Unraveling the mystery of canopy dieback caused by citrus disease Huanglongbing and its link to hypoxia stress.

Devastating citrus disease Huanglongbing (HLB) is without existing cures. Herein, we present results demonstrating the possible mechanisms (hypoxia stress) behind HLB-triggered shoot dieback by comparing the transcriptomes, hormone profiles, and key enzyme activities in buds of severely and mildly symptomatic 'Hamlin' sweet orange (Citrus sinensis). Within six months (October - May) in field conditions, severe trees had 23% bud dieback, greater than mild trees (11%), with a concomitant reduction in canopy density. In February, differentially expressed genes (DEGs) associated with responses to osmotic stress, low oxygen levels, and cell death were upregulated, with those for photosynthesis and cell cycle downregulated in severe versus mild trees. For severe trees, not only were the key markers for hypoxia, including anaerobic fermentation, reactive oxygen species (ROS) production, and lipid oxidation, transcriptionally upregulated, but also alcohol dehydrogenase activity was significantly greater compared to mild trees, indicating a link between bud dieback and hypoxia. Tricarboxylic acid cycle revival, given the upregulation of glutamate dehydrogenase and alanine aminotransferase DEGs, suggests that ROS may also be generated during hypoxia-reoxygenation. Greater (hormonal) ratios of abscisic acid to cytokinins and jasmonates and upregulated DEGs encoding NADPH oxidases in severe versus mild trees indicate additional ROS production under limited oxygen availability due to stomata closure. Altogether, our results provided evidence that as HLB progresses, excessive ROS produced in response to hypoxia and during hypoxia-reoxygenation likely intensify the oxidative stress in buds leading to cell death, contributing to marked bud and shoot dieback and decline of the severely symptomatic sweet orange trees.

Canopy Density, but Not Bacterial Titers, Predicts Fruit Yield in Huanglongbing-Affected Sweet Orange Trees.

In Florida, almost all citrus trees are affected with Huanglongbing (HLB), caused by Candidatus Liberibacter asiaticus (CLas). We characterized various parameters of HLB-affected sweet orange trees in response to yield-improving nutritional treatment, including canopy volume, canopy density and CLas Ct values, and found that the treatment improved yield and maintained canopy density for over three years, whereas untreated HLB-affected trees declined in canopy density. The nutritional treatment did not affect CLas titer or the tree canopy volume suggesting that canopy density is a better indicator of fruit yield. To further validate the importance of canopy density, we evaluated three independent orchards (different in tree age or variety) to identify the specific traits that are correlated with fruit yields. We found that canopy density and fruit detachment force (FDF), were positively correlated with fruit yields in independent trials. Canopy density accurately distinguished between mild and severe trees in three field trials. High and low producing HLB trees had the same Ct values. Ct values did not always agree with CLas number in the phloem, as visualized by transmission electron microscopy. Our work identifies canopy density as an efficient trait to predict yields of HLB-affected trees and suggests canopy health is more relevant for yields than the CLas population.

Poor shoot and leaf growth in Huanglongbing-affected sweet orange is associated with increased investment in defenses.

