Predicting compound activity from phenotypic profiles and chemical structures.

Predicting assay results for compounds virtually using chemical structures and phenotypic profiles has the potential to reduce the time and resources of screens for drug discovery. Here, we evaluate the relative strength of three high-throughput data sources-chemical structures, imaging (Cell Painting), and gene-expression profiles (L1000)-to predict compound bioactivity using a historical collection of 16,170 compounds tested in 270 assays for a total of 585,439 readouts. All three data modalities can predict compound activity for 6-10% of assays, and in combination they predict 21% of assays with high accuracy, which is a 2 to 3 times higher success rate than using a single modality alone. In practice, the accuracy of predictors could be lower and still be useful, increasing the assays that can be predicted from 37% with chemical structures alone up to 64% when combined with phenotypic data. Our study shows that unbiased phenotypic profiling can be leveraged to enhance compound bioactivity prediction to accelerate the early stages of the drug-discovery process.

A general approach to identify cell-permeable and synthetic anti-CRISPR small molecules.

The need to control the activity and fidelity of CRISPR-associated nucleases has resulted in a demand for inhibitory anti-CRISPR molecules. The small-molecule inhibitor discovery platforms available at present are not generalizable to multiple nuclease classes, only target the initial step in the catalytic activity and require high concentrations of nuclease, resulting in inhibitors with suboptimal attributes, including poor potency. Here we report a high-throughput discovery pipeline consisting of a fluorescence resonance energy transfer-based assay that is generalizable to contemporary and emerging nucleases, operates at low nuclease concentrations and targets all catalytic steps. We applied this pipeline to identify BRD7586, a cell-permeable small-molecule inhibitor of SpCas9 that is twofold more potent than other inhibitors identified to date. Furthermore, unlike the reported inhibitors, BRD7586 enhanced SpCas9 specificity and its activity was independent of the genomic loci, DNA-repair pathway or mode of nuclease delivery. Overall, these studies describe a general pipeline to identify inhibitors of contemporary and emerging CRISPR-associated nucleases.

Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science.

Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph-based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these "knowledge graphs" (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open-access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open-source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object-oriented classification and graph-oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.

Progress toward a universal biomedical data translator.

Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based "Translator" system capable of integrating existing biomedical data sets and "translating" those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system's architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems.

Phenotypic Screening for Small Molecules that Protect ß-Cells from Glucolipotoxicity.

Type 2 diabetes is marked by progressive ß-cell failure, leading to loss of ß-cell mass. Increased levels of circulating glucose and free fatty acids associated with obesity lead to ß-cell glucolipotoxicity. There are currently no therapeutic options to address this facet of ß-cell loss in obese type 2 diabetes patients. To identify small molecules capable of protecting ß-cells, we performed a high-throughput screen of 20,876 compounds in the rat insulinoma cell line INS-1E in the presence of elevated glucose and palmitate. We found 312 glucolipotoxicity-protective small molecules (1.49% hit rate) capable of restoring INS-1E viability, and we focused on 17 with known biological targets. 16 of the 17 compounds were kinase inhibitors with activity against specific families including but not limited to cyclin-dependent kinases (CDK), PI-3 kinase (PI3K), Janus kinase (JAK), and Rho-associated kinase 2 (ROCK2). 7 of the 16 kinase inhibitors were PI3K inhibitors. Validation studies in dissociated human islets identified 10 of the 17 compounds, namely, KD025, ETP-45658, BMS-536924, AT-9283, PF-03814735, torin-2, AZD5438, CP-640186, ETP-46464, and GSK2126458 that reduced glucolipotoxicity-induced ß-cell death. These 10 compounds decreased markers of glucolipotoxicity including caspase activation, mitochondrial depolarization, and increased calcium flux. Together, these results provide a path forward toward identifying novel treatments to preserve ß-cell viability in the face of glucolipotoxicity.

The Use of Informer Sets in Screening: Perspectives on an Efficient Strategy to Identify New Probes.

