WEBVTT - The Viral Universe Inside Us 0:00:04.480 --> 0:00:08.160 Viruses are in the air we breathe, in the water 0:00:08.240 --> 0:00:11.080 we drink. They're in the ground we walk on there, 0:00:11.080 --> 0:00:15.200 on our skin, they're in our bellies. They have us surrounded, 0:00:16.440 --> 0:00:20.400 and the wild thing is we've only identified a fraction 0:00:20.480 --> 0:00:25.000 of them. In other words, not only are we surrounded 0:00:25.040 --> 0:00:29.360 and permeated by viruses, we're surrounded and permeated by viral 0:00:29.480 --> 0:00:33.120 dark matter, by viruses that we don't even know exist. 0:00:34.120 --> 0:00:36.159 We have lots of viruses in us and we have 0:00:36.200 --> 0:00:41.480 no idea what they're doing, and potentially in that dark matter, 0:00:42.400 --> 0:00:45.960 there are some answers to the questions on what are 0:00:46.000 --> 0:00:46.960 they doing there. 0:00:48.440 --> 0:00:52.000 I'm Jacob Goldstein, and this is Incubation. Today, on our 0:00:52.040 --> 0:00:54.840 final episode of season two, we're going out to the 0:00:54.920 --> 0:00:58.520 scientific frontier to talk about all the viruses we don't 0:00:58.600 --> 0:01:12.319 know about in the world and in our bodies. In 0:01:12.360 --> 0:01:14.400 the second half of the show today, I'll be speaking 0:01:14.400 --> 0:01:18.160 with a researcher who has recently discovered hundreds of families 0:01:18.160 --> 0:01:22.000 of viruses that live inside the human gut, and he's 0:01:22.040 --> 0:01:25.160 found a link that suggests some of those viruses could 0:01:25.240 --> 0:01:29.760 actually help kids stay healthy. But first I'm going to 0:01:29.840 --> 0:01:33.440 talk with Ken Steadman he's a professor of biology at 0:01:33.560 --> 0:01:37.920 Portland State University. He studies viral dark matter, which basically 0:01:37.959 --> 0:01:42.319 means he goes looking for viruses in wild places. To start, 0:01:42.520 --> 0:01:44.959 I asked him, how do you look for a virus 0:01:45.000 --> 0:01:46.520 that nobody knows exists? 0:01:46.959 --> 0:01:49.440 A couple of different ways. All viruses that we know of, 0:01:49.480 --> 0:01:52.000 by definition, have to have a host that they infect. 0:01:52.480 --> 0:01:55.520 What we do is we'll go and collect samples in 0:01:55.600 --> 0:01:59.320 the craziest places we can find, usually volcanic hot springs, 0:01:59.760 --> 0:02:01.559 and then we bring them back to lab and see 0:02:01.560 --> 0:02:05.320 if they infect our favorite microbes that also happen to 0:02:05.360 --> 0:02:07.000 grow in these hot springs. 0:02:07.560 --> 0:02:10.040 I've read a little bit about your work at last 0:02:10.080 --> 0:02:13.000 in Volcanic National Park in northern California, So tell me 0:02:13.000 --> 0:02:16.000 about what's going on there. Tell me about Boiling Springs Lake. 0:02:16.240 --> 0:02:19.160 So, Boiling Springs Lake I like to describe as the 0:02:19.200 --> 0:02:21.560 biggest hot spring in the world that nobody has ever 0:02:21.600 --> 0:02:25.040 heard of. It's a slight exaggeration. The low temperature in 0:02:25.080 --> 0:02:27.679 the lake is about one hundred and thirty hundred and 0:02:27.760 --> 0:02:28.960 forty degrees fahrenheit. 0:02:29.360 --> 0:02:32.679 And so what does that mean for finding weird viruses? 0:02:32.880 --> 0:02:35.120 Well, hang on just a second, that's the temperature. I 0:02:35.120 --> 0:02:36.960 haven't told you about the pH yet, have I Wait 0:02:37.000 --> 0:02:37.400 a minute. 0:02:37.520 --> 0:02:41.600 If you like the temperature, you're gonna love the pH exactly. 0:02:41.720 --> 0:02:43.000 So the pH is about two. 0:02:43.200 --> 0:02:46.320 pH of two means it's it's acidic. It's highly acidic. 