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.

Immediate-Early Genes as Influencers in Genetic Networks and their Role in Alzheimer's Disease.

Immediate-early genes (IEGs) are a class of activity-regulated genes (ARGs) that are transiently and rapidly activated in the absence of de novo protein synthesis in response to neuronal activity. We explored the role of IEGs in genetic networks to pinpoint potential drug targets for Alzheimer's disease (AD). Using a combination of network analysis and genome-wide association study (GWAS) summary statistics we show that (1) IEGs exert greater topological influence across different human and mouse gene networks compared to other ARGs, (2) ARGs are sparsely involved in diseases and significantly more mutational constrained compared to non-ARGs, (3) Many AD-linked variants are in ARGs gene regions, mainly in MARK4 near FOSB, with an AD risk eQTL that increases MARK4 expression in cortical areas, (4) MARK4 holds an influential place in a dense AD multi-omic network and a high AD druggability score. Our work on IEGs' influential network role is a valuable contribution to guiding interventions for diseases marked by dysregulation of their downstream targets and highlights MARK4 as a promising underexplored AD-target.

Bacterial Wars-a tool for the prediction of bacterial predominance based on network analysis measures.

Bacterial Wars (BW) is a network-based tool that applies a two-step pipeline to display information on the competition of bacterial species found in the same microbiome. It utilizes antimicrobial peptide (AMP) sequence similarities to obtain a relationship between species. The working hypothesis (putative AMP defense) is that friendly species share sequence similarity among the putative AMPs of their proteomes and are therefore immune to their AMPs. This may not happen in competing bacterial species with dissimilar putative AMPs. Similarities in the putative AMPs of bacterial proteomes may be thus used to predict predominance. The tool provides insights as to which bacterial species are more likely to 'die' in a competing environmental niche.

Preliminary In Vitro and In Vivo Insights of In Silico Candidate Repurposed Drugs for Alzheimer's Disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disease and is the most common type of dementia. Although a considerably large amount of money has been invested in drug development for AD, no disease modifying treatment has been detected so far. In our previous work, we developed a computational method to highlight stage-specific candidate repurposed drugs against AD. In this study, we tested the effect of the top 13 candidate repurposed drugs that we proposed in our previous work in a severity stage-specific manner using an in vitro BACE1 assay and the effect of a top-ranked drug from the list of our previous work, tetrabenazine (TBZ), in the 5XFAD as an AD mouse model. From our in vitro screening, we detected 2 compounds (clomiphene citrate and Pik-90) that showed statistically significant inhibition against the activity of the BACE1 enzyme. The administration of TBZ at the selected dose and therapeutic regimen in 5XFAD in male and female mice showed no significant effect in behavioral tests using the Y-maze and the ELISA immunoassay of Aß40. To our knowledge, this is the first time the drug tetrabenazine has been tested in the 5XFAD mouse model of AD in a sex-stratified manner. Our results highlight 2 drugs (clomiphene citrate and Pik-90) from our previous computational work for further investigation.

The Cyprus Institute of Neurology and Genetics, an emerging paradigm of a gender egalitarian organisation.

Females are underrepresented in the science, technology, engineering, mathematics and medicine (STEMM) disciplines globally and although progress has been made, the gender gap persists. Our aim was to explore gender parity in the context of gender representation and internal collaboration at the Cyprus Institute of Neurology and Genetics (CING), a leading national biomedical organisation accredited as an equal opportunity employer. Towards this aim we (1) explored trends in gender parity within the different departments, positions and qualifications and in student representation in the CING's postgraduate school and, (2) investigated the degree of collaboration between male and female researchers within the Institute and the degree of influence within its co-authorship network. We recorded an over-representation of females both in the CING employees and the postgraduate students. The observed female over-representation in pooled CING employees was consistent with a similar over-representation in less senior positions and was contrasted with an observed male over-representation in only one middle rank and culminated in gender equality in the top rank in employee hierarchy. In terms of collaboration, both males and females tended to collaborate with each other without any significant preference to either inter-group or intra-group collaboration. Further comparison of the two groups with respect to their influence in the network in terms of occupying the positions of highest centrality scores, indicated that both gender and seniority level (head vs non-head) were significant in shaping the authors' influence, with no significant difference in those belonging in the same seniority level with respect to their gender. To conclude, our study has validated the formal recognition of the CING's policies and procedures pertinent to its egalitarian culture through the majority of the metrics of gender equality assessed in this study and has provided an extendable paradigm for evaluating gender parity in academic organizations.

