Experimental Evolution, Gene Networks and Bacterial Defences - Professor Tiffany Taylor

The Director of the Milner Centre for Evolution, Professor Turi King, talks to Professor Tiffany Taylor about her research which centres on three main and interlinking areas, experimental evolution, gene networks and bacterial defences.

Episode Transcript

(00:02):
Hello and welcome. You are listening to a podcast by the Milner Centre for Evolution
at the University of Bath. I'm Professor Turi King, your host, and today I'm talking
to Tiffany Taylor, professor of microbial ecology and evolution at the Milner Centre for Evolution.
We're going to be discussing Tiffany's research, which is in three main and interlinking areas,

(00:23):
experimental evolution, gene networks and bacterial defences.
But first, Tiffany, tell me about how you came to be in this field. Was there
something that sparked your interest and sent you down to this path, or did you just kind
of fall into it as you were doing university?Yes, I think a little bit of both. It was never

(00:44):
really on my agenda growing up that I was going to be a scientist or be an academic,
but I had, as many children do, a sort of natural curiosity for the living world.
I really loved animals and animal facts and David Attenborough documentaries. And I
think as I went through my education, I started to think about universities,
I felt like zoology would be a really good fit for me. And upon visiting lots of universities,

(01:05):
I sort of fell in love with the city of Edinburgh.But it's while I was there that I became sort of
more exposed to evolutionary biology and sort of fundamental evolution. And
I was really captivated by a particular lecture by someone called Andy Gardner,
he’s now a professor at St. Andrews, and he was delivering a lecture on social evolution,

(01:27):
which is this idea of how interactions between individuals, things like cooperation, altruism,
selfishness, or cheating, how those interactions can evolve. And this is like a real big challenge
that Darwin noted within, On The Origin of Species, because he couldn't really explain,
given how natural selection works, to maximize an individual's fitness,
how can you explain how behaviours like altruism, where you are helping another at the cost

(01:52):
yourself? How can those evolve? And there are lots of examples of altruism throughout nature.
So, Andy was delivering this lecture showing how mathematical models
could really beautifully predict how and when you might expect these behaviours to evolve using,
sort of, natural data sets. And importantly, through cleverly designed experiments. And that
was sort of my first exposure to experimental evolution, which is kind of the thread that

(02:14):
sort of stayed with me throughout my career.And it was while then I was at Edinburgh that
I switched to an evolutionary biology degree, and that sort of defined the trajectory from there on.
So, I know you did your PhD at Oxford, what did you do that in?
So that I did in social evolution again. So, it was on understanding how the behaviour of

(02:36):
dispersal can evolve and the role of competition between individuals in the
environment in driving dispersal behaviours.And so, this idea that if you're competing
with someone that's very related to you, so a kin member, that competition is going to come
at greater cost than if you're competing with someone that you're unrelated to. And that's
because of this term known as inclusive fitness. So basically, your fitness is

(02:58):
determined not just by the genes that you carry, that you can pass on to the next generation,
but of those relatives that also can pass on those shared genes to the next generation.
And so, by helping the survival of your relatives, you're actually improving the survival of those
genes from generation to generation. So that cost of sort of taking resources from a direct
kin member is actually larger, because you're sacrificing the continuation of those genes. So,

(03:22):
we were able to sort of show using experimental models combined with mathematical models,
that competition between kin was indeed playing a role there.
And I think something that really interestingly came out of that actually was a follow on during
my postdoc, where I was sort of presenting on this work, and I got chatting to a cancer biologist,
and we sort of saw a lot of overlaps in sort of how some of the problems that are seen with

(03:44):
metastasis in cancers, which is sort of a form of dispersal, if you think about it,
and whether or not these sort of same features might be playing out within tumours,
as they were in our petri dishes.And so, we put together a short,
sort of, experiment to sort of show that those overlaps do exist, and actually you can use
similar approaches. So that was really nice.And then another thing that came out of my PhD
work, I did quite a bit of work on bacteriophage coevolution. And again, understanding the role of

(04:10):
phage in dispersal patterns in these bacteria. So, whether or not they might drive dispersal, because
obviously you want to escape those parasites, but also a common target for those phages, which are
viruses of bacteria, are the external structures that they use to move around. So, the flagella,
which is its swimming tail or its pili, which are like grappling hooks on the outside, so they'll

