Network analysis of gene lists for finding reproducible prognostic breast cancer gene signatures

Many genome-scale studies in molecular biology deliver results in the form of a ranked list of gene names, accordingly to some scoring method. There is always the question how many top-ranked genes to consider for further analysis, for example, in order creating a diagnostic or predictive gene signature for a disease. This question is usually approached from a statistical point of view, without considering any biological properties of top-ranked genes or how they are related to each other functionally. Here we suggest a new method for selecting a number of genes in a ranked gene list such that this set forms the Optimally Functionally Enriched Network (OFTEN), formed by known physical interactions between genes or their products. The method allows associating a network with the gene list, providing easier interpretation of the results and classifying the genes or proteins accordingly to their position in the resulting network. We demonstrate the method on four breast cancer datasets and show that 1) the resulting gene signatures are more reproducible from one dataset to another compared to standard statistical procedures and 2) the overlap of these signatures has significant prognostic potential. The method is implemented in BiNoM Cytoscape plugin (http://binom.curie.fr).

The antihypertensive effect of cysteine

Hypertension is a leading cause of morbidity and mortality worldwide. Individuals with hypertension are at an increased risk for stroke, heart disease and kidney failure. Essential hypertension results from a combination of genetic and lifestyle factors. One such lifestyle factor is diet, and its role in the control of blood pressure has come under much scrutiny. Just as increased salt and sugar are known to elevate blood pressure, other dietary factors may have antihypertensive effects. Studies including the Optimal Macronutrient Intake to Prevent Heart Disease (OmniHeart) study, Multiple Risk Factor Intervention Trial (MRFIT), International Study of Salt and Blood Pressure (INTERSALT) and Dietary Approaches to Stop Hypertension (DASH) study have demonstrated an inverse relationship between dietary protein and blood pressure. One component of dietary protein that may partially account for its antihypertensive effect is the nonessential amino acid cysteine. Studies in hypertensive humans and animal models of hypertension have shown that N-acetylcysteine, a stable cysteine analogue, lowers blood pressure, which substantiates this idea. Cysteine may exert its antihypertensive effects directly or through its storage form, glutathione, by decreasing oxidative stress, improving insulin resistance and glucose metabolism, lowering advanced glycation end products, and modulating levels of nitric oxide and other vasoactive molecules. Therefore, adopting a balanced diet containing cysteine-rich proteins may be a beneficial lifestyle choice for individuals with hypertension. An example of such a diet is the DASH diet, which is low in salt and saturated fat; includes whole grains, poultry, fish and nuts; and is rich in vegetables, fruits and low-fat dairy products.

The clinical utility of serum CA 19-9 in the diagnosis, prognosis and management of pancreatic adenocarcinoma: An evidence based appraisal

BACKGROUND

Serum carbohydrate antigen (CA 19-9) is the most common tumor marker assessed in pancreatic cancer patients; nevertheless few articles have comprehensively evaluated the evidence for its utility in pancreatic cancer management.

METHODS

Literature search was performed using Medline with keywords "pancreatic cancer", "tumor markers", "CA 19-9", "diagnosis", "screening", "prognosis", "resectability" and "recurrence". All English language articles pertaining to the role of CA 19-9 in pancreatic cancer were critically analyzed to determine its utility as a biomarker for pancreatic cancer.

RESULTS

Serum CA 19-9 is the most extensively validated pancreatic cancer biomarker with multiple clinical applications. CA 19-9 serum levels have a sensitivity and specificity of 79-81% and 82-90% respectively for the diagnosis of pancreatic cancer in symptomatic patients; but are not useful as a screening marker because of low positive predictive value (0.5-0.9%). Pre-operative CA 19-9 serum levels provide useful prognostic information as patients with normal levels (<37 U/mL) have a prolonged median survival (32-36 months) compared to patients with elevated levels (>37 U/mL) (12-15 months). A CA 19-9 serum level of <100 U/mL implies likely resectable disease whereas levels >100 U/mL suggest unresectablity or metastatic disease. Normalization or a decrease in post-operative CA 19-9 serum levels by ≥20-50% from baseline following surgical resection or chemotherapy is associated with prolonged survival compared to failure of CA 19-9 serum levels to normalize or an increase. Important limitations to CA 19-9 serum level evaluation in pancreatic cancer include poor sensitivity, false negative results in Lewis negative phenotype (5-10%) and increased false positivity in the presence of obstructive jaundice (10-60%).

CONCLUSIONS

CA 19-9 is the most extensively studied and validated serum biomarker for the diagnosis of pancreatic cancer in symptomatic patients. CA 19-9 serum levels can provide important information with regards to prognosis, overall survival, and response to chemotherapy as well as predict post-operative recurrence. However, non-specific expression in several benign and malignant diseases, false negative results in Lewis negative genotype and an increased false positive results in the presence of obstructive jaundice severely limit the universal applicability of serum CA 19-9 levels in pancreatic cancer management.

The clinical utility of serum CA 19-9 in the diagnosis, prognosis and management of pancreatic adenocarcinoma: An evidence based appraisal

BACKGROUND

Serum carbohydrate antigen (CA 19-9) is the most common tumor marker assessed in pancreatic cancer patients; nevertheless few articles have comprehensively evaluated the evidence for its utility in pancreatic cancer management.

METHODS

Literature search was performed using Medline with keywords "pancreatic cancer", "tumor markers", "CA 19-9", "diagnosis", "screening", "prognosis", "resectability" and "recurrence". All English language articles pertaining to the role of CA 19-9 in pancreatic cancer were critically analyzed to determine its utility as a biomarker for pancreatic cancer.

RESULTS

Serum CA 19-9 is the most extensively validated pancreatic cancer biomarker with multiple clinical applications. CA 19-9 serum levels have a sensitivity and specificity of 79-81% and 82-90% respectively for the diagnosis of pancreatic cancer in symptomatic patients; but are not useful as a screening marker because of low positive predictive value (0.5-0.9%). Pre-operative CA 19-9 serum levels provide useful prognostic information as patients with normal levels (<37 U/mL) have a prolonged median survival (32-36 months) compared to patients with elevated levels (>37 U/mL) (12-15 months). A CA 19-9 serum level of <100 U/mL implies likely resectable disease whereas levels >100 U/mL suggest unresectablity or metastatic disease. Normalization or a decrease in post-operative CA 19-9 serum levels by ≥20-50% from baseline following surgical resection or chemotherapy is associated with prolonged survival compared to failure of CA 19-9 serum levels to normalize or an increase. Important limitations to CA 19-9 serum level evaluation in pancreatic cancer include poor sensitivity, false negative results in Lewis negative phenotype (5-10%) and increased false positivity in the presence of obstructive jaundice (10-60%).

CONCLUSIONS

CA 19-9 is the most extensively studied and validated serum biomarker for the diagnosis of pancreatic cancer in symptomatic patients. CA 19-9 serum levels can provide important information with regards to prognosis, overall survival, and response to chemotherapy as well as predict post-operative recurrence. However, non-specific expression in several benign and malignant diseases, false negative results in Lewis negative genotype and an increased false positive results in the presence of obstructive jaundice severely limit the universal applicability of serum CA 19-9 levels in pancreatic cancer management.

