The Director of the Milner Centre for Evolution, Professor Turi King, talks to Dr Lauren Cowley whose research centres around public health and infectious diseases. She works closely with the UK Health Security Agency.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:02):
Hello and welcome. You are listening to a podcast by the Miller Centre for
Evolution at the University of Bath. I'm Professor Turi King, your host,
and today I'm talking to Lauren Cowley, who is a senior lecturer and whose research centres
around public health and infectious diseases. She works closely with the UK Health Security Agency.
(00:25):
But first, Lauren, tell me about how you came to be in this field,
was there something that was a turning point that got you into looking at infectious disease?
I think I've always been interested in human health and medicine. And during my undergrad
degrees, when I was studying molecular biology, I started to focus a lot on microbiology. And I
started to get interested in pathogenic bacteria. And then I started to think about how we could use
(00:49):
sequencing technology to track pathogenic bacteria and the epidemiology of pathogenic bacteria.
So, what is that doing? Is it basically looking at the sequences of bacteria and
trying to understand what makes them pathogenic? Which basically means causes disease really.
Yeah. So, it can be looking at causative genes in disease, but also looking at
(01:12):
tracking relatedness of bacteria and grouping them by how closely related
they are in time and space and then linking that to whether they're parts of an outbreak or not.
So that was your undergraduate, what did you do for your PhD?
I started thinking about that during my undergraduate, but I applied for a PhD based on
(01:32):
those interests, and my PhD was on Shiga toxigenic E. coli and the genomics of Shiga toxigenic E.
coli. And I used sequencing data to look at susceptibility and resistance to bacteriophage.
So, tell me about this E. coli, why are we interested in it?
So, it's a diarrheal pathogen, so it causes bloody diarrhoea. It can be very nasty; it can even
(01:53):
cause kidney failure and death. And there's been quite a few outbreaks of Shiga toxigenic E. coli
that have been big public health issues that we've tried to manage. You may have heard of
outbreaks that are associated with hamburgers or sometimes salads. So, it's a foodborne pathogen.
So, what were you hoping to understand about it then?
So we were looking at susceptibility and resistance to a particular group of
(02:17):
bacteriophage called the typing phage’s, that are used for phage typing of Shiga toxigenic E. coli,
which is a typing method that we used before whole genome sequencing, where we wanted to group
strains on relatedness to try and track them and see whether groups of strains were likely to be
related in terms of their source or transmission.And how does that help? So,
(02:39):
for public health, how does that help?So, we're interested in tracking the transmission
of this diarrheal pathogen and finding the source. If there's a particular animal source or a farm
that is the cause of the outbreak, we need to find which strains are related to each other
and whether they're related to something that has come from a particular place or a particular food.
(03:00):
And then once we know that we can isolate that food or place and stop people getting ill.
So, you worked at what was known as Public Health England, and you were involved in the
implementation of whole genome sequencing to bacteria, for people who don't know,
what is that and why is that useful?So, we use whole genome sequencing to
track the evolution of bacteria and sequence the genetic code. So, what you would do is you would
(03:24):
take that bacterial strain, extract the DNA and sequence it, and then you can look at the genetic
code and see what mutations are shared between strains, to see how related things are to each
other and group them based on their relatedness.And then we might be able to tell based on this
where a strain has come from, if it's very closely related to something that's
(03:45):
come from a particular farm or a particular animal, we can then use that information to
isolate the source of a foodborne outbreak.And presumably you can look to see how it's
evolving. Can you see whether or not it's becoming like more pathogenic or not?
Yeah. And we can also track things like the acquisition of novel plasmids that might carry
resistance determinants to antibiotics. It gives us insight into exactly what
(04:09):
is happening in terms of the evolution of the strain and how the genome is changing.
I suppose it can be really helpful, because if you have an outbreak somewhere else, you can
sequence and go, oh, actually, that traces back to this one. I wonder how that's gotten there. Then
hopefully that's really useful in terms of kind of going, okay, how can we contain this essentially.
Yeah. So, it's all about containment and tracking when there's been a transmission of a particular
(04:33):
pathogen from one place to another. And I think we saw a lot of this during the pandemic, where we
were tracing particular lineages that move between different countries. And that was all done through
whole genome sequencing, where we could say, okay, well, this is the same strain that shares
the same mutations, and we're seeing it popping up in a different place. So, we can infer from
that that there has been some transmission that's happened and that we need to intervene on that.
