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Improving drug discovery using image-based multiparametric analysis of the epigenetic landscape.

ABSTRACT: High-content phenotypic screening has become the approach of choice for drug discovery due to its ability to extract drug-specific multi-layered data. In the field of epigenetics, such screening methods have suffered from a lack of tools sensitive to selective epigenetic perturbations. Here we describe a novel approach, Microscopic Imaging of Epigenetic Landscapes (MIEL), which captures the nuclear staining patterns of epigenetic marks and employs machine learning to accurately distinguish between such patterns. We validated the MIEL platform across multiple cells lines and using dose-response curves, to insure the fidelity and robustness of this approach for high content high throughput drug discovery. Focusing on noncytotoxic glioblastoma treatments, we demonstrated that MIEL can identify and classify epigenetically active drugs. Furthermore, we show MIEL was able to accurately rank candidate drugs by their ability to produce desired epigenetic alterations consistent with increased sensitivity to chemotherapeutic agents or with induction of glioblastoma differentiation.

SUBMITTER: Farhy C

PROVIDER: S-EPMC6908434 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Improving drug discovery using image-based multiparametric analysis of the epigenetic landscape.

Farhy Chen C Hariharan Santosh S Ylanko Jarkko J Orozco Luis L Zeng Fu-Yue FY Pass Ian I Ugarte Fernando F Forsberg E Camilla EC Huang Chun-Teng CT Andrews David W DW Terskikh Alexey V AV

eLife 20191022

High-content phenotypic screening has become the approach of choice for drug discovery due to its ability to extract drug-specific multi-layered data. In the field of epigenetics, such screening methods have suffered from a lack of tools sensitive to selective epigenetic perturbations. Here we describe a novel approach, Microscopic Imaging of Epigenetic Landscapes (MIEL), which captures the nuclear staining patterns of epigenetic marks and employs machine learning to accurately distinguish betwe ...[more]

PMID: 31637999

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Project description:Inflammatory bowel diseases (IBD) with chronic infiltration of immune cells in the gastrointestinal tract are common and largely incurable. The therapeutic targeting of IBD has been hampered by the complex causality of the disease, with environmental insults like cholesterol-enriched Western diets playing a critical role. To address this drug development challenge, we report an easy-to-handle dietary cholesterol-based in vivo assay that allows the screening of immune-modulatory therapeutics in transgenic zebrafish models. An improvement in the feeding strategy with high cholesterol diet (HCD) selectively induces a robust and consistent infiltration of myeloid cells in larvae intestines that is highly suitable for compound discovery efforts. Using transgenics with fluorescent reporter expression in neutrophils, we take advantage of the unique zebrafish larvae clarity to monitor an acute inflammatory response in a whole organism context with a fully functional innate immune system. The use of semi-automated image acquisition and processing combined with quantitative image analysis allows categorizing anti- or pro-inflammatory compounds based on a leukocytic inflammation index. Our HCD gut inflammation (HCD-GI) assay is simple, cost- and time-effective as well as highly physiological which makes it unique when compared to chemical-based zebrafish models of IBD. Besides, diet is a highly controlled, selective and targeted trigger of intestinal inflammation that avoids extra-intestinal outcomes and reduces the chances of chemical-induced toxicity during screenings. We show the validity of this assay for a screening platform by testing two dietary phenolic acids, namely gallic acid (GA; 3,4,5-trihydroxybenzoic acid) and ferulic acid (FA; 4-hydroxy-3-methoxycinnamic acid), with well described anti-inflammatory actions in animal models of IBD. Analysis of common IBD therapeutics (Prednisolone and Mesalamine) proved the fidelity of our IBD-like intestinal inflammation model. In conclusion, the HCD-GI assay can facilitate and accelerate drug discovery efforts on IBD, by identification of novel lead molecules with immune modulatory action on intestinal neutrophilic inflammation. This will serve as a jumping-off point for more profound analyses of drug mechanisms and pathways involved in early IBD immune responses.

Dataset Information

Improving drug discovery using image-based multiparametric analysis of the epigenetic landscape.

Publications

Improving drug discovery using image-based multiparametric analysis of the epigenetic landscape.

Similar Datasets

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