Comparative metabolomic analysis of the phloem sap of nine citrus relatives with different degrees of susceptibility to Huanglongbing disease.

Comparative metabolomic analysis of the phloem sap of nine citrus relatives with different degrees of susceptibility to Huanglongbing disease. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparative metabolomic analysis of the phloem sap of nine citrus relatives with different degrees of susceptibility to Huanglongbing disease. MARIA C. HERRANZ, JOSE ANTONIO NAVARRO, ANTONELLA LOCASCIO, LEANDRO PEÑA, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3965075/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Citrus Huanglongbing (HLB) disease, also known as “citrus greening”, is currently considered the most devastating citrus disease due to its rapid spread, and high severity. Presently, research efforts are focused on searching for either curative treatments or resistant cultivars to combat HLB-associated bacterium ‘ Candidatus Liberibacter asiaticus’ ( C Las). Metabolomics can help to unravel the mechanisms supporting the potential tolerance/resistance of citrus relatives. Herein, we carried out a metabolomic analysis to determine whether the level of resistance of nine citrus-related genotypes is influenced by their pre-existing metabolic background before infection. For this purpose, the healthy phloem of nine Citrinae genotypes previously categorized according to their different responses to HLB was analyzed. A total of 53 different metabolites were targeted, including amino acids, organic and inorganic acids, and sugars. Interestingly, we observed that resistant and partially resistant genotypes exhibited higher accumulations of organic acids such as quinic acid and citric acid. In contrast, the amount of total sugars showed a clear upward trend in the susceptible genotypes. Notably, within this last group of metabolites, sugar acids displayed a trend toward an average percentage increase in both partially resistant and resistant accessions, being more evident in the resistant group. Changes potentially associated with the level of resistance were observed in certain amino acids within the aspartate and glutamate families. However, only lysine levels were significantly higher in the susceptible samples. The evaluation of five genes associated with lysine catabolism by RT-qPCR revealed differences in transcript abundance between resistant and susceptible samples. These findings open a new avenue of opportunity for identifying metabolites and/or substances that could aid in developing resistance strategies to this devastating disease. Huanglongbing Candidatus Liberibacter citrus phloem sap Metabolomics lysine catabolism flavin-dependent monooxygenase 1 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Contribution to the field Modern citriculture is currently threatened by the disease Huanglongbing (HLB), also known as "citrus greening", the most ravaging bacterial disease affecting worldwide citrus production and quality. Presently, research efforts are focused on searching for either curative treatments or resistant cultivars to HLB. Metabolic analysis carried out in this work opens a new interesting line of research about the involvement of lysine catabolism in tolerance and resistance to HLB. Our study describes general changes potentially associated with HLB tolerance and, in particular, in the transcriptional responses of two enzymes related to the catabolism of the above-mentioned amino acid. This could be a starting point to accelerate the creation on-demand of new citrus cultivars resistant to this epidemic disease. I hope you consider interesting enough this work to be evaluated for European Journal of Plant Pathology. Introduction Modern citriculture is currently threatened by the disease Huanglongbing (HLB), also known as "citrus greening". Due to its rapid dispersal, severity, and fast progression, HLB is currently considered the most devastating bacterial disease affecting worldwide citrus production and quality. HLB has been reported in Asia, the Middle East, Africa, and the Americas but it is expected to continue spreading. Hence, the European Commission qualified HLB as a quarantine priority pest in 2019 (Gabriel et al., 2020 ). The disease is associated with at least three uncultured alpha-proteobacteria species of the genus Candidatus Liberibacter, Ca. L. asiaticus ( C Las), Ca . L. africanus ( C Laf), and Ca . L. americanus ( C Lam), which are vectored by two psyllids species into the phloem cells. C Las and C Lam are naturally spread in citrus by the Asian psyllid Diaphorina citri Kuwayama, whereas C Laf is transmitted by Trioza erytreae (Bové, 2006 ; Gottwald, 2010 ). The three bacterial species are phloem-limited but differ in their thermosensitivity, the severity of the induced symptoms, and each appears to have independently evolved on the continent indicated in its name (Nelson et al., 2013 ; Wang et al., 2017 ). To date, there have been no successful established methods for long-term commercial control of HLB, making it progressively more challenging to curb the spread of this disease. Given the absence of a cure, the majority of citrus producers have opted to coexist with infected trees rather than completely removing them. Within the range of disease control strategies that have been assessed to reduce the impact of the disease are antimicrobial compounds potential resistance inducers, phytohormones, thermotherapy improved nutritional programming (ENP) and symptomatic branch trimming (Gottwald et al., 2012; Zhang et al., 2014; Doud et al., 2017; Killiny et al., 2020). Unfortunately, implementing all these approaches widely in the field would be excessively costly. Hence, there is a requirement to conduct further assessment of disease control strategies aimed at addressing pathogen spread on three distinct levels; pathogen, host and vector. Bacterial diseases of plants are typically very challenging to control. Unluckily, the HLB pathosystem is problematic for laboratory studies. Advances in understanding the pathogenesis of C Ls have been hindered because the bacteria had not been successfully cultured long-term (Ha et al., 2019 ; Zuñiga et al., 2020 ), and pure cultures have yet to be maintained. Additionally, the specific characteristics of HLB infection, such as the uneven distribution of the bacteria, the low C Las titer during the initial phase of the latent period, and the influence of the environment on C Las replication, not only hampered the development of detection methods and treatments, but also led to confusing results in the resistance analysis (Alves et al., 2021 ). As a consequence, there is an urgent need for unequivocal resistant citrus cultivars, which can be utilized in breeding programs or directly as potential rootstocks or interstocks, to mitigate the harmful effects caused by this devastating disease. A wide variety of HLB resistance levels, including citrus relatives described as full-resistant, has been reported in the family Rutaceae , subfamily Aurantioideae (Folimonova et al., 2009 ; Cifuentes-Arenas et al., 2019 ; Wang, 2019 ). However, they are phylogenetically distant from commercial citrus that breeding hybrids are unviable. Taking all of the above into consideration, Alves et al. ( 2021 ; 2022 ) tested for C Las resistance a diverse collection of close cross- and/or graft-compatible citrus relatives belonging to the subtribe Citrinae. Utilizing an accurate and consistent evaluation system, they have demonstrated full resistance to C Las in a small group of the analyzed species (Alves et al., 2021 ; 2022 ). Omics-based approaches constitute a powerful tool to understand the citrus response to the pathogen, and for performing comparative analyses among citrus cultivars with different levels of resistance to HLB. In particular, metabolomics has been previously utilized to study the mechanisms supporting this resistance/tolerance. For example, Killiny ( 2016 ), reported that the phloem sap of HLB-resistant curry leaf tree contains lower amounts of most metabolites compared to the partially resistant citrus orange jasmine and the susceptible Valencia sweet orange. Different studies on others citrus cultivars have shown a positive correlation between tolerance to HLB and most amino acids and leaf volatiles, while a negative correlation was observed for several organic acids (Hijaz et al., 2016 ; Killiny and Hijaz, 2016 ; Killiny et al., 2017 ). Valim & Killiny ( 2017 ) analyzed the fatty acid composition of the phloem sap of 12 citrus varieties with different tolerance toward HLB and found that capric acid was higher in “Sugar Belle” and Carrizo citrange tolerant hybrids. Interestingly, Albrecht et al. ( 2020 ) demonstrated that rootstock influences the metabolic response to CLas in grafted sweet orange trees. Other comparative metabolomics studies have suggested that tolerance is linked to preserving plant growth and phloem formation rather than activating plant defense mechanisms to overcome the disease (Suh et al., 2021 ). Herein, we have taken advantage of the strict evaluation of the resistance to C Las of different Citrinae genotypes carried out by Alves et al. ( 2021 ; 2022 ). We selected three genotypes from each of the defined categories: susceptible, partially resistant, and resistant. Notably, the latter two categories included cultivars formerly classified as Microcitrus spp . and Eremocitrus glauca , which are now in the genus Citrus and are actively employed in some breeding programs worldwide. However, the basis of HLB resistance in these citrus relatives remains enigmatic. To determine whether the level of resistance depends on the metabolic background of the different cultivars before infection, we carried out a metabolomics analysis of healthy phloem plants. After trimethylsilyl derivatization, we quantified 20 amino acids, 12 organic and inorganic acids, 15 sugars and sugar acids, 2 amines, 2 nitrogenated bases, and 2 unknown compounds. In general, the metabolites identified in our analysis of HLB-resistant and partially resistant citrus genotypes were similar to those described in other reports on HLB-tolerant citrus. Remarkably, we observed some intriguing differences regarding the potential role of lysine catabolism in HLB resistance. Identifying specific metabolic steps and pathways involved in tolerance will not only enhance our understanding of plant-pathogen interactions but also provide valuable insights into the potential metabolic compounds that could be harnessed against C Las. This knowledge has the potential to expedite the development of new citrus cultivars that are naturally resistant to these fastidious bacteria. Materials and methods Plant material and plant growing conditions The nine citrus varieties used in the present study were maintained in a greenhouse with a temperature of 30 ºC and a relative humidity of 35%. These Citrinae species were previously assessed and classified into three categories; susceptible, partially resistant, and resistant by Alves et al. ( 2021 ). They include Citrus x sinensis 'Pera', Poncirus trifoliata 'Rubidoux' and Citrus x sinensis ´Valencia Midknight' (susceptible), Microcitrus australasica hybrid, Microcitrus virgata hybrid and Microcitrus garrawayae (partially resistant), Microcitrus australis hybrid, Eremocitrus glauca and Microcitrus warburgiana hybrid (resistant) (Table 1 ) (Alves et al., 2022 ). All of these citrus varieties were 3-year-old scions, approximately 50–60 cm in height and grafted on Citrus macrophylla with the exception of the Microcitrus australis hybrid which was propagated on Citrus volkameriana . At least three independent mature trees were used for each species and hybrids. All scions were propagated using buds from a single donor mother plant per genotype, and were kept at an insect-proof climate-controlled greenhouse at the Institute for Plant Molecular and Cell Biology (UPV-CSIC, Valencia, Spain) with temperatures maintained between 25–27ºC. The plants were grown in 8 L pots filled with coir, irrigated and fertilized twice a week, and sprayed monthly with preventive insecticides. Table 1 Citrinae genotypes/accessions used in this study Category Accession Abbreviation Susceptible Citrus x sinensis ´Pera ´ S1 Poncirus trifoliata Rubidoux S2 Citrus sinensis valencia Midknight S3 Partially resistant Microcitrus australasica PR1 Microcitrus virgata PR2 Microcitrus garrawayae PR3 Resistant Microcitrus australis R1 Erimocitrus glauca R2 Microcitrus warburgiana R3 Phloem sap collection Stems measuring 10–20 cm in lengh with a diameter of 0.2–0.3 cm were collected from greenhouse plants. Phloem sap was collected using the centrifugation method described by Hijaz & Killiny, ( 2014 ). Briefly, the bark was manually removed from the sprig and washed with deionized water to eliminate any potencial contamination from the xylem sap. To collect the phloem sap, five small pieces of the bark were placed in a 0.5-mL microcentrifuge tube with a small hole at the bottom, and this tube was then nested into a 2-mL microcentrifuge tube. The sample was centrifuged at 12,000 rpm for 15 minutes at room temperature, and the collected