(04:57):
And one of the things I find really interesting, because it's not my area,
but I know that within bacteria, it's not just kind of like the bacterial genome, but there are
these little things called plasmids. So, talk to me about plasmids and why they're so interesting.
Yeah. So, a plasmid is a circular piece of DNA that is external to the chromosome of
a bacteria. And what's really cool and interesting about it is that they can
(05:19):
be horizontally transferred. So that means that bacteria can pass this circular piece of DNA to
another strain that isn't related to it. So, they just have to come into contact with that strain.
And this means that there can be rapid spread of particular pieces of DNA throughout. Lots and
lots of different species of bacteria that aren't closely related to each other. So,
(05:40):
things like antibiotic resistance can be carried on plasmids, virulence factors can be carried on
plasmids, and they can pass between lots and lots of different bacteria really fast
without being directly related to each other.And that's presumably, again, really important
because if you can track where that's going and you can see kind of how it's moved,
(06:00):
then presumably that helps you in terms of looking at things like how virulent
something is and whether or not it's going to be antibiotic resistant, that kind of thing.
Yeah, we can use whole genome sequencing to say whether a strain carries certain genes
that we know are associated with disease or associated with resistance to antibiotics,
it's a little bit harder to track plasmids, in particular, because they don't evolve in the
(06:25):
same way as chromosomes, they recombine a lot, so they change their composition very quickly. But we
can definitely use whole genome sequencing to see what is carried on those plasmids.
And you work in this really interesting up and coming area, which is combining
both sequencing data and machine learning to understand bacterial resistance to things like
(06:46):
antibiotics. So, what is machine learning and how do you apply it in your research?
So, machine learning is where you basically give some data to a computer, which will build a model
around that data and try and fit that data to a given problem. So, if we're thinking about
that in terms of genomes and DNA as our data, we can ask the machine to look at the data in
(07:09):
lots of different small bite sized chunks.It will then try and learn which bite sized
chunks are associated with particular labels and then predict those labels if it's given
a new genome and break that genome down into small chunks and then say, yes, it has
the same chunks as other examples of genomes that have a particular disease, for example.
(07:30):
And yeah, so we can use this technology to build predictive models that may say this
genome looks like it causes disease, or this genome looks like it has resistance, or this
genome looks like it comes from this country. Yeah. So, it can be a useful tool to automate
these inferences that we might make from genomes.So that's presumably quite handy because it means
(07:51):
that you give it to the computer and you go write what's in here as opposed to doing the sequencing
painfully, kind of going through it and going, right, does it have this one? Does it have this
one? Presumably what happens is the computer spits it out for you and goes, yeah, it has this. No,
it doesn't have that.Yeah.
And that can be really quite handy, I'm guessing.It can be, but obviously you should take it with a
(08:12):
grain of salt because it is a computer trying to learn whatever you've told it to learn,
but it doesn't mean that it's using the right features or right parts
of the genome to learn that association.So, you may ask it to look at which genomes
are associated with disease versus ones that aren't associated with disease. And it might
learn something else about the genomes that have caused disease, not the causal gene that
(08:35):
is causing disease. So, you do have to be careful about how you interpret machine learning results
and predictive models, because it may not be learning exactly what you want it to learn.
And I think that for the current state of machine learning, we do need the right
people to interpret them. We do need people who understand how to use them in biology.
(08:56):
So, this is what the rest of us think of, kind of, as an AI. What is it that
you're using it for at the moment?So, at the moment I'm mostly working
on geographical source attribution. So, this refers back to what we were talking about in
terms of trying to find the source of an outbreak. So, if you're looking at a foodborne outbreak,
for example, and you have strains that have been sequence from the outbreak, can you from
(09:18):
the genome of them tell where they've come from in the world, by looking at how related they are to
things that have come from that place previously.So, you can input the genomes into the model,
and it will output a prediction of where it's come from. And we can use this in foodborne
disease management and epidemiology because we ultimately want to track where things have
come from and then stop them continuing to produce strains that cause disease.
