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

eQTL networks unveil enriched mRNA master integrators downstream of complex disease-associated SNPs

The causal and interplay mechanisms of Single Nucleotide Polymorphisms (SNPs) associated with complex diseases (complex disease SNPs) investigated in genome-wide association studies (GWAS) at the transcriptional level (mRNA) are poorly understood despite recent advancements such as discoveries reported in the Encyclopedia of DNA Elements (ENCODE) and Genotype-Tissue Expression (GTex). Protein interaction network analyses have successfully improved our understanding of both single gene diseases (Mendelian diseases) and complex diseases. Whether the mRNAs downstream of complex disease genes are central or peripheral in the genetic information flow relating DNA to mRNA remains unclear and may be disease-specific. Using expression Quantitative Trait Loci (eQTL) that provide DNA to mRNA associations and network centrality metrics, we hypothesize that we can unveil the systems properties of information flow between SNPs and the transcriptomes of complex diseases. We compare different conditions such as naïve SNP assignments and stringent linkage disequilibrium (LD) free assignments for transcripts to remove confounders from LD. Additionally, we compare the results from eQTL networks between lymphoblastoid cell lines and liver tissue. Empirical permutation resampling (p<0.001) and theoretic Mann-Whitney U test (p<10(-30)) statistics indicate that mRNAs corresponding to complex disease SNPs via eQTL associations are likely to be regulated by a larger number of SNPs than expected. We name this novel property mRNA hubness in eQTL networks, and further term mRNAs with high hubness as master integrators. mRNA master integrators receive and coordinate the perturbation signals from large numbers of polymorphisms and respond to the personal genetic architecture integratively. This genetic signal integration contrasts with the mechanism underlying some Mendelian diseases, where a genetic polymorphism affecting a single protein hub produces a divergent signal that affects a large number of downstream proteins. Indeed, we verify that this property is independent of the hubness in protein networks for which these mRNAs are transcribed. Our findings provide novel insights into the pleiotropy of mRNAs targeted by complex disease polymorphisms and the architecture of the information flow between the genetic polymorphisms and transcriptomes of complex diseases.

Transcriptomic Heterogeneity in Cancer as a Consequence of Dysregulation of the Gene-Gene Interaction Network

Many pathways are dysregulated in cancer. Dysregulation of the regulatory network results in less control of transcript levels in the cell. Hence, dysregulation is reflected in the heterogeneity of the transcriptome: the more dysregulated the pathway, the more the transcriptomic heterogeneity. We identify four scenarios for a transcriptomic heterogeneity increase (i.e., pathway dysregulation) in cancer: (1) activation of a molecular switch, (2) a structural change in a regulator, (3) a temporal change in a regulator, and (4) weakening of gene–gene interactions. These mechanisms are statistically motivated, explored in silico, and their plausibility to occur in vivo illustrated by means of oncogenomics data of breast cancer studies.

Correcting for the study bias associated with protein-protein interaction measurements reveals differences between protein degree distributions from different cancer types

Protein-protein interaction (PPI) networks are associated with multiple types of biases partly rooted in technical limitations of the experimental techniques. Another source of bias are the different frequencies with which proteins have been studied for interaction partners. It is generally believed that proteins with a large number of interaction partners tend to be essential, evolutionarily conserved, and involved in disease. It has been repeatedly reported that proteins driving tumor formation have a higher number of PPI partners. However, it has been noticed before that the degree distribution of PPI networks is biased toward disease proteins, which tend to have been studied more often than non-disease proteins. At the same time, for many poorly characterized proteins no interactions have been reported yet. It is unclear to which extent this study bias affects the observation that cancer proteins tend to have more PPI partners. Here, we show that the degree of a protein is a function of the number of times it has been screened for interaction partners. We present a randomization-based method that controls for this bias to decide whether a group of proteins is associated with significantly more PPI partners than the proteomic background. We apply our method to cancer proteins and observe, in contrast to previous studies, no conclusive evidence for a significantly higher degree distribution associated with cancer proteins as compared to non-cancer proteins when we compare them to proteins that have been equally often studied as bait proteins. Comparing proteins from different tumor types, a more complex picture emerges in which proteins of certain cancer classes have significantly more interaction partners while others are associated with a smaller degree. For example, proteins of several hematological cancers tend to be associated with a higher number of interaction partners as expected by chance. Solid tumors, in contrast, are usually associated with a degree distribution similar to those of equally often studied random protein sets. We discuss the biological implications of these findings. Our work shows that accounting for biases in the PPI network is possible and increases the value of PPI data.

Deregulated SLC2A1 Promotes Tumor Cell Proliferation and Metastasis in Gastric Cancer

Gastric cancer (GC) is one of the common reasons of cancer-related death with few biomarkers for diagnosis and prognosis. Solute carrier family 2 (facilitated glucose transporter) member 1 protein SLC2A1, also known as glucose transporter type 1 (GLUT1), has been associated with tumor progression, metastasis, and poor prognosis in many human solid tumors. However, little is reported about its clinical significance and biological functions in GC. Here we observed a strong up-regulation of SLC2A1 in patients with GC and found that SLC2A1 was significantly correlated with depth of invasion and clinical stage. Additionally, over-expression of SLC2A1 in GC cells promotes cellular proliferation and metastasis in vitro and enhances tumor growth in vivo as well as enhancement of glucose utilization. Meanwhile, elevated SLC2A1 also contributes to tumor metastasis in vitro. Our results indicate SLC2A1 exhibits a pivotal role in tumor growth, metastasis and glucose metabolism, and also suggest SLC2A1 as a promising target for gastric cancer therapy.

Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action

A drug exerts its effects typically through a signal transduction cascade, which is non-linear and involves intertwined networks of multiple signaling pathways. Construction of such a signaling pathway network (SPNetwork) can enable identification of novel drug targets and deep understanding of drug action. However, it is challenging to synopsize critical components of these interwoven pathways into one network. To tackle this issue, we developed a novel computational framework, the Drug-specific Signaling Pathway Network (DSPathNet). The DSPathNet amalgamates the prior drug knowledge and drug-induced gene expression via random walk algorithms. Using the drug metformin, we illustrated this framework and obtained one metformin-specific SPNetwork containing 477 nodes and 1,366 edges. To evaluate this network, we performed the gene set enrichment analysis using the disease genes of type 2 diabetes (T2D) and cancer, one T2D genome-wide association study (GWAS) dataset, three cancer GWAS datasets, and one GWAS dataset of cancer patients with T2D on metformin. The results showed that the metformin network was significantly enriched with disease genes for both T2D and cancer, and that the network also included genes that may be associated with metformin-associated cancer survival. Furthermore, from the metformin SPNetwork and common genes to T2D and cancer, we generated a subnetwork to highlight the molecule crosstalk between T2D and cancer. The follow-up network analyses and literature mining revealed that seven genes (CDKN1A, ESR1, MAX, MYC, PPARGC1A, SP1, and STK11) and one novel MYC-centered pathway with CDKN1A, SP1, and STK11 might play important roles in metformin’s antidiabetic and anticancer effects. Some results are supported by previous studies. In summary, our study 1) develops a novel framework to construct drug-specific signal transduction networks; 2) provides insights into the molecular mode of metformin; 3) serves a model for exploring signaling pathways to facilitate understanding of drug action, disease pathogenesis, and identification of drug targets.

