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Detection and Seasonal Variations of Huanglongbing Disease in Navel Orange Trees using Direct Ionization Mass Spectrometry Wen Li, Ya-Nan Yao, Lin Wu, and Bin Hu J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.8b06427 • Publication Date (Web): 08 Feb 2019 Downloaded from http://pubs.acs.org on February 11, 2019
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Journal of Agricultural and Food Chemistry
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Detection and Seasonal Variations of Huanglongbing Disease in Navel
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Orange Trees using Direct Ionization Mass Spectrometry Wen Li,2# Ya-Nan Yao,1# Lin, Wu,1 Bin Hu1*
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1Institute
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Engineering Research Center for On-line Source Apportionment System of Air Pollution,
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Jinan University, Guangzhou 510632, China
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2Institute
of Mass Spectrometer and Atmospheric Environment, and Guangdong Provincial
of Laboratory Animal Science, Jinan University, Guangzhou 510632, China
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#These
authors contributed equally to this work.
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*Corresponding author:
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Dr. Bin Hu
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Institute of Mass Spectrometer and Atmospheric Environment, Jinan University
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Guangzhou 510632, China
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Tel: +86-20-8522 5991
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Fax: +86-20-8522 5991
17
Email:
[email protected] 1 ACS Paragon Plus Environment
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Abstract
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Citrus greening disease (Huanglongbing, HLB) is the most destructive disease of citrus. In
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this work, we have established a metabolite-based mass spectrometric (MS) method for rapid
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detection of HLB in Navel orange trees. Without sample pretreatment, characteristic mass
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spectra can be directly obtained from the raw plant samples using the direct MS method. The
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whole detection process can be accomplished within one minute. By monitoring and
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comparisons of the healthy and infected plants through a whole year, characteristic MS peaks
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of metabolites are found to be specific responses from infected plants and thus could be used
27
as biomarkers for detection of HLB. Therefore, HLB could be directly detected in the
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asymptomatic samples such as stems using this metabolite-based direct MS method. In
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addition, principal component analysis (PCA) and partial least squares discriminant analysis
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(PLS-DA) modes of metabolites from healthy and infected trees were established for
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investigating differentiation and seasonal variations of HLB in leaves, veins and stems,
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providing valuable information for understanding the HLB in different seasons.
33 34
Keywords: Huanglongbing Disease, Seasonal Variation, Navel Orange, Mass Spectrometry
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INTRODUCTION
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Citrus productions such as oranges, lemons, limes and grapefruits are one of the most
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important economic agricultural activities in the world. However, significant economic losses
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in citrus productions have occurred due to citrus greening disease, also well known as
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Huanglongbing (HLB), which has become the most destructive disease of citrus in the world
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and presents unprecedented challenges (1, 2). Typically, the severe disease symptoms in
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HLB-infected trees such as yellow new leaves, misshapen fruits, blotchy leaves; and the
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infected trees can be death eventually (3, 4). Furthermore, HLB can be spread by movement
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of infected citrus to healthy citrus by grafting, insect (i.e., Diaphorina citri), and dodder (5, 6).
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In Asia, HLB is associated with the bacterium Candidatus Liberibacter asiaticus (C. Las),
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which is nonculturable Gram-negative bacterial species that making the research and
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treatment of HLB very difficult; because there is no successful treatment method (4, 7).
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Alternately, mathematical models also have been established for simulating the transmission
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and development of HLB (5, 8).
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Various traditional methods, including microscopic techniques and molecular techniques,
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have been developed for detecting HLB in the citrus (9). For the microscopic techniques, the
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possibility of false negatives from sampling, many trees that are determined to be uninfected,
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because HLB pathogen loads are distributed unevenly in plant tissues, and can fluctuate with
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time (10). Currently, the widely accepted method used for the identification of the HLB is
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based on polymerase chain reaction (PCR) (11). However, HLB detection poses an enormous
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challenge because infected citrus tresses can remain asymptomatic, even for years (10).
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Therefore, transmission of the HLB from infected trees to their healthy neighbors can be
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diminished or even prevented through target removal if detection of HLB in early stage.
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Small-molecule metabolites are naturally produced by all living organisms. Plant
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metabolites can serve as indicators for minoring the health and environment of plants (12, 13). 3 ACS Paragon Plus Environment
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Compared to other methods, detection of HLB disease via metabolites would be
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advantageous for direct sample analysis, primarily due to abundant molecular information (9,
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14). Among different methodologies used for analysis of metabolites, mass spectrometry (MS)
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is an excellent analytical platform due to its high specificity, sensitivity and versatility (14).
