Human Proteinpedia enables sharing of human protein data

Proteomic characterization of a candidate polygenic driver of metabolism in non-small cell lung cancer

Proteome analysis revealed signatures of co-expressed upregulated metabolism proteins highly conserved between primary and non-small cell lung cancer (NSCLC) patient-derived xenograft tumors (Li et al. 2014, Nat. Communications 5:5469). The C10 signature is encoded by seven genes (ADSS, ATP2A2, CTPS1, IMPDH2, PKM2, PTGES3, SGPL1) and DNA alterations in C10-encoding genes are associated with longer survival in a subset of NSCLC. To explore the C10 signature as an oncogenic driver and address potential mechanisms of action, C10 protein expression and protein-protein interactions were determined. In independent NSCLC cohorts, the coordinated expression of C10 proteins was significant and mutations in C10 genes were associated with better outcome. Affinity purification-mass spectrometry and in vivo proximity-based biotin identification defined a C10 interactome involving 667 proteins including candidate drug targets and clusters associated with glycolysis, calcium homeostasis, and nucleotide and sphingolipid metabolism. DNA alterations in genes encoding C10 interactome components were also found to be associated with better survival. These data support the notion that the coordinated upregulation of the C10 signature impinges metabolic processes that collectively function as an oncogenic driver in NSCLC.

Circulating heart failure biomarkers beyond natriuretic peptides: review from the Biomarker Study Group of the Heart Failure Association (HFA), European Society of Cardiology (ESC)

New biomarkers are being evaluated for their ability to advance the management of patients with heart failure. Despite a large pool of interesting candidate biomarkers, besides natriuretic peptides virtually none have succeeded in being applied into the clinical setting. In this review, we examine the most promising emerging candidates for clinical assessment and management of patients with heart failure. We discuss high-sensitivity cardiac troponins (Tn), procalcitonin, novel kidney markers, soluble suppression of tumorigenicity 2 (sST2), galectin-3, growth differentiation factor-15 (GDF-15), cluster of differentiation 146 (CD146), neprilysin, adrenomedullin (ADM), and also discuss proteomics and genetic-based risk scores. We focused on guidance and assistance with daily clinical care decision-making. For each biomarker, analytical considerations are discussed, as well as performance regarding diagnosis and prognosis. Furthermore, we discuss potential implementation in clinical algorithms and in ongoing clinical trials.

Identifying intracellular signaling modules and exploring pathways associated with breast cancer recurrence

Exploring complex modularization of intracellular signal transduction pathways is critical to understanding aberrant cellular responses during disease development and drug treatment. IMPALA (Inferred Modularization of PAthway LAndscapes) integrates information from high throughput gene expression experiments and genome-scale knowledge databases to identify aberrant pathway modules, thereby providing a powerful sampling strategy to reconstruct and explore pathway landscapes. Here IMPALA identifies pathway modules associated with breast cancer recurrence and Tamoxifen resistance. Focusing on estrogen-receptor (ER) signaling, IMPALA identifies alternative pathways from gene expression data of Tamoxifen treated ER positive breast cancer patient samples. These pathways were often interconnected through cytoplasmic genes such as IRS1/2, JAK1, YWHAZ, CSNK2A1, MAPK1 and HSP90AA1 and significantly enriched with ErbB, MAPK, and JAK-STAT signaling components. Characterization of the pathway landscape revealed key modules associated with ER signaling and with cell cycle and apoptosis signaling. We validated IMPALA-identified pathway modules using data from four different breast cancer cell lines including sensitive and resistant models to Tamoxifen. Results showed that a majority of genes in cell cycle/apoptosis modules that were up-regulated in breast cancer patients with short survivals (< 5 years) were also over-expressed in drug resistant cell lines, whereas the transcription factors JUN, FOS, and STAT3 were down-regulated in both patient and drug resistant cell lines. Hence, IMPALA identified pathways were associated with Tamoxifen resistance and an increased risk of breast cancer recurrence. The IMPALA package is available at https://dlrl.ece.vt.edu/software/ .

Analysis of Protein-Protein Interaction Networks through Computational Approaches

The interactions among proteins and genes are extremely important for cellular functions. Molecular interactions at protein or gene levels can be used to construct interaction networks in which the interacting species are categorized based on direct interactions or functional similarities. Compared with the limited experimental techniques, various computational tools make it possible to analyze, filter, and combine the interaction data to get comprehensive information about the biological pathways. By the efficient way of integrating experimental findings in discovering PPIs and computational techniques for prediction, the researchers have been able to gain many valuable data on PPIs, including some advanced databases. Moreover, many useful tools and visualization programs enable the researchers to establish, annotate, and analyze biological networks. We here review and list the computational methods, databases, and tools for protein-protein interaction prediction.

The development and clinical applications of proteomics: an Indian perspective

INTRODUCTION

Proteomic research has been extensively used to identify potential biomarkers or targets for various diseases. Advances in mass spectrometry along with data analytics have led proteomics to become a powerful tool for exploring the critical molecular players associated with diseases, thereby, playing a significant role in the development of proteomic applications for the clinic.

AREAS COVERED

This review presents recent advances in the development and clinical applications of proteomics in India toward understanding various diseases including cancer, metabolic diseases, and reproductive diseases. Keywords combined with 'clinical proteomics in India' 'proteomic research in India' and 'mass spectrometry' were used to search PubMed.

EXPERT OPINION

The past decade has seen a significant increase in research in clinical proteomics in India. This approach has resulted in the development of proteomics-based marker technologies for disease management in the country. The majority of these investigations are still in the discovery phase and efforts have to be made to address the intended clinical use so that the identified potential biomarkers reach the clinic. To move toward this necessity, there is a pressing need to establish some key infrastructure requirements and meaningful collaborations between the clinicians and scientists which will enable more effective solutions to address health issues specific to India.

The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics

Skyline is a freely available, open-source Windows client application for accelerating targeted proteomics experimentation, with an emphasis on the proteomics and mass spectrometry community as users and as contributors. This review covers the informatics encompassed by the Skyline ecosystem, from computationally assisted targeted mass spectrometry method development, to raw acquisition file data processing, and quantitative analysis and results sharing.

