Chemogenomic profiling predicts antifungal synergies
Abstract Chemotherapies, HIV infections, and treatments to block organ transplant rejection are creating a population of immunocompromised individuals at serious risk of systemic fungal infections. Since single‐agent therapies are susceptible to failure due to either inherent or acquired resistance,...
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer Nature
2009-12-01
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| Series: | Molecular Systems Biology |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/msb.2009.95 |
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| _version_ | 1849225798411616256 |
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| author | Gregor Jansen Anna Y Lee Elias Epp Amélie Fredette Jamie Surprenant Doreen Harcus Michelle Scott Elaine Tan Tamiko Nishimura Malcolm Whiteway Michael Hallett David Y Thomas |
| author_facet | Gregor Jansen Anna Y Lee Elias Epp Amélie Fredette Jamie Surprenant Doreen Harcus Michelle Scott Elaine Tan Tamiko Nishimura Malcolm Whiteway Michael Hallett David Y Thomas |
| author_sort | Gregor Jansen |
| collection | DOAJ |
| description | Abstract Chemotherapies, HIV infections, and treatments to block organ transplant rejection are creating a population of immunocompromised individuals at serious risk of systemic fungal infections. Since single‐agent therapies are susceptible to failure due to either inherent or acquired resistance, alternative therapeutic approaches such as multi‐agent therapies are needed. We have developed a bioinformatics‐driven approach that efficiently predicts compound synergy for such combinatorial therapies. The approach uses chemogenomic profiles in order to identify compound profiles that have a statistically significant degree of similarity to a fluconazole profile. The compounds identified were then experimentally verified to be synergistic with fluconazole and with each other, in both Saccharomyces cerevisiae and the fungal pathogen Candida albicans. Our method is therefore capable of accurately predicting compound synergy to aid the development of combinatorial antifungal therapies. |
| format | Article |
| id | doaj-art-d304a14adf0e4f5ebae26e5db0a183d8 |
| institution | Kabale University |
| issn | 1744-4292 |
| language | English |
| publishDate | 2009-12-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Molecular Systems Biology |
| spelling | doaj-art-d304a14adf0e4f5ebae26e5db0a183d82025-08-24T11:58:58ZengSpringer NatureMolecular Systems Biology1744-42922009-12-015111310.1038/msb.2009.95Chemogenomic profiling predicts antifungal synergiesGregor Jansen0Anna Y Lee1Elias Epp2Amélie Fredette3Jamie Surprenant4Doreen Harcus5Michelle Scott6Elaine Tan7Tamiko Nishimura8Malcolm Whiteway9Michael Hallett10David Y Thomas11Department of Biochemistry, Faculty of Medicine, McGill UniversityMcGill Centre for Bioinformatics, McGill UniversityGenetics Group, Biotechnology Research Institute, National Research Council of CanadaDepartment of Biochemistry, Faculty of Medicine, McGill UniversityDepartment of Biochemistry, Faculty of Medicine, McGill UniversityGenetics Group, Biotechnology Research Institute, National Research Council of CanadaMcGill Centre for Bioinformatics, McGill UniversityDepartment of Biochemistry, Faculty of Medicine, McGill UniversityDepartment of Biochemistry, Faculty of Medicine, McGill UniversityGenetics Group, Biotechnology Research Institute, National Research Council of CanadaMcGill Centre for Bioinformatics, McGill UniversityDepartment of Biochemistry, Faculty of Medicine, McGill UniversityAbstract Chemotherapies, HIV infections, and treatments to block organ transplant rejection are creating a population of immunocompromised individuals at serious risk of systemic fungal infections. Since single‐agent therapies are susceptible to failure due to either inherent or acquired resistance, alternative therapeutic approaches such as multi‐agent therapies are needed. We have developed a bioinformatics‐driven approach that efficiently predicts compound synergy for such combinatorial therapies. The approach uses chemogenomic profiles in order to identify compound profiles that have a statistically significant degree of similarity to a fluconazole profile. The compounds identified were then experimentally verified to be synergistic with fluconazole and with each other, in both Saccharomyces cerevisiae and the fungal pathogen Candida albicans. Our method is therefore capable of accurately predicting compound synergy to aid the development of combinatorial antifungal therapies.https://doi.org/10.1038/msb.2009.95antifungalchemical genomicsdrug profilingsynergy predictor |
| spellingShingle | Gregor Jansen Anna Y Lee Elias Epp Amélie Fredette Jamie Surprenant Doreen Harcus Michelle Scott Elaine Tan Tamiko Nishimura Malcolm Whiteway Michael Hallett David Y Thomas Chemogenomic profiling predicts antifungal synergies Molecular Systems Biology antifungal chemical genomics drug profiling synergy predictor |
| title | Chemogenomic profiling predicts antifungal synergies |
| title_full | Chemogenomic profiling predicts antifungal synergies |
| title_fullStr | Chemogenomic profiling predicts antifungal synergies |
| title_full_unstemmed | Chemogenomic profiling predicts antifungal synergies |
| title_short | Chemogenomic profiling predicts antifungal synergies |
| title_sort | chemogenomic profiling predicts antifungal synergies |
| topic | antifungal chemical genomics drug profiling synergy predictor |
| url | https://doi.org/10.1038/msb.2009.95 |
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