Large-scale and high-resolution mass spectrometry-based proteomics profiling defines molecular subtypes of esophageal cancer for therapeutic targeting.
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ABSTRACT:
SUBMITTER: Liu W
PROVIDER: S-EPMC8368010 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Esophageal cancer (EC) is anaggressive cancer type without clinically relevant molecular subtypes defined, which hinders the development of effective strategies forEC treatment. Here, we reporteda large-scale, high-resolution massspectrometry-based proteomicand phosphoproteomics profiling of EC tumor and the corresponding adjacent non-tumor tissues, revealing a catalog of proteins, phosphositesas well as pathways that are dysregulated in ECs. The EC cohort was stratified into two molecular subtypes, S1 and S2, based on proteomic analysis, withthe S2 subtype characterized by the up-regulation of spliceosome and ribosomal proteins, and being more aggressive.Moreover, we identified a subtype signature composed of ELOA and SCAF4 and constructed a subtype diagnostic model, the accuracy of which was independently validated in a large cohort of EC patients (n=295). Potential drugs were predicted for treating patients in S2 subtype, and threecandidate drugs, GW8510, Menadione and Sulconazole,were validated to significantly inhibit the proliferation and migration of EC cell lines . On the basis of nude mice with xenograft tumors, we found that treatment with the three drugs markedly reduced the size of tumors. Finally, the three drugs were shown to reverse the expression patterns of most proteins that are dysregulated in S2 subtype in KYSE150 cells.Taken together, our proteomic analysis defined subtypesand subtype signature of EC, and providedmolecular basis for finding potential treatmentsfor EC.
2021-07-05 | PXD021701 |
Project description:Human monocyte is an important cell type which is involved in various complex human diseases. To better understand the biology of human monocytes and facilitate further studies, we developed the first comprehensive proteome knowledge base specifically for human monocytes by integrating both in vivo and in vitro datasets. The top 2000 expressed genes from in vitro datasets and 779 genes from in vivo experiments were integrated into this study. Altogether, a total of 2237 unique monocyte-expressed genes were cataloged. Biological functions of these monocyte-expressed genes were annotated and classified via Gene Ontology (GO) analysis. Furthermore, by extracting the overlapped genes from in vivo and in vitro datasets, a core gene list including 541 unique genes was generated. Based on the core gene list, further gene-disease associations, pathway and network analyses were performed. Data analyses based on multiple bioinformatics tools produced a large body of biologically meaningful information, and revealed a number of genes such as SAMHD1, G6PD, GPD2 and ENO1, which have been reported to be related to immune response, blood biology, bone remodeling, and cancer respectively. As a unique resource, this study can serve as a reference map for future in-depth research on monocytes biology and monocyte-involved human diseases.
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Project description:Data Access Committee EGAC00001002236
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Project description:BACKGROUND:Retinoblastoma is an ocular neoplastic cancer caused primarily due to the mutation/deletion of RB1 gene. Due to the rarity of the disease very limited information is available on molecular changes in primary retinoblastoma. High throughput analysis of retinoblastoma transcriptome is available however the proteomic landscape of retinoblastoma remains unexplored. In the present study we used high resolution mass spectrometry-based quantitative proteomics to identify proteins associated with pathogenesis of retinoblastoma. METHODS:We used five pooled normal retina and five pooled retinoblastoma tissues to prepare tissue lysates. Equivalent amount of proteins from each group was trypsin digested and labeled with iTRAQ tags. The samples were analyzed on Orbitrap Velos mass spectrometer. We further validated few of the differentially expressed proteins by immunohistochemistry on primary tumors. RESULTS:We identified and quantified a total of 3587 proteins in retinoblastoma when compared with normal adult retina. In total, we identified 899 proteins that were differentially expressed in retinoblastoma with a fold change of ?2 of which 402 proteins were upregulated and 497 were down regulated. Insulin growth factor 2 mRNA binding protein 1 (IGF2BP1), chromogranin A, fetuin A (ASHG), Rac GTPase-activating protein 1 and midkine that were found to be overexpressed in retinoblastoma were further confirmed by immunohistochemistry by staining 15 independent retinoblastoma tissue sections. We further verified the effect of IGF2BP1 on cell proliferation and migration capability of a retinoblastoma cell line using knockdown studies. CONCLUSIONS:In the present study mass spectrometry-based quantitative proteomic approach was applied to identify proteins differentially expressed in retinoblastoma tumor. This study identified the mitochondrial dysfunction and lipid metabolism pathways as the major pathways to be deregulated in retinoblastoma. Further knockdown studies of IGF2BP1 in retinoblastoma cell lines revealed it as a prospective therapeutic target for retinoblastoma.
