Supplemental Data. Naseem et al. Plant Cell. (2012) 10 ...€¦ · 22/05/2012 · Supplemental Data. Naseem et al. Plant Cell. (2012) 10.1105/tpc.112.098335 7 (A) Fully active input

Supplemental Data. Naseem et al. Plant Cell. (2012) 10.1105/tpc.112.098335

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Supplemental Figure 1. Key steps in the implementation of SQUAD methodology for investigating plant hormones and disease signalling network. (A) Based on Boolean logic an integrated hormone disease network is established in Cell Designer (a structure diagram editor). (B) Loading of CellDesigner based network into SQUAD software, indicating the number of nodes and edges. The loaded network is subjected to identify the number of steady states. (C) Fixation of basic parameters and simulation of the impact of individual nodes on the network. (D) Calculation of the activities assigned to nodes after selecting input node(s). (E) Activation states can be examined as trajectories or heat maps over arbitrary units of time. (F) Modelling output is experimentally validated in targeted experiments. Each of these steps involves details evaluation of input, calculations by SQUAD using heuristics and specific algorithms as well as different analysis steps on the output (including further bioinformatical analysis).


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Supplemental Figure 2. Infection modelling of virulent pathogen Pst and fitness of the pathogen. (A) Taking Pst as fully active input node (with activation value of 1.0 ( ) dark red, upper


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left corner), the SQUAD simulation, while using our assembled network (Figure 1B) as a simulatory platform, assigned activation states to nodes of the anticipated network. Activation states for nodes are shown in terms of trajectories with x-axis: Arbitrary units of time (SQUAD simulation is shown in 12 steps), y-axis: activation states ranging from no (0) to full (1) activation. Each trajectory represents an individual node (colour codes with their corresponding maximum activations are shown in lower inset in panel (A), full names in Supplementary Figure 1). Fully virulent pathogen (Pst ) besides other nodes activates SA, Auxin, JA, ET, ABA and PR-1 while cytokinin ( ) got no activation. Supplementary Table 2 gives a comprehensive list of nodes and their respective activation over time upon the infection of Pst. (B) The fitness of the pathogen affects switching of systems states in host-pathogen interaction. Assigning full activation to Pst simulates a virulent mode of pathogen infection (left) Partial activation simulates a less virulent mode of pathogen infection (right). Red arrows show the level of activation: either full or partial (see above for colour codes designated for activated nodes). Compromised virulence could be due to genetic factors such as mutants of TSS or environmental factors such as humidity, temperature or pH. Models for both types of infection clearly reflect qualitative and quantitative changes in network dynamics.


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Supplemental Figure 3. Modelling the impact of plant hormone cytokinin on the pathogenicity of Pst . (A) Modelling of the impact of cytokinin on the infection by Pst in Arabidopsis.


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Simultaneous activation (upper left corner) of the nodes of Pst (Assigned full activation of 1.0 during SQUAD simulation) and cytokinins (Assigned full activation of 1.0 during SQUAD simulation) results in the activation of nodes of the assembled network (Figure 1B). The y-axis shows the state of activation of various nodes, while x-axis denotes the arbitrary units of time. Symbols for the trajectories of various nodes are shown in Figure 2A-lower inset. Additional nodes activated due to the effect of cytokinins are given with their maximum activation states on the right (inset). Application of cytokinins changes states for marker node of PR-1 from 0.24 (Pst, without cytokinin; Figure 2A) to 0.47 (Pst with cytokinin; Figure 3B). For global changes as a consequence of the infection by Pst together with the application of cytokinin see Supplementary Table 3. (B) Simulation of dose dependent cytokinin activation and resulting network response. Full activation simulates the effect of cytokinin hyper-accumulation while decrease in in-put activation reflects a scenario of overexpression of cytokinin oxidase (cytokinins degrading enzyme). The inset in each model depicts the level of PR-1 marker node activation and hence the predicted state of immunity at a given activation state of cytokinin. Red arrows point to states of activation, see above for trajectories denoted in colour codes.


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Supplemental Figure 4. Effectors of Pst DC3000 change hormonal profile in Arabidopsis and inhibit PTI.


