Tudor staphylococcal nuclease is a docking platform for stress granule components and is essential for SnRK1 activation in Arabidopsis

Article Tudor staphylococcal nuclease is a docking platform for stress granule components and is essential for SnRK1activation in Arabidopsis Emilio Gutierrez-Beltran 1,2,* , Pernilla H Elander 3 , Kerstin Dalman 3 , Guy W Dayhoff II 4 , Panagiotis N Moschou 5,6,7 , Vladimir N Uversky 8,9 , Jose L Crespo 1 & Peter V Bozhkov 3,** Abstract Tudor staphylococcal nuclease (TSN; also known as Tudor-SN, p100, or SND1) is a multifunctional, evolutionarily conserved regulator of gene expression, exhibiting cytoprotective activity in animals and plants and oncogenic activity in mammals. During stress, TSN stably associates with stress granules (SGs), in a poorly understood process. Here, we show that in the model plant Arabidopsis thaliana, TSN is an intrinsically disordered protein (IDP) acting as a scaffold for a large pool of other IDPs, enriched for conserved stress granule components as well as novel or plantspecific SG-localized proteins. While approximately 30% of TSN interactors are recruited to stress granules de novo upon stress perception, 70% form a protein–protein interaction network present before the onset of stress. Finally, we demonstrate that TSN and stress granule formation promote heat-induced activation of the evolutionarily conserved energy-sensing SNF1-related protein kinase 1(SnRK1), the plant orthologue of mammalian AMP-activated protein kinase (AMPK). Our results establish TSN as a docking platform for stress granule proteins, with an important role in stress signalling. Keywords heat stress; intrinsically disordered regions; SnRK1/SNF1/AMPK; stress granules; tudor staphylococcal nuclease Subject Categories Plant Biology; Proteomics; RNA Biology DOI 10.15252/embj.2020105043 | Received 18 March 2020 | Revised 23 June 2021 | Accepted 1July 2021 | Published online 21 July 2021 The EMBO Journal (2021)40:e105043 Introduction Upon stress perception, eukaryotic cells compartmentalize specific mRNA molecules stalled in translation initiation into a type of evolutionary conserved membrane-less organelles called stress granules (SGs) (Thomas et al, 2011; Protter & Parker, 2016). In these biomolecular condensates, mRNA molecules are stored, degraded, or kept silent to prevent energy expenditure on the production of useless, surplus, or even harmful proteins under stress conditions. Recent research in yeast Saccharomyces cerevisiae and animal models established the molecular composition of SGs. SGs typically contain translationally arrested mRNAs, small ribosomal subunits, various translation initiation factors (eIF), poly(A)-binding protein (PAB), and a variety of RNA-binding proteins (RBPs) and non-RNA- binding proteins (Buchan & Parker, 2009). SGs play a major role in translational repression by sequestering, stabilizing and storing mRNA molecules, as well as by indirectly modulating signalling pathways (Protter & Parker, 2016; Mahboubi & Stochaj, 2017). Accordingly, SGs have a pro-survival function during stress and relate to cancer and human disease (Wolozin, 2012; Anderson et al, 2015; Wolozin & Ivanov, 2019). Apart from components of SGs, proteomic and genetic screens in yeast and animal models have identified proteins modulating SG assembly, which is a highly coordinated process driven by the collective interactions of a core protein–RNA network (Ohn et al, 2008; Buchan et al, 2011; Martinez et al, 2013; Jain et al, 2016; Yang et al, 2020). A recent model for the assembly of mammalian and yeast SGs encompasses two major steps: first the formation of a dense stable SG core by a liquid–liquid phase separation (LLPS) followed by accumulation of proteins into a peripheral shell (Jain et al, 2016; Markmiller et al, 2018). Although the molecular 1Instituto de Bioqu ımica Vegetal y Fotos ıntesis, Consejo Superior de Investigaciones Cient ıficas (CSIC)-Universidad de Sevilla, Sevilla, Spain 2Departamento de Bioqu ımica Vegetal y Biolog ıa Molecular, Facultad de Biolog ıa, Universidad de Sevilla, Sevilla, Spain 3Department of Molecular Sciences, Uppsala BioCenter, Swedish University of Agricultural Sciences and Linnean Center for Plant Biology, Uppsala, Sweden 4Department of Chemistry, College of Art and Sciences, University of South Florida, Tampa, FL, USA 5Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology - Hellas, Heraklion, Greece 6Department of Plant Biology, Uppsala BioCenter, Swedish University of Agricultural Sciences and Linnean Center for Plant Biology, Uppsala, Sweden 7Department of Biology, University of Crete, Heraklion, Greece 8Department of Molecular Medicine and USF Health Byrd Alzheimer’s Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA 9Institute for Biological Instrumentation of the Russian Academy of Sciences, Federal Research Center “Pushchino Scientific Center for Biological Research of the Russian Academy of Sciences”, Pushchino, Russia *Corresponding author. Tel: +34 954489595; E-mail: [email protected] **Corresponding author. Tel: +46 18673228; E-mail: [email protected] ª2021 The Authors. Published under the terms of the CC BY 4.0license The EMBO Journal 40:e105043 |2021 1of 21 mechanisms underlying SG assembly remain currently unclear, all of the proposed models converge on the idea that SG assembly is driven by a combination of homotypic and heterotypic interactions involving intrinsically disordered regions (IDRs) (Guillen-Boixet et al, 2020; Schmit et al, 2021). Several lines of evidence also suggest that the dynamics of SGs are controlled, at least in part, by SG remodellers, that include ATP-dependent complexes or ubiquitin-related proteins (Seguin et al, 2014; Jain et al, 2016; Marmor-Kollet et al, 2020). In plants, little is known about the molecular composition and function of SGs as well as their assembly and cross-talk with other signalling pathways. Previous studies in Arabidopsis thaliana (Arabidopsis) revealed formation of SGs under heat, hypoxia and salt stress (Sorenson & Bailey-Serres, 2014; Yan et al, 2014; Gutierrez-Beltran et al, 2015b) and both proteome and metabolome compositions of heat-induced SGs (Kosmacz et al, 2019). To date, a few protein components of plant SGs have been functionally characterized and most of them have direct homologues in yeast and/or mammalian SG proteomes. These include RNA-binding protein 47 (RBP47), Oligouridylate Binding Protein 1 (UBP1) and Tudor Staphylococcal Nuclease (TSN) (Sorenson & Bailey-Serres, 2014; Gutierrez-Beltran et al, 2015b; Kosmacz et al, 2018). Two recent lines of evidence suggest that TSN might be important for assembly and/or function of SGs. First, TSN localizes in SGs in such distant lineages as protozoa, animals and plants (Zhu et al, 2013; Yan et al, 2014; Gao et al, 2015; Gutierrez-Beltran et al, 2015b; Cazares- Apatiga et al, 2017). Second, TSN interacts with proteins constituting the core of SGs, such as PAB1, eIF4E and eIF5A in different organisms including mammalian and Bombyx mori cells (Weissbach & Scadden, 2012; Zhu et al, 2013; Gao et al, 2014). The domain architecture of TSN is conserved in all studied organisms and includes a tandem repeat of four Staphylococcal Nuclease (SN) domains at the N terminus followed by a Tudor domain and a partial SN domain at the C terminus (Abe et al, 2003; Gutierrez-Beltran et al, 2016). TSN is known to be critically involved in the regulation of virtually all pathways of gene expression, ranging from transcription to RNA silencing (Gutierrez-Beltran et al, 2016; Chou et al, 2017). For example, Arabidopsis TSN1 and TSN2, two functionally redundant TSN homologues, have been described to play two antagonistic roles in RNA metabolism during stress (Gutierrez-Beltran et al, 2015a). Yet, the cellular level of TSN protein itself should be carefully regulated. While its depletion triggers cell death (Sundstro ¨met al, 2009; Gutierrez-Beltran et al, 2016; Cui et al, 2018), increased expression of TSN is closely associated with various types of cancer, adding it to a shortlist of most potent oncogenes and attractive targets for anti-cancer therapy (Jariwala et al, 2017; Yu et al, 2017). In yeast and mammals, the universal molecular components of SGs co-exist with other, cell type- and stress stimuli-specific proteins, suggesting that SGs might play additional, yet unexplored, roles during stress. For example, SG formation in both yeast and human cells mediates target of rapamycin (TOR) signalling under stress by sequestering both TOR complex (TORC1) and downstream kinases (Takahara & Maeda, 2012; Wippich et al, 2013). By contrast, sequestration of the pleiotropic adaptor protein Receptor For Activated C Kinase 1 (RACK1) in SGs inhibits the stress-induced activation of the c-Jun N-terminal kinases (JNK) cascade that triggers apoptotic death (Arimoto et al, 2008). In yet another scenario, sequestration of the coiled-coil containing Rho-associated protein kinase 1 (ROCK1) into SGs promotes cell survival by abolishing JNK-mediated cell death (Tsai & Wei, 2010). In summary, SG formation can alter signalling pathways by protein sequestration during stress conditions, but whether such a mode of regulation exists in plants remains elusive. Here, we isolated TSN-interacting proteins from Arabidopsis plants subjected to heat and salt stress, and further combined microscopy, reverse genetics and bioinformatics to advance our understanding of the regulation and molecular function of SGs in plants. We show that TSN engages its highly disordered N-terminal region in providing a platform for docking homologues of key components of yeast and mammalian SGs, as well as novel or plant-specific SG- localized proteins. TSN forms a large disorder-enriched protein– protein interaction network under non-stress conditions, that is poised to enable rapid SG assembly in response to stress. Finally, our data demonstrate that TSN and formation of SGs confer heatinduced activation of the catalytic a-subunit (SnRK1a) of SnRK1 heterotrimeric complex, thus linking TSN and SGs to the energy status of the plant cells. Results Generation and characterization of Arabidopsis TAPa-expressing lines As a first step to investigate the role of TSN in SGs, we used TSN2, one of the two Arabidopsis TSN isoforms, as bait for alternative tandem affinity purification (TAPa; Fig EV1A) (Rubio et al, 2005). TSN2 and green fluorescent protein (GFP; negative control) were tagged at their C-termini with TAPa epitope containing two copies of the immunoglobulin-binding domain of protein A from Staphylococcus aureus, a human rhinovirus 3 protease cleavage site, a 6- histidine repeat and 9-Myc epitopes (Figs 1A and EV1A). The resulting TSN2-TAPa and GFP-TAPa vectors were introduced into Arabidopsis Columbia (Col) background. Two lines per construct showing readily detectable expression by immunoblot were selected for further studies (Fig EV1B). To verify whether the presence of TAPa epitope could affect intracellular localization and functionality of TSN protein, we performed two additional experiments. First, the immunostaining of root cells from 5-day-old seedlings using a-Myc revealed that similar to native TSN (Yan et al, 2014; Gutierrez-Beltran et al, 2015b), TSN2-TAPa displayed diffuse cytoplasmic localization under no stress (NS) conditions but redistributed to cytoplasmic foci following heat stress (HS) (Fig 1B). In contrast, GFP-TAPa remained cytoplasmic regardless of conditions (Fig 1B). Co-localization analysis of the TSN2-TAPa and SG marker eIF4E confirmed that the HS-induced TSN2-TAPa foci are SGs (Fig EV1C). Second, expression of TSN2- TAPa in tsn1 tsn2 seedlings complemented previously reported root cell death phenotype under HS caused by TSN deficiency (Fig EV1D) (Gutierrez-Beltran et al, 2015b). Heat stress induction in these experiments was confirmed by expression analysis of HSP101 and HSF, two HS marker genes (Pecinka et al, 2010) (Appendix Fig S1). Collectively, these data demonstrate that C-terminally TAPatagged TSN retains localization and function of its native counterpart when expressed in Arabidopsis and therefore can be used as a bait for the isolation of TSN-interacting proteins. 2of 21 The EMBO Journal 40:e105043 |2021 ª2021 The Authors The EMBO Journal Emilio Gutierrez-Beltran et al Isolation of TSN2-interacting proteins Since previously we identified TSN2 as a robust marker of SGs induced by HS (Gutierrez-Beltran et al, 2015b), we initially analysed TSN2 interactomes isolated from fully expanded leaves of 18-day- old plants growing under NS conditions (23°C) or subjected to HS (39°C for 60 min). To examine the efficiency of purifying TSN2- TAPa and GFP-TAPa proteins from the corresponding transgenic NS HS TSN2-TAPa GFP-TAPa A B Nos ter 2x35S::TMVU1 TAPa IgG-BD 3C His myc TSN GFP C TSN2 no stress pool (TSN2_NS) TSN2 heat-stress pool (TSN2_HS) Stress-dependent interactors Stress-sensitive interactors TSN Stress-independent interactors TSN F RBPs Prion-like domain ATPase activity TSN2_NS (277) 153 (55%) 22 (7.9%) 27 (9.7%) TSN2_HS (149) 79 (55%) 12 (8.0%) 6 (4.0%) Yeast SGs (159) 39 (24%) 23 (14.5%) 24 (15.0%) Human SGs (411) 224 (55%) 32 (7.8%) 37 (9.0%) D 219 TSN2_NS 21 19 387 109 16 Human SGs Yeast SGs 23 E BIP2 CCT1 CCT3 CCT6B CCT6A CCT7 CCT8 CCT22 CH60C CH60A eIF2 eIF4E5 eEF1A HEN2 MCM4 PAB8 PAB4 31 117245 HS NS HS-independent TSN2 interactors eIF3B HSP7C HSP702 PAB4 RBP47 SYIM SCYL2B UBP1 CML12 HSP7C* PAB4* TCTP* RBP47* UBP1* Group 1 Group 2 Group 1= Homologues of human and/or yeast SG proteins (including SG remodelers). . Group 2= Arabidopsis RBP47-SG proteome (*= overlap with Group 1) HS-dependent TSN2 interactors BRN1 BTR1 CDKC-2 eEF1B eEF1B HEN4 MED14 NRPB3 NRPB1 RPS10 RPS14 RBP1 SpoU SYEC ACLA3 BPA1 CP19A eEF1G1* eEF1D2* GSTF7 TSN1* Group 1 Group 2 HS-sensitive TSN2 interactors SYIC SYIM SYVM1 SYDC1 UBP4 UPL2 UBP6 UBP13 UEV1D UBP9 UBP1 Group 1 Group 2 RH23 RH14 RH11 RH12 RH8 RH6 RENT1 RBP57 RPS11 RL321 RPS16 RPS7 RPS163 RPS81 RK20 SUMO1 SUMO2 DRG3 LRP1A M2K5 MPK3 MYO17 NTF2B PTBP3 PAB8* RHM1 RHM2 RH23* RH11* VPS4 VCS Figure 1. ª2021 The Authors The EMBO Journal 40:e105043 |2021 3of 21 Emilio Gutierrez-Beltran et al The EMBO Journal plants, we performed TAPa approach (Fig EV1A) on a small scale using 10-day-old seedlings. Immunoblot analysis with a-Myc confirmed that both TAPa-tagged proteins could be efficiently purified (Fig EV1E). Mass spectrometry-based label-free quantitative proteomics analysis yielded 2,535 hits across all samples. The relative abundance of proteins was determined using MaxQuant intensity-based absolute quantification (iBAQ), which reports summed intensity values of the identified peptides divided by the number of theoretical peptides (Tyanova et al, 2016; Esgleas et al, 2020). In order to identify specific interactors of TSN2, we filtered the results using a two-step procedure. First, we selected proteins specifically enriched in either TSN2_NS or TSN2_HS pools compared with the GFP pool. Thereafter, proteins were filtered based on subcellular localization according