Project description:Bulk RNA sequencing was used to characterize transcriptional changes associated with the dysfunctional metabolic state in murine gingival fibroblasts (mGF). The mGF were treated with BSA control, palmitate, IL-1β, or palmitate plus IL-1β. Gene ontology analysis demonstrated enrichment of pathways related to innate immune activation, oxidative stress, mitochondrial dysfunction, ER stress, and purine metabolism. Palmitate disrupts the Cd73-adenosine axis while promoting mitochondrial dysfunction, oxidative stress, and Perk-mediated ER stress in gingival fibroblasts. Adenosine signaling protects against lipotoxic-induced ER stress, highlighting the relevance of the Cd73-adenosine pathway for metabolic and inflammatory diseases.
Project description:Proteins have been extracted from kidney calculi with SDS buffer. Proteins were seperated by 1D-SDS-PAGE and stained with coomassie. Each gel lane was cut into 5 (or 6) pieces. Proteins were digested in gel with trypsin. Tryptic peptides were analysed by nanoLC-MS/MS on an ion trap instrument (6340 Ion Trap, Agilent) with CID. Peak lists were generated with Mascot Distiller, and database searching was performed with Mascot. The data from 5 (or 6) LC-MS/MS runs from one patient ware merged into one mgf file, which was searched against IPI (human) database.
Project description:In Bacillus thuringiensis CT-43, five insecticidal crystal proteins (ICPs, Cry protein) are encoded. We extracted the Cry proteins, ran the SDS PAGE (two Cry protein bands were observed), and tried to identify the composition of the two Cry protein bands in the SDS PAGE. The bioinformatics pipeline is described as follows: First, we converted the original mass spectrum files to the mgf file (peaks file), then the mgf files were searched against the Bacillusthuringiensis CT-43 protein database using Mascot (version 2.3.02). The search parameters were: i) trypsin was chosen as the enzyme with one missed cleavage allowed; ii) the fixed modifications of carbamidomethylation were set as Cys, and variable modifications of oxidation as Met; iii) peptide tolerance was set as 0.05 Da, and MS/MS tolerance was set as 0.1 Da. The peptide charge was set as Mr, and monoisotopic mass was chosen. An automatic decoy database search strategy was employed to estimate the false discovery rate (FDR). The FDR was calculated as the false positive matches divided by the total matches. In the final search results, the FDR was less than 1.5%.
Project description:An experiment was designed to use a computer program to create lithography masks using a pseudo-random pattern generator. The data in this file are results from immunosignaturing 8 different monoclonals using a 10,000 peptide random-sequence microarray. Peptides were synthesized by Sigma Aldrich, and printed onto glass slides and used to test several different parameters.
Project description:1D-LC-MS/MS analysis of OGE-prefractionated and TMT labeled mouse samples consiting of 2 unstimulated and 2 stimulated T-cell samples. The acquired raw-files were converted to the mascot generic file (mgf) format using the msconvert tool (part of ProteoWizard, version 3.0.4624 (2013-6-3)). Using the MASCOT algorithm (Matrix Science, Version 2.4.0), the mgf files were searched against a decoy database containing normal and reverse sequences of the predicted SwissProt entries of mus musculus (www.ebi.ac.uk, release date 16/05/2012) and commonly observed contaminants (in total 33,832 sequences) generated using the SequenceReverser tool from the MaxQuant software (Version 1.0.13.13). The precursor ion tolerance was set to 10 ppm and fragment ion tolerance was set to 0.01 Da. The search criteria were set as follows: full tryptic specificity was required (cleavage after lysine or arginine residues unless followed by proline), 2 missed cleavages were allowed, carbamidomethylation (C), TMT6plex (K and peptide n-terminus) were set as fixed modification and oxidation (M) as a variable modification. Next, the database search results were imported to the Scaffold Q+ software (version 4.1.1, Proteome Software Inc., Portland, OR) and the protein false identification rate was set to 1% based on the number of decoy hits. Specifically, peptide identifications were accepted if they could be established at greater than 94.0% probability to achieve an FDR less than 1.0% by the scaffold local FDR algorithm. Protein identifications were accepted if they could be established at greater than 6.0% probability to achieve an FDR less than 1.0% and contained at least 1 identified peptide. Protein probabilities were assigned by the Protein Prophet program (Nesvizhskii, et al, Anal. Chem. 2003; 75(17):4646-58). Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony. Proteins sharing significant peptide evidence were grouped into clusters. For quantification, acquired reporter ion intensities in the experiment were globally normalized across all acquisition runs. Individual quantitative samples were normalized within each acquisition run. Intensities for each peptide identification were normalized within the assigned protein. The reference channels were normalized to produce a 1:1 fold change. All normalization calculations were performed using medians to multiplicatively normalize data. A list of identified and quantified proteins is available in the xls file.
