Vincent Racaniello: Viruses and Vaccines | Lex Fridman Podcast #216

00:00:00.000 | The following is a conversation with Vincent Recaniello,

00:00:03.400 | professor of microbiology and immunology at Columbia.

00:00:08.120 | Vincent is one of the best educators in biology

00:00:10.840 | and in general that I've ever had the pleasure

00:00:12.960 | of speaking with.

00:00:14.360 | I highly recommend you check out his

00:00:16.680 | This Week in Virology podcast

00:00:19.040 | and watch his introductory lectures on YouTube.

00:00:22.520 | In particular, the playlist I recommend

00:00:24.920 | is called Virology Lectures 2021.

00:00:28.720 | To support this podcast,

00:00:30.040 | please check out the sponsors in the description.

00:00:32.720 | As a side note, please allow me to say a few words

00:00:36.400 | about the COVID vaccines.

00:00:38.320 | Some people are scared of a virus hurting

00:00:41.000 | or killing somebody they love.

00:00:43.280 | Some are scared of their government betraying them,

00:00:46.720 | their leaders blinded by power and greed.

00:00:49.520 | I have both of these fears.

00:00:52.160 | And two, I'm afraid, as FDR said, of fear itself.

00:00:57.320 | Fear manifests as anger and anger leads to division

00:01:01.320 | in the hands of charismatic leaders

00:01:03.400 | who then manufacture "truth" in quotes

00:01:06.800 | that maximize controversy and a sense of imminent crisis

00:01:10.360 | that only they can save us from.

00:01:12.320 | And though I'm sometimes mocked for this,

00:01:16.120 | I still believe that love, compassion, empathy

00:01:20.720 | is the way out from this vicious downward spiral

00:01:23.640 | of division.

00:01:25.520 | I personally took the vaccine

00:01:27.320 | based on my understanding of the data,

00:01:29.480 | deciding that for me,

00:01:30.600 | the risk of negative effects from COVID short-term

00:01:33.800 | and long-term are far worse than the negative effects

00:01:37.160 | from the mRNA vaccine.

00:01:39.680 | I read, I thought, I decided, for me.

00:01:44.520 | But I never have and never will talk down to people

00:01:48.280 | who don't take the vaccine.

00:01:50.160 | I'm humble enough to know just how little I know,

00:01:53.520 | how wrong I have been and will be

00:01:56.280 | on many of my beliefs and ideas.

00:01:59.040 | I think dogmatic certainty and division

00:02:02.000 | is more destructive in the long-term than any virus.

00:02:05.600 | The solution for me, personally, like I said,

00:02:08.400 | is to choose empathy and compassion

00:02:10.520 | towards all fellow human beings,

00:02:13.240 | no matter who they voted for.

00:02:15.600 | I hope you do the same.

00:02:17.280 | Read, think, and try to imagine

00:02:20.720 | that what you currently think is the truth

00:02:22.920 | may be totally wrong.

00:02:24.840 | This mindset is one that opens you to discovery,

00:02:27.840 | innovation, and wisdom.

00:02:29.600 | I hope my conversation with Vincent Racaniello

00:02:33.120 | is a useful resource for just this kind of exploration.

00:02:36.080 | He doesn't talk down to people,

00:02:38.000 | and he's the most knowledgeable virologist

00:02:40.480 | I've ever spoken to.

00:02:42.160 | He has no political agenda,

00:02:44.040 | no desire to mock those who disagree with him.

00:02:47.280 | He just loves biology

00:02:49.080 | and explaining the fundamental mechanisms

00:02:51.360 | of how biological systems work.

00:02:54.120 | That's a great person to listen to

00:02:55.800 | and learn from with an open mind.

00:02:58.080 | I hope you join me in doing so,

00:02:59.960 | and no matter what,

00:03:01.200 | try to put more love out there in the world.

00:03:03.840 | This is the Lex Friedman Podcast,

00:03:06.600 | and here is my conversation with Vincent Racaniello.

00:03:10.080 | You mentioned in one of your lectures on virology

00:03:14.560 | that there are more viruses in a liter of coastal seawater

00:03:17.960 | than people on Earth.

00:03:20.640 | In the Nature article titled "Microbiology by Numbers,"

00:03:25.640 | it says there are 10 to the 31 viruses on Earth.

00:03:29.680 | Also, it says that the rate of viral infection in the ocean

00:03:34.440 | stands at 10 to the 23 infections per second,

00:03:39.280 | and these infections remove 20 to 40%

00:03:42.560 | of all bacterial cells each day.

00:03:45.240 | There's a war going on.

00:03:46.880 | What do you make of these numbers?

00:03:50.320 | Why are there so many viruses?

00:03:52.440 | - So the numbers you're quoting,

00:03:54.640 | they're in my first virology lecture, right?

00:03:57.960 | 'Cause people don't know these numbers,

00:04:00.760 | and they get, whoa, they get wowed by them,

00:04:02.600 | so I love to give them.

00:04:04.200 | - By the way, sorry to interrupt,

00:04:05.800 | but as I was saying offline,

00:04:08.720 | you have one of the best introductory lectures on virology

00:04:12.360 | that I've ever seen, introductory lectures period,

00:04:14.760 | so I highly recommend people find you on YouTube

00:04:17.920 | and watch it if you're curious at all about viruses.

00:04:20.520 | Yeah, there's a lot of times throughout watching it,

00:04:25.000 | I felt like, whoa.

00:04:26.560 | - Yeah, that's my goal is to,

00:04:28.320 | and it's what my students tell me.

00:04:29.840 | One student once said, "Every day after every lecture,

00:04:33.240 | "I could go home and tell my roommate something

00:04:35.940 | "she didn't know and blew her away."

00:04:38.880 | So the number of viruses is really an amazing number.

00:04:42.160 | So that number, 10 to the 31,

00:04:44.040 | is actually just the bacterial viruses in the ocean.

00:04:48.840 | So there are viruses that infect everything on the planet,

00:04:51.480 | including bacteria.

00:04:52.440 | There are a lot of bacteria in the ocean,

00:04:54.760 | and so 10 to the 31 is from basically

00:04:57.400 | particle counts of seawater all over the world.

00:05:00.240 | So there are more viruses than 10 to the 31,

00:05:02.600 | but just in the ocean, and that number is so big.

00:05:06.960 | First of all, the mass exceeds that of elephants

00:05:10.360 | on the planet by a thousandfold.

00:05:12.920 | And if you lined up those viruses end to end,

00:05:15.960 | they would go 200 million light years into space.

00:05:19.840 | It's so big a number.

00:05:21.240 | It's amazing.

00:05:23.100 | And then, yes, 10 to the 20-some infections per second

00:05:27.660 | of these viruses killing bacteria

00:05:30.340 | and releasing all this organic matter,

00:05:32.800 | and that's part of this,

00:05:34.600 | what we call the biogeochemical pump,

00:05:37.320 | cycling of material in the ocean.

00:05:39.780 | The bacteria die, they start to sink,

00:05:42.720 | and then they get metabolized and converted

00:05:46.480 | to compounds that are needed.

00:05:48.280 | A lot of it gets released as carbon dioxide and so forth.

00:05:51.040 | So these are actually really important cycles

00:05:52.920 | that are catalyzed by the virus.

00:05:54.600 | - Well, it's so wild that nature has developed

00:05:56.440 | a mechanism for mass murder of bacteria.

00:05:59.760 | - That's one way to look at it,

00:06:00.940 | but it's just what happened, right?

00:06:02.920 | - It's interesting.

00:06:03.760 | I mean, I wonder what the evolutionary advantage

00:06:06.480 | of such fast cycling of life is.

00:06:10.920 | Is it just an accident of evolution

00:06:13.480 | that viruses are so numerous,

00:06:16.280 | or is it a feature, not a bug?

00:06:20.920 | - So the fast is, it's not all fast.

00:06:25.440 | Not all viruses are fast.

00:06:26.760 | Some are 20 minutes per cycle.

00:06:28.840 | Some take weeks per cycle.

00:06:31.860 | But that's just per second.

00:06:34.680 | There's so many viruses in the ocean

00:06:36.680 | that that's what you get per second,

00:06:37.940 | no matter how fast the cycle is.

00:06:40.360 | But I look at it this way.

00:06:42.840 | Viruses were probably the first organic entities

00:06:47.060 | to evolve on the planet.

00:06:49.240 | Long ago, billion years ago,

00:06:51.840 | just as the Earth cooled and organic molecules

00:06:54.520 | began to form, I think these self,

00:06:58.900 | we call them self-replicators.

00:07:01.460 | They're just short things that today would look like RNA,

00:07:05.040 | which is the basis of many viruses, right?

00:07:08.840 | They evolved and they were able to replicate.

00:07:10.880 | Of course, they were just naked molecules.

00:07:13.280 | They had no protection.

00:07:14.600 | And it was just RNA-based.

00:07:17.160 | And that's tough because RNA is pretty fragile in the world,

00:07:21.400 | and it probably didn't get very big as a consequence.

00:07:25.560 | But then proteins evolved,

00:07:28.240 | and I'm skipping like hundreds of millions

00:07:30.160 | of years of evolution.

00:07:31.200 | Proteins evolved maybe without a cell,

00:07:34.400 | maybe with a cell.

00:07:36.100 | But then to make a cell,

00:07:38.920 | there probably were some RNA-based cells early on,

00:07:41.440 | but they were pretty simple.

00:07:42.920 | But the cells that we know of today,

00:07:44.480 | even bacteria and single-celled eukaryotes,

00:07:48.080 | they have very long DNA genomes.

00:07:51.400 | And you need a lot of DNA to make a complicated cell.

00:07:55.000 | And so we think at some point, the RNA became DNA.

00:08:00.000 | And probably one of the earliest enzymes that arose

00:08:04.160 | is the enzyme that could copy that RNA into DNA,

00:08:06.540 | which we now know today as reverse transcriptase,

00:08:09.120 | which my former boss, David Baltimore

00:08:11.640 | and Howard Temin co-discovered.

00:08:14.220 | And that enzyme arose and copied RNA to DNA,

00:08:19.220 | and then you could build big cells with,

00:08:22.140 | 'cause DNA can be millions and millions

00:08:23.980 | of bases in length.

00:08:25.100 | And RNA, the longest RNA we know of is 40,000 bases,

00:08:30.100 | not much bigger than the SARS-CoV-2.

00:08:32.940 | - What would you say is the magic moment along that line?

00:08:36.240 | I saw it was one or two billion,

00:08:39.960 | maybe three billion years it took to go from bacteria

00:08:44.960 | to the complex organism.

00:08:49.280 | It seems like Earth had a very long time,

00:08:52.480 | but not a very long time without life,

00:08:54.880 | and then a very long time with very primitive life.

00:08:59.960 | Maybe I'm discriminating, calling bacteria primitive life.

00:09:03.000 | - Yeah, people would object to you doing that for sure.

00:09:06.240 | - But it seems like complex organisms,

00:09:07.820 | when it starts becoming something like,

00:09:11.080 | I don't know what's a good, not animal-like,

00:09:13.340 | but more complexity than just like a single cell.

00:09:16.900 | What do you think is the magic there?

00:09:20.060 | What's the hardest thing?

00:09:21.020 | If you were trying to engineer Earth and build life

00:09:23.660 | and build the simulations,

00:09:25.120 | obviously we're living in a video game, what this is.

00:09:27.940 | So if you were trying to build this video,

00:09:29.180 | what's the hardest part along this evolution pathway?

00:09:32.100 | - So bacteria are mostly single cells.

00:09:36.140 | They do make colonies, they get together in biofilms,

00:09:39.180 | which are really important,

00:09:41.180 | but they're all single bacteria in that,

00:09:43.900 | and the key is making an organism

00:09:46.820 | where cells do different things.

00:09:49.420 | We have skin cells and eye cells and brain cells.

00:09:51.780 | Bacteria never do that, and the reason is probably energy.

00:09:55.580 | Bacteria can't make enough energy to do that.

00:09:58.980 | And so there was another cell existing at the time,

00:10:03.540 | the archaea,

00:10:04.380 | and the idea is that a bacteria went into an archaea

00:10:10.220 | and became the modern-day mitochondria,

00:10:13.180 | the energy factory of the cell,

00:10:15.020 | and that now let that cell develop

00:10:17.980 | into more and more complicated organisms

00:10:19.900 | like we have today.

00:10:20.740 | It was all about energy.

00:10:21.860 | - So the mitochondria, the energy,

00:10:23.780 | the mitochondria is the magic thing.

00:10:26.480 | - I think so.

00:10:27.320 | It's actually not my idea, it's Nick Jones.

00:10:29.500 | Have you heard of Nick Jones?

00:10:30.620 | He's an evolutionary biologist in the UK,

00:10:33.540 | and he's done experimental work on this,

00:10:36.460 | and it's his idea that the defining point

00:10:39.460 | was the ability to make a lot of energy,

00:10:41.860 | which a mitochondria can do.

00:10:43.140 | It's basically a whole bacteria inside of a bigger cell,

00:10:46.260 | and that becomes what we now call eukaryotes,

00:10:48.260 | and that they can get more and more complicated.

00:10:51.700 | So let me bring you back to the viruses.

00:10:53.220 | I wanna finish that story.

00:10:54.340 | - Yeah, which points of viruses come along?

00:10:56.320 | - So remember, we have these precellular,

00:10:58.840 | they're called precellular replicons, right?

00:11:01.520 | And so we have a precellular stage

00:11:05.260 | where we have these self-replicating molecules,

00:11:08.740 | and then cells arise,

00:11:10.620 | and then the self-replicating molecules invade the cells.

00:11:15.620 | Why?

00:11:16.620 | Because it's a hospitable environment.

00:11:18.060 | I mean, they didn't know that.

00:11:19.260 | They just went in,

00:11:20.100 | and it turned out it was beneficial for them,

00:11:21.800 | so it stuck, and they replicate inside the cell now

00:11:25.640 | where they have pools of everything they need.

00:11:27.540 | They get more and more complicated,

00:11:29.300 | and then they steal proteins from the cell

00:11:31.940 | to build a protective shell.

00:11:34.300 | And then they can be released as virus particles.

00:11:37.020 | They're now protected.

00:11:37.900 | They can move from host to host,

00:11:40.020 | and because they're at the earliest stages

00:11:43.640 | of cellular evolution,

00:11:45.580 | they can diversify to infect anything that arises,

00:11:48.500 | and that is why I think there's so many of them,

00:11:52.380 | and everything on the planet is infected

00:11:54.620 | because the ancestor of everything

00:11:56.140 | was infected many years ago.

00:11:57.540 | - So it's easier to steal than to build from scratch.

00:12:01.740 | So it's easier to sort of break into somebody else's thing

00:12:05.340 | and steal their proteins.

00:12:06.460 | - Yes.

00:12:07.580 | My colleague, Dixon de Pommier, calls viruses safe crackers.

00:12:11.460 | - Safe crackers.

00:12:12.740 | So it's just, from an evolutionary perspective,

00:12:15.980 | yeah, it's easier to steal because you can select,

00:12:20.780 | but then you have to figure out mechanisms for stealing,

00:12:23.380 | for breaking into, for cracking the safe.

00:12:25.700 | - Well, you don't have to figure out.

00:12:26.980 | It just happens, right?

00:12:28.620 | Because molecules are so diverse

00:12:30.980 | that a molecule gets into a cell,

00:12:33.420 | and if there's a protein that sticks to it,

00:12:35.820 | it's gonna stick, and that gives an advantage.

00:12:39.460 | There's no planning.

00:12:41.180 | There's no thinking about it, right?

00:12:43.100 | It just happens.

00:12:45.020 | - Oh, we'll return to that.

00:12:46.460 | (Lex laughing)

00:12:48.940 | But these numbers are crazy.

00:12:50.900 | So what, as these more complex organisms evolved,

00:12:55.900 | let's take us humans as an example,

00:12:58.060 | should we be afraid of these high numbers?

00:13:01.420 | Should we be worried that there's

00:13:02.700 | so many viruses in the world?

00:13:04.140 | - Well, to a certain extent.

00:13:06.300 | I mean, it's twofold.

00:13:08.260 | They're good and bad, right?

00:13:09.340 | Viruses are no, there's no question they can be bad,

00:13:12.060 | and we know that because they've infected

00:13:14.060 | and caused disease throughout history,

00:13:15.500 | but we're also, you and I are full of viruses

00:13:17.900 | that don't hurt us at all and probably help us,

00:13:20.180 | and every organism is the same,

00:13:21.980 | so they are clearly beneficial as a consequence.

00:13:25.860 | So I think, so every living thing on the planet

00:13:30.860 | has multiple viruses infecting everything you can see,

00:13:35.780 | and most of them I think we don't worry about

00:13:39.580 | because they can't infect us.

00:13:41.340 | They're unable.

00:13:42.180 | In fact, now you can actually take your feces

00:13:46.020 | and send them to a company,

00:13:47.060 | and they will sequence your viruses in your feces for you,

00:13:49.980 | your fecal virome, right?

00:13:52.260 | And the most common virus in human feces

00:13:57.260 | is a plant virus that infects peppers.

00:14:00.540 | It's called pepper model mosaic virus,

00:14:03.140 | and that's 'cause people eat a lot of peppers,

00:14:05.060 | and it just passes right through you.

00:14:06.700 | Cabbage is full of viruses from the insects

00:14:09.180 | that walk on the cabbage in the fields.

00:14:10.900 | We eat them.

00:14:12.060 | They just pass us.

00:14:13.140 | So I think most of the viruses we don't need to worry about

00:14:16.380 | except when we're talking about species

00:14:19.520 | that are closest to us, mammals, of course.

00:14:22.580 | And I think the most numerous ones

00:14:25.380 | are the most concerning.

00:14:26.580 | They're viruses like bats.

00:14:28.380 | Bats are 20% of mammals, and rodents are 40% of mammals.

00:14:33.380 | And we humans live nearby, right?

00:14:37.100 | And we know throughout history,

00:14:38.620 | many viruses have come from bats and from rodents to people,

00:14:41.500 | no question about it.

00:14:42.340 | - So there's a proximity in terms of just living together

00:14:44.620 | and a proximity genetically too,

00:14:46.260 | so it's more likely a virus will jump from a bat

00:14:49.420 | than a rodent.

00:14:50.260 | - And birds too.

00:14:51.080 | Birds can give us their viruses.

00:14:52.940 | That's happened.

00:14:53.940 | Influenza viruses come from birds mainly.

00:14:57.140 | So I think those are the three species,

00:15:00.700 | not species, it's higher than species obviously,

00:15:02.720 | but those are the three I would worry about

00:15:04.300 | in terms of getting their viruses.

00:15:05.980 | And we don't really know what's out there, right?

00:15:09.540 | We have very little clue about what viruses.

00:15:12.520 | And I've for years wanted to capture wild mice

00:15:16.500 | in my backyard and see what viruses they have

00:15:18.660 | 'cause no one knows.

00:15:19.620 | And it's an easy--

00:15:21.820 | - We can't ask them, so you mean map,

00:15:23.780 | like is there a way to--

00:15:24.620 | - I can't ask them, yeah.

00:15:25.440 | No, I would have to sacrifice them and take tissue

00:15:27.620 | and then bring it in the lab and do genome sequencing.

00:15:30.060 | - So you can do a thorough sequencing

00:15:32.180 | to determine which viruses.

00:15:33.820 | Is there a sufficiently good categorization of viruses

00:15:37.420 | where you'd be--

00:15:38.260 | - That's a very good question.

00:15:40.100 | So whenever you do sequence, right?

00:15:42.660 | You get some environmental sample

00:15:44.180 | and you extract nucleic acid and you sequence it.

00:15:47.140 | What you do is you run it past the database.

00:15:49.700 | The gold standard is the GenBank database

00:15:52.140 | which is maintained here in the US.

00:15:54.340 | And you see if you get any hits.

00:15:56.620 | And then you can say, ah, look,

00:15:58.540 | this sequence is similar to this virus

00:16:01.100 | and you can classify all the viruses you see.

00:16:03.540 | The problem is 90% of your sequence is dark matter.

00:16:08.540 | It doesn't hit with anything.

00:16:11.140 | It's probably a lot of it is unknown viruses

00:16:14.500 | and that's gonna be hard to figure out

00:16:16.060 | 'cause someone's gonna have to go after it

00:16:17.620 | and sort it through.

00:16:18.940 | So yes, you can find a lot of viruses

00:16:21.600 | and the numbers you get are astounding.

00:16:22.940 | You can find thousands of new viruses

00:16:25.180 | just by looking in various life forms.

00:16:28.140 | But there are many more that we don't pick up

00:16:30.380 | because they're not in the database.

00:16:32.180 | - Maybe this is a good time to take a quick tangent.

00:16:36.100 | What do you think about AlphaFold2?

00:16:38.020 | I don't know if you've been paying attention to that.