to the Arabidopsis subcellular database SUBA version 4 (Hooper et al, 2017), excluding proteins localized to chloroplasts. As a result, we obtained 277 and 149 proteins representing presumptively physiologically relevant interactomes of TSN2 under NS and HS settings, respectively (TSN2_NS and TSN2_HS pools; Dataset EV1). TSN forms a network of SG protein–protein interactions before the onset of stress The comparison of TSN2_NS and TSN2_HS protein pools enabled classification of TSN interactors into one of the three classes (Fig 1C): (i) stress-independent interactors, which always associate with TSN regardless of conditions; (ii) stress-dependent interactors, which associate with TSN only under HS; and (iii) stress-sensitive interactors, whose association with TSN is lost during HS. Although SGs are microscopically visible only under stress conditions (Jain et al, 2016), analysis of eggNOG orthologs (Huerta-Cepas et al, 2019) revealed that ˜20% of proteins from both TSN2_NS and TSN2_HS pools are known components of human or yeast SGs [Fig 1D and E (group 1) and Fig EV2A; Dataset EV2] (Jain et al, 2016). Furthermore, the in silico analysis showed a significant degree of similarity in the functional distribution of composite proteins between TSN2_NS and TSN2_HS pools and both yeast and human SG proteomes. All of them were enriched in RNA-binding proteins (RBPs), proteins with predicted prion-like domains and proteins with ATPase activity (Fig 1F, Dataset EV2). Apart from the overlaps between TSN2_NS and TSN2_HS pools and yeast and human SG proteomes, 7.5% (21 hits) and 10.7% (16 hits) proteins from TSN2_NS or TSN2_HS pools, respectively, were shared with the recently published Arabidopsis SG proteome [RBP47-SG proteome; Fig 1E (group 2) and Fig EV2B; Dataset EV2] isolated from heatstressed (30 min at 42°C) seedlings expressing GFP-RBP47 (Kosmacz et al, 2019). However, larger parts, i.e. 77% (214 proteins) and 79% (118 proteins) of TSN2_NS and TSN_HS protein pools, respectively, were not shared with either yeast, human or Arabidopsis RBP47-SG proteomes (Dataset EV2), representing ample resource for finding novel or plant-specific SG components. Interestingly, 89% (245/277) of hits from the TSN2_NS pool were absent in the TSN2_HS pool, thus constituting the HS-sensitive part of the TSN2 interactome (Fig 1C and E). A significant part of the HS-sensitive pool was represented by the homologues of yeast or human SG remodellers (Fig 1E, proteins marked in grey colour), including protein chaperones [e.g. cpn60 chaperonin proteins (CCTs) or heat shock proteins, such as CH60s and BIP2], multiple RNA and DNA helicases (e.g. RH, MCM and RENT1) or ubiquitinrelated proteins (e.g. SUMO1, SUMO2, UPLs or UBPs). The remaining, smaller part of the TSN2_NS pool (11%, 31/277 proteins) was shared with the TSN2_HS pool and represented HS-independent TSN2 interactors (Fig 1C and E). The latter class of proteins included UBP1, RBP47, PAB4 and TCTP, among others. Lastly, 78% (117/ 149) proteins from the TSN2_HS pool, including several RBPs (HEN4 or BRN1), individual subunits of eEF1 elongation factor (eEF1B and eEF1Bc), or DNA-directed RNA polymerase II subunits (NRPB1 and NRPB3), were absent from the TSN2_NS pool, representing HS-dependent TSN2 interactors (Fig 1C and E). We additionally retrieved publicly available direct protein– protein interaction (PPI) data for all proteins found in our proteomic studies. Both TSN2_NS and TSN2_HS protein pools formed a dense network of protein–protein interactions, comprising 239 and 120 nodes and 1,059 and 177 edges, respectively (Appendix Fig S2). In this context, the average number of interactions per protein for these two pools was 8.86 (P<1×10 16 ) and 2.95 (P= 7.5 ×10 07 ), respectively. Together with our findings that known SG remodellers interact with TSN in Arabidopsis cells in the absence of stress (Fig 1E), these new results pointed to a pre-existing steady-state network of protein–protein interactions as a basal mechanism during SG formation, where TSN could act as a protein assembly platform. ◀Figure 1. Tandem affinity purification and characterization of the Arabidopsis TSN2-interacting proteins. A Schematic illustration of the expression cassette in TAPa vector. The vector allows translational fusion of TSN or GFP at their C termini to the TAPa tag. The expression is driven by two copies of the cauliflower mosaic virus 35S promoter (2×35S) and a tobacco mosaic virus (TMV) U1X translational enhancer. The TAPa tag consists of two copies of the protein A IgG-binding domain (IgG-BD), an eight amino acid sequence corresponding to the 3C protease cleavage site (3C), a 6-histidine stretch (His), and nine repeats of the Myc epitope (myc). A Nos terminator (Nos ter) sequence is located downstream of each expression cassette. B Immunolocalization of TSN2-TAPa and GFP-TAPa fusion proteins in root cells of 5-day-old seedlings. The seedlings were grown under no stress (NS) conditions (23°C) or incubated for 40 min at 39°C (HS) and then immunostained with a-Myc. Scale bars = 10 lm. C Schematic representation of three classes of TSN-interacting proteins, i.e. stress-dependent, stress-sensitive and stress-independent interactors, identified upon the comparison of TSN2_NS and TSN2_HS protein pools. D Venn diagram showing the comparison of TSN2_NS interactome with human and yeast SG proteomes (Jain et al,2016). E Venn diagram showing the comparison between TSN2_NS and TSN2_HS protein pools. TSN2-interacting proteins are divided into three classes: HS-sensitive, HS- independent and HS-dependent. Within each class, the proteins are further classified into two groups. Group 1contains known human or yeast SG proteins (Jain et al,2016), including SG remodellers (marked in grey colour), whereas group 2represents components of recently isolated Arabidopsis RBP47-SG proteome (Kosmacz et al,2019). The full lists of TSN2-interacting proteins, including previously uncharacterized and potentially novel SG components not belonging to either group 1or group 2, are provided in Dataset EV1. F Frequency of RBPs and proteins with prion-like domains or ATPase activity found in TSN2_NS and TSN2_HS protein pools in comparison with yeast and human SG proteomes (Jain et al,2016). 4of 21 The EMBO Journal 40:e105043 |2021 ª2021 The Authors The EMBO Journal Emilio Gutierrez-Beltran et al TSN is a scaffold protein for SG components Stress granules are constituted by a dynamic shell and a more stable core (Jain et al, 2016). Core proteins have been suggested to act as a scaffold for other SG components (Guillen-Boixet et al, 2020; Schmit et al, 2021). In a previous study, we observed that TSN did not exchange between the cytoplasm and SG foci upon a fluorescence recovery after photobleaching (FRAP) analysis, suggesting its role as a scaffold protein (Gutierrez-Beltran et al, 2015b). Deletion of scaffold-like molecules is known to have a strong effect on the composition of membrane-less organelles (Espinosa et al, 2020; Xing et al, 2020). With this in mind and in order to gain a better insight into presumably scaffolding role of TSN in SGs, we investigated the effect of TSN deficiency on the interactome of another plant SG marker protein, RBP47 (Weber et al, 2008; Kosmacz et al, 2019). For this, we immunoprecipitated GFP-RBP47- bound protein complexes from fully expanded leaves of 18-day-old WT and double tsn1 tsn2 knockout plants growing under NS conditions (23°C) or subjected to HS (39°C for 60 min) (Appendix Fig S3). In these experiments, we