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It should say something. Yeah. Okay.

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Can you just start by saying your name and what your current title is?

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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.

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So that was from 2018 and obviously during the COVID times as mine, I only just finished a few months ago, basically.

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Okay. Well, congratulations on finishing. Thank you.

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And so, first of all, can you just tell me a little bit about yourself from when you first got interested

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in in science generally and to specialise in the area that you're interested in that?

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Yeah. So I think with, as with a lot of scientists with COVID, it was a very accident, accidental thing.

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Well, I mean, okay, so first of all, the fact that the pandemic happened,

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you know, that in itself was already just a very sort of random event that way.

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Right. But originally I was interested in immunology that was as an undergraduate student.

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And then I did clinical school and I decided that I thought acute situations were really thrilling.

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So I thought that if I could try and find some research topic that would combine both of them, that would be the best.

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And then that's how I ended up. By acute situations, you mean kind of life or death relationship with the patient?

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Yeah. Yeah. So, so, yes, yes. So, so more emergency situations.

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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,

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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,

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and that's usually due to your immune response going a bit haywire.

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So I thought, well, that seems to be a really good fit, right? So, so let me see.

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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.

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And then, you know, a year or so and obviously COVID started.

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And so given that sepsis is, you know, all that infectious diseases and immune responses, then, you know,

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actually, if you think about COVID, it's a bit of a very similar parallel topic or a bit of an extension.

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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.

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So it was a very neat kind of marriage of the of the topics, and that's how I ended up getting into it.

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Okay. So so before we go on to talk more about the COVID project,

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let's talk a little bit more about sepsis and what your what the take of Julia night's lab was on approaching it.

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What? So what was it we didn't understand about sepsis?

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And what was your particular project aimed at trying to discover?

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Yeah. So the idea with sepsis is that it's an infectious disease problem, right?

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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,

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the bacteria or the virus, you know, the invasive aspect, or there's an ASP,

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there's an issue with the host response of the way the human system behaves.

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And so we looked at it from the perspective of the human right and we specifically looked at the immune response.

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So within that, the main problem that we faced as researchers, I think, was that sepsis is a bit of a broad, vague definition.

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So operationally it's very helpful because it defines the limits of what you can do as a doctor.

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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.

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So, you know, you move, you escalate in terms of the amount of medical care you give the people.

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But what that doesn't really tell you is what the underlying biology is that's going wrong.

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Right? So that's so then you just have this broad umbrella. And so what our aim was to do,

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our aim was basically to see if we could understand the patient in groups with the condition

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a bit better by giving a proper biological mechanism behind different groups of patients.

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So to stratify them, so to speak, that was the idea behind the lab.

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So the idea being that different people might respond,

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they might catch the same bacteria or the same virus, but their body responds in a different way.

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What according to their genetic makeup. Right.

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Well, so that, you know, the genetics is then obviously a need to answer or any potential potential question to investigate, let's say.

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And I think, yeah, there's been a lot of genetics done in sepsis.

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I would say that none of it has been very fruitful.

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But yeah, you know, so we look at every layer, so everything from the genetics to just upstream of that and then,

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you know, all the way through to the immune system and so on. And I focussed quite a lot on the immune system.

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Okay. So it was a what? I'm sorry. I'm going to dive down into this a bit. Yeah, sure, sure.

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The immune system has many components. What are the particular components of the immune system that you've been interested in?

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Yeah. So that was a bit of a journey as well, right. Because so obviously, first of all,

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there was a lot of literature already on how different aspects of the immune system were dysregulated in the septic response.

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So for example, I think, you know,

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when people get vaccines with COVID that people may have heard of T cells and B cells being thrown around these kind of terms.

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Right, because of the pandemic.

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And so there was a whole literature about t cell dysfunction in sepsis, for example, and that the T cells were overactive or undirected.

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What was wrong? Yeah. Mostly underactive, I would say.

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Mostly underactive, but then. The way we looked at it was a little bit different.

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So our initial approach was to take blood from patients, total blood.

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And it turns out that the proportion of T cells that are in your blood is actually very, very low.

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Right. So so in the in the initial experiments that we were running, this is before I even joined the lab,

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the initial experiments from Juliane and others in the group.

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If you take the whole blood and then you just profile the total amount of RNA in the blood,

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and then you see if you can stratify patients with some various machine learning based algorithms and advanced statistical analysis and so on.

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Then you can it seems like we were getting meaningful subgroups of patients, right?

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Meaningful in the sense that certain gene expression signatures which were very reproducible,

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seemed to mark out groups of patients who would deteriorate much more quickly than others.

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Right. So it seemed like we were getting some meaningful biological signals.

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You know what I was saying about getting the biological mechanism behind the different groups, right?

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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.

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Let's dive deeper into it. So that initial finding was when you just took blood in bulk.

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Right. And so my question was very much, well, what are the cellular contributions to that signal that you're seeing?

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Which cells do the signals derive from which cells are therefore most likely, you know, malfunctioning in some way or another?

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How are they malfunctioning? Those are the questions that I wanted to address in the fleet.

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Right. So one so for ranked cells, have we got swimming around in our blood?

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Right. Right. So you could maybe broadly split it into what we call lymphoid and myeloid lineages.

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So lymphoid, you know, the T cells in the B cells,

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these are lymphocytes that people are both maybe have heard of and that those are the cells which are really exciting.

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And I think, you know, more historically, immuno, immuno immunologists were particularly excited about them in the sixties, in the seventies,

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because those were the cells where the exciting aspects of immunity seemed to reside in,

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such as the ideas of immune memory and immune specificity, the fact that you can target a pathogen very specifically.

