Skip to main content
NCBI home page
Plant Physiology logoLink to Plant Physiology
. 2022 Sep 2;190(4):2115–2121. doi: 10.1093/plphys/kiac411

Uncoupling differential water usage from drought resistance in a dwarf Arabidopsis mutant

Daniel N Ginzburg 1, Flavia Bossi 2, Seung Y Rhee 3,✉
PMCID: PMC9706424  PMID: 36053183

Abstract

Understanding the molecular and physiological mechanisms of how plants respond to drought is paramount to breeding more drought-resistant crops. Certain mutations or allelic variations result in plants with altered water-use requirements. To correctly identify genetic differences which confer a drought phenotype, plants with different genotypes must be subjected to equal levels of drought stress. Many reports of advantageous mutations conferring drought resistance do not control for soil water content (SWC) variations across genotypes and may therefore need to be re-examined. Here, we reassessed the drought phenotype of the Arabidopsis (Arabidopsis thaliana) dwarf mutant, chiquita1-1 (chiq1-1, also called constitutively stressed 1 (cost1)), by growing mutant seedlings together with the wild-type to ensure uniform soil water availability across genotypes. Our results demonstrate that the dwarf phenotype conferred by loss of CHIQ1 function results in constitutively lower water usage per plant, but not increased drought resistance. Our study provides an easily reproducible, low-cost method to measure and control for SWC and to compare drought-resistant genotypes more accurately.

Because some genotypes use water at different rates, an accurate comparison of drought phenotypes requires that all genotypes are exposed to uniform soil water content throughout drought treatment.

Introduction

Among the various stresses plants endure in both natural and cultivated environments, drought stress has the greatest impact on plant productivity (Hu and Xiong, 2014). From an agricultural context, drought can be defined as the state of insufficient water availability to sustain maximum plant growth (Deikman et al., 2012). The impact of drought on global crop yields has intensified recently and is projected to intensify even more so in the future (Lesk et al., 2016; Leng and Hall, 2019). Identifying and engineering more drought-resistant crops is, therefore, necessary to provide sufficient food to a growing population (Godfray et al., 2010).

Plants employ various mechanisms in response to drought. The specific responses to drought are influenced by the degree of stress, plant species, genotype within species, and developmental stage (Hu and Xiong, 2014). Some species and genotypes respond by hastening the completion of their life cycle before the onset of more severe stress (“drought escape”) (Lawlor, 2013). Others respond by conserving or acquiring more water (“drought avoidance”), or by maintaining metabolic homeostasis to prevent or repair damaged cells and tissues (“drought tolerance”) (Lawlor, 2013). The many terms used throughout the literature to describe plant responses to water deficit (e.g. drought resistance, drought tolerance, and drought avoidance) are often used interchangeably, resulting in ambiguity and a deviation from established terminology (Lawlor, 2013; Blum, 2014). This problem is compounded by results which could imply one or more forms of drought resistance (which encompasses escape, tolerance, and avoidance; Lawlor, 2013) depending on the available data. For example, in response to reduced soil water availability, a plant could respond by increasing root growth (a drought avoidance response; Levitt, 1980) or via osmotic adjustment to maintain cell turgor (a drought tolerance response; Morgan, 1984). Without establishing which of these mechanisms is involved, we cannot ascertain which specific drought resistance response is responsible for an observed phenotype (Verslues et al., 2006). The term “drought resistance” is therefore used here to not imply any specific mechanism(s) in response to drought.

Despite the well-reasoned need to evaluate drought responses of mutant lines at equal levels of drought stress as that of wild-type controls (Verslues et al., 2006; Lawlor, 2013; Turner, 2019), there are many claims of increased drought resistance that do not include this essential comparison (e.g. Hsieh et al., 2002; Zhang et al., 2009; Bao et al., 2020; Hong et al., 2020). In all such cases, mutant seedlings that survived longer or had greater rates of recovery after drought were not grown in the same pots with control plants nor was percent soil water content (SWC) kept uniform across genotypes. Therefore, the plants were evaluated at potentially unequal levels of drought stress. This situation is particularly problematic for plants that may use water at different rates, such as dwarf plants.

