Cryptococcus neoformans Dual GDP-Mannose Transporters and Their Role in Biology and Virulence

Wang, Zhuo A.; Griffith, Cara L.; Skowyra, Michael L.; Salinas, Nichole D.; Williams, M. P.; Maier, Ezekiel J.; Gish, Stacey R.; Liu, Hong; Brent, Michael R.; Doering, Tamara L.

doi:10.1128/ec.00054-14

Cited by 26 publications

(32 citation statements)

References 63 publications

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“…While FLAG has been used in C . neoformans by others [ 54 , 55 ], we could find no record of the nuceloplasmin NLS being employed; perhaps it is this element that is toxic. We cannot be certain which of these explanations, if any, are the reason for the differences in this work and that published for C .…”

Section: Discussionmentioning

confidence: 67%

Targeted Genome Editing via CRISPR in the Pathogen Cryptococcus neoformans

et al. 2016

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Low rates of homologous integration have hindered molecular genetic studies in Cryptococcus neoformans over the past 20 years, and new tools that facilitate genome manipulation in this important pathogen are greatly needed. To this end, we have investigated the use of a Class 2 CRISPR system in C. neoformans (formerly C. neoformans var. grubii). We first expressed a derivative of the Streptococcus pyogenes Cas9 nuclease in C. neoformans, and showed that it has no effect on growth, production of virulence factors in vitro, or virulence in a murine inhalation model. In proof of principle experiments, we tested the CAS9 construct in combination with multiple self-cleaving guide RNAs targeting the well-characterized phosphoribosylaminoamidazole carboxylase-encoding ADE2 gene. Utilizing combinations of transient and stable expression of our constructs, we revealed that functionality of our CRISPR constructs in C. neoformans is dependent upon the CAS9 construct being stably integrated into the genome, whilst transient expression of the guide RNA is sufficient to enhance rates of homologous recombination in the CAS9 genetic background. Given that the presence of the CRISPR nuclease does not influence virulence in a murine inhalation model, we have successfully demonstrated that this system is compatible with studies of C. neoformans pathogenesis and represents a powerful tool that can be exploited by researchers in the field.

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Section: Discussionmentioning

confidence: 67%

Targeted Genome Editing via CRISPR in the Pathogen Cryptococcus neoformans

et al. 2016

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“…To determine if Yap1 was required for virulence of C. neoformans , we utilized a mouse inhalation model of infection (32) to compare infection isogenic wild-type C. neoformans (K99α:wt) and two independent yap1 Δ derivatives. No significant difference in the virulence of the yap1 Δ strains compared to wild-type could be seen as P values for the difference between wild-type and either independent yap1 Δ strain were >0.01.…”

Section: Resultsmentioning

confidence: 99%

“…C. neoformans strains were tested for virulence as described previously (32) using a mouse inhalation model. Isogenic wild-type (KN99α) and two yap1 Δ ::G418 R derivatives were grown overnight in rich medium and washed in phosphate-buffered saline.…”

Section: Methodsmentioning

confidence: 99%

Cryptococcus neoformans Yap1 is required for normal fluconazole and oxidative stress resistance

Paul

Doering

Moye‐Rowley

2015

Fungal Genetics and Biology

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Cryptococcus neoformans is a pathogen that is the most common cause of fungal meningitis. As with most fungal pathogens, the most prevalent clinical antifungal used to treat Cryptococcosis is orally administered fluconazole. Resistance to this antifungal is an increasing concern in treatment of fungal disease in general. Our knowledge of the specific determinants involved in fluconazole resistance in Cryptococcus is limited. Here we report the identification of an important genetic determinant of fluconazole resistance in Cryptococcus neoformans that encodes a basic region-leucine zipper transcription factor homologous to Saccharomyces cerevisiae Yap1. Expression of a codon-optimized form of the Cn YAP1 cDNA in S. cerevisiae complemented defects caused by loss of the endogenous S. cerevisiae YAP1 gene and activated transcription from a reporter gene construct. Mutant strains of C. neoformans lacking YAP1 were hypersensitive to a range of oxidative stress agents but importantly also to fluconazole. Loss of Yap1 homologues from other fungal pathogens like Candida albicans or Aspergillus fumigatus was previously found to cause oxidant hypersensitivity but had no detectable effect on fluconazole resistance. Our data provide evidence for a unique biological role of Yap1 in wild-type fluconazole resistance in C. neoformans.

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“…In the course of this work, we generated a rich resource for systems biology of fungal virulence. We increased the number of publically available RNA-seq profiles from C. neoformans TF-deletion studies 20-fold, more than doubled the total number of Cryptococcus expression profiles (including microarrays), presented the first time course of expression during capsule induction, and generated virulence- (Cottrell et al 2007;Wang et al 2014), and Ugt1 is a UDP-Gal transporter (Moyrand et al 2007); transporters for the other precursors have not been identified. (B) Network-derived regulatory relationships between the TFs shown in A, using the same colors and symbols except that TFs are circles.…”

Section: Discussionmentioning

confidence: 99%

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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Cryptococcus neoformans Dual GDP-Mannose Transporters and Their Role in Biology and Virulence

Cited by 26 publications

References 63 publications

Targeted Genome Editing via CRISPR in the Pathogen Cryptococcus neoformans

Targeted Genome Editing via CRISPR in the Pathogen Cryptococcus neoformans

Cryptococcus neoformans Yap1 is required for normal fluconazole and oxidative stress resistance

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

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