Comparative Proteomic Analysis of Plant Acclimation to Six Different Long-Term Environmental Changes

Comparative Proteomic Analysis of Plant Acclimation to Six Different Long-Term Environmental Changes Abstract Plants are constantly challenged in their natural environment by a range of changing conditions. We investigated the acclimation processes and adaptive plant responses to various long-term mild changes and compared them directly within one experimental set-up. Arabidopsis thaliana plants were grown in hydroponic culture for 10 d under controlled abiotic stress (15°C, 25°C, salt and osmotic) and in nutrient deficiency (nitrate and phosphate). Plant growth was monitored and proteomic experiments were performed. Resource allocation between tissues altered during the plants’ response. The growth patterns and induced changes of the proteomes indicated that the underlying mechanisms of the adaptation processes are highly specific to the respective environmental condition. Our results indicated differential regulation of response to salt and osmotic treatment, while the proteins in the changed temperature regime showed an inverse, temperature-sensitive control. There was a high correlation of protein level between the nutrient-deficient treatments, but the enriched pathways varied greatly. The proteomic analysis also revealed new insights into the regulation of proteins specific to the shoot and the root. Our investigation revealed unique strategies of plant acclimation to the different applied treatments on a physiological and proteome level, and these strategies are quite distinct in tissues below and above ground. Introduction Human nutrition relies almost entirely on agriculture and its productivity, but the sustainability of the agricultural system is doubtful. A conservative estimate projects that there will be approximately 9 billion inhabitants on Earth in 2050 (Alexandratos and Bruinsma 2012). Based on this estimate, agricultural production will have to increase by 50–100% until 2050, but historical rates of yield growth for most crops are only half of what would be required to achieve this (Curtis and Halford 2014). Increasing food production is technically very challenging (Raines 2011, Dockter et al. 2014), and climate change further endangers the whole process. A mild scenario suggests a 2°C rise in global temperature, and the effects of global warming on food production will be only partially and temporarily compensated by the ability of the existing crops to acclimate and adapt (Challinor et al. 2014). Increased temperature will be coupled with further stresses (i.e. drought, floods, increased salt concentration in soil, nutrient limitations, etc.) and, by the end of the 21st century, many densely populated areas will be seriously arid regions (Dai 2013). Plants are inevitably exposed to the changing environmental conditions; hence, they have to cope with them in order to minimize the loss of their fitness. The acclimation and adaptation responses have been extensively investigated, but the underlying mechanisms are complex and still poorly understood. Increased knowledge about pathways and regulated proteins of the cellular machinery would facilitate efforts to optimize rational breeding. Nutrient availability is one of the key factors that can substantially reduce plant growth and, as a result, yield. Liebig’s law of the minimum states that the growth of plants is highly controlled by single nutrients, which are the least available (Liebig 1840, Liebig 1855). Although it is a good estimate, there are other hypotheses that discuss the relationship between nutrient availability and plant growth (Agren et al. 2012). Nitrogen and phosphorus are two essential macronutrients, and are often limiting factors in agriculture without the application of fertilizers (Masclaux-Daubresse et al. 2010, Agren et al. 2012). Stress response in plants has been studied extensively in recent decades, illustrating an awareness of the importance of a better understanding of the underlying adaptive responses. Traditionally, whole-plant responses were studied with transcriptomic approaches (Agarwal et al. 2014), as that method promises to deliver the most complete picture. However, based on transcriptional changes, it is not always possible to explain the current metabolic status of a biological system. Transcripts can be seen as snapshots, which are followed by protein changes and, therefore, a remodeling of the cellular machinery over time (Stitt et al. 2010, Baerenfaller et al. 2012, Guerreiro et al. 2014). Proteomics allows quantification of the most abundant proteins in a biological sample. Thereby proteins involved in the main processes, which are usually also the most resource-demanding processes, are well covered, in contrast to low abundancy transcription factors. Thus, proteomics is a powerful tool to investigate how plants adjust their metabolism and physiology in response to a recently altered environment. Proteomic experiments carried out in different stress conditions, tissues, organelles and plant species have been reviewed several times (Kosová et al. 2011, Rodziewicz et al. 2014, Ghosh and Xu, 2014, Janmohammadi et al. 2015, Jorrín-Novo et al. 2015). Research on early stress response to an unnaturally strong abiotic stressor is the main focus in the field of Arabidopsis research. However, such experiments may be unsuitable to investigate how plants rewire their cellular machinery and acclimate to long-term environmental changes (i.e. climate change). Further, the extrapolation of data obtained from different experiments can be biased and occasionally contradictory due to the different experimental, technical and statistical approaches. Thus, it remains challenging to compare responses to different environmental conditions and draw universal conclusions on stress responses (Dupae et al. 2014) similarly to other ‘omics’ research areas (Deyholos, 2010, Rest et al. 2016). The aim of our research was to examine a more realistic scenario of long-term acclimation processes of plants under mild environmental perturbations. We conducted a comparative analysis that involved several abiotic stresses and nutrient limitations in a well-defined experimental set-up, which allowed us to analyze and compare them with each other directly. The acclimation process of Arabidopsis thaliana plants was monitored under moderately changed environmental conditions: temperature changes (±5°C), phosphate and nitrogen deficiency, elevated salt and higher osmotic pressure. All these different conditions were successfully established in a hydroponic system, and comparative analyses were carried out. Results Plant growth under changed environmental conditions We compared plant responses to a set of environmental perturbations. Treatments were chosen in which plants were able to adjust their metabolism and sustain growth under the unfavorable environmental conditions. Plants were grown for 18 d in hydroponics under standard full nutrient conditions at 20°C and then subjected to different treatments {temperature (15 and 25°C), mild salt (50 mmol l−1 NaCl), osmotic stress [5% (w/v) polyethylene glycol (PEG)6000], or phosphate (P0) or nitrogen deficiency (N0)} for an additional 10 d. The applied environmental shifts affected growth, but the plants remained green and did not display chlorotic or dead tissues after 10 d (Fig. 1). Compared with the control plants, growth of plants exposed to 25°C appeared similar or slightly enhanced., However, in all other conditions plants showed a reduction in leaf area at the end of the 10 d treatment (Figs. 1, 2, 4). FW and DW were determined (Fig. 2); at 25°C, FW and DW of the shoot were elevated, while the other treatments had a negative impact and the biomass was reduced compared with the control. We correlated FW and DW with the surface area of the plants, and the correlation was high between all three parameters (Supplementary Fig. S1). Leaf area surface was chosen as a non-destructive approximation of biomass production. The DW/FW ratio was approximately similar in all the conditions except for at 15°C, which showed a higher value in the shoot indicating a relatively lower water content in the leaves (Fig. 3A). Fig. 1 View largeDownload slide Plants grown in six environmentally changed conditions for 10 d. Pictures were taken on the second, sixth and 10th day of the treatment [20th, 24th and 28th day after germination (DAG) respectively]. The scale bar corresponds to 1 cm. Fig. 1 View largeDownload slide Plants grown in six environmentally changed conditions for 10 d. Pictures were taken on the second, sixth and 10th day of the treatment [20th, 24th and 28th day after germination (DAG) respectively]. The scale bar corresponds to 1 cm. Fig. 2 View largeDownload slide Weight of plants at the end of the 10 d treatment. (A) 15°C treatment. (B) 25°C treatment. (C) Osmotic treatment with 5% PEG6000. (D) Salt treatment with 50 mmol l−1 NaCl. (E) Nitrogen deficiency (N0). (F) Phosphate deficiency (P0). Bars represent the mean values (n = 9) and the error bars the SEs. Fig. 2 View largeDownload slide Weight of plants at the end of the 10 d treatment. (A) 15°C treatment. (B) 25°C treatment. (C) Osmotic treatment with 5% PEG6000. (D) Salt treatment with 50 mmol l−1 NaCl. (E) Nitrogen deficiency (N0). (F) Phosphate deficiency (P0). Bars represent the mean values (n = 9) and the error bars the SEs. Fig. 3 View largeDownload slide Ratio of biomass production between roots and shoots under changed conditions. (A) DW/FW ratio of the shoot and root and (B) R/S of the FW and DW at the end of the 10 d treatment expressed as a percentage relative to the control conditions. Bars represent the mean values (n = 9) and the error bars the SEs. Fig. 3 View largeDownload slide Ratio of biomass production between roots and shoots under changed conditions. (A) DW/FW ratio of the shoot and root and (B) R/S of the FW and DW at the end of the 10 d treatment expressed as a percentage relative to the control conditions. Bars represent the mean values (n = 9) and the error bars the SEs. Fig. 4 View largeDownload slide Leaf area and relative growth rate (RGR) measurements of plants grown in six environmentally changed conditions for 10 d (18–28 DAG). On the left y-axis is the leaf area in cm2, on the right y-axis the RGR is depicted. (A) 15°C treatment. (B) 25°C treatment. (C) Osmotic treatment with 5% PEG6000. (D) Salt treatment with 50 mmol l−1 NaCl. (E) Nitrogen deficiency. (F) Phosphorus deficiency. Bars represent the mean values (n = 10–18) and the error bars the SEs. Fig. 4 View largeDownload slide Leaf area and relative growth rate (RGR) measurements of plants grown in six environmentally changed conditions for 10 d (18–28 DAG). On the left y-axis is the leaf area in cm2, on the right y-axis the RGR is depicted. (A) 15°C treatment. (B) 25°C treatment. (C) Osmotic treatment with 5% PEG6000. (D) Salt treatment with 50 mmol l−1 NaCl. (E) Nitrogen deficiency. (F) Phosphorus deficiency. Bars represent the mean values (n = 10–18) and the error bars the SEs. Growth of roots was measured as biomass at the end of the experiment. In contrast to the shoot, the growth of roots was relatively robust in all experiments. In nutrient-limiting conditions (N0 and P0) root biomass increased, whereas osmotic stress, salt stress and reduced temperature resulted in unchanged or slightly lower biomass compared with the plants in the control environment. Under elevated temperatures (25°C), root biomass increased significantly by 50%. The root to shoot ratio (R/S) in the treatments was never below the ratio detected in the control (Fig. 3B). At 25°C and with osmotic and salt treatments, plants invested slightly more in the root than the shoot. Plants grown in N0, in P0 and at 15°C invested significantly more resources in the roots (Fig. 3B). The relative growth rate (RGR) curves showed distinct patterns for each applied treatment (Fig. 4). RGR analysis revealed a sigmoid-like growth pattern in control conditions and under abiotic stress, but this was changed under nutrient deficiency (Fig. 4E, F). At 25°C, plants grew faster than the control plants initially for the first 6 d and the RGRs converged towards the end of the experiment (Fig. 4B). Under mild salt and osmotic stresses, the plants maintained a control-level growth in the first 2 d before it declined (Fig. 4C, D). At 15°C, RGR dropped initially by >50% and recovered only partially (Fig. 4A). Nutrient deficiency resulted in different growth responses. In N0, growth was not affected for the first 2 d after the introduced perturbation, but growth was reduced at later time points. At the end of the treatment, nitrogen-limited plants and control plants had a similar RGR. Phosphate limitation initially had a strong negative effect on growth, but later seemed to promote plant growth in the middle of the treatment. At later time points, growth compared with control plants was reduced again (Fig. 4E, F). Induced proteomic alterations in plants grown under changed environmental conditions Plant growth was compromised under the applied environmental changes except that of plants at 25°C, and the dynamics of the observed growth responses suggested different adaptation processes. Therefore, we aimed to elucidate the rewired cellular machinery of the newly adapted physiological state by a quantitative analysis of proteomes at the end of the treatments (10 d). The unfractionated single-tube method was used with root and shoot samples of each treatment. A total of 2,400–3,500 proteins were detected in every sample, with about 500 more proteins in roots than in shoots (Table 1). In a comparison between the control and treatment groups, 150–1,087 changed quantitatively with a P-value ≤0.05 (changing proteins) and 6–441 of these changed with a fold change (FC) exceeding ±1.5 (changing proteins with ±1.5 FC) (Table 1). Nutrient deficiency tended to induce more pronounced changes in the proteome than the mild abiotic stresses, although the number of changing proteins was the highest in the 15°C treatment. Mild osmotic stress affected the shoot proteome the least, even though the same treatment had a strong impact on the root proteome. Table 1 Number of proteins identified in the environmental treatments in the shoot and the root Threshold  All  P ≤ 0.05  FC ±1.5  All  P ≤ 0.05  FC ±1.5  All  P ≤ 0.05  FC ±1.5  Treatment  25°C  15°C  Osmotic  Shoot  2,406  551  68  2,880  1,087  114  2,697  150  6  Root  2,905  819  100  3,256  484  75  3,173  792  236  Treatment  Salt  N0  P0  Shoot  2,860  835  91  2827  917  227  2,829  705  122  Root  3,260  311  84  3483  1,027  441  3,485  1,007  272  Threshold  All  P ≤ 0.05  FC ±1.5  All  P ≤ 0.05  FC ±1.5  All  P ≤ 0.05  FC ±1.5  Treatment  25°C  15°C  Osmotic  Shoot  2,406  551  68  2,880  1,087  114  2,697  150  6  Root  2,905  819  100  3,256  484  75  3,173  792  236  Treatment  Salt  N0  P0  Shoot  2,860  835  91  2827  917  227  2,829  705  122  Root  3,260  311  84  3483  1,027  441  3,485  1,007  272  Every protein (all), proteins with significant expression (P ≤ 0.05) and proteins with significantly changed expression beyond the ±1.5 fold change threshold. For all corresponding samples, around 40% of the detected proteins were found in both roots and shoots. Among these, only 10% changed with a ±1.5 FC in both tissues (Table 2), but the vast majority (84%) of these proteins changed their abundance in the same direction (Supplementary Fig. 2). Table 2 Overlap of proteome changes between roots and shoots Threshold  All  FC ±1.5  Overlap  All  FC ±1.5  Overlap  All  FC ±1.5  Overlap  Treatment  15°C      25°C      Osmotic  Shoot  1,820  53  7 (9.6%)  1,490  36  7 (10.8%)  1,738  5  0 (0%)  Root    20      29      79  Treatment  Salt      N0      P0  Shoot  1,812  41  5 (7.9%)  1807  141  23 (7.5%)  1,808  72  20 (11.4%)  Root    22      163      103  Threshold  All  FC ±1.5  Overlap  All  FC ±1.5  Overlap  All  FC ±1.5  Overlap  Treatment  15°C      25°C      Osmotic  Shoot  1,820  53  7 (9.6%)  1,490  36  7 (10.8%)  1,738  5  0 (0%)  Root    20      29      79  Treatment  Salt      N0      P0  Shoot  1,812  41  5 (7.9%)  1807  141  23 (7.5%)  1,808  72  20 (11.4%)  Root    22      163      103  The overlap between proteins with a significant high (P ≤ 0.05, FC ±1.5) abundance change in every treatment. The subgroup of proteins, which were detected in the shoot and the root, was included, and the significance and fold change threshold (FC ±1.5) was applied on them. Responses on the proteome level between treatments To see if different treatments have similar effects on the proteome, all 12 experiments were clustered based on changing proteins (P ≤ 0.05). Roots and shoots grouped together on distinct branches, suggesting that tissue-specific proteomes differ more than the treatment-induced changes (Fig. 5A). In roots, the altered nutrient conditions (N0 and P0) cluster together with osmotic stress, while the mild salt stress did not cluster closely with other treatments. Both temperature treatments grouped together. The grouping in the leaves differed; salt and cold clustered together, as N0 and P0 again did, whereas heat and osmotic stress were separate from the others. Fig. 5 View largeDownload slide Cluster and correlation analysis of proteomics changes. Heatmap and correlation matrix of each tissue from every experiment based on significantly changing proteins (P < 0.05) in the shoot (S) and root (R) separately. (A) Heatmap and clustering of every treatment, shoot (S) and root (R) separately. The coloring corresponds to the fold change value of the individual proteins: red represents a strong increase and black represents a strong decrease in protein abundance compared with the wild type. The blue trace and its distance from the center in each column also represents the fold change in the protein amount. (B) Pearson correlation between the proteome changes of each tissue from every experiment. The pairwise Pearson correlation coefficient was calculated between all experiments. Fig. 5 View largeDownload slide Cluster and correlation analysis of proteomics changes. Heatmap and correlation matrix of each tissue from every experiment based on significantly changing proteins (P < 0.05) in the shoot (S) and root (R) separately. (A) Heatmap and clustering of every treatment, shoot (S) and root (R) separately. The coloring corresponds to the fold change value of the individual proteins: red represents a strong increase and black represents a strong decrease in protein abundance compared with the wild type. The blue trace and its distance from the center in each column also represents the fold change in the protein amount. (B) Pearson correlation between the proteome changes of each tissue from every experiment. The pairwise Pearson correlation coefficient was calculated between all experiments. Correlation was calculated between two experiments with all possible pairwise combinations based on proteins, which changed in both treatments (Fig. 5B). Initially, we expected that the two nutrient limitations (Nguyen et al. 2015) and the osmotic and salt treatments might have induced similar responses. However, the correlation was mostly higher between tissues than between