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Metabolomic Responses of Arabidopsis Suspension Cells to Bicarbonate under Light and Dark Conditions

Abstract

Global CO2 level presently recorded at 400 ppm is expected to reach 550 ppm in 2050, an increment likely to impact plant growth and productivity. Using targeted LC-MS and GC-MS platforms we quantified 229 and 29 metabolites, respectively in a time-course study to reveal short-term responses to different concentrations (1, 3, and 10 mM) of bicarbonate (HCO3) under light and dark conditions. Results indicate that HCO3 treatment responsive metabolomic changes depend on the HCO3 concentration, time of treatment, and light/dark. Interestingly, 3 mM HCO3 concentration treatment induced more significantly changed metabolites than either lower or higher concentrations used. Flavonoid biosynthesis and glutathione metabolism were common to both light and dark-mediated responses in addition to showing concentration-dependent changes. Our metabolomics results provide insights into short-term plant cellular responses to elevated HCO3 concentrations as a result of ambient increases in CO2 under light and dark.

Introduction

Photosynthesis in green plants involves CO2 fixation. The rising atmospheric CO2 concentration over the past 150 years has posed significant effects on plant metabolism, physiology and productivity1,2,3. Elevated CO2 increases leaf area and number, branching, plant size, biomass, growth rates, C: N ratio and non-structural carbohydrates, in addition to decreased N-compounds such as amino acids and reduced allocation to phenolic compounds in long term4. In many C3 plant species, the effects of elevated CO2 in the long-term may result in enhanced photosynthesis, but little information are available on short-term plant cellular responses to CO2 changes at the metabolomic scale.

Many studies have focused on harnessing the potential of -omics platforms to provide insights into aspects of plant cell metabolism and physiology in response to CO2 or light conditions. Omics tools enable systemic view of cellular processes for investigation of metabolic networks and regulatory mechanisms. For instance, transcriptomic studies conducted in Arabidopsis thaliana5,6, rice (Oryza sativa)7, and Populus euramericana8 have provided meaningful insights into the molecular basis of plant response to elevated CO2. A recent study conducted in Physcomitrella patens reported transcriptomic changes under elevated CO2 conditions, indicating a genome-scale pathway changes9. Although it is still rare, proteomic studies of plant elevated CO2 responses were conducted in Aster tripolium10 and Rumex obtusifolius11, which indicated high expression of antioxidative enzymes and the involvement of organic acids.

Similarly, several studies have focused on the diurnal regulation of plant cellular metabolism. In photosynthetic organisms, the daily light/dark cycle is a major environmental regulator as they rely on solar energy to drive anabolic processes to store the fixed carbon or its catabolism during the dark period. Other light controlled processes include cell division, stress sensitivity, chemotaxis, nutrient uptake and phototaxis12. Light-intensity induced changes in A. thaliana transcriptome indicated that 20% of transcripts were H2O2- and ABA-responsive within 20–60 s of light stress13. Currently, metabolomic studies of plant diurnal processes are limited. In seagrass Zostera marina, the diurnal metabolomic changes were recorded in anoxic conditions14. Following an integrative approach of combining transcriptomic and metabolomic datasets into in silico metabolic models in rice, light-specific metabolic changes were observed15. Although HCO3-responsive guard and mesophyll cell responses were cataloged in canola leaves16, nothing is yet known about the temporal metabolomic changes associated with differential HCO3 levels or under light and dark conditions.