00:16:40.260 | With them, DeepMind solving the protein folding problem

00:16:44.180 | and then also releasing, first of all,

00:16:47.820 | open sourcing the code,

00:16:49.100 | which is for me as a software person, I love.

00:16:52.260 | And then second of all,

00:16:53.340 | also making like 300,000 predictions or something like that

00:16:57.860 | for different protein structures and releasing that data.

00:17:01.460 | On the side of, 'cause you're saying there's dark matter.

00:17:06.620 | Is there something, first, what are your general thoughts,

00:17:12.940 | level of excitement about their work?

00:17:15.900 | And second, how can that be applied to viruses?

00:17:19.420 | Do you think we'll be able to explore the dark matter

00:17:21.660 | of virology using machine learning?

00:17:24.540 | - Absolutely, 'cause in all this dark sequence,

00:17:26.820 | you can translate it and make a protein.

00:17:29.900 | You can see what a protein looks like.

00:17:31.500 | It has what we call an open reading frame, right?

00:17:34.020 | A start and a stop.

00:17:35.300 | And right now it's just a bunch of amino acids.

00:17:37.520 | But if we could fold it,

00:17:39.820 | maybe the fold would be like something we already know,

00:17:43.220 | some protein fold, which gives you a lot of clues, right?

00:17:46.500 | 'Cause there are only so many protein folds in biology

00:17:49.700 | and that dark matter is probably one of them.

00:17:52.660 | So I think that's very exciting because for years,

00:17:55.240 | I followed structural biologists for years.

00:17:59.580 | And in the beginning,

00:18:01.980 | we couldn't even solve structures of viruses.

00:18:04.020 | They're too big.

00:18:04.860 | We could do small molecules like myoglobin.

00:18:06.820 | That was the first one done.

00:18:07.900 | Took years to do that.

00:18:09.420 | Then as computational power increased,

00:18:13.060 | then they could start to do viruses, but it took a long time.

00:18:16.820 | X-ray crystallography,

00:18:18.460 | depending on getting crystals of the virus, right?

00:18:21.580 | And now we can do cryo-electron microscopy,

00:18:24.480 | which is much faster.

00:18:26.380 | You could solve, the spike of SARS-CoV-2

00:18:28.660 | was solved in two months by Jason McClelland here

00:18:31.460 | in Austin, actually, at the beginning of the pandemic.

00:18:34.900 | But you're limited.

00:18:35.780 | You can't do huge proteins.

00:18:38.780 | You can only do moderately sized ones.

00:18:41.180 | So, or actually, you can do viruses,

00:18:45.500 | but you can't do small proteins.

00:18:47.060 | So that's speeded it up, but it's still too fast to solve.

00:18:50.740 | You get a new protein, you want to solve its structure.

00:18:52.760 | So if we could predict it,

00:18:53.900 | and I know from talking to structural biologists,

00:18:55.980 | this has been their holy grail from day one.

00:18:58.300 | They want to be able to take a sequence of a protein,

00:19:00.940 | put it in a computer and have the structure put out

00:19:03.340 | without having to do all the experiment.

00:19:05.220 | So that's why this is very exciting that you can predict it.

00:19:08.620 | I mean, it's not finished, obviously,

00:19:10.780 | and there's more to do, but I think it will be a day

00:19:12.940 | where you could take any amino acid sequence

00:19:15.540 | and predict what it's going to look like.

00:19:17.340 | - See, but aren't structural biologists going to get greedy?

00:19:20.380 | So once you have that,

00:19:21.740 | don't you want to go more complicated then?

00:19:24.060 | Don't you want to go,

00:19:25.460 | 'cause that's just the first step, right?

00:19:27.660 | To go from amino acid to structure,

00:19:29.500 | then there's multiple protein interactions.

00:19:32.140 | Like, how do you get to the virus?

00:19:34.380 | - Well, so that's what the ultimate goal

00:19:37.020 | of getting a structure is, that then you can do experiments

00:19:39.700 | and figure out what the structure means, right?

00:19:42.420 | So many, in the old days,

00:19:44.860 | structural biology was a career in itself.

00:19:48.180 | You worked with people who had a system

00:19:49.980 | and just solved proteins for them,

00:19:51.340 | and then you moved on to another one.

00:19:52.500 | You didn't really do any experiments.

00:19:54.180 | The other people got to do all the interesting experiments.

00:19:56.980 | Now, young structural biologists are multifaceted.

00:20:01.980 | They solve the structure, and then they say,

00:20:04.220 | "What happens if we change this amino acid?

00:20:06.500 | "Oh, look, it blocks binding to the receptor.

00:20:08.940 | "This must be the receptor binding interface."

00:20:11.260 | So that's the exciting stuff, absolutely,

00:20:13.420 | is doing the experiment.

00:20:14.500 | - I wonder if you can do some kinds of simulations

00:20:18.820 | of different proteins or multi-protein systems

00:20:23.820 | going to war against each other,

00:20:26.340 | like to try to figure out...

00:20:29.620 | Reinforcement learning is used in AlphaZero, for example,

00:20:35.380 | to learn chess and Go,

00:20:37.060 | and that's using the self-play mechanism,

00:20:39.220 | where the thing plays against itself

00:20:41.340 | and learns better and better.

00:20:42.820 | I wonder if you can simulate almost evolution in that way

00:20:46.660 | for primitive biological systems,

00:20:49.580 | have them in simulation fight each other,

00:20:52.620 | and then see what comes out,

00:20:53.620 | like a super dangerous virus comes out,

00:20:55.660 | or super Chuck Norris type of thing

00:20:58.500 | that defends against the super dangerous virus,

00:21:00.660 | and it's all in simulation.

00:21:01.820 | - So an example would be,

00:21:04.100 | we have all these variants of SARS-CoV-2 arising, right?

00:21:07.860 | Which look to be selected by immune responses,

00:21:12.860 | but we know what amino acids are changing in the spike,

00:21:17.300 | and how they block antibody binding.

00:21:19.100 | You could simulate that.

00:21:20.300 | You could say, "What is the antibody looking at?"

00:21:24.940 | Where antibodies bind on proteins are called epitopes,

00:21:27.380 | right?

00:21:28.220 | You could map them all and change them in a simulation

00:21:30.380 | one by one, and go back and forth

00:21:32.060 | between the antibody and the virus.

00:21:33.780 | So all these, evolution is what we call an arms race, right?

00:21:38.380 | The virus changes, and then it evades the host,

00:21:41.620 | and then the host can change.

00:21:43.220 | The host takes longer to change though, unfortunately.

00:21:45.220 | It takes geological time, but it can,

00:21:47.940 | and then the virus can change,

00:21:49.620 | and it can go back and forth.

00:21:50.620 | And we can see evidence of this in genome sequences

00:21:54.340 | of both viruses and their hosts.

00:21:56.220 | And so you can take a protein in a host

00:22:00.020 | that is a receptor for multiple viruses,

00:22:02.700 | and you can see all the impacts of virus pressure on it,

00:22:05.700 | and you could simulate that for sure.

00:22:07.260 | And that's just one thing that you could do.

00:22:08.980 | You could simulate changes in, say, an enzyme

00:22:13.660 | that makes it resistant to a drug,

00:22:15.060 | and predict all the drug resistance.

00:22:17.060 | But the problem is, people like me,

00:22:21.220 | the experimental virologist, don't know how to do

00:22:23.380 | any of that, so we need to collaborate with people.

00:22:27.220 | I guess-- - Oh, with other humans.

00:22:29.660 | - We do that, we do that. - Okay.

00:22:31.420 | - But with people from a field that we're not used to,

00:22:35.660 | I suppose people who, would it be AI, I suppose?

00:22:39.060 | - Yeah, machine learning people.

00:22:39.980 | - Machine learning people, and you would say,

00:22:42.020 | "Look, this is the biological problem.

00:22:44.300 | "Is there a way we can use your tools to attack it?"

00:22:46.900 | - The problem is those people are antisocial introverts

00:22:51.660 | that have a place like this, and try to hide

00:22:55.820 | from other people in the world.

00:22:57.100 | It's very difficult to find in the wild.

00:23:00.020 | - Okay, so outside of doing amazing,

00:23:02.980 | brilliant lectures online, you host and produce

00:23:07.980 | five, I would say, related podcasts,

00:23:10.940 | including my favorite this week in virology,

00:23:14.500 | also this week in parasitism,

00:23:17.080 | this week in microbiology, and so on.

00:23:19.420 | So you're a good person to ask,

00:23:21.780 | "What are the categories of small things,

00:23:24.600 | "small biological things in this world that can kill you,

00:23:27.980 | "kill us humans?"

00:23:30.660 | - You said most viruses are friendly,

00:23:34.060 | or at least not unfriendly,

00:23:36.500 | but let's look at the unfriendly ones,

00:23:39.640 | in viruses and bacteria and those kinds of things.

00:23:42.180 | When you look at the full spectrum

00:23:43.620 | of things that can kill you,

00:23:45.260 | can you kind of paint a brief picture?

00:23:47.260 | - Yeah, I think the big picture is that

00:23:50.740 | the things that can kill you are a minority

00:23:52.780 | of everything that's out there.

00:23:54.340 | And we're talking about molecules.

00:23:57.260 | So we have in us proteins that can kill us,

00:24:00.540 | prions, that are just, it's a protein in us,

00:24:04.620 | and if it misfolds, it makes all of its other copies

00:24:08.220 | misfold, and then you die of a neurological disease.

00:24:11.740 | That's pretty rare.

00:24:12.740 | So there are proteins, there are viruses.

00:24:16.540 | As I said, only certain ones can kill us.

00:24:20.260 | But even if we get those from animals,

00:24:23.000 | it's not straightforward.

00:24:24.500 | If you look at SARS-CoV-2, right?

00:24:26.820 | This is probably a once in 100 year pandemic, I would say.

00:24:31.180 | Equivalent to 1918 in its devastation.

00:24:34.780 | And in between there have been smaller pandemics

00:24:36.580 | of other viruses, but it doesn't happen all that often.

00:24:39.640 | So we have a lot of viruses, we have a lot of bacteria

00:24:42.520 | of various sorts that can cause infections in us.

00:24:46.280 | And it's a limited number, right?

00:24:49.280 | You're streptococci and staphylococci and clostridia,

00:24:52.660 | we could go on and on.

00:24:54.140 | But we know how to handle those,

00:24:56.420 | as long as we have antimicrobials.

00:24:58.300 | It's just that we abuse them and we get resistance,

00:25:00.420 | so that can be a problem.

00:25:02.180 | Then we have fungi.

00:25:04.060 | Not mushrooms, but much smaller fungi

00:25:06.420 | that multiply submicroscopic,

00:25:09.480 | or just at the microscopic level.

00:25:11.420 | They can, in dry climates of the US,

00:25:13.940 | you can inhale their spores and they can grow in your lung

00:25:16.300 | if you're immunosuppressed and so forth.

00:25:18.840 | So those are the tiny guys.

00:25:21.100 | And then we have parasites,

00:25:22.340 | which we do this week in parasitism,

00:25:24.980 | where single cells, even worms of various sorts,

00:25:29.980 | can invade you and cause all sorts of problems.

00:25:33.400 | - I was kind of terrified to listen to that podcast.

00:25:37.220 | What's it like?

00:25:39.020 | - What you learn is that you travel somewhere

00:25:46.260 | and you can get infected and bring it back home.

00:25:48.700 | Here in the US, we do have certain kinds of parasites,

00:25:52.640 | but because of our lifestyle,

00:25:54.980 | we more or less have avoided them.

00:25:56.580 | For example, there's a parasite called toxoplasma,

00:26:00.460 | which is infected most of the world, actually,

00:26:03.960 | because a lot of people like to eat raw meat

00:26:06.180 | and you would get it from raw meat.

00:26:08.420 | And we're not as fond of that here in the US.

00:26:12.300 | We like to cook our meat,

00:26:13.340 | but that could be a consequence of eating raw meat.

00:26:16.420 | - Is that what leads to, what is it called, toxoplasmosis?

00:26:19.860 | - Yeah, so toxoplasmosis,

00:26:22.800 | it's mainly a big issue is if you're pregnant

00:26:27.300 | and you get toxo,

00:26:28.420 | then your fetus is gonna be very badly malformed.

00:26:32.540 | It's gonna have brain defects and so forth.

00:26:35.020 | And animals can get it as well.

00:26:39.500 | So there are a lot of parasites of that nature,

00:26:41.300 | which you often acquire by food,

00:26:43.300 | eating food of different sorts.

00:26:45.020 | And it usually happens elsewhere.

00:26:46.780 | We just, on this week in parasitism, we do a case.

00:26:50.880 | So Daniel Griffin is a resident physician.

00:26:54.360 | He's a doctor, a real doctor, right?

00:26:56.500 | And every month he comes up with a case.

00:26:58.700 | Okay, this is a person I saw.

00:27:00.920 | And last month this young lady had traveled somewhere

00:27:05.020 | and she ate raw fish.

00:27:09.660 | It was somewhere Southeast Asia or something.

00:27:11.980 | And she ended up with red bumps all over her skin.

00:27:15.900 | And it turned out it was a parasite from the fish

00:27:17.760 | that moved around in her.

00:27:19.900 | And they're very easy to cure.

00:27:21.740 | We have the right doctors and the right drugs.

00:27:23.740 | You can cure all these.

00:27:24.580 | - What about diagnose?

00:27:26.020 | Like connect the red spots to the fact that it's a parasite?

00:27:28.820 | - Very easy if you have the right diagnostics.

00:27:31.260 | Now Daniel often goes to parts of the world

00:27:33.100 | where they don't have diagnostics

00:27:34.580 | and he has to use other mechanisms.

00:27:37.240 | He may have to take a bit and look at it under a microscope.

00:27:40.340 | And then he may not be able to get the drug

00:27:41.960 | depending on where he is.

00:27:43.840 | But often he sees patients who come back to the US

00:27:47.180 | and they get diarrhea or they have a fever.

00:27:49.640 | And he says, "Where have you been?"

00:27:50.700 | And he can put two and two together.

00:27:52.220 | And so we let our listeners do that

00:27:53.820 | and they all send in guesses.

00:27:55.260 | And it's wonderful to hear them go through this.

00:27:57.860 | So there are a lot of parasites--

00:27:58.700 | - Solve the puzzle and solve the case study.

00:27:59.900 | - That can get you.

00:28:00.740 | You have to be careful about eating when you go overseas.

00:28:03.380 | - And water too?

00:28:04.660 | - Water as well.

00:28:05.540 | And in parts of Africa there are parasites in the lakes.

00:28:09.220 | And if you go swimming they can invade you.

00:28:11.820 | And in fact they can go into your hair follicles

00:28:14.100 | and burrow in and get into your bloodstream.

00:28:15.980 | - That's exciting.

00:28:16.980 | So Daniel is interesting 'cause he's very adventurous

00:28:20.320 | and he's not afraid of any of this.

00:28:22.800 | So there's a famous lake in Africa, Lake Malawi,

00:28:26.040 | which harbors a lot of these parasites.

00:28:28.480 | And he said, "Oh yeah, yeah, I just make sure

00:28:30.240 | "I towel off vigorously when I get out."

00:28:32.240 | (laughing)

00:28:33.200 | - Vigorously.

00:28:34.040 | - Get rid of them.

00:28:34.880 | And that was the name of an episode.

00:28:37.180 | But you know food is--

00:28:38.020 | - Towel off vigorously?

00:28:38.960 | - You know sushi, you can get worms from sushi.

00:28:43.400 | And the solution is to freeze it.

00:28:46.800 | And many sushi restaurants now have liquid nitrogen.

00:28:49.880 | They snap freeze their sushi

00:28:51.320 | and that kills all the parasites.

00:28:53.000 | And a study was actually done in Japan

00:28:56.400 | showing that freezing does not alter the taste of sushi

00:28:59.240 | because it's--

00:29:00.080 | - Uh-oh.

00:29:00.900 | - Except for you see a big industry there.

00:29:01.740 | - Wow, that's brilliant.

00:29:02.580 | (laughing)

00:29:03.400 | That's brilliant.

00:29:04.440 | Yeah, I was thinking about, you know,

00:29:07.760 | I'm so boring and bland,

00:29:09.880 | especially when I'm now in Texas here

00:29:12.640 | and I've been eating quite a bit of barbecue.

00:29:14.280 | I realized I really haven't explored the culinary world.

00:29:18.960 | And I've been curious to travel and taste different foods.

00:29:22.300 | Is there something you can say by way of advice,

00:29:25.080 | you know, channeling Daniel, I guess,

00:29:28.800 | if you were to travel in the world,

00:29:31.060 | if eating is the thing that gets you the parasites,

00:29:34.580 | what's good advice for eating in strange parts of the world,

00:29:39.200 | Mongolia, India, China?

00:29:41.780 | Is there something you could say by way of advice?

00:29:43.840 | - I think Daniel would say

00:29:45.240 | make sure your food is cooked, right?

00:29:47.560 | - Cooked, but that's so boring.

00:29:49.060 | - Yeah, it's unfortunate.

00:29:50.480 | And he would agree with you

00:29:52.240 | because, you know, many vegetables are delicious.

00:29:55.560 | Salads even are delicious, not cooked,

00:29:57.880 | but they can have parasites in them.

00:29:59.560 | Meats, fish, people like to have uncooked fish.

00:30:04.320 | So if you wanna be really safe and boring,

00:30:06.100 | just make sure everything is cooked.

00:30:07.960 | Now we have a case this week on Twip

00:30:11.040 | of a young man who went,

00:30:13.280 | I forgot where he went, but he stayed in a hotel.

00:30:15.640 | I think, oh, Oaxaca, Mexico.

00:30:17.320 | - Mm-hmm.

00:30:18.160 | - Stayed in a hotel.

00:30:19.340 | And he said, he came back with diarrhea and fever.

00:30:23.760 | And he said, "I don't know where, I stayed in the hotel.

00:30:26.480 | "I just ate hotel food, lots of vegetables and fruits."

00:30:30.040 | And probably they weren't washed with clean water,

00:30:32.720 | you know, he got something from that.

00:30:34.880 | The bottom line is most of these infections

00:30:38.520 | with parasites can be diagnosed,

00:30:40.200 | and you can be treated, and you'll be fine.

00:30:42.720 | So if you really wanna experience the cuisine,

00:30:47.520 | I don't think you should worry about it.

00:30:49.240 | That's what Daniel would say.

00:30:50.680 | - Let's return to the basics.

00:30:51.960 | We're gonna jump around all over the place.

00:30:54.840 | What are the basic principles of virology?

00:30:58.720 | Maybe a good place to start is what is a virus?

00:31:02.160 | - That's great.

00:31:03.600 | I mean, I talk in my first lecture for 20 minutes

00:31:05.840 | before I get to that.

00:31:06.880 | And I wonder if I should put it up front,

00:31:10.040 | but it's kind of a boring definition.

00:31:12.100 | So if you do that, first people will turn off.

00:31:14.840 | So first you tell them about all the millions

00:31:17.040 | and billions of viruses around.

00:31:18.920 | So a virus, we have a very specific definition,

00:31:22.720 | 'cause it's different from everything else on the planet.

00:31:25.520 | 'Cause first of all, it's a parasite.

00:31:28.200 | A parasite means you take something from someone else.

00:31:33.800 | You know, we have human parasites

00:31:35.160 | who take money from others, right?

00:31:36.840 | But in biological terms, a parasite takes something

00:31:40.880 | from the host that the host would otherwise use energy

00:31:44.280 | or some building block or something.

00:31:46.200 | - There's never really a symbiotic relationship

00:31:48.540 | between a virus and a host.

00:31:50.420 | - Well, there can be.

00:31:51.980 | So that's the dichotomy, I think,

00:31:53.600 | is that we define them as parasites.

00:31:55.980 | Yet, I just told you 20 minutes ago

00:31:58.740 | that many viruses are probably beneficial.

00:32:01.500 | So I think what it means is at some point

00:32:04.500 | we're gonna have to change our definition, right?

00:32:06.780 | Because after all, definitions we make

00:32:10.220 | are just constructs that make it easier for us to study,

00:32:14.160 | that don't necessarily represent what's right.

00:32:16.760 | - Yeah, like Pluto was a planet at first,

00:32:20.420 | and now it's not a planet anymore,

00:32:21.660 | and a lot of people are very upset.

00:32:23.360 | - But it's only according to us.

00:32:24.700 | There may be another race living somewhere else

00:32:27.020 | who thinks it's a planet, right?

00:32:28.500 | - Well, maybe that's why viruses are attacking humans.

00:32:30.700 | They're very angry.

00:32:31.540 | They weren't calling them parasites.

00:32:33.980 | - So right now our definition includes parasite

00:32:37.980 | because a virus cannot do anything without a cell.

00:32:41.380 | If this mug were full of viruses,

00:32:44.020 | it would not do anything for years.

