Integrated multi-omics and causal inference framework with experimental validation reveals key drivers of air pollution–induced acute kidney injury
Notice bibliographique
Résumé
BACKGROUND: Air pollution has emerged as a significant risk factor for acute kidney injury (AKI), yet the molecular mechanisms underlying this association remain poorly defined. This study aimed to elucidate the nephrotoxic effects of representative air pollutants and identify molecular targets involved in pollutant-induced AKI. METHODS: We developed a multi-layered computational and experimental framework integrating omics-based target prediction, network toxicology, machine learning, Mendelian randomization (MR), single-cell profiling, molecular docking with dynamic simulations, and analysis of pollutant-exposed model. Nine representative air pollutants were selected based on environmental relevance and suspected nephrotoxicity. A diagnostic gene signature was constructed using multiple machine learning algorithms, and key targets were evaluated through transcriptome-wide MR. Pollutant-protein interactions were assessed using molecular docking and dynamics simulations. Single-cell data and in vivo transcriptomes from pollutant-exposed models were used to construct a pollutant-target-cell type network. Finally, experimental validation was performed using in vitro exposure of mouse proximal tubular cells. RESULTS: Nephrotoxicity predictions revealed substantial heterogeneity among pollutants, with carbon monoxide, benzene, and ozone exhibiting the highest toxic potential. A total of 49 overlapping genes were identified and found to be enriched in pathways related to inflammation and oxidative stress. A 38-gene diagnostic model demonstrated strong predictive performance across datasets, highlighting a set of core targets potentially involved in both the pathogenesis and prognosis of air pollution-induced AKI. Transcriptome-wide MR analysis further prioritized five genes - NPPA, TGIF1, IL18, CRLS1, and KLF2 - with significant causal associations with AKI. Single-cell transcriptomic profiling revealed that proximal tubular, immune, and endothelial cells are particularly susceptible to pollutant-induced injury. Molecular docking and dynamic simulations identified high-affinity pollutant-protein interactions. In vitro experiments showed that exposure of mouse proximal tubular cells to PM 2.5 and benzene reduced cell viability, induced apoptosis, and significantly dysregulated key genes, providing experimental support for computational predictions. CONCLUSION: This study provides novel mechanistic insights into air pollution-induced AKI by identifying key genes, pathways, and susceptible renal cell types. The integrative framework combining multi-omics, causal inference, and experimental validation establishes a robust foundation for future translational research and therapeutic development targeting environmentally driven kidney injury.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».