phloem sap was stored at -80 ºC until further analysis. Primary metabolite analysis Primary metabolite analysis was conducted at the Metabolomics Platform of the Institute for Plant Molecular and Cell Biology (UPV-CSIC, Valencia, Spain). The method used was a modification of the one originally described by Roessner et al. ( 2000 ). For the analysis, one µL of each sample of phloem sap was dried. The dry residues were redissolved in 40 µL of 20 mg/mL methoxyamine hydrochloride in pyridine and incubated for 90 minutes at 37 ºC. Then, 70 µL MSTFA (N-methyl-N-[trimethylsilyl]trifluoroacetamide) and 6 µL of a retention time standard mixture (3.7% [w/v] mix of fatty acid methyl esters ranging from 8 to 24C) were added and the samples were incubated for 30 minutes at 37 ºC. Sample volumes of 2 µL were injected in split1:30 and splitless mode in a 6890 N gas chromatograph (Agilent Technologies Inc. Santa Clara, CA) coupled to a Pegasus4D TOF mass spectrometer (LECO, St. Joseph, MI). Gas chromatography was performed on a BPX35 (30 m × 0.32 mm × 0.25 µm) column (SGE Analytical Science Pty Ltd., Australia) with helium as the carrier gas, constant flow 2 mL/min. The liner was set at 250°C. The oven program was 85°C for 2 min, 8°C/min ramp until 360°C. Mass spectra were collected at 6.25 spectra s − 1 in the m/z range 35–900 and ionization energy of 70 eV. Chromatograms and mass spectra were evaluated using the CHROMATOF program (LECO, St. Joseph, MI). Metabolites were identified by comparison with a custom library made with commercial standards. Data analyses were performed using Metaboanalyst, version 5.0 ( https://www.metaboanalyst.ca ). Logarithmic transformation and autoscaling scaling were employed as normalization to perform the principal component analysis and the hierarchical clustering and also to generate the heatmap. In the context of hierarchical clustering, the Euclidean distance metric and the Ward clustering algorithm were utilized as parameters to identify features with significantly different accumulation. Real-time quantitative reverse transcription PCR Total RNA was extracted from a pool of three leaves of each Citrinae species using RIBOzol Reagent. Remnant genomic DNA was removed by DNase I treatment. First-strand cDNA was synthesized from 0.5 µg of total RNA using RevertAid H Minus Reverse Transcriptase and oligo(dT) (Thermo Fisher Scientific, Carlsbad, CA, USA). Real-time quantitative PCR (qPCR) was carried out using QuantStudio 3 Real-Time PCR machine (Applied Biosystems, Waltham, MA, USA) and PyroTaq EvaGreen qPCR Supermix (Solis BioDyne, Tartu, Estonia), specific oligonucleotides primers, and recommended qPCR cycles as follows: initial denaturation for 12 min at 95°C, followed by 50 cycles of 15 s at 95°C and 60 s at 60°C. Specific oligonucleotides primers were designed using Primer3web version 4.1.0 ( https://bioinfo.ut.ee/primer3 ). Oligonucleotide efficiencies were tested by qRT-PCR using ten-fold serial dilutions of the corresponding cDNA. Each technical replicate was run in triplicate. SAND (SAND family protein) and GAPC2 (Glyceraldehyde-3-phosphate dehydrogenase C2) genes were used as endogenous controls (Mafra et al., 2012 ; Wang et al., 2016 ; Máximo et al., 2017 ; Shi et al., 2018 ). The primer sequences of both target and reference genes are shown in Supplementary Table S1 . Statistical analysis The comparison of transcript abundance of the different genes analyzed across the different samples was carried out using GraphPad Prism version 6.00 for Windows, GraphPad Software, La Jolla California USA, ( www.graphpad.com ). A non-parametric Kruskal-Wallis one-way ANOVA was performed. Statistically significant differences were considered for p-values less than 0.05. A post hoc test, the Dunn test for multiple comparissons, was conducted to identify which specific groups differ from each other. Results Metabolic variations in the phloem of HLB-resistant, partially resistant, and susceptible citrus accessions Considering that the citrus phloem sap is the plant tissue in which C Las must survive and grow, we performed a comparative metabolomics analysis of the phloem sap from nine Citrinae genotypes exhibiting different levels of resistance to C Las. These genotypes included three susceptible (S1 to S3), three partially resistant (PR1 to PR3), and three full-resistant (R1 to R3) accessions (Table 1 ). Partial resistance was assigned to species/hybrids showing C Las infection in a fraction of tested scions and/or presenting a delayed infection, without ever reaching the bacterial titer found in susceptible genotypes (Alves et al., 2021 , 2022 ). A diverse range of molecules were detected after trimethylsilyl derivatization and classified for subsequent analysis. They included 20 amino acids, 12 organic and inorganic acids, 15 sugars and sugar acids, 2 amines, 2 nitrogenated bases, and 2 unknown compounds (Supplementary Table S2 A and S2B). Their retention time, quantification ion and injection mode (split 1:10/ splitless) are shown in Supplementary Table S3 . The higher percentage of detectable metabolites were sugars, except for the HLB-resistant R2 Eremocitrus glauca , in which organic acids were more abundant (Fig. 1 a). We conducted a principal component analysis (PCA) and a total of 7 principal components (PCs) were extracted, accounting for 88.8% of the total variance. The total variation explained by the first two principal components was 56%, with PC1 contributing 40% and PC2 contributing 16% (Supplementary Fig. S1 a). Notably, the partially resistant Microcitrus australasica (PR1) appeared as an outlier in PC1, and this separation was primarily attributed to the elevated levels of many compounds in PR1, as confirmed by the loading plot (see Supplementary Fig. S1 b). This distinct profile was also evident in the heatmap, which displayed the fold change for each compound identified through gas chromatography and mass spectrometry analysis (Fig. 1 b). To mitigate the influence of PC1 on the variance, we opted to exclude this outlier from the subsequent PCA analysis. As shown in Supplementary Fig. S1 c, the PCA analysis without PR1 revealed that susceptible samples clustered in the right lower quadrant, indicating their similarity in terms of metabolite profiles. Organic and inorganic acid accumulate in higher amounts in both partially resistant and resistant citrus samples A total of eleven organic acids and one inorganic acid (phosphoric acid) were detected in the phloem sap samples. Remarkably, the average percentage of the total organic and inorganic acids was higher in both partially resistant and resistant samples when compared to the susceptible ones (Fig. 2 a and Supplementary Table S4). Among the detected organic acids were dicarboxylic acids such as fumaric, malic, malonic, oxalic, and succinic. Malic and quinic acid were generally the most abundant organic acids, however, only quinic acid was found to be over-represented in PR and R samples, with the exception of the R3 sample (Supplementary Fig. S2 ). While the percentage of malic acid was similar in susceptible and resistant samples, we observed differences in quinic acid accumulation between both categories. Quinic acid was the most abundant organic acid in 83% of the resistant and partially resistant samples, as well as in one susceptible sample. On average, the percentage of this organic acid was higher in resistant cultivars. Additionally, the average of each organic acid percentage within the three categories revealed that oxalic acid, fumaric acid, threonic acid, lactic acid, and quininic acid were elevated in susceptible samples. By contrast, citric acid and anthranilic acid were higher in resistant cultivars (Supplementary Figs. S3a y b). The distribution of individual organic acid within each accession did not exhibit any differential pattern among the three categories (Supplementary Fig. 2). PCA analysis generated using the organic and inorganic acids did not reveal any separation among the groups with different resistance levels (data not shown). However, when PCA analysis was conducted by excluding PR samples, it distinguished resistant and susceptible samples in two distinct groups, with PC1 contributing 25.4% of the variation (Fig. 2 b). Resistant varieties clustered on the left side of the PCA plot, indicating a distinct organic acid composition in the phloem sap compared to susceptible varieties. The corresponding loading plot showed that the majority of organic acid levels in resistant cultivars were higher than in the susceptible ones, although a high variability is patent (Supplementary Fig. S4). In conclusion, the percentage of total organic acids was higher in the R and PR samples. Quinic acid was in general the most abundant organic acid and displayed a tendency to have higher content in PR and R compared to S samples. Sugars showed a higher accumulation in the susceptible citrus genotypes The average percentage of total sugars showed a noticeable increasing trend in the susceptible group although differences were not statistically significant (Fig. 3 a and Supplementary Table S5). The identified sugars included monosaccharides such as fructose, glucose, and rhamnose, disaccharides such as sucrose, and trisaccharides such as raffinose. Additionally, several sugar alcohols, like galactinol and myo-inositol, as well as sugar acids were also detected. Interestingly, there was a trend toward an average percentage increase of sugar acids in PR and R accessions, being more pronounced in the R group (Fig. 3 b). This finding agrees with that previously reported in Sugar Belle mandarin (Killiny et al., 2017 ), and the fact that sugar acids are induced after C Las infection in Cleopatra mandarin (Albrecht et al., 2016 ) and sweet orange (Hijaz et al., 2013 ). The PCA analysis based on sugars and sugar derivatives is shown in Supplementary Fig. S5a. The total variation explained by the first two principal components was 49.7% with PC1 contributing 32.2% and PC2 contributing 17.5%. The loading plot analysis revealed that the sugar composition of susceptible samples was different from the rest of the samples. However, within the susceptible category, there appeared to be higher variability compared to the resistant varieties (Supplementary Fig. S5b). The most abundant sugars in the citrus phloem sap were fructose, sucrose, glucose, and the sugar alcohol myoinositol (Fig. 3 c). While glucose exhibited a trend of higher levels in the susceptible samples, the differences compared to the other categories were not statistically significant. By contrast, myoinositol was found at higher levels in the resistant samples (Fig. 3 c and Supplementary Fig. S6). The rest of the minority sugar and sugar derivatives clustered to the left of the loading plot together with the resistant and partially resistant samples (Supplementary Fig. S5b). Some of them, such as glycerol, and the detected three unidentified monosaccharides showed an upward trend in the resistant cultivars (Fig. 3 d). Thus, we observed an upward trend in the total sugar levels of the susceptible genotypes. It's worth noting that there is an observable trend in the average percentage increase of sugar acids in PR and R accessions, with a more pronounced effect in the R group. Low amount of lysine in phloem sap as a possible marker for HLB resistance Seventeen proteinogenic amino acids and three non-proteinogenic amino acids (GABA, 4-hydroxyproline, and ornithine) were detected in the phloem sap. The percentage of non-proteinogenic amino acids accounted for approximately 25% of the total amino acids in all the samples, regardless of their resistance levels. In terms of proteinogenic amino acids, the quantity of essential amino acids was considerably lesser than non-essential amino acids, with no significant differences among the samples (Supplementary Fig. S7). Neither significant differences in the percentage of the total amino acids nor a trend bias was observed comparing the three categories (Fig. 4 a and Supplementary Table S6). Non-essential amino acid distribution was more homogeneous among the susceptible species, with proline being the most abundant amino acid in 78% of the samples (Supplementary Fig. S8a). On the other hand, the distribution of most of the essential amino acid was very similar among the three categories, with the exception of lysine levels, which were significantly higher in susceptible samples (Supplementary Fig. S9). Although the levels of other amino acid did not show statistically significant differences among the three categories, the average percentage showed changes that could be potentially associated with the level of tolerance. The levels of other three amino acids from the aspartate family, threonine, asparagine, and aspartic acid were present at higher levels in the resistant cultivars (Figs. 4 b and 4 c). Four amino acids of the glutamate family, including glutamic acid and 4-hydroxyproline were also abundant in resistant varieties, in contrast to proline and ornithine, which had lower representation in this category (Figs. 4 c and 4 d and Supplementary Figs. S8a and S8b). Finally, the level of alanine from the pyruvate family was