Browse
Submit Data
Databases
API
Help

Dataset Information

15 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Proteomic Profiling of Iron-Treated Ovarian Cells Identifies AKT Activation that Modulates the CLEAR Network.

ABSTRACT: Although iron is essential for cell survival, dysregulated levels can contribute to cancer development or even cell death. The underlying mechanisms mediating these events remain unclear. Herein, proteomic alterations are assessed in iron-treated ovarian cell lines using reverse phase protein array (RPPA) technology and potential functional responses via ingenuity pathway analysis (IPA). Using these approaches, upregulation of pathways modulating organismal death with alterations in mTOR, MAPK, and AKT signaling in HEY ovarian cancer cells in contrast to T80 non-malignant ovarian cells is noted. Since modulation of cell death is mediated in part via microphthalmia-associated transcription factor (MiTF) family, which regulates lysosomal biogenesis and autophagosome formation by upregulating expression of coordinated lysosomal expression and regulation (CLEAR) network, expression changes in these factors in response to iron are investigated. Increased transcription factor EB (TFEB) in T80 (relative to HEY), accompanied by its nuclear translocation and increased CLEAR network gene expression with iron, is identified. Inhibition of AKT alters these responses in contrast to mTOR inhibition, which has little effect. Collectively, these findings support use of RPPA/IPA technology to predict functional responses to iron and further implicate AKT pathway and MiTF members in iron-induced cellular responses in ovarian cells.

SUBMITTER: Rockfield S

PROVIDER: S-EPMC8374680 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Comparative proteomic profiling identifies potential prognostic factors for human clear cell renal cell carcinoma.

Project description:The identification of markers for disease diagnostic, prognostic, or predictive purposes will have a great effect in improving patient management. Proteomic?based approaches for biomarker discovery are promising strategies used in cancer research. In this study, we performed quantitative proteomic analysis on four patients including clear cell renal cell carcinoma (ccRCC) and paired adjacent non?cancerous renal tissues using label?free quantitative proteomics and liquid chromatography?tandem mass spectrometry (LC?MS/MS) to identify differentially expressed proteins. Among 3,061 identified non?redundant proteins, we found that 210 proteins were differentially expressed (83 overexpressed and 127 underexpressed) in ccRCC tissue when compared with normal kidney tissues. Two most significantly dysregulated proteins (PCK1 and SNRPF) were chosen to be confirmed by western blotting. Pathway analysis of 210 differentially expressed proteins showed that dysregulated proteins are related to many cancer?related biological processes such as oxidative phosphorylation, glycolysis and amino acid synthetic pathways. Online survival analysis indicated the prognostic value of these dysregulated proteins. In conclusion, we identified some potential diagnostic biomarkers for ccRCC and an in?depth understanding of their involved biological pathways may help pave the way to discover new therapeutic strategies for ccRCC.

| S-EPMC5112614 | biostudies-literature

Integrative Kinome Profiling Identifies mTORC1/2 Inhibition as Treatment Strategy in Ovarian Clear Cell Carcinoma.

Project description:Purpose: Advanced-stage ovarian clear cell carcinoma (OCCC) is unresponsive to conventional platinum-based chemotherapy. Frequent alterations in OCCC include deleterious mutations in the tumor suppressor ARID1A and activating mutations in the PI3K subunit PIK3CA In this study, we aimed to identify currently unknown mutated kinases in patients with OCCC and test druggability of downstream affected pathways in OCCC models.Experimental Design: In a large set of patients with OCCC (n = 124), the human kinome (518 kinases) and additional cancer-related genes were sequenced, and copy-number alterations were determined. Genetically characterized OCCC cell lines (n = 17) and OCCC patient-derived xenografts (n = 3) were used for drug testing of ERBB tyrosine kinase inhibitors erlotinib and lapatinib, the PARP inhibitor olaparib, and the mTORC1/2 inhibitor AZD8055.Results: We identified several putative driver mutations in kinases at low frequency that were not previously annotated in OCCC. Combining mutations and copy-number alterations, 91% of all tumors are affected in the PI3K/AKT/mTOR pathway, the MAPK pathway, or the ERBB family of receptor tyrosine kinases, and 82% in the DNA repair pathway. Strong p-S6 staining in patients with OCCC suggests high mTORC1/2 activity. We consistently found that the majority of OCCC cell lines are especially sensitive to mTORC1/2 inhibition by AZD8055 and not toward drugs targeting ERBB family of receptor tyrosine kinases or DNA repair signaling. We subsequently demonstrated the efficacy of mTORC1/2 inhibition in all our unique OCCC patient-derived xenograft models.Conclusions: These results propose mTORC1/2 inhibition as an effective treatment strategy in OCCC. Clin Cancer Res; 24(16); 3928-40. ©2018 AACR.

| S-EPMC6098697 | biostudies-literature

Proteomic Profiling Identifies Distinct Regulation of Proteins in Obese Diabetic Patients Treated with Metformin.

