Plant Physiology

doi: 10.1104/pp.15.01941pmid: N/A

The editors and staff of Plant Physiology thank all of those listed below whose insight and contributions in reviewing manuscripts from December 13, 2014, to December 8, 2015, have helped make the journal a success. We would also like to thank the Monitoring Editors who have concluded their term of service, and we welcome those Monitoring Editors who are joining the Ed Board in 2016. Reviewers Mark Aarts Javier Abadia Steffen Abel George Aggelis Birgit Agne Tariq Akhtar Kiba Akinori Armando Albert Nick Albert Doug Allen Saleh Alseekh Chris Ambrose Anna Amtmann Gynheung An Ajith Anand Charles Anderson Carl Andre Gerco Angenent Carla Antonio Takashi Aoyama Frederic Aparicio Heidi Appel Wagner Araujo Patricio Arce-Johnson Juan Arellano Albrecht von Arnim Eva-Mari Aro Henk Van As Maria Asensi-Fabado Tom Ashfield Sarah Assman Stefania Astolfi Ross Atkinson Brian Atwell Kyaw Aung Adi Avni Brian Ayre Vasudevan Ayyapan Andreas Bachmair Kyoungwhan Back Katja Baerenfaller Sacha Baginsky Julia Bailey-Serres Soren Bak Salma Balazadeh K. Balestrasse Janneke Balk Marilyn Ball Steven Ball Carlos Ballare Daniel Ballhorn Frantisek Baluska Mark Banfield Francois Barbier Alice Barkan Bronwyn Barkla Cornelius Barry Andrea Barta Csengele Barta Dudy Bar-Zvi Khurram Bashir Tobias Baskin Hank Bass George Bassel Gilles Basset Roberto Bassi Philip Bates Giovanni Battista Petra Bauer Ute Baumann Isabel Baurle Ivan Baxter Frederic Beaudoin Claude Becker Dirk Becker Gerold Beckers Philip Becraft Gerrit Beemster Mary Beilby Christophe Belin Catherine Bellini Mohammed Bendahmane Philip Benfey Adam Benham Alan Bennett Malcolm Bennett Andrew Bent Leónie Bentsink Kenneth Berendzen Susanne Berger Gerald Berkowitz Carl Bernacchi Karine Berthelot Magdalena Bezanilla Rishikesh Bhalerao Prem Bhalla Gerd Bienert Brad Binder James Birchler Hannah Birke Ton Bisseling Elison Blancaflor James Blande Mike Blatt Miguel Blazquez Anna Block Maryse Block Eduardo Blumwald Leonor Boavida Frederik Boernke Christine Boettcher Jochen Bogs Kirsten Bomblies Linda Bonen Jean-Jacques Bono Justin Borevitz Maurice Bosch Javier Botto Arezki Boudaoud Marie Boudsocq Ralph Bours Harro Bouwmeester Joseph Bozell Benjamin Brachi Kent Bradford Siobhan Brady Peter Bramley Federica Brandizzi Veronique Brault Hans-Peter Braun Pascal Braun Melissa Brazier-Hicks Frank Van Breusegem Jean-Francois Briat Winslow Briggs Anne Britt Craig Brodersen Peter Brodersen Timothy Brodribb Yariv Brotman David Brummell Frederic Brunner Judy Brusslan Peter Buchner Thomas Buckhout Heike Bücking Thomas Buckley Claudia Buechel Julia Buitink Vincent Bulone James Bunce Robert Burnap James Burnell Meike Burow Florian Busch Wolfgang Busch Bernadette Byrne Ricardo Cabeza Edgar Cahoon Chao Cai Giampiero Cai Edward Calabrese Judy Callis Robin Cameron Ana Campilho Steven Cannon Jiashu Cao Allan Caplan Francesca Cardinale Pierre Carol Jorge Casal Paula Casati Simone Castellarin Christopher Cazzonelli Francisco Cejudo Mauro Centritto Tomas Cermak Heriberto Cerutti Stefano Cesco Swapnajit Chakravarty Caren Chang Fang Chang Kent Chapman Joe Chappell Clint Chapple Fabien Chardon Amy Charkowski Yee-yung Charng Guillaume Charrier Chris Chastain Sudip Chattopadhyay Youssef Chebli E. Chehab Feng Chen Jin-Gui Chen Kunsong Chen Rujin Chen Shaoliang Chen Sixue Chen Xiao-Ya Chen Xu Chen Xuemei Chen Yi-Fang Chen Z. Jeffrey Chen Zhixiang Chen Zhong-Hua Chen Zhukuan Cheng Alice Cheung Yukako Chiba Tzyy-Jen Chiou Daniel Chitwood Myeong-Je Cho Brendan Choat Surinder Chopra Yves Choquet Chengcai Chu Taijoon Chung Mee-Len Chye Hannes Claeys Bruno Clair Steven Clark Nicole Clay Stephan Clemens Tom Clemente Steven Clouse Jeremy Coate Jerry Cohen Eva Collakova Jose Colmenero Timothy Colmer Thomas Colquhoun Luca Comai Clara Conicella Brendon Conlan Erin Connolly C. Peter Constabel Lucio Conti Douglas Cook Daniel Cosgrove Alex Costa George Coupland Asaph Cousins Grant Cramer Nigel Crawford Jose Crespo Pedro Crevillén Roberta Croce Michael Crowley Maria Helena Cruz de Carvalho Hongchang Cui Catherine Curie John Cushman Sean Cutler Ann Cuypers Mingqiu Dai Luca Dall'Osto Howard Damude Olga Danilevskaya Cristian Danna Maheshi Dassanayake John D'Auria Julia Davies Kevin Davies David Day Marc De Block Stefan de Folter Barend H.J. de Graaf Hans de Jong Giulia De Lorenzo Vincenzo De De Luca Miguel de Lucas Ruud de Maagd Roberto De Michele Sylvia de Pater Bert De Rybel Ive De Smet Lieven De Veylder David de Vleesschauwer Seth DeBolt Jorg Degenhardt Katayoon Dehesh Jan Dekker Stephen Dellaporta Massimo Delledonne Alison DeLong Serge Delrot Vadim Demidchik Jurgen Denecke Xing Wang Deng David Lee Des Marais Anastasia Desyatova Timothy Devarenne Alessandra Devoto Ralph Dewey Christophe D'Hulst Veronica Di Antonio Diaz-Espejo Rebecca Dickstein Petra Dietrich Karl-Josef Dietz Biao Ding Shi-You Ding Yong Ding Zhaojun Ding Randy Dinkins Jose Dinneny Christina Dixelius Ram Dixit Michael Djordjevic Grazyna Dobrowolska Ian Dodd Peter Doermann Frank Dohleman Liam Dolan Rudy Dolferus Aiwu Dong Juan Dong Xinnian Dong Stéphan Dorey Carl Douglas A. Bruce Downie Allan Downie David Drew Gary Drews Qiaohong Duan Christian Dubos Paul Dupree Amanda Durbak Joerrg Durner John Dyer Peter Eastmond Oliver Ebenhoeh Kai Edel Patrick Edger Anna Edlund Robert Edwards Juergen Ehlting David Ehrhardt Cornelia Eisenach Nabil Elrouby Akira Endo Tsuyoshi Endo Karl-Heinz Engel Juergen Engelberth Cawas Engineer Yuval Eshed Richard Espley Jose Estevez Thomas Eulgem John Evans Matthew Evans Frank Ewers Marc Faget Noah Fahlgren Ahmed Faik Chuanzhu Fan Christian Fankhauser Eva Farre Sara Farrona Bruno Favery Jose Feijo David Fell Baomin Feng Jian Feng Alisdair Fernie Simone Ferrari Joerg Fettke Sophie Filleur Giovanni Finazzi Iris Finkemeier Scott Finlayson Fabio Fiorani Andreas Fischer Rainer Fischer Urs Fischer Emmanouil Flemetakis Andrew Fleming Francisco Florencio Miguel Flores Victor Flors Robert Fluhr Elizabeth Fontes Eloise Foo Giuseppe Forlani Ana Fortes Fabrice Foucher Christine Foyer Jean-Marie Frachisse Jose Franco-Zorrilla F. 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Gordon Shino Goto Cecilia Gotor Clare Gough Aska Goverse Steffen Graether Markus Grebe Beverley Green Jean Greenberg Christopher Grefen Brian Gregory Stephan Greiner Jonathan Gressel Peter Gresshoff Donald Grierson Lawrence Griffing Howard Griffiths Erwin Grill Louis Grillet Bernhard Grimm Steven Groot Andrew Groover Erich Grotewold Anil Grover Jose Gualberto Frank Gubler Mary Lou Guerinot Damian Guerra Thomas Guilfoyle Fang-Qing Guo Hongwei Guo Jingxin Guo Xingqi Guo Yan Guo Ryan Gutierrez Jose Gutierrez-Marcos Caroline Gutjahr Charles Guy Jeffrey Habben Young Hae Rainer Haeusler Karin Hage-Ahmed Candace Haigler Mohammad-Reza Hajirezaei Sarah Hake Nigel Halford Barbara Halkier Robert Hall Claire Halpin Takahiro Hamada Thorsten Hamann Kim Hammond-Kosack Fangpu Han Kyung-Hwan Han Mitsumasa Hanaoka Robert Hancock L. Curtis Hannah Johannes Hanson Maureen Hanson Yoshie Hanzawa John Harada Jeremy Harbinson Jesper Harholt Stacey Harmer Alice Harmon Smadar Harpaz-Saad Philip Harris Maria Harrison Jonathan Hart James Hartwell Mike Hasegawa Toshihiro Hasegawa Karl Hasenstein Elizabeth Haswell Bettina Hause Felix Hauser Michel Havaux Michael Havey Martha Hawes Manajit Hayer-Hartl Sheng Ynag He Yuehui He Yuke He Zuhua He Joshua Heazlewood Peter Hedden Rainer Hedrich Marc Heijde Ingo Heilmann Sabine Heinhorst Jan Hejátko Yrjo Helariutta Ruediger Hell Piers Hemsley Ian Henderson Lars Hennig Amelia Henry Goetz Hensel Rainer Herdrich Antonio Heredia Eliot Herman Georgina Hernandez Alistair Hetherington Julian Hibberd Glenn Hicks Tetsuya Higashiyama Kyoko Higuchi Lena Hileman Henk Hilhorst Pierre Hilson Andreas Hiltbrunner Dirk Hincha Michael Hippler Hiro-Yuki Hirano Bertrand Hirel Tatsuro Hirose Candice Hirsch Kendal Hirschi Heribert Hirt Frank Hochholdinger Michael Hodges Rainer Hoefgen Monica Hofte Saskia Hogenhout Martin Hohmann-Marriott N. 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All Rights Reserved. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Minorsky, Peter V.

doi: 10.1104/pp.15.01942pmid: N/A

A Role for Proton Pumping Pyrophosphatase in Phloem Loading Plant productivity is determined in large part by the partitioning of assimilates between sites of production and sites of utilization. Recently, a role for proton-pumping pyrophosphatases (H+-PPases) in phloem loading and biomass partitioning has been proposed. H+-PPases have a well-documented role in hydrolyzing pyrophosphate (PPi) and capturing the released energy to pump H+ across the tonoplast and endomembranes to create proton motive force (pmf). In companion cells (CCs) of the phloem, however, H+-PPases localize to the plasma membrane rather than endomembranes. It has been suggested that plasma-membrane-localized H+-PPase could contribute to phloem pmf to help energize phloem loading and enhance long-distance transport. Although this is an attractive model, H+-PPases on the CC plasma membrane are thermodynamically unable to operate hydrolytically to pump H+ into the apoplasm, based on estimations of the free energy of the H+-PPase pump action. In contrast, the reverse reaction, in which the plasma membrane pmf is used to synthesize PPi, is thermodynamically feasible. Thus, rather than hydrolyzing PPi to create pmf, pmf is utilized to synthesize PPi. This additional PPi in the CCs may promote Suc oxidation and ATP synthesis, which the plasma membrane P-type ATPase can use to create more pmf for loading of Suc into the phloem via Suc-H+ symporters. To test this model, Khadilkar et al. (pp. 401–414) generated transgenic Arabidopsis (Arabidopsis thaliana) plants with constitutive and CC-specific overexpression of AVP1, a gene encoding type 1 ARABIDOPSIS VACUOLAR PYROPHOSPHATASE1. They report that plants with both constitutive and CC-specific overexpression accumulated more biomass in shoot and root systems. Further experiments employing 14C-labeling showed that these genetic modifications enhanced photosynthesis, phloem loading, phloem transport, and delivery to sink organs. The authors suggest that the growth enhancement mediated by AVP1 overexpression is attributable to its role in phloem CCs. These findings also support the hypothesis that H+-PPases function as PPi synthases in the phloem. Small Molecules That Affect Vein Patterning Leaf veins play a critical role in transporting water, nutrients, and signals. Numerous regulators of vein patterning in Arabidopsis have been identified by a combination of genetic screens, inhibitor studies, and vascular cell profiling. Among the venation factors identified are those with roles in auxin signaling and transport, leaf development, and cell biological processes, including sterol and lipid biosynthesis. To identify other regulators of vein patterning, Carland et al. (pp. 338–353) screened more than 5000 structurally diverse small molecules for compounds that alter Arabidopsis (Arabidopsis thaliana) leaf vein patterns. Many perturbations to vein patterning were thereby observed, including vein networks with an open reticulum; decreased or increased vein number and thickness; and misaligned, misshapen, or nonpolar vascular cells. Further characterization of several individual active compounds suggests that their targets include hormone cross talk, hormone-dependent transcription, and PIN-FORMED trafficking. Profilin and Plant Cell Elongation The actin cytoskeleton of plant cells plays an important role in many cellular processes, including cell expansion and morphogenesis, vesicle trafficking, and the response to biotic and abiotic signals. Plant cells tightly regulate the turnover and rearrangement of the actin cytoskeleton networks in the cytoplasm by means of a plethora of actin-binding proteins, but the exact mechanisms are poorly understood. One of the more important of these actin-binding proteins is profilin, a small, conserved actin-monomer binding protein present in all eukaryotic cells. The consequences of profilin activity on actin filament turnover differ based on cellular conditions and the presence of other actin-binding proteins. In vitro studies show that the profilin-actin complex associates with the barbed ends of filaments and promotes actin polymerization by lowering the critical concentration and increasing nucleotide exchange on G-actin. When barbed ends are occupied by capping protein, profilin acts as an actin-monomer sequestering protein. These opposing effects of profilin might be a regulatory mechanism for profilin modulation of actin dynamics in cells. In Arabidopsis, at least five PROFILIN genes have been identified, but there has not been a critical examination of the impact of the loss of profilin on the organization and dynamics of single actin filaments in plant cells in vivo. Cao et al. (pp. 220–233) have now examined the role of PROFILIN1 (PRF1) in regulating actin dynamics in the epidermal cells of Arabidopsis hypocotyls during cell elongation. They report that reduced PRF1 levels enhanced cell and organ growth. Contrary to expectations, the overall frequency of nucleation events in prf1 mutants was dramatically decreased. Pharmacological evidence using inhibitors of formin, another actin-binding protein, provide evidence that Arabidopsis PRF1 contributes to actin dynamics by modulating formin-mediated actin nucleation and filament elongation during axial cell expansion. Induced Crassulacean Acid Metabolism Transcription Plants have evolved a range of mechanisms to cope with drought, including a specialized type of photosynthesis termed Crassulacean acid metabolism (CAM). CAM is associated with stomatal closure during the day as atmospheric CO2 is assimilated primarily during the night, thus reducing transpirational water loss. The tropical herbaceous perennial species Talinum triangulare is capable of transitioning from C3 photosynthesis to weakly expressed CAM in response to drought stress. Brilhaus et al. (pp. 102–122) now report concerning the transcriptional regulation of this transition. They found highly elevated levels of CAM-cycle enzyme transcripts and their metabolic products in T. triangulare leaves upon water deprivation. It also appears that in order to support the CAM-cycle and the synthesis of compatible solutes during drought stress, carbohydrate metabolism is reprogrammed to reduce the use of reserves for growth. The authors have identified candidate transcription factors to mediate this photosynthetic plasticity, which may contribute in the future to the design of more drought-tolerant crops via engineered CAM. A Hormetic Response of Arabidopsis to a Synthetic Elicitor Hormesis is a common but poorly understood phenomenon characterized by low-dose stimulatory effects of agents that are toxic or inhibitory at higher doses. Rodriguez-Salus et al. (pp. 444–458) have revealed a new example of hormesis: they report that low doses of 2-(5-bromo-2-hydroxy-phenyl)-thiazolidine-4-carboxylicacid (BHTC) enhance root growth in Arabidopsis, while high doses of this compound inhibit root growth. BHTC is a synthetic elicitor, a drug-like compound that induces plant immune responses but which is structurally distinct from natural defense elicitors. BHTC induces disease resistance in plants against bacterial, oomycete, and fungal pathogens. BHTC-induced hormesis in Arabidopsis roots, like its effects on defense, is partially dependent on the WRKY70 transcription factor. By mRNA sequencing, the authors have uncovered a dramatic difference between transcriptional profiles triggered by low and high doses of BHTC. Only high levels of BHTC induce typical defense-related transcriptional changes. In contrast, low BHTC levels trigger a transcriptional response manifested by the suppression of photosynthesis- and respiration-related genes in the nucleus, chloroplasts, and mitochondria. Development-related nuclear genes are also induced. Considered together, these results link plant defense signaling to hormetic developmental responses and provide a genetic and transcriptional framework for future studies on the mechanistic basis of plant hormesis. Author notes www.plantphysiol.org/cgi/doi/10.1104/pp.15.01942 © 2016 American Society of Plant Biologists. All Rights Reserved. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Hepler, Peter K.

