scholarly journals Estabelecimento de sistema bacteriano de expressão de peptídeos derivados da enzima vegetal RuBisCO

2019 ◽  
Vol 22 ◽  
Author(s):  
Jessica Audrey Feijó Corrêa ◽  
Svetlana Yurgel ◽  
Chibuike Udenigwe ◽  
Fernando Bittencourt Luciano
Keyword(s):  
Escherichia Coli ◽  
In Silico ◽  
E Coli

Resumo O objetivo do presente estudo foi estabelecer um sistema bacteriano de expressão de peptídeos derivados da proteólise simulada in silico da enzima ribulose-1,5-bisfosfato carboxilase oxigenase (RuBisCO), proveniente de soja, visando viabilizar um método sustentável de produção dessas moléculas para futura aplicação industrial. Inicialmente, foi conferida à cepa Escherichia coli S17-1 cálcio-competência para propagação do plasmídeo de expressão pET-30a(+) contendo o inserto codificante da sequência peptídica GSIKAFKEATKVDKVVVLWTALVPR. Após extração de DNA plasmidial, o material foi transformado em células de alto rendimento E. coli Rosetta™(DE3)pLysS. As células Rosetta portando o plasmídeo de expressão foram induzidas e a produção dos peptídeos foi verificada por meio de eletroforese em gel vertical, confirmando o estabelecimento de um sistema de expressão viável para peptídeos heterólogos. Assim, a produção em maior escala de peptídeos derivados de RuBisCO – associando-se futuramente etapas de purificação e ativação – torna-se possível. Além disso, o método aqui estabelecido pode também ser aplicado utilizando diferentes sequências peptídicas com atividade antimicrobiana.

scholarly journals Metabolic Engineering of Escherichia coli for Enhanced Production of Succinic Acid, Based on Genome Comparison and In Silico Gene Knockout Simulation

2005 ◽  
Vol 71 (12) ◽  
pp. 7880-7887 ◽  
Author(s):  
Sang Jun Lee ◽  
Dong-Yup Lee ◽  
Tae Yong Kim ◽  
Byung Hun Kim ◽  
Jinwon Lee ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Succinic Acid ◽  
In Silico ◽  
Acid Production ◽  
Gene Knockout ◽  
Fermentation Products ◽  
E Coli ◽  
Metabolic Analysis ◽  
Enhanced Production ◽  
Succinic Acid Production

ABSTRACT Comparative analysis of the genomes of mixed-acid-fermenting Escherichia coli and succinic acid-overproducing Mannheimia succiniciproducens was carried out to identify candidate genes to be manipulated for overproducing succinic acid in E. coli. This resulted in the identification of five genes or operons, including ptsG, pykF, sdhA, mqo, and aceBA, which may drive metabolic fluxes away from succinic acid formation in the central metabolic pathway of E. coli. However, combinatorial disruption of these rationally selected genes did not allow enhanced succinic acid production in E. coli. Therefore, in silico metabolic analysis based on linear programming was carried out to evaluate the correlation between the maximum biomass and succinic acid production for various combinatorial knockout strains. This in silico analysis predicted that disrupting the genes for three pyruvate forming enzymes, ptsG, pykF, and pykA, allows enhanced succinic acid production. Indeed, this triple mutation increased the succinic acid production by more than sevenfold and the ratio of succinic acid to fermentation products by ninefold. It could be concluded that reducing the metabolic flux to pyruvate is crucial to achieve efficient succinic acid production in E. coli. These results suggest that the comparative genome analysis combined with in silico metabolic analysis can be an efficient way of developing strategies for strain improvement.

scholarly journals In Silico Design for Systems-Based Metabolic Engineering In Escherichia Coli for The Bioconversion of Aerobic Glycerol to Succinic Acid

2020 ◽  
Author(s):  
Albert Enrique Tafur Rangel ◽  
Wendy Lorena Rios Guzman ◽  
Carmen Elvira Ojeda Cuella ◽  
Daissy Esther Mejia Perez ◽  
Ross Carlson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Escherichia Coli ◽  
Metabolic Engineering ◽  
Succinic Acid ◽  
In Silico ◽  
Rational Design ◽  
Industrial Biotechnology ◽  
E Coli ◽  
Cell Factories ◽  
Transcriptomics Data

