Unraveling Sterol-dependent Membrane Phenotypes by Analysis of Protein Abundance-ratio Distributions in Different Membrane Fractions Under Biochemical and Endogenous Sterol Depletion
2013; Elsevier BV; Volume: 12; Issue: 12 Linguagem: Inglês
10.1074/mcp.m113.029447
ISSN1535-9484
AutoresHenrik Zauber, Witold Szymański, Waltraud X. Schulze,
Tópico(s)Receptor Mechanisms and Signaling
ResumoDuring the last decade, research on plasma membrane focused increasingly on the analysis of so-called microdomains. It has been shown that function of many membrane-associated proteins involved in signaling and transport depends on their conditional segregation within sterol-enriched membrane domains. High throughput proteomic analysis of sterol-protein interactions are often based on analyzing detergent resistant membrane fraction enriched in sterols and associated proteins, which also contain proteins from these microdomain structures. Most studies so far focused exclusively on the characterization of detergent resistant membrane protein composition and abundances. This approach has received some criticism because of its unspecificity and many co-purifying proteins. In this study, by a label-free quantitation approach, we extended the characterization of membrane microdomains by particularly studying distributions of each protein between detergent resistant membrane and detergent-soluble fractions (DSF). This approach allows a more stringent definition of dynamic processes between different membrane phases and provides a means of identification of co-purifying proteins. We developed a random sampling algorithm, called Unicorn, allowing for robust statistical testing of alterations in the protein distribution ratios of the two different fractions. Unicorn was validated on proteomic data from methyl-β-cyclodextrin treated plasma membranes and the sterol biosynthesis mutant smt1. Both, chemical treatment and sterol-biosynthesis mutation affected similar protein classes in their membrane phase distribution and particularly proteins with signaling and transport functions. During the last decade, research on plasma membrane focused increasingly on the analysis of so-called microdomains. It has been shown that function of many membrane-associated proteins involved in signaling and transport depends on their conditional segregation within sterol-enriched membrane domains. High throughput proteomic analysis of sterol-protein interactions are often based on analyzing detergent resistant membrane fraction enriched in sterols and associated proteins, which also contain proteins from these microdomain structures. Most studies so far focused exclusively on the characterization of detergent resistant membrane protein composition and abundances. This approach has received some criticism because of its unspecificity and many co-purifying proteins. In this study, by a label-free quantitation approach, we extended the characterization of membrane microdomains by particularly studying distributions of each protein between detergent resistant membrane and detergent-soluble fractions (DSF). This approach allows a more stringent definition of dynamic processes between different membrane phases and provides a means of identification of co-purifying proteins. We developed a random sampling algorithm, called Unicorn, allowing for robust statistical testing of alterations in the protein distribution ratios of the two different fractions. Unicorn was validated on proteomic data from methyl-β-cyclodextrin treated plasma membranes and the sterol biosynthesis mutant smt1. Both, chemical treatment and sterol-biosynthesis mutation affected similar protein classes in their membrane phase distribution and particularly proteins with signaling and transport functions. The plasma membrane incorporates a broad spectrum of proteins covering mainly different structural, signaling or transport functionalities. Being the first semipermeable cell barrier to its surrounding environment the plasma membrane is important for