Medicago truncatula Natural Resistance-Associated Macrophage Protein1 Is Required for Iron Uptake by Rhizobia-Infected Nodule Cells

Iron is critical for symbiotic nitrogen fixation (SNF) as a key component of multiple ferroproteins involved in this biological process. In the model legume Medicago truncatula, iron is delivered by the vasculature to the infection/maturation zone (zone II) of the nodule, where it is released to the apoplast. From there, plasma membrane iron transporters move it into rhizobia-containing cells, where iron is used as the cofactor of multiple plant and rhizobial proteins (e.g. plant leghemoglobin and bacterial nitrogenase). MtNramp1 (Medtr3g088460) is the M. truncatula Natural Resistance-Associated Macrophage Protein family member, with the highest expression levels in roots and nodules. Immunolocalization studies indicate that MtNramp1 is mainly targeted to the plasma membrane. A loss-of-function nramp1 mutant exhibited reduced growth compared with the wild type under symbiotic conditions, but not when fertilized with mineral nitrogen. Nitrogenase activity was low in the mutant, whereas exogenous iron and expression of wild-type MtNramp1 in mutant nodules increased nitrogen fixation to normal levels. These data are consistent with a model in which MtNramp1 is the main transporter responsible for apoplastic iron uptake by rhizobia-infected cells in zone II.SNF is carried out by the endosymbiosis between legumes and diazotrophic bacteria called rhizobia (van Rhijn and Vanderleyden, 1995). Detection of rhizobial nodulation (Nod) factors by the legume plant results in curling of a root hair around the rhizobia and development of an infection thread that will deliver the rhizobia to the developing root nodule primordium, which is also triggered by Nod factors (Kondorosi et al., 1984; Brewin, 1991; Oldroyd, 2013). Rhizobia are eventually released into the cytoplasm of host plant cells via endocytosis, resulting in an organelle-like structure known as the symbiosome, which consists of bacteria surrounded by a plant membrane called the symbiosome membrane (SM; Roth and Stacey, 1989; Vasse et al., 1990). Rhizobia within symbiosomes eventually differentiate into nitrogen-fixing bacteroids that produce and export ammonium to the plant for assimilation (Vasse et al., 1990).Two main developmental programs for nodulation have been described (Sprent, 2007). In the determinate type, e.g. in soybean (Glycine max), the nodule meristem is active only transiently, which gives rise to a spherical nodule. In the indeterminate nodules, e.g. in alfalfa (Medicago sativa) and pea (Pisum sativum), the meristem(s) remain active for much longer, resulting in cylindrical and/or branched nodules of indeterminate morphology. Indeterminate nodules can be divided in spatiotemporal zones that facilitate the study of the nodulation process. At least four zones are observed in a mature indeterminate nodule (Vasse et al., 1990). Zone I is the meristematic region that drives nodule growth. In zone II, rhizobia are released from the infection thread and differentiate into bacteroids. Zone III is the site of nitrogen fixation. Finally, Zone IV is the senescence zone, where bacteroids are degraded and nutrients are recycled. Some authors describe two more zones: the interzone, a transition zone between zones II and III (Vasse et al., 1990; Roux et al., 2014), and zone V, where saprophytic rhizobia live on the nutrients released by senescent cells (Timmers et al., 2000).Nodulation and nitrogen fixation are tightly regulated processes (for review, see Oldroyd, 2013; Udvardi and Poole, 2013; Downie, 2014) and require a relatively large supply of nutrients from the host: photosynthates, macronutrients such as phosphate and sulfate, amino acids, at least prior to nitrogen fixation, and metal micronutrients (Udvardi and Poole, 2013). Among the latter, iron is one of the most critical (Brear et al., 2013; González-Guerrero et al., 2014). The activity of some of the most abundant and important enzymes in SNF directly depends on iron as cofactor. Nitrogenase, the enzyme directly responsible for nitrogen fixation, needs iron-sulfur clusters and an iron-molybdenum cofactor to reduce N₂ (Miller et al., 1993). The hemoprotein leghemoglobin, which controls O₂ levels in the nodule (Ott et al., 2005), represents around 20% of total nodule protein (Appleby, 1984). Similarly, different types of superoxide dismutase, including an Fe-superoxide