treatments. The correlation was strong between the proteome changes of the shoot (R = 0.898) and the root (R = 0.736) in N0 and P0, but it was lower if we compared the shoot and root of the same treatment. The correlation between the shoot and the root of N-limited plants was low (R = 0.222). However, in P0 it was higher (R = 0.611); it was still lower than the above-mentioned values. The vast majority of the changing proteins were up- or down-regulated in the same direction (Fig. 6E, F). The mild osmotic and salt stresses showed a distinct tendency; changes in the shoot proteome under osmotic stress correlated highly positive (R = 0.909) with the shoot proteome of the salt treatment. In contrast, almost no correlation was detected between roots of the plants exposed to salt and osmotic treatments (Fig. 6C, D). Fig. 6 View largeDownload slide Fold change of overlapping changing proteins between two experiments. Fold change of significantly changing proteins (P < 0.05) detected in 15°C and 25°C, osmotic (5% PEG6000) and salt (50 mmol l−1 NaCl), and nitrate and phosphate deficiency experiments. (A) Shoot in 15 and 25°C. (B) Root at 15 and 25°C. (C) Shoot in osmotic and salt treartment. (D) Root in osmotic and salt treatment. (E) Shoot in nitrogen and phosphate deficiency. (F) Root in nitrogen and phosphate deficiency. Proteins with log2FC larger than ± 0.58 (±1.5 FC) are depicted in red. Fold change of protein is the ratio of its normalized abundance in the treatment divided by its normalized abundance in the control. Fig. 6 View largeDownload slide Fold change of overlapping changing proteins between two experiments. Fold change of significantly changing proteins (P < 0.05) detected in 15°C and 25°C, osmotic (5% PEG6000) and salt (50 mmol l−1 NaCl), and nitrate and phosphate deficiency experiments. (A) Shoot in 15 and 25°C. (B) Root at 15 and 25°C. (C) Shoot in osmotic and salt treartment. (D) Root in osmotic and salt treatment. (E) Shoot in nitrogen and phosphate deficiency. (F) Root in nitrogen and phosphate deficiency. Proteins with log2FC larger than ± 0.58 (±1.5 FC) are depicted in red. Fold change of protein is the ratio of its normalized abundance in the treatment divided by its normalized abundance in the control. The proteins which were found to be significantly changed in both temperature treatments (15 and 25°C) revealed a strong negative correlation (Fig. 5B); it was high between the shoot (R = −0.735) and the root (R = −0.811). This strong negative correlation was reflected in the opposite FC of individual proteins (Fig. 6A, B). This became even more pronounced if only proteins with ±1.5 FC were included (Fig. 7; Supplementary Table S1). If they were up-regulated at 15°C, they were down-regulated at 25°C, and vice versa irrespective of the tissue. Fig. 7 View largeDownload slide Antagonistic regulation of proteins under changed temperature conditions. Changing proteins with a log2FC ± 0.58 (±1.5 FC) detected in the 15 and 25°C treatment. The shoot and root were combined. Fold change of protein is the ratio of its normalized abundance in the treatment divided by its normalized abundance in the control. Fig. 7 View largeDownload slide Antagonistic regulation of proteins under changed temperature conditions. Changing proteins with a log2FC ± 0.58 (±1.5 FC) detected in the 15 and 25°C treatment. The shoot and root were combined. Fold change of protein is the ratio of its normalized abundance in the treatment divided by its normalized abundance in the control. Commonly changed proteins under different growth conditions We separated treatments according to tissues and tested whether any significantly (P ≤ 0.05) changed protein with ±1.5 FC could be observed in several different treatments. Most proteins (78%, shoot; 79%, root) seemed to respond only to single conditions (Fig. 8). However, a number of proteins responded to several treatments in roots and shoots (Tables 3, 4). Table 3 Proteins detected in three and more treatments in the root AGI  Gene name  Stress-related (ref.)  15°C  25°C  Salt  Osmotic  N0  P0  AT1G12780  UDP-Glc epimerase 1    −1.182  0.5057  −0.57    −1.09  −0.995  AT1G13080  Cyt. P450 (CYP71B2)  Gao et al. (2008)  −0.89  0.6214    0.8759      AT1G33590        0.8626      −0.834  −0.706  AT1G52050        −0.428  −0.759  0.7892  0.8424    AT1G56680      0.3514  −0.84  −1.731  −0.868    0.4739  AT1G65970  Thioredoxin-dependent peroxidase 2  Kumar et al. (2015)  −1.222  0.7598    −0.889  −2.272    AT1G70850        0.8861      0.7188  0.7918  AT1G73260  Kunitz trypsin inhibitor  J. Li et al. (2008)  −0.625  1.0958      −1.156  −0.761  AT1G78830      −0.393  0.7223      −0.837  1.2307  AT1G80240        0.3334  −0.721  −0.978  −0.597  0.3122  AT2G01520  MLP-like protein 34    −0.742  1.7859      1.126  0.8097  AT2G41100  Calmodulin-like 12  Sistrunk et al. (1994)  0.6213    −1.609  −0.607      AT2G43920  Harmless to ozone layer 2    3.4238        −0.996  3.0006  AT3G01290  Hypersensitive induced reaction 2  Qi and Katagiri (2012)  −0.679  0.4753      1.1093  1.3853  AT3G03640  Beta glucosidase 25    0.6722      −0.665    0.6329  AT3G05950      −1.471  1.1552  −0.359  2.0416      AT3G06850  Dark inducible 3 (DIN3)    −0.6    −0.519  −0.431  −1.315  −0.683  AT3G16390  Jacalin-related lection 31  Kissen and Bones (2009)  1.1808  −1.595    0.4742  1.2289  1.8704  AT3G16430  Nitrile specifier protein3  Yamada et al. (2011)    0.9232  −0.673    −0.656  −0.358  AT3G18000  N-Methyltransferase 1  Zhang et al. 2010  1.2  −1.155  1.5038    1.0821  1.2313  AT3G49120  Peroxidase CB  Mammarella et al. (2014)    1.0668    0.5915  −0.774    AT3G59480        0.7952  1.597    −2.16  −2.226  AT4G25250  Pectinmethylesterase inhibitor 4    −0.288  −0.916  1.0882  −1.437      AT4G25340  FK506 binding protein      −0.589    0.6332  −1.744    AT4G32460      0.6248  −1.414  0.7571        AT4G37410  Cyt. P450(CYP81F4)      0.8251  −0.625      −0.813  AT4G39800  Myo-inositol-1-phosphate synthase  Ahmad et al. (2015)  0.7534        2.1125  1.0627  AT5G15970  Cold-responsive 6.6  Gorsuch et al. (2010)  1.0886    0.9574  1.2687      AT5G38940  Fructokinase 7      0.6765  −1.469      −0.727  AGI  Gene name  Stress-related (ref.)  15°C  25°C  Salt  Osmotic  N0  P0  AT1G12780  UDP-Glc epimerase 1    −1.182  0.5057  −0.57    −1.09  −0.995  AT1G13080  Cyt. P450 (CYP71B2)  Gao et al. (2008)  −0.89  0.6214    0.8759      AT1G33590        0.8626      −0.834  −0.706  AT1G52050        −0.428  −0.759  0.7892  0.8424    AT1G56680      0.3514  −0.84  −1.731  −0.868    0.4739  AT1G65970  Thioredoxin-dependent peroxidase 2  Kumar et al. (2015)  −1.222  0.7598    −0.889  −2.272    AT1G70850        0.8861      0.7188  0.7918  AT1G73260  Kunitz trypsin inhibitor  J. Li et al. (2008)  −0.625  1.0958      −1.156  −0.761  AT1G78830      −0.393  0.7223      −0.837  1.2307  AT1G80240        0.3334  −0.721  −0.978  −0.597  0.3122  AT2G01520  MLP-like protein 34    −0.742  1.7859      1.126  0.8097  AT2G41100  Calmodulin-like 12  Sistrunk et al. (1994)  0.6213    −1.609  −0.607      AT2G43920  Harmless to ozone layer 2    3.4238        −0.996  3.0006  AT3G01290  Hypersensitive induced reaction 2  Qi and Katagiri (2012)  −0.679  0.4753      1.1093  1.3853  AT3G03640  Beta glucosidase 25    0.6722      −0.665    0.6329  AT3G05950      −1.471  1.1552  −0.359  2.0416      AT3G06850  Dark inducible 3 (DIN3)    −0.6    −0.519  −0.431  −1.315  −0.683  AT3G16390  Jacalin-related lection 31  Kissen and Bones (2009)  1.1808  −1.595    0.4742  1.2289  1.8704  AT3G16430  Nitrile specifier protein3  Yamada et al. (2011)    0.9232  −0.673    −0.656  −0.358  AT3G18000  N-Methyltransferase 1  Zhang et al. 2010  1.2  −1.155  1.5038    1.0821  1.2313  AT3G49120  Peroxidase CB  Mammarella et al. (2014)    1.0668    0.5915  −0.774    AT3G59480        0.7952  1.597    −2.16  −2.226  AT4G25250  Pectinmethylesterase inhibitor 4    −0.288  −0.916  1.0882  −1.437      AT4G25340  FK506 binding protein      −0.589    0.6332  −1.744    AT4G32460      0.6248  −1.414  0.7571        AT4G37410  Cyt. P450(CYP81F4)      0.8251  −0.625      −0.813  AT4G39800  Myo-inositol-1-phosphate synthase  Ahmad et al. (2015)  0.7534        2.1125  1.0627  AT5G15970  Cold-responsive 6.6  Gorsuch et al. (2010)  1.0886    0.9574  1.2687      AT5G38940  Fructokinase 7      0.6765  −1.469      −0.727  Proteins with a significant high FC [P ≤ 0.05, log2FC ± 0.58 (±1.5)]. FCs in italics are below the threshold of expression. Stress-relatedness was defined based on the TAIR database. Table 4 Proteins detected in three and more treatments in the shoot AGI  Gene name  Stress-related (ref.)  15°C  25°C  Salt  Osmotic  N0  P0  AT1G02930  Glutathione S-transferase 6  Tolin et al. (2012)  0.7156    0.9598    1.9187  0.9888  AT1G12770  Embryo defective1586    0.6765        −1.586  −1.7  AT1G15500  TLC ATP/ADP transporter    0.2513  −0.592  −0.368    −1.323  −0.839  AT1G23130      −0.818    −0.695    0.8027    AT1G45201  Triacylglycerol lipase-like 1    −0.367  0.6091  0.5741    0.7251  0.6531  AT1G48570      0.9036        −1.795  −1.367  AT1G54100  Aldehyde dehydrogenase 7B4  Li-Beisson et al. (2013)    0.4339  1.0444  0.6075  0.8159    AT2G22400      1.0165        −0.817  −0.779  AT2G33380  Responsive desiccation 20  Blée et al. (2014)    1.242  1.9917    1.4771  0.6182  AT2G39800  Delta1-pyrroline-5-carboxylate synthase1  Székely et al. (2008)      1.1731  0.9249  −0.687  0.3999  AT3G06980    D. Li et al. (2008)  0.4166  −0.905      −1.546  −1.185  AT3G18000  N-Methyltransferase 1  Zhang et al. (2010)  0.8744    0.9442    −1.753  −1.271  AT3G18680      0.4387  −0.75  −0.514    −1.869  −1.292  AT3G19710  Branched-chain aminotransferase 4  Less and Galili (2008)    0.9781      1.1274  0.7424  AT3G28220      1.014    1.0413    0.2963  0.8688  AT3G44750  Histone deacetylase 3  Han et al. (2016)  0.6684  −0.932      −3.21    AT3G47450  Nitric oxide synthase 1  Xie et al. (2013)      −0.915    −1.342  −1.833  AT3G53460  Chloroplast RNA-binding protein 29  Kupsch et al. (2012)  0.6085  −0.745  −0.545    −1.2  −0.705  AT4G02990  Belaya Smert  Robles et al. (2012)  0.64        −1.604  −1.677  AT4G11960  PGR5-like B  Lehtimäki et al. (2010)  −2.351  0.9835  0.9692  0.5063  0.9617    AT4G14090    Pourcel et al. (2010)  1.263  −0.363  1.4066    2.7953  1.2214  AT4G15530  Pyruvate orthophosphate dikinase      0.6258      1.2293  0.7914  AT4G22485        −0.778  0.5988    0.9335  0.3724  AT4G22880  Anthocyanidin synthase  Bharti et al. (2015)      1.1193    2.1873  0.6336  AT4G36390      0.8676  −0.649      −1.306  −1.018  AT5G08610  Pigment defective 340    1.0774  −0.765  −0.389    −1.623  −1.554  AT5G13930  Chalcone synthase  An et al. (2016)  0.7273  −0.682      1.808  0.7347  AT5G22580      −0.724  0.7569  −0.729        AT5G23900        −0.976      −1.417  −1.261  AT5G54180  Plastid transcriptionally active 15    0.7059        −0.802  −0.795  AGI  Gene name  Stress-related (ref.)  15°C  25°C  Salt  Osmotic  N0  P0  AT1G02930  Glutathione S-transferase 6  Tolin et al. (2012)  0.7156    0.9598    1.9187  0.9888  AT1G12770  Embryo defective1586    0.6765        −1.586  −1.7  AT1G15500  TLC ATP/ADP transporter    0.2513  −0.592  −0.368    −1.323  −0.839  AT1G23130      −0.818    −0.695    0.8027    AT1G45201  Triacylglycerol lipase-like 1    −0.367  0.6091  0.5741    0.7251  0.6531  AT1G48570      0.9036        −1.795  −1.367  AT1G54100  Aldehyde dehydrogenase 7B4  Li-Beisson et al. (2013)    0.4339  1.0444  0.6075  0.8159    AT2G22400      1.0165        −0.817  −0.779  AT2G33380  Responsive desiccation 20  Blée et al. (2014)    1.242  1.9917    1.4771  0.6182  AT2G39800  Delta1-pyrroline-5-carboxylate synthase1  Székely et al. (2008)      1.1731  0.9249  −0.687  0.3999  AT3G06980    D. Li et al. (2008)  0.4166  −0.905      −1.546  −1.185  AT3G18000  N-Methyltransferase 1  Zhang et al. (2010)  0.8744    0.9442    −1.753  −1.271  AT3G18680      0.4387  −0.75  −0.514    −1.869  −1.292  AT3G19710  Branched-chain aminotransferase 4  Less and Galili (2008)    0.9781      1.1274  0.7424  AT3G28220      1.014    1.0413    0.2963  0.8688  AT3G44750  Histone deacetylase 3  Han et al. (2016)  0.6684  −0.932      −3.21    AT3G47450  Nitric oxide synthase 1  Xie et al. (2013)      −0.915    −1.342  −1.833  AT3G53460  Chloroplast RNA-binding protein 29  Kupsch et al. (2012)  0.6085  −0.745  −0.545    −1.2  −0.705  AT4G02990  Belaya Smert  Robles et al. (2012)  0.64        −1.604  −1.677  AT4G11960  PGR5-like B  Lehtimäki et al. (2010)  −2.351  0.9835  0.9692  0.5063  0.9617    AT4G14090    Pourcel et al. (2010)  1.263  −0.363  1.4066    2.7953  1.2214  AT4G15530  Pyruvate orthophosphate dikinase      0.6258      1.2293  0.7914  AT4G22485        −0.778  0.5988    0.9335  0.3724  AT4G22880  Anthocyanidin synthase  Bharti et al. (2015)      1.1193    2.1873  0.6336  AT4G36390      0.8676  −0.649      −1.306  −1.018  AT5G08610  Pigment defective 340    1.0774  −0.765  −0.389    −1.623  −1.554  AT5G13930  Chalcone synthase  An et al. (2016)  0.7273  −0.682      1.808  0.7347  AT5G22580      −0.724  0.7569  −0.729        AT5G23900        −0.976      −1.417  −1.261  AT5G54180  Plastid transcriptionally active 15    0.7059        −0.802  −0.795  Proteins with a significant high FC [P ≤ 0.05, log2FC ± 0.58 (±1.5)]. FCs in italics are below the threshold of expression. Stress-relatedness was defined based on the TAIR database Fig. 8 View largeDownload slide Proteins specific to one or multiple treatments. Number of changing proteins with ±1.5 FC detected in one or multiple treatments. (A) In the shoot and (B) in the root. Fig. 8 View largeDownload slide Proteins specific to one or multiple treatments. Number of changing proteins with ±1.5 FC detected in one or multiple treatments. (A) In the shoot and (B) in the root. In roots, 29 proteins (Table 3) changed significantly in three or more treatments. One of them was an N-methyltransferase (NMT1, At3g18000), which had a significant fold change in every root experiment except the osmotic stress. In the shoot, similarly to roots, there was also only a small fraction of significantly changed proteins (30) which were found in three or more treatments (Table 4). One of them was again NMT1; it was up-regulated in salt and at 15°C and down-regulated in N0 and P0. This protein changed under virtually all changed conditions except the osmotic stress. These changes were not only stress specific, but, for instance in nutrient deficiency, it showed opposite protein levels indicating different roles in different tissues. Gene Ontology (GO) enrichment and analysis of single proteins with the highest significant FC under changed temperature To detect processes that might be affected by adaptation to the applied mild stresses, we performed a GO enrichment analysis on the changing proteins with ±1.5 FC. In response to the 15°C treatment, ‘cold stress’-related GOterms were enriched; in shoots ‘acclimation to the cold’ (GO:0009631), and in roots the ‘response to cold’ (GO:0009409). Several transport GOterms were enriched with the highest significance level (GO:0006810 ‘transport’ and GO:0015250 ‘water channel activity’) and the proteins contained in these categories were all down-regulated in the shoot and root. In the shoot, in particular, a strong reduction of aquaporins (Supplementary Table 2) was observed. There might be a link between the water transport and the induced response to water deprivation in both tissues (GO:0009414 ‘response to water deprivation’). Besides the transport processes, RNA metabolic pathways were also enriched in the shoot and the root (GO:0004004 ‘ATP-dependent RNA helicase activity’; GO:0008168 ‘methyltransferase activity’; and GO:0010501 ‘RNA secondary structure unwinding’). In the 15°C treatment, photosynthetic light reaction proteins were reduced in the shoot. Two of the proteins with the largest decrease in amount were a photosynthetic enzyme, a putative large chain protein of the ribulose-1,5-bisphosphate carboxylase/oxygenase (At2g07732), and a thylakoid transmembrane protein (PGRL1B, At4g11960). PGRL1B belonged to the above-mentioned group of proteins which were detected with a significantly high FC in more than three treatments, but everywhere else it was up-regulated. In the root, the top up-regulated proteins are mainly involved in cold and other stress responses: KIN1 (At5g15960) is a cold- and ABA-inducible protein, RNA HELICASE 25 (At5g08620) controls gene expression during cold, salt and drought stress, and the COLD REGULATED 78 (At5g52310), as its name shows, is regulated by lower temperature. A protein (HARMLESS TO OZONE LAYER 2, AT2G43920) described to a lesser extent was up-regulated the most; it was also one of the commonly changed protein under several treatments. Similarly, the two proteins with the highest decrease in protein abundance (At1g77520 and At3g05950) are also not well characterized. The 25°C treatment induced different responses in the shoot and the root. ‘Heat response’ (GO:0009408), ‘response to hydrogen peroxide’ (GO:0009408) and ‘response to virus’ (GO:0009615) were enriched in both tissues. In the shoot, ‘proteins of the ribosome’ (GO:0005840) and participating in ‘translation’ (GO:0006412) had a lower protein level, while in the root no significant enrichment of these terms was detected. In the warmer environment, flavonoid biosynthesis was down-regulated in the shoot (GO:0009813). The warmer environment induced fewer changes in the proteome of the shoot; it had the lowest number of changing proteins with ±1.5 FC after the osmotic treatment. There was no clear pattern in changes of abundance of stress-related proteins. One heat shock protein (HSP70, At3g12580) had an elevated FC in the shoot and the root, but two flavonoid proteins (At5g13930 and At5g08640) were decreased. The top up-regulated protein in the shoot is a cytosolic β-amylase (BAM5, At4g15210), which was shown previously to be sugar induced (Mita et al. 1995) and to respond to heat and salt stress (Monroe et al. 2014). It was also up-regulated in our salt treatment. The top up-regulated protein in the root is HOP3 (At4g12400), which participates in heat acclimation and is a potential interaction partner of chaperone proteins, i.e. the above-mentioned HSP70 (Fellerer et al. 2011). GOterm enrichment and analysis of single proteins with the highest significant FC under higher salt and osmotic environment The elevated salt condition induced response processes in the shoot and the root (GO:0006952 ‘defense response’; GO:0009611 ‘response to wounding’; GO:0009753 ‘response to jasmonic acid’; and GO:0009620 ‘response to fungus’), but they reacted in different directions; together with other stress-related terms, they were mainly down-regulated in the root. The response in the shoot included proteins which belonged to the enriched terms of water deprivation (GO:0009414 ‘response to water deprivation’ and GO:0009269 ‘response to desiccation’). Similar GOterms were enriched to those in the 15°C treatment; three aquaporins were detected in both experiments, but less than at 15°C. However, their protein abundance increased in the 50 mmol l−1 NaCl environment. Salt stress groups were enriched in the shoot (GO:0042538 ‘hyperosmotic salinity response’ and GO:0009651 ‘response to salt