To demonstrate how controlling for SWC can affect the onset and degree of stress symptoms, and ultimately interpretation of results, we re-evaluated the drought phenotype of a dwarf Arabidopsis (Arabidopsis thaliana) mutant recently implicated in drought tolerance. Bao et al. implicated CONSTITUTIVELY STRESSED 1 (COST1) (originally characterized and named CHIQUITA1 [CHIQ1] [Bossi et al., 2017]) in drought tolerance when grown in pots separate from the wild-type (Bao et al., 2020). Here, we reassessed the drought phenotype associated with loss of CHIQ1 function when chiq1-1 seedlings were grown together with the wild-type. Contrary to the previous report (Bao et al., 2020), we found that chiq1-1 plants do not exhibit increased resistance to drought, despite constitutive lower water usage per plant, compared to the wild-type. Our study provides an easily reproducible, low-cost experimental approach to more accurately compare drought-resistant genotypes, which will advance efforts to breed more resilient crops against climate change.

Results

chiq1-1 mutant plants are not more drought resistant than the wild-type

We evaluated chiq1-1’s water requirements and survival during drought to determine whether CHIQ1 is involved in drought resistance or if chiq1-1 plants simply use less water (Figure 1A). In all experiments, seedlings were spaced uniformly in each pot. Additionally, water was provided from above and uniformly throughout each pot. When grown in pots with only a single genotype (either all wild-type or all chiq1-1), chiq1-1 plants survive longer during drought than wild-type plants (Figure 1B), consistent with the previous study (Bao et al., 2020). We next asked whether this phenotype was due to increased resistance to drought, or rather due to differences in the rate of water use between genotypes. We found that chiq1-1 plants take up less water from the soil under both well-watered and drought conditions based on average daily water loss and daily SWC levels, respectively (Figure 1, C and D). Reintroducing wild-type CHIQ1 into the mutant background complemented the water-use and survival phenotypes observed in the chiq1-1 null mutant (Figure 1, B–D).

Figure 1.

Figure 1

Open in a new tab

chiq1-1 plants use less water and survive longer than the wild-type when grown separately. A, Timeline and description of experimental design in which each pot contained only a single genotype. Seedlings were spaced uniformly in each pot, and water was provided from above and uniformly throughout each pot. B, Representative images of Col-0, chiq1-1, and the complemented line proCHIQ1:CHIQ1-YFP (in chiq1-1 background) grown in separate pots in control and drought conditions (12 days since last watering). C, Average daily water loss of all timepoints by genotype in well-watered (control) conditions, starting from 28 DAS (n = 31–46; N = 4). Black asterisk indicates statistical significance (P-value< 0.05) using Dunnett’s test with Col-0 as control. Error bars represent the 95% confidence interval. D, Percent SWC by genotype during drought. Light-colored bands represent 95% confidence intervals of (n = 4–6; N = 2–3). Representative Col-0 (bottom) and chiq1-1 (top) images are shown at 0, 12, and 17 days since the last watering. DAS, days after sowing; SWC, soil water content; n, number of biological replicates per experiment; N, number of independent experiments.

When chiq1-1 plants were grown in pots together with the wild-type such that SWC was always equal for both genotypes (Figure 2A), the visual onset of stress symptoms and duration of survival was uniform across genotypes (Figure 2B; Supplemental Movie S1). Photosystem II (PSII) quantum efficiency (FV/FM), a commonly used metric to quantify plant stress (Baker, 2008), and leaf relative water content (LRWC) as a percent of control decreased uniformly in both genotypes, when planted together, as a result of withholding water (Figure 2, C and D). Additionally, relative expression of the drought response genes RESPONSIVE TO DESICCATION 22 (RD22) and RESPONSIVE TO DESICCATION 29A (RD29A) (Huang et al., 2018; Yu et al., 2019) in chiq1-1 was not significantly different from that in the wild type as a result of drought (Figure 2, E and F). Relative expression of COLD-REGULATED 15A (COR15A), another commonly used drought response gene (Huang et al., 2018), did not change significantly in either genotype in response to drought (Supplemental Figure S1). Differences in pot size and plant density across experiment types (Figures 1, A and 2, A) did not affect the chiq1-1 dwarf phenotype compared to the wild-type, indicated by indistinguishable rosette areas of chiq1-1 seedlings relative to wild-type across pot types (Supplemental Figure S2). Together, these results indicate that CHIQ1 is not involved in drought resistance, but rather that chiq1-1 plants constitutively use less water per plant, resulting in a slower decrease in soil water availability and a delayed onset of stress symptoms when grown separately from the wild-type.

