Viral immune modulators perturb the human molecular network by common and unique strategies.

Viruses must enter host cells to replicate, assemble and propagate. Because of the restricted size of their genomes, viruses have had to evolve efficient ways of exploiting host cell processes to promote their own life cycles and also to escape host immune defence mechanisms. Many viral open reading frames (viORFs) with immune-modulating functions essential for productive viral growth have been identified across a range of viral classes. However, there has been no comprehensive study to identify the host factors with which these viORFs interact for a global perspective of viral perturbation strategies. Here we show that different viral perturbation patterns of the host molecular defence network can be deduced from a mass-spectrometry-based host-factor survey in a defined human cellular system by using 70 innate immune-modulating viORFs from 30 viral species. The 579 host proteins targeted by the viORFs mapped to an unexpectedly large number of signalling pathways and cellular processes, suggesting yet unknown mechanisms of antiviral immunity. We further experimentally verified the targets heterogeneous nuclear ribonucleoprotein U, phosphatidylinositol-3-OH kinase, the WNK (with-no-lysine) kinase family and USP19 (ubiquitin-specific peptidase 19) as vulnerable nodes in the host cellular defence system. Evaluation of the impact of viral immune modulators on the host molecular network revealed perturbation strategies used by individual viruses and by viral classes. Our data are also valuable for the design of broad and specific antiviral therapies.

A proteogenomic approach to map the proteome of an unsequenced pathogen - Leishmania donovani.

Visceral leishmaniasis or kala azar is the most severe form of leishmaniasis and is caused by the protozoan parasite Leishmania donovani. There is no published report on L. donovani genome sequence available till date, although the genome sequences of three related Leishmania species are already available. Thus, we took a proteogenomic approach to identify proteins from two different life stages of L. donovani. From our analysis of the promastigote (insect) and amastigote (human) stages of L. donovani, we identified a total of 22,322 unique peptides from a homology-based search against proteins from three Leishmania species. These peptides were assigned to 3711 proteins in L. infantum, 3287 proteins in L. major, and 2433 proteins in L. braziliensis. Of the 3711 L. donovani proteins that were identified, the expression of 1387 proteins was detectable in both life stages of the parasite, while 901 and 1423 proteins were identified only in promastigotes and amastigotes life stages, respectively. In addition, we also identified 13 N-terminally and one C-terminally extended proteins based on the proteomic data search against the six-frame translated genome of the three related Leishmania species. Here, we report results from proteomic profiling of L. donovani, an organism with an unsequenced genome.

Identification of Novel Phosphorylation Motifs Through an Integrative Computational and Experimental Analysis of the Human Phosphoproteome.

Protein phosphorylation occurs in certain sequence/structural contexts that are still incompletely understood. The amino acids surrounding the phosphorylated residues are important in determining the binding of the kinase to the protein sequence. Upon phosphorylation these sequences also determine the binding of certain domains that specifically bind to phosphorylated sequences. Thus far, such 'motifs' have been identified through alignment of a limited number of well identified kinase substrates. RESULTS: Experimentally determined phosphorylation sites from Human Protein Reference Database were used to identify 1,167 novel serine/threonine or tyrosine phosphorylation motifs using a computational approach. We were able to statistically validate a number of these novel motifs based on their enrichment in known phosphopeptides datasets over phosphoserine/threonine/tyrosine peptides in the human proteome. There were 299 novel serine/threonine or tyrosine phosphorylation motifs that were found to be statistically significant. Several of the novel motifs that we identified computationally have subsequently appeared in large datasets of experimentally determined phosphorylation sites since we initiated our analysis. Using a peptide microarray platform, we have experimentally evaluated the ability of casein kinase I to phosphorylate a subset of the novel motifs discovered in this study. Our results demonstrate that it is feasible to identify novel phosphorylation motifs through large phosphorylation datasets. Our study also establishes peptide microarrays as a novel platform for high throughput kinase assays and for the validation of consensus motifs. Finally, this extended catalog of phosphorylation motifs should assist in a systematic study of phosphorylation networks in signal transduction pathways.

Quantitative tissue proteomics of esophageal squamous cell carcinoma for novel biomarker discovery.