00:32:46.340 | It would eventually probably lose its infectivity,

00:32:49.020 | but it's not gonna reproduce here.

00:32:50.460 | It needs cells.

00:32:52.020 | And to the first people who discovered viruses,

00:32:54.220 | that was astounding that they didn't just reproduce,

00:32:57.340 | divide on their own like bacteria.

00:32:59.600 | So a virus needs to get inside of a cell, inside the cell.

00:33:03.660 | It can't just hang around on the surface.

00:33:05.500 | It needs to get in in order to make more of itself.

00:33:09.140 | And so we call it an obligate intracellular parasite

00:33:13.020 | because it needs to get in a cell,

00:33:15.020 | and then it takes things from the cell

00:33:17.300 | in the form of all kinds of molecules and processes

00:33:20.220 | and energy and so forth to make new viruses.

00:33:23.340 | - Obligate means it's obligated to be inside the cell.

00:33:26.220 | - Absolutely.

00:33:27.060 | It will not reproduce outside of the cell.

00:33:30.780 | So this mug of viruses can in no way be living,

00:33:35.780 | in my opinion.

00:33:36.900 | However, once it gets inside of a cell,

00:33:39.140 | now the cell is a virus-infected cell.

00:33:41.220 | It's alive.

00:33:42.300 | So a virus, in my view, has two phases, right?

00:33:45.340 | It's this non-living particulate phase

00:33:48.180 | that everyone is used to.

00:33:49.560 | I'll send you, you need a virus for your table.

00:33:52.700 | I'll send you a nice model.

00:33:54.540 | I think it would look good here.

00:33:55.380 | - Which, yes, definitely.

00:33:56.220 | - You don't have to go with all this other stuff.

00:33:57.780 | - Yeah, well, these are all mechanical.

00:33:59.540 | There's no biology here.

00:34:01.260 | - So you wouldn't want a virus here?

00:34:02.460 | - No, I'd want a virus, of course.

00:34:04.020 | - I'll send you one and then you can look at it.

00:34:06.820 | 'Cause now that we have the three-dimensional structures

00:34:09.900 | solved by structural biologists,

00:34:12.580 | we take the coordinates and we put it in a 3D printer

00:34:14.740 | and you can make amazing models, right?

00:34:16.860 | Of any virus.

00:34:18.900 | - And so there's a huge variety of viruses?

00:34:21.500 | - Huge, that we know of,

00:34:23.300 | which is only a fraction of what's out there.

00:34:24.820 | - What's the category?

00:34:25.740 | So there's RNA, there's DNA viruses.

00:34:27.860 | What are those?

00:34:29.020 | What's DNA and RNA?

00:34:30.260 | - Two broad categorizations.

00:34:33.900 | RNA, these are genetic material.

00:34:36.940 | Can be two different chemicals.

00:34:39.180 | So RNA, everything else on the planet besides viruses

00:34:43.100 | is all DNA-based.

00:34:44.140 | You and I are DNA-based.

00:34:45.300 | Everything on the planet today is DNA-based,

00:34:47.340 | except some viruses are RNA-based.

00:34:50.180 | And that's because, as I mentioned earlier,

00:34:53.140 | the first life that arose on the planet was RNA-based.

00:34:57.260 | - Yeah, so these are like old school viruses.

00:34:59.500 | - These are old school.

00:35:00.620 | We call relics, yeah.

00:35:02.620 | Relics, and this has got a name.

00:35:04.500 | It's called the RNA world, which I think is great.

00:35:06.780 | - Is it big still, or are the relics dying out?

00:35:09.660 | - Oh no, the relics, in my opinion,

00:35:11.540 | are the most successful viruses, the RNA viruses.

00:35:14.860 | And SARS-CoV-2 is an RNA virus.

00:35:16.700 | We can talk about why they're so successful.

00:35:19.040 | But you have, broadly speaking, viruses with RNA,

00:35:22.740 | genetic information, which are relics.

00:35:24.940 | Of course, they're contemporary.

00:35:26.420 | They have adapted to the modern world

00:35:28.900 | and the modern organisms living in it.

00:35:31.140 | And then we have DNA-based viruses,

00:35:33.220 | which are extremely conservative and slow.

00:35:36.440 | They're very successful.

00:35:37.780 | Everyone has a herpes virus infection,

00:35:40.020 | but they don't get the news like the RNA viruses do.

00:35:44.500 | The HIVs and the influenza viruses

00:35:46.780 | and the SARS coronaviruses, they get all the press,

00:35:49.860 | and they're RNA-based,

00:35:51.100 | 'cause RNA lets you change more so than DNA.

00:35:54.340 | - So they evolve much faster, the RNA viruses.

00:35:57.220 | - Much faster.

00:35:58.300 | And in fact, when I have a lecture on evolution,

00:36:01.900 | I don't know if you've listened to that when you should.

00:36:04.180 | It's really, I think it's really interesting.

00:36:06.580 | RNA viruses exist at their error threshold,

00:36:12.740 | which means they can't make any more mutations

00:36:17.120 | when they reproduce, otherwise they're dead.

00:36:19.540 | They would go extinct. - Wow.

00:36:20.860 | - They're evolving at their error threshold.

00:36:23.580 | DNA viruses are hundreds of times lower

00:36:26.300 | than their error thresholds.

00:36:28.420 | And we know this.

00:36:29.260 | We can do an experiment to find that out.

00:36:31.140 | Now, why that is is a good question.

00:36:33.580 | But that's the reason why RNA viruses

00:36:38.260 | are far more successful.

00:36:39.420 | They infect many more hosts,

00:36:41.660 | and they're very, I would say, slippery.

00:36:44.140 | They can change hosts really quickly,

00:36:46.460 | because in any animal harboring an RNA virus,

00:36:49.500 | like let's say a bat in some cave somewhere,

00:36:52.940 | it's not just one genome.

00:36:55.180 | It's millions of different genomes of all kinds,

00:36:58.660 | all within the framework of, say, coronavirus,

00:37:01.260 | but they're all different.

00:37:02.580 | And one genome in there might just be right

00:37:04.780 | for infecting a person if it ever encountered that person.

00:37:08.220 | I mean, that's the thing that--

00:37:09.420 | - Or there could be a large number.

00:37:11.260 | There's a tiny fraction, but a large number of them.

00:37:15.020 | And they're all operating at the threshold of error.

00:37:18.020 | - That's right. - That's fascinating.

00:37:19.020 | It's like little, it's like startups,

00:37:21.560 | little entrepreneurs, like a startup world.

00:37:23.900 | - Yes, and many of them fail.

00:37:25.180 | - Yeah, many of them fail. - Many of the changes fail.

00:37:26.020 | - And then there's the DNA viruses

00:37:27.820 | that are like the IBM and the Google.

00:37:29.580 | - Exactly, exactly. - The big corporations.

00:37:31.780 | - It's very good, I like that. - They become conservative

00:37:33.260 | with the bureaucracies and all that kind of stuff.

00:37:35.020 | - And a lot of baggage. - Yeah.

00:37:36.660 | - Yeah, it's expensive for them to reproduce, yeah.

00:37:39.480 | And they don't move quickly.

00:37:40.540 | Yes, the RNA viruses are the fast-moving members.

00:37:43.820 | So that's what a virus is.

00:37:45.460 | We call them oddly intracellular parasites.

00:37:48.900 | And then I told you there's DNA and RNA,

00:37:50.820 | but then let's go further.

00:37:52.980 | The nucleic acid's not naked,

00:37:54.800 | because naked nucleic acid in the world isn't good.

00:37:59.880 | I mean, it existed in the pre-cellular world,

00:38:03.500 | but there probably weren't a lot of threats to it then.

00:38:07.180 | Naked nucleic acid doesn't last long in the environment.

00:38:09.740 | So they're covered, the nucleic acid is covered.

00:38:12.180 | It can be covered with a protein shell, a pure protein shell,

00:38:15.840 | or it can have a membrane around it,

00:38:19.320 | which would be lipids from the host cell.

00:38:22.380 | - So lipids, so it's a fatty membrane.

00:38:26.220 | - Fatty membrane, yeah.

00:38:27.060 | So our cells are coated with fatty membranes, right?

00:38:30.500 | Our cells, the outer plasma membrane, right?

00:38:32.540 | That's the same-- - But viruses can be too.

00:38:34.340 | So they're kind of like cells,

00:38:35.780 | but without the ability to do the mitochondria stuff.

00:38:38.620 | - Some are, some are.

00:38:39.940 | They don't have nuclei, they don't have mitochondria.

00:38:42.800 | But they do have a nucleic acid, they have a membrane.

00:38:46.000 | And then, of course, there's spikes in the membrane

00:38:48.620 | that allow them to attach to cells.

00:38:51.220 | And so that completes our two different kinds of viruses.

00:38:53.420 | - So they all have attachment mechanisms,

00:38:55.740 | like ways to, like keys into the--

00:38:59.260 | - They all have to get into cells.

00:39:01.300 | There are a couple of exceptions, though.

00:39:05.020 | There are viruses of fungi and plants.

00:39:10.020 | So let's do the fungi.

00:39:11.940 | Fungi would be like yeast.

00:39:14.580 | The yeast cell wall is pretty hard to get through.

00:39:18.740 | So viruses typically don't attach to a yeast

00:39:22.060 | and get inside.

00:39:22.900 | Rather, they just live in the yeast forever.

00:39:25.700 | And they multiply as mostly nucleic acids,

00:39:28.740 | and as the yeast divide, they go into the daughter cells.

00:39:31.380 | And that's how they exist.

00:39:32.580 | Plant viruses, also the plant cell wall,

00:39:35.660 | would be very hard to get across by binding a protein.

00:39:40.460 | So plant viruses get into plants

00:39:42.900 | either by pests that inject them in.

00:39:47.340 | They're sucking sap out,

00:39:48.480 | and they inject virus at the same time.

00:39:49.980 | Or farmers, they have contaminated farm equipment,

00:39:52.620 | and they roll over the plants and introduces viruses.

00:39:55.840 | So those fungi and plant viruses,

00:39:57.740 | they don't have this specific receptor binding

00:40:00.580 | to get them into the cell.

00:40:01.500 | But everything else, yeah, the virus binds

00:40:03.620 | to something on the surface, very specific.

00:40:05.420 | It's taken into the cell, because that's what cells do.

00:40:09.060 | When things bind their exterior, they take it in.

00:40:12.620 | 'Cause in most cases, it's good.

00:40:14.300 | It's something they need.

00:40:15.820 | And so the virus slips in.

00:40:17.220 | I guess you'd call that a Trojan horse, right?

00:40:19.260 | - Trojan horse.

00:40:20.080 | It's so hard to not anthropomorphize this whole thing.

00:40:23.340 | - It is hard.

00:40:24.180 | - So obviously, they don't know any of this.

00:40:26.980 | It's not an actual Trojan horse.

00:40:29.420 | So they're not getting actually tricked

00:40:32.780 | in the way humans trick each other.

00:40:34.620 | - No, it's all passive.

00:40:35.740 | And it's just, through so many years of evolution,

00:40:38.620 | you select something that works, and it continues.

00:40:42.940 | And what survives then goes on

00:40:44.940 | with perhaps a slightly different approach.

00:40:48.300 | - I love this idea of passive.

00:40:49.780 | Of course, according to Sam Harris,

00:40:52.100 | so for a sufficiently intelligent alien civilization

00:40:54.900 | observing humans, our behavior might seem passive too,

00:40:58.140 | 'cause they understand fully exactly what we're doing.

00:41:00.820 | And then there's no free will,

00:41:02.260 | and we're all just operating in the same way a cell does,

00:41:05.100 | but just a much higher level of complexity.

00:41:07.820 | - Yeah, so I love the distinction

00:41:10.100 | between active and passive.

00:41:11.820 | - I mean, the point is, I think anthropomorphizing

00:41:14.820 | to a certain extent is fine,

00:41:15.940 | 'cause it helps people understand.

00:41:17.860 | But when you start to say,

00:41:19.580 | I think the virus is doing that

00:41:21.100 | because then you're putting a human lens on it,

00:41:24.700 | and you may be wrong.

00:41:26.580 | Because you don't know why things happen for a virus.

00:41:29.940 | So right now, we have variants emerging,

00:41:33.460 | and people say, well, I think it's because the antibodies

00:41:35.980 | are selecting for variants.

00:41:38.180 | That's a good idea,

00:41:39.140 | but it may not be the only thing that's going on.

00:41:41.500 | - You start imagining them coming to the table negotiating.

00:41:44.780 | Yeah, you get into trouble with that.

00:41:48.420 | - That's why I tell my students,

00:41:49.380 | be careful about the anthropomorphizing,

00:41:51.820 | because you're gonna apply your values to a virus,

00:41:55.980 | and you have different value.

00:41:57.100 | You're a human, and what you think is the reason

00:42:00.580 | for this outcome may not be right, that's all.

00:42:02.380 | Just be open-minded about it.

00:42:04.260 | - In both directions.

00:42:05.220 | I actually, one of the things I push back on

00:42:08.100 | is in the space of robotics,

00:42:10.300 | most people in robotics try to not anthropomorphize.

00:42:14.580 | For example, they don't give a gender or a name to robots.

00:42:17.500 | They really try to see it as a machine.

00:42:20.060 | And to me, that makes sense in one way,

00:42:23.860 | but it totally doesn't make sense in another.

00:42:25.500 | If that robot is to interact,

00:42:27.660 | operate in the human world and interact with humans,

00:42:30.780 | we have to anthropomorphize it

00:42:33.940 | in order to understand as an engineering problem,

00:42:37.740 | how should it operate in a human world?

00:42:42.140 | Now, the difference with viruses, the scale of operation,

00:42:46.060 | it doesn't make sense to treat them as human-like,

00:42:49.300 | 'cause the scale of operation is much smaller.

00:42:51.620 | But with robots, you're in the same time scale,

00:42:54.300 | the same spatial scale.

00:42:55.380 | - Of course, in the movies,

00:42:56.300 | they always give them names and personalities.

00:42:58.500 | - Yeah, well, yeah, that's the,

00:43:00.460 | but that's my argument, is we should do the same

00:43:03.020 | when you're trying to solve

00:43:03.940 | the engineering problem of robotics too.

00:43:06.340 | It's not just for the movies.

00:43:07.780 | Well, let me ask you this,

00:43:08.820 | because you've said controversially, not really,

00:43:12.300 | that viruses are not living.

00:43:14.720 | Defend yourself.

00:43:18.220 | (laughs)

00:43:19.140 | So are viruses alive or not?

00:43:20.660 | - So I've seen many people say, oh, they have to be.

00:43:22.900 | They have nucleic acids, they evolve, they mutate.

00:43:27.180 | That's all true, but they don't do it on their own.

00:43:29.620 | The particles in my mug are just not doing any of that

00:43:32.260 | unless they get into a cell.

00:43:33.980 | So a virus-infected cell is alive.

00:43:36.220 | I totally agree with that,

00:43:37.820 | because in fact, when a virus gets in a cell,

00:43:40.820 | it converts it into a virus-making factory, if you will.

00:43:45.300 | It's no longer a cell.

00:43:47.260 | Some people call it a virus cell.

00:43:48.940 | I don't really like that, but it's fine.

00:43:51.220 | So that's what I'm talking about.

00:43:53.020 | The particle is not alive.

00:43:54.880 | You can have your virus-infected cell as alive,

00:43:57.780 | but the particle, it just would not do anything

00:44:01.260 | forever without getting inside of a cell.

00:44:03.860 | But once it's in a cell, it is alive then,

00:44:07.060 | but it's no longer a particle.

00:44:08.260 | It's taken apart and nucleic acid is moving

00:44:10.740 | around the cells, making proteins.

00:44:12.940 | Eventually it makes new particles.

00:44:14.140 | And then those particles released from the cell,

00:44:16.480 | they're not living anymore.

00:44:17.980 | So I think it's kind of like a spore,

00:44:21.340 | a spore of a, or a seed.

00:44:25.220 | Although the seed doesn't work because the seeds,

00:44:28.140 | the cells in the seed have the ability

00:44:29.860 | to make their own energy and so forth.

00:44:31.980 | But a bacterial spore, and it's the same thing,

00:44:34.460 | doesn't do anything unless you add water and nutrients

00:44:36.580 | and then it starts to divide.

00:44:37.620 | But it doesn't need to get into a cell.

00:44:39.100 | It's very different from a virus.

00:44:40.700 | So that's why the particle.

00:44:42.500 | And when people think of virus,

00:44:44.500 | they're always thinking of the particle.

00:44:47.380 | And that's why I say it can't be alive,

00:44:49.300 | 'cause the particle can't do anything on its own.

00:44:51.380 | But if you think of a virus as an organism

00:44:53.420 | with a particle phase in a cell,

00:44:55.940 | then it makes sense to be alive.

00:44:58.420 | - And by the way, when you say particle,

00:44:59.900 | you're referring to that structure

00:45:01.180 | that you've been mentioning, some kind of membrane or not,

00:45:03.460 | that has been called, what is that,

00:45:05.620 | a viron particle or something?

00:45:06.820 | - Virion. - Virion.

00:45:07.660 | - So what you should have here, I'll send you one,

00:45:10.300 | and then you can refer to it.

00:45:11.820 | - What's the sexiest one to have?

00:45:13.660 | Like what, in terms of particles to have on a table?

00:45:17.940 | - Well, unfortunately the ones that you can 3D print.

00:45:21.460 | - Oh, they're not going to be super--

00:45:23.100 | - They're the ones that we know the structures of, right?

00:45:26.380 | So someone sent me last year a 3D model of SARS-CoV-2,

00:45:31.380 | and it's beautiful.

00:45:32.220 | It's actually cracked open so you can see the RNA,

00:45:34.740 | and the spikes are sticking out,

00:45:37.020 | and they even put some antibodies sticking onto the spikes.

00:45:40.300 | - That's super cool. - I mean, when I show this

00:45:42.060 | on a livestream, people love this.

00:45:43.540 | They go, "Oh my God, that's beautiful."

00:45:44.980 | It is, it's absolutely gorgeous.

00:45:46.660 | I have that, I have my virus that I worked on

00:45:49.560 | most of my career, poliovirus, I have a 3D model of that,

00:45:52.860 | which I actually just had made.

00:45:54.220 | It's gorgeous, and you can have it made

00:45:56.020 | in any color you want, right?

00:45:57.620 | - What would you say is the most fascinating,

00:46:01.500 | terrifying, surprising, beautiful virus to you?

00:46:05.140 | So of all the viruses you looked at,

00:46:08.380 | sometimes when you just sit late at night

00:46:10.940 | with a glass of wine, looking over the sunset,

00:46:13.980 | which virus do you think about?

00:46:16.260 | - So fulfilling all of those adjectives is hard, right?

00:46:21.300 | Fascinating, exciting, terrifying.

00:46:24.940 | - Well, the terrifying is an optional one, I think,

00:46:27.140 | 'cause maybe that puts a lot of pressure.

00:46:30.020 | - I'd say terrifying, to me, I'm not terrified

00:46:34.260 | because I think we can handle most viruses,

00:46:37.620 | as you see with this brand new one

00:46:39.100 | that emerged a year ago, we can handle it.

00:46:42.460 | - From a virology perspective.

00:46:44.140 | - Yeah, I mean, the human perspective

00:46:45.640 | is a different story, right?

00:46:46.860 | That's always an issue, but.

00:46:49.820 | So I think there are a couple

00:46:52.860 | of different categories of virus.

00:46:55.660 | So we could do the terrifying,

00:46:57.420 | and I think rabies is a terrifying virus

00:47:00.060 | because unless you're vaccinated,

00:47:02.340 | 100% certainty you're gonna die.

00:47:04.700 | So you get bitten by a rabid raccoon or bat or dog,

00:47:09.780 | whatever, and there's still 70,000 deaths a year

00:47:14.780 | of rabies throughout the world

00:47:16.300 | because there are a lot of feral dogs running around

00:47:18.020 | that are infected, unless you're vaccinated,

00:47:20.740 | you're gonna die, there's nothing we can do.

00:47:23.140 | But we do have a vaccine which we can actually give you

00:47:26.460 | even after you've been bitten,

00:47:27.820 | which is the only vaccine that works that way.

00:47:31.140 | It's a therapeutic, right?

00:47:32.900 | It will treat your illness

00:47:34.860 | 'cause the disease takes so long to develop.

00:47:37.320 | Eventually you get all kinds of neurological issues

00:47:41.240 | and paralysis and so forth, but it takes time

00:47:44.780 | and you can be vaccinated,

00:47:45.980 | it will prevent that in the meanwhile.

00:47:47.440 | So people always say, what's the most lethal virus?

00:47:50.300 | Is it Ebola?

00:47:51.140 | I said, no, it's actually rabies.

00:47:53.620 | Unless you're vaccinated, it will kill you.