Figure 2.

Figure 2

Open in a new tab

chiq1-1 plants display equal drought resistance to the wild-type when grown together. A, Timeline and description of experimental design in which each pot contained one Col-0 and one chiq1-1 seedling. Seedlings were planted in the corners and at equal distances from the center of the pot. Water was provided from above and uniformly throughout each pot. B, Representative images of pots containing one Col-0 and chiq1-1 seedling over the course of drought. All four pots within each day panel are biological replicates. Day numbers represent days since last watering. Arrows point toward the chiq1-1 seedling. C, Photosystem II (PSII) quantum efficiency (FV/FM) as a function of drought (n = 14–46; N = 3–4). Percent SWC at each time-point is overlaid in the black dashed line (n = 32–48; N = 2–3). D, Relative water content of the seventh true leaf as a percent of well-watered controls at 4, 10, and 14 days since last watering. Error bars represent the 95% confidence interval. E and F, Relative expression fold-change of drought response marker genes RD22 and RD29A. Values were first normalized by the housekeeping gene AT1G13320, then by Col-0/Day 4/Control sample using the ΔΔCt method. Each circle represents an average of 3 technical replicates of a pooled sample of 3-4 biological replicates from an independent experiment (N = 3–4). Error bars represent the standard error of the mean. Letters in (C–F) represent significantly different groups (P-value  < 0.05) as determined by two- (C and D) or three-way (E and F) ANOVA followed by Tukey’s HSD test. DAS, days after sowing; SWC, soil water content; RT-qPCR, reverse transcription quantitative polymerase chain reaction; LRWC, leaf relative water content; n, number of biological replicates; N, number of independent experiments.

Discussion

Plants with reduced size often survive longer in response to water deprivation (Lawlor, 2013; Puértolas et al., 2017; Turner, 2019). We previously showed that chiq1-1 plants have smaller organs than the wild-type (Bossi et al., 2017, 2022). In this study, we found that the reduction in plant size from lacking CHIQ1 does not confer drought resistance. This is contrary to what was recently published (Bao et al., 2020), where wild-type and chiq1-1 plants were grown and droughted in different pots with the implicit assumption that SWC was equal in all pots after withholding water. This assumption can dramatically alter the conclusions drawn regarding drought resistance, as illustrated in this study. We showed that chiq1-1 plants use less water than the wild-type. Therefore, the SWC in pots containing only Columbia-0 (Col-0) or only chiq1-1 was different as a function of time after withholding water. Because developmental transitions are not affected by CHIQ1 function (Bossi et al., 2017, 2022), and both chiq1-1 and wild-type plants continue to grow after 28 days after sowing (DAS; Figure 2B; Supplemental Movie S1), the prolonged survival of chiq1-1 seedlings when grown separately from the wild-type is not due to genotype-specific differences in plant development but rather only due to differences in available SWC.

When we grew chiq1-1 plants in the same pot as the wild-type, such that both genotypes were always forced to cope with equal levels of SWC, chiq1-1 plants were qualitatively and quantitatively no more resistant than the wild-type to drought stress. Specifically, chiq1-1 seedlings did not exhibit greater drought tolerance or avoidance than the wild-type, as evidenced by the uniform decrease in PSII quantum efficiency and LRWC, respectively. While plant density was much greater in the single-genotype approach (12 seedlings/pot) compared to the mixed-genotype approach (2 seedlings/pot), chiq1-1 rosette size relative to the wild-type at the onset of stress was consistent across approaches (Supplemental Figure S2).

The absence of a chiq1-1 drought phenotype, when grown together with the wild-type, was observed both physiologically and molecularly, the latter in direct contrast to the expression profiles of RD22, RD29A, and COR15A in the wild-type and chiq1-1 when grown and droughted in separate pots (Bao et al., 2020). This is not to say that chiq1-1 is not potentially advantageous in an agronomic context (e.g. in a monoculture environment in which all plants are chiq1-1). Indeed, daily water usage in both well-watered and drought conditions demonstrates that the dwarf chiq1-1 plants constitutively use less water than the wild-type. However, when situated in an environment more competitive for water use, chiq1-1 plants fare no better than their wild-type neighbors. The tremendous yield gains of the Green Revolution (Hedden, 2003) were underpinned by dwarfing traits. Further elucidating the mechanisms of how they use less water, for example, if there are differences in water use efficiency or carbon isotope discrimination, will be important for engineering the next generation of high-yielding, stress-resilient crops (Richards et al., 2010).

