O-GlcNAc transferase regulates collagen deposition and fibrosis resolution in idiopathic pulmonary fibrosis.

Background: Idiopathic pulmonary fibrosis (IPF) is a chronic pulmonary disease that is characterized by an excessive accumulation of extracellular matrix (ECM) proteins (e.g. collagens) in the parenchyma, which ultimately leads to respiratory failure and death. While current therapies exist to slow the progression, no therapies are available to resolve fibrosis. Methods: We characterized the O-linked N-Acetylglucosamine (O-GlcNAc) transferase (OGT)/O-GlcNAc axis in IPF using single-cell RNA-sequencing (scRNA-seq) data and human lung sections and isolated fibroblasts from IPF and non-IPF donors. The underlying mechanism(s) of IPF were further investigated using multiple experimental models to modulate collagen expression and accumulation by genetically and pharmacologically targeting OGT. Furthermore, we hone in on the transforming growth factor-beta (TGF-ß) effector molecule, Smad3, by co-expressing it with OGT to determine if it is modified and its subsequent effect on Smad3 activation. Results: We found that OGT and O-GlcNAc levels are upregulated in patients with IPF compared to non-IPF. We report that the OGT regulates collagen deposition and fibrosis resolution, which is an evolutionarily conserved process demonstrated across multiple species. Co-expression of OGT and Smad3 showed that Smad3 is O-GlcNAc modified. Blocking OGT activity resulted in decreased phosphorylation at Ser-423/425 of Smad3 attenuating the effects of TGF-ß1 induced collagen expression/deposition. Conclusion: OGT inhibition or knockdown successfully blocked and reversed collagen expression and accumulation, respectively. Smad3 is discovered to be a substrate of OGT and its O-GlcNAc modification(s) directly affects its phosphorylation state. These data identify OGT as a potential target in pulmonary fibrosis resolution, as well as other diseases that might have aberrant ECM/collagen accumulation.

A CRISPR/Cas9-Based Assay for High-Throughput Studies of Cancer-Induced Innervation.

The aggressive nature of certain cancers and their adverse effects on patient outcomes have been linked to cancer innervation, where neurons infiltrate and differentiate within the cancer stroma. Recently we demonstrated how cancer plasticity and TGFß signaling could promote breast cancer innervation that is associated with increased cancer aggressivity. Despite the promising potential of cancer innervation as a target for anti-cancer therapies, there is currently a significant lack of effective methods to study cancer-induced neuronal differentiation, hindering the development of high-throughput approaches for identifying new targets or pharmacological inhibitors against cancer innervation. To overcome this challenge, we used CRISPR-based endogenous labeling of the neuronal marker ß3-tubulin in neuronal precursors to investigate cancer-induced neuronal differentiation in nerve-cancer cocultures and provide a tool that allows for better standardization and reproducibility of studies about cancer-induced innervation. Our approach demonstrated that ß3-tubulin gene editing did not affect neuronal behavior and enabled accurate reporting of cancer-induced neuronal differentiation dynamics in high-throughput settings, which makes this approach suitable for screening large cohorts of cells or testing various biological contexts. In a more context-based approach, by combining this method with a cell model of breast cancer epithelial-mesenchymal transition, we revealed the role of cancer cell plasticity in promoting neuronal differentiation, suggesting that cancer innervation represents an underexplored path for epithelial-mesenchymal transition-mediated cancer aggressivity.

Improving Visualization of cAMP Gradients Using Algorithmic Modelling.

A ubiquitous second messenger molecule, cAMP is responsible for orchestrating many different cellular functions through a variety of pathways. FÓ§rster resonance energy transfer (FRET) probes have been used to visualize cAMP spatial gradients in pulmonary microvascular endothelial cells (PMVECs). However, FRET probes have inherently low signal-to-noise ratios; multiple sources of noise can obscure accurate visualization of cAMP gradients using a hyperspectral imaging system. FRET probes have also been used to measure cAMP gradients in 3D; however, it can be difficult to differentiate between true FRET signals and noise. To further understand the effects of noise on experimental data, a model was developed to simulate cAMP gradients under experimental conditions. The model uses a theoretical cAMP heatmap generated using finite element analysis. This heatmap was converted to simulate the FRET probe signal that would be detected experimentally with a hyperspectral imaging system. The signal was mapped onto an image of unlabeled PMVECs. The result was a time lapse model of cAMP gradients obscured by autofluorescence, as visualized with FRET probes. Additionally, the model allowed the simulated expression level of FRET signal to be varied. This allowed accurate attribution of signal to FRET and autofluorescence. Comparing experimental data to the model results at different levels of FRET efficiency has allowed improved understanding of FRET signal specificity and how autofluorescence interferes with FRET signal detection. In conclusion, this model can more accurately determine cAMP gradients in PMVECs. This work was supported by NIH award P01HL066299, R01HL58506 and NSF award 1725937.

mRNA detection in living cells: A next generation cancer stem cell identification technique.

Cancer stem cells (CSC) are a distinct subpopulation within a tumor shown to drive tumor progression, metastasis, and recurrence. A review of the literature reveals poor consensus, with the use of a wide variety of surface markers and functional assays to identify and isolate cancer stem cells. Utilizing a novel technology that enables live-cell mRNA quantitation, we have demonstrated the ability to identify and sort viable CSC based on markers associated with stemness in pluripotent cells. Fresh tumor samples from a variety of cancer types were examined by flow cytometry for Nanog expression. Levels of CSC detected ranged from 6% to 19%. This method of CSC detection was cross-validated with other commonly used surface markers with good correlation. Matrigel invasion assays confirmed that CSC isolated using this method are both highly motile and invasive. This approach simplifies the process of identifying viable CSC from fresh tumor tissue, providing a level of accuracy not previously available. This method may also provide a valuable tool for screening and validating new CSC biomarkers.