Human Proteinpedia: a unified discovery resource for proteomics research

Databases for Protein-Protein Interactions

Protein-protein interaction networks have a crucial role in biological processes. Proteins perform multiple functions in forming physical and functional interactions in cellular systems. Information concerning an enormous number of protein interactions in a wide range of species has accumulated and has been integrated into various resources for molecular biology and systems biology. This chapter provides a review of the representative databases and the major computational methods used for protein-protein interactions.

Proteome analysis using machine learning approaches and its applications to diseases

With the tremendous developments in the fields of biological and medical technologies, huge amounts of data are generated in the form of genomic data, images in medical databases or as data on protein sequences, and so on. Analyzing this data through different tools sheds light on the particulars of the disease and our body's reactions to it, thus, aiding our understanding of the human health. Most useful of these tools is artificial intelligence and deep learning (DL). The artificially created neural networks in DL algorithms help extract viable data from the datasets, and further, to recognize patters in these complex datasets. Therefore, as a part of machine learning, DL helps us face all the various challenges that come forth during protein prediction, protein identification and their quantification. Proteomics is the study of such proteins, their structures, features, properties and so on. As a form of data science, Proteomics has helped us progress excellently in the field of genomics technologies. One of the major techniques used in proteomics studies is mass spectrometry (MS). However, MS is efficient with analysis of large datasets only with the added help of informatics approaches for data analysis and interpretation; these mainly include machine learning and deep learning algorithms. In this chapter, we will discuss in detail the applications of deep learning and various algorithms of machine learning in proteomics.

Human carbonic anhydrases and post-translational modifications: a hidden world possibly affecting protein properties and functions

Human carbonic anhydrases (CAs) have become a well-recognized target for the design of inhibitors and activators with biomedical applications. Accordingly, an enormous amount of literature is available on their biochemical, functional and structural aspects. Nevertheless post-translational modifications (PTMs) occurring on these enzymes and their functional implications have been poorly investigated so far. To fill this gap, in this review we have analysed all PTMs occurring on human CAs, as deriving from the search in dedicated databases, showing a widespread occurrence of modification events in this enzyme family. By combining these data with sequence alignments, inspection of 3 D structures and available literature, we have summarised the possible functional implications of these PTMs. Although in some cases a clear correlation between a specific PTM and the CA function has been highlighted, many modification events still deserve further dedicated studies.

Finding the Keys to the CAR: Identifying Novel Target Antigens for T Cell Redirection Immunotherapies

Oncology immunotherapy has been a significant advancement in cancer treatment and involves harnessing and redirecting a patient’s immune response towards their own tumour. Specific recognition and elimination of tumour cells was first proposed over a century ago with Paul Erlich’s ‘magic bullet’ theory of therapy. In the past decades, targeting cancer antigens by redirecting T cells with antibodies using either bispecific T cell engagers (BiTEs) or chimeric antigen receptor (CAR) T cell therapy has achieved impressive clinical responses. Despite recent successes in haematological cancers, linked to a high and uniformly expressed CD19 antigen, the efficacy of T cell therapies in solid cancers has been disappointing, in part due to antigen escape. Targeting heterogeneous solid tumours with T cell therapies will require the identification of novel tumour specific targets. These targets can be found among a range of cell-surface expressed antigens, including proteins, glycolipids or carbohydrates. In this review, we will introduce the current tumour target antigen classification, outline existing approaches to discover novel tumour target antigens and discuss considerations for future design of antibodies with a focus on their use in CAR T cells.

HTS-Net: An integrated regulome-interactome approach for establishing network regulation models in high-throughput screenings

High-throughput RNAi screenings (HTS) allow quantifying the impact of the deletion of each gene in any particular function, from virus-host interactions to cell differentiation. However, there has been less development for functional analysis tools dedicated to RNAi analyses. HTS-Net, a network-based analysis program, was developed to identify gene regulatory modules impacted in high-throughput screenings, by integrating transcription factors-target genes interaction data (regulome) and protein-protein interaction networks (interactome) on top of screening z-scores. HTS-Net produces exhaustive HTML reports for results navigation and exploration. HTS-Net is a new pipeline for RNA interference screening analyses that proves better performance than simple gene rankings by z-scores, by re-prioritizing genes and replacing them in their biological context, as shown by the three studies that we reanalyzed. Formatted input data for the three studied datasets, source code and web site for testing the system are available from the companion web site at http://htsnet.marseille.inserm.fr/. We also compared our program with existing algorithms (CARD and hotnet2).

Characterization of human pineal gland proteome

The pineal gland is a neuroendocrine gland located at the center of the brain. It is known to regulate various physiological functions in the body through secretion of the neurohormone melatonin. Comprehensive characterization of the human pineal gland proteome has not been undertaken to date. We employed a high-resolution mass spectrometry-based approach to characterize the proteome of the human pineal gland. A total of 5874 proteins were identified from the human pineal gland in this study. Of these, 5820 proteins were identified from the human pineal gland for the first time. Interestingly, 1136 proteins from the human pineal gland were found to contain a signal peptide domain, which indicates the secretory nature of these proteins. An unbiased global proteomic profile of this biomedically important organ should benefit molecular research to unravel the role of the pineal gland in neuropsychiatric and neurodegenerative diseases.

