publication . Article . Other literature type . Conference object . 2015

Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis.

Phelim Bradley; Paolo Piazza; Thomas Kohl; Tim E. A. Peto; Tim E. A. Peto; Tanya Golubchik; Stefan Niemann; Luke W. Anson; Simon Heys; Antonina A. Votintseva; ...
Open Access
  • Published: 21 Dec 2015 Journal: Nature Communications, volume 6 (eissn: 2041-1723, Copyright policy)
  • Publisher: Springer Science and Business Media LLC
  • Country: United Kingdom
Abstract
The rise of antibiotic-resistant bacteria has led to an urgent need for rapid detection of drug resistance in clinical samples, and improvements in global surveillance. Here we show how de Bruijn graph representation of bacterial diversity can be used to identify species and resistance profiles of clinical isolates. We implement this method for Staphylococcus aureus and Mycobacterium tuberculosis in a software package (‘Mykrobe predictor') that takes raw sequence data as input, and generates a clinician-friendly report within 3 minutes on a laptop. For S. aureus, the error rates of our method are comparable to gold-standard phenotypic methods, with sensitivity/s...
Subjects
free text keywords: General Biochemistry, Genetics and Molecular Biology, General Physics and Astronomy, General Chemistry, Article, Drug resistance, Microbiology, Biology, Staphylococcus aureus, medicine.disease_cause, medicine, Nanopore sequencing, Antibiotic resistance, Tuberculosis, medicine.disease, Staphylococcal infections, Computational biology, Mycobacterium tuberculosis, biology.organism_classification, Whole genome sequencing
Funded by
EC| PATHONGEN-TRACE
Project
PATHONGEN-TRACE
Next Generation Genome Based High Resolution Tracing of Pathogens
  • Funder: European Commission (EC)
  • Project Code: 278864
  • Funding stream: FP7 | SP1 | HEALTH
,
RCUK| MODERNISING MEDICAL MICROBIOLOGY: ESTABLISHING HOW NEW TECHNOLOGIES CAN BE OPTIMALLY INTEGRATED INTO MICROBIOLOGY
Project
  • Funder: Research Council UK (RCUK)
  • Project Code: G0800778
  • Funding stream: MRC
,
WT| The Genetic Analysis of Populations.
Project
  • Funder: Wellcome Trust (WT)
  • Project Code: 100956
  • Funding stream: Genetics, Genomics and Population Research
,
WT| Modernising Medical Microbiology: Establishing how new technologies can be optimally integrated into microbiology.
Project
  • Funder: Wellcome Trust (WT)
  • Project Code: 087646
  • Funding stream: Immune System in Health and Disease
,
WT| Understanding the genetic basis of common human diseases: core funding for the Wellcome Trust Centre for Human Genetics.
Project
  • Funder: Wellcome Trust (WT)
  • Project Code: 090532
  • Funding stream: Cellular and Molecular Neuroscience
64 references, page 1 of 5

1. Nathan, C. & Cars, O. Antibiotic resistance-problems, progress, and prospects. N. Engl. J. Med. 371, 1761-1763 (2014). [OpenAIRE]

2. Didelot, X., Bowden, R., Wilson, D. J., Peto, T. E. & Crook, D. W. Transforming clinical microbiology with bacterial genome sequencing. Nat. Rev. Genet. 13, 601-612 (2012).

3. Gordon, N. et al. Prediction of Staphylococcus aureus Antimicrobial Resistance by Whole-Genome Sequencing. J. Clin. Microbiol. 52, 1182-1191 (2014). [OpenAIRE]

4. Segata, N. et al. Metagenomic microbial community profiling using unique clade-specific marker genes. Nat. Methods 9, 811-814 (2012).

5. Huson, D. H., Auch, A. F., Qi, J. & Schuster, S. C. MEGAN analysis of metagenomic data. Genome Res. 17, 377-386 (2007).

6. Leopold, S. R., Goering, R. V., Witten, A., Harmsen, D. & Mellmann, A. Bacterial whole genome sequencing revisited: portable, scalable and standardized analysis for typing and detection of virulence and antibiotic resistance genes. J. Clin. Microbiol. 52, 2365-2370 (2014). [OpenAIRE]

7. Kohl, T. A. et al. Whole-genome-based Mycobacterium tuberculosis surveillance: a standardized, portable, and expandable approach. J. Clin. Microbiol. 52, 2479-2486 (2014).

8. Koser, C. U. et al. Whole-genome sequencing for rapid susceptibility testing of M. tuberculosis. N. Engl. J. Med. 369, 290-292 (2013).

9. Pop, M. Genome assembly reborn: recent computational challenges. Brief. Bioinform. 10, 354-366 (2009).

10. Bertels, F., Silander, O. K., Pachkov, M., Rainey, P. B. & van Nimwegen, E. Automated reconstruction of whole-genome phylogenies from short-sequence reads. Mol. Biol. Evol. 31, 1077-1088 (2014). [OpenAIRE]

11. Steiner, A., Stucki, D., Coscolla, M., Borrell, S. & Gagneux, S. KvarQ: targeted and direct variant calling from fastq reads of bacterial genomes. BMC Genomics 15, 881 (2014). [OpenAIRE]

12. Inouye, M. et al. SRST2: rapid genomic surveillance for public health and hospital microbiology labs. Genome Med. 6, 90 (2014).

13. Iqbal, Z., Caccamo, M., Turner, I., Flicek, P. & McVean, G. De novo assembly and genotyping of variants using colored de Bruijn graphs. Nat. Genet. 44, 226-232 (2012). [OpenAIRE]

14. Everitt, R. G. et al. Mobile elements drive recombination hotspots in the core genome of Staphylococcus aureus. Nat. Commun. 5, 3956 (2014).

