1 00:00:00,200 --> 00:00:01,360 Good evening. 2 00:00:01,360 --> 00:00:03,360 I am Professor Jonathan Van-Tam, 3 00:00:03,360 --> 00:00:05,760 Deputy Chief Medical Officer for England. 4 00:00:05,760 --> 00:00:08,560 And many of you may have seen me 5 00:00:08,560 --> 00:00:11,360 giving public health announcements from the podium 6 00:00:11,360 --> 00:00:14,200 at Number 10 Downing Street. 7 00:00:14,200 --> 00:00:16,640 But tonight is different. 8 00:00:21,520 --> 00:00:26,400 Tonight, I am speaking to you as a scientist and a doctor, 9 00:00:26,400 --> 00:00:28,720 not a government adviser. 10 00:00:28,720 --> 00:00:32,800 Welcome to the Royal Institution Christmas Lectures. 11 00:00:32,800 --> 00:00:35,520 Tonight, we're going viral! 12 00:00:52,080 --> 00:00:55,480 APPLAUSE 13 00:01:07,800 --> 00:01:13,480 Wherever there's life on this Earth, there are also viruses. 14 00:01:13,480 --> 00:01:16,920 And there are actually more viruses on the Earth 15 00:01:16,920 --> 00:01:20,280 than there are stars up in our universe. 16 00:01:20,280 --> 00:01:25,080 And to be perfectly truthful, most of them are harmless to us. 17 00:01:25,080 --> 00:01:29,000 And some of them play vital, hidden roles, for example, 18 00:01:29,000 --> 00:01:31,880 recycling nutrients back into the oceans. 19 00:01:33,080 --> 00:01:34,920 But some are deadly 20 00:01:34,920 --> 00:01:38,280 and just one infection 21 00:01:38,280 --> 00:01:42,480 can lead to a global pandemic. 22 00:01:46,760 --> 00:01:49,880 The first respiratory virus pandemic 23 00:01:49,880 --> 00:01:53,160 of the 20th century was in 1918, 24 00:01:53,160 --> 00:01:57,000 and it was the H1N1 influenza virus. 25 00:01:57,000 --> 00:02:00,080 And we think that that killed 50 million 26 00:02:00,080 --> 00:02:04,440 people around the world, and that's more than actually were killed 27 00:02:04,440 --> 00:02:09,960 by World War I, which had taken place over the four preceding years. 28 00:02:09,960 --> 00:02:12,880 And at the time, there were no vaccines. 29 00:02:13,920 --> 00:02:17,440 All we had was isolation, 30 00:02:17,440 --> 00:02:18,840 quarantine, 31 00:02:18,840 --> 00:02:20,880 face coverings. 32 00:02:20,880 --> 00:02:24,080 And keeping social distance. 33 00:02:25,280 --> 00:02:26,880 Does that sound familiar? 34 00:02:26,880 --> 00:02:28,520 FAINT CHUCKLING 35 00:02:28,520 --> 00:02:32,680 Well, since then, we've had many crises. 36 00:02:32,680 --> 00:02:36,200 We had the HIV pandemic in the 1980s. 37 00:02:36,200 --> 00:02:40,760 We've had crises due to Ebola and also to Zika, 38 00:02:40,760 --> 00:02:45,840 and now we find ourselves here in the middle of a pandemic 39 00:02:45,840 --> 00:02:50,480 caused by the SARS-CoV-2 virus, SARS coronavirus. 40 00:02:50,480 --> 00:02:54,120 The disease that comes from it is called Covid-19, 41 00:02:54,120 --> 00:02:58,840 and right now, we're still battling the latest twist in that story, 42 00:02:58,840 --> 00:03:02,240 in terms of the Omicron variant. 43 00:03:02,240 --> 00:03:06,480 That's the kind of bad news but the good news is, in the past 44 00:03:06,480 --> 00:03:11,760 two years, we have had absolutely amazing scientific learning 45 00:03:11,760 --> 00:03:15,520 and achievements on an unprecedented scale. 46 00:03:15,520 --> 00:03:17,680 For those of you who are going to be scientists, 47 00:03:17,680 --> 00:03:20,560 being a scientist is a team game. 48 00:03:20,560 --> 00:03:23,760 You can't know it all and do it all on your own. 49 00:03:23,760 --> 00:03:26,600 And for that reason, over these three lectures that are coming 50 00:03:26,600 --> 00:03:29,960 up now, I am going to be joined by some top 51 00:03:29,960 --> 00:03:34,440 scientists from around the world who have each played a vital 52 00:03:34,440 --> 00:03:38,760 role in this pandemic and getting it under control. 53 00:03:38,760 --> 00:03:43,640 Now, to explain the secrets of viruses 54 00:03:43,640 --> 00:03:47,240 and how our immune system fights them, 55 00:03:47,240 --> 00:03:51,520 our first expert tonight is somebody who helped develop 56 00:03:51,520 --> 00:03:55,880 the AstraZeneca vaccine at Oxford University. 57 00:03:55,880 --> 00:03:59,120 Will you please welcome Professor Katie Ewer! 58 00:04:10,080 --> 00:04:13,720 So I'm an immunologist, which means I study how our bodies respond 59 00:04:13,720 --> 00:04:17,120 when viruses attack us, and I also develop vaccines that help to 60 00:04:17,120 --> 00:04:20,080 train our immune system to learn to fight back. 61 00:04:20,080 --> 00:04:23,000 Now as we've seen over the last two years, rapid development 62 00:04:23,000 --> 00:04:26,840 of vaccines against Covid-19 around the world, that success has been 63 00:04:26,840 --> 00:04:31,560 built on decades of research to understand infection and immunology. 64 00:04:31,560 --> 00:04:34,600 But before we can all understand a bit more of that tonight, 65 00:04:34,600 --> 00:04:37,720 there's a few things that we all need to know first. 66 00:04:37,720 --> 00:04:40,560 What are viruses? How do they work? 67 00:04:40,560 --> 00:04:43,120 And how does our immune system fight them off? 68 00:04:43,120 --> 00:04:47,400 Well, to start with, let's see just how small viruses are. 69 00:04:47,400 --> 00:04:50,560 And what we've got here is a model of a human hair that we're going 70 00:04:50,560 --> 00:04:54,560 to enlarge about 4,000 times, so after three... 71 00:04:54,560 --> 00:04:57,880 ALL: Three, two, one! 72 00:04:57,880 --> 00:05:00,800 WHIRRING 73 00:05:09,280 --> 00:05:12,640 So in real life, this human hair would be about 80 micrometres across 74 00:05:12,640 --> 00:05:15,440 but when we've blown it up 4,000 times, 75 00:05:15,440 --> 00:05:18,720 it's now about 30cm across, and at this scale, 76 00:05:18,720 --> 00:05:22,280 we can start to see lots of things on our body in a lot more detail. 77 00:05:22,280 --> 00:05:23,960 So for example, here, we've got 78 00:05:23,960 --> 00:05:26,800 some little models of human skin cells and these have been 79 00:05:26,800 --> 00:05:29,920 blown up 4,000 times as well, just like our hair. 80 00:05:29,920 --> 00:05:32,800 So if we look here, you can see now, on this scale, 81 00:05:32,800 --> 00:05:35,880 a human skin cell is nearly the same size as my hand. 82 00:05:35,880 --> 00:05:38,160 And we've got some other bits over here as well. 83 00:05:38,160 --> 00:05:40,800 So if we were to blow up an E coli bacteria, 84 00:05:40,800 --> 00:05:44,360 this is a bacteria that can causes infection in all of us, 85 00:05:44,360 --> 00:05:48,000 in real life, E coli would be about two micrometres across 86 00:05:48,000 --> 00:05:49,600 but blown up at this scale... 87 00:05:50,640 --> 00:05:53,440 ..it's like this, it's about this size. 88 00:05:53,440 --> 00:05:56,120 But viruses are even smaller than bacteria. 89 00:05:56,120 --> 00:06:00,360 And if we were to blow up a SARS-CoV-2 virus, 90 00:06:00,360 --> 00:06:03,480 the same amount as we've blown up this hair, 91 00:06:03,480 --> 00:06:07,080 they would be about the same size as these poppy seeds. 92 00:06:07,080 --> 00:06:10,600 So still really difficult to see. 93 00:06:10,600 --> 00:06:13,160 So in order to see viruses in a bit more detail, we are 94 00:06:13,160 --> 00:06:15,520 going to have to zoom in even further. Now to do this, 95 00:06:15,520 --> 00:06:18,760 we've had some help from the pupils at Biddenham International School, 96 00:06:18,760 --> 00:06:21,400 and they've been incredibly busy crafting these beautiful 97 00:06:21,400 --> 00:06:24,080 models for us of different types of viruses, 98 00:06:24,080 --> 00:06:26,760 so that we can see their shapes in more detail. 99 00:06:26,760 --> 00:06:30,000 In fact, we've almost got like a whole virus zoo here. 100 00:06:30,000 --> 00:06:33,440 So viruses can have these quite simple geometric shapes 101 00:06:33,440 --> 00:06:37,040 but as we know, their primary purpose is to replicate, 102 00:06:37,040 --> 00:06:40,360 and when they do that, it can be really bad for human health. 103 00:06:40,360 --> 00:06:41,840 So, JVT, do you want to tell us 104 00:06:41,840 --> 00:06:43,840 a bit more about some of these viruses? 105 00:06:43,840 --> 00:06:46,160 Yeah. I recognise some of these 106 00:06:46,160 --> 00:06:51,160 and this one is the one I've spent most of my life studying. 107 00:06:51,160 --> 00:06:55,240 It's the influenza virus, and this is the one that gave us 108 00:06:55,240 --> 00:06:59,520 a pandemic in 2009, the swine flu pandemic. 109 00:06:59,520 --> 00:07:02,560 It's also the one that gave us the 1918 pandemic 110 00:07:02,560 --> 00:07:05,400 that I talked about at the beginning, and this one 111 00:07:05,400 --> 00:07:09,800 will give you a sore throat, a cough, a runny nose, and a fever. 112 00:07:09,800 --> 00:07:13,560 Now, this one over here, I'm afraid, Katie, 113 00:07:13,560 --> 00:07:17,360 if you get this one, you're going to get diarrhoea and vomiting. 114 00:07:17,360 --> 00:07:20,280 This one is the norovirus. 115 00:07:20,280 --> 00:07:24,120 And this one up here, this is a real nasty. 116 00:07:24,120 --> 00:07:26,320 This is the rabies virus, 117 00:07:26,320 --> 00:07:29,840 and this one attacks the nervous system, 118 00:07:29,840 --> 00:07:33,440 and it also, I'm afraid, can attack the brain. 119 00:07:33,440 --> 00:07:35,520 It's really very serious, that one. 120 00:07:35,520 --> 00:07:38,800 So as we've seen here, we've got lots of different viruses, they can 121 00:07:38,800 --> 00:07:42,560 create lots of different symptoms in different parts of our bodies. 122 00:07:42,560 --> 00:07:46,240 But all of these viruses are built of the same stuff. 123 00:07:46,240 --> 00:07:49,240 On their outside, they have a protein coat. 124 00:07:49,240 --> 00:07:52,080 But, what's on the inside of a virus? 125 00:07:52,080 --> 00:07:54,200 So, we're going to look at that in a bit more detail 126 00:07:54,200 --> 00:07:58,200 and in order to do that, I would like a volunteer from the audience. 127 00:07:59,400 --> 00:08:01,360 Erm, 128 00:08:01,360 --> 00:08:03,080 here in the red top with the monkey on. Yeah. 129 00:08:03,080 --> 00:08:04,360 Would you like to come down? 130 00:08:11,120 --> 00:08:13,880 So, what's your name? Emily. Hello, Emily. 131 00:08:13,880 --> 00:08:17,760 What we've got here is a coronavirus. 