Enhancing in silico strain design predictions through next generation metabolic modeling approaches

2021 ◽  
pp. 107806
Author(s):  
Adil Alsiyabi ◽  
Niaz Bahar Chowdhury ◽  
Dianna Long ◽  
Rajib Saha
Keyword(s):  
In Silico ◽  
Metabolic Modeling ◽  
Next Generation ◽  
Modeling Approaches ◽  
Strain Design

scholarly journals Metabolic Modeling of Pectobacterium parmentieri SCC3193 Provides Insights into Metabolic Pathways of Plant Pathogenic Bacteria

2019 ◽  
Vol 7 (4) ◽  
pp. 101 ◽  
Author(s):  
Sabina Zoledowska ◽  
Luana Presta ◽  
Marco Fondi ◽  
Francesca Decorosi ◽  
Luciana Giovannetti ◽  
...  
Keyword(s):  
In Silico ◽  
Pathogenic Bacteria ◽  
Metabolic Modeling ◽  
Metabolic Model ◽  
Metabolic Adaptation ◽  
Ecological Niches ◽  
Plant Colonization ◽  
Phenotypic Data ◽  
Plant Pathogenic Bacteria ◽  
Metabolic Versatility

Understanding plant–microbe interactions is crucial for improving plants’ productivity and protection. Constraint-based metabolic modeling is one of the possible ways to investigate the bacterial adaptation to different ecological niches and may give insights into the metabolic versatility of plant pathogenic bacteria. We reconstructed a raw metabolic model of the emerging plant pathogenic bacterium Pectobacterium parmentieri SCC3193 with the use of KBase. The model was curated by using inParanoind and phenotypic data generated with the use of the OmniLog system. Metabolic modeling was performed through COBRApy Toolbox v. 0.10.1. The curated metabolic model of P. parmentieri SCC3193 is highly reliable, as in silico obtained results overlapped up to 91% with experimental data on carbon utilization phenotypes. By mean of flux balance analysis (FBA), we predicted the metabolic adaptation of P. parmentieri SCC3193 to two different ecological niches, relevant for the persistence and plant colonization by this bacterium: soil and the rhizosphere. We performed in silico gene deletions to predict the set of essential core genes for this bacterium to grow in such environments. We anticipate that our metabolic model will be a valuable element for defining a set of metabolic targets to control infection and spreading of this plant pathogen.

scholarly journals In-Silico Prediction of Candidate SNPs in TRIOBP, TMC1 and EYA4 Genes Causing Hereditary Deafness in Three Sudanese Patients Using Next Generation Sequencing

2014 ◽  
Vol 03 (02) ◽  
pp. 1-10
Author(s):  
Mahmoud Eltayeb Koko Musa ◽  
Yousuf Hasan Yousuf Bakhit ◽  
Ashraf Yahia Osman Mohamed ◽  
Asaad Tageldein Idris ◽  
Ashraf Ahmed Mohamed Ahmed Fadul ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
In Silico ◽  
Candidate Snps ◽  
In Silico Prediction ◽  
Next Generation ◽  
Hereditary Deafness ◽  
Generation Sequencing

scholarly journals A Model Study of In Silico Proficiency Testing for Clinical Next-Generation Sequencing

2016 ◽  
Vol 140 (10) ◽  
pp. 1085-1091 ◽  
Author(s):  
Eric J. Duncavage ◽  
Haley J. Abel ◽  
Jason D. Merker ◽  
John B. Bodner ◽  
Qin Zhao ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Proficiency Testing ◽  
In Silico ◽  
Absolute Difference ◽  
Ion Torrent ◽  
Next Generation ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Clinical Laboratories ◽  
Generation Sequencing

