Biology subjects

Zachariou, M.

Publications and source records attributed to Zachariou, M..

5 recordsLinked to original sources

bioRxiv2024

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

Abstract/SummaryImmediate-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 Alzheimers 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. HighlightsO_LIImmediate-early genes are topologically influential in brain genetic networks in mouse and human. C_LIO_LIActivity-regulated Genes (ARGs) are highly constrained with sparse gene-disease relevance. C_LIO_LIThere are several AD-associated variants in ARGs gene regions, mainly in MARK4 near FOSB. C_LIO_LIGWAS and network analysis of ARGs pinpoint MARK4 as a promising underexplored AD target. C_LI

bioinformatics↗

bioRxiv2023

DReAmocracy: A Method to Capitalize 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 drugs from clinical trial studies in different phases. To our knowledge, the aggregation of the proposed lists of drugs by previous studies has not been extensively exploited towards the generation of a dynamic reference matrix regarding the disease-related frequencies of drug features such as the modes of action, the initial indications and the targeted pathways. To fill this knowledge gap, we performed a 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. As a testbed, we applied this method to a group of neurodegenerative diseases (Alzheimers, Parkinsons, Huntingtons 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 Alzheimers disease include agomelatine and mirtazapine, for Parkinsons disease apomorphine and pramipexole, for Huntingtons fluphezine and perphezine, and for Multiple Sclerosis zonisamide and disopyramide. Overall, DReAmocracy is a methodology that leverages the existing experimental and/or computational knowledge (1) to reveal trends in selected tracks of drug discovery research that includes modes of action, targeted pathways and initial indications for the investigated drugs and (2) to score new candidate drugs for repurposing against a selected disease.

bioinformatics↗

bioRxiv2020

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.

bioinformatics↗

bioRxiv2020

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

Understanding disease underlying biological mechanisms and respective interactions remains an elusive, time consuming and costly task. The realization of computational methodologies that can propose pathway/mechanism communities and reveal respective relationships can be of great value as it can help expedite the process of identifying how perturbations in a single pathway can affect other pathways. Random walks is a stochastic approach that can be used for both efficient discovery of strong connections and identification of communities formed in networks. The approach has grown in popularity as it efficiently exposes key network components and reveals strong interactions among genes, proteins, metabolites, pathways and drugs. Using random walks in biology, we need to overcome two key challenges: 1) construct disease-specific biological networks by integrating information from available data sources as they become available, and 2) provide guidance to the walker so as it can follow plausible trajectories that comply with inherent biological constraints. In this work, we present a methodology called PathWalks, where a random 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 present maps for Alzheimers Disease and Idiopathic Pulmonary Fibrosis and we use them as case-studies for identifying pathway communities through the application of PathWalks. In the case of Alzheimers Disease, the most visited pathways are the "Alzheimers disease" and the "Calcium signaling" pathways which have indeed the strongest association with Alzheimers Disease. Interestingly however, in the top-20 visited pathways we identify the "Kaposi sarcoma-associated herpesvirus infection" (HHV-8) and the "Human papillomavirus infection" (HPV) pathways suggesting that viruses may be involved in the development and progression of Alzheimers. Similarly, most of the highlighted pathways in Idiopathic Pulmonary Fibrosis are backed by the bibliography. We establish that "MAPK signaling" and "Cytokine-cytokine receptor interaction" pathways are the most visited. However, the "NOD receptor signaling" pathway is also in the top-40 edges. In Idiopathic Pulmonary Fibrosis samples, increased NOD receptor signaling has been associated with augmented concentrations of certain strains of Streptococcus. Additional experimental evidence is required however to further explore and ascertain the above indications.

bioinformatics↗

bioRxiv2019

Empirically Constrained Network Models for Contrast-dependent Modulation of Gamma Rhythm in V1

Here we present experimentally constrained computational models of gamma rhythm and use these to investigate gamma oscillation instability. To this end, we extracted empirical constraints for PING (Pyramidal Interneuron Network Gamma) models from monkey single-unit and LFP responses recorded during contrast variation. These constraints implied weak rather than strong PING, connectivity between excitatory (E) and inhibitory (I) cells within specific bounds, and input strength variations that modulated E but not I cells. Constrained models showed valid behaviours, including gamma frequency increases with contrast and power saturation or decay at high contrasts. The route to gamma instability involved increased heterogeneity of E cells with increasing input triggering a breakdown of I cell pacemaker function. We illustrate the models capacity to resolve disputes in the literature. Our work is relevant for the range of cognitive operations to which gamma oscillations contribute and could serve as a basis for future, more complex models.

neuroscience↗