(09:44):
So, what's something that you're working on now?So, I'm working with collaborators on updating
some of the machine learning models that we've built recently. So, we've recently published a
paper where we produced some models that could predict the geographical source of
salmonella strains. But these models were produced based on data from 2014 to 2019. So,
(10:05):
we're now using more recent data to be accurate for current populations of salmonella.
I'm also working with some other collaborators on a large Shigella project,
which is another diarrheal pathogen that is using genome wide association to look
for parts of the genome that are associated with antibiotic resistance and tolerance.
So how does that work? How do you use the genome wide association studies to do that?
(10:29):
It's similar to machine learning in terms of you feed into essentially a statistical model, the
entirety of a genome, and you train that model to look for parts of the genome that are associated
with a particular disease. And these can be snips or genes. And the model will then output
areas of the genome that are more associated with a disease or a phenotype of interest.
(10:54):
So again, this is looking for bits of genomes that are going to be of interest in terms of
presumably tracking if there's an outbreak and whether or not this is serious or not?
Yeah. So, our motivation is actually to find clues about the evolution of resistance to
antibiotics and what happens in the genome to make resistant populations. If you look at Shigella,
(11:16):
it's a pathogen that is quite often associated with high antimicrobial use. So, there's a lot
of selection for resistance. So, we're looking at how that affects the genome and what parts
of the genome are selected for.And what do you hope to use
that knowledge for eventually?Well once we know what parts of
the genome are particularly associated with resistance and surviving and high
(11:39):
antimicrobial use, we can look for those parts of the genome in global populations and find
clones that may become more dangerous in terms of surviving antibiotic pressures.
So, what's next for you?I'm writing a few grants
at the moment. I'm interested in how we might use bacteriophage therapy,
(12:00):
as antibiotic resistance becomes more and more common, and how we might use machine learning for
prediction of susceptibility and resistance for strains, so that we know whether phage
therapy will be successful or not.And I'm also interested in looking
at genome rearrangement in salmonella. So how the rearrangement of particular
(12:21):
segments of the genome affects how well salmonella survives in different hosts.
So, tell me about this phage therapy?So, phage therapy is when you use bacteriophage
which are viruses that infect bacteria to kill pathogenic bacteria. And it's really
good because they can be very specific. So, you can use them to only kill the pathogenic bacteria
(12:43):
that you're interested in killing. Instead of using broad spectrum antibiotics that may kill
off your whole microbiome. And they also are appealing because we're now finding that there
is more and more resistance to antibiotics. And we need to find alternative therapies.
And it's quite an old idea. I remember reading about this, it was something that was kind of
coming up, you know, in the last century. And it's something we're revisiting now,
(13:06):
isn't it? In light of antibiotic resistance.Yeah. So, it still is used quite commonly in
Eastern Europe. And we're now trying to develop ways to pick the bacteria phage
and develop things called bacteria phage cocktails, where you use a whole range of
bacteria phage in your therapy to make sure that you definitely target the pathogenic bacteria.
(13:27):
Yeah. So, it's a promising and interesting field that hopefully
we can use the latest technologies to improve.So, if there's one thing, you get to the end of
your kind of academic life and you look back and you go, I did that, what would it be?
So, I was involved in the response to the Ebola outbreak that happened in 2014 to 2016. So,
(13:52):
I went out twice, once to Sierra Leone and once to Guinea to work on the response.
So, the first time I went out, I was working in diagnostic labs. So, diagnosing samples as Ebola
positive or negative. And then the second time I went out to Guinea, I was implementing whole
genome sequencing for surveillance of Ebola in-country, which was a really, really exciting
(14:15):
opportunity. And it was really at the forefront of using sequencing for genomic epidemiology in an
outbreak like that and actually being able to use it to track transmission chains and intervene on
active chains that were happening in the country.And I think management of outbreaks and infectious
diseases is my passion because it's often a very unequal in terms of global populations,
(14:37):
in terms of who really suffers from infectious diseases. So, if we can improve public health
around infectious diseases, then we'll definitely be doing a better service to the world.
Lauren, thank you so much for talking with me.This was a podcast by the Miller Centre for
Evolution at the University of Bath. I'm Turi King and thank you for listening. If you have
(14:59):
any thoughts or comments on this or any other episodes, please contact us via our social
media. For more information about the Milner Centre for Evolution, you can visit our website.