Stbd1-deficient mice display insulin resistance associated with enhanced hepatic ER-mitochondria contact.

Starch binding domain-containing protein 1 (STBD1) is an endoplasmic reticulum (ER)-resident, glycogen-binding protein. In addition to glycogen, STBD1 has been shown to interact with several proteins implicated in glycogen synthesis and degradation, yet its function in glycogen metabolism remains largely unknown. In addition to the bulk of the ER, STBD1 has been reported to localize at regions of physical contact between mitochondria and the ER, known as Mitochondria-ER Contact sites (MERCs). Given the emerging correlation between distortions in the integrity of hepatic MERCs and insulin resistance, our study aimed to delineate the role of STBD1 in vivo by addressing potential abnormalities in glucose metabolism and ER-mitochondria communication associated with insulin resistance in mice with targeted inactivation of Stbd1 (Stbd1KO). We show that Stbd1KO mice at the age of 24 weeks displayed reduced hepatic glycogen content and aberrant control of glucose homeostasis, compatible with insulin resistance. In line with the above, Stbd1-deficient mice presented with increased fasting blood glucose and insulin levels, attenuated activation of insulin signaling in the liver and skeletal muscle and elevated liver sphingomyelin content, in the absence of hepatic steatosis. Furthermore, Stbd1KO mice were found to exhibit enhanced ER-mitochondria association and increased mitochondrial fragmentation in the liver. Nevertheless, the enzymatic activity of hepatic respiratory chain complexes and ER stress levels in the liver were not altered. Our findings identify a novel important role for STBD1 in the control of glucose metabolism, associated with the integrity of hepatic MERCs.

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.

Network-based stage-specific drug repurposing for Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disease and the most common type of dementia. With no disease-curing drugs available and an ever-growing AD-related healthcare burden, novel approaches for identifying therapies are needed. In this work, we propose stage-specific candidate repurposed drugs against AD by using a novel network-based method for drug repurposing against different stages of AD severity. For each AD stage, this approach a) ranks the candidate repurposed drugs based on a novel network-based score emerging from the weighted sum of connections in a network resembling the structural similarity with failed, approved or currently ongoing drugs b) re-ranks the candidate drugs based on functional, structural and a priori information according to a recently developed method by our group and c) checks and re-ranks for permeability through the Blood Brain Barrier (BBB). Overall, we propose for further experimental validation 10 candidate repurposed drugs for each AD stage comprising a set of 26 elite candidate repurposed drugs due to overlaps between the three AD stages. We applied our methodology in a retrospective way on the known clinical trial drugs till 2016 and we show that we were able to highly rank a drug that did enter clinical trials in the following year. We expect that our proposed network-based drug-repurposing methodology will serve as a paradigm for application for ranking candidate repurposed drugs in other brain diseases beyond AD.

Age-Related Exosomal and Endogenous Expression Patterns of miR-1, miR-133a, miR-133b, and miR-206 in Skeletal Muscles.

Skeletal muscle growth and maintenance depend on two tightly regulated processes, myogenesis and muscle regeneration. Both processes involve a series of crucial regulatory molecules including muscle-specific microRNAs, known as myomiRs. We recently showed that four myomiRs, miR-1, miR-133a, miR-133b, and miR-206, are encapsulated within muscle-derived exosomes and participate in local skeletal muscle communication. Although these four myomiRs have been extensively studied for their function in muscles, no information exists regarding their endogenous and exosomal levels across age. Here we aimed to identify any age-related changes in the endogenous and muscle-derived exosomal myomiR levels during acute skeletal muscle growth. The four endogenous and muscle-derived myomiRs were investigated in five skeletal muscles (extensor digitorum longus, soleus, tibialis anterior, gastrocnemius, and quadriceps) of 2-week-1-year-old wild-type male mice. The expression of miR-1, miR-133a, and miR-133b was found to increase rapidly until adolescence in all skeletal muscles, whereas during adulthood it remained relatively stable. By contrast, endogenous miR-206 levels were observed to decrease with age in all muscles, except for soleus. Differential expression of the four myomiRs is also inversely reflected on the production of two protein targets; serum response factor and connexin 43. Muscle-derived exosomal miR-1, miR-133a, and miR-133b levels were found to increase until the early adolescence, before reaching a plateau phase. Soleus was found to be the only skeletal muscle to release exosomes enriched in miR-206. In this study, we showed for the first time an in-depth longitudinal analysis of the endogenous and exosomal levels of the four myomiRs during skeletal muscle development. We showed that the endogenous expression and extracellular secretion of the four myomiRs are associated to the function and size of skeletal muscles as the mice age. Overall, our findings provide new insights for the myomiRs' significant role in the first year of life in mice.

