experimental variable to measure photosystem 1

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Article Contents

The plasticity of peripheral photosystem i subunits—fascinating new insights from cryogenic electron microscopy, the structure of photosynthetic rcs in light of evolution—the evolution of plasticity, the plasticity of the donor side, formation of psi–cyt b 6 f supercomplex structures and putative function.

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The Plasticity of Photosystem I

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Michael Hippler, Nathan Nelson, The Plasticity of Photosystem I, Plant and Cell Physiology , Volume 62, Issue 7, July 2021, Pages 1073–1081, https://doi.org/10.1093/pcp/pcab046

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Most of life’s energy comes from sunlight, and thus, photosynthesis underpins the survival of virtually all life forms. The light-driven electron transfer at photosystem I (PSI) is certainly the most important generator of reducing power at the cellular level and thereby largely determines the global amount of enthalpy in living systems (Nelson 2011). The PSI is a light-driven plastocyanin:ferredoxin oxidoreductase, which is embedded into thylakoid membranes of cyanobacteria and chloroplasts of eukaryotic photosynthetic organism. Structural determination of complexes of the photosynthetic machinery is vital for the understanding of its mode of action. Here, we describe new structural and functional insights into PSI and associated light-harvesting proteins, with a focus on the plasticity of PSI.

The core of photosystem I (PSI) is highly conserved from cyanobacteria to vascular plants ( Jordan et al. 2001 , Ben-Shem et al. 2003 , Nelson and Ben-Shem 2004 , Amunts et al. 2007 ). The subunits PsaA and PsaB are arranged in a central heterodimer binding the reaction center (RC) P700 and components of the electron transport chain (ETC), A 0 [monomeric form of chlorophyll (Chl) a], A 1 (phylloquinone) and the [4Fe–4S] cluster Fx are forming the PSI core. The Fx cluster is connected to two additional [4Fe–4S] clusters, termed FA and FB, which are bound through the stromal peripheral subunit PsaC. Light-driven charge separation is stabilized within the PSI core via a cascade of rapid electron transfer reactions in the picosecond to nanosecond time range from P700 via A0 and A1 to the [4Fe–4S] centers Fx, FA and FB. The core of PSI further harbors approximately 100 Chls ( Amunts et al. 2007 ), which serve as antenna system to collect light energy. This core antenna is extended by additional Chl-binding proteins that form the light-harvesting complex (LHCI). The structural plasticity of PSI is mainly reflected in the architecture of the peripheral subunits and their composition.

Until a few years ago the leading technique in structural biology was X-ray crystallography. It has supplied the scientific community with >100,000 3D structures, beginning in the 1950s with the discoveries generating multiple Nobel prizes and genuinely revolutionizing our knowledge in molecular biology. However, X-ray crystallography has its limitations, such as the requirement for large amounts, (milligram quantities) of purified sample and the need to crystallize these samples, which limited the scope of these studies and, also failed to provide knowledge on dynamic properties. By contrast, single-particle cryogenic electron microscopy (cryo EM) does not have these limitations. Importantly, cryo EM has undergone a technological revolution in the last 8 years (Amunts et al. 2014, Kuhlbrandt 2014 ). The method provided detailed modeling of macromolecules and their complexes. As a result, the number of cryo EM studies has increased dramatically and the method has become a leading tool for structure determination, especially for supramolecular complexes and membrane proteins, categories of samples that have been refractory to crystallization. Recently, the structure of spinach photosystem II–LHCII supercomplex solved at 3.2 Å resolution through single-particle cryo EM ( Wei et al. 2016 ) as well as several other photosynthetic supercomplexes from various organisms ( Pan et al. 2018 , Pi et al. 2018 , Kubota-Kawai et al. 2019 , Qin et al. 2019 , Su et al. 2019 , Suga et al. 2019 ). Recently, we solved the structure of Chlamydomonas PSI by cryo EM at 2.54 Å resolution, a resolution not described before (Caspy et al., in preparation). The quantity of the obtained density maps was comparable to high-resolution maps obtained by X-ray crystallography. With this technology in hand, we can address structural aspects that had escaped detection.

Hydrogen is by far the most abundant element in the Universe; hence, the chemistry of the Universe is highly reductive. Therefore, oxygen that is produced by supernovas and other violent cosmic events and encompasses only 0.04% of the elements is unlikely to survive in its free form and terns in water. Consequently, life on Earth initially evolved under anaerobic conditions and only the onset of oxygenic photosynthesis could result in the presence of oxygen in the Earth’s atmosphere. Oxygenic photosynthesis was probably initiated about 3.5 billion years ago, but photosynthetic organisms probably harnessed sun light and turned its energy into chemical energy at the onset of life on Earth about four billion years ago ( Nelson 2013 , Cardona et al. 2019 ). This leaves a relatively short time for the initial evolution of photosynthetic organisms that arguably operated with highly involved protein complexes reminiscent of the current photosynthetic pigment–protein complexes ( Nelson and Junge 2015 ). One of the plausible explanations for this initial fast evolution is that Earth received ready life-building blocks from the reminiscence of supernovas that supplied the material for the generation of the solar system (Panspermia). What was the likely make-up of the initial photosynthetic RC? We argued that it was a large monomeric Chl–protein complex that operated as a symmetric homodimeric structure that like most advances in evolution turned into pseudo-symmetric and finally deviated from symmetry ( Ben-Shem et al. 2004a ). The RC of the ancient green bacterium Chlorobium may represent the initial steps in the evolution of photosynthetic RCs ( Büttner et al. 1992 ). Fig. 1 depicts a scenario for the common evolution of all photosynthetic RCs. Clearly, all the heterodimeric RCs are likely to have evolved from a homodimeric one. The primordial RC may have been a homodimeric unit resembling the core complex of the current RCs in Chlorobi, heliobacteria and acidobacteria ( Büttner et al. 1992 , Nelson 2013 , Nelson and Junge 2015 ). All of those evolutionary events proceeded under strict anaerobic conditions and under a limited requirement for generating reduced components and high demand for ATP. Cyclic photophosphorylation is ideal for accomplishing this task. We proposed that, under the condition of limited electron acceptors, the reduced quinone placed in an identical position to the one present in all known PSI structures dissociates and donates electrons to the b/c1 complex for cyclic photophosphorylation ( Büttner et al. 1992 , Nelson and Junge 2015 ). Indeed, it was observed that, in contrast to isolated PSI from cyanobacteria, plants and green algae, the isolated Chlorobium RC lacks quinone, suggesting that it dissociated and was lost during the preparation of the RC ( Frankenberg et al. 1996 , Gisriel et al. 2017 , Chen et al. 2020a ). The recently published structures of Helicobacter and Chlorobium RC clearly show that the quinone access is blocked by an arginine residue ( Fig. 2B ). However, alternative conformation of this arginine salt bridging a neighboring aspartic acid provides ready access to FX in an identical position, as in higher plant PSI ( Mazor et al. 2017 ; Fig. 2C ). We suggest that this arginine serves as a switch between linear and cyclic electron transport.

Proposed model for the function of the Chlorobium homodimeric reaction center and cytochrome b–c1 complex in NAD photoreduction and cyclic photophosphorylation (adapted from Nelson and Ben-Shem 2002 ). P840 is the primary electron donor. A O is the primary electron acceptor; MQ, menaquinone; F X , FeS center ‘X’; F A and F B , FeS centers ‘A’ and ‘B’.

Potential quinone-binding site in Chlorobium tepidum PSI. (a) The quinone-binding site in PsaA of Pisum sativum PSI ( Mazor et al. 2017 ; PDB-5L8R). (b) A potential quinone-binding site in PsaA of C. tepidum PSI. The structures of plant and Chlorobium (PDB-6M32) were superimposed using Coot. The quinone was added to the Chlorobium structure at an identical position to the plant PsaA. (c) The Chlorobium structure shown in (b) underwent ‘Sphere Refine’ in coot centered on the quinone under map Refinement Weight of 1%.

Homodimeric RCs probably preceded the more complex heterodimers of the type I and type II RCs. The evolution of a heterodimeric structure likely commenced with gene duplication, leading to the advanced RCs found in cyanobacteria and plants ( Büttner et al. 1992 ). Such gene duplications and subsequent independent evolution of the two genes are well known to occur in higher-order systems, which develop intricate regulatory processes to better cope with environmental changes ( Ben-Shem et al. 2004b ). Accordingly, the primordial PSI-like homodimeric RC was the origin of all RCs, including those of PSI and PSII as well as that of bacteria, and it functioned primarily in cyclic photophosphorylation ( Blankenship 1992 , Büttner et al. 1992 , Nelson and Junge 2015 ). The primordial PsaA and PsaB subunits diverged to the current PSI core complex of cyanobacteria, algae and plants. Concomitantly, PsaB split into D1 and CP43, and PsaA split into D2 and CP47 to form the core complex of PSII. During evolution of the various RCs, more subunits were added ( Fig. 3 ), leading to current complexes that contain up to 20 different subunits. Each evolved to maximize light absorption and energy conversion and to minimize photo-oxidative stress under greatly variable light intensities and temperatures and often-hostile environments.

Supersession of minimal C. merolae PSI and Dunaliella PSI. The minimal structure of C. merolae PSI (magenta PDB-7BLZ) and minimal Dunaliella PSI (green PDB 6RHZ) were aligned using PsaA as reference subunit.

The evolutionary advantage of deviation from symmetry using gene duplication and heterodimerization is the establishment of specific peripheral binding sites that are unique for each monomer yet preserving tight conservation in the center. This was clearly revealed in the structure of PSI of cyanobacteria where the structure of PsaA and PsaB is highly preserved yet PsaK binds exclusively to PsaA ( Malavath et al. 2018 ). In eukaryotic PSI, PsaG, a homolog of PsaK, binds exclusively to PsaB. The other peripheral subunits also bind to their neighbors in an asymmetric fashion. In contrast to the changes in the periphery, the center of all PSI complexes is strictly conserved maintaining high but not perfect symmetry. This was probably advantageous in comparison with the perfect symmetry of the homodimeric RCs. As reviewed by Suga and Shen ( Suga and Shen 2020 ), the presence of PSI-IsiA ( Toporik et al. 2019 ) and tetrameric PSI complexes ( Kato et al. 2019 , Zheng et al. 2019 , Chen et al. 2020b ) reflect the structural plasticity of cyanobacterial PSI that is not observed in eukaryotic PSI. It took almost two billion years to generate a next step in PSI evolution that took place during the onset of eukaryotic cells. The emergence of a ubiquitous Chl–protein complex (LHC) that builds a single polypeptide forming three transmembrane helices and binding about 13 Chl molecules and 2–3 carotenes was the evolutionary game changer ( Standfuss et al. 2005 ).

Red algae, such as Cyanidioschyzon merolae , represent the initial step in the evolution of RCs in eukaryotic cells ( Nikolova et al. 2017 ). While PSII of C. merolae is very similar to the cyanobacterial homolog, PSI exhibits an addition of 3–5 LHCs to the core structure ( Pi et al. 2018 , Antoshvili et al. 2019 ). The large form of C. merolae PSI contains two LHCs that are bound to PsaA subunit at the PsaL pole and three LHCs (Lhcr1–3) are mainly bound to PsaB subunit at the PsaF pole. The structure of a minimal version of this RC isolated from cells grown at 25°C contains only three LHCs (Klaiman et al., unpublished; PDB-7BLZ). A minimal structure of PSI containing four LHCs was isolated from the green algae Dunaliella salina ( Perez-Boerema et al. 2020 ). All of them are bound at the PsaF pole at identical positions to those of PSI from higher plants. Fig. 3 shows superposition of the two minimal structures revealing significant shift in their position and apparent different binding sites. Fig. 4 shows superposition of the large PSI form C. merolae and D. salina containing six LHCs showing major movements in Lhcr4 and Lhcr5. There are only four LHCs present in all the structures of PSI from higher plants suggesting that the potential LHC-binding sites at the PsaL pole are redundant and not being used ( Mazor et al. 2015 , Qin et al. 2015 , Mazor et al. 2017 , Pi et al. 2018 ). The minimal structure of Dunaliella PSI shown in Fig. 3 is fully active in light-induced plastocyanin (Pc)–ferredoxin oxidoreductase activity and may represent not only assembly intermediate but also a stepping stone in the evolution of the complex in the green lineage. Green algae PSI is very versatile and in various organisms contains up to 10 LHCs: 2 in the PsaL pole and up to 8 arranged in two crescents at the PsaF pole ( Ozawa et al. 2018 , Kubota-Kawai et al. 2019 , Qin et al. 2019 , Su et al. 2019 , Suga et al. 2019 ). The two LHC-binding sites at the PsaL poles are quite loose and during the purification of the complex are readily dissociated. This loose binding site may have physiological role for reducing the antennae size in response to high light and other stress conditions ( Kubota-Kawai et al. 2019 , Suga et al. 2019 ) and/or for the remodeling of electron transfer ( Steinbeck et al. 2018 ). Recently, an additional Lhca1–a4 dimer bound on the PsaB–PsaI–PsaH side of PSI has also been identified in an isolated PSI–LHCI complex from Arabidopsis thaliana as evidenced by negative-staining single-particle EM analysis ( Crepin et al. 2020 ), suggesting that this structure is also conserved in vascular plants. Notably, the structural organization of PSI–LHCI in the moss Physcomitrella patens also consists of two different rows of the LHCI belt as in Chlamydomonas reinhardtii , but the outer one is shifted toward the PsaK side ( Iwai et al. 2018 , Pinnola et al. 2018 ). In addition, there is evidence for one trimeric LHCII protein and one monomeric Lhcb9 protein position alongside PsaL/K, filling the gap between these subunits and the outer LHCI belt. The versatility of the PSI structure is overstressed in secondary symbionts, such as the diatom that contains 16 FCPI subunits ( Nagao et al. 2020 ).

Supersession of large C. merolae PSI and Dunaliella PSI. The large C. merolae PSI (magenta PDB 5ZGB) and large Dunaliella PSI (green PDB 6SL5) were aligned using PsaA as the reference subunit.

State transition where under excess light LHCII complexes depart from PSII and bind to PSI is common to most organisms of the green linage ( Rochaix 2014 ). The structure of the maize PSI complex with LHCII representing state II transition was published ( Pan et al. 2018 ). The structure of Dunaliella PSI together with excitation transfer analysis revealed that PsaL and PsaO are crucial for the efficiency of this process ( Caspy et al. 2020b ). PsaO readily dissociates during the purification of PSI, and it is not present in most published structures ( Qin et al. 2015 , Mazor et al. 2017 , Suga et al. 2019 ). The Large  Dunaliella  PSI supercomplex disclosed not only the correct excitation pathways from LHCII to PSI but pointed out the crucial role of PsaO ( Caspy et al. 2020b ). Thus, it was quite surprising to detect a tightly bound PsaO in the structure of C. merolae PSI regardless of the growth conditions, purification procedure and structural determination by X-ray crystallography or cryo EM ( Pi et al. 2018 , Antoshvili et al. 2019 ). Moreover, the lack of LHCII precludes red algae from having state transition resembling the organisms of the eukaryotic green linage. It is possible that PsaO of red algae provides a binding site to another membrane protein complex. Another unexpected finding was that removal of PsaF from the PSI complex in C. merolae did not impact electron transfer with the endogenous cytochrome c 6 (Cyt c 6 ) but slowed down the electron transfer reaction with pea plastocyanin (Pc) considerably ( Antoshvili et al. 2019 ), indicating that also the donor side of PSI underwent significant changes during evolution.

The electron transfer reaction from the lumenal electron donors Pc or Cyt c 6 to PSI is a well-studied reaction ( Busch and Hippler 2011 ) and has been recently structurally depicted for the intermolecular complex between Pc and PSI from pea (see Fig. 5 and Caspy et al. 2020a , Caspy et al. 2020b ). Light-induced charge separation in PSI leads to the formation of P700 + . The oxidized primary donor is then reduced by electron transfer from the Pc or Cyt c 6 . The Pc can be replaced by Cyt c 6 in some cyanobacteria and green algae when the availability of copper is limiting ( Wood 1978 , Ho and Krogmann 1984 , Merchant and Bogorad 1986 , Sandmann 1986 ). Although electron transfer can be described via electrostatic and hydrophobic interactions, the binding of the Pc or Cyt c 6 to PSI is mechanistically different in eukaryotic and prokaryotic organisms.

Electrostatic and hydrophobic interactions between PSI and Pc. A membrane plane view of the electrostatic interactions between PsaF lysine residues and Pc-negative patches and of the hydrophobic surface that is created at the PsaA and PsaB boundary. PsaA is colored in red, PsaB in green, PsaF in dark blue and Pc in cyan. The copper (Cu + ) ion is shown as a sphere, and the ETC components are colored in purple (adapted from Caspy et al. 2020a ).

From the crystal structures of the cyanobacterial and eukaryotic PSI, it appears that the flat lumenal surface of the donor side of PSI is mainly formed by the loop regions that connect the transmembrane domains of subunits PsaA and PsaB ( Jordan et al. 2001 , Ben-Shem et al. 2003 ). The lumenal loops of PsaA/B harbor the key residues PsaA-W651 and PsaB-W627 for docking of the lumenal donors and electron transfer into P700 + . The two Trp residues have their indole groups stacked at van der Waals distance ( Jordan et al. 2001 ) and are directly situated above P700 ( Jordan et al. 2001 , Ben-Shem et al. 2003 ). In vitro studies of site-directed mutants of Chlamydomonas have shown that the alteration in these tryptophans drastically decreases the affinity of PSI for Pc or Cyt c 6 , preventing the formation of an intermolecular complex between the two donors and PSI ( Sommer et al. 2002 , Sommer et al. 2004 ). This strongly suggests that hydrophobic interactions between the PsaA-W651 and PsaB-W627 and the northern face of Pc or Cyt c 6 are important for stable binding of the donors. In addition, the helix-loop-helix motif of subunit PsaF contributes a significant structural element to this side.

This structural element formed by the PsaF subunit provides an essential recognition site for efficient binding and electron transfer between PSI and the electron donors in eukaryotic organisms. Nelson and Bengis revealed the importance of plant subunit III (PsaF) for the binding of Pc already in 1977 ( Bengis and Nelson 1977 ). Several approaches have later shown that the positively charged N-terminal domain of the PsaF subunit of eukaryotic PSI that is absent in prokaryotes is responsible for the electrostatic interaction with the electron donors ( Farah et al. 1995 , Hippler et al. 1996 , Hippler et al. 1998 , Hippler et al. 1999 ). This domain with its basic side Lys chains provides an interphase for binding of the soluble electron donors ( Hippler et al. 1996 ). Its α-helical structure in helix-loop-helix motive ( Ben-Shem et al. 2003 ) promotes interaction between Lys16 and 23 of the PsaF ( Chlamydomonas nomenclature) and the conserved negatively charged residues present in Pc and Cyt c 6 (southern negative patches). The occurrence of a pre-formed electron transfer complex between the two donors and PSI is dependent on the presence of the eukaryotic positively charged domain of subunit PsaF as well as the presence of the sandwiched Trp residues from PsaA and PsaB. The formation of a stable complex between PSI and Pc can be measured via flash photolysis, as a fast laser flash will lead to fast microsecond intra-molecular electron transfer between the bound donor and P700 + . In the subsequent slower phase, the remaining PSI complexes are re-reduced by the soluble donor in a bimolecular reaction with second-order kinetics. The changes in amplitude of the two faster kinetic components in regard to the donor concentration reflect the binding equilibrium and allows the calculation of the dissociation constant K D according to Drepper et al. (1996) . It has been measured that dissociation constant for oxidized Pc is about six times larger than that observed for reduced Pc ( Drepper et al. 1996 ). As a consequence, the midpoint redox potential of Pc bound to PSI is 50–60 mV higher as compared to soluble Pc, resulting in a decrease in the driving force within the inter-molecular electron transfer complex. How can this be rationalized? Recent structural data obtained for the PSI–Pc intermolecular complex at 2.74 Å resolution revealed strong hydrophobic interactions between PSI and PSI–Pc ( Caspy et al. 2020a , Caspy et al. 2021 ) ( Fig. 5 ). This is in line with the functional analyses of PsaA-W651 and PsaB-W627 mutants (see above). It was also confirmed by mutating residues PsaA-R647 and PsaA-D648 and functional analyses of the electron transport between Pc and PSI. PsaA-R647 and PsaA-D648 are close to PsaA-W651, and their alteration diminishes the binding affinity of Pc to PSI and the electron transfer rates, underpinning the importance of this area for Pc binding ( Caspy et al. 2021 ). The hydrophobic interaction leads to an exclusion of water molecules from PsaA-PsaB/Pc interface within the PSI–Pc complex. This is in accordance with a low reorganization energy λ of about 418 meV that was determined for the intermolecular electron transfer between Pc and PSI ( Hippler et al. 1995 ), supporting the hydrophobic environment at the contact side of both proteins. Upon oxidation of Pc ( Caspy et al. 2021 ), a slight tilt of bound oxidized Pc allows water molecules to accommodate the space between Pc and PSI. This conclusion was derived by modeling oxidized Populus nigra Pc into the position of reduced bound Pc by superimposing the two conformations based on unchanged core region (Asp61–Asn64) ( Caspy et al. 2021 ). Now, it is foreseen that the incoming water destabilizes the hydrophobic contact and drives Pc dissociation. This scenario is consistent with the weaker binding of oxidized and stronger binding of reduced Pc toward PSI. Why is this of importance and how does it impact photosynthetic electron transfer?

