Why use genomics in an epidemic? With Sam Sheppard
Sam Sheppard of the University of Bath introduces genomic epidemiology at the ARTICnetwork and CLIMB-BIG-DATA workshop on COVID-19 data analysis, held on 14–15 January 2021. He traces the transition from older strain-typing methods to whole-genome comparisons, explaining how cheaper sequencing, portable instruments and shared computing have changed what researchers can do. The talk connects these developments to outbreak investigation, coronavirus surveillance and international collaboration, followed by a question about strains and lineages.
Listen to Episode 43: Why use genomics in an epidemic? with Sam Sheppard on SoundCloud
In this episode
From identifying species to distinguishing strains
Sheppard starts with clinical microbiology: working out which organism is causing disease in samples such as blood, urine, sputum or cerebrospinal fluid. Identifying the species is often relatively straightforward, but it does not answer every question. He uses E. coli as an example of an organism that can live commensally in the human gut yet cause serious disease in certain circumstances.
Strain typing provides finer distinctions within a species. The approaches discussed include serotyping, phage typing, restriction and amplified fragment length polymorphism, pulsed-field gel electrophoresis and multilocus sequence typing. The last typically sequences seven genes around a pathogen’s genome. Whether a typing method can distinguish strains determines how well it can help identify a possible outbreak.
Why whole genomes became the typing approach of choice
The attraction of sequencing an entire genome is not new; historically, cost was the obstacle. Sheppard gives approximate sequencing costs of $5,000 per megabase in 2001, falling to well below $1 per megabase by the time of this January 2021 talk. That decline makes whole-genome sequencing a practical alternative to older typing methods.
He describes this as a transition from molecular epidemiology to genomic epidemiology: comparing pathogen genomes to understand relationships between strains. Older data need not simply be discarded, because some analyses remain comparable and researchers can still refer to earlier typing studies. The central point is resolution: a method that cannot tell two strains apart limits what researchers can infer about their relationship.
Sequencing in laboratories and in the field
Sheppard recalls sending DNA to specialist central laboratories when he began sequencing genomes around 15 years earlier. Central services still have advantages, but laboratories can now operate their own instruments. He identifies Illumina as the dominant platform by data output and MiSeq as common in laboratories, with larger instruments tending to remain in specialist facilities.
Oxford Nanopore provides a different example: a mobile-phone-sized instrument that produces long reads and can travel with a miniaturised laboratory in a suitcase. This makes sequencing near an outbreak possible with relatively little laboratory space.
Sharing results also becomes easier. DNA sequences are more straightforward to exchange between laboratories than electrophoresis gels. Open-access sequence archives support comparisons, some centralised analysis and collaboration across countries.
CLIMB supplies shared computing capacity
Generating more genomes creates a storage and analysis problem. Sheppard describes the CLIMB Consortium’s starting point around seven years before the talk: individual laboratories did not all have enough computing capacity to compare large genomic datasets. CLIMB, followed by CLIMB-BIG-DATA, provides shared resources for storing and analysing those data, alongside an environment for sharing algorithms and pipelines.
From a user’s perspective, access is through a desktop or laptop connected to a cloud distributed across multiple UK sites. Researchers create virtual machines that provide more processing power and storage than their local computers may offer. Large-memory machines are particularly important for analyses with substantial RAM requirements.
What genomic epidemiology can reveal
Sheppard groups the applications into several levels. Genome comparisons can help establish epidemiological links, investigate outbreaks and examine transmission. Researchers can also look beyond human populations for possible sources of infection or reservoirs of pathogen strains.
For surveillance and hypothesis generation, he discusses the timing of strain emergence, primary infection versus relapse or reinfection, and mixed infections involving multiple strains. Broader population questions include which strains are prevalent, whether particular strains dominate particular places, and how mutations might contribute to emergence, spread or virulence.
At national scale, relatedness trees and genetic variation can be examined alongside changes in strain frequency over time. His closing argument is that international collaboration, using shared infrastructure or comparable analyses, extends this work to global patterns of disease emergence and transmission.
Strains and lineages are not a fixed hierarchy
A panellist asks Sheppard to clarify his use of “strain” and explain how it differs from “lineage”. He describes strain as a legacy microbiology term for a variant within a species that shows different phenotypes or genotypes in a laboratory setting. It is useful even though its meaning is somewhat broad.
Lineage carries an evolutionary meaning: it directs attention to a tree and to relationships between isolates, usually through sequence clusters. Sheppard does not impose a rule that a lineage must contain multiple strains, or that a strain must contain multiple lineages. His emphasis is on the different associations of the terms rather than a strict hierarchy.
Highlights
- 00:00:00 — Introduction to the ARTICnetwork and CLIMB-BIG-DATA workshop held on 14–15 January 2021.
- 00:00:17 — Sam Sheppard introduces his role at Bath and begins with pathogen identification and molecular epidemiology.
- 00:16:45 — A panellist asks how strains differ from lineages and when to use each term.
- 00:17:02 — Sheppard contrasts the microbiological meaning of strain with the evolutionary meaning of lineage.
- 00:18:12 — Closing subscription request and production credits, including editing by Nick Waters.
In their own words
But the real difference is that strain is this kind of legacy microbiology term, and lineage is a much more kind of evolutionary term that makes us think of a phylogeny.
— Sam Sheppard, 00:17:02
Who is talking
- Sam Sheppard (guest, University of Bath; CLIMB Consortium)
Also mentioned: Tom Connor, Nick Waters.
Tools and resources mentioned
Serotyping, Phage typing, Restriction fragment length polymorphism, Amplified fragment length polymorphism, Pulsed-field gel electrophoresis, Multilocus sequence typing, Illumina MiSeq, Oxford Nanopore sequencing instrument, CLIMB, CLIMB-BIG-DATA.
Questions this episode answers
Why sequence pathogen genomes during an epidemic?
Sheppard explains that genome comparisons help researchers relate strains, investigate outbreaks, examine possible infection sources and track strain frequencies over time. They also allow researchers to examine genetic changes that might be associated with transmissibility or virulence.
What is the difference between a strain and a lineage?
In Sheppard’s explanation, strain is a broad microbiology term describing variation within a species, while lineage emphasises evolutionary relationships and clustering on a tree. He does not treat them as a fixed hierarchy.
What does CLIMB provide for genomic epidemiology?
CLIMB provides shared computing for storing and analysing large genomic datasets, with virtual machines accessed over the internet. It also supports sharing analytical tools and offers processing power, storage and large-memory resources beyond what individual laboratories may have.
How can sequencing be taken to an outbreak location?
Sheppard describes a small Oxford Nanopore instrument used with a miniaturised laboratory that can fit into a suitcase. Its portability allows sequencing in the field or in relatively small laboratory spaces.
This page was written from a machine transcript of the episode and checked against it. Listen to the episode for the whole conversation.
Show notes
Sam Sheppard from the University of Bath presents at the ARTICnetwork & CLIMB-BIG-DATA workshop on COVID-19 data analysis, motivating why we should use genomics in an epidemic. He gives background on typing schemes, different ways of sequencing and challenges such as how you can analyse large mounts of genomic data. Resources: https://sheppardlab.com/ https://www.climb.ac.uk/artic-and-climb-big- data-joint-workshop/