Project description:MO3.13 is an immortal human-human hybrid cell line that express phenotypic characteristics of primary oligodendrocytes. It was created by fusing a 6-thioguanine-resistant mutant of the human rhabdomyosarcoma RD (cancer of skeletal muscle) with adult human oligodendrocytes by a lectin-enhanced polyethylene glycol procedure. In contrast to the tumor parent, MO3.13 expressed surface immunoreactivity for galactosyl cerebroside(GS) and intracellular immunoreactivity for myelin basic protein (MBP), proteolipid protein (PLP), and glial fibrillary acidic protein (GFAP). MO3.13 also exhibit the markers of immature oligodendrocytes GalC (galactosylceramidase) and CNPase. Upon differentiation, the MO3.13 cells have been also shown to express the MBP and MOG markers. Data analysis: Each MS raw file was processed using ProteoWizard for the generation of a MGF file. These were processed in SearchGUI, which runs the search engines Open Mass Spectrometry Search Algorithm (OMSSA) and X!Tandem against the UniProt human protein database (release 2013_08, 20,266 sequences). Search parameters were: peptide and fragment ion mass accuracy 10 ppm and 0.5 Da, respectively; protein and peptide FDRs 1%; two miss cleavages; trypsin as enzyme; fixed modifications: cysteine carbamidomethylation; variable modifications: methionine oxidation. In order to generate one single proteome dataset of MO3.13, resulting data was processed in PeptideShaker.
Project description:We used phosphoproteomics to compare the responses of the ERK1/2 inhibitors, SCH772984 and GDC0994, and the MKK1/2 inhibitor, trametinib. These are compared with responses to the MKK1/2 inhibitor, selumetinib (AZD6244), previously measured by our lab in the same metastatic melanoma cell line. In three replicate experiments, we quantified a total of 12,805 class I phosphosites on 3,819 proteins in the trametinib-SCH772984-DMSO experiment, and 7,074 class I phosphosites on 2,453 in the GDC0994-SCH772984-DMSO experiment. This included 466 phosphosites that reproducibly decreased in response to at least one inhibitor in the trametinib-SCH772984-DMSO experiment and 414 phosphosites in the GDC0994-SCH772984-DMSO experiment. The results demonstrate linearity in signaling through the MAP kinase pathway. By comparing multiple inhibitors targeted to multiple tiers of protein kinases in the MAPK pathway, we gain insight into regulation and new targets of the oncogenic BRAF driver pathway in cancer cells, and a useful approach for evaluating the specificity of drugs and drug candidates. SILAC Experimental Design Experiment 1 Replicate 1: Heavy – DMSO, Medium – SCH772984, Light – Trametinib Replicate 2: Heavy – SCH772984, Medium – Trametinib, Light – DMSO Replicate 3: Heavy – Trametinib, Medium – DMSO, Light – SCH772984 SILAC Experimental Design Experiment 2 Replicate 1: Heavy – DMSO, Medium – SCH772984, Light – GDC0994 Replicate 2: Heavy – SCH772984, Medium – GDC0994, Light – DMSO Replicate 3: Heavy – GDC0994, Medium – DMSO, Light – SCH772984 File List 1. Zipped MaxQuant search results folder containing index and output folders for each raw file, ‘combined’ output folder, and mqpar.xml MaxQuant search parameters file 2. Individual raw files of phosphopeptide-enriched ERLIC fractions 3. Zipped MaxQuant version used for analysis 4. FASTA file containing Uniprot human identifications 5. Instructions for viewing annotated spectra
Project description:To find out the binding partners of EhRacM, crosslinking and coimmunoprecipitation were performed. Then, eluted samples were submitted to Mass Spectrometry and Proteomics Core Facility, Johns Hopkins University School of Medicine and analyzed by mass spctrometry (shotgun proteomics). The coimmunoprecipitation and following analysis were conducted three times.You can find the data of HA-EhRacM (1st) from "2022_7_32_54_PM_FileSize_500998639_Byte_F010303.mzid.gz", "2022_7_32_54_PM_FileSize_500998639_Byte_F010303.mzid_2022_7_32_54_PM_File_Size__500998639__Byte__F010303.MGF", "RO-CS-LE_220912_NozakiT_MS_Rac16_WT.mzML", and "RO-CS-LE_220912_NozakiT_MS_Rac16_WT.RAW". You can find the data of pEhExHA (mock) (1st) from "2022_5_02_01_AM_FileSize_662636563_Byte_F010297.mzid.gz", "2022_5_02_01_AM_FileSize_662636563_Byte_F010297.mzid_2022_5_02_01_AM_File_Size__662636563__Byte__F010297.MGF", "RO-CS-LE_220912_NozakiT_MS_pEhExHA.mzML", and "RO-CS-LE_220912_NozakiT_MS_pEhExHA.RAW".You can find the data of HA-EhRacM (2nd) from "2022_7_35_46_AM_FileSize_794949236_Byte_F009769.mzid.gz", "2022_7_35_46_AM_FileSize_794949236_Byte_F009769.mzid_2022_7_35_46_AM_File_Size__794949236__Byte__F009769.MGF", "JS-QE-CS_221108_NozakiT_MS_S1_RA.msf", "JS-QE-CS_221108_NozakiT_MS_S1_RA.RAW", "JS-QE-CS_221108_NozakiT_MS_S1_RA.mzML". You can find the data of pEhExHA (mock) (2nd) from "2022_2_58_28_AM_FileSize_846222756_Byte_F009771.mzid.gz", "2022_2_58_28_AM_FileSize_846222756_Byte_F009771.mzid_2022_2_58_28_AM_File_Size__846222756__Byte__F009771.MGF", "JS-QE-CS_221108_NozakiT_MS_S4_RA.msf", "JS-QE-CS_221108_NozakiT_MS_S4_RA.RAW", and "JS-QE-CS_221108_NozakiT_MS_S4_RA.mzML". You can find the data of HA-EhRacM (3rd) from "2023_12_15_11_AM_FileSize_1380630557_Byte_F012916.mzid.gz", "2023_12_15_11_AM_FileSize_1380630557_Byte_F012916.mzid_2023_12_15_11_AM_File_Size__1380630557__Byte__F012916.MGF", "JS-E480-CS_230518_NozakiT_MS_S1_DDA_10pct.msf", "JS-E480-CS_230518_NozakiT_MS_S1_DDA_10pct.mzML", and "JS-E480-CS_230518_NozakiT_MS_S1_DDA_10pct.RAW". You can find the data of pEhExHA (mock) (3rd) from "2023_6_35_51_PM_FileSize_1732075728_Byte_F012915.mzid.gz", "2023_6_35_51_PM_FileSize_1732075728_Byte_F012915.mzid_2023_6_35_51_PM_File_Size__1732075728__Byte__F012915.MGF", "JS-E480-CS_230518_NozakiT_MS_S4_DDA_10pct.msf", "JS-E480-CS_230518_NozakiT_MS_S4_DDA_10pct.mzML", and "JS-E480-CS_230518_NozakiT_MS_S4_DDA_10pct.RAW".