132 00:08:17,760 --> 00:08:21,080 And we want to know what's inside it. 133 00:08:21,080 --> 00:08:24,400 Can you think of a way that we might be able to open this virus up? 134 00:08:24,400 --> 00:08:27,000 Fuse here. Something to light it with. 135 00:08:27,000 --> 00:08:29,280 Yeah, shall be have a go at lighting it? 136 00:08:29,280 --> 00:08:32,320 Would you like to put these on for me? OK. 137 00:08:32,320 --> 00:08:34,640 And this is just a clicky lighter, like that. 138 00:08:34,640 --> 00:08:37,240 So you hold that and what I'd like you to do is hold it 139 00:08:37,240 --> 00:08:40,720 right at the end here, and then, we're going to have a countdown, OK. 140 00:08:40,720 --> 00:08:44,160 So we're going to light the fuse. Go on, press it really hard! 141 00:08:44,160 --> 00:08:46,440 That's it. Great. Now, come stand over here with me. 142 00:08:46,440 --> 00:08:50,600 ALL: Three, two, one. 143 00:08:50,600 --> 00:08:52,120 CLICKING 144 00:08:52,120 --> 00:08:53,440 LAUGHTER 145 00:08:53,440 --> 00:08:56,000 Thank you very much. You did a great job there, Emily. 146 00:08:56,000 --> 00:08:57,920 Do you want to give me your glasses back? 147 00:08:57,920 --> 00:08:59,680 Thank you. You go and sit down. 148 00:09:02,520 --> 00:09:06,200 So inside the virus is its genetic material, 149 00:09:06,200 --> 00:09:10,240 either DNA or more commonly for viruses, RNA. 150 00:09:10,240 --> 00:09:14,520 And on here is the recipe that viruses need to replicate. 151 00:09:14,520 --> 00:09:18,640 And it's shown here as the genetic code, which for viruses, for RNA, 152 00:09:18,640 --> 00:09:21,320 is written as A, G, C and U. 153 00:09:21,320 --> 00:09:22,560 And as you can see, 154 00:09:22,560 --> 00:09:25,760 there are lots and lots of letters written on this piece of paper. 155 00:09:25,760 --> 00:09:28,800 It's quite a long roll so, JVT, can I have a hand to unroll this? 156 00:09:28,800 --> 00:09:32,640 And we'll just see quite how big this recipe is. 157 00:09:35,920 --> 00:09:38,560 You can see it's really long. 158 00:09:38,560 --> 00:09:41,560 There we are. Right at the end. 159 00:09:41,560 --> 00:09:47,440 So, Katie, how many letters are on the code for this virus? 160 00:09:47,440 --> 00:09:49,560 Does anyone in the audience want to have a guess? 161 00:09:49,560 --> 00:09:52,160 Do you want to shout out how many letters they think are on here? 162 00:09:52,160 --> 00:09:53,520 1,000 million? 163 00:09:53,520 --> 00:09:54,760 AUDIENCE OFFERS SUGGESTIONS 164 00:09:56,040 --> 00:09:59,280 Well actually, some of you were pretty close. 165 00:09:59,280 --> 00:10:01,720 It's about 30,000 letters. 166 00:10:01,720 --> 00:10:06,040 Now, that might sound like a lot but if we were to turn that into digital 167 00:10:06,040 --> 00:10:10,000 information and store it as a file, it would be less than one megabyte. 168 00:10:10,000 --> 00:10:12,680 So these are the simple instructions that the virus needs 169 00:10:12,680 --> 00:10:14,720 to be able to replicate. 170 00:10:14,720 --> 00:10:18,440 But the thing about viruses, they can't do this by themselves. 171 00:10:18,440 --> 00:10:22,360 They need a host to be able to reproduce and survive. 172 00:10:22,360 --> 00:10:25,440 So Katie, if viruses 173 00:10:25,440 --> 00:10:29,320 needs another organism to multiply 174 00:10:29,320 --> 00:10:32,640 and survive, are they alive? 175 00:10:32,640 --> 00:10:34,080 Or are they dead? 176 00:10:34,080 --> 00:10:36,920 Let's just see what the audience think. 177 00:10:36,920 --> 00:10:41,200 First of all, if you think a virus is alive, 178 00:10:41,200 --> 00:10:42,720 put your hand up. 179 00:10:44,800 --> 00:10:46,160 OK. 180 00:10:46,160 --> 00:10:49,040 That's about 30% or so, I'm guessing. 181 00:10:49,040 --> 00:10:52,280 And if you think a virus is dead, 182 00:10:52,280 --> 00:10:53,680 put your hand up. 183 00:10:55,240 --> 00:10:57,120 Oh, that might be slightly more. 184 00:10:57,120 --> 00:11:00,440 And if you're really not sure, and you can't decide, 185 00:11:00,440 --> 00:11:02,120 put your hand up now. 186 00:11:03,840 --> 00:11:06,200 Just a few. Katie, what's the answer? 187 00:11:06,200 --> 00:11:08,560 Well, JVT, I'm going to let you in on a little secret 188 00:11:08,560 --> 00:11:11,840 which is that, actually, scientists can't decide either. 189 00:11:11,840 --> 00:11:15,000 What we know is that viruses are very, very ancient, 190 00:11:15,000 --> 00:11:17,480 they've been important to human life for a very long time. 191 00:11:17,480 --> 00:11:20,120 We've evolved closely with them and, of course, as we know, 192 00:11:20,120 --> 00:11:22,440 they are capable of causing disease in humans. 193 00:11:22,440 --> 00:11:24,560 But one thing scientists do agree on 194 00:11:24,560 --> 00:11:27,120 is that viruses are on the boundary of life. 195 00:11:27,120 --> 00:11:31,280 So, why don't we compare viruses to some organisms that we know 196 00:11:31,280 --> 00:11:34,120 really are alive, like us, like humans? 197 00:11:34,120 --> 00:11:37,560 How does viral RNA compare to human DNA? 198 00:11:38,680 --> 00:11:43,000 Well, I've shrunk this genetic code for the coronavirus onto just one 199 00:11:43,000 --> 00:11:47,040 single piece of A4, so all of those 30,000 letters that you saw on 200 00:11:47,040 --> 00:11:51,000 the roll that JVT held up are now shrunk down onto this piece 201 00:11:51,000 --> 00:11:55,120 of paper and to get it all on one piece of A4, it's absolutely tiny. 202 00:11:55,120 --> 00:11:56,480 So... 203 00:11:56,480 --> 00:11:59,720 ..now let's see on exactly the same scale, what the 204 00:11:59,720 --> 00:12:02,360 genetic code of a human being looks like, 205 00:12:02,360 --> 00:12:05,200 and to do that, we're going to go to the human genome library. 206 00:12:08,040 --> 00:12:12,520 And here it is. It's absolutely enormous! 207 00:12:12,520 --> 00:12:15,960 In total, we've got 118 books here, 208 00:12:15,960 --> 00:12:18,640 and every book has 1,000 pages. 209 00:12:18,640 --> 00:12:21,200 So, just to give you an idea... 210 00:12:21,200 --> 00:12:22,840 WHISPERING: Oh, my God! 211 00:12:24,400 --> 00:12:28,480 ..if we open one of these up and I get my little Handycam here... 212 00:12:29,760 --> 00:12:32,440 ..you can see again, that we've used exactly the same 213 00:12:32,440 --> 00:12:38,080 writing that we used, and again, that writing is really, really tiny. 214 00:12:38,080 --> 00:12:40,760 So there are a total of three billion letters 215 00:12:40,760 --> 00:12:44,560 in the human genome - that's 100,000 times more than we saw 216 00:12:44,560 --> 00:12:47,240 in the coronavirus genome, and if we were to turn this 217 00:12:47,240 --> 00:12:51,160 into digital information, it would take up about three gigabytes as a 218 00:12:51,160 --> 00:12:52,960 normal electronic file. 219 00:12:52,960 --> 00:12:56,600 So this just shows you exactly how simple viruses are. 220 00:12:56,600 --> 00:13:00,120 And their simplicity is part of the key to their success. 221 00:13:00,120 --> 00:13:03,680 So how does something so small and so simple 222 00:13:03,680 --> 00:13:06,800 take over and make us so sick? 223 00:13:06,800 --> 00:13:10,160 Well, in order to understand this in a little bit more detail, 224 00:13:10,160 --> 00:13:12,840 we need to go inside the cell. 225 00:13:15,120 --> 00:13:18,080 We're now going to imagine that the cells in our body are a factory, 226 00:13:18,080 --> 00:13:22,480 and in order to get in, we need to go through this security door. 227 00:13:22,480 --> 00:13:26,000 Now, the cells of our body have lots of doors on their surface 228 00:13:26,000 --> 00:13:29,120 and it's a way for different things to get in and out of the cells. 229 00:13:29,120 --> 00:13:31,400 But in order for things to get into the cell, 230 00:13:31,400 --> 00:13:33,520 they need to use a special key. 231 00:13:33,520 --> 00:13:35,840 I'm going to see if this key will open this door. 232 00:13:39,760 --> 00:13:40,760 And we're in! 233 00:13:42,320 --> 00:13:44,840 And now, we're inside the cell factory. 234 00:13:44,840 --> 00:13:47,640 Now over here, we have the boss's office. 235 00:13:47,640 --> 00:13:51,040 This is like the nucleus of the cell where all the genetic information 236 00:13:51,040 --> 00:13:55,080 that our bodies need to produce new cells and proteins is stored. 237 00:13:55,080 --> 00:13:58,360 And over here, we've got the cell factory, and this is like the 238 00:13:58,360 --> 00:14:01,600 ribosome of the cell and this is where all the proteins are produced. 239 00:14:01,600 --> 00:14:05,800 So if our cells are working properly, what should happen 240 00:14:05,800 --> 00:14:08,520 is the instructions go from the boss's office, across the 241 00:14:08,520 --> 00:14:12,640 factory floor, to the cell factory here, and the instructions are 242 00:14:12,640 --> 00:14:16,880 given to produce the products that our bodies need to build and repair. 243 00:14:16,880 --> 00:14:18,360 For example, 244 00:14:18,360 --> 00:14:22,360 we might need a spanner to repair cells, to repair muscles or bones. 245 00:14:23,400 --> 00:14:27,240 Or, we might need enzymes like this that can break down 246 00:14:27,240 --> 00:14:29,840 things in our body that are no longer required. 247 00:14:29,840 --> 00:14:32,360 And this is how our cells work when everything is normal 248 00:14:32,360 --> 00:14:34,280 and we're happy and healthy. 249 00:14:34,280 --> 00:14:37,320 But what happens when a virus infects a cell? 250 00:14:37,320 --> 00:14:41,280 I would like another volunteer from the audience, please. 251 00:14:41,280 --> 00:14:44,120 Erm, in the blue T-shirt here. Why don't you come down? 252 00:14:52,640 --> 00:14:57,160 What's your name? Tyler. Hello, Tyler. So, congratulations. 253 00:14:57,160 --> 00:15:01,120 You have just volunteered to be a coronavirus particle! Thank you! 254 00:15:01,120 --> 00:15:02,720 LAUGHTER 255 00:15:02,720 --> 00:15:06,200 So we're going to make you look like a coronavirus particle now. 256 00:15:06,200 --> 00:15:09,320 The first thing we're going to do is give you a protein coat. 257 00:15:12,280 --> 00:15:14,800 So this protein coat is on the surface of all viruses, 258 00:15:14,800 --> 00:15:16,920 including coronaviruses. 259 00:15:16,920 --> 00:15:20,400 Now the other thing that a virus needs, of course, is its RNA, 260 00:15:20,400 --> 00:15:23,040 its genetic information here. USB stick. 