FragViz: visualization of fragmented networks

BACKGROUND

Researchers in systems biology use network visualization to summarize the results of their analysis. Such networks often include unconnected components, which popular network alignment algorithms place arbitrarily with respect to the rest of the network. This can lead to misinterpretations due to the proximity of otherwise unrelated elements.

RESULTS

We propose a new network layout optimization technique called FragViz which can incorporate additional information on relations between unconnected network components. It uses a two-step approach by first arranging the nodes within each of the components and then placing the components so that their proximity in the network corresponds to their relatedness. In the experimental study with the leukemia gene networks we demonstrate that FragViz can obtain network layouts which are more interpretable and hold additional information that could not be exposed using classical network layout optimization algorithms.

CONCLUSIONS

Network visualization relies on computational techniques for proper placement of objects under consideration. These algorithms need to be fast so that they can be incorporated in responsive interfaces required by the explorative data analysis environments. Our layout optimization technique FragViz meets these requirements and specifically addresses the visualization of fragmented networks, for which standard algorithms do not consider similarities between unconnected components. The experiments confirmed the claims on speed and accuracy of the proposed solution.

Trefoil factor: implications for clinical laboratory diagnostics

Trefoil factors (TFF), expressed physiologically in the secretory cells of gastrointestinal mucosa and other mucosal tissues, compose a unique peptide family with important biologic functions. Aberrant expression and/or continued secretion of TFF might exist concurrently with the processes of digestive system inflammation, tumor or other related pathological changes. Previous pathological studies have shown that the abnormal expression of TFF is associated with the prognosis of digestive system diseases. The results obtained from gene expression profiling, tumor secretomics and bioinformatic analysis have positively confirmed that clinical detection of TFF peptides or mRNAs has an excellent diagnostic performance to detect digestive system diseases, especially for the monitoring of recurrence or metastasis of tumors and for the estimation of therapeutic efficacy and prognosis. Therefore, TFF is a potential biomarker for diagnosis, treatment and prognosis of digestive system diseases.

MatrixDB, the extracellular matrix interaction database

MatrixDB (http://matrixdb.ibcp.fr) is a freely available database focused on interactions established by extracellular proteins and polysaccharides. Only few databases report protein-polysaccharide interactions and, to the best of our knowledge, there is no other database of extracellular interactions. MatrixDB takes into account the multimeric nature of several extracellular protein families for the curation of interactions, and reports interactions with individual polypeptide chains or with multimers, considered as permanent complexes, when appropriate. MatrixDB is a member of the International Molecular Exchange consortium (IMEx) and has adopted the PSI-MI standards for the curation and the exchange of interaction data. MatrixDB stores experimental data from our laboratory, data from literature curation, data imported from IMEx databases, and data from the Human Protein Reference Database. MatrixDB is focused on mammalian interactions, but aims to integrate interaction datasets of model organisms when available. MatrixDB provides direct links to databases recapitulating mutations in genes encoding extracellular proteins, to UniGene and to the Human Protein Atlas that shows expression and localization of proteins in a large variety of normal human tissues and cells. MatrixDB allows researchers to perform customized queries and to build tissue- and disease-specific interaction networks that can be visualized and analyzed with Cytoscape or Medusa.

Candidate gene prioritization by network analysis of differential expression using machine learning approaches

Background

Discovering novel disease genes is still challenging for diseases for which no prior knowledge - such as known disease genes or disease-related pathways - is available. Performing genetic studies frequently results in large lists of candidate genes of which only few can be followed up for further investigation. We have recently developed a computational method for constitutional genetic disorders that identifies the most promising candidate genes by replacing prior knowledge by experimental data of differential gene expression between affected and healthy individuals.

To improve the performance of our prioritization strategy, we have extended our previous work by applying different machine learning approaches that identify promising candidate genes by determining whether a gene is surrounded by highly differentially expressed genes in a functional association or protein-protein interaction network.

Results

We have proposed three strategies scoring disease candidate genes relying on network-based machine learning approaches, such as kernel ridge regression, heat kernel, and Arnoldi kernel approximation. For comparison purposes, a local measure based on the expression of the direct neighbors is also computed. We have benchmarked these strategies on 40 publicly available knockout experiments in mice, and performance was assessed against results obtained using a standard procedure in genetics that ranks candidate genes based solely on their differential expression levels (Simple Expression Ranking). Our results showed that our four strategies could outperform this standard procedure and that the best results were obtained using the Heat Kernel Diffusion Ranking leading to an average ranking position of 8 out of 100 genes, an AUC value of 92.3% and an error reduction of 52.8% relative to the standard procedure approach which ranked the knockout gene on average at position 17 with an AUC value of 83.7%.

Conclusion

In this study we could identify promising candidate genes using network based machine learning approaches even if no knowledge is available about the disease or phenotype.

A transcriptional network signature characterizes lung cancer subtypes

BACKGROUND

Transcriptional networks play a central role in cancer development. The authors described a systems biology approach to cancer classification based on the reverse engineering of the transcriptional network surrounding the 2 most common types of lung cancer: adenocarcinoma (AC) and squamous cell carcinoma (SCC).

METHODS

A transcriptional network classifier was inferred from the molecular profiles of 111 human lung carcinomas. The authors tested its classification accuracy in 7 independent cohorts, for a total of 422 subjects of Caucasian, African, and Asian descent.

RESULTS

The model for distinguishing AC from SCC was a 25-gene network signature. Its performance on the 7 independent cohorts achieved 95.2% classification accuracy. Even more surprisingly, 95% of this accuracy was explained by the interplay of 3 genes (KRT6A, KRT6B, KRT6C) on a narrow cytoband of chromosome 12. The role of this chromosomal region in distinguishing AC and SCC was further confirmed by the analysis of another group of 28 independent subjects assayed by DNA copy number changes. The copy number variations of bands 12q12, 12q13, and 12q12-13 discriminated these samples with 84% accuracy.

CONCLUSIONS

These results suggest the existence of a robust signature localized in a relatively small area of the genome, and show the clinical potential of reverse engineering transcriptional networks from molecular profiles.

Membrane proteomic analysis of pancreatic cancer cells

Background

Pancreatic cancer is one of the most aggressive human tumors due to its high potential of local invasion and metastasis. The aim of this study was to characterize the membrane proteomes of pancreatic ductal adenocarcinoma (PDAC) cells of primary and metastatic origins, and to identify potential target proteins related to metastasis of pancreatic cancer.

Methods

Membrane/membrane-associated proteins were isolated from AsPC-1 and BxPC-3 cells and identified with a proteomic approach based on SDS-PAGE, in-gel tryptic digestion and liquid chromatography with tandem mass spectrometry (LC-MS/MS). X! Tandem was used for database searching against the SwissProt human protein database.

