Dataset Information

Multiplex matrix network analysis of protein complexes in the human TCR signalosome.

ABSTRACT: Multiprotein complexes transduce cellular signals through extensive interaction networks, but the ability to analyze these networks in cells from small clinical biopsies is limited. To address this, we applied an adaptable multiplex matrix system to physiologically relevant signaling protein complexes isolated from a cell line or from human patient samples. Focusing on the proximal T cell receptor (TCR) signalosome, we assessed 210 pairs of PiSCES (proteins in shared complexes detected by exposed surface epitopes). Upon stimulation of Jurkat cells with superantigen-loaded antigen-presenting cells, this system produced high-dimensional data that enabled visualization of network activity. A comprehensive analysis platform generated PiSCES biosignatures by applying unsupervised hierarchical clustering, principal component analysis, an adaptive nonparametric with empirical cutoff analysis, and weighted correlation network analysis. We generated PiSCES biosignatures from 4-mm skin punch biopsies from control patients or patients with the autoimmune skin disease alopecia areata. This analysis distinguished disease patients from the controls, detected enhanced basal TCR signaling in the autoimmune patients, and identified a potential signaling network signature that may be indicative of disease. Thus, generation of PiSCES biosignatures represents an approach that can provide information about the activity of protein signaling networks in samples including low-abundance primary cells from clinical biopsies.

SUBMITTER: Smith SE

PROVIDER: S-EPMC5336380 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Multiplex matrix network analysis of protein complexes in the human TCR signalosome.

Smith Stephen E P SE Neier Steven C SC Reed Brendan K BK Davis Tessa R TR Sinnwell Jason P JP Eckel-Passow Jeanette E JE Sciallis Gabriel F GF Wieland Carilyn N CN Torgerson Rochelle R RR Gil Diana D Neuhauser Claudia C Schrum Adam G AG

Science signaling 20160802 439

Multiprotein complexes transduce cellular signals through extensive interaction networks, but the ability to analyze these networks in cells from small clinical biopsies is limited. To address this, we applied an adaptable multiplex matrix system to physiologically relevant signaling protein complexes isolated from a cell line or from human patient samples. Focusing on the proximal T cell receptor (TCR) signalosome, we assessed 210 pairs of PiSCES (proteins in shared complexes detected by expose ...[more]

PMID: 27485017

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Project description:Phosphoinositide 3-kinases (PI3Ks) play a central role in adaptive immunity by transducing signals from the T cell antigen receptor (TCR) via production of PIP3. PI3Kδ is a heterodimer composed of a p110δ catalytic subunit associated with a p85α or p85β regulatory subunit and is preferentially engaged by the TCR upon T cell activation. The molecular mechanisms leading to PI3Kδ recruitment and activation at the TCR signalosome remain unclear. In this study, we have used quantitative mass spectrometry, biochemical approaches and CRISPR-Cas9 gene editing to uncover the p110δ interactome in primary CD4+ T cells. Moreover, we have determined how the PI3Kδ interactome changes upon the differentiation of small naïve T cells into T cell blasts expanded in the presence of IL-2. Our interactomic analyses identified multiple constitutive and inducible PI3Kδ-interacting proteins, some of which were common to naïve and previously-activated T cells. Our data reveals that PI3Kδ rapidly interacts with as many as seven adaptor proteins upon TCR engagement, including the Gab-family proteins, GAB2 and GAB3, a CD5-CBL signalosome and the transmembrane proteins ICOS and TRIM. Our results also suggest that PI3Kδ pre-forms complexes with the adaptors SH3KBP1 and CRKL in resting cells that could facilitate the localization and activation of p110δ at the plasma membrane by forming ternary complexes during early TCR signalling. Furthermore, we identify interactions that were not previously known to occur in CD4+ T cells, involving BCAP, GAB3, IQGAP3 and JAML. We used CRISPR-Cas9-mediated gene knockout in primary T cells to confirm that BCAP is a positive regulator of PI3K-AKT signalling in CD4+ T cell blasts. Overall, our results provide evidence for a large protein network that regulates the recruitment and activation of PI3Kδ in T cells. Finally, this work shows how the PI3Kδ interactome is remodeled as CD4+ T cells differentiate from naïve T cells to activated T cell blasts. These activated T cells upregulate additional PI3Kδ adaptor proteins, including BCAP, GAB2, IQGAP3 and ICOS. This rewiring of TCR-PI3K signalling that occurs upon T cell differentiation may serve to reduce the threshold of activation and diversify the inputs for the PI3K pathway in effector T cells.

Project description:BACKGROUND: Understanding cellular systems requires the knowledge of a protein's subcellular localization (SCL). Although experimental and predicted data for protein SCL are archived in various databases, SCL prediction remains a non-trivial problem in genome annotation. Current SCL prediction tools use amino-acid sequence features and text mining approaches. A comprehensive analysis of protein SCL in human PPI and metabolic networks for various subcellular compartments is necessary for developing a robust SCL prediction methodology. RESULTS: Based on protein-protein interaction (PPI) and metabolite-linked protein interaction (MLPI) networks of proteins, we have compared, contrasted and analysed the statistical properties across different subcellular compartments. We integrated PPI and metabolic datasets with SCL information of human proteins from LOCATE and GOA (Gene Ontology Annotation) and estimated three statistical properties: Chi-square (?2) test, Paired Localisation Correlation Profile (PLCP) and network topological measures. For the PPI network, Pearson's chi-square test shows that for the same SCL category, twice as many interacting protein pairs are observed than estimated when compared to non-interacting protein pairs (?2 = 1270.19, P-value < 2.2 × 10(-16)), whereas for MLPI, metabolite-linked protein pairs having the same SCL are observed 20% more than expected, compared to non-metabolite linked proteins (?2 = 110.02, P-value < 2.2 x10(-16)). To address the issue of proteins with multiple SCLs, we have specifically used the PLCP (Pair Localization Correlation Profile) measure. PLCP analysis revealed that protein interactions are majorly restricted to the same SCL, though significant cross-compartment interactions are seen for nuclear proteins. Metabolite-linked protein pairs are restricted to specific compartments such as the mitochondrion (P-value < 6.0e-07), the lysosome (P-value < 4.7e-05) and the Golgi apparatus (P-value < 1.0e-15). These findings indicate that the metabolic network adds value to the information in the PPI network for the localisation process of proteins in human subcellular compartments. CONCLUSIONS: The MLPI network differs significantly from the PPI network in its SCL distribution. The PPI network shows passive protein interaction, possibly due to its high false positive rate, across different subcellular compartments, which seem to be absent in the MLPI network, as the MLPI network has evolved to maintain high substrate specificity for proteins.

Dataset Information

Multiplex matrix network analysis of protein complexes in the human TCR signalosome.

Publications

Multiplex matrix network analysis of protein complexes in the human TCR signalosome.

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