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In recent years, liquid chromatography (LC) and gas chromatography (GC) coupled with MS
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analysis have been employed to understand plant’s responses to HLB (10, 15, 16). However,
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based on these traditional MS-based methods, direct methods for determining biomarkers of
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plant tissues poses a challenge, mainly because conventional MS cannot directly analyze raw
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bulk samples such as leaves, veins and fruits. In the last decade, the concept of ambient
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ionization MS was created since the development of desorption electrospray ionization (DESI)
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and direct analysis in real-time (DART) for direct sample analysis with no or little sample
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pre-treatment (17, 18). To date, some new ambient ionization techniques have also been
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developed for rapid analysis of raw food and agricultural samples (19-22).
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In the present study, a direct ionization mass spectrometric method (23-25) was
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established for the first time for rapid detection of HLB in Navel orange. In the direct MS
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method, spray ionization was induced from a tip of raw biological tissue by application of a
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spray voltage and spray solvent, such direct MS method has been successfully used for direct
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analysis of raw biological tissues without sample preparation in our previous work (25-28).
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Navel orange is a mutation of sweet orange. Ganzhou in Jiangxi Province is the top Navel
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orange producing area in China with an annual production of approximately 1.2 million tons
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(29). However, this producing-area has also recently suffered from the widespread outbreak
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of HLB and the output has significantly decreased in recent years (30). In this work, leaves,
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veins and stems from healthy and infected Navel orange trees were cut as the tip, and then
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connected a high voltage and application of some organic solvent to form spray ionization.
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Thus, the raw plant materials could be directly analyzed. In order to evaluate the HLB effects
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on Navel orange trees, healthy and infected samples were monitored through a whole year.
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Meanwhile, infected and healthy Navel orange trees as well as of different parts of tissues at
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different seasons were further differentiated based on their characteristic ions and statistical
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analysis. Overall, the direct MS method allows rapid analysis of raw plant materials without
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sample preparation, and thus rapidly provide valuable information for assessment of the HLB
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in different seasons.
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EXPERIMENTAL
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Chemicals and Reagents. All the chemicals and primers were purchased from Sigma (St.
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Louis, MO, USA). HPLC-grade organic reagents were purchased from Tedia (Fairfeild, OH,
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USA). The water was treated in Milli-Q water purification system (Millipore, Bedford, MA,
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USA).
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Plant Materials. The plant materials of Navel orange (Citrus sinensis [L.] Osbeck cv.
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Newhall; grafted on citrange) including leaves, veins and stems were collected from five
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healthy and five infected-HLB trees from the Navel orange-producing area (Xingguo,
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Ganzhou, China). The healthy and infected-HLB trees were identified by local agricultural
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specialists and further confirmed by polymerase chain reaction (PCR) detection. The
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collections of plant materials were distributed in Winter (Jan), Spring (Apr), Summer (Jul)
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and Fall (Oct) in 2018, respectively. The leaves, veins and stems were collected from mature
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leaves and tree branches from five directions, including the east, south, middle, west and
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north, respectively. All the HLB-infected leaves and vein samples are symptomatic samples,
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and the HLB-infected stems were also collected from symptomatic tree branches. All fresh
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samples were directly analyzed after washing their surface using Milli-Q water. All the plant
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materials are single-use. All the waste plants materials were processed by sterilization
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treatment to prevent the spread of disease.
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PCR Analysis. The HLB-infected and healthy trees were confirmed by a real-time
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fluorescent PCR system (Light cycler 96, Roche, Hamilton, NJ, USA) followed the protocols
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(11, 31). The PCR detection revealed cycle threshold (Ct) values at 27.56 - 33.86 for HLB-
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infected samples, and Ct values > 40.0 for all healthy samples. In this study, the Ct values
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under 35 with typical amplification curves are considered positive samples, while higher
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numbers (Ct values > 40.0) without typical amplification curves are considered negative
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samples; the Ct values between 35.0 and 40.0 were considered ambiguous and then were re-
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detected according to the standard method (31). PCR detections were performed in triplicate
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and repeated three times.
