scholarly journals Hexokinase 2-driven glycolysis in pericytes activates their contractility leading to tumor blood vessel abnormalities

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ya-Ming Meng ◽  
Xue Jiang ◽  
Xinbao Zhao ◽  
Qiong Meng ◽  
Sangqing Wu ◽  
...  
Keyword(s):  
Blood Vessel ◽  
Cell Sorting ◽  
Metabolic Flux ◽  
Magnetic Beads ◽  
Underlying Mechanism ◽  
Cell Interaction ◽  
Flux Analysis ◽  
Hexokinase 2 ◽  
Normal Tissues ◽  
Poor Patient

AbstractDefective pericyte-endothelial cell interaction in tumors leads to a chaotic, poorly organized and dysfunctional vasculature. However, the underlying mechanism behind this is poorly studied. Herein, we develop a method that combines magnetic beads and flow cytometry cell sorting to isolate pericytes from tumors and normal adjacent tissues from patients with non-small cell lung cancer (NSCLC) and hepatocellular carcinoma (HCC). Pericytes from tumors show defective blood vessel supporting functions when comparing to those obtained from normal tissues. Mechanistically, combined proteomics and metabolic flux analysis reveals elevated hexokinase 2(HK2)-driven glycolysis in tumor pericytes, which up-regulates their ROCK2-MLC2 mediated contractility leading to impaired blood vessel supporting function. Clinically, high percentage of HK2 positive pericytes in blood vessels correlates with poor patient overall survival in NSCLC and HCC. Administration of a HK2 inhibitor induces pericyte-MLC2 driven tumor vasculature remodeling leading to enhanced drug delivery and efficacy against tumor growth. Overall, these data suggest that glycolysis in tumor pericytes regulates their blood vessel supporting role.

scholarly journals A Multi-Pronged Computational Pipeline for Prioritizing Drug Target Strategies for Latent Tuberculosis

2020 ◽  
Vol 8 ◽  
Author(s):  
Ushashi Banerjee ◽  
Santhosh Sankar ◽  
Amit Singh ◽  
Nagasuma Chandra
Keyword(s):  
Drug Targets ◽  
Metabolic Flux ◽  
Latent Tuberculosis ◽  
Interaction Network ◽  
New Drugs ◽  
Prolonged Treatment ◽  
Flux Analysis ◽  
Protein Protein Interaction ◽  
Candidate Drug ◽  
Global Population

Tuberculosis is one of the deadliest infectious diseases worldwide and the prevalence of latent tuberculosis acts as a huge roadblock in the global effort to eradicate tuberculosis. Most of the currently available anti-tubercular drugs act against the actively replicating form of Mycobacterium tuberculosis (Mtb), and are not effective against the non-replicating dormant form present in latent tuberculosis. With about 30% of the global population harboring latent tuberculosis and the requirement for prolonged treatment duration with the available drugs in such cases, the rate of adherence and successful completion of therapy is low. This necessitates the discovery of new drugs effective against latent tuberculosis. In this work, we have employed a combination of bioinformatics and chemoinformatics approaches to identify potential targets and lead candidates against latent tuberculosis. Our pipeline adopts transcriptome-integrated metabolic flux analysis combined with an analysis of a transcriptome-integrated protein-protein interaction network to identify perturbations in dormant Mtb which leads to a shortlist of 6 potential drug targets. We perform a further selection of the candidate targets and identify potential leads for 3 targets using a range of bioinformatics methods including structural modeling, binding site association and ligand fingerprint similarities. Put together, we identify potential new strategies for targeting latent tuberculosis, new candidate drug targets as well as important lead clues for drug design.

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