GProX, a User-Friendly Platform for Bioinformatics Analysis and Visualization of Quantitative Proteomics Data

The coordinated and synchronized cardiac muscle contraction relies on an efficient gap junction-mediated intercellular communication (GJIC) between cardiomyocytes, which involves the rapid anisotropic impulse propagation through connexin (Cx)-containing channels, namely of Cx43, the most abundant Cx in the heart. Expectedly, disturbing mechanisms that affect channel activity, localization and turnover of Cx43 have been implicated in several cardiomyopathies, such as myocardial ischemia. Besides gap junction-mediated intercellular communication, Cx43 has been associated with channel-independent functions, including modulation of cell adhesion, differentiation, proliferation and gene transcription. It has been suggested that the role played by Cx43 is dictated by the nature of the proteins that interact with Cx43. Therefore, the characterization of the Cx43-interacting network and its dynamics is vital to understand not only the molecular mechanisms underlying pathological malfunction of gap junction-mediated intercellular communication, but also to unveil novel and unanticipated biological functions of Cx43. In the present report, we applied a quantitative SWATH-MS approach to characterize the Cx43 interactome in rat hearts subjected to ischemia and ischemiareperfusion. Our results demonstrate that, in the heart, Cx43 interacts with proteins related with various biological processes such as metabolism, signaling and trafficking.

Section: Methodsmentioning

confidence: 99%

Interacting Network of the Gap Junction (GJ) Protein Connexin43 (Cx43) is Modulated by Ischemia and Reperfusion in the Heart*

Martins‐Marques

Anjo

Pereira

et al. 2015

“…4A). We analyzed the regulated MMA sites using the Graphical Proteomics Data Explorer (GProX) suite (59), which revealed that MMA sites in general can be clustered into three distinct categories; MMA sites with a protracted down-regulated expression throughout the time-course experiment (Cluster 1; Fig. 4B), MMA sites down-regulated only after prolonged (16 h) ActD treatment (Cluster 2; Fig.…”

Section: Identification Of Endogenous Arginine Mono-methylation (Mma)mentioning

confidence: 99%

Proteomic Analysis of Arginine Methylation Sites in Human Cells Reveals Dynamic Regulation During Transcriptional Arrest

Sylvestersen

Horn

Jungmichel

et al. 2014

The covalent attachment of methyl groups to the side-chain of arginine residues is known to play essential roles in regulation of transcription, protein function, and RNA metabolism. The specific N-methylation of arginine residues is catalyzed by a small family of gene products known as protein arginine methyltransferases; however, very little is known about which arginine residues become methylated on target substrates. Here we describe a proteomics methodology that combines single-step immunoenrichment of methylated peptides with high-resolution mass spectrometry to identify endogenous arginine mono-methylation (MMA) sites. We thereby identify 1027 site-specific MMA sites on 494 human proteins, discovering numerous novel mono-methylation targets and confirming the majority of currently known MMA substrates. Nuclear RNA-binding proteins involved in RNA processing, RNA localization, transcription, and chromatin remodeling are predominantly found modified with MMA. Despite this, MMA sites prominently are located outside RNA-binding domains as compared with the proteome-wide distribution of arginine residues. Quantification of arginine methylation in cells treated with Actinomycin D uncovers strong site-specific regulation of MMA sites during transcriptional arrest. Interestingly, several MMA sites are down-regulated after a few hours of transcriptional arrest. In contrast, the corresponding dimethylation or protein expression levels are not altered, confirming that MMA sites contain regulated functions on their own. Collectively, we present a site-specific MMA data set in human cells and demonstrate for the first time that MMA is a dynamic post-translational modification regulated during transcriptional arrest by a hitherto uncharacterized arginine demethylase. Molecular & Cellular Proteomics

“…Unsupervised Clustering by GProX and Gene Ontology Enrichment Analysis-The mean ratios of the via PAL-qLC-MS/MS identified protein abundances as well as the mean fluorescence intensity ratio of the proteins identified via the flow cytometry screening panel (of one representative donor) were subjected to unsupervised clustering (GProX) based on the fuzzy c-means algorithm as implemented in the Mfuzz package (31,32).…”

Section: The Human Naive Cd4 ؉ T-cell Surface Atlasmentioning

confidence: 99%

A Combined Omics Approach to Generate the Surface Atlas of Human Naive CD4+ T Cells during Early T-Cell Receptor Activation

Graessel

Hauck

Toerne

et al. 2015

Naive CD4؉ T cells are the common precursors of multiple effector and memory T-cell subsets and possess a high plasticity in terms of differentiation potential. This stemcell-like character is important for cell therapies aiming at regeneration of specific immunity. Naive CD4ϩ T cells are the common precursors for all other T-helper cell subsets and it is of fundamental importance for specific immunity that their differentiation process is well directed. A complex signaling network is engaged upon antigen recognition that triggers the differentiation process of stemcell-like, plastic, antigen-unexperienced naive T cells into antigen-specific, functional distinct T-cell subphenotypes (1). The differentiation process of naive T cells is tightly regulated in healthy individuals. Pathology develops under dysregulated effector responses such as overshooting responses leading to impaired tolerance (2) or ineffective control of infections (3). Naive T cells are defined by CD45RA expression and they are early cellular targets of immune modulation regarding the differentiation process and the development of long lasting, sustainable therapeutic strategies. In contrast, memory T cells express CD45RO and cover already committed cells such as T helper 1 and T helper 2 cells. Therefore, we chose to investigate the naive CD4 ϩ T cell (CD45RA) and its phenotype during T-cell receptor (TCR) 1 activation. The differentiation From the ‡Center of Allergy and Environment (ZAUM), Technische