Project PXD006372

PRIDE Assigned Tags:
Biological Dataset

Summary

Title

Proteome-wide analysis of cysteine oxidation reveals metabolic sensitivity to redox stress, PART 2

Description

Reactive oxygen species (ROS) are increasingly recognised as important regulators of cellular biology through the oxidative modification of protein cysteine residues. Comprehensive identification of redox-regulated proteins and cellular pathways are crucial to understand ROS-mediated events. Here, we present a new Stable Isotope Cysteine Labelling with IodoAcetamide (SICyLIA) MS-based proteomic workflow to assess protein cysteine oxidation in diverse cellular models and primary tissues. This approach informs on all possible cysteine oxidative modifications and achieves proteome-wide sensitivity with unprecedented depth without using enrichment steps. Our results suggest that acute and chronic oxidative stress cause metabolic adaptation through direct oxidation of metabolic and mitochondrial proteins. Analysis of chronically stressed fumarate hydratase-deficient mouse kidneys identified oxidation of proteins circulating in bio fluids, through which redox stress may affect whole-body physiology. Obtaining accurate peptide oxidation profiles from a complex organ using SICyLIA holds promise for future application to patient-derived samples in studies of human disease. PART 1 Hydrogen Peroxide PART 2 Primary immortalised kidney epithelial cells fumarate hydratase (FH) deficient PART 3 kidney tissues fumarate hydratase (FH) deficient

Sample Processing Protocol

Cells were lysed in 4%SDS containing 55 mM heavy (13C2D2H2INO) or light (12C2H4INO) iodoacetamide in 100 mM Tris-HCl buffer (pH 8.5). For each independent experiment, 150 µg of differentially labelled protein extracts were mixed using a label-swap replication strategy, finally obtaining 4 biological replicates for each experimental model. Reversibly oxidised thiols were reduced with 70mM DTT, incubated at room temperature (24°C) for 45 minutes and diluted 1:2 using 50 mM ammonium bicarbonate solution (pH 7.0). Newly generated free thiols were subsequently alkylated using 80 mM NEM. Alkylated proteins were then precipitated in two steps using 24% and 10% solution of Trichloroacetic acid. Pellets were finally washed with water until the supernatant reached neutral pH, then reconstituted in 50 µl of 8 M urea solution, and submitted to two step digestion (Endoproteinase Lys-C + Trypsin). Aliquots of digests were used for with dimethyl labelling quantitation for Cysteine containing peptides normalisation. Both IAM and dimethyl-modified protein digests were fractionated using high pH reverse phase chromatography using a 2.1 mm column. Solvent A (98% water, 2% Acetonitrile) and solvent B (90% Acetonitrile and 10% water) were adjusted to pH 10 using ammonium hydroxide. Samples were injected manually through a Rheodyne valve onto the RP-HPLC column equilibrated with 4% solvent B and kept at this percentage for 6 minutes. A two-step gradient was applied at a flow-rate of 200 µl/min (from 4–27% B in 36 minutes, then from 27-48% B in 8 minutes) followed by a 5 minute washing step at 80% solvent B and a 10 minute re-equilibration step, for a total run time of 65 minutes. Column eluate was monitored at 220 and 280 nm, and collected in 21 fractions. Fractionated tryptic digests were separated by nanoscale C18 reverse-phase liquid chromatography using an EASY-nLC II 1200 (Thermo Scientific) coupled to a Q-Exactive HF mass spectrometer (Thermo Scientific). Elution was carried out using a binary gradient with buffer A (2% acetonitrile) and B (80% acetonitrile), both containing 0.1% formic acid. Samples were loaded with 8 µl of buffer A into a 20cm fused silica emitter packed in-house with ReproSil-Pur C18-AQ, 1.9 μm resin. Packed emitter was kept at 35 °C by means of a column oven integrated into the nanoelectrospray ion source. Peptides were eluted at a flow rate of 300 nl/min using 3 different gradients optimised for set of fractions 1-7, 8-15 and 16-21. Two-step gradients were used, all with 20 minutes for step one and 7 minutes for step two. Percentages of buffer B are changed according to the following table. F1-7(%B) F8-14(%B) F15-21(%B) Start 2 4 6 Step1 20 23 28 Step2 39 43 48 All gradients were followed by a washing step (100% B) for 10 minutes followed by a 5 minute re-equilibration step (5%), for a total run time of 40 minutes. Eluting peptides were electrosprayed into the mass spectrometer using a nanoelectrospray ion source. An Active Background Ion Reduction Device was used to decrease air contaminants signal level. Data were acquired with Xcalibur software. Ionisation conditions used include: spray voltage 2.1 kV, ion transfer tube temperature 250 °C. Data acquisition was carried out in positive ion mode using data dependent acquisition. A full scan (FT-MS) over mass range of 375-1400 m/z was acquired at 60,000 resolution at 200 m/z, with a target value of 3,000,000 ions for a maximum injection time of 20 ms. Higher energy collisional dissociation fragmentation was performed on the 15 most intense ions, for a maximum injection time of 50 ms, or a target value of 50,000 ions. Multiply charged ions having intensity greater than 12,000 counts were selected through a 1.5 m/z window and fragmented using normalised collision energy of 27. Former target ions selected for MS/MS were dynamically excluded for 25 s.