metabolite transport as well as initiation point of several signaling processes (1Keinath N.F. Kierszniowska S. Lorek J. Bourdais G. Kessler S.A. Asano H.S. Grossniklaus U. Schulze W.X. Robatzek S. Panstruga. R. PAMP (pathogen-associated molecular pattern)-induced changes in plasma membrane compartmentalization reveal novel components of plant immunity.J. Biol. Chem. 2010; 285: 39140-39149Abstract Full Text Full Text PDF PubMed Scopus (229) Google Scholar, 2Shlomo I.B. Hsu S.Y. Rauch R. Kowalski H.W. Hsueh A.J.W. Signaling receptome: a genomic and evolutionary perspective of plasma membrane receptors involved in signal transduction.Sci. STKE. 2003; 2003: RE9PubMed Google Scholar, 3Staubach S. Hanisch. F.G. 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Order of lipid phases in model and plasma membranes.Proc. Natl. Acad. Sci. U.S.A. 2009; 106: 16645-16650Crossref PubMed Scopus (326) Google Scholar). After a decade of research on these structures, microdomains turned out to be particularly involved in signaling and transport processes incorporating a specific set of proteins. Microdomains provide subcompartments in the plasma membrane with specific physicochemical properties that on specific sterol protein interactions might alter protein activity or PPIs. With the discovery of microdomains the fluid lipid mosaic model was extended by distinguishing two plasma membrane phases, an ordered phase of lower density (Lo phase) enriched in sterols, sphingolipids and long chain fatty acids and a disordered phase of higher density (Ld phase). From isolated plasma membranes a lower density and a higher density membrane fraction can be separated in a sucrose gradient after treatment with non-ionic detergents. The resulting detergent resistant membrane fraction (DRM) 1The abbreviations used are:DRMdetergent resistant membrane fractionDSFdetergent soluble fractionSPsoluble protein fractionIMintracellular membrane fractionPPIprotein-protein interaction mβcd methyl-β-cyclodextrinFDRfalse discovery rateLophase liquid ordered phaseLdphase liquid disordered phase. 1The abbreviations used are:DRMdetergent resistant membrane fractionDSFdetergent soluble fractionSPsoluble protein fractionIMintracellular membrane fractionPPIprotein-protein interaction mβcd methyl-β-cyclodextrinFDRfalse discovery rateLophase liquid ordered phaseLdphase liquid disordered phase. is related to Lo phase and high density detergent soluble membrane fraction (DSF) relates to Lo phases. Although it is still under debate how well DRMs represent native plasma membrane microdomains (10Shogomori H. Brown D.A. Use of detergents to study membrane rafts: the good, the bad, and the ugly.Biol. Chem. 2003; 384: 1259-1263Crossref PubMed Scopus (170) Google Scholar, 11Tanner, W., Malinsky, J., Opekarova, M., In plant and animal cells, detergent-resistant membranes do not define functional membrane rafts. Plant Cell 23, 1191–1193Google Scholar, 12Edidin M. The state of lipid rafts: from model membranes to cells.Annu. Rev. Biophys. Biomol. Struct. 2003; 32: 257-283Crossref PubMed Scopus (1136) Google Scholar), research on protein-sterol interactions is possible by usage of sterol depleting agents like methyl-β-cyclodextrin mβcd (13Pike L.J. Miller J.M. Cholesterol depletion delocalizes phosphatidylinositol bisphosphate and inhibits hormone-stimulated phosphatidylinositol turnover.J. Biol. Chem. 1998; 273: 22298-22304Abstract Full Text Full Text PDF PubMed Scopus (348) Google Scholar). Therefore mβcd is suitable for detecting false positive cholesterol protein interactions in DRM studies (14Zidovetzki R. Levitan I. Use of cyclodextrins to manipulate plasma membrane cholesterol content: Evidence, misconceptions and control strategies.Biochim. Biophys. Acta. 2007; 1768: 1311-1324Crossref PubMed Scopus (812) Google Scholar, 15Kierszniowska S. Seiwert B. Schulze W.X. Definition of Arabidopsis sterol-rich membrane microdomains by differential treatment with methyl-beta-cyclodextrin and quantitative proteomics.Mol. Cell. Proteomics. 