Effect of the Interaction between Elevated Carbon Dioxide and Iron Limitation on Proteomic Profiling of Soybean
Abstract
:1. Introduction
2. Results
2.1. Interactive Effects of eCO2 and Fe-Limitation on Plant Biomass
2.2. Interactive Effects of eCO2 and Fe-Limitation on Photosynthesis Parameters
2.3. Interactive Effects of eCO2 and Fe-Limitation on Sugar Content
2.4. Functional Categories of Differentially Expressed Proteins
2.5. Metabolic Pathways Related to the Interaction of eCO2 and Fe-Limitation
3. Discussion
4. Materials and Methods
4.1. Plant Material and Growth Conditions
4.2. Evaluation of Plant Biomass
4.3. Leaf Gas Exchange Parameters
4.4. Root and Leaf Carbohydrates
4.5. Protein Extraction and LC−MS/MS Analysis
4.6. Database Search and Protein Quantification
4.7. Statistical Analysis
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Meehl, G.A.; Washington, W.M.; Santer, B.D.; Collins, W.D.; Arblaster, J.M.; Hu, A.; Lawrence, D.M.; Teng, H.; Buja, L.E.; Strand, W.G. Climate Change Projections for the Twenty-First Century and Climate Change Commitment in the CCSM3. J. Clim. 2006, 19, 2597–2616. [Google Scholar] [CrossRef] [Green Version]
- Yu, J.; Fan, N.; Li, R.; Zhuang, L.; Xu, Q.; Huang, B. Proteomic Profiling for Metabolic Pathways Involved in Interactive Effects of Elevated Carbon Dioxide and Nitrogen on Leaf Growth in a Perennial Grass Species. J. Proteome Res. 2019, 18, 2446–2457. [Google Scholar] [CrossRef]
- Zheng, G.; Chen, J.; Li, W. Impacts of CO2 elevation on the physiology and seed quality of soybean. Plant Divers. 2020, 42, 44–51. [Google Scholar] [CrossRef]
- Briat, J.-F.; Dubos, C.; Gaymard, F. Iron nutrition, biomass production, and plant product quality. Trends Plant Sci. 2015, 20, 33–40. [Google Scholar] [CrossRef] [PubMed]
- Jin, C.W.; Du, S.T.; Chen, W.W.; Li, G.X.; Zhang, Y.S.; Zheng, S.J. Elevated Carbon Dioxide Improves Plant Iron Nutrition through Enhancing the Iron-Deficiency-Induced Responses under Iron-Limited Conditions in Tomato. Plant Physiol. 2009, 150, 272–280. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Jin, C.W.; Liu, Y.; Mao, Q.Q.; Wang, Q.; Du, S.T. Mild Fe-deficiency improves biomass production and quality of hydroponic-cultivated spinach plants (Spinacia oleracea L.). Food Chem. 2013, 138, 2188–2194. [Google Scholar] [CrossRef]
- Takahashi, M.; Nakanishi, H.; Kawasaki, S.; Nishizawa, N.K.; Mori, S. Enhanced tolerance of rice to low iron availability in alkaline soils using barley nicotianamine aminotransferase genes. Nat. Biotechnol. 2001, 19, 466–469. [Google Scholar] [CrossRef]
- Myers, S.S.; Zanobetti, A.; Kloog, I.; Huybers, P.; Leakey, A.D.B.; Bloom, A.J.; Carlisle, E.; Dietterich, L.H.; Fitzgerald, G.; Hasegawa, T.; et al. Increasing CO2 threatens human nutrition. Nature 2014, 510, 139–142. [Google Scholar] [CrossRef] [Green Version]
