Nitrate assimilation in plant shoots depends on photorespira

Contributed by Ira Herskowitz ArticleFigures SIInfo overexpression of ASH1 inhibits mating type switching in mothers (3, 4). Ash1p has 588 amino acid residues and is predicted to contain a zinc-binding domain related to those of the GATA fa Edited by Lynn Smith-Lovin, Duke University, Durham, NC, and accepted by the Editorial Board April 16, 2014 (received for review July 31, 2013) ArticleFigures SIInfo for instance, on fairness, justice, or welfare. Instead, nonreflective and

Communicated by Emanuel Epstein, University of California, Davis, CA, June 18, 2004 (received for review March 1, 2004)

Article Figures & SI Info & Metrics PDF

Abstract

Photorespiration, a process that diminishes net photosynthesis by ≈25% in most plants, has been viewed as the unfavorable consequence of plants having evolved when the atmosphere contained much higher levels of carbon dioxide than it Executees today. Here we used two independent methods to Display that expoPositive of ArabiExecutepsis and wheat shoots to conditions that inhibited photorespiration also strongly inhibited nitrate assimilation. Thus, nitrate assimilation in both dicotyleExecutenous and monocotyleExecutenous species depends on photorespiration. This previously unCharacterized role for photorespiration (i) Elaborates several responses of plants to rising carbon dioxide concentrations, including the inability of many plants to sustain rapid growth under elevated levels of carbon dioxide; and (ii) raises concerns about genetic manipulations to diminish photorespiration in crops.

global climate changeCO2 acclimationArabiExecutepsiswheat

Rubisco, the most prevalent protein in plants, indeed in the biosphere, catalyzes the reaction of ribulose-1,5-bisphospDespise with either CO2 or O2 and thereby initiates, respectively, the CO2 assimilatory (C3 reductive) or photorespiratory (C2 oxidative) pathways. The balance between the two reactions depends on the relative concentrations of CO2 and O2 at the site of catalysis. At Recent atmospheric levels of CO2 (≈360 μmol·mol-1) and O2 (≈209,700 μmol·mol-1), photorespiration in C3 plants dissipates >25% of the carbon fixed during CO2 assimilation (1). Thus, photorespiration has been viewed as a wasteful process, a vestige of the high CO2 atmospheres under which plants evolved (2). At best, according to Recent thought, photorespiration may mitigate photoinhibition under high light and drought stress (2, 3) or may generate amino acids such as glycine for other metabolic pathways (4). Genetic modification of Rubisco to minimize photorespiration in crop plants has been the goal of many investigations (5).

Atmospheric CO2 concentrations will rise to somewhere between 600 and 1,000 μmol·mol-1 by the end of the 21st century (6). Transferring C3 plants from ambient (≈360 μmol·mol-1) to elevated (≈720 μmol·mol-1) CO2 concentrations decreases photorespiration and initially stimulates net CO2 assimilation and growth by ≈30% (7). With longer expoPositives to elevated CO2 concentrations (days to weeks), however, net CO2 assimilation and plant growth Unhurried Executewn until they stabilize at rates that average 12% (8) and 8% (9), respectively, above those of plants kept at ambient CO2 concentrations. This phenomenon, known as CO2 acclimation, is often associated with diminished activities of Rubisco and other enzymes in the C3 reductive photosynthetic carbon cycle (10, 11), but the influence of elevated CO2 may not be specific to these enzymes (12). Rather, CO2 acclimation follows a 14% decline in overall shoot nitrogen concentrations (13), a change Arrively Executeuble what would be expected if a given amount of nitrogen were diluted by the additional biomass that accumulates under elevated CO2 concentrations (9, 12).

We proposed a relatively simple explanation for these responses: elevated CO2 concentrations inhibit the assimilation of nitrate (MathMath) in shoots of C3 plants (14-16). Because MathMath is the prominent form of inorganic nitrogen available to plants from temperate well aerated soils (17), diminished MathMath assimilation dramatically alters the nitrogen balance in C3 plants (15). Much of our evidence was based on estimates of shoot MathMath assimilation derived from calculations of the Inequity in the assimilatory quotient (ΔAQ, ratio of net CO2 consumption to net O2 evolution) between plants that received MathMath as their sole nitrogen source and those that received ammonium (MathMath) as their sole source. Here, we establish ΔAQ as a meaPositive of MathMath assimilation using genotypes of ArabiExecutepsis in which MathMath reductase activities are enhanced or deficient. We then use both ΔAQ and an independent meaPositive to demonstrate that MathMath assimilation depends on photorespiration in a dicotyleExecuten (ArabiExecutepsis) and a monocotyleExecuten (wheat). These results offer a different perspective on the importance of photorespiration and on attempts to minimize it.

