www.sciencemag.org/content/344/6185/711/suppl/DC1

Supplementary Material for

Border Control—A Membrane-Linked Interactome of Arabidopsis

Alexander M. Jones, Yuanhu Xuan, Meng Xu, Rui-Sheng Wang, Cheng-Hsun Ho, Sylvie Lalonde, Chang Hun You, Maria I. Sardi, Saman A. Parsa, Erika Smith-Valle, Tianying

Su, Keith A. Frazer, Guillaume Pilot, Réjane Pratelli, Guido Grossmann, Biswa R. Acharya, Heng-Cheng Hu, Cawas Engineer, Florent Villiers, Chuanli Ju, Kouji Takeda,

Zhao Su, Qunfeng Dong, Sarah M. Assmann, Jin Chen, June M. Kwak, Julian I. Schroeder, Reka Albert, Seung Y. Rhee,* Wolf B. Frommer*

Corresponding author. E-mail: wfrommer@stanford.edu (W.B.F.); srhee@carnegiescience.edu (S.Y.R.)

Published 16 May 2014, Science 344, 711 (2014)

DOI: 10.1126/science.1251358

This PDF file includes:

Materials and Methods

Supplementary Text

Figs. S1 to S30

Full Reference List Other Supplementary Material for this manuscript includes the following: (available at www.sciencemag.org/content/344/6185/711/suppl/DC1)

Tables S1 to S6 as separate Excel files

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Supplementary Material for Border control – a membrane-linked interactome of Arabidopsis

Alexander M. Jones1, Yuanhu Xuan1, Meng Xu1, Rui-Sheng Wang2

, Cheng-Hsun Ho1, Sylvie Lalonde1,

Chang Hun You1, Maria I. Sardi1†, Saman A. Parsa1, Erika Smith-Valle1∞, Tianying Su1§, Keith A. Frazer1, Guillaume Pilot1, 3, Réjane Pratelli1, 3, Guido Grossmann1‡, Biswa R. Acharya4, Heng-Cheng Hu5±, Cawas Engineer6, Florent Villiers5, Chuanli Ju5, Kouji Takeda5, Zhao Su4, Qunfeng Dong7, Sarah M. Assmann4, Jin Chen1, 8, June M. Kwak5+, Julian I. Schroeder6, Reka Albert2, Seung Y. Rhee1*, and Wolf B. Frommer1*

Affiliations: 1 Dept. Plant Biology, Carnegie Institution for Science, CA 94305, USA 2 Dept. Physics, Pennsylvania State University, University Park, PA 16802, USA 3 Dept. Plant Pathology, Physiology and Weed Science, Virginia Tech, Blacksburg, VA 24061, USA 4 Dept. Biology, Pennsylvania State University, University Park, PA 16802, USA 5 Dept. Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA 6 Division of Biological Sciences, Cell and Developmental Biology, UCSD, La Jolla, CA 92093, USA 7 Dept. Biological Sciences, University of North Texas, Denton, TX 76203, USA 8 MSU-DOE Plant Research Lab and Dept. Computer Science and Engineering, Michigan State

University, MI 48824, USA † Present Address: University of Wisconsin-Madison, Madison, WI 53715, USA

∞ Present Address: Five Prime Therapeutics, Inc., South San Francisco, CA 94080, USA

§ Present Address: Department of Biology, Stanford University, Stanford, CA 94305, USA

‡ Present Address: Centre for Organismal Studies, Universität Heidelberg, 69120 Heidelberg, Germany; Cluster of Excellence CellNetworks, 69120 Heidelberg, Germany

± Present Address: Department of Agronomy, Iowa State University, Ames, IA 50011, USA + Additional affiliation: Center for Plant Aging Research, Institute for Basic Science, Department of New

Biology, Daegu Gyeongbuk Institute of Science & Technology, Daegu 711-873, Republic of Korea

* Correspondence to: Wolf B. Frommer, E-mail: wfrommer@stanford.edu and Seung Y. Rhee, E-mail: srhee@carnegiescience.edu

This PDF contains: Supporting Online Material 1 to 5

Figs. S1 to S30

References (43-83)

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Table of Contents

Supporting Online Material 1: Materials and Methods……………………………………................... p4

1.1 Mating-based Split-Ubiquitin System………………………………………...…………………….p4

1.2 Cloning of Arabidopsis ORFs…………………………………………...……………………….....p4

1.3 Yeast Transformation………………………………………………………………………..……...p5

1.4 Cub prescreen to remove auto-activating and non-activatable fusions..............................................p5

1.5 Primary Interaction Screen………………………………………………………….........................p6

1.6 Secondary Interaction Screen………………………………………………………….....................p6

1.7 Tertiary Interaction Screen………………………………………………………….........................p6

1.8 β -galactosidase assay………….……………………………………………………….......…….....p7

1.9 Localization of MIND1 hub proteins in Arabidopsis mesophyll protoplasts…………..……..…….p7

1.10 Split GFP assay in tobacco leaves………………………………........………………………...….p7

1.11 Plant materials and growth conditions…………………………………………….........................p7

1.12 Cloning and Functional expression of KAT1 and AP2C1 in Xenopus oocytes. …………….……p8

1.13 Electrophysiological Measurements. ……………………………….......…………………………p8

Supporting Online Material 2: Identification of Positive Interactions…………….…………………....p8

2.1 ORF clone libraries screened……………………………………………………….....................….p8

Figure S1: Characterization of proteins screened…………………………………………………….....p9

2.2 Overview of the statistical analyses of the split-ubiquitin screens....…..............................………...p9

Figure S2: Statistical analyses of mbSUS screens...........................……………………...…………....p10

2.3 Identification of positive interactions from the primary screen…………………..........…………..p10

Figure S3: Analysis of primary screen interaction plates……………………………........…………....p11

2.4 Identification of positive interactions from the secondary screen………………………………....p12

2.5 Identification of positive interactions from the tertiary screen………………………………….....p13

Figure S4: mbSUS secondary and tertiary screens…………………………….……….......……...…. p14

Figure S5: Results of mbSUS secondary and tertiary screens and split GFP orthogonal validations ...p15

2.6 Orthogonal validation of mbSUS using split GFP assay………….…………………………….....p15

Supporting Online Material 3: MIND1 Network Characterization……………..………..................... p16

3.1 Network topological properties……………………………………………………................….…p16

Figure S6: Topological properties of MIND1……………………………………………………….....p17

Figure S7: Topological properties of MIND1NH………………………….…………………………....p18

3.2 Shared biological features of MIND1 interaction partners.……………………..………………....p18

Figure S8: Expression Correlation between interacting and non-interacting protein pairs....................p19

Figure S9: GO functional similarity between interacting and non-interacting protein pairs...………...p20

3.3 Comparison of MIND1 with other Networks………………………………………………….......p20

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Figure S10: Comparison of MIND1 with other Arabidopsis interactome data ….…………................p21

3.4 Statistically enriched GO categories in MIND1……………………………………..............……..p21

Figure S11: Representation of selected GO terms in MIND1……….....………………………….......p22

3.5 Enriched Functions in Protein Interaction Partners……………………………………………......p23

Figure S12: The MIND1NH network neighborhood of Derlin1 ……………………....…………….....p24

3.6 Hierarchical clustering ………………………………………………………….............................p24

Figure S13: Hierarchical clustering of the matrices of MIND1 and MIND1NH ……..………………..p25

3.7 MIND1 NH modules…………...…………………………………………………………..…….…p25

Figure S14: Blondel and MCL clustering of MIND1 NH……..………………………………………..p26

3.8 High degree (hub) proteins in MIND1 …………………………………………...............…...…...p26

Figure S15: Sub-cellular localization of hub proteins in Arabidopsis mesophyll protoplasts ……….…….…p29

3.9 Statistically enriched interactions within and between protein families in MIND1…………….....p29

Figure S16: Family-level (Type) Interaction Matrix Heat Map…………………………………….…p31

Figure S17: Family-level (Sub-type) Interaction family-based-network. …………………..………....p32

3.10 MIND1NH interactions within and between subcellular compartments…………………...……...p32

3.11 Enriched interactions within and between GO groups in MIND1 and MIND1NH..………….…...p32

3.12 MIND1 and MIND1 NH Intra-GO interactions compared to other networks………………..…....p33

Figure S18: MIND1 and MIND1 NH Intra-GO interactions compared to other networks…..……….....p33

Supporting Online Material 4: Membrane Biology in MIND1…………..…………….....…………...p34

4.1 MIND1 interactions within integrated hormone signaling sub-networks…………………...….…p34

Figure S19: Integrated ABA signaling sub-network in MIND1……………………………………….p34

Figure S20: Integrated IAA signaling sub-network in MIND1………………………………………...p35

Figure S21: Integrated brassinosteroid signaling sub-network in MIND1………………….……….…p36

Figure S22: Integrated cytokinin signaling sub-network in MIND1…………………..…………….…p37

Figure S23: Integrated ethylene signaling sub-network in MIND1…………………………………....p38

Figure S24: Integrated gibberellin signaling sub-network in MIND1………………………………....p39

Figure S25: Integrated JA signaling sub-network in MIND1………………………………...………..p40

Figure S26: Integrated SA signaling sub-network in MIND1………………………………………….p41

4.2 RLK-GTPase Interactions in MIND1………………………………………..…............................p42

Figure S27: Signaling motifs involving small GTPase signaling proteins ……………………..….....p42

4.3 SNARE proteins and BRI1 regulation……………….……………………………...............…….p44

Figure S28: Overexpression of SYP22 in bri1-5 mutant background. ….…………………………….p44

Figure S29: Brassinosteroid sensitivity of vamp727 and syp22 single mutants…………....................p44

Figure S30: Schematic representation of hypothesized roles of VAMP727 and SYP22………..…....p45

Supporting Online Material 5: Annotations for Supplementary Tables 1-6…………………………..p46

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5.1 Supplementary Table 1……………………………………………………………………………p46

5.2 Supplementary Table 2……………………………………………………………………………p47

5.3 Supplementary Table 3……………………………………………………………………………p49

5.4 Supplementary Table 4……………………………………………………………………………p49

5.5 Supplementary Table 5……………………………………………………………………………p50

5.6 Supplementary Table 6……………………………………………………………………………p50

Supporting Online Material 1: Materials and Methods

1.1 Mating-based Split-Ubiquitin System

The mating-based Split-Ubiquitin System (mbSUS) is a type of yeast two-hybrid assay that uses a ubiquitin protein that is split into two halves, and in which each domain is fused to two proteins whose interaction is to be interrogated. On its N-terminus, one of the two ubiquitin halves (C-terminus of Ubiquitin – Cub) is fused to a membrane anchor or membrane protein X; on its C-terminus, the Cub is fused to an artificial transcription factor PLV. When either a soluble or a membrane protein Y that is fused to the other half of ubiquitin (N-terminus of Ubiquitin – Nub) interacts with the membrane associated X-Cub-PLV fusion, the two halves of ubiquitin are brought into proximity and can reconstitute the full ubiquitin protein. This reconstitution occurs at the cytosol-facing side of a yeast membrane (cytosolic side of ER, Golgi, vesicles, vacuole, organelle, or plasma membrane) and is recognized by ubiquitin-specific proteases that trigger the release of the PLV from the membrane-bound X-Cub-PLV. PLV then moves into the nucleus and activates three marker genes: HIS3, ADE2 and lacZ. A positive readout in assays of marker gene expression indicates an interaction between the protein pair fused to Nub and Cub moieties (HIS3 is used as an auxotrophy complementation marker, ADE2 prevents the accumulation of red pigments in ade2 mutants, and LacZ can be detected using classical β–galactosidase assays). The Nub- and Cub-fusions can be expressed independently in MATα and MATa yeast strains and subsequently be combined by mating between the two strains. High-throughput screening for protein-protein interactions is accomplished by a combinatorial mating approach. In our experiments, libraries of Nub and Cub fusions were expressed in the haploid strains THY.AP5 [MATα URA3 leu2 trp1 his3 loxp::ade2] and THY.AP4 [MATa ura3 leu2 lexA::lacZ::trp1 lexA::HIS3 lexA::ADE2], respectively. Libraries are then screened combinatorially in diploid yeast cells after mating on high-density arrays of 1,536 yeast colonies grown on agar media for complementation of the his3 auxotrophy. Screen details have been described previously (5).

We use the terms ‘interaction’ and ‘protein-protein interaction’ here to mean potential protein-protein interaction detected in an mbSUS assay, since yeast assays only provide information on the potential of two coexpressed proteins interacting with each other in yeast, but do not prove occurrence of such interactions in planta. Coexpression in the same plant cell to sufficient amounts and correct relative orientation are additional parameters required for successful interaction in planta. Functionality of such an interaction requires additional tests. Importantly, an interaction that was positive in the mbSUS assays, but did not meet the stringent criteria for inclusion in the network-level analyses (see 2.4), may still be a valid interaction in planta. All interactions, along with associated confidence levels, are available for investigation at www.associomics.org.

