Cross-species signaling pathways analysis inspire animal model selections for drug screening and target prediction in vascular aging diseases.

Age is a significant contributing factor to the occurrence and progression of cardiovascular disease (CVD). Pharmacological treatment can effectively alleviate CVD symptoms caused by aging. However, 90% of the drugs have failed in clinics because of the loss of drug effects or the occurrence of the side effects. One of the reasons is the disparity between animal models used and the actual physiological levels in humans. Therefore, we integrated multiple datasets from single-cell and bulk-seq RNA-sequencing data in rats, monkeys, and humans to identify genes and pathways with consistent/differential expression patterns across these three species. An approach called "Cross-species signaling pathway analysis" was developed to select suitable animal models for drug screening. The effectiveness of this method was validated through the analysis of the pharmacological predictions of four known anti-vascular aging drugs used in animal/clinical experiments. The effectiveness of drugs was consistently observed between the models and clinics when they targeted pathways with the same trend in our analysis. However, drugs might have exhibited adverse effects if they targeted pathways with opposite trends between the models and the clinics. Additionally, through our approach, we discovered four targets for anti-vascular aging drugs, which were consistent with their pharmaceutical effects in literatures, showing the value of this approach. In the end, software was established to facilitate the use of "Cross-species signaling pathway analysis." In sum, our study suggests utilizing bioinformatics analysis based on disease characteristics can help in choosing more appropriate animal models.