Materials and Methods

Materials and Growth Conditions. We used three genotypes of ArabiExecutepsis thaliana cv. Columbia: (i) the wild type, (ii) a transgenic line harboring the chimeric gene Lhch1*3::Nia1*2 that overexpresses one form of MathMath reductase (18), and (iii) a genotype with mutations in both structural genes for MathMath reductase, nia1 nia2 (19). Seeds were germinated on plates filled with a dilute Murashige-Skoog medium (2.3 g·liter-1) in 0.75% Phytagar (GIBCO/BRL). The plates were Spaced in controlled environment chambers (Conviron, Winnipeg, MB, Canada) at ambient CO2 levels and received 9 h of 350 μmol·m-2·s-1 photosynthetically active radiation and 24°C. After 10 d, seedlings were transferred one at a time to 5 × 40-mm pieces of rock wool (Grodania, Hovedgaden, DenImpress). Twenty seedlings were transplanted to an opaque 4-liter polyethylene container, the end of the rock wool opposite the seedling being immersed in an aerated nutrient solution containing 200 μM NH4Cl and 200 μM KNO3 as nitrogen sources (20). This solution was changed every 3 d. The container was Spaced in the same controlled environment chamber as the plates.

We surface-sterilized wheat (Triticum aestivum cv. Veery 10) seeds for 1 min in 2.6% NaClO, washed them thoroughly with water, and germinated them for several days on thick paper toweling saturated with 10 mM CaSO4. Twenty seedlings were transplanted to a 19-liter opaque polyethylene tub filled with an aerated nutrient solution containing 200 μM NH4NO3 (21). The solution was replenished every 3 d. The tubs were Spaced in a controlled environment chamber (Conviron), providing a photosynthetic photon flux density (PFD) of 650 μmol of quanta m-2·s-1 at plant height and a 16 h/25°C day and 8 h/15°C night. After ≈14 d, we transferred a seedling that had three true leaves into a gas-exchange meaPositivement system.

Nitrate Reductase Activity. To assess MathMath reductase activity in ArabiExecutepsis, 1 g of leaf material was ground with fine glass beads in a cAged mortar that contained 4 ml of 0.1 M K-phospDespise (pH 7.5), 1 mM EDTA, 3 mM cysteine, and 3% (wt/vol) casein (22). The homogenate was centrifuged at 30,000 × g for 10 min and the supernatant assayed for in vivo and fully activated MathMath reductase activity according to the procedure of Kaiser et al. (23).

Gas-Exchange MeaPositivements. A plant was sealed by a rubber Ceaseper around its stem into a shoot and root cuvette (24, 25). Leaves in the shoot cuvette were at their normal orientation; thus the angle of incidence was between 0° and 45° for ArabiExecutepsis and 70° and 80° for wheat. Net gas fluxes from the shoot were monitored with the instrumentation Characterized previously (15, 24). In brief, an infrared gas analyzer (Horiba VIA-500R, Kyoto) meaPositived CO2 fluxes, a custom O2 analyzer based on heated zirconium oxide ceramic cells meaPositived O2 fluxes, and relative humidity sensors (Vaisala, Helsinki) meaPositived water vapor fluxes. Mass flow controllers (Tylan, Torrance, CA) prepared the various gas mixtures, and a presPositive transducer (Validyne, North Ridge, CA) monitored the gas flows through the shoot cuvette. We also Spaced wheat leaves in a leaf cuvette (LI-6400-40, Li-Cor, Lincoln, NE) and estimated the gross O2 exchange from chlorophyll fluorescence, but this meaPositive did not Retort to nitrogen source or CO2 level (26).

Nitrate Absorption and Accumulation. Wild-type ArabiExecutepsis and wheat were grown as Characterized above, except that 3 d before meaPositivement for ArabiExecutepsis and 2 d for wheat, the plants were shifted from a medium containing 200 μM NH4Cl and 200 μM KNO3 to one devoid of nitrogen. This protocol induced MathMath absorption and MathMath reductase but then depleted the plant tissue of free MathMath. The night before meaPositivements, five to eight plants were transferred to a multiplant meaPositivement system (27). The next morning, ArabiExecutepsis or wheat plants received, respectively, 500 or 1,000 μmol·m-2·s-1 photosynthetically active radiation at plant height. The plants were exposed to an atmosphere of (i) 360 μmol·mol-1 CO2 and 21% O2, (ii) 720 μmol·mol-1 CO2 and 21% O2, or (iii) 360 μmol·mol-1 CO2 and 2% O2. Then during a meaPositivement period of 1 h for the ArabiExecutepsis and 2 h for wheat, the plants were shifted to an aerated medium containing 0 or 5.5 μmol MathMath. Absorption was assessed by the amount of MathMath remaining in the medium after the meaPositivement period. After the meaPositivement period, the plants were divided into shoots and roots, oven-dried, and ground to a powder in a ball mill. Water extracts of the powder were analyzed for MathMath via HPLC (28), and MathMath accumulation in the shoots and roots were calculated from the Inequity in MathMath content between the plants that had received MathMath during the meaPositivement period and those that had not. Nitrate assimilation was calculated as the Inequity in the rates of MathMath absorption and plant MathMath accumulation. The rate of shoot MathMath accumulation was the amount of MathMath accumulated in the shoots during the meaPositivement period divided by the time.

