Vir2Drug: a drug repurposing framework based on protein similarities between pathogens.

We draw from the assumption that similarities between pathogens at both pathogen protein and host protein level, may provide the appropriate framework to identify and rank candidate drugs to be used against a specific pathogen. Vir2Drug is a drug repurposing tool that uses network-based approaches to identify and rank candidate drugs for a specific pathogen, combining information obtained from: (a) ranked pathogen-to-pathogen networks based on protein similarities between pathogens, (b) taxonomy distance between pathogens and (c) drugs targeting specific pathogen's and host proteins. The underlying pathogen networks are used to screen drugs by means of specific methodologies that account for either the host or pathogen's protein targets. Vir2Drug is a useful and yet informative tool for drug repurposing against known or unknown pathogens especially in periods where the emergence for repurposed drugs plays significant role in handling viral outbreaks, until reaching a vaccine. The web tool is available at: https://bioinformatics.cing.ac.cy/vir2drug, https://vir2drug.cing-big.hpcf.cyi.ac.cy.

Ranking of cell clusters in a single-cell RNA-sequencing analysis framework using prior knowledge.

Prioritization or ranking of different cell types in a single-cell RNA sequencing (scRNA-seq) framework can be performed in a variety of ways, some of these include: i) obtaining an indication of the proportion of cell types between the different conditions under study, ii) counting the number of differentially expressed genes (DEGs) between cell types and conditions in the experiment or, iii) prioritizing cell types based on prior knowledge about the conditions under study (i.e., a specific disease). These methods have drawbacks and limitations thus novel methods for improving cell ranking are required. Here we present a novel methodology that exploits prior knowledge in combination with expert-user information to accentuate cell types from a scRNA-seq analysis that yield the most biologically meaningful results with respect to a disease under study. Our methodology allows for ranking and prioritization of cell types based on how well their expression profiles relate to the molecular mechanisms and drugs associated with a disease. Molecular mechanisms, as well as drugs, are incorporated as prior knowledge in a standardized, structured manner. Cell types are then ranked/prioritized based on how well results from data-driven analysis of scRNA-seq data match the predefined prior knowledge. In additional cell-cell communication perturbations between disease and control networks are used to further prioritize/rank cell types. Our methodology has substantial advantages to more traditional cell ranking techniques and provides an informative complementary methodology that utilizes prior knowledge in a rapid and automated manner, that has previously not been attempted by other studies. The current methodology is also implemented as an R package entitled Single Cell Ranking Analysis Toolkit (scRANK) and is available for download and installation via GitHub (https://github.com/aoulas/scRANK).

DReAmocracy: A Method to Capitalise on Prior Drug Discovery Efforts to Highlight Candidate Drugs for Repurposing.

In the area of drug research, several computational drug repurposing studies have highlighted candidate repurposed drugs, as well as clinical trial studies that have tested/are testing drugs in different phases. To the best of our knowledge, the aggregation of the proposed lists of drugs by previous studies has not been extensively exploited towards generating a dynamic reference matrix with enhanced resolution. To fill this knowledge gap, we performed weight-modulated majority voting of the modes of action, initial indications and targeted pathways of the drugs in a well-known repository, namely the Drug Repurposing Hub. Our method, DReAmocracy, exploits this pile of information and creates frequency tables and, finally, a disease suitability score for each drug from the selected library. As a testbed, we applied this method to a group of neurodegenerative diseases (Alzheimer's, Parkinson's, Huntington's disease and Multiple Sclerosis). A super-reference table with drug suitability scores has been created for all four neurodegenerative diseases and can be queried for any drug candidate against them. Top-scored drugs for Alzheimer's Disease include agomelatine, mirtazapine and vortioxetine; for Parkinson's Disease, they include apomorphine, pramipexole and lisuride; for Huntington's, they include chlorpromazine, fluphenazine and perphenazine; and for Multiple Sclerosis, they include zonisamide, disopyramide and priralfimide. Overall, DReAmocracy is a methodology that focuses on leveraging the existing drug-related experimental and/or computational knowledge rather than a predictive model for drug repurposing, offering a quantified aggregation of existing drug discovery results to (1) reveal trends in selected tracks of drug discovery research with increased resolution that includes modes of action, targeted pathways and initial indications for the investigated drugs and (2) score new candidate drugs for repurposing against a selected disease.

