Mapping the transcriptional regulatory network of a fungal pathogen by exploiting transcription factor perturbation

<i>Cryptococcus neoformans</i> is a fungus that can cause life-threatening infections, in part by producing a protective capsule around itself. In this study, we analyzed how cryptococcal genes are turned on and off by its many transcription factors (TFs), the proteins that control gene activity. By studying mutant strains lacking 120 TFs and applying a powerful network analysis method, we found that no single TF is dedicated primarily to controlling capsule formation. Instead, the TFs that affect the capsule also influence many other processes. We also compared the cryptococcal network to that of a well-studied model yeast. We found that the yeast TF whose predicted protein sequence is most similar to a cryptococcal TF often regulates completely unrelated sets of target genes, while TFs with less sequence similarity often have more shared targets. This work shows the value of network-based approaches for uncovering hidden biological relationships important for infection and disease.