The difference in binding affinities of oxidized and reduced Pc will actually lead to a faster off rate for unbinding of oxidized Pc. It had been suggested that the unbinding of Pc from PSI is the limiting step in the electron transfer between Cyt b 6   f in vivo ( Drepper et al. 1996 ). C. reinhardtii mutants were used to investigate binding dynamics and electron transfer between Pc and PSI and cytochrome f oxidation kinetics. Using fast absorption spectroscopy, it was shown that the Pc, Cyt b 6   f and PSI pools almost approach redox equilibrium in vivo ( Finazzi et al. 2005 ). Under such conditions, it was also found that electron flow between PSI and the cytochrome complex was not affected by a 5-fold lower binding affinity of Pc to PSI (mutant PsaA-W651S, Sommer et al. 2004 , Finazzi et al. 2005 ). By contrast, electron flow from PSI to the Cyt b 6   f complex was very sensitive to a 2- to 3-fold decrease in the rate of Pc release from PSI (mutant PsaB-E613N; Sommer et al. 2002 , Finazzi et al. 2005 ), suggesting that this is indeed a kinetic step that might limit electron transfer between Cyt b 6   f complex and PSI in vivo. Residues PsaB-D612 and PsaB-E613 of PSI also conferred pH-dependent binding of Pc and Cyt c 6 ( Kuhlgert et al. 2012 ), indicating that the release of donors is pH dependent, where the off rate decreases with decreasing pH. According to recent structural data ( Caspy et al. 2020a ), pea PsaB-Glu611 (Glu 613 in C. reinhardtii ) created a salt bridge with PsaF-Arg94, which immobilized the helix-loop-helix motif of PsaF, along with PsaB Glu 450 and Lys 451, PsaF Arg 129 and PsaJ Asp 35. Amino acid substitution of PsaB D612H and E613H in C. reinhardtii reduced the dissociation constant of Pc by more than five times over, likely due to stronger PSI–Pc binding ( Kuhlgert et al. 2012 ). It is possible that mutating Asp 612 and Glu 613 induced a conformational orientation change in the helix-loop-helix of PsaF that increased the binding affinity of Pc toward PSI. In the intermolecular complex PSI–Pc complex, the N-terminal domain of PsaF is close but not in direct contact with the southern part of Pc ( Caspy et al. 2020a ) ( Fig. 5 ). Upon oxidation, the slight tilt of bound oxidized Pc brings the southern part Pc close to the positively charged helix-loop-helix motif of PsaF, which helps in unbinding ( Caspy et al. 2021 ). It is suggested the binding and electron transfer between Pc and PSI can be described by the following four steps: (i) binding of Pc to PSI is enabled via electrostatic interactions between Pc and PsaF, (ii) stabilization of the reduced Pc–PSI complex is due to the hydrophobic, as well as polar and electrostatic, interface provided by the PsaA and PsaB boundaries, (iii) electron transfer takes place from Pc to P700 + within the intermolecular complex, and (iv) dissociation of bound oxidized Pc is driven via its conformational change, which destabilizes its hydrophobic interaction with PsaA and PsaB interfaces additionally supported through electrostatic pulling toward PsaF ( Caspy et al. 2021 ). This mechanistically explains how this molecular machine has been able to optimize the electron transfer by promoting fast turnover.

Is there evidence for complex formation between PSI and its donors in cyanobacteria? Several observations suggest that cyanobacteria may also have evolved a binding motif that leads to a preferential docking of the donor into one position at PSI being optimal for fast electron transfer. First, for both acidic and basic cyanobacterial donors, a similar first-order phase of microsecond electron transfer to P700 + is apparent. Second, the structures of cyanobacterial Pc (e.g. from Synechococcus sp. PCC 7942; Inoue et al. 1999 ) and Cyt c 6 (e.g. from Synechococcus elongatus ; Beissinger et al. 1998 ) are similar to the eukaryotic homologues and exhibit a hydrophobic surface region with the exception of one basic residue, an arginine, at position 87 of prokaryotic Pc and 66 of Cyt c 6 (numbering for equivalent residues from C. reinhardtii ) ( Hippler and Drepper 2006 ), which project at the edge of this surface. The respective position of these residues relative to the assumed electron transfer site via the copper liganding histidine of Pc and the exposed heme edge of Cyt c 6 is very similar ( Hippler and Drepper 2006 ). Superposing the hydrophobic surfaces as well as the position of the positive arginine residue via structural alignment supports the functional equivalence of the two analogous donors. Specific molecular recognition could be envisioned via short-range interaction of these positive residues with a hydrophilic group at the binding site of PSI (see also above). Third, an alignment, based on the location of the acidic patches and the resulting dipole moment for Pc and Cyt c 6 from green algae ( Frazao et al. 1995 ), yielded similar relative orientations between the two eukaryotic donors and the cyanobacterial donors. This suggests that despite changes in electrostatic interactions between the prokaryotic and eukaryotic donors and PSI, the relative orientation of Pc and Cyt c 6 within the inter-molecular complex with PSI has been conserved. This may suggest that the specificity of the complex formation was already achieved by cyanobacteria before endosymbiosis and the subsequent introduction of a new binding domain in eukaryotic PsaF ( Hippler and Drepper 2006 ). Electron transfer between acidic donors and PSI in cyanobacteria has been described in vitro by monophasic kinetics. The reported electron transfer rates were ∼2 orders of magnitude slower than that in green algae and higher plants. Moreover, the rate was also independent of the presence of subunit PsaF ( Hatanaka et al. 1993 , Xu et al. 1994 , Hippler et al. 1996 ). This absence of a first-order electron transfer was interpreted in terms of a collisional mechanism for the bimolecular reaction ( Hervas et al. 1995 , Hervas et al. 1996 , Navarro et al. 1997 ). However, microsecond first-order electron transfer between PSI and donors at higher (millimolar) concentrations has been observed in vivo in S. elongatus ( Baymann et al. 2001 , Viola et al. 2019 ) and Synechocystis sp. PCC6803 ( Viola et al. 2019 ). This suggests that, although the on rate is slower in these cyanobacteria, they have already evolved the formation of a transient complex that has a lifetime being about 10- to 20-fold longer than the half-life of intercomplex electron transfer. Whereas in the eukaryotic system, as outlined above, the electron transfer between Pc and PSI was tuned for fast turnover, which is probably an adaptation toward a more complex bioenergetic environment, reflected by the presence of chloroplasts and mitochondria in the same cell and which adds another layer of regulation on photosynthetic control.

Linear electron flow (LEF) and particularly cyclic electron transfer (CEF) promote acidification of the lumen. In consequence, this accelerates binding and electron transfer between Pc and PSI as discussed above ( Kuhlgert et al. 2012 ) but slows down unbinding. The pathway of CEF shares at least PQ, Cyt b 6   f , Pc, PSI and FDX with that of LEF. In Chlamydomonas as well as in Arabidopsis, the existence of protein supercomplex composed of PSI–LHCI and the Cyt b 6  f complex has been proposed ( Iwai et al. 2010 , Yadav et al. 2017 , Steinbeck et al. 2018 ). In situ cryo-electron tomography revealed the native architecture of thylakoid membranes ( Engel et al. 2015 ). Apparently, PSI and PSII are strictly segregated at the borders between appressed and non-appressed membrane domains, requiring Pc for long-range electron transfer ( Haehnel et al. 1989 , Kirchhoff et al. 2011 , Hohner et al. 2020 ). The formation of such a supercomplex would shorten the distance between PSI and Cyt b 6   f complex and possibly accelerate Pc turnover by creating a microenvironment diminishing diffusion distance of Pc and PSI, thereby promoting instantaneous reduction via Cytf (see above). Notably, in C. reinhardtii , remodeling of the LHCA antenna is suggested as requirement for formation of this supercomplex ( Steinbeck et al. 2018 ). Hence, the plasticity of the antenna might not only be linked to light-harvesting capability but also to functional requirements that favor the formation of large PSI-containing protein supercomplexes. This is likely driven by the necessity to control photosynthetic electron flow to adjust the generation of reducing power and possibly to promote CEF. The CEF process, which has been first recognized by Arnon (1959) , facilitates the re-equilibration of the ATP poise and diminishes over-reduction of the PSI acceptor side. In microalgae and vascular plants, CEF is suggested to operate via an NAD(P)H dehydrogenase-dependent and/or a PROTON GRADIENT REGULATION 5 (PGR5)-related pathway ( Shikanai 2007 , Alric, 2010 , Peltier et al., 2010 ). The PGR5-dependent CEF pathway ( Munekage et al. 2002 ) requires the PGR5 protein for its function. In an alternative CEF pathway model, electron transfer to a quinone bound to the Q i site of Cyt b 6   f via its proximal heme c i and an FNR bound to the complex ( Jajoo et al. 2005 , Joliot and Joliot 2006 , Hasan et al. 2013 ) have been proposed. Such a model is strengthens by the fact that binding of FNR to the Cyt b 6   f has been shown ( Zhang et al. 2001 ). There is evidence that the association of FNR with the thylakoid membrane and its association with CEF supercomplexes are impaired in the absence of PGR5 and/or PGRL1, implying that both proteins, directly or indirectly, contribute to the recruitment of FNR to the thylakoid membrane ( Mosebach et al. 2017 ). New data suggest that PGR5 is required for facilitating electron input from the reducing side of PSI, likely via FDX/FNR, into the Q i site of the Cyt b 6   f ( Buchert et al. 2020 ). There is evidence that this type of regulation is directly linked to the onset of CEF by changing from a canonical Q cycle during LEF to an alternative Q cycle during CEF ( Buchert et al. 2020 ). The electron transfer within the Cyt b 6  f complex, driven by the Q cycle ( Mitchell 1976 ), increases the number of pumped protons per electron, which accelerates lumen acidification and at the same time slows down Q o site turnover. In the alternative Q cycle during CEF, electron input into the stromal side would liberate one PQH 2 at each Q o site turnover and would further push lumen acidification. Notably, in photosynthetic electron transfer, the rate limiting step is the oxidation of plastoquinol at the Q o site of the Cyt b 6  f complex ( Stiehl and Witt 1969 ), which itself is pH dependent and deaccelerates when the pH is becoming more acidic, in turn, leading to the downregulation of photosynthetic electron transport and photosynthetic control. The protonmotive force can be also modulated via other ion fluxes ( Armbruster et al. 2014 , Kunz et al. 2014 , Herdean et al. 2016 ). However, lumen acidification is required for acclimation to excess light by promoting a mechanism designated as nonphotochemical quenching ( Niyogi and Truong 2013 ). On the contrary, large spiking Δψ amplitudes were shown to have adverse effects by promoting the formation of singlet oxygen at PSII during light fluctuations ( Davis et al. 2016 ) and an over-acidified lumen destabilizes the oxygen-evolving complex in PSII ( Krieger and Weis 1993 ). Thus, the formation of protein supercomplexes composed of PSI–LHCI and the Cyt b 6 f complex could critically contribute to the regulation of photosynthetic control and therefore promote photo-protection.

This work was supported by The Israel Science Foundation (Grant No. 569/17), and by German-Israeli Foundation for Scientific Research and Development (GIF) to N.N and M.H., Grant no. G-1483-207/2018. M.H. acknowledges support from German Science Foundation (DFG, Hi 739/13-2).

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Emerging approaches to measure photosynthesis from the leaf to the ecosystem

Matthew h. siebers.

1 United States Department of Agriculture, Global Change and Photosynthesis Research Unit, Agricultural Research Service, Urbana, IL 61801, U.S.A.

2 Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, U.S.A.

3 Departments of Plant Biology and Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, U.S.A.

Nuria Gomez-Casanovas

Katherine meacham-hensold, caitlin e. moore.

4 Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL 61801, U.S.A.

5 Institute for Sustainability, Energy & Environment, University of Illinois at Urbana-Champaign, Urbana, IL 61801, U.S.A.

6 School of Agriculture and Environment, The University of Western Australia, Crawley, WA 6009, Australia

Carl J. Bernacchi

Measuring photosynthesis is critical for quantifying and modeling leaf to regional scale productivity of managed and natural ecosystems. This review explores existing and novel advances in photosynthesis measurements that are certain to provide innovative directions in plant science research. First, we address gas exchange approaches from leaf to ecosystem scales. Leaf level gas exchange is a mature method but recent improvements to the user interface and environmental controls of commercial systems have resulted in faster and higher quality data collection. Canopy chamber and micrometeorological methods have also become more standardized tools and have an advanced understanding of ecosystem functioning under a changing environment and through long time series data coupled with community data sharing. Second, we review proximal and remote sensing approaches to measure photosynthesis, including hyperspectral reflectance- and fluorescence-based techniques. These techniques have long been used with aircraft and orbiting satellites, but lower-cost sensors and improved statistical analyses are allowing these techniques to become applicable at smaller scales to quantify changes in the underlying biochemistry of photosynthesis. Within the past decade measurements of chlorophyll fluorescence from earth-orbiting satellites have measured Solar Induced Fluorescence (SIF) enabling estimates of global ecosystem productivity. Finally, we highlight that stronger interactions of scientists across disciplines will benefit our capacity to accurately estimate productivity at regional and global scales. Applying the multiple techniques outlined in this review at scales from the leaf to the globe are likely to advance understanding of plant functioning from the organelle to the ecosystem.

Introduction

The terrestrial biosphere consists of an assemblage of diverse ecosystems. Its complexity is illustrated with a diversity of plants with distinct canopy structures subject to changing environmental conditions. Life on earth relies on the energy captured by these ecosystems through photosynthesis, which accounts for the single largest flux associated with the global carbon cycle [ 1 ]. Photosynthesis varies among plant functional types (e.g. C3 vs. C4) and over a wide range of spatial and temporal scales associated with changes in light, temperature, water and nutrients [ 2 , 3 ]. Global climate change driven by anthropogenic activities is having profound impacts on terrestrial ecosystems, with global temperatures rising faster than worst-case predictions [ 4 ]. Increasing agricultural demands associated with a growing population requires a doubling of crop yields by 2050 to keep up with demands [ 5 ], yet current rates of improvement fall short of this goal [ 6 , 7 ], which is likely to suffer with continued global warming [ 8–11 ].

Photosynthesis is a highly complex and relatively inefficient process, yet it is a critical component of the biosphere. Understanding photosynthetic responses over a range of spatial and temporal scales is needed to understand current and to predict future global carbon cycling. This understanding will also lead to improving photosynthesis, which can lead to higher productivity to meet growing agricultural demands [ 12 , 13 ]. These goals can only be achieved through the ability to measure photosynthesis over time and space, yet photosynthesis is difficult to measure directly. This is due to the multiple processes that are represented by the exchange of CO 2 between plants/ecosystems and the surrounding air. For example, at the leaf scale CO 2 is removed from the air by photosynthesis but this is partially countered by photorespiration and respiration, both of which release CO 2 [ 14 , 15 ]. The combined fluxes of these three processes represents net carbon assimilation ( A ) and partitioning this net flux into the component fluxes is challenging [ 16 ]. Scaling beyond the leaf only presents additional challenges. At canopy or ecosystem scales, respiration from non-photosynthetic tissues and heterotrophic organisms also release CO 2 , which combined with A provide measures of Net Ecosystem Exchange (NEE; Table 1 ). In this review, we outline the current and emerging approaches to measure photosynthesis at multiple scales and address the challenges and opportunities at each scale ( Figure 1 ). We begin with a focus on the well-established and widely used gas exchange techniques and follow with more recent approaches made available through recent technological advances.

(A) Leaf-level gas exchange with one measured representative photosynthetic CO 2 response curve. (B) Canopy photosynthesis chamber situated over a soybean field with representative diurnal Net Ecosystem Productivity (NEP) data (Image Credit: Anthony DiGrado). (C) Ecosystem-scale eddy covariance system situated over sorghum with representative Net Ecosystem Exchange (NEE; negative values signify downward flux from atmosphere toward land surface) partitioned into Gross Primary Productivity (GPP) and Ecosystem Respiration (ER). (D) leaf hyperspectral point sensor being used on the model crop tobacco and representative spectral reflectance measurements. (E) A hyperspectral imaging sensor measuring plots of the model crop tobacco and an example hypercube showing the visible surface and spectral information for each pixel with depth of image. (F) aircraft and satellite depicted over the earth surface and a map of GPP (public domain image courtesy of GeoEye/NASA SeaWIFS project). Other than where indicated, all images were taken by authors.

Table 1

In general, photosynthesis is defined as the process why which plants capture light energy and atmospheric CO 2 to synthesize complex carbohydrates. Photosynthesis supports the production of food, fiber, wood, grain fed to livestock, and fuel for humanity and regulates the concentration of CO 2 in the atmosphere. Quantifying global terrestrial photosynthesis is essential to understanding the global CO 2 cycle in a changing environment and the climate system.

Gas exchange

The fundamentals of gas exchange at any scale are relatively similar and require the ability to measure gas concentrations in air surrounding and the flow rate in which the air interacts with photosynthetic tissue. In addition to these measurements, numerous assumptions, corrections, and parameterizations are required to fully exploit the power of this technique [ 16 , 17 ]. Gas exchange methods have been applied at scales ranging from the organelle (e.g. [ 18 , 19 ]) to the whole ecosystem/region [ 20 ] to provide a basic understanding of how leaves, plants, and ecosystems function and respond to their environment ( Figure 1 ). Historically, gas exchange measurements were limited to enclosed sampling chambers, ranging from sections of leaves to whole plant canopies, where the rate of CO 2 exchange was measured over time. With the advent of micrometeorological techniques, gas exchange measurements at large scales (e.g. whole ecosystems) were developed that removed the need for enclosures ( Table 2 ). Despite errors, uncertainties and challenges associated with gas exchange, the various techniques are the current ‘gold standard’ by which emerging techniques are compared. This section provides an overview of gas exchange measurements at the leaf to ecosystem scales as a baseline in the understanding of emerging techniques.

Table 2

The global eddy covariance network, called FLUXNET ( https://fluxnet.org/about/ ), includes measurement sites linked across regional networks in North, Central and South America, Europe, Asia, Africa, and Australia.

Leaf scale gas exchange

Knowledge of leaf photosynthetic physiology stems from the development and application of leaf-level gas exchange systems [ 16 ]. Gas exchange technology has matured to the point where commercial gas exchange systems are widely available from many vendors. In addition to providing the key variables necessary to assess leaf scale carbon assimilation, these systems now provide the opportunity to precisely control the environmental conditions surrounding the photosynthetic tissue and to measure more than just carbon assimilation, including but not limited to transpiration, intercellular CO 2 concentrations, and stomatal conductance. Gas exchange techniques have been used for decades and most recent advancements have focused on improvements in accuracy, precision, usability, environmental control, and reduction in time to stable measurements. Despite the ease with which leaf level gas exchange can be measured, the importance of understanding gas exchange theory to ensure proper measurement and analysis cannot be overstated.

Gas exchange systems are the most commonly utilized technique for leaf scale photosynthetic measurements. While systems provide measures of A , various techniques can be applied to separate fluxes of photosynthesis, photorespiration, and respiration. However, many challenges exist with gas exchange that limit the wide application of the technique. These include cost, usability, data processing requirements, and time needed for ensuring quality measurements. Off-the-shelf gas exchange systems cost tens of thousands of dollars and require frequent maintenance that challenges their widespread use. Most gas exchange systems limit the area of measurement to, at most, several cm 2 , which presents issues related to scaling photosynthesis beyond a small section of one leaf. Typical measurements of in situ gas exchange require a minimum of 2–3 min to allow for both the system and the leaf to stabilize. Using these systems to measure beyond a simple survey of gas exchange, for example to measure light response or CO 2 response curves of A , requires substantially more time for each leaf. Recent techniques that exploit improved instrument precision can reduce the time for some measurements but generally at the expense of accuracy, and often require more advanced post-processing [ 21 ].

