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Article

Primary Metabolite Profile Changes in Coffea spp. Promoted by Single and Combined Exposure to Drought and Elevated CO2 Concentration

by
Ana M. Rodrigues
1,†,
Tiago Jorge
1,
Sonia Osorio
2,
Delphine M. Pott
2,
Fernando C. Lidon
3,
Fábio M. DaMatta
4,
Isabel Marques
5,
Ana I. Ribeiro-Barros
3,5,*,
José C. Ramalho
3,5,* and
Carla António
1,*,†
1
Plant Metabolomics Laboratory, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB NOVA), 2780-157 Oeiras, Portugal
2
Instituto de Hortofruticultura Subtropical y Mediterránea “La Mayora”, Departamento de Biología Molecular y Bioquímica, Universidad de Málaga—Consejo Superior de Investigaciones Científicas (IHSM-UMA-CSIC), 29071 Málaga, Spain
3
GeoBioSciences, GeoTechnologies and GeoEngineering (GeoBioTec), Faculdade de Ciências e Tecnologia (FCT), Universidade NOVA de Lisboa (UNL), 2829-516 Monte de Caparica, Portugal
4
Departamento de Biologia Vegetal, Universidade Federal Viçosa (UFV), Viçosa 36570-090, Brazil
5
Plant Stress & Biodiversity Lab, Centro de Estudos Florestais (CEF), Instituto Superior Agronomia (ISA), Universidade de Lisboa (ULisboa), Tapada da Ajuda, 1349-017 Lisboa, Portugal
*
Authors to whom correspondence should be addressed.
†
Current address: Plant Metabolomics Lab Portugal, Centro de Estudos Florestais (CEF), Instituto Superior Agronomia (ISA), Universidade de Lisboa (ULisboa), Tapada da Ajuda, 1349-017 Lisboa, Portugal.
Metabolites 2021, 11(7), 427; https://doi.org/10.3390/metabo11070427
Submission received: 17 June 2021 / Revised: 24 June 2021 / Accepted: 25 June 2021 / Published: 29 June 2021
(This article belongs to the Special Issue Metabolomics to Elucidate the Metabolic Mechanisms of Plant Responses to Variable Environmental Stresses)
Browse Figures
Versions Notes

Abstract

:
Climate change scenarios pose major threats to many crops worldwide, including coffee. We explored the primary metabolite responses in two Coffea genotypes, C. canephora cv. Conilon Clone 153 and C. arabica cv. Icatu, grown at normal (aCO2) or elevated (eCO2) CO2 concentrations of 380 or 700 ppm, respectively, under well-watered (WW), moderate (MWD), or severe (SWD) water deficit conditions, in order to assess coffee responses to drought and how eCO2 can influence such responses. Primary metabolites were analyzed with a gas chromatography time-of-flight mass spectrometry metabolomics platform (GC-TOF-MS). A total of 48 primary metabolites were identified in both genotypes (23 amino acids and derivatives, 10 organic acids, 11 sugars, and 4 other metabolites), with differences recorded in both genotypes. Increased metabolite levels were observed in CL153 plants under single and combined conditions of aCO2 and drought (MWD and SWD), as opposed to the observed decreased levels under eCO2 in both drought conditions. In contrast, Icatu showed minor differences under MWD, and increased levels (especially amino acids) only under SWD at both CO2 concentration conditions, although with a tendency towards greater increases under eCO2. Altogether, CL153 demonstrated large impact under MWD, and seemed not to benefit from eCO2 in either MWD and SWD, in contrast with Icatu.

Graphical Abstract

1. Introduction

Coffee is an important agricultural commodity worldwide that provides income and employment to millions of people in developing countries. Coffee is cultivated in over 80 countries throughout the tropics with ca. 99% of the production based on Coffea arabica L. (Arabica coffee) and Coffea canephora Pierre ex Froehner (Robusta coffee) [1,2]. Estimated global climate change scenarios will impose adverse environmental conditions with deep impacts on crops, mainly due to water constraints and heat stress [3]. In this context, the coffee sector is expected to face serious challenges in the upcoming decades as several studies have demonstrated the climate change sensitivity of coffee species, particularly C. arabica, with impacts on suitable cultivation areas, yield, and bean quality, as well as increased pest and disease incidence and economic losses [4,5,6,7,8,9].
One of the major components of global climate change is an increase in air CO2 concentration, coupled with rising air temperatures (with the worst estimate pointing to a global average increase of up to 936 ppm CO2 and 4.5 °C by 2100), together with more frequent and prolonged drought events associated with unpredictable rainfall patterns [10]. Such events are expected to threaten the sustainability of agricultural production on a global scale and be responsible for severe constraints to plant growth and productivity [11]. Nevertheless, there are interesting and potentially positive effects of elevated CO2 concentration (eCO2) on several crop plants, usually associated with increased photosynthetic activity [12,13]. That is the case for coffee, under adequate water availability [14,15,16,17], which displays greater C-investment in reproductive structures [18], ultimately increasing productivity [1]. Furthermore, eCO2 was recently reported to increase coffee plant resilience to heat stress [19,20,21], while preserving leaf mineral balance [22] and bean quality [23]. Additionally, although the future positive impact of eCO2 on plants subject to water deficits can be species-dependent [24], studies reported that eCO2 can clearly reduce drought impact on plant photosynthesis, growth, and yields [25,26,27]). These effects were reported to occur in Coffea spp. as well, where eCO2 mitigated the negative impacts of drought on photosynthetic performance and components [28,29,30,31,32].
Under field conditions, plants are often exposed to a combination of different environmental changes. In this case, the plant response to combined changing conditions should be regarded not as a sum of each one applied independently, but as a unique response that can reflect a positive (i.e., increased tolerance) or negative (i.e., susceptibility) interaction between them [33,34,35]. Abiotic stresses can deeply impact plant metabolism and cause a reconfiguration of the metabolic network in order to allow for the maintenance of metabolic homeostasis and the production of compounds that can mitigate the effects of stress [36,37]. Drought stress is one of the major threats to agriculture, since it constrains a wide number of physiological, morphological, and biochemical processes, impacting growth, nutrient uptake, C-assimilation, and partitioning, and ultimately yield and quality [28,38,39]. Hence, it is essential to understand the plant response to this critical adverse environmental condition. Metabolic adjustments in response to drought include the regulation of photosynthesis and the maintenance of cell osmotic potential through the accumulation of metabolites such as phytohormone abscisic acid and osmolytes, including amino acids, soluble sugars, the raffinose family of oligosaccharides (RFOs), polyols, and polyamines [36,40,41,42,43]. Osmolyte accumulation in response to abiotic stress conditions has an important role in the maintenance of cell turgor—via a decrease in the osmotic potential of the cell—and protection against oxidative damage by a decrease in the levels of reactive oxygen species (ROS) to restore cellular redox balance [43]. Moreover, stress-tolerant plants usually show higher levels of some of these stress-related metabolites, even under normal growth conditions, to keep their metabolism prepared for adverse environmental conditions [41].
Complementing the previous studies of our team, and regarding the coffee plant response to single and combined exposure to drought and/or eCO2 at the physiological, biochemical, and molecular level [28,29,32,44], here we further explored the changes in gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) primary metabolite profiling in plants exposed to increasing water deficit severity, and how eCO2 might influence drought response and plant resilience in Coffea spp. To date, GC-MS is the most widely accepted analytical technology used in post-genomic plant metabolomics studies [43,45,46,47]. Genotypes from the two main coffee producing species, C. canephora cv. Conilon Clone 153 (CL153) and C. arabica cv. Icatu (Icatu), were grown under normal atmospheric (aCO2) or elevated (eCO2) air CO2 concentration, coupled with either well-watered conditions (WW), or moderate (MWD) or severe (SWD) water deficit conditions. Our findings provide important and timely evidence regarding the role of eCO2 in the response to drought in these species, contributing to improvements in knowledge regarding coffee plant performance in the face of ongoing and future global climate changes.