00:47:55.980 | - Maybe it's good to linger,

00:47:57.780 | 'cause we'll talk about vaccines a few times today.

00:48:01.180 | It's good to linger on cases where vaccines

00:48:05.340 | have clearly, undoubtedly helped human civilization.

00:48:10.340 | And it seems like rabies is a good example.

00:48:15.420 | - No, rabies is great because everyone knows

00:48:18.320 | what happens when somebody gets rabies, right?

00:48:21.400 | You have fear of water, hydrophobia,

00:48:24.480 | your body becomes spastic and stiff and jerks around

00:48:31.200 | and you lose consciousness, you can't, no more--

00:48:35.520 | - It's not a fun ride to death.

00:48:36.680 | - It's horrible, it's a horrible way to die.

00:48:39.240 | So I think most people know that,

00:48:40.920 | it's been popularized enough in media, right?

00:48:44.120 | So that nobody would probably object to getting,

00:48:47.080 | oh, I was just bit by this raccoon and it ran off.

00:48:51.020 | Okay, well, we should assume it's rabid,

00:48:53.060 | we should immunize you, and most people are okay with that.

00:48:56.360 | 'Cause they know the consequences.

00:48:57.580 | And it's also pretty rare, right?

00:48:59.920 | It's not like something that you're trying

00:49:01.820 | to get into the arms of 250, 300 million Americans,

00:49:06.820 | that's hard, but the few thousand every year, it's easy.

00:49:11.700 | - So the transmissibility is difficult, right?

00:49:13.820 | It has to, oh, it's not airborne, so--

00:49:16.900 | - It's not airborne, it just, you have to be bitten.

00:49:19.900 | Although some people claim you could walk into a cave

00:49:24.780 | and the bats breathing out rabies virus could infect you,

00:49:28.340 | but I don't really think that's well substantiated.

00:49:32.620 | I think it's a bite.

00:49:33.460 | - How would you do a study on that?

00:49:34.820 | - Yeah, it's very hard to do.

00:49:36.380 | You'd have to collect the vapors in the cave

00:49:38.460 | and show that they're infectious, which,

00:49:40.760 | and by the way, someone emailed me the other day,

00:49:43.220 | you'll like this, they said,

00:49:44.460 | "Why can't we just immunize all the bats in the world

00:49:47.100 | against these viruses?"

00:49:49.340 | And I said, "Well, how would you do that?

00:49:50.540 | There are caves everywhere, right?"

00:49:52.380 | - Yeah.

00:49:53.300 | - He said, "Well, maybe you could just go and aerosolize."

00:49:56.620 | - Yeah.

00:49:57.460 | - It's pretty dangerous.

00:49:58.500 | - And then all the bats should have vaccine passports

00:50:01.540 | to make sure that they're all--

00:50:02.380 | - Yeah, and so you have to get their consent

00:50:03.900 | before you do it.

00:50:05.380 | But we do immunize wildlife against rabies.

00:50:12.100 | We have rabies vaccines for wild animals.

00:50:15.060 | There are a whole bunch of them that get rabies.

00:50:17.380 | And we put it in bait and drop it

00:50:20.340 | from helicopters in the woods,

00:50:21.540 | and it drops down the incidence of rabies in people.

00:50:24.420 | - Wow.

00:50:25.260 | - You know, people hiking get bitten and so forth.

00:50:27.140 | It drops the incidence, so we can do that.

00:50:28.980 | - I didn't know that.

00:50:30.340 | I always wondered how much medical care

00:50:32.220 | we're doing for animals in the wild,

00:50:34.020 | because I've recently become more and more aware

00:50:37.480 | that animals are living in extreme poverty.

00:50:41.100 | - Mm-hmm.

00:50:41.940 | - Like, you don't know, you think, like, natural,

00:50:44.300 | it's great, you know, like,

00:50:47.580 | like when animals are living on a farm, it's terrible.

00:50:51.740 | But then you also have to compare to, like,

00:50:53.700 | what life is like in the, or like the zoo.

00:50:56.300 | You have to compare what life is like in the wild.

00:50:58.740 | - Well, life in the wild is very tough, I think.

00:51:01.580 | Most animals have to, well, the carnivores anyway,

00:51:03.740 | they have to catch their food every day, right?

00:51:06.140 | - And then there's the viruses there.

00:51:08.180 | - They have viruses as well.

00:51:09.180 | - So the rabies immunization is the only

00:51:12.460 | one I'm aware of for wild animals.

00:51:16.380 | We do immunize lots of other animals.

00:51:20.940 | We immunize chickens and pigs and cows,

00:51:24.980 | even fish, farmed fish, we pick each fish up

00:51:28.500 | and give it an injection, you know, when it's a small fish.

00:51:31.580 | But that's mostly so that the farmers get a good yield.

00:51:36.220 | We don't really care about the animals, right?

00:51:38.380 | We want a good yield for market.

00:51:41.020 | And then there's some examples where we immunize animals

00:51:46.020 | to prevent spillovers into people.

00:51:48.960 | So there's a disease called Hendra in Australia,

00:51:53.340 | which was discovered in the '90s,

00:51:57.540 | and it turns out there are bats,

00:51:58.980 | fruit bats, that have this virus.

00:52:01.020 | The bats are fine, but sometimes they fly into horse stalls

00:52:05.140 | and the horses get infected.

00:52:06.340 | These are, in Australia, it was initially race horses,

00:52:09.020 | which are very expensive, right?

00:52:11.260 | The horses got infected and they died,

00:52:12.900 | and the humans who would take care of them would die also.

00:52:15.060 | So now they immunize the horses to prevent,

00:52:19.260 | well, to save the horses.

00:52:20.380 | Probably that's the motivation,

00:52:21.620 | 'cause these horses are hundreds of thousands of dollars.

00:52:24.580 | And then the people don't get sick

00:52:25.780 | because the horses don't get sick.

00:52:27.580 | You don't wanna immunize all the people

00:52:29.340 | because it's too rare, but that approach

00:52:31.940 | is called the one world health approach,

00:52:33.940 | which means everything's connected on the planet,

00:52:37.180 | and we have to think of everything in the grander scheme,

00:52:39.300 | not just us.

00:52:40.940 | - Yeah, so you can immunize some things

00:52:42.900 | along the trajectory that a virus would take.

00:52:44.980 | - Exactly.

00:52:45.820 | - So not some things, some living beings.

00:52:48.180 | - In the Arabian Peninsula,

00:52:49.860 | they have a MERS coronavirus issue every month.

00:52:52.580 | There are a couple of cases where a camel

00:52:55.820 | will infect a human, and the human can get very sick.

00:52:59.140 | It's a respiratory disease, very much like COVID.

00:53:03.180 | And so camels are very common there.

00:53:05.780 | They're raced, they're used as pets, they're eaten.

00:53:10.500 | So there's a lot of human-camel contact,

00:53:12.500 | but the number of cases are rare to a month,

00:53:15.060 | so you don't wanna immunize all the humans,

00:53:16.740 | so the idea would be to immunize the camels.

00:53:18.940 | So--

00:53:21.140 | - (laughs) I like it.

00:53:21.960 | So, okay, so you put rabies,

00:53:23.660 | but Ebola also is a famously deadly one.

00:53:28.660 | - Right.

00:53:30.700 | - What is it?

00:53:31.520 | I don't know, 50, 60% of it's--

00:53:33.180 | - Could be 50 to 90, but that's in Africa,

00:53:37.160 | where the healthcare isn't great.

00:53:38.860 | You saw when cases of Ebola came to the US,

00:53:42.880 | we could take care of it.

00:53:43.800 | We knew how to take care of it.

00:53:45.160 | We had fancy hospitals and so forth,

00:53:46.800 | and now we have a vaccine.

00:53:47.920 | So we can, and the vaccine is really good,

00:53:52.260 | but there are many governments in Africa

00:53:54.960 | that are suspicious of us,

00:53:56.960 | and they don't wanna use our vaccine, so they--

00:53:58.960 | - So there's a vaccine for Ebola.

00:54:00.640 | - There is, yeah.

00:54:01.480 | - And the effectiveness and safety of it,

00:54:05.320 | how much is understood?

00:54:06.400 | - So this is difficult because there's not a lot of Ebola.

00:54:11.400 | It's not a continuous, ongoing thing.

00:54:14.080 | There are sporadic outbreaks here and there.

00:54:16.600 | - Of a few thousand people.

00:54:17.720 | - At most, at most, usually a few hundred,

00:54:19.880 | and the biggest ever, in fact,

00:54:23.040 | this is why we didn't for years have an Ebola vaccine.

00:54:25.480 | The US military, together with Canada,

00:54:27.920 | developed an Ebola vaccine for service people.

00:54:30.580 | They wanted to say, "Well, we're sending people

00:54:32.080 | "into these Ebola areas.

00:54:33.360 | "We want a vaccine for them."

00:54:34.740 | So they had developed it through all the preclinical,

00:54:38.160 | which means before it goes into people,

00:54:41.000 | and that stopped because there was no money

00:54:43.280 | to do a phase one and a phase two and a phase three.

00:54:46.360 | In fact, for phase two and three,

00:54:47.840 | you need to have infections going on

00:54:49.520 | 'cause you're looking at how well

00:54:50.800 | the vaccine prevents infections, right?

00:54:53.000 | So then there was a West African outbreak in 2015,

00:54:57.040 | 2013, 2015.

00:54:58.720 | The most cases ever, 25,000.

00:55:00.740 | So they got to test the vaccine,

00:55:02.460 | but they only put it in a few thousand people.

00:55:07.460 | It's not like it's been in hundreds of thousands of people

00:55:10.500 | like the COVID vaccines has been.

00:55:12.100 | So it looks like it has high efficacy,

00:55:15.740 | but we'd like to have more data.

00:55:18.580 | Side effects maybe are not so great.

00:55:21.300 | There are a couple of different available vaccines.

00:55:23.600 | Some have been tested more than others.

00:55:25.860 | I would say this would probably not be

00:55:28.700 | widely accepted in the US.

00:55:31.440 | - But then neither would be something

00:55:34.860 | over 50% deadliness of a virus.

00:55:38.480 | - No, I think if you were,

00:55:39.960 | in fact, many physicians work in countries

00:55:42.560 | that have Ebola, so they get vaccinated

00:55:44.200 | because they understand the choice.

00:55:46.000 | - Yeah, right, it's always about the choice.

00:55:48.520 | So--

00:55:49.440 | - So then one more thing to answer the interesting,

00:55:53.240 | what are some of the viruses you really are fascinated by?

00:55:57.060 | There are a number of viruses that have clearly been shown

00:56:01.300 | to alter host behavior, and that's how they spread.

00:56:04.660 | I think those are fascinating.

00:56:05.900 | For example, there's some viruses of plants

00:56:09.980 | that are spread by aphids,

00:56:13.420 | and the aphid bites the plant,

00:56:16.780 | the virus reproduces in the plant,

00:56:18.900 | then it somehow engineers the plant

00:56:21.660 | to give off volatile organics to attract more aphids,

00:56:26.300 | which will spread the virus.

00:56:28.060 | Isn't that amazing?

00:56:31.460 | - Yeah.

00:56:32.300 | - So that's altering the behavior.

00:56:34.140 | (laughing)

00:56:35.100 | Altering because somehow the virus infecting the plant cells

00:56:38.700 | gives off these organics and attracts aphids.

00:56:40.820 | And furthermore, somehow when the aphid bites,

00:56:44.740 | it tastes horrible, so they immediately leave

00:56:47.780 | with the virus they've just picked up

00:56:49.220 | and go to another plant to spread it.

00:56:51.260 | So they're attracted and then repulsed at the same time.

00:56:53.780 | - And obviously you don't want to anthropomorphize this,

00:56:55.940 | like a strategy they're taking on.

00:56:57.580 | Somehow this worked out.

00:56:58.700 | - It worked out this way.

00:56:59.860 | It just evolved.

00:57:00.700 | And you know, evolution is sometimes hard to trace, right?

00:57:04.300 | Like Darwin famously said, he could never figure out

00:57:07.300 | how an eye evolved from a single cell, right?

00:57:09.340 | But it did.

00:57:11.180 | - The more complicated, complex the holistic organism is

00:57:16.140 | that the virus invades, the less able it is

00:57:19.620 | to control that organism, right?

00:57:21.420 | So I wonder if there's viruses that can control

00:57:23.460 | human behavior, you know, to induce more spread of the virus.

00:57:28.460 | - Well, I don't see why not.

00:57:34.340 | - There's not enough humans, I suppose,

00:57:35.740 | to like evolve through.

00:57:37.100 | - Well, we can't do the experiment to test it, right?

00:57:39.300 | We have to observe.

00:57:40.140 | And that's always hard when you're observing

00:57:41.780 | 'cause there's so many things that can confound

00:57:45.100 | what you're looking at.

00:57:45.940 | - Yeah, change human behavior, yeah.

00:57:47.540 | - I mean, there's so many things that impinge

00:57:49.100 | on our behavior, but yeah, I think it's possible.

00:57:54.100 | I think it's highly possible.

00:57:56.540 | If it does it in a plant, why not change

00:57:59.260 | some other organism's behavior?

00:58:00.780 | I think it's fine.

00:58:01.620 | Anyway, those fascinate me.

00:58:02.460 | There are lots of examples of those that are fascinating

00:58:06.060 | and how they work, people are trying to figure out.

00:58:08.820 | But there's not a lot of money to work on, you know,

00:58:10.660 | insect and plant viruses unless you're going to the USDA.

00:58:13.540 | So they don't get a lot of work moving forward.

00:58:17.100 | - Well, if you understand some of those viruses,

00:58:19.820 | is that transferable to human viruses, that understanding?

00:58:23.780 | - I think some of it could be, sure.

00:58:25.580 | I think the general principles, for example,

00:58:28.340 | how does the virus cause volatile organics to be made?

00:58:32.300 | It must be turning on some genes.

00:58:34.980 | And you could learn principles from that,

00:58:37.420 | how the virus might do that, sure.

00:58:39.020 | I think everything is broadly applicable.

00:58:41.340 | So to say it's not useful to study viruses

00:58:45.620 | of insects and plants is just wrong.

00:58:48.100 | Because in science, you probably know this,

00:58:50.540 | maybe in your field it's the same.

00:58:52.980 | If you're curious, you're gonna run into interesting things

00:58:55.420 | that you never planned on, right?

00:58:57.220 | - That's why people, like, you can criticize,

00:58:59.740 | why do we want to go on Mars?

00:59:01.340 | Why do we want to colonize Mars?

00:59:03.620 | Well, it's like, why do you want to go to the moon?

00:59:06.680 | The reality is when you do really difficult things,

00:59:10.780 | engineering things, like all these inventions

00:59:13.180 | along the way are created.

00:59:14.380 | It's kind of fascinating how basically just,

00:59:17.460 | pick a thing that everyone can agree is kind of cool

00:59:22.460 | and is really hard and do that.

00:59:24.460 | And then you'll have like thousands of inventions

00:59:26.940 | that have nothing to do with the thing.

00:59:28.220 | - That's right.

00:59:29.060 | I think you should let curious scientists

00:59:32.060 | just follow what they're interested in to a certain extent.

00:59:35.220 | You can't, you know, in science we say,

00:59:37.500 | we have translational research where we say,

00:59:39.460 | okay, here's some money, go cure cancer or diabetes

00:59:42.080 | or heart disease, whatever, right?

00:59:43.620 | And that's fine.

00:59:44.580 | But that often doesn't work out very well.

00:59:46.740 | What works better is to say, you have a good lab,

00:59:49.900 | you have a good track record, here's some money,

00:59:52.180 | do something, and that's where PCR, CRISPR,

00:59:55.980 | recombinant DNA, all that stuff

00:59:58.060 | which has made the field explode, that's all it came from.

01:00:00.820 | Not from people saying, I want to cure genetic diseases

01:00:04.340 | by gene editing, but by saying, what are these repeated

01:00:07.420 | things in this bacterium doing?

01:00:09.860 | - Yep.

01:00:11.180 | Can I ask you a big philosophical question?

01:00:13.340 | So there's these deadly viruses

01:00:17.220 | that are not very transmissible, Ebola, rabies,

01:00:21.580 | and then there's these less deadly viruses

01:00:23.820 | that are very transmissible, like COVID,

01:00:28.820 | I guess kind of borderline, but why isn't there

01:00:34.080 | super transmissible, super deadly viruses?

01:00:37.140 | - I think if you compare SARS-1 and 2,

01:00:40.380 | you get somewhat of an answer, right?

01:00:42.620 | SARS-1 was more deadly.

01:00:45.580 | In fact, over half of the time when people were infected,

01:00:50.420 | they ended up in the hospital 'cause they were that sick.

01:00:53.460 | And then the peak of virus shedding from them happened

01:00:58.220 | long after they went in the hospital.

01:00:59.660 | So it's easy to contain the infection

01:01:02.780 | when you're in a hospital, right?

01:01:04.640 | There was not much pre-symptomatic or asymptomatic shedding

01:01:10.300 | with SARS-1.

01:01:11.380 | - And shedding means you become infectious.

01:01:14.020 | - So in a respiratory virus, you inhale the droplets

01:01:17.700 | of the virus and they reproduce

01:01:19.440 | in your upper respiratory tract,

01:01:20.860 | what we call the nasopharynx, right?

01:01:22.940 | The nose and going back to that little cavity

01:01:24.920 | just above your mouth.

01:01:27.020 | So the virus reproduces really well.

01:01:28.480 | And then as you talk and sneeze and cough,

01:01:30.140 | you expel droplets and then those are inhaled

01:01:32.620 | by other people and then they reproduce.

01:01:35.220 | And for SARS-2, we now know there's a lot of reproduction

01:01:39.680 | just before you feel anything, if at all.

01:01:42.380 | So there's a lot of shedding and transmission

01:01:45.620 | before you get symptomatic.

01:01:47.740 | And as many people don't ever get symptomatic, right?

01:01:50.260 | So they spread really easily.

01:01:52.060 | So that explains why some viruses can transmit

01:01:56.020 | a lot better than others.

01:01:57.440 | And if one happens to knock you out,

01:02:00.100 | then you're never gonna transmit

01:02:01.460 | 'cause you're in the hospital like SARS-1.

01:02:03.460 | - But why can't you have both?

01:02:05.900 | Why can't you just wait a while before it knocks you out?

01:02:08.740 | But when it knocks you out, it really kills you.

01:02:11.280 | - That is a philosophical question, right?

01:02:13.280 | Because we could talk about why we haven't observed it.

01:02:16.720 | I mean, one issue is that if you're killed too quickly

01:02:21.720 | by a highly lethal virus,

01:02:25.920 | you're not gonna transmit it very well, right?

01:02:28.020 | So Ebola can kill you quite rapidly.

01:02:31.120 | And most of the transmission occurs

01:02:33.280 | when people are being cared for at home or in hospitals.

01:02:38.720 | Doctors and nurses get virus, but people walking around,

01:02:41.980 | you're not walking around when you have Ebola,

01:02:43.740 | you're too sick.

01:02:45.220 | You have black bloody diarrhea, you're vomiting,

01:02:48.260 | you're bleeding from your skin and mucus membranes.

01:02:53.260 | You're not walking around, you're not going to parties.

01:02:55.500 | So I think that's part of it,

01:02:57.980 | that if the infection is too lethal,

01:03:00.420 | you're simply not a good transmitter.

01:03:02.580 | And I think transmission is probably

01:03:05.740 | one of the most powerful selection forces for viruses,

01:03:09.760 | because a virus always has to find a new host.

01:03:12.760 | If it doesn't, it's a startup that fails, right?

01:03:16.420 | If it doesn't find a new host, it's gone.

01:03:18.580 | And so anything that makes the virus transmit better

01:03:22.560 | is gonna help it.

01:03:23.640 | And if killing you, being less lethal is part of that,

01:03:27.520 | that works too.

01:03:28.360 | - So there's a strong selection pressure

01:03:30.720 | against being lethal.

01:03:32.360 | - I think there's a strong selection pressure

01:03:34.900 | against being lethal and being more transmissible.

01:03:38.700 | Those two seem to work in opposite ways.

01:03:41.600 | And now we don't have a lot of data to support this.

01:03:43.440 | This is kind of a thought experiment,

01:03:46.520 | but there is one experiment done in Australia

01:03:49.600 | many years ago.

01:03:51.840 | I don't know if you know this, but in the 1800s,

01:03:54.700 | the hunters in Australia imported a rabbit from Europe

01:03:57.400 | so they could hunt it,

01:03:58.460 | because the native rabbit in Australia

01:04:01.040 | was too fast for them, they couldn't shoot them.

01:04:03.420 | So they brought in this European rabbit

01:04:05.560 | and they reproduced out of control.

01:04:07.800 | Within a couple of years, they were everywhere,

01:04:10.320 | millions of rabbits in all the watering holes,

01:04:13.440 | and now they had a problem.

