scholarly journals Cellular Genome-scale Metabolic Modeling Identifies New Potential Drug Targets Against Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Oveis Jamialahmadi ◽  
Ehsan Salehabadi ◽  
Sameereh Hashemi-Najafabadi ◽  
Ehsan Motamedian ◽  
Fatemeh Bagheri ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Hepg2 Cells ◽  
Drug Targets ◽  
Cancer Metabolism ◽  
Essential Gene ◽  
Metabolic Modeling ◽  
Essential Genes ◽  
Potential Drug ◽  
Genome Scale ◽  
Potential Drug Targets

Abstract Hepatocellular carcinoma is the third leading cause of cancer related mortality worldwide. Often this hepatic cancer is associated with fatty liver disease and insulin resistance with genetic predisposition are its major driver. Genome-scale metabolic modeling (GEM) is a promising approach to understand cancer metabolism and to identify new drug targets. Here, we used TRFBA-CORE, an algorithm generating a model using key growth-correlated reactions. Specifically, we generated a HepG2 cell-specific GEM by integrating this cell line transcriptomic data with a generic human metabolic model to predict potential drug targets for hepatocellular carcinoma (HCC). A total of 108 essential genes for growth were predicted by TRFBA-CORE. These genes were enriched for metabolic pathways involved in cholesterol, sterols and steroids biosynthesis. Furthermore, we silenced a predicted essential gene, 11-beta dehydrogenase hydroxysteroid type 2 (HSD11B2), in HepG2 cells resulting in a reduction in cell viability. To further identify novel potential drug targets in HCC, we examined the effect of 9 drugs targeting the essential genes, and observed that most drugs inhibited the growth of HepG2 cells. Interestingly, some of these drugs in this model performed better than Sorafenib, the first line therapeutic against HCC.

Predicting essential genes for identifying potential drug targets in Aspergillus fumigatus

2014 ◽  
Vol 50 ◽  
pp. 29-40 ◽  
Author(s):  
Yao Lu ◽  
Jingyuan Deng ◽  
Judith C. Rhodes ◽  
Hui Lu ◽  
Long Jason Lu
Keyword(s):  
Aspergillus Fumigatus ◽  
Drug Targets ◽  
Essential Genes ◽  
Potential Drug ◽  
Potential Drug Targets

Genome-scale reconstruction of the Streptococcus pyogenes M49 metabolic network reveals growth requirements and indicates potential drug targets

2016 ◽  
Vol 232 ◽  
pp. 25-37 ◽  
Author(s):  
Jennifer Levering ◽  
Tomas Fiedler ◽  
Antje Sieg ◽  
Koen W.A. van Grinsven ◽  
Silvio Hering ◽  
...  
Keyword(s):  
Metabolic Network ◽  
Streptococcus Pyogenes ◽  
Drug Targets ◽  
Potential Drug ◽  
Growth Requirements ◽  
Genome Scale ◽  
Potential Drug Targets

scholarly journals Exploring gene knockout strategies to identify potential drug targets using genome-scale metabolic models

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Abhijit Paul ◽  
Rajat Anand ◽  
Sonali Porey Karmakar ◽  
Surender Rawat ◽  
Nandadulal Bairagi ◽  
...  
Keyword(s):  
Cell Lines ◽  
Drug Targets ◽  
Gene Knockout ◽  
Single Gene ◽  
Potential Drug ◽  
Growth Reduction ◽  
Gene Ranking ◽  
Metabolic Models ◽  
Genome Scale ◽  
Potential Drug Targets

AbstractResearch on new cancer drugs is performed either through gene knockout studies or phenotypic screening of drugs in cancer cell-lines. Both of these approaches are costly and time-consuming. Computational framework, e.g., genome-scale metabolic models (GSMMs), could be a good alternative to find potential drug targets. The present study aims to investigate the applicability of gene knockout strategies to be used as the finding of drug targets using GSMMs. We performed single-gene knockout studies on existing GSMMs of the NCI-60 cell-lines obtained from 9 tissue types. The metabolic genes responsible for the growth of cancerous cells were identified and then ranked based on their cellular growth reduction. The possible growth reduction mechanisms, which matches with the gene knockout results, were described. Gene ranking was used to identify potential drug targets, which reduce the growth rate of cancer cells but not of the normal cells. The gene ranking results were also compared with existing shRNA screening data. The rank-correlation results for most of the cell-lines were not satisfactory for a single-gene knockout, but it played a significant role in deciding the activity of drug against cell proliferation, whereas multiple gene knockout analysis gave better correlation results. We validated our theoretical results experimentally and showed that the drugs mitotane and myxothiazol can inhibit the growth of at least four cell-lines of NCI-60 database.

scholarly journals The early-acting glycosome biogenic protein Pex3 is essential for trypanosome viability

2019 ◽  
Vol 2 (4) ◽  
pp. e201900421 ◽  
Author(s):  
Hiren Banerjee ◽  
Barbara Knoblach ◽  
Richard A Rachubinski
Keyword(s):  
Drug Targets ◽  
Essential Gene ◽  
Protein Secondary Structure ◽  
Infectious Agents ◽  
Potential Drug ◽  
Peroxisomal Membrane ◽  
Protein Receptor ◽  
Trypanosomatid Parasites ◽  
Biochemical Analyses ◽  
Potential Drug Targets

Trypanosomatid parasites are infectious agents for diseases such as African sleeping sickness, Chagas disease, and leishmaniasis that threaten millions of people, mostly in the emerging world. Trypanosomes compartmentalize glycolytic enzymes to an organelle called the glycosome, a specialized peroxisome. Functionally intact glycosomes are essential for trypanosomatid viability, making glycosomal proteins as potential drug targets against trypanosomatid diseases. Peroxins (Pex), of which Pex3 is the master regulator, control glycosome biogenesis. Although Pex3 has been found throughout the eukaryota, its identity has remained stubbornly elusive in trypanosomes. We used bioinformatics predictive of protein secondary structure to identify trypanosomal Pex3. Microscopic and biochemical analyses showed trypanosomal Pex3 to be glycosomal. Interaction of Pex3 with the peroxisomal membrane protein receptor Pex19 observed for other eukaryotes is replicated by trypanosomal Pex3 and Pex19. Depletion of Pex3 leads to mislocalization of glycosomal proteins to the cytosol, reduced glycosome numbers, and trypanosomatid death. Our findings are consistent with Pex3 being an essential gene in trypanosomes.

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