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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.

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That's basically how the vaccines work, right? So that's the lymphoid side of things.

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And so in a sense, there's also a very much neglected other side,

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which is the myeloid lineage where you have cells such as things called granulocyte or monocytes.

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We ended up looking a lot at the granular science, but those cells I think are a little bit more poorly understood.

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And I think traditionally the remit has been more with the haematologists rather than the immunologists looking at that.

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And that's because the cells have been ditched by the immunologists to some extent.

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But I think in recent years there's been a lot more excitement in looking at those myeloid cells,

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which are which also another, you know, another bit of terminology.

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But if you broadly divide immunity into innate and adaptive, so adaptive being the immune system,

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which again has that specific function, has the memory function.

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So those are those lymphocytes, whereas the innate arm is the arm that responds more quickly and has a more non-specific response.

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And those are those myeloid cells more. So I would say. Yeah.

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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.

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Yeah. So the first way we did it was what we tried to actually go in with a slightly more unbiased lens, right?

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So, so this was one of the more initial questions that I was challenged when I first started the application.

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So around the time I started there were these very exciting new technologies that

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were coming along called single cell RNA sequencing or single cell sequencing,

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we can call them. So that is to say, imagine if you could literally take every individual cell in your sample or your body,

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the sample material working with and you could actually profile all of them individually.

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Look at all of the RNA or DNA molecules, whatever your choices, all of those molecules in that individual cell.

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Right. So it's a very, very powerful technology.

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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.

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And so when I started the PhD, this was starting to become quite a hot topic.

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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.

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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,

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you might have some guesses based on prior literature, but you can be sure you know 100% sure, right?

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You might want to use techniques such as this kind of single cell sequencing so that

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you can profile all of the cells and then you're sure not to miss any of the signals,

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per se, right? Yeah. And.

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Yeah. And so how far would you go with that? Well, it would fail.

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Yeah. Yeah. So because I was just starting, I'd never done this kind of work before.

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We we didn't get very far. We just ran some pilot experiments.

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It took more than a year to set up because in parallel, other than the lab side,

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other than the technological side, we were also basically setting up the clinical study.

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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.

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So we actually spent a lot of time working out the ethics and the clinical study and then starting to actually recruit patients.

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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.

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So you were you involved personally involved in recruiting the patients as well?

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Yes. So because of my clinical background, I basically did the entire study from scratch.

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Right. I wrote it up from scratch.

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And then I met all the people in the hospital, all the all the teams involved, to all the research nurses on the ground.

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And of course, as the research team, I wasn't actually meeting the patients directly, not in terms of the recruitment,

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but I was liaising very closely with that, closely with the research nurse teams who would then contact the patients.

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And then we also did a lot of work, for example, with patient groups, you know, patient groups with sepsis,

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so that we could understand from their perspective what they would want to be involved in,

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whether they thought this was useful at all, you know, engage them a bit of education.

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Yeah. Yeah. So I was doing a lot of that in the first year, actually. Excellent.

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So, yes, so we get let's get to to the arrival of COVID.

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Can you remember I mean, obviously, you come from from from Hong Kong yourself.

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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.

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And I think people I've interviewed have had a range of initial reactions from, Oh, it can be like sighs.

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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.

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So what what was your how did you first hear about it and what was your initial reaction?

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So I first heard about it towards the end of 2019.

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I definitely remember it was it was still December then.

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And I think the initial reaction was I wasn't too worried in the sense that it didn't seem too close yet.

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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.

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It's just a bit impossible to predict. So in that sense, I wasn't necessarily very worried.

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I think I was also then then, you know, in another month or two's time, by the time January,

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February 2020 rolled around, it definitely seemed like it was a bit more serious.

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Right. I think at that point, I still wasn't very worried, at least to the extent that.

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I felt like I was personally quite prepared for issues of infection spreading through

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communities because I'd lived through sores myself and I thought that that was a.

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Preparation experience, if you if you if you will. You know, we we all survived through that one.

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But then, obviously. They were very different in the sense that this time, you know, it was a full on pandemic.

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Right. It really spread across the world. I think I didn't necessarily see that coming.

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But then I guess the question is for the people who say they saw it coming.

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You know, how how much predictive accuracy do you have if something else happens the next time around?

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Right. And, you know, you can always say that, oh, this one looks really bad.

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This one looks really bad. Is it always going to be really bad? It's very hard to say.

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Right. So I was. I think I was generally more worried during the thing than like in terms of any anticipatory anxiety.

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Yes, yes, yes, yes. Okay. We'll talk bit more about that later.

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So at what point did Julian and the lab decide that you were ideally placed in the way to to tackle,

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you know, to use the techniques that you were using to tackle COVID itself?

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Yes. In fact, before the virus actually hit the UK in January 2020, when things were actually still quite normal in the U.K.,

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we were already looking into it because Julian had some collaborations with a group in Beijing,

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which was a virology group, and so they were obviously, you know, working hard on the problem already.

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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,

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leveraging what we'd done on sepsis before, you know,

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whether there were similar similarities and whether we could parallel up parallels, any kind of work.

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So we'd already put in a little bit of thought there. And then, of course, COVID actually hit, right?

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And so. I think we were.

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Not necessarily ahead of the game, but it was just because of that collaboration that it just prompted us to think quite early,

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you know, before it even hit the UK. And so, I mean a study was set up with the title combat.

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You just tell me what that, what that stood for.

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And we'll talk a bit more about how it how it came together.

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Yeah. Yeah. So the combat was the name for the consortium, the COVID Multi-omics Blood Atlas.