The use of single-cell types eliminates the ‘averaging effect’ of metabolomes that occurs with tissues, organs or whole plants17. In one such study using Chlamydomonas reinhardtii cells, 128 metabolites with significant differences between high- and low-CO2 treated cells were detected, of which 82 included amino acids, lipids, and carbohydrates18. Recently, C. reinhardtii transcriptome and proteome were studied under varying CO2 concentrations. Furthermore, it was observed that varying CO2 concentrations had an effect on 25% of the transcriptome in C. reinhardtii19. Proteomic studies revealed the role of 22 extracellular proteins, which were only expressed under low CO2 conditions20. In addition, transcriptional coordination of physiological responses in Nannochloropsis oceanica CCMP1779, a marine unicellular alga was recorded under light/dark cycles12. In another unicellular dinoflagellate (Symbiodinium sps.), the transcriptomic changes of cells in response to light intensity under different trophic conditions were investigated21. Furthermore, the diurnal transcriptome of C. reinhardtii undergoing cellular and metabolomic differentiation was captured22. Here we chose NaHCO3 treatment as a CO2 enrichment model system to investigate its effect on Arabidopsis cellular metabolome under varying concentration, duration of exposure, and light and dark conditions.

Results

The dark and light responsive metabolomes and their responses to HCO3 treatment

Our targeted high performance liquid chromatography (HPLC)-multiple reaction monitoring mass spectrometry (MRM-MS) approach allowed us to detect and quantify a total of 229 metabolites in A. thaliana suspension cells at various time-points across four biological replicates (Supplementary Tables S1–3). The polar to semi-polar metabolites covered various pathways of secondary metabolites, amino acids, purines, flavonoids, 2-oxocarboxylic acids, central carbon metabolites, glutathione, and beta-alanine metabolism that were spread over the KEGG metabolic map (Supplementary Figure S1). Another 29 metabolites were identified and quantified using the gas chromatography (GC) platform to include cyanoamino acid, pantothenate, fatty acids, beta-alanine, galactose, glycolysis, branched-chain amino acid, arginine, and proline metabolism among others (Supplementary Table S4).

The HCO3 treated suspension cell metabolomes showed quantitative variations based on HCO3 concentrations (1, 3, and 10 mM), light and dark, and the time of treatment. Across the treatments, the shared metabolites showed differential levels of changes (fold change cut-offs of <0.8 and >1.2 at p < 0.05) (Supplementary Table S3). The significantly changing metabolites revealed 156 changing metabolites (either increase or decrease), which are common to both light and dark conditions (Fig. 1A). These common metabolites are enriched in flavones, flavonol, and flavonoid biosynthesis, amino acid (Arg, Pro, Tyr, Ala, Asp and Glu) metabolism, alkaloid biosynthesis and glutathione metabolism. On the other hand, 28 significantly changed metabolites are enriched in pyridine biosynthesis and 32 in one carbon pool by folate, folate biosynthesis and amino acid metabolism. Concentration dependent metabolomic changes revealed shared pathways (i.e., flavonoid, alkaloid, glutathione metabolism), with 3 mM treatment induced the highest number (176) of significant changes enriched in pyrimidine and flavonoid metabolism (Fig. 1B). For 10 mM treatment, beta-alanine, Arg and Pro and Cys and Met metabolism showed enrichment. In addition, the commonly enriched pathways based on 79 commonly shared metabolites showed concentration-independent changes belong to flavone, flavonol and other flavonoid biosynthesis, purine metabolism, glutathione metabolism and amino acid metabolism (Ala, Asp, Glu, Cys, and Met) (Fig. 1B).

Figure 1
figure 1

Light and HCO3 concentration-dependent common and unique metabolites during the treatment.

(A) A two-way Venn diagram showing common and unique metabolites in light and dark conditions. (B) A three-way Venn diagram showing common and unique metabolites in different concentrations (1, 3, and 10 mM) in the time-course study.

Grouped metabolomic changes in HCO3 treated cells

Within subject-ANOVA analysis using the four factors including treatment (control vs. HCO3), light conditions (light and dark), concentration (1, 3, and 10 mM), time-course (0–120 min) and interactions indicated significant changes (p < 0.05) in 89, 123, 228, 112 and 28 metabolites, respectively (Supplementary Table S5). Taurine metabolism was significantly affected by time, whereas flavonoid biosynthesis, glutathione and phenylalanine metabolism were associated with light dependent changes. Furthermore, both taurine metabolism and flavonoid metabolism were associated with HCO3 treatment-specific changes, whereas glyoxylate and dicarboxylate metabolism, Ala, Asp and Glu metabolism and taurine metabolism were responsive to interactions of all factors (Table 1). However, the highest numbers of metabolites were enriched in flavonoid biosynthesis, glutathione metabolism, beta-Ala metabolism, purine and pyrimidine metabolism, Phe and Tyr metabolism.

