Variation in leaf transcriptome responses to elevated ozone corresponds with physiological sensitivity to ozone across maize inbred lines.

We examine the impact of sustained elevated ozone concentration on the leaf transcriptome of 5 diverse maize inbred genotypes, which vary in physiological sensitivity to ozone (B73, Mo17, Hp301, C123, and NC338), using long reads to assemble transcripts and short reads to quantify expression of these transcripts. More than 99% of the long reads, 99% of the assembled transcripts, and 97% of the short reads map to both B73 and Mo17 reference genomes. Approximately 95% of the genes with assembled transcripts belong to known B73-Mo17 syntenic loci and 94% of genes with assembled transcripts are present in all temperate lines in the nested association mapping pan-genome. While there is limited evidence for alternative splicing in response to ozone stress, there is a difference in the magnitude of differential expression among the 5 genotypes. The transcriptional response to sustained ozone stress in the ozone resistant B73 genotype (151 genes) was modest, while more than 3,300 genes were significantly differentially expressed in the more sensitive NC338 genotype. There is the potential for tandem duplication in 30% of genes with assembled transcripts, but there is no obvious association between potential tandem duplication and differential expression. Genes with a common response across the 5 genotypes (83 genes) were associated with photosynthesis, in particular photosystem I. The functional annotation of genes not differentially expressed in B73 but responsive in the other 4 genotypes (789) identifies reactive oxygen species. This suggests that B73 has a different response to long-term ozone exposure than the other 4 genotypes. The relative magnitude of the genotypic response to ozone, and the enrichment analyses are consistent regardless of whether aligning short reads to: long read assembled transcripts; the B73 reference; the Mo17 reference. We find that prolonged ozone exposure directly impacts the photosynthetic machinery of the leaf.

Plant biochemistry influences tropospheric ozone formation, destruction, deposition, and response.

Tropospheric ozone (O3) is among the most damaging air pollutant to plants. Plants alter the atmospheric O3 concentration in two distinct ways: (i) by the emission of volatile organic compounds (VOCs) that are precursors of O3; and (ii) by dry deposition, which includes diffusion of O3 into vegetation through stomata and destruction by nonstomatal pathways. Isoprene, monoterpenes, and higher terpenoids are emitted by plants in quantities that alter tropospheric O3. Deposition of O3 into vegetation is related to stomatal conductance, leaf structural traits, and the detoxification capacity of the apoplast. The biochemical fate of O3 once it enters leaves and reacts with aqueous surfaces is largely unknown, but new techniques for the tracking and identification of initial products have the potential to open the black box.

Age-dependent increase in α-tocopherol and phytosterols in maize leaves exposed to elevated ozone pollution.

Tropospheric ozone is a major air pollutant that significantly damages crop production. Crop metabolic responses to rising chronic ozone stress have not been well studied in the field, especially in C4 crops. In this study, we investigated the metabolomic profile of leaves from two diverse maize (Zea mays) inbred lines and the hybrid cross during exposure to season-long elevated ozone (~100 nl L-1) in the field using free air concentration enrichment (FACE) to identify key biochemical responses of maize to elevated ozone. Senescence, measured by loss of chlorophyll content, was accelerated in the hybrid line, B73 × Mo17, but not in either inbred line (B73 or Mo17). Untargeted metabolomic profiling further revealed that inbred and hybrid lines of maize differed in metabolic responses to ozone. A significant difference in the metabolite profile of hybrid leaves exposed to elevated ozone occurred as leaves aged, but no age-dependent difference in leaf metabolite profiles between ozone conditions was measured in the inbred lines. Phytosterols and α-tocopherol levels increased in B73 × Mo17 leaves as they aged, and to a significantly greater degree in elevated ozone stress. These metabolites are involved in membrane stabilization and chloroplast reactive oxygen species (ROS) quenching. The hybrid line also showed significant yield loss at elevated ozone, which the inbred lines did not. This suggests that the hybrid maize line was more sensitive to ozone exposure than the inbred lines, and up-regulated metabolic pathways to stabilize membranes and quench ROS in response to chronic ozone stress.

Metabolite and transcript profiling of Guinea grass (Panicum maximum Jacq) response to elevated [CO2] and temperature.

INTRODUCTION: By mid-century, global atmospheric carbon dioxide concentration ([CO2]) is predicted to reach 600 µmol mol-1 with global temperatures rising by 2 °C. Rising [CO2] and temperature will alter the growth and productivity of major food and forage crops across the globe. Although the impact is expected to be greatest in tropical regions, the impact of climate-change has been poorly studied in those regions. OBJECTIVES: This experiment aimed to understand the effects of elevated [CO2] (600 µmol mol-1) and warming (+ 2 °C), singly and in combination, on Panicum maximum Jacq. (Guinea grass) metabolite and transcript profiles. METHODS: We created a de novo assembly of the Panicum maximum transcriptome. Leaf samples were taken at two time points in the Guinea grass growing season to analyze transcriptional and metabolite profiles in plants grown at ambient and elevated [CO2] and temperature, and statistical analyses were used to integrate the data. RESULTS: Elevated temperature altered the content of amino acids and secondary metabolites. The transcriptome of Guinea grass shows a clear time point separations, with the changes in the elevated temperature and [CO2] combination plots. CONCLUSION: Field transcriptomics and metabolomics revealed that elevated temperature and [CO2] result in alterations in transcript and metabolite profiles associated with environmental response, secondary metabolism and stomatal function. These metabolic responses are consistent with greater growth and leaf area production under elevated temperature and [CO2]. These results show that tropical C4 grasslands may have unpredicted responses to global climate change, and that warming during a cool growing season enhances growth and alleviates stress.

Variable selection in omics data: A practical evaluation of small sample sizes.

In omics experiments, variable selection involves a large number of metabolites/ genes and a small number of samples (the n < p problem). The ultimate goal is often the identification of one, or a few features that are different among conditions- a biomarker. Complicating biomarker identification, the p variables often contain a correlation structure due to the biology of the experiment making identifying causal compounds from correlated compounds difficult. Additionally, there may be elements in the experimental design (blocks, batches) that introduce structure in the data. While this problem has been discussed in the literature and various strategies proposed, the over fitting problems concomitant with such approaches are rarely acknowledged. Instead of viewing a single omics experiment as a definitive test for a biomarker, an unrealistic analytical goal, we propose to view such studies as screening studies where the goal of the study is to reduce the number of features present in the second round of testing, and to limit the Type II error. Using this perspective, the performance of LASSO, ridge regression and Elastic Net was compared with the performance of an ANOVA via a simulation study and two real data comparisons. Interestingly, a dramatic increase in the number of features had no effect on Type I error for the ANOVA approach. ANOVA, even without multiple test correction, has a low false positive rates in the scenarios tested. The Elastic Net has an inflated Type I error (from 10 to 50%) for small numbers of features which increases with sample size. The Type II error rate for the ANOVA is comparable or lower than that for the Elastic Net leading us to conclude that an ANOVA is an effective analytical tool for the initial screening of features in omics experiments.