Elution Profile Analysis of SDS-induced Subcomplexes by Quantitative Mass Spectrometry
2014; Elsevier BV; Volume: 13; Issue: 5 Linguagem: Inglês
10.1074/mcp.o113.033233
ISSN1535-9484
AutoresYves Texier, Grischa Toedt, Matteo Gorza, Dorus A. Mans, Jeroen van Reeuwijk, Nicola Horn, Jason R. Willer, Nicholas Katsanis, Ronald Roepman, Toby J. Gibson, Marius Ueffing, Karsten Boldt,
Tópico(s)14-3-3 protein interactions
ResumoAnalyzing the molecular architecture of native multiprotein complexes via biochemical methods has so far been difficult and error prone. Protein complex isolation by affinity purification can define the protein repertoire of a given complex, yet, it remains difficult to gain knowledge of its substructure or modular composition. Here, we introduce SDS concentration gradient induced decomposition of protein complexes coupled to quantitative mass spectrometry and in silico elution profile distance analysis. By applying this new method to a cellular transport module, the IFT/lebercilin complex, we demonstrate its ability to determine modular composition as well as sensitively detect known and novel complex components. We show that the IFT/lebercilin complex can be separated into at least five submodules, the IFT complex A, the IFT complex B, the 14–3-3 protein complex and the CTLH complex, as well as the dynein light chain complex. Furthermore, we identify the protein TULP3 as a potential new member of the IFT complex A and showed that several proteins, classified as IFT complex B-associated, are integral parts of this complex. To further demonstrate EPASIS general applicability, we analyzed the modular substructure of two additional complexes, that of B-RAF and of 14-3-3-ε. The results show, that EPASIS provides a robust as well as sensitive strategy to dissect the substructure of large multiprotein complexes in a highly time- as well as cost-effective manner. Analyzing the molecular architecture of native multiprotein complexes via biochemical methods has so far been difficult and error prone. Protein complex isolation by affinity purification can define the protein repertoire of a given complex, yet, it remains difficult to gain knowledge of its substructure or modular composition. Here, we introduce SDS concentration gradient induced decomposition of protein complexes coupled to quantitative mass spectrometry and in silico elution profile distance analysis. By applying this new method to a cellular transport module, the IFT/lebercilin complex, we demonstrate its ability to determine modular composition as well as sensitively detect known and novel complex components. We show that the IFT/lebercilin complex can be separated into at least five submodules, the IFT complex A, the IFT complex B, the 14–3-3 protein complex and the CTLH complex, as well as the dynein light chain complex. Furthermore, we identify the protein TULP3 as a potential new member of the IFT complex A and showed that several proteins, classified as IFT complex B-associated, are integral parts of this complex. To further demonstrate EPASIS general applicability, we analyzed the modular substructure of two additional complexes, that of B-RAF and of 14-3-3-ε. The results show, that EPASIS provides a robust as well as sensitive strategy to dissect the substructure of large multiprotein complexes in a highly time- as well as cost-effective manner. Understanding the orchestration and dynamics of cellular function on the molecular level is one of the challenges in biology. It is now clear that cell regulatory decisions are made by molecular switching events in large but highly dynamic, often coalescing, protein complexes (1.Scott J.D. Pawson T. Cell signaling in space and time: where proteins come together and when they're apart.Science. 2009; 326: 1220-1224Crossref PubMed Scopus (464) Google Scholar, 2.Van Roey K. Dinkel H. Weatheritt R.J. Gibson T.J. Davey N.E. The switches.ELM resource: a compendium of conditional regulatory interaction interfaces.Sci. Signal. 2013; 6: rs7Crossref PubMed Scopus (91) Google Scholar). Protein complex isolation by affinity purification is a common technique, used for the identification of the protein composition of these molecular machines (3.Kocher T. Superti-Furga G. Mass spectrometry-based functional proteomics: from molecular machines to protein networks.Nat. Methods. 