used free GFP-expressing plants as control and followed the same label-free quantitative proteomics procedure as described above for the TSN2-TAPa experiments. Notably, TSN2 was identified in both RBP47_NS and RBP47_HS protein pools (Dataset EV3). We discovered that in the absence of stress, deletion of TSN resulted in more than 10-fold increase in the RBP47 interactome size accompanied by complete renewal of its protein composition (Fig 2A and B; Dataset EV3). Although TSN deficiency did not significantly affect the size of the RBP47 interactome under HS conditions, it induced almost complete renewal of the protein pool (Fig 2A and B; Dataset EV3). Apart from that, comparison of RBP47 interactomes and TSN2 interactomes revealed ˜11% (31 proteins) overlap in the protein composition between the TSN2_NS pool and RBP47_NS pool isolated from tsn1 tsn2 plants (Fig 2B). Furthermore, 11 out of 31 shared proteins are homologous to the yeast ATP-driven remodelling complexes [Sheet RBP47_NS (tsn1 tsn2), Dataset EV3]. Taken together, these data demonstrate massive reorganization of the RBP47 interactome induced by TSN deficiency, providing evidence for the role of TSN as a scaffold during SG formation. TSN-interacting proteins co-localize with TSN2in cytoplasmic foci To ascertain the SG localization of TSN2-interacting proteins identified by mass spectroscopy, we selected 16 of the most interesting proteins (Fig 3A). These included homologues of well-known yeast and animal SG-associated proteins (eIF4E5, PAB4 and the ribosomal subunit RPS11) and hypothetical plant-specific SG components with a role in fundamental eukaryotic pathways (e.g. SKP1, MCA-Ia, TCTP and both SnRK1a1 and SnRK1a2 isoforms). First, we performed co-localization studies to investigate whether selected TSN-interacting proteins were translocated to TSN2 foci under stress. To this end, protoplasts were isolated from Nicotiana benthamiana (N.benthamiana) leaves co-transformed with RFPTSN2 and individual GFP-TSN-interacting proteins. Cotransformation of the cytoplasmic protein GFP-ADH2 or the SG marker GFP-UBP1 with RFP-TSN2 was used as a negative and positive control, respectively (Fig 3B and C). The degree of colocalization was calculated using pixel correlation analysis (Fig 3C) (French et al, 2008). As shown in Figs 3B and C, and EV3; Appendix Fig S4, all selected proteins co-localized with TSN2 in punctate foci upon HS. Next, to elucidate whether these proteins are associated with TSN2 in the heat-induced SGs, we performed bimolecular fluorescence complementation (BiFC) analyses in N. benthamiana leaf cells or protoplasts co-transformed with cYFP-TSN2 and individual nYFP- TSN-interacting proteins. Fluorescence complementation was observed in 10 out of 16 shortlisted TSN-interacting proteins. The YFP signal exhibited diffuse cytoplasmic localization under control conditions (23°C; Appendix Fig S5) and redistributed to punctate foci upon HS (Fig 3D). A 277 TSN2_NS RBP47_NS (WT) RBP47_HS (tsn1 tsn2) RBP47_HS (WT) RBP47_NS (tsn1 tsn2) TSN2_HS 68 55 24 293 149 B Figure 2. TSN deficiency promotes a massive reorganization of the RBP47 interactome. A Venn diagram showing the comparison between RBP47_NS and RBP47_HS protein pools isolated from WT and tsn1tsn2plants. B Circos plot showing the comparison between four RBP47 interactomes and two TSN2interactomes. ª2021 The Authors The EMBO Journal 40:e105043 |2021 5of 21 Emilio Gutierrez-Beltran et al The EMBO Journal To further corroborate the association of TSN with novel plant SG components in planta, translationally controlled tumour protein (TCTP) and an uncharacterized RNA-binding protein (RBP) were selected. TCTP was previously observed in both nuclei and cytoplasm (Betsch et al, 2017). GFP-tagged RBP and TCTP proteins relocalized to cytoplasmic foci under HS in Arabidopsis root tip cells (Fig EV4A). The SG identity of the RBP and TCTP foci was validated by co-localization analysis with the SG marker eIF4E (Fig EV4B) (Gutierrez-Beltran et al, 2015b). Subsequently, a Fo ¨rster resonance energy transfer (FRET) assay in heat-stressed N. benthamiana leaves confirmed that TSN2 interacted with TCTP and RBP (Fig EV4C). Finally, TSN co-immunoprecipitated with both TCTP and RBP but not with GFP (negative control) in Arabidopsis protein extracts, confirming the in vivo protein–protein interaction (Fig EV4D). Taken together, these findings further reinforce the view that TSN plays a scaffolding role in recruiting a wide range of proteins to SGs. TSN associates with SG proteins via the highly disordered N-terminal region Studies in mammalian and yeast cells have suggested that SGs are multicomponent viscous liquid droplets formed in the cytoplasm by LLPS (Kroschwald et al, 2015; Protter & Parker, 2016). Although the molecular details underlying intracellular LLPS are largely obscure, recent evidence suggests that IDRs mediate this process (Posey et al, RACK1B SnRK1 1 RH12 SnRK1 2 eIF4E5 PAB4 TCTPRPS11 RBP TSN2 AGI code Gene name At2g23350 PAB4 At1g51580 RBP At5g18110 eIF4E5 At2g36160 RPS11 At1g78570 RHM1 At3g16640 TCTP At1g02170 MCA-Ia At1g48630 RACK1B At1g07360 C3H4 At5g13480 FY At1g75950 SKP1 At3g15980 COB23 At2g25110 SDF2 At3g61240 RH12 At3g01090 SnRK1 1 At3g29160 SnRK1 2 AB D GFP-ADH2 RFP-TSN2 Merge NS HS GFP-UBP1 RFP-TSN2 Merge Crs rp PSC coefficient SKP1 EV TIPs EV TSN2 -1 -0,5 0 0,5 1 Figure 3. TSN2and its interactors are localized in heat-induced SGs. A A list of TSN-interacting proteins selected for the co-localization analysis. B Co-localization of RFP-TSN2(red) with GFP-ADH2(negative control) and GFP-UBP1(positive control) in N. benthamiana protoplasts incubated under 23°C (NS) or at 39°C for 40 min (HS). Scale bars = 5lm. C Pearson and Spearman coefficients (r p and r s , respectively) of co-localization (PSC) of RFP-TSN2with individual GFP-tagged TSN-interacting proteins listed in Aand with both negative and positive control proteins (denoted by red arrowheads) under HS. Data represent means SD of at least five replicate measurements from three independent experiments. D BiFC between cYFP-TSN2and nYFP-TSN-interacting proteins in N. benthamiana leaf cells or protoplasts after HS (39°C for 40 min). BiFC analysis of cYFP-TSN2and nYFP-TSN-interacting proteins (TIPs) with empty vectors (EV) encoding nYFP and cYFP, respectively, was used as a negative control. Only one representative example of BiFC between cYFP and nYFP-TIP is shown. Scale bars = 5lm. 6of 21 The EMBO Journal 40:e105043 |2021 ª2021 The Authors The EMBO Journal Emilio Gutierrez-Beltran et al 2018; Alberti et al, 2019; Yang et al, 2020). In this context, we estimated the predicted enrichment of IDRs and propensity of proteins for LLPS for both TSN2_NS and TSN2_HS interactomes as compared to GFP-TAPa control protein pools using IUPred and PSPredictor algorithms, respectively (preprint: Sun et al, 2019; Erdos & Dosztanyi, 2020). The analysis revealed significant enhancement of IDR frequency (Fig 4A) and propensity for LLPS (Fig 4B) in both TSN interactomes in agreement with the scaffolding role of TSN in the formation of phase-separated granules. In mammalian cells, IDRs of G3BP or hnRNPA1 regulate SG assembly via LLPS (Molliex et al, 2015; Guillen-Boixet et al, 2020; Yang et al, 2020). Considering this fact as well as that TSN was shown to modulate the integrity of SGs in Arabidopsis (Gutierrez- Beltran et al, 2015b), we evaluated the per-residue intrinsically disordered propensities of TSN2 itself by six commonly used predictors, including