doi: 10.1104/pp.15.01506pmid: 26722019

The cytoskeleton is well recognized as an ever-present component of all eukaryotic cells; more recently, it has been identified in prokaryotic cells as well (van den Ent et al., 2001; Wickstead and Gull, 2011). The cytoskeleton gives order to a cell, and in large cells, where diffusion may become limiting, it provides a means to move components around, thus facilitating reactions (Verchot-Lubicz and Goldstein, 2010). Increasingly, we also see that the cytoskeleton in eukaryotic cells participates in the uptake of material from outside of the cell (e.g. endocytosis and phagocytosis; Yutin et al., 2009). And inside the cell, where organelles, membrane systems, and macromolecular complexes are exquisitely and dynamically organized in minute detail, it is largely the cytoskeleton that is responsible for this organization. Because the cytoskeleton participates in so many processes, it becomes a matter of consequence to understand how it is controlled. Indeed, this is a topic of major interest at present and one whose solution will contribute fundamentally to our understanding of many aspects of cell growth and development. Among several possible control elements, it has been widely known for many years that ions, in particular calcium, can exert a profound effect on the structure and activity of both the actomyosin and tubulin cytoskeletons (Hepler, 2005; Hepler and Winship, 2015). One of the best examples is the stimulation of the contraction of striated muscle by calcium, where, through its binding to troponin C, tropomyosin is displaced along the actin filament, exposing myosin-binding sites and permitting contraction to occur (Alberts et al., 2008). There are numerous other examples found in both plant and animal cells involving calcium regulation of the actin cytoskeleton. In addition, it is also well established that calcium can have profound effects on microtubules (MTs). Indeed, the demonstration in 1972 by Richard Weisenberg that elevated calcium caused MT depolymerization was transformative (Weisenberg, 1972). Whereas biochemists until that time had been unsuccessful in obtaining in vitro polymerization of MTs, this now became possible. But for me, it raised intriguing possibilities concerning a role for calcium in the control of cellular processes such as mitosis and cytokinesis (Hepler and Wayne, 1985; Hepler, 2005). It also got me to think more widely about the role of calcium as a general signaling agent. In the 30 years since Randy Wayne and I reviewed this topic (Hepler and Wayne, 1985), it is now apparent that calcium reaches into countless events and processes and can be viewed as a universal signaling agent in plant cell growth and development (Edel and Kudla, 2015). In advance, I must tell you that I will not review the broad scope of calcium research today, which is vast; there are many reviews to which the interested reader is directed (Hetherington and Brownlee, 2004; Kudla et al., 2010; Verret et al., 2010; Hashimoto and Kudla, 2011; Hamel et al., 2014; Edel and Kudla, 2015). I will also not discuss the role of small GTPase proteins, even though they can have profound effects on calcium and the cytoskeleton in plant cells. Again, the interested reader is directed to pertinent reviews on this topic (Gu et al., 2005; Nibau et al., 2006; Craddock et al., 2012; Oda and Fukuda, 2013; Li et al., 2015). Rather, in this article, I will focus on the role of ions in the control of the cytoskeleton in plant cells, giving attention to those actin- and tubulin-binding proteins that are modulated by calcium and/or protons. Before discussing ion regulation, I briefly consider, first, how calcium and protons emerged as signaling agents and, second, aspects of cytoskeleton evolution in the progression from prokaryotic to eukaryotic cells. CALCIUM AND PROTONS AS SIGNALING AGENTS Calcium Calcium is an abundant element in the earth’s crust; however, high concentrations are harmful and indeed fatal (Case et al., 2007; Kazmierczak et al., 2013; Blackstone, 2015). Most important is the reaction of calcium with phosphate, forming a highly insoluble precipitate, thus incapacitating phosphate-based energy metabolism. The importance of this reaction is made apparent by the realization that all forms of life use phosphate energy metabolism in one form or another (Harold, 2014). But beyond this, high calcium condenses chromatin, aggregates proteins, and in general impairs a host of intracellular activities such as mitochondrial function and chromosome motion (Case et al., 2007; Blackstone, 2015). Indeed, calcium is so abundant and cellular chemistry so sensitive that one wonders how the first protocell arose. For a cell living in today’s ocean, the calcium concentration within the cell is 5 orders of magnitude lower than that in the ocean (e.g. from 0.1 to 10,000 µm; Blackstone, 2015). Energetically, it would seem to be a tall order to imagine that the first protocell would have been able to reduce the concentration of free calcium by 5 orders of magnitude. A possible way around this apparent impasse emerges from the evidence indicating that the early oceans or bodies of water in which life arose were more alkaline than the oceans of today (Kazmierczak et al., 2013). Alkalinity would favor the formation of calcium carbonate precipitates and thus reduce the free concentration of the ion, possibly by a few orders of magnitude. Over geological time, as the oceans became less alkaline, cells could progressively improve their ability to exclude calcium. Through the evolution of pumps and carriers that remove calcium from the cell, and of an impermeable plasma membrane that minimizes leakage, cells became able to establish and maintain a 100,000-fold gradient in the concentration of calcium between the low level in the cytosol and the high level in the extracellular milieu. Although not as well studied as in eukaryotic cells, it appears that prokaryotic cells, including both bacteria and archea, possess calcium pumps and exchangers that extrude calcium from the cytoplasm either to the outside or to internal storage bodies (e.g. acidocalcisomes; Dominguez, 2004; Docampo and Moreno 2011; Dominguez et al., 2015). As a result, these prokaryotic cells, similar to eukaryotic cells, possess a low internal concentration of calcium, in the vicinity of 0.1 to 0.3 µm (Tisa and Adler, 1995; Watkins et al., 1995; Dominguez, 2004; Dominguez et al., 2015). Some of the calcium pumps in prokaryotes are similar to the phosphorylation or P-type ATPases of eukaryotic cells. These include the plasma membrane proton pump, the calcium pump, and the sodium/potassium pump. Other pumps may be more closely related to the F-type ATPases, such as those found in mitochondria (Dominguez, 2004; Dominguez et al., 2015). Additionally, prokaryotic cells have several proteins that possess binding pockets with a high affinity for calcium. The best known are those proteins with the canonical calcium-binding EF-hands. Here, a loop of a dozen amino acids, which is set off by two α-helices, provides an environment with neutral oxygen, carbonyl, and carboxyl residues that permit the loop to bind calcium with enormous selectivity over magnesium (Kawasaki et al., 1998; Nelson and Chazin, 1998; Zhou et al., 2006; Dominguez et al., 2015). Thus, despite the presence of 1 mm (1,000 µm) magnesium, the flexible coordination in the loop allows it to preferentially respond to a change in calcium in the physiological range (0.1–1 µm). The best known EF-hand protein is calmodulin, which occurs ubiquitously in plants and animals. Typically, as in calmodulin, in which there are four EF-hands organized in two pairs, the binding of calcium induces a conformational change exposing a hydrophobic core that then permits its interaction with another protein, the presumed response element (Nelson and Chazin, 1998). This subsequent reaction can be either positive or negative. For muscle contraction, the binding of calcium to troponin C stimulates contraction and is thus positive (Alberts et al., 2008). However, for the KINESIN-LIKE CALCIUM-BINDING PROTEIN (KCBP), which participates in several different processes in plants, the binding of calcium to calmodulin renders the motor inactive; here, calcium is a negative regulator (Deavours et al., 1998; Narasimhulu and Reddy, 1998). To summarize, it seems evident that several important aspects of calcium regulation, which originated in prokaryotes, have been maintained in eukaryotic cells, using protein complexes that arose early and that have been retained through evolution from prokaryotes to eukaryotes. Given the enormous concentration gradient (100,000-fold) across the cell membrane, the situation was poised for exploitation. Thus, the evolution of a calcium-specific channel, which would allow a relatively small number of atoms to enter the cell, could elevate the internal ion concentration in ways that would have little or no effect on phosphate-based energy metabolism but would still be sufficient to activate an EF-hand protein such as calmodulin (Hepler and Wayne, 1985; Hepler, 2005). To give an idea about what is involved, imagine a cubic micrometer of cytoplasm (e.g. an Escherichia coli cell) in which the free calcium is 0.1 µm; this would amount to only 60 free atoms of calcium. If the concentration then increases 10-fold (to 1 µm), that would still only be 600 calcium atoms. Small changes such as these could occur and be sufficient to trigger a developmental event despite the constant surveillance by phosphate. In addition to its selectivity over magnesium, calcium possesses remarkably faster on/off rates than magnesium (Hepler and Wayne, 1985). This property arises because magnesium has a much smaller atomic radius than calcium (65 versus 99 pm) and because the electrostatic field is inversely proportional to its atomic radius. Magnesium, therefore, will bind water much more strongly than will calcium, but it will shed water much more slowly than calcium. These properties mean that, in any given association, calcium will react much more rapidly than magnesium, explaining its participation in muscle contraction and many other events where speed is important. Speed of reaction and signal propagation may also be important in plants. A recent example comes from the study of Choi et al. (2014), who report that salt stress in Arabidopsis (Arabidopsis thaliana) roots induced calcium waves that traveled relatively long distances and at rapid rates (approximately 400 µm s−1). In this example, the calcium wave moved through the cortex and endodermal layers of the root, where its propagation was dependent on the activity of a vacuole-localized two-pore channel. When considered as a whole, we see that calcium signaling takes different forms. However, all of these different forms derive from the element’s unique chemical properties. Protons A great deal of attention is given to the changes in calcium concentration, or the presence of localized gradients in calcium, and the role that these concentration differences play in controlling a host of activities; this is referred to as amplitude modulation. It is also important to recognize the existence of changes in proton concentration and localized proton gradients and their contribution to cell growth and development (Felle, 2001). Because water, which is the solvent for life, weakly ionizes, it yields a solution containing 0.1 µm protons and hydroxyl ions. At pH 7, the proton concentration approximately equals the basal level of calcium and, thus, might seem poised to serve as a second messenger. However, we should be cautious about extrapolating from these data (Felle, 2001). For example, although the extracellular environment of the cell wall is usually acidic (pH 5), giving a proton concentration around 10 µm, the resulting gradient with the inside is 100-fold, whereas for calcium it is 100,000-fold. For calcium, when a selective channel in the plasma membrane opens, a huge influx ensues, whereas for protons, the driving force is much reduced. With regard to protons, while selective channels are found widely in animals (Decoursey, 2003; Beg et al., 2008) and in some marine and freshwater algae (Taylor et al., 2012), an inward-directed proton channel has not been found in flowering plants (Decoursey, 2003; Taylor et al., 2012). Nevertheless, protons can permeate cell membranes. Another major difference between these ions is the substantially greater mobility for protons than for calcium (Feijó et al., 1999; Decoursey, 2003). The early, classic work of Hodgkin and Keynes (1957) showed that calcium is largely bound within the cytoplasm, exhibiting limited and slow mobility. By contrast, protons move quite freely, indeed much faster than expected for a monovalent cation in solution (Decoursey, 2003). One explanation may be prototropic transfer or proton hopping, where a proton within an electric field binds to one side of the water molecule, while another proton on the other side leaves the water molecule (for review, see Decoursey, 2003). This process can occur along a file of water molecules, thus allowing for the rapid displacement of the proton independent of diffusion. As a consequence, a localized proton gradient will dissipate faster than a similarly localized calcium gradient. Finally, the way in which calcium and protons affect a response element in any signal transduction scheme is likely to be different. For calcium, as noted earlier, there are specific binding proteins, notably those with EF-hands, such as calmodulin, which can transmit the calcium signal to the response element. Such a mechanism is thus far unknown for protons. Rather, it appears that changes in proton concentration modify the protonation state of amino acids and, thereby, affect the overall charge of a protein and its interaction with other proteins and substrates. Dumetz et al. (2008) report that, in general, protein interactions increase with increasing acidity (i.e. decreasing pH). Despite these issues, proton gradients do exist and emerge as potential signaling factors (Feijó et al., 1999). Through the activity of the plasma membrane proton ATPase, plant cells establish proton gradients, where the gradient emerges as the single most important factor in creating the membrane potential and in driving ion and nutrient transport (Sze et al., 1999; Palmgren, 2001). Proton-pumping enzymes are already well established in prokaryotes, including both bacteria and archea, with the most prevalent enzyme being the F1F0 ATPase of bacteria or the A1A0 ATPase of archea (Harold, 2014). The former is particularly well known in oxidative phosphorylation in mitochondria and photophosphorylation in plastids. This enzyme either uses ATP to pump out protons, thus creating a proton motive force that can be used to drive other transport reactions, or can use the gradient to synthesize ATP. Curiously, and in contrast to that of mitochondria and plastids, the plasma membrane proton ATPase on plant cells is not closely related to the F1F0 of bacteria (Harold, 2014); rather, it belongs to the P-type ATPase, being more closely related to the sarcoplasmic/endoplasmic reticulum (ER) calcium pump and the sodium/potassium ATPase (Dominguez et al., 2015). Importantly, the P-type ATPase also occurs in prokaryotes, where it participates in pumping calcium and other metals (Dominguez et al., 2015); these enzymes would appear to be the evolutionary ancestor of the eukaryotic proton pump. EVOLUTION OF THE CYTOSKELETON Actin and Tubulin Ancestors For some time, it seemed plausible that a cytoskeleton, such as we know it in eukaryotic cells, did not exist in prokaryotic cells. The argument supporting this conclusion asserted that the prokaryotic cells were very small, and therefore that activities requiring the movement of components within the cell could be satisfied simply by diffusion. However, this notion has been changed by the relatively recent discovery that bacteria and archea possess a cytoskeleton (van den Ent et al., 2001; Doolittle and York, 2002; Carballido-Lopez, 2006; Erickson, 2007; Derman et al., 2009; Erickson et al., 2010; Bernander et al., 2011; Ettema et al., 2011; Wickstead and Gull, 2011; Ingerson-Mahar and Gitai, 2012). Briefly, proteins have been found, notably FtsZ and MreB (also ParM and FtsA), that are members of the tubulin and actin families, respectively, common to virtually all eukaryotic cells. Although far diverged in overall sequence, these ancestral cytoskeletal proteins share similarities with their descendants in eukaryotic cells, not only in their three-dimensional shape but in the amino acid sequences necessary for binding nucleotides and for protein-protein interactions (Carballido-Lopez, 2006; Erickson, 2007). FtsZ and MreB, while not forming structures resembling MTs and microfilaments (MFs), nevertheless do form extended filaments and appear to undergird a system of transport and polarity analogous to that of the eukaryotic cytoskeleton. The prokaryotic cytoskeleton thus participates in processes similar to those with which the eukaryotic cytoskeleton is involved. For example, FtsZ contributes to cell division by forming the Z ring that physically separates the cell into two daughter cells (Erickson et al., 2010). The Z ring consists of curved filaments lying in the plane of the plasma membrane but oriented perpendicular to the long axis of the cell. The constriction of these filaments appears to be driven by the hydrolysis of GTP, which causes a bending in the FtsZ subunit (Li et al., 2013). These events, occurring at a midpoint normal to the longitudinal axis of the filament, generate a curvature that powers the constriction force. It is important to note that FtsZ-generated constrictions and divisions occur commonly in eukaryotic cells, specifically in mitochondria and plastids, organelles that are derived from endosymbiosis (Osteryoung and Pyke, 2014). The early actin-related proteins also take part in activities in prokaryotic cells that are manifestly cytoskeletal. For example, MreB contributes to cell shape formation, where it assists in the formation of the peptidoglycan cell wall (Carballido-Lopez, 2006). Another actin-related protein, ParM, spans the length of an E. coli cell and appears to participate in the separation of genetic elements (plasmids; Garner et al., 2004; Bharat et al., 2015). These polarized ParM filaments grow but then can undergo rapid shortening (dynamic instability; Garner et al., 2004). During a growth phase, the plasmids, which are attached at their ends by FtsA, are maximally pushed apart. Thus, when the FtsZ ring completes cytokinesis, these plasmids, which are maximally separated by ParM, will be segregated to the daughter cells. Despite the clear similarity between prokaryotic and eukaryotic cytoskeletons, and despite the fact that they are involved in operations that are similar, the specific mechanisms by which these activities are achieved are different. For example, thus far, none of the canonical actin- or tubulin-binding proteins has been identified in the prokaryotic cytoskeleton, including notably the motor proteins (i.e. myosin, kinesin, and dynein). Thus, motility or cell shape changes induced in prokaryotes by their cytoskeleton are caused by polymerization/depolymerization, lateral association of the elements themselves, or nucleotide-dependent changes. While, presumably, there are ancillary proteins involved, to the best of current knowledge, they do not fall into the families of actin- and tubulin-binding proteins well known in studies of the eukaryotic cytoskeleton (Wickstead and Gull, 2011). Calcium Regulation of the Prokaryotic Cytoskeleton With regard to regulating the cytoskeleton, calcium seems far less involved in prokaryotes compared with eukaryotes. Nevertheless, calcium has been implicated as a potential general regulator in chemotaxis and motility in E. coli and Bacillus subtilis cells (Tisa and Adler, 1995; Tisa et al., 2000). Thus, agents that repel bacteria cause a spike in calcium, which then causes the cell to tumble and change direction. However, cells challenged with an attractant maintain their basal level of calcium, with a concomitant suppression of tumbling. Calcium also affects the polymerization of FtsZ (Chatterjee and Chakrabarti, 2014). In E. coli, the effect requires millimolar levels of calcium and thus is not likely physiological. However, in Vibrio cholerae, an enhancement of FtsZ polymerization starts at 0.4 µm and may emerge as an in vivo regulatory mechanism (Chatterjee and Chakrabarti, 2014). Comparing with studies on eukaryotic tubulin, I note that the ability of calcium to stimulate the polymerization of FtsZ is nearly the opposite of its effect on eukaryotic tubulin, where 0.6 µm calcium initiates depolymerization (Weisenberg, 1972). Therefore, it appears that some aspects of prokaryotic motility are regulated by calcium; future studies may uncover more examples. Cytoskeletal Changes in the Evolution of the Eukaryotic Cell The evolution of the eukaryotic cell, with its marked increase in size over the prokaryotic cell, with its elaboration on internal membranes and organelles, and with its ability to phagocytose and endocytose, demanded substantial modifications in its cytoskeleton, the cellular component best suited to support such a significant change in cell development and organization (Yutin et al., 2009). Among transitional organisms, the recent report by Spang et al. (2015) provides evidence for an archean organism (Lokiarchaeum) that possesses several eukaryotic signature proteins, including notable actin, which bears considerable similarity to that of eukaryotic cells. In addition, they note the presence of proteins with gelsolin-like domains, with the implication that the villin/gelsolin family of proteins might be present (Spang et al., 2015). This is interesting because, as I will note in detail below, some members of the villin/gelsolin family are calcium regulated and play a pivotal role in controlling actin dynamics in plant cells. However, caution is warranted until more work is done. Even in primitive eukaryotic genera such as Giardia, none of the canonical actin-binding proteins has been identified, despite that fact that the organism has actin and produces obvious filaments that bear a structural relationship to the typical actin filaments of multicellular organisms (Paredez et al., 2011). The evolution of the eukaryotic cell brought with it a marked change in the extent of the cytoskeleton. With large size, diffusion becomes limiting, and thus ways to transport macromolecules and organelles become necessary. A good example of an enlarged eukaryotic cell is found in Nitella, in which the internode cells are several centimeters long and within which the cytoplasm exhibits extremely fast cytoplasmic streaming (approximately 70 µm s−1; Hepler and Palevitz, 1974; Shimmen and Yokota, 1994; Verchot-Lubicz and Goldstein, 2010). Very long bundles of actin, bound to the immobile chloroplasts in the stationary ectoplasm, extend the length of the cell (Kersey and Wessells, 1976). Myosin XI, the fastest myosin examined thus far, drives the circulatory streaming pattern (Kashiyama et al., 2000; Tominaga et al., 2003). The radiation of cytoskeletal structures accompanying the evolution of the eukaryotic cell occurred with a remarkable conservation of the cytoskeleton itself. In prokaryotes, there are several actin analogs that differ not only from eukaryotic actin but substantially from one another. By contrast, eukaryotic actins are highly conserved (Meagher et al., 1999; McCurdy et al., 2001; Galkin et al., 2002; Yutin et al., 2009); between birds and mammals, the actin sequence conservation is nearly 100%, and between some widely spaced organisms such as yeast and mammals, the sequence conservation is approximately 90%. The strength of this conservation was brought home to us in a study spearheaded by Barry Palevitz, who was then a postdoctoral fellow in my laboratory. We were attempting to determine the identity of filaments in the alga Nitella using the binding of heavy meromyosin from rabbit skeletal muscle as the assay. Strikingly, the results showed that muscle myosin bound to the filaments of Nitella, producing the well-known arrowhead pattern (Palevitz et al., 1974). The binding pattern plus the further observation that the heavy meromyosin arrowheads were removed with the addition of ATP provided convincing evidence that the filaments were actin (for a historical account, see Dietrich, 2015). From an evolutionary point of view, these studies indicated that there must be a considerable degree of conservation in plant actin relative to its mammalian counterpart. Similarly, eukaryotic tubulin is also highly conserved between different taxa. Although demonstrating cross-phylum conservation of tubulin was not an objective of mine, research in my laboratory nevertheless benefited from this fact. Before the advent of expressed protein markers, we microinjected Tradescantia stamen hair cells with fluorescently labeled pig brain tubulin and were able to see that it incorporated into the plant MT arrays, including the preprophase band, the spindle apparatus, and the phragmoplast (Zhang et al., 1990a). These observations underscore the depth of tubulin conservation. The extraordinary conservation of actin and tubulin likely reflects the need to interact with multiple binding proteins, which themselves are also quite well conserved (Meagher et al., 1999; McCurdy et al., 2001; Yutin et al., 2009). Any modification in the actin or tubulin sequence itself that compromises its association with these several binding proteins may be harmful in ways that ensure its negative selection. By comparison, the apparent lack of conservation in prokaryotic actin and tubulin, perhaps somewhat indirectly, suggests that these organisms do not possess a cohort of specific binding proteins that regulate their function. Although future work could turn up contrary evidence, at the moment, there appears to be a substantial difference in how prokaryotic and eukaryotic cytoskeletons are regulated. CALCIUM AND PROTON REGULATION OF ACTOMYOSIN Calcium Regulation of Myosin Earlier, I made reference to the well-known role of calcium in the control of muscle contraction. In plants, calcium also plays a central role in the regulation of intracellular motility; cytoplasmic streaming, which occurs in nearly all plant cells, is permitted by basal levels (approximately 0.1 µm) and inhibited by elevated levels (approximately 1 µm) of calcium. In rapidly streaming characean internode cells, elicitation of an action potential causes streaming to abruptly stop, in a process that is reversible (Tazawa and Kishimoto, 1968). Studies in the 1960s using ion replacement methods both on Nitella internode cells and those of angiosperms directed attention to calcium, rather than potassium or magnesium, as a potential streaming regulator (for review, see Hepler, 2005). The landmark study by Williamson and Ashley (1982), using internode cells that had been microinjected with the calcium-sensitive photoprotein aequorin, was pivotal and conclusive; it showed a sharp and significant rise in the intracellular calcium concentration virtually simultaneously with the elicitation of the action potential. This study, quickly confirmed by Kikuyama and Tazawa (1983), provided compelling evidence that the rise in calcium concentration from submicromolar to several micromolar inhibited cytoplasmic streaming. More recent studies have provided insight about how this inhibition is achieved. First, I note that, in plants, the molecule involved in cytoplasmic streaming is myosin XI and that it bears similarity to myosin V of mammals and yeast, which is also calcium regulated. Also, myosin XI, at least from tobacco (Nicotiana tabacum), is a processive motor that walks along the actin filament in 35-nm steps (Tominaga et al., 2003). Yokota et al. (1999) isolated a myosin from lily (Lilium longiflorum) pollen tubes that showed motility as well as F-actin-stimulated ATPase activity in basal levels of calcium but was inhibited by calcium concentrations above 1 µm. That study also identified a myosin-associated peptide, shown to be calmodulin, that dissociated in the presence of elevated calcium. In further studies, Tominaga et al. (2012), using an in vitro motility assay in which fluorescently labeled actin filaments were allowed to move over attached myosin, revealed that, in the presence of elevated calcium, the step size became reduced, resulting from the calcium-dependent detachments of the calmodulin light chains. In this example, there are six calmodulin light chains per myosin and they bind to the so-called IQ domain on the neck region, where IQ refers to the first two amino acids (commonly Ile and Gln). When it binds calcium, the ensuing shape change causes calmodulin to detach from the neck region of the myosin. These detachments appear to progressively disable the motor function of myosin, first leading to a shortened power stroke but eventually causing an inhibition of streaming (Tominaga et al., 2012; Tominaga and Ito, 2015). These changes are reversible, so that when the excess calcium is sequestered, calmodulin dissociates from calcium and rebinds the IQ domains on the neck region, restoring the motile activity of the myosin. While the main focus on myosin XI has been on its role in generating cytoplasmic streaming, it is also important to note that this motor is essential for tip growth in protonemal cells of Physcomitrella patens by a mechanism that is independent of streaming. Normally growing protonemal cells do not exhibit streaming, showing only slow saltatory motion of organelles. However, when the two myosin XI genes are silenced, tip growth is inhibited (Vidali et al., 2010). Localization studies indicate that both myosin and actin aggregate in the cell apex (Vidali et al., 2010) and further that myosin fluctuates in amount, quite possibly controlling the organization of actin (Furt et al., 2013). These are relatively recent results and require more studies to resolve the regulatory mechanisms. Nevertheless, given the presence of the well-established calmodulin-binding sites (the IQ domains) on the neck region of myosin XI, it seems likely that calcium will be involved, possibly in controlling the interaction between myosin and actin and/or in regulating how this complex controls protonemal growth. To the extent that myosin XI of plants resembles myosin V of yeast and animals, there might be additional ways in which calcium can modulate motor activity. Studies of myosin Va draw attention to the globular tail domain, showing that its position within the three-dimensional scheme of the protein can become a powerful regulator of myosin ATPase activity (Krementsov et al., 2004; Li et al., 2008; Donovan and Bretscher, 2015). At very low calcium concentrations, the activity of myosin Va is blocked, apparently because, at these low levels of calcium, the molecule folds such that the myosin head binds to the tail, causing a loss in ATPase activity (Krementsov et al., 2004; Donovan and Bretscher, 2015). Therefore, somewhat paradoxically, calcium at the physiological level (0.1 µm) is needed to unfold and activate myosin Va ATPase, whereas when the concentration is increased above 1 µm, it inhibits myosin. While it is puzzling how the cell could generate subphysiological concentrations of calcium, we must be careful not to extrapolate too far from in vitro data about what is occurring in vivo. For example, there may be cofactors present in the cell that change the sensitivity to calcium in ways that also modulate the head-to-tail binding activity. The important point, however, is that, similar to myosin Va, myosin XI from both Arabidopsis and tobacco appears to possess the head-to-tail binding that inhibits its ATPase activity (Li and Nebenführ, 2007; Avisar et al., 2012). While the focus above has been on myosin XI, plants possess a second myosin, designated myosin VIII. Although less well studied than myosin XI, important roles for myosin VIII are suggested from studies showing its association with the expanding cell plate and with the region in the cell cortex to which the cell plate will fuse (Wu and Bezanilla, 2014). A myosin (Radford and White, 1998), likely myosin VIII (Reichelt et al., 1999), also localizes to plasmodesmata, suggesting that it may function in intercellular transport. Given that myosin VIII is also similar to myosin V and that it contains four calmodulin-binding IQ regions (Knight and Kendrick-Jones, 1993), it is reasonable to suggest that it too is regulated by calcium. Calcium Regulation of Profilin Profilin is a small (12–15 kD) but abundant protein that binds G-actin or unpolymerized actin and plays an important role in controlling the polymerization of F-actin (Vidali and Hepler, 2001). In particular, the profilin/G-actin complex, when additionally bound with ATP, provides subunits to the barbed or growing end of an actin filament and thus contributes to the elongation of that filament. Given that a significant percentage of actin in plants is present in the depolymerized state (Li et al., 2015), it becomes important to understand how this condition is regulated. It is reasonable to ask, therefore, why doesn’t all the G-actin form into F-actin, given the plentiful supply of ATP? At least one part of the explanation involves calcium. Kovar et al. (2000) show that calcium concentrations of 1 µm or higher, with a saturation at 5 µm, effectively sequester the profilin/G-actin complex and prevent it from undergoing polymerization. The basis for this phenomenon might have more to do with the binding of calcium to actin than to profilin. Actin, at least from animal sources, is known to have a high-affinity site that is occupied by either calcium or magnesium as well as some low-affinity sites (Carlier et al., 1986). When bound with calcium, polymerization is blocked, whereas when bound with magnesium, especially at the low-affinity sites, polymerization is promoted (Carlier et al., 1986). Calcium, under these conditions, might also affect the profilin/actin conformation in a way that renders the complex unfavorable for polymerization (Porta and Borgstahl, 2012). It follows, therefore, in regions of elevated calcium (e.g. the apex of the pollen tube or root hair), that the local ionic conditions will prevent polymerization. Supporting evidence comes from Snowman et al. (2002), who show that calcium, working through the profilin/actin complex, contributes to the depolymerization of actin during a self-incompatibility reaction in poppy (Papaver rhoeas) pollen. However, the authors are quick to note that the profilin/actin/calcium complex can only account for part of the depolymerization of F-actin; other factors are likely involved (Snowman et al., 2002). Nevertheless, these data help us understand why there is a lack of F-actin in regions of high calcium such as the tip of pollen tubes and root hairs (to be discussed later). Calcium Regulation of Villin/Gelsolin Several years ago, Kohno and Shimmen (1987) provided evidence for calcium fragmentation of actin MFs in lily pollen tubes, leading to speculation that a calcium-sensitive actin-binding protein might be involved. Subsequently, two actin-binding proteins were identified, one at 135 kD and the second at 115 kD, which were shown to be homologs of villin (Vidali et al., 1999; Yokota et al., 2003). Although these proteins were initially isolated from pollen, villin has been shown to be present in many plant species and cell types (Klahre et al., 2000; Bao et al., 2012). These observations raised an awareness concerning its ionic regulation, because villin from animal sources (e.g. mammalian intestinal epithelium) typically contains six gelsolin repeats that are well known for their sensitivity to calcium. Initially, it had been shown that the lily 135-kD villin bound to actin and appeared to participate in the polymerization and bundling of actin MFs. Upon further analysis, it became apparent that the presence of calcium and calmodulin, in the range of 1 µm, reduced this binding and promoted depolymerization (Yokota et al., 2000). More recently, it has become apparent that plant villins, like their animal counterparts, are extremely sensitive to calcium without added calmodulin. Thus, they bind and bundle MFs at 0.1 µm calcium but fragment them at 1 µm calcium (Qu et al., 2014; Huang et al., 2015). At the elevated level of calcium, they may also cap the barbed end, preventing plus end assembly. Structure analysis indicates that, in low calcium (0.1 µm), the six segments of gelsolin form a compact configuration that sterically prevents certain interactions with actin (Burtnick et al., 1997). However, in elevated calcium (1 µm), the N and C termini of the gelsolin complex consisting of the six gelsolin segments, which heretofore were joined, are now released, opening up the gelsolin complex and allowing interactions with actin (Burtnick et al., 1997). In total, there are eight calcium-binding sites on gelsolin, which are divided into two categories (Choe et al., 2002). The first category, called type 1, of which there are two such sites, participate in a shared coordination of calcium between gelsolin and actin. The second category, called type 2, of which there are six (i.e. one on each gelsolin segment), is fully contained within gelsolin. When the calcium concentration increases, the type 2 sites, perhaps especially that on the sixth gelsolin segment (Choe et al., 2002), cause structural rearrangements that promote the binding between gelsolin and actin and further lead to the fragmentation of actin (Burtnick et al., 1997; Choe et al., 2002). A more systematic analysis of five members of the Arabidopsis villin family shows that villin-5, which is preferentially expressed in pollen, bundles and protects actin filaments from depolymerization in low concentrations of calcium (10 nm), possibly by promoting lateral binding between adjacent filaments (Zhang et al., 2010). However, at 10 µm calcium, it stimulates severing of F-actin and facilitates barbed end capping to prevent new polymerization. Similar results have been obtained with villin-3 (Khurana et al., 2010), villin-2 (Bao et al., 2012), and villin-4 (Zhang et al., 2011). Of the four calcium-sensitive villins in Arabidopsis, villin-2 appears to be the most sensitive, with statistically significant severing at only 0.1 µm calcium (Bao et al., 2012). However, not all villins display calcium-sensitive severing; parallel studies on villin-1 reveal that, while it stabilizes actin filaments, it does not nucleate, sever, or cap F-actin, being quite insensitive to the calcium concentration (Khurana et al., 2010). Additionally, these authors show that villin-1, while promoting F-actin bundling, fails to protect the bundles against the severing action of villin-3 in the presence of elevated calcium (1–10 µm; Khurana et al., 2010). In addition to villin, smaller members of that family of proteins, including notably gelsolin, have been identified in plants. Xiang et al. (2007) isolated a particularly small (29-kD) actin-binding protein in lily that contains only two of the gelsolin repeats (G-1 and G-2). But like its larger family members, it proves to be a calcium-sensitive protein that fragments F-actin and caps the plus ends (Xiang et al., 2007). Yet another member of this family of proteins is designated PrABP-80, which was isolated from field poppy pollen (Huang et al., 2004). The protein contains peptides homologous to villin, but the lack of a head piece, together with its inability to bundle F-actin, identifies PrABP-80 as a plant gelsolin. It too exhibits calcium-dependent severing of F-actin as well as plus end capping. Calcium Regulation of MT-Associated Proteins That Also Bind Actin At least two proteins, which were originally identified as plant-specific microtubule-associated proteins (MAPs; Hamada, 2014), have been found to bind MFs and to exhibit calcium-sensitive fragmentation of F-actin. The first is MAP18, which in pollen tubes is an F-actin-binding and -severing protein that is activated by an increase in intracellular calcium (Zhu et al., 2013). The second is MICROTUBULE-DESTABILIZING PROTEIN25 (MDP25), which again influences MT behavior (see below) but is also recognized as a powerful modulator of actin MFs (Qin et al., 2014). Like MAP18, MDP25 fragments F-actin in a calcium-sensitive manner. Somewhat curiously, mutants in pollen lacking the wild-type gene grow significantly faster than the wild type; however, they are less successful in causing fertilization (Qin et al., 2014). Also, the mutants, when compared with the wild type, show more prominent arrays of actin in the subapical region of the pollen tube. Structural studies further reveal that MDP25, and possibly MAP18, are plasma membrane-localized proteins that detach and move into the cytosol in the presence of elevated calcium (Li et al., 2011). Calcium and Proton Regulation of Actin-Depolymerizing Factor and LIM Domain Proteins ACTIN-DEPOLYMERIZING FACTOR (ADF) is a small (15-kD) actin-binding protein that plays a prime role in the control of actin dynamics and is essential for cell growth and development (Dong et al., 2001; Allwood et al., 2002; Chen et al., 2002; Augustine et al., 2008; Bou Daher et al., 2011). Studies on pollen tubes identified ADF as an abundant actin-binding protein in tobacco (Chen et al., 2002), lily (Allwood et al., 2002), and Arabidopsis (Bou Daher et al., 2011). In tobacco, overexpression analysis indicates that pollen tube growth is inhibited by elevated levels of ADF (Chen et al., 2002). Localization data indicate that, when expressed at only a moderate level, ADF appears to preferentially stain a meshwork of actin close to the tip of the pollen tube. Because ADF is recognized as being regulated by the concentration of protons, Chen et al. (2002) point out that its localization is remarkably close to the position of the alkaline band in the pollen tube. The study in lily, while emphasizing the importance of ADF for actin remodeling, also provides evidence that a related protein, called ACTIN-INTERACTING PROTEIN (AIP), plays a crucial role when working together with ADF. Also in lily, more recent data indicate that both ADF and AIP localize to a subapical region occupied by both the alkaline band and the cortical actin fringe (Lovy-Wheeler et al., 2006). These two proteins also participate in tip growth in protonemata of the moss P. patens (Augustine et al., 2011). ADF/AIP thus stimulates actin severing and the production of new plus ends, which then become focal points for new actin filament formation and growth. That ADF is regulated by the proton concentration appears well established in both plants and animals (Yonezawa et al., 1985; Carlier et al., 1997; Bernstein et al., 2000; Bernstein and Bamburg, 2004). In plant cells as the pH increases above pH 7, ADF becomes increasingly more effective in severing and promoting actin depolymerization at the minus or pointed end of the actin filament while supporting growth at the plus end (Carlier et al., 1997; Gungabissoon et al., 1998; Allwood et al., 2002; Chen et al., 2002). In addition to protons, calcium might serve as a regulator of ADF activity, where it participates in the control of phosphorylation, with the Ser residue located at position 6 being the target. In ADFs derived from both tobacco (NtADF1; Chen et al., 2002) and maize (Zea mays; ZmADF3; Smertenko et al., 1998), the phosphorylated state has been found to be inactive, whereas the dephosphorylated state promotes F-actin turnover. From studies of calcium-enhanced phosphorylation of ZmADF3, Smertenko et al. (1998) suggest that this reaction is regulated by a calcium-dependent protein kinase. Consistently, a partially purified fraction from bean (Phaseolus vulgaris) suspension culture cells with phosphorylation activity is enriched in calcium-dependent protein kinases, and this phosphorylation activity is blocked by antibodies to calcium-dependent protein kinase (Allwood et al., 2001). However, such regulation is not ubiquitous; for example, ADF from lily pollen (LlADF1), which retains the conserved Ser at position 6, is not phosphorylated (Allwood et al., 2002). Further complication comes from studies of P. patens, which show that neither a phosphomimetic nor an unphosphorylatable form of ADF supports normal growth; these data suggest that a balance between phosphorylation and dephosphorylation is required to achieve normal growth (Augustine et al., 2008). A final class of actin-associated proteins to be considered here are the LIM domain proteins, which are suggested to play a role in bundling or stabilizing F-actin (Thomas et al., 2009). Studies on a LIM domain protein from lily pollen tubes (LlLIM1) indicate that low calcium (170 nm) and slightly acidic pH (optimum at 6.25) support the stabilizing and bundling properties of this actin-binding protein (Wang et al., 2008). When conditions deviate from these levels (i.e. the calcium concentration increases and the proton concentration decreases), the stability of F-actin is reduced. Comparing protons and calcium as regulators, Wang et al. (2008) argue that protons are more important. CALCIUM REGULATION OF MTs The study by Weisenberg (1972), showing that MTs depolymerized when the calcium concentration exceeds 0.6 µm, sparked an intense interest in the mechanism of action of calcium. In a subsequent breakthrough, Marcum et al. (1978) identified a calcium-dependent regulatory protein, later known as calmodulin. Building support for the idea that calmodulin is the intermediary factor that binds calcium and then stimulates the depolymerization of MTs, they showed that calmodulin plus 10 µm calcium inhibited MT assembly in vitro, whereas without calmodulin the same level of calcium had little effect (Marcum et al., 1978). These authors also showed that low calcium (less than 1 µm) in the presence of calmodulin failed to inhibit MT polymerization. Thus, to depolymerize MTs, both calmodulin