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Project description:Introduction: Serratia marcescens, an opportunistic human pathogen, is reported as an important cause of nosocomial infection and outbreaks. Although the genome of S. marcescens (ATCC 13880) was completely sequenced by 2014, there are no studies on the proteomic profile of the organism. The objective of the present study is to analyze the protein profile of S. marcescens (ATCC 13880) using a high resolution mass spectrometry (MS). Methods: Serratia marcescens ATCC 13880 strain was grown in Luria-Bertani broth and the protein extracted was subjected to trypsin digestion, followed by basic reverse phase liquid chromatography fractionation. The peptide fractions were then analysed using Orbitrap Fusion Mass Spectrometry and the raw MS data were processed in Proteome Discoverer software. Results: The proteomic analysis identified 15?009 unique peptides mapping to 2541 unique protein groups, which corresponds to approximately 54% of the computationally predicted protein-coding genes. Bioinformatic analysis of these identified proteins showed their involvement in biological processes such as cell wall organization, chaperone-mediated protein folding and ATP binding. Pathway analysis revealed that some of these proteins are associated with bacterial chemotaxis and beta-lactam resistance pathway. Conclusion: To the best of our knowledge, this is the first high-throughput proteomics study of S. marcescens (ATCC 13880). These novel observations provide a crucial baseline molecular profile of the S. marcescens proteome which will prove to be helpful for the future research in understanding the host-pathogen interactions during infection, elucidating the mechanism of multidrug resistance, and developing novel diagnostic markers or vaccine for the disease.
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Project description:Mass spectrometry plays a key role in drug metabolite identification, an integral part of drug discovery and development. The development of high-resolution (HR) MS instrumentation with improved accuracy and stability, along with new data processing techniques, has improved the quality and productivity of metabolite identification processes. In this minireview, HR-MS-based targeted and non-targeted acquisition methods and data mining techniques (e.g. mass defect, product ion, and isotope pattern filters and background subtraction) that facilitate metabolite identification are examined. Methods are presented that enable multiple metabolite identification tasks with a single LC/HR-MS platform and/or analysis. Also, application of HR-MS-based strategies to key metabolite identification activities and future developments in the field are discussed.
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Project description:Tandem mass tag (TMT)-based mass spectrometry (MS) enables deep proteomic profiling of more than 10,000 proteins in complex biological samples but requires up to 100 μg protein in starting materials during a standard analysis. Here, we present a streamlined protocol to quantify more than 9000 proteins with 0.5 μg protein per sample by 16-plex TMT coupled with two-dimensional liquid chromatography and tandem mass spectrometry (LC/LC-MS/MS). In this protocol, we optimized multiple conditions to reduce sample loss, including processing each sample in a single tube to minimize surface adsorption, increasing digestion enzymes to shorten proteolysis and function as carriers, eliminating a desalting step between digestion and TMT labeling, and developing miniaturized basic pH LC for prefractionation. By profiling 16 identical human brain tissue samples of Alzheimer's disease (AD), vascular dementia (VaD), and non-dementia controls, we directly compared this new microgram-scale protocol to the standard-scale protocol, quantifying 9116 and 10,869 proteins, respectively. Importantly, bioinformatics analysis indicated that the microgram-scale protocol had adequate sensitivity and reproducibility to detect differentially expressed proteins in disease-related pathways. Thus, this newly developed protocol is of general application for deep proteomics analysis of biological and clinical samples at sub-microgram levels.
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Project description:Large-scale protein sequencing methods rely on enzymatic digestion of complex protein mixtures to generate a collection of peptides for mass spectrometric analysis. Here we examine the use of multiple proteases (trypsin, LysC, ArgC, AspN, and GluC) to improve both protein identification and characterization in the model organism Saccharomyces cerevisiae. Using a data-dependent, decision tree-based algorithm to tailor MS(2) fragmentation method to peptide precursor, we identified 92 095 unique peptides (609 665 total) mapping to 3908 proteins at a 1% false discovery rate (FDR). These results were a significant improvement upon data from a single protease digest (trypsin) - 27 822 unique peptides corresponding to 3313 proteins. The additional 595 protein identifications were mainly from those at low abundances (i.e., < 1000 copies/cell); sequence coverage for these proteins was likewise improved nearly 3-fold. We demonstrate that large portions of the proteome are simply inaccessible following digestion with a single protease and that multiple proteases, rather than technical replicates, provide a direct route to increase both protein identifications and proteome sequence coverage.
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Project description:Apolipoprotein-CIII (apo-CIII) is a glycoprotein involved in lipid metabolism and its levels are associated with cardiovascular disease risk. Apo-CIII sialylation is associated with improved plasma triglyceride levels and its glycosylation may have an effect on the clearance of triglyceride-rich lipoproteins by directing these particles to different metabolic pathways. Large-scale sample cohort studies are required to fully elucidate the role of apo-CIII glycosylation in lipid metabolism and associated cardiovascular disease. In this study, we revisited a high-throughput workflow for the analysis of intact apo-CIII by ultrahigh-resolution MALDI FT-ICR MS. The workflow includes a chemical oxidation step to reduce methionine oxidation heterogeneity and spectrum complexity. Sinapinic acid matrix was used to minimize the loss of sialic acids upon MALDI. MassyTools software was used to standardize and automate MS data processing and quality control. This method was applied on 771 plasma samples from individuals without diabetes allowing for an evaluation of the expression levels of apo-CIII glycoforms against a panel of lipid biomarkers demonstrating the validity of the method. Our study supports the hypothesis that triglyceride clearance may be regulated, or at least strongly influenced by apo-CIII sialylation. Interestingly, the association of apo-CIII glycoforms with triglyceride levels was found to be largely independent of body mass index. Due to its precision and throughput, the new workflow will allow studying the role of apo-CIII in the regulation of lipid metabolism in various disease settings.
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