A System-wide Approach to Monitor Responses to Synergistic BRAF and EGFR Inhibition in Colorectal Cancer Cells

Intrinsic and/or acquired resistance represents one of the great challenges in targeted cancer therapy. A deeper understanding of the molecular biology of cancer has resulted in more efficient strategies, where one or multiple drugs are adopted in novel therapies to tackle resistance. This beneficial effect of using combination treatments has also been observed in colorectal cancer patients harboring the BRAF(V600E) mutation, whereby dual inhibition of BRAF(V600E) and EGFR increases antitumor activity. Notwithstanding this success, it is not clear whether this combination treatment is the only or most effective treatment to block intrinsic resistance to BRAF inhibitors. Here, we investigate molecular responses upon single and multi-target treatments, over time, using BRAF(V600E) mutant colorectal cancer cells as a model system. Through integration of transcriptomic, proteomic and phosphoproteomics data we obtain a comprehensive overview, revealing both known and novel responses. We primarily observe widespread up-regulation of receptor tyrosine kinases and metabolic pathways upon BRAF inhibition. These findings point to mechanisms by which the drug-treated cells switch energy sources and enter a quiescent-like state as a defensive response, while additionally compensating for the MAPK pathway inhibition.

Cracking the Sugar Code by Mass Spectrometry : An Invited Perspective in Honor of Dr. Catherine E. Costello, Recipient of the 2017 ASMS Distinguished Contribution Award

The structural study of glycans and glycoconjugates is essential to assign their roles in homeostasis, health, and disease. Once dominated by nuclear magnetic resonance spectroscopy, mass spectrometric methods have become the preferred toolbox for the determination of glycan structures at high sensitivity. The patterns of such structures in different cellular states now allow us to interpret the sugar codes in health and disease, based on structure-function relationships. Dr. Catherine E. Costello was the 2017 recipient of the American Society for Mass Spectrometry's Distinguished Contribution Award. In this Perspective article, we describe her seminal work in a historical and geographical context and review the impact of her research accomplishments in the field.8 ᅟ Graphical abstract.

Understanding Leishmania parasites through proteomics and implications for the clinic

INTRODUCTION

Leishmania spp. are causative agents of leishmaniasis, a broad-spectrum neglected vector-borne disease. Genomic and transcriptional studies are not capable of solving intricate biological mysteries, leading to the emergence of proteomics, which can provide insights into the field of parasite biology and its interactions with the host. Areas covered: The combination of genomics and informatics with high throughput proteomics may improve our understanding of parasite biology and pathogenesis. This review analyses the roles of diverse proteomic technologies that facilitate our understanding of global protein profiles and definition of parasite development, survival, virulence and drug resistance mechanisms for disease intervention. Additionally, recent innovations in proteomics have provided insights concerning the drawbacks associated with conventional chemotherapeutic approaches and Leishmania biology, host-parasite interactions and the development of new therapeutic approaches. Expert commentary: With progressive breakthroughs in the foreseeable future, proteome profiles could provide target molecules for vaccine development and therapeutic intervention. Furthermore, proteomics, in combination with genomics and informatics, could facilitate the elimination of several diseases. Taken together, this review provides an outlook on developments in Leishmania proteomics and their clinical implications.

The physical characteristics of human proteins in different biological functions

The physical properties of gene products are the foundation of their biological functions. In this study, we systematically explored relationships between physical properties and biological functions. The physical properties including origin time, evolution pressure, mRNA and protein stability, molecular weight, hydrophobicity, acidity/alkaline, amino acid compositions, and chromosome location. The biological functions are defined from 4 aspects: biological process, molecular function, cellular component and cell/tissue/organ expression. We found that the proteins associated with basic material and energy metabolism process originated earlier, while the proteins associated with immune, neurological system process etc. originated later. Tissues may have a strong influence on evolution pressure. The proteins associated with energy metabolism are double-stable. Immune and peripheral cell proteins tend to be mRNA stable/protein unstable. There are very few function items with double-unstable of mRNA and protein. The proteins involved in the cell adhesion tend to consist of large proteins with high proportion of small amino acids. The proteins of organic acid transport, neurological system process and amine transport have significantly high hydrophobicity. Interestingly, the proteins involved in olfactory receptor activity tend to have high frequency of aromatic, sulfuric and hydroxyl amino acids.

Proteomic Data Storage and Sharing

With the advent of high-throughput genomic and proteomic techniques, there is a massive amount of multidimensional data being generated and has increased several orders of magnitude. But the amount of data that is cataloged in the central repositories and shared publicly with the scientific community does not correlate the same rate at which the data is generated. Here, in this chapter, we discuss various proteomics data repositories that are freely accessible to the researchers for further downstream meta-analysis.

Proteomics in India: the clinical aspect

Proteomics has emerged as a highly promising bioanalytical technique in various aspects of applied biological research. In Indian academia, proteomics research has grown remarkably over the last decade. It is being extensively used for both basic as well as translation research in the areas of infectious and immune disorders, reproductive disorders, cardiovascular diseases, diabetes, eye disorders, human cancers and hematological disorders. Recently, some seminal works on clinical proteomics have been reported from several laboratories across India. This review aims to shed light on the increasing use of proteomics in India in a variety of biological conditions. It also highlights that India has the expertise and infrastructure needed for pursuing proteomics research in the country and to participate in global initiatives. Research in clinical proteomics is gradually picking up pace in India and its future seems very bright.

A dual specificity kinase, DYRK1A, as a potential therapeutic target for head and neck squamous cell carcinoma

Despite advances in clinical management, 5-year survival rate in patients with late-stage head and neck squamous cell carcinoma (HNSCC) has not improved significantly over the past decade. Targeted therapies have emerged as one of the most promising approaches to treat several malignancies. Though tyrosine phosphorylation accounts for a minority of total phosphorylation, it is critical for activation of signaling pathways and plays a significant role in driving cancers. To identify activated tyrosine kinase signaling pathways in HNSCC, we compared the phosphotyrosine profiles of a panel of HNSCC cell lines to a normal oral keratinocyte cell line. Dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 1A (DYRK1A) was one of the kinases hyperphosphorylated at Tyr-321 in all HNSCC cell lines. Inhibition of DYRK1A resulted in an increased apoptosis and decrease in invasion and colony formation ability of HNSCC cell lines. Further, administration of the small molecular inhibitor against DYRK1A in mice bearing HNSCC xenograft tumors induced regression of tumor growth. Immunohistochemical labeling of DYRK1A in primary tumor tissues using tissue microarrays revealed strong to moderate staining of DYRK1A in 97.5% (39/40) of HNSCC tissues analyzed. Taken together our results suggest that DYRK1A could be a novel therapeutic target in HNSCC.