1.2 Cloning of Arabidopsis ORFs

The mbSUS screen relies on a Gateway Entry clone library of full-length Arabidopsis ORF clones (Figure

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S1)(5). Individual Gateway Entry clones were mobilized using Gateway recombination into either a Cub fusion Gateway Destination vector (pMetYC_GW) or a Nub fusion Gateway Destination vector (XN21_GW, XN22_GW, NX32_GW or NX33_GW). Details of the generation of the clone libraries have been described previously (5). Briefly, ORFs (without the stop codon) were amplified by standard PCR techniques in 96-PCR plates (E&K Scientific) using KOD DNA polymerase (Novagen). Primers used for PCR contained sequences for Gateway attB1 and attB2 sites and 19 - 25 ORF-specific nucleotides. PCR products were gel-purified with NucleoSpin 96 Extract II kits (Machery-Nagel). PCR products were cloned into pDONR221 or pDONR221-f1 using BP clonase II enzyme (Invitrogen). The resulting clones were then sequenced from both ends using M13 forward 5´-d(GTTTTCCCAGTCACGAC)-3´and reverse 5´-d(CAGGAAACAGCTATGAC)-3´primers. Clones with correct sequence and clones with silent mutations and/or with no more than a single amino acid change were accepted for the screen. Resulting Nub- and Cub-clones were verified by restriction digest analysis (PvuII) and recombined into the mbSUS Destination vectors using LR clonase II (Invitrogen). Clones received from third parties (including ABRC) were reconfirmed by restriction analysis. Over 2,000 Entry clones as well as all Nub and Cub clones have been donated to the Arabidopsis Biological Resource Center (ABRC) for distribution (see www.associomics.org for more information).

1.3 Yeast Transformation

Yeast strains THY.AP5 and THY.AP4 were transformed with Nub- and Cub-fusions, respectively. The LiAc transformation protocol (43) was modified for use in 96-well format. Both strains were incubated in 15 mL of YPD medium supplemented with 20 mg/L adenine hemisulfate at 30ºC for ~24 hrs with shaking. Cultures were then diluted to OD600 0.1 in 300 mL of the same medium and allowed to grow to a final OD600 0.5-0.6 (~12 hrs). Cells were harvested by centrifugation and resuspended in 1.8 mL 100 mM LiAc in Tris-EDTA (10 mM Tris, 2 mM EDTA pH 7.5). A transformation master mix was prepared using the competent yeast cell solution by adding 7 mL of 50% (w/v) polyethylene glycol 4000 (Fluka #81240), 0.8 mL 1M LiAc in Tris-EDTA pH 7.5, and 0.3 ml freshly denatured ssDNA (8-10 mg/ml). In each well of a 96-well microplate, 3 µL of expression clone plasmid DNA (50-100 ng/µL) was combined with 97 µl of the master mix and was incubated for 30 minutes at 30ºC followed by a heat shock for 13 minutes at 42°C. Cells were then washed twice with water, resuspended in 75 µL water. Then 10 µL of cell solution was spotted on the appropriate agar medium using a Hydra96 (Art Robbins Instruments, Sunnyvale). Cells with Nub constructs were grown on Synthetic Complete (SC) media plus 240 mg/L leucine, 20 mg/L adenine hemisulfate, and 20 mg/L histidine-HCL (Nub medium). Cells with Cub constructs were grown on SC media plus 20 mg/L tryptophan, 20 mg/L histidine-HCL, 20 mg/L adenine hemisulfate, and 20 mg/L uracil (Cub medium). After 3 days of growth at 30°C, colonies were picked and grown in liquid Nub or Cub media in 96-well culture blocks. After 3 days, cells were transferred onto Nub or Cub agar media for further analysis.

1.4 Cub prescreen to remove autoactivating and non-activatable protein fusions

Before performing the binary screen, a prescreen was performed to eliminate ‘autoactivating’ and ‘non-activatable’ Cub fusions. In mbSUS, interaction tests are carried out with a mutated Nub, termed NubG, which does not interact with the Cub unless the fused proteins promote physical proximity. All THY.AP4 strains with Cub clones were mated to THY.AP5 strains with NubG alone in a ‘negative control’ test. Cub clones from the resulting diploid strains that showed activation of marker genes were deemed autoactivating and removed from further analysis. Examples of this type include Cub fusions that can diffuse to the nucleus (e.g. Cub fused to a soluble protein) or that release the PLV transcription factor through proteolytic activity independent of Nub interaction. The wild-type variant of the Nub, termed NubWT, interacts with the Cub domain even in the absence of interacting fusion proteins. All THY.AP4 strains expressing Cub clones were also mated to THY.AP5 strains containing NubWT alone as a ‘positive control’ interaction test. Cub clones from the resulting diploid strains that did not show activation of marker genes were deemed non-activatable and removed from the screen. Examples of this type of protein include Cub fusions in which the Cub moiety is inaccessible or the expression level or

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stability is low. Cub clones were pre-screened in a high-density yeast colony array (384 format) to identify clones that led to constitutive activation or inactivation of the mbSUS marker genes. All arraying (i.e. pinnings) was performed using the RoToR HDA pinning robot (Singer Instruments, UK) on Singer Plusplates (Singer Instruments, UK).

1.5 Primary Interaction Screen

Details of the screening procedure have been described previously (5). Cells were mated on YPD media and grown for 24 hrs at 30ºC, replicated onto SC media supplemented with 20 mg/L adenine hemisulfate, and 20 mg/L histidine-HCL (Diploid medium), grown for 72 hrs at 30ºC, and pinned onto SC media or SC media supplemented with 500 µM methionine, in duplicate. After 72 hrs of growth at 30°C, plates were scanned at 48-bit 300 dpi on flatbed scanner (Epson Expression 10000xL). Colony growth was quantified using GenePix 6.1 software (Molecular Devices) to convert quantitative aspects of the colony images into pixel values.

High-density arrays of 1,536 yeast strains carrying Nub clones were mated with a corresponding strain carrying a single Cub clone. Included in the Nub clone arrays were strains carrying the free NubWT as a positive control, and free NubG as a negative control. Each Cub fusion clone was grown in 15 ml of Cub medium for 72 hrs at 30ºC. The Cub culture was then distributed into the wells of a 96-well plate and pinned on Cub agar plates in a 1,536 array and grown for 72 hrs. A Nub clone array was then pinned on top of the Cub array on YPD agar media. After 24 hrs at 30°C, diploid cells were selected by replica-plating the cells onto selection medium on which only diploids can grow (Diploid). Colonies of diploid yeast carrying Cub- and Nub-fusions were pinned after 72 hrs onto interaction selection plates (SC medium) to assay the activation of the HIS3 and ADE2 marker genes (Figure S3A). The Cub fusion is under the control of the methionine repressible Met25 promoter; thus addition of methionine to the interaction selection plates reduces Cub-fusion expression levels and leads to increased stringency of the selection for a given interaction pair. Each 1,536 diploid array was screened in duplicate on interaction selection plates at each of two methionine concentrations (0 or 500 µM methionine). All plates were scanned, resulting images were converted to black and white, and colony growth was quantified using GenePix v6.1 software (Molecular Devices, Sunnyvale). An analysis pipeline (described below) of GenePix v6.1 image data from the primary screen identified 30,426 potential positive interactions.

1.6 Secondary Interaction Screen

In the secondary screen, the 30,426 potential positive interactions from the primary screen were retested in twelve additional mbSUS interaction tests. The haploid yeast strains expressing Nub or Cub clones were re-arrayed from the yeast Nub and Cub clone libraries into paired 96-well culture arrays using the liquid handling robot Biomek FX (Beckman). The new yeast clone arrays were then combined into 384 (4 x 96) spot Nub and Cub arrays. Each 384 Nub array was then arrayed twice into a 1,536 array along with one 384 array of free NubWT (positive control) and one 384 array of free NubG (negative control) clones (Figure S4). Corresponding 384 Cub arrays were arrayed four times into a 1,536 array. Thus, each protein pair was screened twice in each interaction selection plate alongside a positive and negative control test. Matings and interaction tests were carried out in 1,536 arrays as described above, except that each 1,536 diploid array was screened on interaction selection plates three times at each of two methionine concentrations (0 or 500 µM methionine), for a total of 12 additional interactions tests (2 tests/array x 3 replicate plates x 2 methionine conditions). An analysis (described below) of GenePix v6.1 image data from the secondary screen identified 12,102 high-confidence interactions (positive in both 0 and 500 µM methionine assays) as the MIND1 network.

1.7 Tertiary Interaction Screen

To confirm the reproducibility of the secondary mbSUS screen results, 7,770 interactions from the secondary screen were selected randomly for retesting in six additional mbSUS interaction tests. Protein pairs for these interactions, along with positive (Cub fusion clone against NubWT) and negative (Cub

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fusion clone against NubG) controls, were re-arrayed from the yeast Nub and Cub clone libraries into paired 96-well culture arrays using the liquid handling robot Biomek FX (Beckman). The new yeast clone arrays were scaled up to paired 384 Nub and Cub arrays. Matings and interaction tests were carried out as described above except for the use of 384 arrays and the addition of a β-galactosidase assay to test for the activation of the lacZ marker gene (see below). Each gene pair was screened twice in each of the three interaction tests (growth assay with 0 or 500 µM methionine and the β-galactosidase assay) for a total of six tests. An analysis pipeline (described below) of GenePix v6.1 image data from the tertiary screen categorized interactions from the secondary screen as positive or negative in each of the three tertiary screen assays (interaction selection plates with 0 µM methionine or 500 µM methionine and β-galactosidase assay plates with 0 µM methionine).

1.8 β -galactosidase assay

Diploid colonies on YPD plates were pinned onto a nitrocellulose membrane overlaid onto diploid agar plates. Diploid colonies were allowed to grow for 48 hrs at 30°C. The nitrocellulose membrane with colonies was removed and placed for 10 seconds into liquid nitrogen and allowed to thaw for another 10 seconds. The freeze and thaw cycle was repeated three times. The membrane was placed into a petri plate containing a filter paper soaked in Z-Buffer (Na2HPO4 7H2O 16.1g/L, NaH2PO4 H2O 5.5g/L, KCl 0.75g/L, MgSO4 7H2O 0.25g/L pH 7.2), X-Gal (0.42g in 4.2mL DMF), and 2-mercaptoethanol (2.7mL/1L Z-Buffer). The plates were then placed into a 37ºC incubator until blue color clearly distinguished positive from negative controls (3-5 hrs). Plates were scanned at 300 dpi and 48 bit on a flatbed scanner. The blue coloration of X-gal was quantified with GenePix software after conversion of the cyan channel to grey scale (5).

1.9 Localization of MIND1 hub proteins in Arabidopsis mesophyll protoplasts

C-terminal GFP-fusions of selected genes corresponding to 16 hubs were generated by Gateway recombination cloning using pDONR221 vectors as donor for cDNA (lacking stop codon) and p2GWF7 (44) (CaMV 35S promoter, C-terminal GFP-tag; obtained from VIB-PSB, Gent, Belgium) as destination tagging vector. Arabidopsis mesophyll protoplasts were isolated from rosette leaves of four-week-old Col-0 plants and transfected with the constructs, using the method described by (45) with modifications (tape-sandwich method) according to Wu et al., 2009 (46). After 12-20 hr incubation in WI buffer (4 mM MES pH 5.7, 0.5 M mannitol, 20 mM KCl) at 25ºC in the dark, the cells were imaged as cell suspensions in chambered cover glasses (Nunc, USA) using an SP5 laser scanning confocal microscope (Leica, Germany) by taking stacks of optical sections. Maximum intensity projections of image stacks were created using Fiji software (47).

1.10 Split-GFP interaction assay in tobacco leaves

A randomly selected subset of 195 interactions was retested in a tobacco transient expression system using the split GFP assay (48). For transient expression, all binary constructs were introduced into A. tumefaciens strains (GV3101 or C58C1). Agrobacterium cells containing each split GFP fusion construct were grown in liquid yeast extract peptone (YEP) medium (10 g of peptone, 5 g of NaCl, and 10 g yeast extract in 1 L volume) supplemented with antibiotics (spectinomycin 50 µg/ml and Rifampicin 50 µg/ml). Cultured cells were centrifuged at 4,000 rpm for 5 min at RT, and then the cell pellet was resuspended in infiltration buffer (10 mM MES, pH 5.6, 10 mM MgCl2, 200 µM acetosyringone) after removing supernatant. Cell density was adjusted with infiltration buffer to give an OD600 of ~0.5. Agrobacterium harboring the Tomato bushy stunt virus P19 silencing suppressor was co-infiltrated in order to minimize gene-silencing effects on protein expression. Aliquots (0.5 ml) of Agrobacterium cells carrying a split GFP fusion construct and the P19 construct, respectively, were mixed, and then a syringe was used to infiltrate the mixture into the abaxial side of N. benthamiana leaves. Plants were incubated in a growth chamber for 36 to 48 hours.