2007; 4: 807-815Crossref PubMed Scopus (186) Google Scholar), contributing to the elucidation of spatial and temporal patterns of large protein networks and functional modules within these networks (4.Kiel C. Vogt A. Campagna A. Chatr-aryamontri A. Swiatek-de Lange M. Beer M. Bolz S. Mack A.F. Kinkl N. Cesareni G. Serrano L. Ueffing M. Structural and functional protein network analyses predict novel signaling functions for rhodopsin.Mol. Syst. Biol. 2011; 7: 551Crossref PubMed Scopus (33) Google Scholar). Interaction data derived from different protein complex analyses are the basis for predictions of biological pathways or disease mechanisms concerning those proteins (5.Boldt K. Mans D.A. Won J. van Reeuwijk J. Vogt A. Kinkl N. Letteboer S.J. Hicks W.L. Hurd R.E. Naggert J.K. Texier Y. den Hollander A.I. Koenekoop R.K. Bennett J. Cremers F.P. Gloeckner C.J. Nishina P.M. Roepman R. Ueffing M. Disruption of intraflagellar protein transport in photoreceptor cilia causes Leber congenital amaurosis in humans and mice.J. Clin. Invest. 2011; 121: 2169-2180Crossref PubMed Scopus (79) Google Scholar). Still, in most cases it is difficult or even impossible to determine how, or even if the co-purified proteins assemble as a single module in a cell, limiting the fine-grained description of the complex structure (6.Gingras A.C. Gstaiger M. Raught B. Aebersold R. Analysis of protein complexes using mass spectrometry.Nat. Rev. Mol. Cell Biol. 2007; 8: 645-654Crossref PubMed Scopus (558) Google Scholar, 7.Gavin A.C. Aloy P. Grandi P. Krause R. Boesche M. Marzioch M. Rau C. Jensen L.J. Bastuck S. Dumpelfeld B. Edelmann A. Heurtier M.A. Hoffman V. Hoefert C. Klein K. Hudak M. Michon A.M. Schelder M. Schirle M. Remor M. Rudi T. Hooper S. Bauer A. Bouwmeester T. Casari G. Drewes G. Neubauer G. Rick J.M. Kuster B. Bork P. Russell R.B. Superti-Furga G. Proteome survey reveals modularity of the yeast cell machinery.Nature. 2006; 440: 631-636Crossref PubMed Scopus (2116) Google Scholar). The possibility to integrate module and submodule information in higher order protein networks is extremely valuable for their understanding and opens the route to define pathways of information flow within and between discrete molecular machines. Zooming in on a protein mutated in early childhood blindness, lebercilin, we have previously identified proteins of the intraflagellar transport (IFT) machinery to interact with lebercilin (5.Boldt K. Mans D.A. Won J. van Reeuwijk J. Vogt A. Kinkl N. Letteboer S.J. Hicks W.L. Hurd R.E. Naggert J.K. Texier Y. den Hollander A.I. Koenekoop R.K. Bennett J. Cremers F.P. Gloeckner C.J. Nishina P.M. Roepman R. Ueffing M. Disruption of intraflagellar protein transport in photoreceptor cilia causes Leber congenital amaurosis in humans and mice.J. Clin. Invest. 2011; 121: 2169-2180Crossref PubMed Scopus (79) Google Scholar). IFT appears as a physical entity driving vesicular trafficking through the connecting cilium that bridges the inner and the outer segment of vertebrate photoreceptors (8.Fliegauf M. Benzing T. Omran H. When cilia go bad: cilia defects and ciliopathies.Nat. Rev. Mol. Cell Biol. 2007; 8: 880-893Crossref PubMed Scopus (907) Google Scholar). IFT, like many other multiprotein complexes, is an example for a functionally fairly well described molecular machine with yet unknown molecular topology and mechanical properties. To determine composition, as well as protein complex topology of lebercilin and its interaction with IFT components, we developed a novel workflow, which we termed "elution profile analysis of SDS-induced subcomplexes by quantitative mass spectrometry" (EPASIS) 1The abbreviations used are: EPASIS, Elution profile analysis of SDS-induced subcomplexes by quantitative mass spectrometry; AP, Affinity purification; BN-PAGE, Blue Native Polyacrylamide gel electrophoresis; CMC, Critical micellar concentration; CTLH, C-terminal to Lissencephaly type-1-like homology motif; EPD, Elution profile distance; GPCR, G-protein coupled receptor; IFT, Intraflagellar transport; IFT-A, Intraflagellar transport complex A; IFT-B, Intraflagellar transport complex B; PCP, Protein correlation profiling; PPM, Parts Per Million; SF-TAP, Strep-FLAG-tandem affinity purification; FLAG-IP, FLAG immunoprecipitation. 