PR10 and two PR10-like clusters with a high synteny across different species of five-needle pines. Of ten PmPR10 genes, PmPR10-3.1 was selected and expressed in Escherichia coli. The purified recombinant protein exhibited inhibitory effects on spore hyphal growth of fungal pathogens C. ribicola, Phoma exigua and P. argillacea by in-vitro antifungal analysis. Genetic variation analysis detected a total of 21 single nucleotide polymorphisms (SNPs) within PmPR10-3.1 in a collection of P. monticola seed families. A nonsynonymous SNP (t178g) showed significant association with relative levels of quantitative disease resistance (QDR), explaining about 8.7% of phenotypic variation as the peak value across all SNPs. Our results provide valuable insight into the genetic architecture underlying P. monticola QDR, and imply that PmPR10-3.1 may function as an important component in conifer basal immunity for non-specific resistance to a wide spectrum of pathogens.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Additional file 4: Figure S3. of Transcriptome analysis of the white pine blister rust pathogen Cronartium ribicola: de novo assembly, expression profiling, and identification of candidate effectors

Scatter plot of relative levels of transcript expression as measured by RNA-seq and qRT-PCR. Relative levels of transcript expression are presented as log2 (fold-change). Both RNA-seq and qRT-PCR data were analyzed based on three biological replicates. Pearson coefficient correlation analysis between data from RNA-seq and qRT-PCR shows R2 = 0.94 (p-value < 0.00001). (XLSX 6464 kb

Locations of seed families and geographic distribution of genetic subgroups.

<p>A total of 124 seed families were samples in 16 seed (sub) zones. Each pie chart represents the proportion of genetic subgroups (GG-1 to GG-9) as identified by STRUCTURE in a given area.</p

Principal coordinate analysis (PCoA) of whitebark pine populations using GenAlEx version 6.5.

<p>Seed zone (subzone) designations were listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0167986#pone.0167986.s001" target="_blank">S1 Table</a>. Three regions are shown by colors, Green: British Columbia (BC), Canada; red: Washington State (WA); blue, Oregon state (OR), USA.</p

Genetic Diversity and Population Structure of Whitebark Pine (<i>Pinus albicaulis</i> Engelm.) in Western North America

<div><p>Whitebark pine (WBP, <i>Pinus albicaulis</i> Engelm.) is an endangered conifer species due to heavy mortality from white pine blister rust (WPBR, caused by <i>Cronartium ribicola</i>) and mountain pine beetle (<i>Dendroctonus ponderosae</i>). Information about genetic diversity and population structure is of fundamental importance for its conservation and restoration. However, current knowledge on the genetic constitution and genomic variation is still limited for WBP. In this study, an integrated genomics approach was applied to characterize seed collections from WBP breeding programs in western North America. RNA-seq analysis was used for <i>de novo</i> assembly of the WBP needle transcriptome, which contains 97,447 protein-coding transcripts. Within the transcriptome, single nucleotide polymorphisms (SNPs) were discovered, and more than 22,000 of them were non-synonymous SNPs (ns-SNPs). Following the annotation of genes with ns-SNPs, 216 ns-SNPs within candidate genes with putative functions in disease resistance and plant defense were selected to design SNP arrays for high-throughput genotyping. Among these SNP loci, 71 were highly polymorphic, with sufficient variation to identify a unique genotype for each of the 371 individuals originating from British Columbia (Canada), Oregon and Washington (USA). A clear genetic differentiation was evident among seed families. Analyses of genetic spatial patterns revealed varying degrees of diversity and the existence of several genetic subgroups in the WBP breeding populations. Genetic components were associated with geographic variables and phenotypic rating of WPBR disease severity across landscapes, which may facilitate further identification of WBP genotypes and gene alleles contributing to local adaptation and quantitative resistance to WPBR. The WBP genomic resources developed here provide an invaluable tool for further studies and for exploitation and utilization of the genetic diversity preserved within this endangered conifer and other five-needle pines.</p></div

Bayesian clustering analysis for estimation of population structure using program STRUCTURE.

<p>ΔK plots by Evanno’s method. Graph of delta K values (y-axe) against assumed sub-populations (x-axe) showing the ideal number of groups present in a set of whitebark pine seed families collected for a breeding program. Genotypic data were collected for 71 ns-SNP loci across all genotyped individuals. The highest peak shows the best K = 9.</p

Association of whitebark pine genetic subgroups with relative levels of disease severity post inoculation by <i>Cronartium ribicola</i>.

<p>(a) Mean values of relative levels of disease severity were shown for seedlings with nine genetic subgroups (GG1 to GG9). Standard error (SE) was calculated based on the entire subpopulation of each genetic subgroup. Statistical difference is significant (T-test and One-way ANOVA test, * <i>P</i> < 0.05, ** <i>P</i> <0.01) between subgroups labelled with different letters.</p

Phylogenetic relationships among whitebark pine populations.

<p>Nei’s standard genetic distances with sample size correction (Dst) (Nei 1972) were calculated using genotypic data of 71 SNP loci. A consensus dendrogram was constructed using the unweighted pair-group method with arithmetic mean (UPGMA). Bootstrap values are indicated on the nodes as percentages as tested with 1000 bootstrap replicates.</p