1.11 Plant materials and growth conditions

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The ORF region of VAMP727 was recombined into the pEarleyGate 104 binary destination vector in a Gateway LR reaction and then Agrobacterium mediated transformation into Arabidopsis Col-0 was used to generate VAMP727 overexpressing transgenic plants (49). Surface sterilized seeds were sown on half-strength Murashige and Skoog medium with 1.2% agar (MS medium) or MS medium supplemented with epibrassinolide (Sigma). After two days of stratification at 4 ºC, seedlings were incubated in a 24 h low light chamber (~80 µE/m2) for five days. Seedling plates were scanned and hypocotyl lengths were measured using ImageJ software.

1.12 Cloning and Functional expression of KAT1 and AP2C1 in Xenopus oocytes

The cDNAs of KAT1 and AP2C1 were cloned into pOO2 GW cloning vector (50). In vitro transcription was performed using the mMESSAGE mMACHINE T7 Ultra kit (Ambion) following the manufacturer’s instructions. Different capped and polyadenylated cRNA constructs were produced. Xenopus laevis oocytes were obtained from lab of Miriam Goodman by surgery manually, or ordered from Ecocyte Bio Science (Austin, TX) and kept in ND96 solution (2 mM KCl, 96 mM NaCl, 1 mM MgCl2, 1.8 mM CaCl2, 5 mM HEPES-NaOH (pH 7.4)), containing gentamycin (50 µg/µl). The oocytes were injected by Roboinjector (Multi Channel Systems, Reutlingen, Germany; (51, 52)) with a final volume of 50 nl of various cRNAs. For co-injections, cRNA mixtures were systematically prepared at an equal concentration ratio before injection. Injected oocytes were then maintained at 19 °C for 3–5 days in ND96 solution.

1.13 Electrophysiological Measurements

Whole-cell currents from oocytes were recorded 3-4 days after injections as described (53) using the two-microelectrode voltage-clamp technique (TEVC) Roboocyte system (Multi Channel Systems)(51, 52). The bath solution contained, unless stated otherwise, 100 mM KCl, 1 mM CaCl2, 1.5 mM MgCl2, and 10 mM HEPES-NaOH (pH 7.4) and was continuously perfused through the chamber. All experiments were performed with at least three biological repeats and were performed at room temperature (20 –21 °C).

Supporting Online Material 2: Identification of positive interactions

2.1 ORF clone libraries screened

A summary of all genes that were screened as Nub and Cub fusions is presented in Table S1. To focus on regulatory events at the membrane, the clone library generated for this large-scale interactome screen was designed to contain predominantly membrane proteins and signaling proteins (Figure S1).

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Figure S1: Characterization of proteins screened (A) Percentages of protein types assayed in the primary screen. (B) Frequency of candidates with a defined number of transmembrane (TM) domains of screened ORFs (all clones tested), ORFs present in the network of positive interactions (MIND1, described below) and in the network excluding hub proteins (MIND1NH, described below). The number of TM domains was defined as the number given by the consensus algorithm of the ARAMEMNON database (54) and other topology data sources (see (5)).

2.2 Overview of the statistical analyses used to analyze split-ubiquitin screens

We performed three rounds of large-scale split-ubiquitin screens to identify and evaluate the reproducibility of protein interactions. Positive interactions were determined based on optical intensities of yeast colonies compared with those of negative and positive controls. Each screen had different distributions of optical intensities of test and control interactions. To determine positive interactions for each round of screen, we used statistical analyses that were most appropriate based on the experimental design and distributions of the data. (Figure S2). The primary screen contained interaction tests that were dominated by negative interactions. The optical intensity of test interactions in most plates was a right-skewed unimodal distribution. In order to select the potential positive interactions, we used the distribution of the negative and positive control data as references to choose a cut-off point that can separate them best. We then adjusted the cut-off threshold to maximize our selection of tests that repeatedly tested positive (at least 3 positives out of 4 tests) in the data set. The secondary screen tested all positive interactions from the primary screen. The number of tests was greatly reduced compared to the primary screen. Thus, more controls were tested in the secondary screen. An interaction was deemed as positive when its mean optical intensity (from independent replicates) is significantly higher in comparison with that of negative controls. The tertiary screen contained a subset of interactions from the primary screen in addition to positive and negative controls. We used the Gaussian mixture model (55, 56) to separate the two peaks of the test interactions.

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Figure S2. Statistical analyses of split-ubiquitin screens. Three rounds of split-ubiquitin screens had different distributions of optical intensities of both tests and controls, and three discriminative analysis approaches were used to identify positive interactions.

2.3 Identification of positive interactions from the primary screen

We developed a computational pipeline to determine positive interactions using the following statistical methods. We first classified images from interaction selection plates into three distinct types: ‘bimodal’, ‘too much growth’ and ‘reduced positive control’. In the majority of plates, diploid clones corresponding to the positive control (Cub clone with free NubWT clone) grew large colonies, and diploid clones corresponding to experimental interaction tests grew few to no colonies (Figure S3A). This type of plate was termed ‘bimodal’ i.e. interaction tests and positive control tests have distinct distributions (Figure S3B). Figure S3C shows the histogram of the distribution of the optical density of the interaction tests in the example plate shown in Figure S3A. Two uncommon types of plates indicated that the Cub being tested showed signs of being an autoactivator or non-activatable despite having passed the pre-screen.

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Plates showing signs of Cub autoactivation showed large colonies in all tests, were not bimodal, and were termed ‘too much growth’. Plates showing signs of Cub non-activation showed low growth for the positive control tests (i.e. Cub clone tested against Nub-WT clone), were not bimodal, and were termed ‘reduced positive control’ plates.

Figure S3: Analysis of primary screen interaction plates. (A) GenePix-processed image of an interaction plate with a bimodal distribution. Experimental interaction tests are in the center (bounded in a blue rectangle) and positive control tests are present in the outer two rows/columns (bounded in red rectangles). (B) Diagram of a typical bimodal distribution. In blue: typical distribution of optical density of experimental test interactions; in red: typical distribution of optical density of positive control interactions. (C) Histogram of optical density for all interactions of the plate in (A) showing a bimodal distribution.

We applied two filters defined as bimodal index and variance index to categorize the plate data. The bimodal index is modified from (57) and identifies whether a distribution is bimodal. The bimodal index is defined as:

where µt and µn are the means of test and positive control (NubWT) interactions on a plate, and σt and σn represent the standard deviation of test and positive control (NubWT) interactions, respectively. If the value of the bimodal index was >1.0, the plate was classified as ‘bimodal’. Then, we applied the variance index to categorize the non-bimodal index plates into two types, ‘too much growth’ and ‘reduced positive

€

β =µ t − µ n

σ t2 +σn

2

  12  

control’ plates. Variance index is a normalized variance (variance/mean) used as a measure of dispersion. A ‘too much growth’ plate typically has a high variance index compared to a ‘reduced positive control’ plate. A threshold of variance index was used to classify these two types of plates. If the variance index was ≥ 1,035 (determined empirically by manual examination of sample plates), the plate was classified as ‘too-much-growth’ and was removed from further analysis.

The ‘bimodal plates’ were processed to generate an ‘Interaction Confidence Score’ (ICS) for each test interaction. First, we used the Expectation Maximization (EM) algorithm (58) to set up a boundary that divided the bimodal distribution into two distributions. The left distribution included most of the test interactions and a small proportion of positive control interactions. The right distribution included most of the positive control interactions and a small proportion of test interactions. The two distributions were used to compute p-values for each interaction. We used two models to compute the p-values for the test interactions. One model assumed that the distributions of pixel intensities of both the test and the positive control interactions were normal. Alternatively, we considered a gamma distribution model based on long tails in both of the distributions. Distributions of the test interactions varied from one plate to another, complicating the choice of a single distribution type to fit all data. However, a comparative analysis showed similar results for both normal and gamma fitting procedures (IST/SEMATECH e-Handbook of Statistical Methods, http://www.itl.nist.gov/div898/handbook/), and therefore it was deemed appropriate to use the results from the normal distributions in further analyses. We computed two p-values (positive p-value from the ‘positive control’ distribution and negative p-value from the ‘test interaction’ distribution) of every test interaction vi against the two distributions. Then, we computed the ICS of vi with the following equation:

ICS(vi) = -log2 (neg_p-value(vi)) x pos_p-value(vi)

We applied a threshold for the ICS to derive the PPI network, in which the interactions with ICS higher than the threshold were considered to be positive. To determine the best ICS threshold to select positive interactions, we generated a repeat positive (RP) rate curve, which indicated a threshold of 0.75 to be the ICS value at which the RP rate is maximized. To generate the RP curves, we defined a repeat positive as the clone pairs that show positive interactions in at least 3 out of 4 tests and stochastic positive (SP) interactions as the clone pairs that show positive interactions in only 1 out of 4 tests at a given threshold. An ICS threshold of 0.75 was selected because the RP rate at this threshold is maximal (1.6%) and highest relative to the SP rate (2.3%). For the ‘reduced positive control’ plates, a separate positive control distribution does not exist and the ICS score for each test interaction was a single p-value based solely on the test interaction distribution. The threshold for this ICS was set to 0.01. Only the 30,426 PPI test interactions that passed the 0.75 ICS threshold were considered positive in the primary screen. Because the mbSUS PPI between Arabidopsis proteins are detected in yeast, we consider them to be potential protein-protein interactions in planta.

2.4 Identification of positive interactions from the secondary screen

The entire set of 30,426 PPI from the primary screen was re-evaluated in a second round of split-ubiquitin interaction tests to select the highest-confidence PPIs that were consistently positive (Figure S4). To identify positive interactions in the secondary screen, we compared colony optical densities between test interactions and negative control interactions after normalization of the colony optical densities within and between plates. Optical densities of positive and negative control colonies were used as reference points for normalization. For each plate, the scale between the median densities of positive and negative controls was first adjusted to one. Then optical densities between the plates were normalized by setting the negative control median densities of each plate to zero.

density.norm =density.raw – median (neg) / median (wt) – median (neg)

  13  

where: density.norm: Normalized optical density

density.raw: Unnormalized raw optical density

median (wt): Median optical density of all positive controls in a single plate

median (neg): Median optical density of all negative controls in a single plate

An interaction was deemed positive when its normalized optical density was significantly higher than that of negative controls. Six replicates for each interaction test (growth on 0 and 500 µM methionine interaction selection plates) were compared to their respective three negative controls using Student’s t-test, followed by the false discovery rate (FDR) adjustment following the Benjamini–Hochberg procedure. The threshold for positive interactions was FDR q-value ≤ 0.01. Primary screen PPIs that did not retest positive in either of the secondary screen conditions were defined as negative while those that tested positive in only one of the conditions were considered partial-positive. Only those 12,102 PPIs that were positive in both 0 and 500 µM methionine tests were considered to be high-confidence interactions and were included in MIND1 (Figure S5A). Our approach here selected only for interactions that are the most reproducible in the mbSUS assays, and thus might exclude many potential positive interactions. 2.5 Identification of positive interactions from the tertiary screen

A tertiary screen was performed on a random subset of 7,770 interactions from the primary screen that included positive, partial-positive, and negative PPIs from the secondary screen to evaluate the reproducibility of the secondary screen results. To identify positive interaction tests in the tertiary screen, we employed model-based classification of interaction tests based on distributions of yeast colony optical densities in interaction plates. We used a shift-and-scale method to normalize optical densities for all colonies on all plates using the median values for positive and negative control interactions on individual plates as reference data points. To normalize the optical density of each interaction in a plate, the median of negative controls was subtracted from the raw colony optical density value and divided by the difference between the medians of positive and negative controls in each plate.

(Raw Valuex - Medianneg)/(Medianwt-Medianneg)

After applying this transformation to colony images from each of the plates, median values of negative and positive controls were normalized to 0 and 1, respectively, across the plates.

The test interactions showed a bimodal distribution. The distributions of negative and positive test interactions were assumed to be normal. We applied a Gaussian mixture model to classify each test interaction (Figure S4). An Expectation-Maximization (EM) algorithm (58) was applied to estimate parameters of each normal distribution component and determine whether a component was negative or positive based on its normalized median optical density relative to that of the negative control distribution. To avoid overfitting, the number of Gaussian components was determined by the Bayesian Information Criterion (BIC), which exerts a penalty on the number of parameters a model uses. The modeling process was performed using mclust software (55, 56). We performed model-based classification based on the optimal fitting results. For each optical density value x, we calculated the probability that it belongs to component i: Pi(x). If a test interaction was more likely to belong to a positive component, it was classified as positive. Otherwise, it was classified as negative.

If Ppositive(x)>0.5, x is a positive interaction.

If Ppositive(x)<0.5, x is a negative interaction.

