Differential modeling for cancer microarray data

Capturing the changes between two biological phenotypes is a crucial task in understanding the mechanisms of various diseases. Most of the existing computational approaches depend on testing the changes in the expression levels of each single gene individually. In this work, we proposed novel computational approaches to identify the differential genes between two phenotypes. These approaches aim to quantitatively characterize the differences between two phenotypes and can provide better insights and understanding of various diseases. The purpose of this thesis is three-fold. Firstly, we review the state-of-the-art approaches for differential analysis of gene expression data. Secondly, we propose a novel differential network analysis approach that is composed of two algorithms, namely, DiffRank and DiffSubNet, to identify differential hubs and differential subnetworks, respectively. In this approach, two datasets are represented as two networks , and then the problem of identifying differential genes is transformed to the problem of comparing two networks to identify the most differential network omponents. Studying such networks can provide valuable knowledge about the data. The DiffRank algorithm ranks the nodes of two networks based on their differential behavior using two novel differential measures: differential connectivity and differential betweenness centrality for each node. These measures are propagated through the network and are optimized to capture the local and global structural changes between two networks. Then, we integrated the results of this algorithm into the proposed differential subnetwork algorithm which is called DiffSubNet. This algorithm aims to identify sets of differentially connected nodes. We demonstrated the effectiveness of these algorithms on synthetic datasets and real-world applications and showed that these algorithms identified meaningful and valuable information compared to some of the baseline methods that can be used for such a task. Thirdly, we propose a novel differential co-clustering approach to efficiently find arbitrarily positioned difeferntial (or discriminative) co-clusters from large datasets. The goal of this approach is to discover a distinguishing set of gene patterns that are highly correlated in a subset of the samples (subspace co-expressions) in one phenotype but not in the other. This approach is useful when the biological samples are assumed to be heterogenous or have multiple subtypes. To achieve this goal, we propose a novel co-clustering algorithm, Ranking-based Arbitrarily Positioned Overlapping Co-Clustering (RAPOCC), to efficiently extract significant co-clusters. This algorithm optimizes a novel ranking-based objective function to find arbitrarily positioned co-clusters, and it can extract large and overlapping co-clusters containing both positively and negatively correlated genes. Then, we extend this algorithm to discover discriminative co-clusters by incorporating the class information into the co-cluster search process. The novel discriminative co-clustering algorithm is called Discriminative RAPOCC (Di-RAPOCC), to efficiently extract the discriminative co-clusters from labeled datasets. We also characterize the discriminative co-clusters and propose three novel measures that can be used to evaluate the performance of any discriminative subspace algorithm. We evaluated the proposed algorithms on several synthetic and real gene expression datasets, and our experimental results showed that the proposed algorithms outperformed several existing algorithms available in the literature. The shift from single gene analysis to the differential gene network analysis and differential co-clustering can play a crucial role in future analysis of gene expression and can help in understanding the mechanism of various diseases

A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer

Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single gene classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single gene classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single gene classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single gene sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single gene classifiers for predicting outcome in breast cancer

MicroRNA regulation of bovine monocyte inflammatory and metabolic networks in an in vivo infection model.

peer-reviewedBovine mastitis is an inflammation-driven disease of the bovine mammary gland that costs the global dairy industry several billion dollars per annum. Because disease susceptibility is a multi-factorial complex phenotype, an integrative biology approach is required to dissect the molecular networks involved. Here, we report such an approach, using next generation sequencing combined with advanced network and pathway biology methods to simultaneously profile mRNA and miRNA expression at multiple time-points (0, 12, 24, 36 and 48h) in both milk and blood FACS-isolated CD14+ monocytes from animals infected in vivo with Streptococcus uberis. More than 3,700 differentially expressed (DE) genes were identified in milk-isolated monocytes (MIMs), a key immune cell recruited to the site of infection during mastitis. Up-regulated genes were significantly enriched for inflammatory pathways, while down-regulated genes were enriched for non-glycolytic metabolic pathways. Monocyte transcriptional changes in the blood, however, were more subtle but highlighted the impact of this infection systemically. Genes up-regulated in blood-isolated-monocytes (BIMs) showed a significant association with interferon and chemokine signalling. Furthermore, twenty-six miRNAs were differentially expressed in MIMs and three in BIMs. Pathway analysis revealed that predicted targets of down-regulated miRNAs were highly enriched for roles in innate immunity (FDR < 3.4E-8) in particular TLR signalling, while up-regulated miRNAs preferentially targeted genes involved in metabolism. We conclude that during S. uberis infection miRNAs are key amplifiers of monocyte inflammatory response networks and repressors of several metabolic pathways.This study was funded in part by Teagasc RMIS 6018 and United States Department of Agriculture ARS funding 3625-32000-102-00. NL is supported by a Teagasc Walsh Fellowship