261 00:15:23,040 --> 00:15:25,080 Thank you. 262 00:15:25,080 --> 00:15:28,440 But what else does coronavirus have on its surface 263 00:15:28,440 --> 00:15:30,440 that's really important? 264 00:15:30,440 --> 00:15:32,200 Shout out if you know. 265 00:15:32,200 --> 00:15:33,720 AUDIENCE SHOUTS SUGGESTIONS 266 00:15:33,720 --> 00:15:37,080 Spike, yes! Somebody said "spike". You were exactly right. 267 00:15:37,080 --> 00:15:39,520 So have you got any spikes on your coat? 268 00:15:39,520 --> 00:15:41,360 Yes, you have. Right. 269 00:15:41,360 --> 00:15:43,520 That might come in useful later. 270 00:15:43,520 --> 00:15:45,120 But we're inside the cell. 271 00:15:45,120 --> 00:15:48,480 And we've got a virus inside the cell. This isn't what we want. 272 00:15:48,480 --> 00:15:50,480 Come on, out, out, out through that door. Come on. 273 00:15:50,480 --> 00:15:53,240 We don't want any viruses inside our cells. Out you go! 274 00:15:53,240 --> 00:15:56,560 And we're going to lock our virus outside. 275 00:15:57,800 --> 00:15:59,000 FAINT LAUGHTER 276 00:15:59,000 --> 00:16:01,240 So now, of course, we're inside, the virus is outside 277 00:16:01,240 --> 00:16:03,680 so we should all be safe, of course, shouldn't we? 278 00:16:03,680 --> 00:16:07,640 Well, maybe our volunteer, our virus, can get in? 279 00:16:07,640 --> 00:16:10,000 So, our virus over there, our coronavirus, 280 00:16:10,000 --> 00:16:12,240 do you want to see if you can get in through the door? 281 00:16:14,200 --> 00:16:15,240 DOOR RATTLES 282 00:16:15,240 --> 00:16:17,120 FAINT LAUGHTER 283 00:16:19,120 --> 00:16:20,840 LAUGHTER 284 00:16:23,160 --> 00:16:25,320 Well done. He's done it! 285 00:16:27,480 --> 00:16:28,920 Well done. 286 00:16:34,120 --> 00:16:35,960 So that was difficult, Tyler, 287 00:16:35,960 --> 00:16:39,240 but you did manage to get in and how did you manage to open the door? 288 00:16:39,240 --> 00:16:41,200 With the spike protein. Exactly. 289 00:16:41,200 --> 00:16:45,320 So the spike is the key to opening the doors on the cells. 290 00:16:45,320 --> 00:16:47,360 But the doors that are opened by the key 291 00:16:47,360 --> 00:16:49,240 are specific for the coronavirus, 292 00:16:49,240 --> 00:16:52,400 and not all cells have the right door or the receptor on their 293 00:16:52,400 --> 00:16:56,240 surface - they need that matching lock. Now... 294 00:16:56,240 --> 00:16:58,880 ..what does the virus do now it's inside the cell? 295 00:16:59,920 --> 00:17:03,600 Could go to the boss's office. But what do viruses really want to do? 296 00:17:03,600 --> 00:17:05,760 AUDIENCE SHOUTS SUGGESTIONS 297 00:17:05,760 --> 00:17:07,680 Yeah, they want to take over and replicate 298 00:17:07,680 --> 00:17:10,720 so let's go back over to our cell factory. 299 00:17:10,720 --> 00:17:13,520 And our coronavirus is going to insert its own genetic 300 00:17:13,520 --> 00:17:15,920 material into the cell factory. 301 00:17:15,920 --> 00:17:18,520 Now, let's see what the machine's going to make this time. 302 00:17:18,520 --> 00:17:20,360 What's the first thing to come out, Tyler? 303 00:17:20,360 --> 00:17:21,880 An envelope. 304 00:17:21,880 --> 00:17:23,480 A USB. 305 00:17:23,480 --> 00:17:25,720 Yeah. So we've got some more genetic material. 306 00:17:28,760 --> 00:17:32,240 Another spike protein. Some more spike proteins. And what else? 307 00:17:33,520 --> 00:17:37,960 Right. And now we've got some pieces of the protein coat from the virus. 308 00:17:37,960 --> 00:17:40,160 So we're starting to make all the different pieces 309 00:17:40,160 --> 00:17:45,400 we need for the virus to make copies of itself. And now we can see, OK. 310 00:17:45,400 --> 00:17:47,440 OK, I don't think this is what's meant to happen. 311 00:17:47,440 --> 00:17:49,960 Come over here with me, Tyler. I'm not sure what's going on. 312 00:17:49,960 --> 00:17:52,640 Come over here. We've got more and more pieces of... 313 00:17:52,640 --> 00:17:54,080 BANG! 314 00:17:54,080 --> 00:17:56,360 ..that protein being produced by the cell. 315 00:17:56,360 --> 00:17:58,440 And our factory is in big trouble. 316 00:17:58,440 --> 00:18:02,920 Our cell factory has just exploded, and this is not good for us 317 00:18:02,920 --> 00:18:05,960 because all of those pieces of virus have now been released 318 00:18:05,960 --> 00:18:09,880 and now go and infect new cells, and what does that mean? 319 00:18:09,880 --> 00:18:11,880 We've all just contracted Covid-19. 320 00:18:13,200 --> 00:18:16,560 Tyler, thank you so much for your help. You did a great job. 321 00:18:16,560 --> 00:18:17,880 Go back up. 322 00:18:17,880 --> 00:18:19,160 I'll take your coat. 323 00:18:26,000 --> 00:18:30,480 OK. So what viruses do to us 324 00:18:30,480 --> 00:18:35,920 depends upon the types of cells that they can actually break into. 325 00:18:35,920 --> 00:18:41,720 And that's often determined by the shape of the key they use. 326 00:18:41,720 --> 00:18:44,960 And here is a model 327 00:18:44,960 --> 00:18:49,600 of the SARS-CoV-2 spike protein, 328 00:18:49,600 --> 00:18:51,680 this red piece here. 329 00:18:51,680 --> 00:18:54,840 And that is the specific shape 330 00:18:54,840 --> 00:18:59,040 that allows it to bind onto this piece here. 331 00:19:00,640 --> 00:19:03,160 And the piece I'm holding, the blue piece, 332 00:19:03,160 --> 00:19:05,920 that's an ACE2 receptor 333 00:19:05,920 --> 00:19:09,280 that sits on the outside of human cells. 334 00:19:09,280 --> 00:19:13,200 But those receptors are most common 335 00:19:13,200 --> 00:19:16,640 in the lungs and in the airways. 336 00:19:16,640 --> 00:19:19,280 And that's why SARS-CoV-2 337 00:19:19,280 --> 00:19:23,760 causes a primarily respiratory infection. 338 00:19:23,760 --> 00:19:26,720 Children have fewer 339 00:19:26,720 --> 00:19:30,640 of these ACE2 receptors than adults. 340 00:19:30,640 --> 00:19:35,720 And it probably explains why the disease is more serious in adults. 341 00:19:35,720 --> 00:19:40,600 Viruses can seem like pretty nasty invaders 342 00:19:40,600 --> 00:19:43,840 but we do have an immune system 343 00:19:43,840 --> 00:19:47,400 and that immune system can fight back. 344 00:19:48,360 --> 00:19:50,160 Katie. 345 00:19:51,880 --> 00:19:55,240 So our immune system is made up of lots of different organs 346 00:19:55,240 --> 00:19:57,480 and cells and proteins, and it's what 347 00:19:57,480 --> 00:20:01,320 protects our bodies from infection with bacteria and viruses. 348 00:20:01,320 --> 00:20:04,360 The thing about our immune system is that if it's running smoothly, 349 00:20:04,360 --> 00:20:07,720 we really don't notice it's there. 350 00:20:07,720 --> 00:20:11,280 The first line of defence in our bodies against bacteria 351 00:20:11,280 --> 00:20:14,680 and viruses coming in, is actually our skin. 352 00:20:14,680 --> 00:20:17,000 And the skin forms a physical barrier that stops 353 00:20:17,000 --> 00:20:19,520 everything on the outside getting inside our bodies. 354 00:20:19,520 --> 00:20:22,280 But of course, there are weak points in that - 355 00:20:22,280 --> 00:20:26,360 entrances into our body, like our mouth and our nose. 356 00:20:26,360 --> 00:20:29,760 So we have a second line of defence inside our mouth and nose. 357 00:20:29,760 --> 00:20:32,040 Does anybody know what that is? 358 00:20:32,040 --> 00:20:33,520 Snot! 359 00:20:33,520 --> 00:20:35,120 Did somebody say "snot"? 360 00:20:35,120 --> 00:20:38,200 That's right! Bring on the snot! 361 00:20:38,200 --> 00:20:41,320 Now, I'm going to need another volunteer to help me with this. 362 00:20:41,320 --> 00:20:43,680 Does anybody want to come down and help me? 363 00:20:43,680 --> 00:20:47,080 Erm, in the hat up there. Would you like to come down? 364 00:20:47,080 --> 00:20:48,480 APPLAUSE 365 00:20:51,680 --> 00:20:54,560 OK. So you go and stand down the other side for me. 366 00:20:56,440 --> 00:20:59,120 OK. What's your name? Isaac. Hi, Isaac. 367 00:20:59,120 --> 00:21:01,680 Right, what we're going to do here 368 00:21:01,680 --> 00:21:05,720 is use our nose to look at how much snot the average adult 369 00:21:05,720 --> 00:21:07,760 makes in a day. 370 00:21:07,760 --> 00:21:10,600 So we've got a range of beakers here, going from very big 371 00:21:10,600 --> 00:21:12,120 down to very small. 372 00:21:12,120 --> 00:21:13,880 I want you to try and guess how much snot 373 00:21:13,880 --> 00:21:16,720 the average adult makes each day. What do you think? 374 00:21:16,720 --> 00:21:19,040 Erm, the big one. So you're going for a big one? OK. 375 00:21:19,040 --> 00:21:20,960 Pick the one you think is right, 376 00:21:20,960 --> 00:21:24,920 and if you put it underneath our model of the nose 377 00:21:24,920 --> 00:21:27,560 and we switch on the snot, we should find out. 378 00:21:27,560 --> 00:21:30,560 So, let's have another countdown. 379 00:21:30,560 --> 00:21:33,400 ALL: Three, two, one! 380 00:21:33,400 --> 00:21:35,320 WHOOSHING 381 00:21:35,320 --> 00:21:37,400 Oh. It's coming out pretty quickly. 382 00:21:38,960 --> 00:21:42,040 Is this going to be the right size? What do we think? 383 00:21:42,040 --> 00:21:45,440 It's still going, Isaac. I think you might need another beaker here. 384 00:21:45,440 --> 00:21:47,600 Do you want to get ready with another one? 385 00:21:47,600 --> 00:21:51,680 Right, swap those over. Excellent. Yeah. That's it. 386 00:21:51,680 --> 00:21:55,240 Well done. OK. Should be enough now. Those are our two biggest beakers. 387 00:21:55,240 --> 00:21:58,120 That's it. 388 00:21:58,120 --> 00:22:01,520 Is it stopping? No. Quick, get ready with another one! 389 00:22:01,520 --> 00:22:04,480 Well done. Excellent. Keep going! 390 00:22:05,680 --> 00:22:07,520 A lot of snot! 391 00:22:08,760 --> 00:22:10,400 LAUGHTER 392 00:22:10,400 --> 00:22:14,960 Go on, we might as well do the tiny one now. We've done all the others! 393 00:22:14,960 --> 00:22:16,240 Thank you, Isaac. 394 00:22:16,240 --> 00:22:18,280 So it turns out that all of them 395 00:22:18,280 --> 00:22:21,640 hold the entire adult output of snot for a single day! 396 00:22:21,640 --> 00:22:23,720 Isaac, thank you so much! 397 00:22:30,840 --> 00:22:35,160 So as we've seen, the average adult produces around two litres of snot, 398 00:22:35,160 --> 00:22:38,840 or to give it its proper name, mucus, every day. 399 00:22:38,840 --> 00:22:42,000 But actually, when we're well, most of it isn't in our nose, 400 00:22:42,000 --> 00:22:45,640 it remains in our airways and it has a really important role there. 