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 Regulation of OmpA Translation and Shigella dysenteriae Virulence by an RNA Thermometer

2019 ◽  
Vol 88 (3) ◽  
Author(s):  
Erin R. Murphy ◽  
Johanna Roßmanith ◽  
Jacob Sieg ◽  
Megan E. Fris ◽  
Hebaallaha Hussein ◽  
...  
Keyword(s):  
In Silico ◽  
Bacterial Species ◽  
Regulation Of Gene Expression ◽  
Structure And Function ◽  
Shigella Dysenteriae ◽  
Content Type ◽  
Bacterial Physiology ◽  
Wide Range ◽  
Rna Thermometer ◽  
And Function

ABSTRACT RNA thermometers are cis-acting riboregulators that mediate the posttranscriptional regulation of gene expression in response to environmental temperature. Such regulation is conferred by temperature-responsive structural changes within the RNA thermometer that directly result in differential ribosomal binding to the regulated transcript. The significance of RNA thermometers in controlling bacterial physiology and pathogenesis is becoming increasingly clear. This study combines in silico, molecular genetics, and biochemical analyses to characterize both the structure and function of a newly identified RNA thermometer within the ompA transcript of Shigella dysenteriae. First identified by in silico structural predictions, genetic analyses have demonstrated that the ompA RNA thermometer is a functional riboregulator sufficient to confer posttranscriptional temperature-dependent regulation, with optimal expression observed at the host-associated temperature of 37°C. Structural studies and ribosomal binding analyses have revealed both increased exposure of the ribosomal binding site and increased ribosomal binding to the ompA transcript at permissive temperatures. The introduction of site-specific mutations predicted to alter the temperature responsiveness of the ompA RNA thermometer has predictable consequences for both the structure and function of the regulatory element. Finally, in vitro tissue culture-based analyses implicate the ompA RNA thermometer as a bona fide S. dysenteriae virulence factor in this bacterial pathogen. Given that ompA is highly conserved among Gram-negative pathogens, these studies not only provide insight into the significance of riboregulation in controlling Shigella virulence, but they also have the potential to facilitate further understanding of the physiology and/or pathogenesis of a wide range of bacterial species.

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.

scholarly journals Modeling the Differences in Biochemical Capabilities ofPseudomonasSpecies by Flux Balance Analysis: How Good Are Genome-Scale Metabolic Networks at Predicting the Differences?

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Parizad Babaei ◽  
Tahereh Ghasemi-Kahrizsangi ◽  
Sayed-Amir Marashi
Keyword(s):  
In Silico ◽  
Metabolic Networks ◽  
Closely Related Species ◽  
Systematic Analysis ◽  
Reconstruction Process ◽  
Flux Balance ◽  
Wide Range ◽  
Balance Analysis ◽  
Metabolic Models ◽  
Genome Scale

To date, several genome-scale metabolic networks have been reconstructed. These models cover a wide range of organisms, from bacteria to human. Such models have provided us with a framework for systematic analysis of metabolism. However, little effort has been put towards comparing biochemical capabilities of closely related species using their metabolic models. The accuracy of a model is highly dependent on the reconstruction process, as some errors may be included in the model during reconstruction. In this study, we investigated the ability of threePseudomonasmetabolic models to predict the biochemical differences, namely, iMO1086, iJP962, and iSB1139, which are related toP. aeruginosaPAO1,P. putidaKT2440, andP. fluorescensSBW25, respectively. We did a comprehensive literature search for previous works containing biochemically distinguishable traits over these species. Amongst more than 1700 articles, we chose a subset of them which included experimental results suitable forin silicosimulation. By simulating the conditions provided in the actual biological experiment, we performed case-dependent tests to compare thein silicoresults to the biological ones. We found out that iMO1086 and iJP962 were able to predict the experimental data and were much more accurate than iSB1139.

scholarly journals A Genome-Scale Metabolic Model of 2,3-Butanediol Production by Thermophilic Bacteria Geobacillus icigianus