- Burgess, P.; Huang, B. Leaf protein abundance associated with improved drought tolerance by elevated carbon dioxide in creeping bentgrass. J. Am. Soc. Hortic. Sci. 2016, 141, 85–96. [Google Scholar] [CrossRef] [Green Version]
- Burgess, P.; Huang, B. Root protein metabolism in association with improved root growth and drought tolerance by elevated carbon dioxide in creeping bentgrass. Field Crops Res. 2014, 165, 80–91. [Google Scholar] [CrossRef]
- Yu, J.; Li, R.; Fan, N.; Yang, Z.; Huang, B. Metabolic pathways involved in carbon dioxide enhanced heat tolerance in bermudagrass. Front. Plant Sci. 2017, 8, 1506. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Robin, A.; Vansuyt, G.; Hinsinger, P.; Meyer, J.M.; Briat, J.F.; Lemanceau, P. Chapter 4 Iron Dynamics in the Rhizosphere: Consequences for Plant Health and Nutrition. In Advances in Agronomy; Academic Press: Cambridge, MA, USA, 2008; Volume 99, pp. 183–225. [Google Scholar]
- Haase, S.; Rothe, A.; Kania, A.; Wasaki, J.; Römheld, V.; Engels, C.; Kandeler, E.; Neumann, G. Responses to Iron Limitation in Hordeum vulgare L. as Affected by the Atmospheric CO2 Concentration. J. Environ. Qual. 2008, 37, 1254–1262. [Google Scholar] [CrossRef] [PubMed]
- Donnini, S.; Prinsi, B.; Negri, A.S.; Vigani, G.; Espen, L.; Zocchi, G. Proteomic characterization of iron deficiency responses in Cucumis sativus L. roots. BMC Plant Biol. 2010, 10, 1–15. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Meisrimler, C.-N.; Wienkoop, S.; Lyon, D.; Geilfus, C.-M.; Lüthje, S. Long-term iron deficiency: Tracing changes in the proteome of different pea (Pisum sativum L.) cultivars. J. Proteom. 2016, 140, 13–23. [Google Scholar] [CrossRef]
- López-Millán, A.-F.; Grusak, M.; Abadia, A.; Abadía, J. Iron deficiency in plants: An insight from proteomic approaches. Front. Plant Sci. 2013, 4, 254. [Google Scholar] [CrossRef] [Green Version]
- Thompson, M.; Gamage, D.; Hirotsu, N.; Martin, A.; Seneweera, S. Effects of Elevated Carbon Dioxide on Photosynthesis and Carbon Partitioning: A Perspective on Root Sugar Sensing and Hormonal Crosstalk. Front. Physiol. 2017, 578, 1–13. [Google Scholar] [CrossRef] [Green Version]
- Lin, X.Y.; Ye, Y.Q.; Fan, S.K.; Jin, C.W.; Zheng, S.J. Increased Sucrose Accumulation Regulates Iron-Deficiency Responses by Promoting Auxin Signaling in Arabidopsis Plants. Plant Physiol. 2016, 170, 907–920. [Google Scholar] [CrossRef] [Green Version]
- Chen, T.; Zhang, L.; Shang, H.; Liu, S.; Peng, J.; Gong, W.; Shi, Y.; Zhang, S.; Li, J.; Gong, J. iTRAQ-based quantitative proteomic analysis of cotton roots and leaves reveals pathways associated with salt stress. PLoS ONE 2016, 11, e0148487. [Google Scholar] [CrossRef]
- Ainsworth, E.A.; Rogers, A.; Vodkin, L.O.; Walter, A.; Schurr, U. The Effects of Elevated CO2 Concentration on Soybean Gene Expression. An Analysis of Growing and Mature Leaves. Plant Physiol. 2006, 142, 135–147. [Google Scholar] [CrossRef] [Green Version]