Increased entropy of signal transduction in the cancer metastasis phenotype

Studies into the statistical properties of biological networks have led to important biological insights, such as the presence of hubs and hierarchical modularity. There is also a growing interest in studying the statistical properties of networks in the context of cancer genomics. However, relatively little is known as to what network features differ between the cancer and normal cell physiologies, or between different cancer cell phenotypes. Based on the observation that frequent genomic alterations underlie a more aggressive cancer phenotype, we asked if such an effect could be detectable as an increase in the randomness of local gene expression patterns. Using a breast cancer gene expression data set and a model network of protein interactions we derive constrained weighted networks defined by a stochastic information flux matrix reflecting expression correlations between interacting proteins. Based on this stochastic matrix we propose and compute an entropy measure that quantifies the degree of randomness in the local pattern of information flux around single genes. By comparing the local entropies in the non-metastatic versus metastatic breast cancer networks, we here show that breast cancers that metastasize are characterised by a small yet significant increase in the degree of randomness of local expression patterns. We validate this result in three additional breast cancer expression data sets and demonstrate that local entropy better characterises the metastatic phenotype than other non-entropy based measures. We show that increases in entropy can be used to identify genes and signalling pathways implicated in breast cancer metastasis. Further exploration of such integrated cancer expression and protein interaction networks will therefore be a fruitful endeavour.Comment: 5 figures, 2 Supplementary Figures and Table

A Network Integration Approach to Predict Conserved Regulators Related to Pathogenicity of Influenza and SARS-CoV Respiratory Viruses

Respiratory infections stemming from influenza viruses and the Severe Acute Respiratory Syndrome corona virus (SARS-CoV) represent a serious public health threat as emerging pandemics. Despite efforts to identify the critical interactions of these viruses with host machinery, the key regulatory events that lead to disease pathology remain poorly targeted with therapeutics. Here we implement an integrated network interrogation approach, in which proteome and transcriptome datasets from infection of both viruses in human lung epithelial cells are utilized to predict regulatory genes involved in the host response. We take advantage of a novel "crowd-based" approach to identify and combine ranking metrics that isolate genes/proteins likely related to the pathogenicity of SARS-CoV and influenza virus. Subsequently, a multivariate regression model is used to compare predicted lung epithelial regulatory influences with data derived from other respiratory virus infection models. We predicted a small set of regulatory factors with conserved behavior for consideration as important components of viral pathogenesis that might also serve as therapeutic targets for intervention. Our results demonstrate the utility of integrating diverse 'omic datasets to predict and prioritize regulatory features conserved across multiple pathogen infection models

Network-based approaches to explore complex biological systems towards network medicine

Network medicine relies on different types of networks: from the molecular level of protein–protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein–protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs—including long non-coding RNAs (lncRNAs) —competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes—called switch genes—critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes

Comparative genomic analysis of novel Acinetobacter symbionts : A combined systems biology and genomics approach

Acknowledgements This work was supported by University of Delhi, Department of Science and Technology- Promotion of University Research and Scientific Excellence (DST-PURSE). V.G., S.H. and U.S. gratefully acknowledge the Council for Scientific and Industrial Research (CSIR), University Grant Commission (UGC) and Department of Biotechnology (DBT) for providing research fellowship.Peer reviewedPublisher PD

Topological Analysis of Biological Pathways: Genes, MicroRNAs and Pathways Involved in Hepatocellular Carcinoma

abstract: Rewired biological pathways and/or rewired microRNA (miRNA)-mRNA interactions might also influence the activity of biological pathways. Here, rewired biological pathways is defined as differential (rewiring) effect of genes on the topology of biological pathways between controls and cases. Similarly, rewired miRNA-mRNA interactions are defined as the differential (rewiring) effects of miRNAs on the topology of biological pathways between controls and cases. In the dissertation, it is discussed that how rewired biological pathways (Chapter 1) and/or rewired miRNA-mRNA interactions (Chapter 2) aberrantly influence the activity of biological pathways and their association with disease. This dissertation proposes two PageRank-based analytical methods, Pathways of Topological Rank Analysis (PoTRA) and miR2Pathway, discussed in Chapter 1 and Chapter 2, respectively. PoTRA focuses on detecting pathways with an altered number of hub genes in corresponding pathways between two phenotypes. The basis for PoTRA is that the loss of connectivity is a common topological trait of cancer networks, as well as the prior knowledge that a normal biological network is a scale-free network whose degree distribution follows a power law where a small number of nodes are hubs and a large number of nodes are non-hubs. However, from normal to cancer, the process of the network losing connectivity might be the process of disrupting the scale-free structure of the network, namely, the number of hub genes might be altered in cancer compared to that in normal samples. Hence, it is hypothesized that if the number of hub genes is different in a pathway between normal and cancer, this pathway might be involved in cancer. MiR2Pathway focuses on quantifying the differential effects of miRNAs on the activity of a biological pathway when miRNA-mRNA connections are altered from normal to disease and rank disease risk of rewired miRNA-mediated biological pathways. This dissertation explores how rewired gene-gene interactions and rewired miRNA-mRNA interactions lead to aberrant activity of biological pathways, and rank pathways for their disease risk. The two methods proposed here can be used to complement existing genomics analysis methods to facilitate the study of biological mechanisms behind disease at the systems-level.Dissertation/ThesisDoctoral Dissertation Molecular and Cellular Biology 201