scholarly journals Roadmap for the use of base editors to decipher drug mechanism of action

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257537
Author(s):  
Estel Aparicio-Prat ◽  
Dong Yan ◽  
Marco Mariotti ◽  
Michael Bassik ◽  
Gaelen Hess ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Mechanism Of Action ◽  
Large Scale ◽  
Reporter System ◽  
Experimental Conditions ◽  
Drug Mechanism ◽  
Β2 Adrenergic Receptor ◽  
Set Up ◽  
G Protein Coupled ◽  
Early Drug

CRISPR base editors are powerful tools for large-scale mutagenesis studies. This kind of approach can elucidate the mechanism of action of compounds, a key process in drug discovery. Here, we explore the utility of base editors in an early drug discovery context focusing on G-protein coupled receptors. A pooled mutagenesis screening framework was set up based on a modified version of the CRISPR-X base editor system. We determine optimized experimental conditions for mutagenesis where sgRNAs are delivered by cell transfection or viral infection over extended time periods (>14 days), resulting in high mutagenesis produced in a short region located at -4/+8 nucleotides with respect to the sgRNA match. The β2 Adrenergic Receptor (B2AR) was targeted in this way employing a 6xCRE-mCherry reporter system to monitor its response to isoproterenol. The results of our screening indicate that residue 184 of B2AR is crucial for its activation. Based on our experience, we outline the crucial points to consider when designing and performing CRISPR-based pooled mutagenesis screening, including the typical technical hurdles encountered when studying compound pharmacology.

scholarly journals Roadmap for the use of Base Editors to Decipher Drug Mechanism of Action

2020 ◽  
Author(s):  
Estel Aparicio Prat ◽  
Dong Yan ◽  
Marco Mariotti ◽  
Michael Bassik ◽  
Gaelen Hess ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Mechanism Of Action ◽  
Large Scale ◽  
Reporter System ◽  
Experimental Conditions ◽  
Base Editing ◽  
Drug Mechanism ◽  
Set Up ◽  
G Protein Coupled ◽  
Early Drug

Abstract Background: CRISPR base editors are powerful tools for large-scale mutagenesis studies. This kind of approach can elucidate the mechanism of action of compounds, a key process in drug discovery. Here, we explore the utility of base editors in an early drug discovery context, and we focus on G-protein coupled receptors.Results: We set up a pooled mutagenesis screening framework based on a modified version of the CRISPR-X base editor system. We determine optimized experimental conditions for mutagenesis where sgRNAs are delivered by cell transfection or viral infection over extended time periods (>14 days), resulting in high mutagenesis produced in a short region located at -4/+8 nucleotides with respect to the sgRNA match. We thus target the Beta 2 Adrenergic Receptor (B2AR) and employ a 6xCRE-mCherry reporter system to monitor its activity. The results of our screening indicate that residue 184 of B2AR is crucial for its activation. Based on our experience, we then outline the crucial points to consider when designing and performing CRISPR-based pooled mutagenesis screening, including the typical technical hurdles encountered when studying compound pharmacology. Conclusions: The base editing technology has a great potential to help deciphering the mechanism of action of drugs, and it is a very powerful tool in drug discovery. Here we show an application of pooled mutagenesis screening to study B2AR, and we provide a roadmap for successfully applying this approach to other target proteins.

scholarly journals An In-Silico Approach for Designing a Potential Antagonistic Molecule Targeting β2-adrenoreceptor Having Therapeutic Significance

2020 ◽  
Vol 10 (2) ◽  
pp. 2063-2069
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Active Sites ◽  
De Novo ◽  
Cellular Level ◽  
Ligand Molecule ◽  
Best Fitting ◽  
Β2 Adrenergic Receptor ◽  
External Stimuli ◽  
G Protein Coupled