dismutase, control the free radicals produced during SNF (Rubio et al., 2007). Other ferroproteins are involved in energy transduction and recycling related to the nitrogen fixation process (Ruiz-Argüeso et al., 1979; Preisig et al., 1996).Despite its importance, iron is a growth-limiting nutrient for plants in most soils (Grotz and Guerinot, 2006), especially in alkaline soils. As a result, iron deficiency is prevalent in plants and hampers crop production and human health (Grotz and Guerinot, 2006; Mayer et al., 2008). This is even more so when legumes are nodulated (Terry et al., 1991; Tang et al., 1992). The relatively high iron demand of nodules can trigger the iron deficiency response, i.e. increase in iron reductase activities in the root epidermis and acidification of the surrounding soil (Terry et al., 1991; Andaluz et al., 2009). Consequently, knowing how iron homeostasis is maintained in nodulated legumes, including how this micronutrient is delivered to the nodule, is important for understanding and improving SNF.Taking advantage of state-of-the-art metal visualization methods, the pathway for iron delivery to the nodule has been elucidated (Rodríguez-Haas et al., 2013). Synchrotron-based x-ray fluorescence studies on Medicago truncatula indeterminate nodules indicate that most of the iron is delivered by the vasculature to the apoplast of zone II. In zone III, iron is mostly localized within bacteroids. Therefore, a number of transporters must exist that move iron through the plasma membrane of plant cells and the SM of infected cells. Several transporters have been hypothesized to mediate iron transport through the SM. Soybean Divalent Metal Transporter1 (GmDMT1) is a nodule-induced Natural Resistance-Associated Macrophage Protein (Nramp) that was found in the soybean SM using specific antibodies (Kaiser et al., 2003). However, biochemical studies on Nramp transporters suggest that they transport substrates into the cytosol (Nevo and Nelson, 2006), rather than outwards or into symbiosomes. More recently, the study of stationary endosymbiont nodule1 (sen1) mutants in Lotus japonicus indicated that SEN1, a yeast (Saccharomyces cerevisiae) Cross Complements CSG1/Arabidopsis (Arabidopsis thaliana) Vacuolar Iron Transporter1 homolog, could play a role in delivering iron across the SM (Hakoyama et al., 2012), albeit this is merely based on the mutant plant phenotype and the role of members of this family in other organisms.Very little is known about the molecular identity of transporters that mediate iron uptake from the nodule apoplast. Based on known plant metal transporters and their biochemistry, the most likely candidates are members of the Nramp and Zinc-Regulated Transporter1, Iron-Regulated Transporter1-Like Protein (ZIP) families, because these can transport divalent metals into the cytosol (Vert et al., 2002; Nevo and Nelson, 2006). Moreover, given that the expression of at least one Nramp transporter (GmDMT1) is activated by nodulation (Kaiser et al., 2003), it is possible that members of this family might mediate iron uptake into rhizobia-containing cells. Nramp transporters are ubiquitous divalent transition metal importers (Nevo and Nelson, 2006). Phenotypical and electrophysiological studies indicate that they have a wide range of possible biological (Fe²⁺, Mn²⁺, Zn²⁺, Cu²⁺, Co²⁺, and Ni²⁺) and nonbiological (Pb²⁺ and Cd²⁺) substrates (Belouchi et al., 1997; Curie et al., 2000; Thomine et al., 2000; Mizuno et al., 2005; Rosakis and Köster, 2005; Cailliatte et al., 2009). In plants, Nramp transporters have been associated with a number of biological roles, such as Fe²⁺ and Mn²⁺ uptake from soil (Curie et al., 2000; Cailliatte et al., 2010), Mn²⁺ long-distance trafficking (Yamaji et al., 2013), metal remobilization during germination (Lanquar et al., 2005), Cd²⁺ and Ni²⁺ tolerance (Mizuno et al., 2005; Cailliatte et al., 2009), and the immune response (Segond et al., 2009), in addition to participating in SNF (Kaiser et al., 2003).In this study, M. truncatula MtNramp1 (Medtr3g088460) was identified as the Nramp transporter gene expressed at the highest levels in nodules. MtNramp1 protein was localized in the plasma membrane of nodule cells in zone II, where the expression reached its maximum. Its role in iron uptake and its importance for SNF were established using a loss-of-function mutant, nramp1-1. This work adds to our understanding of how apoplastic metals are imported into nodule cells.     

