Interactome-transcriptome analysis reveals the high centrality of genes differentially expressed in lung cancer tissues

Identification of HLA ligands and T-cell epitopes for immunotherapy of lung cancer

INTRODUCTION

Lung cancer is the most common cancer worldwide. Every year, as many people die of lung cancer as of breast, colon and rectum cancers combined. Because most patients are being diagnosed in advanced, not resectable stages and therefore have a poor prognosis, there is an urgent need for alternative therapies. Since it has been demonstrated that a high number of tumor- and stromal-infiltrating cytotoxic T cells (CTLs) is associated with an increased disease-specific survival in lung cancer patients, it can be assumed that immunotherapy, e.g. peptide vaccines that are able to induce a CTL response against the tumor, might be a promising approach.

METHODS

We analyzed surgically resected lung cancer tissues with respect to HLA class I- and II-presented peptides and gene expression profiles, aiming at the identification of (novel) tumor antigens. In addition, we tested the ability of HLA ligands derived from such antigens to generate a CTL response in healthy donors.

RESULTS

Among 170 HLA ligands characterized, we were able to identify several potential targets for specific CTL recognition and to generate CD8+ T cells which were specific for peptides derived from cyclin D1 or protein-kinase, DNA-activated, catalytic polypeptide and lysed tumor cells loaded with peptide.

CONCLUSIONS

This is the first molecular analysis of HLA class I and II ligands ex vivo from human lung cancer tissues which reveals known and novel tumor antigens able to elicit a CTL response.

Cancer networks and beyond: interpreting mutations using the human interactome and protein structure

Over recent years, with the advances in next-generation sequencing, a large number of cancer mutations have been identified and accumulated in public repositories. Coupled to this is our increased ability to generate detailed interactome maps that help to enrich our knowledge of the biological implications of cancer mutations. As a result, network analysis approaches have become an invaluable tool to predict and interpret mutations that are associated with tumour survival and progression. Our understanding of cancer mechanisms is further enhanced by mapping protein structure information to such networks. Here we review the current methodologies for annotating the functional impacts of cancer mutations, which range from analysis of protein structures to protein-protein interaction network studies.

FOG2 protein down-regulation by transforming growth factor-β1-induced microRNA-200b/c leads to Akt kinase activation and glomerular mesangial hypertrophy related to diabetic nephropathy

Glomerular hypertrophy is a hallmark of diabetic nephropathy. Akt kinase activated by transforming growth factor-β1 (TGF-β) plays an important role in glomerular mesangial hypertrophy. However, the mechanisms of Akt activation by TGF-β are not fully understood. Recently, miR-200 and its target FOG2 were reported to regulate the activity of phosphatidylinositol 3-kinase (the upstream activator of Akt) in insulin signaling. Here, we show that TGF-β activates Akt in glomerular mesangial cells by inducing miR-200b and miR-200c, both of which target FOG2, an inhibitor of phosphatidylinositol 3-kinase activation. FOG2 expression was reduced in the glomeruli of diabetic mice as well as TGF-β-treated mouse mesangial cells (MMC). FOG2 knockdown by siRNAs in MMC activated Akt and increased the protein content/cell ratio suggesting hypertrophy. A significant increase of miR-200b/c levels was detected in diabetic mouse glomeruli and TGF-β-treated MMC. Transfection of MMC with miR-200b/c mimics significantly decreased the expression of FOG2. Conversely, miR-200b/c inhibitors attenuated TGF-β-induced decrease in FOG2 expression. Furthermore, miR-200b/c mimics increased the protein content/cell ratio, whereas miR-200b/c inhibitors abrogated the TGF-β-induced increase in protein content/cell. In addition, down-regulation of FOG2 by miR-200b/c could activate not only Akt but also ERK, which was also through PI3K activation. These data suggest a new mechanism for TGF-β-induced Akt activation through FOG2 down-regulation by miR-200b/c, which can lead to glomerular mesangial hypertrophy in the progression of diabetic nephropathy.

Notch increases the shedding of HB-EGF by ADAM12 to potentiate invadopodia formation in hypoxia

Hypoxia increases the levels of ADAM12 in a Notch-dependent manner, leading to increased ectodomain shedding of HB-EGF and subsequent promotion of invadopodia formation.

Notch regulates cell–cell contact-dependent signaling and is activated by hypoxia, a microenvironmental condition that promotes cellular invasion during both normal physiology and disease. The mechanisms by which hypoxia and Notch regulate cellular invasion are not fully elucidated. In this paper, we show that, in cancer cells, hypoxia increased the levels and activity of the ADAM12 metalloprotease in a Notch signaling–dependent manner, leading to increased ectodomain shedding of the epidermal growth factor (EGF) receptor (EGFR) ligand heparin-binding EGF-like growth factor. Released HB-EGF induced the formation of invadopodia, cellular structures that aid cancer cell invasion. Thus, we describe a signaling pathway that couples cell contact–dependent signaling with the paracrine activation of the EGFR, indicating cross talk between the Notch and EGFR pathways in promoting cancer cell invasion. This signaling pathway might regulate the coordinated acquisition of invasiveness by neighboring cells and mediate the communication between normoxic and hypoxic areas of tumors to facilitate cancer cell invasion.