and elevated calcium (e.g. 1 µm) are necessary. While these results seem inconsistent with the earlier report showing that only calcium was needed to depolymerize MTs (Weisenberg, 1972), it must be appreciated, especially given the ubiquity of calmodulin, that some of this protein was likely already present in these early in vitro preparations, thereby rendering MTs sensitive to the experimental changes in calcium. Because tubulin, like actin, is highly conserved, it will come as no surprise that plant MTs are also sensitive to calcium and calmodulin. In relatively early work, Cyr and coworkers showed that MTs in lysed carrot (Daucus carota) cell protoplasts were markedly destabilized by calcium plus calmodulin but not in either calcium or calmodulin alone (Cyr, 1991; Fisher and Cyr, 1993; Durso and Cyr, 1994). Given the ever-present cortical MTs and their role in controlling cell shape and growth, and given the ability of various growth-modulating stimuli to generate calcium transients, it is reasonable to expect multiple arrays of calcium response elements and signaling pathways that connect these events. I draw attention to one example, namely the ability of roots to respond to mechanical stimuli (i.e. touch) and change their direction of growth. In earlier work, Lee et al. (2005) had shown that mechanical perturbation, through an induced calcium spike (Knight et al., 1991), up-regulated several genes, including notably those for calmodulin and calmodulin-like proteins. More recently, Wang et al. (2011) established a connection between a calmodulin-like protein (CML24) and the organization and orientation of cortical MTs. Plants harboring mutations in CML24 (cml24-2 and cml24-4) showed reduced root length and altered orientation of MTs in epidermal cells. Nevertheless, observations like these are not straightforward to interpret, because calmodulin and its relatives also regulate other activities. For example, CML24 itself binds to the IQ region of myosin VIII (Abu-Abied et al., 2006). These early reports may be only the tip of the iceberg; future work will likely uncover many examples in which calcium, working through appropriate calcium-binding proteins, will affect MTs and MFs in ways that impact plant growth and development. In addition to calmodulin and related proteins, there are others involved in linking calcium to the control of MT organization and stability; one is MAP18 (Wang et al., 2007). I made reference above to this protein in its role as an actin-binding protein. Although not initially identified as a calcium-binding protein, it is recognized as being the same as PCaP2 and similar to PCaP1, which are calcium-binding proteins shown to be associated with the plasma membrane in Arabidopsis (Kato et al., 2010, 2013; Hamada, 2014). PCaP1, which has been renamed MDP25, is a negative regulator, facilitating the destabilization of cortical MTs in the presence of elevated calcium (0.5 µm; Li et al., 2011; Qin et al., 2012). Because cortical MTs participate in the process of cellulose orientation and deposition, it seems likely that MDP25 plays a role in that process. Specifically, Li et al. (2011) draw attention to in vivo studies showing that blue light causes a rapid reduction in cell elongation in cucumber (Cucumis sativus) hypocotyls while simultaneously inducing an increase in the intracellular calcium (Shinkle and Jones, 1988; Baum et al., 1999). While it can be appreciated that these factors are likely important players, more work is needed to establish the functional relationship between calcium, cortical MT stability/organization, and cell elongation. Having noted that MAP18 and MDP25 bind to both MTs and MFs, it is important to recognize that the colocalization and coordination of these two cytoskeletal elements may be crucial to several aspects of plant growth and development (Collings, 2008). Indeed, even before these biochemical and physiological studies had emerged, observations from electron micrographs of different plant cells had shown that cortical MTs often possess coaligned MFs (Hardham et al., 1980; Lancelle et al., 1987; Ding et al., 1991a). Although these initial observations were confined to the cortical cytoskeleton, close associations between actin and tubulin have been observed in the preprophase band (Ding et al., 1991b) and the phragmoplast (Kakimoto and Shibaoka, 1988). That the MFs adjacent to MTs are actin has been confirmed, at least in pollen tubes, by immunogold labeling with anti-actin antibody (Lancelle and Hepler, 1991). In addition to regulating the assembly and structure of MTs in plant cells, calcium contributes to the activity of at least some of the kinesins. The best studied example thus far is KCBP (Reddy and Day, 2011; Ganguly and Dixit, 2013). Reddy and coworkers isolated KCBP from Arabidopsis, showing that it is a minus end-directed motor protein and a member of the class 14 kinesins, a group that is particularly large in plants (Reddy et al., 1996a, 1996b; Narasimhulu et al., 1997; Song et al., 1997). A similar kinesin motor protein emerged from the studies of Oppenheimer et al. (1997) on the zwichel mutant in Arabidopsis, which has impaired trichome development. Reddy and coworkers have further shown that KCBP possesses a calmodulin-binding domain, which together with calcium is involved in the regulation of the motor. Thus, in the absence of calcium, KCBP binds and moves along MTs, whereas in the presence of calcium, the MT-binding affinity as well as ATPase activity decline. More recent work indicates that a calmodulin-binding helix at the C terminus of KCBP is responsible for the negative regulatory activity. When bound with calcium/calmodulin, the helix comes to reside between the motor and the MT, thus blocking motor activity (Vinogradova et al., 2008). Interestingly, KCBP has yet another protein, which has been named KCBP-INTERACTING CALCIUM BINDING PROTEIN (KIC), that contributes to the calcium signal (Reddy et al., 2004). When KIC binds calcium, it too is able to deliver the signal to KCBP and negatively regulate this motor protein. In this instance, KIC is thought to work as an allosteric trap (Vinogradova et al., 2009); when bound to calcium, it sterically prevents the motor from binding to an MT. KCBP associates with different MT arrays, and under different circumstances. For example, the protein is up-regulated during division and seen to be associated with the preprophase band, the mitotic apparatus, and the phragmoplast (Bowser and Reddy, 1997; Smirnova et al., 1998; Dymek et al., 2006; Buschmann et al., 2015). In addition, KCBP localizes with cortical MTs in cotton (Gossypium hirsutum) fibers (Preuss et al., 2003) and cytoplasmic MTs in elongating spruce (Picea abies) pollen tubes (Lazzaro et al., 2013). Recent studies have identified a KCBP family member in P. patens, where it appears to dimerize and become a processive minus end MT motor (Jonsson et al., 2015). The authors suggest that this activity may compensate for the lack of cytoplasmic dynein in P. patens and higher plants (Jonsson et al., 2015). I also draw attention to recently published work showing that KCBP interacts with actin MFs as well as MTs (Tian et al., 2015). It has been known for some years that both MTs and MFs, as well as KCBP (Oppenheimer et al., 1997), participate in the control of trichome development. It also has been known that KCBP possesses a MYOSIN TAIL HOMOLOGY DOMAIN4 (MyTH4; Abdel-Ghany et al., 2005), and while it has been thought that this might participate in binding to actin, clear evidence for this activity had not been forthcoming. Tian et al. (2015) now provide evidence that KCBP may be the link between MTs and MFs. First, it is important to note that the N terminus of KCBP includes both MyTH4 and a domain known primarily in the animal literature for its ability to bind to a complex of actin-associated proteins, which includes band 4.1, ezrin, radixin, and moesin, called FERM (Kerber and Cheney, 2011). Using truncated versions of full-length KCBP, Tian et al. (2015) report that the MyTH4 domain binds MTs while the FERM domain binds F-actin. Of course, further work is needed to resolve the functional significance of these interactions. Nevertheless, these results add to our widening appreciation of the likely importance of the interaction between MTs and MFs in the control of various developmental processes (Collings, 2008). Ideas about the specific function of KCBP have been gleaned from studies involving the microinjection of an affinity-purified antibody that constitutively activates the protein (Narasimhulu et al., 1997; Narasimhulu and Reddy, 1998). This particular antibody, which was raised against a 23-amino acid peptide that contains the calcium/calmodulin-binding domain, interferes with calcium/calmodulin regulation but does not affect MT binding; its presence thus activates KCBP motor activity. Injection of this antibody into dividing stamen hair cells of Tradescantia yields a series of results that depend on the mitotic stage at the time of injection (Vos et al., 2000). When injected during late prophase, the constitutive activation of KCBP induces the breakdown of the nuclear envelope in 2 to 10 min but then arrests cells in metaphase. However, injection of cells in late metaphase did not cause arrest, and moreover, these cells progressed normally into anaphase. Nevertheless, these injected late metaphase cells often failed to form a phragmoplast or complete cytokinesis. At the moment, we do not have a good explanation for these diverse activities, except to say that KCBP, through continuous fine-tuning of its activity, possibly by local calcium gradients, plays a pivotal role in the formation and function of both the mitotic and cytokinetic apparatuses. More recently, Lazzaro et al. (2013) have microinjected the antibody into growing pollen tubes of spruce, observing that MTs become bundled and that the vacuole was repositioned. Cytoplasmic streaming first slows before stopping completely, together with the inhibition of cell elongation. These phenomena might also be influenced by the local ion conditions, where spatially defined gradients of calcium, together with activated calmodulin, are simply overridden by microinjection of the activating antibody. While KCBP emerges as the best studied calcium-sensitive MT motor protein in plants, there are likely to be others. For example, the calmodulin-binding protein IQD1, recently identified in Arabidopsis, has been found to interact with a KINESIN LIGHT CHAIN-RELATED PROTEIN1 (KLCR1; Bürstenbinder et al., 2013). Further structural studies revealed that GFP-tagged IQD1 localizes to MTs, suggesting that IQD1 recruits KLCR1 and calmodulin to MTs. Further work is warranted, but it seems increasingly clear that calcium and calmodulin will be involved with the activity of plant kinesins other than just KCBP. Finally, it seems likely that additional examples of calcium/calmodulin regulation will emerge for other plant kinesins. Among the class 14 kinesins, there are several that possess calponin homology domains (for review, see Richardson et al., 2006; Collings, 2008; Reddy and Day, 2011; Schneider and Persson, 2015). These kinesins are interesting for two reasons: first, they are often noted for binding actin and thus could link MTs and MFs (Wills et al., 1994; Schneider and Persson, 2015); and second, the calponin homology domain can be involved in binding to calmodulin (Wills et al., 1994). Should the latter be shown to be present, then there is a strong likelihood that calcium modulation will be found. INTEGRATED ION/CYTOSKELETAL ACTIVITIES In the previous sections, I note several different examples in which either actin MFs and/or MTs are controlled in their polymerization or structural organization by changes in calcium, protons, or both. In this section, I attempt to integrate the ion and cytoskeletal activities and to show how these components work together and contribute to our understanding of plant cell growth and development. I will discuss two examples: the first focuses on tip growth, especially in pollen tubes, and the second on cell division. Tip Growth in Pollen Tubes One of the best examples showing an interaction between ion fluxes and gradients and the structure and activity of the cytoskeleton occurs in the apex of tip-growing cells, especially the pollen tube (Cole and Fowler, 2006; Hepler et al., 2006; Campanoni and Blatt, 2007; Krichevsky et al., 2007; Cheung and Wu, 2008; Qin and Yang, 2011; Rounds and Bezanilla, 2013; Cai et al., 2015; Hepler and Winship, 2015). It has been known for over 50 years that calcium is essential for pollen tube growth (Brewbaker and Kwack, 1963; Steinhorst and Kudla, 2013). It is also known that pollen tubes grow best in an acidic environment (Holdaway-Clarke et al., 2003). It is widely appreciated that pollen tubes possess a striking gradient of free calcium focused at the extreme apex of the tube (Fig. 1A; Rathore et al., 1991; Miller et al., 1992; Hepler et al., 2006). They also possess a proton gradient, in which the extreme tip exhibits a slightly acidic domain, with an alkaline band being situated a few micrometers back from the tip (Fig. 1B; Feijó et al., 1999). Not only do these standing gradients persist during growth, but in many instances they oscillate in magnitude, with the same period as the growth rate but with differing phase relationships. Thus, the increase in calcium slightly follows the increase in growth rate (Messerli et al., 2000; Cárdenas et al., 2008), as does maximal acidity at the tip (Lovy-Wheeler et al., 2006), whereas the increase in the alkaline band precedes the increase in growth rate (Lovy-Wheeler et al., 2006). Figure 1. Open in new tabDownload slide Lily pollen tubes, which have been treated with different reporters or stains, show the distribution of free calcium (A), pH (B), F-actin (C), and the actin-binding protein ADF (D). A, Microinjection of the calcium-sensitive dye Fura-2-dextran allows one to image the distribution of free calcium in a living lily pollen tube. Ratiometric analysis reveals that the calcium is maximal at the extreme apex, where the concentrations are 1 to 10 µm. Back from the tip, the calcium concentration drops sharply, reaching the basal level of 0.15 µm within 15 to 20 µm. From Rounds et al. (2011). B, In this instance, the living pollen tube has been microinjected with BCECF-dextran, a pH-sensitive dye. The ratiometric image reveals a slightly acidic region at the extreme apex followed by a prominent alkaline band (orange/red; pH 7.5), starting a few micrometers behind the tip and extending rearward for an additional 10 to 20 µm. Thereafter, the pH approaches neutrality. From Rounds et al. (2011). C, In this image, the lily pollen tube has been preserved through rapid freeze fixation, followed by rehydration and staining with an anti-actin antibody. The resulting confocal fluorescence image reveals the striking cortical actin fringe that begins a few micrometers back from the tip and extends rearward for 5 to 10 µm. The MFs in the fringe are organized as a palisade in which the individual elements are aligned parallel to the long axis of the pollen tube. Behind the fringe, the MFs are also longitudinally oriented, but they appear to be much less dense than in the fringe. From Lovy-Wheeler et al. (2005). D, In this example, a lily pollen tube, which has been preserved through rapid freeze fixation and rehydration, has been stained with an antibody to lily ADF1. The stained region starts a few micrometers back from the apex and extends rearward 3 to 5 µm. By comparison with C, it is obvious that ADF colocalizes with the actin fringe, perhaps especially the forward edge of the fringe. Also note that ADF (D) and the actin fringe (C) colocalize with the alkaline band (B). From Lovy-Wheeler et al. (2006). Bar, 10 μm. Figure 1. Open in new tabDownload slide Lily pollen tubes, which have been treated with different reporters or stains, show the distribution of free calcium (A), pH (B), F-actin (C), and the actin-binding protein ADF (D). A, Microinjection of the calcium-sensitive dye Fura-2-dextran allows one to image the distribution of free calcium in a living lily pollen tube. Ratiometric analysis reveals that the calcium is maximal at the extreme apex, where the concentrations are 1 to 10 µm. Back from the tip, the calcium concentration drops sharply, reaching the basal level of 0.15 µm within 15 to 20 µm. From Rounds et al. (2011). B, In this instance, the living pollen tube has been microinjected with BCECF-dextran, a pH-sensitive dye. The ratiometric image reveals a slightly acidic region at the extreme apex followed by a prominent alkaline band (orange/red; pH 7.5), starting a few micrometers behind the tip and extending rearward for an additional 10 to 20 µm. Thereafter, the pH approaches neutrality. From Rounds et al. (2011). C, In this image, the lily pollen tube has been preserved through rapid freeze fixation, followed by rehydration and staining with an anti-actin antibody. The resulting confocal fluorescence image reveals the striking cortical actin fringe that begins a few micrometers back from the tip and extends rearward for 5 to 10 µm. The MFs in the fringe are organized as a palisade in which the individual elements are aligned parallel to the long axis of the pollen tube. Behind the fringe, the MFs are also longitudinally oriented, but they appear to be much less dense than in the fringe. From Lovy-Wheeler et al. (2005). D, In this example, a lily pollen tube, which has been preserved through rapid freeze fixation and rehydration, has been stained with an antibody to lily ADF1. The stained region starts a few micrometers back from the apex and extends rearward 3 to 5 µm. By comparison with C, it is obvious that ADF colocalizes with the actin fringe, perhaps especially the forward edge of the fringe. Also note that ADF (D) and the actin fringe (C) colocalize with the alkaline band (B). From Lovy-Wheeler et al. (2006). Bar, 10 μm. The position and localization of the cytoskeleton strongly reflect the corresponding position of the specific ion gradients and the pertinent binding proteins. Thus, actin MFs are generally not observed in the extreme apex of the tube, especially at the polar axis (Fig. 1C; Kroeger et al., 2009). A likely reason is that the calcium concentration at the extreme tip, which can be as high as 10 µm (Messerli et al., 2000), will activate villin/gelsolin and fragment MFs as well as cap newly exposed plus ends, thus preventing further polymerization of actin in this region (Huang et al., 2015). The high calcium will also affect the profilin/G-actin complex and further prevent polymerization (Kovar et al., 2000). Affirmation of that statement derives from the inspection of pollen tubes injected with fluorescent DNase, a probe that binds G-actin; direct imaging reveals a high concentration of G-actin in the apex of the pollen tube (Cárdenas et al., 2008). The high calcium will also activate calmodulin and inhibit several different proteins or processes, such as MT polymerization and myosin-dependent cytoplasmic streaming. These conclusions find support from the use of TA-calmodulin, a fluorescent derivative that increases its signal in the presence of activated calmodulin. Whereas total calmodulin is evenly spread throughout the pollen tube, those molecules bound with calcium occur predominantly at the tube apex (Rato et al., 2004). Finally, the high calcium will activate MAP18 and MDP25; as noted earlier, these proteins destabilize MTs and fragment MFs (Li et al., 2011; Zhu et al., 2013; Qin et al., 2014). There are thus several combinations of proteins that, in the presence of calcium, ensure that the extreme apex of the pollen tube will be mostly free of cytoskeletal polymer. A particularly intriguing cytoskeletal feature of the pollen tube apex is the cortical actin fringe, which is found a few micrometers back from the extreme apex (Fig. 1C; Lovy-Wheeler et al., 2005; Vidali et al., 2009; Dong et al., 2012; Rounds et al., 2014). Although most clearly depicted in lily pollen tubes, the cortical actin fringe has also been observed in tobacco pollen tubes, both in fixed (Lovy-Wheeler et al., 2005) and living (Vidali et al., 2009) preparations. The fringe consists of a palisade of longitudinally oriented MFs positioned close to the plasma membrane (Fig. 1C; Lovy-Wheeler et al., 2006). Because this region, although close to the apex, is nevertheless separated from it, its calcium level is lower than that at the extreme apex. At these lower calcium concentrations, villin might serve as a cross-linking and stabilizing factor to support the formation of MFs. Importantly, this region contains the alkaline band (Fig. 1B; Feijó et al., 1999), which is produced by the closely positioned plasma membrane proton ATPase (Lefebvre et al., 2005; Certal et al., 2008). Notably, this is also where some isoforms of ADF (Fig. 1D) and AIP (Chen et al., 2002; Lovy-Wheeler et al., 2006; Bou Daher et al., 2011) are localized, which because of the alkalinity will enhance the turnover of actin. However, in marked contrast to villin/gelsolin, which caps plus ends, ADF/AIP will promote the growth of new MFs from the newly exposed plus ends. Indeed, the combined colocalization ADF/AIP and the alkaline band seems likely to ensure that the cortical actin fringe remains in a state of constant renewal and growth. Deviations from these conditions, however, will lead to growth inhibition. In a recent study, Wilkins et al. (2015) show that poppy pollen tubes undergoing a self-incompatible induction of programmed cell death exhibit a rapid acidification in the apical cytoplasm, together with an abrupt inhibition of growth. The authors argue that the increased acidity down-regulates ADF and thus prevents actin turnover, which is necessary for normal growth (Wilkins et al., 2015). Several studies support the importance of the cortical actin fringe for rapid and oscillatory growth of the pollen tube. For example, treatment of the pollen tubes with low concentrations of latrunculin B (2 nm), or injection with low concentrations of profilin or DNase, do not block cytoplasmic streaming but do inhibit growth (Vidali et al., 2001). This low level of latrunculin B destroys the cortical actin fringe but has little effect on the longitudinal cables of actin in the shank of the tube (Vidali et al., 2001, 2009; Rounds et al., 2014). Also, the streaming, which under normal conditions occurs in the reverse fountain pattern, in the presence of 2 nm latrunculin B shifts to a circulatory pattern, with large plastids that heretofore had been prevented from entering the clear zone now freely flowing through the extreme apex of the tube (Vidali et al., 2001). More recently, Dong et al. (2012) have shown a strong correlation between the emergence of rapid growth and the appearance of the fringe. They further note that shorter fringes are associated with more rapid growth. Moreover, when pollen tubes undergo changes in growth direction, the actin fringe is most intense on the faster growing side (Dong et al., 2012). Most recently, Rounds et al. (2014) find, in studies in which growth has been reversibly inhibited, that the fringe reemerges together with the reinitiation of growth. From these results, we see that the apical actin fringe plays a pivotal role in the elongation of the pollen tube, in the maintenance of the apical clear zone, and likely in the generation of the reverse fountain streaming pattern. Direct inspection of the tube apex using image correlation spectroscopy and fluorescence recovery after photobleaching provides evidence that the vesicles move forward along the cortex, presumably along the MFs of the apical fringe (Bove et al., 2008; Chebli et al., 2013). While these studies and those of Zonia and Munnik (2008) suggest that vesicles are delivered to an annular region at the tube tip and not the polar axis, on balance, the evidence favors the idea that material is actually secreted maximally at or near the polar axis (Lee et al., 2008; McKenna et al., 2009; Rojas et al., 2011; Rounds et al., 2014). These ideas fit with the notion that the apical calcium gradient (Fig. 1A), while effectively destroying the cytoskeleton at the tube tip, stimulates the secretion of vesicles along the polar axis, thereby accounting for the observed elongation of the tube. Therefore, while the apical fringe of actin probably transports vesicles close to the tip, it actually drops them off in an annular zone a micrometer or two back from the apex. For this reason, I suggest that processes of diffusion take over, with the calcium gradient creating a favored spot for vesicle fusion and secretion (Fig. 1A). Thus, as vesicles immediately adjacent to the high point of the calcium gradient fuse and release their contents at the tip plasma membrane, neighboring vesicles will move into the vacated space, creating a diffusive flow from the annular region toward the tube apex. By marked contrast to the apex, in the shank of the pollen tube only 10 to 20 µm behind the apex in lily, the calcium (Pierson et al., 1996) and proton (Feijó et al., 1999) concentrations are at basal levels (approximately 0.1 µm for both; Fig. 1, A and B); therefore, both MFs and MTs will form structured arrays with less turnover. Although often ignored, it is important to emphasize that electron and fluorescence microscopy studies indicate that well-organized arrays of longitudinally oriented cortical MTs occur in the shank of the pollen tube (Lancelle et al., 1987; Lancelle and Hepler, 1992; Cheung et al., 2008). In addition, we commonly see cortical MTs coaligned with MFs in the pollen tube cortex (Lancelle et al., 1987; Pierson et al., 1989). The pollen tube thus provides a compelling example in which the ion gradients and the cytoskeleton fit closely together and where the known properties of the actin- and tubulin-binding proteins under specific ion conditions largely account for the observed cytoskeletal organization (Cheung and Wu, 2007; Hepler and Winship, 2015). These observations also underscore the concept of local ionic domains as key regulatory components in large cell systems (Berridge 2006). Cell Division (Mitosis and Cytokinesis) For several reasons, it has seemed plausible that calcium contributes to the regulation of both mitosis and cytokinesis. To begin, MTs are a central component of the mitotic and cytokinetic apparatuses. Thus, for chromosomes to move toward the spindle poles during anaphase, the kinetochore MTs must break down, in a process that might contribute to the forces that move the chromosomes (McIntosh et al., 2010). Given the remarkable sensitivity of MTs to calcium at the physiological level (Weisenberg, 1972), it becomes attractive to suggest that small increases in the calcium concentration contribute to their depolymerization. It is additionally pertinent that, even though the nuclear envelope has broken down, the mitotic/meiotic apparatus in different plant and animal cells remains surrounded by the ER (Fig. 2A; Hepler, 1980; Hepler and Wolniak, 1984; Bobinnec et al., 2003; Parry et al., 2005; Whitaker, 2006, 2008), with some elements interpenetrating the mitotic apparatus specifically along the kinetochore MTs (Fig. 2, B and C; Hepler, 1980). During anaphase, this closely positioned ER might elevate the calcium concentration locally around the kinetochore fibers, thereby driving MT depolymerization and contributing to the movement of the chromosomes to the spindle poles (Hepler and Wolniak, 1984; Whitaker, 2006). Figure 2. Open in new tabDownload slide A, This image shows an electron micrograph of a dividing barley leaf mesophyll cell in metaphase that has been fixed in glutaraldehyde and postfixed in osmium tetroxide plus potassium ferricyanide. The osmium/ferricyanide treatment markedly contrasts the ER and reveals that, while the nuclear envelope has broken down, the mitotic spindle remains surrounded by ER. The image further shows that ER accumulates at the spindle poles and that some elements extend into the spindle interior, usually along kinetochore MTs. Bar = 1 µm. From Hepler (1980). B and C, At higher magnification than in A, these images show that tubular elements of ER extend along the full length of kinetochore MTs. In B, the ER has been contrasted by postfixation with osmium/ferricyanide, whereas in C, only standard glutaraldehyde/osmium fixation has been used. Both images show the close apposition of interpenetrating ER with kinetochore MTs. C also shows some dictyosome vesicles (D). Bars = 0.5 µm. From Hepler (1980). Figure 2. Open in new tabDownload slide A, This image shows an electron micrograph of a dividing barley leaf mesophyll cell in metaphase that has been fixed in glutaraldehyde and postfixed in osmium tetroxide plus potassium ferricyanide. The osmium/ferricyanide treatment markedly contrasts the ER and reveals that, while the nuclear envelope has broken down, the mitotic spindle remains surrounded by ER. The image further shows that ER accumulates at the spindle poles and that some elements extend into the spindle interior, usually along kinetochore MTs. Bar = 1 µm. From Hepler (1980). B and C, At higher magnification than in A, these images show that tubular elements of ER extend along the full length of kinetochore MTs. In B, the ER has been contrasted by postfixation with osmium/ferricyanide, whereas in C, only standard glutaraldehyde/osmium fixation has been used. Both images show the close apposition of interpenetrating ER with kinetochore MTs. C also shows some dictyosome vesicles (D). Bars = 0.5 µm. From Hepler (1980). Supporting evidence comes from studies in which dividing stamen hair cells of Tradescantia have been microinjected with controlled amounts of calcium. By increasing the calcium concentration in an early anaphase cell from a basal level to 1 µm, it is possible to transiently increase chromosome motion 2-fold (Fig. 3A), whereas an increase to approximately 2 µm transiently slows motion (Fig. 3B; Zhang et al., 1990b). Consistently, injection of the elevated level at one end of an anaphase cell slows chromosome motion toward the proximal pole while speeding it toward the distal pole (Fig. 3C; Zhang et al., 1990b). In this experiment, the injection of calcium has likely established a diffusion gradient across the cell, with the concentration being approximately 2 µm at the proximal pole and 1 µm at the distal pole. Taken together, these results support the idea that added calcium facilitates kinetochore MT depolymerization, but this process can only be manipulated over a very limited range. That is, 1 µm enhances depolymerization and accelerates chromosome motion, whereas 2 µm is too drastic and causes excessive decay of the kinetochore fibers so that directed pole-ward motion is slowed or stopped. In support of the above interpretation, we showed subsequently, in cells preinjected with fluorescent brain tubulin to label the MTs, that injection of calcium caused a transient decline in fluorescence (Zhang et al., 1992). Injection of 2 µm calcium causes a distinct decay in the fluorescence of the spindle fibers, whereas injection of 1 µm calcium generates a lesser decline in fluorescence. From these results, it seems reasonable to suggest that chromosome motion is controlled by the local calcium concentration (Zhang et al., 1992). Figure 3. Open in new tabDownload slide Iontophoretic injection of controlled amounts of calcium during early anaphase produces a clear effect on chromosome motion. In A and B, the injection needle was inserted into the mid plane of the cell, whereas in C, it was inserted into the spindle pole. The microinjection needle contained 20 mm CaCl2 and 100 mm KCl. The magnitude of the current was then varied in order to produce the desired level of calcium. A, Application of positive current of 1 nA for 10 s, which produces an increase in calcium to approximately 1 µm, causes the chromosomes to increase their rate of motion from 1.1 to 2.1 µm min−1 for approximately 20 s. B, However, application of 2 nA for 10 s causes a brief slowing of chromosome motion. C, Injection of calcium at 2 nA for 10 s to one of the spindle poles causes a brief slowing of chromosomes to the proximal pole while simultaneously accelerating motion to the distal pole. From Zhang et al. (1990b). Figure 3. Open in new tabDownload slide Iontophoretic injection of controlled amounts of calcium during early anaphase produces a clear effect on chromosome motion. In A and B, the injection needle was inserted into the mid plane of the cell, whereas in C, it was inserted into the spindle pole. The microinjection needle contained 20 mm CaCl2 and 100 mm KCl. The magnitude of the current was then varied in order to produce the desired level of calcium. A, Application of positive current of 1 nA for 10 s, which produces an increase in calcium to approximately 1 µm, causes the chromosomes to increase their rate of motion from 1.1 to 2.1 µm min−1 for approximately 20 s. B, However, application of 2 nA for 10 s causes a brief slowing of chromosome motion. C, Injection of calcium at 2 nA for 10 s to one of the spindle poles causes a brief slowing of chromosomes to the proximal pole while simultaneously accelerating motion to the distal pole. From Zhang et al. (1990b). There is also reason to suspect that calcium plays a major role in cytokinesis. The phragmoplast, which is a complex of cytoskeletal (MTs and MFs) and membrane (ER and vesicles) elements (Staehelin and Hepler, 1996; McMichael and Bednarek, 2013), gives rise to the new cell plate that eventually separates the recently formed daughter nuclei. Both MTs and MFs are oriented perpendicular to the plane of the plate, with their respective plus ends being proximal to the plate (Kakimoto and Shibaoka, 1988; Ho et al., 2011). For MTs, at least in some examples, the plus ends overlap (Hepler and Jackson, 1968). Through processes not fully understood, during late anaphase the cell plate starts to form in the central part of the cell, like an island emerging in a sea of cytoplasm. It seems likely that the cytoskeletal elements, particularly MTs and their associated kinesin motors (Lee et al., 2001; Lee and Liu, 2013; McMichael and Bednarek, 2013), drive Golgi vesicles to the plane of the plate, where the vesicles fuse to form the new cell wall. During successive stages, the plate grows centrifugally, with new vesicles being added to the expanding edge. At the same time, or more likely in anticipation, the cytoskeletal elements form anew at the edge while decaying in the more central, mature parts of the cell plate. The placement and fusion of the expanding plate along the side wall involves complex positioning events. The place of fusion is usually forecast by the preprophase band of MTs (Pickett-Heaps and Northcote, 1966). But we also know that MFs associate with the preprophase band (Palevitz, 1987; Cleary et al., 1992) and, indeed, come to mark its edges on the cortical side wall (Sano et al., 2005). As the cell plate approaches the side wall, actin MFs make connections between elements of the expanding phragmoplast and the actin on the edges of the preprophase band site and possibly play a key role in guiding the plate (Valster and Hepler, 1997; Wu and Bezanilla, 2014). The recent work by Wu and Bezanilla (2014) provides important new information showing a coordination of MTs and MFs in the guidance process. Specifically, they provide evidence that myosin VIII binds to the plus ends of MTs. In addition, myosin VIII binds to the preprophase band site on the cell cortex. Yet further pertinent observations show that MFs are polymerized at the phragmoplast mid zone, with some MFs growing normal to the plane of the cell plate while others grow toward the cell periphery. When taken together, Wu and Bezanilla (2014) suggest that the myosin VIII/MT complex links to the preprophase band site via MFs, where the motor activity of myosin VIII, by moving along MFs, effectively reels in the expanding phragmoplast. The reason for providing the above description is to emphasize the number of potential targets for calcium action and to suggest the likelihood that it plays a role. The assembly and disassembly of both MTs and MFs, the activity of motor proteins (kinesins and myosin VIII), as well as the secretory processes of cell plate fusion are all candidates for regulation by calcium. Supporting evidence involves studies in which BAPTA [1,2-bis(o-aminophenoxy)ethane-N,N,N′,N′-tetraacetic acid] buffers of differing affinities for calcium were microinjected into stamen hair cells undergoing late anaphase and cytokinesis (Jürgens et al., 1994). The results show that all the buffers, but especially dibromo BAPTA (1.5 µm), markedly slow the process of centrifugal cell plate expansion or cause distortion and even complete dissolution of the young cell plate. These results are consistent with local calcium gradients being associated with cell plate formation. Presumably, these local gradients are controlled by the extensive elements of the ER that reside around and among the cytoskeletal elements of the phragmoplast (Hepler, 1982). Studies on dividing cells in general underscore the concept of domains for the local control of calcium (Whitaker, 2006, 2008). For example, in Drosophila embryos, convincing images show prominent elements of ER surrounding the spindle apparatus (Bobinnec et al., 2003; Parry et al., 2005, 2006); it seems plausible that these membranes locally control the uptake and release of calcium. While some imaging studies show changes in calcium, particularly associated with nuclear envelope breakdown (Parry et al., 2005) and again at the onset of anaphase (Groigno and Whitaker, 1998), overall there are relatively few data, and moreover, the calcium changes observed are small and spatially localized (Parry et al., 2005). This is particularly true in plants, where robust images of calcium transients associated with the stages of cell division are rare, even though these same methods reveal striking ion gradients when applied to pollen tubes. In stamen hair cells, my laboratory reported calcium elevations associated with anaphase in dividing cells injected with the absorbance indicator arsenazo III (Hepler and Callaham, 1987). However, these results could not be confirmed in subsequent studies using the more sensitive indicator Fura-2-dextran (P.K. Hepler, unpublished data). But that does not rule out the possible occurrence of local calcium transients. Given that calmodulin is dispersed uniformly throughout the dividing plant cell (Vos and Hepler, 1998), it would take only small, and highly localized, pulses of calcium to initiate important events such as MT depolymerization. I predict, therefore, that with newer and more sensitive probes, together with those that are targeted to certain domains, such as the ER surfaces along the kinetochore MTs or close to the spindle pole, reproducible calcium transients will at least be detected. CONCLUSION Ions, especially calcium, play a major role in the control of the structure and activity of the cytoskeleton and, thus, contribute to many aspects of plant growth and development that are dependent on the cytoskeleton. Prokaryotes evolved mechanisms that markedly lowered the calcium concentration within the cell, with the result that phosphate precipitation and the inhibition of energy metabolism were minimized. In so doing, prokaryotes made possible the subsequent emergence of calcium as a signaling factor. Despite this tight control of calcium in prokaryotic cells and the presence of different signaling pathways, calcium has apparently not been recruited extensively into the regulation of the prokaryotic cytoskeleton. With the emergence of the eukaryotic cell, the calcium paradigm has been taken to a new level, where calcium is now recognized as a universal signaling agent. The factors driving that transformation, which would already have been occurring in prokaryotes, were the evolution of proteins, or specific motifs (EF-hands), that are able to select calcium by several orders of magnitude over closely related magnesium. Also, the much higher speed of reaction inherent with calcium in binding to proteins, compared with magnesium, would have been a major selective advantage favoring the calcium pathways. Given these circumstances, it should come as no surprise that calcium signaling emerged as a common and robust process for many events. The evolution of the cytoskeleton charts a more checkered course. Although prokaryotic tubulins and actins appeared early, they have undergone significant changes during the progression from prokaryotes to eukaryotes. A major event appears to be the radiation of binding proteins that play pivotal roles in the control of ever so many processes. Perhaps most noticeable are the motor proteins, kinesins, dyneins, and myosins. But also of crucial importance are those proteins controlling nucleation, polymerization, depolymerization, bundling, membrane binding, and association with specific organelles or sites. It is in the activation and deactivation of these proteins and the events they promote where we see calcium and, to a lesser extent, protons performing key functions. If we take villin/gelsolin as a good example, we see that when the calcium concentration is at a basal level (0.1 µm), villin will bundle, stabilize, and even promote the formation of actin MFs. However, when the concentration increases to 1 µm, it fragments MFs and caps their plus ends, preventing MF formation. Thus, a relatively modest increase in the actual number of calcium atoms leads to a profound change in actin structure and activity. Major factors in the broad scheme of calcium-cytoskeleton interaction are the membrane compartments that sequester calcium and the channels and pumps that control ion movement. Here, it appears that evolutionary processes within eukaryotic cells have produced exquisitely defined systems so that remarkably different events can occur with a single cell, being separated by only a few micrometers. This is the concept of local domains and needs to be part of our thinking as we attempt to unravel the ways in which calcium and protons affect plant growth and development (Berridge, 2006). Despite the fact that we have some impressive probes for examining small structural regions of the cell, we nevertheless run into barriers that prevent us from making the necessary observations. One barrier is the inherent resolution limit of 0.2 µm for the normal light microscope. This barrier, fortunately, is being surmounted (Chen et al., 2014). Together with selective targeting of probes through molecular methods, we can hope for an improved level of spatial detection that could not be dreamed about just a few years ago. It is an exciting time in which the stage is being set for new discoveries. Even with the probes and microscopes we currently have, significant progress has been made in sorting out the ionic and cytoskeletal events that control the growth of the pollen tube. But looking ahead, I would love the opportunity to revisit the dividing cell and see if I could make sense out of the calcium signals as they pertain to the events of mitosis and cytokinesis. ACKNOWLEDGMENTS Many colleagues have generously helped me during the writing of this article. Through communications, usually by e-mail, I have been made aware of ideas and publications whose inclusion have greatly improved the final product. A few have read early drafts and provided detailed criticisms. I list all these people in alphabetical order and sincerely thank them for the help they provided: Tobias Baskin, University of Massachusetts, Amherst; Magdalena Bezanilla, University of Massachusetts, Amherst; Maurice Bosch, Institute of Biological, Environmental, and Rural Sciences, Aberystwyth, UK; Jon Calame, Eastport, ME; Zac Cande, University of California, Berkeley; Alice Cheung, University of Massachusetts, Amherst; Delfina Dominguez, University of Texas, El Paso; Noni Franklin-Tong, University of Birmingham, Birmingham, UK; Takahiro Hamada, University of Tokyo; Frank Harold, University of Washington, Seattle; Alenka Lovy-Wheeler, Tufts Medical School, Boston; Bo Liu, University of California, Davis; Cornelius Lütz, University of Innsbruck, Innsbruck, Austria, Ursula Lütz-Meindl, University of Salzburg, Salzburg, Austria; Alexander Paredez, University of Washington, Seattle; Anireddy Reddy, Colorado State University, Fort Collins; René Schneider, Max-Planck-Institute, Postdam-Golm, Germany; Louis Tisa, University of New Hampshire, Durham; Luis Vidali, Worcester Polytechnical Institute, Worcester, MA; Patricia Wadsworth, University of Massachusetts, Amherst; Lawrence Winship, Hampshire College, Amherst, MA; Shu-Zon Wu, University of Massachusetts, Amherst; and Dahong Zhang, Oregon State University, Corvallis. I also thank Mike Blatt, Editor-in-Chief of Plant Physiology, for inviting me to write a Founders’ Review. Finally, I thank the National Institutes of Health, the U.S. Department of Agriculture, and especially the National Science Foundation for the many years of prior research support they provided throughout my career. Glossary MT microtubule EF elongation factor MF microfilament ER endoplasmic reticulum LITERATURE CITED Abdel-Ghany SE , Day IS, Simmons MP, Kugrens P, Reddy ASN ( 2005 ) Origin and evolution of kinesin-like calmodulin-binding protein . Plant Physiol 138 : 1711 – 1722 Google Scholar Crossref Search ADS PubMed WorldCat Abu-Abied M , Golomb L, Belausov E, Huang S, Geiger B, Kam Z, Staiger CJ, Sadot E ( 2006 ) Identification of plant cytoskeleton-interacting proteins by screening for actin stress fiber association in mammalian fibroblasts . 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J Exp Bot 59 : 861 – 873 Google Scholar Crossref Search ADS PubMed WorldCat Author notes * Address correspondence to [email protected]. www.plantphysiol.org/cgi/doi/10.1104/pp.15.01506 © 2016 American Society of Plant Biologists. All Rights Reserved. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Schopfer, Peter; Palme, Klaus

