Dataset Information

A simple method for analyzing actives in random RNAi screens: introducing the "H Score" for hit nomination & gene prioritization.

ABSTRACT: Due to the numerous challenges in hit identification from random RNAi screening, we have examined current practices with a discovery of a variety of methodologies employed and published in many reports; majority of them, unfortunately, do not address the minimum associated criteria for hit nomination, as this could potentially have been the cause or may well be the explanation as to the lack of confirmation and follow up studies, currently facing the RNAi field. Overall, we find that these criteria or parameters are not well defined, in most cases arbitrary in nature, and hence rendering it extremely difficult to judge the quality of and confidence in nominated hits across published studies. For this purpose, we have developed a simple method to score actives independent of assay readout; and provide, for the first time, a homogenous platform enabling cross-comparison of active gene lists resulting from different RNAi screening technologies. Here, we report on our recently developed method dedicated to RNAi data output analysis referred to as the BDA method applicable to both arrayed and pooled RNAi technologies; wherein the concerns pertaining to inconsistent hit nomination and off-target silencing in conjugation with minimal activity criteria to identify a high value target are addressed. In this report, a combined hit rate per gene, called "H score", is introduced and defined. The H score provides a very useful tool for stringent active gene nomination, gene list comparison across multiple studies, prioritization of hits, and evaluation of the quality of the nominated gene hits.

SUBMITTER: Bhinder B

PROVIDER: S-EPMC3678258 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature

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Publications

A simple method for analyzing actives in random RNAi screens: introducing the "H Score" for hit nomination & gene prioritization.

Bhinder Bhavneet B Djaballah Hakim H

Combinatorial chemistry & high throughput screening 20121101 9

Due to the numerous challenges in hit identification from random RNAi screening, we have examined current practices with a discovery of a variety of methodologies employed and published in many reports; majority of them, unfortunately, do not address the minimum associated criteria for hit nomination, as this could potentially have been the cause or may well be the explanation as to the lack of confirmation and follow up studies, currently facing the RNAi field. Overall, we find that these crite ...[more]

PMID: 22934950

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Project description:The prevalence of lung disease due to infections with nontuberculous mycobacteria (NTM) has been increasing and surpassed tuberculosis (TB) in some countries. Treatment outcomes are often unsatisfactory, highlighting an urgent need for new anti-NTM medications. Although NTM in general do not respond well to TB specific drugs, the similarities between NTM and Mycobacterium tuberculosis at the molecular and cell structural level suggest that compound libraries active against TB could be leveraged for NTM drug discovery. Here we tested this hypothesis. The Pathogen Box from the Medicines for Malaria Venture (MMV) is a collection of 400 diverse drug-like compounds, among which 129 are known to be active against M. tuberculosis. By screening this compound collection against two NTM species, Mycobacterium abscessus and Mycobacterium avium, we showed that indeed the hit rates for NTM among TB active compounds is significantly higher compared to compounds that are not active against TB. MIC/dose response confirmation identified 10 top hits. Bactericidal activity determination demonstrated attractive potency for a subset of the confirmed hits. In vivo pharmacokinetic profiling showed that some of the compounds present reasonable starting points for medicinal chemistry programs. Three of the top hits were oxazolidinones, suggesting the potential for repositioning this class of protein synthesis inhibitors to replace linezolid which suffers from low potency. Two hits were inhibitors of the trehalose monomycolate transporter MmpL3, suggesting that this transmembrane protein may be an attractive target for NTM. Other hits are predicted to target a range of functions, including cell division (FtsZ), DNA gyrase (GyrB), dihydrofolate reductase, RNA polymerase and ABC transporters. In conclusion, our study showed that screening TB active compounds for activity against NTM resulted in high hit rates, suggesting that this may be an attractive approach to kick start NTM drug discovery projects. In addition, the work identified a series of novel high value NTM hits with associated candidate targets which can be followed up in hit-to-lead projects for the discovery of new NTM antibiotics.

Project description:BackgroundRNAi screens via pooled short hairpin RNAs (shRNAs) have recently become a powerful tool for the identification of essential genes in mammalian cells. In the past years, several pooled large-scale shRNA screens have identified a variety of genes involved in cancer cell proliferation. All of those studies employed microarray analysis, utilizing either the shRNA's half hairpin sequence or an additional shRNA-associated 60 nt barcode sequence as a molecular tag. Here we describe a novel method to decode pooled RNAi screens, namely barcode tiling array analysis, and demonstrate how this approach can be used to precisely quantify the abundance of individual shRNAs from a pool.ResultsWe synthesized DNA microarrays with six overlapping 25 nt long tiling probes complementary to each unique 60 nt molecular barcode sequence associated with every shRNA expression construct. By analyzing dilution series of expression constructs we show how our approach allows quantification of shRNA abundance from a pool and how it clearly outperforms the commonly used analysis via the shRNA's half hairpin sequences. We further demonstrate how barcode tiling arrays can be used to predict anti-proliferative effects of individual shRNAs from pooled negative selection screens. Out of a pool of 305 shRNAs, we identified 28 candidate shRNAs to fully or partially impair the viability of the breast carcinoma cell line MDA-MB-231. Individual validation of a subset of eleven shRNA expression constructs with potential inhibitory, as well as non-inhibitory, effects on the cell line proliferation provides further evidence for the accuracy of the barcode tiling approach.ConclusionsIn summary, we present an improved method for the rapid, quantitative and statistically robust analysis of pooled RNAi screens. Our experimental approach, coupled with commercially available lentiviral vector shRNA libraries, has the potential to greatly facilitate the discovery of putative targets for cancer therapy as well as sensitizers of drug toxicity.

Dataset Information

A simple method for analyzing actives in random RNAi screens: introducing the "H Score" for hit nomination & gene prioritization.

Publications

A simple method for analyzing actives in random RNAi screens: introducing the "H Score" for hit nomination & gene prioritization.

Similar Datasets

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