Dataset Information

Mass Spectrometry Utilities: Fast post processing of mass spectrometry data.

ABSTRACT: Development of Mass Spectrometry Utilities as a tool to complex MS data output as obtained from MaxQuant. As proof of concept the proteolytic degradation of glycerophosphoryl diester phosphodiesterase GlpQ by the protein quality control protease DegP is analyzed. Mass spectrometry was used to monitor proteolysis over time in the absence and presence of a peptidic allosteric activator of DegP. The software’s output clearly shows the increased proteolytic activity of DegP in the presence of the activating peptide, identifies statistically significant products of the proteolysis and offers insights into substrate specificity.

INSTRUMENT(S): LTQ Orbitrap Elite

ORGANISM(S): Escherichia Coli

SUBMITTER: Farnusch Kaschani

LAB HEAD: Farnusch Kaschani

PROVIDER: PXD009326 | Pride | 2018-08-06

REPOSITORIES: Pride

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Utilities for Mass Spectrometry Analysis of Proteins (UMSAP): Fast post-processing of mass spectrometry data.

Bravo-Rodriguez Kenny K Hagemeier Birte B Drescher Lea L Lorenz Marian M Rey Juliana J Meltzer Michael M Kaschani Farnusch F Kaiser Markus M Ehrmann Michael M

Rapid communications in mass spectrometry : RCM 20181001 19

<h4>Rationale</h4>Mass spectrometry (MS) is an invaluable tool for the analysis of proteins. However, the sheer amount of data generated in MS studies demands dedicated data-processing tools that are efficient and require minimal user intervention.<h4>Methods</h4>Utilities for Mass Spectrometry Analysis of Proteins (UMSAP) is a graphical user interface designed for efficient post-processing of MS result files. The software is written in Tcl/Tk and can be used in Windows, OS X or Linux. No third ...[more]

PMID: 30062799

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Project description:Silencing of tumor suppressor genes plays a vital role in head and neck carcinogenesis. Aberrant hypermethylation in the promoter region of some known or putative tumor suppressor genes (TSGs) occurs frequently during the development of various cancers including head and neck squamous cell carcinoma (HNSCC). In this study we used an expanded mRNA expression profiling approach followed by microarray expression analysis to identify epigenetically inactivated genes in HNSCC. Two HNSCC cell lines were treated with 5-aza-2M-bM-^@M-^Y-deoxycytidine followed by microarray analysis to identify epigenetically silenced genes in HNSCC. 1960, 614, and 427 genes were upregulated in HNSCC cell lines JHU-012, JHU-011 and the combination of both cell lines, respectively. HNSCC tumor and normal mucosal samples were used for gene profiling by a 47K mRNA gene expression array and we found, 7140 genes were downregulated in HNSCC tumors compared to normal mucosa as determined by microarray analysis and were integrated with cell line data. Integrative analysis defined 126 candidate genes, of which only seven genes showed differentially methylation in tumors and no methylation in normal mucosa after bisulfite sequencing. After validation by QMSP, one gene, GNG7, was confirmed as being highly methylated in tumors and unmethylated in normal mucosal and salivary rinse samples demonstrating cancer-specific methylation in HNSCC tissues. TXNIP and TUSC2 were partially methylated in tumors and normal salivary rinses but unmethylated in normal mucosa. We concluded GNG7 as a highly specific promoter methylated gene associated with HNSCC. In addition, TXNIP and TUSC2 are also potential biomarkers for HNSCC. We performed pharmacological unmasking analysis on two HNSCC cancer cell lines JHU-O11 and JHU-O12 by treating cells with or without 5-aza-dC, followed by RNA extraction and microarray analysis using Affymetrix U133 Plus 2.0. The array data were analyzed by initially dChip and then SAM. We performed a four-phase strategy to obtain the unmasked genes in the cells treated with 5-aza and downregulated genes in primary tumors. In the first phase, we compared the cell lines, either JHU-012 or JHU-011, before treatment to the cell lines treated with 5-aza, in order to identify genes that were reexpressed M-bM-^IM-% 2 fold. We found 1960 genes that were upregulated by 5-aza-dC in JHU-O12 cell line. SAM output was obtained at a delta value of 2.05 with a false discovery rate (FDR) of 10% and the d-score cut-off was 1.17. 614 reexpressed genes were found in 5 aza-treated JHU-O11 (SAM output; delta: 2.089, FDR%: 10, d-score cut-off: 2.8). 427 genes were commonly upregulated in both cell lines when the cell lines were normalized and analyzed together (SAM output; delta: 1.44, FDR%: 10, d-score cut-off: 1.88). In the second phase of our analysis, we further extracted RNA and performed the 47K mRNA expression array analysis on 13 primary HNSCC tumors samples and 5 normal mucosa samples from non-cancer control patients. After initial dChip and SAM analysis (SAM output; delta: 1.247, FDR%: 5.24, d-score cut-off:0.24), we found 7140 downregulated genes in primary HNSCC tumors compared with normal mucosa. In the third phase, we investigated these three data sets: a) SAM output of 1960 upregulated genes after 5-aza treatment of JHU-012 vs SAM output of 7140 downregulated genes in primary HNSCC: We found that 210 genes that were upregulated by 5-aza-dC in the JHU-O12 cell line and showed downregulation in tumor samples, b) SAM output of 614 upregulated genes after 5-aza treatment of JHU-011 vs SAM output of 7140 downregulated genes in primary HNSCC: We found 79 genes that were upregulated by 5-aza-dC in the JHU-O11 cell line and showed downregulation in tumor samples, c) SAM output from analyzing both cell lines together in the same SAM computation, of 427 upregulated genes after 5-aza treatment of JHU-011 and JHU-012 vs SAM output of 7140 downregulated genes in primary HNSCC: We found 44 genes that were upregulated by 5-aza-dC in JHU-O11 and JHU-012 cell lines and showed downregulation in tumor samples, suggesting that methylation might be involved in gene downregulation. In the fourth phase of our strategy, we rank-ordered the results of upregulated genes obtained from these 3 data sets and found 126 common genes. We then examined promoter regions of the 126 genes for CpG islands and performed bisulfite sequencing analysis of the promoter region of these genes. We found that 7 genes showed a differential methylation pattern between normal and neoplastic samples. After validation of these genes in a cohort of 33 HNSCC patients and normal salivary and mucosal samples from healthy people by QMSP, we found 3 genes of interest.

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