Hello and welcome. You are listening to a podcast by the Miller Centre for
Evolution at the University of Bath. I'm Professor Turi King, your host,
and today I'm talking to Lauren Cowley, who is a senior lecturer and whose research centres
around public health and infectious diseases. She works closely with the UK Health Security Agency.
(00:25):
But first, Lauren, tell me about how you came to be in this field,
was there something that was a turning point that got you into looking at infectious disease?
I think I've always been interested in human health and medicine. And during my undergrad
degrees, when I was studying molecular biology, I started to focus a lot on microbiology. And I
started to get interested in pathogenic bacteria. And then I started to think about how we could use
(00:49):
sequencing technology to track pathogenic bacteria and the epidemiology of pathogenic bacteria.
So, what is that doing? Is it basically looking at the sequences of bacteria and
trying to understand what makes them pathogenic? Which basically means causes disease really.
Yeah. So, it can be looking at causative genes in disease, but also looking at
(01:12):
tracking relatedness of bacteria and grouping them by how closely related
they are in time and space and then linking that to whether they're parts of an outbreak or not.
So that was your undergraduate, what did you do for your PhD?
I started thinking about that during my undergraduate, but I applied for a PhD based on
(01:32):
those interests, and my PhD was on Shiga toxigenic E. coli and the genomics of Shiga toxigenic E.
coli. And I used sequencing data to look at susceptibility and resistance to bacteriophage.
So, tell me about this E. coli, why are we interested in it?
So, it's a diarrheal pathogen, so it causes bloody diarrhoea. It can be very nasty; it can even
(01:53):
cause kidney failure and death. And there's been quite a few outbreaks of Shiga toxigenic E. coli
that have been big public health issues that we've tried to manage. You may have heard of
outbreaks that are associated with hamburgers or sometimes salads. So, it's a foodborne pathogen.
So, what were you hoping to understand about it then?
So we were looking at susceptibility and resistance to a particular group of
(02:17):
bacteriophage called the typing phage’s, that are used for phage typing of Shiga toxigenic E. coli,
which is a typing method that we used before whole genome sequencing, where we wanted to group
strains on relatedness to try and track them and see whether groups of strains were likely to be
related in terms of their source or transmission.And how does that help? So,
(02:39):
for public health, how does that help?So, we're interested in tracking the transmission
of this diarrheal pathogen and finding the source. If there's a particular animal source or a farm
that is the cause of the outbreak, we need to find which strains are related to each other
and whether they're related to something that has come from a particular place or a particular food.
(03:00):
And then once we know that we can isolate that food or place and stop people getting ill.
So, you worked at what was known as Public Health England, and you were involved in the
implementation of whole genome sequencing to bacteria, for people who don't know,
what is that and why is that useful?So, we use whole genome sequencing to
track the evolution of bacteria and sequence the genetic code. So, what you would do is you would
(03:24):
take that bacterial strain, extract the DNA and sequence it, and then you can look at the genetic
code and see what mutations are shared between strains, to see how related things are to each
other and group them based on their relatedness.And then we might be able to tell based on this
where a strain has come from, if it's very closely related to something that's
(03:45):
come from a particular farm or a particular animal, we can then use that information to
isolate the source of a foodborne outbreak.And presumably you can look to see how it's
evolving. Can you see whether or not it's becoming like more pathogenic or not?
Yeah. And we can also track things like the acquisition of novel plasmids that might carry
resistance determinants to antibiotics. It gives us insight into exactly what
(04:09):
is happening in terms of the evolution of the strain and how the genome is changing.
I suppose it can be really helpful, because if you have an outbreak somewhere else, you can
sequence and go, oh, actually, that traces back to this one. I wonder how that's gotten there. Then
hopefully that's really useful in terms of kind of going, okay, how can we contain this essentially.
Yeah. So, it's all about containment and tracking when there's been a transmission of a particular
(04:33):
pathogen from one place to another. And I think we saw a lot of this during the pandemic, where we
were tracing particular lineages that move between different countries. And that was all done through
whole genome sequencing, where we could say, okay, well, this is the same strain that shares
the same mutations, and we're seeing it popping up in a different place. So, we can infer from
that that there has been some transmission that's happened and that we need to intervene on that.