401 00:22:45,640 --> 00:22:48,120 It's there to trap anything that comes into our bodies 402 00:22:48,120 --> 00:22:50,640 and stop it getting into our lungs. 403 00:22:50,640 --> 00:22:53,560 So to have another look at this in a bit more detail, I'm going 404 00:22:53,560 --> 00:22:55,200 to need one more volunteer. 405 00:22:55,200 --> 00:22:58,040 Who wants to come down and help me? 406 00:22:58,040 --> 00:23:00,920 In the golden top, there. Would you like to come down? 407 00:23:07,000 --> 00:23:10,200 What's your name? Marta. Marta? Hi, Marta. 408 00:23:10,200 --> 00:23:12,440 Right. So, we're going to have a look now at what happens 409 00:23:12,440 --> 00:23:14,840 when viruses attack our airways. 410 00:23:14,840 --> 00:23:18,280 And we've got a demonstration here of the inside of our airways 411 00:23:18,280 --> 00:23:21,840 and now, it's snot-free, so there's no snot in our airways. 412 00:23:21,840 --> 00:23:25,280 We've just got the simple lining of our lungs and nose. 413 00:23:25,280 --> 00:23:27,320 So I'm going to give you these viruses. 414 00:23:27,320 --> 00:23:30,280 We're going to throw these at it and see what happens. 415 00:23:30,280 --> 00:23:31,440 Go on! 416 00:23:31,440 --> 00:23:33,520 And another one! 417 00:23:37,320 --> 00:23:38,640 Excellent! 418 00:23:38,640 --> 00:23:41,920 So now you can see, when there's no snot in our airways, 419 00:23:41,920 --> 00:23:46,320 these viruses are able to stick onto our airways really, really well. 420 00:23:46,320 --> 00:23:48,680 So let's see what happens 421 00:23:48,680 --> 00:23:51,760 if we line our airways with snot. 422 00:23:51,760 --> 00:23:53,080 Have another go! 423 00:23:58,720 --> 00:24:00,240 OK. Are you ready for this? 424 00:24:00,240 --> 00:24:02,240 OK. Let's have another go now 425 00:24:02,240 --> 00:24:05,880 and see what happens when we try and get virus in. 426 00:24:07,480 --> 00:24:08,520 Try again. 427 00:24:10,000 --> 00:24:12,040 Ohh! 428 00:24:12,040 --> 00:24:13,280 APPLAUSE 429 00:24:13,280 --> 00:24:15,520 Well done! Ohh! 430 00:24:15,520 --> 00:24:17,840 APPLAUSE 431 00:24:26,320 --> 00:24:28,840 So now what you can see is where there's snot, 432 00:24:28,840 --> 00:24:31,280 it makes it much harder for the virus to bind on. 433 00:24:31,280 --> 00:24:32,760 Where there isn't any snot, you can 434 00:24:32,760 --> 00:24:35,360 see that they're still able to stick. 435 00:24:35,360 --> 00:24:38,240 And snot's absolutely amazing because it contains special 436 00:24:38,240 --> 00:24:42,360 antiviral and antibacterial enzymes which can destroy them. 437 00:24:42,360 --> 00:24:45,840 Now, I bet you'll never look at snot in the same way again! 438 00:24:45,840 --> 00:24:48,640 Thank you very much, Marta, for your help! 439 00:24:53,640 --> 00:24:56,680 Now we've seen how wonderful snot is, but our immune systems 440 00:24:56,680 --> 00:24:59,920 also have a search and destroy army of cells and proteins. 441 00:25:01,560 --> 00:25:04,720 Now, the first part of this, you've probably heard of - 442 00:25:04,720 --> 00:25:07,240 the first component is antibodies. 443 00:25:07,240 --> 00:25:11,320 These are Y-shaped proteins that attach to the surface of the virus, 444 00:25:11,320 --> 00:25:14,360 clump them together and stop the virus from being able 445 00:25:14,360 --> 00:25:16,600 to break into our cells. 446 00:25:16,600 --> 00:25:18,320 But of course antibodies can only bind 447 00:25:18,320 --> 00:25:20,840 if they have the perfect shape to do so. 448 00:25:20,840 --> 00:25:22,680 And of course, as we now know, 449 00:25:22,680 --> 00:25:24,960 this is a problem because viruses 450 00:25:24,960 --> 00:25:27,640 have all those different shapes that we saw earlier. 451 00:25:27,640 --> 00:25:31,560 So, how do our bodies create millions of antibodies 452 00:25:31,560 --> 00:25:35,520 that are exactly the right shape to combat a new disease 453 00:25:35,520 --> 00:25:38,560 and new virus that we've never seen before? 454 00:25:38,560 --> 00:25:43,000 Well, it all starts with a very simple game of chance. 455 00:25:43,000 --> 00:25:44,920 And we're going to play that game now. 456 00:25:44,920 --> 00:25:47,040 And I'm going to need everybody in the audience to be 457 00:25:47,040 --> 00:25:49,280 a volunteer for this one. OK? 458 00:25:52,600 --> 00:25:54,160 So you, in the audience, 459 00:25:54,160 --> 00:25:57,280 you are all going to be a special type of white blood cell 460 00:25:57,280 --> 00:26:01,480 called B cells and we have millions of them all over our bodies. 461 00:26:01,480 --> 00:26:03,800 If we took all of our B cells in our body and lumped them all 462 00:26:03,800 --> 00:26:08,000 together in one big clump, it would be about the size of our brain. 463 00:26:08,000 --> 00:26:10,240 And B cells are absolutely amazing 464 00:26:10,240 --> 00:26:11,800 because they all have 465 00:26:11,800 --> 00:26:14,480 different-shaped antibodies on their surface. 466 00:26:14,480 --> 00:26:19,160 Now, what I would like you all to do is look underneath your seats. 467 00:26:19,160 --> 00:26:20,440 And hopefully, 468 00:26:20,440 --> 00:26:23,320 you should all have a little model of an antibody under there. 469 00:26:23,320 --> 00:26:24,880 So, if you've got your antibody, 470 00:26:24,880 --> 00:26:27,040 could you hold it up in the air for me, please? 471 00:26:28,080 --> 00:26:29,440 Great! 472 00:26:29,440 --> 00:26:33,360 What we want to know is which one of you has the right antibody to 473 00:26:33,360 --> 00:26:35,240 attach to our specific virus? 474 00:26:36,840 --> 00:26:40,680 So, here is our target virus. 475 00:26:40,680 --> 00:26:43,960 And what we need is the antibodies to bind onto it, 476 00:26:43,960 --> 00:26:47,800 so we've made a big one here. 477 00:26:47,800 --> 00:26:50,640 And we're going to now play a game. Over to you, Katie. 478 00:26:50,640 --> 00:26:51,920 Great. 479 00:26:51,920 --> 00:26:55,000 And the shape of this spike protein is going to be exactly the same as 480 00:26:55,000 --> 00:26:57,040 the spike protein that we saw the virus use 481 00:26:57,040 --> 00:26:59,560 to get into the cell earlier. So what we are going to do is 482 00:26:59,560 --> 00:27:02,880 I would like everybody in the audience to stand up, please. 483 00:27:06,240 --> 00:27:07,520 And we're going to find out 484 00:27:07,520 --> 00:27:10,400 whether anybody here has got the right shape for our spike protein. 485 00:27:10,400 --> 00:27:12,800 We're going to start on this side, on the red side, 486 00:27:12,800 --> 00:27:16,160 so JVT, what's the first part of the pattern that people need to find? 487 00:27:16,160 --> 00:27:18,120 Three red blocks. 488 00:27:18,120 --> 00:27:21,600 So, if you've got three red blocks on your antibody model, 489 00:27:21,600 --> 00:27:23,120 stay standing up. 490 00:27:23,120 --> 00:27:25,760 OK, so everybody, I think, is still standing up at this point. 491 00:27:25,760 --> 00:27:26,880 What's next, JVT? 492 00:27:26,880 --> 00:27:29,640 So, next is two yellow blocks. 493 00:27:29,640 --> 00:27:32,880 So if you haven't got two yellow blocks, sit down. 494 00:27:32,880 --> 00:27:35,520 So we've lost a few people now. 495 00:27:35,520 --> 00:27:36,960 And what's next? 496 00:27:36,960 --> 00:27:40,160 Next is one green block. 497 00:27:40,160 --> 00:27:42,840 So if you haven't got a green, sit down. 498 00:27:42,840 --> 00:27:46,240 So what have we got on the blue part of the antibody model? 499 00:27:46,240 --> 00:27:49,560 So now we need two blue blocks. 500 00:27:49,560 --> 00:27:53,080 We're down to just a few final competitors. Oh! 501 00:27:53,080 --> 00:27:56,440 And our very last piece? The very last piece... 502 00:27:56,440 --> 00:27:59,280 ..three purple blocks. 503 00:27:59,280 --> 00:28:03,160 OK. So have we got anybody left? Yes! 504 00:28:03,160 --> 00:28:06,400 We have a winner here in the middle. Come on down! Come on down! 505 00:28:11,320 --> 00:28:14,600 Excellent. Turn round and face everybody! 506 00:28:14,600 --> 00:28:17,120 What's your name? Sophia. 507 00:28:17,120 --> 00:28:18,960 Right. So what we're going to do now 508 00:28:18,960 --> 00:28:20,920 is we're going to take your antibody, 509 00:28:20,920 --> 00:28:22,840 come round the side and have a look. 510 00:28:22,840 --> 00:28:27,400 We're going to pop it on there and see if it binds perfectly 511 00:28:27,400 --> 00:28:29,160 to the spike protein. 512 00:28:30,360 --> 00:28:31,760 And yes, it does! 513 00:28:31,760 --> 00:28:34,440 So you're the one 514 00:28:34,440 --> 00:28:39,280 with the right antibody out of all those B cells. 515 00:28:39,280 --> 00:28:40,560 Well done! 516 00:28:40,560 --> 00:28:43,640 So as we've seen, Sophia's got the best antibody 517 00:28:43,640 --> 00:28:45,760 to fit onto our spike protein. 518 00:28:45,760 --> 00:28:48,400 But of course, just one little antibody like this 519 00:28:48,400 --> 00:28:51,520 isn't going to be enough to fight off the virus infection. 520 00:28:51,520 --> 00:28:54,960 There's one more step in the process that needs to happen. 521 00:28:57,120 --> 00:29:01,160 Now, THIS is the model of a plasma cell. 522 00:29:01,160 --> 00:29:04,480 It's another really important cell within our immune system 523 00:29:04,480 --> 00:29:08,160 and its job is to produce very large quantities of antibodies 524 00:29:08,160 --> 00:29:10,960 of the right type to fight off an infection. 525 00:29:10,960 --> 00:29:14,560 So if you would like to take your winning antibody, please, 526 00:29:14,560 --> 00:29:17,720 and put it into the top of the plasma cell here. 527 00:29:17,720 --> 00:29:21,440 And then if you pull the lever there, and see what happens. 528 00:29:21,440 --> 00:29:23,920 Pull it forwards towards you. Brilliant. 529 00:29:23,920 --> 00:29:28,040 And now we've got lots and lots of antibody to fight off our virus. 530 00:29:28,040 --> 00:29:30,960 So thank you very much, Sophia. You did a great job. 531 00:29:34,960 --> 00:29:37,680 We have one more weapon in our immune system that can stop 532 00:29:37,680 --> 00:29:41,400 viruses in their tracks and these are the killers in our bodies. 533 00:29:41,400 --> 00:29:44,360 And this is actually my area of expertise in research - 534 00:29:44,360 --> 00:29:46,160 killer T cells. 