The use of internal calibrants (the so called lock mass approach) provides much greater accuracy in mass spectrometry based proteomics. However, the polydimethylcyclosiloxane (PCM) peaks commonly used for this purpose are quite unreliable, leading to missing calibrant peaks in spectra and correspondingly lower mass measurement accuracy. Therefore, we here introduce a universally applicable and robust internal calibrant, the tripeptide Asn3. We show that Asn3 is a substantial improvement over PCM both in terms of consistent detection and resulting mass measurement accuracy. Asn3 is also very easy to adopt in the lab, as it requires only minor adjustments to the analytical setup. Data analysis: For mass measurement accuracy (MMA) calculations and comparisons, the following Mascot workflow was used. From the MS/MS data in each LC run, Mascot Generic Files were created using Distiller software (version 2.4.3.3, Matrix Science, London, UK, www.matrixscience.com/distiller.html). These peak lists were then searched with the Mascot search engine (Matrix Science) using the Mascot Daemon interface (version 2.4.0, Matrix Science). Spectra were searched against the Swiss-Prot database (version 13_04 of UniProtKB/Swiss-Prot protein database containing 20,232 sequence entries of human proteins) concatenated with its reversed sequence database. Variable modifications were set to pyro-glutamate formation of amino terminal glutamine and acetylation of the protein N-terminus, whereas fixed modifications only included oxidation of methionine. Mass tolerance on peptide ions was set to 10 ppm (with Mascot’s C13 option set to 1), and the mass tolerance on peptide fragment ions was set to 20 millimass units (mmu), except for the space-charge effect experiment(LMA5) where an extra search was done with a setting of 3 mmu. The peptide charge was set to 1+,2+,3+ and instrument setting was put on ESI-QUAD. Enzyme was set to trypsin allowing for one missed cleavage, and cleavage was allowed when arginine or lysine is followed by proline. Only peptides that were ranked one and scored above the threshold score, set at 99% confidence, were withheld. All data was processed and managed by ms_lims.

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).

Mtb was grown as either an actively growing normal culture or as a hypoxia-induced dormant culture. Whole cell lysates of live bacteria were obtained by mechanical disruption with silica beads. ATP-binding proteins were covalently labeled with desthiobiotin-tagged ATP (ActivX, Thermo Pierce). This chemoproteomic approach profiled the ATP-binding proteins present in either metabolic state. Labeled proteins were enriched via streptavidin affinity chromatography, digested with trypsin and subjected to tandem mass spectrometry (LC-MS/MS) for the identification of proteins containing a desthiobiotin modification of lysine (K +196 Da). Differential abundance of nucleotide binding proteins between the two growth conditions were quantified using label-free spectral counting and normalized spectral abundance factors (NSAF). DATABASE SEARCHING: Tandem mass spectra were extracted, charge state deconvoluted and deisotoped by Xcalibur version 2.2 SP1. All MS/MS samples were analyzed using Mascot (Matrix Science, London, UK; version 2.3.02) and SEQUEST (Thermo Fisher Scientific, San Jose, CA, USA; version v.27, rev. 11). Mascot and SEQUEST were set up to search the MtbReverse041712 database (7992 entries) assuming the digestion enzyme Trypsin. Parameters for both search engines were set to a fragment ion mass tolerance of 1.0 Da and a parent ion tolerance of 2.5 Da. Oxidation of methionine, iodoacetamide derivative of cysteine and the desthiobiotin modification of lysine were specified in Mascot and SEQUEST as variable modifications. PROTEIN IDENTIFICATION AND LABEL-FREE QUANTITATION-- Scaffold (version Scaffold_3.6.1, Proteome Software Inc., Portland, OR) was used to validate MS/MS based peptide and protein identifications. Peptide identifications were accepted if they exceeded specific database search engine thresholds. Mascot identifications required ion scores to be greater than the associated identity scores and 50, 65, 65 and 65 for singly, doubly, triply and quadruply charged peptides. SEQUEST identifications required deltaCn scores of greater than 0.2 and XCorr scores of greater than 1.8, 2.0, 3.0 and 4.0 for singly, doubly, triply and quadruply charged peptides. Proteins identifications were accepted if they contained at least one identified peptide in at least two biological replicates. Peptide spectra meeting the most minimum requirement were manually inspected for quality. Quantification of proteins was performed on normalized spectral abundance factors for each protein (NSAF). BLAST-BASED SEQUENCE DESCRIPTION: The most relevant description for each of the sequences was acquired based on the significant BLAST results. The homologs for the sequences were retrieved using the Blastp algorithm and the nonredundant database of NCBI. The Blast2GO suite was used. GENE ONTOLOGY ANNOTATION: The Pfam domains were mapped to Gene Ontology (GO) terms using the lookup table provided by Pfam2go (http://www.geneontology.org/external2go/pfam2go). An in-house script was written to retrieve GO annotations based on the root term as "molecular function" and their distance from the root term. PFAM DOMAIN-BASED ANNOTATION: The InterPro and Pfam IDs corresponding to the GO term "ATP binding" (GO ID:0005524) were retrieved using the QuickGO (www.ebi.ac.uk/QuickGO/) and InterPro BioMart web services (http://www.ebi.ac.uk/interpro/biomart/martview). The ATP-binding associated domains were queried against the ATP-binding proteome data sets according to spectral quality (high, medium, low confidence). Their Pfam and InterPro descriptions, were identified using the InterProscan Web service, which was accessed via the Pipeline Pilot (Accelrys) implementation in the sequence analysis collection.