Project description:Background: Obesity and type 2 diabetes mellitus (T2DM) are characterized by underlying low-grade chronic inflammation. Metformin has been used as the first line of therapy in T2DM as it decreases hepatic glucose production and glucose intestinal absorption, enhances insulin sensitivity and weight loss, and is known to ameliorate inflammation. The mechanisms through which metformin exerts its effect remain unclear. Proteomics has emerged as a unique approach to explore the biological changes associated with diseases, including T2DM. It provides insight into the circulating biomarkers/mediators which could be utilized for disease screening, diagnosis, and prognosis. Methods: This study evaluated the proteomic changes in obese (Ob), obese diabetics (OD), and obese diabetic patients on metformin (ODM) using a 2D DIGE MALDI-TOF mass spectrometric approach. Results: Significant changes in sixteen plasma proteins (15 up and 1 down, ANOVA, p ≤ 0.05; fold change ≥ 1.5) were observed in the ODM group when compared to the Ob and OD groups. Bioinformatic network pathway analysis revealed that the majority of these altered plasma proteins are involved in distinct pathways involving acute-phase response, inflammation, and oxidative response and were centered around HNF4A, ERK, JNK, and insulin signaling pathways. Conclusions: Our study provides important information about the possible biomarkers altered by metformin treatment in obese patients with and without T2DM. These altered plasma proteins are involved in distinct pathways involving acute-phase response, inflammation, and oxidative response and were centered around HNF4A, ERK, JNK, and insulin signaling pathways. The presented proteomic profiling approach may help in identifying potential biomarkers/mediators affected by metformin treatment in T2DM and inform the understanding of metformin's mechanisms of action.

| S-EPMC10609691 | biostudies-literature

Quantitative proteomic profiling identifies global protein network dynamics in murine embryonic heart development.

Project description:Defining the mechanisms that govern heart development is essential for identifying the etiology of congenital heart disease. Here, quantitative proteomics was used to measure temporal changes in the proteome at critical stages of murine embryonic heart development. Global temporal profiles of the over 7,300 proteins uncovered signature cardiac protein interaction networks that linked protein dynamics with molecular pathways. Using this integrated dataset, we identified and demonstrated a functional role for the mevalonate pathway in regulating the cell cycle of embryonic cardiomyocytes. Overall, our proteomic datasets are a resource for studying events that regulate embryonic heart development and contribute to congenital heart disease.

| S-EPMC10330608 | biostudies-literature

Multivariate proteomic profiling identifies novel accessory proteins of coated vesicles.

Project description:Despite recent advances in mass spectrometry, proteomic characterization of transport vesicles remains challenging. Here, we describe a multivariate proteomics approach to analyzing clathrin-coated vesicles (CCVs) from HeLa cells. siRNA knockdown of coat components and different fractionation protocols were used to obtain modified coated vesicle-enriched fractions, which were compared by stable isotope labeling of amino acids in cell culture (SILAC)-based quantitative mass spectrometry. 10 datasets were combined through principal component analysis into a "profiling" cluster analysis. Overall, 136 CCV-associated proteins were predicted, including 36 new proteins. The method identified >93% of established CCV coat proteins and assigned >91% correctly to intracellular or endocytic CCVs. Furthermore, the profiling analysis extends to less well characterized types of coated vesicles, and we identify and characterize the first AP-4 accessory protein, which we have named tepsin. Finally, our data explain how sequestration of TACC3 in cytosolic clathrin cages causes the severe mitotic defects observed in auxilin-depleted cells. The profiling approach can be adapted to address related cell and systems biological questions.

| S-EPMC3317806 | biostudies-other

Comparative network-based recovery analysis and proteomic profiling of neurological changes in valproic acid-treated mice.

Project description:Despite its prominence for characterization of complex mixtures, LC-MS/MS frequently fails to identify many proteins. Network-based analysis methods, based on protein-protein interaction networks (PPINs), biological pathways, and protein complexes, are useful for recovering non-detected proteins, thereby enhancing analytical resolution. However, network-based analysis methods do come in varied flavors for which the respective efficacies are largely unknown. We compare the recovery performance and functional insights from three distinct instances of PPIN-based approaches, viz., Proteomics Expansion Pipeline (PEP), Functional Class Scoring (FCS), and Maxlink, in a test scenario of valproic acid (VPA)-treated mice. We find that the most comprehensive functional insights, as well as best non-detected protein recovery performance, are derived from FCS utilizing real biological complexes. This outstrips other network-based methods such as Maxlink or Proteomics Expansion Pipeline (PEP). From FCS, we identified known biological complexes involved in epigenetic modifications, neuronal system development, and cytoskeletal rearrangements. This is congruent with the observed phenotype where adult mice showed an increase in dendritic branching to allow the rewiring of visual cortical circuitry and an improvement in their visual acuity when tested behaviorally. In addition, PEP also identified a novel complex, comprising YWHAB, NR1, NR2B, ACTB, and TJP1, which is functionally related to the observed phenotype. Although our results suggest different network analysis methods can produce different results, on the whole, the findings are mutually supportive. More critically, the non-overlapping information each provides can provide greater holistic understanding of complex phenotypes.

| S-EPMC3805323 | biostudies-literature

Comprehensive genomic profiling (CGP) of ovarian clear cell carcinomas (OCCC) identifies clinically relevant genomic alterations (CRGA) and targeted therapy options.

