A flow cytometry-based screen identifies MBNL1 modulators that rescue splicing defects in myotonic dystrophy type I.

Myotonic dystrophy Type 1 (DM1) is a rare genetic disease caused by the expansion of CTG trinucleotide repeats ((CTG)exp) in the 3' untranslated region of the DMPK gene. The repeat transcripts sequester the RNA binding protein Muscleblind-like protein 1 (MBNL1) and hamper its normal function in pre-mRNA splicing. Overexpressing exogenous MBNL1 in the DM1 mouse model has been shown to rescue the splicing defects and reverse myotonia. Although a viable therapeutic strategy, pharmacological modulators of MBNL1 expression have not been identified. Here, we engineered a ZsGreen tag into the endogenous MBNL1 locus in HeLa cells and established a flow cytometry-based screening system to identify compounds that increase MBNL1 level. The initial screen of small molecule compound libraries identified more than thirty hits that increased MBNL1 expression greater than double the baseline levels. Further characterization of two hits revealed that the small molecule HDAC inhibitors, ISOX and vorinostat, increased MBNL1 expression in DM1 patient-derived fibroblasts and partially rescued the splicing defect caused by (CUG)exp repeats in these cells. These findings demonstrate the feasibility of this flow-based cytometry screen to identify both small molecule compounds and druggable targets for MBNL1 upregulation.

A dataset of images and morphological profiles of 30 000 small-molecule treatments using the Cell Painting assay.

Background: Large-scale image sets acquired by automated microscopy of perturbed samples enable a detailed comparison of cell states induced by each perturbation, such as a small molecule from a diverse library. Highly multiplexed measurements of cellular morphology can be extracted from each image and subsequently mined for a number of applications. Findings: This microscopy dataset includes 919 265 five-channel fields of view, representing 30 616 tested compounds, available at "The Cell Image Library" (CIL) repository. It also includes data files containing morphological features derived from each cell in each image, both at the single-cell level and population-averaged (i.e., per-well) level; the image analysis workflows that generated the morphological features are also provided. Quality-control metrics are provided as metadata, indicating fields of view that are out-of-focus or containing highly fluorescent material or debris. Lastly, chemical annotations are supplied for the compound treatments applied. Conclusions: Because computational algorithms and methods for handling single-cell morphological measurements are not yet routine, the dataset serves as a useful resource for the wider scientific community applying morphological (image-based) profiling. The dataset can be mined for many purposes, including small-molecule library enrichment and chemical mechanism-of-action studies, such as target identification. Integration with genetically perturbed datasets could enable identification of small-molecule mimetics of particular disease- or gene-related phenotypes that could be useful as probes or potential starting points for development of future therapeutics.

Correlating chemical sensitivity and basal gene expression reveals mechanism of action.

Changes in cellular gene expression in response to small-molecule or genetic perturbations have yielded signatures that can connect unknown mechanisms of action (MoA) to ones previously established. We hypothesized that differential basal gene expression could be correlated with patterns of small-molecule sensitivity across many cell lines to illuminate the actions of compounds whose MoA are unknown. To test this idea, we correlated the sensitivity patterns of 481 compounds with ∼19,000 basal transcript levels across 823 different human cancer cell lines and identified selective outlier transcripts. This process yielded many novel mechanistic insights, including the identification of activation mechanisms, cellular transporters and direct protein targets. We found that ML239, originally identified in a phenotypic screen for selective cytotoxicity in breast cancer stem-like cells, most likely acts through activation of fatty acid desaturase 2 (FADS2). These data and analytical tools are available to the research community through the Cancer Therapeutics Response Portal.

Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset.

