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Effects of Environmental Stresses on the Growth of Arabidopsis thaliana Rosette Leaves

Effects of Environmental Stresses on the Growth of Arabidopsis thaliana Rosette Leaves INTRODUCTIONPlant phenotype is the result of the interaction between genotype and growth environments. Studying the phenotype of plants under various growth environments is the core of understanding plant function. Recent research indicates that in the context of rapid climate change, phenotypes play a key role in plant adaptation to various growth factors (Hu et al. 2008; de Jong et al. 2012; Gratani et al. 2014; Scharr et al. 2016), and biotic and abiotic stresses lead to phenotypic changes in plants. Therefore, identifying the plant phenotypic signatures under biotic or abiotic stress conditions is helpful for the early detection of biotic or abiotic stresses, which have great economic benefits for plant production (Pauli et al. 2016).Leaves are the energy factories of plants and play an important role in the process of plant survival and growth. Through photosynthesis, leaves convert solar into chemical energy, which can then be used for further metabolism and ultimately in the production of food, feed, and fuel (Xu et al. 2009; Rodriguez et al. 2014). Since most photosynthesis occurs in leaves, it is important to characterize them in terms of size, shape, and number, which are regulated by the growth environment and genetic factors (Gonzalez et al. 2010, 2012; Mishra et al. 2012; Weraduwage et al. 2015). Many functional genomic studies have been carried out to improve agricultural and forestry crops using high-throughput genomic tools. However, there are relatively few studies on the phenotypic characteristics (e.g., the size and morphological structure of organs) of plants with specific genotypes under various growth conditions (Rahaman et al. 2015). Therefore, the study of the relationships between phenotypes, genes, and environments should be expanded. It will deepen our understanding of the relationships between the observable plant phenotypes and their physiological status, and the effects of different growth environments on plant growth, yield, and quality (Ke 2014; Orgogozo et al. 2015).Studying the effects of environmental factors on plant phenotype is helpful in understanding the biological functions of plant development, serving in breeding to develop cultivars with ideal phenotypes. However, the majority of experiments testing the response of plants to changes in environmental conditions have focused on a single stress treatment applied to plants under controlled conditions (Wang & Zhou 2021). In contrast, in practice, a number of different stresses can occur simultaneously. These may include irradiance, temperature, humidity, or water availability and may alter plant metabolism in a novel manner that may be different from that caused by each of the different stresses applied individually. A. thaliana is an ideal model for studying those problems and has been widely used for this purpose (Minervini et al. 2014). As a model organism, A. thaliana has several advantages, such as its small size, short life cycle, and known gene sequence, which make it easy to conduct experiments in the laboratory. By cultivating A. thaliana under a controllable environment, the change regulation of its phenotypes can be used to guide the studies of other plants, and provide references for outdoor experiments, where the environmental conditions and other factors are difficult to control. Moreover, analyzing the phenotypic characteristics of A. thaliana under various growth environments can provide a theoretical framework for quantifying and understanding plant phenotypic differences caused by environmental stresses (Boyes et al. 2001).Earlier studies did not analyze the effect of different growth environments (temperature, humidity, light intensity) on A. thaliana phenotypes, which could not adequately explain the relationship between A. thaliana phenotypes and environments, and the effect of different growth environments on A. thaliana growth. Moreover, different environmental factors and phenotypic characteristics have rarely been integrated into a single model, and predicting and analyzing the influence of environmental factors on phenotypic characteristics has been challenging.Using image analysis to nondestructively analyze plant leaf size in greenhouses, a number of studies have been conducted (Fahlgren et al. 2015; Ge et al. 2016). Compared to destructive sampling, image analysis enables the measurements of leaf size multiple times during the plant growth cycle, and also allows the quantification of dynamic traits such as growth rate and leaf expansion rate (Liang et al. 2018).The aim of this paper was to study the effect of environmental factors of temperature, humidity, and light intensity on the phenotypic characteristics of A. thaliana through a multi-factor orthogonal test, which may be helpful in optimizing the parameters for phenotyping and selecting new improved genotypes with the desired traits in cultivation under specific climatic conditions.MATERIALS AND METHODSCultivation of A. thaliana and leaf image acquisition under various growth environmentsFrom November 2015 to December 2016, the area of individual leaves and the number of leaves of A. thaliana in nine different growth environments were measured at Nanjing Forestry University, China. Temperature, humidity, and light intensity were selected as environmental factors for the experiments, and orthogonal tests of three factors at three levels were carried out. Seeds of A. thaliana Col-0 were sown into nursery substrate of peat fibers: 0–10 mm, and cultured in a climate chamber (RXZ-500B, Ningbo Jiangnan Instrument Factory, Zhejiang, China). The environmental parameters (temperature; humidity; light intensity) could be individually configured in the chamber, so various combinations of these parameters could be simulated. Nine sets of experiments were conducted by setting the environmental parameters of the climate chamber and 50 samples were taken in each experiment (Table 1). After A. thaliana leaf emerged, the growth image of A. thaliana was collected every day by a CCD camera (JAI, model: GO-5000C-PGE). A total of 20,200 original images were obtained, which were used to extract the leaf area of A. thaliana. Figure 1 shows the growth images of A. thaliana on the 20th day after the leaf emerged in the 38th pot in all nine sets of experiments.Figure 1Example images of A. thaliana leaves at 20th day after emergence under various growth environments (temperature; humidity; light intensity). Note: the blue squares in each picture represent calibration plateTable 1Trial environment parameters of each groupTrial numberTrial factorsTrial factorsTHItemperature (°C)humidity (%)light intensity (μmol·m−2·s−1)1225027822 °C; 50%; 278 μmol·m−2·s−1222659222 °C; 65%; 92 μmol·m−2·s−13228018422 °C; 80%; 184 μmol·m−2·s−14255018425 °C; 50%; 184 μmol·m−2·s−15256527825 °C; 65%; 278 μmol·m−2·s−1625809225 °C; 80%; 92 μmol·m−2·s−1728509228 °C; 50%; 92 μmol·m−2·s−18286518428 °C; 65%; 184 μmol·m−2·s−19288027828 °C; 80%; 278 μmol·m−2·s−1Image analysis and feature extractionMATLAB (version R2010, MathWorks) was used for image analysis. Rosette leaves of A. thaliana were measured at regular intervals during the whole life cycle using image analysis. Firstly, individual leaves were segmented from the plant using an interactive segmentation process. Then, the total number of pixels in the region of the individual leaf area was extracted using the image region property measurement function (regionprops) in MATLAB's Image Processing Toolbox. Finally, the size of the individual leaf was obtained by the proportional relationship calculated from a blue square standard plate (10 mm × 10 mm) with the following equation:(1)A=PL×AB/PBA = {P_L} \times {A_B}/{P_B}where A is the A. thaliana leaf area (mm2); PL is the number of pixels of the A. thaliana leaf area (pixel); AB is the area of the standard plate (mm2); and PB is the number of pixels of the standard plate (pixel).The smallest leaf was visible when the individual leaf area was approximately 0.5 mm2 (Cookson et al. 2010). In this paper, the number of leaves was calculated every 2 days during the period from two visible leaves to bolting.Growth equation of rosette leaves of A. thalianaIndividual leaf area and leaf numbers were analyzed and fitted using a logistic model and a linear regression model. The regression equations between leaf area, leaf number, and growth time were established by SPSS software (IBM SPSS statistics 19.0). The equations of the models were as follows (Cookson et al. 2010; Karadavut et al. 2010; Jiao et al. 2018):(2)logistic model Area=a1+b⋅e−k⋅t{{\rm logistic} \;{\rm model}}\;Area = {a \over {1 + b \cdot {e^{ - k \cdot t}}}}(3)linear regression model N=k1⋅t+k2{\rm{linear}}\;{\rm{regression}}\;{\rm{model}}\;N = {k_1} \cdot t + {k_2}where Area is the fitted values of leaf area (mm2); t is the growth time (d); k is the growth rate (mm2·d−1); a is the final leaf size (mm2); b is a constant scale; N is the number of leaves; k1 is a slope, which represents leaf emergence rate; and k2 is the intercept term.The final sizes of the leaf area under different growth environments were determined according to the established growth equations. Then, the effects of various growth environments on the final leaf size and the leaf number were analyzed.RESULTS AND DISCUSSIONCurve fitting to the final A. thaliana leaf size and the number of leaves in the rosette in the various growth environmentsLeaf 6 (the leaf number was determined by the order of the emergence) of each plant was taken as the target to analyze the effects of growth environments on the final leaf size of A. thaliana. Scatter plots of leaf 6 for each experiment under various growth conditions are shown in Figure 2.Figure 2Scatter plots of leaf 6 areas for each experiment under various growth environments (temperature; humidity; light intensity)It could be learned from Figure 2 that in the early stage, the growth and increase of leaf area were faster than in the maturation stage, producing a sigmoid curve. Moreover, the fitted values of leaf 6 agreed well with the measured values, with no obvious outliers. The results of regression analysis between leaf area and growth time (equation 2) were given in Table 2. The final size of leaf 6 for each experiment was determined by the parameter a in the regression equations. The minimum final size of leaf 6 was 84.75 mm2 under the condition of temperature 28 °C, humidity 50%, and light intensity 92 μmol·m−2·s−1. The maximum final size of leaf 6 was 174.31 mm2 under the condition of temperature 25 °C, humidity 50%, and light intensity 184 μmol·m−2·s−1. Therefore, the final leaf size was greatly affected by its growth environments. Studying the effect of growth environment factors on the final leaf size could provide basic data for breeding the ideal plant type.Table 2Regression equations of leaf number under various growth environments (temperature; humidity; light intensity)Trial factorsGrowth regression equationTrial factorsGrowth regression equation22 °C; 50%; 278 μmol·m−2·s−1N = 0.68*t + 0.1722 °C; 80%; 184 μmol·m−2·s−1N = 0.65*t + 0.4022 °C; 65%; 92 μmol·m−2·s−1N = 0.47*t + 0.2225 °C; 65%; 278 μmol·m−2·s−1N = 0.68*t + 0.8025 °C; 50%; 184 μmol·m−2·s−1N = 0.67*t + 0.9925 °C; 80%; 92 μmol·m−2·s−1N = 0.45*t + 0.6128 °C; 80%; 278 μmol·m−2·s−1N = 0.71*t + 0.7428 °C; 65%; 184 μmol·m−2·s−1N = 0.57*t + 0.6328 °C; 50%; 92 μmol·m−2·s−1N = 0.38*t + 1.53The regression equations of the number of leaves per rosette with time were established using equation (3). The trends of the leaf number of A. thaliana under various growth environments with time are shown in Figure 3. It could be learned that there was a linear correlation between the leaf number and the growth time. The leaf emergence rate (the slope of the fitted line) under various growth conditions is shown in Table 2. The minimum leaf emergence rate was 0.38 leaves per day under the temperature of 28 °C, humidity 50%, and light intensity 92 μmol·m−2·s−1, while the maximum leaf emergence rate was 0.71 leaves per day under the temperature of 28 °C, humidity 80%, and light intensity 278 μmol·m−2·s−1. Therefore, the leaf emergence rate was also greatly affected by its growth environments.Figure 3The variation trends of the number of leaves with time under various growth environments (temperature; humidity; light intensity)Impact of growth conditions on the final leaf size of A. thaliana and the number of rosette leavesRange analysis and variance analysis of the orthogonal test were conducted to evaluate the effect of various environmental factors (temperature, humidity, and light intensity) on the individual leaf final size and the number of rosette leaves of A. thaliana. The results are shown in Tables 3–5.Table 3Range analysis results of the final size and the number of the rosette leaves of A. thalianaTrial numberTrial factorsEstimated resultsTHIFinal leaf size per mm2Leaf numbertemperature (°C)humidity (%)light intensity (μmol·m−2·s−1)Trial design12250278129.03212226592102.061632280184104.261842550184174.31155256527895.9915625809285.9014728509284.751282865184125.301192880278139.1812Final leaf sizeK1335.35388.09272.71K2356.2323.35403.87K3349.23329.34364.21K¯1{{\bar K}_1}111.78129.3690.90K¯2{{\bar K}_2}118.73107.78134.62K¯3{{\bar K}_3}116.41109.78121.40Range R6.9521.5843.72Leaf number analysisK1554842K2444244K3354448K¯1{{\bar K}_1}181614K¯2{{\bar K}_2}151415K¯3{{\bar K}_3}121516Range R622Table 4Analysis of variance for the final rosette leaf size of A. thalianaSourceSSDFMSF-valueFαSig.T75.11237.560.026F0.05(2,2) = 19.0F0.1(2,2) = 9.0H853.192426.600.30I3018.7721509.391.06Error2860.5421430.27Total6807.618Table 5Analysis of variance for the number of rosette leaves of A. thalianaSourceSSDFMSF-valueFαSig.T66.89233.4542.88F0.05(2,2) = 19.0F0.1(2,2) = 9.0**H6.2223.113.99I6.2223.113.99Error1.5620.78Total80.898The increase in the individual leaf area resulted in the increase in the rosette area, which intensified the interception and utilization of light energy and provided higher above-ground biomass. It could be concluded by range analysis (Table 3) that the order of environmental factors affecting the final size of A. thaliana leaves was: light intensity (I) > humidity (H) > temperature (T). So the light intensity was the main factor affecting the final size of the individual leaf. Compared with the light intensity of 184 μmol·m−2·s−1, the final size of leaf 6 decreased by 32.84% and 9.82% at 92 and 278 μmol·m−2·s−1, respectively. This was consistent with the study results of Cookson et al. (2005), where low light condition caused a significant reduction in the final leaf area. Under the light intensity of 92 and 278 μmol·m−2·s−1, the duration of leaf 6 expansion increased by 2 days and by 1 day, respectively, compared with that under the condition of 184 μmol·m−2·s−1. Under the condition of 92 μmol·m−2·s−1 light intensity, the leaf 6 expansion rate decreased by 5.99 mm2·d−1 compared with that under 184 μmol·m−2·s−1 light intensity. These indicated that the reduction in individual leaf area was associated with a reduction in leaf expansion rate and an increase in the duration of leaf expansion (Cookson & Granier 2006). However, under the condition of 278 μmol·m−2·s−1 light intensity, the leaf 6 expansion rate increased by 0.21 mm2·d−1 compared with that under 184 μmol·m−2·s−1 light intensity. This may be caused by temperature and humidity, which need to be further studied. Light intensity also significantly affected the leaf emergence rate of A. thaliana. Compared with 278 μmol·m−2·s−1, the A. thaliana leaf emergence rates were reduced by 0.26 and 0.06 leaves per day under the condition of 92 and 184 μmol·m−2·s−1 light intensity, respectively. Analysis of variance (Table 4) showed that the effect of light intensity (I) on the final leaf size was greater. However, the effect of temperature, humidity, and light intensity was not significant, which was quite different from the growth image in Figure 1, mainly because it represents the image on day 20, and the emergence time and growth rate of leaves under different conditions was different, moreover, the analysis concern the final size of the leaf. It could be concluded, that the order of environmental factors affecting the final size of A. thaliana leaves was: light intensity (I) > humidity (H) > temperature (T), which was consistent with that obtained by range analysis.The increase in the leaf number caused an increase in the rosette area. It could be concluded by range analysis that the order of environmental factors affecting the leaf number of A. thaliana was: temperature (T) > humidity (H) = light intensity (I). Compared with 22 °C, the number of rosette leaves was reduced by 19.97% and 38.19% under the conditions of 25 °C and 28 °C, respectively. This was consistent with the study results of Crawford et al. (2012), where the leaf number of A. thaliana grown at high temperature (28 °C) was lower than that at low temperature (22 °C). Compared with 22 °C and 25 °C, A. thaliana