Results

We identified 221 & 208 proteins from AsPC-1 and BxPC-3 cells, respectively, most of which are membrane or membrane-associated proteins. A hundred and nine proteins were found in both cell lines while the others were present in either AsPC-1 or BxPC-3 cells. Differentially expressed proteins between two cell lines include modulators of cell adhesion, cell motility or tumor invasion as well as metabolic enzymes involved in glycolysis, tricarboxylic acid cycle, or nucleotide/lipid metabolism.

Conclusion

Membrane proteomes of AsPC-1 (metastatic) and BxPC-3 (primary) cells are remarkably different. The differentially expressed membrane proteins may serve as potential targets for diagnostic and therapeutic interventions.

Protein connectivity and protein complexity promotes human gene duplicability in a mutually exclusive manner

It has previously been reported that protein complexity (i.e. number of subunits in a protein complex) is negatively correlated to gene duplicability in yeast as well as in humans. However, unlike in yeast, protein connectivity in a protein–protein interaction network has a positive correlation with gene duplicability in human genes. In the present study, we have analyzed 1732 human and 1269 yeast proteins that are present both in a protein–protein interaction network as well as in a protein complex network. In the human case, we observed that both protein connectivity and protein complexity complement each other in a mutually exclusive manner over gene duplicability in a positive direction. Analysis of human haploinsufficient proteins and large protein complexes (complex size >10) shows that when protein connectivity does not have any direct association with gene duplicability, there exists a positive correlation between gene duplicability and protein complexity. The same trend, however, is not found in case of yeast, where both protein connectivity and protein complexity independently guide gene duplicability in the negative direction. We conclude that the higher rate of duplication of human genes may be attributed to organismal complexity either by increasing connectivity in the protein–protein interaction network or by increasing protein complexity.

A yeast's eye view of mammalian reproduction: cross-species gene co-expression in meiotic prophase

Background

Meiotic prophase is a critical stage in sexual reproduction. Aberrant chromosome recombination during this stage is a leading cause of human miscarriages and birth defects. However, due to the experimental intractability of mammalian gonads, only a very limited number of meiotic genes have been characterized. Here we aim to identify novel meiotic genes important in human reproduction through computational mining of cross-species and cross-sex time-series expression data from budding yeast, mouse postnatal testis, mouse embryonic ovary, and human fetal ovary.

Results

Orthologous gene pairs were ranked by order statistics according to their co-expression profiles across species, allowing us to infer conserved meiotic genes despite obvious differences in cellular synchronicity and composition in organisms. We demonstrated that conserved co-expression networks could successfully recover known meiotic genes, including homologous recombination genes, chromatin cohesion genes, and genes regulating meiotic entry. We also showed that conserved co-expression pairs exhibit functional connections, as evidenced by the annotation similarity in Gene Ontology and overlap with physical interactions. More importantly, we predicted six new meiotic genes through their co-expression linkages with known meiotic genes, and subsequently used the genetically more amenable yeast system for experimental validation. The deletion mutants of all six genes showed sporulation defects, equivalent to a 100% validation rate.

Conclusions

We identified evolutionarily conserved gene modules in meiotic prophase by integrating cross-species and cross-sex expression profiles from budding yeast, mouse, and human. Our co-expression linkage analyses confirmed known meiotic genes and identified several novel genes that might be critical players in meiosis in multiple species. These results demonstrate that our approach is highly efficient to discover evolutionarily conserved novel meiotic genes, and yeast can serve as a valuable model system for investigating mammalian meiotic prophase.

Profiling protein expression and interactions: proximity ligation as a tool for personalized medicine

The ability to detect very low levels of expressed proteins has enormous potential for early diagnostics and intervention at curable stages of disease. An extended range of targets such as interacting or post-translationally modified proteins can further improve the potential for diagnostics and patient stratification, and for monitoring response to treatment. These are critical building blocks for personalized treatment strategies to manage disease. The past few decades have seen a remarkably improved understanding of the molecular basis of disease in general, and of tumour formation and progression in particular. This accumulated knowledge creates opportunities to develop drugs that specifically target molecules or molecular complexes critical for survival and expansion of tumour cells. However, tumours are highly variable between patients, necessitating the development of diagnostic tools to individualize treatment through parallel analysis of sets of biomarkers. The proximity ligation assay (PLA) can address many of the requirements for advanced molecular analysis. The method builds on the principle that recognition of target proteins by two, three or more antibodies can bring in proximity DNA strands attached to the antibodies. The DNA strands can then participate in ligation reactions, giving rise to molecules that are amplified for highly sensitive detection. PLA is particularly well suited for sensitive, specific and multiplexed analysis of protein expression, post-translational modifications and protein-protein interactions. The analysis of this extended range of biomarkers will prove critical for the development and implementation of personalized medicine.

The grand challenge to decipher the cancer proteome

The quest to decipher protein alterations in cancer has spanned well over half a century. The vast dynamic range of protein abundance coupled with a plethora of isoforms and disease heterogeneity have been formidable challenges. Progress in cancer proteomics has substantially paralleled technological developments. Advances in analytical techniques and the implementation of strategies to de-complex the proteome into manageable components have allowed proteins across a wide dynamic range to be explored. The massive amounts of data that can currently be collected through proteomics allow the near-complete definition of cancer subproteomes, which reveals the alterations in signalling and developmental pathways. This allows the discovery of predictive biomarkers and the annotation of the cancer genome based on proteomic findings. There remains a considerable need for infrastructure development and the organized collaborative efforts to efficiently mine the cancer proteome.

Discovery of putative pancreatic cancer biomarkers using subcellular proteomics

Pancreatic cancer (PC) is a highly aggressive disease that frequently remains undetected until it has progressed to an advanced, systemic stage. Successful treatment of PC is hindered by the lack of early detection. The application of proteomic analysis to PC combined with subcellular fractionation has introduced new possibilities in the field of biomarker discovery. We utilized matched pairs of pancreas tumor and non-tumor pancreas from patients undergoing tumor resection. The tissues were treated to obtain cellular protein fractions corresponding to cytosol, membrane, nucleus and cytoskeleton. The fractions were then separated by molecular weight and digested with trypsin, followed by liquid chromatography and tandem mass spectrometry. The spectra obtained were searched using Sequest engine and combined into a single analysis file to obtain a semi-quantitative number, spectral count, using Scaffold software. We identified 2393 unique proteins in non-tumor and cancer pancreas. Utilizing PLGEM statistical analysis we determined 104 proteins were significantly changed in cancer. From these, we further validated four secreted proteins that are up-regulated in cancer and have potential for development as minimally-invasive diagnostic markers. We conclude that subcellular fractionation followed by gel electrophoresis and tandem mass spectrometry is a powerful strategy for identification of differentially expressed proteins in pancreatic cancer.

