Multimodal perturbation analyses of cyclin-dependent kinases reveal a network of synthetic lethalities associated with cell-cycle regulation and transcriptional regulation.

Cell-cycle control is accomplished by cyclin-dependent kinases (CDKs), motivating extensive research into CDK targeting small-molecule drugs as cancer therapeutics. Here we use combinatorial CRISPR/Cas9 perturbations to uncover an extensive network of functional interdependencies among CDKs and related factors, identifying 43 synthetic-lethal and 12 synergistic interactions. We dissect CDK perturbations using single-cell RNAseq, for which we develop a novel computational framework to precisely quantify cell-cycle effects and diverse cell states orchestrated by specific CDKs. While pairwise disruption of CDK4/6 is synthetic-lethal, only CDK6 is required for normal cell-cycle progression and transcriptional activation. Multiple CDKs (CDK1/7/9/12) are synthetic-lethal in combination with PRMT5, independent of cell-cycle control. In-depth analysis of mRNA expression and splicing patterns provides multiple lines of evidence that the CDK-PRMT5 dependency is due to aberrant transcriptional regulation resulting in premature termination. These inter-dependencies translate to drug-drug synergies, with therapeutic implications in cancer and other diseases.

Cell Index Database (CELLX): a web tool for cancer precision medicine.

The Cell Index Database, (CELLX) (http://cellx.sourceforge.net) provides a computational framework for integrating expression, copy number variation, mutation, compound activity, and meta data from cancer cells. CELLX provides the computational biologist a quick way to perform routine analyses as well as the means to rapidly integrate data for offline analysis. Data is accessible through a web interface which utilizes R to generate plots and perform clustering, correlations, and statistical tests for associations within and between data types for ~20,000 samples from TCGA, CCLE, Sanger, GSK, GEO, GTEx, and other public sources. We show how CELLX supports precision oncology through indications discovery, biomarker evaluation, and cell line screening analysis.

Correlation set analysis: detecting active regulators in disease populations using prior causal knowledge.

BACKGROUND: Identification of active causal regulators is a crucial problem in understanding mechanism of diseases or finding drug targets. Methods that infer causal regulators directly from primary data have been proposed and successfully validated in some cases. These methods necessarily require very large sample sizes or a mix of different data types. Recent studies have shown that prior biological knowledge can successfully boost a method's ability to find regulators. RESULTS: We present a simple data-driven method, Correlation Set Analysis (CSA), for comprehensively detecting active regulators in disease populations by integrating co-expression analysis and a specific type of literature-derived causal relationships. Instead of investigating the co-expression level between regulators and their regulatees, we focus on coherence of regulatees of a regulator. Using simulated datasets we show that our method performs very well at recovering even weak regulatory relationships with a low false discovery rate. Using three separate real biological datasets we were able to recover well known and as yet undescribed, active regulators for each disease population. The results are represented as a rank-ordered list of regulators, and reveals both single and higher-order regulatory relationships. CONCLUSIONS: CSA is an intuitive data-driven way of selecting directed perturbation experiments that are relevant to a disease population of interest and represent a starting point for further investigation. Our findings demonstrate that combining co-expression analysis on regulatee sets with a literature-derived network can successfully identify causal regulators and help develop possible hypothesis to explain disease progression.

Small molecule ago-allosteric modulators of the human glucagon-like peptide-1 (hGLP-1) receptor.

Following our previous publication describing the biological profiles, we herein describe the structure-activity relationships of a core set of quinoxalines as the hGLP-1 receptor agonists. The most potent and efficacious compounds are 6,7-dichloroquinoxalines bearing an alkyl sulfonyl group at the C-2 position and a secondary alkyl amino group at the C-3 position. These findings serve as a valuable starting point for the discovery of more drug-like small molecule agonists for the hGLP-1 receptor.

Small-molecule agonists for the glucagon-like peptide 1 receptor.

The peptide hormone glucagon-like peptide (GLP)-1 has important actions resulting in glucose lowering along with weight loss in patients with type 2 diabetes. As a peptide hormone, GLP-1 has to be administered by injection. Only a few small-molecule agonists to peptide hormone receptors have been described and none in the B family of the G protein coupled receptors to which the GLP-1 receptor belongs. We have discovered a series of small molecules known as ago-allosteric modulators selective for the human GLP-1 receptor. These compounds act as both allosteric activators of the receptor and independent agonists. Potency of GLP-1 was not changed by the allosteric agonists, but affinity of GLP-1 for the receptor was increased. The most potent compound identified stimulates glucose-dependent insulin release from normal mouse islets but, importantly, not from GLP-1 receptor knockout mice. Also, the compound stimulates insulin release from perfused rat pancreas in a manner additive with GLP-1 itself. These compounds may lead to the identification or design of orally active GLP-1 agonists.