Identification of viral-mediated pathogenic mechanisms in neurodegenerative diseases using network-based approaches.

During the course of a viral infection, virus-host protein-protein interactions (PPIs) play a critical role in allowing viruses to replicate and survive within the host. These interspecies molecular interactions can lead to viral-mediated perturbations of the human interactome causing the generation of various complex diseases. Evidences suggest that viral-mediated perturbations are a possible pathogenic etiology in several neurodegenerative diseases (NDs). These diseases are characterized by chronic progressive degeneration of neurons, and current therapeutic approaches provide only mild symptomatic relief; therefore, there is unmet need for the discovery of novel therapeutic interventions. In this paper, we initially review databases and tools that can be utilized to investigate viral-mediated perturbations in complex NDs using network-based analysis by examining the interaction between the ND-related PPI disease networks and the virus-host PPI network. Afterwards, we present our theoretical-driven integrative network-based bioinformatics approach that accounts for pathogen-genes-disease-related PPIs with the aim to identify viral-mediated pathogenic mechanisms focusing in multiple sclerosis (MS) disease. We identified seven high centrality nodes that can act as disease communicator nodes and exert systemic effects in the MS-enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways network. In addition, we identified 12 KEGG pathways, 5 Reactome pathways and 52 Gene Ontology Immune System Processes by which 80 viral proteins from eight viral species might exert viral-mediated pathogenic mechanisms in MS. Finally, our analysis highlighted the Th17 differentiation pathway, a disease communicator node and part of the 12 underlined KEGG pathways, as a key viral-mediated pathogenic mechanism and a possible therapeutic target for MS disease.

Multi-omics data integration and network-based analysis drives a multiplex drug repurposing approach to a shortlist of candidate drugs against COVID-19.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is undeniably the most severe global health emergency since the 1918 Influenza outbreak. Depending on its evolutionary trajectory, the virus is expected to establish itself as an endemic infectious respiratory disease exhibiting seasonal flare-ups. Therefore, despite the unprecedented rally to reach a vaccine that can offer widespread immunization, it is equally important to reach effective prevention and treatment regimens for coronavirus disease 2019 (COVID-19). Contributing to this effort, we have curated and analyzed multi-source and multi-omics publicly available data from patients, cell lines and databases in order to fuel a multiplex computational drug repurposing approach. We devised a network-based integration of multi-omic data to prioritize the most important genes related to COVID-19 and subsequently re-rank the identified candidate drugs. Our approach resulted in a highly informed integrated drug shortlist by combining structural diversity filtering along with experts' curation and drug-target mapping on the depicted molecular pathways. In addition to the recently proposed drugs that are already generating promising results such as dexamethasone and remdesivir, our list includes inhibitors of Src tyrosine kinase (bosutinib, dasatinib, cytarabine and saracatinib), which appear to be involved in multiple COVID-19 pathophysiological mechanisms. In addition, we highlight specific immunomodulators and anti-inflammatory drugs like dactolisib and methotrexate and inhibitors of histone deacetylase like hydroquinone and vorinostat with potential beneficial effects in their mechanisms of action. Overall, this multiplex drug repurposing approach, developed and utilized herein specifically for SARS-CoV-2, can offer a rapid mapping and drug prioritization against any pathogen-related disease.

Novel clinical, molecular and bioinformatics insights into the genetic background of autism.

BACKGROUND: Clinical classification of autistic patients based on current WHO criteria provides a valuable but simplified depiction of the true nature of the disorder. Our goal is to determine the biology of the disorder and the ASD-associated genes that lead to differences in the severity and variability of clinical features, which can enhance the ability to predict clinical outcomes. METHOD: Novel Whole Exome Sequencing data from children (n = 33) with ASD were collected along with extended cognitive and linguistic assessments. A machine learning methodology and a literature-based approach took into consideration known effects of genetic variation on the translated proteins, linking them with specific ASD clinical manifestations, namely non-verbal IQ, memory, attention and oral language deficits. RESULTS: Linear regression polygenic risk score results included the classification of severe and mild ASD samples with a 81.81% prediction accuracy. The literature-based approach revealed 14 genes present in all sub-phenotypes (independent of severity) and others which seem to impair individual ones, highlighting genetic profiles specific to mild and severe ASD, which concern non-verbal IQ, memory, attention and oral language skills. CONCLUSIONS: These genes can potentially contribute toward a diagnostic gene-set for determining ASD severity. However, due to the limited number of patients in this study, our classification approach is mostly centered on the prediction and verification of these genes and does not hold a diagnostic nature per se. Substantial further experimentation is required to validate their role as diagnostic markers. The use of these genes as input for functional analysis highlights important biological processes and bridges the gap between genotype and phenotype in ASD.