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So the COVID being COVID right, the multi omega pot is where when I was saying talking about single cell sequencing earlier on.

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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,

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you just sequence all of them first and then see,

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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.

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So your, your genome, right. Is your whole whole genetic makeup.

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Right? Or you can see the transcriptome, which is the way the genes are expressed.

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And that's how all of the genes are expressed as the transcriptome rather than each individual one.

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And so that's the omics.

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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,

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at the level of the RNA, at the level of the proteins and so on and so forth.

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And because we were heavily focussed on blood sampling, so that was the Blood Atlas.

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So we were building this entire atlas of how the human was responding to the

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virus through a blood window and with these many different layers of the data.

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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.

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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,

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you know, basic comparison is you compare people with COVID with healthy individuals.

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Right. And then there was a tricky thing as to how you could get age appropriate controls.

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Right? Because actually a lot of the people who are being hospitalised with COVID or

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especially getting critical illness were people on the older age or older spectrum.

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Right. It wasn't as many 20, 30 year olds. There were some.

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But that was, you know, fewer, fewer proportion at a lower proportion for sure.

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So then there was a very tricky thing of having to find older, healthy controls.

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And actually we had some recruitment drives where we distributed posters to various colleges asking for various professors,

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you know, of slightly more advanced ages to participate.

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And it was it was quite it was actually we got quite a doozy asking responses.

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And so what do you need? It from them was a blood sample.

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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.

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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.

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So that ranged from individuals who were hospitalised but didn't necessarily need much additional medical support other than just sort of monitoring.

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And then we had people were critically ill in intensive care from COVID.

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So those who were actually septic. Right.

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And then we had some individuals who were in the community who caught COVID, didn't even need to be hospitalised.

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So we had the whole we spend a whole range of severities and then we still had a couple of more

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comparisons where we managed to source some cohorts of patients who had previously had flu,

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the flu infection and were critically ill as well in intensive care.

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So that provided a very, very good control to the COVID critically ill COVID patients,

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because then you had two groups of patients which were both critically ill,

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but from different pathogens, and you could see how the immune response was going differently.

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And then there was a final one where we had some or all cause or, you know,

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mostly bacterial causes really of septic patients who were also very, very ill.

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And again, they were not caused by COVID, but illness.

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And we could compare those immune responses again with the individuals who had COVID.

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So we had a whole range of comparisons. So combat, as its name suggests, was a consortium.

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I mean, I've I've seen the publication that came out at the end of this, which we'll talk about.

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It's got a lot of names on it. How was that consortium put together?

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So I think it was very organic, right? I think a lot of things really started in Julian's lab.

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And between us, the two of us, you know, having meetings are just talking about, well, what are we going to do about this?

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But it soon became clear that we would need just a lot of on the ground support logistically.

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Right. And especially if you consider the context, which is that this was a an acute public health emergency.

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Right. I mean, I think that's one of the things that made it particularly impressive was, you know, it's there you know,

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we were very proud to be part of and proud of achievement, essentially, which is that it was hectic already enough as it were.

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Right. So in a way, the list of names. Includes, I think, just everyone who was even more peripherally involved in the project.

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For example, all of the all of the a lot of the ground stop and the hospital who participated and contributed back.

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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,

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sample processing teams which were equipped to deal with coded samples,

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as I say, equipped because, you know, this was at the beginning of the pandemic.

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We were much more worried about the pathogen than we currently are now. Right.

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So, you know, then at that point, things had to be done at very high biosecurity levels and so on.

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And so, you know,

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we had sample processing teams in place and then the teams which would actually generate data and then data analysis teams and so on.

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So it was it was quite organic in the beginning.

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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,

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it was such a big thing and it was it was a great scientific opportunity as well.

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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.

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And you personally, I mean, again,

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I've looked at the contributions of all these different people and your name tags up under most of the that the headings.

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So were you playing a coordinating role. Yeah.

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So I think I was I was definitely involved in basically every facet of the, of the project, as it were.

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I wasn't really the coordinator per se in the sense that I wasn't supervising the project.

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So that was really much more of Julian's role.

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But in terms of then all of the ins and outs of because if you think about the way the study came about,

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so it's a combat was the name of the sort of the study, the group, right.

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The majority of the patients were recruited under that initial study that I'd set up in the first year of my people.

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So. I was probably I was the best place person to know everything about the study write.

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You know, I'd written it up from scratch. I had designed it up from scratch like that.

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So I knew where all the ethics, you know, what it was all about, what the framework was.

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I knew all the research teams, so it was very, very close liaising with them.

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In fact,

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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.

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But I very distinctly remember the first two patients,

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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.

254
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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.

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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

256
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constantly in addition to obviously the round the clock scientific work.

257
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Right. So, yeah. Mm hmm. And and so, I mean, is it too soon to jump to the the outcome?

258
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No, I don't think it is. What what what?

259
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Once you know, I think we should talk a little bit about the analysis.

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So you collected huge amounts of data, as you say, in this rather unbiased way, which just finding out what was there.

261
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How did you go about the analysis? Right.

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So yeah, I guess there are maybe two things that are worth sort of flagging up.

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One is that they're a more practical, logistical thing.

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But it was it's to say that we had a lot of analysis teams because there was so much data,

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as you said, that we needed many people and a lot of different expertise.

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It was multi-omics, right? So there was data from, you know, gene expression that was data from protein and so on.

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And I wasn't necessarily an expert in all of them.

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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.

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And then what was very interesting was that around the same time as how well these things develop in parallel, right?

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So we we started to have these technologies to probe biological samples in this data heavyweight in the lab.

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And so then at the same time, people were coming up with very interesting computational methods to analyse the data.