Citrus disease Huanglongbing (HLB) causes sparse (thinner) canopies due to reduced leaf and shoot biomass. Herein, we present results demonstrating the possible mechanisms behind compromised leaf growth of HLB-affected 'Valencia' sweet orange trees by comparing morphological, transcriptome, and phytohormone profiles at different leaf development phases (1. buds at the start of the experiment; 2. buds on day 5; . 3. leaf emergence; 4. leaf expansion; and 5. leaf maturation) to healthy trees. Over a period of 3 months (in greenhouse conditions), HLB-affected trees had ≈40% reduction in growth traits such as tree height, number of shoots per tree, shoot length, internode length, and leaf size compared to healthy trees. In addition, buds from HLB-affected trees lagged by ≈1 week in sprouting as well as leaf growth. Throughout the leaf development, high accumulation of defense hormones, salicylic acid (SA) and abscisic acid (ABA), and low levels of growth-promoting hormone (auxin) were found in HLB-affected trees compared to healthy trees. Concomitantly, HLB-affected trees had upregulated differentially expressed genes (DEGs) encoding SA, ABA, and ethylene-related proteins in comparison to healthy trees. The total number of cells per leaf was lower in HLB-affected trees compared to healthy trees, which suggests that reduced cell division may coincide with low levels of growth-promoting hormones leading to small leaf size. Both bud dieback and leaf drop were higher in HLB-affected trees than in healthy trees, with concomitant upregulated DEGs encoding senescence-related proteins in HLB-affected trees that possibly resulted in accelerated aging and cell death. Taken together, it can be concluded that HLB-affected trees had a higher tradeoff of resources on defense over growth, leading to sparse canopies and a high tree mortality rate with HLB progression.

Nontargeted metabolomics-based multiple machine learning modeling boosts early accurate detection for citrus Huanglongbing.

Early accurate detection of crop disease is extremely important for timely disease management. Huanglongbing (HLB), one of the most destructive citrus diseases, has brought about severe economic losses for the global citrus industry. The direct strategies for HLB identification, such as quantitative real-time polymerase chain reaction (qPCR) and chemical staining, are robust for the symptomatic plants but powerless for the asymptomatic ones at the early stage of affection. Thus, it is very necessary to develop a practical method used for the early detection of HLB. In this study, a novel method combining ultra-high performance liquid chromatography/mass spectrometry (UHPLC/MS)-based nontargeted metabolomics and machine learning (ML) was developed for conducting the early detection of HLB for the first time. Six ML algorithms were selected to build the classifiers. Regularized logistic regression (LR-L2) and gradient-boosted decision tree (GBDT) outperformed with the highest average accuracy of 95.83% to not only classify healthy and infected plants but identify significant features. The proposed method proved to be practical for early detection of HLB, which tackled the shortcomings of low sensitivity in the conventional methods and avoid the problems such as lighting condition interference in spectrum/image recognition-based ML methods. Additionally, the discovered biomarkers were verified by the metabolic pathway analysis and content change analysis, which was remarkably consistent with the previous reports.

An efficient RNA isolation procedure and identification of reference genes for normalization of gene expression in blueberry.

Application of transcriptomics approaches can greatly enhance our understanding of blueberry physiology. The success of transcriptomics approaches is dependent on the extraction of high-quality RNA which is complicated by the abundance of polyphenolics and polysaccharides in blueberry. Additionally, transcriptomics requires the accurate quantification of transcript abundance. Quantitative real-time polymerase chain reaction (qRT-PCR) is a robust method to determine transcript abundance. Normalization of gene expression using stably expressed reference genes is essential in qRT-PCR. An evaluation of the stability of expression of reference genes has not yet been reported in blueberry. The objectives of this study were to develop an effective procedure for extracting RNA from different organs and to evaluate potential reference genes for qRT-PCR analyses in blueberry. RNA of high quality and yield was extracted from eight and six organs of rabbiteye and southern highbush blueberry, respectively, using a modified cetyltrimethyl ammonium bromide-based method. The expression stability of 12 reference genes was evaluated. UBIQUITIN-CONJUGATING ENZYME (UBC28), RNA HELICASE-LIKE (RH8), CLATHRIN ADAPTER COMPLEXES MEDIUM SUBUNIT FAMILY PROTEIN (CACSa), and POLYUBIQUITIN (UBQ3b) were the most stably expressed genes across multiple organs in both blueberry species. Further, the expression stability of the reference genes in the branch abscission zone following treatment with fruit abscission-inducing compounds was analyzed. CACSa, RH8, and UBC28 were the most stably expressed genes in the abscission zone under abscission-inducing conditions. We suggest a preliminary evaluation of UBC28, CACSa, RH8, and UBQ3b to identify the most suitable reference genes for the experimental conditions under consideration in blueberry.