1. Heat waves are increasing in frequency and intensity globally with negative consequences for biological function. Assessing the effect of extreme heat on species requires an understanding of their adaptive capacity for mitigating physiological damage. Where long-term exposure to hot conditions in natural populations provides sufficient selection pressure, populations should exhibit signals of adaptive thermotolerance to temperature extremes. 2. Using quantitative proteomics, we tested this idea in the widespread and commercially-important tree species Eucalyptus grandis (Flooded Gum). Seedlings from six natural populations of E. grandis spanning a 2000 km gradient were exposed to a four-day extreme heat treatment (42-24°C day-night cycle) in experimental growth chambers. Populations differed in their long-term exposure to extreme heat conditions, defined both as the number of days annually ≥15°C above mean annual temperature (MAT), and average number of days annually with temperature maxima ≥ 35°C between 1960-1990. 3. Long-term exposure to extreme heat conditions in the field predicted the protein-level response of E. grandis to experimental heat waves. Relationships between long-term extreme heat exposure and protein increases were positive and linear for all combinations of extreme heat (days ≥15°C above MAT, mean days with temperature maxima ≥35°C annually) and expression (all differentially expressed proteins, isolated heat shock proteins, proteins involved in molecular stress responses). 4. Although extreme climate events are typically rare (e.g. 1 day 15° ≥ MAT per 5 years in some populations in our study), E. grandis populations sampled from across a 2000km range exhibit a clear capacity to increase expression of proteins involved in heat stress in response to simulated heat wave exposure. Presumably they respond similarly under natural heat wave conditions. 5. We show that a long-lived species with a broad environmental niche exhibits adaptive variation in protein response to temperature extremes at the population level. This implies that restoration, translocation and silvicultural programs should consider the molecular response of source populations to climatic extremes to maximise success under future climates. 6. Tree populations with low exposure to extreme heat conditions may be limited in their ability to respond to heat wave events, potentially limiting their adaptive capacity to withstand novel climate conditions.

Usage Notes

Protein expression under heat wave conditionsProtein ratios from quantitative proteomics of leaf samples collected after a four-day heat wave (42 degree) and control (28 degree) treatment for Eucalyptus grandis. Protein identity and quantity were determined using Proteome Discoverer V1.3 software (Thermo Scientific, United States) run from a local MASCOT server (Matrix Science, London, U.K.). The E. grandis protein sequences available through UniProt (http://www.uniprot.org, n = 44,150 proteins; October 2016) were used as a reference search database. The following parameters were used for peptide to spectrum matching in MASCOT: MS tolerance, ±10 ppm; enzyme, trypsin with one missed cleavage; fragment mass error, 0.1 Da; static modifications, carbamidomethylation of cysteine and 10-plex TMT tags on lysine residues and peptide N-termini; variable modifications, oxidation of methionine and deamidation of asparagine and glutamine. Spectra were also searched against a reversed-sequenced decoy database to determine false discovery rates (FDR) and filtered at a maximum FDR cut-off of 1% at the protein level. Only peptides below the MASCOT significance threshold filter of p = 0.05 were included in the final dataset, and proteins with at least two unique peptides were regarded as confident identifications. Relative quantification of proteins was achieved by pairwise comparison of TMT intensities, using the ratio of the labels for each of the treatment replicates (numerator) versus the labels of their corresponding pooled control reference (denominator).Appendix_s2.xlsx

Acquired raw data as used for the protein identification of differential spots from the quantitative analysis of the response of the barley root proteome to salinity stress. Acquisition of peptide mass fingerprint data was performed on a Reflex III MALDI-TOF device (Bruker Daltonics, Bremen, Germany, www.bruker.com/) operating in reflector mode using the FlexControl software. Spectra were calibrated using external calibration, with subsequent internal mass correction. Protein identification was performed with the Mascot search engine (Matrix Science, London, United Kingdom, www.matrixscience.com/home.html), by searching the barley EST (expressed sequence tag) Gene Index in the TIGR database (compbio.dfci.harvard.edu/cgi-bin/tgi/gimain.pl?gudb=barley) and the HarvEST database (harvest.ucr.edu/). Parameters for the search were: monoisotopic mass accuracy 100 ppm tolerance, missed cleavages 1, allowed variable modifications: oxidation (Met), propionamide (Cys) and carbamidomethyl (Cys). When this approach failed, the samples were subjected to analysis by nanoLC-ESI-Q-TOF MS/MS and de novo sequencing (nanoLC ESI-Q-TOF MS/MS, Waters Corporation, Milford, USA, www.waters.com), following Amme et al. (2006) using the MassLynx software. A 10 ppm peptide, 0.1 Da fragment tolerance, one missed cleavage and variable oxidation (Met), propionamide (Cys) and carbamidomethyl (Cys) were used as the search parameters. Database searches were conducted against the barley EST Gene Index of the TIGR database and the HarvEST database.

Dataset 4. List of proteins identified in human whole brain. Database searches was performed using an in-house Mascot server (version 2.3.02, Matrix Science, MA, USA) with a precursor ion tolerance of 10 ppm and fragment peak tolerance of 0.05 Da. Uniprot human database (downloaded on 15th July of 2015, 90478 sequences and 35.890.546 residues) was used for database search. Two missed trypsin cleavage sites were tolerated. Carbamidomethylation (C) was set as fixed modification and deamidation (NQ) and oxidation (M) were set as variable modifications. Peptides with Mascot score >15 were used to generate the peptide list in all tested human conditions. (XLS 286 kb)

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.

Relating protein concentration to cell-type-specific responses is one of the remaining challenges for obtaining a quantitative systems level understanding of mammalian signaling. Here we used mass-spectrometry (MS)- and antibody-based quantitative proteomic approaches to measure protein abundances for 75% of a hand-curated reconstructed ErbB network of 198 proteins, in two established cell types (HEK293 and MCF-7) and in primary keratinocyte cells. Comparison with other quantitative studies allowed building a set of ErbB network proteins expressed in all cells and another which are cell-specific and could impart specific properties to the network. As a proof-of-concept of the importance of protein concentration, we generated a small simplified mathematical model encompassing ligand binding, followed by receptor dimerization, activation, and degradation. The model predicts ErbB phosphorylation in HEK293, MCF-7, and keratinocyte cells simply by incorporating cell-type-specific ErbB1, ErbB2, and caveolin-1 abundances but otherwise contains similar rate constants. Altogether, the data provide a resource for protein abundances and localization to be included in larger mathematical models, enabling the generation of cell-type-specific computational models. Shotgun MS/MS analysis was conducted on HEK293 cells and on primary keratinocytes and SRM was used for all 3 cell types. Acquired data were analyzed using the Proteome Discoverer software suite (v1.3.0.339, Thermo Fisher Scientific), and the Mascot search engine (v2.3, Matrix Science) was used for peptide identification. Data were searched against an in-house generated database containing all proteins corresponding to human in the Swissprot human database (as of 3/2013) with 600 added common contaminants, as previously described, with a total number of 37 694 sequences in the database. A precursor ion mass tolerance of 7 ppm at the MS1 level was used, and up to three miscleavages for trypsin were allowed. The fragment ion mass tolerance was set to 0.5 Da. Oxidation of methionine and protein acetylation at the N-terminal was defined as variable modification. Carbamidomethylation on cysteines was set as a fix modification. The identified peptides were filtered using a FDR < 5%.

