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

Hyperactivity of the mechanistic target of rapamycin complex 1 (mTORC1) is implicated in a variety of diseases such as cancer and diabetes. Treatment may benefit from effective mTORC1 inhibition, which can be achieved by preventing arginine from disrupting the cytosolic arginine sensor for mTORC1 subunit 1 (CASTOR1)-GTPase-activating proteins toward RAGS subcomplex 2 (GATOR2) complex through binding with CASTOR1. An attractive idea is to determine analogues of arginine that are as competent as arginine in binding with CASTOR1, but without disrupting the CASTOR1-GATOR2 interaction.

Seeking mTORC1 Inhibitors Through Molecular Dynamics Simulation of Arginine Analogs Inhibiting CASTOR1.

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.

Insulin is an essential and versatile hormone taking part in the control of blood glucose levels and protein anabolism. Abnormal levels of circulating insulin in the body can be problematic. Insulin resistance means the body fails to react with high or normal level insulin, causing our body to produce more insulin through feedback, and is the main cause of many chronic diseases such as type 2 diabetes and obesity. Pre-diabetes or obesity often occurs in people with high insulin resistance. Thus, quantification of insulin levels is essential for the early diagnosis and treatment of diabetes mellitus and obesity. Immunoassays and chromatography assays are currently reliable methods for insulin detection, although they are time-consuming, expensive, and require complex procedures, centralized instruments as well as trained personnel. Modern biosensing technologies have demonstrated success and huge potential for the quantification of insulin. This review provides a summary of the biological significance of insulin with a focus on the role of insulin resistance and its consequences in pre-diabetes/diabetes and obesity. The current practice for insulin detection followed by recent advances in developing biosensors for detection of insulin are reviewed, compared, and discussed from the aspects of detection principle, analytical performances, and challenges. Finally, future perspectives in the quantification of insulin in clinical settings are proposed.

Insulin quantification towards early diagnosis of prediabetes/diabetes.

In the present study, an efficient  drug screening platform is established based on FRET technique. We transfected cancer cells with FRET-based caspase-3 (C3) sensor and validated the cell lines by detecting the change in FRET signal caused by the  drug-induced cell apoptosis. Furthermore, the C3 expressing cancer cells were then injected into zebrafish embryos and nude mice to establish the corresponding  xenograft models. We found that cancer cell lines expressing C3 were effective in detecting cell death following drug treatment, including the detection of the tipping point of apoptosis. The drug-induced cell apoptosis was also observed in both zebrafish embryos and nude mice xenograft models. Overall, the FRET-based platform, through  imaging, is potentially useful to improve drug screening efficiency.

An  Fluorescence Resonance Energy Transfer-Based Imaging Platform for Targeted Drug Discovery and Cancer Therapy.

[This corrects the article DOI: 10.3389/fbioe.2022.839078.].

Corrigendum: An  fluorescence resonance energy transfer-based imaging platform for targeted drug discovery and cancer therapy.

To investigate the anti-coronavirus potential and the corresponding mechanisms of the two ingredients of Reduning Injection: quercetin and luteolin.

Active Ingredients of Reduning Injection Maintain High Potency against SARS-CoV-2 Variants.

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.

Type 2 diabetes is rapidly emerging as a global public health problem. While blood glucose monitoring has been the primary method of managing diabetes for decades, the increasing global prevalence of the disease suggests that there might be a need to identify additional biomarkers for a more precise early diagnosis. Herein, a microneedle patch based wearable sensor is developed for the purpose of diabetic diagnosis. Utilizing methacrylic acid modified gelatin and polyvinyl alcohol in the fabrication of microneedles has improved their mechanical properties for skin penetration and increased swelling capacity for interstitial fluid extraction, thanks to the double crosslinking mechanism. The fabricated microneedles are further integrated with test paper functionalized with enzyme and dye molecules to detect multiple signature biomarkers of diabetes in vivo through a colorimetric reaction. Such a wearable microneedle patch  holds significant promise for the real-time monitoring of various biomarkers related to chronic diseases and aging.

Wearable Microneedle Patch for Colorimetric Detection of Multiple Signature Biomarkers in vivo Toward Diabetic Diagnosis.

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..

Protocol for live-cell Förster resonance energy transfer imaging to reveal the bistable insulin response of single C2C12-derived myotubes.

The stimulus-response curve is usually modeled by the Hill function due to its simplicity and clear molecular mechanisms (Michaelis-Menten type of kinetics). Unfortunately, the mechanisms do not explain why the stimulus is ubiquitously measured by logarithmic dose rather than the dose itself and why the log(dose)-response curve possesses such fine properties as symmetry and wide adjustability. Here, the dose-response is considered from a holistic perspective spanning multiple biological levels from molecules to the whole organism, which reveals that an appropriate model for log(dose) response is the cumulative normal distribution (CND) function, which had only statistical implication previously but now possess mechanistic-statistical duality. The present CND model establishes a connection between single-cell all-or-none responses and the graded response at the tissue/organism level, reveals the raison d'être of the logarithmic transformation, explains why log(dose)-response curve possesses many fine properties, and reveals new mechanisms of tissue/organism dose-response, including homogeneity-induced sensitivity. It also provides new insights into vital biological processes, such as the insulin dose-response.

Article	Year
Seeking mTORC1 Inhibitors Through Molecular Dynamics Simulation of Arginine Analogs Inhibiting CASTOR1. Cancer genomics & proteomics\| PubMed ID: 31659101	2019
Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models. iScience\| PubMed ID: 34553136	2021
Insulin quantification towards early diagnosis of prediabetes/diabetes. Biosensors & bioelectronics\| PubMed ID: 35114469	2022
An Fluorescence Resonance Energy Transfer-Based Imaging Platform for Targeted Drug Discovery and Cancer Therapy. Frontiers in bioengineering and biotechnology\| PubMed ID: 35237583	2022
Corrigendum: An fluorescence resonance energy transfer-based imaging platform for targeted drug discovery and cancer therapy. Frontiers in bioengineering and biotechnology\| PubMed ID: 36237215	2022
Active Ingredients of Reduning Injection Maintain High Potency against SARS-CoV-2 Variants. Chinese journal of integrative medicine\| PubMed ID: 36374439	2023
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
Wearable Microneedle Patch for Colorimetric Detection of Multiple Signature Biomarkers in vivo Toward Diabetic Diagnosis. Advanced healthcare materials\| PubMed ID: 38353398	2024
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
A more holistic view of the logarithmic dose-response curve offers greater insights into insulin responses. The Journal of biological chemistry\| PubMed ID: 39617270	2025

Guanyu Wang

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