Network modeling links breast cancer susceptibility and centrosome dysfunction

Microarray expression profile analysis of long noncoding RNAs in premature brain injury: A novel point of view

Long noncoding RNAs (lncRNAs) are abundant in the central nervous system and have a key role in brain function as well as many neurological disorders. However, the regulatory function of lncRNAs in the premature brain has not been well studied. This study described the expression profile of lncRNAs in premature mice using microarray technology. 1999 differentially expressed lncRNAs and 955 differentially expressed mRNAs were identified. Gene Ontology (GO) and pathway analysis showed that these lncRNAs were involved in multiple biological processes, including the nervous system development and inflammatory response. Additionally, the lncRNA-mRNA-network and TF-gene-lncRNA-network were constructed to identify core regulatory lncRNAs and transcription factors. The sex-determining region of Y chromosome (SRY) gene may be a key transcription factor that regulates premature brain development and injury. This study for the first time represents an expression profile of differentially expressed lncRNAs in the premature brain and may provide a novel point of view into the mechanisms of premature brain injury.

Prediction of disease-gene-drug relationships following a differential network analysis

Great efforts are being devoted to get a deeper understanding of disease-related dysregulations, which is central for introducing novel and more effective therapeutics in the clinics. However, most human diseases are highly multifactorial at the molecular level, involving dysregulation of multiple genes and interactions in gene regulatory networks. This issue hinders the elucidation of disease mechanism, including the identification of disease-causing genes and regulatory interactions. Most of current network-based approaches for the study of disease mechanisms do not take into account significant differences in gene regulatory network topology between healthy and disease phenotypes. Moreover, these approaches are not able to efficiently guide database search for connections between drugs, genes and diseases. We propose a differential network-based methodology for identifying candidate target genes and chemical compounds for reverting disease phenotypes. Our method relies on transcriptomics data to reconstruct gene regulatory networks corresponding to healthy and disease states separately. Further, it identifies candidate genes essential for triggering the reversion of the disease phenotype based on network stability determinants underlying differential gene expression. In addition, our method selects and ranks chemical compounds targeting these genes, which could be used as therapeutic interventions for complex diseases.

PR-Set7 is Degraded in a Conditional Cul4A Transgenic Mouse Model of Lung Cancer

Background and objective

Maintenance of genomic integrity is essential to ensure normal organismal development and to prevent diseases such as cancer. PR-Set7 (also known as Set8) is a cell cycle regulated enzyme that catalyses monomethylation of histone 4 at Lys20 (H4K20me1) to promote chromosome condensation and prevent DNA damage. Recent studies show that CRL4CDT2-mediated ubiquitylation of PR-Set7 leads to its degradation during S phase and afer DNA damage. Tis might occur to ensure appropriate changes in chromosome structure during the cell cycle or to preserve genome integrity afer DNA damage.

Methods

We developed a new model of lung tumor development in mice harboring a conditionally expressed allele of Cul4A. We have therefore used a mouse model to demonstrate for the frst time that Cul4A is oncogenic in vivo. With this model, staining of PR-Set7 in the preneoplastic and tumor lesions in AdenoCre-induced mouse lungs was performed. Meanwhile we identifed higher protein level changes of γ-tubulin and pericentrin by IHC.

Results

Te level of PR-Set7 down-regulated in the preneoplastic and adenocarcinomous lesions following over-expression of Cul4A. We also identifed higher levels of the proteins pericentrin and γ-tubulin in Cul4A mouse lungs induced by AdenoCre.

Conclusion

PR-Set7 is a direct target of Cul4A for degradation and involved in the formation of lung tumors in the conditional Cul4A transgenic mouse model.

The principle of conformational signaling

Specific conformations of signaling proteins can serve as “signals” in signal transduction by being recognized by receptors.

A Practical Guide for Exploring Opportunities of Repurposing Drugs for CNS Diseases in Systems Biology

Systems biology has shown its potential in facilitating pathway-focused therapy development for central nervous system (CNS) diseases. An integrated network can be utilized to explore the multiple disease mechanisms and to discover repositioning opportunities. This review covers current therapeutic gaps for CNS diseases and the role of systems biology in pharmaceutical industry. We conclude with a Multiple Level Network Modeling (MLNM) example to illustrate the great potential of systems biology for CNS diseases. The system focuses on the benefit and practical applications in pathway centric therapy and drug repositioning.

A Computational Network Biology Approach to Uncover Novel Genes Related to Alzheimer's Disease

Recent advances in the fields of genetics and genomics have enabled the identification of numerous Alzheimer's disease (AD) candidate genes, although for many of them the role in AD pathophysiology has not been uncovered yet. Concomitantly, network biology studies have shown a strong link between protein network connectivity and disease. In this chapter I describe a computational approach that, by combining local and global network analysis strategies, allows the formulation of novel hypotheses on the molecular mechanisms involved in AD and prioritizes candidate genes for further functional studies.

Differential expression profiles of long non-coding RNAs reveal potential biomarkers for identification of human gastric cancer

Gastric cancer (GC) is one of the most lethal malignancies worldwide. To reduce its high mortality, sensitive and specific biomarkers for early detection are urgently needed. Recent studies have reported that tumor-specific long non-coding RNAs (lncRNAs) seem to be potential biomarkers for the early diagnosis and treatment of cancer. In the present study, lncRNA and mRNA expression profiling of GC specimens and their paired adjacent non-cancerous tissues was performed. Differentially expressed lncRNAs and mRNAs were identified through microarray analysis. The function of differential mRNA was determined by gene ontology and pathway analysis and the functions of lncRNAs were studied by constructing a co-expression network to find the relationships with corresponding mRNAs. We connected the co-expression network, mRNA functions, and the results of the microarray profile differential expression and selected 14 significantly differentially expressed key lncRNAs and 21 key mRNAs. Quantitative RT-PCR (qRT-PCR) was conducted to verify these key RNAs in 50 newly diagnosed GC patients. The data showed that RP5-919F19, CTD-2541M15 and UCA1 was significantly higher expressed. AP000459, LOC101928316, RP11-167N4 and LINC01071 expression was significantly lower in 30 advanced GC tumor tissues than adjacent non-tumor tissues P<0.05. Then, we further validated the above significant differential expression candidate lncRNAs in 20 early stage GC patients. Results showed that CTD-2541M15 and UCA1 were significantly higher expressed, AP000459, LINC01071 and MEG3 expression was significantly lower in 20 early stage GC patient tumor tissues than adjacent non-tumor tissues (P<0.05). In addition, expression of these lncRNAs shows gradual upward trend from early stage GC to advanced GC. Furthermore, conditional logistic regression analysis revealed the aberrant expression of CTD-2541M15, UCA1 and MEG3 closely linked with GC. There is a set of differentially expressed lncRNAs in GC which may be associated with the progression and development of GC. The differential expression profiles of lncRNAs in GC may be promising biomarkers for the early detection and early screening of high‑risk populations.

FERAL: network-based classifier with application to breast cancer outcome prediction

MOTIVATION

Breast cancer outcome prediction based on gene expression profiles is an important strategy for personalize patient care. To improve performance and consistency of discovered markers of the initial molecular classifiers, network-based outcome prediction methods (NOPs) have been proposed. In spite of the initial claims, recent studies revealed that neither performance nor consistency can be improved using these methods. NOPs typically rely on the construction of meta-genes by averaging the expression of several genes connected in a network that encodes protein interactions or pathway information. In this article, we expose several fundamental issues in NOPs that impede on the prediction power, consistency of discovered markers and obscures biological interpretation.

