Daniel Wipf

Carole Pfister

Arnaud Mounier

Nathalie Leborgne-Castel

Wolf B Frommer

Pierre-Emmanuel Courty
email: pierre-emmanuel.courty@inrae.fr

Identification of Putative Interactors of Arabidopsis Sugar Transporters

Keywords: sugar transporter, interactome, protein-protein interactions

Hexoses and disaccharides are the key carbon sources for essentially all physiological processes across kingdoms. In plants, sucrose, and in some cases raffinose and stachyose, are transported from the site of synthesis in leaves, the sources, to all other organs that depend on import, the sinks. Sugars also play key roles in interactions with beneficial and pathogenic microbes. Sugar transport is mediated by transport proteins that fall into superfamilies. Sugar transporter (ST) activity is tuned at different levels, including transcriptional and posttranslational levels. Understanding the ST interactome has a great potential to uncover important players in biologically and physiologically relevant processes, including, but not limited to Arabidopsis thaliana. Here, we combined ST interactions and coexpression studies to identify potentially relevant interaction networks

functions are quite diverse, but the role of the individual members is still poorly characterized [START_REF] Scholz-Starke | AtSTP6, a new pollen-specific H + -monosaccharide symporter from Arabidopsis[END_REF][START_REF] Schneidereit | AtSTP11, a pollen tube-specific monosaccharide transporter in Arabidopsis[END_REF]. SWEETs were recently identified in plants, animals and some fungi [START_REF] Chen | Transport of sugars[END_REF][START_REF] Tao | Structure of a eukaryotic SWEET transporter in a homotrimeric complex[END_REF]; they derive from another ancient class of transport proteins already found in archaea named Semi-SWEETs [START_REF] Hu | Phylogenetic evidence for a fusion of archaeal and bacterial SemiSWEETs to form eukaryotic SWEETs and identification of SWEET hexose transporters 10[END_REF]. SWEETs are uniporters that mediate in-or efflux of sugars (sucrose, glucose, fructose). SWEETs are involved in many physiological processes including phloem loading, seed filling, nectar secretion, pollen nutrition [9-13], and play crucial roles as susceptibility factors in plant-microbe interactions [START_REF] Chen | Sugar transporters for intercellular exchange and nutrition of pathogens[END_REF][START_REF] Doidy | Sugar transporters in plants and in their interactions with fungi[END_REF][START_REF] Yamada | Regulation of sugar transporter activity for antibacterial defense in Arabidopsis[END_REF][START_REF] Oliva | Broad-spectrum resistance to bacterial blight in rice using genome editing[END_REF].

ST activity is determined by the number of transporters located in the membrane and by their transport rate (number of substrates transported per second), and is tightly controlled at transcriptional, post-transcriptional, translational and post-translational levels [START_REF] Liesche | Sucrose transporter regulation at the transcriptional, posttranscriptional and post-translational level[END_REF][START_REF] Qiyu | Carbon export from leaves is controlled via ubiquitination and phosphorylation of sucrose transporter SUC2[END_REF].

Several reports address the regulation of ST activity at the RNA level (e.g., SWEETs) [START_REF] Miao | Genome-wide analyses of SWEET family proteins reveal involvement in fruit development and abiotic/biotic stress responses in banana[END_REF] and by post-translational protein modifications [START_REF] Roblin | Regulation of a plant plasma membrane sucrose transporter by phosphorylation[END_REF][22][23][24][25]. In addition, the activity of several STs seems to be regulated via direct protein-protein-interaction (PPI) [26][27][28][29][30][31][32]. A significant fraction of cellular proteins exists in oligomeric states. Oligomerization may serve a variety of purposes -oligomerization may be advantageous for clustering transporters, for transporter stability in the membrane, may play roles in their delivery to target membranes and endocytosis and may have regulatory roles. A prominent example is the phosphorylation-mediated allosteric regulation, triggered by ammonium in a time-and concentration-dependent manner, of AMT activity [33]. Concerning ST, the importance of oligomerization has previously been demonstrated for SUTs/SUCs [34] and SWEETs [28]. ST interactions could contribute to many biological functions as signaling, sugar homeostasis at the cellular and organism levels and nutrient transfer in plant microbe interactions. Only few experimental reports about the role of PPI for physiological function have been published; e.g. the interaction between a Flowering Locus T-like protein (StSP6A) and a SWEET in potato, linking sugar transport to photoperiodic pathways in the context of the regulation of source-sink relations [START_REF] Abelenda | Source-sink regulation is mediated by interaction of an FT homolog with a SWEET protein in potato[END_REF], the interaction of tomato SUT2 (SlSUT2) with proteins involved in brassinosteroid signaling or synthesis that affects arbuscular mycorrhiza formation [START_REF] Bitterlich | The sucrose transporter SlSUT2 from tomato interacts with brassinosteroid functioning and affects arbuscular mycorrhiza formation[END_REF][START_REF] Bitterlich | Challenges in nanogrinding of active pharmaceutical ingredients[END_REF].