Putative Antimicrobial Peptides Within Bacterial Proteomes Affect Bacterial Predominance: A Network Analysis Perspective.

The predominance of bacterial taxa in the gut, was examined in view of the putative antimicrobial peptide sequences (AMPs) within their proteomes. The working assumption was that compatible bacteria would share homology and thus immunity to their putative AMPs, while competing taxa would have dissimilarities in their proteome-hidden AMPs. A network-based method ("Bacterial Wars") was developed to handle sequence similarities of predicted AMPs among UniProt-derived protein sequences from different bacterial taxa, while a resulting parameter ("Die" score) suggested which taxa would prevail in a defined microbiome. T he working hypothesis was examined by correlating the calculated Die scores, to the abundance of bacterial taxa from gut microbiomes from different states of health and disease. Eleven publicly available 16S rRNA datasets and a dataset from a full shotgun metagenomics served for the analysis. The overall conclusion was that AMPs encrypted within bacterial proteomes affected the predominance of bacterial taxa in chemospheres.

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.

Generalized linear models provide a measure of virulence for specific mutations in SARS-CoV-2 strains.

This study aims to highlight SARS-COV-2 mutations which are associated with increased or decreased viral virulence. We utilize genetic data from all strains available from GISAID and countries' regional information, such as deaths and cases per million, as well as COVID-19-related public health austerity measure response times. Initial indications of selective advantage of specific mutations can be obtained from calculating their frequencies across viral strains. By applying modelling approaches, we provide additional information that is not evident from standard statistics or mutation frequencies alone. We therefore, propose a more precise way of selecting informative mutations. We highlight two interesting mutations found in genes N (P13L) and ORF3a (Q57H). The former appears to be significantly associated with decreased deaths and cases per million according to our models, while the latter shows an opposing association with decreased deaths and increased cases per million. Moreover, protein structure prediction tools show that the mutations infer conformational changes to the protein that significantly alter its structure when compared to the reference protein.

Empirically constrained network models for contrast-dependent modulation of gamma rhythm in V1.

Gamma oscillations are thought to play a key role in neuronal network function and neuronal communication, yet the underlying generating mechanisms have not been fully elucidated to date. At least partly, this may be due to the fact that even in simple network models of interconnected inhibitory (I) and excitatory (E) neurons, many parameters remain unknown and are set based on practical considerations or by convention. Here, we mitigate this problem by requiring PING (Pyramidal Interneuron Network Gamma) models to simultaneously satisfy a broad set of criteria for realistic behaviour based on empirical data spanning both the single unit (spikes) and local population (LFP) levels while unknown parameters are varied. By doing so, we were able to constrain the parameter ranges and select empirically valid models. The derived model constraints implied weak rather than strong PING as the generating mechanism for gamma, connectivity between E and I neurons within specific bounds, and variations of the external input to E but not I neurons. Constrained models showed valid behaviours, including gamma frequency increases with contrast and power saturation or decay at high contrasts. Using an empirically-validated model we studied the route to gamma instability at high contrasts. This involved increased heterogeneity of E neurons with increasing input triggering a breakdown of I neuron pacemaker function. Further, we illustrate the model's capacity to resolve disputes in the literature concerning gamma oscillation properties and GABA conductance proxies. We propose that the models derived in our study will be useful for other modelling studies, and that our approach to the empirical constraining of PING models can be expanded when richer empirical datasets become available. As local gamma networks are the building blocks of larger networks that aim to understand complex cognition through their interactions, there is considerable value in improving our models of these building blocks.