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.     

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.     

Putrescine Alleviates Iron Deficiency via NO-Dependent Reutilization of Root Cell-Wall Fe in Arabidopsis

Plants challenged with abiotic stress show enhanced polyamines levels. Here, we show that the polyamine putrescine (Put) plays an important role to alleviate Fe deficiency. The adc2-1 mutant, which is defective in Put biosynthesis, was hypersensitive to Fe deficiency compared with wild type (Col-1 of Arabidopsis [Arabidopsis thaliana]). Exogenous Put decreased the Fe bound to root cell wall, especially to hemicellulose, and increased root and shoot soluble Fe content, thus alleviating the Fe deficiency-induced chlorosis. Intriguingly, exogenous Put induced the accumulation of nitric oxide (NO) under both Fe-sufficient (+Fe) and Fe-deficient (-Fe) conditions, although the ferric-chelate reductase (FCR) activity and the expression of genes related to Fe uptake were induced only under -Fe treatment. The alleviation of Fe deficiency by Put was diminished in the hemicellulose-level decreased mutant-xth31 and in the noa1 and nia1nia2 mutants, in which the endogenous NO levels are reduced, indicating that both NO and hemicellulose are involved in Put-mediated alleviation of Fe deficiency. However, the FCR activity and the expression of genes related to Fe uptake were still up-regulated under -Fe+Put treatment compared with -Fe treatment in xth31, and Put-induced cell wall Fe remobilization was abolished in noa1 and nia1nia2, indicating that Put-regulated cell wall Fe reutilization is dependent on NO. From our results, we conclude that Put is involved in the remobilization of Fe from root cell wall hemicellulose in a process dependent on NO accumulation under Fe-deficient condition in Arabidopsis.Iron is an essential element for plant growth and development, and iron deficiency is the most common micronutrient deficiency in the world. To cope with iron deficiency, plants have evolved two distinct mechanisms for Fe acquisition from the rhizosphere. Strategy I, found in all dicots and monocots with the exception of graminaceous species, is characterized by (1) release of protons to acidify the rhizosphere, which is mediated in Arabidopsis (Arabidopsis thaliana) by the proton-translocating ATPase AHA2 (ARABIDOPSIS PLASMA MEMBRANE H⁺-ATPASE ISOFORM 2; Curie and Briat, 2003; Santi and Schmidt, 2009); (2) inducing ferric chelate reductase activity mediated by FRO2 (FERRIC REDUCTASE OXIDASE2; Robinson et al., 1999); and (3) uptake of Fe²⁺ by the metal transporter IRT1 (IRON REGULATED TRANSPORTER1; Eide et al., 1996; Vert et al., 2002). Strategy II, utilized by graminaceous monocots (Römheld and Marschner, 1986), is characterized by enhanced release of phytosiderophores that form chelates with Fe(III) (Curie and Briat, 2003). However, in addition to Fe acquisition, the mechanisms underlying the mobilization of Fe(III) also are a major challenge for us to understand.Recently, accumulating evidence has shown that phenolic compounds are important for iron mobilization. Rodríguez-Celma et al. (2013) showed that secretion of phenolics is critical for Arabidopsis Fe acquisition from low bioavailability sources, and then Fourcroy et al. (2014) and Schmidt et al. (2014) demonstrated that coumarins are the active compounds in this process. Schmid et al. (2014) confirmed that secretion of coumarins is an essential aspect of Arabidopsis Fe acquisition and provided extensive information on metabolomic changes elicited by Fe deficiency. However, under certain conditions Fe is not readily available, and Fe is difficult to mobilize; thus, Fe stored in the plant needs to be reutilized. For example, phenolics are secreted to remobilize the root apoplastic Fe and improve Fe nutrition in red clover (Trifolium pratense) and