Fewer than 200 proteins are targeted by cancer drugs approved by the Food and Drug Administration (FDA). We integrate Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteogenomics data from ...1,043 patients across 10 cancer types with additional public datasets to identify potential therapeutic targets. Pan-cancer analysis of 2,863 druggable proteins reveals a wide abundance range and identifies biological factors that affect mRNA-protein correlation. Integration of proteomic data from tumors and genetic screen data from cell lines identifies protein overexpression- or hyperactivation-driven druggable dependencies, enabling accurate predictions of effective drug targets. Proteogenomic identification of synthetic lethality provides a strategy to target tumor suppressor gene loss. Combining proteogenomic analysis and MHC binding prediction prioritizes mutant KRAS peptides as promising public neoantigens. Computational identification of shared tumor-associated antigens followed by experimental confirmation nominates peptides as immunotherapy targets. These analyses, summarized at https://targets.linkedomics.org, form a comprehensive landscape of protein and peptide targets for companion diagnostics, drug repurposing, and therapy development.
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•Integrating tumor proteogenomics with cell line data reveals pan-cancer druggable targets•Proteogenomic discovery of synthetic lethality facilitates targeting tumor suppressor loss•Computational workflows enable effective tumor antigen identification•Web portal provides access to identified targets and their supporting data
Integrating pan-cancer proteogenomic data from 1,043 patients across 10 cancer types, genetic screen data from cell lines, and tumor antigen predictions unveils a comprehensive landscape of protein and peptide targets for drug repurposing and therapy development.
TNFα has been identified as playing an important role in pathologic complications associated with diabetic retinopathy and retinal inflammation, such as retinal leukostasis. However, the ...transcriptional effects of TNFα on retinal microvascular endothelial cells and the different signaling pathways involved are not yet fully understood. In the present study, RNA-seq was used to profile the transcriptome of human retinal microvascular endothelial cells (HRMEC) treated for 4 hours with TNFα in the presence or absence of the NFAT-specific inhibitor INCA-6, in order to gain insight into the specific effects of TNFα on RMEC and identify any involvement of NFAT signaling. Differential expression analysis revealed that TNFα treatment significantly upregulated the expression of 579 genes when compared to vehicle-treated controls, and subsequent pathway analysis revealed a TNFα-induced enrichment of transcripts associated with cytokine-cytokine receptor interactions, cell adhesion molecules, and leukocyte transendothelial migration. Differential expression analysis comparing TNFα-treated cells to those co-treated with INCA-6 revealed 10 genes whose expression was significantly reduced by the NFAT inhibitor, including those encoding the proteins VCAM1 and CX3CL1 and cytokines CXCL10 and CXCL11. This study identifies the transcriptional effects of TNFα on HRMEC, highlighting its involvement in multiple pathways that contribute to retinal leukostasis, and identifying a previously unknown role for NFAT-signaling downstream of TNFα.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
DOI: 10.1002/pmic.201900335
Deep learning holds great potential in proteomics research. In article number 1900335, Bo Wen et al. provide a comprehensive overview of deep learning applications in ...proteomics. The cover art illustrates some of the applications reviewed in the article. The protein sequence under the GPU chip represents the large amount of proteomics data used for deep learning. The brain in the center of the chip symbolizes artificial intelligence. The blocks spread out from the chip summarizes the main applications described in the article, including PTM prediction, protein structure prediction, de novo peptide sequencing, retention time prediction, MS/MS spectrum prediction, and MHC‐peptide binding prediction. The glowing connections imply high‐speed calculation. The glowing cloud depicts the great potential of deep learning in the analysis of proteomics data.
