scholarly journals Identification of Acinetobacter baumannii loci for capsular polysaccharide (KL) and lipooligosaccharide outer core (OCL) synthesis in genome assemblies using curated reference databases compatible with Kaptive

2019 ◽  
Author(s):  
Kelly L. Wyres ◽  
Sarah M. Cahill ◽  
Kathryn E. Holt ◽  
Ruth M. Hall ◽  
Johanna J. Kenyon
Keyword(s):  
Public Health ◽  
In Silico ◽  
Capsular Polysaccharide ◽  
Gene Clusters ◽  
Outer Core ◽  
Command Line ◽  
Antibiotic Resistant ◽  
Link Type ◽  
Reference Sequences ◽  
Genome Assemblies

AbstractMultiply antibiotic resistant Acinetobacter baumannii infections are a global public health concern and accurate tracking of the spread of specific lineages is needed. Variation in the composition and structure of capsular polysaccharide (CPS), a critical determinant of virulence and phage susceptibility, makes it an attractive epidemiological marker. The outer core (OC) of lipooligosaccharide also exhibits variation. To take better advantage of the untapped information available in whole genome sequences, we have created a curated reference database of the 92 publicly available gene clusters at the locus encoding proteins responsible for biosynthesis and export of CPS (K locus), and a second database for the 12 gene clusters at the locus for outer core biosynthesis (OC locus). Each entry has been assigned a unique KL or OCL number, and is fully annotated using a simple, transparent and standardised nomenclature. These databases are compatible with Kaptive, a tool for in silico typing of bacterial surface polysaccharide loci, and their utility was validated using a) >630 assembled A. baumannii draft genomes for which the KL and OCL regions had been previously typed manually, and b) 3386 A. baumannii genome assemblies downloaded from NCBI. Among the previously typed genomes, Kaptive was able to confidently assign KL and OCL types with 100% accuracy. Among the genomes retrieved from NCBI, Kaptive detected known KL and OCL in 87% and 90% of genomes, respectively indicating that the majority of common KL and OCL types are captured within the databases; 13 KL were not detected in any public genome assembly. The failure to assign a KL or OCL type may indicate incomplete or poor-quality genomes. However, further novel variants may remain to be documented. Combining outputs with multi-locus sequence typing (Institut Pasteur scheme) revealed multiple KL and OCL types in collections of a single sequence type (ST) representing each of the two predominant globally-distributed clones, ST1 of GC1 and ST2 of GC2, and in collections of other clones comprising >20 isolates each (ST10, ST25, and ST140), indicating extensive within-clone replacement of these loci. The databases are available at https://github.com/katholt/Kaptive and will be updated as further locus types become available.Data Summary1. Databases including fully annotated gene cluster sequences for A. baumannii K loci and OC loci are available for download at https://github.com/katholt/Kaptive2. The Kaptive software, which can be used to screen new genomes against the K and O locus database is available at https://github.com/katholt/Kaptive (command-line code) and http://kaptive.holtlab.net/ (interactive web service).3. Details of the Kaptive search results validating in silico serotyping of K and O loci using our approach are provided as supplementary files, Dataset 1 (92 KL reference sequences and 12 OCL reference sequences), Dataset 2 (642 genomes assembled from reads available in NCBI SRA) and Dataset 3 (3415 genome assemblies downloaded from NCBI GenBank).Impact statementThe ability to identify and track closely related isolates is key to understanding, and ultimately controlling, the spread of multiply antibiotic resistant A. baumannii causing difficult to treat infections, which are an urgent public health threat. Extensive variation in the KL and OCL gene clusters responsible for biosynthesis of capsule and the outer core of lipooligosaccharide, respectively, are potentially highly informative epidemiological markers. However, clear, well-documented identification of each variant and simple-to-use tools and procedures are needed to reliably identify them in genome sequence data. Here, we present curated databases compatible with the available web-based and command-line Kaptive tool to make KL and OCL typing readily accessible to assist epidemiological surveillance of this species. As many bacteriophage recognise specific properties of the capsule and attach to it, capsule typing is also important in assessing the potential of specific phage for therapy on a case by case basis.

Updated analysis of the surface carbohydrate gene clusters in the diverse panel of Acinetobacter baumannii isolates.