A deep understanding of a drug’s mechanisms of actions is essential not only in the discovery of new treatments but also in minimizing adverse effects. Here, we develop a computational framework, the Drug-specific Signaling Pathway Network (DSPathNet), to reconstruct a comprehensive signaling pathway network (SPNetwork) impacted by a particular drug. To illustrate this computational approach, we used metformin, an anti-diabetic drug, as an example. Starting from collecting the metformin-related upstream genes and inferring the metformin-related downstream genes, we built one metformin-specific SPNetwork via random walk based algorithms. Our evaluation of the metformin-specific SPNetwork by using disease genes and genotyping data from genome-wide association studies showed that our DSPathNet approach was efficient to synopsize drug’s key components and their relationship involved in the type 2 diabetes and cancer, even the metformin anticancer activity. This work presents a novel computational framework for constructing individual drug-specific signal transduction networks. Furthermore, its successful application to the drug metformin provides some valuable insights into the mode of metformin action, which will facilitate our understanding of the molecular mechanisms underlying drug treatments, disease pathogenesis, and identification of novel drug targets and repurposed drugs.

The Use of Chemical-Chemical Interaction and Chemical Structure to Identify New Candidate Chemicals Related to Lung Cancer

Lung cancer causes over one million deaths every year worldwide. However, prevention and treatment methods for this serious disease are limited. The identification of new chemicals related to lung cancer may aid in disease prevention and the design of more effective treatments. This study employed a weighted network, constructed using chemical-chemical interaction information, to identify new chemicals related to two types of lung cancer: non-small lung cancer and small-cell lung cancer. Then, a randomization test as well as chemical-chemical interaction and chemical structure information were utilized to make further selections. A final analysis of these new chemicals in the context of the current literature indicates that several chemicals are strongly linked to lung cancer.

A predicted protein interactome identifies conserved global networks and disease resistance subnetworks in maize

Interactomes are genome-wide roadmaps of protein-protein interactions. They have been produced for humans, yeast, the fruit fly, and Arabidopsis thaliana and have become invaluable tools for generating and testing hypotheses. A predicted interactome for Zea mays (PiZeaM) is presented here as an aid to the research community for this valuable crop species. PiZeaM was built using a proven method of interologs (interacting orthologs) that were identified using both one-to-one and many-to-many orthology between genomes of maize and reference species. Where both maize orthologs occurred for an experimentally determined interaction in the reference species, we predicted a likely interaction in maize. A total of 49,026 unique interactions for 6004 maize proteins were predicted. These interactions are enriched for processes that are evolutionarily conserved, but include many otherwise poorly annotated proteins in maize. The predicted maize interactions were further analyzed by comparing annotation of interacting proteins, including different layers of ontology. A map of pairwise gene co-expression was also generated and compared to predicted interactions. Two global subnetworks were constructed for highly conserved interactions. These subnetworks showed clear clustering of proteins by function. Another subnetwork was created for disease response using a bait and prey strategy to capture interacting partners for proteins that respond to other organisms. Closer examination of this subnetwork revealed the connectivity between biotic and abiotic hormone stress pathways. We believe PiZeaM will provide a useful tool for the prediction of protein function and analysis of pathways for Z. mays researchers and is presented in this paper as a reference tool for the exploration of protein interactions in maize.

miR-330-3p controls cell proliferation by targeting early growth response 2 in non-small-cell lung cancer

Non-small-cell lung cancer (NSCLC) is one of the most common lung cancers, and microRNAs (miRNAs) have been reported to play essential roles in NSCLC. Recent studies have indicated that miR-330-3p expression is up-regulated in NSCLC samples and in tissues of NSCLC brain metastasis. In this study, up-regulation of miR-330-3p expression was confirmed in NSCLC and 20 NSCLC patient samples. Furthermore, miR-330-3p was over-expressed in NSCLC cell lines A549 and H23, and the promotive function of miR-330-3p was investigated in regulating NSCLC cell proliferation and cell cycle distribution. To identify potential target genes of miR-330-3p in NSCLC, the miRNA target prediction databases were used. Luciferase activity assay and real-time RT-PCR analysis confirmed that miR-330-3p is negatively correlated with the expression of early growth response 2 (EGR2). Moreover, it was also found that EGR2 mRNA contains two potential binding sites for miR-330-3p. Knock-down of EGR2 with siRNA was demonstrated to have a similar effect as the over-expression of miR-330-3p in NSCLC cell lines. Taken together, our results show that EGR2 is a target of miR-330-3p.

Molecular Pathways: Targeting Cellular Energy Metabolism in Cancer via Inhibition of SLC2A1 and LDHA

Reprogramming of cellular energy metabolism is widely accepted to be one of the main hallmarks of cancer. The aberrant expression pattern of key regulators in the glycolysis pathway in cancer cells corroborates with the hypothesis that most cancer cells utilize aerobic glycolysis as their main ATP production method instead of mitochondrial oxidative phosphorylation. Overexpression of SLC2A1 and LDHA, both important regulators of the glycolysis pathway, was detected in the premalignant lesions and tumors of lung cancer patients, suggesting the involvement of these proteins in early carcinogenesis and tumor progression in cancer. Preclinical studies demonstrated that inhibiting SLC2A1 or LDHA led to diminished tumor growth in vitro and in vivo. SLC2A1 and LDHA inhibitors, when administered in combination with other chemotherapeutic agents, showed synergistic antitumor effects by resensitizing chemoresistant cancer cells to the chemotherapies. These results indicate that disrupting SLC2A1, LDHA, or other regulators in cancer cell energetics is a very promising approach for new targeted therapies.