01:04:14.920 | So they decided to use a virus

01:04:16.520 | to get rid of these excess rabbits.

01:04:19.080 | And they used a virus, a pox virus called myxoma virus,

01:04:22.860 | which is a natural virus of a different kind of rabbit.

01:04:26.220 | But for these European rabbits, it was quite lethal.

01:04:28.640 | And it's spread by mosquitoes.

01:04:30.080 | So they said, "Okay, let's release this virus."

01:04:33.860 | And the first year, 99.2% of the rabbits were killed.

01:04:38.860 | But that 0.8% that were left had some form of resistance.

01:04:45.160 | They were variants.

01:04:46.400 | Every organism, not just viruses, makes mutants.

01:04:49.080 | And there were some variants of the rabbits

01:04:51.020 | that could survive infection.

01:04:52.780 | And then in subsequent years, the virus became less lethal,

01:04:56.600 | and then the mosquitoes had a better shot

01:04:59.140 | of transmitting it from one rabbit to another

01:05:01.100 | if the rabbit lived longer.

01:05:02.300 | That's the selection, probably.

01:05:04.180 | And so in the end, the rabbits lived on.

01:05:06.500 | The virus was there.

01:05:07.460 | It evolved to be more transmissible and less lethal.

01:05:11.420 | So that's the only-- - Life is amazing.

01:05:13.180 | - That's the only data. - Life on Earth is amazing.

01:05:15.060 | - It is, it is.

01:05:15.940 | If you take the time to look at it and see what's happened,

01:05:19.420 | it is amazing.

01:05:20.740 | - It's also humbling that it just makes you realize

01:05:22.740 | humans are just a small part of the picture.

01:05:25.260 | - Of course.

01:05:26.360 | And we're wrecking it, aren't we?

01:05:28.320 | - Well, I mean, that's, we're not really,

01:05:32.500 | I mean, viruses are wrecking it in some ways.

01:05:34.780 | Part of this, we're not really wrecking anything.

01:05:36.860 | It's all part of it.

01:05:38.180 | - But you know when, the ways that humans exist

01:05:41.460 | encourages viruses to infect us, right?

01:05:43.900 | When we were hunter-gatherers,

01:05:45.820 | living in bands of 100 people, very few viruses,

01:05:49.620 | because it was hard for the virus to go

01:05:51.720 | from one band to another.

01:05:53.180 | And perhaps a hunter would, one of these humans

01:05:55.860 | would get an animal and bring a virus into camp,

01:05:58.600 | and some people would die,

01:05:59.560 | but it would never spread to another.

01:06:02.160 | And then when we started to congregate in cities,

01:06:04.500 | we figured out agriculture and so forth

01:06:06.640 | and how to harvest animals.

01:06:07.760 | Then we could get bigger and bigger populations,

01:06:09.480 | and the viruses went crazy.

01:06:10.960 | And they went from animals to us.

01:06:12.560 | So measles went from cows to humans

01:06:15.120 | when humans learned to domesticate cows

01:06:17.480 | and started gathering in big cities.

01:06:20.680 | - Yeah, but now that humans are able to communicate

01:06:24.880 | and travel globally, the virus has become

01:06:28.240 | more and more dangerous, transmissible.

01:06:30.260 | Thereby, if you look at Earth as an organism,

01:06:33.960 | thereby pushing humans to be more innovative,

01:06:36.780 | create Alpha, Fold 2, and 3, and 4, and 5,

01:06:39.600 | create better systems, and eventually there's rockets

01:06:41.800 | that keep flying from Earth.

01:06:43.960 | And eventually the virus is becoming super dangerous

01:06:47.460 | and threatening all of human civilization,

01:06:49.680 | will force it to become a multi-planetary species,

01:06:52.520 | and its organism starts expanding.

01:06:54.520 | So I think it's a feature, not a bug.

01:06:55.960 | I don't know.

01:06:57.520 | - Well, I think that we have our early,

01:07:00.720 | probably most of the, we're studying viruses since 1900.

01:07:05.720 | Most of that time was because of diseases they caused.

01:07:09.680 | The first viruses discovered, yellow fever,

01:07:12.920 | virus smallpox, polio virus, influenza virus,

01:07:17.920 | those were all because people got sick,

01:07:21.120 | and they said, "Oh, look, this is a virus

01:07:22.800 | "that's associated with it."

01:07:24.600 | And so we got good at learning how to take care

01:07:28.880 | of these infections, making vaccines, and so forth

01:07:31.040 | over the years, and it's only in the last 20 years

01:07:33.240 | that we recognize that there are more viruses out there

01:07:36.120 | that are far more interesting, perhaps,

01:07:38.240 | but we've learned how to deal with the bad ones, for sure.

01:07:41.120 | - So we talked about what is a virus.

01:07:43.500 | We talked about some of the most dangerous

01:07:45.560 | and deadly viruses.

01:07:47.080 | Can we zoom in and talk about COVID-19 virus?

01:07:51.320 | - Sure.

01:07:52.160 | - What your preferred name is, but maybe for this--

01:07:53.680 | - Well, there's two names, right?

01:07:54.600 | The virus is SARS-CoV-2, which is hard, it's long, right?

01:07:58.360 | And then COVID-19 is the disease.

01:08:00.440 | So you could say the virus of COVID-19, that's fine.

01:08:03.280 | - The virus of COVID-19.

01:08:04.680 | But for the purpose of this conversation,

01:08:06.240 | we'll every once in a while just say COVID.

01:08:08.280 | - It's fine, no problem.

01:08:10.520 | - What is this virus from,

01:08:12.740 | I don't know how many ways we can talk about it.

01:08:16.640 | I think from a basic structural,

01:08:19.680 | like the very end structure,

01:08:22.960 | biological structure perspective, what is it?

01:08:25.920 | What are its variants?

01:08:28.080 | Can you describe the basics,

01:08:30.080 | the important characteristics of the virus?

01:08:32.060 | - So viruses are classified by humans,

01:08:35.020 | just to make it easier to keep track of them, right?

01:08:38.280 | So this is a coronavirus,

01:08:40.160 | which is because when they were first discovered,

01:08:45.160 | I think the first ones were animal coronaviruses.

01:08:48.280 | They looked at them in the electron microscope

01:08:50.440 | and it looked like the solar corona,

01:08:51.960 | and that's all there is to it.

01:08:53.500 | And I have to say that early in the outbreak,

01:08:56.340 | the place with the highest seropositivity in the US

01:09:00.260 | for a while, 68% was a working class neighborhood

01:09:03.800 | in New York City called Corona.

01:09:05.500 | Can you beat that, right?

01:09:08.120 | - That's crazy, yeah.

01:09:09.600 | - So coronaviruses, they have membranes, right?

01:09:12.220 | We talked about membranes,

01:09:13.180 | they have spike proteins in the membrane

01:09:15.000 | so they can attach to cells.

01:09:16.320 | And inside, they have RNA.

01:09:19.040 | And they are the viruses with the longest RNA

01:09:22.000 | that we know of.

01:09:23.360 | None other comes close.

01:09:25.120 | For some reason, they're able to maintain 30,000,

01:09:29.620 | so SARS-CoV-2 RNA is 30,000 bases of RNA.

01:09:33.840 | And some of the other coronas are even longer, 40,000.

01:09:37.240 | - This is a, coronas are family of viruses

01:09:40.920 | that included the one you mentioned before, version one.

01:09:46.320 | - So SARS-CoV-1, yeah.

01:09:47.800 | - CoV-1 and I guess other ones as well.

01:09:49.520 | - So the first, we first learned of them in animals.

01:09:52.520 | A lot of animals, pigs and cows and horses

01:09:56.840 | have coronaviruses.

01:09:58.240 | And then in the '60s, we discovered a couple

01:10:01.980 | of human coronaviruses that just cause colds,

01:10:05.240 | very mild colds that you wouldn't even think twice about.

01:10:08.960 | And then suddenly, in 2003, there's this outbreak

01:10:14.160 | of severe respiratory disease in China.

01:10:18.600 | And it started in November

01:10:21.560 | and they didn't tell the world until February.

01:10:24.000 | And that was really bad because it was already spreading

01:10:27.220 | by the time they told people about it.

01:10:30.080 | But this went to 29 different countries.

01:10:34.620 | Only 8,000 people were infected and then it stopped.

01:10:37.480 | And that was the first time we saw an epidemic coronavirus

01:10:43.080 | and what they did afterwards is they said,

01:10:45.760 | okay, it looks like it came from the meat markets.

01:10:48.400 | They have live meat markets in Guangzhou

01:10:50.640 | in the south of China where you can go and pick out

01:10:54.000 | an animal and the guy will slaughter it for you

01:10:56.440 | and give it to you.

01:10:57.280 | And then of course, there's blood everywhere

01:10:58.640 | and that's how they got infected.

01:11:00.600 | And they figured out that there's this animal

01:11:02.760 | called a palm civet that was the source of virus.

01:11:05.780 | The palm civets are shipped in from the countryside

01:11:08.420 | and the palm civets somehow in the countryside

01:11:10.620 | got it from a bat.

01:11:11.660 | So they went looking in caves in the countryside

01:11:13.880 | and they found in one cave all the viruses

01:11:16.840 | that could make up SARS-1.

01:11:19.120 | And that was 2000, I would say took about five,

01:11:22.360 | eight years after that outbreak.

01:11:24.320 | So that was the first hint that bats have coronaviruses

01:11:29.320 | that can infect people and cause problems, right?

01:11:32.920 | And after that, we should have been ready.

01:11:35.640 | - So didn't they already start developing vaccines

01:11:38.160 | after then? - Yes.

01:11:39.000 | So some people started making vaccines.

01:11:41.320 | They tested them in mice, but they never got into people.

01:11:46.320 | And some people started working on antiviral drugs.

01:11:50.900 | Nothing ever came of them because industry,

01:11:55.240 | there's no disease, it's gone.

01:11:57.520 | Why should we make vaccines and drugs?

01:11:59.440 | And NIH in the US, you submit a grant and they say,

01:12:03.060 | "Ah, it's too risky.

01:12:03.960 | "There's none of this virus around."

01:12:05.560 | So people were really short-sighted

01:12:07.400 | because I always say we could have had antivirals

01:12:10.280 | for this, absolutely, for sure, no question.

01:12:14.280 | In fact, the one antiviral that's in phase three,

01:12:17.160 | it's called molnupiravir.

01:12:20.240 | It's the only one that you can take orally, it's a pill.

01:12:23.120 | It looks really good.

01:12:24.240 | That was developed five years ago,

01:12:26.320 | but never taken into humans.

01:12:28.000 | It could have been ready.

01:12:29.320 | So we dropped the ball.

01:12:31.240 | And then the next decade, 2012, MERS coronavirus

01:12:35.920 | comes up in the Arabian Peninsula.

01:12:38.960 | This comes from camels and infects people,

01:12:41.600 | but probably the camels got it from bats originally

01:12:44.840 | some time ago.

01:12:46.300 | But that never transmits from person to person, very rarely.

01:12:50.600 | Every new little outbreak is a new infection from a camel.

01:12:54.600 | So that was 2012.

01:12:57.280 | And now here we are, 2019,

01:12:59.320 | the new outbreak of respiratory disease in China.

01:13:02.720 | And this one really goes all over the world

01:13:06.480 | where SARS-1 could not.

01:13:07.840 | It's a coronavirus.

01:13:09.400 | It's different enough from SARS-1

01:13:11.160 | that it has very different properties.

01:13:13.000 | - But it still has a membrane,

01:13:14.640 | it still has a very long RNA in the middle,

01:13:17.880 | and then it still has the spike proteins.

01:13:19.960 | - That's right.

01:13:20.800 | - What are the little unique things

01:13:24.840 | that make it that much more effective?

01:13:27.800 | - That make it cause a pandemic of millions of people

01:13:30.960 | as opposed to SARS-1?

01:13:32.560 | Well, the genome is 20% different from SARS-1.

01:13:38.560 | And in those bases, there are things

01:13:40.920 | that make it different from SARS-1.

01:13:42.920 | It binds the same receptor, ACE2, on the cell surface.

01:13:45.800 | That's remarkable.

01:13:48.000 | It has a lot of the same proteins.

01:13:50.100 | They look similar.

01:13:51.160 | If you look at the structure of the spikes,

01:13:53.080 | they look similar,

01:13:54.660 | but there's enough amino acid differences

01:13:57.000 | to make the bio...

01:13:58.520 | And what it is, we don't know

01:14:00.040 | because how do you figure that out?

01:14:03.200 | You need to study animals 'cause you can't infect people.

01:14:06.720 | And the animal models aren't great.

01:14:08.940 | - So the way you figure that out

01:14:11.600 | is you figure out how those differences,

01:14:14.960 | what functional, like how the difference in the amino acids

01:14:18.560 | lead to functional difference of the virus.

01:14:20.840 | - That's right. - Like how it attaches,

01:14:22.080 | how it breaks the cell wall.

01:14:23.160 | - Exactly.

01:14:24.000 | - And how the hell do you figure that out?

01:14:26.080 | I guess there's models of interaction.

01:14:29.960 | - You need to, first you need an animal

01:14:31.760 | of some kind to infect, right?

01:14:33.120 | You can use mice.

01:14:34.880 | People have used ferrets, guinea pigs, non-human primates,

01:14:39.880 | all of the above, non-human primates are very expensive,

01:14:43.240 | so not many people do that.

01:14:44.600 | And then you can put the virus in the respiratory tract.

01:14:48.440 | But in fact, none of them get sick like people do.

01:14:51.560 | Many people with COVID get a mild disease,

01:14:55.520 | but 20% get a very severe, longer lasting disease

01:14:59.560 | and they can die from it, right?

01:15:00.760 | No animal does that yet.

01:15:03.000 | So we have no insight into what's controlling that.

01:15:05.440 | But if you just wanna look at the very first part

01:15:07.480 | of infection and the shedding and the transmission,

01:15:10.880 | you can do it in any one of several animal models.

01:15:14.640 | Ferrets are really good for transmission.

01:15:16.640 | They have nasal structures like humans

01:15:19.600 | and you can put them in cages next to each other

01:15:22.760 | and they'll transmit the virus really nicely.

01:15:24.880 | So you can study that.

01:15:26.640 | But the other thing that's important that we should mention

01:15:29.760 | is how do you manipulate these viruses?

01:15:33.180 | So these are RNA viruses.

01:15:35.700 | You can't manipulate RNA.

01:15:38.740 | We don't know how to do it.

01:15:40.820 | But DNA, because of the recombinant DNA revolution

01:15:45.740 | that occurred in the '70s, we can change DNA any way we want.

01:15:50.740 | We can change a single base, we can cut out bases,

01:15:53.680 | we can put other things in really easily.

01:15:56.980 | And if I may give it a personal aspect,

01:16:01.700 | when I went to MIT as a postdoc in 1979,

01:16:05.860 | David Balthamer said, "Here's what I want you to do.

01:16:08.980 | "The moratorium on recombinant DNA experiments

01:16:12.540 | "on viruses has just been lifted.

01:16:14.700 | "I want you to make a DNA copy of polio

01:16:18.140 | "and see if you put that in a cell,

01:16:19.780 | "whether it will start an infection."

01:16:21.720 | So okay, so I made a DNA copy of polio virus.

01:16:27.340 | It's only 7,500 bases, it's much smaller than corona.

01:16:31.780 | And I took that DNA and I put it in a piece of DNA

01:16:35.620 | from a bacteria called a plasmid.

01:16:38.020 | And you can grow plasmids in many, many bacteria,

01:16:41.460 | make lots of them and purify the DNA really easily.

01:16:44.620 | And I took that DNA and I sequenced it

01:16:48.720 | because we didn't know the genome sequence

01:16:50.900 | of polio at the time.

01:16:52.740 | And that took me a year, by the way,

01:16:54.660 | 'cause the techniques we had were really archaic

01:16:57.320 | and nowadays you could do it in 15 minutes, right?

01:17:00.020 | It's amazing.

01:17:01.540 | And I took the DNA, I put it into cells and out came polio.

01:17:05.680 | So that's the start.

01:17:08.020 | Now, since then, everybody has taken that technique

01:17:10.740 | and used it for their virus.

01:17:11.740 | You can now do it with SARS-CoV-2.

01:17:13.220 | You make a DNA copy of any RNA virus,

01:17:16.020 | you can modify it and you put it back into cells

01:17:19.060 | and you'll get your modified virus out.

01:17:21.460 | So that's an important part of understanding

01:17:23.820 | the properties of the virus, let's say, in an animal.

01:17:26.660 | By changing the virus, you're changing a DNA copy,

01:17:28.860 | you're making the virus then and putting it into the animal.

01:17:32.540 | - Can you clarify, so even an RNA virus,

01:17:35.380 | you can take and turn it into DNA?

01:17:38.080 | - Yes.

01:17:38.920 | - And then that allows you to modify it?

01:17:41.020 | - Yes.

01:17:41.860 | - What's that mapping?

01:17:44.300 | Well, no, no, no, what's the process

01:17:46.020 | of going from RNA to DNA?

01:17:48.360 | - Reverse transcription.

01:17:49.700 | - That's reverse transcription.

01:17:51.220 | Oh, so you actually go through the process

01:17:53.180 | of reverse transcription to do this?

01:17:54.740 | - Yes, remember David Baltimore and Howard Timmons

01:17:57.780 | had discovered this enzyme in the '70s.

01:18:00.140 | They got the Nobel Prize for that.

01:18:01.780 | And when I went to David's lab at MIT,

01:18:04.620 | he had the enzyme in the freezer.

01:18:06.420 | He said, "Here, take this and make a DNA copy of polio."

01:18:08.860 | - Yeah, I didn't make the connection

01:18:10.220 | that you can use that kind of thing for an RNA virus.

01:18:14.060 | - And so that's--

01:18:14.900 | - And then modify it.

01:18:15.980 | - See, any DNA virus already exists as DNA,

01:18:18.260 | so you can modify it.

01:18:19.820 | But for RNA viruses, it was difficult.

01:18:22.340 | And so then from that point on, for influenza,

01:18:25.660 | every other RNA virus and coronaviruses,

01:18:28.020 | people made DNA copies,

01:18:29.860 | and that's what they used to modify

01:18:31.500 | and ask questions about what things are doing, right?

01:18:34.580 | What's this gene doing?

01:18:35.540 | What if we take it out, what happens?

01:18:36.900 | - Can you do the same thing with COVID?

01:18:39.580 | Is it take the RNA and then--

01:18:41.180 | - Of course, and in fact, in January 2020,

01:18:44.140 | as soon as the genome sequence was released from China,

01:18:47.580 | the labs all over were synthesizing

01:18:50.300 | this 30,000 base DNA and getting--

01:18:54.260 | - What can you figure out without infecting anything?

01:18:58.220 | Just turning into, with the reverse transcription,

01:19:01.660 | turning it to DNA, modifying stuff,

01:19:03.500 | and then putting it into a cell.

01:19:05.460 | What can you figure out from that?

01:19:08.420 | - Well, you could, let's say you can cut out a gene.

01:19:11.580 | You see some genes in the sequence.

01:19:13.220 | I don't know what these genes do.

01:19:14.660 | Let's cut them out.

01:19:15.980 | And then you could cut them out of the DNA.

01:19:18.780 | You put the DNA in cells and maybe you get virus out.

01:19:22.020 | And you go, oh, clearly that gene's not needed

01:19:25.420 | for the virus to reproduce, at least in cells, right?

01:19:27.780 | Or maybe you take the gene out

01:19:29.180 | and you never get any virus, so it's lethal.

01:19:31.580 | - Is there a nice systematic ways of doing this?

01:19:33.500 | Do people kind of automate it?

01:19:35.460 | - Absolutely, and we, I mean, the problem with SARS,

01:19:40.020 | the COVID virus is it's 30,000 bases.

01:19:42.540 | There's a lot of stuff there.

01:19:44.420 | And what makes it more difficult is that you have to,

01:19:48.460 | it's been classified as a BSL-3 agent,

01:19:52.860 | biosafety level three.

01:19:54.580 | And so not everyone has a lab that's capable of doing that.

01:19:58.940 | So it limits the number of people who can do experiments.

01:20:02.160 | We're lucky to have a few in New York City,

01:20:05.720 | but not every place has them.

01:20:07.500 | So you cannot work with a virus just out on the bench

01:20:11.520 | like we do with many other viruses.

01:20:13.060 | You have to wear a suit and have to have special procedures

01:20:15.780 | and containment and so forth.

01:20:16.900 | So it makes it difficult to do basic experiments

01:20:19.220 | on the virus.

01:20:20.060 | - But when it's a pandemic, there's a lot of money,

01:20:22.740 | there's a lot of incentive to work on it harder.

01:20:25.540 | - And also you don't need to work on the virus.

