It should say something. Yeah. Okay.
Can you just start by saying your name and what your current title is?
Yeah. So I'm Andrew Kwok, I'm actually now a clinical lecturer at the Chinese University of Hong Kong and I previously did a PhD at Oxford University.
So that was from 2018 and obviously during the COVID times as mine, I only just finished a few months ago, basically.
Okay. Well, congratulations on finishing. Thank you.
And so, first of all, can you just tell me a little bit about yourself from when you first got interested
in in science generally and to specialise in the area that you're interested in that?
Yeah. So I think with, as with a lot of scientists with COVID, it was a very accident, accidental thing.
Well, I mean, okay, so first of all, the fact that the pandemic happened,
you know, that in itself was already just a very sort of random event that way.
Right. But originally I was interested in immunology that was as an undergraduate student.
And then I did clinical school and I decided that I thought acute situations were really thrilling.
So I thought that if I could try and find some research topic that would combine both of them, that would be the best.
And then that's how I ended up. By acute situations, you mean kind of life or death relationship with the patient?
Yeah. Yeah. So, so, yes, yes. So, so more emergency situations.
Right. And so in that case, I looked up some projects on the university's website and it turns out that the lab that I was in at the end,
so that was Professor Julian Knight's lab. So he has done a lot of work on sepsis, which is a life threatening condition when you get an infection,
and that's usually due to your immune response going a bit haywire.
So I thought, well, that seems to be a really good fit, right? So, so let me see.
And he very kindly took me on. So that's what I was originally embarking the journey on, which was a PhD to work on sepsis.
And then, you know, a year or so and obviously COVID started.
And so given that sepsis is, you know, all that infectious diseases and immune responses, then, you know,
actually, if you think about COVID, it's a bit of a very similar parallel topic or a bit of an extension.
And in fact, you could very well say that technically people who get COVID, who get very, very ill, are essentially septic patients as well.
So it was a very neat kind of marriage of the of the topics, and that's how I ended up getting into it.
Okay. So so before we go on to talk more about the COVID project,
let's talk a little bit more about sepsis and what your what the take of Julia night's lab was on approaching it.
What? So what was it we didn't understand about sepsis?
And what was your particular project aimed at trying to discover?
Yeah. So the idea with sepsis is that it's an infectious disease problem, right?
So if you think about then you split into two halves, then either there's a problem with the pathogen as the invading, you know,
the bacteria or the virus, you know, the invasive aspect, or there's an ASP,
there's an issue with the host response of the way the human system behaves.
And so we looked at it from the perspective of the human right and we specifically looked at the immune response.
So within that, the main problem that we faced as researchers, I think, was that sepsis is a bit of a broad, vague definition.
So operationally it's very helpful because it defines the limits of what you can do as a doctor.
So if you tell people that, okay, you know, we have a septic patient, that they're very ill, maybe they're going to need intensive care support.
So, you know, you move, you escalate in terms of the amount of medical care you give the people.
But what that doesn't really tell you is what the underlying biology is that's going wrong.
Right? So that's so then you just have this broad umbrella. And so what our aim was to do,
our aim was basically to see if we could understand the patient in groups with the condition
a bit better by giving a proper biological mechanism behind different groups of patients.
So to stratify them, so to speak, that was the idea behind the lab.
So the idea being that different people might respond,
they might catch the same bacteria or the same virus, but their body responds in a different way.
What according to their genetic makeup. Right.
Well, so that, you know, the genetics is then obviously a need to answer or any potential potential question to investigate, let's say.
And I think, yeah, there's been a lot of genetics done in sepsis.
I would say that none of it has been very fruitful.
But yeah, you know, so we look at every layer, so everything from the genetics to just upstream of that and then,
you know, all the way through to the immune system and so on. And I focussed quite a lot on the immune system.
Okay. So it was a what? I'm sorry. I'm going to dive down into this a bit. Yeah, sure, sure.
The immune system has many components. What are the particular components of the immune system that you've been interested in?
Yeah. So that was a bit of a journey as well, right. Because so obviously, first of all,
there was a lot of literature already on how different aspects of the immune system were dysregulated in the septic response.
So for example, I think, you know,
when people get vaccines with COVID that people may have heard of T cells and B cells being thrown around these kind of terms.
Right, because of the pandemic.
And so there was a whole literature about t cell dysfunction in sepsis, for example, and that the T cells were overactive or undirected.
What was wrong? Yeah. Mostly underactive, I would say.
Mostly underactive, but then. The way we looked at it was a little bit different.
So our initial approach was to take blood from patients, total blood.
And it turns out that the proportion of T cells that are in your blood is actually very, very low.
Right. So so in the in the initial experiments that we were running, this is before I even joined the lab,
the initial experiments from Juliane and others in the group.
If you take the whole blood and then you just profile the total amount of RNA in the blood,
and then you see if you can stratify patients with some various machine learning based algorithms and advanced statistical analysis and so on.
Then you can it seems like we were getting meaningful subgroups of patients, right?
Meaningful in the sense that certain gene expression signatures which were very reproducible,
seemed to mark out groups of patients who would deteriorate much more quickly than others.
Right. So it seemed like we were getting some meaningful biological signals.