Integrative analysis of mutational and transcriptional profiles reveals driver mutations of metastatic breast cancers

Despite the explosion in the numbers of cancer genomic studies, metastasis is still the major cause of cancer mortality. In breast cancer, approximately one-fifth of metastatic patients survive 5 years. Therefore, detecting the patients at a high risk of developing distant metastasis at first diagnosis is critical for effective treatment strategy. We hereby present a novel systems biology approach to identify driver mutations escalating the risk of metastasis based on both exome and RNA sequencing of our collected 78 normal-paired breast cancers. Unlike driver mutations occurring commonly in cancers as reported in the literature, the mutations detected here are relatively rare mutations occurring in less than half metastatic samples. By supposing that the driver mutations should affect the metastasis gene signatures, we develop a novel computational pipeline to identify the driver mutations that affect transcription factors regulating metastasis gene signatures. We identify driver mutations in ADPGK, NUP93, PCGF6, PKP2 and SLC22A5, which are verified to enhance cancer cell migration and prompt metastasis with in vitro experiments. The discovered somatic mutations may be helpful for identifying patients who are likely to develop distant metastasis.

Characterization of host response to Cryptococcus neoformans through quantitative proteomic analysis of cryptococcal meningitis co-infected with HIV

Cryptococcal meningitis is the most common opportunistic fungal infection causing morbidity and mortality (>60%) in HIV-associated immunocompromised individuals caused by Cryptococcus neoformans. Molecular mechanisms of cryptococcal infection in brain have been studied using experimental animal models and cell lines. There are limited studies for the molecular understanding of cryptococcal meningitis in human brain. The proteins involved in the process of invasion and infection in human brain still remains obscure. To this end we carried out mass spectrometry-based quantitative proteomics of frontal lobe brain tissues from cryptococcal meningitis patients and controls to identify host proteins that are associated with the pathogenesis of cryptococcal meningitis. We identified 317 proteins to be differentially expressed (≥2-fold) from a total of 3423 human proteins. We found proteins involved in immune response and signal transduction to be differentially expressed in response to cryptococcal infection in human brain. Immune response proteins including complement factors, major histocompatibility proteins, proteins previously known to be involved in fungal invasion to brain such as caveolin 1 and actin were identified to be differentially expressed in cryptococcal meningitis brain tissues co-infected with HIV. We also validated the expression status of 5 proteins using immunohistochemistry. Overexpression of major histocompatibility complexes, class I, B (HLA-B), actin alpha 2 smooth muscle aorta (ACTA2) and caveolin 1 (CAV1) and downregulation of peripheral myelin protein 2 (PMP2) and alpha crystallin B chain (CRYAB) in cryptococcal meningitis were confirmed by IHC-based validation experiments. This study provides the brain proteome profile of cryptococcal meningitis co-infected with HIV for a better understanding of the host response associated with the disease.

Designing biomedical proteomics experiments: state-of-the-art and future perspectives

With the current expanded technical capabilities to perform mass spectrometry-based biomedical proteomics experiments, an improved focus on the design of experiments is crucial. As it is clear that ignoring the importance of a good design leads to an unprecedented rate of false discoveries which would poison our results, more and more tools are developed to help researchers designing proteomic experiments. In this review, we apply statistical thinking to go through the entire proteomics workflow for biomarker discovery and validation and relate the considerations that should be made at the level of hypothesis building, technology selection, experimental design and the optimization of the experimental parameters.

Calcium calmodulin dependent kinase kinase 2 - a novel therapeutic target for gastric adenocarcinoma

Gastric cancer is one of the most common gastrointestinal malignancies and is associated with poor prognosis. Exploring alterations in the proteomic landscape of gastric cancer is likely to provide potential biomarkers for early detection and molecules for targeted therapeutic intervention. Using iTRAQ-based quantitative proteomic analysis, we identified 22 proteins that were overexpressed and 17 proteins that were downregulated in gastric tumor tissues as compared to the adjacent normal tissue. Calcium/calmodulin-dependent protein kinase kinase 2 (CAMKK2) was found to be 7-fold overexpressed in gastric tumor tissues. Immunohistochemical labeling of tumor tissue microarrays for validation of CAMKK2 overexpression revealed that it was indeed overexpressed in 94% (92 of 98) of gastric cancer cases. Silencing of CAMKK2 using siRNA significantly reduced cell proliferation, colony formation and invasion of gastric cancer cells. Our results demonstrate that CAMKK2 signals in gastric cancer through AMPK activation and suggest that CAMKK2 could be a novel therapeutic target in gastric cancer.

Interpreting functional effects of coding variants: challenges in proteome-scale prediction, annotation and assessment

Accurate assessment of genetic variation in human DNA sequencing studies remains a nontrivial challenge in clinical genomics and genome informatics. Ascribing functional roles and/or clinical significances to single nucleotide variants identified from a next-generation sequencing study is an important step in genome interpretation. Experimental characterization of all the observed functional variants is yet impractical; thus, the prediction of functional and/or regulatory impacts of the various mutations using in silico approaches is an important step toward the identification of functionally significant or clinically actionable variants. The relationships between genotypes and the expressed phenotypes are multilayered and biologically complex; such relationships present numerous challenges and at the same time offer various opportunities for the design of in silico variant assessment strategies. Over the past decade, many bioinformatics algorithms have been developed to predict functional consequences of single nucleotide variants in the protein coding regions. In this review, we provide an overview of the bioinformatics resources for the prediction, annotation and visualization of coding single nucleotide variants. We discuss the currently available approaches and major challenges from the perspective of protein sequence, structure, function and interactions that require consideration when interpreting the impact of putatively functional variants. We also discuss the relevance of incorporating integrated workflows for predicting the biomedical impact of the functionally important variations encoded in a genome, exome or transcriptome. Finally, we propose a framework to classify variant assessment approaches and strategies for incorporation of variant assessment within electronic health records.