Colorectal cancer drug target prediction using ontology-based inference and network analysis

Identification of novel drug targets is a critical step in drug development. Many recent studies have produced multiple types of data, which provides an opportunity to mine the relationships among them to predict drug targets. In this study, we present a novel integrative approach that combines ontology reasoning with network-assisted gene ranking to predict new drug targets. We utilized colorectal cancer (CRC) as a proof-of-concept use case to illustrate the approach. Starting from FDA-approved CRC drugs and the relationships among disease, drug, gene, pathway, and SNP in an ontology representing PharmGKB data, we inferred 113 potential CRC drug targets. We further prioritized these genes based on their relationships with CRC disease genes in the context of human protein-protein interaction networks. Thus, among the 113 potential drug targets, 15 were selected as the promising drug targets, including some genes that are supported by previous studies. Among them, EGFR, TOP1 and VEGFA are known targets of FDA-approved drugs. Additionally, CCND1 (cyclin D1), and PTGS2 (prostaglandin-endoperoxide synthase 2) have reported to be relevant to CRC or as potential drug targets based on the literature search. These results indicate that our approach is promising for drug target prediction for CRC treatment, which might be useful for other cancer therapeutics.

Genome-wide characterization of essential, toxicity-modulating and no-phenotype genes in S. cerevisiae

Based on the requirements for an organism's viability, genes can be classified into essential genes and non-essential genes. Non-essential genes can be further classified into toxicity-modulating genes and no-phenotype genes based on the fitness phenotype of yeast cells when the gene is deleted under DNA-damaging conditions. In this study, graph theoretical approaches were used to characterize essential, toxicity-modulating and no-phenotype genes for S. cerevisiae in the physical interaction (PI) network and the perturbation sensitivity (PS) network. We also gained previously published biological datasets to gain a more complete understanding of the differences and relationships between essential, toxicity-modulating genes and no-phenotype genes. The analysis results indicate that toxicity-modulating genes have similar properties as essential genes, and toxicity-modulating genes might represent a middle ground between essential genes and no-phenotype genes, suggesting that cells initiate highly coordinated responses to damage that are similar to those needed for vital cellular functions. These findings may elucidate the mechanisms for understanding toxicity-modulating processes relevant to certain diseases.

Loss of GDF10/BMP3b as a prognostic marker collaborates with TGFBR3 to enhance chemotherapy resistance and epithelial-mesenchymal transition in oral squamous cell carcinoma

Growth differentiation factor-10 (GDF10), commonly referred as BMP3b, is a member of the transforming growth factor-β (TGF-β) superfamily. GDF10/BMP3b has been considered as a tumor suppressor, however, little is known about the molecular mechanism of its roles in tumor suppression in oral cancer. Clinical significance of GDF10 downregulation in oral squamous cell carcinoma (OSCC) was evaluated using three independent cohorts of OSCC patients. The molecular mechanisms of GDF10 in the suppression of cell survival, cell migration/invasion and epithelial-mesenchymal transition (EMT) were investigated by using oral cancer cell lines. The present study shows that GDF10 is downregulated during oral carcinogenesis, and GDF10 expression is also an independent risk factor for overall survival of OSCC patients. Overexpression of GDF10 attenuates cell proliferation, transformation, migration/invasion, and EMT. GDF10-inhibited EMT is mediated by ERK signaling but not by typical TGF-β signaling. In addition, overexpression of GDF10 promotes DNA damage-induced apoptosis and sensitizes the response to all-trans retinoic acid (ATRA) and camptothecin (CPT). Intriguingly, the expression of GDF10 is induced by type III TGF-β receptor (TGFBR3) through TGF-β-SMAD2/3 signaling. Our findings suggest that TGFBR3 is an upstream activator of GDF10 expression and they share the same signaling to inhibit EMT and migration/invasion. These results support that GDF10 acts as a hinge to collaborate with TGFBR3 in the transition of EMT-MET program. Taken together, we illustrated the clinical significance and the molecular mechanisms of tumor-suppressive GDF10 in OSCC.

Genomic analysis of host - Peste des petits ruminants vaccine viral transcriptome uncovers transcription factors modulating immune regulatory pathways

Peste des petits ruminants (PPR), is an acute transboundary viral disease of economic importance, affecting goats and sheep. Mass vaccination programs around the world resulted in the decline of PPR outbreaks. Sungri 96 is a live attenuated vaccine, widely used in Northern India against PPR. This vaccine virus, isolated from goat works efficiently both in sheep and goat. Global gene expression changes under PPR vaccine virus infection are not yet well defined. Therefore, in this study we investigated the host-vaccine virus interactions by infecting the peripheral blood mononuclear cells isolated from goat with PPRV (Sungri 96 vaccine virus), to quantify the global changes in the transcriptomic signature by RNA-sequencing. Viral genome of Sungri 96 vaccine virus was assembled from the PPRV infected transcriptome confirming the infection and demonstrating the feasibility of building a complete non-host genome from the blood transcriptome. Comparison of infected transcriptome with control transcriptome revealed 985 differentially expressed genes. Functional analysis showed enrichment of immune regulatory pathways under PPRV infection. Key genes involved in immune system regulation, spliceosomal and apoptotic pathways were identified to be dysregulated. Network analysis revealed that the protein - protein interaction network among differentially expressed genes is significantly disrupted in infected state. Several genes encoding TFs that govern immune regulatory pathways were identified to co-regulate the differentially expressed genes. These data provide insights into the host - PPRV vaccine virus interactome for the first time. Our findings suggested dysregulation of immune regulatory pathways and genes encoding Transcription Factors (TFs) that govern these pathways in response to viral infection.

The roles of post-translational modifications in the context of protein interaction networks

Among other effects, post-translational modifications (PTMs) have been shown to exert their function via the modulation of protein-protein interactions. For twelve different main PTM-types and associated subtypes and across 9 diverse species, we investigated whether particular PTM-types are associated with proteins with specific and possibly “strategic” placements in the network of all protein interactions by determining informative network-theoretic properties. Proteins undergoing a PTM were observed to engage in more interactions and positioned in more central locations than non-PTM proteins. Among the twelve considered PTM-types, phosphorylated proteins were identified most consistently as being situated in central network locations and with the broadest interaction spectrum to proteins carrying other PTM-types, while glycosylated proteins are preferentially located at the network periphery. For the human interactome, proteins undergoing sumoylation or proteolytic cleavage were found with the most characteristic network properties. PTM-type-specific protein interaction network (PIN) properties can be rationalized with regard to the function of the respective PTM-carrying proteins. For example, glycosylation sites were found enriched in proteins with plasma membrane localizations and transporter or receptor activity, which generally have fewer interacting partners. The involvement in disease processes of human proteins undergoing PTMs was also found associated with characteristic PIN properties. By integrating global protein interaction networks and specific PTMs, our study offers a novel approach to unraveling the role of PTMs in cellular processes.