The marine toxin okadaic acid induces alterations in the expression level of cancer-related genes in human neuronal cells

Okadaic acid (OA) is one of the most common and highly distributed marine toxins. It can be accumulated in several molluscs and other marine organisms and cause acute gastrointestinal symptoms after oral consumption by humans, called diarrheic shellfish poisoning. However other toxic effects beyond these gastrointestinal symptoms were also reported. Thus, OA was found to induce important chromosomal abnormalities and other genetic injuries that can lead to severe pathologies, including cancer. Furthermore, the relationship between OA and carcinogenic processes has been previously demonstrated in in vivo studies with rodents, and also suggested in human epidemiological studies. In this context, further research is required to better understand the underlying mechanisms of OA-related tumourigenesis. In a previous study, we identified 247 genes differentially expressed in SHSY5Y neuroblastoma cells exposed to 100nM OA at different times (3, 24 and 48h) by means of suppression subtractive hybridization. These genes were involved in relevant cell functions such as signal transduction, cell cycle, metabolism, and transcription and translation processes. However, due to the high potential percentage of false positives that may be obtained by this approach, results from SSH are recommended to be analyzed by an independent method. In the present study, we selected ten genes related to cancer initiation or progression, directly or indirectly, for further quantitative PCR analysis (ANAPC13, PTTG1, CALM2, CLU, HN1, MALAT1, MAPRE2, MLLT11, SGA-81M and TAX1BP1). Results obtained showed important alterations in the expression patterns of all the genes evaluated at one or more treatment times, providing, for the first time, a possible explanation at the molecular level of the potential relationship between the consumption of OA-contaminated shellfish and the incidence of different cancers in humans. Nevertheless, given the complexity of this process, more exhaustive studies are required before drawing any final conclusion.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

Linking Interactome to Disease

The introduction of high-throughput gene expression profiling technologies (DNA microarrays) in molecular biology and their expected applications to the clinic have allowed the design of predictive signatures linked to a particular clinical condition or patient outcome in a given clinical setting. However, it has been shown that such signatures are prone to several problems: (i) they are heavily unstable and linked to the set of patients chosen for training; (ii) data topology is problematic with regard to the data dimensionality (too many variables for too few samples); (iii) diseases such as cancer are provoked by subtle misregulations which cannot be readily detected by current analysis methods. To find a predictive signature generalizable for multiple datasets, a strategy of superimposition of a large scale of protein-protein interaction data (human interactome) was devised over several gene expression datasets (a total of 2,464 breast cancer tumors were integrated), to find discriminative regions in the interactome (subnetworks) predicting metastatic relapse in breast cancer. This method, Interactome-Transcriptome Integration (ITI), was applied to several breast cancer DNA microarray datasets and allowed the extraction of a signature constituted by 119 subnetworks. All subnetworks have been stored in a relational database and linked to Gene Ontology and NCBI EntrezGene annotation databases for analysis. Exploration of annotations has shown that this set of subnetworks reflects several biological processes linked to cancer and is a good candidate for establishing a network-based signature for prediction of metastatic relapse in breast cancer.

Expression of protein tyrosine kinase 6 (PTK6) in nonsmall cell lung cancer and their clinical and prognostic significance

Aim:

The aim of the study was to validate the expression of protein tyrosine kinase 6 (PTK6) in nonsmall cell lung cancer (NSCLC), and to evaluate its clinicopathological and prognostic significance.

Methods:

We first conducted a meta-analysis on the mRNA profiling data sets of NSCLC in the Oncomine database. Then, one of the most significantly upregulated tyrosine kinase targets, PTK6, was further validated by immunohistochemistry in 104 primary NSCLC tumors. Furthermore the association between PTK6 expression, the clinical parameters, and overall survival was further analyzed.

Results:

Using the Oncomine database, we identified a list of tyrosine kinase genes related to NSCLC, among which PTK6 was the second most overexpressed gene (median rank = 915, P = 2.9 × 10⁻⁵). We further confirmed that NSCLC tumors had a higher expression level of PTK6 than normal pulmonary tissues. Moreover, high PTK6 expression correlated positively with shorter overall survival time, but not with other clinicopathological characteristics. In the multivariate Cox regression model, high PTK6 expression was demonstrated to be an independent prognostic factor for NSCLC patients.

Conclusion:

Our results validated that PTK6 was found to be overexpressed in a proportion of NSCLC samples, and was associated with a poor prognosis, suggesting that this subgroup of NSCLC patients might benefit from PTK6 inhibitors in the future.

Semantically predicting protein functions based on protein functional connectivity

BACKGROUND

The current availability of public protein-protein interaction (PPI) databases which are usually modelled as PPI networks has led to the rapid development of protein function prediction approaches. The existing network-based prediction approaches mainly focus on the topological similarities between immediately interacting proteins, neglecting the protein functional connectivity which is the functional tightness between proteins. In this paper, we attempt to predict the functions of unannotated proteins based on PPI networks by incorporating the protein functional connectivity, as well as the similarity of protein functions, into the prediction procedure.

RESULTS

An approach named Semantic protein function Prediction based on protein Functional Connectivity (SPFC) is proposed to achieve a higher accuracy in predicting functions of unannotated protein. We define the functional connectivity and function addition for each protein, and incorporate them into the prediction. We evaluated the SPFC on real PPI datasets and the experiment results show that the SPFC method is more effective in function prediction than other network-based approaches.

CONCLUSION

Incorporating the functional connectivity of each protein into the function prediction can significantly improve the accuracy of protein prediction.