Identification of Entry Factors Involved in Hepatitis C Virus Infection Based on Host-Mimicking Short Linear Motifs

Host factors that facilitate viral entry into cells can, in principle, be identified from a virus-host protein interaction network, but for most viruses information for such a network is limited. To help fill this void, we developed a bioinformatics approach and applied it to hepatitis C virus (HCV) infection, which is a current concern for global health. Using this approach, we identified short linear sequence motifs, conserved in the envelope proteins of HCV (E1/E2), that potentially can bind human proteins present on the surface of hepatocytes so as to construct an HCV (envelope)-host protein interaction network. Gene Ontology functional and KEGG pathway analyses showed that the identified host proteins are enriched in cell entry and carcinogenesis functionalities. The validity of our results is supported by much published experimental data. Our general approach should be useful when developing antiviral agents, particularly those that target virus-host interactions.

Viruses recruit host proteins, called entry factors, to help gain entry to host cells. Identification of entry factors can provide targets for developing antiviral drugs. By exploring the concept that short linear peptide motifs involved in human protein-protein interactions may be mimicked by viruses to hijack certain host cellular processes and thereby assist viral infection/survival, we developed a bioinformatics strategy to computationally identify entry factors of hepatitis C virus (HCV) infection, which is a worldwide health problem. Analysis of cellular functions and biochemical pathways indicated that the human proteins we identified usually play a role in cell entry and/or carcinogenesis, and results of the analysis are generally supported by experimental studies on HCV infection, including the ~80% (15 of 19) prediction rate of known HCV hepatocyte entry factors. Because molecular mimicry is a general concept, our bioinformatics strategy is a timely approach to identify new targets for antiviral research, not only for HCV but also for other viruses.

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.

Data sources for in vivo molecular profiling of human phenotypes

Molecular profiling of human diseases has been approached at the genetic (DNA), expression (RNA), and proteomic (protein) levels. An important goal of these efforts is to map observed molecular patterns to specific, mechanistic organic entities, such as loci in the genome, individual RNA molecules or defined proteins or protein assemblies. Importantly, such maps have been historically approached in the more intuitive context of a theoretical individual cell, but diseases are better described in reality using an in vivo framework, namely a library of several tissue-specific maps. In this article, we review the existing data atlases that can be used for this purpose and identify critical gaps that could move the field forward from cellular to in vivo dimensions. WIREs Syst Biol Med 2016, 8:472-484. doi: 10.1002/wsbm.1354 For further resources related to this article, please visit the WIREs website.

Synovial fluid proteome in rheumatoid arthritis

BACKGROUND

Rheumatoid arthritis (RA) is a chronic autoinflammatory disorder that affects small joints. Despite intense efforts, there are currently no definitive markers for early diagnosis of RA and for monitoring the progression of this disease, though some of the markers like anti CCP antibodies and anti vimentin antibodies are promising. We sought to catalogue the proteins present in the synovial fluid of patients with RA. It was done with the aim of identifying newer biomarkers, if any, that might prove promising in future.

METHODS

To enrich the low abundance proteins, we undertook two approaches-multiple affinity removal system (MARS14) to deplete some of the most abundant proteins and lectin affinity chromatography for enrichment of glycoproteins. The peptides were analyzed by LC-MS/MS on a high resolution Fourier transform mass spectrometer.

RESULTS

This effort was the first total profiling of the synovial fluid proteome in RA that led to identification of 956 proteins. From the list, we identified a number of functionally significant proteins including vascular cell adhesion molecule-1, S100 proteins, AXL receptor protein tyrosine kinase, macrophage colony stimulating factor (M-CSF), programmed cell death ligand 2 (PDCD1LG2), TNF receptor 2, (TNFRSF1B) and many novel proteins including hyaluronan-binding protein 2, semaphorin 4A (SEMA4D) and osteoclast stimulating factor 1. Overall, our findings illustrate the complex and dynamic nature of RA in which multiple pathways seems to be participating actively.

CONCLUSIONS

The use of high resolution mass spectrometry thus, enabled identification of proteins which might be critical to the progression of RA.

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.

Lgr5 expression, cancer stem cells and pancreatic cancer: results from biological and computational analyses

AIMS

To determine the relationship between Lgr5 and other stemness markers and pathologic features in pancreatic ductal adenocarcinoma (PDAC) samples.

MATERIALS & METHODS

In 69 samples, Lgr5 was analyzed by qRT-PCR together with a panel of 29 genes. Bioinformatic analysis was carried out to identify a possible pathway regulating Lgr5 expression in PDAC.

RESULTS

Lgr5 expression was not associated with the expression of tested cancer stem cell markers. Moreover, it was not an independent predictor of survival neither at univariate analysis (p = 0.21) nor at multivariate analysis (p = 0.225).

CONCLUSION

Based on the lack of correlation between Lgr5 and tested cancer stem cell markers, Lgr5 does not seem to be a potential stemness marker or prognostic factor in PDAC.

Making proteomics data accessible and reusable: current state of proteomics databases and repositories

Compared to other data-intensive disciplines such as genomics, public deposition and storage of MS-based proteomics, data are still less developed due to, among other reasons, the inherent complexity of the data and the variety of data types and experimental workflows. In order to address this need, several public repositories for MS proteomics experiments have been developed, each with different purposes in mind. The most established resources are the Global Proteome Machine Database (GPMDB), PeptideAtlas, and the PRIDE database. Additionally, there are other useful (in many cases recently developed) resources such as ProteomicsDB, Mass Spectrometry Interactive Virtual Environment (MassIVE), Chorus, MaxQB, PeptideAtlas SRM Experiment Library (PASSEL), Model Organism Protein Expression Database (MOPED), and the Human Proteinpedia. In addition, the ProteomeXchange consortium has been recently developed to enable better integration of public repositories and the coordinated sharing of proteomics information, maximizing its benefit to the scientific community. Here, we will review each of the major proteomics resources independently and some tools that enable the integration, mining and reuse of the data. We will also discuss some of the major challenges and current pitfalls in the integration and sharing of the data.

