A Major Role for Capsule-Independent Phagocytosis-Inhibitory Mechanisms in Mammalian Infection by Cryptococcus neoformans

Chun, Cheryl D.; Brown, Jessica C. S.; Madhani, Hiten D.

doi:10.1016/j.chom.2011.02.003

Cited by 85 publications

(118 citation statements)

References 31 publications

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“…Our data support a previous report (Chun et al 2011) that Gat201 activates ECM2201, encoding a TF, and we report for the first time that the ecm2201 mutant is hypocapsular (Fig. 2).…”

Section: Chip-experiments Validate Netprophet Predictionssupporting

confidence: 81%

“…S3; Supplemental File 5). Previously, binding specificity was known for only 2 TFs in Cryptococcus (Chun et al 2011;O'Meara et al 2014), both of which strongly support our independently derived PWMs.…”

Section: Netprophet Predicts Functional Direct Binding Of Tfs To Thesupporting

confidence: 56%

“…Usv101 also represses several sugar transporters while activating HXT1, a hexose transporter that has a hypercapsular phenotype (Chikamori and Fukushima 2005). Usv101 represses, and Gat201 activates, BLP1, which is involved in a capsule-independent anti-phagocytic mechanism (Chun et al 2011). The opposing effects of Usv101 and Gat201 on BLP1, which promotes fungal survival during infection, are consistent with their generally opposing roles in regulating virulence and their opposite capsule phenotypes.…”

Section: Chip-experiments Validate Netprophet Predictionsmentioning

confidence: 50%

“…2). Gat201 has the same effect on capsule size as Nrg1 and works with Nrg1 to stimulate expression of GAT204, which encodes a second TF involved in the capsule-independent anti-phagocytic mechanism (Chun et al 2011). Both Gat201 and Nrg1 repress some genes involved in cell wall synthesis, but Nrg1 also activates other genes involved in cell wall synthesis, consistent with cell wall remodeling during capsule induction.…”

Section: Chip-experiments Validate Netprophet Predictionsmentioning

confidence: 69%

“…To validate NetProphet in C. neoformans, we focused on Gat201, the only cryptococcal TF for which ChIP data was available (Chun et al 2011); Nrg1, a well-studied capsule regulator (Cramer et al 2006); and Usv101, a capsule regulator described here for the first time. We epitope-tagged the last two and carried out ChIP-seq.…”

Section: Netprophet Predicts Functional Direct Binding Of Tfs To Thementioning

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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“…Our data support a previous report (Chun et al 2011) that Gat201 activates ECM2201, encoding a TF, and we report for the first time that the ecm2201 mutant is hypocapsular (Fig. 2).…”

Section: Chip-experiments Validate Netprophet Predictionssupporting

confidence: 81%

Section: Netprophet Predicts Functional Direct Binding Of Tfs To Thesupporting

confidence: 56%

Section: Chip-experiments Validate Netprophet Predictionsmentioning

confidence: 50%

Section: Chip-experiments Validate Netprophet Predictionsmentioning

confidence: 69%

Section: Netprophet Predicts Functional Direct Binding Of Tfs To Thementioning

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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Ecogenomics of Human and Animal Basidiomycetous Yeast Pathogens

Sun¹,

Hagen²,

Xu³

et al. 2013

The Ecological Genomics of Fungi

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Cryptococcus extracellular vesicles properties and their use as vaccine platforms

Rizzo

Wong

Gazi

et al. 2021

J of Extracellular Vesicle

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Whereas extracellular vesicle (EV) research has become commonplace in different biomedical fields, this field of research is still in its infancy in mycology. Here we provide a robust set of data regarding the structural and compositional aspects of EVs isolated from the fungal pathogenic species Cryptococcus neoformans, C. deneoformans and C. deuterogattii . Using cutting‐edge methodological approaches including cryogenic electron microscopy and cryogenic electron tomography, proteomics, and flow cytometry, we revisited cryptococcal EV features and suggest a new EV structural model, in which the vesicular lipid bilayer is covered by mannoprotein‐based fibrillar decoration, bearing the capsule polysaccharide as its outer layer. About 10% of the EV population is devoid of fibrillar decoration, adding another aspect to EV diversity. By analysing EV protein cargo from the three species, we characterized the typical Cryptococcus EV proteome. It contains several membrane‐bound protein families, including some Tsh proteins bearing a SUR7/PalI motif. The presence of known protective antigens on the surface of Cryptococcus EVs, resembling the morphology of encapsulated virus structures, suggested their potential as a vaccine. Indeed, mice immunized with EVs obtained from an acapsular C. neoformans mutant strain rendered a strong antibody response in mice and significantly prolonged their survival upon C. neoformans infection.

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A Major Role for Capsule-Independent Phagocytosis-Inhibitory Mechanisms in Mammalian Infection by Cryptococcus neoformans

Cited by 85 publications

References 31 publications

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

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

Ecogenomics of Human and Animal Basidiomycetous Yeast Pathogens

Cryptococcus extracellular vesicles properties and their use as vaccine platforms

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