Accelerating surveillance and research of antimicrobial resistance – an online repository for sharing of antimicrobial susceptibility data associated with whole genome sequences

Matamoros, Sébastien; Hendriksen, René S.; Pataki, Bálint Ármin; Pakseresht, Nima; Rossello, Marc; Silvester, Nicole C.; Amid, Clara; Cochrane, Guy; Csabai, István; Lund, Ole; Schultsz, Constance

doi:10.1101/532267

Cited by 4 publications

(7 citation statements)

References 13 publications

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“…Furthermore, not all genomic data is submitted to the online open genomic repositories of INSDC and shared globally due to difficulties to submit, a lack of appreciation for its value, access to local or national repositories, fear of being data being published by others, or privacy of the data (83). Nonetheless, today the NCBI and the European Bioinformatics Institute (EMBL-EBI) can accommodate AST data along with the WGS information, to facilitate a global monitoring of AMR in bacteria to strengthen global public health (84, 85).…”

Section: Online Repositories To Host and Link Genome And Antimicrobiamentioning

confidence: 99%

“…At European Nucleotide Archive (ENA), a mechanism to host and link submitted genomic and AST data has been developed by the EU COMPARE partners and EMBL-EBI (85). Briefly, the EMBL-EBI system allows submitted genomes and associated metadata in the ENA to be stored as open access or privately in a secured login protected repository with named data hubs (86).…”

Section: European Nucleotide Archive Repositorymentioning

confidence: 99%

“…The system is designed to accommodate submission of susceptibility data from both dilution or diffusion methods. Novel software has been developed to validate conformity of the AST data to ensure harmonization of the data (85). The submitted genomic and AST data could be analyzed by using existing bioinformatics infrastructure and implemented cloud-based bioinformatics workflows in specific an extended version of the Bacterial Analysis Pipeline consisting of ContigAnalyzer-1.0, KmerFinder-2.1, MLST-1.6, ResFinder-2.1, VirulenceFinder-1.2, PlasmidFinder-1.2, pMLST-1.4 (87) with the inclusion of also the cgMLSTFinder 1.0.…”

Section: European Nucleotide Archive Repositorymentioning

confidence: 99%

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Using Genomics to Track Global Antimicrobial Resistance

Hendriksen

Bortolaia

Tate

et al. 2019

Front. Public Health

328

261

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The recent advancements in rapid and affordable DNA sequencing technologies have revolutionized diagnostic microbiology and microbial surveillance. The availability of bioinformatics tools and online accessible databases has been a prerequisite for this. We conducted a scientific literature review and here we present a description of examples of available tools and databases for antimicrobial resistance (AMR) detection and provide future perspectives and recommendations. At least 47 freely accessible bioinformatics resources for detection of AMR determinants in DNA or amino acid sequence data have been developed to date. These include, among others but not limited to, ARG-ANNOT, CARD, SRST2, MEGARes, Genefinder, ARIBA, KmerResistance, AMRFinder, and ResFinder. Bioinformatics resources differ for several parameters including type of accepted input data, presence/absence of software for search within a database of AMR determinants that can be specific to a tool or cloned from other resources, and for the search approach employed, which can be based on mapping or on alignment. As a consequence, each tool has strengths and limitations in sensitivity and specificity of detection of AMR determinants and in application, which for some of the tools have been highlighted in benchmarking exercises and scientific articles. The identified tools are either available at public genome data centers, from GitHub or can be run locally. NCBI and European Nucleotide Archive (ENA) provide possibilities for online submission of both sequencing and accompanying phenotypic antimicrobial susceptibility data, allowing for other researchers to further analyze data, and develop and test new tools. The advancement in whole genome sequencing and the application of online tools for real-time detection of AMR determinants are essential to identify control and prevention strategies to combat the increasing threat of AMR. Accessible tools and DNA sequence data are expanding, which will allow establishing global pathogen surveillance and AMR tracking based on genomics. There is however, a need for standardization of pipelines and databases as well as phenotypic predictions based on the data.