Our work highlights the importance of ensuring that comparisons between genotypes are made at equal levels of drought stress by subjecting all genotypes to uniform levels of stress. Besides growing genotypes together in the same pot, SWC can also be controlled for gravimetrically (Granier et al., 2006; de Ollas et al., 2019), or via soil moisture probes (Miralles-Crespo and van Iersel, 2011).

Drought is the single biggest source of crop production loss, resulting in billions of dollars in losses worldwide (FAO, 2021). The easily-reproducible and cost-effective methods we used to evaluate chiq1-1’s drought phenotype can be used to evaluate various specific mechanisms of drought resistance and will serve as a valuable resource to the plant science community in designing future experiments to screen for genotypes more resilient to drought.

Materials and methods

Plant materials and growth conditions

Water content of fresh PRO-MIX HP Mycorrhizae potting soil (Premier Tech Horticulture, Quakertown, PA, USA) was determined by drying three samples of fresh soil at 45°C for 1 week. Average water content of fresh soil was calculated as 1 – (dry weight/fresh weight). To determine soil water holding capacity (100% SWC), eight pots were filled with fresh soil, weighed, saturated with water, covered, and then left to drip until pots reached pot capacity (cessation of dripping). They were then weighed again and averaged to determine the mean water-holding capacity of the soil.

Wild-type Arabidopsis (Arabidopsis thaliana) accession Col-0 and chiq1-1 mutant (SALK_064001) seeds were obtained from the Arabidopsis Biological Resource Center (ABRC). CHIQ1 complementation lines were obtained as described in Bossi et al. (2022). Seeds were stratified in water at 4°C for 4 days before planting. Seedlings were grown in a growth chamber under a 16:8 h light:dark cycle at 22°C, 40% relative huminidy, and ∼100-μmol m−2 s−1 photosynthetic photon flux density (PPFD) measured at pot-level. Water was added to pots from above and applied uniformly throughout pots to ensure uniform water availability for all seedlings. Pots and flats were rotated daily Monday–Friday to avoid positional effects. All experiments were conducted at least 3 times independently.

Single genotype per pot drought experiment

All pots were filled with an equal amount of PRO-MIX HP Mycorrhizae potting soil by weight. Seeds were planted such that each pot contained 12 seedlings of a single genotype (Col-0, chiq1-1, proCHIQ1:CHIQ1-YFP (in chiq1-1 background), or 35Spro:CHIQ1-FLAG (in chiq1-1 background)). To obtain 12 seedlings per pot for the single-genotype per pot experiments, 3–4 seeds were planted in each of 12 locations within a pot. After seeding, pots were put into flats and were covered for 1 week, after which covers were removed and each pot was thinned to contain 12 seedlings, one seedling in each seeded location. At 28 DAS, pots were either subjected to drought (withholding of water) or were maintained at 70% SWC as controls. All pots were weighed daily Monday–Friday to determine water loss in both control and drought conditions. Statistical differences in weekday water usage per day across genotypes was determined by one-way analysis of variance (ANOVA) followed by Dunnett’s test (P < 0.05) setting Col-0 as control and using the DunnettTest() function within the DescTools package in R version 3.6.3.

Multiple genotypes per pot drought experiment

For experiments directly comparing drought resistance between Col-0 and chiq1-1 plants, one seedling each of Col-0 and chiq1-1 was planted together in individual pots, each at an equal distance to the center of the pot. All pots were filled with an equal amount of PRO-MIX HP Mycorrhizae potting soil by weight. At 28 DAS, half of the pots were subjected to drought (withholding of water) and the other half were maintained at 70% SWC as controls. Droughted pots were weighed daily Monday–Friday to determine % SWC as a function of time.

