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Atherosclerosis is an important risk factor in the development of cardiovascular diseases. In addition to increased plasma lipid concentrations, irregular/oscillatory shear stress and inflammatory processes trigger atherosclerosis. Inhibitors of the transcription modulatory bromo- and extra-terminal domain (BET) protein family (BETi) could offer a possible therapeutic approach due to their epigenetic mechanism and anti-inflammatory properties. In this study, the influence of laminar shear stress, inflammation and BETi treatment on human endothelial cells was investigated using global protein expression profiling by ion mobility separation-enhanced data independent acquisition mass spectrometry (IMS-DIA-MS). For this purpose, primary human umbilical cord derived vascular endothelial cells were treated with TNFα to mimic inflammation and exposed to laminar shear stress in the presence or absence of the BRD4 inhibitor JQ1. IMS-DIA-MS detected over 4037 proteins expressed in endothelial cells. Inflammation, shear stress and BETi led to pronounced changes in protein expression patterns with JQ1 having the greatest effect. To our knowledge, this is the first proteomics study on primary endothelial cells, which provides an extensive database for the effects of shear stress, inflammation and BETi on the endothelial proteome.
Enormous amounts of data are being generated by modern methods such as transcriptome or exome sequencing and microarray profiling. Primary analyses such as quality control, normalization, statistics and mapping are highly complex and need to be performed by specialists. Thereafter, results are handed back to biomedical researchers, who are then confronted with complicated data lists. For rather simple tasks like data filtering, sorting and cross-association there is a need for new tools which can be used by non-specialists. Here, we describe CrossQuery, a web tool that enables straight forward, simple syntax queries to be executed on transcriptome sequencing and microarray datasets. We provide deep-sequencing data sets of stem cell lines derived from the model fish Medaka and microarray data of human endothelial cells. In the example datasets provided, mRNA expression levels, gene, transcript and sample identification numbers, GO-terms and gene descriptions can be freely correlated, filtered and sorted. Queries can be saved for later reuse and results can be exported to standard formats that allow copy-and-paste to all widespread data visualization tools such as Microsoft Excel. CrossQuery enables researchers to quickly and freely work with transcriptome and microarray data sets requiring only minimal computer skills. Furthermore, CrossQuery allows growing association of multiple datasets as long as at least one common point of correlated information, such as transcript identification numbers or GO-terms, is shared between samples. For advanced users, the object-oriented plug-in and event-driven code design of both server-side and client-side scripts allow easy addition of new features, data sources and data types.
Enormous amounts of data are being generated by modern methods such as transcriptome or exome sequencing and microarray profiling. Primary analyses such as quality control, normalization, statistics and mapping are highly complex and need to be performed by specialists. Thereafter, results are handed back to biomedical researchers, who are then confronted with complicated data lists. For rather simple tasks like data filtering, sorting and cross-association there is a need for new tools which can be used by non-specialists. Here, we describe CrossQuery, a web tool that enables straight forward, simple syntax queries to be executed on transcriptome sequencing and microarray datasets. We provide deepsequencing data sets of stem cell lines derived from the model fish Medaka and microarray data of human endothelial cells. In the example datasets provided, mRNA expression levels, gene, transcript and sample identification numbers, GO-terms and gene descriptions can be freely correlated, filtered and sorted. Queries can be saved for later reuse and results can be exported to standard formats that allow copy-and-paste to all widespread data visualization tools such as Microsoft Excel. CrossQuery enables researchers to quickly and freely work with transcriptome and microarray data sets requiring only minimal computer skills. Furthermore, CrossQuery allows growing association of multiple datasets as long as at least one common point of correlated information, such as transcript identification numbers or GO-terms, is shared between samples. For advanced users, the object-oriented plug-in and event-driven code design of both server-side and client-side scripts allow easy addition of new features, data sources and data types.