RESULTS

To overcome these issues, we propose FERAL, a network-based classifier that hinges upon the Sparse Group Lasso which performs simultaneous selection of marker genes and training of the prediction model. An important feature of FERAL, and a significant departure from existing NOPs, is that it uses multiple operators to summarize genes into meta-genes. This gives the classifier the opportunity to select the most relevant meta-gene for each gene set. Extensive evaluation revealed that the discovered markers are markedly more stable across independent datasets. Moreover, interpretation of the marker genes detected by FERAL reveals valuable mechanistic insight into the etiology of breast cancer.

AVAILABILITY AND IMPLEMENTATION

All code is available for download at: http://homepage.tudelft.nl/53a60/resources/FERAL/FERAL.zip.

Microarray analysis of Long non-coding RNA expression profiles in human gastric cells and tissues with Helicobacter pylori Infection

Background

Although Helicobacter pylori (H.pylori) is the dominant gastrointestinal pathogen, the genetic and molecular mechanisms underlying H.pylori-related diseases have not been fully elucidated. Long non-coding RNAs (lncRNAs) have been identified in eukaryotic cells, many of which play important roles in regulating biological processes and pathogenesis. However, the expression changes of lncRNAs in human infected by H.pylori have been rarely reported. This study aimed to identify the dysregulated lncRNAs in human gastric epithelial cells and tissues infected with H.pylori.

Methods

The aberrant expression profiles of lncRNAs and mRNAs in GES-1 cells with or without H.pylori infection were explored by microarray analysis. LncRNA-mRNA co-expression network was constructed based on Pearson correlation analysis. Gene Ontology (GO) and KEGG Pathway analyses of aberrantly expressed mRNAs were performed to identify the related biological functions and pathologic pathways. The expression changes of target lncRNAs were validated by qRT-PCR to confirm the microarray data in both cells and clinical specimens.

Results

Three hundred three lncRNAs and 565 mRNAs were identified as aberrantly expressed transcripts (≥2 or ≤0.5-fold change, P < 0.05) in cells with H.pylori infection compared to controls. LncRNA-mRNA co-expression network showed the core lncRNAs/mRNAs which might play important roles in H.pylori-related pathogenesis. GO and KEGG analyses have indicated that the functions of aberrantly expressed mRNAs in H.pylori infection were related closely with inflammation and carcinogenesis. QRT-PCR data confirmed the expression pattern of 8 (n345630, XLOC_004787, n378726, LINC00473, XLOC_005517, LINC00152, XLOC_13370, and n408024) lncRNAs in infected cells. Additionally, four down-regulated (n345630, XLOC_004787, n378726, and LINC00473) lncRNAs were verified in H.pylori-positive gastric samples.

Conclusion

Our study provided a preliminary exploration of lncRNAs expression profiles in H.pylori-infected cells by microarray. These dysregulated lncRNAs might contribute to the pathological processes during H.pylori infection.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-015-0159-0) contains supplementary material, which is available to authorized users.

Integrated Genomic and Network-Based Analyses of Complex Diseases and Human Disease Network

A disease phenotype generally reflects various pathobiological processes that interact in a complex network. The highly interconnected nature of the human protein interaction network (interactome) indicates that, at the molecular level, it is difficult to consider diseases as being independent of one another. Recently, genome-wide molecular measurements, data mining and bioinformatics approaches have provided the means to explore human diseases from a molecular basis. The exploration of diseases and a system of disease relationships based on the integration of genome-wide molecular data with the human interactome could offer a powerful perspective for understanding the molecular architecture of diseases. Recently, subnetwork markers have proven to be more robust and reliable than individual biomarker genes selected based on gene expression profiles alone, and achieve higher accuracy in disease classification. We have applied one of these methodologies to idiopathic dilated cardiomyopathy (IDCM) data that we have generated using a microarray and identified significant subnetworks associated with the disease. In this paper, we review the recent endeavours in this direction, and summarize the existing methodologies and computational tools for network-based analysis of complex diseases and molecular relationships among apparently different disorders and human disease network. We also discuss the future research trends and topics of this promising field.

Gonadal Transcriptome Analysis in Sterile Double Haploid Japanese Flounder

Sterility is a serious problem that can affect all bionts. In teleosts, double haploids (DHs) induced by mitogynogenesis are often sterile. This sterility severely restricts the further application of DHs for production of clones, genetic analysis, and breeding. However, sterile DH individuals are good source materials for investigation of the molecular mechanisms of gonad development, especially for studies into the role of genes that are indispensable for fish reproduction. Here, we used the Illumina sequencing platform to analyze the transcriptome of sterile female DH Japanese flounder in order to identify major genes that cause sterility and to provide a molecular basis for an intensive study of gonadal development in teleosts. Through sequencing, assembly, and annotation, we obtained 52,474 contigs and found that 60.7% of these shared homologies with existing sequences. A total of 1225 differentially expressed unigenes were found, including 492 upregulated and 733 downregulated genes. Gene Ontology and KEGG analyses showed that genes showing significant upregulation, such as CYP11A1, CYP11B2, CYP17, CYP21, HSD3β, bcl2l1, and PRLR, principally correlated with sterol metabolic process, steroid biosynthetic process, and the Jak-stat signaling pathway. The significantly downregulated genes were primarily associated with immune response, antigen processing and presentation, cytokine–cytokine receptor interaction, and protein digestion and absorption. Using a co-expression network analysis, we conducted a comprehensive comparison of gene expression in the gonads of fertile and sterile female DH Japanese flounder. Identification of genes showing significantly different expression will provide further insights into DH reproductive dysfunction and oocyte maturation processes in teleosts.

Long noncoding ribonucleic acid specific for distant metastasis of gastric cancer is associated with TRIM16 expression and facilitates tumor cell invasion in vitro

BACKGROUND AND AIM

Increasing evidence has indicated that long noncoding ribonucleic acids (lncRNAs) play a major role in cancers. Although certain lncRNAs has been reported to play a role in gastric cancer (GC), specific lncRNAs involved in distant metastasis of GC remain unknown.

METHODS

Differentially expressed mRNAs and lncRNAs between stage IV and non-stage IV GC were obtained by microarray. Gene ontology and pathway analysis were used to study functions of differential mRNAs. Algorithms were used to predict potential gene targets of cis or trans-acting lncRNAs. Network analysis was performed to analyze each pair of gene-lncRNA, gene-gene, or lncRNA-lncRNA interactions. Expression of lncRNA special for distant metastasis of GC (SDMGC) and target gene TRIM16 were tested in GC tissues and cell lines. RNAi and overexpression were used to observe the biological functions of SDMGC and TRIM16 on GC cells.

RESULTS

502 mRNAs and 440 lncRNAs were found to be differentially expressed. 74 gene ontology terms and 38 pathways were associated with the dysregulated transcripts. Fourteen core factors were determined by network analysis. Expression of SDMGC and TRIM16 was upregulated in the distant metastasis tissues, compared with primary GC tissues, which were positive correlation. Silencing of SDMGC or TRIM16 was demonstrated to decrease cell invasion and migration, while upregulated of SDMGC or TRIM16 could promote cell invasion and migration. However, little effect on proliferation, cell cycle, colony formation, and apoptosis was found.