UNLABELLED: Identifying genetic alterations that prime a cancer cell to respond to a particular therapeutic agent can facilitate the development of precision cancer medicines. Cancer cell-line (CCL) profiling of small-molecule sensitivity has emerged as an unbiased method to assess the relationships between genetic or cellular features of CCLs and small-molecule response. Here, we developed annotated cluster multidimensional enrichment analysis to explore the associations between groups of small molecules and groups of CCLs in a new, quantitative sensitivity dataset. This analysis reveals insights into small-molecule mechanisms of action, and genomic features that associate with CCL response to small-molecule treatment. We are able to recapitulate known relationships between FDA-approved therapies and cancer dependencies and to uncover new relationships, including for KRAS-mutant cancers and neuroblastoma. To enable the cancer community to explore these data, and to generate novel hypotheses, we created an updated version of the Cancer Therapeutic Response Portal (CTRP v2). SIGNIFICANCE: We present the largest CCL sensitivity dataset yet available, and an analysis method integrating information from multiple CCLs and multiple small molecules to identify CCL response predictors robustly. We updated the CTRP to enable the cancer research community to leverage these data and analyses.

Toward performance-diverse small-molecule libraries for cell-based phenotypic screening using multiplexed high-dimensional profiling.

High-throughput screening has become a mainstay of small-molecule probe and early drug discovery. The question of how to build and evolve efficient screening collections systematically for cell-based and biochemical screening is still unresolved. It is often assumed that chemical structure diversity leads to diverse biological performance of a library. Here, we confirm earlier results showing that this inference is not always valid and suggest instead using biological measurement diversity derived from multiplexed profiling in the construction of libraries with diverse assay performance patterns for cell-based screens. Rather than using results from tens or hundreds of completed assays, which is resource intensive and not easily extensible, we use high-dimensional image-based cell morphology and gene expression profiles. We piloted this approach using over 30,000 compounds. We show that small-molecule profiling can be used to select compound sets with high rates of activity and diverse biological performance.

Connecting Small Molecules with Similar Assay Performance Profiles Leads to New Biological Hypotheses.

High-throughput screening allows rapid identification of new candidate compounds for biological probe or drug development. Here, we describe a principled method to generate "assay performance profiles" for individual compounds that can serve as a basis for similarity searches and cluster analyses. Our method overcomes three challenges associated with generating robust assay performance profiles: (1) we transform data, allowing us to build profiles from assays having diverse dynamic ranges and variability; (2) we apply appropriate mathematical principles to handle missing data; and (3) we mitigate the fact that loss-of-signal assay measurements may not distinguish between multiple mechanisms that can lead to certain phenotypes (e.g., cell death). Our method connected compounds with similar mechanisms of action, enabling prediction of new targets and mechanisms both for known bioactives and for compounds emerging from new screens. Furthermore, we used Bayesian modeling of promiscuous compounds to distinguish between broadly bioactive and narrowly bioactive compound communities. Several examples illustrate the utility of our method to support mechanism-of-action studies in probe development and target identification projects.

An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules.

The high rate of clinical response to protein-kinase-targeting drugs matched to cancer patients with specific genomic alterations has prompted efforts to use cancer cell line (CCL) profiling to identify additional biomarkers of small-molecule sensitivities. We have quantitatively measured the sensitivity of 242 genomically characterized CCLs to an Informer Set of 354 small molecules that target many nodes in cell circuitry, uncovering protein dependencies that: (1) associate with specific cancer-genomic alterations and (2) can be targeted by small molecules. We have created the Cancer Therapeutics Response Portal (http://www.broadinstitute.org/ctrp) to enable users to correlate genetic features to sensitivity in individual lineages and control for confounding factors of CCL profiling. We report a candidate dependency, associating activating mutations in the oncogene ß-catenin with sensitivity to the Bcl-2 family antagonist, navitoclax. The resource can be used to develop novel therapeutic hypotheses and to accelerate discovery of drugs matched to patients by their cancer genotype and lineage.

Utility-aware screening with clique-oriented prioritization.