2021 ◽  
Author(s):  
Johanna J Kenyon ◽  
Ruth M. Hall
Keyword(s):  
Acinetobacter Baumannii ◽  
Capsular Polysaccharide ◽  
Gene Clusters ◽  
Outer Core ◽  
The Novel

To enhance the utility of the genetically diverse panel of Acinetobacter baumannii isolates reported recently by Galac and co-workers (AAC 64: e00840-20) and to identify the novel KL and OCL, all of the gene clusters that direct the biosynthesis of capsular polysaccharide and of the outer core of lipooligosaccharide, respectively, were re-examined. The nine KL and one OCL previously recorded as novel were identified and nine further novel KL and two OCL were found.

scholarly journals ECTyper: in silico Escherichia coli serotype and species prediction from raw and assembled whole-genome sequence data

2021 ◽  
Vol 7 (12) ◽  
Author(s):  
Kyrylo Bessonov ◽  
Chad Laing ◽  
James Robertson ◽  
Irene Yong ◽  
Kim Ziebell ◽  
...  
Keyword(s):  
Public Health ◽  
Escherichia Coli ◽  
In Silico ◽  
Type Species ◽  
Outbreak Detection ◽  
Health Concern ◽  
Whole Genome ◽  
Content Type ◽  
Link Type ◽  
O Antigens

Escherichia coli is a priority foodborne pathogen of public health concern and phenotypic serotyping provides critical information for surveillance and outbreak detection activities. Public health and food safety laboratories are increasingly adopting whole-genome sequencing (WGS) for characterizing pathogens, but it is imperative to maintain serotype designations in order to minimize disruptions to existing public health workflows. Multiple in silico tools have been developed for predicting serotypes from WGS data, including SRST2, SerotypeFinder and EToKi EBEis, but these tools were not designed with the specific requirements of diagnostic laboratories, which include: speciation, input data flexibility (fasta/fastq), quality control information and easily interpretable results. To address these specific requirements, we developed ECTyper (https://github.com/phac-nml/ecoli_serotyping) for performing both speciation within Escherichia and Shigella , and in silico serotype prediction. We compared the serotype prediction performance of each tool on a newly sequenced panel of 185 isolates with confirmed phenotypic serotype information. We found that all tools were highly concordant, with 92–97 % for O-antigens and 98–100 % for H-antigens, and ECTyper having the highest rate of concordance. We extended the benchmarking to a large panel of 6954 publicly available E. coli genomes to assess the performance of the tools on a more diverse dataset. On the public data, there was a considerable drop in concordance, with 75–91 % for O-antigens and 62–90 % for H-antigens, and ECTyper and SerotypeFinder being the most concordant. This study highlights that in silico predictions show high concordance with phenotypic serotyping results, but there are notable differences in tool performance. ECTyper provides highly accurate and sensitive in silico serotype predictions, in addition to speciation, and is designed to be easily incorporated into bioinformatic workflows.

scholarly journals Easily phylotyping E. coli via the EzClermont web app and command-line tool

2018 ◽  
Author(s):  
Nicholas R. Waters ◽  
Florence Abram ◽  
Fiona Brennan ◽  
Ashleigh Holmes ◽  
Leighton Pritchard
Keyword(s):  
Supplementary Information ◽  
Validation Dataset ◽  
Command Line ◽  
E Coli ◽  
Link Type ◽  
Command Line Tool ◽  
Pcr Method ◽  
Web App ◽  
Local Use ◽  
Genome Assemblies

SummaryThe Clermont PCR method of phylotyping Escherichia coli has remained a useful classification scheme despite the proliferation of higher-resolution sequence typing schemes. We have implemented an in silico Clermont PCR method as both a web app and as a command-line tool to allow researchers to easily apply this phylotyping scheme to genome assemblies easily.Availability and ImplementationEzClermont is available as a web app at http://www.ezclermont.org. For local use, EzClermont can be installed with pip or installed from the source code at https://github.com/nickp60/ezclermont. All analysis was done with version [email protected], [email protected] informationTable S1: test dataset; S2: validation dataset; S3: results.

scholarly journals Easy phylotyping of Escherichia coli via the EzClermont web app and command-line tool

2020 ◽  
Vol 2 (9) ◽  
Author(s):  
Nicholas R. Waters ◽  
Florence Abram ◽  
Fiona Brennan ◽  
Ashleigh Holmes ◽  
Leighton Pritchard
Keyword(s):  
Escherichia Coli ◽  
Type Species ◽  
Whole Genome ◽  
Command Line ◽  
Content Type ◽  
Link Type ◽  
Command Line Tool ◽  
Pcr Method ◽  
Web App ◽  
Genome Assemblies

The Clermont PCR method for phylotyping Escherichia coli remains a useful classification scheme even though genome sequencing is now routine, and higher-resolution sequence typing schemes are now available. Relating present-day whole-genome E. coli classifications to legacy phylotyping is essential for harmonizing the historical literature and understanding of this important organism. Therefore, we present EzClermont – a novel in silico Clermont PCR phylotyping tool to enable ready application of this phylotyping scheme to whole-genome assemblies. We evaluate this tool against phylogenomic classifications, and an alternative software implementation of Clermont typing. EzClermont is available as a web app at www.ezclermont.org, and as a command-line tool at https://nickp60.github.io/EzClermont/.