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And so one of the more interesting things to highlight was that and strength, I think,

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of the study was that we were trying out some very novel analytical methods to try to integrate the many different layers of data.

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And so there were, you know, a number of people on, on that team.

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And I was part of that team. I wasn't leading that team.

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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,

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you know, an abstract mathematical notion as well. So that was a very, very exciting opportunity.

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Yeah. And from looking at the paper, you really work very hard on the visualisation side of it,

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making those the results clear or the analysis clear through different kinds of data visualisation.

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Yes. And I think this is something that I learned quite a lot from Julian as well, I think,

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over the course of years, which is that the data needs to be presented very clearly.

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Right. There needs to be not not only does there need to be no ambiguity, but it needs to really flow.

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Right, because otherwise it's hard to follow the story as given how complex it already is.

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Right. If I was just describing, I'd describe the multi-omics.

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I'd describe how many cohorts of patients we had. Right.

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You know, if you imagine just intersecting those two things, you already have a big, you know, a large combination of variables there.

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Right. So imagine how difficult it is to follow that. Right.

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It's like if you're watching a TV series and you have many different characters except, you know,

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it's all condensed in this paper as well with all this data you're trying to interpret.

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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,

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there would be common threads that you could sort of follow to help you really to guide through that the reading of the story.

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And what were the key headlines of the of the findings from the study?

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So the key headlines we will highlight, too, I think one of them was that.

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So if you think about COVID, I will SARS-CoV-2 write the virus itself.

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Very often when we think about immune responses, there is some amount of tailoring of your immune response to the type of a pathogen.

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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.

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And because of the uniqueness of our cohort, the fact that we had those many different comparative groups and then we had different pathogens,

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what we could really see was that COVID would elicit, in fact, of course, as expected, atypical antiviral response.

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But what was quite interesting was that the severity of illness did not seem to track that antiviral response.

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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,

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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.

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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.

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But that was one of the more novel pieces that we could get from combining so many different layers of data,

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which I think other people have also seen now.

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But then this was one of the more initial central reports of of this COVID, this aspect of the COVID response.

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And it's so different people have variations in that that kind of percent of of genetic

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material that you're talking about which influence severely the disease impacts.

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And. Yes, yes. And this was a very uniquely COVID thing.

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Right.

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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.

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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.

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Right. So we were able to do it through this kind of multi-omics layering.

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And I think the second thing maybe then to highlight rather than the biology, but more something from the technical aspects.

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So what I was briefly alluding to earlier, but I think in a way, the the paper was also a.

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A demonstration of the success of using some of these more novel analytical algorithms.

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Right. So it was a bit of a technical achievement. And as as one of the examples, we use many, many different methods,

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but one of the examples we use the kind of tensor decomposition method, which is to say, if you imagine your many different datasets.

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Right. So where you have each dataset, you can imagine there are two dimensions, right?

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One dimension is the number of samples that you have. Right? So, you know, you have a table, right?

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You have a table where the columns of the samples, let's say, and the rows are the measurements that you take.

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Right. So a nice 2D table.

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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.

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Right? So one is, you know, blood cells, one is the blood proteins and so on.

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And if you combine all these tables and make it 3D, then then you have what's mathematically called the tensor, right?

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And so how might you go about decomposing that tensor to find structure in the data?

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So that's, you know, not a trivial question to answer, right?

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So we were able to really apply these kind of methods to these datasets.

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And I think that was a real demonstration of the power of some of these new algorithms.

329
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Do you and I should perhaps fast is beginning it, but it sounds like a silly question.

330
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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

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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.

332
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Yeah. So I'll give you two examples.

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One not immediately related to the study, but it'll make sense.

334
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But so there are a lot of anti COVID treatments now which are given based on our understanding of the

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biology of the immune system and not necessarily aspects which are unique to COVID individually.

336
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But they were trialled because they've been very well understood those aspects of the immune system for many years now,

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and they've been used in many other different settings and therefore they will, you know, are already licensed products basically.

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And so people trialled them on COVID patients and you know, some of them work, some of them didn't.

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And then the ones that have worked, they're obviously now currently being used.

340
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But that's all thanks to things like dexamethasone, the sterile steroid dexamethasone, which.

341
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Right, dexamethasone. But I was thinking.

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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.

343
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Right.

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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.

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And that was, you know, years, decades and decades of work.

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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.

347
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You know, what is the what are the systems in biology that work that make humans work the way they do?

348
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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.

349
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And then you have these kind of drugs right now.

350
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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.

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But then to highlight, as I was saying earlier, that AP one pathway, which, you know,

352
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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.

353
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But so one of the thoughts would be that Long-covid is obviously a big issue, right?

354
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A lot of people have it. It's something like 5% or maybe even more people get it.

355
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And I think it's really poorly understood. Right. Exactly what's going on now in our cohort.

356
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You remember I said that we had some individuals who were ill with COVID but didn't need to be hospitalised.

357
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And in fact, some of these individuals were probably in what we call the convalescent phase already of the illness.

358
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They were already recovering. But what was very interesting was when you looked in these patients,

359
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it seemed like they would still show signatures of that COVID response in terms of that AP one program that I was explaining.

360
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And in fact, in some cases that seemed to be even more highly activated, that program than in the acute phase of the illness.

361
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And yet this program was not activated in other aspects of critical illness.

362
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Right. So you can see how now you're starting to get hints of, well,

363
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is this going to be one of those pathways that you really need to probe into

364
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and understand what its relationship is with longer term sequelae of COVID,

365
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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?