(04:32):
commonly lose those in order to resist infection.So, there's a trade-off there is, do you escape
and maintain those structures, or do you lose those structures and hopefully
resist the phage? And so, I did a little bit of work on that as well. And again,
that's come through to what I'm doing now as these interactions between bacteria and their viruses.
So, this is really interesting because if you're working with somebody who's

(04:52):
looking at cancer metastasis, this is like an application of the work that you're doing. So,
tell me a little bit more about that study because that's really interesting.
Yeah. So, we sort of did in two parts. Firstly, we laid out this sort of perspective piece that
tried to take what we had learned from experimental evolution in bacteria,
which at this stage was fairly well established. And whether or not you could utilize other model

(05:14):
systems to explore the relevance and the generalizability of these principles,
which we are assuming is general, but, you know, it hasn't been tested in so many systems.
So, what we did is we put spheroids, which are like these in vitro tumours, essentially,
these little balls of cancerous cells. You allow them to adhere to the bottom of a tissue dish. And
then we put them under either high or low nutrient conditions. With the idea being in high nutrients,

(05:37):
there should be lower competition for those nutrients. And in low nutrients there's higher
competition. And we sort of maintained these spheroids and transferred them over multiple
generations. And what you found is that those that were maintained in a high nutrient environment
were moving less away from… So, when you let them adhere, they sort of spread out away
from that central spheroid, and they would move away less in high nutrient conditions

(06:00):
than they would in low nutrient conditions. And this would adapt over time.
So, if you took those cells that had adapted, some would move faster than others. And that
sort of motility, is the initial phases of metastasis, that's what they have to do first,
is sort of migrate away from the tumour in order to get into the bloodstream.
It was a very simple experimental setup, but it just showed how just something as simple as access
to nutrients and competition between individuals might drive this process. And it was nice because

(06:23):
we had originally predicted this result. We had sort of a thought experiment, and we thought this
is what might happen. And then we followed up with experiment and luckily were able to show
that was true. So yeah, it was it was very cool to sort of translate that work to another system.
So, is that something that people can now use further, where they can kind of go,
okay, so if they're in low nutrient they're more likely to metastasize?

(06:44):
Yeah. And this is sort of, it wasn't revolutionary that idea, I think it had been seen, but I think
it was more the connections to that sort of evolutionary pressure that had not
necessarily been appreciated. And so, this work is ongoing, because there is this strange result
that comes out of that might be, that if you feed a tumour, you actually reduce the likelihood that
it would metastasize, which is a strange idea.And there's other evolutionary principles that

(07:08):
come with that. The problems of drug resistance is obviously something that's also faced within
cancer treatment. And so, if you over treat a tumour, again, you might be selecting for
those resistant phenotypes. And there has absolutely been some research that's been
done and followed up that has shown that actually by reducing the amount that you treat a patient,
you might not get rid of the cancer, but you actually prolong the prognosis.

(07:30):
So, there are really interesting evolutionary principles that can be brought into that. And
there are like brilliant research groups that are continuing that work. But for me, I was drawn back
into the microbial world because I just find bacteria really, really fascinating and also
much easier to use in the lab. I think anything eukaryotic I have kind of been scared off of.

(07:50):
So, I know you first came here to Bath as a Royal Society Dorothy Hodgkin Research fellow,
and I know you've been working on these kind of three main areas ever since. So first,
experimental evolution. So, talk me through what that is and how do you study it.
So experimental evolution in essence is really just about trying as much as possible to control

(08:16):
every element in an experiment, such that you're able to manipulate one variable to understand how
that variable might drive evolutionary processes.Okay. So, a lot of the way that evolution is
studied is looking for correlations and trends in data, because usually you're
looking at processes that have taken perhaps millions of years. So, it's very hard to do
any sort of experimental manipulations. But with experimental evolution, it exploits the

(08:41):
fact that the rate of evolution depends on a population size and on the rate of replication.
Okay. So, things that have very large effective population sizes and that have very fast
replication rates are going to evolve much faster than things that have small effective
population sizes and slow replication rates. So, with experimental evolution,

(09:03):
you use things such as microbes, which can exist in very large population sizes in very
small spaces, and they replicate very quickly.And also, they're brilliant because you can freeze
them down over time, which provides a frozen fossil record. So that means that if you're
evolving or adapting something in the lab and you want to see how these changes have happened,
firstly you can go back and genome sequence, which of course is really, really powerful. But we can

