Genetic alterations that reduce the function of the immunoregulatory cytokine IL-10 contribute to colitis in mouse and man. Myeloid cells such as macrophages (M?s) and dendritic cells (DCs) play an essential role in determining the relative abundance of IL-10 versus inflammatory cytokines in the gut. As such, using small molecules to boost IL-10 production by DCs-M?s represents a promising approach to increase levels of this cytokine specifically in gut tissues. Toward this end, we screened a library of well-annotated kinase inhibitors for compounds that enhance production of IL-10 by murine bone-marrow-derived DCs stimulated with the yeast cell wall preparation zymosan. This approach identified a number of kinase inhibitors that robustly up-regulate IL-10 production including the Food and Drug Administration (FDA)-approved drugs dasatinib, bosutinib, and saracatinib that target ABL, SRC-family, and numerous other kinases. Correlating the kinase selectivity profiles of the active compounds with their effect on IL-10 production suggests that inhibition of salt-inducible kinases (SIKs) mediates the observed IL-10 increase. This was confirmed using the SIK-targeting inhibitor HG-9-91-01 and a series of structural analogs. The stimulatory effect of SIK inhibition on IL-10 is also associated with decreased production of the proinflammatory cytokines IL-1?, IL-6, IL-12, and TNF-?, and these coordinated effects are observed in human DCs-M?s and anti-inflammatory CD11c(+) CX3CR1(hi) cells isolated from murine gut tissue. Collectively, these studies demonstrate that SIK inhibition promotes an anti-inflammatory phenotype in activated myeloid cells marked by robust IL-10 production and establish these effects as a previously unidentified activity associated with several FDA-approved multikinase inhibitors.
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.
Understanding the structure-activity relationships (SARs) of small molecules is important for developing probes and novel therapeutic agents in chemical biology and drug discovery. Increasingly, multiplexed small-molecule profiling assays allow simultaneous measurement of many biological response parameters for the same compound (e.g., expression levels for many genes or binding constants against many proteins). Although such methods promise to capture SARs with high granularity, few computational methods are available to support SAR analyses of high-dimensional compound activity profiles. Many of these methods are not generally applicable or reduce the activity space to scalar summary statistics before establishing SARs. In this article, we present a versatile computational method that automatically extracts interpretable SAR rules from high-dimensional profiling data. The rules connect chemical structural features of compounds to patterns in their biological activity profiles. We applied our method to data from novel cell-based gene-expression and imaging assays collected on more than 30,000 small molecules. Based on the rules identified for this data set, we prioritized groups of compounds for further study, including a novel set of putative histone deacetylase inhibitors.
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.
Type-1 diabetes (T1D) is an autoimmune disease in which insulin-secreting pancreatic beta cells are destroyed by the immune system. An emerging strategy to regenerate beta-cell mass is through transdifferentiation of pancreatic alpha cells to beta cells. We previously reported two small molecules, BRD7389 and GW8510, that induce insulin expression in a mouse alpha cell line and provide a glimpse into potential intermediate cell states in beta-cell reprogramming from alpha cells. These small-molecule studies suggested that inhibition of kinases in particular may induce the expression of several beta-cell markers in alpha cells. To identify potential lineage reprogramming protein targets, we compared the transcriptome, proteome, and phosphoproteome of alpha cells, beta cells, and compound-treated alpha cells. Our phosphoproteomic analysis indicated that two kinases, BRSK1 and CAMKK2, exhibit decreased phosphorylation in beta cells compared to alpha cells, and in compound-treated alpha cells compared to DMSO-treated alpha cells. Knock-down of these kinases in alpha cells resulted in expression of key beta-cell markers. These results provide evidence that perturbation of the kinome may be important for lineage reprogramming of alpha cells to beta cells.
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 ?-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.
Although genetic and non-genetic studies in mouse and human implicate the CD40 pathway in rheumatoid arthritis (RA), there are no approved drugs that inhibit CD40 signaling for clinical care in RA or any other disease. Here, we sought to understand the biological consequences of a CD40 risk variant in RA discovered by a previous genome-wide association study (GWAS) and to perform a high-throughput drug screen for modulators of CD40 signaling based on human genetic findings. First, we fine-map the CD40 risk locus in 7,222 seropositive RA patients and 15,870 controls, together with deep sequencing of CD40 coding exons in 500 RA cases and 650 controls, to identify a single SNP that explains the entire signal of association (rs4810485, P?=?1.4×10(-9)). Second, we demonstrate that subjects homozygous for the RA risk allele have ?33% more CD40 on the surface of primary human CD19+ B lymphocytes than subjects homozygous for the non-risk allele (P?=?10(-9)), a finding corroborated by expression quantitative trait loci (eQTL) analysis in peripheral blood mononuclear cells from 1,469 healthy control individuals. Third, we use retroviral shRNA infection to perturb the amount of CD40 on the surface of a human B lymphocyte cell line (BL2) and observe a direct correlation between amount of CD40 protein and phosphorylation of RelA (p65), a subunit of the NF-?B transcription factor. Finally, we develop a high-throughput NF-?B luciferase reporter assay in BL2 cells activated with trimerized CD40 ligand (tCD40L) and conduct an HTS of 1,982 chemical compounds and FDA-approved drugs. After a series of counter-screens and testing in primary human CD19+ B cells, we identify 2 novel chemical inhibitors not previously implicated in inflammation or CD40-mediated NF-?B signaling. Our study demonstrates proof-of-concept that human genetics can be used to guide the development of phenotype-based, high-throughput small-molecule screens to identify potential novel therapies in complex traits such as RA.
