Dataset Information

Mouse cerebellum (phospho)proteome LC-MSMS

ABSTRACT: Cerebella from wild-type and cGKI knockout mice, on 129/Sv background, were subjected to protein digestion using trypsin o/n and chemically labeled with dimethyl labeling. After labeling the samples were mixed and subjected to both strong cation exchange and phosphopeptide enrichment. The SCX fractions and the enriched phosphopeptides were then analyzed on an Orbitrap mass spectrometer. Data analysis: For each raw data file recorded by the mass spectrometer, peak lists were generated using Proteome Discoverer (version 1.3, Thermo Scientific, Bremen, Germany) using a standardized workflow. Peak lists, generated in Proteome Discoverer, were searched against a Swiss-Prot database (version 2.3.02, taxonomy Mus musculus, 32402 protein entries) supplemented with frequently observed contaminants, using Mascot (version 2.3.02 Matrix Science, London, UK). The database search was performed by using the following paramenters: a mass tolerance of 50 ppm for the precursor masses and +/-0.6 Da for CID/ETD fragment ions and +/-0.05 Da for HCD fragments. Enzyme speciﬁcity was set to Trypsin with 2 missed cleavages allowed. Carbarmidomethylation of cysteines was set as fixed modification, oxidation of methionine and dimethyl labeling (L, I, H) of lysine residues and N termini, and phosphorylation (S, T, Y) (for the phosphoproteome analysis) were used as variable modifications. Percolator was used to ﬁlter the PSMs for <1% false discovery-rate. Phosphorylation sites were localized by applying phosphoRS (pRS) (v2.0). Triplex dimethyl labeling was used as quantification method, with a mass precision for the 2 ppm for consecutive precursor mass scan. A retention time tolerance of 0.5 min was used to account effect of deuterium on retention time. To further filter for high quality data we used the following parameters: high confidence peptide spectrum matches, minimal Mascot score of 20, minimal peptide length of 6 and only unique rank 1 peptide. For the phosphopeptides analysis, the search rank 1 peptide was added to the previous filters. For the identification and quantitation of the proteins, only the unique peptides were considered.

INSTRUMENT(S): LTQ Orbitrap Elite, Q Exactive

ORGANISM(S): Mus Musculus (mouse)

SUBMITTER: Eleonora Corradini

PROVIDER: PXD000502 | Pride | 2014-06-17

REPOSITORIES: Pride

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Publications

Alterations in the cerebellar (Phospho)proteome of a cyclic guanosine monophosphate (cGMP)-dependent protein kinase knockout mouse.

Corradini Eleonora E Vallur Raghavan R Raaijmakers Linsey M LM Feil Susanne S Feil Robert R Heck Albert J R AJ Scholten Arjen A

Molecular & cellular proteomics : MCP 20140612 8

The cyclic nucleotide cyclic guanosine monophosphate (cGMP) plays an important role in learning and memory, but its signaling mechanisms in the mammalian brain are not fully understood. Using mass-spectrometry-based proteomics, we evaluated how the cerebellum adapts its (phospho)proteome in a knockout mouse model of cGMP-dependent protein kinase type I (cGKI). Our data reveal that a small subset of proteins in the cerebellum (∼3% of the quantified proteins) became substantially differentially ex ...[more]

PMID: 24925903

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Project description:Here we evaluate and explore a peptide-centric antibody generated to selectively enrich peptides containing the cAMP-dependent protein kinase A (PKA) consensus motif. This targeted phospho-proteomic strategy is used to profile temporal quantitative changes of protein PKA substrates in Jurkat T-lymphocytes upon prostaglandin E2 stimulation, which increases intracellular cAMP activating PKA. Our method combines ultra-high specificity PKA-motif-based immunoaffinity purification with cost-efficient stable isotope dimethyl labeling. The data set coprises of 4 raw files, 3 of them (IP, IP_2 and IP_3) are the LC-MSMS technical replicates of the eluate of the immunoprecipitation, while the MIX raw files correpsond to the LC-MSMS analysis of the pre-IP cell lysate digest.Each raw data file was processed and quantified with Proteome Discoverer (version 1.3, Thermo Scientific). Peak lists containing HCD and ETD fragmentation were generated with a signal-to-noise threshold of 1.5. The ETD non-fragment filter was also taken into account with the following settings: the precursor peak was removed within a 4 Da window, charged reduced precursors were removed within a 2 Da window, and neutral losses from charged reduced precursors were removed within a 2 Da window and the maximum neutral loss mass was set to 120 Da. All generated peak lists of the IP were searched against a concatenated forward-decoy Swissprot human database (version 2012_09, 40,992 sequences) while the MIX file was searched against a Swissprot database version 2012_09 with taxonomy Homo sapiens (20,235 sequences) by the use of Mascot software (version 2.3.02 Matrix Science). The database search was performed with the following parameters: a mass tolerance of ±50 ppm for precursor masses; ±0.6 Da for ETD-ion trap fragment ions; ±0.05 Da for HCD and ETD-Orbi trap fragment ions, allowing two missed cleavages, cysteine carbamidomethylation as fixed modification. Light, intermediate and heavy dimethylation of peptide N-termini and lysine residues; methionine oxidation; phosphorylation on serine and threonine (only for the IP) were set as variable modifications. The enzyme was specified as Lys-C and the fragment ion type was specified as electrospray ionization FTMS-ECD, ETD-TRAP, and ESI-QUAD-TOF for the corresponding mass spectra. The phosphorylation site localization of the identified phosphopeptides was performed by the phosphoRS algorithm 2.0. The dimethyl-based quantitation method was chosen in Proteome Discoverer, with mass precision requirement of 2 ppm for consecutive precursor mass measurements. We applied 0.5 min of retention time tolerance for isotope pattern multiplets and allowed spectra with maximum 1 missing channels to be quantified. After identification and quantification, we combined all results originating from the same biological replica and filtered them with the following criteria: (i) mass deviations of ±10 ppm, (ii) Mascot Ion Score of at least 20, (iii) a minimum of 6 amino-acid residues per peptide and (iv) position rank 1. Mascot results of the MIX analysis were filtered with the same parameters and with the integrated Percolator based filter using an FDR <1% (based on PSMs).