The tertiary screen consisted of six interaction tests with two replicates for each of three assays. If an interaction was positive in one out of the two replicates for a given assay, it was considered positive for that assay. The rate of positive interactions for each secondary screen category (negative, partial-positive,

  14  

and positive) in the tertiary screen is shown in Figure S5B. Overall, the results of the tertiary screen reproduced the results of the secondary screen well in that only 12% of secondary screen positives were negative in all three tertiary screen assays and 80% were positive in at least two tertiary screen assays (Figure S5B).

Figure S4: mbSUS secondary and tertiary screens. (TOP Left to Right) Analysis of interaction plates from the secondary screen. The yeast strains containing the Cub and Nub clones from the primary screen PPIs that were positive were re-arrayed, mated, and retested in a secondary interaction screen. In the secondary interaction screen, each plate tested 1,536 protein pairs, including 384 positive controls (red circles), 384 negative controls (blue circles), and 768 test interactions from the primary screen. The secondary screen tested each interaction twelve times; six each at two methionine conditions. Raw intensity values for yeast colony optical densities were normalized and then compared to intensity values for positive and negative control optical densities. Positive tests from the secondary screen resulted in the MIND1 network. (BOTTOM left to right) The yeast strains containing the Cub and Nub clones from a random subset of the primary screen PPIs were re-arrayed, mated, and retested in a tertiary interaction screen. In the tertiary screen, each plate tested 384 protein pairs, including 8 positive controls (red circles), 32 negative controls (blue bars), and 344 test interactions from the primary screen. The tertiary screen tested each interaction on two interaction plates for each of three interaction assays. Raw intensity values for yeast colony optical densities were normalized and then compared to intensity values for positive and negative control optical densities. Normalized data of the tertiary screen results was fitted using a Gaussian mixture model.

  15  

Figure S5. Results of mbSUS secondary and tertiary screens. (A) The 30,426 PPI from the primary screen were retested in a secondary mbSUS screen and categorized into negative, partial-positive, and positive PPIs depending on the number of positive tests out of two secondary screen conditions. (B) A subset of 7,770 PPI from the primary screen were retested in a tertiary mbSUS screen and the proportion of positive tests out of three tertiary screen assays is shown (i.e. 0/3 – 3/3).

2.6 Orthogonal validation of mbSUS using split-GFP assay

We tested a subset of MIND1 interactions in planta using the orthogonal split-GFP protein interaction assay (Figure 1C). A randomly chosen subset of 179 MIND1NH interactions were re-tested in the split-GFP assay along with 16 MIND1 interactions involving hubs. Because interactions involving hubs have lower specificity than interactions not involving hubs, they were not included in split-GFP assays at a rate proportional to MIND1. However, the two sets of interactions had similar positive re-test rates: 31.2% for MIND1NH interactions and 37.5% for MIND1 hub interactions. Together MIND1 interactions tested positive in 31.8% of split-GFP interaction tests, (95% CI: (25.6%, 38.5%), bootstrap analysis). We can estimate the in planta false positive rate of MIND1 by comparison to the rate of split-GFP validation for two reference sets of protein pairs. A positive reference set of 49 independently validated positive interactions - randomly sampled from all primary screen protein pairs reported positive in the literature - was used to estimate the true positive rate (38.8% tested positive, 95% CI: (24.5%, 53.1%), bootstrap analysis) of interactions tested in the split-GFP assay. A negative reference set of interactions - randomly sampled from all primary screen protein pairs - was used to estimate the false positive rate (2.5% tested positive, 95% CI: (0%, 5.8%), bootstrap analysis) of interactions tested in the split-GFP assay. MIND1 interactions test positive at a rate that is not significantly different compared to independently reported known interactions (positive reference set, p-value = 0.364, permutation test) and over 12-fold higher than non-interacting protein pairs (negative reference set, p-value = 0, permutation test).

The MIND1 in planta false positive rate was then estimated using the formula:

€

FPRMIND1 =FP

FP +TP=1−

PRMIND1 − FPRsplit−GFP

sensitivitysplit−GFP − FPRsplit−GFP

  16  

where

FPRMIND1 = MIND1 in planta false positive rate;

PRMIND1 = Positive Rate of MIND1 subset in the split-GFP assay;

FPRsplit-GFP = False Positive Rate of the negative reference set in the split-GFP assay; and

sensitivitysplit-GFP = Positive rate of the positive reference set in the split-GFP assay.

The estimated MIND1 in planta false positive rate was 19.2% (95%CI: (0, 46.6%), bootstrap analysis) (59)).

Confidence intervals (CI) were estimated by the basic bootstrap approach of random sampling 10,000 times with replacement from the original samples and deriving empirical quantiles from the bootstrap distribution (59). Statistical significance was determined by permutation test against the null hypothesis that rearrangements of labels on the observed data points have no effect on the outcome. (60). The detailed steps of the permutation test are: Step 1. Compute a statistic, which is the difference between the positive rates of two verification results of the split-GFP assays (MIND1 vs NRS, MIND1 vs PRS, or PRS vs NRS); Step 2. Combine the two verification result datasets into a single group; Step 3. Randomly sample two permuted sets from the combined group with replacement; Step 4. Compute the same statistic from the permuted sets; Step 5. Repeat step 3 and 4 10,000 times; Step 6. Count instances where the statistic is equal or higher in permuted sets than observed; and Step 7. Compute the p-value, which is the ratio between the number of instances in Step 6 to the number of permutations (10,000 in this study).

Supporting Online Material 3: MIND Network Characterization

3.1 Network topological properties

We used the software NetworkX (http://networkx.lanl.gov/), a Python package for analysis of complex networks, to examine the topological properties of our membrane-linked interaction networks. The network of MIND1 contains 12,102 interactions between 1,523 proteins. The average degree of MIND1, i.e. the average number of interacting partners (61), is 15.9, and network density, i.e. the fraction of positive interactions out of all possible pairwise interactions, is 0.0104. These values are higher than those in the Arabidopsis protein interaction network AI-1 (13) and protein-protein interaction networks of other organisms (14, 62). The maximum degree of the proteins in the MIND1 network is 535. There are 46 hub proteins with degrees over 70, involving 8,857 interactions (73% of the total number of interactions).

The degree of the proteins in this network follows a heavy-tailed distribution (Figure S6A, B), consistent with other protein-protein interaction networks (13, 14, 62). Similar to other protein interaction networks (13, 14, 62), which generally have a large connected component and a few small components, MIND1 has a large connected component of 1,498 proteins and 11 small components of 4 proteins or fewer. The average distance between two proteins is 3.12 edges, and the average clustering coefficient (a measure of the density of triangles in the network, reflecting the local order in the network) (63) is 0.048. We compared these values to a frequently used null model, Erdos-Rényi random networks (61), with the same number of nodes and edges as MIND1. The average distance and the average clustering coefficient of Erdos-Rényi random networks are 2.92 and 0.0104, respectively (ratio γ=3.12/2.92=1.07, λ=0.0480/0.0104=4.62). This combination of comparable-to-random average distance with a higher-than-random clustering coefficient indicates that the MIND1 network is small world (63).

To decipher the relationship between node degree and clustering coefficient, we grouped proteins with the same degree and determined the average clustering coefficient in each group. As shown in Figure S6C, proteins with lower degrees (roughly k < 70) consistently have high clustering coefficients. The clustering

  17  

coefficients of the proteins with higher degrees (roughly k > 70) have large variances. Previously published protein interaction networks from other organisms exhibited a tendency for proteins with high degrees (hubs) to interact with proteins with low degrees (disassortativity) (64). Disassortativity is also observed in the MIND1 network for nodes with degree k > 20 (Figure S6D). Interestingly, low-degree nodes (roughly k < 20) show a positive correlation between the degrees of connected nodes. Overall the network is disassortative with assortativity value of −0.391.

 Figure S6. Topological properties of MIND1. (A) Degree distribution of the proteins, i.e. the fraction of proteins with degree k plotted versus k. (B) Cumulative degree distribution of the proteins, i.e. the fraction of proteins with degree larger than K plotted versus K. (C) Average clustering coefficient of proteins with the same degree. (D) Average degree of neighbors of proteins with the same degree.

Because the MIND1 network is dominated by interactions involving hub proteins (degree > 70) that exhibit low specificity in their interaction partners, we also performed analyses on the MIND network after excluding the hub proteins (MIND1 non-hub or MIND1NH). The MIND1NH network has 3,354 interactions between 1,326 proteins. The average degree of this network is 5.06 and network density is 0.0038. The maximum degree of the proteins in the network is 66. The degree of the proteins in MIND1NH follows a power-law distribution (Figure S7A and Figure S7B), similar as in MIND1. The MIND1NH network has a large connected component of 1,247 proteins and 34 smaller connected

  18  

components. The average distance among proteins is 4.35, and the average clustering coefficient is 0.014. The average distance and the average clustering coefficient of Erdos-Rényi random networks with the same number of nodes and edges as MIND1NH are 4.34 and 0.0037, respectively (ratio γ=4.35/4.34=1.00, λ=0.014/0.0037=3.78). As in MIND1, there is a distinct negative correlation between the degree and the clustering coefficient of proteins in MIND1NH (Figure S7C). The network is slightly disassortative with the assortativity value of −0.125 (Figure S7D).

Figure S7. Topological properties of MIND1NH. (A) Degree distribution of the proteins, i.e. the fraction of proteins with degree k plotted versus k. (B) Cumulative degree distribution of the proteins, i.e. the fraction of proteins with degrees larger than K plotted versus K. (C) Average clustering coefficient of proteins with the same degree. (D) The average degree of neighbors of proteins with the same degree.

3.2 Shared biological features of MIND interaction partners

Many important cellular processes are mediated through protein–protein interactions. Thus, interacting proteins may be more likely to share biological features. We investigated the biological relationships between interacting proteins in MIND1 and MIND1NH by examining the similarity of their mRNA expression levels and their Gene Ontology (GO) functional annotations. We expect interacting proteins to be co-expressed and be involved in similar functions. For comparison, we defined non-interacting protein pairs as the Nub-Cub pairs that did not show interaction in any of our assays.

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We downloaded a compendium of normalized Affymetrix data from 1,436 microarray experiments from TAIR (ftp://ftp.arabidopsis.org/home/tair/Microarrays/analyzed_data/) and calculated the pairwise Pearson Correlation Coefficient (PCC) over the entire compendium for each protein pair for which expression profiles were available. Figure S8A provides cumulative fractions of interacting and non-interacting pairs with PCCs above 0.5. To test whether interacting protein pairs were enriched in co-expressed pairs, we compared the fraction of interacting protein pairs with PCCs > 0.5 to the fraction of non-interacting protein pairs with PCCs > 0.5. MIND1NH contains a higher fraction of co-expressed pairs (PCC > 0.6) compared to MIND1 and non-interacting protein pairs (Figure S8A). Compared with non-interacting pairs, MIND1NH, but not the complete MIND1 network, was significantly enriched in highly co-expressed protein pairs (PCC > 0.7, p-value = 2.2 E-5, Fisher’s exact test), confirming the expectation, at least for the network excluding hubs, that interacting proteins tended to be co-expressed (Figure S8B).

Figure S8. Expression Correlation between interacting and non-interacting protein pairs (A) Cumulative fraction of protein pairs with different PCCs from 0.5 to 1.0. (B) Fraction of protein pairs with PCC > 0.7. P = p-value from Fisher’s exact test.

We also explored whether interacting protein pairs tended to have similar functions. There are a number of measures to numerically evaluate the functional similarity of genes or proteins based on their annotations. We used the measure GS2 (GO-based similarity of gene sets) developed in (65). In addition to having accuracy comparable to other semantic methods (15, 66), this measure is faster and can be used for analysis of large-scale gene sets. The daily snapshot of the GO tree and the Arabidopsis gene annotations was downloaded from the GO web site (http://www.geneontology.org) on Nov 08, 2013. Figure S9A gives the cumulative fractions of interacting and non-interacting pairs with different GO functional similarities > 0.5. MIND1NH and MIND1 contained many more functionally similar pairs than non-interacting pairs. The fractions of interacting and non-interacting pairs with functional similarity > 0.5 are shown in Figure S9B. Although only MIND1NH exhibited enriched expression correlation among protein pairs, both MIND1NH and MIND1 were significantly enriched in functionally similar protein pairs compared with non-interacting pairs (Figure S9, P < 2.2 E-16 for both MIND1NH and MIND1, Fisher’s exact test). The contrast between protein pair expression correlation and functional similarity for the complete MIND1 network suggests that hubs tend to share functional similarity with interacting partners despite lacking similar expression patterns, potentially owing to the reduced specificity of their interactions or diversification of roles of the interacting proteins. Overall, the results are consistent with the expectation that interacting proteins tend to be involved in similar functions.

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Figure S9. GO functional similarity between interacting and non-interacting protein pairs. (A) Cumulative fractions of protein pairs with increasing similarities from 0.5 to 1.0. (B) Fraction of protein pairs with similarities > 0.5. P = p-value from Fisher’s exact test.