2. Results

The GC-TOF-MS analysis identified 48 primary metabolites in CL153 and Icatu leaves, including 23 amino acids and derivatives, 10 organic acids, 11 sugars, and 4 other metabolites (Figure 1 and Figure 2, Supplementary Tables S1 and S2).
In general, a larger number of significant changes in primary metabolite levels were observed in CL153 than in Icatu due to single exposure to eCO2 (WW plants), and MWD (both CO2 concentrations). However, under SWD, both genotypes showed a large (and similar) number of significant changes under both aCO2 or eCO2. Additionally, in CL153 SWD plants under eCO2, those changes reflected mostly a decrease in metabolite levels (as opposed to Icatu) (Figure 1c and Figure 2c). Lower metabolite levels were observed under eCO2 as compared with aCO2 in both MWD or SWD (Figure 1d), whereas Icatu revealed opposite patterns under eCO2, as compared with aCO2 (Figure 2d).
In CL153 plants, the effect of single exposure to eCO2 led to a significant increase in aspartate, glutamate, methionine, pyroglutamate, 2-oxoglutarate, citrate, quinate, and glucose (up to 5-fold) (Figure 1a). Additionally, exposure to single drought conditions caused a higher number of significant changes in primary metabolite levels (Figure 1b). Under MWD, CL153 plants grown under aCO2 revealed significant increases (up to 5-fold) in most amino acids (γ-aminobutyric acid (GABA), glutamate, histidine, isoleucine, lysine, pyroglutamate, threonine, tryptophan, tyramine, tyrosine, and valine). A similar response was observed under SWD with significant increases in amino acids (up to 11-fold), together with a significant increase in glucose (5-fold), and a significant decline in sucrose, citrate, and phosphoric acid. Interestingly, CL153 plants grown under eCO2 and subjected to MWD and SWD conditions (Figure 1c) showed a markedly different response than under WW conditions, with reduction of a large number of metabolites. These eCO2 plants also showed a different pattern under MWD and SWD as compared with their aCO2 counterparts, revealing significant declines in the levels of asparagine, aspartate, glycine, glutamate (only at SWD), histidine (only at MWD), proline (only at MWD), pyroglutamate, and serine. Nevertheless, under eCO2, significant increases in glucose (2-fold, in MWD), and in fructose (4-fold), trehalose (5-fold), tryptophan, (up to 7-fold), and valine (4-fold) were observed under SWD. Significant reductions were observed regarding pyruvate (under MWD), and in citrate, glycerate, and threonate under SWD.
Furthermore, a global reduction in relative metabolite abundance was observed when comparing eCO2 to aCO2 for each water deficit treatment in regards to amino acid levels, particularly in MWD (Figure 1d). Still, under the combined exposure to SWD and eCO2, only glutamate and pyroglutamate showed a significant decrease, whereas pyruvate and sucrose showed a significant increase (up to 4-fold).
Analysis of C. arabica cv. Icatu showed a different pattern of metabolite variation when compared with the C. canephora genotype CL153. In Icatu plants, the single eCO2 effect promoted significant changes in a low number of metabolites (in isoleucine, fructose, and spermine, up to 2-fold) (Figure 2a). Furthermore, in this genotype, only a few metabolites showed altered abundance under MWD, irrespective of the air CO2 concentration (Figure 2b). Most significant changes were observed in plants under SWD and aCO2 conditions (Figure 2b,c). Overall, these significant variations included an increase in arginine, fructose, GABA, histidine, isoleucine, lysine, phenylalanine, proline, tryptophan, tyrosine, and valine (up to 9-fold), and a significant decrease in 2-oxoglutarate, aspartate, galactinol, glutamate, glycerate, serine, sucrose, and threonate.
In opposition to what was observed in CL153, comparison between the combined effect of MWD and SWD in plants grown under eCO2 (Figure 2d) led to few significant changes in primary metabolite levels. These changes included an increase in aspartate (up to 4-fold), pyroglutamate (2-fold, in MWD under eCO2), putrescine, glycerate, threonate (2-fold, in SWD under eCO2), and a significant decrease in arginine and fructose (in MWD under eCO2).
Unsupervised principal component analysis (PCA) was applied to GC-TOF-MS relative primary metabolite data to identify major sources of variation in the data (Figure 3). The CL153 SWD plants clustered separately in relation to WW and MWD plants, whereas a distinction between CO2 concentration conditions could only be observed for MWD plants.
In Icatu plants (Figure 3b), a clear separation was observed for SWD plants along PC1, in relation to WW and MWD plants. In addition, as observed for CL153 plants, only MWD plants showed a clear distinction between plants grown under aCO2 and eCO2.
To further explore and identify potential stress responsive metabolites in these Coffea spp. genotypes, a supervised partial least square discrimination analysis (PLS-DA) was performed (Figure 4). As observed in the PCA, both genotypes showed a clear discrimination between WW and SWD plants, particularly in Icatu plants, regardless of CO2 concentration. PLS-DA further confirmed a clear distinction between MWD plants grown under different CO2 concentrations.
The score plots revealed a high number of highly correlated metabolites responsible for the cluster of SWD samples in the negative quadrants of component 1 for both genotypes. These metabolites include mostly amino acids and sugars that significantly increased in plants exposed to single and combined drought and eCO2 conditions, including the the branched chained amino acids (BCAAs) valine and isoleucine, arginine, GABA, lysine, phenylalanine, trehalose, tryptophan, and tyrosine for CL153 plants, and arginine, BCAAs, fructose, GABA, lysine, proline, tryptophan, and tyrosine for Icatu plants.

