Investigation of the Relationship between the Metabolic Profile of

Article pubs.acs.org/jpr

Investigation of the Relationship between the Metabolic Profile of Tobacco Leaves in Different Planting Regions and Climate Factors Using a Pseudotargeted Method Based on Gas Chromatography/ Mass Spectrometry Yanni Zhao,† Chunxia Zhao,† Xin Lu,*,† Huina Zhou,‡ Yanli Li,† Jia Zhou,† Yuwei Chang,† Junjie Zhang,† Lifeng Jin,‡ Fucheng Lin,‡ and Guowang Xu*,† †

CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, 116023 Dalian, China ‡ China Tobacco Gene Research Center, Zhengzhou Tobacco Research Institute of CNTC, No.2 Fengyang Street, 450001, Zhengzhou, China S Supporting Information *

ABSTRACT: An improved pseudotargeted method using gas chromatography/mass spectrometry (GC/MS) was developed to investigate the metabolic profile of tobacco leaves from three planting regions (Yunnan, Guizhou, and Henan provinces). The analytical characteristics of the method with regard to reproducibility, precision, linearity, and stability were satisfactory for metabolic profiling study. Partial least-squares-discriminant analysis and hierarchical cluster analysis demonstrated that the metabolic profiles of tobacco from the Yunnan and Guizhou regions were different from that from the Henan province. The amino acid (e.g., phenylalanine, leucine, and tyrosine) and carbohydrate (e.g., fructose, trehalose, and sucrose) contents were the highest in Henan tobacco. The highest contents of organic acids (e.g., isocitrate, citrate, and fumarate) of the TCA cycle and antioxidants (e.g., quinate, chlorogenic acid, and ascorbate) were found in Guizhou tobacco. The correlation coefficients between metabolite content and climate factors (rainfall, sunshine, and temperature) demonstrated that drought facilitated the accumulation of sugars and amino acids. The content of TCA cycle intermediates could be influenced by multiple climate factors. This study demonstrates that the pseudotargeted method with GC/MS is suitable for the investigation of the metabolic profiling of tobacco leaves and the assessment of differential metabolite levels related to the growing regions. KEYWORDS: metabolic profiling, pseudotargeted, climate, tobacco leaf, GC/MS, metabolomics

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INTRODUCTION

planting conditions and diverse genotypes on tobacco metabolite content. The influence of salinity dosage and duration on the tobacco metabolome included widespread disturbance of metabolic pathways, including the TCA cycle, glycolysis, shikimate metabolism, and some secondary types of metabolism.5 Under a combination of drought and heat shock stresses, variations in the physiological metabolism of tobacco

Tobacco (Nicotiana tabacum L.), a dicotyledonous solanaceae plant, is an important agricultural crop and the main raw material of tobacco commodities around the world. The chemical components of tobacco leaves are closely related to the quality and flavor of cigarettes.1−3 It is well known that many conditions, including biological factors (e.g., endophytic fungi and bacteria, symbionts in rhizospheres, and plant pathogens) and abiotic factors (e.g., salt, moisture, and temperature), have a significant influence on tobacco growth, yield, and quality.4 Several studies have investigated the effect of © XXXX American Chemical Society

Special Issue: Agricultural and Environmental Proteomics Received: August 3, 2013

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dx.doi.org/10.1021/pr400799a | J. Proteome Res. XXXX, XXX, XXX−XXX