(09:23):
also do is revive these ancestral frozen samples and then understand and measure how those fitness
changes have changed over time by competing them with old strains under different conditions. And
you can sort of get a really strong idea of the causal drivers of evolutionary processes rather
than looking for trends and correlations in data.So, you're doing really fundamental evolution

(09:45):
because you're looking at these kinds of underlying mechanisms and the processes
of evolution, like, in real time?Yes. And it's in real time, which
makes it so brilliant and fun and fascinating.So some of the work that we do and the modal
system that we use in the lab looks at the rescue of swimming motility in bacteria, and that happens

(10:06):
over the course of a few days, just sort of 2 to 3 days is what you'll see there, which is remarkable
to see because you start off, we’ve engineered these bacteria to lose their ability to swim. So,
the way that we've done that is by removing an essential regulator, which job is to switch on or
off all of the genes associated with swimming motility. And then we put it on very strong
selection for motility and very, very rapidly, with just a few mutations, it can basically borrow

(10:30):
a regulator from somewhere else and just plug it in to fix… It's kind of like putting a band-aid
over a massive gaping hole, but it does it and it fixes it, and then it can compensate over time.
And so being able to just see because it's such an obvious phenotype motility, it's really exciting
to suddenly see these motile mutants emerge. And then you can sequence them, you can phenotype
them, you can look at their fitness, you can do all sorts of cool stuff with them. And so that's

(10:52):
really allowed us, as you say, to get right down to the molecular detail of what are the underlying
mutations and drivers going on that can, kind of, determine these evolutionary pathways. And it's
quite strange for me because my background is very, a sort of high level phenotypic,
even throughout my PhD, I never really understood genetics that well. And the more I've gone in,
the more I've sort of, kind of been fascinated by the smaller and smaller detail. Until now,

(11:16):
we're getting to sort of single point mutations and how that's changing things.
And that's great because actually it allows us to utilize genetic engineering to also understand
how different, sort of, genetic starting points might change the sort of end point that you end
up in. And I think that's also really powerful to, sort of, not just rely on what you can get
your hands on, but actually you can create what you need and then see how that evolves over time.

(11:39):
So where are the bacteria getting these little band-aids from? How are they acquiring them?
So, the way that these regulators evolve is by what we call duplication and specialization. So
throughout evolutionary history, perhaps deep in evolutionary history, there was one copy of this
regulator that did everything. And then there'll be a random event where there's two copies,

(12:02):
and then suddenly one of those can specialize into a different task, so it's not one doing every job,
they can start specializing. And that happens again and again and again.
But what that means, underlying all of that, is that all of these are quite closely related, they
look quite similar. And the way that they work is by connecting to sites on the DNA that basically
allows transcription of those genes of interest. So, they recruit the copying enzyme to that site.

(12:26):
So because they share this ancestry and because they all look quite similar,
what that means is that if you lose one of those regulator, or if we delete that regulator,
it can borrow a regulator from another site, and actually it can still interact with those
same DNA sites, perhaps to its slightly less degree, but it still retains that capability.
And so quite quickly, if that interaction is useful for natural selection,
it can act to reinforce that connection. And as such, it can sort of quickly repair these,

(12:50):
sort of, broken networks over time.So that segues really nicely into one
of the other areas that I know you're looking at, which is gene networks. So, what are they
and what are you hoping to learn about them?So, genes themselves are organized into groups,
genes that have similar function or related function. And they can be regulated or switched
on or off all together at the same time. And that's much more efficient than regulating the

(13:14):
expression of every single gene in that network.What that means, though, is that where you have
these hubs that are responsible for regulating a huge group of genes,
they become what's known as evolutionary hotspots, because just a few mutations at
those sites can have a really big impact on the expression of all the genes in that network.
So, what we're trying to do, through this model system of rescuing swimming motility, is

(13:37):
understand which genes or regulators are available to natural selection to borrow, which are more
likely to be used? Why are they more likely to be used? And how does its evolutionary history,
sort of, determine that pattern? And can we make any predictions, if we looked at the genome and
said, okay, if we removed that gene, which do we think is most likely to repair that lost function?