You know what I was saying about getting the biological mechanism behind the different groups, right?
So that was that got us very excited. And that's basically how I joined the lab, right? Because I saw that I thought, hey, this is really meaningful.
Let's dive deeper into it. So that initial finding was when you just took blood in bulk.
Right. And so my question was very much, well, what are the cellular contributions to that signal that you're seeing?
Which cells do the signals derive from which cells are therefore most likely, you know, malfunctioning in some way or another?
How are they malfunctioning? Those are the questions that I wanted to address in the fleet.
Right. So one so for ranked cells, have we got swimming around in our blood?
Right. Right. So you could maybe broadly split it into what we call lymphoid and myeloid lineages.
So lymphoid, you know, the T cells in the B cells,
these are lymphocytes that people are both maybe have heard of and that those are the cells which are really exciting.
And I think, you know, more historically, immuno, immuno immunologists were particularly excited about them in the sixties, in the seventies,
because those were the cells where the exciting aspects of immunity seemed to reside in,
such as the ideas of immune memory and immune specificity, the fact that you can target a pathogen very specifically.
And then if you've had that infection once before, you know, a few months down the line, you see it again, you're able to respond very well.
That's basically how the vaccines work, right? So that's the lymphoid side of things.
And so in a sense, there's also a very much neglected other side,
which is the myeloid lineage where you have cells such as things called granulocyte or monocytes.
We ended up looking a lot at the granular science, but those cells I think are a little bit more poorly understood.
And I think traditionally the remit has been more with the haematologists rather than the immunologists looking at that.
And that's because the cells have been ditched by the immunologists to some extent.
But I think in recent years there's been a lot more excitement in looking at those myeloid cells,
which are which also another, you know, another bit of terminology.
But if you broadly divide immunity into innate and adaptive, so adaptive being the immune system,
which again has that specific function, has the memory function.
So those are those lymphocytes, whereas the innate arm is the arm that responds more quickly and has a more non-specific response.
And those are those myeloid cells more. So I would say. Yeah.
And so you were looking to see if these myeloid cells were producing the distinctive signatures that had turned up in your analysis of whole blood.
Yeah. So the first way we did it was what we tried to actually go in with a slightly more unbiased lens, right?
So, so this was one of the more initial questions that I was challenged when I first started the application.
So around the time I started there were these very exciting new technologies that
were coming along called single cell RNA sequencing or single cell sequencing,
we can call them. So that is to say, imagine if you could literally take every individual cell in your sample or your body,
the sample material working with and you could actually profile all of them individually.
Look at all of the RNA or DNA molecules, whatever your choices, all of those molecules in that individual cell.
Right. So it's a very, very powerful technology.
It's been around for more than ten years now, but then it's really taken off over the last five years or so, I would say.
And so when I started the PhD, this was starting to become quite a hot topic.
I wanted to, you know, really try it out as well. And the beauty of it is that it gives you a relatively unbiased lens into the question.
Right, that you're asking. So because if you have your blood population of cells and you don't necessarily know which cells are the most relevant,
you might have some guesses based on prior literature, but you can be sure you know 100% sure, right?
You might want to use techniques such as this kind of single cell sequencing so that
you can profile all of the cells and then you're sure not to miss any of the signals,
per se, right? Yeah. And.
Yeah. And so how far would you go with that? Well, it would fail.
Yeah. Yeah. So because I was just starting, I'd never done this kind of work before.
We we didn't get very far. We just ran some pilot experiments.
It took more than a year to set up because in parallel, other than the lab side,
other than the technological side, we were also basically setting up the clinical study.
We'd had previous clinical studies in place, but there wasn't quite the ethics for us to take the kind of samples we wanted.
So we actually spent a lot of time working out the ethics and the clinical study and then starting to actually recruit patients.
Before we could even run some of the experiments. We got to the stage where we had some initial pilot data and then COVID hit.
So you were you involved personally involved in recruiting the patients as well?
Yes. So because of my clinical background, I basically did the entire study from scratch.
Right. I wrote it up from scratch.
And then I met all the people in the hospital, all the all the teams involved, to all the research nurses on the ground.
And of course, as the research team, I wasn't actually meeting the patients directly, not in terms of the recruitment,
but I was liaising very closely with that, closely with the research nurse teams who would then contact the patients.
And then we also did a lot of work, for example, with patient groups, you know, patient groups with sepsis,
so that we could understand from their perspective what they would want to be involved in,
whether they thought this was useful at all, you know, engage them a bit of education.
Yeah. Yeah. So I was doing a lot of that in the first year, actually. Excellent.
So, yes, so we get let's get to to the arrival of COVID.
Can you remember I mean, obviously, you come from from from Hong Kong yourself.
Yes. So you must perhaps more alert to what was going on in Wuhan and spreading out into China, maybe a little earlier than some people were.
And I think people I've interviewed have had a range of initial reactions from, Oh, it can be like sighs.
It'll just go away, it won't come here. And other people saying, This looks really bad and we should we need to do something.
So what what was your how did you first hear about it and what was your initial reaction?
So I first heard about it towards the end of 2019.
I definitely remember it was it was still December then.