Abstract BackgroundGlycerol has become an interesting carbon source for industrial processes as consequence of the biodiesel business growth since it has shown promising results in terms of biomass/substrate yields. Selecting the appropriate metabolic targets to build efficient cell factories and maximize the desired chemical production in as little time as possible is a major challenge in industrial biotechnology. The engineering of microbial metabolism following rational design has been widely studied. However, it is a cost-, time-, and laborious-intensive process because of the cell network complexity; thus, to be proficient is needed known in advance the effects of gene deletions.ResultsAn in silico experiment was performed to model and understand the effects of metabolic engineering over the metabolism by transcriptomics data integration. In this study, systems-based metabolic engineering to predict the metabolic engineering targets was used in order to increase the bioconversion of glycerol to succinic acid by Escherichia coli. Transcriptomics analysis suggest insights of how increase the glycerol utilization of the cell for further design efficient cell factories. Three models were used; an E. coli core model, a model obtained after the integration of transcriptomics data obtained from E. coli growing in an optimized culture media, and a third one obtained after integration of transcriptomics data obtained from E. coli after adaptive laboratory evolution experiments. A total of 2402 strains were obtained from these three models. Fumarase and pyruvate dehydrogenase were frequently predicted in all the models, suggesting that these reactions are essential to increasing succinic acid production from glycerol. Finally, using flux balance analysis results for all the mutants predicted, a machine learning method was developed to predict new mutants as well as to propose optimal metabolic engineering targets and mutants based on the measurement of importance of each knockout’s (feature’s) contribution.ConclusionsThe combination of transcriptome, systems metabolic modeling, and machine learning analyses revealed versatile molecular mechanisms involved in the utilization of glycerol. These data provide a platform to improve the prediction of metabolic engineering targets to design efficient cell factories. Our results may also work a guide platform for the selection/engineering of microorganisms for production of interesting chemical compounds.

scholarly journals New Approaches for Escherichia coli Genotyping

Pathogens ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 73
Author(s):  
Roman Kotłowski ◽  
Katarzyna Grecka ◽  
Barbara Kot ◽  
Piotr Szweda
Keyword(s):  
Escherichia Coli ◽  
In Silico ◽  
In Silico Analysis ◽  
Point Of View ◽  
Discriminatory Power ◽  
Gene Encoding ◽  
High Genetic Diversity ◽  
Flic Gene ◽  
E Coli ◽  
Complete Sequences

Easy-to-perform, fast, and inexpensive methods of differentiation of Escherichia coli strains beyond the species level are highly required. Herein two new, original tools for genotyping of E. coli isolates are proposed. The first of the developed method, a PCR-RFLP (polymerase chain reaction-restriction fragment length polymorphism) test uses a highly variable fliC gene, encoding the H antigen as a molecular target. The designing of universal pair of primers and selection of the optimal restriction enzyme RsaI was preceded by in silico comparative analysis of the sequences of the genes coding for 53 different serotypes of H-antigen (E. coli flagellin). The target fragments of E. coli genomes for MLST method were selected on the basis of bioinformatics analysis of complete sequences of 16 genomes of E. coli. Initially, seven molecular targets were proposed (seven pairs of primers) and five of them were found useful for effective genotyping of E. coli strains. Both developed methods revealed high differentiation power, and a high genetic diversity of the strains tested was observed. Within the group of 71 strains tested, 29 and 47 clusters were revealed with fliC RFLP-PCR and MLST methods, respectively. Differentiation of the strains with the reference BOX-PCR method revealed 31 different genotypes. The in silico analysis revealed that the discriminatory power of the new MLST method is comparable to the Pasteur and Achtman schemes and is higher than the discriminatory power of the method developed by Clermont. From the epidemiology point of view, the outcomes of our investigation revealed that in most cases, the patients were infected with unique strains, probably from environmental sources. However, some strains isolated from different patients of the wards of pediatrics, internal medicine, and neurology were classified to the same genotype when the results of all three methods were taken into account. It could suggest that they were transferred between the patients.

scholarly journals Genome-Scale Analysis of the Uses of the Escherichia coli Genome: Model-Driven Analysis of Heterogeneous Data Sets

