Walnut Witches'-Broom Disease Threatens Butternut Restoration Efforts in Indiana.

Due to the devastating effects of butternut canker disease, efforts to protect the endangered butternut (Juglans cinerea) tree through resistance breeding have been a primary focus of forest restoration efforts. Walnut witches'-broom (WWB) disease poses a serious threat to these restoration efforts. This study sought to confirm the presence of the WWB disease phytoplasma, 'Candidatus Phytoplasma pruni', in butternut research plantings in Indiana using molecular methods and Sanger sequencing and to identify butternut families affected by the disease. We also sought to better understand the incidence of the WWB phytoplasma in asymptomatic trees and asymptomatic branches of symptomatic trees to better direct management decisions. Sanger sequencing confirmed the presence of the WWB phytoplasma in the butternut restoration plantings, the first confirmation in Indiana based on sequencing to our knowledge, in both symptomatic and some asymptomatic trees. In addition, the WWB phytoplasma was detected in asymptomatic branches of symptomatic trees, indicating that phytoplasma infection is not necessarily localized to symptomatic tissues in a tree. Trees with positive molecular confirmation of the WWB phytoplasma consisted of 23 different butternut families and one family of Japanese walnut (J. ailantifolia), which is considered to be one of the most susceptible species to WWB disease. Based on these findings, future studies should prioritize identifying the hybridity and pedigrees of families and their susceptibility to WWB disease to aid in butternut restoration efforts.

Machine Learning-Based Presymptomatic Detection of Rice Sheath Blight Using Spectral Profiles.

Early detection of plant diseases, prior to symptom development, can allow for targeted and more proactive disease management. The objective of this study was to evaluate the use of near-infrared (NIR) spectroscopy combined with machine learning for early detection of rice sheath blight (ShB), caused by the fungus Rhizoctonia solani. We collected NIR spectra from leaves of ShB-susceptible rice (Oryza sativa L.) cultivar, Lemont, growing in a growth chamber one day following inoculation with R. solani, and prior to the development of any disease symptoms. Support vector machine (SVM) and random forest, two machine learning algorithms, were used to build and evaluate the accuracy of supervised classification-based disease predictive models. Sparse partial least squares discriminant analysis was used to confirm the results. The most accurate model comparing mock-inoculated and inoculated plants was SVM-based and had an overall testing accuracy of 86.1% (N = 72), while when control, mock-inoculated, and inoculated plants were compared the most accurate SVM model had an overall testing accuracy of 73.3% (N = 105). These results suggest that machine learning models could be developed into tools to diagnose infected but asymptomatic plants based on spectral profiles at the early stages of disease development. While testing and validation in field trials are still needed, this technique holds promise for application in the field for disease diagnosis and management.

Distinctive Gene Expression Patterns Define Endodormancy to Ecodormancy Transition in Apricot and Peach.

Dormancy is a physiological state that plants enter for winter hardiness. Environmental-induced dormancy onset and release in temperate perennials coordinate growth cessation and resumption, but how the entire process, especially chilling-dependent dormancy release and flowering, is regulated remains largely unclear. We utilized the transcriptome profiles of floral buds from fall to spring in apricot (Prunus armeniaca) genotypes with contrasting bloom dates and peach (Prunus persica) genotypes with contrasting chilling requirements (CR) to explore the genetic regulation of bud dormancy. We identified distinct gene expression programming patterns in endodormancy and ecodormancy that reproducibly occur between different genotypes and species. During the transition from endo- to eco-dormancy, 1,367 and 2,102 genes changed in expression in apricot and peach, respectively. Over 600 differentially expressed genes were shared in peach and apricot, including three DORMANCY ASSOCIATED MADS-box (DAM) genes (DAM4, DAM5, and DAM6). Of the shared genes, 99 are located within peach CR quantitative trait loci, suggesting these genes as candidates for dormancy regulation. Co-expression and functional analyses revealed that distinctive metabolic processes distinguish dormancy stages, with genes expressed during endodormancy involved in chromatin remodeling and reproduction, while the genes induced at ecodormancy were mainly related to pollen development and cell wall biosynthesis. Gene expression analyses between two Prunus species highlighted the conserved transcriptional control of physiological activities in endodormancy and ecodormancy and revealed genes that may be involved in the transition between the two stages.