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(A) Fully active input signal of Pst DC3000 is shown in red on the left y-axis while activation of hormonal nodes is given as trajectories on the right y-axis. The x-axis denotes arbitrary units of time (upper panel). The impact of TTSS mutant of Pst on the hormonal profile of the plant, bacterial input signal is given as red line with black dots (lower panel). Hormonal nodes with their respective colour codes are shown at the bottom. (B) Effectors inhibit the level of immunity the plant achieves upon interacting with bacterial elicitors. PTI is shown as red line with dots, and modelled with full activation of elicitor nodes flag. And EF-Tu while effector nodes were deleted from the network. ETI is shown as black line with open circles and modelled such that all bacterial effectors were kept fully active while bacterial elicitors were removed from the network. PTI under effectors is shown for the condition where both elicitors and effectors were switched to full activation.


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Supplemental Figure 5. Robustness and redundancy in ETI and its operational overlap with PTI. During the course of infection Pst via TTSS (Type three secretory system) injects effector molecules to the plant cell. Recognition of effectors by cell surveillance receptors triggers additional layers of immunity i.e. ETI (Effectors Triggered Immunity) besides PTI. To demonstrate properties of robustness and redundancy (salient feature of ETI), we selected activities (y-axis) of PR-1 (marker node for immunity; green line trajectory with filled dots), NPR1 (relevant node in the immunity against Pst, upstream of PR1; blue line trajectory with cross line filling) and miR393 (a target of effectors in regulating PTI; red line trajectory filled with horizontal lines) over arbitrary units of time (x-axis). Activities of nodes were determined with SQUAD simulations for: (A) Wild type condition of Pst being input activating node (B) HopAI1 Pst mutant by activating Pst as input node while deleting HopAI1 effector from the network (C) Pst DC3000 as input activating node while deleting Avr PtoB from the network (D) Pst as input activating node while deleting both Avr PtoB and HopA1. (E) Pst being input activating node while deleting all effectors nodes (to mimic a TTSS deficient mutant) (F) Venn diagram showing overlap between cellular components upon which both PTI and ETI operate. Operating components of both types of immunity were determined by activating PAMPs and effectors as input nodes during SQUAD simulation, respectively.


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Supplemental Figure 6. Cytokinin has no cytotoxic or antimicrobial effects on the multiplication of Pst DC3000. Pst DC3000 were grown on KB medium in the presence of 10µM (red cross sign) and 5µM (green triangles) cytokinins. Pst cultures with no cytokinin (blue rectangle) and with tetracycline (black dot) were adopted as negative and positive control, respectively. The growth of Pst is shown as OD at 600nm in log scale at the y-axis while the x-axis represents hours of incubation at 37ºC on shaking condition.


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Supplemental Figure 7. System stability analysis and resulting robustness of the network topology. To demonstrate stability in our system of nodes connected through edges (Figure 1B), we monitored: (A) the activity of PR-1 (marker node and index of immunity in our analysis), (B) NPR1 (important node shaping immunity against the infection of Pst in Arabidopsis and close to PR-1 in network topology ) and (C) miR393 (distal node with no direct connection to PR-1, modulating auxin signalling to mediate resistance against Pst) during Pst infection as input activating node for the following conditions: 1) intact (without any deletion of node from the network) network; 2) complete removal of SA (hub-node with maximum interactions) from the network; 3) deletion of DELLA-protein (moderately connected node) from the network; 4) deletion of Oxop (less connected node of minimal interaction i.e. linear node ). This is only one example, we tested various modifications systematically. In general hub-nodes are having global impacts while less connected nodes are contributing less to change in system states. Level of node activation is determined by input signal strength and necessarily may not be the same for all nodes of network.