And I think the initial reaction was I wasn't too worried in the sense that it didn't seem too close yet.
And you know, as with a lot of these things, you kind of have to just let things run their course to see how it pans out.
It's just a bit impossible to predict. So in that sense, I wasn't necessarily very worried.
I think I was also then then, you know, in another month or two's time, by the time January,
February 2020 rolled around, it definitely seemed like it was a bit more serious.
Right. I think at that point, I still wasn't very worried, at least to the extent that.
I felt like I was personally quite prepared for issues of infection spreading through
communities because I'd lived through sores myself and I thought that that was a.
Preparation experience, if you if you if you will. You know, we we all survived through that one.
But then, obviously. They were very different in the sense that this time, you know, it was a full on pandemic.
Right. It really spread across the world. I think I didn't necessarily see that coming.
But then I guess the question is for the people who say they saw it coming.
You know, how how much predictive accuracy do you have if something else happens the next time around?
Right. And, you know, you can always say that, oh, this one looks really bad.
This one looks really bad. Is it always going to be really bad? It's very hard to say.
Right. So I was. I think I was generally more worried during the thing than like in terms of any anticipatory anxiety.
Yes, yes, yes, yes. Okay. We'll talk bit more about that later.
So at what point did Julian and the lab decide that you were ideally placed in the way to to tackle,
you know, to use the techniques that you were using to tackle COVID itself?
Yes. In fact, before the virus actually hit the UK in January 2020, when things were actually still quite normal in the U.K.,
we were already looking into it because Julian had some collaborations with a group in Beijing,
which was a virology group, and so they were obviously, you know, working hard on the problem already.
And so we had some discussions and communications and we had sort of some ideas of what we could offer on our side, you know,
leveraging what we'd done on sepsis before, you know,
whether there were similar similarities and whether we could parallel up parallels, any kind of work.
So we'd already put in a little bit of thought there. And then, of course, COVID actually hit, right?
And so. I think we were.
Not necessarily ahead of the game, but it was just because of that collaboration that it just prompted us to think quite early,
you know, before it even hit the UK. And so, I mean a study was set up with the title combat.
You just tell me what that, what that stood for.
And we'll talk a bit more about how it how it came together.
Yeah. Yeah. So the combat was the name for the consortium, the COVID Multi-omics Blood Atlas.
So the COVID being COVID right, the multi omega pot is where when I was saying talking about single cell sequencing earlier on.
So this idea that if you can sequence molecules and get an unbiased view, right, as in so you don't know what the bag of molecules are,
you just sequence all of them first and then see,
that's when in biology we call that a lot of omics because it's and that's to say it encompasses the whole thing, right.
So your, your genome, right. Is your whole whole genetic makeup.
Right? Or you can see the transcriptome, which is the way the genes are expressed.
And that's how all of the genes are expressed as the transcriptome rather than each individual one.
And so that's the omics.
And so it was an idea of multi-omics in the sense that we had layers of data which were all encompassing at the level of the DNA,
at the level of the RNA, at the level of the proteins and so on and so forth.
And because we were heavily focussed on blood sampling, so that was the Blood Atlas.
So we were building this entire atlas of how the human was responding to the
virus through a blood window and with these many different layers of the data.
So. So the idea was to take blood from COVID patients and what compare that with people with other infections or people with no infections.
How did that work? Yeah. So one of the strengths of the set up and I think our cohorts was that we had obviously the single,
you know, basic comparison is you compare people with COVID with healthy individuals.
Right. And then there was a tricky thing as to how you could get age appropriate controls.
Right? Because actually a lot of the people who are being hospitalised with COVID or
especially getting critical illness were people on the older age or older spectrum.
Right. It wasn't as many 20, 30 year olds. There were some.
But that was, you know, fewer, fewer proportion at a lower proportion for sure.
So then there was a very tricky thing of having to find older, healthy controls.
And actually we had some recruitment drives where we distributed posters to various colleges asking for various professors,
you know, of slightly more advanced ages to participate.
And it was it was quite it was actually we got quite a doozy asking responses.
And so what do you need? It from them was a blood sample.
Is that right? Yes. Oh, it. That was it. Yes, that was it. But it was you know, it was not easy to be able to find such a targeted age group in a way.
Right. So that was one route we had. And then we also had a contrast between people who were very ill from COVID and people who are less ill.
So that ranged from individuals who were hospitalised but didn't necessarily need much additional medical support other than just sort of monitoring.
And then we had people were critically ill in intensive care from COVID.
So those who were actually septic. Right.
And then we had some individuals who were in the community who caught COVID, didn't even need to be hospitalised.
So we had the whole we spend a whole range of severities and then we still had a couple of more
comparisons where we managed to source some cohorts of patients who had previously had flu,
the flu infection and were critically ill as well in intensive care.
So that provided a very, very good control to the COVID critically ill COVID patients,
because then you had two groups of patients which were both critically ill,
but from different pathogens, and you could see how the immune response was going differently.
And then there was a final one where we had some or all cause or, you know,
mostly bacterial causes really of septic patients who were also very, very ill.
And again, they were not caused by COVID, but illness.
And we could compare those immune responses again with the individuals who had COVID.
