Calcineurin B-Like Protein-Interacting Protein Kinase CIPK21 Regulates Osmotic and Salt Stress Responses in Arabidopsis

The role of calcium-mediated signaling has been extensively studied in plant responses to abiotic stress signals. Calcineurin B-like proteins (CBLs) and CBL-interacting protein kinases (CIPKs) constitute a complex signaling network acting in diverse plant stress responses. Osmotic stress imposed by soil salinity and drought is a major abiotic stress that impedes plant growth and development and involves calcium-signaling processes. In this study, we report the functional analysis of CIPK21, an Arabidopsis (Arabidopsis thaliana) CBL-interacting protein kinase, ubiquitously expressed in plant tissues and up-regulated under multiple abiotic stress conditions. The growth of a loss-of-function mutant of CIPK21, cipk21, was hypersensitive to high salt and osmotic stress conditions. The calcium sensors CBL2 and CBL3 were found to physically interact with CIPK21 and target this kinase to the tonoplast. Moreover, preferential localization of CIPK21 to the tonoplast was detected under salt stress condition when coexpressed with CBL2 or CBL3. These findings suggest that CIPK21 mediates responses to salt stress condition in Arabidopsis, at least in part, by regulating ion and water homeostasis across the vacuolar membranes.Drought and salinity cause osmotic stress in plants and severely affect crop productivity throughout the world. Plants respond to osmotic stress by changing a number of cellular processes (Xiong et al., 1999; Xiong and Zhu, 2002; Bartels and Sunkar, 2005; Boudsocq and Lauriére, 2005). Some of these changes include activation of stress-responsive genes, regulation of membrane transport at both plasma membrane (PM) and vacuolar membrane (tonoplast) to maintain water and ionic homeostasis, and metabolic changes to produce compatible osmolytes such as Pro (Stewart and Lee, 1974; Krasensky and Jonak, 2012). It has been well established that a specific calcium (Ca²⁺) signature is generated in response to a particular environmental stimulus (Trewavas and Malhó, 1998; Scrase-Field and Knight, 2003; Luan, 2009; Kudla et al., 2010). The Ca²⁺ changes are primarily perceived by several Ca²⁺ sensors such as calmodulin (Reddy, 2001; Luan et al., 2002), Ca²⁺-dependent protein kinases (Harper and Harmon, 2005), calcineurin B-like proteins (CBLs; Luan et al., 2002; Batistič and Kudla, 2004; Pandey, 2008; Luan, 2009; Sanyal et al., 2015), and other Ca²⁺-binding proteins (Reddy, 2001; Shao et al., 2008) to initiate various cellular responses.Plant CBL-type Ca²⁺ sensors interact with and activate CBL-interacting protein kinases (CIPKs) that phosphorylate downstream components to transduce Ca²⁺ signals (Liu et al., 2000; Luan et al., 2002; Batistič and Kudla, 2004; Luan, 2009). In several plant species, multiple members have been identified in the CBL and CIPK family (Luan et al., 2002; Kolukisaoglu et al., 2004; Pandey, 2008; Batistič and Kudla, 2009; Weinl and Kudla, 2009; Pandey et al., 2014). Involvement of specific CBL-CIPK pair to decode a particular type of signal entails the alternative and selective complex formation leading to stimulus-response coupling (D’Angelo et al., 2006; Batistič et al., 2010).Several CBL and CIPK family members have been implicated in plant responses to drought, salinity, and osmotic stress based on genetic analysis of Arabidopsis (Arabidopsis thaliana) mutants (Zhu, 2002; Cheong et al., 2003, 2007; Kim et al., 2003; Pandey et al., 2004, 2008; D’Angelo et al., 2006; Qin et al., 2008; Tripathi et al., 2009; Held et al., 2011; Tang et al., 2012; Drerup et al., 2013; Eckert et al., 2014). A few CIPKs have also been functionally characterized by gain-of-function approach in crop plants such as rice (Oryza sativa), pea (Pisum sativum), and maize (Zea mays) and were found to be involved in osmotic stress responses (Mahajan et al., 2006; Xiang et al., 2007; Yang et al., 2008; Tripathi et al., 2009; Zhao et al., 2009; Cuéllar et al., 2010).In this report, we examined the role of the Arabidopsis CIPK21 gene in osmotic stress response by reverse genetic analysis. The loss-of-function mutant plants became hypersensitive to salt and mannitol stress conditions, suggesting that CIPK21 is involved in the regulation of osmotic stress response in Arabidopsis. These findings are further supported by an enhanced tonoplast targeting of the cytoplasmic CIPK21 through interaction with the vacuolar Ca²⁺ sensors CBL2 and CBL3 under salt stress condition.     