One of the largest families of membrane proteins, the G protein-coupled receptors (GPCRs) has been a very important target of drug discovery as they are involved in having a regulatory role in a variety of signaling pathways at the cellular level in response to external stimuli. Modern in-silico and crystallographic approaches have further made it easier to peep into their structures. In this study, β2 adrenergic receptor (β2AR) has been targeted, and a new ligand molecule using the de-novo approach has been proposed. Using 1-Amino-3-(2,3-dihydro-1H-indol-4-yloxy)-propan-2-ol, the best fitting binding fragments were established with a significant dissociation constant value of 5-7 nanomolar. The flexibility of specific active sites was also investigated, and it was observed that residues 114 (V), 117 (V), 203 (S), 286 (W), and 289 (F) played a crucial role in accommodating ligand for the best binding. Upon examination of the bioavailability parameters, the ligand var9 exhibited significant inhibitory characteristics having lower toxicity values and high drug likeliness properties. Findings certainly hold significance in terms of targeting GPCRs in getting insight into structure-based drug designing and drug discovery.

Development of a High-Content Screening Assay Panel to Accelerate Mechanism of Action Studies for Oncology Research

2012 ◽  
Vol 17 (8) ◽  
pp. 1005-1017 ◽  
Author(s):  
Danli L. Towne ◽  
Emily E. Nicholl ◽  
Kenneth M. Comess ◽  
Scott C. Galasinski ◽  
Philip J. Hajduk ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Mechanism Of Action ◽  
Large Scale ◽  
Morphological Changes ◽  
Single Cells ◽  
High Content Screening ◽  
Individual Compound ◽  
Screening Assay ◽  
Oncology Research ◽  
Protein Functions

Efficient elucidation of the biological mechanism of action of novel compounds remains a major bottleneck in the drug discovery process. To address this need in the area of oncology, we report the development of a multiparametric high-content screening assay panel at the level of single cells to dramatically accelerate understanding the mechanism of action of cell growth–inhibiting compounds on a large scale. Our approach is based on measuring 10 established end points associated with mitochondrial apoptosis, cell cycle disruption, DNA damage, and cellular morphological changes in the same experiment, across three multiparametric assays. The data from all of the measurements taken together are expected to help increase our current understanding of target protein functions, constrain the list of possible targets for compounds identified using phenotypic screens, and identify off-target effects. We have also developed novel data visualization and phenotypic classification approaches for detailed interpretation of individual compound effects and navigation of large collections of multiparametric cellular responses. We expect this general approach to be valuable for drug discovery across multiple therapeutic areas.

scholarly journals Domain-invariant features for mechanism of action prediction in a multi-cell-line drug screen

2019 ◽  
Vol 36 (5) ◽  
pp. 1607-1613 ◽  
Author(s):  
Joseph C Boyd ◽  
Alice Pinheiro ◽  
Elaine Del Nery ◽  
Fabien Reyal ◽  
Thomas Walter
Keyword(s):  
Drug Discovery ◽  
Single Cell ◽  
Cell Line ◽  
Cell Lines ◽  
Mechanism Of Action ◽  
Molecular Subtype ◽  
Supplementary Information ◽  
Breast Cancer Cell Lines ◽  
Molecular Heterogeneity ◽  
Drug Mechanism

Abstract Motivation High-content screening is an important tool in drug discovery and characterization. Often, high-content drug screens are performed on one single-cell line. Yet, a single-cell line cannot be thought of as a perfect disease model. Many diseases feature an important molecular heterogeneity. Consequently, a drug may be effective against one molecular subtype of a disease, but less so against another. To characterize drugs with respect to their effect not only on one cell line but on a panel of cell lines is therefore a promising strategy to streamline the drug discovery process. Results The contribution of this article is 2-fold. First, we investigate whether we can predict drug mechanism of action (MOA) at the molecular level without optimization of the MOA classes to the screen specificities. To this end, we benchmark a set of algorithms within a conventional pipeline, and evaluate their MOA prediction performance according to a statistically rigorous framework. Second, we extend this conventional pipeline to the simultaneous analysis of multiple cell lines, each manifesting potentially different morphological baselines. For this, we propose multi-task autoencoders, including a domain-adaptive model used to construct domain-invariant feature representations across cell lines. We apply these methods to a pilot screen of two triple negative breast cancer cell lines as models for two different molecular subtypes of the disease. Availability and implementation https://github.com/jcboyd/multi-cell-line or https://zenodo.org/record/2677923. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Integrated Genomic and Metabolomic Approach to the Discovery of Potential Anti-Quorum Sensing Natural Products from Microbes Associated with Marine Samples from Singapore