Evolutionary analysis reveals low coverage as the major challenge for protein interaction network alignment

Local alignments of protein interaction networks have found little conservation among several species. While this could be a consequence of the incompleteness of interaction data-sets and presence of error, an intriguing prospect is that the process of network evolution is sufficient to erase any evidence of conservation. Here, we aim to test this hypothesis using models of network evolution and also investigate the role of error in the results of network alignment. We devised a distance metric based on summary statistics to assess the fit between experimental and simulated network alignments. Our results indicate that network evolution alone is unlikely to account for the poor quality alignments given by real data. Alignments of simulated networks undergoing evolution are considerably (4 to 5 times) larger than real alignments. We compare several error models in their ability to explain this discrepancy. Our estimates of false negative rates vary from 20 to 60% dependent on whether incomplete proteome sampling is taken into account or not. We also find that false positives appear to affect network alignments little compared to false negatives indicating that incompleteness, not spurious links, is the major challenge for interactome-level comparisons.

Protein networks reveal detection bias and species consistency when analysed by information-theoretic methods

We apply our recently developed information-theoretic measures for the characterisation and comparison of protein–protein interaction networks. These measures are used to quantify topological network features via macroscopic statistical properties. Network differences are assessed based on these macroscopic properties as opposed to microscopic overlap, homology information or motif occurrences. We present the results of a large–scale analysis of protein–protein interaction networks. Precise null models are used in our analyses, allowing for reliable interpretation of the results. By quantifying the methodological biases of the experimental data, we can define an information threshold above which networks may be deemed to comprise consistent macroscopic topological properties, despite their small microscopic overlaps. Based on this rationale, data from yeast–two–hybrid methods are sufficiently consistent to allow for intra–species comparisons (between different experiments) and inter–species comparisons, while data from affinity–purification mass–spectrometry methods show large differences even within intra–species comparisons.

From NPC therapeutic target identification to potential treatment strategy

Nasopharyngeal carcinoma (NPC) is relatively rare in Western countries but is a common cancer in southern Asia. Many differentially expressed genes have been linked to NPC; however, how to prioritize therapeutic targets and potential drugs from unsorted gene lists remains largely unknown. We first collected 558 upregulated and 993 downregulated NPC genes from published microarray data and the primary literatures. We then postulated that conversion of gene signatures into the protein-protein interaction network and analyzing the network topologically could provide insight into key regulators involved in tumorigenesis of NPC. Of particular interest was the presence of cliques, called fully connected subgraphs, in the inferred NPC networks. These clique-based hubs, connecting with more than three queries and ranked higher than other nodes in the NPC protein-protein interaction network, were further narrowed down by pathway analysis to retrieve 24 upregulated and 6 downregulated bottleneck genes for predicting NPC carcinogenesis. Moreover, additional oncogenes, tumor suppressor genes, genes involved in protein complexes, and genes obtained after functional profiling were merged with the bottleneck genes to form the final gene signature of 38 upregulated and 10 downregulated genes. We used the initial and final NPC gene signatures to query the Connectivity Map, respectively, and found that target reduction through our pipeline could efficiently uncover potential drugs with cytotoxicity to NPC cancer cells. An integrative Web site (http://140.109.23.188:8080/NPC) was established to facilitate future NPC research. This in silico approach, from target prioritization to potential drugs identification, might be an effective method for various cancer researches.

A Poisson model for random multigraphs

MOTIVATION

Biological networks are often modeled by random graphs. A better modeling vehicle is a multigraph where each pair of nodes is connected by a Poisson number of edges. In the current model, the mean number of edges equals the product of two propensities, one for each node. In this context it is possible to construct a simple and effective algorithm for rapid maximum likelihood estimation of all propensities. Given estimated propensities, it is then possible to test statistically for functionally connected nodes that show an excess of observed edges over expected edges. The model extends readily to directed multigraphs. Here, propensities are replaced by outgoing and incoming propensities.

RESULTS

The theory is applied to real data on neuronal connections, interacting genes in radiation hybrids, interacting proteins in a literature curated database, and letter and word pairs in seven Shaskespearean plays.

AVAILABILITY

All data used are fully available online from their respective sites. Source code and software is available from http://code.google.com/p/poisson-multigraph/.

A novel tissue-specific meta-analysis approach for gene expression predictions, initiated with a mammalian gene expression testis database

BACKGROUND

In the recent years, there has been a rise in gene expression profiling reports. Unfortunately, it has not been possible to make maximum use of available gene expression data. Many databases and programs can be used to derive the possible expression patterns of mammalian genes, based on existing data. However, these available resources have limitations. For example, it is not possible to obtain a list of genes that are expressed in certain conditions. To overcome such limitations, we have taken up a new strategy to predict gene expression patterns using available information, for one tissue at a time.

RESULTS

The first step of this approach involved manual collection of maximum data derived from large-scale (genome-wide) gene expression studies, pertaining to mammalian testis. These data have been compiled into a Mammalian Gene Expression Testis-database (MGEx-Tdb). This process resulted in a richer collection of gene expression data compared to other databases/resources, for multiple testicular conditions. The gene-lists collected this way in turn were exploited to derive a 'consensus' expression status for each gene, across studies. The expression information obtained from the newly developed database mostly agreed with results from multiple small-scale studies on selected genes. A comparative analysis showed that MGEx-Tdb can retrieve the gene expression information more efficiently than other commonly used databases. It has the ability to provide a clear expression status (transcribed or dormant) for most genes, in the testis tissue, under several specific physiological/experimental conditions and/or cell-types.

CONCLUSIONS

Manual compilation of gene expression data, which can be a painstaking process, followed by a consensus expression status determination for specific locations and conditions, can be a reliable way of making use of the existing data to predict gene expression patterns. MGEx-Tdb provides expression information for 14 different combinations of specific locations and conditions in humans (25,158 genes), 79 in mice (22,919 genes) and 23 in rats (14,108 genes). It is also the first system that can predict expression of genes with a 'reliability-score', which is calculated based on the extent of agreements and contradictions across gene-sets/studies. This new platform is publicly available at the following web address: http://resource.ibab.ac.in/MGEx-Tdb/.

Musite, a tool for global prediction of general and kinase-specific phosphorylation sites

Reversible protein phosphorylation is one of the most pervasive post-translational modifications, regulating diverse cellular processes in various organisms. High throughput experimental studies using mass spectrometry have identified many phosphorylation sites, primarily from eukaryotes. However, the vast majority of phosphorylation sites remain undiscovered, even in well studied systems. Because mass spectrometry-based experimental approaches for identifying phosphorylation events are costly, time-consuming, and biased toward abundant proteins and proteotypic peptides, in silico prediction of phosphorylation sites is potentially a useful alternative strategy for whole proteome annotation. Because of various limitations, current phosphorylation site prediction tools were not well designed for comprehensive assessment of proteomes. Here, we present a novel software tool, Musite, specifically designed for large scale predictions of both general and kinase-specific phosphorylation sites. We collected phosphoproteomics data in multiple organisms from several reliable sources and used them to train prediction models by a comprehensive machine-learning approach that integrates local sequence similarities to known phosphorylation sites, protein disorder scores, and amino acid frequencies. Application of Musite on several proteomes yielded tens of thousands of phosphorylation site predictions at a high stringency level. Cross-validation tests show that Musite achieves some improvement over existing tools in predicting general phosphorylation sites, and it is at least comparable with those for predicting kinase-specific phosphorylation sites. In Musite V1.0, we have trained general prediction models for six organisms and kinase-specific prediction models for 13 kinases or kinase families. Although the current pretrained models were not correlated with any particular cellular conditions, Musite provides a unique functionality for training customized prediction models (including condition-specific models) from users' own data. In addition, with its easily extensible open source application programming interface, Musite is aimed at being an open platform for community-based development of machine learning-based phosphorylation site prediction applications. Musite is available at http://musite.sourceforge.net/.

