Genetic interaction networks mediate individual statin drug response in Saccharomyces cerevisiae

ABSTRACTDetermination of genetic interaction networks (GINs) surrounding drug targets identifies buffering genes and provides molecular insight into drug response in individuals. Here we used backcross methodology to create Saccharomyces cerevisiae deletion libraries in three genetic backgrounds resistant to statins, which are additional to the statin-sensitive S288C deletion library that has provided much of what is known about GINs in eukaryotes. Whole genome sequencing and linkage group analysis confirmed the genomic authenticity of the new deletion libraries. Statin response was probed by drug-gene interactions with atorvastatin and cerivastatin treatments, as well as gene-gene interactions with the statin target HMG1 and HMG2 genes or the sterol homeostatic ARV1 gene. The 20 GINs generated from these interactions were not conserved by function or topology across the four genetic backgrounds. Centrality measures and hierarchical agglomerative clustering identified master regulators that if removed collapsed the networks. Community structure distinguished a characteristic early secretory pathway pattern of gene usage in each genetic background. ER stress in statin-resistant backgrounds was buffered by protein folding genes, which was confirmed by reduced activation of the unfolded protein response in statin-resistant backgrounds relative to the statin-sensitive S288C background. These network analyses of new gene deletion libraries provide insight into the complexity of GINs underlying individual drug response.

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Publisher Correction: Genetic interaction networks mediate individual statin drug response in Saccharomyces cerevisiae

npj Systems Biology and Applications ◽

10.1038/s41540-020-0129-9 ◽

2020 ◽

Vol 6 (1) ◽

Author(s):

Bede P. Busby ◽

Eliatan Niktab ◽

Christina A. Roberts ◽

Jeffrey P. Sheridan ◽

Namal V. Coorey ◽

...

Keyword(s):

Saccharomyces Cerevisiae ◽

Drug Response ◽

Genetic Interaction ◽

Interaction Networks ◽

Statin Drug

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The Chemical Genetic Interactions of Statin Drugs with Their Target Genes in Saccharomyces cerevisiae

10.26686/wgtn.16968850.v1 ◽

2021 ◽

Author(s):

◽

Bede P Busby

Keyword(s):

Saccharomyces Cerevisiae ◽

Target Genes ◽

Genetic Interaction ◽

Retrograde Transport ◽

Interaction Networks ◽

Query Gene ◽

Yeast Saccharomyces Cerevisiae ◽

Chemical Genetic ◽

Genome Wide ◽

Statin Drugs

<p>Statins, competitive inhibitors of the rate limiting cholesterol/ergosterol enzymes HMG-CoA reductase (HMG1 and HMG2), are the most widely prescribed human therapeutic drugs. They are effective in lowering cholesterol levels in atherosclerosis and related syndromes. However, statins exhibit a range of pleiotropic side effects whose mechanisms are poorly understood. This study investigates statin pleiotropy by analysis of genetic interaction networks in yeast, Saccharomyces cerevisiae, which shows high homology to mammalian pathways affected by statins. Synthetic genetic array (SGA) analysis allows elucidation of functional genetic networks of genes of interest ("query genes") by measurement of genetic epistasis in double mutants of the query gene with the genome - wide deletion mutant array of ~4800 non-essential strains. Chemicalgenetic profiling is similar where a SMP may effectively replace the query gene in genome wide epistatic analysis. The genetic interaction networks resulting from use of HMG1 and HMG2 as query genes for SGA analysis were compared to the chemical-genetic profiles of atorvastatin, cerivastatin and lovastatin. The genes ARV1, BTS1, OPI3 displaying phenotypic enhancements (i.e. their deletion caused major growth inhibition) with statins became essential in the presence of all the statins. Two mitochondrial genes, COX17 and MMM1, showed phenotypic suppressions (i.e. their deletion allowed better growth) in common to all three statin drugs. An attractive hypothesis is that major pleiotropic effects of statins could be due to variation in function or expression of these enhancing or suppressing genes. Other processes compensating statin use were also elucidated. For example, when HMG1 and its epistatically interacting genes are shut down by deletion coupled with inhibition of HMG2 with statin, there is strong evidence that the cell attempts to maintain membrane/lipid homeostasis via anterograde and retrograde transport mechanisms, including the mobilisation of lipid storage droplets. To aid refinement of genetic analysis in this and future studies, a more direct phenotypic assay was developed for quantifying ergosterol. Such an assay may be used as a phenotype to map the effect of up - and downstream - genes, or network genes affecting ergosterol levels. This assay was used to quantify ergosterol in a drug - resistant mutant developed by others aiding confirmation of the drug target.</p>