2020 ◽  
Vol 8 (7) ◽  
pp. 1002
Author(s):  
Mikhail Kulyashov ◽  
Sergey E. Peltek ◽  
Ilya R. Akberdin
Keyword(s):  
Citrate Synthase ◽  
Thermophilic Bacteria ◽  
Carbon Sources ◽  
Metabolic Model ◽  
Biofuel Production ◽  
Biotechnological Applications ◽  
Metabolic Potential ◽  
A Genome ◽  
Wide Range ◽  
Genome Scale

The thermophilic strain of the genus Geobacillus, Geobacillus icigianus is a promising bacterial chassis for a wide range of biotechnological applications. In this study, we explored the metabolic potential of Geobacillus icigianus for the production of 2,3-butanediol (2,3-BTD), one of the cost-effective commodity chemicals. Here we present a genome-scale metabolic model iMK1321 for Geobacillus icigianus constructed using an auto-generating pipeline with consequent thorough manual curation. The model contains 1321 genes and includes 1676 reactions and 1589 metabolites, representing the most-complete and publicly available model of the genus Geobacillus. The developed model provides new insights into thermophilic bacterial metabolism and highlights new strategies for biotechnological applications of the strain. Our analysis suggests that Geobacillus icigianus has a potential for 2,3-butanediol production from a variety of utilized carbon sources, including glycerine, a common byproduct of biofuel production. We identified a set of solutions for enhancing 2,3-BTD production, including cultivation under anaerobic or microaerophilic conditions and decreasing the TCA flux to succinate via reducing citrate synthase activity. Both in silico predicted metabolic alternatives have been previously experimentally verified for closely related strains including the genus Bacillus.

scholarly journals Genome scale metabolic modeling reveals the metabolic potential of three Type II methanotrophs of the genus Methylocystis

2019 ◽  
Vol 54 ◽  
pp. 191-199 ◽  
Author(s):  
Sergio Bordel ◽  
Yadira Rodríguez ◽  
Anna Hakobyan ◽  
Elisa Rodríguez ◽  
Raquel Lebrero ◽  
...  
Keyword(s):  
Metabolic Modeling ◽  
Type Ii ◽  
Metabolic Potential ◽  
Genome Scale

scholarly journals Genome-scale metabolic modeling reveals SARS-CoV-2-induced host metabolic reprogramming and identifies metabolic antiviral targets

2021 ◽  
Author(s):  
Kuoyuan Cheng ◽  
Laura Riva ◽  
Sanju Sinha ◽  
Lipika Ray Pal ◽  
Nishanth Ulhas Nair ◽  
...  
Keyword(s):  
Metabolic Modeling ◽  
Metabolic Reprogramming ◽  
Integrated Analysis ◽  
Sequencing Data ◽  
Human Patient ◽  
Medical Needs ◽  
Wide Range ◽  
Genome Scale ◽  
Host Metabolism

AbstractTremendous progress has been made to control the COVID-19 pandemic, including the development and approval of vaccines as well as the drug remdesivir, which inhibits the SARS-CoV-2 virus that causes COVID-19. However, remdesivir confers only mild benefits to a subset of patients, and additional effective therapeutic options are needed. Drug repurposing and drug combinations may represent practical strategies to address these urgent unmet medical needs. Viruses, including coronaviruses, are known to hijack the host metabolism to facilitate their own proliferation, making targeting host metabolism a promising antiviral approach. Here, we describe an integrated analysis of 12 published in vitro and human patient gene expression datasets on SARS-CoV-2 infection using genome-scale metabolic modeling (GEM). We find that SARS-CoV-2 infection can induce recurrent and complicated metabolic reprogramming spanning a wide range of metabolic pathways. We next applied the GEM-based metabolic transformation algorithm (MTA) to predict anti-SARS-CoV-2 targets that counteract the virus-induced metabolic changes. These predictions are enriched for validated targets from various published experimental drug and genetic screens. Further analyzing the RNA-sequencing data of remdesivir-treated Vero E6 cell samples that we generated, we predicted metabolic targets that act in combination with remdesivir. These predictions are enriched for previously reported synergistic drugs with remdesivir. Since our predictions are based in part on human patient data, they are likely to be clinically relevant. We provide our top high-confidence candidate targets for their evaluation in further studies, demonstrating host metabolism-targeting as a promising antiviral strategy.

