Precision phenotyping of pines for resistance against Fusarium circinatum

Abstract

Pine trees are globally important agricultural and ecological species, with Pinus patula being a key species in South Africa’s economically valuable forestry industry. Natural and commercial pine forests are significantly threatened by the pitch canker fungus, Fusarium circinatum. In the nursery, F. circinatum infection manifests as a wilt, thereby hindering pine production. Since Pinus spp. exhibit a wide range of inter- and intra-specific variation in resistance, insights into the pine resistance phenotype and the pine-F. circinatum interaction may aid in producing resistant planting stock and mitigating F. circinatum damage. Currently, this pine variation is quantified by visually measuring lesion lengths, which is predictive of the pine response phenotype. Visual estimates, however, are limited by subjectivity, inaccuracy, and low reproducibility. This highlights the requirement for a more reliable, precise disease phenotyping method. Quantifying fungal load in infected plants holds promise for greater sensitivity, specificity, timeliness, and high-throughput. To the best of our knowledge, this phenotyping method has not been explored for quantifying F. circinatum resistance in pines. Therefore, our study aimed to develop and optimise a quantitative real-time PCR (qPCR) precision phenotyping tool to quantify the fungal load of F. circinatum in different Pinus patula (susceptible) and Pinus tecunumanii high (intermediately resistant) and low elevation (resistant) families. We also assessed the application of this tool on F1 hybrid pine genotypes of unknown resistance. As an initial approach to estimate resistance, lesion lengths of plants artificially inoculated with F. circinatum were measured weekly for 8 weeks to calculate percentage live stem. The visible lesion length measurements showed the expected differences in resistance for the pine species as well as variation within and between each pine species and family. Next, the fungal load from artificially inoculated pine samples was quantified at 1, 2, 3, 7, 14, 21, and 28 days post-inoculation (dpi) to calculate percentage fungal load, which was compared to percentage live stem. To assess the efficacy of our precision phenotyping tool, we identified correlations between the median percentage live stem and median percentage fungal load. Strong correlations were observed between the median percentage fungal load and median percentage live stem at 3 and 49 dpi, respectively, (0.66, p<0.01) as well as at 28 and 21 dpi, respectively (−0.76, p<0.01). We also observed variation between the F1 genotypes, which allowed them to be classified into three phenotypic groups: susceptible, intermediately resistant, and resistant. We concluded that this phenotyping tool has the potential for phenotypic classes to be developed on a continuum from highly susceptible to highly resistant, to timely and accurately class different pine individuals. This study represents the first step in developing an optimal qPCR precision phenotyping tool to capture the subtle variation in pine responses to F. circinatum. Future studies would involve optimising this tool further and using it to help characterise the genetic architecture of the pine defence response and identify quantitative trait loci governing resistance against F. circinatum.