1The abbreviations used are: EPASIS, Elution profile analysis of SDS-induced subcomplexes by quantitative mass spectrometry; AP, Affinity purification; BN-PAGE, Blue Native Polyacrylamide gel electrophoresis; CMC, Critical micellar concentration; CTLH, C-terminal to Lissencephaly type-1-like homology motif; EPD, Elution profile distance; GPCR, G-protein coupled receptor; IFT, Intraflagellar transport; IFT-A, Intraflagellar transport complex A; IFT-B, Intraflagellar transport complex B; PCP, Protein correlation profiling; PPM, Parts Per Million; SF-TAP, Strep-FLAG-tandem affinity purification; FLAG-IP, FLAG immunoprecipitation.. The approach is a combination of affinity purification (AP) with mild destabilization by sodium dodecylsulfate (SDS), enabling gradual decomposition of protein complexes, with quantitative mass spectrometry (MS) and in silico elution profile distance analysis (EPD, Fig. 1). HEK293-T cells were grown in DMEM (PAA, Pasching, Austria) supplemented with 10% fetal bovine serum and 0.5% Penicillin/Streptomycin. Cells were seeded, grown overnight and then transfected with the corresponding SF-TAP-tagged (9.Gloeckner C.J. Boldt K. Schumacher A. Roepman R. Ueffing M. A novel tandem affinity purification strategy for the efficient isolation and characterisation of native protein complexes.Proteomics. 2007; 7: 4228-4234Crossref PubMed Scopus (177) Google Scholar) DNA constructs using PEI reagent (Polysciences, Warrington, PA) according to the manufacturer's instructions. 48 h later, cells were harvested in lysis buffer containing 0.5% Nonidet-P40 (N P-40), protease inhibitor mixture (Roche, Freiburg, Germany), and phosphatase inhibitor cocktails II and III (Sigma-Aldrich, Taufkirchen, Germany) in TBS (30 mm Tris-HCl, pH 7.4, and 150 mm NaCl) for 20 min at 4 °C. Cell debris and nuclei were removed by centrifugation at 10,000 × g for 10 min. For protein complex destabilization, the cleared lysates were transferred to anti-FLAG M2 agarose (Sigma-Aldrich). After one hour of incubation, the resin was washed three times using wash buffer (TBS containing 0.1% N P-40 and phosphatase inhibitor cocktails II and III, Sigma-Aldrich). For the SDS-destabilization of the protein complexes, the resin was then incubated 3 min with each concentration of SDS (0.00025%, 0.0025%, 0.005%, 0.01%, and 0.1% for lebercilin or 0.00025%, 0.002%, 0.004%, 0.008%, 0.016%, 0.05% for 14–3-3-ε and B-RAF) in SDS-elution buffer (TBS containing phosphatase inhibitor cocktails II and III) at 4 °C. The flow through was collected and precipitated by methanol-chloroform. After every elution step a single wash step was performed. Subsequent to the SDS gradient, the remaining proteins were eluted from the resin by incubation for 3 min with FLAG peptide (200 μg/ml; Sigma-Aldrich) in wash buffer. A schematic representation is given in Fig. 1. For SF-TAP, the cleared lysates were incubated for one hour at 4 °C with Strep-Tactin superflow (IBA, Göttingen, Germany). Subsequently, the resin was washed three times in wash buffer (TBS containing 0.1% N P-40 and phosphatase inhibitor cocktails II and III, Sigma-Aldrich). Proteins were eluted with desthiobiotin (2 mm in TBS). For the second purification step, the eluates were transferred to anti-FLAG M2 agarose (Sigma) and incubated for one hour at 4 °C. The beads were washed three times with wash buffer and proteins were eluted with FLAG peptide (200 μg/ml, Sigma-Aldrich) in wash buffer. Equal amounts of eluates were separated by SDS-PAGE and electrophoretically transferred to PVDF membranes. Membranes were blocked in 5% nonfat milk in TBS/0.1% Tween20 (Sigma-Aldrich) and incubated with anti-TRAF3IP1 (1:1000, mouse, Abnova, Taipei City, Taiwan), anti-14-3-3-ε (1:1000, rabbit, Santa Cruz, Santa Cruz), anti-C20orf11 (GID8, 1:1000, rabbit, Sigma-Aldrich), and anti-FLAG-HRP (1:1000, mouse, Sigma-Aldrich). Secondary antibodies from Jackson Immunoresearch (West Grove, PA) were applied (1:15,000) and protein bands were visualized using ECL plus (GE Healthcare, Freiburg, Germany). See Supplemental Fig. 9. After precipitation of the proteins by methanol-chloroform, a tryptic in-solution digestion was performed as described