had the shortest growth cycle at 28 °C, indicating that as the temperature increased, the life cycle of A. thaliana was shortened. Moreover, the reduction in the duration of the phase of leaf initiation could result in a reduction in leaf number (Cookson & Granier 2006). Therefore, as the temperature increased, the leaf number had a declining trend. Analysis of the variance (Table 5) showed that the effect of temperature on the leaf number was highly significant. This was because low light intensity inhibited growth and development, especially when the humidity was too high. It could be concluded, by F-value, that the order of environmental factors affecting the leaf number of A. thaliana was: temperature > humidity = light intensity, which was consistent with that obtained by the range analysis.In order to intuitively analyze the rules and trends of the effect of environmental factors on the individual leaf size of A. thaliana and the leaf number, the environmental factor level was taken as the abscissa and the average value K¯{\bar {\rm K}}of the final size of the individual leaf, and leaf number at each environmental factor level was taken as the ordinate. In this way, the environmental factors and the test results were obtained, as shown in Figure 4.Figure 4Trend charts of environmental factors levelIn Figure 4, the final size of leaf 6 reached its maximum value at 25 °C, and increased by 5.85%, and 1.95% compared with that at 22 °C and 28 °C, respectively. With the increase in temperature, the final leaf size exhibited little difference. This means that the effect of temperature on the final leaf size was not significant. When the relative humidity increased from 50% to 65%, the final size of leaf 6 decreased by 16.68%, and when the relative humidity increased from 65% to 80%, the final size of this leaf increased by 1.82%. Therefore, a low humidity (50%) was beneficial to the growth and development of leaf 6, and high humidity (80%) inhibited the growth. Compared with 184 μmol·m−2·s−1, the final size of leaf 6 was reduced by 32.84% and 9.82% at 92 and 278 μmol·m−2·s−1, respectively, which indicated that the final size of this leaf decreased significantly under low-light conditions.In Figure 4, the number of leaves was reduced by 19.97% and 38.19% at 25 °C and 28 °C, respectively, compared with 22 °C. At the relative humidity of 65%, the number of leaves increased by 12.5% and 6.67% compared with 50%, and 80%, respectively. When the light intensity increased from 92 μmol·m−2·s−1 to 184 μmol·m−2·s−1 and 278 μmol·m−2·s−1, the number of leaves increased by 6.67% and 6.25%, respectively. The analysis of the number of leaves under the influence of various environmental factors showed that the influence of temperature was significant, while the influence of humidity and light intensity was small.The optimum combination of environmental factors could be determined by analyzing the above test results, as shown in Table 6. The optimization conditions of the final leaf size and leaf number obtained by individual leaf analysis were inconsistent. Therefore, the optimum environmental parameters for the growth of A. thaliana rosette leaves must be determined according to the major and minor order of environmental factors.Table 6Optimization table for the environmental factorsCharacteristicCombination of optimal growth conditionsFinal leaf size25 °C, 50% humidity, 184 μmol·m−2·s−1Leaf number22 °C, 50% humidity, 278 μmol·m−2·s−1The temperature was the major factor influencing the leaf number, but for the final leaf size, it was a secondary factor. Therefore, the temperature of 22 °C was selected as the optimum growth temperature according to the test result of leaf number. The effects of humidity on the final leaf size and leaf number were both the secondary factor, and the value of 50% was selected as the optimum growth humidity. Light intensity was the major factor affecting the final leaf size, but for the leaf number, it was a secondary factor. Therefore, the light intensity 184 μmol·m−2·s−1 was selected as the optimum growth according to the test result of the final leaf size. Finally, the optimum growth environments for the rosette leaves growth of A. thaliana were determined, which were temperature 22 °C, relative humidity 50%, and light intensity 184 μmol·m−2·s−1.CONCLUSIONSThe main motivation for the present experiments was to study how the temperature, relative humidity, and light intensity affected the final leaf size and leaf number of A. thaliana, which was a notable advantage of our research analyzing the effects of multi environmental factors on the phenotypes of A. thaliana, since previous studies had mostly focused on the impact of single environmental factors on the plant growth. The effects of various environmental factors on the final size and the number of leaves of A. thaliana were determined, the cognitive scope of its physiological function was expanded, as well as the optimum growth environment for specific genotype A. thaliana was validated.The dynamics of individual leaf area and leaf number as a function of time were analyzed and fitted by the statistical analysis method.In this paper, an effective method of phenotypic assessment was developed, which can be helpful in optimizing phenotyping procedures applicable for breeding new cultivars adapted to given climatic conditions, or to select genotypes with the desired traits suitable for cultivation under specific conditions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Horticultural Research de Gruyter

Effects of Environmental Stresses on the Growth of Arabidopsis thaliana Rosette Leaves

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de Gruyter
