Update on new methods for identifying infectious diseases€¦ · genomics & syndromic arrays...
Transcript of Update on new methods for identifying infectious diseases€¦ · genomics & syndromic arrays...
"Update on new methods for
identifying infectious diseases"
Jane Greatorex
• Overview of developments in infectious disease testing through time
• Personal perspective – rapid diagnostics, automation,genomics & syndromic arrays
• Future directions & challenges
Development of microbial diagnosticsSome milestones
• The growth of bacteria on solid media
Development of microbial diagnosticsSome milestones
Development of microbial diagnosticsSome milestones
• Selective media
• 1905 Maconkey – bile salts
• 1921 Muller –iodine & sodium thiosulphate
• 1960s Thayer Martin - antibiotics
Development of microbial diagnosticsSome milestones
• Diagnostic media
• The growth of viruses in tissue culture: CPE
Virology
• Serology & EM
Virology
• PCR
• RT-PCR
• Array technology
Molecular biology
• Viral sequencing
• Direct from samples
• Real time
Genomics
Potential improvements to service
• Less hands on time for our staff
• Simplification of data handling
• Detection of minority variants
•Capacity for automation and batch testing
• Potentially better quality data
Bringing new technology to a diagnostic microbiology laboratory
• MUST fit into or improve the work flow
• Ideally does not involve purchase of capitol equipment
• Ideally does not involve major retraining of staff
• Should bring benefit to both patients and clinical service
• HIV resistance testing using viral sequence
HIV timelines
1981 – AIDS first described – 121 cases reported
1984 – HIV implicated as the causative agent
1985 – FDA approves ELISA test for diagnosing AIDS –First main use for screening blood products
1986 – > one million Americans now infected
1987 – Release of AZT for treatment
2000s – Genotypic testing for resistance
1990s – Other drugs and subsequent resistance
2000s – Triple therapy
Extract nucleic acid & ascertain viral load
Reverse transcribe + amplify genes (nested PCR)
Capillary sequence genes using set of overlapping primers
Generate contig and use database to determine presence & significance of resistance mutations
Next Gen or ultra deep sequencing
HIV resistance testing and next gen sequencing
Clinical rationale:Studies indicate that low level (> 1% - < 25%) variants in newly infected individuals may have clinical consequences
Low level resistance mutations in individuals on failing treatment may be indicative of resistance buildup
Implementation of NGS at PHE Cambridge
• Keep the system amplicon based
• Allows for low viral loads
• Maintains the status quo as far as laboratory staff concerned
• Use system which is relatively low cost, produceshigh quality data & is amenable to automation
Raw fastq files, already QC’ed by the laboratory
Alignment to reference (K03455) using SMALT
Fixing INDEL alignments (bespoke program)
Intermediate BAM file
Consensus sequence (bespoke script) Codon population analysis (bespoke script)
HIV informatics analysis pipeline
QC of runs and samples (bespoke script)
Extract nucleic acid & ascertain viral load
Reverse transcribe + amplify genes (nested PCR)
Subsequent procedures at Addenbrookes Hospital
Clean amplicon and deep sequence using MiSeq
Pipeline automatically generates resistance and quality control reports
Economies of scale
* Piggy backing onto existing MiSeq runs
* Having a large enough demand
Impact on laboratory workflow - data handling and processing:
• Previously data arrived as ABI files and had to be processed via software to edit the sequences and assemble contig for submission to database.
• Now, .fasta files received, uploaded directly to Stanford, enter sample number, date and press “Analyze”. Results entered directly into LIMS.
• Data quality greatly improved
23Ultra deep sequencing - a practical approachPresentation title
- edit in Header and Footer23 Genomic Medicine: Pharmacogenomics III 2017
Pragmatic rationale for replacing capillary sequencing with NGS
• Nothing to suggest that the NGS does not perform at least as well as capillary sequencing at the 20% level
• In general data quality high and performed well on DNA which gave poor capillary sequencing results
• No retraining required for staff and immediate time savingsto be made
MiSeq data can be used to produce high quality data to replace our consensus
sequencing
Can NGS improve patient care?
• At baseline?