535 00:29:46,160 --> 00:29:48,840 So antibodies are really good at going round our bodies and 536 00:29:48,840 --> 00:29:52,960 mopping up free virus, but once the virus gets inside our cells, 537 00:29:52,960 --> 00:29:55,280 only T cells can see them. 538 00:29:55,280 --> 00:29:59,440 And killer T cells act as assassins, going round our body, 539 00:29:59,440 --> 00:30:02,400 finding infected cells, and eliminating the virus 540 00:30:02,400 --> 00:30:04,640 in a really precise and accurate way. 541 00:30:04,640 --> 00:30:07,920 So we've got this amazing video here that shows exactly how this 542 00:30:07,920 --> 00:30:11,320 happens in real life. So in the green, you've got a T cell. 543 00:30:11,320 --> 00:30:14,040 It goes around the body with these finger-like protrusions on its 544 00:30:14,040 --> 00:30:17,800 surface, and it's actually feeling the surface of the host cell, 545 00:30:17,800 --> 00:30:22,160 looking for any indication that this cell might be infected with virus. 546 00:30:22,160 --> 00:30:25,360 And if it finds that it's infected, it attaches to the 547 00:30:25,360 --> 00:30:28,680 surface of the cell, makes holes and injects enzymes 548 00:30:28,680 --> 00:30:31,040 and poisons to kill the cell. 549 00:30:31,040 --> 00:30:33,520 This destroys the cell and everything in it, 550 00:30:33,520 --> 00:30:35,240 including the virus. 551 00:30:35,240 --> 00:30:38,200 The most important thing about our immune system 552 00:30:38,200 --> 00:30:41,200 is its incredible ability to generate memory. 553 00:30:41,200 --> 00:30:44,000 This means that once it's seen a threat like a virus once, 554 00:30:44,000 --> 00:30:47,280 it's incredibly good at remembering how to attack it. 555 00:30:47,280 --> 00:30:50,680 And THIS is the key to vaccines. 556 00:30:50,680 --> 00:30:54,840 Vaccines train our immune system to recognise virus and to 557 00:30:54,840 --> 00:30:58,520 produce the right antibodies and T cells to fight off the infection, 558 00:30:58,520 --> 00:31:02,040 and this is something we're going to explore in the last lecture. 559 00:31:03,320 --> 00:31:07,000 Thanks, Katie. That was a really fantastic description of what 560 00:31:07,000 --> 00:31:10,480 viruses are and how they get in, and how we fight back. 561 00:31:10,480 --> 00:31:13,400 And tell me something, how did you feel 562 00:31:13,400 --> 00:31:16,080 when you knew for the first time 563 00:31:16,080 --> 00:31:20,600 that the AstraZeneca vaccine was going to make antibodies 564 00:31:20,600 --> 00:31:23,960 and help us on this journey we've been on? 565 00:31:23,960 --> 00:31:26,200 Oh! I think more than anything, 566 00:31:26,200 --> 00:31:29,000 I just felt incredibly relieved that the vaccine we'd spent 567 00:31:29,000 --> 00:31:30,760 so long and put so much hard work into, 568 00:31:30,760 --> 00:31:32,960 actually worked, because there's 569 00:31:32,960 --> 00:31:36,400 never any guarantee when you make a new vaccine, that it will work. 570 00:31:36,400 --> 00:31:38,480 So it was a big relief. 571 00:31:38,480 --> 00:31:41,320 And tell us, what are you working on now in the lab? 572 00:31:41,320 --> 00:31:44,800 So I've been working on malaria for a very long time, the world 573 00:31:44,800 --> 00:31:46,840 desperately needs a new malaria vaccine. 574 00:31:46,840 --> 00:31:49,040 And I hope that one of the things that will come 575 00:31:49,040 --> 00:31:51,200 out of the Covid pandemic is that we can make 576 00:31:51,200 --> 00:31:53,880 vaccines for diseases like malaria as quickly as we've been able to 577 00:31:53,880 --> 00:31:55,560 make them for diseases like Covid. 578 00:31:55,560 --> 00:31:58,760 That's brilliant. Thank you so much for joining us this evening. 579 00:31:58,760 --> 00:32:00,440 Thank you. Thank you. 580 00:32:06,480 --> 00:32:09,560 So now we know what a virus is, 581 00:32:09,560 --> 00:32:11,320 how big it is, 582 00:32:11,320 --> 00:32:13,120 how it can get inside, 583 00:32:13,120 --> 00:32:15,040 and how we fight back. 584 00:32:15,040 --> 00:32:18,880 Let's turn our attention instead to how we know 585 00:32:18,880 --> 00:32:22,920 when somebody's been infected with a virus. 586 00:32:22,920 --> 00:32:25,240 Because that's quite important, firstly, 587 00:32:25,240 --> 00:32:27,880 if we're going to treat them, and maybe also if we're going 588 00:32:27,880 --> 00:32:31,240 to keep them away from other people, so they don't spread it. 589 00:32:31,240 --> 00:32:33,840 So let's talk about diagnostic tests. 590 00:32:33,840 --> 00:32:36,840 But really, if you're going to talk about diagnostic tests, 591 00:32:36,840 --> 00:32:40,400 then what we need is a virologist! 592 00:32:40,400 --> 00:32:44,920 And our next expert this evening can help us with that - 593 00:32:44,920 --> 00:32:46,920 it's Professor Ravi Gupta. 594 00:32:48,520 --> 00:32:49,760 Thank you. 595 00:32:51,680 --> 00:32:54,960 Hi. My name's Ravi. I'm a virologist as well as a hospital doctor. 596 00:32:54,960 --> 00:32:58,040 And I've spent the last 15 years of my career trying to understand 597 00:32:58,040 --> 00:33:01,920 HIV, but also improve treatments worldwide, and most recently, 598 00:33:01,920 --> 00:33:05,000 we achieved the world's second cure of HIV in the so-called 599 00:33:05,000 --> 00:33:07,640 London patient, or Adam Castillejo. 600 00:33:07,640 --> 00:33:10,440 Since then, of course, the coronavirus pandemic hit 601 00:33:10,440 --> 00:33:13,640 and we dropped everything to work on this new disease, and even now, 602 00:33:13,640 --> 00:33:15,800 my team is working on the omicron variant 603 00:33:15,800 --> 00:33:18,920 and trying to understand why it's spreading so fast. 604 00:33:18,920 --> 00:33:20,640 From my point of view as a doctor, 605 00:33:20,640 --> 00:33:23,600 the first thing we need to know when treating HIV is, of course, is 606 00:33:23,600 --> 00:33:27,480 the patient infected? And for that you need a test, of course. 607 00:33:27,480 --> 00:33:31,000 Now, designing tests depends on where that virus is sitting or 608 00:33:31,000 --> 00:33:32,840 residing or replicating. 609 00:33:32,840 --> 00:33:36,760 In the case of HIV, this replicates in white blood cells, 610 00:33:36,760 --> 00:33:39,360 as we know, and so we do blood tests. 611 00:33:39,360 --> 00:33:42,800 Now, SARS-CoV-2, of course, replicates or infects 612 00:33:42,800 --> 00:33:46,680 cells in the respiratory tract, either the lungs or the nose. 613 00:33:46,680 --> 00:33:49,280 And for that reason, we've got a giant nose here 614 00:33:49,280 --> 00:33:51,320 and it's a replica of mine, apparently! 615 00:33:51,320 --> 00:33:53,120 So we're going to take this apart 616 00:33:53,120 --> 00:33:56,400 and show you where SARS-CoV-2 is actually replicating. 617 00:33:56,400 --> 00:33:59,600 OK. So, hope you can see here 618 00:33:59,600 --> 00:34:02,080 that most of the nose is actually inside the head, 619 00:34:02,080 --> 00:34:05,320 the part inside, the soft part of the nose is actually very small. 620 00:34:05,320 --> 00:34:09,000 And the virus, really, is sitting way back there in cells that 621 00:34:09,000 --> 00:34:12,160 express the receptor ACE2, and so you can imagine, 622 00:34:12,160 --> 00:34:14,960 this is not easy to test for with a swab. 623 00:34:14,960 --> 00:34:18,880 And I think to demonstrate this, I am going to need a volunteer. 624 00:34:18,880 --> 00:34:22,160 Oh. OK. How about you in the first row? 625 00:34:25,400 --> 00:34:29,000 Hello. What's your name? Helena. Helena. 626 00:34:29,000 --> 00:34:30,520 Thanks for joining us, Helena. 627 00:34:30,520 --> 00:34:33,560 Now, JVT, please can I have a swab? 628 00:34:33,560 --> 00:34:36,160 Don't be afraid, it's quite large. 629 00:34:36,160 --> 00:34:38,440 But this is my nose, it's going to hurt me, not you. 630 00:34:38,440 --> 00:34:40,920 Thank you very much. Right. 631 00:34:40,920 --> 00:34:43,400 Let's just imagine now 632 00:34:43,400 --> 00:34:47,080 that this individual was infected yesterday. 633 00:34:47,080 --> 00:34:49,440 OK? By someone else who was positive. 634 00:34:49,440 --> 00:34:51,440 We're going to try and do a test at day one, 635 00:34:51,440 --> 00:34:53,360 so let's see if we can find any virus. 636 00:34:53,360 --> 00:34:55,040 Would you like to take that for me? 637 00:34:55,040 --> 00:34:57,160 And then, just put it into that nostril, 638 00:34:57,160 --> 00:34:59,280 you're going to be pushing it forward, that's it. 639 00:34:59,280 --> 00:35:01,240 Pushing it...oh, that hurts! 640 00:35:01,240 --> 00:35:02,920 Keep going, keep going. 641 00:35:02,920 --> 00:35:04,760 Until you feel some resistance. 642 00:35:04,760 --> 00:35:07,720 Just twist it round, gently. There you go. All right. 643 00:35:07,720 --> 00:35:09,840 And then you can take it out. 644 00:35:09,840 --> 00:35:11,360 OK. 645 00:35:11,360 --> 00:35:14,120 Oh, what do we have here? Right, there's some virus on there. 646 00:35:14,120 --> 00:35:15,680 So that's day one. 647 00:35:15,680 --> 00:35:18,600 Right, the person at day one is probably not feeling too unwell, 648 00:35:18,600 --> 00:35:21,040 they're probably going about their daily business, 649 00:35:21,040 --> 00:35:24,200 but there's some virus in there, as you can see. What about day four? 650 00:35:24,200 --> 00:35:27,200 So day four, the person may be feeling a bit unwell, 651 00:35:27,200 --> 00:35:29,040 cough, some fever. 652 00:35:29,040 --> 00:35:32,880 Let's test at that time point and see what we find. 653 00:35:32,880 --> 00:35:35,320 OK. So, same again. 654 00:35:35,320 --> 00:35:38,880 Let's try and get more virus out this time. Here we go. 655 00:35:38,880 --> 00:35:40,880 There we go. Right, two hands, I think. 656 00:35:40,880 --> 00:35:42,640 Very good. All the way to the back. 657 00:35:42,640 --> 00:35:44,960 So, remember, you need to go quite far. 658 00:35:44,960 --> 00:35:46,680 And twist it around. OK. 659 00:35:46,680 --> 00:35:49,120 Let's withdraw that, gently. 660 00:35:49,120 --> 00:35:52,360 Fantastic. Wonderful. Thank you very much. 661 00:35:52,360 --> 00:35:54,400 That's a day-four sample 662 00:35:54,400 --> 00:35:57,040 and you can see there's quite a lot of virus on that swab. 663 00:35:57,040 --> 00:35:58,480 Not what about by day ten? 664 00:35:58,480 --> 00:36:02,160 By now, the person's feeling better, maybe completely well, 665 00:36:02,160 --> 00:36:05,000 and may still get a test because, of course, 666 00:36:05,000 --> 00:36:06,760 people get sick at different stages. 