web applications for rapid microbial taxonomy identification
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Web applications for rapid microbial taxonomy
identification Ole Lund
Center for Genomic Epidemiology
Whole genome sequence based Diagnostics
Infectious diseases are responsible for >25% of all global deaths
An increasing number of infectious diseases have a global epidemiology (e.g. SARS, avian flu, influenza, Salmonella etc.).
Rapid detection, identification and exchange of comparable information between public health laboratories globally, are crucial to avoid or control global and local spread.
Sample Antibioticresistance
Culturing ID Typing
1-2 days 1-2 days 1-2 days 1 – several weeks
Routine microbial diagnostic
Sample Culturing
IDResistance
Typing+
Muchmore
1-2 days ½-1 day
Whole genome sequence based diagnostic
Bacterial genomics
• Sequencing a bacterial genome cost ~$100 (on a desk top sequencer)
• Equipment will cost less than $100 000
• In Denmark 1 million clinical microbiology isolates are handled each year
– EU/USA ~100 million
– Globally ~ 1 billion (10 billion needed)
• Future limiting factor will not be sequencing but handling the sequences
K-mer based method works well for species identification
Benchmarking of methods for genomic taxonomy. Larsen MV, Cosentino S, Lukjancenko O, Saputra D, Rasmussen S, HasmanH, Sicheritz-Pontén T, Aarestrup FM, Ussery DW, Lund O. J Clin Microbiol. 2014 May;52(5):1529-39.
Multilocus Sequence Typing of Total Genome Sequenced Bacteria. Larsen MV, Cosentino S, Rasmussen S, Friis C, Hasman H, Marvig RL, Jelsbak L, Pontén TS, Ussery DW, Aarestrup FM, Lund O. J Clin Microbiol. 2012 Apr;50(4):1355-61.
MLST typing
Genotyping using whole-genome sequencing is a realistic alternative to surveillance based on phenotypic antimicrobial susceptibility testing. Zankari E, Hasman H, Kaas RS, Seyfarth AM, Agersø Y, Lund O, Larsen MV, Aarestrup FM. J Antimicrob Chemother. 2013 68:771-7. Identification of acquired antimicrobial resistance genes. Zankari E, Hasman H, Cosentino S, Vestergaard M, Rasmussen S, Lund O, Aarestrup FM, Larsen MV. J Antimicrob Chemother. 2012 67:2640-4.
Antimicrobial resistance
Pheno typing by machine learning
• Earlier methods are all based on alignment to a database of genes with known (pheno-) types.
• Andreatta et al. took a radically different approach and sorted genomes of Gamma-Proteobacteria into pathogenic or non-pathogenic, and looked for gene families that were statistically associated with either pathogenic or non-pathogenic bacteria (Andreatta et al. 2010).
• First example of using machine learning techniques to determine the phenotype from WGS.
• Extended to work for all species of bacteria and using raw sequencing data as input (Cosentino et al. 2013).
Phylogeny of the isolates by the NDtree method.
Joensen K G et al. J. Clin. Microbiol. 2014;52:1501-1510
3.0
0508R6762_IonTorrent_2
0508R6707_IonTorrent_1
0508R6707_MiSeq_2
0507R6701_HiSeq
0508R6701_IonTorrent_1
0508R6701_IonTorrent_2
0508R6701_MiSeq_2
0508R6762_MiSeq_1
0508R6762_IonTorrent_1
0508R6762_HiSeq
NCTC_13348_IonTorrent_2
0508R6707_IonTorrent_2
NCTC_13348_IonTorrent_1
0508R6762_MiSeq_2
NCTC_13348_MiSeq_1
NCTC_13348_MiSeq_2
0508R6707_HiSeq
0508R6707_MiSeq_1
0508R6701_MiSeq_1
NCTC_13348_MiSeq_3
0.5
0508R6762_IonTorrent_2
0508R6707_MiSeq_1
0508R6707_IonTorrent_1
NCTC_13348_MiSeq_1
NCTC_13348_MiSeq_3
0508R6701_MiSeq_1
0508R6762_MiSeq_2
0508R6701_IonTorrent_1
0508R6707_HiSeq
0508R6762_HiSeq
NCTC_13348_MiSeq_2
0508R6707_IonTorrent_2
NCTC_13348_IonTorrent_1
0508R6701_IonTorrent_2
0508R6762_MiSeq_1
0508R6762_IonTorrent_1
NCTC_13348_IonTorrent_2
0508R6701_MiSeq_2
0508R6707_MiSeq_2
0507R6701_HiSeq
0.08
0508R6762_IonTorrent_2
0508R6762_HiSeq
0507R6701_HiSeq
0508R6707_IonTorrent_1
0508R6701_MiSeq_1
0508R6707_IonTorrent_2
0508R6707_MiSeq_2
NCTC13348_Miseq1
0508R6707_MiSeq_1
0508R6701_MiSeq_2
0508R6707_HiSeq
NCTC13348_IonTorrent1
NCTC13348_IonTorrent2
0508R6762_MiSeq_2
0508R6762_MiSeq_1
0508R6762_IonTorrent_1
NCTC13348_Miseq3
0508R6701_IonTorrent_1
NCTC13348_Miseq2
0508R6701_IonTorrent_2
SNPtree CSIPhylogeny NDtree
Closereference
Remotereference
Salmonella TyphimuriumDT104
PLoS One. 2014 Aug 11;9(8):e104984.