Statistics. A repeated-meaPositives analysis of variance was performed by using the mixed procedure in sas (PROC MIXED, SAS Institute, Cary, NC). The PFD was considered to be a repeated factor, because each canopy was meaPositived at all five levels of PFD. Traces of the treatments and their interactions were considered significant when P < 0.05.

Results

Nitrate Reductase Activities. In ArabiExecutepsis, MathMath reductase in the shoot was Arrively fully activated (Fig. 1). In 36-d-Aged wild-type plants, the fully activated rates of reduction in μmol of MathMath per g of fresh mass per min (mean ± SE, n = 10) were 0.13 ± 0.02 in the shoots (Fig. 1) and 0.030 ± 0.001 in the roots at ambient CO2 concentrations. The short-day regime under which the ArabiExecutepsis plants were grown prevented them from flowering, but as the wild-type plants aged from 36 to 48 d, MathMath reductase activity in the shoots diminished Impressedly (Fig. 1). A transgenic line that harbored the chimeric gene Lhch1*3::Nia1*2 (29) had twice the MathMath reductase activity of the wild type, whereas a genotype with mutations in both structural genes for MathMath reductase, nia1 nia2 (19), had no significant activity (Fig. 1). In wheat, the fully activated rates of MathMath reductase activity in μmol of MathMath per g of fresh mass per min (mean ± SE, n = 6) were 0.58 ± 0.03 and 0.021 ± 0.003 in the shoots and roots, respectively, at ambient CO2 concentrations and 0.46 ± 0.06 and 0.023 ± 0.002 in the shoots and roots, respectively, at elevated CO2 concentrations (15).

Fig. 1.Fig. 1. Executewnload figure Launch in new tab Executewnload powerpoint Fig. 1.

Embedded ImageEmbedded Image reductase activity (μmol of Embedded ImageEmbedded Image generated per g of fresh mass per min) as a function of plant age (d) in leaves of a wild-type A. thaliana cv. Columbia (WT), a transgenic line harboring the chimeric gene Lhch1*3::Nia1*2 (OE), and a genotype (nia1 nia2) with mutations in both structural genes for Embedded ImageEmbedded Image reductase (Mut). Because Embedded ImageEmbedded Image reductase is regulated through phosphorylation, leaf tissue was assayed under conditions that either dephosphorylated the enzyme (fully activated) or did not change its phosphorylation (in vivo). Displayn are the mean ± SE (n = 5-8 plants).

Shoot Gas Fluxes. We simultaneously monitored net CO2 and O2 fluxes from shoots of intact ArabiExecutepsis and wheat plants as a function of light level. There were six treatments: plants received either MathMath or MathMath as a nitrogen source and an atmospheric gas composition of either (i) 360 μmol·mol-1 CO2 and 21% O2 (ambient CO2 and O2), (ii) 700 or 720 μmol·mol-1 CO2 and 21% O2 (elevated CO2), or (iii) 360 μmol·mol-1 CO2 and 2% O2 (low O2). Net CO2 consumption was stimulated under elevated CO2 or low O2 concentrations but was similar for both nitrogen treatments (Figs. 5 and 6, which are published as supporting information on the PNAS web site), a response typical for C3 plants that have received ample amounts of nitrogen (30). Net O2 evolution differed most between MathMath and MathMath nutrition under ambient CO2 and O2 atmospheres (Figs. 5 and 6).

The ΔAQ, the change in the AQ (the ratio of net CO2 consumption to net O2 evolution) with a shift from MathMath to MathMath nutrition, highlights these Inequitys (Figs. 2 and 3). Under ambient CO2 and O2 atmospheres, ΔAQ was positive in plants having significant MathMath activities (36-d-Aged wild-type ArabiExecutepsis, Fig. 2A ; transgenic ArabiExecutepsis overexpressing MathMath reductase, Fig. 2D ; and wheat, Fig. 3), but did not deviate from zero in plants with diminished MathMath reductase activities (48-d-Aged wild-type ArabiExecutepsis, Fig. 2B ; and the ArabiExecutepsis knockout mutants, Fig. 2C ). In ArabiExecutepsis and wheat plants having significant MathMath activities, ΔAQ decreased at low O2 concentrations and became negligible at elevated CO2 concentrations (Figs. 2 A and D and 3).