2009; 8: 612-623Abstract Full Text Full Text PDF PubMed Scopus (102) Google Scholar, 16Ilangumaran S. Hoessli D.C. Effects of cholesterol depletion by cyclodextrin on the sphingolipid microdomains of the plasma membrane.Biochem. J. 1998; 335: 433-440Crossref PubMed Scopus (401) Google Scholar, 17Mathay C. Pierre M. Pittelkow M.R. Depiereux E. Nikkels A.F. Colige A. Poumay Y. Transcriptional profiling after lipid raft disruption in keratinocytes identifies critical mediators of atopic dermatitis pathways.J. Invest. Dermatol. 2011; 131: 46-58Abstract Full Text Full Text PDF PubMed Scopus (40) Google Scholar, 18Roche, Y., Gerbeau-Pissot, P., Buhot, B., Thomas, D., Bonneau, L., Gresti, J., Mongrand, S., Perrier-Cornet, J. M., Simon-Plas, F., Depletion of phytosterols from the plant plasma membrane provides evidence for disruption of lipid rafts. FASEB J. 22, 3980–3991Google Scholar, 19Lingwood D. Simons K. Detergent resistance as a tool in membrane research.Nat. Protoc. 2007; 2: 2159Crossref PubMed Scopus (223) Google Scholar). Proteins depleted on mβcd treatment are finally considered to be sterol dependent (15Kierszniowska S. Seiwert B. Schulze W.X. Definition of Arabidopsis sterol-rich membrane microdomains by differential treatment with methyl-beta-cyclodextrin and quantitative proteomics.Mol. Cell. Proteomics. 2009; 8: 612-623Abstract Full Text Full Text PDF PubMed Scopus (102) Google Scholar, 16Ilangumaran S. Hoessli D.C. Effects of cholesterol depletion by cyclodextrin on the sphingolipid microdomains of the plasma membrane.Biochem. J. 1998; 335: 433-440Crossref PubMed Scopus (401) Google Scholar, 17Mathay C. Pierre M. Pittelkow M.R. Depiereux E. Nikkels A.F. Colige A. Poumay Y. Transcriptional profiling after lipid raft disruption in keratinocytes identifies critical mediators of atopic dermatitis pathways.J. Invest. Dermatol. 2011; 131: 46-58Abstract Full Text Full Text PDF PubMed Scopus (40) Google Scholar). To compare the mβcd treatment for disturbing the sterol distribution in the Lo fraction, we studied the sterol biosynthesis deficient mutant smt1. (20Schrick K. Mayer U. Martin G. Bellini C. Kuhnt C. Schmidt J. Jurgens G. Interactions between sterol biosynthesis genes in embryonic development of Arabidopsis.Plant J. 2002; 31: 61-73Crossref PubMed Scopus (91) Google Scholar) smt1 carries a point mutation in the smt1 locus, encoding the sterol methyltransferase 1 and it exhibits a dwarf-like phenotype on whole plant level (20Schrick K. Mayer U. Martin G. Bellini C. Kuhnt C. Schmidt J. Jurgens G. Interactions between sterol biosynthesis genes in embryonic development of Arabidopsis.Plant J. 2002; 31: 61-73Crossref PubMed Scopus (91) Google Scholar). In total, three sterol methyl transferases are encoded in Arabidopsis where SMT1 catalyzes the first step in the sterol biosynthesis by adding a methyl group at C24 of the sterol precursor cycloartenol. SMT2 and SMT3 act at later steps and were shown to be functionally redundant as C-24 sterol methyltransferases at the branching in sterol synthesis that either leads to sitosterol or campesterol (21Carland F. Fujioka S. Nelson T. The sterol methyltransferases SMT1, SMT2, and SMT3 influence Arabidopsis development through nonbrassinosteroid products.Plant Physiol. 2010; 153: 741-756Crossref PubMed Scopus (108) Google Scholar). The total sterol composition in smt1 mutants was shown to be different from wild type, with the major phytosterols like sitosterol, stigmasterol, and brassicasterol being strongly depleted. In contrast, other sterol species remained unaltered and some even increased (20Schrick K. Mayer U. Martin G. Bellini C. Kuhnt C. Schmidt J. Jurgens G. Interactions between sterol biosynthesis genes in embryonic development of Arabidopsis.Plant J. 2002; 31: 61-73Crossref PubMed Scopus (91) Google Scholar, 21Carland F. Fujioka S. Nelson T. The sterol methyltransferases SMT1, SMT2, and SMT3 influence Arabidopsis development through nonbrassinosteroid products.Plant Physiol. 