Expression constructs and cell line generation
Drosophila ORFs of interest were PCR amplified from Drosophila cDNA pools using the Phusion Polymerase (Finnzymes), sequenced and inserted into a Gateway compatible entry vector (pENTR-D-TOPO, Invitrogen). Using LR recombination, the ORFs were transferred from the entry vector into in-house designed expression vectors allowing inducible expression of the bait protein fused to a triple hemagglutinin (HA) affinity-tag from the Metallothionein promoter. For generation of inducibly expressing cell lines, the expression construct was transfected using the Transfecten reagent (Qiagen) to Drosophila Kc167 cells cultivated in Schneiders S2 medium (Invitrogen) containing 10% FBS and 50 μg/ml penicillin, 50 μg/ml streptomycin. In all, 10 μg/ml Blasticidin was used for selection for 5 weeks. Cell pools were tested for positive expression by western blotting using anti-HA antibodies (Covance). For bait expression, the cells were exposed to 600 μM CuSO4 overnight.
Prior to AP, Kc167 cells were grown in shaking flasks in Schneider S2 medium containing 10% FBS. The cells were serum starved in 2% FBS overnight and bait expression was induced using 600 μM CuSO4 for at least 16 h. Cells were either treated with 100 nM insulin for 20 min or left untreated before harvest. For AP, the cell pellets were lysed on ice for 30 min in 10 ml HNN (50 mM HEPES pH 7.5, 5 mM EDTA, 250 mM NaCl, 0.5% NP-40, 1 mM PMSF, 50 mM NaF, 1.5 mM Na3VO4, protease inhibitor cocktail (Roche), 3 mM DSP) using a tight-fitting Dounce homogenizer. Following cell lysis, reactive DSP was quenched by adding 1 ml 1M Tris, pH 7.5. Insoluble material was removed from the lysate by centrifugation and the supernatant was precleared using 100 μl Protein A-Sepharose (Sigma) for 1 h at 4 °C on a rotating shaker. After removal of the Protein A-Sepharose, 100 μl anti-HA agarose (Sigma) was added to the extracts and incubated for 4 h at 4 °C on a rotating shaker. Immunoprecipitates were washed 4 × with 20 bedvolumes of lysis buffer and 3 × with 20 bedvolumes of buffer without detergent and protease inhibitor. The proteins were released from the beads by adding 3 × 150 μl 0.2 M Glycine, pH 2.5. Following neutralization using 100 μl 1 M NH4CO3, the eluates were treated with 5 mM TCEP for 30 min at 37 °C and alkylated with 10 mM Iodacetamide for 30 min at RT in the dark. For tryptic digest, 1 μg trypsin was added to the eluate and incubated at 37 °C overnight. The tryptic digest was acidified to pH<3 using TFA and purified using C18 Microspincolumns (Harvard Apparatus) according to the protocol of the manufacturer. Dried peptides were resolved in 0.1% formic acid containing 1% acetonitrile and injected into the mass spectrometer.
LC–MS/MS analysis of affinity-purified samples was performed on an LTQ-FT-ICR mass spectrometer (Thermo Electron), which was connected to an online electrospray ion source. Peptide separation was carried out using an Eksigent Tempo nano LC System (Eksigent Technologies) equipped with a RP-HPLC column (75 μm × 15 cm) packed in-house with C18 resin (Magic C18 AQ 3 μm; Michrom BioResources) using a linear gradient from 96% solvent A (0.15% formic acid, 2% acetonitrile) and 4% solvent B (98% acetonitrile, 0.15% formic acid) to 35% solvent B over 60 min at a flow rate of 0.3 μl/min. The data acquisition mode was set to obtain one high-resolution MS scan in the ICR cell at a resolution of 100 000 full width at half maximum (at m/z 400) followed by MS/MS scans in the linear ion trap of the three most intense ions (overall cycle time of 1 s). To increase the efficiency of MS/MS attempts, the charged state screening modus was enabled to exclude unassigned and singly charged ions. Only MS precursors that exceeded a threshold of 150 ion counts were allowed to trigger MS/MS scans. The ion accumulation time was set to 500 ms (MS) and 250 ms (MS/MS) using a target setting of 106 (for MS) and 104 (for MS/MS) ions. After every sample, a peptide mixture containing 200 fmol of [Glu1]-Fibrinopeptide B human (Sigma, Buchs) was analyzed by LC–MS/MS to constantly monitor the performance of the LC–MS/MS system.
