Biology subjects

Abbassi-Daloii, T.

Publications and source records attributed to Abbassi-Daloii, T..

3 recordsLinked to original sources

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↗

bioRxiv2024

From spreadsheet lab data templates to knowledge graphs: A FAIR data journey in the domain of AMR research

While awareness of FAIR (Findable, Accessible, Interoperable, and Reusable) principles has expanded across diverse domains, there remains a notable absence of impactful narratives regarding the practical application of FAIR data. This gap is particularly evident in the context of in-vitro and in-vivo experimental studies associated with the drug discovery and development process. Despite the structured nature of these data, reliance on classic methods such as spreadsheet-based visualization and analysis has limited the long-term reuse opportunities for such datasets. In response to this challenge, our work presents a representative journey towards FAIR data, characterized by structured, conventional spreadsheet-based lab data templates and the adoption of a knowledge graph framework for breaking data silos in the field of early antimicrobial resistance research. Here, we illustrate a tailored application of a "FAIRification framework" facilitating the practical implementation of FAIR principles. By showcasing the feasibility and benefits of transitioning to FAIR data practices, our work aims to encourage broader adoption and integration of FAIR principles within a research lab setting.

bioinformatics↗

bioRxiv2023

Collaborative network analysis for the interpretation of transcriptomics data in rare diseases, an application to Huntington's disease

BackgroundRare diseases may affect the quality of life of patients and in some cases be life-threatening. Therapeutic opportunities are often limited, in part because of the lack of understanding of the molecular mechanisms that can cause disease. This can be ascribed to the low prevalence of rare diseases and therefore the lower sample sizes available for research. A way to overcome this is to integrate experimental rare disease data with prior knowledge using network-based methods. Taking this one step further, we hypothesized that combining and analyzing the results from multiple network-based methods could provide data-driven hypotheses of pathogenicity mechanisms from multiple perspectives. ResultsWe analyzed a Huntingtons disease (HD) transcriptomics dataset using six network-based methods in a collaborative way. These methods either inherently reported enriched annotation terms or their results were fed into enrichment analyses. The resulting significantly enriched Reactome pathways were then summarized using the ontological hierarchy which allowed the integration and interpretation of outputs from multiple methods. Among the resulting enriched pathways, there are pathways that have been shown previously to be involved in HD and pathways whose direct contribution to disease pathogenesis remains unclear and requires further investigation. ConclusionsIn summary, our study shows that collaborative network analysis approaches are well-suited to study rare diseases, as they provide hypotheses for pathogenic mechanisms from multiple perspectives. Applying different methods to the same case study can uncover different disease mechanisms that would not be apparent with the application of a single method.

bioinformatics↗