3. Discussion

GC-TOF-MS analysis allowed for the characterization of the primary metabolome of CL153 and Icatu leaves under single and combined exposure to increasing CO2 concentration (eCO2) and water deficit (MWD and SWD) conditions. Unsupervised exploratory PCA revealed differential metabolite responses to single and combined stresses; this effect was clearer in Icatu plants, and PLS-DA analyses allowed us to further identify stress-responsive metabolites whose levels significantly varied upon single and/or combined exposure to drought and eCO2.
The significant increase in CL153 alone of most amino acids upon single exposure to MWD indicated that Icatu was not affected by MWD, and thus, supports previous studies reporting its strong drought resilience [32]. However, under SWD, both genotypes showed relevant changes in the levels of most amino acids, although in Icatu these changes were relatively similar under both air CO2 concentrations. This is also in agreement with the reported drought resilience of this genotype under both CO2 concentrations [32]. The BCAAs isoleucine and valine significantly increased in Icatu under SWD at both air CO2 concentrations, and under MWD only at aCO2, whereas in CL153 an increase was only observed under SWD and eCO2. BCAAs are well-known to accumulate under abiotic stress conditions and play the role of osmolytes during osmotic stress [36,52,53]. BCAAs are also responsible for an alternative supply of electrons and respiratory substrates to the plant respiratory chain under stress conditions, ultimately contributing to drought stress tolerance in plants [54,55].
Lysine, a product of the aspartate pathway, also significantly increased in response to SWD in CL153 plants (only under aCO2), and Icatu (under aCO2 and eCO2). Lysine catabolism can channel electrons to the mitochondrial electron transport chain in addition to feeding the operation of the tricarboxylic acid (TCA) cycle under carbon starvation [54], as would be the case in these plants due to a strong reduction of photosynthetic C-assimilation under limited water availability [32].
Metabolites from the glutamate family pathway significantly increased in Icatu, including the osmolytes proline and GABA, under SWD irrespective of CO2 concentration. Osmolyte accumulation has an important role in abiotic stress responses; namely, maintenance of cell turgor via a decrease in the osmotic potential, protection against oxidative damage by decreasing the levels of reactive oxygen species (ROS), and maintenance of the cellular redox balance [41,42,43]. Accumulation of proline is considered an adaptive mechanism in drought-tolerant coffee genotypes. For instance, C. canephora clones (IC-2, IC-3, IC-4, IC-6, IC-8, and R-4) subjected to drought-stress revealed an accumulation of proline with increasing exposure to soil drying, followed by a decline upon re-watering [56]. Moreover, Silva et al. [57] showed that proline accumulation was higher in the leaves of a drought-tolerant clone of C. canephora than in a susceptible genotype. Proline levels were also found to increase in C. arabica cvs. Catuaí and Mundo Novo, C. canephora cv. Apoatã, and a graft of Mundo Novo shoot on Apoatã root in response to drought [58]. Arginine, another member of the glutamate family, showed a significant increase in response to the harshest water deficit level under both CO2 concentrations and in both genotypes. Increased levels of arginine were suggested to contribute to drought tolerance [59]. Arginine is a precursor of polyamines, molecules strongly associated with many biological processes including protection from osmotic stress [60]. TCA cycle-derived amino acids of the glutamate and aspartate families are central regulators of carbon and nitrogen metabolism [61]. In fact, a high level of glutamine synthetase activity has been found in the leaves of C. arabica cv. Catuaí during grain expansion [62], which has been linked to the capacity of coffee plants to assimilate nitrogen, a useful trait to improve coffee management in commercial orchards.
In Icatu, amino acids derived from the shikimate pathway (e.g., phenylalanine, tryptophan, and tyrosine) accumulated under single exposure to SWD under aCO2; tryptophan and tyrosine also increased in SWD under eCO2 conditions. The accumulation of these metabolites is a valuable source of carbon-skeletons for the phenylpropanoid pathway and further biosynthesis of secondary metabolites [63,64]. Tryptophan is reported to play an important role as an osmolyte, but also in stomatal regulation and reactive oxygen species (ROS) scavenging under drought conditions [65].
A significant increase in putrescine occurred in Icatu under the interaction of SWD and eCO2. Several drought experiments have shown a correlation between resistance to dehydration and the levels of putrescine—a plant growth regulator that helps to balance chlorophyll concentrations—and the accumulation of osmolytes and soluble phenolic compounds under drought conditions [66].
Osmoprotective sugars were also affected by drought and a significant increase was observed in fructose in Icatu and in glucose, trehalose, and sucrose in CL153 (particularly under SWD and eCO2 conditions), which might have contributed to dehydration tolerance [36,37].
The metabolite responses of each genotype to water deficit and eCO2, together with the PCA and PLS-DA analysis, showed that eCO2 clearly altered the impact of MWD in both genotypes, as shown by the overlap of the MWD under eCO2 cluster with WW controls. However, MWD plants under aCO2 did not show such proximity to WW controls. This observation is in line with the positive effect of eCO2 regarding the water deficit imposition reported earlier [28,32], mainly in Icatu plants. In addition, the clear separation of SWD plants from the WW and MWD clusters, particularly in Icatu plants, showed that the production of metabolites was greatly altered by SWD. Nevertheless, Icatu plants maintained (or increased) their metabolite levels under both CO2 concentrations (within MWD and SWD), contrasting with the decreases observed in CL153 under eCO2, which is in line with the high tolerance of Icatu plants to water deficit under both CO2 concentrations [32].
Physiological and biochemical studies demonstrated an absence of impact on Icatu plants in most parameters under SWD, irrespective of CO2 concentration, and a greater tolerance than CL153 under aCO2. This resilience of Icatu was associated with the preservation of photosynthetic performance (e.g., photosynthetic capacity, Amax; photochemical efficiency of PSII, Fv/Fm) and the maintenance or reinforcement of several chloroplast components (e.g., cytochromes electron carriers, photosystems (PSs), and RuBisCO activities) [32,44]. Such intrinsic tolerance (reinforced under eCO2) was further associated with a wide abundance of proteins related to the photosynthetic apparatus (as part of photosystems, light harvesting complexes, protective cyclic electron flow, and RuBisCO activase), which was not observed in CL153 [32].
Limited photochemical energy use can generate ROS leading to damage to the photosynthetic apparatus (e.g., D1 protein, lipids, electron transport) [67,68]. Therefore, at the cellular level, drought tolerance is often associated with the activation of photoprotective and antioxidative mechanisms. This has previously been shown to be a key response in the acclimation of coffee plants to a wide variety of abiotic stresses, such as high irradiance [69,70], cold [71,72,73], and temperature [19,21,31]. Antioxidant enzymes coupled with strong lipid dynamics usually contribute to the maintenance of photosynthetic activity in coffee genotypes (including in Icatu). For instance, drought experiments using C. canephora plants of Clone 120 (drought-tolerant) and Clone 109 (drought-sensitive), subjected to repetitive cycles of drought conditions for 14 days, revealed an oxidative stress response on the sensitive clone, presumably leading to programmed cell death, while acclimation of the tolerant genotype seems to be related to antioxidant secondary metabolites and ABA response [74].

4. Materials and Methods

4.1. Plant Material and Growth Conditions

Plants of two cropped genotypes (Brazil) from the two main coffee-producing species, Coffea canephora Pierre ex A. Froehner cv. Conilon Clone 153 (CL153) and C. arabica L. cv. Icatu Vermelho (an introgressed cultivar resulting from a cross of C. canephora and C. arabica cv. Bourbon Vermelho, which was then further crossed with C. arabica cv. Mundo Novo) were used, as described in [32]. Briefly, plants were grown for seven years in 80 L pots in two walk-in growth chambers (EHHF 10000, ARALAB, Portugal), under controlled temperature conditions (25/20 °C, day/night, ±1 °C), irradiance (ca. 750 μmol m−2 s−1 at the upper part of the plant), relative humidity (70% ± 2%), photoperiod (12 h), and either ambient (aCO2, 380 ± 5 ppm) or elevated (eCO2, 700 ± 5 ppm) air CO2 concentration. Plants were grown without restriction of nutrients [14] or root growth, and with adequate water availability provided by watering the plants every two days.

4.2. Water Deficit Imposition

Within each chamber, plants were divided into three groups (n = 6 to 8) and maintained under well-irrigated conditions (WW), or exposed to moderate (MWD) and severe water deficit (SWD), as described in [32]. Water deficit was gradually imposed over two weeks by partially withholding irrigation (that is, by replacing only part of the amount of lost water per pot). Water availability levels were established as a function of leaf water potential at predawn (Ψpd), as WW: Ψpd > −0.35 MPa; MWD −1.5 < Ψpd < 2.5 MPa; and SWD Ψpd < 3.5 MPa, corresponding to ca. 80% (WW), 25% (MWD), or 10% (SWD) of maximal pot water availability, respectively [75]. Ψpd was determined immediately after leaf excision in 5–6 plants per treatment, using a pressure chamber (Model 1000, PMS Instrument Co., Albany, OR, USA).
When the required Ψpd was reached for MWD or SWD conditions, pot moisture was maintained for another two weeks by adding adequate water amounts according to each watering treatment before sampling. Exceptionally, the Icatu 700-plants (eCO2) under MWD conditions were exposed to total water withholding in the last five days of the four-week period, in order to further force the reduction of Ψpd values, which did not shift below −0.6 MPa until that stage.
For metabolomic analysis, newly matured leaves from the upper third part (well illuminated) were collected, immediately frozen in liquid nitrogen, and stored at −80 °C.

4.3. GC-TOF-MS Primary Metabolite Profiling

Primary metabolites were extracted and derivatized following a well-established protocol [76]. Frozen leaves were ground to a fine powder with a mortar and pestle and under liquid nitrogen. A total of 50 mg fresh weight (FW) of finely homogenized leaf material was weighed into each 1.5 mL safe-lock polypropylene microfuge tube and 700 µL of ice-cold methanol and 30 μL of ribitol (0.2 mg mL–1 ribitol in water), as the internal standard, were added to each tube. Samples were vortex-mixed and incubated in a shaker (ThermoMixer, Eppendorf, Hamburg, Germany) for 15 min at 70 °C and 950 rpm, and subsequently centrifuged (12,000× g, 10 min, 20 °C). The supernatant was collected, mixed with 375 μL chloroform and 750 μL water, and vortex-mixed. After centrifugation (2200× g, 15 min, 20 °C), 150 mL of the polar aqueous/methanol phase was evaporated to dryness using a centrifugal concentrator for 3 h at 30 °C (Vacufuge Plus, Eppendorf, Hamburg, Germany), and stored at −80 °C. Dried polar extracts were derivatized with 40 μL of 20 mg mL−1 methoxyamine hydrochloride in pyridine, followed by 70 μL of N-methyl-N-trimethylsilyltrifluoroacetamide (TMS derivatization) and 20 μL mL−1 of a mixture of fatty acid methyl esters (FAMES). Biological variations were controlled by analyzing quality control (QC) standards; namely, FAMES internal standard markers and a QC standard solution of 41 pure reference compounds, throughout the analysis. After GC-TOF-MS analysis, samples were subsequently evaluated using AMDIS (Automated Mass Spectral Deconvolution and Identification System software, version 2.71). Primary metabolites were annotated using the TagFinder software [77] and a reference library of ambient mass spectra and retention indices from the Golm Metabolome Database (http://gmd.mpimp-golm.mpg.de/, accessed on 25 June 2021) [78,79]. Metadata information following minimum reporting standard guidelines of the Metabolomics Standard Initiative (MSI) can be found in Supplementary Table S3.