While experimental techniques such as the two-hybrid system have provided a partial view of ST interactome maps [START_REF] Deng | Prediction of protein function using protein -protein interaction data[END_REF][START_REF] Kemmeren | Integrating functional genomics data[END_REF][START_REF] Uetz | A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae[END_REF][START_REF] Ito | A comprehensive two-hybrid analysis to explore the yeast protein interactome[END_REF][START_REF] Rain | The protein-protein interaction map of Helicobacter pylori[END_REF][START_REF] Giot | A protein interaction map of Drosophila melanogaster[END_REF][START_REF] Li | A map of the interactome network of the metazoan Caenorhabditis elegans[END_REF][START_REF] Rual | Towards a proteome-scale map of the human protein-protein interaction network[END_REF][START_REF] Geisler-Lee | A predicted interactome for Arabidopsis[END_REF][START_REF] Simonis | Empirically controlled mapping of the Caenorhabditis elegans protein-protein interactome network[END_REF], understanding the ST interactome has a great potential to provide new insights into plant development, plant physiology, plant interactions with their abiotic and biotic environments. To address these questions, we screened the Membranebased Interaction Network Database (MIND) for Arabidopsis thaliana protein interactions [48] to identify candidate ST-interactors potentially involved in the regulation of carbon allocation in a wide range of conditions including abiotic or biotic stress. These candidates are putative interactors, as the MIND is based on a heterologous system and requires in planta validation.

The Membrane-based Interaction Network Database (MIND)

Membrane proteins mediate fundamental roles in many biological processes. Membrane proteins allow for transport of ions and metabolites, and protein trafficking across subcellular membranes. Some of the transporters (called transceptors) detect environmental stimuli and transduce signals into the cells; some catalyze chemical reactions [49]. The regulation of transport activity and the transduction of environmental signals depend to a substantial extent on interactions of membrane proteins with themselves (homodimerization), with other membrane proteins and / or with soluble proteins [50]. The mating-based split-ubiquitin system (mbSUS) paved the way for major advances in the identification of membrane protein interactions [START_REF] Miller | Large-scale identification of yeast integral membrane protein interactions[END_REF]. mbSUS identified homo-, hetero-, and oligomeric interactions in Arabidopsis, e.g., among K + channels [START_REF] Obrdlik | K + channel interactions detected by a genetic system optimized for systematic studies of membrane protein interactions[END_REF], between the Acyl-CoAbinding protein ACBP6 and the plasmodesmata-located protein PDLP8 [START_REF] Zi-Wei | Arabidopsis thaliana Acyl-CoA-binding protein ACBP6 interacts with plasmodesmata-located protein PDLP8[END_REF], among subunits of glutamate-like receptors (GLRs) [54], among aquaporins and kinase receptors [55]. Using mbSUS in yeast, 12.102 high-confidence membrane/signaling protein interactions were identified and recorded in MIND (https://associomics.dpb.carnegiescience.edu/Associomics/Home.html) [50,[START_REF] Jones | Border control -a membrane-linked interactome of Arabidopsis[END_REF]. More than 99% of the putative PPIs identified were previously unknown [START_REF] Jones | Border control -a membrane-linked interactome of Arabidopsis[END_REF]. MIND data were partially validated in orthogonal in planta split-green fluorescent protein interaction assays at a rate of 32%, similar as the confirmation rate obtained for published interactions (38%) [START_REF] Jones | Border control -a membrane-linked interactome of Arabidopsis[END_REF].