Esophageal squamous cell carcinoma (ESCC) is among the top ten most frequent malignancies worldwide. In this study, our objective was to identify potential biomarkers for ESCC through a quantitative proteomic approach using the isobaric tags for relative and absolute quantitation (iTRAQ) approach. We compared the protein expression profiles of ESCC tumor tissues with the corresponding adjacent normal tissue from ten patients. LC-MS/MS analysis of strong cation exchange chromatography fractions was carried out on an Accurate Mass QTOF mass spectrometer, which led to the identification of 687 proteins. In all, 257 proteins were identified as differentially expressed in ESCC as compared to normal. We found several previously known protein biomarkers to be upregulated in ESCC including thrombospondin 1 (THBS1), periostin 1 (POSTN) and heat shock 70 kDa protein 9 (HSPA9) confirming the validity of our approach. In addition, several novel proteins that had not been reported previously were identified in our screen. These novel biomarker candidates included prosaposin (PSAP), plectin 1 (PLEC1) and protein disulfide isomerase A 4 (PDIA4) that were further validated to be overexpressed by immunohistochemical labeling using tissue microarrays. The success of our study shows that this mass spectrometric strategy can be applied to cancers in general to develop a panel of candidate biomarkers, which can then be validated by other techniques.

A proteogenomic analysis of Anopheles gambiae using high-resolution Fourier transform mass spectrometry.

Anopheles gambiae is a major mosquito vector responsible for malaria transmission, whose genome sequence was reported in 2002. Genome annotation is a continuing effort, and many of the approximately 13,000 genes listed in VectorBase for Anopheles gambiae are predictions that have still not been validated by any other method. To identify protein-coding genes of An. gambiae based on its genomic sequence, we carried out a deep proteomic analysis using high-resolution Fourier transform mass spectrometry for both precursor and fragment ions. Based on peptide evidence, we were able to support or correct more than 6000 gene annotations including 80 novel gene structures and about 500 translational start sites. An additional validation by RT-PCR and cDNA sequencing was successfully performed for 105 selected genes. Our proteogenomic analysis led to the identification of 2682 genome search-specific peptides. Numerous cases of encoded proteins were documented in regions annotated as intergenic, introns, or untranslated regions. Using a database created to contain potential splice sites, we also identified 35 novel splice junctions. This is a first report to annotate the An. gambiae genome using high-accuracy mass spectrometry data as a complementary technology for genome annotation.

Systematic evaluation of alternating CID and ETD fragmentation for phosphorylated peptides.

CID has become a routine method for fragmentation of peptides in shotgun proteomics, whereas electron transfer dissociation (ETD) has been described as a preferred method for peptides carrying labile PTMs. Though both of these fragmentation techniques have their obvious advantages, they also have their own drawbacks. By combining data from CID and ETD fragmentation, some of these disadvantages can potentially be overcome because of the complementarity of fragment ions produced. To evaluate alternating CID and ETD fragmentation, we analyzed a complex mixture of phosphopeptides on an LTQ-Orbitrap mass spectrometer. When the CID and ETD-derived spectra were searched separately, we observed 2504, 491, 2584, and 3249 phosphopeptide-spectrum matches from CID alone, ETD alone, decision tree-based CID/ETD, and alternating CID and ETD, respectively. Combining CID and ETD spectra prior to database searching should, intuitively, be superior to either method alone. However, when spectra from the alternating CID and ETD method were merged prior to database searching, we observed a reduction in the number of phosphopeptide-spectrum matches. The poorer identification rates observed after merging CID and ETD spectra are a reflection of a lack of optimized search algorithms for carrying out such searches and perhaps inherent weaknesses of this approach. Thus, although alternating CID and ETD experiments for phosphopeptide identification are desirable for increasing the confidence of identifications, merging spectra prior to database search has to be carefully evaluated further in the context of the various algorithms before adopting it as a routine strategy.

The interactome of a PTB domain-containing adapter protein, Odin, revealed by SILAC.

Signal transduction pathways are tightly controlled by positive and negative regulators. We have previously identified Odin (also known as ankyrin repeat and sterile alpha motif domain-containing 1A; gene symbol ANKS1A) as a negative regulator of growth factor signaling; however, the mechanisms through which Odin regulates these pathways remain to be elucidated. To determine how Odin negatively regulates growth factor signaling, we undertook a proteomic approach to systematically identify proteins that interact with Odin using the SILAC strategy. In this study, we identified 18 molecules that were specifically associated in a protein complex with Odin. Our study established that the complete family of 14-3-3 proteins occur in a protein complex with Odin, which is also supported by earlier reports that identified a few members of the 14-3-3 family as Odin interactors. Among the novel protein interactors of Odin were CD2-associated protein, SH3 domain kinase binding protein 1 and DAB2 interacting protein. We confirmed 8 of the eighteen interactions identified in the Odin protein complex by co-immunoprecipitation experiments. Finally, a literature-based network analysis revealed that Odin interacting partners are involved in various cellular processes, some of which are key molecules in regulating receptor endocytosis.