Small-molecule discovery typically involves large-scale screening campaigns, spanning multiple compound collections. However, such activities can be cost- or time-prohibitive, especially when using complex assay systems, limiting the number of compounds tested. Further, low hit rates can make the process inefficient. Sparse coverage of chemical structure or biological activity space can lead to limited success in a primary screen and represents a missed opportunity by virtue of selecting the "wrong" compounds to test. Thus, the choice of screening collections becomes of paramount importance. In this perspective, we discuss the utility of generating "informer sets" for small-molecule discovery, and how this strategy can be leveraged to prioritize probe candidates. While many researchers may assume that informer sets are focused on particular targets (e.g., kinases) or processes (e.g., autophagy), efforts to assemble informer sets based on historical bioactivity or successful human exposure (e.g., repurposing collections) have shown promise as well. Another method for generating informer sets is based on chemical structure, particularly when the compounds have unknown activities and targets. We describe our efforts to screen an informer set representing a collection of 100,000 small molecules synthesized through diversity-oriented synthesis (DOS). This process enables researchers to identify activity early and more extensively screen only a few chemical scaffolds, rather than the entire collection. This elegant and economic outcome is a goal of the informer set approach. Here, we aim not only to shed light on this process, but also to promote the use of informer sets more widely in small-molecule discovery projects.

Knowledge Beacons: Web services for data harvesting of distributed biomedical knowledge.

AVAILABILITY: The API and associated software is open source and currently available for access at https://github.com/NCATS-Tangerine/translator-knowledge-beacon.

Plasticity of ether lipids promotes ferroptosis susceptibility and evasion.

Ferroptosis-an iron-dependent, non-apoptotic cell death process-is involved in various degenerative diseases and represents a targetable susceptibility in certain cancers1. The ferroptosis-susceptible cell state can either pre-exist in cells that arise from certain lineages or be acquired during cell-state transitions2-5. However, precisely how susceptibility to ferroptosis is dynamically regulated remains poorly understood. Here we use genome-wide CRISPR-Cas9 suppressor screens to identify the oxidative organelles peroxisomes as critical contributors to ferroptosis sensitivity in human renal and ovarian carcinoma cells. Using lipidomic profiling we show that peroxisomes contribute to ferroptosis by synthesizing polyunsaturated ether phospholipids (PUFA-ePLs), which act as substrates for lipid peroxidation that, in turn, results in the induction of ferroptosis. Carcinoma cells that are initially sensitive to ferroptosis can switch to a ferroptosis-resistant state in vivo in mice, which is associated with extensive downregulation of PUFA-ePLs. We further find that the pro-ferroptotic role of PUFA-ePLs can be extended beyond neoplastic cells to other cell types, including neurons and cardiomyocytes. Together, our work reveals roles for the peroxisome-ether-phospholipid axis in driving susceptibility to and evasion from ferroptosis, highlights PUFA-ePL as a distinct functional lipid class that is dynamically regulated during cell-state transitions, and suggests multiple regulatory nodes for therapeutic interventions in diseases that involve ferroptosis.

A High-Throughput Platform to Identify Small-Molecule Inhibitors of CRISPR-Cas9.

The precise control of CRISPR-Cas9 activity is required for a number of genome engineering technologies. Here, we report a generalizable platform that provided the first synthetic small-molecule inhibitors of Streptococcus pyogenes Cas9 (SpCas9) that weigh <500 Da and are cell permeable, reversible, and stable under physiological conditions. We developed a suite of high-throughput assays for SpCas9 functions, including a primary screening assay for SpCas9 binding to the protospacer adjacent motif, and used these assays to screen a structurally diverse collection of natural-product-like small molecules to ultimately identify compounds that disrupt the SpCas9-DNA interaction. Using these synthetic anti-CRISPR small molecules, we demonstrated dose and temporal control of SpCas9 and catalytically impaired SpCas9 technologies, including transcription activation, and identified a pharmacophore for SpCas9 inhibition using structure-activity relationships. These studies establish a platform for rapidly identifying synthetic, miniature, cell-permeable, and reversible inhibitors against both SpCas9 and next-generation CRISPR-associated nucleases.