Characterization of the Possible Roles for B Class MADS Box Genes in Regulation of Perianth Formation in Orchid

To investigate sepal/petal/lip formation in Oncidium Gower Ramsey, three paleoAPETALA3 genes, O. Gower Ramsey MADS box gene5 (OMADS5; clade 1), OMADS3 (clade 2), and OMADS9 (clade 3), and one PISTILLATA gene, OMADS8, were characterized. The OMADS8 and OMADS3 mRNAs were expressed in all four floral organs as well as in vegetative leaves. The OMADS9 mRNA was only strongly detected in petals and lips. The mRNA for OMADS5 was only strongly detected in sepals and petals and was significantly down-regulated in lip-like petals and lip-like sepals of peloric mutant flowers. This result revealed a possible negative role for OMADS5 in regulating lip formation. Yeast two-hybrid analysis indicated that OMADS5 formed homodimers and heterodimers with OMADS3 and OMADS9. OMADS8 only formed heterodimers with OMADS3, whereas OMADS3 and OMADS9 formed homodimers and heterodimers with each other. We proposed that sepal/petal/lip formation needs the presence of OMADS3/8 and/or OMADS9. The determination of the final organ identity for the sepal/petal/lip likely depended on the presence or absence of OMADS5. The presence of OMADS5 caused short sepal/petal formation. When OMADS5 was absent, cells could proliferate, resulting in the possible formation of large lips and the conversion of the sepal/petal into lips in peloric mutants. Further analysis indicated that only ectopic expression of OMADS8 but not OMADS5/9 caused the conversion of the sepal into an expanded petal-like structure in transgenic Arabidopsis (Arabidopsis thaliana) plants.The ABCDE model predicts the formation of any flower organ by the interaction of five classes of homeotic genes in plants (Yanofsky et al., 1990; Jack et al., 1992; Mandel et al., 1992; Goto and Meyerowitz, 1994; Jofuku et al., 1994; Pelaz et al., 2000, 2001; Theißen and Saedler, 2001; Pinyopich et al., 2003; Ditta et al., 2004; Jack, 2004). The A class genes control sepal formation. The A, B, and E class genes work together to regulate petal formation. The B, C, and E class genes control stamen formation. The C and E class genes work to regulate carpel formation, whereas the D class gene is involved in ovule development. MADS box genes seem to have a central role in flower development, because most ABCDE genes encode MADS box proteins (Coen and Meyerowitz, 1991; Weigel and Meyerowitz, 1994; Purugganan et al., 1995; Rounsley et al., 1995; Theißen and Saedler, 1995; Theißen et al., 2000; Theißen, 2001).The function of B group genes, such as APETALA3 (AP3) and PISTILLATA (PI), has been thought to have a major role in specifying petal and stamen development (Jack et al., 1992; Goto and Meyerowitz, 1994; Krizek and Meyerowitz, 1996; Kramer et al., 1998; Hernandez-Hernandez et al., 2007; Kanno et al., 2007; Whipple et al., 2007; Irish, 2009). In Arabidopsis (Arabidopsis thaliana), mutation in AP3 or PI caused identical phenotypes of second whorl petal conversion into a sepal structure and third flower whorl stamen into a carpel structure (Bowman et al., 1989; Jack et al., 1992; Goto and Meyerowitz, 1994). Similar homeotic conversions for petal and stamen were observed in the mutants of the AP3 and PI orthologs from a number of core eudicots such as Antirrhinum majus, Petunia hybrida, Gerbera hybrida, Solanum lycopersicum, and Nicotiana benthamiana (Sommer et al., 1990; Tröbner et al., 1992; Angenent et al., 1993; van der Krol et al., 1993; Yu et al., 1999; Liu et al., 2004; Vandenbussche et al., 2004; de Martino et al., 2006), from basal eudicot species such as Papaver somniferum and Aquilegia vulgaris (Drea et al., 2007; Kramer et al., 2007), as well as from monocot species such as Zea mays and Oryza sativa (Ambrose et al., 2000; Nagasawa et al., 2003; Prasad and Vijayraghavan, 2003; Yadav et al., 2007; Yao et al., 2008). This indicated that the function of the B class genes AP3 and PI is highly conserved during evolution.It has been thought that B group genes may have arisen from an ancestral gene through multiple gene duplication events (Doyle, 1994; Theißen et al., 1996, 2000; Purugganan, 1997; Kramer et al., 1998; Kramer and Irish, 1999; Lamb and Irish, 2003; Kim et al., 2004; Stellari et al., 2004; Zahn et al., 2005; Hernandez-Hernandez et al., 2007). In the gymnosperms, there was a single putative B class lineage that duplicated to generate the paleoAP3 and PI lineages in angiosperms (Kramer et al., 1998; Theißen et al., 2000; Irish, 2009). The paleoAP3 lineage is composed of AP3 orthologs identified in lower eudicots, magnolid dicots, and monocots (Kramer et al., 1998). Genes in this lineage contain the conserved paleoAP3- and PI-derived motifs in the C-terminal end of the proteins, which have been thought to be characteristics of the B class ancestral gene (Kramer et al., 1998; Tzeng and Yang, 2001; Hsu and Yang, 2002). The PI lineage is composed of PI orthologs that contain a highly conserved PI motif identified in most plant species (Kramer et al., 1998). Subsequently, there was a second duplication at the base of the core eudicots that produced the euAP3 and TM6 lineages, which have been subject to substantial sequence changes in eudicots during evolution (Kramer et al., 1998; Kramer and Irish, 1999). The paleoAP3 motif in the C-terminal end of the proteins was retained in the TM6 lineage and replaced by a conserved euAP3 motif in the euAP3 lineage of most eudicot species (Kramer et al., 1998). In addition, many lineage-specific duplications for paleoAP3 lineage have occurred in plants such as orchids (Hsu and Yang, 2002; Tsai et al., 2004; Kim et al., 2007; Mondragón-Palomino and Theißen, 2008, 2009; Mondragón-Palomino et al., 2009), Ranunculaceae, and Ranunculales (Kramer et al., 2003; Di Stilio et al., 2005; Shan et al., 2006; Kramer, 2009).Unlike the A or C class MADS box proteins, which form homodimers that regulate flower development, the ability of B class proteins to form homodimers has only been reported in gymnosperms and in the paleoAP3 and PI lineages of some monocots. For example, LMADS1 of the lily Lilium longiflorum (Tzeng and Yang, 2001), OMADS3 of the orchid Oncidium Gower Ramsey (Hsu and Yang, 2002), and PeMADS4 of the orchid Phalaenopsis equestris (Tsai et al., 2004) in the paleoAP3 lineage, LRGLOA and LRGLOB of the lily Lilium regale (Winter et al., 2002), TGGLO of the tulip Tulipa gesneriana (Kanno et al., 2003), and PeMADS6 of the orchid P. equestris (Tsai et al., 2005) in the PI lineage, and GGM2 of the gymnosperm Gnetum gnemon (Winter et al., 1999) were able to form homodimers that regulate flower development. Proteins in the euAP3 lineage and in most paleoAP3 lineages were not able to form homodimers and had to interact with PI to form heterodimers in order to regulate petal and stamen development in various plant species (Schwarz-Sommer et al., 1992; Tröbner et al., 1992; Riechmann et al., 1996; Moon et al., 1999; Winter et al., 2002; Kanno et al., 2003; Vandenbussche et al., 2004; Yao et al., 2008). In addition to forming dimers, AP3 and PI were able to interact with other MADS box proteins, such as SEPALLATA1 (SEP1), SEP2, and SEP3, to regulate petal and stamen development (Pelaz et al., 2000; Honma and Goto, 2001; Theißen and Saedler, 2001; Castillejo et al., 2005).Orchids are among the most important plants in the flower market around the world, and research on MADS box genes has been reported for several species of orchids during the past few years (Lu et al., 1993, 2007; Yu and Goh, 2000; Hsu and Yang, 2002; Yu et al., 2002; Hsu et al., 2003; Tsai et al., 2004, 2008; Xu et al., 2006; Guo et al., 2007; Kim et al., 2007; Chang et al., 2009). Unlike the flowers in eudicots, the nearly identical shape of the sepals and petals as well as the production of a unique lip in orchid flowers make them a very special plant species for the study of flower development. Four clades (1–4) of genes in the paleoAP3 lineage have been identified in several orchids (Hsu and Yang, 2002; Tsai et al., 2004; Kim et al., 2007; Mondragón-Palomino and Theißen, 2008, 2009; Mondragón-Palomino et al., 2009). Several works have described the possible interactions among these four clades of paleoAP3 genes and one PI gene that are involved in regulating the differentiation and formation of the sepal/petal/lip of orchids (Tsai et al., 2004; Kim et al., 2007; Mondragón-Palomino and Theißen, 2008, 2009). However, the exact mechanism that involves the orchid B class genes remains unclear and needs to be clarified by more experimental investigations.O. Gower Ramsey is a popular orchid with important economic value in cut flower markets. Only a few studies have been reported on the role of MADS box genes in regulating flower formation in this plant species (Hsu and Yang, 2002; Hsu et al., 2003; Chang et al., 2009). An AP3-like MADS gene that regulates both floral formation and initiation in transgenic Arabidopsis has been reported (Hsu and Yang, 2002). In addition, four AP1/AGAMOUS-LIKE9 (AGL9)-like MADS box genes have been characterized that show novel expression patterns and cause different effects on floral transition and formation in Arabidopsis (Hsu et al., 2003; Chang et al., 2009). Compared with other orchids, the production of a large and well-expanded lip and five small identical sepals/petals makes O. Gower Ramsey a special case for the study of the diverse functions of B class MADS box genes during evolution. Therefore, the isolation of more B class MADS box genes and further study of their roles in the regulation of perianth (sepal/petal/lip) formation during O. Gower Ramsey flower development are necessary. In addition to the clade 2 paleoAP3 gene OMADS3, which was previously characterized in our laboratory (Hsu and Yang, 2002), three more B class MADS box genes, OMADS5, OMADS8, and OMADS9, were characterized from O. Gower Ramsey in this study. Based on the different expression patterns and the protein interactions among these four orchid B class genes, we propose that the presence of OMADS3/8 and/or OMADS9 is required for sepal/petal/lip formation. Further sepal and petal formation at least requires the additional presence of OMADS5, whereas large lip formation was seen when OMADS5 expression was absent. Our results provide a new finding and information pertaining to the roles for orchid B class MADS box genes in the regulation of sepal/petal/lip formation.     

A Host RNA Helicase-Like Protein,AtRH8, Interacts with the Potyviral Genome-Linked Protein,VPg, Associates with the Virus Accumulation Complex,and Is Essential for Infection

The viral genome-linked protein, VPg, of potyviruses is a multifunctional protein involved in viral genome translation and replication. Previous studies have shown that both eukaryotic translation initiation factor 4E (eIF4E) and eIF4G or their respective isoforms from the eIF4F complex, which modulates the initiation of protein translation, selectively interact with VPg and are required for potyvirus infection. Here, we report the identification of two DEAD-box RNA helicase-like proteins, PpDDXL and AtRH8 from peach (Prunus persica) and Arabidopsis (Arabidopsis thaliana), respectively, both interacting with VPg. We show that AtRH8 is dispensable for plant growth and development but necessary for potyvirus infection. In potyvirus-infected Nicotiana benthamiana leaf tissues, AtRH8 colocalizes with the chloroplast-bound virus accumulation vesicles, suggesting a possible role of AtRH8 in viral genome translation and replication. Deletion analyses of AtRH8 have identified the VPg-binding region. Comparison of this region and the corresponding region of PpDDXL suggests that they are highly conserved and share the same secondary structure. Moreover, overexpression of the VPg-binding region from either AtRH8 or PpDDXL suppresses potyvirus accumulation in infected N. benthamiana leaf tissues. Taken together, these data demonstrate that AtRH8, interacting with VPg, is a host factor required for the potyvirus infection process and that both AtRH8 and PpDDXL may be manipulated for the development of genetic resistance against potyvirus infections.Plant viruses are obligate intracellular parasites that infect many agriculturally important crops and cause severe losses each year. One of the common characteristics of plant viruses is their relatively small genome that encodes a limited number of viral proteins, making them dependent on host factors to fulfill their infection cycles (Maule et al., 2002; Whitham and Wang, 2004; Nelson