Circulating small RNA signatures differentiate accurately the subtypes of muscular dystrophies: small-RNA next-generation sequencing analytics and functional insights.

Muscular dystrophies are a group of rare and severe inherited disorders mainly affecting the muscle tissue. Duchene Muscular Dystrophy, Myotonic Dystrophy types 1 and 2, Limb Girdle Muscular Dystrophy and Facioscapulohumeral Muscular Dystrophy are some of the members of this family of disorders. In addition to the current diagnostic tools, there is an increasing interest for the development of novel non-invasive biomarkers for the diagnosis and monitoring of these diseases. miRNAs are small RNA molecules characterized by high stability in blood thus making them ideal biomarker candidates for various diseases. In this study, we present the first genome-wide next-generation small RNA sequencing in serum samples of five different types of muscular dystrophy patients and healthy individuals. We identified many small RNAs including miRNAs, lncRNAs, tRNAs, snoRNAs and snRNAs, that differentially discriminate the muscular dystrophy patients from the healthy individuals. Further analysis of the identified miRNAs showed that some miRNAs can distinguish the muscular dystrophy patients from controls and other miRNAs are specific to the type of muscular dystrophy. Bioinformatics analysis of the target genes for the most significant miRNAs and the biological role of these genes revealed different pathways that the dysregulated miRNAs are involved in each type of muscular dystrophy investigated. In conclusion, this study shows unique signatures of small RNAs circulating in five types of muscular dystrophy patients and provides a useful resource for future studies for the development of miRNA biomarkers in muscular dystrophies and for their involvement in the pathogenesis of the disorders.

Identifying Novel Osteoarthritis-Associated Genes in Human Cartilage Using a Systematic Meta-Analysis and a Multi-Source Integrated Network.

Osteoarthritis, the most common joint disorder, is characterised by deterioration of the articular cartilage. Many studies have identified potential therapeutic targets, yet no effective treatment has been determined. The aim of this study was to identify and rank osteoarthritis-associated genes and micro-RNAs to prioritise those most integral to the disease. A systematic meta-analysis of differentially expressed mRNA and micro-RNAs in human osteoarthritic cartilage was conducted. Ingenuity pathway analysis identified cellular senescence as an enriched pathway, confirmed by a significant overlap (p < 0.01) with cellular senescence drivers (CellAge Database). A co-expression network was built using genes from the meta-analysis as seed nodes and combined with micro-RNA targets and SNP datasets to construct a multi-source information network. This accumulated and connected 1689 genes which were ranked based on node and edge aggregated scores. These bioinformatic analyses were confirmed at the protein level by mass spectrometry of the different zones of human osteoarthritic cartilage (superficial, middle, and deep) compared to normal controls. This analysis, and subsequent experimental confirmation, revealed five novel osteoarthritis-associated proteins (PPIB, ASS1, LHDB, TPI1, and ARPC4-TTLL3). Focusing future studies on these novel targets may lead to new therapies for osteoarthritis.

Performance evaluation of pipelines for mapping, variant calling and interval padding, for the analysis of NGS germline panels.

BACKGROUND: Next-generation sequencing (NGS) represents a significant advancement in clinical genetics. However, its use creates several technical, data interpretation and management challenges. It is essential to follow a consistent data analysis pipeline to achieve the highest possible accuracy and avoid false variant calls. Herein, we aimed to compare the performance of twenty-eight combinations of NGS data analysis pipeline compartments, including short-read mapping (BWA-MEM, Bowtie2, Stampy), variant calling (GATK-HaplotypeCaller, GATK-UnifiedGenotyper, SAMtools) and interval padding (null, 50 bp, 100 bp) methods, along with a commercially available pipeline (BWA Enrichment, Illumina®). Fourteen germline DNA samples from breast cancer patients were sequenced using a targeted NGS panel approach and subjected to data analysis. RESULTS: We highlight that interval padding is required for the accurate detection of intronic variants including spliceogenic pathogenic variants (PVs). In addition, using nearly default parameters, the BWA Enrichment algorithm, failed to detect these spliceogenic PVs and a missense PV in the TP53 gene. We also recommend the BWA-MEM algorithm for sequence alignment, whereas variant calling should be performed using a combination of variant calling algorithms; GATK-HaplotypeCaller and SAMtools for the accurate detection of insertions/deletions and GATK-UnifiedGenotyper for the efficient detection of single nucleotide variant calls. CONCLUSIONS: These findings have important implications towards the identification of clinically actionable variants through panel testing in a clinical laboratory setting, when dedicated bioinformatics personnel might not always be available. The results also reveal the necessity of improving the existing tools and/or at the same time developing new pipelines to generate more reliable and more consistent data.