Bioinformatics analysis reveals connection of squamous cell carcinoma and adenocarcinoma of the lung

Squamous cell carcinoma and adenocarcinoma are the major histological types of non-small cell lung cancer. Because they differ on the basis of histopathological and clinical characteristics and their relationship with smoking, their etiologies may be different; for example, different tumor suppressor genes may be related to the genesis of each type. We used microarray data to construct three regulatory networks to identify potential genes related to lung adenocarcinoma and squamous cell carcinoma and investigated the similarity and specificity of them. In the network, some of the observed transcription factors and target genes had been previously proven to be related to lung adenocarcinoma and squamous cell carcinoma. We also found some new transcription factors and target genes related to SCC. The results demonstrated that regulatory network analysis is useful in connection analysis between lung adenocarcinoma and squamous cell carcinoma.

Phosphotyrosine signaling proteins that drive oncogenesis tend to be highly interconnected

Mutation and overexpression of receptor tyrosine kinases or the proteins they regulate serve as oncogenic drivers in diverse cancers. To better understand receptor tyrosine kinase signaling and its link to oncogenesis, we used protein microarrays to systematically and quantitatively measure interactions between virtually every SH2 or PTB domain encoded in the human genome and all known sites of tyrosine phosphorylation on 40 receptor tyrosine kinases and on most of the SH2 and PTB domain-containing adaptor proteins. We found that adaptor proteins, like RTKs, have many high affinity bindings sites for other adaptor proteins. In addition, proteins that drive cancer, including both receptors and adaptor proteins, tend to be much more highly interconnected via networks of SH2 and PTB domain-mediated interactions than nononcogenic proteins. Our results suggest that network topological properties such as connectivity can be used to prioritize new drug targets in this well-studied family of signaling proteins.

Disentangling function from topology to infer the network properties of disease genes

BACKGROUND

The topological features of disease genes within interaction networks are the subject of intense study, as they shed light on common mechanisms of pathology and are useful for uncovering additional disease genes. Computational analyses typically try to uncover whether disease genes exhibit distinct network features, as compared to all genes.

RESULTS

We demonstrate that the functional composition of disease gene sets is an important confounding factor in these types of analyses. We consider five disease sets and show that while they indeed have distinct topological features, they are also enriched in functions that a priori exhibit distinct network properties. To address this, we develop a computational framework to assess the network properties of disease genes based on a sampling algorithm that generates control gene sets that are functionally similar to the disease set. Using our function-constrained sampling approach, we demonstrate that for most of the topological properties studied, disease genes are more similar to sets of genes with similar functional make-up than they are to randomly selected genes; this suggests that these observed differences in topological properties reflect not only the distinguishing network features of disease genes but also their functional composition. Nevertheless, we also highlight many cases where disease genes have distinct topological properties even when accounting for function.

CONCLUSIONS

Our approach is an important first step in extracting the residual topological differences in disease genes when accounting for function, and leads to new insights into the network properties of disease genes.

The Core Diseasome

Large amounts of protein-protein interaction (PPI) data are available. The human PPI network currently contains over 56 000 interactions between 11 100 proteins. It has been demonstrated that the structure of this network is not random and that the same wiring patterns in it underlie the same biological processes and diseases. In this paper, we ask if there exists a subnetwork of the human PPI network such that its topology is the key to disease formation and hence should be the primary object of therapeutic intervention. We demonstrate that such a subnetwork exists and can be obtained purely computationally. In particular, by successively pruning the entire human PPI network, we are left with a "core" subnetwork that is not only topologically and functionally homogeneous, but is also enriched in disease genes, drug targets, and it contains genes that are known to drive disease formation. We call this subnetwork the Core Diseasome. Furthermore, we show that the topology of the Core Diseasome is unique in the human PPI network suggesting that it may be the wiring of this network that governs the mutagenesis that leads to disease. Explaining the mechanisms behind this phenomenon and exploiting them remains a challenge.

Chapter 4: Protein interactions and disease

Proteins do not function in isolation; it is their interactions with one another and also with other molecules (e.g. DNA, RNA) that mediate metabolic and signaling pathways, cellular processes, and organismal systems. Due to their central role in biological function, protein interactions also control the mechanisms leading to healthy and diseased states in organisms. Diseases are often caused by mutations affecting the binding interface or leading to biochemically dysfunctional allosteric changes in proteins. Therefore, protein interaction networks can elucidate the molecular basis of disease, which in turn can inform methods for prevention, diagnosis, and treatment. In this chapter, we will describe the computational approaches to predict and map networks of protein interactions and briefly review the experimental methods to detect protein interactions. We will describe the application of protein interaction networks as a translational approach to the study of human disease and evaluate the challenges faced by these approaches.

Chapter 5: Network biology approach to complex diseases

Complex diseases are caused by a combination of genetic and environmental factors. Uncovering the molecular pathways through which genetic factors affect a phenotype is always difficult, but in the case of complex diseases this is further complicated since genetic factors in affected individuals might be different. In recent years, systems biology approaches and, more specifically, network based approaches emerged as powerful tools for studying complex diseases. These approaches are often built on the knowledge of physical or functional interactions between molecules which are usually represented as an interaction network. An interaction network not only reports the binary relationships between individual nodes but also encodes hidden higher level organization of cellular communication. Computational biologists were challenged with the task of uncovering this organization and utilizing it for the understanding of disease complexity, which prompted rich and diverse algorithmic approaches to be proposed. We start this chapter with a description of the general characteristics of complex diseases followed by a brief introduction to physical and functional networks. Next we will show how these networks are used to leverage genotype, gene expression, and other types of data to identify dysregulated pathways, infer the relationships between genotype and phenotype, and explain disease heterogeneity. We group the methods by common underlying principles and first provide a high level description of the principles followed by more specific examples. We hope that this chapter will give readers an appreciation for the wealth of algorithmic techniques that have been developed for the purpose of studying complex diseases as well as insight into their strengths and limitations.