3.3 Comparison of MIND with other networks

We performed pairwise comparisons among five existing protein-protein interaction networks in Arabidopsis (MIND1, MIND1NH, AI-1 (13), and the published data sets LCI (13, 67) and G-protein interactome (68)). Data for MIND interactions are mostly complementary to data from published Arabidopsis interactomes (Figure S10; (13, 67, 68). This is most likely due to the complementary set of proteins (here membrane-bound and signaling proteins) that were tested (Figure S10) and the use of a distinct assay type (e.g. here the mbSUS yeast two-hybrid versus the traditional yeast two-hybrid).

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Figure S10. Comparison of MIND with other Arabidopsis interactome data. (A) Venn Diagram of overlap between MIND1 (left) or MIND1NH (right) and two previously reported large PPI networks: AI-1 and literature-curated interactions (LCI). MIND1 contains 12,102 protein-protein interactions (edges) among 1,523 Arabidopsis proteins (nodes). This network is the result of the primary and secondary mbSUS screens. MIND1NH is MIND1 excluding hub proteins (degree >70) and contains 3,354 protein-protein interactions among 1,326 Arabidopsis proteins. AI-1 is a reported Arabidopsis interactome dataset (13), containing 6,205 interactions among 2,774 Arabidopsis proteins. LCI is a literature-curated interactome consisting of 5,723 interactions among 2,695 Arabidopsis proteins. This dataset is the union of LCIBINARY (13) and protein-protein interactions curated by TAIR (67). (B) Summary of overlap of MIND nodes and edges with other interactomes. ‘G-protein’ contains 539 interactions among 433 proteins that are mainly composed of interactions of G-protein subunits and their effectors and scaffold proteins (68).

3.4 Statistically enriched GO categories in MIND1

MIND1

AI-1 LCI

1523 Nodes12102 Edges

212 N11 E

160 N1 E37 N

0 E

2774 N6205 E

2695 N5723 E585 N

102 E

MIND1NH

AI-1 LCI

1326 Nodes3354 Edges

185 N5 E

148 N1 E34 N

0 E

2774 N6205 E

2695 N5723 E585 N

102 E

Overlap among

networksMIND1NH

Literature curated PPI

(LCI)

Arabidopsis G-protein

interactome

MIND1NH1326 Nodes3354 Edges

MIND1

Arabidopsis Interactome map (AI-1)

MIND1Arabidopsis Interactome map (AI-1)

2774 N6205 E

585 N102 E

85 N1 E

86 N3 E

433 N539 E

Literature curated PPI

(LCI)

2695 N5723 E

Arabidopsis G-protein

interactom

185 N5 E

1326 N3354 E

1523 N12102 E

35 N1 E

40 N1 E

212 N11 E

160 N1 E

148 N1 E

A

B

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We used GO slim categories to characterize the representation of protein functions in MIND1. The statistical enrichment of proteins in a GO category was obtained by comparison of proteins in MIND1 to those in the Arabidopsis genome (Figure S11 top) as well as the proteins tested (Figure S11 bottom). There were five specific GO biological process categories that are statistically enriched in MIND1 compared to the genome (Figure S11): transport, signal transduction, response to abiotic or biotic stimulus, protein metabolism (modification), and response to stress (Figure S11). As expected, GO cellular components involving membranes were highly enriched compared to the genome as are GO molecular functions relating to transport and signaling (Figure S11). Only the GO biological process transport was enriched in MIND1 compared to both the genome and the proteins tested for interactions.

Figure S11: All enriched/depleted GO slim terms in MIND1. Yellow highlight indicates GO slim terms enriched in MIND and blue highlight shows GO slim terms depleted in MIND1. GO slim annotations were obtained from the TAIR database (http://www.arabidopsis.org/). Statistical significance level for each GO slim term was determined

  23  

by comparing the number of genes annotated to the term in MIND1 with the same measure in Arabidopsis genome (top) or all proteins tested in the primary screen (bottom) (hypergeometric test). FDR was controlled following the Benjamini–Hochberg procedure.

3.5 Enriched Functions in Protein Interaction Partners

Protein-protein interactions mediate signal transduction in cellular systems. Functional analysis of the neighbors (interaction partners) of proteins in a protein interaction network can help to identify signaling pathways and generate functional hypotheses. We wrote an R script implemented in the topGO environment (69) to systematically explore the functional enrichment in neighbors for each protein in MIND1NH. We evaluated the functions enriched in the neighbor set of each protein in the network on the basis of the TAIR GO annotation using the elim method provided with topGO. As described in the tutorial of topGO (http://www.bioconductor.org/packages/release/bioc/vignettes/topGO/inst/doc/topGO. pdf, page 19), the p-values returned by the elim method can be interpreted as corrected (or not affected) by multiple hypothesis testing. We performed Fisher’s exact test to assess the significance of over-representation of GO categories of a node’s neighbor set compared to the set of all proteins in the network. Because MIND1NH consisted of interactions between Nub- and Cub-fusions, we performed the functional enrichment analysis of the Nub neighbors of each Cub protein against only the Nub proteins in the network and functional enrichment of the Cub neighbors of each Nub protein against only the Cub proteins in the network (Table S2). Proteins with an enriched functional annotation in its neighbors can be hypothesized to share or have a role in that function. For example, 4 out of 5 Nub neighbors of AT1G16000 have the annotation GO Biological Process hyperosmotic response (p-value = 4.6E-6, Fisher’s exact test, Table S2), and thus AT1G16000 is potentially involved in the cellular response to hyperosmotic stress. Example nodes for which the enriched annotation in neighbors matched with known functions include magnesium transporter 9 (MGT9, AT5G64560) and plasma membrane intrinsic protein 1C (PIP1C, AT1G01620). MGT9, as a Cub protein, has 11 Nub neighbors and 6 of them are annotated with the GO biological process metal ion transport (p-value = 8.2E-5, Fisher’s exact test, Table S2). The Cub neighbors of PIP1C (Nub-fused protein) were significantly enriched with the GO molecular function auxin:hydrogen symporter activity (p-value = 4.4E-5, Fisher’s exact test, Table S2).

As a further example, the neighbors of Derlin1 (DER1, AT4G29330) were enriched for the GO biological processes protein secretion (p-value = 7.69 E-04, Fisher’s exact test), response to heat (p-value = 7.08 E-03, Fisher’s exact test), protein folding (p-value = 1.45 E-03, Fisher’s exact test) and protein transport (p-value = 7.13 E-03, Fisher’s exact test, Table S2). These functional annotations are consistent with the ER-associated protein degradation (ERAD) function of DER1 homologs in yeast (Der1p, (70)) and mammals (Der1-3, (71-73)). However, examining the DER1 neighbors in MIND1NH revealed interaction partners involved in several steps of ER protein regulation beyond the retrotranslocation associated with ERAD (Figure S12). For example, DER1 interacted with homologs of Sec61/SecY anterograde translocation complex components (AT2G45070 and AT2G34250), Sec12 and Emp24 proteins involved in ER to golgi protein transport (AT1G69460 and PHF1 AT3G52190) as well as several proteins involved in protein processing and five SNARE proteins (Figure S12). Thus, DER1 was broadly associated with ER protein regulation components in MIND1NH, consistent with the more general role in ER homeostasis proposed for the yeast homolog Dfm1p (74).  

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Figure S12. The MIND1NH network neighborhood of Derlin1 (DER1). (A) The neighbors of DER1 are enriched for GO annotations related to protein secretion, folding, and transport. P-values are derived using Fisher’s exact test and all Nub-fusion proteins in the network. (B) The neighborhood of DER1 in MIND1NH includes many proteins related to protein regulation in the secretory pathway. Membrane and soluble proteins are colored according to functional groupings and the size of the nodes is proportional to node degree as indicated.

3.6 Hierarchical clustering

Complex networks from many fields of science exhibit modularity, also referred to as clustering or community structure (15). Modularity means that nodes are organized into modules, such that nodes within the same module are densely connected and there are few edges connecting to nodes in different modules. Network modules tend to represent relatively independent functional units from each other in the network, and thus module detection is important for understanding the function and structure of the networks. Modular structure has been observed in protein-protein interactions of many organisms (48, 75, 76). We used three different methods to examine the modularity of our membrane protein interaction

  25  

networks: hierarchical clustering, the Blondel community detection method, and the MCL clustering algorithm.

To obtain an overview of the network structure, we defined two matrices for MIND1 and MIND1NH respectively. The rows of the matrices correspond to the Nub proteins, and the columns of the matrices correspond to the Cub proteins. If there is an interaction between a Nub protein and a Cub protein, then the entry is 1; otherwise 0. We then used a hierarchical agglomerative average linkage clustering with uncentered correlation coefficients (77) as the distance matrix to cluster the matrices of MIND1 and MIND1NH (Figure S13). The data were visualized using Matrix2png (http://www.chibi.ubc.ca/matrix2png/)(78). Interactions are shown as red, and non-interactions as black.

Figure S13. Hierarchical clustering of the matrices of MIND1 and MIND1NH. The rows of the matrices correspond to the Nub proteins, and the columns of the matrices correspond to the Cub proteins. Interactions are shown as red, and non-interactions as black.

3.7 MIND1NH Modules

In protein-protein interaction networks, highly cohesive protein clusters (i.e. neighborhoods) could represent protein complexes or functional modules. We used two module detection methods beyond hierarchical clustering to detect additional functional modules in our membrane protein interaction network. The Blondel community detection method (22) implemented in Network Workbench (http://nwb.cns.iu.edu/) is a heuristic method based on modularity optimization; it identified 95 modules containing at least four proteins in MINDNH (Table S4) and 852 (64.3%) proteins were assigned to one of these 95 modules. The Markov clustering (MCL) algorithm (http://www.micans.org/mcl/) is a fast and scalable unsupervised network-clustering algorithm based on simulation of stochastic flow in graphs. It has an inflation parameter (which should be larger than 1.0) to control the granularity or ‘tightness’ of clusters. The MCL algorithm with inflation parameter 5.0 identified 84 modules containing at least four

  26  

proteins in MINDNH (Table S4) and 671 proteins (50.6%) were assigned to one of these modules. The size distributions of modules detected by the two methods are shown in Figure S14A. Both methods returned modules of similar sizes, with the exception of two large modules detected only by the Blondel method (Table S4). We used the GOTermFinder software (http://go.princeton.edu/cgi-bin/GOTermFinder) to check the functional enrichment of each module with all GO annotations in the genome as background. The significantly enriched GO terms and the corresponding p-values (Fisher’s exact test) after Bonferroni adjustment for multiple hypothesis testing are listed in Table S4. Figure S14B-D give three module examples that are enriched with significant GO terms.

Figure S14. Blondel and MCL clustering of MIND1. (A) The size distributions of modules in MIND1NH derived by the Blondel method and the MCL algorithms. (B) An aquaporin module found by the Blondel method. (C) A magnesium transporter module found by the Blondel method. (D) A module enriched in phospholipid metabolic process proteins found by the MCL algorithm (MCL module 5). Proteins are colored according to functional groupings. P-values are derived from Fisher’s exact test and adjusted for multiple hypothesis testing using the Bonferroni method.

3.8 High degree hub proteins in MIND1

  27  

Because the super-cluster consisting largely of interactions with MIND1 hubs dominated the hierarchical clustering, we pursued further characterization of MIND1 hubs. In MIND1 there are 46 proteins with a degree >70, which account for 73% of the total number of interactions. Hub proteins in PPI networks are considered to be more likely to perform important functions, because the corresponding genes often show evidence of slower evolution and are often essential (79). We examined the Ka/Ks ratio for MIND1 hubs compared to their orthologs in the Arabidopsis lyrata genome (Plant Genome Duplication Database (PGDD), http://chibba.agtec.uga.edu/duplication/, accessed November 14, 2013) and found that the ratio of the number of non-synonymous substitutions / non-synonymous site (Ka) to that of synonymous substitutions / synonymous site (Ks) - considered to be an indicator of selective pressure acting on protein-coding genes - is low for MIND1 hubs (mean Ka/Ks = 0.15). This indicates that MIND1 hubs may indeed be subject to purifying selection. Compared to Ka/Ks ratios for core cell cycle genes, MIND1 hub Ka/Ks falls between Cyclin Dependent Kinases (mean Ka/Ks = 0.073) and Cyclins (mean Ka/Ks = 0.241)(23). Ka/Ks for MIND1 hubs were also significantly lower than Ka/Ks for all Arabidopsis proteins with Arabidopsis lyrata orthologs (mean Ka/Ks = 0.21, p-value = 0.028, Student’s t-test). Of the 33 MIND1 hubs that have a known or inferred molecular function, 21 can be considered to have generalist functions in protein modification (e.g. chaperones/thioredoxins, signal peptidases, proteases) or protein sorting (e.g. SNAREs, cornichon, Table S3). Although we cannot exclude the possibility that the high-degree of an individual hub is an artifact of the split-ubiquitin system, we can hypothesize that the ‘unknown’ hubs could also function as generalist protein regulators. Although assigning such functionality to uncharacterized MIND1 hubs awaits further experimentation, we performed sub-cellular localization of 16 MIND1 hubs, including nine with unknown molecular function. All but one of the hub GFP-fusions expressed in Arabidopsis protoplasts localized to endomembranes, consistent with potential functions in membrane protein modification or sorting (Figure S15 and Table S3). Interestingly, the other MIND1 hub, a calmodulin-binding protein with unknown molecular function (IQD6), localized to microtubules (Figure S15, Table S3). A hypothesized role for IQD6 in generalist protein regulation working at microtubules (e.g. as a scaffold connecting microtubules to other microtubule associated proteins) also awaits further experimentation.