Data Processing Protocol

The MS Raw data obtained were processed with MaxQuant software version 1.5.5.1 and searched with Andromeda search engine, querying SwissProt Uniprot Mus musculus (20/06/2016 57,25820,273 entries) database. First and main searches were performed with precursor mass tolerances of 20 ppm and 4.5 ppm, respectively, and MS/MS tolerance of 20 ppm. The minimum peptide length was set to 6 amino acids and specificity for trypsin cleavage was required, allowing up to two missed cleavage sites. Methionine oxidation and N-terminal acetylation were specified as variable modifications, no fixed modifications were specified. The peptide, protein and site false discovery rate (FDR) was set to 1 %. Modification by light and heavy iodoacetamide on cysteines (carbamidomethylation) was set as “label type” modification in Andromeda configuration. Compositions set in the software were: H N O Cx(2)Hx(2) for heavy and H(3)NOC(2) for light label. Dimethylated samples were processed using: DimethLys0/Nter0 and DimethLys8/Nter8 as light and heavy labels, respectively. For samples that required protein expression normalisation, both datasets (iodoacetamide heavy/light and dimethyl heavy/light) were processed at the same time in MaxQuant using “Parameter Groups” option. Quantitation of cysteine oxidation at peptide level was done using thereported in the MaxQuant output “peptide.txt” file was used for the analysis. MaxQuant output was further processed and analysed using Perseus software version 1.5.2.6 and 1.5.5.3. Peptides with Cys count lower than one were excluded, together with “Reverse” and “Potential Contaminant” flagged peptides. Protein level quantitation was done using the “ProteinGroups.txt” file. From the “ProteinGroups.txt” file Again “Reverse” and “Potential Contaminant” flagged proteins were removed, and at least one uniquely assigned peptide and a minimum ratio count of 2 were required for a protein to be quantified. Only cysteine containing peptides and protein groups that have been robustly quantified (in 4/4 replicate experiments) were used for the analysis. MaxQuant output was further processed and analysed using Perseus software version 1.5.2.6 and 1.5.5.3. For the quantitative analysis of the FH samples, only cysteine containing peptides uniquely assigned to one protein group within each replicate experiment were normalised.

Contact

Sergio Lilla, Beatson Institute for Cancer Research
Sara Rossana Zanivan, CRUK - Beatson Institute for Cancer Research - Switchback Rd, Bearsden, Glasgow G61 1BD - United Kingdom ( lab head )

Submission Date

25/04/2017

Publication Date

06/03/2018

Publication

    van der Reest J, Lilla S, Zheng L, Zanivan S, Gottlieb E. Proteome-wide analysis of cysteine oxidation reveals metabolic sensitivity to redox stress. Nat Commun. 2018 9(1):1581 PubMed: 29679077