Most methods of deciding which hits from a screen to send for confirmatory testing assume that all confirmed actives are equally valuable and aim only to maximize the number of confirmed hits. In contrast, "utility-aware" methods are informed by models of screeners' preferences and can increase the rate at which the useful information is discovered. Clique-oriented prioritization (COP) extends a recently proposed economic framework and aims--by changing which hits are sent for confirmatory testing--to maximize the number of scaffolds with at least two confirmed active examples. In both retrospective and prospective experiments, COP enables accurate predictions of the number of clique discoveries in a batch of confirmatory experiments and improves the rate of clique discovery by more than 3-fold. In contrast, other similarity-based methods like ontology-based pattern identification (OPI) and local hit-rate analysis (LHR) reduce the rate of scaffold discovery by about half. The utility-aware algorithm used to implement COP is general enough to implement several other important models of screener preferences.

A small-molecule screening strategy to identify suppressors of statin myopathy.

The reduction of plasma low-density lipoprotein levels by HMG-CoA reductase inhibitors, or statins, has had a revolutionary impact in medicine, but muscle-related side effects remain a dose-limiting toxicity in many patients. We describe a chemical epistasis approach that can be useful in refining the mechanism of statin muscle toxicity, as well as in screening for agents that suppress muscle toxicity while preserving the ability of statins to increase the expression of the low-density lipoprotein receptor. Using this approach, we identified one compound that attenuates the muscle side effects in both cellular and animal models of statin toxicity, likely by influencing Rab prenylation. Our proof-of-concept screen lays the foundation for truly high-throughput screens that could help lead to the development of clinically useful adjuvants that can one day be co-administered with statins.

Master data management: getting your house in order.

The availability of high-throughput techniques combined with more exploratory and confirmatory studies in small-molecule science (e.g., probe- and drug-discovery) creates a significant need for structured approaches to data management. The probe- and drug-discovery scientific processes start and end with lower-throughput experiments, connected often by high-throughput cheminformatics, screening, and small-molecule profiling experiments. A rigorous and disciplined approach to data management ensures that data can be used to ask complex questions of assay results, and allows many questions to be answered computationally, without the need for significant manual effort. A structured approach to recording scientific experimental design and observations involves using a consistently maintained set of 'master data' or 'metadata'. Master data include sets of tightly controlled terminology used to describe an experiment, including both materials and methods. Master data can be used at the level of an individual laboratory or with a scope as extensive as a whole community of scientists. Consistent use of master data increases experimental power by allowing data analysis to connect all parts of the discovery life cycle, across experiments performed by different researchers and from different laboratories, thus decreasing the opportunity cost for making novel connections between results. Despite the promise of this increased experimental power, challenges remain in implementation and consistent use of master data management (MDM) techniques in the laboratory. In this paper, we discuss how specific MDM techniques can enhance the quality and utility of scientific data at a project, laboratory, and institutional level. We present a model for storage and exploitation of master data, practical applications of these techniques in the research context of small-molecule science, and specific benefits of MDM to small-molecule screening aimed at probe- and drug-discovery.

Enhancing the rate of scaffold discovery with diversity-oriented prioritization.

MOTIVATION: In high-throughput screens (HTS) of small molecules for activity in an in vitro assay, it is common to search for active scaffolds, with at least one example successfully confirmed as an active. The number of active scaffolds better reflects the success of the screen than the number of active molecules. Many existing algorithms for deciding which hits should be sent for confirmatory testing neglect this concern. RESULTS: We derived a new extension of a recently proposed economic framework, diversity-oriented prioritization (DOP), that aims-by changing which hits are sent for confirmatory testing-to maximize the number of scaffolds with at least one confirmed active. In both retrospective and prospective experiments, DOP accurately predicted the number of scaffold discoveries in a batch of confirmatory experiments, improved the rate of scaffold discovery by 8-17%, and was surprisingly robust to the size of the confirmatory test batches. As an extension of our previously reported economic framework, DOP can be used to decide the optimal number of hits to send for confirmatory testing by iteratively computing the cost of discovering an additional scaffold, the marginal cost of discovery. CONTACT: swamidass@wustl.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Small molecules of different origins have distinct distributions of structural complexity that correlate with protein-binding profiles.