  28  

  29  

Figure S15: Sub-cellular localization of hub proteins in Arabidopsis mesophyll protoplasts. Left: Localization of C-terminal GFP fusions of the indicated proteins. Representative maximum intensity projections of confocal Z-stacks are shown. Middle (where applicable): Merged image of GFP signal (green) and chlorophyll autofluorescence (magenta). Right: Brightfield image of the same cell. Scale bars, 5 µm (AT1G14700, AT1G29060, AT1G65690, AT2G27290, AT3G13175, AT3G18800, AT4G05370, AT4G30500, AT5G11890, AT5G49540, AT5G52420), and 10 µm (AT1G17280, AT3G10640, AT3G12180, AT4G05370, AT4G35950). AT2G26180: IQD6-GFP forms filaments in the cytosol that colocalize with mCherry-labeled α-tubulin TuA5. Right: intensity profile along the dotted line in the merged image is shown.

3.9 Statistically enriched interactions within and between protein families in MIND1NH

We examined the interaction patterns within and between MIND1NH protein families, because analyzing PPI networks at the level of protein families has several advantages. If multiple members of one family interact with multiple members of another family, then an interaction motif can be hypothesized, particularly if the proteins in the two families interact with each other more than expected by chance (i.e. statistically enriched connections). Furthermore, because many protein families are only partially represented in the mbSUS screen, considering interactions at the family level is valuable as it can expand association predictions to members not included in the screen. Family level analysis can also be used to make predictions of functional redundancy, either when multiple members of a protein family interact with each other or with the same partner protein.

MIND1NH proteins were grouped into families by sequence-based family assignments and by manually curated family annotations. For sequence-based family assignment, TAIR10 proteome sequences were downloaded and BLAST (Basic Local Alignment Search Tool) was run to generate pairwise alignments

  30  

of protein sequences. The Markov Clustering algorithm (MCL http://micans.org/mcl/) was used to cluster protein sequences based on sequence similarity. MCL has an inflation parameter (which should be larger than 1.0) controlling the granularity or ‘tightness’ of clusters. MCL with the inflation parameter 2.0 (MCL2.0) generated 6,972 families, of which 2,591 families contain four or more proteins. The proteins in MIND1NH fall into 761 families. MCL with the inflation parameter 5.0 (MCL5.0) generated 8,019 families, of which 2,830 families contained four or more proteins. The proteins in MIND1NH fall into 861 families. In the manually curated family annotations, proteins in MIND1NH were classified into 58 protein family ‘types’, and 350 protein family ‘sub-types’ based on gene descriptions found at TAIR. Proteins whose molecular function is unknown were labeled as “unknown”.

To identify intra- and inter-family associations in MIND1NH, we compared the number of interactions within and between families in MIND1NH with those in randomized networks with the same overall structure as MIND1NH. Due to the mbSUS experimental design in which Nub fusions can only be tested against Cub fusions, MIND1NH is a bipartite network, (i.e. a network with two node types in which edges among nodes of the same type are not allowed). We generated 1,000 randomized MIND1NH networks using an edge-shuffling method, which rewires randomly selected edge pairs in the network while maintaining each protein’s degree and the bipartite nature of the network. A non-parametric fit technique (ksdensity in Matlab) was used to estimate the distribution of the numbers of the intra- and inter-family interactions in bipartite random networks, and the p-value for MIND1NH intra- and inter-family interactions was obtained from this probability density (Table S5). The p-values were adjusted for multiple comparisons by using the command P.adjust in R (https://stat.ethz.ch/R-manual/R-devel/library/stats/html/p.adjust.html) and the Benjamini & Hochberg procedure to control the false discovery rate.

There are 12 families with significantly enriched intra-family interactions according to the MCL2.0 family assignment and also 13 families according to the MCL5.0 assignments (FDR<0.05). Nine out of the 12 MCL2.0 families are common to the results based on MCL5.0 families, indicating that the MCL family assignment is reliable, and that the enriched intra-family interactions are robust. According to the manual assignment of protein families, there are 5 family types and 14 family sub-types with significantly enriched intra-family interactions (FDR < 0.05). Three of the sub-type families with enriched intra-family interactions, Mg2+ transporter, aquaporin, and COPT, were also supported by the results of MCL2.0 and MCL5.0 (Table S5). Two family pairs based on MCL2.0, and 6 family pairs based on MCL5.0 had significantly more inter-family interactions than in random networks (FDR<0.05). Two manually curated family type pairs (Figure S14) and 4 family sub-type pairs (Figure S15) were significantly enriched in inter-family interactions. We also compared the average degree of each family from all four family groupings to 1,000 randomized groups of proteins with the same size (Table S5). We detected 11 families with high and 8 families with low average degree (i.e. the mean number of interactions was significantly higher or lower than the mean of the whole network or random families with the same size; Table S5).

Prevalent intra-family interactions indicate potential heterodimerization patterns within a family. The MIND1NH networks contain two families with statistical enrichment of intra-family interactions, the aquaporins, for which the formation of hetero-tetramers is well established (80), and the MRS2 family of magnesium transporters, for which the formation of a homo-pentamer is known at least for the bacterial homolog CorA (81). The predicted existence of hetero-oligomers suggested by our results is a potential mechanism of diversifying function across cell- or tissue-types (Table S5). The average degree of a protein family could itself be indicative of the relative specificity or generality of the functional role of its members. For example, E3 ligases (by family type annotation) have a significantly lower average degree in MIND1NH (~2.4) compared to that in random networks (~5.0), suggesting a relative specificity for their role in targeting proteins for degradation.

  31  

Figure S16: Family-level (Type) Interaction Matrix Heat Map. Only families that have more than 2 proteins are included. The colors above the diagonal represent the Z-scores of observed interactions between two families. The colors below the diagonal indicate the observed numbers of interactions between two families. The colors in the diagonal represent the Z-score and the observed number of interactions within families. A positive Z-score indicates a larger number of interactions than expected, and a negative Z-score indicates a smaller number of interactions than expected as compared to random networks.

  32  

Figure S17: Family-level (Sub-type) Interaction family-based-network. The nodes represent the manually curated family sub-types and the edges indicate the interactions between two families. Only families that have more than 2 proteins are included, and only those family pairs that have 5 or more interactions are listed. The size of the nodes is proportional to the number of proteins in the families, and the width of the edges is proportional to the number of interactions between two families. Abbreviations: Acyl Lipid Metab - Acyl Lipid Metabolism Family, CPA2 - Monovalent Cation:Proton Antiporter−2 (CPA2)_Family, CRR - Cysteine rich repeat secretory protein, emp24 - emp24/gp25L/p24 family/GOLD family protein, Endo 70 – Endomembrane protein 70, LEA - Late embryogenesis abundant (LEA) hydroxyproline−rich glycoprotein family, Methyltransferases - S−adenosyl−L−methionine−dependent methyltransferases superfamily protein, SNARE Golgi - SNARE associated Golgi protein family, WCOR413 - cold acclimation protein WCOR413 family.

3.10 MIND1NH interactions within and between subcellular compartments

To assess interaction patterns across different subcellular compartments, the SUBAcon consensus localization prediction was used to group MIND1NH proteins by cell compartment (82) (http://suba.plantenergy.uwa.edu.au). We then examined the number of protein interactions within subcellular compartments and the number of protein interactions between two different subcellular compartments. Figure 1E summarizes interaction patterns across different subcellular compartments. The proteins in MIND1NH are predominantly plasma membrane localized and these proteins interact most extensively with themselves and with cytosolic proteins (Figure 1E).

3.11 Enriched interactions within and between GO groups in MIND1 and MIND1NH

To identify statistically enriched interactions between different biological processes, i.e. whether proteins involved in certain biological processes (BP) are more likely to interact with each other than by chance, we first classified proteins into different BP terms using the BP domain annotations from GO. To avoid overly generic groupings, we used only the GO terms at or below 3 distances from the root term (GO term “Biological Process”, GO:0000004). For each BP term, all genes annotated to it or its children were considered to belong to this BP. Only BP terms containing at least 4 proteins were included. BPs that consisted of similar protein members (similarity defined as jaccard index greater or equal to 0.8) were clustered into grouped BP terms (Table S6). The proteins in MIND1 were classified into 636 BP terms. We next performed an all-versus-all BP level comparison on the MIND1 network and then built a BP network consisting only of enriched connections of BPs (Figure 2, Table S6). To determine which BP pairs show enriched protein interactions between each other, we calculated the number of protein

  33  

interactions between each pair of BP terms, and then compared it with the same measurements calculated from a randomized network. To reduce the computing time and the number of statistical tests, only BP pairs that have 20 or more interactions were included. We randomized the MIND1 network using an edge-shuffling method, which randomly shuffled the edges within the network while keeping individual node degrees unchanged and taking into consideration the bipartite (Nubs vs Cubs) nature of the network. We generated 10,000 random PPI networks and used a FDR q-value < 0.05 as threshold to determine whether protein interactions between a pair of BPs were significantly enriched compared to random networks. The same procedure was repeated for MIND1NH (Figure 2, Table S6). The network consisting only of enriched BP-BP connections revealed potential functional connections both between distinct BP processes and within BP processes (Figure 2).

3.12 MIND1 and MIND1 NH intra-GO interactions compared to other networks

The percentage of BPs enriched for intra-BP interactions in MIND1 and MIND1 NH is lower relative to that found in AI-1 and LCI (Figure S18) (13). Random sampling analysis showed that this result is not caused by differences in network size. Possible explanations include the prevalence of interactions involving proteins without BP annotations in MIND1 or a biologically driven feature of protein interactions at membranes. Although AI-1 and LCI cover predominantly soluble proteins, LCI showed the highest percentage of biological processes with enriched intra-BP interactions. This is likely due to the bias of small-scale hypothesis-driven investigations, which tend to screen interactions among proteins with functions in the same biological process (14, 83).

Figure S18. MIND1 Intra-GO interactions compared to other networks. Classification of proteins according to biological processes showed that proteins in MIND1 are less likely to interact within GO Biological Processes compared to proteins in AI-1 and LCI. The number of intra-BP interactions in each PPI network was compared to the same measure from 10,000 random networks generated by degree-preserved edge shuffling. A threshold of FDR<=0.05 was used to determine whether a GO BP contains significantly enriched number of interactions.

  34  

Supporting Online Material 4: Membrane Biology in MIND1

4.1 MIND1 interactions within integrated hormone signaling sub-networks

The Arabidopsis Hormone Database 2.0 (http://ahd.cbi.pku.edu.cn/) is a repository of genes related to the eight major phytohormones (abscisic acid, auxin, brassinosteroid, cytokinin, ethylene, gibberellin, jasmonic acid (JA), and salicylic acid (SA)) based on genetic evidence, GO annotations, and expression data. These data types were integrated with MIND1 network to generate hormone-signaling sub-networks with greater support than from MIND1 data alone (Figures S19 – S26).

Figure S19: Integrated ABA signaling sub-network in MIND1. MIND1 contains 311 proteins that are related to ABA signaling: 23 from genetic evidence, 25 from GO annotations, and 288 from expression data. A subset of these genes is found in the ABA signaling sub-network within MIND1. Interactions involving hub proteins are not shown, and proteins that only interact with hubs are shown at left.