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

Comparative Study on Mitogen Activated Protein Kinase of Plasmodium Species by Using in silico Method

2012 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Mohd Fakharul Zaman Raja Yahya ◽  
Hasidah Mohd Sidek
Keyword(s):  
Amino Acid ◽  
In Silico ◽  
Nuclear Export ◽  
Nuclear Export Signal ◽  
Plasmodium Species ◽  
Mitogen Activated Protein Kinase ◽  
Multiple Sequence ◽  
Wide Range ◽  
Mitogen Activated Protein ◽  
Human Plasmodium

Malaria parasites, Plasmodium can infect a wide range of hosts including humans and rodents. There are two copies of mitogen activated protein kinases (MAPKs) in Plasmodium, namely MAPK1 and MAPK2. The MAPKs have been studied extensively in the human Plasmodium, P. falciparum. However, the MAPKs from other Plasmodium species have not been characterized and it is therefore the premise of presented study to characterize the MAPKs from other Plasmodium species-P. vivax, P. knowlesi, P. berghei, P. chabaudi and P.yoelli using a series of publicly available bioinformatic tools. In silico data indicates that all Plasmodium MAPKs are nuclear-localized and contain both a nuclear localization signal (NLS) and a Leucine-rich nuclear export signal (NES). The activation motifs of TDY and TSH were found to be fully conserved in Plasmodium MAPK1 and MAPK2, respectively. The detailed manual inspection of a multiple sequence alignment (MSA) construct revealed a total of 17 amino acid stack patterns comprising of different amino acids present in MAPKJ and MAPK2 respectively, with respect to rodent and human Plasmodia. It is proposed that these amino acid stack patterns may be useful in explaining the disparity between rodent and human Plasmodium MAPKs. 

Comparative Study on Mitogen Activated Protein Kinase of Plasmodium Species by Using In silico Method

2012 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Mohd Fakharul Zaman Raja Yahya ◽  
Hasidah Mohd Sidek
Keyword(s):  
Amino Acid ◽  
In Silico ◽  
Nuclear Export ◽  
Nuclear Export Signal ◽  
Plasmodium Species ◽  
Mitogen Activated Protein Kinase ◽  
Multiple Sequence ◽  
Bioinformatic Tools ◽  
Wide Range ◽  
Human Plasmodium

Malaria parasites, Plasmodium can infect a wide range ofhosts including humans and rodents. There are two copies ofmitogen activated protein kinases (MAPKs) in Plasmodium, namely MAPK1 and MAPK2. The MAPKs have been studied extensively in the human Plasmodium, P. falciparum. However, the MAPKs from other Plasmodium species have not been characterized and it is therefore the premise ofpresented study to characterize the MAPKs from other Plasmodium species-P. vivax, P. knowlesi, P. berghei, P. chabaudi and P.yoelli using a series ofpublicly available bioinformatic tools. In silico data indicates that all Plasmodium MAPKs are nuclear-localizedandcontain both a nuclear localization signal (NLS) anda Leucine-rich nuclear export signal (NES). The activation motifs ofTDYand TSH werefound to befully conserved in Plasmodium MAPK1 and MAPK2, respectively. The detailed manual inspection ofa multiple sequence alignment (MSA) construct revealed a total of 17 amino acid stack patterns comprising ofdifferent amino acids present in MAPK1 and MAPK2 respectively, with respect to rodent and human Plasmodia. 1t is proposed that these amino acid stack patterns may be useful in explaining the disparity between rodent and human Plasmodium MAPKs.

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