scholarly journals Population simulations of COVID-19 outbreaks provide tools for risk assessment and continuity planning

JAMIA Open ◽  
2021 ◽  
Author(s):  
Bo Peng ◽  
Rowland W Pettit ◽  
Christopher I Amos
Keyword(s):  
Public Health ◽  
Risk Assessment ◽  
Emergency Response ◽  
Robustness Analysis ◽  
Health Policies ◽  
Command Line ◽  
Public Health Policies ◽  
Continuity Planning ◽  
Quantitative Estimates ◽  
Community Infection

Abstract Objectives We developed COVID-19 Outbreak Simulator (https://ictr.github.io/covid19-outbreak-simulator/) to quantitatively estimate the effectiveness of preventative and interventive measures to prevent and battle COVID-19 outbreaks for specific populations. Materials and methods Our simulator simulates the entire course of infection and transmission of the virus among individuals in heterogeneous populations, subject to operations and influences, such as quarantine, testing, social distancing, and community infection. It provides command-line and Jupyter notebook interfaces and a plugin system for user-defined operations. Results The simulator provides quantitative estimates for COVID-19 outbreaks in a variety of scenarios and assists the development of public health policies, risk-reduction operations, and emergency response plans. Discussion Our simulator is powerful, flexible, and customizable, although successful applications require realistic estimation and robustness analysis of population-specific parameters. Conclusion Risk assessment and continuity planning for COVID-19 outbreaks are crucial for the continued operation of many organizations. Our simulator will be continuously expanded to meet this need.

scholarly journals The Serological Cross-Detection of Bat-Borne Hantaviruses: A Valid Strategy or Taking Chances?

Viruses ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1188
Author(s):  
Renata Carvalho de Oliveira ◽  
Jorlan Fernandes ◽  
Elba Regina de Sampaio Lemos ◽  
Fernando de Paiva Conte ◽  
Rodrigo Nunes Rodrigues-da-Silva
Keyword(s):  
Public Health ◽  
In Silico ◽  
Data Bank ◽  
Distinct Species ◽  
Geographic Range ◽  
Ab Initio Methods ◽  
Success Rates ◽  
B Cell Epitopes ◽  
Crystallographic Structures ◽  
A Minor

Bats are hosts of a range of viruses, and their great diversity and unique characteristics that distinguish them from all other mammals have been related to the maintenance, evolution, and dissemination of these pathogens. Recently, very divergent hantaviruses have been discovered in distinct species of bats worldwide, but their association with human disease remains unclear. Considering the low success rates of detecting hantavirus RNA in bat tissues and that to date no hantaviruses have been isolated from bat samples, immunodiagnostic tools could be very helpful to understand pathogenesis, epidemiology, and geographic range of bat-borne hantaviruses. In this sense, we aimed to identify in silico immunogenic B-cell epitopes present on bat-borne hantaviruses nucleoprotein (NP) and verify if they are conserved among them and other selected members of Mammantavirinae, using a combination of (the three most used) different prediction algorithms, ELLIPRO, Discotope 2.0, and PEPITO server. To support our data, we in silico modeled 3D structures of NPs from representative members of bat-borne hantaviruses, using comparative and ab initio methods due to the absence of crystallographic structures of studied proteins or similar models in the Protein Data Bank. Our analysis demonstrated the antigenic complexity of the bat-borne hantaviruses group, showing a low sequence conservation of epitopes among members of its own group and a minor conservation degree in comparison to Orthohantavirus, with a recognized importance to public health. Our data suggest that the use of recombinant rodent-borne hantavirus NPs to cross-detect antibodies against bat- or shrew-borne viruses could underestimate the real impact of this virus in nature.

scholarly journals Looking for the X Factor in Bacterial Pathogenesis: Association of orfX-p47 Gene Clusters with Toxin Genes in Clostridial and Non-Clostridial Bacterial Species

Toxins ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 19 ◽  
Author(s):  
Maria B. Nowakowska ◽  
François P. Douillard ◽  
Miia Lindström
Keyword(s):  
Gene Cluster ◽  
In Silico ◽  
Botulinum Neurotoxin ◽  
Bacterial Species ◽  
Gene Clusters ◽  
Bacterial Pathogenesis ◽  
Toxin Gene ◽  
Toxin Complex ◽  
Analytical Tools ◽  
Bacillus Isolate