Journal of Proteome Research

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were discovered, such as suppression of photosynthesis, enhancement of respiration, and induction of a large number of defense genes.6 Genetic differences can also cause changes in tobacco leaf traits independent of environmental factors. Transgenic tobacco plants expressing the C. W80 GPX-like protein in chloroplasts (TpGPX) or the cytosol (TcGPX) were used to investigate physiological changes during environmental stress. The capacity for photosynthesis and tolerance to oxidative stress were higher in transgenic tobacco plants than in wild plants.7 In addition, the levels of flavor-related metabolites are affected by the varieties used by cigarette brands.8 Metabolomics is a comprehensive technique used to investigate the metabolic response of plants to genetic aberrance9,10 or environmental factors11,12 and unravel the relationship between metabolic networks and plant phenotypes.13−15 Common technologies, including gas chromatography/mass spectrometry (GC/MS),16,17 liquid chromatography/mass spectrometry (LC/MS),18,19 and nuclear magnetic resonance (NMR), 5,20 have been established in prior metabolomics studies. The GC/MS platform is often employed to investigate changes in plant metabolic phenotypes because of its reproducibility, broad dynamic range, and high accuracy.21−24 Available deconvolution software programs (e.g., AMDIS and ChromaTOF) can be utilized to solve overlapping peaks, and various commercial mass spectra libraries based on the GC/MS workstation system can be used for convenient identification of metabolites.25−27 The primary metabolites of leaf tissue include some small polar molecules (e.g., sugar, amino acids, and organic acids), which are flavor precursors and affect the quality of the tobacco leaf. The GC capillary column is a suitable tool for separating these polar features after derivatization. In general, metabolic profiling methods based on GC/MS contain a full scan mode and a selected ion monitoring (SIM) mode. The GC/MS full scan method has low sensitivity because of its wide mass scan range and difficulty of peak alignment, in addition to the low resolution of overlapping chromatographic peaks. The GC/MS method using the SIM targeted mode has high sensitivity and is usually applied to analyze several specific compounds, although this method does not satisfy the requirements of metabolic profiling analysis. The GC/SIM/MS pseudotargeted method, which integrates the advantages of targeted and untargeted methods, is promising because of its high sensitivity, accurate quantification, and wide linear range.28 Some factors in the reported pseudotargeted method still need improvements, such as the loss of lowabundance metabolites in the quality control (QC) sample, the malformed peak profile arising from retention time drift, and the errors of characteristic ions. Some improvements have been made in this study. First, a QC sample with a doubled concentration was employed to find more trace components and to improve sensitivity. In addition, SIM group ions were studied to prevent the false peak alignment caused by retention time drift. Moreover, both inhouse and commercial software programs were exploited to better determine the characteristics of the quantitative ions. With these modifications, an improved pseudotargeted method was established and used to investigate differences in the metabolic phenotype of leaves and the metabolic signatures associated with geographical factors. The alterative tendency and the corresponding pathways of different metabolites were investigated and linked with climate factors associated with the different planting districts.

Article

MATERIALS AND METHODS

Reagents and Plant Materials

HPLC-grade methanol, acetonitrile, and isopropanol were supplied by Merck (Germany). HPLC-grade chloroform was manufactured by SK Chemicals (Korea). Ultrapure water was acquired from a Mill-Q system (Millipore, USA). Derivatization reagents, including methoxyamine hydrochloride, N-methyl-N(trimethylsilyl)-trifluoroacetamide (MSTFA), and pyridine, were supplied by Sigma-Aldrich (USA). 4-Hydroxy-3-methoxybenzoic acid was used as an internal standard and obtained from Sigma-Aldrich (USA). Standards for structure identification, including amino acids, organic acids, sugars, and phenols were produced by Sigma-Aldrich, Alfa Aesar (USA), and J&K Scientific (China), among others. Fifty-three fresh flue-cured tobacco leaves were acquired from the Yunnan, Guizhou, and Henan provinces. These provinces represent the main growing districts of three tobacco flavor types (strong, medium, and delicate flavor) in China (Figure S1 in the Supporting Information).29 The mature tobacco leaves were harvested from July to August (Table S1 in the Supporting Information). The time of collection for tobacco leaf was unified at about 10 a.m. Between five and seven duplicates of each sample were collected from the same field and immediately frozen in liquid nitrogen for further processing. The meteorological conditions, including the temperature (°C), rainfall (mm), and sun exposure (h), at each growth stage were recorded in detail by the sampling staff. The total rainfall was the highest in the Guizhou province. Drought occurred in the Henan province during the vigorous growth period, and the least total rainfall was observed in the Henan growing region. The temperature of the Henan sampling district during the total growth period was higher than that of the other provinces. There was not much difference in the total amount of sun exposure between the Yunnan and Henan provinces; Guizhou province had the least sun exposure among the three planting regions (Figure S2 in the Supporting Information). The QC sample was generated by pooling equal amounts of all samples. Sample Preparation