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The Chemical Genetic Interactions of Statin Drugs with Their Target Genes in Saccharomyces cerevisiae

10.26686/wgtn.16968850 ◽

2021 ◽

Author(s):

◽

Bede P Busby

Keyword(s):

Saccharomyces Cerevisiae ◽

Target Genes ◽

Genetic Interaction ◽

Retrograde Transport ◽

Interaction Networks ◽

Query Gene ◽

Yeast Saccharomyces Cerevisiae ◽

Chemical Genetic ◽

Genome Wide ◽

Statin Drugs

<p>Statins, competitive inhibitors of the rate limiting cholesterol/ergosterol enzymes HMG-CoA reductase (HMG1 and HMG2), are the most widely prescribed human therapeutic drugs. They are effective in lowering cholesterol levels in atherosclerosis and related syndromes. However, statins exhibit a range of pleiotropic side effects whose mechanisms are poorly understood. This study investigates statin pleiotropy by analysis of genetic interaction networks in yeast, Saccharomyces cerevisiae, which shows high homology to mammalian pathways affected by statins. Synthetic genetic array (SGA) analysis allows elucidation of functional genetic networks of genes of interest ("query genes") by measurement of genetic epistasis in double mutants of the query gene with the genome - wide deletion mutant array of ~4800 non-essential strains. Chemicalgenetic profiling is similar where a SMP may effectively replace the query gene in genome wide epistatic analysis. The genetic interaction networks resulting from use of HMG1 and HMG2 as query genes for SGA analysis were compared to the chemical-genetic profiles of atorvastatin, cerivastatin and lovastatin. The genes ARV1, BTS1, OPI3 displaying phenotypic enhancements (i.e. their deletion caused major growth inhibition) with statins became essential in the presence of all the statins. Two mitochondrial genes, COX17 and MMM1, showed phenotypic suppressions (i.e. their deletion allowed better growth) in common to all three statin drugs. An attractive hypothesis is that major pleiotropic effects of statins could be due to variation in function or expression of these enhancing or suppressing genes. Other processes compensating statin use were also elucidated. For example, when HMG1 and its epistatically interacting genes are shut down by deletion coupled with inhibition of HMG2 with statin, there is strong evidence that the cell attempts to maintain membrane/lipid homeostasis via anterograde and retrograde transport mechanisms, including the mobilisation of lipid storage droplets. To aid refinement of genetic analysis in this and future studies, a more direct phenotypic assay was developed for quantifying ergosterol. Such an assay may be used as a phenotype to map the effect of up - and downstream - genes, or network genes affecting ergosterol levels. This assay was used to quantify ergosterol in a drug - resistant mutant developed by others aiding confirmation of the drug target.</p>

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Clinical Identification of Dysregulated Circulating microRNAs and Their Implication in Drug Response in Triple Negative Breast Cancer (TNBC) by Target Gene Network and Meta-Analysis

Genes ◽

10.3390/genes12040549 ◽

2021 ◽

Vol 12 (4) ◽

pp. 549

Author(s):

Amal Qattan ◽

Taher Al-Tweigeri ◽

Wafa Alkhayal ◽

Kausar Suleman ◽

Asma Tulbah ◽

...

Keyword(s):

Breast Cancer ◽

Drug Resistance ◽

Triple Negative Breast Cancer ◽

Drug Response ◽

Triple Negative ◽

Meta Analysis ◽

Integrated Analysis ◽

Circulating Mirnas ◽

Persistent Problem ◽

Network Analyses

Resistance to therapy is a persistent problem that leads to mortality in breast cancer, particularly triple-negative breast cancer (TNBC). MiRNAs have become a focus of investigation as tissue-specific regulators of gene networks related to drug resistance. Circulating miRNAs are readily accessible non-invasive potential biomarkers for TNBC diagnosis, prognosis, and drug-response. Our aim was to use systems biology, meta-analysis, and network approaches to delineate the drug resistance pathways and clinical outcomes associated with circulating miRNAs in TNBC patients. MiRNA expression analysis was used to investigate differentially regulated circulating miRNAs in TNBC patients, and integrated pathway regulation, gene ontology, and pharmacogenomic network analyses were used to identify target genes, miRNAs, and drug interaction networks. Herein, we identified significant differentially expressed circulating miRNAs in TNBC patients (miR-19a/b-3p, miR-25-3p, miR-22-3p, miR-210-3p, miR-93-5p, and miR-199a-3p) that regulate several molecular pathways (PAM (PI3K/Akt/mTOR), HIF-1, TNF, FoxO, Wnt, and JAK/STAT, PD-1/PD-L1 pathways and EGFR tyrosine kinase inhibitor resistance (TKIs)) involved in drug resistance. Through meta-analysis, we demonstrated an association of upregulated miR-93, miR-210, miR-19a, and miR-19b with poor overall survival outcomes in TNBC patients. These results identify miRNA-regulated mechanisms of drug resistance and potential targets for combination with chemotherapy to overcome drug resistance in TNBC. We demonstrate that integrated analysis of multi-dimensional data can unravel mechanisms of drug-resistance related to circulating miRNAs, particularly in TNBC. These circulating miRNAs may be useful as markers of drug response and resistance in the guidance of personalized medicine for TNBC.