Macrophage migration inhibitory factor - a therapeutic target in gallbladder cancer

Background

Poor prognosis in gallbladder cancer is due to late presentation of the disease, lack of reliable biomarkers for early diagnosis and limited targeted therapies. Early diagnostic markers and novel therapeutic targets can significantly improve clinical management of gallbladder cancer.

Methods

Proteomic analysis of four gallbladder cancer cell lines based on the invasive property (non-invasive to highly invasive) was carried out using the isobaric tags for relative and absolute quantitation labeling-based quantitative proteomic approach. The expression of macrophage migration inhibitory factor was analysed in gallbladder adenocarcinoma tissues using immunohistochemistry. In vitro cellular assays were carried out in a panel of gallbladder cancer cell lines using MIF inhibitors, ISO-1 and 4-IPP or its specific siRNA.

Results

The quantitative proteomic experiment led to the identification of 3,653 proteins, among which 654 were found to be overexpressed and 387 were downregulated in the invasive cell lines (OCUG-1, NOZ and GB-d1) compared to the non-invasive cell line, TGBC24TKB. Among these, macrophage migration inhibitory factor (MIF) was observed to be highly overexpressed in two of the invasive cell lines. MIF is a pleiotropic proinflammatory cytokine that plays a causative role in multiple diseases, including cancer. MIF has been reported to play a central role in tumor cell proliferation and invasion in several cancers. Immunohistochemical labeling of tumor tissue microarrays for MIF expression revealed that it was overexpressed in 21 of 29 gallbladder adenocarcinoma cases. Silencing/inhibition of MIF using siRNA and/or MIF antagonists resulted in a significant decrease in cell viability, colony forming ability and invasive property of the gallbladder cancer cells.

Conclusions

Our findings support the role of MIF in tumor aggressiveness and suggest its potential application as a therapeutic target for gallbladder cancer.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-015-1855-z) contains supplementary material, which is available to authorized users.

A mass spectrometric-derived cell surface protein atlas

Cell surface proteins are major targets of biomedical research due to their utility as cellular markers and their extracellular accessibility for pharmacological intervention. However, information about the cell surface protein repertoire (the surfaceome) of individual cells is only sparsely available. Here, we applied the Cell Surface Capture (CSC) technology to 41 human and 31 mouse cell types to generate a mass-spectrometry derived Cell Surface Protein Atlas (CSPA) providing cellular surfaceome snapshots at high resolution. The CSPA is presented in form of an easy-to-navigate interactive database, a downloadable data matrix and with tools for targeted surfaceome rediscovery (http://wlab.ethz.ch/cspa). The cellular surfaceome snapshots of different cell types, including cancer cells, resulted in a combined dataset of 1492 human and 1296 mouse cell surface glycoproteins, providing experimental evidence for their cell surface expression on different cell types, including 136 G-protein coupled receptors and 75 membrane receptor tyrosine-protein kinases. Integrated analysis of the CSPA reveals that the concerted biological function of individual cell types is mainly guided by quantitative rather than qualitative surfaceome differences. The CSPA will be useful for the evaluation of drug targets, for the improved classification of cell types and for a better understanding of the surfaceome and its concerted biological functions in complex signaling microenvironments.

Characterization of traumatic brain injury in human brains reveals distinct cellular and molecular changes in contusion and pericontusion

Traumatic brain injury (TBI) contributes to fatalities and neurological disabilities worldwide. While primary injury causes immediate damage, secondary events contribute to long-term neurological defects. Contusions (Ct) are primary injuries correlated with poor clinical prognosis, and can expand leading to delayed neurological deterioration. Pericontusion (PC) (penumbra), the region surrounding Ct, can also expand with edema, increased intracranial pressure, ischemia, and poor clinical outcome. Analysis of Ct and PC can therefore assist in understanding the pathobiology of TBI and its management. This study on human TBI brains noted extensive neuronal, astroglial and inflammatory changes, alterations in mitochondrial, synaptic and oxidative markers, and associated proteomic profile, with distinct differences in Ct and PC. While Ct displayed petechial hemorrhages, thrombosis, inflammation, neuronal pyknosis, and astrogliosis, PC revealed edema, vacuolation of neuropil, axonal loss, and dystrophic changes. Proteomic analysis demonstrated altered immune response, synaptic, and mitochondrial dysfunction, among others, in Ct, while PC displayed altered regulation of neurogenesis and cytoskeletal architecture, among others. TBI brains displayed oxidative damage, glutathione depletion, mitochondrial dysfunction, and loss of synaptic proteins, with these changes being more profound in Ct. We suggest that analysis of markers specific to Ct and PC may be valuable in the evaluation of TBI pathobiology and therapeutics. We have characterized the primary injury in human traumatic brain injury (TBI). Contusions (Ct) - the injury core displayed hemorrhages, inflammation, and astrogliosis, while the surrounding pericontusion (PC) revealed edema, vacuolation, microglial activation, axonal loss, and dystrophy. Proteomic analysis demonstrated altered immune response, synaptic and mitochondrial dysfunction in Ct, and altered regulation of neurogenesis and cytoskeletal architecture in PC. Ct displayed more oxidative damage, mitochondrial, and synaptic dysfunction compared to PC.

Web resources for mass spectrometry-based proteomics

With the development of high-resolution and high-throughput mass spectrometry (MS) technology, a large quantum of proteomic data is continually being generated. Collecting and sharing these data are a challenge that requires immense and sustained human effort. In this report, we provide a classification of important web resources for MS-based proteomics and present rating of these web resources, based on whether raw data are stored, whether data submission is supported, and whether data analysis pipelines are provided. These web resources are important for biologists involved in proteomics research.

