Biology subjects

Gkoutos, G.

Publications and source records attributed to Gkoutos, G..

4 recordsLinked to original sources

bioRxiv2023

Modelling cognitive outcomes in the UK Biobank: education, noradrenaline and frontoparietal networks

Education is often used as a surrogate measure of so called cognitive reserve (CR) benefiting cognitive functioning in later years. In line with Robertsons theory we tested here a hypothesis that education acting on the noradrenergic system strengthen the right fronto-parietal networks to facilitate CR and maintain cognition throughout the lifetime. We used machine learning and mediation analysis to model interactions between neurobiological features (genetic variants in noradrenergic signalling, structural and functional fronto-parietal connectivity) and education (proxy of CR) on cognitive outcomes (general cognitive ability score) in the UK Biobank cohort. We show that: (1) interactions between education and neurobiological variables better explain cognitive outcomes than either factor alone; (2) among the neurobiological features selected using variable importance testing, measures of right fronto-parietal connectivity are the strongest mediators of the effect of education on cognitive outcomes. Our findings offer novel insights into neurobiological basis of CR by pointing to between-networks connectivity, representing connections linking the default mode network with the right fronto-parietal network as the key facilitator of CR.

neuroscience↗

bioRxiv2023

Genetically mediated associations between chronotype and neuroimaging phenotypes in the UK Biobank: a Mendelian randomisation study

Chronotype impacts numerous physiological and disease traits, from metabolic syndrome to schizophrenia. The suprachiasmatic nucleus (SCN) maintains transcriptional-translational feedback loop (TTFL) which acts as a central chronobiological pacemaker, regulating 24-hour cycles throughout the human body. However, each tissue maintains its own peripheral clock, and both endogenous hormones and neurotransmitters and exogenous environmental cues regulate the SCNs central clock. The extent to which brain regions outside the SCN influence the core TTFL is unknown. Here, we investigated how genetic variability affecting brain regions outside the SCN may indirectly influence chronotype, using Mendelian randomization and causal inference. We performed genome wide association studies (GWAS) based on image derived phenotypes (IDPs) from neuroimaging data (grey matter volume, thickness and surface area, microstructural white matter measures; 42,062 participants), and additionally for sleep duration and morning/evening chronotype (361,739 participants). Significant, single nucleotide polymorphisms (SNPs) associating with each phenotype were entered into 2-sample Mendelian randomization performed using inverse-variance weighted methods (exposure versus outcome): 1) chronotype versus each IDP, 2) sleep versus each IDP and 3) each IDP versus chronotype. Subsequently, we investigated genes where significant instrumental SNPs were located for circadian periodic cycling, interaction with TTFL genes in common biological pathways (genetic, physical, or functional interaction), and enrichment of traits from UK Biobank and GWAS Catalogs. We found three associations with chronotype (morning/evening diurnal preference) outside the SCN based on genetically predicted (FAM76B, DENND1A, CDH11) regional differences in brain volume. Specifically, genetically predicted lower inferior temporal gyrus volume linked to morning phenotype, while lower volume of the superior parietal lobule and angular gyrus linked to evening preference. In addition, evening chronotype exposure influenced superior temporal gyrus volume, and both increased sleep duration and evening chronotype influenced thalamic volume. We conclude that genetically mediated associations between chronotype and brain regions outside SCN exist suggesting novel zeitgeber mechanisms.

neuroscience↗

bioRxiv2021

DeepMOCCA: A pan-cancer prognostic model identifies personalized prognostic markers through graph attention and multi-omics data integration

Combining multiple types of genomic, transcriptional, proteomic, and epigenetic datasets has the potential to reveal biological mechanisms across multiple scales, and may lead to more accurate models for clinical decision support. Developing efficient models that can derive clinical outcomes from high-dimensional data remains problematical; challenges include the integration of multiple types of omics data, inclusion of biological background knowledge, and developing machine learning models that are able to deal with this high dimensionality while having only few samples from which to derive a model. We developed DeepMOCCA, a framework for multi-omics cancer analysis. We combine different types of omics data using biological relations between genes, transcripts, and proteins, combine the multi-omics data with background knowledge in the form of protein-protein interaction networks, and use graph convolution neural networks to exploit this combination of multi-omics data and background knowledge. DeepMOCCA predicts survival time for individual patient samples for 33 cancer types and outperforms most existing survival prediction methods. Moreover, DeepMOCCA includes a graph attention mechanism which prioritizes driver genes and prognostic markers in a patient-specific manner; the attention mechanism can be used to identify drivers and prognostic markers within cohorts and individual patients. Author summaryLinking the features of tumors to a prognosis for the patient is a critical part of managing cancer. Many methods have been applied to this problem but we still lack accurate prognostic markers for many cancers. We now have more information than ever before on the state of the cancer genome, the epigenetic changes in tumors, and gene expression at both RNA and protein levels. Here, we address the question of how this data can be used to predict cancer survival and discover which tumor genes make the greatest contribution to the prognosis in individual tumor samples. We have developed a computational model, DeepMOCCA, that uses artificial neural networks underpinned by a large graph constructed from background knowledge concerning the functional interactions between genes and their products. We show that with our method, DeepMOCCA can predict cancer survival time based entirely on features of the tumor at a cellular and molecular level. The method confirms many existing genes that affect survival but for some cancers suggests new genes, either not implicated in survival before or not known to be important in that particular cancer. The ability to predict the important features in individual tumors provided by our method raises the possibility of personalized therapy based on the gene or network dominating the prognosis for that patient.

bioinformatics↗

bioRxiv2018

Vec2SPARQL: integrating SPARQL queries and knowledge graph embeddings

Recent developments in machine learning have lead to a rise of large number of methods for extracting features from structured data. The features are represented as a vectors and may encode for some semantic aspects of data. They can be used in a machine learning models for different tasks or to compute similarities between the entities of the data. SPARQL is a query language for structured data originally developed for querying Resource Description Framework (RDF) data. It has been in use for over a decade as a standardized NoSQL query language. Many different tools have been developed to enable data sharing with SPARQL. For example, SPARQL endpoints make your data interoperable and available to the world. SPARQL queries can be executed across multiple endpoints. We have developed a Vec2SPARQL, which is a general framework for integrating structured data and their vector space representations. Vec2SPARQL allows jointly querying vector functions such as computing similarities (cosine, correlations) or classifications with machine learning models within a single SPARQL query. We demonstrate applications of our approach for biomedical and clinical use cases. Our source code is freely available at https://github.com/bio-ontology-research-group/vec2sparql and we make a Vec2SPARQL endpoint available at http://sparql.bio2vec.net/.

bioinformatics↗