4.4. Experimental Design and Statistical Analyses

Plants from each coffee genotype were subjected to six treatment combinations, reflecting the single and combined exposure to two CO2 concentration (aCO2 or eCO2) and three levels of water availability (WW, MWD or SWD). The experiment was implemented using a completely randomized design, with six to eight plants per treatment in individual pots.
Statistical analyses were performed in R and R Studio software [48,49]. R Packages used to perform statistical analysis include “agricolae” [80], “gplots” [50], and “mixOmics” [51].
The relative abundance of primary metabolite levels was normalized to the internal standard (ribitol) and the fresh weight/dry weight ratio of the samples. Within each genotype, one-way ANOVA at a 95% confidence level was used to assess differences between treatments. Subsequently, fold-changes between conditions and the control for each metabolite were determined and Log10 transformed for heatmap plotting.
Principal component analysis (PCA) was applied using unit variance scaling, and to further maximize the discrimination between the sample groups, supervised partial least squares discriminant analysis (PLS-DA) was performed using the leave-one-out cross-validation embedded in the “mixOmics” package.

5. Conclusions

Overall, our metabolite profiling findings showed significant drought impacts on two widely traded coffee species. Notably, eCO2 clearly reduced metabolite changes under MWD in Icatu (but not in CL153), resulting in metabolic profiles close to those of their WW plants. These findings are relevant in terms of the coffee plant response to future climate change scenarios (particularly in regard to the role of eCO2), and also highlight the need for complementary and integrative studies, from ecophysiology to omics, to better clarify the role of specific metabolites in the resilience of this important tropical crop.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/metabo11070427/s1, Table S1: Fold changes in relative levels of primary metabolites in leaves of Coffea canephora cv. Conilon: Clone 153, Table S2: Fold changes in relative levels of primary metabolites in leaves of Coffea arabica cv. Icatu, Table S3: Metabolomics Standards Initiative compliant metadata.

Author Contributions

Conceptualization, A.I.R.-B., F.C.L., F.M.D., I.M. and J.C.R.; methodology, A.I.R.-B., C.A. and J.C.R.; software, A.M.R.; validation, C.A.; formal analysis, A.M.R.; investigation, A.M.R., D.M.P., J.C.R. and T.J.; resources, C.A., J.C.R. and S.O.; data curation, A.M.R.; writing—original draft preparation, A.M.R.; writing—review and editing, A.I.R.-B., C.A., D.M.P., F.C.L., F.M.D., I.M., J.C.R., S.O. and T.J.; visualization, A.M.R.; supervision, C.A.; project administration, A.I.R.-B. and J.C.R.; funding acquisition, A.I.R.-B. and J.C.R. All authors have read and agreed to the published version of the manuscript.