PONDR-VLXT, PONDR-VL3, PONDR-VSL2, IUpred_- short, IUpred_long and PONDR-FIT (Meng et al, 2015). Figure 4C shows that TSN2 is expected to have 11 disordered regions if averaged for six predictors (score above 0.5). Thus, the SN region (tandem repeat of four N-terminally located SN domains) of TNS2 is predicted to be highly disordered, whereas the Tudor domain is predicted to be one of the most ordered parts of the protein. This observation was confirmed using the D 2 P 2 database providing information about the predicted disorder and selected disorder-related functions (Appendix Fig S6A) (Oates et al, 2013). Notably, similar results were obtained for the TSN1 protein isoform which is functionally redundant with TSN2 (Appendix Fig S6A and B) (dit Frey et al, 2010). To investigate whether it is the highly disordered part of TSN which is required for interaction with SG components, we compared the association of the SN region, the Tudor region (composed of the Tudor domain and the fifth, partial SN domain; Fig 4C) and fulllength TSN (as a control) with four different TSN-interacting proteins in heat-stressed N. benthamiana leaves using BiFC (Fig 4D). The experiment revealed reconstitution of fluorescent signal with all four TSN interactors in case of both full-length TSN2 and SN region, whereas none of the interactors could form a complex with Tudor region (Fig 4D). Furthermore, expression of either fulllength TSN2 or SN region yielded identical, punctate BiFC localization pattern. Taken together, these results prompted us to hypothesize that TSN protein could recruit SG components via IDRs, promoting rapid coalescence of microscopically visible SGs upon stress exposure. Arabidopsis SG-associated proteins are common targets of both TSN1and TSN2isoforms TSN1 and TSN2 proteins were suggested to be redundant in conferring Arabidopsis stress tolerance (dit Frey et al, 2010; Gutierrez- Beltran et al, 2015b). To investigate whether this redundancy is conserved at the SG level, we isolated the TSN1 interactome from unstressed plants using the same TAPa procedure as described above for TSN2 (Fig EV1A, B and E). As a result, we obtained the TSN1_NS pool enriched in 215 proteins (Dataset EV1). Out of these, 110 (51%) were TSN1-specific, whereas the remaining fraction (105 proteins, 49%) represented common interactors of TSN1 and TSN2, reflecting their functional redundancy (Fig 5A). Notably, the pool of common interactors of TSN1 and TSN2 was enriched in homologues of human and/or yeast SG proteins, such as PAB4, small ribosomal subunits, RNA or DNA helicases or CCT proteins (group 1, Fig 5A). In addition, the common TSN1 and TSN2 interactors included many recently identified members of Arabidopsis RBP47-SG proteome (group 2, Fig 5A) (Kosmacz et al, 2019), as well as novel plant SG components (group 3, Fig 5A) verified in the current study through either BiFC analysis or co-localization or by using both methods (Figs 3, EV3 and EV4). To corroborate the proteomics results, we chose DEAD-box ATP- dependent RNA helicase 12 (RH12), as a common interactor of TSN1 and TSN2. RH12 is a nucleocytoplasmic protein associated with SGs under stress (Chantarachot et al, 2020). First, we confirmed the molecular interaction between two isoforms of TSN and RH12 by coimmunoprecipitation in cell extracts from Agrobacterium-infiltrated N. benthamiana leaves. RH12 co-immunoprecipitated with both TSN1 and TSN2 but not with GFP (Fig 5B). Second, we produced Arabidopsis lines stably expressing GFP-RH12 under its native promoter and observed re-localization of the fusion protein to heatinduced SGs in root tip cells (Fig 5C and Appendix Fig S7). Taken together, these data are consistent with TSN1 and TSN2 as functionally redundant in providing a scaffold platform for the recruitment of a wide range of plant SG components. Identification of a salt stress-induced TSN2interactome TSN2 localizes to SGs under salt stress (Yan et al, 2014). To investigate the differences between salt stress- and HS-induced TSN interactomes as a proxy for SG proteome variability under different types of stresses, we purified TSN2-interacting proteins from salt-treated Arabidopsis plants using our standard TAPa purification procedure. The resulting TSN2_NaCl protein pool was much (9.3–17 times) smaller than both TSN2_NS and TSN2_HS pools, and contained only 16 protein hits (Dataset EV1), 5 and 7 of which were shared with TSN2_NS and TSN2_HS pools, respectively (Fig 6A). Apart from the presence of well-defined mammalian and/or yeast SG proteins, such as HSP70, all three protein pools contained RBP47. To corroborate this result, we performed co-immunoprecipitation of native TSN using protein extracts prepared from GFP-RBP47- expressing Arabidopsis seedlings exposed to heat (60 min at 39°C) or salt (60 min, 200 mM NaCl) stress. TSN co-immunoprecipated with GFP-RBP47 under both stresses, as well as in the absence of stress (Fig 6B), suggesting that RBP47 is a constitutive interactor of TSN that might be recruited to SGs under various types of stresses. Since we have found that TSN exhibits stress type-dependent variation in both size and composition of its interactome (Fig 6A; Dataset EV1), we then addressed SG recruitment of TSN-interacting proteins to SGs in a stress-type-specific manner using confocal microscopy. To this end, we examined the localization of RBP47 (present in all three TSN2 interactomes), as well as UBP1, TCTP and SnRK1a2 (all present in both TSN_NS and TSN_HS pools but absent in TSN2_NaCl pool) in root tip cells of 5-day-old Arabidopsis seedlings expressing GFP-tagged fusions of these proteins. Analysis revealed that while RBP47 and UBP1 were localized to both HS- and salt-induced cytoplasmic puncta, TCTP and SnRK1a2 showed punctate localization only under HS (Fig 6C). These data point to heterogeneity of SG composition in plants and additionally demonstrate that some SG resident proteins might not associate with TSN in SGs (e.g. UBP1 absent in the TSN2_NaCl protein pool). ª2021 The Authors The EMBO Journal 40:e105043 |2021 7of 21 Emilio Gutierrez-Beltran et al The EMBO Journal TSN interacts with and mediates assembly of SnRK1ain heat SGs The evolutionary conserved subfamily of yeast sucrose nonfermenting-1 protein kinase (SNF1)/mammalian AMP-activated protein kinase (AMPK)/plant SNF1-related kinase 1 (SnRK1) plays a central role in metabolic responses to declined energy levels in response to nutritional and environmental stresses (Broeckx et al, 2016). These kinases typically function as a heterotrimeric complex composed of two regulatory subunits, band c, and an a-catalytic subunit. In Arabidopsis, the a-catalytic subunit of SnRK1 is encoded A C D B Figure 4. The highly disordered region of TSN2is required for interaction with SG proteins. A, B % IDR (A) and propensity for LLPS (B) in TSN2_NS and TSN2_HS interactomes versus corresponding GFP-TAPa controls (C_NS and C-HS) using IUPred and PSPredictor algorithms, respectively. Upper and lower quartiles, medians and extreme points are shown. The number of protein sequences included to the analyses was 566,277,995 and 149 for C_NS, TSN2_NS, C_HS and TSN2_HS, respectively. Pvalues denote statistically significant differences for comparisons to controls (two-tailed t-test). C Disorder profiles of TSN2generated by PONDR-VLXT, PONDR-VL3, PONDR-VSL2, IUPred-short, IUPred-long and PONDR-FIT and a consensus disorder profile (based on mean values of six predictors). SN, staphylococcal nuclease region composed of four N-terminally situated SN domains. C-terminally situated Tudor region is composed of the domain of the same name and a partial SN domain. D BiFC between cYFP-TSN2(full-length), cYFP-SN or cYFP-Tudor and nYFP-TSN-interacting proteins in N. benthamiana protoplasts after HS (39°C for 40 min). Scale bars = 10 lm. Boxplots show quantification of the reconstituted YFP signal. AU, arbitrary units. Upper and lower box boundaries represent the first and third quantiles, respectively, horizontal lines mark the median, and whiskers mark the highest and lowest values. Three independent experiments, each containing seven individual measurements, were performed. ***P<0.001 versus Tudor (one-way ANOVA). 