br Data experimental design materials and methods br Data

Data, experimental design, materials and methods
Data, experimental design, materials and methods
Acknowledgments This work was supported by the Molecular and Cell Biology Research Center (MCBRC), Mazandaran University of Medical Sciences, Iran (grant number 1658); and the Iran National Science Foundation (INSF), Iran (grant number 91002930).
Specifications table
Value of the data
Experimental design, materials and methods
Acknowledgments We thank to Dr. Spiros D. Garbis and Dr. Theodoros Roumeliotis for their training on SEC fractionation for previous collaborative project at Academy of Athens, Greece. This work was supported by Czech Science Foundation Project no. GAP503/12/0369 to IK. Salary of VP and IS was covered by the Czech Science Foundation Project no. 14-19250S (to PB); IS was also partly supported by State Budget of the Czech Republic (LO1413). The mass spectrometric analyses were carried out with the support of Proteomics Core Facility of CEITEC – Central European Institute of Technology, ID number CZ.1.05/1.1.00/02.0068, financed from European Regional Development Fund.
Value of the data
Experimental design, materials and methods
Conflict of interest
Acknowledgements The authors thank the Foundation for Innovative New Diagnostics (FIND), for financial support in sample collection in Uganda, and the Belgian Directorate General for Development Cooperation, grant 1.5.093.06N, for financial support in patients׳ sample collection in the Democratic Republic of the Congo. The authors acknowledge Dr Veerle Lejon, Prof. Philippe Büscher, Dr Diuedonné Mumba Ngoyi, Dr Enock Matovu and Prof. John Enyaru for the collection of the CSF samples analysed in the present study. The authors also acknowledge the PRIDE team for their support for MS data deposition to ProteomeXchange Consortium (identifier PXD001082).
[describe in 3–5 bulleted points why this data is of value to the scientific community] Collection of eggshell matrix samples and preparation for MS analyses Eggs were collected on brown-laying hens at 5, 6, 7 and 16h after previous oviposition as described in [1]. These time intervals correspond respectively to the primary events of mineralization with widespread deposition of amorphous s1p receptor (ACC), ACC transformation into crystalline calcite aggregates, formation of larger calcite crystal units and rapid growth of calcite and development of a columnar structure with preferential crystal orientation [3]. Eggs were broken and forming eggshells were washed with water, air dried and stored at −20°C until protein extraction. Eggshell matrix proteins were extracted as described in [1]. A total of 24 individual eggshell protein extracts were used. Six samples collected at the same time point were pooled in equal amounts for each time (5h p.o., 6h p.o., 7h p.o. and 16h p.o.). The four pooled samples (51µg of proteins/sample) were fractionated on a 4–20% SDS-PAGE gel (8.3cm×7.3cm×1.5mm). Proteins were stained with Coomassie blue and the entire SDS-PAGE lanes were sectioned into 15 bands for each individual pooled sample. Excised proteins were in-gel digested with bovine trypsin (Roche Diagnostics GmbH, Mannhiem, Germany) as previously described [4] and analyzed by nanoscale liquid chromatography-tandem mass spectrometry (nanoLC–MS/MS).
Nano LC MS/MS analyses All experiments were performed on a linear ion trap Fourier Transform Mass Spectrometer (FT-MS) LTQ Orbitrap Velos (Thermo Fisher Scientific, Bremen, Germany) coupled to an Ultimate® 3000 RSLC Ultra-High Pressure Liquid Chromatographer (Dionex, Amsterdam, The Netherlands) as previously described in [1,4]. Raw data files were converted to MGF as previously described [4]. The identification of proteins was established using MASCOT search engine (v 2.3, Matrix Science, London, UK). The peptide and fragment masses obtained were matched automatically against the chordata section of nr NCBI database (2132453 sequences, downloaded on 2013/05/13). Enzyme specificity was set to trypsin with two missed cleavages using carbamidomethylcysteine, oxidation of methionine and N-terminal protein acetylation as variable modifications. The tolerance of the ions was set to 5ppm for parent and 0.8Da for fragment ion matches. Mascot results obtained from the target and decoy database were incorporated in Scaffold 3 software (version 3.4, Proteome Software, Portland, USA). Peptide identifications were accepted if they could be established at greater than 95.0% probability as specified by the Peptide Prophet algorithm [5]. Peptides were considered distinct if they differed in sequence. Protein identifications were accepted if they could be established at greater than 95.0% probability as specified by the Protein Prophet algorithm [6] and contained at least one sequence-unique peptide for global inventory and at least two sequence-unique peptides for quantitative analysis. A false discovery rate was calculated as <1% at the peptide or protein level. The abundance of identified proteins was estimated by calculating the emPAI using Scaffold 4 Q+ software (version 4.2, Proteome Software, Portland, USA). Additionally, label-free quantitative proteomic analyses based on spectral counting method, Scaffold 3 Q+ software (version 3.4, Proteome Software, Portland, USA) was used to quantify the proteins at the four different stages of calcification. All proteins with greater than two sequence-unique peptides, identified in database with high confidence were considered for protein quantification. To eliminate quantitative ambiguity within protein groups, we ignored all the spectra matching any peptide which is shared between proteins. Thereby, quantification performed with normalized spectral counts was carried out on distinct proteins.