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Additional file 2: Table S1. Pigment recovery of all-trans-β-Car, Lut and Chl a. Table S2. Reproducibility of ΣDoL and NLP calculated from TQ-MS data. Table S3. Peak assignment and calculation of BPIi, BPIi(norm) and DoLi for individual isotopologs of all-trans-β-Car from a non-labeled Arabidopsis plant obtained by FTICR-MS. Table S4. Peak assignment and calculation of BPIi, BPIi(norm) and DoLi for individual isotopologs of all-trans-β-Car from a non-labeled Arabidopsis plant obtained by TQ-MS. Table S5. Peak assignment and calculation of BPIi, BPIi(norm) and DoLi for individual isotopologs of all-trans-β-Car from a 13C-labeled Arabidopsis plant obtained by FTICR-MS. Table S6. Peak assignment and calculation of BPIi, BPIi(norm) and DoLi for individual isotopologs of all-trans-β-Car from a 13C-labeled Arabidopsis plant obtained by TQ-MS. Table S7. Peak assignment and calculation of BPIi, BPIi(norm) and DoLi for individual isotopologs of Lut from a non-labeled Arabidopsis plant obtained by FTICR-MS. Table S8. Peak assignment and calculation of BPIi, BPIi(norm) and DoLi for individual isotopologs of Lut from a non-labeled Arabidopsis plant obtained by TQ-MS. Table S9. Peak assignment and calculation of BPIi, BPIi(norm) and DoLi for individual isotopologs of Lut from a 13C-labeled Arabidopsis plant obtained by FTICR-MS. Table S10. Peak assignment and calculation of BPIi, BPIi(norm) and DoLi for individual isotopologs of Lut from a 13C-labeled Arabidopsis plant obtained by TQ-MS. Table S11. Peak assignment and calculation of BPIi, BPIi(norm) and DoLi for individual isotopologs of Chl a from a non-labeled Arabidopsis plant obtained by FTICR-MS. Table S12. Peak assignment and calculation of BPIi, BPIi(norm) and DoLi for individual isotopologs of Chl a from a non-labeled Arabidopsis plant obtained by TQ-MS. Table S13. Peak assignment and calculation of BPIi, BPIi(norm) and DoLi for individual isotopologs of Chl a from a 13C-labeled Arabidopsis plant obtained by FTICR-MS. Table S14. Peak assignment and calculation of BPIi, BPIi(norm) and DoLi for individual isotopologs of Chl a from a 13C-labeled Arabidopsis plant obtained by TQ-MS. Table S15. Peak assignment and calculation of BPIi, BPIi(norm) and DoLi for individual isotopologs of Chl b from a non-labeled Arabidopsis plant obtained by FTICR-MS. Table S16. Peak assignment and calculation of BPIi, BPIi(norm) and DoLi for individual isotopologs of Chl b from a non-labeled Arabidopsis plant obtained by TQ-MS. Table S17. Peak assignment and calculation of BPIi, BPIi(norm) and DoLi for individual isotopologs of Chl b from a 13C-labeled Arabidopsis plant obtained by FTICR-MS. Table S18. Peak assignment and calculation of BPIi, BPIi(norm) and DoLi for individual isotopologs of Chl b from a 13C-labeled Arabidopsis plant obtained by TQ-MS.

Photosynthesis acclimates quickly to the fluctuating environment in order to optimize the absorption of sunlight energy, specifically the photosynthetic photon fluence rate (PPFR), to fuel plant growth. The conversion efficiency of intercepted PPFR to photochemical energy (ɛe) and to biomass (ɛc) are critical parameters to describe plant productivity over time. However, they mask the link of instantaneous photochemical energy uptake under specific conditions, that is, the operating efficiency of photosystem II (Fq′/Fm′), and biomass accumulation. Therefore, the identification of energy- and thus resource-efficient genotypes under changing environmental conditions is impeded. We long-term monitored Fq′/Fm′ at the canopy level for 21 soybean (Glycine max (L.) Merr.) and maize (Zea mays) genotypes under greenhouse and field conditions using automated chlorophyll fluorescence and spectral scans. Fq′/Fm′ derived under incident sunlight during the entire growing season was modeled based on genotypic interactions with different environmental variables. This allowed us to cumulate the photochemical energy uptake and thus estimate ɛe noninvasively. ɛe ranged from 48% to 62%, depending on the genotype, and up to 9% of photochemical energy was transduced into biomass in the most efficient C4 maize genotype. Most strikingly, ɛe correlated with shoot biomass in seven independent experiments under varying conditions with up to r = 0.68. Thus, we estimated biomass production by integrating photosynthetic response to environmental stresses over the growing season and identified energy-efficient genotypes. This has great potential to improve crop growth models and to estimate the productivity of breeding lines or whole ecosystems at any time point using autonomous measuring systems., Cumulative photochemical energy uptake throughout a fluctuating growing season reveals genotypic differences in photosynthetic performance, respiratory losses, and biomass production efficiency.

