scholarly journals A large scale evaluation of TBProfiler and Mykrobe for antibiotic resistance prediction in Mycobacterium tuberculosis

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6857 ◽  
Author(s):  
Pierre Mahé ◽  
Meriem El Azami ◽  
Philippine Barlas ◽  
Maud Tournoud
Keyword(s):  
Antibiotic Resistance ◽  
Mycobacterium Tuberculosis ◽  
Large Scale ◽  
Predictive Power ◽  
Predictive Performance ◽  
Systematic Evaluation ◽  
Whole Genome Sequencing Data ◽  
Attractive Alternative ◽  
Sequencing Data ◽  
Trade Offs

Recent years saw a growing interest in predicting antibiotic resistance from whole-genome sequencing data, with promising results obtained for Staphylococcus aureus and Mycobacterium tuberculosis. In this work, we gathered 6,574 sequencing read datasets of M. tuberculosis public genomes with associated antibiotic resistance profiles for both first and second-line antibiotics. We performed a systematic evaluation of TBProfiler and Mykrobe, two widely recognized softwares allowing to predict resistance in M. tuberculosis. The size of the dataset allowed us to obtain confident estimations of their overall predictive performance, to assess precisely the individual predictive power of the markers they rely on, and to study in addition how these softwares behave across the major M. tuberculosis lineages. While this study confirmed the overall good performance of these tools, it revealed that an important fraction of the catalog of mutations they embed is of limited predictive power. It also revealed that these tools offer different sensitivity/specificity trade-offs, which is mainly due to the different sets of mutation they embed but also to their underlying genotyping pipelines. More importantly, it showed that their level of predictive performance varies greatly across lineages for some antibiotics, therefore suggesting that the predictions made by these softwares should be deemed more or less confident depending on the lineage inferred and the predictive performance of the marker(s) actually detected. Finally, we evaluated the relevance of machine learning approaches operating from the set of markers detected by these softwares and show that they present an attractive alternative strategy, allowing to reach better performance for several drugs while significantly reducing the number of candidate mutations to consider.

scholarly journals MTBseq: a comprehensive pipeline for whole genome sequence analysis of Mycobacterium tuberculosis complex isolates

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5895 ◽  
Author(s):  
Thomas Andreas Kohl ◽  
Christian Utpatel ◽  
Viola Schleusener ◽  
Maria Rosaria De Filippo ◽  
Patrick Beckert ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Mycobacterium Tuberculosis ◽  
Genome Sequence ◽  
Sequence Data ◽  
Whole Genome Sequence ◽  
Whole Genome Sequencing Data ◽  
Phylogenomic Analysis ◽  
Whole Genome ◽  
Sequencing Data ◽  
Desktop Computer

Analyzing whole-genome sequencing data of Mycobacterium tuberculosis complex (MTBC) isolates in a standardized workflow enables both comprehensive antibiotic resistance profiling and outbreak surveillance with highest resolution up to the identification of recent transmission chains. Here, we present MTBseq, a bioinformatics pipeline for next-generation genome sequence data analysis of MTBC isolates. Employing a reference mapping based workflow, MTBseq reports detected variant positions annotated with known association to antibiotic resistance and performs a lineage classification based on phylogenetic single nucleotide polymorphisms (SNPs). When comparing multiple datasets, MTBseq provides a joint list of variants and a FASTA alignment of SNP positions for use in phylogenomic analysis, and identifies groups of related isolates. The pipeline is customizable, expandable and can be used on a desktop computer or laptop without any internet connection, ensuring mobile usage and data security. MTBseq and accompanying documentation is available from https://github.com/ngs-fzb/MTBseq_source.

scholarly journals Identification of gene fusion events in Mycobacterium tuberculosis that encode chimeric proteins

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
James Gallant ◽  
Jomien Mouton ◽  
Roy Ummels ◽  
Corinne ten Hagen-Jongman ◽  
Nastassja Kriel ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Gene Fusion ◽  
Large Scale ◽  
Chimeric Protein ◽  
Whole Genome Sequencing Data ◽  
Chimeric Proteins ◽  
Gene Fusions ◽  
Sequencing Data ◽  
Putative Gene ◽  
Develop Software

