Dataset Information

Selenium-Encoded Isotopic Signature Targeted Profiling.

ABSTRACT: Selenium (Se), as an essential trace element, plays crucial roles in many organisms including humans. The biological functions of selenium are mainly mediated by selenoproteins, a unique class of selenium-containing proteins in which selenium is inserted in the form of selenocysteine. Due to their low abundance and uneven tissue distribution, detection of selenoproteins within proteomes is very challenging, and therefore functional studies of these proteins are limited. In this study, we developed a computational method, named as selenium-encoded isotopic signature targeted profiling (SESTAR), which utilizes the distinct natural isotopic distribution of selenium to assist detection of trace selenium-containing signals from shotgun-proteomic data. SESTAR can detect femtomole quantities of synthetic selenopeptides in a benchmark test and dramatically improved detection of native selenoproteins from tissue proteomes in a targeted profiling mode. By applying SESTAR to screen publicly available datasets from Human Proteome Map, we provide a comprehensive picture of selenoprotein distributions in human primary hematopoietic cells and tissues. We further demonstrated that SESTAR can aid chemical-proteomic strategies to identify additional selenoprotein targets of RSL3, a canonical inducer of cell ferroptosis. We believe SESTAR not only serves as a powerful tool for global profiling of native selenoproteomes, but can also work seamlessly with chemical-proteomic profiling strategies to enhance identification of target proteins, post-translational modifications, or protein-protein interactions.

SUBMITTER: Gao J

PROVIDER: S-EPMC6107865 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Selenium-Encoded Isotopic Signature Targeted Profiling.

Gao Jinjun J Yang Fan F Che Jinteng J Han Yu Y Wang Yankun Y Chen Nan N Bak Daniel W DW Lai Shuchang S Xie Xiao X Weerapana Eranthie E Wang Chu C

ACS central science 20180716 8

Selenium (Se), as an essential trace element, plays crucial roles in many organisms including humans. The biological functions of selenium are mainly mediated by selenoproteins, a unique class of selenium-containing proteins in which selenium is inserted in the form of selenocysteine. Due to their low abundance and uneven tissue distribution, detection of selenoproteins within proteomes is very challenging, and therefore functional studies of these proteins are limited. In this study, we develop ...[more]

PMID: 30159393

Similar Datasets

Project description:The use of gene expression signatures to classify compounds, identify efficacy or toxicity, and differentiate close analogs relies on the sensitivity of the method to identify modulated genes. We used a novel ligation-based targeted whole transcriptome expression profiling assay, TempO-Seq®, to determine whether previously unreported compound-responsive genes could be identified and incorporated into a broad but specific compound signature. TempO-Seq exhibits 99.6% specificity, single cell sensitivity, and excellent correlation with fold differences measured by RNA-Seq (R2 = 0.9) for 20,629 targets. Unlike many expression assays, TempO-Seq does not require RNA purification, cDNA synthesis, or capture of targeted RNA, and lacks a 3' end bias. To investigate the sensitivity of the TempO-Seq assay to identify significantly modulated compound-responsive genes, we derived whole transcriptome profiles from MCF-7 cells treated with the histone deacetylase inhibitor Trichostatin A (TSA) and identified more than 9,000 differentially expressed genes. The TSA profile for MCF-7 cells overlapped those for HL-60 and PC-3 cells in the Connectivity Map (cMAP) database, suggesting a common TSA-specific expression profile independent of baseline gene expression. A 43-gene cell-independent TSA signature was extracted from cMAP and confirmed in TempO-Seq MCF-7 data. Additional genes that were not previously reported to be TSA responsive in the cMAP database were also identified. TSA treatment of 5 cell types revealed 1,136 differentially expressed genes in common, including 785 genes not previously reported to be TSA responsive. We conclude that TSA induces a specific expression signature that is consistent across widely different cell types, that this signature contains genes not previously associated with TSA responses, and that TempO-Seq provides the sensitive differential expression detection needed to define such compound-specific, cell-independent, changes in expression.