The function of proteins is frequently modulated by chemical modifications introduced after translation from RNA. These post-translational modifications (PTMs) have been shown to also influence the interaction between proteins carrying them. We tested whether specific PTM-types characterized by attaching different chemical groups are associated with proteins with characteristic and possibly strategic positions within the network of all protein interactions in cellular systems. Based on network-theoretic analyses of PTMs in the context of protein interaction networks of nine selected species, we indeed observed distinctive properties of twelve PTM-types tested. Phosphorylation was found associated with proteins in central locations with the broadest interaction scope, while glycosylation was more prominent in proteins at the periphery of the web of all protein interactions. The involvement in disease processes of human proteins undergoing PTMs was also found associated with characteristic protein interaction network properties. Our study highlights common and specific roles of the various PTM types in the orchestration of molecular interactions in cells.

Characterization of proteins in S. cerevisiae with subcellular localizations

Acquiring comprehensive knowledge of protein in various subcellular localizations is one of the fundamental goals in cell biology and proteomics.

Decreased expression of sestrin 2 predicts unfavorable outcome in colorectal cancer

Sestrin 2 is a conserved antioxidant protein that is involved in p53‑dependent antioxidant defenses and protects cells against oxidative stresses. The present study was conducted to examine the expression of sestrin 2 in colorectal cancer (CRC) and investigate a possible relationship between sestrin 2 expression and prognosis in CRC. The expression of sestrin 2 in human CRC tissues and cell lines was evaluated by immunohistochemical or immunofluorescent staining and western blot analysis. The correlations between sestrin 2 expression in human CRC tissues and clinicopathological variables, including overall survival (OS) and disease‑free survival (DFS), were analyzed. Both human CRC tissues and cell lines showed a decreased expression of sestrin 2. Furthermore, a low expression of sestrin 2 was significantly correlated with advanced tumor stage, lymphatic invasion, lymph node metastasis, vascular invasion and liver metastasis. Survival analysis showed that patients with low sestrin 2 staining had a significantly worse DFS and OS. Additionally, early or advanced stage CRC patients with a low expression of sestrin 2 had a shorter survival. In univariate analysis, the patients with low sestrin 2 expression, advanced tumor stage, lymphatic invasion, lymphatic node metastasis, vascular invasion, liver metastasis and peritoneal metastasis had shorter OS and DFS. In multivariate analysis, only low sestrin 2 expression, advanced tumor stage, lymphatic node metastasis, vascular invasion and liver metastasis remained as independent prognostic factors of poor OS and DFS. The findings suggested that a decreased expression of sestrin 2 is associated with an unfavorable prognosis, which suggests that it is a novel and crucial predictor for CRC metastasis.

An Arf-Egr-C/EBPβ pathway linked to ras-induced senescence and cancer

Oncogene-induced senescence (OIS) protects normal cells from transformation by Ras, whereas cells lacking p14/p19(Arf) or other tumor suppressors can be transformed. The transcription factor C/EBPβ is required for OIS in primary fibroblasts but is downregulated by H-Ras(V12) in immortalized NIH 3T3 cells through a mechanism involving p19(Arf) loss. Here, we report that members of the serum-induced early growth response (Egr) protein family are also downregulated in 3T3(Ras) cells and directly and redundantly control Cebpb gene transcription. Egr1, Egr2, and Egr3 recognize three sites in the Cebpb promoter and associate transiently with this region after serum stimulation, coincident with Cebpb induction. Codepletion of all three Egrs prevented Cebpb expression, and serum induction of Egrs was significantly blunted in 3T3(Ras) cells. Egr2 and Egr3 levels were also reduced in Ras(V12)-expressing p19(Arf) null mouse embryonic fibroblasts (MEFs), and overall Egr DNA-binding activity was suppressed in Arf-deficient but not wild-type (WT) MEFs, leading to Cebpb downregulation. Analysis of human cancers revealed a strong correlation between EGR levels and CEBPB expression, regardless of whether CEBPB was increased or decreased in tumors. Moreover, overexpression of Egrs in tumor cell lines induced CEBPB and inhibited proliferation. Thus, our findings identify the Arf-Egr-C/EBPβ axis as an important determinant of cellular responses (senescence or transformation) to oncogenic Ras signaling.

Network analysis in the identification of special mechanisms between small cell lung cancer and non-small cell lung cancer

BACKGROUND

To explore the similar and different pathogenesis between non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC).

METHODS

This study used bioinformatics methods, including functional enrichment analysis, compared the topological features of SCLC and NSCLC in the human protein interaction network in a system aspect, and analyzed the highly intense modules from an integrated network.

RESULTS

This study included 5082 and 2781 significantly different expression genes for NSCLC and SCLC, respectively. The differently expressed genes of NSCLC are mainly distributed in the extracellular region and synapse. By contrast, the genes of SCLC are located in the organelle, macromolecular complex, membrane-enclosed lumen, cell part, envelope, and synapse. Compared with SCLC, the differently expressed genes of NSCLC act in the biological regulation, multicellular organismal process, and viral reproduction and locomotion, which show that NSCLC is more likely to cause a wide range of cancer cell proliferation and virus infection than SCLC. The network topological properties of SCLC and NSCLC are similar, except the average shortest path length, which indicates that most of the genes of the two lung cancers play a similar function in the entire body. The commonly expressed genes show that all of the genes in the module may also cause NSCLC and SCLC, simultaneously.

CONCLUSIONS

The proteins in module will involve the same or similar biological functions and the interactions among them induce the occurrence of lung cancer. Moreover, a potential biomarker of SCLC is the interaction between APIP and apoptotic protease activating factor (APAF)1, which share a common module.

Identification of intrinsically disordered regions in PTEN and delineation of its function via a network approach

Intrinsically disordered proteins (IDPs) are proteins that lack stable higher order structures for the entire protein molecule or a significant portion of it. The discovery of IDPs evolved as an antithesis to the conventional structure-function paradigm wherein a higher order structure dictates protein function. Over the last decade, a number of proteins with functionally relevant unstructured regions have been discovered, which includes tumor suppressor PTEN. The protein domains that lack structure provide "hot-spots" for post-translational modifications (PTMs) and protein-protein interactions (PPIs), which facilitate their regulation and participation in multiple cellular processes. Consequently, dysregulation in IDPs contribute to aberrant cellular pathophysiology. Herein, we present PTEN and its translational isoform PTEN-L as a hybrid protein possessing ordered domain and intrinsically disordered C-terminal and an N-terminal tails. We review the role of intrinsic disorder in PTEN function and propose a methodology for the use of intrinsic disorder to study PTEN-regulated higher order protein-networks by associating basic principles of network biology to functional pathway analysis at the systems level.

