Tracking the Distribution of Brucella abortus in Egypt Based on Core Genome SNP Analysis and In Silico MLVA-16

Brucellosis, caused by the bacteria of the genus Brucella, is one of the most neglected common zoonotic diseases globally with a public health significance and a high economic loss among the livestock industry worldwide. Since little is known about the distribution of B. abortus in Egypt, a total of 46 B. abortus isolates recovered between 2012–2020, plus one animal isolate from 2006, were analyzed by examining the whole core genome single nucleotide polymorphism (cgSNP) in comparison to the in silico multilocus variable number of tandem repeat analysis (MLVA). Both cgSNP analysis and MLVA revealed three clusters and one isolate only was distantly related to the others. One cluster identified a rather widely distributed outbreak strain which is repeatedly occurring for at least 16 years with marginal deviations in cgSNP analysis. The other cluster of isolates represents a rather newly introduced outbreak strain. A separate cluster comprised RB51 vaccine related strains, isolated from aborted material. The comparison with MLVA data sets from public databases reveals one near relative from Argentina to the oldest outbreak strain and a related strain from Spain to a newly introduced outbreak strain in Egypt. The distantly related isolate matches with a strain from Portugal in the MLVA profile. Based on cgSNP analysis the oldest outbreak strain clusters with strains from the UK. Compared to the in silico analysis of MLVA, cgSNP analysis using WGS data provides a much higher resolution of genotypes and, when correlated to the associated epidemiological metadata, cgSNP analysis allows the differentiation of outbreaks by defining different outbreak strains. In this respect, MLVA data are error-prone and can lead to incorrect interpretations of outbreak events.

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Metabolic potential of microbial mats and microbialites: Autotrophic capabilities described by an in silico stoichiometric approach from shared genomic resources

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720016500207 ◽

2016 ◽

Vol 14 (04) ◽

pp. 1650020 ◽

Cited By ~ 5

Author(s):

Daniel Cerqueda-García ◽

Luisa I. Falcón

Keyword(s):

In Silico ◽

Microbial Mats ◽

Tricarboxylic Acid Cycle ◽

Calvin Cycle ◽

Reducing Power ◽

In Silico Analysis ◽

Metagenomic Data ◽

Data Sets ◽

Metabolic Potential ◽

C And N

Microbialites and microbial mats are complex communities with high phylogenetic diversity. These communities are mostly composed of bacteria and archaea, which are the earliest living forms on Earth and relevant to biogeochemical evolution. In this study, we identified the shared metabolic pathways for uptake of inorganic C and N in microbial mats and microbialites based on metagenomic data sets. An in silico analysis for autotrophic pathways was used to trace the paths of C and N to the system, following an elementary flux modes (EFM) approach, resulting in a stoichiometric model. The fragility was analyzed by the minimal cut sets method. We found four relevant pathways for the incorporation of CO2 (Calvin cycle, reverse tricarboxylic acid cycle, reductive acetyl-CoA pathway, and dicarboxylate/4-hydroxybutyrate cycle), some of them present only in archaea, while nitrogen fixation was the most important source of N to the system. The metabolic potential to incorporate nitrate to biomass was also relevant. The fragility of the network was low, suggesting a high redundancy of the autotrophic pathways due to their broad metabolic diversity, and highlighting the relevance of reducing power source. This analysis suggests that microbial mats and microbialites are “metabolic pumps” for the incorporation of inorganic gases and formation of organic matter.

