ExposomeMap-PM Version 1.0
Le résumé fourni par la source
Background: Air pollution is one of the leading global environmental health risks, associated with millions of premature deaths annually. It represents a substantial share of disease burden across respiratory, cardiovascular and neurological conditions. Despite well-established epidemiological associations, the underlying molecular mechanisms remain only partially understood. There is the need for systematic approaches toward mechanistic understanding that connect particulate matter exposure to downstream pathways and pathophysiological effects. Results: We present ExposomeMap-PM (https://disease-maps.io/markopolo), a model of mechanistic progression from fine and ultrafine particle exposure to tissue-specific pathologies across respiratory, cardiovascular and neurological systems. The model identifies key components, including alveolar deposition, oxidative stress, barrier disruption, mitochondrial dysfunction, and pro-inflammatory signalling. Adverse outcomes are associated with conditions such as asthma, chronic obstructive pulmonary disease, atherosclerosis and neurodegeneration. Differential expression values from transcriptomics and proteomics datasets are overlaid onto the protein, mRNA and gene nodes of the ExposomeMap-PM, enabling a pathway-level view of the particle-induced effects and highlighting coherently perturbed blocks. Methods: The approach combines the adverse outcome pathway concept from toxicology with the disease map concept from systems biomedicine. The model was constructed in CellDesigner using Systems Biology Graphical Notation standard and made available online via the MINERVA Platform to facilitate exploration and reuse. Conclusions: The ExposomeMap-PM provides a mechanistic layer of understanding, a missing step between epidemiological evidence of particle-induced harmful effects and the molecular events that drive it and connect to outcomes. This reusable and extendable resource bridges toxicological and biomedical perspectives, advancing the understanding of how fine and ultrafine particles contribute to the onset and progression of complex diseases. It supports community annotation and is accessible online for browsing, community feedback and data visualisation.
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