elevated in resistant and partially resistant cultivars. The PCA analysis that emerged from amino acid data is presented in Supplementary Fig. S10a. The total variation explained by the first two principal components was 59.7%, with PC1 contributing 39.4% and PC2 contributing 20.3%. Resistant and susceptible samples clustered into two well-defined groups. Additionally, partially resistant sample PR2, clustered with the susceptible samples, while PR3 clustered with the resistant ones. The corresponding loading plot showed that susceptible samples were high in proline, lysine, and the non-proteinogenic amino acid ornithine. In contrast, resistant samples were high in 4-hydroxyproline, glutamic acid, threonine, and histidine, although many of the amino acid levels detected in PR2 were elevated (Supplementary Fig. S10b). Therefore, lysine decrease in phloem sap could be considered as a potential marker of HLB resistance. Expression of genes of lysine catabolism is affected in resistant citrus samples. Based on our data, lysine was the only amino acid present at significantly higher levels in the phloem sap of susceptible samples in comparison with the resistant ones. Lysine and its catabolic intermediates have been involved in plant responses to abiotic and biotic stresses (Arruda & Barreto 2020 ). Thus, we conducted RT-qPCR analyses to investigate the potential involvement of the lysine catabolic pathway in determining the tolerance levels of the accessions analyzed. We evaluated the expression of five genes including two genes of the saccharopine pathway (lysine-ketoglutarate reductase/saccharopine dehydrogenase ( LKR/SDH ) and aldehyde dehydrogenase 7B4 ( ALDH7B4 )), two genes associated with a pathway recently link to plant systemic acquired resistance (SAR) (SAR-deficient 4 ( SARD4 )) and flavin-dependent monooxygenase 1 ( FMO 1 ) and, one gene implicated in the connection between these two branches of lysine catabolism (pyrroline-5-carboxylate reductase ( P5CR ). As shown in Fig. 5 a, the transcript abundance of the FMO1 gene was significantly higher in two resistant samples, Eremocitrus glauca (R2), and Microcitrus warburgiana hybrid (R3), in comparison to all the susceptible samples. However, in the resistant Microcitrus australis (R1), the level of this transcript was only significantly higher compared with one of the susceptible genotypes, Poncirus trifoliata Rubidoux (S2). On the other hand, the transcript of ALDH7B4 was downregulated in the resistant cultivars. All the observed differences were significant except between Citrus x sinensis ´Pera´ (S1) and Microcitrus australis hybrid (R1) (Fig. 5 b). The expression of the rest of the analyzed genes did not exhibit significant differences among the three categories. Our findings suggest that lysine catabolism is influenced in the resistant plants. Among the five genes we analyzed, FMO1 and ALDH7B4 displayed significant alterations in the accumulation of their transcripts when compared to susceptible cultivars. These changes in gene expression might be indicative of the role of lysine catabolism in the resistance of these citrus accessions to CLas infection. Discussion While significant efforts have been dedicated to study the bases of tolerance/resistance to the devastating HLB disease, we are still a long way from a comprehensive understanding of this process. To gain further insights into the underlying mechanisms of the disease, omics technologies constitute an indispensable research tool to understand plant tolerance to this pathogen. In this study, we use metabolomics to try to gain insight into the mechanism underpinning the resistance, by analysing the composition of phloem sap in citrus varieties with different level of susceptibility to HLB (Alves et al., 2021 ; 2022 ). Regarding organic acids, we observed a global upward trend in both the resistant and partially resistant cultivars. This aligns with the finding of Killiny et al. ( 2017 ), who conducted a metabolite profile analysis of the “Sugar Belle” mandarin hybrid to investigate its relative tolerance to HLB in comparison to some of its ancestors. Their research revealed that “Sugar Belle” exhibited elevated levels of phosphoric and some organic acids, including malic and threonic acid. Furthermore, previous studies have also reported an increase in the presence of various organic acids in resistant varieties (Killiny, 2017 ), and C Las-infected leaves from susceptible cultivars (Albrecht et al., 2016 ). Therefore, our data, in line with these previous studies, suggest that organic acids may have a role in nutrient uptake from the soil and serve as a priming strategy for those cultivars to be more tolerant. In our analysis, we found that malic and quinic acid were the most abundant organic acids, consistent with prior research (Jones et al., 2012 ). Notably, only quinic acid showed a trend of higher content in PR and R samples compared to S samples. This metabolite has previously been link to defence responses in citrus leaves and has been detected in fruits of semi-tolerant varieties to CLas (Jones et al., 2012 ; Killiny, 2017 ), although its specific role in countering this pathogen remains unknown. Additionally, the average percentage of each organic acid within the three categories revealed trends that may be associated with the level of tolerance. Among them, citric acid and anthranilic acid, found at higher accumulation levels in resistant cultivars in our analysis, have been reported as metabolites involved in stress tolerance and plant protection (Zahan et al., 2021 ; Köllner et al., 2010 ). Higher levels of sugars have been previously associated with HLB susceptibility (Albrecht et al., 2016 ; Killiny, 2016 ). Although there was not a consistent pattern in sugar composition among the different categories in our analysis, we did observe an upward trend in total sugar levels in the susceptible genotypes. However, these metabolites are unlikely to be the limiting factor for the HLB vector, since no clear correlation between glucose, fructose, sucrose, and citrus susceptibility to C Las was found (Killiny, 2016 , 2017 ). It´s worth mentioning that effects on carbohydrate metabolism have been previously described in different phloem-related plant-pathosystems, including HLB (Albrecht et al., 2016 ). Irrespective of their role in protein biosynthesis, numerous amino acids have been reported to participate in plant response to different stresses (Trovato et al., 2021 ). In our study, we have observed distinct trends in the levels of certain amino acids that could be coupled to the degree of HLB resistance, with a majority of them belonging to the glutamate and aspartate families. Higher constitutive concentrations of amino acids from the glutamate family have previously been associated with tolerance and resistance to C Las. In our analysis, glutamate and 4-hydroxyproline showed an upward tendency in resistant cultivars. Both of these amino acids have been formerly correlated with defence responses and resistance to plant pathogens (Albrecht et al., 2020 ; Qiu et al., 2020 ; Kim et al., 2021 ; Deepak et al. 2007 , 2010 ). In contrast, two other members of this family, ornithine and proline, displayed a downward trend in the resistant samples. Supporting this, ornithine has been associated with susceptibility in various pathosystems (Dhodary et al. 2022 ; Jiménez-Bremont et al., 2014 ). Proline upregulation, on the other hand, has been connected to C Las tolerance, as well as other biotic and abiotic stresses, and has been observed in infected plants compared to controls (Albrecht et al., 2020 ; Chin et al., 2020 ). However, in a metabolic profiling of the phloem sap from fourteen varieties with different levels of tolerance to C Las, only one tolerant variety exhibited higher proline levels (Killiny, 2017 ). The variability in the metabolic composition of the different varieties and the conditions used in the different studies make it challenging to draw consistent conclusions. Nonetheless, it is worth noting that C Las lacks the ability to synthesize proline, phenylalanine, tryptophan, cysteine, tyrosine, and histidine and other essential translation components. These components are crucial for the bacterium's replication and metabolic activity, and it must acquire them from the host (Mendonça et al., 2017 ; Zuñiga et al., 2020 ). The aspartate family pathway encompasses the synthesis of amino acids such as lysine, asparagine, and threonine (Yang & Ludewig, 2014 ). Our metabolomics analysis revealed higher levels of threonine in the resistant cultivars as well as increased levels of asparagine in both the resistant and partially resistant cultivars. The essential role of threonine as a sensor of different metabolic and environmental signals and translator of these signals into specific functional outputs, including stress tolerance, has been previously reported (Muthuramalingam et al., 2018 ; Killiny, 2017 ; Killiny et al., 2017 ). However, it's worth noting that lower amounts of threonine in resistant Rutaceae genotypes in comparison to susceptible cultivars, have also been described (Killiny, 2016 ). Pepper asparagine synthase 1, the enzyme required for the production of asparagine from aspartate, was reported as essential for stress response (Hwang et al., 2011 ). Lysine is a limiting essential amino acid in plants, and its biosynthesis has been a matter of study aimed at enhancing the nutritional value of crops. As mentioned above, lysine catabolism research has recently focused on tolerance to biotic and abiotic stresses. Lysine can be catabolized through several metabolic pathways (Fig. 6 ). In some plants, lysine is converted into the alkaloid cadaverine (Hartmann & Zeier, 2018 ; Arruda & Barreto, 2020 ). A second ubiquitous catabolic pathway in plants is the saccharopine pathway rendering acetyl-CoA and glutamate. Furthermore, the third pathway, with a central role in plant immunity, leads to the generation of N-hydroxypipecolic acid (NHP), a metabolite recently involved in plant SAR (Hartmann et al., 2018 ). Contradictory results have been reported describing the lysine accumulation levels in response to pathogens (e.g. Návarová et al., 2013 ; Albrecht et al., 2016 , 2020 ; Killiny & Hijaz, 2016 ; Killiny, 2016 ). According to our results, among the essential amino acids, lysine represented on average 19.26%, 12.66%, and 11.40% in susceptible, partially resistant, and resistant samples, respectively. The significant difference in lysine levels between the resistant and susceptible cultivars, suggests a potential key role in plant defense. Regarding the synthesis of NHP (N-hydroxypipecolic acid), several enzymes have been associated with its production. The aminotransferase ALD1 (AGD2-like defense response protein 1), (Hartmann & Zeier, 2018 ; Holmes et al., 2019 ), a reductase, SARD4 (Ding et al., 2016 ); and FMO1, an NHP-synthesizing pipecolate N-hydroxylase (Hartmann & Zeier, 2018 ; Holmes et al., 2019 ). Additionally, the saccharopine pathway, which is associated with both abiotic and biotic stress responses, can produce pipecolate through two enzymatic reactions catalyzed by lysine-ketoglutarate reductase/saccharopine dehydrogenase (LKR/SDH) and pyrroline-5-carboxylate reductase (P5CR) (Arruda & Barreto, 2020 ). These authors explored whether both pathways contribute to SAR activation by comparing the transcriptional response of key genes, including LKR/SDH, ALD1, SARD4 , and FMO1 in Arabidopsis thaliana under various biotic and abiotic conditions. Under most biotic stresses, all the analyzed genes were upregulated. Conversely, only the saccharopine pathway was upregulated under abiotic conditions. The observed differences in lysine levels between susceptible and resistant categories led us to study the potential involvement of both the saccharopine and NHP pathways in HLB tolerance. We monitored transcriptional response of key genes, including LKR/SDH, ALDH7B4, P5CR, SARD 4, and FMO1 , in the leaves of citrus plants. Our results revealed significant upregulation of FMO1 in two of the resistant samples compared to the susceptible ones. As previously mentioned, FMO1 is the final enzyme of a pathogen-inducible L-lysine catabolic pathway in Arabidopsis thaliana . Koch et al. ( 2006 ) reported that the overexpression of FMO1 under the control of the 35S promoter increases the basal resistance to Pseudomonas syringae . Additionally, Mishina & Zeier ( 2006 ) demonstrated the crucial role of this enzyme in the establishing of SAR. Afterward, supporting this, it was reported that FMO1 catalyzes the conversion of pipecolic acid to NHP, which is a critical amino acid with a central role in the establishment of SAR (Hartmann et al., 2018 ). Interestingly, these authors did not detect this metabolite in unstressed Arabidopsis thaliana ; however, NHP strongly accumulated in the infected leaves. This metabolite was also not detected in any healthy phloem sap samples we analysed. Concerning the pipecolic acid, we did not obtain any conclusive result indicating significant differences among categories. Another lysine-catabolite is the α-amino adipic acid (Fig. 6 ). Its biosynthesis is dependent on