8of 21 The EMBO Journal 40:e105043 |2021 ª2021 The Authors The EMBO Journal Emilio Gutierrez-Beltran et al by two functionally redundant genes, SnRK1a1and SnRK1a2 (Baena-Gonzalez et al, 2007). We found that SnRK1a1 and SnRK1a2 (also known as KIN10 and KIN11, respectively) are TSN-interacting proteins re-localized to SGs upon HS (Figs 3, 4D and 6C). To dissect the functional relevance of TSN binding and SG localization of SnRK1a1 and SnRK1a2 proteins, we first corroborated the interaction with TSN2 using two different approaches. First, we performed co-immunoprecipitation of native TSN from protein extracts prepared from heat-stressed Arabidopsis plants expressing GFP- SnRK1a1 and GFP-SnRK1a2. We found that native TSN coimmunoprecipitated with both GFP-SnRK1a1 and GFP-SnRK1a2 but not with free GFP, which was used as a negative control (Fig 7A). In addition, co-immunoprecipitation analysis confirmed our proteomics data suggesting that SnRK1ais bound to TSN also in the absence of stress (Fig 7B). Second, a FRET assay demonstrated that TSN2 directly interacts with both SnRK1a1 and SnRK1a2inN. benthamiana leaves under HS (Fig 7C). To explore the molecular link between SnRK1aand TSN2, we first examined the potential localization of SnRK1 in the root tip cells of heat-stressed WT and TSN-deficient (tsn1 tsn2) plants. As shown in Fig 7D, TSN is dispensible for localization of either GFP- SnRK1a1 or GFP-SnRK1a2 to cytoplasmic foci. However, we observed a significant decrease in the number of SnRK1afoci and a simultaneous increase in their size in tsn1 tsn2 compared with WT plants (Fig 7E and F). The HS induction in WT and tsn1 tsn2 plants was confirmed by expression analysis of HSP101 and HSF (Appendix Fig S1). We conclude that TSN takes part in the assembly of SnRK1aisoforms in Arabidopsis heat SGs. Stress granules are dynamic structures where many proteins move continuously (Mahboubi & Stochaj, 2017). To investigate the role of TSN in the SnRK1adynamics, we measured SnRK1amobility within heat-induced SGs in the root tip cells of WT and tsn1 tsn2 plants using FRAP. While SnRK1a2 revealed a lack of any fluorescent signal recovery in a TSN-independent manner, TSN deficiency led to a significant decrease in both signal recovery rate and proportion of the initial signal recovery of SnRK1a1 (Figs 7G and H, and EV5A). Thus, we conclude that TSN is required for full mobility of the SnRK1a1 isoform. Catalytic and regulatory domains of SnRK1a1exhibit differential behaviour in SGs To investigate the role of N-terminal catalytic and C-terminal regulatory domains of SnRK1a1 (hereafter designated as SnRK1a1 CD and 110 172 105 TSN1_NS TSN2_NS A RH12 NS HS B Common interactors of TSN1 and TSN2 CCT22 eEF1B eIF2 eIF4E5 HEN2 LRP1A PAB4 RBP47 RH6 RH12 RH8 RH37 DRG3 eEF1B * EPN2 ECT2 LRP1A* PAB4* RBP47* RH37* RHM2 TCTP TSN1 UBP1* VCS C3H4 eIF4E5 MCA-Ia RH12 SDF2 SKP1 SnRK1 1 SnRK1 2 TCTP Group 1 Group 2 -GFP -Myc Input GFP-TSN1 GFP-TSN2 GFP Myc-RH12 Co-Ip GFP-TSN1 GFP-TSN2 GFP Myc-RH12 C Group 1 = Homologues of human and/or yeast SG proteins Group 2 = Arabidopsis RBP47-SG proteome (*= overlap with Group 1) Group 3 = Novel plant SG components validated in Fig. 3, EV3 and EV4 Group 3 RPS11 RPS23 RPS25 RPS73 RPS163 RPS81 RR14 RR15 SYVM1 SYIC SYDC1 UBP1 UBP4 UPL2 Figure 5. Interactomes of Arabidopsis TSN1and TSN2largely overlap. A Venn diagram showing the overlap between TSN1and TSN2interactomes isolated by TAPa from Arabidopsis plants grown under NS conditions. Common interactors of TSN1and TSN2are classified into three groups: (i) homologues of human and/or yeast SG proteins, (ii) proteins constituting recently isolated Arabidopsis RBP47-SG proteome (Kosmacz et al, 2019) and (iii) novel plant SG components validated in Figs 3,EV3and EV4. The full lists of TSN1- and TSN2-interacting proteins, including as yet uncharacterized and potentially novel SG components not belonging to any of the three groups, are provided in Dataset EV1. B Co-immunoprecipitation (Co-Ip) of the two TSN isoforms and RH12 in protein extracts prepared from N. benthamiana leaves agro-infiltrated with GFP-TSN1or GFPTSN2and Myc-RH12. Free GFP was used as a negative control. Input and Co-Ip fractions were analysed by immunoblotting using a-Myc and a-GFP. C Localization of RH12 in root cells of 5-day-old Arabidopsis seedlings expressing GFP-RH12 under control of the native promoter. The seedlings were grown under 23°C (NS) or incubated at 39°C for 60 min (HS). Scale bars = 10 lm. Source data are available online for this figure. ª2021 The Authors The EMBO Journal 40:e105043 |2021 9of 21 Emilio Gutierrez-Beltran et al The EMBO Journal dried under vacuum and analysed by LC-MS using Nano LC-MS/MS (Dionex Ultimate 3000 RLSC nano System, Thermo Fisher) interfaced with Eclipse (Thermo Fisher). Samples were loaded onto a fused silica trap column Acclaim PepMap 100, 75 lm×2 cm (Thermo Fisher). After washing for 5 min at 5 µl/min with 0.1% TFA, the trap column was brought in-line with an analytical column (Nanoease MZ peptide BEH C18, 130A, 1.7 lm, 75 lm×250 mm; Waters) for LC-MS/MS. Peptides were fractionated at 300 nL/min using a segmented linear-gradient 4–15% of buffer B in buffer A over 30 min (A: 0.2% formic acid and B: 0.16% formic acid, 80% acetonitrile), and then 15%-25%, 25%-50% and 50–90% over 40, 44 and 44 min, respectively. Buffer B then returned at 4% for 5 min for the next run. The scan sequence began with an MS1 spectrum [Orbitrap analysis, resolution 120,000, scan range from m/z 375–1,500, automatic gain control (AGC) target 1E6, maximum injection time 100 ms]. The top duty cycle (3 s) scheme and dynamic exclusion of 60 s were used for the selection of parent ions of 2–7 charges for MS/MS. Parent masses were isolated in the quadrupole with an isolation window of 1.2 m/z, AGC target 1E5, and fragmented with higherenergy collisional dissociation with a normalized collision energy of 30%. The fragments were scanned in Orbitrap with 15,000 resolution. The MS/MS scan range was determined by the charge state of the parent ion, with a lower limit set at 110 amu. Mass spectrometry data analysis The raw data from TAPa and on-bead-digestion were processed using MaxQuant software (version 1.6.10.43) (Tyanova et al, 2016) and searched against an TAIR11 protein database. The following modifications were selected for the search: carbamidomethyl (C; fixed), acetyl (N-term; variable) and oxidation (M; variable). For both the full-scan MS spectra (MS1) and the MS/MS spectra (MS2), the mass error tolerances were set to 20 ppm. Trypsin was selected as a protease with a maximum of two miscleavages. For protein identification, a minimum of one unique peptide with a peptide length of at least seven amino acids and a false discovery rate below 0.01 was required. The match between runs function was enabled, and a time window of one min was set. Label-free