0:02:46.440 --> 0:02:48.960 So not great for soaking is what you're not great for. 0:02:49.280 --> 0:02:51.400 We've seen people walking up there and they're a swimming 0:02:52.320 --> 0:02:55.200 gear and we tell them not a real good idea. 0:02:55.360 --> 0:02:57.680 So you go to this hot, acidic lake and what 0:02:57.680 --> 0:02:58.399 what do you do there? 0:02:58.880 --> 0:03:02.120 We just took about two hundred liters worth of water 0:03:02.160 --> 0:03:06.160 from the lake and then purified all of the virus 0:03:06.160 --> 0:03:11.399 sized particles in it, then determined what their genetic sequences were, 0:03:11.560 --> 0:03:15.400 what we call them meta genome, but basically all the viruses, 0:03:16.120 --> 0:03:17.440 what genes do they have in. 0:03:17.919 --> 0:03:23.280 So you're basically just what pouring this acid into a 0:03:23.320 --> 0:03:25.920 machine and saying, tell me all the genes that are in. 0:03:25.840 --> 0:03:28.639 Here or or less. Yeah. So one of the things 0:03:28.680 --> 0:03:32.600 about viruses which makes virus is incredibly unique is they 0:03:32.639 --> 0:03:34.440 have what we like to call we call it a 0:03:34.560 --> 0:03:39.040 very on it's the virus structure. So the lunar lander 0:03:39.160 --> 0:03:40.520 module kind of thing. 0:03:40.520 --> 0:03:43.360 Right, your classic virus looks like a little lunar lander 0:03:43.640 --> 0:03:46.000 like a pod, and then little legs coming out right. 0:03:46.040 --> 0:03:48.560 Absolutely, and it's relatively small. 0:03:48.600 --> 0:03:50.760 So what you do is sayge right, that's the classic phase. 0:03:50.760 --> 0:03:52.520 That's the thing that lands on the bacterium and then 0:03:52.600 --> 0:03:54.600 inserts its genetic material. 0:03:54.360 --> 0:03:57.240 Injects it exactly. But even if you think about no 0:03:57.480 --> 0:04:01.680 Sarscobe two virus that causes COVID nineteen also is a 0:04:01.720 --> 0:04:04.440 little bag which has genes on the inside of it. 0:04:04.760 --> 0:04:06.200 So you break up in the bag and you throw 0:04:06.200 --> 0:04:09.400 it into the machine and then it gives you back 0:04:10.720 --> 0:04:14.800 hundreds of thousands of sequences in our case now millions 0:04:14.840 --> 0:04:18.680 of sequences with the newest technology, so millions of genes, 0:04:19.040 --> 0:04:21.520 hundreds of thousands of genes. But they're not genes, they're 0:04:21.560 --> 0:04:25.039 gene fragments, they're little pieces. Now, at first you just 0:04:25.080 --> 0:04:29.080 want to look at what those little pieces are relative 0:04:29.200 --> 0:04:31.320 to known sequences. 0:04:31.800 --> 0:04:32.320 Uh huh. 0:04:32.560 --> 0:04:34.680 That the dark matter is going to be, you know, 0:04:35.000 --> 0:04:37.480 those little pieces that don't match anything, and the light 0:04:37.520 --> 0:04:40.320 matter is going to be stuff that does Ninety plus 0:04:40.360 --> 0:04:42.400 percent of the sequences that we got back of our 0:04:42.480 --> 0:04:45.400 hundreds of thousands of sequences didn't match anything. 0:04:45.680 --> 0:04:48.520 And what did you think when you saw that, Oh. 0:04:48.360 --> 0:04:50.960 It's like other environments, other people seemed very similar things. 0:04:51.000 --> 0:04:54.240 So you do this with seawater, you do this with 0:04:54.680 --> 0:04:59.080 things you find in soil. Ninety odd percent plus or 0:04:59.120 --> 0:05:01.080 minus don't match anything. 0:05:01.720 --> 0:05:05.080 Does that mean that we don't know about ninety percent 0:05:05.160 --> 0:05:07.800 of the viruses that are out in the world. Is 0:05:07.839 --> 0:05:09.680 that broadly what that implies? 0:05:09.760 --> 0:05:11.400 That is exactly what it implies. 0:05:11.600 --> 0:05:14.400 And it's not just in a weirdo boiling acid lake. 0:05:14.400 --> 0:05:16.680 How about just in the dirt. If I just went 0:05:16.720 --> 0:05:19.880 into my yard and dug up some dirt and send 0:05:19.920 --> 0:05:21.080 it to somebody who could put it in one of 0:05:21.080 --> 0:05:25.039 your machines. What percentage of the viruses in my backyard 0:05:25.080 --> 0:05:26.440 are known to science? 