The freshly frozen tobacco leaves were ground to a uniform powder, filtered using a 80-mesh sieve, and stored at −80 °C until the metabolic study. The leaf powder (10 mg) was added to a 2 mL Eppendorf tube and soaked in 1.5 mL of an extraction solvent containing isopropanol/acetonitrile/water (3/3/2 v/v/v) with 25 μL (0.1 mg/mL) of 4-hydroxy-3methoxybenzoic acid as an internal standard. The sample was vortexed for 4 min to adequately extract the metabolites and centrifuged at 14 000 rpm for 10 min. Prior to derivatization, 500 μL of the supernatant was freeze-dried in Labconco vacuum concentrators for 6 h. The dry residue was dissolved in 100 μL of methoxyamine solution (20 mg/mL in pyridine) and placed in a 37 °C water bath for 90 min to perform the oximation reaction for protecting carbonyl, thus reducing the ring reaction of sugars and decreasing the number of isomers. The silylation reaction for increasing the volatility of the metabolites was performed by adding 80 μL of MSTFA to the sample and incubating it for 60 min in a 37 °C water bath. 150 μL of the supernatant was transferred to a conical insert of a 2 mL glass vial for subsequent GC/MS analysis. The injection volume was 1 μL. B



Article

GC/MS Analysis

mixtures based on their reproducibility, peak areas, and numbers: 100% methanol, methanol/chloroform/water (5:2:2 v/v/v), isopropanol/acetonitrile/water (3:3:2 v/v/v), water/ methanol/acetonitrile (3:1:1) and acetonitrile/water (4:1 v/v) (Table S2 in the Supporting Information). Fresh tobacco leaf tissue (10 mg) was dissolved in different solvents and ultrasonically extracted for 60 min. Ten duplicates of each solvent were analyzed to monitor reproducibility. A data set was obtained using XCMS software,34 and the total area was used to normalize the features. The repeatability of three solvents, that is, 100% methanol, acetonitrile/isopropanol/water (3/3/2 v/v/ v), and acetonitrile/water (4/1, v/v), was satisfactory; RSD values of less than 30% were present in over 90% of the total ion peak values. The ion peak values obtained from these three extraction solvents were similar. Therefore, the influence of the total area was emphasized. The total ion peak areas were (3.46, 4.16, and 3.75) × 108 for 100% methanol, isopropanol/ acetonitrile/water (3:3:2 v/v/v), and acetonitrile/water (4/1, v/v), respectively. Isopropanol/acetonitrile/water (3:3:2 v/v/v) was chosen as the extraction solvent for the fresh tobacco leaves in this study. An effective extraction protocol can improve the efficiency of extraction and also improve reproducibility. Ultrasonic extraction is a widely used method,35,36 although a long duration is needed and the ultrasonic effect is not uniform. Shaking and vortexing methods can decrease analysis time37 because of the severe movement applied. The reproducibility, total ion peak areas, and numbers obtained by the ultrasound, shake, and vortex methods were compared (Table S3 in the Supporting Information). The total ion peak areas and numbers were similar among the three methods, whereas the reproducibility was highest for the vortex method. The vortex protocol was therefore applied to analyze the fresh tobacco leaves in this study. The vortex time was also investigated (Table S4 in the Supporting Information). The total ion peak numbers were similar for the five durations (0.5, 1, 2, 4, and 6 min), whereas the total peak area and reproducibility were better at 4 and 6 min than at other vortex durations. To decrease the extraction time, we used a 4 min vortex time for this study.