Table 1 Within subject-ANOVA showing enriched pathways based on significantly changed metabolites for time, light, treatment, concentrations, and their interactions.

Hierarchical clustering analysis (HCA), performed to classify all metabolites based on treatments, light conditions, concentrations, and time-points revealed three distinct clusters (Fig. 2). The first cluster mostly reflected the increased metabolites under the dark conditions, while the last cluster reflected increased metabolites across all the concentrations and conditions. Similarly, when all samples were clustered by HCA as a function of metabolite responses, they were clustered into several regions of high and low correlations (Spearman) (Fig. 3A). For 3 mM treatment, all the samples under dark and light conditions correlated well among each other (R2 < 0.9) in a condition specific manner, i.e., light-independent HCO3 response, whereas for 1 mM and 10 mM treatments, the correlations were low (Fig. 3A). Similarly, metabolite pair-wise (Spearman) correlation map indicated strong correlation among sugar and nucleotide phosphates and amino acids during the time-course study (Fig. 3B).

Figure 2
figure 2

Hierarchical cluster analysis (HCA) analysis of the mean values of metabolite contents.

Results are from four biological replicates showing 229 metabolites common to all the treatments depicting the data structure dependent on light, concentrations, and time course (0–120 min) of HCO3 treatment. Red and green indicate high and low concentrations of metabolites, respectively. Values were subjected to average linkage clustering (Euclidean distance).

Figure 3
figure 3

Correlation among samples and metabolites in the study.

(A) Samples pair-wise correlation heat map for control and HCO3 treated time-course profiling study (0, 5, 15, 30 60, 120 min). Columns and rows refer to the samples represented as a function of metabolites. Clustering on correlation coefficients demonstrate the grouping of samples based on their ‘metabotype’. (B) Metabolites pair-wise correlation heat map for control and HCO3 treated time-course profiling study (0, 5, 15, 30 60, 120 min). Columns and rows refer to metabolites arranged based on the Pearson correlation co-efficient. Highly correlated (red) metabolites in both cell-types belong to sugars and amino acids as compared to the lowly correlated ones (green).

Temporal metabolomic changes in HCO3 treated cells

Short time-series expression miner (STEM) analyses showed that under both light and dark conditions the most significant pattern (p < 0.05) was a consistent increase (upward trend) throughout the time-course (Fig. 4). In fact, under dark conditions the number of metabolites displaying this pattern was directly proportional to the 1, 3, and 10 mM HCO3concentrations, with 75, 92 and 104 metabolites, respectively (Supplementary Table S6). Pathways enriched in the dark across the three concentrations include purine and pyrimidine metabolism, flavonoid biosynthesis, beta-alanine metabolism, tyrosine metabolism and alkaloid metabolism. This is completely opposite to what was observed under the light conditions, where 80, 65 and 63 metabolites (enriched with flavone, flavonol and other flavonoid biosynthesis, alkaloid metabolism, and tyrosine metabolism) showed increasing patterns with 1, 3, and 10 mM HCO3, respectively. In contrast, the number of metabolites for the other two-patterns (i.e., bell-shaped with initial increase followed by decrease in later time-points and two-peaked patterns) showed consistent decreases. The bell-shaped pattern showed concentration-dependent increases in the number of metabolites i.e., 45, 48, and 64 metabolites for 1, 3, and 10 mM HCO3, respectively, usually enriched with beta-alanine metabolism (Supplementary Table S6). While a few of the models demonstrated clear and linear patterns of changes, a majority of the metabolites showed mostly biphasic patterns with two peaks and two troughs.