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Direct MS Analysis. Direct MS analysis of raw plant materials was performed as described
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in our previous work (25, 32). Briefly, the schematic diagram of experimental setup for direct
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MS analysis of tissue samples is shown in Figure 1. Raw plant materials including leaves,
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vein (midrib) and stems were cut into as sharp tips and placed in the front of the mass
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spectrometer (Waters Synapt G2-Si, Milford, MA, USA) by using a metal clip with distances
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of 10 mm horizontal from the sample tip to the MS inlet. The temperature of MS inlet was 80
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ºC. No sweep gas and Aux gas was used in this direct MS method. The high voltage supply
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from the mass spectrometer was connected to a metal clip, as shown the photo of the setup in
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Figure S1. Without any homogenization, by application of a high spray voltage (3.5 kV) and
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spray solvent (methanol, 5.0 μL) to the center of the tissue sample, spray ionization could be
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induced from the tissue tip-end sample to generate characteristic mass spectra. The
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mechanism of this kind of direct MS methods was revealed that the analytes in the raw
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samples were extracted by the solvents and then the sprayed out from the tip-end of substrate
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under the strong electric filed (32, 33).
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Statistical Analysis. Principal component analysis (PCA) and partial least squares
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discriminant analysis (PLS-DA) were carried out using SIMCA-13 (Umetrics, Sweden) as
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described previously (26, 27). For each MS spectrum, the normalized intensities of those
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monoisotopic peaks at the mass range from m/z 200 to m/z 1500 with signal intensities higher
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than 5.0 % were input to the SIMCA for the statistical analysis in this work. P-values were
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calculated for a two-tailed test in this work.
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RESULTS AND DISCUSSION
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Direct MS Analysis of Raw Plant Materials. Typical MS spectra of healthy and HLB-
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infected leaves, veins and stems obtained are shown in Figure 2 (in Winter) and Figure S3-5
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(in Spring, Summer and Fall). Two significant peaks at m/z 381 and m/z 649 were found in
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these spectra obtained from all the plant materials including healthy and infected leaves,
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veins and stems. Interestingly, it is noted that relative abundances of the peak at m/z 1259 was
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higher in healthy than infected samples. Upon the MS/MS experiments (Figure S2a), the loss
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of 610 Da from the ions at m/z 1259 suggests that peak at m/z 1259 was the dimer of the ions
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at m/z 649 as potassium adducts ([2M+K]+ at m/z 1259; [M+K]+ at m/z 649), probably due to
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its high concentration. The fragment ions at m/z 381 and m/z 487 were observed in the
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MS/MS spectrum of m/z 649. According to these fragments and literatures (34, 35), the peak
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at m/z 649 is good agreement with some possible potassiated flavonoids [M+K]+ species such
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as kaempferol-3-O-sophoroside and luteolin-7-O-sophoroside which could generate these
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fragments by loss of some function groups or/and retro-Diels-Alder cleavage of flavonoid
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ring C in the MS/MS experiments (34-36). The MS/MS spectrum of the ions at m/z 381
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shows the sole peak at m/z 219 (Figure S2c), suggesting that the m/z 381 could be
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disaccharides such as sucrose (m/z 381, [M+K]+) in citrus (37). Flavonoids and disaccharides
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are the most important metabolites in citrus plant (34, 38). These results suggest that the
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peaks at m/z 649 and m/z 381 are two characteristic peaks of biomarkers for detection of HLB,
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since their responses are very strong and visibly different in healthy and infected samples.
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Stable spray ionization with strong signal is crucial for direct MS analysis of raw
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samples, since the direct MS method becomes a potential rapid method for metabolic analysis
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without chromatographic separation. One example of the reproducibility test was investigated,
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as shown in Figure 3. In this test, a total of nine individual healthy leaves were analyzed by
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direct MS. The coefficient of variations (CVs) of peak heights of total ion current (TIC)
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chromatogram and two selected-ion chromatograms (SICs) of the ions at m/z 381 and m/z 649
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were obtained at 14.5 % and 12.1 %, respectively, which are acceptable for direct analysis of
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raw samples. From the TIC, it is noted that direct analysis of single sample could be
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completed within one minute, showing the rapid response of analytes from the raw sample.
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The plant materials are single-use, considering the signal reduction by reloading solvent (32,
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39).