Neurotrophin receptor TrkB promotes lung adenocarcinoma metastasis

Lung cancer is notorious for its ability to metastasize, but the pathways regulating lung cancer metastasis are largely unknown. An in vitro system designed to discover factors critical for lung cancer cell migration identified brain-derived neurotrophic factor, which stimulates cell migration through activation of tropomyosin-related kinase B (TrkB; also called NTRK2). Knockdown of TrkB in human lung cancer cell lines significantly decreased their migratory and metastatic ability in vitro and in vivo. In an autochthonous lung adenocarcinoma model driven by activated oncogenic Kras and p53 loss, TrkB deficiency significantly reduced metastasis. Hypoxia-inducible factor-1 directly regulated TrkB expression, and, in turn, TrkB activated Akt signaling in metastatic lung cancer cells. Finally, TrkB expression was correlated with metastasis in patient samples, and TrkB was detected more often in tumors that did not have Kras or epidermal growth factor receptor mutations. These studies demonstrate that TrkB is an important therapeutic target in metastatic lung adenocarcinoma.

Pathway reconstruction of airway remodeling in chronic lung diseases: a systems biology approach

Airway remodeling is a pathophysiologic process at the clinical, cellular, and molecular level relating to chronic obstructive airway diseases such as chronic obstructive pulmonary disease (COPD), asthma and mustard lung. These diseases are associated with the dysregulation of multiple molecular pathways in the airway cells. Little progress has so far been made in discovering the molecular causes of complex disease in a holistic systems manner. Therefore, pathway and network reconstruction is an essential part of a systems biology approach to solve this challenging problem. In this paper, multiple data sources were used to construct the molecular process of airway remodeling pathway in mustard lung as a model of airway disease. We first compiled a master list of genes that change with airway remodeling in the mustard lung disease and then reconstructed the pathway by generating and merging the protein-protein interaction and the gene regulatory networks. Experimental observations and literature mining were used to identify and validate the master list. The outcome of this paper can provide valuable information about closely related chronic obstructive airway diseases which are of great importance for biologists and their future research. Reconstructing the airway remodeling interactome provides a starting point and reference for the future experimental study of mustard lung, and further analysis and development of these maps will be critical to understanding airway diseases in patients.

Identification of novel elements of the Drosophila blisterome sheds light on potential pathological mechanisms of several human diseases

Main developmental programs are highly conserved among species of the animal kingdom. Improper execution of these programs often leads to progression of various diseases and disorders. Here we focused on Drosophila wing tissue morphogenesis, a fairly complex developmental program, one of the steps of which – apposition of the dorsal and ventral wing sheets during metamorphosis – is mediated by integrins. Disruption of this apposition leads to wing blistering which serves as an easily screenable phenotype for components regulating this process. By means of RNAi-silencing technique and the blister phenotype as readout, we identify numerous novel proteins potentially involved in wing sheet adhesion. Remarkably, our results reveal not only participants of the integrin-mediated machinery, but also components of other cellular processes, e.g. cell cycle, RNA splicing, and vesicular trafficking. With the use of bioinformatics tools, these data are assembled into a large blisterome network. Analysis of human orthologues of the Drosophila blisterome components shows that many disease-related genes may contribute to cell adhesion implementation, providing hints on possible mechanisms of these human pathologies.

Predicting disease-related proteins based on clique backbone in protein-protein interaction network

Network biology integrates different kinds of data, including physical or functional networks and disease gene sets, to interpret human disease. A clique (maximal complete subgraph) in a protein-protein interaction network is a topological module and possesses inherently biological significance. A disease-related clique possibly associates with complex diseases. Fully identifying disease components in a clique is conductive to uncovering disease mechanisms. This paper proposes an approach of predicting disease proteins based on cliques in a protein-protein interaction network. To tolerate false positive and negative interactions in protein networks, extending cliques and scoring predicted disease proteins with gene ontology terms are introduced to the clique-based method. Precisions of predicted disease proteins are verified by disease phenotypes and steadily keep to more than 95%. The predicted disease proteins associated with cliques can partly complement mapping between genotype and phenotype, and provide clues for understanding the pathogenesis of serious diseases.

Human proteins characterization with subcellular localizations

Proteins are responsible for performing the vast majority of cellular functions which are critical to a cell's survival. The knowledge of the subcellular localization of proteins can provide valuable information about their molecular functions. Therefore, one of the fundamental goals in cell biology and proteomics is to analyze the subcellular localizations and functions of these proteins. Recent large-scale human genomics and proteomics studies have made it possible to characterize human proteins at a subcellular localization level. In this study, according to the annotation in Swiss-Prot, 8842 human proteins were classified into seven subcellular localizations. Human proteins in the seven subcellular localizations were compared by using topological properties, biological properties, codon usage indices, mRNA expression levels, protein complexity and physicochemical properties. All these properties were found to be significantly different in the seven categories. In addition, based on these properties and pseudo-amino acid compositions, a machine learning classifier was built for the prediction of protein subcellular localization. The study presented here was an attempt to address the aforementioned properties for comparing human proteins of different subcellular localizations. We hope our findings presented in this study may provide important help for the prediction of protein subcellular localization and for understanding the general function of human proteins in cells.

Characterization of essential genes by topological properties in the perturbation sensitivity network

Genes that are indispensable for survival are called essential genes. In recent years, the analysis of essential genes has become extremely important for understanding the way a cell functions. With the advent of large-scale gene expression profiling technologies, it is now possible to profile transcriptional changes in the entire genome of Saccharomyces cerevisiae. Notwithstanding the accumulation of gene expression profiling in recent years, only a few studies have used these data to construct the network for S. cerevisiae. In this paper, based on the transcriptional profiling of the S. cerevisiae genome in hundreds of different gene disruptions, the perturbation sensitivity (PS) network is constructed. A scale-free topology with node degree following a power-law distribution is shown in the PS network. Twelve topological properties are used to investigate the characteristics of essential and non-essential genes in the PS network. Most of the properties are found to be statistically discriminative between essential and non-essential genes. In addition, the F-score is used to estimate the essentiality of each property, and the core number demonstrates the highest F-score among all properties.

EgoNet: identification of human disease ego-network modules

BACKGROUND

Mining novel biomarkers from gene expression profiles for accurate disease classification is challenging due to small sample size and high noise in gene expression measurements. Several studies have proposed integrated analyses of microarray data and protein-protein interaction (PPI) networks to find diagnostic subnetwork markers. However, the neighborhood relationship among network member genes has not been fully considered by those methods, leaving many potential gene markers unidentified. The main idea of this study is to take full advantage of the biological observation that genes associated with the same or similar diseases commonly reside in the same neighborhood of molecular networks.