Three different label-free proteome quantification methods - APEX, emPAI and iBAQ - were evaluated to measure proteome-wide protein concentrations in the cell. All the methods were applied to a sample from Escherichia coli chemostat culture. A Pearson squared correlation of approximately 0.6 among the three quantification methods was demonstrated. Importantly, the sum of quantified proteins by iBAQ and emPAI corresponded with the Lowry total protein quantification, demonstrating applicability of label-free methods for an accurate calculation of protein concentrations at the proteome level. The iBAQ method showed the best correlation between biological replicates, a normal distribution among all protein abundances, and the lowest variation among ribosomal protein abundances, which are expected to have equal amounts. Absolute quantitative proteome data enabled us to evaluate metabolic cost for protein synthesis and apparent catalytic activities of enzymes by integration with flux analysis. All the methods demonstrated similar ATP costs for protein synthesis for different cellular processes and that costs for expressing biomass synthesis related proteins were higher than those for energy generation. Importantly, catalytic activities of energy metabolism enzymes were an order or two higher than those of monomer synthesis. Interestingly, a staircase-like protein expression was demonstrated for most of the transcription units. Fragment MS/MS spectra from raw files were extracted as MSM files and then merged to peak lists using the Raw2MSM version 1.7 [29], selecting top six peaks for 100 Da. MSM files for the three technical replicates of the same sample were concatenated to generate a single large peak list file with a MultiRawPrepare.pl script (http://msquant.alwaysdata.net) and subsequently searched with the Mascot 2.2 search engine (Matrix Science, London, UK) against the E. coli K-12 MG1655 protein sequence database downloaded 22.09.2009 from EcoGene 2.0 (http://ecogene.org), supplemented with common contaminants. Search parameters were as follows: two missed trypsin cleavage, fixed modification was set as carbamidomethyl (C), variable modifications were set as oxidation (M) and acetyl (protein N-term), 5 ppm precursor mass tolerance and 0.6 Da MS/MS mass tolerance. In order to estimate the false discovery rate (FDR) decoy search option was allowed. The Mascot search results were validated by the PeptideProphet and ProteinProphet algorithms [30] before the absolute protein expression indexes (APEX) [19] were calculated by the APEX Quantitative Proteomics Tool [25]. An estimated false positive rate (FPR) cut-off of less than 5% was used, which corresponded to the ProteinProphet probability p greater than 0.5. FPR less than 5% was chosen, as this resulted in a reasonable number of quantified proteins (1220), comparable with the iBAQ dataset (1334 proteins). Limiting the FPR to less than 1% would result in the loss of more than 200 proteins.

