The fidelity of intracellular signaling hinges on the organization of dynamic activity architectures. Spatial compartmentation was first proposed over 30 years ago to explain how diverse G protein-coupled receptors achieve specificity despite converging on a ubiquitous messenger, cyclic adenosine monophosphate (cAMP). However, the mechanisms responsible for spatially constraining this diffusible messenger remain elusive. Here, we reveal that the type I regulatory subunit of cAMP-dependent protein kinase (PKA), RIα, undergoes liquid-liquid phase separation (LLPS) as a function of cAMP signaling to form biomolecular condensates enriched in cAMP and PKA activity, critical for effective cAMP compartmentation. We further show that a PKA fusion oncoprotein associated with an atypical liver cancer potently blocks RIα LLPS and induces aberrant cAMP signaling. Loss of RIα LLPS in normal cells increases cell proliferation and induces cell transformation. Our work reveals LLPS as a principal organizer of signaling compartments and highlights the pathological consequences of dysregulating this activity architecture.

Drug development has been hampered by a high failure rate in clinical trials due to our incomplete understanding of drug functions across organs and species. Therefore, elucidating species- and tissue-specific drug functions can provide insights into therapeutic efficacy, potential adverse effects, and interspecies differences necessary for effective translational medicine. Here, we present PharmOmics, a drug knowledgebase and analytical tool that is hosted on an interactive web server. Using tissue- and species-specific transcriptome data from human, mouse, and rat curated from different databases, we implemented a gene-network-based approach for drug repositioning. We demonstrate the potential of PharmOmics to retrieve known therapeutic drugs and identify drugs with tissue toxicity using in silico performance assessment. We further validated predicted drugs for nonalcoholic fatty liver disease in mice. By combining tissue- and species-specific in vivo drug signatures with gene networks, PharmOmics serves as a complementary tool to support drug characterization and network-based medicine.

BACKGROUND: Uveal melanoma (UM) is the most common intraocular malignancy in adults. In contrast to cutaneous melanoma (CM), there is no standard therapy, and the efficacy and safety of dual checkpoint blockade with nivolumab and ipilimumab is not well defined. METHODS: We conducted a retrospective analysis of patients with metastatic UM (mUM) who received treatment with ipilimumab plus nivolumab across 14 academic medical centers. Toxicity was graded using National Cancer Institute Common Terminology Criteria for Adverse Events V.5.0. Progression-free survival (PFS) and overall survival (OS) were calculated using Kaplan-Meier methodology. RESULTS: 89 eligible patients were identified. 45% had received prior therapy, which included liver directed therapy (29%), immunotherapy (21%), targeted therapy (10%) and radiation (16%). Patients received a median 3 cycles of ipilimumab plus nivolumab. The median follow-up time was 9.2 months. Overall response rate was 11.6%. One patient achieved complete response (1%), 9 patients had partial response (10%), 21 patients had stable disease (24%) and 55 patients had progressive disease (62%). Median OS from treatment initiation was 15 months and median PFS was 2.7 months. Overall, 82 (92%) of patients discontinued treatment, 34 due to toxicity and 27 due to progressive disease. Common immune-related adverse events were colitis/diarrhea (32%), fatigue (23%), rash (21%) and transaminitis (21%). CONCLUSIONS: Dual checkpoint inhibition yielded higher response rates than previous reports of single-agent immunotherapy in patients with mUM, but the efficacy is lower than in metastatic CM. The median OS of 15 months suggests that the rate of clinical benefit may be larger than the modest response rate.

Bipolar disorder is a heritable mental illness with complex etiology. We performed a genome-wide association study of 41,917 bipolar disorder cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. Bipolar disorder risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics and anesthetics. Integrating expression quantitative trait locus data implicated 15 genes robustly linked to bipolar disorder via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of bipolar disorder subtypes indicated high but imperfect genetic correlation between bipolar disorder type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of bipolar disorder, identify novel therapeutic leads and prioritize genes for functional follow-up studies.