- Kobayashi, K.; Baba, S.; Obayashi, T.; Sato, M.; Toyooka, K.; Keränen, M.; Aro, E.M.; Fukaki, H.; Ohta, H.; Sugimoto, K.; et al. Regulation of root greening by light and auxin/cytokinin signaling in Arabidopsis. Plant Cell 2012, 24, 1081–1095. [Google Scholar] [CrossRef]
- Kaur, G.; Shukla, V.; Kumar, A.; Kaur, M.; Goel, P.; Singh, P.; Shukla, A.; Meena, V.; Kaur, J.; Singh, J.; et al. Integrative analysis of hexaploid wheat roots identifies signature components during iron starvation. J. Exp. Bot. 2019, 70, 6141–6161. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Jiang, C.D.; Gao, H.Y.; Zou, Q.; Shi, L. Effects of iron deficiency on photosynthesis and photosystem II function in soybean leaf. J. Plant Physiol. Mol. Biol. 2007, 33, 53–60. [Google Scholar]
- Therby-Vale, R.; Lacombe, B.; Rhee, S.Y.; Nussaume, L.; Rouached, H. Mineral nutrient signaling controls photosynthesis: Focus on iron deficiency-induced chlorosis. Trends Plant Sci. 2021, 27, 502–509. [Google Scholar] [CrossRef] [PubMed]
- Andaluz, S.; López-Millán, A.-F.; De las Rivas, J.; Aro, E.-M.; Abadía, J.; Abadía, A. Proteomic profiles of thylakoid membranes and changes in response to iron deficiency. Photosynth. Res. 2006, 89, 141–155. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Austen, N.; Walker, H.J.; Lake, J.A.; Phoenix, G.K.; Cameron, D.D. The Regulation of Plant Secondary Metabolism in Response to Abiotic Stress: Interactions Between Heat Shock and Elevated CO2. Front. Plant Sci. 2019, 10, 1463. [Google Scholar] [CrossRef] [Green Version]
- Ramakrishna, A.; Ravishankar, G.A. Influence of abiotic stress signals on secondary metabolites in plants. Plant Signal. Behav. 2011, 6, 1720–1731. [Google Scholar] [CrossRef]
- Falcone Ferreyra, M.L.; Rius, S.; Casati, P. Flavonoids: Biosynthesis, biological functions, and biotechnological applications. Front. Plant Sci. 2012, 3, 222. [Google Scholar] [CrossRef] [Green Version]
- Ahmed, U.; Rao, M.J.; Qi, C.; Xie, Q.; Noushahi, H.A.; Yaseen, M.; Shi, X.; Zheng, B. Expression Profiling of Flavonoid Biosynthesis Genes and Secondary Metabolites Accumulation in Populus under Drought Stress. Molecules 2021, 26, 5546. [Google Scholar] [CrossRef]
- Lan, P.; Li, W.; Wen, T.N.; Shiau, J.Y.; Wu, Y.C.; Lin, W.; Schmidt, W. iTRAQ protein profile analysis of Arabidopsis roots reveals new aspects critical for iron homeostasis. Plant Physiol. 2011, 155, 821–834. [Google Scholar] [CrossRef] [Green Version]
- Perkowska, I.; Potrykus, M.; Siwinska, J.; Siudem, D.; Lojkowska, E.; Ihnatowicz, A. Interplay between Coumarin Accumulation, Iron Deficiency and Plant Resistance to Dickeya spp. Int. J. Mol. Sci. 2021, 22, 6449. [Google Scholar] [CrossRef]