0:05:26.720 --> 0:05:27.080 Roughly? 0:05:29.320 --> 0:05:35.159 Wow, eighty percent are dark matter are unknown. I love that. 0:05:35.600 --> 0:05:37.560 It's keeps us employed. 0:05:38.160 --> 0:05:43.120 Yeah, so okay, so you get this result back it's 0:05:43.360 --> 0:05:46.760 ninety percent is unknown. What like? And so what you 0:05:46.960 --> 0:05:48.880 just have is like a genetic mess that you don't 0:05:48.920 --> 0:05:50.479 know what to do with, because it's not like each 0:05:50.560 --> 0:05:53.120 little fragment is like, oh, that's a new virus. It's 0:05:53.120 --> 0:05:55.960 just these are weird fragments that we don't understand. 0:05:56.040 --> 0:06:00.120 Yeah, exactly weird fragments if we don't understand. But one 0:06:00.160 --> 0:06:03.120 of the other things that we found is some of 0:06:03.120 --> 0:06:08.760 the fragments that we could actually identify didn't look like 0:06:09.279 --> 0:06:13.680 sequences that we should have found, Meaning not only are 0:06:13.720 --> 0:06:16.720 they different than anythings that's been found before. 0:06:16.520 --> 0:06:19.320 They are like too weird, They're like, wait, that doesn't 0:06:19.320 --> 0:06:20.080 make any sense. 0:06:20.120 --> 0:06:23.120 How could that even be? Exactly did you think you 0:06:23.160 --> 0:06:25.200 had made a mistake of some sort so that the 0:06:25.240 --> 0:06:28.440 machine was broken. We thought that we had absolutely screwed 0:06:28.480 --> 0:06:31.279 up in this case. So we've got genetic material virus, 0:06:31.320 --> 0:06:33.720 you've got RNA viruses, you got DNA viruses, right, So. 0:06:33.680 --> 0:06:36.880 Basically a virus is just like a bag with genetic 0:06:36.920 --> 0:06:40.560 material in it. And there's some viruses have DNA and 0:06:40.600 --> 0:06:43.800 some viruses have RNA. And even though these are like 0:06:43.839 --> 0:06:47.280 two types of viruses, sort of historically evolutionarily, they're like 0:06:47.320 --> 0:06:48.839 really different from each other. 0:06:48.920 --> 0:06:53.360 Right, DNA viruses and RNA viruses we always thought were 0:06:53.480 --> 0:06:57.400 completely different relative to each other. And if you think 0:06:57.440 --> 0:07:03.480 about the evolutionary relationship between between RNA viruses and DNA viruses, 0:07:04.080 --> 0:07:07.320 there basically seems to be almost none. 0:07:07.480 --> 0:07:11.360 Like how big is the gap? Sort of whatever evolutionarily, 0:07:11.440 --> 0:07:14.120 how different are DNA and RNA viruses? 0:07:14.200 --> 0:07:18.840 So the difference between DNA and RNA viruses is probably 0:07:20.360 --> 0:07:23.080 billions of years evolutionarily speaking. 0:07:23.080 --> 0:07:28.000 Okay, I was gonna say, like, it's like as big 0:07:28.000 --> 0:07:30.760 as the difference between mammals and reptiles, but it's way 0:07:30.800 --> 0:07:31.400 bigger than that. 0:07:31.840 --> 0:07:36.200 It's probably more like the difference between you know, bacteria 0:07:36.280 --> 0:07:39.280 and people, bacterian people exactly, much more like that in 0:07:39.360 --> 0:07:40.640 terms of evolutionary difference. 0:07:41.040 --> 0:07:44.680 Wow. Okay, So there are these profoundly different things. 0:07:45.440 --> 0:07:48.280 So we sequenced a bunch of DNA put into our machine, 0:07:48.360 --> 0:07:51.480 you know, said hey, get some DNA sequences, and then 0:07:52.080 --> 0:07:55.880 some of those proxially a couple of thousand sequences that 0:07:55.960 --> 0:07:59.520 actually match. Something in those sequences were things that look 0:07:59.680 --> 0:08:03.080 like RNA viruses in terms of their sequence. 0:08:03.080 --> 0:08:05.680 But it's DNA that you're But we sequenced DNA. 0:08:06.400 --> 0:08:09.840 Yeah, but we and when I say we, mostly a 0:08:09.840 --> 0:08:12.880 graduate student working in our group, Jeff Deemer. He then 0:08:13.000 --> 0:08:15.960 started to try and put some of these pieces together. 