previously (10.Gloeckner C.J. Boldt K. Ueffing M. Strep/FLAG tandem affinity purification (SF-TAP) to study protein interactions.Curr. Protoc. Protein Sci. 2009; (Chapter 19, Unit19 20)Crossref PubMed Scopus (47) Google Scholar). LC-MS/MS analysis was performed on a NanoRSLC3000 HPLC system (Dionex, Idstein, Germany) coupled to a LTQ OrbitrapXL, or to a LTQ OrbitrapVelos mass spectrometer (Thermo Fisher Scientific, Bonn, Germany) by a nano spray ion source. Tryptic peptide mixtures were automatically injected and loaded at a flow rate of 6 μl/min in 98% buffer C (0.1% trifluoroacetic acid in HPLC-grade water) and 2% buffer B (80% actetonitrile and 0.08% formic acid in HPLC-grade water) onto a nano trap column (75 μm i.d. × 2 cm, packed with Acclaim PepMap100 C18, 3 μm, 100 Å; Dionex). After 5 min, peptides were eluted and separated on the analytical column (75 μm i.d. × 25 cm, Acclaim PepMap RSLC C18, 2 μm, 100 Å; Dionex) by a linear gradient from 2% to 35% of buffer B in buffer A (2% acetonitrile and 0.1% formic acid in HPLC-grade water) at a flow rate of 300 nl/min over 33 min for EPASIS samples, respectively over 80 min for SF-TAP samples. Remaining peptides were eluted by a short gradient from 35% to 95% buffer B in 5 min. The eluted peptides were analyzed by using a LTQ Orbitrap XL, or a LTQ OrbitrapVelos mass spectrometer. From the high-resolution mass spectrometry prescan with a mass range of 300–1500, the ten most intense peptide ions were selected for fragment analysis in the linear ion trap if they exceeded an intensity of at least 200 counts and if they were at least doubly charged. The normalized collision energy for collision-induced dissociation was set to a value of 35, and the resulting fragments were detected with normal resolution in the linear ion trap. The lock mass option was activated and set to a background signal with a mass of 445.12002 (11.Olsen J.V. de Godoy L.M. Li G. Macek B. Mortensen P. Pesch R. Makarov A. Lange O. Horning S. Mann M. Parts per million mass accuracy on an Orbitrap mass spectrometer via lock mass injection into a C-trap.Mol. Cell. Proteomics. 2005; 4: 2010-2021Abstract Full Text Full Text PDF PubMed Scopus (1241) Google Scholar). Every ion selected for fragmentation was excluded for 20 s by dynamic exclusion. For qualitative results, the raw data were analyzed using Mascot (Matrix Science, Boston, USA; version 2.4.0) and Scaffold (version Scaffold_4.0.3, Proteome Software Inc., Portland, USA). Tandem mass spectra were extracted, charge state deconvoluted and deisotoped by extract_msn.exe version 5.0. All MS/MS samples were analyzed using Mascot. Mascot was set up to search the SwissProt_2012_05 database (selected for Homo sapiens, 2012_05, 20245 entries) assuming the digestion enzyme trypsin. Mascot was searched with a fragment ion mass tolerance of 1.00 Da and a parent ion tolerance of 10.0 PPM. Carbamidomethyl of cysteine was specified in Mascot as a fixed modification. Deamidation of asparagine and glutamine and oxidation of methionine were specified in Mascot as variable modifications. Scaffold was used to validate MS/MS based peptide and protein identifications. Peptide identifications were accepted if they could be established at greater than 95.0% probability by the Peptide Prophet algorithm (12.Keller A. Nesvizhskii A.I. Kolker E. Aebersold R. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.Anal. Chem. 2002; 74: 5383-5392Crossref PubMed Scopus (3886) Google Scholar) with Scaffold delta-mass correction. Protein identifications were accepted if they could be established at greater than 99.0% probability and contained at least two identified peptides. Protein probabilities were assigned by the Protein Prophet algorithm (13.Nesvizhskii A.I. Keller A. Kolker E. Aebersold R. A statistical model for identifying proteins by tandem mass spectrometry.Anal. Chem. 2003; 75: 4646-4658Crossref PubMed Scopus (3621) Google Scholar). Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony. Furthermore, proteins were only considered to be specific protein complex components if they were not detected in the control experiments. For quantitative analysis, MS raw data were processed using the MaxQuant software (version 1.3.0.5 (14.Cox J. Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.Nat. Biotechnol. 2008; 26: 1367-1372Crossref PubMed Scopus (9154) Google Scholar)). Trypsin/P was set as cleaving enzyme. Cysteine carbamidomethylation was selected as fixed modification, methionine oxidation, and protein acetylation were allowed as variable modifications. Two missed cleavages per peptide were allowed. The peptide and protein false discovery rates were set to 1%. The initial mass tolerance for precursor ions was set to 6 ppm and the first search option was enabled with 10 ppm precursor mass tolerance. The fragment ion mass tolerance was set to 0.5 Da. The human subset of the human proteome reference set provided by SwissProt (Release 2012_01 534,242 entries) was used for peptide and protein identification. Contaminants like keratins were automatically detected by enabling the MaxQuant contaminant database search. A minimum number of two unique peptides with a minimum length of seven amino acids needed to be detected to perform protein quantification. Only unique peptides were selected for quantification. For label-free quantification the minimum LFQ count was set to three, the re-quantify option was chosen. The option match between runs was enabled with a time window of 2 min, fast LFQ was disabled (see also parameters.txt in supplementary File S1). Statistical analysis of the data was carried out using R (15.R-Core-Team R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, 2012Google Scholar). For the eight IFT/lebercilin vector experiments (48 measurements), in total 175471 unique peptides with minimum peptide length of seven amino acids were identified by searching against the forward version of the database and only 239 unique peptides were identified by searching against a reversed version of the database which indicates a peptide false positive identification rate of 0.14% (239/175471). Without filtering, 1081 proteins were detected for the forward search and ten for the reverse search leading to an indicated protein false positive identification rate of 0.93% (10/1081). To reduce the number of false positive protein identifications, proteins were considered as detected, if they were identified by at least two unique peptides, had a minimal MS/MS spectra count of three (760/1081) and were not flagged as contaminant by MaxQuant (731/760). Additionally, three repeated experiments (18 measurements) using an empty vector as control were performed and the same filter criteria were applied. The Euclidian distance between proteins both detected in the control and the IFT/lebercilin experiments was calculated and the proteins were excluded from further considerations if they showed a distance less than 0.1 in two or more out of 24 comparisons between both experiments (205/731). Finally, proteins had to be present in at least 5/8 (62.5%) repeated experiments, resulting in a total of 290 Proteins that were further analyzed. For the protein complexes of 14-3-3-ε and B-RAF, five experiments were performed. Additionally, five repeated experiments using the SF-TAP vector as a control were performed with the same SDS-gradient. The statistical analysis was performed as described above, leading to a total number of 135 proteins for 14-3-3-ε and 32 proteins for B-RAF, which were further analyzed. Protein intensities for all SDS concentrations of an experiment were combined and the values log2-transformed. To investigate the linear relationship between data points, regression lines determined by minimizing the sum of squares of the Euclidean distance of points to the fitted line ("orthogonal regression") are shown in Fig. 2 (supplemental Fig. S1 and S2 for 14-3-3-ε and B-RAF). Correlations between repeated experiments were estimated using the Pearson correlation coefficient together with its 95% confidence interval. To investigate the safe isolation of elution profiles for different SDS concentrations, Spearman's correlation scores were calculated and plotted in supplemental Fig. S3 (supplemental Fig. S4 and S5 for 14-3-3-ε and B-RAF). Consensus profiles of known marker protein groups (supplemental Table S1 for lebercilin and supplemental Table S2 and S3 for 14-3-3-ε and B-RAF) were calculated by averaging the normalized cumulative intensities of the protein group per concentration step for all experiments, similar to Andersen et al. (16.Andersen J.S. Wilkinson C.J. Mayor T. Mortensen P. Nigg E.A. Mann M. Proteomic characterization of the human centrosome by protein correlation profiling.Nature. 