So we had a whole range of comparisons. So combat, as its name suggests, was a consortium.
I mean, I've I've seen the publication that came out at the end of this, which we'll talk about.
It's got a lot of names on it. How was that consortium put together?
So I think it was very organic, right? I think a lot of things really started in Julian's lab.
And between us, the two of us, you know, having meetings are just talking about, well, what are we going to do about this?
But it soon became clear that we would need just a lot of on the ground support logistically.
Right. And especially if you consider the context, which is that this was a an acute public health emergency.
Right. I mean, I think that's one of the things that made it particularly impressive was, you know, it's there you know,
we were very proud to be part of and proud of achievement, essentially, which is that it was hectic already enough as it were.
Right. So in a way, the list of names. Includes, I think, just everyone who was even more peripherally involved in the project.
For example, all of the all of the a lot of the ground stop and the hospital who participated and contributed back.
But, of course, also a lot of the key players, such as the research nurses who put in a lot of hard work best, we had, you know,
sample processing teams which were equipped to deal with coded samples,
as I say, equipped because, you know, this was at the beginning of the pandemic.
We were much more worried about the pathogen than we currently are now. Right.
So, you know, then at that point, things had to be done at very high biosecurity levels and so on.
And so, you know,
we had sample processing teams in place and then the teams which would actually generate data and then data analysis teams and so on.
So it was it was quite organic in the beginning.
But as the project grew, you know, we just reached out to more and more people and then I think people were obviously keen because it was,
it was such a big thing and it was it was a great scientific opportunity as well.
And so that's how the team just grew. And then eventually it got to the point where, well, this is clearly the size of a consortium.
And you personally, I mean, again,
I've looked at the contributions of all these different people and your name tags up under most of the that the headings.
So were you playing a coordinating role. Yeah.
So I think I was I was definitely involved in basically every facet of the, of the project, as it were.
I wasn't really the coordinator per se in the sense that I wasn't supervising the project.
So that was really much more of Julian's role.
But in terms of then all of the ins and outs of because if you think about the way the study came about,
so it's a combat was the name of the sort of the study, the group, right.
The majority of the patients were recruited under that initial study that I'd set up in the first year of my people.
So. I was probably I was the best place person to know everything about the study write.
You know, I'd written it up from scratch. I had designed it up from scratch like that.
So I knew where all the ethics, you know, what it was all about, what the framework was.
I knew all the research teams, so it was very, very close liaising with them.
In fact,
in the study we I don't even know what the actual number of COVID patients we've got in the Biobank now is because we've been continually recruiting.
But I very distinctly remember the first two patients,
which I basically cycled the blood samples from the hospital to the lab because that was how on the ground it was at that point.
Right. So so I was involved in everything from, you know, those initial bits with the study to then of course generating the data and then analysing.
And so even though I wasn't coordinating, I was basically in I can imagine like all of the meetings it was it was meetings
constantly in addition to obviously the round the clock scientific work.
Right. So, yeah. Mm hmm. And and so, I mean, is it too soon to jump to the the outcome?
No, I don't think it is. What what what?
Once you know, I think we should talk a little bit about the analysis.
So you collected huge amounts of data, as you say, in this rather unbiased way, which just finding out what was there.
How did you go about the analysis? Right.
So yeah, I guess there are maybe two things that are worth sort of flagging up.
One is that they're a more practical, logistical thing.
But it was it's to say that we had a lot of analysis teams because there was so much data,
as you said, that we needed many people and a lot of different expertise.
It was multi-omics, right? So there was data from, you know, gene expression that was data from protein and so on.
And I wasn't necessarily an expert in all of them.
And in fact, I don't think any of us were really right. So we had to really split the task up and divide and conquer that way.
And then what was very interesting was that around the same time as how well these things develop in parallel, right?
So we we started to have these technologies to probe biological samples in this data heavyweight in the lab.
And so then at the same time, people were coming up with very interesting computational methods to analyse the data.
And so one of the more interesting things to highlight was that and strength, I think,
of the study was that we were trying out some very novel analytical methods to try to integrate the many different layers of data.
And so there were, you know, a number of people on, on that team.
And I was part of that team. I wasn't leading that team.
But, you know, we tried a lot of new mathematical ways to to combine the layers of data in something that would be meaningful rather than just,
you know, an abstract mathematical notion as well. So that was a very, very exciting opportunity.
Yeah. And from looking at the paper, you really work very hard on the visualisation side of it,
making those the results clear or the analysis clear through different kinds of data visualisation.
Yes. And I think this is something that I learned quite a lot from Julian as well, I think,
over the course of years, which is that the data needs to be presented very clearly.
Right. There needs to be not not only does there need to be no ambiguity, but it needs to really flow.
Right, because otherwise it's hard to follow the story as given how complex it already is.
Right. If I was just describing, I'd describe the multi-omics.
I'd describe how many cohorts of patients we had. Right.
You know, if you imagine just intersecting those two things, you already have a big, you know, a large combination of variables there.
Right. So imagine how difficult it is to follow that. Right.
It's like if you're watching a TV series and you have many different characters except, you know,
it's all condensed in this paper as well with all this data you're trying to interpret.