the LKR/SDH and ALDH7B4 saccharopine enzymes. In our analysis, ALDH7B4 exhibited a decreased transcriptional response in the resistant genotypes. It's interesting to note that Návarová et al. ( 2013 ) previously suggested that the saccharopine pathway leading to α-amino adipic acid has no critical role in the resistance and SAR establishment after Pseudomonas syringae infection. Upstream of ALDH7B4, LKR/SDH converts lysine to α-aminoadipate semialdehyde and glutamate, which is a precursor for several metabolites related to stress including pipecolic acid (Arruda & Barreto, 2020 ). Although this is a small picture especially due to the number of samples analysed, it allows us to speculate about the potential roles of the upregulation of FMO1 and the downregulation of ALDH7B4 transcriptional responses. This differential regulation could create a more favourable scenario to combat the bacteria, potentially leading to increase resistance in certain cultivars. Somehow, it could be a plant strategy to anticipate defence mechanisms and gain an advantage over the pathogen. By identifying specific metabolic steps and defining the pathways involved in tolerance and resistance, we can develop powerful tools not only for discovering potential antimicrobial compounds against C Las but also for expediting the development of new citrus cultivars with enhanced resistance to this devastating disease. Declarations Author contributions M.C.H and V.P. conceived and designed the experiments. M.C.H. performed the experiments. M.C.H., J.A.N., A.L., P.M., J.F.M. and V.P. analyzed, and interpreted the data. M.C.H. wrote the manuscript. All authors reviewed and edited the manuscript. Funding This work was funded by grant no. 817526 (PRE-HLB) from the European Union H2020 Innovation Action Program. Acknowledgments Thanks are due to B. Alquézar for her valuable support with the sampling, L. Corachán-Valencia for her technical assistance and to A. Espinosa (Metabolomics service, IBMCP, UPV-CSIC) for the excellent technical support with the UPLC-PDAQ/TOF-MS analyses. 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Supplementary Files SupplementaryFigs.Herranzetal.EJPP.pdf SupplementaryFigLegends.docx SupplementaryTablesHerranzetal.EJPP.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revisions 02 May, 2024 Reviewers agreed at journal 22 Feb, 2024 Reviewers invited by journal 22 Feb, 2024 Editor invited by journal 22 Feb, 2024 First submitted to journal 19 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3965075","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274513857,"identity":"ed946a15-fb30-455f-b2d8-3f8d8bacea65","order_by":0,"name":"MARIA C. 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MARCOS","email":"","orcid":"","institution":"IATA: Instituto de Agroquimica y Tecnologia de Alimentos","correspondingAuthor":false,"prefix":"","firstName":"JOSE","middleName":"F.","lastName":"MARCOS","suffix":""},{"id":274513863,"identity":"57533406-0562-4eb8-bfde-405a3b8a62d7","order_by":6,"name":"VICENTE PALLAS","email":"","orcid":"","institution":"Instituto de Biología Molecular y Celular de Plantas: Instituto de Biologia Molecular y Celular de Plantas","correspondingAuthor":false,"prefix":"","firstName":"VICENTE","middleName":"","lastName":"PALLAS","suffix":""}],"badges":[],"createdAt":"2024-02-17 21:12:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3965075/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3965075/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51631796,"identity":"a6c0b089-0783-4ebb-a269-7207fa949676","added_by":"auto","created_at":"2024-02-26 09:44:22","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":415815,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e Percentage distribution of the main groups of metabolites found in the phloem sap of \u003cem\u003eCitrus x sinensis ´Pera´\u003c/em\u003e (S1), \u003cem\u003ePoncirus trifoliata Rubidoux\u003c/em\u003e (S2), \u003cem\u003eCitrus sinensis valencia Midknight \u003c/em\u003e(S3), \u003cem\u003eMicrocitrus australasica\u003c/em\u003e (PR1), \u003cem\u003eMicrocitrus virgata\u003c/em\u003e (PR2), \u003cem\u003eMicrocitrus garrawayae\u003c/em\u003e(PR3), \u003cem\u003eMicrocitrus australis\u003c/em\u003e (R1), \u003cem\u003eEmocitrus glauca\u003c/em\u003e (R2) and \u003cem\u003eMicrocitrus warburgiana\u003c/em\u003e (R3). Proteinogenic amino-acids (PAAs), Non-proteinogenic amino-acids (NPAAs). \u003cstrong\u003e(b) \u003c/strong\u003eTwo-way hierarchical cluster analysis and the heatmap showing the distribution of different citrus varieties using all of the detected compounds in phloem saps. Rows represents the different cultivars; Susceptible in green (S1, S2 and S3), Partially Resistant in red (PR1, PR2 and PR3) and Resistant in blue (R1, R2 and R3). Columns represent compound concentrations. Red and blue indicate high and low concentration, respectively. Colour density indicating levels of fold change was displayed\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3965075/v1/b09ef2c9818f851ad7893149.jpg"},{"id":51631528,"identity":"a91c018e-9798-4aa7-b5ae-919dfbe3fad4","added_by":"auto","created_at":"2024-02-26 09:36:22","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":201898,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e Bar graph represents the average percentage of total organic acids calculated from the Total Ion Chromatogram (TIC) vs. the three analyzed categories: Susceptible (S), Partially Resistant (PR), and Resistant (R) (no significant difference in one-way ANOVA; α 0.05, P 0.28). \u003cstrong\u003e(b)\u003c/strong\u003e Scot plot of principal component analysis (PCA) visualizing recognizable differences in the composition of all detected phloem organic and inorganic acids between the resistant (R1, R2 and R3 in blue) and susceptible (S1, S2 and S3 in green) samples after derivatization with Trimethylsilyl (TMS)\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3965075/v1/e62c44be0b2b4b7a79c3afba.jpg"},{"id":51631534,"identity":"b8ae8928-cf34-48e1-af62-9b42c7548a32","added_by":"auto","created_at":"2024-02-26 09:36:22","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":387046,"visible":true,"origin":"","legend":"\u003cp\u003eBar graph represents the average percentage of total sugars and sugar acids (no significant difference in one-way ANOVA; α 0.05, P.0.43) \u003cstrong\u003e(a)\u003c/strong\u003e and sugar acids (no significant difference in one-way ANOVA; α 0.05, P.0.39) \u003cstrong\u003e(b)\u003c/strong\u003e vs the three analyzed categories; Susceptible (S), Partially Resistant (PR), and Resistant (R). Graphics \u003cstrong\u003e(c)\u003c/strong\u003e and \u003cstrong\u003e(d)\u003c/strong\u003e represent the average of each detected sugar in percentage vs each category. Different scale depicts the most abundant \u003cstrong\u003e(C)\u003c/strong\u003e and the minority sugars \u003cstrong\u003e(D) \u003c/strong\u003edetected in the phloem sap. The percentages were calculated from the Total Ion Chromatogram (TIC)\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3965075/v1/b585095866e39e18297ec5eb.jpg"},{"id":51631533,"identity":"994e85c0-3983-4e38-bff7-9ad10a7dc954","added_by":"auto","created_at":"2024-02-26 09:36:22","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":546274,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e Bar graph represents the average percentage of total detected amino acids in the three analyzed categories; Susceptible (S), Partially Resistant (PR), and Resistant (R). The percentages were calculated from the Total Ion Chromatogram (TIC). (No significant difference in one-way ANOVA; α 0.05, P.0.54). Graphics represent the average of each detected essential amino acid over the total essential amino acids \u003cstrong\u003e(b)\u003c/strong\u003e, non-essential amino acid over the total non-essential amino acids \u003cstrong\u003e(c)\u003c/strong\u003e and non-proteinogenic amino acid over the total non-proteinogenic amino acids \u003cstrong\u003e(d)\u003c/strong\u003ein percentage within each analyzed categories. The percentage of each component was calculated from the Total Ion Chromatogram (TIC). The asterisk (*) indicating lysine, means the only amino acid in which we observed significant differences between susceptible (S) and resistant (R) samples (t student analysis for lysine, S vs R (α 0.05, P 0.0035)). Colours in (A), (B) and (C) represent the different amino acid families; Aspartate family (red); Glutamate family (green); Pyruvate family (blue); Aromatic amino acid family (yellow) and Serine family (purple)\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3965075/v1/5e3673473f955c55d86175b7.jpg"},{"id":51631798,"identity":"6cc1999d-980c-4a5c-bb8c-1320554ada04","added_by":"auto","created_at":"2024-02-26 09:44:22","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":167492,"visible":true,"origin":"","legend":"\u003cp\u003eRelative Quantification (RQ) plot of the\u003cstrong\u003e \u003c/strong\u003etarget genes flavin-dependent monooxygenase 1 (\u003cem\u003eFMO1)\u003c/em\u003e \u003cstrong\u003e(a)\u003c/strong\u003e and aldehyde dehydrogenase 7B4 (\u003cem\u003eALDH7B4\u003c/em\u003e) \u003cstrong\u003e(b)\u003c/strong\u003e in the leaf samples from the nine Citrinae genotypes. Expression was normalized to Glyceraldehyde-3-phosphate dehydrogenase C2 (GAPC2) and a SAND family protein (SAND). A non-parametric Kruskal-Wallis one-way ANOVA was performed. Statistically significant differences were considered for p-values less than 0.05. A post hoc test, the Dunn test for multiple comparissons, was conducted to identify which specific groups differ from each other. (α 0,05; * P\u0026lt; 0,05; ** P \u0026lt; 0,01; *** P\u0026lt; 0,001; **** P\u0026lt; 0,0001)\u003c/p\u003e","description":"","filename":"Fig.5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3965075/v1/0b3147ffdffd822eff155766.jpg"},{"id":51631529,"identity":"cd914c67-c964-45c4-8648-8ffb402b0a4f","added_by":"auto","created_at":"2024-02-26 09:36:22","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":110883,"visible":true,"origin":"","legend":"\u003cp\u003eLysine catabolism including three metabolic routes. Cadaverine pathway (yellow), Sacchaporine pathway (green) and the N-hydroxyl L-pipecolic acid route (red). Lysine decarboxylase (LDC), lysine-ketoglutarate reductase/ saccharopine dehydrogenase (LKR/SDH), aldehyde dehydrogenase 7B4 (ALDH7B4), AGD2-like defense response protein 1 (ALD1), SAR-deficient 4 (SARD4), flavin-dependent monooxygenase 1 (FMO 1), Pirroline-carboxylate reductase (P5CR). α-amino-adipic semialdehyde (AAS), α-amino adipic acid (Aad), Δ1-piperideine-6-carboxylic acid (1,6-DP). (SAR) systemic acquired resistance\u003c/p\u003e","description":"","filename":"Fig.6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3965075/v1/88595e8ccfdc0c2bdaa992d3.jpg"},{"id":51632187,"identity":"026eb3de-a9f7-4f28-ae16-d98d7eaf9069","added_by":"auto","created_at":"2024-02-26 09:52:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":772795,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3965075/v1/308e17b3-35c7-4ee3-872e-cae0c9cc5b21.pdf"},{"id":51631797,"identity":"ea597f81-1336-42cf-b1f7-6d9c1eded51d","added_by":"auto","created_at":"2024-02-26 09:44:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3728065,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigs.Herranzetal.EJPP.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3965075/v1/3d7df31560c3b2cbc1c00d10.pdf"},{"id":51631526,"identity":"3fd3cea6-c60b-46f3-8b81-dae4639c79b6","added_by":"auto","created_at":"2024-02-26 09:36:22","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":13823,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigLegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-3965075/v1/24f7cc3084e9dc2f0aafb0ed.docx"},{"id":51631530,"identity":"5dac4d16-197a-40ce-bd7d-21f850da79e3","added_by":"auto","created_at":"2024-02-26 09:36:22","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":629805,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTablesHerranzetal.EJPP.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3965075/v1/f3228796121a2720b2c0726f.pdf"}],"financialInterests":"","formattedTitle":"Comparative metabolomic analysis of the phloem sap of nine citrus relatives with different degrees of susceptibility to Huanglongbing disease.","fulltext":[{"header":"Contribution to the field","content":"\u003cp\u003eModern citriculture is currently threatened by the disease Huanglongbing (HLB), also known as \u0026quot;citrus greening\u0026quot;, the most ravaging bacterial disease affecting worldwide citrus production and quality. Presently, research efforts are focused on searching for either curative treatments or resistant cultivars to HLB.\u003c/p\u003e\n\u003cp\u003eMetabolic analysis carried out in this work opens a new interesting line of research about the involvement of lysine catabolism in tolerance and resistance to HLB. Our study describes general changes potentially associated with HLB tolerance and, in particular, in the transcriptional responses of two enzymes related to the catabolism of the above-mentioned amino acid. This could be a starting point to accelerate the creation on-demand of new citrus cultivars resistant to this epidemic disease. I hope you consider interesting enough this work to be evaluated for European Journal of Plant Pathology.