[Bioinformatic screening of key genes controlling the development and progression of lung cancer]

BACKGROUND AND OBJECTIVE

Lung cancer is the most common cancer in the world. The gene expression profiling of lung cancer has been extensively investigated. However, only a few studies have identified the possible pathways and significant genes related to lung cancer. The aim of this study is to explore the large number of lung cancer-related microarray datasets and identify the crucial genes that can benefit the understanding of the progression and development of this disease.

METHODS

To identify the genes that effected lung cancer at the mRNA level, gene set enrichment analysis was used to analyze six selected gene expression datasets.

RESULTS

Among the six gene expression datasets, 3 up-regulated and 26 down-regulated pathways were found by gene set enrichment analysis. We found 11 significant genes with P < 0.05 from the results of tight junction meta-analysis of the six data sets.

CONCLUSIONS

The tight junction pathway plays an important role in the study of the occurrence and development of lung cancer. Significant genes within the pathways will be further discussed in future studies.

Transcriptional regulatory networks in human lung adenocarcinoma

Lung adenocarcinoma (AC) is the most common histological subtype of lung cancer worldwide and its absolute incidence is increasing markedly. Transcriptional regulation is one of the most fundamental processes in lung AC development. However, high-throughput functional analyses of multiple transcription factors and their target genes in lung AC are rare. Thus, the objective of our study was to interpret the mechanisms of human AC through the regulatory network using the GSE2514 microarray data. Our results identified the genes peroxisome proliferator activated receptor-γ (PPARG), CCAAT/enhancer binding protein β (CEBPB), ets variant 4 (ETV4), Friend leukemia virus integration 1 (FLI1), T-cell acute lymphocytic leukemia 1 (TAL1) and nuclear factor of kappa light polypeptide gene enhancer in B-cells 1 (NFKB1) as hub nodes in the transcriptome network. Among these genes, it appears that: PPARG promotes the PPAR signaling pathway via the upregulation of lipoprotein lipase (LPL) expression, but suppresses the cell cycle pathway via downregulation of growth arrest and DNA-damage-inducible, γ (GADD45G) expression; ETV4 stimulates matrix metallopeptidase 9 (MMP9) expression to induce the bladder cancer pathway; FLI upregulates transforming growth factor, β receptor II (TGFBR2) expression to activate TGF-β signaling and upregulates cyclin D3 (CCND3) expression to promote the cell cycle pathway; NFKB1 upregulates interleukin 1, β (IL-1B) expression and initiates the prostate cancer pathway; CEBPB upregulates IL-6 expression and promotes pathways in cancer; and TAL1 promotes kinase insert domain receptor (KDR) expression to promote the TGF-β signaling pathway. This transcriptional regulation analysis may provide an improved understanding of the molecular mechanisms and potential therapeutic targets in the treatment of lung AC.

Biological function through network topology: a survey of the human diseasome

Molecular network data are increasingly becoming available, necessitating the development of well performing computational tools for their analyses. Such tools enabled conceptually different approaches for exploring human diseases to be undertaken, in particular, those that study the relationship between a multitude of biomolecules within a cell. Hence, a new field of network biology has emerged as part of systems biology, aiming to untangle the complexity of cellular network organization. We survey current network analysis methods that aim to give insight into human disease.

Exploiting protein-protein interaction networks for genome-wide disease-gene prioritization

Complex genetic disorders often involve products of multiple genes acting cooperatively. Hence, the pathophenotype is the outcome of the perturbations in the underlying pathways, where gene products cooperate through various mechanisms such as protein-protein interactions. Pinpointing the decisive elements of such disease pathways is still challenging. Over the last years, computational approaches exploiting interaction network topology have been successfully applied to prioritize individual genes involved in diseases. Although linkage intervals provide a list of disease-gene candidates, recent genome-wide studies demonstrate that genes not associated with any known linkage interval may also contribute to the disease phenotype. Network based prioritization methods help highlighting such associations. Still, there is a need for robust methods that capture the interplay among disease-associated genes mediated by the topology of the network. Here, we propose a genome-wide network-based prioritization framework named GUILD. This framework implements four network-based disease-gene prioritization algorithms. We analyze the performance of these algorithms in dozens of disease phenotypes. The algorithms in GUILD are compared to state-of-the-art network topology based algorithms for prioritization of genes. As a proof of principle, we investigate top-ranking genes in Alzheimer's disease (AD), diabetes and AIDS using disease-gene associations from various sources. We show that GUILD is able to significantly highlight disease-gene associations that are not used a priori. Our findings suggest that GUILD helps to identify genes implicated in the pathology of human disorders independent of the loci associated with the disorders.

Cancer develops, progresses and responds to therapies through restricted perturbation of the protein-protein interaction network

The products of genes mutated or differentially expressed in cancer tend to occupy central positions within the network of protein-protein interactions, or the interactome network. Integration of different types of gene and protein relationships has considerably increased the understanding of the mechanisms of carcinogenesis, while also enhancing the applicability of expression signatures. In this scenario, however, it remains unknown how cancer develops, progresses and responds to therapies in a potentially controlled manner at the systems level. Here, by applying the concepts of load transfer and cascading failures in power grids, we examine the impact and transmission of cancer-related gene expression changes in the interactome network. Relative to random perturbations, this study reveals topological robustness associated with all cancer conditions. In addition, experimental perturbation of a central cancer node, which consists of over-expression of the α-synuclein (SNCA) protein in MCF7 breast cancer cells, also reveals robustness. Conversely, a search for proteins with an opposite topological impact identifies the autophagy pathway. Mechanistically, the existence of smaller shortest paths among cancer-related proteins appears to be a topological feature that partially contributes to the restricted perturbation of the network. Together, the results of this study suggest that cancer develops, progresses and responds to therapies following controlled, restricted perturbation of the interactome network.