By combining mass-spectrometry-based proteomics and phosphoproteomics with genomics, epi-genomics, and transcriptomics, proteogenomics provides comprehensive molecular characterization of cancer. ...Using this approach, the Clinical Proteomic Tumor Analysis Consortium (CPTAC) has characterized over 1,000 primary tumors spanning 10 cancer types, many with matched normal tissues. Here, we present LinkedOmicsKB, a proteogenomics data-driven knowledge base that makes consistently processed and systematically precomputed CPTAC pan-cancer proteogenomics data available to the public through ∼40,000 gene-, protein-, mutation-, and phenotype-centric web pages. Visualization techniques facilitate efficient exploration and reasoning of complex, interconnected data. Using three case studies, we illustrate the practical utility of LinkedOmicsKB in providing new insights into genes, phosphorylation sites, somatic mutations, and cancer phenotypes. With precomputed results of 19,701 coding genes, 125,969 phosphosites, and 256 genotypes and phenotypes, LinkedOmicsKB provides a comprehensive resource to accelerate proteogenomics data-driven discoveries to improve our understanding and treatment of human cancer. A record of this paper’s transparent peer review process is included in the supplemental information.
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•CPTAC proteogenomics data from 1,043 cancer patients across 10 cancer types•40,000 web pages dedicated to genes, proteins, mutations, and phenotypes•User-friendly visualization tools for efficient data exploration and analysis•Practical utility demonstrated through three informative case studies
LinkedOmicsKB makes consistently processed and systematically precomputed CPTAC pan-cancer proteogenomics data easily accessible to the public through a web portal. With approximately 40,000 gene-, protein-, mutation-, and phenotype-centric web pages, it enables anyone with internet access to conduct meaningful inquiries into CPTAC data, facilitating data-driven scientific discoveries.
Diabetic retinopathy (DR) is triggered by retinal cell damage stimulated by the diabetic milieu, including increased levels of intraocular free fatty acids. Free fatty acids may serve as an initiator ...of inflammatory cytokine release from Müller cells, and the resulting cytokines are potent stimulators of retinal endothelial pathology, such as leukostasis, vascular permeability, and basement membrane thickening. Our previous studies have elucidated a role for peroxisome proliferator-activated receptor-β/δ (PPARβ/δ) in promoting several steps in the pathologic cascade in DR, including angiogenesis and expression of inflammatory mediators. Furthermore, PPARβ/δ is a known target of lipid signaling, suggesting a potential role for this transcription factor in fatty acid-induced retinal inflammation. Therefore, we hypothesized that PPARβ/δ stimulates both the induction of inflammatory mediators by Müller cells as well the paracrine induction of leukostasis in endothelial cells (EC) by Müller cell inflammatory products. To test this, we used the PPARβ/δ inhibitor, GSK0660, in primary human Müller cells (HMC), human retinal microvascular endothelial cells (HRMEC) and mouse retina. We found that palmitic acid (PA) activation of PPARβ/δ in HMC leads to the production of pro-angiogenic and/or inflammatory cytokines that may constitute DR-relevant upstream paracrine inflammatory signals to EC and other retinal cells. Downstream, EC transduce these signals and increase their synthesis and release of chemokines such as CCL8 and CXCL10 that regulate leukostasis and other cellular events related to vascular inflammation in DR. Our results indicate that PPARβ/δ inhibition mitigates these upstream (MC) as well as downstream (EC) inflammatory signaling events elicited by metabolic stimuli and inflammatory cytokines. Therefore, our data suggest that PPARβ/δ inhibition is a potential therapeutic strategy against early DR pathology.
•The PPARβ/δ inhibitor GSK0660 inhibits palmitic acid-stimulated inflammatory mediator production by Müller cells.•GSK0660 inhibits TNFα-induced leukocyte adhesion in both HRMEC and mouse retina.•GSK0660 blocks TNFα-induced leukostasis by modulating the levels of CCL8 and CXCL10.•GSK0660's effects on retinal cells are mediated by both PPARβ/δ-dependent and PPARβ/δ-independent pathways.
The peroxisome proliferator-activated receptor beta/delta (PPARβ/δ) is a transcription factor with roles in metabolism, angiogenesis, and inflammation. It has yet undefined roles in retinal ...inflammation and diabetic retinopathy (DR). We used RNA-seq to better understand the role of the antagonist and inverse agonist of PPARβ/δ, GSK0660, in TNFα-induced inflammation. Understanding the underlying mechanisms of vascular inflammation could lead to new treatments for DR.