quantification was selected using iBAQ (calculated as the sum of the intensities of the identified peptides divided by the number of observable peptides of a protein) (Schwanhausser et al, 2011). The proteinGroups.txt file, an output of MaxQuant, was further analysed using Perseus 1.16.10.43 (Tyanova et al, 2016). The iBAQ values were normalized to summed total iBAQ value of all proteins of that sample and log 2 transformed. After filtering out the protein groups with no valid Quan value, the missing values were replaced with a random normal distribution of small values. The non-paired two-tailed t-test (Tusher et al, 2001) was used to calculate significant differences between the two samples. Identified proteins were considered as interaction partners if their MaxQuant iBAQ values displayed a >1.5- or 2-fold change enrichment and P<0.05 (t-test) when compared to the control. Furthermore, at least two unique peptides were required per protein group. Protoplast and plant transformation Protoplasts were isolated from leaves of 15- to 20-day-old N. benthamiana transiently expressing the corresponding fluorescent proteins, as described previously (Wu et al, 2009). The cell walls were digested by incubation in enzymatic solution containing 1% (w/v) Cellulose R-10, 0.25% (w/v) Macerozyme R-10, 20 mM MESHOK pH 5.7, 400 mM Mannitol, 10 mM CaCl 2 , 20 mM KCl, 0.1% (w/v) Bovine serum albumin (BSA) for 60 min. Protoplasts were separated from debris by centrifugation (100 g, 3 min, 4°C), washed two times with ice-cold W5 buffer (154 mM NaCl, 125 mM CaCl 2 ,5 mM KCl and 2 mM MES-KOH pH 5.7) and resuspended in ice-cold W5 buffer at a density of 2.5 ×10 5 protoplasts/ml. The protoplast suspension was incubated for 15 min on ice before HS at 39°C. Arabidopsis Col plants were transformed as described previously (Clough & Bent, 1998) using Agrobacterium tumefaciens (Agrobacterium) strain GV3101. In Figs 5–8, plants from the T2 and T3 generations were used. Transgenic plants were confirmed by genotyping. For transient expression in N. benthamiana mesophyll cells, Agrobacterium strain GV3101 was transformed with the appropriate binary vectors by electroporation as described previously (Gutierrez-Beltran et al, 2017). Positive clones were grown in Luria- Bertani until reaching OD 600 = 0.4 and were pelleted after centrifugation at 3,000 gfor 10 min. Cells were resuspended in MM (10 mM MES, pH 5.7, 10 mM MgCl 2 supplemented with 0.2 mM acetosyringone) until OD 600 = 0.4, incubated at room temperature for 2 h and infiltrated in N. benthamiana leaves using a 1 ml hypodermic syringe. Leaves were analysed after 48 h using a Zeiss 780 confocal microscope with the 40×water-immersion objective. The excitation/emission wavelength was 480/508 nm for GFP and 561/610 nm for RFP. Bimolecular fluorescence complementation (BiFC) For BiFC assays, Agrobacterium strains GV3101 carrying cYFP-TSN2 cYFP-SN or cYFP-Tudor and the corresponding nYFP-TSN-interacting proteins were co-infiltrated into N. benthamiana leaves (OD 600 = 0.3). Fluorescence images were obtained 48 h after infiltration using a Leica TCS Sp2/DMRE confocal microscope, with an excitation wavelength of 514 nm. Transient expression of proteins in N. benthamiana leaves via agroinfiltration was performed as previously described (Gutierrez-Beltran et al, 2017). Immunocytochemistry and imaging For immunocytochemistry, roots of 5-day-old Arabidopsis seedlings were fixed for 60 min at room temperature with 4% (w/v) paraformaldehyde in 50 mM PIPES, pH 6.8, 5 mM EGTA, 2 mM MgCl 2 and 0.4% Triton X-100. The fixative was washed away with phosphate-buffered saline buffer supplemented with Tween-20 (PBST), and cells were treated for 8 min at room temperature with a solution of 2% (w/v), driselase (Sigma-Aldrich) in 0.4 M mannitol, 5 mM EGTA, 15 mM MES pH 5.0, 1 mM PMSF, 10 lg/ml leupeptin and 10 lg/ml pepstatin A. Thereafter, roots were washed twice, 10 min each, in PBST and then in 1% (w/v) BSA in PBST for 30 min before overnight incubation with a primary antibody (rabbit a-eIF4E diluted 1:500 or rabbit/mouse a-Myc diluted 1:500). The specimens were then washed three times for 90 min in PBST and incubated overnight with the corresponding secondary antibody [goat a-rabbit/mouse fluorescein isothiocyanate (FITC)/rhodamine conjugate] diluted 1:200. After washing in PBST, the specimens were mounted in Vectashield mounting medium (Vector Laboratories). 16 of 21 The EMBO Journal 40:e105043 |2021 ª2021 The Authors The EMBO Journal Emilio Gutierrez-Beltran et al Staining with FDA and SYTOX Orange (both from Molecular Probes, Invitrogen) was performed on 5-day-old Arabidopsis seedlings. FDA and SYTOX Orange were added to final concentrations of 250 nM and 2 mg/ml, respectively, in water. After 10 min of incubation in the dark, the samples were washed twice with half-strength liquid MS medium supplemented with 1% (w/v) sucrose, pH 5.7, and observed immediately. For the CHX treatment, the protoplast suspension or seedling roots were incubated with 200 ng/l1 drug for 30 min and then heat-stressed at 39°C. Förster resonance energy transfer (FRET) The assay was performed as described previously (Moschou et al, 2013). FRET was performed using Zeiss 780 laser scanning confocal microscope and a plan-apochromat 20×/0.8 M27 objective. FRET acceptor photobleaching mode of Zeiss 780 ZEN software was used, with the following parameters: acquisition of 10 pre-bleach images, one bleach scan and 80 post-bleach scans. Bleaching was performed using 488, 514 and 561-nm laser lines at 100% transmittance and 40 iterations. Pre- and post-bleach scans were at minimum possible laser power (0.8% transmittance) for the 458 nm or 514 nm (4.7%) and 5% for 561 nm; 512 ×512 8-bit pixel format; pinhole of 181 lm and zoom factor of 2.0. Fluorescence intensity was measured in the ROIs corresponding to the bleached region. One ROI was measured outside the bleached region to serve as the background. The background values were subtracted from the fluorescence recovery values, and the resulting values were normalized by the first post-bleach time point. Three pre-bleach and three post-bleach intensities were averaged and used for calculations using the formula FRET eff =(D post -D pre )/D post , where D is intensity in arbitrary units. Fluorescence recovery after photobleaching (FRAP) The assay was performed as described previously (Moschou et al, 2013). Five-day-old seedlings were grown on sterile plates containing half-strength MS with 1% (w/v) sucrose. For HS treatment, plates were incubated for 60 min on a thermoblock at 39°C. GFP fluorescence was detected using a water-corrected 403 objective. During analyses, the FRAP mode of Zeiss 780 ZEN software was set up for the acquisition of one pre-bleach image, one bleach scan and 40 post-bleach scans. In FRAP of SGs, the width of the bleached region was 2 mm. The following settings were used for f photobleaching: 10–20 iterations, 10–60 s per frame and 75% transmittance with the 458- to 561-nm laser lines of the argon laser. Prebleach and post-bleach scans were at the minimum possible laser power (1.4 to 20% transmittance) for 488 or 561 nm and at 0% for all other laser lines, 512 ×512 pixel format and zoom factor of 5.1. Analyses of fluorescence intensities during FRAP were performed in regions of interest corresponding to the size of the bleached region. One region of interest was measured outside the bleached region to serve as the background. The background values were subtracted from the fluorescence recovery