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Supplemental Figure 8. Network topology for hormones disease networks in Arabidopsis. All nodes shown are according to the available information from literature and databases. Squad assigns activation (→) and inhibition (→) to nodes of the network. Detailed structure analysis of the assembled network is possible. Hormone biosynthesis pathways are often linear, hub nodes including SA, JA, FLS2, Della Protein and PR1 as well as well different crosstalk possibilities became apparent. Following are abbreviations and full names of all nodes presented in the network topology: JA-res.genes (Jasmonic acid responsive genes), JAZ (jasmonate ZIM-domain), MYC2 (a basic helix-loop helix transcription factor), JAZ-deg. Comp. (jasmonate ZIM-domain degrading complex), FLS2(flagellin sensitive 2), Auxin, NADPH-Oxi (NADPH-Oxidase), NPR1(non-expressor of PR1), CKX (cytokinin Oxidase), TIR1 (transport inhibitor response1), ROS (reactive oxygen species), ABA (abscisic acid), JA (jasmonic acid), MKS1 (MAP Kinase substrate 1), Callose, Della-Prot. (Della-Protein), MAPK (Mitogen activated protein kinase), GRX480 (Glutaredoxin), PAD4 (Phytoalexin deficient 4), Resistance, WRKY22, WRKY70 and WRKY62 (transcription factors with W-box binding domain), miR393 (microRNA 393), SA (salicylic acid), PR1(pathogenesis related protein), Stom.Clos.(stomata closure), TGA-TF (TGACG motif binding [TGA] transcription factors) , Aux/IAA (Auxin/Indole-3 acetic acid ), ET (ethylene), ATK (Aspartate kinase), ASD (Aspartate semialdehyde dehydrogenase), HSD (homoserine dehydrogenase), HSK (homoserine kinase), CTL (cystathionine beta-lyase), MTS (Methionine synthase), HMT (Homocysteine Smethyltransferase), ACS (1-Aminocyclopropane 1- carboxylate synthase), MAT (Methionine adenosyltransferase), TMO (CYP79B3 tryptophan monooyxygenase),


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TPM (tryptamine monooxygenase), RPT (tRNA isopentenyl transferase), IAD (Indoleacetaldoxime dehydrogenase), IAN (Indole-3-acetonitrile nitrilase), IAO (Indole-3-acetaldehyde oxidase), ICO (IBA-CoA oxidase ), Aux (Auxin ), HMS (Hydroxy 3-methylglutaryl Co-A synthase), HMR (Hydroxy 3-methylglutaryl Co-A synthase), MNK (Melvonate kinase), MDD (Melvonate Diphosphate decarboxylase), IDI (Iso-pentenyl Diphosphate isomerase), DPS (Diphosphate synthase), PES (Phytoene synthase), PED (Phytoene desaturase), CED (Carotene desaturase), LBC (Lycopene Beta cyclase), BRH (Beta ring hydroxylase), ADO (Antheraxinthin deepoxidase), ZEO (Zeaxanthin epoxidase), AED (Antheraxanthin epoxidase) Xanthoxin dehydrogenase AAO (Abscisic acid aldehyde oxidase), ABA (Abscicic acid), AGT (Abscisic acid glycosyltransferase) CTH (Cytokinin trans-hydroxylase), IPT (Isopentenyl transferase) CK (Cytokinin) , ICS (Isocharismate synthase), PAL (Phenylalanine amonia layase), DPS (3-Deoxy 7-phosphoheptulonate synthase) PCT (3-Phosphoshikimate 1- carboxyvinyl tranferase), PSP (Phospholipase), LOX (Lipooxygenase), AOS (Allene oxide synthase), AOC (Allene oxide cyclase), OPR (Oxophytodienpate reductase), JA (Jasmonic Acid), EDS (ent-copalyl diphosphate synthase), EKS (ent-kaurene synthase), EKO (entkaurene oxidase), EUO (ent-kaurenoate oxidase), G20O (gibberellin 20-oxidase) and G3O (gibberellin 3-oxidase).


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Supplemental Table 1. Node interactions and literature support. Node to node interaction References

