Konstantina Dimitrakopoulou

An in silico method for detecting overlapping functional modules from composite biological networks

BackgroundThe ever-increasing flow of gene expression and protein-protein interaction (PPI) data has assisted in understanding the dynamics of the cell. The detection of functional modules is the first step in deciphering the apparent modularity of biological networks. However, most network-partitioning algorithms consider only the topological aspects and ignore the underlying functional relationships.ResultsIn the current study we integrate proteomics and microarray data of yeast, in the form of a weighted PPI graph. We partition the enriched PPI network with the novel DetMod algorithm and we identify 335 modules. One of the main advantages of DetMod is that it manages to capture the inter-module cross-talk by allowing a controlled degree of overlap among the detected modules. The obtained modules are densely connected in terms of protein interactions, while their members share up to a high degree similar biological process GO terms.Moreover, known protein complexes are largely incorporated in the assessed modules. Finally, we display the prevalence of our method against modules resulting from other computational approaches.ConclusionThe successful integration of heterogeneous data and the concept of the proposed algorithm provide confident functional modules. We also proved that our approach is superior to methods restricted to PPI data only.

Dynamic gene network reconstruction from gene expression data in mice after influenza A (H1N1) infection.

BackgroundThe immune response to viral infection is a temporal process, represented by a dynamic and complex network of gene and protein interactions. Here, we present a reverse engineering strategy aimed at capturing the temporal evolution of the underlying Gene Regulatory Networks (GRN). The proposed approach will be an enabling step towards comprehending the dynamic behavior of gene regulation circuitry and mapping the network structure transitions in response to pathogen stimuli.ResultsWe applied the Time Varying Dynamic Bayesian Network (TV-DBN) method for reconstructing the gene regulatory interactions based on time series gene expression data for the mouse C57BL/6J inbred strain after infection with influenza A H1N1 (PR8) virus. Initially, 3500 differentially expressed genes were clustered with the use of k-means algorithm. Next, the successive in time GRNs were built over the expression profiles of cluster centroids. Finally, the identified GRNs were examined with several topological metrics and available protein-protein and protein-DNA interaction data, transcription factor and KEGG pathway data.ConclusionsOur results elucidate the potential of TV-DBN approach in providing valuable insights into the temporal rewiring of the lung transcriptome in response to H1N1 virus.

Integromics network meta-analysis on cardiac aging offers robust multi-layer modular signatures and reveals micronome synergism

BackgroundThe avalanche of integromics and panomics approaches shifted the deciphering of aging mechanisms from single molecular entities to communities of them. In this orientation, we explore the cardiac aging mechanisms – risk factor for multiple cardiovascular diseases - by capturing the micronome synergism and detecting longevity signatures in the form of communities (modules).For this, we developed a meta-analysis scheme that integrates transcriptome expression data from multiple cardiac-specific independent studies in mouse and human along with proteome and micronome interaction data in the form of multiple independent weighted networks. Modularization of each weighted network produced modules, which in turn were further analyzed so as to define consensus modules across datasets that change substantially during lifespan. Also, we established a metric that determines - from the modular perspective - the synergism of microRNA-microRNA interactions as defined by significantly functionally associated targets.ResultsThe meta-analysis provided 40 consensus integromics modules across mouse datasets and revealed microRNA relations with substantial collective action during aging. Three modules were reproducible, based on homology, when mapped against human-derived modules. The respective homologs mainly represent NADH dehydrogenases, ATP synthases, cytochrome oxidases, Ras GTPases and ribosomal proteins. Among various observations, we corroborate to the involvement of miR-34a (included in consensus modules) as proposed recently; yet we report that has no synergistic effect. Moving forward, we determined its age-related neighborhood in which HCN3, a known heart pacemaker channel, was included. Also, miR-125a-5p/-351, miR-200c/-429, miR-106b/-17, miR-363/-92b, miR-181b/-181d, miR-19a/-19b, let-7d/-7f, miR-18a/-18b, miR-128/-27b and miR-106a/-291a-3p pairs exhibited significant synergy and their association to aging and/or cardiovascular diseases is supported in many cases by a disease database and previous studies. On the contrary, we suggest that miR-22 has not substantial impact on heart longevity as proposed recently.ConclusionsWe revised several proteins and microRNAs recently implicated in cardiac aging and proposed for the first time modules as signatures. The integromics meta-analysis approach can serve as an efficient subvening signature tool for more-oriented better-designed experiments. It can also promote the combinational multi-target microRNA therapy of age-related cardiovascular diseases along the continuum from prevention to detection, diagnosis, treatment and outcome.

OLYMPUS: An automated hybrid clustering method in time series gene expression. Case study: Host response after Influenza A (H1N1) infection

The increasing flow of short time series microarray experiments for the study of dynamic cellular processes poses the need for efficient clustering tools. These tools must deal with three primary issues: first, to consider the multi-functionality of genes; second, to evaluate the similarity of the relative change of amplitude in the time domain rather than the absolute values; third, to cope with the constraints of conventional clustering algorithms such as the assignment of the appropriate cluster number. To address these, we propose OLYMPUS, a novel unsupervised clustering algorithm that integrates Differential Evolution (DE) method into Fuzzy Short Time Series (FSTS) algorithm with the scope to utilize efficiently the information of population of the first and enhance the performance of the latter. Our hybrid approach provides sets of genes that enable the deciphering of distinct phases in dynamic cellular processes. We proved the efficiency of OLYMPUS on synthetic as well as on experimental data. The discriminative power of OLYMPUS provided clusters, which refined the so far perspective of the dynamics of host response mechanisms to Influenza A (H1N1). Our kinetic model sets a timeline for several pathways and cell populations, implicated to participate in host response; yet no timeline was assigned to them (e.g. cell cycle, homeostasis). Regarding the activity of B cells, our approach revealed that some antibody-related mechanisms remain activated until day 60 post infection. The Matlab codes for implementing OLYMPUS, as well as example datasets, are freely accessible via the Web (http://biosignal.med.upatras.gr/wordpress/biosignal/).

