Quantitative Disease Resistance to White Pine Blister Rust at Southwestern White Pine’s (Pinus strobiformis) Northern Range

White pine blister rust, caused by the non-native, invasive fungal pathogen Cronartium ribicola, is a significant cause of mortality in white pines (Pinus subgenus Strobus) in North America. Along with climate-driven range contraction, mortality from blister rust can seriously impact the abundance and distribution of the nine white pine species native to the United States and Canada. Very little evaluation of this disease in southwestern white pine (Pinus strobiformis) has been previously undertaken, but genetic resistance to the disease has been documented, including major gene resistance (MGR) conferred by a dominant R gene. Data is emerging suggesting that the species also has quantitative disease resistance (QR). Our results suggest QR occurs at low frequency, with perhaps 10% of trees having a moderate level (> 35% survival). We assessed progeny arrays from 40 P. strobiformis families (1873 seedlings), originating from three populations, inoculated with C. ribicola. Subsequently, the seedlings were assessed for signs, symptoms and resulting impact in a common garden trial over a 7.5-year period to determine the types and frequency of resistance in a portion of this species’ range. There was a high incidence of both stem symptoms and mortality in the P. strobiformis families tested, and families ranged in survival from 0 to 84.6%. Three families had > 70% survival, representing perhaps the highest documented QR to date in a North American white pine species. Approximately 29.1% of the 441 surviving seedlings showed no stem symptoms, and of the approximately 70.8% of seedlings surviving with infections only few (24 of 316) had infections of moderate to high severity. QR traits associated with improved survival were primarily related to lower severity of infection, a reduced number of stem symptoms, and an increased number of bark reactions. Despite the high overall susceptibility, the presence of QR appears to be at a frequency and level useful to forest managers involved in restoration and reforestation efforts.

Breeding With Major and Minor Genes: Genomic Selection for Quantitative Disease Resistance

Disease resistance in plants is mostly quantitative, with both major and minor genes controlling resistance. This research aimed to optimize genomic selection (GS) models for use in breeding programs that are needed to select both major and minor genes for resistance. In this study, stripe rust (Puccinia striiformis Westend. f. sp. tritici Erikss.) of wheat (Triticum aestivum L.) was used as a model for quantitative disease resistance. The quantitative nature of stripe rust is usually phenotyped with two disease traits, infection type (IT) and disease severity (SEV). We compared two types of training populations composed of 2,630 breeding lines (BLs) phenotyped in single-plot trials from 4 years (2016–2020) and 475 diversity panel (DP) lines from 4 years (2013–2016), both across two locations. We also compared the accuracy of models using four different major gene markers and genome-wide association study (GWAS) markers as fixed effects. The prediction models used 31,975 markers that are replicated 50 times using a 5-fold cross-validation. We then compared GS models using a marker-assisted selection (MAS) to compare the prediction accuracy of the markers alone and in combination. GS models had higher accuracies than MAS and reached an accuracy of 0.72 for disease SEV. The major gene and GWAS markers had only a small to nil increase in the prediction accuracy more than the base GS model, with the highest accuracy increase of 0.03 for the major markers and 0.06 for the GWAS markers. There was a statistical increase in the accuracy using the disease SEV trait, BLs, population type, and combining years. There was also a statistical increase in the accuracy using the major markers in the validation sets as the mean accuracy decreased. The inclusion of fixed effects in low prediction scenarios increased the accuracy up to 0.06 for GS models using significant GWAS markers. Our results indicate that GS can accurately predict quantitative disease resistance in the presence of major and minor genes.