Outbreak analysis of billions of strains: Real-time tracking of all microbial genomes
• OX values
• O10
– Number of earlier isolates (from within the last year) with less than 10 SNP differences to the current isolate
• Do not need to be updated
• Mapped genomes may be stores as binary files
• Search can/should be restricted to those that cluster to the same template
Evergreen Trees
• User submitted samples compared against all close-matching sample clusters
• Ever growing trees from the clusters
• Users can see all previous samples their sample is closely related to
Global Data Exchange
Global repositories*
* Providers you will bet your life on will provided High bandwidth programic access to deposition/retrieval forever: SRA/ENA/??
Hospital
Food safety agency
National CDC
Analysis www servers
Sequence + Meta data
Animal health
Sample
IDResistance
Type+
Everything
minutes
Metagenomic based diagnostic with non batch mode sequencing (nanopore technologies)
Rapid whole genome sequencing for the detection and characterization of microorganisms directly from clinical samples. Hasman H, Saputra D, Sicheritz-Ponten T, Lund O, Svendsen CA, Frimodt-Møller N, AarestrupFM. J Clin Microbiol. 2014 Jan;52(1):139-46
It is not important to know where you are but where you are not
• Analysis of absence/presence of specific strains/species may be more important for diagnosis of infectious diseases than the general composition of the microbiomenormally associated with metagenomics
Whole genome sequencing
• Is it a game changer in the combat against infectious diseases
• Game changer? - what is new with WGS?– Typing with ultimate resolution (bar epigenetics?)
• Resolution = 1/mutation rate = 1 year
– Can (soon) be done in a day– Instant deep pheno-typing (e. g. resistance/virulence
genes)– With falling prices surveillance may be ubiquitous
• Everything is under constant surveillance– People, animals, planes, places, doorhandles …
– Information can be shared instantly around the globe
Transmission do not have to be zero
• But R0:
– The number of secondary infections that a case on the average give rise to
• Have to be below 1
Game changer?
• Can WGS + IT be used to set R0 to less than 1 for some pathogens in some areas?
• Which are the best cases?
ThanksDTU Systems Biology/CBS/Lund group
Mette Voldby Larsen
Martin Thomsen
Johanne Ahrenfeldt
Vanessa Jurtz
Jose L. Bellod Cisneros
Johanne Ahrenfeldt
Anna Maria Malberg Tetzschner
Ex members
Salvatore Consentiono
Student helpers
Jamie Neubert Pedersen
Valentin Ibanez
Rosa Allesøe
Camilla Lemvigh
DTU Systems Biology/CBS
Dave Ussery
Thomas Ponten
Dhany Saputra
Simon Rasmussen
Thomas Nordahl Petersen
DTU DMAC
Laurent Gautier
Marlene Dalgaard
DTU Food
Frank Aarestrup
Henrik Hasman
Rene S. Hendriksen
Shinny Leekitcharoenphon
Rolf Sommer Kaas
Marlene Hansen
Katrine Grimstrup Joensen
Oksana Lukjancenko
Copenhagen University/CMP
Thor Theander
Michael Alifrangis
Sidsel Nag
KCMC Moshi, Tanzania
Gibson Kibiki
Happiness Kumburu
Tolbert Sonda
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