2010; 153: 741-756Crossref PubMed Scopus (108) Google Scholar). So far, it remains unclear how the altered sterol-composition of the smt1 mutant affects sterol-protein interactions. In this study, using the newly developed algorithm Unicorn, we compared changes in protein distributions between DRM and DSF after biochemical mβcd treatment and on endogenous alterations in sterol composition in smt1 to improve understanding of sterol–protein interactions. detergent resistant membrane fraction detergent soluble fraction soluble protein fraction intracellular membrane fraction protein-protein interaction mβcd methyl-β-cyclodextrin false discovery rate phase liquid ordered phase phase liquid disordered phase. detergent resistant membrane fraction detergent soluble fraction soluble protein fraction intracellular membrane fraction protein-protein interaction mβcd methyl-β-cyclodextrin false discovery rate phase liquid ordered phase phase liquid disordered phase. Heterozygous seeds from the point mutation line cphT357 (20Schrick K. Mayer U. Martin G. Bellini C. Kuhnt C. Schmidt J. Jurgens G. Interactions between sterol biosynthesis genes in embryonic development of Arabidopsis.Plant J. 2002; 31: 61-73Crossref PubMed Scopus (91) Google Scholar) with a point mutation at T357 in the smt1 locus were germinated and homozygous seedlings exhibiting the typical extreme dwarf-like phenotype of sterol-biosynthesis mutants (20Schrick K. Mayer U. Martin G. Bellini C. Kuhnt C. Schmidt J. Jurgens G. Interactions between sterol biosynthesis genes in embryonic development of Arabidopsis.Plant J. 2002; 31: 61-73Crossref PubMed Scopus (91) Google Scholar, 22Schrick K. Nguyen D. Karlowski W. Mayer K. START lipid/sterol-binding domains are amplified in plants and are predominantly associated with homeodomain transcription factors.Genome Biol. 2004; 5: R41Crossref PubMed Google Scholar) were selected. Callus cultures were initialized from leafs on 6.8% agar in full mineral MS medium (23Jouanneau J.P. Peaud-Lenoel C. Growth and synthesis of proteins in cell suspensions of a kinetin dependent tobacco.Plant Physiol. 1967; 20: 834-850Crossref Scopus (79) Google Scholar) with the freshly added components 3% sucrose, 200 mg/l myoinositol, 1 mg/l 2,4-dichlorophenoxyacetic acid, and 0.25 mg/l kinetin for smt1 and corresponding wild type Ler-0 (A. thaliana). Suspension cultures were prepared from callus, which was chopped and added to medium without agar. Arabidopsis cell suspension cultures were subcultured to fresh medium every week. Cultures were harvested for protein extraction after 4 days of growth in fresh medium. Frozen cell powder was mixed with two volumes of cold extraction buffer (100 mm Hepes-KOH pH 7.5, 250 mm sucrose, 3 mm KCl, 0.1 mm EDTA, 1 mm dithiothreitol (DTT) and freshly added protease inhibitor mixture (Thermo Scientific) for final concentration of 200 μl/l. Completely thawed homogenate was filtered through one layer of Miracloth. Supernatant after short centrifugation was directed to ultra centrifugation at 100,000 × g for 40 min. After this step, the obtained supernatant was used to extract soluble proteins and pellet containing microsomal fraction was resuspended in buffer (5 mm KH2 PO4, 0.33 m sucrose, 3 mm KCL, 0.1 mm EDTA, 1 mm DTT, protein inhibitor mixture). Plasma membrane (PM) and intracellular membranes (IM) containing organelle membranes could be purified using aqueous two-phase system (24Schindler J. Nothwang H.G. Aqueous polymer two-phase systems: Effective tools for plasma membrane proteomics.Proteomics. 2006; 6: 5409-5417Crossref PubMed Scopus (86) Google Scholar). IM and PM pellets were resuspended in 25 mm Tris buffer (pH 7.5; 150 mm NaCl; 5 m M EDTA; 1 mm DTT). The wild-type PM fraction was treated with 15 mm mβcd for 30 min to deplete sterols. The treated PM pellet was recovered and resuspended after sample dilution and ultracentrifugation. (15Kierszniowska S. Seiwert B. Schulze W.X. Definition of Arabidopsis sterol-rich membrane microdomains by differential treatment with methyl-beta-cyclodextrin and quantitative proteomics.Mol. Cell. Proteomics. 