The clinical utility of serum CA 19-9 in the diagnosis, prognosis and management of pancreatic adenocarcinoma: An evidence based appraisal

BACKGROUND

Serum carbohydrate antigen (CA 19-9) is the most common tumor marker assessed in pancreatic cancer patients; nevertheless few articles have comprehensively evaluated the evidence for its utility in pancreatic cancer management.

METHODS

Literature search was performed using Medline with keywords "pancreatic cancer", "tumor markers", "CA 19-9", "diagnosis", "screening", "prognosis", "resectability" and "recurrence". All English language articles pertaining to the role of CA 19-9 in pancreatic cancer were critically analyzed to determine its utility as a biomarker for pancreatic cancer.

RESULTS

Serum CA 19-9 is the most extensively validated pancreatic cancer biomarker with multiple clinical applications. CA 19-9 serum levels have a sensitivity and specificity of 79-81% and 82-90% respectively for the diagnosis of pancreatic cancer in symptomatic patients; but are not useful as a screening marker because of low positive predictive value (0.5-0.9%). Pre-operative CA 19-9 serum levels provide useful prognostic information as patients with normal levels (<37 U/mL) have a prolonged median survival (32-36 months) compared to patients with elevated levels (>37 U/mL) (12-15 months). A CA 19-9 serum level of <100 U/mL implies likely resectable disease whereas levels >100 U/mL suggest unresectablity or metastatic disease. Normalization or a decrease in post-operative CA 19-9 serum levels by ≥20-50% from baseline following surgical resection or chemotherapy is associated with prolonged survival compared to failure of CA 19-9 serum levels to normalize or an increase. Important limitations to CA 19-9 serum level evaluation in pancreatic cancer include poor sensitivity, false negative results in Lewis negative phenotype (5-10%) and increased false positivity in the presence of obstructive jaundice (10-60%).

CONCLUSIONS

CA 19-9 is the most extensively studied and validated serum biomarker for the diagnosis of pancreatic cancer in symptomatic patients. CA 19-9 serum levels can provide important information with regards to prognosis, overall survival, and response to chemotherapy as well as predict post-operative recurrence. However, non-specific expression in several benign and malignant diseases, false negative results in Lewis negative genotype and an increased false positive results in the presence of obstructive jaundice severely limit the universal applicability of serum CA 19-9 levels in pancreatic cancer management.

HOXB7 promotes invasion and predicts survival in pancreatic adenocarcinoma

BACKGROUND

The homeobox gene HOXB7 is overexpressed across a range of cancers and promotes tumorigenesis through varying effects on proliferation, survival, invasion, and angiogenesis. Although published microarray data suggest HOXB7 is overexpressed in pancreatic ductal adenocarcinoma (PDAC), its function in pancreatic cancer has not been studied.

METHODS

HOXB7 message and protein levels were examined in PDAC cell lines and patient samples, as well as in normal pancreas. HOXB7 protein expression in patient tumors was determined by immunohistochemistry and correlated with clinicopathologic factors and survival. The impact of HOXB7 on cell proliferation, growth, and invasion was assessed by knockdown and overexpression in PDAC cell lines. Candidate genes whose expression levels were altered following HOXB7 knockdown were determined by microarray analysis.

RESULTS

HOXB7 message and protein levels were significantly elevated in PDAC cell lines and patient tumor samples relative to normal pancreas. Evaluation of a tissue microarray of 145 resected PDACs found high HOXB7 protein expression was correlated with lymph node metastasis (P = .034) and an independent predictor of worse overall survival in multivariate analysis (hazard ratio = 1.56, 95% confidence interval = 1.02-2.39). HOXB7 knockdown or overexpression in PDAC cell lines resulted in decreased or increased invasion, respectively, without influencing proliferation or cell viability.

CONCLUSIONS

HOXB7 is frequently overexpressed in PDAC, specifically promotes invasive phenotype, and is associated with lymph node metastasis and worse survival outcome. HOXB7 and its downstream targets may represent novel clinical biomarkers or targets of therapy for inhibiting the invasive and metastatic capacity of PDAC.

Potential molecular approaches for the early diagnosis of lung cancer (review)

Lung cancer is the leading cause of mortality from cancer among men and women worldwide. More individuals die each year of lung cancer than of colon, breast and prostate cancer combined. Despite new diagnostic techniques, the overall 5-year survival rate remains at approximately 15% and the majority of patients still present with advanced disease. Therefore, lung cancer is the most lethal cancer at present. Diagnosing and treating cancer at its early stages, ideally during the precancerous stages, could increase the 5-year survival rate by 3-4‑fold, with the possibility of cure. To date, no screening method has been shown to decrease the disease-specific mortality rate. This review describes issues related to early lung cancer screening and their rationale, the management of primary cancers detected by screening and the different approaches that have been tested for cancer screening; these include imaging techniques, bronchoscopies and molecular screening, such as analysis of epigenomics using different noninvasive or invasive sources, such as blood, sputum, bronchoscopic samples and exhaled breath.

Common genetic variants and gene-expression changes associated with bipolar disorder are over-represented in brain signaling pathway genes

BACKGROUND

Despite high heritability, the genetic variants influencing bipolar disorder (BD) susceptibility remain largely unknown. Low statistical power to detect the small effect-size alleles believed to underlie much of the genetic risk and possible heterogeneity between cohorts are an increasing concern. Integrative biology approaches might offer advantages over genetic analysis alone by combining different genomic datasets at the higher level of biological processes rather than the level of specific genetic variants or genes. We employed this strategy to identify biological processes involved in BD etiopathology.

METHOD

Three genome-wide association studies and a brain gene-expression study were combined with the Human Protein Reference Database protein-protein interaction network data. We used bioinformatic analysis to search for biological networks with evidence of association on the basis of enrichment among both genetic and differential-expression associations with BD.

RESULTS

We identified association with gene networks involved in transmission of nerve impulse, Wnt, and Notch signaling. Three features stand out among these genes: 1) they localized to the human postsynaptic density, which is crucial for neuronal function; 2) their mouse knockouts present altered behavioral phenotypes; and 3) some are known targets of the pharmacological treatments for BD.

CONCLUSIONS

Genetic and gene-expression associations of BD cluster in discrete regions of the protein-protein interaction network. We found replicated evidence for association for networks involving several interlinked signaling pathways. These genes are promising candidates to generate animal models and pharmacological interventions. Our results demonstrate the potential advantage of integrative biology analyses of BD datasets.

A network synthesis model for generating protein interaction network families

In this work, we introduce a novel network synthesis model that can generate families of evolutionarily related synthetic protein-protein interaction (PPI) networks. Given an ancestral network, the proposed model generates the network family according to a hypothetical phylogenetic tree, where the descendant networks are obtained through duplication and divergence of their ancestors, followed by network growth using network evolution models. We demonstrate that this network synthesis model can effectively create synthetic networks whose internal and cross-network properties closely resemble those of real PPI networks. The proposed model can serve as an effective framework for generating comprehensive benchmark datasets that can be used for reliable performance assessment of comparative network analysis algorithms. Using this model, we constructed a large-scale network alignment benchmark, called NAPAbench, and evaluated the performance of several representative network alignment algorithms. Our analysis clearly shows the relative performance of the leading network algorithms, with their respective advantages and disadvantages. The algorithm and source code of the network synthesis model and the network alignment benchmark NAPAbench are publicly available at http://www.ece.tamu.edu/bjyoon/NAPAbench/.