RNA was isolated from human retinal microvascular endothelial cells treated with a vehicle, TNFα, or TNFα plus GSK0660. RNA-seq was performed with a 50 bp single read protocol. The differential expression was determined using edgeR and gene ontology, and a pathway analysis was performed using DAVID. RNA-seq validation was performed using qRT-PCR using the primers for ANGPTL4, CCL8, NOV, CXCL10, and PDPK1.
TNFα differentially regulated 1,830 transcripts, many of which are involved in the cytokine-cytokine receptor interaction, chemokine signaling, and inflammatory response. Additionally, TNFα highly upregulated genes involved in leukocyte recruitment, including CCL5, CX3CL1, and CXCL10. GSK0660 differentially regulated 273 transcripts in TNFα-treated cells compared to TNFα alone. A pathway analysis revealed the enrichment of cytokine-cytokine receptor signaling. In particular, GSK0660 blocks the TNFα-induced upregulation of CCL8, a chemokine involved in leukocyte recruitment.
TNFα regulates several genes related to retinal leukostasis in retinal endothelial cells. GSK0660 blocks the effect of TNFα on the expressions of cytokines involved in leukocyte recruitment, including CCL8, CCL17, and CXCL10 and it may therefore block TNFα-induced retinal leukostasis.
The objective of this study was to determine the role of individual NFAT isoforms in TNFα-induced retinal leukostasis. To this end, human retinal microvascular endothelial cells (HRMEC) transfected ...with siRNA targeting individual NFAT isoforms were treated with TNFα, and qRT-PCR was used to examine the contribution of each isoform to the TNFα-induced upregulation of leukocyte adhesion proteins. This showed that NFATc1 siRNA increased ICAM1 expression, NFATc2 siRNA reduced CX3CL1, VCAM1, SELE, and ICAM1 expression, NFATc3 siRNA increased CX3CL1 and SELE expression, and NFATc4 siRNA reduced SELE expression. Transfected HRMEC monolayers were also treated with TNFα and assayed using a parallel plate flow chamber, and both NFATc2 and NFATc4 knockdown reduced TNFα-induced cell adhesion. The effect of isoform-specific knockdown on TNFα-induced cytokine production was also measured using protein ELISAs and conditioned cell culture medium, and showed that NFATc4 siRNA reduced CXCL10, CXCL11, and MCP-1 protein levels. Lastly, the CN/NFAT-signaling inhibitor INCA-6 was shown to reduce TNFα-induced retinal leukostasis in vivo. Together, these studies show a clear role for NFAT-signaling in TNFα-induced retinal leukostasis, and identify NFATc2 and NFATc4 as potentially valuable therapeutic targets for treating retinopathies in which TNFα plays a pathogenic role.
To develop new therapies against ocular neovascularization (NV), we tested the effect of peroxisome proliferator-activated receptor-β/δ (PPAR-β/δ) agonism and antagonism on angiogenic behaviors and ...in human retinal microvascular endothelial cells (HRMEC) and on preretinal NV in rat oxygen-induced retinopathy (OIR).
HRMECs were treated with the PPAR-β/δ agonist GW0742 and the antagonist GSK0660. Messenger RNA levels of a PPAR-β/δ target gene, angiopoietin-like-4 (angptl4) were assayed by qRT-PCR. HRMEC proliferation and tube formation were assayed according to standard protocols. OIR was induced in newborn rats by exposing them to alternating 24-hour episodes of 50% and 10% oxygen for 14 days. OIR rats were treated with GW0742 or GSK0660. Angptl4 protein levels were assessed by ELISA and preretinal NV was quantified by adenosine diphosphatase staining.
GW0742 significantly increased angptl4 mRNA, and GSK0660 significantly decreased angptl4 mRNA. GW0742 had no effect on HRMEC proliferation, but caused a significant and dose-responsive increase in tube formation. GSK0660 significantly reduced serum-induced HRMEC proliferation and tube formation in a dose-dependent manner. Intravitreal injection of GW0742 significantly increased total retinal Angptl4 protein, but intravitreal injection of GSK0660 had no effect. Intravitreal injection of GW0742 significantly increased retinal NV, as did GW0742 administered by oral gavage. Conversely, both intravitreal injection and intraperitoneal injection of GSK0660 significantly reduced retinal NV.