0:08:16.840 --> 0:08:20.760 What he found was those pieces that looked like RNA 0:08:21.280 --> 0:08:29.360 viruses were connected genetically to sequences that looked like DNA viruses. 0:08:29.840 --> 0:08:35.320 Okay, and connected like physically like that they were physically 0:08:34.920 --> 0:08:37.760 almost like the one piece of a chain of genetic 0:08:37.800 --> 0:08:38.520 material exactly. 0:08:38.880 --> 0:08:40.880 And then what we did is we went back to 0:08:40.920 --> 0:08:43.200 the samples that we collected from Boiling Springs Lake, and 0:08:43.200 --> 0:08:46.239 instead of pouring them into the machine to get the sequences, 0:08:47.200 --> 0:08:51.800 we then made many many copies of whatever this piece was. 0:08:51.840 --> 0:08:53.640 And this piece was to show that were actual connected 0:08:53.679 --> 0:08:57.520 to each other. So there are these what we're now 0:08:57.559 --> 0:09:02.320 calling cruci viruses that appear to have evolved by DNA 0:09:02.400 --> 0:09:04.120 viruses and RNA viruses coming together. 0:09:04.960 --> 0:09:07.720 Okay, so we thought these were like totally different kinds 0:09:07.720 --> 0:09:10.560 of viruses, but now you have discovered this new kind 0:09:10.559 --> 0:09:12.680 of virus that's kind of like a cross between the 0:09:12.720 --> 0:09:16.080 two of them. Right, what does that mean? Like, what 0:09:16.080 --> 0:09:18.320 does it mean for how we think about RNA viruses 0:09:18.360 --> 0:09:19.439 and DNA viruses. 0:09:20.520 --> 0:09:25.120 It means that there's communication between them, and there's this recombination. 0:09:25.200 --> 0:09:29.640 So it's not billions of years of evolutionary difference, which 0:09:29.679 --> 0:09:33.080 is what we thought. Now it looks as if they 0:09:33.160 --> 0:09:36.960 can be exchanging genetic information with each other, which is 0:09:37.280 --> 0:09:41.480 really kind of revolutionary in terms of thinking about virus 0:09:41.480 --> 0:09:46.040 evolution and what it means is. We always thought DNA 0:09:46.080 --> 0:09:48.560 viruses evolved like this and RNA viruses evolved like this, 0:09:49.000 --> 0:09:51.240 But if they can exchange genes with each other, that 0:09:51.400 --> 0:09:54.360 kind of throws a lot of what we think about 0:09:54.480 --> 0:09:57.840 virus evolution kind of out the window. Turns out that 0:09:58.200 --> 0:10:01.080 these viruses in and of them els are just so 0:10:01.520 --> 0:10:06.040 different from any other virus anybody's ever seen before, in 0:10:06.120 --> 0:10:09.640 terms of their shape, in terms of their genes, what 0:10:09.800 --> 0:10:10.320 is in them? 0:10:11.160 --> 0:10:14.720 So you and your colleagues found this, this crucivirus in 0:10:14.800 --> 0:10:18.360 the boiling acid Lake. I know that since then a 0:10:18.440 --> 0:10:21.520 number of other of these cruciviruses have been found. So 0:10:21.720 --> 0:10:24.320 just give me the landscape. Give me what we know 0:10:24.400 --> 0:10:27.080 so far of like where are they, what are they doing, etc. 0:10:27.679 --> 0:10:30.280 We do not know what they're doing. Crucy virus has 0:10:30.280 --> 0:10:35.240 been found in boiling Springs Lake, Antarctic lakes, in deep 0:10:35.320 --> 0:10:39.319 sea sediments off the coast of Greenland, in Korean air samples, 0:10:39.800 --> 0:10:45.720 isopods off the coast of Oregon, monkey feces, in dragonfly guts, 0:10:45.760 --> 0:10:49.960 soil just outside the lab at Portland State University. Basically 0:10:50.400 --> 0:10:54.640 anywhere that we have looked, we've found these crucy viruses. 0:10:55.200 --> 0:10:59.160 Very low amounts of them, but seem to be very ubiquitous. 0:10:59.280 --> 0:11:00.880 So where are the everywhere? 