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1.  The KLK5 protease suppresses breast cancer by repressing the mevalonate pathway 
Oncotarget  2013;5(9):2390-2403.
Kallikrein-related peptidase 5 (KLK5) displays aberrant expression in cancer. However, any functional association is missing. Here, we show that reconstitution of KLK5 expression in non-expressing MDA-MB-231 breast cancer cells suppresses malignancy in vitro and in vivo dose-dependently. Reactivation of KLK5 suppressed key EMT genes. Unexpectedly, we identified altered expression of genes encoding enzymes of the mevalonate pathway typical of those observed upon cholesterol starvation. Consistently, we found that SREBF1, the master regulator of the mevalonate pathway was induced. KLK5 re-expression leads to reduced cellular cholesterol and fatty acid synthesis and enhanced uptake of LDL-cholesterol. Suppression of the mevalonate pathway in KLK5 transfectants was further shown by reduced synthesis of isoprenoids. Indeed, we found diminished levels of active RhoA, a signaling oncoprotein that requires prenylation for activation. We propose that reduced RhoA activation plays a dominant role in suppression of malignancy by KLK5, since geranylgeranyl pyrophosphate restored active RhoA in KLK5-reverted cells resulting in increased malignancy. For the first time, we suggest that a protease may suppress breast cancer by modulating the mevalonate pathway.
PMCID: PMC4058013  PMID: 24158494
Kallikrein-related peptidase 5 (KLK5); breast cancer; mevalonate pathway; oncogenic signaling
2.  Integration of High-Volume Molecular and Imaging Data for Composite Biomarker Discovery in the Study of Melanoma 
BioMed Research International  2014;2014:145243.
In this work the effects of simple imputations are studied, regarding the integration of multimodal data originating from different patients. Two separate datasets of cutaneous melanoma are used, an image analysis (dermoscopy) dataset together with a transcriptomic one, specifically DNA microarrays. Each modality is related to a different set of patients, and four imputation methods are employed to the formation of a unified, integrative dataset. The application of backward selection together with ensemble classifiers (random forests), followed by principal components analysis and linear discriminant analysis, illustrates the implication of the imputations on feature selection and dimensionality reduction methods. The results suggest that the expansion of the feature space through the data integration, achieved by the exploitation of imputation schemes in general, aids the classification task, imparting stability as regards the derivation of putative classifiers. In particular, although the biased imputation methods increase significantly the predictive performance and the class discrimination of the datasets, they still contribute to the study of prominent features and their relations. The fusion of separate datasets, which provide a multimodal description of the same pathology, represents an innovative, promising avenue, enhancing robust composite biomarker derivation and promoting the interpretation of the biomedical problem studied.
PMCID: PMC3914284  PMID: 24527435
3.  Integrative workflows for metagenomic analysis 
The rapid evolution of all sequencing technologies, described by the term Next Generation Sequencing (NGS), have revolutionized metagenomic analysis. They constitute a combination of high-throughput analytical protocols, coupled to delicate measuring techniques, in order to potentially discover, properly assemble and map allelic sequences to the correct genomes, achieving particularly high yields for only a fraction of the cost of traditional processes (i.e., Sanger). From a bioinformatic perspective, this boils down to many GB of data being generated from each single sequencing experiment, rendering the management or even the storage, critical bottlenecks with respect to the overall analytical endeavor. The enormous complexity is even more aggravated by the versatility of the processing steps available, represented by the numerous bioinformatic tools that are essential, for each analytical task, in order to fully unveil the genetic content of a metagenomic dataset. These disparate tasks range from simple, nonetheless non-trivial, quality control of raw data to exceptionally complex protein annotation procedures, requesting a high level of expertise for their proper application or the neat implementation of the whole workflow. Furthermore, a bioinformatic analysis of such scale, requires grand computational resources, imposing as the sole realistic solution, the utilization of cloud computing infrastructures. In this review article we discuss different, integrative, bioinformatic solutions available, which address the aforementioned issues, by performing a critical assessment of the available automated pipelines for data management, quality control, and annotation of metagenomic data, embracing various, major sequencing technologies and applications.