stress’). The photosystem was affected in salt stress: two light-harvesting complex (LHC) proteins were up-regulated (At2g05100 and At3g27690) together with the previously mentioned thylakoid transmembrane PGRL1B, but the protein level of two CP12 enzymes was decreased. This was accompanied by the increased content of two VEGETATIVE STORAGE PROTEIN (VSP) isoforms: VSP1 (At5g24780) and VSP2 (At5g24770). The VSPs are acid phosphatases which mobilize nutrient sources (the abundance of VSP2 was decreased in P0), but they also play an active role against herbivores in biotic stress responses (Liu et al. 2005). CALEOSIN 3 (RD20, At2g33380) was highly up-regulated in salt stress. It is involved in the stress response of the plants (Blée et al. 2014) and had a significantly high FC in several other treatments (Table 4). The long-term osmotic treatment showed a unique response and acclimation process of the plants. Due to the very low number (six) of changing proteins with ±1.5 FC in the shoot, we focused on the root only. The GOterm with the highest significance level was the ‘plastoglobule’ (GO:0010287); nine proteins were increased in this cellular compartment. The expected stress-related GOterms ‘response to abscisic acid’ (GO:0009737), ‘response to osmotic stress’ (GO:0006970) and ‘response to hypoxia’ (GO:0001666) were up-regulated and several cold response proteins were also up-regulated. Proteins related to the thylakoid membranes (GO:0009579) were enriched in the osmotic stress; their protein concentration was increased significantly. RNA, DNA and protein metabolism were negatively affected, and a large part of the changing DNA metabolic proteins consisted of chromatin and histone proteins. Cell wall metabolism was impacted; UDP-XYL SYNTHASE 6 (UXS6, At2g28760) was the most up-regulated protein in the root, while other cell wall enzymes were highly down-regulated, e.g. FASCICLIN-LIKE ARABINOGALACTAN PROTEIN 11 (FLA11, At5g03170). Next to the cell wall and histone proteins, response proteins were also expressed. THIOGLUCOSIDE GLUCOHYDROLASE 1 (TGG1, At5g26000) was highly up-regulated; it is considered to play a role mainly in herbivore resistance by producing toxic compounds (Badenes-Perez et al. 2012). GOterm enrichment and analysis of single proteins with the highest significant FC under changed nutrient availability When nitrogen was omitted (N0), translation and RNA metabolism were suppressed. Among the enriched terms in the shoot, the down-regulated pathways with the highest significance belonged to these processes: 32 proteins related to ‘translation’ (GO:0006412), 14 proteins to ‘RNA binding’ (GO:0003723) and 25 proteins to ‘cytosolic ribosome’ (GO:0022626). In the root, similar tendencies were detected, but DNA metabolism was also highly affected. A total of 97 proteins of the ‘nucleus’ (GO:0005634) were down-regulated and, among the enriched pathways with the highest significance, were the chromatin structure terms (GO:0003682 ‘chromatin binding’; GO:0042393 ‘histone binding’, etc.). GOterms covering the ‘cellular response to nitrogen starvation’ (GO:0006995) and to ‘nitrate’ (GO:0010167) and ‘nitrate transmembrane transporter activity’ (GO:0015112) were enriched significantly, while proteins of nitrate assimilation (GO:0042128) were down-regulated. Among the most increased proteins were anthocyanin metabolism (At4g14090 and At5g54060) and stress response genes (ERD1, At5g54060; and CPK32, At3g57530). Several nitrate transporter (NRT2, At1g08090; NRT2.5, At1g12940; and WR3, At5g50200) were up-regulated together with ammonium transporters (AMT1;1, At4g13510; and AMT1;3, At3g24300) in the root (AMT1 in the shoot too). The levels of glutamate-ammonia ligases (GLN1;1, At5g37600; GLN1;4, At5g16570; and GLN1;5, At1g48470), which play a role in glutamine synthesis, were elevated. While glutamine synthesis was enhanced, the catabolism of glutamine was down-regulated. Glutamate synthase (GLT1, At5g53460) and glutamate dehydrogenases (GDH1, At5g18170; and GDH2, At5g07440) showed a decreased protein abundance compared with the control. Glutamate synthase, which was enhanced, produces glutamate from glutamine, which can be a substrate for glutamate dehydrogenase, which was down-regulated. Like N0, phosphate deficiency (P0) decreased the level of RNA metabolic and translational proteins in the shoot, but they were increased in roots. The defense and response pathways (GO:0050832 ‘defense response to fungus’ and GO:0009753 ‘response to jasmonic acid’ etc.) were enriched in leaves with high significance. These included ‘cellular response to phosphate starvation’ (GO:0016036) in both tissues. Stress and response terms were up-regulated (i.e. GO:0009737 ‘response to abscisic acid’ and GO:0051707 ‘response to other organism’) or down-regulated (i.e. GO:0009409 ‘response to cold’ and GO:0009646 ‘response to absence of light’). The term with the highest significance value was oxygen binding (GO:0019825), which covered several cytochromes (CYP706A1, At4g22690; CYP71B7, AT1G13110; CYP76C7, At3g61040; and CYP81F4, At4g37410) and a hemoglobin (HB1, At2g16060). Some of the top down-regulated proteins had a helicase, ribosomal or unknown function. As in N0, in the shoot of P0 DNA, RNA and protein metabolism were strongly down-regulated. The plants were actively responding to the phosphate starvation; there were two sulfolipid biosynthetic proteins [SULFOQUINOVOSYL DIACYLGLYCEROL 1 (SQD1), At4g33030; SULFOQUINOVOSYL DIACYLGLYCEROL 2 (SQD2), At5g01220], which were highly up-regulated in the shoot and the root. Co-expression showed a possible interaction (data not shown) with GLYCEROPHOSPHODIESTER PHOSPHODIESTERASE (GDPD1, At3g02040); its protein content increased in both tissues, it had an extremely high FC (log2FC= 7.61) in the root and it is an important factor in maintenance of the cellular homoeostasis in phosphate deficiency (Wang et al. 2008, Cheng et al. 2011). Like the nitrate transporters in the N0 root, phosphate transporters (ATPT2, At2g38940; PHT1;1, At5g43350; and APT1, At5g43370) were highly up-regulated in the root during phosphate starvation. Discussion Plant responses to mild environmental perturbations Arabidopsis thaliana plants were exposed to a mildly altered environment for 10 d and the applied treatments provided us with new insights about the acclimation and adaptation of plants to long-term changes. In the field of stress proteomics, where experiments are categorized (Kosová et al. 2011, Zhang et al. 2012, Ghosh and Xu 2014, Janmohammadi et al. 2015), approximately one-third addressed long-term stresses, and these stresses were mainly harsh. For instance, among the listed cold stress experiments (Janmohammadi et al. 2015), in only one out of 22 were the plants exposed to a temperature >10°C. In our study, however, the 15°C environment already induced significant changes in both the physiology and the proteome of the plants. It might be realistic that plants in their natural environment are exposed not only to severe and sudden stresses, but also to prolonged moderate impacts. Our set-up allowed us to investigate each response individually, and compare them directly. During the experiments under a changed environment, all plants preserved their physiological integrity, illustrated by the fact that they were able to grow continuously throughout the experiments; no signs of irreversible tissue damage were observed. Transcripts usually respond rapidly and thus are excellent for investigation of the plants’ immediate sensing and response to altered conditions. Therefore, transcriptional changes at a certain time point could be interpreted as the intention to use the plasticity potential of the plant during acclimation. However, proteins are the integrated result of the transcript levels over time, and they do not really follow a diurnal abundance fluctuation even if transcripts do (Baerenfaller et al. 2012, Guerreiro et al. 2014). Proteins rather describe and represent the newly established biological machinery over a longer time period. On the level of growth (RGR, FW and DW) and the proteome, our experiments showed distinct patterns, indicating different dynamic adjustment. The resource allocation of plants culminates in biomass produced as roots and shoots. Environmental changes had a more profound effect on shoot biomass than on that of the root (Figs. 2, 3). Except for the 25°C treatment, shoot growth was reduced by approximately 50%, whereas root biomass was mainly preserved or even increased. This observation is a common response of plants in agriculture (Richard-Molard et al. 2008, Wang et al. 2009). The R/S was, in all treatments, higher than in the controls, indicating a preferential investment in root biomass. Different dynamic growth patterns during the acclimation of plants to different environmental perturbations Although shoot biomass at the end of the treatments was similar for some treatments, they often resulted from different temporal growth patterns. The RGR showed unique patterns for each of the applied conditions. At 15°C, the shoot growth was suppressed constantly from the first day onwards. The initial drop in RGR is not surprising considering that enzymatic reactions are highly temperature sensitive. As a rule of thumb, a reduction of 10°C halves the enzyme reaction rate. Obviously, this is a rough estimate, and biochemical processes at a whole-plant level are affected by many other parameters. Nevertheless, the fast drop in growth under reduced temperature suggests that some catalytic reactions were limiting. The RGR increased later, which might reflect the adaptation and increased synthesis of limiting enzymes. However, the cold-treated plants never reached the control RGR, implying that the rate-limiting steps (enzymes) cannot be fully compensated for. In contrast, increasing the temperature by 5°C had a positive impact on plant biomass production. The RGR showed that this growth promotion was largely dependent on the accelerated growth rate at the beginning of the treatment, whereas the RGR decreased to control levels later. This observation is again in agreement with an increased enzymatic reaction rate under increased temperature, if we assume rather constant enzyme amounts at the beginning of the treatment. Over the time of the treatment, plants probably optimize their enzyme quantity. This is in agreement with our proteomics results. For example, photosynthesis-related proteins, which significantly decreased in the 25°C and increased in the 15°C treatment (Supplementary Table S5, S6). Together with other results (increased RGR, down-regulation of anthocyanin proteins, etc.), this implies that a warmer environment was not necessarily a stress but rather a positive stimulus. Other acclimation patterns were observed for both osmotic stresses (PEG and salt). At the beginning of the treatment, no reduction of growth rate was observed in the shoots, and growth was only negatively affected later during the treatment. In contrast to the temperature treatments, the osmotic stresses were applied to the roots and might have consequences later on the shoots. This effect could be due to the different times the sodium and chloride ions or PEG molecules require to diffuse, be transported and accumulate in the shoots. While the ions can reach other tissues rapidly, the large PEG molecules might not even be able to overcome cellular obstacles, or they do it slowly (Blum 2008). Alternatively, the treatments could first affect root metabolism, which is sensed and transmitted to the shoots later, and this would change resource allocation between shoots and roots. For the salt treatment, it is probably a combination of the two above-mentioned effects. However, in a more osmotic environment, it is more likely that the effect on shoot growth is a secondary effect on altered root metabolism. This is also substantiated by the observation that the shoot proteome in the PEG treatment was largely unaffected, whereas the salt treatment induced substantial changes in the shoot proteome. Interestingly, the RGR of the salt-treated plants approached that of the control plants at the end of the experiment, suggesting an efficient compensation response. The two nutrient-limiting conditions differed in their effect on RGR. In N0, the RGR remained the same as in control plants at the beginning, probably because nitrogen was not limiting during the first days, but this was followed by a huge drop in growth, probably due to depleted free inorganic nitrogen sources. The RGR recovered over time until it converged again towards the control levels after 10 d, suggesting a gradual adaptation. This process had to rely on salvage and reuse of internal available nitrogen sources (e.g. proteins). The RGR response on phosphate limitation was somehow counterintuitive as it first dropped below the control level and recovered for 4 d to the control level, with a decline at the end of the treatment. As the phosphate supply was omitted, it must have been sensed immediately by the plants and initiated a successful temporal growth stop in the shoots. That might be explained by relatively high internal phosphate storage pools in the vacuole (Liu et al. 2016), which first had to be made accessible. The analysis of the rewired proteomic machinery revealed crucial differences of the plants’ acclimation processes The number of significantly changed proteins with a ±1.5 FC was higher in the nutrient-deficient treatments. This suggests that they might have induced a greater change in the plant proteome, although the growth measurements (FW, DW and RGR) did not necessarily reflect that. However, if the number of significantly changing proteins is used as an indicator for the complexity of proteome remodeling, the 15°C treatment in the shoot induced the greatest perturbation in the proteome. Among the proteins with the highest significant FC at 15°C, stress-related proteins were over-represented, which was quite unique compared with the other mild abiotic treatments, also implying that a 5°C decrease can already have a strong effect on plants. Interestingly, while the osmotic treatment promoted strong changes of the proteome in the root, the shoot seemed almost unaffected. Similar phenomena were observed in comparative proteomic analysis of roots and shoots of drought-stressed soybean seedlings (Mohammadi et al. 2012). The uptake of the long PEG6000 molecules is very slow and mostly concentrated in the roots, especially if root damage is avoided and the concentration is not high (Blum 2008). The relatively unchanged proteome in the shoots suggests that their growth retardation is mostly an indirect effect of changed metabolism in the roots, resulting in resource allocation towards the roots. It raises the question of whether stresses are local or systemic, whether the stress was communicated throughout the plant or the observed shoot phenotype could have been a secondary effect. Here, four out of six treatments (salt, PEG and nutrients) were applied to the roots through changes in the media and, as expected, they had an effect on the whole plant. In the case of PEG treatment, the stress response on the proteome level remained local. This illustrates that the observed phenotypes in one tissue are not necessarily easy to explain with measured factors (e.g. transcript or protein abundances) in the same tissue. Obviously, it is important to treat multicellular organisms as a system to reach conclusions about cause and effect. Although our experimental set-up did not allow us to apply a tissue-specific temperature treatment, plants in nature are exposed to different temperatures below and above ground, and this should be considered in future experiments. The response processes between the shoot and root were different in every treatment, indicating a highly tissue-specific adaptation mechanism. To our knowledge, only a few experiments were performed where the impact of the treatment on the proteome of both organs was analyzed. It was investigated in rice, soybean, barley and cotton (Lee et al. 2010, Sobhanian et al. 2010, Moller et al. 2011, Mohammadi et al. 2012, Chen et al. 2016), and no major overlap of the changing proteins was detected and reported between the two organs. There was a relatively small overlapping fraction of changing proteins in both tissue, which often changed similarly. This implies regulatory processes or certain pathways, which have a system-wide function. The clustering and correlation analysis verified the tissue specificity and the mildness of the treatments. Responses to nutrient deficiencies (N0 and P0) overlapped to a certain extent on the proteomic level; they always clustered together and showed a high correlation. N and P metabolism are linked; the N:P ratio (supply, demand, etc.) is an important factor of growth (Agren et al. 2012, Menge et al. 2012). Nutrient limitations can induce either similar or opposite responses (Nguyen et al. 2015), but in our experimental set-up the changes in abundance of proteins, which were detected in both nutrient treatments, were similar. However, plants exposed to N0 and P0 showed a different growth response. In contrast to our initial hypothesis, the responses to salt and osmotic treatment were quite different on the proteome level, especially in the root. Both are often considered to have a similar effect on plants and are at least discussed together (Zhu 2002, Fujita et al. 2006, Zhang et al. 2006, Golldack et al. 2014), but there is some evidence at the transcriptomic level that already suggested otherwise (Kreps et al. 2002, Patade et al. 2012). Due to the low number of changed proteins under the osmotic stress, the overlap between the two treatments was also low. Only two proteins changed with a ±1.5 FC in both treatments. However, both proteins (At2g39800 and At1g54100) were characterized previously as stress-responsive proteins. They are induced by several external stimuli, which include water deprivation and elevated salt level (Székely et al. 2008, Li-Beisson et al. 2013). The observed high correlation between salt and PEG treatment in the shoots (Fig. 6C) suggests the same tendencies of adaptation, with a more accentuated response in the salt treatment. This could be caused by a harsher environmental challenge for the plant under salt conditions. However, this is not supported by the observed biomass production. It is more likely that the salt treatments evoke a multilevel response including simultaneous ion toxicity and water deprivation, whereas PEG induced responses to water deprivation only. In the shoots, the PEG treatment affected the proteome only slightly, but the root proteome was seriously affected. There were many overlapping proteins with much higher FCs, enabling us to draw a conclusion about the differences of the plant’s response in the two experiments (Fig. 6D). In contrast to the long PEG molecules, which are probably not taken up by the plants, the excess salt ions might have a direct effect on the ionic homeostasis of cells across the whole plant. In osmotic stress induced by PEG, the changed osmotic environment inhibits uptake of water and nutrients by the plants from the surroundings of the roots (Albert et al. 2012). This is supported by the