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Differentiation of Healthy and Infected Trees Throughout A Year. To rapid identify the
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healthy and infected trees, PCA plots of the healthy and infected samples, including leaves,
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veins and stems, were generated from the first and second principal components based on
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their MS data. As shown in Figure 4a-c, the clusters of healthy and infected samples are well
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separated in PCA plots. Interestingly, the clusters of infected samples from different seasons
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are more concentrated than healthy samples, suggesting that there are some significant
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metabolites in infected trees through a whole year. In addition, it is found that PCA plots
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from leaves were the most concentrated clusters than veins and stems in infected samples,
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because it is the fact that the most obvious symptoms of HLB are the yellow leaves. To better
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understanding the HLB in different plant parts, the PCA plots of leaves, vein and stems
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obtained from healthy and infected Navel trees at different seasons were further analyzed in
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this study, since the early visible symptoms of HLB are leaves and veins (40) and stems play
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an important role in transcriptional of HLB with no yellowing symptoms (41). As shown in
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Figure S6 and S7, the clusters of samples from different parts (i.e., leaves, veins and stems)
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successfully separated and concentrated through a year, revealing that there are the unique
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characteristics of different parts in the samples.
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To further seek the significant biomarkers for detection of HLB in plant materials,
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healthy and infected samples are grouped into two different groups for PLS-DA analysis with
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variable importance in the projection (VIP) scores which is useful multivariate analysis for
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identification of biomarkers (42). As shown in Figure S8, the clusters of healthy and infected
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samples were successfully separated in from different parts (i.e., leaves, veins and stems) due
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to their characteristics MS spectra. According to the VIP values ( cut-off ≥ 1.0 ), various
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peaks with different m/z values could be biomarker candidates for different parts as listed in
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Table S1. A Venn diagram was further constructed to illustrate the overlapping these
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significant metabolites (VIP values > 1.0 ) from the different parts (Figure S9), indicating
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similarities and differences of significant biomarkers for detection of HLB in different parts
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of the citrus tree. In this work, our focus was the detection of significant differences among
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the MS features for rapid differentiation of healthy and HLB-infected Navel orange trees
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more than the direct MS identification of compounds from these raw samples.
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To quantitatively compare the healthy and infected samples, the characteristic ions at m/z
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381 and m/z 649 were chosen as the internal reference compounds for comparison of their
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responses, as summarized in Figure 5. It could be found that the ratios (m/z 381/649) obtained
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from infected samples were significantly higher than those obtained from healthy samples ( p
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< 0.01), which confirmed the fact that sucrose remained at high levels compared to healthy
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samples (37). Furthermore, there results also reveal that HLB could be rapidly detected from
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asymptomatic stems based on the characteristic ions.
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Seasonal Variations of Healthy and Infected Trees. To gain insight into the seasonal
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variations of HLB, the clusters of samples obtained from each season are found to be
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concentrated (Figure 4a-c), indicating that the seasonal variation of metabolites in infected
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samples are obvious. To be more clearly, PCA plots of the healthy samples, including leaves,
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veins and stems from the four seasons were generated from the first and second principal
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components based on their MS data, as shown in Figure S10a-c. Interestingly, seasonal
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variations of healthy samples including leaves, veins and stems are distinctly distributions in
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four clusters in which the clusters were almost distributed in different quadrants, respectively.
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Furthermore, the ratios of biomarkers (i.e., m/z 381 and m/z 649) against the four seasons
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(Figure 5a) have naturally fluctuated. The seasonal flush and rebound can be explained as
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periodic growth of healthy trees ξ(t) with the periods (5):
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ξ(t) = ξ0 (1 + υsinωt)
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where ξ0 is the baseline rate of growth, υ is the seasonal forcing, and ω is the period.
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Compared with the healthy samples, the seasonal variations of HLB is quite complicated due
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to the growth of trees, development of HLB disease, and the other changes (5, 43, 44).
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Although the multiple factors, the metabolites obtained from infected samples clearly
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reflected the seasonal variations of HLB, including asymptomatic stems and symptomatic
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leaves and veins. The clusters of PCA plots from each season were also concentrated,
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respectively, as shown in Figure S10d-e.
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The significant differences of seasonal variations were found by monitoring of the ratios
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of m/z 381/649 (Figure 5). Particularly, the ratios were found to be 3.8 - 10.2 from infected
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samples in fall and winter when the ratios were found to be 0.24 - 1.25 from healthy samples.