PMCID: PMC4237130  PMID: 25478562
metagenomics; bioinformatics; distributed computing; cloud computing; workflow engines
4.  HuR-Regulated mRNAs Associated with Nuclear hnRNP A1-RNP Complexes 
Post-transcriptional regulatory networks are dependent on the interplay of many RNA-binding proteins having a major role in mRNA processing events in mammals. We have been interested in the concerted action of the two RNA-binding proteins hnRNP A1 and HuR, both stable components of immunoselected hnRNP complexes and having a major nuclear localization. Specifically, we present here the application of the RNA-immunoprecipitation (RIP)-Chip technology to identify a population of nuclear transcripts associated with hnRNP A1-RNPs as isolated from the nuclear extract of either HuR WT or HuR-depleted (KO) mouse embryonic fibroblast (MEF) cells. The outcome of this analysis was a list of target genes regulated via HuR for their association (either increased or reduced) with the nuclear hnRNP A1-RNP complexes. Real time PCR analysis was applied to validate a selected number of nuclear mRNA transcripts, as well as to identify pre-spliced transcripts (in addition to their mature mRNA counterpart) within the isolated nuclear hnRNP A1-RNPs. The differentially enriched mRNAs were found to belong to GO categories relevant to biological processes anticipated for hnRNP A1 and HuR (such as transport, transcription, translation, apoptosis and cell cycle) indicating their concerted function in mRNA metabolism.
PMCID: PMC3821614  PMID: 24152440
RNA-binding proteins (RBPs); ribonucleoprotein (hnRNP) complexes; post-transcriptional regulation; mRNA processing; RNA-immunoprecipitation (RIP)-Chip technology; mouse embryonic fibroblasts (MEFs)
5.  A Comparative Genomic Study in Schizophrenic and in Bipolar Disorder Patients, Based on Microarray Expression Profiling Meta-Analysis 
The Scientific World Journal  2013;2013:685917.
Schizophrenia affecting almost 1% and bipolar disorder affecting almost 3%–5% of the global population constitute two severe mental disorders. The catecholaminergic and the serotonergic pathways have been proved to play an important role in the development of schizophrenia, bipolar disorder, and other related psychiatric disorders. The aim of the study was to perform and interpret the results of a comparative genomic profiling study in schizophrenic patients as well as in healthy controls and in patients with bipolar disorder and try to relate and integrate our results with an aberrant amino acid transport through cell membranes. In particular we have focused on genes and mechanisms involved in amino acid transport through cell membranes from whole genome expression profiling data. We performed bioinformatic analysis on raw data derived from four different published studies. In two studies postmortem samples from prefrontal cortices, derived from patients with bipolar disorder, schizophrenia, and control subjects, have been used. In another study we used samples from postmortem orbitofrontal cortex of bipolar subjects while the final study was performed based on raw data from a gene expression profiling dataset in the postmortem superior temporal cortex of schizophrenics. The data were downloaded from NCBI's GEO datasets.
PMCID: PMC3608181  PMID: 23554570
6.  Application of an Integrative Computational Framework in Trancriptomic Data of Atherosclerotic Mice Suggests Numerous Molecular Players 
Advances in Bioinformatics  2012;2012:453513.
Atherosclerosis is a multifactorial disease involving a lot of genes and proteins recruited throughout its manifestation. The present study aims to exploit bioinformatic tools in order to analyze microarray data of atherosclerotic aortic lesions of ApoE knockout mice, a model widely used in atherosclerosis research. In particular, a dynamic analysis was performed among young and aged animals, resulting in a list of 852 significantly altered genes. Pathway analysis indicated alterations in critical cellular processes related to cell communication and signal transduction, immune response, lipid transport, and metabolism. Cluster analysis partitioned the significantly differentiated genes in three major clusters of similar expression profile. Promoter analysis applied to functional related groups of the same cluster revealed shared putative cis-elements potentially contributing to a common regulatory mechanism. Finally, by reverse engineering the functional relevance of differentially expressed genes with specific cellular pathways, putative genes acting as hubs, were identified, linking functionally disparate cellular processes in the context of traditional molecular description.