PPAR-β/δ activation exacerbates, and its inhibition reduces, preretinal NV. PPAR-β/δ may regulate preretinal NV through a prodifferentiation/maturation mechanism that depends on Angptl4. Pharmacologic inhibition of PPAR-β/δ may provide a rational basis for therapeutic targeting of ocular NV.
The purpose of this study was to investigate the hypoxia-induced Vegf120, Vegf164 and Vegf188 mRNA expression profiles in rat Müller cells (MC), astrocytes, retinal pigmented epithelial cells (RPE) ...and retinal microvascular endothelial cells (RMEC) and correlate these findings to VEGF secreted protein. Cultured cells were exposed to normoxia or hypoxia. Total RNA was isolated from cell lysates and Vegf splice variant mRNA copy numbers were assayed by a validated qRT-PCR external calibration curve method. mRNA copy numbers were normalized to input total RNA. Conditioned medium was collected from cells and assayed for total VEGF protein by ELISA. Hypoxia increased total Vegf mRNA and secreted protein in all the retinal cell types, with the highest levels observed in MC and astrocytes ranking second. Total Vegf mRNA levels in hypoxic RPE and RMEC were comparable; however, the greatest hypoxic induction of each Vegf splice variant mRNA was observed in RMEC. RPE and RMEC ranked 3rd and 4th respectively, in terms of secreted total VEGF protein in hypoxia. The Vegf120, Vegf164 and Vegf188 mRNA splice variants were all increased in hypoxic cells compared to normoxic controls. In normoxia, the relative Vegf splice variant mRNA levels ranked from highest to lowest for each cell type were Vegf164 > Vegf120 > Vegf188. Hypoxic induction did not alter this ranking, although it did favor an increased stoichiometry of Vegf164 mRNA over the other two splice variants. MC and astrocytes are likely to be the major sources of total Vegf, Vegf164 splice variant mRNAs, and VEGF protein in retinal hypoxia.
•Vegf120, Vegf164 and Vegf188 are three VEGF splice variants expressed in the rat retina.•Splice variant-specific expression of Vegf in four retinal cell types was assayed in response to hypoxia.•Total VEGF protein in the conditioned medium of hypoxic and normoxic cells was assayed.•Vegf164 was the most abundant splice variant in all four retinal cell types.•Of the four cell types, Müller cells produced the highest total Vegf mRNA and protein levels.
Omics characterization of pancreatic adenocarcinoma tissue is complicated by the highly heterogeneous and mixed populations of cells. We evaluate the feasibility and potential benefit of using a ...coring method to enrich specific regions from bulk tissue and then perform proteogenomic analyses.
We used the Biopsy Trifecta Extraction (BioTExt) technique to isolate cores of epithelial-enriched and stroma-enriched tissue from pancreatic tumor and adjacent tissue blocks. Histology was assessed at multiple depths throughout each core. DNA sequencing, RNA sequencing, and proteomics were performed on the cored and bulk tissue samples. Supervised and unsupervised analyses were performed based on integrated molecular and histology data.
Tissue cores had mixed cell composition at varying depths throughout. Average cell type percentages assessed by histology throughout the core were better associated with KRAS variant allele frequencies than standard histology assessment of the cut surface. Clustering based on serial histology data separated the cores into three groups with enrichment of neoplastic epithelium, stroma, and acinar cells, respectively. Using this classification, tumor overexpressed proteins identified in bulk tissue analysis were assigned into epithelial- or stroma-specific categories, which revealed novel epithelial-specific tumor overexpressed proteins.
Our study demonstrates the feasibility of multi-omics data generation from tissue cores, the necessity of interval H&E stains in serial histology sections, and the utility of coring to improve analysis over bulk tissue data.
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