Citrus metabolomics with the ZenoTOF 7600 system

Metabolomic profiling of mandarin fruit using the Zeno trap with the ZenoTOF 7600 system

Chen Xi ², He Guangyun¹, Hou Xue¹，Zhao Xianglong², Liu Bingjie², Guo Lihai^2
1Institute of Quality Standard and Testing Technology for Agro-Products, Sichuan Academy of Agricultural Sciences
²SCIEX, China

Abstract

In this study, the generation of highly sensitive MS/MS spectra in the ZenoTOF 7600 system from SCIEX improved nontargeted compound identification in citrus fruit extracts from mandarin oranges grown in the Sichuan province of China. The higher-quality fragmentation spectra resulting from the Zeno trap enabled higher confidence in MS/MS library matches. As a result, 142 citrus components were identified through spectral matching against the Natural Products HR-MS/MS Library from SCIEX, as highlighted in Figure 1. 

Introduction

Commercial citrus fruits undergo continuous agricultural practices to improve their flavor, appearance and nutritional value, for example. As these attributes are closely associated with the citrus metabolite composition, metabolomics can be used to monitor changes in metabolite profiles from preharvest practices that are targeted toward optimizing fruit quality.^1,2 Combined MS techniques such as GC-MS, LC-PDA and LC-MS/MS are typically required to characterize the citrus metabolome. However, the diversity of the analytes necessitates a highly sensitive method that is capable of accurately identifying the known metabolites and the lesser abundant or even novel components to better understand their role in developing different quality attributes during citrus fruit growth.

Metabolomics is critical to ongoing research on the effects of agricultural practices on fruit plants and the soil environment, especially since the chemical products used are often not trademarked and include limited to no information about their sources of origin in China. 

Key features of the ZenoTOF 7600 system metabolomics workflow
 

• The Zeno trap offers improvement in MS/MS sensitivity and ion duty cycle, allowing for greater efficiency and confidence in identifying low-level or even previously undetected compounds in complex matrices such as citrus pulp
  
  
• Signal-to-noise enhancements increase the quality of chromatographic peaks extracted from fragment masses in Zeno MRM^HR, which enables greater confidence in the quantification of even low-level compounds
  
  
• Filtering in SCIEX OS software on key attributes such as mass error, isotopic abundance ratio, retention time and library scores expedites targeted compound identification

Experimental methods

Sample collection: Chun Jian mandarin orange fruit trees in the Sichuan province of China were divided into two groups: treated with a sweetening agent and an untreated control group. After ripening, 20 batches, each comprised of 3 kg of fruits, were harvested from each group. Triplicate fruits were selected from a batch in each group for homogenization into pulp after peeling.

Sample preparation: In a 50 mL centrifuge tube, 15 mL of methanol with 0.1% formic acid was added to ~5 g of homogenized pulp. The sample was vortexed, sonicated for 30 minutes and centrifuged at 8000 rpm for 5 minutes, and the supernatant was transferred to another 50 mL tube. The extraction was repeated twice, and the supernatants were combined and diluted up to 50 mL with extraction solvent. Upon shaking, the mixture was filtered through a 0.22 μm filter and stored at 4°C before LC-MS/MS analysis. Quality control (QC) samples were prepared by spiking a composite matrix comprised of pooling 100 µL extracts from both the treated and untreated groups into 5 mL centrifuge tubes.

Chromatography: Reverse-phase separation was conducted with a Waters HSS T3 C18 column (100 x 3 mm, 1.7 µm). A flow rate of 0.3 mL/minute and an injection volume of 1 μL was used. The 25-minute elution gradient is shown in Table 1.

Mass spectrometry: The ZenoTOF 7600 system from SCIEX was operated utilizing a data dependent acquisition (DDA) strategy in positive ionization mode. Table 2 shows the acquisition method parameters for the mass spectrometer.

Data processing: All data were acquired and processed using SCIEX OS software 2.2. The Natural Products HR-MS/MS Library from SCIEX was used to perform library searching. 

Results

Data quality

Data reproducibility is critical in omics-based research. In this experiment, nine QC samples interspersed between samples demonstrated good reproducibility in peak areas (Table 3).

Compound identification and quantification

The diversity of metabolites complicates the analysis of the citrus metabolome, some of which may be present at very low levels or difficult to detect in complex matrices such as citrus pulp. When the Zeno trap was activated, previously low abundance fragments increased in intensity by as much as 10 times. This improvement in MS/MS sensitivity resulted in the production of much higher quality MS/MS spectra for library matching against published databases and enabled the identification of compounds at previously unreachable levels (Figures 1 and 2). As shown in Figure 3, the additional level of sensitivity from the Zeno trap improved the peak quality in MRM^HR extracted ion chromatograms (XICs), which enabled confident quantification of even low-level compounds.

Figure 3. The intensity of the analyte peaks for syringin (positive) and tryptophan (negative) in the MRM^HR XIC increased by a factor of >10 when the Zeno trap was turned on.

Simple and rapid data review using SCIEX OS software

Suspect and/or nontargeted screening often yields a substantial list of candidate compounds that is both time-consuming and labor-intensive to review. SCIEX OS software allows the user to define confidence thresholds on critical confirmation criteria such as mass error, isotopic ratio matches and MS/MS library matches, which are collectively assessed to identify a compound. When combined with the enhanced filtering capability in SCIEX OS software, 142 natural components—including sugars, organic acids, lipids, flavonoids and their derivatives, amino acids and their derivatives, nucleosides and nucleotides, phenols, terpenoids, coumarin and its derivatives and vitamins— were identified from citrus pulp in a fast and simple screening workflow (Figure 4).

Distinguishing metabolite profiles between citrus fruits undergoing different preharvest treatments

Different metabolite compositions were observed between the treated (sample) and untreated (control) citrus fruits based on the heatmap distribution of peak areas for 25 citrus components that were identified from the nontargeted screening workflow (Figure 5). Aside from the upregulation observed for a few metabolites (such as caffeic acid) in the sample group as compared to the control group, most compounds (which included citric acid and a variety of lipids) were downregulated in the treated citrus fruits as compared to the control.

The sweet and sour flavors of citrus are mainly controlled by the ratio of sugar and organic acid content in citrus fruits.⁴ Citric acid is one of the most abundant organic acids in citrus fruits.⁵ During fruit development, citric acid is synthesized from soluble sugars through the tricarboxylic acid cycle.⁶ The downregulation of citric acid observed here suggests that preharvest sweetening may have suppressed the production of this metabolite in the tricarboxylic acid cycle, which in turn would increase the sugaracid ratio to strengthen the sweetness of the fruit.

Conclusion

Using the ZenoTOF 7600 system, this study established a method for identifying compounds in citrus fruit, and the method was applied to examine the impact of preharvest sweetening on the metabolome of treated mandarin oranges. Acquiring data with the Zeno trap enabled at high scan speeds in the ZenoTOF 7600 system increased the MS/MS sensitivity by about 10 times, which in turn increased the confidence and accuracy of the identification and quantification of less abundant and novel compounds. 

A single LC-MS/MS method capable of identifying structurally diverse components of the citrus metabolome—such as sugars, organic acids, amino acids, flavonoids, limonoids and lipids—has been established. Retrospective mining of this data may reveal novel endogenous metabolites, such as the natural sweeteners recently identified in citrus fruits using a similar metabolomicsbased screening strategy.⁷ This type of analysis could enable the discovery of new natural, non-caloric sugar substitutes for artificial sweeteners, which is a growing demand in the food and beverage market.

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