PMCID: PMC3502768  PMID: 23193398
7.  Elucidating the identity of resistance mechanisms to prednisolone exposure in acute lymphoblastic leukemia cells through transcriptomic analysis: A computational approach 
It has been shown previously that glucocorticoids exert a dual mechanism of action, entailing cytotoxic, mitogenic as well as cell proliferative and anti-apoptotic responses, in a dose-dependent manner on CCRF-CEM cells at 72 h. Early gene expression response implies a dose-dependent dual mechanism of action of prednisolone too, something reflected on cell state upon 72 h of treatment.
In this work, a generic, computational microarray data analysis framework is proposed, in order to examine the hypothesis, whether CCRF-CEM cells exhibit an intrinsic or acquired mechanism of resistance and investigate the molecular imprint of this, upon prednisolone treatment. The experimental design enables the examination of both the dose (0 nM, 10 nM, 22 uM, 700 uM) effect of glucocorticoid exposure and the dynamics (early and late, namely 4 h, 72 h) of the molecular response of the cells at the transcriptomic layer.
In this work, we demonstrated that CCRF-CEM cells may attain a mixed mechanism of response to glucocorticoids, however, with a clear preference towards an intrinsic mechanism of resistance. Specifically, at 4 h, prednisolone appeared to down-regulate apoptotic genes. Also, low and high prednisolone concentrations up-regulates genes related to metabolism and signal-transduction in both time points, thus favoring cell proliferative actions. In addition, regulation of NF-κB-related genes implies an inherent mechanism of resistance through the established link of NF-κB inflammatory role and GC-induced resistance. The analysis framework applied here highlights prednisolone-activated regulatory mechanisms through identification of early responding sets of genes. On the other hand, study of the prolonged exposure to glucocorticoids (72 h exposure) highlights the effect of homeostatic feedback mechanisms of the treated cells.
Overall, it appears that CCRF-CEM cells in this study exhibit a diversified, combined pattern of intrinsic and acquired resistance to prednisolone, with a tendency towards inherent resistant characteristics, through activation of different molecular courses of action.
PMCID: PMC3313905  PMID: 22185641
acute lymphoblastic leukemia; DNA microarray analysis; gene ontology; glucocorticoid resistance; intrinsic vs. acquired
8.  Escherichia coli genome-wide promoter analysis: Identification of additional AtoC binding target elements 
BMC Genomics  2011;12:238.
Studies on bacterial signal transduction systems have revealed complex networks of functional interactions, where the response regulators play a pivotal role. The AtoSC system of E. coli activates the expression of atoDAEB operon genes, and the subsequent catabolism of short-chain fatty acids, upon acetoacetate induction. Transcriptome and phenotypic analyses suggested that atoSC is also involved in several other cellular activities, although we have recently reported a palindromic repeat within the atoDAEB promoter as the single, cis-regulatory binding site of the AtoC response regulator. In this work, we used a computational approach to explore the presence of yet unidentified AtoC binding sites within other parts of the E. coli genome.
Through the implementation of a computational de novo motif detection workflow, a set of candidate motifs was generated, representing putative AtoC binding targets within the E. coli genome. In order to assess the biological relevance of the motifs and to select for experimental validation of those sequences related robustly with distinct cellular functions, we implemented a novel approach that applies Gene Ontology Term Analysis to the motif hits and selected those that were qualified through this procedure. The computational results were validated using Chromatin Immunoprecipitation assays to assess the in vivo binding of AtoC to the predicted sites. This process verified twenty-two additional AtoC binding sites, located not only within intergenic regions, but also within gene-encoding sequences.