Dissecting the gene network of dietary restriction to identify evolutionarily conserved pathways and new functional genes

Dietary restriction (DR), limiting nutrient intake from diet without causing malnutrition, delays the aging process and extends lifespan in multiple organisms. The conserved life-extending effect of DR suggests the involvement of fundamental mechanisms, although these remain a subject of debate. To help decipher the life-extending mechanisms of DR, we first compiled a list of genes that if genetically altered disrupt or prevent the life-extending effects of DR. We called these DR–essential genes and identified more than 100 in model organisms such as yeast, worms, flies, and mice. In order for other researchers to benefit from this first curated list of genes essential for DR, we established an online database called GenDR (http://genomics.senescence.info/diet/). To dissect the interactions of DR–essential genes and discover the underlying lifespan-extending mechanisms, we then used a variety of network and systems biology approaches to analyze the gene network of DR. We show that DR–essential genes are more conserved at the molecular level and have more molecular interactions than expected by chance. Furthermore, we employed a guilt-by-association method to predict novel DR–essential genes. In budding yeast, we predicted nine genes related to vacuolar functions; we show experimentally that mutations deleting eight of those genes prevent the life-extending effects of DR. Three of these mutants (OPT2, FRE6, and RCR2) had extended lifespan under ad libitum, indicating that the lack of further longevity under DR is not caused by a general compromise of fitness. These results demonstrate how network analyses of DR using GenDR can be used to make phenotypically relevant predictions. Moreover, gene-regulatory circuits reveal that the DR–induced transcriptional signature in yeast involves nutrient-sensing, stress responses and meiotic transcription factors. Finally, comparing the influence of gene expression changes during DR on the interactomes of multiple organisms led us to suggest that DR commonly suppresses translation, while stimulating an ancient reproduction-related process.

Dietary restriction has been shown to extend lifespan in diverse, evolutionarily distant species, yet its underlying mechanisms remain unknown. We first constructed a database of genes essential for the life-extending effects of dietary restriction in various model organisms and then studied their interactions using a variety of network and systems biology approaches. This enabled us to predict novel genes related to dietary restriction, which we validated experimentally in yeast. By comparing large-scale data compilations (interactomes and transcriptomes) from multiple organisms, we were able to condense this -omics information to the most conserved essential elements, eliminating species-specific adaptive responses. These results lead us to the rather surprising conclusion that lifespan extension by a restricted diet commonly may exploit an ancient rejuvenation process derived from gametogenesis.

Feasibility of fecal microRNAs as novel biomarkers for pancreatic cancer

INTRODUCTION

Pancreatic cancer (PCA) is an aggressive tumor that associates with high mortality rates. Majority of PCA patients are diagnosed usually at late tumor stages when the therapeutic options are limited. MicroRNAs (miRNA) are involved in tumor development and are commonly dysregulated in PCA. As a proof-of-principle study, we aimed to evaluate the potential of fecal miRNAs as biomarkers for pancreatic cancer.

MATERIALS AND METHODS

Total RNA was extracted from feces using Qiagen's miRNA Mini Kit. For miRNA expression analyses we selected a subset of 7 miRNAs that are frequently dysregulated in PCA (miR-21, -143, -155, -196a, -210, -216a, -375). Subsequently, expression levels of these miRNAs were determined in fecal samples from controls (n = 15), chronic pancreatitis (n = 15) and PCA patients (n = 15) using quantitative TaqMan-PCR assays.

RESULTS

All selected miRNAs were detectable in fecal samples with high reproducibility. Four of seven miRNAs (miR-216a, -196a, -143 und -155) were detected at lower concentrations in feces of PCA patients when compared to controls (p<0.05). Analysis of fecal miRNA expression in controls and patients with chronic pancreatitis and PCA revealed that the expression of miR-216a, -196a, -143 und -155 were highest in controls and lowest in PCA. The expression of the remaining three miRNAs (miR-21, -210 and -375) remained unchanged among controls and the patients with either chronic pancreatitis or PCA.

CONCLUSION

Our data provide novel evidence for the differential expression of miRNAs in feces of patients with PCA. If successfully validated in large-scale prospective studies, the fecal miRNA biomarkers may offer novel tools for PCA screening research.

Rewiring the dynamic interactome

Transcriptomics continues to provide ever-more evidence that in morphologically complex eukaryotes, each protein-coding genetic locus can give rise to multiple transcripts that differ in length, exon content and/or other sequence features. In humans, more than 60% of loci give rise to multiple transcripts in this way. Motifs that mediate protein-protein interactions can be present or absent in these transcripts. Analysis of protein interaction networks has been a valuable development in systems biology. Interactions are typically recorded for representative proteins or even genes, although exploratory transcriptomics has revealed great spatiotemporal diversity in the output of genes at both the transcript and protein-isoform levels. The increasing availability of high-resolution protein structures has made it possible to identify the domain-domain interactions that underpin many protein interactions. To explore the impact of transcript and isoform diversity we use full-length human cDNAs to interrogate the protein-coding transcriptional output of genes, identifying variation in the inclusion of protein interaction domains. We map these data to a set of high-quality protein interactions, and characterise the variation in network connectivity likely to result. We find strong evidence for altered interaction potential in nearly 20% of genes, suggesting that transcriptional variation can significantly rewire the human interactome.

Effect of Pueraria tuberosa DC. (Indian Kudzu) on blood pressure, fibrinolysis and oxidative stress in patients with stage 1 hypertension

The Indian Kudzu (Pueraria tuberosa DC.) is an important medicinal plant widely used in Indian and Chinese traditional systems of medicine. The present study is an attempt to evaluate effect of its tubers on blood pressure, coagulation parameters and antioxidant status in patients with stage 1 (primary) hypertension. In a long-term, single blinded, placebo controlled study; 15 patients with stage 1 hypertension (group 1), were administered 3 g P. tuberosa in two divided doses while another 15 patients (group II) were administered matched placebo for a period of twelve weeks. A significant fall of 25, 11 and 16 mmHg was observed in systolic (p < 0.001), diastolic (p < 0.05) and mean (p < 0.001) blood pressure, respectively at the end of the study. Along with blood pressure reduction, there was a significant (p < 0.01) reduction in plasma fibrinogen and significant enhancement of plasma fibrinolytic activity (p < 0.001) and serum total antioxidant status (p < 0.05). It was tolerated well without any untoward side effects.

KRas induces a Src/PEAK1/ErbB2 kinase amplification loop that drives metastatic growth and therapy resistance in pancreatic cancer

Early biomarkers and effective therapeutic strategies are desperately needed to treat pancreatic ductal adenocarcinoma (PDAC), which has a dismal 5-year patient survival rate. Here, we report that the novel tyrosine kinase PEAK1 is upregulated in human malignancies, including human PDACs and pancreatic intraepithelial neoplasia (PanIN). Oncogenic KRas induced a PEAK1-dependent kinase amplification loop between Src, PEAK1, and ErbB2 to drive PDAC tumor growth and metastasis in vivo. Surprisingly, blockade of ErbB2 expression increased Src-dependent PEAK1 expression, PEAK1-dependent Src activation, and tumor growth in vivo, suggesting a mechanism for the observed resistance of patients with PDACs to therapeutic intervention. Importantly, PEAK1 inactivation sensitized PDAC cells to trastuzumab and gemcitabine therapy. Our findings, therefore, suggest that PEAK1 is a novel biomarker, critical signaling hub, and new therapeutic target in PDACs.