Expression of Nestin associates with BRCA1 mutations, a basal-like phenotype and aggressive breast cancer

We here examined whether Nestin, by protein and mRNA levels, could be a predictor of BRCA1 related breast cancer, a basal-like phenotype, and aggressive tumours. Immunohistochemical staining of Nestin was done in independent breast cancer hospital cohorts (Series I-V, total 1257 cases). Also, TCGA proteomic data (n = 103), mRNA microarray data from TCGA (n = 520), METABRIC (n = 1992), and 6 open access breast cancer datasets (n = 1908) were analysed. Patients with Nestin protein expression in tumour cells more often had BRCA1 germline mutations (OR 8.7, p < 0.0005, Series III), especially among younger patients (<40 years at diagnosis) (OR 16.5, p = 0.003). Nestin protein positivity, observed in 9–28% of our hospital cases (Series I-IV), was independently associated with reduced breast cancer specific survival (HR = 2.0, p = 0.035) and was consistently related to basal-like differentiation (by Cytokeratin 5, OR 8.7–13.8, p < 0.0005; P-cadherin OR 7.0–8.9, p < 0.0005; EGFR staining, OR 3.7–8.2, p ≤ 0.05). Nestin mRNA correlated significantly with Nestin protein expression (ρ = 0.6, p < 0.0005), and high levels were seen in the basal-like intrinsic subtype. Gene expression signalling pathways linked to high Nestin were explored, and revealed associations with stem-like tumour features. In summary, Nestin was strongly associated with germline BRCA1 related breast cancer, a basal-like phenotype, reduced survival, and stemness characteristics.

Aging Integromics: Module-Based Markers of Heart Aging from Multi-omics Data

In the emerging Systems Medicine field, the study of aging is re-evaluated and contextualized through the combination of ‘omics’ investigations (i.e. transcriptomic, proteomic, metabolomics, fluxomic). In particular, heart aging is a highly complex process in terms of molecular changes and the role of microRNAs (miRNAs) as key gene regulators has recently arisen.

Detecting Perturbed Subpathways towards Mouse Lung Regeneration Following H1N1 Influenza Infection

It has already been established by the systems-level approaches that the future of predictive disease biomarkers will not be sketched by plain lists of genes or proteins or other biological entities but rather integrated entities that consider all underlying component relationships. Towards this orientation, early pathway-based approaches coupled expression data with whole pathway interaction topologies but it was the recent approaches that zoomed into subpathways (local areas of the entire biological pathway) that provided more targeted and context-specific candidate disease biomarkers. Here, we explore the application potential of PerSubs, a graph-based algorithm which identifies differentially activated disease-specific subpathways. PerSubs is applicable both for microarray and RNA-Seq data and utilizes the Kyoto Encyclopedia of Genes and Genomes (KEGG) database as reference for biological pathways. PerSubs operates in two stages: first, identifies differentially expressed genes (or uses any list of disease-related genes) and in second stage, treating each gene of the list as start point, it scans the pathway topology around to build meaningful subpathway topologies. Here, we apply PerSubs to investigate which pathways are perturbed towards mouse lung regeneration following H1N1 influenza infection.

Supervised method for construction of microRNA-mRNA networks: Application in cardiac tissue aging dataset

MicroRNAs play an important role in regulation of gene expression, but still detection of their targets remains a challenge. In this work we present a supervised regulatory network inference method with aim to identify potential target genes (mRNAs) of microRNAs. Briefly, the proposed method exploiting mRNA and microRNA expression trains Random Forests on known interactions and subsequently it is able to predict novel ones. In parallel, we incorporate different available data sources, such as Gene Ontology and ProteinProtein Interactions, to deliver biologically consistent results. Application in both benchmark data and an experiment studying aging showed robust performance.

Network-based modular markers of aging across different tissues

Aging is a highly complex biological process and a risk factor for many diseases. Motivated by the high availability of diverse high-throughput data in the mouse model organism, we provide a systemic view of the age-related mechanisms. In particular, we present a robust network-based integrative approach that provides, based on protein interaction and microarray data, reliable modules that alter significantly in terms of expression during aging. Our modular meta-analysis provides novel information about the involvement of several established as well as recently reported longevity-associated pathways across different tissues.

Multi-scale modeling of gene regulatory networks via integration of temporal and topological biological data

Regulome is the dynamic network representation of the regulatory interplay among genes, proteins and other cellular components that control cellular processes. Reconstruction of gene regulatory networks (GRN) delineates one of the main objectives of Systems Biology towards understanding the organization of regulome. Significant progress has been reported the last years regarding GRN reconstruction methods, but the majority of them either consider information originating solely from gene expression data or/and are applied on a small fraction of the experimental dataset. In this paper, we will describe an integrative method, utilizing both temporal information arriving from time-series gene expression profiles, as well as topological properties of protein networks. The proposed methodology detects relations among either groups of genes or specific genes depending on the level of abstraction or resolution requested. Application on real data proved the ability of the method to extract relations in accordance with current biological knowledge as well as discriminate between different experimental conditions.