366
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And so, you know, these are the kind of fundamental things which maybe in another five years, ten years or something, you know,

367
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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.

368
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This is the pathway that we should really act on to treat people. So that's one example.

369
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But one can imagine that with such an enormous dataset, actually,

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you've generated enough new questions for lots of people to explore for some time to come.

371
00:33:17,980 --> 00:33:22,620
Yeah, no, for sure. And I think so. So that's where there's strengths and limitations, right.

372
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But with these kind of studies, I think the one of the biggest limitations ultimately is that we're talking about observational data, right?

373
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So we have many different patient groups and we collect the data and it's observational, but it's not necessarily perturbation.

374
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So we haven't given people any drugs or we're not trialling things on mice in the lab or something like that.

375
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Right? So you can never be 100% sure where the various effects that you observe are necessarily causal.

376
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And so that's where, you know, you have to go back to the lab,

377
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you have to develop your model systems and then build on the questions that we've really come up with in the study.

378
00:33:59,830 --> 00:34:02,950
But of course, this was, you know, such a big project on its own already, right?

379
00:34:04,570 --> 00:34:09,640
Yeah. So did that become the topic of your your dphil?

380
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And suddenly chunks of it. Yeah. So, so I would say about yeah.

381
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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

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sepsis work which I actually ended up going back to basically after completing the combat project.

383
00:34:31,720 --> 00:34:37,570
Yeah. So what was the timeline of it. When, when did you regard the the Copan project as complete.

384
00:34:39,490 --> 00:34:46,330
Yeah. Well let's say yeah, that's a good question. I mean, you know, combat finished combat was released earlier this year.

385
00:34:46,330 --> 00:34:53,530
Right. And of course, we were months before that it had already been completed and been in the pipeline for being published.

386
00:34:55,120 --> 00:35:06,219
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,

387
00:35:06,220 --> 00:35:09,280
I would say mostly because I was still doing various things on on on COVID,

388
00:35:09,490 --> 00:35:13,870
just not necessarily on the combat project, but all the strands that we were still trying to follow up on.

389
00:35:14,350 --> 00:35:25,809
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

390
00:35:25,810 --> 00:35:30,969
a ridiculously good learning experience and what an amazing opportunity in terms of the science,

391
00:35:30,970 --> 00:35:34,930
you know, meeting the people that I did and learning from everybody, you know, in the team, that was a huge privilege.

392
00:35:36,350 --> 00:35:40,180
Yeah. You're not the only person who's said so.

393
00:35:40,220 --> 00:35:45,950
Some of them were slightly reluctant to say it, but that COVID did open up amazing opportunities.

394
00:35:45,950 --> 00:35:48,229
But there are a number of people in their research,

395
00:35:48,230 --> 00:35:57,980
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.

396
00:35:57,980 --> 00:36:03,170
But clearly, in the in the research area, it did open it opened lots of doors.

397
00:36:04,470 --> 00:36:11,030
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.

398
00:36:11,030 --> 00:36:14,839
I because people always ask me, right, you know, they say, well, how is it, you know, during that time?

399
00:36:14,840 --> 00:36:21,440
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.

400
00:36:22,160 --> 00:36:27,830
But I said that honestly, I wouldn't complain to the extent that I was so lucky in the sense that the work well,

401
00:36:27,830 --> 00:36:31,459
I know so many of my other friends who are so heavily affected.

402
00:36:31,460 --> 00:36:36,020
Right. You know, their projects can run and so on. And then of course, the pandemic itself was also raging.

403
00:36:36,020 --> 00:36:39,679
You know, people were worried about their personal safety and health. Right. Myself as well.

404
00:36:39,680 --> 00:36:43,129
But, you know, so there were so many all the things everyone had to contend with.

405
00:36:43,130 --> 00:36:46,280
I count myself as having been so lucky in that period. Definitely.

406
00:36:47,240 --> 00:36:53,060
But it was. Yeah, you did. You were rough. What was your personal life like through that?

407
00:36:53,120 --> 00:36:56,240
Through that period? What kind of hours were you working? Where were you living?

408
00:36:56,840 --> 00:37:01,850
How did you cope with with the social distancing regulations, all that kind of thing?

409
00:37:03,140 --> 00:37:08,990
So for me, it was a it was such a surreal experience because because of the lockdown.

410
00:37:10,670 --> 00:37:14,749
There was basically not much contact with anybody.

411
00:37:14,750 --> 00:37:22,010
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.

412
00:37:22,020 --> 00:37:25,549
So I was I was given one of those essential worker status letters.

413
00:37:25,550 --> 00:37:30,470
I still remember that. I think I still have a copy of that because I thought it was such a strange thing.

414
00:37:30,480 --> 00:37:33,950
It was such a strange feeling to be holding that piece of paper and then thinking, okay, well,

415
00:37:33,950 --> 00:37:38,090
I'll take the bus in the morning, you know, at seven and then get into the lab and then get the day going.

416
00:37:39,230 --> 00:37:44,180
It was very much round the clock work for many months.

417
00:37:44,660 --> 00:37:55,580
And, you know, I remember having had conversations with team members with some sort of, you could say quite hilarious comments being thrown around,

418
00:37:55,580 --> 00:38:01,909
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.

419
00:38:01,910 --> 00:38:05,680
But then, you know, we were already working really at the limits, right?

420
00:38:05,690 --> 00:38:08,810
As in we were going full throttle. So.

421
00:38:08,980 --> 00:38:13,550
So it's very, very intense. The rough one.