Project description:•MTOR pathway genes are often mutated in ovarian clear cell carcinomas (OCCC).•11.2% of OCCC have targetable alterations only in the mTOR pathway.•MTOR pathway mutations in OCCC can underlie robust, lasting responses to everolimus.

| S-EPMC5357688 | biostudies-literature

Proteomic profiling reveals that collismycin A is an iron chelator.

Project description:Collismycin A (CMA), a microbial product, has anti-proliferative activity against cancer cells, but the mechanism of its action remains unknown. Here, we report the identification of the molecular target of CMA by ChemProteoBase, a proteome-based approach for drug target identification. ChemProteoBase profiling showed that CMA is closely clustered with di-2-pyridylketone 4,4-dimethyl-3-thiosemicarbazone, an iron chelator. CMA bound to both Fe(II) and Fe(III) ions and formed a 2:1 chelator-iron complex with a redox-inactive center. CMA-induced cell growth inhibition was completely canceled by Fe(II) and Fe(III) ions, but not by other metal ions such as Zn(II) or Cu(II). Proteomic and transcriptomic analyses showed that CMA affects the glycolytic pathway due to the accumulation of HIF-1α. These results suggest that CMA acts as a specific iron chelator, leading to the inhibition of cancer cell growth.

| S-EPMC5138588 | biostudies-literature

Network analysis identifies an HSP90-central hub susceptible in ovarian cancer.

Project description:Epithelial ovarian cancer (EOC) is usually detected at an advanced stage and is frequently lethal. Although many patients respond to initial surgery and standard chemotherapy consisting of a platinum-based agent and a taxane, most experience recurrence and eventually treatment-resistant disease. Although there have been numerous efforts to apply protein-targeted agents in EOC, these studies have so far documented little efficacy. Our goal was to identify broadly susceptible signaling proteins or pathways in EOC.As a new approach, we conducted data-mining meta-analyses integrating results from multiple siRNA screens to identify gene targets that showed significant inhibition of cell growth. On the basis of this meta-analysis, we established that many genes with such activity were clients of the protein chaperone HSP90. We therefore assessed ganetespib, a clinically promising second-generation small-molecule HSP90 inhibitor, for activity against EOC, both as a single agent and in combination with cytotoxic and targeted therapeutic agents.Ganetespib significantly reduced cell growth, induced cell-cycle arrest and apoptosis in vitro, inhibited growth of orthotopic xenografts and spontaneous ovarian tumors in transgenic mice in vivo, and inhibited expression and activation of numerous proteins linked to EOC progression. Importantly, paclitaxel significantly potentiated ganetespib activity in cultured cells and tumors. Moreover, combined treatment of cells with ganetespib and siRNAs or small molecules inhibiting genes identified in the meta-analysis in several cases resulted in enhanced activity.These results strongly support investigation of ganetespib, a single-targeted agent with effects on numerous proteins and pathways, in augmenting standard EOC therapies.

| S-EPMC3778161 | biostudies-other

Quantitative proteomic profiling identifies protein correlates to EGFR kinase inhibition.

Project description:Clinical oncology is hampered by lack of tools to accurately assess a patient's response to pathway-targeted therapies. Serum and tumor cell surface proteins whose abundance, or change in abundance in response to therapy, differentiates patients responding to a therapy from patients not responding to a therapy could be usefully incorporated into tools for monitoring response. Here, we posit and then verify that proteomic discovery in in vitro tissue culture models can identify proteins with concordant in vivo behavior and further, can be a valuable approach for identifying tumor-derived serum proteins. In this study, we use stable isotope labeling of amino acids in culture (SILAC) with proteomic technologies to quantitatively analyze the gefitinib-related protein changes in a model system for sensitivity to EGF receptor (EGFR)-targeted tyrosine kinase inhibitors. We identified 3,707 intracellular proteins, 1,276 cell surface proteins, and 879 shed proteins. More than 75% of the proteins identified had quantitative information, and a subset consisting of 400 proteins showed a statistically significant change in abundance following gefitinib treatment. We validated the change in expression profile in vitro and screened our panel of response markers in an in vivo isogenic resistant model and showed that these were markers of gefitinib response and not simply markers of phospho-EGFR downregulation. In doing so, we also were able to identify which proteins might be useful as markers for monitoring response and which proteins might be useful as markers for a priori prediction of response.

| S-EPMC3959865 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data