proteomics clustering analysis (Fig. 5A), where in the root the osmotic treatment grouped together with the nutrient deficiencies. As a response to the excess salt, the photosynthetic apparatus might have been even positively affected, and this observation coincides with earlier proteomic findings in other plant species, mainly in the salt-tolerant mangrove (Zhu et al. 2012, Wang et al. 2014). Two LHC proteins together with the thylakoid transmembrane PGRL1B protein had an increased protein level, while the concentration of two CP12 enzymes decreased. The down-regulation of the latter might reduce complex formation and subsequently increase the activity of two Calvin–Benson cycle enzymes (GAPDH and PRK) (López-Calcagno et al. 2014). The data revealed an unexpected group of proteins related to thylakoid membranes which was enriched upon the osmotic treatment. Although these proteins should not be detected in the root in theory, most of them were expressed in most of the root experiments and this was confirmed by gene expression data (Supplementary Fig. S4), illustrating the occasional deceptive naming convention of GOterms. The PEG treatment might have also affected the cell wall in the roots. Roots are considered the primary site of sensing drought and osmotic stress, and PEG-induced osmotic stress changes the properties of the cell wall in the root, especially the porosity (Sharp et al. 2004, Yang et al. 2013). UDP-xylose synthase 6, the protein with the highest FC, is involved in synthesis of xylan, a major plant cell wall polysaccharide (Kuang et al. 2016), while the abundance of other cell wall-related enzymes was decreased. Plants can sense temperature. Several pathways and protein abundances are known to be affected and regulated by temperature changes (Patel and Franklin 2009). However, most of them are described as either cold or heat dependent. We observed around 100 changing proteins with a ±1.5 FC in each condition and tissue, and the majority were tissue and condition dependent. However, a subset of 20 proteins showed an inverse pattern between both temperature regimes. Further, including all significantly changing proteins in the analysis resulted in a high negative correlation between abundance and temperature. We tested whether the observed correlation was linked to growth rather than temperature. Seventeen out of the 20 proteins were found with a significant FC in other treatments as well, and the change in abundance was growth independent (Supplementary Table S3), suggesting that these 20 proteins are temperature dependent. Thereby, they are promising to be used to study in more detail over a finer temperature gradient with a bigger range. Is there a core stress response in plants? The majority of the proteins with changed abundances were rather specific for the treatment and only observed once or twice (94% in the shoot and 95% in the root). That could be interpreted as meaning that every change in the environment triggers specific responses even if two treatments are considered to be of a similar nature. The underlying reason might be found in our experimental set-up, where plants operate under tolerable changed conditions without experiencing fatal stresses. The latter would probably result in chlorosis, necrotic tissues or abscission, which then would result in similar responses. On the other hand, there were still several key proteins (Tables 3, 4) which changed in three or more treatments. A cold stress might induce water deficit. Similarly, phosphate and nitrate limitations are cross-linked (Agren et al. 2012). Drought changes not only the osmotic, but also the ionic status of the cell. Further, upon environmental stresses, reactive oxygen species, which are chemically highly reactive and harmful molecules, are always generated, with the resultant consequences. The highly specific responses to treatments at the proteome level do not exclude common response characteristics between the treatments at earlier time points. The protein NMT1 (At3g18000) came the closest to the definition of a “‘master responder”’, as it was detected with significantly high FC in nine out 12 treatments, in both shoots and roots, but it showed no significant regulation in osmotic stress. However, in a different experimental set-up in earlier experiments it was shown also to be part of the response to osmotic stress (Zhang et al. 2010). NMT1 is involved in phospholipid metabolism and catalyzes key methylation steps in choline biosynthesis (BeGora et al. 2010, Eastmond et al. 2010). The gene expression of NMT1 follows a similar stress-responsive pattern, and might be a good marker of lipid metabolism in stress (Supplementary Fig. S3). The overall large response of NMT1 levels in environmentally changed conditions makes it a good target for more specific experiments to elucidate the exact role of this enzyme. We hypothesized that similar alterations between treatments might occur on a higher hierarchical level, e.g. in the same pathway but within homologous proteins or in alternative pathways (e.g. salvage pathways). The GOterm analysis revealed the expected defense, response and acclimation processes. Further, an activation of DNA, RNA and protein metabolic processes was observed in most of the experiments. There was an up-regulation of these processes at 15°C, especially in the shoot, indicating more active transcription and translation. The salt treatment affected fewer of these major metabolic pathways; the protein synthesis machinery, which might have been intensified earlier, may have been stopped until the end of the 10th day. The down-regulation of the few proteins at 25°C rather suggests that the plants were not necessarily stressed or the acclimation process was completed already. In other experiments (osmotic, N0 and P0), the abundance of proteins, which belonged to translation, ribosome biogenesis, RNA binding, helicase activity, etc., was decreased, which can be the result of the unavailability of the necessary building blocks. There were several changes in the proteome, which could be linked to physiological processes. The lower water content in the shoot of the cold-treated plants can be the result or consequence of down-regulation of aquaporins. Transporter activity, which is responsible for the transfer of the necessary nutrient, increased in the N0 and P0 environments, as was previously shown (Lezhneva et al. 2014, Ayadi et al. 2015). However, nitrate assimilation was decreased in N0. The unavailability of nitrate was sensed by the plants; they did not invest in the whole assimilation machinery, but rather directed their resources into the synthesis of the nutrient transporters to increase nutrient uptake. Like the ammonium and nitrate transporters in N0, the phosphate transporter level was increased in the roots of plants exposed to phosphate starvation. SQD proteins, which are key enzymes involved in sulfolipid biosynthesis, crucial for photosynthetic membranes (Yu et al. 2002), were extremely elevated in both tissues. Together with the increased amount of glycerophosphodiester phosphodiesterase (GDPD1, At3g02040) they maintain the phosphate homeostasis during phosphate starvation (Cheng et al. 2011). Together with the abundant transporters, these proteins probably contribute to the rapid incorporation and metabolism of phosphate, if any source becomes available. While the phosphorus is limited, the plants also enhance alternative pathways; the phosphoenolpyruvate carboxylase (PEPC, At1g53310), which had an increased protein level in the shoot and root of the plants exposed to phosphate starvation, provides a metabolic bypass for the cytosolic pyruvate kinase together with malate dehydrogenase and NAD-malic enzyme, which are ADP limited, to facilitate the supply of pyruvate (Gregory et al. 2009, Plaxton and Tran, 2011). Further, phosphatases were up-regulated for acquisition of phosphate from intra- and/or extracellular sources. In the root, the protein abundances of purple acid phosphatases (PAPs) were increased, and PAP17 (At3g17790), which had the highest FC, had been previously shown to be strongly induced upon phosphate starvation (Del Pozo et al. 1999). The up-regulation of this array of different proteins shows the complex response of the plants induced by phosphate starvation and their adjustment to the nutrient-limiting environment. While metabolism is adjusted to be prepared for the rapid intake and incorporation of phosphate, simultaneously every available phosphorus, which can be temporary dispensable (vacuole, phospholipids, sulfolipids, etc.), is mobilized. Future perspectives Plants have adapted to cope with environmental changes, and these adaptations seem to be specific based on biomass allocation, growth strategy and proteome changes. Environment perturbations usually occur simultaneously, and plants are often exposed to a combination of stresses in the field. Transcriptomic data already revealed that the combination of different treatment-induced response processes that could have not been predicted from the single stress experiment (Rasmussen et al. 2013, Barah et al. 2015). Some proteomic experiments had already been carried out recently in maize and soybean with the application of a short-term strong heat and drought stress combined (Das et al. 2016, Zhao et al. 2016), but we still lack a deep understanding of the mechanism of the plant response to a combination of different stresses. These investigations should be performed with a combination of “‘omics”’ methods to allow the building of predictive scenarios of how plants adopt to environmental changes. Materials and Methods Plant growth Arabidopsis thaliana wild-type plants (Col-0) seeds were individually distributed into cut 0.5 ml tubes containing 0.65% (w/v) phytoagar. They were grown hydroponically in Cramer’s solution (Gibeaut et al. 1997, Tocquin et al. 2003) in a control environment for 18 d in a Percival AR95 growth chamber (CLF Plant Climatics) with a 12 h photoperiod, 20°C and 60% relative humidity. Light intensity was uniform at 150 μmol quanta m−2 s−1. The tubes were soaked in nutrient solution (pH was adjusted to 6), which contained the following macronutrients: 1.5 mmol l−1 Ca(NO3)2, 1.25 mmol l−1 KNO3, 0.75 mmol l−1 Mg(SO4), 0.5 mmol l−1 KH2PO4, 1 mmol l−1 (NH4)2SO4, 72 μmol l−1 C10H12FeN2NaO8 and 100 μmol l−1 Na2O3Si, and micronutrients: 50 μmol l−1 KCl, 10 μmol l−1 MnSO4, 1.5 μmol l−1 CuSO4, 2 μmol l−1 ZnSO4, 50 μmol l−1 H3BO3 and 0.075 μmol l−1 (NH4)6Mo7O24. Eighteen days after germination, plants were exposed to different treatments: 50 mmol l−1 NaCl or 5% (w/v) PEG6000 dissolved in hydroponic solution, ±5°C in an E-41L2 growth chamber (CLF Plant Climatics) and no phosphate and nitrate in the hydroponic solution. The plants were grown under changed conditions for an additional 10 d. Growth analysis During a 12 d period (from 16 to 28 d after germination) rosettes were photographed to quantify the leaf area. Leaf areas of plants were evaluated with ImageJ software. The growth of each plant was described with a polynomial (third-order) model: y = ax3 + bx2 + cx + d. RGR is defined as the relative increase in leaf area over the time period (GR) and was calculated by taking the difference between the polynomial transformation of the leaf area at the end (Gn) and at the beginning of the time frame (Gn–1) divided by Gn– 1: GRn = (Gn – Gn– 1/Gn– 1 (Poorter 1989, Heinen 1999, Paine et al. 2012, Ruts et al. 2013). Protein extraction and gel-free digestion for mass spectrometry (MS) analysis Proteins were purified and digested using a modified filter-aided sample preparation (FASP) protocol (Wisniewski et al. 2009); a Microcon 30 kDa centrifugal filter with an Ultracel-30 membrane was used (Merck Millipore). Whole rosettes were frozen in liquid N2, ground to a powder and aliquoted. The hydroponic system allowed us to harvest the roots in a similar way; they were cut off, quickly dried with tissue paper and finally frozen and ground in liquid N2. Proteins were extracted from aliquoted powder (20–50 μg) in SDS lysis buffer [4% (w/v) SDS, 100 mmol l−1 Tris–HCl pH 8.2] with a 1 : 10 sample to buffer ratio, incubated for 30 min at room temperature without dithiothreitol (DTT), which was added afterwards to a final concentration of 0.1 mol−1, sonicated for 12 min with a Bioruptor sonificator (Diagenode) and boiled for 5 min at 95°C. The protein concentration was measured with Qubit (Invitrogen). A 200 μl aliquot of 8 mol l−1 urea in 100 mol l−1 Tris–HCl pH 8.2 was mixed with 30 μl of sample, and loaded on the filter by centrifugation at 14,000×g. Samples were washed once with 200 μl of 8 mol l−1 urea and centrifuged at 14,000×g. Thiol groups of proteins were blocked with 100 μl of 0.05 mol l−1 iodoacetamide for 1 min at room temperature followed by removal of the chemicals by centrifugation at 14,000×g. SDS, urea and other digestion-incompatible chemicals were washed away twice with 200 μl of 0.5 mol l−1 NaCl followed by centrifugation at 14,000×g. Proteins were digested with trypsin in a 1 : 100 ratio of trypsin to protein in 0.05 mol l−1 triethylammonium bicarbonate overnight on the filter. Peptides were harvested by centrifugation at 14,000×g in a collection tube and acidified with trifluoroacetic acid (TFA) to a final concentration of 0.5%. Peptide purification for mass spectrometry analysis The digested peptides were purified and desalted on C18 Solid Phase Extraction columns (Sep-Pak), which were attached to a QIAvac 24 Plus (QIAGEN) vacuum manifold. Columns were washed once with 1 ml of 100% (v/v) methanol, once with 1 ml of 60 % (v/v) acetonitrile (ACN), 0.1% (v/v) TFA and twice with 1 ml of 3% (v/v) ACN and 0.1% (v/v) TFA solution. A 600–1,000 μl aliquot of 3% (v/v) ACN and 0.1% (v/v) TFA solution was added to the peptides and loaded on the column. The column was washed three times with 1 ml of 3% (v/v) ACN and 0.1% (v/v) TFA. Peptides were eluted with 200 μl of 60% (v/v) ACN, 0.1% (v/v) TFA, lyophilized, and redissolved in 40 μl of 3% (v/v) ACN and 0.1% (v/v) formic acid. Mass spectrometry analysis A 5–10 μl aliquot of peptide solution was analyzed on a Q-Exactive mass spectrometer (Thermo Scientific) coupled to an EASY-nLC1000 (Thermo Scientific). Instrument parameters followed the “‘sensitive”’ method published by Kelstrup et al. (2012) with slight modifications. Full scan MS spectra were acquired in profile mode from 300 to 1,700 m/z with an automatic gain control target of 3e6, Orbitrap resolution of 70,000 (at 200 m/z) and maximum injection time of 120 ms. The 12 most intense multiply charged (z = +2 to +8) precursor ions from each full scan were selected for higher energy collisional dissociation fragmentation. Precursor was accumulated with an automatic gain control value of 5e4 and a maximum injection time of 120 ms, and fragmented with a normalized collision energy of 28 (arbitrary unit). Generated fragment ions were scanned with Orbitrap resolution of 35,000 (at 200 m/z) from a fixed first mass of 120 m/z. The isolation window for precursor ions was set to 2.0 m/z and the underfill ratio was 2% (referring to an intensity of 8.3e3). Each fragmented precursor ion was set onto the dynamic exclusion list for 30 s. Peptide separation was achieved by reversed phase HPLC on a C18 column (150 mm×75 μm, 1.9 μm, C-18 AQ, 120 Å). Samples were separated with a linear gradient of 130 min from 3% to 25% solvent B [0.1% (v/v) formic acid in ACN] in solvent A [0.1% (v/v) formic acid in H2O] with a flow rate of 250 nl min−1. Protein identification and protein quantification using ProgenesisQI for proteomics For every stress experiment and tissue, Progenesis experiments were set up separately. Raw MS files were loaded into ProgenesisQI for Proteomics (v.4.0.4265.42984). The aligning reference was a combined pool of all respective samples. From each peptide ion, a maximum of the top five tandem mass spectra were exported using charge deconvolution and the deisotoping option, and a maximum number of 200 peaks per MS/MS. The Mascot generic file (.mgf) was searched with Mascot Server v.2.4.1 (www.matrixscience.com) using the parameters 10 p.p.m. for precursor ion mass tolerance and 0.2 Da for fragment ion tolerance. Trypsin was used as the protein-cleaving enzyme; two missed cleavages were allowed. Carbamidomethylation of cysteine was specified as a fixed modification, and oxidation of methionine was selected as a variable modification. A forward and reversed TAIR10 database concatenated to 260 known MS contaminants was searched in order to evaluate the false discovery rate (FDR) using the target-decoy strategy (Käll et al. 2008). The mascot result was loaded into Scaffold v4.1.1 using local FDR and protein cluster analysis. The spectrum report was loaded into ProgenesisQI for Proteomics. A between-group analysis was used with a Control and Stress condition group (4–6 replicates). Normalization was kept with default settings. Quantification included proteins identified with at least two features and one unique peptide. Proteins were grouped with Progenesis and quantified based on non-conflicting features. Normalized abundance from all non-conflicting peptide ions of the same protein group were summed individually for each sample. The parametric test (two tailed t-test) on the transformed (hyperbolic arcsine transformation) normalized protein abundance was applied. FCs were calculated using the group means of the protein sums. The proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (Vizcaíno et al. 2014) partner repository with the data set identifier PXD006797 (reviewer account details: Username: reviewer34305@ebi.ac.uk, Password: OopPQSdq). Bioinformatic analysis GO term enrichment analyses were based on TAIR 10 (May 2015) GO term association lists excluding associations based on GO evidence codes IEA (Inferred from Electronic Annotation) or RCA (Inferred from Reviewed Computational Analysis). GO categories over-represented by genes with a significantly high (±1.5) FC in particular responses in a given condition were determined by a hypergeometric test against all proteins detected in that condition. Only GO categories represented by at least five genes in the background list were considered. The assignments and tests were performed in R (R Development Core Team 2010). 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( 2012) Physiological and proteomic characterization of salt tolerance in a mangrove plant, Bruguiera gymnorrhiza (L.) Lam. Tree Physiol.  32: 1378– 1388. Google Scholar CrossRef Search ADS PubMed  Abbreviations Abbreviations ACN acetonitrile FC fold change GO Gene Ontology MS mass spectrometry N0 nitrate-deficient treatment P0 phosphate-deficient treatment PEG polyethylene glycol R/S root to shoot ratio RGR relative growth rate TFA trifluoroacetic acid © The Author(s) 2017. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email: journals.permissions@oup.com http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Plant and Cell Physiology Oxford University Press

Comparative Proteomic Analysis of Plant Acclimation to Six Different Long-Term Environmental Changes

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Oxford University Press