CONCLUSIONS

SDMGC is obviously upregulated in stage IV GC and may represent a new marker and therapeutic target for GC treatment.

Transcriptome Profiling and Molecular Pathway Analysis of Genes in Association with Salinity Adaptation in Nile Tilapia Oreochromis niloticus

Nile tilapia Oreochromis niloticus is a freshwater fish but can tolerate a wide range of salinities. The mechanism of salinity adaptation at the molecular level was studied using RNA-Seq to explore the molecular pathways in fish exposed to 0, 8, or 16 (practical salinity unit, psu). Based on the change of gene expressions, the differential genes unions from freshwater to saline water were classified into three categories. In the constant change category (1), steroid biosynthesis, steroid hormone biosynthesis, fat digestion and absorption, complement and coagulation cascades were significantly affected by salinity indicating the pivotal roles of sterol-related pathways in response to salinity stress. In the change-then-stable category (2), ribosomes, oxidative phosphorylation, signaling pathways for peroxisome proliferator activated receptors, and fat digestion and absorption changed significantly with increasing salinity, showing sensitivity to salinity variation in the environment and a responding threshold to salinity change. In the stable-then-change category (3), protein export, protein processing in endoplasmic reticulum, tight junction, thyroid hormone synthesis, antigen processing and presentation, glycolysis/gluconeogenesis and glycosaminoglycan biosynthesis—keratan sulfate were the significantly changed pathways, suggesting that these pathways were less sensitive to salinity variation. This study reveals fundamental mechanism of the molecular response to salinity adaptation in O. niloticus, and provides a general guidance to understand saline acclimation in O. niloticus.

The putative oncogene CEP72 inhibits the mitotic function of BRCA1 and induces chromosomal instability

BRCA1 is a tumor-suppressor gene associated with, but not restricted to, breast and ovarian cancer and implicated in various biological functions. During mitosis, BRCA1 and its positive regulator Chk2 are localized at centrosomes and are required for the regulation of microtubule plus end assembly, thereby ensuring faithful mitosis and numerical chromosome stability. However, the function of BRCA1 during mitosis has not been defined mechanistically. To gain insights into the mitotic role of BRCA1 in regulating microtubule assembly, we systematically identified proteins interacting with BRCA1 during mitosis and found the centrosomal protein Cep72 as a novel BRCA1-interacting protein. CEP72 is frequently upregulated in colorectal cancer tissues and overexpression of CEP72 mirrors the consequences of BRCA1 loss during mitosis. In detail, the overexpression of CEP72 causes an increase in microtubule plus end assembly, abnormal mitotic spindle formation and the induction of chromosomal instability. Moreover, we show that high levels of Cep72 counteract Chk2 as a positive regulator of BRCA1 to ensure proper mitotic microtubule assembly. Thus, CEP72 represents a putative oncogene in colorectal cancer that might negatively regulate the mitotic function of BRCA1 to ensure chromosomal stability.

Large-scale RNA-Seq Transcriptome Analysis of 4043 Cancers and 548 Normal Tissue Controls across 12 TCGA Cancer Types

The Cancer Genome Atlas (TCGA) has accrued RNA-Seq-based transcriptome data for more than 4000 cancer tissue samples across 12 cancer types, translating these data into biological insights remains a major challenge. We analyzed and compared the transcriptomes of 4043 cancer and 548 normal tissue samples from 21 TCGA cancer types, and created a comprehensive catalog of gene expression alterations for each cancer type. By clustering genes into co-regulated gene sets, we identified seven cross-cancer gene signatures altered across a diverse panel of primary human cancer samples. A 14-gene signature extracted from these seven cross-cancer gene signatures precisely differentiated between cancerous and normal samples, the predictive accuracy of leave-one-out cross-validation (LOOCV) were 92.04%, 96.23%, 91.76%, 90.05%, 88.17%, 94.29%, and 99.10% for BLCA, BRCA, COAD, HNSC, LIHC, LUAD, and LUSC, respectively. A lung cancer-specific gene signature, containing SFTPA1 and SFTPA2 genes, accurately distinguished lung cancer from other cancer samples, the predictive accuracy of LOOCV for TCGA and GSE5364 data were 95.68% and 100%, respectively. These gene signatures provide rich insights into the transcriptional programs that trigger tumorigenesis and metastasis, and many genes in the signature gene panels may be of significant value to the diagnosis and treatment of cancer.

A novel lncRNA, LUADT1, promotes lung adenocarcinoma proliferation via the epigenetic suppression of p27

Long noncoding RNAs (lncRNAs) are known to regulate the development and progression of various cancers. However, few lncRNAs have been well characterized in lung adenocarcinoma (LUAD). Here, we identified the expression profile of lncRNAs and protein-coding genes via microarrays analysis of paired LUAD tissues and adjacent non-tumor tissues from five female non-smokes with LUAD. A total of 498 lncRNAs and 1691 protein-coding genes were differentially expressed between LUAD tissues and paired adjacent normal tissues. A novel lncRNA, LUAD transcript 1 (LUADT1), which is highly expressed in LUAD and correlates with T stage, was characterized. Both in vitro and in vivo data showed that LUADT1 knockdown significantly inhibited proliferation of LUAD cells and induced cell cycle arrest at the G0–G1 phase. Further analysis indicated that LUADT1 may regulate cell cycle progression by epigenetically inhibiting the expression of p27. RNA immunoprecipitation and chromatin immunoprecipitation assays confirmed that LUADT1 binds to SUZ12, a core component of polycomb repressive complex 2, and mediates the trimethylation of H3K27 at the promoter region of p27. The negative correlation between LUADT1 and p27 expression was confirmed in LUAD tissue samples. These data suggested that a set of lncRNAs and protein-coding genes were differentially expressed in LUAD. LUADT1 is an oncogenic lncRNA that regulates LUAD progression, suggesting that dysregulated lncRNAs may serve as key regulatory factors in LUAD progression.

The cancer cell map initiative: defining the hallmark networks of cancer

Progress in DNA sequencing has revealed the startling complexity of cancer genomes, which typically carry thousands of somatic mutations. However, it remains unclear which are the key driver mutations or dependencies in a given cancer and how these influence pathogenesis and response to therapy. Although tumors of similar types and clinical outcomes can have patterns of mutations that are strikingly different, it is becoming apparent that these mutations recurrently hijack the same hallmark molecular pathways and networks. For this reason, it is likely that successful interpretation of cancer genomes will require comprehensive knowledge of the molecular networks under selective pressure in oncogenesis. Here we announce the creation of a new effort, The Cancer Cell Map Initiative (CCMI), aimed at systematically detailing these complex interactions among cancer genes and how they differ between diseased and healthy states. We discuss recent progress that enables creation of these cancer cell maps across a range of tumor types and how they can be used to target networks disrupted in individual patients, significantly accelerating the development of precision medicine.