Figure 4
figure 4

Short Time series Expression Miner (STEM) analysis displaying patterns of metabolite changes in dark and light conditions across the HCO3 treatment concentrations.

The numbers in the bottom left corner indicate number of metabolites following the pattern, while the numbers on top left indicate the serial number of the predicted model out of the 10 generated models, where only the top four models for each conditions are shown.

Global metabolic and metabolite-specific responses to HCO3 treatment

In OPLS-DA, the supervised multivariate linear models revealed grouped and differential responses of the suspension cells to HCO3 with an interpretable visualization in addition to providing the important metabolites, which helped in differentiating the various treatments (groups). Upon OPLS-DA evaluation, the resulting score plots indicated great separation of control and HCO3 treatment (Fig. 5A), slight separation between dark and light-treated conditions (Fig. 5B), 3 mM standing out of the three concentrations (Fig. 5C), and a clear separation at 0 time point controls and the other time-points (Fig. 5D). In addition, flavonoids such as neohesperidin, isoliquiritigenin, and luteolin showed treatment and light-dependent changes in the time-course study (Fig. 5A–D). GC-MS based profiling of primary metabolites revealed the contribution of individual metabolites in the loading plots, i.e., sugars (glucose, galactose, trehalose and melezitose), amino acids (Val, Ala, Gly), and fatty acids (stearic and palmitic acid) among others (Fig. 6A). OPLS-DA analyses based variable importance in projections (VIP) scores indicated the roles of sugars (glucose, galactose, trehalose), amino acids (Ala, beta-Ala, Gly, pyroglutamic acid), lactic acid, ethanolamine, boric and phosphoric acids in the HCO3 responsiveness (Fig. 6B). To further validate the models, we performed random permutation tests with the OPLS-DA model. In accordance with the OPLS-DA models, all the components and validation with 1000 random permutation tests generated intercepts with acceptable (>0.9) variance (R2) and the cross-validated variance (Q2) values (>0.85).

Figure 5
figure 5

Orthogonal partial least square discriminant (OPLS-DA) analysis of metabolites.

Alterations in A. thaliana suspension cells showed the effect of HCO3. (A) treatment, (B) light conditions, (C) concentration, and (D) time-course upon HCO3 treatment. OPLS-DA was performed using four replicates data of relative metabolite abundance in samples at 0, 5, 15, 30, 60, 120 min, and the generated PC1 and PC2 were plotted.

Figure 6
figure 6

Principal component analysis of the GC-MS-based metabolites.

(A) Loading plot displaying the contribution of individual metabolites; (B) Variable importance in projections (VIP) scores of top 10 metabolites obtained from the PLS-DA analysis.

Discussion

Increases in atmospheric CO2 concentrations provide additional resources for photosynthesis, which leads to high accumulation of photosynthate10,23. Carbonic anhydrase (CA) converts CO2 into HCO3 for incorporation into cellular metabolism and acts as an upstream regulator of CO224. CA catalyzes the reaction CO2 + H2O ↔ HCO3 + H+ in plants leading to accumulated HCO3, where rapid equilibration leads to increased concentration of CO2 around RuBisCO. In addition, the fundamental function of PEPCase is to catalyze the conversion of HCO3 and phosphoenolpyruvate (PEP) to oxaloacetate. In dicots, PEPCases are activated by glucose 6-phosphate (G6P) and inhibited by malate or Asp25. Furthermore, the dark fixation of CO2/HCO3 is important for plant growth26. Our results showed wide-spread shifts in cellular metabolism at all four levels, i.e., in response to treatment (control and HCO3) (Fig. 6A), light conditions (dark and light) (Fig. 1A), concentrations (1, 3, and 10 mM) (Fig. 1B) and time-course (0, 5, 15, 30, 60, 120 min) (Table 1). We observed large-scale HCO3 concentration-dependent metabolomic changes in the photomixotrophic A. thaliana suspension cells. In addition, transcriptomic reprogramming in response to modulation of light can occur in a matter of seconds in A. thaliana13. We observed more synchronized and comparable responses at 3 mM HCO3 as compared to low (1 mM) or high (10 Mm) HCO3, possibly because those concentrations are either too low to elicit a cellular response or too high that they trigger stress responses and suppress other metabolic processes. Evidently, saturated photosynthesis takes place in soybean leaf mesophyll cells under light at 10 mM HCO3 27. Moreover, photosynthetic activities in photosystem II (PSII) are known to increase when treated with 1 mM HCO3, but not when treated with 12 mM HCO3, indicating that higher concentration may not be activating PSII, but only playing a protective role against excessive damage28. In addition, dose-dependency of HCO3 (increments from 2 mM to 5 mM HCO3) in inducing malate and citrate synthesis in plant cells is also known29. Thus, in this study the observed metabolomic responses not only reflect HCO3 dependent responses but also species-specific responses.