0:11:01.160 --> 0:11:01.480 Love it? 0:11:01.600 --> 0:11:03.079 What are they doing? We don't know. 0:11:03.720 --> 0:11:05.280 Are they in my body right now? 0:11:06.280 --> 0:11:08.280 Probably in your body right now. 0:11:09.080 --> 0:11:12.440 So these things are all around us, all over the world, 0:11:13.040 --> 0:11:17.120 possibly in our guts, and nobody knows what they're doing. 0:11:17.640 --> 0:11:21.200 That is exactly correct. I love it me too. 0:11:23.240 --> 0:11:26.080 So what do we know about like what they're doing. 0:11:26.360 --> 0:11:29.440 We're trying to figure out what they infect. We think 0:11:29.679 --> 0:11:34.640 they're infecting microbial EU carry out, So things like fungi 0:11:35.720 --> 0:11:39.760 or protus, these paramesia things you know swimming around in lakes. 0:11:40.040 --> 0:11:44.800 Are there are those things? Also? Are there also organisms 0:11:44.840 --> 0:11:45.880 like that in our bodies? 0:11:46.080 --> 0:11:46.920 There definitely are? 0:11:47.080 --> 0:11:48.760 Is that part of the microflora? 0:11:49.000 --> 0:11:53.240 Yeah, we have. We have a euchreytic microflora. Mostly these 0:11:53.280 --> 0:11:55.840 are going to be fungi, some kinds of yeats, et cetera. 0:11:57.320 --> 0:12:00.600 But there are many other of the And again this 0:12:00.679 --> 0:12:03.839 is something which has been not very well studied, so 0:12:03.880 --> 0:12:06.839 you kind of put in environmental viruses have not been 0:12:06.840 --> 0:12:11.640 well studied. These microbial EU carry outs have not been 0:12:11.760 --> 0:12:15.439 very well studied. So you put those two together, extremely 0:12:15.480 --> 0:12:16.599 poorly studied. 0:12:16.880 --> 0:12:20.559 Very dark. It's very dark matter. 0:12:20.600 --> 0:12:23.640 Very dark matter, but at the same time really exciting 0:12:23.640 --> 0:12:25.400 because there's so much to discover. 0:12:25.800 --> 0:12:29.080 Like why does microbial dark matter matter? 0:12:29.600 --> 0:12:33.520 Besides being cool, I think it's an area where we 0:12:33.559 --> 0:12:37.920 can make discoveries. There's so much we don't know. We 0:12:38.040 --> 0:12:40.400 have lots of viruses in US and we have no 0:12:40.520 --> 0:12:46.200 idea what they're doing, and potentially in that dark matter 0:12:47.120 --> 0:12:50.680 there are some answers to the questions on what are 0:12:50.720 --> 0:12:53.280 they doing there? So I think that that's a very 0:12:53.320 --> 0:12:54.839 important thing to think about. 0:12:55.040 --> 0:12:56.679 Not just how are they making us sick, but how 0:12:56.720 --> 0:12:58.840 are they keeping us healthy? How might they get out 0:12:58.840 --> 0:13:04.000 of balance at times and contribute in indirect ways to sickness? 0:13:04.200 --> 0:13:07.839 Certainly seems plausible. We know that happens with the bacteria 0:13:07.840 --> 0:13:08.320 in our gut. 0:13:08.440 --> 0:13:11.080 Yeah, I think that that's a very reasonable thing to 0:13:11.080 --> 0:13:14.959 think about. And then just in a larger ecological sense, 0:13:15.120 --> 0:13:17.880 you know, understanding the ecology. There's still so much that 0:13:17.920 --> 0:13:22.720 we don't know. I think understanding that virus' role in 0:13:22.800 --> 0:13:27.079 not just us, but also in life on our planet. 0:13:27.800 --> 0:13:30.920 I think understanding that dark matter will really help us 0:13:31.840 --> 0:13:36.640 understand what's going on with all of these different pirates. 0:13:44.720 --> 0:13:46.880 I appreciate your time. It was a fun conversation. 0:13:46.960 --> 0:13:49.880 Yeah, it was fun conversation for me too. I learned things, 0:13:50.080 --> 0:13:51.200 So thank you for that good. 0:13:54.440 --> 0:13:58.120 Ken Stedman is a biology professor and extreme virologist at 0:13:58.200 --> 0:14:02.679 Portland State University. His work and his team's work are 0:14:02.720 --> 0:14:10.560 expanding our idea of what a virus can be in 0:14:10.600 --> 0:14:14.440 a minute, discovering hundreds of kinds of new viruses that 0:14:14.520 --> 0:14:30.680 live in the human gut. I'm going to go out 0:14:30.680 --> 0:14:35.440 on a limb and say the most underrated viruses are phages. 