15. Howe, R. A., Andrews, J. M. & Testing, B. W. P. O. S. BSAC standardized disc susceptibility testing method (version 11). J. Antimicrob. Chemother. 67, 2783-2784 (2012).

64 references, page 1 of 5
Abstract
The rise of antibiotic-resistant bacteria has led to an urgent need for rapid detection of drug resistance in clinical samples, and improvements in global surveillance. Here we show how de Bruijn graph representation of bacterial diversity can be used to identify species and resistance profiles of clinical isolates. We implement this method for Staphylococcus aureus and Mycobacterium tuberculosis in a software package (‘Mykrobe predictor') that takes raw sequence data as input, and generates a clinician-friendly report within 3 minutes on a laptop. For S. aureus, the error rates of our method are comparable to gold-standard phenotypic methods, with sensitivity/s...
Subjects
free text keywords: General Biochemistry, Genetics and Molecular Biology, General Physics and Astronomy, General Chemistry, Article, Drug resistance, Microbiology, Biology, Staphylococcus aureus, medicine.disease_cause, medicine, Nanopore sequencing, Antibiotic resistance, Tuberculosis, medicine.disease, Staphylococcal infections, Computational biology, Mycobacterium tuberculosis, biology.organism_classification, Whole genome sequencing
Funded by
EC| PATHONGEN-TRACE
Project
PATHONGEN-TRACE
Next Generation Genome Based High Resolution Tracing of Pathogens
  • Funder: European Commission (EC)
  • Project Code: 278864
  • Funding stream: FP7 | SP1 | HEALTH
,
RCUK| MODERNISING MEDICAL MICROBIOLOGY: ESTABLISHING HOW NEW TECHNOLOGIES CAN BE OPTIMALLY INTEGRATED INTO MICROBIOLOGY
Project
  • Funder: Research Council UK (RCUK)
  • Project Code: G0800778
  • Funding stream: MRC
,
WT| The Genetic Analysis of Populations.
Project
  • Funder: Wellcome Trust (WT)
  • Project Code: 100956
  • Funding stream: Genetics, Genomics and Population Research
,
WT| Modernising Medical Microbiology: Establishing how new technologies can be optimally integrated into microbiology.
Project
  • Funder: Wellcome Trust (WT)
  • Project Code: 087646
  • Funding stream: Immune System in Health and Disease
,
WT| Understanding the genetic basis of common human diseases: core funding for the Wellcome Trust Centre for Human Genetics.
Project
  • Funder: Wellcome Trust (WT)
  • Project Code: 090532
  • Funding stream: Cellular and Molecular Neuroscience
64 references, page 1 of 5

1. Nathan, C. & Cars, O. Antibiotic resistance-problems, progress, and prospects. N. Engl. J. Med. 371, 1761-1763 (2014). [OpenAIRE]

2. Didelot, X., Bowden, R., Wilson, D. J., Peto, T. E. & Crook, D. W. Transforming clinical microbiology with bacterial genome sequencing. Nat. Rev. Genet. 13, 601-612 (2012).

3. Gordon, N. et al. Prediction of Staphylococcus aureus Antimicrobial Resistance by Whole-Genome Sequencing. J. Clin. Microbiol. 52, 1182-1191 (2014). [OpenAIRE]

4. Segata, N. et al. Metagenomic microbial community profiling using unique clade-specific marker genes. Nat. Methods 9, 811-814 (2012).

5. Huson, D. H., Auch, A. F., Qi, J. & Schuster, S. C. MEGAN analysis of metagenomic data. Genome Res. 17, 377-386 (2007).

6. Leopold, S. R., Goering, R. V., Witten, A., Harmsen, D. & Mellmann, A. Bacterial whole genome sequencing revisited: portable, scalable and standardized analysis for typing and detection of virulence and antibiotic resistance genes. J. Clin. Microbiol. 52, 2365-2370 (2014). [OpenAIRE]

7. Kohl, T. A. et al. Whole-genome-based Mycobacterium tuberculosis surveillance: a standardized, portable, and expandable approach. J. Clin. Microbiol. 52, 2479-2486 (2014).

8. Koser, C. U. et al. Whole-genome sequencing for rapid susceptibility testing of M. tuberculosis. N. Engl. J. Med. 369, 290-292 (2013).

9. Pop, M. Genome assembly reborn: recent computational challenges. Brief. Bioinform. 10, 354-366 (2009).

10. Bertels, F., Silander, O. K., Pachkov, M., Rainey, P. B. & van Nimwegen, E. Automated reconstruction of whole-genome phylogenies from short-sequence reads. Mol. Biol. Evol. 31, 1077-1088 (2014). [OpenAIRE]

11. Steiner, A., Stucki, D., Coscolla, M., Borrell, S. & Gagneux, S. KvarQ: targeted and direct variant calling from fastq reads of bacterial genomes. BMC Genomics 15, 881 (2014). [OpenAIRE]

12. Inouye, M. et al. SRST2: rapid genomic surveillance for public health and hospital microbiology labs. Genome Med. 6, 90 (2014).

13. Iqbal, Z., Caccamo, M., Turner, I., Flicek, P. & McVean, G. De novo assembly and genotyping of variants using colored de Bruijn graphs. Nat. Genet. 44, 226-232 (2012). [OpenAIRE]

14. Everitt, R. G. et al. Mobile elements drive recombination hotspots in the core genome of Staphylococcus aureus. Nat. Commun. 5, 3956 (2014).

15. Howe, R. A., Andrews, J. M. & Testing, B. W. P. O. S. BSAC standardized disc susceptibility testing method (version 11). J. Antimicrob. Chemother. 67, 2783-2784 (2012).

64 references, page 1 of 5
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