The derivatized sample was injected into a Shimadzu QP 2010 GC tandem quadrupole MS (Kyoto, Japan). The GC separation was performed on an Agilent DB-5 MS fused silica capillary column (30 m × 0.25 mm × 0.25 μm). The column temperature was 70 °C for the first 3 min and then increased at 5 °C/min to 310 °C for 5 min. The injection temperature was set as 300 °C, and the injection volume was 1 μL with a 10:1 split ratio. Helium (99.9995%, China) was applied as a carrier gas. The column flow was 1.2 mL/min, and the column was equipped with a linear velocity control model. Prior to the instrumental analysis, the mass spectrometer was tuned using perfluorotributylamine (PFTBA) to obtain optimum performance. The mass spectra scanning scope was set to 33−600 m/z in the full scan mode with a scan speed of 5 scans s−1 and a solvent cut time of 5.6 min based on the retention time of the pyridine solvent. The temperatures of the interface and the ion source were adjusted to 280 and 240 °C, respectively. The detector voltage was maintained at 1.2 kV, and the electron impact (EI) model was selected to achieve ionization of the metabolites at 70 eV. Statistical Analysis

A partial least-squares-discriminant analysis (PLS-DA) was employed to summarize the systematic alteration of samples using SIMCA-P 11 software (Sweden). A permutation test was applied to evaluate the reliability of the model and prevent overfitting.30 To explain the metabolic features, we used a nonparametric test (Mann−Whitney U test) to identify the significantly different compounds with p < 0.05 among the various groups using PASW Statistics 18 (USA). Metabolic pathway enrichment analysis was performed to confirm the important pathways related to the metabolic phenotype using a freely available website.31 Subsequently, hierarchical cluster analysis (HCA) was performed to determine the relationship between the different metabolites using MeV. V.4.8.1 software.32 The software VANTED was used to visualize the pathway map of the metabolites.33 Box plots were generated using Origin 8.0 software to display the variation tendency of compounds of interest. Prior to the identification of compounds, deconvolution analysis was performed on the data to improve the resolution of coeluting peaks. The metabolites were confirmed using two key procedures. First, several mass spectra libraries were applied to aid in the identification of compounds based on mass spectral matching (e.g., NIST, Mainlib, Replib, Wiley, and Fiehn). The structure was further confirmed using the mass spectra, retention time (RT), and retention index (RI) of commercial standards.

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Establishment of the Pseudo-Targeted Method

To improve the sensitivity of low-abundance metabolites in the QC sample, we evaluated a QC sample with a doubled concentration in full-scan mode (Figure S3 in the Supporting Information), and the data were transformed into netCDF format by the Shimadzu workstation. The peaks were deconvoluted using AMDIS (Automated Mass Spectral Deconvolution, NIST) software. A peak table that included the scanning spot, signal-noise ratio, retention time, peak intensity, and the start and end points of the peaks was generated after peaks with a signal-noise ratio of less than 20 were eliminated. An in-house software program based on an algorithm of a bi-Gaussian chromatographic peak mode28 was used to choose the characteristic ions. Because the selection of characteristic ions for some coeluted chromatographic peaks was difficult, the Leco ChromaTOF and AMDIS software programs were also used. In total, 37 low-abundance metabolites that were not detected in a normal-concentration QC sample were added to the established peak table. To prevent false peak alignment resulting from retention time drift, ions from the SIM group were studied (Figure S3 in the Supporting Information). The first and last ions of a group

RESULTS AND DISCUSSION

Optimization of Pretreatment Conditions

The prevalent pretreatment protocol for plant metabolomics studies has been liquid−liquid extraction combined with mechanical vibration, which can efficiently destroy the plant cell wall through cell swelling to release intracellular metabolites. Various pretreatment factors have significant effects on the abundance and classification of metabolites. The extraction solvents, methods, and time were optimized extensively in this study. To account for the influence of the extraction solvents, we compared the following five broadly used extraction solvent C



Article

Figure 1. Total ion current chromatograms of the QC sample in full scan mode (A) and pseudotargeted mode (B).

were added to the adjacent former group and the latter group, respectively. The total ion current chromatograms from the full scan mode and the pseudotargeted mode of the QC sample are shown in Figure 1. This pseudotargeted method was used to analyze the metabolic profile of fresh tobacco leaves and to evaluate the influence of climate factors on these metabolites. This analysis included a SIM acquired method with 56 groups and a quantity method with 343 peaks.