Colorectal cancer atlas: An integrative resource for genomic and proteomic annotations from colorectal cancer cell lines and tissues

In order to advance our understanding of colorectal cancer (CRC) development and progression, biomedical researchers have generated large amounts of OMICS data from CRC patient samples and representative cell lines. However, these data are deposited in various repositories or in supplementary tables. A database which integrates data from heterogeneous resources and enables analysis of the multidimensional data sets, specifically pertaining to CRC is currently lacking. Here, we have developed Colorectal Cancer Atlas (http://www.colonatlas.org), an integrated web-based resource that catalogues the genomic and proteomic annotations identified in CRC tissues and cell lines. The data catalogued to-date include sequence variations as well as quantitative and non-quantitative protein expression data. The database enables the analysis of these data in the context of signaling pathways, protein–protein interactions, Gene Ontology terms, protein domains and post-translational modifications. Currently, Colorectal Cancer Atlas contains data for >13 711 CRC tissues, >165 CRC cell lines, 62 251 protein identifications, >8.3 million MS/MS spectra, >18 410 genes with sequence variations (404 278 entries) and 351 pathways with sequence variants. Overall, Colorectal Cancer Atlas has been designed to serve as a central resource to facilitate research in CRC.

Silencing of high-mobility group box 2 (HMGB2) modulates cisplatin and 5-fluorouracil sensitivity in head and neck squamous cell carcinoma

Dysregulation of protein expression is associated with most diseases including cancer. MS-based proteomic analysis is widely employed as a tool to study protein dysregulation in cancers. Proteins that are differentially expressed in head and neck squamous cell carcinoma (HNSCC) cell lines compared to the normal oral cell line could serve as biomarkers for patient stratification. To understand the proteomic complexity in HNSCC, we carried out iTRAQ-based MS analysis on a panel of HNSCC cell lines in addition to a normal oral keratinocyte cell line. LC-MS/MS analysis of total proteome of the HNSCC cell lines led to the identification of 3263 proteins, of which 185 proteins were overexpressed and 190 proteins were downregulated more than twofold in at least two of the three HNSCC cell lines studied. Among the overexpressed proteins, 23 proteins were related to DNA replication and repair. These included high-mobility group box 2 (HMGB2) protein, which was overexpressed in all three HNSCC lines studied. Overexpression of HMGB2 has been reported in various cancers, yet its role in HNSCC remains unclear. Immunohistochemical labeling of HMGB2 in a panel of HNSCC tumors using tissue microarrays revealed overexpression in 77% (54 of 70) of tumors. The HMGB proteins are known to bind to DNA structure resulting from cisplatin-DNA adducts and affect the chemosensitivity of cells. We observed that siRNA-mediated silencing of HMGB2 increased the sensitivity of the HNSCC cell lines to cisplatin and 5-FU. We hypothesize that targeting HMGB2 could enhance the efficacy of existing chemotherapeutic regimens for treatment of HNSCC. All MS data have been deposited in the ProteomeXchange with identifier PXD000737 (http://proteomecentral.proteomexchange.org/dataset/PXD000737).

Meta-Analysis of Large-Scale Toxicogenomic Data Finds Neuronal Regeneration Related Protein and Cathepsin D to Be Novel Biomarkers of Drug-Induced Toxicity

Undesirable toxicity is one of the main reasons for withdrawing drugs from the market or eliminating them as candidates in clinical trials. Although numerous studies have attempted to identify biomarkers capable of predicting pharmacotoxicity, few have attempted to discover robust biomarkers that are coherent across various species and experimental settings. To identify such biomarkers, we conducted meta-analyses of massive gene expression profiles for 6,567 in vivo rat samples and 453 compounds. After applying rigorous feature reduction procedures, our analyses identified 18 genes to be related with toxicity upon comparisons of untreated versus treated and innocuous versus toxic specimens of kidney, liver and heart tissue. We then independently validated these genes in human cell lines. In doing so, we found several of these genes to be coherently regulated in both in vivo rat specimens and in human cell lines. Specifically, mRNA expression of neuronal regeneration-related protein was robustly down-regulated in both liver and kidney cells, while mRNA expression of cathepsin D was commonly up-regulated in liver cells after exposure to toxic concentrations of chemical compounds. Use of these novel toxicity biomarkers may enhance the efficiency of screening for safe lead compounds in early-phase drug development prior to animal testing.

MI-PVT: A Tool for Visualizing the Chromosome-Centric Human Proteome

We have developed the web-based Michigan Proteome Visualization Tool (MI-PVT) to visualize and compare protein expression and isoform-level function across human chromosomes and tissues (http://guanlab.ccmb.med.umich.edu/mipvt). As proof of principle, we have populated the tool with Human Proteome Map (HPM) data. We were able to observe many biologically interesting features. From the vantage point of our chromosome 17 team, for example, we found more than 300 proteins from chromosome 17 expressed in each of the 30 tissues and cell types studied, with the highest number of expressed proteins being 685 in testis. Comparisons of expression levels across tissues showed low numbers of proteins expressed in esophagus, but esophagus had 12 cytoskeletal proteins coded on chromosome 17 with very high expression (>1000 spectral counts). This customized MI-PVT should be helpful for biologists to browse and study specific proteins and protein data sets across tissues and chromosomes. Users can upload any data of interest in MI-PVT for visualization. Our aim is to integrate extensive mass-spectrometric proteomic data into the tool to facilitate finding chromosome-centric protein expression and correlation across tissues.

Targeted proteomics for biomarker discovery and validation of hepatocellular carcinoma in hepatitis C infected patients

Hepatocellular carcinoma (HCC)-related mortality is high because early detection modalities are hampered by inaccuracy, expense and inherent procedural risks. Thus there is an urgent need for minimally invasive, highly specific and sensitive biomarkers that enable early disease detection when therapeutic intervention remains practical. Successful therapeutic intervention is predicated on the ability to detect the cancer early. Similar unmet medical needs abound in most fields of medicine and require novel methodological approaches. Proteomic profiling of body fluids presents a sensitive diagnostic tool for early cancer detection. Here we describe such a strategy of comparative proteomics to identify potential serum-based biomarkers to distinguish high-risk chronic hepatitis C virus infected patients from HCC patients. In order to compensate for the extraordinary dynamic range in serum proteins, enrichment methods that compress the dynamic range without surrendering proteome complexity can help minimize the problems associated with many depletion methods. The enriched serum can be resolved using 2D-difference in-gel electrophoresis and the spots showing statistically significant changes selected for identification by liquid chromatography-tandem mass spectrometry. Subsequent quantitative verification and validation of these candidate biomarkers represent an obligatory and rate-limiting process that is greatly enabled by selected reaction monitoring (SRM). SRM is a tandem mass spectrometry method suitable for identification and quantitation of target peptides within complex mixtures independent on peptide-specific antibodies. Ultimately, multiplexed SRM and dynamic multiple reaction monitoring can be utilized for the simultaneous analysis of a biomarker panel derived from support vector machine learning approaches, which allows monitoring a specific disease state such as early HCC. Overall, this approach yields high probability biomarkers for clinical validation in large patient cohorts and represents a strategy extensible to many diseases.

