live-cell-frster-resonance-energy-transfer-imaging-metabolically

Live-Cell F&#246;rster Resonance Energy Transfer Imaging of Metabolically Regulated Akt Activation Dynamics in HepG2 Cells

Metabolically regulated Akt activation is a critical node in the insulin signaling cascade and provides valuable insights into the relationship between ...

The Chinese University of Hong Kong

Southern University of Science and Technology

Early quantitative structure-activity relationship (QSAR) technologies have unsatisfactory versatility and accuracy in fields such as drug discovery because they are based on traditional machine learning and interpretive expert features. The development of Big Data and deep learning technologies significantly improve the processing of unstructured data and unleash the great potential of QSAR. Here we discuss the integration of wet experiments (which provide experimental data and reliable verification), molecular dynamics simulation (which provides mechanistic interpretation at the atomic/molecular levels), and machine learning (including deep learning) techniques to improve QSAR models. We first review the history of traditional QSAR and point out its problems. We then propose a better QSAR model characterized by a new iterative framework to integrate machine learning with disparate data input. Finally, we discuss the application of QSAR and machine learning to many practical research fields, including drug development and clinical trials.

Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models.

To satisfy both the safety and rapidity of glycemic control, muscles' insulin response must be bistable, as theoretically predicted. Here, we test the bistability hypothesis by combining cellular experiments (to measure the threshold values ) with mathematical modeling (to test the relevance of bistability ). We examine bistability in C2C12 myotubes by both single-cell analysis (Fӧrster resonance energy transfer) and cultured cells analysis (immunoblot). These technologies demonstrate bistable insulin response, with typical switch-on and switch-off thresholds of approximately 300 and 100 pM, respectively. Our mathematical model demonstrates the indispensability of bistability in interpreting experimental data, reveals fine details of plasma glucose-insulin dynamics, and explains unclear phenomena. These results suggest that the body's ability to simultaneously avoid both hypoglycemia and hyperglycemia is mediated by bistability. The switch-on threshold is a promising biomarker for metabolic complications due to its deep quantitative connection with body composition, which is easy to measure.

Bistable insulin response: The win-win solution for glycemic control.

Innovative therapeutic strategies for esophageal squamous cell carcinoma (ESCC) are urgently required due to the limited effectiveness of standard chemotherapies. C-Terminal Binding Protein 1 (CtBP1) has been implicated in various cancers, including ESCC. However, the precise expression patterns and functional roles of CtBP1 in ESCC remain inadequately characterized. In this study, we aimed to investigate CtBP1 expression and its role in the resistance of ESCC to paclitaxel, an effective chemotherapeutic agent. Western blotting and immunofluorescence were applied to assess CtBP1 expression in the TE-1 and KYSE-50 cell lines. We observed the marked expression of CtBP1, which was associated with enhanced proliferation, invasion, and metastasis in these cell lines. Further, we successfully generated paclitaxel resistant ESCC cell lines and conducted cell viability assays. We employed the CRISPR/Cas9 genome editing system to disable the CtBP1 gene in ESCC cell lines. Through the analysis of the drug dose-response curve, we assessed the sensitivity of these cell lines in different treatment groups. Remarkably, CtBP1-disabled cell lines displayed not only improved sensitivity but also a remarkable inhibition of proliferation, invasion, and metastasis. This demonstrates that CtBP1 may promote ESCC cell malignancy and confer paclitaxel resistance. In summary, our study opens a promising avenue for targeted therapies, revealing the potential of CtBP1 inhibition to enhance the effectiveness of paclitaxel treatment for the personalized management of ESCC.

CRISPR/Cas9-Mediated CtBP1 Gene Editing Enhances Chemosensitivity and Inhibits Metastatic Potential in Esophageal Squamous Cell Carcinoma Cells.

Early detection of complex diseases like hepatocellular carcinoma remains challenging due to their network-driven pathology. Dynamic network biomarkers (DNB) based on monitoring changes in molecular correlations may enable earlier predictions. However, DNB analysis often overlooks disease heterogeneity.

Early warning and diagnosis of liver cancer based on dynamic network biomarker and deep learning.

Based on our hypothesis that myotubes exhibit a bistable response to insulin, here we present a protocol for finely measuring Akt phosphorylation in single myotubes under insulin stimulation. We describe steps to stably express a Förster resonance energy transfer (FRET)-based Akt biosensor in C2C12-derived myotubes and perform single-cell FRET imaging. This protocol highlights its potential for precision medicine in analyzing protein phosphorylation dynamics at the single-cell level. For complete details on the use and execution of this protocol, please refer to Akhtar et al..

Article	Year
Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models. iScience\| PubMed ID: 34553136	2021
Bistable insulin response: The win-win solution for glycemic control. iScience\| PubMed ID: 36465102	2022
CRISPR/Cas9-Mediated CtBP1 Gene Editing Enhances Chemosensitivity and Inhibits Metastatic Potential in Esophageal Squamous Cell Carcinoma Cells. International journal of molecular sciences\| PubMed ID: 37762332	2023
Early warning and diagnosis of liver cancer based on dynamic network biomarker and deep learning. Computational and structural biotechnology journal\| PubMed ID: 38213892	2023
Protocol for live-cell Förster resonance energy transfer imaging to reveal the bistable insulin response of single C2C12-derived myotubes. STAR protocols\| PubMed ID: 38829736	2024

Javed Akhtar

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