The epithelial barrier's primary role is to protect against entry of foreign and pathogenic elements. Global and targeted approaches were applied to nasal swabs from healthy and COVID-19-confirmed cases within 24 hours post-positive-confirmation and at 3 weeks post-infection to observe changes in proteome and metabolome. We found that the tryptophan/kynurenine metabolism pathway is a pinch-point regulator of canonical and non-canonical transcription activation, macrophage release of cytokines and significant changes in the immune and metabolic status with increasing severity and disease course. Methods Nasal epithelial swabs were self-collected by participants in this study. Swabs were resuspended in 80% methanol with 6mg of 1.0 mm zirconium beads and used cell shearing to extract proteins and metabolites. The method is described in Wasinger et al., 2020 [1]. Proteins were pelleted and the supernatant containing metabolites stored at -80°C until required. Protein pellet was resuspended in digestion buffer and 50 µg enzymatically treated with trypsin overnight at room temperature. Proteomic mass spectrometry Mass spectrometry was carried out using a QExactive (Thermo Electron, Bremen, Germany) run in DDA mode using 1.5 μg (2.0 μL from 10μL) as previously described [2]. Peptides were eluted using a linear gradient of H2O:CH3CN (98:2, 0.1% formic acid) to H2O:CH3CN (64:36, 0.1% formic acid) at 250 nL min-1 over 60 min. Statistical Analysis Proteins were identified using Mascot Daemon v2.5.1 (Matrix Science, London, UK) searched against the SwissProt and SARV19 database (downloaded February 2021, containing 563,972 sequences; and July 2020, containing 271,909 sequences, respectively). Search parameters were set to carbamidomethyl (C); variable modifications, oxidation (M), phospho (STY); enzyme, semi-Trypsin; and maximum missed cleavages, 1; peptide tolerance, ± 5 ppm; fragment tolerance, 0.05 Da. Scaffold software (version 4.6.1, Proteome Software Inc., Portland, OR, USA) was used to compare the proteome. Peptide identifications were accepted if they could be established at greater than 95% probability using the Scaffold delta-mass correction. Protein identifications were accepted if they could be established at less than 1% false discovery rate (FDR) and contained at least 2 identified peptides. Expression changes across the samples were measured via spectral count, normalised to total ion count. ANOVA was used to report abundance changes controlled by the Benjamini-Hochberg procedure for multiple comparisons, with p-values set to <0.05. The studies reached a power ≥ 90% and were calculated using PASS software based on a mean abundance values and standard deviation between groups. The proteomic dataset of differentially abundant proteins was assessed for enriched pathways using Ingenuity Pathway Analysis (IPA® Qiagen, CA, USA). The core analysis was carried out using the default settings with only direct relationships and only experimentally observed confidence considered based on the IPA knowledge base (genes only). The P-value for the correlation between identified proteins and a given canonical pathway was calculated by Fisher's exact test. Targeted proteins were analysed using Skyline Software, and peptides were accepted based on retention time and sequence with at least 3 transitions required. Peak area under curve of the parent ion was used to assess relative abundance of the marker panel. Log2 transformed data were evaluated using Student T-test, and Receiver Operating Characteristic (ROC) probability curves to measure ability to distinguish between binary classifiersPRM targeted analysis applied transitions listed in Attachment. Quantification of Kynurenine Pathway Mixed standards and 100 µl aliquots of Nasal methanolic extracts were spiked with an internal standard mixture containing labelled KP metabolites; dried, and reconstituted in 100 µl of water, filtered through 4 mm syringe filters with 0.2 μm membrane into reduced volume LC vials; 20 µl aliquots were injected for analysis. MRM LC-MS/MS analysis was conducted using a TSQ Vantage mass spectrometer (Thermo, USA) connected to Vanquish (Thermo-Dionex USA) solvent delivery/autosampler system. Chromatographic separation was achieved using a Kinetex™ PFP column (150mm x 2 mm, 1.7μm, 100 Å, Phenomenex USA) by reverse phase gradient elution at 25˚C using a gradient of 0.1% formic acid to 10% methanol over 2 min, then ramped to 60% B to 4min, and then ramped to 100%B by 8mins. Quantification of NAD+ome metabolites LC-MS/MS analysis was conducted using a TSQ Vantage mass spectrometer (Thermo, USA) connected to Vanquish (Thermo-Dionex USA) solvent delivery/autosampler system. Chromatographic separation was achieved using a Kinetex™ PFP column (150mm x 2 mm, 1.7μm, 100 Å, Phenomenex USA) by reverse phase gradient elution at 25˚C. The mobile phase consisted of aqueous 0.1% formic acid (A) and methanol (B). The gradient elution was programmed as follows: start at 10 % B, hold 2 minutes, ramp to 60%B in 4min, then to 100%B in 8min. In 0.4min set to 10 % B and equilibrate for 5.6 min. Total run time is 20 min. Mass spectrometric detection was performed using multiple reaction monitoring (MRM) with heated electrospray ionization (HESI) source in positive mode. MSD parameters were optimised using Anthranilic acid direct infusion, and the tune file created was used in the created method. The conditions were: ion spray voltage, 4,000 V; vaporizer temperature 300˚C, capillary temperature 300˚C, collision argon gas 1 Torr, sheath and auxiliary gas valves (nitrogen) set at 20 and 10 arbitrary units respectively. The MRM transitions for all analytes were optimised using a syringe infusion pump and are shown in Attachment 1. Data acquisition and processing were performed with Xcalibur™ (version 2.2, 2011 Thermo Fischer Scientific, Waltham MA). NAD+ome LCMS/MS assay of nasal epithelial (NE) swab extracts Methods followed Bustamante et al. [3]. LC-MS/MS analysis was conducted using a TSQ Vantage mass spectrometer (Thermo, USA) connected to Vanquish (Thermo-Dionex, USA) solvent delivery system/autosampler using an adaptation of a previously published method by Bustamante et al. [5]. Isotopically enriched (2H) internal standards were purchased from Toronto Research Chemicals and primary standards from Sigma-Aldrich. HESI-MS parameters: Ion spray voltage 4,000 V; vaporizer temperature 300˚C, capillary temperature 300˚C, collision gas 1.0 Torr. These parameters were optimised using NMN solution in positive ion mode. Calibrators of known concentrations (0, 0.02, 0.04, 0.06, 0.08, 0.1, 0.2, 0.3, 0.4 μM) of NADOME metabolites were prepared by mixing aliquots of standards with a fixed volume of internal standard mixture. Similarly, NE extracts were mixed with internal std. cocktail, dried and reconstituted in 50 µl of 100 mM ammonium acetate in water. Samples were filtered into LC vials and 20μL injected for analysis. Data acquisition and processing were performed with Xcalibur™ (version 2.2, 2011 Thermo Fischer Scientific, Waltham MA). Mobile phases consisted of 5mM ammonium acetate in water pH 9.5 (A); 100 % Acetonitrile (B) according to Table S5 using a Phenomenex Luna 3 µm NH2 100 Å 150 x 2 mm column. Racemic amino acid analysis Methods were adapted from Ayon et al. [4]. Briefly, 40 µl of colon biopsies extracts were mixed with 2H4-alanine as internal standard. Samples were dried and derivatised with 20 µl of 10mM Marfey’s reagent in acetone and 5 µl of triethylamine and incubated at 37˚C for 3 hours, the reaction was quenched with 10 µl 0.5 M HCl. Samples were diluted with 120 µl of 30 % ACN in 0.1% aqueous formic acid. Phenomenex SPE Strata-X cartridges (30 mg) were preconditioned with methanol, followed by 0.1 % formic acid in water, and samples were loaded and washed with 0.1 % formic acid in water, and then eluted with 70 % acetonitrile in 0.1 % aqueous formic acid. Eluants were dried and reconstituted in 0.1 % aqueous formic acid before analysis. LC-MS/MS analysis was conducted using a TSQ Vantage mass spectrometer as described in Attachment 1. GCMS/MS assay of nasal epithelial (NE) swabs of picolinic and quinolinic acid GC-MS analysis was carried out using Agilent Technologies GCMS system comprising 5973inert MSD coupled to 6890 GC oven and 7683 series autosampler. Chromatographic column Agilent J&W DB5-MS UI 30mx 0.25mm x 0.25μm. Methods followed those described by Smythe et al. [5]. Single Ion Monitoring (SIM) GC-MS assay of picolinic and quinolinic acid in nasal swab extracts. Picolinic and quinolinic acid in NE extracts were assayed by GC–MS in electron-capture negative ionization mode; a very sensitive method with on-column limit of detection for QUIN and PIC < 1 femtomol on column (Smythe et al. 2003). Briefly, standards and NS extracts (100-200μl) were spiked with 2H4 -Pic and 2H3-Quin in 13x100mm glass cell culture tubes, and dried in a Speedvac before derivatisation with 60μL TFAA and 60μL of HFP. Capped tubes were placed in a heating block at at 60°C for 30 min to produce the hexafluoro-isopropyl esters of the respective acids. Samples were then dissolved in 80μl of toluene, washed with 1ml of 5% sodium bicarbonate and 1ml of water to remove by-products. The upper toluene layer was passed through anhydrous sodium sulphate mini columns (approx. 500 mg) into autosampler vials, and 2μl were injected into the GC/MS system. Sample concentrations of Pic and Quin were calculated from the standard curves generated.
Monitored SIM ions for 2H4 -Pic, Pic, 2H3-Quin and Quin are m/z 277, m/z 273, m/z 467 and m/z 470 respectively.
Injector temperature 250˚C, transfer line temperature 280˚C; run time 15.2 minutes using T program below: GC-MS analysis was carried out using Agilent Technologies GCMS system comprising 5973inert MSD coupled to 6890 GC oven and 7683 series autosampler. Chromatographic column

