Editorial: An Omics Perspective on Fungal Infection: Toward Next-Generation Diagnosis and Therapy

Fungal infections are estimated to occur in over a billion people each year, and evidence suggests the rate is increasing. Vaccines are unavailable, and despite progress in diagnosis and therapy, the management of fungal infections is a challenging endeavor associated with unacceptable mortality rates, particularly in immunocompromised hosts (Kontoyiannis et al., 2010; Pagano et al., 2010). Importantly, the risk of infection and its clinical outcome vary significantly even among patients with similar predisposing conditions (Carvalho et al., 2010). Concerns over excessive prescription of antifungals and the remarkable burden conveyed to healthcare systems have encouraged efforts to decipher the molecular and cellular causes underlying variable susceptibility to infection (Cunha and Carvalho, 2012). The combination of omics technologies and advanced computational methods, together with the use of both established and alternative in vitro and in vivo models of infection (Brunke et al., 2015), provides comprehensive views of the architecture and dynamics of host-fungus interaction networks at a level of complexity previously unanticipated. As a result of our deepened understanding of the biological, biochemical and biophysical molecular processes regulating the host-fungus interaction, several targets with potential usefulness in personalized medical interventions have been proposed (Oliveira-Coelho et al., 2015). The present Research Topic brings together 10 articles covering multiple aspects of the host-fungus interaction with emphasis on the application of omics-based technologies to project novel or improve current diagnostic and therapeutic approaches. Smeekens et al. point out the advent of omics platforms and the development of systems biology tools to study antifungal immunity (Smeekens et al.). Much research has been performed on host genetics and fungal infection (Cunha et al., 2013; Smeekens et al., 2013b), but only recently have these data been integrated into functional genomics approaches driving unbiased identification and quantification of targets controlling susceptibility to infection (Smeekens et al., 2013a; Kumar et al., 2014). With the increasing number and quality of data repositories, the generation of multi-scale host-fungus interaction models through systems biology is expected to support personalized medicine interventions (Dix et al., 2016). A substantial proportion of genetic markers associated with the risk of fungal infection are within immune-related genes (Lupianez et al., 2015), often implicated in the nuclear factor (NF)-κB