doi: 10.1104/pp.15.01403pmid: 26537564

How do cortical microtubules shape plant cells? This has been an important question ever since the microtubular cytoskeleton was found to orientate the deposition of cellulose microfibrils in the primary cell wall and control long-term anisotropic cell expansion under isotropic turgor pressure (Green, 1962). In axial plant organs, longitudinal microtubule/microfibril arrays hamper expansion in length and favor expansion in girth, while transverse microtubule/microfibril arrays have the opposite effect (Baskin, 2001). By generating mechanical anisotropy in the cell wall, microtubule orientation controls the ratio of longitudinal versus circumferential cell expansion (the allometric ratio). A recent study by Chen et al. (2014) concludes that auxin inhibits cell growth by causing a rapid reorientation of microtubules from a transverse to a longitudinal orientation in cells of the submeristematic root zone and the elongation zone of the hypocotyl of Arabidopsis (Arabidopsis thaliana) seedlings. This conclusion warrants attention because the cell expansion that drives auxin-mediated organ elongation is generally thought to be controlled by the regulated breaking of bonds between existing cell wall polymers by chemical means (wall loosening; Cosgrove, 2005). It has been clear for more than 25 years that auxin does induce rapid changes in the orientation of microtubules in growing cells, either during straight growth (Bergfeld et al., 1988) or tropic bending (Nick et al., 1990). These studies form part of a group of investigations that show that numerous growth-affecting endogenous, environmental, and even artificial physical factors have very similar effects on microtubule orientation: during active cell expansion or related mechanical strains, microtubules are aligned against the direction of expansion, and they are aligned with it during the inhibition of expansion (Fischer and Schopfer, 1997). An important insight that emerges from this extensive evidence is that the type of reorientation elicited by a particular factor depends on its physiological context, thereby allowing auxin to induce either transverse or longitudinal microtubule orientations depending on whether elongation growth is promoted (as in shoot organs) or inhibited (as in roots). Clearly, ordered microtubule reorientations require the input of directional information (Williamson, 1990). Auxin signaling as such cannot provide this information, but the directional growth responses produced by auxin can. This brings us to the crucial question: is the orientation of microtubules determined by the effector signal directly or by changes in growth? The answer given by Chen et al. (2014) comes as a surprise: the inhibition of cell expansion is mediated by the rapid AUXIN-BINDING PROTEIN1 (ABP1)-dependent action of auxin on microtubules. The authors imply that it is microtubular reorientation per se that is responsible for the sudden growth inhibition caused by auxin in roots and hypocotyls rather than any changes in cell wall structure. Related microtubule reorientations at the concave side of gravitropically curving roots are interpreted in a similar way. Coming as an even bigger surprise, Chen and collaborators do not provide any evidence to support the claim made in their title, nor do they touch the obvious question of how microtubule reorientation from transverse to longitudinal might produce the growth inhibition elicited by auxin in the Arabidopsis root so quickly (Evans et al., 1994). Modification of the allometric ratio by changing the orientation of newly deposited cellulose microfibrils happens over hours and, therefore, is much too slow to account directly for the inhibition of cell expansion by auxin. Accompanying data on the growth changes produced by auxin in their experiments are not included in the report, nor are specifications of the investigated cell layers, despite the fact that the different cell layers show different responses to hormones (Ubeda-Tomás et al., 2012). Hence, critical questions regarding the quantitative relationship between microtubule reorientation and cell elongation remain unanswered. The gap between the experimental data presented and the far-reaching conclusions derived from them has been pointed out by Baskin (2015). There is, to our knowledge, no evidence for any fast growth-controlling mechanisms involving microtubules. There is, on the other hand, ample evidence for a causal relationship between wall-relaxing processes (such as the secretion or activation of wall-loosening enzymes or the generation of reactive oxygen species) and the rapid regulation of cell growth by auxin (Cosgrove, 2005; Perrot-Rechenmann, 2010). Modification of the allometric ratio by changing the orientation of newly deposited cellulose microfibrils appears much too slow (happening over hours) to account for the inhibition of cell expansion within minutes (Evans et al., 1994). Gravitropic bending of maize (Zea mays) roots has been shown to proceed normally even after the disassembly or immobilization of microtubules, and the inhibition of bending prohibits unilateral microtubule reorientation (Balu¡ka et al., 1996). Chen et al. (2014) do not consider any of this evidence. So, can the data presented by Chen and collaborators be explained without coming into conflict with previously published results? The answer to this question is straightforward and has long been accessible in the pertinent literature: the observed changes in microtubule pattern are trivial consequences of either growth inhibition or the auxin insensitivity of growth in abp1 (Tromas et al., 2009). Consider the following points. First, mechanical forces can reorientate microtubules. There is a large body of experimental evidence that indicates that microtubule reorientations in single cells or tissues can be induced by oriented mechanical forces causing oriented stresses and strains in the affected cell walls (Landrein and Hamant, 2013). For example, Fisher and Cyr (2000) subjected protoplasts, embedded in an elastic agarose matrix, to mechanically induced stretching. The originally randomly oriented microtubules responded to this treatment by aligning at right angles to the major tensive force vector. Similarly, growing coleoptile segments respond to mechanical bending by reorientating the microtubules of epidermal cells at right angles to the direction of tension (extended side) and parallel to the direction of compression (compressed side; Fischer and Schopfer, 1997). Second, anisotropic cell growth mirrors patterns of stress and strain within the cell wall (Hamant and Traas, 2010). In biophysical terms, turgid plant cells can be regarded as pressurized vessels surrounded by an elastically stretched wall. Auxin-driven cell enlargement is brought about by changing the yielding properties of the wall and the resulting expansion in the direction of growth (Cosgrove, 2005). Third, experiments with maize coleoptiles have shown that auxin, in addition to effecting growth-related microtubule reorientations, strongly promotes their responsivity to mechanical forces. The epidermal microtubules of auxin-deprived coleoptile segments barely respond to bending stresses but reorientate rapidly after a 1-h treatment with auxin (Fischer and Schopfer, 1997). Hence, cell wall strains generated by growth or applied stresses interact in orientating microtubules in a synergistic manner, pointing to a common signaling mechanism activated by changes in strain rate. Summing up, there is well-founded evidence for the conclusion that the microtubule reorientations observed by Chen and collaborators occur as a result of changes in the physical strain pattern that underlies the auxin-induced changes in cell expansion. In agreement with established knowledge, the primary effect of auxin may be a rapid inhibition of cell wall loosening, mediated by the production of hydrogen peroxide (Ivanchenko et al., 2013). Based on these arguments and the weight of published evidence, we conclude that Chen and collaborators have inversed cause and effect. Their observation that the inactivation of ABP1 (and downstream components of the ABP1 pathway) causes microtubules to become unresponsive to auxin and lose their transverse pattern in roots may be explained as trivial consequences of growth inhibition (Tromas et al., 2009). Serious questions now hang over the roles for ABP1 in auxin signaling and auxin-controlled development (Gao et al., 2015; Liu, 2015). However, this does not come as a complete surprise. Hayashi et al. (2008) previously showed that the auxin-induced growth inhibition of root and hypocotyl in Arabidopsis can be suppressed by α[2,4-dimethylphenly-ethyl-2-oxo]-IAA (auxinole). This antagonist specifically competes with auxin at the TIR1-AUX/IAA-type receptor complexes and does not bind to ABP1, thereby suggesting that ABP1 does not act as a receptor in these pathways. There are lessons here, not the least that literature from the pre-Arabidopsis era remains a relevant and valuable source of information. LITERATURE CITED Balu¡ka F , Hauskrecht M, Barlow PW, Sievers A ( 1996 ) Gravitropism of the primary root of maize: a complex pattern of differential cellular growth in the cortex independent of the microtubular cytoskeleton . Planta 198 : 310 – 318 Google Scholar Crossref Search ADS PubMed WorldCat Baskin TI ( 2001 ) On the alignment of cellulose microfibrils by cortical microtubules: a review and a model . 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This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Tang, Fang; Yang, Shengming; Liu, Jinge; Zhu, Hongyan

