Overview on the experimental workflow and data processing. (A) Experimental workflow. In all, 16 genes linked to dInR/TOR pathway were PCR amplified from cDNA pools. By recombinatorial cloning, the Drosophila ORFs were transferred into an in-house designed expression vector for inducible expression of epitope-tagged bait proteins. Bait expression was induced and cells were starved overnight in 2% FBS. Half of the cell population was exposed to 100 nM insulin for 20 min. The cells were lysed in the presence of DSP cross-linker and bait proteins were purified by anti-HA AP. All AP–MS/MS experiments were performed as two independent replicates. (B) Data processing of the AP–MS/MS data set for detection of specific PPIs using the SAINT algorithm. Following database search, spectrum counts for each identified protein were extracted from the LC–MS/MS data. Based on normalized spectrum counts, SAINT models the spectrum count distribution of each bait–prey interaction and calculates scores based on the relative abundance of each hit. In all, 24 independently control AP–MS experiments (using GFP as a bait protein) were performed in parallel to model false interaction distribution. (C) SAINT scores (data from two biological replicates) were plotted from dTOR purified in the absence (x axis) or the presence (y axis) of insulin to illustrate the specificity increase by the SAINT filtering approach. (D) Overlap with orthologous PPI data and known Drosophila PPIs. The PPI data obtained in this study were compared with available literature and database information covering known D. melanogaster and related Homo sapiens interactions (see also Materials and methods).

Quantitative analysis of insulin-sensitive PPIs. (A) Schematic overview on the experimental approach for monitoring insulin-sensitive bait–prey interactions using label-free MS and MS1 alignment (for details see Materials and methods). (B) Examples of dilution experiments in combination with label-free quantification and multiple LC–MS alignment. AP–MS analysis was performed on insulin-treated and -untreated Kc167 cell lines expressing Chico (dIRS) and p60 (Pi3K21B). Protein abundance profiles were normalized against the individual bait profiles of Chico and p60 (black lines). Bait–prey interactions showing changes in abundance upon insulin stimulation are presented as red (increase) and blue (decrease) profiles. Lines in green refer to unaffected protein interactions. Error bars indicate s.e.m. of the average MS1 signal intensity of an individual profile in a particular dilution. (C) Overview on enrichment factor distribution of all quantified protein interactions. The average enrichment factor (AEF) of every PPI were calculated from replicate experiments and plotted over the entire data set. All interactions with a SAINT score >0.8 are shown (see also Materials and methods). (D) List of all observed insulin-sensitive interactions. Pearson product-moment coefficient from linear regression analysis (r) as well as AEF calculated based on profile information between two replicates (Exp. A and B, respectively) are listed.

Modularity and insulin sensitivity of the dInR/TOR interaction proteome. High confidence PPI data are visualized using Cytoscape v2.8.1. Bait proteins are represented as hexagons, preys as circles. Names correspond to flybase gene symbols. Interactions that are increased or decreased upon insulin treatment are represented with arrow symbols and edge colors as indicated. Nodes have been color coded according to the functional classification given in the pie chart. Proteins are grouped into functional modules based on prior knowledge and network topology.

Analysis of dTOR complexes by quantitative AP–MS. (A) Workflow of AP–MS analysis for dTOR complex analysis. Peptide precursor m/z values obtained from tryptic in silico digest of dTOR core components were used for inclusion list-LC/MS/MS experiments. Based on all successfully identified peptide features, label-free quantification was performed and average signal intensities of the three most intense peptides (TOP3) were calculated to represent protein abundances. (B) Abundance distribution of proteins identified in dTOR and dGβL (upper panel), dRictor and dRaptor purifications (lower panel) relative to the bait. The average TOP3 signal intensity was used to infer protein abundances within individual AP–MS/MS experiments (data are listed in Supplementary Table 10). The average signal intensities of dTOR core components in each of the indicated purifications were calculated relative to corresponding bait intensity (set to 10E⁵) from four purification experiments and are shown in log scale in the bar chart. Error bars represent standard deviation in log scale.

Functional analysis of dInR/TOR pathway interaction proteome for the regulation of dTOR activity. (A) Effect of RNAi-mediated depletion of network components on the phosphorylation levels of S6K, PKB and d4E-BP. Identified network components were depleted by RNAi in Drosophila Kc167 cells as indicated next to the heat map. Total cell extracts were analyzed by immunoblotting and the band intensities obtained with anti-P-d4E-BP, anti-P-S6K and anti-P-PKB were quantified (see Materials and methods). The band intensities observed in total extracts of cells treated with RNAi against EGFP were set to 100%. Each frame represents the average signal intensity of at least two independent experiments. For further information, see Supplementary Figure 4. (B) Knockdown experiments of lqfR and CG16908 in Kc167 cells. Kc167 cells were treated with dsRNA as indicated and whole-cell lysates were analyzed after insulin stimulation. Individual or simultaneous depletion of LqfR or CG16908 in Kc167 cells severely reduced the phosphorylation of S6K^T398 (P-S6K), PKB^S505 (P-PKB) and d4E-BP^T37/47 (P-d4E-BP/Thor). Depletion of Tor served as positive control and depletion of EGFP as negative control. The anti-α-tubulin (Tub) staining showed equal loading. Numbers on the right side indicate molecular weight markers (in kDa).

Regulation of cell growth by LqfR/Tel2 and CG16908/Tti1. (A) Knockdown experiments of lqfR and CG16908 in the Drosophila eye. ey-GAL4 was used to drive expression of short hairpin UAS constructs in the Drosophila eye. Depletion of lqfR (UAS-lqfR^RNAi) or CG16908 (UAS-CG16908^RNAi) resulted in a severe reduction of eye size, suggesting that both proteins might act as positive growth regulators. A short hairpin construct against CG1315, which does not affect eye size, was used as control (control). (B) Flp-FRT-based mutagenesis of lqfR and CG16908. Flp-FRT-mediated recombination was used to create mutant clones of Tor, lqfR or CG16908 (white tissue) surrounded by wild-type tissue (red tissue) in the Drosophila eye. In comparison to control clones (white tissue, first image; the FRT chromosome carries no mutation), clones mutant for lqfR (white tissue, third image) or CG16908 (white tissue, fourth image) are severely smaller and show a growth disadvantage similar to clones mutant for Tor (white tissue, second image), suggesting a positive growth regulating function of lqfR and the product of CG16908. Exact genotypes are (1) y w ey-Flp; FRT82B, cl^3R3w⁺/FRT82B, (2) y w ey-Flp; FRT40A, cl^2L3 w⁺/FRT40A, Tor^2L1, (3) y w ey-Flp; FRT82B, cl^3R3w⁺/FRT82B, lqfR^Δ117, (4) y w ey-Flp; FRT82B, cl^3R3w⁺/FRT82B, CG16908^MB01483.