In vivo identification of GTPase interactors by mitochondrial relocalizationand proximity biotinylation
The ~160 small GTPases that comprise the Ras superfamily include many major regulators of growth, membrane traffic and the cytoskeleton, and a wide range of diseases are caused by mutations in particular members. They function as switchable landmarks with the active GTP-bound form recruiting to the membrane a specific set of effector proteins. The location and level of the GTP-bound form is precisely controlled by upstream regulators that promote acquisition of GTP (GEFs) or its hydrolysis to GDP (GAPs). We report here MitoID, a method for identifying effectors and regulators by performing in vivo proximity biotinylation with mitochondrial-localized forms of the GTPases. Applying this to 11 human Rab GTPases identified many known effectors and GAPs, as well as putative novel interactors, with examples of the latter validated for Rab2, Rab5 and Rab9. We also show that MitoID can efficiently identify effectors and GAPs of members of other GTPase families such as Cdc42, RhoA, Rac1, Rheb, RalB and N-Ras, and can also identify GEFs by use of GDP-bound forms.
Sample Processing Protocol
Affinity capture of biotinylated proteins The protocol for isolating proteins biotinylated by BirA* was adapted from the BioID method (Roux et al., 2012). Briefly, HEK293T cells were grown in two 175 cm2 flasks to ~50% confluence and transfected with 25 μg plasmid and 75 μl FuGENE6 in 2 ml Opti-MEM according to the manufacturers instructions (Promega). One day after transfection, biotin was added to 50 μM, and the cells incubated for a further day. Cells were pelleted by centrifugation (1000g, 5 min), washed once in ice-cold PBS and resuspended in lysis buffer (25 mM Tris pH 7.4, 150 mM NaCl, 1 mM EDTA, 1%(v/v) Triton X-100, 1 mM PMSF, 1 cOmplete protease inhibitor cocktail tablet (Roche)/50 ml buffer), and incubated for 30-60 min at 4°C with rotation. After centrifugation at 10,000 g for 10 min at 4°C, the supernatants were added to 500 μl Dynabeads MyOne Streptavidin C1 beads (Invitrogen) that had been pre-washed twice in the same buffer. The beads were incubated at 4°C overnight, washed twice in Wash-Buffer 1 (2% SDS PAGE, cOmplete inhibitors), three times in Wash-Buffer 2(1% (v/v) Triton X-100, 0.1% (w/v) deoxycholate, 500 mM NaCl, 1 mM EDTA, 50 mMHEPES, cOmplete inhibitors, pH 7.5), and three times in Wash-Buffer 3 (50 mM TrispH 7.4, 50 mM NaCl, cOmplete inhibitors). Finally, the beads were incubated in 75 μlSDS sample buffer containing 3 mM biotin at 98°C for 5 min to dissociate the biotinylated proteins from the beads. 1 mM β-mercaptoethanol was subsequently added to the SDS sample buffer and 20 μl of the sample was analysed by SDSPAGE and mass spectrometry with the remainder reserved for immunoblotting. These experiments were performed in three biologically separate replicates. Mass spectrometry Samples obtained from affinity chromatography and proximity biotinylation were loaded on 4-20% Tris-glycine SDS-PAGE gels and run for 1-2 centimeters. Proteins were stained with Coomassie Instant Blue, the entire gel lane cut into eight slices that were placed in a 96-well plate and destained with 50% v/v acetonitrile and 50 mM ammonium bicarbonate, reduced with 10 mM DTT, and alkylated with 55 mM iodoacetamide. Digestion was with 6 ng/μl trypsin (Promega, UK) overnight at 37°C, and peptides extracted in 2% v/v formic acid 2% v/v acetonitrile, and analysed by nano-scale capillary LC-MS/MS (Ultimate U3000 HPLC, Thermo Scientific Dionex) at a flow of ~ 300 nL/min. A C18 Acclaim PepMap100 5 μm, 100 μm x 20 mm 31nanoViper (Thermo Scientific Dionex), trapped the peptides prior to separation on aC18 Acclaim PepMap100 3 μm, 75 μm x 250 mm nanoViper. Peptides were eluted with an acetonitrile gradient. The analytical column outlet was interfaced via a nanoflowelectrospray ionisation source with a linear ion trap mass spectrometer (Orbitrap Velos, Thermo Scientific). Data dependent analysis was performed using a resolution of 30,000 for the full MS spectrum, followed by ten MS/MS spectra in the linear ion trap. MS spectra were collected over a m/z range of 300–2000. MS/MS scans were collected using a threshold energy of 35 for collision-induced dissociation.
Data Processing Protocol
Analysis of mass spectrometry data For analysis of spectral counts and calculation of WD scores LC-MS/MS data were searched against the UniProt KB database using Mascot (Matrix Science), with a precursor tolerance of 5 ppm and a fragment ion mass tolerance of 0.8 Da. Two missed enzyme cleavages and variable modifications for oxidised methionine, carbamidomethyl cysteine, pyroglutamic acid, phosphorylated serine, threonine and tyrosine were included. MS/MS data were validated using the Scaffold programme (Proteome Software Inc). To score the significance of interactors total spectral counts which were converted into D- and WD- scores using the CompPASS platform (Sowa et al., 2009). The D-score assigns more confidence to proteins that are found in replicate experiments and that interact with fewer baits (in this case, fewer Rabs) and is, thus, a measure of specificity and reproducibility. The WD-score, in addition, takes into account that some preys (effectors) may interact with a subset of related baits (Rabs) but that in this case the total spectral counts found in this sub-set of baits will be higher than the general background level. For analysis of spectral intensities and generation of volcano plots LC-MS/MS raw files were processed in MaxQuant (version184.108.40.206) and the peptide lists generated searched against the human reviewed Uniprot FASTA database using the Andromeda search engine embedded in MaxQuant (Cox and Mann, 2008; Cox et al., 2011). Enzyme specificity for trypsin was selected (cleavage at the C-terminal side of lysine and arginine amino acid residues, unless proline is present on the carboxyl side of the cleavage site) and a maximum of two missed cleavages were allowed. Cysteine carbamidomethylation was set as a fixed modification, while phosphorylation of serine, threonine and tyrosine, and oxidation of methionine were set as variable modifications. Peptides were identified with an initial precursor mass deviation of up to 10 ppm and a fragment mass deviation of 0.2 Da. For label-free protein quantitation (MaxLFQ) we required a minimum ratio count of 1, with two minimum and two average comparisons, which enabled normalization of this large dataset (Hein et al., 2015). A false discovery rate (FDR), determined by searching a reverse sequence database, of 0.01 was used at both the protein and peptide level. Data from the Maxquant analysis was analysed on the Perseus platform (Tyanova et al., 2016). Protein identifications were filtered, removing hits to the reverse decoy database as well as proteins only identified by modified peptides. We required that each protein be quantified in two out of the three replicates from the AP-MS samples of at least one bait. Protein LFQ intensities were logarithmized and missing values imputed by values simulating noise around the detection limit using the Perseus default settings (Tyanova et al., 2016). Two sample t-tests were performed in which baits were compared against the entire dataset of GDP-locked GTPases. The false discovery rate was set at 0.05 (5%).
Gillingham AK, Bertram J, Begum F, Munro S. In vivo identification of GTPase interactors by mitochondrial relocalization and proximity biotinylation. Elife. 2019 8 PubMed: 31294692