422
00:38:13,670 --> 00:38:19,430
One of the roughest parts was the fact that I just didn't have much social contact

423
00:38:19,430 --> 00:38:27,650
because most of my friends also had left Oxford either just as things had,

424
00:38:27,890 --> 00:38:32,360
you know, started getting bad or right after lockdowns had happened.

425
00:38:32,750 --> 00:38:36,500
But then because of the stuff I was doing, you know, I had to be there to do it right.

426
00:38:36,980 --> 00:38:44,960
So I was basically completely on my own for months for that that whole stretch between March to July,

427
00:38:44,990 --> 00:38:50,450
August time before before the first sort of opening up right in the UK and.

428
00:38:51,840 --> 00:38:55,200
I mean, I think in a way I was able to keep going because I was so busy.

429
00:38:55,500 --> 00:38:58,829
But it was very, very strange. It was definitely very strange.

430
00:38:58,830 --> 00:39:03,510
And I think I guess I was kept sane by just, you know, making sure that I would still talk to people,

431
00:39:03,510 --> 00:39:07,290
even though I didn't necessarily have the physical contact. I would still call people.

432
00:39:08,220 --> 00:39:09,330
I again,

433
00:39:09,330 --> 00:39:16,530
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,

434
00:39:16,530 --> 00:39:21,510
you know, work from home or couldn't work. Of course, there were many more who were much more unfortunate that way.

435
00:39:22,140 --> 00:39:26,820
And where were you? Where were you living at the time? So I was just living in the city.

436
00:39:27,060 --> 00:39:33,660
I was not that close to the lab. Well, so this is what sort of what a lockdown does, right?

437
00:39:33,990 --> 00:39:38,700
My normal morning commute would take while the daily commute would be 45 to

438
00:39:38,700 --> 00:39:43,380
45 minutes to an hour from where I left it to the lab on about an enduring.

439
00:39:43,560 --> 00:39:46,890
Yes, but I change at some point.

440
00:39:47,400 --> 00:39:55,049
And then with COVID, it became something like 20, 25 minutes at half the time, literally, because there were just no people around.

441
00:39:55,050 --> 00:39:58,230
Right. So no stops or anything. You just it was a straight shot kind of. Yeah.

442
00:39:58,230 --> 00:40:04,110
So that was that was a very noticeable what even a flat to or a college room or what sort of.

443
00:40:04,110 --> 00:40:08,399
Yes, I was I was in a flat. I was I was renting a flat with a college friend.

444
00:40:08,400 --> 00:40:12,990
But then he'd gone home on your own. Yes, exactly.

445
00:40:12,990 --> 00:40:16,170
Exactly. Yes. He'd he'd returned to Italy and so I was on my own.

446
00:40:17,380 --> 00:40:20,460
Yeah. And your family were your family in Hong Kong?

447
00:40:21,060 --> 00:40:23,640
Yes. So my entire family was in Hong Kong.

448
00:40:24,390 --> 00:40:29,490
And I think especially during 2020, Hong Kong didn't have a particularly rough time, so I wasn't very worried.

449
00:40:30,330 --> 00:40:35,310
And this was another thing that I always mentioned to other people, but I said that I was,

450
00:40:35,550 --> 00:40:39,900
in a way, as scary as it was, you know, personal safety public health wise.

451
00:40:41,130 --> 00:40:48,180
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,

452
00:40:48,180 --> 00:40:52,360
very early on about how the elderly were by far the ones most at risk.

453
00:40:52,360 --> 00:40:58,650
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.

454
00:40:58,920 --> 00:41:03,970
Yeah, but living on my own. I didn't have that baggage necessarily and.

455
00:41:05,620 --> 00:41:11,200
And and so. Yes. So you completed your your PhD.

456
00:41:12,220 --> 00:41:15,850
Have you go back to clinical work now or are you are you still doing this research?

457
00:41:16,540 --> 00:41:19,659
Yeah, so I am actually. I'm with the Chinese University of Hong Kong now.

458
00:41:19,660 --> 00:41:29,410
And so they've been very supportive of the junior staff who are aspiring to do both clinical work and research work like myself.

459
00:41:29,830 --> 00:41:34,510
And so I'm in a position where I spend about three quarters of my time doing clinical work,

460
00:41:34,810 --> 00:41:39,370
but then the other one quarter of the time I'm actually attached to a lab and so I'm doing the

461
00:41:39,370 --> 00:41:44,290
research there and now I'm sort of more full time into that analysis rather than the wet lab work,

462
00:41:44,290 --> 00:41:48,189
because it's a bit hard to combine clinical work where you have very heavy contact time.

463
00:41:48,190 --> 00:41:53,079
You have to see the patients day in, day out kind of thing and then also try to run experiments at the same time.

464
00:41:53,080 --> 00:41:56,020
Right? The only way you could do that is basically probably run the experiments at night.

465
00:41:56,020 --> 00:42:04,690
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,

466
00:42:04,690 --> 00:42:13,210
you know, slaving away with the pipettes. Yeah. And are you continuing with the same ideas that you worked through during your Ph.D.?

467
00:42:13,900 --> 00:42:21,370
So it's been a bit of a stretch. I've actually made a switch to neuroscience, but then, yeah, so radically different.

468
00:42:21,370 --> 00:42:25,149
But I think actually at the end of the day, to me it's all biology.

469
00:42:25,150 --> 00:42:31,810
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.

470
00:42:32,140 --> 00:42:38,170
And so really applying those kind of lenses to interrogating neurobiological questions rather than immunological questions.

471
00:42:38,410 --> 00:42:45,790
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.

472
00:42:45,790 --> 00:42:49,959
So in recent years, we've there's been quite an explosion, I would say,

473
00:42:49,960 --> 00:42:54,760
of understanding that there are actually many links between the nervous system and immune system.