0:14:36.160 --> 0:14:40.320 Phages are the viruses that infect bacteria. They're the most 0:14:40.360 --> 0:14:44.720 abundant biological entity on Earth and their killers. 0:14:45.360 --> 0:14:49.880 Every other bacterium on Earth gets killed by a virus 0:14:49.960 --> 0:14:50.400 every day. 0:14:50.600 --> 0:14:52.800 Actually, that's wild to think about. 0:14:53.640 --> 0:14:54.880 It really sucks for them. 0:14:55.080 --> 0:14:58.800 Shiraz ali Sha studies the phages that live inside people. 0:15:00.160 --> 0:15:04.640 Your researcher on a project called COPSAC, the Copenhagen Prospective 0:15:04.720 --> 0:15:09.080 Studies for Asthma in Childhood. The project is following hundreds 0:15:09.120 --> 0:15:12.800 of kids from birth into childhood to try to understand 0:15:12.840 --> 0:15:16.840 the causes of asthma. Shiraz focuses on the human virum, 0:15:17.040 --> 0:15:19.520 the universe of viruses that live in the human gut 0:15:19.920 --> 0:15:22.680 and he told me that studying the viroom from birth 0:15:22.960 --> 0:15:24.000 is really important. 0:15:24.640 --> 0:15:27.920 In the first year of life, the baby has an 0:15:27.920 --> 0:15:30.800 immune system that has not yet matured, so it does 0:15:30.840 --> 0:15:34.080 not know how to distinguish friend from foe. What happens 0:15:34.120 --> 0:15:35.720 in the first year of life is that the immune 0:15:35.760 --> 0:15:38.000 system is still trying to get to know what is 0:15:38.040 --> 0:15:40.120 it supposed to attack and what is it not supposed 0:15:40.120 --> 0:15:42.400 to attack. And it seems that there's more and more 0:15:42.440 --> 0:15:44.600 evidence showing that if you are not exposed to a 0:15:44.600 --> 0:15:48.920 diverse array of good bacteria in the body and on 0:15:48.960 --> 0:15:51.160 the body within the first year of life, then the 0:15:51.200 --> 0:15:53.720 immune system is not properly trained, and then you're way 0:15:53.760 --> 0:15:58.160 more prone to chronic inflammatory or immune diseases in the future, 0:15:58.920 --> 0:16:02.720 like asthma like asthma, like allergy like asthma, even stuff 0:16:02.720 --> 0:16:06.760 like depression and anxiety, inflammation linked heart disease, most definitely cancer, 0:16:06.840 --> 0:16:09.080 most definitely diabetes, most definitely yes. 0:16:09.160 --> 0:16:11.760 So okay, so now you're getting into some of what 0:16:11.840 --> 0:16:14.560 you study, right, tell me about your work on this. 0:16:14.960 --> 0:16:18.080 So this is a place called COPSAC Copenhagen Perspective Studies 0:16:18.080 --> 0:16:19.080 for Asthma in Childhood. 0:16:19.200 --> 0:16:22.600 It's a place where they're trying to understand how asthma 0:16:22.640 --> 0:16:23.640 works in kids. 0:16:23.520 --> 0:16:25.840 Exactly, Okay, and so and so. The way that they 0:16:25.880 --> 0:16:28.080 do this is basically, they have a bunch of kids 0:16:28.080 --> 0:16:31.000 that were born in twenty ten and they've been following 0:16:31.000 --> 0:16:33.320 them since the moms got pregnant and today they're like 0:16:33.360 --> 0:16:34.120 fifteen years old. 0:16:34.240 --> 0:16:34.400 Right. 