2009; 8: 612-623Abstract Full Text Full Text PDF PubMed Scopus (102) Google Scholar) Protein content in PM fraction was determined by Bradford assay (25Bradford M.M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding.Anal. Biochem. 1976; 72: 248-254Crossref PubMed Scopus (215632) Google Scholar). All samples were adjusted to equal protein amounts of 100 μg. All PM fractions were mixed for 30 min with TritonX-100 at a protein/detergent ratio of 1:15 with final detergent concentration being diluted below 1%. (15Kierszniowska S. Seiwert B. Schulze W.X. Definition of Arabidopsis sterol-rich membrane microdomains by differential treatment with methyl-beta-cyclodextrin and quantitative proteomics.Mol. Cell. Proteomics. 2009; 8: 612-623Abstract Full Text Full Text PDF PubMed Scopus (102) Google Scholar) Detergent resistant membranes (DRM) and a detergent soluble fraction (DSF) could be separated in a sucrose gradient (1.8 mm; 1.6 mm; 1.4 mm, and 0.15 mm) using ultra centrifugation at 150,000 × g for 18 h. From the four collected fractions, DRM, DSF, SP, and IM proteins were extracted using methanol/chloroform extraction. An overview of the sample preparation is provided in Fig. 1. Extracted proteins were dissolved in 6 m urea, 2 m thiourea, pH 8 and protein concentration of SP and IM fractions determined. Total protein content from DRM and DSF fraction and 75 μg of protein from SP and IM fraction was reduced, carbamidomethylated (26Sechi S. Chait B.T. Modification of cysteine residues by alkylation. A tool in peptide mapping and protein identification.Anal. Chem. 1998; 70: 5150-5158Crossref PubMed Scopus (355) Google Scholar) and subsequently digested with LysC for 3 h at room temperature. Afterward, samples were diluted four times with 2 mm Tris-HCl pH 8 and trypsin was added for overnight digestion at room temperature (27Olsen J.V. Ong S.E. Mann M. Trypsin cleaves exclusively C-terminal to arginine and lysine residues.Mol. Cell. Proteomics. 2004; 3: 608-614Abstract Full Text Full Text PDF PubMed Scopus (869) Google Scholar, 28Kierszniowska S. Walther D. Schulze W.X. Ratio-dependent significance thresholds in reciprocal 15N-labeling experiments as a robust tool in detection of candidate proteins responding to biological treatment.Proteomics. 2009; 9: 1916-1924Crossref PubMed Scopus (29) Google Scholar). The reaction was stopped by adding trifluoroacetic acid to reach a pH of around 2. Tryptic peptides were desalted over C18 Stop and Go Extraction tips (Empore Disk; Varian Inc, Palo Alto, CA) (29Rappsilber J. Ishihama Y. Mann M. Stop and go extraction tips for matrix-assisted laser desorption/ionization, nanoelectrospray, and LC/MS sample pretreatment in proteomics.Anal. Chem. 2003; 75: 663-670Crossref PubMed Scopus (1795) Google Scholar). Injections containing 25 μg of protein, were analyzed by LC-MS/MS using nano-flow HPLC (Easy nLC, Thermo Scientific, Waltham, MA) and an Orbitrap hybrid mass spectrometer (LTQ-Orbitrap XL, Thermo Scientific, Waltham, MA) as mass analyzer. Peptides were eluted from 75 μm analytical column (Reprosil C18, Dr. Maisch GmbH) on a linear gradient, running from 5 to 80% acetonitrile in 90 min at a flow rate of 250 nL/min. Up to five data-dependent MS/MS spectra were acquired in the linear ion trap for each FTMS full-scan spectrum, acquired at 60,000 full-width half-maximum resolution settings with an overall cycle time of ∼1 s and precursor mass tolerance of 10 ppm. Raw file peak extraction, protein identification, and quantitation of peptides was performed by MaxQuant (30Cox 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 (9150) Google Scholar) (version 1.3.0.5) using a protein sequence database of Arabidopsis thaliana (TAIR10, 35386 entries, www.arabidopsis.org). For protein identification, carbamidomethylation and N-terminal protein acetylation were used as fixed modifications and methionine oxidation as a variable modification. Multiplicity was set to 1 for label-free