Fourier-transform infrared (FT-IR) spectroscopy analysis discriminates asymptomatic and symptomatic Norway spruce trees.

Conifer trees, including Norway spruce, are threatened by fungi of the Heterobasidion annosum species complex, which severely affect timber quality and cause economic losses to forest owners. The timely detection of infected trees is complicated, as the pathogen resides within the heartwood and sapwood of infected trees. The presence of the disease and the extent of the wood decay often becomes evident only after tree felling. Fourier-transform infrared (FT-IR) spectroscopy is a potential method for non-destructive sample analysis that may be useful for identifying infected trees in this pathosystem. We performed FT-IR analysis of 18 phloem, 18 xylem, and 18 needle samples from asymptomatic and symptomatic Norway spruce trees. FT-IR spectra from 1066 - 912 cm-1 could be used to distinguish phloem, xylem, and needle tissue extracts. FT-IR spectra collected from xylem and needle extracts could also be used to discriminate between asymptomatic and symptomatic trees using spectral bands from 1657 - 994 cm-1 and 1104 - 994 cm-1, respectively. A partial least squares regression model predicted the concentration of condensed tannins, a defense-related compound, in phloem of asymptomatic and symptomatic trees. This work is the first to show that FT-IR spectroscopy can be used for the identification of Norway spruce trees naturally infected with Heterobasidion spp.

Association of the phenylpropanoid pathway with dormancy and adaptive trait variation in apricot (Prunus armeniaca).

Trees use many mechanisms to adapt and respond to stressful conditions. The phenylpropanoid pathway in particular is known to be associated with a diverse suite of plant stress responses. In this study, we explored the relationship between the phenylpropanoid pathway metabolite production, gene expression and adaptive trait variation associated with floral bud reactivation during and following dormancy in Prunus armeniaca L. (apricot). Concentrations of eight phenylpropanoid metabolites were measured during chill accumulation and at developmental stages corresponding to the emergence of sepals and petals in floral buds of varieties that differ phenotypically in bloom date (BD). A significant interaction effect of chill hours and BD phenotype on the concentration of each of the compounds was observed (mixed analysis of variance, P < 0.05), with the concentration of most phenylpropanoid metabolites dropping precipitously when sepals and petals emerged. While phenylpropanoid biosynthetic gene expression patterns were more variable in general, expression changed over time and was impacted, although to a lesser degree, by BD phenotype. Furthermore, separation of BD phenotypic groups was most pronounced when early and late BD varieties were at different developmental stages, i.e., 800 chill hours. Taken together, these results suggest that the phenylpropanoid pathway is associated with floral bud reactivation in apricot. Furthermore, we show that the phenylpropanoid pathway is also impacted by phenological trait variation associated with dormancy. A better understanding of how apricot and other perennial tree species respond and adapt to environmental perturbations will be critical for improvement programs aimed at identifying and breeding trees more suitable for rapidly changing environments.

Corrigendum: Identification of Quercus agrifolia (coast live oak) resistant to the invasive pathogen Phytophthora ramorum in native stands using Fourier-transform infrared (FT-IR) spectroscopy.

[This corrects the article on p. 521 in vol. 5, PMID: 25352852.].

Constitutive phenolic biomarkers identify naïve Quercus agrifolia resistant to Phytophthora ramorum, the causal agent of sudden oak death.

