Computational prediction of Homo sapiens–Candida albicans protein–protein interactions reveal key virulence factors using dual RNA-Seq data analysis

Abstract

A prevalent pathobiont, Candida albicans, accounts for approximately 70% of fungal infections worldwide owing to its virulence traits that culminate in devastating fatalities within healthcare facilities. Protein–protein interactions (PPIs) between Homo sapiens and C. albicans play a pivotal role in infection and disease progression. Additionally, scarcity of information on H. sapiens–C. albicans protein–protein interactions makes it difficult to understand the molecular mechanisms underlying infection and host immune responses. Investigating these PPIs can provide crucial insights into host–pathogen relationships and facilitate the development of novel therapeutic interventions. To address this challenge, we utilized computational techniques based on homology and domain to project 56,515 human-fungal pathogen protein–protein interactions (HF-PPIs) involving 6830 human and 486 C. albicans proteins. We have identified 16 key virulence factors of C. albicans, including SOD1, ERG10, GFA1, and VPS4, as potential therapeutic targets. As evidenced by dual RNA-Seq data acquired at various stages of infection such as 15, 30, 60, 120, and 240 min, these fungal genes interact with down-regulated human immunomodulatory genes specifically, ADRM1, DAXX, RYBP, SGTA, and SRGN. In addition to their intrinsically disordered regions, these human genes are particularly susceptible to fungal manipulation. Through the identification of experimentally validated virulence factors and their interaction partners, this investigation constructs HF-PPI between H. sapiens and C. albicans. Our knowledge of human-fungal pathogen protein–protein interactions will be improved by integrating computational and experimental data in order to facilitate the development of efficient fungal infection prevention and treatment protocols.