Dataset Information

Protein kinase C-beta regulates environment mediated drug resistance in B cell malignancies

ABSTRACT: Environment-mediated drug resistance (EMDR) limits the efficacy of cancer therapies by providing anti-apoptotic signals from a non-malignant microenvironment to tumour cells. We show that EMDR is regulated by protein kinase C in stromal cells is required for EMDR. We used plasma membrane profiling to demonstrate that PKC-beta regulates numerous plasma membrane proteins required for cell-cell communications and EMDR.

INSTRUMENT(S): Orbitrap Fusion

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Stromal Cell

DISEASE(S): Chronic Lymphocytic Leukemia

SUBMITTER: James Williamson

LAB HEAD: Ingo Ringshuasen

PROVIDER: PXD011062 | Pride | 2021-09-08

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	PJL_MM-IR_CLL-PMP_F.mzML	Mzml
	PJL_MM-IR_CLL-PMP_F.mzid.gz	Mzid
	PJL_MM-IR_CLL-PMP_F.pride.mgf.gz	Mgf
	PJL_MM-IR_CLL-PMP_F1.raw	Raw
	PJL_MM-IR_CLL-PMP_F10.raw	Raw

Items per page:

1 - 5 of 15

Publications

Stromal cell protein kinase C-β inhibition enhances chemosensitivity in B cell malignancies and overcomes drug resistance.

Park Eugene E Chen Jingyu J Moore Andrew A Mangolini Maurizio M Santoro Antonella A Boyd Joseph R JR Schjerven Hilde H Ecker Veronika V Buchner Maike M Williamson James C JC Lehner Paul J PJ Gasparoli Luca L Williams Owen O Bloehdorn Johannes J Stilgenbauer Stephan S Leitges Michael M Egle Alexander A Schmidt-Supprian Marc M Frietze Seth S Ringshausen Ingo I

Science translational medicine 20200101 526

Overcoming drug resistance remains a key challenge to cure patients with acute and chronic B cell malignancies. Here, we describe a stromal cell-autonomous signaling pathway, which contributes to drug resistance of malignant B cells. We show that protein kinase C (PKC)-β-dependent signals from bone marrow-derived stromal cells markedly decrease the efficacy of cytotoxic therapies. Conversely, small-molecule PKC-β inhibitors antagonize prosurvival signals from stromal cells and sensitize tumor ce ...[more]

PMID: 31941829

Similar Datasets

Project description:Despite more than a century fighting against tuberculosis, the World Health Organisation has estimated that around 1.7 million people died of tuberculosis in 2016 and over a quarter of the world’s population is infected. One of the critical hurdles for stopping tuberculosis transmission is early and effective diagnosis of patients with the active pulmonary disease. Although important innovations in molecular diagnosis have been recently developed (e.g. Xpert MTB/RIF, Cepheid Inc., USA), there are no suitable tests for population screening at point-of-care. The current tuberculosis diagnosis pipeline presents a highly variable performance and requires access to reference laboratory facilities. A non-sputum based rapid test with high specificity and sensitivity could save ~400,000 lives per year. Therefore, new biomarkers for diagnosis are urgently required for identifying patients with early symptoms and to expedite treatment. Variable sensitivity and specificity can be overcome using a combination of multiple biomarkers (5). Proteins, as ultimate biological effectors, are ideal candidates for diagnostic biomarkers; consequently, proteomic studies are a crucial platform for biomarker discovery in tuberculosis. This work aims to develop a multi-marker panel for tuberculosis diagnosis with high performance capable of differentiating tuberculosis patients from relevant controls. Quantitative Multidimensional Protein Identification Technology (qMudPIT) is applied for biomarker discovery identifying candidates for early diagnosis of tuberculosis. The multidimensional method optimised in this work led to the identification of 5022 plasma proteins and 3577 quantified proteins using iTRAQ labelling. Known and completely novel markers for active tuberculosis in plasma were identified including a peptide derived from Mycobacterium tuberculosis. Complementary statistical and bioinformatic analysis were applied to prioritise candidates for validation in one or two independent cohorts. The plasma proteomic profile here described represents a power strategy for biomarker discovery and the panel proposed has the potential to be translated to a rapid test and which might contribute to tuberculosis control.

Project description:Novel biomarkers to identify infectious patients transmitting Mycobacterium tuberculosis are urgently needed to control the global tuberculosis (TB) pandemic. We hypothesized that proteins released into the plasma in active pulmonary TB are clinically useful biomarkers to distinguish TB cases from healthy individuals and patients with other respiratory infections. We applied a highly sensitive non-depletion tandem mass spectrometry discovery approach to investigate plasma protein expression in pulmonary TB cases compared to healthy controls in South African and Peruvian cohorts. . Plasma samples were analysed from 11 untreated male patients with active pulmonary TB and 10 male healthy control samples. Each plasma sample was initially separated into four segments by size exclusion chromatography, and each segment was processed individually. Analyses of plasma segments were performed in twelve iTRAQ (isobaric tags for relative and absolute quantification) 8-plex experimental sets in a block randomised design comprising three experimental sets. Each iTRAQ experiment contained a bridging master-pool plasma sample run in every experiment. Healthy controls were matched to TB samples by age, ethnicity, and smoking status within each iTRAQ set. Protein abundances from the plasma segments and multi-consensus reports were combined and adjusted for experimental batch effects. Protein abundances from the remaining combined plasma segment proteomes between experimental sets and the combined multi-consensus proteomes were analysed by complementary bioinformatic approaches to identify candidate diagnostic protein biomarkers. In total, 4,696 protein identifications were made across all iTRAQ experiments, at 5% FDR (false discovery rate). This comprised 2,332 unique host-derived proteins and 22 Mtb-derived proteins. Of these, 594 host proteins had a quantification result for every sample analysed and therefore comprised the complete quantified proteome. Subsequent bioinformatic analysis identified 118 differentially expressed proteins by three complementary bioinformatic pipelines and were taken forward in subsequent validation work as strong candidate biomarkers of pulmonary TB.