L1000CDS2: LINCS L1000 characteristic direction signatures search engine

Abstract

The library of integrated network-based cellular signatures (LINCS) L1000 data set currently comprises of over a million gene expression profiles of chemically perturbed human cell lines. Through unique several intrinsic and extrinsic benchmarking schemes, we demonstrate that processing the L1000 data with the characteristic direction (CD) method significantly improves signal to noise compared with the MODZ method currently used to compute L1000 signatures. The CD processed L1000 signatures are served through a state-of-the-art web-based search engine application called L1000CDS2. The L1000CDS2 search engine provides prioritization of thousands of small-molecule signatures, and their pairwise combinations, predicted to either mimic or reverse an input gene expression signature using two methods. The L1000CDS2 search engine also predicts drug targets for all the small molecules profiled by the L1000 assay that we processed. Targets are predicted by computing the cosine similarity between the L1000 small-molecule signatures and a large collection of signatures extracted from the gene expression omnibus (GEO) for single-gene perturbations in mammalian cells. We applied L1000CDS2 to prioritize small molecules that are predicted to reverse expression in 670 disease signatures also extracted from GEO, and prioritized small molecules that can mimic expression of 22 endogenous ligand signatures profiled by the L1000 assay. As a case study, to further demonstrate the utility of L1000CDS2, we collected expression signatures from human cells infected with Ebola virus at 30, 60 and 120 min. Querying these signatures with L1000CDS2 we identified kenpaullone, a GSK3B/CDK2 inhibitor that we show, in subsequent experiments, has a dose-dependent efficacy in inhibiting Ebola infection in vitro without causing cellular toxicity in human cell lines. In summary, the L1000CDS2 tool can be applied in many biological and biomedical settings, while improving the extraction of knowledge from the LINCS L1000 resource.

HDAC inhibition ameliorates cone survival in retinitis pigmentosa mice

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Formatting biological big data for modern machine learning in drug discovery

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Cross-species molecular dissection across alcohol behavioral domains

“…Large-scale studies of gene expression signatures for small molecules have aided the discovery-based efforts for neoteric uses of existing therapeutics (Lamb et al, 2006). Taking advantage of a constantly increasing volume of data and available resources, several studies have predicted the efficacy of existing compounds for previously non-indicated conditions, which can be further validated in a suitable laboratory model (Duan et al, 2016; Dudley et al, 2011; Liu, Lee, Salazar Hernandez, Mazitschek & Ozcan, 2015). Repurposing or repositioning of medications broadens their respective clinical utility and provides opportunities to examine previously unknown mechanisms of action.…”