Computational profiling of the gut-brain axis: microflora dysbiosis insights to neurological disorders.

Almost 2500 years after Hippocrates' observations on health and its direct association to the gastrointestinal tract, a paradigm shift has recently occurred, making the gut and its symbionts (bacteria, fungi, archaea and viruses) a point of convergence for studies. It is nowadays well established that the gut microflora's compositional diversity regulates via its genes (the microbiome) the host's health and provides preliminary insights into disease progression and regulation. The microbiome's involvement is evident in immunological and physiological studies that link changes in its biodiversity to its contributions to the host's phenotype but also in neurological investigations, substantiating the aptly named gut-brain axis. The definitive mechanisms of this last bidirectional interaction will be our main focus because it presents researchers with a new conundrum. In this review, we prospect current literature for computational analysis methodologies that accommodate the need for better understanding of the microbiome-gut-brain interactions and neurological disorder onset and progression, through cross-disciplinary systems biology applications. We will present bioinformatics tools used in exploring these synergies that help build and interpret microbial 16S ribosomal RNA data sets, produced by shotgun and high-throughput sequencing of healthy and neurological disorder samples stored in biological databases. These approaches provide alternative means for researchers to form hypotheses to their inquests faster, cheaper and swith precision. The goal of these studies relies on the integration of combined metagenomics and metabolomics assessments. An accurate characterization of the microbiome and its functionality can support new diagnostic, prognostic and therapeutic strategies for neurological disorders, customized for each individual host.

Systems Bioinformatics: increasing precision of computational diagnostics and therapeutics through network-based approaches.

Systems Bioinformatics is a relatively new approach, which lies in the intersection of systems biology and classical bioinformatics. It focuses on integrating information across different levels using a bottom-up approach as in systems biology with a data-driven top-down approach as in bioinformatics. The advent of omics technologies has provided the stepping-stone for the emergence of Systems Bioinformatics. These technologies provide a spectrum of information ranging from genomics, transcriptomics and proteomics to epigenomics, pharmacogenomics, metagenomics and metabolomics. Systems Bioinformatics is the framework in which systems approaches are applied to such data, setting the level of resolution as well as the boundary of the system of interest and studying the emerging properties of the system as a whole rather than the sum of the properties derived from the system's individual components. A key approach in Systems Bioinformatics is the construction of multiple networks representing each level of the omics spectrum and their integration in a layered network that exchanges information within and between layers. Here, we provide evidence on how Systems Bioinformatics enhances computational therapeutics and diagnostics, hence paving the way to precision medicine. The aim of this review is to familiarize the reader with the emerging field of Systems Bioinformatics and to provide a comprehensive overview of its current state-of-the-art methods and technologies. Moreover, we provide examples of success stories and case studies that utilize such methods and tools to significantly advance research in the fields of systems biology and systems medicine.

ChemBioServer 2.0: an advanced web server for filtering, clustering and networking of chemical compounds facilitating both drug discovery and repurposing.

SUMMARY: ChemBioServer 2.0 is the advanced sequel of a web server for filtering, clustering and networking of chemical compound libraries facilitating both drug discovery and repurposing. It provides researchers the ability to (i) browse and visualize compounds along with their physicochemical and toxicity properties, (ii) perform property-based filtering of compounds, (iii) explore compound libraries for lead optimization based on perfect match substructure search, (iv) re-rank virtual screening results to achieve selectivity for a protein of interest against different protein members of the same family, selecting only those compounds that score high for the protein of interest, (v) perform clustering among the compounds based on their physicochemical properties providing representative compounds for each cluster, (vi) construct and visualize a structural similarity network of compounds providing a set of network analysis metrics, (vii) combine a given set of compounds with a reference set of compounds into a single structural similarity network providing the opportunity to infer drug repurposing due to transitivity, (viii) remove compounds from a network based on their similarity with unwanted substances (e.g. failed drugs) and (ix) build custom compound mining pipelines. AVAILABILITY AND IMPLEMENTATION: http://chembioserver.vi-seem.eu.