and Citovsky, 2005; Decroocq et al., 2006). In order to establish a successful infection, the invading virus must recruit an array of host proteins (host factors) to translate and replicate its genome and to move locally from cell to cell via the plasmodesmata and systemically via the vascular system. It has been suggested that down-regulation or mutation of some of the required host factors may result in recessively inherited resistance to viruses (Kang et al., 2005b).Potyviruses, belonging to the genus Potyvirus in the family Potyviradae, constitute the largest group of plant viruses (Rajamäki et al., 2004). Potyviruses have a single positive-strand RNA genome approximately 10 kb in length, with a viral genome-linked protein (VPg) covalently attached to the 5′ end and a poly(A) tail at the 3′ end (Urcuqui-Inchima et al., 2001; Rajamäki et al., 2004). The viral genome contains a single open reading frame (ORF) that translates into a polypeptide with a molecular mass of approximately 350 kD, which is cleaved into 10 mature proteins by viral proteases (Urcuqui-Inchima et al., 2001). Recently, a novel viral protein resulting from a frameshift in the P3 cistron has been reported (Chung et al., 2008). Of the 11 viral proteins, VPg is a multifunctional protein and the only other viral protein present in the viral particles (virions) besides the coat protein and the cylindrical inclusion protein (CI; Oruetxebarria et al., 2001; Puustinen et al., 2002; Gabrenaite-Verkhovskaya et al., 2008). The nonstructural protein is linked to the viral RNA by a phosphodiester bond between the 5′ terminal uridine residue of the RNA and the O⁴-hydroxyl group of amino acid Tyr (Murphy et al., 1996; Oruetxebarria et al., 2001; Puustinen et al., 2002). Mutation of the Tyr residue that links VPg to the viral RNA abolishes virus infectivity completely (Murphy et al., 1996). In infected cells, VPg and its precursor NIa are present in the nucleus and in the membrane-associated virus replication vesicles in the cytoplasm (Carrington et al., 1993; Rajamäki and Valkonen, 2003; Cotton et al., 2009). As a component of the replication complex, VPg may serve as a primer for viral RNA replication (Puustinen and Mäkinen, 2004) and as an analog of the m⁷G cap of mRNAs for the viral genome to recruit the translation complex for translation (Michon et al., 2006; Beauchemin et al., 2007; Khan et al., 2008). Furthermore, VPg has been suggested to be an avirulence factor for recessive resistance genes in diverse plant species (Moury et al., 2004; Kang et al., 2005b; Bruun-Rasmussen et al., 2007). Thus, VPg plays a pivotal role in the virus infection process. The molecular identification of VPg-interacting host proteins and the subsequent functional characterization of such interactions may advance knowledge of the intricate virus replication mechanisms and help develop novel antiviral strategies.Previous studies have shown that VPg and its precursor NIa interact with several host proteins, including three essential components of the host protein translation apparatus (Thivierge et al., 2008). The first protein is the cellular translation initiation factor eIF4E or its isoform eIF(iso)4E, identified through a yeast two-hybrid screen using VPg as a bait (Wittmann et al., 1997; Schaad et al., 2000). The protein complex of VPg and eIF4E is an essential component for virus infectivity (Robaglia and Caranta, 2006). Mutations and knockout of eIF4E or eIF(iso)4E confer resistance to infection (Lellis et al., 2002; Ruffel et al., 2002; Nicaise et al., 2003; Gao et al., 2004; Kang et al., 2005a; Ruffel et al., 2005; Decroocq et al., 2006; Bruun-Rasmussen et al., 2007). It is well known that potyviruses recruit selectively one of the eIF4E isoforms, depending on specific virus-host combinations (German-Retana et al., 2008). For instance, in Arabidopsis (Arabidopsis thaliana), eIF(iso)4E is required for infection by Turnip mosaic virus (TuMV), Plum pox virus (PPV), and Lettuce mosaic virus, while eIF4E is indispensable for infection by Clover yellow vein virus (Duprat et al., 2002; Lellis et al., 2002; Sato et al., 2005; Decroocq et al., 2006). The second cellular protein interacting with VPg is another translation initiation factor, eIF4G. Analysis of Arabidopsis knockout mutants for eIF4G or its isomers eIF(iso)4G1 and eIF(iso)4G2 has yielded results supporting the idea that the recruitment of eIF4G for potyvirus infection is also isoform dependent (Nicaise et al., 2007). Recently, poly(A)-binding protein (PABP), the translation initiation factor that bridges the 5′ and 3′ termini of the mRNA into proximity, has been proposed to be essential for efficient multiplication of TuMV (Dufresne et al., 2008). PABP was previously documented to interact with NIa, a VPg precursor containing both VPg and the proteinase NIa-Pro (Léonard et al., 2004). As the translation factors eIF(iso)4E and PABP have been found to be internalized in virus-induced vesicles, it has been suggested that the interactions between VPg and these translation factors are crucial for viral RNA translation and/or replication (Beauchemin and Laliberté, 2007; Beauchemin et al., 2007; Cotton et al., 2009). Besides these three translation factors, a Cys-rich plant protein, potyvirus VPg-interaction protein, was also found to associate with VPg (Dunoyer et al., 2004). This plant-specific VPg-interacting host protein contains a PHD finger domain and acts as an ancillary factor to support potyvirus infection and movement (Dunoyer et al., 2004).In this study, we describe the identification of an Arabidopsis DEAD-box RNA helicase (DDX), AtRH8, and a peach (Prunus persica) DDX-like protein, PpDDXL, both interacting with the potyviral VPg protein. Using the atrh8 mutant, we demonstrate that AtRH8 is not required for plant growth and development in Arabidopsis but is necessary for infection by two plant potyviruses, PPV and TuMV. Furthermore, we present evidence that AtRH8 colocalizes with the virus accumulation complex in potyvirus-infected leaf tissues, which reveals a possible role of AtRH8 in virus infection. Finally, we have identified the VPg-binding region (VPg-BR) of AtRH8 and PpDDX and show that overexpression of the VPg-BR either from AtRH8 or PpDDXL suppresses virus accumulation.     