A role of uridylation pathway for blockade of let-7 microRNA biogenesis by Lin28B

The precise control of microRNA (miRNA) biosynthesis is crucial for gene regulation. Lin28A and Lin28B are selective inhibitors of biogenesis of let-7 miRNAs involved in development and tumorigenesis. Lin28A selectively inhibits let-7 biogenesis through cytoplasmic uridylation of precursor let-7 by TUT4 terminal uridyl transferase and subsequent degradation by Dis3l2 exonuclease. However, a role of this uridylation pathway remains unclear in let-7 blockade by Lin28B, a paralog of Lin28A, while Lin28B is reported to engage a distinct mechanism in the nucleus to suppress let-7. Here we revisit a functional link between Lin28B and the uridylation pathway with a focus on let-7 metabolism in cancer cells. Both Lin28A and Lin28B interacted with Dis3l2 in the cytoplasm, and silencing of Dis3l2 upregulated uridylated pre-let-7 in both Lin28A- and Lin28B-expressing cancer cell lines. In addition, we found that amounts of let-7 precursors influenced intracellular localization of Lin28B. Furthermore, we found that MCPIP1 (Zc3h12a) ribonuclease was also involved in degradation of both non-uridylated and uridylated pre-let-7. Cancer transcriptome analysis showed association of expression levels of Lin28B and uridylation pathway components, TUT4 and Dis3l2, in various human cancer cells and hepatocellular carcinoma. Collectively, these results suggest that cytoplasmic uridylation pathway actively participates in blockade of let-7 biogenesis by Lin28B.

GESearch: An Interactive GUI Tool for Identifying Gene Expression Signature

The huge amount of gene expression data generated by microarray and next-generation sequencing technologies present challenges to exploit their biological meanings. When searching for the coexpression genes, the data mining process is largely affected by selection of algorithms. Thus, it is highly desirable to provide multiple options of algorithms in the user-friendly analytical toolkit to explore the gene expression signatures. For this purpose, we developed GESearch, an interactive graphical user interface (GUI) toolkit, which is written in MATLAB and supports a variety of gene expression data files. This analytical toolkit provides four models, including the mean, the regression, the delegate, and the ensemble models, to identify the coexpression genes, and enables the users to filter data and to select gene expression patterns by browsing the display window or by importing knowledge-based genes. Subsequently, the utility of this analytical toolkit is demonstrated by analyzing two sets of real-life microarray datasets from cell-cycle experiments. Overall, we have developed an interactive GUI toolkit that allows for choosing multiple algorithms for analyzing the gene expression signatures.

Revealing Pathway Dynamics in Heart Diseases by Analyzing Multiple Differential Networks

Development of heart diseases is driven by dynamic changes in both the activity and connectivity of gene pathways. Understanding these dynamic events is critical for understanding pathogenic mechanisms and development of effective treatment. Currently, there is a lack of computational methods that enable analysis of multiple gene networks, each of which exhibits differential activity compared to the network of the baseline/healthy condition. We describe the iMDM algorithm to identify both unique and shared gene modules across multiple differential co-expression networks, termed M-DMs (multiple differential modules). We applied iMDM to a time-course RNA-Seq dataset generated using a murine heart failure model generated on two genotypes. We showed that iMDM achieves higher accuracy in inferring gene modules compared to using single or multiple co-expression networks. We found that condition-specific M-DMs exhibit differential activities, mediate different biological processes, and are enriched for genes with known cardiovascular phenotypes. By analyzing M-DMs that are present in multiple conditions, we revealed dynamic changes in pathway activity and connectivity across heart failure conditions. We further showed that module dynamics were correlated with the dynamics of disease phenotypes during the development of heart failure. Thus, pathway dynamics is a powerful measure for understanding pathogenesis. iMDM provides a principled way to dissect the dynamics of gene pathways and its relationship to the dynamics of disease phenotype. With the exponential growth of omics data, our method can aid in generating systems-level insights into disease progression.

Integrative network-based approach identifies key genetic elements in breast invasive carcinoma

BACKGROUND

Breast cancer is a genetically heterogeneous type of cancer that belongs to the most prevalent types with a high mortality rate. Treatment and prognosis of breast cancer would profit largely from a correct classification and identification of genetic key drivers and major determinants driving the tumorigenesis process. In the light of the availability of tumor genomic and epigenomic data from different sources and experiments, new integrative approaches are needed to boost the probability of identifying such genetic key drivers. We present here an integrative network-based approach that is able to associate regulatory network interactions with the development of breast carcinoma by integrating information from gene expression, DNA methylation, miRNA expression, and somatic mutation datasets.

RESULTS

Our results showed strong association between regulatory elements from different data sources in terms of the mutual regulatory influence and genomic proximity. By analyzing different types of regulatory interactions, TF-gene, miRNA-mRNA, and proximity analysis of somatic variants, we identified 106 genes, 68 miRNAs, and 9 mutations that are candidate drivers of oncogenic processes in breast cancer. Moreover, we unraveled regulatory interactions among these key drivers and the other elements in the breast cancer network. Intriguingly, about one third of the identified driver genes are targeted by known anti-cancer drugs and the majority of the identified key miRNAs are implicated in cancerogenesis of multiple organs. Also, the identified driver mutations likely cause damaging effects on protein functions. The constructed gene network and the identified key drivers were compared to well-established network-based methods.

CONCLUSION

The integrated molecular analysis enabled by the presented network-based approach substantially expands our knowledge base of prospective genomic drivers of genes, miRNAs, and mutations. For a good part of the identified key drivers there exists solid evidence for involvement in the development of breast carcinomas. Our approach also unraveled the complex regulatory interactions comprising the identified key drivers. These genomic drivers could be further investigated in the wet lab as potential candidates for new drug targets. This integrative approach can be applied in a similar fashion to other cancer types, complex diseases, or for studying cellular differentiation processes.

MyProteinNet: build up-to-date protein interaction networks for organisms, tissues and user-defined contexts

The identification of the molecular pathways active in specific contexts, such as disease states or drug responses, often requires an extensive view of the potential interactions between a subset of proteins. This view is not easily obtained: it requires the integration of context-specific protein list or expression data with up-to-date data of protein interactions that are typically spread across multiple databases. The MyProteinNet web server allows users to easily create such context-sensitive protein interaction networks. Users can automatically gather and consolidate data from up to 11 different databases to create a generic protein interaction network (interactome). They can score the interactions based on reliability and filter them by user-defined contexts including molecular expression and protein annotation. The output of MyProteinNet includes the generic and filtered interactome files, together with a summary of their network attributes. MyProteinNet is particularly geared toward building human tissue interactomes, by maintaining tissue expression profiles from multiple resources. The ability of MyProteinNet to facilitate the construction of up-to-date, context-specific interactomes and its applicability to 11 different organisms and to tens of human tissues, make it a powerful tool in meaningful analysis of protein networks. MyProteinNet is available at http://netbio.bgu.ac.il/myproteinnet.