Abstract Mycobacterium tuberculosis is a facultative intracellular pathogen responsible for causing tuberculosis. The harsh environment in which M. tuberculosis survives requires this pathogen to continuously adapt in order to maintain an evolutionary advantage. However, the apparent absence of horizontal gene transfer in M. tuberculosis imposes restrictions in the ways by which evolution can occur. Large-scale changes in the genome can be introduced through genome reduction, recombination events and structural variation. Here, we identify a functional chimeric protein in the ppe38–71 locus, the absence of which is known to have an impact on protein secretion and virulence. To examine whether this approach was used more often by this pathogen, we further develop software that detects potential gene fusion events from multigene deletions using whole genome sequencing data. With this software we could identify a number of other putative gene fusion events within the genomes of M. tuberculosis isolates. We were able to demonstrate the expression of one of these gene fusions at the protein level using mass spectrometry. Therefore, gene fusions may provide an additional means of evolution for M. tuberculosis in its natural environment whereby novel chimeric proteins and functions can arise.

scholarly journals Integrating Culture-based Antibiotic Resistance Profiles with Whole-genome Sequencing Data for 11,087 Clinical Isolates

2019 ◽  
Vol 17 (2) ◽  
pp. 169-182 ◽  
Author(s):  
Valentina Galata ◽  
Cédric C. Laczny ◽  
Christina Backes ◽  
Georg Hemmrich-Stanisak ◽  
Susanne Schmolke ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Clinical Isolates ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data

scholarly journals gplas: a comprehensive tool for plasmid analysis using short-read graphs

2020 ◽  
Vol 36 (12) ◽  
pp. 3874-3876 ◽  
Author(s):  
Sergio Arredondo-Alonso ◽  
Martin Bootsma ◽  
Yaïr Hein ◽  
Malbert R C Rogers ◽  
Jukka Corander ◽  
...  
Keyword(s):  
Large Scale ◽  
Sequence Data ◽  
Bacterial Genome ◽  
Workflow Management ◽  
Supplementary Information ◽  
Whole Genome Sequencing Data ◽  
Network Partitioning ◽  
Sequencing Data ◽  
Genetic Traits ◽  
Short Read

Abstract Summary Plasmids can horizontally transmit genetic traits, enabling rapid bacterial adaptation to new environments and hosts. Short-read whole-genome sequencing data are often applied to large-scale bacterial comparative genomics projects but the reconstruction of plasmids from these data is facing severe limitations, such as the inability to distinguish plasmids from each other in a bacterial genome. We developed gplas, a new approach to reliably separate plasmid contigs into discrete components using sequence composition, coverage, assembly graph information and network partitioning based on a pruned network of plasmid unitigs. Gplas facilitates the analysis of large numbers of bacterial isolates and allows a detailed analysis of plasmid epidemiology based solely on short-read sequence data. Availability and implementation Gplas is written in R, Bash and uses a Snakemake pipeline as a workflow management system. Gplas is available under the GNU General Public License v3.0 at https://gitlab.com/sirarredondo/gplas.git. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Draft Genome Sequences of Ciprofloxacin-Resistant Salmonella enterica Strains with Multiple-Antibiotic Resistance, Isolated from Imported Foods

2017 ◽  
Vol 5 (45) ◽  
Author(s):  
Ashraf A. Khan ◽  
Bijay K. Khajanchi ◽  
Sana A. Khan ◽  
Christopher A. Elkins ◽  
Steven L. Foley
Keyword(s):  
Antibiotic Resistance ◽  
Salmonella Enterica ◽  
Draft Genome ◽  
Resistance Mechanisms ◽  
Whole Genome Sequencing Data ◽  
Sequencing Data ◽  
Genome Sequences ◽  
Content Type ◽  
Imported Foods ◽  
High Level

ABSTRACT We report here the draft genome sequences of 15 ciprofloxacin-resistant Salmonella enterica strains with resistance to multiple other antibiotics, including aminoglycosides, β-lactams, sulfonamides, tetracycline, and trimethoprim, isolated from different imported foods. Three strains (NCTR75, NCTR281, and NCTR350) showed a high level of ciprofloxacin resistance compared to that of the other isolates. The whole-genome sequencing data provide a better understanding of the antibiotic resistance mechanisms and virulence properties of these isolates.

scholarly journals Network analysis reveals differential metabolic functionality in antibiotic-resistantPseudomonas aeruginosa

2018 ◽  
Author(s):  
Laura J. Dunphy ◽  
Phillip Yen ◽  
Jason A. Papin
Keyword(s):  
Antibiotic Resistance ◽  
Carbon Sources ◽  
Growth Dynamics ◽  
Metabolic Phenotype ◽  
Metabolic Adaptation ◽  
Whole Genome Sequencing Data ◽  
Sequencing Data ◽  
Drug Induced ◽  
Antibiotic Resistant ◽  
A Genome