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© The Author(s) 2017. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email: journals.permissions@oup.com
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0032-0781
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1471-9053
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10.1093/pcp/pcx206
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Abstract

Abstract Plants are constantly challenged in their natural environment by a range of changing conditions. We investigated the acclimation processes and adaptive plant responses to various long-term mild changes and compared them directly within one experimental set-up. Arabidopsis thaliana plants were grown in hydroponic culture for 10 d under controlled abiotic stress (15°C, 25°C, salt and osmotic) and in nutrient deficiency (nitrate and phosphate). Plant growth was monitored and proteomic experiments were performed. Resource allocation between tissues altered during the plants’ response. The growth patterns and induced changes of the proteomes indicated that the underlying mechanisms of the adaptation processes are highly specific to the respective environmental condition. Our results indicated differential regulation of response to salt and osmotic treatment, while the proteins in the changed temperature regime showed an inverse, temperature-sensitive control. There was a high correlation of protein level between the nutrient-deficient treatments, but the enriched pathways varied greatly. The proteomic analysis also revealed new insights into the regulation of proteins specific to the shoot and the root. Our investigation revealed unique strategies of plant acclimation to the different applied treatments on a physiological and proteome level, and these strategies are quite distinct in tissues below and above ground. Introduction Human nutrition relies almost entirely on agriculture and its productivity, but the sustainability of the agricultural system is doubtful. A conservative estimate projects that there will be approximately 9 billion inhabitants on Earth in 2050 (Alexandratos and Bruinsma 2012). Based on this estimate, agricultural production will have to increase by 50–100% until 2050, but historical rates of yield growth for most crops are only half of what would be required to achieve this (Curtis and Halford 2014). Increasing food production is technically very challenging (Raines 2011, Dockter et al. 2014), and climate change further endangers the whole process. A mild scenario suggests a 2°C rise in global temperature, and the effects of global warming on food production will be only partially and temporarily compensated by the ability of the existing crops to acclimate and adapt (Challinor et al. 2014). Increased temperature will be coupled with further stresses (i.e. drought, floods, increased salt concentration in soil, nutrient limitations, etc.) and, by the end of the 21st century, many densely populated areas will be seriously arid regions (Dai 2013). Plants are inevitably exposed to the changing environmental conditions; hence, they have to cope with them in order to minimize the loss of their fitness. The acclimation and adaptation responses have been extensively investigated, but the underlying mechanisms are complex and still poorly understood. Increased knowledge about pathways and regulated proteins of the cellular machinery would facilitate efforts to optimize rational breeding. Nutrient availability is one of the key factors that can substantially reduce plant growth and, as a result, yield. Liebig’s law of the minimum states that the growth of plants is highly controlled by single nutrients, which are the least available (Liebig 1840, Liebig 1855). Although it is a good estimate, there are other hypotheses that discuss the relationship between nutrient availability and plant growth (Agren et al. 2012). Nitrogen and phosphorus are two essential macronutrients, and are often limiting factors in agriculture without the application of fertilizers (Masclaux-Daubresse et al. 2010, Agren et al. 2012). Stress response in plants has been studied extensively in recent decades, illustrating an awareness of the importance of a better understanding of the underlying adaptive responses. Traditionally, whole-plant responses were studied with transcriptomic approaches (Agarwal et al. 2014), as that method promises to deliver the most complete picture. However, based on transcriptional changes, it is not always possible to explain the current metabolic status of a biological system. Transcripts can be seen as snapshots, which are followed by protein changes and, therefore, a remodeling of the cellular machinery over time (Stitt et al. 2010, Baerenfaller et al. 2012, Guerreiro et al. 2014). Proteomics allows quantification of the most abundant proteins in a biological sample. Thereby proteins involved in the main processes, which are usually also the most resource-demanding processes, are well covered, in contrast to low abundancy transcription factors. Thus, proteomics is a powerful tool to investigate how plants adjust their metabolism and physiology in response to a recently altered environment. Proteomic experiments carried out in different stress conditions, tissues, organelles and plant species have been reviewed several times (Kosová et al. 2011, Rodziewicz et al. 2014, Ghosh and Xu, 2014, Janmohammadi et al. 2015, Jorrín-Novo et al. 2015). Research on early stress response to an unnaturally strong abiotic stressor is the main focus in the field of Arabidopsis research. However, such experiments may be unsuitable to investigate how plants rewire their cellular machinery and acclimate to long-term environmental changes (i.e. climate change). Further, the extrapolation of data obtained from different experiments can be biased and occasionally contradictory due to the different experimental, technical and statistical approaches. Thus, it remains challenging to compare responses to different environmental conditions and draw universal conclusions on stress responses (Dupae et al. 2014) similarly to other ‘omics’ research areas (Deyholos, 2010, Rest et al. 2016). The aim of our research was to examine a more realistic scenario of long-term acclimation processes of plants under mild environmental perturbations. We conducted a comparative analysis that involved several abiotic stresses and nutrient limitations in a well-defined experimental set-up, which allowed us to analyze and compare them with each other directly. The acclimation process of Arabidopsis thaliana plants was monitored under moderately changed environmental conditions: temperature changes (±5°C), phosphate and nitrogen deficiency, elevated salt and higher osmotic pressure. All these different conditions were successfully established in a hydroponic system, and comparative analyses were carried out. Results Plant growth under changed environmental conditions We compared plant responses to a set of environmental perturbations. Treatments were chosen in which plants were able to adjust their metabolism and sustain growth under the unfavorable environmental conditions. Plants were grown for 18 d in hydroponics under standard full nutrient conditions at 20°C and then subjected to different treatments {temperature (15 and 25°C), mild salt (50 mmol l−1 NaCl), osmotic stress [5% (w/v) polyethylene glycol (PEG)6000], or phosphate (P0) or nitrogen deficiency (N0)} for an additional 10 d. The applied environmental shifts affected growth, but the plants remained green and did not display chlorotic or dead tissues after 10 d (Fig. 1). Compared with the control plants, growth of plants exposed to 25°C appeared similar or slightly enhanced., However, in all other conditions plants showed a reduction in leaf area at the end of the 10 d treatment (Figs. 1, 2, 4). FW and DW were determined (Fig. 2); at 25°C, FW and DW of the shoot were elevated, while the other treatments had a negative impact and the biomass was reduced compared with the control. We correlated FW and DW with the surface area of the plants, and the correlation was high between all three parameters (Supplementary Fig. S1). Leaf area surface was chosen as a non-destructive approximation of biomass production. The DW/FW ratio was approximately similar in all the conditions except for at 15°C, which showed a higher value in the shoot indicating a relatively lower water content in the leaves (Fig. 3A). Fig. 1 View largeDownload slide Plants grown in six environmentally changed conditions for 10 d. Pictures were taken on the second, sixth and 10th day of the treatment [20th, 24th and 28th day after germination (DAG) respectively]. The scale bar corresponds to 1 cm. Fig. 1 View largeDownload slide Plants grown in six environmentally changed conditions for 10 d. Pictures were taken on the second, sixth and 10th day of the treatment [20th, 24th and 28th day after germination (DAG) respectively]. The scale bar corresponds to 1 cm. Fig. 2 View largeDownload slide Weight of plants at the end of the 10 d treatment. (A) 15°C treatment. (B) 25°C treatment. (C) Osmotic treatment with 5% PEG6000. (D) Salt treatment with 50 mmol l−1 NaCl. (E) Nitrogen deficiency (N0). (F) Phosphate deficiency (P0). Bars represent the mean values (n = 9) and the error bars the SEs. Fig. 2 View largeDownload slide Weight of plants at the end of the 10 d treatment. (A) 15°C treatment. (B) 25°C treatment. (C) Osmotic treatment with 5% PEG6000. (D) Salt treatment with 50 mmol l−1 NaCl. (E) Nitrogen deficiency (N0). (F) Phosphate deficiency (P0). Bars represent the mean values (n = 9) and the error bars the SEs. Fig. 3 View largeDownload slide Ratio of biomass production between roots and shoots under changed conditions. (A) DW/FW ratio of the shoot and root and (B) R/S of the FW and DW at the end of the 10 d treatment expressed as a percentage relative to the control conditions. Bars represent the mean values (n = 9) and the error bars the SEs. Fig. 3 View largeDownload slide Ratio of biomass production between roots and shoots under changed conditions. (A) DW/FW ratio of the shoot and root and (B) R/S of the FW and DW at the end of the 10 d treatment expressed as a percentage relative to the control conditions. Bars represent the mean values (n = 9) and the error bars the SEs. Fig. 4 View largeDownload slide Leaf area and relative growth rate (RGR) measurements of plants grown in six environmentally changed conditions for 10 d (18–28 DAG). On the left y-axis is the leaf area in cm2, on the right y-axis the RGR is depicted. (A) 15°C treatment. (B) 25°C treatment. (C) Osmotic treatment with 5% PEG6000. (D) Salt treatment with 50 mmol l−1 NaCl. (E) Nitrogen deficiency. (F) Phosphorus deficiency. Bars represent the mean values (n = 10–18) and the error bars the SEs. Fig. 4 View largeDownload slide Leaf area and relative growth rate (RGR) measurements of plants grown in six environmentally changed conditions for 10 d (18–28 DAG). On the left y-axis is the leaf area in cm2, on the right y-axis the RGR is depicted. (A) 15°C treatment. (B) 25°C treatment. (C) Osmotic treatment with 5% PEG6000. (D) Salt treatment with 50 mmol l−1 NaCl. (E) Nitrogen deficiency. (F) Phosphorus deficiency. Bars represent the mean values (n = 10–18) and the error bars the SEs. Growth of roots was measured as biomass at the end of the experiment. In contrast to the shoot, the growth of roots was relatively robust in all experiments. In nutrient-limiting conditions (N0 and P0) root biomass increased, whereas osmotic stress, salt stress and reduced temperature resulted in unchanged or slightly lower biomass compared with the plants in the control environment. Under elevated temperatures (25°C), root biomass increased significantly by 50%. The root to shoot ratio (R/S) in the treatments was never below the ratio detected in the control (Fig. 3B). At 25°C and with osmotic and salt treatments, plants invested slightly more in the root than the shoot. Plants grown in N0, in P0 and at 15°C invested significantly more resources in the roots (Fig. 3B). The relative growth rate (RGR) curves showed distinct patterns for each applied treatment (Fig. 4). RGR analysis revealed a sigmoid-like growth pattern in control conditions and under abiotic stress, but this was changed under nutrient deficiency (Fig. 4E, F). At 25°C, plants grew faster than the control plants initially for the first 6 d and the RGRs converged towards the end of the experiment (Fig. 4B). Under mild salt and osmotic stresses, the plants maintained a control-level growth in the first 2 d before it declined (Fig. 4C, D). At 15°C, RGR dropped initially by >50% and recovered only partially (Fig. 4A). Nutrient deficiency resulted in different growth responses. In N0, growth was not affected for the first 2 d after the introduced perturbation, but growth was reduced at later time points. At the end of the treatment, nitrogen-limited plants and control plants had a similar RGR. Phosphate limitation initially had a strong negative effect on growth, but later seemed to promote plant growth in the middle of the treatment. At later time points, growth compared with control plants was reduced again (Fig. 4E, F). Induced proteomic alterations in plants grown under changed environmental conditions Plant growth was compromised under the applied environmental changes except that of plants at 25°C, and the dynamics of the observed growth responses suggested different adaptation processes. Therefore, we aimed to elucidate the rewired cellular machinery of the newly adapted physiological state by a quantitative analysis of proteomes at the end of the treatments (10 d). The unfractionated single-tube method was used with root and shoot samples of each treatment. A total of 2,400–3,500 proteins were detected in every sample, with about 500 more proteins in roots than in shoots (Table 1). In a comparison between the control and treatment groups, 150–1,087 changed quantitatively with a P-value ≤0.05 (changing proteins) and 6–441 of these changed with a fold change (FC) exceeding ±1.5 (changing proteins with ±1.5 FC) (Table 1). Nutrient deficiency tended to induce more pronounced changes in the proteome than the mild abiotic stresses, although the number of changing proteins was the highest in the 15°C treatment. Mild osmotic stress affected the shoot proteome the least, even though the same treatment had a strong impact on the root proteome. Table 1 Number of proteins identified in the environmental treatments in the shoot and the root Threshold  All  P ≤ 0.05  FC ±1.5  All  P ≤ 0.05  FC ±1.5  All  P ≤ 0.05  FC ±1.5  Treatment  25°C  15°C  Osmotic  Shoot  2,406  551  68  2,880  1,087  114  2,697  150  6  Root  2,905  819  100  3,256  484  75  3,173  792  236  Treatment  Salt  N0  P0  Shoot  2,860  835  91  2827  917  227  2,829  705  122  Root  3,260  311  84  3483  1,027  441  3,485  1,007  272  Threshold  All  P ≤ 0.05  FC ±1.5  All  P ≤ 0.05  FC ±1.5  All  P ≤ 0.05  FC ±1.5  Treatment  25°C  15°C  Osmotic  Shoot  2,406  551  68  2,880  1,087  114  2,697  150  6  Root  2,905  819  100  3,256  484  75  3,173  792  236  Treatment  Salt  N0  P0  Shoot  2,860  835  91  2827  917  227  2,829  705  122  Root  3,260  311  84  3483  1,027  441  3,485  1,007  272  Every protein (all), proteins with significant expression (P ≤ 0.05) and proteins with significantly changed expression beyond the ±1.5 fold change threshold. For all corresponding samples, around 40% of the detected proteins were found in both roots and shoots. Among these, only 10% changed with a ±1.5 FC in both tissues (Table 2), but the vast majority (84%) of these proteins changed their abundance in the same direction (Supplementary Fig. 2). Table 2 Overlap of proteome changes between roots and shoots Threshold  All  FC ±1.5  Overlap  All  FC ±1.5  Overlap  All  FC ±1.5  Overlap  Treatment  15°C      25°C      Osmotic  Shoot  1,820  53  7 (9.6%)  1,490  36  7 (10.8%)  1,738  5  0 (0%)  Root    20      29      79  Treatment  Salt      N0      P0  Shoot  1,812  41  5 (7.9%)  1807  141  23 (7.5%)  1,808  72  20 (11.4%)  Root    22      163      103  Threshold  All  FC ±1.5  Overlap  All  FC ±1.5  Overlap  All  FC ±1.5  Overlap  Treatment  15°C      25°C      Osmotic  Shoot  1,820  53  7 (9.6%)  1,490  36  7 (10.8%)  1,738  5  0 (0%)  Root    20      29      79  Treatment  Salt      N0      P0  Shoot  1,812  41  5 (7.9%)  1807  141  23 (7.5%)  1,808  72  20 (11.4%)  Root    22      163      103  The overlap between proteins with a significant high (P ≤ 0.05, FC ±1.5) abundance change in every treatment. The subgroup of proteins, which were detected in the shoot and the root, was included, and the significance and fold change threshold (FC ±1.5) was applied on them. Responses on the proteome level between treatments To see if different treatments have similar effects on the proteome, all 12 experiments were clustered based on changing proteins (P ≤ 0.05). Roots and shoots grouped together on distinct branches, suggesting that tissue-specific proteomes differ more than the treatment-induced changes (Fig. 5A). In roots, the altered nutrient conditions (N0 and P0) cluster together with osmotic stress, while the mild salt stress did not cluster closely with other treatments. Both temperature treatments grouped together. The grouping in the leaves differed; salt and cold clustered together, as N0 and P0 again did, whereas heat and osmotic stress were separate from the others. Fig. 5 View largeDownload slide Cluster and correlation analysis of proteomics changes. Heatmap and correlation matrix of each tissue from every experiment based on significantly changing proteins (P < 0.05) in the shoot (S) and root (R) separately. (A) Heatmap and clustering of every treatment, shoot (S) and root (R) separately. The coloring corresponds to the fold change value of the individual proteins: red represents a strong increase and black represents a strong decrease in protein abundance compared with the wild type. The blue trace and its distance from the center in each column also represents the fold change in the protein amount. (B) Pearson correlation between the proteome changes of each tissue from every experiment. The pairwise Pearson correlation coefficient was calculated between all experiments. Fig. 5 View largeDownload slide Cluster and correlation analysis of proteomics changes. Heatmap and correlation matrix of each tissue from every experiment based on significantly changing proteins (P < 0.05) in the shoot (S) and root (R) separately. (A) Heatmap and clustering of every treatment, shoot (S) and root (R) separately. The coloring corresponds to the fold change value of the individual proteins: red represents a strong increase and black represents a strong decrease in protein abundance compared with the wild type. The blue trace and its distance from the center in each column also represents the fold change in the protein amount. (B) Pearson correlation between the proteome changes of each tissue from every experiment. The pairwise Pearson correlation coefficient was calculated between all experiments. Correlation was calculated between two experiments with all possible pairwise combinations based on proteins, which changed in both treatments (Fig. 5B). Initially, we expected that the two nutrient limitations (Nguyen et al. 2015) and the osmotic and salt treatments might have induced similar responses. However, the correlation was mostly higher between tissues than between treatments. The correlation was strong between the proteome changes of the shoot (R = 0.898) and the root (R = 0.736) in N0 and P0, but it was lower if we compared the shoot and root of the same treatment. The correlation between the shoot and the root of N-limited plants was low (R = 0.222). However, in P0 it was higher (R = 0.611); it was still lower than the above-mentioned values. The vast majority of the changing proteins were up- or down-regulated in the same direction (Fig. 6E, F). The mild osmotic and salt stresses showed a distinct tendency; changes in the shoot proteome under osmotic stress correlated highly positive (R = 0.909) with the shoot proteome of the salt treatment. In contrast, almost no correlation was detected between roots of the plants exposed to salt and osmotic treatments (Fig. 6C, D). Fig. 6 View largeDownload slide Fold change of overlapping changing proteins between two experiments. Fold change of significantly changing proteins (P < 0.05) detected in 15°C and 25°C, osmotic (5% PEG6000) and salt (50 mmol l−1 NaCl), and nitrate and phosphate deficiency experiments. (A) Shoot in 15 and 25°C. (B) Root at 15 and 25°C. (C) Shoot in osmotic and salt treartment. (D) Root in osmotic and salt treatment. (E) Shoot in nitrogen and phosphate deficiency. (F) Root in nitrogen and phosphate deficiency. Proteins with log2FC larger than ± 0.58 (±1.5 FC) are depicted in red. Fold change of protein is the ratio of its normalized abundance in the treatment divided by its normalized abundance in the control. Fig. 6 View largeDownload slide Fold change of overlapping changing proteins between two experiments. Fold change of significantly changing proteins (P < 0.05) detected in 15°C and 25°C, osmotic (5% PEG6000) and salt (50 mmol l−1 NaCl), and nitrate and phosphate deficiency experiments. (A) Shoot in 15 and 25°C. (B) Root at 15 and 25°C. (C) Shoot in osmotic and salt treartment. (D) Root in osmotic and salt treatment. (E) Shoot in nitrogen and phosphate deficiency. (F) Root in nitrogen and phosphate deficiency. Proteins with log2FC larger than ± 0.58 (±1.5 FC) are depicted in red. Fold change of protein is the ratio of its normalized abundance in the treatment divided by its normalized abundance in the control. The proteins which were found to be significantly changed in both temperature treatments (15 and 25°C) revealed a strong negative correlation (Fig. 5B); it was high between the shoot (R = −0.735) and the root (R = −0.811). This strong negative correlation was reflected in the opposite FC of individual proteins (Fig. 6A, B). This became even more pronounced if only proteins with ±1.5 FC were included (Fig. 7; Supplementary Table S1). If they were up-regulated at 15°C, they were down-regulated at 25°C, and vice versa irrespective of the tissue. Fig. 7 View largeDownload slide Antagonistic regulation of proteins under changed temperature conditions. Changing proteins with a log2FC ± 0.58 (±1.5 FC) detected in the 15 and 25°C treatment. The shoot and root were combined. Fold change of protein is the ratio of its normalized abundance in the treatment divided by its normalized abundance in the control. Fig. 7 View largeDownload slide Antagonistic regulation of proteins under changed temperature conditions. Changing proteins with a log2FC ± 0.58 (±1.5 FC) detected in the 15 and 25°C treatment. The shoot and root were combined. Fold change of protein is the ratio of its normalized abundance in the treatment divided by its normalized abundance in the control. Commonly changed proteins under different growth conditions We separated treatments according to tissues and tested whether any significantly (P ≤ 0.05) changed protein with ±1.5 FC could be observed in several different treatments. Most proteins (78%, shoot; 79%, root) seemed to respond only to single conditions (Fig. 8). However, a number of proteins responded to several treatments in roots and shoots (Tables 3, 4). Table 3 Proteins detected in three and more treatments in the root AGI  Gene name  Stress-related (ref.)  15°C  25°C  Salt  Osmotic  N0  P0  AT1G12780  UDP-Glc epimerase 1    −1.182  0.5057  −0.57    −1.09  −0.995  AT1G13080  Cyt. P450 (CYP71B2)  Gao et al. (2008)  −0.89  0.6214    0.8759      AT1G33590        0.8626      −0.834  −0.706  AT1G52050        −0.428  −0.759  0.7892  0.8424    AT1G56680      0.3514  −0.84  −1.731  −0.868    0.4739  AT1G65970  Thioredoxin-dependent peroxidase 2  Kumar et al. (2015)  −1.222  0.7598    −0.889  −2.272    AT1G70850        0.8861      0.7188  0.7918  AT1G73260  Kunitz trypsin inhibitor  J. Li et al. (2008)  −0.625  1.0958      −1.156  −0.761  AT1G78830      −0.393  0.7223      −0.837  1.2307  AT1G80240        0.3334  −0.721  −0.978  −0.597  0.3122  AT2G01520  MLP-like protein 34    −0.742  1.7859      1.126  0.8097  AT2G41100  Calmodulin-like 12  Sistrunk et al. (1994)  0.6213    −1.609  −0.607      AT2G43920  Harmless to ozone layer 2    3.4238        −0.996  3.0006  AT3G01290  Hypersensitive induced reaction 2  Qi and Katagiri (2012)  −0.679  0.4753      1.1093  1.3853  AT3G03640  Beta glucosidase 25    0.6722      −0.665    0.6329  AT3G05950      −1.471  1.1552  −0.359  2.0416      AT3G06850  Dark inducible 3 (DIN3)    −0.6    −0.519  −0.431  −1.315  −0.683  AT3G16390  Jacalin-related lection 31  Kissen and Bones (2009)  1.1808  −1.595    0.4742  1.2289  1.8704  AT3G16430  Nitrile specifier protein3  Yamada et al. (2011)    0.9232  −0.673    −0.656  −0.358  AT3G18000  N-Methyltransferase 1  Zhang et al. 2010  1.2  −1.155  1.5038    1.0821  1.2313  AT3G49120  Peroxidase CB  Mammarella et al. (2014)    1.0668    0.5915  −0.774    AT3G59480        0.7952  1.597    −2.16  −2.226  AT4G25250  Pectinmethylesterase inhibitor 4    −0.288  −0.916  1.0882  −1.437      AT4G25340  FK506 binding protein      −0.589    0.6332  −1.744    AT4G32460      0.6248  −1.414  0.7571        AT4G37410  Cyt. P450(CYP81F4)      0.8251  −0.625      −0.813  AT4G39800  Myo-inositol-1-phosphate synthase  Ahmad et al. (2015)  0.7534        2.1125  1.0627  AT5G15970  Cold-responsive 6.6  Gorsuch et al. (2010)  1.0886    0.9574  1.2687      AT5G38940  Fructokinase 7      0.6765  −1.469      −0.727  AGI  Gene name  Stress-related (ref.)  15°C  25°C  Salt  Osmotic  N0  P0  AT1G12780  UDP-Glc epimerase 1    −1.182  0.5057  −0.57    −1.09  −0.995  AT1G13080  Cyt. P450 (CYP71B2)  Gao et al. (2008)  −0.89  0.6214    0.8759      AT1G33590        0.8626      −0.834  −0.706  AT1G52050        −0.428  −0.759  0.7892  0.8424    AT1G56680      0.3514  −0.84  −1.731  −0.868    0.4739  AT1G65970  Thioredoxin-dependent peroxidase 2  Kumar et al. (2015)  −1.222  0.7598    −0.889  −2.272    AT1G70850        0.8861      0.7188  0.7918  AT1G73260  Kunitz trypsin inhibitor  J. Li et al. (2008)  −0.625  1.0958      −1.156  −0.761  AT1G78830      −0.393  0.7223      −0.837  1.2307  AT1G80240        0.3334  −0.721  −0.978  −0.597  0.3122  AT2G01520  MLP-like protein 34    −0.742  1.7859      1.126  0.8097  AT2G41100  Calmodulin-like 12  Sistrunk et al. (1994)  0.6213    −1.609  −0.607      AT2G43920  Harmless to ozone layer 2    3.4238        −0.996  3.0006  AT3G01290  Hypersensitive induced reaction 2  Qi and Katagiri (2012)  −0.679  0.4753      1.1093  1.3853  AT3G03640  Beta glucosidase 25    0.6722      −0.665    0.6329  AT3G05950      −1.471  1.1552  −0.359  2.0416      AT3G06850  Dark inducible 3 (DIN3)    −0.6    −0.519  −0.431  −1.315  −0.683  AT3G16390  Jacalin-related lection 31  Kissen and Bones (2009)  1.1808  −1.595    0.4742  1.2289  1.8704  AT3G16430  Nitrile specifier protein3  Yamada et al. (2011)    0.9232  −0.673    −0.656  −0.358  AT3G18000  N-Methyltransferase 1  Zhang et al. 2010  1.2  −1.155  1.5038    1.0821  1.2313  AT3G49120  Peroxidase CB  Mammarella et al. (2014)    1.0668    0.5915  −0.774    AT3G59480        0.7952  1.597    −2.16  −2.226  AT4G25250  Pectinmethylesterase inhibitor 4    −0.288  −0.916  1.0882  −1.437      AT4G25340  FK506 binding protein      −0.589    0.6332  −1.744    AT4G32460      0.6248  −1.414  0.7571        AT4G37410  Cyt. P450(CYP81F4)      0.8251  −0.625      −0.813  AT4G39800  Myo-inositol-1-phosphate synthase  Ahmad et al. (2015)  0.7534        2.1125  1.0627  AT5G15970  Cold-responsive 6.6  Gorsuch et al. (2010)  1.0886    0.9574  1.2687      AT5G38940  Fructokinase 7      0.6765  −1.469      −0.727  Proteins with a significant high FC [P ≤ 0.05, log2FC ± 0.58 (±1.5)]. FCs in italics are below the threshold of expression. Stress-relatedness was defined based on the TAIR database. Table 4 Proteins detected in three and more treatments in the shoot AGI  Gene name  Stress-related (ref.)  15°C  25°C  Salt  Osmotic  N0  P0  AT1G02930  Glutathione S-transferase 6  Tolin et al. (2012)  0.7156    0.9598    1.9187  0.9888  AT1G12770  Embryo defective1586    0.6765        −1.586  −1.7  AT1G15500  TLC ATP/ADP transporter    0.2513  −0.592  −0.368    −1.323  −0.839  AT1G23130      −0.818    −0.695    0.8027    AT1G45201  Triacylglycerol lipase-like 1    −0.367  0.6091  0.5741    0.7251  0.6531  AT1G48570      0.9036        −1.795  −1.367  AT1G54100  Aldehyde dehydrogenase 7B4  Li-Beisson et al. (2013)    0.4339  1.0444  0.6075  0.8159    AT2G22400      1.0165        −0.817  −0.779  AT2G33380  Responsive desiccation 20  Blée et al. (2014)    1.242  1.9917    1.4771  0.6182  AT2G39800  Delta1-pyrroline-5-carboxylate synthase1  Székely et al. (2008)      1.1731  0.9249  −0.687  0.3999  AT3G06980    D. Li et al. (2008)  0.4166  −0.905      −1.546  −1.185  AT3G18000  N-Methyltransferase 1  Zhang et al. (2010)  0.8744    0.9442    −1.753  −1.271  AT3G18680      0.4387  −0.75  −0.514    −1.869  −1.292  AT3G19710  Branched-chain aminotransferase 4  Less and Galili (2008)    0.9781      1.1274  0.7424  AT3G28220      1.014    1.0413    0.2963  0.8688  AT3G44750  Histone deacetylase 3  Han et al. (2016)  0.6684  −0.932      −3.21    AT3G47450  Nitric oxide synthase 1  Xie et al. (2013)      −0.915    −1.342  −1.833  AT3G53460  Chloroplast RNA-binding protein 29  Kupsch et al. (2012)  0.6085  −0.745  −0.545    −1.2  −0.705  AT4G02990  Belaya Smert  Robles et al. (2012)  0.64        −1.604  −1.677  AT4G11960  PGR5-like B  Lehtimäki et al. (2010)  −2.351  0.9835  0.9692  0.5063  0.9617    AT4G14090    Pourcel et al. (2010)  1.263  −0.363  1.4066    2.7953  1.2214  AT4G15530  Pyruvate orthophosphate dikinase      0.6258      1.2293  0.7914  AT4G22485        −0.778  0.5988    0.9335  0.3724  AT4G22880  Anthocyanidin synthase  Bharti et al. (2015)      1.1193    2.1873  0.6336  AT4G36390      0.8676  −0.649      −1.306  −1.018  AT5G08610  Pigment defective 340    1.0774  −0.765  −0.389    −1.623  −1.554  AT5G13930  Chalcone synthase  An et al. (2016)  0.7273  −0.682      1.808  0.7347  AT5G22580      −0.724  0.7569  −0.729        AT5G23900        −0.976      −1.417  −1.261  AT5G54180  Plastid transcriptionally active 15    0.7059        −0.802  −0.795  AGI  Gene name  Stress-related (ref.)  15°C  25°C  Salt  Osmotic  N0  P0  AT1G02930  Glutathione S-transferase 6  Tolin et al. (2012)  0.7156    0.9598    1.9187  0.9888  AT1G12770  Embryo defective1586    0.6765        −1.586  −1.7  AT1G15500  TLC ATP/ADP transporter    0.2513  −0.592  −0.368    −1.323  −0.839  AT1G23130      −0.818    −0.695    0.8027    AT1G45201  Triacylglycerol lipase-like 1    −0.367  0.6091  0.5741    0.7251  0.6531  AT1G48570      0.9036        −1.795  −1.367  AT1G54100  Aldehyde dehydrogenase 7B4  Li-Beisson et al. (2013)    0.4339  1.0444  0.6075  0.8159    AT2G22400      1.0165        −0.817  −0.779  AT2G33380  Responsive desiccation 20  Blée et al. (2014)    1.242  1.9917    1.4771  0.6182  AT2G39800  Delta1-pyrroline-5-carboxylate synthase1  Székely et al. (2008)      1.1731  0.9249  −0.687  0.3999  AT3G06980    D. Li et al. (2008)  0.4166  −0.905      −1.546  −1.185  AT3G18000  N-Methyltransferase 1  Zhang et al. (2010)  0.8744    0.9442    −1.753  −1.271  AT3G18680      0.4387  −0.75  −0.514    −1.869  −1.292  AT3G19710  Branched-chain aminotransferase 4  Less and Galili (2008)    0.9781      1.1274  0.7424  AT3G28220      1.014    1.0413    0.2963  0.8688  AT3G44750  Histone deacetylase 3  Han et al. (2016)  0.6684  −0.932      −3.21    AT3G47450  Nitric oxide synthase 1  Xie et al. (2013)      −0.915    −1.342  −1.833  AT3G53460  Chloroplast RNA-binding protein 29  Kupsch et al. (2012)  0.6085  −0.745  −0.545    −1.2  −0.705  AT4G02990  Belaya Smert  Robles et al. (2012)  0.64        −1.604  −1.677  AT4G11960  PGR5-like B  Lehtimäki et al. (2010)  −2.351  0.9835  0.9692  0.5063  0.9617    AT4G14090    Pourcel et al. (2010)  1.263  −0.363  1.4066    2.7953  1.2214  AT4G15530  Pyruvate orthophosphate dikinase      0.6258      1.2293  0.7914  AT4G22485        −0.778  0.5988    0.9335  0.3724  AT4G22880  Anthocyanidin synthase  Bharti et al. (2015)      1.1193    2.1873  0.6336  AT4G36390      0.8676  −0.649      −1.306  −1.018  AT5G08610  Pigment defective 340    1.0774  −0.765  −0.389    −1.623  −1.554  AT5G13930  Chalcone synthase  An et al. (2016)  0.7273  −0.682      1.808  0.7347  AT5G22580      −0.724  0.7569  −0.729        AT5G23900        −0.976      −1.417  −1.261  AT5G54180  Plastid transcriptionally active 15    0.7059        −0.802  −0.795  Proteins with a significant high FC [P ≤ 0.05, log2FC ± 0.58 (±1.5)]. FCs in italics are below the threshold of expression. Stress-relatedness was defined based on the TAIR database Fig. 8 View largeDownload slide Proteins specific to one or multiple treatments. Number of changing proteins with ±1.5 FC detected in one or multiple treatments. (A) In the shoot and (B) in the root. Fig. 8 View largeDownload slide Proteins specific to one or multiple treatments. Number of changing proteins with ±1.5 FC detected in one or multiple treatments. (A) In the shoot and (B) in the root. In roots, 29 proteins (Table 3) changed significantly in three or more treatments. One of them was an N-methyltransferase (NMT1, At3g18000), which had a significant fold change in every root experiment except the osmotic stress. In the shoot, similarly to roots, there was also only a small fraction of significantly changed proteins (30) which were found in three or more treatments (Table 4). One of them was again NMT1; it was up-regulated in salt and at 15°C and down-regulated in N0 and P0. This protein changed under virtually all changed conditions except the osmotic stress. These changes were not only stress specific, but, for instance in nutrient deficiency, it showed opposite protein levels indicating different roles in different tissues. Gene Ontology (GO) enrichment and analysis of single proteins with the highest significant FC under changed temperature To detect processes that might be affected by adaptation to the applied mild stresses, we performed a GO enrichment analysis on the changing proteins with ±1.5 FC. In response to the 15°C treatment, ‘cold stress’-related GOterms were enriched; in shoots ‘acclimation to the cold’ (GO:0009631), and in roots the ‘response to cold’ (GO:0009409). Several transport GOterms were enriched with the highest significance level (GO:0006810 ‘transport’ and GO:0015250 ‘water channel activity’) and the proteins contained in these categories were all down-regulated in the shoot and root. In the shoot, in particular, a strong reduction of aquaporins (Supplementary Table 2) was observed. There might be a link between the water transport and the induced response to water deprivation in both tissues (GO:0009414 ‘response to water deprivation’). Besides the transport processes, RNA metabolic pathways were also enriched in the shoot and the root (GO:0004004 ‘ATP-dependent RNA helicase activity’; GO:0008168 ‘methyltransferase activity’; and GO:0010501 ‘RNA secondary structure unwinding’). In the 15°C treatment, photosynthetic light reaction proteins were reduced in the shoot. Two of the proteins with the largest decrease in amount were a photosynthetic enzyme, a putative large chain protein of the ribulose-1,5-bisphosphate carboxylase/oxygenase (At2g07732), and a thylakoid transmembrane protein (PGRL1B, At4g11960). PGRL1B belonged to the above-mentioned group of proteins which were detected with a significantly high FC in more than three treatments, but everywhere else it was up-regulated. In the root, the top up-regulated proteins are mainly involved in cold and other stress responses: KIN1 (At5g15960) is a cold- and ABA-inducible protein, RNA HELICASE 25 (At5g08620) controls gene expression during cold, salt and drought stress, and the COLD REGULATED 78 (At5g52310), as its name shows, is regulated by lower temperature. A protein (HARMLESS TO OZONE LAYER 2, AT2G43920) described to a lesser extent was up-regulated the most; it was also one of the commonly changed protein under several treatments. Similarly, the two proteins with the highest decrease in protein abundance (At1g77520 and At3g05950) are also not well characterized. The 25°C treatment induced different responses in the shoot and the root. ‘Heat response’ (GO:0009408), ‘response to hydrogen peroxide’ (GO:0009408) and ‘response to virus’ (GO:0009615) were enriched in both tissues. In the shoot, ‘proteins of the ribosome’ (GO:0005840) and participating in ‘translation’ (GO:0006412) had a lower protein level, while in the root no significant enrichment of these terms was detected. In the warmer environment, flavonoid biosynthesis was down-regulated in the shoot (GO:0009813). The warmer environment induced fewer changes in the proteome of the shoot; it had the lowest number of changing proteins with ±1.5 FC after the osmotic treatment. There was no clear pattern in changes of abundance of stress-related proteins. One heat shock protein (HSP70, At3g12580) had an elevated FC in the shoot and the root, but two flavonoid proteins (At5g13930 and At5g08640) were decreased. The top up-regulated protein in the shoot is a cytosolic β-amylase (BAM5, At4g15210), which was shown previously to be sugar induced (Mita et al. 1995) and to respond to heat and salt stress (Monroe et al. 2014). It was also up-regulated in our salt treatment. The top up-regulated protein in the root is HOP3 (At4g12400), which participates in heat acclimation and is a potential interaction partner of chaperone proteins, i.e. the above-mentioned HSP70 (Fellerer et al. 2011). GOterm enrichment and analysis of single proteins with the highest significant FC under higher salt and osmotic environment The elevated salt condition induced response processes in the shoot and the root (GO:0006952 ‘defense response’; GO:0009611 ‘response to wounding’; GO:0009753 ‘response to jasmonic acid’; and GO:0009620 ‘response to fungus’), but they reacted in different directions; together with other stress-related terms, they were mainly down-regulated in the root. The response in the shoot included proteins which belonged to the enriched terms of water deprivation (GO:0009414 ‘response to water deprivation’ and GO:0009269 ‘response to desiccation’). Similar GOterms were enriched to those in the 15°C treatment; three aquaporins were detected in both experiments, but less than at 15°C. However, their protein abundance increased