Surveying Rubisco Diversity and Temperature Response to Improve Crop Photosynthetic Efficiency

The threat to global food security of stagnating yields and population growth makes increasing crop productivity a critical goal over the coming decades. One key target for improving crop productivity and yields is increasing the efficiency of photosynthesis. Central to photosynthesis is Rubisco, which is a critical but often rate-limiting component. Here, we present full Rubisco catalytic properties measured at three temperatures for 75 plants species representing both crops and undomesticated plants from diverse climates. Some newly characterized Rubiscos were naturally “better” compared to crop enzymes and have the potential to improve crop photosynthetic efficiency. The temperature response of the various catalytic parameters was largely consistent across the diverse range of species, though absolute values showed significant variation in Rubisco catalysis, even between closely related species. An analysis of residue differences among the species characterized identified a number of candidate amino acid substitutions that will aid in advancing engineering of improved Rubisco in crop systems. This study provides new insights on the range of Rubisco catalysis and temperature response present in nature, and provides new information to include in models from leaf to canopy and ecosystem scale.In a changing climate and under pressure from a population set to hit nine billion by 2050, global food security will require massive changes to the way food is produced, distributed, and consumed (Ort et al., 2015). To match rising demand, agricultural production must increase by 50 to 70% in the next 35 years, and yet the gains in crop yields initiated by the green revolution are slowing, and in some cases, stagnating (Long and Ort, 2010; Ray et al., 2012). Among a number of areas being pursued to increase crop productivity and food production, improving photosynthetic efficiency is a clear target, offering great promise (Parry et al., 2007; von Caemmerer et al., 2012; Price et al., 2013; Ort et al., 2015). As the gatekeeper of carbon entry into the biosphere and often acting as the rate-limiting step of photosynthesis, Rubisco, the most abundant enzyme on the planet (Ellis, 1979), is an obvious and important target for improving crop photosynthetic efficiency.Rubisco is considered to exhibit comparatively poor catalysis, in terms of catalytic rate, specificity, and CO₂ affinity (Tcherkez et al., 2006; Andersson, 2008), leading to the suggestion that even small increases in catalytic efficiency may result in substantial improvements to carbon assimilation across a growing season (Zhu et al., 2004; Parry et al., 2013; Galmés et al., 2014a; Carmo-Silva et al., 2015). If combined with complimentary changes such as optimizing other components of the Calvin Benson or photorespiratory cycles (Raines, 2011; Peterhansel et al., 2013; Simkin et al., 2015), optimized canopy architecture (Drewry et al., 2014), or introducing elements of a carbon concentrating mechanism (Furbank et al., 2009; Lin et al., 2014a; Hanson et al., 2016; Long et al., 2016), Rubisco improvement presents an opportunity to dramatically increase the photosynthetic efficiency of crop plants (McGrath and Long, 2014; Long et al., 2015; Betti et al., 2016). A combination of the available strategies is essential for devising tailored solutions to meet the varied requirements of different crops and the diverse conditions under which they are typically grown around the world.Efforts to engineer an improved Rubisco have not yet produced a “super Rubisco” (Parry et al., 2007; Ort et al., 2015). However, advances in engineering precise changes in model systems continue to provide important developments that are increasing our understanding of Rubisco catalysis (Spreitzer et al., 2005; Whitney et al., 2011a, 2011b; Morita et al., 2014; Wilson et al., 2016), regulation (Andralojc et al., 2012; Carmo-Silva and Salvucci, 2013; Bracher et al., 2015), and biogenesis (Saschenbrecker et al., 2007; Whitney and Sharwood, 2008; Lin et al., 2014b; Hauser et al., 2015; Whitney et al., 2015).A complementary approach is to understand and exploit Rubisco natural diversity. Previous characterization of Rubisco from a limited number of species has not only demonstrated significant differences in the underlying catalytic parameters, but also suggests that further undiscovered diversity exists in nature and that the properties of some of these enzymes could be beneficial if present in crop plants (Carmo-Silva et al., 2015). Recent studies clearly illustrate the variation possible among even closely related species (Galmés et al., 2005, 2014b, 2014c; Kubien et al., 2008; Andralojc et al., 2014; Prins et al., 2016).Until recently, there have been relatively few attempts to characterize the consistency, or lack thereof, of temperature effects on in vitro Rubisco catalysis (Sharwood and Whitney, 2014), and often studies only consider a subset of Rubisco catalytic properties. This type of characterization is particularly important for future engineering efforts, enabling specific temperature effects to be factored into any attempts to modify crops for a future climate. In addition, the ability to coanalyze catalytic properties and DNA or amino acid sequence provides the opportunity to correlate sequence and biochemistry to inform engineering studies (Christin et al., 2008; Kapralov et al., 2011; Rosnow et al., 2015). While the amount of gene sequence information available grows rapidly with improving technology, knowledge of the corresponding biochemical variation resulting has yet to be determined (Cousins et al., 2010; Carmo-Silva et al., 2015; Sharwood and Whitney, 2014; Nunes-Nesi et al., 2016).This study aimed to characterize the catalytic properties of Rubisco from diverse species, comprising a broad range of monocots and dicots from diverse environments. The temperature dependence of Rubisco catalysis was evaluated to tailor Rubisco engineering for crop improvement in specific environments. Catalytic diversity was analyzed alongside the sequence of the Rubisco large subunit gene, rbcL, to identify potential catalytic switches for improving photosynthesis and productivity. In vitro results were compared to the average temperature of the warmest quarter in the regions where each species grows to investigate the role of temperature in modulating Rubisco catalysis.     