01:20:27.320 | You can take bits of it and work.

01:20:29.220 | You could take, say, just the spike, right?

01:20:31.180 | And say, can we make a vaccine with just the spike?

01:20:33.860 | 'Cause that doesn't require BSL-3.

01:20:36.040 | So yes.

01:20:36.880 | - So like building a vaccine requires you to figure out

01:20:40.420 | how, or antiviral drugs,

01:20:42.700 | how to attack various structural parts of the virus

01:20:45.420 | and the functional parts of the virus.

01:20:47.020 | - Right.

01:20:47.860 | You have to decide on a target.

01:20:50.060 | - Yeah.

01:20:50.900 | - Like, I'm gonna make an antiviral.

01:20:52.340 | What am I gonna target in the virus?

01:20:55.460 | And there are a few things that make more sense than others.

01:21:00.460 | Usually we like to target enzymes.

01:21:03.380 | I don't know if you remember your biochemistry,

01:21:05.560 | but enzymes are catalytic.

01:21:07.820 | You don't need a lot of them to do a lot of things.

01:21:10.460 | So they're typically in low concentrations

01:21:12.900 | in a virus-infected cell.

01:21:14.360 | So it's easier to inhibit them with a drug.

01:21:17.120 | And the coronas have a couple of enzymes that we can target.

01:21:20.940 | So you have to figure that out ahead of time

01:21:23.620 | and decide what to go after.

01:21:25.080 | And then you can look for drugs that inhibit

01:21:27.300 | what you're interested in.

01:21:28.380 | It's not that hard to do.

01:21:29.920 | - There's just something beautiful about biology,

01:21:34.040 | about the mechanisms of biology.

01:21:36.100 | And I kind of regret falling in love

01:21:40.020 | with computer science so much

01:21:41.820 | that I left that biology textbook on the shelf

01:21:46.280 | and left it behind.

01:21:47.360 | But hopefully we'll return to it now.

01:21:49.480 | 'Cause I think one of the things you learn

01:21:51.920 | even in computer science,

01:21:53.000 | that studying biology and certainly neurobiology,

01:21:58.000 | you get inspired.

01:22:02.760 | Here's a mechanism of incredible complexity

01:22:05.160 | that works really well, is very robust,

01:22:07.520 | is very effective, efficient.

01:22:09.500 | It inspires you to come up with techniques

01:22:11.800 | that you can engineer in the machine.

01:22:13.760 | - That's what drives the field forward

01:22:15.760 | when people improvise and come up with new technologies

01:22:20.760 | that really make a difference.

01:22:22.720 | And we have a bunch of those now.

01:22:25.240 | - What's the difference between the coronavirus family

01:22:28.440 | and the other popular family, influenza virus family?

01:22:33.080 | (laughing)

01:22:35.040 | I mean, if I were, 'cause you mentioned

01:22:37.320 | we should have done a lot more

01:22:38.480 | in terms of vaccine development,

01:22:39.920 | that kind of thing for coronaviruses.

01:22:42.320 | But if I were back then, from my understanding,

01:22:46.440 | the thing we should all be afraid of is influenza.

01:22:49.840 | Like some strong variants coming out from that family.

01:22:53.840 | That seems like the one that will destroy

01:22:55.800 | human civilization or hurt us really badly.

01:23:00.240 | I don't know if you agree with this sense,

01:23:02.520 | but maybe you can also just clarify

01:23:06.480 | what to use as the difference between the families.

01:23:09.560 | - So it's an interesting difference.

01:23:11.000 | They both have membranes, right?

01:23:14.480 | So then they have spike proteins embedded in them.

01:23:18.080 | And they're different spikes.

01:23:20.760 | In fact, for influenza, there are two main ones.

01:23:24.320 | They're called the HA and the NA.

01:23:26.080 | But what's inside is RNA, but it's very different RNA.

01:23:32.240 | And here we have to explain that.

01:23:35.720 | So viruses with RNA can have three different kinds of RNA.

01:23:40.680 | They can have what we call plus RNA.

01:23:44.520 | They can have minus RNA, or they could have plus minus,

01:23:49.880 | actually two strands hybridized together.

01:23:54.600 | The plus RNA simply means that if you put

01:23:59.600 | that plus RNA in a cell, your cell has ribosomes in it

01:24:03.720 | that make the proteins that you need.

01:24:05.800 | The ribosomes will immediately latch onto the plus RNA

01:24:08.600 | and begin to make proteins.

01:24:09.960 | A minus RNA is not the right strand to make proteins.

01:24:15.360 | So it has to be copied first.

01:24:17.240 | And then the plus minus is both together.

01:24:19.640 | So the SARS coronaviruses,

01:24:22.600 | all the coronaviruses have plus RNA.

01:24:25.320 | So as soon as that RNA gets in the cell,

01:24:26.840 | boom, it starts an infectious cycle.

01:24:28.480 | Same thing with poliovirus, by the way, which I worked on.

01:24:31.280 | Influenza viruses are negative stranded.

01:24:34.920 | So they cannot be translated when they get in the cell.

01:24:37.800 | So that's tough for the virus

01:24:40.000 | because the cell actually cannot make plus RNA

01:24:45.000 | from minus RNA.

01:24:47.000 | It doesn't have the enzyme to do it.

01:24:49.160 | So the virus has to carry it in, inside the virus particle.

01:24:53.600 | And then when the minus RNA is in the cell,

01:24:55.600 | the virus enzyme makes plus RNAs and those get translated.

01:24:59.400 | So it's a big difference.

01:25:00.320 | And then in the influenza viruses,

01:25:03.400 | not only is it minus RNA, but it's in pieces.

01:25:07.040 | It's in eight pieces.

01:25:09.480 | We call that segmented,

01:25:10.880 | whereas the corona is in one long piece of RNA.

01:25:14.880 | - So what is that?

01:25:15.720 | Is that they're like floating separately?

01:25:17.880 | - Yeah, so the genes are on separate pieces.

01:25:19.600 | They're all packaged inside that virus particle

01:25:21.800 | of influenza virus, but they're in pieces.

01:25:23.760 | And why that's important is because

01:25:26.400 | if two different influenza viruses

01:25:29.320 | infect the same cell,

01:25:31.520 | the pieces as they reproduce can mix

01:25:33.760 | and out can come a virus with a new assortment of pieces.

01:25:38.200 | And that allows influenza virus

01:25:40.240 | to undergo extremely high frequency evolution.

01:25:43.840 | That's why we get pandemics.

01:25:45.640 | When we have a new flu pandemic,

01:25:47.040 | it's because somewhere in some animal,

01:25:49.960 | two viruses have reassorted and made a new virus

01:25:53.120 | that we hadn't seen before.

01:25:55.320 | - So you're talking about kind of biological characteristics,

01:26:00.320 | but what, am I incorrect in my intuition

01:26:03.680 | that are from the things I've heard

01:26:05.720 | that the influenza family of viruses is more dangerous?

01:26:09.920 | Like what makes it more dangerous to humans?

01:26:12.600 | - Well, it depends on the,

01:26:16.120 | there are many flavors or vintages of influenza virus.

01:26:19.440 | Some are dangerous and some are not, right?

01:26:21.240 | It depends on which one.

01:26:22.600 | Some, like the 1918 apparently was very lethal,

01:26:26.400 | killed a lot of people.

01:26:28.320 | But more contemporary viruses,

01:26:30.120 | we had a pandemic in 2009 of influenza.

01:26:35.120 | That wasn't such a lethal virus.

01:26:39.300 | We don't know exactly why,

01:26:40.680 | but it didn't kill that many people.

01:26:42.320 | It transmitted pretty well.

01:26:43.800 | - Is that the bird flu one?

01:26:45.240 | - They're all deriving,

01:26:47.640 | that one was called swine influenza.

01:26:49.760 | - Swine, that's right, swine, yeah.

01:26:50.600 | - It seemed to have started in a pig,

01:26:52.320 | but it had bird, it had RNAs from bird influenza viruses.

01:26:57.080 | These viruses are all reassortants of different viruses

01:27:00.640 | from pigs and birds and humans.

01:27:03.080 | But influenza can cause pneumonia

01:27:07.200 | and can kill you as does SARS-CoV-2.

01:27:10.000 | So it depends on the virus.

01:27:11.560 | So there is another influenza virus

01:27:13.680 | that's currently circulating.

01:27:15.000 | So right now we have the 2009 pandemic virus,

01:27:18.640 | that's still around.

01:27:20.080 | And then the 1968 pandemic virus,

01:27:23.880 | which was the one before 2009,

01:27:25.800 | that one is still around too.

01:27:27.440 | And that's more lethal.

01:27:29.120 | And depending on the season,

01:27:30.400 | some seasons the 2009 virus predominates,

01:27:33.840 | some seasons the 1968.

01:27:36.240 | And when the '68 is around, you get more lethality.

01:27:38.840 | - So we're living with an influenza family.

01:27:41.720 | We haven't exterminated them.

01:27:44.000 | - Right, we never will, never exterminate them.

01:27:46.320 | - Why?

01:27:47.160 | - Well, because every shorebird in the world

01:27:49.560 | is infected with them.

01:27:50.840 | You know, gulls and terns and ducks

01:27:53.200 | and all sorts of things.

01:27:54.840 | - Why can't we develop strong vaccines

01:27:58.280 | that defend against--

01:27:59.520 | - Oh, we could do that, sure.

01:28:01.440 | But that would not eliminate them from humans.

01:28:04.960 | Even if you had the best vaccine,

01:28:06.840 | you would never get rid of it in people

01:28:08.920 | because there would always be someone

01:28:11.040 | who's not vaccinated or in which the vaccine didn't work.

01:28:14.440 | No vaccine is 100%.

01:28:16.280 | - Right.

01:28:17.120 | Well, you just contradicted yourself.

01:28:18.760 | You said the perfect vaccine.

01:28:20.920 | - Imperfect, imperfect.

01:28:23.200 | - But then you said, even if you had the perfect vaccine,

01:28:26.640 | yeah, some people wouldn't get vaccinated.

01:28:29.000 | But I understand what you mean.

01:28:30.760 | I actually was asking, how difficult is it

01:28:32.680 | to make vaccines like that?

01:28:35.240 | It seems like it's very difficult to do that

01:28:36.920 | for the influenza virus.

01:28:38.880 | - So it's really easy to make an old-school vaccine.

01:28:43.000 | So the way the first influenza vaccines were made,

01:28:47.520 | it was actually Jonas Salk worked on them in the '40s.

01:28:51.120 | You just grow lots of virus,

01:28:53.320 | and you grow it in eggs, by the way, chicken eggs.

01:28:55.960 | - Nice.

01:28:57.000 | Literally?

01:28:57.920 | Wait, wait.

01:28:58.760 | - Yeah, chicken, embryonated, so they get fertilized,

01:29:00.960 | and there's a 10 or 12-day embryo in it,

01:29:03.080 | and you put virus in it, it grows up,

01:29:04.920 | and then you harvest it.

01:29:05.760 | You get about 10 mLs of fluid.

01:29:08.480 | And then you take that, you treat it with formaldehyde

01:29:11.520 | or formalin, and it inactivates the virus

01:29:14.400 | so it's no longer infectious.

01:29:16.600 | And you just inject that into people.

01:29:18.480 | And that was the first flu vaccine

01:29:19.840 | that was made for the US Army, actually,

01:29:22.200 | and then it got moved over to people.

01:29:24.240 | We still use that old-school tech today.

01:29:27.400 | - So you're taking, can you help me out here?

01:29:30.680 | Okay, so this is a good time to talk about vaccines.

01:29:34.480 | Okay, so you're talking about,

01:29:37.000 | you're taking the actual virus,

01:29:39.200 | you put it in an egg, you let it grow up.

01:29:42.520 | It's very funny that you put it in an egg.

01:29:44.080 | It's very poetic.

01:29:46.720 | And then how do you make it not infection,

01:29:51.720 | not effective or whatever?

01:29:53.960 | - Not infectious.

01:29:54.800 | - Not infectious, is that the right term here?

01:29:56.640 | - Yeah.

01:29:57.480 | - So how do you make it not infectious?

01:29:58.920 | - You can treat it with any number of chemicals

01:30:02.000 | that'll disrupt the particle so it no longer infects.

01:30:05.400 | - So that step of disrupting the particle,

01:30:09.040 | is that very specific to a particular variant particle?

01:30:12.920 | - No, the same collection of chemicals

01:30:14.880 | you can use for all kinds of,

01:30:16.280 | and which have been used for SARS-CoV-2 vaccines also.

01:30:19.400 | Same technology.

01:30:20.520 | - Okay, so what are, there's several things to ask.

01:30:23.680 | So you called it old-school in a way

01:30:25.360 | that's slightly dismissive,

01:30:27.640 | like people talk about Windows 98 or something.

01:30:30.040 | (both laugh)

01:30:31.880 | So is there risks involved with it,

01:30:34.840 | or is it just difficult to produce large amounts?

01:30:37.400 | Does it take a lot of eggs?

01:30:38.860 | - It's very easy.

01:30:40.160 | I mean, you could do it in cells and culture,

01:30:41.660 | but eggs were convenient.

01:30:43.040 | And in the 1940s, we didn't have cells in culture.

01:30:46.280 | We didn't know how to do that,

01:30:47.280 | so we had to use something else.

01:30:49.720 | It's easy to do,

01:30:52.320 | but the process of inactivating the virus with a chemical

01:30:56.960 | makes it not the best vaccine you can make.

01:30:59.980 | The flu vaccines that we have today,

01:31:02.480 | which are mostly based on this inactivation,

01:31:05.760 | is called inactivated virus vaccines.

01:31:08.520 | - Oh, so like the kind of thing it presents

01:31:12.880 | to the immune system to train on

01:31:14.960 | is not close to the actual virus.

01:31:19.560 | - Yes, that's what we think.

01:31:20.520 | So that's why probably the flu vaccines

01:31:22.400 | are just not very good.

01:31:23.960 | 60% efficiency at the best, right,

01:31:29.040 | which is not really good.

01:31:30.080 | - What does it mean?

01:31:31.200 | What is the measure of efficiency for a vaccine?

01:31:33.980 | - Well, how it does in the general population

01:31:37.500 | at preventing influenza.

01:31:38.800 | - At preventing?

01:31:40.800 | - Illness, not infection.

01:31:41.920 | We usually don't measure infection

01:31:44.840 | when we're testing a vaccine.

01:31:46.080 | We just measure sickness.

01:31:47.680 | That's really easy to score, right?

01:31:50.240 | You do a trial and you say,

01:31:52.000 | "If you feel sick, give us a call."

01:31:53.880 | We'll tell you what to do.

01:31:55.280 | - So yeah, I mean, what's sickness?

01:31:58.040 | Sickness is the presence of symptoms.

01:32:01.560 | - So this is good time to say what a symptom is, okay?

01:32:04.960 | A symptom is what you only can feel.

01:32:08.900 | Only you can feel an upset stomach

01:32:11.580 | or a sore throat or that sort of thing.

01:32:13.380 | - It's the lived experience of a symptom.

01:32:15.380 | - Whereas a sign is something that someone could measure

01:32:19.060 | and tell that you're infected,

01:32:20.500 | like virus in your nasopharynx or something else, right?

01:32:25.500 | Signs and symptoms.

01:32:27.100 | And so in a vaccine trial,

01:32:28.460 | they tell you if you have any of these symptoms,

01:32:31.820 | they give you a paper with the exact symptoms listed

01:32:35.040 | to make sure you're picking them up, right?

01:32:36.880 | So for flu, it would probably be fever, sore throat, cough.

01:32:41.520 | You call them and then they will do a PCR

01:32:44.160 | and make sure you've got flu and not some other virus

01:32:46.580 | that makes similar symptoms.

01:32:48.160 | And then they would say,

01:32:50.940 | "Are you a vaccine or non-vaccine arm?"

01:32:53.780 | And they count up all the infections

01:32:55.800 | and see how the vaccine did, basically.

01:32:57.720 | - That's so fascinating because the reporting,

01:33:02.120 | so symptom is what you feel.

01:33:03.820 | - Yes, for sure.

01:33:04.660 | - And certainly the mind has the ability

01:33:08.300 | to conjure up feelings.

01:33:10.180 | - Oh yes, absolutely.

01:33:11.860 | - And so like culturally,

01:33:13.760 | maybe there was a time in our culture

01:33:16.860 | where it was looked down upon to feel sick

01:33:21.860 | or something like that, like toughen up kind of thing.

01:33:25.140 | And so then you probably have very few symptoms

01:33:28.560 | being reported.

01:33:29.480 | - Absolutely, absolutely.

01:33:31.040 | - And now is like much more, I don't know,

01:33:34.820 | perhaps you're much more likely to report symptoms.

01:33:37.880 | Now it's fascinating 'cause then it changes.

01:33:40.800 | - Oh, it is definitely a perception

01:33:42.120 | because your symptom may be nothing to me

01:33:45.120 | or vice versa, right?

01:33:46.080 | And so when you're doing this,

01:33:47.480 | it's a little bit of a imprecise science

01:33:51.120 | because and even it's a cultural thing.

01:33:54.120 | In some countries, something that would make us feel horrible

01:33:57.840 | they wouldn't even bother reporting.

01:33:59.240 | No, I didn't have any symptoms.

01:34:00.480 | So it's a little bit imprecise and it clouds the results.

01:34:03.440 | So if you can measure things, it's always better.

01:34:05.840 | But you start out with a symptom.

01:34:07.760 | And if you say, if someone tells you this virus,

01:34:11.120 | 20% of the people are asymptomatic,

01:34:15.760 | they don't report symptoms,

01:34:18.240 | that number is probably not a constant.

01:34:22.000 | It depends where you did the study.

01:34:24.220 | It could be different in China versus South America,

01:34:26.900 | Europe, et cetera, yeah.

01:34:28.420 | - I mean, I was trying to,

01:34:29.580 | so I took two shots of the Pfizer vaccine.

01:34:32.420 | I had zero symptoms.

01:34:34.580 | - Wow.

01:34:35.420 | - So and I was wondering,

01:34:36.780 | well, see, but that's my feelings, right?

01:34:38.740 | This is not, 'cause I felt fine.

01:34:41.100 | I was waiting.

01:34:42.060 | - Did you have pain at the injection site?

01:34:44.140 | - No, it was kind of pleasant.

01:34:48.380 | - You felt nothing the next day, no?

01:34:50.260 | - Nothing. - Okay.

01:34:51.420 | - No tightness, no exhaustion, no.

01:34:53.380 | But see, like I have an insane sleeping schedule.

01:34:56.780 | I already put myself through crazy stuff.

01:34:59.300 | That said, maybe I was expecting something really bad.

01:35:03.300 | Like I was waiting and therefore didn't feel it.

01:35:06.780 | But I also got allergy shots.

01:35:10.300 | And those, I was out all next day,

01:35:14.620 | like exhausted for some reason.

01:35:16.460 | So that gave me like a sense like,

01:35:19.580 | okay, at least sometimes I can feel shitty.

01:35:22.660 | That's good to know.

01:35:24.500 | - Sure, sure.

01:35:25.340 | - And then with the vaccine, it didn't.

01:35:27.220 | But the question is like,

01:35:29.020 | how much does my mind come into play there?

01:35:32.620 | The expectations of symptoms,

01:35:34.980 | the expectations of not feeling well,

01:35:39.340 | how does that affect the sort of the self-reporting

01:35:41.340 | of the symptoms?

01:35:42.180 | - I think it's definitely a variable there,

01:35:44.620 | but there's certainly many people

01:35:46.460 | that don't feel anything after the vaccines.

01:35:48.460 | And there's some that have a whole range of things

01:35:52.020 | like soreness and fever, et cetera.

01:35:54.620 | Yeah.

01:35:55.460 | - So, okay, you were talking about

01:35:56.300 | the old school developments like the egg.

01:35:58.620 | - Right.

01:35:59.460 | - What's better than that?

01:36:02.380 | - So then the next generation of vaccines

01:36:04.460 | which arose in the '50s were what we call

01:36:07.900 | replication competent, where the virus,

01:36:11.020 | you take it and it's actually reproducing in you.

01:36:13.980 | - Yeah, that sounds safe.

01:36:16.100 | - And it can be somewhat problematic, yes,

01:36:19.060 | as you might imagine, 'cause once you put that virus in you,

01:36:22.340 | you have no more control, right?

01:36:24.180 | It's not like you have a kill switch in it,

01:36:25.780 | which actually would be a great idea to put in.

01:36:28.660 | - Like nanobots?

01:36:31.620 | What composite?

01:36:32.460 | - No, you could just put something in there.

01:36:34.220 | If you added a drug, you would shut it off, right?

01:36:37.940 | And people are thinking about that

01:36:39.920 | because now we're engineering viruses

01:36:41.780 | to treat cancers and other diseases.