Quantifying the underlying landscape and paths of cancer

Cancer is a disease regulated by the underlying gene networks. The emergence of normal and cancer states as well as the transformation between them can be thought of as a result of the gene network interactions and associated changes. We developed a global potential landscape and path framework to quantify cancer and associated processes. We constructed a cancer gene regulatory network based on the experimental evidences and uncovered the underlying landscape. The resulting tristable landscape characterizes important biological states: normal, cancer and apoptosis. The landscape topography in terms of barrier heights between stable state attractors quantifies the global stability of the cancer network system. We propose two mechanisms of cancerization: one is by the changes of landscape topography through the changes in regulation strengths of the gene networks. The other is by the fluctuations that help the system to go over the critical barrier at fixed landscape topography. The kinetic paths from least action principle quantify the transition processes among normal state, cancer state and apoptosis state. The kinetic rates provide the quantification of transition speeds among normal, cancer and apoptosis attractors. By the global sensitivity analysis of the gene network parameters on the landscape topography, we uncovered some key gene regulations determining the transitions between cancer and normal states. This can be used to guide the design of new anti-cancer tactics, through cocktail strategy of targeting multiple key regulation links simultaneously, for preventing cancer occurrence or transforming the early cancer state back to normal state.

Interactions between Hyaluronan and Its Receptors (CD44, RHAMM) Regulate the Activities of Inflammation and Cancer

The glycosaminoglycan hyaluronan (HA), a major component of extracellular matrices, and cell surface receptors of HA have been proposed to have pivotal roles in cell proliferation, migration, and invasion, which are necessary for inflammation and cancer progression. CD44 and receptor for HA-mediated motility (RHAMM) are the two main HA-receptors whose biological functions in human and murine inflammations and tumor cells have been investigated comprehensively. HA was initially considered to be only an inert component of connective tissues, but is now known as a “dynamic” molecule with a constant turnover in many tissues through rapid metabolism that involves HA molecules of various sizes: high molecular weight HA (HMW HA), low molecular weight HA, and oligosaccharides. The intracellular signaling pathways initiated by HA interactions with CD44 and RHAMM that lead to inflammatory and tumorigenic responses are complex. Interestingly, these molecules have dual functions in inflammations and tumorigenesis. For example, the presence of CD44 is involved in initiation of arthritis, while the absence of CD44 by genetic deletion in an arthritis mouse model increases rather than decreases disease severity. Similar dual functions of CD44 exist in initiation and progression of cancer. RHAMM overexpression is most commonly linked to cancer progression, whereas loss of RHAMM is associated with malignant peripheral nerve sheath tumor growth. HA may similarly perform dual functions. An abundance of HMW HA can promote malignant cell proliferation and development of cancer, whereas antagonists to HA-CD44 signaling inhibit tumor cell growth in vitro and in vivo by interfering with HMW HA-CD44 interaction. This review describes the roles of HA interactions with CD44 and RHAMM in inflammatory responses and tumor development/progression, and how therapeutic strategies that block these key inflammatory/tumorigenic processes may be developed in rodent and human diseases.

Spatiotemporal 16p11.2 protein network implicates cortical late mid-fetal brain development and KCTD13-Cul3-RhoA pathway in psychiatric diseases

The psychiatric disorders autism and schizophrenia have a strong genetic component, and copy number variants (CNVs) are firmly implicated. Recurrent deletions and duplications of chromosome 16p11.2 confer a high risk for both diseases, but the pathways disrupted by this CNV are poorly defined. Here we investigate the dynamics of the 16p11.2 network by integrating physical interactions of 16p11.2 proteins with spatiotemporal gene expression from the developing human brain. We observe profound changes in protein interaction networks throughout different stages of brain development and/or in different brain regions. We identify the late mid-fetal period of cortical development as most critical for establishing the connectivity of 16p11.2 proteins with their co-expressed partners. Furthermore, our results suggest that the regulation of the KCTD13-Cul3-RhoA pathway in layer 4 of the inner cortical plate is crucial for controlling brain size and connectivity and that its dysregulation by de novo mutations may be a potential determinant of 16p11.2 CNV deletion and duplication phenotypes.

Assessing associations between the AURKA-HMMR-TPX2-TUBG1 functional module and breast cancer risk in BRCA1/2 mutation carriers

While interplay between BRCA1 and AURKA-RHAMM-TPX2-TUBG1 regulates mammary epithelial polarization, common genetic variation in HMMR (gene product RHAMM) may be associated with risk of breast cancer in BRCA1 mutation carriers. Following on these observations, we further assessed the link between the AURKA-HMMR-TPX2-TUBG1 functional module and risk of breast cancer in BRCA1 or BRCA2 mutation carriers. Forty-one single nucleotide polymorphisms (SNPs) were genotyped in 15,252 BRCA1 and 8,211 BRCA2 mutation carriers and subsequently analyzed using a retrospective likelihood approach. The association of HMMR rs299290 with breast cancer risk in BRCA1 mutation carriers was confirmed: per-allele hazard ratio (HR) = 1.10, 95% confidence interval (CI) 1.04-1.15, p = 1.9 x 10(-4) (false discovery rate (FDR)-adjusted p = 0.043). Variation in CSTF1, located next to AURKA, was also found to be associated with breast cancer risk in BRCA2 mutation carriers: rs2426618 per-allele HR = 1.10, 95% CI 1.03-1.16, p = 0.005 (FDR-adjusted p = 0.045). Assessment of pairwise interactions provided suggestions (FDR-adjusted pinteraction values > 0.05) for deviations from the multiplicative model for rs299290 and CSTF1 rs6064391, and rs299290 and TUBG1 rs11649877 in both BRCA1 and BRCA2 mutation carriers. Following these suggestions, the expression of HMMR and AURKA or TUBG1 in sporadic breast tumors was found to potentially interact, influencing patients' survival. Together, the results of this study support the hypothesis of a causative link between altered function of AURKA-HMMR-TPX2-TUBG1 and breast carcinogenesis in BRCA1/2 mutation carriers.

Systematic identification of molecular links between core and candidate genes in breast cancer

Despite the remarkable progress achieved in the identification of specific genes involved in breast cancer (BC), our understanding of their complex functioning is still limited. In this manuscript, we systematically explore the existence of direct physical interactions between the products of BC core and associated genes. Our aim is to generate a protein interaction network of BC-associated gene products and suggest potential molecular mechanisms to unveil their role in the disease. In total, we report 599 novel high-confidence interactions among 44 BC core, 54 BC candidate/associated and 96 newly identified proteins. Our findings indicate that this network-based approach is indeed a robust inference tool to pinpoint new potential players and gain insight into the underlying mechanisms of those proteins with previously unknown roles in BC. To illustrate the power of our approach, we provide initial validation of two BC-associated proteins on the alteration of DNA damage response as a result of specific re-wiring interactions. Overall, our BC-related network may serve as a framework to integrate clinical and molecular data and foster novel global therapeutic strategies.

Next-generation sequencing reveals low-dose effects of cationic dendrimers in primary human bronchial epithelial cells

Gene expression profiling has developed rapidly in recent years with the advent of deep sequencing technologies such as RNA sequencing (RNA Seq) and could be harnessed to predict and define mechanisms of toxicity of chemicals and nanomaterials. However, the full potential of these technologies in (nano)toxicology is yet to be realized. Here, we show that systems biology approaches can uncover mechanisms underlying cellular responses to nanomaterials. Using RNA Seq and computational approaches, we found that cationic poly(amidoamine) dendrimers (PAMAM-NH2) are capable of triggering down-regulation of cell-cycle-related genes in primary human bronchial epithelial cells at doses that do not elicit acute cytotoxicity, as demonstrated using conventional cell viability assays, while gene transcription was not affected by neutral PAMAM-OH dendrimers. The PAMAMs were internalized in an active manner by lung cells and localized mainly in lysosomes; amine-terminated dendrimers were internalized more efficiently when compared to the hydroxyl-terminated dendrimers. Upstream regulator analysis implicated NF-κB as a putative transcriptional regulator, and subsequent cell-based assays confirmed that PAMAM-NH2 caused NF-κB-dependent cell cycle arrest. However, PAMAM-NH2 did not affect cell cycle progression in the human A549 adenocarcinoma cell line. These results demonstrate the feasibility of applying systems biology approaches to predict cellular responses to nanomaterials and highlight the importance of using relevant (primary) cell models.

