PKC signaling regulates drug resistance of the fungal pathogen Candida albicans via circuitry comprised of Mkc1, calcineurin, and Hsp90.
Abstract
Fungal pathogens exploit diverse mechanisms to survive exposure to antifungal drugs.
This poses concern given the limited number of clinically useful antifungals and the
growing population of immunocompromised individuals vulnerable to life-threatening
fungal infection. To identify molecules that abrogate resistance to the most widely
deployed class of antifungals, the azoles, we conducted a screen of 1,280 pharmacologically
active compounds. Three out of seven hits that abolished azole resistance of a resistant
mutant of the model yeast Saccharomyces cerevisiae and a clinical isolate of the leading
human fungal pathogen Candida albicans were inhibitors of protein kinase C (PKC),
which regulates cell wall integrity during growth, morphogenesis, and response to
cell wall stress. Pharmacological or genetic impairment of Pkc1 conferred hypersensitivity
to multiple drugs that target synthesis of the key cell membrane sterol ergosterol,
including azoles, allylamines, and morpholines. Pkc1 enabled survival of cell membrane
stress at least in part via the mitogen activated protein kinase (MAPK) cascade in
both species, though through distinct downstream effectors. Strikingly, inhibition
of Pkc1 phenocopied inhibition of the molecular chaperone Hsp90 or its client protein
calcineurin. PKC signaling was required for calcineurin activation in response to
drug exposure in S. cerevisiae. In contrast, Pkc1 and calcineurin independently regulate
drug resistance via a common target in C. albicans. We identified an additional level
of regulatory control in the C. albicans circuitry linking PKC signaling, Hsp90, and
calcineurin as genetic reduction of Hsp90 led to depletion of the terminal MAPK, Mkc1.
Deletion of C. albicans PKC1 rendered fungistatic ergosterol biosynthesis inhibitors
fungicidal and attenuated virulence in a murine model of systemic candidiasis. This
work establishes a new role for PKC signaling in drug resistance, novel circuitry
through which Hsp90 regulates drug resistance, and that targeting stress response
signaling provides a promising strategy for treating life-threatening fungal infections.
Type
Journal articleSubject
AnimalsAntifungal Agents
Calcineurin
Candida albicans
Drug Resistance, Fungal
Fungal Proteins
HSP90 Heat-Shock Proteins
Immunoblotting
Mice
Microbial Sensitivity Tests
Mitogen-Activated Protein Kinases
Protein Kinase C
Reverse Transcriptase Polymerase Chain Reaction
Saccharomyces cerevisiae
Signal Transduction
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https://hdl.handle.net/10161/4605Published Version (Please cite this version)
10.1371/journal.ppat.1001069Publication Info
LaFayette, Shantelle L; Collins, Cathy; Zaas, Aimee K; Schell, Wiley A; Betancourt-Quiroz,
Marisol; Gunatilaka, AA Leslie; ... Cowen, Leah E (2010). PKC signaling regulates drug resistance of the fungal pathogen Candida albicans via
circuitry comprised of Mkc1, calcineurin, and Hsp90. PLoS Pathog, 6(8). pp. e1001069. 10.1371/journal.ppat.1001069. Retrieved from https://hdl.handle.net/10161/4605.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
John Robert Perfect
James B. Duke Distinguished Professor of Medicine
Research in my laboratory focuses around several aspects of medical mycology. We
are investigating antifungal agents (new and old) in animal models of candida and
cryptococcal infections. We have examined clinical correlation of in vitro antifungal
susceptibility testing and with in vivo outcome. Our basic science project examines
the molecular pathogenesis of cryptococcal infections. We have developed a molecular
foundation for C. neoformans, including transformation systems, gene disr
Wiley Alexander Schell
Associate Professor Emeritus in Medicine
Aimee Kirsch Zaas
Professor of Medicine
Medical education Genomic applications for diagnosis of infectious diseases Genomic
applications for prediction of infectious diseases
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