A Signaling Network of Thyroid-Stimulating Hormone.

Human thyroid stimulating hormone (TSH) is a glycoprotein secreted by the anterior part of the pituitary gland. TSH plays an important physiological role in the regulation of hypothalamic-pituitary-thyroid axis by modulating the release of the thyroid hormones from the thyroid gland. It induces iodine uptake by the thyroid, promotes thyroid epithelial differentiation and growth, and protects thyroid cells from apoptosis. Impairment of TSH signal transduction pathway leads to thyroid disorders such as goitre, hypothyroidism and hyperthyroidism, which can have complex clinical manifestations. TSH signaling is largely effected through two separate pathways, the adenylate cyclase and the phospholipase C pathways. In spite of its biomedical importance, a concise signaling map of TSH pathway is not available in the public domain. Therefore, we have generated a detailed signaling map of TSH pathway by systematically cataloging the molecular reactions induced by TSH including protein-protein interactions, post-translational modifications, protein translocation events and activation/inhibition reactions. We have cataloged 40 molecular association events, 42 enzyme-substrate reactions and 16 protein translocation events in TSH signaling pathway resource. Additionally, we have documented 208 genes, which are differentially regulated by TSH. We have provided the details of TSH pathway through NetPath (http://www.netpath.org), which is a publicly available resource for human signaling pathways developed by our group. We have also depicted the map of TSH signaling using NetSlim criteria (http://www.netpath.org/netslim/) and provided pathway maps in Wikipathways (http://www.wikipathways.org/). We anticipate that the availability of TSH pathway as a community resource will enhance further biomedical investigations into the function and effects of this important hormone.

The BioPAX community standard for pathway data sharing.

Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. The rapid growth of the volume of pathway data has spurred the development of databases and computational tools to aid interpretation; however, use of these data is hampered by the current fragmentation of pathway information across many databases with incompatible formats. BioPAX, which was created through a community process, solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. Using BioPAX, millions of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases. This large amount of pathway data in a computable form will support visualization, analysis and biological discovery.

SILAC-based quantitative proteomic approach to identify potential biomarkers from the esophageal squamous cell carcinoma secretome.

The identification of secreted proteins that are differentially expressed between non-neoplastic and esophageal squamous cell carcinoma (ESCC) cells can provide potential biomarkers of ESCC. We used a SILAC-based quantitative proteomic approach to compare the secretome of ESCC cells with that of non-neoplastic esophageal squamous epithelial cells. Proteins were resolved by SDS-PAGE, and tandem mass spectrometry analysis (LC-MS/MS) of in-gel trypsin-digested peptides was carried out on a high-accuracy qTOF mass spectrometer. In total, we identified 441 proteins in the combined secretomes, including 120 proteins with > 2-fold upregulation in the ESCC secretome vs. that of non-neoplastic esophageal squamous epithelial cells. In this study, several potential protein biomarkers previously known to be increased in ESCC including matrix metalloproteinase 1, transferrin receptor, and transforming growth factor beta-induced 68 kDa were identified as overexpressed in the ESCC-derived secretome. In addition, we identified several novel proteins that have not been previously reported to be associated with ESCC. Among the novel candidate proteins identified, protein disulfide isomerase family a member 3 (PDIA3), GDP dissociation inhibitor 2 (GDI2), and lectin galactoside binding soluble 3 binding protein (LGALS3BP) were further validated by immunoblot analysis and immunohistochemical labeling using tissue microarrays. This tissue microarray analysis showed overexpression of protein disulfide isomerase family a member 3, GDP dissociation inhibitor 2, and lectin galactoside binding soluble 3 binding protein in 93%, 93% and 87% of 137 ESCC cases, respectively. Hence, we conclude that these potential biomarkers are excellent candidates for further evaluation to test their role and efficacy in the early detection of ESCC.

Identifying targets of miR-143 using a SILAC-based proteomic approach.

Although the targets of most miRNAs have not been experimentally identified, microRNAs (miRNAs) have begun to be extensively characterized in physiological, developmental and disease-related contexts in recent years. Thus far, mainly computational approaches have been employed to predict potential targets for the large majority of miRNAs. Although miRNAs exert a major influence on the efficiency of translation of their targets in animals, most studies describing experimental identification of miRNA target genes are based on detection of altered mRNA levels. miR-143 is a miRNA involved in tumorigenesis in multiple types of cancer, smooth muscle cell fate and adipocyte differentiation. Only a few miR-143 targets are experimentally verified, so we employed a SILAC-based quantitative proteomic strategy to systematically identify potential targets of miR-143. In total, we identified >1200 proteins from MiaPaCa2 pancreatic cancer cells, of which 93 proteins were downregulated >2-fold in miR-143 mimic transfected cells as compared to controls. Validation of 34 of these candidate targets in luciferase assays showed that 10 of them were likely direct targets of miR-143. Importantly, we also carried out gene expression profiling of the same cells and observed that the majority of the candidate targets identified by proteomics did not show a concomitant decrease in mRNA levels confirming that miRNAs affect the expression of most targets through translational inhibition. Our study clearly demonstrates that quantitative proteomic approaches are important and necessary for identifying miRNA targets.