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eModern citriculture is currently threatened by the disease Huanglongbing (HLB), also known as \"citrus greening\". Due to its rapid dispersal, severity, and fast progression, HLB is currently considered the most devastating bacterial disease affecting worldwide citrus production and quality. HLB has been reported in Asia, the Middle East, Africa, and the Americas but it is expected to continue spreading. Hence, the European Commission qualified HLB as a quarantine priority pest in 2019 (Gabriel et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The disease is associated with at least three uncultured alpha-proteobacteria species of the genus \u003cem\u003eCandidatus\u003c/em\u003e Liberibacter, \u003cem\u003eCa.\u003c/em\u003e L. asiaticus (\u003cem\u003eC\u003c/em\u003eLas), \u003cem\u003eCa\u003c/em\u003e. L. africanus (\u003cem\u003eC\u003c/em\u003eLaf), and \u003cem\u003eCa\u003c/em\u003e. L. americanus (\u003cem\u003eC\u003c/em\u003eLam), which are vectored by two psyllids species into the phloem cells. \u003cem\u003eC\u003c/em\u003eLas and \u003cem\u003eC\u003c/em\u003eLam are naturally spread in citrus by the Asian psyllid \u003cem\u003eDiaphorina citri\u003c/em\u003e Kuwayama, whereas \u003cem\u003eC\u003c/em\u003eLaf is transmitted by \u003cem\u003eTrioza erytreae\u003c/em\u003e (Bov\u0026eacute;, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Gottwald, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The three bacterial species are phloem-limited but differ in their thermosensitivity, the severity of the induced symptoms, and each appears to have independently evolved on the continent indicated in its name (Nelson et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo date, there have been no successful established methods for long-term commercial control of HLB, making it progressively more challenging to curb the spread of this disease. Given the absence of a cure, the majority of citrus producers have opted to coexist with infected trees rather than completely removing them. Within the range of disease control strategies that have been assessed to reduce the impact of the disease are antimicrobial compounds potential resistance inducers, phytohormones, thermotherapy improved nutritional programming (ENP) and symptomatic branch trimming (Gottwald et al., 2012; Zhang et al., 2014; Doud et al., 2017; Killiny et al., 2020). Unfortunately, implementing all these approaches widely in the field would be excessively costly. Hence, there is a requirement to conduct further assessment of disease control strategies aimed at addressing pathogen spread on three distinct levels; pathogen, host and vector.\u003c/p\u003e \u003cp\u003eBacterial diseases of plants are typically very challenging to control. Unluckily, the HLB pathosystem is problematic for laboratory studies. Advances in understanding the pathogenesis of \u003cem\u003eC\u003c/em\u003eLs have been hindered because the bacteria had not been successfully cultured long-term (Ha et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zu\u0026ntilde;iga et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and pure cultures have yet to be maintained. Additionally, the specific characteristics of HLB infection, such as the uneven distribution of the bacteria, the low \u003cem\u003eC\u003c/em\u003eLas titer during the initial phase of the latent period, and the influence of the environment on \u003cem\u003eC\u003c/em\u003eLas replication, not only hampered the development of detection methods and treatments, but also led to confusing results in the resistance analysis (Alves et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs a consequence, there is an urgent need for unequivocal resistant citrus cultivars, which can be utilized in breeding programs or directly as potential rootstocks or interstocks, to mitigate the harmful effects caused by this devastating disease. A wide variety of HLB resistance levels, including citrus relatives described as full-resistant, has been reported in the family \u003cem\u003eRutaceae\u003c/em\u003e, subfamily \u003cem\u003eAurantioideae\u003c/em\u003e (Folimonova et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Cifuentes-Arenas et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, they are phylogenetically distant from commercial citrus that breeding hybrids are unviable. Taking all of the above into consideration, Alves et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) tested for \u003cem\u003eC\u003c/em\u003eLas resistance a diverse collection of close cross- and/or graft-compatible citrus relatives belonging to the subtribe Citrinae. Utilizing an accurate and consistent evaluation system, they have demonstrated full resistance to \u003cem\u003eC\u003c/em\u003eLas in a small group of the analyzed species (Alves et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOmics-based approaches constitute a powerful tool to understand the citrus response to the pathogen, and for performing comparative analyses among citrus cultivars with different levels of resistance to HLB. In particular, metabolomics has been previously utilized to study the mechanisms supporting this resistance/tolerance. For example, Killiny (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), reported that the phloem sap of HLB-resistant curry leaf tree contains lower amounts of most metabolites compared to the partially resistant citrus orange jasmine and the susceptible Valencia sweet orange. Different studies on others citrus cultivars have shown a positive correlation between tolerance to HLB and most amino acids and leaf volatiles, while a negative correlation was observed for several organic acids (Hijaz et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Killiny and Hijaz, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Killiny et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Valim \u0026amp; Killiny (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) analyzed the fatty acid composition of the phloem sap of 12 citrus varieties with different tolerance toward HLB and found that capric acid was higher in \u0026ldquo;Sugar Belle\u0026rdquo; and Carrizo citrange tolerant hybrids. Interestingly, Albrecht et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) demonstrated that rootstock influences the metabolic response to CLas in grafted sweet orange trees. Other comparative metabolomics studies have suggested that tolerance is linked to preserving plant growth and phloem formation rather than activating plant defense mechanisms to overcome the disease (Suh et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHerein, we have taken advantage of the strict evaluation of the resistance to \u003cem\u003eC\u003c/em\u003eLas of different Citrinae genotypes carried out by Alves et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). We selected three genotypes from each of the defined categories: susceptible, partially resistant, and resistant. Notably, the latter two categories included cultivars formerly classified as \u003cem\u003eMicrocitrus spp\u003c/em\u003e. and \u003cem\u003eEremocitrus glauca\u003c/em\u003e, which are now in the genus \u003cem\u003eCitrus\u003c/em\u003e and are actively employed in some breeding programs worldwide. However, the basis of HLB resistance in these citrus relatives remains enigmatic. To determine whether the level of resistance depends on the metabolic background of the different cultivars before infection, we carried out a metabolomics analysis of healthy phloem plants. After trimethylsilyl derivatization, we quantified 20 amino acids, 12 organic and inorganic acids, 15 sugars and sugar acids, 2 amines, 2 nitrogenated bases, and 2 unknown compounds. In general, the metabolites identified in our analysis of HLB-resistant and partially resistant citrus genotypes were similar to those described in other reports on HLB-tolerant citrus. Remarkably, we observed some intriguing differences regarding the potential role of lysine catabolism in HLB resistance. Identifying specific metabolic steps and pathways involved in tolerance will not only enhance our understanding of plant-pathogen interactions but also provide valuable insights into the potential metabolic compounds that could be harnessed against \u003cem\u003eC\u003c/em\u003eLas. This knowledge has the potential to expedite the development of new citrus cultivars that are naturally resistant to these fastidious bacteria.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant material and plant growing conditions\u003c/h2\u003e \u003cp\u003eThe nine citrus varieties used in the present study were maintained in a greenhouse with a temperature of 30 \u0026ordm;C and a relative humidity of 35%. These Citrinae species were previously assessed and classified into three categories; susceptible, partially resistant, and resistant by Alves et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). They include \u003cem\u003eCitrus x sinensis 'Pera', Poncirus trifoliata 'Rubidoux'\u003c/em\u003e and \u003cem\u003eCitrus x sinensis \u0026acute;Valencia Midknight'\u003c/em\u003e (susceptible), \u003cem\u003eMicrocitrus australasica\u003c/em\u003e hybrid, \u003cem\u003eMicrocitrus virgata hybrid\u003c/em\u003e and \u003cem\u003eMicrocitrus garrawayae\u003c/em\u003e (partially resistant), \u003cem\u003eMicrocitrus australis\u003c/em\u003e hybrid, \u003cem\u003eEremocitrus glauca\u003c/em\u003e and \u003cem\u003eMicrocitrus warburgiana\u003c/em\u003e hybrid (resistant) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (Alves et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). All of these citrus varieties were 3-year-old scions, approximately 50\u0026ndash;60 cm in height and grafted on \u003cem\u003eCitrus macrophylla\u003c/em\u003e with the exception of the \u003cem\u003eMicrocitrus australis\u003c/em\u003e hybrid which was propagated on \u003cem\u003eCitrus volkameriana\u003c/em\u003e. At least three independent mature trees were used for each species and hybrids. All scions were propagated using buds from a single donor mother plant per genotype, and were kept at an insect-proof climate-controlled greenhouse at the Institute for Plant Molecular and Cell Biology (UPV-CSIC, Valencia, Spain) with temperatures maintained between 25\u0026ndash;27\u0026ordm;C. The plants were grown in 8 L pots filled with coir, irrigated and fertilized twice a week, and sprayed monthly with preventive insecticides.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCitrinae genotypes/accessions used in this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccession\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbbreviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSusceptible\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCitrus x sinensis \u0026acute;Pera\u003c/em\u003e\u0026acute;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePoncirus trifoliata Rubidoux\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCitrus sinensis valencia Midknight\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePartially resistant\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMicrocitrus australasica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePR1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMicrocitrus virgata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePR2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMicrocitrus garrawayae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePR3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eResistant\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMicrocitrus australis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eErimocitrus glauca\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMicrocitrus warburgiana\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePhloem sap collection\u003c/h2\u003e \u003cp\u003eStems measuring 10\u0026ndash;20 cm in lengh with a diameter of 0.2\u0026ndash;0.3 cm were collected from greenhouse plants. Phloem sap was collected using the centrifugation method described by Hijaz \u0026amp; Killiny, (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Briefly, the bark was manually removed from the sprig and washed with deionized water to eliminate any potencial contamination from the xylem sap. To collect the phloem sap, five small pieces of the bark were placed in a 0.5-mL microcentrifuge tube with a small hole at the bottom, and this tube was then nested into a 2-mL microcentrifuge tube. The sample was centrifuged at 12,000 rpm for 15 minutes at room temperature, and the collected phloem sap was stored at -80 \u0026ordm;C until further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePrimary metabolite analysis\u003c/h2\u003e \u003cp\u003ePrimary metabolite analysis was conducted at the Metabolomics Platform of the Institute for Plant Molecular and Cell Biology (UPV-CSIC, Valencia, Spain). The method used was a modification of the one originally described by Roessner et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). For the analysis, one \u0026micro;L of each sample of phloem sap was dried. The dry residues were redissolved in 40 \u0026micro;L of 20 mg/mL methoxyamine hydrochloride in pyridine and incubated for 90 minutes at 37 \u0026ordm;C. Then, 70 \u0026micro;L MSTFA (N-methyl-N-[trimethylsilyl]trifluoroacetamide) and 6 \u0026micro;L of a retention time standard mixture (3.7% [w/v] mix of fatty acid methyl esters ranging from 8 to 24C) were added and the samples were incubated for 30 minutes at 37 \u0026ordm;C.