Fungal infections are estimated to occur in over a billion people each year, and recent evidence suggests the rate is increasing. Vaccines are not available and despite the improved diagnosis and therapy, the treatment of fungal infections, particularly in immunocompromised hosts, is a challenging endeavor. Importantly, the risk of infection and its clinical outcome vary significantly even among patients with similar predisposing factors. This has prompted the study of the interaction of fungi with the host in an attempt to decipher the molecular and cellular causes underlying variable susceptibility to infection. The last decade has seen remarkable innovations in technology and computational methods. The combination of new “omics” technologies, including genomics, transcriptomics, proteomics and metabolomics, together with the use of both established and alternative in vivo models of infection, provides nearly comprehensive views of the architecture and dynamics of systems-wide host-fungus molecular interaction networks during infection. By providing a deeper understanding of the overall complexity of the biological, biochemical, and biophysical molecular processes regulating the host-fungus interaction, this knowledge is expected to allow the design and validation of predictive models of infection initiation, progression, and outcome, eventually contributing to new clinical strategies based on personalized diagnostics and treatment. The integration of individual traits into clinically applicable processes to predict the risk and progression of fungal infection, and the efficacy of antifungal prophylaxis and therapy, holds the promise of pioneering innovations benefiting patients suffering from or at risk of fungal infections. Here, we encourge contributions dealing with recent advances in “omics” technologies applied to the investigation of the host-fungus interactions, thereby laying the foundation for methodologies integrating omics and clinical data to support personalized diagnostic and therapeutic interventions.
Fungal infections are estimated to occur in over a billion people each year, and recent evidence suggests the rate is increasing. Vaccines are not available and despite the improved diagnosis and therapy, the treatment of fungal infections, particularly in immunocompromised hosts, is a challenging endeavor. Importantly, the risk of infection and its clinical outcome vary significantly even among patients with similar predisposing factors. This has prompted the study of the interaction of fungi with the host in an attempt to decipher the molecular and cellular causes underlying variable susceptibility to infection. The last decade has seen remarkable innovations in technology and computational methods. The combination of new “omics” technologies, including genomics, transcriptomics, proteomics and metabolomics, together with the use of both established and alternative in vivo models of infection, provides nearly comprehensive views of the architecture and dynamics of systems-wide host-fungus molecular interaction networks during infection. By providing a deeper understanding of the overall complexity of the biological, biochemical, and biophysical molecular processes regulating the host-fungus interaction, this knowledge is expected to allow the design and validation of predictive models of infection initiation, progression, and outcome, eventually contributing to new clinical strategies based on personalized diagnostics and treatment. The integration of individual traits into clinically applicable processes to predict the risk and progression of fungal infection, and the efficacy of antifungal prophylaxis and therapy, holds the promise of pioneering innovations benefiting patients suffering from or at risk of fungal infections. Here, we encourge contributions dealing with recent advances in “omics” technologies applied to the investigation of the host-fungus interactions, thereby laying the foundation for methodologies integrating omics and clinical data to support personalized diagnostic and therapeutic interventions.