474
00:42:55,210 --> 00:42:59,050
And these are many things that we're not really aware of consciously on a day to day basis.

475
00:42:59,050 --> 00:43:05,140
Right. But these kind of cross talks are probably very relevant biologically, and I think there's a huge amount to be discovered there.

476
00:43:05,140 --> 00:43:09,490
So who knows? But maybe that's the kind of stuff that I'll be getting into in the years to come.

477
00:43:10,690 --> 00:43:13,870
That sounds really exciting. I shall have to come back to you again.

478
00:43:13,910 --> 00:43:20,840
But you've done some more work on. So.

479
00:43:23,250 --> 00:43:29,520
Yeah. Oh, yes. Sorry, I just on the question that I'd written about the another published your science paper.

480
00:43:29,900 --> 00:43:37,920
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.

481
00:43:39,590 --> 00:43:44,210
Oh, yes. The. Yes. Yes. More recently released one.

482
00:43:44,270 --> 00:43:48,410
Yes, very. Yes, I think it was. Yes, yes. Yes. That's in the last month or so.

483
00:43:48,460 --> 00:43:56,000
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.

484
00:43:56,000 --> 00:43:59,190
And then, you know, he was building on the.

485
00:44:00,440 --> 00:44:10,799
So that was. It was more more clinically translational related than mechanistically biologically related, I would say.

486
00:44:10,800 --> 00:44:13,950
Right. But yes. So so now we're going back to sepsis, right. Rather than COVID.

487
00:44:14,100 --> 00:44:21,399
Yes, yes. But. Right at the beginning, we were talking about how you could take some blood, just whole blood from patients,

488
00:44:21,400 --> 00:44:26,440
and then you could stratify them into at least two groups that's called amino groups one and two, right?

489
00:44:26,440 --> 00:44:31,150
Where, let's say group one does more poorly. They deteriorate more quickly.

490
00:44:31,160 --> 00:44:35,799
Right. And one of the questions we were interested in was to say,

491
00:44:35,800 --> 00:44:46,060
have we captured essentially an axis along which individuals with infectious disease present in terms of their severity and,

492
00:44:46,450 --> 00:44:50,169
you know, obviously some kind of biological meaning because we're using gene expression as a readout.

493
00:44:50,170 --> 00:44:54,640
Right. And so we did this. We derived this originally in sepsis patients.

494
00:44:55,030 --> 00:45:02,560
So then the question was, can we extend this over to other settings of either critical illness or infectious disease?

495
00:45:03,280 --> 00:45:09,009
And so what that new project was about was this generalisation.

496
00:45:09,010 --> 00:45:18,220
And we basically managed to take a lot of publicly available datasets as well as dataset that we generated ourselves and show that,

497
00:45:19,720 --> 00:45:23,770
yes, that original description that we had, we called them the sepsis response signatures.

498
00:45:23,770 --> 00:45:28,239
You could in fact recapitulate that signature in COVID patients and flu patients

499
00:45:28,240 --> 00:45:34,360
and sepsis patients or even potentially in patients who didn't have infection.

500
00:45:34,370 --> 00:45:40,870
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.

501
00:45:41,050 --> 00:45:47,410
Much like how, you know, if you have a cough and a fever, you think, and then eventually you need oxygen support.

502
00:45:47,410 --> 00:45:51,680
You think that the person who needs oxygen support probably has the worse illness than just the cough.

503
00:45:51,680 --> 00:45:56,140
Right. Except we've got this on a biological axis. So that was the kind of work that we were trying to do.

504
00:45:56,500 --> 00:45:58,360
And along with that,

505
00:45:58,360 --> 00:46:08,769
there was a very big technical challenge as to how you could actually do this analysis in these different datasets in a robust manner,

506
00:46:08,770 --> 00:46:13,870
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.

507
00:46:14,050 --> 00:46:19,420
And so that was a huge challenge that Eddie overcame. And so thanks to some amazing work that he put in there,

508
00:46:19,630 --> 00:46:25,510
I think we're able to apply this kind of framework now a lot more generally to a lot of different settings and datasets.

509
00:46:27,070 --> 00:46:32,530
So that holds the promise that you could simply take a blood sample from somebody who looked a bit

510
00:46:32,530 --> 00:46:40,720
poorly and have a sense of how closely you needed to monitor them because of how far it might develop.

511
00:46:41,230 --> 00:46:44,559
Right, exactly. Definitely. So that's definitely one of the ideas.

512
00:46:44,560 --> 00:46:52,810
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

513
00:46:52,810 --> 00:46:57,879
because the basis of this is actually just measuring the expression of seven genes in the blood.

514
00:46:57,880 --> 00:47:00,880
So it's a very small we've managed to shrink it down to a very, very small number.

515
00:47:00,880 --> 00:47:04,750
So, you know, we started off with profiling everything, right? The omics, the unbiased nature.

516
00:47:05,080 --> 00:47:11,200
But of course, you know, you go from that big sea of data to wanting to pinpoint what some of the specific factors.

517
00:47:11,200 --> 00:47:13,509
And we've managed to successfully narrow down seven genes.

518
00:47:13,510 --> 00:47:17,649
So you can imagine if you wanted to measure seven in a patient's blood, that's not too difficult.

519
00:47:17,650 --> 00:47:22,180
Right. You could turn that around within an hour or two. And so that might be something you could do by the bedside.

520
00:47:22,180 --> 00:47:24,010
So we find that to be very exciting prospect.

521
00:47:24,550 --> 00:47:30,040
The second thing, a very key, I think, extension beyond just saying, oh, these patients are going to do poorly.