667 00:36:06,760 --> 00:36:08,640 So let's see what happens at day ten. 668 00:36:08,640 --> 00:36:10,840 Let's repeat that swab here. 669 00:36:10,840 --> 00:36:13,640 OK. Big twist. And let's pull that back. 670 00:36:13,640 --> 00:36:14,920 You're very good at this. 671 00:36:14,920 --> 00:36:17,640 Right. Fantastic. Thank you very much. 672 00:36:17,640 --> 00:36:20,720 Right, let's pop that on there. All right. Thank you very much. 673 00:36:20,720 --> 00:36:22,280 That was excellent swabbing! 674 00:36:26,480 --> 00:36:30,960 So what you can see from this experiment is that 675 00:36:30,960 --> 00:36:33,000 when somebody's infected, 676 00:36:33,000 --> 00:36:35,400 virus builds up in the body, 677 00:36:35,400 --> 00:36:38,640 and then, as they get better, it clears again. 678 00:36:38,640 --> 00:36:41,320 And on the day-one swab, 679 00:36:41,320 --> 00:36:44,280 there's a fair bit of virus 680 00:36:44,280 --> 00:36:48,240 but on the day-four swab, there's a lot of virus, 681 00:36:48,240 --> 00:36:50,840 and on the day-ten swab, 682 00:36:50,840 --> 00:36:53,120 there's not very much at all. 683 00:36:53,120 --> 00:36:56,440 And this is important in two ways - 684 00:36:56,440 --> 00:36:59,560 first of all, it's important 685 00:36:59,560 --> 00:37:02,920 in terms of how the diagnostic 686 00:37:02,920 --> 00:37:07,200 tests perform, and also, as doctors, 687 00:37:07,200 --> 00:37:11,560 when we get a specimen from a patient and it's positive, 688 00:37:11,560 --> 00:37:16,560 we also need to think about when that specimen was taken 689 00:37:16,560 --> 00:37:21,240 compared with when the patient first got ill with symptoms. 690 00:37:21,240 --> 00:37:23,840 Because it might change the result, you can see that. 691 00:37:23,840 --> 00:37:29,040 OK. So, we have a situation where on day one, you may not have very much 692 00:37:29,040 --> 00:37:31,960 virus present but you really need to know if that person's infected 693 00:37:31,960 --> 00:37:35,760 because they would then isolate and stop spreading this virus. 694 00:37:35,760 --> 00:37:39,720 So it's really important to have an incredibly sensitive test. 695 00:37:39,720 --> 00:37:43,200 So the gold standard that we have at the moment is something called PCR. 696 00:37:43,200 --> 00:37:44,720 Who's heard of PCR? 697 00:37:44,720 --> 00:37:49,200 Fantastic. Right, it stands for Polymerase Chain Reaction. 698 00:37:49,200 --> 00:37:53,320 And this has been around for more than 40 years, and the secret 699 00:37:53,320 --> 00:37:56,800 of this is the fact that it is trying to identify bits of 700 00:37:56,800 --> 00:38:00,440 the genetic code of the virus, it's not looking for the spike protein or 701 00:38:00,440 --> 00:38:03,880 any other bits of structure of the virus, it's looking for the code. 702 00:38:03,880 --> 00:38:08,080 What we're going to look for when we're testing is a short signature - 703 00:38:08,080 --> 00:38:10,800 a signature that's only a few bases long, 704 00:38:10,800 --> 00:38:14,000 something like a genetic fingerprint. So... 705 00:38:14,000 --> 00:38:17,440 ..we know what the sequence is going to be of that fingerprint 706 00:38:17,440 --> 00:38:21,360 but we need to find a way of fishing that out of a sample 707 00:38:21,360 --> 00:38:23,120 and the sample is in this bucket, 708 00:38:23,120 --> 00:38:25,360 the genetic material from viruses and bacteria 709 00:38:25,360 --> 00:38:27,560 from our nose are all in there together, 710 00:38:27,560 --> 00:38:31,120 and we need to find a way of fishing out SARS-CoV-2 but nothing else. 711 00:38:31,120 --> 00:38:33,040 And that requires us to understand 712 00:38:33,040 --> 00:38:35,080 another concept, which is base pairing. 713 00:38:35,080 --> 00:38:39,400 Now, base pairing is something that we have in our genetic code 714 00:38:39,400 --> 00:38:42,640 and that's how we get double-stranded DNA or the DNA helix 715 00:38:42,640 --> 00:38:46,000 and I will show you how that works because up here, 716 00:38:46,000 --> 00:38:51,560 looking at our target sequence of SARS-CoV-2, we can add complementary 717 00:38:51,560 --> 00:38:53,800 bases so the A pairs with the T, 718 00:38:53,800 --> 00:38:57,320 and, for example, a G pairs with C. OK? 719 00:38:57,320 --> 00:39:01,880 So based on that fact, we can design these things called primers. 720 00:39:01,880 --> 00:39:05,680 And primers are really just types of bait, in a way. 721 00:39:05,680 --> 00:39:10,160 We are using our knowledge of the target sequence to design 722 00:39:10,160 --> 00:39:12,400 something that will help us pull that out. 723 00:39:12,400 --> 00:39:16,360 So what we are going to do now is use this primer, OK, 724 00:39:16,360 --> 00:39:20,360 to fish out SARS-CoV-2, and nothing else. 725 00:39:20,360 --> 00:39:22,600 And so this should recognise 726 00:39:22,600 --> 00:39:25,600 a complementary sequence in that bucket. 727 00:39:25,600 --> 00:39:28,320 So we are going to have to untangle a few things here. 728 00:39:28,320 --> 00:39:32,360 It is Christmas so there's some tinsel there. Right. So... 729 00:39:32,360 --> 00:39:35,200 ..as you can see, using my primer, 730 00:39:35,200 --> 00:39:37,840 I have been able to fish out 731 00:39:37,840 --> 00:39:43,120 a sequence and it looks suspiciously like the SARS-CoV-2 sequence 732 00:39:43,120 --> 00:39:44,960 that we have on the board. 733 00:39:44,960 --> 00:39:46,760 So let's prop this here. 734 00:39:46,760 --> 00:39:48,800 And you can see that, hey presto, 735 00:39:48,800 --> 00:39:53,680 we have pulled out from a mixed bag of different pieces of genetic 736 00:39:53,680 --> 00:39:57,000 material, a SARS-CoV-2 specific sequence, 737 00:39:57,000 --> 00:40:00,280 and of course, it comes with the primer attached 738 00:40:00,280 --> 00:40:02,120 because that's how we fished it out. 739 00:40:02,120 --> 00:40:04,880 So that's the first step but this really hasn't helped us, has it? 740 00:40:04,880 --> 00:40:07,120 All we've done is fish something out that we can't see, 741 00:40:07,120 --> 00:40:08,880 there's only one molecule there. 742 00:40:08,880 --> 00:40:11,800 And we need to make many, many more in order to be able to detect 743 00:40:11,800 --> 00:40:15,240 it with modern technology, so we're going to have to do something. 744 00:40:15,240 --> 00:40:18,160 Hm, and this comes back to the polymerase that I mentioned earlier. 745 00:40:18,160 --> 00:40:20,520 So, I think we're going to need a volunteer for this. 746 00:40:20,520 --> 00:40:23,560 Let's go to this side, in the red jumper. Come forward, please. 747 00:40:28,880 --> 00:40:32,280 Right. What's your name? Alexandra. Alexandra. 748 00:40:32,280 --> 00:40:34,280 Alexandra, everybody. OK. 749 00:40:34,280 --> 00:40:37,240 Right, you're going to become an enzyme, you're a protein, 750 00:40:37,240 --> 00:40:39,000 like a little robot in a cell. 751 00:40:39,000 --> 00:40:40,640 You're going to add 752 00:40:40,640 --> 00:40:43,920 little letters or shapes onto this strand 753 00:40:43,920 --> 00:40:45,640 so that they match up, all right? 754 00:40:45,640 --> 00:40:49,400 So what I'm going to ask you to do is just find shapes that match 755 00:40:49,400 --> 00:40:52,200 the ones that you've got here. And just pop them onto the Velcro. 756 00:40:52,200 --> 00:40:56,040 OK. Let's see you do the first one. OK. 757 00:40:56,040 --> 00:40:58,280 Excellent. As Alexandra does this, 758 00:40:58,280 --> 00:41:01,360 I'm just going to explain the type of polymerase we use because 759 00:41:01,360 --> 00:41:05,760 we don't just use a standard human one, we use a polymerase that's 760 00:41:05,760 --> 00:41:10,080 derived from special bacteria that live in really high temperatures 761 00:41:10,080 --> 00:41:13,640 and that were first isolated in Yellowstone National Park 762 00:41:13,640 --> 00:41:16,520 from a bacterium called Thermus aquaticus, 763 00:41:16,520 --> 00:41:19,160 and the name of the polymerase is therefore TAC. 764 00:41:19,160 --> 00:41:22,520 Now, in order to make more of this, 765 00:41:22,520 --> 00:41:25,200 we are going to have to find a way of putting our primers back on 766 00:41:25,200 --> 00:41:27,800 because remember, the primers were the reason this worked 767 00:41:27,800 --> 00:41:29,240 in the first place. 768 00:41:29,240 --> 00:41:32,000 Now there's nowhere to put those primers on at the moment, is there? 769 00:41:32,000 --> 00:41:34,800 So what we're going to do is break those two apart. 770 00:41:34,800 --> 00:41:37,240 And the way we do that is with heat. 771 00:41:37,240 --> 00:41:38,880 So, can we have some heat, please? 772 00:41:38,880 --> 00:41:42,800 It's going to take a temperature of something like 95 degrees in order 773 00:41:42,800 --> 00:41:46,440 to break the hydrogen bonds between these two strands, and once 774 00:41:46,440 --> 00:41:51,280 we reach 95 degrees, we're going to be able to separate these two. 775 00:41:52,320 --> 00:41:53,360 As you can see here. 776 00:41:55,960 --> 00:41:59,320 You've now got two strands and, lo and behold, 777 00:41:59,320 --> 00:42:02,640 the primers can attach, but before that happens, we need to drop 778 00:42:02,640 --> 00:42:04,120 it down to 60 degrees, please, 779 00:42:04,120 --> 00:42:06,720 because otherwise these primers won't bind. 780 00:42:06,720 --> 00:42:10,360 So we're down to 60. Let's put the primers back on. 781 00:42:10,360 --> 00:42:13,080 As we can see here. Right. 782 00:42:13,080 --> 00:42:16,720 Now, you've got the ability for our polymerase friend here to add 783 00:42:16,720 --> 00:42:20,320 more bases, so would you like to add some more on? 784 00:42:20,320 --> 00:42:22,160 So. 785 00:42:22,160 --> 00:42:23,840 It's amazing that, 786 00:42:23,840 --> 00:42:26,960 a polymerase from a bacterium in the middle of a hot spring 787 00:42:26,960 --> 00:42:28,720 in the middle of the US, 788 00:42:28,720 --> 00:42:32,880 now underpins the great majority of our molecular biology 789 00:42:32,880 --> 00:42:36,440 and a lot of our scientific advances would not have been possible 790 00:42:36,440 --> 00:42:39,800 without polymerases, and I cannot stress that enough to you. 791 00:42:39,800 --> 00:42:41,720 It's an incredible achievement. 792 00:42:41,720 --> 00:42:46,040 All right. Thanks very much for that. You were wonderful. Bye-bye. 793 00:42:50,120 --> 00:42:51,400 OK, folks. 