MIND also predicted PPIs within the membrane proteome of Arabidopsis roots that were confirmed by Size Exclusion Chromatography -Mass Spectrometry (SEC-MS) [START_REF] Gilbert | Global identification of protein complexes within the membrane proteome of Arabidopsis roots using a SEC-MS approach[END_REF]. Split GFP, antibody-pulldown assays and Förster resonance energy transfer (FRET) for PPI studies are orthologous assays that can be used to validate candidates present in the MIND database.

MIND allowed the identification of several interactions that were further confirmed by orthogonal systems, as for example between the possible cargo-receptor Cornichon and a

Golgi-located sodium transporter in rice [START_REF] Rosas-Santiago | Identification of rice cornichon as a possible cargo receptor for the Golgi-localized sodium transporter OsHKT1;3[END_REF], as well as Cornichon with GLRs in Arabidopsis pollen [START_REF] Wudick | CORNICHON sorting and regulation of GLR channels underlie pollen tube Ca 2+ homeostasis[END_REF].

Identification of putative sugar transporter interactors

The present analysis did not retrieve all of the known interactions among STs [34,[START_REF] Schulze | Interactions between co-expressed Arabidopsis sucrose transporters in the split-ubiquitin system[END_REF]. This is not surprising as MIND was generated with a subset of the Arabidopsis proteome, and focused on interactions between membrane proteins. MIND thus did not cover all possible interactions (not all STs included, also not all possible interactors included). The total interaction network must thus be substantially larger. Despite the importance of STs in carbon allocation and plant-microbe interactions [61-64], there is a knowledge gap regarding ST activity regulation. Therefore, we used MIND to perform an in silico search to identify potential interactors of the 79 Arabidopsis STs (9 AtSUCs, 17 AtSWEETs, and 53

AtMSTs; Figure 1). We ranked STs based on the number of their potential predicted interactors with 2 + , 3 + or 4 + Interaction confidence, respectively. "Interaction confidence", Fi, corresponds to the number of repeats in which a particular interaction tested positively for all three reporter genes (HIS3, ADE2 and LACZ) in MIND [START_REF] Jones | Border control -a membrane-linked interactome of Arabidopsis[END_REF]. For example, 4 + Interaction confidence corresponds to the activation of the three reporter genes in two independent biological replicates. A 1 + Interaction confidence corresponds to the activation of a single reporter gene; it was not included in our analysis as it can lead to many false positives.

The resulting ST interactome is a complex scale-free network with a dense central hub where large interaction nodes group together (Figure 1). Nodes with fewer putative interactors are present in the periphery of the hub (Figure 1). Out of the 79 STs, 34 (43%) had at least one interaction (see online Supplemental Table S1), revealing a total of 920 interactors with proteins such as a nitrate transporter (AtNRT1;1), AtRBOHD (Respiratory Burst Oxidase Homolog protein D) and the QC-SNARE SFT12 (soluble N-ethylmaleimidesensitive factor attachment receptor SFT12) (see below) (see online Supplemental Table S2).

In the SUT family, 4 out of the 9 STs (45%) tested had putative interactors, while in the SWEET family, 10 STs out of 17 (59%) had putative interactors. In the largest family, namely the MSTs, 33 STs out of 53 (38%) could interact with other proteins.