- Jia, X.-m.; Zhu, Y.-f.; Hu, Y.; Zhang, R.; Cheng, L.; Zhu, Z.-l.; Zhao, T.; Zhang, X.; Wang, Y.-x. Integrated physiologic, proteomic, and metabolomic analyses of Malus halliana adaptation to saline–alkali stress. Hortic. Res. 2019, 6, 91. [Google Scholar] [CrossRef] [PubMed]
- Zhang, X.; Högy, P.; Wu, X.; Schmid, I.; Wang, X.; Schulze, W.X.; Jiang, D.; Fangmeier, A. Physiological and proteomic evidence for the interactive effects of post-anthesis heat stress and elevated CO2 on wheat. Proteomics 2018, 18, 1800262. [Google Scholar] [CrossRef] [PubMed]
- Kapoor, D.; Singh, S.; Kumar, V.; Romero, R.; Prasad, R.; Singh, J. Antioxidant enzymes regulation in plants in reference to reactive oxygen species (ROS) and reactive nitrogen species (RNS). Plant Gene 2019, 19, 100182. [Google Scholar] [CrossRef]
- Dalton, D.A.; Boniface, C.; Turner, Z.; Lindahl, A.; Kim, H.J.; Jelinek, L.; Govindarajulu, M.; Finger, R.E.; Taylor, C.G. Physiological roles of glutathione S-transferases in soybean root nodules. Plant Physiol. 2009, 150, 521–530. [Google Scholar] [CrossRef] [Green Version]
- Vaish, S.; Gupta, D.; Mehrotra, R.; Mehrotra, S.; Basantani, M.K. Glutathione S-transferase: A versatile protein family. 3 Biotech. 2020, 10, 321. [Google Scholar] [CrossRef]
- Höhner, R.; Barth, J.; Magneschi, L.; Jaeger, D.; Niehues, A.; Bald, T.; Grossman, A.; Fufezan, C.; Hippler, M. The metabolic status drives acclimation of iron deficiency responses in Chlamydomonas reinhardtii as revealed by proteomics based hierarchical clustering and reverse genetics. Mol. Cell. Proteom. 2013, 12, 2774–2790. [Google Scholar] [CrossRef] [Green Version]
- Ribeiro, D.M.; Araújo, W.L.; Fernie, A.R.; Schippers, J.H.; Mueller-Roeber, B. Action of gibberellins on growth and metabolism of Arabidopsis plants associated with high concentration of carbon dioxide. Plant Physiol. 2012, 160, 1781–1794. [Google Scholar] [CrossRef] [Green Version]
- Li, C.R.; Gan, L.J.; Xia, K.; Zhou, X.; Hew, C.S. Responses of carboxylating enzymes, sucrose metabolizing enzymes and plant hormones in a tropical epiphytic CAM orchid to CO2 enrichment. Plant Cell Environ. 2002, 25, 369–377. [Google Scholar] [CrossRef]
- Teng, N.; Wang, J.; Chen, T.; Wu, X.; Wang, Y.; Lin, J. Elevated CO2 induces physiological, biochemical and structural changes in leaves of Arabidopsis thaliana. New Phytol. 2006, 172, 92–103. [Google Scholar] [CrossRef]
- Liu, J.; Zhang, J.; He, C.; Duan, A. Genes responsive to elevated CO2 concentrations in triploid white poplar and integrated gene network analysis. PLoS ONE 2014, 9, e0098300. [Google Scholar] [CrossRef]
- Bai, J.; Jin, K.; Qin, W.; Wang, Y.; Yin, Q. Proteomic Responses to Alkali Stress in Oats and the Alleviatory Effects of Exogenous Spermine Application. Front. Plant Sci. 2021, 12, 627129. [Google Scholar] [CrossRef] [PubMed]
