Nucleus-Independent Chemical Shift (NICS) as a Criterion for the Design of New Antifungal Benzofuranones.
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ABSTRACT:
SUBMITTER: Zermeno-Macias MLA
PROVIDER: S-EPMC8401487 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background: The CRISPR/Cas9 toolbox has recently been expanded to include approaches for modulating gene expression. To successfully build on this work, and apply it for answering biological questions, it is important to establish it in a broad range of circumstances. Genome-scale CRISPR interference (CRISPRi) has been used in human cells lines, however the rules for designing effective guide RNAs (gRNAs) in different organisms are not well known. We sought to determine rules that determine gRNA effectiveness at transcriptional repression in Saccharomyces cerevisiae. Results: We created an inducible single plasmid CRISPRi system for gene repression in yeast, and used it to analyze fitness effects of gRNAs under 18 small molecule treatments. Our approach correctly identified previously-described chemical-genetic interactions, as well as a new mechanism of suppressing fluconazole toxicity by repression of the ERG25 gene. Assessment of multiple target loci across treatments allowed us to determine generalizable features associated with gRNA efficacy. Guides that target regions with low nucleosome occupancy and high chromatin accessibility were clearly more effective. We also found the best region to target gRNAs was between the transcription start site (TSS) and 200bp upstream of the TSS. Finally, unlike nuclease-proficient Cas9 in human cells, point mutations were tolerated equally well by truncated (18 nt specificity sequence) and full length (20 nt) gRNAs, however, 18 nt gRNAs were generally less potent than full length gRNAs. Conclusions: Our results establish a powerful functional genomics screening method, provide rules for designing effective gRNAs for gene repression, and show that 18 nt and 20 nt gRNAs exhibit similar tolerance to mismatches in the target sequence. These findings will enable effective library design and genome-wide screening in many genetic backgrounds. An expression construct was created for inducible CRISPRi in yeast. Key features include ORFs expressing dCas9-Mxi1 and the tetracycline repressor (TetR), as well as a tetracycline inducible gRNA locus containing the RPR1 promoter with a TetO site, a NotI site for cloning new gRNA specificity sequences, and the constant part of the gRNA. When yeast containing this plasmid are grown in the absence of anhydrotetracycline (ATc) TetR binds the gRNA promoter and prevents PolIII from binding and transcribing the gRNA. This in turn prevents dCas9-Mxi1 from binding the target site. In the presence of ATc, TetR dissociates and gRNA is expressed, allowing dCas9-Mxi1 to bind its target locus, and repress gene expression. gRNA libraries were cloned into this construct and transformed into yeast to create pools. Experiments were conducted in which yeast pools were grown in inducing (+ATc) and non-inducing conditions (-ATc) in the presence of different drugs. After multiple generations of growth in these conditions, yeast plasmids were minipreped and the gRNA locus was PCRed and sequenced via MiSeq. Counts of each gRNA were compared in different conditions.
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2016-01-18 | GSE71490 | GEO
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Project description:BackgroundResidual feed intake (RFI) is one measure of feed efficiency, which is usually obtained by multiple regression of feed intake (FI) on measures of production, body weight gain and tissue composition. If phenotypic regression is used, the resulting RFI is generally not genetically independent of production traits, whereas if RFI is computed using genetic regression coefficients, RFI and production traits are independent at the genetic level. The corresponding regression coefficients can be easily derived from the result of a multiple trait model that includes FI and production traits. However, this approach is difficult to apply in the case of multiple repeated measurements of FI and production traits. To overcome this difficulty, we used a structured antedependence approach to account for the longitudinality of the data with a phenotypic regression model or with different genetic and environmental regression coefficients [multi- structured antedependence model (SAD) regression model].ResultsAfter demonstrating the properties of RFI obtained by the multi-SAD regression model, we applied the two models to FI and production traits that were recorded for 2435 French Large White pigs over a 10-week period. Heritability estimates were moderate with both models. With the multi-SAD regression model, heritability estimates were quite stable over time, ranging from 0.14 ± 0.04 to 0.16 ± 0.05, while heritability estimates showed a U-shaped profile with the phenotypic regression model (ranging from 0.19 ± 0.06 to 0.28 ± 0.06). Estimates of genetic correlations between RFI at different time points followed the same pattern for the two models but higher estimates were obtained with the phenotypic regression model. Estimates of breeding values that can be used for selection were obtained by eigen-decomposition of the genetic covariance matrix. Correlations between these estimated breeding values obtained with the two models ranged from 0.66 to 0.83.ConclusionsThe multi-SAD model is preferred for the genetic analysis of longitudinal RFI because, compared to the phenotypic regression model, it provides RFI that are genetically independent of production traits at all time points. Furthermore, it can be applied even when production records are missing at certain time points.
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