<scp>SRM</scp> targeted proteomics in search for biomarkers of <scp>HCV</scp>‐induced progression of fibrosis to cirrhosis in <scp>HALT</scp>‐<scp>C</scp> patients

Qin, Shizhen; Zhou, Yong; Lok, Anna S.; Tsodikov, Alex; Yan, Xiaowei; Gray, Li; Yuan, Min; Moritz, Robert L.; Galas, David J.; Omenn, Gilbert S.; Hood, Leroy

doi:10.1002/pmic.201100601

Cited by 34 publications

(35 citation statements)

References 32 publications

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“…Five proteins (A1BG, CFH, IGFALS, PROC, and RBP4) were identified and verified with selected reaction monitoring as potential serum biomarkers that separate HCVinfected patients from healthy individuals or distinguish HCV patients at different stages of fibrosis (30). All five proteins were detected with significant differential abundance in our analysis, and observed with the same expression trends.…”

Section: Functional Classification and Comparison With Previousmentioning

confidence: 53%

Advancing the High Throughput Identification of Liver Fibrosis Protein Signatures Using Multiplexed Ion Mobility Spectrometry

Baker

Burnum-Johnson

Jacobs

et al. 2014

Molecular & Cellular Proteomics

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Rapid diagnosis of disease states using less invasive, safer, and more clinically acceptable approaches than presently employed is a crucial direction for the field of medicine. While MS-based proteomics approaches have attempted to meet these objectives, challenges such as the enormous dynamic range of protein concentrations in clinically relevant biofluid samples coupled with the need to address human biodiversity have slowed their employment. Herein, we report on the use of a new instrumental platform that addresses these challenges by coupling technical advances in rapid gas phase multiplexed ion mobility spectrometry separations with liquid chromatography and MS to dramatically increase measurement sensitivity and throughput, further enabling future high throughput To date pre-clinical and clinical applications of MS-based proteomic techniques analyzing complex biofluids have fallen short of expectations, largely due to deficiencies in both analytical sensitivity and throughput. These deficiencies result in measurements typically failing to confidently detect and quantify proteins at moderate to low concentrations, or not providing sufficient sample analysis throughput for statistical relevance. Targeted MS analyses with higher sensitivity are currently utilized to address these shortcomings (1, 2); however, these studies often only analyze a small list of proteins identified as biologically significant. While targeted MS measurements are increasingly common in clinical applications (3, 4), the limited number of proteins they examine does not necessarily reflect the biodiversity across a population, making broad untargeted measurements essential in developing individual disease metrics for diagnosis (5). As the future of medicine proceeds toward a personal profiling approach (6, 7), the potential for robust high throughput clinical measurements based upon MS is highly attractive, though only if its deficiencies can be addressed. MSAn initial step in attaining broad untargeted measurements that increasingly retain the benefits of targeted analyses exploits technological advances such as faster separations, more effective ion sources, detectors with greater dynamic range, and MS measurements with both higher resolution and accuracy. Advanced liquid-phase separations have already

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Section: Functional Classification and Comparison With Previousmentioning

confidence: 53%

Advancing the High Throughput Identification of Liver Fibrosis Protein Signatures Using Multiplexed Ion Mobility Spectrometry

Baker

Burnum-Johnson

Jacobs

et al. 2014

Molecular & Cellular Proteomics

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“…We found that alpha-1-B-glycoprotein (A1BG) and T4 surface glycoprotein precursor (TSGP) were both up-regulated in DEW and SP, and the expression was over two-fold higher in SP than DEW (Figure 1; Table 1). A1BG was reportedly involved in a number of diseases such as endometrial cancer, cervical cancer and cervical squamous cell carcinoma (30), and was also elevated in hepatic fibrosis patients, which allowed such patients to be easily distinguished from HI (31). TSGP was associated with T-cell costimulation, positive regulation of interleukin-2 biosynthesis, T-cell receptor complex formation, extracellular matrix structural constituents, the transmembrane receptor protein tyrosine kinase signaling pathway, early endosomes, innate immune responses, and proteinaceous extracellular matrix and MHC class II protein complexes.…”