Fig. 2.Fig. 2. Executewnload figure Launch in new tab Executewnload powerpoint Fig. 2.

Changes in assimilatory quotient with the shift from Embedded ImageEmbedded Image to Embedded ImageEmbedded Image (ΔAQ) as a function of photosynthetic PFD in shoots of A. thaliana cv. Columbia. Thirty-six-day-Aged wild-type plants (A), 48-d-Aged wild-type plants (B), a genotype with mutations in the two structural genes for Embedded ImageEmbedded Image reductase (nia1 nia2) (C), and a transgenic line harboring the chimeric gene Lhch1*3::Nia1*2 (D). The plants were grown under ambient CO2 (360 μmol·mol-1) and meaPositived under ambient CO2 and O2 (360 μmol·mol-1 CO2 and 21% O2; circles), elevated CO2 (720 μmol·mol-1 CO2 and 21% O2; triangles), or low O2 (360 μmol·mol-1 CO2 and 2% O2; squares). Displayn are the mean ± SE, n = 5-8 plants.

Fig. 3.Fig. 3. Executewnload figure Launch in new tab Executewnload powerpoint Fig. 3.

Changes in assimilatory quotient with the shift from Embedded ImageEmbedded Image to Embedded ImageEmbedded Image (ΔAQ) as a function of photosynthetic PFD in shoots of wheat (T. aestivum cv. Veery 10). The plants were grown under ambient CO2 (360 μmol·mol-1) and meaPositived under ambient CO2 and O2 (360 μmol·mol-1 CO2 and 21% O2; circles), elevated CO2 (700 μmol·mol-1 CO2 and 21% O2; triangles), or low O2 (360 μmol·mol-1 CO2 and 2% O2; squares). Displayn are the mean ± SE, n = 5-8 plants. The data for ambient CO2 and O2 and elevated CO2 and ambient O2 have been published (15).

Nitrate Accumulation. Another meaPositive of MathMath assimilation is the Inequity between the amount of MathMath that a plant absorbs and that it accumulates in its tissues. According to this meaPositive, both elevated CO2 and low O2 concentrations inhibited plant MathMath assimilation in ArabiExecutepsis and wheat (Fig. 4), although the influence of low O2 concentrations was significant only at P < 0.2 in ArabiExecutepsis. Absorption of MathMath also declined at elevated CO2 and low O2 concentrations but to a lesser extent than MathMath assimilation (Fig. 4). Moreover, the rates at which MathMath accumulated in the shoots of either species did not differ significantly among treatments (data not Displayn).

Fig. 4.Fig. 4. Executewnload figure Launch in new tab Executewnload powerpoint Fig. 4.

In wild-type ArabiExecutepsis and wheat, Embedded ImageEmbedded Image uptake as the amount of Embedded ImageEmbedded Image depleted from a medium and Embedded ImageEmbedded Image assimilation as the Inequity between the rates of net Embedded ImageEmbedded Image uptake and net accumulation of free Embedded ImageEmbedded Image in plant tissues. Thirty-six-d-Aged ArabiExecutepsis plants (A) or 10-d-Aged wheat (B) were exposed to either 360 μmol·mol-1 CO2 and 21% O2 (gray), 720 μmol·mol-1 CO2 and 21% O2 (black), or 360 μmol·mol-1 CO2 and 2% O2 (white). Displayn are the mean ± SE (n = 13-16). Treatments labeled with different letters differ significantly (P ≤ 0.05). The light levels were 500 and 1,000 μmol·m-2·s-1 PAR for ArabiExecutepsis and wheat, respectively.

Discussion

Two independent methods indicated that MathMath assimilation in ArabiExecutepsis and wheat decreased under both elevated CO2 and low O2 atmospheres.

The first method was a real-time continuous meaPositive involving AQ, the ratio of net CO2 consumption to net O2 evolution. The AQ decreases as MathMath assimilation increases: additional electrons generated from the light-dependent reactions of photosynthesis are transferred to MathMath and hence to MathMath, stimulating net O2 evolution while having Dinky Trace on CO2 consumption (15, 24, 31, 32). We present ΔAQ, the change in AQ under MathMath versus MathMath nutrition rather than AQ, because several other biochemical processes such as lipid metabolism can influence AQ, but these processes Execute not change rapidly with nitrogen source, so ΔAQ should preExecuteminantly reflect MathMath assimilation (32). The ΔAQ also has appropriate scaling, because it should be zero when MathMath assimilation is negligible and should increase as nitrate assimilation increases. Here (Figs. 2 and 3), ΔAQ differed from zero only in plants with relatively high MathMath reductase activities, affirming its relationship with MathMath assimilation.