(13:57):
And so, we think about all the features that might go into determining those rewiring patterns.
So, is there anything that's, kind of, come out of that research that surprised you… or?
Well, we were really lucky in that it ended up leading to sort of unexpected line of research,
which is this predictability of evolution. So, what we found when we repeated this experiment

(14:19):
again and again, this rescue of the flagella motility, is that the same mutation was popping
up every time. And that was really unexpected.So, within experimental evolution,
because you're working under very controlled conditions, you might expect parallel evolution,
which is where you get the same mutation every time, perhaps at the genetic level, the same gene
mutating every time. But you wouldn't expect the same nucleotide to be mutating every time. And so,

(14:42):
we knew something strange was going on there.And so, I had a brilliant PhD student,
who’s still working with me now, James Horton, who, kind of, became obsessed in understanding
what was really going on to drive this, sort of, level of parallel evolution.
And he's gone further and further into the molecular detail. And basically, he has uncovered
this mutational hotspot. That means that mutations at that site are much more likely to occur than

(15:02):
elsewhere in the genome. And that's just to do with the order of nucleotides that precede it.
But what's really interesting there is because he's gone into such detail, he's learned a lot
about how to both break and build these mutational hotspots. And so, what we're
hoping is that this knowledge will help us search for these mutational hotspots within genomes,
and also potentially build these hotspots for use in something like synthetic biology, where we're

(15:25):
actually having that control over how synthetic organisms might adapt to different environmental
conditions, might be really powerful.And that's actually really, again,
fundamental because knowing where mutations are likely to happen… We know that mutations
are important, like things like cancers developing. So, are you thinking about you

(15:46):
might be able to apply it to other fields as well?Well, I hope so. Whether or not I would actually
go out and apply this to other fields, or whether hopefully I would collaborate with
others that might be able to utilize, sort of, the knowledge that we've learned and build this
into other systems is sort of more what I hope will happen, but I think it does have
potentially quite far reaching applications.For example, it would be brilliant in vaccine

(16:07):
development if we could have some foresight as to which mutations we expect to occur before they
happen, but we're also very far away from that. I think we are really building the foundations in
terms of understanding where and when evolution is predictable, and it's important to say that it's
only really when these mutations, strong mutation biases synergize with natural selection. So,

(16:28):
with the mutation that confers some sort of benefit that you get this really,
really strong predictive power. But where we are at the moment is that sometimes in our
experimental work we might see parallel evolution, and at the moment we either assume it's to do with
strong natural selection and very few mutational options, or we might assume its mutation bias,
but we can only see it in retrospect, we can't predict where those features are going to be. So,

(16:51):
I think this is just a first step towards that.So, I know you're also combining this and
looking at bacterial defences and antibiotic resistance. So, tell me what you're doing there?
Okay. So, this is fairly recent work that, kind of, brings me back to some of the work I did
during my PhD, looking at these bacteria, phage interactions. So, bacteria can be infected with

(17:13):
viruses known as phages, just like us. And also, just like us they have these defence systems,
which are a bit like our own immune systems that can allow them to clear these infections.
So at the moment, this is a really big growing field, defence systems are being discovered all
the time, but we don't really have a great understanding as to why some bacteria have

(17:34):
certain defence system combinations compared to others, how they might work together,
it's sort of like a big open field. And you might be familiar with some of these defence systems
because they're used quite often as genetic engineering tools, things like CRISPR-Cas or
restriction modification enzymes. Their origins are all as these bacterial defence systems.
But the thing is, bacteria and genetic material entering bacterial cells isn't

(17:56):
always a bad thing. So obviously virus is a bad thing because the way that that works is
they inject their DNA into the cells, that DNA will hijack the bacterial replication machinery,
make lots of copies of that virus, not burst out the cell and kill the bacteria.
But a really important adaptive mechanism for bacteria is horizontal gene transfer. So that's
where they can pick up DNA and genes from the environment that can potentially facilitate

(18:20):
their own adaptation. So, they can really rapidly acquire a gene that, for example,
will give them something like antibiotic resistance. Well, that's bad for us,
it's obviously really good for the bacteria.So, we're trying to understand how these defence
systems work in order to allow the good stuff to keep coming in and the bad stuff to stay out,
and also how they work as a collective. So, for example, whether or not they're being