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In Silico Analysis of Gardnerella Genomospecies Detected in the Setting of Bacterial Vaginosis

Clinical Chemistry ◽

10.1373/clinchem.2019.305474 ◽

2019 ◽

Vol 65 (11) ◽

pp. 1375-1387 ◽

Cited By ~ 5

Author(s):

Robert F Potter ◽

Carey-Ann D Burnham ◽

Gautam Dantas

Keyword(s):

Bacterial Vaginosis ◽

In Silico ◽

In Silico Analysis ◽

Gardnerella Vaginalis ◽

Phylogenomic Analysis ◽

Data Sets ◽

Separate Species ◽

Accessory Genes ◽

Causative Agents ◽

Multiple Species

Abstract BACKGROUND Gardnerella vaginalis is implicated as one of the causative agents of bacterial vaginosis, but it can also be isolated from the vagina of healthy women. Previous efforts to study G. vaginalis identified 4 to 6 clades, but average nucleotide identity analysis indicates that G. vaginalis may be multiple species. Recently, Gardnerella was determined to be 13 genomospecies, with Gardnerella piottii, Gardnerella leopoldii, and Gardnerella swidsinkii delineated as separate species. METHODS We accessed 103 publicly available genomes annotated as G. vaginalis. We performed comprehensive taxonomic and phylogenomic analysis to quantify the number of species called G. vaginalis, the similarity of their core genes, and their burden of their accessory genes. We additionally analyzed publicly available metatranscriptomic data sets of bacterial vaginosis to determine whether the newly delineated genomospecies are present, and to identify putative conserved features of Gardnerella pathogenesis. RESULTS Gardnerella could be classified into 8 to 14 genomospecies depending on the in silico classification tools used. Consensus classification identified 9 different Gardnerella genomospecies, here annotated as GS01 through GS09. The genomospecies could be readily distinguished by the phylogeny of their shared genes and burden of accessory genes. All of the new genomospecies were identified in metatranscriptomes of bacterial vaginosis. CONCLUSIONS Multiple Gardnerella genomospecies operating in isolation or in concert with one another may be responsible for bacterial vaginosis. These results have important implications for future efforts to understand the evolution of the Gardnerella genomospecies, host–pathogen interactions of the genomospecies during bacterial vaginosis, diagnostic assay development for bacterial vaginosis, and metagenomic investigations of the vaginal microbiota.

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IN SILICO ANALYSIS OF VARIABLE NUMBER OF TANDEM REPEATS IN ERWINIA AMYLOVORA GENOME

Acta Horticulturae ◽

10.17660/actahortic.2011.896.13 ◽

2011 ◽

pp. 115-118 ◽

Cited By ~ 5

Author(s):

T. Dreo ◽

M. Ravnikar ◽

J.E. Frey ◽

T.H.M. Smits ◽

B. Duffy

Keyword(s):

In Silico ◽

Tandem Repeats ◽

Erwinia Amylovora ◽

In Silico Analysis ◽

Variable Number ◽

Silico Analysis

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A systematic review with in silico analysis on transcriptomic profile of gallbladder carcinoma

Seminars in Oncology ◽

10.1053/j.seminoncol.2020.02.012 ◽

2020 ◽

Vol 47 (6) ◽

pp. 398-408

Author(s):

Sonam Tulsyan ◽

Showket Hussain ◽

Balraj Mittal ◽

Sundeep Singh Saluja ◽

Pranay Tanwar ◽

...

Keyword(s):

Systematic Review ◽

Gallbladder Carcinoma ◽

In Silico ◽

In Silico Analysis ◽

Transcriptomic Profile ◽

Silico Analysis

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In Silico Analysis of Single Nucleotide Polymorphism in Human Prothrombin Gene

10.1055/s-0039-1680245 ◽

2019 ◽

Author(s):

I. Farah ◽

A. El-Mubark ◽

M. Osman ◽

A. Soliman ◽

F. Ali ◽

...