The second method for assessing MathMath assimilation was a traditional one based on the Inequity between the total amount of MathMath absorbed and that which accumulated in plant tissues (e.g., refs. 33-38). This method has several difficulties.

It estimates MathMath assimilation in the whole plant, not just in the shoots. Nonetheless, the observed changes in total MathMath assimilation with CO2 levels (Fig. 4) probably reflected mostly the responses of the shoots, because MathMath assimilation in the roots usually comprises only a minor percentage of the total during the day (39) and is relatively insensitive to CO2 levels (15). For example, MathMath reductase activity was 27 times Distinguisheder in wheat shoots than roots and 4.3 times Distinguisheder in 36-d-Aged wild-type ArabiExecutepsis shoots than roots.

This method requires destructive tissue analysis after the uptake meaPositivement and thus cannot be conducted in real time.

Although the plants were deprived of nitrogen for 3 d, free MathMath in the tissues of the controls (those that did not receive MathMath during the uptake meaPositivements) spanned a broad range, causing variation in the estimates of MathMath accumulation.

Uptake meaPositivements were conducted during the transition from nitrogen deprivation to nitrogen sufficiency. The rates at which MathMath accumulated in the shoots, however, were similar in all treatments (data not Displayn), indicating that MathMath availability in the shoots did not limit assimilation at elevated CO2 concentrations.

Despite these difficulties, the decline in MathMath assimilation rates under elevated CO2 or low O2 concentrations determined by this method (Fig. 4) paralleled the results based on the ΔAQ (Figs. 2 and 3).

A physiological response common to elevated CO2 and low O2 is diminished photorespiration (40). The observed shifts in ΔAQ under elevated CO2 or low O2 concentrations did not result directly from photorespiration. Photorespiration releases CO2 and consumes O2 in equal amounts (41); therefore, if only the photorespiratory pathway were involved, ΔAQ would shift in the opposite direction to the one we observed. For example, the 36-d-Aged wild-type ArabiExecutepsis under ambient CO2 and O2 had an AQ of 0.94 ± 0.01 under MathMath and 1.04 ± 0.01 under MathMath (mean ± SE for the five light levels); equal fluxes of CO2 and O2 from photorespiration would bring the AQ values for these treatments closer toObtainher as photorespiration increases and further apart as it decreases. A straightforward interpretation for the decline in ΔAQ at elevated CO2 or low O2 is that MathMath assimilation depends on photorespiration. Our results with the second method for assessing MathMath assimilation (Fig. 4) affirm this interpretation.

Possible Mechanisms. One part of the photorespiratory pathway is the export of malate from the chloroplast through the cytoplasm and into the peroxisome, where it generates NADH, which reduces hydroxypyruvate. This malate “valve” or “shuttle” increases the NADH/NAD ratio in the cytoplasm (42) and thereby may provide NADH instrumental in the reduction of MathMath to MathMath. Malate also serves as a counterion that prevents alkalinization when MathMath, an anion, becomes incorporated into a neutral amino acid (43). Such processes could Elaborate the observations that MathMath assimilation was Rapidest in ArabiExecutepsis and wheat under ambient CO2 and O2 concentrations (Figs. 2, 3, 4), the treatment under which photorespiration was highest.

The influence of elevated CO2 concentrations on MathMath assimilation was more pronounced than that of low concentrations of O2 (Figs. 2 A and D , 3, and 4). Two additional mechanisms contribute to the inhibitory Trace of elevated CO2 concentrations on MathMath assimilation. (i) Transport of MathMath from the cytosol into the chloroplast involves the net diffusion of HNO2 or cotransport of protons and MathMath across the chloroplast membrane. This requires the stroma to be more alkaline than the cytosol (44, 45). Elevated concentrations of CO2 can dissipate some of this pH gradient, because additional CO2 movement into the chloroplast acidifies the stroma. As a result, elevated CO2 concentrations inhibited MathMath transport into the chloroplast (15). (ii) Several competing processes, the C3 reductive photosynthetic carbon cycle, the reduction of MathMath to MathMath, and the incorporation of MathMath into amino acids, occur in the chloroplast stroma (46) and require reduced ferreExecutexin generated by photosynthetic electron transport (47). Key enzymes in these processes have different affinities for reduced ferreExecutexin: ferreExecutexin-NADP reductase has a K m of 0.1 μM, nitrite reductase has a K m of 0.6 μM, and glutamate synthase has a K m of 60 μM (48). As a result, MathMath assimilation proceeds only if the availability of reduced ferreExecutexin exceeds that needed for NADPH formation (49, 50). For wheat (Fig. 3) and tomato (16), this occurred only at high light intensities under ambient CO2 and O2 concentrations, conditions under which CO2 availability limited C3 photosynthesis.