Focus on Ethylene: Ethylene and the Regulation of Physiological and Morphological Responses to Nutrient Deficiencies

To cope with nutrient deficiencies, plants develop both morphological and physiological responses. The regulation of these responses is not totally understood, but some hormones and signaling substances have been implicated. It was suggested several years ago that ethylene participates in the regulation of responses to iron and phosphorous deficiency. More recently, its role has been extended to other deficiencies, such as potassium, sulfur, and others. The role of ethylene in so many deficiencies suggests that, to confer specificity to the different responses, it should act through different transduction pathways and/or in conjunction with other signals. In this update, the data supporting a role for ethylene in the regulation of responses to different nutrient deficiencies will be reviewed. In addition, the results suggesting the action of ethylene through different transduction pathways and its interaction with other hormones and signaling substances will be discussed.When plants suffer from a mineral nutrient deficiency, they develop morphological and physiological responses (mainly in their roots) aimed to facilitate the uptake and mobilization of the limiting nutrient. After the nutrient has been acquired in enough quantity, these responses need to be switched off to avoid toxicity and conserve energy. In recent years, different plant hormones (e.g. ethylene, auxin, cytokinins, jasmonic acid, abscisic acid, brassinosteroids, GAs, and strigolactones) have been implicated in the regulation of these responses (Romera et al., 2007, 2011, 2015; Liu et al., 2009; Rubio et al., 2009; Kapulnik et al., 2011; Kiba et al., 2011; Iqbal et al., 2013; Zhang et al., 2014).Before the 1990s, there were several publications relating ethylene and nutrient deficiencies (cited in Lynch and Brown [1997] and Romera et al. [1999]) without establishing a direct implication of ethylene in the regulation of nutrient deficiency responses. In 1994, Romera and Alcántara (1994) published an article in Plant Physiology suggesting a role for ethylene in the regulation of Fe deficiency responses. In 1999, Borch et al. (1999) showed the participation of ethylene in the regulation of P deficiency responses. Since then, evidence has been accumulating in support of a role for ethylene in the regulation of both Fe (Romera et al., 1999, 2015; Waters and Blevins, 2000; Lucena et al., 2006; Waters et al., 2007; García et al., 2010, 2011, 2013, 2014; Yang et al., 2014) and P deficiency responses (Kim et al., 2008; Lei et al., 2011; Li et al., 2011; Nagarajan and Smith, 2012; Wang et al., 2012, 2014c). Both Fe and P may be poorly available in most soils, and plants develop similar responses under their deficiencies (Romera and Alcántara, 2004; Zhang et al., 2014). More recently, a role for ethylene has been extended to other deficiencies, such as K (Shin and Schachtman, 2004; Jung et al., 2009; Kim et al., 2012), S (Maruyama-Nakashita et al., 2006; Wawrzyńska et al., 2010; Moniuszko et al., 2013), and B (Martín-Rejano et al., 2011). Ethylene has also been implicated in both N deficiency and excess (Tian et al., 2009; Mohd-Radzman et al., 2013; Zheng et al., 2013), and its participation in Mg deficiency has been suggested (Hermans et al., 2010).In this update, we will review the information supporting a role for ethylene in the regulation of different nutrient deficiency responses. For information relating ethylene to other aspects of plant mineral nutrition, such as N₂ fixation and responses to excess of nitrate or essential heavy metals, the reader is referred to other reviews (for review, see Maksymiec, 2007; Mohd-Radzman et al., 2013; Steffens, 2014).     

Patterns of Metabolite Changes Identified from Large-Scale Gene Perturbations in Arabidopsis Using a Genome-Scale Metabolic Network

Metabolomics enables quantitative evaluation of metabolic changes caused by genetic or environmental perturbations. However, little is known about how perturbing a single gene changes the metabolic system as a whole and which network and functional properties are involved in this response. To answer this question, we investigated the metabolite profiles from 136 mutants with single gene perturbations of functionally diverse Arabidopsis (Arabidopsis thaliana) genes. Fewer than 10 metabolites were changed significantly relative to the wild type in most of the mutants, indicating that the metabolic network was robust to perturbations of single metabolic genes. These changed metabolites were closer to each other in a genome-scale metabolic network than expected by chance, supporting the notion that the genetic perturbations changed the network more locally than globally. Surprisingly, the changed metabolites were close to the perturbed reactions in only 30% of the mutants of the well-characterized genes. To determine the factors that contributed to the distance between the observed metabolic changes and the perturbation site in the network, we examined nine network and functional properties of the perturbed genes. Only the isozyme number affected the distance between the perturbed reactions and changed metabolites. This study revealed patterns of metabolic changes from large-scale gene perturbations and relationships between characteristics of the perturbed genes and metabolic changes.Rational and quantitative assessment of metabolic changes in response to genetic modification (GM) is an open question and in need of innovative solutions. Nontargeted metabolite profiling can detect thousands of compounds, but it is not easy to understand the significance of the changed metabolites in the biochemical and biological context of the organism. To better assess the changes in metabolites from nontargeted metabolomics studies, it is important to examine the changed metabolites in the context of the genome-scale metabolic network of the organism.Metabolomics is a technique that aims to quantify all the metabolites in a biological system (Nikolau and Wurtele, 2007; Nicholson and Lindon, 2008; Roessner