Abstract
The chronic wound microenvironment consists of a complex milieu of host cells, microbial species, and metabolites. While much is known about wound microbiomes, our knowledge of metabolic landscapes influencing wound healing is limited. Furthermore, integrating complex datasets into predictive models of wound healing is almost non-existent. Microbial rRNA and total metabolites were extracted from 45 diabetic foot ulcers (DFU) debridement samples from 13 patients, with 25 from non-healing wounds and 20 from healing wounds that remained closed for over 30 days. 16S rRNA sequencing and global metabolomics were performed and clinical metadata collected. Healing outcome was modeled as a function of three blocks of features (N = 21 clinical, 634 microbiome, and 865 metabolome) using DIABLO (Data Integration Analysis for Biomarker Discovery using Latent Components). The final model selected 176 features (N = 15 clinical, 8 microbiome, and 153 metabolome) and the correct clinical outcome was predicted with an accuracy of nearly 94%. These results indicate that integrating multi-omics data with clinical metadata can predict clinical wound healing with low error rates. Furthermore, the biomarkers selected within the model offer novel insights into wound microenvironment composition which may reveal innovative therapeutic approaches and improve treatment efficacy in difficult-to-heal wounds.