Early warning systems augmented by bacterial genomics
Vitali Sintchenko A B C D E F and Nadine Holmes A D E G
+ Author Affiliations
- Author Affiliations
A Centre for Infectious Diseases and Microbiology – Public Health
B Institute of Clinical Pathology and Medical Research – Pathology West
C NSW Health Pathology and Westmead Hospital
D Marie Bashir Institute for Infectious Diseases and Biosecurity
E Sydney Medical School – Westmead, The University of Sydney
Tel: +61 2 9845 6255
Fax: + 61 2 9893 8659
F Email: vitali.sintchenko@sydney.edu.au
G Email: nadine.holmes@sydney.edu.au
Microbiology Australia 35(1) 44-48 https://doi.org/10.1071/MA14012
Published: 17 February 2014
Abstract
The number of microbial threats – in the form of newly identified pathogens, infections crossing the species barrier to people, diseases adapting to new environments, transmissible drug-resistance genes and microbial agents appearing in more virulent forms – has multiplied to an unprecedented degree. The epidemiology of well-known infectious diseases has also been changing due to the globalisation of trade and in response to immunisation campaigns. This evolving epidemiology presents new challenges to countries' healthcare systems, in terms of both understanding and monitoring of determinants of infections, as well as in terms of service provision and the implementation of appropriate prevention measures. In this article we discuss the concepts of early warning systems and genome sequencing for public health laboratory surveillance and outbreak detection and response. The added value of these new means of surveillance can be seen when clinical and public health laboratory data is harmonised, aggregated and shared.
References
[1] Dato, V. et al. (2004) How outbreaks of infectious disease are detected: a review of surveillance systems and outbreaks. Public Health Rep. 119, 464–471.| How outbreaks of infectious disease are detected: a review of surveillance systems and outbreaks.Crossref | GoogleScholarGoogle Scholar | 15313109PubMed |
[2] Wallinga, J. and Teunis, P. (2004) Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. Am. J. Epidemiol. 160, 509–516.
| Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures.Crossref | GoogleScholarGoogle Scholar | 15353409PubMed |
[3] Svoboda, T. et al. (2004) Public health measures to control the spread of the severe acute respiratory syndrome during the outbreak in Toronto. N. Engl. J. Med. 350, 2352–2361.
| Public health measures to control the spread of the severe acute respiratory syndrome during the outbreak in Toronto.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2cXks1Gjt7w%3D&md5=8a25303e6b8e3c52e6d4025af8a9c18aCAS | 15175437PubMed |
[4] Buckeridge, D.L. (2007) Outbreak detection through automated surveillance: a review of the determinants of detection. J. Biomed. Inform. 40, 370–379.
| Outbreak detection through automated surveillance: a review of the determinants of detection.Crossref | GoogleScholarGoogle Scholar | 17095301PubMed |
[5] Fournier, P.-E. et al. (2007) Bacterial genome sequencing and its use in infectious diseases. Lancet Infect. Dis. 7, 711–723.
| Bacterial genome sequencing and its use in infectious diseases.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXhtlKhsbfN&md5=4979e5a7e90d304c58d0e36754c12144CAS | 17961857PubMed |
[6] Mellmann, A. et al. (2006) Automated DNA sequence-based early warning system for the detection of methicillin-resistant Staphylococcus aureus outbreaks. PLoS Med. 3, e33.
| Automated DNA sequence-based early warning system for the detection of methicillin-resistant Staphylococcus aureus outbreaks.Crossref | GoogleScholarGoogle Scholar | 16396609PubMed |
[7] Sintchenko, V. et al. (2012) Improving resolution of public health surveillance for human Salmonella enterica serovar Typhimurium infection: 3 years of prospective multiple-locus variable-number tandem-repeat analysis (MLVA). BMC Infect. Dis. 12, 78.
| Improving resolution of public health surveillance for human Salmonella enterica serovar Typhimurium infection: 3 years of prospective multiple-locus variable-number tandem-repeat analysis (MLVA).Crossref | GoogleScholarGoogle Scholar | 22462487PubMed |
[8] Swaminathan, B. et al. (2006) Building PulseNet International: an interconnected system of laboratory networks to facilitate timely public health recognition and response to foodborne disease outbreaks and emerging foodborne diseases. Foodborne Pathog. Dis. 3, 36–50.
