Toward an Integrated Model of Capsule Regulation in Cryptococcus neoformans

Haynes, Brian C.; Skowyra, Michael L.; Spencer, Sarah J.; Gish, Stacey R.; Williams, M. P.; Held, E; Brent, Michael R.; Doering, Tamara L.

doi:10.1371/journal.ppat.1002411

Cited by 92 publications

(124 citation statements)

References 66 publications

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“…6B; p Ͼ 0.675), and lung inflammation in both groups was comparable (data not shown). Acapsular cryptococci typically do not proliferate within this period (60).…”

Section: Resultsmentioning

confidence: 99%

Unusual Galactofuranose Modification of a Capsule Polysaccharide in the Pathogenic Yeast Cryptococcus neoformans

Heiß

Skowyra

Liu

et al. 2013

Journal of Biological Chemistry

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Background: Galactofuranose, the five-membered ring form of galactose, occurs in the encapsulated pathogenic fungus Cryptococcus neoformans but not in humans. Results: We established the position of galactofuranose within a capsule polysaccharide and characterized cryptococci lacking this modification. Conclusion: Galactofuranose occurs in an unusual linkage but is not required for growth or virulence. Significance: This work fills a gap in knowledge about a pathogen-specific modification.

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“…6B; p Ͼ 0.675), and lung inflammation in both groups was comparable (data not shown). Acapsular cryptococci typically do not proliferate within this period (60).…”

Section: Resultsmentioning

confidence: 99%

Unusual Galactofuranose Modification of a Capsule Polysaccharide in the Pathogenic Yeast Cryptococcus neoformans

Heiß

Skowyra

Liu

et al. 2013

Journal of Biological Chemistry

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“…In the second stage, we deleted 17 genes encoding putative DNA binding proteins based on the correlation of their expression levels with capsule size in various conditions (Supplemental Table S1, "Correlation"; Haynes et al 2011). We grew these 28 deletion mutants and three others (Supplemental Table S1, "Other") in capsule-inducing conditions and assayed them for capsule size.…”

Section: Expression-based Network Mapping Predicts Tfs Involved In Camentioning

confidence: 99%

“…We compared this result to our previous strategy of using the correlation of gene expression with capsule thicknesses to predict TF capsule involvement by using our previously published correlation scores (Haynes et al 2011 Figure 2. TF mutants have significant virulence-related phenotypes.…”

Section: Phenoprophet Accurately Predicts Which Tfs Will Have Alteredmentioning

confidence: 99%

“…PSA101 is the most heavily regulated of the genes we analyzed, suggesting that it is a key point at which transcriptional regulation affects biosynthesis. Of the two genes encoding GDP-mannose transporters, GMT1, which has a much greater effect on capsule size , is regulated by the major capsule regulators Cir1 (Jung et al 2006), Rim101 (O'Meara et al 2014, and Ada2 (Haynes et al 2011). GMT2, whose deletion shows a phenotype only when GMT1 is also inactivated , is repressed by Usv101 and activated by Rds2, a TF without a significant capsule thickness phenotype.…”

Section: Network Analysis Reveals Mechanisms Of Capsule Biosynthesis mentioning

confidence: 99%

“…A split-marker strategy (Fu et al 2006) was used to replace specific genomic targets with drug resistance cassettes as in Haynes et al (2011) and to incorporate HA tags; see Supplemental Methods for details and our gene naming protocol. New gene names were CLR, capsule-linked regulator; MLR, melanin-linked regulator; and CCD, capsule-correlated DNA-binding protein.…”

Section: Gene Manipulation and Namingmentioning

confidence: 99%

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

et al. 2015

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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...

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