Marine Drugs ◽  
2019 ◽  
Vol 17 (1) ◽  
pp. 72 ◽  
Author(s):  
Ji Ong ◽  
Hui Goh ◽  
Swee Lim ◽  
Li Pang ◽  
Joyce Chin ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Quorum Sensing ◽  
Large Scale ◽  
Marine Ecosystem ◽  
Gene Clusters ◽  
Integrated Approach ◽  
Rrna Gene ◽  
Dependent Manner ◽  
Bacterial Strains ◽  
Reporter System

With 70% of the Earth’s surface covered in water, the marine ecosystem offers immense opportunities for drug discovery and development. Due to the decreasing rate of novel natural product discovery from terrestrial sources in recent years, many researchers are beginning to look seaward for breakthroughs in new therapeutic agents. As part of an ongoing marine drug discovery programme in Singapore, an integrated approach of combining metabolomic and genomic techniques were initiated for uncovering novel anti-quorum sensing molecules from bacteria associated with subtidal samples collected in the Singapore Strait. Based on the culture-dependent method, a total of 102 marine bacteria strains were isolated and the identities of selected strains were established based on their 16S rRNA gene sequences. About 5% of the marine bacterial organic extracts showed quorum sensing inhibitory (QSI) activity in a dose-dependent manner based on the Pseudomonas aeruginosa QS reporter system. In addition, the extracts were subjected to mass spectrometry-based molecular networking and the genome of selected strains were analysed for known as well as new biosynthetic gene clusters. This study revealed that using integrated techniques, coupled with biological assays, can provide an effective and rapid prioritization of marine bacterial strains for downstream large-scale culturing for the purpose of isolation and structural elucidation of novel bioactive compounds.

scholarly journals Document Embedding Centroids: New and Versatile Semantic Descriptors for Compounds

2020 ◽  
Author(s):  
John Santa Maria ◽  
Scott Gleim ◽  
Eugen Lounkine ◽  
Jeremy Jenkins
Keyword(s):  
Drug Discovery ◽  
Mechanism Of Action ◽  
Nearest Neighbor ◽  
Biomedical Literature ◽  
Chemical Descriptors ◽  
Target Activity ◽  
Early Drug ◽  
Novel Algorithm ◽  
Chemical Matter

We describe a novel algorithm for generating representational embeddings of chemical matter based on the biomedical literature/semantic contexts in which they occur. We then demonstrate that these chemical descriptors have utility in nearest neighbor retrieval for early drug discovery tasks such as mechanism of action and target activity predictions.

scholarly journals Gene Expression Data Mining Reveals the Involvement of GPR55 and Its Endogenous Ligands in Immune Response, Cancer, and Differentiation

2021 ◽  
Vol 22 (24) ◽  
pp. 13328
Author(s):  
Artur Wnorowski ◽  
Jakub Wójcik ◽  
Maciej Maj
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Immune Cell ◽  
Cell Lineage ◽  
Expression Data ◽  
Experimental Conditions ◽  
Pathological Conditions ◽  
Pituitary Adenylate Cyclase ◽  
G Protein Coupled

G protein-coupled receptor 55 (GPR55) is a recently deorphanized lipid- and peptide-sensing receptor. Its lipidic endogenous agonists belong to lysoglycerophospholipids, with lysophosphatidylinositol (LPI) being the most studied. Peptide agonists derive from fragmentation of pituitary adenylate cyclase-activating polypeptide (PACAP). Although GPR55 and its ligands were implicated in several physiological and pathological conditions, their biological function remains unclear. Thus, the aim of the study was to conduct a large-scale re-analysis of publicly available gene expression datasets to identify physiological and pathological conditions affecting the expression of GPR55 and the production of its ligands. The study revealed that regulation of GPR55 occurs predominantly in the context of immune activation pointing towards the role of the receptor in response to pathogens and in immune cell lineage determination. Additionally, it was revealed that there is almost no overlap between the experimental conditions affecting the expression of GPR55 and those modulating agonist production. The capacity to synthesize LPI was enhanced in various types of tumors, indicating that cancer cells can hijack the motility-related activity of GPR55 to increase aggressiveness. Conditions favoring accumulation of PACAP-derived peptides were different than those for LPI and were mainly related to differentiation. This indicates a different function of the two agonist classes and possibly the existence of a signaling bias.