794 00:42:51,400 --> 00:42:55,800 So we started with one strand and we ended up with four here, 795 00:42:55,800 --> 00:42:58,120 after a cycle of PCR. 796 00:42:58,120 --> 00:43:02,080 The number of strands will double each time, so if we were to heat 797 00:43:02,080 --> 00:43:07,400 things up again, to 95 and cool down again, we would get eight strands. 798 00:43:07,400 --> 00:43:10,920 And then, after another round, we'd get 16, and then 32. 799 00:43:10,920 --> 00:43:12,280 Can anybody tell me 800 00:43:12,280 --> 00:43:16,600 how many we would have after 30 rounds of this PCR cycling? 801 00:43:16,600 --> 00:43:18,320 You can shout out. 802 00:43:18,320 --> 00:43:20,880 A lot! A lot! That's correct! 803 00:43:20,880 --> 00:43:22,920 500 million. A million. 804 00:43:22,920 --> 00:43:24,320 A billion? 805 00:43:24,320 --> 00:43:25,560 AUDIENCE SHOUTS SUGGESTIONS 806 00:43:25,560 --> 00:43:28,000 500 million. It's about a billion. OK. 807 00:43:28,000 --> 00:43:32,000 So you'd get a billion copies after 30 rounds of PCR - that would take 808 00:43:32,000 --> 00:43:33,280 you around three hours. 809 00:43:33,280 --> 00:43:37,120 That's incredible amplification and it's quite easy to detect a billion 810 00:43:37,120 --> 00:43:42,200 particles, and that explains to you why PCR is so sensitive because 811 00:43:42,200 --> 00:43:45,840 it takes a tiny amount, makes lots of it, and then you can detect it. 812 00:43:45,840 --> 00:43:47,080 Thanks, Ravi. 813 00:43:47,080 --> 00:43:52,720 So, now you can see that, you know, use of PCR in the Covid pandemic has 814 00:43:52,720 --> 00:43:55,000 been an absolute revelation to us 815 00:43:55,000 --> 00:43:58,880 but it's an incredible technique that we're now using 816 00:43:58,880 --> 00:44:02,480 to diagnose lots of other diseases, 817 00:44:02,480 --> 00:44:05,160 including TB, HIV. 818 00:44:05,160 --> 00:44:11,240 We can use PCR at the scenes of crimes to detect what's gone on. 819 00:44:11,240 --> 00:44:16,360 We can use it to actually tissue type for organ transplants. 820 00:44:16,360 --> 00:44:21,800 And PCR is becoming quicker and cheaper as the years go by. 821 00:44:21,800 --> 00:44:24,160 But as you saw from Ravi's demonstration, 822 00:44:24,160 --> 00:44:27,680 it's still quite complicated. 823 00:44:27,680 --> 00:44:30,680 And it still takes three hours 824 00:44:30,680 --> 00:44:33,600 to make those billion copies. 825 00:44:33,600 --> 00:44:36,760 It's not the quickest thing in the world. 826 00:44:36,760 --> 00:44:42,640 What if we need to diagnose people faster than PCR? 827 00:44:42,640 --> 00:44:45,480 And maybe in a simpler way? 828 00:44:45,480 --> 00:44:49,040 Can you think of another test that works for that? Give it a shout out. 829 00:44:49,040 --> 00:44:50,280 AUDIENCE: Lateral flow! 830 00:44:50,280 --> 00:44:52,280 Lateral flow. What's a lateral flow? 831 00:44:52,280 --> 00:44:54,680 Ah! Yes, all right. Ravi! 832 00:44:54,680 --> 00:44:56,680 Lateral flows. OK. All right. 833 00:44:56,680 --> 00:44:59,280 So who's taken a lateral flow? You all know what it is. 834 00:44:59,280 --> 00:45:01,040 Right, of course, everybody knows what it is 835 00:45:01,040 --> 00:45:03,320 because you had one to come in here, didn't you? 836 00:45:03,320 --> 00:45:08,480 OK, right. So here, to demonstrate this 837 00:45:08,480 --> 00:45:10,680 is a giant-sized lateral flow device, 838 00:45:10,680 --> 00:45:13,640 generated by our colleagues here at the RI, 839 00:45:13,640 --> 00:45:15,800 and it even has a barcode that works, 840 00:45:15,800 --> 00:45:20,440 and it has a test strip with a C on it that means control, 841 00:45:20,440 --> 00:45:23,360 and this means that the test has worked once you put the sample 842 00:45:23,360 --> 00:45:25,560 through at the top. And the T, if this goes positive, 843 00:45:25,560 --> 00:45:27,840 it means there's coronavirus in the sample. 844 00:45:27,840 --> 00:45:31,000 So I think we're going to need a volunteer for this. 845 00:45:31,000 --> 00:45:32,360 Someone in the middle now. 846 00:45:32,360 --> 00:45:34,880 I'm trying to stay away from the red jumpers! OK. 847 00:45:34,880 --> 00:45:36,840 Red jumper, in the middle. 848 00:45:40,680 --> 00:45:43,000 Come over this side. Right. 849 00:45:43,000 --> 00:45:45,920 What's your name? Max. Max. 850 00:45:45,920 --> 00:45:47,160 Max, thanks for coming down. 851 00:45:47,160 --> 00:45:49,960 Right, you are going to demonstrate this giant lateral flow test 852 00:45:49,960 --> 00:45:51,040 to everybody. 853 00:45:51,040 --> 00:45:54,480 Right, so what I'm going to do is give you a sample. Here. 854 00:45:54,480 --> 00:45:58,800 And I'm going to ask you to pour this into the sample well, here. 855 00:45:58,800 --> 00:46:01,560 Normally, this would be liquid but it's balls at this time. OK. 856 00:46:01,560 --> 00:46:03,600 Right, and we're going to count down from three. 857 00:46:03,600 --> 00:46:06,640 ALL: Three, two, one. Go! 858 00:46:06,640 --> 00:46:09,800 RATTLING 859 00:46:14,600 --> 00:46:18,040 So as you can see, the control line has come on 860 00:46:18,040 --> 00:46:21,880 but the test line hasn't, and this shows that the test has worked but 861 00:46:21,880 --> 00:46:23,800 there doesn't seem to be any virus in there, 862 00:46:23,800 --> 00:46:25,120 so this is a negative test. 863 00:46:25,120 --> 00:46:28,080 Right, Max, now we're going to give you a second sample, OK? 864 00:46:28,080 --> 00:46:30,400 You did really well the first time. 865 00:46:30,400 --> 00:46:32,840 Let's see how your luck is running tonight. 866 00:46:32,840 --> 00:46:36,560 Let's pour that down through the sample well. Go! 867 00:46:36,560 --> 00:46:38,560 RATTLING 868 00:46:42,400 --> 00:46:44,240 Excellent. Thank you very much, Max. 869 00:46:44,240 --> 00:46:48,800 As you can see, this is now a positive test result 870 00:46:48,800 --> 00:46:52,160 because the test has worked and you have the red lights coming on - 871 00:46:52,160 --> 00:46:54,200 in other words, the test is positive. 872 00:46:54,200 --> 00:46:56,880 So this is a sample that had coronavirus in it. 873 00:46:56,880 --> 00:46:58,640 Thanks very much for coming down. 874 00:47:04,960 --> 00:47:07,760 OK. So we've seen a positive test result 875 00:47:07,760 --> 00:47:09,920 but how does this thing actually work? 876 00:47:09,920 --> 00:47:12,080 Let's look inside. 877 00:47:12,080 --> 00:47:16,280 Imagine these balls representing virus particles. 878 00:47:16,280 --> 00:47:18,120 They're covered in spike protein, aren't they? 879 00:47:18,120 --> 00:47:20,080 They're going to pass through the test. 880 00:47:20,080 --> 00:47:24,440 Now, on this red line, in a real lateral flow device, 881 00:47:24,440 --> 00:47:27,880 we have immobilised or placed antibodies to spike, 882 00:47:27,880 --> 00:47:30,320 and you saw how those were generated and how specific 883 00:47:30,320 --> 00:47:33,480 and tightly they are bound, in Katie's talk. 884 00:47:33,480 --> 00:47:36,800 So those antibodies are placed along there and as the spike goes 885 00:47:36,800 --> 00:47:40,160 through, it will bind to the antibody and trigger a colour 886 00:47:40,160 --> 00:47:43,920 change, and that's how the lateral test works in reality. 887 00:47:43,920 --> 00:47:46,600 Ravi, that was fantastic and I've always wanted to know what 888 00:47:46,600 --> 00:47:48,800 was inside a lateral flow device 889 00:47:48,800 --> 00:47:52,280 and finally, you have explained it to me. So... 890 00:47:52,280 --> 00:47:54,280 ..as you can see, 891 00:47:54,280 --> 00:47:57,840 we've got one test that's super-duper sensitive, 892 00:47:57,840 --> 00:47:59,720 but takes a long while. 893 00:47:59,720 --> 00:48:04,240 And we've got the lateral flow tests which aren't quite as sensitive, 894 00:48:04,240 --> 00:48:06,880 but are really quick and really practical. 895 00:48:06,880 --> 00:48:09,720 And that's one of the things in medicine 896 00:48:09,720 --> 00:48:14,080 and indeed, in controlling this coronavirus pandemic - 897 00:48:14,080 --> 00:48:16,840 you have to choose the tests 898 00:48:16,840 --> 00:48:18,600 that fit the purpose. 899 00:48:18,600 --> 00:48:22,320 And lateral flow tests, we all know, we've all used them, they're very 900 00:48:22,320 --> 00:48:25,560 quick and simple, but the really accurate ones 901 00:48:25,560 --> 00:48:27,840 are the PCR tests. 902 00:48:27,840 --> 00:48:32,160 And perhaps that now explains to you why the advice is that if you 903 00:48:32,160 --> 00:48:34,720 have a positive lateral flow test, 904 00:48:34,720 --> 00:48:38,200 you still need to go and get a PCR. 905 00:48:38,200 --> 00:48:41,640 So is there a way of getting the best of both worlds - 906 00:48:41,640 --> 00:48:44,680 something that's quick like lateral flow tests, 907 00:48:44,680 --> 00:48:47,120 and something that's as sensitive as PCR? 908 00:48:47,120 --> 00:48:48,760 Now, early in the Covid pandemic, 909 00:48:48,760 --> 00:48:53,440 our hospital was overrun by hundreds of patients with symptoms, 910 00:48:53,440 --> 00:48:56,040 and we needed a really quick test that could be 911 00:48:56,040 --> 00:48:58,280 done near the patients, to give a really quick result 912 00:48:58,280 --> 00:49:00,960 that was accurate, because if we got it wrong, people could be 913 00:49:00,960 --> 00:49:03,400 transmitting coronavirus to each other within hospital, 914 00:49:03,400 --> 00:49:05,440 which is a really dangerous scenario. 915 00:49:05,440 --> 00:49:08,640 So there was immense pressure to find such a system. 916 00:49:08,640 --> 00:49:13,000 And during my HIV work, I had used a test developed by an inventor, and 917 00:49:13,000 --> 00:49:17,560 here to explain this test is the inventor, Dr Helen Lee. 918 00:49:27,560 --> 00:49:29,240 Hello. 919 00:49:32,400 --> 00:49:34,440 Helen, welcome. Thank you. 920 00:49:34,440 --> 00:49:37,440 Could you explain to us what the original purpose of the machine was? 921 00:49:37,440 --> 00:49:42,400 Well, originally this machine was to test for HIV in Africa, 922 00:49:42,400 --> 00:49:44,680 in the villages. 923 00:49:44,680 --> 00:49:49,040 And we had to check whether the HIV treatment was working and 924 00:49:49,040 --> 00:49:53,360 also whether the babies that are born to HIV-positive mothers 925 00:49:53,360 --> 00:49:55,760 is infected or not. 926 00:49:55,760 --> 00:50:00,240 And we take the test to the patient rather than the other way around. 