High-resolution specificity profiling and off-target prediction for site-specific DNA recombinases.

The development of site-specific recombinases (SSRs) as genome editing agents is limited by the difficulty of altering their native DNA specificities. Here we describe Rec-seq, a method for revealing the DNA specificity determinants and potential off-target substrates of SSRs in a comprehensive and unbiased manner. We applied Rec-seq to characterize the DNA specificity determinants of several natural and evolved SSRs including Cre, evolved variants of Cre, and other SSR family members. Rec-seq profiling of these enzymes and mutants thereof revealed previously uncharacterized SSR interactions, including specificity determinants not evident from SSR:DNA structures. Finally, we used Rec-seq specificity profiles to predict off-target substrates of Tre and Brec1 recombinases, including endogenous human genomic sequences, and confirmed their ability to recombine these off-target sequences in human cells. These findings establish Rec-seq as a high-resolution method for rapidly characterizing the DNA specificity of recombinases with single-nucleotide resolution, and for informing their further development.

A GPX4-dependent cancer cell state underlies the clear-cell morphology and confers sensitivity to ferroptosis.

Clear-cell carcinomas (CCCs) are a histological group of highly aggressive malignancies commonly originating in the kidney and ovary. CCCs are distinguished by aberrant lipid and glycogen accumulation and are refractory to a broad range of anti-cancer therapies. Here we identify an intrinsic vulnerability to ferroptosis associated with the unique metabolic state in CCCs. This vulnerability transcends lineage and genetic landscape, and can be exploited by inhibiting glutathione peroxidase 4 (GPX4) with small-molecules. Using CRISPR screening and lipidomic profiling, we identify the hypoxia-inducible factor (HIF) pathway as a driver of this vulnerability. In renal CCCs, HIF-2α selectively enriches polyunsaturated lipids, the rate-limiting substrates for lipid peroxidation, by activating the expression of hypoxia-inducible, lipid droplet-associated protein (HILPDA). Our study suggests targeting GPX4 as a therapeutic opportunity in CCCs, and highlights that therapeutic approaches can be identified on the basis of cell states manifested by morphological and metabolic features in hard-to-treat cancers.

CTD2 Dashboard: a searchable web interface to connect validated results from the Cancer Target Discovery and Development Network.

Database URL: https://ctd2-dashboard.nci.nih.gov/.

Small-molecule inhibitors directly target CARD9 and mimic its protective variant in inflammatory bowel disease.

Advances in human genetics have dramatically expanded our understanding of complex heritable diseases. Genome-wide association studies have identified an allelic series of CARD9 variants associated with increased risk of or protection from inflammatory bowel disease (IBD). The predisposing variant of CARD9 is associated with increased NF-κB-mediated cytokine production. Conversely, the protective variant lacks a functional C-terminal domain and is unable to recruit the E3 ubiquitin ligase TRIM62. Here, we used biochemical insights into CARD9 variant proteins to create a blueprint for IBD therapeutics and recapitulated the mechanism of the CARD9 protective variant using small molecules. We developed a multiplexed bead-based technology to screen compounds for disruption of the CARD9-TRIM62 interaction. We identified compounds that directly and selectively bind CARD9, disrupt TRIM62 recruitment, inhibit TRIM62-mediated ubiquitinylation of CARD9, and demonstrate cellular activity and selectivity in CARD9-dependent pathways. Taken together, small molecules targeting CARD9 illustrate a path toward improved IBD therapeutics.

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.

A Structural and Mathematical Modeling Analysis of the Likelihood of Antibody-Dependent Enhancement in Influenza.