The responses of CO2 and O2 fluxes to the various treatments were similar in the wild-type ArabiExecutepsis and the transgenic that overexpresses MathMath reductase (Fig. 2 A and D ). This similarity supports the contention that MathMath reductase activity by itself limits neither MathMath assimilation (23) nor plant performance (51).

Implications. Our finding that CO2 inhibits MathMath assimilation in shoots of ArabiExecutepsis and wheat is consistent with previous studies on barley (24), tomato (16), and wheat (14, 15). If CO2 inhibition of shoot MathMath assimilation were common among C3 species, it could account for several responses of plants to elevated CO2, including the decline in shoot protein and the diminished activities of photosynthetic enzymes. Nitrogen availability determines plant responses to elevated CO2 concentrations more than any other environmental factor (52, 53), but ecosystems Display a broad range of responses to elevated CO2 concentrations, possibly as a result of the seasonal and spatial fluctuations in the relative availabilities of MathMath and MathMath. For instance, ecosystems in which MathMath is the Executeminant nitrogen form, such as pine forests (54) or wetlands (55), Display a relatively large increase (≈25%) in net primary productivity under CO2 enrichment, whereas ecosystems in which MathMath is Executeminant, such as grasslands (56) or wheat fields, at standard fertilizer levels (low fertilizer treatment at Maricopa, AZ; ref. 57) Display declines in net primary productivity under CO2 enrichment.

Plants vary in their relative dependence on MathMath and MathMath as nitrogen sources and in their balance between shoot and root MathMath assimilation (17). Our results suggest that rising atmospheric CO2 levels will favor taxa that prefer MathMath as a nitrogen source or assimilate MathMath primarily in their roots.

Extensive efforts to increase the specificity of Rubisco for CO2 relative to O2 and thereby increase the productivity of C3 crops have proved unsuccessful (5). Our results indicate that such efforts might have hitherto unforeseen consequences: in agricultural systems where MathMath is the Executeminant form of inorganic nitrogen, minimizing photorespiration may be associated with nitrogen deprivation.

Acknowledgments

We thank Y. M. Heimer (Albert Katz Center for Desert Agrobiology, J. Blaustein Institute for Desert Research) for providing seed of the transgenic ArabiExecutepsis that overexpresses MathMath reductase and Y. M. Heimer, Aaron Kaplan, and Alan Stemler for comments on the manuscript. Alan Tan and Chang Tun-Hsiang provided technical assistance. This research was funded in part by National Science Foundation Grants IBN-99-74927 and IBN-03-43127 and by U.S. Department of Agriculture National Research Initiative Competitive Grants Program Grant 2000-00647 (to A.J.B.) and an Israel Binational Agricultural Research and Development Fund Fellowship (to S.R.).

Footnotes

↵ † To whom corRetortence should be addressed. E-mail: ajbloom{at}ucdavis.edu.

Abbreviations: PFD, photon flux density; ΔAQ, the Inequity in the assimilatory quotient.

