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Li J, Kolberg K, Schlecht U, St Onge RP, Aparicio AM, Horecka J, Davis RW, Hillenmeyer ME, Harvey CJB. A biosensor-based approach reveals links between efflux pump expression and cell cycle regulation in pleiotropic drug resistance of yeast. J Biol Chem 2018; 294:1257-1266. [PMID: 30514758 DOI: 10.1074/jbc.ra118.003904] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Revised: 10/19/2018] [Indexed: 11/06/2022] Open
Abstract
Multidrug resistance is highly conserved in mammalian, fungal, and bacterial cells, is characterized by resistance to several unrelated xenobiotics, and poses significant challenges to managing infections and many cancers. Eukaryotes use a highly conserved set of drug efflux transporters that confer pleiotropic drug resistance (PDR). To interrogate the regulation of this critical process, here we developed a small molecule-responsive biosensor that couples transcriptional induction of PDR genes to growth rate in the yeast Saccharomyces cerevisiae Using diverse PDR inducers and the homozygous diploid deletion collection, we applied this biosensor system to genome-wide screens for potential PDR regulators. In addition to recapitulating the activity of previously known factors, these screens identified a series of genes involved in a variety of cellular processes with significant but previously uncharacterized roles in the modulation of yeast PDR. Genes identified as down-regulators of the PDR included those encoding the MAD family of proteins involved in the mitotic spindle assembly checkpoint (SAC) complex. Of note, we demonstrated that genetic disruptions of the mitotic spindle assembly checkpoint elevate expression of PDR-mediating efflux pumps in response to exposure to a variety of compounds that themselves have no known influence on the cell cycle. These results not only establish our biosensor system as a viable tool for investigating PDR in a high-throughput fashion, but also uncover critical control mechanisms governing the PDR response and a previously uncharacterized link between PDR and cell cycle regulation in yeast.
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Affiliation(s)
- Jian Li
- Department of Biochemistry, Stanford Genome Technology Center, Stanford University School of Medicine, Palo Alto, California 94304
| | - Kristen Kolberg
- Department of Biochemistry, Stanford Genome Technology Center, Stanford University School of Medicine, Palo Alto, California 94304
| | - Ulrich Schlecht
- Department of Biochemistry, Stanford Genome Technology Center, Stanford University School of Medicine, Palo Alto, California 94304
| | - Robert P St Onge
- Department of Biochemistry, Stanford Genome Technology Center, Stanford University School of Medicine, Palo Alto, California 94304
| | - Ana Maria Aparicio
- Department of Biochemistry, Stanford Genome Technology Center, Stanford University School of Medicine, Palo Alto, California 94304
| | - Joe Horecka
- Department of Biochemistry, Stanford Genome Technology Center, Stanford University School of Medicine, Palo Alto, California 94304
| | - Ronald W Davis
- Department of Biochemistry, Stanford Genome Technology Center, Stanford University School of Medicine, Palo Alto, California 94304
| | - Maureen E Hillenmeyer
- Department of Biochemistry, Stanford Genome Technology Center, Stanford University School of Medicine, Palo Alto, California 94304
| | - Colin J B Harvey
- Department of Biochemistry, Stanford Genome Technology Center, Stanford University School of Medicine, Palo Alto, California 94304.
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Celaj A, Schlecht U, Smith JD, Xu W, Suresh S, Miranda M, Aparicio AM, Proctor M, Davis RW, Roth FP, St Onge RP. Quantitative analysis of protein interaction network dynamics in yeast. Mol Syst Biol 2017; 13:934. [PMID: 28705884 PMCID: PMC5527849 DOI: 10.15252/msb.20177532] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Many cellular functions are mediated by protein–protein interaction networks, which are environment dependent. However, systematic measurement of interactions in diverse environments is required to better understand the relative importance of different mechanisms underlying network dynamics. To investigate environment‐dependent protein complex dynamics, we used a DNA‐barcode‐based multiplexed protein interaction assay in Saccharomyces cerevisiae to measure in vivo abundance of 1,379 binary protein complexes under 14 environments. Many binary complexes (55%) were environment dependent, especially those involving transmembrane transporters. We observed many concerted changes around highly connected proteins, and overall network dynamics suggested that “concerted” protein‐centered changes are prevalent. Under a diauxic shift in carbon source from glucose to ethanol, a mass‐action‐based model using relative mRNA levels explained an estimated 47% of the observed variance in binary complex abundance and predicted the direction of concerted binary complex changes with 88% accuracy. Thus, we provide a resource of yeast protein interaction measurements across diverse environments and illustrate the value of this resource in revealing mechanisms of network dynamics.