RESULTS

We present EgoNet, a novel method based on egocentric network-analysis techniques, to exhaustively search and prioritize disease subnetworks and gene markers from a large-scale biological network. When applied to a triple-negative breast cancer (TNBC) microarray dataset, the top selected modules contain both known gene markers in TNBC and novel candidates, such as RAD51 and DOK1, which play a central role in their respective ego-networks by connecting many differentially expressed genes.

CONCLUSIONS

Our results suggest that EgoNet, which is based on the ego network concept, allows the identification of novel biomarkers and provides a deeper understanding of their roles in complex diseases.

Tetraspanin TSPAN12 regulates tumor growth and metastasis and inhibits β-catenin degradation

Ablation of tetraspanin protein TSPAN12 from human MDA-MB-231 cells significantly decreased primary tumor xenograft growth, while increasing tumor apoptosis. Furthermore, TSPAN12 removal markedly enhanced tumor-endothelial interactions and increased metastasis to mouse lungs. TSPAN12 removal from human MDA-MB-231 cells also caused diminished association between FZD4 (a key canonical Wnt pathway receptor) and its co-receptor LRP5. The result likely explains substantially enhanced proteosomal degradation of β-catenin, a key effecter of canonical Wnt signaling. Consistent with disrupted canonical Wnt signaling, TSPAN12 ablation altered expression of LRP5, Naked 1 and 2, DVL2, DVL3, Axin 1, and GSKβ3 proteins. TSPAN12 ablation also altered expression of several genes regulated by β-catenin (e.g. CCNA1, CCNE2, WISP1, ID4, SFN, ME1) that may help to explain altered tumor growth and metastasis. In conclusion, these results provide the first evidence for TSPAN12 playing a role in supporting primary tumor growth and suppressing metastasis. TSPAN12 appears to function by stabilizing FZD4-LRP5 association, in support of canonical Wnt-pathway signaling, leading to enhanced β-catenin expression and function.

Integrative computational and experimental approaches to establish a post-myocardial infarction knowledge map

Vast research efforts have been devoted to providing clinical diagnostic markers of myocardial infarction (MI), leading to over one million abstracts associated with “MI” and “Cardiovascular Diseases” in PubMed. Accumulation of the research results imposed a challenge to integrate and interpret these results. To address this problem and better understand how the left ventricle (LV) remodels post-MI at both the molecular and cellular levels, we propose here an integrative framework that couples computational methods and experimental data. We selected an initial set of MI-related proteins from published human studies and constructed an MI-specific protein-protein-interaction network (MIPIN). Structural and functional analysis of the MIPIN showed that the post-MI LV exhibited increased representation of proteins involved in transcriptional activity, inflammatory response, and extracellular matrix (ECM) remodeling. Known plasma or serum expression changes of the MIPIN proteins in patients with MI were acquired by data mining of the PubMed and UniProt knowledgebase, and served as a training set to predict unlabeled MIPIN protein changes post-MI. The predictions were validated with published results in PubMed, suggesting prognosticative capability of the MIPIN. Further, we established the first knowledge map related to the post-MI response, providing a major step towards enhancing our understanding of molecular interactions specific to MI and linking the molecular interaction, cellular responses, and biological processes to quantify LV remodeling.

Heart attack, known medically as myocardial infarction, often occurs as a result of partial shortage of blood supply to a portion of the heart, leading to the death of heart muscle cells. Following myocardial infarction, complications might arise, including arrhythmia, myocardial rupture, left ventricular dysfunction, and heart failure. Although myocardial infarction can be quickly diagnosed using a various number of tests, including blood tests and electrocardiography, there have been no available prognostic tests to predict the long-term outcome in response to myocardial infarction. Here, we present a framework to analyze how the left ventricle responds to myocardial infarction by combining protein interactome and experimental results retrieved from published human studies. The framework organized current understanding of molecular interactions specific to myocardial infarction, cellular responses, and biological processes to quantify left ventricular remodeling process. Specifically, our knowledge map showed that transcriptional activity, inflammatory response, and extracellular matrix remodeling are the main functional themes post myocardial infarction. In addition, text analytics of relevant abstracts revealed differentiated protein expressions in plasma or serum expressions from patients with myocardial infarction. Using this data, we predicted expression levels of other proteins following myocardial infarction.

The prediction of candidate genes for cervix related cancer through gene ontology and graph theoretical approach

With rapidly changing technology, prediction of candidate genes has become an indispensable task in recent years mainly in the field of biological research. The empirical methods for candidate gene prioritization that succors to explore the potential pathway between genetic determinants and complex diseases are highly cumbersome and labor intensive. In such a scenario predicting potential targets for a disease state through in silico approaches are of researcher's interest. The prodigious availability of protein interaction data coupled with gene annotation renders an ease in the accurate determination of disease specific candidate genes. In our work we have prioritized the cervix related cancer candidate genes by employing Csaba Ortutay and his co-workers approach of identifying the candidate genes through graph theoretical centrality measures and gene ontology. With the advantage of the human protein interaction data, cervical cancer gene sets and the ontological terms, we were able to predict 15 novel candidates for cervical carcinogenesis. The disease relevance of the anticipated candidate genes was corroborated through a literature survey. Also the presence of the drugs for these candidates was detected through Therapeutic Target Database (TTD) and DrugMap Central (DMC) which affirms that they may be endowed as potential drug targets for cervical cancer.

Molecular profiling of premalignant lesions in lung squamous cell carcinomas identifies mechanisms involved in stepwise carcinogenesis

Lung squamous cell carcinoma (SCC) is thought to arise from premalignant lesions in the airway epithelium; therefore, studying these lesions is critical for understanding lung carcinogenesis. Previous microarray and sequencing studies designed to discover early biomarkers and therapeutic targets for lung SCC had limited success identifying key driver events in lung carcinogenesis, mostly due to the cellular heterogeneity of patient samples examined and the interindividual variability associated with difficult to obtain airway premalignant lesions and appropriate normal control samples within the same patient. We performed RNA sequencing on laser-microdissected representative cell populations along the SCC pathologic continuum of patient-matched normal basal cells, premalignant lesions, and tumor cells. We discovered transcriptomic changes and identified genomic pathways altered with initiation and progression of SCC within individual patients. We used immunofluorescent staining to confirm gene expression changes in premalignant lesions and tumor cells, including increased expression of SLC2A1, CEACAM5, and PTBP3 at the protein level and increased activation of MYC via nuclear translocation. Cytoband enrichment analysis revealed coordinated loss and gain of expression in chromosome 3p and 3q regions, respectively, during carcinogenesis. This is the first gene expression profiling study of airway premalignant lesions with patient-matched SCC tumor samples. Our results provide much needed information about the biology of premalignant lesions and the molecular changes that occur during stepwise carcinogenesis of SCC, and it highlights a novel approach for identifying some of the earliest molecular changes associated with initiation and progression of lung carcinogenesis within individual patients.