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These results showed that biomarkers allow unequivocal differentiation of the healthy and
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infected samples. In fact, flavonoids are the most important secondary metabolites of citrus
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(38). The changes of flavonoids and disaccharides could be explained due to sucrose and
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starch metabolism was highly linked with HLB disease (45). Sugar and starch metabolism
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have been linked to a possible pathogenetic mechanism of HLB (46), it is also reported that
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some key genes involved in sucrose and starch metabolism were induced by HLB (45).
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Therefore, HLB highly repressed photosynthesis in leaves and thus causing yellow leaves
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that highly linked with disease symptoms in which infected leaves, veins and fruits are
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yellowing and asymmetrical chlorosis except for stems, as shown in Figure S11. Starch
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biosynthesis and degradation were clearly induced by HLB in leaves (47, 48), which may
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also enhance the accumulation of sugar. These results suggested that metabolic changes of
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HLB infected trees could be predictable. Investigation on seasons vibrations of HLB provides
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further understanding and confirmation of the population dynamics of HLB in citrus trees and
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the seasonal development of HLB, because it is fact that the season is one of the important
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factors that could impact the distribution, development and level of HLB. The seasonal
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variations of HLB can also help to provide valuable information for the prediction,
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assessment, and management of the infected trees (49, 50). Requiring no sample pre-
245
treatment, the direct MS method has additional advantages including readiness for
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miniaturization and integration, simple maintenance, easy operation, and toward to field
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detection by coupling the miniature and portable mass spectrometer (51). In addition, these
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results also indicated that this direct MS method could be extended for rapid detection HLB
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in other citrus plants.
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ACKNOLEDGEMENTS
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This work was supported by the Fundamental Research Funds for the Central Universities
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(Grant No. 21618341), the National Natural Science Foundation of China (Grant No.
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21804053), and the Foundation for New Faculty Start-up Grant of Jinan University (B.H.).
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REFERENCES
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1. Bove, J. M., Huanglongbing: A destructive, newly-emerging, century-old disease of citrus. J Plant Pathol 2006, 88, 7-37. 2. Wang, N.; Trivedi, P., Citrus Huanglongbing: A Newly Relevant Disease Presents Unprecedented Challenges. Phytopathology 2013, 103, 652-665. 3. Gottwald, T. R., Current Epidemiological Understanding of Citrus Huanglongbing. Annu Rev Phytopathol 2010, 48, 119-139. 4. Munir, S.; He, P. F.; Wu, Y. X.; He, P. B.; Khan, S.; Huang, M.; Cui, W. Y.; He, P. J.; He, Y. Q., Huanglongbing Control: Perhaps the End of the Beginning. Microb Ecol 2018, 76, 192-204. 11 ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316