in the 50 mmol l−1 NaCl environment. Salt stress groups were enriched in the shoot (GO:0042538 ‘hyperosmotic salinity response’ and GO:0009651 ‘response to salt stress’). The photosystem was affected in salt stress: two light-harvesting complex (LHC) proteins were up-regulated (At2g05100 and At3g27690) together with the previously mentioned thylakoid transmembrane PGRL1B, but the protein level of two CP12 enzymes was decreased. This was accompanied by the increased content of two VEGETATIVE STORAGE PROTEIN (VSP) isoforms: VSP1 (At5g24780) and VSP2 (At5g24770). The VSPs are acid phosphatases which mobilize nutrient sources (the abundance of VSP2 was decreased in P0), but they also play an active role against herbivores in biotic stress responses (Liu et al. 2005). CALEOSIN 3 (RD20, At2g33380) was highly up-regulated in salt stress. It is involved in the stress response of the plants (Blée et al. 2014) and had a significantly high FC in several other treatments (Table 4). The long-term osmotic treatment showed a unique response and acclimation process of the plants. Due to the very low number (six) of changing proteins with ±1.5 FC in the shoot, we focused on the root only. The GOterm with the highest significance level was the ‘plastoglobule’ (GO:0010287); nine proteins were increased in this cellular compartment. The expected stress-related GOterms ‘response to abscisic acid’ (GO:0009737), ‘response to osmotic stress’ (GO:0006970) and ‘response to hypoxia’ (GO:0001666) were up-regulated and several cold response proteins were also up-regulated. Proteins related to the thylakoid membranes (GO:0009579) were enriched in the osmotic stress; their protein concentration was increased significantly. RNA, DNA and protein metabolism were negatively affected, and a large part of the changing DNA metabolic proteins consisted of chromatin and histone proteins. Cell wall metabolism was impacted; UDP-XYL SYNTHASE 6 (UXS6, At2g28760) was the most up-regulated protein in the root, while other cell wall enzymes were highly down-regulated, e.g. FASCICLIN-LIKE ARABINOGALACTAN PROTEIN 11 (FLA11, At5g03170). Next to the cell wall and histone proteins, response proteins were also expressed. THIOGLUCOSIDE GLUCOHYDROLASE 1 (TGG1, At5g26000) was highly up-regulated; it is considered to play a role mainly in herbivore resistance by producing toxic compounds (Badenes-Perez et al. 2012). GOterm enrichment and analysis of single proteins with the highest significant FC under changed nutrient availability When nitrogen was omitted (N0), translation and RNA metabolism were suppressed. Among the enriched terms in the shoot, the down-regulated pathways with the highest significance belonged to these processes: 32 proteins related to ‘translation’ (GO:0006412), 14 proteins to ‘RNA binding’ (GO:0003723) and 25 proteins to ‘cytosolic ribosome’ (GO:0022626). In the root, similar tendencies were detected, but DNA metabolism was also highly affected. A total of 97 proteins of the ‘nucleus’ (GO:0005634) were down-regulated and, among the enriched pathways with the highest significance, were the chromatin structure terms (GO:0003682 ‘chromatin binding’; GO:0042393 ‘histone binding’, etc.). GOterms covering the ‘cellular response to nitrogen starvation’ (GO:0006995) and to ‘nitrate’ (GO:0010167) and ‘nitrate transmembrane transporter activity’ (GO:0015112) were enriched significantly, while proteins of nitrate assimilation (GO:0042128) were down-regulated. Among the most increased proteins were anthocyanin metabolism (At4g14090 and At5g54060) and stress response genes (ERD1, At5g54060; and CPK32, At3g57530). Several nitrate transporter (NRT2, At1g08090; NRT2.5, At1g12940; and WR3, At5g50200) were up-regulated together with ammonium transporters (AMT1;1, At4g13510; and AMT1;3, At3g24300) in the root (AMT1 in the shoot too). The levels of glutamate-ammonia ligases (GLN1;1, At5g37600; GLN1;4, At5g16570; and GLN1;5, At1g48470), which play a role in glutamine synthesis, were elevated. While glutamine synthesis was enhanced, the catabolism of glutamine was down-regulated. Glutamate synthase (GLT1, At5g53460) and glutamate dehydrogenases (GDH1, At5g18170; and GDH2, At5g07440) showed a decreased protein abundance compared with the control. Glutamate synthase, which was enhanced, produces glutamate from glutamine, which can be a substrate for glutamate dehydrogenase, which was down-regulated. Like N0, phosphate deficiency (P0) decreased the level of RNA metabolic and translational proteins in the shoot, but they were increased in roots. The defense and response pathways (GO:0050832 ‘defense response to fungus’ and GO:0009753 ‘response to jasmonic acid’ etc.) were enriched in leaves with high significance. These included ‘cellular response to phosphate starvation’ (GO:0016036) in both tissues. Stress and response terms were up-regulated (i.e. GO:0009737 ‘response to abscisic acid’ and GO:0051707 ‘response to other organism’) or down-regulated (i.e. GO:0009409 ‘response to cold’ and GO:0009646 ‘response to absence of light’). The term with the highest significance value was oxygen binding (GO:0019825), which covered several cytochromes (CYP706A1, At4g22690; CYP71B7, AT1G13110; CYP76C7, At3g61040; and CYP81F4, At4g37410) and a hemoglobin (HB1, At2g16060). Some of the top down-regulated proteins had a helicase, ribosomal or unknown function. As in N0, in the shoot of P0 DNA, RNA and protein metabolism were strongly down-regulated. The plants were actively responding to the phosphate starvation; there were two sulfolipid biosynthetic proteins [SULFOQUINOVOSYL DIACYLGLYCEROL 1 (SQD1), At4g33030; SULFOQUINOVOSYL DIACYLGLYCEROL 2 (SQD2), At5g01220], which were highly up-regulated in the shoot and the root. Co-expression showed a possible interaction (data not shown) with GLYCEROPHOSPHODIESTER PHOSPHODIESTERASE (GDPD1, At3g02040); its protein content increased in both tissues, it had an extremely high FC (log2FC= 7.61) in the root and it is an important factor in maintenance of the cellular homoeostasis in phosphate deficiency (Wang et al. 2008, Cheng et al. 2011). Like the nitrate transporters in the N0 root, phosphate transporters (ATPT2, At2g38940; PHT1;1, At5g43350; and APT1, At5g43370) were highly up-regulated in the root during phosphate starvation. Discussion Plant responses to mild environmental perturbations Arabidopsis thaliana plants were exposed to a mildly altered environment for 10 d and the applied treatments provided us with new insights about the acclimation and adaptation of plants to long-term changes. In the field of stress proteomics, where experiments are categorized (Kosová et al. 2011, Zhang et al. 2012, Ghosh and Xu 2014, Janmohammadi et al. 2015), approximately one-third addressed long-term stresses, and these stresses were mainly harsh. For instance, among the listed cold stress experiments (Janmohammadi et al. 2015), in only one out of 22 were the plants exposed to a temperature >10°C. In our study, however, the 15°C environment already induced significant changes in both the physiology and the proteome of the plants. It might be realistic that plants in their natural environment are exposed not only to severe and sudden stresses, but also to prolonged moderate impacts. Our set-up allowed us to investigate each response individually, and compare them directly. During the experiments under a changed environment, all plants preserved their physiological integrity, illustrated by the fact that they were able to grow continuously throughout the experiments; no signs of irreversible tissue damage were observed. Transcripts usually respond rapidly and thus are excellent for investigation of the plants’ immediate sensing and response to altered conditions. Therefore, transcriptional changes at a certain time point could be interpreted as the intention to use the plasticity potential of the plant during acclimation. However, proteins are the integrated result of the transcript levels over time, and they do not really follow a diurnal abundance fluctuation even if transcripts do (Baerenfaller et al. 2012, Guerreiro et al. 2014). Proteins rather describe and represent the newly established biological machinery over a longer time period. On the level of growth (RGR, FW and DW) and the proteome, our experiments showed distinct patterns, indicating different dynamic adjustment. The resource allocation of plants culminates in biomass produced as roots and shoots. Environmental changes had a more profound effect on shoot biomass than on that of the root (Figs. 2, 3). Except for the 25°C treatment, shoot growth was reduced by approximately 50%, whereas root biomass was mainly preserved or even increased. This observation is a common response of plants in agriculture (Richard-Molard et al. 2008, Wang et al. 2009). The R/S was, in all treatments, higher than in the controls, indicating a preferential investment in root biomass. Different dynamic growth patterns during the acclimation of plants to different environmental perturbations Although shoot biomass at the end of the treatments was similar for some treatments, they often resulted from different temporal growth patterns. The RGR showed unique patterns for each of the applied conditions. At 15°C, the shoot growth was suppressed constantly from the first day onwards. The initial drop in RGR is not surprising considering that enzymatic reactions are highly temperature sensitive. As a rule of thumb, a reduction of 10°C halves the enzyme reaction rate. Obviously, this is a rough estimate, and biochemical processes at a whole-plant level are affected by many other parameters. Nevertheless, the fast drop in growth under reduced temperature suggests that some catalytic reactions were limiting. The RGR increased later, which might reflect the adaptation and increased synthesis of limiting enzymes. However, the cold-treated plants never reached the control RGR, implying that the rate-limiting steps (enzymes) cannot be fully compensated for. In contrast, increasing the temperature by 5°C had a positive impact on plant biomass production. The RGR showed that this growth promotion was largely dependent on the accelerated growth rate at the beginning of the treatment, whereas the RGR decreased to control levels later. This observation is again in agreement with an increased enzymatic reaction rate under increased temperature, if we assume rather constant enzyme amounts at the beginning of the treatment. Over the time of the treatment, plants probably optimize their enzyme quantity. This is in agreement with our proteomics results. For example, photosynthesis-related proteins, which significantly decreased in the 25°C and increased in the 15°C treatment (Supplementary Table S5, S6). Together with other results (increased RGR, down-regulation of anthocyanin proteins, etc.), this implies that a warmer environment was not necessarily a stress but rather a positive stimulus. Other acclimation patterns were observed for both osmotic stresses (PEG and salt). At the beginning of the treatment, no reduction of growth rate was observed in the shoots, and growth was only negatively affected later during the treatment. In contrast to the temperature treatments, the osmotic stresses were applied to the roots and might have consequences later on the shoots. This effect could be due to the different times the sodium and chloride ions or PEG molecules require to diffuse, be transported and accumulate in the shoots. While the ions can reach other tissues rapidly, the large PEG molecules might not even be able to overcome cellular obstacles, or they do it slowly (Blum 2008). Alternatively, the treatments could first affect root metabolism, which is sensed and transmitted to the shoots later, and this would change resource allocation between shoots and roots. For the salt treatment, it is probably a combination of the two above-mentioned effects. However, in a more osmotic environment, it is more likely that the effect on shoot growth is a secondary effect on altered root metabolism. This is also substantiated by the observation that the shoot proteome in the PEG treatment was largely unaffected, whereas the salt treatment induced substantial changes in the shoot proteome. Interestingly, the RGR of the salt-treated plants approached that of the control plants at the end of the experiment, suggesting an efficient compensation response. The two nutrient-limiting conditions differed in their effect on RGR. In N0, the RGR remained the same as in control plants at the beginning, probably because nitrogen was not limiting during the first days, but this was followed by a huge drop in growth, probably due to depleted free inorganic nitrogen sources. The RGR recovered over time until it converged again towards the control levels after 10 d, suggesting a gradual adaptation. This process had to rely on salvage and reuse of internal available nitrogen sources (e.g. proteins). The RGR response on phosphate limitation was somehow counterintuitive as it first dropped below the control level and recovered for 4 d to the control level, with a decline at the end of the treatment. As the phosphate supply was omitted, it must have been sensed immediately by the plants and initiated a successful temporal growth stop in the shoots. That might be explained by relatively high internal phosphate storage pools in the vacuole (Liu et al. 2016), which first had to be made accessible. The analysis of the rewired proteomic machinery revealed crucial differences of the plants’ acclimation processes The number of significantly changed proteins with a ±1.5 FC was higher in the nutrient-deficient treatments. This suggests that they might have induced a greater change in the plant proteome, although the growth measurements (FW, DW and RGR) did not necessarily reflect that. However, if the number of significantly changing proteins is used as an indicator for the complexity of proteome remodeling, the 15°C treatment in the shoot induced the greatest perturbation in the proteome. Among the proteins with the highest significant FC at 15°C, stress-related proteins were over-represented, which was quite unique compared with the other mild abiotic treatments, also implying that a 5°C decrease can already have a strong effect on plants. Interestingly, while the osmotic treatment promoted strong changes of the proteome in the root, the shoot seemed almost unaffected. Similar phenomena were observed in comparative proteomic analysis of roots and shoots of drought-stressed soybean seedlings (Mohammadi et al. 2012). The uptake of the long PEG6000 molecules is very slow and mostly concentrated in the roots, especially if root damage is avoided and the concentration is not high (Blum 2008). The relatively unchanged proteome in the shoots suggests that their growth retardation is mostly an indirect effect of changed metabolism in the roots, resulting in resource allocation towards the roots. It raises the question of whether stresses are local or systemic, whether the stress was communicated throughout the plant or the observed shoot phenotype could have been a secondary effect. Here, four out of six treatments (salt, PEG and nutrients) were applied to the roots through changes in the media and, as expected, they had an effect on the whole plant. In the case of PEG treatment, the stress response on the proteome level remained local. This illustrates that the observed phenotypes in one tissue are not necessarily easy to explain with measured factors (e.g. transcript or protein abundances) in the same tissue. Obviously, it is important to treat multicellular organisms as a system to reach conclusions about cause and effect. Although our experimental set-up did not allow us to apply a tissue-specific temperature treatment, plants in nature are exposed to different temperatures below and above ground, and this should be considered in future experiments. The response processes between the shoot and root were different in every treatment, indicating a highly tissue-specific adaptation mechanism. To our knowledge, only a few experiments were performed where the impact of the treatment on the proteome of both organs was analyzed. It was investigated in rice, soybean, barley and cotton (Lee et al. 2010, Sobhanian et al. 2010, Moller et al. 2011, Mohammadi et al. 2012, Chen et al. 2016), and no major overlap of the changing proteins was detected and reported between the two organs. There was a relatively small overlapping fraction of changing proteins in both tissue, which often changed similarly. This implies regulatory processes or certain pathways, which have a system-wide function. The clustering and correlation analysis verified the tissue specificity and the mildness of the treatments. Responses to nutrient deficiencies (N0 and P0) overlapped to a certain extent on the proteomic level; they always clustered together and showed a high correlation. N and P metabolism are linked; the N:P ratio (supply, demand, etc.) is an important factor of growth (Agren et al. 2012, Menge et al. 2012). Nutrient limitations can induce either similar or opposite responses (Nguyen et al. 2015), but in our experimental set-up the changes in abundance of proteins, which were detected in both nutrient treatments, were similar. However, plants exposed to N0 and P0 showed a different growth response. In contrast to our initial hypothesis, the responses to salt and osmotic treatment were quite different on the proteome level, especially in the root. Both are often considered to have a similar effect on plants and are at least discussed together (Zhu 2002, Fujita et al. 2006, Zhang et al. 2006, Golldack et al. 2014), but there is some evidence at the transcriptomic level that already suggested otherwise (Kreps et al. 2002, Patade et al. 2012). Due to the low number of changed proteins under the osmotic stress, the overlap between the two treatments was also low. Only two proteins changed with a ±1.5 FC in both treatments. However, both proteins (At2g39800 and At1g54100) were characterized previously as stress-responsive proteins. They are induced by several external stimuli, which include water deprivation and elevated salt level (Székely et al. 2008, Li-Beisson et al. 2013). The observed high correlation between salt and PEG treatment in the shoots (Fig. 6C) suggests the same tendencies of adaptation, with a more accentuated response in the salt treatment. This could be caused by a harsher environmental challenge for the plant under salt conditions. However, this is not supported by the observed biomass production. It is more likely that the salt treatments evoke a multilevel response including simultaneous ion toxicity and water deprivation, whereas PEG induced responses to water deprivation only. In the shoots, the PEG treatment affected the proteome only slightly, but the root proteome was seriously affected. There were many overlapping proteins with much higher FCs, enabling us to draw a conclusion about the differences of the plant’s response in the two experiments (Fig. 6D). In contrast to the long PEG molecules, which are probably not taken up by the plants, the excess salt ions might have a direct effect on the ionic homeostasis of cells across the whole plant. In osmotic stress induced by PEG, the