Here we describe a high coverage dataset of whole fly through a one-dimensional gel electrophoresis LC-MS/MS approach. By combining the datasets of two kinds of SDS-PAGE and two kinds of tagmata, the high coverage analysis resulted in the identification of 5,262 genes, corresponding to 38.23% of the whole encoding genes. Sample info: 4-day adult male Canton-S Drosophila melanogaster was used in this study. To reduce sample complexity, fly heads and bodies (including reproductive system, genitalia and gonad) were sequenced separately. Protein extraction and analysis: Fly heads and bodies were isolated using prechilled size-exclusion sieves and distributed into 1.5-mL tubes separately. Approximately 120 ug of each sample were lysed in 200 ul lysis buffer containing 2% SDS, 10% glycine, 1% bromophenol blue, 10 mM DTT, and 0.01% protease inhibitor cocktail (Roche) and 0.5-mm glass beads by vortexing at the highest speed for 5 min. After boiling for 5min, 20 mM iodoacetamide was used to alkylate the proteins. Samples prepared as described above were separated by 1D SDS-PAGE, and the lanes were excised into fractions, followed by in-gel tryptic digestion. All prepared peptides were further analyzed on an LTQ-Orbitrap Velos hybrid mass spectrometer (Thermo Electron, San Jose, CA) coupled with UPLC (nano Acquity Ultra Performance LC, Waters). Protein search:The acquired MS/MS spectra were searched against the flybase database (http://flybase.org/, release 5.4, 24,043 entries) using Mascot (version 2.3, Matrix Science). The results were filtered by PepDistiller (v. 1.26) with a false discovery rate (FDR) lower than 1% at the peptide spectrum match level using the target-decoy strategy.

Plasmodium falciparum schizont proteins were extracted using SDS, digested with trypsin and phosphopeptides were enriched using IMAC. Phosphopeptides were analysed using either decision tree or data dependent neutral loss triggered ETD acquisition. All raw MS data files were processed and converted into MGF file format using Proteome Discoverer 1.1 (Thermo Scientific). A precursor filter of 600-10000 Da and a non-fragment filter were applied to ETD spectra to remove un-reacted precursor peaks, charge reduced precursor peaks, neutral losses from charge reduced precursors and FT Overtones using default settings. All ion trap spectra with less than 15 fragmentation peaks were removed and a signal to noise filter of 3 was applied to all spectra. All datasets were searched using Mascot v2.2 (Matrix Science) against a combined Human (IPI, 2010) and Plasmodium falciparum (GeneDB) sequence database (79,637 sequences) using the following search parameters: trypsin with a maximum of 3 missed cleavages, 10 ppm for MS mass tolerance, 0.5 Da for MS/MS mass tolerance, with Acetyl (Protein N-term), Oxidation (M), Deamidated (NQ), Carbamidomethyl (C) and Phospho ST set as variable modifications. ETD spectra were searched using c, z and y ion series and CID data was searched using b and y ion series. All searches used Mascot’s automated decoy database searching.