Canopy and ecosystem scale gas exchange

Scaling gas exchange measurements to the canopy or whole ecosystem presents significantly more challenges than at the leaf level, yet there are also more options ( Table 2 ). Canopy chambers work in much the same way as leaf chambers, although at a larger scale. The general principle follows that of leaf-level measurements, although chambers are required to be much larger to encompass multiple plants and the potential is greater for errors associated with leaks or pressure fluctuations [ 17 ]. Canopy chambers have been extensively used to measure CO 2 fluxes for a wide range of vegetation types and their strengths lie in their ability to address small-scale spatial variability ( Table 2 ). Furthermore, canopy chambers have been used both as a measurement and treatment system in global change studies to impose treatments as open-top chambers and acting as sample chambers when enclosed (e.g. [ 22 ]). Canopy chambers, however, can be limited in sampling frequency and spatial integration ( Table 2 ) while also having a profound impact on the canopy microclimate.

Micrometeorological approaches to gas exchange lack the need for chambers but require large spatial areas (>4 Ha) and a sensor suite that can measure the upward/downward movement of air coupled with the gas concentrations in the air [ 20 ]. The dominant micrometeorological technique, eddy covariance (EC), provides near-continuous measurements of NEE integrated over large spatial areas, called the flux footprint, with minimal disturbance ( Table 2 ) [ 20 ]. Air flow over a canopy consists of numerous rotating eddies. Measuring the speed and CO 2 concentrations of the eddies moving air upward and downward, provides the basic data needed to calculate fluxes of the footprint, which varies with wind speed and direction [ 23 ]. EC requires several important considerations to ensure the NEE data are robust and reliable [ 24 ], including ensuring sufficient atmospheric turbulence [ 23 ], applying corrections to exclude data fluxes extending beyond the area of measurements [ 25 , 26 ], and ensuring all measured fluxes follow the laws of thermodynamics [ 27 , 28 ] ( Table 3 ). Because of inevitable gaps in data collection associated with field instrumentation, gap filling strategies are used to complete the time-series of flux data ( Table 4 ). In addition to NEE, EC can apply to any measurable component of the atmosphere provided high temporal resolution sensors (≥10 Hz) exist (e.g. water vapor, methane, etc.). A global EC flux network, called FLUXNET, provides data from over 900 sites globally, allowing for a link between ecosystem and global NEE. This network provides unprecedented insights into environmental and biological drivers of ecosystem NEE [ 3 , 20 , 29–33 ]. Among other purposes, the long-term measurements of NEE from this network have improved understanding of ecosystem responses to climate and land-use change [ 34 ], and the data are essential to validate remote sensing and modeling products that scale to regions and the globe [ 35 , 36 ].

Table 3

Some of these challenges include ensuring sufficient atmospheric turbulent conditions are met [ 23 ], applying footprint corrections to exclude data when a significant portion of fluxes occur outside the ecosystem region of interest [ 25 , 26 ], and quantifying energy balance closure at the site [ 27 , 28 ]. Improving the robustness of NEE estimates from flux towers is an area of active research in the flux community, and one which will lead to greater understanding of ecosystem photosynthesis across a diversity of biomes.

Table 4

Good reliability of annual sum of the net CO 2 exchange refers to methods that ranked the best based on a several statistical metrics to predict annual fluxes as reported in References [ 122 , 123 ]. These statistical metrics include Root Mean Square Error, Bias Error and the annual CO 2 flux sum among others and were evaluated by comparing the filled NEE data with the observed values.

Whether using chamber-based or micrometeorological approaches, measured NEE provides an opportunity to explore changes in ecosystem-scale gas exchange at high temporal frequency. Photosynthesis at the ecosystem scale is generally defined as gross primary productivity (GPP), which is only one component of NEE. GPP is derived as the difference between measured NEE and modeled ecosystem respiration (ER; Table 5 ). Obtaining GPP from NEE involves modeling ER using temperature and light response functions; a process typically referred to as flux partitioning [ 24 , 32 , 37 , 38 ]. Flux partitioning allows for the investigation over time of GPP and ER in response to a variety of conditions [ 39–41 ]. A challenge with flux partitioning is introduced by the inhibitory effect of light on leaf respiration rates, known as the Kok effect [ 42 ]. In the light, autotrophic respiration can be significantly lower than at night resulting in GPP estimation errors when ignored [ 43 ].

Table 5

Both methods assume that any difference between daytime and nighttime Reco is due to temperature alone.

Recent micrometeorological approaches have attempted to measure GPP using a sulfur-containing analog of CO 2 , carbonyl sulfide (COS) that acts as natural ‘tracer’ molecule for GPP. This molecule enters a leaf in the same manner as CO 2 and is broken down by the enzyme carbonic anhydrase. Because of this, COS ‘uptake’ should scale with GPP, removing the need for partitioning NEE into the GPP and respiration components [ 44 ]. Studies using this method are showing promising insights with GPP estimated using CO 2 vs. COS measurements agreeing within 15% in forests and crops [ 45 ]. Another study that investigated variability in COS uptake and release in forests found agreement to within 3.5% between the two methods when GPP was high [ 46 ]. These results suggest an opportunity to use indirect methods for assessing GPP at larger scales, although recent work also suggests that photosynthetic tissues are not the only sink for COS [ 46–48 ].

Remote and proximal sensing

Obtaining photosynthetic carbon uptake measurements using gas exchange systems is laborious resulting in efforts to replace this technique with other high-throughput methods. There exists a rapid growth in plant phenotyping greenhouses with the goal of automated measurement capabilities [ 49 ] at scales ranging from leaf to globe ( Figure 1 ). Even with the most modern technologies, direct monitoring of leaf or plant level gas exchange would require substantial effort and resources. Thus, there are emerging technologies that provide means to infer plant responses to their growth environments that overcome the limitation of gas exchange [ 50–53 ]. Commercial sensors are available that provide information about plant canopy architecture and volume, which is important to infer growth over time [ 54 ], yet disentangling the underlying factors that lead to this growth requires physiological understanding. In the field, plot-level estimations of photosynthetic traits have been successfully estimated using a variety of platforms [ 55–57 ]. However, there needs to be improvements to the precision, accuracy, repeatability, and data pipeline before we can use these methods to estimate photosynthesis. Nonetheless, these new methods have a large potential impact on leaf to canopy understanding of plant physiology, ecosystem functioning and improving breeding efforts to maximize crop yields. In this section, we will discuss emerging technologies to monitor photosynthesis using spectral reflectance or fluorescence techniques. We will first outline the tools used for these approaches followed by a description of how these tools are being used.

Hyperspectral approaches to measure photosynthesis

Hyperspectral analysis is a non-destructive means of analysis that uses light reflected from vegetation to infer leaf, plant, canopy, or ecosystem performance. At the leaf and single-plant level, spectral sensors funnel light reflected from vegetation through a holographic diffraction grating, which separates light by wavelength across the electromagnetic spectrum [ 58 ]. Hyperspectral imaging data is in three ‘cubed’ dimensions with spectral wavelength (z) across spatial co-ordinates (x,y). Depending on the size of a single-pixel hyperspectral cameras can image vegetation from the whole plant to ecosystem scale [ 58 ].

Reflected light has become a powerful tool to characterize plant traits, including photosynthesis, given the varying response of light to leaf structure and pigment content at different wavelengths. In the near infrared (770–1300 nm), differences in chlorophyll and plant nitrogen content indicate a variety of vegetation stressors such as nutrient deficiency [ 59 , 60 ], plant disease status [ 61 , 62 ], and ozone damage [ 63 ], while the short wave infrared (SWIR1; 1300–2500 nm) indicates plant water status based traits [ 64 , 65 ]. In the past, discrete spectral reflectance indices were used as proxies for crop status [ 66 ]. However, computational and technological advances make it possible to derive photosynthetic capacities (maximum rate of carboxylation for C3 and C4 plants, V cmax and V max , respectively; and maximum rate of electron transport, J max ) and make predictions about photosynthetic performance scaling from the leaf [ 67–71 ] to the plot [ 72 , 73 ] and ecosystem scales [ 74 ].

One significant advance is the commercial availability of high-resolution fiber optic leaf clip-attachments. Hyperspectral radiometers typically contain a radiometrically calibrated light source and standardized white and dark reference panels for calibration. Leaf-level reflective intensity is compared with the reference material. Computer models (discussed later) are then used to correlate portions of the leaf's reflective spectrum with traditional measurements of gas exchange. Hyperspectral data can provide significant information about leaf photosynthesis at a fraction of the time compared with gas exchange [ 67–71 , 75 ]. These measurements can offer insight for upscaling to the plot level using field push carts [ 76 ] or drones mounted with hyperspectral cameras for breeding and research trials.

In addition to the hyperspectral methods mentioned above, recently handheld multispectral tools (e.g. FluroPen, Photo Systems Instruments, Drásov, Czech Republic; MultispeQ, PHOTOSYNQ INC. East Lancing MI, U.S.A.; and LI-600, LiCOR Biosciences Lincoln NE, U.S.A.) are used to monitor fluorescence and other parameters associated with leaves. Compared with hyperspectral leaf clips or fluorescence chambers sold with gas exchange units, these leaf tools can be used to more quickly and inexpensively screen for the vitality of photosynthetic systems under biotic and abiotic stresses (e.g. [ 77 ]). Furthermore, these tools provide opportunity, in some cases, to specify wavebands of interest for specific phenotypes that can extend beyond photosynthetic measurements.

Inspired by the successful leaf-level estimations of photosynthetic capacities, hyperspectral imaging (HSI) techniques are increasingly applied to canopy-scale measurements [ 73 , 78 ]. Imaging hyperspectral spectrometers provide more spatial information than a leaf-clip portable radiometer. Because of this, these sensors are being utilized to reveal variability in photosynthetic traits of interest across leaves, plants, and/or over large geographic areas [ 72 , 74 ]. These HSI sensors can scan individual plants in a few seconds [ 79 ] or provide analysis spanning several km 2 if mounted on aircraft or Earth-orbiting satellites [ 80 , 81 ]. Compared with point-based portable radiometers, these HSI sensors result in the accumulation of large amounts of data that need to be processed in an innovative way.

To link reflectance spectra to photosynthetic physiological parameters, data processing pipelines must be tailored to specific sensing platforms. These data pipelines are critical to applications such as field phenotyping in a high-throughput manner. For leaf-level estimations of photosynthetic variables using reflectance spectra, great efforts have been made to select statistical techniques that can provide the best predictive power [ 75 ]. Partial Least Square Regression (PLSR) [ 82 ] is currently the most common technique used to relate reflectance spectra to photosynthesis associated parameters [ 68 , 71 ] due to its ability to reduce tens to hundreds of spectral bands to just a few orthogonal principle components (also known as latent variables). There are also other machine learning algorithms such as Artificial Neural Network (ANN)-based regression and Least Absolute Shrinkage and Selection Operator (LASSO) that have been used to estimate photosynthesis [ 83 ]. The availability of these machine learning and empirical algorithms also poses a dilemma regarding the most effective approach. Collectively harnessing the strengths of individual empirical or machine learning algorithms through regression stacking shows promise [ 72 ] although further studies are needed to test its effectiveness across more plant species. For estimations of photosynthesis using reflectance spectra at the plot and ecosystem levels, further data processing steps are necessary to account for spurious variations in reflectance caused by sun-target-sensor geometry, canopy structure, leaf scattering, atmospheric contaminations, and background soil [ 75 ]. These steps are required to ensure that only reflectance data associated with photosynthesis are used for estimations. Although Radiative Transfer Models (RTMs) such as PROSAIL [ 84 ] are developed to remove those spurious variations, few of them can be directly used in the proximal sensing setting [ 85 ]. However, these RTMs provide an alternative way to reduce hyperspectral data into several meaningful leaf traits, such as chlorophyll concentration, that can serve as a proxy for photosynthesis. For example, RTMs-inverted traits were shown to explain up to 60% of variation in photosynthetic physiology in a crop species [ 72 ].

Remote-sensing products that measure GPP are traditionally based on the Light-Use Efficiency (LUE) concept of ecosystem modeling [ 86 ] and empirical models that rely on the relationships between remote sensing-derived variables and GPP [ 87–90 ]. These methods provide reasonable estimates of GPP compared with measured EC fluxes, however, new emerging spectral sensing technologies including Solar-Induced chlorophyll Fluorescence (SIF) are providing potential for estimating GPP at the ecosystem scale [ 91–93 ]. A fraction of solar radiation absorbed by chlorophyll is emitted as fluorescence, hence SIF is more physiologically based than other traditional remote sensing products [ 94 ] as it is a direct product of the photosynthetic process [ 95–97 ]. While pulse amplitude modulated chlorophyll fluorescence has long been used to measure photochemical efficiencies and heat dissipation in individual leaves [ 98 ], this should not be confused with SIF, which relies on measuring of the radiance chlorophyll fluorescence from an ecosystem.

Passive SIF measurements were first applied at the satellite scale ( Table 6 ) [ 99 ] to assess regional and global scale patterns of SIF alongside GPP [ 91–93 ] and is now being implemented at flux towers across multiple ecosystem types to determine the physiological and structural relationship between SIF and photosynthesis at this scale [ 100–103 ]. Likewise, the near-infrared radiance of vegetation index (NIR v ) has shown promising accuracy at detecting photosynthetic variability at the hourly scale over crop and forest system [ 104 , 105 ]. Therefore, both SIF and NIR v should enable real-time monitoring of productivity and stress.

Table 6

SIF measurement was first applied at the satellite scale [ 99 ] to assess regional and global scale patterns of SIF alongside GPP [ 91–93 ]. Currently, it is being implemented at flux towers across multiple ecosystem types to determine the physiological and structural relationship between SIF and photosynthesis at this scale [ 100–103 ]. For comparison, the EC method has a spatial resolution between hundred meters and several kilometers, and a continuous temporal resolution (half-hour) with a fine spatial coverage at the ecosystem and landscape scales.

The relationship between SIF and GPP is primarily dominated by absorbed photosynthetic active radiation (APAR) [ 106 , 107 ], implying that the correlation between SIF and GPP is the highest when photosynthesis is primarily light-limited [ 108 , 109 ]. However, GPP is also controlled by environmental factors other than light, and recent insights suggest that SIF responded to environmental stresses in a similar way as GPP, encouraging the application of SIF to estimate photosynthesis [ 94 ]. A relationship between SIF and GPP was similar among ecosystems although the relationship was stronger for grasslands than forests, savannas and croplands, and for C4 grasslands and crops than C3 ecosystems [ 94 ]. This quasi-universal relationship indicates that SIF could be a valuable tool for inferring GPP of the land surface. More collaborative studies between the EC and remote sensing communities are needed to evaluate why the relationship between SIF and GPP varies among ecosystems and under differing environmental conditions to improve the ability of SIF products to estimate ecosystem GPP robustly to scale regionally and globally.

Much progress has been made to understand the relationship between SIF and GPP but many challenges remain [ 109–111 ]. Higher spatial and temporal resolution SIF measurements are needed to coincide with the continuous GPP measurements [ 112 ]. Promising solutions to these challenges would be to develop remote sensing approaches that can cross-calibrate and blend multi-source SIF and reflectance measurements for a consistent record in both spatial and temporal domains. For example, combining satellite SIF with satellite reflectance was used to generate a spatially and temporally continuous SIF dataset [ 113 ]. Another solution is to improve SIF sensor designs to facilitate measurements at a much higher spatial and temporal resolutions. For example, the Fluorescence Imaging Spectrometer (FLORIS) onboard the Fluorescence EXplorer (FLEX) satellite can provide SIF at a better spatial resolution than its predecessors ( Table 6 ) [ 114 ] and the newly launched Orbiting Carbon Observatory 3 instrument (OCO-3) allow for more coverage globally at higher definition [ 115 ].

Interestingly, much of the work on remote sensing has initiated with large-scale measurements, yet there is a tremendous need to increase throughput of measurements at leaf and plot scales, particularly for application in high throughput phenotyping facilities. Whether these techniques are fully scalable remains uncertain, yet the opportunity for multidisciplinary research has advanced the versatility of the tools outlined in this review beyond their original users. Moving forward, simplifying data collection through ‘turn-key’ sensors and standardizing data analysis pipelines for the variety of techniques outlined here are certain to advance understanding of plant function from molecular to global scale.

• Monitoring Photosynthesis at every scale, from leaf to ecosystem, is an important task given the challenges of climate change and growing human populations.
• In the past 5 years there have been significant improvements to the technology and computational tools used to measure photosynthesis at every scale. And new facilities and equipment are being used around the world to monitor photosynthesis.
• Hyperspectral imaging at the leaf, and canopy scale paired with improved computational modeling allows for rapid estimates of important biochemical parameters.
• Micrometeorological approaches to estimate Gross Primary Productivity have been improved by the uses of sulfur tracing elements.
• Monitoring Solar Induced Fluorescence is a promising satellite-based method that should enable real-time monitoring of global ecosystem productivity.

Acknowledgements

This work is supported by funding from Global Change and Photosynthesis Research Unit of the USDA Agricultural Research Service. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the U.S. Department of Agriculture. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. USDA is an equal opportunity provider and employer.

Abbreviations

Competing Interests

The authors declare that there are no competing interests associated with the manuscript.

Author Contribution

M.H.S., N.G.-C., and C.J.B. conceived the outline, all authors contributed to the organization and writing of the manuscript, M.H.S. and N.G-C. Edited the manuscript, and C.J.B. supervised the project.

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Uncovering the photosystem I assembly pathway in land plants

Photosystem I (PSI) is one of two large pigment–protein complexes responsible for converting solar energy into chemical energy. This study reveals the previously unknown major PSI assembly pathway in land plants.

• Aihong Zhang
• Congming Lu

Architecture of symbiotic dinoflagellate photosystem I–light-harvesting supercomplex in Symbiodinium

Here the authors determine the cryoEM structure of Symbiodinium photosystem I, revealing a distinct architecture and pigment network of this light-harvesting supercomplex.

• Long-Sheng Zhao
• Yu-Zhong Zhang

Anoxygenic phototroph of the Chloroflexota uses a type I reaction centre

Cultivation of a new anoxygenic phototrophic bacterium from Boreal Shield lake water—representing a transition form in the evolution of photosynthesis—offers insights into how the major modes of phototrophy diversified.

• J. M. Tsuji
• J. D. Neufeld

News and Comment

Shining light on dinoflagellate photosystem I

Dinoflagellates are ecologically important and essential to corals and other cnidarians as phytosymbionts, but their photosystems had been underexplored. Recently, photosystem I (PSI) of dinoflagellate Symbiodinium sp. was structurally characterized using cryo-Electron Microscopy (cryo-EM). These analyses revealed a distinct organization of the PSI supercomplex, including two previously unidentified subunits, PsaT and PsaU, and shed light on interactions between light harvesting antenna proteins and the PSI core. These results have implications with respect to the evolution of dinoflagellates and their association with cnidarians.

• Shuaishuai Wu
• Arthur R. Grossman

A novel photosystem assembly line worker

The assembly of large protein–pigment photosystem supercomplexes relies on several assembly factors. Zhang et al. describe a novel assembly factor that evolved during the terrestrialization of land plants.

• Lisa Heinke

Deciphering the molecular mechanism of photosystem I assembly in land plants

Photosystem I (PSI) and PSII are two large pigment–protein complexes that are responsible for converting solar energy into chemical energy. We identify the PSI assembly factor PBF8 and show that it mediates two key consecutive steps in PSI assembly, revealing major aspects of the PSI assembly pathway in land plants.

The revolution evolution

Machine-learning algorithms for protein structure prediction can now generate models directly from sequences. However, photosynthetic assemblies represent a challenge due to additional levels of complexity arising from their multi-protein nature and presence of cofactors.

Photosynthesis: Complex flexibilities

The core of the photosynthetic complex photosystem I had been assumed to require contact with its associated light-harvesting complex I to function. But a mutant Arabidopsis line lacking the components of this complex shows that a plant's photosynthetic apparatus is more adaptable to changes in its environment than previously thought.

• Dario Leister

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AP®︎/College Biology

Course: ap®︎/college biology   >   unit 3.

