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Abstract: Protein biomarker discovery for early detection of head and neck squamous cell carcinoma (HNSCC) is a crucial unmet need to improve patient outcomes. Mass spectrometry-based proteomics has emerged as a promising tool for identification of biomarkers in different cancer types. Proteins secreted from cancer cells can serve as potential biomarkers for early diagnosis. In the current study, we have used isobaric tag for relative and absolute quantitation (iTRAQ) labeling methodology coupled with high resolution mass spectrometry to identify and quantitate secreted proteins from a panel of head and neck carcinoma cell lines. In all, we identified 2,472 proteins, of which 225 proteins were secreted at higher or lower abundance in HNSCC-derived cell lines. Of these, 148 were present in higher abundance and 77 were present in lower abundance in the cancer-cell derived secretome. We detected a higher abundance of some previously known markers for HNSCC including insulin like growth factor binding protein 3, IGFBP3 (11-fold) and opioid growth factor receptor, OGFR (10-fold) demonstrating the validity of our approach. We also identified several novel secreted proteins in HNSCC including olfactomedin-4, OLFM4 (12-fold) and hepatocyte growth factor activator, HGFA (5-fold). IHC-based validation was conducted in HNSCC using tissue microarrays which revealed overexpression of IGFBP3 and OLFM4 in 70% and 75% of the tested cases, respectively. Our study illustrates quantitative proteomics of secretome as a robust approach for identification of potential HNSCC biomarkers. This article is part of a Special Issue entitled: An Updated Secretome. [Copyright &y& Elsevier]

We have used unbiased phosphoproteomic approaches, based on quantitative mass spectrometry using stable isotope labeling with amino acids in cell culture (SILAC), to identify tyrosine phosphorylated proteins in isogenic human bronchial epithelial cells (HBECs) and human lung adenocarcinoma cell lines, expressing either of the two mutant alleles of EGFR (L858R and Del E746-A750), or a mutant KRAS allele, which are common in human lung adenocarcinomas. Tyrosine phosphorylation of signaling molecules was greater in HBECs expressing the mutant EGFRs than in cells expressing WT EGFR or mutant KRAS. Receptor tyrosine kinases (such as EGFR, ERBB2, MET, and IGF1R), and Mig-6, an inhibitor of EGFR signaling, were more phosphorylated in HBECs expressing mutant EGFR than in cells expressing WT EGFR or mutant RAS. Phosphorylation of some proteins differed in the two EGFR mutant-expressing cells; for example, some cell junction proteins (β-catenin, plakoglobin, and E-cadherin) were more phosphorylated in HBECs expressing L858R EGFR than in cells expressing Del EGFR. There were also differences in degree of phosphorylation at individual tyrosine sites within a protein; for example, a previously uncharacterized phosphorylation site in the nucleotide-binding loop of the kinase domains of EGFR (Y727), ERBB2 (Y735). or ERBB4 (Y733), is phosphorylated significantly more in HBECs expressing the deletion mutant than in cells expressing the wild type or L858R EGFR. Signaling molecules not previously implicated in ERBB signaling, such as polymerase transcript release factor (PTRF), were also phosphorylated in cells expressing mutant EGFR. Bayesian network analysis of these and other datasets revealed that PTRF might be a potentially important component of the ERBB signaling network. [ABSTRACT FROM AUTHOR]

Background: Cellular function and diversity are orchestrated by complex interactions of fundamental biomolecules including DNA, RNA and proteins. Technological advances in genomics, epigenomics, transcriptomics and proteomics have enabled massively parallel and unbiased measurements. Such high-throughput technologies have been extensively used to carry out broad, unbiased studies, particularly in the context of human diseases. Nevertheless, a unified analysis of the genome, epigenome, transcriptome and proteome of a single human cell type to obtain a coherent view of the complex interplay between various biomolecules has not yet been undertaken. Here, we report the first multi-omic analysis of human primary naïve CD4+ T cells isolated from a single individual. Results: Integrating multi-omics datasets allowed us to investigate genome-wide methylation and its effect on mRNA/protein expression patterns, extent of RNA editing under normal physiological conditions and allele specific expression in naïve CD4+ T cells. In addition, we carried out a multi-omic comparative analysis of naïve with primary resting memory CD4+ T cells to identify molecular changes underlying T cell differentiation. This analysis provided mechanistic insights into how several molecules involved in T cell receptor signaling are regulated at the DNA, RNA and protein levels. Phosphoproteomics revealed downstream signaling events that regulate these two cellular states. Availability of multi-omics data from an identical genetic background also allowed us to employ novel proteogenomics approaches to identify individual-specific variants and putative novel protein coding regions in the human genome. Conclusions: We utilized multiple high-throughput technologies to derive a comprehensive profile of two primary human cell types, naïve CD4+ T cells and memory CD4+ T cells, from a single donor. Through vertical as well as horizontal integration of whole genome sequencing, methylation arrays, RNA-Seq, miRNA-Seq, proteomics, and phosphoproteomics, we derived an integrated and comparative map of these two closely related immune cells and identified potential molecular effectors of immune cell differentiation following antigen encounter. [ABSTRACT FROM AUTHOR]