Proteomics research in India: an update

After a successful completion of the Human Genome Project, deciphering the mystery surrounding the human proteome posed a major challenge. Despite not being largely involved in the Human Genome Project, the Indian scientific community contributed towards proteomic research along with the global community. Currently, more than 76 research/academic institutes and nearly 145 research labs are involved in core proteomic research across India. The Indian researchers have been major contributors in drafting the "human proteome map" along with international efforts. In addition to this, virtual proteomics labs, proteomics courses and remote triggered proteomics labs have helped to overcome the limitations of proteomics education posed due to expensive lab infrastructure. The establishment of Proteomics Society, India (PSI) has created a platform for the Indian proteomic researchers to share ideas, research collaborations and conduct annual conferences and workshops. Indian proteomic research is really moving forward with the global proteomics community in a quest to solve the mysteries of proteomics. A draft map of the human proteome enhances the enthusiasm among intellectuals to promote proteomic research in India to the world.This article is part of a Special Issue entitled: Proteomics in India.

The molecular genetic architecture of attention deficit hyperactivity disorder

Attention deficit hyperactivity disorder (ADHD) is a common childhood behavioral condition which affects 2-10% of school age children worldwide. Although the underlying molecular mechanism for the disorder is poorly understood, familial, twin and adoption studies suggest a strong genetic component. Here we provide a state-of-the-art review of the molecular genetics of ADHD incorporating evidence from candidate gene and linkage designs, as well as genome-wide association (GWA) studies of common single-nucleotide polymorphisms (SNPs) and rare copy number variations (CNVs). Bioinformatic methods such as functional enrichment analysis and protein-protein network analysis are used to highlight biological processes of likely relevance to the aetiology of ADHD. Candidate gene associations of minor effect size have been replicated across a number of genes including SLC6A3, DRD5, DRD4, SLC6A4, LPHN3, SNAP-25, HTR1B, NOS1 and GIT1. Although case-control SNP-GWAS have had limited success in identifying common genetic variants for ADHD that surpass critical significance thresholds, quantitative trait designs suggest promising associations with Cadherin13 and glucose-fructose oxidoreductase domain 1 genes. Further, CNVs mapped to glutamate receptor genes (GRM1, GRM5, GRM7 and GRM8) have been implicated in the aetiology of the disorder and overlap with bioinformatic predictions based on ADHD GWAS SNP data regarding enriched pathways. Although increases in sample size across multi-center cohorts will likely yield important new results, we advocate that this must occur in parallel with a shift away from categorical case-control approaches that view ADHD as a unitary construct, towards dimensional approaches that incorporate endophenotypes and statistical classification methods.

Coupling enrichment methods with proteomics for understanding and treating disease

Owing to recent advances in proteomics analytical methods and bioinformatics capabilities there is a growing trend toward using these capabilities for the development of drugs to treat human disease, including target and drug evaluation, understanding mechanisms of drug action, and biomarker discovery. Currently, the genetic sequences of many major organisms are available, which have helped greatly in characterizing proteomes in model animal systems and humans. Through proteomics, global profiles of different disease states can be characterized (e.g. changes in types and relative levels as well as changes in PTMs such as glycosylation or phosphorylation). Although intracellular proteomics can provide a broad overview of physiology of cells and tissues, it has been difficult to quantify the low abundance proteins which can be important for understanding the diseased states and treatment progression. For this reason, there is increasing interest in coupling comparative proteomics methods with subcellular fractionation and enrichment techniques for membranes, nucleus, phosphoproteome, glycoproteome as well as low abundance serum proteins. In this review, we will provide examples of where the utilization of different proteomics-coupled enrichment techniques has aided target and biomarker discovery, understanding the drug targeting mechanism, and mAb discovery. Taken together, these improvements will help to provide a better understanding of the pathophysiology of various diseases including cancer, autoimmunity, inflammation, cardiovascular disease, and neurological conditions, and in the design and development of better medicines for treating these afflictions.

Downregulation of S100 Calcium Binding Protein A9 in Esophageal Squamous Cell Carcinoma

The development of esophageal squamous cell carcinoma (ESCC) is poorly understood and the major regulatory molecules involved in the process of tumorigenesis have not yet been identified. We had previously employed a quantitative proteomic approach to identify differentially expressed proteins in ESCC tumors. A total of 238 differentially expressed proteins were identified in that study including S100 calcium binding protein A9 (S100A9) as one of the major downregulated proteins. In the present study, we carried out immunohistochemical validation of S100A9 in a large cohort of ESCC patients to determine the expression and subcellular localization of S100A9 in tumors and adjacent normal esophageal epithelia. Downregulation of S100A9 was observed in 67% (n = 192) of 288 different ESCC tumors, with the most dramatic downregulation observed in the poorly differentiated tumors (99/111). Expression of S100A9 was restricted to the prickle and functional layers of normal esophageal mucosa and localized predominantly in the cytoplasm and nucleus whereas virtually no expression was observed in the tumor and stromal cells. This suggests the important role that S100A9 plays in maintaining the differentiated state of epithelium and suggests that its downregulation may be associated with increased susceptibility to tumor formation.