\u003c/p\u003e \u003cp\u003eSample volumes of 2 \u0026micro;L were injected in split1:30 and splitless mode in a 6890 N gas chromatograph (Agilent Technologies Inc. Santa Clara, CA) coupled to a Pegasus4D TOF mass spectrometer (LECO, St. Joseph, MI). Gas chromatography was performed on a BPX35 (30 m \u0026times; 0.32 mm \u0026times; 0.25 \u0026micro;m) column (SGE Analytical Science Pty Ltd., Australia) with helium as the carrier gas, constant flow 2 mL/min. The liner was set at 250\u0026deg;C. The oven program was 85\u0026deg;C for 2 min, 8\u0026deg;C/min ramp until 360\u0026deg;C. Mass spectra were collected at 6.25 spectra s\u0026thinsp;\u0026minus;\u0026thinsp;1 in the m/z range 35\u0026ndash;900 and ionization energy of 70 eV. Chromatograms and mass spectra were evaluated using the CHROMATOF program (LECO, St. Joseph, MI). Metabolites were identified by comparison with a custom library made with commercial standards.\u003c/p\u003e \u003cp\u003eData analyses were performed using Metaboanalyst, version 5.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.metaboanalyst.ca\u003c/span\u003e\u003cspan address=\"https://www.metaboanalyst.ca\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Logarithmic transformation and autoscaling scaling were employed as normalization to perform the principal component analysis and the hierarchical clustering and also to generate the heatmap. In the context of hierarchical clustering, the Euclidean distance metric and the Ward clustering algorithm were utilized as parameters to identify features with significantly different accumulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eReal-time quantitative reverse transcription PCR\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from a pool of three leaves of each Citrinae species using RIBOzol Reagent. Remnant genomic DNA was removed by DNase I treatment. First-strand cDNA was synthesized from 0.5 \u0026micro;g of total RNA using RevertAid H Minus Reverse Transcriptase and oligo(dT) (Thermo Fisher Scientific, Carlsbad, CA, USA). Real-time quantitative PCR (qPCR) was carried out using QuantStudio 3 Real-Time PCR machine (Applied Biosystems, Waltham, MA, USA) and PyroTaq EvaGreen qPCR Supermix (Solis BioDyne, Tartu, Estonia), specific oligonucleotides primers, and recommended qPCR cycles as follows: initial denaturation for 12 min at 95\u0026deg;C, followed by 50 cycles of 15 s at 95\u0026deg;C and 60 s at 60\u0026deg;C. Specific oligonucleotides primers were designed using Primer3web version 4.1.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinfo.ut.ee/primer3\u003c/span\u003e\u003cspan address=\"https://bioinfo.ut.ee/primer3\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Oligonucleotide efficiencies were tested by qRT-PCR using ten-fold serial dilutions of the corresponding cDNA. Each technical replicate was run in triplicate. \u003cem\u003eSAND\u003c/em\u003e (SAND family protein) and \u003cem\u003eGAPC2\u003c/em\u003e (Glyceraldehyde-3-phosphate dehydrogenase C2) genes were used as endogenous controls (Mafra et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; M\u0026aacute;ximo et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The primer sequences of both target and reference genes are shown in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe comparison of transcript abundance of the different genes analyzed across the different samples was carried out using GraphPad Prism version 6.00 for Windows, GraphPad Software, La Jolla California USA, (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://www.metaboanalyst.ca\" target=\"_blank\"\u003ewww.graphpad.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.graphpad.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A non-parametric Kruskal-Wallis one-way ANOVA was performed. Statistically significant differences were considered for p-values less than 0.05. A post hoc test, the Dunn test for multiple comparissons, was conducted to identify which specific groups differ from each other.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMetabolic variations in the phloem of HLB-resistant, partially resistant, and susceptible citrus accessions\u003c/h2\u003e \u003cp\u003eConsidering that the citrus phloem sap is the plant tissue in which \u003cem\u003eC\u003c/em\u003eLas must survive and grow, we performed a comparative metabolomics analysis of the phloem sap from nine Citrinae genotypes exhibiting different levels of resistance to \u003cem\u003eC\u003c/em\u003eLas. These genotypes included three susceptible (S1 to S3), three partially resistant (PR1 to PR3), and three full-resistant (R1 to R3) accessions (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Partial resistance was assigned to species/hybrids showing \u003cem\u003eC\u003c/em\u003eLas infection in a fraction of tested scions and/or presenting a delayed infection, without ever reaching the bacterial titer found in susceptible genotypes (Alves et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A diverse range of molecules were detected after trimethylsilyl derivatization and classified for subsequent analysis. They included 20 amino acids, 12 organic and inorganic acids, 15 sugars and sugar acids, 2 amines, 2 nitrogenated bases, and 2 unknown compounds (Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA and S2B). Their retention time, quantification ion and injection mode (split 1:10/ splitless) are shown in Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e. The higher percentage of detectable metabolites were sugars, except for the HLB-resistant R2 \u003cem\u003eEremocitrus glauca\u003c/em\u003e, in which organic acids were more abundant (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eWe conducted a principal component analysis (PCA) and a total of 7 principal components (PCs) were extracted, accounting for 88.8% of the total variance. The total variation explained by the first two principal components was 56%, with PC1 contributing 40% and PC2 contributing 16% (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea). Notably, the partially resistant \u003cem\u003eMicrocitrus australasica\u003c/em\u003e (PR1) appeared as an outlier in PC1, and this separation was primarily attributed to the elevated levels of many compounds in PR1, as confirmed by the loading plot (see Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb). This distinct profile was also evident in the heatmap, which displayed the fold change for each compound identified through gas chromatography and mass spectrometry analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). To mitigate the influence of PC1 on the variance, we opted to exclude this outlier from the subsequent PCA analysis. As shown in Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ec, the PCA analysis without PR1 revealed that susceptible samples clustered in the right lower quadrant, indicating their similarity in terms of metabolite profiles.\u003c/p\u003e \u003cp\u003e \u003cb\u003eOrganic and inorganic acid accumulate in higher amounts in both partially resistant and resistant citrus samples\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA total of eleven organic acids and one inorganic acid (phosphoric acid) were detected in the phloem sap samples. Remarkably, the average percentage of the total organic and inorganic acids was higher in both partially resistant and resistant samples when compared to the susceptible ones (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and Supplementary Table S4). Among the detected organic acids were dicarboxylic acids such as fumaric, malic, malonic, oxalic, and succinic.\u003c/p\u003e \u003cp\u003eMalic and quinic acid were generally the most abundant organic acids, however, only quinic acid was found to be over-represented in PR and R samples, with the exception of the R3 sample (Supplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). While the percentage of malic acid was similar in susceptible and resistant samples, we observed differences in quinic acid accumulation between both categories. Quinic acid was the most abundant organic acid in 83% of the resistant and partially resistant samples, as well as in one susceptible sample. On average, the percentage of this organic acid was higher in resistant cultivars. Additionally, the average of each organic acid percentage within the three categories revealed that oxalic acid, fumaric acid, threonic acid, lactic acid, and quininic acid were elevated in susceptible samples. By contrast, citric acid and anthranilic acid were higher in resistant cultivars (Supplementary Figs. S3a y b). The distribution of individual organic acid within each accession did not exhibit any differential pattern among the three categories (Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003ePCA analysis generated using the organic and inorganic acids did not reveal any separation among the groups with different resistance levels (data not shown). However, when PCA analysis was conducted by excluding PR samples, it distinguished resistant and susceptible samples in two distinct groups, with PC1 contributing 25.4% of the variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Resistant varieties clustered on the left side of the PCA plot, indicating a distinct organic acid composition in the phloem sap compared to susceptible varieties. The corresponding loading plot showed that the majority of organic acid levels in resistant cultivars were higher than in the susceptible ones, although a high variability is patent (Supplementary Fig. S4).\u003c/p\u003e \u003cp\u003eIn conclusion, the percentage of total organic acids was higher in the R and PR samples. Quinic acid was in general the most abundant organic acid and displayed a tendency to have higher content in PR and R compared to S samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSugars showed a higher accumulation in the susceptible citrus genotypes\u003c/h2\u003e \u003cp\u003eThe average percentage of total sugars showed a noticeable increasing trend in the susceptible group although differences were not statistically significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and Supplementary Table S5). The identified sugars included monosaccharides such as fructose, glucose, and rhamnose, disaccharides such as sucrose, and trisaccharides such as raffinose. Additionally, several sugar alcohols, like galactinol and myo-inositol, as well as sugar acids were also detected. Interestingly, there was a trend toward an average percentage increase of sugar acids in PR and R accessions, being more pronounced in the R group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). This finding agrees with that previously reported in Sugar Belle mandarin (Killiny et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and the fact that sugar acids are induced after \u003cem\u003eC\u003c/em\u003eLas infection in Cleopatra mandarin (Albrecht et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and sweet orange (Hijaz et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe PCA analysis based on sugars and sugar derivatives is shown in Supplementary Fig. S5a. The total variation explained by the first two principal components was 49.7% with PC1 contributing 32.2% and PC2 contributing 17.5%. The loading plot analysis revealed that the sugar composition of susceptible samples was different from the rest of the samples. However, within the susceptible category, there appeared to be higher variability compared to the resistant varieties (Supplementary Fig. S5b). The most abundant sugars in the citrus phloem sap were fructose, sucrose, glucose, and the sugar alcohol myoinositol (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). While glucose exhibited a trend of higher levels in the susceptible samples, the differences compared to the other categories were not statistically significant. By contrast, myoinositol was found at higher levels in the resistant samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec and Supplementary Fig. S6). The rest of the minority sugar and sugar derivatives clustered to the left of the loading plot together with the resistant and partially resistant samples (Supplementary Fig. S5b). Some of them, such as glycerol, and the detected three unidentified monosaccharides showed an upward trend in the resistant cultivars (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003eThus, we observed an upward trend in the total sugar levels of the susceptible genotypes. It's worth noting that there is an observable trend in the average percentage increase of sugar acids in PR and R accessions, with a more