Genetic Evidence Gene Ontology ABA ResponsiveMIND1 ABA Signaling subnetwork

MIND1 hub

MIND1 hubinteractors

ANAC089

CPK3

AT3G28040

AT2G17972

LECRKA4.1

OCP3

AT3G19100

NCRK

SWEET1

AT2G32380

NEK3

AT5G38990

DUF26!Ib

AT1G67470

HA2

PIP2;5

AT3G14860

PIP1;4

CPD

AT4G03820

CPK15

CORI1

AT5G66440

AT1G51800PAD3

ALMT1

AT1G04360AT4G31830

KAT1

AP2C1

AAP6

CYP705A4

BAT5

ETR1

MRP5

NRT1.9

AT1G68400RING2NPSN11

AT1G52190

AMT2;1YSL5

PIN7EMB1273

MTP1

KCS20AT1G57680

AT5G56040

AT3G08630

RGS1

CESA2

EMB1923

ABCG11TIP2;2

AT4G37220

AT1G16860

SRF8AT1G50290

KUP6

VTI12BAM2

BAF07!1

ZF14

AT4G34220

ESL1

INT1

AT1G11330

AT2G23450

LECRKA4.2

AUX1

CHX20

AT1G11210DUF26!Ib

PRA1.F4AT3G16180

AT3G02420CER5

AT5G57100SWEET16

PTR4

ABCG23

AT1G32410

IPCS2

EVR

COR413!PM1

SUC4

DUF26!Iberd6!like1TBL36

ROP10 HOS3!1

FEI1CERK1

AT5G13400RABB1B WAT1

CYP707A2ADS2 ANNAT1AT3G53190 NRT1.8

CESA8DUF26!If ProT1BCBAT5G47530

DUF26!IbAT3G56370

AT4G12000ELIP1

AT2G37050

AT1G72300

AT5G19130VSR1AT4G13530

RBOHDRAB11A

NRAMP3AT5G10290 AT5G42420

AT5G07340

HAEAT5G17210

LRR1

NHL1

AT5G49760

CPK30

AT2G42390

GRH1AT1G65690AT5G47180

ATH5

AT1G66760

ERD2

AT3G06470

AT1G12500AGP16

DTX28

AT5G35460HHP2

AT5G52420UBC32

CYP81D8

AT1G62330

AAP3

PK3

ABCG16

PERK15ERD4

TTH1DGL1

ABCG10 MHX1

ABCG20 MYB33AT2G01970

HHP1

AMT1;1

ELIP2

CPK6AT5G10020

AT1G49230PYR1

AT1G71940

AT1G54540AT2G01680CAS

erd6!like5

ABCG6SWEET15

STP4

  35  

Figure S20: Integrated IAA signaling sub-network in MIND1. MIND1 contains 130 proteins that are related to IAA signaling: 15 from genetic evidence, 16 from GO annotations, and 111 from expression data. A subset of these genes is found in the IAA signaling sub-network within MIND1. Interactions involving hub proteins are not shown, and proteins that only interact with hubs are shown at left.

Genetic Evidence Gene Ontology IAA ResponsiveMIND1 IAA Signaling subnetwork

MIND1 hub

MIND1 hubinteractors

AP2C1

ARGOS

MLO6

AT5G66440

AT4G20390

ALMT1

PYR1

PIN4

FAO3

AT4G08300

ALS3AT5G47610

NRT1.9BAF07-1

ABCG2

BRH1

AT1G72300AT3G56370

AT4G31330

SWEET2

AVP1

COR413-PM1MLO4

ProT2CBRLK1

PGP1

PTR5

CLC-BMLO14

IPCS2AAP3

AT1G72200

ProT1

PERK15PIN6

AT2G41610PIN7

AT5G49760

AUX1

RKF3

AT2G41820

CASP5

AT3G47960

AT4G15620

ZF14

AT5G13400

AAP6

HHP2

UCC2

MLO15

NHL3ETR1CYP81D8

AT1G50740

AMT1;2

AT1G68400

PIN5

  36  

Figure S21: Integrated brassinosteroid signaling sub-network in MIND1. MIND1 contains 133 proteins that are related to brassinosteroid signaling: 9 from genetic evidence, 9 from GO annotations, and 125 from expression data. A subset of these genes is found in the brassinosteroid signaling sub-network within MIND1. Interactions involving hub proteins are not shown, and proteins that only interact with hubs are shown at left.

Genetic Evidence Gene Ontology Brassinosteroid ResponsiveMIND1 Brassinosteroid Signaling subnetwork

MIND1 hub

MIND1 hubinteractors

AAP3KUP6

AT2G19130

LRR1BCB

AT1G52190AT1G12460

AT3G07540

AT2G41820

ZF14

CYP707A2

AT5G10290

LUT1

RABH1c

ARK3

AT5G66080

WAT1

MSBP1

AT4G32250

SRF6

CYP83A1|REF2

AT2G25310

AT1G07650CRK10

KCS20

AMT1;1

AT1G12500

CRK42

ARL

TET02

AOS

ATARFB1A

PIN4

ELIP2

AT1G64150

CYP85A2

AT5G13400

AAP6

PERK15

DWF1

AT2G28960

UTR2PAP3

AT5G52420ATH5

  37  

Figure S22: Integrated cytokinin signaling sub-network in MIND1. MIND1 contains 221 proteins that are related to cytokinin signaling: 2 from genetic evidence, 4 from GO annotations, and 215 from expression data. A subset of these genes is found in the cytokinin signaling sub-network within MIND1. Interactions involving hub proteins are not shown, and proteins that only interact with hubs are shown at left.

Genetic Evidence Gene Ontology Cytokinin ResponsiveMIND1 Cytokinin Signaling subnetwork

MIND1 hub

MIND1 hubinteractors

AUX1

AT3G49190

BAF07-1

PUP1ESL1

LFR

ATL15

NPSN13

ZF14

BRH1

AAP3ETR2

AT2G40460PTR1

PHT4;1

NIP2;1ENODL9IPCS2

AT3G16670

AT1G07650

AT1G72200

PUP4

AT2G39360

AT4G31330AT2G01680

CESTAT3G08680

TIP4;1

AT1G64150

AT1G11540CRK10AAP5

CPK7MRP5

AT4G08300

ELIP1AMT1;1

AT2G45980CYP83A1|REF2

AT4G37220

AT1G71110

AT2G35710AT1G51805

KUP6AT4G33940AT5G12420

ZCF61AT4G03600AT4G36180

AT3G28220

NHL3

PAP3

UTR2

ATH5

AT1G27290

BCB

AT3G52070

DAD1

ARGOS

AT1G30080

GPT1

RLK902

CORI1

AT2G42290

MLO6

AT4G15620

PUP18

STP1

CYP71B4

CASP

EMB1273

ANAC089

LOH2

NDF6

PPT2

LECRKA4.1

KT1

ENODL15

AT3G11810PEPC1

AT5G47530

OLEO2

NHL1

AT2G24170

RLCK-XII

PRA1.E

BUS1|CYP79F1|SPS1

AT5G40020

AT4G31340

AMT1;2

AT5G67130

EXO70B2

MLO15

DUF26-Ib

AT3G18215AT1G49230

CYP79B2

LECRK1

AVP2

UTR3

LHT4HHP1

PIP1;3NAP5

AT3G27770

FAO3

DTX1

AT5G56100

NRT1.5AT3G45700LGO

AT1G52190

ATH6

AT5G37890

  38  

Figure S23: Integrated Ethylene signaling sub-network in MIND1. MIND1 contains 100 proteins that are related to ethylene signaling: 13 from genetic evidence, 8 from GO annotations, and 88 from expression data. A subset of these genes is found in the ethylene signaling sub-network within MIND1. Interactions involving hub proteins are not shown, and proteins that only interact with hubs are shown at left.

Genetic Evidence Gene Ontology Ethylene Responsive

MIND1 Ethylene Signaling subnetwork

MIND1 hub

MIND1 hubinteractors

ARGOS

AT4G15620

LPAT2

AT4G26770

CESA3

AT5G47180AT5G13400

CSLA04

AT3G08600

AAP3

AT2G01680

AT2G26730SRF8

DTX40

PIN2ATH3

AT5G05820

FAO3

ARL

  39  

Figure S24: Integrated gibberellin signaling sub-network in MIND1. MIND1 contains 276 proteins that are related to gibberellin signaling: 1 from genetic evidence, 4 from GO annotations, and 271 from expression data. A subset of these genes is found in the gibberellin signaling sub-network within MIND1. Interactions involving hub proteins are not shown, and proteins that only interact with hubs are shown at left.

Genetic Evidence Gene Ontology Gibberellin ResponsiveMIND1 Gibberellin Signaling subnetworkMIND1

hubMIND1 hubinteractors

AT5G48360TIP1;1

KCS20

BAF14-1

AT3G02880ETR1

ERD4CNGC6

ABCG15

INT4

AT3G16180

AT1G11210

AT4G36180

ELIP1

AT4G15620

SIP2;1EVR

DUF26-If

CRK7PIP2;3

VSR1

AT5G01960AT2G39360

RCI2C

SWEET3

LHT2

AT1G51805AT2G42770

AT1G52190

AT5G10290LECRKA4.2

RBOHCAT2G26900

AT5G58150AT4G27435

PRK2A

AT1G50290

BAM2

NIP7;1

PUP1

AT4G31330MLO5

AT3G08680

RKF3SWEET15

erd6-like1BAF09-1

GPT1

AT1G14390

AMT1;4

AT1G27700

IRX15CYP83A1|REF2

AT3G27770

AHA8

AT1G68220KUP9

AT1G08590NRT1:2|NTL1

AT1G72180

NPSN11erd6-like5

CHX18

BIR1AT3G63380

NIP4;1

HA2 PIN7

AUX1

PTR1

AT2G22180

DUF26-Ib

TET11

NEK3

AT3G56370

DTX34

AT1G25510

ABCG20

AGP16

CYP79B2

AT2G41705

PIP1;5MSBP2

ATH5AT5G52420AT1G16170

AT5G11890

HHP2

NHL1

AT5G47530

AT2G24170

AT1G30080

ATG1

NRAMP3

BCB

AT5G11680

PTR5

CYP708A3

SWEET7ANNAT1

CYP707A2

ProT1LRR1

ENODL15

AT1G12500

HAE

CYP716A1

SSI2

CYP85A2

AT1G64150

ABCG13

AT5G13400

AAP6

COR413-PM1

ELIP2

PIN4

BAF10-1

MRP5

PIP1;3

HHP1

SWEET5

AT5G49130

MRS2-1

AT1G54540

AOX2

AT3G22060

AT3G06170

AT2G41820

DUF26-Ib

AT1G49200

NAT5

CESA7

PDLP3

AT1G70280

WAT1

CSLA05

BAF07-1

RING2

AT1G32740

SRF4

RING

MLO4

NRT1.5

LHT4

LOH2

AT3G26110

PAP1

AT5G62710

CYP71B4

DJ1A

PERK4

CHX19

DWF1

SULTR1;3

AT5G63030

AT2G36330

SULTR1;2

PRR1

CYP71B22

CLC-B

CESA8 AT1G15740

FAO3

SVL4

VATG3

WNK2 ANK1

AT1G16260

RLK

AT5G56100

NRT1;1

AT2G16595

GRP18

OLEO2

AT2G25890

SWEET1

  40  

Figure S25: Integrated JA signaling sub-network in MIND1. MIND1 contains 164 proteins that are related to JA signaling: 8 from genetic evidence, 8 from GO annotations, and 156 from expression data. A subset of these genes is found in the JA signaling sub-network within MIND1. Interactions involving hub proteins are not shown, and proteins that only interact with hubs are shown at left.

Genetic Evidence Gene Ontology JA ResponsiveMIND1 JA Signaling subnetwork

MIND1 hub

MIND1 hubinteractors

MLO6

AT4G28330

HHP1

PIP1;3

CRR23

MRS2-2

ARGOSAT2G29180

NHL1BCB

TPC1AT3G18250

RPH1

AT2G33250

CORI1

ATARFB1A

HA2

AP2C1

AOS

GRH1

AT5G66440

RING

TIP1;1

AT2G39360

AAP3AT1G61610

AT2G23200

AT2G40460

NRT1.8

AGB1

WAKL10

ZCF61

EVR

ERD2

AT3G53810

AT1G64150AT3G18800 DTX33

AT2G23450 DHAR1LECRKA4.1

ERD4

AT2G01680

COR413-PM1

EDS5AT5G24010

NRT1:2|NTL1

ESL1

AT4G21570MSL3

ARL

AT5G52050

DUF26-Ib

ALMT1

CESA3

AT2G12400

BUS1|CYP79F1|SPS1

AT1G65690

PAP3

AT3G28220

LRR1

CYP81D8

RLK902

CYP79B2

AGP16

AT1G49230

LECRK1

DUF26-Ib

AT1G49200

  41  

Figure S26: Integrated SA signaling sub-network in MIND1. MIND1 contains 234 proteins that are related to SA signaling: 6 from genetic evidence, 2 from GO annotations, and 228 from expression data. A subset of these genes is found in the SA signaling sub-network within MIND1. Interactions involving hub proteins are not shown, and proteins that only interact with hubs are shown at left.