construct a linear curve to calculate the correlation coefficients. The Pearson correlations of all metabolites in different weight samples (2, 5, 10, 15, 20, 30, 40, 50, and 70 mg) were calculated to evaluate the linearity response. On the basis of the distribution plot of the Pearson correlations, the correlation coefficients of 84.6% of the metabolites were higher than 0.9, and these metabolites occupied 98.35% of total area (Figure 2D). The linear correlations between the area of the metabolites and the concentrations of the samples were satisfactory. The stability of derivatized samples at room temperature was investigated at 3, 6, 12, 24, 36, 48, and 60 h (Table 1). The RSD values of more than 90% of the metabolites were lower than 30%, and the RSD value of the total area was less than 5%. Therefore, the derivatized sample was stable for up to 60 h. On the basis of these results, we found that the reproducibility and precision of the analytical method were acceptable for the investigation of metabolic profiling of tobacco leaves.

Analytical Method Validation

The reproducibility of the analytical method based on the pseudotargeted SIM mode was evaluated by continually analyzing eight QC replicates. A peak table was generated using the SIM integration table, and all metabolites were normalized using the internal standard. The RSD distribution plot for all metabolites was used to evaluate the reproducibility of the analytical method (Figure 2A). The RSD values of 97.0% of the ion peaks were less than 30%, which accounted for 99.7% of the total peak area. The intraday precision was investigated using five successive injections of a QC sample on the same day. Acceptable precision was determined using the RSD distribution plot, which showed that 98.7% of the metabolic features had an RSD value 0.5) and negatively correlated with rainfall (correlation coefficient r < −0.5). According to Figure 6, the responses of carbohydrates involved in glycolysis and sugar metabolism were highest in Henan. Because drought appeared during the early period of plant growth in Henan (Figure S2 in the Supporting Information), sugars were likely used as osmoprotective molecules to preserve the protein structure of the cells and reverse the accumulation of reactive oxygen species (ROS).38−40 Moreover, the contents of ribitol, tagatose, and xylose from group B were positively correlated with the amount of rainfall during maturation. Notably, the contents of xylitol, threitol, trehalose, and xylulose from cluster C were positively

reference standards (Table S5 in the Supporting Information). A Venn diagram of the different metabolites illustrated that 82 metabolites in the Yunnan province samples and 81 metabolites in the Guizhou province samples were significantly different from samples from the Henan growing region (Figure S5 in the Supporting Information). Between the Yunnan and Guizhou providences, 77 significantly different metabolites were discovered. Additionally, 33 altered metabolites, including some carbohydrates, amino acids, and organic acids, were significantly different among the three pairwise comparisons. HCA was preformed based on the Pearson correlation coefficients between metabolites to visualize the trends of the differential metabolites (Figure 4). Most metabolites had the highest abundance in the Henan province samples and the lowest abundance in the Yunnan province samples. To determine the relationships and trends among the differential metabolites in the three planting regions, we divided a HCA plot into six groups. For zones B, C, and F, most of the metabolites had the highest abundance in the Henan samples and the lowest abundance in the Yunnan samples, although each region had its own characteristics. In group B, most of the metabolites were significantly different between any other two regions; this group included some saccharides, amino acids, and sugar alcohols. In zone C, the contents of some monosaccharides (such as glucose, fructose, and xylose) were significantly different between the Henan and Yunnan samples. For group F, significant differences occurred between the Henan samples and samples from the other two provinces, while the levels of metabolites in the Guizhou and Yunnan samples were not significantly different. The contents of the corresponding metabolites from group A and group D (e.g., TCA cycle intermediates and antioxidants) were highest in samples from the Guizhou regions and lowest in Yunnan samples; however, no significant differences in the contents of these metabolites were observed between the Guizhou samples and samples from the other two provinces. It was clear that a small number of metabolites clustered in group E (e.g., βsitosterol, linoleic acid, and 2,4-dihydroxy-butanoic acid) were most abundant in samples from Yunnan province; p < 0.05 in the nonparametric test between Yunnan samples and those from the other regions. In addition, related metabolic pathways with differential metabolites were visualized by metabolic pathway enrichment analysis (Figure S6 in the Supporting Information); 54 metabolic pathways (galactose metabolism, F