- Soares, J.; Deuchande, T.; Valente, L.M.P.; Pintado, M.; Vasconcelos, M.W. Growth and nutritional responses of bean and soybean genotypes to elevated CO2 in a controlled environment. Plants 2019, 8, 465. [Google Scholar] [CrossRef] [Green Version]
- López-Millán, A.; Morales, F.; Gogorcena, Y.; Abadía, A.; Abadía, J. Metabolic responses in iron deficient tomato plants. J. Plant Physiol. 2009, 166, 375–384. [Google Scholar] [CrossRef]
- Wu, X.; Xiong, E.; Wang, W.; Scali, M.; Cresti, M. Universal sample preparation method integrating trichloroacetic acid/acetone precipitation with phenol extraction for crop proteomic analysis. Nat. Protoc. 2014, 9, 362–374. [Google Scholar] [CrossRef] [PubMed]
- Bradford, M.M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 1976, 72, 248–254. [Google Scholar] [CrossRef]
- Osório, H.; Silva, C.; Ferreira, M.; Gullo, I.; Máximo, V.; Barros, R.; Mendonça, F.; Oliveira, C.; Carneiro, F. Proteomics Analysis of Gastric Cancer Patients with Diabetes Mellitus. J. Clin. Med. 2021, 10, 407. [Google Scholar] [CrossRef]
- Casanova, M.R.; Osório, H.; Reis, R.L.; Martins, A.; Neves, N.M. Chondrogenic differentiation induced by extracellular vesicles bound to a nanofibrous substrate. NPJ Regen. Med. 2021, 6, 79. [Google Scholar] [CrossRef]
- Schwacke, R.; Ponce-Soto, G.Y.; Krause, K.; Bolger, A.M.; Arsova, B.; Hallab, A.; Gruden, K.; Stitt, M.; Bolger, M.E.; Usadel, B. MapMan4: A Refined Protein Classification and Annotation Framework Applicable to Multi-Omics Data Analysis. Mol. Plant 2019, 12, 879–892. [Google Scholar] [CrossRef]
- Thimm, O.; Bläsing, O.; Gibon, Y.; Nagel, A.; Meyer, S.; Krüger, P.; Selbig, J.; Müller, L.A.; Rhee, S.Y.; Stitt, M. mapman: A user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J. 2004, 37, 914–939. [Google Scholar] [CrossRef]
- Hooper, C.M.; Castleden, I.R.; Tanz, S.K.; Aryamanesh, N.; Millar, A.H. SUBA4: The interactive data analysis centre for Arabidopsis subcellular protein locations. Nucleic Acids Res. 2017, 45, 1064–1074. [Google Scholar] [CrossRef]
Treatments | Two-Way ANOVA | |||||||
---|---|---|---|---|---|---|---|---|
Measurements | Fe+ELE | Fe+AMB | Fe-ELE | Fe-AMB | d.f. | CO2 | Fe | CO2 × Fe |
Total biomass (g plant−1) | 3.14 ± 0.19 a | 2.14 ± 0.15 b | 2.91 ± 0.12 a | 1.18 ± 0.08 c | 1 | 94.29; <0.001 | 18.04; <0.001 | 6.59; 0.021 |
Pn (µmol m−2 s−1) | 11.21 ± 0.43 a | 11.43 ± 0.55 a | 9.21 ± 0.40 b | 8.90 ± 0.80 b | 1 | 0.006; 0.939 | 15.17; <0.001 | 0.21; 0.654 |
gs (mol m−2 s−1) | 0.13 ± 0.01 a | 0.37 ± 0.03 b | 0.12 ± 0.01 a | 0.30 ± 0.02 c | 1 | 85.74; <0.001 | 6.568; 0.021 | 5.42; 0.033 |
Tr (mol m−2 s−1) | 2.46 ± 0.19 a | 5.22 ± 0.36 b | 2.28 ± 0.16 a | 4.13 ± 0.29 d | 1 | 54.19; <0.001 | 3.84; 0.068 | 4.44; 0.051 |
Sugar (roots, μmol g FW−1) | 26.17 ± 3.72 a | 10.92 ± 1.12 b | 19.89 ± 2.36 c | 8.75 ± 0.92 b | 1 | 9.90; 0.009 | 4.82; 0.051 | 1.85; 0.201 |