Broadly neutralizing monoclonal antibodies (bNAbs) for viral infections, such as HIV, respiratory syncytial virus (RSV), and influenza, are increasingly entering clinical development. For influenza, most neutralizing antibodies target influenza virus hemagglutinin. These bNAbs represent an emerging, promising modality for treatment and prophylaxis of influenza due to their multiple mechanisms of antiviral action and generally safe profile. Preclinical work in other viral diseases, such as dengue, has demonstrated the potential for antibody-based therapies to enhance viral uptake, leading to enhanced viremia and worsening of disease. This phenomenon is referred to as antibody-dependent enhancement (ADE). In the context of influenza, ADE has been used to explain several preclinical and clinical phenomena. Using structural and viral kinetics modeling, we assess the role of ADE in the treatment of influenza with a bNAb.

DiSCoVERing Innovative Therapies for Rare Tumors: Combining Genetically Accurate Disease Models with In Silico Analysis to Identify Novel Therapeutic Targets.

PURPOSE: We used human stem and progenitor cells to develop a genetically accurate novel model of MYC-driven Group 3 medulloblastoma. We also developed a new informatics method, Disease-model Signature versus Compound-Variety Enriched Response ("DiSCoVER"), to identify novel therapeutics that target this specific disease subtype. EXPERIMENTAL DESIGN: Human neural stem and progenitor cells derived from the cerebellar anlage were transduced with oncogenic elements associated with aggressive medulloblastoma. An in silico analysis method for screening drug sensitivity databases (DiSCoVER) was used in multiple drug sensitivity datasets. We validated the top hits from this analysis in vitro and in vivo RESULTS: Human neural stem and progenitor cells transformed with c-MYC, dominant-negative p53, constitutively active AKT and hTERT formed tumors in mice that recapitulated Group 3 medulloblastoma in terms of pathology and expression profile. DiSCoVER analysis predicted that aggressive MYC-driven Group 3 medulloblastoma would be sensitive to cyclin-dependent kinase (CDK) inhibitors. The CDK 4/6 inhibitor palbociclib decreased proliferation, increased apoptosis, and significantly extended the survival of mice with orthotopic medulloblastoma xenografts. CONCLUSIONS: We present a new method to generate genetically accurate models of rare tumors, and a companion computational methodology to find therapeutic interventions that target them. We validated our human neural stem cell model of MYC-driven Group 3 medulloblastoma and showed that CDK 4/6 inhibitors are active against this subgroup. Our results suggest that palbociclib is a potential effective treatment for poor prognosis MYC-driven Group 3 medulloblastoma tumors in carefully selected patients. Clin Cancer Res; 22(15); 3903-14. ©2016 AACR.

Inhibition of DYRK1A Stimulates Human ß-Cell Proliferation.

Restoring functional ß-cell mass is an important therapeutic goal for both type 1 and type 2 diabetes (1). While proliferation of existing ß-cells is the primary means of ß-cell replacement in rodents (2), it is unclear whether a similar principle applies to humans, as human ß-cells are remarkably resistant to stimulation of division (3,4). Here, we show that 5-iodotubercidin (5-IT), an annotated adenosine kinase inhibitor previously reported to increase proliferation in rodent and porcine islets (5), strongly and selectively increases human ß-cell proliferation in vitro and in vivo. Remarkably, 5-IT also increased glucose-dependent insulin secretion after prolonged treatment. Kinome profiling revealed 5-IT to be a potent and selective inhibitor of the dual-specificity tyrosine phosphorylation-regulated kinase (DYRK) and cell division cycle-like kinase families. Induction of ß-cell proliferation by either 5-IT or harmine, another natural product DYRK1A inhibitor, was suppressed by coincubation with the calcineurin inhibitor FK506, suggesting involvement of DYRK1A and nuclear factor of activated T cells signaling. Gene expression profiling in whole islets treated with 5-IT revealed induction of proliferation- and cell cycle-related genes, suggesting that true proliferation is induced by 5-IT. Furthermore, 5-IT promotes ß-cell proliferation in human islets grafted under the kidney capsule of NOD-scid IL2Rg(null) mice. These results point to inhibition of DYRK1A as a therapeutic strategy to increase human ß-cell proliferation.

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.