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A humanized yeast phenomic model of deoxycytidine kinase to predict genetic buffering of nucleoside analog cytotoxicity

View ORCID ProfileSean M. Santos, Mert Icyuz, Ilya Pound, Doreen William, Jingyu Guo, View ORCID ProfileBrett A. McKinney, View ORCID ProfileMichael Niederweis, John Rodgers, View ORCID ProfileJohn L. Hartman IV
doi: https://doi.org/10.1101/700153
Sean M. Santos
1Department of Genetics, University of Alabama at Birmingham, Birmingham, AL
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Mert Icyuz
1Department of Genetics, University of Alabama at Birmingham, Birmingham, AL
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Ilya Pound
1Department of Genetics, University of Alabama at Birmingham, Birmingham, AL
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Doreen William
2Department of Microbiology, University of Alabama at Birmingham, Birmingham, AL
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Jingyu Guo
1Department of Genetics, University of Alabama at Birmingham, Birmingham, AL
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Brett A. McKinney
1Department of Genetics, University of Alabama at Birmingham, Birmingham, AL
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Michael Niederweis
2Department of Microbiology, University of Alabama at Birmingham, Birmingham, AL
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John Rodgers
1Department of Genetics, University of Alabama at Birmingham, Birmingham, AL
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John L. Hartman IV
1Department of Genetics, University of Alabama at Birmingham, Birmingham, AL
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  • For correspondence: jhartman@uab.edu
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Abstract

Knowledge about synthetic lethality can be applied to enhance the efficacy of anti-cancer therapies in individual patients harboring genetic alterations in their cancer that specifically render it vulnerable. We investigated the potential for high-resolution phenomic analysis in yeast to predict such genetic vulnerabilities by systematic, comprehensive, and quantitative assessment of drug-gene interaction for gemcitabine and cytarabine, substrates of deoxycytidine kinase that have similar molecular structures yet distinct anti-tumor efficacy. Human deoxycytidine kinase (dCK) was conditionally expressed in the S. cerevisiae genomic library of knockout and knockdown (YKO/KD) strains, to globally and quantitatively characterize differential drug-gene interaction for gemcitabine and cytarabine. Pathway enrichment analysis revealed that autophagy, histone modification, chromatin remodeling, and apoptosis-related processes influence gemcitabine specifically, while drug-gene interaction specific to cytarabine was less enriched in Gene Ontology. Processes having influence over both drugs were DNA repair and integrity checkpoints and vesicle transport and fusion. Non-GO-enriched genes were also informative. Yeast phenomic and cancer cell line pharmacogenomics data were integrated to identify yeast-human homologs with correlated differential gene expression and drug-efficacy, thus providing a unique resource to predict whether differential gene expression observed in cancer genetic profiles are causal in tumor-specific responses to cytotoxic agents.

  • List of abbreviations and glossary of terms

    AraC
    cytarabine; cytosine arabinoside
    CPPs
    Cell proliferation parameters: parameters of the logistic growth equation used to fit cell proliferation data obtained by Q-HTCP. The CPPs used to assess gene interaction in this study were K (carrying capacity) and L (time required to reach half of carrying capacity) [7-9,38].
    DAmP
    Decreased Abundance of mRNA Production: refers to a method of making YKD alleles, where the 3’ UTR of essential genes is disrupted, reducing mRNA stability and gene dosage [291].
    dCK
    deoxycytidine kinase
    dCMP
    deoxycytidine monophosphate
    DE
    Deletion enhancer: gene loss of function (knockout or knockdown) that results in enhancement / increase of drug sensitivity [9].
    dFdC
    2’,2’-difluoro 2’-deoxycytidine, gemcitabine
    dNTP
    deoxyribonucleotide triphosphate
    DS
    Deletion suppressor: gene loss of function (knockout or knockdown) that results in suppression / reduction of drug sensitivity [9].
    ESCRT
    endosomal sorting complex required for transport
    GARP complex
    Golgi-associated retrograde protein complex.
    gCSI
    The Genentech Cell Line Screening Initiative: One of two pharmacogenomics datasets used in this study (https://pharmacodb.pmgenomics.ca/datasets/4).
    GDSC1000
    Genomics of Drug Sensitivity in Cancer: One of two pharmacogenomics datasets used in this study (https://pharmacodb.pmgenomics.ca/datasets/5)
    GO
    Gene ontology
    GTF
    Gene ontology term finder: an algorithm to assess GO term enrichment amongst a list of genes; applied to REMc (clustering) results [41].
    GTA
    Gene ontology term averaging: an assessment of GO term function obtained by averaging the gene interaction values for all genes of a GO term
    GTA value
    Gene ontology term average value
    gtaSD
    standard deviation of GTA value
    GTA score
    (GTA value - gtaSD)
    HaL
    hematopoietic & lymphoid tissue
    HDAC
    Histone deacetylase complex
    HLD
    Human-like media with dextrose [8]: the yeast media used in this study.
    INT
    Interaction score
    NDK
    nucleoside diphosphate kinase
    OES
    Overexpression sensitivity: refers to association of increased gene expression with drug sensitivity in pharmacogenomics data [33].
    PharmacoDB
    The resource used for cancer pharmacogenomics analysis [33].
    PPOD
    Princeton protein orthology database
    Q-HTCP
    Quantitative high throughput cell array phenotyping: a method of imaging, image analysis, and growth curve fitting to obtain cell proliferation parameters [7,38].
    Ref
    Reference: the genetic background from which the YKO/KD library was derived
    REMc
    Recursive expectation maximization clustering: a probabilistic clustering algorithm that determines a discrete number of clusters from a data matrix [40].
    RNR
    ribonucleotide reductase
    SD
    Standard deviation
    SGA
    Synthetic genetic array
    SGD
    Saccharomyces genome database
    UES
    Underexpression sensitivity: refers to association of reduced gene expression with drug sensitivity in pharmacogenomics data [33].
    YKO
    Yeast knockout
    YKD
    Yeast knockdown: DAmP alleles
    YKO/KD
    Yeast knockout or knockdown
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    A humanized yeast phenomic model of deoxycytidine kinase to predict genetic buffering of nucleoside analog cytotoxicity
    Sean M. Santos, Mert Icyuz, Ilya Pound, Doreen William, Jingyu Guo, Brett A. McKinney, Michael Niederweis, John Rodgers, John L. Hartman IV
    bioRxiv 700153; doi: https://doi.org/10.1101/700153
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    A humanized yeast phenomic model of deoxycytidine kinase to predict genetic buffering of nucleoside analog cytotoxicity
    Sean M. Santos, Mert Icyuz, Ilya Pound, Doreen William, Jingyu Guo, Brett A. McKinney, Michael Niederweis, John Rodgers, John L. Hartman IV
    bioRxiv 700153; doi: https://doi.org/10.1101/700153

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