Project description:In this project, we aim to pair-wise analyze the genomes, transcriptomes and proteomes of in-bred rats originating from two different genetic backgrounds. These two strains are Brown Norway (BN-Lx) and Spontaneously Hypertensive Rats (SHR). First, we re-sequenced the genomes for both BN and SHR rats, followed by RNA-seq and proteomics of their liver tissues. We then append novel predicted gene models, non-synonymous SNPs and INDELs (derived from genome re-sequencing), as well as transcript variants such as RNA-editing and alternative splicing (derived from RNA-seq) that can diversify existing protein sequences onto the ENSEMBL rat FASTA (Build 68) to build an enhanced database. For proteomics studies, equal amount of liver lysates were digested with trypsin, LysC, GluC, AspN and chymotrypsin and were individually fractionated with strong cationic exchange chromatography. Doubly- and triply-charged fractions were analyzed with an Triple-TOF 5600 with collision-activated dissociation (CAD); while electron-transfer dissociation (ETD) was applied for fractions containing triple charges and above with a LTQ-Orbitrap Velos. Data analysis: Peak List generation: For Wiff files generated from TripleTOF 5600, tandem MS spectra were de-isotoped, charge- deconvoluted and peak lists converted to Mascot generic format (MGF) files using AB Sciex Data Converter (version 1.1). For data generated from the LTQ-Orbitrap Velos, Raw files were converted to MGF files using Proteome Discoverer (version 1.3). The non-fragment filter was used to simplify ETD spectra and the Top N filter for the HCD spectra. Three MGF files were generated (one for HCD, one for ETD IT and one for ETD FT). The files with an orbitrap readout were deisotoped and charge de-convoluted. Database Searching: All MGF files were queried with Mascot search engine (version 2.3) via Proteome Discoverer version 1.3 (PD 1.3, Thermo Fisher) for submission. The spectra were searched against in-house database (NGS_COMBINED). One of the five different enzymes used (Trypsin/P, LysC/P, Chymotrypsin, GluC-DE and AspN_ambic) were selected for each file and up to 9 missed cleavages were allowed. Cysteine carbamidomethylation was set as fixed modification, and oxidation of methionine and acetylation of the N-term as variable modifications. Peptide tolerance was initially set to 50 ppm and the MS/MS tolerance was set to 0.1 Da (for TOF readout), 0.02 Da (orbitrap readout) and 0.5 Da (ion trap readout). All peptide-spectrum matches (PSMs) were evaluated with Percolator for validation. We classified each PSM based on their q value. For proteins identification, we used set a high stringency filter of q = 0 (0% FDR). For peaks lists that do not yield any peptide matches, we exported them with PD 1.3 for further analysis. De novo search with PEAKS: Unassigned peak lists that are exported were re-analyzed with another software suite i.e. PEAKS Studio (version 6.0). The identification workflows is as follows. Peak lists were first filtered with a quality value of 0.65 as suggested by the manufacturer followed by de novo spectra interpretation. In this step, both peptide tolerance and MS/MS tolerance were set according to MASCOT search. To broaden the search space for these unassigned spectra, we additionally set de-amidation of asparagine and glutamine, and pyro-glu from glutamic acid and glutamine as variable modifications, on top of the other modifications indicated above. Maximum allowed variable PTM per peptide was set to 3. Finally de novo interpreted PSMs were submitted to PEAKS DB database matching, this time allowing semi-enzymatic specificity and a maximum cleavages per peptide of 2. Database used was set to NGS_COMBINED. FDR was estimated using decoy-fusion. The genomics and transcriptomics data are already deposited in the respective EBI repositories. Some of these data are derived from an already published manuscript. For the genomics data (from: Genetic basis of transcriptome differences between the founder strains of the rat HXB/BXH recombinant inbred panel by Simonis et al PMID:22541052) DNA data in Sequence Read Archive (SRA): BN-Lx genome: ERP001355 http://www.ebi.ac.uk/ena/data/view/ERP001355, SHR genome: ERP001371, BN reference genome: ERP000510, http://www.ebi.ac.uk/ena/data/view/ERP000510. RNA data in ArrayExpress: BN-Lx and SHR fragment RNA-seq data: E-MTAB-1029 http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-1029, BN-Lx and SHR paired-end RNA-seq data: to be submitted.

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Dataset Information

Mouse cerebellum (phospho)proteome LC-MSMS

Publications

Alterations in the cerebellar (Phospho)proteome of a cyclic guanosine monophosphate (cGMP)-dependent protein kinase knockout mouse.

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