For directed mass spectrometry, LC–MS/MS experiments were carried out on an Orbitrap Velos mass spectrometer coupled online to a nano-LC systems (Proxeon Biosystems) and an electrospray ion source (Proxeon Biosystems). LC settings (flow rates and buffer composition) were identical to those described before. Survey full MS spectra were acquired in the Orbitrap with a resolution of 60 000 full width at half maximum (at m/z 400), followed by MS/MS spectra acquired in the linear ion trap of the five most intense ions. Another five MS/MS spectra were triggered on target m/z derived from in silico digests of dTOR core components (dTOR, dRaptor, dRictor, CG3004/dGβL, dSIN1, Lobe, dS6k, Thor/d4E-BP, CG16908, Unkempt, LqfR) using trypsin as protease. The charge state screening modus was enabled to exclude singly charged and uncharged ions. General settings were similar to FT-MS measurements, except CID-based fragmentation was triggered when the precursor exceeded 500 ion counts. The dynamic exclusion duration was set to 15 s. The ion accumulation time was set to 300 ms (MS) and 50 ms (MS/MS). All MS raw data can be accessed via the following ftp site:
MS2 peptide assignment
Acquired MS2 scans were searched against the Drosophila
Flybase database version 5.7 using the SORCERER-SEQUEST (TM) search algorithm, which was run on the SageN Sorcerer (Thermo Electron). Data represented in Supplementary Table 9
were searched using MASCOT against a decoy database. In silico
trypsin digestion was performed after lysine and arginine (unless followed by proline) tolerating two missed cleavages in fully tryptic peptides. Database search parameters were set to allow phosphorylation (+79.9663 Da) of serine, threonine and tyrosine as a variable modification and carboxyamidomethylation (+57.021464 Da) of cysteine residues as fixed modification. Furthermore, a variable modification of lysine residues (+145.01975) from the carboxyamidomethylated cleaved DSP cross-linker was considered. The fragment mass tolerance was set 0.5 Da and the precursor mass tolerance to 10 p.p.m. Search results were evaluated on the Trans Proteomic Pipeline using Peptide Prophet (v3.0) and Protein Prophet (Keller et al, 2002
; Nesvizhskii et al, 2003
). For SEQUEST searches, a minimum peptide probability corresponding to <5% false discovery rate (FDR) was required for protein identification. For MASCOT searches, only peptides with a scores of 31 corresponding to a 5% FDR were accepted.