Genetic Evidence Gene Ontology SA ResponsiveMIND1 SA Signaling subnetwork

EVRSWEET1

AMT1;1DUF26-Ib

GDU3

AT2G45340

AT5G11650

AT3G53810

AT1G14260CPK7

TAP2

AT1G61420

WAKL10

AT2G40270

PUP5

AT4G12980

AT5G56040

DUF26-Ib

AT5G19930PIN7

AT1G50740AT5G59670AT1G11330

AT2G39360

AT5G38210

AT5G22540

AT5G47610

CYP83A1|REF2

AUX1

DUF26-Ib

TIP2;2AT3G08600

ABCG11

AT1G12460

AT3G56370

AT1G68220

TIP1;1

AT4G08300AT5G47420

CYP81K1

AT4G14420AT1G70280

EDS5

CER5

AT1G52600

erd6-like14AT2G33580

AT5G41180RFO1 PIP1;5

AT1G34640

ATH5VPS60.1

CYP81D8

AT1G29060

UTR2AT1G65690

WAK3

PAP3

AT4G38690

ARK3

AT4G15620

AT1G79630

DUF26-Ib

AT3G27770

AT4G23740

AT1G51800

AT3G13950

NDH18

LECRK1

AT1G67470

HHP1ATH6 STP4

TWD1

PIP2;1

PIP1;4 PIP1;2

PIP2;7PIP2;5

AT3G59780PIP2;8

PIP1;3

AT5G25260CA1

AT5G10290

RABH1c

AT2G41820

DHAR1

RCI2F

DWF1

CESA2

AT3G52070

CER10

PERK15

AT3G28130

OPT1

BCB

AT2G17972CRR23

ALF5

AT5G57345AT3G19100

CASUTR3

AT5G51560

CIPK14SEC61 BETA

AT4G34220

AT3G18250

ATKT2

RLK

AT1G16260

MLO3

ERECTA

CSLA05

PDLP3

NDF6

AT5G38990

BCA4

AT5G13400

AT2G42360

CYP85A2

SSI2

RHA1a

AT1G17200

PLDgamma3

RGS1

AT1G48210

PIP2;3

LPAT4

FRK1

AT5G07340

BIR1

AT5G61570

CYP708A3

AT2G16595

AT5G08050

COR413-PM1

DUF26-Ib

AT4G16980

RKL1

ANAC089

AT2G33250

AT3G28040

LOH2

FEI1

SFP1

LECRKA4.1

AT2G29180

MIND1hub

MIND1 hub interactors

  42  

4.2 RLK-GTPase interactions in MIND

Figure S27. Possible signaling motifs involving small GTPase signaling proteins. (A) Multiple interactions between RLKs and small GTPase signaling proteins in MIND1NH (size of node is proportional to degree). (B) Additional interactions in the MIND1 network are dominated by the hub Rab5-interacting protein AT5G49540 (size

  43  

of node is proportional to degree). (C) Elaboration of small GTPase interaction motifs by combining with other interactome data (literature curated interactome (LCI, (13) (67)) and (AI-1MAIN (13).

  44  

4.3 SNARE proteins and BRI1 regulation

Figure S28. Overexpression of SYP22 in bri1-5 mutant background results in reduced plant stature. (Top) Phenotype of bri1-5, SYP22 overexpression (OX) plants and (Bottom) SYP22 transcript levels of SYP22 overexpression (OX) lines compared to the bri1-5 mutant.

Figure S29. Brassinosteroid sensitivity of vamp727 and syp22 single mutants is similar to wild type. (A) Wild type (WT), vamp727, and syp22 plants grown on 0.5X MS media for 5 days with 10 nM or without brassinolide (BL). (B) Hypocotyl length of vamp727 and syp22 plants grown under different concentrations of BL. Error bars indicate standard deviations (n > 9 plants).

  45  

Figure S30. Schematic representation of hypothesized roles of VAMP727 and SYP22. One may hypothesize that BRI1 is endocytosed from the plasma membrane (PM) to endosomes and either recycled from endosomes to the plasma membrane or stored/degraded in vacuoles. VAMP727 helps BRI1 to traffic to the early endosome based on VAMP727 plasma membrane localization, while VAMP727 and SYP22 together relocate BRI1 from recycling vesicles to either the plasma membrane or the vacuole. TGN = Trans Golgi Network.

  46  

Supporting Online Material 5: Annotations for Supplementary Tables 1-6

5.1 Supplementary Table 1

Tab 1: MIND1 Col A. MIND1 Potential Protein-Protein Interaction by AGI - The putative interactions listed here comprise the Membrane-based Interactome Network Database (MIND). Col B. MIND1 vs MIND1 non-hub Col C. Tertiary Screen (0-3 assays) Col D. Split-GFP Assay Col E. Nub (AGI) - The Split-Ubiquitin System is not symmetrical. One half of an interacting pair is an Arabidopsis clone fused to the N-terminus of ubiquitin (Nub). We term these clones Nub clones. Any protein can be tested as a Nub clone. Col F. Nub AGI degree MIND1 (out of 1070) -Degree is the number of interactions (edges) found for a given Nub or Cub (node) in the network. Nodes with many edges are considered Hubs and individual Hub interactions may not be highly specific and should be interpreted with this in mind. Col G. Nub Gene Name Col H. Nub Gene Sub-Type Col I. Nub Gene Type Col J. Nub Membrane Topology Type - There are different types of protein topology in the membrane. 0 - soluble 2 - NoutCin (1 transmembrane domain) 3 - NinCout (1 transmembrane domain) 4 - NinCout (>1 transmembrane domain) 5 - NinCin (>1 transmembrane domain) 6 - NoutCout (>1 transmembrane domain) 7 - NoutCin (>1 transmembrane domain) Col K. Nub #Trans-membrane Domains - Membrane domains predicted by aramemnon Col L. SUBACON unique localization for Nub - Subcellular localization predicted by SUBAcon Col M. GO Biological Process Annotation from experimental evidence - GO Experimental Evidence Codes: EXP,IDA,IPI,IMP,IGI and IEP Col N. Cub (AGI) - The Split-Ubiquitin System is not symmetrical. One half of an interacting pair is an Arabidopsis clone fused to the C-terminus of ubiquitin (Cub) and the PLV transcription factor that controls marker gene expression. We term these clones Cub clones. Only some proteins can be tested as Cub clones, (mostly membrane proteins). Col O. Cub AGI degree MIND1 (out of 3233) - Degree is the number of interactions (edges) found for a given Nub or Cub (node) in the network. Nodes with many edges are considered Hubs and individual Hub interactions may not be highly specific and should be interpreted with this in mind. Col P. Cub Gene Name Col Q. Cub Gene Sub-Type Col R. Cub Gene Type Col S. Cub Membrane Topology Type - There are different types of protein topology in the membrane. 0 - soluble 2 - NoutCin (1 transmembrane domain) 3 - NinCout (1 transmembrane domain) 4 - NinCout (>1 transmembrane domain) 5 - NinCin (>1 transmembrane domain) 6 - NoutCout (>1 transmembrane domain) 7 - NoutCin (>1 transmembrane domain) Col T. Cub #Trans-membrane Domains - Membrane domains predicted by aramemnon Col U. SUBACON unique localization for Cub - Subcellular localization predicted by SUBAcon

  47  

Col V. GO Biological Process Annotation from experimental evidence - GO Experimental Evidence Codes: EXP,IDA,IPI,IMP,IGI and IEP Col W. Coexpressed in any ATTED condition - Coexpression Analysis from ATTED. Cutoff for coexpression was <1000 Col X. Coexpressed over all ATTED conditions Tab 2: MIND1 Node Attributes Col A. AGI Col B. Gene Name (short version) Col C. Gene Sub-Type Col D. Gene Type Col E. MIND1 degree Col F. Degree as Nub Col G. Degree as Cub Col H. Membrane Topology Type - There are different types of protein topology in the membrane. 0 - soluble 2 - NoutCin (1 transmembrane domain) 3 - NinCout (1 transmembrane domain) 4 - NinCout (>1 transmembrane domain) 5 - NinCin (>1 transmembrane domain) 6 - NoutCout (>1 transmembrane domain) 7 - NoutCin (>1 transmembrane domain) Col I. #Trans-membrane Domains - Membrane domains predicted by aramemnon Col J. SUBACON unique localization - Subcellular localization predicted by SUBAcon Col K. Gene Name Tab 3. AGI Screened Col A. AGI Col B. Gene Type Col C. Gene Subtype Col D. # of Transmembrane Domains Col E. Cloned as Nub Col F. Cloned as Cub Col G. Cub Compatible with Screen? If No why? Col H. Cub Passed Prescreen? Col I. Screened as Nub Col J. Screened as Cub Col K. Nub found in 1º screen Col L. Cub found in 1º screen Tab 4. Split-GFP Results Col A. Nub AGI Col B. Cub AGI Col C. Split-GFP Result (1=positive, 0=negative) Col D. Interaction type (PRS = positive reference set, NRS = negative reference set) 5.2 Supplementary Table 2 Tab 1. BP-all: GO Biological Process enrichment in the neighbors of MIND1NH proteins Col A. Proteins Col B. Degree

  48  

Col C. Descriptions Col D. GO IDs in neighbors Col E. GO terms in neighbors Col F. P value Col G. Neighbors Tab 2. MF-all: GO Molecular Function enrichment in the neighbors of MIND1NH proteins Col A. Proteins Col B. Degree Col C. Descriptions Col D. GO IDs in neighbors Col E. GO terms in neighbors Col F. P value Col G. Neighbors Tab 3. CC-all: GO Cellular Component enrichment in the neighbors of MIND1NH proteins Col A. Proteins Col B. Degree Col C. Descriptions Col D. GO IDs in neighbors Col E. GO terms in neighbors Col F. P value Col G. Neighbors Tab 4. BP-Nubs: GO Biological Process enrichment in the Cub neighbors of MIND1NH Nub proteins Col A. Proteins Col B. Degree Col C. Descriptions Col D. GO IDs in neighbors Col E. GO terms in neighbors Col F. P value Col G. Neighbors Tab 5. MF-Nubs: GO Molecular Function enrichment in the Cub neighbors of MIND1NH Nub proteins Col A. Proteins Col B. Degree Col C. Descriptions Col D. GO IDs in neighbors Col E. GO terms in neighbors Col F. P value Col G. Neighbors Tab 6. CC-Nubs: GO Cellular Component enrichment in the Cub neighbors of MIND1NH Nub proteins Tab 7. BP-Cubs: GO Biological Process enrichment in the Nub neighbors of MIND1NH Cub proteins Col A. Proteins Col B. Degree Col C. Descriptions Col D. GO IDs in neighbors Col E. GO terms in neighbors Col F. P value

  49  

Col G. Neighbors Tab 8. MF-Cubs: GO Molecular Function enrichment in the Nub neighbors of MIND1NH Cub proteins Col A. Proteins Col B. Degree Col C. Descriptions Col D. GO IDs in neighbors Col E. GO terms in neighbors Col F. P value Col G. Neighbors Tab 9. CC-Cubs: GO Cellular Component enrichment in the Nub neighbors of MIND1NH Cub proteins Col A. Proteins Col B. Degree Col C. Descriptions Col D. GO IDs in neighbors Col E. GO terms in neighbors Col F. P value Col G. Neighbors 5.3 Supplementary Table 3 Tab 1. MIND1 Hubs Col A. MIND1 Hub AGI Col B. MIND1 Degree Col C. Ka/Ks vs A. lyrata Col D. Hypothesized generalist protein regulator? Col E. Protein Modification/ Sorting Col F. Gene Model Name Col G. Localization of GFP fusion protein Col H. Annotation Col I. Gene Model Description 5.4 Supplementary Table 4 Tab 1. Blondel – Modules detected using Blondel method Col A. Module # Col B. AGI# Col C. Descriptions Col D. GO ID Col E. GO terms Col F. Corrected P-values by Bonferroni correction Col G. Uncorrected P-values Tab 2. MCL – Modules detected using MCL method Col A. Module # Col B. AGI# Col C. Descriptions Col D. GO ID Col E. GO terms Col F. Corrected P-values by Bonferroni correction

  50  

Col G. Uncorrected P-values 5.5 Supplementary Table 5 Tab 1. High and Low-Degree Families Col A. Type Col B. Family category Col C. Family Name Col D. Family size Col E. average degree Col F. std degree Col G. max degree Col H. min degree Col I. random degree Col J. P-values Col K. Corrected P-values by FDR Tab 2. Enriched Intra-family PPI – Families enriched for protein-protein interactions within the family Col A. Family category Col B. Annotated family names Col C. # proteins Col D. MIND1nh Col E. Random networks Col F. P-values Col G. Corrected P-values by FDR Col H. Z-scores Tab 3. Enriched Inter-family PPI - Family pairs enriched for protein-protein interactions between them Col A. Family category Col B. Family 1 Col C. Family category Col D. Family 2 Col E. Prot. in F.1 Col F. Prot. in F.2 Col G. MIND1nh Col H. Random networks Col I. P-value Col J. Corrected P-values by FDR Col K. Z-scores Tab 4. MCL family AGI annotations – AGIs annotated to gene families Col A. Gene AGI Col B. Gene Family Code from MCL groupings 2.0 Col C. Gene Family Code from MCL groupings 5.0 5.6 Supplementary Table 6 Tab 1. BP-BP network edges Col A. Network Col B. GO group A Col C. Short name

  51  

Col D. GO group A names Col E. Group A members Col F. GO Group B Col G. Short name Col H. GO group B names Col I. Group B members Tab 2. BP-BP network nodes Col A. Network Col B. GO group Col C. Short name Col D. GO group names Col E. GO group members

1

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