Transcription profiling of Epstein-Barr virus nuclear antigen (EBNA)-1 expressing cells suggests targeting of chromatin remodeling complexes

The Epstein-Barr virus (EBV) encoded nuclear antigen (EBNA)-1 regulates virus replication and transcription, and participates in the remodeling of the cellular environment that accompanies EBV induced B-cell immortalization and malignant transformation. The putative cellular targets of these effects of EBNA-1 are largely unknown. To address this issue we have profiled the transcriptional changes induced by short- and long-term expression of EBNA-1 in the EBV negative B-cell lymphoma BJAB. Three hundred and nineteen cellular genes were regulated in a conditional transfectant shortly after EBNA-1 induction while a ten fold higher number of genes was regulated upon continuous EBNA-1 expression. Promoter analysis of the differentially regulated genes demonstrated a significant enrichment of putative EBNA-1 binding sites suggesting that EBNA-1 may directly influence the transcription of a subset of genes. Gene ontology analysis of forty seven genes that were consistently regulated independently on the time of EBNA-1 expression revealed an unexpected enrichment of genes involved in the maintenance of chromatin architecture. The interaction network of the affected gene products suggests that EBNA-1 may promote a broad rearrangement of the cellular transcription landscape by altering the expression of key components of chromatin remodeling complexes.

Personalized medicine: marking a new epoch in cancer patient management

Personalized medicine (PM) is defined as "a form of medicine that uses information about a person's genes, proteins, and environment to prevent, diagnose, and treat disease." The promise of PM has been on us for years. The suite of clinical applications of PM in cancer is broad, encompassing screening, diagnosis, prognosis, prediction of treatment efficacy, patient follow-up after surgery for early detection of recurrence, and the stratification of patients into cancer subgroup categories, allowing for individualized therapy. PM aims to eliminate the "one size fits all" model of medicine, which has centered on reaction to disease based on average responses to care. By dividing patients into unique cancer subgroups, treatment and follow-up can be tailored for each individual according to disease aggressiveness and the ability to respond to a certain treatment. PM is also shifting the emphasis of patient management from primary patient care to prevention and early intervention for high-risk individuals. In addition to classic single molecular markers, high-throughput approaches can be used for PM including whole genome sequencing, single-nucleotide polymorphism analysis, microarray analysis, and mass spectrometry. A common trend among these tools is their ability to analyze many targets simultaneously, thus increasing the sensitivity, specificity, and accuracy of biomarker discovery. Certain challenges need to be addressed in our transition to PM including assessment of cost, test standardization, and ethical issues. It is clear that PM will gradually continue to be incorporated into cancer patient management and will have a significant impact on our health care in the future.

Expression profile and protein translation of TMEM16A in murine smooth muscle

Recently, overexpression of the genes TMEM16A and TMEM16B has been shown to produce currents qualitatively similar to native Ca(2+)-activated Cl(-) currents (I(ClCa)) in vascular smooth muscle. However, there is no information about this new gene family in vascular smooth muscle, where Cl(-) channels are a major depolarizing mechanism. Qualitatively similar Cl(-) currents were evoked by a pipette solution containing 500 nM Ca(2+) in smooth muscle cells isolated from BALB/c mouse portal vein, thoracic aorta, and carotid artery. Quantitative PCR using SYBR Green chemistry and primers specific for transmembrane protein (TMEM) 16A or the closely related TMEM16B showed TMEM16A expression as follows: portal vein > thoracic aorta > carotid artery > brain. In addition, several alternatively spliced variant transcripts of TMEM16A were detected. In contrast, TMEM16B expression was very low in smooth muscle. Western blot analysis with different antibodies directed against TMEM16A revealed a number of products with a consistent band at ∼120 kDa, except portal vein, where an 80-kDa band predominated. TMEM16A protein was identified in the smooth muscle layers of 4-μm-thick slices of portal vein, thoracic aorta, and carotid artery. In isolated myocytes, fluorescence specific to a TMEM16A antibody was detected diffusely throughout the cytoplasm, as well as near the membrane. The same antibody used in Western blot analysis of lysates from vascular tissues also recognized an ∼147-kDa mouse TMEM16A-green fluorescent protein (GFP) fusion protein expressed in HEK 293 cells, which correlated to a similar band detected by a GFP antibody. Patch-clamp experiments revealed that I(ClCa) generated by transfection of TMEM16A-GFP in HEK 293 cells displayed remarkable similarities to I(ClCa) recorded in vascular myocytes, including slow kinetics, steep outward rectification, and a response similar to the pharmacological agent niflumic acid. This study shows that TMEM16A expression is robust in murine vascular smooth muscle cells, consolidating the view that this gene is a viable candidate for the native Ca(2+)-activated Cl(-) channel in this cell type.

Comprehensive transcriptome analysis of mouse embryonic stem cell adipogenesis unravels new processes of adipocyte development

Background

The current epidemic of obesity has caused a surge of interest in the study of adipose tissue formation. While major progress has been made in defining the molecular networks that control adipocyte terminal differentiation, the early steps of adipocyte development and the embryonic origin of this lineage remain largely unknown.

Results

Here we performed genome-wide analysis of gene expression during adipogenesis of mouse embryonic stem cells (ESCs). We then pursued comprehensive bioinformatic analyses, including de novo functional annotation and curation of the generated data within the context of biological pathways, to uncover novel biological functions associated with the early steps of adipocyte development. By combining in-depth gene regulation studies and in silico analysis of transcription factor binding site enrichment, we also provide insights into the transcriptional networks that might govern these early steps.

Conclusions

This study supports several biological findings: firstly, adipocyte development in mouse ESCs is coupled to blood vessel morphogenesis and neural development, just as it is during mouse development. Secondly, the early steps of adipocyte formation involve major changes in signaling and transcriptional networks. A large proportion of the transcription factors that we uncovered in mouse ESCs are also expressed in the mouse embryonic mesenchyme and in adipose tissues, demonstrating the power of our approach to probe for genes associated with early developmental processes on a genome-wide scale. Finally, we reveal a plethora of novel candidate genes for adipocyte development and present a unique resource that can be further explored in functional assays.

Network-based relating pharmacological and genomic spaces for drug target identification

BACKGROUND

Identifying drug targets is a critical step in pharmacology. Drug phenotypic and chemical indexes are two important indicators in this field. However, in previous studies, the indexes were always isolated and the candidate proteins were often limited to a small subset of the human genome.