doi: 10.1104/pp.15.01661pmid: 26582727

Abstract Rj4 is a dominant gene in soybeans (Glycine max) that restricts nodulation by many strains of Bradyrhizobium elkanii. The soybean-B. elkanii symbiosis has a low nitrogen-fixation efficiency, but B. elkanii strains are highly competitive for nodulation; thus, cultivars harboring an Rj4 allele are considered favorable. Cloning the Rj4 gene is the first step in understanding the molecular basis of Rj4-mediated nodulation restriction and facilitates the development of molecular tools for genetic improvement of nitrogen fixation in soybeans. We finely mapped the Rj4 locus within a small genomic region on soybean chromosome 1, and validated one of the candidate genes as Rj4 using both complementation tests and CRISPR/Cas9-based gene knockout experiments. We demonstrated that Rj4 encodes a thaumatin-like protein, for which a corresponding allele is not present in the surveyed rj4 genotypes, including the reference genome Williams 82. Our conclusion disagrees with the previous report that Rj4 is the Glyma.01G165800 gene (previously annotated as Glyma01g37060). Instead, we provide convincing evidence that Rj4 is Glyma.01g165800-D, a duplicated and unique version of Glyma.01g165800, that has evolved the ability to control symbiotic specificity. Legumes are capable of forming a root nodule symbiosis with nitrogen-fixing soil bacteria called rhizobia. Remarkably, this symbiosis shows a high level of specificity (Broughton et al., 2000; Perret et al., 2000; Wang et al., 2012). The specificity occurs at both between- and within-species levels, such that each legume species or genotype can establish an efficient symbiosis with only a specific group of rhizobial species or strains. Genetic control of symbiosis specificity is complex, involving an exchange of multiple molecular signals between the symbiotic partners. Understanding the molecular mechanisms underlying symbiosis specificity would allow for development of tools for genetic improvement of biological nitrogen fixation in legumes. In most but not all legumes, bacterial infection and nodule organogenesis is mediated by specific perception of bacterially derived lipo-chitooligosaccharides (called Nod factors) by the cognate plant receptors (Lerouge et al., 1990; Geurts et al., 1997; Limpens et al., 2003; Radutoiu et al., 2003, 2007). The Nod factors carry various species-specific chemical decorations, and this structural variation is widely thought to play an important role in defining the recognition specificity at the species level (Lerouge et al., 1990; Perret et al., 2000; Radutoiu et al., 2007). In addition to Nod factors, rhizobial surface polysaccharides, such as exopolysaccharides, lipopolysaccharides, capsular polysaccharides, and cyclic glucans, are also important for development of infected root nodules and for modulating host specificity (D’Haeze and Holsters, 2004; Jones et al., 2008; Deakin and Broughton, 2009). Recently, an exopolysaccharide receptor has been identified in Lotus japonicus that controls rhizobial infection and distinguishes between compatible and incompatible exopolysaccharides (Kawaharada et al., 2015). Despite their unique attributes, the legume-rhizobial interactions share many common features with pathogenic plant-bacterial interactions (D’Haeze and Holsters, 2004; Deakin and Broughton, 2009). As such, plant immunity triggered by microbe-associated molecular patterns or bacterial effector proteins also plays a key role in regulation of strain-specific nodulation (D’Haeze and Holsters, 2004; Deakin and Broughton, 2009; Yang et al., 2010; Wang et al., 2012). It has been demonstrated in soybeans (Glycine max) that plants use classical NBS-LRR resistance genes to restrict nodulation with certain rhizobial strains (Yang et al., 2010). In this case, the host range of rhizobial symbionts is determined by the presence of type III effectors in the bacteria and the corresponding resistance genes in the plant. In this report, we describe positional cloning of the soybean Rj4 gene. The Rj4 gene was first identified in 1972 (Vest and Caldwell, 1972) and subject to extensive study in the 1980s and 1990s (e.g. Devine and O’Neill, 1986; Devine et al., 1990; Sadowsky and Cregan, 1992). Soybean genotypes carrying an Rj4 allele restrict nodulation by many strains of Bradyrhizobium japonicum and Bradyrhizobium elkanii (Sadowsky and Cregan, 1992). B. elkanii is a poor symbiotic partner of soybeans because of its low nitrogen-fixation efficiency. In addition, many of the strains also produce rhizobitoxine, a compound that induces chlorosis in the host plant. Thus, cultivars with the Rj4 genotype are favorable in soils where the B. elkanii population is dominant because Rj4 stops those cultivars from forming invaded nodules with it. The Rj4 allele is frequently present in Gly soja, the wild progenitor of soybean, but less frequent in the modern cultivars from North America (Devine and Breithaupt, 1981). We mapped the Rj4 locus within a small genomic region on soybean chromosome 1 (Tang et al., 2014), and validated one of the candidate genes as Rj4 using both complementation tests and CRISPR/Cas9-based gene knockout experiments. We showed that Rj4 encodes a thaumatin-like protein that does not have a corresponding allele in the analyzed rj4 genetic backgrounds. This conclusion disagrees with the previous report that Rj4 is the Glyma.01G165800 gene (Hayashi et al., 2014). Instead, we provide convincing evidence that Rj4 is a duplicate copy of Glyma.01G165800. RESULTS AND DISCUSSION Fine Mapping of the Rj4 Locus We previously mapped the dominant Rj4 locus to soybean chromosome 1 based on an F2 segregating population derived from the cross between Hill (Rj4/Rj4) and Williams (rj4/rj4) (Tang et al., 2014). Hill restricts nodulation by B. elkanii USDA61 (Nod−), whereas Williams nodulates normally with this strain (Nod+; Fig. 1, A and B). In the Rj4 background, the USDA61 strain could occasionally induce the formation of nodule primordium, but, in contrast to the compatible interaction, cortical cell division ceased at an early stage due to a lack of bacterial infection (Fig. 1, C and D). Phenotyping and genotyping of approximately 5,000 F2 plants enabled us to delimit the Rj4 locus within a 45-kb genomic region according to the reference genome sequence of Williams 82 (rj4/rj4; Schmutz et al., 2010; version Wm82.a2.v1). The 45-kb genomic sequence contains four predicted genes, including Glyma.01G165600, Glyma.01G165700, Glyma.01G165800, and Glyma.01G165900 (Fig. 1E). Sequence comparisons did not detect any nonsynonymous nucleotide substitutions between the parental alleles of Glyma.01G165600 and Glyma.01G165900, while Glyma.01G165700 represents a truncated and presumably nonfunctional gene in both parents. However, we identified five amino acid substitutions and two amino acid insertions/deletions between the two parental protein isoforms of Glyma.01G165800. These amino acid substitutions are correlated with the nodulation phenotypes based on the association analysis of 48 soybean genotypes, including 40 G. max and eight G. soja lines (Tang et al., 2014). Glyma.01G165800 (previously annotated as Glyma01g37060) encodes a thaumatin-like protein, a member of the pathogenesis-related protein family 5 (PR-5) that plays an important role in plant defense (van Loon et al., 2006). Based on these data, we suggested that the Hill allele of Glyma.01G165800 is possibly a candidate gene of Rj4 (Tang et al., 2014). Shortly after the publication of Tang et al. (2014), Hayashi et al. (2014) reported that Glyma.01G165800 was indeed the Rj4 gene. However, as described below, we were never able to validate this candidate gene. Figure 1. Open in new tabDownload slide Rj4-mediated nodulation restriction in soybean and fine mapping of the Rj4 locus. A, Nod+ phenotype of Williams (rj4/rj4) by B. elkanii USDA61. B, Nod− phenotype of Hill (Rj4/Rj4) by B. elkanii USDA61. C, In the compatible Williams/USDA61 interaction, nodule developed normally and contained bacteria. D, In the incompatible interaction, occasionally nodule primordia were formed, but the nodule primordia did not contain bacteria. E, The Rj4 locus was delimited to a 45-kb genomic region on chromosome 1 containing four predicted genes, of which Glyma.01G165800 was considered as a candidate gene. Map is drawn to scale. Figure 1. Open in new tabDownload slide Rj4-mediated nodulation restriction in soybean and fine mapping of the Rj4 locus. A, Nod+ phenotype of Williams (rj4/rj4) by B. elkanii USDA61. B, Nod− phenotype of Hill (Rj4/Rj4) by B. elkanii USDA61. C, In the compatible Williams/USDA61 interaction, nodule developed normally and contained bacteria. D, In the incompatible interaction, occasionally nodule primordia were formed, but the nodule primordia did not contain bacteria. E, The Rj4 locus was delimited to a 45-kb genomic region on chromosome 1 containing four predicted genes, of which Glyma.01G165800 was considered as a candidate gene. Map is drawn to scale. Complementation Tests Failed to Validate the Hill Version of Glyma.01G165800 as Rj4 To validate the candidate gene Glyma.01G165800, we developed genomic constructs that contain the Hill allele of Glyma.01G165800 under the control of its native promoter as well as driven by the Cauliflower mosaic virus 35S promoter. These gene constructs were transferred to the rj4 genetic background (Williams) via Agrobacterium rhizogenes-mediated hairy root transformation, followed by assaying the nodulation phenotypes after inoculation with B. elkanii USDA61. The transformation experiments were performed without antibiotic selection; thus, the hairy roots induced by A. rhizogenes contained both transgenic and nontransgenic, which can be readily distinguished by assaying the expression of a GUSPlus gene present in the modified binary plasmid pEarleyGate100. As shown in Figure 2, nodules were formed on the transgenic roots expressing the Hill allele of Glyma.01G165800. We repeated similar complementation tests a number of times over a 3-year period from 2012 to 2015 and obtained the same results. Thus, our results appeared not to support Rj4 as an allele of Glyma.01G165800, which contradicted the conclusion reported by Hayashi et al. (2014). As described later in this article, we further confirmed our conclusion by developing a CRISPR/Cas9 construct to knock out the Glyma.01G165800 gene in the Hill (Rj4/Rj4) background. Figure 2. Open in new tabDownload slide Complementation test using the Hill allele of Glyma.01G165800. A, Introduction of the Hill allele of Glyma.01G165800 into Williams (rj4/rj4) failed to block nodulation on the transgenic hairy roots (blue) by USDA61. B, Examples of transgenic hairy roots expressing Glyma.01G165800 of Hill in Williams. The transgenic hairy roots were first identified by GUS staining of a small portion of the root segments, followed by isolation of RNA from the transgenic hairy roots. Figure 2. Open in new tabDownload slide Complementation test using the Hill allele of Glyma.01G165800. A, Introduction of the Hill allele of Glyma.01G165800 into Williams (rj4/rj4) failed to block nodulation on the transgenic hairy roots (blue) by USDA61. B, Examples of transgenic hairy roots expressing Glyma.01G165800 of Hill in Williams. The transgenic hairy roots were first identified by GUS staining of a small portion of the root segments, followed by isolation of RNA from the transgenic hairy roots. Identification of Insertion/Deletion Polymorphisms Between the Rj4 and rj4 Genotypes Since the genotype of the reference genome (Williams 82) is rj4/rj4, it is very likely that rj4 represents a null allele in this genetic background. To address this possibility, we screened a bacterial artificial chromosome (BAC) library derived from PI468916, a G. soja genotype harboring an Rj4 allele. BLAST search against the BAC ends database of PI468916 identified two BAC clones (GSS_Ba124A02 and GSS_Ba201P23) that cover the 45-kb reference genomic region where the Rj4 locus was mapped. Sequencing and assembly of both BACs identified numerous insertions in the PI468916 genome at this locus, including an approximately 10-kb large insertion plus several small insertions of several hundred bp (Fig. 3A). In particular, we identified a second copy of Glyma.01G165800 in the PI468916 genotype. The two duplicates are separated by approximately 7.5-kb nongenic sequences and share approximately 96% identity at the amino acid level (Fig. 3B). To distinguish between the two copies, we named the second copy of Glyma.01G165800 as Glyma.01G165800-D. Glyma.01G165800-D expressed at a very low level in the soybean root and was barely detectable under noninoculated conditions. However, its expression in the root was significantly enhanced 3 d post rhizobial inoculation (Fig. 3C). The similar expression pattern was also observed for Glyma.01G165800. Figure 3. Open in new tabDownload slide Identification of a duplicate gene of Glyma.01G165800 in the Rj4 genotypes. A, Sequence analysis identified a total of approximately 13-kb insertions in the Rj4 genotype PI468916 compared to the reference genome Williams 82, including an approximately 10-kb large insertion indicated by the shaded bar plus several small insertions (not shown). The insertion contains a duplicate copy of Glyma.01G165800, which we called Glyma.01G165800-D. B, Alignment of predicted protein sequences of Glyma.01G165800 and Glyma.01G165800-D of Hill. Gray-highlighted letters indicated amino acid substitutions between the two proteins. C, RT-PCR (left) and quantitative RT-PCR (right) analyses indicated that the expression of Hill-Glyma.01G165800-D was not detectable in the noninfected roots, but the expression was induced after inoculation. Similar expression pattern was also observed for Glyma.01G165800 in Hill. Figure 3. Open in new tabDownload slide Identification of a duplicate gene of Glyma.01G165800 in the Rj4 genotypes. A, Sequence analysis identified a total of approximately 13-kb insertions in the Rj4 genotype PI468916 compared to the reference genome Williams 82, including an approximately 10-kb large insertion indicated by the shaded bar plus several small insertions (not shown). The insertion contains a duplicate copy of Glyma.01G165800, which we called Glyma.01G165800-D. B, Alignment of predicted protein sequences of Glyma.01G165800 and Glyma.01G165800-D of Hill. Gray-highlighted letters indicated amino acid substitutions between the two proteins. C, RT-PCR (left) and quantitative RT-PCR (right) analyses indicated that the expression of Hill-Glyma.01G165800-D was not detectable in the noninfected roots, but the expression was induced after inoculation. Similar expression pattern was also observed for Glyma.01G165800 in Hill. PCR amplification of genomic DNA from the same 48 Rj4 and rj4 genotypes (Tang et al., 2014) using gene-specific primers revealed that all the Rj4 genotypes possess both Glyma.01G165800 and Glyma.01G165800-D, while Glyma.01G165800-D is missing in all the tested rj4 genotypes (Supplemental Table S1). Based on these observations, we postulated that either Glyma.01G165800-D or both Glyma.01G165800 and Glyma.01G165800-D are required for Rj4-mediated nodulation restriction. CRISPR/Cas9-Mediated Gene Knockout Revealed That Glyma.01G165800-D But Not Glyma.01G165800 Is Required for Rj4-Mediated Nodulation Restriction We used the CRISPR/Cas9-based reverse genetics tool (Doudna and Charpentier, 2014) to knock out the Glyma.01G165800 and Glyma.01G165800-D genes in the Hill (Rj4/Rj4) genetic background. Glyma.01G165800 and Glyma.01G165800-D differ significantly at the 5′ coding region (Fig. 3B), allowing us to design gRNA vectors that specifically target individual genes. The vectors were introduced to A. rhizogenes K599 for hairy root transformation, followed by assaying the nodulation capacity of the hairy roots with B. elkanii USDA61. The transgenic roots were first identified by GUS staining, followed by DNA sequencing to validate the targeted DNA insertions/deletions. For each vector, we generated at least 100 independent transgenic roots from more than 50 plants. For both genes, we successfully generated transgenic roots with both alleles being mutated, including homozygous roots containing two homogenous mutated alleles as well as heterozygous roots with two or more heterogeneous mutated alleles (Supplemental Text S1). Our experiments showed that the knockout of Glyma.01G165800 did not abolish Rj4 function; all the mutant roots were not able to restore nodulation with B. elkanii USDA61. In contrast, the knockout of Glyma.01G165800-D completely abolished Rj4 function, enabling all the mutant roots to form nodules after inoculation with USDA61. Examples of phenotypes are shown in Figure 4, A and B, with their mutant genotypes illustrated in Figure 4, C and D, respectively. Furthermore, we transferred the Hill version of Glyma.01G165800-D to the rj4 genetic background of Williams, and the transgenic roots successfully blocked the nodulation with USDA61 (Fig. 5). Taken together, these experiments unambiguously indicated that Glyma.01G165800-D but not Glyma.01G165800 is required for Rj4-mediated nodulation restriction. Figure 4. Open in new tabDownload slide CRISPR/Cas9-mediated gene knockout of Glyma.01G165800 and Glyma.01G165800-D in the Hill (Rj4/Rj4) background. A, Knockout of Glyma.01G165800 did not affect Rj4-mediated nodulation restriction in Hill. Mutant hairy roots (blue), same as the wild-type roots, were not able to nodulate with USDA61. B, Knockout of Glyma.01G165800-D completely abolished Rj4-mediated nodulation restriction in Hill. Mutant hairy roots restored the ability to nodulate with USDA61. C, Sequence analysis indicated a single nucleotide deletion (indicated by an arrow) in one of the transgenic hairy roots shown in A. D, Sequence analysis indicated a single nucleotide insertion in one of the transgenic hairy roots (indicated by an arrow) shown in B. Figure 4. Open in new tabDownload slide CRISPR/Cas9-mediated gene knockout of Glyma.01G165800 and Glyma.01G165800-D in the Hill (Rj4/Rj4) background. A, Knockout of Glyma.01G165800 did not affect Rj4-mediated nodulation restriction in Hill. Mutant hairy roots (blue), same as the wild-type roots, were not able to nodulate with USDA61. B, Knockout of Glyma.01G165800-D completely abolished Rj4-mediated nodulation restriction in Hill. Mutant hairy roots restored the ability to nodulate with USDA61. C, Sequence analysis indicated a single nucleotide deletion (indicated by an arrow) in one of the transgenic hairy roots shown in A. D, Sequence analysis indicated a single nucleotide insertion in one of the transgenic hairy roots (indicated by an arrow) shown in B. Figure 5. Open in new tabDownload slide Complementation test using the Hill allele of Glyma.01G165800-D. A, Introduction of the Hill allele of Glyma.01G165800-D into Williams (rj4/rj4) successfully blocked the nodulation of the transgenic hairy roots (blue) by USDA61. B, Examples of transgenic hairy roots expressing Glyma.01G165800-D of Hill in Williams. Figure 5. Open in new tabDownload slide Complementation test using the Hill allele of Glyma.01G165800-D. A, Introduction of the Hill allele of Glyma.01G165800-D into Williams (rj4/rj4) successfully blocked the nodulation of the transgenic hairy roots (blue) by USDA61. B, Examples of transgenic hairy roots expressing Glyma.01G165800-D of Hill in Williams. Similar to that of Glyma.01G165800, Glyma.01G165800-D contains three exons and two introns and is predicted to encode a protein of 296 amino acids. Sequence comparison identified a total of 13 amino acid substitutions between the two duplicate copies (Fig. 3B). These polymorphisms primarily occur at the N termini of the proteins, suggesting that these amino acid residues play an important role in Rj4-mediated nodulation restriction in soybeans. CONCLUSION We report here the isolation of the Rj4 gene that controls nodulation specificity in soybeans. We found that the Rj4 alleles are present in the Rj4 genetic backgrounds, while rj4 alleles are null alleles in the surveyed rj4 genotypes. Rj4 encodes a thaumatin-like protein but not the one previously reported by Hayashi et al. (2014). It is difficult to reconcile the different conclusions, given that we used similar experimental systems and biological materials. Resembling the soybean Rj2 gene that encodes a typical NBS-LRR protein, the Rj4-mediated nodulation restriction also relies on the bacterial type III secretion system (T3SS; Okazaki et al., 2009). Recently, a putative type III effector from B. elkanii USDA61 has been identified that is involved in the recognition process (Faruque et al., 2015). Furthermore, B. elkanii USDA61 was able to induce bacterial infection and nodule formation independent of Nod factor perception, and this ability is dependent on the T3SS of USDA61 (Okazaki et al., 2013). All these findings appeared to support that Rj4 triggers gene-for-gene resistance against the rhizobial infection through recognition of the cognate bacterial effectors. The fact that Rj4 is not an R gene but encodes a thaumatin-like protein is kind of surprising. It will be intriguing to understand how a thaumatin-like protein is involved in the effector-triggered immunity in plant-microbe interactions. MATERIALS AND METHODS Plant Materials and Nodulation Assay The F2 mapping population was derived from the cross between Hill (Rj4/Rj4) and Williams (rj4/rj4). Plants were grown in sterilized perlite-turface mix in a growth chamber programmed for 16 h light at 26°C and 8 h dark at 23°C. Roots of 1-week-old seedlings were inoculated with B. elkanii USDA61. Nodulation assays were performed 2 to 3 weeks postinoculation. Genetic mapping of the Rj4 locus was described by Tang et al. (2014). Complementation Tests and CRISPR/Cas9-Mediated Gene Knockout The Hill versions of Glyma.01G165800 and Glyma.01G165800-D were used for complementation tests. The genomic DNA of the candidate genes, including both introns and exons, was PCR amplified and cloned into the binary vector pEarleyGate100. The expression of these genes was driven by the Cauliflower mosaic virus 35S promoter. To facilitate the identification of the transgenic hairy roots, we also pre-engineered a GUSPlus gene expression cassette (amplified from pCAMBIA1305.1) into the pEarleyGate100 vector. The primers used for amplification of the GUSPlus gene cassette were 5′GCAGGACCGGACGGGGCGAACTCGCCGTAAAGACTGG3′ and 5′TCGTCCGTCTGCGGGAGCACTGATAGTTTAATTCCCGAT3′. Purified PCR product was ligated into the AfeI and KpnI digested pEarleyGate100 DNA using the In-Fusion Advantage PCR Cloning Kits (Clontech). The primer pairs used for amplification of Glyma.01G165800 were 5′AAAAAAGCAGGCTTCCATCGTCCTTTGCCTCTTTC3′ and 5′AAGAAAGCTGGGTCTTAACAAAAGCACGGAGGGGAAATG3′, and for Glyma.01G165800-D were 5′AAAAAAGCAGGCTTCTACTTCTCCAACCCCTCACG3′ and 5′AAGAAAGCTGGGTCTCAATCTGTCCAAAGTGGGGCGTG3′. Purified PCR products were first ligated into the pENTR vector using Gateway BP clonase (Invitrogen) and then cloned into the destination vector pEarleyGate100 using Gateway LR clonase (Invitrogen). For developing the CRISPR/Cas9 gene knockout constructs, we used the pHSE401 vector described by Xing et al. (2014). Two pairs of oligos were designed to specifically target Glyma.01G165800 and Glyma.01G165800-D, respectively. For Glyma.01G165800, we used the oligos 5′ATTGATGGGAAATTCAACTAAAA3′and 5′AAACTTTTAGTTGAATTTCCCAT3′. For Glyma.01G165800-D, we used the oligos 5′ATTGATGGCAAGTTCGACTAAAA3′ and 5′AAACTTTTAGTCGAACTTGCCAT3′. The underlined sequences represent the targeted sites. The oligo pairs were first annealed to produce a double-stranded fragment with 4-nt 5′ overhangs at both ends and then ligated into the BsaI digested pHSE401 vector. We also amplified the GUSPlus gene cassette from pCAMBIA1305.1 and ligated into the pHSE401 vector to facilitate the identification of transgenic hairy roots. For this purpose, the GUSPlus gene cassette was amplified using the primer pair 5′AATTGATTGACAACGAATTGAACTCGCCGTAAAGACTGG3′ and 5′GCTAAGATCGGCCGCAGCGCACTGATAGTTTAATTCCCGAT3′. The purified PCR product was then ligated into the EcoRI and AsiSI digested pHSE401 vector using the In-Fusion Advantage PCR Cloning Kits (Clontech). For CRISPR/Cas9-based knockout experiments, to validate the targeted DNA insertions/deletions, the transgenic roots were first identified by GUS staining, followed by DNA isolation, PCR amplification, and DNA sequencing. If the initial sequencing indicated the p,resence of multiple heterogeneous mutant alleles, the PCR product was ligated into pGEM T-Easy Vector System (Promega), and 10 to 15 clones were selected for sequencing. Hairy Root Transformation of Soybean Agrobacterium rhizogenes-mediated hairy root transformation was performed according to the procedures described by Kereszt et al. (2007). Briefly, the A. rhizogenes strain K599 containing individual binary vectors was injected into the cotyledonary node of 1-week-old seedlings. The inoculated seedlings were grown in a growth chamber with at least 90% humidity. The main roots were cut when the hairy roots were long enough to support the plant growth. The composite plants were then inoculated with the rhizobial strain USDA61. Nodulation phenotypes were recorded three weeks after inoculation. Analysis of Gene Expression Total RNA was isolated by the Qiagen Plant RNeasy mini kit. Two micrograms of RNA was used to perform reverse transcription (RT)-PCR reactions using M-MLV reverse transcriptase (Invitrogen) in a 20-μL reaction mixture. Two microliters of the RT reaction was used as a template in a 20-μL PCR solution. The PCR primers were as follows: Glyma.01G165800 specific, 5′CATCGTGACTATGGCAACAA3′ and 5′CCGCTTCCCCTGGATATTTCTTGATA3′; Glyma.01G165800-D specific, 5′TACTTCTCCAACCCCTCACG3′ and 5′CCGCTTCCCCTGGATATTTCTTGATA3′; and GmActin, 5′GAGCTATGAATTGCCTGATGG3′ and 5′CGTTTCATGAATTCCAGTAGC3′. Real-time quantitative PCR was carried out according to the instructions of the SsoAdvanced SYBR Green Supermix Kit (Bio-Rad) using a CFX Connect Real-Time System (Bio-Rad). The reaction mixture was heated at 95°C for 10 min and then followed by 40 cycles of 95°C for 15 s, 61°C for 15 s, and 72°C for 30 s. Three biological replicates were used in all the experiments. The ATP synthase subunit 1 (GmATS1) mRNA was amplified as a reference gene (Hayashi et al., 2014). The primers used in the analysis were as follows: for Glyma.01G165800, 5′TCGTGACTATGGCAACAACT3′ and 5′GCTGCACTTGTTGGTGATG3′; for Glyma.01G165800-D, 5′AGTGCACTTTGTGTCCCATA3′ and 5′GCTGCACTTGTTGATGATGG3′; and for GmATS1, 5′GCGATTCTTAAGCCAGCCTTT3′ and 5′ ACACACCCTGGAAACTGGTGA3′. Anatomical Analysis Soybean (Glycine max) roots with nodules or nodule primordia were harvested at 2 weeks after inoculation and immediately fixed in 50% ethyl alcohol, 5% glacial acetic acid, and 10% formaldehyde for 24 h at 4°C. The tissues were dehydrated in a graded ethanol series followed by a graded series of xylene. Following this, the roots were infiltrated with several changes of paraffin at 60°C and embedded in paraffin. Embedded tissues were sectioned (10 μm thick) with a microtome, stained with trypan blue, and examined with bright-field optics. Accession Numbers Sequence data generated from this project can be found in the GenBank/EMBL data libraries under accession numbers KU144684 to KU144689. Supplemental Data The following supplemental materials are available. Supplemental Table S1. Presence (+) or absence (−) of Glyma.01G165800-D (Rj4) in 48 soybean genotypes. Supplemental Text S1. Examples of the genotypes of Glyma.01G165800 and Glyma.01G165800-D knocked out hairy roots and their phenotypes ACKNOWLEDGMENTS We thank Dr. Peter van Berkum (U.S. Department of Agriculture Agricultural Research Service, Beltsville, MD) for the B. elkanii strain USDA61 and Dr. Qijun Chen (China Agricultural University, Beijing, China) for providing the CRISPR/Cas9 binary vectors. Glossary BAC bacterial artificial chromosome RT reverse transcription LITERATURE CITED Broughton WJ , Jabbouri S, Perret X ( 2000 ) Keys to symbiotic harmony . J Bacteriol 182 : 5641 – 5652 Google Scholar Crossref Search ADS PubMed WorldCat Deakin WJ , Broughton WJ ( 2009 ) Symbiotic use of pathogenic strategies: rhizobial protein secretion systems . 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H.Z. conceived the project; F.T., S.Y., and J.L. performed research; F.T., S.Y., J.L, and H.Z. analyzed data; and F.T. and H.Z. wrote the article. www.plantphysiol.org/cgi/doi/10.1104/pp.15.01661 © 2016 American Society of Plant Biologists. All Rights Reserved. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Minguet-Parramona, Carla; Wang, Yizhou; Hills, Adrian; Vialet-Chabrand, Silvere; Griffiths, Howard; Rogers, Simon; Lawson, Tracy; Lew, Virgilio L.; Blatt, Michael R.

doi: 10.1104/pp.15.01607pmid: 26628748

Abstract Oscillations in cytosolic-free Ca2+ concentration ([Ca2+]i) have been proposed to encode information that controls stomatal closure. [Ca2+]i oscillations with a period near 10 min were previously shown to be optimal for stomatal closure in Arabidopsis (Arabidopsis thaliana), but the studies offered no insight into their origins or mechanisms of encoding to validate a role in signaling. We have used a proven systems modeling platform to investigate these [Ca2+]i oscillations and analyze their origins in guard cell homeostasis and membrane transport. The model faithfully reproduced differences in stomatal closure as a function of oscillation frequency with an optimum period near 10 min under standard conditions. Analysis showed that this optimum was one of a range of frequencies that accelerated closure, each arising from a balance of transport and the prevailing ion gradients across the plasma membrane and tonoplast. These interactions emerge from the experimentally derived kinetics encoded in the model for each of the relevant transporters, without the need of any additional signaling component. The resulting frequencies are of sufficient duration to permit substantial changes in [Ca2+]i and, with the accompanying oscillations in voltage, drive the K+ and anion efflux for stomatal closure. Thus, the frequency optima arise from emergent interactions of transport across the membrane system of the guard cell. Rather than encoding information for ion flux, these oscillations are a by-product of the transport activities that determine stomatal aperture. Stomata in the leaf epidermis are the main pathway both for CO2 entry for photosynthesis and for foliar water loss by transpiration. Guard cells surround the stomatal pore and regulate the aperture, balancing the often conflicting demands for CO2 and water conservation. Guard cells open and close the pore by expanding and contracting through the uptake and loss, respectively, of osmotic solutes, notably of K+, Cl−, and malate2− (Mal2−; Pandey et al., 2007; Kim et al., 2010; Roelfsema and Hedrich, 2010; Lawson and Blatt, 2014). These transport processes comprise the final effectors of a regulatory network that coordinates transport across the plasma membrane and tonoplast, and maintains the homeostasis of the guard cell. A number of well-defined signals—including light, CO2, drought and the water stress hormone abscisic acid (ABA)—act on this network, altering transport, solute content, turgor and cell volume, and ultimately stomatal aperture. Much research has focused on stomatal closure, underscoring both Ca2+-independent and Ca2+-dependent signaling. Of the latter, elevated cytosolic-free Ca2+ concentration ([Ca2+]i) inactivates inward-rectifying K+ channels (IK,in) to prevent K+ uptake and activates Cl− (anion) channels (ICl) at the plasma membrane to depolarize the membrane and engage K+ efflux through outward-rectifying K+ channels (IK,out; Keller et al., 1989; Blatt et al., 1990; Thiel et al., 1992; Lemtiri-Chlieh and MacRobbie, 1994). ABA, and most likely CO2 (Kim et al., 2010), elevate [Ca2+]i by facilitating Ca2+ entry at the plasma membrane to trigger Ca2+ release from endomembrane stores, a process often described as Ca2+-induced Ca2+ release (Grabov and Blatt, 1998, 1999). The hormone promotes Ca2+ influx by activating Ca2+ channels (ICa) at the plasma membrane, even in isolated membrane patches (Hamilton et al., 2000, 2001), which is linked to reactive oxygen species (Kwak et al., 2003; Wang et al., 2013). In parallel, cADP-ribose and nitric oxide promote endomembrane Ca2+ release and [Ca2+]i elevation (Leckie et al., 1998; Neill et al., 2002; Garcia-Mata et al., 2003; Blatt et al., 2007). Best estimates indicate that endomembrane release accounts for more than 95% of the Ca2+ entering the cytosol to raise [Ca2+]i (Chen et al., 2012; Wang et al., 2012). One feature of stomatal response to ABA, and indeed to a range of stimuli both hormonal as well as external, is its capacity for oscillations both in membrane voltage and [Ca2+]i. Guard cell [Ca2+]i at rest is typically around 100 to 200 nm, as it is in virtually all living cells. In response to ABA, [Ca2+]i can rise above 1 μm—and locally, most likely above 10 μm—often in cyclic transients of tens of seconds to several minutes’ duration in association with oscillations in voltage and stomatal closure (Gradmann et al., 1993; McAinsh et al., 1995; Webb et al., 1996; Grabov and Blatt, 1998, 1999; Staxen et al., 1999; Allen et al., 2001). In principle, cycling in voltage and [Ca2+]i arises as closure is accelerated with a controlled release of K+, Cl−, and Mal2− from the guard cell and is subject to extracellular ion concentrations (Gradmann et al., 1993; Chen et al., 2012). However, it has been proposed that these, and similar oscillations in a variety of plant cell models, serve as physiological signals in their own right (McAinsh et al., 1995; Ehrhardt et al., 1996; Taylor et al., 1996). In support of such a signaling role, experiments designed to impose [Ca2+]i (and voltage) oscillations in guard cells have yielded an optimal frequency for closure with a period near 10 min (Allen et al., 2001). Nonetheless, the studies offer no mechanistic explanation for this optimum that could validate a causal role in signaling, and none has been forthcoming since. Here we address questions of how such optimal frequencies in [Ca2+]i oscillation arise and their relevance for stomatal closure, using quantitative systems analysis of guard cell transport and homeostasis. Our findings indicate that oscillations in voltage and [Ca2+]i, and their optima associated with stomatal closure, are most simply explained as emerging from the interactions between ion transporters that drive stomatal closure. Thus, we conclude that these oscillations do not control, but are a by-product of the transport that determines stomatal aperture. RESULTS Figure 1, A to C, shows the model outputs for voltage, [Ca2+]i and aperture at times late in the daylight period of the simulated diurnal cycle when the stomata close (Chen et al., 2012; Wang et al., 2012). The overview illustrates the onset of oscillations, initially as rapid transitions in voltage—and coupled, but limited oscillations in [Ca2+]i—with a gradual shift over roughly 1.5 h as these transients contract and accelerate. The oscillations culminate with long membrane depolarizations and hyperpolarizations, each of several minutes’ duration, accompanied by large transients in [Ca2+]i from a baseline near 200 nm to values in excess of 1 μm. This entire sequence of oscillations is stably repeated with each diurnal cycle for any given set of model parameters (Chen et al., 2012). At their onset, the voltage transients initially traverse a range of approximately 50 mV with a cycle period near 0.3 min. These oscillations, and the accompanying variations in [Ca2+]i, are clipped in a saw-tooth pattern (Fig. 1D). The subsequent, long voltage oscillations, by contrast, traverse roughly 100 mV, show more extensive relaxations and dwell times at each voltage extreme, and are closely matched by long cycles of [Ca2+]i elevation and recovery in which [Ca2+]i rises and then decays as Ca2+ release is followed by its sequestration and export from the cytosol (Chen et al., 2012). In effect, these extended time periods are long enough to permit the rise and fall in [Ca2+]i to “catch up” with the voltage cycle. Fourier analysis of the oscillations in voltage and [Ca2+]i (Fig. 1E) showed a range of oscillation frequencies between 0.3 and 2 mHz, and resonance frequencies near 3.5, 6, 7, and 9 mHz. The latter series is most evident in the [Ca2+]i oscillations, as is to be expected from the nonsinusoidal waveform in [Ca2+]i. The major peaks of both voltage and [Ca2+]i align with one another, confirming their close association, and the spread between approximately 0.3 mHz and the major peak at 1.9 mHz (=8.9 min) suggests complex relationships originating with the ensemble of transport activities within the cell, an interpretation that was drawn previously from observing the interactions between the several transporters in simulation (Chen et al., 2012). Figure 1. Open in new tabDownload slide Macroscopic outputs of the OnGuard Arabidopsis model. Outputs resolved over a standard diurnal cycle (12 h light:12 h dark; dark period indicated by bar above) with 10 mm KCl, 1 mm CaCl2, and pH 6.5 outside. The full set of model parameters and initializing variables will be found in Wang et al. (2012) and may be downloaded with the OnGuard software at www.psrg.org.uk. A to C, Outputs for the diurnal period 8.8 to 12.5 h from the start of the diurnal cycle (dark period indicated by bar above) for plasma membrane and tonoplast voltage (A), [Ca2+]i (B), and stomatal aperture and the rate of opening/closing in stomatal aperture (C; =ƊAperture/Ɗt). Positive rates here indicate opening, and negative rates indicate closing. D, Expanded view of the rapid cycles in voltage, [Ca2+]i, aperture, and the rates of opening/closing corresponding to the period (9.2–9.3 h) highlighted by the gray bars in A to C cross-referenced by numbers. Note the periodic, step-like decrease in aperture and its association with the periods of membrane depolarization and elevated [Ca2+]i. Trials with diurnal fluence rates weighted to peak at 2, 6, or 10 h yielded a comparable series of oscillations, indicating that the kinetics of decay in primary ATP-dependent transport have no substantive influence on this behavior. E, Fourier spectral analysis of oscillation frequencies in voltage and [Ca2+]i for the data in A to C. The plots show the relative amplitude of the component frequencies. Frequencies within the gray bars correspond to periodicities of approximately 6 to 26 min and show a prominent amplitude at 8.9 min (=1.9 mHz). Figure 1. Open in new tabDownload slide Macroscopic outputs of the OnGuard Arabidopsis model. Outputs resolved over a standard diurnal cycle (12 h light:12 h dark; dark period indicated by bar above) with 10 mm KCl, 1 mm CaCl2, and pH 6.5 outside. The full set of model parameters and initializing variables will be found in Wang et al. (2012) and may be downloaded with the OnGuard software at www.psrg.org.uk. A to C, Outputs for the diurnal period 8.8 to 12.5 h from the start of the diurnal cycle (dark period indicated by bar above) for plasma membrane and tonoplast voltage (A), [Ca2+]i (B), and stomatal aperture and the rate of opening/closing in stomatal aperture (C; =ƊAperture/Ɗt). Positive rates here indicate opening, and negative rates indicate closing. D, Expanded view of the rapid cycles in voltage, [Ca2+]i, aperture, and the rates of opening/closing corresponding to the period (9.2–9.3 h) highlighted by the gray bars in A to C cross-referenced by numbers. Note the periodic, step-like decrease in aperture and its association with the periods of membrane depolarization and elevated [Ca2+]i. Trials with diurnal fluence rates weighted to peak at 2, 6, or 10 h yielded a comparable series of oscillations, indicating that the kinetics of decay in primary ATP-dependent transport have no substantive influence on this behavior. E, Fourier spectral analysis of oscillation frequencies in voltage and [Ca2+]i for the data in A to C. The plots show the relative amplitude of the component frequencies. Frequencies within the gray bars correspond to periodicities of approximately 6 to 26 min and show a prominent amplitude at 8.9 min (=1.9 mHz). It is clear from Figure 1 that the consequence of both the short and long oscillations was to promote solute loss and stomatal closure in simulation. For the long oscillations, the effect was a step-wise decay in aperture with bursts of rapid closure separated by periods in which there is little change in aperture. However, even the short oscillations in voltage and [Ca2+]i associated with periods of solute loss and small declines in aperture. Most striking is the difference in rates of stomatal closure with the expanded cycles in voltage and [Ca2+]i. Figure 2 summarizes the closure rate as a function of oscillation period. To avoid the potential complication of the changing solute gradients, the rates of closure here are corrected in inverse ratio with the decline in the sum of the thermodynamic driving forces for K+ and anion flux. This correction has no qualitative effect on the overall pattern. Remarkably, the results showed that closure followed a sharp, biphasic function of oscillation frequency with a maximum closure rate at a cycle period of 8.9 min (=1.9 mHz). Closure rate fell off steeply to either side of this value, with a plateau in rates at longer cycle periods. Also plotted is the relationship of closure rate with the time fraction in the depolarized phase of the cycle. Maximum rate associated with a mean time fraction around 0.50 to 0.55 in the depolarized phase, in other words rapid closure occurred with a near-symmetric cycle of elevated [Ca2+]i (depolarized voltage) and resting [Ca2+]i (hyperpolarized voltage). These characteristics match closely those previously identified in experiments: Allen et al. (2001) reported maximum closure with an imposed [Ca2+]i oscillation frequency near 10 min and a mean time fraction of 0.5 in the depolarized phase. In short, the model reproduces these phenomenological observations faithfully, without ad hoc manipulation or refinements in any of the system parameters. Figure 2. Open in new tabDownload slide Aperture closing rate as a function of the oscillation cycle period. Data from Figure 1 for each of the long oscillatory cycles and from a selection of the early, rapid cycles are plotted together with H+-ATPase current dynamics. H+-ATPase current determined as the difference in current at 0 mV between the hyperpolarized and depolarized phases of each oscillation cycle. Closing rates shown are corrected proportionally for the decay in K+ and anion electrochemical driving force during closure, which elevated the closing rates by approximately 20% to 25% for cycle periods of 10 min and longer. Inset, Closing rate as a function of the relative time fraction spent in the depolarized phase of the oscillation cycle. Note the slight hysteresis loop near the maximum rates of closure. Figure 2. Open in new tabDownload slide Aperture closing rate as a function of the oscillation cycle period. Data from Figure 1 for each of the long oscillatory cycles and from a selection of the early, rapid cycles are plotted together with H+-ATPase current dynamics. H+-ATPase current determined as the difference in current at 0 mV between the hyperpolarized and depolarized phases of each oscillation cycle. Closing rates shown are corrected proportionally for the decay in K+ and anion electrochemical driving force during closure, which elevated the closing rates by approximately 20% to 25% for cycle periods of 10 min and longer. Inset, Closing rate as a function of the relative time fraction spent in the depolarized phase of the oscillation cycle. Note the slight hysteresis loop near the maximum rates of closure. To gain a better understanding of the drivers behind this optimum in closure rate, we examined the activities of the plasma membrane H+- and Ca2+-ATPases, as well as the IK,in, IK,out, ICa, ICl, and ALMT Mal2− channels at the plasma membrane. Figure 3 shows cycle period as a function of the plasma membrane currents carried by the H+-ATPase, the Ca2+-ATPase, and each of the predominant channels known to integrate with the voltage and [Ca2+]i oscillations. A corresponding analysis for the tonoplast VH+-ATPase, VH+-PPase, and Ca2+-ATPase, as well as the major tonoplast K+ and anion channels, is included in Supplemental Figure S1. Currents in each case were determined at the oscillation cycle limits and at fixed voltages outside the range in which the transporters are sensitive to voltage itself. The approach therefore avoids the complications of kinetic limitation by voltage and [Ca2+]i. Thus, for the H+-ATPase, for example, the current was determined for 0 mV, at which the pump is essentially independent of membrane voltage and close to its positive maximum output, and when the free-running voltage was at its most negative, that corresponds to low [Ca2+]i values near 200 nm, so minimizing its interference. It is obvious that oscillations in the model were closely tied to plasma membrane H+-ATPase and, to a lesser extent, to tonoplast VH+-ATPase activity. An association with the VH+-ATPase is consistent with experimental evidence that the det3 mutant, which suppresses this transporter, also eliminates Ca2+-evoked oscillations and stomatal closure (Allen et al., 2000). No strong dependence on Ca2+-ATPases, VH+-PPase, or the various ion channel activities was evident. The results also indicated that the H+-ATPase activity must decline substantially in order to facilitate the slow oscillations and accelerate stomatal closure, an observation in agreement with studies of the ost2 H+-ATPase mutant that remains constitutively active and prevents stomatal closure (Merlot et al., 2007 and below). Figure 3. Open in new tabDownload slide Oscillation cycle period is a well-defined function of H+-ATPase activity but is largely independent of the Ca2+-ATPase and the major channel currents at the plasma membrane. Data are from each of the longer oscillations in Figure 1 and from a selection of the short oscillations. A, H+-ATPase current at 0 mV and the maximum hyperpolarization in the cycle. Note the discontinuity in cycle period around 0.7 pA. B, Ca2+-ATPase current at 0 mV and minimum hyperpolarization in the cycle. C and D, Ca2+ ICa and IK,in currents at −200 mV and maximum hyperpolarization in the cycle. E, ICl current at −60 mV and minimum hyperpolarization in the cycle. Data for Mal2− flux were equivalent to that for ICl, and for the outward-rectifying K+ channels were complementary to those for IK,in and have been omitted from display. Figure 3. Open in new tabDownload slide Oscillation cycle period is a well-defined function of H+-ATPase activity but is largely independent of the Ca2+-ATPase and the major channel currents at the plasma membrane. Data are from each of the longer oscillations in Figure 1 and from a selection of the short oscillations. A, H+-ATPase current at 0 mV and the maximum hyperpolarization in the cycle. Note the discontinuity in cycle period around 0.7 pA. B, Ca2+-ATPase current at 0 mV and minimum hyperpolarization in the cycle. C and D, Ca2+ ICa and IK,in currents at −200 mV and maximum hyperpolarization in the cycle. E, ICl current at −60 mV and minimum hyperpolarization in the cycle. Data for Mal2− flux were equivalent to that for ICl, and for the outward-rectifying K+ channels were complementary to those for IK,in and have been omitted from display. With the exception of the outward-rectifying K+ channel, all of these transporters are sensitive to [Ca2+]i. The H+-ATPase, especially, is strongly suppressed by [Ca2+]i elevation (Kinoshita et al., 1995; Chen et al., 2012; Hills et al., 2012). Therefore, to explore how critically stomatal closure rate correlated with [Ca2+]i, we plotted the dynamic variation in plasma membrane H+-ATPase activity together with closing rate as a function of cycle period (Fig. 2). A close match was found between these two variables, indicating that the [Ca2+]i-driven feedback on the H+-ATPase features strongly in the link between cycle period and stomatal closure rates. The observation is important, because the plasma membrane H+-ATPase, the tonoplast VH+-ATPase and VH+-PPase, and the Ca2+-ATPases are all moderated externally in simulation, their activities declining with the daylight (Blatt et al., 2014; Wang et al., 2014). Yet only the H+-ATPase and the VH+-ATPase activities showed an obvious correspondence to the cycle period (Fig. 3; Supplemental Fig. S1), and, of these pumps, only the H+-ATPase is sensitive to [Ca2+]i (Hills et al., 2012). Why a connection to the Ca2+-ATPases should fail to surface is obvious when considering that, in the model, both of these pumps are much more strongly affected by changes in Ca2+ substrate availability, which varies by roughly 10-fold during the oscillations in [Ca2+]i. Thus, as Chen et al. (2012) noted before, it is the overall decline in energy-dependent transport activity following a period of solute accumulation—and specifically of the dominant H+-ATPase—that is prerequisite to initiate the oscillatory behavior in the model. We examined the effects of extracellular Ca2+ and K+ concentrations that have been used to manipulate voltage and [Ca2+]i and to drive their oscillations experimentally (Grabov and Blatt, 1999; Allen et al., 2001). Reducing extracellular Ca2+ to 0.1 mm in the model eliminated both voltage and [Ca2+]i oscillations, leading to a much slower rate of stomatal closure. This behavior is entirely consistent with the requirement for Ca2+ entry to trigger endomembrane Ca2+ release and elevate [Ca2+]i (Grabov and Blatt, 1998). Marginally higher external Ca2+ concentrations in the model returned [Ca2+]i and voltage transients of irregular frequency spanning cycle periods of 8 to 85 min, and elevating external Ca2+ concentrations to 3 mm yielded sets of sharply defined oscillation frequencies and faster stomatal closure rates (Fig. 4). Analysis of closure rate as a function of oscillation period with 3 mm Ca2+ outside showed the most prominent of these was displaced to a cycle period near 12 min (=1.3 mHz), consistent with greater driving force for Ca2+ influx and the longer times needed to clear the cytosol of Ca2+ following each period of elevated [Ca2+]i. Varying extracellular K+ concentration in the model, by contrast, had less impact on oscillation characteristics, although raising K+ concentration broadened the range of the most prominent frequencies (Supplemental Fig. S2). This characteristic was primarily associated with the change in cycle number: over the range from 1 to 20 mm K+, the model returned a difference in the total number of cycles that corresponds with the variation in the aperture differential between the open and closed pore at each concentration (Hills et al., 2012). With 1 mm or less K+ outside, the model returned maximum apertures below 3 μm that closed to 1.8 μm with a single cycle in [Ca2+]i elevation; in 20 mm K+, the pore opened to more than 6 μm and closed to 1.8 μm with 14 cycles of [Ca2+]i elevation. However, with 30 mm K+, outside-the-membrane voltage situated positive of the range effective in promoting Ca2+ influx to trigger a rise in [Ca2+]i. As a result, the stomatal aperture closed only gradually. Again, these characteristics are broadly consistent with the very large body of literature on stomatal aperture and its dependence on external Ca2+ and K+ (McAinsh et al., 1990; Fricker et al., 1991; Willmer and Fricker, 1996; Hills et al., 2012), and specifically with experimental manipulations of voltage and [Ca2+]i. Furthermore, the predictions of changes in oscillation period find direct support in experimental recordings, albeit in Vicia and Commelina, that show oscillation frequencies that varied with extracellular K+ (Gradmann et al., 1993) and Ca2+ (McAinsh et al., 1995) concentrations. Most important, however, the analysis with different external Ca2+ and K+ concentrations points to oscillation frequencies that are not immutable and are closely tied to the ionic drivers that contribute to flux across the plasma membrane. Figure 4. Open in new tabDownload slide Outputs of the OnGuard Arabidopsis model with different extracellular Ca2+ concentrations. Outputs resolved as in Figure 1 but with 0.3 and 3 mm Ca2+ outside. A to C. Macroscopic outputs with 0.3 mm Ca2+ outside during the period from 8.8 to 17.2 h following the start of the diurnal cycle with plasma membrane and tonoplast voltage (A), [Ca2+]i (B), and stomatal aperture and the rate of opening/closing in stomatal aperture (C; =ƊAperture/Ɗt). Positive rates here indicate opening, and negative rates indicate closing. Note the irregular oscillations in voltage and [Ca2+]i and reduced rates of stomatal closure that extend into the dark period. Results with 3 mm Ca2+ are visually almost identical to those in Figure 1. D, Fourier analysis of oscillations in membrane [Ca2+]i for the data in A to C and in with 3 mm Ca2+ outside. Data from Figure 1 are replotted in gray for reference. Note the loss in 0.3 mm Ca2+ of the prominent frequency near 1.9 mHz and the resolution of frequencies between approximately 0.5 and 1.8 mHz in 3 mm Ca2+. Higher order resonance frequencies are also well-resolved near 3 and 5 mHz when Ca2+ is elevated outside. Figure 4. Open in new tabDownload slide Outputs of the OnGuard Arabidopsis model with different extracellular Ca2+ concentrations. Outputs resolved as in Figure 1 but with 0.3 and 3 mm Ca2+ outside. A to C. Macroscopic outputs with 0.3 mm Ca2+ outside during the period from 8.8 to 17.2 h following the start of the diurnal cycle with plasma membrane and tonoplast voltage (A), [Ca2+]i (B), and stomatal aperture and the rate of opening/closing in stomatal aperture (C; =ƊAperture/Ɗt). Positive rates here indicate opening, and negative rates indicate closing. Note the irregular oscillations in voltage and [Ca2+]i and reduced rates of stomatal closure that extend into the dark period. Results with 3 mm Ca2+ are visually almost identical to those in Figure 1. D, Fourier analysis of oscillations in membrane [Ca2+]i for the data in A to C and in with 3 mm Ca2+ outside. Data from Figure 1 are replotted in gray for reference. Note the loss in 0.3 mm Ca2+ of the prominent frequency near 1.9 mHz and the resolution of frequencies between approximately 0.5 and 1.8 mHz in 3 mm Ca2+. Higher order resonance frequencies are also well-resolved near 3 and 5 mHz when Ca2+ is elevated outside. Finally, we examined the consequences of altering the [Ca2+]i sensitivity of the SLAC1 Cl− current (ICl) on [Ca2+]i oscillations and stomatal closure in simulation. A few recent studies have coined a new term “Ca2+ priming” (Kim et al., 2010), but the phenomenon it describes of modulation in [Ca2+]i and other signal transduction processes—that is, of sensitivity control—is not new (see Trewavas [1992] and Berridge and Dupont [1994]; and for guard cells Armstrong et al. [1995], Grabov et al. [1997], Garcia-Mata et al. [2003], and Chen et al. [2010]). To date, meaningful kinetic data that describe the modulation of the [Ca2+]i sensitivity of ICl are available only for the effects of protein phosphorylation in Vicia (Chen et al., 2010). These studies showed that protein phosphatase antagonism increases the [Ca2+]i sensitivity of ICl with roughly a 200 nm reduction in the apparent K Ca for activation of the current. In the Arabidopsis (Arabidopsis thaliana) model of OnGuard we examined so far, the K Ca was set to 600 nm. As a test, we reduced this parameter to 400 nm and ran model again to generate the outputs in Figure 5. Compared to that in Figure 1, increasing the [Ca2+]i sensitivity of ICl led to roughly a 20% reduction in the maximum aperture but had little effect on the maximum rate of stomatal closure. Most evident, however, was a halving in the number of cycles in [Ca2+]i and voltage oscillations and a substantial lengthening of their periods. Fourier analysis (Fig. 5D) showed that the prominent frequency of oscillations in [Ca2+]i was displaced to a cycle period of 10.4 min (=1.6 mHz), some 2.5 min longer than observed with a K Ca of 600 nm. In short, the effect of increasing the sensitivity of ICl to [Ca2+]i was to extend the period of oscillations and reduce their number, but without substantial effect on closure rate. Thus, again, the results predict that the oscillation-frequency optimum is not immutable, that closure rate is not tied to a specific oscillation-frequency optimum, and that these frequencies depend on the ionic transporters that contribute to flux across the plasma membrane. Figure 5. Open in new tabDownload slide Outputs of the OnGuard Arabidopsis model with the [Ca2+]i sensitivity of the SLAC1 Cl− channel increased by reducing its K Ca from 600 nm to 400 nm. Compare outputs with those resolved in Figure 1. A to C, Macroscopic outputs for the period from 8.7 to 12.4 h following the start of the diurnal cycle with plasma membrane and tonoplast voltage (A), [Ca2+]i (B), and stomatal aperture and the rate of opening/closing in stomatal aperture (C; =ƊAperture/Ɗt). Positive rates here indicate opening, and negative rates indicate closing. Note the elongation in voltage and [Ca2+]i oscillations and their reduction in number. D, Fourier analysis of oscillations in [Ca2+]i for the data in A to C. Data from Figure 1 are replotted in gray for reference. Note the shift in prominent frequency to 1.6 mHz (=12.4 min). Higher order frequencies are also well-resolved and displaced to lower values. Figure 5. Open in new tabDownload slide Outputs of the OnGuard Arabidopsis model with the [Ca2+]i sensitivity of the SLAC1 Cl− channel increased by reducing its K Ca from 600 nm to 400 nm. Compare outputs with those resolved in Figure 1. A to C, Macroscopic outputs for the period from 8.7 to 12.4 h following the start of the diurnal cycle with plasma membrane and tonoplast voltage (A), [Ca2+]i (B), and stomatal aperture and the rate of opening/closing in stomatal aperture (C; =ƊAperture/Ɗt). Positive rates here indicate opening, and negative rates indicate closing. Note the elongation in voltage and [Ca2+]i oscillations and their reduction in number. D, Fourier analysis of oscillations in [Ca2+]i for the data in A to C. Data from Figure 1 are replotted in gray for reference. Note the shift in prominent frequency to 1.6 mHz (=12.4 min). Higher order frequencies are also well-resolved and displaced to lower values. DISCUSSION Chen et al. (2012) analyzed the mechanistic coupling between voltage, Ca2+ influx, and elevated [Ca2+]i in detail and their connection to osmotic solute efflux during closure. They noted that membrane voltage is the common denominator that connects [Ca2+]i with the osmotic solute flux. At its negative extreme, voltage favors K+ and anion uptake but also triggers Ca2+ entry to elevate [Ca2+]i. At its positive extreme, voltage activates the channels mediating K+ and anion efflux as well as engaging Ca2+-ATPases to restore [Ca2+]i to its resting level. Thus, the model gives quantitative understanding to a previous interpretation (Blatt, 2000) that the processes driving guard cell oscillations in vivo can be thought of as a cycle of four steps: (1) with resting [Ca2+]i low, negative voltage triggers Ca2+ influx across the plasma membrane, stimulating endomembrane Ca2+ release to elevate [Ca2+]i; (2) the rise in [Ca2+]i inactivates the Ca2+ channels, shuts down IK,in to prevent solute uptake, inactivates the H+-ATPase, and activates ICl to promote membrane depolarization; (3) depolarization promotes K+ and Cl− efflux and, with Ca2+ influx suppressed, engages the Ca2+-ATPases and CAX transporters to sequester Ca2+ and restore [Ca2+]i to near-resting levels; and finally, (4) with the fall in [Ca2+]i, ICl declines, and IK,in and the H+-ATPase recover sufficiently, the latter repolarizing the membrane to facilitate K+ and H+-coupled anion uptake. In short, voltage forms the core of this intrinsic feedback between membrane transporters operating across a common membrane. The cycle also incorporates a second feedback loop of [Ca2+]i-dependent controls on several of the major transporters that dominate osmotic solute flux and on the H+-ATPase, not just on the Ca2+ channels at the plasma membrane and those facilitating endomembrane Ca2+ release. Our analysis of the SLAC1 Cl− channel (Fig. 5) and variations in extracellular Ca2+ and K+ concentrations illustrate a few of the predictions that arise from a quantitative analysis using OnGuard, and there are unquestionably others that can be drawn from similarly challenging these models. Overall, the most important and overarching of these is that voltage and [Ca2+]i interact so as to oscillate in syncopation, driving with them K+ and anion efflux to accelerate stomatal closure. Even so, the existence of an optimal [Ca2+]i oscillation frequency to drive stomatal closure is not obvious. Its emergence in simulation was entirely unexpected and underscores two points. First, the relationships between [Ca2+]i, voltage, and solute flux are highly nonlinear, as is amply demonstrated in the voltage-dependence of the component currents (Chen et al., 2012; Wang et al., 2012). Thus, only by quantitative systems analysis can the interactions between these different transporters, ion buffering and metabolism be fully understood. Second, analysis of the voltage and [Ca2+]i oscillations exposes a close dependence on primary, ATP-dependent transport and the requirement in the model for its suppression to initiate and sustain the oscillations and accelerate stomatal closure. Clearly, the model predicts that the [Ca2+]i and voltage oscillations are critically dependent on H+-ATPase activity for their initiation and maintenance. It is difficult to assign an absolute value to the H+-ATPase suppression needed for voltage and [Ca2+]i to oscillate, but a rough estimate suggests values of 0.1- to 0.4-fold of the maximum pump output, in other words around 60% to 90% inhibition of the pump activity. The maximum current output of the modeled H+-ATPase in Arabidopsis is approximately 2 pA per cell when [Ca2+]i is 100 nm and the cytosolic pH is 7.6. So oscillations in voltage and [Ca2+]i require that the pump output decline to roughly two-thirds or less of this value in order to enter the window of activity giving maximal stomatal closure rates (Figs. 2 and 3). If we consider the H+ extrusion rates in Arabidopsis with fusicoccin and the ost2 mutant to represent the maximum output—at which stomata do not close—then the H+-ATPase activity of the wild type is within range of this estimate (Merlot et al., 2007), although clearly a value based on the mean output of the wild type represents an overestimate on H+-ATPase activity permissive for rapid stomatal closure. In summary, the simulations reported here provide a close match to experimental data that have indicated the coupling between voltage, the Ca2+ channels, and endomembrane Ca2+ release in driving the [Ca2+]i cycle. Unexpectedly, OnGuard simulations support the perception that [Ca2+]i oscillations associate with an accelerated rate in stomatal closure and that the most effective bandwidths fall within a narrow range of frequencies near 1.9 mHz (=8.9 min oscillation period) under our standard model conditions. What is all the more important, therefore, is that the model outputs offer a truly mechanistic basis from which to understand the origins of this frequency optimum. An analysis of the outputs does not support the perception that this frequency is in any way unique or that it acts to initiate or control a special signaling mechanism for osmotic ion efflux. Indeed, the range of oscillation frequencies in the model depends on the Ca2+ and K+ concentrations outside, both in the model and published experiments, and the model predicts an oscillation frequency optimum that is subject to the Ca2+ sensitivity of the SLAC1 current. These observations underscore the lack of any uniqueness to the frequency optimum. All of the oscillations find their origins in the underlying relationships between voltage and current through each of the dominant transporters at the two membranes (Chen et al., 2012). More useful, then, is to recognize that the oscillations in voltage and [Ca2+]i simply reflect a spectrum of frequencies that emerge from the balance of intrinsic transport activities of the guard cell, from their variation with H+-ATPase and associated ATP-dependent transport activities, and from the several ionic driving forces across the guard cell membranes. It happens that the slower frequencies are sufficiently long-lived to enhance K+, Cl−, and Mal2− efflux from the guard cells and accelerate stomatal closure. Whether the oscillatory output may signal other metabolic changes independent of transport, such as might depend on use- or frequency-encoded phosphorylation and gene expression (DeKoninck and Schulman, 1998; Flavell et al., 2006; Shalizi et al., 2006), remains an open question. What is clear, however, is that an optimum in [Ca2+]i oscillations is a by-product, rather than a cause of, accelerated stomatal closure. MATERIALS AND METHODS Investigation of the modality of the [Ca2+]i oscillations, their frequencies, link to stomatal dynamics, and mechanism, was carried out here on the Arabidopsis (Arabidopsis thaliana) version of the OnGuard model, briefly overviewed below. The study centered on the [Ca2+]i oscillatory pattern spontaneously generated over a diurnal pattern under conditions detailed in the figures and text. Analysis of their origin, frequencies, and mechanism was carried out by a systematic examination of their correlation with plasma and tonoplast membrane voltages, stomatal aperture at different fluency regimes, and with the activities of the plasma membrane H+- and Ca2+-ATPases as well as IK,in, IK,out, ICa, ICl, and ALMT Mal2− channels at the plasma membrane. Examination of the frequency components of the [Ca2+]i oscillations was carried out by Fourier spectral analysis throughout. Model Overview A substantial body of data now exists that has led to an integrated and quantitative systems description of guard cells (Chen et al., 2012; Hills et al., 2012; Wang et al., 2012, 2014; Blatt et al., 2014). These quantitative models, generated with the OnGuard software (freely available at www.psrg.org.uk), codify the complexity inherent in the interactions between transport, metabolism, and buffering reactions of the guard cell, enabling a deep, mechanistic understanding inaccessible from intuition alone. OnGuard is built on user-definable libraries for transporter kinetics, chemical buffering, macromolecular binding, and metabolic reactions, and it includes the macroscopic equations essential to couple these processes to solute content, cell volume, turgor, and stomatal aperture (Hills et al., 2012). OnGuard models of Vicia and Arabidopsis reproduce the range of known properties of these guard cells with respect to ion transport, solute content, and stomatal aperture. They have yielded unexpected and emergent outputs, including counterintuitive changes in [Ca2+]i and cytosolic pH over the diurnal cycle, and an exchange of vacuolar Mal2− with Cl− subject to the availability of the inorganic anion, all of which have direct support in independent experimental data (Raschke and Schnabl, 1978; MacRobbie, 1991, 1995, 2000, 2006; Thiel et al., 1992; Blatt and Armstrong, 1993; Willmer and Fricker, 1996; Frohnmeyer et al., 1998; Dodd et al., 2005, 2006). Furthermore, OnGuard models have demonstrated true predictive power, for example, in guiding experiments that led Wang et al. (2012) to explain how eliminating the SLAC1 Cl− channel, which slows stomatal closure, also suppresses current through inward-rectifying K+ channels to slow stomatal opening. Model Construction Construction of the Vicia and Arabidopsis models is detailed in previous publications (Chen et al., 2012; Hills et al., 2012; Wang et al., 2012; Blatt et al., 2014). Hills et al. (2012) provide a complete list of the transporters with short summaries of their characteristics, their parameterization, the associated genes and proteins, and details of sensitivity analyses (see Appendix 2 and Supplemental Tables 1 to 6 therein). For the present task, we used the Arabidopsis model (Wang et al., 2012) to analyze the [Ca2+]i and voltage oscillations associated with stomatal closure. Again, Wang et al. (2012) provide the complete parameterization list for all of the transport and metabolic reactions in their supplemental material. In both models, the activities of all primary ATP-dependent transporters and organic solute synthesis in the guard cell are coupled to fluence rate. The activities of all other enzymatic, transport, and buffering processes are dictated by their inherent kinetics, binding, and regulatory parameters defined by the associated kinetic equations. Thus, the dynamic behavior of these transporters arises entirely from interactions between the component transport, buffering, and metabolic processes within the system as a whole. Oscillations in voltage and [Ca2+]i, which are observed in the models near the end of the daylight period shortly before and during stomatal closure, are a case in point. These oscillations follow on the decline in ATPase activities and reflect a release of the energy stored in the various ionic gradients that are generated during the daylight period. Chen et al. (2012) have analyzed the underlying mechanisms coupling voltage, Ca2+ influx and elevated [Ca2+]i with osmotic solute flux during these oscillations in detail. This study also demonstrated that the simulated oscillations are a stable and reproducible feature of the diurnal cycle. Therefore, we focus here on the macroscopic characteristics of the oscillations, their frequency, and their dependence on extracellular Ca2+ and K+ concentrations. The concentration parameters are directly accessible by the user in OnGuard and were employed previously to manipulate voltage and [Ca2+]i experimentally, as noted above. Supplemental Data Supplemental Figure S1. Oscillation cycle period is a well-defined function of vacuolar VH+-ATPase activity but shows little evidence of a dependence on the Ca2+-ATPase or any of the major, [Ca2+]i-sensitive currents at the tonoplast. Supplemental Figure S2. Fourier spectral analysis of oscillation frequencies in membrane voltage and [Ca2+]i with 5 and 20 mm K+ concentrations outside. Glossary [Ca2+]i cytosolic-free [Ca2+] ABA abscisic acid IK,in inward-rectifying K+ channels ICl Cl− (anion) channels IK,out outward-rectifying K+ channels ICa Ca2+ channels LITERATURE CITED Allen GJ , Chu SP, Harrington CL, Schumacher K, Hoffmann T, Tang YY, Grill E, Schroeder JI ( 2001 ) A defined range of guard cell calcium oscillation parameters encodes stomatal movements . 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Plant Physiol 160 : 1956 – 1967 Google Scholar Crossref Search ADS PubMed WorldCat Webb AAR , McAinsh MR, Mansfield TA, Hetherington AM ( 1996 ) Carbon dioxide induces increases in guard cell cytosolic free calcium . Plant J 9 : 297 – 304 Google Scholar Crossref Search ADS WorldCat Willmer C , Fricker MD ( 1996 ) Stomata, Vol 2. Chapman and Hall , London , pp 1 – 375 Author notes 1 This work was supported by the Biotechnology and Biological Sciences Research Council (grant nos. BB/L019205/1 and BB/M001601/1 to M.R.B., BB/L001276/1 to M.R.B. and S.R., and BB/I001187/1 to H.G. and T.L.) and EU OPTIMA (project 289642 Ph.D. studentship to C.M.-P.). 2 These authors contributed equally to the article. * Address correspondence to [email protected] and [email protected]. The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantphysiol.org) is: Michael R. Blatt ([email protected]). C.M.-P. and Y.W. tested OnGuard models, quantifying and validating the outputs; A.H. developed OnGuard and Fourier analysis routines for output analysis; S.V.-C., H.G., S.R., and T.L. contributed to discussion of output and Fourier analysis; M.R.B. developed the concepts and carried out the Fourier analysis; V.L.L. and M.R.B. wrote the article. [CC-BY] Article Free via Creative Commons CC-BY 4.0 license. www.plantphysiol.org/cgi/doi/10.1104/pp.15.01607 © 2016 The Authors. All Rights Reserved. © The Author(s) 2016. Published by Oxford University Press on behalf of American Society of Plant Biologists. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