Image capture and timelapse generation

Images were taken every 2 hours from directly above pots using a Raspberry Pi Zero W (Raspberry Pi Foundation, Cambridge UK) and an Arducam M12 lens (model B0031; https://arducam.com). Images were captured using the camera.capture() Python function and were taken at 2-hour intervals using the command-line job scheduler, crontab (Unix). To remove lens distortion, images were corrected in Adobe Photoshop CS6 (Adobe Systems, Inc., San Jose, CA, USA) using the “Lens correction” feature. All images were then stitched together into a time series video using Davinci Resolve 17 (Blackmagic Design, Port Melbourne, Victoria, Australia). Rosette areas were measured using ImageJ.

Chlorophyll fluorescence measurements

Chlorophyll fluorescence parameters were measured between 9:30–10:00 am Pacific Time on the seventh true leaf of each sample using a chlorophyll fluorometer (OS30p+, Opti-Sciences, Inc. Hudson, New Hampshire). After dark-adapting leaves for 30 minutes, a weak modulated light (0.1 μmol m−2 s−1 PPFD) was applied to measure minimum fluorescence (F0). Maximum fluorescence (FM) was measured after applying a saturating light pulse (6,000 μmol m−2 s−1 PPFD) of 1 second to the sampled region. FV/FM was calculated as (FM−F0)/FM. Statistical differences in FV/FM values between genotypes as a function of time were determined by two-way ANOVA followed by Tukey’s honestly significant difference test (P < 0.05) using the lsmeans() function within the lsmeans package in R version 3.6.3.

Reverse transcription quantitative polymerase chain reaction (RT–qPCR)

At 4, 10, and 14 days of drought treatment, a 0.38 cm2 disc was excised from the seventh true leaf of well-watered and droughted plants and immediately frozen in liquid nitrogen. Leaves from four plants of the same genotype, condition, and time point were pooled for each experiment. Four independent experiments were conducted. Frozen tissue was homogenized and mRNA was extracted using the QIAGEN Plant RNeasy Plant Mini Kit (QIAGEN, Hilden, Germany). mRNA was reverse transcribed using the Protoscript II First Strand cDNA Synthesis Kit (New England BioLabs, Ipswich, MA, USA). RT–qPCR reactions were performed in a Roche LightCycler 480 II 96-well plates (Roche Diagnostics, Indianapolis, IN, USA) using a 10 minute pre-incubation period at 95°C followed by 40 cycles of denaturation for 5 seconds at 95°C, annealing for 10 seconds at 60°C, and extension for 10 seconds at 72°C. Amplification of target genes was measured each cycle by SYBR Green I fluorescent dye. All RT–qPCR reactions were performed in triplicate in a Roche LightCycler 480 II (Roche Sequencing and Life Sciences, Indianapolis, IN, USA) using the LightCycler 480 SYBR Green I Master reaction mix. Transcript abundance was normalized by the housekeeping gene AT1G13320 (Czechowski et al., 2005) and relative expression fold change was calculated using the delta-delta Ct (ΔΔCt) method (Livak and Schmittgen, 2001). Statistical differences in relative expression fold change were determined by three-way ANOVA followed by Tukey’s honestly significant difference test (P-value < 0.05) using the lsmeans() function within the lsmeans package in R version 3.6.3. All primers used in this study are listed in Supplemental Table S1.

Leaf relative water content (LRWC)

The remaining leaf tissue from which discs were cut out for RT–qPCR was weighed, floated on deionized water for 2 hours, reweighed, and then dried for 48 hours at 65°C. LRWC was calculated as (pre-float weight−dry weight)/(post-float weight−dry weight) * 100. Statistical differences in LRWC as a percent of control were determined by two-way ANOVA followed by Tukey’s honestly significant difference test (P-value < 0.05) using the lsmeans() function within the lsmeans package in R version 3.6.3.

Accession numbers

CHIQ1/COST1 (AT2G45260), RD22 (AT5G25610), RD29A (AT5G52310), and COR15A (AT2G42540).

Supplemental data

The following materials are available in the online version of this article.

Supplemental Movie S1. Visual onset and progression of drought stress are uniform between the wild-type and chiq1-1 plants when grown in shared pots.

Supplemental Figure S1. Relative expression fold-change of drought response marker gene COR15A.