(04:57):
And one of the things I find really interesting, because it's not my area,
but I know that within bacteria, it's not just kind of like the bacterial genome, but there are
these little things called plasmids. So, talk to me about plasmids and why they're so interesting.
Yeah. So, a plasmid is a circular piece of DNA that is external to the chromosome of
a bacteria. And what's really cool and interesting about it is that they can
(05:19):
be horizontally transferred. So that means that bacteria can pass this circular piece of DNA to
another strain that isn't related to it. So, they just have to come into contact with that strain.
And this means that there can be rapid spread of particular pieces of DNA throughout. Lots and
lots of different species of bacteria that aren't closely related to each other. So,
(05:40):
things like antibiotic resistance can be carried on plasmids, virulence factors can be carried on
plasmids, and they can pass between lots and lots of different bacteria really fast
without being directly related to each other.And that's presumably, again, really important
because if you can track where that's going and you can see kind of how it's moved,
(06:00):
then presumably that helps you in terms of looking at things like how virulent
something is and whether or not it's going to be antibiotic resistant, that kind of thing.
Yeah, we can use whole genome sequencing to say whether a strain carries certain genes
that we know are associated with disease or associated with resistance to antibiotics,
it's a little bit harder to track plasmids, in particular, because they don't evolve in the
(06:25):
same way as chromosomes, they recombine a lot, so they change their composition very quickly. But we
can definitely use whole genome sequencing to see what is carried on those plasmids.
And you work in this really interesting up and coming area, which is combining
both sequencing data and machine learning to understand bacterial resistance to things like
(06:46):
antibiotics. So, what is machine learning and how do you apply it in your research?
So, machine learning is where you basically give some data to a computer, which will build a model
around that data and try and fit that data to a given problem. So, if we're thinking about
that in terms of genomes and DNA as our data, we can ask the machine to look at the data in
(07:09):
lots of different small bite sized chunks.It will then try and learn which bite sized
chunks are associated with particular labels and then predict those labels if it's given
a new genome and break that genome down into small chunks and then say, yes, it has
the same chunks as other examples of genomes that have a particular disease, for example.
(07:30):
And yeah, so we can use this technology to build predictive models that may say this
genome looks like it causes disease, or this genome looks like it has resistance, or this
genome looks like it comes from this country. Yeah. So, it can be a useful tool to automate
these inferences that we might make from genomes.So that's presumably quite handy because it means
(07:51):
that you give it to the computer and you go write what's in here as opposed to doing the sequencing
painfully, kind of going through it and going, right, does it have this one? Does it have this
one? Presumably what happens is the computer spits it out for you and goes, yeah, it has this. No,
it doesn't have that.Yeah.
And that can be really quite handy, I'm guessing.It can be, but obviously you should take it with a
(08:12):
grain of salt because it is a computer trying to learn whatever you've told it to learn,
but it doesn't mean that it's using the right features or right parts
of the genome to learn that association.So, you may ask it to look at which genomes
are associated with disease versus ones that aren't associated with disease. And it might
learn something else about the genomes that have caused disease, not the causal gene that
(08:35):
is causing disease. So, you do have to be careful about how you interpret machine learning results
and predictive models, because it may not be learning exactly what you want it to learn.
And I think that for the current state of machine learning, we do need the right
people to interpret them. We do need people who understand how to use them in biology.
(08:56):
So, this is what the rest of us think of, kind of, as an AI. What is it that
you're using it for at the moment?So, at the moment I'm mostly working
on geographical source attribution. So, this refers back to what we were talking about in
terms of trying to find the source of an outbreak. So, if you're looking at a foodborne outbreak,
for example, and you have strains that have been sequence from the outbreak, can you from
(09:18):
the genome of them tell where they've come from in the world, by looking at how related they are to
things that have come from that place previously.So, you can input the genomes into the model,
and it will output a prediction of where it's come from. And we can use this in foodborne
disease management and epidemiology because we ultimately want to track where things have
come from and then stop them continuing to produce strains that cause disease.
