Profiling of the effects of antifungal agents on yeast cells based on morphometric analysis

Gebre, Abraham A; Okada, Hiroki; Kim, Cholgwang; Kubo, Kosuke; Ohnuki, Shinsuke; Ohya, Yoshikazu

doi:10.1093/femsyr/fov040

Cited by 25 publications

(12 citation statements)

References 88 publications

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“…Interestingly, our fitness data revealed a growth defect of the CUT494/SUT053/SUT468 Δ strain in YP+2% Glycerol (Fig 2C). To test the hypothesis that CUT494/SUT053/SUT468 Δ has a role in membrane stability by targeting synthesis of ERG, we used azole antifungal agents that inhibit various steps in the ERG biosynthesis pathway [40]. When the fitness of CUT494/SUT053/SUT468Δ was tested in medium supplemented with either fluconazole (Fig 3A and C) or miconazole (Fig 3B and D), a slow growth phenotype was identified compared to the WT and the other deletion mutants in the same cluster (Fig 3).…”

Section: Resultsmentioning

confidence: 99%

Functional and transcriptional profiling of non-coding RNAs in yeast reveal context-dependent phenotypes and widespreadin transeffects on the protein regulatory network

Balarezo-Cisneros

Parker

Fraczek

et al. 2020

Preprint

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24Non-coding RNAs (ncRNAs), including the more recently identified Stable Unannotated 25 Transcripts (SUTs) and Cryptic Unstable Transcripts (CUTs), are increasingly being shown to 26 play pivotal roles in the transcriptional and post-transcriptional regulation of genes in 27 eukaryotes. Here, we carried out a large-scale screening of ncRNAs in Saccharomyces 28 cerevisiae, and provide evidence for SUT and CUT function. Phenotypic data on 372 ncRNA 29 deletion strains in 23 different growth conditions were collected, identifying ncRNAs 30 responsible for significant cellular fitness changes. Transcriptome profiles were assembled for 31 18 haploid ncRNA deletion mutants and 2 essential ncRNA heterozygous deletants. Guided 32 by the resulting RNA-seq data we analysed the genome-wide dysregulation of protein coding 33 genes and non-coding transcripts. Novel functional ncRNAs, SUT125, SUT126, SUT035 and 34 SUT532 that act in trans by modulating transcription factors were identified. Furthermore, we 35 described the impact of SUTs and CUTs in modulating coding gene expression in response 36 of different environmental conditions, regulating important biological process such as 37 respiration (SUT125, SUT126, SUT035, SUT432), steroid biosynthesis (CUT494, SUT530, 38 SUT468) or rRNA processing (SUT075 and snR30). Overall, this data captures and integrates 39 the regulatory and phenotypic network of ncRNAs and protein coding genes, providing 40 genome-wide evidence of the impact of ncRNAs on cellular homeostasis. 41 42 Author Summary 43 44 The yeast genome contains 25% of non-coding RNA molecules (ncRNAs), which do not 45 translate into proteins but are involved in regulation of gene expression. ncRNAs can affect 46 nearby genes by physically interfering with their transcription (cis mode of action), or they 50 regulation are still lacking. Here, we used the ncRNA yeast deletion collection to score their 51 impact on cellular function in different environmental conditions. A group of 20 ncRNAs 52 mutants with broad fitness diversity were selected to investigate their effect on the protein and 53 ncRNA expression network. We showed a high correlation between altered phenotypes and 54 global transcriptional changes, in an environmental dependent manner. We confirmed the 55 widespread trans acting expressional regulation of ncRNAs in the genome and their role in 56 affecting transcription factors. These findings support the notion of the involvement on ncRNAs 57 in fine tuning the cellular expression via regulations of TFs, as an advantageous RNA-58 mediated mechanism that can be fast and cost-effective for the cells. 59 65 expression. More recently, RNA transcripts which are not translated into protein, have been 66 described to have a prominent role as epigenetic modifiers [1,2]. There are an increasing 67 number of examples of these non-coding RNA (ncRNA) transcripts regulating gene 68 expression positively and negatively [3-10]. 69 RNA interference (RNAi) was the first understood example of ncRNA involvement in 70 epigene...

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Section: Resultsmentioning

confidence: 99%

Functional and transcriptional profiling of non-coding RNAs in yeast reveal context-dependent phenotypes and widespreadin transeffects on the protein regulatory network

Balarezo-Cisneros

Parker

Fraczek

et al. 2020

Preprint

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“…Although not yet used in such applications, Naïve Bayesian and Random Forest algorithms have been recently trained with chemical genetics data to predict drug–drug interactions ∗∗37 , ∗∗38 . Finally, although single-cell morphological profiling can be very powerful for MoA identification on its own ∗26 , 39 , it has not been used yet as a readout for large-scale chemical genetic screens in microbes. Small-scale screens do exist [40] and morphological profiling of wildtype cells has been combined only to a limited degree with growth-based chemical genetics [41] .…”

Section: Chemical Genetics In Moa Identificationmentioning

confidence: 99%

Chemical genetics in drug discovery

Cacace

Kritikos

Typas

2017

Current Opinion in Systems Biology

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Chemical-genetic approaches are based on measuring the cellular outcome of combining genetic and chemical perturbations in large-numbers in tandem. In these approaches the contribution of every gene to the fitness of an organism is measured upon exposure to different chemicals. Current technological advances enable the application of chemical genetics to almost any organism and at an unprecedented throughput. Here we review the underlying concepts behind chemical genetics, present its different vignettes and illustrate how such approaches can propel drug discovery.

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“…The approach was upgraded for investigating fission yeast [89] and, in addition to the originally developed 501 parameters for cell wall morphology, nuclear DNA, and actin, 610 parameters for the morphology of some other subcellular components, were proposed [90]. Also, it was demonstrated to be useful for studies of antifungal drugs [91] [92].…”

Section: Single Cellsmentioning

confidence: 99%

Image Analysis in Microbiology: A Review

Pushchino¹

2016

JCC

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This review is focused on using computer image analysis as a means of objective and quantitative characterizing optical images of the macroscopic (e.g. microbial colonies) and the microscopic (e.g. single cell) objects in the microbiological research. This is the way of making many visual inspection assays more objective and less time and labor consuming. Also, it can provide new visually inaccessible information on relation between some optical parameters and various biological features of the microbial cultures. Of special interest is application of image analysis in fluorescence microscopy as it opens new ways of using fluorescence based methodology for single microbial cell studies. Examples of using image analysis in the studies of both the macroscopic and the microscopic microbiological objects obtained by various imaging techniques are presented and discussed.

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Profiling of the effects of antifungal agents on yeast cells based on morphometric analysis

Cited by 25 publications

References 88 publications

Functional and transcriptional profiling of non-coding RNAs in yeast reveal context-dependent phenotypes and widespreadin transeffects on the protein regulatory network

Functional and transcriptional profiling of non-coding RNAs in yeast reveal context-dependent phenotypes and widespreadin transeffects on the protein regulatory network

Chemical genetics in drug discovery

Image Analysis in Microbiology: A Review

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