Differential proteomic analysis of human glioblastoma and neural stem cells reveals HDGF as a novel angiogenic secreted factor

Presence in glioblastomas of cancer cells with normal neural stem cell (NSC) properties, tumor initiating capacity, and resistance to current therapies suggests that glioblastoma stem-like cells (GSCs) play central roles in glioblastoma development. We cultured human GSCs endowed with all features of tumor stem cells, including tumor initiation after xenograft and radio-chemoresistance. We established proteomes from four GSC cultures and their corresponding whole tumor tissues (TTs) and from human NSCs. Two-dimensional difference gel electrophoresis and tandem mass spectrometry revealed a twofold increase of hepatoma-derived growth factor (HDGF) in GSCs as compared to TTs and NSCs. Western blot analysis confirmed HDGF overexpression in GSCs as well as its presence in GSC-conditioned medium, while, in contrast, no HDGF was detected in NSC secretome. At the functional level, GSC-conditioned medium induced migration of human cerebral endothelial cells that can be blocked by anti-HDGF antibodies. In vivo, GSC-conditioned medium induced neoangiogenesis, whereas HDGF-targeting siRNAs abrogated this effect. Altogether, our results identify a novel candidate, by which GSCs can support neoangiogenesis, a high-grade glioma hallmark. Our strategy illustrates the usefulness of comparative proteomic analysis to decipher molecular pathways, which underlie GSC properties.

HINT: High-quality protein interactomes and their applications in understanding human disease

Background

A global map of protein-protein interactions in cellular systems provides key insights into the workings of an organism. A repository of well-validated high-quality protein-protein interactions can be used in both large- and small-scale studies to generate and validate a wide range of functional hypotheses.

Results

We develop HINT (http://hint.yulab.org) - a database of high-quality protein-protein interactomes for human, Saccharomyces cerevisiae, Schizosaccharomyces pombe, and Oryza sativa. These were collected from several databases and filtered both systematically and manually to remove low-quality/erroneous interactions. The resulting datasets are classified by type (binary physical interactions vs. co-complex associations) and data source (high-throughput systematic setups vs. literature-curated small-scale experiments). We find strong sociological sampling biases in literature-curated datasets of small-scale interactions. An interactome without such sampling biases was used to understand network properties of human disease-genes - hubs are unlikely to cause disease, but if they do, they usually cause multiple disorders.

Conclusions

HINT is of significant interest to researchers in all fields of biology as it addresses the ubiquitous need of having a repository of high-quality protein-protein interactions. These datasets can be utilized to generate specific hypotheses about specific proteins and/or pathways, as well as analyzing global properties of cellular networks. HINT will be regularly updated and all versions will be tracked.

Screening of candidate genes for primary open angle glaucoma

PURPOSE

Primary open-angle glaucoma (POAG) is one of the leading causes of irreversible blindness in the world. To make progress in understanding POAG, it is necessary to identify more POAG-causing genes.

METHODS

Using haplotype analysis, we found that mutational region is located on chromosome 2 in two families. Furthermore, we screened 11 candidate genes on chromosome 2 by protein-protein interaction (PPI) analysis, including mutS homolog 6 (MSH6), mutS homolog 2 (MSH2), v-rel reticuloendotheliosis viral oncogene homolog (REL), endothelial PAS domain protein 1 (EPAS1), vaccinia related kinase 2 (VRK2), F-box protein 11 (FBXO11), EGF containing fibulin-like extracellular matrix protein 1 (EFEMP1), reticulon 4 (RTN4), RAB1A, member RAS oncogene family (RAB1A), ARP2 actin-related protein 2 homolog (ACTR2), and calmodulin 2 (phosphorylase kinase, delta; CALM2). These 11 genes are all predicted to be related to trabecular meshwork changes and progressive loss of retinal ganglion cells in POAG patients.

RESULTS

According to our study, FBXO11 and VRK2 may interact with tumor protein p53 to regulate mitochondrial membrane permeability, mitochondrial membrane organization, and apoptosis. MSH2 is responsible for repairing DNA mismatches and RTN4 is for neuronal regeneration. Therefore, they are supposed to play a negative role in cellular process in POAG. CALM2 may be involved in retinal ganglion cell death and oxidative damage to cell communication.

CONCLUSIONS

The results demonstrate that the genes above may be associated with pathogenesis of POAG.

NSort/DB: an intranuclear compartment protein database

Distinct substructures within the nucleus are associated with a wide variety of important nuclear processes. Structures such as chromatin and nuclear pores have specific roles, while others such as Cajal bodies are more functionally varied. Understanding the roles of these membraneless intra-nuclear compartments requires extensive data sets covering nuclear and compartment-associated proteins. NSort/DB is a database providing access to intra- or sub-nuclear compartment associations for the mouse nuclear proteome. Based on resources ranging from large-scale curated data sets to detailed experiments, this data set provides a high-quality set of annotations of non-exclusive association of nuclear proteins with structures such as promyelocytic leukaemia bodies and chromatin. The database is searchable by protein identifier or compartment, and has a documented web service API. The search interface, web service and data download are all freely available online at http://www.nsort.org/db/. Availability of this data set will enable systematic analyses of the protein complements of nuclear compartments, improving our understanding of the diverse functional repertoire of these structures.

Prediction and analysis of the protein interactome in Pseudomonas aeruginosa to enable network-based drug target selection

Pseudomonas aeruginosa (PA) is a ubiquitous opportunistic pathogen that is capable of causing highly problematic, chronic infections in cystic fibrosis and chronic obstructive pulmonary disease patients. With the increased prevalence of multi-drug resistant PA, the conventional “one gene, one drug, one disease” paradigm is losing effectiveness. Network pharmacology, on the other hand, may hold the promise of discovering new drug targets to treat a variety of PA infections. However, given the urgent need for novel drug target discovery, a PA protein-protein interaction (PPI) network of high accuracy and coverage, has not yet been constructed. In this study, we predicted a genome-scale PPI network of PA by integrating various genomic features of PA proteins/genes by a machine learning-based approach. A total of 54,107 interactions covering 4,181 proteins in PA were predicted. A high-confidence network combining predicted high-confidence interactions, a reference set and verified interactions that consist of 3,343 proteins and 19,416 potential interactions was further assembled and analyzed. The predicted interactome network from this study is the first large-scale PPI network in PA with significant coverage and high accuracy. Subsequent analysis, including validations based on existing small-scale PPI data and the network structure comparison with other model organisms, shows the validity of the predicted PPI network. Potential drug targets were identified and prioritized based on their essentiality and topological importance in the high-confidence network. Host-pathogen protein interactions between human and PA were further extracted and analyzed. In addition, case studies were performed on protein interactions regarding anti-sigma factor MucA, negative periplasmic alginate regulator MucB, and the transcriptional regulator RhlR. A web server to access the predicted PPI dataset is available at http://research.cchmc.org/PPIdatabase/.