• Photosynthesis
• Intro to photosynthesis
• Breaking down photosynthesis stages
• Conceptual overview of light dependent reactions

The light-dependent reactions

• The Calvin cycle
• Photosynthesis evolution
• Photosynthesis review

Introduction

• Plants carry out a form of photosynthesis called oxygenic photosynthesis . In oxygenic photosynthesis, water molecules are split to provide a source of electrons for the electron transport chain, and oxygen gas is released as a byproduct. Plants organize their photosynthetic pigments into two separate complexes called photosystems (photosystems I and II), and they use chlorophylls as their reaction center pigments.
• Purple sulfur bacteria, in contrast, carry out anoxygenic photosynthesis , meaning that water is not used as an electron source and oxygen gas is not produced. Instead, these bacteria use hydrogen sulfide ( H 2 S ‍   ) as an electron source and produce elemental sulfur as a byproduct. In addition, purple sulfur bacteria have only one photosystem, and they use chlorophyll-like molecules called bacteriochlorophylls as reaction center pigments 1 , 2 , 3 ‍   .

Overview of the light-dependent reactions

• Light absorption in PSII. When light is absorbed by one of the many pigments in photosystem II, energy is passed inward from pigment to pigment until it reaches the reaction center. There, energy is transferred to P680, boosting an electron to a high energy level. The high-energy electron is passed to an acceptor molecule and replaced with an electron from water. This splitting of water releases the O 2 ‍   we breathe.
• ATP synthesis. The high-energy electron travels down an electron transport chain, losing energy as it goes. Some of the released energy drives pumping of H + ‍   ions from the stroma into the thylakoid interior, building a gradient. ( H + ‍   ions from the splitting of water also add to the gradient.) As H + ‍   ions flow down their gradient and into the stroma, they pass through ATP synthase, driving ATP production in a process known as chemiosmosis .
• Light absorption in PSI. The electron arrives at photosystem I and joins the P700 special pair of chlorophylls in the reaction center. When light energy is absorbed by pigments and passed inward to the reaction center, the electron in P700 is boosted to a very high energy level and transferred to an acceptor molecule. The special pair's missing electron is replaced by a new electron from PSII (arriving via the electron transport chain).
• NADPH formation. The high-energy electron travels down a short second leg of the electron transport chain. At the end of the chain, the electron is passed to NADP + ‍   (along with a second electron from the same pathway) to make NADPH.

What is a photosystem?

Photosystem i vs. photosystem ii.

• Special pairs. The chlorophyll a special pairs of the two photosystems absorb different wavelengths of light. The PSII special pair absorbs best at 680 nm, while the PSI special absorbs best at 700 nm. Because of this, the special pairs are called P680 and P700 , respectively.
• Primary acceptor . The special pair of each photosystem passes electrons to a different primary acceptor. The primary electron acceptor of PSII is pheophytin, an organic molecule that resembles chlorophyll, while the primary electron acceptor of PSI is a chlorophyll called A 0 ‍   7 , 8 ‍   .
• Source of electrons . Once an electron is lost, each photosystem is replenished by electrons from a different source. The PSII reaction center gets electrons from water, while the PSI reaction center is replenished by electrons that flow down an electron transport chain from PSII.

Photosystem II

Electron transport chains and photosystem i, some electrons flow cyclically, attribution:, works cited:.

• Lodish, H., Berk, A., Zipursky, S. L., Matsudaira, P., Baltimore, D., and Darnell, J. (2000). Molecular analysis of photosystems. In Molecular cell biology (4th ed., section 16.4). New York, NY: W. H. Freeman. Retrieved from http://www.ncbi.nlm.nih.gov/books/NBK21484/ .
• Boundless. (2015, July 21). Anoxygenic photosynthetic bacteria. In Boundless microbiology . Retrieved from https://www.boundless.com/microbiology/textbooks/boundless-microbiology-textbook/microbial-evolution-phylogeny-and-diversity-8/nonproteobacteria-gram-negative-bacteria-105/anoxygenic-photosynthetic-bacteria-551-7338/ .
• Purple sulfur bacteria. (2015, July 16). Retrieved October 24, 2015 from Wikipedia: https://en.wikipedia.org/wiki/Purple_sulfur_bacteria .
• Soda lake. (2015, September 26). Retrieved October 24, 2015 from Wikipedia: https://en.wikipedia.org/wiki/Soda_lake .
• Gutierrez, R. Bio41 Week 7 Biochemistry Lectures 11 and 12. Bio41. 2009.
• Berg, J. M., Tymoczko, J. L., and Stryer, L. (2002). Accessory pigments funnel energy into reaction centers. In Biochemistry (5th ed., section 19.5). New York, NY: W. H. Freeman. Retrieved from http://www.ncbi.nlm.nih.gov/books/NBK22604/ .
• Pheophytin. (2015, February 11). Retrieved October 28, 2015 from Wikipedia: https://en.wikipedia.org/wiki/Pheophytin .
• Photosystem I. (2016, June 25). Retrieved from Wikipedia on July 22, 2016: https://en.wikipedia.org/wiki/Photosystem_I .
• Berg, J. M., Tymoczko, J. L., and Stryer, L. (2002). Two photosystems generate a proton gradient and NADPH in oxygenic photosynthesis. In Biochemistry (5th ed., section 19.3). New York, NY: W. H. Freeman. Retrieved from http://www.ncbi.nlm.nih.gov/books/NBK22538/#_A2681_ .
• Joliot, P. and Johnson, G. N. (2011). Regulation of cyclic and linear electron flow in higher plants. PNAS, 108(32), 13317-13322. http://dx.doi.org/10.1073/pnas.1110189108 .
• Johnson, Giles N. (2011). Physiology of PSI cyclic electron transport in higher plants. Biochimica et Biophysica Acta - Bioenergetics , 1807 (8), 906-911. http://dx.doi.org/doi:10.1016/j.bbabio.2010.11.009 .
• Berg, J. M., Tymoczko, J. L., and Stryer, L. (2002). A proton gradient across the thylakoid membrane drives ATP synthesis. In Biochemistry (5th ed., section 19.4). New York, NY: W. H. Freeman. Retrieved from http://www.ncbi.nlm.nih.gov/books/NBK22519/ .
• Takahashi, S., Milward, S. E., Fan, D.-Y., Chow, W. S., and Badger, M. R. (2008). How does cyclic electron flow alleviate photoinhibition in Arabidopsis? Plant Physiology , 149 (3), 1560-1567. http://dx.doi.org/10.1104/pp.108.134122 .

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8.2 The Light-Dependent Reactions of Photosynthesis

Learning objectives.

By the end of this section, you will be able to do the following:

• Explain how plants absorb energy from sunlight
• Describe short and long wavelengths of light
• Describe how and where photosynthesis takes place within a plant

How can light energy be used to make food? When a person turns on a lamp, electrical energy becomes light energy. Like all other forms of kinetic energy, light can travel, change form, and be harnessed to do work. In the case of photosynthesis, light energy is converted into chemical energy, which photoautotrophs use to build basic carbohydrate molecules ( Figure 8.9 ). However, autotrophs only use a few specific wavelengths of sunlight.

What Is Light Energy?

The sun emits an enormous amount of electromagnetic radiation (solar energy in a spectrum from very short gamma rays to very long radio waves). Humans can see only a tiny fraction of this energy, which we refer to as “visible light.” The manner in which solar energy travels is described as waves. Scientists can determine the amount of energy of a wave by measuring its wavelength (shorter wavelengths are more powerful than longer wavelengths)—the distance between consecutive crest points of a wave. Therefore, a single wave is measured from two consecutive points, such as from crest to crest or from trough to trough ( Figure 8.10 ).

Visible light constitutes only one of many types of electromagnetic radiation emitted from the sun and other stars. Scientists differentiate the various types of radiant energy from the sun within the electromagnetic spectrum. The electromagnetic spectrum is the range of all possible frequencies of radiation ( Figure 8.11 ). The difference between wavelengths relates to the amount of energy carried by them.

Each type of electromagnetic radiation travels at a particular wavelength. The longer the wavelength, the less energy it carries. Short, tight waves carry the most energy. This may seem illogical, but think of it in terms of a piece of moving heavy rope. It takes little effort by a person to move a rope in long, wide waves. To make a rope move in short, tight waves, a person would need to apply significantly more energy.

The electromagnetic spectrum ( Figure 8.11 ) shows several types of electromagnetic radiation originating from the sun, including X-rays and ultraviolet (UV) rays. The higher-energy waves can penetrate tissues and damage cells and DNA, which explains why both X-rays and UV rays can be harmful to living organisms.

Absorption of Light

Light energy initiates the process of photosynthesis when pigments absorb specific wavelengths of visible light. Organic pigments, whether in the human retina or the chloroplast thylakoid, have a narrow range of energy levels that they can absorb. Energy levels lower than those represented by red light are insufficient to raise an orbital electron to an excited (quantum) state. Energy levels higher than those in blue light will physically tear the molecules apart, in a process called bleaching. Our retinal pigments can only “see” (absorb) wavelengths between 700 nm and 400 nm of light, a spectrum that is therefore called visible light. For the same reasons, plants, pigment molecules absorb only light in the wavelength range of 700 nm to 400 nm; plant physiologists refer to this range for plants as photosynthetically active radiation.

The visible light seen by humans as white light actually exists in a rainbow of colors. Certain objects, such as a prism or a drop of water, disperse white light to reveal the colors to the human eye. The visible light portion of the electromagnetic spectrum shows the rainbow of colors, with violet and blue having shorter wavelengths, and therefore higher energy. At the other end of the spectrum toward red, the wavelengths are longer and have lower energy ( Figure 8.13 ).

Understanding Pigments

Different kinds of pigments exist, and each absorbs only specific wavelengths (colors) of visible light. Pigments reflect or transmit the wavelengths they cannot absorb, making them appear a mixture of the reflected or transmitted light colors.

Chlorophylls and carotenoids are the two major classes of photosynthetic pigments found in plants and algae; each class has multiple types of pigment molecules. There are five major chlorophylls: a , b , c and d and a related molecule found in prokaryotes called bacteriochlorophyll . Chlorophyll a and chlorophyll b are found in higher plant chloroplasts and will be the focus of the following discussion.

With dozens of different forms, carotenoids are a much larger group of pigments. The carotenoids found in fruit—such as the red of tomato (lycopene), the yellow of corn seeds (zeaxanthin), or the orange of an orange peel (β-carotene)—are used as advertisements to attract seed dispersers. In photosynthesis, carotenoids function as photosynthetic pigments that are very efficient molecules for the disposal of excess energy. When a leaf is exposed to full sun, the light-dependent reactions are required to process an enormous amount of energy; if that energy is not handled properly, it can do significant damage. Therefore, many carotenoids reside in the thylakoid membrane, absorb excess energy, and safely dissipate that energy as heat.

Each type of pigment can be identified by the specific pattern of wavelengths it absorbs from visible light: This is termed the absorption spectrum . The graph in Figure 8.14 shows the absorption spectra for chlorophyll a , chlorophyll b , and a type of carotenoid pigment called β-carotene (which absorbs blue and green light). Notice how each pigment has a distinct set of peaks and troughs, revealing a highly specific pattern of absorption. Chlorophyll a absorbs wavelengths from either end of the visible spectrum (blue and red), but not green. Because green is reflected or transmitted, chlorophyll appears green. Carotenoids absorb in the short-wavelength blue region, and reflect the longer yellow, red, and orange wavelengths.

Many photosynthetic organisms have a mixture of pigments, and by using these pigments, the organism can absorb energy from a wider range of wavelengths. Not all photosynthetic organisms have full access to sunlight. Some organisms grow underwater where light intensity and quality decrease and change with depth. Other organisms grow in competition for light. Plants on the rainforest floor must be able to absorb any bit of light that comes through, because the taller trees absorb most of the sunlight and scatter the remaining solar radiation ( Figure 8.15 ).

When studying a photosynthetic organism, scientists can determine the types of pigments present by generating absorption spectra. An instrument called a spectrophotometer can differentiate which wavelengths of light a substance can absorb. Spectrophotometers measure transmitted light and compute from it the absorption. By extracting pigments from leaves and placing these samples into a spectrophotometer, scientists can identify which wavelengths of light an organism can absorb. Additional methods for the identification of plant pigments include various types of chromatography that separate the pigments by their relative affinities to solid and mobile phases.

How Light-Dependent Reactions Work

The overall function of light-dependent reactions is to convert solar energy into chemical energy in the form of NADPH and ATP. This chemical energy supports the light-independent reactions and fuels the assembly of sugar molecules. The light-dependent reactions are depicted in Figure 8.16 . Protein complexes and pigment molecules work together to produce NADPH and ATP. The numbering of the photosystems is derived from the order in which they were discovered, not in the order of the transfer of electrons.

The actual step that converts light energy into chemical energy takes place in a multiprotein complex called a photosystem , two types of which are found embedded in the thylakoid membrane: photosystem II (PSII) and photosystem I (PSI) ( Figure 8.17 ). The two complexes differ on the basis of what they oxidize (that is, the source of the low-energy electron supply) and what they reduce (the place to which they deliver their energized electrons).

Both photosystems have the same basic structure; a number of antenna pigments to which the chlorophyll molecules are bound surround the reaction center where the photochemistry takes place. Each photosystem is serviced by the light-harvesting complex , which passes energy from sunlight to the reaction center; it consists of multiple antenna pigments that contain a mixture of 300 to 400 chlorophyll a and b molecules as well as other pigments like carotenoids. The absorption of a single photon or distinct quantity or “packet” of light by any of the chlorophylls pushes that molecule into an excited state. In short, the light energy has now been captured by biological molecules but is not stored in any useful form yet. The energy is transferred from chlorophyll to chlorophyll until eventually (after about a millionth of a second), it is delivered to the reaction center. Up to this point, only energy has been transferred between molecules, not electrons.

Visual Connection

What is the initial source of electrons for the chloroplast electron transport chain?

• carbon dioxide

The reaction center contains a pair of chlorophyll a molecules with a special property. Those two chlorophylls can undergo oxidation upon excitation; they can actually give up an electron in a process called a photoact . It is at this step in the reaction center during photosynthesis that light energy is converted into an excited electron. All of the subsequent steps involve getting that electron onto the energy carrier NADPH for delivery to the Calvin cycle where the electron is deposited onto carbon for long-term storage in the form of a carbohydrate. PSII and PSI are two major components of the photosynthetic electron transport chain , which also includes the cytochrome complex . The cytochrome complex, an enzyme composed of two protein complexes, transfers the electrons from the carrier molecule plastoquinone (Pq) to the protein plastocyanin (Pc), thus enabling both the transfer of protons across the thylakoid membrane and the transfer of electrons from PSII to PSI.

The reaction center of PSII (called P680 ) delivers its high-energy electrons, one at the time, to the primary electron acceptor , and through the electron transport chain (Pq to cytochrome complex to plastocyanine) to PSI. P680’s missing electron is replaced by extracting a low-energy electron from water; thus, water is “split” during this stage of photosynthesis, and PSII is re-reduced after every photoact. Splitting one H 2 O molecule releases two electrons, two hydrogen atoms, and one atom of oxygen. However, splitting two molecules is required to form one molecule of diatomic O 2 gas. About 10 percent of the oxygen is used by mitochondria in the leaf to support oxidative phosphorylation. The remainder escapes to the atmosphere where it is used by aerobic organisms to support respiration.

As electrons move through the proteins that reside between PSII and PSI, they lose energy. This energy is used to move hydrogen atoms from the stromal side of the membrane to the thylakoid lumen. Those hydrogen atoms, plus the ones produced by splitting water, accumulate in the thylakoid lumen and will be used synthesize ATP in a later step. Because the electrons have lost energy prior to their arrival at PSI, they must be re-energized by PSI, hence, another photon is absorbed by the PSI antenna. That energy is relayed to the PSI reaction center (called P700 ). P700 is oxidized and sends a high-energy electron to NADP + to form NADPH. Thus, PSII captures the energy to create proton gradients to make ATP, and PSI captures the energy to reduce NADP + into NADPH. The two photosystems work in concert, in part, to guarantee that the production of NADPH will roughly equal the production of ATP. Other mechanisms exist to fine-tune that ratio to exactly match the chloroplast’s constantly changing energy needs.

Generating an Energy Carrier: ATP

As in the intermembrane space of the mitochondria during cellular respiration, the buildup of hydrogen ions inside the thylakoid lumen creates a concentration gradient . The passive diffusion of hydrogen ions from high concentration (in the thylakoid lumen) to low concentration (in the stroma) is harnessed to create ATP, just as in the electron transport chain of cellular respiration. The ions build up energy because of diffusion and because they all have the same electrical charge, repelling each other.

To release this energy, hydrogen ions will rush through any opening, similar to water jetting through a hole in a dam. In the thylakoid, that opening is a passage through a specialized protein channel called the ATP synthase. The energy released by the hydrogen ion stream allows ATP synthase to attach a third phosphate group to ADP, which forms a molecule of ATP ( Figure 8.17 ). The flow of hydrogen ions through ATP synthase is called chemiosmosis because the ions move from an area of high to an area of low concentration through a semi-permeable structure of the thylakoid.

Link to Learning

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Experimental in vivo measurements of light emission in plants: a perspective dedicated to David Walker

• Published: 13 October 2012
• Volume 114 , pages 69–96, ( 2012 )

Cite this article

• Hazem M. Kalaji 1 ,
• Vasilij Goltsev 2 ,
• Karolina Bosa 3 ,
• Suleyman I. Allakhverdiev 4 , 5 ,
• Reto J. Strasser 6 , 7 , 8 &
• Govindjee 9  

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This review is dedicated to David Walker (1928–2012), a pioneer in the field of photosynthesis and chlorophyll fluorescence. We begin this review by presenting the history of light emission studies, from the ancient times. Light emission from plants is of several kinds: prompt fluorescence (PF), delayed fluorescence (DF), thermoluminescence, and phosphorescence. In this article, we focus on PF and DF. Chlorophyll a fluorescence measurements have been used for more than 80 years to study photosynthesis, particularly photosystem II (PSII) since 1961. This technique has become a regular trusted probe in agricultural and biological research. Many measured and calculated parameters are good biomarkers or indicators of plant tolerance to different abiotic and biotic stressors. This would never have been possible without the rapid development of new fluorometers. To date, most of these instruments are based mainly on two different operational principles for measuring variable chlorophyll a fluorescence: (1) a PF signal produced following a pulse-amplitude-modulated excitation and (2) a PF signal emitted during a strong continuous actinic excitation. In addition to fluorometers, other instruments have been developed to measure additional signals, such as DF, originating from PSII, and light-induced absorbance changes due to the photooxidation of P700, from PSI, measured as the absorption decrease (photobleaching) at about 705 nm, or increase at 820 nm. In this review, the technical and theoretical basis of newly developed instruments, allowing for simultaneous measurement of the PF and the DF as well as other parameters is discussed. Special emphasis has been given to a description of comparative measurements on PF and DF. However, DF has been discussed in greater details, since it is much less used and less known than PF, but has a great potential to provide useful qualitative new information on the back reactions of PSII electron transfer. A review concerning the history of fluorometers is also presented.

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Acknowledgments

This work was supported by grants to one of the authors (Suleyman Allakhverdiev) from the Russian Foundation for Basic Research, the Russian Ministry of Science and Education and the Molecular and Cell Biology Programs of the Russian Academy of Sciences, and by BMBF, Bilateral Cooperation between Germany and Russia. Hazem Kalaji thanks Richard Poole and Paul Davis of Hansatech Instruments Company for supporting him with appropriate literature for this review, and Beniamino Barbieri of ISS Inc. (USA) and David Jameson (University of Hawaii at Manoa, USA) for helping him collect data related to the history of fluorometry and fluorometer development. Govindjee thanks Jawaharlal Nehru University, New Delhi, India, for providing him with a Visiting Professorship in early 2012, where this paper was being finalized; he is highly grateful to George Papageorgiou and Alexandrina Stirbet for reading and commenting on the various drafts of this paper. Vasilij Goltsev thanks the Bulgarian National Science Fund, for financial support. Reto J. Strasser thanks the Swiss National Science Foundation for a 3-year fellowship for advanced scientists and for long-term support of the Bioenergetics Laboratory of the University of Geneva. Since his retirement in 2009, the Weed Research Laboratory at Nanjing Agricultural University (NAU) has regularly supported him as a Chair Professor. Support by the NSF of China is also highly acknowledged by him. As a part time Professor Extra-Ordinarius at the North-West University Potchefstroom 2520 Republic of South Africa, he has had the chance to work with the physiologically best defined and reproducible plants in green houses and optimally regulated open top chambers.