Recently, microarray analyses conducted in moss (P. patens) gametophytes revealed that the expression levels of 814 genes were affected under elevated CO2, where most transcriptional reprogramming occurred in photosynthetic regulation, carbon metabolism, and stress responses9. In our metabolomic data sets, pyrimidine metabolism and folate metabolism showed unique responses to light and dark conditions, respectively, whereas flavonoid metabolism, glutathione, and alkaloid metabolism were enriched in both dark and light conditions. In fact, light induced phenylpropanoid metabolism and flavonoid biosynthesis in A. thaliana roots, as a high-irradiance response is well documented30. Increased folate metabolism in dark conditions indicates stress response as reiterated from the involvement of folate in seedling establishment of A. thaliana in dark31. Interestingly, lactic acid, a biomarker for concentration-dependent response to HCO3, displayed the highest VIP score derived from PLSDA analyses. Lactic acid is a known indicator of hypoxia stress in maize roots32. Hence, lactic acid production is also indicative of anoxic and stressful conditions leading to anaerobic respiration in photosynthetic suspension cells in response to HCO3. Similarly, induction of secondary metabolites, e.g., alkaloids by elevated CO2 is known in many alkaloid producing plants33. Furthermore, in cyanobacterial model Cyanothece sp. PCC 7822, proteomics and transcriptomics indicated that proteins for nitrogenase and the pentose phosphate pathway were increased in the dark, whereas glycolysis and TCA cycle were more prominent in the light34. Furthermore, Glu, a proteogenic amino acid and indicator of protein abundance in plant cells displayed HCO3 concentration dependent accumulation patterns. We observed the levels of benzoic acid, a phenylpropanoid metabolism derivative, accumulated only in the dark, where its levels were depleted by HCO3 under light (Supplementary Table S4). Thus, not only does HCO3 perturbs light-dependent secondary metabolism, but also results in shifts in respiration, transcription (light-induced changes in pyrimidine metabolism and concentration-dependent changes in purine metabolism), amino acid (Ala and pyroglutamic acid with VIP > 1), sugar (glucose, galactose, trehalose with VIP > 1) and folate metabolism.

Significant changes in glutathione metabolism under light (Fig. 2A) at both 1 mM and 10 mM HCO3 (Fig. 2B) indicate the involvement of redox regulation in presence of HCO3 (Table 1). Diurnal and light-mediated regulation of sulfur and glutathione metabolism in A. thaliana is known35. Upon HCO3 treatment, glutathione metabolism, an integral part of redox regulation showed changes in both light and concentration-dependent manner. Increased oxidative stress in C3 plants36 and decreased ascorbate by high CO211 further supports redox associated changes upon HCO3 treatment. Changes in taurine and flavonoid metabolism (Fig. 5, Table 1) with time further substantiate the role of antioxidants in amelioration of elevated HCO3 induced oxidative responses. In a recent study, up-regulation of genes encoding enzymes involved in oxidative signaling and ROS scavenging such as glutathione-S-transferase, peroxidases, catalases, Cys and thioredoxin-pathway were reported under elevated CO29. Thus, elevated CO2 can enhance maintenance of the redox potential due to elevated rates of CO2 assimilation and electron transport, as well as low photorespiration37. In a nutshell, cellular redox regulation is intricately associated with HCO3 responses, and eventually with elevated CO2 conditions. In addition, using HCO3 feeding/enrichment as an alternative to elevated CO2, we show the usefulness of this system in A. thaliana suspension cell cultures which can be extended to other cell-types and plant cultures.