rice (Oryza sativa) (Jin et al., 2007; Bashir et al., 2011). Moreover, Lei et al. (2014) reported that the cell wall can be an important Fe source during periods of limited Fe supply. As the first barrier to encounter the soil environment, the cell wall is a pivotal site for most cationic ions in plants (Lozano-Rodríguez et al., 1997; Carrier et al., 2003). Hemicellulose contributes to the overall Al/Cd accumulation in the cell wall of Arabidopsis (Zhu et al., 2012, 2013) and also acts as a Fe pool (Lei et al., 2014). Over 75% of Fe in the root is retained in the cell wall (Bienfait et al., 1985), especially in the hemicellulose fraction (Lei et al., 2014). Thus, the cell wall is not only a site to immobilize an element and restrict its entrance into the cell, but also can serve as a pool to provide the nutrient when the supply from the growth medium is limited. However, the upstream mechanism of Fe reutilization through the cell wall, especially hemicellulose, is still far from clear.The responses to Fe deficiency in plants involve numerous phytohormones and signaling molecules, including auxin (Römheld and Marschner, 1981; Chen et al., 2010), ethylene (García et al., 2010; Wu et al., 2011), and NO (Graziano and Lamattina, 2007; Chen et al., 2010). Polyamines share common substrates with nitric oxide (NO) (Shi and Chan, 2014), and polyamines like spermidine and spermine rapidly induce a burst of NO in various plant species, indicating that NO is a potential intermediate of polyamine-mediated signaling.Polyamines, including putrescine (Put), spermidine, and spermine, are low M_r natural compounds with nitrogen-containing aliphatic structure and influence basic physiological and developmental events, such as cell division and differentiation, rhizogenesis, leaf senescence, zygotic, somatic embryogenesis, and development of flowers and fruits (Feirer et al., 1984; Galston et al., 1995; Bouchereau et al., 1999; Kakkar et al., 2000; Tun et al., 2001; Shi and Chan, 2014). The metabolism of polyamines in plant tissues is subject to strict regulation, and polyamine levels in plant roots change upon exposure to abiotic stress such as salt, drought, low and high temperature, heavy metals (Cu, Cr, Fe, and Ni), and oxidative stresses (Liu et al., 2005; Cheng et al., 2009; Wimalasekera et al., 2011; Tavladoraki et al., 2012).Ample evidence demonstrates the involvement of Put in responses to various types of abiotic stress, such as mineral deficiency in barley (Hordeum vulgare) leaves (Smith, 1973), high osmotic pressure in barley, corn, wheat, and wild oat leaves (Flores and Galston, 1982a), low pH in peeled oat (Avena sativa L. var Victory) leaf (Young and Galston, 1983), potassium deficiency in oat shoot and Arabidopsis thaliana (L.) Heynh (Young and Galston, 1984; Watson and Malmberg, 1996), and cadmium toxicity in oat and bean leaves (Weinstein et al., 1986). In animals, Put is produced either from Orn by Orn decarboxylase or from Arg by Arg decarboxylase (ADC) (Hanfrey et al., 2001). As there is no detectable Orn decarboxylase activity in Arabidopsis, the ADC route is critical for Put biosynthesis. Although there are two genes responsible for ADC activity, Urano et al. (2004) reported that the expression of ADC2 correlates well with the increment of free Put, indicating ADC2 plays an important role in Put biosynthesis in Arabidopsis. However, the role of Put under Fe deficiency in plants remains unknown.In this study, we found that Fe deficiency results in enhanced Put levels. Further, whereas exogenous Put alleviated Fe deficiency, the adc2-1 mutant, in which endogenous Put is decreased, exhibited a Fe deficiency-sensitive phenotype. We demonstrated that Put acts upstream of NO to decrease the Fe binding capacity of the cell wall, especially that of hemicellulose, thus resulting in greater Fe reutilization.     