2003; 426: 570-574Crossref PubMed Scopus (1051) Google Scholar). The elution profile distance between a protein and a consensus profile was calculated as:epd(x,c)=∑i=1n(xi−Ci)2n−1 with x being the cumulative intensity of a protein, c the average cumulative intensity of the consensus profile and the number of the fraction, i. Dividing the Euclidean distance by the maximum possible cumulative elution profile distance ( n−1), allows to compare EPASIS experiments with different numbers of SDS-concentrations. Elution profile distances (EPD) to consensus profiles were calculated for all detected proteins (lebercilin n = 290, 14-3-3-ε n = 135, B-RAF n = 32). A stepwise (n = 1000) parameter search was performed to estimate the optimal EPD threshold to maximize the specificity and sensitivity to assign known subcomplex members to the consensus profile (lebercilin supplemental Fig. S6, 14-3-3-ε supplemental Fig. S7, B-RAF supplemental Fig. S8). For lebercilin, 60 new candidate proteins for the reference subcomplexes were identified (27 for 14-3-3-ε and six for B-RAF), by using the identified EPD threshold of 0.11 (0.064 for 14-3-3-ε and 0.155 for B-RAF). To perform nonmetric multidimensional scaling, the elution profiles were averaged across the experiments (n = 8 for lebercilin and n = 5 for 14-3-3-ε and B-RAF) and Euclidean distances between them were calculated. A stable solution was estimated by using random starts (17.Oksanen, J., Blanchet, F. G., Kindt, R., Legendre, P., Minchin, P. R., O'Hara, R. B., Simpson, G. L., Solymos, P., Stevens, M. H. H., Wagner, H., (2013) vegan: Community Ecology Package. R package version 2.1–27/r2470.Google Scholar) and the best ordination (stress: 0.03 for lebercilin and 0.04 for 14-3-3-ε and B-RAF) was selected (Fig. 3B for lebercilin, Fig. 4B and 4D for 14-3-3-ε and B-RAF).Fig. 4EPASIS of the protein complexes of 14-3-3-ε and B-RAF. A, Cumulative elution profiles of the reference proteins of the 14-3-3-ε complex by increasing the SDS-concentration from 0.00025% to 0.05%. The consensus group of the 14–3-3 proteins is shown in red and is the first group eluting from the complex. In blue the group of kinesin heavy chain proteins (Kin-HC) is shown and elutes slightly before the kinesin light chain proteins (Kin-LC, green). The consensus group eluting at the highest SDS concentration is a group of microtubule affinity-regulating kinases (MARK, violet). B, Nonmetric multidimensional scaling ordination plot of the proteins eluting from the 14-3-3-ε complex, based on Euclidean distances of elution profiles (stress 0.04). Data points (n = 135) present the average of replicated data (n = 5). C, Cumulative elution profiles of three consensus protein groups of the B-RAF complex induced by increasing concentrations of SDS (0.00025%-0.05%). Two HSP90 proteins elute first from the complex (blue). The second eluting consensus group consists of the two mitogen activated kinases MAPK1 and MAPK2 (green). The strongest binding consensus proteins are the 14–3-3 proteins (red). D, Nonmetric multidimensional scaling ordination plot of the proteins eluting from the B-RAF complex, based on Euclidean distances of elution profiles (stress 0.04). Data points (n = 32) present the average of replicated data (n = 5).View Large Image Figure ViewerDownload Hi-res image Download (PPT) Following FLAG-based AP of Strep/FLAG tandem affinity purification tag (SF-TAP)-fused (9.Gloeckner C.J. Boldt K. Schumacher A. Roepman R. Ueffing M. A novel tandem affinity purification strategy for the efficient isolation and characterisation of native protein complexes.Proteomics. 