pronounced effect in the R group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLow amount of lysine in phloem sap as a possible marker for HLB resistance\u003c/h2\u003e \u003cp\u003eSeventeen proteinogenic amino acids and three non-proteinogenic amino acids (GABA, 4-hydroxyproline, and ornithine) were detected in the phloem sap. The percentage of non-proteinogenic amino acids accounted for approximately 25% of the total amino acids in all the samples, regardless of their resistance levels. In terms of proteinogenic amino acids, the quantity of essential amino acids was considerably lesser than non-essential amino acids, with no significant differences among the samples (Supplementary Fig. S7). Neither significant differences in the percentage of the total amino acids nor a trend bias was observed comparing the three categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea and Supplementary Table S6). Non-essential amino acid distribution was more homogeneous among the susceptible species, with proline being the most abundant amino acid in 78% of the samples (Supplementary Fig. S8a). On the other hand, the distribution of most of the essential amino acid was very similar among the three categories, with the exception of lysine levels, which were significantly higher in susceptible samples (Supplementary Fig. S9). Although the levels of other amino acid did not show statistically significant differences among the three categories, the average percentage showed changes that could be potentially associated with the level of tolerance. The levels of other three amino acids from the aspartate family, threonine, asparagine, and aspartic acid were present at higher levels in the resistant cultivars (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Four amino acids of the glutamate family, including glutamic acid and 4-hydroxyproline were also abundant in resistant varieties, in contrast to proline and ornithine, which had lower representation in this category (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed and Supplementary Figs. S8a and S8b). Finally, the level of alanine from the pyruvate family was elevated in resistant and partially resistant cultivars.\u003c/p\u003e \u003cp\u003eThe PCA analysis that emerged from amino acid data is presented in Supplementary Fig. S10a. The total variation explained by the first two principal components was 59.7%, with PC1 contributing 39.4% and PC2 contributing 20.3%. Resistant and susceptible samples clustered into two well-defined groups. Additionally, partially resistant sample PR2, clustered with the susceptible samples, while PR3 clustered with the resistant ones. The corresponding loading plot showed that susceptible samples were high in proline, lysine, and the non-proteinogenic amino acid ornithine. In contrast, resistant samples were high in 4-hydroxyproline, glutamic acid, threonine, and histidine, although many of the amino acid levels detected in PR2 were elevated (Supplementary Fig. S10b).\u003c/p\u003e \u003cp\u003eTherefore, lysine decrease in phloem sap could be considered as a potential marker of HLB resistance.\u003c/p\u003e \u003cp\u003e \u003cb\u003eExpression of genes of lysine catabolism is affected in resistant citrus samples.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBased on our data, lysine was the only amino acid present at significantly higher levels in the phloem sap of susceptible samples in comparison with the resistant ones. Lysine and its catabolic intermediates have been involved in plant responses to abiotic and biotic stresses (Arruda \u0026amp; Barreto \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thus, we conducted RT-qPCR analyses to investigate the potential involvement of the lysine catabolic pathway in determining the tolerance levels of the accessions analyzed. We evaluated the expression of five genes including two genes of the saccharopine pathway (lysine-ketoglutarate reductase/saccharopine dehydrogenase (\u003cem\u003eLKR/SDH\u003c/em\u003e) and aldehyde dehydrogenase 7B4 (\u003cem\u003eALDH7B4\u003c/em\u003e)), two genes associated with a pathway recently link to plant systemic acquired resistance (SAR) (SAR-deficient 4 (\u003cem\u003eSARD4\u003c/em\u003e)) and flavin-dependent monooxygenase 1 (\u003cem\u003eFMO 1\u003c/em\u003e) and, one gene implicated in the connection between these two branches of lysine catabolism (pyrroline-5-carboxylate reductase (\u003cem\u003eP5CR\u003c/em\u003e). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, the transcript abundance of the \u003cem\u003eFMO1\u003c/em\u003e gene was significantly higher in two resistant samples, \u003cem\u003eEremocitrus glauca\u003c/em\u003e (R2), and \u003cem\u003eMicrocitrus warburgiana\u003c/em\u003e hybrid (R3), in comparison to all the susceptible samples. However, in the resistant \u003cem\u003eMicrocitrus australis\u003c/em\u003e (R1), the level of this transcript was only significantly higher compared with one of the susceptible genotypes, \u003cem\u003ePoncirus trifoliata Rubidoux\u003c/em\u003e (S2). On the other hand, the transcript of \u003cem\u003eALDH7B4\u003c/em\u003e was downregulated in the resistant cultivars. All the observed differences were significant except between \u003cem\u003eCitrus x sinensis \u0026acute;Pera\u0026acute;\u003c/em\u003e (S1) and \u003cem\u003eMicrocitrus australis\u003c/em\u003e hybrid (R1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). The expression of the rest of the analyzed genes did not exhibit significant differences among the three categories.\u003c/p\u003e \u003cp\u003eOur findings suggest that lysine catabolism is influenced in the resistant plants. Among the five genes we analyzed, FMO1 and ALDH7B4 displayed significant alterations in the accumulation of their transcripts when compared to susceptible cultivars. These changes in gene expression might be indicative of the role of lysine catabolism in the resistance of these citrus accessions to CLas infection.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWhile significant efforts have been dedicated to study the bases of tolerance/resistance to the devastating HLB disease, we are still a long way from a comprehensive understanding of this process. To gain further insights into the underlying mechanisms of the disease, omics technologies constitute an indispensable research tool to understand plant tolerance to this pathogen. In this study, we use metabolomics to try to gain insight into the mechanism underpinning the resistance, by analysing the composition of phloem sap in citrus varieties with different level of susceptibility to HLB (Alves et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding organic acids, we observed a global upward trend in both the resistant and partially resistant cultivars. This aligns with the finding of Killiny et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), who conducted a metabolite profile analysis of the \u0026ldquo;Sugar Belle\u0026rdquo; mandarin hybrid to investigate its relative tolerance to HLB in comparison to some of its ancestors. Their research revealed that \u0026ldquo;Sugar Belle\u0026rdquo; exhibited elevated levels of phosphoric and some organic acids, including malic and threonic acid. Furthermore, previous studies have also reported an increase in the presence of various organic acids in resistant varieties (Killiny, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and \u003cem\u003eC\u003c/em\u003eLas-infected leaves from susceptible cultivars (Albrecht et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Therefore, our data, in line with these previous studies, suggest that organic acids may have a role in nutrient uptake from the soil and serve as a priming strategy for those cultivars to be more tolerant.\u003c/p\u003e \u003cp\u003eIn our analysis, we found that malic and quinic acid were the most abundant organic acids, consistent with prior research (Jones et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Notably, only quinic acid showed a trend of higher content in PR and R samples compared to S samples. This metabolite has previously been link to defence responses in citrus leaves and has been detected in fruits of semi-tolerant varieties to CLas (Jones et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Killiny, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), although its specific role in countering this pathogen remains unknown. Additionally, the average percentage of each organic acid within the three categories revealed trends that may be associated with the level of tolerance. Among them, citric acid and anthranilic acid, found at higher accumulation levels in resistant cultivars in our analysis, have been reported as metabolites involved in stress tolerance and plant protection (Zahan et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; K\u0026ouml;llner et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHigher levels of sugars have been previously associated with HLB susceptibility (Albrecht et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Killiny, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Although there was not a consistent pattern in sugar composition among the different categories in our analysis, we did observe an upward trend in total sugar levels in the susceptible genotypes. However, these metabolites are unlikely to be the limiting factor for the HLB vector, since no clear correlation between glucose, fructose, sucrose, and citrus susceptibility to \u003cem\u003eC\u003c/em\u003eLas was found (Killiny, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It\u0026acute;s worth mentioning that effects on carbohydrate metabolism have been previously described in different phloem-related plant-pathosystems, including HLB (Albrecht et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIrrespective of their role in protein biosynthesis, numerous amino acids have been reported to participate in plant response to different stresses (Trovato et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In our study, we have observed distinct trends in the levels of certain amino acids that could be coupled to the degree of HLB resistance, with a majority of them belonging to the glutamate and aspartate families. Higher constitutive concentrations of amino acids from the glutamate family have previously been associated with tolerance and resistance to \u003cem\u003eC\u003c/em\u003eLas. In our analysis, glutamate and 4-hydroxyproline showed an upward tendency in resistant cultivars. Both of these amino acids have been formerly correlated with defence responses and resistance to plant pathogens (Albrecht et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Qiu et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Deepak et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In contrast, two other members of this family, ornithine and proline, displayed a downward trend in the resistant samples. Supporting this, ornithine has been associated with susceptibility in various pathosystems (Dhodary et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jim\u0026eacute;nez-Bremont et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Proline upregulation, on the other hand, has been connected to \u003cem\u003eC\u003c/em\u003eLas tolerance, as well as other biotic and abiotic stresses, and has been observed in infected plants compared to controls (Albrecht et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chin et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, in a metabolic profiling of the phloem sap from fourteen varieties with different levels of tolerance to \u003cem\u003eC\u003c/em\u003eLas, only one tolerant variety exhibited higher proline levels (Killiny, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The variability in the metabolic composition of the different varieties and the conditions used in the different studies make it challenging to draw consistent conclusions. Nonetheless, it is worth noting that \u003cem\u003eC\u003c/em\u003eLas lacks the ability to synthesize proline, phenylalanine, tryptophan, cysteine, tyrosine, and histidine and other essential translation components. These components are crucial for the bacterium's replication and metabolic activity, and it must acquire them from the host (Mendon\u0026ccedil;a et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zu\u0026ntilde;iga et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe aspartate family pathway encompasses the synthesis of amino acids such as lysine, asparagine, and threonine (Yang \u0026amp; Ludewig, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Our metabolomics analysis revealed higher levels of threonine in the resistant cultivars as well as increased levels of asparagine in both the resistant and partially resistant cultivars. The essential role of threonine as a sensor of different metabolic and environmental signals and translator of these signals into specific functional outputs, including stress tolerance, has been previously reported (Muthuramalingam et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Killiny, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Killiny