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Figure 4. Heat map of differential metabolites and box plots of representative metabolites. The range of p values for metabolites using the nonparametric test is shown with symbols * (0.001 < p < 0.05) and ** (p < 0.001). Red and green reflect the relative concentration of these metabolites.

during water deficiency,42 and interfering with the attack of pests.43 There were three groups in the HCA of amino acids and climate factors (Figure 5B). For group A, the effect of climate factors on the content of alanine was not important because the absolute values of the correlation coefficients were less than 0.05. The relationships between the amino acids of

influenced by temperatures during vigorous growth and maturation. Amino acids can be a source of nitrogen for plant nutrition. Amino acids also have vital effects related to the physiological metabolism of plants, such as providing respite from the toxicity of heavy metal ions,41 impeding protein degradation G



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Figure 5. Heat maps of the correlation coefficients between metabolite contents and climate conditions. (A) Carbohydrate content and climate conditions, (B) amino acid content and climate conditions, and (C) content of TCA cycle intermediates and climate conditions.

cluster B and climate conditions were as follows. The temperature during root extension had large negative correlations with cysteine, glutamic acid, and tryptophan content. In contrast, rainfall had a positive effect on the content of these amino acids; of note is the influence of rainfall during root extension on glutamic acid and glycine content. Cluster C contained branched-chain amino acids (leucine, isoleucine, and valine), aromatic amino acids (tyrosine and phenylalanine), and amino acids from the glutamate family (proline and 5-aminovaleric acid). The content of most amino acids was positively correlated with temperature and negatively correlated with rainfall. Upon dehydration stress, the interaction of cell molecules damages cell membranes and proteins because of the reduced cell volume and the increased cytoplasmic concentration.38 The levels of amino acids, especially proline, branched chain amino acids, and aromatic amino acids, increase under drought stress.39,42 The content of most amino acids was highest in the Henan samples, which may be the result of drought in the early growth stage of the plants (Figure S2 in the Supporting Information). Because temperature had a positive effect on amino acid content in cluster C, it can be assumed that high temperature benefits nitrogenous metabolism and enhances the accumulation of amino acids.

The tricarboxylic acid (TCA) cycle promotes ATP synthesis as a type of respiration.44 Additionally, 2-oxoglutarate and oxaloacetate, which are important intermediates in the TCA cycle, can provide carbon skeletons for nitrogen supersession.45 The contents of TCA cycle intermediates (e.g., citric acid, isocitric acid, and α-ketoglutaric acid) were positively correlated with rainfall, especially rainfall during root extension and total rainfall (Figure 5C). The hours of sun exposure during maturation were positively correlated with the contents of corresponding metabolites of the TCA cycle. In contrast, clear negative correlations were observed between the contents of TCA cycle intermediates and other sun exposure conditions (sun exposure during root extension and vigorous growth as well as total sun exposure). Meteorological information revealed that rainfall was abundant and hours of sun exposure were limited in the Guizhou planting region (Figure S2 in the Supporting Information), which may have increased the levels of metabolites in the TCA cycle. Antioxidants play important roles in the scavenging of reactive oxygen and protecting the enzyme structure in plants. Numerous studies have found that various environmental stresses (e.g., high temperature, dehydration, pests, and UV stress) generate active oxygen species (ROS) in the cell, which H



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Figure 6. Metabolic pathway plot of the differential metabolites among three regions. Black, red, and blue indicate the relative concentration of metabolites in Yunnan, Henan, and Guizhou provinces. #, △, and □ represent p values of metabolites using nonparametric test that were less than 0.05 in Henan vs Guizhou, Yunnan vs Henan, Yunnan vs Guizhou, respectively.

