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London Calling: Day 2 (afternoon) writeups

Sun 27th May 2018

Alex Dainis at London Calling 2018

A blog summarising presentations from the London Calling Conference, May 2018.

Thanks for visiting our writeup page. We'll  continue to update it over the coming days so please check back soon!

Plenary: Alex Dainis, Stanford University:  Haplotyping of key cardiac disease genes using long-read sequencing

[content available only within the nanopore community]

Plenary: Hayley Wilson: Shaking up the short reads – using MinION to enhance outbreak investigations

Hayley Wilson, from the University of Cambridge, gave an amazing plenary talk outlining the potential use of Nanopore sequencing in the UK's National Health Service (NHS). Hayley opened with the statement “Despite the fact people have sequenced in all sorts of locations and even space, [Nanopore sequencing] wasn’t happening in the NHS despite the benefits”. Furthermore, Hayley pointed out that not only was there no routine sequencing in the NHS, but that there is no routine sequencing in microbiology full stop. In an effort to explain why this may be, Hayley said that even though clinicians in hospitals have started to become receptive to the idea of short turnaround times, and thus potential for rapid and clinically relevant diagnostics, there are still barriers to the widespread adoption of routine sequencing within the NHS, these barriers predominantly being the requirement of a lab trained operator and no allocated funding within the NHS. 

Hayley’s talk focused on a specific methicillin-resistant Staphylococcus aureus (MRSA) outbreak she and her team were able to monitor and track using Oxford Nanopore’s MinION sequencing platform. Hayley gave a great overview of the history of MRSA in the British healthcare system; she described how it has been a considerable public health problem in the UK for over 30 years, and that it can both asymptomatically colonise 20-30% of the population and cause clinical infection in others. Interestingly, Hayley described how asymptomatic carriage can precede infection and that the UK has a zero tolerance policy to nosocomial MRSA blood stream infections. Breaching this zero tolerance policy can have major consequences that can go as far as withholding funding required from treatment, thus making the prevention of hospital-acquired bloodstream MRSA infections a high priority and a perfect target for sequencing-based intervention strategies.

In February 2018, the infection control team that Hayley is associated with logged a sudden increase in positive nasal swabs across two separate, but neighbouring, wards. This was thought to be strange at the time, as all the newly-positive patients had been negative for nasal carriage on admission and previous screenings; also, the patient and staff populations in the two wards were separate. The only link between the two wards was via shared equipment such as hoists; the clinicians wanted to know if this was a definite carriage outbreak, or if different strain types were seen in each ward. Hayley and her team set out to attempt to answer this question and also find out how quickly whole genome sequencing can provide information during an outbreak, what information can be obtained in what timeframe, and what real-time benefits a MinION has over other pipelines such as culture and AMR testing.
MRSA isolates were obtained from Public Health England on day 1 and sub-cultured prior to DNA extraction of the morning of day 2. Library prep using native barcoding was performed so that 9 isolates could be sequenced concurrently, and by the afternoon of day 2 sequencing was underway. After 24 hours of sequencing, approximately 2 Gb of data had been generated and data analysis began. By day 4, Hayley and her team had gone from 9 samples undergoing culture, to genomes with coverages ranging from 32–80x. By day 5, this sequencing data had been transformed into full assemblies where 7 of the 9 samples had two contigs, the largest of which was the size of the MRSA genome; the other was thought to be a shared genetic element. A single sample assembled into one contig and the remaining sample assembled into many small contigs, raising concerns. Furthermore, Multi Locus Sequence Typing (MLST) was undertaken using Krocus and identified the predominant sequence type in the outbreak to be ST22, whilst the sample that assembled into multiple contigs (sample 9) was identified as a novel sequence type. In order to investigate these findings further, Hayley uploaded the assemblies to WGSA, a web based program for visualisation, antimicrobial resistance detection and MLST. The assembly and MLST anomalies detected in sample 9 were shown to be due to misidentification of the original isolate and was a different, but near visually identical, species known as Staphlococcus haemolyticus. Hayley said that this was a clear advantage of sequencing over visual identification: not only would a patient be incorrectly grouped into an outbreak cohort, but they also may have had to undergo unnecessary decolonisation and isolation procedures, costing more than the sequencing itself.