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Section: Online Repositories To Host and Link Genome And Antimicrobiamentioning

confidence: 99%

Section: European Nucleotide Archive Repositorymentioning

confidence: 99%

Section: European Nucleotide Archive Repositorymentioning

confidence: 99%

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Using Genomics to Track Global Antimicrobial Resistance

Hendriksen

Bortolaia

Tate

et al. 2019

Front. Public Health

328

261

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“…This work proposes a machine learning approach that can predict the minimum 23 inhibitory concentration (MIC) for a given antibiotic, here ciprofloxacin, on the basis of 24 genome-wide mutation profiles alongside profiles of acquired resistance genes. We 25 analyzed 704 Escherichia coli WGS samples coming from different countries along with 26 their MIC measurements for ciprofloxacin.…”

mentioning

confidence: 99%

“…This is a common problem for many studies aiming to predict resistance 173 from WGS data. Solving this would require continuous updating of databases and an 174 adequate database structure, the latter we have addressed previously(24). Potentially, 175 these efforts could allow machine learning methods to enter routine clinical and 176 epidemiological practices to continuously improve predictions.177 Our approach could work for other antibiotics too if an adequate amount of diverse178 data is collected that includes the full range of susceptibility and resistance values for179 the antibiotic under study.…”

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Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning

Pataki

Matamoros

Putten

et al. 2019

Preprint

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A possible way to slow down the antibiotic resistance crisis is to be 17 more strict when it comes to antibiotics prescriptions. For accurate antibiotic pre-18 scriptions, antibiotic susceptibility data are needed. With the increasing availability 19 of next-generation sequencing (NGS), bacterial whole genome sequencing (WGS) is 20 becoming a feasible alternative to traditional phenotyping for the detection and surveil-21 lance of AMR. 22 Pataki et al. the flexibility to follow potential adjustments in definitions of susceptible and resistant 44 categories (breakpoints). 45 KEYWORDS: AMR, MIC, machine learning, antibiotics, personalized medicine, 46 ciprofloxacin 47 102 RESULTS 103 Data 704 E. coli genomes were analyzed in this study which had MIC measurement 104 for ciprofloxacin (24). Paired-end sequencing was performed on all of them and the 105 results were stored in FASTQ format. The samples originated from Denmark, Italy, USA, 106 UK, and Vietnam. 266 out of the 704 E. coli genomes had no country metadata available

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Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning

Pataki

Matamoros

Putten

et al. 2020

Sci Rep

Self Cite

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It is important that antibiotics prescriptions are based on antimicrobial susceptibility data to ensure effective treatment outcomes. The increasing availability of next-generation sequencing, bacterial whole genome sequencing (WGS) can facilitate a more reliable and faster alternative to traditional phenotyping for the detection and surveillance of AMR. This work proposes a machine learning approach that can predict the minimum inhibitory concentration (MIC) for a given antibiotic, here ciprofloxacin, on the basis of both genome-wide mutation profiles and profiles of acquired antimicrobial resistance genes. We analysed 704 Escherichia coli genomes combined with their respective MIC measurements for ciprofloxacin originating from different countries. The four most important predictors found by the model, mutations in gyrA residues Ser83 and Asp87, a mutation in parC residue Ser80 and presence of the qnrS1 gene, have been experimentally validated before. Using only these four predictors in a linear regression model, 65% and 93% of the test samples’ MIC were correctly predicted within a two- and a four-fold dilution range, respectively. The presented work does not treat machine learning as a black box model concept, but also identifies the genomic features that determine susceptibility. The recent progress in WGS technology in combination with machine learning analysis approaches indicates that in the near future WGS of bacteria might become cheaper and faster than a MIC measurement.

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Accelerating surveillance and research of antimicrobial resistance – an online repository for sharing of antimicrobial susceptibility data associated with whole genome sequences

Cited by 4 publications

References 13 publications

Using Genomics to Track Global Antimicrobial Resistance

Using Genomics to Track Global Antimicrobial Resistance

Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning

Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning

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