Fast and robust group-wise eQTL mapping using sparse graphical models

Background

Genome-wide expression quantitative trait loci (eQTL) studies have emerged as a powerful tool to understand the genetic basis of gene expression and complex traits. The traditional eQTL methods focus on testing the associations between individual single-nucleotide polymorphisms (SNPs) and gene expression traits. A major drawback of this approach is that it cannot model the joint effect of a set of SNPs on a set of genes, which may correspond to hidden biological pathways.

Results

We introduce a new approach to identify novel group-wise associations between sets of SNPs and sets of genes. Such associations are captured by hidden variables connecting SNPs and genes. Our model is a linear-Gaussian model and uses two types of hidden variables. One captures the set associations between SNPs and genes, and the other captures confounders. We develop an efficient optimization procedure which makes this approach suitable for large scale studies. Extensive experimental evaluations on both simulated and real datasets demonstrate that the proposed methods can effectively capture both individual and group-wise signals that cannot be identified by the state-of-the-art eQTL mapping methods.

Conclusions

Considering group-wise associations significantly improves the accuracy of eQTL mapping, and the successful multi-layer regression model opens a new approach to understand how multiple SNPs interact with each other to jointly affect the expression level of a group of genes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0421-z) contains supplementary material, which is available to authorized users.

Querying Co-regulated Genes on Diverse Gene Expression Datasets Via Biclustering

Rapid development and increasing popularity of gene expression microarrays have resulted in a number of studies on the discovery of co-regulated genes. One important way of discovering such co-regulations is the query-based search since gene co-expressions may indicate a shared role in a biological process. Although there exist promising query-driven search methods adapting clustering, they fail to capture many genes that function in the same biological pathway because microarray datasets are fraught with spurious samples or samples of diverse origin, or the pathways might be regulated under only a subset of samples. On the other hand, a class of clustering algorithms known as biclustering algorithms which simultaneously cluster both the items and their features are useful while analyzing gene expression data, or any data in which items are related in only a subset of their samples. This means that genes need not be related in all samples to be clustered together. Because many genes only interact under specific circumstances, biclustering may recover the relationships that traditional clustering algorithms can easily miss. In this chapter, we briefly summarize the literature using biclustering for querying co-regulated genes. Then we present a novel biclustering approach and evaluate its performance by a thorough experimental analysis.

Aberrant expression of long noncoding RNAs in chronic thromboembolic pulmonary hypertension

Chronic thromboembolic pulmonary hypertension (CTEPH) is one of the primary causes of severe pulmonary hypertension. In order to identify long noncoding RNAs (lncRNAs) that may be involved in the development of CTEPH, comprehensive lncRNA and messenger RNA (mRNA) profiling of endothelial tissues from the pulmonary arteries of CTEPH patients was conducted with microarray analysis. Differential expression of 185 lncRNAs was observed in the CTEPH tissues compared with healthy control tissues. Further analysis identified 464 regulated enhancer-like lncRNAs and overlapping, antisense or nearby mRNA pairs. Coexpression networks were subsequently constructed and investigated. The expression levels of the lncRNAs, NR_036693, NR_027783, NR_033766 and NR_001284, were significantly altered. Gene ontology and pathway analysis demonstrated the potential role of lncRNAs in the regulation of central process, including inflammatory response, response to endogenous stimulus and antigen processing and presentation. The use of bioinformatics may help to uncover and analyze large quantities of data identified by microarray analyses, through rigorous experimental planning, statistical analysis and the collection of more comprehensive data regarding CTEPH. The results of the present study provided evidence which may be helpful in future studies on the diagnosis and management of CTEPH.

Tumor antigens as proteogenomic biomarkers in invasive ductal carcinomas

Background

The majority of genetic biomarkers for human cancers are defined by statistical screening of high-throughput genomics data. While a large number of genetic biomarkers have been proposed for diagnostic and prognostic applications, only a small number have been applied in the clinic. Similarly, the use of proteomics methods for the discovery of cancer biomarkers is increasing. The emerging field of proteogenomics seeks to enrich the value of genomics and proteomics approaches by studying the intersection of genomics and proteomics data. This task is challenging due to the complex nature of transcriptional and translation regulatory mechanisms and the disparities between genomic and proteomic data from the same samples. In this study, we have examined tumor antigens as potential biomarkers for breast cancer using genomics and proteomics data from previously reported laser capture microdissected ER+ tumor samples.

Results

We applied proteogenomic analyses to study the genetic aberrations of 32 tumor antigens determined in the proteomic data. We found that tumor antigens that are aberrantly expressed at the genetic level and expressed at the protein level, are likely involved in perturbing pathways directly linked to the hallmarks of cancer. The results found by proteogenomic analysis of the 32 tumor antigens studied here, capture largely the same pathway irregularities as those elucidated from large-scale screening of genomics analyses, where several thousands of genes are often found to be perturbed.

Conclusion

Tumor antigens are a group of proteins recognized by the cells of the immune system. Specifically, they are recognized in tumor cells where they are present in larger than usual amounts, or are physiochemically altered to a degree at which they no longer resemble native human proteins. This proteogenomic analysis of 32 tumor antigens suggests that tumor antigens have the potential to be highly specific biomarkers for different cancers.

Transcriptional profiling and co-expression network analysis identifies potential biomarkers to differentiate chronic hepatitis B and the caused cirrhosis

Liver cirrhosis is one of the most common non-neoplastic causes of mortality worldwide. Chronic hepatitis B (CHB) is a major cause of liver cirrhosis in China. To find biomarkers for the diagnosis of CHB caused cirrhosis (HBC), we examined the transcriptional profiling of CHB and HBC. The leukocyte samples of CHB (n = 5) and HBC (n = 5) were analyzed by microarray. The results showed that 2128 mapped genes were differentially expressed between CHB and HBC (fold change ≥ 2.0, p < 0.05). Gene ontology (GO) analysis indicated that these 2128 differentially expressed genes (DEGs) were enriched for immune response and cell formation functions mostly. Moreover, co-expression networks using the k-core algorithm were established to determine the core genes, which may play important roles in the progression of CHB to HBC. There were markedly different gene co-expression patterns in CHB and HBC. We validated the five core genes, CASP1, TGFBI, IFI30, HLA-DMA and PAG1 in CHB (n = 60) and HBC (n = 60) by quantitative RT-PCR. The expression of the five genes were consistent with microarray, and there were statistically significant co-expression patterns of TGFβ1, PAG1 and HLA-DMA mRNA (Pearson correlation coefficient >0.6). Furthermore, we constructed an mRNA panel of TGFBI, IFI30, HLA-DMA and PAG1 (TIPH HBCtest) by means of a logistic regression model, and evaluated the TIPH HBCtest for HBC diagnosis by area under the receiver operating characteristic curve (AUC) analysis, which showed a higher accuracy (AUC = 0.903). This study suggested that there are particular transcriptional profiles, gene co-expression patterns and core genes in CHB and HBC. The TIPH HBC test may be useful in the diagnosis of HBC from CHB.