(18:43):
infected with a virus that they've encountered before, and therefore they might be able to
use these more targeted defence systems like CRISPR-Cas, or whether it’s a virus
that they've never seen before, and as such, they have to utilize other defence systems.
So, there's lots and lots of open questions there. And so what I've got at the moment is a project
that's looking at how if a bacteria has acquired one of these defence systems from the environment,

(19:05):
which we know happens quite frequently, what are the environmental conditions
that actually will allow that defence system to persist within a population?
Because you might think it's just seems like a really good idea, everything should maintain
it. But actually, there are certain costs associated with expressing and maintaining
these defence systems, if you're not using it appropriately. So, it can really quickly be lost.
So, we're using actually a combination of some synthetic biology, because we have synthetically

(19:28):
engineered a defence system that we've taken from one bacteria, stripped it back down to its
bare necessities, and then put that into a naive bacteria that doesn't have the defence system.
And then we're trying to understand how that changes the way that it interacts with phage.
So, what's next for you then?Well, I have some new exciting
projects starting this year. So, I've got a few new people coming to the lab to look at

(19:52):
how multiple defence systems in bacteria might be regulated altogether, to sort of provide this
holistic defence response to bacteria or response to other genetic material coming into the cell.
So that's really interesting in terms of understanding the role of these multiple
defence systems in applications such as phage therapy, for example. We have this huge global

(20:14):
challenge with antibiotic resistance at the moment and how we can develop new strategies to treat
bacterial infections that are potentially harmless as long as you have the right drugs. But once
those drugs disappear, can actually become fatal.So, phage therapy is something that's being quite
rigorously explored at the moment. And what that does is it utilizes these phages, these viruses

(20:35):
of bacteria that can kill them, as a way to treat bacterial infections. But the thing that's
interesting about them is not that… bacteria can still evolve resistance to these phages, but
because phages themselves are an evolving entity, they can evolve to become infective again and so
you end up in these arms races, but it means that they can potentially stay effective for longer.
But there are lots of things that we don't actually understand about those evolutionary

(20:58):
dynamics that are likely to be really important as to whether or not phage therapy is effective,
and things like defence systems are going to play into that because they're going to determine
whether or not certain phages that we might use are likely to be effective. And potentially the
transfer, if we're going to be using combined phages and antibiotic treatment approaches,
whether or not those genes that might confer antibiotic resistance might be

(21:21):
conferred more or less readily. So that's one of the things that we're looking at.
And another really exciting project that I have starting is something quite different, but it's
about understanding how to teach evolution better in the classroom. And specifically,
I want to look at the role of practicals in experimental evolution and doing that.
So, this is funded by the Evolution Education Trust. And one of the issues I have at the moment

(21:45):
with the way that evolution is taught in the curriculum, is that I think it's not taught in the
same way as other sciences, in that it’s lacking a lot of these practical components that the other
sciences have. And I think that can convey this incorrect message that is perhaps less robust or
doesn't follow the typical scientific method.And it also sends this message that evolution

(22:05):
is important for understanding our past, but it doesn't do a great job of conveying the message
that actually it's really critical for these big global future challenges that we face,
things like antibiotic resistance, but also adaptation to a changing global environment
or vaccine development for that matter. So, there's all sorts of these real-world,
modern-day applications that I think are missing from the national curriculum.

(22:26):
And so what this project aims to do is to take our model system of repairing the swimming motility
and take into the classroom, allowing children to evolve bacteria themselves over just a few days,
look at these motility phenotypes evolve and try and use that as a way to, 1: allow them to
see that evolution can happen on incredibly short timescales. Allow them to contextualize evolution,

(22:48):
and hopefully allow them to make these connections with these long evolutionary processes that it's
very, very hard to understand in deep time, but actually be able to allow them to see
it on their bench in the classroom.So, I'm excited, but we're going to
try and do lots of methods to understand how to optimize it. What age is it best
appropriate? How do we make sure it's accessible as possible? So, this is a whole new challenge,
but one I'm really excited to start on.Tiffany, thank you so much for talking

(23:12):
with me. This was a podcast by the Milner Centre for Evolution at the University of
Bath. I'm Turi King and thank you for listening. If you have any thoughts or
comments on this or any other episodes, please contact us via our X channel @MilnerCentre.
For more information about the Milner Centre for Evolution, you can visit our website.