scholarly journals In silicoidentification of metabolic enzyme drug targets inBurkholderia pseudomallei

2015 ◽  
Author(s):  
Jean F. Challacombe
Keyword(s):  
In Silico ◽  
Drug Targets ◽  
Culture Media ◽  
Metabolic Modeling ◽  
Host Cells ◽  
Natural Resistance ◽  
Metabolic Enzyme ◽  
Chronic Infections ◽  
Genome Scale ◽  
Nutrient Conditions

AbstractThe intracellular pathogenBurkholderia pseudomallei,which is endemic to parts of southeast Asia and northern Australia, causes the disease melioidosis. Although acute infections can be treated with antibiotics, melioidosis is difficult to cure, and some patients develop chronic infections or a recrudescence of the disease months or years after treatment of the initial infection.B. pseudomalleistrains have a high level of natural resistance to a variety of antibiotics, and with limited options for new antibiotics on the horizon, new alternatives are needed. The aim of the present study was to characterize the metabolic capabilities ofB. pseudomallei, identify metabolites crucial for pathogen survival, understand the metabolic interactions that occur between pathogen and host cells, and determine if metabolic enzymes produced by the pathogen might be potential antibacterial targets. This aim was accomplished through genome scale metabolic modeling under different external conditions: 1) including all nutrients that could be consumed by the model, and 2) providing only the nutrients available in culture media. Using this approach, candidate chokepoint enzymes were identified, then knocked outin silicounder the different nutrient conditions. The effect of each knockout on the metabolic network was examined. When five of the candidate chokepoints were knocked outin silico, the flux through theB. pseudomalleinetwork was decreased, depending on the nutrient conditions. These results demonstrate the utility of genome-scale metabolic modeling methods for drug target identification inB. pseudomallei.

In silico genome-scale modeling and analysis for identifying anti-tubercular drug targets

2010 ◽  
Vol 72 (2) ◽  
pp. 121-129 ◽  
Author(s):  
Dong-Yup Lee ◽  
Bevan Kai Sheng Chung ◽  
Faraaz N.K. Yusufi ◽  
Suresh Selvarasu
Keyword(s):  
In Silico ◽  
Drug Targets ◽  
Modeling And Analysis ◽  
Scale Modeling ◽  
Genome Scale

scholarly journals In Silico Prediction of Novel Probiotic Species Limiting Pathogenic Vibrio Growth Using Constraint-Based Genome Scale Metabolic Modeling

2021 ◽  
Vol 11 ◽  
Author(s):  
Neelakantan Thulasi Devika ◽  
Ashok Kumar Jangam ◽  
Vinaya Kumar Katneni ◽  
Prasanna Kumar Patil ◽  
Suganya Nathamuni ◽  
...  
Keyword(s):  
In Silico ◽  
Bacterial Species ◽  
Metabolic Modeling ◽  
Growth Enhancement ◽  
Phenotypic Analysis ◽  
Metabolic Potential ◽  
Shrimp Culture ◽  
Pathogenic Vibrio ◽  
Wide Range ◽  
Genome Scale

The prevalence of bacterial diseases and the application of probiotics to prevent them is a common practice in shrimp aquaculture. A wide range of bacterial species/strains is utilized in probiotic formulations, with proven beneficial effects. However, knowledge of their role in inhibiting the growth of a specific pathogen is restricted. In this study, we employed constraint-based genome-scale metabolic modeling approach to screen and identify the beneficial bacteria capable of limiting the growth of V. harveyi, a common pathogen in shrimp culture. Genome-scale models were built for 194 species (including strains from the genera Bacillus, Lactobacillus, and Lactococcus and the pathogenic strain V. harveyi) to explore the metabolic potential of these strains under different nutrient conditions in a consortium. In silico-based phenotypic analysis on 193 paired models predicted six candidate strains with growth enhancement and pathogen suppression. Growth simulations reveal that mannitol and glucoronate environments mediate parasitic interactions in a pairwise community. Furthermore, in a mannitol environment, the shortlisted six strains were purely metabolite consumers without donating metabolites to V. harveyi. The production of acetate by the screened species in a paired community suggests the natural metabolic end product’s role in limiting pathogen survival. Our study employing in silico approach successfully predicted three novel candidate strains for probiotic applications, namely, Bacillus sp 1 (identified as B. licheniformis in this study), Bacillus weihaiensis Alg07, and Lactobacillus lindneri TMW 1.1993. The study is the first to apply genomic-scale metabolic models for aquaculture applications to detect bacterial species limiting Vibrio harveyi growth.

scholarly journals Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data

Metabolites ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 14
Author(s):  
Anurag Passi ◽  
Juan D. Tibocha-Bonilla ◽  
Manish Kumar ◽  
Diego Tec-Campos ◽  
Karsten Zengler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Metabolic Pathways ◽  
Drug Targets ◽  
Metabolic Modeling ◽  
Annotation Data ◽  
Dynamic Resolution ◽  
Different Types ◽  
Eukaryotic Organisms ◽  
Genome Scale

Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.