0:16:34.760 --> 0:16:36.840 What they're doing is they're recording as much data on 0:16:36.880 --> 0:16:39.960 these children as possible as humanly possible, like where do 0:16:40.000 --> 0:16:42.120 they go to date hair, how many siblings do they have, 0:16:42.800 --> 0:16:45.160 but also blood tests, you know, which chemicals do they 0:16:45.160 --> 0:16:48.200 have in their bodies in their pee, what bacteria do 0:16:48.240 --> 0:16:50.440 they have in their poop, in their lungs, et cetera, 0:16:50.480 --> 0:16:52.840 et cetera. So we have like jigabtes upon jigabat also 0:16:52.880 --> 0:16:54.520 their own genes, their own genomes we also have. 0:16:54.640 --> 0:16:57.360 And so, just to be clear, it's the idea of 0:16:57.440 --> 0:17:00.840 doing all this and starting before the child is even born. 0:17:01.200 --> 0:17:03.920 Is the question they're trying to answer, why do some 0:17:04.000 --> 0:17:05.840 people get asthma and others don't? 0:17:06.400 --> 0:17:10.040 Exactly Because even though asthma is such a common childhood 0:17:10.200 --> 0:17:13.520 kind of disease, it's very poorly understrue. And this is 0:17:13.560 --> 0:17:15.520 not only the case for asthma. It's also the case 0:17:15.560 --> 0:17:18.360 for all the other chronic diseases basically that kill adults, 0:17:18.400 --> 0:17:21.840 like cancer, heart disease, diabetes, you know, chronic respiratory disease, 0:17:22.000 --> 0:17:25.240 multiple scrosis, you know, all of these. And so maybe 0:17:25.359 --> 0:17:28.239 by collecting all of this data on the children, we 0:17:28.240 --> 0:17:30.800 can start predicting based on the data, who's going to 0:17:30.840 --> 0:17:33.000 get which disease, and based on that, maybe we can 0:17:33.000 --> 0:17:35.280 figure out, Okay, if we do this, this and this, 0:17:35.440 --> 0:17:37.719 maybe we can avoid that and that and that chronic disease. 0:17:38.119 --> 0:17:40.040 Every time the kids visit us, and they do so 0:17:40.119 --> 0:17:43.080 once a year, we take as money samples as we 0:17:43.119 --> 0:17:43.840 possibly can. 0:17:44.119 --> 0:17:46.800 Right, So you have this whole poop library going over 0:17:46.880 --> 0:17:49.720 the kid's whole lifetime that you can sort of examine 0:17:49.720 --> 0:17:52.520 over time. Yes, and how many kids are in this cohort? 0:17:52.720 --> 0:17:54.240 So we have two horts and what I'm going to 0:17:54.240 --> 0:17:56.080 talk about today. The data is from the corps AC 0:17:56.080 --> 0:17:58.239 twenty ten cohorts. So they were born in twenty ten. 0:17:58.240 --> 0:18:01.160 They're like fourteen years old now, right, And the twenty 0:18:01.160 --> 0:18:02.840 ten cohort is seven hundred kids. 0:18:02.920 --> 0:18:05.480 So the cohort you're following is seven hundred kids who 0:18:05.480 --> 0:18:08.280 were born in twenty ten. You're coming into this as 0:18:08.320 --> 0:18:13.120 a person who has been studying viruses that attack bacteria 0:18:13.359 --> 0:18:16.520 for purposes here, and so when you get there. 0:18:17.040 --> 0:18:17.640 What do you do? 0:18:18.960 --> 0:18:22.080 I get there and then my boss he basically explains 0:18:22.119 --> 0:18:23.840 me some of the studies that they've been doing on 0:18:23.880 --> 0:18:26.600 the bacteria in the gut so far. And one of 0:18:26.640 --> 0:18:28.760 the major studies that they did just like one year 0:18:28.800 --> 0:18:32.080 before I came was that they found that in one 0:18:32.160 --> 0:18:35.080 year old, when you're basically still a baby, the bacteria 0:18:35.119 --> 0:18:37.080 that you have in your gut when you're a baby 0:18:37.200 --> 0:18:39.720 end up determining whether or not you get asthma as 0:18:39.720 --> 0:18:42.720 a five year old. And I was like, what, I mean, 0:18:42.760 --> 0:18:45.719 how is that even possible? And so what the general 0:18:45.760 --> 0:18:48.480 picture is that if you have only a few different 0:18:48.480 --> 0:18:51.280 bacteria in your gut when you're one year old, then 0:18:51.320 --> 0:18:53.480 you have much higher risk of getting asthma as a 0:18:53.480 --> 0:18:55.679 five year old, right, But if you have like loads 0:18:55.720 --> 0:18:57.800 and loads of different bacteria in your gut when you're 0:18:57.840 --> 0:19:00.880 one year old, then you're much more protected from asthma 0:19:01.280 --> 0:19:03.520 as a five year old. And so basically that that 0:19:03.560 --> 0:19:06.479 got me thinking, Wow, that means that most bacteria are 0:19:06.480 --> 0:19:08.520 actually good for us. I mean, there are few bacteria, 0:19:08.520 --> 0:19:10.919 maybe one hundred species in total that can cause infection, 0:19:11.240 --> 0:19:13.280 But the total number of bacteria in nature is like 0:19:13.280 --> 0:19:15.800 one hundred million species at least, So those other one 0:19:15.840 --> 0:19:17.879 hundred million are not causing. It's just one out of a 0:19:17.920 --> 0:19:19.160 million bacterium. 