This file contains information about mass spectrometry data for the manuscript entitled “Protein Arginine Methyltransferase 5 Governs Expression of Early Cardiomyocyte Markers during Zebrafish Development." 1) peptide and protein identifications based on mass spectrometry; 2) a collection of protein sequences, peptide sequences (with modifications), and structures for capturing the scores associated with ranked peptide matches for each spectrum searched. The goal of the mass spectrometry analysis was to confirm or reject identification of protein arginine methyltransferase 5 (Prmt5). Isoforms of Prtmt5 were detected by immunoblotting. Mass spectrometry was used to confirm that the protein identified by immunoblotting was indeed Prmt5.Prmt5 expression fluctuates during zebrafish development.To investigate the role of Prmt5 in zebrafish development, we sought to determine its mRNA and protein expression during early stages of zebrafish embryogenesis. Western blot analysis revealed that Prmt5 protein expression can be detected as early as 6 hrs post-fertilization (hpf), gradually increases 9.3-fold by 18 hpf, and decreases 2 to 3-fold between 24 and 72 hpf.Similarly, real-time RT-PCR analysis at the same developmental stages showed that prmt5 mRNA can be detected 1 hpf and its levels increase slightly by 18 to 72 hpf. Remarkably, despite the lack of any significant change in prmt5 mRNA levels between 18 and 72 hpf, Prmt5 protein expression showed dynamic changes at 18 and 72 hpf, which were accompanied by a change in mobility of Prmt5 protein at 72 hpf compared to other developmental stages.To gain further insight into the unique pattern of Prmt5 protein expression and to understand the shift in its size, we used mass spectrometry to analyze the two Prmt5 isoforms, which were detected at 24 and 72 hpf. Matrix-assisted laser-desorption ionization (MALDI) mass spectrometry was performed for detection of PRTM5 (Ultraflextreme, Bruker Daltonics, Bremen, Germany). Mass spectra were collected in the range from 500 to 3,500 m/z values, positive mode, single protonated, and with a collection of a minimum of 10,000 laser shots for every single spectrum by using FlexControl software (Bruker Daltonics, Bremen, Germany). Tryptic autodigestion peptides 842.51, 1045.56, and 2211.10 Da were used for internal caibration. Analysis of spectra was performed with FlexAnalysis software (Bruker Daltonics, Bremen, Germany). Collected mass lists were used for identification by using ProFound and Mascot search engines (http://prowl.rockefeller.edu/prowl-cgi/profound.exe and https://www.matrixscience.com), with mass tolerance +/-0.1 Da, and no restriction of species, pI, and molecular mass. For identification, the number of matched peptides and coverage of the identified protein with detected peptides were also considered. NCBI database (version of 2019) was used in the searches. The significance of identification was set to p0.95. Identified PRTM5 peptides were matched and assigned to PTRM5 peptides generated by in silico digestion with trypsin as described in the maniuscript.We found unique peptides present in Prmt5 protein isolated at 24 hpf, but not in Prmt5 protein isolated at 72 hpf. Amino acid sequence alignment of the four available zebrafish Prmt5 variants indicated that all of the identified peptides were present only in Prmt5 variant 1 (Accesssion no. Q503E3). Mobility of Prmt5 at 72 hpf indicated a lower apparent molecular weight compared to the 24 hpf zebrafish Prmt5 variant 1. In addition, the predicted molecular weights of the already identified zebrafish cDNAs varied in size between 69.41 kDa for variant 1 (Accesssion no. Q503E3), 70.73 kDa for variant 2 (Accesssion no. A0A8M6Z3C8), 72.71 kDa for variant 3 (Accesssion no. A0A8M9PS04), and 85.36 kDa for variant 4 (Accesssion no. Q68EH4). Taken together, these results suggest that the Prmt5 protein expressed at 72 hpf corresponds to a novel splice variant of a smaller size with a predicted molecular weight lower than 69.41 kDa, and which lacks the 6 peptides identified by mass spectrometry.Table 1. Prmt5 peptide sequences expressed at 24 but not at 72 hpf.Peptide sequences Residues Position Computed Mass1- LSPWIETDSELTTERR 82-97 1931.9582- WLGEPIKAAILPTSIFLTNK 217-236 2211.2663- HSEKDLR 268-274 883.4514- EWTSPEK 418-424 875.4025- ADIIVSELLGSFGDNELSPECLDGAQHFLK 425-454 3216.5636- EVTLSIKPETHSPGMFSWFPILFPLK 556-581 3000.581Total computed mass of peptides in the Prmt5 isoform observed at 24 hpf. 12960.69The total computed mass of 12,960.69 Da, assigned to extra peptides in Prmt5 variant 1 (69.41 kDa, accesssion no. Q503E3) present at 24 hpf, is missing in the shorter version of Prmt5 variant found at 72 hpf.

SILAC analysis of human primary bladder smooth muscle cells treated with platelet-derived growth factor for 0, 4, and 24 h. Platelet-derived growth factor-BB (PDGF-BB) is a mitogen and motogen that has been implicated in the proliferation, migration and synthetic activities of smooth muscle cells (SMC) that characterize pathologic tissue remodeling in hollow organs. To explore the signals induced by PDGF on a global scale, we performed expression profiling and quantitative proteomics analysis of PDGF-treated human visceral SMC. 1695 genes and 241 proteins were identified as differentially expressed in PDGF-treated primary bladder SMC versus non-treated cells. Analysis of gene expression data revealed MYC, JUN, EGR1, MYB and RUNX1 as the transcription factors most significantly networked with upregulated genes; DDIT3, NFAT5, and SOX5 were most networked with downregulated genes. For protein identification and quantification, raw mass spectrometric data were analyzed with MaxQuant software (version 1.0.13.13). The parameters were set as follows. In the Quant module, SILAC triplets was selected; oxidation (M) and acetyl (Protein N-term) were set as variable modification; carbamidomethyl (C) was set as fixed modification; concatenated IPI human database (version 3.52) (74,190 forward sequences and 74,190 reverse sequences) was used for database searching; all other parameters were default. Tandem mass spectra were searched by Mascot (version 2.2.0.4) (Matrix Science, Boston, MA). In the Identify module, all parameters were default, except that maximal peptide posterior error probability was set as 0.05. False discovery rates for protein and peptide identifications were both set at 0.01.

Datasets contain protein identification and raw data from MALDI protein identifications. Identified proteins resulted from a comparison of GLC1 and GLC1 SCLC cell lines using 2D DIGE. MALDI-TOF-MS analyses were performed on an UltraFlexTM II (Bruker Daltonics) instrument according to the instructions of the manufacturer. The instrument was equipped with a scoutTM MTP MALDI target. The spectra were acquired in the positive ion mode according to the settings given by the manufacturer. For external calibration, a peptide standard (m/z 757.399, 1296.684, 1619.822, 2093.086 and 3147.471) was used. The MALDI-PMF spectra were processed using the FlexAnalysis™ 2.4 software (Bruker Daltonics) and converted in the .xml format. For peak detection, the spectra were subjected to an internal recalibration using 13 different monoisotopic masses from autolysis products of trypsin and fragments of keratins ranging from m/z 842.509 – 2825.406. Following parameters were applied: snap peak detection algorithm, signal to noise threshold of 6, maximal number of peaks 100, quality factor threshold 50 and baseline subtraction TopHat. The generated mass lists were subsequently sent to ProteinScapeTM 1.3 (Bruker Daltonics, Bremen, Germany), triggering database searches using ProFound (Version 2002.03.01, Proteometrics LLC) and MASCOT (Version 2.3.02, Matrix Science, London, UK). The following search parameters were selected: fixed cysteine modification with propionamide, variable modification due to methionine oxidation, one maximal missed cleavage sites in case of incomplete trypsin hydrolysis and no details about 2-DE derived protein mass and pI. Using the Score booster function of ProteinScapeTM the mass lists were recalibrated and background masses removed using a list containing 44 masses occurring in a minimum of 10% of generated peak lists. The database searches were run with a mass tolerance of 40 ppm using UniProt’s human complete proteome set (downloaded on 26.10.2012) containing 68.109 protein entries. The used database is a composite database consisting of the UniProtKB entries and a duplicate of the same database, in which the amino acid sequence of each protein entry was randomly shuffled. Proteins reaching Profound score > 1.5 or Mascot score > 64 were considered as identified. Using these criteria one decoy database entry was found by the search engines indicating high confidence of protein identifications. If several database entries of homologues proteins matched these criteria only the entry with the highest score was reported.

