The leaf ionome as a multivariable system to detect a plant's physiological status

Abstract

The contention that quantitative profiles of biomolecules contain information about the physiological state of the organism has motivated a variety of high-throughput molecular profiling experiments. However, unbiased discovery and validation of biomolecular signatures from these experiments remains a challenge. Here we show that the Arabidopsis thaliana (Arabidopsis) leaf ionome, or elemental composition, contains such signatures, and we establish statistical models that connect these multivariable signatures to defined physiological responses, such as iron (Fe) and phosphorus (P) homeostasis. Iron is essential for plant growth and development, but potentially toxic at elevated levels. Because of this, shoot Fe concentrations are tightly regulated and show little variation over a range of Fe concentrations in the environment, making them a poor probe of a plant's Fe status. By evaluating the shoot ionome in plants grown under different Fe nutritional conditions, we have established a multivariable ionomic signature for the Fe response status of Arabidopsis. This signature has been validated against known Fe-response proteins and allows the high-throughput detection of the Fe status of plants with a false negative/positive rate of 18%/16%. A "metascreen" of previously collected ionomic data from 880 Arabidopsis mutants and natural accessions for this Fe response signature successfully identified the known Fe mutants frd1 and frd3. A similar approach has also been taken to identify and use a shoot ionomic signature associated with P homeostasis. This study establishes that multivariable ionomic signatures of physiological states associated with mineral nutrient homeostasis do exist in Arabidopsis and are in principle robust enough to detect specific physiological responses to environmental or genetic perturbations.

Fig. 1.

Biochemical assessment of the Fe response status and Fe accumulation of Arabidopsis (Col-0): immunoblot showing IRT1 protein accumulation in roots (A), ferric chelate reducatse activity in roots (B), and Fe accumulation in leaves (C) of Arabidopsis Col-0 plants grown under Fe-deficient (1 μM FeHBED) and Fe-sufficient (10 μM FeHBED) conditions. Data for the ferric chelate reductase activity represents the mean ± SD of three batches of roots from approximately three plants each. The immunoblot represents IRT1 protein in a pooled sample of roots from ≈18 plants with 10 μg of total protein loaded in each lane, and the experiment replicated twice with a representative blot shown. Box plots represent the interquartile range (IQR), the bisecting line represents the median, the whiskers represent 1.5 times the IQR, the dots represent outlier points, and the data are from 21 individual plants. All plants were grown for 5 weeks in soil.

Fig. 2.

Box plot of the Mn, Co, Zn, Mo, and Cd concentrations of Arabidopsis (Col-0) shoots when grown with varying concentrations of FeHBED. For each concentration, the box represents the interquartile range (IQR), the bisecting line represents the median, the whiskers represent 1.5 times the IQR, and the dots represent outlier points. All five elements are significantly different (P < 0.001) between treatments. Plants (n = 21) were grown for 5 weeks in soil.

Fig. 3.

PCA based on the shoot concentrations of Mn, Co, Zn, Mo, and Cd of Arabidopsis (Col-0) plants grown with low (1 μM FeHBED) or high (10 μM FeHBED) Fe fertilization. The analysis was performed on data from n = 200 plants for each group. Green circles and black triangles represent data from plants grown under low and high Fe conditions, respectively.

Fig. 4.

Receiver–operator curves of the various logistic regression models. Black line, simple model built and tested on training set; gray lines, simple models built on the bootstrapped set and tested on training set; blue line, simple model built on training set and tested on validation set; brown line, model built solely by using shoot Fe concentrations and tested on the full training set; red dot, cutoff level chosen for further studies; dotted line, unity, a model with no predictive ability.