Proteomic-based approach to gain insight into reprogramming of THP-1 cells exposed to Leishmania donovani over an early temporal window

Leishmania donovani, a protozoan parasite, is the causative agent of visceral leishmaniasis. It lives and multiplies within the harsh environment of macrophages. In order to investigate how intracellular parasite manipulate the host cell environment, we undertook a quantitative proteomic study of human monocyte-derived macrophages (THP-1) following infection with L. donovani. We used the isobaric tags for relative and absolute quantification (iTRAQ) method and liquid chromatography-tandem mass spectrometry (LC-MS/MS) to compare expression profiles of noninfected and L. donovani-infected THP-1 cells. We detected modifications of protein expression in key metabolic pathways, including glycolysis and fatty acid oxidation, suggesting a global reprogramming of cell metabolism by the parasite. An increased abundance of proteins involved in gene transcription, RNA splicing (heterogeneous nuclear ribonucleoproteins [hnRNPs]), histones, and DNA repair and replication was observed at 24 h postinfection. Proteins involved in cell survival and signal transduction were more abundant at 24 h postinfection. Several of the differentially expressed proteins had not been previously implicated in response to the parasite, while the others support the previously identified proteins. Selected proteomics results were validated by real-time PCR and immunoblot analyses. Similar changes were observed in L. donovani-infected human monocyte-derived primary macrophages. The effect of RNA interference (RNAi)-mediated gene knockdown of proteins validated the relevance of the host quantitative proteomic screen. Our findings indicate that the host cell proteome is modulated after L. donovani infection, provide evidence for global reprogramming of cell metabolism, and demonstrate the complex relations between the host and parasite at the molecular level.

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.

Recent advances in proteomics: towards the human proteome

After the successful completion of the Human Genome project in 2003, the next major challenge was to understand when and where the encoded proteins were expressed, and to generate a map of the complex, interconnected pathways, networks and molecular systems (the human proteome) that, taken together, control the workings of all cells, tissues, organs and organisms. Proteomics will be fundamental for such studies. This review summarizes the key discoveries that laid down the foundations for proteomics as we now know it, and describes key recent technological advances that will undoubtedly contribute to achieving the initial goal of the Human Proteome Organization of identifying and characterizing at least one protein product and representative post-translational modifications, single amino acid polymorphisms and splice variant isoforms from the 20,300 human protein-coding genes within the next 10 years. Successful unraveling of the human proteome will undoubtedly improve our understanding of human biology at the cellular level and lay the foundations for improved diagnostic, prognostic, therapeutic and preventive medical outcomes as we enter the era of personalized medicine.

Host response profile of human brain proteome in toxoplasma encephalitis co-infected with HIV

Background

Toxoplasma encephalitis is caused by the opportunistic protozoan parasite Toxoplasma gondii. Primary infection with T. gondii in immunocompetent individuals remains largely asymptomatic. In contrast, in immunocompromised individuals, reactivation of the parasite results in severe complications and mortality. Molecular changes at the protein level in the host central nervous system and proteins associated with pathogenesis of toxoplasma encephalitis are largely unexplored. We used a global quantitative proteomic strategy to identify differentially regulated proteins and affected molecular networks in the human host during T. gondii infection with HIV co-infection.

Results

We identified 3,496 proteins out of which 607 proteins were differentially expressed (≥1.5-fold) when frontal lobe of the brain from patients diagnosed with toxoplasma encephalitis was compared to control brain tissues. We validated differential expression of 3 proteins through immunohistochemistry, which was confirmed to be consistent with mass spectrometry analysis. Pathway analysis of differentially expressed proteins indicated deregulation of several pathways involved in antigen processing, immune response, neuronal growth, neurotransmitter transport and energy metabolism.

Conclusions

Global quantitative proteomic approach adopted in this study generated a comparative proteome profile of brain tissues from toxoplasma encephalitis patients co-infected with HIV. Differentially expressed proteins include previously reported and several new proteins in the context of T. gondii and HIV infection, which can be further investigated. Molecular pathways identified to be associated with the disease should enhance our understanding of pathogenesis in toxoplasma encephalitis.

Electronic supplementary material

The online version of this article (doi:10.1186/1559-0275-11-39) contains supplementary material, which is available to authorized users.

ComPPI: a cellular compartment-specific database for protein-protein interaction network analysis

Here we present ComPPI, a cellular compartment-specific database of proteins and their interactions enabling an extensive, compartmentalized protein–protein interaction network analysis (URL: http://ComPPI.LinkGroup.hu). ComPPI enables the user to filter biologically unlikely interactions, where the two interacting proteins have no common subcellular localizations and to predict novel properties, such as compartment-specific biological functions. ComPPI is an integrated database covering four species (S. cerevisiae, C. elegans, D. melanogaster and H. sapiens). The compilation of nine protein–protein interaction and eight subcellular localization data sets had four curation steps including a manually built, comprehensive hierarchical structure of >1600 subcellular localizations. ComPPI provides confidence scores for protein subcellular localizations and protein–protein interactions. ComPPI has user-friendly search options for individual proteins giving their subcellular localization, their interactions and the likelihood of their interactions considering the subcellular localization of their interacting partners. Download options of search results, whole-proteomes, organelle-specific interactomes and subcellular localization data are available on its website. Due to its novel features, ComPPI is useful for the analysis of experimental results in biochemistry and molecular biology, as well as for proteome-wide studies in bioinformatics and network science helping cellular biology, medicine and drug design.