Using a diverse collection of small molecules generated from a variety of sources, we measured protein-binding activities of each individual compound against each of 100 diverse (sequence-unrelated) proteins using small-molecule microarrays. We also analyzed structural features, including complexity, of the small molecules. We found that compounds from different sources (commercial, academic, natural) have different protein-binding behaviors and that these behaviors correlate with general trends in stereochemical and shape descriptors for these compound collections. Increasing the content of sp(3)-hybridized and stereogenic atoms relative to compounds from commercial sources, which comprise the majority of current screening collections, improved binding selectivity and frequency. The results suggest structural features that synthetic chemists can target when synthesizing screening collections for biological discovery. Because binding proteins selectively can be a key feature of high-value probes and drugs, synthesizing compounds having features identified in this study may result in improved performance of screening collections.

Small-Molecule Suppressors of Cytokine-Induced beta-Cell Apoptosis.

Pancreatic beta-cell apoptosis is a critical event during the development of type-1 diabetes. The identification of small molecules capable of preventing cytokine-induced apoptosis could lead to avenues for therapeutic intervention. We developed a set of phenotypic cell-based assays designed to identify such small-molecule suppressors. Rat INS-1E cells were simultaneously treated with a cocktail of inflammatory cytokines and a collection of 2,240 diverse small molecules and screened using an assay for cellular ATP levels. Forty-nine top-scoring compounds included glucocorticoids, several pyrazole derivatives, and known inhibitors of glycogen synthase kinase-3beta. Two compounds were able to increase cellular ATP levels, reduce caspase-3 activity and nitrite production, and increase glucose-stimulated insulin secretion in the presence of cytokines. These results indicate that small molecules identified by this screening approach may protect beta cells from autoimmune attack and may be good candidates for therapeutic intervention in early stages of type-1 diabetes.

An economic framework to prioritize confirmatory tests after a high-throughput screen.

How many hits from a high-throughput screen should be sent for confirmatory experiments? Analytical answers to this question are derived from statistics alone and aim to fix, for example, the false discovery rate at a predetermined tolerance. These methods, however, neglect local economic context and consequently lead to irrational experimental strategies. In contrast, the authors argue that this question is essentially economic, not statistical, and is amenable to an economic analysis that admits an optimal solution. This solution, in turn, suggests a novel tool for deciding the number of hits to confirm and the marginal cost of discovery, which meaningfully quantifies the local economic trade-off between true and false positives, yielding an economically optimal experimental strategy. Validated with retrospective simulations and prospective experiments, this strategy identified 157 additional actives that had been erroneously labeled inactive in at least one real-world screening experiment.

ChemBank: a small-molecule screening and cheminformatics resource database.

ChemBank (http://chembank.broad.harvard.edu/) is a public, web-based informatics environment developed through a collaboration between the Chemical Biology Program and Platform at the Broad Institute of Harvard and MIT. This knowledge environment includes freely available data derived from small molecules and small-molecule screens and resources for studying these data. ChemBank is unique among small-molecule databases in its dedication to the storage of raw screening data, its rigorous definition of screening experiments in terms of statistical hypothesis testing, and its metadata-based organization of screening experiments into projects involving collections of related assays. ChemBank stores an increasingly varied set of measurements derived from cells and other biological assay systems treated with small molecules. Analysis tools are available and are continuously being developed that allow the relationships between small molecules, cell measurements, and cell states to be studied. Currently, ChemBank stores information on hundreds of thousands of small molecules and hundreds of biomedically relevant assays that have been performed at the Broad Institute by collaborators from the worldwide research community. The goal of ChemBank is to provide life scientists unfettered access to biomedically relevant data and tools heretofore available primarily in the private sector.