Insidious changes in stromal matrix fuel cancer progression

Reciprocal interactions between tumor and stromal cells propel cancer progression and metastasis. A complete understanding of the complex contributions of the tumor stroma to cancer progression necessitates a careful examination of the extracellular matrix (ECM), which is largely synthesized and modulated by cancer-associated fibroblasts. This structurally supportive meshwork serves as a signaling scaffold for a myriad of biologic processes and responses favoring tumor progression. The ECM is a repository for growth factors and cytokines that promote tumor growth, proliferation, and metastasis through diverse interactions with soluble and insoluble ECM components. Growth factors activated by proteases are involved in the initiation of cell signaling pathways essential to invasion and survival. Various transmembrane proteins produced by the cancer stroma bind the collagen and fibronectin-rich matrix to induce proliferation, adhesion, and migration of cancer cells, as well as protease activation. Integrins are critical liaisons between tumor cells and the surrounding stroma, and with their mechano-sensing ability, induce cell signaling pathways associated with contractility and migration. Proteoglycans also bind and interact with various matrix proteins in the tumor microenvironment to promote cancer progression. Together, these components function to mediate cross-talk between tumor cells and fibroblasts ultimately to promote tumor survival and metastasis. These stromal factors, which may be expressed differentially according to cancer stage, have prognostic utility and potential. This review examines changes in the ECM of cancer-associated fibroblasts induced through carcinogenesis, and the impact of these changes on cancer progression. The implication is that cancer progression, even in epithelial cancers, may be based in large part on changes in signaling from cancer-associated stromal cells. These changes may provide early prognostic indicators to further stratify patients during treatment or alter the timing of their follow-up visits and observations.

Increased hepcidin expression in non-small cell lung cancer tissue and serum is associated with clinical stage

BACKGROUND

Hepcidin is a small secreted peptide that plays a key role in iron metabolism. A high level of hepcidin expression may be implicated in colorectal cancer; however, the relationship between hepcidin and lung cancer has not yet been studied.

METHODS

Serum hepcidin-25, bone morphogenetic protein (BMP)-2, and interleukin (IL)-6 concentration in 53 patients and 16 non-cancerous individuals was measured by enzyme-linked immune sorbent assay. Reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) was utilized to study the expression of hepcidin mRNA in paired tumor and non-tumor lung tissues in surgical specimens from 65 patients with non small cell lung cancer (NSCLC), as well as in six types of lung cancer cell lines and human bronchial epithelial (HBE) cells. Hepcidin protein expression and cellular localization in NSCLC was determined by immunohistochemistry.

RESULTS

The serum hepcidin-25 concentration was higher in patients with NSCLC than in non-cancerous individuals, and was positively correlated with serum BMP2 concentration, but negatively with serum IL-6 levels. Serum hepcidin was also correlated with lymph node metastasis and clinical stage. Hepcidin mRNA expression was higher in cancerous tissues of NSCLC than in normal pulmonary tissues (P = 0.001). Hepcidin mRNA levels in four lung carcinoma cell lines were higher than in HBE cells. Immunohistochemistry showed that hepcidin protein was increased in cancerous tissues of NSCLC.

CONCLUSIONS

The level of hepcidin expression increased in NSCLC tissue and serum. Serum hepcidin-25 level was associated with lymph node metastasis and tumor clinical stage in patients with NSCLC.

Protein-protein interaction networks (PPI) and complex diseases

The physical interaction of proteins which lead to compiling them into large densely connected networks is a noticeable subject to investigation. Protein interaction networks are useful because of making basic scientific abstraction and improving biological and biomedical applications. Based on principle roles of proteins in biological function, their interactions determine molecular and cellular mechanisms, which control healthy and diseased states in organisms. Therefore, such networks facilitate the understanding of pathogenic (and physiologic) mechanisms that trigger the onset and progression of diseases. Consequently, this knowledge can be translated into effective diagnostic and therapeutic strategies. Furthermore, the results of several studies have proved that the structure and dynamics of protein networks are disturbed in complex diseases such as cancer and autoimmune disorders. Based on such relationship, a novel paradigm is suggested in order to confirm that the protein interaction networks can be the target of therapy for treatment of complex multi-genic diseases rather than individual molecules with disrespect the network.

Patterns of TPD52 overexpression in multiple human solid tumor types analyzed by quantitative PCR

Tumor protein D52 (TPD52) is located at chromosome 8q21, a region that is frequently gained or amplified in multiple human cancer types. TPD52 has been suggested as a potential target for new anticancer therapies. In order to analyze TPD52 expression in the most prevalent human cancer types, we employed quantitative PCR to measure TPD52 mRNA levels in formalin-fixed tissue samples from more than 900 cancer tissues obtained from 29 different human cancer types. TPD52 was expressed at varying levels in all tested normal tissues, including skin, lymph node, lung, oral mucosa, breast, endometrium, ovary, vulva, myometrium, liver, pancreas, stomach, kidney, prostate, testis, urinary bladder, thyroid gland, brain, muscle and fat tissue. TPD52 was upregulated in 18/29 (62%) tested cancer types. Strongest expression was found in non-seminoma (56-fold overexpression compared to corresponding normal tissue), seminoma (42-fold), ductal (28-fold) and lobular breast cancer (14-fold). In these tumor types, TPD52 upregulation was found in the vast majority (>80%) of tested samples. Downregulation was found in 11 (38%) tumor types, most strongly in papillary renal cell cancer (-8-fold), leiomyosarcoma (-6-fold), clear cell renal cell cancer (-5-fold), liposarcoma (-5-fold) and lung cancer (-4-fold). These results demonstrate that TPD52 is frequently and strongly upregulated in many human cancer types, which may represent candidate tumor types for potential anti-TPD52 therapies.

Co-expression network analysis and genetic algorithms for gene prioritization in preeclampsia

BACKGROUND

In this study, we explored the gene prioritization in preeclampsia, combining co-expression network analysis and genetic algorithms optimization approaches. We analysed five public projects obtaining 1,146 significant genes after cross-platform and processing of 81 and 149 microarrays in preeclamptic and normal conditions, respectively.

METHODS

After co-expression network construction, modular and node analysis were performed using several approaches. Moreover, genetic algorithms were also applied in combination with the nearest neighbour and discriminant analysis classification methods.