António, Carla; Päpke, Carola; Rocha, Marcio; Diab, Houssein; Limami, Anis M.; Obata, Toshihiro; Fernie, Alisdair R.; van Dongen, Joost T.

doi: 10.1104/pp.15.00266pmid: 26553649

Abstract Based on enzyme activity assays and metabolic responses to waterlogging of the legume Lotus japonicus, it was previously suggested that, during hypoxia, the tricarboxylic acid cycle switches to a noncyclic operation mode. Hypotheses were postulated to explain the alternative metabolic pathways involved, but as yet, a direct analysis of the relative redistribution of label through the corresponding pathways was not made. Here, we describe the use of stable isotope-labeling experiments for studying metabolism under hypoxia using wild-type roots of the crop legume soybean (Glycine max). [13C]Pyruvate labeling was performed to compare metabolism through the tricarboxylic acid cycle, fermentation, alanine metabolism, and the γ-aminobutyric acid shunt, while [13C]glutamate and [15N]ammonium labeling were performed to address the metabolism via glutamate to succinate. Following these labelings, the time course for the redistribution of the 13C/15N label throughout the metabolic network was evaluated with gas chromatography-time of flight-mass spectrometry. Our combined labeling data suggest the inhibition of the tricarboxylic acid cycle enzyme succinate dehydrogenase, also known as complex II of the mitochondrial electron transport chain, providing support for the bifurcation of the cycle and the down-regulation of the rate of respiration measured during hypoxic stress. Moreover, up-regulation of the γ-aminobutyric acid shunt and alanine metabolism explained the accumulation of succinate and alanine during hypoxia. Plants are sessile, unable to relocate when exposed to diverse environmental and seasonal stimuli, and hence must be able to respond rapidly to survive stress conditions. Flooding or waterlogging of the soil is a common environmental condition that can greatly affect crop production and quality by blocking the entry of oxygen into the soil so that roots and other belowground organs cannot maintain respiration. In recent decades, the number of extreme floodings has strongly increased, which is especially tragic because most arable land worldwide is located in regions that are threatened by regular flooding events (Voesenek and Bailey-Serres, 2015). In plant heterotrophic tissues, respiratory metabolism is composed of various pathways, including glycolysis, the mitochondrial tricarboxylic acid cycle, and the mitochondrial electron transport chain. Under normal conditions, the conversion of Glc to pyruvate in the cytosol involves an initial input of ATP and produces the reduced cofactor NADH. The reactions of the tricarboxylic acid cycle occur within the mitochondrial matrix and lead to the complete oxidation of pyruvate, moving electrons from organic acids to the oxidized redox cofactors NAD+ and FAD, forming the reducing equivalents NADH and FADH2 and concomitantly releasing carbon dioxide (Tovar-Méndez et al., 2003; Millar et al., 2011). Finally, the reduced cofactors generated during glycolysis and the tricarboxylic acid cycle are subsequently oxidized by the mitochondrial electron transport chain to fuel ATP synthesis by a process known as oxidative phosphorylation (Fernie et al., 2004; Plaxton and Podesta, 2006). The tricarboxylic acid cycle turnover rate depends greatly on the rate of NADH reoxidation by the mitochondrial electron transport chain and on the cellular rate of ATP utilization (Plaxton and Podesta, 2006). Besides supporting ATP synthesis, the reactions of the tricarboxylic acid cycle also contribute to the production of key metabolic intermediates for use in many other fundamental biosynthetic processes elsewhere in the cell (Fernie et al., 2004; Sweetlove et al., 2010; van Dongen et al., 2011; Araújo et al., 2012). Nevertheless, the control and regulation of the carbon flux through the tricarboxylic acid cycle are still poorly understood in plants, and noncyclic modes have been described to operate under certain circumstances (Rocha et al., 2010; Sweetlove et al., 2010; Araújo et al., 2012). Upon hypoxia, respiratory energy (ATP) production via oxidative phosphorylation by the mitochondrial electron transport chain goes down. To compensate for this, the glycolytic flux increases and Glc is consumed faster in an attempt to produce ATP via the glycolytic pathway, a process known as the Pasteur effect. To survive short-term hypoxia during flooding or waterlogging, plants must generate sufficient ATP and regenerate NADP+ and NAD+, which are required for glycolysis (Narsai et al., 2011; van Dongen et al., 2011). In addition to the accumulation of ethanol and lactate in oxygen-deprived plant tissues, metabolites such as Ala, succinate, and γ-aminobutyric acid (GABA) have also been shown to accumulate (Sousa and Sodek, 2003; Kreuzwieser et al., 2009; van Dongen et al., 2009; Rocha et al., 2010; Zabalza et al., 2011), although hardly anything is known about the fate of these products of hypoxic metabolism. However, the relative abundance of these products of hypoxic metabolism varies between plant species, genotypes, and tissues and can change throughout the course of oxygen limitation stress as well (Narsai et al., 2011). A model describing metabolic changes during hypoxia has been described previously for waterlogged roots of the highly flood-tolerant model crop legume Lotus japonicus (Rocha et al., 2010): upon waterlogging, the rate of pyruvate production is enhanced due to the activation of glycolysis (Pasteur effect) and the concomitant production of ATP via substrate-level phosphorylation. At the same time, the fermentation pathway is activated with the accumulation of lactate via lactate dehydrogenase and ethanol via two subsequent reactions catalyzed by pyruvate decarboxylase and alcohol dehydrogenase (Tadege et al., 1999). The amount of pyruvate produced can be reduced via alanine aminotransferease (AlaAT), which catalyzes the reversible reaction interconverting pyruvate and Glu to Ala and 2-oxoglutarate (2OG). Concomitantly, 2OG was suggested to reenter the tricarboxylic acid cycle to be used to produce another ATP and also succinate, which accumulates in the cell (Rocha et al., 2010). This Ala pathway provides a means for the role of Ala accumulation during hypoxia via reorganization of the tricarboxylic acid cycle. Furthermore, given that the use of this strategy prevents pyruvate accumulation, the continued operation of glycolysis during waterlogging can occur. It should be noted, however, that measurements of metabolite levels alone do not provide information about the actual activity of the metabolic pathways involved. Furthermore, the previous studies did not reveal which enzymes of the tricarboxylic acid cycle change their activity that leads to reorganization of the tricarboxylic acid cycle. To overcome this, analysis of metabolism using isotope-labeled substrates has proven to be essential for understanding the control and regulation of metabolic networks, and it has often been observed that significant changes in carbon flow are sometimes associated with only small adjustments in metabolite abundance (Schwender et al., 2004; Ratcliffe and Shachar-Hill, 2006). Metabolomics studies that require extensive metabolite labeling utilize uniformly labeled stable isotope tracers. Alternatively, detailed analysis of central carbon metabolism can make use of positional labeling as well. Following the extraction of labeled metabolites, the 13C label redistribution is measured usually with NMR or gas chromatography-mass spectrometry methods (Jorge et al., 2015). Schwender and Ohlrogge (2002) used both labeling approaches to investigate embryo development in Brassica napus seeds. While uniformly labeled [13C6]Glc and [13C12]Suc were applied to determine the metabolic flux through the major pathways of carbon metabolism, positionally labeled [1,2-13C]Glc was used to specifically outline the glycolytic/oxidative pentose phosphate pathway network during embryo development (Schwender and Ohlrogge, 2002). Gas chromatography-mass spectrometry analysis was used in this study to evaluate the 13C enrichment and isotopomer composition. In earlier studies of hypoxic metabolism, positionally labeled [1-13C]Glc was used to specifically investigate energy metabolism and pH regulation in hypoxic maize (Zea mays) root tips (Roberts et al., 1992; Edwards et al., 1998). In this study, we performed stable isotope labeling experiments using wild-type soybean (Glycine max) roots in order to better understand the dynamics of metabolism in operation in plant cells under hypoxic conditions. For this, we used fully labeled 13C and 15N tracers rather than positional labeling, as this allowed us to cover a broad view of the central carbon and nitrogen metabolic network. The labeling pattern of metabolites was subsequently measured with gas chromatography-time of flight-mass spectrometry (GC-TOF-MS). Our studies confirm the activity of Ala metabolism while revealing the parallel activity of the GABA shunt. The results provide evidence that the bifurcation of the tricarboxylic acid cycle results from the inhibition of the tricarboxylic acid cycle enzyme succinate dehydrogenase (SDH), also known as complex II of the mitochondrial electron transport chain (mETC). RESULTS Metabolite Profiling and Isotope Redistribution Analysis To obtain an overview of the metabolic responses of soybean roots under our experimental conditions, we first carried out a broad metabolite profiling study with GC-TOF-MS (Lisec et al., 2006; Fig. 1). Using this approach, over 30 metabolites from the central primary metabolism were characterized, including sugars, amino acids, and intermediates of the tricarboxylic acid cycle. Figure 1 shows the changes of selected primary metabolites in soybean root extracts expressed as the ratio between values obtained with hypoxia and normoxia conditions. Significant accumulation (Student’s t test, P < 0.05) of Fru, Glc, lactate, Val, Leu, Ser, Phe, Ala, and Pro was observed in soybean roots after 6 h of hypoxic treatment (Fig. 1; Table I). Asp, Asn, and the organic acid fumarate were shown to decrease significantly after 6 h of hypoxic treatment (Student’s t test, P < 0.05; Fig. 1; Table I). These data showed that after 6 h, an increase in many amino acids was observed in hypoxic soybean roots while intermediates of the tricarboxylic acid cycle remained mostly unchanged, except for succinate, which accumulated. Figure 1. Open in new tabDownload slide Relative abundance of metabolites in soybean root pieces during a 6-h time course of hypoxia treatment determined with GC-TOF-MS. The relative metabolite levels are normalized to an internal standard (ribitol) and the fresh weight of the samples and are depicted on a primary metabolite map. The gray bars represent the ratio of metabolite levels between hypoxia and normoxia conditions at each time interval. The values are means ± se of six biological replicates. Asterisks indicate that these values showed significant differences from the control (normoxia) in Student’s t test (P < 0.05). AlaT, Ala aminotransferase; GABA-T, γ-aminobutyric acid trans-aminase; GOGAT, Glu synthase; ICL, isocitrate lyase; ME, malic enzyme; MS, malate synthase; OGDH, 2-oxoglutarate dehydrogenase; PEP, phosphoenolpyruvate; PEPC, phosphoenolpyruvate carboxylase; SSADH, succinic semialdehyde dehydrogenase. Figure 1. Open in new tabDownload slide Relative abundance of metabolites in soybean root pieces during a 6-h time course of hypoxia treatment determined with GC-TOF-MS. The relative metabolite levels are normalized to an internal standard (ribitol) and the fresh weight of the samples and are depicted on a primary metabolite map. The gray bars represent the ratio of metabolite levels between hypoxia and normoxia conditions at each time interval. The values are means ± se of six biological replicates. Asterisks indicate that these values showed significant differences from the control (normoxia) in Student’s t test (P < 0.05). AlaT, Ala aminotransferase; GABA-T, γ-aminobutyric acid trans-aminase; GOGAT, Glu synthase; ICL, isocitrate lyase; ME, malic enzyme; MS, malate synthase; OGDH, 2-oxoglutarate dehydrogenase; PEP, phosphoenolpyruvate; PEPC, phosphoenolpyruvate carboxylase; SSADH, succinic semialdehyde dehydrogenase. GC-TOF-MS primary metabolite profiling of soybean root tissue following incubation in different substrates Table I. GC-TOF-MS primary metabolite profiling of soybean root tissue following incubation in different substrates Relative values are ratios between hypoxia and normoxia conditions and are represented as means ± se of six independent measurements. Values in boldface indicate significant differences from the control (normoxia) in Student’s t test (P < 0.05). False color imaging was performed on log10-transformed GC-TOF-MS metabolite data. A, Control (no substrate) versus pyruvate data. B, Control (no substrate) versus Glu data. The control data (no substrate) are the same and shown twice only for comparison purposes between pyruvate and Glu treatments. Open in new tab Table I. GC-TOF-MS primary metabolite profiling of soybean root tissue following incubation in different substrates Relative values are ratios between hypoxia and normoxia conditions and are represented as means ± se of six independent measurements. Values in boldface indicate significant differences from the control (normoxia) in Student’s t test (P < 0.05). False color imaging was performed on log10-transformed GC-TOF-MS metabolite data. A, Control (no substrate) versus pyruvate data. B, Control (no substrate) versus Glu data. The control data (no substrate) are the same and shown twice only for comparison purposes between pyruvate and Glu treatments. Open in new tab Isotope tracer experiments were next performed by assessing how 13C isotopes were redistributed among metabolites following the incubation of excised soybean roots in [13C]pyruvate or [13C]Glu under hypoxic conditions. The same experiment was performed using the corresponding 12C substrates as controls to assess the effect of substrate provision alone, and the labeling pattern of metabolites was subsequently measured with GC-TOF-MS. Metabolite profiling analysis revealed that the metabolic changes induced by low oxygen were independent from the supply of labeled/unlabeled substrate. Similar levels of the characteristic hypoxia-responsive metabolites Ala, GABA, lactate, succinate, and Asp were observed in all treatments (Table I; Supplemental Table S1). The [13C]pyruvate labeling experiment revealed a rapid 13C label incorporation into lactate and Ala after 3 h of hypoxic treatment. Label incorporation doubled after 6 h (Fig. 2; Tables II and III; Supplemental Table S2). To a lesser extent, 13C label was also redistributed from pyruvate to citrate and 2OG, a process that was apparently independent of the oxygen concentration, since similar levels were observed under both normoxic and hypoxic conditions. In addition, considerable 13C label was incorporated into Glu (via 2OG). Figure 2. Open in new tabDownload slide Total 13C accumulation in primary metabolites in soybean roots following [13C]pyruvate feeding for a period of 6 h. Black and gray bars represent normoxia and hypoxia conditions, respectively. All data are given in nmol g−1 fresh weight, and asterisks indicate that these values showed significant differences from the control (normoxia) in Student’s t test (P < 0.05). Red and blue color represent the transfer of carbon through the pathways of AlaAT and the GABA shunt, respectively. The black dashed lines indicate that pyruvate and 2OG can participate in different reactions; however, the data are the same. The three carbons donated by pyruvate to Ala are highlighted in red. The two carbons donated by acetyl-CoA to citrate are highlighted in blue, at which point they become randomized and no longer can be traced. Abbreviations not defined in Figure 1 are as follows: GAD, Glu decarboxylase; LDH, lactate dehydrogenase. Figure 2. Open in new tabDownload slide Total 13C accumulation in primary metabolites in soybean roots following [13C]pyruvate feeding for a period of 6 h. Black and gray bars represent normoxia and hypoxia conditions, respectively. All data are given in nmol g−1 fresh weight, and asterisks indicate that these values showed significant differences from the control (normoxia) in Student’s t test (P < 0.05). Red and blue color represent the transfer of carbon through the pathways of AlaAT and the GABA shunt, respectively. The black dashed lines indicate that pyruvate and 2OG can participate in different reactions; however, the data are the same. The three carbons donated by pyruvate to Ala are highlighted in red. The two carbons donated by acetyl-CoA to citrate are highlighted in blue, at which point they become randomized and no longer can be traced. Abbreviations not defined in Figure 1 are as follows: GAD, Glu decarboxylase; LDH, lactate dehydrogenase. Total 13C accumulation in primary metabolites in soybean roots following incubation in [13C]pyruvate for 3 and 6 h Table II. Total 13C accumulation in primary metabolites in soybean roots following incubation in [13C]pyruvate for 3 and 6 h Values are means ± se (nmol g−1 fresh weight) of six independent measurements. Values in boldface showed significant differences from the control (normoxia) in Student’s t test (P < 0.05). Metabolite . Total 13C Label . 3 h . 6 h . Normoxia . Hypoxia . Normoxia . Hypoxia . Fumarate 0.016 ± 0.000 1.352E-05 ± 0.000 0.034 ± 0.000 8.003E-05 ± 0.000 Citrate 0.182 ± 0.006 0.176 ± 0.011 0.634 ± 0.022 0.623 ± 0.019 2OG 0.240 ± 0.028 0.207 ± 0.025 0.543 ± 0.041 0.606 ± 0.032 GABA 0.532 ± 0.016 0.889 ± 0.024 3.514 ± 0.214 6.141 ± 0.313 Succinate 0.595 ± 0.013 0.816 ± 0.020 0.786 ± 0.021 1.556 ± 0.096 Ala 0.619 ± 0.052 1.054 ± 0.048 0.799 ± 0.049 2.217 ± 0.098 Lactate 0.645 ± 0.048 2.180 ± 0.113 1.025 ± 0.123 7.036 ± 0.224 Asp 0.866 ± 0.057 0.506 ± 0.033 1.393 ± 0.053 1.087 ± 0.073 Glu 1.122 ± 0.086 1.482 ± 0.075 1.656 ± 0.069 2.598 ± 0.146 Malate 2.464 ± 0.134 0.611 ± 0.015 3.810 ± 0.173 4.184 ± 0.168 Pyruvate 17.346 ± 0.788 19.188 ± 0.526 19.851 ± 0.631 22.369 ± 1.020 Metabolite . Total 13C Label . 3 h . 6 h . Normoxia . Hypoxia . Normoxia . Hypoxia . Fumarate 0.016 ± 0.000 1.352E-05 ± 0.000 0.034 ± 0.000 8.003E-05 ± 0.000 Citrate 0.182 ± 0.006 0.176 ± 0.011 0.634 ± 0.022 0.623 ± 0.019 2OG 0.240 ± 0.028 0.207 ± 0.025 0.543 ± 0.041 0.606 ± 0.032 GABA 0.532 ± 0.016 0.889 ± 0.024 3.514 ± 0.214 6.141 ± 0.313 Succinate 0.595 ± 0.013 0.816 ± 0.020 0.786 ± 0.021 1.556 ± 0.096 Ala 0.619 ± 0.052 1.054 ± 0.048 0.799 ± 0.049 2.217 ± 0.098 Lactate 0.645 ± 0.048 2.180 ± 0.113 1.025 ± 0.123 7.036 ± 0.224 Asp 0.866 ± 0.057 0.506 ± 0.033 1.393 ± 0.053 1.087 ± 0.073 Glu 1.122 ± 0.086 1.482 ± 0.075 1.656 ± 0.069 2.598 ± 0.146 Malate 2.464 ± 0.134 0.611 ± 0.015 3.810 ± 0.173 4.184 ± 0.168 Pyruvate 17.346 ± 0.788 19.188 ± 0.526 19.851 ± 0.631 22.369 ± 1.020 Open in new tab Table II. Total 13C accumulation in primary metabolites in soybean roots following incubation in [13C]pyruvate for 3 and 6 h Values are means ± se (nmol g−1 fresh weight) of six independent measurements. Values in boldface showed significant differences from the control (normoxia) in Student’s t test (P < 0.05). Metabolite . Total 13C Label . 3 h . 6 h . Normoxia . Hypoxia . Normoxia . Hypoxia . Fumarate 0.016 ± 0.000 1.352E-05 ± 0.000 0.034 ± 0.000 8.003E-05 ± 0.000 Citrate 0.182 ± 0.006 0.176 ± 0.011 0.634 ± 0.022 0.623 ± 0.019 2OG 0.240 ± 0.028 0.207 ± 0.025 0.543 ± 0.041 0.606 ± 0.032 GABA 0.532 ± 0.016 0.889 ± 0.024 3.514 ± 0.214 6.141 ± 0.313 Succinate 0.595 ± 0.013 0.816 ± 0.020 0.786 ± 0.021 1.556 ± 0.096 Ala 0.619 ± 0.052 1.054 ± 0.048 0.799 ± 0.049 2.217 ± 0.098 Lactate 0.645 ± 0.048 2.180 ± 0.113 1.025 ± 0.123 7.036 ± 0.224 Asp 0.866 ± 0.057 0.506 ± 0.033 1.393 ± 0.053 1.087 ± 0.073 Glu 1.122 ± 0.086 1.482 ± 0.075 1.656 ± 0.069 2.598 ± 0.146 Malate 2.464 ± 0.134 0.611 ± 0.015 3.810 ± 0.173 4.184 ± 0.168 Pyruvate 17.346 ± 0.788 19.188 ± 0.526 19.851 ± 0.631 22.369 ± 1.020 Metabolite . Total 13C Label . 3 h . 6 h . Normoxia . Hypoxia . Normoxia . Hypoxia . Fumarate 0.016 ± 0.000 1.352E-05 ± 0.000 0.034 ± 0.000 8.003E-05 ± 0.000 Citrate 0.182 ± 0.006 0.176 ± 0.011 0.634 ± 0.022 0.623 ± 0.019 2OG 0.240 ± 0.028 0.207 ± 0.025 0.543 ± 0.041 0.606 ± 0.032 GABA 0.532 ± 0.016 0.889 ± 0.024 3.514 ± 0.214 6.141 ± 0.313 Succinate 0.595 ± 0.013 0.816 ± 0.020 0.786 ± 0.021 1.556 ± 0.096 Ala 0.619 ± 0.052 1.054 ± 0.048 0.799 ± 0.049 2.217 ± 0.098 Lactate 0.645 ± 0.048 2.180 ± 0.113 1.025 ± 0.123 7.036 ± 0.224 Asp 0.866 ± 0.057 0.506 ± 0.033 1.393 ± 0.053 1.087 ± 0.073 Glu 1.122 ± 0.086 1.482 ± 0.075 1.656 ± 0.069 2.598 ± 0.146 Malate 2.464 ± 0.134 0.611 ± 0.015 3.810 ± 0.173 4.184 ± 0.168 Pyruvate 17.346 ± 0.788 19.188 ± 0.526 19.851 ± 0.631 22.369 ± 1.020 Open in new tab Fractional enrichment of metabolites labeled in soybean root tissue following incubation in [13C]pyruvate for 3 and 6 h Table III. Fractional enrichment of metabolites labeled in soybean root tissue following incubation in [13C]pyruvate for 3 and 6 h Values are means ± se (%) of six independent measurements. Values in boldface indicate significant differences from the control (normoxia) in Student’s t test (P < 0.05). Metabolite . 13C Enrichment . 3 h . 6 h . Normoxia . Hypoxia . Normoxia . Hypoxia . Fumarate 0.842 ± 0.004 0.001 ± 0.000 1.173 ± 0.004 0.004 ± 0.000 Citrate 0.959 ± 0.027 1.013 ± 0.021 2.990 ± 0.111 3.074 ± 0.019 2OG 1.259 ± 0.060 1.195 ± 0.073 2.557 ± 0.346 2.992 ± 0.241 Malate 1.296 ± 0.075 1.155 ± 0.052 4.088 ± 0.068 4.025 ± 0.095 GABA 3.258 ± 0.133 2.495 ± 0.214 8.761 ± 0.118 11.322 ± 0.159 Succinate 4.458 ± 0.187 4.986 ± 0.114 6.274 ± 0.210 7.173 ± 0.226 Asp 5.875 ± 0.136 5.463 ± 0.121 8.205 ± 0.235 10.184 ± 0.083 Glu 8.756 ± 0.050 8.654 ± 0.054 11.754 ± 0.061 14.748 ± 0.090 Lactate 10.976 ± 0.125 11.592 ± 0.103 17.275 ± 0.053 18.492 ± 0.109 Ala 14.576 ± 0.074 14.316 ± 0.084 19.429 ± 0.049 20.327 ± 0.037 Pyruvate 89.586 ± 0.167 87.576 ± 0.145 88.386 ± 0.195 89.420 ± 0.030 Metabolite . 13C Enrichment . 3 h . 6 h . Normoxia . Hypoxia . Normoxia . Hypoxia . Fumarate 0.842 ± 0.004 0.001 ± 0.000 1.173 ± 0.004 0.004 ± 0.000 Citrate 0.959 ± 0.027 1.013 ± 0.021 2.990 ± 0.111 3.074 ± 0.019 2OG 1.259 ± 0.060 1.195 ± 0.073 2.557 ± 0.346 2.992 ± 0.241 Malate 1.296 ± 0.075 1.155 ± 0.052 4.088 ± 0.068 4.025 ± 0.095 GABA 3.258 ± 0.133 2.495 ± 0.214 8.761 ± 0.118 11.322 ± 0.159 Succinate 4.458 ± 0.187 4.986 ± 0.114 6.274 ± 0.210 7.173 ± 0.226 Asp 5.875 ± 0.136 5.463 ± 0.121 8.205 ± 0.235 10.184 ± 0.083 Glu 8.756 ± 0.050 8.654 ± 0.054 11.754 ± 0.061 14.748 ± 0.090 Lactate 10.976 ± 0.125 11.592 ± 0.103 17.275 ± 0.053 18.492 ± 0.109 Ala 14.576 ± 0.074 14.316 ± 0.084 19.429 ± 0.049 20.327 ± 0.037 Pyruvate 89.586 ± 0.167 87.576 ± 0.145 88.386 ± 0.195 89.420 ± 0.030 Open in new tab Table III. Fractional enrichment of metabolites labeled in soybean root tissue following incubation in [13C]pyruvate for 3 and 6 h Values are means ± se (%) of six independent measurements. Values in boldface indicate significant differences from the control (normoxia) in Student’s t test (P < 0.05). Metabolite . 13C Enrichment . 3 h . 6 h . Normoxia . Hypoxia . Normoxia . Hypoxia . Fumarate 0.842 ± 0.004 0.001 ± 0.000 1.173 ± 0.004 0.004 ± 0.000 Citrate 0.959 ± 0.027 1.013 ± 0.021 2.990 ± 0.111 3.074 ± 0.019 2OG 1.259 ± 0.060 1.195 ± 0.073 2.557 ± 0.346 2.992 ± 0.241 Malate 1.296 ± 0.075 1.155 ± 0.052 4.088 ± 0.068 4.025 ± 0.095 GABA 3.258 ± 0.133 2.495 ± 0.214 8.761 ± 0.118 11.322 ± 0.159 Succinate 4.458 ± 0.187 4.986 ± 0.114 6.274 ± 0.210 7.173 ± 0.226 Asp 5.875 ± 0.136 5.463 ± 0.121 8.205 ± 0.235 10.184 ± 0.083 Glu 8.756 ± 0.050 8.654 ± 0.054 11.754 ± 0.061 14.748 ± 0.090 Lactate 10.976 ± 0.125 11.592 ± 0.103 17.275 ± 0.053 18.492 ± 0.109 Ala 14.576 ± 0.074 14.316 ± 0.084 19.429 ± 0.049 20.327 ± 0.037 Pyruvate 89.586 ± 0.167 87.576 ± 0.145 88.386 ± 0.195 89.420 ± 0.030 Metabolite . 13C Enrichment . 3 h . 6 h . Normoxia . Hypoxia . Normoxia . Hypoxia . Fumarate 0.842 ± 0.004 0.001 ± 0.000 1.173 ± 0.004 0.004 ± 0.000 Citrate 0.959 ± 0.027 1.013 ± 0.021 2.990 ± 0.111 3.074 ± 0.019 2OG 1.259 ± 0.060 1.195 ± 0.073 2.557 ± 0.346 2.992 ± 0.241 Malate 1.296 ± 0.075 1.155 ± 0.052 4.088 ± 0.068 4.025 ± 0.095 GABA 3.258 ± 0.133 2.495 ± 0.214 8.761 ± 0.118 11.322 ± 0.159 Succinate 4.458 ± 0.187 4.986 ± 0.114 6.274 ± 0.210 7.173 ± 0.226 Asp 5.875 ± 0.136 5.463 ± 0.121 8.205 ± 0.235 10.184 ± 0.083 Glu 8.756 ± 0.050 8.654 ± 0.054 11.754 ± 0.061 14.748 ± 0.090 Lactate 10.976 ± 0.125 11.592 ± 0.103 17.275 ± 0.053 18.492 ± 0.109 Ala 14.576 ± 0.074 14.316 ± 0.084 19.429 ± 0.049 20.327 ± 0.037 Pyruvate 89.586 ± 0.167 87.576 ± 0.145 88.386 ± 0.195 89.420 ± 0.030 Open in new tab The [13C]Glu labeling experiment, on the other hand, revealed a 13C label incorporation mainly in GABA, succinate, and Asn (via Asp) immediately after 3 h of hypoxic treatment (Fig. 3; Tables IV and V; Supplemental Table S3). The accumulation of 13C label in succinate provides additional evidence that the GABA shunt is active during hypoxia. Furthermore, while almost undetectable 13C label was observed in fumarate under hypoxia, 13C label was observed in malate, presumably by incorporation via oxaloacetate (OAA), which is in agreement with an anticlockwise operation of the tricarboxylic acid cycle during hypoxia (Figs. 2 and 3). Figure 3. Open in new tabDownload slide Total 13C accumulation in primary metabolites in soybean roots following [13C]Glu feeding for a period of 6 h. Black and gray bars represent normoxia and hypoxia conditions, respectively. All data are given in nmol g−1 fresh weight, and asterisks indicate that these values showed significant differences from the control (normoxia) in Student’s t test (P < 0.05). Red and blue color represent the transfer of carbon through the pathways of AlaAT and the GABA shunt, respectively. The black dashed line indicates that 2OG can participate in different reactions; however, the data are the same. The five carbons donated by Glu to 2OG are highlighted in red. Abbreviations are as defined in Figures 1 and 2. Figure 3. Open in new tabDownload slide Total 13C accumulation in primary metabolites in soybean roots following [13C]Glu feeding for a period of 6 h. Black and gray bars represent normoxia and hypoxia conditions, respectively. All data are given in nmol g−1 fresh weight, and asterisks indicate that these values showed significant differences from the control (normoxia) in Student’s t test (P < 0.05). Red and blue color represent the transfer of carbon through the pathways of AlaAT and the GABA shunt, respectively. The black dashed line indicates that 2OG can participate in different reactions; however, the data are the same. The five carbons donated by Glu to 2OG are highlighted in red. Abbreviations are as defined in Figures 1 and 2. Total 13C accumulation in primary metabolites in soybean roots following incubation in [13C]Glu for 3 and 6 h Table IV. Total 13C accumulation in primary metabolites in soybean roots following incubation in [13C]Glu for 3 and 6 h Values are means ± se (nmol g−1 fresh weight) of six independent measurements. Values in boldface indicate significant differences from the control (normoxia) in Student’s t test (P < 0.05). Metabolite . Total 13C Label . 3 h . 6 h . Normoxia . Hypoxia . Normoxia . Hypoxia . Fumarate 0.011 ± 0.000 9.864E-06 ± 0.000 0.016 ± 0.000 2.316E-04 ± 0.000 Pro 0.026 ± 0.016 0.115 ± 0.024 0.099 ± 0.014 0.813 ± 0.113 Ala 0.029 ± 0.006 0.072 ± 0.011 0.089 ± 0.022 0.588 ± 0.019 2OG 0.097 ± 0.017 0.533 ± 0.033 0.115 ± 0.023 0.752 ± 0.073 Succinate 0.150 ± 0.034 1.655 ± 0.115 0.381 ± 0.053 3.497 ± 0.068 Asp 0.535 ± 0.086 0.365 ± 0.075 1.626 ± 0.169 2.848 ± 0.146 GABA 0.789 ± 0.048 5.547 ± 0.113 4.695 ± 0.123 13.480 ± 0.224 Glu 1.196 ± 0.388 8.764 ± 0.426 8.191 ± 0.331 10.394 ± 0.570 Malate 2.433 ± 0.078 1.167 ± 0.065 8.974 ± 0.241 14.489 ± 0.432 Asn 3.726 ± 0.153 17.862 ± 0.065 21.974 ± 1.036 34.674 ± 1.011 Metabolite . Total 13C Label . 3 h . 6 h . Normoxia . Hypoxia . Normoxia . Hypoxia . Fumarate 0.011 ± 0.000 9.864E-06 ± 0.000 0.016 ± 0.000 2.316E-04 ± 0.000 Pro 0.026 ± 0.016 0.115 ± 0.024 0.099 ± 0.014 0.813 ± 0.113 Ala 0.029 ± 0.006 0.072 ± 0.011 0.089 ± 0.022 0.588 ± 0.019 2OG 0.097 ± 0.017 0.533 ± 0.033 0.115 ± 0.023 0.752 ± 0.073 Succinate 0.150 ± 0.034 1.655 ± 0.115 0.381 ± 0.053 3.497 ± 0.068 Asp 0.535 ± 0.086 0.365 ± 0.075 1.626 ± 0.169 2.848 ± 0.146 GABA 0.789 ± 0.048 5.547 ± 0.113 4.695 ± 0.123 13.480 ± 0.224 Glu 1.196 ± 0.388 8.764 ± 0.426 8.191 ± 0.331 10.394 ± 0.570 Malate 2.433 ± 0.078 1.167 ± 0.065 8.974 ± 0.241 14.489 ± 0.432 Asn 3.726 ± 0.153 17.862 ± 0.065 21.974 ± 1.036 34.674 ± 1.011 Open in new tab Table IV. Total 13C accumulation in primary metabolites in soybean roots following incubation in [13C]Glu for 3 and 6 h Values are means ± se (nmol g−1 fresh weight) of six independent measurements. Values in boldface indicate significant differences from the control (normoxia) in Student’s t test (P < 0.05). Metabolite . Total 13C Label . 3 h . 6 h . Normoxia . Hypoxia . Normoxia . Hypoxia . Fumarate 0.011 ± 0.000 9.864E-06 ± 0.000 0.016 ± 0.000 2.316E-04 ± 0.000 Pro 0.026 ± 0.016 0.115 ± 0.024 0.099 ± 0.014 0.813 ± 0.113 Ala 0.029 ± 0.006 0.072 ± 0.011 0.089 ± 0.022 0.588 ± 0.019 2OG 0.097 ± 0.017 0.533 ± 0.033 0.115 ± 0.023 0.752 ± 0.073 Succinate 0.150 ± 0.034 1.655 ± 0.115 0.381 ± 0.053 3.497 ± 0.068 Asp 0.535 ± 0.086 0.365 ± 0.075 1.626 ± 0.169 2.848 ± 0.146 GABA 0.789 ± 0.048 5.547 ± 0.113 4.695 ± 0.123 13.480 ± 0.224 Glu 1.196 ± 0.388 8.764 ± 0.426 8.191 ± 0.331 10.394 ± 0.570 Malate 2.433 ± 0.078 1.167 ± 0.065 8.974 ± 0.241 14.489 ± 0.432 Asn 3.726 ± 0.153 17.862 ± 0.065 21.974 ± 1.036 34.674 ± 1.011 Metabolite . Total 13C Label . 3 h . 6 h . Normoxia . Hypoxia . Normoxia . Hypoxia . Fumarate 0.011 ± 0.000 9.864E-06 ± 0.000 0.016 ± 0.000 2.316E-04 ± 0.000 Pro 0.026 ± 0.016 0.115 ± 0.024 0.099 ± 0.014 0.813 ± 0.113 Ala 0.029 ± 0.006 0.072 ± 0.011 0.089 ± 0.022 0.588 ± 0.019 2OG 0.097 ± 0.017 0.533 ± 0.033 0.115 ± 0.023 0.752 ± 0.073 Succinate 0.150 ± 0.034 1.655 ± 0.115 0.381 ± 0.053 3.497 ± 0.068 Asp 0.535 ± 0.086 0.365 ± 0.075 1.626 ± 0.169 2.848 ± 0.146 GABA 0.789 ± 0.048 5.547 ± 0.113 4.695 ± 0.123 13.480 ± 0.224 Glu 1.196 ± 0.388 8.764 ± 0.426 8.191 ± 0.331 10.394 ± 0.570 Malate 2.433 ± 0.078 1.167 ± 0.065 8.974 ± 0.241 14.489 ± 0.432 Asn 3.726 ± 0.153 17.862 ± 0.065 21.974 ± 1.036 34.674 ± 1.011 Open in new tab Fractional enrichment of metabolites labeled in soybean root tissue following incubation in [13C]Glu for 3 and 6 h Table V. Fractional enrichment of metabolites labeled in soybean root tissue following incubation in [13C]Glu for 3 and 6 h Values are means ± se (%) of six independent measurements. Values in boldface indicate significant differences from the control (normoxia) in Student’s t test (P < 0.05). Metabolite . 13C Enrichment . 3 h . 6 h . Normoxia . Hypoxia . Normoxia . Hypoxia . Fumarate 0.598 ± 0.004 0.001 ± 0.000 0.727 ± 0.005 0.009 ± 0.000 Ala 0.680 ± 0.020 0.975 ± 0.017 2.169 ± 0.099 5.412 ± 0.025 Pro 1.046 ± 0.211 2.487 ± 0.199 4.010 ± 0.231 11.576 ± 0.179 Succinate 1.124 ± 0.038 10.055 ± 0.068 2.997 ± 0.059 16.135 ± 0.074 Malate 1.283 ± 0.054 2.215 ± 0.036 9.630 ± 0.290 13.955 ± 0.325 Asn 2.020 ± 0.215 2.872 ± 0.099 3.488 ± 0.198 4.681 ± 0.326 2OG 2.896 ± 0.119 16.870 ± 0.311 3.209 ± 0.328 20.542 ± 0.075 Asp 3.599 ± 0.033 3.947 ± 0.041 9.588 ± 0.076 26.703 ± 0.088 GABA 4.793 ± 0.140 15.574 ± 0.098 11.655 ± 0.050 24.857 ± 0.124 Glu 9.345 ± 0.054 51.211 ± 0.074 58.177 ± 0.036 59.028 ± 0.048 Metabolite . 13C Enrichment . 3 h . 6 h . Normoxia . Hypoxia . Normoxia . Hypoxia . Fumarate 0.598 ± 0.004 0.001 ± 0.000 0.727 ± 0.005 0.009 ± 0.000 Ala 0.680 ± 0.020 0.975 ± 0.017 2.169 ± 0.099 5.412 ± 0.025 Pro 1.046 ± 0.211 2.487 ± 0.199 4.010 ± 0.231 11.576 ± 0.179 Succinate 1.124 ± 0.038 10.055 ± 0.068 2.997 ± 0.059 16.135 ± 0.074 Malate 1.283 ± 0.054 2.215 ± 0.036 9.630 ± 0.290 13.955 ± 0.325 Asn 2.020 ± 0.215 2.872 ± 0.099 3.488 ± 0.198 4.681 ± 0.326 2OG 2.896 ± 0.119 16.870 ± 0.311 3.209 ± 0.328 20.542 ± 0.075 Asp 3.599 ± 0.033 3.947 ± 0.041 9.588 ± 0.076 26.703 ± 0.088 GABA 4.793 ± 0.140 15.574 ± 0.098 11.655 ± 0.050 24.857 ± 0.124 Glu 9.345 ± 0.054 51.211 ± 0.074 58.177 ± 0.036 59.028 ± 0.048 Open in new tab Table V. Fractional enrichment of metabolites labeled in soybean root tissue following incubation in [13C]Glu for 3 and 6 h Values are means ± se (%) of six independent measurements. Values in boldface indicate significant differences from the control (normoxia) in Student’s t test (P < 0.05). Metabolite . 13C Enrichment . 3 h . 6 h . Normoxia . Hypoxia . Normoxia . Hypoxia . Fumarate 0.598 ± 0.004 0.001 ± 0.000 0.727 ± 0.005 0.009 ± 0.000 Ala 0.680 ± 0.020 0.975 ± 0.017 2.169 ± 0.099 5.412 ± 0.025 Pro 1.046 ± 0.211 2.487 ± 0.199 4.010 ± 0.231 11.576 ± 0.179 Succinate 1.124 ± 0.038 10.055 ± 0.068 2.997 ± 0.059 16.135 ± 0.074 Malate 1.283 ± 0.054 2.215 ± 0.036 9.630 ± 0.290 13.955 ± 0.325 Asn 2.020 ± 0.215 2.872 ± 0.099 3.488 ± 0.198 4.681 ± 0.326 2OG 2.896 ± 0.119 16.870 ± 0.311 3.209 ± 0.328 20.542 ± 0.075 Asp 3.599 ± 0.033 3.947 ± 0.041 9.588 ± 0.076 26.703 ± 0.088 GABA 4.793 ± 0.140 15.574 ± 0.098 11.655 ± 0.050 24.857 ± 0.124 Glu 9.345 ± 0.054 51.211 ± 0.074 58.177 ± 0.036 59.028 ± 0.048 Metabolite . 13C Enrichment . 3 h . 6 h . Normoxia . Hypoxia . Normoxia . Hypoxia . Fumarate 0.598 ± 0.004 0.001 ± 0.000 0.727 ± 0.005 0.009 ± 0.000 Ala 0.680 ± 0.020 0.975 ± 0.017 2.169 ± 0.099 5.412 ± 0.025 Pro 1.046 ± 0.211 2.487 ± 0.199 4.010 ± 0.231 11.576 ± 0.179 Succinate 1.124 ± 0.038 10.055 ± 0.068 2.997 ± 0.059 16.135 ± 0.074 Malate 1.283 ± 0.054 2.215 ± 0.036 9.630 ± 0.290 13.955 ± 0.325 Asn 2.020 ± 0.215 2.872 ± 0.099 3.488 ± 0.198 4.681 ± 0.326 2OG 2.896 ± 0.119 16.870 ± 0.311 3.209 ± 0.328 20.542 ± 0.075 Asp 3.599 ± 0.033 3.947 ± 0.041 9.588 ± 0.076 26.703 ± 0.088 GABA 4.793 ± 0.140 15.574 ± 0.098 11.655 ± 0.050 24.857 ± 0.124 Glu 9.345 ± 0.054 51.211 ± 0.074 58.177 ± 0.036 59.028 ± 0.048 Open in new tab Our metabolite redistribution analysis was further investigated by performing a [15N]ammonium (15NH4 +) labeling experiment for 36 h to study the redistribution of nitrogen through the pathways of both Glu and Ala synthesis. The 15NH4 + labeling experiment revealed an inhibition of the reversible reaction of aspartate aminotransferase (AspAT) activity upon hypoxia in the direction of Asp and Asn synthesis, as only negligible amounts of 15NH4 + were incorporated into Asp and Asn (Fig. 4; Tables VI and VII; Supplemental Table S4). On the other hand, during the 24-h 15NH4 + labeling period, considerable incorporation of 15N label into Ala and GABA in hypoxic soybean roots was observed. Figure 4. Open in new tabDownload slide Total 15N accumulation in primary metabolites in soybean roots following 15NH4 + feeding for a period of 36 h. Black and gray bars represent normoxia and hypoxia conditions, respectively. All data are given in nmol g−1 fresh weight, and asterisks indicate that these values showed significant differences from the control (normoxia) in Student’s t test (P < 0.05). Red color represent the transfer of nitrogen through the pathways of GOGAT and the GABA shunt. The black dashed line indicates that 2OG can participate in different reactions. GS, Gln synthetase. Other abbreviations are as defined in Figures 1 and 2. Figure 4. Open in new tabDownload slide Total 15N accumulation in primary metabolites in soybean roots following 15NH4 + feeding for a period of 36 h. Black and gray bars represent normoxia and hypoxia conditions, respectively. All data are given in nmol g−1 fresh weight, and asterisks indicate that these values showed significant differences from the control (normoxia) in Student’s t test (P < 0.05). Red color represent the transfer of nitrogen through the pathways of GOGAT and the GABA shunt. The black dashed line indicates that 2OG can participate in different reactions. GS, Gln synthetase. Other abbreviations are as defined in Figures 1 and 2. Total 15N accumulation in primary metabolites in soybean roots following incubation in 15NH4 + for 3, 12, 24, and 36 h Table VI. Total 15N accumulation in primary metabolites in soybean roots following incubation in 15NH4 + for 3, 12, 24, and 36 h Values are means ± se (nmol g−1 fresh weight) of five independent measurements. Values in boldface indicate significant differences from the control (normoxia) in Student’s t test (P < 0.05). Metabolite . Total 15N Label . 3 h . 12 h . 24 h . 36 h . Normoxia . Hypoxia . Normoxia . Hypoxia . Normoxia . Hypoxia . Normoxia . Hypoxia . Ser 0.150 ± 0.001 0.118 ± 0.001 0.475 ± 0.001 0.242 ± 0.001 0.792 ± 0.001 0.282 ± 0.001 1.326 ± 0.001 0.559 ± 0.001 Gly 0.244 ± 0.001 0.189 ± 0.002 0.907 ± 0.001 0.443 ± 0.001 1.247 ± 0.001 0.533 ± 0.002 2.491 ± 0.001 0.879 ± 0.001 Ala 0.412 ± 0.000 0.650 ± 0.001 0.920 ± 0.000 2.738 ± 0.001 1.563 ± 0.000 6.576 ± 0.001 3.010 ± 0.000 8.330 ± 0.001 Asp 0.760 ± 0.001 0.258 ± 0.001 2.054 ± 0.001 0.396 ± 0.001 3.557 ± 0.001 0.509 ± 0.001 5.365 ± 0.001 0.574 ± 0.001 GABA 1.552 ± 0.002 2.011 ± 0.002 2.436 ± 0.003 3.501 ± 0.001 2.880 ± 0.002 4.904 ± 0.002 3.248 ± 0.003 7.368 ± 0.001 Gln 1.602 ± 0.001 0.181 ± 0.000 1.973 ± 0.001 0.209 ± 0.001 3.388 ± 0.001 0.309 ± 0.000 6.163 ± 0.001 0.368 ± 0.001 Glu 3.762 ± 0.000 3.277 ± 0.000 4.588 ± 0.001 3.982 ± 0.001 5.854 ± 0.000 4.025 ± 0.000 6.629 ± 0.001 3.367 ± 0.001 Asn 7.049 ± 0.002 7.360 ± 0.003 58.114 ± 0.002 5.282 ± 0.001 26.618 ± 0.002 3.782 ± 0.003 33.835 ± 0.002 6.009 ± 0.001 Metabolite . Total 15N Label . 