ATK > ASD Yoshioka et al., 2001 and Plant Cyc Database ASD > HSD Curien et al., 2005 and Plant Cyc Database HSD > HSK Lee M and Leustek 1999 and Plant Cyc Database HSK > CTL Kim et al., 2002 and Plant Cyc Database CTL > MTS/HMT Ranocha et al., 2000 and Plant Cyc Database MTS > MAT Abel et al., 1995 and Plant Cyc Database MAT > ACS Lincoln 1991 and Plant Cyc Database ACS > ET Lincoln 1991 and Plant Cyc Database PPS > PES Lindgren et al., 2003 and AraCyc Database PES > PED Bartley et al., 1999 and AraCyc Database PED > CED Pogson et al., 1996 and AraCyc Database CED > LBC Cunningham et al., 199 and Plant Cyc Database LBC > BRH Kim and Dellapenna 2006 and AraCyc Database BRH > ZEO Hieber et al., 2000 and AraCyc Database ZEO > AED Frechilla et al., 1999 and AraCyc Database AED > XDH Nambara et al., 2005 and AraCyc Database XDH > AAO Gonzalez et al., 2002 and Plant Cyc Database AAO > ABA Seo et al., 2000 and AraCyc Database AGT | ABA Jackson et al., 2002 and AraCyc Database IPT > CTH Kakimoto et al., 2001 AraCyc Database CTH > t-Zeatin (CK) Takei et al., 2004 and AraCyc Database CKX | CK Werner et al. 2003 LOXs > AOS Feussner and Wasternack 2002 and AraCyc Database AOS > AOC Feussner and Laudert et al., 1996 and AraCyc Database AOC > OPRs Hofmann et al., 2006 and Plant Cyc Database OPRs > OPCs Hooks et al., 1999 and AraCyc Database OPCs > Jasmonate Reymond and Farmer 1998 and AraCyc Database EDS > EKS Fleet et al., 2003 and AraCyc Database EKS > EKOs Helliwell et al., 2001 and AraCyc Database EKOs > EUOs Davidson et al., 2003 and AraCyc Database EUOs > G20/3 Os Lange et al., 1994 and AraCyc Database GOs > GA Lange et al., 1994 and AraCyc Database TMO,TPM,IAD > IAN,IAO Ouyang et al., 2000 and AraCyc Database IAN,IAO > Auxin Normanly et al., 1993 and AraCyc Database IAA-Synthase | Auxin Müller and Weiler 2000 and AraCyc Database SKK > PCT Singh et al., 2007 and Schmid et al., 1995 PCT > ICSs Wildermuth et al., 2001 and AraCyc Database


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ICS,PAL > SA Shah 2003 and Mauch and Slusarenko 1996 ET | ETR Kendrick and Chang 2008 and Kieber et al., 1993 ET > DELLA Achard et al., 2003 ET | ETR > EIN2 Kendrick and Chang 2008 and Alonso et al., 1999 EIN2 | SCFcomp > EIN3 Solano et al., 1998 and Kendrick and Chang 2008 ETR1 > AHPs Urao et al., 2000 and Müller and Sheen 2007 EIN2 > NPR1 Leon-Reyes et al., 2009 and Pieterse et al., 2009 EIN3 > ERF1 Solano et al., 1998 and Kendrick and Chang 2008 ERF1 > PDF 1.2 Pré, M. et al 2008 and Pieterse et al., 2009 ABA | SA Flors et al., 2007 ABA > OST1 Kinase. Mustilli et al., 2002 OST1 K > Stom. Clos Melotto et al. 2006 Stom. Clos > Resistance Melotto et al. 2006 and Pieterse et al., 2009 ABA > MYC2 Anderson et al., 2004 and Abe et al., 2003 GA > GID1 Zentella et al., 2007 GID1 > SCF | DELLA Griffiths et al., 2006 GA > SA Navarro et al., 2008 and Alonso-Ramı´rez et al, 2009 DELLA | JAZ Hou et al., 2010 and Navarro et al., 2008 DELLA > ABA Zentella et al., 2007 DELLA| GA Zentella et al., 2007 DELLA| SA Navarro et al., 2008 and Alonso-Ramı´rez et al, 2009 DELLA | ROS Achard et al., 2008 and Grant and Jones 2009 Auxin | Cytokinin Nordstrom et al., 2004 and Liu et al., 2010 Auxin > TIR1 Dharmasiri et al., 2005 AUX/IAA | ARFs Benjamins and Scheres 2008 and Ulmasov et al., 1997 Auxin > SCFTIR1|AUX/IAA Tiwari et al., 2001 and Santner and Estelle 2009 Auxin > JA Liu et al., 2006 Auxin > AFB1| SA Robert-Seilaniantz et al, 2011 Auxin > Ethylene Arteca and Arteca 2008 JA > SCF-COI Katsir et al., 2008 JA > SCF-COI | JAZ Pieterse et al., 2009 and Katsir et al., 2008 | JAZ > MYC2 Lorenzo and Solano 2005 and Pré et al. 2008 | JAZ > ERF1 Lorenzo and Solano 2005 MYC2 > LOX2 Mao et al., 2007 and Bari et al., 2009 WRKY 62 | LOX2 Pieterse et al., 2009 and Mao et al., 2007 GRX480 | PDF 1.2 Ndamukong et al., 2007 and Bari et al., 2009 WRKY70| PDF1.2 Li et al., 2006 MYC2 | SA Laurie-Berry et al., 2006 MYC2 | PR-1 Kazan and Manners 2008 and Laurie-Berry et al., 2006