METHODOLOGY/PRINCIPAL FINDINGS

Based on the correlations observed in pharmacological and genomic spaces, we develop a computational framework, drugCIPHER, to infer drug-target interactions in a genome-wide scale. Three linear regression models are proposed, which respectively relate drug therapeutic similarity, chemical similarity and their combination to the relevance of the targets on the basis of a protein-protein interaction network. Typically, the model integrating both drug therapeutic similarity and chemical similarity, drugCIPHER-MS, achieved an area under the Receiver Operating Characteristic (ROC) curve of 0.988 in the training set and 0.935 in the test set. Based on drugCIPHER-MS, a genome-wide map of drug biological fingerprints for 726 drugs is constructed, within which unexpected drug-drug relations emerged in 501 cases, implying possible novel applications or side effects.

CONCLUSIONS/SIGNIFICANCE

Our findings demonstrate that the integration of phenotypic and chemical indexes in pharmacological space and protein-protein interactions in genomic space can not only speed the genome-wide identification of drug targets but also find new applications for the existing drugs.

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.

Identifying potential survival strategies of HIV-1 through virus-host protein interaction networks

Background

The National Institute of Allergy and Infectious Diseases has launched the HIV-1 Human Protein Interaction Database in an effort to catalogue all published interactions between HIV-1 and human proteins. In order to systematically investigate these interactions functionally and dynamically, we have constructed an HIV-1 human protein interaction network. This network was analyzed for important proteins and processes that are specific for the HIV life-cycle. In order to expose viral strategies, network motif analysis was carried out showing reoccurring patterns in virus-host dynamics.

Results

Our analyses show that human proteins interacting with HIV form a densely connected and central sub-network within the total human protein interaction network. The evaluation of this sub-network for connectivity and centrality resulted in a set of proteins essential for the HIV life-cycle. Remarkably, we were able to associate proteins involved in RNA polymerase II transcription with hubs and proteasome formation with bottlenecks. Inferred network motifs show significant over-representation of positive and negative feedback patterns between virus and host. Strikingly, such patterns have never been reported in combined virus-host systems.

Conclusions

HIV infection results in a reprioritization of cellular processes reflected by an increase in the relative importance of transcriptional machinery and proteasome formation. We conclude that during the evolution of HIV, some patterns of interaction have been selected for resulting in a system where virus proteins preferably interact with central human proteins for direct control and with proteasomal proteins for indirect control over the cellular processes. Finally, the patterns described by network motifs illustrate how virus and host interact with one another.

Wnt signaling regulates mitochondrial physiology and insulin sensitivity

Mitochondria serve a critical role in physiology and disease. The genetic basis of mitochondrial regulation in mammalian cells has not yet been detailed. We performed a large-scale RNAi screen to systematically identify genes that affect mitochondrial abundance and function. This screen revealed previously unrecognized roles for >150 proteins in mitochondrial regulation. We report that increased Wnt signals are a potent activator of mitochondrial biogenesis and reactive oxygen species (ROS) generation, leading to DNA damage and acceleration of cellular senescence in primary cells. The signaling protein insulin receptor substrate-1 (IRS-1), shown here to be a transcriptional target of Wnt, is induced in this setting. The increased level of IRS-1 drives activation of mitochondrial biogenesis; furthermore, in insulin-responsive cell types, it enhances insulin signaling, raising the possibility that Wnt proteins may be used to modulate glucose homeostasis. Our results identify a key component of the mitochondrial regulatory apparatus with a potentially important link to metabolic and degenerative disorders.

Granins as disease-biomarkers: translational potential for psychiatric and neurological disorders

The identification of biomarkers represents a fundamental medical advance that can lead to an improved understanding of disease pathogenesis, and holds the potential to define surrogate diagnostic and prognostic endpoints. Because of the inherent difficulties in assessing brain function in patients and objectively identifying neurological and cognitive/emotional symptoms, future application of biomarkers to neurological and psychiatric disorders is extremely desirable. This article discusses the biomarker potential of the granin family, a group of acidic proteins present in the secretory granules of a wide variety of endocrine, neuronal and neuroendocrine cells: chromogranin A (CgA), CgB, Secretogranin II (SgII), SgIII, HISL-19 antigen, 7B2, NESP55, VGF and ProSAAS. Their relative abundance, functional significance, and secretion into the cerebrospinal fluid (CSF), saliva, and the general circulation have made granins tractable targets as biomarkers for many diseases of neuronal and endocrine origin, recently impacting diagnosis of a number of neurological and psychiatric disorders including amyotrophic lateral sclerosis (ALS), Alzheimer's disease, frontotemporal dementia, and schizophrenia. Although research has not yet validated the clinical utility of granins as surrogate endpoints for the progression or treatment of neurological or psychiatric disease, a growing body of experimental evidence indicates that the use of granins as biomarkers might be of great potential clinical interest. Advances that further elucidate the mechanism(s) of action of granins, coupled with improvements in biomarker technology and direct clinical application, should increase the translational effectiveness of this family of proteins in disease diagnosis and drug discovery.

Integration of pathway knowledge into a reweighted recursive feature elimination approach for risk stratification of cancer patients

MOTIVATION

One of the main goals of high-throughput gene-expression studies in cancer research is to identify prognostic gene signatures, which have the potential to predict the clinical outcome. It is common practice to investigate these questions using classification methods. However, standard methods merely rely on gene-expression data and assume the genes to be independent. Including pathway knowledge a priori into the classification process has recently been indicated as a promising way to increase classification accuracy as well as the interpretability and reproducibility of prognostic gene signatures.

RESULTS

We propose a new method called Reweighted Recursive Feature Elimination. It is based on the hypothesis that a gene with a low fold-change should have an increased influence on the classifier if it is connected to differentially expressed genes. We used a modified version of Google's PageRank algorithm to alter the ranking criterion of the SVM-RFE algorithm. Evaluations of our method on an integrated breast cancer dataset comprising 788 samples showed an improvement of the area under the receiver operator characteristic curve as well as in the reproducibility and interpretability of selected genes.

AVAILABILITY

The R code of the proposed algorithm is given in Supplementary Material.

Identifying differentially regulated subnetworks from phosphoproteomic data

Background

Various high throughput methods are available for detecting regulations at the level of transcription, translation or posttranslation (e.g. phosphorylation). Integrating these data with protein networks should make it possible to identify subnetworks that are significantly regulated. Furthermore, such integration can support identification of regulated entities from often noisy high throughput data. In particular, processing mass spectrometry-based phosphoproteomic data in this manner may expose signal transduction pathways and, in the case of experiments with drug-treated cells, reveal the drug's mode of action.

Results

Here, we introduce SubExtractor, an algorithm that combines phosphoproteomic data with protein network information from STRING to identify differentially regulated subnetworks and individual proteins. The method is based on a Bayesian probabilistic model combined with a genetic algorithm and rigorous significance testing. The Bayesian model accounts for information about both differential regulation and network topology. The method was tested with artificial data and subsequently applied to a comprehensive phosphoproteomics study investigating the mode of action of sorafenib, a small molecule kinase inhibitor.