Among the largest interaction nodes, we identified AtSWEET5 (112 putative interactors), AtSWEET7 (57 putative interactors), MST At1g54730 (56 putative interactors), AtSUC2 (31 putative interactors) and AtSUC4 (30 putative interactors). We detected two new putative interactions between STs not proposed before: between MST At3g05160 and AtSTP4, and between At3g05160 and AtSUC4. At3g05160 is a member of the Early Responsive to Dehydration 6-Like (ERD6 like) sub-family of MSTs. Members of the ERD6-like had been characterized as tonoplastic glucose exporters [START_REF] Poschet | A novel Arabidopsis vacuolar glucose exporter is involved in cellular sugar homeostasis and affects the composition of seed storage compounds[END_REF]. The interaction of At3g05160 with the tonoplastic sucrose importer AtSUC4 and the monosaccharide plasma membrane STP4 may be involved in the control of cellular sugar homeostasis in response to different stimuli by these interactions. This hypothesis is reinforced by the induction of AtSTP4 during pathogen infection [START_REF] Fotopoulos | The monosaccharide transporter gene, AtSTP4, and the cellwall invertase, Atbetafruct1, are induced in Arabidopsis during infection with the fungal biotroph Erysiphe cichoracearum[END_REF] in order to transport sugars into the host cells and to reduce availability of sugars to the pathogen. All these putative interactions await independent validation (Box 1).

Identification of genes co-expressed with sugar transporters and coding for ST interactors

Co-expression network analysis (ATTED-II; http://atted.jp) [START_REF] Obayashi | ATTED-II in 2018: A Plant Coexpression Database Based on Investigation of Statistical Property of the Mutual Rank Index[END_REF] allowed to capture patterns of transcriptome organization whereby gene clusters and co-expression across diverse conditions are identified. Co-expression can indicate that genes are controlled by the same transcriptional regulatory pathway, may be functionally related, or be members of the same pathway or protein complex [68]. Here, within the set of proteins interacting with STs defined with MIND, we identified a small subset of genes encoding proteins that are coexpressed with a ST using the ATTED-II database (Table 1). Out of the 34 genes of interacting STs, two did not show co-expression. Eight genes (Table 1) were significantly co-expressed with at least one other gene represented in ATTED-II, and the encoded proteins interact as well (MIND). Functions of proteins encoded by genes with a significant correlation with an ST expression indicated possible crosslinks to ammonium transport, cell trafficking and signaling, and hormone regulation related to sugar transport.

mRNA levels of the transmembrane protein gene encoded by At1G27290 were found to correlate with three STs (AtSTP4, AtESL1 and AtSFP1). The mRNA levels of the sugar transporter gene At1G67300 correlated with an uncharacterized Xanthine/uracil permease family protein, highlighting a possible crosslink between sugar and nitrogen transport/metabolism. Interestingly, the previously mentioned transmembrane protein (AT1G27290) is predicted to be an interactor of the dual-affinity nitrate transceptor AtNRT1;1 [69] in MIND. In roots, AtNRT1;1 is involved in nitrate uptake from the soil and nitrate signaling, participating in the regulation of primary root growth [START_REF] Krouk | Nitrate-regulated auxin transport by NRT1.1 defines a mechanism for nutrient sensing in plants[END_REF]. In addition to nitrate uptake, AtNRT1;1 functions as a nitrate sensor, regulating the primary nitrate response. In addition, evidence has been provided that AtNRT1.1 is associated with a modification of auxin transport in roots depending on nitrate concentration, defining a mechanism connecting hormone and signaling without any competition. When comparing the root RNA levels of AtSTP4 (At3G19930) between wild type and the chl1-5 mutant of AtNRT1;1, no RNA was detected in the mutant, indicating a loss of the connection between NRT1;1 and sugar transporter regulation [START_REF] Muños | Transcript profiling in the chl1-5 mutant of Arabidopsis reveals a role of the nitrate transporter NRT1.1 in the regulation of another nitrate transporter, NRT2.1[END_REF]. Beside its role in the acquisition and sensing of nitrate from the soil, AtNRT1;1 is also expressed in guard cells promoting stomatal opening in the presence of nitrate. Finally, the possible link between Xanthine/uracil permease and the At1G6730 ST, as well as the indirect link of AtNRT1;1 and key putative sugar transport-related genes such as AtSTP4, AtESL1 and AtSFP1 through interaction with the transmembrane protein At1G27290 may indicate close regulatory connections between C and N transport and signaling for potential fine-tuning of the C/N ratio [START_REF] Sato | Carbon and nitrogen metabolism regulated by the ubiquitinproteasome system[END_REF][START_REF] Sulpice | Impact of the carbon and nitrogen supply on relationships and connectivity between metabolism and biomass in a broad panel of Arabidopsis accessions[END_REF].