Unraveling patterns of site-to-site synonymous rates variation and associated gene properties of protein domains and families

In protein-coding genes, synonymous mutations are often thought not to affect fitness and therefore are not subject to natural selection. Yet increasingly, cases of non-neutral evolution at certain synonymous sites were reported over the last decade. To evaluate the extent and the nature of site-specific selection on synonymous codons, we computed the site-to-site synonymous rate variation (SRV) and identified gene properties that make SRV more likely in a large database of protein-coding gene families and protein domains. To our knowledge, this is the first study that explores the determinants and patterns of the SRV in real data. We show that the SRV is widespread in the evolution of protein-coding sequences, putting in doubt the validity of the synonymous rate as a standard neutral proxy. While protein domains rarely undergo adaptive evolution, the SRV appears to play important role in optimizing the domain function at the level of DNA. In contrast, protein families are more likely to evolve by positive selection, but are less likely to exhibit SRV. Stronger SRV was detected in genes with stronger codon bias and tRNA reusage, those coding for proteins with larger number of interactions or forming larger number of structures, located in intracellular components and those involved in typically conserved complex processes and functions. Genes with extreme SRV show higher expression levels in nearly all tissues. This indicates that codon bias in a gene, which often correlates with gene expression, may often be a site-specific phenomenon regulating the speed of translation along the sequence, consistent with the co-translational folding hypothesis. Strikingly, genes with SRV were strongly overrepresented for metabolic pathways and those associated with several genetic diseases, particularly cancers and diabetes.

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.

The Zebrafish GenomeWiki: a crowdsourcing approach to connect the long tail for zebrafish gene annotation

A large repertoire of gene-centric data has been generated in the field of zebrafish biology. Although the bulk of these data are available in the public domain, most of them are not readily accessible or available in nonstandard formats. One major challenge is to unify and integrate these widely scattered data sources. We tested the hypothesis that active community participation could be a viable option to address this challenge. We present here our approach to create standards for assimilation and sharing of information and a system of open standards for database intercommunication. We have attempted to address this challenge by creating a community-centric solution for zebrafish gene annotation. The Zebrafish GenomeWiki is a 'wiki'-based resource, which aims to provide an altruistic shared environment for collective annotation of the zebrafish genes. The Zebrafish GenomeWiki has features that enable users to comment, annotate, edit and rate this gene-centric information. The credits for contributions can be tracked through a transparent microattribution system. In contrast to other wikis, the Zebrafish GenomeWiki is a 'structured wiki' or rather a 'semantic wiki'. The Zebrafish GenomeWiki implements a semantically linked data structure, which in the future would be amenable to semantic search. Database URL: http://genome.igib.res.in/twiki.

Proteomic analysis of human osteoarthritis synovial fluid

Background

Osteoarthritis is a chronic musculoskeletal disorder characterized mainly by progressive degradation of the hyaline cartilage. Patients with osteoarthritis often postpone seeking medical help, which results in the diagnosis being made at an advanced stage of cartilage destruction. Sustained efforts are needed to identify specific markers that might help in early diagnosis, monitoring disease progression and in improving therapeutic outcomes. We employed a multipronged proteomic approach, which included multiple fractionation strategies followed by high resolution mass spectrometry analysis to explore the proteome of synovial fluid obtained from osteoarthritis patients. In addition to the total proteome, we also enriched glycoproteins from synovial fluid using lectin affinity chromatography.

Results

We identified 677 proteins from synovial fluid of patients with osteoarthritis of which 545 proteins have not been previously reported. These novel proteins included ADAM-like decysin 1 (ADAMDEC1), alanyl (membrane) aminopeptidase (ANPEP), CD84, fibulin 1 (FBLN1), matrix remodelling associated 5 (MXRA5), secreted phosphoprotein 2 (SPP2) and spondin 2 (SPON2). We identified 300 proteins using lectin affinity chromatography, including the glycoproteins afamin (AFM), attractin (ATRN), fibrillin 1 (FBN1), transferrin (TF), tissue inhibitor of metalloproteinase 1 (TIMP1) and vasorin (VSN). Gene ontology analysis confirmed that a majority of the identified proteins were extracellular and are mostly involved in cell communication and signaling. We also confirmed the expression of ANPEP, dickkopf WNT signaling pathway inhibitor 3 (DKK3) and osteoglycin (OGN) by multiple reaction monitoring (MRM) analysis of osteoarthritis synovial fluid samples.

Conclusions

We present an in-depth analysis of the synovial fluid proteome from patients with osteoarthritis. We believe that the catalog of proteins generated in this study will further enhance our knowledge regarding the pathophysiology of osteoarthritis and should assist in identifying better biomarkers for early diagnosis.

Differential proteomic analysis of synovial fluid from rheumatoid arthritis and osteoarthritis patients

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.

CEG: a database of essential gene clusters

Background

Essential genes are indispensable for the survival of living entities. They are the cornerstones of synthetic biology, and are potential candidate targets for antimicrobial and vaccine design.

Description

Here we describe the Cluster of Essential Genes (CEG) database, which contains clusters of orthologous essential genes. Based on the size of a cluster, users can easily decide whether an essential gene is conserved in multiple bacterial species or is species-specific. It contains the similarity value of every essential gene cluster against human proteins or genes. The CEG_Match tool is based on the CEG database, and was developed for prediction of essential genes according to function. The database is available at http://cefg.uestc.edu.cn/ceg.

Conclusions

Properties contained in the CEG database, such as cluster size, and the similarity of essential gene clusters against human proteins or genes, are very important for evolutionary research and drug design. An advantage of CEG is that it clusters essential genes based on function, and therefore decreases false positive results when predicting essential genes in comparison with using the similarity alignment method.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-14-769) contains supplementary material, which is available to authorized users.