Funding

This work received funding support from the European Union’s Horizon 2020 research and innovation program (grant agreement No. 727934, project BreedCAFS), and from national funds from Fundação para a Ciência e a Tecnologia (FCT), Portugal, through the project PTDC/ASP-AGR/31257/2017, and the research units UIDB/00239/2020 (CEF), UIDP/04035/2020 (GeoBioTec), and UID/Multi/04551/2013 (GREEN-IT). C.A. acknowledges the FCT Investigator Programme (contract IF/00376/2012/CP0165/CT0003). A.M.R. acknowledges FCT and the ITQB NOVA international Ph.D. programme ‘Plants for Life’ (PD/00035/2013) for the Ph.D. fellowship (PD/BD/114417/2016). T.J. acknowledges FCT and the ITQB NOVA international Ph.D. programme ‘Plants for Life’ for the Ph.D. fellowship (PD/BD/113475/2015). Fellowships from the Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil (CNPq) and Fundação de Amparo à Pesquisa do Estado de Minas Gerais, Brazil (FAPEMIG, project CRA-RED-00053-16) to F.M.D. are also gratefully acknowledged.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available in article and Supplementary Materials.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. FAO Statistical Pocketbook; Coffee Food and Agricultural Organization of the United Nations: Rome, Italy, 2015.
  2. DaMatta, F.M.; Rahn, E.; Läderach, P.; Ghini, R.; Ramalho, J.C. Why could the coffee crop endure climate change and global warming to a greater extent than previously estimated? Clim. Chang. 2019, 152, 167–178. [Google Scholar] [CrossRef] [Green Version]
  3. Ramirez-Villegas, J.; Challinor, A. Assessing relevant climate data for agricultural applications. Agric. For. Meteorol. 2012, 161, 26–45. [Google Scholar] [CrossRef] [Green Version]
  4. Magrach, A.; Ghazoul, J. Climate and pest-driven geographic shifts in global coffee production: Implications for forest cover, biodiversity and carbon storage. PLoS ONE 2015, 10, e0133071. [Google Scholar] [CrossRef] [PubMed]
  5. Ovalle-Rivera, O.; Läderach, P.; Bunn, C.; Obersteiner, M.; Schroth, G. Projected shifts in Coffea arabica suitability among major global producing regions due to climate change. PLoS ONE 2015, 10, e0124155. [Google Scholar] [CrossRef] [Green Version]
  6. van der Vossen, H.; Bertrand, B.; Charrier, A. Next generation variety development for sustainable production of arabica coffee (Coffea arabica L.): A review. Euphytica 2015, 204, 243–256. [Google Scholar] [CrossRef]
  7. Läderach, P.; Ramirez–Villegas, J.; Navarro-Racines, C.; Zelaya, C.; Martinez-Valle, A.; Jarvis, A. Climate change adaptation of coffee production in space and time. Clim. Chang. 2017, 141, 47–62. [Google Scholar] [CrossRef] [Green Version]
  8. Davis, A.P.; Chadburn, H.; Moat, J.; O’Sullivan, R.; Hargreaves, S.; Lughadha, E.N. High extinction risk for wild coffee species and implications for coffee sector sustainability. Sci. Adv. 2019, 5, eaav3473. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  9. de Sousa, K.; van Zonneveld, M.; Holmgren, M.; Kindt, R.; Ordoñez, J.C. The future of coffee and cocoa agroforestry in a warmer Mesoamerica. Sci. Rep. 2019, 9, 8828. [Google Scholar] [CrossRef]
  10. IPCC Core Writing Team. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; IPCC: Geneva, Switzerland, 2014. [Google Scholar]
  11. DaMatta, F.M.; Grandis, A.; Arenque, B.C.; Buckeridge, M.S. Impacts of climate changes on crop physiology and food quality. Food Res. Int. 2010, 43, 1814–1823. [Google Scholar] [CrossRef]
  12. Ainsworth, E.A.; Rogers, A. The response of photosynthesis and stomatal conductance to rising [CO2]: Mechanisms and environmental interactions. Plant Cell Environ. 2007, 30, 258–270. [Google Scholar] [CrossRef] [PubMed]
  13. Kirschbaum, M.U.F. Does enhanced photosynthesis enhance growth? Lessons learned from CO2 enrichment studies. Plant Physiol. 2011, 155, 117–124. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Ramalho, J.C.; Rodrigues, A.P.; Semedo, J.N.; Pais, I.P.; Martins, L.D.; Simões-Costa, M.C.; Leitão, A.E.; Fortunato, A.S.; Batista-Santos, P.; Palos, I.M.; et al. Sustained photosynthetic performance of Coffea spp. under long-term enhanced [CO2]. PLoS ONE 2013, 8, e82712. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Ghini, R.; Torre-Neto, A.; Dentzien, A.F.M.; Guerreiro-Filho, O.; Lost, R.; Patrício, F.R.A.; Prado, J.S.M.; Thomaziello, R.A.; Bettiol, W.; DaMatta, F.M. Coffee growth, pest and yield responses to free-air CO2 enrichment. Clim. Chang. 2015, 132, 307–320. [Google Scholar] [CrossRef]
  16. DaMatta, F.M.; Godoy, A.G.; Menezes-Silva, P.E.; Martins, S.C.V.; Sanglard, L.M.V.P.; Morais, L.E.; Torre-Neto, A.; Ghini, R. Sustained enhancement of photosynthesis in coffee trees grown under free-air CO2 enrichment conditions: Disentangling the contributions of stomatal, mesophyll, and biochemical limitations. J. Exp. Bot. 2016, 67, 341–352. [Google Scholar] [CrossRef] [Green Version]
  17. DaMatta, F.M.; Avila, R.T.; Cardoso, A.A.; Martins, S.C.V.; Ramalho, J.C. Physiological and agronomic performance of the coffee crop in the context of climate change and global warming: A review. J. Agric. Food Chem. 2018, 66, 5264–5274. [Google Scholar] [CrossRef]
  18. Rakocevic, M.; Braga, K.S.M.; Batista, E.R.; Maia, A.H.N.; Scholz, M.B.S.; Filizola, H.F. The vegetative growth assists to reproductive responses of Arabic coffee trees in a long-term FACE experiment. Plant Growth Reg. 2020, 91, 305–316. [Google Scholar] [CrossRef]
  19. Martins, M.Q.; Rodrigues, W.P.; Fortunato, A.S.; Leitão, A.E.; Rodrigues, A.P.; Pais, I.P.; Martins, L.D.; Silva, M.J.; Reboredo, F.H.; Partelli, F.L.; et al. Protective response mechanisms to heat stress in interaction with high [CO2] conditions in Coffea spp. Front. Plant Sci. 2016, 7, 947. [Google Scholar] [CrossRef] [Green Version]
  20. Rodrigues, W.P.; Martins, M.Q.; Fortunato, A.S.; Rodrigues, A.P.; Semedo, J.N.; Simoes-Costa, M.C.; Pais, I.P.; Leitão, A.E.; Colwell, F.; Goulão, L.; et al. Long-term elevated air [CO2] strengthens photosynthetic functioning and mitigates the impact of supra-optimal temperatures in tropical Coffea arabica and Coffea canephora species. Glob. Chang. Biol. 2016, 22, 415–431. [Google Scholar] [CrossRef]
  21. Scotti-Campos, P.; Pais, I.P.; Ribeiro-Barros, A.I.; Martins, L.D.; Tomaz, M.A.; Rodrigues, W.P.; Campostrini, E.; Semedo, J.N.; Fortunato, A.S.; Martins, M.Q.; et al. Lipid profile adjustments may contribute to warming acclimation and to heat impact mitigation by elevated [CO2] in Coffea spp. Environ. Exp. Bot. 2019, 167, 103856. [Google Scholar] [CrossRef]
  22. Martins, L.D.; Tomaz, M.A.; Lidon, F.C.; DaMatta, F.M.; Ramalho, J.C. Combined effects of elevated [CO2] and high temperature on leaf mineral balance in Coffea spp. plants. Clim. Chang. 2014, 126, 365–379. [Google Scholar] [CrossRef]
  23. Ramalho, J.C.; Pais, I.P.; Leitão, A.E.; Guerra, M.; Reboredo, F.H.; Máguas, C.M.; Carvalho, M.L.; Scotti-Campos, P.; Ribeiro-Barros, A.I.; Lidon, F.C.; et al. Can elevated air [CO2] conditions mitigate the predicted warming impact on the quality of coffee bean? Front. Plant Sci. 2018, 9, 287. [Google Scholar] [CrossRef] [Green Version]
  24. Tausz-Posch, S.; Tausz, M.; Bourgault, M. Elevated [CO2] effects on crops: Advances in understanding acclimation, nitrogen dynamics and interactions with drought and other organisms. Plant Biol. 2020, 22, 38–51. [Google Scholar] [CrossRef] [PubMed]
  25. Koutavas, A. CO2 fertilization and enhanced drought resistance in Greek firs from Cephalonia Island, Greece. Glob. Chang. Biol. 2013, 19, 529–539. [Google Scholar] [CrossRef]
  26. Wang, A.; Lam, S.K.; Hao, X.; Li, F.Y.; Zong, Y.; Wang, H.; Li, P. Elevated CO2 reduces the adverse effects of drought stress on a high-yielding soybean (Glycine max (L.) Merr.) cultivar by increasing water use efficiency. Plant Physiol. Biochem. 2018, 132, 660–665. [Google Scholar] [CrossRef] [PubMed]
