Mechanisms of glacial‐to‐future atmospheric CO 2 effects on plant immunity

Summary The impacts of rising atmospheric CO2 concentrations on plant disease have received increasing attention, but with little consensus emerging on the direct mechanisms by which CO2 shapes plant immunity. Furthermore, the impact of sub‐ambient CO 2 concentrations, which plants have experienced repeatedly over the past 800 000 yr, has been largely overlooked. A combination of gene expression analysis, phenotypic characterisation of mutants and mass spectrometry‐based metabolic profiling was used to determine development‐independent effects of sub‐ambient CO 2 (sa CO 2) and elevated CO 2 (eCO 2) on Arabidopsis immunity. Resistance to the necrotrophic Plectosphaerella cucumerina (Pc) was repressed at sa CO 2 and enhanced at eCO 2. This CO 2‐dependent resistance was associated with priming of jasmonic acid (JA)‐dependent gene expression and required intact JA biosynthesis and signalling. Resistance to the biotrophic oomycete Hyaloperonospora arabidopsidis (Hpa) increased at both eCO 2 and sa CO 2. Although eCO 2 primed salicylic acid (SA)‐dependent gene expression, mutations affecting SA signalling only partially suppressed Hpa resistance at eCO 2, suggesting additional mechanisms are involved. Induced production of intracellular reactive oxygen species (ROS) at sa CO 2 corresponded to a loss of resistance in glycolate oxidase mutants and increased transcription of the peroxisomal catalase gene CAT2, unveiling a mechanism by which photorespiration‐derived ROS determined Hpa resistance at saCO2. By separating indirect developmental impacts from direct immunological effects, we uncover distinct mechanisms by which CO 2 shapes plant immunity and discuss their evolutionary significance.


Fig. S1
Effects of CO 2 on plant development. Data represent average leaf numbers (± SE; n = 8) plotted against time (days) at ambient CO 2 (aCO 2 ; 400 ppm; dashed line), subambient CO 2 (saCO 2 ; 200 ppm; dotted line) and elevated CO 2 (eCO 2 ; 1200 ppm; straight line). Inserts show typical rosette sizes of 4.5-week old plants. Red and blue lines illustrate differences in absolute age at the 8-and 18-leaf stage, respectively. Shown are results from a representative experiment that was repeated twice.

Fig. S2
Representative examples of the four different Hpa colonization classes that were used to quantify Arabidopsis resistance. To visualise Hpa colonisation, leaves were stained with lactophenol trypan-blue, as described previously (Luna et al. 2012). Class I is defined by a lack of hyphal growth; Class II sustains hyphal development, but not the production of asexual conidiospores; Class III is characterised by extensive hyphal colonisation and the formation of conidiophores and asexual condiospores; Class IV is similar as class III, but with additional formation of sexual oospores (> 10 per leaf). Black bars indicate scales.  Levels of SA-inducible PR1 gene expression in 8-leaf Col-0 at saCO 2 (200 ppm) and aCO 2 (400 ppm). Shown are box plots of relative transcript values (n = 3; means are indicated by X) at 24 hours after treatment. (b) Quantification of Hpa resistance at saCO 2 and aCO 2 in Col-0, the SA insensitive npr1-1 mutant, and the SA production mutant sid2-1 at the 8leaf stage. Shown are relative numbers of leaves (n > 50) in Hpa colonization classes of increasing severity (I-IV) at 7 dpi. Letters -(a) ANOVA with Tukey HSD post hoc analysis -or asterisks -(b) Fisher's exact test -indicate statistically significant differences between conditions (P < 0.05). The pathogenicity assays with sid2-1 and npr1-1 were repeated with similar results.   Shown are relative numbers of leaves (n > 50) in Hpa colonization classes of increasing severity (I-IV) at 7 dpi. Asterisks indicate statistically significant differences between CO 2 conditions (Fisher's exact test; P < 0.05).

Methods S1: supplemental Materials and Methods
Chemicals and reagents. All chemicals and solvents (higher analytical MS grade) used in this study were purchased from Sigma-Aldrich (UK), except JA which was obtained from OlChemim (http://www.olchemim.cz/).

Targeted quantification of hormones.
SA and JA were quantified by UPLC-Q-TOF-MS E , using a previous method (Pétriacq et al., 2016). Briefly, phytohormones were double-extracted from frozen leaf material °C with an injection volume of 10 μL. Buffer (50% methanol) was injected between treatments and between ESIand ESI + ionization modes for stabilization of the electrospray ionization source.
Ions were detected over a mass range of 50 -1200 Da, using a scan time of 0.2 s (ESIand ESI + ) with the instrument operating in sensitivity mode for the MS full scan (i.e. without collision energy). Collision energy was ramped in the transfer cell from 5 to 45 eV (MS E ), using the following conditions:

ESI -ESI +
Capillary voltage (kV) -3 + 3 Sampling cone voltage (V) -25 + 25 Extraction cone voltage (V) -4.5 + 10 Source Temperature (°C) 120 120 Desolvation Temperature (°C) 350 350 Desolvation gas flow (L h -1 ) 800 800 Cone gas flow (L h -1 ) 60 60 Prior to analysis, the Q-TOF detector was calibrated with a solution of sodium formate. During each run, accurate mass measurements were ensured by infusing leucine enkephalin peptide as an internal reference (10 s scan frequency, cone voltage of 40 V and a capillary voltage of 3 kV). The system was controlled by MassLynx v 4.1 software (Waters). , this analysis resulted in 3 subsets of markers, whose intensity was influenced by CO2, Hpa, or the CO2 x Hpa interaction (Fig. S6b). Hierarchal clustering (Pearson's correlation; MeV) allowed visual selection of ion clusters that are induced directly by saCO2 or primed for augmented induction after Hpa inoculation, as detailed in Fig. S6c. Putative identities of the selected ion markers were based on m/z values at stringent accuracy (< 30 ppm), the using METLIN chemical database (Smith et al., 2005).