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SA > NPR1 Mou et al., 2003 and Dong 2004 NPR1 > TGA-TF Loake and Grant 2007 and Mou et al., 2003 NPR1 > GRX480 > TGA > PR1

Ndamukong et al., 2007

NPR1 > WRKY 62 Mao et al., 2007 GRX480 > TGA| PDF1.2 Ndamukong et al., 2007 and Bari et al., 2009 NPR1 > WRKY 70 Li et al., 2004 SA > AUX/IAA Wang et al., 2007 WRKY 70 > PR-1 Li et al., 2004, 2006 WRKY 11 | WRKY70 Journot-Catalino et al., 2006 WRKY 17| WRKY 70 Journot-Catalino et al., 2006 WRKY11 and 17 > JA Journot-Catalino et al., 2006 WRKY 25 | PR1 Zheng et al., 2007 SA | AOS Pan et al., 1998 B ARRs > TGA > PR-1 Choi et al., 2010 B ARRs > CKX Müller and Sheen 2007 CKX | PR1 Choi et al., 2010 A ARRS > PhyB Müller and Sheen 2007 PhyB > SA Genoud et al., 2001 Pst DC3000 > Flagellin Zipfel et al., 2004 Flagellin > FLS2 Zipfel et al., 2004 Flag > FLS2 > BAK1 Chinchilla et al., 2007 > BAK1 > MAPK1,2,3,4 Zipfel et al., 2006 Pst DC3000 > EF > EFR Zipfel et al., 2006 and Nekrasov et al., 2009 > EFR > MAPK4 and 6 Nekrasov et al., 2009 > BAK1 > ....MAPK1 > PR1 Gust et al., 2007 and Andreasson et al., 2005 FLS2 > BAK > NADPH-Oxi Torres et al., 1998 and Mersmann et al., 2010 BAK > NADPH-Oxi > ROS Panstruga et al., 2009 and Torres et al., 1998 ROS > SA Torres et al., 1998 and Draper 1997 SA > ROS Klessig et al., 2000 and Torres et al., 1998 FLS2 > BAK > DELLA Navarro et al., 2008 and Grant and Jones 2009 DELLA | ROS Grant and Jones 2009 and Achard et al., 2008 FLS2 > BAK > mirRNA393 Navarro et al., 2006 miRNA 393|ARF1and TIR1 Pieterse et al., 2009 and Navarro et al., 2006 AvrPtoB | mir393 Navarro et al., 2008 MAPK4 > MKS1 Andreasson et al., 2005 MAPK4 | PAD4 and EDS1 Andreasson et al., 2005 and Brodersen et al., 2006 MSK1| WRKY 25 and 33 Andreasson et al., 2005 WRKY25 and 33 | PR1 Zhang et al., 2007 and loke and Grant 2007 MAPK4 > WRKY25 Loke and Grant 2007 and Andreasson et al., 2005


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> = Activation, potentiation, stabilization, de-repression or other positive attribute and | = Inhibition, degredation, repression or a negative attribute

Supplemental Table 1 shows the nature of inter-nodal interactions in hormone disease networks. It is supported by the primary literature (see the following reference list). Reference list: Lea, P. (1997). "Primary nitrogen metabolism." Plant Biochemistry, Eds Dey & Harborne, Academic Press, Harcourt Brace & Co, Publishers, London. Yoshioka Y, Kurei S, Machida Y (2001). "Identification of a monofunctional aspartate kinase gene of Arabidopsis thaliana with spatially and temporally regulated expression." Genes Genet Syst 76(3);189-98. Curien G, Ravanel S, Robert M, Dumas R (2005). "Identification of six novel allosteric effectors of Arabidopsis thaliana aspartate kinase-homoserine dehydrogenase isoforms. Physiological context sets the specificity." J Biol Chem 280(50);41178-83. Lee M, Leustek T (1999). "Identification of the gene encoding homoserine kinase from Arabidopsis thaliana and characterization of the recombinant enzyme derived from the gene." Arch Biochem Biophys 1999;372(1);135-42. Kim J, Lee M, Chalam R, Martin MN, Leustek T, Boerjan W (2002). "Constitutive overexpression of cystathionine gamma-synthase in Arabidopsis leads to accumulation of soluble methionine and S-methylmethionine." Plant Physiol 2002;128(1);95-107.