Overexpression of the DEC1 protein induces senescence in vitro and is related to better survival in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a leading cause of cancer-related death in China and has limited effective therapeutic options except for early surgery, since the underlying molecular mechanism driving its precursor lesions towards invasive ESCC is not fully understood. Cellular senescence is the state of the permanent growth arrest of a cell, and is considered as the initial barrier of tumor development. Human differentiated embryo chondrocyte expressed gene 1 (Dec1) is an important transcription factor that related to senescence. In this study, DEC1 immunohistochemical analysis was performed on tissue microarray blocks constructed from ESCC combined with adjacent precursor tissues of 241 patients. Compared with normal epithelia, DEC1 expression was significantly increased in intraepithelial neoplasia and DEC1 expression was significantly decreased in ESCC in comparison with intraepithelial neoplasia. In vitro, DEC1 overexpression induced cellular senescence, and it inhibited cell growth and colony formation in ESCC cell line EC9706. Fresh esophagectomy tissue sections from five ESCC patients were detected by immunohistochemistry of DEC1 and senescence-associated β-galactosidase (SA-β-Gal) activity, and strongly positive expression of DEC1 was correlated to more senescent cells in these fresh tissue sections. Kaplan – Meier method analysis of the 241 patients revealed that DEC1 expression levels were significantly correlated with the survival of ESCC patients after surgery. The expression levels of DEC1 were also correlated with age, tumor embolus, depth of invasion of ESCC, lymph metastasis status and pTNMs. These results suggest that DEC1 overexpression in precursor lesions of ESCC is a protective mechanism by inducing cellular senescence in ESCC initiation, and DEC1 may be a potential prognostic marker of ESCC.

atBioNet--an integrated network analysis tool for genomics and biomarker discovery

Background

Large amounts of mammalian protein-protein interaction (PPI) data have been generated and are available for public use. From a systems biology perspective, Proteins/genes interactions encode the key mechanisms distinguishing disease and health, and such mechanisms can be uncovered through network analysis. An effective network analysis tool should integrate different content-specific PPI databases into a comprehensive network format with a user-friendly platform to identify key functional modules/pathways and the underlying mechanisms of disease and toxicity.

Results

atBioNet integrates seven publicly available PPI databases into a network-specific knowledge base. Knowledge expansion is achieved by expanding a user supplied proteins/genes list with interactions from its integrated PPI network. The statistically significant functional modules are determined by applying a fast network-clustering algorithm (SCAN: a Structural Clustering Algorithm for Networks). The functional modules can be visualized either separately or together in the context of the whole network. Integration of pathway information enables enrichment analysis and assessment of the biological function of modules. Three case studies are presented using publicly available disease gene signatures as a basis to discover new biomarkers for acute leukemia, systemic lupus erythematosus, and breast cancer. The results demonstrated that atBioNet can not only identify functional modules and pathways related to the studied diseases, but this information can also be used to hypothesize novel biomarkers for future analysis.

Conclusion

atBioNet is a free web-based network analysis tool that provides a systematic insight into proteins/genes interactions through examining significant functional modules. The identified functional modules are useful for determining underlying mechanisms of disease and biomarker discovery. It can be accessed at: http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm285284.htm.

Deciphering a global network of functionally associated post-translational modifications

This study is the first large-scale comparative analysis of multiple types of post-translational modifications in different eukaryotic species. The resulting network of co-evolving and functionally associated modifications reveals the global landscape of post-translational regulation.

In all, 115 149 non-redundant post-translational modifications (PTMs) of 13 different types were collected from 8 eukaryotes.

Comparison of evolution speed reveals that carboxylation is the most conserved while SUMOylation is the fastest evolving PTM type.

Co-evolution of PTM pairs that co-occur within proteins reveals a vastly interconnected global network of functionally associated PTM types in eukaryotes.

Central to the network of functionally associated PTM types appear phosphorylation, acetylation, ubiquitination and O-linked glycosylation that control both temporal events and processes that govern protein localization.

Various post-translational modifications (PTMs) fine-tune the functions of almost all eukaryotic proteins, and co-regulation of different types of PTMs has been shown within and between a number of proteins. Aiming at a more global view of the interplay between PTM types, we collected modifications for 13 frequent PTM types in 8 eukaryotes, compared their speed of evolution and developed a method for measuring PTM co-evolution within proteins based on the co-occurrence of sites across eukaryotes. As many sites are still to be discovered, this is a considerable underestimate, yet, assuming that most co-evolving PTMs are functionally associated, we found that PTM types are vastly interconnected, forming a global network that comprise in human alone >50 000 residues in about 6000 proteins. We predict substantial PTM type interplay in secreted and membrane-associated proteins and in the context of particular protein domains and short-linear motifs. The global network of co-evolving PTM types implies a complex and intertwined post-translational regulation landscape that is likely to regulate multiple functional states of many if not all eukaryotic proteins.

Concerted perturbation observed in a hub network in Alzheimer's disease

Alzheimer’s disease (AD) is a progressive neurodegenerative disease involving the alteration of gene expression at the whole genome level. Genome-wide transcriptional profiling of AD has been conducted by many groups on several relevant brain regions. However, identifying the most critical dys-regulated genes has been challenging. In this work, we addressed this issue by deriving critical genes from perturbed subnetworks. Using a recent microarray dataset on six brain regions, we applied a heaviest induced subgraph algorithm with a modular scoring function to reveal the significantly perturbed subnetwork in each brain region. These perturbed subnetworks were found to be significantly overlapped with each other. Furthermore, the hub genes from these perturbed subnetworks formed a connected hub network consisting of 136 genes. Comparison between AD and several related diseases demonstrated that the hub network was robustly and specifically perturbed in AD. In addition, strong correlation between the expression level of these hub genes and indicators of AD severity suggested that this hub network can partially reflect AD progression. More importantly, this hub network reflected the adaptation of neurons to the AD-specific microenvironment through a variety of adjustments, including reduction of neuronal and synaptic activities and alteration of survival signaling. Therefore, it is potentially useful for the development of biomarkers and network medicine for AD.

Identification of significant pathways in gastric cancer based on protein-protein interaction networks and cluster analysis

Gastric cancer is one of the most common and lethal cancers worldwide. However, despite its clinical importance, the regulatory mechanisms involved in the aggressiveness of this cancer are still poorly understood. A better understanding of the biology, genetics and molecular mechanisms of gastric cancer would be useful in developing novel targeted approaches for treating this disease. In this study we used protein-protein interaction networks and cluster analysis to comprehensively investigate the cellular pathways involved in gastric cancer. A primary immunodeficiency pathway, focal adhesion, ECM-receptor interactions and the metabolism of xenobiotics by cytochrome P450 were identified as four important pathways associated with the progression of gastric cancer. The genes in these pathways, e.g., ZAP70, IGLL1, CD79A, COL6A3, COL3A1, COL1A1, CYP2C18 and CYP2C9, may be considered as potential therapeutic targets for gastric cancer.

Computational analysis of target hub gene repression regulated by multiple and cooperative miRNAs

MicroRNA (miRNA) target hubs are genes that can be simultaneously targeted by a comparatively large number of miRNAs, a class of non-coding RNAs that mediate post-transcriptional gene repression. Although the details of target hub regulation remain poorly understood, recent experiments suggest that pairs of miRNAs can cooperate if their binding sites reside in close proximity. To test this and other hypotheses, we established a novel approach to investigate mechanisms of collective miRNA repression. The approach presented here combines miRNA target prediction and transcription factor prediction with data from the literature and databases to generate a regulatory map for a chosen target hub. We then show how a kinetic model can be derived from the regulatory map. To validate our approach, we present a case study for p21, one of the first experimentally proved miRNA target hubs. Our analysis indicates that distinctive expression patterns for miRNAs, some of which interact cooperatively, fine-tune the features of transient and long-term regulation of target genes. With respect to p21, our model successfully predicts its protein levels for nine different cellular functions. In addition, we find that high abundance of miRNAs, in combination with cooperativity, can enhance noise buffering for the transcription of target hubs.