Increased levels of metabolites from glycolysis/gluconeogenesis, galactose, starch and sucrose metabolism along the time-course (Supplementary Table S3) are consistent with previous results of CO2 induced accumulation of sugars and starch38. Although maintenance of a C/N balance in plants is complex and mediated by multiple mechanisms39, we observed metabolic flow in favor of primary metabolism with increased HCO3. In contrast, metabolites showing decreases throughout the time-course belonged to fatty acid, vitamin B6, pantothenate metabolism among others (p < 0.1). Phe is involved in biosynthesis of secondary metabolites (phenylpropanoids and flavonoids). Its increase possibly indicated increased pathway fluxes to generate more defense-related metabolites. Furthermore, elevated CO2 is known to associate with increased secondary metabolites, e.g., phenolics, flavonoids, alkaloids and terpenoids38. Increased secondary metabolism is a characteristic of light signal, which induce systems-wide changes in rice transcriptome15. Thus, HCO3 not only regulates primary metabolism, but also modulates secondary metabolism in plant cells indicating a genome-wide metabolic shifts in light-, concentration-, and time-dependent manner.

As time-resolved single-cell type metabolomic studies are rare, the concentration, light, and time-dependent HCO3 induced metabolomic changes reported here in A. thaliana suspension cells would help us understand CO2 responsive metabolic changes. Typical cellular HCO3 induced responses are characterized by significant changes in flavonoid, glutathione, and Phe metabolism in light- and concentration-dependent manner, indicating functional and temporal behavior in the cells. Validation of these regulated metabolic pathways by means of traditional and omics approaches is essential for a systems biology perspective on HCO3 response. The results of this study have shown the utility of metabolomics tools towards improved understanding of the plant CO2 sensing, utilization, and response mechanisms under light and dark conditions.

Materials and Methods

Plant materials and culture conditions

A. thaliana var. Landsberg erecta (LER) suspension cell cultures were obtained from Dr. Joshua Heazlewood (University of Western Australia, Australia) and maintained in 250 ml flasks under illumination of 130 μmol m−2 s−1 on an orbital shaker with constant shaking at 120 rpm, 22 °C. They were sub-cultured weekly by tenfold dilution into fresh Murashige and Skoog (MS) medium40 supplemented with 3% sucrose, kinetin (0.05 mg/L) and α-naphthalene acetic acid (NAA) (0.5 mg/L).

HCO3 treatment of suspension cells

The suspension cells were treated with NaHCO3 at a final concentration of 1, 3, or 10 mM. For controls, NaNO3 (NO3 is a macronutrient in MS media) was added in same concentration to nullify the effect of Na+ during the treatment. Furthermore, as HCO3 induced significant pH changes of the MS media (pH 5.8), we added 50 mM (for 1 mM and 3 mM HCO3) and 100 mM (for 10 mM HCO3) MES buffer (pH 5.8) to the MS media, and the pH-stabilized MS media were used for both control and HCO3 treatments. In addition, the buffered MS media were sonicated to remove atmospheric gases prior to addition of HCO3. Cells were incubated at 25 °C for 0, 5, 15, 30, 60, and 120 min on a shaker, and four replicates (n = 4) were generated for each data point after treatment with HCO3 (treatment) and control. After treatment, 100 ml cells at a concentration of 3 × 106 ml−1 were filtered using filter paper mounted on funnels, quickly blotted dry using Kim wipes and immediately snap-frozen in liquid nitrogen and stored in a −80 °C freezer until metabolite extraction.