Additional file 1: Fig. S1. Cultivation of Arabidopsis plants in plastic cups. Fig. S2. Rosette leaves of Arabidopsis plants before and after 7-d exposure to the different airs (day 8). Fig. S3. 15 Arabidopsis plants before and after 7-d exposure to 13CO2 in the labeling chamber. Fig. S4. 15 Arabidopsis plants before and after 7-d exposure to normal CO2 in the labeling chamber. Fig. S5. Flow rates of CO2-free air and CO2 as well as real-time readings of the CO2 concentration and overpressure inside the labeling chamber. Fig. S6. CO2 concentration measured by LI-840. Fig. S7. Conditions inside the 13CO2 labeling chamber. Fig. S8. Simulation of carotenoid isotopolog distribution based on the natural 13C abundance. Fig. S9. Mass spectra of Lut, all-trans-β-Car and Chl a from the spike tests of TQ-MS. Fig. S10. Mass spectra of 9-cis-β-Car extracted from non-labeled and 13C-labeled Arabidopsis plants. Fig. S11. Correlation between ΣDoL of different pigments extracted from Arabidopsis leaves after 7-d 13CO2 labeling.

Plants are subjected to strong fluctuations in light intensity in their natural growth environment, caused both by unpredictable changes due to weather conditions and movement of clouds and upper canopy leaves and predictable changes during day-night cycle. The mechanisms of long-term acclimation to fluctuating light (FL) are still not well understood. Here, we used quantitative mass spectrometry to investigate long-term acclimation of low light-grown Arabidopsis thaliana to a FL condition that induces mild photooxidative stress. On the third day of exposure to FL, young and mature leaves were harvested in the morning and at the end of day for proteome analysis using a stable isotope labeling approach. We identified 2,313 proteins, out of which 559 proteins exhibited significant changes in abundance in at least one of the four experimental groups (morning-young, morning-mature, end-of-day-young, end-of-day-mature). A core set of 49 proteins showed significant responses to FL in three or four experimental groups, which included enhanced accumulation of proteins involved in photoprotection, cyclic electron flow around photosystem I, photorespiration, and glycolysis, while specific glutathione transferases and proteins involved in translation and chlorophyll biosynthesis were reduced in abundance. In addition, we observed pathway- and protein-specific changes predominantly at the end of day, whereas few changes were observed exclusively in the morning. Comparison of the proteome data with the matching transcript data revealed gene- and protein-specific responses, with several chloroplast-localized proteins decreasing in abundance despite increased gene expression under FL. Together, our data shows moderate but widespread alterations of protein abundance during acclimation to FL and suggests an important role of post-transcriptional regulation of protein abundance.

Photosynthetic phenotyping requires quick characterization of dynamic traits when measuring large plant numbers in a fluctuating environment. Here, we evaluated the light-induced fluorescence transient (LIFT) method for its capacity to yield rapidly fluorometric parameters from 0.6 m distance. The close approximation of LIFT to conventional chlorophyll fluorescence (ChlF) parameters is shown under controlled conditions in spinach leaves and isolated thylakoids when electron transport was impaired by anoxic conditions or chemical inhibitors. The ChlF rise from minimum fluorescence (Fo) to maximum fluorescence induced by fast repetition rate (Fm−FRR) flashes was dominated by reduction of the primary electron acceptor in photosystem II (QA). The subsequent reoxidation of QA− was quantified using the relaxation of ChlF in 0.65 ms (Fr1) and 120 ms (Fr2) phases. Reoxidation efficiency of QA− (Fr1/Fv, where Fv = Fm−FRR − Fo) decreased when electron transport was impaired, while quantum efficiency of photosystem II (Fv/Fm) showed often no significant effect. ChlF relaxations of the LIFT were similar to an independent other method. Under increasing light intensities, Fr2′/Fq′ (where Fr2′ and Fq′ represent Fr2 and Fv in the light-adapted state, respectively) was hardly affected, whereas the operating efficiency of photosystem II (Fq′/Fm′) decreased due to non-photochemical quenching. Fm−FRR was significantly lower than the ChlF maximum induced by multiple turnover (Fm−MT) flashes. However, the resulting Fv/Fm and Fq′/Fm′ from both flashes were highly correlated. The LIFT method complements Fv/Fm with information about efficiency of electron transport. Measurements in situ and from a distance facilitate application in high-throughput and automated phenotyping., Photosynthesis Research, 140 (2), ISSN:0166-8595, ISSN:1573-5079