• At point of virological failure?
• At other time points?
26 Genomic Medicine: Pharmacogenomics III 2017
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All PI NRTI NNRTI
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Changes in susceptibility reports usingUDS > 2%
in naive patients
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Changes in susceptibility reports using UDSin antiretroviral-experienced patients
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•UDS adds significant numbers of resistance mutations to both treatment failure and baseline reports
•These mutations may alter the report but the significance of this is yet to be elucidated
• Low level resistance mutations are seen in patients on certain regimens
• Reporting at the > 2% cutoff – the repercussions need discussion
Challenges:
• Bioinformatics
•Quality control and assurance
• Sustainability - dealing with innovation
• Reporting, utility & communication
Additional information that can be gathered from the UDS data:
• Mother and child admitted together, child presenting with AIDS defining illness.
• On sequencing:
• Mother = subtype CRF02_AG, only resistance mutation = L10I (by consensus & “all variants”)
• Child = subtype CRF02_AG, L10I by consensus BUT by when looking at all minority variants they had E138G (3.1%) & Y181C (10%) mutations. These made the virus resistant to all of the NNRTIs. Hospital informed.
30 Genomic Medicine: Pharmacogenomics III 2017
The need for a HIV-1 WGS assay• HIV resistance testing must take place within 2-3 weeksof diagnosis
• Performed by ~17 labs nationally, not all of whomroutinely perform integrase or tropism testing
• New BHIVA guidelines autumn 2015:
• All HIV + infected individuals should be treated
• Change in recommended frontline drugs to include integrase inhibitors
• All drug target resistance polymorphisms in a single assay
* Potential novel mutations revealed
* Opportunity to identify minority variants
• Opportunity to automate and streamline existing workflows in line with Pathology modernisation
Assay development
> 1000 primers originally identified and designed
Bioinformatics used to select “best primer”
> 1000 possible combinations
Further refinement
Amplicon production & assessment by sequencing
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What does the future hold?
• Faster single molecule sequencing
• Instant phylogenetics
• Real time transmission and epidemiology
• Sequencing in situ from DBS?
• Real time viral evolution
•WGS!
Syndromic approaches to infectious disease diagnosis
Adeno #1Adeno 40/41Astro#1Sapo#1Sapo#2Enterovirus#1ParaechovirusHEV#1IC PDV controlRotavirus camNoro gpIINoro gpINoro gp 1 mod33C difficle GDHE coli vtx 1BT control manE coli vtx 2Campy spp NEWSalmonella ttrCryptosporidium spp CP2GFP controlAdeno#2Adeno 40/41Astro Liu #2Giardia lambia #1Rotavirus Liu #2Enterovirus #2 BrHepatitis A #1HEV #2MS2 controlRotarix NSP2 BrisCMV BrRnase P controlSalmonella hil AShigella ipa HEnteroagg E coliCampy jejuniCampy coliYersinnia enterocoliticaBacterial 16SAeromonas hydrophilaVibrio choleraDientamoeba fragilisEntamoeba histolyticaCryptosporidium spp #2Giardia lamlia #2 rev
Advantages of the syndromic array approach
• In a single assay determine the causative organism OR rule out infectious agent
• Rapid response in critical cases
• Rapid response in outbreak settings
• Flexibility - -assays added in or out, seasonality
Full lab automation
Lessons learnt
Lessons learnt
• The best new technologies are the simplest ones
• That new technologies are accepted quickest when there are clear clinical advantages to their implementation
• Be sure the future is sustainable & affordable
• Be brave
Cambridge PHE laboratory:Jane GreatorexNick GleadallNatasha Moore (student)East Anglian Genetics Service:Howard MartinKim BruggerSarah Dowson (student)
Birmingham PHE laboratory:Erasmus SmitLi XuSteve WilsonSusan Jackson
Colindale PHE laboratory:Tamyo MbisaDavid BibbyColindale Bioinformatics:Anthony UnderwoodKieren LithgowRichard MyersProject management:Ethan Cuthbert
Additional support, consultation John PaulJon GreenTom StewartJohn KentSaheer GharbiaPaul Kellam