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Department of Plant Physiology, Faculty of Agriculture and Biology, Warsaw University of Life Sciences SGGW, Nowoursynowska 159, 02-776, Warsaw, Poland

Hazem M. Kalaji

Department of Biophysics and Radiobiology, Faculty of Biology St. Kliment Ohridski University of Sofia, 8 Dr.Tzankov Blvd., 1164, Sofia, Bulgaria

Vasilij Goltsev

Department of Pomology, Faculty of Horticulture and Landscape Architecture, Warsaw University of Life Sciences SGGW, Nowoursynowska 159, 02-776, Warsaw, Poland

Karolina Bosa

Institute of Plant Physiology, Russian Academy of Sciences, Botanicheskaya Street 35, Moscow, 127276, Russia

Suleyman I. Allakhverdiev

Institute of Basic Biological Problems, Russian Academy of Sciences, Pushchino, Moscow Region, 142290, Russia

Bioenergetics Laboratory, University of Geneva, 1254, Jussy, Geneva, Switzerland

Reto J. Strasser

Weed Research Laboratory, Nanjing Agricultural University, Nanjing, China

Research Unit Environmental Science and Management, North-West University (Potchefstroom Campus), Potchefstroom, 2520, Republic of South Africa

Department of Biochemistry, Department of Plant Biology, and Center of Biophysics & Computational Biology, University of Illinois at Urbana-Champaign, 265 Morrill Hall, 505 South Goodwin Avenue, Urbana, IL, 61801-3707, USA

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Appendix 1 (by V. Goltsev)

Df measurement.

In view of the not-so-common use of DF, we describe here in some details this method and analysis of data. Two experimental approaches are used for the measurement and the analysis of the DF signal: (a) monitoring of relaxation of DF intensity in the dark (the so-called “dark DF decay”); and (b) recording of “DF induction curve” (IC) during the transition of dark-adapted samples to light-adapted state. In the first approach, the samples are pre-illuminated by short (single turnover) light pulse (flash) or by continuous light to form redox states of PSII that lead to emission of DF, for example S 3 Z + P680 Q – A Q B . Here, S 3 is one of the oxidized S -states of the oxygen evolving complex of PSII (Joliot and Kok 1975 ). After turning off the actinic light, the rate of DF quanta emission and the kinetics of DF decay are analyzed. Such an approach is usually applied for measuring the DF kinetic components decaying in ns (Christen et al. 2000 ), in μs (Grabolle and Dau 2005 ; Buchta et al. 2007 ), in ms (Goltsev et al. 1980 ) or in s (Rutherford et al. 1984 ; Rutherford and Inoue 1984 ). To evaluate long-lived light emission (>seconds or minutes), the samples are excited by continuous light (Hideg et al. 1991 ; Katsumata et al. 2008 ; Berden-Zrimec et al. 2008 ).

When a DF emission is monitored during illumination of a dark-adapted sample by continuous light, the DF induction curve is measured, as is done for recording the OJIP transients of PF. Both the measurements reflect changes in the photosynthesis machinery during dark-to-light adaptation. The same population of Chl-proteins of the PSII antennae complexes that emit PF emits DF quanta. The main difference is that the quantum yield of PF is 3-10 orders of magnitude higher than that of DF, and the DF quanta cannot be distinguished from those of PF during illumination. One effective experimental approach that allows one to distinguish between the two types of light emission is the separation of the two processes as follows: PF is recorded simultaneously with illumination and DF—after turning off the actinic light. For measurement of DF induction, it is necessary to use alternate light/dark cycles. During the light period, PF can be measured, a short time interval after the light is turned off (to avoid measuring PF), DF dark decay is measured (see Fig.  10 ).

Reconstruction of kinetics of prompt (chlorophyll) fluorescence ( PF ) and delayed (chlorophyll) fluorescence ( DF ) signals measured simultaneously by a M-PEA instrument (Hansatech, UK; see Fig.  8 ) device during dark-to-light adaptation in bean leaves. The data acquisition for the two signals, PF in the light and DF in the dark, was every 0.01 ms in the 0–0.3 ms range, every 0.1 ms in the 0.3–3 ms range, every 1 ms in the 3–30 ms, and up to 30 s; after this time, data was acquired every 10 s. The black dots show values of PF signal, at specific points, during the O–J–I–P transition—i.e., on the PF induction curve (see the top vertical panel ). Points marked as DD (“dark drops”) show the first PF values recorded after short dark periods during which DF was measured. Sheets perpendicular to the back plane show DF dark decays at different times of the O–J–I–P transition. Arrows show DF induction curves, recorded at different decay times. This figure was drawn by two of the coauthors (VG and RS) from their own original data

During each dark interval, the DF signal shows a polyphasic decrease. In most analog phosphoroscope-based DF-measuring devices, the quanta emitted during each dark interval are collected, integrated and presented as a value proportional to DF intensity at definite times. The time course of the measured signal at intermittent illumination of dark-adapted samples is presented as a DF induction curve. The digitalization of the measuring signal (Gaevsky and Morgun 1993 ; Zaharieva and Goltsev 2003 ) and the use of fast analog–digital converter devices (<50 μs) allows analysis of the kinetics of DF relaxation at each dark interval during induction (Fig.  11 ). To construct DF induction curves, a distinct dark time period is chosen within which the values of DF intensities are averaged and used as a single point of DF induction curve. Selecting points from different decay intervals, one can construct induction curves that show DF kinetic components with different lifetimes. In Fig.  11 , five DF induction curves are shown that used the following time points in DF decay curves, i.e., after 20 μs, 90 μs, 0.9, 2.3 and 23 ms of the start of dark interval. Thus, the time course of different components (measured at different delay intervals) of the DF decay can be monitored during the dark-to-light transitions.

Induction curves of delayed fluorescence ( DF ) recorded in 20–90 µs ( left panel ) and in 100–900 µs dark decay window ( right panel ) as a function of actinic light intensity. Primary leaves of decapitated bean plants were dark adapted for 1 h and then illuminated by red (625 nm) actinic light of different intensities from 500 to 4,000 µmol photons m −2  s −1 . DF intensities are normalized to maximal values for each curve. The “I’s” (inflection peaks) refer to the induction maxima, and “D’s” (dips) to the minima. This figure was drawn by one of the authors (VG), using his own original data

Origin of DF induction phases

The DF induction curve reflects processes that occur in the photosynthetic machinery of plants during illumination after a period of dark adaptation. Usually, induction maxima are well pronounced after 5 to 15 min of dark adaptation. A stationary level of DF is reached after the 2-3 min of actinic light (Veselovskii and Veselova 1990 ; Radenovic et al. 1994 ).

The DF induction curve is extremely complex: it is multiphasic. Even 61 years after its discovery (Strehler and Arnold 1951 ), the reasons for the changes in the intensity of delayed light quanta emission during the induction transients, are not clear. DF intensity passes through several maxima and minima before reaching a stationary level. The main factors affecting the DF induction shape are: (1) The photosynthetic sample: plant species; (2) structural status of the sample (whole plant, isolated chloroplast suspension, membrane particles); (3) physiological state of the sample (chemical and physical treatments); (4) measurement details: e.g., dark adaptation duration; actinic light intensity; recording period (duration of time interval when DF is measured; dark interval before DF recording). Thus, measuring conditions determine which kinetic components of DF are being measured in an experiment (Zaharieva and Goltsev 2003 ).

There is no consensus nomenclature of the maxima that are observed in the DF induction curve, and there is no consensus about the number and interpretation of these maxima. We use here the nomenclature proposed by V. Goltsev and coworkers (Goltsev and Yordanov 1997 ; Goltsev et al. 1998 , 2005 , 2009 ; Zaharieva and Goltsev 2003 ) where the maxima (denoted by I) and minima (denoted by D) are numbered in a sequence according to their position in the DF induction curve (I 1 , D 1 , I 2 , D 2 ).

The DF induction curve is easily divided into two main phases, a fast phase and a slow phase (Itoh et al. 1971 ; Itoh and Murata 1973 ; Malkin and Barber 1978 ) (Fig.  11 ). The fast phase that lasts for about 300 ms coincides with the OJIP transient of PF, and then there is the slow phase that occurs in the minute range, reaching a stationary level at the end. Using a mechanical phosphoroscope with fast signal digitalization (~50 μs) and electromechanical light “cutter” (opening time <1 ms), it is possible to resolve details in the structure of the fast phase. Thus, when DF is measured starting with 5.5 ms of illumination (the working cycle being 11–5.5 ms light and 5.5 ms dark and induction, see Goltsev et al. 2003 ), two maxima I 1 and I 2 (sometime with a minimum D 1 in between) are observed in the fast phase; after this DF drops to a minimum labeled as D 2 (Goltsev and Yordanov 1997 ; Goltsev et al. 1998 , 2003 ). After a small step, labeled as I 3 , the slow phase begins. During this phase, DF rises to a maximum I 4 and then, through several transient maxima (I 5 and I 6 ), DF intensity decreases to a stationary level S (Itoh and Murata 1973 ; Goltsev et al. 2003 ).

For DF that decays in 100-μs time interval, the time position of the first induction maximum I 1 as well as a ratio of I 1 /I 2 are highly dependent on light intensity. At 4,000 μmol photons m −2 s −1 the I 1 maximum appears at about 3 ms of illumination and it is shifted up to 15 ms at lower light intensity (500 μmol photons m −2 s −1 ). A similar effect is observed in the induction curve of sub-millisecond DF component (Fig.  11 , right panel).

When DF is compared with PF transient and with the kinetics of the signal of “reflection” of modulated light at 820 nm (called MR820—this photoinduced signal is caused by the appearance and disappearance of the oxidized form of P700 and of plastocyanin, see Schansker et al. 2003 ), the maximum I 1 coincides with the PF increase from the J-level ( F j ) to the I level ( F I ) and with decrease of MR820 reflecting P700 and plastocyanin oxidation (Schansker et al. 2003 ). The growth of DF intensity up to I 1 probably reflects the accumulation of S 3 ZP680 + Q – A and S 3 Z + P680 Q – A states that have a relatively high yield of DF emission. The kinetics of DF decreases after the maximum I 1 to the minimum D 2 is similar to that of the PF rise from J to I and P phases, and it, possibly, is caused by the formation of “closed” PSII states S i ZP680 Q – A Q 2− B that are not able to do charge recombination in sub-ms and ms time interval and, thus, DF formation. Another process that probably has a part in the kinetics of the fast phase of the DF induction is photooxidation of P700 and of plastocyanin (PC) as a result of the activity of PSI due to the lack of electrons in the plastoquinone pool (Schansker et al. 2003 ). The accumulation of positive charges in the inner part of thylakoid membrane in the form of P700 + and PC + may lead to the formation of a transmembrane potential (Satoh and Katoh 1983 ). Thus, the appearance of I 1 , like the transition from F o to F j and F I , can be related to two phenomena: (1) photochemical —accumulation of certain light-emitting states of the PSII RC, and (2) non - photochemical —increase in the DF due to the electrical gradient formed by PSI when P700 is oxidized (Pospisil and Dau 2002; Vredenberg et al. 2006 ).

The I 2 maximum (usually, at high light intensities; visible only as a shoulder) is probably related to the prolonged reopening of PSII RCs by the accelerated electron transfer from the reduced Q B when the PQ pool is actively reoxidized by PSI before the full reduction of the PQ pool (I 2 –D 2 transition in the DF induction curve that coincides with the I–P phase in PF transient and with the slow increase phase in the MR820 ( M odulated light R eflectance at 820 nm) signal. The relative size of this maximum depends on the ratio between the flow of excitation trapping in the RCs of PSII and of the intersystem electron transfer. The share of I 2 increases under several conditions: at lower actinic light, with the decrease in the size of the PSII antenna; and with increase in temperatures (Zaharieva et al. 2001 ).

After about 300–500 ms of illumination, the plastoquinone pool is reduced and most of the Q A is in its reduced state, Q –, A Chl fluorescence is maximal (P step) and MR 820 signal reaches its maximal level. At this moment of induction (phase D 2 ), DF is emitted from RCs in “closed” state Z + P680 Q – A Q 2− B (Gaevsky and Morgun 1993 ; Zaharieva and Goltsev 2003 ; Goltsev et al. 2005 ). This is “deactivation” type of light emission (see the main text) and is a result of charge recombination in Z + P680 Q – A Q 2− B or S i ZP680 Q – A Q 2− B PSII states. During this induction phase, the amplitude of the sub-ms DF components decreases, and the lifetime of the ms component increases (Zaharieva and Goltsev 2003 ). In the presence of an artificial electron acceptor (potassium ferricyanide) and uncouplers of phosphorylation, this increase in the lifetime of DF is insignificant and no I 2 –D 2 is observed. This indicates that the I 1 –I 2 –D 2 phase correlates with the processes of reduction of the PQ pool, and the JIP phase of PF transient (Schansker et al. 2003 ).

The peak I 3 was first discovered with a phosphoroscope-based DF instrument with low actinic light (~1,200 μmol photons m −2  s −1 (Goltsev et al. 2003 ) but it is not visible if DF is recorded at high actinic light (4,000 μmol photons m −2  s −1 ); it is visible as a small shoulder after the D 2 phase in the DF induction curve with exposure to 1,000 μmol photons m −2  s −1 light intensity. The source of DF emission of this phase is weakly luminescent “closed” PSII states.

The increase of DF to the next maximum, labeled as I 4 , is well pronounced at relatively low excitation light. The DF growth during the D 2 –I 4 phase coincides with a slight decrease in the PF intensity and reduction of MR signals caused by oxidation of P700 (Goltsev et al. 2005 ). The accumulation of P700 + suggests that at this time the light-induced activation of the ferredoxin: NADP + -oxidoreductase takes place (Harbinson and Hedley 1993 ; Schansker et al. 2003 ), i.e., the linear electron transport is activated, and the transmembrane proton gradient starts to accumulate. The increase of the DF intensity in the slow phase (toward the I 4 maximum) is associated with the formation of a proton gradient (Wraight and Crofts 1971 ; Evans and Crofts 1973 ) that increases the rate constant of radiative recombination in the PSII RCs.

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Kalaji, H.M., Goltsev, V., Bosa, K. et al. Experimental in vivo measurements of light emission in plants: a perspective dedicated to David Walker. Photosynth Res 114 , 69–96 (2012). https://doi.org/10.1007/s11120-012-9780-3

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Accepted : 03 September 2012

Published : 13 October 2012

Issue Date : December 2012

DOI : https://doi.org/10.1007/s11120-012-9780-3

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Investigating the Rate of Photosynthesis ( AQA A Level Biology )

Revision note.

Biology & Environmental Systems and Societies

Apparatus & Techniques: Investigating the Rate of Photosynthesis

• Investigations to determine the effects of light intensity, carbon dioxide concentration and temperature on the rate of photosynthesis can be carried out using aquatic plants , such as Elodea or Cabomba (types of pondweed )
• Light intensity – change the distance ( d ) of a light source from the plant (light intensity is proportional to 1/ d 2 )
• Carbon dioxide concentration – add different quantities of sodium hydrogencarbonate (NaHCO 3 ) to the water surrounding the plant, this dissolves to produce CO 2
• Temperature (of the solution surrounding the plant) – place the boiling tube containing the submerged plant in water baths of different temperatures
• For example, when investigating the effect of light intensity on the rate of photosynthesis, a glass tank should be placed in between the lamp and the boiling tube containing the pondweed to absorb heat from the lamp – this prevents the solution surrounding the plant from changing temperature
• Distilled water
• Aquatic plant, algae or algal beads
• Sodium hydrogen carbonate solution
• Thermometer
• Test tube plug
• This will ensure oxygen gas given off by the plant during the investigation form bubbles and do not dissolve in the water
• This will ensure that the plant contains all the enzymes required for photosynthesis and that any changes of rate are due to the independent variable
• Ensure the pondweed is submerged in sodium hydrogen carbonate solution (1%) – this ensures the pondweed has a controlled supply of carbon dioxide (a reactant in photosynthesis)
• Cut the stem of the pondweed cleanly just before placing into the boiling tube
• Measure the volume of gas collected in the gas-syringe in a set period of time (eg. 5 minutes)
• Change the independent variable (ie. change the light intensity, carbon dioxide concentration or temperature depending on which limiting factor you are investigating) and repeat step 5
• Record the results in a table and plot a graph of volume of oxygen produced per minute against the distance from the lamp (if investigating light intensity), carbon dioxide concentration, or temperature

The effect of light intensity on an aquatic plant is measured by the volume of oxygen produced

Results - Light Intensity

• The closer the lamp, the higher the light intensity (intensity ∝ 1/ d 2 )
• Therefore, the volume of oxygen produced should increase as the light intensity is increased
• This is when the light stops being the limiting factor and the temperature or concentration of carbon dioxide is limiting the rate of photosynthesis
• The effect of these variables could then be measured by increasing the temperature of water (by using a water bath) or increasing the concentration of sodium hydrogen carbonate respectively
• Rate of photosynthesis = volume of oxygen produced ÷ time elapsed

Limitations

• Immobilised algae beads are beads of jelly with a known surface area and volume that contain algae, therefore it is easier to ensure a standard quantity
• Immobilised algae beads are easy and cheap to grow, they are also easy to keep alive for several weeks and can be reused in different experiments
• The method is the same for algae beads though it is important to ensure sufficient light coverage for all beads

Light intensity – the distance of the light source from the plant (intensity ∝ 1/ d 2 )

Temperature - changing the temperature of the water bath the test tube sits in

Carbon dioxide - the amount of NaHCO 3 dissolved in the water the pondweed is in

Also remember that the variables not being tested (the control variables) must be kept constant.

Required Practical: Affecting the Rate of Dehydrogenase Activity

• The light-dependent reactions of photosynthesis take place in the thylakoid membrane and involve the release of high-energy electrons from chlorophyll a molecules
• These electrons are picked up by the electron acceptor NADP in a reaction catalysed by dehydrogenase
• However, if a redox indicator (such as DCPIP or methylene blue ) is present, the indicator takes up the electrons instead of NADP
• DCPIP: oxidised ( blue ) → accepts electrons → reduced ( colourless )
• Methylene blue: oxidised ( blue ) → accepts electrons → reduced ( colourless )
• The colour of the reduced solution may appear green because chlorophyll produces a green colour
• When light is at a higher intensity, or at more preferable light wavelengths, the rate of photoactivation of electrons is faster, therefore the rate of reduction of the indicator is faster

Light activates electrons from chlorophyll molecules during the light-dependent reaction. Redox indicators accept the excited electrons from the photosystem, becoming reduced and therefore changing colour.

• Isolation medium
• Pestel and mortar
• Aluminium Foil

Method - Measuring light as a limiting factor

• This produces a concentrated leaf extract that contains a suspension of intact and functional chloroplasts
• The medium must have the same water potential as the leaf cells so the chloroplasts don’t shrivel or burst and contain a buffer to keep the pH constant
• The medium should also be ice-cold (to avoid damaging the chloroplasts and to maintain membrane structure)
• The room should be at an adequate temperate for photosynthesis and maintained throughout, as should carbon dioxide concentration
• If different intensities of light are used, they must all be of the same wavelength (same colour of light) - light intensity is altered by changing the distance between the lamp and the test tube
• If different wavelengths of light are used, they must all be of the same light intensity - the lamp should be the same distance in all experiments
• DCPIP of methylene blue indicator is added to each tube, as well as a small volume of the leaf extract
• A control that is not exposed to light (wrapped in aluminium foil) should also be set up to ensure the affect on colour is due to the light
• This is a measure of the rate of photosynthesis
• A graph should be plotted of absorbance against time for each distance from the light
• This is because the lowered light intensity will slow the rate of photoionisation of the chlorophyll pigment, so the overall rate of the light dependent reaction will be slower
• This means that less electrons are released by the chlorophyll, hence the DCPIP accepts less electrons. This means that it will take longer to turn from blue to colourless
• A higher rate of decrease, shown by a steep gradient on the graph, indicates that the dehydrogenase is highly active.
• This experiment is not measuring the rate of dehydrogenase activity directly (through measuring the rate of substrate use or product made) but is instead predicting what the rate would be by measuring the rate of electron transfer from the photosystems
• It is therefore important to control the amount of leaf used to produce the chloroplast sample and also how much time is spent crushing the leaf to release the chloroplast
• It is also a good idea to measure a specific wavelength absorption by each sample on the colorimeter before and after the experiment so you can get a more accurate change in oxidised DCPIP concentration
• Results should also be repeated and the mean value calculated
• The time taken to go colourless is subjective to each person observing and therefore one person should be assigned the task of deciding when this is

In chemistry the acronym ‘OILRIG’ is used to remember if something is being oxidised or reduced. Oxidation Is Loss (of electrons) and Reduction Is Gain (of electrons). Therefore the oxidised state is when it hasn’t accepted electrons and the reduced state has accepted electrons.