Metabolite sample preparation and analysis

Authentic standard metabolites, listed in Supplementary Table S1 were obtained from Sigma-Aldrich (St. Louis, MO, USA) and their stocks were stored in −80 °C. Serial dilutions of the standards were tested over a range of 10 pmol μL−1 to 1 nmol μL−1 for the linearity of detection in the mass spectrometer. Metabolite extractions from 20 mg of freeze dried suspension cells were performed following Fiehn et al.41. During extraction internal standards were added, i.e., 33 pmol lidocaine (positive mode), 210 pmol camphor-10-sulfonic acid and 100 pmol ribitol (negative mode) to aid in retention time correction and peak abundance quantification. Extracts were dissolved in 100 μL ddH2O and aliquoted for HPLC (MRM)-MS and GC-MS analysis.

Targeted profiling by HPLC-MRM-MS

The targeted metabolite profiling of the samples were performed using HPLC-MRM-MS as described16. An Agilent 1100 HPLC system (Agilent Technologies, Santa Clara, CA, USA) was used with an autosampler (Agilent, Santa Clara, CA, USA) to inject the samples. A C18-reverse phase analytical column (Gemini 5 μ; 150 × 2.0 mm, Phenomenex, Torrance, CA, USA) with the following gradient program helped separation of the compounds using two solvents i.e., 0.1% formic acid in H2O (solvent A) and 0.1% formic acid in acetonitrile (solvent B) for 1:99, v/v, at 0 min; 1:99, v/v, at 0.2 min; 99.5:0.5, v/v at 31 min; 99.5:0.5, v/v at 34 min; 1:99, v/v, at 34.2 min; 1:99 at 60 min at room temperature at a flow rate of 500 μL/min. The HPLC was coupled to a hybrid triple quadrupole-ion trap 4000 Q-TRAP system equipped with a TurboIonSpray (TIS) interface (AB Sciex Inc., Foster City, CA, USA). The electrospray ionization (ESI) parameters were as previously described16, while the optimized MRM assay parameters are in Supplementary Table S1. QQQ scans were acquired as MRM experiments, where we monitored 59 (5.8 min), 40 (9.9 min), 45 (7.9 min), 31 (12.5 min), and 18 (23.8 min) MRM transitions over five periods (segments) in positive ionization, and 66 and 11 MRM transitions for two periods in negative ionization42. Each MRM transition was obtained with a dwell times specific for compounds, and a total cycle time was 1–1.5 s to ensure at least 10 data points across the peak. Only water served as blank solutions, while ddH2O was injected during washes. Pooled samples from the entire study served as quality control for monitoring the chromatography and MS conditions. Data imported using AnalystTM software version 1.5.1 (AB Sciex, Foster City, CA, USA) while peak areas were integrated and quantified using the IntelliQuan algorithm of the MultiQuantTM software version 2.1 (AB Sciex, Foster City, CA, USA).

Profiling of metabolomes by GC-MS

GC-MS was performed following Lisec et al.43, where 20 μl of aliquoted extracts were dried and were sequentially derivatised with 100 μl of 20 mg ml−1 methoxyamine hydrochloride (MeOX) in pyridine, followed by shaking at 55 °C for 90 min, then overnight incubation, which was followed by trimethylsilylation by adding 100 μl of N-methyl-N-trimethylsilyl-trifluoroacetamide (MSTFA) to the mixture and incubation for 30 min at 37 °C, 30 min at 60 °C, and finally for 120 min at 100 °C to obtain optimized derivatization Samples were injected at a split ratio of 1:25 (250 °C) with a He carrier gas flow of 2 ml/min in a GC-MS system consisting of an autosampler, a TRACETM 1310 gas chromatograph (Thermo Scientific, San Jose, CA, USA) in-line with a TSQ8000 Triple Quadrupole mass spectrometer (Thermo Scientific, San Jose, CA, USA) equipped with an electron ionization (EI) source. A TR-5MS capillary column (30 m × 0.25 mm × 0.25 μm, Thermo Scientific, Waltham, MA) was used and the temperature program was essentially as described16. A standard n-alkane mixture (C8-C40) was injected at the beginning and end of the analysis for tentative identification and to monitor any shifts in retention indices (RI). The GC-MS data obtained from Xcalibur™ software version 2.0 (Thermo Finnigan, Austin, TX, USA) in raw format were aligned and processed as described44. Filtered raw GC-MS data sets comprised of data from four biological replicates (n = 4) and 29 curated analytes with less than 10% missing values, which were imputed using 1/3rd of minimum values in each sample. Metabolite identification and quantification were performed as described16 using MSRI spectral libraries of Golm Metabolome Database available from Max-Planck-Institute for Plant Physiology, Golm, Germany (http://csbdb.mpimp-golm.mpg.de/csbdb/gmd/gmd.html)45, Automated Mass Spectral Deconvolution and Identification System (AMDIS, National Institute of Standards and Technology, USA) and NIST Mass Spectral Reference Library (NIST11/2011; National Institute of Standards and Technology, USA) library.