Efforts to develop more effective therapies for acute leukemia may benefit from high-throughput screening systems that reflect the complex physiology of the disease, including leukemia stem cells (LSCs) and supportive interactions with the bone marrow microenvironment. The therapeutic targeting of LSCs is challenging because LSCs are highly similar to normal hematopoietic stem and progenitor cells (HSPCs) and are protected by stromal cells in vivo. We screened 14,718 compounds in a leukemia-stroma co-culture system for inhibition of cobblestone formation, a cellular behavior associated with stem-cell function. Among those compounds that inhibited malignant cells but spared HSPCs was the cholesterol-lowering drug lovastatin. Lovastatin showed anti-LSC activity in vitro and in an in vivo bone marrow transplantation model. Mechanistic studies demonstrated that the effect was on target, via inhibition of HMG-CoA reductase. These results illustrate the power of merging physiologically relevant models with high-throughput screening.
Target-identification and mechanism-of-action studies have important roles in small-molecule probe and drug discovery. Biological and technological advances have resulted in the increasing use of cell-based assays to discover new biologically active small molecules. Such studies allow small-molecule action to be tested in a more disease-relevant setting at the outset, but they require follow-up studies to determine the precise protein target or targets responsible for the observed phenotype. Target identification can be approached by direct biochemical methods, genetic interactions or computational inference. In many cases, however, combinations of approaches may be required to fully characterize on-target and off-target effects and to understand mechanisms of small-molecule action.
Using a diverse collection of small molecules we recently found that compound sets from different sources (commercial; academic; natural) have different protein-binding behaviors, and these behaviors correlate with trends in stereochemical complexity for these compound sets. These results lend insight into structural features that synthetic chemists might target when synthesizing screening collections for biological discovery. We report extensive characterization of structural properties and diversity of biological performance for these compounds and expand comparative analyses to include physicochemical properties and three-dimensional shapes of predicted conformers. The results highlight additional similarities and differences between the sets, but also the dependence of such comparisons on the choice of molecular descriptors. Using a protein-binding dataset, we introduce an information-theoretic measure to assess diversity of performance with a constraint on specificity. Rather than relying on finding individual active compounds, this measure allows rational judgment of compound subsets as groups. We also apply this measure to publicly available data from ChemBank for the same compound sets across a diverse group of functional assays. We find that performance diversity of compound sets is relatively stable across a range of property values as judged by this measure, both in protein-binding studies and functional assays. Because building screening collections with improved performance depends on efficient use of synthetic organic chemistry resources, these studies illustrate an important quantitative framework to help prioritize choices made in building such collections.
High-content screening for small-molecule inducers of insulin expression identified the compound BRD7389, which caused alpha-cells to adopt several morphological and gene expression features of a beta-cell state. Assay-performance profile analysis suggests kinase inhibition as a mechanism of action, and we show that biochemical and cellular inhibition of the RSK kinase family by BRD7389 is likely related to its ability induce a beta-cell-like state. BRD7389 also increases the endocrine cell content and function of donor human pancreatic islets in culture.
We show that natural products target proteins with a high number of protein-protein functional interactions (high biological network connectivity) and that these protein targets have higher network connectivity than disease genes. This feature may facilitate disruption of essential biological pathways, resulting in competitor death. This result also suggests that additional sources of small molecules will be required to discover drugs targeting the root causes of human disease in the future.
The mechanism by which cells decide to skip mitosis to become polyploid is largely undefined. Here we used a high-content image-based screen to identify small-molecule probes that induce polyploidization of megakaryocytic leukemia cells and serve as perturbagens to help understand this process. Our study implicates five networks of kinases that regulate the switch to polyploidy. Moreover, we find that dimethylfasudil (diMF, H-1152P) selectively increased polyploidization, mature cell-surface marker expression, and apoptosis of malignant megakaryocytes. An integrated target identification approach employing proteomic and shRNA screening revealed that a major target of diMF is Aurora kinase A (AURKA). We further find that MLN8237 (Alisertib), a selective inhibitor of AURKA, induced polyploidization and expression of mature megakaryocyte markers in acute megakaryocytic leukemia (AMKL) blasts and displayed potent anti-AMKL activity in vivo. Our findings provide a rationale to support clinical trials of MLN8237 and other inducers of polyploidization and differentiation in AMKL.
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