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10th December 2012 @ 03:26
Biological evaluation of OSM-S-96 through 105 performed by Vicky Avery's lab by Sabine Fletcher.

Compound Structures

PDF summarising data:
PDF Summary of Curves

Excel sheet of raw data:
Raw Biological Data

Comments from Sabine:
Unfortunately, none of the cpds was active enough on 3D7 to obtain an
IC50 curve (only cpd OSM-S-103 showed 61% inhibition at the highest dose).

Slight solubility issues etc. after thorough vortexing were as follows:

OSM-S-100: very few, tiny undissolved crystals
OSM-S-101: few floating specks that looked like dust / cotton wool
OSM-S-104: few floating specks of different colour: white (cotton
wool?) large black 'chip' (plastic from lid?) and tiny orange specks (undissolved crystals?)
OSM-S-105: had cotton wool in lid. I 'flushed' the cotton wool
thoroughly with DMSO to dissolve all compound on the cotton wool and
then discarded the wool.

Places where these results are discussed:

(need to insert)
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3rd December 2012 @ 22:37

Yeas-based genetic sensitivity assay performed by the Nislow Lab at The University of Toronto. Data acquired by Marinella Gebbia and subsequent gene set analysis performed by Anna Lee.

Relevant Compounds:

Chemdraw of Nislow Compounds
Picture of Compound Structures

The Excel file contains the log2 intensity ratios (control/treatment) which are proportional to the strains sensitivity to the compound. It also has a list of sensitive genes for all compound tested and a description of the cellular compartment, the metabolic process and the gene function for each strain.

Raw data intensity ratios

The Spotfire file contains all the data plots.

Spotfire data plots

The enrichment maps show the GO biological processes that are enriched amongst the genes associated with compound-sensitivity. Maps are *not* provided for compounds that did not yield any significant enrichment (FDR<= 0.1).

OSM-S-9 Map
OSM-S-12 Map
OSM-S-31 Map
OSM-S-39 Map
OSM-S-51 Map
OSM-S-56 Map

The Word document contains a more detailed description of the enrichment maps, and details about how they were generated.

Figure Legend and SI

Marinella also kindly provided the Spotfire plots (Powerpoint file):

Spotfire Plots

and said: "on the Y axis you have the log2ratios (control/treatment) and on the X axis the genes."

Associated discussion of these data: GHI20

Update. Additional data sent 18th Feb. File attached "Dataset S35 Nislow4". Excel file of the fitness defect scores for all deletion strains.

File lists the significantly sensitive strains derived from Tag4 microarray hybridizations as described in Lee AY, St.Onge RP, Proctor MJ, et al. Mapping the Cellular Response to Small Molecules Using Chemogenomic Fitness Signatures. Science2014;344(6180):208-211. doi:10.1126/science.1250217.

Raw data normalization and removal of problematic tags Each probe on the Genflex tag16k array (Affymetrix, Santa Clara, CA), i.e. the Tag4 array, is represented by 5 replicate features. These replicates allow the removal of outliers that may for example, arise from small debris in the hybridization solution. To identify and remove probes defined as outliers, we used a previously described masking algorithm. We next defined the ‘raw average’ of each tag as the average of all remaining probe replicates for a particular tag. We then removed all tags corresponding to the control strain. For each array, uptags and downtags were normalized separately, as were heterozygous and homozygous strains, creating 4 sets: uptag/het, uptag/hom, downtag/het, downtag/hom. To simplify our dataset, we removed strains where the deletions no longer correspond to valid genes according to the Saccharomyces Genome Database (SGD).

The fitness defect (FD) score We devised a fitness defect (FD) score that quantifies the sensitivity of each deletion strain to a chemical perturbation by comparing the signal of a strain following chemical treatment to the signal of the strain from control samples (i.e. DMSO-treated samples). Specifically, log2ratios were calculated for each strain as follows: log2ratio = log2[ / ]

Identification of significant chemical-genetic interactions: We defined significant chemical-genetic interactions by identifying FD scores that deviated significantly from other FD scores in a given screen (heterozygous and homozygous strains were considered separately). This approach is based on the assumption that, at chemical concentrations that only minimally inhibit growth of the pool, most strains will not exhibit a fitness defect.

Forward link to PRR Assay performed to verify these data.

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