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

2019 ◽  
Author(s):  
Sean M. Santos ◽  
Mert Icyuz ◽  
Ilya Pound ◽  
Doreen William ◽  
Jingyu Guo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Synthetic Lethality ◽  
Gene Interaction ◽  
Genetic Alterations ◽  
Molecular Structures ◽  
Cytotoxic Agents ◽  
Deoxycytidine Kinase ◽  
Pathway Enrichment Analysis ◽  
Differential Gene

AbstractKnowledge 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.

scholarly journals A Humanized Yeast Phenomic Model of Deoxycytidine Kinase to Predict Genetic Buffering of Nucleoside Analog Cytotoxicity

Genes ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 770 ◽  
Author(s):  
Santos ◽  
Icyuz ◽  
Pound ◽  
William ◽  
Guo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Differential Gene Expression ◽  
Synthetic Lethality ◽  
Gene Interaction ◽  
Genetic Alterations ◽  
Cytotoxic Agents ◽  
Deoxycytidine Kinase ◽  
Pathway Enrichment Analysis ◽  
Differential Gene

Knowledge about synthetic lethality can be applied to enhance the efficacy of anticancer 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 antitumor efficacy. Human deoxycytidine kinase (dCK) was conditionally expressed in the Saccharomyces 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-gene ontology (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.

scholarly journals Analysis of lung transcriptome in calves infected with Bovine Respiratory Syncytial Virus and treated with antiviral and/or cyclooxygenase inhibitor

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246695
Author(s):  
Maxim Lebedev ◽  
Heather A. McEligot ◽  
Victoria N. Mutua ◽  
Paul Walsh ◽  
Francisco R. Carvallo Chaigneau ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Gene Expression ◽  
Respiratory Syncytial Virus ◽  
Differential Gene Expression ◽  
Lung Tissue ◽  
Bovine Respiratory Syncytial Virus ◽  
Pathway Enrichment Analysis ◽  
Adaptive Immune ◽  
Differential Gene ◽  
Syncytial Virus

Bovine Respiratory Syncytial virus (BRSV) is one of the major infectious agents in the etiology of the bovine respiratory disease complex. BRSV causes a respiratory syndrome in calves, which is associated with severe bronchiolitis. In this study we describe the effect of treatment with antiviral fusion protein inhibitor (FPI) and ibuprofen, on gene expression in lung tissue of calves infected with BRSV. Calves infected with BRSV are an excellent model of human RSV in infants: we hypothesized that FPI in combination with ibuprofen would provide the best therapeutic intervention for both species. The following experimental treatment groups of BRSV infected calves were used: 1) ibuprofen day 3–10, 2) ibuprofen day 5–10, 3) placebo, 4) FPI day 5–10, 5) FPI and ibuprofen day 5–10, 6) FPI and ibuprofen day 3–10. All calves were infected with BRSV on day 0. Daily clinical evaluation with monitoring of virus shedding by qRT-PCR was conducted. On day10 lung tissue with lesions (LL) and non-lesional (LN) was collected at necropsy, total RNA extracted, and RNA sequencing performed. Differential gene expression analysis was conducted with Gene ontology (GO) and KEGG pathway enrichment analysis. The most significant differential gene expression in BRSV infected lung tissues was observed in the comparison of LL with LN; oxidative stress and cell damage was especially noticeable. Innate and adaptive immune functions were reduced in LL. As expected, combined treatment with FPI and Ibuprofen, when started early, made the most difference in gene expression patterns in comparison with placebo, especially in pathways related to the innate and adaptive immune response in both LL and LN. Ibuprofen, when used alone, negatively affected the antiviral response and caused higher virus loads as shown by increased viral shedding. In contrast, when used with FPI Ibuprofen enhanced the specific antiviral effect of FPI, due to its ability to reduce the damaging effect of prostanoids and oxidative stress.

scholarly journals A yeast phenomic model for the influence of Warburg metabolism on genetic buffering of doxorubicin

2019 ◽  
Author(s):  
Sean M. Santos ◽  
John L. Hartman
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Mutant Strain ◽  
Warburg Effect ◽  
Gene Knockout ◽  
Gene Interaction ◽  
Chemotherapy Response ◽  
Cellular Processes ◽  
Differential Gene ◽  
The Warburg Effect