Protein phosphorylation is one of the major molecular mechanisms regulating protein activity and function throughout the cell. Pannexin 1 (PANX1) is a large-pore channel permeable to ATP and other cellular metabolites. Its tyrosine phosphorylation and subsequent activation have been found to play critical roles in diverse cellular conditions, including neuronal cell death, acute inflammation, and smooth muscle contraction. Specifically, the non-receptor kinase Src has been reported to phosphorylate Tyr198 and Tyr308 of mouse PANX1 (equivalent to Tyr199 and Tyr309 of human PANX1), resulting in channel opening and ATP release. Although the Src-dependent PANX1 activation mechanism has been widely discussed in the literature, independent validation of the tyrosine phosphorylation of PANX1 has been lacking. Here, we show that commercially available antibodies against the two phosphorylation sites mentioned above—which were used to identify endogenous PANX1 phosphorylation at these two sites—are nonspecific and should not be used to interpret results related to PANX1 phosphorylation. We further provide evidence that neither tyrosine residue is a major phosphorylation site for Src kinase in heterologous expression systems. We call on the field to re-examine the existing paradigm of tyrosine phosphorylation-dependent activation of the PANX1 channel. Methods Purified hPANX1 protein with/without co-expressing mSrc Y529F mutant was resolved in SDS-PAGE gel and the band corresponding to hPANX1 protein was cut and subjected to in-gel digestion with trypsin. Half of each digested sample was analyzed by nano LC-MS/MS with a Waters M-Class HPLC system interfaced with a ThermoFisher Fusion Lumos mass spectrometer. Peptides were loaded on a trapping column and eluted over a 75µm analytical column at 350nL/min; both columns were packed with Luna C18 resin (Phenomenex). The mass spectrometer was operated in data-dependent mode, with the Orbitrap operating at 60,000 FWHM and 15,000 FWHM for MS and MS/MS respectively. The instrument was run with a 3 s cycle for MS and MS/MS. Data were searched using a local copy of Mascot (Matrix Science) with the following parameters. Enzyme: Trypsin/P; Database: SwissProt Human (concatenated forward and reverse plus common contaminants); Fixed modification: Carbamidomethyl (C) Variable modifications: Oxidation (M), Acetyl (N-term), Pyro-Glu (N-term Q), Deamidation (N/Q); Mass values: Monoisotopic; Peptide Mass Tolerance: 10 ppm; Fragment Mass Tolerance: 0.02 Da; Max Missed Cleavages: 2. Mascot DAT files were parsed into Scaffold (Proteome Software) for validation, filtering and to create a non-redundant list per sample. Data were filtered using 1% protein and peptide FDR and requiring at least two unique peptides per protein.

Proteome analysis of a novel type of virus. The virus was grown in A. castellanii amobea before being purified. Viral particules were enriched after centrifugation and proteins solubilised by SDS before being stacked in the top of a SDS-PGE gel. After in-gel digestion, resulting peptides were injected for a 120min nanoLC-MS/MS analysis using an Ultimate U3000 system and a LTQ-Orbitrap Velos pro hybrid mass spectrometer (Top 20).Data processing and bioinformatics: Data were processed automatically using Mascot Daemon software (version 2.3.2, Matrix Science). Concomitant searches against Pandoravirus and A. castellanii protein sequence databanks as well as classical contaminants database and the corresponding reversed databases were performed using Mascot (version 2.4). ESI-TRAP was chosen as the instrument, trypsin/P as the enzyme and 2 missed cleavage allowed. Precursor and fragment mass error tolerances were set respectively at 10 ppm and 0.6 Da. Peptide modifications allowed during the search were: carbamidomethyl (C, fixes) acetyl (N-ter, variable), oxidation (M, variable) and deamidation (NQ, variable). The IRMa software (Dupierris et al., Bioinformatics, 2009, 25:1980-1, version 1.30.4) was used to filter the results: selection of rank 1 peptides, peptide identification FDR < 1% (as calculated by employing the reverse database strategy), and minimum of 1 specific peptide per identified protein group.

Cells grown in 10 cm dishes at 80-90% confluency were lysed with 500 μL NP-40 buffer (150 mM NaCl, 0.5% NP-40, 10 mM Tris-HCl at pH 7.5, and 0.5 mM EDTA containing a protease inhibitor cocktail), followed by sonication. To identify the proteins in the anti-FLAG (METTL16) antibody-immunoprecipitated samples, Nano-liquid chromatography-nanospray tandem mass spectrometry (Nano-LC/MS/MS) of protein identification was performed by the Mass Spectrometry and Proteomics Facility at the Ohio State University (Columbus, Ohio), using a Thermo Scientific orbitrap Fusion mass spectrometer equipped with an nanospray FAIMS Pro™ Sources operated in positive ion mode. Data were searched using Mascot Daemon (Matrix Science, version 2.7.0; Boston, MA) via ProteomeDiscoverer (version 2.4; Thermo Scientific) and the database searched against the most recent Uniprot databases. Label free quantitation was performed using the spectral count approach, in which the relative protein quantitation was measured by comparing the number of MS/MS spectra identified from the same protein in each of the multiple LC/MSMS datasets. Scaffold was used for data analysis.