  27. Gamar, M.I.A.; Kisiala, A.; Emery, R.J.N.; Yeung, E.C.; Stone, S.L.; Qaderi, M.M. Elevated carbon dioxide decreases the adverse effects of higher temperature and drought stress by mitigating oxidative stress and improving water status in Arabidopsis thaliana. Planta 2019, 250, 1191–1214. [Google Scholar] [CrossRef]
  28. Avila, R.T.; Almeida, W.L.; Costa, L.C.; Machado, K.L.; Barbosa, M.L.; de Souza, R.P.; Martino, P.B.; Juárez, M.A.T.; Marçal, D.M.S.; Martins, S.C.V.; et al. Elevated air [CO2] improves photosynthetic performance and alters biomass accumulation and partitioning in drought-stressed coffee plants. Environ. Exp. Bot. 2020, 177, 104137. [Google Scholar] [CrossRef]
  29. Avila, R.T.; Cardoso, A.A.; de Almeida, W.L.; Costa, L.C.; Machado, K.L.G.; Barbosa, M.L.; de Souza, R.P.B.; Oliveira, L.A.; Batista, D.S.; Martins, S.C.V.; et al. Coffee plants respond to drought and elevated [CO2] through changes in stomatal function, plant hydraulic conductance, and aquaporin expression. Environ. Exp. Bot. 2020, 177, 104148. [Google Scholar] [CrossRef]
  30. Marques, I.; Fernandes, I.; David, P.H.C.; Paulo, O.S.; Goulao, L.F.; Fortunato, A.S.; Lidon, F.C.; DaMatta, F.M.; Ramalho, J.C.; Ribeiro-Barros, A.I. Transcriptomic leaf profiling reveals differential responses of the two most traded coffee species to elevated [CO2]. Int. J. Mol. Sci. 2020, 21, 9211. [Google Scholar] [CrossRef]
  31. Marques, I.; Fernandes, I.; Paulo, O.S.; Lidon, F.C.; DaMatta, F.M.; Ramalho, J.C.; Ribeiro-Barros, A.I. A transcriptomic approach to understanding the combined impacts of supra-optimal temperatures and CO2 revealed different responses in the polyploid Coffea arabica and its diploid progenitor C. canephora. Int. J. Mol. Sci. 2021, 22, 3125. [Google Scholar] [CrossRef] [PubMed]
  32. Semedo, J.N.; Rodrigues, A.P.; Lidon, F.C.; Pais, I.P.; Marques, I.; Gouveia, D.; Armengaud, J.; Silva, M.J.; Martins, S.; Semedo, M.C.; et al. Intrinsic non-stomatal resilience to drought of the photosynthetic apparatus in Coffea spp. is strengthened by elevated air [CO2]. Tree Physiol. 2021, 41, 708–727. [Google Scholar] [CrossRef]
  33. Mittler, R. Abiotic stress, the field environment and stress combination. Trends Plant Sci. 2006, 11, 15–19. [Google Scholar] [CrossRef] [PubMed]
  34. Mittler, R.; Blumwald, E. Genetic engineering for modern agriculture: Challenges and perspectives. Annu. Rev. Plant Biol. 2010, 61, 443–462. [Google Scholar] [CrossRef] [Green Version]
  35. Suzuki, N.; Rivero, R.M.; Shulaev, V.; Blumwald, E.; Mittler, R. Abiotic and biotic stress combinations. New Phytol. 2014, 203, 32–43. [Google Scholar] [CrossRef] [PubMed]
  36. Obata, T.; Fernie, A.R. The use of metabolomics to dissect plant responses to abiotic stresses. Cell. Mol. Life Sci. 2012, 69, 3225–3243. [Google Scholar] [CrossRef] [Green Version]
  37. Jorge, T.F.; António, C. Plant Metabolomics in a Changing World: Metabolite Responses to Abiotic Stress Combinations, Plant, Abiotic Stress and Responses to Climate Change. In Plant, Abiotic Stress and Responses to Climate Change; Andjelkovic, V., Ed.; InTechOpen Publishing: London, UK, 2017; pp. 111–132. [Google Scholar] [CrossRef] [Green Version]
  38. Chaves, M.M.; Oliveira, M.M. Mechanisms underlying plant resilience to water deficits: Prospects for water-saving agriculture. J. Exp. Bot. 2004, 55, 2365–2384. [Google Scholar] [CrossRef] [Green Version]
  39. Fahad, S.; Bajwa, A.A.; Nazir, U.; Anjum, S.A.; Farooq, A.; Zohaib, A.; Sadia, S.; Nasim, W.; Adkins, S.; Saud, S.; et al. Crop production under drought and heat stress: Plant responses and management options. Front. Plant Sci. 2017, 8, 1147. [Google Scholar] [CrossRef] [Green Version]
  40. Hare, P.D.; Cress, W.A.; van Staden, J. Dissecting the roles of osmolyte accumulation during stress. Plant Cell Environ. 1998, 21, 535–553. [Google Scholar] [CrossRef]
  41. Krasensky, J.; Jonak, C. Drought, salt and temperature stress induced metabolic rearrangements and regulatory networks. J. Exp. Bot. 2012, 63, 1593–1608. [Google Scholar] [CrossRef] [Green Version]
  42. Arbona, V.; Manzi, M.; de Ollas, C.; Gómez-Cadenas, A. Metabolomics as a tool to investigate abiotic stress tolerance in plants. Int. J. Mol. Sci. 2013, 14, 4885–4911. [Google Scholar] [CrossRef]
  43. Jorge, T.F.; Rodrigues, J.A.; Caldana, C.; Sçhimt, R.; van Dongen, J.; Thomas-Oates, J.; António, C. Mass spectrometry-based plant metabolomics: Metabolite responses to abiotic stress. Mass Spec. Rev. 2016, 35, 620–649. [Google Scholar] [CrossRef] [PubMed]
  44. Dubberstein, D.; Lidon, F.C.; Rodrigues, A.P.; Semedo, J.N.; Marques, I.; Rodrigues, W.P.; Gouveia, D.; Armengaud, J.; Semedo, M.C.; Martins, S.; et al. Resilient and sensitive key points of the photosynthetic machinery of Coffea spp. to the single and superimposed exposure to severe drought and heat stresses. Front. Plant Sci. 2020, 11, 1049. [Google Scholar] [CrossRef]
  45. Fiehn, O.; Kopka, J.; Dörmann, P.; Tretheway, R.N.; Willmitzer, L. Metabolite profiling of plant functional genomics. Nat. Biotechnol. 2000, 18, 1157–1161. [Google Scholar] [CrossRef]
  46. Fernie, A. Metabolome characterisation in plant system analysis. Funct. Plant Biol. 2003, 30, 111–120. [Google Scholar] [CrossRef] [PubMed]
  47. Kopka, J.; Fernie, A.; Weckwerth, W.; Gibon, Y.; Stitt, M. Metabolite profiling in plant biology: Platforms and destinations. Genome Biol. 2004, 5, 109. [Google Scholar] [CrossRef] [Green Version]
  48. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2020; Available online: https://www.R-project.org/ (accessed on 24 November 2020).
  49. RStudio Team. RStudio: Integrated development for R; RStudio, Inc.: Boston, MA, USA, 2020; Available online: http://www.rstudio.com/ (accessed on 24 November 2020).
  50. Warnes, G.R.; Bolker, B.; Bonebakker, L.; Gentleman, R.; Huber, W.; Liaw, A.; Thomas, L.; Maechler, M.; Magnusson, A.; Moeller, S.; et al. gplots: Various R Programming Tools for Plotting Data; R Package Version 3.0.3; 2020. Available online: https://CRAN.R-project.org/package=gplots (accessed on 24 November 2020).
  51. Rohart, F.; Gautier, B.; Singh, A.; Lê Cao, K.-A. mixOmics: An R package for ‘omics feature selection and multiple data integration. PLoS Comput. Biol. 2017, 13, e1005752. [Google Scholar] [CrossRef] [Green Version]
  52. Mata, A.T.; Jorge, T.F.; Pires, M.V.; António, C. Drought stress tolerance in plants: Insights from metabolomics. In Drought Stress Tolerance in Plants, Molecular and Genetic Perspectives; Hossain, M.A., Wani, S.H., Bhattachajee, S., Burritt, D.J., Phan, L.S., Eds.; Tran. Humana Press, Springer International Publishing: Cham, Switzerland, 2016; Volume 2, pp. 187–216. [Google Scholar] [CrossRef]
  53. Persson, T.; Van Nguyen, T.; Alloisio, N.; Pujic, P.; Berry, A.M.; Normand, P.; Pawlowski, K. The N-metabolites of roots and actinorhizal nodules from Alnus glutinosa and Datisca glomerata: Can D. glomerata change N-transport forms when nodulated? Symbiosis 2016, 70, 149–157. [Google Scholar] [CrossRef]
  54. Araújo, W.L.; Ishizaki, K.; Nunes-Nesi, A.; Larson, T.R.; Tohge, T.; Krahnert, I.; Witt, S.; Obata, T.; Schauer, N.; Graham, I.A.; et al. Identification of the 2-hydroxyglutarate and isovaleryl-CoA dehydrogenases as alternative electron donors linking lysine catabolism to the electron transport chain of Arabidopsis mitochondria. Plant Cell 2010, 22, 1549–1563. [Google Scholar] [CrossRef] [Green Version]
  55. Pires, M.V.; Pereira Júnior, A.A.; Medeiros, D.B.; Daloso, D.M.; Pham, P.A.; Barros, K.A.; Engqvist, M.K.; Florian, A.; Krahnert, I.; Maurino, V.G.; et al. The influence of alternative pathways of respiration that utilize branched-chain amino acids following water shortage in Arabidopsis. Plant Cell Environ. 2016, 39, 1304–1319. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Tamirat, W. Review on role of proline on coffee under drought conditions. J. Environ. Earth Sci. 2019, 9, 10. [Google Scholar] [CrossRef]