927 00:50:00,240 --> 00:50:05,160 So, Helen, can you explain to us how this works? OK, it's very simple. 928 00:50:06,240 --> 00:50:08,800 I'm sure you have all done a lateral flow test and have seen this 929 00:50:08,800 --> 00:50:10,920 before, right? 930 00:50:10,920 --> 00:50:14,000 A tube with a swab in, yes? 931 00:50:14,000 --> 00:50:16,400 So what I have done is that I have 932 00:50:16,400 --> 00:50:19,680 transferred with a pipette 300 microlitre 933 00:50:19,680 --> 00:50:26,760 exactly into this separate tube, so you can see I have this cartridge. 934 00:50:26,760 --> 00:50:31,560 I put it in here. And then the second cartridge I put it here. 935 00:50:31,560 --> 00:50:33,960 And the third one I put it here. 936 00:50:33,960 --> 00:50:38,040 But you can see, I cannot put it in the wrong way and now I am 937 00:50:38,040 --> 00:50:42,160 going to put the detection cartridge in. 938 00:50:42,160 --> 00:50:47,760 And this, in the NHS sites, some of them call it the ballerina 939 00:50:47,760 --> 00:50:50,280 because it dances and twists. 940 00:50:50,280 --> 00:50:55,680 And you'll see. So then I put this sample here. Now, I'm ready. 941 00:50:55,680 --> 00:50:59,080 All I had to do to load it is this simple. 942 00:50:59,080 --> 00:51:04,280 OK. So now I just close the lid and then what happens is that this 943 00:51:04,280 --> 00:51:08,800 particular tablet will prompt me to do the next thing. 944 00:51:08,800 --> 00:51:11,480 So now I just say start. 945 00:51:11,480 --> 00:51:15,600 It's a combination of PCR and lateral flow. 946 00:51:15,600 --> 00:51:21,080 It amplifies like PCR but at a constant temperature and so 947 00:51:21,080 --> 00:51:26,200 the target also gets to be amplified to billions of copies, but 948 00:51:26,200 --> 00:51:30,640 then the detection is visual, just like a lateral flow. 949 00:51:30,640 --> 00:51:33,960 If it is negative, it has only one line. 950 00:51:33,960 --> 00:51:36,560 That shows that the chemistry worked. 951 00:51:36,560 --> 00:51:41,880 And if it's positive, then you have two more lines, showing that 952 00:51:41,880 --> 00:51:44,240 you have Covid. 953 00:51:44,240 --> 00:51:46,960 Can you tell us then what the benefits are of this machine? 954 00:51:46,960 --> 00:51:51,320 Well, the main thing is that it is accurate. It's rapid. 955 00:51:51,320 --> 00:51:54,240 It gives you the results in about an hour. 956 00:51:54,240 --> 00:51:57,560 Can it be used for any other viruses or other diseases? Yes. 957 00:51:57,560 --> 00:52:03,880 We just developed a multiplex test which detects Covid plus flu A 958 00:52:03,880 --> 00:52:07,960 and flu B, so it is a multiplex test. 959 00:52:07,960 --> 00:52:12,480 It's going to be shortly in the hospitals, in the care homes 960 00:52:12,480 --> 00:52:14,600 and even in some schools. 961 00:52:14,600 --> 00:52:18,560 Fantastic, so that's incredible, you've got a test that can do 962 00:52:18,560 --> 00:52:21,000 multiple different viruses and tell 963 00:52:21,000 --> 00:52:23,720 you fairly quickly what the diagnosis is. 964 00:52:23,720 --> 00:52:28,120 That's incredible for a machine that was developed initially to test for 965 00:52:28,120 --> 00:52:31,600 HIV in resource-limited settings, where there was poor access to PCR. 966 00:52:31,600 --> 00:52:33,480 Thank you very much. 967 00:52:34,680 --> 00:52:36,120 Thank you. 968 00:52:39,360 --> 00:52:43,160 Ravi, what a tour de force on diagnostics. 969 00:52:43,160 --> 00:52:49,200 If I asked you two years ago whether you thought we could get to 970 00:52:49,200 --> 00:52:52,280 where we've got to now with diagnostics for coronavirus, 971 00:52:52,280 --> 00:52:54,120 what would you have said to me? 972 00:52:54,120 --> 00:52:56,600 Well, I wouldn't have believed you, because of course to bring 973 00:52:56,600 --> 00:52:59,560 tests from development all the way through to clinical use takes 974 00:52:59,560 --> 00:53:03,480 a very, very long time, a lot of validation, a lot of studies. 975 00:53:03,480 --> 00:53:06,480 And so the rapidity of this has just been incredible. 976 00:53:06,480 --> 00:53:07,840 I'm really stunned at how 977 00:53:07,840 --> 00:53:10,600 many different tests we have of high-level accuracy. 978 00:53:10,600 --> 00:53:13,160 Thank you so much, Ravi, thanks for being with us this evening. 979 00:53:13,160 --> 00:53:14,360 Thank you very much. 980 00:53:18,400 --> 00:53:23,560 We've seen that rapid testing can increase the quality of 981 00:53:23,560 --> 00:53:28,120 patient care, but could there be 982 00:53:28,120 --> 00:53:31,960 even faster ways of testing? 983 00:53:31,960 --> 00:53:35,960 Let's bring on our next set of guests. 984 00:53:35,960 --> 00:53:39,280 Mark and Scott and Jodie and Millie. 985 00:53:46,520 --> 00:53:49,720 What we've got here is we've got four Christmas stockings 986 00:53:49,720 --> 00:53:51,880 with four different things. 987 00:53:51,880 --> 00:53:56,680 Actually, the things are on the backs, on the little sensor 988 00:53:56,680 --> 00:54:03,080 panels, and in two of these Christmas stockings we've got old 989 00:54:03,080 --> 00:54:05,080 sweaty socks. 990 00:54:05,080 --> 00:54:11,720 But one of the old sweaty socks was worn by somebody who had Covid-19. 991 00:54:11,720 --> 00:54:15,480 There's no Covid-19 around this evening, please don't be 992 00:54:15,480 --> 00:54:22,120 afraid, but the smell of Covid-19... Have you ever smelled Covid-19? 993 00:54:22,120 --> 00:54:24,920 ..might still be on one of the socks. 994 00:54:24,920 --> 00:54:31,320 And on the other two specimens we've got wound specimens and one of those 995 00:54:31,320 --> 00:54:38,160 wound specimens is infected with a nasty bacteria called Pseudomonas. 996 00:54:38,160 --> 00:54:41,360 Pseudomonas can be quite serious for patients who've got damaged 997 00:54:41,360 --> 00:54:45,040 immune systems. And the other swab has got nothing on it. 998 00:54:45,040 --> 00:54:47,280 It's not infected. 999 00:54:47,280 --> 00:54:50,120 And so what we are going to do is see if our little friends 1000 00:54:50,120 --> 00:54:55,760 here can actually find these infections for us. 1001 00:54:55,760 --> 00:54:58,520 So let's start with Covid. 1002 00:54:58,520 --> 00:55:00,840 Which dog have we got for Covid? This is Millie. 1003 00:55:00,840 --> 00:55:02,240 So Millie, this is Millie. 1004 00:55:02,240 --> 00:55:04,720 Millie's been specially trained to sniff out Covid. 1005 00:55:04,720 --> 00:55:06,160 Over to you , Mark. 1006 00:55:06,160 --> 00:55:07,760 Millie. 1007 00:55:11,680 --> 00:55:15,880 She's found something. Yes, good girl. Very good. Good girl. 1008 00:55:22,640 --> 00:55:24,800 Was she right? 1009 00:55:24,800 --> 00:55:27,960 Yes, she was. Well done. Jodie. Jodie, here. 1010 00:55:27,960 --> 00:55:31,960 Right, Jodie, you've got an act to follow now. It's your go. 1011 00:55:33,160 --> 00:55:35,640 Jodie is looking for Pseudomonas. 1012 00:55:45,960 --> 00:55:47,960 Hey, good girl. Good girl. 1013 00:56:01,200 --> 00:56:03,160 So there you have it. 1014 00:56:04,640 --> 00:56:10,840 Dogs that have been trained to sniff out infectious diseases. 1015 00:56:10,840 --> 00:56:15,680 Now, I hope we are about to be able to be joined by Dr Claire Guest 1016 00:56:15,680 --> 00:56:20,120 online. And here she is. Good evening, Claire, can you hear me OK? 1017 00:56:20,120 --> 00:56:23,520 I can indeed, good evening, everyone. Thanks for joining us. 1018 00:56:23,520 --> 00:56:28,640 Now, you know a lot more about using dogs in medical detection 1019 00:56:28,640 --> 00:56:32,280 than I do, so I'm just going to ask you the questions. 1020 00:56:32,280 --> 00:56:35,440 How could these dogs be used in the future? 1021 00:56:35,440 --> 00:56:39,000 Well, what you've just seen is the biosensor with a fluffy coat 1022 00:56:39,000 --> 00:56:43,560 and a waggy tail and as we know, dogs have been used for many years 1023 00:56:43,560 --> 00:56:46,600 keeping us safe by finding drugs and explosives. 1024 00:56:46,600 --> 00:56:50,360 What the dogs have done here is found the odour of disease, so when 1025 00:56:50,360 --> 00:56:53,840 we have a particular disease or condition our odour changes 1026 00:56:53,840 --> 00:56:57,400 and the dogs with their incredible sense of smell can be trained 1027 00:56:57,400 --> 00:56:59,520 to detect this and warn us. 1028 00:56:59,520 --> 00:57:03,720 So we can't smell these diseases but the dogs can? Absolutely. 1029 00:57:03,720 --> 00:57:08,960 They've got 350 million sensory receptors dedicated to olfaction. 1030 00:57:08,960 --> 00:57:12,240 Now, I don't know if anyone would like to guess but us humans 1031 00:57:12,240 --> 00:57:15,760 have got five million, which means we can smell a teaspoon of 1032 00:57:15,760 --> 00:57:17,320 sugar in a cup of tea. 1033 00:57:17,320 --> 00:57:21,440 These dogs can smell a teaspoon of sugar in the volume of water held 1034 00:57:21,440 --> 00:57:25,760 in two Olympic-sized swimming pools, a quite incredible sense of smell. 1035 00:57:25,760 --> 00:57:29,320 And the dogs are really happy to put this nose to work to find disease. 1036 00:57:29,320 --> 00:57:31,760 For example, Millie the Covid dog, she could go out now, she 1037 00:57:31,760 --> 00:57:34,440 could have screened every person as you came in this afternoon. 1038 00:57:34,440 --> 00:57:38,080 So these dogs, because they are very rapid, they could screen 1039 00:57:38,080 --> 00:57:40,960 hundreds of people at a time. Thank you, Claire. 1040 00:57:40,960 --> 00:57:43,000 Thanks for joining us this evening. 1041 00:57:43,000 --> 00:57:46,000 And thanks to Jodie and Millie and thanks to Mark and Scott. 1042 00:57:52,960 --> 00:57:57,800 So I think you've seen in this lecture just a first glimpse 1043 00:57:57,800 --> 00:58:01,240 of how the pandemic has really been 1044 00:58:01,240 --> 00:58:05,520 transformed by scientific achievements. 1045 00:58:05,520 --> 00:58:09,400 And in the next lecture, we're going to think about how these 1046 00:58:09,400 --> 00:58:15,280 viruses spread, why some humans spread them better than others and 1047 00:58:15,280 --> 00:58:19,200 what we can do to stop them. That's for next time. 1048 00:58:19,200 --> 00:58:21,360 Thank you for being such a great audience. 1049 00:58:21,360 --> 00:58:24,840 I hope you've had a good first lecture. Thanks for joining us.