Functional Annotation of Chemical Libraries across Diverse Biological Processes

Piotrowski, Jeff S.; Li, Sheena C.; Deshpande, Raamesh; Simpkins, Scott W.; Nelson, J. Daniel; Yashiroda, Yoko; Barber, Jacqueline M.; Safizadeh, Hamid; Wilson, Erin; Okada, Hiroki; Gebre, Abraham A; Kubo, Kosuke; Torres, Nikko P.; LeBlanc, Marissa A.; Andrusiak, Kerry; Okamoto, Reika; Yoshimura, Mami; DeRango-Adem, Eva; Leeuwen, Jolanda van; Shirahige, Katsuhiko; Baryshnikova, Anastasia; Brown, Grant W.; Hirano, Hiroyuki; Costanzo, Michael; Andrews, Brenda; Ohya, Yoshikazu; Osada, Hiroyuki; Yoshida, Minoru; Myers, Chad L.; Boone, Charles

doi:10.1101/112557

Cited by 9 publications

(28 citation statements)

References 63 publications

(4 reference statements)

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

Section: Methodsmentioning

confidence: 99%

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

Ranjan

Westrick

Jain

et al. 2019

Plant Biotechnology Journal

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103

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Summary Sclerotinia sclerotiorum , a predominately necrotrophic fungal pathogen with a broad host range, causes a significant yield‐limiting disease of soybean called Sclerotinia stem rot. Resistance mechanisms against this pathogen in soybean are poorly understood, thus hindering the commercial deployment of resistant varieties. We used a multiomic approach utilizing RNA ‐sequencing, gas chromatography–mass spectrometry‐based metabolomics and chemical genomics in yeast to decipher the molecular mechanisms governing resistance to S. sclerotiorum in soybean. Transcripts and metabolites of two soybean recombinant inbred lines, one resistant and one susceptible to S. sclerotiorum were analysed in a time course experiment. The combined results show that resistance to S. sclerotiorum in soybean is associated in part with an early accumulation of JA ‐Ile ((+)‐7‐iso‐jasmonoyl‐L‐isoleucine), a bioactive jasmonate, increased ability to scavenge reactive oxygen species, and importantly, a reprogramming of the phenylpropanoid pathway leading to increased antifungal activities. Indeed, we noted that phenylpropanoid pathway intermediates, such as 4‐hydroxybenzoate, cinnamic acid, ferulic acid and caffeic acid, were highly accumulated in the resistant line. In vitro assays show that these metabolites and total stem extracts from the resistant line clearly affect S. sclerotiorum growth and development. Using chemical genomics in yeast, we further show that this antifungal activity targets ergosterol biosynthesis in the fungus, by disrupting enzymes involved in lipid and sterol biosynthesis. Overall, our results are consistent with a model where resistance to S. sclerotiorum in soybean coincides with an early recognition of the pathogen, leading to the modulation of the redox capacity of the host and the production of antifungal metabolites.

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Section: Methodsmentioning

confidence: 99%

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

Ranjan

Westrick

Jain

et al. 2019

Plant Biotechnology Journal

Self Cite

103

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Section: Resultsmentioning

confidence: 91%

“…In total, we screened more than 13,000 compounds across 5 different compound collections using this optimized screening approach enabled by COMPRESS-GI [35]. We were able to make high-confidence mode-of-action predictions for the targeted bioprocess through comparison to genetic interaction profiles for a total of more than 1500 compounds [35, 36], supporting the utility of compressed mutant profiles for functional characterization. The predicted modes of action spanned a diverse set of functional categories (Fig 6d), and we collected additional phenotypic data supporting our predictions for novel compounds in several different classes including tubulin inhibitors, cell cycle, and cell wall targeting compounds [35].…”

Section: Resultsmentioning

confidence: 91%

“…Based on the encouraging results from our in silico evaluation, we designed a compressed mutant pool based on the COMPRESS-GI selection and used this as a basis for a large-scale chemical genetic screening effort (Fig 6a). The details of this effort and specific findings related to the screened compounds are described in our companion paper [35], but we briefly highlight the impact of the mutant selection here. Our chemical-genetic screening platform utilizes barcode sequencing technology, which leverages the fact that each mutant strain is barcoded with a unique 20bp identifier adjacent to a common priming site [34].…”

Section: Resultsmentioning

confidence: 99%

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Efficient strategies for screening large-scale genetic interaction networks

Deshpande

Nelson

Simpkins

et al. 2017

Preprint

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Large-scale genetic interaction screening is a powerful approach for unbiased characterization of gene function and understanding systems-level cellular organization. While genome-wide screens are desirable as they provide the most comprehensive interaction profiles, they are resource and time-intensive and sometimes infeasible, depending on the species and experimental platform. For these scenarios, optimal methods for more efficient screening while still producing the maximal amount of information from the resulting profiles are of interest.To address this problem, we developed an optimal algorithm, called COMPRESS-GI, which selects a small but informative set of genes that captures most of the functional information contained within genome-wide genetic interaction profiles. The utility of this algorithm is demonstrated through an application of the approach to define a diagnostic mutant set for large-scale chemical genetic screens, where more than 13,000 compound screens were achieved through the increased throughput enabled by the approach. COMPRESS-GI can be broadly applied for directing genetic interaction screens in other contexts, including in species with little or no prior genetic-interaction data.

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“…C hemical-genetic interaction profiling in model organisms, such as yeast, has emerged as a powerful strategy to reveal functional insights into compounds, genes, and cellular processes. In these studies, the mechanism of action of a compound can be deduced by comparing its chemical-genetic interaction profile (the quantitative landscape of the effects of a panel of individual genes on the efficacy of this particular compound) to the profiles of compounds with known cellular targets to identify the most similar profiles [1][2][3][4][5] . Likewise, the function of a gene can be inferred by comparing its chemical-genetic interaction profile (the quantitative landscape of the effects of this particular gene on the efficacy of a panel of compounds) to the profiles of genes with known functions 6 .…”

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confidence: 99%

Quantitative and multiplexed chemical-genetic phenotyping in mammalian cells with QMAP-Seq

et al. 2020

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Chemical-genetic interaction profiling in model organisms has proven powerful in providing insights into compound mechanism of action and gene function. However, identifying chemical-genetic interactions in mammalian systems has been limited to low-throughput or computational methods. Here, we develop Quantitative and Multiplexed Analysis of Phenotype by Sequencing (QMAP-Seq), which leverages next-generation sequencing for pooled high-throughput chemical-genetic profiling. We apply QMAP-Seq to investigate how cellular stress response factors affect therapeutic response in cancer. Using minimal automation, we treat pools of 60 cell types—comprising 12 genetic perturbations in five cell lines—with 1440 compound-dose combinations, generating 86,400 chemical-genetic measurements. QMAP-Seq produces precise and accurate quantitative measures of acute drug response comparable to gold standard assays, but with increased throughput at lower cost. Moreover, QMAP-Seq reveals clinically actionable drug vulnerabilities and functional relationships involving these stress response factors, many of which are activated in cancer. Thus, QMAP-Seq provides a broadly accessible and scalable strategy for chemical-genetic profiling in mammalian cells.

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Functional Annotation of Chemical Libraries across Diverse Biological Processes

Abstract: Abstract

Cited by 9 publications

References 63 publications

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

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

Efficient strategies for screening large-scale genetic interaction networks

Quantitative and multiplexed chemical-genetic phenotyping in mammalian cells with QMAP-Seq

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