So. So, yeah. So that was, you know, we did spend a lot of time on that to polish the, the presentation such that in the final format,
there would be common threads that you could sort of follow to help you really to guide through that the reading of the story.
And what were the key headlines of the of the findings from the study?
So the key headlines we will highlight, too, I think one of them was that.
So if you think about COVID, I will SARS-CoV-2 write the virus itself.
Very often when we think about immune responses, there is some amount of tailoring of your immune response to the type of a pathogen.
So that is to say a virus usually elicits a antiviral immune response and then you have antibacterial ones and anti-fungal ones and so on.
And because of the uniqueness of our cohort, the fact that we had those many different comparative groups and then we had different pathogens,
what we could really see was that COVID would elicit, in fact, of course, as expected, atypical antiviral response.
But what was quite interesting was that the severity of illness did not seem to track that antiviral response.
So it was other things, right? And of which we identified a very specific set of genes which belong to what we call that, like, you know,
an entire program or module of genes which seem to belong to a pathway where a very central regulator was a gene or a protein complex known as AP one.
Now, to be completely honest, I think it's still a little bit unknown as to how that in the end really impacts your response.
But that was one of the more novel pieces that we could get from combining so many different layers of data,
which I think other people have also seen now.
But then this was one of the more initial central reports of of this COVID, this aspect of the COVID response.
And it's so different people have variations in that that kind of percent of of genetic
material that you're talking about which influence severely the disease impacts.
And. Yes, yes. And this was a very uniquely COVID thing.
Right.
Because you could say, you know, people with flu, that the other flu cohort that we had, they were similarly ill and that was also a viral illness.
And yet they didn't have this response. So this was very unique to the kind of immune response to COVID, which was what we were trying to get at.
Right. So we were able to do it through this kind of multi-omics layering.
And I think the second thing maybe then to highlight rather than the biology, but more something from the technical aspects.
So what I was briefly alluding to earlier, but I think in a way, the the paper was also a.
A demonstration of the success of using some of these more novel analytical algorithms.
Right. So it was a bit of a technical achievement. And as as one of the examples, we use many, many different methods,
but one of the examples we use the kind of tensor decomposition method, which is to say, if you imagine your many different datasets.
Right. So where you have each dataset, you can imagine there are two dimensions, right?
One dimension is the number of samples that you have. Right? So, you know, you have a table, right?
You have a table where the columns of the samples, let's say, and the rows are the measurements that you take.
Right. So a nice 2D table.
But then imagine you had many of these kind of data sets of the same patients, the same samples, but the measurements are all different.
Right? So one is, you know, blood cells, one is the blood proteins and so on.
And if you combine all these tables and make it 3D, then then you have what's mathematically called the tensor, right?
And so how might you go about decomposing that tensor to find structure in the data?
So that's, you know, not a trivial question to answer, right?
So we were able to really apply these kind of methods to these datasets.
And I think that was a real demonstration of the power of some of these new algorithms.
Do you and I should perhaps fast is beginning it, but it sounds like a silly question.
Obviously it's it's good to know why things work the way they do but but what was the the hope or the expectation that by acquiring this
knowledge in the world of research and and drug development might be able to do in future to to actually tackle these kinds of conditions.
Yeah. So I'll give you two examples.
One not immediately related to the study, but it'll make sense.
But so there are a lot of anti COVID treatments now which are given based on our understanding of the
biology of the immune system and not necessarily aspects which are unique to COVID individually.
But they were trialled because they've been very well understood those aspects of the immune system for many years now,
and they've been used in many other different settings and therefore they will, you know, are already licensed products basically.
And so people trialled them on COVID patients and you know, some of them work, some of them didn't.
And then the ones that have worked, they're obviously now currently being used.
But that's all thanks to things like dexamethasone, the sterile steroid dexamethasone, which.
Right, dexamethasone. But I was thinking.
Yeah, I was thinking maybe it's like even more niche ones, things like drugs called Elysium Mab, which inhibits a very specific IL6 receptor pathway.
Right.
And you know, that's only thanks to knowing how il6 signalling really works, what it really means in the immune system and inflammatory response.
And that was, you know, years, decades and decades of work.
And in a way, what we are doing, what we did and what we continue to do right, is to build on these kind of fundamental aspects.
You know, what is the what are the systems in biology that work that make humans work the way they do?
And by understanding these things, eventually you will crack these you will get these kind of insights, like the whole sex pathway and so on.
And then you have these kind of drugs right now.
Obviously, the study, I don't think, you know, we didn't go straight from this study to a drug that's a really, really long process.
But then to highlight, as I was saying earlier, that AP one pathway, which, you know,
I think is probably a story which would be worth following up, it's just that I'm post patient and haven't followed it up.
But so one of the thoughts would be that Long-covid is obviously a big issue, right?
A lot of people have it. It's something like 5% or maybe even more people get it.
And I think it's really poorly understood. Right. Exactly what's going on now in our cohort.
You remember I said that we had some individuals who were ill with COVID but didn't need to be hospitalised.
And in fact, some of these individuals were probably in what we call the convalescent phase already of the illness.