5. Chiyaka, C.; Singer, B. H.; Halbert, S. E.; Morris, J. G.; van Bruggen, A. H. C., Modeling huanglongbing transmission within a citrus tree. P Natl Acad Sci USA 2012, 109, 12213-12218. 6. Clark, K.; Franco, J. Y.; Schwizer, S.; Pang, Z. Q.; Hawara, E.; Liebrand, T. W. H.; Pagliaccia, D.; Zeng, L. P.; Gurung, F. B.; Wang, P. C.; Shi, J. X.; Wang, Y. S.; Ancona, V.; van der Hoorn, R. A. L.; Wang, N.; Coaker, G.; Ma, W. B., An effector from the Huanglongbing-associated pathogen targets citrus proteases. Nat Commun 2018, 9, 1718. 7. Weinert, M. P.; Jacobson, S. C.; Grimshaw, J. F.; Bellis, G. A.; Stephens, P. M.; Gunua, T. G.; Kame, M. F.; Davis, R. I., Detection of Huanglongbing (citrus greening disease) in Timor-Leste (East Timor) and in Papua New Guinea. Australas Plant Path 2004, 33, 135-136. 8. Taylor, R. A.; Mordecai, E. A.; Gilligan, C. A.; Rohr, J. R.; Johnson, L. R., Mathematical models are a powerful method to understand and control the spread of Huanglongbing. PeerJ 2016, 4, e2642. 9. Valdes, R. A.; Ortiz, J. C. D.; Beache, M. B.; Cabello, J. A.; Chavez, E. C.; Pagaza, Y. R.; Fuentes, Y. M. O., A review of techniques for detecting Huanglongbing (greening) in citrus. Can J Microbiol 2016, 62, 803-811. 10. Aksenov, A. A.; Pasamontes, A.; Peirano, D. J.; Zhao, W. X.; Dandekar, A. M.; Fiehn, O.; Ehsani, R.; Davis, C. E., Detection of Huanglongbing Disease Using Differential Mobility Spectrometry. Anal Chem 2014, 86, 2481-2488. 11. Li, W. B.; Hartung, J. S.; Levy, L., Quantitative real-time PCR for detection and identification of Candidatus Liberibacter species associated with citrus huanglongbing. J Microbiol Meth 2006, 66, 104-115. 12. Wang, Y. N.; Chan, K. K. J.; Chan, W., Plant Uptake and Metabolism of Nitrofuran Antibiotics in Spring Onion Grown in Nitrofuran-Contaminated Soil. J Agr Food Chem 2017, 65, 4255-4261. 13. Li, W.; Chan, C.-K.; Liu, Y.; Yao, J.; Mitić, B.; Kostić, E. N.; Milosavljević, B.; Davinić, I.; Orem, W. H.; Tatu, C. A.; Dedon, P. C.; Pavlović, N. M.; Chan, W., Aristolochic Acids as Persistent Soil Pollutants: Determination of Risk for Human Exposure and Nephropathy from Plant Uptake. J Agr Food Chem 2018, 66, 11468-11476. 14. Villas-Boas, S. G.; Mas, S.; Akesson, M.; Smedsgaard, J.; Nielsen, J., Mass spectrometry in metabolome analysis. Mass Spectrom Rev 2005, 24, 613-646. 15. Kiefl, J.; Kohlenberg, B.; Hartmann, A.; Obst, K.; Paetz, S.; Krammer, G.; Trautzsch, S., Investigation on Key Molecules of Huanglongbing (HLB)-Induced Orange Juice Off-flavor. J Agr Food Chem 2018, 66, 2370-2377. 16. Hung, W. L.; Wang, Y., A Targeted Mass Spectrometry-Based Metabolomics Approach toward the Understanding of Host Responses to Huanglongbing Disease. J Agr Food Chem 2018, 66, 10651-10661. 17. Takats, Z.; Wiseman, J. M.; Gologan, B.; Cooks, R. G., Mass spectrometry sampling under ambient conditions with desorption electrospray ionization. Science 2004, 306, 471-473. 18. Cody, R. B.; Laramee, J. A.; Durst, H. D., Versatile new ion source for the analysis of materials in open air under ambient conditions. Anal Chem 2005, 77, 2297-2302. 19. Lu, H. Y.; Zhang, H.; Chingin, K.; Xiong, J. L.; Fang, X. W.; Chen, H. W., Ambient mass spectrometry for food science and industry. Trac-Trend Anal Chem 2018, 107, 99-115. 20. Black, C.; Chevallier, O. P.; Elliott, C. T., The current and potential applications of Ambient Mass Spectrometry in detecting food fraud. Trac-Trend Anal Chem 2016, 82, 268-278. 21. Funasaki, M.; Oliveira, R. S.; Zanotto, S. P.; Carioca, C. R. F.; Simas, R. C.; Eberlin, M. N.; Alberici, R. M., Brazil Nut Oil: Quality Control via Triacylglycerol Profiles Provided by Easy Ambient Sonic-Spray Ionization Mass Spectrometry. J Agr Food Chem 2012, 60, 11263-11267. 22. Li, L.; Li, W.; Hu, B., Electrostatic-Field Induced Tip-Electrospray Ionization Mass Spectrometry for Direct Analysis of Raw Food Materials. J Mass Spectrom 2019, 54, 73-80. 23. Liu, J. J.; Wang, H.; Cooks, R. G.; Ouyang, Z., Leaf Spray: Direct Chemical Analysis of Plant Material and Living Plants by Mass Spectrometry. Anal Chem 2011, 83, 7608-7613. 24. Chan, S. L. F.; Wong, M. Y. M.; Tang, H. W.; Che, C. M.; Ng, K. M., Tissue-spray ionization mass spectrometry for raw herb analysis. Rapid Commun Mass Spectrom 2011, 25, 2837-2843. 25. Hu, B.; Lai, Y. H.; So, P. K.; Chen, H. W.; Yao, Z. P., Direct ionization of biological tissue for mass spectrometric analysis. Analyst 2012, 137, 3613-3619. 12 ACS Paragon Plus Environment