quantitation. Standard settings in MaxQuant involving peptide false-discovery rate of 0.01, minimum peptide length of 6 amino acids, MSMS mass tolerance of 0.5 Da, two maximal allowed missed cleavages, and enabled retention time correlation, using a time window of 2 min were used. The full list of all identified peptides in all replicates and treatments can be downloaded as supplemental Table S2. Peptide lists derived from MaxQuant (evidence.txt) were directed to cRacker (31Zauber H. Schulze W.X. Proteomics wants cRacker: automated standardized data analysis of LC–MS derived proteomic data.J. Proteome Res. 2012; 11: 5548-5555Crossref PubMed Scopus (27) Google Scholar) analysis for normalization between samples and for merging peptide intensities to protein intensities using razor protein identifications from MaxQuant. Peptides, which were quantified in less than 70% among all fraction specific samples, were filtered out. The principal steps of the reference protein normalization were: (1) All peptide intensities within each sample were normalized to fraction of total ion-intensity sums. (2) To compensate effects of normalization because of number of detected peptides per samples, intensity was proportionally corrected to reciprocal number of identified peptides in each sample using the integrated normalization option in cRacker (31Zauber H. Schulze W.X. Proteomics wants cRacker: automated standardized data analysis of LC–MS derived proteomic data.J. Proteome Res. 2012; 11: 5548-5555Crossref PubMed Scopus (27) Google Scholar). (3) Peptide intensities with missing values in more than the permitted numbers of samples within replicates of each fractions were filtered out. (4) Remaining normalized peptide intensities were median scaled and median averaged. cRracker settings used in this study are available as supplemental File S1. We developed a bootstrap based algorithm, named Unicorn, to statistically test differences between abundance ratios between DRM/DSF fractions in wild type compared with DRM/DSF abundance ratios on mβcd treatment or in the sterol-biosynthesis mutant smt1. Unicorn uses preprocessed protein intensities, relative standard deviation and protein specific counts of analyzed peptides from cRacker (files “proteinlist.csv,” “proteinlist-sd.csv,” and “n-protein.csv”). In Unicorn, the first step involves a random generation of an ion-intensity distribution matching the median, standard deviations (DRM sd + DSF sd) and numbers of analyzed peptides (n1 + n2) per protein of all measured DRM/DSF protein intensity ratios. Second, the two resulting pools of ion-intensity ratios from wildtype and smt1 mutant or mβcd treatment were subjected to pairwise t-testing (Fig. 1). To control variability in using such a stochastic approach, the analysis was iterated 10 times and p values were collected. A score for each protein was calculated by multiplying number of positive tests (α = 0.05) with the log2 transformed abundance ratio of a protein. A threshold score indicating significantly different ratios at a false discovery rate lower than 1% was calculated using randomized p value and ratio matrixes for each experiment. Protein candidates were filtered for DRM/DSF specific proteins using cross comparisons with IM or SP fraction (t test; α = 0.01). The full Unicorn algorithm was written in R and is designed to work semi-automatically with cRacker output. The R scripts of Unicorn can be provided on demand. Statistical analysis was performed using free available scripting language R (32R Development Core TeamR: A Language and Environment for Statistical Computing.Vienna Austria R Foundation Statistical Computing. 