Next-Generation Computational Genetic Analysis: Multiple Complement Alleles Control Survival after Candida albicans Infection

Abstract

ABSTRACT Candida albicans is a fungal pathogen that causes severe disseminated infections that can be lethal in immunocompromised patients. Genetic factors are known to alter the initial susceptibility to and severity of C. albicans infection. We developed a next-generation computational genetic mapping program with advanced features to identify genetic factors affecting survival in a murine genetic model of hematogenous C. albicans infection. This computational tool was used to analyze the median survival data after inbred mouse strains were infected with C. albicans , which provides a useful experimental model for identification of host susceptibility factors. The computational analysis indicated that genetic variation within early classical complement pathway components ( C1q , C1r , and C1s ) could affect survival. Consistent with the computational results, serum C1 binding to this pathogen was strongly affected by C1rs alleles, as was survival of chromosome substitution strains. These results led to a combinatorial, conditional genetic model, involving an interaction between C5 and C1r/s alleles, which accurately predicted survival after infection. Beyond applicability to infectious disease, this information could increase our understanding of the genetic factors affecting susceptibility to autoimmune and neurodegenerative diseases.