3 h . 12 h . 24 h . 36 h . Normoxia . Hypoxia . Normoxia . Hypoxia . Normoxia . Hypoxia . Normoxia . Hypoxia . Ser 0.150 ± 0.001 0.118 ± 0.001 0.475 ± 0.001 0.242 ± 0.001 0.792 ± 0.001 0.282 ± 0.001 1.326 ± 0.001 0.559 ± 0.001 Gly 0.244 ± 0.001 0.189 ± 0.002 0.907 ± 0.001 0.443 ± 0.001 1.247 ± 0.001 0.533 ± 0.002 2.491 ± 0.001 0.879 ± 0.001 Ala 0.412 ± 0.000 0.650 ± 0.001 0.920 ± 0.000 2.738 ± 0.001 1.563 ± 0.000 6.576 ± 0.001 3.010 ± 0.000 8.330 ± 0.001 Asp 0.760 ± 0.001 0.258 ± 0.001 2.054 ± 0.001 0.396 ± 0.001 3.557 ± 0.001 0.509 ± 0.001 5.365 ± 0.001 0.574 ± 0.001 GABA 1.552 ± 0.002 2.011 ± 0.002 2.436 ± 0.003 3.501 ± 0.001 2.880 ± 0.002 4.904 ± 0.002 3.248 ± 0.003 7.368 ± 0.001 Gln 1.602 ± 0.001 0.181 ± 0.000 1.973 ± 0.001 0.209 ± 0.001 3.388 ± 0.001 0.309 ± 0.000 6.163 ± 0.001 0.368 ± 0.001 Glu 3.762 ± 0.000 3.277 ± 0.000 4.588 ± 0.001 3.982 ± 0.001 5.854 ± 0.000 4.025 ± 0.000 6.629 ± 0.001 3.367 ± 0.001 Asn 7.049 ± 0.002 7.360 ± 0.003 58.114 ± 0.002 5.282 ± 0.001 26.618 ± 0.002 3.782 ± 0.003 33.835 ± 0.002 6.009 ± 0.001 Open in new tab Table VI. Total 15N accumulation in primary metabolites in soybean roots following incubation in 15NH4 + for 3, 12, 24, and 36 h Values are means ± se (nmol g−1 fresh weight) of five independent measurements. Values in boldface indicate significant differences from the control (normoxia) in Student’s t test (P < 0.05). Metabolite . Total 15N Label . 3 h . 12 h . 24 h . 36 h . Normoxia . Hypoxia . Normoxia . Hypoxia . Normoxia . Hypoxia . Normoxia . Hypoxia . Ser 0.150 ± 0.001 0.118 ± 0.001 0.475 ± 0.001 0.242 ± 0.001 0.792 ± 0.001 0.282 ± 0.001 1.326 ± 0.001 0.559 ± 0.001 Gly 0.244 ± 0.001 0.189 ± 0.002 0.907 ± 0.001 0.443 ± 0.001 1.247 ± 0.001 0.533 ± 0.002 2.491 ± 0.001 0.879 ± 0.001 Ala 0.412 ± 0.000 0.650 ± 0.001 0.920 ± 0.000 2.738 ± 0.001 1.563 ± 0.000 6.576 ± 0.001 3.010 ± 0.000 8.330 ± 0.001 Asp 0.760 ± 0.001 0.258 ± 0.001 2.054 ± 0.001 0.396 ± 0.001 3.557 ± 0.001 0.509 ± 0.001 5.365 ± 0.001 0.574 ± 0.001 GABA 1.552 ± 0.002 2.011 ± 0.002 2.436 ± 0.003 3.501 ± 0.001 2.880 ± 0.002 4.904 ± 0.002 3.248 ± 0.003 7.368 ± 0.001 Gln 1.602 ± 0.001 0.181 ± 0.000 1.973 ± 0.001 0.209 ± 0.001 3.388 ± 0.001 0.309 ± 0.000 6.163 ± 0.001 0.368 ± 0.001 Glu 3.762 ± 0.000 3.277 ± 0.000 4.588 ± 0.001 3.982 ± 0.001 5.854 ± 0.000 4.025 ± 0.000 6.629 ± 0.001 3.367 ± 0.001 Asn 7.049 ± 0.002 7.360 ± 0.003 58.114 ± 0.002 5.282 ± 0.001 26.618 ± 0.002 3.782 ± 0.003 33.835 ± 0.002 6.009 ± 0.001 Metabolite . Total 15N Label . 3 h . 12 h . 24 h . 36 h . Normoxia . Hypoxia . Normoxia . Hypoxia . Normoxia . Hypoxia . Normoxia . Hypoxia . Ser 0.150 ± 0.001 0.118 ± 0.001 0.475 ± 0.001 0.242 ± 0.001 0.792 ± 0.001 0.282 ± 0.001 1.326 ± 0.001 0.559 ± 0.001 Gly 0.244 ± 0.001 0.189 ± 0.002 0.907 ± 0.001 0.443 ± 0.001 1.247 ± 0.001 0.533 ± 0.002 2.491 ± 0.001 0.879 ± 0.001 Ala 0.412 ± 0.000 0.650 ± 0.001 0.920 ± 0.000 2.738 ± 0.001 1.563 ± 0.000 6.576 ± 0.001 3.010 ± 0.000 8.330 ± 0.001 Asp 0.760 ± 0.001 0.258 ± 0.001 2.054 ± 0.001 0.396 ± 0.001 3.557 ± 0.001 0.509 ± 0.001 5.365 ± 0.001 0.574 ± 0.001 GABA 1.552 ± 0.002 2.011 ± 0.002 2.436 ± 0.003 3.501 ± 0.001 2.880 ± 0.002 4.904 ± 0.002 3.248 ± 0.003 7.368 ± 0.001 Gln 1.602 ± 0.001 0.181 ± 0.000 1.973 ± 0.001 0.209 ± 0.001 3.388 ± 0.001 0.309 ± 0.000 6.163 ± 0.001 0.368 ± 0.001 Glu 3.762 ± 0.000 3.277 ± 0.000 4.588 ± 0.001 3.982 ± 0.001 5.854 ± 0.000 4.025 ± 0.000 6.629 ± 0.001 3.367 ± 0.001 Asn 7.049 ± 0.002 7.360 ± 0.003 58.114 ± 0.002 5.282 ± 0.001 26.618 ± 0.002 3.782 ± 0.003 33.835 ± 0.002 6.009 ± 0.001 Open in new tab Fractional enrichment of metabolites labeled in soybean root tissue following incubation in 15NH4 + for 3, 12, 24, and 36 h Table VII. Fractional enrichment of metabolites labeled in soybean root tissue following incubation in 15NH4 + for 3, 12, 24, and 36 h Values are means ± se (%) of five independent measurements. Values in boldface indicate significant differences from the control (normoxia) in Student’s t test (P < 0.05). Metabolite . 15N Enrichment . 3 h . 12 h . 24 h . 36 h . Normoxia . Hypoxia . Normoxia . Hypoxia . Normoxia . Hypoxia . Normoxia . Hypoxia . Ser 3.317 ± 0.159 2.653 ± 0.054 10.137 ± 0.083 5.337 ± 0.070 15.143 ± 0.109 6.248 ± 0.064 22.239 ± 0.093 12.575 ± 0.070 Gly 4.620 ± 0.080 3.357 ± 0.136 19.528 ± 0.071 8.151 ± 0.082 22.239 ± 0.090 10.350 ± 0.196 43.460 ± 0.081 17.678 ± 0.082 Asn 4.992 ± 0.159 5.237 ± 0.224 25.658 ± 0.178 8.965 ± 0.105 39.167 ± 0.159 14.239 ± 0.284 44.798 ± 0.198 20.173 ± 0.105 Ala 7.366 ± 0.057 7.431 ± 0.067 17.675 ± 0.029 36.894 ± 0.058 29.037 ± 0.037 78.806 ± 0.087 54.075 ± 0.039 86.075 ± 0.058 Asp 15.143 ± 0.095 5.070 ± 0.060 37.024 ± 0.078 7.751 ± 0.051 65.471 ± 0.095 9.861 ± 0.060 87.500 ± 0.058 10.980 ± 0.051 GABA 23.128 ± 0.441 28.379 ± 0.275 45.956 ± 0.331 58.608 ± 0.108 54.768 ± 0.241 71.036 ± 0.175 60.236 ± 0.231 81.949 ± 0.108 Gln 64.167 ± 0.050 12.070 ± 0.058 67.725 ± 0.080 16.538 ± 0.107 70.732 ± 0.070 17.725 ± 0.060 79.955 ± 0.030 22.495 ± 0.082 Glu 69.943 ± 0.029 60.858 ± 0.049 77.435 ± 0.141 74.686 ± 0.147 81.015 ± 0.019 64.342 ± 0.038 87.615 ± 0.101 46.660 ± 0.147 Metabolite . 15N Enrichment . 3 h . 12 h . 24 h . 36 h . Normoxia . Hypoxia . Normoxia . Hypoxia . Normoxia . Hypoxia . Normoxia . Hypoxia . Ser 3.317 ± 0.159 2.653 ± 0.054 10.137 ± 0.083 5.337 ± 0.070 15.143 ± 0.109 6.248 ± 0.064 22.239 ± 0.093 12.575 ± 0.070 Gly 4.620 ± 0.080 3.357 ± 0.136 19.528 ± 0.071 8.151 ± 0.082 22.239 ± 0.090 10.350 ± 0.196 43.460 ± 0.081 17.678 ± 0.082 Asn 4.992 ± 0.159 5.237 ± 0.224 25.658 ± 0.178 8.965 ± 0.105 39.167 ± 0.159 14.239 ± 0.284 44.798 ± 0.198 20.173 ± 0.105 Ala 7.366 ± 0.057 7.431 ± 0.067 17.675 ± 0.029 36.894 ± 0.058 29.037 ± 0.037 78.806 ± 0.087 54.075 ± 0.039 86.075 ± 0.058 Asp 15.143 ± 0.095 5.070 ± 0.060 37.024 ± 0.078 7.751 ± 0.051 65.471 ± 0.095 9.861 ± 0.060 87.500 ± 0.058 10.980 ± 0.051 GABA 23.128 ± 0.441 28.379 ± 0.275 45.956 ± 0.331 58.608 ± 0.108 54.768 ± 0.241 71.036 ± 0.175 60.236 ± 0.231 81.949 ± 0.108 Gln 64.167 ± 0.050 12.070 ± 0.058 67.725 ± 0.080 16.538 ± 0.107 70.732 ± 0.070 17.725 ± 0.060 79.955 ± 0.030 22.495 ± 0.082 Glu 69.943 ± 0.029 60.858 ± 0.049 77.435 ± 0.141 74.686 ± 0.147 81.015 ± 0.019 64.342 ± 0.038 87.615 ± 0.101 46.660 ± 0.147 Open in new tab Table VII. Fractional enrichment of metabolites labeled in soybean root tissue following incubation in 15NH4 + for 3, 12, 24, and 36 h Values are means ± se (%) of five independent measurements. Values in boldface indicate significant differences from the control (normoxia) in Student’s t test (P < 0.05). Metabolite . 15N Enrichment . 3 h . 12 h . 24 h . 36 h . Normoxia . Hypoxia . Normoxia . Hypoxia . Normoxia . Hypoxia . Normoxia . Hypoxia . Ser 3.317 ± 0.159 2.653 ± 0.054 10.137 ± 0.083 5.337 ± 0.070 15.143 ± 0.109 6.248 ± 0.064 22.239 ± 0.093 12.575 ± 0.070 Gly 4.620 ± 0.080 3.357 ± 0.136 19.528 ± 0.071 8.151 ± 0.082 22.239 ± 0.090 10.350 ± 0.196 43.460 ± 0.081 17.678 ± 0.082 Asn 4.992 ± 0.159 5.237 ± 0.224 25.658 ± 0.178 8.965 ± 0.105 39.167 ± 0.159 14.239 ± 0.284 44.798 ± 0.198 20.173 ± 0.105 Ala 7.366 ± 0.057 7.431 ± 0.067 17.675 ± 0.029 36.894 ± 0.058 29.037 ± 0.037 78.806 ± 0.087 54.075 ± 0.039 86.075 ± 0.058 Asp 15.143 ± 0.095 5.070 ± 0.060 37.024 ± 0.078 7.751 ± 0.051 65.471 ± 0.095 9.861 ± 0.060 87.500 ± 0.058 10.980 ± 0.051 GABA 23.128 ± 0.441 28.379 ± 0.275 45.956 ± 0.331 58.608 ± 0.108 54.768 ± 0.241 71.036 ± 0.175 60.236 ± 0.231 81.949 ± 0.108 Gln 64.167 ± 0.050 12.070 ± 0.058 67.725 ± 0.080 16.538 ± 0.107 70.732 ± 0.070 17.725 ± 0.060 79.955 ± 0.030 22.495 ± 0.082 Glu 69.943 ± 0.029 60.858 ± 0.049 77.435 ± 0.141 74.686 ± 0.147 81.015 ± 0.019 64.342 ± 0.038 87.615 ± 0.101 46.660 ± 0.147 Metabolite . 15N Enrichment . 3 h . 12 h . 24 h . 36 h . Normoxia . Hypoxia . Normoxia . Hypoxia . Normoxia . Hypoxia . Normoxia . Hypoxia . Ser 3.317 ± 0.159 2.653 ± 0.054 10.137 ± 0.083 5.337 ± 0.070 15.143 ± 0.109 6.248 ± 0.064 22.239 ± 0.093 12.575 ± 0.070 Gly 4.620 ± 0.080 3.357 ± 0.136 19.528 ± 0.071 8.151 ± 0.082 22.239 ± 0.090 10.350 ± 0.196 43.460 ± 0.081 17.678 ± 0.082 Asn 4.992 ± 0.159 5.237 ± 0.224 25.658 ± 0.178 8.965 ± 0.105 39.167 ± 0.159 14.239 ± 0.284 44.798 ± 0.198 20.173 ± 0.105 Ala 7.366 ± 0.057 7.431 ± 0.067 17.675 ± 0.029 36.894 ± 0.058 29.037 ± 0.037 78.806 ± 0.087 54.075 ± 0.039 86.075 ± 0.058 Asp 15.143 ± 0.095 5.070 ± 0.060 37.024 ± 0.078 7.751 ± 0.051 65.471 ± 0.095 9.861 ± 0.060 87.500 ± 0.058 10.980 ± 0.051 GABA 23.128 ± 0.441 28.379 ± 0.275 45.956 ± 0.331 58.608 ± 0.108 54.768 ± 0.241 71.036 ± 0.175 60.236 ± 0.231 81.949 ± 0.108 Gln 64.167 ± 0.050 12.070 ± 0.058 67.725 ± 0.080 16.538 ± 0.107 70.732 ± 0.070 17.725 ± 0.060 79.955 ± 0.030 22.495 ± 0.082 Glu 69.943 ± 0.029 60.858 ± 0.049 77.435 ± 0.141 74.686 ± 0.147 81.015 ± 0.019 64.342 ± 0.038 87.615 ± 0.101 46.660 ± 0.147 Open in new tab Respiration Rate Measurements The rate of respiratory oxygen consumption was measured on soybean root pieces in normoxic and hypoxic conditions (Fig. 5). Our data show a strong reduction of approximately 40% under hypoxic conditions compared with normoxic conditions. Figure 5. Open in new tabDownload slide Respiratory oxygen consumption rates of soybean roots during normoxia and hypoxia conditions. Values are means ± se of at least 45 independent measurements (of freshly excised roots 50 min in buffer). FW, Fresh weight. Figure 5. Open in new tabDownload slide Respiratory oxygen consumption rates of soybean roots during normoxia and hypoxia conditions. Values are means ± se of at least 45 independent measurements (of freshly excised roots 50 min in buffer). FW, Fresh weight. DISCUSSION Low-Oxygen Stress Induces a Highly Conserved Metabolic Response in Plants Metabolite profiling confirmed that, in soybean roots, a short-term hypoxic treatment (up to 6 h) already induces fermentation with an increase in lactate and Ala and several responses in most central metabolites, such as carbohydrates, glycolytic intermediates, and amino acids (Fig. 1; Sousa and Sodek, 2003; Bailey-Serres and Voesenek, 2008; Narsai et al., 2011; Bailey-Serres et al., 2012). Ser, derived from the glycolytic intermediate 3-phosphoglycerate, increased; so did Phe (derived from phosphoenolpyruvate) and Val, Leu, and Ala (derived from pyruvate). The amino acids Glu and Pro, which are derived from the tricarboxylic acid cycle intermediate 2OG, increase during hypoxia. On the other hand, the amino acids Asp and Asn, derived from the tricarboxylic acid cycle intermediate OAA, decrease during hypoxia. We compared our metabolite profiling data with other previously reported studies of different hypoxic treatments applied to root material in several plant systems, namely pea (Pisum sativum; Zabalza et al., 2011), L. japonicus (Rocha et al., 2010), Arabidopsis (Arabidopsis thaliana; van Dongen et al., 2009), and the hybrid Populus × canescens (Kreuzwieser et al., 2009). We observed that most of the known metabolic changes upon hypoxia are conserved across the different species analyzed, such as the activation of fermentation with an increase in lactate, Ala, and the accumulation of GABA and succinate (Supplemental Fig. S1). Therefore, we suggest that conclusions from soybean experiments are likely to be valid for many other plant species as well. Feeding Isotope-Labeled Substrate Does Not Affect the Metabolic Response to Hypoxia Even though metabolite changes in response to low oxygen concentrations suggest a regulation of primary metabolism through alternative pathways such as a noncyclic operation of the tricarboxylic acid cycle (Rocha et al., 2010), the existing data in the literature are mainly nonquantitative metabolite levels that do not provide final proof of the direction of the carbon flow through the metabolic pathways. To overcome this, metabolic flux analyses that make use of robust protocols for quantifying steady-state metabolic fluxes have been developed and applied to reveal novel aspects of the fluxes through the tricarboxylic acid cycle and associated pathways (Schwender et al., 2004, 2006; Ratcliffe and Shachar-Hill, 2006; Williams et al., 2008). Following a similar approach, we performed feeding experiments utilizing uniformly stable isotope-labeled precursors to evaluate the relative isotope redistribution within the different primary metabolic pathways in hypoxic soybean roots. To check if providing additional 13C-labeled substrates to the plant tissue affected the metabolic response to hypoxia, we first tested for changes in the metabolite levels (both 12C and 13C isotopes combined) in soybean roots under our hypoxic conditions (Table I; Supplemental Table S1). This experiment revealed that the metabolite responses to hypoxia were independent from the supply of isotope-labeled substrate, as the changes of metabolite levels were similar in all of the experimental treatments (Table I; Supplemental Table S1). Because in our experiments we used uniformly precursor substrates, all pathways in which the precursor is involved will be marked. Nevertheless, although uniformly stable isotope experiments will provide a more broad overview of changes in various metabolic pathways, it will simultaneously make it more difficult to specifically calculate the dynamics of the redistribution of label due to the mixing of pathways. Alternatively, the more expensive positional isotope tracers can be used. The latter will give more detailed information about the redistribution of label through specific pathways only. However, one problem with positional labeled molecules is that the label can get too rapidly lost due to decarboxylation reactions to provide effective information in some instances (Kruger and von Schaewen, 2003). Both Ala Metabolism and the GABA Shunt Are Activated during Hypoxia We first addressed the isotope redistribution from 13C-labeled pyruvate through the tricarboxylic acid cycle, fermentation, and Ala synthesis (Fig. 2). As expected by the induction of fermentation, the redistribution of label from pyruvate to lactate increased strongly during hypoxia. Similarly, the incorporation of 13C in Ala increased during hypoxic conditions. In contrast, the redistribution of label into the tricarboxylic acid cycle intermediates citrate and 2OG was not affected by the oxygen concentration, whereas a progressive increase of label in succinate was observed during the course of the hypoxic treatment. The identical profiles of label incorporation in citrate and 2OG indicate that the metabolic pathway via aconitase and isocitrate dehydrogenase remains active during low-oxygen stress. This observation is opposed to our previous suggestion that these reactions were likely inhibited during hypoxia (Rocha et al., 2010). The feeding experiments using [13C]Glu as precursor confirmed the suggested activation of AlaAT during hypoxia and the concomitant link between 2OG and succinate metabolism by the tricarboxylic acid cycle (Fig. 3). Apparently, hypoxia does not induce a complete shift between the two pathways that lead to 2OG production but, rather, activates the Ala pathway in addition to the existing tricarboxylic acid cycle reactions during hypoxia. In addition to the activation of the Ala pathway, both the [13C]pyruvate and [13C]Glu feeding experiments revealed considerable GABA shunt activity during hypoxia, also explaining the increase in the redistribution of 13C to succinate during hypoxia (Figs. 2 and 3). Interpretation of the fractional enrichment of the metabolites from the GABA shunt revealed the following sequence after [13C]pyruvate feeding for 6 h (Table III): Glu (15%) to GABA (11%) to succinate (7%); after [13C]Glu feeding for 6 h, the 13C enrichment was as follows (Table V): Glu (59%) to GABA (25%) to succinate (16%). The steadily decreasing fractional enrichment of the subsequent metabolites from the GABA shunt pathway are characteristic of a linear pathway and support the conclusion that the GABA shunt is active upon hypoxia, although a precise quantitative comparison of the activities of the Ala pathway and the GABA shunt is not possible from our data. As shown previously for Medicago truncatula roots (Limami et al., 2008), the reaction from 2OG to Glu is likely catalyzed by Glu synthase, which uses Gln along with [13C]2OG to generate a mixture of [13C]Glu and [12C]Glu while using NADH as reducing power, thus regenerating NAD+ (Figs. 2 and 3). Interpretation of the fractional enrichment sequence of the metabolites in the pathway from 2OG to succinate is more complex after [13C]Glu feeding for 6 h (Table V): Glu (59%) to 2OG (21%) to succinate (16%), indicating that a linear pathway from 2OG produced by AlaAT to succinate is possible. On the other hand, after 6 h of [13C]pyruvate feeding, the following fractional enrichment series was observed (Table III): pyruvate (89%) to citrate (3.07%) to 2OG (2.99%) to succinate (7.17%). This latter series indicates that the pathway via pyruvate to 2OG and succinate is not likely explained via a simple linear reaction pathway. Therefore, although it is not possible to calculate exactly the relative extent of both Ala and GABA pathways, this provides additional evidence that the GABA shunt is involved in the production of succinate during hypoxia. Quantitative comparison between the activity of both pathways will require the use of positional labeling in future experiments, since positional labeling of precursors allows us to better distinguish between specific metabolic pathways than do uniformly labeled substrates (Roberts et al., 1992; Edwards et al., 1998). Previously, a link between GABA and the tricarboxylic acid cycle was discussed to be unlikely during hypoxic stress because the reaction from GABA to succinate requires NAD+, which becomes limiting during hypoxic conditions (Rocha et al., 2010). Moreover, the drop in cytosolic pH that occurs during hypoxia (Felle, 2005) was expected to inhibit the activity of the enzyme succinate semialdehyde dehydrogenase (pH optimum of 9), which catalyzes the reaction converting GABA to succinate (Satya Narayan and Nair, 1989; Shelp et al., 1995). However, our results clearly indicate that the predicted inhibition of the GABA shunt activity does not affect the isotope between GABA and succinate. In contrast, the data support the hypothesis that the consumption of protons by Glu decarboxylase for the production of GABA can help to stabilize the cytosolic pH during exposure to different stress conditions, including hypoxia (Turano and Fang, 1998; Shelp et al., 2012). Hypoxia Leads to the Inhibition of SDH and Concomitantly of Respiration While label in succinate accumulated during hypoxia, very little 13C label was detected in fumarate in both the [13C]pyruvate and [13C]Glu feeding experiments (Figs. 2 and 3; Supplemental Fig. S2). In contrast, 13C incorporation in malate occurred in both experiments also during hypoxia. Of course, the interpretation of isotope accumulation in the organic acids of the tricarboxylic acid cycle can be complicated by the occurrence of sometimes large pools of unlabeled metabolites in different cellular compartments or the simultaneous occurrence of metabolites in various metabolic pathways, like malate and OAA that can also be produced from pyruvate and phosphoenolpyruvate via pyruvate-orthophosphate dikinase and phosphoenolpyruvate carboxylase (Setién et al., 2014) or malate production via the glyoxylate cycle. Indeed, the expression of pyruvate-orthophosphate dikinase genes was shown to be slightly up-regulated in low-oxygen experiments with rice (Oryza sativa) or Arabidopsis (Mustroph et al., 2010; Narsai et al., 2011), indicating that the reaction from pyruvate to phosphoenolpyruvate is an option. However, it should be noted that no conclusion can be drawn about the activity of these pathways based on these gene expression data, and further experiments are required to describe the pathways that lead to label accumulation in malate under hypoxia. Especially when label accumulation occurs very slowly (as for fumarate), the labeling efficiency should be assessed critically. For example, variations in the exchange of labeled compounds between different cellular compartments can conceal the redistribution of 13C through a metabolic pathway, especially when the concentration of labeled metabolites is low, such as in the case of mitochondrial fumarate. Having said so, the observation that 13C label in fumarate decreased to almost undetectable values during hypoxia (Tables III and V; Supplemental Fig. S2) can also be explained by the reduction of fumarate synthesis upon hypoxia as compared with the normoxic treatment, probably as a consequence of the inhibition during low-oxygen stress of the enzyme SDH, also known as complex II of the mETC. Inhibition of SDH will disrupt the tricarboxylic acid cycle, which leads to a noncyclic reaction pathway that has actually been described earlier to occur in plants and green algae in response to various conditions, including low oxygen availability (Vanlerberghe et al., 1989, 1990, 1991; Vanlerberghe and Turpin, 1990; Hanning and Heldt, 1993; Schwender et al., 2006; Steuer et al., 2007; Boyle and Morgan, 2009; Tcherkez et al., 2009; Sweetlove et al., 2010; Grafahrend-Belau et al., 2013; Ma et al., 2014). Interestingly, similar observations were made on the relative levels of the metabolites associated with the tricarboxylic acid cycle in antisense SDH tomato (Solanum lycopersicum) plants, which were deficient in the expression of the iron-sulfur subunit of SDH (Araújo et al., 2011). In these transgenic lines, the activity of the tricarboxylic acid cycle was clearly reduced, with high accumulation of succinate in comparison with wild-type plants, while fumarate was not detected. The inhibition of SDH will not only affect the net output of redox equivalents by the tricarboxylic acid cycle reactions (NADH and FADH) as substrates for the mETC but also the direct input of electrons into the mETC via SDH itself. As a causal response, this is anticipated to result in a decrease of the activity of the mETC, leading to a reduction in the rate of respiratory oxygen consumption, as was shown previously in plants deficient in SDH expression (Araújo et al., 2011). Indeed, the inhibition of respiratory activity was measured during hypoxic conditions as compared with normoxic conditions (in a buffer solution that is in equilibrium with air; Fig. 5). It should be noted that the oxygen concentration in the hypoxic solution is about 2 orders of magnitude higher than the K m for oxygen of cytochrome c oxidase (0.1–0.15 µm). These results thus provide a mechanistic explanation for the proactive down-regulation of respiratory oxygen consumption during hypoxia that was discussed previously (Geigenberger et al., 2000; Geigenberger, 2003; Gupta et al., 2009; Zabalza et al., 2009; Armstrong and Beckett, 2011; Nikoloski and van Dongen, 2011). Ala Metabolism and the GABA Shunt Are Activated at the Expense of the Production of Other Amino Acids The 15NH4 + labeling experiment was performed to study isotope redistribution through the pathways of both Glu and Ala synthesis. Ammonium is assimilated into Gln and sequentially converted into Glu. The synthesis or regeneration of Glu is an important issue in hypoxic tissues to maintain AlaAT and Glu decarboxylase pathways. These two pathways contribute to mitigate the damaging effects of hypoxia. AlaAT contributes to save carbon derived from glycolysis by using pyruvate competitively with fermentation, because ethanol is released to the rhizosphere and the carbon is lost for the plant (Bailey-Serres and Voesenek, 2008). 15NH4 + labeling revealed that the reversible reaction of AspAT activity is strongly inhibited upon hypoxia in the direction of Asp synthesis, confirmed by the very low 15N label redistributed to Asp and Asn, in favor of an increased metabolic activity of AlaAT and Glu decarboxylase, both of which use Glu as an amino donor. This observation is supported by the high 15N enrichment patterns observed in Ala and GABA synthesis during hypoxia (Fig. 4). Furthermore, the slow 15N enrichment in Asn is in agreement with its function as nitrogen storage and transport, resulting in lower nitrogen assimilation under low oxygen availability. A slow 15N enrichment was also observed in Ser and Gly during hypoxia, suggesting that the Glu:glyoxylate aminotransferase activity might be higher under normoxia (Ricoult et al., 2006; Limami et al., 2008); however, further directed studies will be required to verify this hypothesis. Is the Redox Status of the Cell Involved in the Regulation of Tricarboxylic Acid Cycle Activity? Evidence from our results here, as well as from other publications (Vanlerberghe et al., 1989), point out that the activity of the tricarboxylic acid cycle is modulated when the oxygen availability to a cell decreases. The subsequent question that arises is how this regulation is mediated. It was discussed previously that hypoxia-mediated changes in the redox status of the cell could be involved in regulating low-oxygen stress responses (Considine and Foyer, 2014). Although it is not possible to fully answer the question of how the tricarboxylic acid cycle is controlled upon hypoxia with our current experimental data, our results provide interesting indications that give rise to the hypothesis that redox-mediated control of the tricarboxylic acid cycle might be involved. Recently, mitochondrial thioredoxins were identified as master regulators of the carbon flow through the tricarboxylic acid cycle in plants (Daloso et al., 2015). Based on 13C metabolic flux analysis in Arabidopsis thioredoxin mutants and in vitro activity studies on enzymes of the tricarboxylic acid cycle, it was shown that the activity of SDH and fumarase is deactivated by reduced thioredoxin, while ATP-citrate lyase (ACL) is activated. Via the enzyme ACL, citrate is converted to acetyl-CoA and OAA in the cytosol (Klinman and Rose, 1971). Here, OAA reacts to Asp. This pathway was shown previously to exist in soybean (Allen and Young, 2013) and is up-regulated upon hypoxia in mammalian cells (Metallo et al., 2011). However, while ACL might be involved in the synthesis of malate and Asp under hypoxia, it cannot explain the labeling of these metabolites, because the two carbons that are derived from labeled pyruvate in citrate are released as the two-carbon moiety by the action of ACL (Klinman and Rose, 1971). Taken altogether, it is reasonable to suggest a hypothesis that the tricarboxylic acid cycle activity is modulated upon hypoxia via a redox-mediated mechanism in which thioredoxin is involved. CONCLUSION Upon hypoxia, a series of drastic metabolic adaptations are initiated in plants. Of these, the best known is the activation of fermentation and the up-regulation of glycolytic activity to increase the yield of ATP from the glycolytic pathway. Our evaluation of isotope redistribution following 13C and 15N feeding demonstrates the existence of an alternative carbon flux that explains the accumulation of Ala, GABA, and succinate upon hypoxia via pathways mediated by an Ala and GABA shunt. These alternative pathways go hand-in-hand with the bifurcation of the tricarboxylic acid cycle into separate oxidative and reductive pathways. Concomitantly, the net production of redox equivalents in the mitochondria decreases, which could explain the reduction of respiratory oxygen consumption during hypoxic conditions. Future analysis of the regulation of this part of respiratory metabolism might include positional labeling experiments to provide more detailed information about the relative activity of the different alternative pathways that are identified here. MATERIALS AND METHODS Plant Material and Growth Conditions Wild-type soybean (Glycine max ‘IAC-17’) plants were grown from seeds in the greenhouse under natural light and temperature. After 2 weeks of germination in the greenhouse, soybean roots were inoculated with Bradyrhizobium elkanii strain DSM 11554 before being transferred into pots containing hydroponic culture substrate granules (Lecaton Original; Fibo Exclay Deutschland). B. elkanii were grown in liquid culture using a medium solution composed of 0.5 g L−1 KH2PO4, 0.8 g L−1 MgSO40·7H2O, 0.1 g L−1 NaCl, 0.01 g L−1 FeCl30·6H2O, 0.8 g L−1 yeast extract, 10 g L−1 mannitol, and 5 mL of 0.5% (w/v) Bromotimol Blue in ethanol (pH 6.8) prior to inoculation. Plants were watered daily and supplied twice per week with 200 mL of nitrogen-containing nutrient solution (5 mm KNO3, 0.5 mm KCl, 0.25 mm KH2PO4, 0.25 mm K2HPO4, 1 mm MgSO4, 0.05 mm FeEDTA, and trace elements 9.1 μm MnCl2, 0.046 mm H3BO3, 0.765 μm ZnCl2, 0.56 μm NaMoO4, and 0.32 μm CuCl2) as described previously (Hoagland and Arnon, 1950). After 4 weeks, mature nodules were observed. Introduction of Label and Sampling The introduction of 13C label was performed by adding [U-13C3]pyruvate (99 atom % 13C) or [U-13C5]Glu (98 atom % 13C; Euriso-Top) at a final concentration of 20 mm to 25 mL of buffer solution (10 mm MES + KOH, pH 6.5). Unlabeled control samples were prepared by adding [12C]pyruvate or [12C]Glu at a final concentration of 20 mm. Nodules were removed, and root pieces of a pool of six independent plants were incubated for 45 min in buffer solution, without label, in order to adapt the root pieces to the experimental normoxic (approximately 250 µm oxygen) and hypoxic (approximately 50 µm oxygen) conditions. Normoxic and hypoxic conditions were obtained by supplying air and an oxygen/nitrogen mixture to the flasks, respectively. Samples were then harvested at different time points (0, 3, and 6 h) after the addition of 13C label or 12C unlabeled substrate. A control experiment without the addition of substrate was also performed. Root pieces were washed three times with buffer (10 mm MES + KOH, pH 6.5) and snap frozen in liquid nitrogen. Samples were stored at −80°C prior to extraction and GC-TOF-MS analysis. The introduction of 15N label was performed by adding 15NH4 + (98 atom % or greater 15N; Euriso-Top) at a final concentration of 20 mm to 25 mL of buffer solution (10 mm MES + KOH, pH 6.5). Nodules were removed, and root pieces of a pool of five independent plants were incubated for 45 min in buffer solution, without label, in order to adapt the root pieces to the experimental normoxic (approximately 250 µm oxygen) and hypoxic (approximately 50 µm oxygen) conditions. Normoxic and hypoxic conditions were obtained by supplying air and an oxygen/nitrogen mixture to the flasks, respectively. Samples were then harvested at different time points (0, 3, 24, and 36 h) after the addition of 15NH4 +. Root pieces were washed three times with buffer (10 mm MES + KOH, pH 6.5) and snap frozen in liquid nitrogen. Samples were stored at −80°C prior to extraction and GC-TOF-MS analysis. Extraction of Metabolites and GC-TOF-MS Metabolite Profiling Analysis of 13C/15N Labeling Primary metabolites were extracted using a methanol/chloroform extraction procedure as described previously in the literature (Lisec et al., 2006). Briefly, a total of 20 mg (fresh weight) of homogenized soybean root material was extracted in 280 μL of 100% (v/v) methanol with 12 μL of ribitol (0.2 mg mL–1 ribitol in water) as an internal standard. Extracts were incubated for 15 min at 70°C on a shaker (950 rpm) and then centrifuged at room temperature at 12,000g for 10 min. The supernatant was transferred to a new tube, mixed with 150 μL of chloroform and 300 μL of water, and vortex mixed. Extracts were centrifuged at room temperature at 12,000g for 15 min. Aliquots (150 μL) of the polar (upper) phase were evaporated to dryness using a centrifugal concentrator, and metabolites were subsequently derivatized and analyzed using an established GC-TOF-MS protocol (Lisec et al., 2006). GC-TOF-MS chromatograms were evaluated using TagFinder (Luedemann et al., 2008). Analytes were manually identified using the TargetFinder plug-in of the TagFinder software and a reference library of ambient and 13C- or 15N-labeled mass spectra and retention indices from the Golm Metabolome Database (http://gmd.mpimp-golm.mpg.de/; Kopka et al., 2005; Schauer et al., 2005). A peak intensity matrix containing all available mass isotopomers of characteristic mass fragments that represented the primary metabolites under investigation was generated by TagFinder. This matrix was processed using the CORRECTOR software tool, a TagFinder-based high-throughput tool for the mass isotope correction of GC-TOF-MS flux profiling experiments (http://www.mpimp-golm.mpg.de/10871/Supplementary_Materials). Fractional 15N enrichments of mass fragments were calculated using this processing tool as described previously (Huege et al., 2011). Fractional 13C enrichments were evaluated by determination of the intensities of the 12C spectral fragments, and the isotopic spectral fragments of unlabeled controls were compared with the fragmentation patterns of metabolites detected in the chromatograms of the 13C-fed soybean roots as described by Roessner-Tunali et al. (2004). The total 13C and 15N label present in a metabolite pool (expressed as nmol 13C- or 15N-labeled metabolite g−1 fresh weight) was calculated by multiplying the absolute concentration of that metabolite determined after GC-TOF-MS analysis by its mean 13C fractional enrichment (Roessner-Tunali et al., 2004). Respiratory Oxygen Consumption Measurements Respiratory oxygen consumption was measured on excised root pieces (2–6 mm long; total mass approximately 25 mg). Prior to the measurements, excised roots were preincubated in HEPES buffer (100 mm, pH 7.4) for 50 min to reduce wound-stress responses. Respiration was determined after transfer of the roots to 1 mL of well-stirred fresh HEPES buffer (100 mm, pH 7.4) in a closed vial at 25°C connected to an OXY-4 multichannel optical oxygen sensor (PreSens). Supplemental Data The following supplemental materials are available. Supplemental Figure S1. Metabolite responses of different species to different hypoxia treatments. Supplemental Figure S2. GC-TOF-MS fumarate signal under all experimental conditions. Supplemental Table S1. GC-TOF-MS relative values and two-way ANOVA for primary metabolites. Supplemental Table S2. Absolute concentrations of selected primary metabolites used to determine the total 13C label accumulated following incubation in [13C]pyruvate. Supplemental Table S3. Absolute concentrations of selected primary metabolites used to determine the total 13C label accumulated following incubation in [13C]Glu. Supplemental Table S4. Absolute concentrations of selected primary metabolites used to determine the total 15N label accumulated following incubation in 15NH4 +. Glossary GABA γ-aminobutyric acid 2OG 2-oxoglutarate GC-TOF-MS gas chromatography-time of flight-mass spectrometry mETC mitochondrial electron transport chain OAA oxaloacetate 15NH4+ [15N]ammonium LITERATURE CITED Allen DK , Young JD ( 2013 ) Carbon and nitrogen provisions alter the metabolic flux in developing soybean embryos . Plant Physiol 161 : 1458 – 1475 Google Scholar Crossref Search ADS PubMed WorldCat Araújo WL , Nunes-Nesi A, Nikoloski Z, Sweetlove LJ, Fernie AR ( 2012 ) Metabolic control and regulation of the tricarboxylic acid cycle in photosynthetic and heterotrophic plant tissues . Plant Cell Environ 35 : 1 – 21 Google Scholar Crossref Search ADS PubMed WorldCat Araújo WL , Nunes-Nesi A, Osorio S, Usadel B, Fuentes D, Nagy R, Balbo I, Lehmann M, Studart-Witkowski C, Tohge T, et al. 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Plant Physiol 149 : 1087 – 1098 Google Scholar Crossref Search ADS PubMed WorldCat Author notes 1 This work was supported by the Max Planck Society and the Federal Ministry of Education and Research (HydromicPro), by the Ministère de la Recherche et Technologies and the QUALISEM program funded by the Région des Pas de la Loire (to A.M.L. and H.D.), by the FCT Investigator program of the Fundação para a Ciência e a Tecnologia (grant no. IF/00376/2012/CP0165/CT0003 to C.A.), and by the Instituto de Tecnologia Química e Biológica António Xavier research unit GREEN-it, Bioresources for Sustainability (grant no. UID/Multi/04551/2013 to C.A.). 2 These authors contributed equally to the article. * Address correspondence to [email protected]. The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantphysiol.org) is: Carla António ([email protected]). J.T.v.D. conceived the original screening and research plans; J.T.v.D. supervised the experiments; C.A., C.P., and J.T.v.D. designed the experiments; C.A. and C.P. performed the experiments; C.A. and C.P. analyzed the data; M.R. and H.D. provided assistance in data analysis; J.T.v.D. conceived the project; C.A. wrote the article with contributions of all the coauthors; A.M.L., A.R.F., and T.O. complemented the writing and data analysis. [OPEN] Articles can be viewed without a subscription. www.plantphysiol.org/cgi/doi/10.1104/pp.15.00266 © 2016 American Society of Plant Biologists. All Rights Reserved. © The Author(s) 2016. Published by Oxford University Press on behalf of American Society of Plant Biologists. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