Keyword(s):

Single Nucleotide Polymorphism ◽

In Silico ◽

In Silico Analysis ◽

Nucleotide Polymorphism ◽

Single Nucleotide ◽

Prothrombin Gene ◽

Silico Analysis

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Enalos Suite of Tools: Enhancing Cheminformatics and Nanoinfor - matics through KNIME

Current Medicinal Chemistry ◽

10.2174/0929867327666200727114410 ◽

2020 ◽

Vol 27 (38) ◽

pp. 6523-6535 ◽

Cited By ~ 3

Author(s):

Antreas Afantitis ◽

Andreas Tsoumanis ◽

Georgia Melagraki

Keyword(s):

Data Analysis ◽

Virtual Screening ◽

In Silico ◽

Model Development ◽

In Silico Analysis ◽

Material Design ◽

Efficient Solutions ◽

Biological Data ◽

Cost Efficient ◽

Silico Analysis

Drug discovery as well as (nano)material design projects demand the in silico analysis of large datasets of compounds with their corresponding properties/activities, as well as the retrieval and virtual screening of more structures in an effort to identify new potent hits. This is a demanding procedure for which various tools must be combined with different input and output formats. To automate the data analysis required we have developed the necessary tools to facilitate a variety of important tasks to construct workflows that will simplify the handling, processing and modeling of cheminformatics data and will provide time and cost efficient solutions, reproducible and easier to maintain. We therefore develop and present a toolbox of >25 processing modules, Enalos+ nodes, that provide very useful operations within KNIME platform for users interested in the nanoinformatics and cheminformatics analysis of chemical and biological data. With a user-friendly interface, Enalos+ Nodes provide a broad range of important functionalities including data mining and retrieval from large available databases and tools for robust and predictive model development and validation. Enalos+ Nodes are available through KNIME as add-ins and offer valuable tools for extracting useful information and analyzing experimental and virtual screening results in a chem- or nano- informatics framework. On top of that, in an effort to: (i) allow big data analysis through Enalos+ KNIME nodes, (ii) accelerate time demanding computations performed within Enalos+ KNIME nodes and (iii) propose new time and cost efficient nodes integrated within Enalos+ toolbox we have investigated and verified the advantage of GPU calculations within the Enalos+ nodes. Demonstration data sets, tutorial and educational videos allow the user to easily apprehend the functions of the nodes that can be applied for in silico analysis of data.

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In Silico Study of 1, 4 Alpha Glucan Branching Enzyme and Substrate Docking Studies

Current Proteomics ◽

10.2174/1570164616666190401204009 ◽

2020 ◽

Vol 17 (1) ◽

pp. 40-50

Author(s):

Farzane Kargar ◽

Amir Savardashtaki ◽

Mojtaba Mortazavi ◽

Masoud Torkzadeh Mahani ◽

Ali Mohammad Amani ◽

...

Keyword(s):

Phylogenetic Tree ◽

In Silico ◽

Alpha Helix ◽

In Silico Analysis ◽

Glycogen Storage ◽

Docking Studies ◽

Random Coil ◽

Special Topic ◽

Industrial Biotechnology ◽

In Silico Study

Background: The 1,4-alpha-glucan branching protein (GlgB) plays an important role in the glycogen biosynthesis and the deficiency in this enzyme has resulted in Glycogen storage disease and accumulation of an amylopectin-like polysaccharide. Consequently, this enzyme was considered a special topic in clinical and biotechnological research. One of the newly introduced GlgB belongs to the Neisseria sp. HMSC071A01 (Ref.Seq. WP_049335546). For in silico analysis, the 3D molecular modeling of this enzyme was conducted in the I-TASSER web server. Methods: For a better evaluation, the important characteristics of this enzyme such as functional properties, metabolic pathway and activity were investigated in the TargetP software. Additionally, the phylogenetic tree and secondary structure of this enzyme were studied by Mafft and Prabi software, respectively. Finally, the binding site properties (the maltoheptaose as substrate) were studied using the AutoDock Vina. Results: By drawing the phylogenetic tree, the closest species were the taxonomic group of Betaproteobacteria. The results showed that the structure of this enzyme had 34.45% of the alpha helix and 45.45% of the random coil. Our analysis predicted that this enzyme has a potential signal peptide in the protein sequence. Conclusion: By these analyses, a new understanding was developed related to the sequence and structure of this enzyme. Our findings can further be used in some fields of clinical and industrial biotechnology.

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