| Building PulseNet International: an interconnected system of laboratory networks to facilitate timely public health recognition and response to foodborne disease outbreaks and emerging foodborne diseases.Crossref | GoogleScholarGoogle Scholar | 16602978PubMed |
[9] Kirk, M.D. et al. (2010) Surveillance for outbreaks of gastroenteritis in long-term care facilities, Australia, 2002-2008. Clin. Infect. Dis. 51, 907–914.
| Surveillance for outbreaks of gastroenteritis in long-term care facilities, Australia, 2002-2008.Crossref | GoogleScholarGoogle Scholar | 20825308PubMed |
[10] Niesters, H.G. et al. (2013) Laboratory-based surveillance in the molecular era: the TYPENED model, a joint data-sharing platform for clinical and public health laboratories. Eurosurveillance 18, 20 387.
| 1:STN:280:DC%2BC3szktlKisQ%3D%3D&md5=799d444aeadb1c1e2f431c41c607a856CAS |
[11] Didelot, X. et al. (2012) Transforming clinical microbiology with bacterial genome sequencing. Nat. Rev. Genet. 13, 601–612.
| Transforming clinical microbiology with bacterial genome sequencing.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XhtFCrt7jE&md5=b3706a80c91e61d8387c2668d4497745CAS | 22868263PubMed |
[12] Ford, C.B. et al. (2011) Use of whole genome sequencing to estimate the mutation rate of Mycobacterium tuberculosis during latent infection. Nat. Genet. 43, 482–486.
| Use of whole genome sequencing to estimate the mutation rate of Mycobacterium tuberculosis during latent infection.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXltFyqur0%3D&md5=c68e8cd0d2c19bf917ba1578dc2ae12dCAS | 21516081PubMed |
[13] Okoro, C.K. et al. (2012) High-resolution single nucleotide polymorphism analysis distinguishes recrudescence and reinfection in recurrent invasive nontyphoidal Salmonella typhimurium disease. Clin. Infect. Dis. 54, 955–963.
| High-resolution single nucleotide polymorphism analysis distinguishes recrudescence and reinfection in recurrent invasive nontyphoidal Salmonella typhimurium disease.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XktVGmtrs%3D&md5=8cfb49c0b37a485ced559fa46f6d636fCAS | 22318974PubMed |
[14] Snitkin, E.S. et al. (2012) Tracking a hospital outbreak of carbapenem-resistant Klebsiella pneumonia with whole-genome sequencing. Science Transl. Med. 4, 148ra116.
| Tracking a hospital outbreak of carbapenem-resistant Klebsiella pneumonia with whole-genome sequencing.Crossref | GoogleScholarGoogle Scholar |
[15] Jolley, K.A. et al. (2012) Resolution of a meningococcal disease outbreak from whole-genome sequence data with rapid web-based analysis methods. J. Clin. Microbiol. 50, 3046–3053.
| Resolution of a meningococcal disease outbreak from whole-genome sequence data with rapid web-based analysis methods.Crossref | GoogleScholarGoogle Scholar | 22785191PubMed |
[16] Walker, T.M. et al. (2013) Contact investigations for outbreaks of Mycobacterium tuberculosis: advances through whole genome sequencing. Clin. Microbiol. Infect. 19, 796–802.
| Contact investigations for outbreaks of Mycobacterium tuberculosis: advances through whole genome sequencing.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXhtlWlurbI&md5=45a6e9278aba684881b51bcab00d016cCAS | 23432709PubMed |
[17] Schűrch, A.C. et al. (2010) High-resolution typing by integration of genome sequencing data in a large tuberculosis cluster. J. Clin. Microbiol. 48, 3403–3406.
| High-resolution typing by integration of genome sequencing data in a large tuberculosis cluster.Crossref | GoogleScholarGoogle Scholar |
[18] Gardy, J.L. et al. (2011) Whole-genome sequencing and social-network analysis of a tuberculosis outbreak. N. Engl. J. Med. 364, 730–739.
| Whole-genome sequencing and social-network analysis of a tuberculosis outbreak.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXis1ymsb0%3D&md5=17f8737ecb384cf889db5a4e31e6e36fCAS | 21345102PubMed |
[19] Roetzer, A. et al. (2013) Whole genome sequencing versus traditional genotyping for investigation of a Mycobacterium tuberculosis outbreak: a longitudinal molecular epidemiology study. PLoS Med. 10, e1001387.