Context.—Most current proficiency testing challenges for next-generation sequencing assays are methods-based proficiency testing surveys that use DNA from characterized reference samples to test both the wet-bench and bioinformatics/dry-bench aspects of the tests. Methods-based proficiency testing surveys are limited by the number and types of mutations that either are naturally present or can be introduced into a single DNA sample. Objective.—To address these limitations by exploring a model of in silico proficiency testing in which sequence data from a single well-characterized specimen are manipulated electronically. Design.—DNA from the College of American Pathologists reference genome was enriched using the Illumina TruSeq and Life Technologies AmpliSeq panels and sequenced on the MiSeq and Ion Torrent platforms, respectively. The resulting data were mutagenized in silico and 26 variants, including single-nucleotide variants, deletions, and dinucleotide substitutions, were added at variant allele fractions (VAFs) from 10% to 50%. Participating clinical laboratories downloaded these files and analyzed them using their clinical bioinformatics pipelines. Results.—Laboratories using the AmpliSeq/Ion Torrent and/or the TruSeq/MiSeq participated in the 2 surveys. On average, laboratories identified 24.6 of 26 variants (95%) overall and 21.4 of 22 variants (97%) with VAFs greater than 15%. No false-positive calls were reported. The most frequently missed variants were single-nucleotide variants with VAFs less than 15%. Across both challenges, reported VAF concordance was excellent, with less than 1% median absolute difference between the simulated VAF and mean reported VAF. Conclusions.—The results indicate that in silico proficiency testing is a feasible approach for methods-based proficiency testing, and demonstrate that the sensitivity and specificity of current next-generation sequencing bioinformatics across clinical laboratories are high.

Next generation safety assessment strategy based on combined use of in silico tools

2018 ◽  
Vol 295 ◽  
pp. S162
Author(s):  
A. Granitzny ◽  
S. Heinz ◽  
T. Kawamoto ◽  
A. Fuchs ◽  
R. Fautz
Keyword(s):  
In Silico ◽  
Safety Assessment ◽  
Next Generation ◽  
Assessment Strategy ◽  
Combined Use ◽  
In Silico Tools

scholarly journals Interrogation of the Perturbed Gut Microbiota in Gouty Arthritis Patients Through in silico Metabolic Modeling

2020 ◽  
Author(s):  
Michael A. Henson
Keyword(s):  
Amino Acids ◽  
Uric Acid ◽  
Amino Acid ◽  
Gut Microbiota ◽  
In Silico ◽  
Gouty Arthritis ◽  
Metabolic Modeling ◽  
Patient Specific ◽  
Surprising Result ◽  
Sulfur Containing

Recent studies have shown perturbed gut microbiota associated with gouty arthritis, a metabolic disease in which an imbalance between uric acid production and excretion leads to the deposition of uric acid crystals in joints. To mechanistically investigate altered microbiota metabolism in gout disease, 16S rRNA gene amplicon sequence data from stool samples of gout patients and healthy controls were computationally analyzed through bacterial community metabolic modeling. Patient-specific models were used to cluster samples according to their metabolic capabilities and to generate statistically significant partitioning of the samples into a Bacteroides-dominated, high gout cluster and a Faecalibacterium-elevated, low gout cluster. The high gout cluster samples were predicted to allow elevated synthesis of the amino acids D-alanine and L-alanine and byproducts of branched-chain amino acid catabolism, while the low gout cluster samples allowed higher production of butyrate, the sulfur-containing amino acids L-cysteine and L-methionine and the L-cysteine catabolic product H2S. The models predicted an important role for metabolite crossfeeding, including the exchange of acetate, D-lactate and succinate from Bacteroides to Faecalibacterium to allow higher butyrate production differences than would be expected based on taxa abundances in the two clusters. The surprising result that the high gout cluster could underproduce H2S despite having a higher abundance of H2S-synthesizing bacteria was rationalized by reduced L-cysteine production from Faecalibacterium in this cluster. Model predictions were not substantially altered by constraining uptake rates with different in silico diets, suggesting that sulfur-containing amino acid metabolism generally and H2S more specifically could be novel gout disease markers.

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 Next-generation muscle-directed gene therapy by in silico vector design

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
S. Sarcar ◽  
W. Tulalamba ◽  
M. Y. Rincon ◽  
J. Tipanee ◽  
H. Q. Pham ◽  
...  
Keyword(s):  
Gene Therapy ◽  
In Silico ◽  
Next Generation
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