Sugar (leaves, μmol g FW−1) | 52.22 ± 4.03 a | 33.62 ± 1.21 b | 46.70 ± 2.54 a | 34.80 ± 2.44 b | 1 | 37.71; <0.001 | 0.761; 0.406 | 1.82; 0.210 |
Bin Code | Bin Name | Fe+ELE vs. Fe+AMB | p-Value | Fe-AMB vs. Fe+AMB | p-Value | Fe-ELE vs. Fe+AMB | p-Value | Fe-ELE vs. Fe-AMB | p-Value |
---|---|---|---|---|---|---|---|---|---|
1.1.1 | PS. Light reaction. Photosystem II | - | - | UP | 2.9 × 10−5 | UP | 1.7 × 10−5 | - | - |
1.1.2 | PS. Light reaction. Photosystem I | - | - | UP | 1.5 × 10−2 | UP | 3.8 × 10−5 | - | - |
1.3 | PS. Calvin cycle | - | - | - | - | UP | 4.4 × 10−4 | - | - |
4.1 | Glycolysis. Cytosolic branch | - | - | - | - | UP | 1.5 × 10−3 | UP | 1.7 × 10−2 |
10 | Cell wall | - | - | DOWN | 5.6 × 10−7 | DOWN | 1.6 × 10−3 | - | - |
13.1.3.4 | Amino acid metabolism. Synthesis. Aspartate family. Methionine | - | - | - | - | DOWN | 6.9 × 10−3 | - | - |
15.2 | Metal handling. Binding, chelation, and storage | - | - | DOWN | 3.3 × 10−3 | DOWN | 4.3 × 10−7 | - | - |
16.1 | Secondary metabolism. Isoprenoids | - | - | DOWN | 8.3 × 10−3 | UP | 1.6 × 10−4 | ||
16.2.1 | Secondary metabolism. Phenylpropanoids. Lignin biosynthesis | - | - | DOWN | 1.8 × 10−3 | DOWN | 2.6 × 10−2 | - | - |
16.8 | Secondary metabolism. Flavonoids | - | - | DOWN | 1.4 × 10−2 | - | - | UP | 3.1 × 10−4 |
17.6 | Hormone metabolism. Gibberellin | UP | 1.4 × 10−4 | DOWN | 8.3 × 10−3 | - | - | UP | 3.6 × 10−4 |
20.1 | Stress. Biotic | - | - | DOWN | 5.6 × 10−3 | - | - | UP | 2.1 × 10−3 |
20.2.1 | Stress. Abiotic. Heat | - | - | DOWN | 4.6 × 10−2 | - | - | - | - |
21.2.1.3 | Redox. Ascorbate and glutathione. Ascorbate. l-galactose-1-phosphate phosphatase | - | - | - | - | UP | 2.1 × 10−2 | - | - |
26.9 | Misc. Glutathione S-transferases | - | - | UP | 1.5 × 10−2 | - | - | DOWN | 1.3 × 10−3 |
26.12 | Misc. Peroxidases | - | - | DOWN | 2.7 × 10−3 | - | - | UP | 1.3 × 10−3 |
26.13 | Misc. Acid and other phosphatases | - | - | DOWN | 6.1 × 10−3 | - | - | UP | 3.6 × 10−4 |
26.14 | Misc. Oxygenase | - | - | DOWN | 4.2 × 10−2 | - | - | - | - |
27.3 | RNA. Regulation of transcription | DOWN | 9.4 × 10−3 | - | - | - | - | - | - |
27.4 | RNA.RNA binding | DOWN | 4.4 × 10−2 | - | - | - | - | - | - |
29.2.1 | Protein. Synthesis. Ribosomal protein | DOWN | 4.2 × 10−2 | UP | 2.9 × 10−5 | - | - | DOWN | 1.0 × 10−14 |
34 | Transport | - | - | UP | 3.1 × 10−2 | - | - | DOWN | 3.7 × 10−2 |
Bin Code | Bin Name | Fe+ELE vs. Fe+AMB | p-Value | Fe-AMB vs. Fe+AMB | p-Value | Fe-ELE vs. Fe+AMB | p-Value | Fe-ELE vs. Fe-AMB | p-Value |
---|---|---|---|---|---|---|---|---|---|
1.1.1 | PS. Light reaction. Photosystem II | - | - | DOWN | 1.9 × 10−4 | - | - | - | - |
1.1.4 | PS. Light reaction. Photosystem I | - | - | DOWN | 3.0 × 10−2 | - | - | - | - |
2.1 | Major CHO metabolism. Synthesis | - | - | DOWN | 1.0 × 10−2 | - | - | UP | 1.2 × 10−4 |
3.4 | Minor CHO metabolism. Myo-inositol | - | - | - | - | - | - | UP | 2.0 × 10−2 |