2003 ◽  
Vol 185 (21) ◽  
pp. 6392-6399 ◽  
Author(s):  
Timothy E. Allen ◽  
Markus J. Herrgård ◽  
Mingzhu Liu ◽  
Yu Qiu ◽  
Jeremy D. Glasner ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Protein Synthesis ◽  
In Silico ◽  
Half Life ◽  
Data Sets ◽  
Expression Data ◽  
E Coli ◽  
Model Driven ◽  
Life Data ◽  
Genome Scale

ABSTRACT The recent availability of heterogeneous high-throughput data types has increased the need for scalable in silico methods with which to integrate data related to the processes of regulation, protein synthesis, and metabolism. A sequence-based framework for modeling transcription and translation in prokaryotes has been established and has been extended to study the expression state of the entire Escherichia coli genome. The resulting in silico analysis of the expression state highlighted three facets of gene expression in E. coli: (i) the metabolic resources required for genome expression and protein synthesis were found to be relatively invariant under the conditions tested; (ii) effective promoter strengths were estimated at the genome scale by using global mRNA abundance and half-life data, revealing genes subject to regulation under the experimental conditions tested; and (iii) large-scale genome location-dependent expression patterns with approximately 600-kb periodicity were detected in the E. coli genome based on the 49 expression data sets analyzed. These results support the notion that a structured model-driven analysis of expression data yields additional information that can be subjected to commonly used statistical analyses. The integration of heterogeneous genome-scale data (i.e., sequence, expression data, and mRNA half-life data) is readily achieved in the context of an in silico model.

scholarly journals Improved Genomic Identification, Clustering, and Serotyping of Shiga Toxin-Producing Escherichia coli Using Cluster/Serotype-Specific Gene Markers

2022 ◽  
Vol 11 ◽  
Author(s):  
Xiaomei Zhang ◽  
Michael Payne ◽  
Sandeep Kaur ◽  
Ruiting Lan
Keyword(s):  
Escherichia Coli ◽  
Shiga Toxin ◽  
In Silico ◽  
Genomic Analysis ◽  
Comparative Genomic ◽  
Specific Gene ◽  
Metagenomic Sequencing ◽  
Accurate Identification ◽  
Gene Markers ◽  
E Coli

Shiga toxin-producing Escherichia coli (STEC) have more than 470 serotypes. The well-known STEC O157:H7 serotype is a leading cause of STEC infections in humans. However, the incidence of non-O157:H7 STEC serotypes associated with foodborne outbreaks and human infections has increased in recent years. Current detection and serotyping assays are focusing on O157 and top six (“Big six”) non-O157 STEC serogroups. In this study, we performed phylogenetic analysis of nearly 41,000 publicly available STEC genomes representing 460 different STEC serotypes and identified 19 major and 229 minor STEC clusters. STEC cluster-specific gene markers were then identified through comparative genomic analysis. We further identified serotype-specific gene markers for the top 10 most frequent non-O157:H7 STEC serotypes. The cluster or serotype specific gene markers had 99.54% accuracy and more than 97.25% specificity when tested using 38,534 STEC and 14,216 non-STEC E. coli genomes, respectively. In addition, we developed a freely available in silico serotyping pipeline named STECFinder that combined these robust gene markers with established E. coli serotype specific O and H antigen genes and stx genes for accurate identification, cluster determination and serotyping of STEC. STECFinder can assign 99.85% and 99.83% of 38,534 STEC isolates to STEC clusters using assembled genomes and Illumina reads respectively and can simultaneously predict stx subtypes and STEC serotypes. Using shotgun metagenomic sequencing reads of STEC spiked food samples from a published study, we demonstrated that STECFinder can detect the spiked STEC serotypes, accurately. The cluster/serotype-specific gene markers could also be adapted for culture independent typing, facilitating rapid STEC typing. STECFinder is available as an installable package (https://github.com/LanLab/STECFinder) and will be useful for in silico STEC cluster identification and serotyping using genome data.