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26. Wong, H. Y.; Wong, M. Y. M.; Hu, B.; So, P. K.; Chan, C. O.; Mok, D. K. W.; Yao, Z. P., Rapid differentiation of &ITGanoderma&IT species by direct ionization mass spectrometry. Anal Chim Acta 2018, 999, 99-106. 27. Wong, H. Y.; Hu, B.; So, P. K.; Chan, C. O.; Mok, D. K. W.; Xin, G. Z.; Li, P.; Yao, Z. P., Rapid authentication of Gastrodiae rhizoma by direct ionization mass spectrometry. Anal Chim Acta 2016, 938, 90-97. 28. Hu, B.; Yao, Z. P., Detection of native proteins using solid-substrate electrospray ionization mass spectrometry with nonpolar solvents. Anal Chim Acta 2018, 1004, 51-57. 29. Yang, C.; Chen, H.; Chen, H. L.; Zhong, B. L.; Luo, X. Z.; Chun, J., Antioxidant and Anticancer Activities of Essential Oil from Gannan Navel Orange Peel. Molecules 2017, 22, 1391. 30. Rao, G.; Huang, L.; Liu, M.; Chen, T.; Chen, J.; Luo, Z.; Xu, F.; Xu, X.; Yao, M., Identification of Huanglongbing-infected navel oranges based on laser-induced breakdown spectroscopy combined with different chemometric methods. Appl. Opt. 2018, 57, 8738-8742. 31. GB/T 28062-2011 Detection of Candidatus Liberibacter asiaticus using the real-time fluorescent PCR. In Administration of Quality Supervision, Inspection and Quarantine of People's Republic of China; Standardization Administration of China: Beijing, 2012. 32. Huang, Z.; Yao, Y.-N.; Li, W.; Hu, B., Analytical properties of electrospray ionization mass spectrometry with solid substrates and nonpolar solvents. Anal Chim Acta 2019, 1050, 105-112. 33. Hu, B.; So, P. K.; Yao, Z. P., Analytical Properties of Solid-substrate Electrospray Ionization Mass Spectrometry. J Am Soc Mass Spectrom 2013, 24, 57-65. 34. Djoukeng, J. D.; Arbona, V.; Argamasilla, R.; Gomez-Cadenas, A., Flavonoid Profiling in Leaves of Citrus Genotypes under Different Environmental Situations. J Agr Food Chem 2008, 56, 11087-11097. 35. Zhang, M. X.; Duan, C. Q.; Zang, Y. Y.; Huang, Z. W.; Liu, G. J., The flavonoid composition of flavedo and juice from the pummelo cultivar (Citrus grandis (L.) Osbeck) and the grapefruit cultivar (Citrus paradisi) from China. Food Chem 2011, 129, 1530-1536. 36. Demarque, D. P.; Crotti, A. E. M.; Vessecchi, R.; Lopes, J. L. C.; Lopes, N. P., Fragmentation reactions using electrospray ionization mass spectrometry: an important tool for the structural elucidation and characterization of synthetic and natural products. Nat Prod Rep 2016, 33, 432455. 37. Fan, J.; Chen, C.; Brlansky, R. H.; Gmitter, F. G.; Li, Z. G., Changes in carbohydrate metabolism in Citrus sinensis infected with 'Candidatus Liberibacter asiaticus'. Plant Pathol 2010, 59, 10371043. 38. Robards, K.; Antolovich, M., Analytical Chemistry of Fruit Bioflavonoids - A Review. Analyst 1997, 122, 11R-34R. 39. Yao, Y.-N.; Hu, B., Analyte-substrate interactions at functionalized tip electrospray ionization mass spectrometry: Molecular mechanisms and applications. J Mass Spectrom 2018, 53, 12221229. 40. Cimo, G.; Lo Bianco, R.; Gonzalez, P.; Bandaranayake, W.; Etxeberria, E.; Syvertsen, J. P., Carbohydrate and Nutritional Responses to Stem Girdling and Drought Stress with Respect to Understanding Symptoms of Huanglongbing in Citrus. Hortscience 2013, 48, 920-928. 41. Aritua, V.; Achor, D.; Gmitter, F. G.; Albrigo, G.; Wang, N., Transcriptional and Microscopic Analyses of Citrus Stem and Root Responses to Candidatus Liberibacter asiaticus Infection. Plos One 2013, 8, e73742. 42. Luan, H. M.; Liu, L. F.; Tang, Z.; Zhang, M. W.; Chua, K. K.; Song, J. X.; Mok, V. C. T.; Li, M.; Cai, Z. W., Comprehensive urinary metabolomic profiling and identification of potential noninvasive marker for idiopathic Parkinson's disease. Sci Rep 2015, 5, 13888. 43. Lee, J. A.; Halbert, S. E.; Dawson, W. O.; Robertson, C. J.; Keesling, J. E.; Singer, B. H., Asymptomatic spread of huanglongbing and implications for disease control. P Natl Acad Sci USA 2015, 112, 7605-7610. 44. Sauer, A. V.; Zanutto, C. A.; Nocchi, P. T. R.; Machado, M. A.; Bock, C. H.; Nunes, W. M. C., Seasonal Variation in Populations of 'Candidatus Liberibacter asiaticus' in Citrus Trees in Parana State, Brazil. Plant Dis 2015, 99, 1125-1132.