Conclusions

SubExtractor reliably identifies differentially regulated subnetworks from phosphoproteomic data by integrating protein networks. The method can also be applied to gene or protein expression data.

Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways

Because mouse models play a crucial role in biomedical research related to the human nervous system, understanding the similarities and differences between mouse and human brain is of fundamental importance. Studies comparing transcription in human and mouse have come to varied conclusions, in part because of their relatively small sample sizes or underpowered methodologies. To better characterize gene expression differences between mouse and human, we took a systems-biology approach by using weighted gene coexpression network analysis on more than 1,000 microarrays from brain. We find that global network properties of the brain transcriptome are highly preserved between species. Furthermore, all modules of highly coexpressed genes identified in mouse were identified in human, with those related to conserved cellular functions showing the strongest between-species preservation. Modules corresponding to glial and neuronal cells were sufficiently preserved between mouse and human to permit identification of cross species cell-class marker genes. We also identify several robust human-specific modules, including one strongly correlated with measures of Alzheimer disease progression across multiple data sets, whose hubs are poorly-characterized genes likely involved in Alzheimer disease. We present multiple lines of evidence suggesting links between neurodegenerative disease and glial cell types in human, including human-specific correlation of presenilin-1 with oligodendrocyte markers, and significant enrichment for known neurodegenerative disease genes in microglial modules. Together, this work identifies convergent and divergent pathways in mouse and human, and provides a systematic framework that will be useful for understanding the applicability of mouse models for human brain disorders.

Modular composition predicts kinase/substrate interactions

Background

Phosphorylation events direct the flow of signals and metabolites along cellular protein networks. Current annotations of kinase-substrate binding events are far from complete. In this study, we scanned the entire human protein sequences using the PROSITE domain annotation tool to identify patterns of domain composition in kinases and their substrates. We identified statistically enriched pairs of strings of domains (signature pairs) in kinase-substrate couples presented in the 2006 version of the PTM database.

Results

The signature pairs enriched in kinase - substrate binding interactions turned out to be highly specific to kinase subtypes. The resulting list of signature pairs predicted kinase-substrate interactions in validation dataset not used in learning with high statistical accuracy.

Conclusions

The method presented here produces predictions of protein phosphorylation events with high accuracy and mid-level coverage. Our method can be used in expanding the currently available drafts of cell signaling pathways and thus will be an important tool in the development of combination drug therapies targeting complex diseases.

The B-lymphoid Grb2 interaction code

The growth factor receptor-bound protein 2 (Grb2) is a ubiquitously expressed and evolutionary conserved adapter protein possessing a plethora of described interaction partners for the regulation of signal transduction. In B lymphocytes, the Grb2-mediated scaffolding function controls the assembly and subcellular targeting of activating as well as inhibitory signalosomes in response to ligation of the antigen receptor. Also, integration of simultaneous signals from B-cell coreceptors that amplify or attenuate antigen receptor signal output relies on Grb2. Hence, Grb2 is an essential signal integrator. The key question remains, however, of how pathway specificity can be maintained during signal homeostasis critically required for the balance between immune cell activation and tolerance induction. Here, we summarize the molecular network of Grb2 in B cells and introduce a proteomic approach to elucidate the interactome of Grb2 in vivo.

Association of elevated blood pressure and impaired vasorelaxation in experimental Sprague-Dawley rats fed with heated vegetable oil

Background

Poor control of blood pressure leads to hypertension which is a major risk factor for development of cardiovascular disease. The present study aimed to explore possible mechanisms of elevation in blood pressure following consumption of heated vegetable oil.

Methods

Forty-two male Sprague-Dawley rats were equally divided into six groups: Group I (control) - normal rat chow, Group II - fresh soy oil, Group III - soy oil heated once, Group IV - soy oil heated twice, Group V - soy oil heated five times, Group VI - soy oil heated ten times. Blood pressure was measured at the baseline level and at a monthly interval for six months. Plasma nitric oxide, heme oxygenase and angiotensin-converting enzyme levels were measured prior to treatment, at month-three and month-six later. At the end of treatment, the rats were sacrificed and thoracic aortas were taken for measurement of vascular reactivity.

Results

Blood pressure increased significantly (p < 0.01) in the repeatedly heated oil groups compared to the control and fresh soy oil groups. Consumption of diet containing repeatedly heated oil resulted higher plasma angiotensin-converting enzyme level and lower nitric oxide content and heme oxygenase concentration. Reheated soy oil groups exhibited attenuated relaxation in response to acetylcholine or sodium nitroprusside, and greater contraction to phenylephrine.

Conclusion

As a result of consumption of repeatedly heated soy oil, an elevation in blood pressure was observed which may be due to the quantitative changes in endothelium dependent and independent factors including enzymes directly involved in the regulation of blood pressure.

Elevated level of anterior gradient-2 in pancreatic juice from patients with pre-malignant pancreatic neoplasia

Background

Pancreatic intraepithelial neoplasias (PanINs) are precursors of malignant pancreatic cancer, an ideal stage for early cancer detection. We applied quantitative proteomics to identify aberrantly elevated proteins in pancreatic juice samples derived from patients with PanIN3.

Results

Twenty proteins were found elevated in all three PanIN juices by at least two-fold. Among these proteins, anterior gradient-2 (AGR2) was found to be 2-10 fold elevated in PanIN3 juice samples analyzed by quantitative proteomics. An ELISA assay was developed to evaluate AGR2 levels in 51 pancreatic juice samples and 23 serum samples from patients with pancreatic cancer, pre-malignant lesions (including PanIN3, PanIN2, Intraductal Papillary Mucinous Neoplasms (IPMNs)) and benign disease controls (including chronic pancreatitis). AGR2 levels in the pancreatic juice samples were found significantly elevated in patients with pre-malignant conditions (PanINs and IPMNs) as well as pancreatic cancer compared to control samples (p ≤ 0.03). By ROC analysis, the AGR2 ELISA achieved 67% sensitivity at 90% specificity in predicting PanIN3 juice samples from the benign disease controls.

Conclusions

These results suggest that elevation of AGR2 levels in pancreatic juice occurs in early pancreatic cancer progression and could be further investigated as a potential candidate juice biomarker for early detection of pancreatic cancer.

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.

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.

Phosphorylated and nonphosphorylated serine and threonine residues evolve at different rates in mammals

Protein phosphorylation plays an important role in the regulation of protein function. Phosphorylated residues are generally assumed to be subject to functional constraint, but it has recently been suggested from a comparison of distantly related vertebrate species that most phosphorylated residues evolve at the rates consistent with the surrounding regions. To resolve the controversy, we infer the ancestral phosphoproteome of human and mouse to compare the evolutionary rates of phosphorylated and nonphosphorylated serine (S), threonine (T), and tyrosine (Y) residues. This approach enables accurate estimation of evolutionary rates as it does not assume deep conservation of phosphorylated residues. We show that phosphorylated S/T residues tend to evolve more slowly than nonphosphorylated S/T residues not only in disordered but also in ordered protein regions, indicating evolutionary conservation of phosphorylated S/T residues in mammals. Thus, phosphorylated S/T residues tend to be subject to stronger functional constraint than nonphosphorylated residues regardless of the protein regions in which they reside. In contrast, phosphorylated Y residues evolve at similar rates as nonphosphorylated ones. We also find that the human lineage has gained more phosphorylated T residues and lost fewer phosphorylated Y residues than the mouse lineage. The cause of the gain/loss imbalance remains a mystery but should be worth exploring.