Protein arginine methyltransferase 5 is a potential oncoprotein that upregulates G1 cyclins/cyclin-dependent kinases and the phosphoinositide 3-kinase/AKT signaling cascade

Increasing evidence suggests that PRMT5, a protein arginine methyltransferase, is involved in tumorigenesis. However, no systematic research has demonstrated the cell-transforming activity of PRMT5. We investigated the involvement of PRMT5 in tumor formation. First, we showed that PRMT5 was associated with many human cancers, through statistical analysis of microarray data in the NCBI GEO database. Overexpression of ectopic PRMT5 per se or its specific shRNA enhanced or reduced cell growth under conditions of normal or low concentrations of serum, low cell density, and poor cell attachment. A stable clone that expressed exogenous PRMT5 formed tumors in nude mice, which demonstrated that PRMT5 is a potential oncoprotein. PRMT5 accelerated cell cycle progression through G1 phase and modulated regulators of G1; for example, it upregulated cyclin-dependent kinase (CDK) 4, CDK6, and cyclins D1, D2 and E1, and inactivated retinoblastoma protein (Rb). Moreover, PRMT5 activated phosphoinositide 3-kinase (PI3K)/AKT and suppressed c-Jun N-terminal kinase (JNK)/c-Jun signaling cascades. However, only inhibition of PI3K activity, and not overexpression of JNK, blocked PRMT5-induced cell proliferation. Further analysis of PRMT5 expression in 64 samples of human lung cancer tissues by microarray and western blot analysis revealed a tight association of PRMT5 with lung cancer. Knockdown of PRMT5 retarded cell growth of lung cancer cell lines A549 and H1299. In conclusion, to the best of our knowledge, we have characterized the cell-transforming activity of PRMT5 and delineated its underlying mechanisms for the first time.

Gene regulatory network from microarray data of colon cancer patients using TSK-type recurrent neural fuzzy network

In this work we applied a TSK-type recurrent neural fuzzy approach to extract regulatory relationship among genes and reconstruct gene regulatory network from microarray data. The identified signature has captured the regulatory relationship among 27 differentially expressed genes from microarray dataset. We applied three different methods viz., feed forward neural fuzzy, modified genetic algorithm and recurrent neural fuzzy, on the same data set for the inference of GRNs and the results obtained are almost comparable. In all tested cases, TRNFN identified more biologically meaningful relations. We found that 87.8% of the total interactions extracted by TRNFN are correct in accordance with the biological knowledge. Our analysis resulted in 2 major outcomes. First, upregulated genes are regulated by more genes than downregulated genes. Second, tumor activators activate other tumor activators and suppress tumor suppressers strongly in the disease environment. These findings will help to elucidate the common molecular mechanism of colon cancer, and provide new insights into cancer diagnostics, prognostics and therapy.

Integrating the Alzheimer's disease proteome and transcriptome: a comprehensive network model of a complex disease

Network models combined with gene expression studies have become useful tools for studying complex diseases like Alzheimer's disease. We constructed a "Core" Alzheimer's disease protein interaction network by human curation of the primary literature. The Core network consisted of 775 nodes and 2,204 interactions. To our knowledge, this is the most comprehensive and accurate protein interaction network yet constructed for Alzheimer's disease. An "Expanded" network was computationally constructed by adding additional proteins that interacted with Core network proteins, and consisted of 4,945 nodes and 26,064 interactions. We then mapped existing gene expression studies to the Core network. This combined data model identified the MAPK/ERK pathway and clathrin-mediated receptor endocytosis as key pathways in Alzheimer's disease. Important proteins in the MAPK/ERK pathway that interacted in the Core network formed a downregulated cluster of nodes, whereas clathrin and several clathrin accessory proteins that interacted in the Core network formed an upregulated cluster of nodes. The MAPK/ERK pathway is a key component in synaptic plasticity and learning, processes disrupted in Alzheimer's. Clathrin and clathrin adaptor proteins are involved in the endocytosis of the APP protein that can lead to increased intracellular levels of amyloid beta peptide, contributing to the progression of Alzheimer's.

The p44/wdr77-dependent cellular proliferation process during lung development is reactivated in lung cancer

During lung development, cells proliferate for a defined length of time before they begin to differentiate. Factors that control this proliferative process and how this growth process is related to lung cancer are currently unknown. Here, we found that the WD40-containing protein (p44/wdr77) was expressed in growing epithelial cells at the early stages of lung development. In contrast, p44/wdr77 expression was diminished in fully differentiated epithelial cells in the adult lung. Loss of p44/wdr77 gene expression led to cell growth arrest and differentiation. Re-expression of p44/wdr77 caused terminally differentiated cells to re-enter the cell cycle. Our findings suggest that p44/wdr77 is essential and sufficient for proliferation of lung epithelial cells. P44/Wdr77 was re-expressed in lung cancer, and silencing p44/wdr77 expression strongly inhibited growth of lung adenocarcinoma cells in tissue culture and abolished growth of lung adenocarcinoma tumor xenografts in mice. The growth arrest induced by loss of p44/wdr77 expression was partially through the p21-Rb signaling. Our results suggest that p44/wdr77 controls cellular proliferation during lung development, and this growth process is reactivated during lung tumorigenesis.