AbstractBackgroundSaccharomyces cerevisiae represses respiration in the presence of adequate glucose, mimicking the Warburg effect, termed aerobic glycolysis. We conducted yeast phenomic experiments to characterize differential doxorubicin-gene interaction, in the context of respiration vs. glycolysis. The resulting systems level biology about doxorubicin cytotoxicity, including the influence of the Warburg effect, was integrated with cancer pharmacogenomics data to identify potentially causal correlations between differential gene expression and anti-cancer efficacy.MethodsQuantitative high-throughput cell array phenotyping (Q-HTCP) was used to measure cell proliferation phenotypes (CPPs) of the yeast gene knockout/knockdown library, treated with escalating doxorubicin concentrations in fermentable and non-fermentable media. Doxorubicin-gene interaction was quantified by departure of the observed and expected phenotypes for the doxorubicin-treated mutant strain, with respect to phenotypes for the untreated mutant strain and both the treated and untreated reference strain. Recursive expectation-maximization clustering (REMc) and Gene Ontology-based analyses of interactions were used to identify functional biological modules that buffer doxorubicin cytotoxicity, and to characterize their Warburg-dependence. Yeast phenomic data was applied to cancer cell line pharmacogenomics data to predict differential gene expression that causally influences the anti-tumor efficacy, and potentially the anthracycline-associated host toxicity, of doxorubicin.ResultsDoxorubicin cytotoxicity was greater with respiration, suggesting the Warburg effect can influence therapeutic efficacy. Accordingly, doxorubicin drug-gene interaction was more extensive with respiration, including increased buffering by cellular processes related to chromatin organization, protein folding and modification, translation reinitiation, spermine metabolism, and fatty acid beta-oxidation. Pathway enrichment was less notable for glycolysis-specific buffering. Cellular processes exerting influence relatively independently, with respect to Warburg status, included homologous recombination, sphingolipid homeostasis, telomere tethering at nuclear periphery, and actin cortical patch localization. Causality for differential gene expression associated with doxorubicin cytotoxicity in tumor cells was predicted within the biological context of the phenomic model.ConclusionsWarburg status influences the genetic requirements to buffer doxorubicin toxicity. Yeast phenomics provides an experimental platform to model the complexity of gene interaction networks that influence human disease phenotypes, as in this example of chemotherapy response. High-resolution, systems level yeast phenotyping is useful to predict the biological influence of functional variation on disease, offering the potential to fundamentally advance precision medicine.

scholarly journals Differential Gene Expression Analysis of Oocytes in a PCOS Mouse Model Revealed the Relation of Ribosomal Pathway

2022 ◽  
Author(s):  
Natsuki Nakanishi ◽  
Satoko Osuka ◽  
Tomohiro Kono ◽  
Hisato Kobayashi ◽  
Shinya Ikeda ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Mouse Model ◽  
Differential Gene Expression ◽  
Polycystic Ovary ◽  
Methylation Status ◽  
Immunohistochemical Analysis ◽  
Pathway Enrichment Analysis ◽  
Sequencing Analysis ◽  
Differential Gene

Abstract Polycystic ovary syndrome (PCOS), a common endocrinal disorder, is associated with impaired oocyte development, which leads to infertility. However, the pathogenesis of PCOS has not been completely elucidated. This study aimed to analyze the differentially expressed genes (DEGs) and epigenetic changes in the oocytes of the PCOS mouse model to identify the etiological factors. In this study, RNA-sequencing analysis revealed that 90 DEGs were upregulated and 27 DEGs were downregulated in the PCOS mouse model. DNA methylation analysis revealed 30 hypomethylated and 10 hypermethylated regions in the PCOS group. However, the DNA methylation status was not correlated with differential gene expression. The pathway enrichment analysis revealed that five DEGs (Rps21, Rpl36, Rpl36a, Rpl37a, and Rpl22l1) were enriched in ribosome-related pathways in the oocytes of the PCOS mouse model, and the immunohistochemical analysis revealed significantly upregulated expression levels of Rps21 and Rpl36. These results suggest that differential gene expression in the oocytes of the PCOS mouse model is related to impaired folliculogenesis. These findings improved our understanding of the pathogenesis of PCOS.

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