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dc.contributor.authorMobegi, Fredrick
dc.contributor.authorCremers, A.J.H.
dc.contributor.authorDe Jonge, M.I.
dc.contributor.authorBentley, S.D.
dc.contributor.authorVan Hijum, S.A.F.T.
dc.contributor.authorZomer, A.
dc.date.accessioned2020-08-24T06:54:05Z
dc.date.available2020-08-24T06:54:05Z
dc.date.issued2017
dc.identifier.citationMobegi, F.M. and Cremers, A.J.H. and De Jonge, M.I. and Bentley, S.D. and Van Hijum, S.A.F.T. and Zomer, A. 2017. Deciphering the distance to antibiotic resistance for the pneumococcus using genome sequencing data. Scientific Reports. 7: Article No. 42808.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/80735
dc.identifier.doi10.1038/srep42808
dc.description.abstract

Advances in genome sequencing technologies and genome-wide association studies (GWAS) have provided unprecedented insights into the molecular basis of microbial phenotypes and enabled the identification of the underlying genetic variants in real populations. However, utilization of genome sequencing in clinical phenotyping of bacteria is challenging due to the lack of reliable and accurate approaches. Here, we report a method for predicting microbial resistance patterns using genome sequencing data. We analyzed whole genome sequences of 1,680 Streptococcus pneumoniae isolates from four independent populations using GWAS and identified probable hotspots of genetic variation which correlate with phenotypes of resistance to essential classes of antibiotics. With the premise that accumulation of putative resistance-conferring SNPs, potentially in combination with specific resistance genes, precedes full resistance, we retrogressively surveyed the hotspot loci and quantified the number of SNPs and/or genes, which if accumulated would confer full resistance to an otherwise susceptible strain. We name this approach the € distance to resistance'. It can be used to identify the creep towards complete antibiotics resistance in bacteria using genome sequencing. This approach serves as a basis for the development of future sequencing-based methods for predicting resistance profiles of bacterial strains in hospital microbiology and public health settings.

dc.languageEnglish
dc.publisherNATURE PUBLISHING GROUP
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectScience & Technology
dc.subjectMultidisciplinary Sciences
dc.subjectScience & Technology - Other Topics
dc.subjectPENICILLIN-BINDING PROTEINS
dc.subjectINVASIVE STREPTOCOCCUS-PNEUMONIAE
dc.subjectBETA-LACTAM ANTIBIOTICS
dc.subjectSICKLE-CELL-DISEASE
dc.subjectFLUOROQUINOLONE RESISTANCE
dc.subjectUNITED-STATES
dc.subjectMOLECULAR EPIDEMIOLOGY
dc.subjectPRESCRIPTION RATES
dc.subjectIN-VITRO
dc.subjectMUTATIONS
dc.titleDeciphering the distance to antibiotic resistance for the pneumococcus using genome sequencing data
dc.typeJournal Article
dcterms.source.volume7
dcterms.source.issn2045-2322
dcterms.source.titleScientific Reports
dc.date.updated2020-08-24T06:54:01Z
curtin.note

© 2017 The Authors. Published in Scientific Reports.

curtin.departmentSchool of Molecular and Life Sciences (MLS)
curtin.accessStatusOpen access
curtin.facultyFaculty of Science and Engineering
curtin.contributor.orcidMobegi, Fredrick [0000-0003-0554-9919]
curtin.contributor.researcheridMobegi, Fredrick [D-1058-2015]
curtin.identifier.article-numberARTN 42808
dcterms.source.eissn2045-2322
curtin.contributor.scopusauthoridMobegi, Fredrick [56479121000]


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