Section: Glycoproteinsmentioning

confidence: 99%

Proteomic profiling change during the early development of silicosis disease

Miao¹,

Ding²,

Xin³

et al. 2016

J. Thorac. Dis.

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Background: Silicosis is one of several severe occupational diseases for which effective diagnostic tools during early development are currently unavailable. In this study we focused on proteomic profiling during the early stages of silicosis to investigate the pathophysiology and identify the proteins involved.Methods: Two-dimensional (2D) gel electrophoresis and MALDI-TOF-MS were used to assess the proteomic differences between healthy individuals (HI), dust-exposed workers without silicosis (DEW) and silicosis patients (SP). Proteins abundances that differed by a factor of two-fold or greater were subjected to more detailed analysis, and enzyme linked to immunosorbent assay (ELISA) was employed to correlate with protein expression data.Results: Compared with HI, 42 proteins were more abundant and 8 were less abundant in DEW, and these were also differentially accumulated in SP. Closer inspection revealed that serine protease granzyme A, alpha-1-B-glycoprotein (A1BG) and the T4 surface glycoprotein precursor (TSGP) were among the up-regulated proteins in DEW and SP. Significant changes in serine proteases, glycoproteins and protooncogenes may be associated with the response to cytotoxicity and infectious pathogens by activation of T cells, positive regulation of extracellular matrix structural constituents and immune response, and fibroblast proliferation. Up-regulation of cytokines included TNFs, interferon beta precursor, interleukin 6, atypical chemokine receptor 2, TNFR13BV, and mutant IL-17F may be involved in the increased and persistent immune response and fibrosis that occurred during silicosis development.Conclusions: Granzymes, glycoproteins, cytokines and immune factors were dramatically involved in the immune response, metabolism, signal regulation and fibrosis during the early development of silicosis.Proteomic profiling has expanded our understanding of the pathogenesis of silicosis, and identified a number of targets that may be potential biomarkers for early diagnosis of this debilitating disease. J Thorac Dis 2016;8(3):329-341 jtd.amegroups.com extracellular apoptosis related factor (ECM), cytokines and lipid-associated proteins were all reportedly involved in the development of silicosis (3,4). Unfortunately, an effective method involving biomarkers for early diagnosis remained unavailable at present.Significant cellular and molecular alterations ensued following inhalation and deposition of silica minerals in lung tissues (5). Lower-level exposure to silica particles triggered reversible inflammatory lesions, whereas highlevel exposure led to long-term effects on inflammation, cell proliferation, deposition of collagen and extracellular matrix components in mesenchymal cells (1). Alveolar macrophages (AM), neutrophils, T-lymphocytes and mast cells were all involved in fibrogenesis, and cross-talk between these cells played an important role in disease progression. In studies on the interaction of mast cells and neutrophils in mice, animals with intact mast cells endured more severe lung lesi...

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“…Blood contains organ-specific gene products that are released into circulation through tissue damage, enzymatic cleavage from the cell membrane, or normal blood secretion, providing a source of clinically useful biomarkers for differentiating normal from diseased organs (15). A comparison with published human tissue expression atlases (data on deep comparative transcriptome analyses of multiple organs) (16)(17)(18) showed that 130 of the 320 neuron-specific genes from Cahoy et al (3), of which our 170 genes are a subset, are n = 3 genes*, Accuracy = 80%*** n = 2 genes*, Accuracy = 80%*** n = 3 genes**, Accuracy = 79%*** n = 2 genes**, Accuracy = 76%*** n = 12 genes*, Accuracy = 89%*** n = 6 genes, Accuracy = 74%*** n = 12 genes, Accuracy = 64%*** Gene Ontology gene sets with consistent differences in relative gene expression (reordering of gene set components) between clusters.…”

Section: Transcriptomic Signatures Distinguishing Cortical and Noncormentioning

confidence: 99%

Cell type-specific genes show striking and distinct patterns of spatial expression in the mouse brain

Ament

Eddy

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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To characterize gene expression patterns in the regional subdivisions of the mammalian brain, we integrated spatial gene expression patterns from the Allen Brain Atlas for the adult mouse with panels of cell type-specific genes for neurons, astrocytes, and oligodendrocytes from previously published transcriptome profiling experiments. We found that the combined spatial expression patterns of 170 neuron-specific transcripts revealed strikingly clear and symmetrical signatures for most of the brain's major subdivisions. Moreover, the brain expression spatial signatures correspond to anatomical structures and may even reflect developmental ontogeny. Spatial expression profiles of astrocyte-and oligodendrocyte-specific genes also revealed regional differences; these defined fewer regions and were less distinct but still symmetrical in the coronal plane. Follow-up analysis suggested that region-based clustering of neuron-specific genes was related to (i) a combination of individual genes with restricted expression patterns, (ii) region-specific differences in the relative expression of functional groups of genes, and (iii) regional differences in neuronal density. Products from some of these neuron-specific genes are present in peripheral blood, raising the possibility that they could reflect the activities of disease-or injury-perturbed networks and collectively function as biomarkers for clinical disease diagnostics.T he mammalian brain can be subdivided into more than 100 anatomically and functionally distinct regions, each containing multiple cell types, including various classes of neurons and glia (1). Despite several decades of modern neuroscience research, we lack a complete understanding of how these brain compartments are specified and maintained or of how structural differences are translated into the diverse functions performed within the brain. Understanding the transcriptional correlates of brain region and cell type diversity holds promise for elucidating brain function and development. Moreover, unique transcriptional signatures for brain regions have potential clinical uses as biomarkers for disease diagnostics (2).Recent technological innovations have enabled researchers to begin systematically characterizing regional differences in brain gene expression. Transcriptome profiling of isolated neurons and glia has revealed that certain genes are specifically expressed in neurons, astrocytes, or oligodendrocytes (3), or even within specific classes of cortical neurons (4). A complementary approach, highthroughput in situ hybridization of more than 20,000 mouse genes (5, 6), allows visualization of expression patterns across the entire brain at the single-cell level, revealing tremendous diversity in the expression profiles of individual genes.The diversity of spatial expression patterns for genes in the adult brain (as well as their distinct functions) suggests that many regional differences have gene expression correlates. Indeed, clustering analysis using 3,041 genes from the Mouse Brain Atlas produce...

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SRM targeted proteomics in search for biomarkers of HCV‐induced progression of fibrosis to cirrhosis in HALT‐C patients

Cited by 34 publications

References 32 publications

Advancing the High Throughput Identification of Liver Fibrosis Protein Signatures Using Multiplexed Ion Mobility Spectrometry

Advancing the High Throughput Identification of Liver Fibrosis Protein Signatures Using Multiplexed Ion Mobility Spectrometry

Proteomic profiling change during the early development of silicosis disease

Cell type-specific genes show striking and distinct patterns of spatial expression in the mouse brain

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