A global view of network of lncRNAs and their binding proteins

Recently, the long non-coding RNAs (lncRNAs) have obtained wide attention because they have broad and crucial functions in regulating complex biological processes. Many lncRNAs functioned by interfacing with corresponding RNA binding proteins and the complexity of lncRNAs' function was attributed to multiple lncRNA-protein interactions. To gain insights into the global relationship between lncRNAs and their binding proteins, here we constructed a lncRNA-protein network (LPN) based on experimentally determined functional interactions between them. This network included 177 lncRNAs, 92 proteins and 683 relationships between them. Cluster analysis of LPN revealed that some proteins (such as AGO and IGFBP families) and lncRNA (such as XIST and MALAT1) were densely connected, suggesting the potential co-regulated mechanism and functional cross-talk of different lncRNAs. We then characterized the lncRNA functions and found that lncRNA binding proteins (LBPs) enriched in many cancer or cancer-related pathways. Finally, we investigated the different topological properties of LBPs in PPIs network. Compared with disease proteins and average ones, LBPs tend to have significantly higher degree, betweenness, and closeness but a relatively lower clustering coefficient, indicating their centrality and essentiality in the context of a biological network.

Proteomic analysis of human vitreous humor

Background

The vitreous humor is a transparent, gelatinous mass whose main constituent is water. It plays an important role in providing metabolic nutrient requirements of the lens, coordinating eye growth and providing support to the retina. It is in close proximity to the retina and reflects many of the changes occurring in this tissue. The biochemical changes occurring in the vitreous could provide a better understanding about the pathophysiological processes that occur in vitreoretinopathy. In this study, we investigated the proteome of normal human vitreous humor using high resolution Fourier transform mass spectrometry.

Results

The vitreous humor was subjected to multiple fractionation techniques followed by LC-MS/MS analysis. We identified 1,205 proteins, 682 of which have not been described previously in the vitreous humor. Most proteins were localized to the extracellular space (24%), cytoplasm (20%) or plasma membrane (14%). Classification based on molecular function showed that 27% had catalytic activity, 10% structural activity, 10% binding activity, 4% cell and 4% transporter activity. Categorization for biological processes showed 28% participate in metabolism, 20% in cell communication and 13% in cell growth. The data have been deposited to the ProteomeXchange with identifier PXD000957.

Conclusion

This large catalog of vitreous proteins should facilitate biomedical research into pathological conditions of the eye including diabetic retinopathy, retinal detachment and cataract.

Neurodevelopmental and neuropsychiatric disorders represent an interconnected molecular system

Many putative genetic factors that confer risk to neurodevelopmental disorders such as autism spectrum disorders (ASDs) and X-linked intellectual disability (XLID), and to neuropsychiatric disorders including attention deficit hyperactivity disorder (ADHD) and schizophrenia (SZ) have been identified in individuals from diverse human populations. Although there is significant aetiological heterogeneity within and between these conditions, recent data show that genetic factors contribute to their comorbidity. Many studies have identified candidate gene associations for these mental health disorders, albeit this is often done in a piecemeal fashion with little regard to the inherent molecular complexity. Here, we sought to abstract relationships from our knowledge of systems level biology to help understand the unique and common genetic drivers of these conditions. We undertook a global and systematic approach to build and integrate available data in gene networks associated with ASDs, XLID, ADHD and SZ. Complex network concepts and computational methods were used to investigate whether candidate genes associated with these conditions were related through mechanisms of gene regulation, functional protein-protein interactions, transcription factor (TF) and microRNA (miRNA) binding sites. Although our analyses show that genetic variations associated with the four disorders can occur in the same molecular pathways and functional domains, including synaptic transmission, there are patterns of variation that define significant differences between disorders. Of particular interest is DNA variations located in intergenic regions that comprise regulatory sites for TFs or miRNA. Our approach provides a hypothetical framework, which will help discovery and analysis of candidate genes associated with neurodevelopmental and neuropsychiatric disorders.

PPI layouts: BioJS components for the display of Protein-Protein Interactions

SUMMARY

We present two web-based components for the display of Protein-Protein Interaction networks using different self-organizing layout methods: force-directed and circular. These components conform to the BioJS standard and can be rendered in an HTML5-compliant browser without the need for third-party plugins. We provide examples of interaction networks and how the components can be used to visualize them, and refer to a more complex tool that uses these components.

AVAILABILITY

http://github.com/biojs/biojs; http://dx.doi.org/10.5281/zenodo.7753.

Integrating in silico resources to map a signaling network

The abundance of publicly available life science databases offers a wealth of information that can support interpretation of experimentally derived data and greatly enhance hypothesis generation. Protein interaction and functional networks are not simply new renditions of existing data: they provide the opportunity to gain insights into the specific physical and functional role a protein plays as part of the biological system. In this chapter, we describe different in silico tools that can quickly and conveniently retrieve data from existing data repositories and we discuss how the available tools are best utilized for different purposes. While emphasizing protein-protein interaction databases (e.g., BioGrid and IntAct), we also introduce metasearch platforms such as STRING and GeneMANIA, pathway databases (e.g., BioCarta and Pathway Commons), text mining approaches (e.g., PubMed and Chilibot), and resources for drug-protein interactions, genetic information for model organisms and gene expression information based on microarray data mining. Furthermore, we provide a simple step-by-step protocol for building customized protein-protein interaction networks in Cytoscape, a powerful network assembly and visualization program, integrating data retrieved from these various databases. As we illustrate, generation of composite interaction networks enables investigators to extract significantly more information about a given biological system than utilization of a single database or sole reliance on primary literature.

Mass spectrometric protein maps for biomarker discovery and clinical research

Among the wide range of proteomic technologies, targeted mass spectrometry (MS) has shown great potential for biomarker studies. To extend the degree of multiplexing achieved by selected reaction monitoring (SRM), we recently developed SWATH MS. SWATH MS is a variant of the emerging class of data-independent acquisition (DIA) methods and essentially converts the molecules in a physical sample into perpetually re-usable digital maps. The thus generated SWATH maps are then mined using a targeted data extraction strategy, allowing us to profile disease-related proteomes at a high degree of reproducibility. The successful application of both SRM and SWATH MS requires the a priori generation of reference spectral maps that provide coordinates for quantification. Herein, we demonstrate that the application of the mass spectrometric reference maps and the acquisition of personalized SWATH maps hold a particular promise for accelerating the current process of biomarker discovery.