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.

Genome-scale metabolic modeling and in silico analysis of lipid accumulating yeastCandida tropicalisfor dicarboxylic acid production

2016 ◽  
Vol 113 (9) ◽  
pp. 1993-2004 ◽  
Author(s):  
Pranjul Mishra ◽  
Gyu-Yeon Park ◽  
Meiyappan Lakshmanan ◽  
Hee-Seok Lee ◽  
Hongweon Lee ◽  
...  
Keyword(s):  
Dicarboxylic Acid ◽  
In Silico ◽  
Acid Production ◽  
In Silico Analysis ◽  
Metabolic Modeling ◽  
Genome Scale ◽  
Silico Analysis

scholarly journals Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer

eLife ◽  
2014 ◽  
Vol 3 ◽  
Author(s):  
Keren Yizhak ◽  
Edoardo Gaude ◽  
Sylvia Le Dévédec ◽  
Yedael Y Waldman ◽  
Gideon Y Stein ◽  
...  
Keyword(s):  
Drug Targets ◽  
Metabolic Modeling ◽  
Molecular Data ◽  
Metabolic Effects ◽  
Proof Of Concept ◽  
Phenotypic Data ◽  
Experimental Proof ◽  
Physiological Models ◽  
Genome Scale ◽  
Computational Basis

Utilizing molecular data to derive functional physiological models tailored for specific cancer cells can facilitate the use of individually tailored therapies. To this end we present an approach termed PRIME for generating cell-specific genome-scale metabolic models (GSMMs) based on molecular and phenotypic data. We build >280 models of normal and cancer cell-lines that successfully predict metabolic phenotypes in an individual manner. We utilize this set of cell-specific models to predict drug targets that selectively inhibit cancerous but not normal cell proliferation. The top predicted target, MLYCD, is experimentally validated and the metabolic effects of MLYCD depletion investigated. Furthermore, we tested cell-specific predicted responses to the inhibition of metabolic enzymes, and successfully inferred the prognosis of cancer patients based on their PRIME-derived individual GSMMs. These results lay a computational basis and a counterpart experimental proof of concept for future personalized metabolic modeling applications, enhancing the search for novel selective anticancer therapies.

scholarly journals Towards the routine use of in silico screenings for drug discovery using metabolic modelling

2020 ◽  
Vol 48 (3) ◽  
pp. 955-969
Author(s):  
Tamara Bintener ◽  
Maria Pires Pacheco ◽  
Thomas Sauter
Keyword(s):  
In Silico ◽  
Drug Targets ◽  
Model Building ◽  
Synthetic Lethality ◽  
Drug Efficacy ◽  
Metabolic Modelling ◽  
New Drug ◽  
Development Costs ◽  
Underlying Mechanisms ◽  
Genome Scale

Currently, the development of new effective drugs for cancer therapy is not only hindered by development costs, drug efficacy, and drug safety but also by the rapid occurrence of drug resistance in cancer. Hence, new tools are needed to study the underlying mechanisms in cancer. Here, we discuss the current use of metabolic modelling approaches to identify cancer-specific metabolism and find possible new drug targets and drugs for repurposing. Furthermore, we list valuable resources that are needed for the reconstruction of cancer-specific models by integrating various available datasets with genome-scale metabolic reconstructions using model-building algorithms. We also discuss how new drug targets can be determined by using gene essentiality analysis, an in silico method to predict essential genes in a given condition such as cancer and how synthetic lethality studies could greatly benefit cancer patients by suggesting drug combinations with reduced side effects.

Phytochemicals for drug discovery in Alzheimer’s disease: In silico Advances

2020 ◽  
Vol 26 ◽  
Author(s):  
Smriti Sharma ◽  
Vinayak Bhatia
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Drug Design ◽  
In Silico ◽  
Drug Targets ◽  
Development Process ◽  
Phytochemical Analysis ◽  
Computational Drug Design ◽  
Novel Drugs ◽  
Game Changer

: The search for novel drugs that can prevent or control Alzheimer’s disease has attracted lot of attention from researchers across the globe. Phytochemicals are increasingly being used to provide scaffolds to design drugs for AD. In silico techniques, have proven to be a game-changer in this drug design and development process. In this review, the authors have focussed on current advances in the field of in silico medicine, applied to phytochemicals, to discover novel drugs to prevent or cure AD. After giving a brief context of the etiology and available drug targets for AD, authors have discussed the latest advances and techniques in computational drug design of AD from phytochemicals. Some of the prototypical studies in this area are discussed in detail. In silico phytochemical analysis is a tool of choice for researchers all across the globe and helps integrate chemical biology with drug design.

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