values, and the resulting values were normalized by the first post-bleach time point. Initial signal recovery (%) = 100 ×(I final,post-bleach I initial,post-bleach )/(I prebleach  I initial,post-bleach ), where Iis the normalized signal intensity (relative to the background intensity). Values were corrected for the artificial loss of fluorescence using values from the neighbouring cells. At least ten cells from different roots were analysed for each FRAP experiment. Protein extraction and immunoblotting Two hundred milligrams of leaf material were mixed with 350 llof extraction buffer (100 mM Tris–HCl, pH 7.5, 150 mM NaCl, 0.1% Nonidet P-40 and 1×Protease inhibitor cocktail (Sigma, P599) and centrifuged for 15 min at 14,000 g.4×Laemmli sample buffer was added to 100 ll supernatant and boiled for 5 min. Equal amounts of supernatant were loaded on 10% poly-acrylamide gels and blotted on a polyvinylidene difluoride (PVDF) membrane. a-Myc and arabbit horseradish peroxidase conjugates (Amersham, GE Healthcare) were used at dilutions 1:1,000 and 1:5,000, respectively. The reaction was developed for 1 min using a Luminata Crescendo Millipore immunoblotting detection system (Millipore, WBLUR0500). For detection of the phosphorylated forms of SnRK1aproteins, 10-day-old seedlings were collected and ground in liquid nitrogen and the proteins were extracted using the following extraction buffer: 25 mM Tris–HCl pH 7.8, 75 mM NaCl, 15 mM EGTA, 10 mM MgCl 2 , 10 mM B-glycerophosphate, 15 mM 4-Nitrophenylphosphate bis, 1 mM DTT, 1 mM NaF, 0.5 mM Na 3 VO 4 , 0.5 mM PMSF, 1% Protease inhibitor cocktail (Sigma, P599) and 0.1% Tween-20. The protein extracts were centrifuged at 14,000 gand 4°C for 10 min and supernatants transferred to a new tube. The protein concentration was measured using Bradford Dye Reagent (Bio-Rad); equal amounts (15 lg) of total protein for each sample were separated by SDS–PAGE (10% acrylamide gel) and transferred to a PVDF membrane (Bio-Rad). The membrane was blocked in TBST buffer containing 5% (w/v) BSA and incubated with primary antibody and secondary antibody. Antibodies used for immunoblotting were as follows: a–Phospho-AMPKa(Thr175) (a-pT175) (dilution 1:1,000, Cell Signaling Technology), a-Kin10 (dilution 1:1,000, Agrisera), a- Kin11 (dilution 1:1,000, Agrisera), a-TSN [dilution 1:1,000, (Sundstro ¨met al, 2009)] and a-Actin (dilution 1:10,000, Agrisera). Co-immunoprecipitation (Co-Ip) Total proteins were extracted from 10-day-old seedlings with no-salt lysis buffer [50 mM Tris, pH 8.0, 0.1% Nonidet P-40 and 1% Protease inhibitor cocktail (Sigma)] at a fresh weight:buffer volume ratio of 1 g:2 ml. After centrifugation at 6,000 gand 4°C for 5 min, 20 llofa-GFP microbeads (Miltenyi Biotec) were added to the resultant supernatant and incubated for 1 h at 4°C on a rotating wheel. Subsequent washing and elution steps were performed according to the manufacturer (lMACS GFP Isolation Kit; Miltenyi Biotec). Immunoblot analysis was done essentially as described above, and immunoprecipitates from transgenic lines expressing free GFP were used as controls. GFP-TSN-interacting proteins and native TSN were detected by mouse a-GFP (monoclonal antibody JL-8; Clontech) and mouse a-TSN (Sundstro ¨met al, 2009) at final dilutions of 1:1,000 and 1:5,000, respectively. Image analysis The image analysis was done using ImageJ v1.41 (NIH) software (http://rsb.info.nih.gov/ij/index.html). For co-localization analyses, we calculated the linear Pearson (rp) and nonlinear Spearman’s ª2021 The Authors The EMBO Journal 40:e105043 |2021 17 of 21 Emilio Gutierrez-Beltran et al The EMBO Journal rank (rs) correlation coefficient (PSC) for the pixels representing the fluorescence signals in both channels (French et al, 2008). Levels of co-localization can range from +1to1 for positive and negative correlations, respectively. Quantitative RT–PCR Total RNA was isolated with RNA plant kit (Bioline) from 10-day- old Arabidopsis seedlings grown on liquid MS medium with or without HS (60 min at 39°C). First-strand cDNA was generated using the iScript cDNA Synthesis kit (Bio-Rad) in a 20-ll reaction mixture containing 1 µg of total RNA. The PCR mixtures were performed in a final volume of 18 ll using the SsoAdvanced Universal SYBR Green Supermix (Bio-Rad). The data were normalized to UBQ10 expression, a constitutively expressed gene that is used as an internal control in Arabidopsis (Ramon et al, 2019; Belda-Palazon et al, 2020; Chantarachot et al, 2020). Relative expression levels were determined as described previously (de la Torre et al, 2013). Bioinformatics In silico analysis of subcellular protein localization was performed using SUBA4 (Hooper et al, 2017). Prion-like domains were identified using PLAAC (Lancaster et al, 2014), with the minimum length for prion domains Lcore = 60, organism background Arabidopsis, and the parameter a=1. The RNA-binding proteins were predicted by the RNApred tool (Kumar et al, 2011). The prediction approach was based on amino acid composition, and the threshold for the support vector machine (SVM) was 0.5. To retrieve protein–protein interaction data, we used STRING database (V10) (Szklarczyk et al, 2015). Only physical protein–protein interactions were considered. Per-residue disorder content was evaluated by PONDR predictors, including PONDR-FIT (Xue et al, 2010) and PONDR-VSL2 (Peng et al, 2005). The intrinsic disorder propensities of TSN were evaluated according to the previously described method (Santamaria et al, 2017; Uversky, 2017). Disorder evaluations together with disorder-related functional information were retrieved from the D 2 P 2 database (http://d2p2.pro/) (Oates et al, 2013). Intrinsically disordered regions were predicted using Iupred2A (Erdos & Dosztanyi, 2020). LLPS predisposition was evaluated using the PSPredictor tool (preprint: Sun et al, 2019). The image analysis was done using ImageJ version 1.52 software (http://rsb.info.nih.gov/ij/index. html). SGs were scored as positive when they had a minimum size of 0.5 lm. SG counting was performed manually with the Cell Counter plugin of ImageJ (http://rsbweb.nih.gov/ij/ plugins/cellcounter.html). Data availability The mass spectrometry data from this publication have been deposited to the JPOST repository. For TSN and RBP47 interactomes, the dataset identifiers are JPST000766 (https://repository.jpostdb.org/ entry/JPST000766) and JPST001103 (https://repository.jpostdb. org/entry/JPST001103), respectively. Expanded View for this article is available online. Acknowledgements We thank Filip Rolland (Katholieke Universiteit Leuven) for providing snrk1a1 / snrk1a2 /+ mutant, Tsuyoshi Nakagawa (Shimane University) for providing Gateway binary vectors, Elena Baena-Gonzalez (Instituto Gulbenkian de Ci^ encia, Portugal) for providing SnRK1a1cDNA and critical discussion and Glenn Hicks (University of California, Riverside) for critical reading of the manuscript. This work was supported by the European Commission (MSCA-IF- ReSGulating- 702473), the Ministerio de Econom ıa y Competitividad (Juan de la Cierva-Incorporacion, IJCI-2016-30763) and University of Seville (VIPPIT- 2020-IV.4and -I.5) to E.G.-B, by the Ministerio de Econom ıa y Competitividad (PGC2018-099048-B-100) to J.L.C., by grants from Knut and Alice Wallenberg Foundation (2018.0026), the Swedish Research Council VR (2019-04250_VR), the Swedish Foundation for Strategic Research (RBP14-0037), and by Crops for the Future Research Programme to P.V.B. 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