and Bowne, 2009). It has been used widely in studies ranging from disease diagnosis (Holmes et al., 2008; DeBerardinis and Thompson, 2012) and drug discovery (Cascante et al., 2002; Kell, 2006) to metabolic reconstruction (Feist et al., 2009; Kim et al., 2012) and metabolic engineering (Keasling, 2010; Lee et al., 2011). Metabolomic studies have demonstrated the possibility of identifying gene functions from changes in the relative concentrations of metabolites (metabotypes or metabolic signatures; Ebbels et al., 2004) in various species including yeast (Saccharomyces cerevisiae; Raamsdonk et al., 2001; Allen et al., 2003), Arabidopsis (Arabidopsis thaliana; Brotman et al., 2011), tomato (Solanum lycopersicum; Schauer et al., 2006), and maize (Zea mays; Riedelsheimer et al., 2012). Metabolomics has also been used to better understand how plants interact with their environments (Field and Lake, 2011), including their responses to biotic and abiotic stresses (Dixon et al., 2006; Arbona et al., 2013), and to predict important agronomic traits (Riedelsheimer et al., 2012). Metabolite profiling has been performed on many plant species, including angiosperms such as Arabidopsis, poplar (Populus trichocarpa), and Catharanthus roseus (Sumner et al., 2003; Rischer et al., 2006), basal land plants such as Selaginella moellendorffii and Physcomitrella patens (Erxleben et al., 2012; Yobi et al., 2012), and Chlamydomonas reinhardtii (Fernie et al., 2012; Davis et al., 2013). With the availability of whole genome sequences of various species, metabolomics has the potential to become a useful tool for elucidating the functions of genes using large-scale systematic analyses (Fiehn et al., 2000; Saito and Matsuda, 2010; Hur et al., 2013).Although metabolomics data have the potential for identifying the roles of genes that are associated with metabolic phenotypes, the biochemical mechanisms that link functions of genes with metabolic phenotypes are still poorly characterized. For example, we do not yet know the principles behind how perturbing the expression of a single gene changes the metabolic system as a whole. Large-scale metabolomics data have provided useful resources for linking phenotypes to genotypes (Fiehn et al., 2000; Roessner et al., 2001; Tikunov et al., 2005; Schauer et al., 2006; Lu et al., 2011; Fukushima et al., 2014). For example, Lu et al. (2011) compared morphological and metabolic phenotypes from more than 5,000 Arabidopsis chloroplast mutants using gas chromatography (GC)- and liquid chromatography (LC)-mass spectrometry (MS). Fukushima et al. (2014) generated metabolite profiles from various characterized and uncharacterized mutant plants and clustered the mutants with similar metabolic phenotypes by conducting multidimensional scaling with quantified metabolic phenotypes. Nonetheless, representation and analysis of such a large amount of data remains a challenge for scientific discovery (Lu et al., 2011). In addition, these studies do not examine the topological and functional characteristics of metabolic changes in the context of a genome-scale metabolic network. To understand the relationship between genotype and metabolic phenotype, we need to investigate the metabolic changes caused by perturbing the expression of a gene in a genome-scale metabolic network perspective, because metabolic pathways are not independent biochemical factories but are components of a complex network (Berg et al., 2002; Merico et al., 2009).Much progress has been made in the last 2 decades to represent metabolism at a genome scale (Terzer et al., 2009). The advances in genome sequencing and emerging fields such as biocuration and bioinformatics enabled the representation of genome-scale metabolic network reconstructions for model organisms (Bassel et al., 2012). Genome-scale metabolic models have been built and applied broadly from microbes to plants. The first step toward modeling a genome-scale metabolism in a plant species started with developing a genome-scale metabolic pathway database for Arabidopsis (AraCyc; Mueller et al., 2003) from reference pathway databases (Kanehisa and Goto, 2000; Karp et al., 2002; Zhang et al., 2010). Genome-scale metabolic pathway databases have been built for several plant species (Mueller et al., 2005; Zhang et al., 2005, 2010; Urbanczyk-Wochniak and Sumner, 2007; May et al., 2009; Dharmawardhana et al., 2013; Monaco et al., 2013, 2014; Van Moerkercke et al., 2013; Chae et al., 2014; Jung et al., 2014). Efforts have been made to develop predictive genome-scale metabolic models using enzyme kinetics and stoichiometric flux-balance approaches (Sweetlove et al., 2008). de Oliveira Dal’Molin et al. (2010) developed a genome-scale metabolic model for Arabidopsis and successfully validated the model by predicting the classical photorespiratory cycle as well as known key differences between redox metabolism in photosynthetic and nonphotosynthetic plant cells. Other genome-scale models have been developed for Arabidopsis (Poolman et al., 2009; Radrich et al., 2010; Mintz-Oron et al., 2012), C. reinhardtii (Chang et al., 2011; Dal’Molin et al., 2011), maize (Dal’Molin et al., 2010; Saha et al., 2011), sorghum (Sorghum bicolor; Dal’Molin et al., 2010), and sugarcane (Saccharum officinarum; Dal’Molin et al., 2010). These predictive models have the potential to be applied broadly in fields such as metabolic engineering, drug target discovery, identification of gene function, study of evolutionary processes, risk assessment of genetically modified crops, and interpretations of mutant phenotypes (Feist and Palsson, 2008; Ricroch et al., 2011).Here, we interrogate the metabotypes caused by 136 single gene perturbations of Arabidopsis by analyzing the relative concentration changes of 1,348 chemically identified metabolites using a reconstructed genome-scale metabolic network. We examine the characteristics of the changed metabolites (the metabolites whose relative concentrations were significantly different in mutants relative to the wild type) in the metabolic network to uncover biological and topological consequences of the perturbed genes.     