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© 2022 Xiang Jiao et al., published by Sciendo
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2300-5009
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10.2478/johr-2022-0008
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Abstract

INTRODUCTIONPlant phenotype is the result of the interaction between genotype and growth environments. Studying the phenotype of plants under various growth environments is the core of understanding plant function. Recent research indicates that in the context of rapid climate change, phenotypes play a key role in plant adaptation to various growth factors (Hu et al. 2008; de Jong et al. 2012; Gratani et al. 2014; Scharr et al. 2016), and biotic and abiotic stresses lead to phenotypic changes in plants. Therefore, identifying the plant phenotypic signatures under biotic or abiotic stress conditions is helpful for the early detection of biotic or abiotic stresses, which have great economic benefits for plant production (Pauli et al. 2016).Leaves are the energy factories of plants and play an important role in the process of plant survival and growth. Through photosynthesis, leaves convert solar into chemical energy, which can then be used for further metabolism and ultimately in the production of food, feed, and fuel (Xu et al. 2009; Rodriguez et al. 2014). Since most photosynthesis occurs in leaves, it is important to characterize them in terms of size, shape, and number, which are regulated by the growth environment and genetic factors (Gonzalez et al. 2010, 2012; Mishra et al. 2012; Weraduwage et al. 2015). Many functional genomic studies have been carried out to improve agricultural and forestry crops using high-throughput genomic tools. However, there are relatively few studies on the phenotypic characteristics (e.g., the size and morphological structure of organs) of plants with specific genotypes under various growth conditions (Rahaman et al. 2015). Therefore, the study of the relationships between phenotypes, genes, and environments should be expanded. It will deepen our understanding of the relationships between the observable plant phenotypes and their physiological status, and the effects of different growth environments on plant growth, yield, and quality (Ke 2014; Orgogozo et al. 2015).Studying the effects of environmental factors on plant phenotype is helpful in understanding the biological functions of plant development, serving in breeding to develop cultivars with ideal phenotypes. However, the majority of experiments testing the response of plants to changes in environmental conditions have focused on a single stress treatment applied to plants under controlled conditions (Wang & Zhou 2021). In contrast, in practice, a number of different stresses can occur simultaneously. These may include irradiance, temperature, humidity, or water availability and may alter plant metabolism in a novel manner that may be different from that caused by each of the different stresses applied individually. A. thaliana is an ideal model for studying those problems and has been widely used for this purpose (Minervini et al. 2014). As a model organism, A. thaliana has several advantages, such as its small size, short life cycle, and known gene sequence, which make it easy to conduct experiments in the laboratory. By cultivating A. thaliana under a controllable environment, the change regulation of its phenotypes can be used to guide the studies of other plants, and provide references for outdoor experiments, where the environmental conditions and other factors are difficult to control. Moreover, analyzing the phenotypic characteristics of A. thaliana under various growth environments can provide a theoretical framework for quantifying and understanding plant phenotypic differences caused by environmental stresses (Boyes et al. 2001).Earlier studies did not analyze the effect of different growth environments (temperature, humidity, light intensity) on A. thaliana phenotypes, which could not adequately explain the relationship between A. thaliana phenotypes and environments, and the effect of different growth environments on A. thaliana growth. Moreover, different environmental factors and phenotypic characteristics have rarely been integrated into a single model, and predicting and analyzing the influence of environmental factors on phenotypic characteristics has been challenging.Using image analysis to nondestructively analyze plant leaf size in greenhouses, a number of studies have been conducted (Fahlgren et al. 2015; Ge et al. 2016). Compared to destructive sampling, image analysis enables the measurements of leaf size multiple times during the plant growth cycle, and also allows the quantification of dynamic traits such as growth rate and leaf expansion rate (Liang et al. 2018).The aim of this paper was to study the effect of environmental factors of temperature, humidity, and light intensity on the phenotypic characteristics of A. thaliana through a multi-factor orthogonal test, which may be helpful in optimizing the parameters for phenotyping and selecting new improved genotypes with the desired traits in cultivation under specific climatic conditions.MATERIALS AND METHODSCultivation of A. thaliana and leaf image acquisition under various growth environmentsFrom November 2015 to December 2016, the area of individual leaves and the number of leaves of A. thaliana in nine different growth environments were measured at Nanjing Forestry University, China. Temperature, humidity, and light intensity were selected as environmental factors for the experiments, and orthogonal tests of three factors at three levels were carried out. Seeds of A. thaliana Col-0 were sown into nursery substrate of peat fibers: 0–10 mm, and cultured in a climate chamber (RXZ-500B, Ningbo Jiangnan Instrument Factory, Zhejiang, China). The environmental parameters (temperature; humidity; light intensity) could be individually configured in the chamber, so various combinations of these parameters could be simulated. Nine sets of experiments were conducted by setting the environmental parameters of the climate chamber and 50 samples were taken in each experiment (Table 1). After A. thaliana leaf emerged, the growth image of A. thaliana was collected every day by a CCD camera (JAI, model: GO-5000C-PGE). A total of 20,200 original images were obtained, which were used to extract the leaf area of A. thaliana. Figure 1 shows the growth images of A. thaliana on the 20th day after the leaf emerged in the 38th pot in all nine sets of experiments.Figure 1Example images of A. thaliana leaves at 20th day after emergence under various growth environments (temperature; humidity; light intensity). Note: the blue squares in each picture represent calibration plateTable 1Trial environment parameters of each groupTrial numberTrial factorsTrial factorsTHItemperature (°C)humidity (%)light intensity (μmol·m−2·s−1)1225027822 °C; 50%; 278 μmol·m−2·s−1222659222 °C; 65%; 92 μmol·m−2·s−13228018422 °C; 80%; 184 μmol·m−2·s−14255018425 °C; 50%; 184 μmol·m−2·s−15256527825 °C; 65%; 278 μmol·m−2·s−1625809225 °C; 80%; 92 μmol·m−2·s−1728509228 °C; 50%; 92 μmol·m−2·s−18286518428 °C; 65%; 184 μmol·m−2·s−19288027828 °C; 80%; 278 μmol·m−2·s−1Image analysis and feature extractionMATLAB (version R2010, MathWorks) was used for image analysis. Rosette leaves of A. thaliana were measured at regular intervals during the whole life cycle using image analysis. Firstly, individual leaves were segmented from the plant using an interactive segmentation process. Then, the total number of pixels in the region of the individual leaf area was extracted using the image region property measurement function (regionprops) in MATLAB's Image Processing Toolbox. Finally, the size of the individual leaf was obtained by the proportional relationship calculated from a blue square standard plate (10 mm × 10 mm) with the following equation:(1)A=PL×AB/PBA = {P_L} \times {A_B}/{P_B}where A is the A. thaliana leaf area (mm2); PL is the number of pixels of the A. thaliana leaf area (pixel); AB is the area of the standard plate (mm2); and PB is the number of pixels of the standard plate (pixel).The smallest leaf was visible when the individual leaf area was approximately 0.5 mm2 (Cookson et al. 2010). In this paper, the number of leaves was calculated every 2 days during the period from two visible leaves to bolting.Growth equation of rosette leaves of A. thalianaIndividual leaf area and leaf numbers were analyzed and fitted using a logistic model and a linear regression model. The regression equations between leaf area, leaf number, and growth time were established by SPSS software (IBM SPSS statistics 19.0). The equations of the models were as follows (Cookson et al. 2010; Karadavut et al. 2010; Jiao et al. 2018):(2)logistic model Area=a1+b⋅e−k⋅t{{\rm logistic} \;{\rm model}}\;Area = {a \over {1 + b \cdot {e^{ - k \cdot t}}}}(3)linear regression model N=k1⋅t+k2{\rm{linear}}\;{\rm{regression}}\;{\rm{model}}\;N = {k_1} \cdot t + {k_2}where Area is the fitted values of leaf area (mm2); t is the growth time (d); k is the growth rate (mm2·d−1); a is the final leaf size (mm2); b is a constant scale; N is the number of leaves; k1 is a slope, which represents leaf emergence rate; and k2 is the intercept term.The final sizes of the leaf area under different growth environments were determined according to the established growth equations. Then, the effects of various growth environments on the final leaf size and the leaf number were analyzed.RESULTS AND DISCUSSIONCurve fitting to the final A. thaliana leaf size and the number of leaves in the rosette in the various growth environmentsLeaf 6 (the leaf number was determined by the order of the emergence) of each plant was taken as the target to analyze the effects of growth environments on the final leaf size of A. thaliana. Scatter plots of leaf 6 for each experiment under various growth conditions are shown in Figure 2.Figure 2Scatter plots of leaf 6 areas for each experiment under various growth environments (temperature; humidity; light intensity)It could be learned from Figure 2 that in the early stage, the growth and increase of leaf area were faster than in the maturation stage, producing a sigmoid curve. Moreover, the fitted values of leaf 6 agreed well with the measured values, with no obvious outliers. The results of regression analysis between leaf area and growth time (equation 2) were given in Table 2. The final size of leaf 6 for each experiment was determined by the parameter a in the regression equations. The minimum final size of leaf 6 was 84.75 mm2 under the condition of temperature 28 °C, humidity 50%, and light intensity 92 μmol·m−2·s−1. The maximum final size of leaf 6 was 174.31 mm2 under the condition of temperature 25 °C, humidity 50%, and light intensity 184 μmol·m−2·s−1. Therefore, the final leaf size was greatly affected by its growth environments. Studying the effect of growth environment factors on the final leaf size could provide basic data for breeding the ideal plant type.Table 2Regression equations of leaf number under various growth environments (temperature; humidity; light intensity)Trial factorsGrowth regression equationTrial factorsGrowth regression equation22 °C; 50%; 278 μmol·m−2·s−1N = 0.68*t + 0.1722 °C; 80%; 184 μmol·m−2·s−1N = 0.65*t + 0.4022 °C; 65%; 92 μmol·m−2·s−1N = 0.47*t + 0.2225 °C; 65%; 278 μmol·m−2·s−1N = 0.68*t + 0.8025 °C; 50%; 184 μmol·m−2·s−1N = 0.67*t + 