Filtering for specific interaction partners
SAINT (Breitkreutz et al, 2010
; Choi et al, 2010
) was used to assign confidence scores to observed PPIs. SAINT performs statistical modeling of the quantitative (in this work, using normalized spectral counts) bait–prey association matrix. It generates distributions for true and false interaction and reports the probability score for classification into the two categories. To take advantage of the control purifications (using GFP as a bait protein) generated in parallel with experimental purifications using bait proteins, the data were analyzed using SAINT 2.0 version of the algorithm (Choi et al, 2010
). In SAINT 2.0, the false interaction distribution for each prey protein is learned with the help of the quantitative prey abundance data observed in control purifications. After simultaneously learning both true and false interaction distributions from the data, the method determines whether the observation of a prey protein in a particular experimental purification indicates true interaction based on that prey's abundance measurement relative to the prey-specific false and true interaction distributions () using Bayes' rule:
Because bait proteins were profiled in two biological replicates for each condition (insulin-treated and -untreated AP–MS experiments), the final SAINT score is computed as an average of the individual probabilities across the replicates. Bait–prey interactions are sorted in a decreasing order of SAINT scores. The FDR associated with a threshold can be approximated from the probabilities in the selected set of interactions (see also ):
In running this data set, the quantitative data for both conditions were pooled into a single data set, where identical baits in different conditions were treated as independent baits, and SAINT was applied to this data. High confidence interactions were selected to meet the requirement that the local FDR is controlled at (1–x
) × 100% (posterior probability x
). In the first step, network components were defined based on a SAINT posterior probability of 0.99. In addition to the SAINT filtering, which removed the majority of contaminants, we excluded 17 known contaminant proteins (Supplementary Table 3
), resulting in the identification of 58 high confidence network components. Protein interactions between high confidence network components were included in the network model, if SAINT probability was at least 0.8. The filtered protein interaction data from this publication have been submitted to the IMEx (http://www.imexconsortium.org
) consortium through IntAct (Aranda et al, 2010
) and assigned the identifier IM-15821.
Detection of insulin-sensitive protein interaction and analysis of dTOR complexes using label-free quantification
Differences in complex composition between stimulation conditions were quantified based on the ratio of MS1 signal intensity under insulin-stimulated and -unstimulated conditions. In order to increase significance of apparent differences, a three-step dilution series was measured by mixing tryptic peptides from insulin-starved samples with peptides from the corresponding insulin-stimulated samples as described in Rinner et al (2007)
. MS1 signal intensities obtained for each peptide mapping to a specific protein were grouped according to the dilution factor (0, 30, 100% insulin-treated sample). The median signal intensity of the 10 most intense peptide precursors of the individual bait proteins were used to calculate factors that were used to normalize prey MS1 abundance profiles relative to the bait abundance. Individual peptide dilution profiles were accepted for further analysis when aligned MS1 features were detected at least twice within the profile. Otherwise, the individual peptide profile was discarded from analysis. In cases where only two data points were observed, the profile was extrapolated to cover a full profile. On each valid peptide profile, a linear regression from dilution factor to MS1 signal intensity was performed to determine the difference between the theoretical and observed protein abundance profile. The Pearson product-moment correlation coefficient (r
) was used to represent the quality of profiles, indicating insulin-regulated interactions. Data points differing by >2 s.d. from the average intensity for a specific dilution point were discarded as outliers before linear regression. Profiles showing a linear regression with r
>0.5 were accepted. The slope inclination of the profile indicates the enrichment of an interaction between insulin-stimulated and -unstimulated condition. Protein profiles were considered as changed when the enrichment factor of the same bait–prey protein profile in both replicate experiment were >1.5 (enriched) or <0.67 (depleted). The validity of a 1.5-fold cutoff was evaluated using triplicate experiments followed by t
-test analysis (see Supplementary Figure 1
and Supplementary Table 9
For analysis of dTOR complexes, defined dTOR core components (dTOR, dRaptor, dRictor, CG3004/dGβL, SIN1, Lobe, CG16908, Unkempt, LqfR) were in silico digested. Predicted double and triply charged tryptic fragment m/z were used as target masses for inclusion list LC–MS/MS analysis. Profile mzXML data for each AP experiment (four purifications per bait protein from insulin-treated and -untreated cells) were used for label-free quantification using Progenesis software Version 3.0 (Nonlinear Dynamics Limited). The raw data were first normalized by the Median TIC. In a second step, the three most intense peptide signal intensities of aligned peptides matching to dTOR core components were extracted from LC–MS maps (TOP3) and the average signal intensity of the TOP3 peptides for each protein in each AP–MS experiment was calculated. The average TOP3 intensities of each protein were then normalized by the average TOP3 intensity of the respective bait protein in each AP–MS experiment. The data were further processed with the Spotfire Decision Site program (TIBCO).