Transpiration and water movement are affected both by stomatal aperture and hydraulic conductance. Previous studies implicated sucrose/hexoses in the regulation of aquaporin genes, which encoded water channels, in plant hydraulic conductivity and stomatal closure [START_REF] Di Pietro | Coordinated post-translational responses of aquaporins to abiotic and nutritional stimuli in Arabidopsis roots[END_REF]. For instance, glucose addition reduced the movement of water from the xylem into the mesophyll, coordinating transpirational water loss via the regulation of several aquaporins [START_REF] Kelly | Sugar and hexokinase suppress expression of PIP aquaporins and reduce leaf hydraulics that preserves leaf water potential[END_REF]. Among STs, At1G54730 mRNA levels were highly correlated with aquaporin AtPIP1;5 transcript levels, consistent with a role of both plasma membrane (PM) transporters in the need for parallel transport of sugar and water according to the Münch hypothesis.

The uncharacterized Xanthine/uracil permease family protein (At1G27290) is also predicted to be an interactor of AtSFT12 (At1g29060) in MIND, which can interact directly with a large number of STs (see online Supplemental Table S2). SFT12 belongs to the SNARE family, proteins that play critical roles in the fusion of endomembranes. More specifically, SFT12 is a Qc-SNARE localized in the Golgi apparatus [START_REF] Tarte | Arabidopsis Qc-SNARE gene AtSFT12 is involved in salt and osmotic stress responses and Na( + ) accumulation in vacuoles[END_REF]. Trafficking-related proteins were found as high-degree hubs (proteins with many interactions) involved in a regulatory association with receptors [START_REF] Jones | Border control -a membrane-linked interactome of Arabidopsis[END_REF]. SFT12 interacted directly with two AtSUCs (SUC2 and SUC4), eight AtSWEETs Another interesting result was that the Phosphoinositide phospholipase C (PLC) -like phosphodiesterase superfamily protein (At4G38690), which was co-expressed and was an interactor of AtINT1/AtINT1, also interacted with AtRBOHD in MIND. AtRBOHD is a membrane NADPH oxidase producing reactive oxygen species, for example during pathogen infection [START_REF] Wang | The fundamental role of NOX family proteins in plant immunity and their regulation[END_REF]. AtRBOHD interacted with AtSWEET11 (3 + interaction confidence), as well as with several proteins involved in the regulation of intracellular trafficking that interact also with several STs (4+ interaction confidence). Interestingly, some of the STs interacted with proteins involved in the regulation of intracellular trafficking. For example, the early endosome marker (the small GTPase Rab5, gene accession At5g49540) appeared to directly interact with 16 STs, and the prenylated Rab acceptor PRA1.E (gene accession At1g08770) had two direct interactions with two STs. Rab5 and PRA1.E interacted with each other, and also with AtSWEET7 (4 + interaction confidence). The calcium-binding Annexin 1 (gene accession At1g35720) interacted with AtRBOHD and AtSWEET7. Annexins are described as regulators of membrane trafficking [86], and Annexin 1 is also involved in the response to salt stress and plant immunity [87]. These examples of ST-interacting proteins, which are related to membrane trafficking, signaling and environmental constraints, provide possible clues about the regulation of STs by PPIs. Localization of ST in mutants for genes encoding these ST-interacting proteins, would be of interest as it could highlight failures in the assembly of ST complexes in the endoplasmic reticulum or in their help for trafficking toward membranes.

Concluding remarks

Our findings highlight the importance of combining interactome and co-expression studies to detect potential crossroads of biological functions. Some STs appear to be crucial nodes and their functional characterization (Box 1) could help to improve our knowledge of their cellular interactions. Understanding these interactions is crucial to follow the sugar trade from cells to organs for plant nutrition and plant-microbe interactions. Interactome and coexpression studies also reveal many targets that have so far not been linked to sugars. Here, we observed that some STs form large nodes of putative interactions while others interact only with few membrane proteins and/or at the periphery of membranes. We identified a large set of 920 candidate proteins interacting with 34 STs with various biological functions.