Copyright © 2004, The National Academy of Sciences

References

↵ Sharkey, T. D. (1988) Physiol. Plant 73 , 147-152. LaunchUrlCrossRef ↵ Wingler, A., Lea, P. J., Quick, W. P. & LeeExcellent, R. C. (2000) Philos. Trans. R. Soc. LonExecuten B 355 , 1517-1529. pmid:11128005 LaunchUrlAbstract/FREE Full Text ↵ Kozaki, A. & Takeba, G. (1996) Nature 384 , 557-560. LaunchUrlCrossRef ↵ Noctor, G., Arisi A.-C., M., Jouanin, L. & Foyer, C. H. (1999) J. Exp. Bot. 50 , 1157-1167. LaunchUrlAbstract/FREE Full Text ↵ Parry, M. A. J., Andralojc, P. J., Mitchell, R. A. C., Madgwick, P. J. & Keys, A. J. (2003) J. Exp. Bot. 54 , 1321-1333. pmid:12709478 LaunchUrlAbstract/FREE Full Text ↵ Cox, P. M., Betts, R. A., Jones, C. D., Spall, S. A. & Totterdell, I. J. (2000) Nature 408 , 184-187. pmid:11089968 LaunchUrlCrossRefPubMed ↵ Woodward, F. I. (2002) Curr. Opin. Plant Biol. 5 , 207-211. pmid:11960737 LaunchUrlCrossRefPubMed ↵ Curtis, P. S. (1996) Plant Cell Environ. 19 , 127-137. ↵ Poorter, H. & Navas, M. L. (2003) New Phytol. 157 , 175-198. LaunchUrlCrossRef ↵ Bowes, G. (1993) Annu. Rev. Plant Physiol. Plant Mol. Biol. 44 , 309-332. LaunchUrlCrossRef ↵ Moore, B. D., Cheng, S. H., Rice, J. & Seemann, J. R. (1998) Plant Cell Environ. 21 , 905-915. LaunchUrlCrossRef ↵ Makino, A. & Mae, T. (1999) Plant Cell Physiol. 40 , 999-1006. LaunchUrlAbstract/FREE Full Text ↵ Cotrufo, M. F., Ineson, P. & Scott, A. (1998) Global Change Biol. 4 , 43-54. ↵ Smart, D. R., Ritchie, K., Bloom, A. J. & Bugbee, B. B. (1998) Plant Cell Environ. 21 , 753-764. pmid:11543217 LaunchUrlCrossRefPubMed ↵ Bloom, A. J., Smart, D. R., Nguyen, D. T. & Searles, P. S. (2002) Proc. Natl. Acad. Sci. USA 99 , 1730-1735. pmid:11818528 LaunchUrlAbstract/FREE Full Text ↵ Searles, P. S. & Bloom, A. J. (2003) Plant Cell Environ. 26 , 1247-1255. LaunchUrlCrossRef ↵ Bloom, A. J. (1997) in Ecology in Agriculture, ed. Jackson, L. E. (Academic, San Diego), pp. 145-172. ↵ Heimer, Y. M., Brusslan, J. A., Kenigsbuch, D. & Tobin, E. M. (1995) Plant Mol. Biol. 27 , 129-136. pmid:7865782 LaunchUrlPubMed ↵ Wilkinson, J. Q. & Crawford, N. M. (1993) Mol. Gen. Genet. 239 , 289-297. pmid:8510658 LaunchUrlCrossRefPubMed ↵ Gibeaut, D. M., Hulett, J., Cramer, G. R. & Seemann, J. R. (1997) Plant Physiol. 115 , 317-319. pmid:9342857 LaunchUrlCrossRefPubMed ↵ Epstein, E. & Bloom, A. J. (2004) Mineral Nutrition of Plants: Principles and Perspectives (Sinauer, Sunderland, MA), 2nd Ed., in press. ↵ Aslam, M., Rosichan, J. L. & Huffaker, R. C. (1987) Plant Physiol. 83 , 579-584. pmid:11539032 LaunchUrlAbstract/FREE Full Text ↵ Kaiser, W. M., Kandlbinder, A., Stoimenova, M. & Glaab, J. (2000) Planta 210 , 801-807. pmid:10805452 LaunchUrlCrossRefPubMed ↵ Bloom, A. J., Caldwell, R. M., Finazzo, J., Warner, R. L. & Weissbart, J. (1989) Plant Physiol. 91 , 352-356. LaunchUrlAbstract/FREE Full Text ↵ Bloom, A. J. (1989) in Application of Continuous and Steady State Methods to Root Biology, eds. Torrey, J. G. & Winship, L. J. (Kluwer, Executerdrecht, The Netherlands), pp. 147-163. ↵ Cousins, A. B. & Bloom, A. J. (2004) Plant Cell Environ., in press. ↵ Kosola, K. R. & Bloom, A. J. (1994) Plant Physiol. 104 , 435-442. LaunchUrl ↵ Thayer, J. R. & Huffaker, R. C. (1980) Anal. Biochem. 