522
00:47:30,040 --> 00:47:37,749
Is that so? This is work in a study, in a in a manuscript, which we are which is now under review.

523
00:47:37,750 --> 00:47:41,530
Right. So we're hopefully going to be releasing yet another one to follow up on this work,

524
00:47:42,100 --> 00:47:50,979
which goes beyond the the data predictions to really understanding some of the underlying biology I was talking about right at the beginning.

525
00:47:50,980 --> 00:47:54,280
What are the biological mechanisms or features that underlie these groups of patients?

526
00:47:54,280 --> 00:47:57,550
And I think we've really managed to get a good crack at that, I would say.

527
00:47:58,360 --> 00:48:07,120
So hopefully that data comes out soon. But basically with that knowledge now we get closer to those pathways.

528
00:48:07,120 --> 00:48:13,719
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.

529
00:48:13,720 --> 00:48:21,490
And for that matter, in terms of the degree of severity in an infectious disease case, infectious disease patient.

530
00:48:21,970 --> 00:48:24,210
And so then the excitement is beyond just that.

531
00:48:24,230 --> 00:48:30,250
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.

532
00:48:30,520 --> 00:48:37,749
So if you have something clinically actionable there, then I think that's really the beauty of doing that bench work that the,

533
00:48:37,750 --> 00:48:45,580
you know, understanding the fundamentals of immunological biology and then bringing it all the way to the clinical use.

534
00:48:46,330 --> 00:48:52,600
Really fantastic. Right. I think we've we've more or less got the hope that they at the end.

535
00:48:53,410 --> 00:48:59,799
I've just got a final question really to ask whether the experience of working through the

536
00:48:59,800 --> 00:49:05,170
pandemic and working on this project has changed your attitude or your approach to your work?

537
00:49:05,170 --> 00:49:08,830
And and is there anything you'd like to see change in the future?

538
00:49:10,740 --> 00:49:17,399
Yeah, I think. Yeah, you know, that's a quite a reflective question.

539
00:49:17,400 --> 00:49:20,969
I feel like I've definitely thought about that a lot and talked to people a lot about it,

540
00:49:20,970 --> 00:49:27,640
but I think you've maybe caught me a little bit off guard at this moment, specifically in time, I would say that, um.

541
00:49:30,570 --> 00:49:33,930
Maybe I'm a little bit less uptight about.

542
00:49:36,630 --> 00:49:43,260
Certain aspects of the research work after the experience because it was sort of very nerve wracking to go through it.

543
00:49:43,260 --> 00:49:46,320
But then it feels like, you know, we've all come out the other end.

544
00:49:46,670 --> 00:49:48,659
Now, on the other hand, you could maybe say that, you know,

545
00:49:48,660 --> 00:49:52,560
I feel I've been lucky enough to you know, we've been lucky enough to come out the other end.

546
00:49:52,680 --> 00:49:56,210
It's been a very, very traumatic experience in many ways for the entire world, I'm sure.

547
00:49:56,220 --> 00:50:04,250
Right. So. So. And the other thing is that, you know, is this just a matter of having gone through the idea that,

548
00:50:04,250 --> 00:50:08,120
you know, maybe you relax a little bit more after that? It's a bit hard to tease that apart.

549
00:50:08,360 --> 00:50:11,929
And so I guess that, like you say, I should just say that this may be part of what you're saying,

550
00:50:11,930 --> 00:50:19,670
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.

551
00:50:21,530 --> 00:50:24,649
Maybe. But I think, you know, when I was doing, I did think about that.

552
00:50:24,650 --> 00:50:28,090
Right. You know, I was just in the thing. You know, these were the things that we need to do.

553
00:50:28,100 --> 00:50:32,600
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.

554
00:50:32,930 --> 00:50:39,860
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.

555
00:50:39,860 --> 00:50:44,080
You know, I'm enthusiastic about the things I do, right? So so I pick and choose carefully, right.

556
00:50:44,090 --> 00:50:49,069
You know, but if I'm if I've made that decision to choose that, then then I will put myself into it.

557
00:50:49,070 --> 00:50:52,880
Right? So, so I think and so in that sense, it was a very rewarding experience to.

558
00:50:54,980 --> 00:50:58,990
In terms of things that could be done differently, I think?

559
00:51:00,020 --> 00:51:02,210
Well, again, this is a it's a tricky one.

560
00:51:02,510 --> 00:51:08,650
And, you know, looking back, obviously, there were many things that we could have either done better or quickly,

561
00:51:08,660 --> 00:51:14,180
more efficiently, had complementary approaches to help what we actually did.

562
00:51:15,320 --> 00:51:17,959
So, you know what I was saying about how you profile a lot of patients.

563
00:51:17,960 --> 00:51:22,610
You have all these data that you observed in different groups, but it's very hard to understand what causes what.

564
00:51:23,060 --> 00:51:29,570
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,

565
00:51:29,960 --> 00:51:34,400
you know, try to actually interrogate some of the questions that you generate from your data.

566
00:51:34,640 --> 00:51:37,940
So in a sense, that's an obvious improvement step, right?

567
00:51:38,120 --> 00:51:42,949
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.

568
00:51:42,950 --> 00:51:47,690
It was it required such a huge team, so many people, so much dedicated effort.

569
00:51:47,990 --> 00:51:50,809
It's easy to say, like, well, why don't we actually add this thing?

570
00:51:50,810 --> 00:51:56,450
But it turns out it's already enough of a sometimes that's that's a separate project like let's say.

571
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Okay. I think that's great. Thanks very much indeed. Thank you, Cody.