PathWalks: identifying pathway communities using a disease-related map of integrated information.

MOTIVATION: Understanding the underlying biological mechanisms and respective interactions of a disease remains an elusive, time consuming and costly task. Computational methodologies that propose pathway/mechanism communities and reveal respective relationships can be of great value as they can help expedite the process of identifying how perturbations in a single pathway can affect other pathways. RESULTS: We present a random-walks-based methodology called PathWalks, where a walker crosses a pathway-to-pathway network under the guidance of a disease-related map. The latter is a gene network that we construct by integrating multi-source information regarding a specific disease. The most frequent trajectories highlight communities of pathways that are expected to be strongly related to the disease under study.We apply the PathWalks methodology on Alzheimer's disease and idiopathic pulmonary fibrosis and establish that it can highlight pathways that are also identified by other pathway analysis tools as well as are backed through bibliographic references. More importantly, PathWalks produces additional new pathways that are functionally connected with those already established, giving insight for further experimentation. AVAILABILITY AND IMPLEMENTATION: https://github.com/vagkaratzas/PathWalks. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Fluorine-18 fluorodeoxyglucose positron emission tomography-based textural features for prediction of event prone carotid atherosclerotic plaques.

BACKGROUND: Texture analysis has been increasingly used in the field of positron emission tomography (PET)/computed tomography (CT) imaging with Fluorine-18 fluorodeoxyglucose (18F-FDG), aiming at assessing tumor heterogeneity. The purpose of the present study is to examine the feasibility of performing texture analysis in carotid arteries, investigate the value of textural features as predictors of potential plaque vulnerability using as reference standards histological and immunohistochemical data and compare their performance with conventional uptake measurements. METHODS: 67 different 18F-FDG PET-based textural features were extracted from carotid images of 21 patients with high-grade carotid stenosis undergoing endarterectomy. To identify the more reliable predictors, univariate logistic regression analysis was performed. The accuracy was satisfactory in case of an Area Under the Receiver Operating Characteristic (ROC) curve (AUC) ≥ 0.80. RESULTS: First measure of information correlation (AUC = 0.87, P < 0.001), large zone low gray level emphasis (AUC = 0.87, P < 0.001), and normalized run length non-uniformity (AUC = 0.84, P < 0.001) were the most optimal textural features for identifying characteristics of plaque vulnerability based on histological analysis. Addition of textural features to target-to-background ratio (TBR) (AUC = 0.74, P = 0.031) resulted in an AUC = 0.92 (P < 0.001), however, this did not reach statistical significance (Pdiff = 0.09). Intensity histogram standard deviation (AUC = 0.87, P < 0.001) and joint variance (AUC = 0.81, P = 0.001) were the most efficient features for signal differential in relation to immunohistochemical findings and provided incremental value compared to TBR (Pdiff = 0.02). CONCLUSION: Texture analysis can be applied in 18F-FDG PET carotid imaging providing valuable information for plaque characterization.

PathwayConnector: finding complementary pathways to enhance functional analysis.

SUMMARY: PathwayConnector is a web-tool that facilitates the construction of complementary pathway-to-pathway networks and subnetworks of them, based on a reference pathway network derived from the rich information available either in KEGG or Reactome database for pathway mapping. Specifically, for a given set of pathways, PathwayConnector (i) finds all the direct connections between them, (ii) adds a minimum set of complementary pathways required to achieve connectivity between the pathways, leading to informative fully connected networks and (ii) provides a series of clustering methods for the further grouping of pathways in to sub-clusters. The proposed web-tool is a simple yet informative tool towards identifying connected groups of pathways that are significantly related to specific diseases. AVAILABILITY AND IMPLEMENTATION: http://bioinformatics.cing.ac.cy/PathwayConnector. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Analyzing Gene Expression Profiles from Ataxia and Spasticity Phenotypes to Reveal Spastic Ataxia Related Pathways.