BRCA1 haploinsufficiency for replication stress suppression in primary cells

BRCA1—a breast and ovarian cancer suppressor gene—promotes genome integrity. To study the functionality of BRCA1 in the heterozygous state, we established a collection of primary human BRCA1^+/+ and BRCA1^mut/+ mammary epithelial cells and fibroblasts. Here we report that all BRCA1^mut/+ cells exhibited multiple normal BRCA1 functions, including the support of homologous recombination- type double-strand break repair (HR-DSBR), checkpoint functions, centrosome number control, spindle pole formation, Slug expression and satellite RNA suppression. In contrast, the same cells were defective in stalled replication fork repair and/or suppression of fork collapse, that is, replication stress. These defects were rescued by reconstituting BRCA1^mut/+ cells with wt BRCA1. In addition, we observed ‘conditional’ haploinsufficiency for HR-DSBR in BRCA1^mut/+ cells in the face of replication stress. Given the importance of replication stress in epithelial cancer development and of an HR defect in breast cancer pathogenesis, both defects are candidate contributors to tumorigenesis in BRCA1-deficient mammary tissue.

BRCA1 is a key breast and ovarian cancer suppressor involved in DSB repair. Here, the authors show that cells heterozygous for several BRCA1 mutations are universally defective in the response to replication stress, which could contribute to the BRCA1 breast cancer development pathway.

Systematic screening reveals a role for BRCA1 in the response to transcription-associated DNA damage

BRCA1 is a breast and ovarian tumor suppressor. Given its numerous incompletely understood functions and the possibility that more exist, we performed complementary systematic screens in search of new BRCA1 protein-interacting partners. New BRCA1 functions and/or a better understanding of existing ones were sought. Among the new interacting proteins identified, genetic interactions were detected between BRCA1 and four of the interactors: TONSL, SETX, TCEANC, and TCEA2. Genetic interactions were also detected between BRCA1 and certain interactors of TONSL, including both members of the FACT complex. From these results, a new BRCA1 function in the response to transcription-associated DNA damage was detected. Specifically, new roles for BRCA1 in the restart of transcription after UV damage and in preventing or repairing damage caused by stabilized R loops were identified. These roles are likely carried out together with some of the newly identified interactors. This new function may be important in BRCA1 tumor suppression, since the expression of several interactors, including some of the above-noted transcription proteins, is repeatedly aberrant in both breast and ovarian cancers.

Modules, networks and systems medicine for understanding disease and aiding diagnosis

Many common diseases, such as asthma, diabetes or obesity, involve altered interactions between thousands of genes. High-throughput techniques (omics) allow identification of such genes and their products, but functional understanding is a formidable challenge. Network-based analyses of omics data have identified modules of disease-associated genes that have been used to obtain both a systems level and a molecular understanding of disease mechanisms. For example, in allergy a module was used to find a novel candidate gene that was validated by functional and clinical studies. Such analyses play important roles in systems medicine. This is an emerging discipline that aims to gain a translational understanding of the complex mechanisms underlying common diseases. In this review, we will explain and provide examples of how network-based analyses of omics data, in combination with functional and clinical studies, are aiding our understanding of disease, as well as helping to prioritize diagnostic markers or therapeutic candidate genes. Such analyses involve significant problems and limitations, which will be discussed. We also highlight the steps needed for clinical implementation.

Network-based identification of biomarkers coexpressed with multiple pathways

Unraveling complex molecular interactions and networks and incorporating clinical information in modeling will present a paradigm shift in molecular medicine. Embedding biological relevance via modeling molecular networks and pathways has become increasingly important for biomarker identification in cancer susceptibility and metastasis studies. Here, we give a comprehensive overview of computational methods used for biomarker identification, and provide a performance comparison of several network models used in studies of cancer susceptibility, disease progression, and prognostication. Specifically, we evaluated implication networks, Boolean networks, Bayesian networks, and Pearson’s correlation networks in constructing gene coexpression networks for identifying lung cancer diagnostic and prognostic biomarkers. The results show that implication networks, implemented in Genet package, identified sets of biomarkers that generated an accurate prediction of lung cancer risk and metastases; meanwhile, implication networks revealed more biologically relevant molecular interactions than Boolean networks, Bayesian networks, and Pearson’s correlation networks when evaluated with MSigDB database.

Study of ZHENG differentiation in hepatitis B-caused cirrhosis: a transcriptional profiling analysis

BACKGROUND

In traditional Chinese medicine (TCM) clinical practice, ZHENG (also known as TCM syndrome) helps to understand the human homeostasis and guide individualized treatment. However, the scientific basis of ZHENG remains unclear due to limitations of current reductionist approaches.

METHODS

We collected the leukocyte samples of three hepatitis B-caused cirrhosis (HBC) patients with dampness-heat accumulation syndrome (DHAS) and three HBC patients with liver depression and spleen deficiency syndrome (LDSDS) for microarray analysis. We generated Gene-Regulatory-Networks (GeneRelNet) from the differentially expressed genes (DEGs) of microarray date. Core genes were validated using anther independent cohort of 40 HBC patients (20 DHAS, 20 LDSDS) with RT-PCR.

RESULTS

There were 2457 mapped genes were differentially expressed between DHAS and LDSDS (Fold change ≥ 2.0, P < 0.05). There were markedly different genes co-expression patterns in DHAS and LDSDS. Furthermore, three differential co-expression genes including purine nucleoside phosphorylase (PNP); aquaporin 7 (AQP7) and proteasome 26S subunit, non-ATPase 2 (PSMD2) were screened by GeneRelNets, and their mRNA expressions were further validated by real time RT-PCR. The results were consistent with microarray. The PNP (P = 0.007), AQP7 (P = 0.038) and PSMD2 (P = 0.009) mRNA expression is significant difference between DHAS and LDSDS using the non-parametric test. Furthermore, we constructed an mRNA panel of PNP, AQP7 and PSMD2 (PAP panel) by logistic regression model, and evaluated the PAP panel to distinguish DHAS from LDSDS by area under the receiver operating characteristic curve (AUC) analysis, which showed a higher accuracy (AUC = 0.835). Gene ontology (GO) analysis indicated that the DHAS is most likely related to system process while the functions overrepresented by LDSDS most related to the response to stimulus.

CONCLUSIONS

This study suggested that there are particular transcriptional profiles, genes co-expressions patterns and functional properties of DHAS and LDSDS, and PNP, AQP7, and PSMD2 may be involved in ZHENG differentiation of DHAS and LDSDS in HBC.

Graph-regularized dual Lasso for robust eQTL mapping

MOTIVATION

As a promising tool for dissecting the genetic basis of complex traits, expression quantitative trait loci (eQTL) mapping has attracted increasing research interest. An important issue in eQTL mapping is how to effectively integrate networks representing interactions among genetic markers and genes. Recently, several Lasso-based methods have been proposed to leverage such network information. Despite their success, existing methods have three common limitations: (i) a preprocessing step is usually needed to cluster the networks; (ii) the incompleteness of the networks and the noise in them are not considered; (iii) other available information, such as location of genetic markers and pathway information are not integrated.

