Model-driven mapping of transcriptional networks reveals the circuitry and dynamics of virulence regulation

Abstract

Key steps in understanding a biological process include identifying genes that are involved and determining how they are regulated. We developed a novel method for identifying transcription factors (TFs) involved in a specific process and used it to map regulation of the key virulence factor of a deadly fungus-its capsule. The map, built from expression profiles of 41 TF mutants, includes 20 TFs not previously known to regulate virulence attributes. It also reveals a hierarchy comprising executive, midlevel, and "foreman" TFs. When grouped by temporal expression pattern, these TFs explain much of the transcriptional dynamics of capsule induction. Phenotypic analysis of TF deletion mutants revealed complex relationships among virulence factors and virulence in mice. These resources and analyses provide the first integrated, systems-level view of capsule regulation and biosynthesis. Our methods dramatically improve the efficiency with which transcriptional networks can be analyzed, making genomic approaches accessible to laboratories focused on specific physiological processes.[Supplemental material is available for this article.]In this paper we present an efficient means of comprehensively mapping the network of transcription factors (TFs) that regulate a particular physiological process. Our approach cycles through deletion of TFs, expression profiling of TF mutants, model construction, and model-directed selection of TFs for the next round of deletion. This predictive genetics approach identifies TFs that affect the process of interest, providing a valuable complement to undirected mutagenesis and screening. Simultaneously, it builds a network model that explains how the TFs affect the process, yielding novel insights into the biological system under study.Mapping the network that regulates a specific process requires knowing which TFs affect that process. One way to identify such TFs is to screen comprehensive mutant libraries, but generating such libraries is not always feasible. Furthermore, genome-scale screening assays must be fast and scalable; such assays may not exist for the process of interest or may be less sensitive than other, more laborious assays. An alternative approach is to map the targets of all TFs encoded in a genome by using methods such as chromatin-immunoprecipitation (ChIP) or large-scale TF deletion and expression analysis. However, undirected, genome-wide approaches are costly and inefficient for probing a specific biological process in detail. We report a model-guided approach that addresses all of these problems by focusing experimental effort on the TFs most likely to be involved in the process of interest. Furthermore, our approach generates a network that provides mechanistic explanations for the phenotypes of TF deletion mutants.Our approach alternates network building by using an algorithm we call NetProphet with identifying relevant TFs by using an algorithm we call PhenoProphet. NetProphet is a validated method for mapping direct, functional regulation that significantly out...

Investigating Conservation of the Cell-Cycle-Regulated Transcriptional Program in the Fungal Pathogen, Cryptococcus neoformans

“…neoformans and phenotypically profiled by single gene knockouts [6,31,32]. This TF deletion collection was profiled over many virulence factor-inducing conditions to discover pathways that regulate disease and drug response genes [32].…”

“…This TF deletion collection was profiled over many virulence factor-inducing conditions to discover pathways that regulate disease and drug response genes [32]. Serial activation of TFs during capsule production has also been studied to elucidate the order in which TFs control virulence gene products [31]. However, the cell cycle has not been investigated in synchronous populations of cells to date.…”

The cAMP/protein kinase A signaling pathway in pathogenic basidiomycete fungi: Connections with iron homeostasis

“…These transcription factors integrate cAMP/PKA signaling with other functions including the pH response pathway and the MAPK pathways for pheromone response and cell wall integrity. There is a wealth of emerging systems biology information on the functions of transcription factors in C. neoformans and this work will likely support the identification of additional targets of cAMP/PKA signaling (Jung et al ., 2015; Maier et al ., 2015). Overall, these findings set the stage for future work to identify new signaling components including transcription factor targets as well the molecular mechanisms of regulation and cross talk.…”

TheCryptococcus neoformansTitan cell is an inducible and regulated morphotype underlying pathogenesis

“…The C 2 H 2 transcription factor Usv101 is a master regulator of C. neoformans pathogenesis that negatively regulates capsule and acts downstream of Swi6, a regulator of cell cycle progression [31][32][33]. Usv101 is additionally predicted to regulate Gpa1 but is not itself directly influenced by cAMP.…”