They were already recovering. But what was very interesting was when you looked in these patients,
it seemed like they would still show signatures of that COVID response in terms of that AP one program that I was explaining.
And in fact, in some cases that seemed to be even more highly activated, that program than in the acute phase of the illness.
And yet this program was not activated in other aspects of critical illness.
Right. So you can see how now you're starting to get hints of, well,
is this going to be one of those pathways that you really need to probe into
and understand what its relationship is with longer term sequelae of COVID,
for example? How does that relate to the the virus? How does it relate to the rest of your immune response, the interaction of that together?
And so, you know, these are the kind of fundamental things which maybe in another five years, ten years or something, you know,
as people follow it up, you get to the point where then you can say, Oh, look, maybe we have some druggable targets here and there.
This is the pathway that we should really act on to treat people. So that's one example.
But one can imagine that with such an enormous dataset, actually,
you've generated enough new questions for lots of people to explore for some time to come.
Yeah, no, for sure. And I think so. So that's where there's strengths and limitations, right.
But with these kind of studies, I think the one of the biggest limitations ultimately is that we're talking about observational data, right?
So we have many different patient groups and we collect the data and it's observational, but it's not necessarily perturbation.
So we haven't given people any drugs or we're not trialling things on mice in the lab or something like that.
Right? So you can never be 100% sure where the various effects that you observe are necessarily causal.
And so that's where, you know, you have to go back to the lab,
you have to develop your model systems and then build on the questions that we've really come up with in the study.
But of course, this was, you know, such a big project on its own already, right?
Yeah. So did that become the topic of your your dphil?
And suddenly chunks of it. Yeah. So, so I would say about yeah.
About a half to two thirds. So of it was of of the of the thesis ended up being on this work and then the rest of it was on other
sepsis work which I actually ended up going back to basically after completing the combat project.
Yeah. So what was the timeline of it. When, when did you regard the the Copan project as complete.
Yeah. Well let's say yeah, that's a good question. I mean, you know, combat finished combat was released earlier this year.
Right. And of course, we were months before that it had already been completed and been in the pipeline for being published.
But I spent about the last let me think a year and a half I think it last the last year and a half of the Ph.D. back to working mostly on sepsis,
I would say mostly because I was still doing various things on on on COVID,
just not necessarily on the combat project, but all the strands that we were still trying to follow up on.
So yeah, I think, you know, in a way I thought this was a very big stroke of luck for me in the PhD in the sense that it was such
a ridiculously good learning experience and what an amazing opportunity in terms of the science,
you know, meeting the people that I did and learning from everybody, you know, in the team, that was a huge privilege.
Yeah. You're not the only person who's said so.
Some of them were slightly reluctant to say it, but that COVID did open up amazing opportunities.
But there are a number of people in their research,
and sometimes it seems it seems almost unfair to to mention that when a lot of people have, you know, have had a hard time with it.
But clearly, in the in the research area, it did open it opened lots of doors.
You know for sure. I think I do talk about that a fair bit with, you know, other people, you know, my friends, my family or anything.
I because people always ask me, right, you know, they say, well, how is it, you know, during that time?
Because it must have been really rough. And I always tell them that it was rough from, you know, in many ways, obviously the lockdowns and everything.
But I said that honestly, I wouldn't complain to the extent that I was so lucky in the sense that the work well,
I know so many of my other friends who are so heavily affected.
Right. You know, their projects can run and so on. And then of course, the pandemic itself was also raging.
You know, people were worried about their personal safety and health. Right. Myself as well.
But, you know, so there were so many all the things everyone had to contend with.
I count myself as having been so lucky in that period. Definitely.
But it was. Yeah, you did. You were rough. What was your personal life like through that?
Through that period? What kind of hours were you working? Where were you living?
How did you cope with with the social distancing regulations, all that kind of thing?
So for me, it was a it was such a surreal experience because because of the lockdown.
There was basically not much contact with anybody.
Right. So it was just going to work. And only a select few of us who were still going to work in the lab were allowed in anyway.
So I was I was given one of those essential worker status letters.
I still remember that. I think I still have a copy of that because I thought it was such a strange thing.
It was such a strange feeling to be holding that piece of paper and then thinking, okay, well,
I'll take the bus in the morning, you know, at seven and then get into the lab and then get the day going.
It was very much round the clock work for many months.
And, you know, I remember having had conversations with team members with some sort of, you could say quite hilarious comments being thrown around,
such as saying, well, if I were a management consultant, I hope that, you know, I wish that this could go even even even more quickly.
But then, you know, we were already working really at the limits, right?
As in we were going full throttle. So.
So it's very, very intense. The rough one.
One of the roughest parts was the fact that I just didn't have much social contact
because most of my friends also had left Oxford either just as things had,
you know, started getting bad or right after lockdowns had happened.
But then because of the stuff I was doing, you know, I had to be there to do it right.
So I was basically completely on my own for months for that that whole stretch between March to July,
August time before before the first sort of opening up right in the UK and.
I mean, I think in a way I was able to keep going because I was so busy.
But it was very, very strange. It was definitely very strange.
And I think I guess I was kept sane by just, you know, making sure that I would still talk to people,
even though I didn't necessarily have the physical contact. I would still call people.