Systematic analysis of protein phosphorylation networks from phosphoproteomic data

In eukaryotes, hundreds of protein kinases (PKs) specifically and precisely modify thousands of substrates at specific amino acid residues to faithfully orchestrate numerous biological processes, and reversibly determine the cellular dynamics and plasticity. Although over 100,000 phosphorylation sites (p-sites) have been experimentally identified from phosphoproteomic studies, the regulatory PKs for most of these sites still remain to be characterized. Here, we present a novel software package of iGPS for the prediction of in vivo site-specific kinase-substrate relations mainly from the phosphoproteomic data. By critical evaluations and comparisons, the performance of iGPS is satisfying and better than other existed tools. Based on the prediction results, we modeled protein phosphorylation networks and observed that the eukaryotic phospho-regulation is poorly conserved at the site and substrate levels. With an integrative procedure, we conducted a large-scale phosphorylation analysis of human liver and experimentally identified 9719 p-sites in 2998 proteins. Using iGPS, we predicted a human liver protein phosphorylation networks containing 12,819 potential site-specific kinase-substrate relations among 350 PKs and 962 substrates for 2633 p-sites. Further statistical analysis and comparison revealed that 127 PKs significantly modify more or fewer p-sites in the liver protein phosphorylation networks against the whole human protein phosphorylation network. The largest data set of the human liver phosphoproteome together with computational analyses can be useful for further experimental consideration. This work contributes to the understanding of phosphorylation mechanisms at the systemic level, and provides a powerful methodology for the general analysis of in vivo post-translational modifications regulating sub-proteomes.

Integration of biological networks and pathways with genetic association studies

Millions of genetic variants have been assessed for their effects on the trait of interest in genome-wide association studies (GWAS). The complex traits are affected by a set of inter-related genes. However, the typical GWAS only examine the association of a single genetic variant at a time. The individual effects of a complex trait are usually small, and the simple sum of these individual effects may not reflect the holistic effect of the genetic system. High-throughput methods enable genomic studies to produce a large amount of data to expand the knowledge base of the biological systems. Biological networks and pathways are built to represent the functional or physical connectivity among genes. Integrated with GWAS data, the network- and pathway-based methods complement the approach of single genetic variant analysis, and may improve the power to identify trait-associated genes. Taking advantage of the biological knowledge, these approaches are valuable to interpret the functional role of the genetic variants, and to further understand the molecular mechanism influencing the traits. The network- and pathway-based methods have demonstrated their utilities, and will be increasingly important to address a number of challenges facing the mainstream GWAS.

Prioritization of candidate genes for attention deficit hyperactivity disorder by computational analysis of multiple data sources

Attention deficit hyperactivity disorder (ADHD) is a common, highly heritable psychiatric disorder characterized by hyperactivity, inattention and increased impulsivity. In recent years, a large number of genetic studies for ADHD have been published and related genetic data has been accumulated dramatically. To provide researchers a comprehensive ADHD genetic resource, we previously developed the first genetic database for ADHD (ADHDgene). The abundant genetic data provides novel candidates for further study. Meanwhile, it also brings new challenge for selecting promising candidate genes for replication and verification research. In this study, we surveyed the computational tools for candidate gene prioritization and selected five tools, which integrate multiple data sources for gene prioritization, to prioritize ADHD candidate genes in ADHDgene. The prioritization analysis resulted in 16 prioritized candidate genes, which are mainly involved in several major neurotransmitter systems or in nervous system development pathways. Among these genes, nervous system development related genes, especially SNAP25, STX1A and the gene-gene interactions related with each of them deserve further investigations. Our results may provide new insight for further verification study and facilitate the exploration of pathogenesis mechanism of ADHD.

Identification of SRC as a potent drug target for asthma, using an integrative approach of protein interactome analysis and in silico drug discovery

Network-biology inspired modeling of interactome data and computational chemistry have the potential to revolutionize drug discovery by complementing conventional methods. We consider asthma, a complex disease characterized by intricate molecular mechanisms, for our study. We aim to integrate prediction of potent drug targets using graph-theoretical methods and subsequent identification of small molecules capable of modulating activity of the best target. In this work, we construct the protein interactome underlying this disease: Asthma Protein Interactome (API). Using a strategy based on network analysis of the interactome, we identify a set of potential drug targets for asthma. Topologically and dynamically, v-src sarcoma (Schmidt-Ruppin A-2) viral oncogene homolog (SRC) emerges as the most central target in API. SRC is known to play an important role in promoting airway smooth muscle cell growth and facilitating migration in airway remodeling. From interactome analysis, and with the reported role in respiratory mechanisms, SRC emerges as a promising drug target for asthma. Further, we proceed to identify leads for SRC from a public database of small molecules. We predict two potential leads for SRC using ligand-based virtual screening methodology.

Comparison of different normalization assumptions for analyses of DNA methylation data from the cancer genome

Nowadays, some researchers normalized DNA methylation arrays data in order to remove the technical artifacts introduced by experimental differences in sample preparation, array processing and other factors. However, other researchers analyzed DNA methylation arrays without performing data normalization considering that current normalizations for methylation data may distort real differences between normal and cancer samples because cancer genomes may be extensively subject to hypomethylation and the total amount of CpG methylation might differ substantially among samples. In this study, using eight datasets by Infinium HumanMethylation27 assay, we systemically analyzed the global distribution of DNA methylation changes in cancer compared to normal control and its effect on data normalization for selecting differentially methylated (DM) genes. We showed more differentially methylated (DM) genes could be found in the Quantile/Lowess-normalized data than in the non-normalized data. We found the DM genes additionally selected in the Quantile/Lowess-normalized data showed significantly consistent methylation states in another independent dataset for the same cancer, indicating these extra DM genes were effective biological signals related to the disease. These results suggested normalization can increase the power of detecting DM genes in the context of diagnostic markers which were usually characterized by relatively large effect sizes. Besides, we evaluated the reproducibility of DM discoveries for a particular cancer type, and we found most of the DM genes additionally detected in one dataset showed the same methylation directions in the other dataset for the same cancer type, indicating that these DM genes were effective biological signals in the other dataset. Furthermore, we showed that some DM genes detected from different studies for a particular cancer type were significantly reproducible at the functional level.