RESULTS

To address the limitations of the existing methods, we propose Graph-regularized Dual Lasso (GDL), a robust approach for eQTL mapping. GDL integrates the correlation structures among genetic markers and traits simultaneously. It also takes into account the incompleteness of the networks and is robust to the noise. GDL utilizes graph-based regularizers to model the prior networks and does not require an explicit clustering step. Moreover, it enables further refinement of the partial and noisy networks. We further generalize GDL to incorporate the location of genetic makers and gene-pathway information. We perform extensive experimental evaluations using both simulated and real datasets. Experimental results demonstrate that the proposed methods can effectively integrate various available priori knowledge and significantly outperform the state-of-the-art eQTL mapping methods.

AVAILABILITY

Software for both C++ version and Matlab version is available at http://www.cs.unc.edu/∼weicheng/.

Hope for GWAS: relevant risk genes uncovered from GWAS statistical noise

Hundreds of genetic variants have been associated to common diseases through genome-wide association studies (GWAS), yet there are limits to current approaches in detecting true small effect risk variants against a background of false positive findings. Here we addressed the missing heritability problem, aiming to test whether there are indeed risk variants within GWAS statistical noise and to develop a systematic strategy to retrieve these hidden variants. Employing an integrative approach, which combines protein-protein interactions with association data from GWAS for 6 common diseases, we found that associated-genes at less stringent significance levels (p < 0.1) with any of these diseases are functionally connected beyond noise expectation. This functional coherence was used to identify disease-relevant subnetworks, which were shown to be enriched in known genes, outperforming the selection of top GWAS genes. As a proof of principle, we applied this approach to breast cancer, supporting well-known breast cancer genes, while pinpointing novel susceptibility genes for experimental validation. This study reinforces the idea that GWAS are under-analyzed and that missing heritability is rather hidden. It extends the use of protein networks to reveal this missing heritability, thus leveraging the large investment in GWAS that produced so far little tangible gain.

A generally applicable translational strategy identifies S100A4 as a candidate gene in allergy

The identification of diagnostic markers and therapeutic candidate genes in common diseases is complicated by the involvement of thousands of genes. We hypothesized that genes co-regulated with a key gene in allergy, IL13, would form a module that could help to identify candidate genes. We identified a T helper 2 (TH2) cell module by small interfering RNA-mediated knockdown of 25 putative IL13-regulating transcription factors followed by expression profiling. The module contained candidate genes whose diagnostic potential was supported by clinical studies. Functional studies of human TH2 cells as well as mouse models of allergy showed that deletion of one of the genes, S100A4, resulted in decreased signs of allergy including TH2 cell activation, humoral immunity, and infiltration of effector cells. Specifically, dendritic cells required S100A4 for activating T cells. Treatment with an anti-S100A4 antibody resulted in decreased signs of allergy in the mouse model as well as in allergen-challenged T cells from allergic patients. This strategy, which may be generally applicable to complex diseases, identified and validated an important diagnostic and therapeutic candidate gene in allergy.

Long noncoding RNAs expression patterns associated with chemo response to cisplatin based chemotherapy in lung squamous cell carcinoma patients

Background

There is large variability among lung squamous cell carcinoma patients in response to treatment with cisplatin based chemotherapy. LncRNA is potentially a new type of predictive marker that can identify subgroups of patients who benefit from chemotherapy and it will have great value for treatment guidance.

Methods

Differentially expressed lncRNAs and mRNA were identified using microarray profiling of tumors with partial response (PR) vs. with progressive disease (PD) from advanced lung squamous cell carcinoma patients treated with cisplatin based chemotherapy and validated by quantitative real-time PCR (qPCR). Furthermore, the expression of AC006050.3-003 was assessed in another 60 tumor samples.

Results

Compared with the PD samples, 953 lncRNAs were consistently upregulated and 749 lncRNAs were downregulated consistently among the differentially expressed lncRNAs in PR samples (Fold Change≥2.0-fold, p <0.05). Pathway analyses showed that some classical pathways, including “Nucleotide excision repair,” that participated in cisplatin chemo response were differentially expressed between PR and PD samples. Coding-non-coding gene co-expression network identified many lncRNAs, such as lncRNA AC006050.3-003, that potentially played a key role in chemo response. The expression of lncRNA AC006050.3-003 was significantly lower in PR samples compared to the PD samples in another 60 lung squamous cell carcinoma patients. Receiver operating characteristic curve analysis revealed that lncRNA AC006050.3-003 was a valuable biomarker for differentiating PR patients from PD patients with an area under the curve of 0.887 (95% confidence interval 0.779, 0.954).

Conclusions

LncRNAs seem to be involved in cisplatin-based chemo response and may serve as biomarkers for treatment response and candidates for therapy targets in lung squamous cell carcinoma.

Toward better benchmarking: challenge-based methods assessment in cancer genomics

Rapid technological development has created an urgent need for improved evaluation of algorithms for the analysis of cancer genomics data. We outline how challenge-based assessment may help fill this gap by leveraging crowd-sourcing to distribute effort and reduce bias.

Randomness and preserved patterns in cancer network

Breast cancer has been reported to account for the maximum cases among all female cancers till date. In order to gain a deeper insight into the complexities of the disease, we analyze the breast cancer network and its normal counterpart at the proteomic level. While the short range correlations in the eigenvalues exhibiting universality provide an evidence towards the importance of random connections in the underlying networks, the long range correlations along with the localization properties reveal insightful structural patterns involving functionally important proteins. The analysis provides a benchmark for designing drugs which can target a subgraph instead of individual proteins.

Identification of gene expression profile in the rat brain resulting from acute alcohol intoxication

This study aimed to identify gene expression profile in the rat brain resulting from acute alcohol intoxication (AAI). Eighteen SD rats were divided into the alcohol-treated group (n = 9) and saline control group (n = 9). Periorbital blood samples were taken to determine their blood alcohol content by gas chromatography. Tissue sections were analyzed by H and E staining and biochemical assays. Real-time reverse transcription PCR was used to validate microarray data. Statistical analysis was carried out using SPSS18.0 software (Version 18.0, SPSS Inc., Chicago, IL, USA). H and E staining demonstrated that alcohol-treated rats showed no obvious pathological changes in nerve cells compared with those in the control group. Biochemical tests revealed that alcohol-treated rats had lower superoxide dismutase activity than those in the control group (167.3 ± 10.3 U/mg vs. 189.2 ± 5.9 U/mg, P < 0.05). Furthermore, the malondialdehyde levels in alcohol-treated rats were higher than those in the control group (3.48 ± 0.24 mmol/mg vs. 2.51 ± 0.23 mmol/mg, P < 0.05). Microarray data presented 366 up-regulated genes and 300 down-regulated genes in the AAI rat brain. Gene ontology analysis identified 31 genes up-regulated and 39 down-regulated among all differentially expressed genes. Twenty-four pathways showed significant differences, including 12 pathways involved with up-regulated genes and 12 pathways involved with down-regulated genes. Selected genes showed significantly different expression in both alcohol-treated and control groups (P < 0.05). Gene expression analysis enabled clustering of alcohol intoxication-related genes by function. These genes expression may be potential targets for treatment or drug screening for acute alcohol intoxication.