01:36:44.860 | And we may wanna put kill switches in them

01:36:46.980 | just to make sure they don't run away.

01:36:48.580 | - Oh, interesting, so you can like deploy a drug

01:36:50.380 | that binds to this virus that would shut it off in the body.

01:36:55.380 | Something like that.

01:36:56.860 | - Something like that, yeah, that would be the idea.

01:36:58.660 | You'd have to engineer it in.

01:36:59.900 | Anyway, these were, the first one was yellow fever vaccine

01:37:03.620 | that was made because that was a big problem.

01:37:06.260 | And this virus, and the way you do this,

01:37:08.900 | back in the old day, was empirical.

01:37:12.680 | So Max Tyler, who did the yellow fever vaccine,

01:37:15.300 | he took the virus, which is a human virus, right?

01:37:18.600 | And he infected, I think he used chick embryos.

01:37:23.800 | And he went from one embryo to another

01:37:26.540 | and just kept passing it, did that hundreds of times.

01:37:29.340 | And every 10 passages, he would take the virus

01:37:32.700 | and put it in a mouse or a monkey, whatever his model was.

01:37:36.580 | And then eventually he got a virus

01:37:38.060 | that didn't cause any disease after 200 and some passages.

01:37:41.900 | And then that was tested in people

01:37:43.880 | and it became the yellow fever vaccine that we use today.

01:37:46.560 | He selected for mutations that made the virus

01:37:50.460 | not cause disease, but still make an immune response.

01:37:55.460 | So those are called replication competent.

01:37:57.660 | We now have the polio vaccine,

01:37:59.500 | which was developed in the '50s after the yellow fever.

01:38:03.200 | Then we had measles, mumps, rubella.

01:38:05.480 | Those are all replication competent vaccines.

01:38:09.220 | And you mentioned that's a good idea.

01:38:12.420 | They are all safe vaccines.

01:38:15.500 | The only one that has had an issue

01:38:18.460 | is the polio replication competent vaccine.

01:38:21.380 | It was called Sabin vaccine or oral polio virus vaccine,

01:38:26.000 | because you take it orally.

01:38:28.980 | It's wonderful because you don't have to inject it.

01:38:31.460 | This is the perfect delivery.

01:38:33.300 | Either intranasal for a respiratory virus

01:38:37.020 | or orally for polio.

01:38:38.020 | It goes into your intestines, it reproduces,

01:38:40.900 | and it gives you wonderful protection against polio.

01:38:43.940 | However, you do shed virus out.

01:38:48.100 | And that virus is no longer a vaccine.

01:38:52.780 | It's reverted genetically in your intestine.

01:38:55.620 | - So you can infect others with polio.

01:38:57.300 | - You can take that virus and then put it into an animal

01:38:59.780 | and give it polio.

01:39:00.620 | And in fact, the parents of some kids

01:39:04.380 | in the '60s and '70s who were immunized

01:39:06.900 | got polio from the vaccine.

01:39:08.380 | The rate was about one

01:39:09.820 | in one and a half million cases of polio.

01:39:13.740 | So it's called vaccine-associated polio.

01:39:15.340 | And I always argue that we may not have

01:39:19.500 | picked the right vaccine.

01:39:21.180 | There was a big fight in the US and other countries

01:39:25.460 | between the inactivated polio

01:39:27.540 | and the infectious polio vaccines,

01:39:29.780 | which ones we should be using,

01:39:31.580 | because we found out that the infectious vaccine

01:39:33.980 | actually caused polio.

01:39:35.300 | And eight to 10 kids a year in the US alone

01:39:38.060 | got polio from the vaccine,

01:39:39.820 | which looking back is really not acceptable in my view,

01:39:43.500 | although the public health community said it was

01:39:46.060 | to get rid of polio.

01:39:47.560 | So now we're close to eradicating polio globally,

01:39:51.420 | but this vaccine-derived polio is a problem.

01:39:55.940 | So now we have to go back to the inactivated vaccine,

01:39:59.220 | which is tough 'cause it's injected.

01:40:01.420 | - So, okay, so the basic high-level,

01:40:05.020 | how vaccines work principle is

01:40:08.340 | you want to deploy something in the body

01:40:11.180 | that's as close to the actual virus as possible,

01:40:13.860 | but doesn't do nearly as much harm.

01:40:15.540 | - That's right.

01:40:16.380 | - And there's like a million, not a million,

01:40:17.780 | but there's a bunch of ways you could possibly do that.

01:40:19.620 | - So those are two ways.

01:40:20.560 | And now, of course, we have modern ways

01:40:21.980 | we can make mRNA vaccines, right?

01:40:25.020 | - What are the modern ways?

01:40:26.980 | Do you want to look mRNA vaccine?

01:40:29.380 | - So that's the most modern,

01:40:31.100 | but even before mRNA vaccines,

01:40:33.140 | we learned that we could use viruses

01:40:36.060 | to deliver proteins from a virus that you want to prevent.

01:40:40.820 | And so the Ebola vaccine,

01:40:43.980 | we took the spike gene of Ebola virus

01:40:46.580 | and put it in a different virus,

01:40:47.980 | and we deliver that to people,

01:40:49.580 | and that's called a vectored vaccine.

01:40:51.980 | And some of the COVID vaccines are vectors

01:40:54.560 | of different kinds, the most famous are adenovirus vectors

01:40:57.680 | carrying the spike gene into the cell.

01:41:00.460 | - Can you explain how the vector vaccine works again?

01:41:03.380 | - So we take a virus that will infect humans,

01:41:08.380 | but will not make you sick.

01:41:11.540 | In the case of adenovirus,

01:41:13.520 | the years and years of people studying it

01:41:16.020 | has told us what genes you could cut out

01:41:18.780 | and allow the virus to infect the cell,

01:41:21.000 | but not cause any disease.

01:41:22.140 | - So instead of doing selection on it,

01:41:24.160 | you actually genetically modify it.

01:41:27.460 | - Yes, you modify the vector, yeah.

01:41:29.220 | So you'd be much more precise about it.

01:41:30.620 | - You'd be very precise,

01:41:31.500 | and then you splice in the gene for the spike,

01:41:34.900 | and then you use that to deliver the gene,

01:41:37.620 | and it becomes produced as protein,

01:41:39.660 | and then you make an immune response.

01:41:40.940 | - And vector is the term for this modified.

01:41:43.180 | - Right.

01:41:44.180 | So we're now using viruses at our bidding.

01:41:47.900 | We're using them as vectors, not just for vaccines.

01:41:50.180 | We can cure monogenic diseases.

01:41:52.380 | That is, if you're born with a genetic disease,

01:41:55.560 | you have a deletion or a mutation in a gene,

01:41:57.820 | a single gene, we can give you the regular gene back

01:42:02.020 | using a virus vector.

01:42:03.120 | Cancers too, we can cure cancers with vectors.

01:42:07.220 | - Wow, really?

01:42:09.980 | Interesting.

01:42:10.940 | - I think in 10 to 15 years,

01:42:12.580 | most cancers will be treatable with viruses, yeah.

01:42:15.220 | Not only can we put things in the vector to kill the tumor,

01:42:21.140 | we can target the vector to the tumor specifically

01:42:25.420 | in a number of ways,

01:42:26.880 | and that makes it less toxic, right?

01:42:28.220 | It doesn't infect all your other cells.

01:42:30.180 | - But it takes time to develop a vector

01:42:33.700 | for a particular thing,

01:42:34.860 | 'cause it requires a deep understanding.

01:42:37.620 | - Yeah, in fact, we have about a dozen different

01:42:40.220 | virus vectors that have been studied for 20 years,

01:42:42.940 | and those are the set of vaccine vectors that we're using.

01:42:46.340 | So it includes adenovirus, vesicular stomatitis virus,

01:42:50.780 | which is a cousin of rabies, but doesn't make people sick.

01:42:55.420 | Influenza virus is being used as a vector,

01:42:58.640 | and even measles virus.

01:43:00.260 | So we're familiar with how to modify those to be vectors,

01:43:04.160 | and those are being used for COVID vaccines.

01:43:07.480 | And then of course, we have the newest,

01:43:10.640 | which is the nucleic acid vaccines.

01:43:12.620 | So years ago, people said,

01:43:15.220 | "Why can't we just inject DNA into people?

01:43:17.760 | "Take the spike and put it in a DNA and inject it."

01:43:22.400 | So people tried many, many different vaccines,

01:43:25.320 | and in fact, there are no human licensed vaccines

01:43:28.320 | that are DNA vaccines,

01:43:29.520 | although there is a West Nile vaccine for horses

01:43:33.120 | that's a DNA-based vaccine.

01:43:35.960 | So if you have a horse, you can give it this vaccine,

01:43:38.200 | but no human.

01:43:39.040 | - Can you clarify, does a DNA vaccine

01:43:42.640 | only work for DNA viruses?

01:43:44.960 | - No, it can work for DNA or RNA,

01:43:46.880 | 'cause remember, for an RNA virus,

01:43:48.440 | we can make a DNA copy of it.

01:43:50.800 | And it will still, when you put that DNA in a cell,

01:43:53.840 | it goes into the nucleus.

01:43:55.480 | - Okay, right.

01:43:56.720 | So you're just skipping a step.

01:43:58.520 | - You get proteins.

01:43:59.360 | - RNA vaccines, you're giving, okay, I got it.

01:44:02.160 | - So those didn't work for human vaccines,

01:44:04.320 | and there were many HIV/AIDS vaccine trials

01:44:07.360 | that used DNA vaccines, didn't work.

01:44:10.880 | And then, a number of years ago,

01:44:13.400 | people started thinking, "How about RNA, RNA vaccines?"

01:44:17.880 | And I first heard this, I thought, "What?"

01:44:20.360 | I've worked with RNA my whole career.

01:44:22.400 | It's so fragile.

01:44:24.320 | If you look at it the wrong way, it breaks.

01:44:26.700 | I mean, that's being facetious, right?

01:44:29.840 | But you have to be very careful,

01:44:31.680 | 'cause your hands are full of enzymes that will degrade RNA.

01:44:35.940 | So I thought, "How could this possibly work,

01:44:38.840 | "injecting it into someone's?"

01:44:40.200 | It's an example of, I was skeptical, and I was wrong.

01:44:43.980 | It turns out that if you modify the RNA properly

01:44:47.040 | and protect it in a lipid capsule,

01:44:51.100 | it actually works as a vaccine.

01:44:52.980 | And people were working on this years

01:44:54.980 | before COVID came around.

01:44:57.120 | They were doing experimental mRNA vaccines,

01:45:00.360 | and there were a couple of companies

01:45:01.540 | that were working on it.

01:45:02.580 | And so at the beginning of 2020, they said, "Let's try it."

01:45:07.500 | And I was skeptical, frankly,

01:45:09.740 | 'cause I just thought RNA would be too labile,

01:45:12.180 | but I was wrong.

01:45:13.660 | - So this is, as we're saying offline,

01:45:16.020 | one of the great things about you

01:45:18.180 | is you're able to say when you're wrong

01:45:20.620 | about intuitions you've had in the past,

01:45:22.300 | which is a beautiful thing for a scientist.

01:45:25.140 | But I still think it's very surprising

01:45:28.140 | that something like that works, right?

01:45:30.060 | - Yeah, I am surprised.

01:45:31.380 | - So you're just launching RNA in a protective membrane.

01:45:35.980 | - Yeah.

01:45:36.820 | - And then, now, one thing is surprising,

01:45:39.780 | that the RNA sort of lasts long enough, right?

01:45:43.860 | - That's right.

01:45:44.900 | - In its structure.

01:45:45.740 | But then the other thing is,

01:45:49.340 | why does it work that that's a good training ground

01:45:53.220 | for the immune system?

01:45:56.140 | Is that obvious?

01:45:58.540 | - Well, I don't think it's obvious to most people,

01:46:01.260 | and it's worth going into, 'cause it's really interesting.

01:46:04.540 | I mean, first of all, they wrap the RNA in fats,

01:46:09.020 | in lipid membranes, right?

01:46:10.580 | And the particular formulation,

01:46:12.740 | they test for years to make sure it's stable,

01:46:16.020 | it lasts a long time after it's injected.

01:46:17.980 | And the two companies that make the current COVID vaccines,

01:46:22.100 | right, Moderna and Pfizer,

01:46:23.220 | they have different lipid formulations, to get to the same.

01:46:26.620 | So that's a real part of it.

01:46:29.020 | And it's not simple.

01:46:30.220 | There are quite a few different lipids

01:46:31.860 | that they put into this coating.

01:46:34.100 | And they test to see how long they protect the RNA

01:46:37.020 | after it's injected, say, into a mouse.

01:46:39.140 | How long does it last?

01:46:40.620 | And the way it works is,

01:46:41.940 | apparently, these lipid nanoparticles,

01:46:46.140 | they get injected into your muscle,

01:46:48.820 | they bump into cells, and they get taken up.

01:46:52.100 | So lipid fat is sticky.

01:46:55.260 | It's greasy, we like to say.

01:46:59.880 | And so your cells are covered with a greasy membrane also.

01:47:03.700 | So when these lipid nanoparticles bump into them,

01:47:06.560 | they stick, and they eventually get taken up.

01:47:08.380 | And they figured this out right at the beginning.

01:47:11.260 | If we put RNA in a lipid nanoparticle,

01:47:13.700 | will it get taken up into a cell?

01:47:15.220 | And the answer was yes.

01:47:16.340 | It was just, let's try it, and it worked.

01:47:18.660 | - So it's basically experiment.

01:47:20.340 | It's not like some deep understanding of biology.

01:47:22.620 | It's experimentally speaking, it just seems to work.

01:47:25.460 | - Yeah, well, they had some idea

01:47:26.800 | that lipids would target this to a cell membrane.

01:47:30.460 | And remember, there's no receptor involved.

01:47:33.860 | Like, the virus has a specific protein

01:47:36.900 | that it attaches to a receptor.

01:47:39.260 | It's not efficient enough to just bump around

01:47:41.620 | and get into a cell.

01:47:43.260 | That's what these things are doing.

01:47:44.940 | And they probably optimize the lipids

01:47:47.260 | to get more efficient uptake.

01:47:49.780 | But it's not as efficient as a virus would be

01:47:51.940 | to get into a cell.

01:47:52.780 | - Right, so you have no specific,

01:47:54.460 | I mean, which is why it's surprising

01:47:57.620 | that you can crack into the safe with a hammer.

01:48:02.620 | (laughs)

01:48:04.260 | Or with some fat.

01:48:06.100 | I mean, that's kind of surprising.

01:48:08.820 | It's kind of amazing that it works.

01:48:11.900 | But so, maybe let's try to talk about this.

01:48:16.900 | So, one of the hesitancies around vaccines,

01:48:21.240 | or basically around any new technology,

01:48:24.660 | is the fact that mRNA is a new idea.

01:48:27.680 | And it's an idea that was shrouded in some skepticism,

01:48:32.180 | as you said, by the scientific community.

01:48:35.300 | 'Cause it's like, it's a cool new technology.

01:48:40.220 | Surprising that it works.

01:48:42.540 | What's your intuition?

01:48:44.380 | I think one nice way to approach this

01:48:46.420 | is try to play devil's advocate and say both sides.

01:48:51.420 | One side is why your intuition says

01:48:56.160 | that it's safe for humans.

01:48:59.080 | And what arguments can you see,

01:49:02.380 | if you could steal man,

01:49:03.520 | an argument why it's unsafe for humans.

01:49:06.680 | Or not unsafe for humans,

01:49:09.340 | but the hesitancy to take an mRNA vaccine is justified.

01:49:14.340 | - So, many people are afraid because it's new technology

01:49:19.540 | and they feel it hasn't been tested.

01:49:22.260 | I mean, in theory, what could go wrong?

01:49:25.160 | This is, the nice thing about mRNA

01:49:29.020 | is that it doesn't last forever.

01:49:30.820 | As opposed to DNA, which doesn't last forever,

01:49:35.020 | but it can last a lot longer.

01:49:38.180 | And it could even go into your DNA, right?

01:49:40.540 | So, mRNA has a shorter lifetime,

01:49:44.340 | maybe days after it's injected into your arm,

01:49:47.300 | then it's gone.

01:49:48.720 | So, that's a good thing,

01:49:49.740 | because it's not gonna be around forever.

01:49:52.040 | So, that would say, okay,

01:49:54.220 | so it's sticking around for your lifetime,

01:49:57.380 | it's not happening.

01:49:58.300 | But what else could happen?

01:50:00.020 | Well, let's see the protein that's made,

01:50:03.100 | could that be an issue?

01:50:05.260 | And again, proteins don't last forever,

01:50:08.980 | they have a finite longevity in the body.

01:50:12.500 | And this one also lasts, perhaps, at the best, a few weeks.

01:50:17.500 | - This is a protein that's made

01:50:18.900 | after the RNA gets into the cell.

01:50:22.060 | - Yeah, so the lipid nanoparticles taken up into a cell

01:50:25.060 | and the mRNA is translated and you get protein made.

01:50:27.860 | - And there's also a question, I'm sorry to interrupt,

01:50:30.300 | where in the body,

01:50:32.300 | so because it's not well targeted,

01:50:35.100 | or I don't know if it's supposed to be targeted,

01:50:38.320 | but it can go throughout the body,

01:50:39.900 | that's one of the concerns. - Right, so it's injected

01:50:41.480 | deep into your deltoid muscle, right here, shoulder.

01:50:45.120 | And the idea is not to put it in a blood vessel,

01:50:49.780 | otherwise it would then, for sure, circulate everywhere.

01:50:52.420 | So, they go deep in a blood vessel

01:50:55.180 | and it's locally injected.

01:50:57.180 | And they did, before this even went into people,

01:51:00.380 | they did experiments in mice,

01:51:01.780 | where they gave them 1,000 times higher concentrations

01:51:05.020 | than they would ever give to people.

01:51:06.820 | And then, when you do that, it can go everywhere, basically.

01:51:09.320 | You can find these nanoparticles

01:51:11.300 | in every tissue of the mouse.

01:51:14.060 | But that's at 1,000-fold higher concentration, right?

01:51:17.180 | So, I think at the levels that we're using in people,

01:51:21.140 | most of it's staying in the muscle,

01:51:22.500 | but sure, small amounts go elsewhere.

01:51:26.060 | - And could there be a lot of harm caused

01:51:28.500 | if it goes elsewhere?

01:51:30.460 | Like, let's say ridiculously high quantities.

01:51:33.140 | I'm trying to understand what is the damage

01:51:35.500 | that could be done from an RNA just floating about.

01:51:39.420 | - So, the RNA itself is not gonna be a problem.

01:51:41.260 | It's the protein that is-- - The protein.

01:51:42.940 | - Encoded in it, right?

01:51:43.940 | This is a viral RNA, which has no sequence in us.

01:51:48.800 | So, there's nothing that it could do.

01:51:50.540 | It's the protein that I would say,

01:51:52.840 | you could ask, what is that gonna do?

01:51:56.160 | And the one property we know about the spike

01:52:00.600 | is that it can cause fusion of cells.

01:52:06.600 | That's how the virus gets in in the beginning.

01:52:09.280 | The spike attaches to the cell by this ACE2 receptor,

01:52:14.280 | and it causes the virus and the cell to fuse.

01:52:19.200 | And that's how the RNA gets out of the particle.

01:52:21.080 | - But so, wait, I'm a bit confused.

01:52:24.120 | So, with this mRNA vaccine with lipids and the RNA,

01:52:28.600 | there's no spike, right?

01:52:30.720 | - The mRNA codes for the spike.

01:52:33.160 | - Oh, the mRNA codes, so it creates the spike.

01:52:35.880 | - Creates a spike.

01:52:36.720 | - And so, that spike could cause fusion of cells.

01:52:39.320 | - Yes, except they modified the spike so it wouldn't.

01:52:43.060 | - Got it.

01:52:45.120 | - They made two amino acid changes in the spike

01:52:46.480 | so it would not fuse.

01:52:47.320 | - So, they understand enough which amino acids

01:52:49.600 | are responsible for the fusion.

01:52:51.080 | - That's right.

01:52:51.920 | - Interesting.

01:52:52.760 | This is so cool.

01:52:53.580 | - So, they modified, so now it's not gonna cause fusion,

01:52:55.600 | so that's not an issue.

01:52:57.200 | It's called the pre-fusion stabilized spike.

01:52:59.680 | - Cool.

01:53:01.880 | - So, the spike, when it binds ACE2,

01:53:04.200 | that top falls off and the spike,

01:53:06.840 | and the part of the spike that causes fusion is now exposed.

01:53:09.320 | And that doesn't happen in this mRNA vaccine.

01:53:12.040 | So, those are the things that could have happened,

01:53:14.940 | but I think they're ruled out by what we've just said.

01:53:18.380 | But there's no better test than putting it into people,

01:53:21.240 | right? - Right.