0:19:18.760 --> 0:19:20.760 That is bad and the other one in a million 0:19:20.800 --> 0:19:24.199 gives him a bad name, and so go on. 0:19:24.760 --> 0:19:27.000 So I was thinking, Okay, if that's the case for bacteria, 0:19:27.040 --> 0:19:29.840 then what about viruses. What if it's the same for viruses. 0:19:29.880 --> 0:19:31.800 What if the only viruses that we know about are 0:19:31.840 --> 0:19:35.040 the ones that cause disease and there are loads of 0:19:35.080 --> 0:19:38.280 other viruses that are actually good for us. That's what 0:19:38.400 --> 0:19:41.639 I was thinking back then. But the funny thing is 0:19:41.680 --> 0:19:44.040 that this other guy called Dennis Nielsen, who is a 0:19:44.080 --> 0:19:47.200 professor at copenha University because he's an expert at figuring 0:19:47.240 --> 0:19:49.960 out which viruses are in a sample, he basically said, Okay, 0:19:50.000 --> 0:19:52.720 you guys found this thing with bacteria, why don't we 0:19:52.720 --> 0:19:54.439 look at the viruses in the gut and maybe we 0:19:54.480 --> 0:19:57.080 can find something similar or even cooler. And so when 0:19:57.080 --> 0:19:59.639 I started copsack, this data set is already in the 0:19:59.680 --> 0:20:03.800 pro being generated. Dennis has taken seven hundred fecal samples 0:20:03.840 --> 0:20:07.239 extracted viral particles, and then he has basically put them 0:20:07.240 --> 0:20:09.640 through a sequencer and we're getting in sequences from each. 0:20:09.560 --> 0:20:13.800 Child's sequences, meaning genetic sequences that allows you to determine 0:20:13.840 --> 0:20:19.560 what viruses. Yeah, exactly, So you get there in twenty seventeen, 0:20:20.000 --> 0:20:23.639 and another researcher is already just starting to look for 0:20:24.880 --> 0:20:29.359 what viruses are in the fecal samples of these kids 0:20:29.359 --> 0:20:31.960 in the study. How do you get involved to what 0:20:32.000 --> 0:20:32.320 do you do? 0:20:32.359 --> 0:20:33.720 What happens back then? 0:20:33.920 --> 0:20:36.080 What people used to do when they got gut VIRAM 0:20:36.200 --> 0:20:38.320 data is that they would then take all the DNA 0:20:38.359 --> 0:20:40.600 sequences that came out of that and they would then 0:20:40.760 --> 0:20:43.120 blast it. Is what it is called against a public 0:20:43.200 --> 0:20:46.840 database of viruses, viruses that scientists have already discovered and 0:20:46.920 --> 0:20:49.159 know about, so that you can figure out which viruses 0:20:49.200 --> 0:20:51.639 are in those samples. The problem is that most of 0:20:51.680 --> 0:20:53.640 the viruses in the human gut at that time were 0:20:53.760 --> 0:20:56.280 unknown to science by I love it. So by doing 0:20:56.320 --> 0:20:58.359 that exercise, you're only going to get like a list 0:20:58.359 --> 0:21:02.040 of contents of maybe ten virus, whereas the actual diversity 0:21:02.040 --> 0:21:03.880 in each sample is going to be like maybe ten 0:21:03.920 --> 0:21:05.800 thousand or maybe a thousand or something. 0:21:05.600 --> 0:21:07.440 Right, But the problem is you don't know what you're 0:21:07.440 --> 0:21:09.959 looking for, right, You just have this random strings of 0:21:09.960 --> 0:21:15.480 genetic material, and if you're trying to find newly discovered viruses, well, 0:21:15.680 --> 0:21:17.960 how do you even do that? In fact, how do 0:21:18.000 --> 0:21:18.439 you do it? 0:21:19.000 --> 0:21:21.320