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.

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.

Selecting variants of unknown significance through network-based gene-association significantly improves risk prediction for disease-control cohorts.

Variants of unknown/uncertain significance (VUS) pose a huge dilemma in current genetic variation screening methods and genetic counselling. Driven by methods of next generation sequencing (NGS) such as whole exome sequencing (WES), a plethora of VUS are being detected in research laboratories as well as in the health sector. Motivated by this overabundance of VUS, we propose a novel computational methodology, termed VariantClassifier (VarClass), which utilizes gene-association networks and polygenic risk prediction models to shed light into this grey area of genetic variation in association with disease. VarClass has been evaluated using numerous validation steps and proves to be very successful in assigning significance to VUS in association with specific diseases of interest. Notably, using VUS that are deemed significant by VarClass, we improved risk prediction accuracy in four large case-studies involving disease-control cohorts from GWAS as well as WES, when compared to traditional odds ratio analysis. Biological interpretation of selected high scoring VUS revealed interesting biological themes relevant to the diseases under investigation. VarClass is available as a standalone tool for large-scale data analyses, as well as a web-server with additional functionalities through a user-friendly graphical interface.

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.

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.

Revealing Clusters of Connected Pathways Through Multisource Data Integration in Huntington's Disease and Spastic Ataxia.

The advancement of scientific and medical research over the past years has generated a wealth of experimental data from multiple technologies, including genomics, transcriptomics, proteomics, and other forms of -omics data, which are available for a number of diseases. The integration of such multisource data is a key component toward the success of precision medicine. In this paper, we are investigating a multisource data integration method developed by our group, regarding its ability to drive to clusters of connected pathways under two different approaches: first, a disease-centric approach, where we integrate data around a disease, and second, a gene-centric approach, where we integrate data around a gene. We have used as a paradigm for the first approach Huntington's disease (HD), a disease with a plethora of available data, whereas for the second approach the GBA2, a gene that is related to spastic ataxia (SA), a phenotype with sparse availability of data. Our paper shows that valuable information at the level of disease-related pathway clusters can be obtained for both HD and SA. New pathways that classical pathway analysis methods were unable to reveal, emerged as necessary "connectors" to build connected pathway stories formed as pathway clusters. The capability to integrate multisource molecular data, concluding to something more than the sum of the existing information, empowers precision and personalized medicine approaches.

Integrating multi-source information on a single network to detect disease-related clusters of molecular mechanisms.

The abundance of available information for each disease from multiple sources (e.g. as genetic, regulatory, metabolic, and protein-protein interaction) constitutes both an advantage and a challenge in identifying disease-specific underlying mechanisms. Integration of multi-source data is a rising topic and a great challenge in precision medicine and is crucial in enhancing disease understanding, identifying meaningful clusters of molecular mechanisms and increasing precision and personalisation towards the goal of Predictive, Preventive and Personalised Medicine (PPPM). The overall aim of this work was to develop a novel network-based integration methodology with the following characteristics: (i) maximise the number of data sources, (ii) utilise holistic approaches to integrate these sources (iii) be simple, flexible and extendable, (iv) be conclusive. Here, we present the case of Alzheimer's disease as a paradigm for illustrating our novel approach. SIGNIFICANCE: In this work we present an integration methodology, which aggregates a large number of the available data sources and types by exploiting the holistic nature of network approaches. It is simple, flexible and extendable generating solid conclusions regarding the molecular mechanisms that underlie the input data. We have illustrated the strength of our proposed methodology using Alzheimer's disease as a paradigm. This method is expected to serve as a stepping-stone for further development of integration methods of multi-source omic-data and to contribute to progress towards the goal of Predictive, Preventive and Personalised Medicine (PPPM). The output of this methodology may act as a reference map of implicated pathways in the disease under investigation, where pathways related to additional omics data from any kind of experiment may be projected. This will increase the precision in the understanding of the disease and may contribute to personalised approaches for patients with different disease-related pathway profile, leading to a more precise, personalised and ideally preventive management of the disease.