Biology subjects

Koetsier, J.

Publications and source records attributed to Koetsier, J..

3 recordsLinked to original sources

bioRxiv2026

User-friendly transcriptomic data analysis with ArrayAnalysis

Transcriptomic profiling has become a cornerstone of modern biomedical research. To make transcriptomic analyses accessible to a broader scientific community, specifically including researchers with limited bioinformatics expertise, we introduced ArrayAnalysis in 2013 as a user-friendly web-based application for microarray data analysis. We now present a major update (https://arrayanalysis.org), introducing a strongly interactive platform that facilitates the dedicated exploration and analysis of both microarray and RNA-seq data, and allows for the generation of publication-ready outputs. Users can perform key analysis steps, including data pre-processing and quality control, differential expression analysis, and gene set analysis, via a sequential, interactive workflow. At each step, the application provides interactive visualizations accompanied by information pages to support interpretation. Users can dynamically adjust figure layouts and colour palettes and export figures as vector graphics and high-resolution raster images. For non-expert users, ArrayAnalysis offers step-by-step guidance to support correct usage and facilitate learning, while for experienced bioinformaticians, it provides a streamlined and flexible workflow ideal for large-scale analyses requiring efficient and consistent processing. ArrayAnalysis is available both as a web application and for local deployment as a desktop application, Docker image, or R package, making it suitable for diverse computational environments, user groups, and analytical purposes. Together, ArrayAnalysis empowers a broad community of biomedical researchers to unlock the full potential of transcriptomic data. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=96 SRC="FIGDIR/small/738193v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@1072123org.highwire.dtl.DTLVardef@11095e0org.highwire.dtl.DTLVardef@1dfaee7org.highwire.dtl.DTLVardef@53d31e_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Association of RhoGEF Ect2 with Desmoplakin Supports RhoA Activity at Intercellular Junctions: Implications for Carvajal Disease

Desmoplakin (DP) is an essential component of the desmosomal adhesion complex, tethering intermediate filaments to sites of intercellular adhesion to confer mechanical integrity to tissues. As a frequent target for mutation in cardiocutaneous syndromes that vary widely in phenotype, DPs roles as a signaling hub are rapidly emerging. Here, we identify the RhoGEF Ect2 as a previously unappreciated binding partner of the desmosomal protein DP. DP is required for the localization of Ect2 to keratinocyte desmosomes and cardiac intercalated discs in vitro and in vivo, where it maintains active RhoA (Rho-GTP) at the membrane. We demonstrate further that Ect2 activity is supported by PKC in a DP-dependent manner in cardiac myocytes. Finally, a truncated form of DP expressed in patients with Carvajal syndrome associated with severe cardiocutaneous defects is impaired in its ability to bind and localize Ect2 to cell junctions in cardiomyocytes and keratinocytes isolated from patients. Our findings delineate an important relationship between a component of the desmosome and a critical regulator of actin cytoskeletal remodeling that could have widespread implications for understanding cardiac and cutaneous health and disease pathogenesis.

cell biology↗

bioRxiv2025

Epigenomic subtypes of late-onset Alzheimer's disease reveal distinct microglial signatures

Growing evidence suggests that clinical, pathological, and genetic heterogeneity in late-onset Alzheimers disease contributes to variable therapeutic outcomes, potentially explaining many trial failures. Advances in molecular subtyping through proteomic and transcriptomic profiling reveal distinct patient subgroups, highlighting disease complexity beyond amyloid-beta plaques and tau tangles. This insight underscores the need to expand molecular subtyping across new molecular layers, to identify novel drug targets for different patient subgroups. In this study, we analyzed genome-wide DNA methylation data from three independent postmortem brain cohorts (n = 831) to identify epigenetic subtypes of late-onset Alzheimers disease. Unsupervised clustering approaches were employed to identify distinct DNA methylation patterns, with subsequent cross-cohort validation to ensure robustness and reproducibility. To explore the cell-type specificity of the identified epigenomic subtypes, we characterized their methylation signatures utilizing DNA methylation profiles derived from purified brain cells. Transcriptomic data from bulk and single-cell RNA sequencing were integrated to examine the functional impact of epigenetic subtypes on gene expression profiles. Finally, we performed statistical analyses to investigate associations between these DNA methylation-defined subtypes and clinical or neuropathological features, aiming to elucidate their biological significance and clinical implications. We identified two distinct epigenomic subtypes of late-onset Alzheimers disease, each defined by reproducible DNA methylation patterns across three cohorts. Both subtypes exhibit cell-type-specific DNA methylation profiles. Subtype 1 and subtype 2 show significant microglial methylation enrichment, with odds ratios (OR) of 1.6 and 1.3, respectively. The minimal overlap between them suggests distinct microglial states. Additionally, subtype 2 displays strong neuronal (OR = 1.6) and oligodendrocyte (OR = 3.6) enrichment. Bulk transcriptomic analyses further highlighted divergent biological mechanisms underpinning these subtypes, with subtype 1 enriched for immune-related processes, and subtype 2 characterized predominantly by neuronal and synaptic functional pathways. Single-cell transcriptional profiling of microglia revealed subtype-specific inflammatory states: subtype 1 represented a state of chronic innate immune hyperactivation with impaired resolution, while subtype 2 exhibited a more dynamic inflammatory profile balancing pro-inflammatory signaling with reparative and regulatory mechanisms. This study highlights the molecular heterogeneity of late-onset Alzheimers disease by identifying two epigenetic subtypes with distinct cell-type-specific DNA methylation patterns. Their alignment with previously defined molecular classifications underscores their relevance in disease pathogenesis. By linking these subtypes to inflammatory microglial activity, our findings provide a foundation for future precision medicine approaches in Alzheimers research and treatment.

genomics↗