Proteomic profiling of serum samples from chikungunya-infected patients provides insights into host response

BACKGROUND

Chikungunya is a highly debilitating febrile illness caused by Chikungunya virus, a single-stranded RNA virus, which is transmitted by Aedes aegypti or Aedes albopictus mosquito species. The pathogenesis and host responses in individuals infected with the chikungunya virus are not well understood at the molecular level. We carried out proteomic profiling of serum samples from chikungunya patients in order to identify molecules associated with the host response to infection by this virus.

RESULTS

Proteomic profiling of serum obtained from the infected individuals resulted in identification of 569 proteins. Of these, 63 proteins were found to be differentially expressed (≥ 2-fold) in patient as compared to control sera. These differentially expressed proteins were involved in various processes such as lipid metabolism, immune response, transport, signal transduction and apoptosis.

CONCLUSIONS

This is the first report providing a global proteomic profile of serum samples from individuals infected with the chikungunya virus. Our data provide an insight into the proteins that are involved as host response factors during an infection. These proteins include clusterin, apolipoproteins and S100A family of proteins.

Effect of posttranslational modifications on enzyme function and assembly

The detailed examination of enzyme molecules by mass spectrometry and other techniques continues to identify hundreds of distinct PTMs. Recently, global analyses of enzymes using methods of contemporary proteomics revealed widespread distribution of PTMs on many key enzymes distributed in all cellular compartments. Critically, patterns of multiple enzymatic and nonenzymatic PTMs within a single enzyme are now functionally evaluated providing a holistic picture of a macromolecule interacting with low molecular mass compounds, some of them being substrates, enzyme regulators, or activated precursors for enzymatic and nonenzymatic PTMs. Multiple PTMs within a single enzyme molecule and their mutual interplays are critical for the regulation of catalytic activity. Full understanding of this regulation will require detailed structural investigation of enzymes, their structural analogs, and their complexes. Further, proteomics is now integrated with molecular genetics, transcriptomics, and other areas leading to systems biology strategies. These allow the functional interrogation of complex enzymatic networks in their natural environment. In the future, one might envisage the use of robust high throughput analytical techniques that will be able to detect multiple PTMs on a global scale of individual proteomes from a number of carefully selected cells and cellular compartments. This article is part of a Special Issue entitled: Posttranslational Protein modifications in biology and Medicine.

Quantitative proteomics for identifying biomarkers for Rabies

INTRODUCTION

Rabies is a fatal acute viral disease of the central nervous system, which is a serious public health problem in Asian and African countries. Based on the clinical presentation, rabies can be classified into encephalitic (furious) or paralytic (numb) rabies. Early diagnosis of this disease is particularly important as rabies is invariably fatal if adequate post exposure prophylaxis is not administered immediately following the bite.

METHODS

In this study, we carried out a quantitative proteomic analysis of the human brain tissue from cases of encephalitic and paralytic rabies along with normal human brain tissues using an 8-plex isobaric tags for relative and absolute quantification (iTRAQ) strategy.

RESULTS AND CONCLUSION

We identified 402 proteins, of which a number of proteins were differentially expressed between encephalitic and paralytic rabies, including several novel proteins. The differentially expressed molecules included karyopherin alpha 4 (KPNA4), which was overexpressed only in paralytic rabies, calcium calmodulin dependent kinase 2 alpha (CAMK2A), which was upregulated in paralytic rabies group and glutamate ammonia ligase (GLUL), which was overexpressed in paralytic as well as encephalitic rabies. We validated two of the upregulated molecules, GLUL and CAMK2A, by dot blot assays and further validated CAMK2A by immunohistochemistry. These molecules need to be further investigated in body fluids such as cerebrospinal fluid in a larger cohort of rabies cases to determine their potential use as antemortem diagnostic biomarkers in rabies. This is the first study to systematically profile clinical subtypes of human rabies using an iTRAQ quantitative proteomics approach.

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.

The application of quantification techniques in proteomics for biomedical research

The systematic analysis of biological processes requires an understanding of the quantitative expression patterns of proteins, their interacting partners and their subcellular localization. This information was formerly difficult to accrue as the relative quantification of proteins relied on antibody-based methods and other approaches with low throughput. The advent of soft ionization techniques in mass spectrometry plus advances in separation technologies has aligned protein systems biology with messenger RNA, DNA, and microarray technologies to provide data on systems as opposed to singular protein entities. Another aspect of quantitative proteomics that increases its importance for the coming few years is the significant technical developments underway both for high pressure liquid chromatography and mass spectrum devices. Hence, robustness, reproducibility and mass accuracy are still improving with every new generation of instruments. Nonetheless, the methods employed require validation and comparison to design fit for purpose experiments in advanced protein analyses. This review considers the newly developed systematic protein investigation methods and their value from the standpoint that relative or absolute protein quantification is required de rigueur in biomedical research.

CAPER: a chromosome-assembled human proteome browsER

High-throughput mass spectrometry and antibody-based experiments have begun to produce a large amount of proteomic data sets. Chromosome-based visualization of these data sets and their annotations can help effectively integrate, organize, and analyze them. Therefore, we developed a web-based, user-friendly Chromosome-Assembled human Proteome browsER (CAPER). To display proteomic data sets and related annotations comprehensively, CAPER employs two distinct visualization strategies: track-view for the sequence/site information and the correspondence between proteome, transcriptome, genome, and chromosome and heatmap-view for the qualitative and quantitative functional annotations. CAPER supports data browsing at multiple scales through Google Map-like smooth navigation, zooming, and positioning with chromosomes as the reference coordinate. Both track-view and heatmap-view can mutually switch, providing a high-quality user interface. Taken together, CAPER will greatly facilitate the complete annotation and functional interpretation of the human genome by proteomic approaches, thereby making a significant contribution to the Chromosome-Centric Human Proteome Project and even the human physiology/pathology research. CAPER can be accessed at http://www.bprc.ac.cn/CAPE .