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Suppressor Analysis of the Saccharomyces cerevisiae Gene REC104 Reveals a Genetic Interaction With REC102

Genetics ◽

10.1093/genetics/151.4.1261 ◽

1999 ◽

Vol 151 (4) ◽

pp. 1261-1272 ◽

Cited By ~ 1

Author(s):

Laura Salem ◽

Natalie Walter ◽

Robert Malone

Keyword(s):

Saccharomyces Cerevisiae ◽

Meiotic Recombination ◽

Null Allele ◽

Genetic Interaction ◽

In Vitro Mutagenesis ◽

Conditional Mutations ◽

Suppressor Analysis

Abstract REC104 is a gene required for the initiation of meiotic recombination in Saccharomyces cerevisiae. To better understand the role of REC104 in meiosis, we used an in vitro mutagenesis technique to create a set of temperature-conditional mutations in REC104 and used one ts allele (rec104-8) in a screen for highcopy suppressors. An increased dosage of the early exchange gene REC102 was found to suppress the conditional recombinational reduction in rec104-8 as well as in several other conditional rec104 alleles. However, no suppression was observed for a null allele of REC104, indicating that the suppression by REC102 is not “bypass” suppression. Overexpression of the early meiotic genes REC114, RAD50, HOP1, and RED1 fails to suppress any of the rec104 conditional alleles, indicating that the suppression might be specific to REC102.

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Significant conservation of synthetic lethal genetic interaction networks between distantly related eukaryotes

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.0806261105 ◽

2008 ◽

Vol 105 (43) ◽

pp. 16653-16658 ◽

Cited By ~ 124

Author(s):

S. J. Dixon ◽

Y. Fedyshyn ◽

J. L. Y. Koh ◽

T. S. K. Prasad ◽

C. Chahwan ◽

...

Keyword(s):

Genetic Interaction ◽

Interaction Networks ◽

Synthetic Lethal

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Genome-wide mapping of unexplored essential regions in the Saccharomyces cerevisiae genome: evidence for hidden synthetic lethal combinations in a genetic interaction network

Nucleic Acids Research ◽

10.1093/nar/gku576 ◽

2014 ◽

Vol 42 (15) ◽

pp. 9838-9853 ◽

Cited By ~ 6

Author(s):

Saeed Kaboli ◽

Takuya Yamakawa ◽

Keisuke Sunada ◽

Tao Takagaki ◽

Yu Sasano ◽

...

Keyword(s):

Saccharomyces Cerevisiae ◽

Genetic Interaction ◽

Interaction Network ◽

Essential Genes ◽

Genetic Interactions ◽

Lethal Gene ◽

Synthetic Lethal ◽

Genome Wide ◽

A Genome ◽

Wide Scale

Abstract Despite systematic approaches to mapping networks of genetic interactions in Saccharomyces cerevisiae, exploration of genetic interactions on a genome-wide scale has been limited. The S. cerevisiae haploid genome has 110 regions that are longer than 10 kb but harbor only non-essential genes. Here, we attempted to delete these regions by PCR-mediated chromosomal deletion technology (PCD), which enables chromosomal segments to be deleted by a one-step transformation. Thirty-three of the 110 regions could be deleted, but the remaining 77 regions could not. To determine whether the 77 undeletable regions are essential, we successfully converted 67 of them to mini-chromosomes marked with URA3 using PCR-mediated chromosome splitting technology and conducted a mitotic loss assay of the mini-chromosomes. Fifty-six of the 67 regions were found to be essential for cell growth, and 49 of these carried co-lethal gene pair(s) that were not previously been detected by synthetic genetic array analysis. This result implies that regions harboring only non-essential genes contain unidentified synthetic lethal combinations at an unexpectedly high frequency, revealing a novel landscape of genetic interactions in the S. cerevisiae genome. Furthermore, this study indicates that segmental deletion might be exploited for not only revealing genome function but also breeding stress-tolerant strains.

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