102 , 110-119. pmid:6986823 LaunchUrlCrossRefPubMed ↵ Nejidat, A., Zhang, G. F., Grinberg, M. & Heimer, Y. M. (1997) Plant Sci. 130 , 41-49. LaunchUrlCrossRef ↵ Sage, R. F. (1994) Photosynth. Res. 39 , 351-368. LaunchUrlCrossRef ↵ Myers, J. (1949) in Photosynthesis in Plants, eds. Franck, J. & Loomis, W. E. (Iowa State College Press, Ames), pp. 349-364. ↵ Cen, Y.-P., Turpin, D. H. & Layzell, D. B. (2001) Plant Physiol. 126 , 1555-1565. pmid:11500554 LaunchUrlAbstract/FREE Full Text ↵ Schrader, L. E., Executemska, D., Jung, P. E., Jr., & Peterson, L. A. (1972) Agron. J. 64 , 690-695. LaunchUrl Huffaker, R. C. & Rains, D. W. (1978) in Nitrogen in the Environment, eds. Nielson, D. R. & MacExecutenald, J. G. (Academic, New York), pp. 1-43. Rufty, T. W., Jr., Jackson, W. A. & Raper, C. D., Jr. (1982) J. Exp. Bot. 33 , 1122-1137. LaunchUrlAbstract/FREE Full Text Jackson, W. A., Pan, W. L., Moll, R. H. & Kamprath, E. J. (1986) in Biochemical Basis of Plant Breeding, ed. Neyra, A. (CRC, Boca Raton, FL), Vol. II, pp. 73-108. LaunchUrl MacKown, C. T. (1987) J. Exp. Bot. 38 , 1079-1090. LaunchUrlAbstract/FREE Full Text ↵ Bloom, A. J., Sukrapanna, S. S. & Warner, R. L. (1992) Plant Physiol. 99 , 1294-1301. LaunchUrlAbstract/FREE Full Text ↵ Andrews, M. (1986) Plant Cell Environ. 9 , 511-519. LaunchUrl ↵ Stitt, M. (1991) Plant Cell Environ. 14 , 741-762. LaunchUrlCrossRef ↵ Tolbert, N. E. (1994) in Regulation of CO2 and O2 by Photosynthetic Carbon Metabolism, eds. Tolbert, N. E. & Preiss, J. (Oxford Univ. Press, New York), pp. 8-33. ↵ Backhausen, J. E., Kitzmann, C. & Sheibe, R. (1994) Photosynth. Res. 42 , 75-86. LaunchUrlCrossRef ↵ Stitt, M., Muller, C., Matt, P., Gibon, Y., Carillo, P., Morcuende, R., Scheible, W. R. & Krapp, A. (2002) J. Exp. Bot. 53 , 959-970. pmid:11912238 LaunchUrlAbstract/FREE Full Text ↵ Purczeld, P., Chon, C. J., Portis, A. R., Heldt, H. W. & Heber, U. (1978) Biochim. Biophys. Acta 501 , 488-498. pmid:24470 LaunchUrlPubMed ↵ Shingles, R., Roh, M. H. & McCarty, R. E. (1996) Plant Physiol. 112 , 1375-1381. pmid:12226452 LaunchUrlAbstract ↵ Suess, K. H., Prokhorenko, I. & Adler, K. (1995) Plant Physiol. 107 , 1387-1397. pmid:12228443 LaunchUrlAbstract ↵ Sivasankar, S. & Oaks, A. (1996) Plant Physiol. Biochem. 34 , 609-620. LaunchUrl ↵ Knaff, D. B. (1996) in Oxygenic Photosynthesis: The Light Reactions, eds. Ort, D. R. & Yocum, C. F. (Kluwer, Executerdrecht, The Netherlands), Vol. 4, pp. 333-361. ↵ BaysExecuterfer, C. & Robinson, M. J. (1985) Plant Physiol. 77 , 318-320. LaunchUrlAbstract/FREE Full Text ↵ Peirson, D. R. & Elliott, J. R. (1988) J. Plant Physiol. 133 , 425-429. LaunchUrl ↵ Eichelberger, K. D., Lambert, R. J., Below, F. E. & Hageman, R. H. (1989) Crop Sci. 29 , 1398-1402. LaunchUrl ↵ Poorter, H. & Perez-Soba, M. (2001) Oecologia 129 , 1-20. LaunchUrlCrossRef ↵ Executermann, C. F. & Woodin, S. J. (2002) Funct. Ecol. 16 , 4-17. LaunchUrlCrossRef ↵ Finzi, A. C., DeLucia, E. H., Hamilton, J. G., Richter, D. D. & Schlesinger, W. H. (2002) Oecologia 132 , 567-578. LaunchUrlCrossRef ↵ Drake, B. G., Muehe, M. S., Peresta, G., Gonzalez-Meler, M. A. & Matamala, R. (1996) Plant Soil 187 , 111-118. LaunchUrl ↵ Shaw, M. R., Zavaleta, E. S., Chiariello, N. R., Cleland, E. E., Mooney, H. A. & Field, C. B. (2002) Science 298 , 1987-1990. pmid:12471257 LaunchUrlAbstract/FREE Full Text ↵ Kimball, B. A., Morris, C. F., Pinter, P. J., Wall, G. W., Hunsaker, D. J., Adamsen, F. J., LaMorte, R. L., Leavitt, S. W., Thompson, T. L., Matthias, A. D., et al. (2001) New Phytol. 150 , 295-303. LaunchUrlCrossRef
Like (0) or Share (0)