Project description:Constitutive MET signaling promotes invasiveness in most primary and recurrent GBM. However, deployment of available MET-targeting agents is confounded by lack of effective biomarkers for selecting suitable patients for treatment. Because endogenous HGF overexpression often causes autocrine MET activation, and also indicates sensitivity to MET inhibitors, we investigated whether it drives the expression of distinct genes which could serve as a signature indicating vulnerability to MET-targeted therapy in GBM.Interrogation of genomic data from TCGA GBM (Student's t test, GBM patients with high and low HGF expression, p ? 0.00001) referenced against patient-derived xenograft (PDX) models (Student's t test, sensitive vs. insensitive models, p ? 0.005) was used to identify the HGF-dependent signature. Genomic analysis of GBM xenograft models using both human and mouse gene expression microarrays (Student's t test, treated vs. vehicle tumors, p ? 0.01) were performed to elucidate the tumor and microenvironment cross talk. A PDX model with EGFR(amp) was tested for MET activation as a mechanism of erlotinib resistance.We identified a group of 20 genes highly associated with HGF overexpression in GBM and were up- or down-regulated only in tumors sensitive to MET inhibitor. The MET inhibitors regulate tumor (human) and host (mouse) cells within the tumor via distinct molecular processes, but overall impede tumor growth by inhibiting cell cycle progression. EGFR (amp) tumors undergo erlotinib resistance responded to a combination of MET and EGFR inhibitors.Combining TCGA primary tumor datasets (human) and xenograft tumor model datasets (human tumor grown in mice) using therapeutic efficacy as an endpoint may serve as a useful approach to discover and develop molecular signatures as therapeutic biomarkers for targeted therapy. The HGF dependent signature may serve as a candidate predictive signature for patient enrollment in clinical trials using MET inhibitors. Human and mouse microarrays maybe used to dissect the tumor-host interactions. Targeting MET in EGFR (amp) GBM may delay the acquired resistance developed during treatment with erlotinib.

Project description:Metabolic flux analysis (MFA) is highly relevant to understanding metabolic mechanisms of various biological processes. While the pace of methodology development in MFA has been rapid, a major challenge the field continues to witness is limited metabolite coverage, often restricted to a small to moderate number of well-known compounds. In addition, isotopic peaks from an enriched metabolite tend to have low abundances, which makes liquid chromatography tandem mass spectrometry (LC-MS/MS) highly useful in MFA due to its high sensitivity and specificity. Previously we have built large-scale LC-MS/MS approaches that can be routinely used for measurement of up to ∼1,900 metabolite/feature levels [Gu et al. Anal. Chem. 2015, 87, 12355-12362. Shi et al. Anal. Chem. 2019, 91, 13737-13745.]. In this study, we aim to expand our previous studies focused on metabolite level measurements to flux analysis and establish a novel comprehensive isotopic targeted mass spectrometry (CIT-MS) method for reliable MFA analysis with broad coverage. As a proof-of-principle, we have applied CIT-MS to compare the steady-state enrichment of metabolites between Myc(oncogene)-On and Myc-Off Tet21N human neuroblastoma cells cultured with U-13C6-glucose medium. CIT-MS is operationalized using multiple reaction monitoring (MRM) mode and is able to perform MFA of 310 identified metabolites (142 reliably detected, 46 kinetically profiled) selected from >35 metabolic pathways of strong biological significance. Further, we developed a novel concept of relative flux, which eliminates the requirement of absolute quantitation in traditional MFA and thus enables comparative MFA under the pseudosteady state. As a result, CIT-MS was shown to possess the advantages of broad coverage, easy implementation, fast throughput, and more importantly, high fidelity and accuracy in MFA. In principle, CIT-MS can be easily adapted to track the flux of other labeled tracers (such as 15N-tracers) in any metabolite detectable by LC-MS/MS and in various biological models (such as mice). Therefore, CIT-MS has great potential to bring new insights to both basic and clinical metabolism research.

Dataset Information

Selenium-Encoded Isotopic Signature Targeted Profiling.

Publications

Selenium-Encoded Isotopic Signature Targeted Profiling.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets