Biology subjects

Mobus, L.

Publications and source records attributed to Mobus, L..

5 recordsLinked to original sources

bioRxiv2026

Integrating Semantic Retrieval, LLM-based Refinement, and Structured Expert Curation for Scalable AOP Gene Mapping

Toxicogenomics can support regulatory toxicology, but its use is limited by the difficulty of translating molecular responses into mechanistic, decision-relevant interpretations. Adverse Outcome Pathways (AOPs) provide a framework for this translation, yet omics applications require scalable mapping of Key Events (KEs) to molecular features. Here, we present an AI-assisted, multi-step workflow for KE-to-gene mapping that uses embedding-based semantic retrieval to identify candidate ontology/pathway terms, large language model-assisted refinement to filter these candidates, and double-independent expert group curation with rule-based consolidation to finalize mappings and derive confidence scores. Compared with earlier NLP-based approaches, the workflow improves KE-to-ontology/pathway mapping performance and generates candidate annotations that better align with expert judgment while substantially reducing the need for manual augmentation. Explicit gene and protein mentions in KE titles were additionally grounded to improve specificity, and each curated mapping was assigned curator reason codes to support transparent, traceable, and confidence-aware reuse. Applied across AOP-Wiki, the workflow produced a comprehensive KE-to-gene set resource covering 1,254 KEs across 523 AOPs and linking 15,833 human genes. Utility is demonstrated through CTD-based AOP fingerprinting of curated reference chemical groups, highlighting expanded coverage and confidence-informed interpretation of chemical-associated gene signatures in an AOP context. The workflow and resulting resource provide a practical bridge between toxicogenomics and AOP-based mechanistic interpretation and support routine updating and future extension to additional omics layers within OECD Omics2AOP.

bioinformatics↗

bioRxiv2025

OpTiles: An R Package for Adaptive Tiling and Methylation Variability Profiling

SummaryOpTiles is an R package that dynamically defines tiling windows based on the distribution of sequenced CpGs, addressing the limitations of traditional fixed-tiling approaches in targeted methylation datasets. By integrating CpG density with intra-region methylation variability, it provides a reliability metric and extended functionality for annotating, prioritizing, and interpreting complex methylation data. AvailabilityOpTiles is implemented in R and source code is freely available at https://github.com/fhaive/OpTiles. Data are available on Zenodo at https://doi.org/10.5281/zenodo.16961293 Contactdario.greco@tuni.fi

bioinformatics↗

bioRxiv2025

Endothelial Sensitivity to Pro-Fibrotic Signals Links Systemic exposure to Pulmonary Fibrosis

Pulmonary fibrosis (PF) is a life-threatening condition characterised by excessive extracellular matrix deposition and tissue scarring. While much of PF research has focused on alveolar epithelial cells and fibroblasts, endothelial cells have emerged as active contributors to the disease initiation, especially in the context of systemic exposure to pro-fibrotic substances. Here, we investigate early transcriptomic and secretory responses of human umbilical vein endothelial cells (HUVEC) to subtoxic doses of bleomycin, a known pro-fibrotic agent, and TGF-beta, a key cytokine in fibrosis. Bleomycin exposure induced a rapid and extensive shift in the endothelial transcriptional programme, including signatures of endothelial to mesenchymal transition, cellular senescence, and immune cell recruitment. These findings suggest endothelial cells as early initiators of pro-fibrotic signals, independent of contributions from other cell types. In contrast, TGF-beta effects were limited and transient, indicating its pro-fibrotic action may require another initial stimulus and interplay with other cells like fibroblasts. This study highlights the sensitivity of endothelial cells to systemic pro-fibrotic exposure and provides a blueprint of early pro-fibrotic mechanisms, emphasising their pivotal role in PF pathogenesis.

molecular biology↗

bioRxiv2024

Methylation and Transcriptomic Profiling Reveals Short Term and Long Term Regulatory Responses in Polarized Macrophages

Macrophage plasticity allows the adoption of distinct functional states in response to environmental cues. While unique transcriptomic profiles define these states, focusing solely on transcription neglects potential long-term effects. The investigation of epigenetic changes can be used to understand how temporary stimuli can result in lasting effects. Moreover, epigenetic alterations play an important role in the pathophysiology of macrophages, including phenomena related to the trained innate immunity, which allow faster and more efficient inflammatory responses upon subsequent encounters with the same pathogen. In this study, we used a multi-omics approach to elucidate the interplay between gene expression and DNA-methylation, unravelling the long-term effects of diverse polarizing environments on macrophage activity. We identified a common core set of genes that are differentially methylated regardless of exposure suggesting a potential mechanism for rapid adaptation to various stimuli. These conserved epigenetic modifications might represent a fundamental state that allows for flexible responses to various environmental cues. Functional analysis revealed that processes requiring rapid responses displayed transcriptomic regulation, whereas functions critical for long-term adaptations exhibited co-regulation at both transcriptomic and epigenetic levels. Our study unveils a novel set of genes critically linked to the long-term effects of macrophage polarization. This discovery underscores the potential of epigenetics in elucidating how macrophages establish long-term memory and influence health outcomes. Highlights- Environmental signals trigger gene changes in macrophages, leaving a long-lasting epigenetic reprogramming - Epigenetic changes and metabolic shifts in polarized macrophages suggest training mechanisms - Common gene set epigenetically altered across different cues, suggest common adaptation to various stimuli Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=87 SRC="FIGDIR/small/599278v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@1006cacorg.highwire.dtl.DTLVardef@deb49eorg.highwire.dtl.DTLVardef@12428e9org.highwire.dtl.DTLVardef@f9e1c9_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

A Multi-Dimensional Approach to Map Disease Relationships Challenges Classical Disease Views

The categorization of human diseases is mainly based on the affected organ system and phenotypic characteristics. This is limiting the view to the pathological manifestations, while it neglects mechanistic relationships that are crucial to develop therapeutic strategies. This work aims to advance the understanding of diseases and their relatedness beyond traditional phenotypic views. Hence, the similarity among 502 diseases is mapped using six different data dimensions encompassing molecular, clinical, and pharmacological information retrieved from public sources. Multiple distance measures and multi-view clustering is used to assess the patterns of disease relatedness. The integration of all six dimensions into a consensus map of disease relationships reveals a divergent disease view from the International Classification of Diseases (ICD), emphasizing novel insights offered by a multi-view disease map. Disease features such as genes, pathways, and chemicals that are enriched in distinct disease groups are identified. Finally, an evaluation of the top similar diseases of three candidate diseases common in the Western population shows concordance with known epidemiological associations and reveals rare features shared between Type 2 diabetes and Alzheimer disease. A revision of disease relationships holds promise for facilitating the reconstruction of comorbidity patterns, repurposing drugs, and advancing drug discovery in the future.

bioinformatics↗