Screening for therapeutic targets of vorinostat by SILAC-based proteomic analysis in human breast cancer cells

Histone deacetylases (HDACs) play critical roles in silencing tumor suppressor genes. HDAC inhibitors reactivate tumor suppressor genes and inhibit tumor cell growth in vitro and in vivo, and several HDAC inhibitors are currently being evaluated in clinical trials for cancer therapy. A comprehensive analysis of proteins regulated by HDAC inhibitors would enhance our ability to define and characterize their essential therapeutic targets. Here, we employed stable isotope labeling with amino acids in cell culture-based quantitative proteomics to identify acetylated proteins in human breast cancer cells. Treatment with the clinically relevant HDAC inhibitor, suberoylanilide hydroxamic acid (vorinostat), induces lysine acetylation of 61 proteins in MDA-MB-231 human breast cancer cells. Suberoylanilide hydroxamic acid not only induces lysine acetylation in chromatin-associated proteins, but also acetylates previously unrecognized nonhistone proteins, including transcriptional factors and regulators, chaperones, cell structure proteins, and glycolytic enzymes in a time-dependent manner. Knowledge of the full repertoire of acetylated proteins will provide a foundation for further defining the functions of HDACs in cancer cells.

Global phosphoproteomic effects of natural tyrosine kinase inhibitor, genistein, on signaling pathways

Genistein is a natural protein tyrosine kinase inhibitor that exerts anti-cancer effect by inducing G2/M arrest and apoptosis. However, the phosphotyrosine signaling pathways mediated by genistein are largely unknown. In this study, we combined tyrosine phosphoprotein enrichment with MS-based quantitative proteomics technology to globally identify genistein-regulated tyrosine phosphoproteins aiming to depict genistein-inhibited phosphotyrosine cascades. Our experiments resulted in the identification of 213 phosphotyrosine sites on 181 genistein-regulated proteins. Many identified phosphoproteins, including nine protein kinases, eight receptors, five protein phosphatases, seven transcriptical regulators and four signal adaptors, were novel inhibitory effectors with no previously known function in the anti-cancer mechanism of genistein. Functional analysis suggested that genistein-regulated protein tyrosine phosphorylation mainly by inhibiting the activity of tyrosine kinase EGFR, PDGFR, insulin receptor, Abl, Fgr, Itk, Fyn and Src. Core signaling molecules inhibited by genistein can be functionally categorized into the canonial Receptor-MAPK or Receptor-PI3K/AKT cascades. The method used here may be suitable for the identification of inhibitory effectors and tyrosine kinases regulated by anti-cancer drugs.

Literature-based discovery of IFN-gamma and vaccine-mediated gene interaction networks

Interferon-gamma (IFN-gamma) regulates various immune responses that are often critical for vaccine-induced protection. In order to annotate the IFN-gamma-related gene interaction network from a large amount of IFN-gamma research reported in the literature, a literature-based discovery approach was applied with a combination of natural language processing (NLP) and network centrality analysis. The interaction network of human IFN-gamma (Gene symbol: IFNG) and its vaccine-specific subnetwork were automatically extracted using abstracts from all articles in PubMed. Four network centrality metrics were further calculated to rank the genes in the constructed networks. The resulting generic IFNG network contains 1060 genes and 26313 interactions among these genes. The vaccine-specific subnetwork contains 102 genes and 154 interactions. Fifty six genes such as TNF, NFKB1, IL2, IL6, and MAPK8 were ranked among the top 25 by at least one of the centrality methods in one or both networks. Gene enrichment analysis indicated that these genes were classified in various immune mechanisms such as response to extracellular stimulus, lymphocyte activation, and regulation of apoptosis. Literature evidence was manually curated for the IFN-gamma relatedness of 56 genes and vaccine development relatedness for 52 genes. This study also generated many new hypotheses worth further experimental studies.

Serum tumor markers in pancreatic cancer-recent discoveries

The low prevalence of pancreatic cancer remains an obstacle to the development of effective screening tools in an asymptomatic population. However, development of effective serologic markers still offers the potential for improvement of diagnostic capabilities, especially for subpopulations of patients with high risk for pancreatic cancer. The accurate identification of patients with pancreatic cancer and the exclusion of disease in those with benign disorders remain important goals. While clinical experience largely dismissed many candidate markers as useful markers of pancreatic cancer, CA19-9 continues to show promise. The present review highlights the development and the properties of different tumor markers in pancreatic cancer and their impact on the diagnostic and treatment of this aggressive disease.

Candidate serological biomarkers for cancer identified from the secretomes of 23 cancer cell lines and the human protein atlas

Although cancer cell secretome profiling is a promising strategy used to identify potential body fluid-accessible cancer biomarkers, questions remain regarding the depth to which the cancer cell secretome can be mined and the efficiency with which researchers can select useful candidates from the growing list of identified proteins. Therefore, we analyzed the secretomes of 23 human cancer cell lines derived from 11 cancer types using one-dimensional SDS-PAGE and nano-LC-MS/MS performed on an LTQ-Orbitrap mass spectrometer to generate a more comprehensive cancer cell secretome. A total of 31,180 proteins was detected, accounting for 4,584 non-redundant proteins, with an average of 1,300 proteins identified per cell line. Using protein secretion-predictive algorithms, 55.8% of the proteins appeared to be released or shed from cells. The identified proteins were selected as potential marker candidates according to three strategies: (i) proteins apparently secreted by one cancer type but not by others (cancer type-specific marker candidates), (ii) proteins released by most cancer cell lines (pan-cancer marker candidates), and (iii) proteins putatively linked to cancer-relevant pathways. We then examined protein expression profiles in the Human Protein Atlas to identify biomarker candidates that were simultaneously detected in the secretomes and highly expressed in cancer tissues. This analysis yielded 6-137 marker candidates selective for each tumor type and 94 potential pan-cancer markers. Among these, we selectively validated monocyte differentiation antigen CD14 (for liver cancer), stromal cell-derived factor 1 (for lung cancer), and cathepsin L1 and interferon-induced 17-kDa protein (for nasopharyngeal carcinoma) as potential serological cancer markers. In summary, the proteins identified from the secretomes of 23 cancer cell lines and the Human Protein Atlas represent a focused reservoir of potential cancer biomarkers.