AbstractMetabolic adaptations accompanying the development of antibiotic resistance in bacteria remain poorly understood. To interrogate this relationship, we profiled the growth of lab-evolved antibiotic-resistant lineages of the opportunistic pathogenPseudomonas aeruginosaacross 190 unique carbon sources. We semi-automatically calculated growth dynamics (maximum growth density, growth rate, and time to mid-exponential phase) of over 2,800 growth curves. These data revealed that the evolution of antibiotic resistance resulted in systems-level changes to growth dynamics and metabolic phenotype. Drug-resistant lineages predominantly displayed decreased growth relative to the ancestral lineage; however, resistant lineages occasionally displayed enhanced growth on certain carbon sources, indicating that adaption to drug can provide a growth advantage in certain environments. A genome-scale metabolic network reconstruction (GENRE) ofP. aeruginosastrain UCBPP-PA14 was paired with whole-genome sequencing data of one of the drug-evolved lineages to predict genes contributing to observed changes in metabolism. Finally, we experimentally validatedin silicopredictions to identify genes mutated in resistantP. aeruginosaaffecting loss of catabolic function. Our results build upon previous mechanistic knowledge of drug-induced metabolic adaptation and provide a framework for the identification of metabolic limitations in antibiotic-resistant pathogens. Robust drug-driven changes in bacterial metabolism have the potential to be exploited to select against antibiotic-resistant populations in chronic infections.

scholarly journals SplitStrains, a tool to identify and separate mixed Mycobacterium tuberculosis infections from WGS data

2021 ◽  
Author(s):  
Einar Gabbasov ◽  
Miguel Moreno-Molina ◽  
Iñaki Comas ◽  
Maxwell Libbrecht ◽  
Leonid Chindelevitch
Keyword(s):  
Public Health ◽  
Mycobacterium Tuberculosis ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Bacterial Pathogens ◽  
Superior Performance ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Multiple Strains

AbstractThe occurrence of multiple strains of a bacterial pathogen such as M. tuberculosis or C. difficile within a single human host, referred to as a mixed infection, has important implications for both healthcare and public health. However, methods for detecting it, and especially determining the proportion and identities of the underlying strains, from WGS (whole-genome sequencing) data, have been limited.In this paper we introduce SplitStrains, a novel method for addressing these challenges. Grounded in a rigorous statistical model, SplitStrains not only demonstrates superior performance in proportion estimation to other existing methods on both simulated as well as real M. tuberculosis data, but also successfully determines the identity of the underlying strains.We conclude that SplitStrains is a powerful addition to the existing toolkit of analytical methods for data coming from bacterial pathogens, and holds the promise of enabling previously inaccessible conclusions to be drawn in the realm of public health microbiology.Author summaryWhen multiple strains of a pathogenic organism are present in a patient, it may be necessary to not only detect this, but also to identify the individual strains. However, this problem has not yet been solved for bacterial pathogens processed via whole-genome sequencing. In this paper, we propose the SplitStrains algorithm for detecting multiple strains in a sample, identifying their proportions, and inferring their sequences, in the case of Mycobacterium tuberculosis. We test it on both simulated and real data, with encouraging results. We believe that our work opens new horizons in public health microbiology by allowing a more precise detection, identification and quantification of multiple infecting strains within a sample.

scholarly journals Population-level genome-wide STR typing in Plasmodium species reveals higher resolution population structure and genetic diversity relative to SNP typing

2021 ◽  
Author(s):  
Jiru Han ◽  
Jacob E Munro ◽  
Anthony Kocoski ◽  
Alyssa E Barry ◽  
Melanie Bahlo
Keyword(s):  
Genetic Diversity ◽  
Large Scale ◽  
Tandem Repeats ◽  
Plasmodium Species ◽  
Whole Genome Sequence ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Genome Wide ◽  
Field Samples

Short tandem repeats (STRs) are highly informative genetic markers that have been used extensively in population genetics analysis. They are an important source of genetic diversity and can also have functional impact. Despite the availability of bioinformatic methods that permit large-scale genome-wide genotyping of STRs from whole genome sequencing data, they have not previously been applied to sequencing data from large collections of malaria parasite field samples. Here, we have genotyped STRs using HipSTR in more than 3,000 Plasmodium falciparum and 174 Plasmodium vivax published whole-genome sequence data from samples collected across the globe. High levels of noise and variability in the resultant callset necessitated the development of a novel method for quality control of STR genotype calls. A set of high-quality STR loci (6,768 from P. falciparum and 3,496 from P. vivax) were used to study Plasmodium genetic diversity, population structures and genomic signatures of selection and these were compared to genome-wide single nucleotide polymorphism (SNP) genotyping data. In addition, the genome-wide information about genetic variation and other characteristics of STRs in P. falciparum and P. vivax have been made available in an interactive web-based R Shiny application PlasmoSTR (https://github.com/bahlolab/PlasmoSTR).

Sign in / Sign up

Export Citation Format

Share Document