PAD4 and EDS1 > SA Feys et al., 2001 PAD4 and EDS1 | JA Brodersen et al., 2006 and Loke and Grant 2007 MAPK4 | SA Petersen et al., 2000 FLS2-BAK1 > Callose Luna et al., 2011 FLS2, EFR > MAPK3,2,1 Panstruga et al., 2009 PstDC3000 > Avr PtoB de toress-Zabala et al., 2007 AvrPtoB | mir393 Navarro et al., 2008 Pst DC > AvrPtoB > ABA Zabala et al., 2007 AvrPtoB | FLS2-BAK1 Shan et al., 2008 T-LRRs > PAD4 and EDS1 Volt et al., 2009 and Aarts et al., 1998 CC-NB-LRRs > NDR1 > SA Century et al., 1997 and Volt et al., 2009 AvrPtoB > CC-NB-LRR Collier and Moffett 2009 AvrRpm1 > CC-NB-LRR Panstruga et al., 2009 and Collier and Moffett 2009 Pst > Avr Rpt2 > Auxin Chen et al., 2007 Avr Rpt2 > CC-NB-LRR Panstruga et al., 2009 and Collier and Moffett 2009 Pst > HopAIA > TIRNB-LRR Zhang et al., 2007 Pst > Coronatine > COI | JAZ Panstruga et al., 2009 and Block et al., 2005 HopAI1 | MAPK3 and MAPK6 Zhang et al., 2007


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Gonzalez-Guzman M, Apostolova N, Belles JM, Barrero JM, Piqueras P, Ponce MR, Micol JL, Serrano R, Rodriguez PL (2002). "The short-chain alcohol dehydrogenase ABA2 catalyzes the conversion of xanthoxin to abscisic aldehyde." Plant Cell 14(8);1833-46. PMID: 12172025 Seo M, Peeters AJ, Koiwai H, Oritani T, Marion-Poll A, Zeevaart JA, Koornneef M, Kamiya Y, Koshiba T (2000). "The Arabidopsis aldehyde oxidase 3 (AAO3) gene product catalyzes the final step in abscisic acid biosynthesis in leaves." Proc Natl Acad Sci U S A 97(23);12908-13. PMID: 11050171 Jackson RG, Kowalczyk M, Li Y, Higgins G, Ross J, Sandberg G, Bowles DJ (2002). "Over-expression of an Arabidopsis gene encoding a glucosyltransferase of indole-3-acetic acid: phenotypic characterisation of transgenic lines." Plant J 32(4);573-83. PMID: 12445128 Kakimoto T (2001). "Identification of plant cytokinin biosynthetic enzymes as dimethylallyl diphosphate:ATP/ADP isopentenyltransferases." Plant Cell Physiol 42(7);677-85. PMID: 11479373 Takei K, Yamaya T, Sakakibara H (2004). "Arabidopsis CYP735A1 and CYP735A2 encode cytokinin hydroxylases that catalyze the biosynthesis of trans-Zeatin." J Biol Chem 279(40);41866-72. PMID: 15280363 Werner, T., Motyka, V., Laucou, V., Smets, R., Van Onckelen, H., Schmülling, T. (2003) Cytokinin-deficient transgenic Arabidopsis plants show multiple developmental alterations indicating opposite functions of cytokinins in regulating shoot and root meristem activity Plant Cell. 15: 2532-2550 Laudert D, Pfannschmidt U, Lottspeich F, Hollander-Czytko H, Weiler EW (1996). "Cloning, molecular and functional characterization of Arabidopsis thaliana allene oxide synthase (CYP 74), the first enzyme of the octadecanoid pathway to jasmonates." Plant Mol Biol 31(2);323-35. PMID: 8756596 Feussner I, Wasternack C (2002). "The lipoxygenase pathway." Annu Rev Plant Biol 53;275-97. PMID: 12221977 Hofmann E, Zerbe P, Schaller F (2006). "The Crystal Structure of Arabidopsis thaliana Allene Oxide Cyclase: Insights into the Oxylipin Cyclization Reaction." Plant Cell 18(11);3201-17. PMID: 17085685 Hooks MA, Kellas F, Graham IA (1999). "Long-chain acyl-CoA oxidases of Arabidopsis." Plant J 20(1);1-13. PMID: 10571860 Reymond P, Farmer EE (1998). "Jasmonate and salicylate as global signals for defense gene expression." Curr Opin Plant Biol 1(5);404-11. PMID: 10066616 Fleet CM, Yamaguchi S, Hanada A, Kawaide H, David CJ, Kamiya Y, Sun TP (2003). "Overexpression of AtCPS and AtKS in Arabidopsis confers increased ent-kaurene production but no increase in bioactive gibberellins." Plant Physiol 132(2);830-9. PMID: 12805613