In Silico Evaluation of Various Signal Peptides to Improve Secretion of Humulin Protein in E. coli Host

2020 ◽  
Vol 17 ◽  
Author(s):  
Soudabe Kavousipour ◽  
Mahadi Barazesh ◽  
Shiva Mohammadi ◽  
Meghdad Abdollahpour- Alitappeh ◽  
Shirzad Fallahi ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Signal Peptide ◽  
In Silico ◽  
Signal Sequence ◽  
Expression Vectors ◽  
Secretory Proteins ◽  
Signal Peptides ◽  
Heterologous Proteins ◽  
Genetic Characteristics ◽  
E Coli

Background:: Escherichia coli host has been the workhorse for the production of heterologous proteins due to simplicity of use, low cost, availability of various expression vectors, and widespread knowledge on its genetic characteristics, but without a suitable signal sequence, this host cannot be used for production secretory proteins. Humulin is a form of insulin used to treat hyperglycemia caused by types 1 and 2 diabetes. To improve expression and make a straightforward production of Humulin protein, we chose a series of signal peptides. Objective:: aim our study to predict the most excellent signal peptides to express secretory Humulin in E. coli organisms. Method:: Therefore, to forecast the most excellent signal peptides for expression of Humulin in Escherichia coli, 47 signal sequences from bacteria organisms were elected and the most imperative elements of them were studied. Hence, signal peptide probability along with physicochemical features was evaluated by signal 4.1, and Portparam, PROSO II servers respectively. Later, the in-silico cloning in a known pET28a plasmid system also estimated the possibility of best signal peptide+ Humulin expression in E.Coli. Results:: The outcomes demonstrated among 47 signal peptides only 2 signal peptides can be suggested as suitable signal peptides. Conclusion:: Ultimately protein yebF precursor (YEBF_ECOLI) and protein yebF precursor (YEBF_YERP3) were suggested severally; as the most excellent signal peptides to express Humulin (With D scores 0.812 and 0.623 respectively). Although verification of these results want experimental analysis.

scholarly journals Description and Interpretation of Adaptive Evolution of Escherichia coli K-12 MG1655 by Using a Genome-Scale In Silico Metabolic Model

2003 ◽  
Vol 185 (21) ◽  
pp. 6400-6408 ◽  
Author(s):  
Stephen S. Fong ◽  
Jennifer Y. Marciniak ◽  
Bernhard Ø. Palsson
Keyword(s):  
Escherichia Coli ◽  
Adaptive Evolution ◽  
Growth Rates ◽  
In Silico ◽  
A Priori ◽  
E Coli ◽  
End Point ◽  
In Silico Model ◽  
Uptake Rates ◽  
Genome Scale

ABSTRACT Genome-scale in silico metabolic networks of Escherichia coli have been reconstructed. By using a constraint-based in silico model of a reconstructed network, the range of phenotypes exhibited by E. coli under different growth conditions can be computed, and optimal growth phenotypes can be predicted. We hypothesized that the end point of adaptive evolution of E. coli could be accurately described a priori by our in silico model since adaptive evolution should lead to an optimal phenotype. Adaptive evolution of E. coli during prolonged exponential growth was performed with M9 minimal medium supplemented with 2 g of α-ketoglutarate per liter, 2 g of lactate per liter, or 2 g of pyruvate per liter at both 30 and 37°C, which produced seven distinct strains. The growth rates, substrate uptake rates, oxygen uptake rates, by-product secretion patterns, and growth rates on alternative substrates were measured for each strain as a function of evolutionary time. Three major conclusions were drawn from the experimental results. First, adaptive evolution leads to a phenotype characterized by maximized growth rates that may not correspond to the highest biomass yield. Second, metabolic phenotypes resulting from adaptive evolution can be described and predicted computationally. Third, adaptive evolution on a single substrate leads to changes in growth characteristics on other substrates that could signify parallel or opposing growth objectives. Together, the results show that genome-scale in silico metabolic models can describe the end point of adaptive evolution a priori and can be used to gain insight into the adaptive evolutionary process for E. coli.

scholarly journals Synthesis and in Silico Modelling of the Potential Dual Mechanistic Activity of Small Cationic Peptides Potentiating the Antibiotic Novobiocin against Susceptible and Multi-Drug Resistant Escherichia coli

2020 ◽  
Vol 21 (23) ◽  
pp. 9134
Author(s):  
Ilaria Passarini ◽  
Pedro Ernesto de Resende ◽  
Sarah Soares ◽  
Tadeh Tahmasi ◽  
Paul Stapleton ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
In Silico ◽  
Mammalian Cells ◽  
Antimicrobial Agents ◽  
Efflux Pump ◽  
Coli Strain ◽  
Cell Permeability ◽  
Cationic Peptides ◽  
Drug Resistant ◽  
E Coli