scholarly journals High Content Phenotypic Profiling in Oesophageal Adenocarcinoma Identifies Selectively Active Pharmacological Classes of Drugs for Repurposing and Chemical Starting Points for Novel Drug Discovery

2020 ◽  
Author(s):  
Rebecca E Hughes ◽  
Richard J R Elliott ◽  
Alison F Munro ◽  
Ashraff Makda ◽  
J Robert O’Neill ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Cell Lines ◽  
Cancer Biology ◽  
Large Scale ◽  
Oesophageal Adenocarcinoma ◽  
Phenotypic Screening ◽  
Chemical Library ◽  
Drug Mechanism ◽  
Screening Platform ◽  
Selective Activity

AbstractOesophageal adenocarcinoma (OAC) is a highly heterogeneous disease, dominated by large-scale genomic rearrangements and copy number alterations. Such characteristics have hampered conventional target-directed drug discovery and personalized medicine strategies contributing to poor outcomes for patients diagnosed with OAC. We describe the development and application of a phenotypic-led OAC drug discovery platform incorporating image-based, high-content cell profiling and associated image-informatics tools to classify drug mechanism-of-action (MoA). We applied a high-content Cell Painting assay to profile the phenotypic response of 19,555 compounds across a panel of six OAC cell lines representing the genetic heterogeneity of disease, a pre-neoplastic Barrett’s oesophagus line and a non-transformed squamous oesophageal line. We built an automated phenotypic screening and high-content image analysis pipeline to identify compounds that selectively modified the phenotype of OAC cell lines. We further trained a machine-learning model to predict the MoA of OAC selective compounds using phenotypic fingerprints from a library of reference compounds.We identified a number of phenotypic clusters enriched with similar pharmacological classes e.g. Methotrexate and three other antimetabolites which are highly selective for OAC cell lines. We further identify a small number of hits from our diverse chemical library which show potent and selective activity for OAC cell lines and which do not cluster with the reference library of known MoA, indicating they may be selectively targeting novel oesophageal cancer biology. Our results demonstrate that our OAC phenotypic screening platform can identify existing pharmacological classes and novel compounds with selective activity for OAC cell phenotypes.

scholarly journals Multiplexed single-cell profiling of post-perturbation transcriptional responses to define cancer vulnerabilities and therapeutic mechanism of action

2019 ◽  
Author(s):  
James M. McFarland ◽  
Brenton R. Paolella ◽  
Allison Warren ◽  
Kathryn Geiger-Schuller ◽  
Tsukasa Shibue ◽  
...  
Keyword(s):  
Single Cell ◽  
Cancer Cell ◽  
Mechanism Of Action ◽  
Treatment Time ◽  
Marker Gene ◽  
Sequencing Data ◽  
Transcriptional Responses ◽  
Experimental Conditions ◽  
Drug Mechanism ◽  
Genetic Perturbations

AbstractAssays to study cancer cell responses to pharmacologic or genetic perturbations are typically restricted to using simple phenotypic readouts such as proliferation rate or the expression of a marker gene. Information-rich assays, such as gene-expression profiling, are generally not amenable to efficient profiling of a given perturbation across multiple cellular contexts. Here, we developed MIX-Seq, a method for multiplexed transcriptional profiling of post-perturbation responses across a mixture of samples with single-cell resolution, using SNP-based computational demultiplexing of single-cell RNA-sequencing data. We show that MIX-Seq can be used to profile responses to chemical or genetic perturbations across pools of 100 or more cancer cell lines, and combine it with Cell Hashing to further multiplex additional experimental conditions, such as multiple post-treatment time points or drug doses. Analyzing the high-content readout of scRNA-seq reveals both shared and context-specific transcriptional response components that can identify drug mechanism of action and can be used to predict long-term cell viability from short-term transcriptional responses to treatment.

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