(09:44):
So, what's something that you're working on now?So, I'm working with collaborators on updating
some of the machine learning models that we've built recently. So, we've recently published a
paper where we produced some models that could predict the geographical source of
salmonella strains. But these models were produced based on data from 2014 to 2019. So,
(10:05):
we're now using more recent data to be accurate for current populations of salmonella.
I'm also working with some other collaborators on a large Shigella project,
which is another diarrheal pathogen that is using genome wide association to look
for parts of the genome that are associated with antibiotic resistance and tolerance.
So how does that work? How do you use the genome wide association studies to do that?
(10:29):
It's similar to machine learning in terms of you feed into essentially a statistical model, the
entirety of a genome, and you train that model to look for parts of the genome that are associated
with a particular disease. And these can be snips or genes. And the model will then output
areas of the genome that are more associated with a disease or a phenotype of interest.
(10:54):
So again, this is looking for bits of genomes that are going to be of interest in terms of
presumably tracking if there's an outbreak and whether or not this is serious or not?
Yeah. So, our motivation is actually to find clues about the evolution of resistance to
antibiotics and what happens in the genome to make resistant populations. If you look at Shigella,
(11:16):
it's a pathogen that is quite often associated with high antimicrobial use. So, there's a lot
of selection for resistance. So, we're looking at how that affects the genome and what parts
of the genome are selected for.And what do you hope to use
that knowledge for eventually?Well once we know what parts of
the genome are particularly associated with resistance and surviving and high
(11:39):
antimicrobial use, we can look for those parts of the genome in global populations and find
clones that may become more dangerous in terms of surviving antibiotic pressures.
So, what's next for you?I'm writing a few grants
at the moment. I'm interested in how we might use bacteriophage therapy,
(12:00):
as antibiotic resistance becomes more and more common, and how we might use machine learning for
prediction of susceptibility and resistance for strains, so that we know whether phage
therapy will be successful or not.And I'm also interested in looking
at genome rearrangement in salmonella. So how the rearrangement of particular
(12:21):
segments of the genome affects how well salmonella survives in different hosts.
So, tell me about this phage therapy?So, phage therapy is when you use bacteriophage
which are viruses that infect bacteria to kill pathogenic bacteria. And it's really
good because they can be very specific. So, you can use them to only kill the pathogenic bacteria
(12:43):
that you're interested in killing. Instead of using broad spectrum antibiotics that may kill
off your whole microbiome. And they also are appealing because we're now finding that there
is more and more resistance to antibiotics. And we need to find alternative therapies.
And it's quite an old idea. I remember reading about this, it was something that was kind of
coming up, you know, in the last century. And it's something we're revisiting now,
(13:06):
isn't it? In light of antibiotic resistance.Yeah. So, it still is used quite commonly in
Eastern Europe. And we're now trying to develop ways to pick the bacteria phage
and develop things called bacteria phage cocktails, where you use a whole range of
bacteria phage in your therapy to make sure that you definitely target the pathogenic bacteria.
(13:27):
Yeah. So, it's a promising and interesting field that hopefully
we can use the latest technologies to improve.So, if there's one thing, you get to the end of
your kind of academic life and you look back and you go, I did that, what would it be?
So, I was involved in the response to the Ebola outbreak that happened in 2014 to 2016. So,
(13:52):
I went out twice, once to Sierra Leone and once to Guinea to work on the response.
So, the first time I went out, I was working in diagnostic labs. So, diagnosing samples as Ebola
positive or negative. And then the second time I went out to Guinea, I was implementing whole
genome sequencing for surveillance of Ebola in-country, which was a really, really exciting
(14:15):
opportunity. And it was really at the forefront of using sequencing for genomic epidemiology in an
outbreak like that and actually being able to use it to track transmission chains and intervene on
active chains that were happening in the country.And I think management of outbreaks and infectious
diseases is my passion because it's often a very unequal in terms of global populations,
(14:37):
in terms of who really suffers from infectious diseases. So, if we can improve public health
around infectious diseases, then we'll definitely be doing a better service to the world.
Lauren, thank you so much for talking with me.This was a podcast by the Miller Centre for
Evolution at the University of Bath. I'm Turi King and thank you for listening. If you have
(14:59):
any thoughts or comments on this or any other episodes, please contact us via our social
media. For more information about the Milner Centre for Evolution, you can visit our website.
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