| Whole genome sequencing versus traditional genotyping for investigation of a Mycobacterium tuberculosis outbreak: a longitudinal molecular epidemiology study.Crossref | GoogleScholarGoogle Scholar | 23424287PubMed |
[20] Mellmann, A. et al. (2011) Prospective genomic characterization of the German enterohemorrhagic Escherichia coli O124:H4 outbreak by rapid next generation sequencing technology. PLoS ONE 6, e22751.
| Prospective genomic characterization of the German enterohemorrhagic Escherichia coli O124:H4 outbreak by rapid next generation sequencing technology.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXhtVCnsr%2FP&md5=e629ed6bd6779f3f40ae78d5a51e49bfCAS | 21799941PubMed |
[21] Sherry, N.L. et al. (2013) Outbreak investigation using high-throughput genome sequencing within a diagnostic microbiology laboratory. J. Clin. Microbiol. 51, 1396–1401.
| Outbreak investigation using high-throughput genome sequencing within a diagnostic microbiology laboratory.Crossref | GoogleScholarGoogle Scholar | 23408689PubMed |
[22] Eyre, D.W. et al. (2012) A pilot study of rapid benchtop sequencing of Staphylococcus aureus and Clostridium difficile for outbreak detection and surveillance. BMJ Open 2, e001124.
| A pilot study of rapid benchtop sequencing of Staphylococcus aureus and Clostridium difficile for outbreak detection and surveillance.Crossref | GoogleScholarGoogle Scholar | 22674929PubMed |
[23] Gilmour, M.W. et al. (2013) Public health genomics and the new molecular epidemiology of bacterial pathogens. Public Health Genomics 16, 25–30.
| Public health genomics and the new molecular epidemiology of bacterial pathogens.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC3srhs1ynuw%3D%3D&md5=42fc1d80a4b9a6d67366ce60cca3154cCAS | 23548714PubMed |
[24] Köser, C.U. et al. (2012) Rapid whole-genome sequencing for investigation of a neonatal MRSA outbreak. N. Engl. J. Med. 366, 2267–2275.
| Rapid whole-genome sequencing for investigation of a neonatal MRSA outbreak.Crossref | GoogleScholarGoogle Scholar | 22693998PubMed |
[25] Underwood, A.P. et al. (2013) Public health value of next-generation DNA sequencing of enterohaemorrhagic Escherichia coli from an outbreak. J. Clin. Microbiol. 51, 232–237.
| Public health value of next-generation DNA sequencing of enterohaemorrhagic Escherichia coli from an outbreak.Crossref | GoogleScholarGoogle Scholar | 23135946PubMed |
[26] Sintchenko, V. and Coiera, E. (2011) Translational web robots for pathogen genome analysis. Microbial Informatics and Experimentation 1, 10.
| Translational web robots for pathogen genome analysis.Crossref | GoogleScholarGoogle Scholar | 22587672PubMed |
[27] Okeke, I.N. and Wain, J. (2008) Postgenomic challenges for collaborative research in infectious diseases. Nat. Rev. Microbiol. 6, 858–864.
| Postgenomic challenges for collaborative research in infectious diseases.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXht1CmtrrI&md5=7def4b661e75e514776de9cb3c855378CAS | 18711428PubMed |
[28] Sintchenko, V. et al. (2009) Towards bioinformatics assisted infectious disease control. BMC Bioinform. 10, S10.
| Towards bioinformatics assisted infectious disease control.Crossref | GoogleScholarGoogle Scholar |
[29] Lipkin, W.I. (2013) The changing face of pathogen discovery and surveillance. Nat. Rev. Microbiol. 11, 133–141.
| The changing face of pathogen discovery and surveillance.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XhvVymurvE&md5=f7a62e148983cd3adb210809f600caf8CAS | 23268232PubMed |
[30] Aarestrup, F.M. et al. (2012) Integrating genome-based informatics to modernize global disease monitoring, information sharing, and response. Emerg. Infect. Dis. 18, e1.
| Integrating genome-based informatics to modernize global disease monitoring, information sharing, and response.Crossref | GoogleScholarGoogle Scholar | 23092707PubMed |
[31] Maiden, M.C.J. et al. (2013) MLST revisited: the gene-by-gene approach to bacterial genomics. Nat. Rev. Microbiol. 11, 728–736.
| MLST revisited: the gene-by-gene approach to bacterial genomics.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXhtlalu7nJ&md5=03bf50cdbfb5d694a97d049e9112b88aCAS |