Cationic antimicrobial peptides have attracted interest, both as antimicrobial agents and for their ability to increase cell permeability to potentiate other antibiotics. However, toxicity to mammalian cells and complexity have hindered development for clinical use. We present the design and synthesis of very short cationic peptides (3–9 residues) with potential dual bacterial membrane permeation and efflux pump inhibition functionality. Peptides were designed based upon in silico similarity to known active peptides and efflux pump inhibitors. A number of these peptides potentiate the activity of the antibiotic novobiocin against susceptible Escherichia coli and restore antibiotic activity against a multi-drug resistant E. coli strain, despite having minimal or no intrinsic antimicrobial activity. Molecular modelling studies, via docking studies and short molecular dynamics simulations, indicate two potential mechanisms of potentiating activity; increasing antibiotic cell permeation via complexation with novobiocin to enable self-promoted uptake, and binding the E. coli RND efflux pump. These peptides demonstrate potential for restoring the activity of hydrophobic drugs.

scholarly journals In silico Prediction and Prioritization of Novel Selective Antimicrobial Drug Targets in Escherichia coli

Antibiotics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 632
Author(s):  
Frida Svanberg Frisinger ◽  
Bimal Jana ◽  
Stefano Donadio ◽  
Luca Guardabassi
Keyword(s):  
Escherichia Coli ◽  
Gut Microbiota ◽  
In Silico ◽  
Drug Targets ◽  
3D Structure ◽  
Opportunistic Pathogens ◽  
Antimicrobial Drugs ◽  
E Coli ◽  
Essential Proteins ◽  
Alignment Length

Novel antimicrobials interfering with pathogen-specific targets can minimize the risk of perturbations of the gut microbiota (dysbiosis) during therapy. We employed an in silico approach to identify essential proteins in Escherichia coli that are either absent or have low sequence identity in seven beneficial taxa of the gut microbiota: Faecalibacterium, Prevotella, Ruminococcus, Bacteroides, Lactobacillus, Lachnospiraceae and Bifidobacterium. We identified 36 essential proteins that are present in hyper-virulent E. coli ST131 and have low similarity (bitscore < 50 or identity < 30% and alignment length < 25%) to proteins in mammalian hosts and beneficial taxa. Of these, 35 are also present in Klebsiella pneumoniae. None of the proteins are targets of clinically used antibiotics, and 3D structure is available for 23 of them. Four proteins (LptD, LptE, LolB and BamD) are easily accessible as drug targets due to their location in the outer membrane, especially LptD, which contains extracellular domains. Our results indicate that it may be possible to selectively interfere with essential biological processes in Enterobacteriaceae that are absent or mediated by unrelated proteins in beneficial taxa residing in the gut. The identified targets can be used to discover antimicrobial drugs effective against these opportunistic pathogens with a decreased risk of causing dysbiosis.

scholarly journals Effects of carbamate pesticides intermediates on Escherichia coli membrane architecture: An in vitro and in silico approach

2021 ◽  
Vol 36 (3) ◽  
pp. e2021020
Author(s):  
Pushpendra Singh ◽  
Manish Kumar Tripathi ◽  
Mohammad Yasir ◽  
Ashish Ranjan ◽  
Rahul Shrivastava
Keyword(s):  
Escherichia Coli ◽  
Membrane Protein ◽  
Outer Membrane ◽  
Outer Membrane Protein ◽  
In Silico ◽  
Synthetic Analogue ◽  
Methyl Isocyanate ◽  
Kcal Mole ◽  
E Coli

Methyl isocyanate (MIC), a low molecular weight synthetic aliphatic compound, having an isocyanate group (−NCO), has industrial application. In this study, the effects of methyl isocyanate and its mechanism on outer membrane protein of Escherichia coli were observed using experimental and computational methods. In vitro exposure of N-succinimidyl N-methylcarbamate (NSNM) a synthetic analogue of MIC on E. coli to a final concentration of 2 mM was found to affect the growth curve pattern and changes in cell morphology. Molecular docking studies of MIC and NSNM with E. coli outer membrane protein (OmpW, OmpX, OmpF OmpA), and periplasmic domain (PAL) were performed. The in-silico results revealed that outer membrane protein OmpF showed the highest negative binding energy, i.e. ∆G -4.11 kcal/mole and ∆G -3.19 kcal/mole by NSNM and MIC as compared to other proteins. Our study concludes that methyl isocyanate retains lethal toxicity which leads to cell death due to the membrane protein damage of E. coli membrane.

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