Although a large proportion of these proteins has no known biological function to date, several are involved either in transport or in cellular processes as trafficking or signaling.

Since MIND represents only a subset of all possible interactions, the network must be substantially larger. The MIND database is based on a library of 3233 membrane proteins and soluble signaling proteins, whereas at least several thousands of other membrane proteins exist and that many membrane proteins will interact with soluble proteins.

Glossary

Fi: Confidence of the interaction according to MIND (2 + , 3 + and 4 + ), with 4 + the strongest interaction between two partners [START_REF] Zi-Wei | Arabidopsis thaliana Acyl-CoA-binding protein ACBP6 interacts with plasmodesmata-located protein PDLP8[END_REF]. The 1 + Interaction confidence was excluded from the present analysis.

Interactant: defined as Boolean; "True" if the connected node is a ST, "False" if the connected node is an NonST-PROT.

Interactome: biological networks/interactions formed by and between molecules within a cell.

MST: MonoSaccharide Transporter.

Node: A connection point that participates in a network. Here, it could be ST-type and NonST-PROT.

SUT: SUcrose Transporter. Also called SUC: SUcrose Carriers SWEET: Sugars Will Eventually be Exported Transporter.

Membrane trafficking:

Process by which proteins and other macromolecules are distributed throughout cell organelles, and released to or internalized from the extracellular space, using membrane-bound vesicles. S2), the protein sequences encoding 9 AtSUTs, 17 AtSWEETs and 53

AtMSTs (see online Supplemental Table S1) were used to interrogate the MIND Database (https ://associomics.dpb.carnegiescience.edu/Associomics/Home.html).

Building the sugar transporters -protein interaction network. The sugar transportersmembrane proteins (Interactant, INTPROT) interaction network was built from a list of interaction tuples (STi, INTPROTi, Fi). The interaction network is an unoriented graph that includes nodes and edges with attributes computed using the NetworkX Python package of Python 3 [START_REF] Hagberg | Exploring network structure, dynamics, and function using networks[END_REF]. The attribute definitions have been summarized in the Glossary and examples of attributes of a network of interactions between sugar transporters and interacting proteins are presented in Figure 1. Visualization of the interaction network was carried out via the Gephi software [START_REF] Bastian | Gephi: an open source software for exploring and manipulating networks[END_REF] using the Fruchterman Reingold algorithm. The node sizes are proportional to their regular degree, and the color of the edges depends on their interaction confidence.

Calculations and presentations. All calculations were performed with Jupyter Notebooks [START_REF] Kluyver | Jupyter Notebooks -a publishing format for reproducible computational workflows[END_REF] and rendered with the Bokeh Python library [START_REF] Scmidt | Bokeh: Python library for interactive visualization URL[END_REF]. SUT, SWEET and MST family members are respectively represented by blue, orange and green dots. The larger the size of the node, the higher the number of interactants with the sugar transporter. The 4 + interaction confidence is represented by red lines, the 3 + interaction confidence is represented by blue lines and the 2 + interaction confidence by green lines.

Box 1. What is next?

Investigating experimentally membrane protein-protein interactions is a challenge, not least because of the partial hydrophobicity of membrane proteins. This explains why only a small number of membrane protein interactions are known. After identifying proteinprotein interactions of high interest through the combined MIND-ATTED approach, several genetic, biochemical and in-silico techniques could be used and/or combined to study specific interactions in Eukaryotes, for example: Atomic Force Microscopy (AFM) [START_REF] Ivanov | Atomic Force Microscopy Study of Protein-Protein Interactions in the Cytochrome CYP11A1 (P450scc)-Containing Steroid Hydroxylase System[END_REF], Blue