The K-Segment of Maize DHN1 Mediates Binding to Anionic Phospholipid Vesicles and Concomitant Structural Changes

Dehydrins (DHNs; late embryogenesis abundant D11 family) are a family of intrinsically unstructured plant proteins that accumulate in the late stages of seed development and in vegetative tissues subjected to water deficit, salinity, low temperature, or abscisic acid treatment. We demonstrated previously that maize (Zea mays) DHNs bind preferentially to anionic phospholipid vesicles; this binding is accompanied by an increase in α-helicity of the protein, and adoption of α-helicity can be induced by sodium dodecyl sulfate. All DHNs contain at least one “K-segment,” a lysine-rich 15-amino acid consensus sequence. The K-segment is predicted to form a class A2 amphipathic α-helix, a structural element known to interact with membranes and proteins. Here, three K-segment deletion proteins of maize DHN1 were produced. Lipid vesicle-binding assays revealed that the K-segment is required for binding to anionic phospholipid vesicles, and adoption of α-helicity of the K-segment accounts for most of the conformational change of DHNs upon binding to anionic phospholipid vesicles or sodium dodecyl sulfate. The adoption of structure may help stabilize cellular components, including membranes, under stress conditions.When plants encounter environmental stresses such as drought or low temperature, various responses take place to adapt to these conditions. Typical responses include increased expression of chaperones, signal transduction pathway and late embryogenesis abundant (LEA) proteins, osmotic adjustment, and induction of degradation and repair systems (Ingram and Bartels, 1996).Dehydrins (DHNs; LEA D11 family) are a subfamily of group 2 LEA proteins that accumulate to high levels during late stages of seed development and in vegetative tissues subjected to water deficit, salinity, low temperature, or abscisic acid (ABA) treatment (Svensson et al., 2002). Some DHNs are expressed constitutively during normal growth (Nylander et al., 2001; Rorat et al., 2004, 2006; Rodriguez et al., 2005). DHNs exist in a wide range of photosynthetic organisms, including angiosperms, gymnosperms, algae, and mosses (Svensson et al., 2002). DHNs are encoded by a dispersed multigene family and are differentially regulated, at least in higher plants. For example, 13 Dhn genes have been identified in barley (Hordeum vulgare), dispersed over seven genetic map locations (Choi et al., 1999; Svensson et al., 2002) and regulated variably by drought, low temperature, and embryo development (Tommasini et al., 2008). DHNs are localized in various subcellular compartments, including cytosol (Roberts et al., 1993), nucleus (Houde et al., 1995), chloroplast (Artus et al., 1996), vacuole (Heyen et al., 2002), and proximal to the plasma membrane and protein bodies (Asghar et al., 1994; Egerton-Warburton et al., 1997; Puhakainen et al., 2004). Elevated expression of Dhn genes generally has been correlated with the acquisition of tolerance to abiotic stresses such as drought (Whitsitt et al., 1997), salt (Godoy et al., 1994; Jayaprakash et al., 1998), chilling (Ismail et al., 1999a), or freezing (Houde et al., 1995; Danyluk et al., 1998; Fowler et al., 2001). The differences in expression and tissue location suggest that individual members of the Dhn multigene family have somewhat distinct biological functions (Close, 1997; Zhu et al., 2000; Nylander et al., 2001). Many studies have observed a positive correlation between the accumulation of DHNs and tolerance to abiotic stresses (Svensson et al., 2002). However, overexpression of a single DHN protein has not, in general, been sufficient to confer stress tolerance (Puhakainen et al., 2004).DHNs are subclassified by sequence motifs referred to as the K-segment (Lys-rich consensus sequence), the Y-segment (N-terminal conserved sequence), the S-segment (a tract of Ser residues), and the φ-segment (Close, 1996). Because of high hydrophilicity, high content of Gly (>20%), and the lack of a defined three-dimensional structure in the pure form (Lisse et al., 1996), DHNs have been categorized as “intrinsically disordered/unstructured proteins” or “hydrophilins” (Wright and Dyson, 1999; Garay-Arroyo et al., 2000; Tompa, 2005; Kovacs et al., 2008). On the basis of compositional and biophysical properties and their link to abiotic stresses, several functions of DHNs have been proposed, including ion sequestration (Roberts et al., 1993), water retention (McCubbin et al., 1985), and stabilization of membranes or proteins (Close, 1996, 1997). Observations from in vitro experiments include DHN binding to lipid vesicles (Koag et al., 2003; Kovacs et al., 2008) or metals (Svensson et al., 2000; Heyen et al., 2002; Kruger et al., 2002; Alsheikh et al., 2003; Hara et al., 2005), protection of membrane lipid against peroxidation (Hara et al., 2003), retention of hydration or ion sequestration (Bokor et al., 2005; Tompa et al., 2006), and chaperone activity against the heat-induced inactivation and aggregation of various proteins (Kovacs et al., 2008).Intrinsically disordered/unstructured proteins that lack a well-defined three-dimensional structure have recently been recognized to be prevalent in prokaryotes and eukaryotes (Oldfield et al., 2005). They fulfill important functions in signal transduction, gene expression, and binding to targets such as protein, RNA, ions, and membranes (Wright and Dyson, 1999; Tompa, 2002; Dyson and Wright, 2005). The disorder confers structural flexibility and malleability to adapt to changes in the protein environment, including water potential, pH, ionic strength, and temperature, and to undergo structural transition when complexed with ligands such as other proteins, DNA, RNA, or membranes (Prestrelski et al., 1993; Uversky, 2002). Structural changes from disorder to ordered functional structure also can be induced by the folding of a partner protein (Wright and Dyson, 1999; Tompa, 2002; Mouillon et al., 2008).The idea that DHNs interact with membranes is consistent with many immunolocalization studies, which have shown that DHNs accumulate near the plasma membrane or membrane-rich areas surrounding lipid and protein bodies (Asghar et al., 1994; Egerton-Warburton et al., 1997; Danyluk et al., 1998; Puhakainen et al., 2004). The K-segment is predicted to form a class A2 amphipathic α-helix, in which hydrophilic and hydrophobic residues are arranged on opposite faces (Close, 1996). The amphipathic α-helix is a structural element known to interact with membranes and proteins (Epand et al., 1995). Also, in the presence of helical inducers such as SDS and trifluoroethanol (Dalal and Pio, 2006), DHNs take on α-helicity (Lisse et al., 1996; Ismail et al., 1999b). We previously examined the binding of DHN1 to liposomes and found that DHNs bind preferentially to anionic phospholipids and that this binding is accompanied by an increase in α-helicity of the protein (Koag et al., 2003). Similarly, a mitochondrial LEA protein, one of the group III LEA proteins, recently has been shown to interact with and protect membranes subjected to desiccation, coupled with the adoption of amphipathic α-helices (Tolleter et al., 2007).Here, we explore the basis of DHN-vesicle interaction using K-segment deletion proteins. This study reveals that the K-segment is necessary and sufficient for binding to anionic phospholipid vesicles and that the adoption of α-helicity of DHN proteins can be attributed mainly to the K-segment.