Quantitative proteomics for identifying biomarkers for tuberculous meningitis

INTRODUCTION

Tuberculous meningitis is a frequent extrapulmonary disease caused by Mycobacterium tuberculosis and is associated with high mortality rates and severe neurological sequelae. In an earlier study employing DNA microarrays, we had identified genes that were differentially expressed at the transcript level in human brain tissue from cases of tuberculous meningitis. In the current study, we used a quantitative proteomics approach to discover protein biomarkers for tuberculous meningitis.

METHODS

To compare brain tissues from confirmed cased of tuberculous meningitis with uninfected brain tissue, we carried out quantitative protein expression profiling using iTRAQ labeling and LC-MS/MS analysis of SCX fractionated peptides on Agilent's accurate mass QTOF mass spectrometer.

RESULTS AND CONCLUSIONS

Through this approach, we identified both known and novel differentially regulated molecules. Those described previously included signal-regulatory protein alpha (SIRPA) and protein disulfide isomerase family A, member 6 (PDIA6), which have been shown to be overexpressed at the mRNA level in tuberculous meningitis. The novel overexpressed proteins identified in our study included amphiphysin (AMPH) and neurofascin (NFASC) while ferritin light chain (FTL) was found to be downregulated in TBM. We validated amphiphysin, neurofascin and ferritin light chain using immunohistochemistry which confirmed their differential expression in tuberculous meningitis. Overall, our data provides insights into the host response in tuberculous meningitis at the molecular level in addition to providing candidate diagnostic biomarkers for tuberculous meningitis.

The development of selected reaction monitoring methods for targeted proteomics via empirical refinement

Software advancements in the last several years have had a significant impact on proteomics from method development to data analysis. Herein, we detail a method, which uses our in-house developed software tool termed Skyline, for empirical refinement of candidate peptides from targeted proteins. The method consists of four main steps from generation of a testable hypothesis, method development, peptide refinement, to peptide validation. The ultimate goal is to identify the best performing peptide in terms of ionization efficiency, reproducibility, specificity, and chromatographic characteristics to monitor as a proxy for protein abundance. It is important to emphasize that this method allows the user to perform this refinement procedure in the sample matrix and organism of interest with the instrumentation available. Finally, the method is demonstrated in a case study to determine the best peptide to monitor the abundance of surfactant protein B in lung aspirates.

A dynamic view of domain-motif interactions

Many protein-protein interactions are mediated by domain-motif interaction, where a domain in one protein binds a short linear motif in its interacting partner. Such interactions are often involved in key cellular processes, necessitating their tight regulation. A common strategy of the cell to control protein function and interaction is by post-translational modifications of specific residues, especially phosphorylation. Indeed, there are motifs, such as SH2-binding motifs, in which motif phosphorylation is required for the domain-motif interaction. On the contrary, there are other examples where motif phosphorylation prevents the domain-motif interaction. Here we present a large-scale integrative analysis of experimental human data of domain-motif interactions and phosphorylation events, demonstrating an intriguing coupling between the two. We report such coupling for SH3, PDZ, SH2 and WW domains, where residue phosphorylation within or next to the motif is implied to be associated with switching on or off domain binding. For domains that require motif phosphorylation for binding, such as SH2 domains, we found coupled phosphorylation events other than the ones required for domain binding. Furthermore, we show that phosphorylation might function as a double switch, concurrently enabling interaction of the motif with one domain and disabling interaction with another domain. Evolutionary analysis shows that co-evolution of the motif and the proximal residues capable of phosphorylation predominates over other evolutionary scenarios, in which the motif appeared before the potentially phosphorylated residue, or vice versa. Our findings provide strengthening evidence for coupled interaction-regulation units, defined by a domain-binding motif and a phosphorylated residue.

Domain-motif interactions are instrumental for many central cellular processes, and are therefore tightly regulated. Phosphorylation events are known modulators of protein-protein interactions in general, including domain-motif interactions. Here, we addressed the association of phosphorylation and domain-motif interaction taking a motif-centred view. We integrated human domain-motif interaction and phosphorylation data for four representative domains (SH2, WW, SH3 and PDZ), and showed that the adjacency between phosphorylation and domain-motif interactions is extensive, suggesting interesting functional links between them that extend the classical and widely studied phospho-regulation of SH2 or WW domain-motif interactions. Furthermore, we show that such interaction-regulation units may function as double switches, concurrently enabling interaction of the motif with one domain and disabling interaction with another domain. These latter interaction-regulation units are more conserved in evolution than the individual units comprising them. Assuming that the four analyzed domain-motif interaction types are reliable representatives of such interactions, our results support the existence of units comprising motifs and associated phosphorylation sites, in which the regulation of domain-motif interaction is inherent.

Comprehensive proteomic analysis of human bile

Bile serves diverse functions from metabolism to transport. In addition to acids and salts, bile is composed of proteins secreted or shed by the hepatobiliary system. Although there have been previous efforts to catalog biliary proteins, an in-depth analysis of the bile proteome has not yet been reported. We carried out fractionation of non-cancerous bile samples using a multipronged approach (SDS-PAGE, SCX and OFFGEL) followed by MS analysis on an LTQ-Orbitrap Velos mass spectrometer using high resolution at both MS and MS/MS levels. We identified 2552 proteins - the largest number of proteins reported in human bile till date. To our knowledge, there are no previous studies employing high-resolution MS reporting a more detailed catalog of any body fluid proteome in a single study. We propose that extensive fractionation coupled to high-resolution MS can be used as a standard methodology for in-depth characterization of any body fluid. This catalog should serve as a baseline for the future studies aimed at discovering biomarkers from bile in gallbladder, hepatic, and biliary cancers.

