"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

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

All PI NRTI NNRTI

32 33 34 33

2 1 0 1

N= 34 patients

Changes in susceptibility reports usingUDS > 2%

in naive patients

Change

No Change

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

All PI NRTI NNRTI

2

7

2

9

7

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N= 9 patients

Changes in susceptibility reports using UDSin antiretroviral-experienced patients

Change

No Change

•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

34

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