2009; 1 (9/18/2009)Google Scholar). All experimental data are based on three biological replicates and up to three technical replicates. For testing the efficiency of filtering co-purifying proteins as well as for analyzing protein subcellular localization distribution, information on subcellular location was derived from SUBA3 (33Tanz S.K. Castleden I. Hooper C.M. Vacher M. Small I. Millar H.A. SUBA3: a database for integrating experimentation and prediction to define the SUBcellular location of proteins in Arabidopsis.Nucleic Acids Res. 2013; 41: D1185-D1191Crossref PubMed Scopus (239) Google Scholar) and functional information was annotated based on MapMan functional categories (34Thimm O. Blasing O. Gibon Y. Nagel A. Meyer S. Kruger P. Selbig J. Muller L.A. Rhee S.Y. Stitt M. MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes.Plant J. 2004; 37: 914-939Crossref PubMed Scopus (2527) Google Scholar). A correlation analysis of protein abundances in DRM and DSF in replicates from mβcd and smt1 analysis was done in R by calculating pairwise Pearson correlation coefficients using the function “cor” supplied by the R base distribution. For a detailed comparison, responding candidate proteins were manually assigned to functional protein groups based on protein descriptions supplied in TAIR (35Poole R.L. The TAIR database.Methods Mol. Biol. 2007; 406: 179-212PubMed Google Scholar). The algorithm Unicorn was developed to enable statistical testing of protein abundance distributions between two fractions, here DRM and DSF membrane subcompartments. For proving reproducibility of the results obtained from the Unicorn algorithm, effects of mβcd treatment and smt1 on protein DRM/DSF distribution were analyzed in 10 iterations. Numbers of intersecting proteins with significantly altered, sterol-dependent distribution ratios and significant Unicorn-scores were compared. Unicorn-score distributions analyzing estimated densities were highly reproducible between iterations (Figs. 2A, 2B). For every iteration a threshold score (applying a false discovery rate (FDR) ≤ 1%) was calculated. Between all iterations this threshold was stable, showing only minor variation. In a pairwise analysis of all iterations, 94 and 87% of the responding proteins were overlapping in mβcd treatment and smt1 respectively (Figs. 2C, 2D). Proteins were considered as responding to mβcd or smt1 (sterol-dependent proteins) if their distribution between DRM and DSF was significantly different from untreated control or wild type (FDR ≤ 1%). Across all iterations, the overlap of unique proteins between the iterations is smaller but still, the largest fraction of commonly identified responding proteins is significant in all iterations (Figs. 2E, 2F). The number of nonoverlapping proteins between iterations is related to the stochastic nature of random sampling. Therefore, some ratio comparisons expressing lower scores, can by chance fall below the stringent score threshold. If a less stringent analysis needs to be applied we propose running several iterations to identify the proteins in this lower scoring range. However, with increasing number of iterations, false positives are also likely to accumulate. Therefore, only one random sampling iteration was applied for analyzing mβcd and smt1. Comparing the score distributions from mβcd treatment and smt1, more proteins in the lower scoring range were positively tested in smt1, explaining the slightly higher number of differentially abundant proteins in total for smt1. Without including prior information, the ratio-based testing algorithm does not allow to distinguish whether effects mainly in DRM or DSF caused the distribution differences. Because we can assume that on the mβcd treatment as well as in the smt1 mutant the observed alterations in protein distributions were especially induced effects on sterol composition of membranes, in the following sections we will interpret changes in DRM/DSF ratios as especially induced by changes in abundance in the sterol enriched DRM fraction. In total, 3028 proteins were quantified, out of which distribution ratios between DRM and DSF were compared for 1184 proteins in mβcd treatment versus control, and 1788 in the smt1 versus wild type. Several proteins showed a significantly depleted DRM/DSF ratio both on mβcd treatment and in t
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