Supplemental Figure S2. chiq1-1 rosette area relative to the wild-type when grown in pots of single-genotype (12 plants/pot) or mixed-genotype (2 plants/pot).

Supplemental Table S1. Primers used for RT-qPCR.

Supplementary Material

kiac411_Supplementary_Data
Click here for additional data file. (8.1MB, zip)

Acknowledgments

We thank the Arabidopsis Biological Resource Center (ABRC) for providing chiq1-1 (SALK_064001) mutant seeds, A. Malkovskiy for helpful advice on imaging, Y. Dorone for helpful suggestions, J.D. Klein for instruction on conducting drought experiments, and I. Villa and G. Materassi-Shultz for plant growth facility support. This work was done on the ancestral land of the Muwekma Ohlone Tribe, which was and continues to be of great importance to the Ohlone people.

Funding

This work was supported by grants from the National Science Foundation (IOS-1546838, IOS-1026003) and the US Department of Energy, Office of Science, Office of Biological and Environmental Research, Genomic Science Program grant nos. DE-SC0018277, DE-SC0008769, DE-SC0020366, and DE-SC0021286.

Conflict of interest statement. None declared.

Contributor Information

Daniel N Ginzburg, Department of Plant Biology, Carnegie Institution for Science, Stanford, CA, USA.

Flavia Bossi, Department of Plant Biology, Carnegie Institution for Science, Stanford, CA, USA.

Seung Y Rhee, Department of Plant Biology, Carnegie Institution for Science, Stanford, CA, USA.

Data availability statement

All study data are included in the main text and supporting information.

 

F.B., S.Y.R., and D.G. conceived the project. D.G. performed the drought studies. F.B. and S.Y.R. provided intellectual contribution and input into manuscript organization. D.G. wrote the manuscript and F.B. and S.Y.R. edited the manuscript.

The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (https://academic.oup.com/plphys/pages/general-instructions) is Seung Y. Rhee (srhee@carnegiescience.edu).