Pancreatic cancer is the fourth leading cause of cancer-related death in the world. The etiology of pancreatic cancer is heterogeneous with a wide range of alterations that have already been reported at the level of the genome, transcriptome, and proteome. The past decade has witnessed a large number of experimental studies using high-throughput technology platforms to identify genes whose expression at the transcript or protein levels is altered in pancreatic cancer. Based on expression studies, a number of molecules have also been proposed as potential biomarkers for diagnosis and prognosis of this deadly cancer. Currently, there are no repositories which provide an integrative view of multiple Omics data sets from published research on pancreatic cancer. Here, we describe the development of a web-based resource, Pancreatic Cancer Database (http://www.pancreaticcancerdatabase.org), as a unified platform for pancreatic cancer research. PCD contains manually curated information pertaining to quantitative alterations in miRNA, mRNA, and proteins obtained from small-scale as well as high-throughput studies of pancreatic cancer tissues and cell lines. We believe that PCD will serve as an integrative platform for scientific community involved in pancreatic cancer research. [ABSTRACT FROM AUTHOR]

Background Rheumatoid arthritis and osteoarthritis are two common musculoskeletal disorders that affect the joints. Despite high prevalence rates, etiological factors involved in these disorders remain largely unknown. Dissecting the molecular aspects of these disorders will significantly contribute to improving their diagnosis and clinical management. In order to identify proteins that are differentially expressed between these two conditions, a quantitative proteomic profiling of synovial fluid obtained from rheumatoid arthritis and osteoarthritis patients was carried out by using iTRAQ labeling followed by high resolution mass spectrometry analysis. Results We have identified 575 proteins out of which 135 proteins were found to be differentially expressed by ⩾3-fold in the synovial fluid of rheumatoid arthritis and osteoarthritis patients. Proteins not previously reported to be associated with rheumatoid arthritis including, coronin- 1A (CORO1A), fibrinogen like-2 (FGL2), and macrophage capping protein (CAPG) were found to be upregulated in rheumatoid arthritis. Proteins such as CD5 molecule-like protein (CD5L), soluble scavenger receptor cysteine-rich domain-containing protein (SSC5D), and TTK protein kinase (TTK) were found to be upregulated in the synovial fluid of osteoarthritis patients. We confirmed the upregulation of CAPG in rheumatoid arthritis synovial fluid by multiple reaction monitoring assay as well as by Western blot. Pathway analysis of differentially expressed proteins revealed a significant enrichment of genes involved in glycolytic pathway in rheumatoid arthritis. Conclusions We report here the largest identification of proteins from the synovial fluid of rheumatoid arthritis and osteoarthritis patients using a quantitative proteomics approach. The novel proteins identified from our study needs to be explored further for their role in the disease pathogenesis of rheumatoid arthritis and osteoarthritis. Sartaj Ahmad and Raja Sekhar Nirujogi contributed equally to this article. [ABSTRACT FROM AUTHOR]

Introduction: Candida albicans and Candida glabrata are the two most common opportunistic pathogens which are part of the normal flora in humans. Clinical diagnosis of infection by these organisms is still largely based on culturing of these organisms. In order to identify species-specific protein expression patterns, we carried out a comparative proteomic analysis of C. albicans and C. glabrata. Methods: We used 'isobaric tag for relative and absolute quantitation' (iTRAQ) labeling of cell homogenates of C. albicans and C. glabrata followed by LC-MS/MS analysis using a quadrupole time-of-flight mass spectrometer. The MS/MS data was searched against a protein database comprised of known and predicted proteins reported from these two organisms. Subsequently, we carried out a bioinformatics analysis to group orthologous proteins across C. albicans and C. glabrata and calculated protein abundance changes between the two species. Results and Conclusions: We identified 500 proteins from these organisms, the large majority of which corresponded to predicted transcripts. A number of proteins were observed to be significantly differentially expressed between the two species including enolase (Eno1), fructose-bisphosphate aldolase (Fba1), CCT ring complex subunit (Cct2), pyruvate kinase (Cdc19), and pyruvate carboxylase (Pyc2). This study illustrates a strategy for investigating protein expression patterns across closely related organisms by combining orthology information with quantitative proteomics. [ABSTRACT FROM AUTHOR]

The article presents a comprehensive literature survey which explores the potential biochemical markers of pancreatic cancer. It cites the methodology used, including the identification of relevant datasets on overexpression of messenger ribonucleic acids (mRNA) or proteins. It details the genes found in body fluids, plasma membrane, precursor lesions and chronic pancreatitis. It also mentions the importance of validation using deoxyribonucleic acid (DNA) microarrays.