This study, by tracing a number of putative AtoC binding sites, has indicated an AtoC-related cross-regulatory function. This highlights the significance of computational genome-wide approaches in elucidating complex patterns of bacterial cell regulation.
PMCID: PMC3118216  PMID: 21569465
9.  Exploiting Statistical Methodologies and Controlled Vocabularies for Prioritized Functional Analysis of Genomic Experiments: the StRAnGER Web Application 
StRAnGER is a web application for the automated statistical analysis of annotated gene profiling experiments, exploiting controlled biological vocabularies, like the Gene Ontology or the KEGG pathways terms. Starting from annotated lists of differentially expressed genes and gene enrichment scores, regarding the terms of each vocabulary, StRAnGER repartitions and reorders the initial distribution of terms to define a new distribution of elements. Each element pools terms holding the same enrichment score. The new distribution thus derived, is reordered in a decreasing order to the right, according to the observation score of the elements, while elements with the same score, are sorted again in a decreasing order of their enrichment scores. By applying bootstrapping techniques, a corrected measure of the statistical significance of these elements is derived, which enables the selection of terms mapped to these elements, unambiguously associated with respective significant gene sets. The selected terms are immunized against the bias infiltrating statistical enrichment analyses, producing technically very high statistical scores, due to the finite nature of the data population. Besides their high statistical score, another selection criterion for the terms is the number of their members, something that incurs a biological prioritization in line with a Systems Biology context. The output derived, represents a detailed ranked list of significant terms, which constitute a starting point for further functional analysis.
PMCID: PMC3032379  PMID: 21293737
ontological analysis; pathway analysis; gene enrichment analysis
10.  A transcriptomic computational analysis of mastic oil-treated Lewis lung carcinomas reveals molecular mechanisms targeting tumor cell growth and survival 
BMC Medical Genomics  2009;2:68.
Mastic oil from Pistacia lentiscus variation chia, a blend of bioactive terpenes with recognized medicinal properties, has been recently shown to exert anti-tumor growth activity through inhibition of cancer cell proliferation, survival, angiogenesis and inflammatory response. However, no studies have addressed its mechanisms of action at genome-wide gene expression level.
To investigate molecular mechanisms triggered by mastic oil, Lewis Lung Carcinoma cells were treated with mastic oil or DMSO and RNA was collected at five distinct time points (3-48 h). Microarray expression profiling was performed using Illumina mouse-6 v1 beadchips, followed by computational analysis. For a number of selected genes, RT-PCR validation was performed in LLC cells as well as in three human cancer cell lines of different origin (A549, HCT116, K562). PTEN specific inhibition by a bisperovanadium compound was applied to validate its contribution to mastic oil-mediated anti-tumor growth effects.
In this work we demonstrated that exposure of Lewis lung carcinomas to mastic oil caused a time-dependent alteration in the expression of 925 genes. GO analysis associated expression profiles with several biological processes and functions. Among them, modifications on cell cycle/proliferation, survival and NF-κB cascade in conjunction with concomitant regulation of genes encoding for PTEN, E2F7, HMOX1 (up-regulation) and NOD1 (down-regulation) indicated some important mechanistic links underlying the anti-proliferative, pro-apoptotic and anti-inflammatory effects of mastic oil. The expression profiles of Hmox1, Pten and E2f7 genes were similarly altered by mastic oil in the majority of test cancer cell lines. Inhibition of PTEN partially reversed mastic oil effects on tumor cell growth, indicating a multi-target mechanism of action. Finally, k-means clustering, organized the significant gene list in eight clusters demonstrating a similar expression profile. Promoter analysis in a representative cluster revealed shared putative cis-elements suggesting a common regulatory transcription mechanism.
Present results provide novel evidence on the molecular basis of tumor growth inhibition mediated by mastic oil and set a rational basis for application of genomics and bioinformatic methodologies in the screening of natural compounds with potential cancer chemopreventive activities.