Label-free quantitative proteomics and N-glycoproteomics analysis of KRAS-activated human bronchial epithelial cells

Mutational activation of KRAS promotes various malignancies, including lung adenocarcinoma. Knowledge of the molecular targets mediating the downstream effects of activated KRAS is limited. Here, we provide the KRAS target proteins and N-glycoproteins using human bronchial epithelial cells with and without the expression of activated KRAS (KRAS(V12)). Using an OFFGEL peptide fractionation and hydrazide method combined with subsequent LTQ-Orbitrap analysis, we identified 5713 proteins and 608 N-glycosites on 317 proteins in human bronchial epithelial cells. Label-free quantitation of 3058 proteins (≥2 peptides; coefficient of variation (CV) ≤ 20%) and 297 N-glycoproteins (CV ≤ 20%) revealed the differential regulation of 23 proteins and 14 N-glycoproteins caused by activated KRAS, including 84% novel ones. An informatics-assisted IPA-Biomarker® filter analysis prioritized some of the differentially regulated proteins (ALDH3A1, CA2, CTSD, DST, EPHA2, and VIM) and N-glycoproteins (ALCAM, ITGA3, and TIMP-1) as cancer biomarkers. Further, integrated in silico analysis of microarray repository data of lung adenocarcinoma clinical samples and cell lines containing KRAS mutations showed positive mRNA fold changes (p < 0.05) for 61% of the KRAS-regulated proteins, including biomarker proteins, CA2 and CTSD. The most significant discovery of the integrated validation is the down-regulation of FABP5 and PDCD4. A few validated proteins, including tumor suppressor PDCD4, were further confirmed as KRAS targets by shRNA-based knockdown experiments. Finally, the studies on KRAS-regulated N-glycoproteins revealed structural alterations in the core N-glycans of SEMA4B in KRAS-activated human bronchial epithelial cells and functional role of N-glycosylation of TIMP-1 in the regulation of lung adenocarcinoma A549 cell invasion. Together, our study represents the largest proteome and N-glycoproteome data sets for HBECs, which we used to identify several novel potential targets of activated KRAS that may provide insights into KRAS-induced adenocarcinoma and have implications for both lung cancer therapy and diagnosis.

Genes2FANs: connecting genes through functional association networks

Background

Protein-protein, cell signaling, metabolic, and transcriptional interaction networks are useful for identifying connections between lists of experimentally identified genes/proteins. However, besides physical or co-expression interactions there are many ways in which pairs of genes, or their protein products, can be associated. By systematically incorporating knowledge on shared properties of genes from diverse sources to build functional association networks (FANs), researchers may be able to identify additional functional interactions between groups of genes that are not readily apparent.

Results

Genes2FANs is a web based tool and a database that utilizes 14 carefully constructed FANs and a large-scale protein-protein interaction (PPI) network to build subnetworks that connect lists of human and mouse genes. The FANs are created from mammalian gene set libraries where mouse genes are converted to their human orthologs. The tool takes as input a list of human or mouse Entrez gene symbols to produce a subnetwork and a ranked list of intermediate genes that are used to connect the query input list. In addition, users can enter any PubMed search term and then the system automatically converts the returned results to gene lists using GeneRIF. This gene list is then used as input to generate a subnetwork from the user’s PubMed query. As a case study, we applied Genes2FANs to connect disease genes from 90 well-studied disorders. We find an inverse correlation between the counts of links connecting disease genes through PPI and links connecting diseases genes through FANs, separating diseases into two categories.

Conclusions

Genes2FANs is a useful tool for interpreting the relationships between gene/protein lists in the context of their various functions and networks. Combining functional association interactions with physical PPIs can be useful for revealing new biology and help form hypotheses for further experimentation. Our finding that disease genes in many cancers are mostly connected through PPIs whereas other complex diseases, such as autism and type-2 diabetes, are mostly connected through FANs without PPIs, can guide better strategies for disease gene discovery. Genes2FANs is available at: http://actin.pharm.mssm.edu/genes2FANs.

SurvNet: a web server for identifying network-based biomarkers that most correlate with patient survival data

An important task in biomedical research is identifying biomarkers that correlate with patient clinical data, and these biomarkers then provide a critical foundation for the diagnosis and treatment of disease. Conventionally, such an analysis is based on individual genes, but the results are often noisy and difficult to interpret. Using a biological network as the searching platform, network-based biomarkers are expected to be more robust and provide deep insights into the molecular mechanisms of disease. We have developed a novel bioinformatics web server for identifying network-based biomarkers that most correlate with patient survival data, SurvNet. The web server takes three input files: one biological network file, representing a gene regulatory or protein interaction network; one molecular profiling file, containing any type of gene- or protein-centred high-throughput biological data (e.g. microarray expression data or DNA methylation data); and one patient survival data file (e.g. patients' progression-free survival data). Given user-defined parameters, SurvNet will automatically search for subnetworks that most correlate with the observed patient survival data. As the output, SurvNet will generate a list of network biomarkers and display them through a user-friendly interface. SurvNet can be accessed at http://bioinformatics.mdanderson.org/main/SurvNet.

Involvement of microRNA families in cancer

Collecting representative sets of cancer microRNAs (miRs) from the literature we show that their corresponding families are enriched in sets of highly interacting miR families. Targeting cancer genes on a statistically significant level, such cancer miR families strongly intervene with signaling pathways that harbor numerous cancer genes. Clustering miR family-specific profiles of pathway intervention, we found that different miR families share similar interaction patterns. Resembling corresponding patterns of cancer miRs families, such interaction patterns may indicate a miR family’s potential role in cancer. As we find that the number of targeted cancer genes is a naïve proxy for a cancer miR family, we design a simple method to predict candidate miR families based on gene-specific interaction profiles. Assessing the impact of miR families to distinguish between (non-)cancer genes, we predict a set of 84 potential candidate families, including 75% of initially collected cancer miR families. Further confirming their relevance, predicted cancer miR families are significantly indicated in increasing, non-random numbers of tumor types.

Posterior association networks and functional modules inferred from rich phenotypes of gene perturbations

Combinatorial gene perturbations provide rich information for a systematic exploration of genetic interactions. Despite successful applications to bacteria and yeast, the scalability of this approach remains a major challenge for higher organisms such as humans. Here, we report a novel experimental and computational framework to efficiently address this challenge by limiting the 'search space' for important genetic interactions. We propose to integrate rich phenotypes of multiple single gene perturbations to robustly predict functional modules, which can subsequently be subjected to further experimental investigations such as combinatorial gene silencing. We present posterior association networks (PANs) to predict functional interactions between genes estimated using a Bayesian mixture modelling approach. The major advantage of this approach over conventional hypothesis tests is that prior knowledge can be incorporated to enhance predictive power. We demonstrate in a simulation study and on biological data, that integrating complementary information greatly improves prediction accuracy. To search for significant modules, we perform hierarchical clustering with multiscale bootstrap resampling. We demonstrate the power of the proposed methodologies in applications to Ewing's sarcoma and human adult stem cells using publicly available and custom generated data, respectively. In the former application, we identify a gene module including many confirmed and highly promising therapeutic targets. Genes in the module are also significantly overrepresented in signalling pathways that are known to be critical for proliferation of Ewing's sarcoma cells. In the latter application, we predict a functional network of chromatin factors controlling epidermal stem cell fate. Further examinations using ChIP-seq, ChIP-qPCR and RT-qPCR reveal that the basis of their genetic interactions may arise from transcriptional cross regulation. A Bioconductor package implementing PAN is freely available online at http://bioconductor.org/packages/release/bioc/html/PANR.html.