maturation periods may have caused the slow growth of leaves and a large accumulation of nicotine.48 Compared with samples from the Guizhou and Henan provinces, the levels of two alkaloids (nicotine and nornicotine) were highest in plants from the Henan location because of the early drought (Figure 7).

then attack unsaturated fatty acid in the cell membrane and induce overoxidation of membrane lipids. The accumulation of antioxidant metabolites can block the formation of ROS.46 The levels of four important antioxidant compounds (chlorogenic acid, 4-hydroxycinnamic acid, quinic acid, and ascorbic acid) were highest in samples from the Guizhou province (Figure 7). Alkaloids are important secondary metabolites that help plants resist biotic and abiotic stress. It is well known that the great majority of alkaloids (e.g., nicotine, nornicotine, and myosmine) can be detected in tobacco leaves. In addition, nicotine accounts for 90−95% of the total alkaloids in tobacco leaves and is thus used as the main indicator of the quality of tobacco leaves.47 Drought during the vigorous growth and

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CONCLUSIONS An improved pseudotargeted method based on GC/SIM/MS was developed to investigate the metabolic profile of tobacco leaves. The detection sensitivity of low-abundance metabolites was further improved, and false SIM ion alignment caused by retention time drift was prevented. The extraction solvents used I



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Figure 7. Box plots of antioxidant and alkaloid content. * and ** indicate p values for the metabolites based on a nonparametric test in two growing districts of 0.001 < p < 0.05 and p < 0.001, respectively.

and the methods and timing of pretreatments were optimized. The developed analytical method was fully validated and used to investigate tobacco leaf metabolic profiles in different planting regions. Multivariate analysis indicated that the metabolic phenotype of tobacco leaves in the Yunnan province was similar to those from Guizhou. Differences between samples from the Henan province and the other two regions were obvious. A nonparametric test indicated that the levels of carbohydrates, amino acids, TCA cycle intermediates, and antioxidants were significantly different in the various growing districts. Pearson correlation analyses between climate parameters and the content of different metabolites were utilized to explain the alteration of the corresponding metabolites. The contents of most carbohydrates and amino acids were positively correlated with temperature and negatively correlated with rainfall. Because the Henan growing region experienced drought in the early stage of plant growth, the contents of most carbohydrates and amino acids were highest in the Henan samples; osmotic adjustment of these substances is important under drought stress. The content of TCA cycle metabolites was positively correlated with rainfall and hours of sun exposure during maturation; these components were negatively correlated with other sun exposure conditions. In the Guizhou samples, antioxidants such as chlorogenic acid, 4-hydroxycinnamic acid, quinic acid, and ascorbic acid accumulated to reduce oxidative damage to the plant cells. The high levels of alkaloids observed in the Henan samples were related to

drought. It was concluded that the climate factors in various planting regions most likely have a great effect on the levels of primary and secondary metabolites in tobacco leaves. These conclusions were obtained using field experimentation. To further validate the relationship between climate factors and metabolite content, we will investigate tobacco plants using controlled climate conditions. In the interim, this systemic biology platform that integrates the metabolome, transcriptome, and proteome will help to completely elucidate physiological mechanisms.

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ASSOCIATED CONTENT

S Supporting Information *

Sample information; optimization of extraction solvents, methods, and time; differential metabolites between any two planting locations; three growing districts of fresh tobacco leaves; climate conditions of Yunnan, Guizhou, and Henan provinces; schematic summary of the pseudo-targeted method; distribution of the RSD values for all metabolites from 13 QC samples; Venn diagram of the significantly different variables between any two regions; and pathway enrichment analysis of differential metabolites using the MetaboAnalyst. This material is available free of charge via the Internet at http://pubs.acs.org. J



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AUTHOR INFORMATION

Corresponding Authors

*(G.X.) Tel/fax: 0086-411-84379530. E-mail: [email protected]. cn. *(X.L.) Tel/fax: 0086-411-84379559. E-mail: luxin001@dicp. ac.cn. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This study was supported by the foundations (nos. 21175132 and 31000137) and the creative research group project (no. 21021004) from National Natural Science Foundation of China.

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