2007; 7: 4228-4234Crossref PubMed Scopus (177) Google Scholar) lebercilin from HEK293T cells, we destabilized the purified protein complexes by treatment with very low concentrations of SDS. This approach was previously described in combination with Blue Native polyacrylamide gel electrophoresis (BN-PAGE) (18.Klodmann J. Sunderhaus S. Nimtz M. Jansch L. Braun H.P. Internal architecture of mitochondrial complex I from Arabidopsis thaliana.Plant Cell. 2010; 22: 797-810Crossref PubMed Scopus (153) Google Scholar). The underlying mechanism is based on hydrophobic interaction of monomeric SDS with the proteins, starting way below critical micellar concentration (CMC) (19.Garavito R.M. Ferguson-Miller S. Detergents as tools in membrane biochemistry.J. Biol. Chem. 2001; 276: 32403-32406Abstract Full Text Full Text PDF PubMed Scopus (450) Google Scholar), and resulting in a partial destabilization of the tertiary structure (20.Bhuyan A.K. On the mechanism of SDS-induced protein denaturation.Biopolymers. 2010; 93: 186-199Crossref PubMed Scopus (163) Google Scholar). The destabilization leads to the sequential elution of proteins, depending on the sensitivity of their interaction with IFT/lebercilin to SDS and employs the fact that low concentrations of SDS can be used to destabilize noncovalent binding of proteins. Assuming that the binding affinity of proteins within a single submodule is higher than their affinity to proteins outside a module, a stepwise increase of the SDS concentration will lead to early decomposition of labile interactions at low concentrations of SDS, whereas binding within a submodule, stabilized by affinity, avidity and possible binding partners as well as docking motifs, decomposes at higher concentrations. It is important to mention here, that the stability of interactions within a submodule does not need to be equal. To discriminate a submodule of a larger protein complex, its resistance to dissociation only needs to be higher than the stability of its interaction with the bait protein. To increase the sensitivity, robustness and feasibility of the approach we applied highly sensitive MS in combination with label-free based quantification (21.Luber C.A. Cox J. Lauterbach H. Fancke B. Selbach M. Tschopp J. Akira S. Wiegand M. Hochrein H. O'Keeffe M. Mann M. Quantitative proteomics reveals subset-specific viral recognition in dendritic cells.Immunity. 2010; 32: 279-289Abstract Full Text Full Text PDF PubMed Scopus (447) Google Scholar) and a refined in silico protein correlation profiling (PCP) approach. The latter procedure is based on calculating the similarity of elution profiles to a consensus profile of known complex members (16.Andersen J.S. Wilkinson C.J. Mayor T. Mortensen P. Nigg E.A. Mann M. Proteomic characterization of the human centrosome by protein correlation profiling.Nature. 2003; 426: 570-574Crossref PubMed Scopus (1051) Google Scholar). The destabilization of the IFT/lebercilin complex resulted in at least five different subcomplexes coeluting with distinguishable profiles (Fig. 3, supplemental Fig. S9 and supplemental Table S4), confirming already postulated submodules (5.Boldt K. Mans D.A. Won J. van Reeuwijk J. Vogt A. Kinkl N. Letteboer S.J. Hicks W.L. Hurd R.E. Naggert J.K. Texier Y. den Hollander A.I. Koenekoop R.K. Bennett J. Cremers F.P. Gloeckner C.J. Nishina P.M. Roepman R. Ueffing M. Disruption of intraflagellar protein transport in photoreceptor cilia causes Leber congenital amaurosis in humans and mice.J. Clin. Invest. 2011; 121: 2169-2180Crossref PubMed Scopus (79) Google Scholar). Consensus profiles for known modules of the IFT/lebercilin complex were calculated and the EPD to these consensus profiles was determined. Candidate proteins were selected based on a short EPD (≤0.11, supplemental Fig. S6) to a consensus profile. The robustness of the approach is demonstrated by the high reproducibility of the results obtained from eight independent experiments (Fig. 2, supplemental Fig. S3). By using consensus profiles for IFT complex A (IFT-A) and IFT complex B (IFT-B), we can verify that IFT-A and IFT-B exist as two discrete submodules, eluting from the IFT/lebercilin complex at different SDS concentrations with clearly separated profile values. The IFT-A elutes at a very low SDS concentration (0.0025%) with an EPD-value of less than or equal to 0.02 for all known IFT-A proteins (n = 6) to its consensus profile (EPDIFT-A), while all IFT-B proteins had values greater than or equal to 0.382 for the IFT-A consensus profile. IFT-B components elute mainly at a SDS concentration of 0.005%, showing tightly clustered elution profiles for all the
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