Assessment of resolution parameters for CID-based shotgun proteomic experiments on the LTQ-Orbitrap mass spectrometer.

Shotgun proteomics has been used extensively for characterization of a number of proteomes. High-resolution Fourier transform mass spectrometry (FTMS) has emerged as a powerful tool owing to its high mass accuracy and resolving power. One of its major limitations, however, is that the confidence level of peptide identification and sensitivity cannot be maximized simultaneously. Although it is generally assumed that higher resolution is better for peptide identifications, the precise effect of varying resolution as a parameter on peptide identification has not yet been systematically evaluated. We used the Escherichia coli proteome and a standard 48 protein mix to study the effect of different resolution parameters on peptide identifications in the setting of a shotgun proteomics experiment on an LTQ-Orbitrap mass spectrometer. We observed a higher number of peptide-spectrum matches (PSMs) whenever the MS scan was carried out by FT and the MS/MS in the ion-trap (IT) with the maximum PSMs obtained at an MS resolution of 30,000. In contrast, when samples were analyzed by FT for both MS and MS/MS, the number of PSMs was significantly lower (approximately 40% compared with FT-IT experiments) with the maximum PSMs obtained when both the MS and MS/MS resolution were set to 15,000. Thus, a 15K-15K resolution setting may provide the best compromise for studies where both speed and accuracy such as high-throughput post-translational analysis and de novo sequencing are important. We hope that our study will allow researchers to choose between different resolution parameters to achieve their desired results from proteomic analyses.

Comparative proteomics of human embryonic stem cells and embryonal carcinoma cells.

Pluripotent human embryonic stem cells (ESCs) can be differentiated in vitro into a variety of cells which hold promise for transplantation therapy. Human embryonal carcinoma cells (ECCs), stem cells of human teratocarcinomas, are considered a close but malignant counterpart to human ESCs. In this study, a comprehensive quantitative proteomic analysis of ESCs and ECCs was carried out using the iTRAQ method. Using two-dimensional LC and MS/MS analyses, we identified and quantitated approximately 1800 proteins. Among these are proteins associated with pluripotency and development as well as tight junction signaling and TGFbeta receptor pathway. Nearly approximately 200 proteins exhibit more than twofold difference in abundance between ESCs and ECCs. Examples of early developmental markers high in ESCs include beta-galactoside-binding lectin, undifferentiated embryonic cell transcription factor-1, DNA cytosine methyltransferase 3beta isoform-B, melanoma antigen family-A4, and interferon-induced transmembrane protein-1. In contrast, CD99-antigen (CD99), growth differentiation factor-3, cellular retinoic acid binding protein-2, and developmental pluripotency associated-4 were among the highly expressed proteins in ECCs. Several proteins that were highly expressed in ECCs such as heat shock 27 kDa protein-1, mitogen-activated protein kinase kinase-1, nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor like-2, and S100 calcium-binding protein-A4 have also been attributed to malignancy in other systems. Importantly, immunocytochemistry was used to validate the proteomic analyses for a subset of the proteins. In summary, this is the first large-scale quantitative proteomic study of human ESCs and ECCs, which provides critical information about the regulators of these two closely related, but developmentally distinct, stem cells.

NetPath: a public resource of curated signal transduction pathways.

We have developed NetPath as a resource of curated human signaling pathways. As an initial step, NetPath provides detailed maps of a number of immune signaling pathways, which include approximately 1,600 reactions annotated from the literature and more than 2,800 instances of transcriptionally regulated genes - all linked to over 5,500 published articles. We anticipate NetPath to become a consolidated resource for human signaling pathways that should enable systems biology approaches.

Prediction of candidate primary immunodeficiency disease genes using a support vector machine learning approach.