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Affiliation(s)
- Albi Celaj
- Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON, Canada.,Donnelly Centre, University of Toronto, Toronto, ON, Canada.,Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Ulrich Schlecht
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA.,Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | - Justin D Smith
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA.,Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Weihong Xu
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA
| | - Sundari Suresh
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA.,Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | - Molly Miranda
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA.,Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | - Ana Maria Aparicio
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA.,Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael Proctor
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA.,Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | - Ronald W Davis
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA.,Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA.,Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Frederick P Roth
- Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON, Canada .,Donnelly Centre, University of Toronto, Toronto, ON, Canada.,Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.,Canadian Institute for Advanced Research, Toronto, ON, Canada.,Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Robert P St Onge
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA .,Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
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Lee AY, St Onge RP, Proctor MJ, Wallace IM, Nile AH, Spagnuolo PA, Jitkova Y, Gronda M, Wu Y, Kim MK, Cheung-Ong K, Torres NP, Spear ED, Han MKL, Schlecht U, Suresh S, Duby G, Heisler LE, Surendra A, Fung E, Urbanus ML, Gebbia M, Lissina E, Miranda M, Chiang JH, Aparicio AM, Zeghouf M, Davis RW, Cherfils J, Boutry M, Kaiser CA, Cummins CL, Trimble WS, Brown GW, Schimmer AD, Bankaitis VA, Nislow C, Bader GD, Giaever G. Mapping the cellular response to small molecules using chemogenomic fitness signatures. Science 2014; 344:208-11. [PMID: 24723613 DOI: 10.1126/science.1250217] [Citation(s) in RCA: 176] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Genome-wide characterization of the in vivo cellular response to perturbation is fundamental to understanding how cells survive stress. Identifying the proteins and pathways perturbed by small molecules affects biology and medicine by revealing the mechanisms of drug action. We used a yeast chemogenomics platform that quantifies the requirement for each gene for resistance to a compound in vivo to profile 3250 small molecules in a systematic and unbiased manner. We identified 317 compounds that specifically perturb the function of 121 genes and characterized the mechanism of specific compounds. Global analysis revealed that the cellular response to small molecules is limited and described by a network of 45 major chemogenomic signatures. Our results provide a resource for the discovery of functional interactions among genes, chemicals, and biological processes.
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Affiliation(s)
- Anna Y Lee
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
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Schlecht U, Suresh S, Xu W, Aparicio AM, Chu A, Proctor MJ, Davis RW, Scharfe C, St Onge RP. A functional screen for copper homeostasis genes identifies a pharmacologically tractable cellular system. BMC Genomics 2014; 15:263. [PMID: 24708151 PMCID: PMC4023593 DOI: 10.1186/1471-2164-15-263] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2013] [Accepted: 03/10/2014] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Copper is essential for the survival of aerobic organisms. If copper is not properly regulated in the body however, it can be extremely cytotoxic and genetic mutations that compromise copper homeostasis result in severe clinical phenotypes. Understanding how cells maintain optimal copper levels is therefore highly relevant to human health. RESULTS We found that addition of copper (Cu) to culture medium leads to increased respiratory growth of yeast, a phenotype which we then systematically and quantitatively measured in 5050 homozygous diploid deletion strains. Cu's positive effect on respiratory growth was quantitatively reduced in deletion strains representing 73 different genes, the function of which identify increased iron uptake as a cause of the increase in growth rate. Conversely, these effects were enhanced in strains representing 93 genes. Many of these strains exhibited respiratory defects that were specifically rescued by supplementing the growth medium with Cu. Among the genes identified are known and direct regulators of copper homeostasis, genes required to maintain low vacuolar pH, and genes where evidence supporting a functional link with Cu has been heretofore lacking. Roughly half of the genes are conserved in man, and several of these are associated with Mendelian disorders, including the Cu-imbalance syndromes Menkes and Wilson's disease. We additionally demonstrate that pharmacological agents, including the approved drug disulfiram, can rescue Cu-deficiencies of both environmental and genetic origin. CONCLUSIONS A functional screen in yeast has expanded the list of genes required for Cu-dependent fitness, revealing a complex cellular system with implications for human health. Respiratory fitness defects arising from perturbations in this system can be corrected with pharmacological agents that increase intracellular copper concentrations.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Robert P St Onge
- Stanford Genome Technology Center, Department of Biochemistry, Stanford University, 855 S California Avenue, Palo Alto, CA 94304, USA.
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Hyman RW, St Onge RP, Kim H, Tamaresis JS, Miranda M, Aparicio AM, Fukushima M, Pourmand N, Giudice LC, Davis RW. Molecular probe technology detects bacteria without culture. BMC Microbiol 2012; 12:29. [PMID: 22404909 PMCID: PMC3316761 DOI: 10.1186/1471-2180-12-29] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2011] [Accepted: 03/09/2012] [Indexed: 01/29/2023] Open
Abstract
Background Our ultimate goal is to detect the entire human microbiome, in health and in disease, in a single reaction tube, and employing only commercially available reagents. To that end, we adapted molecular inversion probes to detect bacteria using solely a massively multiplex molecular technology. This molecular probe technology does not require growth of the bacteria in culture. Rather, the molecular probe technology requires only a sequence of forty sequential bases unique to the genome of the bacterium of interest. In this communication, we report the first results of employing our molecular probes to detect bacteria in clinical samples. Results While the assay on Affymetrix GenFlex Tag16K arrays allows the multiplexing of the detection of the bacteria in each clinical sample, one Affymetrix GenFlex Tag16K array must be used for each clinical sample. To multiplex the clinical samples, we introduce a second, independent assay for the molecular probes employing Sequencing by Oligonucleotide Ligation and Detection. By adding one unique oligonucleotide barcode for each clinical sample, we combine the samples after processing, but before sequencing, and sequence them together. Conclusions Overall, we have employed 192 molecular probes representing 40 bacteria to detect the bacteria in twenty-one vaginal swabs as assessed by the Affymetrix GenFlex Tag16K assay and fourteen of those by the Sequencing by Oligonucleotide Ligation and Detection assay. The correlations among the assays were excellent.
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Affiliation(s)
- Richard W Hyman
- Departments of Biochemistry, Stanford University, Stanford, CA, USA.
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