Abstract: N-terminal acetylation (Nt-acetylation) occurs on the majority of eukaryotic proteins and is catalysed by N-terminal acetyltransferases (NATs). Nt- acetylation is increasingly recognized as a vital modification with functional implications ranging from protein degradation to protein localization. Very recently, the first human X-linked genetic disorder caused by a mutation in a NAT gene was reported; Ogden syndrome boys harbour a p.Ser37Pro variant in the gene encoding Naa10, the catalytic subunit of the NatA complex, and suffer from global developmental delays and lethality during infancy. Here, we developed a Saccharomyces cerevisiae model by introducing the human wildtype or mutant NatA complex into yeast lacking NatA (NatA-∆). The human NatA complex phenotypically complemented the NatA-∆ strain, while only a partial rescue was observed for the Ogden mutant NatA complex suggesting that hNaa10-S37P is only partially functional in vivo. Furthermore, we performed quantitative Nt-acetylome analyses on a control yeast strain (yNatA), a yeast NatA deletion strain (yNatA-∆), a yeast NatA deletion strain expressing human NatA (hNatA), and a yeast NatA deletion strain expressing mutant human NatA (hNatA-hNaa10-S37P). Interestingly, a reduced degree of Nt-acetylation was specifically observed among a large group of NatA substrates in the yeast expressing mutant hNatA as compared to yeast expressing wildtype hNatA. Furthermore, immunoprecipitated mutant NatA complex displayed a reduced catalytic activity in vitro as compared to the wildtype NatA complex. Combined, these data provide strong support for the functional impairment of hNaa10-S37P in vivo and suggest that reduced Nt-acetylation of one or more target substrates contributes to the pathogenesis of Ogden syndrome. Comparative analysis between human and yeast NatA also provided novel insights into the co-evolution of the NatA complexes and their substrates. For instance, (Met-)Ala- N- termini are more prevalent in the human proteome as compared to the yeast proteome, and hNatA displays a relative preference towards these N-termini as compared to yNatA. Methods: The obtained peptide mixtures were introduced into an LC-MS/MS system, the Ultimate 3000 (Dionex, Amsterdam, The Netherlands) in-line connected to an LTQ Orbitrap XL (Thermo Fisher Scientific, Bremen, Germany). Samples were first loaded on a trapping column (made in-house, 100 µm internal diameter (I.D.) x 20 mm, 5 µm beads C18 Reprosil-HD, Dr. Maisch). After back-flushing from the trapping column, the sample was loaded on a reverse-phase column (made in- house, 75 µm I.D. x 150 mm , 5 µm beads C18 Reprosil-HD, Dr. Maisch). Peptides were loaded with solvent A (0.1% trifluoroacetic acid, 2% acetonitrile), and were separated with a linear gradient from 2% solvent A’ (0.05% formic acid) to 55% solvent B’ (0.05% formic acid and 80% acetonitrile) at a flow rate of 300 nl/min followed by a wash reaching 100% solvent B’. The mass spectrometer was operated in data-dependent mode, automatically switching between MS and MS/MS acquisition for the six most abundant peaks in a given MS spectrum. Full scan MS spectra were acquired in the Orbitrap at a target value of 1E6 with a resolution of 60,000. The six most intense ions were then isolated for fragmentation in the linear ion trap, with a dynamic exclusion of 60 s. Peptides were fragmented after filling the ion trap at a target value of 1E4 ion counts. From the MS/MS data in each LC run, Mascot Generic Files were created using the Mascot Distiller software (version 2.3.01, Matrix Science). While generating these peak lists, grouping of spectra was allowed with a maximum intermediate retention time of 30 s and a maximum intermediate scan count of 5 was used where possible. Grouping was done with 0.005 Da precursor tolerance. A peak list was only generated when the MS/MS spectrum contained more than 10 peaks. There was no de-isotoping and the relative signal to noise limit was set at 2. These peak lists were then searched with the Mascot search engine (Matrix Science) using the Mascot Daemon interface (version 2.3, Matrix Science). Spectra were searched against the yeast (S. cerevisiae) Swiss-Prot database (version 2011_03 of the UniProtKB/Swiss-Prot protein database containing 7,320 yeast sequence entries (525997 sequences in total)). )). 13C2D3- [(13C2)-trideutero-acetylation] at lysines, carbamidomethylation of cysteine and methionine oxidation to methionine-sulfoxide were set as fixed modifications for the N-terminal COFRADIC analyses. Variable modifications were 13C2D3- acetylation and acetylation of protein N-termini. Pyroglutamate formation of N-terminal glutamine was additionally set as a variable modification. Mass tolerance on precursor ions was set to 10 ppm (with Mascot’s C13 option set to 1) and on fragment ions to 0.5 Da. Endoproteinase semi-Arg-C/P (Arg-C specificity with arginine-proline cleavage allowed) was set as enzyme allowing no missed cleavages. The peptide charge was set to 1+, 2+, 3+ and instrument setting was put on ESI-TRAP. Only peptides that were ranked one and scored above the threshold score, set at 99% confidence, were withheld. The estimated false discovery rate by searching decoy databases was typically found to lie between 2 and 4% on the spectrum level [33]. Quantification of the degree of Nt-Ac was performed as described previously (5). All data management was done in ms_lims ([49]). Please note!! the pride result files will show a lot of delta m/z deviations. The modification for the heavy acetylation (+47 Da) used in this experiment is not in Pride. So another heavy acetylation (+45 Da) modification was annotated when creating the xml files.

The study describes comprehensive proteomics data of the cholesteatoma disease. Cholesteatoma is a serious and destructive growth of keratinizing squamous epithelium in the middle ear, with a poorly understood etiopathogenesis. The large scale proteomics approach used in this study highly expands our molecular knowledge of cholesteatoma and implicates several biological functions in its pathology. Human samples of cholesteatoma, neck of cholesteatoma, tympanic membrane, external auditory canal skin, and middle ear mucosa were analyzed. Approximately two thousand unique proteins were identified by label-free nanoLC-MS/MS proteomics. The protein and peptide identification data were obtained by Proteome Discoverer (Thermo Scientific, version 1.3.0.339) processing of LTQ Orbitrap raw files using the Mascot algorithm (Matrix Sciences, version 2.4.0). To yield comprehensive proteome data the tissue biopsy was separated into ten protein fractions, which were MS analysed separately, and the resulting MS files were subsequently merged in the database analysis. Precursor mass tolerance was 5 ppm and fragment mass tolerance was 0.5 Da. Dynamic Modification was Oxidation (M) and Static Modification was Carbamidomethyl (C); annotated in the PRIDE files as mass deviations (e g for double charged peptides m/z deviation of approximately 8.00 and 28.51 for modification of Met and Cys, respectively). FASTA file was SwissProt_2012_03.fasta (containing 20,255 Homo sapiens sequences). MudPit scoring was applied, and up to two missed trypsin cleavages were accepted. Peptide cut off Score was 10 and protein relevance threshold was 20.