4.1 | Glycolysis. Cytosolic branch | UP | 2.0 × 10−3 | - | - | - | - | UP | 2.0 × 10−2 |
11.9.3.3 | Lipid metabolism. Lipid degradation. Lysophospholipases. Glycerophosphodiester phosphodiesterase | - | - | DOWN | 1.7 × 10−3 | - | - | UP | 3.5 × 10−3 |
13.2.4 | Amino acid metabolism. Degradation. Branched chain | - | - | UP | 3.3 × 10−2 | - | - | - | - |
16.8 | Secondary metabolism. Flavonoids | - | - | UP | 6.1 × 10−4 | - | - | - | - |
20.1 | Stress. Biotic | UP | 6.1 × 10−3 | UP | 3.7 × 10−4 | UP | 1.6 × 10−7 | - | - |
20.2.1 | Stress. Abiotic. Heat | - | - | DOWN | 2.7 × 10−3 | - | - | - | - |
26.9 | Misc. Glutathione S-transferases | - | - | UP | 3.8 × 10−2 | - | - | - | - |
26.12 | Misc. Peroxidases | - | - | UP | 1.3 × 10−4 | UP | 1.8 × 10−5 | - | - |
28.1.3 | DNA. Synthesis/chromatin structure. Histone | DOWN | 1.5 × 10−2 | UP | 4.1 × 10−2 | - | - | DOWN | 2.4 × 10−2 |
29.2.1 | Protein. Synthesis. Ribosomal protein | DOWN | 2.8 × 10−14 | UP | 1.9 × 10−13 | - | - | DOWN | 9.5 × 10−14 |
29.2.2 | Protein. Synthesis. Ribosome biogenesis | DOWN | 9.8 × 10−3 | - | - | - | - | DOWN | 3.8 × 10−3 |
29.3.4 | Protein. Targeting. Secretory pathway | - | - | - | - | - | - | DOWN | 2.8 × 10−3 |
29.5.1 | Protein. Degradation. Subtilases | - | - | - | - | - | - | UP | 2.3 × 10−3 |
29.5.3 | Protein. Degradation. Cysteine protease | - | - | UP | 2.5 × 10−3 | UP | 4.1 × 10−3 | - | - |
30.5 | Signaling. G-proteins | - | - | - | - | DOWN | 4.2 × 10−2 | - | - |
33.1 | Development. Storage proteins | UP | 6.5 × 10−6 | UP | 8.3 × 10−3 | - | - | DOWN | 1.4 × 10−2 |
34.9 | Transport. Metabolite transporters at the mitochondrial membrane | DOWN | 7.8 × 10−3 | - | - | - | - | - | - |
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Soares, J.C.; Osório, H.; Pintado, M.; Vasconcelos, M.W. Effect of the Interaction between Elevated Carbon Dioxide and Iron Limitation on Proteomic Profiling of Soybean. Int. J. Mol. Sci. 2022, 23, 13632. https://doi.org/10.3390/ijms232113632
Soares JC, Osório H, Pintado M, Vasconcelos MW. Effect of the Interaction between Elevated Carbon Dioxide and Iron Limitation on Proteomic Profiling of Soybean. International Journal of Molecular Sciences. 2022; 23(21):13632. https://doi.org/10.3390/ijms232113632
Chicago/Turabian StyleSoares, José C., Hugo Osório, Manuela Pintado, and Marta W. Vasconcelos. 2022. "Effect of the Interaction between Elevated Carbon Dioxide and Iron Limitation on Proteomic Profiling of Soybean" International Journal of Molecular Sciences 23, no. 21: 13632. https://doi.org/10.3390/ijms232113632
APA StyleSoares, J. C., Osório, H., Pintado, M., & Vasconcelos, M. W. (2022). Effect of the Interaction between Elevated Carbon Dioxide and Iron Limitation on Proteomic Profiling of Soybean. International Journal of Molecular Sciences, 23(21), 13632. https://doi.org/10.3390/ijms232113632