References

  1. Baker NR (2008) Chlorophyll fluorescence: a probe of photosynthesis in vivo. Annu Rev Plant Biol 59:89–113 [DOI] [PubMed] [Google Scholar]
  2. Bao Y, Song WM, Wang P, Yu X, Li B, Jiang C, Shiu S-H, Zhang H, Bassham DC. ( 2020) COST1 regulates autophagy to control plant drought tolerance. Proc Natl Acad Sci USA 117:7482–7493 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Blum A (2014) Genomics for drought resistance – getting down to earth. Funct Plant Biol 41:1191. [DOI] [PubMed] [Google Scholar]
  4. Bossi F, Fan J, Xiao J, Chandra L, Shen M, Dorone Y, Wagner D, Rhee SY. ( 2017) Systematic discovery of novel eukaryotic transcriptional regulators using sequence homology independent prediction. BMC Genomics 18:480. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bossi F, Jin B, Lazarus E, Cartwright H, Dorone Y, Rhee SY (2022) CHIQUITA1 maintains the temporal transition between proliferation and differentiation in Arabidopsis thaliana. Development. 149: dev200565. [DOI] [PubMed] [Google Scholar]
  6. Czechowski T, Stitt M, Altmann T, Udvardi MK, Scheible WR (2005) Genome-wide identification and testing of superior reference genes for transcript normalization in Arabidopsis. Plant Physiol 139:5–17 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Deikman J, Petracek M, Heard JE (2012) Drought tolerance through biotechnology: improving translation from the laboratory to farmers’ fields. Curr Opin Biotechnol 23:243–250 [DOI] [PubMed] [Google Scholar]
  8. Godfray HCJ, Beddington JR, Crute IR, Haddad L, Lawrence D, Muir JF, Pretty J, Robinson S, Thomas SM, Toulmin C (2010) Food security: the challenge of feeding 9 billion people. Science 327:812–818 [DOI] [PubMed] [Google Scholar]
  9. Granier C, Aguirrezabal L, Chenu K, Cookson SJ, Dauzat M, Hamard P, Thioux J-J, Rolland G, Bouchier-Combaud S, Lebaudy A, et al. (2006) PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit. New Phytol 169:623–635 [DOI] [PubMed] [Google Scholar]
  10. Hedden P (2003) The genes of the Green Revolution. Trends Genet 19:5–9 [DOI] [PubMed] [Google Scholar]
  11. Hong Y, Wang Z, Liu X, Yao J, Kong X, Shi H, Zhu JK (2020) Two chloroplast proteins negatively regulate plant drought resistance through separate pathways. Plant Physiol 182:1007–1021 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Hsieh TH, Lee JT, Charng YY, Chan MT (2002) Tomato plants ectopically expressing Arabidopsis CBF1 show enhanced resistance to water deficit stress. Plant Physiol 130:618–626 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Huang Y, Zhao H, Gao F, Yao P, Deng R, Li C, Chen H, Wu Q (2018) A R2R3-MYB transcription factor gene, FtMYB13, from Tartary buckwheat improves salt/drought tolerance in Arabidopsis. Plant Physiol Biochem 132:238–248 [DOI] [PubMed] [Google Scholar]
  14. Hu H, Xiong L (2014) Genetic engineering and breeding of drought-resistant crops. Annu Rev Plant Biol 65:715–741 [DOI] [PubMed] [Google Scholar]
  15. Lawlor DW (2013) Genetic engineering to improve plant performance under drought: physiological evaluation of achievements, limitations, and possibilities. J Exp Bot 64:83–108 [DOI] [PubMed] [Google Scholar]
  16. Leng G, Hall J (2019) Crop yield sensitivity of global major agricultural countries to droughts and the projected changes in the future. Sci Total Environ 654:811–821 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Lesk C, Rowhani P, Ramankutty N (2016) Influence of extreme weather disasters on global crop production. Nature 529:84–87 [DOI] [PubMed] [Google Scholar]
  18. Levitt J (1980) Responses of Plants to Environmental Stresses. Volume II. Water, Radiation, Salt, and Other Stresses. Academic Press, Cambridge, MA. [Google Scholar]
  19. Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 25:402–408 [DOI] [PubMed] [Google Scholar]
  20. Miralles-Crespo J, van Iersel MW (2011) A calibrated time domain transmissometry soil moisture sensor can be used for precise automated irrigation of container-grown plants. HortScience 46: 889–894 [Google Scholar]
  21. Morgan JM (1984) Osmoregulation and water stress in higher plants. Annu Rev Plant Physiol 35:299–319 [Google Scholar]
  22. de Ollas C, de Ollas C, Segarra-Medina C, González-Guzmán M, Puertolas J, Gómez-Cadenas A (2019) A customizable method to characterize Arabidopsis thaliana transpiration under drought conditions. Plant Methods 15: 89. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Puértolas J, Larsen EK, Davies WJ, Dodd IC (2017) Applying “drought” to potted plants by maintaining suboptimal soil moisture improves plant water relations. J Exp Bot 68:2413–2424 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Richards RA, Rebetzke GJ, Watt M, (Tony) Condon AG, Spielmeyer W, Dolferus R (2010) Breeding for improved water productivity in temperate cereals: phenotyping, quantitative trait loci, markers and the selection environment. Funct Plant Biol 37:85–97 [Google Scholar]
  25. Turner NC (2019) Imposing and maintaining soil water deficits in drought studies in pots. Plant Soil 439: 45–55 [Google Scholar]
  26. Verslues PE, Agarwal M, Katiyar-Agarwal S, Zhu J, Zhu JK (2006) Methods and concepts in quantifying resistance to drought, salt and freezing, abiotic stresses that affect plant water status. Plant J 45:523–539 [DOI] [PubMed] [Google Scholar]
  27. Yu Y, Bi C, Wang Q, Ni Z (2019) Overexpression of TaSIM provides increased drought stress tolerance in transgenic Arabidopsis. Biochem Biophys Res Commun 512:66–71 [DOI] [PubMed] [Google Scholar]
  28. Zhang SW, Li CH, Cao J, Zhang YC, Zhang SQ, Xia YF, Sun DY, Sun Y (2009) Altered architecture and enhanced drought tolerance in rice via the down-regulation of indole-3-acetic acid by TLD1/OsGH3.13 activation. Plant Physiol 151:1889–1901 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

kiac411_Supplementary_Data
Click here for additional data file. (8.1MB, zip)

Data Availability Statement

All study data are included in the main text and supporting information.

Articles from Plant Physiology are provided here courtesy of Oxford University Press

RESOURCES