RESULTS

Significant differences were found in the genes connectivity distribution, both in normal and preeclampsia conditions pointing to the need and importance of examining connectivity alongside expression for prioritization. We discuss the global as well as intra-modular connectivity for hubs detection and also the utility of genetic algorithms in combination with the network information. FLT1, LEP, INHA and ENG genes were identified according to the literature, however, we also found other genes as FLNB, INHBA, NDRG1 and LYN highly significant but underexplored during normal pregnancy or preeclampsia.

CONCLUSIONS

Weighted genes co-expression network analysis reveals a similar distribution along the modules detected both in normal and preeclampsia conditions. However, major differences were obtained by analysing the nodes connectivity. All models obtained by genetic algorithm procedures were consistent with a correct classification, higher than 90%, restricting to 30 variables in both classification methods applied.Combining the two methods we identified well known genes related to preeclampsia, but also lead us to propose new candidates poorly explored or completely unknown in the pathogenesis of preeclampsia, which may have to be validated experimentally.

Manic fringe inhibits tumor growth by suppressing Notch3 degradation in lung cancer

Notch signaling plays an essential role in development as well as cancer. We have previously shown that Notch3 is important for lung cancer growth and survival. Notch receptors are activated through the interaction with their ligands, resulting in proteolytic cleavage of the receptors. This interaction is modulated by Fringe, a family of fucose-specific β1,3 N-acetylglucosaminyltransferases that modify the extracellular subunit of Notch receptors. Studies in developmental models showed that Fringe enhances Notch's response to Delta ligands at the expense of Jagged ligands. We observed that Manic Fringe expression is down-regulated in lung cancer. Since Jagged1, a known ligand for Notch3, is often over-expressed in lung cancer, we hypothesized that Fringe negatively regulates Notch3 activation. In this study, we show that re-expression of Manic Fringe down-regulates Notch3 target genes HES1 and HeyL and reduces tumor phenotype in vitro and in vivo. The mechanism for this phenomenon appears to be related to modulation of Notch3 protein stability. Proteasome inhibition reverses Manic Fringe-induced protein turnover. Taken together, our data provide the first evidence that Manic Fringe functions as a tumor suppressor in the lung and that the mechanism of its anti-tumor activity is mediated by inhibition of Notch3 activation.

MicroRNA 4423 is a primate-specific regulator of airway epithelial cell differentiation and lung carcinogenesis

Smoking is a significant risk factor for lung cancer, the leading cause of cancer-related deaths worldwide. Although microRNAs are regulators of many airway gene-expression changes induced by smoking, their role in modulating changes associated with lung cancer in these cells remains unknown. Here, we use next-generation sequencing of small RNAs in the airway to identify microRNA 4423 (miR-4423) as a primate-specific microRNA associated with lung cancer and expressed primarily in mucociliary epithelium. The endogenous expression of miR-4423 increases as bronchial epithelial cells undergo differentiation into mucociliary epithelium in vitro, and its overexpression during this process causes an increase in the number of ciliated cells. Furthermore, expression of miR-4423 is reduced in most lung tumors and in cytologically normal epithelium of the mainstem bronchus of smokers with lung cancer. In addition, ectopic expression of miR-4423 in a subset of lung cancer cell lines reduces their anchorage-independent growth and significantly decreases the size of the tumors formed in a mouse xenograft model. Consistent with these phenotypes, overexpression of miR-4423 induces a differentiated-like pattern of airway epithelium gene expression and reverses the expression of many genes that are altered in lung cancer. Together, our results indicate that miR-4423 is a regulator of airway epithelium differentiation and that the abrogation of its function contributes to lung carcinogenesis.

Pathways enrichment analysis for differentially expressed genes in squamous lung cancer

Squamous lung cancer (SQLC) is a common type of lung cancer, but its oncogenesis mechanism is not so clear. The aim of this study was to screen the potential pathways changed in SQLC and elucidate the mechanism of it. Published microarray data of GSE3268 series was downloaded from Gene Expression Omnibus (GEO). Significance analysis of microarrays was performed using software R, and differentially expressed genes (DEGs) were harvested. The functions and pathways of DEGs were mapped in Gene Otology and KEGG pathway database, respectively. A total of 2961 genes were filtered as DEGs between normal and SQLC cells. Cell cycle and metabolism were the mainly changed functions of SQLC cells. Meanwhile genes such as MCM, RFC, FEN1, and POLD may induce SQLC through DNA replication pathway, and genes such as PTTG1, CCNB1, CDC6, and PCNA may be involved in SQLC through cell cycle pathway. It is demonstrated that pathway analysis is useful in the identification of target genes in SQLC.

Simple topological features reflect dynamics and modularity in protein interaction networks

The availability of large-scale protein-protein interaction networks for numerous organisms provides an opportunity to comprehensively analyze whether simple properties of proteins are predictive of the roles they play in the functional organization of the cell. We begin by re-examining an influential but controversial characterization of the dynamic modularity of the S. cerevisiae interactome that incorporated gene expression data into network analysis. We analyse the protein-protein interaction networks of five organisms, S. cerevisiae, H. sapiens, D. melanogaster, A. thaliana, and E. coli, and confirm significant and consistent functional and structural differences between hub proteins that are co-expressed with their interacting partners and those that are not, and support the view that the former tend to be intramodular whereas the latter tend to be intermodular. However, we also demonstrate that in each of these organisms, simple topological measures are significantly correlated with the average co-expression of a hub with its partners, independent of any classification, and therefore also reflect protein intra- and inter- modularity. Further, cross-interactomic analysis demonstrates that these simple topological characteristics of hub proteins tend to be conserved across organisms. Overall, we give evidence that purely topological features of static interaction networks reflect aspects of the dynamics and modularity of interactomes as well as previous measures incorporating expression data, and are a powerful means for understanding the dynamic roles of hubs in interactomes.

A better understanding of protein interaction networks would be a great aid in furthering our knowledge of the molecular biology of the cell. Towards this end, large-scale protein-protein physical interaction data have been determined for organisms across the evolutionary spectrum. However, the resulting networks give a static view of interactomes, and our knowledge about protein interactions is rarely time or context specific. A previous prominent but controversial attempt to characterize the dynamic modularity of the interactome was based on integrating physical interaction data with gene activity measurements from transcript expression data. This analysis distinguished between proteins that are co-expressed with their interacting partners and those that are not, and argued that the former are intramodular and the latter are intermodular. By analyzing the interactomes of five organisms, we largely confirm the biological significance of this characterization through a variety of statistical tests and computational experiments. Surprisingly, however, we find that similar results can be obtained using just network information without additionally integrating expression data, suggesting that purely topological characteristics of interaction networks strongly reflect certain aspects of the dynamics and modularity of interactomes.

Topology of molecular interaction networks

Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks.Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs.Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network topology to generic properties such as robustness, it is used to predict specific functions or phenotypes.Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further.