Characterizing the normal proteome of human ciliary body

BACKGROUND

The ciliary body is the circumferential muscular tissue located just behind the iris in the anterior chamber of the eye. It plays a pivotal role in the production of aqueous humor, maintenance of the lens zonules and accommodation by changing the shape of the crystalline lens. The ciliary body is the major target of drugs against glaucoma as its inhibition leads to a drop in intraocular pressure. A molecular study of the ciliary body could provide a better understanding about the pathophysiological processes that occur in glaucoma. Thus far, no large-scale proteomic investigation has been reported for the human ciliary body.

RESULTS

In this study, we have carried out an in-depth LC-MS/MS-based proteomic analysis of normal human ciliary body and have identified 2,815 proteins. We identified a number of proteins that were previously not described in the ciliary body including importin 5 (IPO5), atlastin-2 (ATL2), B-cell receptor associated protein 29 (BCAP29), basigin (BSG), calpain-1 (CAPN1), copine 6 (CPNE6), fibulin 1 (FBLN1) and galectin 1 (LGALS1). We compared the plasma proteome with the ciliary body proteome and found that the large majority of proteins in the ciliary body were also detectable in the plasma while 896 proteins were unique to the ciliary body. We also classified proteins using pathway enrichment analysis and found most of proteins associated with ubiquitin pathway, EIF2 signaling, glycolysis and gluconeogenesis.

CONCLUSIONS

More than 95% of the identified proteins have not been previously described in the ciliary body proteome. This is the largest catalogue of proteins reported thus far in the ciliary body that should provide new insights into our understanding of the factors involved in maintaining the secretion of aqueous humor. The identification of these proteins will aid in understanding various eye diseases of the anterior segment such as glaucoma and presbyopia.

SILAC-based quantitative proteomic analysis of gastric cancer secretome

PURPOSE

Gastric cancer is a commonly occurring cancer in Asia and one of the leading causes of cancer deaths. However, there is no reliable blood-based screening test for this cancer. Identifying proteins secreted from tumor cells could lead to the discovery of clinically useful biomarkers for early detection of gastric cancer.

EXPERIMENTAL DESIGN

A SILAC-based quantitative proteomic approach was employed to identify secreted proteins that were differentially expressed between neoplastic and non-neoplastic gastric epithelial cells. Proteins from the secretome were subjected to SDS-PAGE and SCX-based fractionation, followed by mass spectrometric analysis on an LTQ-Orbitrap Velos mass spectrometer. Immunohistochemical labeling was employed to validate a subset of candidates using tissue microarrays.

RESULTS

We identified 2205 proteins in the gastric cancer secretome of which 263 proteins were overexpressed greater than fourfold in gastric cancer-derived cell lines as compared to non-neoplastic gastric epithelial cells. Three candidate proteins, proprotein convertase subtilisin/kexin type 9 (PCSK9), lectin mannose binding 2 (LMAN2), and PDGFA-associated protein 1 (PDAP1) were validated by immunohistochemical labeling.

CONCLUSIONS AND CLINICAL RELEVANCE

We report here the largest cancer secretome described to date. The novel biomarkers identified in the current study are excellent candidates for further testing as early detection biomarkers for gastric adenocarcinoma.

A call for a fresh new look at the plasma proteome

Serum and plasma from which serum is derived represent a substantial challenge for proteomics due to their complexity. A landmark plasma proteome study was initiated a decade ago by the Human Proteome Organization (HUPO) that had as an objective to examine the capabilities of existing technologies. Given the advances in proteomics and the continued interest in the plasma proteome, it would timely reassess the depth and breadth of analysis of plasma that can be achieved with current methodology and instrumentation. A collaborative project to define the plasma proteome and its variation, with a plan to build a plasma proteome database would be timely.

Access guide to human proteinpedia

Human Proteinpedia (http://www.humanproteinpedia.org) is a publicly available proteome repository for sharing human protein data derived from multiple experimental platforms. It incorporates diverse features of the human proteome including protein-protein interactions, enzyme-substrate relationships, PTMs, subcellular localization, and expression of proteins in various human tissues and cell lines in diverse biological conditions including diseases. Through a publicly distributed annotation system developed especially for proteomic data, investigators across the globe can upload, view, and edit proteomic data even before they are published. Inclusion of information on investigators and laboratories that generated the data, as well as visualization of tandem mass spectra, stained tissue sections, protein/peptide microarrays, fluorescent micrographs, and western blots, ensures quality of proteomic data assimilated in Human Proteinpedia. Many of the protein annotations submitted to Human Proteinpedia have also been made available to the scientific community through Human Protein Reference Database (http://www.hprd.org), another resource developed by our group. In this protocol, we describe how to submit, edit, and retrieve proteomic data in Human Proteinpedia.

Downregulation of cornulin in esophageal squamous cell carcinoma

Early events in the development of esophageal squamous cell carcinoma (ESCC) are poorly understood and many of the key molecules involved have not yet been identified. We previously used isobaric tags for a relative and absolute quantitation (iTRAQ)-based quantitative proteomics approach to identify differentially expressed proteins in ESCC tissue as compared to the adjacent normal mucosa. Cornulin was identified as one of the major downregulated molecules in ESCC. Cornulin is a member of the S100 fused-type protein family, which has an EF-hand calcium binding motif and multiple tandem repeats of specific peptide motifs. Cornulin was 5-fold downregulated in ESCC as compared to normal epithelium mirroring our previous findings in a gene expression study of ESCC. In the present study, we performed immunohistochemical validation of cornulin (CRNN) in a larger set of patients with ESCC. Downregulation of cornulin was observed in 89% (n=239) of 266 different ESCC tissues arrayed on tissue microarrays (TMAs). Expression of cornulin was observed in the prickle and functional cell layers of normal esophageal mucosa, localized predominantly in the cytoplasm and perinuclear region. The large majority of ESCC cases had little or no expression of cornulin in the carcinoma or stroma. These findings suggest that cornulin is an important molecule in normal esophageal pathology and is likely lost during the conversion of normal to neoplastic epithelium.

