Biology subjects

de Kok, T. M.

Publications and source records attributed to de Kok, T. M..

3 recordsLinked to original sources

bioRxiv2026

Network analysis reveals converging molecular mechanisms in genetic epilepsies

Background: Rare genetic epilepsies are often difficult to diagnose and treat. They can arise from changes in many different genes, and the molecular basis linking these changes to epilepsy remains poorly understood. This complexity makes it difficult to select effective treatments and develop new therapies, leaving many patients with poorly controlled seizures and reduced quality of life Methods: To address this gap, we first identified epilepsy-associated genes and conducted a network-based analysis of biomolecular interactions in genetic epilepsy to identify converging pathways with high translational relevance and potential therapeutic targets. To run the analysis, we constructed a human brain-specific molecular interaction network by integrating all human pathways from the WikiPathways database. HotNet2 was then applied to identify highly perturbed clusters through evidence-based weight diffusion of EAGs in the human brain network. Results: We identified 2,496 epilepsy-associated genes (EAGs) from multiple resources. These genes encode coding for metabolite interconversion enzymes (20%), transporters (15%), and protein-modifying enzymes (11%). Furthermore, the network analysis identified 19 subnetworks with more than two nodes and 125 unique genes. Gene enrichment analysis revealed convergence of processes including action potential, synaptic function, nervous system development, TOR signaling, cilium organization, DNA repair, and cell fate specification. We identified 44 first-order neighbors connecting the subnetworks. By integrating drug-target interactions from DrugBank using CyTargetLinker, we highlighted potential therapeutic targets within the S4 subnetwork, associated with cerebral cortex development, as well as CREB1, a connector between subnetworks. Conclusion: Network analysis organized heterogeneous epilepsy-associated genes into biologically related subnetworks and generated hypotheses for further research.

bioinformatics↗

bioRxiv2026

Ontology-Driven Annotation of Key Events Enables Transcriptomics-Based Assessment of Adverse Outcome Pathway Networks

Chemical safety assessment is moving towards new approach methodologies (NAMs) to improve human safety and reduce animal testing. The Adverse Outcome Pathway (AOP) is a NAM framework describing toxicity as a chain of Key Events (KEs). Many AOP-Wiki KEs describe molecular, cellular, or tissue-level processes and can therefore be assessed with gene expression data, making transcriptomics of human in vitro models a NAM data generator. In AOP-Wiki, KEs are described by Key Event Components (KECs). A gene set can be derived when the biological process (BP) component carries a Gene Ontology (GO) term. Most KEs carry no such term, so their perturbation cannot be assessed using omics data. We assigned GO BP terms to the unmapped BP component across the brain, lung, liver and kidney AOP networks. We also replaced obsolete and direction-carrying terms in the pre-existing mapping with current, direction-neutral ones. For liver, we reviewed all pre-existing terms and replaced overly broad terms with better-matched alternatives. We then applied this approach to pesticide-induced hepatotoxicity, using Open TG-GATEs transcriptomics data from primary human hepatocytes exposed to cycloheximide, 2,4-dinitrophenol, hexachlorobenzene, and rotenone. KE perturbation was scored using single-sample Gene Set Enrichment Analysis (ssGSEA). Annotation increased the proportion of transcriptomics-linkable KEs in the liver network from 53% to 77%, enhancing identification of perturbed KEs after exposure. The KEs perturbed by all four compounds fell into five functional categories associated with hepatotoxicity. Our approach resulted in a more relevant AOP network for transcriptomics-based assessment of KE perturbation. These findings show molecular perturbation rather than direct proof of KE activation or causal progression through an AOP. When validated on larger and independent datasets, such networks will support mechanistic interpretation of transcriptomics data in chemical safety assessment.

bioinformatics↗

bioRxiv2018

Adipose tissue in health and disease through the lens of its building blocks

BackgroundHighly specialized cells work in synergy forming tissues to perform functions required for the survival of organisms. Understanding this tissue-specific cellular heterogeneity and homeostasis is essential to comprehend the development of diseases within the tissue and also for developing regenerative therapies. Cellular subpopulations in the adipose tissue have been related to disease development, but efforts towards characterizing the adipose tissue cell type composition are limited due to lack of robust cell surface markers, limited access to tissue samples, and the labor-intensive process required to identify them.\n\nResultsWe propose a framework, identifying cellular heterogeneity while providing state-of-the-art cellular markers for each cell type present in tissues using transcriptomics level analysis. We validate our approach with an independent dataset and present the most comprehensive study of adipose tissue cell type composition to date, determining the relative amounts of 21 different cell types in 779 adipose tissue samples detailing differences across four adipose tissue depots, between genders, across ranges of BMI and in different stages of type-2 diabetes. We also highlight the heterogeneity in reported marker-based studies of adipose tissue cell type composition and provide novel cellular markers to distinguish different cell types within the adipose tissue.\n\nConclusionsOur study provides a systematic framework for studying cell type composition in a given tissue and valuable insights into adipose tissue cell type heterogeneity in health and disease.

systems biology↗