SpliceAid 2: a database of human splicing factors expression data and RNA target motifs

Splicing is the most frequently altered biological process by mutations within gene regions. Information for splicing is recognized by several factors that bind pre-mRNA sequence and, through coordinated interaction, yield mature transcripts. Some in silico methods have been developed to predict if a mutation leads to aberrant splicing patterns. We previously created SpliceAid tool that is able to minimize false positive predictions because it adopts strictly experimental RNA target motifs bound by splicing proteins in humans. In order to improve prediction accuracy and better understand the splicing outcome, the tissue specificity of each splicing regulatory factor has to be taken into account. Here, we have developed SpliceAid 2 by adding the expression data related to the splicing factors extracted from the main proteomic and transcriptomic databases, true 5' and 3' splice sites, polypyrimidine tracts, and branch point sequences. The new version collects 2,220 target sites of 62 human splicing proteins and their expression data in 320 tissues per cell. SpliceAid 2 can be useful to foresee the splicing pattern alteration, to guide the identification of the molecular effect due to the mutations and to understand the tissue-specific alternative splicing. SpliceAid 2 is freely accessible at www.introni.it/spliceaid.html.

Quantitative temporal proteomic analysis of human embryonic stem cell differentiation into oligodendrocyte progenitor cells

Oligodendrocytes (OLs) are glial cells of the central nervous system, which produce myelin. Cultured OLs provide immense therapeutic opportunities for treating a variety of neurological conditions. One of the most promising sources for such therapies is human embryonic stem cells (ESCs) as well as providing a model to study human OL development. For these purposes, an investigation of proteome level changes is critical for understanding the process of OL differentiation. In this report, an iTRAQ-based quantitative proteomic approach was used to study multiple steps during OL differentiation including neural progenitor cells, glial progenitor cells and oligodendrocyte progenitor cells (OPCs) compared to undifferentiated ESCs. Using a 1% false discovery rate cutoff, ∼3145 proteins were quantitated and several demonstrated progressive stage-specific expression. Proteins such as transferrin, neural cell adhesion molecule 1, apolipoprotein E and wingless-related MMTV integration site 5A showed increased expression from the neural progenitor cell to the OPC stage. Several proteins that have demonstrated evidence or been suspected in OL maturation were also found upregulated in OPCs including fatty acid-binding protein 4, THBS1, bone morphogenetic protein 1, CRYAB, transferrin, tenascin C, COL3A1, TGFBI and EPB41L3. Thus, by providing the first extensive proteomic profiling of human ESC differentiation into OPCs, this study provides many novel proteins that are potentially involved in OL development.

Human protein reference database and human proteinpedia as discovery resources for molecular biotechnology

In the recent years, research in molecular biotechnology has transformed from being small scale studies targeted at a single or a small set of molecule(s) into a combination of high throughput discovery platforms and extensive validations. Such a discovery platform provided an unbiased approach which resulted in the identification of several novel genetic and protein biomarkers. High throughput nature of these investigations coupled with higher sensitivity and specificity of Next Generation technologies provided qualitatively and quantitatively richer biological data. These developments have also revolutionized biological research and speed of data generation. However, it is becoming difficult for individual investigators to directly benefit from this data because they are not easily accessible. Data resources became necessary to assimilate, store and disseminate information that could allow future discoveries. We have developed two resources--Human Protein Reference Database (HPRD) and Human Proteinpedia, which integrate knowledge relevant to human proteins. A number of protein features including protein-protein interactions, post-translational modifications, subcellular localization, and tissue expression, which have been studied using different strategies were incorporated in these databases. Human Proteinpedia also provides a portal for community participation to annotate and share proteomic data and uses HPRD as the scaffold for data processing. Proteomic investigators can even share unpublished data in Human Proteinpedia, which provides a meaningful platform for data sharing. As proteomic information reflects a direct view of cellular systems, proteomics is expected to complement other areas of biology such as genomics, transcriptomics, molecular biology, cloning, and classical genetics in understanding the relationships among multiple facets of biological systems.

Automatic annotation of experimentally derived, evolutionarily conserved post-translational modifications onto multiple genomes

New generation sequencing technologies have resulted in significant increases in the number of complete genomes. Functional characterization of these genomes, such as by high-throughput proteomics, is an important but challenging task due to the difficulty of scaling up existing experimental techniques. By use of comparative genomics techniques, experimental results can be transferred from one genome to another, while at the same time minimizing errors by requiring discovery in multiple genomes. In this study, protein phosphorylation, an essential component of many cellular processes, is studied using data from large-scale proteomics analyses of the phosphoproteome. Phosphorylation sites from Homo sapiens, Mus musculus and Drosophila melanogaster phosphopeptide data sets were mapped onto conserved domains in NCBI’s manually curated portion of Conserved Domain Database (CDD). In this subset, 25 phosphorylation sites are found to be evolutionarily conserved between the three species studied. Transfer of phosphorylation annotation of these conserved sites onto sequences sharing the same conserved domains yield 3253 phosphosite annotations for proteins from coelomata, the taxonomic division that spans H. sapiens, M. musculus and D. melanogaster. The method scales automatically, so as the amount of experimental phosphoproteomics data increases, more conserved phosphorylation sites may be revealed.