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Author: Alistair

Alistair graduated from Oxford University with a degree in Biological Sciences. He has taught GCSE/IGCSE Biology, as well as Biology and Environmental Systems & Societies for the International Baccalaureate Diploma Programme. While teaching in Oxford, Alistair completed his MA Education as Head of Department for Environmental Systems & Societies. Alistair has continued to pursue his interests in ecology and environmental science, recently gaining an MSc in Wildlife Biology & Conservation with Edinburgh Napier University.

Practical Biology

A collection of experiments that demonstrate biological concepts and processes.

Observing earthworm locomotion

Practical Work for Learning

Published experiments

Investigating photosynthesis using immobilised algae, class practical.

This procedure offers a method for measuring the rate of photosynthesis which depends directly on the rate of uptake of carbon dioxide by the photosynthetic organism. Hydrogencarbonate indicator changes colour with pH, which is determined by the concentration of carbon dioxide in solution.

The photosynthetic organism is a fast-growing green alga – such as Scenedesmus quadricauda – immobilised in alginate beads. These algal balls make it easy to standardise the amount of photosynthetic tissue in any investigation.

There is scope for students to develop the protocol to investigate a range of factors.

This protocol is adapted with permission from information on the Science and Plants for Schools (SAPS) website (see www.saps.org.uk ).

Lesson organisation

You can make up the algal balls in one lesson, discuss how to set up the main procedure, and carry it through in the next lesson. A preliminary investigation by teacher/ technician will allow you to estimate the amount of algal material and indicator to use to get a result with your equipment in the time available.

Apparatus and Chemicals

For each group of students:.

Transparent containers (glass bottles are ideal) with sealable lids, around 10 cm 3 , 6–12

Clamp stand, boss and clamp

Syringe barrel, 10 cm 3

Beaker, 100 cm 3 , 2

Cocktail stick to stir alginate

150 W lamp ( Note 5 )

Container of water as heat filter ( Note 6 )

Ruler/ tape measure

For the class – set up by technician/ teacher:

Colorimeter ( Note 1 )

Algal suspension, 2.5 cm 3 concentrated for each group ( Note 2 )

Sodium alginate, 2–3%, 100 cm 3 for a class of 30 students ( Note 3 )

Calcium chloride solution, 2%, 50 cm 3 per group

Hydrogencarbonate indicator, 50–100 cm 3 per group ( Note 4 )

Hydrogencarbonate indicator, standard colour scale, if colorimeter not available ( Note 4 )

Light meters, if available

Health & Safety and Technical notes

Do not look directly into the lamps. Do not touch the lamps while hot. Keep flammable material away from the lamps in use.

Read our standard health & safety guidance

1 Colorimeter. There is a linear relationship between absorbance of the indicator (at 550 nm – bright green filter) and pH over the range studied in this procedure.

2 Growing your alga. Prepare a culture of green alga such as unicellular Scenedesmus quadricauda . Make up a solution of algal enrichment medium, and subculture the alga into this. Aerate gently and keep at temperatures between 18–22 °C. Constant illumination ensures faster growth of the alga. After 3–4 weeks, the culture should have a green ‘pea soup’ colour. Subculture the alga again to maintain a healthy culture. You could use other algae, but Scenedesmus should produce 2 to 3 litres of dark green ‘soup’ in about 4 weeks from 50 cm 3 of original culture. (Details from SAPS Sheet 23).

3 Preparing solutions to make alginate beads (Refer to Recipe card 2):

• Dissolve 3 g of sodium alginate in 100 cm 3 of cold, pure water. Stir with a spatula every half hour or so. Leave overnight and stir in the morning.
• Dissolve 4 g of calcium chloride-6-water in 200 cm 3 of pure water in a 250 cm 3 beaker.

4 Hydrogencarbonate indicator. Refer to Recipe card 34 and Hazcard 32. Low hazard once made; must be made fresh by qualified staff using fume cupboard. The indicator is very sensitive to changes in pH, so rinse all apparatus with the indicator before use. Avoid exhaling over open containers of the indicator. Make up a ‘standard colour scale’ of reaction bottles containing buffers from pH 7.6- pH 9.2 with hydrogencarbonate indicator if students will not have access to a colorimeter.

5 Lamps. You need a brighter light than a standard 40 W or 60 W bench light. Low energy bulbs produce too limited a spectrum of light for full activity. 150 W tungsten or halogen lamps are best. 150 W portable halogen lamps have a stand and handle separate from the body of the lamp which makes them safer to handle. But they do produce heat, so you will need a heat filter for the investigation ( Note 6 ).

6 Heat filter. Use a large flat-sided glass vase (available from Ikea or Homebase or other domestic suppliers) or a medical ‘flat’ filled with water. With a high power lamp, the small volume in a medical ‘flat’ may get too hot for comfort.

7 Making alginate beads:

• When making up the alginate or diluting the algal culture it is essential to use pure water; otherwise calcium ions in the water will cause the alginate to 'set' prematurely.
• If your beads are not the size and texture you want, try different mixes with your active material, or different concentrations of sodium alginate (around 2–3%), or make the syringe nozzle narrower (with glass capillary tubing) or wider (by sawing off and adding a plastic tube). Different brands of alginate have different consistencies. You need a viscous mixture that will drip steadily through the syringe. Keep a note of what worked for this supply and keep your syringe barrels with nozzles for next time.

8 Neutral density filters: can be sourced as a film from photographic suppliers (for example, Lee filters www.leefilters.com . A neutral density filter reduces the amount of light transmitted by the same amount at all wavelengths.

Ethical issues

There are no ethical issues associated with this procedure.

SAFETY: Take precautions to avoid burns, fires and dazzle caused by hot, bright lamps. Do not leave the apparatus unattended overnight as the lamps are so hot.

Preparation of algal beads

a Concentrate your active algal culture ( Note 2 ). Do this by leaving 50 cm 3 to settle for 30 minutes in a measuring cylinder until you have a darker green sediment. Carefully pour off the pale green suspension to leave just 5 cm 3 of concentrated culture.

b Make up the solutions you need to prepare alginate beads ( Note 3 ).

c Mix 5 cm 3 of the algal culture with 5 cm 3 of the 3% sodium alginate solution ( Note 3 ) in a very small beaker.

d Clamp a syringe barrel above a beaker of calcium chloride solution (see diagram and Note 7 ) – making sure the tip of the syringe is well above the solution in the beaker.

e Pour the alga/alginate mixture through the syringe barrel so it drips through and forms beads in the beaker. Swirl the beaker gently as the drops fall. ( Note 7 .)

f Allow the beads to harden for a few minutes before straining them out of the beaker through a tea strainer.

g Rinse the beads in distilled water. The algae in the beads will stay alive for several months in a stoppered bottle of distilled water in the refrigerator.

Other materials

h Make up hydrogencarbonate indicator (see Note 4 and Recipe card 34). It is important to make up enough for the whole investigation as the depth of colour of this indicator is so variable. Make a few litres and keep for the duration of the investigations, aerating before each lesson. Make up a ‘standard colour scale’ of reaction bottles containing buffers from pH 7.6- pH 9.2 with hydrogencarbonate indicator if students will not have access to a colorimeter.

a Rinse 6 translucent bottles with hydrogencarbonate indicator solution.

b Add equal numbers of algal balls to each container – around 20.

c Add a standard volume of indicator to each container (7–10 cm 3 ).

d Replace the lid.

e Note the colour of the solution against a standard set of bottles of indicator, and/or measure the absorbance at 550nm using a colorimeter.

f Put one container in the dark. Set up the other containers at different light intensities – either by placing at different distances from the lamp, or by wrapping different neutral density filters ( Note 8 ) around the bottles.

g Place a heat filter between the light and the containers to absorb heat from the lamp. ( Note 6 .)

h After 30 minutes, note the colours of the solution at each distance.

i Once the bottles cover a range of colours, which will probably take more than 60 minutes, take samples from each bottle and measure the absorbance at 550 nm using a colorimeter. Start with the bottle that has changed most – the one at highest light intensity.

j Plot absorbance against light intensity.

k You could measure light intensity at each distance if a light meter is available, or calculate light intensity (1 ÷ distance 2 ).

Teaching notes

It takes at least an hour for colour changes to happen – so students will need to return to the lab at break or after lessons to ‘read’ the results.

Plotting absorbance against light intensity for the above procedure will show a proportional relationship at low light intensities, levelling off at higher intensities which indicates another limiting factor. The method with neutral density filters (rather than changing distance from the lamp) is reportedly more accessible for some students.

Carbon dioxide is unlikely to be the limiting factor here as the indicator contains a relatively high concentration of hydrogencarbonate ions.

Other factors to investigate include:

• amount of algal material: Shake 1 litre of algal culture, and leave 500 cm 3 , 250 cm 3 or 100 cm 3 to settle in measuring cylinders, saving the bottom 5 cm 3 each time. Make up into balls as above – each batch will have a different amount of algae, but the same surface area to volume ratio for the balls.
• surface area to volume ratio: Make smaller balls using a narrower bore to drip the alginate through.
• wavelength of light: Use coloured filters wrapped around the containers, or try different lamps (if you can identify the spectrum of light produced). Coloured filters will also alter the light intensity.

This practical reportedly works well for coursework investigations as students can suggest improvements or developments after some preliminary work with the technique.

Set aside a container of indicator and algal beads to keep in the lab for a few days. Students will be able to see changes to the colour of indicator according to the time of day – algae use up more carbon dioxide than they produce during the day and release carbon dioxide at night when they are not taking up any.

There is a linear response between absorbance of the indicator (at 550nm) and pH over the range studied in this procedure. Using a colorimeter (at 550nm) is one way to quantify this procedure. Alternatively you could set up a ‘standard colour scale’ with buffer solutions and indicator at the same concentration as the reagent in the investigation. Students can then compare their reaction vessels with the standards and interpolate to estimate pH.

You can also use this technique of immobilisation in an alginate bead to study enzymes or yeast cells. The beads can be packed into a column (for example, a syringe barrel) and a suitable substrate passed over the beads. Collect the products at the bottom of the column and use the immobilised enzymes or cells again and again without the need to separate them from the reactants.

Health and safety checked, September 2008

Related experiments

Investigating factors affecting the rate of photosynthesis

Investigating the light dependent reaction in photosynthesis

Adapted with permission from information on the Science and Plants for Schools (SAPS) website (see www.saps.org.uk ).

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Experimental in vivo measurements of light emission in plants: A perspective dedicated to David Walker

Research output : Contribution to journal › Review article › peer-review

This review is dedicated to David Walker (1928-2012), a pioneer in the field of photosynthesis and chlorophyll fluorescence. We begin this review by presenting the history of light emission studies, from the ancient times. Light emission from plants is of several kinds: prompt fluorescence (PF), delayed fluorescence (DF), thermoluminescence, and phosphorescence. In this article, we focus on PF and DF. Chlorophyll a fluorescence measurements have been used for more than 80 years to study photosynthesis, particularly photosystem II (PSII) since 1961. This technique has become a regular trusted probe in agricultural and biological research. Many measured and calculated parameters are good biomarkers or indicators of plant tolerance to different abiotic and biotic stressors. This would never have been possible without the rapid development of new fluorometers. To date, most of these instruments are based mainly on two different operational principles for measuring variable chlorophyll a fluorescence: (1) a PF signal produced following a pulse-amplitude-modulated excitation and (2) a PF signal emitted during a strong continuous actinic excitation. In addition to fluorometers, other instruments have been developed to measure additional signals, such as DF, originating from PSII, and light-induced absorbance changes due to the photooxidation of P700, from PSI, measured as the absorption decrease (photobleaching) at about 705 nm, or increase at 820 nm. In this review, the technical and theoretical basis of newly developed instruments, allowing for simultaneous measurement of the PF and the DF as well as other parameters is discussed. Special emphasis has been given to a description of comparative measurements on PF and DF. However, DF has been discussed in greater details, since it is much less used and less known than PF, but has a great potential to provide useful qualitative new information on the back reactions of PSII electron transfer. A review concerning the history of fluorometers is also presented.

• Delayed fluorescence
• Fluorometers
• Photosystem II
• Prompt fluorescence

ASJC Scopus subject areas

• Biochemistry
• Plant Science
• Cell Biology

Access to Document

• 10.1007/s11120-012-9780-3

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• Link to publication in Scopus
• Link to the citations in Scopus

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• Photosystem II Immunology and Microbiology 100%
• David Walker Keyphrases 100%
• Prompt Fluorescence Keyphrases 100%
• Electron Transfer Material Science 100%
• Thermoluminescence Material Science 100%
• Photosynthesis Immunology and Microbiology 66%
• Electron Transport Immunology and Microbiology 33%
• Absorption Immunology and Microbiology 33%

T1 - Experimental in vivo measurements of light emission in plants

T2 - A perspective dedicated to David Walker

AU - Kalaji, Hazem M.

AU - Goltsev, Vasilij

AU - Bosa, Karolina

AU - Allakhverdiev, Suleyman I.

AU - Strasser, Reto J.

AU - Govindjee,

N1 - Funding Information: Acknowledgments This work was supported by grants to one of the authors (Suleyman Allakhverdiev) from the Russian Foundation for Basic Research, the Russian Ministry of Science and Education and the Molecular and Cell Biology Programs of the Russian Academy of Sciences, and by BMBF, Bilateral Cooperation between Germany and Russia. Hazem Kalaji thanks Richard Poole and Paul Davis of Hansatech Instruments Company for supporting him with appropriate literature for this review, and Beniamino Barbieri of ISS Inc. (USA) and David Jameson (University of Hawaii at Manoa, USA) for helping him collect data related to the history of fluorometry and fluorometer development. Govindjee thanks Jawaharlal Nehru University, New Delhi, India, for providing him with a Visiting Professorship in early 2012, where this paper was being finalized; he is highly grateful to George Papageorgiou and Alexandrina Stirbet for reading and commenting on the various drafts of this paper. Vasilij Goltsev thanks the Bulgarian National Science Fund, for financial support. Reto J. Strasser thanks the Swiss National Science Foundation for a 3-year fellowship for advanced scientists and for long-term support of the Bioenergetics Laboratory of the University of Geneva. Since his retirement in 2009, the Weed Research Laboratory at Nanjing Agricultural University (NAU) has regularly supported him as a Chair Professor. Support by the NSF of China is also highly acknowledged by him. As a part time Professor Extra-Ordinarius at the North-West University Potchefstroom 2520 Republic of South Africa, he has had the chance to work with the physiologically best defined and reproducible plants in green houses and optimally regulated open top chambers.

PY - 2012/12

Y1 - 2012/12

N2 - This review is dedicated to David Walker (1928-2012), a pioneer in the field of photosynthesis and chlorophyll fluorescence. We begin this review by presenting the history of light emission studies, from the ancient times. Light emission from plants is of several kinds: prompt fluorescence (PF), delayed fluorescence (DF), thermoluminescence, and phosphorescence. In this article, we focus on PF and DF. Chlorophyll a fluorescence measurements have been used for more than 80 years to study photosynthesis, particularly photosystem II (PSII) since 1961. This technique has become a regular trusted probe in agricultural and biological research. Many measured and calculated parameters are good biomarkers or indicators of plant tolerance to different abiotic and biotic stressors. This would never have been possible without the rapid development of new fluorometers. To date, most of these instruments are based mainly on two different operational principles for measuring variable chlorophyll a fluorescence: (1) a PF signal produced following a pulse-amplitude-modulated excitation and (2) a PF signal emitted during a strong continuous actinic excitation. In addition to fluorometers, other instruments have been developed to measure additional signals, such as DF, originating from PSII, and light-induced absorbance changes due to the photooxidation of P700, from PSI, measured as the absorption decrease (photobleaching) at about 705 nm, or increase at 820 nm. In this review, the technical and theoretical basis of newly developed instruments, allowing for simultaneous measurement of the PF and the DF as well as other parameters is discussed. Special emphasis has been given to a description of comparative measurements on PF and DF. However, DF has been discussed in greater details, since it is much less used and less known than PF, but has a great potential to provide useful qualitative new information on the back reactions of PSII electron transfer. A review concerning the history of fluorometers is also presented.

AB - This review is dedicated to David Walker (1928-2012), a pioneer in the field of photosynthesis and chlorophyll fluorescence. We begin this review by presenting the history of light emission studies, from the ancient times. Light emission from plants is of several kinds: prompt fluorescence (PF), delayed fluorescence (DF), thermoluminescence, and phosphorescence. In this article, we focus on PF and DF. Chlorophyll a fluorescence measurements have been used for more than 80 years to study photosynthesis, particularly photosystem II (PSII) since 1961. This technique has become a regular trusted probe in agricultural and biological research. Many measured and calculated parameters are good biomarkers or indicators of plant tolerance to different abiotic and biotic stressors. This would never have been possible without the rapid development of new fluorometers. To date, most of these instruments are based mainly on two different operational principles for measuring variable chlorophyll a fluorescence: (1) a PF signal produced following a pulse-amplitude-modulated excitation and (2) a PF signal emitted during a strong continuous actinic excitation. In addition to fluorometers, other instruments have been developed to measure additional signals, such as DF, originating from PSII, and light-induced absorbance changes due to the photooxidation of P700, from PSI, measured as the absorption decrease (photobleaching) at about 705 nm, or increase at 820 nm. In this review, the technical and theoretical basis of newly developed instruments, allowing for simultaneous measurement of the PF and the DF as well as other parameters is discussed. Special emphasis has been given to a description of comparative measurements on PF and DF. However, DF has been discussed in greater details, since it is much less used and less known than PF, but has a great potential to provide useful qualitative new information on the back reactions of PSII electron transfer. A review concerning the history of fluorometers is also presented.

KW - Delayed fluorescence

KW - Fluorometers

KW - Photosystem II

KW - Prompt fluorescence

UR - http://www.scopus.com/inward/record.url?scp=84871609649&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84871609649&partnerID=8YFLogxK

U2 - 10.1007/s11120-012-9780-3

DO - 10.1007/s11120-012-9780-3

M3 - Review article

C2 - 23065335

AN - SCOPUS:84871609649

SN - 0166-8595

JO - Photosynthesis research

JF - Photosynthesis research

• Open access
• Published: 14 August 2024

Optimized nitrogen application ameliorates the photosynthetic performance and yield potential in peanuts as revealed by OJIP chlorophyll fluorescence kinetics

• Pei Guo 1 ,
• Jingyao Ren 1 ,
• Xiaolong Shi 1 ,
• Anning Xu 1 ,
• Ping Zhang 1 ,
• Fan Guo 1 ,
• Yuanyuan Feng 1 ,
• Xinhua Zhao 1 ,
• Haiqiu Yu 1 , 2 &
• Chunji Jiang 1  

BMC Plant Biology volume  24 , Article number:  774 ( 2024 ) Cite this article

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Metrics details

Nitrogen (N) is a crucial element for increasing photosynthesis and crop yields. The study aims to evaluate the photosynthetic regulation and yield formation mechanisms of different nodulating peanut varieties with N fertilizer application.

The present work explored the effect of N fertilizer application rates (N0, N45, N105, and N165) on the photosynthetic characteristics, chlorophyll fluorescence characteristics, dry matter, N accumulation, and yield of four peanut varieties.

The results showed that N application increased the photosynthetic capacity, dry matter, N accumulation, and yield of peanuts. The measurement of chlorophyll a fluorescence revealed that the K-phase, J-phase, and I-phase from the OJIP curve decreased under N105 treatment compared with N0, and W OI , ET 0 /CS M , RE 0 /CS M , ET 0 /RC, RE 0 /RC, φPo, φEo, φRo, and Ψ0 increased, whereas V J , V I , W K , ABS/RC, TR 0 /RC, DI 0 /RC, and φDo decreased. Meanwhile, the photosystem activity and electron transfer efficiency of nodulating peanut varieties decreased with an increase in N (N165). However, the photosynthetic capacity and yield of the non-nodulating peanut variety, which highly depended on N fertilizer, increased with an increase in N.