  57. Silva, V.A.; Antunes, W.C.; Guimarães, B.L.S.; Paiva, R.M.C.; Silva, V.F.; Ferrão, M.A.G.; DaMatta, F.M.; Loureiro, M.E. Physiological response of Conilon coffee clone sensitive to drought grafted onto tolerant rootstock. Pesq. Agropec. Bras. 2010, 45, 457–464. [Google Scholar] [CrossRef] [Green Version]
  58. Santos, A.B.; Mazzafera, P. Dehydrins are highly expressed in water-stressed plants of two coffee species. Trop. Plant Biol. 2012, 5, 218–232. [Google Scholar] [CrossRef]
  59. Kalamaki, M.S.; Alexandrou, D.; Lazari, D.; Merkouropoulos, G.; Fotopoulos, V.; Pateraki, I.; Aggelis, A.; Carrillo-López, A.; Rubio-Cabetas, M.J.; Kanellis, A.K. Over-expression of a tomato N-acetyl-L-glutamate synthase gene (SlNAGS1) in Arabidopsis thaliana results in high ornithine levels and increased tolerance in salt and drought stresses. J. Exp. Bot. 2009, 60, 1859–1871. [Google Scholar] [CrossRef] [Green Version]
  60. Kalamaki, M.S.; Merkouropoulos, G.; Kanellis, A.K. Can ornithine accumulation modulate abiotic stress tolerance in Arabidopsis? Plant Signal. Behav. 2009, 4, 1099–1101. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  61. Fait, A.; Fromm, H.; Walter, D.; Galili, G.; Fernie, A.R. Highway or byway: The metabolic role of the GABA shunt in plants. Trends Plant Sci. 2008, 13, 14–19. [Google Scholar] [CrossRef]
  62. Neto, A.P.; Favarin, J.L.; dos Reis, A.R.; Tezzoto, T.; de Almeida, R.E.M.; Junior, J.L.; Fallo, L.A. Nitrogen metabolism in coffee plants in response to nitrogen supply by fertigation. Theor. Exp. Plant Physiol. 2015, 27, 41–50. [Google Scholar] [CrossRef] [Green Version]
  63. Tohge, T.; Watanabe, M.; Hoefgen, R.; Fernie, A.R. Shikimate and phenylalanine biosynthesis in the green lineage. Front. Plant Sci. 2013, 4, 62. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Hildebrandt, T.M. Synthesis versus degradation: Directions of amino acid metabolism during Arabidopsis abiotic stress response. Plant Mol. Biol. 2018, 98, 121–135. [Google Scholar] [CrossRef]
  65. Bowne, J.B.; Erwin, T.A.; Juttner, J.; Schnurbusch, T.; Langridge, P.; Bacic, A.; Roessner, U. Drought responses of leaf tissues from wheat cultivars of differing drought tolerance at the metabolite level. Mol. Plant 2012, 5, 418–429. [Google Scholar] [CrossRef] [Green Version]
  66. Alcázar, R.; Planas, J.; Saxena, T.; Zarza, X.; Bortolotti, C.; Cuevas, J.; Bitrián, M.; Tiburcio, A.F.; Altabella, T. Putrescine accumulation confers drought tolerance in transgenic Arabidopsis plants over-expressing the homologous Arginine decarboxylase 2 gene. Plant Physiol. Biochem. 2010, 48, 547–552. [Google Scholar] [CrossRef]
  67. Chaves, M.M.; Flexas, J.; Pinheiro, C. Photosynthesis under drought and salt stress: Regulation mechanisms from whole plant to cell. Ann. Bot. 2009, 103, 551–560. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  68. Osakabe, Y.; Osakabe, K.; Shinozaki, K.; Tran, L.S.P. Response of plants to water stress. Front. Plant Sci. 2014, 5, 86. [Google Scholar] [CrossRef] [Green Version]
  69. Ramalho, J.C.; Campos, P.S.; Teixeira, M.; Nunes, M.A. Nitrogen dependent changes in antioxidant systems and in fatty acid composition of chloroplast membranes from Coffea arabica L. plants submitted to high irradiance. Plant Sci. 1998, 135, 115–124. [Google Scholar] [CrossRef]
  70. Ramalho, J.; Campos, P.; Quartin, V.; Silva, M.J.; Nunes, M.A. High irradiance impairments on photosynthetic electron transport, ribulose-1,5-bisphosphate carboxylase/ oxygenase and N assimilation as a function of N availability in Coffea arabica L. plants. J. Plant Physiol. 1999, 154, 319–326. [Google Scholar] [CrossRef]
  71. Fortunato, A.; Lidon, F.; Eichler, P.; Leitão, A.; Pais, I.; Ribeiro, A.; Ramalho, J. Biochemical and molecular characterization of the antioxidative system of Coffea sp. under cold conditions in genotypes with contrasting tolerance. J. Plant Physiol. 2010, 167, 333–342. [Google Scholar] [CrossRef]
  72. Scotti-Campos, P.; Pais, I.P.; Partelli, F.L.; Batista-Santos, P.; Ramalho, J.C. Phospholipids profile in chloroplasts of Coffea spp. genotypes differing in cold acclimation ability. J. Plant Physiol. 2014, 171, 243–249. [Google Scholar] [CrossRef]
  73. Ramalho, J.C.; DaMatta, F.M.; Rodrigues, A.P.; Scotti-Campos, P.; Pais, I.; Batista-Santos, P.; Partelli, F.L.; Ribeiro, A.; Lidon, F.C.; Leitão, A.E. Cold impact and acclimation response of Coffea spp. plants. Theor. Exp. Plant Physiol. 2014, 26, 5–18. [Google Scholar] [CrossRef]
  74. Guedes, F.A.F.; Nobres, P.; Ferreira, D.C.R.; Menezes-Silva, P.E.; Ribeiro-Alves, M.; Correa, R.L.; DaMatta, F.M.; Alves-Ferreira, M. Transcriptional memory contributes to drought tolerance in coffee (Coffea canephora) plants. Environ. Exp. Bot. 2018, 147, 220–233. [Google Scholar] [CrossRef]
  75. Ramalho, J.C.; Rodrigues, A.P.; Lidon, F.C.; Marques, L.M.C.; Leitão, A.E.; Fortunato, A.F.; Pais, I.P.; Silva, M.J.; Scotti-Campos, P.; Lopes, A.; et al. Stress cross-response of the antioxidative system promoted by superimposed drought and cold conditions in Coffea spp. PLoS ONE 2018, 13, e0198694. [Google Scholar] [CrossRef]
  76. Lisec, J.; Schauer, N.; Kopka, J.; Willmitzer, L.; Fernie, A.R. Gas chromatography mass spectrometry–based metabolite profiling in plants. Nat. Protoc. 2006, 1, 387–396. [Google Scholar] [CrossRef]
  77. Luedemann, A.; Strassburg, K.; Erban, A.; Kopka, J. TagFinder for the quantitative analysis of gas chromatography-mass spectrometry (GC-MS)-based metabolite profiling experiments. Bioinformatics 2008, 24, 732–737. [Google Scholar] [CrossRef]
  78. Kopka, J.; Schauer, N.; Krueger, S.; Birkemeyer, C.; Usadel, B.; Bergmüller, E.; Dörmann, P.; Weckwerth, W.; Gibon, Y.; Stitt, M.; et al. [email protected]: The golm metabolome database. Bioinformatics 2005, 21, 1635–1638. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  79. Schauer, N.; Steinhauser, D.; Strelkov, S.; Schomburg, D.; Allison, G.; Moritz, T.; Lundgren, K.; Roessner-Tunali, U.; Forbes, M.G.; Willmitzer, L.; et al. GC-MS libraries for the rapid identification of metabolites in complex biological samples. FEBS Lett. 2005, 579, 1332–1337. [Google Scholar] [CrossRef] [PubMed]
  80. Mendiburu, F. Agricolae: Statistical Procedures for Agricultural Research; R Package Version 1.3-3; 2020; Available online: https://CRAN.R-project.org/package=agricolae (accessed on 24 November 2020).
Metabolites 11 00427 g001 550
Figure 1. Heatmap representing the changes in relative levels of primary metabolites in leaves of C. canephora cv. Conilon Clone 153 (CL153), grown under 380 (aCO2) or 700 (eCO2) ppm CO2, and three water availability levels (WW, well-watered; MWD, moderate water deficit; SWD, severe water deficit). Results indicate (a) single exposure to eCO2, (b) single exposure to each water deficit level under aCO2, (c) exposure to each water deficit level under eCO2, and (d) eCO2 effect within each water deficit level. Relative values (as means of three to six independent measurements) were normalized to the internal standard (ribitol) and the dry weight of the samples; false color imaging was performed on log10-transformed GC-TOF-MS metabolite data. Significant changes using one-way ANOVA are indicated as (○) for p < 0.05. Heatmaps and clustering were performed in R software [48,49] using the “gplots” package [50]. Gray-color squares represent non-detected values. Metabolites are grouped in amino acids and derivatives (AA), organic acids (OA), sugars and sugar alcohols (SS), and others (O).