Twist1 suppresses senescence programs and thereby accelerates and maintains mutant Kras-induced lung tumorigenesis

KRAS mutant lung cancers are generally refractory to chemotherapy as well targeted agents. To date, the identification of drugs to therapeutically inhibit K-RAS have been unsuccessful, suggesting that other approaches are required. We demonstrate in both a novel transgenic mutant Kras lung cancer mouse model and in human lung tumors that the inhibition of Twist1 restores a senescence program inducing the loss of a neoplastic phenotype. The Twist1 gene encodes for a transcription factor that is essential during embryogenesis. Twist1 has been suggested to play an important role during tumor progression. However, there is no in vivo evidence that Twist1 plays a role in autochthonous tumorigenesis. Through two novel transgenic mouse models, we show that Twist1 cooperates with Kras^G12D to markedly accelerate lung tumorigenesis by abrogating cellular senescence programs and promoting the progression from benign adenomas to adenocarcinomas. Moreover, the suppression of Twist1 to physiological levels is sufficient to cause Kras mutant lung tumors to undergo senescence and lose their neoplastic features. Finally, we analyzed more than 500 human tumors to demonstrate that TWIST1 is frequently overexpressed in primary human lung tumors. The suppression of TWIST1 in human lung cancer cells also induced cellular senescence. Hence, TWIST1 is a critical regulator of cellular senescence programs, and the suppression of TWIST1 in human tumors may be an effective example of pro-senescence therapy.

Lung cancer is the most common cause of cancer death worldwide. The Twist1 gene encodes for an essential transcription factor required for embryogenesis and overexpressed in many cancer types. It has yet to be shown in vivo whether Twist1 plays a role in the initiation or maintenance of cancer. Here we demonstrate using novel transgenic mouse models that Twist1 cooperates to induce lung tumorigenesis by suppressing cellular senescence programs. Moreover, the suppression of Twist1 in murine tumors elicited cellular senescence and the loss of a neoplastic phenotype. We found that TWIST1 is commonly overexpressed in human lung cancers. Finally, the inhibition of TWIST1 levels in human lung cancer cells was associated with loss of proliferation, induction of cellular senescence, and the inability to form tumors in mice. Hence, we conclude that TWIST1 is a key regulator of cellular senescence programs during tumorigenesis. The targeted inactivation of TWIST1 may be an effective pro-senescence therapy for human lung adenocarcinomas.

Exploiting multi-layered information to iteratively predict protein functions

BACKGROUND

Similarity based computational methods are a useful tool for predicting protein functions from protein-protein interaction (PPI) datasets. Although various similarity-based prediction algorithms have been proposed, unsatisfactory prediction results have occurred on many occasions. The purpose of this type of algorithm is to predict functions of an unannotated protein from the functions of those proteins that are similar to the unannotated protein. Therefore, the prediction quality largely depends on how to select a set of proper proteins (i.e., a prediction domain) from which the functions of an unannotated protein are predicted, and how to measure the similarity between proteins. Another issue with existing algorithms is they only believe the function prediction is a one-off procedure, ignoring the fact that interactions amongst proteins are mutual and dynamic in terms of similarity when predicting functions. How to resolve these major issues to increase prediction quality remains a challenge in computational biology.

RESULTS

In this paper, we propose an innovative approach to predict protein functions of unannotated proteins iteratively from a PPI dataset. The iterative approach takes into account the mutual and dynamic features of protein interactions when predicting functions, and addresses the issues of protein similarity measurement and prediction domain selection by introducing into the prediction algorithm a new semantic protein similarity and a method of selecting the multi-layer prediction domain. The new protein similarity is based on the multi-layered information carried by protein functions. The evaluations conducted on real protein interaction datasets demonstrated that the proposed iterative function prediction method outperformed other similar or non-iterative methods, and provided better prediction results.

CONCLUSIONS

The new protein similarity derived from multi-layered information of protein functions more reasonably reflects the intrinsic relationships among proteins, and significant improvement to the prediction quality can occur through incorporation of mutual and dynamic features of protein interactions into the prediction algorithm.

Transcriptome network analysis reveals potential candidate genes for esophageal squamous cell carcinoma

The esophageal squamous cell carcinoma (ESCC) is an aggressive tumor with a poor prognosis. Understanding molecular changes in ESCC should improve identification of risk factors with different molecular subtypes and provide potential targets for early detection and therapy. Our study aimed to obtain a molecular signature of ESCC through the regulation network based on differentially expressed genes (DEGs). We used the GSE23400 series to identify potential genes related to ESCC. Based on bioinformatics we constructed a regulation network. From the results, we could establish that many transcription factors and pathways closely related with ESCC were linked by our method. STAT1 also arose as a hub node in our transcriptome network, along with some transcription factors like CCNB1, TAP1, RARG and IFITM1 proven to be related with ESCC by previous studies. In conclusion, our regulation network provided information on important genes which might be useful in investigating the complex interacting mechanisms underlying the disease.