Optimized N application (N105) increased the activity of the photosystem II (PSII) reaction center, improved the electron and energy transfer performance in the photosynthetic electron transport chain, and reduced the energy dissipation of leaves in nodulating peanut varieties, which is conducive to improving the yield. Nevertheless, high N (N165) had a positive effect on the photosystem and yield of non-nodulating peanut. The results provide highly valuable guidance for optimizing peanut N management and cultivation measures.

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Introduction

Peanut ( Arachis hypogaea L.) is one of the most consumed oilseed crops worldwide with high protein content in its seeds [ 1 ]. According to statistics, China's peanut production and planting area account for about 40% and 20% of the world's, respectively, making it the world's largest peanut producer [ 2 ]. Therefore, a stable and sustained increase in peanut yield is essential to ensure food and edible oil security.

Nitrogen (N) is one of the most important elements that influence the growth of plants [ 3 ]. A lack of N severely limits crop productivity. Therefore, application of N fertilizers in the field dramatically increases crop yield [ 4 ]. Research has proven that appropriate N fertilizer application greatly improves crop growth, photosynthesis, biomass production, yield, and N use efficiency (NUE) [ 5 , 6 ]. Nevertheless, excessive application of N fertilizers has caused severe N pollution worldwide and decreased crop NUE [ 7 , 8 ]. The element of N is an important part of photosynthetic organs, and the utilization of nitrogen by crops determines the photosynthetic capacity and function [ 9 ]. A change in the N content of crops has a direct impact on the photosynthetic process of leaves, which includes light absorption, electron transfer and energy distribution [ 10 , 11 ]. Chlorophyll fluorescence is intimately connected to the process of photosynthesis, which has been employed as a reference index to evaluate the relationship between abiotic stress and photosynthesis [ 12 ]. This is an important index that allows further reflection on photosynthesis, such as light absorption and energy transformation processes [ 13 , 14 , 15 ]. Previous studies have employed the JIP-test in chlorophyll fluorescence determination to analyze the response strategies of crops facing different nitrogen environments and the negative effects of nitrogen deficiency on plant photosystem II (PSII) were reported [ 11 , 16 ]. Some studies have indicated that photosynthesis, stomatal conductance, photosynthetic pigment content and soluble sugar concentration of crops decrease under N deficiency conditions [ 17 , 18 , 19 ]. Specifically, N deficiency caused a slight reduction in the electron acceptor pool size, PSII reaction centre activity and photosynthetic enzyme activity of crop leaves [ 20 ]. Appropriate N rates improve leaf PSII performance, which in turn increases photosynthesis and dry matter weight, and greatly contributes to seed yield, especially during the critical period of crop growth (reproductive growth period) [ 21 , 22 ]. Nevertheless, high N treatments were found to be somewhat beneficial in increasing the isoelectric point (PI) value of soybean, while excessive N application resulted in a significant reduction in the photosynthetic capacity and electron transfer efficiency of summer maize [ 23 ].

Legumes can conduct biological N fixation (BNF) through endosymbiotic interactions with bacteria residing in root nodules [ 24 ]. Unlike other crops, the main sources of N in legume crops are soil, fertilizer and BNF [ 25 ]. In particular, under growing conditions where soil N is deficient, peanut can supply more than 60% of its N requirements from BNF, while under high-yielding conditions it can provide about 40% [ 26 ]. The process of BNF is one of the most environmentally friendly and economical sources of N, which can greatly reduce the dependence on fertilizer in peanut production, while saving costs and protecting the environment [ 4 ]. During growth, the podding stage is the most vigorous period of peanut vegetative and reproductive growth; it is also the period with the highest N requirement of peanuts [ 27 ]. The maximum nodule number and dry weight are reached at the podding stage, which has great potential to provide N to peanuts [ 25 ]. However, some studies demonstrated that excessive N applications significantly constrained the BNF of peanuts, resulting in lower NUE, reduced yield, and increased production costs [ 28 , 29 , 30 ]. There are notable differences in nodulation characteristics, N acquisition and utilization among different peanut varieties [ 31 ].

In our previous studies, we investigated the effect of N fertilizer at different growth stages on nodulation characteristics, dry matter, and N accumulation in different nodulating peanut varieties [ 25 , 32 ]. We found that peanuts had the highest number of nodules at eighty days after seedling emergence (the podding stage), and the N supply proportions from root nodules were more than half, which played an important role in the formation of peanut pods. This study sought to further investigate the impact of four N application treatments on the photosynthetic physiological response in peanut varieties with different nodulation during the critical period for pod formation. Four peanut varieties were grown in an outdoor pot experiment to investigate photosynthetic and chlorophyll fluorescence characteristics, and the JIP- test was used to analyze the effects of different application rates of N fertilizer on peanut leaves and the photosynthetic electron transport chain.

Materials and methods

Plant material and treatment.

An outdoor pot experiment was conducted at the experimental base of Shenyang Agricultural University, Shenyang, Liaoning, China (41°82' N, 123°56' E) in 2022 (Fig.  1 A). Peanuts were sown on May 14, 2022 and harvested on September 25, 2022, including the whole growth stages. The region where the test site is located has a temperate semi-humid continental climate, characterised by an average annual rainfall of approximately 878.9 mm and an average annual temperature of 8.7°C (Fig.  1 B). The soil type was brown loam, and the soil nutrient status before the test was as shown in Table S1.

A  Map of the study site. B  Air temperature and precipitation in the growing season of intercropping in 2022. C  Flowchart showing the study design

Four peanut varieties with different nodulation, i.e., Nonghua 5 (NH5, low nodulation, Shenyang Agricultural University), Xianghua 11 (XH11, high nodulation, Hunan Agricultural University), Honghua 16 (HH16, high nodulation, Oil crops Research Institute, Chinese Academy of Agricultural Sciences) and DH9 (non-nodulation, Shenyang Agricultural University), were used as study materials [ 25 , 32 ]. Five seeds were sown in each pot, with a height of 40 cm and a diameter of 27cm, and filled with 23 kg soil. Nitrogen (N) fertilizer was added in the form of urea (containing 46% pure N). According to the local farmers' conventional fertilization amount (105 kg N ha −1 ) and our previous research [ 25 , 32 ], four N fertilization treatments were utilized: no N fertilizer input (N0), low N fertilizer input (N45: 45 kg N ha −1 ), normal N fertilizer input (N105: 105 kg N ha −1 ), and high N fertilizer input (N165: 165 kg N ha −1 ). In addition, the dose of phosphate (P 2 O 5 ) fertilizer and potassium (K 2 O) fertilizer was 6.16 g and 2.31 g per pot, respectively, applied once as basal fertilizer before sowing. No topdressing was applied in the later stage. After seeding emergence, keep one healthy peanut seedling with consistent growth in each pot. One plant in each pot was regarded as one replication; fifteen replicates were used for each treatment of each variety in this study, with a total of 240 pots (Fig.  1 C). The pots were watered according to need with an equal amount of water per pot, normally once or twice a week, except on rainy days.

Measurement items and methods

Photosynthetic parameters.

Based on the results of the previous experiments [ 32 ], samples were determined eighty days after seedling emergence (at the podding stage) during the critical period of peanut growth. The functional leaves (the third fully expanded leaf counted from the top of peanut plants) were selected for the assessment of photosynthetic parameters (Fig.  1 C). Variations of photosynthetic parameters of peanut leaves, including net photosynthetic rate (Pn), transpiration rate (Tr), intercellular CO 2 concentration (Ci), and stomatal conductance (Gs), were measured using a portable photosynthetic system CIRAS-2 (PP Systems, Hitchin, UK), calibrated as follows: the size of the leaf chamber, 1.75 cm 2 (0.7 cm × 2.5 cm); the light intensity of the internal light source in the leaf chamber, 1200 μmol photons m –2 s –1 ; temperature, 25°C; relative humidity, 70%; and CO 2 concentration, 380 μmol mol –1 . All photosynthetic parameters were measured on three replicates per treatment, with each replicate consisting of one pot, each containing one peanut plant.

Chlorophyll a fluorescence

Eighty days after seedling emergence, nine plants from each treatment, without diseases or insect pests and with uniform growth, were selected and labeled as sample plants (4 N treatments × 4 varieties × 9 replicates, totaling 144 pots). A portable plant efficiency analyzer (Handy PEA, Hanstech, UK) was used to determine the fast chlorophyll fluorescence induced kinetic curve (OJIP transient) of the functional leaves of the sample plants. Prior to the start of measurement, plants were acclimated to the light environment, followed by a 30-min period of darkness. The O, K, J, I, and P on the OJIP curve represent the instantaneous fluorescence intensity observed at 0.01, 0.3, 2, 30 and 1000 ms, respectively, and were labelled as F O , F K , F J , F I , and F M . The V t , which represents the relative variable fluorescence at time t, was calculated using the following formula: V t  = (F t – F O )/(F M – F O ). The W OJ , calculated as W OJ  = (F t – F O )/(F J – F O ), denotes the K peak. The W OI was calculated as W OI  = (Ft – F O )/(F I – F O ), which indicates variable fluorescence between steps O and I. The JIP-test parameters W K  = (F K – F O )/(F J – F O ) represent the relative variable fluorescence at the K-step (W K ) to the amplitude F J – F O . Table S2 provides additional OJIP test parameters used in the current study [ 14 , 15 , 33 ].

Photosynthetic pigment content

After measuring the photosynthetic parameters and chlorophyll a fluorescence, functional leaves of peanuts were taken to measure the photosynthetic pigment content. Photosynthetic pigments were determined spectrophotometrically according to Lichtenthaler [ 34 ], including chlorophyll a (Chl a), chlorophyll b (Chl b), chlorophyll a + b (Chl a + b), and chlorophyll a/b (Chl a/b). Measurements were performed in triplicate for each treatment.

Dry matter (DM) and N accumulation

Samples were taken 80 days after seedling emergence, with three plants serving as replicates for each treatment. The peanut plants were uprooted from the pot and washed, and the roots, leaves, stems, and pods were separated. Samples were oven-dried to constant weight at 80℃ and then weighed and pulverized. The N concentration of each peanut organ was measured using the Kjeldahl apparatus. The calculation for N accumulation was based on the multiplication of the concentration value by the dry mass value [ 35 ].

Yield and its components

During the harvest stage (134 days after sowing), six plants from each treatment with consistent growth were selected from the remaining sample to assess the yield and its components, including the average yield per plant, number of pods and full pods, weight of 100 pods, and 100 kernels.

Statistical analysis

Statistical analyses and graphing were completed using Microsoft Excel 2021, SPSS 22, Origin 2021b, and GraphPad Prism 8. Analysis of variance (ANOVA) and least significant difference analysis (LSD) were performed on data from at least three independent experiments [ 32 , 36 ]. A value of p < 0.05 was deemed to be statistically significant. All data were expressed as mean ± standard error (SE) (n ≥ 3).

Compared with N0, nitrogen (N) application increased the net photosynthetic rate (Pn), transpiration rate (Tr), and stomatal conductance (Gs) of different peanut varieties (Fig.  2 ). The Pn and Tr of NH5, XH11, and HH16 reached the maximum at the N105 treatment, and DH9 exhibited significantly higher Pn and Tr than the other treatments under N165 treatment (Fig.  2 A, B ). Compared with the N0 treatment, the N105 treatment dramatically enhanced the Pn of XH11 and HH16 by 51.1% and 51.0%, respectively, and no significant difference in Pn of NH5 was found among N treatments (Fig.  2 A). The N105 treatment significantly increased the Tr of NH5, XH11, and HH16 by 20.4%, 87.3%, and 55.4% respectively compared with the N0 treatment. However, the Tr of DH9 was not significantly different among N treatments. No significant difference was found between varieties (Fig.  2 B). The application of N significantly influenced the intercellular CO 2 concentration (Ci) of NH5 and HH16, reaching a minimum threshold under the N105 treatment; however, no significant difference was found between XH11 and DH9 (Fig.  2 C). The Gs of peanuts increased with an increase in N application, and the Gs under N105 and N165 treatments was significantly higher than that under N45 and N0 treatments (Fig.  2 D).

The photosynthetic parameters of leaves in peanut as affected by different N application rates. A , B , C , and D represent net photosynthetic rate (Pn), transpiration rate (Tr), intercellular CO 2 concentration (Ci), and stomatal conductance (Gs), respectively. The different letters indicate the significant differences ( p  < 0.05) among the N treatments within each variety. In multivariate variance analysis, V and N represent the variety and N fertilizer treatment, respectively, * indicates p  < 0.05, ** p  < 0.01, and ns indicates not significance. Data from each treatment with three replicates were subjected to analysis of variance (ANOVA), and means were compared by the least significant difference (LSD) test ( p  < 0.05). Values are mean ± standard error (SE) ( n  = 3)

Chlorophyll fluorescence kinetics and the parameters of JIP-test

The ojip curves.

As shown in Fig.  3 A, B, the OJIP curves for the K-phase, J-phase, and I-phase were smaller than N0 under higher N (N105 and N165) treatments, and the peak value of peanut varieties was recorded at J-phase and I-phase. The peaks of NH5 and HH16 in J-phase and I-phase were obtained in the N105 treatment, whereas XH11 and DH9 were lowest in the N165 treatment (Fig.  3 B). This demonstrated that N fertilizer on the acceptor side (J step decrease) of photosystem II (PSII) improved the PSII performance. The relative variable fluorescence at the J-step and I-step (V J and V I ) of peanuts was smaller relative to that of the N0 treatment under other N treatments (Fig.  3 C). N treatment did not significantly affect V J and V I in NH5 and HH16 varieties. However, XH11 exhibited a significantly higher value for XH11 in the N0 treatment compared to others treatments. Under N105 and N165 treatments, V J and V I of DH9 were significantly decreased compared with N0.

Changes of the relative variables of chlorophyll a fluorescence rise kinetics (OJIP) in four peanut varieties under different N application rates. A  OJIP curves for chlorophyll fluorescence with normalized [(V t  = (F t – F O ) / (F M – F O )]. B  Kinetic difference (ΔV t ) for chlorophyll fluorescence [ΔV t  = V t(treatment)  – V t (control) ]. C V J and V I are the relative variable fluorescence at the J-step and I-step. The different letters indicate the significant differences ( p  < 0.05) among the N treatments within each variety. In multivariate variance analysis, V and N represent the variety and N fertilizer treatment, respectively, * indicates p  < 0.05, ** p  < 0.01, and ns indicates not significance. Data from each treatment with nine replicates were subjected to analysis of variance (ANOVA), and means were compared by the least significant difference (LSD) test ( p  < 0.05). Values are mean ± SE ( n  = 9)

The O-J phase and O-I phase

The O-J on the OJIP curve were standardized (W OJ ) and the kinetic difference (ΔW OJ ) for chlorophyll fluorescence was calculated (Fig.  4 A). With the continued application of N, the K point of nodulating peanut varieties (NH5, XH11, and HH16) showed a trend in which it first increased and then fell, indicating that normal N fertilizer (N105) enhanced the oxygen evolving complex (OEC) activity of peanuts, while high N fertilizer (N165) damaged it. In addition, the K point of DH9 (non-nodulation) gradually decreased with the increase of N application. Next, we calculated the normalized relative variable fluorescence of K point (W K ) and the difference between control and N treatment (ΔW K ) (Fig.  4 B). Among the varieties with nodulation ability, W K and ΔW K reached their lowest point under the N105 treatment. And there were no significant differences in W K or ΔW K between the other N treatments (N0 and N165) for these nodulating varieties. In contrast, the ΔW K value in the non-nodulating variety DH9 significantly decreased under the N165 compared to the N0.

Changes of the fluorescence O − J phase and O − I phase in four peanut varieties under different N application rates. A  Variable fluorescence between the steps O and J [W OJ  = (F t  − F O ) / (F J  − F O )]; [ΔW OJ  = W OJ (treatment)  − W OJ (control) ]. B  The ratio of relative variable fluorescence of four peanut varieties at the K-step to the amplitude F J – F O as W K  = (F K – F O ) / (F J – F O ). ΔW K  = W K (treatment) – W K (control) . The different letters indicate the significant differences ( p  < 0.05) among the N treatments within each variety. In multivariate variance analysis, V and N represent the variety and N fertilizer treatment, respectively, * indicates p  < 0.05, ** p  < 0.01, and ns indicates not significance. Data from each treatment with nine replicates were subjected to analysis of variance (ANOVA), and means were compared by the least significant difference (LSD) test ( p  < 0.05). Values are mean ± SE ( n  = 9). C Variable fluorescence between the steps O and I [W OI  = (F t – F O ) / (F I – F O )]; [ΔW OI  = W OI (treatment)  − W OI (control) ]

Figure  4 C illustrates that OJIP curves were normalized by O-I (W OI ), with the amplitude of the I-P phase in the W OI  ≥ 1 fraction is indicative of the size of the terminal electron receptor pool on the photosystem I (PSI) receptor side. The graph illustrates that the application of higher N treatments (N105 and N165) increased the size W OI , which was beneficial to improve the efficiency of electron transfer. Meanwhile, the high nodulating variety HH16 exhibited the largest W OI  ≥ 1 fraction under N105, while the low nodulating variety NH5 did not show differences under N treatments.

Pipeline models of specific energy fluxes

Absorbed photon flux per active PSII (ABS/RC), trapped energy flux per active PSII (TR 0 /RC), and dissipated energy (as heat and fluorescence) flux per active PSII PSII (DI 0 /RC) of nodulating varieties (NH5, XH11, and HH16) were lowest under the N105 treatment, and there were no significant changes under N treatments in any variety (Fig.  5 A). While the ABS/RC, TR 0 /RC, electron flux from Q A ‒ to the plastoquinones pool per active PSII (ET 0 /RC), and electron flux from Q A ‒ to the final electron acceptors of PSI per active PSII (RE 0 /RC) of XH11 and HH16 (high nodulation) reached the highest under N165 treatment and the ET 0 /RC of the two varieties was significantly increased by 33.3% and 15.7% compared with N0, respectively (Table S3). The ABS/RC, TR 0 /RC, and DI 0 /RC of DH9 (non-nodulation) demonstrated a decreasing trend with increasing N application. Furthermore, ET 0 /RC and RE 0 /RC in the higher N treatments (N105 and N165) were higher compared to those in lower N treatments (N0 and N45), with RE 0 /RC being significantly increased by 40.9% and 31.8% under N105 and N165 treatments, respectively, compared with N0 treatment.

A  Pipeline models of specific energy fluxes per active PSII reaction center (membrane/specific model) for four peanut varieties under different N application rates. B  Leaf model representing phenomenological energy fluxes per excited cross section for four peanut varieties under different N application rates. C  Spider plots show normalized values of quantum yields and efficiencies & probabilities in four peanut varieties under different N application rates. The different letters indicate the significant differences ( p  < 0.05) among the N treatments within each variety. Data from each treatment with nine replicates were subjected to analysis of variance (ANOVA), and means were compared by the least significant difference (LSD) test ( p  < 0.05)

Leaf model representing phenomenological energy fluxes

The leaf model of phenomenological energy fluxes per excited cross section of four peanut varieties under different N treatments was established, and the results indicated that compared to N0, higher N treatments (N105 and N165) treatments increased the absorption flux per cross section (ABS/CS M ), trapped flux per cross section (TR 0 /CS M ), electron transport flux per cross section (ET 0 /CS M ), electron flux reducing end electron acceptors at the PSI acceptor side per cross section (RE 0 /CS M ), and amount of active PSII RCs per cross section (t = 0) (RC/CS 0 ) of XH11, HH16, and DH9, while decreasing thermal dissipation energy flux per cross section (DI 0 /CS M ) of NH5, XH11, and DH9 (Fig.  5 B). The ET 0 /CS M and RE 0 /CS M of the three nodulating varieties (NH5, XH11, and HH16) were highest in the N105 treatment, and the RE 0 /CS M of the high nodulating varieties (XH11 and HH16) was significantly higher in N105 treatment than under N0 treatment. The ABS/CS M and TR 0 /CS M of the four peanut varieties had no significant difference between N treatments, but there were significant differences between varieties (Table S4).

