Deciphering the Role of Human Genetics and Skin Microbiome in Diabetic Foot Ulcers

Abstract

Diabetes mellitus is a metabolic disease that affects every part of the human body including the skin. One of the most common complications of diabetes is skin disorders, including skin infections and diabetic foot ulcers (DFUs) that account for the majority of diabetes-associated limb amputation and hospitalizations, and result in a reduction in quality of life and economic burden. The etiology of DFU involves the patients’ genetics, microbiome, and environmental factors that together affect the severity, response to treatment, and outcome of ulcers. Long-term high blood sugar in diabetics can lead to changes in skin texture, appearance, and ability to heal, yet the underlying molecular mechanisms are understudied. To date, studies on the genetics of DFUs consider the expression of genes individually and ignore the effect of their coordinated expression in biological systems. Here, we use network analysis and topological properties to systematically investigate the dysregulated gene co-expression patterns in type II diabetic skin with transcriptome profiles of skin samples from the Genotype-Tissue Expression database. Our work reveals a novel mechanism (miR-21-PPARA-NCOA6) associated with the dysregulation of keratinocyte proliferation, differentiation, and migration in diabetic skin, where NCOA6 is the hub gene and KHSRP and SIN3B are key coordinators. Additionally, we build a TF-miRNA-mRNA regulatory network to describe its interactive connections. With respect to the skin microbiome, the impaired immune response of diabetics may fail to prevent bacterial colonization in affected tissue resulting in chronic infection and biofilm production. First, we review the latest literature on DFU microbiology unveiled by next-generation sequencing technologies and discuss the limitations and the promises of these approaches in measuring and monitoring wound progression. Then, we conduct a meta-analysis across publicly available DFU microbiome datasets to assess the effect of demographic and technical factors on the resulting microbiome and leverage this harmonized dataset to train a predictive model for the wound outcome. We show that the wound microbiome predictive model can classify DFUs as healing or seriously infected, and that the presence of two bacterial genera, Actinomyces and Brevibacterium (Actinomycetia), are strong predictors of wound status. Our work also demonstrates the significant impact of the study cohort, geographic location, sampling method, and sequencing region on the observed DFU microbiome, and reveals how such factors can obscure pathogen detection. Lastly, we develop a web-based database called WoundDB, which is a repository for both cross-sectional and longitudinal datasets that will enable the reuse of wound-relevant microbiome data in future studies. This dissertation explores different facets of DFUs including host genetics and skin microbiome, and offers (1) new insights into molecular mechanisms associated with susceptibility to cutaneous diseases in diabetic populations; (2) a deeper understanding of DFU microbiome and molecular sequencing techniques used for profiling; (3) a predictive model that associates DFU healing status with corresponding dynamic microbiome for wound outcome classification; and (4) an online database called WoundDB that provides a user-friendly interface for querying and downloading wound-relevant microbiome data. This work expands our knowledge of the etiology of DFUs towards the development of wound management and diagnostics.