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Input -stimuli SQUAD outputs

Experimental validation

PTI (Flag.) Figure 2

ABA Callose Della prot. MAPK1 MAPK2 MAPK3 PAD4 NADPH-Ox ROS Resistance miR393 WRKY41 SA JA PR1 Stom. Clos.

PAMPs perception is linked to ABA mediated stomata closure. Melotto et al Cell 2006 (126:969-980) Callose deposition is delayed PAMPS mediated defence response. Panstruga et al Cell 2009 (136:978) PAMPS treatment stabilizes DELLA protein. Robatzek and Saijo Genome Biology 2008 (9:304) PAMPs perception and activation of MAPK. Boller and He Science 2009 (324:742-744) PAMPs perception and activation of MAPK. Boller and He Science 2009 (324:742-744) PAMPS perception and downstream signaling Panstruga et al Cell 2009 (136:978) SA dependent PAD4 activation. Panstruga et al Cell 2009 (136:978) PAMPS provoke ROS production due to NADPH-Oxidase activity. Panstruga et al Cell 2009 (136:978) PAMPS cause ROS production as an early recognition event. Panstruga et al Cell 2009 (136:978) PAMPS trigger immunity. Jones and Dangl Nature 2006 (444: 323-329) Flag.22 activates miR393 to suppress auxins signaling. Navarro et al Science 2006 (312:436-439) Flagellin induces WRKY41 Higashi et al Mol Genet Gen. 2008(279:303-312) PAMPS trigger SA accumulation, Tsuda et al. Plant J. 2008 (53:763-775) Flag. 22 activates JA pathway. Navarro et al Plant Physiol. 2004 (135:1113-1128) Flag 22 up-regulates PR1. Zipfel et al Nature 2004 (428:764-767) Stomata closure upon PAMPS perception is part of immunity. Melotto et al Cell 2006 (126:969-980)

SA +/- Pst Figure 3

Aux/IAA GRX480 NPR1 PR1 Resistance TGA WRKY62 WRKY70

SA stabilizes auxin repressor Aux/IAA. Wang et al Current Biol. 2007 (17:1784-1790) GRX interacts with TGA TF. Ndamukong et al Plant J. 2007 (150: 128-139) NPR1 play an important role in SA/JA interaction. Dong. Curr Opin Plant Biol 2004 (7:547-552) Role of SA in SAR against biotrophs Wildermuth Nature 2001(414:562-565) Resistance pathway against biotrophs Gelazebrook. Annu. Rev. Phytopathol. 2005 (43:305-327) TGA is part of SA signalling. Bari and Jones Plant Mol Biol. 2009 (69:473-488) WRKY62 suppresses JA responses. Mao et al Plant Cell Physiol 2007 (48:833-842) Node of conversion to SA from JA mediated defence. Brader and Palva The Plant Cell 2004 (16:319-331)

GA +/- Pst Figure 3

AtGID1 Aux/IAA NPR1 PR1 SA SCF-com WRKY62 Resistance

GID1 causes degradation of Della. Griffiths et al Plant Cell 2006 (18:3399-3414) GA activates SA pathway. Navarro et al Current Biol 2008(18:650-655) NPR1 play an important role in SA/JA interaction. Dong. Curr Opin Plant Biol 2004 (7:547-552) WRKY62 suppresses JA responses. Mao et al Plant Cell Physiol 2007 (48:833-842) Resistance pathway against biotrophs Gelazebrook. Annu. Rev. Phytopathol. 2005 (43:305-327) SCFSLY1/GID2 degrades Della Protein. Grant and Jones Science 2009 (324:750-752) WRKY62 suppresses JA responses. Mao et al Plant Cell Physiol 2007 (48:833-842) GA potentiates SA pathway. Grant and Jones Science 2009 (324:750-752)

