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      Systematic Prediction of Antifungal Drug Synergy by Chemogenomic Screening in Saccharomyces cerevisiae

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          Abstract

          Since the earliest days of using natural remedies, combining therapies for disease treatment has been standard practice. Combination treatments exhibit synergistic effects, broadly defined as a greater-than-additive effect of two or more therapeutic agents. Clinicians often use their experience and expertise to tailor such combinations to maximize the therapeutic effect. Although understanding and predicting biophysical underpinnings of synergy have benefitted from high-throughput screening and computational studies, one challenge is how to best design and analyze the results of synergy studies, especially because the number of possible combinations to test quickly becomes unmanageable. Nevertheless, the benefits of such studies are clear—by combining multiple drugs in the treatment of infectious disease and cancer, for instance, one can lessen host toxicity and simultaneously reduce the likelihood of resistance to treatment. This study introduces a new approach to characterize drug synergy, in which we extend the widely validated chemogenomic HIP–HOP assay to drug combinations; this assay involves parallel screening of comprehensive collections of barcoded deletion mutants. We identify a class of “combination-specific sensitive strains” that introduces mechanisms for the synergies we observe and further suggest focused follow-up studies.

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          Most cited references56

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            The genetic landscape of a cell.

            A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for approximately 75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape provides a key for interpretation of chemical-genetic interactions and drug target identification.
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              RAFT1: a mammalian protein that binds to FKBP12 in a rapamycin-dependent fashion and is homologous to yeast TORs.

              The immunosuppressants rapamycin and FK506 bind to the same intracellular protein, the immunophilin FKBP12. The FKB12-FK506 complex interacts with and inhibits the Ca(2+)-activated protein phosphatase calcineurin. The target of the FKBP12-rapamycin complex has not yet been identified. We report that a protein complex containing 245 kDa and 35 kDa components, designated rapamycin and FKBP12 targets 1 and 2 (RAFT1 and RAFT2), interacts with FKBP12 in a rapamycin-dependent manner. Sequences (330 amino acids total) of tryptic peptides derived from the 245 kDa RAFT1 reveal striking homologies to the yeast TOR gene products, which were originally identified by mutations that confer rapamycin resistance in yeast. A RAFT1 cDNA was obtained and found to encode a 289 kDa protein (2549 amino acids) that is 43% and 39% identical to TOR2 and TOR1, respectively. We propose that RAFT1 is the direct target of FKBP12-rapamycin and a mammalian homolog of the TOR proteins.
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                Author and article information

                Contributors
                Journal
                Front Fungal Biol
                Front Fungal Biol
                Front. Fungal Biol.
                Frontiers in Fungal Biology
                Frontiers Media S.A.
                2673-6128
                02 July 2021
                2021
                : 2
                : 683414
                Affiliations
                [1] 1Faculty of Pharmaceutical Sciences, University of British Columbia , Vancouver, BC, Canada
                [2] 2Department of Chemistry, University of British Columbia , Vancouver, BC, Canada
                [3] 3Donnelly Centre for Cellular and Biomedical Research, University of Toronto , Toronto, ON, Canada
                [4] 4Department of Biochemistry and Molecular Biology, University of British Columbia , Vancouver, BC, Canada
                Author notes

                Edited by: Dominique Sanglard, University of Lausanne, Switzerland

                Reviewed by: Dominic Hoepfner, Novartis Institutes for BioMedical Research, Switzerland; Stephanie Diezmann, University of Bristol, United Kingdom; Rebecca Shapiro, University of Guelph, Canada

                *Correspondence: Corey Nislow corey.nislow@ 123456ubc.ca

                This article was submitted to Fungal Pathogenesis, a section of the journal Frontiers in Fungal Biology

                †These authors share first authorship

                Article
                10.3389/ffunb.2021.683414
                10512392
                37744101
                fb3e5620-a531-413a-8e41-b56ecb12becb
                Copyright © 2021 Gaikani, Smith, Lee, Giaever and Nislow.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 20 March 2021
                : 01 June 2021
                Page count
                Figures: 5, Tables: 3, Equations: 0, References: 56, Pages: 13, Words: 8927
                Funding
                Funded by: Canada Research Chairs, doi 10.13039/501100001804;
                Categories
                Fungal Biology
                Original Research

                drug synergy,drug combinations,drug–gene interaction,antifungal,chemogenomics

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