Network-based characterization of drug-regulated genes, drug targets, and toxicity

Proteins do not exert their effects in isolation of one another, but interact together in complex networks. In recent years, sophisticated methods have been developed to leverage protein-protein interaction (PPI) network structure to improve several stages of the drug discovery process. Network-based methods have been applied to predict drug targets, drug side effects, and new therapeutic indications. In this paper we have two aims. First, we review the past contributions of network approaches and methods to drug discovery, and discuss their limitations and possible future directions. Second, we show how past work can be generalized to gain a more complete understanding of how drugs perturb networks. Previous network-based characterizations of drug effects focused on the small number of known drug targets, i.e., direct binding partners of drugs. However, drugs affect many more genes than their targets - they can profoundly affect the cell's transcriptome. For the first time, we use networks to characterize genes that are differentially regulated by drugs. We found that drug-regulated genes differed from drug targets in terms of functional annotations, cellular localizations, and topological properties. Drug targets mainly included receptors on the plasma membrane, down-regulated genes were largely in the nucleus and were enriched for DNA binding, and genes lacking drug relationships were enriched in the extracellular region. Network topology analysis indicated several significant graph properties, including high degree and betweenness for the drug targets and drug-regulated genes, though possibly due to network biases. Topological analysis also showed that proteins of down-regulated genes appear to be frequently involved in complexes. Analyzing network distances between regulated genes, we found that genes regulated by structurally similar drugs were significantly closer than genes regulated by dissimilar drugs. Finally, network centrality of a drug's differentially regulated genes correlated significantly with drug toxicity.

Protein interaction data curation: the International Molecular Exchange (IMEx) consortium

The International Molecular Exchange (IMEx) consortium is an international collaboration between major public interaction data providers to share literature-curation efforts and make a nonredundant set of protein interactions available in a single search interface on a common website (http://www.imexconsortium.org/). Common curation rules have been developed, and a central registry is used to manage the selection of articles to enter into the dataset. We discuss the advantages of such a service to the user, our quality-control measures and our data-distribution practices.

RESQUE: network reduction using semi-Markov random walk scores for efficient querying of biological networks

MOTIVATION

Recent technological advances in measuring molecular interactions have resulted in an increasing number of large-scale biological networks. Translation of these enormous network data into meaningful biological insights requires efficient computational techniques that can unearth the biological information that is encoded in the networks. One such example is network querying, which aims to identify similar subnetwork regions in a large target network that are similar to a given query network. Network querying tools can be used to identify novel biological pathways that are homologous to known pathways, thereby enabling knowledge transfer across different organisms.

RESULTS

In this article, we introduce an efficient algorithm for querying large-scale biological networks, called RESQUE. The proposed algorithm adopts a semi-Markov random walk (SMRW) model to probabilistically estimate the correspondence scores between nodes that belong to different networks. The target network is iteratively reduced based on the estimated correspondence scores, which are also iteratively re-estimated to improve accuracy until the best matching subnetwork emerges. We demonstrate that the proposed network querying scheme is computationally efficient, can handle any network query with an arbitrary topology and yields accurate querying results.

AVAILABILITY

The source code of RESQUE is freely available at http://www.ece.tamu.edu/~bjyoon/RESQUE/

Proteome-wide prediction of protein-protein interactions from high-throughput data

In this paper, we present a brief review of the existing computational methods for predicting proteome-wide protein-protein interaction networks from high-throughput data. The availability of various types of omics data provides great opportunity and also unprecedented challenge to infer the interactome in cells. Reconstructing the interactome or interaction network is a crucial step for studying the functional relationship among proteins and the involved biological processes. The protein interaction network will provide valuable resources and alternatives to decipher the mechanisms of these functionally interacting elements as well as the running system of cellular operations. In this paper, we describe the main steps of predicting protein-protein interaction networks and categorize the available approaches to couple the physical and functional linkages. The future topics and the analyses beyond prediction are also discussed and concluded.

Diagnostic, prognostic, and predictive biomarkers in pancreatic cancer

Serum CA 19-9 is the only FDA approved biomarker recommended for use in the routine management of pancreatic ductal adenocarcinoma (PDA). Over 2,000 biomarker studies related to pancreatic cancer appear in the literature, highlighting the need to discover and develop improved tests. Diagnostic biomarkers have implications for early detection of PDA, prognostic markers predict patient survival and recurrence patterns, and predictive markers can help personalize treatment regimens.

SH3 domains: modules of protein-protein interactions

Src homology 3 (SH3) domains are involved in the regulation of important cellular pathways, such as cell proliferation, migration and cytoskeletal modifications. Recognition of polyproline and a number of noncanonical sequences by SH3 domains has been extensively studied by crystallography, nuclear magnetic resonance and other methods. High-affinity peptides that bind SH3 domains are used in drug development as candidates for anticancer treatment. This review summarizes the latest achievements in deciphering structural determinants of SH3 function.

ChromoHub: a data hub for navigators of chromatin-mediated signalling

The rapidly increasing research activity focused on chromatin-mediated regulation of epigenetic mechanisms is generating waves of data on writers, readers and erasers of the histone code, such as protein methyltransferases, bromodomains or histone deacetylases. To make these data easily accessible to communities of research scientists coming from diverse horizons, we have created ChromoHub, an online resource where users can map on phylogenetic trees disease associations, protein structures, chemical inhibitors, histone substrates, chromosomal aberrations and other types of data extracted from public repositories and the published literature. The interface can be used to define the structural or chemical coverage of a protein family, highlight domain architectures, interrogate disease relevance or zoom in on specific genes for more detailed information. This open-access resource should serve as a hub for cell biologists, medicinal chemists, structural biologists and other navigators that explore the biology of chromatin signalling.

AVAILABILITY

http://www.thesgc.org/chromohub/.

Quantitative proteomic analysis of human embryonic stem cell differentiation by 8-plex iTRAQ labelling

Analysis of gene expression to define molecular mechanisms and pathways involved in human embryonic stem cells (hESCs) proliferation and differentiations has allowed for further deciphering of the self-renewal and pluripotency characteristics of hESC. Proteins associated with hESCs were discovered through isobaric tags for relative and absolute quantification (iTRAQ). Undifferentiated hESCs and hESCs in different stages of spontaneous differentiation by embryoid body (EB) formation were analyzed. Using the iTRAQ approach, we identified 156 differentially expressed proteins involved in cell proliferation, apoptosis, transcription, translation, mRNA processing, and protein synthesis. Proteins involved in nucleic acid binding, protein synthesis, and integrin signaling were downregulated during differentiation, whereas cytoskeleton proteins were upregulated. The present findings added insight to our understanding of the mechanisms involved in hESC proliferation and differentiation.

DEFOG: discrete enrichment of functionally organized genes

High-throughput biological experiments commonly result in a list of genes or proteins of interest. In order to understand the observed changes of the genes and to generate new hypotheses, one needs to understand the functions and roles of the genes and how those functions relate to the experimental conditions. Typically, statistical tests are performed in order to detect enriched Gene Ontology categories or pathways, i.e. the categories are observed in the genes of interest more often than is expected by chance. Depending on the number of genes and the complexity and quantity of functions in which they are involved, such an analysis can easily result in hundreds of enriched terms. To this end we developed DEFOG, a web-based application that facilitates the functional analysis of gene sets by hierarchically organizing the genes into functionally related modules. Our computational pipeline utilizes three powerful tools to achieve this goal: (1) GeneMANIA creates a functional consensus network of the genes of interest based on gene-list-specific data fusion of hundreds of genomic networks from publicly available sources; (2) Transitivity Clustering organizes those genes into a clear hierarchy of functionally related groups, and (3) Ontologizer performs a Gene Ontology enrichment analysis on the resulting gene clusters. DEFOG integrates this computational pipeline within an easy-to-use web interface, thus allowing for a novel visual analysis of gene sets that aids in the discovery of potentially important biological mechanisms and facilitates the creation of new hypotheses. DEFOG is available at http://www.mooneygroup.org/defog.