I again,
maybe I was lucky in the sense that I had that essential worker status and at least I saw a few people in the lab rather than if you completely,
you know, work from home or couldn't work. Of course, there were many more who were much more unfortunate that way.
And where were you? Where were you living at the time? So I was just living in the city.
I was not that close to the lab. Well, so this is what sort of what a lockdown does, right?
My normal morning commute would take while the daily commute would be 45 to
45 minutes to an hour from where I left it to the lab on about an enduring.
Yes, but I change at some point.
And then with COVID, it became something like 20, 25 minutes at half the time, literally, because there were just no people around.
Right. So no stops or anything. You just it was a straight shot kind of. Yeah.
So that was that was a very noticeable what even a flat to or a college room or what sort of.
Yes, I was I was in a flat. I was I was renting a flat with a college friend.
But then he'd gone home on your own. Yes, exactly.
Exactly. Yes. He'd he'd returned to Italy and so I was on my own.
Yeah. And your family were your family in Hong Kong?
Yes. So my entire family was in Hong Kong.
And I think especially during 2020, Hong Kong didn't have a particularly rough time, so I wasn't very worried.
And this was another thing that I always mentioned to other people, but I said that I was,
in a way, as scary as it was, you know, personal safety public health wise.
I was very fortunate that my grandparents weren't around. I felt like I wasn't as stressed in that way because I think we were all very acutely aware,
very early on about how the elderly were by far the ones most at risk.
Right. So I felt like that would have been a very, very big stressor if, for example, I'd been living with my grandparents, I would have, you know.
Yeah, but living on my own. I didn't have that baggage necessarily and.
And and so. Yes. So you completed your your PhD.
Have you go back to clinical work now or are you are you still doing this research?
Yeah, so I am actually. I'm with the Chinese University of Hong Kong now.
And so they've been very supportive of the junior staff who are aspiring to do both clinical work and research work like myself.
And so I'm in a position where I spend about three quarters of my time doing clinical work,
but then the other one quarter of the time I'm actually attached to a lab and so I'm doing the
research there and now I'm sort of more full time into that analysis rather than the wet lab work,
because it's a bit hard to combine clinical work where you have very heavy contact time.
You have to see the patients day in, day out kind of thing and then also try to run experiments at the same time.
Right? The only way you could do that is basically probably run the experiments at night.
And I think I find it a lot easier to sit in front of the computer to do some analytical work in the evening rather than still be in the lab,
you know, slaving away with the pipettes. Yeah. And are you continuing with the same ideas that you worked through during your Ph.D.?
So it's been a bit of a stretch. I've actually made a switch to neuroscience, but then, yeah, so radically different.
But I think actually at the end of the day, to me it's all biology.
And one of the key things is still thinking about the genomics aspects, the multi-omics that, you know, we were talking about with combat.
And so really applying those kind of lenses to interrogating neurobiological questions rather than immunological questions.
So that's one. But I think certainly longer term, one thing that I'm actually very interested in is the idea of neuro immune connections.
So in recent years, we've there's been quite an explosion, I would say,
of understanding that there are actually many links between the nervous system and immune system.
And these are many things that we're not really aware of consciously on a day to day basis.
Right. But these kind of cross talks are probably very relevant biologically, and I think there's a huge amount to be discovered there.
So who knows? But maybe that's the kind of stuff that I'll be getting into in the years to come.
That sounds really exciting. I shall have to come back to you again.
But you've done some more work on. So.
Yeah. Oh, yes. Sorry, I just on the question that I'd written about the another published your science paper.
I should have mentioned that earlier that looked at predicting outcomes in sepsis from using the the kind of data that you collected during combat.
Oh, yes. The. Yes. Yes. More recently released one.
Yes, very. Yes, I think it was. Yes, yes. Yes. That's in the last month or so.
Yeah. Yeah, yeah. So so that was work that was led by any kind of agamben's, which is a post-doc in Julian's lab.
And then, you know, he was building on the.
So that was. It was more more clinically translational related than mechanistically biologically related, I would say.
Right. But yes. So so now we're going back to sepsis, right. Rather than COVID.
Yes, yes. But. Right at the beginning, we were talking about how you could take some blood, just whole blood from patients,
and then you could stratify them into at least two groups that's called amino groups one and two, right?
Where, let's say group one does more poorly. They deteriorate more quickly.
Right. And one of the questions we were interested in was to say,
have we captured essentially an axis along which individuals with infectious disease present in terms of their severity and,
you know, obviously some kind of biological meaning because we're using gene expression as a readout.
Right. And so we did this. We derived this originally in sepsis patients.
So then the question was, can we extend this over to other settings of either critical illness or infectious disease?
And so what that new project was about was this generalisation.
And we basically managed to take a lot of publicly available datasets as well as dataset that we generated ourselves and show that,
yes, that original description that we had, we called them the sepsis response signatures.
You could in fact recapitulate that signature in COVID patients and flu patients
and sepsis patients or even potentially in patients who didn't have infection.
So just an idea of, you know, critical illness. So we we seem to have identified a kind of blood gene expression, axis of severity of illness.
Much like how, you know, if you have a cough and a fever, you think, and then eventually you need oxygen support.