Quantum yields and efficiencies & probabilities

The N treatment increased maximum quantum yield for primary photochemistry (φPo), quantum yield for electron transport (φEo), quantum yield for reduction of the end electron acceptors at the PSI acceptor side (φRo), and probability that a trapped exciton moves an electron into the electron transport chain Beyond Quinone A (t = 0) (Ψ0) and decreased quantum yield (at t = 0) of energy dissipation (φDo) in peanut (Fig.  5 C). Under N105 treatment, φPo, Ψ0, and φEo of the three nodulating varieties (NH5, XH11, and HH16) reached the maximum values, while φDo reached the lowest value. In contrast, DH9 (non-nodulation) had significantly higher values of φPo, Ψ0, φEo, φRo, and probability that an electron is transported from the reduced intersystem electron acceptors to the final electron acceptors of PSI (δRo) and significantly lower values of φDo compared with N0 under N165 treatment (Table S5). Variations in δRo were significantly different across varieties. The δRo of NH5 decreased with continued use of N, whereas the δRo of XH11, HH16 and DH9 increased with higher N treatments (N105 and N165).

Photosynthetic pigment content in leaves

The applications of N fertilizer increased the photosynthetic pigment content of leaves in the four peanut varieties. There was a significant effect among varieties, N fertilizer treatments, and two-way interaction effects between variety and N fertilizer treatment on the photosynthetic pigment content of peanut leaves (p < 0.01) (Table  1 ). Photosynthetic pigment contents in leaves of NH5 were significantly higher under N165 treatment compared with other treatments, including chlorophyll a (Chl a), chlorophyll b (Chl b), and chlorophyll a + b (Chl a + b), with an increase of 23.4%, 32.8%, and 26.2%, respectively. The XH11 and DH9 had the highest Chl a, Chl b, and Chl a + b under the N105 treatment, which significantly increased by 26.9%, 28.8%, and 27.4% in XH11 and 37.1%, 41.9%, and 37.7% in DH9, respectively, compared with N0. The HH16 exhibited the highest Chl a, Chl a + b, and chlorophyll a/b (Chl a/b) under the N105 treatment, showing significant increases of 13.7%, 12.6%, and 5.6%, respectively, compared with N0.

Dry matter and N accumulation

The N105 and N165 treatments significantly increased both pod dry matter (Pod DM) and pod N accumulation (Pod N) in the four peanut varieties, and dry matter (DM) and N accumulation in vegetative organs (root, stem, and leaf) also increased to varying degrees (Fig.  6 ). No significant difference in Pod DM was found among nodulating varieties NH5, XH11, and HH16 under N105 and N165 treatments (Fig.  6 A), while Pod N was significantly increased by 18.6%, 19.1%, and 12.1% compared with N165, respectively (Fig.  6 B). The DM and N accumulation in DH9 (non-nodulation) reached the maximum under N165 treatment, and Pod DM was significantly increased by 27.1% compared with N105, while the Pod N was not significantly different between the two treatments. Collectively, these data suggest that the N applications, varieties, and interactions between N applications and varieties were significant for N accumulation in various peanut organs.

Dry matter and N accumulation in each organ of peanut as affected by different N application rates. A and B represent dry matter and N accumulation respectively. The different letters indicate the significant differences ( p  < 0.05) among the N treatments within each variety. In multivariate variance analysis, V and N represent the variety and N fertilizer treatment, respectively, * indicates p  < 0.05, ** p  < 0.01, and ns indicates not significance. Data from each treatment with three replicates were subjected to analysis of variance (ANOVA), and means were compared by the least significant difference (LSD) test ( p  < 0.05). Values are mean ± SE ( n  = 3)

Compared with N0, all N treatments increased the single plant yield, pod number per plant, 100-pod weight, and 100-kernel weight among the four peanut varieties (Table  2 ). The yield and full pod number per plant of NH5, XH11, and HH16 reached the maximum under the N105 treatment, which significantly increased by 26.8%, 27.4%, and 30.4% and 41.2%, 32.7%, and 30.9%, respectively, compared with N0, but showed no significant difference compared with N165. Non-nodulating variety DH9 exhibited the highest yield under the N165 treatment, with an increase of 39.7% compared with N0. In addition, NH5 and XH11 had the highest pod number under the N105 treatment, which was significantly enhanced by 29.7% and 31.8%, respectively, compared with N0. Meanwhile, HH16 and DH9 had the highest pod number under the N165 treatment, with an increase of 36.5% and 54.5%, respectively. The 100-kernel weight of the four varieties was the largest under the N165 treatment, and no significant differences in 100-pod weight and 100-kernel weight of HH16 and DH9 were observed among N treatments.

Plant phenotypic plasticity analysis

Analyses of plant plasticity can provide insight into their potential to adapt to changes in the external environment. As illustrated in Fig. 7 , the plasticity index of φPo, one of the fluorescence parameters, was the smallest, and it showed stronger correlations with biomass and N content, and variations in plasticity index differed among peanut varieties. In terms of photosynthetic pigments index and photosynthetic parameters, the smallest plasticity index was detected in Chl a/b and Ci, respectively. The plasticity index of biomass and N content of NH5, HH16, and DH9 was higher than 0.4 (Fig.  7 A, C, D), while the plasticity index of other indexes of NH5 and HH16 was lower than 0.4 (Fig.  7 A, C). The plasticity of fluorescence parameters of the low nodulating variety (NH5) was lower, indicating that it had stable response to N fertilizer. The plasticity index of RE 0 /CS M , Tr, Pod N, and TN of XH11 exceeded 0.4 (Fig.  7 B), and the plasticity index of RE 0 /CS M and V J of DH9 was higher than 0.4 (Fig.  7 D). These results demonstrated that different N application rates did not significantly affect φPo, Chl a/b, and Ci of peanut varieties, and the physiological response of peanut varieties to N fertilizer was different.

Phenotypic plasticity analysis of four peanut varieties with different nodulation under different N application rates. Pod DM, pod dry matter; Pod N, pod N accumulation; TDM, total dry matter; TN, total N accumulation

Correlation analysis

According to the comprehensive analysis among the indexes of four peanut varieties, we then performed a correlation analysis between the traits exhibiting higher plasticity indices (Pn, Tr, RE 0 /CS M , Pod N, and Yield) and the fluorescence parameters with strong responses to N fertilizer application (ET 0 /CS M , TR 0 /CS M , RE 0 /RC, and ET 0 /RC) (Fig.  8 ). The Pod N and Pn were significantly positively correlated with other indexes, and peanut yield exhibited a significantly positive correlation with all indexes except TR 0 /CS M . Except Tr, ET 0 /CS M , and RE 0 /CS M were significantly and positively correlated with other indexes, whereas V J was significantly and negatively correlated with other indexes. Some distinct variations were observed in the correlation between the indices of peanut varieties. No significant correlation with fluorescence parameters indexes was observed for the yield and Pod N of NH5 (low nodulation), Pn and Tr of high nodulating varieties XH11 and HH16 were positively correlated with Pod N. The ET 0 /RC, RE 0 /RC, RE 0 /CS M , and Pn of DH9 (non-nodulation) were significantly positively correlated with yield, while V J was significantly negatively correlated with yield, Pod N and Pn. Higher N treatments (N105 and N165) significantly decreased the V J of DH9, which contributing to the increase its photosynthetic capacity and yield.

Correlation analysis between photosynthetic physiological parameters and yield of four peanut varieties with different nodulation under different N application rates

Nitrogen (N) is a critical element in Calvin cycle enzymes, chlorophyll, and carotenoids, of which the chlorophyll content has a direct effect on the photosynthetic capacity and primary production [ 37 , 38 ]. Previous studies demonstrated that N application is closely related to the photosynthetic capacity, yield, and agronomic traits [ 39 , 40 ], and N deficiency can cause the closure of stomata in leaves, blocking photosynthetic electron transfer and ultimately reducing photosystem II (PSII) performance [ 23 , 41 ]. A lack of N can also reduce chlorophyll content. This can lead to an increase in the allocation of photosynthetic electron transport to photorespiration, thus reducing the progression of photosynthesis, which is not conducive to the accumulation of dry matter (DM) [ 42 ]. Our study found that compared with N0 and N45 (lower N), higher N treatments (N105 and N165) increased the net photosynthetic rate (Pn), transpiration rate (Tr), stomatal conductance (Gs), and photosynthetic pigment content of leaves in the four peanut varieties. Nodulating peanut varieties (NH5, XH11, and HH16) had the highest Pn and Tr in N105 (normal N), while DH9 reached the highest in N165. The Pn of nodulating peanut varieties was lower under the N165 treatment than under the N105 treatment. This may be due to the close arrangement of leaf mesophyll cells caused by excessive N application, which was not conducive to the entry of external CO 2 into the cells, resulting in reduced photosynthetic rate and even toxic effect on crops [ 7 ]. Therefore, applying N fertilizer properly can promote chlorophyll synthesis, improve photosynthesis during plant growth, and ultimately increase crop yield.

The OJIP transient detection technology has been widely employed to study the photosynthetic response of plants under abiotic stresses due to its advantages of being non-destructive and easy to use in practical measurements [ 43 ]. Since the accumulation of DM is closely related to the change in Pn and photosynthetic fluorescence parameters, the JIP parameters of peanut leaves were assessed. It has been hypothesized that the kinetic curves of OJIP can well reflect changes in the primary photochemical reaction and the photosynthetic function of PSII [ 44 ]. The rise of the O-J step on the OJIP curve is correlated with the degree of PSII reaction center closure. Furthermore, the change in fluorescence intensity at the J-step (V J ) can be used as an indicator of the electron transfer from primary acceptor plastoquinone A (Q A ) to secondary acceptor plastoquinone B (Q B ) on the electron acceptor side of PSII [ 43 , 45 ]. Electrons originating from reduced Phe on the acceptor side of PSII are sequentially transferred to the first Q A and second Q B plastoquinone electron acceptors, and subsequently to the mobile pool of plastoquinone within the lipid phase of the thylakoid membrane [ 46 ]. In this study, the O-J step and V J decreased under higher N application (N105 and N165), indicating that N application could improve the openness of the PSII reaction center and electron transfer from Q A to Q B on PSII acceptors. Correlation analysis revealed that V J significantly negatively correlated with Pn and yield, which was consistent with a previous report [ 47 ]. The emergence of the K-phase in OJIP is associated with the damage of oxygen evolving complex (OEC) on the donor side of PSII, which plays a crucial role within PSII and is responsible for the cleavage and oxidation of H 2 O [ 48 , 49 ]. The lowest K point of nodulating peanut varieties (NH5, XH11, and HH16) was found under N105 (normal N) treatment. This indicated that peanuts under the N105 treatment exhibited greater stability in their OEC, which was conducive to maintaining PSII activity. As quantified by the ratio of relative variable fluorescence at the K-step to the amplitude F J – F O (W K and ∆W K ), the OEC was damaged under high N treatment (N165), resulting in a reduced ability to deliver electrons downstream [ 50 ]. The ∆W K of the non-nodulating variety DH9 was the lowest under the N165 treatment and significantly lower than that of N0, indicating that higher application of N may markedly improve the OEC activity on the PSII donor side, thus enhancing the electron transfer efficiency, which is ultimately conducive to yield formation.

A reduction in the secondary electron acceptors Q B , plastoquinones (PQ), cytochrome and plastocyanin (PC) is related to the J-I step of the curve [ 51 ]. The relative variable fluorescence at the I-step (V I ) serves as an indicator of the ability of photosystem I (PSI) and its acceptors to oxidize reduced PQ [ 52 , 53 ]. The V I of the four peanut varieties under N105 and N165 treatments was lower than that under N45 and N0 treatments. Standardization of the O-I step (W OI ) demonstrated that a higher application of N (N105 and N165) increased the size of the terminal electron acceptor pool on the PSI acceptor side and enhanced electron transfer [ 54 ]. The amplitude of the W OI  ≥ 1 of NH5 had no significant difference between the N treatments, indicating that N fertilizer exerted a relatively limited effect on the size of the terminal electron acceptor pool on the PSI acceptor side and the stability of the PSII donor side (K) and acceptor side (J) was stronger, which was conducive to maintaining the influence of N environmental changes on photosynthesis.

Limited N availability disrupts energy transfer within PSII. This occurs through the inactivation of reaction centers, which reduces the efficiency of converting absorbed light energy into usable electrons. Consequently, less excitation energy is captured, ultimately leading to decreased crop yield [ 53 ]. Nodulating peanut varieties (NH5, XH11, and HH16) had the lowest absorbed photon flux (ABS/RC), trapped energy flux (TR 0 /RC), dissipated energy flux per active PSII (DI 0 /RC), and quantum yield (at t = 0) of energy dissipation (φDo) under N105 treatment, while the electron flux from Q A ‒ to the PQ pool per active PSII (ET 0 /CS M ), electron flux reducing end electron acceptors at the PSI acceptor side per cross section (RE 0 /CS M ), maximum quantum yield for primary photochemistry (φPo), quantum yield for electron transport (φEo), quantum yield for reduction of the end electron acceptors at the PSI acceptor side (φRo), and probability that a trapped exciton moves an electron into the electron transport chain Beyond Quinone A (t = 0) (Ψ0) were the highest. Moreover, the N105 treatment promoted the transfer of absorbed energy in PSII, reduced the dissipated energy, and was conducive to improving the photosynthetic efficiency [ 55 ]. Accumulated studies have shown that when plants are exposed to excess light energy, they initiate a photoprotective strategy, maximizing the use of light energy while increasing the loss of light energy to maintain normal plant growth [ 56 ]. In our study, the absorbed photon flux per active PSII (ABS/RC) of nodulating peanut varieties increased under the N165 treatment, indicating that some reaction centers of peanut PSII were deactivated [ 57 ]. The TR 0 /RC, electron flux from Q A ‒ to the PQ pool per active PSII (ET 0 /RC), and DI 0 /RC were higher under the N165 treatment than under the N105 treatment, suggesting that leaves may activate the photoprotection strategy and improve the high electron transfer efficiency (ET 0 /RC) and heat dissipation (DI 0 /RC) ability, which may prevent damage to the photosynthetic mechanism caused by excess light energy and maintain relatively higher photosynthetic capacity [ 58 ]. Meanwhile, non-nodulating DH9 was highly dependent on N fertilizer, and the activity of OEC and PSII reaction center increased and the DI 0 /RC decreased with an increase in N application, which maintains a high electron transfer efficiency, ensuring high photosynthetic efficiency.

N is a critical component of plant growth and development and it is very important to ensure the balance between nitrogen supply and plant growth needs. A previous study reported a significant correlation between the accumulation of total N and both DM and pod yield in peanuts [ 59 ]. In other studies, N deficiency significantly reduced crop height, leaf chlorophyll, DM, and N accumulation [ 60 ], but these effects were alleviated by the application of N fertilizer [ 6 ]. However, excessive N application can negatively affect crop yield and DM [ 59 ]. Our study showed that N application increased DM, N accumulation, yield, and yield components of peanuts, among which the DM, N accumulation, yield, and full pod number per plant of nodulating peanut varieties (NH5, XH11, and HH16) reached the maximum under the N105 treatment. This may be because biological N fixation (BNF) was the main source for meeting N requirements in nodulating peanut varieties in the podding stage [ 26 , 32 ]. The process of BNF in peanuts requires a certain amount of N initiation, and a small amount of N application is conducive to the formation of root nodules [ 61 ]. However, BNF is an energy-consuming process that requires strict control of the number of nodules [ 62 ]. To a certain extent, lower N application cannot simultaneously satisfy the BNF and the normal growth and development process of peanuts (N45), while excessive N application (N165) would reduce the number of root nodules [ 28 ]. Therefore, in our study, the nodulating peanut varieties had a higher yield potential under the N105 treatment, while the non-nodulating peanut variety (DH9) was more dependent on fertilizer for N sources, and thus reached the highest yield under high N treatment (N165).

The change in plant plasticity is an external representation of the change in its life activities. The plasticity index can directly reflect the ability of plants to adapt to environmental changes, and the higher the plasticity, the greater the potential to adapt to the environment [ 63 ]. In our study, the plasticity index of biomass and N content of four peanut varieties were higher, indicating that N fertilizer greatly promoted the growth and yield formation of peanuts. Some differences in the plasticity of various indexes were observed among varieties, among which, the plasticity of fluorescence parameters related to XH11 and DH9 was relatively higher (RE 0 /CS M ), indicating that exogenous N fertilizer could promote the photosynthetic electron transfer in the peanut leaves, thus contributing to the improvement of photosynthetic efficiency.

Correlation analysis showed that Pn and Tr had a positive correlation with Pod N accumulation and yield, indicating that the leaf photosynthetic capacity is closely related to peanut yield, consistent with previous findings in rice [ 40 ], maize [ 23 ], and soybean [ 64 ]. The correlation between photosynthetic indices differed among the peanut varieties. Pn was significantly positively correlated with Pod N in high nodulating varieties (XH11 and HH16) and non-nodulating varieties (DH9), while Pn in low nodulating varieties (NH5) was not significantly different between treatments. However, the correlation of Pn with yield was not significant. This may be due to the higher N absorption capacity of NH5, which increases yield by distributing more N to the pods [ 32 ]. In addition, correlation analysis also indicated that the trapping of light energy (TR) contributes to the increase in peanut Pn, Tr, and Pod N, and the increase in electron transport flux (ET) and electron transfer to PSI receptor side terminal (RE) improves peanut yield. Xu et al. [ 55 ] reached similar conclusions in oilseed rape using different N application rates. Taken together, these results demonstrated that higher activity of PSII RCs, electron transport, quantum yield, and efficiency are conducive to the increase of yield (Fig.  9 ), providing a foundation for elucidating the interrelationship between peanut photosynthesis and crop yield.

A model of the adaptive strategy of the peanut photosystem after the application of nitrogen fertilizer. Chl a, chlorophyll a; Pn, net photosynthetic rate; Gs, stomatal conductance; Tr, transpiration rate; Ci, intercellular CO 2 concentration; PSI, photosystem I; PSII, photosystem II; Q A , primary acceptor plastoquinone A; Q B , the secondary acceptor plastoquinone B; Cy t b 6 f , the cytochrome b 6 f complex; PC, cytochrome and plastocyanin; OEC, oxygen evolving complex; DM, dry matter; TN, total N accumulation

In summary, the present study demonstrates that optimized nitrogen (N) application (N105) can improve the performance of the photosystem II (PSII) donor/acceptor side and the size of the terminal electron acceptor pool of the photosystem I (PSI) acceptor side, enhance photosynthetic electron transport, reduce the dissipated energy, and ultimately promote the photosynthesis efficiency of peanut leaves. Meanwhile, the N105 treatment can improve the photosynthetic pigment content and photosynthetic capacity of peanut leaves, which was conducive to N accumulation and dry matter, and then promote the formation of yield and yield components. However, electron transfer from primary acceptor plastoquinone A (Q A ) to the secondary acceptor plastoquinone B (Q B ) of PSII in nodulating peanut leaves was blocked, the oxygen evolving complex (OEC) was damaged, some reaction centers were deactivated, and photosynthetic capacity decreased under high N application (N165), while the non-nodulating peanut varieties had higher photosynthetic efficiency and yield. This study uncovers the photosynthetic electron transfer response mechanism of peanut varieties with different nodulations under different N fertilizer treatments and provides highly guidance for efficient N fertilizer management in cultivation and production.

Availability of data and materials

Data has been included in the manuscript.

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This study was financially supported by the earmarked fund for China Agricultural Research System (CARS-13) and major project of food crop production based on technological application of Liaoning province (2023JH1/10200002).

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College of Agronomy, Shenyang Agricultural University, Shenyang, China

Pei Guo, Jingyao Ren, Xiaolong Shi, Anning Xu, Ping Zhang, Fan Guo, Yuanyuan Feng, Xinhua Zhao, Haiqiu Yu & Chunji Jiang

Liaoning Agriculture Vocational and Technical College, Yingkou, China

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PG, HY and CJ designed the research. HY, CJ and XZ contributed reagents and materials. PG, JR, XS, AX, PZ, YF and FG conducted the experiments, prepared figures and tables. PG, JR and XS prepared and revised the manuscript. HY and CJ organized and coordinated the whole project.

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Correspondence to Haiqiu Yu or Chunji Jiang .

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Guo, P., Ren, J., Shi, X. et al. Optimized nitrogen application ameliorates the photosynthetic performance and yield potential in peanuts as revealed by OJIP chlorophyll fluorescence kinetics. BMC Plant Biol 24 , 774 (2024). https://doi.org/10.1186/s12870-024-05482-x

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Received : 24 June 2024

Accepted : 05 August 2024

Published : 14 August 2024

DOI : https://doi.org/10.1186/s12870-024-05482-x

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