Spastic ataxia (SA) is a group of rare neurodegenerative diseases, characterized by mixed features of generalized ataxia and spasticity. The pathogenetic mechanisms that drive the development of the majority of these diseases remain unclear, although a number of studies have highlighted the involvement of mitochondrial and lipid metabolism, as well as calcium signaling. Our group has previously published the GBA2 c.1780G > C (p.Asp594His) missense variant as the cause of spastic ataxia in a Cypriot consanguineous family, and more recently the biochemical characterization of this variant in patients' lymphoblastoid cell lines. GBA2 is a crucial enzyme of sphingolipid metabolism. However, it is unknown if GBA2 has additional functions and therefore additional pathways may be involved in the disease development. The current study introduces bioinformatics approaches to better understand the pathogenetic mechanisms of the disease. We analyzed publicly available human gene expression datasets of diseases presented with 'ataxia' or 'spasticity' in their clinical phenotype and we performed pathway analysis in order to: (a) search for candidate perturbed pathways of SA; and (b) evaluate the role of sphingolipid signaling pathway and sphingolipid metabolism in the disease development, through the identification of differentially expressed genes in patients compared to controls. Our results demonstrate consistent differential expression of genes that participate in the sphingolipid pathways and highlight alterations in the pathway level that might be associated with the disease phenotype. Through enrichment analysis, we discuss additional pathways that are connected to sphingolipid pathways, such as PI3K-Akt signaling, MAPK signaling, calcium signaling, and lipid and carbohydrate metabolism as the most enriched for ataxia and spasticity phenotypes.

Investigating the Transition of Pre-Symptomatic to Symptomatic Huntington's Disease Status Based on Omics Data.

Huntington's disease is a rare neurodegenerative disease caused by a cytosine-adenine-guanine (CAG) trinucleotide expansion in the Huntingtin (HTT) gene. Although Huntington's disease (HD) is well studied, the pathophysiological mechanisms, genes and metabolites involved in HD remain poorly understood. Systems bioinformatics can reveal synergistic relationships among different omics levels and enables the integration of biological data. It allows for the overall understanding of biological mechanisms, pathways, genes and metabolites involved in HD. The purpose of this study was to identify the differentially expressed genes (DEGs), pathways and metabolites as well as observe how these biological terms differ between the pre-symptomatic and symptomatic HD stages. A publicly available dataset from the Gene Expression Omnibus (GEO) was analyzed to obtain the DEGs for each HD stage, and gene co-expression networks were obtained for each HD stage. Network rewiring, highlights the nodes that change most their connectivity with their neighbors and infers their possible implication in the transition between different states. The CACNA1I gene was the mostly highly rewired node among pre-symptomatic and symptomatic HD network. Furthermore, we identified AF198444 to be common between the rewired genes and DEGs of symptomatic HD. CNTN6, DEK, LTN1, MST4, ZFYVE16, CEP135, DCAKD, MAP4K3, NUPL1 and RBM15 between the DEGs of pre-symptomatic and DEGs of symptomatic HD and CACNA1I, DNAJB14, EPS8L3, HSDL2, SNRPD3, SOX12, ACLY, ATF2, BAG5, ERBB4, FOCAD, GRAMD1C, LIN7C, MIR22, MTHFR, NABP1, NRG2, OTC, PRAMEF12, SLC30A10, STAG2 and Y16709 between the rewired genes and DEGs of pre-symptomatic HD. The proteins encoded by these genes are involved in various biological pathways such as phosphatidylinositol-4,5-bisphosphate 3-kinase activity, cAMP response element-binding protein binding, protein tyrosine kinase activity, voltage-gated calcium channel activity, ubiquitin protein ligase activity, adenosine triphosphate (ATP) binding, and protein serine/threonine kinase. Additionally, prominent molecular pathways for each HD stage were then obtained, and metabolites related to each pathway for both disease stages were identified. The transforming growth factor beta (TGF-ß) signaling (pre-symptomatic and symptomatic stages of the disease), calcium (Ca2+) signaling (pre-symptomatic), dopaminergic synapse pathway (symptomatic HD patients) and Hippo signaling (pre-symptomatic) pathways were identified. The in silico metabolites we identified include Ca2+, inositol 1,4,5-trisphosphate, sphingosine 1-phosphate, dopamine, homovanillate and L-tyrosine. The genes, pathways and metabolites identified for each HD stage can provide a better understanding of the mechanisms that become altered in each disease stage. Our results can guide the development of therapies that may target the altered genes and metabolites of the perturbed pathways, leading to an improvement in clinical symptoms and hopefully a delay in the age of onset.

Inhibition of Breast Cancer Cell Invasion by Ras Suppressor-1 (RSU-1) Silencing Is Reversed by Growth Differentiation Factor-15 (GDF-15).

Extracellular matrix (ECM)-related adhesion proteins are important in metastasis. Ras suppressor-1 (RSU-1), a suppressor of Ras-transformation, is localized to cell⁻ECM adhesions where it interacts with the Particularly Interesting New Cysteine-Histidine rich protein (PINCH-1), being connected to Integrin Linked Kinase (ILK) and alpha-parvin (PARVA), a direct actin-binding protein. RSU-1 was also found upregulated in metastatic breast cancer (BC) samples and was recently demonstrated to have metastasis-promoting properties. In the present study, we transiently silenced RSU-1 in BC cells, MCF-7 and MDA-MB-231. We found that RSU-1 silencing leads to downregulation of Growth Differentiation Factor-15 (GDF-15), which has been associated with both actin cytoskeleton reorganization and metastasis. RSU-1 silencing also reduced the mRNA expression of PINCH-1 and cell division control protein-42 (Cdc42), while increasing that of ILK and Rac regardless of the presence of GDF-15. However, the downregulation of actin-modulating genes PARVA, RhoA, Rho associated kinase-1 (ROCK-1), and Fascin-1 following RSU-1 depletion was completely reversed by GDF-15 treatment in both cell lines. Moreover, complete rescue of the inhibitory effect of RSU-1 silencing on cell invasion was achieved by GDF-15 treatment, which also correlated with matrix metalloproteinase-2 expression. Finally, using a graph clustering approach, we corroborated our findings. This is the first study providing evidence of a functional association between RSU-1 and GDF-15 with regard to cancer cell invasion.

Bioinformatics methods in drug repurposing for Alzheimer's disease.

Alarming epidemiological features of Alzheimer's disease impose curative treatment rather than symptomatic relief. Drug repurposing, that is reappraisal of a substance's indications against other diseases, offers time, cost and efficiency benefits in drug development, especially when in silico techniques are used. In this study, we have used gene signatures, where up- and down-regulated gene lists summarize a cell's gene expression perturbation from a drug or disease. To cope with the inherent biological and computational noise, we used an integrative approach on five disease-related microarray data sets of hippocampal origin with three different methods of evaluating differential gene expression and four drug repurposing tools. We found a list of 27 potential anti-Alzheimer agents that were additionally processed with regard to molecular similarity, pathway/ontology enrichment and network analysis. Protein kinase C, histone deacetylase, glycogen synthase kinase 3 and arginase inhibitors appear consistently in the resultant drug list and may exert their pharmacologic action in an epidermal growth factor receptor-mediated subpathway of Alzheimer's disease.

mAPKL: R/ Bioconductor package for detecting gene exemplars and revealing their characteristics.

BACKGROUND: So far many algorithms have been proposed towards the detection of significant genes in microarray analysis problems. Several of those approaches are freely available as R-packages though their engagement in gene expression analysis by non-bioinformaticians is usually a frustrating task. Besides, only some of those packages offer a complete suite of tools starting from initial data import and ending to analysis report. Here we present an R/Bioconductor package that implements a hybrid gene selection method along with a bunch of functions to facilitate a thorough and convenient gene expression profiling analysis. RESULTS: mAPKL is an open-source R/Bioconductor package that implements the mAP-KL hybrid gene selection method. The advantage of this method is that selects a small number of gene exemplars while achieving comparable classification results to other well established algorithms on a variety of datasets and dataset sizes. The mAPKL package is accompanied with extra functionalities including (i) solid data import; (ii) data sampling following a user-defined proportion; (iii) preprocessing through several normalization and transformation alternatives; (iv) classification with the aid of SVM and performance evaluation; (v) network analysis of the significant genes (exemplars), including degree of centrality, closeness, betweeness, clustering coefficient as well as the construction of an edge list table; (vi) gene annotation analysis, (vii) pathway analysis and (viii) auto-generated analysis reporting. CONCLUSIONS: Users are able to run a thorough gene expression analysis in a timely manner starting from raw data and concluding to network characteristics of the selected gene exemplars. Detailed instructions and example data are provided in the R package, which is freely available at Bioconductor under the GPL-2 or later license http://www.bioconductor.org/packages/3.1/bioc/html/mAPKL.html.