(

  SWEET1, 3, 5, 7, 9, 12, 15 and 16) and 9 AtMSTs (STP4, INT1, INT4, VGT2, GLT1, SGB1, ESL1, At3g05400 and At4g04750), with 4 + , 3 + or 2 + interaction confidence. One may argue that STs need this Qc-SNARE at the beginning of the secretory pathway en route to their final location by membrane trafficking. For example, SUC2/SUT1 is targeted to the plasma membrane[START_REF] Riesmeier | Evidence for an essential role of the sucrose transporter in phloem loading and assimilate partitioning[END_REF], while AtSWEET16 is addressed to the tonoplast membrane[START_REF] Guo | SWEET17, a facilitative transporter, mediates fructose transport across the tonoplast of Arabidopsis roots and leaves[END_REF][START_REF] Hedrich | Sugar transport across the plant vacuolar membrane: nature and regulation of carrier proteins[END_REF]. Thus, the SNARE may interact with the transporters to help them get to their correct destination. Qc-SNAREs are specifically involved in vesicular transport during salt and osmotic stress responses and influence Na + accumulation in vacuoles[START_REF] Tarte | Arabidopsis Qc-SNARE gene AtSFT12 is involved in salt and osmotic stress responses and Na( + ) accumulation in vacuoles[END_REF]. Since mRNAs of several ST genes are increased in response to environmental cues, thereby possibly impacting carbohydrate allocation[80,[START_REF] Zhou | A novel sugar transporter from Dianthus spiculifolius, DsSWEET12, affects sugar metabolism and confers osmotic and oxidative stress tolerance in Arabidopsis[END_REF], one can hypothesize that PPI between Qc-SNARE and STs could contribute to effective vesicular targeting of STs, or to subcellular dynamics of STs and recycling e.g. at the plasma membrane[START_REF] Garg | Subcellular targeting of plant sucrose transporters is affected by their oligomeric state[END_REF][START_REF] Liesche | Dimerization and endocytosis of the sucrose transporter StSUT1 in mature sieve elements[END_REF][START_REF] Liesche | Recycling of Solanum sucrose transporters expressed in yeast, tobacco, and in mature phloem sieve elements[END_REF] to adjust the composition of vacuolar and plasma membrane and sugar flux to adjust osmolality.

Figure 1 .

 1 Figure 1. Global Arabidopsis interactome of 34 sugar transporters and 296 interacting proteins. To identify a network of proteins interacting with sugar transporters (see online Supplemental TableS2), the protein sequences encoding 9 AtSUTs, 17 AtSWEETs and 53

20 Table 1 .

 201 Native/SDS PolyAcrylamide Gel Electrophoresis (BN/SDS PAGE)[START_REF] Schägger | Blue native electrophoresis for isolation of membrane protein complexes in enzymatically active form[END_REF][START_REF] Camacho-Carvajal | Two-dimensional Blue native/SDS gel electrophoresis of multi-protein complexes from whole cellular lysates: a proteomics approach[END_REF], Coimmunoprecipitation (co-IP)[START_REF] Miernyk | Biochemical approaches for discovering proteinprotein interactions[END_REF], Developing further In-silico tools as for example largescale interactomes integrating responses to multiple conditions, and in diverse Arabidopsis genetic backgrounds, DiHydroFolate Reductase (DHFR) [97], Förster Resonance Energy Transfer (FRET) [98], Membrane Strep-Protein INtEraction experiment (SPINE) [99], Phage display [100], Protein chips [101], Protein Interaction Reporter (PIR) [102], PUPylation-based InTeraction tagging (PUP-IT) [103], Reconstitution of membrane proteins [104], Reverse Ras recruitment System (reverse RRS) [105], Site-directed chemical cross-linking [106], Site-Directed Spin Labeling (SDSL) Electron Paramagnetic Resonance (EPR) spectroscopy [107], Split-ubiquitin yeast two-hybrid system [50,108], SPOT-analysis [109], Surface Plasmon Resonance (SPR) [110],Tandem affinity purification (TAP) [111-113], and X-ray crystallography of protein complexes [114]. Arabidopsis sugar transporters and candidates that are interactors a of and coexpressed b with a given sugar transporter.

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Acknowledgments

CP had a grant from the French Ministry of Research and Higher Education. DW and PEC thank the following institutions for financial support: the division of Plant Health and Environment of the French National Institute for Agriculture, Food and Environment (INRAE), the Burgundy Franche-Comté Regional Council (PUMPER Project). WF is supported by an Alexander von Humboldt Professorship and Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), under Germany´s Excellence Strategy -EXC-2048/1 -project ID 390686111

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