CK +/- Pst Figure 3 Figure 4 Figure 7

PR1 Resistance AHKs , AHPs ARRs

Figure 8BFig. 3 and 4 Muller and Sheen 2007 Muller and Sheen 2007

JA +/- Pst Figure 3

JA. res.gen. JAZ MYC SCF-com

JA responsive genes Dombrecht et al Plant Cell 2007.(19:2255-2245) Negatively regulate JA responses. Grant and Jones Science 2009(324:750-752) MYC differentially regulates JA responses. Dombrecht et al Plant Cell 2007 (19:2255-2245) Degrades JAZ repressor and allows JA signalling. Grant and Jones. Science 2009(324:750-752)

Auxin +/- Pst Figure 3 Figure 7

ARF-1 CKX Dell. prot EIN3 Ethylene JA JA.res.ge MYC SCF-com TIR1

Promote auxin by degradation of Aux/IAA. Hagen and Guilfoyle Plant Mol. Biol 2002(49:373-385) Auxins up regulate CKX genes. Yoshida et al Plant Mol Biol 2009 (70:457-469) Auxins stabilize Della protein vis ethylene pathway Grant and Jones Science 2009(324:750-752) Auxins stabilize Della protein vis ethylene pathway Grant and Jones Science 2009(324:750-752) Auxin potentiate ethylene signalling Pieterse et al Nat. chemical biology 2009 (5:308-316) Auxins promote JA signalling and suppress SA. Wang et al Current Biol. 2007 (1784-1790) Auxin reinforces JA signaling while suppressing SA. Grant and Jones Science 2009(324:750-752) MYC2 differentially regulates JA responses. Dombrecht et al Plant Cell 2007(19:2225-2245) Degrade Aux/IAA , promote Auxin signaling Parry and Estelle Curr. Opin.Cell Biol. 2006 (18:152-156) Promotion of TIR1 negatively regulates SA responses. Wang et al Current Biol. 2007(1784-1790)

ABA+/- Pst Figure 3

JA SCF-COI Sto.clos. MYC2

ABA promotes JA and suppresses SA responses. Pieterse et al Nat. chemical biology 2009(5:308-316) Degrade JAZ, potentiate JA mediated SA antagonism. Bari and Jones .Plant Mol. Biol. 2009 (69:473-488) ABA mediated stomata closure. Melotto et al Cell 2006 (126:969-980) ABA promotes MYC2 to suppress SA responses. Pieterse et al Nat. chemical biology 2009(5:308-316)

Supplemental Table 2. Input signals and SQUAD analysed outputs with experimental validation.


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To assign continuous activity values to each component, static network is automatically converted into a continuous dynamical system based on ordinary differential equations using the equation

(eq. 1)

which describes the change of activation of node over time (DiCara et al., 2007). This transformation is achieved without detailed information on the kinetics by simplifying generic and integrative assumptions: Original discrete step function is translated into a sigmoid response curve whose magnitude depends on three parameters: gain ( ), weights ( ) and the decay ( ). In the absence of kinetic data, for our purpose of SQUAD simulations we used 10 and 1 as default values for gain and decay of the exponential terms, respectively (Figure S2). The weighting factors

account for the three different conditions of modulatory input (a, b, c) to a network node :

(eq.2)

Here, is the set of activators of a node , represents the set of inhibitors affecting , respectively. Formula a is applied if integrates activatory and stimulatory inputs of further nodes as it is the case for hub nodes like SA, CK (see the network structure, Figure 1). Similarly, b is used if the signalling node only has activatory input (PSP, LOX), whereas c is applied if is affected only by inhibitors (SCF. Com, ETR, CTR1). This interpolation between the on and the off state for any node in the system result in the corresponding exponential decaying or bell-like shapes for the activation curves of the signalling nodes. The different steepness and trajectory shapes mirror the resulting complex function regarding the different crosslinking combining activation and inhibition (see DiCara et al., 2007 for further details). Supplemental Methods: Computation of the dynamic behaviour of network components.

Documents

Supplemental Data. Naseem et al. Plant Cell. (2012) 10 ...€¦ · 22/05/2012 · Supplemental Data. Naseem et al. Plant Cell. (2012) 10.1105/tpc.112.098335 7 (A) Fully active input