Integrated multi-omics and causal inference framework with experimental validation reveals key drivers of air pollution–induced acute kidney injury
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".