Screening and early identification of primary immunodeficiency disease (PID) genes is a major challenge for physicians. Many resources have catalogued molecular alterations in known PID genes along with their associated clinical and immunological phenotypes. However, these resources do not assist in identifying candidate PID genes. We have recently developed a platform designated Resource of Asian PDIs, which hosts information pertaining to molecular alterations, protein-protein interaction networks, mouse studies and microarray gene expression profiling of all known PID genes. Using this resource as a discovery tool, we describe the development of an algorithm for prediction of candidate PID genes. Using a support vector machine learning approach, we have predicted 1442 candidate PID genes using 69 binary features of 148 known PID genes and 3162 non-PID genes as a training data set. The power of this approach is illustrated by the fact that six of the predicted genes have recently been experimentally confirmed to be PID genes. The remaining genes in this predicted data set represent attractive candidates for testing in patients where the etiology cannot be ascribed to any of the known PID genes.

Human Protein Reference Database and Human Proteinpedia as discovery tools for systems biology.

Although high-throughput technologies used in biology have resulted in the accumulation of vast amounts of data in the literature, it is becoming difficult for individual investigators to directly benefit from this data because they are not easily accessible. Databases have assumed a crucial role in assimilating and storing information that could enable future discoveries. To this end, our group has developed two resources - Human Protein Reference Database (HPRD) and Human Proteinpedia - that provide integrated information pertaining to human proteins. These databases contain information on a number of features of proteins that have been discovered using various experimental methods. Human Proteinpedia was developed as a portal for community participation to annotate and share proteomic data using HPRD as the scaffold. It allows proteomic investigators to even share unpublished data and provides an effective medium 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, classical genetics, and chemical genetics in understanding the relationships among genome, gene functions, and living systems.

Evaluation of several MS/MS search algorithms for analysis of spectra derived from electron transfer dissociation experiments.

Electron transfer dissociation (ETD) is increasingly becoming popular for high-throughput experiments especially in the identification of the labile post-translational modifications. Most search algorithms that are currently in use for querying MS/MS data against protein databases have been optimized on the basis of matching fragment ions derived from collision induced dissociation of peptides, which are dominated by b and y ions. However, electron transfer dissociation of peptides generates completely different types of fragments: c and z ions. The goal of our study was to test the ability of different search algorithms to handle data from this fragmentation method. We compared four MS/MS search algorithms (OMSSA, Mascot, Spectrum Mill, and X!Tandem) using approximately 170,000 spectra generated from a standard protein mix, as well as from complex proteomic samples which included a large number of phosphopeptides. Our analysis revealed (1) greater differences between algorithms than has been previously reported for CID data, (2) a significant charge state bias resulting in >60-fold difference in the numbers of matched doubly charged peptides, and (3) identification of 70% more peptides by the best performing algorithm than the algorithm identifying the least number of peptides. Our results indicate that the search engines for analyzing ETD derived MS/MS spectra are still in their early days and that multiple search engines could be used to reduce individual biases of algorithms.

PathBuilder--open source software for annotating and developing pathway resources.

SUMMARY: We have developed PathBuilder, an open-source web application to annotate biological information pertaining to signaling pathways and to create web-based pathway resources. PathBuilder enables annotation of molecular events including protein-protein interactions, enzyme-substrate relationships and protein translocation events either manually or through automated importing of data from other databases. Salient features of PathBuilder include automatic validation of data formats, built-in modules for visualization of pathways, automated import of data from other pathway resources, export of data in several standard data exchange formats and an application programming interface for retrieving existing pathway datasets. AVAILABILITY: PathBuilder is freely available for download at http://pathbuilder.sourceforge.net/ under the terms of GNU lesser general public license (LGPL: http://www.gnu.org/copyleft/lesser.html). The software is platform independent and has been tested on Windows and Linux platforms. CONTACT: pandey@jhmi.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Human Protein Reference Database--2009 update.

Human Protein Reference Database (HPRD--http://www.hprd.org/), initially described in 2003, is a database of curated proteomic information pertaining to human proteins. We have recently added a number of new features in HPRD. These include PhosphoMotif Finder, which allows users to find the presence of over 320 experimentally verified phosphorylation motifs in proteins of interest. Another new feature is a protein distributed annotation system--Human Proteinpedia (http://www.humanproteinpedia.org/)--through which laboratories can submit their data, which is mapped onto protein entries in HPRD. Over 75 laboratories involved in proteomics research have already participated in this effort by submitting data for over 15,000 human proteins. The submitted data includes mass spectrometry and protein microarray-derived data, among other data types. Finally, HPRD is also linked to a compendium of human signaling pathways developed by our group, NetPath (http://www.netpath.org/), which currently contains annotations for several cancer and immune signaling pathways. Since the last update, more than 5500 new protein sequences have been added, making HPRD a comprehensive resource for studying the human proteome.