You think that the person who needs oxygen support probably has the worse illness than just the cough.
Right. Except we've got this on a biological axis. So that was the kind of work that we were trying to do.
And along with that,
there was a very big technical challenge as to how you could actually do this analysis in these different datasets in a robust manner,
which would be, you know, it wouldn't give you a false result just because you moved to a different technical platform, a different data set.
And so that was a huge challenge that Eddie overcame. And so thanks to some amazing work that he put in there,
I think we're able to apply this kind of framework now a lot more generally to a lot of different settings and datasets.
So that holds the promise that you could simply take a blood sample from somebody who looked a bit
poorly and have a sense of how closely you needed to monitor them because of how far it might develop.
Right, exactly. Definitely. So that's definitely one of the ideas.
So, you know, again, maybe two quick points on that. The first is that this is something that we imagine to be clinically imminently translatable
because the basis of this is actually just measuring the expression of seven genes in the blood.
So it's a very small we've managed to shrink it down to a very, very small number.
So, you know, we started off with profiling everything, right? The omics, the unbiased nature.
But of course, you know, you go from that big sea of data to wanting to pinpoint what some of the specific factors.
And we've managed to successfully narrow down seven genes.
So you can imagine if you wanted to measure seven in a patient's blood, that's not too difficult.
Right. You could turn that around within an hour or two. And so that might be something you could do by the bedside.
So we find that to be very exciting prospect.
The second thing, a very key, I think, extension beyond just saying, oh, these patients are going to do poorly.
Is that so? This is work in a study, in a in a manuscript, which we are which is now under review.
Right. So we're hopefully going to be releasing yet another one to follow up on this work,
which goes beyond the the data predictions to really understanding some of the underlying biology I was talking about right at the beginning.
What are the biological mechanisms or features that underlie these groups of patients?
And I think we've really managed to get a good crack at that, I would say.
So hopefully that data comes out soon. But basically with that knowledge now we get closer to those pathways.
As I was saying, you know, with IL six or the AP one and so on, with COVID, we have a similar kind of framework that you could work on in sepsis.
And for that matter, in terms of the degree of severity in an infectious disease case, infectious disease patient.
And so then the excitement is beyond just that.
You could say, oh, these are the patients who would deteriorate, but then we can really highlight specific pathways which we think we should target.
So if you have something clinically actionable there, then I think that's really the beauty of doing that bench work that the,
you know, understanding the fundamentals of immunological biology and then bringing it all the way to the clinical use.
Really fantastic. Right. I think we've we've more or less got the hope that they at the end.
I've just got a final question really to ask whether the experience of working through the
pandemic and working on this project has changed your attitude or your approach to your work?
And and is there anything you'd like to see change in the future?
Yeah, I think. Yeah, you know, that's a quite a reflective question.
I feel like I've definitely thought about that a lot and talked to people a lot about it,
but I think you've maybe caught me a little bit off guard at this moment, specifically in time, I would say that, um.
Maybe I'm a little bit less uptight about.
Certain aspects of the research work after the experience because it was sort of very nerve wracking to go through it.
But then it feels like, you know, we've all come out the other end.
Now, on the other hand, you could maybe say that, you know,
I feel I've been lucky enough to you know, we've been lucky enough to come out the other end.
It's been a very, very traumatic experience in many ways for the entire world, I'm sure.
Right. So. So. And the other thing is that, you know, is this just a matter of having gone through the idea that,
you know, maybe you relax a little bit more after that? It's a bit hard to tease that apart.
And so I guess that, like you say, I should just say that this may be part of what you're saying,
that with the degree of responsibility that you had as a Ph.D. student was, I would have said in excess of what you would normally expect.
Maybe. But I think, you know, when I was doing, I did think about that.
Right. You know, I was just in the thing. You know, these were the things that we need to do.
These were the directions that we were going to go, let's just do it right. You know, I was just fully, fully, fully engaged.
And I think maybe you could say this thing hasn't changed, but I still think I guess that's in general how I approach my work, my life.
You know, I'm enthusiastic about the things I do, right? So so I pick and choose carefully, right.
You know, but if I'm if I've made that decision to choose that, then then I will put myself into it.
Right? So, so I think and so in that sense, it was a very rewarding experience to.
In terms of things that could be done differently, I think?
Well, again, this is a it's a tricky one.
And, you know, looking back, obviously, there were many things that we could have either done better or quickly,
more efficiently, had complementary approaches to help what we actually did.
So, you know what I was saying about how you profile a lot of patients.
You have all these data that you observed in different groups, but it's very hard to understand what causes what.
And you might easily make the point to say, well, why don't you build a model, you know, have a mouse model and then,
you know, try to actually interrogate some of the questions that you generate from your data.
So in a sense, that's an obvious improvement step, right?
But at the same time, what I also mentioned earlier, which was that, you know, this is already such a huge project on its own, right.
It was it required such a huge team, so many people, so much dedicated effort.
It's easy to say, like, well, why don't we actually add this thing?
But it turns out it's already enough of a sometimes that's that's a separate project like let's say.
Okay. I think that's great. Thanks very much indeed. Thank you, Cody.