Figure 1. Heatmap representing the changes in relative levels of primary metabolites in leaves of C. canephora cv. Conilon Clone 153 (CL153), grown under 380 (aCO2) or 700 (eCO2) ppm CO2, and three water availability levels (WW, well-watered; MWD, moderate water deficit; SWD, severe water deficit). Results indicate (a) single exposure to eCO2, (b) single exposure to each water deficit level under aCO2, (c) exposure to each water deficit level under eCO2, and (d) eCO2 effect within each water deficit level. Relative values (as means of three to six independent measurements) were normalized to the internal standard (ribitol) and the dry weight of the samples; false color imaging was performed on log10-transformed GC-TOF-MS metabolite data. Significant changes using one-way ANOVA are indicated as (○) for p < 0.05. Heatmaps and clustering were performed in R software [48,49] using the “gplots” package [50]. Gray-color squares represent non-detected values. Metabolites are grouped in amino acids and derivatives (AA), organic acids (OA), sugars and sugar alcohols (SS), and others (O).
Metabolites 11 00427 g001
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Figure 2. Heatmap representing the changes in relative levels of primary metabolites in leaves of Coffea arabica cv. Icatu (Icatu) grown under 380 (aCO2) or 700 (eCO2) ppm CO2, and three water availability levels (WW, well-watered; MWD, moderate water deficit; SWD, severe water deficit). Results indicate (a) single exposure to eCO2, (b) single exposure to each water deficit level under aCO2, (c) exposure to each water deficit level under eCO2, and (d) eCO2 effect within each water deficit level. Relative values (as means of three to six independent measurements) were normalized to the internal standard (ribitol) and the dry weight of the samples; false color imaging was performed on log10-transformed GC-TOF-MS metabolite data. Significant changes using one-way ANOVA are indicated as (○) for p < 0.05. Heatmaps and clustering were performed in R software [48,49] using the “gplots” package [50]. Gray-color squares represent non-detected values. Metabolites are grouped in amino acids and derivatives (AA), organic acids (OA), sugars and sugar alcohols (SS), and others (O).
Figure 2. Heatmap representing the changes in relative levels of primary metabolites in leaves of Coffea arabica cv. Icatu (Icatu) grown under 380 (aCO2) or 700 (eCO2) ppm CO2, and three water availability levels (WW, well-watered; MWD, moderate water deficit; SWD, severe water deficit). Results indicate (a) single exposure to eCO2, (b) single exposure to each water deficit level under aCO2, (c) exposure to each water deficit level under eCO2, and (d) eCO2 effect within each water deficit level. Relative values (as means of three to six independent measurements) were normalized to the internal standard (ribitol) and the dry weight of the samples; false color imaging was performed on log10-transformed GC-TOF-MS metabolite data. Significant changes using one-way ANOVA are indicated as (○) for p < 0.05. Heatmaps and clustering were performed in R software [48,49] using the “gplots” package [50]. Gray-color squares represent non-detected values. Metabolites are grouped in amino acids and derivatives (AA), organic acids (OA), sugars and sugar alcohols (SS), and others (O).
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Figure 3. Principal component analysis (PCA) score plots of the primary metabolite profile of leaves of (a) Coffea canephora cv. Conilon Clone 153 (CL153) and (b) C. arabica cv. Icatu (Icatu), grown under 380 (aCO2) or 700 (eCO2) ppm CO2 and increasing water deficit severity; namely, well-watered (WW), mild water deficit (MWD), and severe water deficit (SWD). Plots performed in R software [48,49] using the “mixOmics” package [51].
Figure 3. Principal component analysis (PCA) score plots of the primary metabolite profile of leaves of (a) Coffea canephora cv. Conilon Clone 153 (CL153) and (b) C. arabica cv. Icatu (Icatu), grown under 380 (aCO2) or 700 (eCO2) ppm CO2 and increasing water deficit severity; namely, well-watered (WW), mild water deficit (MWD), and severe water deficit (SWD). Plots performed in R software [48,49] using the “mixOmics” package [51].
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Figure 4. Partial least square discriminant analysis (PLS-DA) score and correlation circle plots of the primary metabolite profile of leaves of Coffea canephora cv. Conilon Clone 153 (CL153), (a,c), respectively; and of C. arabica cv. Icatu (Icatu), (b,d), respectively, grown under 380 (aCO2) or 700 (eCO2) ppm CO2 and increasing water deficit severity; namely, well-watered (WW), mild water deficit (MWD), and severe water deficit (SWD). Plots performed in R software [48,49] using the “mixOmics” package [51]. Abbreviations: 2-oxoglutarate (2OG), alanine (Ala), arginine (Arg), asparagine (Asn), aspartate (Asp), citrate (Citr), cystine (cys), dehydroascorbate (DhAsc), erythritol (Ery), fructose (Fru), fucose (Fuc), fumarate (Fum), galactinol (Gol), glucose (Glc), glutamate (Glu), glycerate (GAc), glycerol (Glyc), glycerol-3-phosphate (G3P), glycine (Gly), histidine (His), isoleucine (Ile), lysine (Lys), malate (Mal), methionine (Met), myo-Inositol (Ino), nicotinic acid (NAc), ornithine (Orn), phenylalanine (Phe), phosphoric acid (PhA), proline (Pro), putrescine (Put), pyroglutamate (pGlu), pyruvate (Pyr), quinate (QNA), raffinose (Raf), rhamnose (Rham), serine (Ser), spermine (Spm), succinate (Succ), sucrose (Suc), trehalose (Tre), threonate (ThrAc), threonine (Thr), tryptophan (Trp), tyramine (Tym), tyrosine (Tyr), valine (Val).
Figure 4. Partial least square discriminant analysis (PLS-DA) score and correlation circle plots of the primary metabolite profile of leaves of Coffea canephora cv. Conilon Clone 153 (CL153), (a,c), respectively; and of C. arabica cv. Icatu (Icatu), (b,d), respectively, grown under 380 (aCO2) or 700 (eCO2) ppm CO2 and increasing water deficit severity; namely, well-watered (WW), mild water deficit (MWD), and severe water deficit (SWD). Plots performed in R software [48,49] using the “mixOmics” package [51]. Abbreviations: 2-oxoglutarate (2OG), alanine (Ala), arginine (Arg), asparagine (Asn), aspartate (Asp), citrate (Citr), cystine (cys), dehydroascorbate (DhAsc), erythritol (Ery), fructose (Fru), fucose (Fuc), fumarate (Fum), galactinol (Gol), glucose (Glc), glutamate (Glu), glycerate (GAc), glycerol (Glyc), glycerol-3-phosphate (G3P), glycine (Gly), histidine (His), isoleucine (Ile), lysine (Lys), malate (Mal), methionine (Met), myo-Inositol (Ino), nicotinic acid (NAc), ornithine (Orn), phenylalanine (Phe), phosphoric acid (PhA), proline (Pro), putrescine (Put), pyroglutamate (pGlu), pyruvate (Pyr), quinate (QNA), raffinose (Raf), rhamnose (Rham), serine (Ser), spermine (Spm), succinate (Succ), sucrose (Suc), trehalose (Tre), threonate (ThrAc), threonine (Thr), tryptophan (Trp), tyramine (Tym), tyrosine (Tyr), valine (Val).
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Rodrigues, A.M.; Jorge, T.; Osorio, S.; Pott, D.M.; Lidon, F.C.; DaMatta, F.M.; Marques, I.; Ribeiro-Barros, A.I.; Ramalho, J.C.; António, C. Primary Metabolite Profile Changes in Coffea spp. Promoted by Single and Combined Exposure to Drought and Elevated CO2 Concentration. Metabolites 2021, 11, 427. https://doi.org/10.3390/metabo11070427

AMA Style

Rodrigues AM, Jorge T, Osorio S, Pott DM, Lidon FC, DaMatta FM, Marques I, Ribeiro-Barros AI, Ramalho JC, António C. Primary Metabolite Profile Changes in Coffea spp. Promoted by Single and Combined Exposure to Drought and Elevated CO2 Concentration. Metabolites. 2021; 11(7):427. https://doi.org/10.3390/metabo11070427

Chicago/Turabian Style

Rodrigues, Ana M., Tiago Jorge, Sonia Osorio, Delphine M. Pott, Fernando C. Lidon, Fábio M. DaMatta, Isabel Marques, Ana I. Ribeiro-Barros, José C. Ramalho, and Carla António. 2021. "Primary Metabolite Profile Changes in Coffea spp. Promoted by Single and Combined Exposure to Drought and Elevated CO2 Concentration" Metabolites 11, no. 7: 427. https://doi.org/10.3390/metabo11070427

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