Aberrant signaling pathways in squamous cell lung carcinoma

Lung cancer is the second most commonly occurring non-cutaneous cancer in the United States with the highest mortality rate among both men and women. In this study, we utilized three lung cancer microarray datasets generated by previous researchers to identify differentially expressed genes, altered signaling pathways, and assess the involvement of Hedgehog (Hh) pathway. The three datasets contain the expression levels of tens of thousands genes in normal lung tissues and squamous cell lung carcinoma. The datasets were combined and analyzed. The dysregulated genes and altered signaling pathways were identified using statistical methods. We then performed Fisher’s exact test on the significance of the association of Hh pathway downstream genes and squamous cell lung carcinoma.

395 genes were found commonly differentially expressed in squamous cell lung carcinoma. The genes encoding fibrous structural protein keratins and cell cycle dependent genes encoding cyclin-dependent kinases were significantly up-regulated while the ones encoding LIM domains were down. Over 100 signaling pathways were implicated in squamous cell lung carcinoma, including cell cycle regulation pathway, p53 tumor-suppressor pathway, IL-8 signaling, Wnt-β-catenin pathway, mTOR signaling and EGF signaling. In addition, 37 out of 223 downstream molecules of Hh pathway were altered. The P-value from the Fisher’s exact test indicates that Hh signaling is implicated in squamous cell lung carcinoma.

Numerous genes were altered and multiple pathways were dysfunctional in squamous cell lung carcinoma. Many of the altered genes have been implicated in different types of carcinoma while some are organ-specific. Hh signaling is implicated in squamous cell lung cancer, opening the door for exploring new cancer therapeutic treatment using GLI antagonist GANT 61.

DART: Denoising Algorithm based on Relevance network Topology improves molecular pathway activity inference

BACKGROUND

Inferring molecular pathway activity is an important step towards reducing the complexity of genomic data, understanding the heterogeneity in clinical outcome, and obtaining molecular correlates of cancer imaging traits. Increasingly, approaches towards pathway activity inference combine molecular profiles (e.g gene or protein expression) with independent and highly curated structural interaction data (e.g protein interaction networks) or more generally with prior knowledge pathway databases. However, it is unclear how best to use the pathway knowledge information in the context of molecular profiles of any given study.

RESULTS

We present an algorithm called DART (Denoising Algorithm based on Relevance network Topology) which filters out noise before estimating pathway activity. Using simulated and real multidimensional cancer genomic data and by comparing DART to other algorithms which do not assess the relevance of the prior pathway information, we here demonstrate that substantial improvement in pathway activity predictions can be made if prior pathway information is denoised before predictions are made. We also show that genes encoding hubs in expression correlation networks represent more reliable markers of pathway activity. Using the Netpath resource of signalling pathways in the context of breast cancer gene expression data we further demonstrate that DART leads to more robust inferences about pathway activity correlations. Finally, we show that DART identifies a hypothesized association between oestrogen signalling and mammographic density in ER+ breast cancer.

CONCLUSIONS

Evaluating the consistency of prior information of pathway databases in molecular tumour profiles may substantially improve the subsequent inference of pathway activity in clinical tumour specimens. This de-noising strategy should be incorporated in approaches which attempt to infer pathway activity from prior pathway models.

Disease-driven detection of differential inherited SNP modules from SNP network

Detection of the synergetic effects between variants, such as single-nucleotide polymorphisms (SNPs), is crucial for understanding the genetic characters of complex diseases. Here, we proposed a two-step approach to detect differentially inherited SNP modules (synergetic SNP units) from a SNP network. First, SNP-SNP interactions are identified based on prior biological knowledge, such as their adjacency on the chromosome or degree of relatedness between the functional relationships of their genes. These interactions form SNP networks. Second, disease-risk SNP modules (or sub-networks) are prioritised by their differentially inherited properties in IBD (Identity by Descent) profiles of affected and unaffected sibpairs. The search process is driven by the disease information and follows the structure of a SNP network. Simulation studies have indicated that this approach achieves high accuracy and a low false-positive rate in the identification of known disease-susceptible SNPs. Applying this method to an alcoholism dataset, we found that flexible patterns of susceptible SNP combinations do play a role in complex diseases, and some known genes were detected through these risk SNP modules. One example is GRM7, a known alcoholism gene successfully detected by a SNP module comprised of two SNPs, but neither of the two SNPs was significantly associated with the disease in single-locus analysis. These identified genes are also enriched in some pathways associated with alcoholism, including the calcium signalling pathway, axon guidance and neuroactive ligand-receptor interaction. The integration of network biology and genetic analysis provides putative functional bridges between genetic variants and candidate genes or pathways, thereby providing new insight into the aetiology of complex diseases.

Differentially expressed genes in major depression reside on the periphery of resilient gene coexpression networks

The structure of gene coexpression networks reflects the activation and interaction of multiple cellular systems. Since the pathology of neuropsychiatric disorders is influenced by diverse cellular systems and pathways, we investigated gene coexpression networks in major depression, and searched for putative unifying themes in network connectivity across neuropsychiatric disorders. Specifically, based on the prevalence of the lethality–centrality relationship in disease-related networks, we hypothesized that network changes between control and major depression-related networks would be centered around coexpression hubs, and secondly, that differentially expressed (DE) genes would have a characteristic position and connectivity level in those networks. Mathematically, the first hypothesis tests the relationship of differential coexpression to network connectivity, while the second “hybrid” expression-and-network hypothesis tests the relationship of differential expression to network connectivity. To answer these questions about the potential interaction of coexpression network structure with differential expression, we utilized all available human post-mortem depression-related datasets appropriate for coexpression analysis, which spanned different microarray platforms, cohorts, and brain regions. Similar studies were also performed in an animal model of depression and in schizophrenia and bipolar disorder microarray datasets. We now provide results which consistently support (1) that genes assemble into small-world and scale-free networks in control subjects, (2) that this efficient network topology is largely resilient to changes in depressed subjects, and (3) that DE genes are positioned on the periphery of coexpression networks. Similar results were observed in a mouse model of depression, and in selected bipolar- and schizophrenia-related networks. Finally, we show that baseline expression variability contributes to the propensity of genes to be network hubs and/or to be DE in disease. In summary, our results suggest that the small-world and scale-free properties of gene networks are resilient to pathological changes in major depression, and that the network structure may constrain the extent to which a gene may be DE in the illness, hence informing further gene-network-based mechanistic studies of neuropsychiatric disorders.