Breeding with Major and Minor Genes: Genomic Selection for Quantitative Disease Resistance

Most disease resistance in plants is quantitative, with both major and minor genes controlling resistance. This research aimed to optimize genomic selection (GS) models for use in breeding programs needing to select both major and minor genes for resistance. In this experiment, stripe rust (Puccinia striiformis Westend. f. sp. tritici Erikss.) of wheat (Triticum aestivum L.) was used as a model for quantitative disease resistance. The quantitative nature of stripe rust is usually phenotyped with two disease traits, infection type and disease severity. We compared two types of training populations composed of 2,630 breeding lines phenotyped in single plot trials from four years (2016-2020) and 475 diversity panel lines from four years (2013-2016), both across two locations. We also compared the accuracy of models with four different major gene markers and genome-wide association (GWAS) markers as fixed effects. The prediction models used 31,975 markers replicated 50 times using 5-fold cross-validation. We then compared the GS models with marker-assisted selection to compare the prediction accuracy of the markers alone and in combination. The GS models had higher accuracies than marker-assisted selection and reached an accuracy of 0.72 for disease severity. The major gene and GWAS markers had only a small to zero increase in prediction accuracy over the base GS model, with the highest accuracy increase of 0.03 for major markers and 0.06 for GWAS markers. There was a statistical increase in accuracy by using the disease severity trait, the breeding lines, population type, and by combing years. There was also a statistical increase in accuracy using major markers within the validation sets as the mean accuracy decreased. The inclusion of fixed effects in low prediction scenarios increased accuracy up to 0.06 for GS models using significant GWAS markers. Our results indicate that GS can accurately predict quantitative disease resistance in the presence of major and minor genes.

Identification of Quantitative Disease Resistance Loci Toward Four Pythium Species in Soybean

In this study, four recombinant inbred line (RIL) soybean populations were screened for their response to infection by Pythium sylvaticum, Pythium irregulare, Pythium oopapillum, and Pythium torulosum. The parents, PI 424237A, PI 424237B, PI 408097, and PI 408029, had higher levels of resistance to these species in a preliminary screening and were crossed with “Williams,” a susceptible cultivar. A modified seed rot assay was used to evaluate RIL populations for their response to specific Pythium species selected for a particular population based on preliminary screenings. Over 2500 single-nucleotide polymorphism (SNP) markers were used to construct chromosomal maps to identify regions associated with resistance to Pythium species. Several minor and large effect quantitative disease resistance loci (QDRL) were identified including one large effect QDRL on chromosome 8 in the population of PI 408097 × Williams. It was identified by two different disease reaction traits in P. sylvaticum, P. irregulare, and P. torulosum. Another large effect QDRL was identified on chromosome 6 in the population of PI 408029 × Williams, and conferred resistance to P. sylvaticum and P. irregulare. These large effect QDRL will contribute toward the development of improved soybean cultivars with higher levels of resistance to these common soil-borne pathogens.

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.

Auxin Profiling and GmPIN Expression in Phytophthora sojae−Soybean Root Interactions

Auxin (indole-3-acetic acid, IAA) has been implicated as a susceptibility factor in both beneficial and pathogenic molecular plant−microbe interactions. Previous studies have identified a large number of auxin-related genes underlying quantitative disease resistance loci (QDRLs) for Phytophthora sojae. Thus, we hypothesized that auxin may be involved the P. sojae−soybean interaction. The levels of IAA and related metabolites were measured in mycelia and media supernatant as well as in mock and inoculated soybean roots in a time course assay. The expression of 11 soybean Pin-formed (GmPIN) auxin efflux transporter genes was also examined. Tryptophan, an auxin precursor, was detected in the P. sojae mycelia and media supernatant. During colonization of roots, levels of IAA and related metabolites were significantly higher in both moderately resistant Conrad and moderately susceptible Sloan inoculated roots compared with mock controls at 48 h postinoculation (hpi) in one experiment and at 72 hpi in a second, with Sloan accumulating higher levels of the auxin catabolite IAA-Ala than Conrad. Additionally, one GmPIN at 24 hpi, one at 48 hpi, and three at 72 hpi had higher expression in inoculated compared with the mock control roots in Conrad. The ability of resistant cultivars to cope with auxin accumulation may play an important role in quantitative disease resistance. Levels of jasmonic acid (JA), another plant hormone associated with defense responses, were also higher in inoculated roots at these same time points, suggesting that JA also plays a role during the later stages of infection.