The botulinum neurotoxin (BoNT) has been extensively researched over the years in regard to its structure, mode of action, and applications. Nevertheless, the biological roles of four proteins encoded from a number of BoNT gene clusters, i.e., OrfX1-3 and P47, are unknown. Here, we investigated the diversity of orfX-p47 gene clusters using in silico analytical tools. We show that the orfX-p47 cluster was not only present in the genomes of BoNT-producing bacteria but also in a substantially wider range of bacterial species across the bacterial phylogenetic tree. Remarkably, the orfX-p47 cluster was consistently located in proximity to genes coding for various toxins, suggesting that OrfX1-3 and P47 may have a conserved function related to toxinogenesis and/or pathogenesis, regardless of the toxin produced by the bacterium. Our work also led to the identification of a putative novel BoNT-like toxin gene cluster in a Bacillus isolate. This gene cluster shares striking similarities to the BoNT cluster, encoding a bont/ntnh-like gene and orfX-p47, but also differs from it markedly, displaying additional genes putatively encoding the components of a polymorphic ABC toxin complex. These findings provide novel insights into the biological roles of OrfX1, OrfX2, OrfX3, and P47 in toxinogenesis and pathogenesis of BoNT-producing and non-producing bacteria.

scholarly journals THU0008 DEVELOPMENT OF A NOVEL TRANSLATIONAL IN SILICO INDICATION DISCOVERY FRAMEWORK: EXEMPLIFIED BY THE CLINICAL COMPOUND CENERIMOD

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 216.2-217
Author(s):  
D. Hartl ◽  
M. Keller ◽  
A. Klenk ◽  
M. Murphy ◽  
M. Martinic ◽  
...  
Keyword(s):  
Gene Expression ◽  
Animal Models ◽  
Clinical Response ◽  
Gene Expression Data ◽  
In Silico ◽  
Drug Candidate ◽  
Expression Data ◽  
Computer Algorithms ◽  
Clinical Patient ◽  
Link Type

Background:To explore the full therapeutic spectrum of a drug it is crucial to consider its potential effectiveness in all diseases. Serendipitous clinical observations have often shown that approved drugs and those in development to be efficacious in indications different to those originally tested for. Traditional approaches to match a drug candidate with possible indications are mostly based on matching drug mechanistic knowledge with disease pathophysiology. Proof-of-concept trials or elaborate pre-clinical studies in animal models do not allow for a broad assessment due to high costs and slow progress. Gene expression changes in patients or animal models represent a good proxy to comprehensively assess both disease and drug effects. Furthermore, this data type can be integrated with a plethora of publicly available data.Objectives:Generation of a novel in silico framework to support the selection and expansion of potential indications which associate with a compound or approved drug. The framework was exemplified by the clinical compound cenerimod, a potent, selective, and orally active sphingosine-1-phosphate receptor 1 modulator (Piali et al., 2017).Methods:A total of ~13’000 public patient gene expression datasets from ~140 diseases were evaluated against cenerimod gene expression data generated in mouse disease models. To improve comparability of studies across platforms and species, computer algorithms (neural networks) were trained and employed to reduce noise within the data sets and improve signal. The predicted response to cenerimod for individual patients was contrasted against clinical patient characteristics.Results:The neural network algorithm efficiently reduced experimental noise and improved sensitivity in the gene expression data. The results predicted cenerimod to be efficacious in several auto-immune diseases foremost SLE. Additionally, focused analysis on individual patients rather than disease cohorts revealed potential determinants predictive of maximal clinical response, with the highest predicted clinical response for cenerimod in patients with severe inflammatory endotype and/or high SLE Disease Activity Index (SLEDAI).Conclusion:Combining preclinical compound data with the wealth of public disease gene expression data, provides great potential to support indication selection. The novel in silico framework identified SLE as a prime potential indication for cenerimod and supported the cenerimod phase 2b clinical trial in patients with SLE (CARE study,NCT03742037).References:[1]Piali, L., Birker-Robaczewska, M., Lescop, C., Froidevaux, S., Schmitz, N., Morrison, K., … Nayler, O. (2017). Cenerimod, a novel selective S1P1 receptor modulator with unique signaling properties. Pharmacology Research & Perspectives, 5(6), 1–12.https://doi.org/10.1002/prp2.370Disclosure of Interests:Dominik Hartl Shareholder of: Idorsia shares, Employee of: Idorsia employee, Marcel Keller Shareholder of: Idorsia options/shares, Employee of: Idorsia employee, Axel Klenk Shareholder of: Idorsia option/shares, Employee of: Idorsia employee, Mark Murphy Shareholder of: Idorsia shares and stock options, Employee of: Idorsia employee, Marianne Martinic Shareholder of: Idorsia options/shares, Employee of: Idorsia employee, Gabin Pierlot Shareholder of: Idorsia options/shares, Employee of: Idorsia employee, Peter Groenen Shareholder of: Idorsia options/shares, Employee of: Idorsia employee, Daniel Strasser Shareholder of: Idorsia options/shares, Employee of: Idorsia employee

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