Sudden oak death, caused by the invasive pathogen Phytophthora ramorum Werres, de Cock & Man in't Veld, can be deadly for Quercus agrifolia Neé (coast live oak, CLO). However, resistant trees have been observed in natural populations. The objective of this study was to examine if pre-attack (constitutive) levels of phenolic compounds can be used as biomarkers to identify trees likely to be resistant. Naïve trees were selected from a natural population and phloem was sampled for analysis of constitutive phenolics. Following P. ramorum inoculation, trees were phenotyped to determine disease susceptibility and constitutive phenolic biomarkers of resistance were identified. Seasonal variation in phloem phenolics was also assessed in a subset of non-inoculated trees. Four biomarkers, including myricitrin and three incompletely characterized flavonoids, together correctly classified 80% of trees. Biomarker levels were then used to predict survival of inoculated CLO and the proportion of resistant trees within a subset of non-inoculated trees from the same population. Levels of five phenolics were significantly affected by season, but with no pronounced variation in average levels among seasons. These results suggest that pre-infection levels of specific phenolic compounds (i.e., biomarkers) can identify trees naturally resistant to this invasive forest pathogen. Knowledge of resistant trees within natural populations may be useful for conserving and breeding resistant trees and for disease management.

Application of Infrared and Raman Spectroscopy for the Identification of Disease Resistant Trees.

New approaches for identifying disease resistant trees are needed as the incidence of diseases caused by non-native and invasive pathogens increases. These approaches must be rapid, reliable, cost-effective, and should have the potential to be adapted for high-throughput screening or phenotyping. Within the context of trees and tree diseases, we summarize vibrational spectroscopic and chemometric methods that have been used to distinguish between groups of trees which vary in disease susceptibility or other important characteristics based on chemical fingerprint data. We also provide specific examples from the literature of where these approaches have been used successfully. Finally, we discuss future application of these approaches for wide-scale screening and phenotyping efforts aimed at identifying disease resistant trees and managing forest diseases.

Identification of Quercus agrifolia (coast live oak) resistant to the invasive pathogen Phytophthora ramorum in native stands using Fourier-transform infrared (FT-IR) spectroscopy.

Over the last two decades coast live oak (CLO) dominance in many California coastal ecosystems has been threatened by the alien invasive pathogen Phytophthora ramorum, the causal agent of sudden oak death. In spite of high infection and mortality rates in some areas, the presence of apparently resistant trees has been observed, including trees that become infected but recover over time. However, identifying resistant trees based on recovery alone can take many years. The objective of this study was to determine if Fourier-transform infrared (FT-IR) spectroscopy, a chemical fingerprinting technique, can be used to identify CLO resistant to P. ramorum prior to infection. Soft independent modeling of class analogy identified spectral regions that differed between resistant and susceptible trees. Regions most useful for discrimination were associated with carbonyl group vibrations. Additionally, concentrations of two putative phenolic biomarkers of resistance were predicted using partial least squares regression; >99% of the variation was explained by this analysis. This study demonstrates that chemical fingerprinting can be used to identify resistance in a natural population of forest trees prior to infection with a pathogen. FT-IR spectroscopy may be a useful approach for managing forests impacted by sudden oak death, as well as in other situations where emerging or existing forest pests and diseases are of concern.

Mycorrhizal colonization of Palafoxia feayi (Asteraceae) in a pyrogenic ecosystem.

Although a number of factors have predictable effects on mycorrhizal colonization, determining generalized patterns for some variables have remained elusive. In particular, fire has been identified as a major event that may influence plant-mycorrhiza interactions, yet efforts to date have yielded contradictory results. Here, we assess the impact of fire on mycorrhizal colonization in Palafoxia feayi, a plant commonly found in the fireswept, nutrient-poor scrub community of central Florida. We determined soil nutrient conditions and percent colonization patterns for plants growing in replicate plots that were burned 1 to 15 years previously. The results showed a negative relationship between mycorrhizal colonization and time since fire, but there was no effect of fire return interval (lapsed time between successive fires). Soil nutrient analyses corroborated previous studies and showed no change in soil nutrients following fire. In contrast to previous studies of mycorrhizal colonization in Florida scrub, we conclude that fire can affect arbuscular mycorrhizal fungi colonization and we speculate that this is mediated by light availability.