Examining Crosstalk among Transforming Growth Factor ß, Bone Morphogenetic Protein, and Wnt Pathways.

The integration of morphogenic signals by cells is not well understood. A growing body of literature suggests increasingly complex coupling among classically defined pathways. Given this apparent complexity, it is difficult to predict where, when, or even whether crosstalk occurs. Here, we investigated pairs of morphogenic pathways, previously reported to have multiple points of crosstalk, which either do not share (TGFß and Wnt/ß-catenin) or share (TGFß and bone morphogenetic protein (BMP)) core signaling components. Crosstalk was measured by the ability of one morphogenic pathway to cross-activate core transcription factors and/or target genes of another morphogenic pathway. In contrast to previous studies, we found a surprising absence of crosstalk between TGFß and Wnt/ß-catenin. Further, we did not observe expected cross-pathway inhibition in between TGFß and BMP, despite the fact that both use (or could compete) for the shared component SMAD4. Critical to our assays was a separation of timescales, which helped separate crosstalk due to initial signal transduction from subsequent post-transcriptional feedback events. Our study revealed fewer (and different) inter-morphogenic pathway crosstalk connections than expected; even pathways that share components can be insulated from one another.

Improving drug discovery with high-content phenotypic screens by systematic selection of reporter cell lines.

High-content, image-based screens enable the identification of compounds that induce cellular responses similar to those of known drugs but through different chemical structures or targets. A central challenge in designing phenotypic screens is choosing suitable imaging biomarkers. Here we present a method for systematically identifying optimal reporter cell lines for annotating compound libraries (ORACLs), whose phenotypic profiles most accurately classify a training set of known drugs. We generate a library of fluorescently tagged reporter cell lines, and let analytical criteria determine which among them--the ORACL--best classifies compounds into multiple, diverse drug classes. We demonstrate that an ORACL can functionally annotate large compound libraries across diverse drug classes in a single-pass screen and confirm high prediction accuracy by means of orthogonal, secondary validation assays. Our approach will increase the efficiency, scale and accuracy of phenotypic screens by maximizing their discriminatory power.

GSK-3 modulates cellular responses to a broad spectrum of kinase inhibitors.

A fundamental challenge in treating disease is identifying molecular states that affect cellular responses to drugs. Here, we focus on glycogen synthase kinase 3 (GSK-3), a key regulator for many of the hallmark behaviors of cancer cells. We alter GSK-3 activity in colon epithelial cells to test its role in modulating drug response. We find that GSK-3 activity broadly affects the cellular sensitivities to a panel of oncology drugs and kinase inhibitors. Specifically, inhibition of GSK-3 activity can strongly desensitize or sensitize cells to kinase inhibitors (for example, mTOR or PLK1 inhibitors, respectively). Additionally, colorectal cancer cell lines, in which GSK-3 function is commonly suppressed, are resistant to mTOR inhibitors and yet highly sensitive to PLK1 inhibitors, and this is further exacerbated by additional GSK-3 inhibition. Finally, by conducting a kinome-wide RNAi screen, we find that GSK-3 modulates the cell proliferative phenotype of a large fraction (∼35%) of the kinome, which includes ∼50% of current, clinically relevant kinase-targeted drugs. Our results highlight an underappreciated interplay of GSK-3 with therapeutically important kinases and suggest strategies for identifying disease-specific molecular profiles that can guide optimal selection of drug treatment.