Statistical analysis

The individual MS data were assembled into a concatenated data set into a single. csv sheet in Microsoft Excel (Microsoft Corp., Seattle, WA, USA) before statistical analyses using statistical software R (Version 2.9.1, R Development Core Team 2007, https://www.r-project.org/)46 Logarithmic and median transformations of metabolite data were performed using DeviumWeb47 and MetaboAnalyst48, and data were transformed, scaled, missing values imputed, and outlier removed before proceeding to uni- and multi-variate statistical analysis of the data. Raw processed data are available in Supplementary Table S2. ANOVA was performed using DeviumWeb47. p-values were adjusted by Benjamini-Hochberg correction (BH)49. Probability level, α was set to 0.05, adjusted for multiple hypotheses testing using BH to allow for a maximum 5% probability (q = 0.05) in false positive detection (FDR). Metabolite fold changes with cut-offs of 1.2 and 0.8, and significant changes (p < 0.05) were calculated (Supplementary Table S3). Principal component analysis (PCA) and orthogonal partial least square discriminant analysis (OPLS-DA) were performed using DeviumWeb47, where output consisted of score plots to visualize the contrast between different samples and loading plots to explain the cluster separation. The data files were scaled with unit variance without any transformations.

Pathway enrichment analysis and clustering analysis

Pathway enrichment, over-representation, and mapping were performed using functionalities available at MBRole (http://csbg.cnb.csic.es/mbrole/), MSEA (http://www.msea.ca/MSEA/) and MetPA (http://metpa.metabolomics.ca/MetPA/) while ID conversions were performed at Chemical Translation Service (CTS: http://cts.fiehnlab.ucdavis.edu/conversion/batch) (Supplementary Table S1). Short Time series Expression Miner (STEM)50 was used as a Java implementation with a graphical user interface available at http://www.cs.cmu.edu/~jernst/st/ for clustering the metabolite accumulation patterns according to time points. Hierarchical clustering analysis (HCA) using average linkage clustering was performed on Euclidean distances using PermutMatrix51.

Additional Information

How to cite this article: Misra, B. B. et al. Metabolomic Responses of Arabidopsis Suspension Cells to Bicarbonate under Light and Dark Conditions. Sci. Rep. 6, 35778; doi: 10.1038/srep35778 (2016).

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Acknowledgements

This work was supported by the US National Science Foundation grant MCB-1158000 to SC.

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B.B.M. performed the experiments, S.G., Z.Y. and E.A. obtained the data, B.B.M. analyzed the data, B.B.M. drafted the main manuscript text and prepared the figures and tables. S.C. supervised the project and wrote the manuscript. All authors reviewed the manuscript.

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Misra, B., Yin, Z., Geng, S. et al. Metabolomic Responses of Arabidopsis Suspension Cells to Bicarbonate under Light and Dark Conditions. Sci Rep 6, 35778 (2016). https://doi.org/10.1038/srep35778

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