Putting the genomic assemblies into a wider phylogenetic context, 7 of the remaining 8 isolates were very similar in terms of genomic sequence, clustering together on a phylogenetic tree. A single isolate identified as ST59 clustered away from the others, and thus the patient from which it came could be ruled out as an index case.

In order to get a higher resolution picture of the outbreak, Hayley enlisted the help of Tom Rich and Phillipp Reschender at Oxford Nanopore to examine variant calling approaches. Isolates identified as ST22 previously were all shown to all be within 5 SNPs of each other, well below the pre-defined threshold for transmission cut-offs.

Hayley finished by critiquing the approach taken with the benefit of hindsight, such as planning an analysis pipeline to use prior to an outbreak or QC checks of sequencing data using WIMP to make sure the target organism was actually the correct one. Concluding, Hayley said that using the MinION defined an outbreak across two hospital wards within four to five working days; they were also able to multiplex 9 isolates on a single flowcell, with room for many more. Furthermore, she highlighted how real-time sequencing can inform outbreaks and aid clinicians but currently bioinformaticians are a necessity.

Plenary:  Liana Kafetzopoulou

In  the final plenary of London Calling 2018, Liana Kafetzopoulou from Public Health England (PHE) provided an overview on some of her recent work characterising emerging and re-emerging RNA viruses. She started by stating that ‘RNA viruses are in the spotlight’ and cited the 2014 Ebola epidemic, in which some of her colleagues at PHE were involved in characterising using nanopore sequencing. Liana explained how rapid and unbiased identification methods, such as metagenomic sequencing, are vital for the identification and characterisation of emerging pathogens for which little prior knowledge is available.
 
The first part of Liana’s talk concentrated on lab-based metagenomic analysis of Chikungunya and Dengue virus, where the team from PHE demonstrated that nanopore sequencing can elucidate full viral genomes from a relevant range of viral titres.

Using this technique, they were able to get 99% of the genome at 20x coverage even from very low viral titres (Dengue 31.29Ct; Chikungunya 32.52Ct). Next, they compared the coverage plots generated by the short-read and nanopore data, which showed a high level of concordance — both in terms of coverage and percentage of matching reads. Additionally, the metagenomic sequencing strategy employed was shown to identify co-infection of Dengue and Chikungunya, confirming the advantages of this approach compared to targeted techniques. Liana stated that ‘the rapid kit was really impressive because with only a 10 minute library prep we were able to detect both of the viruses in the co-infection sample’. These results provided the confidence to further develop their nanopore sequencing workflow.
 
Liana stated that ‘the next step was to take this assay to the field and test it on something more divergent’. For this, she travelled to the Institute of Lassa Fever Research and Control in Nigeria, a remote and resource-limited location, to sequence Lassa virus. Lassa is a haemorrhagic fever virus typically found in West Africa and endemic in Nigeria. The virus is transmitted through contact with infected rats.
 
Liana showed the local diagnostic workflow from sample receipt, through to inactivation, extraction, PCR amplification and analysis using gel electrophoresis. Positive samples were selected and sequenced using the MinION.
 
In February 2018, during the time the team were on the ground, the largest ever Lassa fever outbreak in Nigeria was reported. Liana explained how they were able to set up the field lab, develop a sequence analysis workflow and deliver complete genomic analysis 35 samples within 8 weeks of the outbreak. Liana highlighted that all of the data was made publicly available, in real-time and more information on this work is available on Virological.org.
 
Because the Lassa genome is so diverse, they first had to create a de novo assembly, which was blasted against the NCBI database to allow selection of the appropriate reference genome to compute the consensus. 
 
Phylogenetic analysis of the L and S segment of the Lassa virus genome indicated that the outbreak was due to independent spill-over from the rodent reservoir and that there was no evidence of human-to-human spread. In one interesting example, the team identified two samples that only differed by one SNP; however, detailed investigation into the origin of the sample identified that it had come from the same patient.
 
Closing her presentation and the conference, Liana outlined some of the challenges encountered during this project which may be faced by others setting up remote, field-based laboratories, which included dealing with heat, power cuts and internet connectivity.
In addition, a range of data processing challenges were also evident in this resource-limited setting such as the amount of data generated and back up demands.
 

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