PMCID: PMC2801511  PMID: 20003503
11.  Gene ARMADA: an integrated multi-analysis platform for microarray data implemented in MATLAB 
BMC Bioinformatics  2009;10:354.
The microarray data analysis realm is ever growing through the development of various tools, open source and commercial. However there is absence of predefined rational algorithmic analysis workflows or batch standardized processing to incorporate all steps, from raw data import up to the derivation of significantly differentially expressed gene lists. This absence obfuscates the analytical procedure and obstructs the massive comparative processing of genomic microarray datasets. Moreover, the solutions provided, heavily depend on the programming skills of the user, whereas in the case of GUI embedded solutions, they do not provide direct support of various raw image analysis formats or a versatile and simultaneously flexible combination of signal processing methods.
We describe here Gene ARMADA (Automated Robust MicroArray Data Analysis), a MATLAB implemented platform with a Graphical User Interface. This suite integrates all steps of microarray data analysis including automated data import, noise correction and filtering, normalization, statistical selection of differentially expressed genes, clustering, classification and annotation. In its current version, Gene ARMADA fully supports 2 coloured cDNA and Affymetrix oligonucleotide arrays, plus custom arrays for which experimental details are given in tabular form (Excel spreadsheet, comma separated values, tab-delimited text formats). It also supports the analysis of already processed results through its versatile import editor. Besides being fully automated, Gene ARMADA incorporates numerous functionalities of the Statistics and Bioinformatics Toolboxes of MATLAB. In addition, it provides numerous visualization and exploration tools plus customizable export data formats for seamless integration by other analysis tools or MATLAB, for further processing. Gene ARMADA requires MATLAB 7.4 (R2007a) or higher and is also distributed as a stand-alone application with MATLAB Component Runtime.
Gene ARMADA provides a highly adaptable, integrative, yet flexible tool which can be used for automated quality control, analysis, annotation and visualization of microarray data, constituting a starting point for further data interpretation and integration with numerous other tools.
PMCID: PMC2771024  PMID: 19860866
12.  KEGGconverter: a tool for the in-silico modelling of metabolic networks of the KEGG Pathways database 
BMC Bioinformatics  2009;10:324.
The KEGG Pathway database is a valuable collection of metabolic pathway maps. Nevertheless, the production of simulation capable metabolic networks from KEGG Pathway data is a challenging complicated work, regardless the already developed tools for this scope. Originally used for illustration purposes, KEGG Pathways through KGML (KEGG Markup Language) files, can provide complete reaction sets and introduce species versioning, which offers advantages for the scope of cellular metabolism simulation modelling. In this project, KEGGconverter is described, implemented also as a web-based application, which uses as source KGML files, in order to construct integrated pathway SBML models fully functional for simulation purposes.
A case study of the integration of six human metabolic pathways from KEGG depicts the ability of KEGGconverter to automatically produce merged and converted to SBML fully functional pathway models, enhanced with default kinetics. The suitability of the developed tool is demonstrated through a comparison with other state-of-the art relevant software tools for the same data fusion and conversion tasks, thus illustrating the problems and the relevant workflows. Moreover, KEGGconverter permits the inclusion of additional reactions in the resulting model which represent flux cross-talk with neighbouring pathways, providing in this way improved simulative accuracy. These additional reactions are introduced by exploiting relevant semantic information for the elements of the KEGG Pathways database. The architecture and functionalities of the web-based application are presented.
KEGGconverter is capable of producing integrated analogues of metabolic pathways appropriate for simulation tasks, by inputting only KGML files. The web application acts as a user friendly shell which transparently enables the automated biochemically correct pathway merging, conversion to SBML format, proper renaming of the species, and insertion of default kinetic properties for the pertaining reactions. The tool is available at:
PMCID: PMC2764712  PMID: 19814801

Results 1-12 (12)