0.9925 °C; 80%; 92 μmol·m−2·s−1N = 0.45*t + 0.6128 °C; 80%; 278 μmol·m−2·s−1N = 0.71*t + 0.7428 °C; 65%; 184 μmol·m−2·s−1N = 0.57*t + 0.6328 °C; 50%; 92 μmol·m−2·s−1N = 0.38*t + 1.53The regression equations of the number of leaves per rosette with time were established using equation (3). The trends of the leaf number of A. thaliana under various growth environments with time are shown in Figure 3. It could be learned that there was a linear correlation between the leaf number and the growth time. The leaf emergence rate (the slope of the fitted line) under various growth conditions is shown in Table 2. The minimum leaf emergence rate was 0.38 leaves per day under the temperature of 28 °C, humidity 50%, and light intensity 92 μmol·m−2·s−1, while the maximum leaf emergence rate was 0.71 leaves per day under the temperature of 28 °C, humidity 80%, and light intensity 278 μmol·m−2·s−1. Therefore, the leaf emergence rate was also greatly affected by its growth environments.Figure 3The variation trends of the number of leaves with time under various growth environments (temperature; humidity; light intensity)Impact of growth conditions on the final leaf size of A. thaliana and the number of rosette leavesRange analysis and variance analysis of the orthogonal test were conducted to evaluate the effect of various environmental factors (temperature, humidity, and light intensity) on the individual leaf final size and the number of rosette leaves of A. thaliana. The results are shown in Tables 3–5.Table 3Range analysis results of the final size and the number of the rosette leaves of A. thalianaTrial numberTrial factorsEstimated resultsTHIFinal leaf size per mm2Leaf numbertemperature (°C)humidity (%)light intensity (μmol·m−2·s−1)Trial design12250278129.03212226592102.061632280184104.261842550184174.31155256527895.9915625809285.9014728509284.751282865184125.301192880278139.1812Final leaf sizeK1335.35388.09272.71K2356.2323.35403.87K3349.23329.34364.21K¯1{{\bar K}_1}111.78129.3690.90K¯2{{\bar K}_2}118.73107.78134.62K¯3{{\bar K}_3}116.41109.78121.40Range R6.9521.5843.72Leaf number analysisK1554842K2444244K3354448K¯1{{\bar K}_1}181614K¯2{{\bar K}_2}151415K¯3{{\bar K}_3}121516Range R622Table 4Analysis of variance for the final rosette leaf size of A. thalianaSourceSSDFMSF-valueFαSig.T75.11237.560.026F0.05(2,2) = 19.0F0.1(2,2) = 9.0H853.192426.600.30I3018.7721509.391.06Error2860.5421430.27Total6807.618Table 5Analysis of variance for the number of rosette leaves of A. thalianaSourceSSDFMSF-valueFαSig.T66.89233.4542.88F0.05(2,2) = 19.0F0.1(2,2) = 9.0**H6.2223.113.99I6.2223.113.99Error1.5620.78Total80.898The increase in the individual leaf area resulted in the increase in the rosette area, which intensified the interception and utilization of light energy and provided higher above-ground biomass. It could be concluded by range analysis (Table 3) that the order of environmental factors affecting the final size of A. thaliana leaves was: light intensity (I) > humidity (H) > temperature (T). So the light intensity was the main factor affecting the final size of the individual leaf. Compared with the light intensity of 184 μmol·m−2·s−1, the final size of leaf 6 decreased by 32.84% and 9.82% at 92 and 278 μmol·m−2·s−1, respectively. This was consistent with the study results of Cookson et al. (2005), where low light condition caused a significant reduction in the final leaf area. Under the light intensity of 92 and 278 μmol·m−2·s−1, the duration of leaf 6 expansion increased by 2 days and by 1 day, respectively, compared with that under the condition of 184 μmol·m−2·s−1. Under the condition of 92 μmol·m−2·s−1 light intensity, the leaf 6 expansion rate decreased by 5.99 mm2·d−1 compared with that under 184 μmol·m−2·s−1 light intensity. These indicated that the reduction in individual leaf area was associated with a reduction in leaf expansion rate and an increase in the duration of leaf expansion (Cookson & Granier 2006). However, under the condition of 278 μmol·m−2·s−1 light intensity, the leaf 6 expansion rate increased by 0.21 mm2·d−1 compared with that under 184 μmol·m−2·s−1 light intensity. This may be caused by temperature and humidity, which need to be further studied. Light intensity also significantly affected the leaf emergence rate of A. thaliana. Compared with 278 μmol·m−2·s−1, the A. thaliana leaf emergence rates were reduced by 0.26 and 0.06 leaves per day under the condition of 92 and 184 μmol·m−2·s−1 light intensity, respectively. Analysis of variance (Table 4) showed that the effect of light intensity (I) on the final leaf size was greater. However, the effect of temperature, humidity, and light intensity was not significant, which was quite different from the growth image in Figure 1, mainly because it represents the image on day 20, and the emergence time and growth rate of leaves under different conditions was different, moreover, the analysis concern the final size of the leaf. It could be concluded, that the order of environmental factors affecting the final size of A. thaliana leaves was: light intensity (I) > humidity (H) > temperature (T), which was consistent with that obtained by range analysis.The increase in the leaf number caused an increase in the rosette area. It could be concluded by range analysis that the order of environmental factors affecting the leaf number of A. thaliana was: temperature (T) > humidity (H) = light intensity (I). Compared with 22 °C, the number of rosette leaves was reduced by 19.97% and 38.19% under the conditions of 25 °C and 28 °C, respectively. This was consistent with the study results of Crawford et al. (2012), where the leaf number of A. thaliana grown at high temperature (28 °C) was lower than that at low temperature (22 °C). Compared with 22 °C and 25 °C, A. thaliana had the shortest growth cycle at 28 °C, indicating that as the temperature increased, the life cycle of A. thaliana was shortened. Moreover, the reduction in the duration of the phase of leaf initiation could result in a reduction in leaf number (Cookson & Granier 2006). Therefore, as the temperature increased, the leaf number had a declining trend. Analysis of the variance (Table 5) showed that the effect of temperature on the leaf number was highly significant. This was because low light intensity inhibited growth and development, especially when the humidity was too high. It could be concluded, by F-value, that the order of environmental factors affecting the leaf number of A. thaliana was: temperature > humidity = light intensity, which was consistent with that obtained by the range analysis.In order to intuitively analyze the rules and trends of the effect of environmental factors on the individual leaf size of A. thaliana and the leaf number, the environmental factor level was taken as the abscissa and the average value K¯{\bar {\rm K}}of the final size of the individual leaf, and leaf number at each environmental factor level was taken as the ordinate. In this way, the environmental factors and the test results were obtained, as shown in Figure 4.Figure 4Trend charts of environmental factors levelIn Figure 4, the final size of leaf 6 reached its maximum value at 25 °C, and increased by 5.85%, and 1.95% compared with that at 22 °C and 28 °C, respectively. With the increase in temperature, the final leaf size exhibited little difference. This means that the effect of temperature on the final leaf size was not significant. When the relative humidity increased from 50% to 65%, the final size of leaf 6 decreased by 16.68%, and when the relative humidity increased from 65% to 80%, the final size of this leaf increased by 1.82%. Therefore, a low humidity (50%) was beneficial to the growth and development of leaf 6, and high humidity (80%) inhibited the growth. Compared with 184 μmol·m−2·s−1, the final size of leaf 6 was reduced by 32.84% and 9.82% at 92 and 278 μmol·m−2·s−1, respectively, which indicated that the final size of this leaf decreased significantly under low-light conditions.In Figure 4, the number of leaves was reduced by 19.97% and 38.19% at 25 °C and 28 °C, respectively, compared with 22 °C. At the relative humidity of 65%, the number of leaves increased by 12.5% and 6.67% compared with 50%, and 80%, respectively. When the light intensity increased from 92 μmol·m−2·s−1 to 184 μmol·m−2·s−1 and 278 μmol·m−2·s−1, the number of leaves increased by 6.67% and 6.25%, respectively. The analysis of the number of leaves under the influence of various environmental factors showed that the influence of temperature was significant, while the influence of humidity and light intensity was small.The optimum combination of environmental factors could be determined by analyzing the above test results, as shown in Table 6. The optimization conditions of the final leaf size and leaf number obtained by individual leaf analysis were inconsistent. Therefore, the optimum environmental parameters for the growth of A. thaliana rosette leaves must be determined according to the major and minor order of environmental factors.Table 6Optimization table for the environmental factorsCharacteristicCombination of optimal growth conditionsFinal leaf size25 °C, 50% humidity, 184 μmol·m−2·s−1Leaf number22 °C, 50% humidity, 278 μmol·m−2·s−1The temperature was the major factor influencing the leaf number, but for the final leaf size, it was a secondary factor. Therefore, the temperature of 22 °C was selected as the optimum growth temperature according to the test result of leaf number. The effects of humidity on the final leaf size and leaf number were both the secondary factor, and the value of 50% was selected as the optimum growth humidity. Light intensity was the major factor affecting the final leaf size, but for the leaf number, it was a secondary factor. Therefore, the light intensity 184 μmol·m−2·s−1 was selected as the optimum growth according to the test result of the final leaf size. Finally, the optimum growth environments for the rosette leaves growth of A. thaliana were determined, which were temperature 22 °C, relative humidity 50%, and light intensity 184 μmol·m−2·s−1.CONCLUSIONSThe main motivation for the present experiments was to study how the temperature, relative humidity, and light intensity affected the final leaf size and leaf number of A. thaliana, which was a notable advantage of our research analyzing the effects of multi environmental factors on the phenotypes of A. thaliana, since previous studies had mostly focused on the impact of single environmental factors on the plant growth. The effects of various environmental factors on the final size and the number of leaves of A. thaliana were determined, the cognitive scope of its physiological function was expanded, as well as the optimum growth environment for specific genotype A. thaliana was validated.The dynamics of individual leaf area and leaf number as a function of time were analyzed and fitted by the statistical analysis method.In this paper, an effective method of phenotypic assessment was developed, which can be helpful in optimizing phenotyping procedures applicable for breeding new cultivars adapted to given climatic conditions, or to select genotypes with the desired traits suitable for cultivation under specific conditions.

Journal

Journal of Horticultural Researchde Gruyter

Published: Jun 1, 2022

Keywords: Arabidopsis thaliana; humidity; image analysis; light intensity; rosette leaves; temperature

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