Analysis of protein interaction data
hairpin lines 25 707 (UAS-lqfRRNAi
), 47 096 (UAS-CG1315RNAi
) and 16908R-1 (UAS-CG16908RNAi
) were obtained from the Vienna Drosophila
RNAi Center and the National Institute of Genetics (Japan), respectively. CG16908MB01483
(Metaxakis et al, 2005
) and the GAL4
driver line ey-GAL4
(Hazelett et al, 1998
) were from the Bloomington Drosophila
Stock Center. The alleles lqfRΔ117
(Lee et al, 2009
) and TOR2L1
(Oldham et al, 2000
) as well as the FRT insertions FRT40A
(Xu and Rubin, 1993
) and the lines y w ey-FLP; FRT40A, w+, cl2L3/CyO
and y w ey-FLP; FRT82B, w+, cl3R3/TM6B, Tb, Hu, y+
(Newsome et al, 2000
) have been described. Lines carrying mutations on FRT chromosomes were established by meiotic recombination.
For , ey-GAL4 females have been crossed to males carrying the different UAS transgene insertions. For , y w ey-FLP; FRT40A, w+, cl2L3/CyO or y w ey-FLP; FRT82B, w+, cl3R3/TM6B, Tb, Hu, y+ females have been crossed to males of the following lines: (1) y w; FRT82B/TM6B, Tb, Hu, y+, (2) y w; FRT40A, Tor2L1/CyO, (3) y w; FRT82B, lqfRΔ117/TM6B, Tb, Hu, y+, (4) y w; FRT82B, CG16908MB01483/TM6B, Tb, Hu, y+.
Generation of dsRNA
Gene fragments fused to T7 promoters were amplified by PCR (for primer sequences see Supplementary Table 11
) and subjected to in vitro
transcription using the Ambion Megascript Kit.
Transfection of dsRNA into Drosophila KC cells and cell lysis
In all, 106 Kc cells were plated in 1 ml serum-free medium (six-well plate) and incubated with 10 μg dsRNA. After 30 min, serum-containing medium was added. Five days after transfection, the cells were stimulated with 40 μg insulin (Sigma) for 10 min, washed with PBS and lysed on ice for 30 min in 30 μl lysis buffer (120 mM NaCl, 50 mM Tris–HCl (pH 8), 20 mM NaF, 1 mM EDTA, 6 mM EGTA, 15 mM Na4P2O7, 1 mM benzamidine, 1% NP-40) supplemented with 30 mM para-nitrophenylphosphate and 30 mM β-glycerolphosphate. After centrifugation, the whole-cell lysates were analyzed by SDS–PAGE.
Immunoblotting and quantification of band signal intensities
Hybond ECL membranes (GE Healthcare) and Immobilon Western detection reagent (Millipore) were used for immunoblotting. The following antibodies were used: rabbit anti-phospho-Drosophila p70 S6K (Thr398; Cell Signaling) at 1:9.000, rabbit anti-phospho-AKT (Ser473; Cell Signaling) at 1:9.000, rabbit anti-phospho-4E-BP1 (Thr37/46; Cell Signaling) at 1:1.000 and mouse anti-α-tubulin (DM1A; Sigma) at 1:100.000.
Scanned images of the immunoblots were processed with ImageJ for quantification of band signal intensities. Therefore, individual bands were selected by a rectangle and the total signal intensity was determined after correcting each pixel within the rectangle by a background intensity value. This value was obtained by calculating the average pixel intensity of a region directly above or below the selected rectangle. Subsequently, the signal intensities of the bands corresponding to P-S6K, P-PKB and P-d4E-BP were further corrected for loading differences by normalization with the corresponding tubulin bands. For the heat map in , the signal intensities of the bands corresponding to P-S6K, P-PKB and P-d4E-BP were averaged for each RNAi experiment after normalization to EGFP RNAi experiments, which were set to 100%.
Note: The two bands, which are recognized by the anti-phospho-PKB antibody, represent the two phosphorylated PKB isoforms and have been quantified simultaneously.