changed osmotic environment inhibits uptake of water and nutrients by the plants from the surroundings of the roots (Albert et al. 2012). This is supported by the proteomics clustering analysis (Fig. 5A), where in the root the osmotic treatment grouped together with the nutrient deficiencies. As a response to the excess salt, the photosynthetic apparatus might have been even positively affected, and this observation coincides with earlier proteomic findings in other plant species, mainly in the salt-tolerant mangrove (Zhu et al. 2012, Wang et al. 2014). Two LHC proteins together with the thylakoid transmembrane PGRL1B protein had an increased protein level, while the concentration of two CP12 enzymes decreased. The down-regulation of the latter might reduce complex formation and subsequently increase the activity of two Calvin–Benson cycle enzymes (GAPDH and PRK) (López-Calcagno et al. 2014). The data revealed an unexpected group of proteins related to thylakoid membranes which was enriched upon the osmotic treatment. Although these proteins should not be detected in the root in theory, most of them were expressed in most of the root experiments and this was confirmed by gene expression data (Supplementary Fig. S4), illustrating the occasional deceptive naming convention of GOterms. The PEG treatment might have also affected the cell wall in the roots. Roots are considered the primary site of sensing drought and osmotic stress, and PEG-induced osmotic stress changes the properties of the cell wall in the root, especially the porosity (Sharp et al. 2004, Yang et al. 2013). UDP-xylose synthase 6, the protein with the highest FC, is involved in synthesis of xylan, a major plant cell wall polysaccharide (Kuang et al. 2016), while the abundance of other cell wall-related enzymes was decreased. Plants can sense temperature. Several pathways and protein abundances are known to be affected and regulated by temperature changes (Patel and Franklin 2009). However, most of them are described as either cold or heat dependent. We observed around 100 changing proteins with a ±1.5 FC in each condition and tissue, and the majority were tissue and condition dependent. However, a subset of 20 proteins showed an inverse pattern between both temperature regimes. Further, including all significantly changing proteins in the analysis resulted in a high negative correlation between abundance and temperature. We tested whether the observed correlation was linked to growth rather than temperature. Seventeen out of the 20 proteins were found with a significant FC in other treatments as well, and the change in abundance was growth independent (Supplementary Table S3), suggesting that these 20 proteins are temperature dependent. Thereby, they are promising to be used to study in more detail over a finer temperature gradient with a bigger range. Is there a core stress response in plants? The majority of the proteins with changed abundances were rather specific for the treatment and only observed once or twice (94% in the shoot and 95% in the root). That could be interpreted as meaning that every change in the environment triggers specific responses even if two treatments are considered to be of a similar nature. The underlying reason might be found in our experimental set-up, where plants operate under tolerable changed conditions without experiencing fatal stresses. The latter would probably result in chlorosis, necrotic tissues or abscission, which then would result in similar responses. On the other hand, there were still several key proteins (Tables 3, 4) which changed in three or more treatments. A cold stress might induce water deficit. Similarly, phosphate and nitrate limitations are cross-linked (Agren et al. 2012). Drought changes not only the osmotic, but also the ionic status of the cell. Further, upon environmental stresses, reactive oxygen species, which are chemically highly reactive and harmful molecules, are always generated, with the resultant consequences. The highly specific responses to treatments at the proteome level do not exclude common response characteristics between the treatments at earlier time points. The protein NMT1 (At3g18000) came the closest to the definition of a “‘master responder”’, as it was detected with significantly high FC in nine out 12 treatments, in both shoots and roots, but it showed no significant regulation in osmotic stress. However, in a different experimental set-up in earlier experiments it was shown also to be part of the response to osmotic stress (Zhang et al. 2010). NMT1 is involved in phospholipid metabolism and catalyzes key methylation steps in choline biosynthesis (BeGora et al. 2010, Eastmond et al. 2010). The gene expression of NMT1 follows a similar stress-responsive pattern, and might be a good marker of lipid metabolism in stress (Supplementary Fig. S3). The overall large response of NMT1 levels in environmentally changed conditions makes it a good target for more specific experiments to elucidate the exact role of this enzyme. We hypothesized that similar alterations between treatments might occur on a higher hierarchical level, e.g. in the same pathway but within homologous proteins or in alternative pathways (e.g. salvage pathways). The GOterm analysis revealed the expected defense, response and acclimation processes. Further, an activation of DNA, RNA and protein metabolic processes was observed in most of the experiments. There was an up-regulation of these processes at 15°C, especially in the shoot, indicating more active transcription and translation. The salt treatment affected fewer of these major metabolic pathways; the protein synthesis machinery, which might have been intensified earlier, may have been stopped until the end of the 10th day. The down-regulation of the few proteins at 25°C rather suggests that the plants were not necessarily stressed or the acclimation process was completed already. In other experiments (osmotic, N0 and P0), the abundance of proteins, which belonged to translation, ribosome biogenesis, RNA binding, helicase activity, etc., was decreased, which can be the result of the unavailability of the necessary building blocks. There were several changes in the proteome, which could be linked to physiological processes. The lower water content in the shoot of the cold-treated plants can be the result or consequence of down-regulation of aquaporins. Transporter activity, which is responsible for the transfer of the necessary nutrient, increased in the N0 and P0 environments, as was previously shown (Lezhneva et al. 2014, Ayadi et al. 2015). However, nitrate assimilation was decreased in N0. The unavailability of nitrate was sensed by the plants; they did not invest in the whole assimilation machinery, but rather directed their resources into the synthesis of the nutrient transporters to increase nutrient uptake. Like the ammonium and nitrate transporters in N0, the phosphate transporter level was increased in the roots of plants exposed to phosphate starvation. SQD proteins, which are key enzymes involved in sulfolipid biosynthesis, crucial for photosynthetic membranes (Yu et al. 2002), were extremely elevated in both tissues. Together with the increased amount of glycerophosphodiester phosphodiesterase (GDPD1, At3g02040) they maintain the phosphate homeostasis during phosphate starvation (Cheng et al. 2011). Together with the abundant transporters, these proteins probably contribute to the rapid incorporation and metabolism of phosphate, if any source becomes available. While the phosphorus is limited, the plants also enhance alternative pathways; the phosphoenolpyruvate carboxylase (PEPC, At1g53310), which had an increased protein level in the shoot and root of the plants exposed to phosphate starvation, provides a metabolic bypass for the cytosolic pyruvate kinase together with malate dehydrogenase and NAD-malic enzyme, which are ADP limited, to facilitate the supply of pyruvate (Gregory et al. 2009, Plaxton and Tran, 2011). Further, phosphatases were up-regulated for acquisition of phosphate from intra- and/or extracellular sources. In the root, the protein abundances of purple acid phosphatases (PAPs) were increased, and PAP17 (At3g17790), which had the highest FC, had been previously shown to be strongly induced upon phosphate starvation (Del Pozo et al. 1999). The up-regulation of this array of different proteins shows the complex response of the plants induced by phosphate starvation and their adjustment to the nutrient-limiting environment. While metabolism is adjusted to be prepared for the rapid intake and incorporation of phosphate, simultaneously every available phosphorus, which can be temporary dispensable (vacuole, phospholipids, sulfolipids, etc.), is mobilized. Future perspectives Plants have adapted to cope with environmental changes, and these adaptations seem to be specific based on biomass allocation, growth strategy and proteome changes. Environment perturbations usually occur simultaneously, and plants are often exposed to a combination of stresses in the field. Transcriptomic data already revealed that the combination of different treatment-induced response processes that could have not been predicted from the single stress experiment (Rasmussen et al. 2013, Barah et al. 2015). Some proteomic experiments had already been carried out recently in maize and soybean with the application of a short-term strong heat and drought stress combined (Das et al. 2016, Zhao et al. 2016), but we still lack a deep understanding of the mechanism of the plant response to a combination of different stresses. These investigations should be performed with a combination of “‘omics”’ methods to allow the building of predictive scenarios of how plants adopt to environmental changes. Materials and Methods Plant growth Arabidopsis thaliana wild-type plants (Col-0) seeds were individually distributed into cut 0.5 ml tubes containing 0.65% (w/v) phytoagar. They were grown hydroponically in Cramer’s solution (Gibeaut et al. 1997, Tocquin et al. 2003) in a control environment for 18 d in a Percival AR95 growth chamber (CLF Plant Climatics) with a 12 h photoperiod, 20°C and 60% relative humidity. Light intensity was uniform at 150 μmol quanta m−2 s−1. The tubes were soaked in nutrient solution (pH was adjusted to 6), which contained the following macronutrients: 1.5 mmol l−1 Ca(NO3)2, 1.25 mmol l−1 KNO3, 0.75 mmol l−1 Mg(SO4), 0.5 mmol l−1 KH2PO4, 1 mmol l−1 (NH4)2SO4, 72 μmol l−1 C10H12FeN2NaO8 and 100 μmol l−1 Na2O3Si, and micronutrients: 50 μmol l−1 KCl, 10 μmol l−1 MnSO4, 1.5 μmol l−1 CuSO4, 2 μmol l−1 ZnSO4, 50 μmol l−1 H3BO3 and 0.075 μmol l−1 (NH4)6Mo7O24. Eighteen days after germination, plants were exposed to different treatments: 50 mmol l−1 NaCl or 5% (w/v) PEG6000 dissolved in hydroponic solution, ±5°C in an E-41L2 growth chamber (CLF Plant Climatics) and no phosphate and nitrate in the hydroponic solution. The plants were grown under changed conditions for an additional 10 d. Growth analysis During a 12 d period (from 16 to 28 d after germination) rosettes were photographed to quantify the leaf area. Leaf areas of plants were evaluated with ImageJ software. The growth of each plant was described with a polynomial (third-order) model: y = ax3 + bx2 + cx + d. RGR is defined as the relative increase in leaf area over the time period (GR) and was calculated by taking the difference between the polynomial transformation of the leaf area at the end (Gn) and at the beginning of the time frame (Gn–1) divided by Gn– 1: GRn = (Gn – Gn– 1/Gn– 1 (Poorter 1989, Heinen 1999, Paine et al. 2012, Ruts et al. 2013). Protein extraction and gel-free digestion for mass spectrometry (MS) analysis Proteins were purified and digested using a modified filter-aided sample preparation (FASP) protocol (Wisniewski et al. 2009); a Microcon 30 kDa centrifugal filter with an Ultracel-30 membrane was used (Merck Millipore). Whole rosettes were frozen in liquid N2, ground to a powder and aliquoted. The hydroponic system allowed us to harvest the roots in a similar way; they were cut off, quickly dried with tissue paper and finally frozen and ground in liquid N2. Proteins were extracted from aliquoted powder (20–50 μg) in SDS lysis buffer [4% (w/v) SDS, 100 mmol l−1 Tris–HCl pH 8.2] with a 1 : 10 sample to buffer ratio, incubated for 30 min at room temperature without dithiothreitol (DTT), which was added afterwards to a final concentration of 0.1 mol−1, sonicated for 12 min with a Bioruptor sonificator (Diagenode) and boiled for 5 min at 95°C. The protein concentration was measured with Qubit (Invitrogen). A 200 μl aliquot of 8 mol l−1 urea in 100 mol l−1 Tris–HCl pH 8.2 was mixed with 30 μl of sample, and loaded on the filter by centrifugation at 14,000×g. Samples were washed once with 200 μl of 8 mol l−1 urea and centrifuged at 14,000×g. Thiol groups of proteins were blocked with 100 μl of 0.05 mol l−1 iodoacetamide for 1 min at room temperature followed by removal of the chemicals by centrifugation at 14,000×g. SDS, urea and other digestion-incompatible chemicals were washed away twice with 200 μl of 0.5 mol l−1 NaCl followed by centrifugation at 14,000×g. Proteins were digested with trypsin in a 1 : 100 ratio of trypsin to protein in 0.05 mol l−1 triethylammonium bicarbonate overnight on the filter. Peptides were harvested by centrifugation at 14,000×g in a collection tube and acidified with trifluoroacetic acid (TFA) to a final concentration of 0.5%. Peptide purification for mass spectrometry analysis The digested peptides were purified and desalted on C18 Solid Phase Extraction columns (Sep-Pak), which were attached to a QIAvac 24 Plus (QIAGEN) vacuum manifold. Columns were washed once with 1 ml of 100% (v/v) methanol, once with 1 ml of 60 % (v/v) acetonitrile (ACN), 0.1% (v/v) TFA and twice with 1 ml of 3% (v/v) ACN and 0.1% (v/v) TFA solution. A 600–1,000 μl aliquot of 3% (v/v) ACN and 0.1% (v/v) TFA solution was added to the peptides and loaded on the column. The column was washed three times with 1 ml of 3% (v/v) ACN and 0.1% (v/v) TFA. Peptides were eluted with 200 μl of 60% (v/v) ACN, 0.1% (v/v) TFA, lyophilized, and redissolved in 40 μl of 3% (v/v) ACN and 0.1% (v/v) formic acid. Mass spectrometry analysis A 5–10 μl aliquot of peptide solution was analyzed on a Q-Exactive mass spectrometer (Thermo Scientific) coupled to an EASY-nLC1000 (Thermo Scientific). Instrument parameters followed the “‘sensitive”’ method published by Kelstrup et al. (2012) with slight modifications. Full scan MS spectra were acquired in profile mode from 300 to 1,700 m/z with an automatic gain control target of 3e6, Orbitrap resolution of 70,000 (at 200 m/z) and maximum injection time of 120 ms. The 12 most intense multiply charged (z = +2 to +8) precursor ions from each full scan were selected for higher energy collisional dissociation fragmentation. Precursor was accumulated with an automatic gain control value of 5e4 and a maximum injection time of 120 ms, and fragmented with a normalized collision energy of 28 (arbitrary unit). Generated fragment ions were scanned with Orbitrap resolution of 35,000 (at 200 m/z) from a fixed first mass of 120 m/z. The isolation window for precursor ions was set to 2.0 m/z and the underfill ratio was 2% (referring to an intensity of 8.3e3). Each fragmented precursor ion was set onto the dynamic exclusion list for 30 s. Peptide separation was achieved by reversed phase HPLC on a C18 column (150 mm×75 μm, 1.9 μm, C-18 AQ, 120 Å). Samples were separated with a linear gradient of 130 min from 3% to 25% solvent B [0.1% (v/v) formic acid in ACN] in solvent A [0.1% (v/v) formic acid in H2O] with a flow rate of 250 nl min−1. Protein identification and protein quantification using ProgenesisQI for proteomics For every stress experiment and tissue, Progenesis experiments were set up separately. Raw MS files were loaded into ProgenesisQI for Proteomics (v.4.0.4265.42984). The aligning reference was a combined pool of all respective samples. From each peptide ion, a maximum of the top five tandem mass spectra were exported using charge deconvolution and the deisotoping option, and a maximum number of 200 peaks per MS/MS. The Mascot generic file (.mgf) was searched with Mascot Server v.2.4.1 (www.matrixscience.com) using the parameters 10 p.p.m. for precursor ion mass tolerance and 0.2 Da for fragment ion tolerance. Trypsin was used as the protein-cleaving enzyme; two missed cleavages were allowed. Carbamidomethylation of cysteine was specified as a fixed modification, and oxidation of methionine was selected as a variable modification. A forward and reversed TAIR10 database concatenated to 260 known MS contaminants was searched in order to evaluate the false discovery rate (FDR) using the target-decoy strategy (Käll et al. 2008). The mascot result was loaded into Scaffold v4.1.1 using local FDR and protein cluster analysis. The spectrum report was loaded into ProgenesisQI for Proteomics. A between-group analysis was used with a Control and Stress condition group (4–6 replicates). Normalization was kept with default settings. Quantification included proteins identified with at least two features and one unique peptide. Proteins were grouped with Progenesis and quantified based on non-conflicting features. Normalized abundance from all non-conflicting peptide ions of the same protein group were summed individually for each sample. The parametric test (two tailed t-test) on the transformed (hyperbolic arcsine transformation) normalized protein abundance was applied. FCs were calculated using the group means of the protein sums. The proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (Vizcaíno et al. 2014) partner repository with the data set identifier PXD006797 (reviewer account details: Username: reviewer34305@ebi.ac.uk, Password: OopPQSdq). Bioinformatic analysis GO term enrichment analyses were based on TAIR 10 (May 2015) GO term association lists excluding associations based on GO evidence codes IEA (Inferred from Electronic Annotation) or RCA (Inferred from Reviewed Computational Analysis). GO categories over-represented by genes with a significantly high (±1.5) FC in particular responses in a given condition were determined by a hypergeometric test against all proteins detected in that condition. Only GO categories represented by at least five genes in the background list were considered. The assignments and tests were performed in R (R Development Core Team 2010). 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( 2012) Physiological and proteomic characterization of salt tolerance in a mangrove plant, Bruguiera gymnorrhiza (L.) Lam. Tree Physiol.  32: 1378– 1388. Google Scholar CrossRef Search ADS PubMed  Abbreviations Abbreviations ACN acetonitrile FC fold change GO Gene Ontology MS mass spectrometry N0 nitrate-deficient treatment P0 phosphate-deficient treatment PEG polyethylene glycol R/S root to shoot ratio RGR relative growth rate TFA trifluoroacetic acid © The Author(s) 2017. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email: journals.permissions@oup.com

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Plant and Cell PhysiologyOxford University Press

Published: Mar 1, 2018

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