et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, it's worth noting that lower amounts of threonine in resistant Rutaceae genotypes in comparison to susceptible cultivars, have also been described (Killiny, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Pepper asparagine synthase 1, the enzyme required for the production of asparagine from aspartate, was reported as essential for stress response (Hwang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLysine is a limiting essential amino acid in plants, and its biosynthesis has been a matter of study aimed at enhancing the nutritional value of crops. As mentioned above, lysine catabolism research has recently focused on tolerance to biotic and abiotic stresses. Lysine can be catabolized through several metabolic pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In some plants, lysine is converted into the alkaloid cadaverine (Hartmann \u0026amp; Zeier, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Arruda \u0026amp; Barreto, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A second ubiquitous catabolic pathway in plants is the saccharopine pathway rendering acetyl-CoA and glutamate. Furthermore, the third pathway, with a central role in plant immunity, leads to the generation of N-hydroxypipecolic acid (NHP), a metabolite recently involved in plant SAR (Hartmann et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Contradictory results have been reported describing the lysine accumulation levels in response to pathogens (e.g. N\u0026aacute;varov\u0026aacute; et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Albrecht et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Killiny \u0026amp; Hijaz, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Killiny, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). According to our results, among the essential amino acids, lysine represented on average 19.26%, 12.66%, and 11.40% in susceptible, partially resistant, and resistant samples, respectively. The significant difference in lysine levels between the resistant and susceptible cultivars, suggests a potential key role in plant defense.\u003c/p\u003e \u003cp\u003eRegarding the synthesis of NHP (N-hydroxypipecolic acid), several enzymes have been associated with its production. The aminotransferase ALD1 (AGD2-like defense response protein 1), (Hartmann \u0026amp; Zeier, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Holmes et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), a reductase, SARD4 (Ding et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); and FMO1, an NHP-synthesizing pipecolate N-hydroxylase (Hartmann \u0026amp; Zeier, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Holmes et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, the saccharopine pathway, which is associated with both abiotic and biotic stress responses, can produce pipecolate through two enzymatic reactions catalyzed by lysine-ketoglutarate reductase/saccharopine dehydrogenase (LKR/SDH) and pyrroline-5-carboxylate reductase (P5CR) (Arruda \u0026amp; Barreto, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These authors explored whether both pathways contribute to SAR activation by comparing the transcriptional response of key genes, including LKR/SDH, \u003cem\u003eALD1, SARD4\u003c/em\u003e, and \u003cem\u003eFMO1\u003c/em\u003e in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e under various biotic and abiotic conditions. Under most biotic stresses, all the analyzed genes were upregulated. Conversely, only the saccharopine pathway was upregulated under abiotic conditions.\u003c/p\u003e \u003cp\u003eThe observed differences in lysine levels between susceptible and resistant categories led us to study the potential involvement of both the saccharopine and NHP pathways in HLB tolerance. We monitored transcriptional response of key genes, including \u003cem\u003eLKR/SDH, ALDH7B4, P5CR, SARD 4, and FMO1\u003c/em\u003e, in the leaves of citrus plants. Our results revealed significant upregulation of \u003cem\u003eFMO1\u003c/em\u003e in two of the resistant samples compared to the susceptible ones. As previously mentioned, FMO1 is the final enzyme of a pathogen-inducible L-lysine catabolic pathway in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e. Koch et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) reported that the overexpression of FMO1 under the control of the 35S promoter increases the basal resistance to \u003cem\u003ePseudomonas syringae\u003c/em\u003e. Additionally, Mishina \u0026amp; Zeier (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) demonstrated the crucial role of this enzyme in the establishing of SAR. Afterward, supporting this, it was reported that FMO1 catalyzes the conversion of pipecolic acid to NHP, which is a critical amino acid with a central role in the establishment of SAR (Hartmann et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Interestingly, these authors did not detect this metabolite in unstressed \u003cem\u003eArabidopsis thaliana\u003c/em\u003e; however, NHP strongly accumulated in the infected leaves. This metabolite was also not detected in any healthy phloem sap samples we analysed. Concerning the pipecolic acid, we did not obtain any conclusive result indicating significant differences among categories.\u003c/p\u003e \u003cp\u003eAnother lysine-catabolite is the α-amino adipic acid (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Its biosynthesis is dependent on the LKR/SDH and ALDH7B4 saccharopine enzymes. In our analysis, \u003cem\u003eALDH7B4\u003c/em\u003e exhibited a decreased transcriptional response in the resistant genotypes. It's interesting to note that N\u0026aacute;varov\u0026aacute; et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) previously suggested that the saccharopine pathway leading to α-amino adipic acid has no critical role in the resistance and SAR establishment after \u003cem\u003ePseudomonas syringae\u003c/em\u003e infection. Upstream of ALDH7B4, LKR/SDH converts lysine to α-aminoadipate semialdehyde and glutamate, which is a precursor for several metabolites related to stress including pipecolic acid (Arruda \u0026amp; Barreto, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough this is a small picture especially due to the number of samples analysed, it allows us to speculate about the potential roles of the upregulation of \u003cem\u003eFMO1\u003c/em\u003e and the downregulation of \u003cem\u003eALDH7B4\u003c/em\u003e transcriptional responses. This differential regulation could create a more favourable scenario to combat the bacteria, potentially leading to increase resistance in certain cultivars. Somehow, it could be a plant strategy to anticipate defence mechanisms and gain an advantage over the pathogen. By identifying specific metabolic steps and defining the pathways involved in tolerance and resistance, we can develop powerful tools not only for discovering potential antimicrobial compounds against \u003cem\u003eC\u003c/em\u003eLas but also for expediting the development of new citrus cultivars with enhanced resistance to this devastating disease.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.C.H and V.P. conceived and designed the experiments. M.C.H. performed the experiments. M.C.H., J.A.N., A.L., P.M., J.F.M. and V.P. analyzed, and interpreted the data. M.C.H. wrote the manuscript. All authors reviewed and edited the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by grant no. 817526 (PRE-HLB) from the European Union H2020 Innovation Action Program.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThanks are due to B. Alqu\u0026eacute;zar for her valuable support with the sampling, L. Corach\u0026aacute;n-Valencia for her technical assistance and to A. Espinosa (Metabolomics service, IBMCP, UPV-CSIC) for the excellent technical support with the UPLC-PDAQ/TOF-MS analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlbrecht, U., Fiehn, O., \u0026amp; Bowman, K. D. (2016). Metabolic variations in different citrus rootstock cultivars associated with different responses to Huanglongbing. \u003cem\u003ePlant Physiology and Biochemistry\u003c/em\u003e, \u003cem\u003e107\u003c/em\u003e, 33\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.plaphy.2016.05.030\u003c/span\u003e\u003cspan address=\"10.1016/j.plaphy.2016.05.030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlbrecht, U., Tripathi, I., \u0026amp; Bowman, K. D. (2020). 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Linking metabolic phenotypes to pathogenic traits among \u003cem\u003eCandidatus\u003c/em\u003e Liberibacter asiaticus and its hosts. \u003cem\u003enpj Systems Biology and Applications\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41540-020-00142-w\u003c/span\u003e\u003cspan address=\"10.1038/s41540-020-00142-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 6.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-plant-pathology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejpp","sideBox":"Learn more about [European Journal of Plant Pathology](http://link.springer.com/journal/10658)","snPcode":"10658","submissionUrl":"https://www.editorialmanager.com/ejpp/default2.aspx","title":"European Journal of Plant Pathology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Huanglongbing, Candidatus Liberibacter, citrus phloem sap, Metabolomics, lysine catabolism, flavin-dependent monooxygenase 1","lastPublishedDoi":"10.21203/rs.3.rs-3965075/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3965075/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCitrus Huanglongbing (HLB) disease, also known as \u0026ldquo;citrus greening\u0026rdquo;, is currently considered the most devastating citrus disease due to its rapid spread, and high severity. Presently, research efforts are focused on searching for either curative treatments or resistant cultivars to combat HLB-associated bacterium \u0026lsquo;\u003cem\u003eCandidatus\u003c/em\u003e Liberibacter asiaticus\u0026rsquo; (\u003cem\u003eC\u003c/em\u003eLas).\u003c/p\u003e \u003cp\u003eMetabolomics can help to unravel the mechanisms supporting the potential tolerance/resistance of citrus relatives. Herein, we carried out a metabolomic analysis to determine whether the level of resistance of nine citrus-related genotypes is influenced by their pre-existing metabolic background before infection. For this purpose, the healthy phloem of nine Citrinae genotypes previously categorized according to their different responses to HLB was analyzed. A total of 53 different metabolites were targeted, including amino acids, organic and inorganic acids, and sugars. Interestingly, we observed that resistant and partially resistant genotypes exhibited higher accumulations of organic acids such as quinic acid and citric acid. In contrast, the amount of total sugars showed a clear upward trend in the susceptible genotypes. Notably, within this last group of metabolites, sugar acids displayed a trend toward an average percentage increase in both partially resistant and resistant accessions, being more evident in the resistant group.\u003c/p\u003e \u003cp\u003eChanges potentially associated with the level of resistance were observed in certain amino acids within the aspartate and glutamate families. However, only lysine levels were significantly higher in the susceptible samples. The evaluation of five genes associated with lysine catabolism by RT-qPCR revealed differences in transcript abundance between resistant and susceptible samples. These findings open a new avenue of opportunity for identifying metabolites and/or substances that could aid in developing resistance strategies to this devastating disease.\u003c/p\u003e","manuscriptTitle":"Comparative metabolomic analysis of the phloem sap of nine citrus relatives with different degrees of susceptibility to Huanglongbing disease.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-26 09:36:17","doi":"10.21203/rs.3.rs-3965075/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2024-05-02T14:49:26+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-02-22T10:34:33+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-22T09:58:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"European Journal of Plant Pathology","date":"2024-02-22T07:47:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Plant Pathology","date":"2024-02-19T09:17:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-plant-pathology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejpp","sideBox":"Learn more about [European Journal of Plant Pathology](http://link.springer.com/journal/10658)","snPcode":"10658","submissionUrl":"https://www.editorialmanager.com/ejpp/default2.aspx","title":"European Journal of Plant Pathology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6cf15807-072b-4362-8986-daec3f4ae1a9","owner":[],"postedDate":"February 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-06-18T15:37:05+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-26 09:36:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3965075","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3965075","identity":"rs-3965075","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}