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45. Balan, B.; Ibanez, A. M.; Dandekar, A. M.; Caruso, T.; Martinelli, F., Identifying Host Molecular Features Strongly Linked With Responses to Huanglongbing Disease in Citrus Leaves. Front Plant Sci 2018, 9, 277. 46. Martinelli, F.; Dandekar, A. M., Genetic Mechanisms of the Devious Intruder Candidatus Liberibacter in Citrus. Front Plant Sci 2017, 8, 904. 47. Albrecht, U.; Bowman, K. D., Gene expression in Citrus sinensis (L.) Osbeck following infection with the bacterial pathogen Candidatus Liberibacter asiaticus causing Huanglongbing in Florida. Plant Sci 2008, 175, 291-306. 48. Martinelli, F.; Reagan, R. L.; Uratsu, S. L.; Phu, M. L.; Albrecht, U.; Zhao, W. X.; Davis, C. E.; Bowman, K. D.; Dandekar, A. M., Gene Regulatory Networks Elucidating Huanglongbing Disease Mechanisms. Plos One 2013, 8, e74256. 49. Sauer, A. V.; Zanutto, C. A.; Nocchi, P. T. R.; Machado, M. A.; Bock, C. H.; Nunes, W. M. C., Seasonal Variation in Populations of 'Candidatus Liberibacter asiaticus' in Citrus Trees in Parana State, Brazil. Plant Dis 2015, 99, 1125-1132. 50. Lopes, S. A.; Luiz, F. Q. B. F.; Oliveira, H. T.; Cifuentes-Arenas, J. C.; Raiol, L. L., Seasonal Variation of 'Candidatus Liberibacter asiaticus' Titers in New Shoots of Citrus in Distinct Environments. Plant Dis 2017, 101, 583-590. 51. Snyder, D. T.; Pulliam, C. J.; Ouyang, Z.; Cooks, R. G., Miniature and Fieldable Mass Spectrometers: Recent Advances. Anal Chem 2016, 88, 2-29.
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Figures Captions
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Figure 1. Schematic diagram of direct MS analysis of raw plant materials.
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Figure 2. Direct MS spectra of the Navel orange samples obtained in winter: (a) healthy
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leaves, (b) healthy veins, (c) healthy stems, (d) infected leaves, (e) infected veins, (f) infected
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stems.
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Figure 3. Reproducibility test of nine repeated analysis of healthy leaves by direct MS: (a)
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TIC; (b) SIC of m/z 381; (c) SIC of m/z 649.
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Figure 4. PCA plots of the healthy and infected Navel orange samples obtained from four
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seasons: (a) leaves, (b) veins, (c) stems.
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Figure 5. Changes of the signal ratios of m/z 381/649 obtained from the healthy and infected
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Navel orange samples in four seasons: (a) scatter graph, (b) radar graph; ( *p < 0.01 ).
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Solvent Ions to MS
HV
++
+
Tissue tip 403 404
+
Figure 1.
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MS inlet
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Figure 2.
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Relative Abundance (%)
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a)
TIC CV: 12.4 %
b)
m/z 381 CV: 14.5 %
c)
m/z 649 CV: 12.1 %
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Figure 3.
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Figure 4.
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Figure 5.
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TOC only
m/z
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