Covshoff, Sarah; Szecowka, Marek; Hughes, Thomas E.; Smith-Unna, Richard; Kelly, Steven; Bailey, Karen J.; Sage, Tammy L.; Pachebat, Justin A.; Leegood, Richard; Hibberd, Julian M.

doi: 10.1104/pp.15.00889pmid: 26527656

Viola, Ivana L.; Camoirano, Alejandra; Gonzalez, Daniel H.

doi: 10.1104/pp.15.01016pmid: 26574599

Abstract TCP proteins integrate a family of transcription factors involved in the regulation of developmental processes and hormone responses. It has been shown that most members of class I, one of the two classes in which the TCP family is divided, contain a conserved Cys that leads to inhibition of DNA binding when oxidized. In this work, we describe that the class-I TCP protein TCP15 inhibits anthocyanin accumulation during exposure of plants to high light intensity by modulating the expression of transcription factors involved in the induction of anthocyanin biosynthesis genes, as suggested by the study of plants that express TCP15 from the 35SCaMV promoter and mutants in TCP15 and the related gene TCP14. In addition, the effect of TCP15 on anthocyanin accumulation is lost after prolonged incubation under high light intensity conditions. We provide evidence that this is due to inactivation of TCP15 by oxidation of Cys-20 of the TCP domain. Thus, redox modulation of TCP15 activity in vivo by high light intensity may serve to adjust anthocyanin accumulation to the duration of exposure to high irradiation conditions. TCP proteins constitute a family of plant-specific transcription factors involved in the regulation of cell proliferation and growth-related processes. TCP transcription factors contain a conserved DNA binding and dimerization domain, the TCP domain, that bears a resemblance to the bHLH domain found in many eukaryotic transcription factors (Cubas et al., 1999). Arabidopsis contains 24 TCP proteins that can be ascribed to classes I or II according to sequence conservation within the TCP domain (Cubas et al., 1999; Martín-Trillo and Cubas, 2010). Class-I proteins participate in the regulation of cell proliferation, leaf and flower development, stem elongation, and responses to hormones. They modulate jasmonic acid biosynthesis and cytokinin, auxin, and gibberellin responses (Martín-Trillo and Cubas, 2010; Manassero et al., 2013). Mutations in genes encoding two closely related class-I proteins from Arabidopsis, TCP14 (At3g47620) and TCP15 (At1g69690), affect several aspects of plant development, among them seed germination (Resentini et al., 2015), leaf shape (Kieffer et al., 2011), inflorescence stem growth (Davière et al., 2014), and gynoecium development (Lucero et al., 2015). Recently, it was also shown that mutations in these genes affect effector-triggered immunity (Kim et al., 2014), thus suggesting a role of class-I TCP proteins in responses to stress. As part of the sequence conservation observed within the TCP domain of class-I proteins, we have previously identified a conserved Cys located at position 20 of the TCP domain. We have shown that this Cys undergoes redox interconversions that render the proteins unable to bind DNA when oxidized (Viola et al., 2013). According to this, we have postulated that class-I TCP proteins may participate in responses to internal or external conditions that cause changes in cell redox parameters. Changes in the production or amounts of redox compounds, mainly due to the generation of reactive oxygen species (ROS), are observed under a variety of stress conditions (Noctor and Foyer, 1998; Grant and Loake, 2000; Foyer and Noctor, 2005; Potters et al., 2009; Frederickson Matika and Loake, 2014). However, little is known about the participation of class-I TCP proteins in responses to stress. A stress condition that causes changes in redox parameters is the growth of plants under high light intensities. Excess light causes changes in the redox state of photosynthetic electron transport chain components and thiol compounds and increases ROS production (Karpinski et al., 1997; Huner et al., 1998; Foyer and Allen, 2003; Li et al., 2009). These changes originate protective responses oriented to prevent cell damage under the new situation. A typical response to high light intensities is the accumulation of anthocyanins, a group of aromatic pigments that have protecting functions against excess light and ROS (Holton and Cornish, 1995; Grotewold, 2006; Albert et al., 2009; Agati et al., 2012; Page et al., 2012). Anthocyanins act as protecting pigments since they absorb light and thus reduce the amount of energy that reaches the photosynthetic apparatus (Hughes et al., 2005; Albert et al., 2009). It has been postulated that anthocyanins also act as antioxidant compounds in plants since they are ROS scavengers (Neill et al., 2002; Nakabayashi et al., 2014). Anthocyanin synthesis is modulated at the transcriptional level through the regulation of anthocyanin biosynthesis genes (i.e. genes that encode enzymes involved in anthocyanin biosynthesis) by transcription factors from the Myb, bHLH, and WD40 families (Winkel, 2006; Guo et al., 2014). These factors form a transcriptional complex, called MBW, which binds to anthocyanin biosynthesis gene promoters and induces gene expression (Quattrochio et al., 2006). Some members of the MBW complex are in turn also regulated by light conditions at the transcriptional and posttranslational levels (Lee et al., 2007; Shin et al., 2007; Dubos et al., 2008; Matsui et al., 2008; Maier et al., 2013; Albert et al., 2014). It was recently shown that the class-II TCP protein TCP3 induces anthocyanin biosynthesis by integrating into the MBW complex (Li and Zachgo, 2013). In this work, we report that the class-I TCP protein TCP15 from Arabidopsis acts as a repressor of anthocyanin accumulation acting on the expression of anthocyanin biosynthesis genes and upstream transcriptional regulators. TCP15 prevents accumulation of anthocyanin under high light intensity after relatively short irradiation times but not after prolonged irradiation. We provide evidence indicating that loss of TCP15 action after prolonged irradiation is due to inactivation of TCP15 by oxidation of Cys-20 of the TCP domain. Redox interconversions of TCP15 and other class-I proteins would then help to modulate the response of plants to high light intensities depending on the duration of these external conditions. RESULTS Expression of a Repressor Form of TCP15 Causes an Increase in Anthocyanin Levels Previously, we described plants that express a fusion of the Arabidopsis TCP transcription factor TCP15 to the EAR repression domain under the control of the TCP15 promoter (p15:TCP15-EAR plants) (Uberti-Manassero et al., 2012). These plants express TCP15-EAR from a 1514-bp fragment containing sequences located upstream of the TCP15 translation start codon. This fragment promotes expression in developing leaves, petioles, and inflorescences (Uberti-Manassero et al., 2012), which is in agreement with expression data from microarray experiments (http://bar.utoronto.ca/). Upon growing these plants under standard conditions, as described in “Materials and Methods”, we observed typical signs of anthocyanin accumulation. Analysis of global gene expression changes in rosettes of these plants revealed the induction of 740 genes. Among them, several genes involved in anthocyanin biosynthesis were observed (Fig. 1; Supplemental Table S1). In fact, 42% (15 out of 36) of the anthocyanin biosynthesis or regulatory genes described by Guo et al. (2014) are induced in p15:TCP15-EAR plants, which is significantly higher than expected by chance (P < 0.001). Four of these genes encode transcription factors involved in the positive regulation of the expression of anthocyanin biosynthesis genes (PAP1, PAP2, TT8, and EGL3) and 11 encode enzymes involved in different steps of anthocyanin biosynthesis from chalcones (Fig. 1). In addition, TT19, a gene encoding a transporter involved in the accumulation of anthocyanins in the vacuole (Kitamura et al., 2004; Sun et al., 2012), and AT5MAT, which encodes an anthocyanin 5-O-glucoside-6″-O-malonyltransferase involved in the malonylation of anthocyanin (D’Auria et al., 2007), are also induced in p15:TCP15-EAR plants (Fig. 1). Notably, genes encoding enzymes involved in the synthesis of isoflavonoids, flavonols, and proanthocyanidins, which share metabolic precursors with anthocyanins, did not show changes in expression or were repressed, as in the case of the flavonol biosynthetic genes FLS2 (Owens et al., 2008; Guo et al., 2014), UGT78D1 (Jones et al., 2003), and UGT71D1 (Lim et al., 2004) (Supplemental Table S1). Figure 1. Open in new tabDownload slide Genes involved in anthocyanin production are induced in p15:TCP15-EAR plants. A, Scheme of the anthocyanin biosynthesis pathway in Arabidopsis. The abbreviated names of the genes encoding enzymes involved in anthocyanin biosynthesis are shown, together with the names of transcription factors involved in the induction of anthocyanin biosynthesis genes. Names boxed in gray indicate genes whose expression is increased in p15:TCP15-EAR plants. B, List of genes involved in anthocyanin accumulation whose expression is induced in p15:TCP15-EAR plants grown under standard light conditions. logFC is the log2 of the fold-change in expression in p15:TCP15-EAR plants relative to wild-type (wt) plants. Details of gene names and AGI codes are given in Supplemental Table S1. Figure 1. Open in new tabDownload slide Genes involved in anthocyanin production are induced in p15:TCP15-EAR plants. A, Scheme of the anthocyanin biosynthesis pathway in Arabidopsis. The abbreviated names of the genes encoding enzymes involved in anthocyanin biosynthesis are shown, together with the names of transcription factors involved in the induction of anthocyanin biosynthesis genes. Names boxed in gray indicate genes whose expression is increased in p15:TCP15-EAR plants. B, List of genes involved in anthocyanin accumulation whose expression is induced in p15:TCP15-EAR plants grown under standard light conditions. logFC is the log2 of the fold-change in expression in p15:TCP15-EAR plants relative to wild-type (wt) plants. Details of gene names and AGI codes are given in Supplemental Table S1. Accumulation of anthocyanins in p15:TCP15-EAR plants was evident after visual inspection (Fig. 2 A, arrowheads). Measurement of anthocyanin levels indicated that, indeed, p15:TCP15-EAR plants accumulate higher anthocyanin levels than wild-type plants (Fig. 2 B). We also measured transcript levels of TT8 and DFR, anthocyanin regulatory and biosynthesis genes, respectively (Fig. 1), by RT-qPCR. The results confirmed the induction of these genes in p15:TCP15-EAR plants (Fig. 2 C) and suggest that TCP15 may be involved in the modulation of anthocyanin levels through the regulation of the expression of anthocyanin biosynthesis and regulatory genes. Figure 2. Open in new tabDownload slide p15:TCP15-EAR plants contain higher anthocyanin levels. A, A photograph of wild-type (wt) and three different lines of p15:TCP15-EAR plants showing accumulation of anthocyanin in leaves (arrowheads). B, Anthocyanin levels in extracts from rosettes of wt and p15:TCP15-EAR plants. Note that the higher anthocyanin content is readily evident by visual inspection of the extracts after chlorophyll extraction, as indicated by the photographs to the right (top: wt; bottom: p15:TCP15-EAR). The bars indicate the mean ± sd of three independent measurements. Columns with different letters are significantly different at P < 0.05 (Student’s t test). C, Relative transcript levels of TT8 and DFR in wt and p15:TCP15-EAR plants measured by RT-qPCR. The value in wild-type plants was set to 1. The bars indicate the mean ± sd of three technical replicates. Columns with different letters are significantly different at P < 0.05 (Student’s t test). The experiment was repeated three times with similar results. Figure 2. Open in new tabDownload slide p15:TCP15-EAR plants contain higher anthocyanin levels. A, A photograph of wild-type (wt) and three different lines of p15:TCP15-EAR plants showing accumulation of anthocyanin in leaves (arrowheads). B, Anthocyanin levels in extracts from rosettes of wt and p15:TCP15-EAR plants. Note that the higher anthocyanin content is readily evident by visual inspection of the extracts after chlorophyll extraction, as indicated by the photographs to the right (top: wt; bottom: p15:TCP15-EAR). The bars indicate the mean ± sd of three independent measurements. Columns with different letters are significantly different at P < 0.05 (Student’s t test). C, Relative transcript levels of TT8 and DFR in wt and p15:TCP15-EAR plants measured by RT-qPCR. The value in wild-type plants was set to 1. The bars indicate the mean ± sd of three technical replicates. Columns with different letters are significantly different at P < 0.05 (Student’s t test). The experiment was repeated three times with similar results. TCP15 Is a Negative Regulator of Anthocyanin Accumulation under High Light Conditions To further evaluate the role of TCP15 in anthocyanin accumulation, we analyzed anthocyanin levels in plants that express TCP15 from the 35SCaMV promoter (35S:TCP15 plants) and in double knock-out mutants in TCP15 and the related TCP protein TCP14. We used the double knock-out mutant with TCP14 for our studies, since it has been shown that there is a high degree of redundancy in the functions of TCP15 and TCP14 (Kieffer et al., 2011; Davière et al., 2014). No differences in anthocyanin levels were observed among mutant, wild-type, and overexpressing plants under standard conditions (Fig. 3 A). We then decided to apply a treatment that induces anthocyanin accumulation to analyze the response of plants in which the function of TCP15 was altered. For this purpose, we treated plants with high light intensity during the light period (see “Materials and Methods” for details) and analyzed anthocyanin levels at different times after the beginning of the treatment. At d 3, wild-type plants showed a 2.5-fold increase in anthocyanin levels (Fig. 3 A). These levels were similar to those observed in p15:TCP15-EAR plants either before or after the high light intensity treatment (Supplemental Figure S1). In two different lines of 35S:TCP15 plants, however, anthocyanin levels were significantly lower than in wild-type plants at d 3 of treatment, when tcp14 tcp15 mutants contained higher anthocyanin levels than wild-type plants (Fig. 3 A). These results suggest that TCP15 is a negative regulator of anthocyanin accumulation. Notably, anthocyanin levels were similar in all plants when analyzed at d 5 (Fig. 3 A). Figure 3. Open in new tabDownload slide TCP15 inhibits anthocyanin accumulation during exposure of plants to high light intensity. A, Anthocyanin levels in wild-type plants (wt), tcp14 tcp15 loss-of-function mutants, and 35S:TCP15 plants (two independent lines) at different times after exposure to high light irradiation. Irradiation was started at the middle of the light period of d 1. Samples were collected at the end of the light period of d 3 and d 5. Control samples (d 0) were collected before the treatment was started. No increase in anthocyanin accumulation was observed in plants kept under normal illumination conditions during the treatment. The bars indicate the mean ± sd of three independent measurements. Columns with different letters are significantly different at P < 0.05 (ANOVA; Tukey test). B, Relative transcript levels of PAP1, TT8, and DFR in wild-type (wt) plants and tcp14 tcp15 mutants after exposure to high light irradiation. The bars indicate the mean ± sd of three biological replicates. Columns with different letters are significantly different at P < 0.05 (ANOVA; Tukey test). C, Relative transcript levels of PAP1, TT8, and DFR in wild-type (wt) and 35S:TCP15 plants (two independent lines) after 3 h of exposure to high light irradiation. The bars indicate the mean ± sd of three biological replicates. Asterisks indicate significant differences compared to wt (P < 0.05; ANOVA). Figure 3. Open in new tabDownload slide TCP15 inhibits anthocyanin accumulation during exposure of plants to high light intensity. A, Anthocyanin levels in wild-type plants (wt), tcp14 tcp15 loss-of-function mutants, and 35S:TCP15 plants (two independent lines) at different times after exposure to high light irradiation. Irradiation was started at the middle of the light period of d 1. Samples were collected at the end of the light period of d 3 and d 5. Control samples (d 0) were collected before the treatment was started. No increase in anthocyanin accumulation was observed in plants kept under normal illumination conditions during the treatment. The bars indicate the mean ± sd of three independent measurements. Columns with different letters are significantly different at P < 0.05 (ANOVA; Tukey test). B, Relative transcript levels of PAP1, TT8, and DFR in wild-type (wt) plants and tcp14 tcp15 mutants after exposure to high light irradiation. The bars indicate the mean ± sd of three biological replicates. Columns with different letters are significantly different at P < 0.05 (ANOVA; Tukey test). C, Relative transcript levels of PAP1, TT8, and DFR in wild-type (wt) and 35S:TCP15 plants (two independent lines) after 3 h of exposure to high light irradiation. The bars indicate the mean ± sd of three biological replicates. Asterisks indicate significant differences compared to wt (P < 0.05; ANOVA). We then measured the response of genes involved in the regulation of anthocyanin biosynthesis (PAP1, TT8) and the anthocyanin biosynthesis gene DFR to high light treatment. As observed in Figure 3 B, tcp14 tcp15 plants showed higher transcript levels of the three genes after 1 h and 2 h of treatment, indicating that mutant plants respond more rapidly to high light conditions. For TT8 and DFR, transcript levels were already higher in tcp14 tcp15 plants before the treatment was started (Fig. 3 B). The increased response of anthocyanin regulatory and biosynthesis genes observed in tcp14 tcp15 plants may explain why these plants accumulate higher anthocyanin levels than wild-type. In the case of 35S:TCP15 plants, the opposite behavior was observed since transcript levels for PAP1, TT8, and DFR were significantly lower than those of wild-type plants after 3 h of incubation under high light intensity (Fig. 3 C). Prolonged High Light Treatment Abolishes the Effects of TCP15 Overexpression It is noteworthy that the effect of modifying TCP15 function on anthocyanin accumulation under high light was lost after five days of treatment (Fig. 3 A). One possibility is that TCP15 regulates the speed of the response to high light, but eventually anthocyanin levels reach a maximum that is similar regardless of the action of TCP15. Another possibility is that a TCP15-independent pathway becomes prevalent after prolonged high light treatment. Finally, a third possibility is that TCP15 action is inhibited by prolonged exposure to high light. To analyze this, we evaluated the effect of high light irradiation on the action of TCP15 in a supposedly different pathway, i.e. regulation of auxin homeostasis. In fact, it has been reported previously that TCP15 regulates auxin homeostasis and that expression of TCP15-EAR induces the expression of the auxin reporter DR5:GUS (Uberti-Manassero et al., 2012). We then crossed DR5:GUS plants with 35S:TCP15 plants and analyzed the effect of high light treatment on DR5:GUS expression. As expected from the results reported for TCP15-EAR, 35S:TCP15 plants showed significantly lower DR5:GUS expression levels than wild-type under normal illumination conditions (Fig. 4 A, left panels). When wild-type plants were kept under high light intensity, a progressive decay in DR5:GUS expression was evident (Fig. 4 A, top panels). This may be related to the fact that auxin responses are attenuated under stress conditions that generate ROS (Blomster et al., 2011; Tognetti et al., 2012; Peer et al., 2013). In turn, treatment of DR5:GUS x 35S:TCP15 plants caused a completely different response, since a significant increase in DR5:GUS expression was observed after prolonged high light treatment (Fig. 4 A, bottom panels). Repression of anthocyanin accumulation by 35S:TCP15 followed a similar pattern (Fig. 4 B), even if the treatment produced opposite behaviors of both processes in wild-type plants (i.e. increase in anthocyanin accumulation and decrease in DR5:GUS expression). This supports the idea that TCP15 is inactivated after prolonged high light irradiation, thus relieving the repression of DR5:GUS expression and anthocyanin accumulation. Figure 4. Open in new tabDownload slide Repression of DR5:GUS by TCP15 is relieved after prolonged exposure to high light irradiation. A, Plants carrying the auxin reporter DR:GUS in a wild-type (DR5:GUS) or a 35S:TCP15 background (DR5:GUS x 35S:TCP15) were exposed to high light irradiation conditions. GUS activity was analyzed by histochemical staining at the beginning of the treatment (day 0) or at the end of the light period of days 2 and 7. B, Anthocyanin levels in the plants treated as described in (A). Wild-type plants (wt) were also included in the assay for comparison. The bars indicate the mean ± sd of three independent measurements. Columns with different letters are significantly different at P < 0.05 (ANOVA; Tukey test). The experiment was repeated three times with similar results. Figure 4. Open in new tabDownload slide Repression of DR5:GUS by TCP15 is relieved after prolonged exposure to high light irradiation. A, Plants carrying the auxin reporter DR:GUS in a wild-type (DR5:GUS) or a 35S:TCP15 background (DR5:GUS x 35S:TCP15) were exposed to high light irradiation conditions. GUS activity was analyzed by histochemical staining at the beginning of the treatment (day 0) or at the end of the light period of days 2 and 7. B, Anthocyanin levels in the plants treated as described in (A). Wild-type plants (wt) were also included in the assay for comparison. The bars indicate the mean ± sd of three independent measurements. Columns with different letters are significantly different at P < 0.05 (ANOVA; Tukey test). The experiment was repeated three times with similar results. A C20S Mutant of TCP15 Causes Durable Repression of Anthocyanin Accumulation under High Light Intensity One of the effects of high light intensity is the accumulation of ROS (Miller et al., 2009). ROS damage cellular structures, cause changes in redox conditions, and act as intermediates in signal transduction pathways involved in plant acclimation. We have previously reported that TCP15 and other class-I TCP proteins undergo redox interconversions that involve a conserved Cys (Cys-20 of the TCP domain) located at the beginning of the HLH motif. Oxidation of this Cys inhibits DNA binding and can be achieved in vivo by treatment of plants with H2O2 (Viola et al., 2013). We then asked whether oxidation of Cys-20 of the TCP domain may be the cause of the apparent inactivation of TCP15 after prolonged incubation under high light conditions. To analyze the role of Cys-20 in TCP15 action, we expressed in plants a constitutively active, redox-insensitive variant of TCP15 obtained by changing Cys-20 to Ser (C20S-TCP15). This replacement does not affect the DNA binding activity of TCP15 under reducing conditions (Viola et al., 2013). TCP15 transcript levels in lines that overexpress C20S-TCP15 were similar to those observed in plants that overexpress native TCP15 (Supplemental Figure S2). As shown in Figure 5 A, expression of C20S-TCP15 under the control of the 35SCaMV promoter caused a decrease in anthocyanin accumulation under high light conditions. At d 2 of treatment, the effect of C20S-TCP15 was similar to the one observed with native TCP15 (Fig. 5 A). However, after more prolonged incubation, repression of anthocyanin accumulation was maintained in 35S:C20S-TCP15 plants but was lost in 35S:TCP15 plants (Fig. 5, A and B). Similar results were obtained when different lines expressing either TCP15 or C20S-TCP15 were used (Fig. 5 C), indicating that this is not a line-specific effect. Repression of the expression of TT8 and DFR was also maintained after prolonged incubation under high light in 35S:C20S-TCP15 plants but not in 35S:TCP15 plants (Fig. 5 D), in agreement with the results of anthocyanin accumulation. The most likely explanation for this observation is that TCP15 becomes inactivated by oxidation of Cys-20 after prolonged exposure of plants to high light intensities. Figure 5. Open in new tabDownload slide Expression of a C20S-TCP15 mutant causes durable repression of anthocyanin accumulation under high irradiation conditions. A, Anthocyanin levels in wild-type (wt), 35S:TCP15 (two independent lines), and 35S:C20S-TCP15 plants at different times after exposure to high light irradiation. The bars indicate the mean ± sd of three independent measurements. B, Anthocyanin levels in wt and three independent lines of 35S:TCP15 and 35S:C20S-TCP15 plants at d 7 of exposure to high light irradiation. The bars indicate the mean ± sd of three independent measurements. C, Photograph of wt, 35S:TCP15, and 35S:C20S-TCP15 plants at d 7 of irradiation showing the decrease in anthocyanin accumulation in 35S:C20S-TCP15 plants. D, Relative transcript levels of TT8 and DFR in wild-type (wt), 35S:TCP15, and 35S:C20S-TCP15 plants at different times after exposure to high light irradiation. The bars indicate the mean ± sd of three biological replicates. Columns with different letters are significantly different at P < 0.05 (ANOVA; Tukey test). Figure 5. Open in new tabDownload slide Expression of a C20S-TCP15 mutant causes durable repression of anthocyanin accumulation under high irradiation conditions. A, Anthocyanin levels in wild-type (wt), 35S:TCP15 (two independent lines), and 35S:C20S-TCP15 plants at different times after exposure to high light irradiation. The bars indicate the mean ± sd of three independent measurements. B, Anthocyanin levels in wt and three independent lines of 35S:TCP15 and 35S:C20S-TCP15 plants at d 7 of exposure to high light irradiation. The bars indicate the mean ± sd of three independent measurements. C, Photograph of wt, 35S:TCP15, and 35S:C20S-TCP15 plants at d 7 of irradiation showing the decrease in anthocyanin accumulation in 35S:C20S-TCP15 plants. D, Relative transcript levels of TT8 and DFR in wild-type (wt), 35S:TCP15, and 35S:C20S-TCP15 plants at different times after exposure to high light irradiation. The bars indicate the mean ± sd of three biological replicates. Columns with different letters are significantly different at P < 0.05 (ANOVA; Tukey test). TCP15 Is Inactivated after Exposure of Plants to High light Intensity To effectively analyze whether TCP15 is inactivated after exposure of plants to high light, we analyzed the DNA binding activity of protein extracts prepared from 35S:TCP15 plants toward a double-stranded oligonucleotide that contains the sequence recognized by TCP15 using electrophoretic mobility shift assays (EMSAs) (Viola et al., 2011). In extracts from nontransformed plants, two to three faint retarded bands, probably arising from the binding of endogenous proteins, were observed (Fig. 6 A). The pattern is similar to the one observed earlier using seedling extracts and the same oligonucleotide (Viola et al., 2013). A significant increase in the intensity of one of the retarded bands was observed when extracts from 35S:TCP15 plants were used, suggesting that this represents the binding of TCP15 to the labeled oligonucleotide (Fig. 6 A, asterisk). The specificity of binding to the TCP15 target site was analyzed in a competition experiment (Fig. 6 B). Binding of 35S:TCP15 extracts was efficiently competed by a 30-fold molar excess of the same, unlabeled oligonucleotide, but not by a similar amount of an oligonucleotide carrying two point mutations in the TCP15 binding site (Fig. 6 B). This strongly suggests that this retarded band arises from TCP15 binding. In wild-type extracts, no significant competition was observed. This indicates that the faint retarded bands observed in wild-type extracts are most likely the consequence of unspecific protein-DNA interactions. Figure 6. Open in new tabDownload slide High light intensity inhibits the DNA binding activity of TCP15 in a redox-dependent manner. A, EMSA showing the binding of proteins to DNA carrying a class-I TCP target site. Extracts from wild-type (wt) and two independent lines of 35S:TCP15 plants were used. The retarded band highlighted with an asterisk indicates binding of the expressed TCP15 to DNA. B, EMSA as in (A) showing the competition of binding by a 30-fold molar excess of unlabeled oligonucleotides carrying an unmodified (BS) or a mutated (BSm) target site. C, EMSA using extracts prepared from 35S:TCP15 and 35S:C20S-TCP15 plants exposed for different times to high light irradiation. D, EMSA using extracts from 35S:TCP15 and 35S:C20S-TCP15 plants exposed for 0 or 2 d to high light irradiation. Before loading in the EMSA, the samples were incubated for 10 min at 25°C either in the absence (−2ME) or presence (+2ME) of 10 mm 2-mercaptoethanol. The experiment was repeated twice with similar results. Figure 6. Open in new tabDownload slide High light intensity inhibits the DNA binding activity of TCP15 in a redox-dependent manner. A, EMSA showing the binding of proteins to DNA carrying a class-I TCP target site. Extracts from wild-type (wt) and two independent lines of 35S:TCP15 plants were used. The retarded band highlighted with an asterisk indicates binding of the expressed TCP15 to DNA. B, EMSA as in (A) showing the competition of binding by a 30-fold molar excess of unlabeled oligonucleotides carrying an unmodified (BS) or a mutated (BSm) target site. C, EMSA using extracts prepared from 35S:TCP15 and 35S:C20S-TCP15 plants exposed for different times to high light irradiation. D, EMSA using extracts from 35S:TCP15 and 35S:C20S-TCP15 plants exposed for 0 or 2 d to high light irradiation. Before loading in the EMSA, the samples were incubated for 10 min at 25°C either in the absence (−2ME) or presence (+2ME) of 10 mm 2-mercaptoethanol. The experiment was repeated twice with similar results. We then performed EMSAs using extracts from plants that were kept under high light conditions for 2 or 4 days. As observed in Figure 6 C (left panel), binding of TCP15 was almost completely abolished under these conditions. However, binding was not affected by a high light treatment when extracts from 35S:C20S-TCP15 plants were used (Fig. 6 C, right panel). This may indicate that TCP15 is inactivated by oxidation after high light treatment of plants. To evaluate this, we treated the extracts with a reducing agent, since this treatment was shown previously to reactivate the oxidized protein (Viola et al., 2013). DTT, as used before in seedling extracts (Viola et al., 2013), was unsuccessful, since incubation in the presence of DTT caused a complete loss of binding in all samples. We speculate that DTT may activate a protease or another factor from the extract that inhibits binding of proteins to DNA. We then used 2-mercaptoethanol (2ME) as reducing agent and observed that the DNA binding activity in extracts from 35S:TCP15 plants subjected to high light intensity conditions was recovered after 2ME treatment (Fig. 6 D, right panel). The results indicate that TCP15 is present in an inactive form in extracts from 35S:TCP15 plants after the high light intensity treatment and that this form can be reactivated by a reducing agent. We also analyzed the effect of high light intensity on the stability of TCP15. For this purpose, we obtained plants that constitutively express a fusion of TCP15 to the red fluorescent protein (RFP) and analyzed protein levels using antibodies against RFP. Figure 7 A shows that these antibodies detected more than one band in 35S:TCP15-RFP plants but not in nontransformed plants. This indicates the presence of truncated products, probably arising from proteolytic processing since the RFP tag is located at the C-terminal end. The band of slowest migration (Fig. 7 A, arrow) corresponds to the expected M r of the full-length protein. TCP15-RFP inhibited anthocyanin accumulation under high light at d 3 of treatment but not at d 5 (Fig. 7 B). This behavior is similar to the one observed with native TCP15. Analysis of protein levels showed that the amount of TCP15-RFP did not change significantly after five days of treatment (Fig. 7 A), indicating that lack of repression is not due to the disappearance of the protein. Figure 7. Open in new tabDownload slide High light intensity does not affect the stability of TCP15. A, Western blot of extracts from wild-type plants (wt) and plants that express a fusion of TCP15 to RFP (35S:TCP15-RFP). The plants were either maintained under control or high light irradiation conditions for five days. Blots were revealed with an antibody against RFP. The left and right panels show images of a Coomassie Brilliant Blue stained gel and a Western blot, respectively, obtained using the same amounts of extracts. The rightmost lane in the left panel shows molecular-weight standards. The arrow in the right panel indicates the expected migration of full-length 35S:TCP15-RFP. B, Anthocyanin levels in wild-type (wt) and 35S:TCP15-RFP plants at different times after exposure to high light irradiation. The bars indicate the mean ± sd of three independent measurements. Columns with different letters are significantly different at P < 0.05 (ANOVA; Tukey test). Figure 7. Open in new tabDownload slide High light intensity does not affect the stability of TCP15. A, Western blot of extracts from wild-type plants (wt) and plants that express a fusion of TCP15 to RFP (35S:TCP15-RFP). The plants were either maintained under control or high light irradiation conditions for five days. Blots were revealed with an antibody against RFP. The left and right panels show images of a Coomassie Brilliant Blue stained gel and a Western blot, respectively, obtained using the same amounts of extracts. The rightmost lane in the left panel shows molecular-weight standards. The arrow in the right panel indicates the expected migration of full-length 35S:TCP15-RFP. B, Anthocyanin levels in wild-type (wt) and 35S:TCP15-RFP plants at different times after exposure to high light irradiation. The bars indicate the mean ± sd of three independent measurements. Columns with different letters are significantly different at P < 0.05 (ANOVA; Tukey test). DISCUSSION TCP15 Is a Repressor of Anthocyanin Accumulation Plants accumulate anthocyanins in response to several stresses and metabolic conditions (Kubasek et al., 1992; Batschauer et al., 1996; Winkel-Shirley, 2002; Lepiniec et al., 2006). One of the functions of anthocyanins is to act as protective pigments to prevent cell damage under conditions of high light intensity; they may also act as antioxidants under situations that cause an increase in ROS accumulation (Grotewold, 2006; Albert et al., 2009; Agati et al., 2012; Page et al., 2012). Expression of a repressor form of TCP15 (TCP15-EAR) in the TCP15 expression domain (i.e. under the TCP15 promoter) causes an increase in anthocyanin levels and in the expression of several anthocyanin biosynthesis genes, suggesting that TCP15 modulates anthocyanin accumulation. This is further supported by the fact that a tcp14 tcp15 mutant accumulates higher anthocyanin levels after exposure to high light intensity. This and the results obtained with plants that express TCP15 from the 35SCaMV promoter indicate that TCP15 is a repressor of anthocyanin accumulation. The fact that anthocyanin levels were similar to wild-type in tcp14 tcp15 mutants under normal illumination suggests that the role of TCP15 and the related protein TCP14 is especially important under conditions that promote anthocyanin accumulation, like the high light intensity treatment used here. Since p15:TCP15-EAR plants have higher anthocyanin levels already under normal conditions, this may indicate the existence of redundancy, probably with other class-I TCP proteins, in the repression of anthocyanin biosynthesis. Anthocyanin biosynthesis is modulated at the transcriptional level by several transcription factors from the Myb, bHLH, and WD40 families (Quattrochio et al., 2006). These factors form the so-called MBW complex that induces the expression of genes encoding different enzymes of the anthocyanin biosynthesis pathway. The genes encoding several of these transcription factors are also induced under conditions that promote anthocyanin biosynthesis, suggesting the existence of upstream components in the signaling pathway. Since several of these genes, such as PAP1, PAP2, TT8, and EGL3, were induced in p15:TCP15-EAR plants, it can be postulated that TCP15 is one of these upstream components. In addition, the fact that a repressor form of TCP15 induces the expression of these genes further suggests that the regulation exerted by TCP15 is indirect and probably acts by inducing the expression of a repressor of PAP1 and other anthocyanin regulatory genes. Known repressors of anthocyanin biosynthesis include AtMYBL2 (Dubos et al., 2008; Matsui et al., 2008), CPC (Zhu et al., 2009), SPL9 (Gou et al., 2011), AtMYB4 (Jin et al., 2000; Fornalé et al., 2014), and AtMYB7 (Fornalé et al., 2014). However, the genes encoding these transcription factors did not show changes in expression in p15:TCP15-EAR plants according to the microarray experiment. It was previously reported that TCP3, a class-II TCP protein, also regulates anthocyanin biosynthesis. In this case, TCP3 acts as an inducer of anthocyanin biosynthesis genes by incorporating into the MBW complex (Li and Zachgo, 2013). This fits into an earlier proposition that class-I and class-II TCP proteins have antagonistic functions (Li et al., 2005). Opposing functions of class-I and class-II TCP proteins have been shown for the regulation of senescence and leaf development associated with jasmonic acid biosynthesis and also suggested for the regulation of cell proliferation (Schommer et al., 2008; Sarvepalli and Nath, 2011; Danisman et al., 2012). The initial proposition of antagonistic functions was made based on the similarities of class-I and class-II TCP recognition sequences and assumed that they were the result of interactions with the same target sites in gene promoters (Kosugi and Ohashi, 2002). Although it is highly probable that this occurs, examples of interactions of class-I and class-II TCPs with the same target sites are scarce. In the case of jasmonic acid biosynthesis, class-I and class-II TCPs do interact with the same target gene (LOX2) but through different promoter elements (Danisman et al., 2012). In addition, it is not completely clear that these interactions always lead to antagonistic effects. In the case of anthocyanin biosynthesis, it has been described that TCP3 interacts with components of the MBW complex and strengthens the activation capacity of Myb proteins bound to this complex (Li and Zachgo, 2013). It would be interesting to analyze whether TCP15 is capable of interacting with members of the MBW complex producing the opposite effect, thus inhibiting anthocyanin biosynthesis. Even if this occurs, our results suggest that the mechanism involved in TCP15 action is different. (1) An effect on transcription through incorporation of TCP15 to the MBW complex would lead to repression by TCP15-EAR and activation by TCP15, which is opposite to a role of TCP15 in inhibiting anthocyanin accumulation, as observed here. (2) A role of TCP15 in interfering or destabilizing the MBW complex or the interaction of the complex with TCP3 through protein-protein interactions would lead to a decrease in anthocyanin levels independently of the TCP15 form, either native TCP15 or TCP15-EAR, that is used. The fact that opposing results are obtained when native or repressor forms are expressed strongly suggests that TCP15 acts by directly modulating the expression of one or more factors that negatively regulate anthocyanin accumulation. However, the primary TCP15 target genes that relate TCP15 action to anthocyanin biosynthesis remain unknown. TCP15 Is Inactivated by Oxidation under High Light Conditions It seemed intriguing that the action of TCP15 on anthocyanin accumulation was lost after prolonged exposure of plants to high light intensity. However, as shown under Results, a persistent inhibition of anthocyanin biosynthesis was observed when a redox-insensitive form of TCP15 was expressed in plants. This form, C20S-TCP15, carries a mutation in a conserved Cys present at the beginning of the HLH motif. It has been reported that oxidation of this Cys inhibits binding of TCP15 to DNA (Viola et al., 2013). Oxidation of Cys-20, located near the DNA binding domain, may disrupt DNA binding due to the formation of intermolecular disulfide bonds between two TCP15 monomers (Viola et al., 2013). This is suggested by the fact that covalent dimers were observed in nonreducing sodium dodecyl sulfate-polyacrylamide gel electrophoresis recombinant proteins treated with oxidizing agents. In addition, Cys-20 from both monomers are probably located at a short distance in dimers according to models of the TCP domain based on its resemblance with the bHLH domain (Viola et al., 2013). Formation of covalent dimers may impose spatial constraints that impair binding of both TCP15 monomers to DNA. It is also possible that oxidation of Cys-20 to sulphenic or sulphinic acid, which is also reversible, may impair DNA binding. Experiments using the two-hybrid system in yeast have shown that the interaction between TCP15 monomers is not disrupted after H2O2 treatment (our unpublished data). Mass spectrometry analysis of TCP15 after inactivation is required to ascertain the nature of the modification that occurs in vivo. The results presented in this work indicate that oxidation of Cys-20 occurs after prolonged exposure of plants to high light intensity. In agreement with this, binding to DNA was lost in extracts from 35S:TCP15 plants exposed to high light and was recovered after treatment of the extracts with a reducing agent, suggesting that TCP15 is present in the extract in its oxidized, inactive form. Indeed, binding was not affected by the high light intensity treatment when C20S-TCP15 was expressed in plants. It has been shown that excess light causes changes in the pools of redox compounds and increase the production of ROS, such as H2O2 and singlet oxygen (Li et al., 2009). Treatment of leaves with H2O2 was shown previously to inactivate endogenous TCP15 activity (Viola et al., 2013). Progressive inactivation of TCP15 after exposure to high light may then be caused by changes in redox conditions of the cellular environment. Even if excess light increases ROS production and promotes anthocyanin accumulation, current evidence does not support the existence of a direct role of ROS in the induction of anthocyanin biosynthesis genes. Contrary to that, Vanderauwera et al. (2005) reported that the induction of anthocyanin biosynthesis genes by high light is impaired in peroxisomal catalase-deficient plants, which accumulate high levels of H2O2. These results may imply that ROS produced during exposure to high light intensity repress, rather than induce, the expression of anthocyanin biosynthesis genes, while our model suggests that ROS act to enhance anthocyanin accumulation after prolonged exposure due to inactivation of TCP15 and related proteins. One possibility is that the effect of ROS depends on the location or the species involved. High light intensity causes redox changes mainly in the chloroplast (Li et al., 2009) and this may affect redox balance in other compartments in a different way than H2O2 from peroxisomes. In addition, catalase-deficient plants are constitutively stressed and this may affect a proper response to ROS produced during the high light intensity treatment. It is noteworthy that inactivation of TCP15 by high light was observed already at d 2, while differences in anthocyanin accumulation persisted until d 3. Gene expression changes, on the other hand, showed correlation with TCP15 activity, since differences with wild-type were lost at d 2. This indicates that, once TCP15 is inactivated, a certain time is required for plants to reach similar anthocyanin levels than wild-type. It should be kept in mind that the treatment applied consists of illumination under high light intensity only during the light period (8 h) and that some recovery may occur during the night. In any case, the results show that TCP15 inactivation precedes anthocyanin accumulation, which is consistent with a role of TCP15 redox changes in modulating anthocyanin levels under high light conditions. The fact that differences in anthocyanin accumulation in tcp14 tcp15 mutants were also evident at d 3 but not at d 5 is also consistent with a role of TCP15 and TCP14 only after relatively short exposure times. After prolonged treatment, inactivation of the proteins in wild-type plants would eliminate the differences with mutant plants. According to our results, we propose that TCP15 and related class-I TCP proteins act to attenuate anthocyanin accumulation after short times of exposition to high light intensity. If high light intensity conditions persist, inhibition of anthocyanin biosynthesis by TCP15 is progressively relieved by oxidation of the protein. This would prevent excessive anthocyanin accumulation after short periods of high light intensity but allow a proper protective response under persistent high illumination. In addition, since TCP15 and other class-I TCP proteins are developmental regulators, high light-intensity-dependent inhibition of class-I TCP protein action may transduce changes in light intensity into morphological or developmental responses. Finally, the redox-dependent modulation of class-I TCP protein action described here for anthocyanin biosynthesis under high light intensity conditions may also operate to adjust the action of TCP proteins under other conditions that change the cellular redox state. MATERIALS AND METHODS Plant Materials Arabidopsis Columbia-0 (Col-0) was used as the wild-type. Plants expressing TCP15 fused to the EAR repressor domain (Hiratsu et al., 2003) under the control of the TCP15 promoter (pTCP15:TCP15-EAR) were reported previously (Uberti-Manassero et al., 2012). The tcp14-4 tcp15-3 double mutant was kindly provided by Dr. Simona Masiero (Universitá degli Studi di Milano, Italy) and was described previously (Kieffer et al., 2011). The DR5:GUS reporter was obtained from the Arabidopsis Biological Resource Center (Ohio State University). DNA Constructs and Plant Transformation To generate 35S:TCP15 and 35S:C20S-TCP15 plants, the TCP15 coding sequence or a mutated version encoding Ser at position 20 of the TCP domain (Viola et al., 2013) were amplified using primers TCP15-F and TCP15-R (Supplemental Table S2). The amplified PCR products were digested with XbaI and XhoI and inserted into a modified binary plasmid pBi121 (which contains an XhoI site at the end of the GUS coding sequence) under the control of the 35SCaMV promoter. To obtain lines expressing TCP15 fused to RFP under the control of the 35SCaMV promoter, the TCP15 coding sequence was cloned into entry vector pENTR-3C (Life Technologies) and then transferred to destination vector pGWB554 (Nakagawa et al., 2007), using the Gateway cloning system (Life Technologies). This vector allows C-terminal fusions of proteins to monomeric red fluorescent protein (mRFP or mCherry). The primers used are listed in Supplemental Table S2. All constructs were checked by DNA sequencing and introduced into Agrobacterium tumefaciens strain LB4404. Arabidopsis plants were transformed by the floral dip procedure (Clough and Bent, 1998). Transformed plants were selected on the basis of kanamycin resistance and genotyping. 35S:TCP15 x DR5:GUS plants were obtained by crossing 35S:TCP15 (line 2) plants with DR5:GUS plants, followed by selfing and selection based on kanamycin resistance. The presence of DR5:GUS in the genome was confirmed by PCR analysis of genomic DNA with primers GUS-F and GUS-R (Supplemental Table S2). Plant-Growth Conditions Plants were grown in soil at 22 to 24°C under a long-day photoperiod (16 h of illumination by a mixture of cool-white and GroLux fluorescent lamps) at an intensity of 100 µE m−2 s−1. For high light intensity treatments, 2-week-old Arabidopsis plants were illuminated by light at 800 µE m−2 s−1, obtained by supplementing normal light with a metal-halide lamp, under a short-day (8 h of illumination) photoperiod. The high light intensity treatment was started at the middle of the illumination period of d 1. Unless otherwise stated, samples were collected at the end of the light period of each day. Experiments were performed in two different growth chambers and we observed that levels and rates of anthocyanin accumulation varied between them, probably because of differences in ambient conditions. The effects described here for the different plant lines under study, however, were consistently observed under both growth conditions. Anthocyanin Measurement Anthocyanin measurement was performed using a method adapted from Neff and Chory (1998). Seventy-five milligrams of tissue were collected from three plants in three replicates. Tissue was ground into a fine powder in liquid nitrogen, extracted with 1 mL of methanol acidified with 1% HCl, incubated overnight at 4°C, and centrifuged at 12,000 rpm for 5 min. Distilled water (0.25 mL) was added to 0.3 mL of supernatants and anthocyanins were separated from chlorophylls by extraction with 0.5 mL of chloroform. Total anthocyanin content in the aqueous phase was measured at 529 nm in a spectrophotometer and the amount of anthocyanin was calculated as mg/gram of fresh weight using a molar extinction coefficient of anthocyanin of 30,000 l/(mol × cm) and a M r of 449.2. Gene-Expression Analysis Total RNA was extracted from entire rosettes with Trizol reagent (Invitrogen). One microgram of total RNA was used for cDNA synthesis with oligo(dT) primer and MMLV reverse transcriptase (Promega) under standard conditions. PCR was performed with an aliquot of the cDNA synthesis reaction with primers specific for the genes under analysis in 20 µL final volume containing 1 µL SYBR Green, 10 pmol of primers, 3 mm MgCl2, and 0.2 µL platinum Taq DNA polymerase (Invitrogen). The amplification was monitored in real time on an MJ Research Chromo4 apparatus. Based on primer efficiency, fold expression was calculated after normalization to actin (ACT2 and ACT8; Charrier et al., 2002) by a comparative Ct method. Triplicate PCR reactions of single lines were analyzed for each gene. Similar results were obtained with at least two additional lines in each case. The primers used for PCR are listed in Supplemental Table S2. GUS staining was performed as described in Uberti-Mansassero et al. (2012). Microarray Results The microarray experiment was performed in triplicate with total RNA isolated from rosettes of 25-day-old wild-type and p15:TCP15-EAR plants grown under standard conditions using Agilent Arabidopsis (V4) 4×44K arrays (two samples in two-color arrays with reciprocal labeling plus one sample in one-color arrays). The data are deposited in the GEO database under Accession nos. GSE57742, GSE57743, and GSE57744. For the analysis of differentially expressed genes, intensities were background-corrected employing the “normexp” method with an offset of 16, except for those probes with log2 background intensity above 10, which were excluded. After normalization using the loess procedure (for two-color arrays) or quantile normalization (for one-color arrays), a MA object (Minus/Average of log2 intensities) was generated. Differentially expressed genes were identified as those with absolute log2 fold change above 1 after applying a FDR-adjusted p-value of 0.05. Genes related with anthocyanin metabolism were extracted manually from the list of differentially expressed genes. DNA-Binding Assays For EMSAs, 10 µg of total protein extracts from transgenic plants prepared as described previously in Viola et al., 2013 were incubated with 50 ng of dsDNA generated by hybridization of the complementary oligonucleotides 5′-AATTCAGATCTGTGGGACCGGGAG-3′ (5′-end labeled with FAM) and 5′-GATCCTCCCGGTCCCACAGATCTG-3′ (TCP15 binding site underlined). Binding reactions were performed in 20 mm HEPES (pH 7.5), 50 mm KCl, 2 mm MgCl, 0.5 mm EDTA, 0.5% Triton X-100, 1 mg of poly(dI-dC), and 10% glycerol. After incubation on ice for 20 min, the reactions were supplemented with 2.5% Ficoll and immediately loaded onto a running gel (5% acrylamide, 0.08% bis-acrylamide in 0.5× TBE plus 2.5% glycerol; 1× TBE is 90 mm Tris-borate, pH 8.3, 2 mm EDTA). The gel was run in 0.5× TBE at 20 mA for 2 h and directly scanned for fluorescence using a Typhoon scanner (GE Healthcare Life Sciences). For competitions, complementary oligonucleotides with changes in positions 3 and 8 of the class-I TCP binding site (5′-GTGGGACC-3′), 5′-AATTCAGATCTGTAGGACTGGGAG-3′ and 5′-GATCCTCCCAGTCCTACAGATCTG-3′, were used. Western Blot Analysis For Western blot analysis, 45 µg of protein extracts were separated through reducing sodium dodecyl sulfate-polyacrylamide gel electrophoresis and then transferred to Hybond-ECL membranes (GE Healthcare Life Sciences). Membranes were subsequently probed with an antibody against RFP (Living colors DsRed Polyclonal Antibody, Clontech) at a dilution of 1:1,000, and developed with anti-rabbit immunoglobin conjugated with horseradish peroxidase using the SuperSignal West Pico Chemiluminescent Substrate (Pierce). Accession Numbers Microarray data from this article can be found in the GEO database under Accession nos. GSE57742, GSE57743, and GSE57744. Supplemental Data The following supplemental materials are available. Supplemental Table S1. Genes involved in anthocyanin biosynthesis showing significantly altered expression in p15:TCP15-EAR plants. Supplemental Table S2. Oligonucleotides used in this study. Supplemental Figure S1. Anthocyanin levels in extracts from wild-type and p15:TCP15-EAR plants after a high light intensity treatment. Supplemental Figure S2. TCP15 transcript levels in 35S:TCP15 and 35S:C20S-TCP15 plants. ACKNOWLEDGMENTS We thank the Arabidopsis Biological Resource Center, Martin Kieffer, and Simona Masiero for seed stocks. We also thank Elina Welchen and Leandro Lucero, from our lab, for generating the 35S:TCP15-RFP line and Agustin Arce for help with the analysis of microarray data. Glossary ROS reactive oxygen species EMSA electrophoretic mobility shift assay 2ME 2-mercaptoethanol RFP red fluorescent protein LITERATURE CITED Agati G , Azzarello E, Pollastri S, Tattini M ( 2012 ) Flavonoids as antioxidants in plants: location and functional significance . Plant Sci 196 : 67 – 76 Google Scholar Crossref Search ADS PubMed WorldCat Albert NW , Davies KM, Lewis DH, Zhang H, Montefiori M, Brendolise C, Boase MR, Ngo H, Jameson PE, Schwinn KE ( 2014 ) A conserved network of transcriptional activators and repressors regulates anthocyanin pigmentation in eudicots . 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A.C. is a CONICET fellow. * Address correspondence to [email protected]. The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantphysiol.org) is: Daniel H. Gonzalez ([email protected]). I.L.V. and D.H.G. designed the experiments and analyzed data. I.L.V. performed the experiments. A.C. performed and analyzed RT-qPCR measurements. D.H.G. wrote the manuscript. I.L.V. made the figures and contributed to writing. [OPEN] This article is available without a subscription. www.plantphysiol.org/cgi/doi/10.1104/pp.15.01016 © 2016 American Society of Plant Biologists. All Rights Reserved. © The Author(s) 2016. Published by Oxford University Press on behalf of American Society of Plant Biologists. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

Medeiros, David B.; Martins, Samuel C.V.; Cavalcanti, João Henrique F.; Daloso, Danilo M.; Martinoia, Enrico; Nunes-Nesi, Adriano; DaMatta, Fábio M.; Fernie, Alisdair R.; Araújo, Wagner L.

doi: 10.1104/pp.15.01053pmid: