Biology subjects

Quackenbush, D.

Publications and source records attributed to Quackenbush, D..

3 recordsLinked to original sources

bioRxiv2025

Remodeling of KRAS interactome induced by clinically relevant RAS inhibitors reveals convergent responses and a KRAS-mediated regulation of directional cell migration

KRAS is commonly mutated in lung, colorectal, and pancreatic cancers. Small molecule inhibitors targeting KRAS with distinct mechanisms-of-action and variable specificities have entered the clinic, but a comprehensive view of their effect on the RAS signaling network has not been reported. Here, we describe the impact of RAS inhibition on the KRAS protein interactome using Proximity-dependent Biotinylation Identification. Two inhibitors were used: panRAS-ON, which forms a ternary complex between cyclophilin and RAS in the GTP bound state; and panKRAS-off, which binds specifically to the KRAS switch II pocket. RAS inhibitors were found to significantly alter 16.5% of proteins in proximity to KRAS. Despite their distinct mechanisms-of-action, the KRAS inhibitors induced highly correlated changes in the KRAS interactomes. Among proteins in close proximity to KRAS, Afadin was found to be highly regulated by RAS inhibition. AFDN has been characterized as a key regulator of cell motility, invasion, and metastasis. Analysis of AFDN phosphorylation revealed that AKT only partially modulates p-Ser1718, while inhibition of KRAS is sufficient to abolish EGF-mediated AFDN phosphorylation. Knockdown of AFDN in a KRAS-driven non-small cell lung cancer model abolished chemotaxis in a transwell migration assay and disrupted directional movement in an EGF-driven wound healing model. These results suggest that KRAS is a central node in regulating growth factor induced cell migration, and that KRAS inhibition plays a broader role than MAPK-mediated cell proliferation and survival.

cancer biology↗

bioRxiv2024

A diffusion model conditioned on compound bioactivity profiles for predicting high-content images

High-content imaging (HCI) provides a rich snapshot of compound-induced phenotypic outcomes that augment our understanding of compound mechanisms in cellular systems. Generative imaging models for HCI provide a route towards anticipating the phenotypic outcomes of chemical perturbations in silico at unprecedented scale and speed. Here, we developed Profile-Diffusion (pDIFF), a generative method leveraging a latent diffusion model conditioned on in silico bioactivity profiles to predict high-content images displaying the cellular outcomes induced by compound treatment. We trained and evaluated a pDIFF model using high-content images from a Cell Painting assay profiling 3750 molecules with corresponding in silico bioactivity profiles. Using a realistic held-out set, we demonstrate that pDIFF provides improved predictions of phenotypic responses of compounds with low chemical similarity to compounds in the training set compared to generative models trained on chemical fingerprints only. In a virtual hit expansion scenario, pDIFF yielded significantly improved expansion outcomes, thus showcasing the potential of the methodology to speed up and improve the search for novel phenotypically active molecules.

bioinformatics↗

bioRxiv2022

A high-throughput COPD bronchosphere model for disease-relevant phenotypic compound screening

COPD is the third leading cause of death worldwide, but current therapies for COPD are only effective at treating the symptoms of the disease rather than targeting the underlying pathways that are driving the pathogenic changes. The lack of targeted therapies for COPD is in part due to a lack of knowledge about drivers of disease progression and the difficulty in building relevant and high throughput models that can recapitulate the phenotypic and transcriptomic changes associated with pathogenesis of COPD. To identify these drivers, we have developed a cigarette smoke extract (CSE)-treated bronchosphere assay in 384-well plate format that exhibits CSE-induced decreases in size and increase in luminal secretion of MUC5AC. Transcriptomic changes in CSE-treated bronchospheres resemble changes that occur in human smokers both with and without COPD compared to healthy groups, indicating that this model can capture human smoking signature. To identify new targets, we ran a small molecule compound deck screening with diversity in target mechanisms of action and identified hit compounds that attenuated CSE induced changes, either decreasing spheroid size or increasing secreted mucus. This work provides insight into the utility of this bronchosphere model in examining human respiratory diseases, the pathways implicated by CSE, and compounds with known mechanisms of action for therapeutic development.

cell biology↗