IC4R008-Phenomics-2015-26111541

Project Title

• Integrating Image-Based Phenomics and Association Analysis to Dissect the Genetic Architecture of Temporal Salinity Responses in Rice

The Background of This Project

• Salinity affects a significant portion of arable land and is particularly detrimental for irrigated agriculture, which provides onethird of the global food supply. Rice (Oryza sativa), the most important food crop, is salt sensitive. The genetic resources for salt tolerance in rice germplasm exist but are underutilized due to the difficulty in capturing the dynamic nature of physiological responses to salt stress. The genetic basis of these physiological responses is predicted to be polygenic.

Plant Culture & Treatment

• Seeds from 373 genotypes from the rice (Oryza sativa) diversity panel were surface sterilized with fungicide, thiram, and germinated on moist paper towels in plastic boxes for 3 d (Famoso et al., 2011; Zhao et al., 2011). Three uniformly germinated seeds of each genotype were transplanted to pots (150-mm diameter 3 200-mm height) filled with 2.6 kg of UC Mix and placed into square containers to allow for water to collect. Plants were thinned to one seedling per pot 6 d after transplanting (DAT). For the first 7 DAT, each pot was watered daily with approximately 150 mL from the top of the pot. Over the course of the three experiments, the greenhouse temperatures during the day averaged 28.8°C (62.02°C, SD) and 26.0°C (61.01°C, SD) at night. Relative humidity was maintained at 63.4% (69.04%, SD) during the day and 69.7%(61.73%, SD) at night (Rotation Atomizer Defensor ABS3, Condair).
• Eight DAT, each pot was watered to a uniform weight so that approximately 600 mL of water was maintained in the soil. For the salt-stressed plants,100 mL of NaCl solution (270 mM NaCl:9.9 mM CaClb) was applied to the square dish, and small holes in the bottom of the pots allowed for the infiltration of salt into the soil through capillary action. Salt treatment was applied in two steps of 45 mM to reach a final concentration of 90 mM at 10 and 13 DAT(Supplemental Fig. S1A). Control plants received 100 mL of water on days 10 and 13 (Supplemental Fig. S2).

Fig.S1

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Research Findings

• Correlation analysis was done using seven biomass-related metrics and three manual measurements. Of the seven biomass-related metrics derived from color images, projected shoot area (PSA), which is defined as the sum of all pixels from all three RGB images, showed the highest correlation with all manual biomass-related measurements(Supplemental Table S2). As expected, PSA showed the strongest positive correlation with total plant area as well (r2 = 0.96, P , 0.001, n = 72; Fig. 1A).Shoot fresh and dry weight showed a strong positive correlation with PSA, although at a slightly lower correlation compared with total plant area (r2 = 0.95, P ,0.001 for both shoot fresh and dry weight; Fig. 1, B and C, respectively). A significant difference in PSA was detected between treatments using a one-way blocked ANOVA (where accession is considered as a block)beginning at day 4 after 90 mM NaCl (P , 0.0028; Fig.1D). These results indicate that PSA is an accurate and sensitive metric for assessing plant biomass accumulation in response to salinity.Notably, the tropical japonica subpopulation showed a significantly lower growth reduction in response to salinity when compared with other subpopulations, suggesting that this varietal group may be an important source for osmotic stress tolerance response during early stages of salinity stress (Fig. 1E).

'Figure 1. Salinity-induced growth responses in a rice diversity panel. A to C, Relationship between PSA and conventional biomass metrics. Pearson correlation analyses were performed between PSA and shoot area (A), shoot fresh mass (B), and shoot dry mass (C). D, Comparisons of PSA between treatments at each of the 18 d of imaging. Differences between treatments at each time point were determined using a one-way blocked ANOVA, where accession is considered as a block (P , 0.0027).E, Comparison of salinity-induced growth response models between each of the five subpopulations defined by Zhao et al.(2011). The salinity-induced growth response was modeled with a decreasing logistic curve, and pairwise comparisons were made between each subpopulation. Aromatic accessions were excluded due to low n. Mean growth responses for each subpopulation are denoted by solid lines, while the SE for each subpopulation is indicated by shadows. TRJ, Tropical japonica;TEJ, temperate japonica; IND, indica; ADM, admix.'

• To determine whether there were any differences in the salinity response among the five subpopulations as classified by Zhao et al. (2011), we calculated the salinity-induced growth response as the ratio of PSA in salt-treated plants over control plants. Aromatic lines were excluded due to small number of accessions. For each subpopulation, the salinity-induced growth response was modeled across all time points with a decreasing logistic curve. Therefore, on day 1 of salt treatment, the growth response is 1, and it begins to decrease after the onset of salinity stress and eventually flattens out as vegetative growth declines and plants transition to reproductive phase. Pairwise comparisons of growth response models revealed significant differences between several subpopulations(Table I).

• To assess the effects of salinity stress on leaf senescence, plants were imaged in a separate fluorescenceimaging chamber.Because the available functions are limited in LemnaGrid software, we developed an open-source processing software called Image Harvest to extract several spectral metrics from the 95,118 fluorescence images. Color ranges that may be indicative of salinity-induced chlorophyll responses were identified by utilizing an ad hoc image segmentation strategy that classified the range of colors present in all fluorescence images into 90 classes of color ranges.Based on our pixel classification strategy, we identified 32 color classes that showed significant differences between treatments after three or more days of salt stress across all 373 accessions (P , 0.00056; Fig. 2A).we performed hierarchal clustering analysis (HCA)using the mean temporal trend for each color class across all 373 accessions for each treatment. No clear distinction between treatments could be observed from hierarchal clustering. Rather, clustering seemed to be driven largely by the behavior of color classes over time (Fig. 2B).

'Figure 2. The development of imagebased fluorescence traits for monitoring chlorophyll responses to salinity.A, Salinity-responsive color classes were identified through comparisons between treatments at each time point via one-way ANOVA. Color classes were considered to be responsive to saline conditions if significant differences between treatments were observed in 3 or more days of 90 mM NaCl stress (P , 0.00056).B, Identification of color classes exhibiting similar trends over 14 d of 90 mM NaCl. HCA with complete linkage was performed using the mean value in each treatment for each color class. The six clusters are depicted to the right of the dendrogram. Labels in red indicate mean response in saline conditions, while those in black indicate control conditions. The right section summarizes the temporal trend captured by each cluster. The mean values for each color class were scaled and centered prior to clustering, so that the mean is 0 and variance is 1,and are represented on the y axis.'

Labs working on this Project

• Department of Agronomy and Horticulture (M.T.C., H.W.), Holland Computing Center (A.C.K.);
• Department of Statistics (D.W.), University of Nebraska, Lincoln, Nebraska 68583;
• The Plant Accelerator,Australian Plant Phenomics Facility, University of Adelaide, Urrbrae, South Australia 5064, Australia (B.B.);
• Phenomics and Bioinformatics Research Centre, University of South Australia, Adelaide, South Australia 5001, Australia (C.J.B.)

Corresponding Author

• Malachy T. Campbell:0000-0002-8257-3595;