Nonreductive Iron Uptake Mechanism in the Marine Alveolate Chromera velia

Chromera velia is a newly cultured photosynthetic marine alveolate. This microalga has a high iron requirement for respiration and photosynthesis, although its natural environment contains less than 1 nm of this metal. We found that this organism uses a novel mechanism of iron uptake, differing from the classic reductive and siderophore-mediated iron uptake systems characterized in the model yeast Saccharomyces cerevisiae and present in most yeasts and terrestrial plants. C. velia has no trans-plasma membrane electron transfer system, and thus cannot reduce extracellular ferric chelates. It is also unable to use hydroxamate siderophores as iron sources. Iron uptake from ferric citrate by C. velia is not inhibited by a ferrous chelator, but the rate of uptake is strongly decreased by increasing the ferric ligand (citrate) concentration. The cell wall contains a large number of iron binding sites, allowing the cells to concentrate iron in the vicinity of the transport sites. We describe a model of iron uptake in which aqueous ferric ions are first concentrated in the cell wall before being taken up by the cells without prior reduction. We discuss our results in relation to the strategies used by the phytoplankton to take up iron in the oceans.Chromera velia is a newly cultured marine alveolate containing a photosynthetic plastid phylogenetically related to vestigial plastids in apicomplexan (Moore et al., 2008). It represents the closest free-living photosynthetic relative to apicomplexan parasites, thus providing a powerful model to study the evolution of eukaryotic adaptability (Moore et al., 2008). To gain further insight into the biology of this organism, the genome of which remains unsequenced, we investigated its iron metabolism and its mechanisms of iron uptake. We compared the data obtained with other phytoplanktonic organisms sharing the same ecological niche, and with a terrestrial unicellular eukaryote, the yeast Saccharomyces cerevisiae. S. cerevisiae is phylogenetically distant from C. velia, but its mechanisms of iron uptake are well characterized, and thus constitutes a useful model in these studies.Iron uptake by terrestrial microorganisms and plants is mostly based on the use of two main strategies, both of which have been previously characterized in S. cerevisiae. The first strategy is the reductive mechanism of uptake. Extracellular ferric complexes are first dissociated by reduction, via trans-plasma membrane electron transfer catalyzed by specialized flavohemoproteins (Fre). Free iron is then imported by a high-affinity permease system (Ftr1) coupled to a copper-dependent oxidase (Fet3), allowing iron to be channeled through the plasma membrane. In the second strategy, the siderophore-mediated mechanism, siderophores excreted by the cells or produced by other bacterial or fungal species are taken up without prior dissociation, via specific, copper-independent high-affinity receptors. Iron is then dissociated from the siderophores inside the cells, probably by reduction (for review, see Kosman, 2003; Philpott, 2006). Chlamydomonas reinhardtii is a photosynthetic eukaryotic model organism for the study of iron homeostasis, which shares with yeast the strategy 1 of iron uptake (copper-dependent reductive iron uptake; Merchant et al., 2006).Much less is known about the strategies used by marine phytoplankton to acquire iron. Some data suggest that these two strategies are used by some marine microalgae (Soria-Dengg and Horstmann, 1995; Allen et al., 2008). However, for most marine unicellular eukaryotes the mechanisms of iron assimilation are completely unknown. The strategies used by these organisms to acquire iron must have evolved to adapt to the very particular conditions that prevail in their surrounding natural environment: The transition metal composition of the ocean differs greatly from that of terrestrial environments (Butler, 1998). In particular, iron levels in surface seawater are extremely low (0.02–1 nm; Turner et al., 2001). Therefore a strategy of iron uptake operating efficiently in a terrestrial environment that contains iron at a micromolar level may be inefficient in a marine environment. No classic iron uptake system with an affinity constant in the nanomolar range has ever been found. Additionally, the marine environment imposes physical limits on the classic strategies of uptake, including the high diffusion rate of the species of interest (siderophores or reduced iron; Völker and Wolf-Gladrow, 1999). It is well known that the low levels of iron limits primary production of phytoplankton and carbon fluxes across vast regions of the world’s oceans (Coale et al., 2004; Pollard et al., 2009). It is thus of particular interest to elucidate the molecular mechanisms underlying acquisition of iron by marine phytoplankton and to determine which iron sources are preferentially assimilated with regards to the yield of carbon fixation.In this study, we investigated the mechanisms of iron uptake by C. velia, and found that this organism uses a nonreductive uptake system of ferric ions, which are first concentrated in the cell wall. Our findings provide a better understanding of the biology of this organism, and highlights the need for further study on the mechanisms of iron acquisition in marine phytoplankton.     

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.