Databases of protein-protein interactions and complexes

In the current understanding, translation of genomic sequences into proteins is the most important path for realization of genome information. In exercising their intended function, proteins work together through various forms of direct (physical) or indirect interaction mechanisms. For a variety of basic functions, many proteins form a large complex representing a molecular machine or a macromolecular super-structural building block. After several high-throughput techniques for detection of protein-protein interactions had matured, protein interaction data became available in a large scale and curated databases for protein-protein interactions (PPIs) are a new necessity for efficient research. Here, their scope, annotation quality, and retrieval tools are reviewed. In addition, attention is paid to portals that provide unified access to a variety of such databases with added annotation value.

Duplicability of self-interacting human genes

Background

There is increasing interest in the evolution of protein-protein interactions because this should ultimately be informative of the patterns of evolution of new protein functions within the cell. One model proposes that the evolution of new protein-protein interactions and protein complexes proceeds through the duplication of self-interacting genes. This model is supported by data from yeast. We examined the relationship between gene duplication and self-interaction in the human genome.

Results

We investigated the patterns of self-interaction and duplication among 34808 interactions encoded by 8881 human genes, and show that self-interacting proteins are encoded by genes with higher duplicability than genes whose proteins lack this type of interaction. We show that this result is robust against the system used to define duplicate genes. Finally we compared the presence of self-interactions amongst proteins whose genes have duplicated either through whole-genome duplication (WGD) or small-scale duplication (SSD), and show that the former tend to have more interactions in general. After controlling for age differences between the two sets of duplicates this result can be explained by the time since the gene duplication.

Conclusions

Genes encoding self-interacting proteins tend to have higher duplicability than proteins lacking self-interactions. Moreover these duplicate genes have more often arisen through whole-genome rather than small-scale duplication. Finally, self-interacting WGD genes tend to have more interaction partners in general in the PIN, which can be explained by their overall greater age. This work adds to our growing knowledge of the importance of contextual factors in gene duplicability.

Dynamic cross-talk analysis among TNF-R, TLR-4 and IL-1R signalings in TNFalpha-induced inflammatory responses

Background

Development in systems biology research has accelerated in recent years, and the reconstructions for molecular networks can provide a global view to enable in-depth investigation on numerous system properties in biology. However, we still lack a systematic approach to reconstruct the dynamic protein-protein association networks at different time stages from high-throughput data to further analyze the possible cross-talks among different signaling/regulatory pathways.

Methods

In this study we integrated protein-protein interactions from different databases to construct the rough protein-protein association networks (PPANs) during TNFα-induced inflammation. Next, the gene expression profiles of TNFα-induced HUVEC and a stochastic dynamic model were used to rebuild the significant PPANs at different time stages, reflecting the development and progression of endothelium inflammatory responses. A new cross-talk ranking method was used to evaluate the potential core elements in the related signaling pathways of toll-like receptor 4 (TLR-4) as well as receptors for tumor necrosis factor (TNF-R) and interleukin-1 (IL-1R).

Results

The highly ranked cross-talks which are functionally relevant to the TNFα pathway were identified. A bow-tie structure was extracted from these cross-talk pathways, suggesting the robustness of network structure, the coordination of signal transduction and feedback control for efficient inflammatory responses to different stimuli. Further, several characteristics of signal transduction and feedback control were analyzed.

Conclusions

A systematic approach based on a stochastic dynamic model is proposed to generate insight into the underlying defense mechanisms of inflammation via the construction of corresponding signaling networks upon specific stimuli. In addition, this systematic approach can be applied to other signaling networks under different conditions in different species. The algorithm and method proposed in this study could expedite prospective systems biology research when better experimental techniques for protein expression detection and microarray data with multiple sampling points become available in the future.

Transcriptional networks characterize ventricular dysfunction after myocardial infarction: a proof-of-concept investigation

There is currently no method powerful enough to identify patients at risk of developing ventricular dysfunction after myocardial infarction (MI). We aimed to identify major mechanisms related to ventricular dysfunction to predict outcome after MI. Based on the combination of domain knowledge, protein-protein interaction networks and gene expression data, a set of potential biomarkers of ventricular dysfunction after MI was identified. Here we propose a new strategy for the prediction of ventricular dysfunction after MI based on "network activity indices" (NAI), which encode gene network-based signatures and distinguishes between prognostic classes. These models outperformed prognostic models based on standard differential expression analysis. NAI-based models reported high classification accuracy, with a maximum area under the receiver operating characteristic curve (AUC) of 0.75. Furthermore, the classification capacity of these models was validated by performing evaluations on an independent patient cohort (maximum AUC=0.75). These results suggest that transcriptional network-based biosignatures can offer both powerful and biologically-meaningful prediction models of ventricular dysfunction after MI. This research reports a new integrative strategy for identifying transcriptional responses that characterize cardiac repair and for predicting clinical outcome after MI. It can be adapted to other clinical domains, such as those constrained by small molecular datasets and limited translational knowledge. Furthermore, it may reflect clinically-meaningful synergistic effects that cannot be identified by standard analyses.

Phosphoproteome analysis reveals regulatory sites in major pathways of cardiac mitochondria

Mitochondrial functions are dynamically regulated in the heart. In particular, protein phosphorylation has been shown to be a key mechanism modulating mitochondrial function in diverse cardiovascular phenotypes. However, site-specific phosphorylation information remains scarce for this organ. Accordingly, we performed a comprehensive characterization of murine cardiac mitochondrial phosphoproteome in the context of mitochondrial functional pathways. A platform using the complementary fragmentation technologies of collision-induced dissociation (CID) and electron transfer dissociation (ETD) demonstrated successful identification of a total of 236 phosphorylation sites in the murine heart; 210 of these sites were novel. These 236 sites were mapped to 181 phosphoproteins and 203 phosphopeptides. Among those identified, 45 phosphorylation sites were captured only by CID, whereas 185 phosphorylation sites, including a novel modification on ubiquinol-cytochrome c reductase protein 1 (Ser-212), were identified only by ETD, underscoring the advantage of a combined CID and ETD approach. The biological significance of the cardiac mitochondrial phosphoproteome was evaluated. Our investigations illustrated key regulatory sites in murine cardiac mitochondrial pathways as targets of phosphorylation regulation, including components of the electron transport chain (ETC) complexes and enzymes involved in metabolic pathways (e.g. tricarboxylic acid cycle). Furthermore, calcium overload injured cardiac mitochondrial ETC function, whereas enhanced phosphorylation of ETC via application of phosphatase inhibitors restored calcium-attenuated ETC complex I and complex III activities, demonstrating positive regulation of ETC function by phosphorylation. Moreover, in silico analyses of the identified phosphopeptide motifs illuminated the molecular nature of participating kinases, which included several known mitochondrial kinases (e.g. pyruvate dehydrogenase kinase) as well as kinases whose mitochondrial location was not previously appreciated (e.g. Src). In conclusion, the phosphorylation events defined herein advance our understanding of cardiac mitochondrial biology, facilitating the integration of the still fragmentary knowledge about mitochondrial signaling networks, metabolic pathways, and intrinsic mechanisms of functional regulation in the heart.