Impaired OXPHOS complex III in breast cancer

We measured the mitochondrial oxidative phosphorylation (mtOXPHOS) activities of all five complexes and determined the activity and gene expression in detail of the Complex III subunits in human breast cancer cell lines and primary tumors. Our analysis revealed dramatic differences in activity of complex III between normal and aggressive metastatic breast cancer cell lines. Determination of Complex III subunit gene expression identified over expression and co-regulation of UQCRFS1 (encoding RISP protein) and UQCRH (encoding Hinge protein) in 6 out of 9 human breast tumors. Analyses of UQCRFS1/RISP expression in additional matched normal and breast tumors demonstrated an over expression in 14 out of 40 (35%) breast tumors. UQCRFS1/RISP knockdown in breast tumor cell line led to decreased mitochondrial membrane potential as well as a decrease in matrigel invasion. Furthermore, reduced matrigel invasion was mediated by reduced ROS levels coinciding with decreased expression of NADPH oxidase 2, 3, 4 and 5 involved in ROS production. These studies provide direct evidence for contribution of impaired mtOXPHOS Complex III to breast tumorigenesis.

Network-based methods for human disease gene prediction

Despite the considerable progress in disease gene discovery, we are far from uncovering the underlying cellular mechanisms of diseases since complex traits, even many Mendelian diseases, cannot be explained by simple genotype-phenotype relationships. More recently, an increasingly accepted view is that human diseases result from perturbations of cellular systems, especially molecular networks. Genes associated with the same or similar diseases commonly reside in the same neighborhood of molecular networks. Such observations have built the basis for a large collection of computational approaches to find previously unknown genes associated with certain diseases. The majority of the methods are based on protein interactome networks, with integration of other large-scale genomic data or disease phenotype information, to infer how likely it is that a gene is associated with a disease. Here, we review recent, state of the art, network-based methods used for prioritizing disease genes as well as unraveling the molecular basis of human diseases.

Protein-protein interactions: making sense of networks via graph-theoretic modeling

The emerging area of network biology is seeking to provide insights into organizational principles of life. However, despite significant collaborative efforts, there is still typically a weak link between biological and computational scientists and a lack of understanding of the research issues across the disciplines. This results in the use of simple computational techniques of limited potential that are incapable of explaining these complex data. Hence, the danger is that the community might begin to view the topological properties of network data as mere statistics, rather than rich sources of biological information. A further danger is that such views might result in the imposition of scientific doctrines, such as scale-free-centric (on the modeling side) and genome-centric (on the biological side) opinions onto this area. Here, we take a graph-theoretic perspective on protein-protein interaction networks and present a high-level overview of the area, commenting on possible challenges ahead.

Interactome networks and human disease

Complex biological systems and cellular networks may underlie most genotype to phenotype relationships. Here, we review basic concepts in network biology, discussing different types of interactome networks and the insights that can come from analyzing them. We elaborate on why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease.

Identification of homogeneous genetic architecture of multiple genetically correlated traits by block clustering of genome-wide associations

Genome-wide association studies (GWAS) using high-density genotyping platforms offer an unbiased strategy to identify new candidate genes for osteoporosis. It is imperative to be able to clearly distinguish signal from noise by focusing on the best phenotype in a genetic study. We performed GWAS of multiple phenotypes associated with fractures [bone mineral density (BMD), bone quantitative ultrasound (QUS), bone geometry, and muscle mass] with approximately 433,000 single-nucleotide polymorphisms (SNPs) and created a database of resulting associations. We performed analysis of GWAS data from 23 phenotypes by a novel modification of a block clustering algorithm followed by gene-set enrichment analysis. A data matrix of standardized regression coefficients was partitioned along both axes--SNPs and phenotypes. Each partition represents a distinct cluster of SNPs that have similar effects over a particular set of phenotypes. Application of this method to our data shows several SNP-phenotype connections. We found a strong cluster of association coefficients of high magnitude for 10 traits (BMD at several skeletal sites, ultrasound measures, cross-sectional bone area, and section modulus of femoral neck and shaft). These clustered traits were highly genetically correlated. Gene-set enrichment analyses indicated the augmentation of genes that cluster with the 10 osteoporosis-related traits in pathways such as aldosterone signaling in epithelial cells, role of osteoblasts, osteoclasts, and chondrocytes in rheumatoid arthritis, and Parkinson signaling. In addition to several known candidate genes, we also identified PRKCH and SCNN1B as potential candidate genes for multiple bone traits. In conclusion, our mining of GWAS results revealed the similarity of association results between bone strength phenotypes that may be attributed to pleiotropic effects of genes. This knowledge may prove helpful in identifying novel genes and pathways that underlie several correlated phenotypes, as well as in deciphering genetic and phenotypic modularity underlying osteoporosis risk.