Construction of human activity-based phosphorylation networks

The landscape of human phosphorylation networks has not been systematically explored, representing vast, unchartered territories within cellular signaling networks. Although a large number of in vivo phosphorylated residues have been identified by mass spectrometry (MS)-based approaches, assigning the upstream kinases to these residues requires biochemical analysis of kinase-substrate relationships (KSRs). Here, we developed a new strategy, called CEASAR, based on functional protein microarrays and bioinformatics to experimentally identify substrates for 289 unique kinases, resulting in 3656 high-quality KSRs. We then generated consensus phosphorylation motifs for each of the kinases and integrated this information, along with information about in vivo phosphorylation sites determined by MS, to construct a high-resolution map of phosphorylation networks that connects 230 kinases to 2591 in vivo phosphorylation sites in 652 substrates. The value of this data set is demonstrated through the discovery of a new role for PKA downstream of Btk (Bruton's tyrosine kinase) during B-cell receptor signaling. Overall, these studies provide global insights into kinase-mediated signaling pathways and promise to advance our understanding of cellular signaling processes in humans.

Vesiclepedia: a compendium for extracellular vesicles with continuous community annotation

Extracellular vesicles (EVs) are membraneous vesicles released by a variety of cells into their microenvironment. Recent studies have elucidated the role of EVs in intercellular communication, pathogenesis, drug, vaccine and gene-vector delivery, and as possible reservoirs of biomarkers. These findings have generated immense interest, along with an exponential increase in molecular data pertaining to EVs. Here, we describe Vesiclepedia, a manually curated compendium of molecular data (lipid, RNA, and protein) identified in different classes of EVs from more than 300 independent studies published over the past several years. Even though databases are indispensable resources for the scientific community, recent studies have shown that more than 50% of the databases are not regularly updated. In addition, more than 20% of the database links are inactive. To prevent such database and link decay, we have initiated a continuous community annotation project with the active involvement of EV researchers. The EV research community can set a gold standard in data sharing with Vesiclepedia, which could evolve as a primary resource for the field.

Proteomics analysis of tumor microenvironment: Implications of metabolic and oxidative stresses in tumorigenesis

Tumorigenesis is always concomitant with microenvironmental alterations. The tumor microenvironment is a heterogeneous and complex milieu, which exerts a variety of stresses on tumor cells for proliferation, survival, or death. Recently, accumulated evidence revealed that metabolic and oxidative stresses both play significant roles in tumor development and progression that converge on a common autophagic pathway. Tumor cells display increased metabolic autonomy, and the hallmark is the exploitation of aerobic glycolysis (termed Warburg effect), which increased glucose consumption and decreased oxidative phosphorylation to support growth and proliferation. This characteristic renders cancer cells more aggressive; they devour tremendous amounts of nutrients from microenvironment to result in an ever-growing appetite for new tumor vessel formation and the release of more "waste," including key determinants of cell fate like lactate and reactive oxygen species (ROS). The intracellular ROS level of cancer cells can also be modulated by a variety of stimuli in the tumor microenvironment, such as pro-growth and pro-inflammatory factors. The intracellular redox state serves as a double-edged sword in tumor development and progression: ROS overproduction results in cytotoxic effects and might lead to apoptotic cell death, whereas certain level of ROS can act as a second-messenger for regulation of such cellular processes as cell survival, proliferation, and metastasis. The molecular mechanisms for cancer cell responses to metabolic and oxidative stresses are complex and are likely to involve multiple molecules or signaling pathways. In addition, the expression and modification of these proteins after metabolic or oxidative stress challenge are diverse in different cancer cells and endow them with different functions. Therefore, MS-based high-throughput platforms, such as proteomics, are indispensable in the global analysis of cancer cell responses to metabolic and oxidative stress. Herein, we highlight recent advances in the understanding of the metabolic and oxidative stresses associated with tumor progression with proteomics-based systems biology approaches.

Unleashing the power of proteomics to develop blood-based cancer markers

BACKGROUND

There is an urgent need for blood-based molecular tests to assist in the detection and diagnosis of cancers at an early stage, when curative interventions are still possible, and to predict and monitor response to treatment and disease recurrence. The rich content of proteins in blood that are impacted by tumor development and host factors provides an ideal opportunity to develop noninvasive diagnostics for cancer.

CONTENT

Mass spectrometry instrumentation has advanced sufficiently to allow the discovery of protein alterations directly in plasma across no less than 7 orders of magnitude of protein abundance. Moreover, the use of proteomics to harness the immune response in the form of seropositivity to tumor antigens has the potential to complement circulating protein biomarker panels for cancer detection. The depth of analysis currently possible in a discovery setting allows the detection of potential markers at concentrations of less than 1 μg/L. Such low concentrations may exceed the limits of detection of ELISAs and thus require the development of clinical assays with exquisite analytical sensitivity. Clearly the availability for discovery and validation of biospecimens that are highly relevant to the intended clinical application and have been collected, processed, and stored with the use of standard operating procedures is of crucial importance to the successful application of proteomics to the development of blood-based tests for cancer.

SUMMARY

The realization of the potential of proteomics to yield blood biomarkers will benefit from a collaborative approach and a substantial investment in resources.

Parallel reaction monitoring for high resolution and high mass accuracy quantitative, targeted proteomics

Selected reaction monitoring on a triple quadrupole mass spectrometer is currently experiencing a renaissance within the proteomics community for its, as yet, unparalleled ability to characterize and quantify a set of proteins reproducibly, completely, and with high sensitivity. Given the immense benefit that high resolution and accurate mass instruments have brought to the discovery proteomics field, we wondered if highly accurate mass measurement capabilities could be leveraged to provide benefits in the targeted proteomics domain as well. Here, we propose a new targeted proteomics paradigm centered on the use of next generation, quadrupole-equipped high resolution and accurate mass instruments: parallel reaction monitoring (PRM). In PRM, the third quadrupole of a triple quadrupole is substituted with a high resolution and accurate mass mass analyzer to permit the parallel detection of all target product ions in one, concerted high resolution mass analysis. We detail the analytical performance of the PRM method, using a quadrupole-equipped bench-top Orbitrap MS, and draw a performance comparison to selected reaction monitoring in terms of run-to-run reproducibility, dynamic range, and measurement accuracy. In addition to requiring minimal upfront method development and facilitating automated data analysis, PRM yielded quantitative data over a wider dynamic range than selected reaction monitoring in the presence of a yeast background matrix because of PRM's high selectivity in the mass-to-charge domain. With achievable linearity over the quantifiable dynamic range found to be statistically equal between the two methods, our investigation suggests that PRM will be a promising new addition to the quantitative proteomics toolbox.