Functional annotation of chemical libraries across diverse biological processes

Abstract

Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells to a compound, revealing chemical-genetic interactions that can elucidate a compound’s mode of action. We developed a highly parallel and unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized, diagnostic mutant collection that is predictive for all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode sequencing protocol, enabling assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened 7 different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action.

Resistance against Sclerotinia sclerotiorum in soybean involves a reprogramming of the phenylpropanoid pathway and up‐regulation of antifungal activity targeting ergosterol biosynthesis

“…Mutant-specific barcodes were amplified using indexed primers. Samples were sequenced on a HiSeq2500 (1 9 50 bp) (Illumina, San Diego CA, USA) rapid run and reads were processed using BEAN-counter (Piotrowski et al, 2017) and EdgeR (Robinson et al, 2009).…”

Diverse lignocellulosic feedstocks can achieve high field‐scale ethanol yields while providing flexibility for the biorefinery and landscape‐level environmental benefits

“…Chemical genomic analysis of the hydrolyzates was performed as described previously, using a collection of 4,000 yeast deletion mutants (Piotrowski et al, 2017), and~3,500 Z. mobilis transposon insertion mutants in 1578 ORFs (Skerker et al, 2013). Cultures (200 µl) of the pooled collection of S. cerevisiae deletion mutants or Z. mobilis transposon insertion mutants were cultured anaerobically in the different biomass hydrolyzates, rich media (YPD for yeast: 10 g/L yeast extract, 20 g/L peptone, and 20 g/L dextrose; rich ZRMG for Z. mobilis: 20 g/L glucose, 10 g/L yeast extract, and 2 g/L KH 2 PO 4 (Skotnicki, Tribe, & Rogers, 1980) or synthetic hydrolyzate (SynH) mimic of AFEX-pretreated corn stover hydrolyzate (Keating et al, 2014) without (SynHv2.6) or with inhibitory compounds (SynHv2.7) in triplicate for 48 hr at 30°C.…”

MOSAIC: a chemical-genetic interaction data repository and web resource for exploring chemical modes of action

“…This website displays data obtained by screening the RIKEN Natural Product Depository (NPDepo) as well as diversity libraries from the NCI/NIH, and the Published Kinase Inhibitor Set from Glaxo-Smith-Kline (Piotrowski et al, 2017). The data housed in the database were derived from the experiments described in (Piotrowski et al, 2017) and were processed and analyzed using BEAN-counter (https://github.com/csbio/BEANcounter) and CG-TARGET (Simpkins et al, 2017) tools. MOSAIC was designed using a combination of HTML, javascript with jquery, AJAX, javascript object notation (JSON), PHP, and MySQL.…”

“…In these approaches, a collection of genetic mutants is assayed for sensitivity or resistance to a compound to generate a compound-specific functional profile. We recently developed a new high-throughput platform for mapping CG interactions in yeast for large-scale compound screening and, using this, we generated a library of CG profiles for various compound collections (Piotrowski et al, 2017). This represents one of the most extensive chemical-genomic datasets to date, including informative profiles for more than 1,000 natural products and derivatives, hundreds of clinically relevant compounds, and other small molecules.…”

“…Methods are available for haploid, homozygous, and heterozygous deletion mutant collections and have incorporated global genetic interaction networks to make mode-of-action predictions (Giaever et al, 1999;Parsons et al, 2004;Parsons et al, 2006;Hillenmeyer et al, 2008;Costanzo et al, 2010;Hoepfner et al, 2014;Lee et al, 2014;Wildenhain et al, 2016). We recently developed a new high-throughput platform for mapping CG interactions in yeast for large-scale compound screening and, using this, we generated a library of CG profiles for various compound collections (Piotrowski et al, 2017). This represents one of the most extensive chemical-genomic datasets to date, including informative profiles for more than 1,000 natural products and derivatives, hundreds of clinically relevant compounds, and other small molecules.…”