01:53:22.360 | - And doing phase one, phase two, and phase three,

01:53:25.120 | and increasing numbers of people,

01:53:26.840 | and asking, what do we see?

01:53:28.920 | Do we have any concerns?

01:53:30.920 | And so, now it's been in many millions of people,

01:53:35.320 | and we don't see, most of the effects you see in a vaccine,

01:53:40.320 | you see in the first couple of months.

01:53:43.240 | Things like the myocarditis with some of the vaccines,

01:53:46.140 | the clotting issues with the AstraZeneca vaccine,

01:53:49.400 | EMBRA, you see those relatively quickly.

01:53:53.540 | And we've seen small numbers of those occur,

01:53:58.320 | but other things we haven't seen,

01:54:01.480 | and you never say never, right?

01:54:04.840 | - Right, so, I mean, this is fascinating, right?

01:54:07.800 | It's like, I drink, I put Splenda in my coffee,

01:54:14.760 | and has supposedly no calories, but it tastes really good.

01:54:19.760 | And despite what, like, rumors, and blogs, and so on,

01:54:26.160 | I have not seen good medical evidence

01:54:28.920 | that it's harmful to you.

01:54:31.400 | But it's like, it tastes too good.

01:54:34.360 | So, I'm thinking, like,

01:54:35.440 | there's gotta be long-term consequences,

01:54:37.760 | but it's very difficult to understand

01:54:40.000 | what the long-term consequences are.

01:54:44.600 | And there's this kind of, like,

01:54:46.520 | distant fear or anxiety about it.

01:54:49.520 | Like, this thing tastes too good, it's too good to be true.

01:54:53.000 | There's gotta be, there's no free launch in this world.

01:54:55.680 | This is the kind of feeling that people have

01:54:57.760 | about the long-term effects of the vaccine.

01:55:00.280 | That you mentioned that there's some intuition

01:55:04.400 | about near-term effects that you want to remove,

01:55:08.960 | like, the diffusion of cells, and all those kinds of things.

01:55:11.480 | But they think, okay, this travels to other cells

01:55:14.280 | in the body, it travels to neurons, or that kind of stuff.

01:55:18.660 | And then, what kind of effect does that have long-term

01:55:20.680 | that's yet to be discovered?

01:55:22.760 | What do you make, I mean, for this vaccine,

01:55:25.120 | but in general, in science, about making statements

01:55:28.240 | about long-term negative effects?

01:55:32.240 | Is that something that weighs heavy on you?

01:55:34.480 | Is that something we can kind of escape

01:55:36.440 | through just large-scale experimentation

01:55:39.040 | with animals and humans?

01:55:41.300 | - Well, if you're really,

01:55:42.960 | if you're concerned about long-term,

01:55:44.240 | then you have to do a long-term experiment, right?

01:55:46.760 | And maybe you don't see something for 50, 60 years.

01:55:50.640 | So, if someone says to you, there are no long-term effects

01:55:54.600 | of the COVID vaccines, they can't say that

01:55:57.480 | because they haven't done the long experiment, right?

01:56:00.320 | There's always the possibility, but you have to weigh it.

01:56:02.880 | It's always, there's no free lunch, right?

01:56:06.160 | There's always a risk-benefit calculation you have to make.

01:56:10.360 | - You can have the study, it goes for 50 years,

01:56:12.520 | and then decide, but I guess what you're doing is,

01:56:15.780 | just like we said, I forget with which one,

01:56:20.120 | with polio, with rabies, I forget,

01:56:22.200 | but you're weighing the side effects of the vaccine

01:56:27.200 | versus the effects of the virus.

01:56:31.080 | And both of them, you don't know long-term effects,

01:56:34.540 | but you're building up intuition as you study,

01:56:38.600 | which, what are the long-term effects?

01:56:41.120 | Like, there's a huge number of people

01:56:45.560 | that have, I don't want to say experts

01:56:51.480 | 'cause I don't like the word,

01:56:52.400 | but people have studied it long enough

01:56:54.440 | to where they build up intuition.

01:56:56.280 | They don't know for sure.

01:56:57.760 | There's basic science being done, there's basic studies,

01:57:00.560 | but you start to build up an intuition

01:57:03.040 | of what might be a problem down the line

01:57:07.280 | and what is not, biologically speaking.

01:57:09.960 | And so, given that map, then considering the virus,

01:57:14.280 | there seems to be a lot of evidence for COVID

01:57:16.800 | having negative effects on all aspects of the body,

01:57:21.800 | and not just even respiratory,

01:57:23.360 | which is kind of interesting.

01:57:24.840 | So, the cognitive stuff, that's terrifying.

01:57:27.080 | - All kinds of systems evolve, yes.

01:57:28.960 | - And then you look at the same thing with the vaccine,

01:57:32.360 | and there seems to be less of that.

01:57:34.680 | But of course, you don't know

01:57:36.320 | if it's some kind of dormant thing that's just going to--

01:57:39.240 | - You won't know.

01:57:40.360 | You have to make a judgment,

01:57:42.560 | and for a lot of people, they can't, right?

01:57:44.880 | 'Cause they don't have the tools to make the judgment.

01:57:47.560 | I totally understand that.

01:57:49.320 | And we have let people down a few times in medicine, right?

01:57:54.320 | And I know two very specific examples.

01:57:59.080 | The first polio vaccine ever made,

01:58:01.880 | the Salk vaccine was released in 1955.

01:58:05.680 | Immediately, within months,

01:58:07.760 | few hundred cases of paralysis in kids who got it,

01:58:10.720 | because it was not properly inactivated.

01:58:14.160 | Now, you have to understand,

01:58:16.560 | parents were dying for a polio vaccine,

01:58:19.000 | 'cause kids were getting paralyzed every summer,

01:58:21.320 | 30,000 kids a year.

01:58:23.720 | And so, they went and took it.

01:58:26.080 | They took the word of the medical establishment

01:58:28.280 | that it was safe, and it wasn't.

01:58:30.840 | Big letdown.

01:58:32.000 | Never going to forget something.

01:58:33.200 | Although, I think a lot of people today

01:58:35.120 | aren't aware of that.

01:58:37.240 | I think that was a big problem that's everlasting.

01:58:39.400 | Then, the attenuated vaccine that we talked about,

01:58:43.360 | the infectious, causing polio.

01:58:46.880 | Yet, parents continued to bring their kids to be vaccinated,

01:58:51.080 | because they were said, "This is the right thing to do."

01:58:53.200 | And I have to say, I was involved in several lawsuits

01:58:55.920 | where parents of a kid who got paralyzed

01:58:59.360 | from the polio vaccine decided to sue the manufacturer

01:59:02.760 | and get some money for their kid.

01:59:05.600 | And so, they got mad.

01:59:08.000 | And I think you could not,

01:59:10.880 | the first issue could have been prevented,

01:59:15.500 | could have been prevented by inactivating it properly.

01:59:19.480 | I think the company just did the wrong thing.

01:59:22.160 | The second, we had evidence for,

01:59:24.240 | and we should probably have not used that vaccine any longer,

01:59:26.920 | but I think that destroys public confidence.

01:59:29.160 | - But those are-- - They're not long-term.

01:59:30.680 | - That's the minority of cases.

01:59:31.640 | - It's a minority, this is a very rare event, yeah.

01:59:33.440 | - But nevertheless, science as an institution

01:59:35.640 | didn't make corrections in that case.

01:59:40.080 | - No, they didn't.

01:59:40.920 | - And so, what do you make of that?

01:59:45.520 | I mean, it's very unfortunate

01:59:46.760 | that those few things can destroy trust.

01:59:49.500 | - But I don't think that lasts 'til today.

01:59:51.920 | I think today is a different era, right?

01:59:54.200 | - Yeah. - And most people

01:59:55.080 | don't know about those stories.

01:59:56.280 | I tell them to you because that's what could happen.

01:59:59.800 | I think it could happen today.

02:00:01.480 | - Yeah.

02:00:02.320 | - If you look at the history of the polio vaccine,

02:00:06.400 | the US Public Health Service wanted kids to be vaccinated.

02:00:11.680 | So they did things that probably weren't correct

02:00:14.360 | to get the vaccine back online, right?

02:00:16.640 | But they did it and they pushed it through.

02:00:19.080 | So the question is, what do we do today?

02:00:23.760 | So I can look at, as we just said,

02:00:27.400 | I can look at what might happen

02:00:29.200 | and I can make reasonable decisions

02:00:33.400 | about the likelihood of them happening.

02:00:35.160 | And I can also say, I don't wanna get COVID of any kind

02:00:39.040 | because I've seen how nasty it can be.

02:00:41.780 | And I decide I'm taking the risk,

02:00:44.120 | whatever small of a long-term effect,

02:00:46.120 | I'm gonna take the risk.

02:00:47.060 | My family took the risk and many other people did.

02:00:50.040 | - Of a vaccine.

02:00:50.940 | - Of getting vaccinated 'cause I think it's very small.

02:00:54.020 | But I understand where people can't make that decision.

02:00:56.960 | And that begs the question, what would they need

02:01:00.000 | to make a decision?

02:01:01.280 | So if you're concerned about an effect in 40 years,

02:01:05.420 | we're not gonna know for 40 years.

02:01:09.180 | - Yeah, so I think if I were to speak,

02:01:11.040 | 'cause I talked to, like I mentioned,

02:01:13.440 | offline to Joe Rogan and his podcast yesterday,

02:01:15.920 | I talked to him all the time about this.

02:01:18.760 | I think the concern is less about the long-term effects

02:01:23.760 | on paper, it's more about the,

02:01:28.760 | people like Anthony Fauci and people at the top

02:01:36.640 | are simply misrepresenting the data

02:01:39.120 | or are not accurately being transparent,

02:01:42.960 | not collecting the data properly,

02:01:45.000 | not reporting on the data properly, not being transparent,

02:01:48.280 | not representing the uncertainties,

02:01:50.360 | not openly saying they were wrong two months ago,

02:01:55.360 | like in a way that's not like dramatic,

02:01:58.080 | but revealing the basic process of science

02:02:00.960 | when you have to do your best under uncertainty,

02:02:03.720 | just also just being inauthentic.

02:02:06.040 | There's a sense, especially with like a younger generation

02:02:09.500 | now, there's a certain way on the internet,

02:02:11.680 | like the internet could smell bullshit

02:02:14.280 | much better than previous generations could.

02:02:16.880 | And so they see there's a kind of inauthenticity

02:02:21.720 | that comes with being, like representing authority.

02:02:26.440 | Like I am a scientist, I'm an expert, I have a PhD,

02:02:30.440 | I have four decades of work,

02:02:31.880 | therefore everyone should listen to me.

02:02:34.040 | And somehow that maps to this feeling of,

02:02:38.920 | well, what are they hiding?

02:02:40.700 | If they're speaking from authority like this,

02:02:43.080 | if everyone is in agreement like this,

02:02:45.680 | that means they all have emails between each other.

02:02:48.160 | They said, we're gonna tell this,

02:02:49.440 | this is the message we're gonna tell the public.

02:02:51.800 | Then what is the truth, the actual truth?

02:02:54.840 | Maybe there's a much bigger uncertainty.

02:02:57.200 | Maybe there's dead people in the basement

02:02:59.960 | that they're hiding from bad mRNA vaccine experiments.

02:03:03.840 | Maybe they're, and then the conspiracy theories

02:03:06.720 | start to grow naturally when there's this kind of mistrust

02:03:10.760 | of that.

02:03:11.840 | So it's less about kind of,

02:03:14.020 | like a deep concern about long-term effects.

02:03:18.980 | It's a concern about long-term effects

02:03:22.540 | if we find out that there's some secret stuff

02:03:27.100 | that we're not being told.

02:03:28.400 | It all lends on that.

02:03:30.040 | So what the heck, I mean,

02:03:31.540 | so I put the blame not on the data,

02:03:33.380 | but basically on the leaders

02:03:35.580 | and the communicators of the science at the top.

02:03:39.260 | - Well, to that, I would say all the data,

02:03:43.780 | as far as I know, are made public.

02:03:46.020 | So you can dive into it.

02:03:48.780 | And I know a lot of people ask me questions,

02:03:51.540 | and I just say, it's right here in the data.

02:03:54.180 | And I know a lot of people can't do that.

02:03:55.820 | They can't dive into it.

02:03:57.460 | But that's one solution for people who are able.

02:04:00.300 | Now, you could argue, well, maybe they've left data out.

02:04:03.460 | Well, then not even I can help

02:04:05.500 | because then they're hiding it from me too.

02:04:07.180 | And I think that's highly unlikely.

02:04:08.580 | I think for the most part,

02:04:09.940 | the FDA requires the release

02:04:11.860 | of all the clinical trial data, right?

02:04:14.260 | - So, okay, so this clinical trial data, that's one thing.

02:04:17.600 | So that's the data that we should be focusing on, right?

02:04:20.180 | So there's a lot of different data sets here.

02:04:24.180 | - So there's preclinical data,

02:04:25.700 | which is everything that was done in the lab

02:04:28.260 | before this vaccine ever went into a human arm.

02:04:30.900 | It's all the cell culture work

02:04:32.260 | that we talked about a little, experiments in animals.

02:04:35.900 | All of that is publicly accessible.

02:04:38.360 | Most of it gets published.

02:04:39.980 | And then there's the initial drug filing,

02:04:42.580 | which is huge, the books of diet.

02:04:45.100 | You can get that and look at it, right?

02:04:46.980 | - This is me sort of asking sort of difficult questions here.

02:04:49.980 | So there's a lot of money to be made by makers of the vaccine.

02:04:56.060 | So for these companies,

02:04:59.820 | obviously there's a distrust of those folks too.

02:05:02.500 | They've done a lot of really good things in this world,

02:05:04.580 | but the incentives are such

02:05:07.420 | that you want to sweep stuff under the rug

02:05:09.980 | if you're not 100% pure in your ethics.

02:05:14.980 | And how hard is it for that data to be fabricated,

02:05:20.460 | manipulated?

02:05:22.940 | Like what's your intuition for the pre-trial stuff?

02:05:26.140 | - I think when you start fabricating,

02:05:29.060 | then you get inconsistencies,

02:05:31.420 | which are pretty easy to pick up.

02:05:34.420 | - When you're talking about some large scale

02:05:36.420 | things of this nature.

02:05:38.020 | - Because then you can look through the data very,

02:05:40.980 | you're gonna, I mean,

02:05:41.860 | we require looking very carefully,

02:05:43.700 | but you'll see inconsistencies from one trial to another.

02:05:46.900 | And that may ring a bell that something's been done.

02:05:50.780 | - Yeah.

02:05:51.620 | It's like the moon landing thing.

02:05:55.180 | Sometimes like going to the moon is easier than faking.

02:05:58.080 | (laughs)

02:05:59.420 | - Right.

02:06:00.260 | - In the sense it might be easier to do large scale trial

02:06:03.420 | and get an effective vaccine versus faking it.

02:06:05.980 | - But you know, when you brought up the for-profit issue,

02:06:08.420 | I think that has always been an issue.

02:06:11.500 | I've always felt that having your health depend

02:06:15.860 | on for-profit industry may not be the best solution.

02:06:20.100 | And I don't know how else to do it.

02:06:23.260 | People tell me I'm a dreamer,

02:06:25.100 | thinking that all medicines could be non-profit.

02:06:28.340 | But I also think that the world should have

02:06:30.540 | one health system that takes care of everyone, right?

02:06:32.820 | Because there are some countries that can't

02:06:34.780 | and other countries have an excess like us.

02:06:37.620 | So I wish we could do that.

02:06:40.260 | - Well, the argument is the speed of which the vaccines

02:06:44.220 | for COVID were produced would never happen

02:06:47.500 | in a non-profit system,

02:06:49.260 | would never happen in a non-capitalist system.

02:06:51.740 | - Oh, I could set up a vaccine production institute

02:06:56.140 | in the US that would get the vaccines done

02:06:58.940 | because you just need to put money into it.

02:07:00.940 | That's what made these vaccines get done, money.

02:07:03.620 | They poured billions of dollars

02:07:05.180 | and they got it done quickly.

02:07:06.980 | But if I set up a non-profit institutes of vaccines

02:07:09.760 | throughout the US, staffed with really talented people,

02:07:12.580 | pay them well, keep them motivated,

02:07:15.060 | you'll get your vaccines.

02:07:15.900 | - No, but that's the thing with capitalism

02:07:18.540 | is that the selection of who to hire,

02:07:22.900 | when you say good people,

02:07:24.980 | capitalism has a machine that fires people

02:07:28.180 | who are not good and selects people that are good.

02:07:30.980 | Coming from the Soviet Union,

02:07:32.740 | the dream of communism is similar to what you're saying,

02:07:36.500 | broadly defined.

02:07:37.380 | It certainly doesn't work in the broad,

02:07:40.060 | the question of whether it works in the healthcare space.

02:07:44.860 | There is some aspect to the machine of capitalism

02:07:49.540 | being the most effective way to select for good people

02:07:53.380 | to effectively produce the thing.

02:07:55.220 | But then of course, a lot of people would argue

02:07:59.020 | even the current healthcare is not,

02:08:01.180 | with regulations, there's some weird mix

02:08:03.660 | where there's a lot of opportunities for inefficiencies,

02:08:07.100 | there's a lot of opportunities for bureaucracy.

02:08:09.020 | So you have the worst of all worlds.

02:08:11.860 | - Can't there be some intermediate that works?

02:08:14.180 | I mean, the other issue that we haven't mentioned

02:08:17.300 | is that politics gets thrown into this

02:08:19.260 | and that really messes up

02:08:21.420 | and it should never be mixed with healthcare,

02:08:23.020 | but it is because a lot of funding comes from the government

02:08:26.860 | so that's another confounding factor.

02:08:29.340 | But I really think I could make a vaccine institute

02:08:33.700 | that if someone didn't do well, I'd fire them.

02:08:36.660 | No, you're not gonna stay if you can't do your job

02:08:39.100 | and do it well, you don't give them incentives,

02:08:41.380 | but it doesn't have to be the two extremes, I think.

02:08:44.700 | There has to be a solution that people don't have

02:08:47.740 | this mistrust for a company making huge profits

02:08:51.580 | off of a drug.

02:08:52.460 | But you know what, it's funny.

02:08:54.120 | It seems that vaccines and antivirals

02:08:57.500 | bear the brunt of this criticism,

02:08:59.540 | yet there are many other pharmaceuticals

02:09:01.340 | that people rely on of all sorts.

02:09:04.340 | They don't seem to question and have issues with those

02:09:06.740 | and they have far more side effects than vaccines.

02:09:09.180 | - It's very strange how we're picking that way,

02:09:11.900 | but I should also say that if you have one big

02:09:16.900 | vaccine institute, one of the other sets of

02:09:24.120 | vaccine conspiracies, I mean, I would say they're

02:09:28.400 | a little farther out into the wild set of ideas,

02:09:32.440 | but that's one way to control the populace

02:09:37.200 | is by injecting substances into them.

02:09:40.300 | People, I mean, part of that, funny enough,

02:09:44.840 | it probably has to do with needles

02:09:46.160 | versus something you put in your mouth.

02:09:48.960 | But there's something about the government,

02:09:50.820 | especially when it's government mandated,

02:09:52.880 | injection of a substance into you.

02:09:54.900 | I don't care what the science says,

02:09:57.800 | if it's 100% effective, 100% safe,

02:10:00.960 | there's a natural distrust of what,

02:10:03.980 | like even if this is effective and safe,

02:10:07.660 | giving the government power to do this,

02:10:11.960 | aren't they gonna start getting ideas down the line for,

02:10:15.080 | you know?

02:10:15.920 | - I think that they can barely govern.

02:10:20.760 | I don't think they're gonna do that,

02:10:22.240 | but you don't have to take, unless you're a federal employee,

02:10:26.000 | you don't have to take a COVID vaccine.

02:10:27.920 | - Yeah, but that largely has to do,

02:10:31.160 | not largely, but there is an individualistic spirit

02:10:36.520 | to the American people.

02:10:41.400 | There's this, like, you're not gonna take my gun away

02:10:44.400 | from me, you're not going, and I think that,

02:10:51.360 | that's something that makes America what it is.

02:10:56.080 | Just coming from the Soviet Union,

02:10:57.500 | there's a power to sort of resisting

02:10:59.940 | the overreach of government.

02:11:01.720 | That's quite interesting, 'cause I'm a believer,

02:11:04.440 | I hope that it's possible to have,

02:11:08.040 | to strive towards a government that works extremely well.

02:11:11.600 | I think at its best, a government represents the people

02:11:14.840 | and functions in a similar way that you're mentioning.

02:11:17.880 | But that, like, pushback,

02:11:20.160 | even if it turns into conspiracy theory sometimes,

02:11:22.600 | I think is actually healthy in the long arc of history.

02:11:25.960 | It can be frustrating sometimes,

02:11:28.100 | but that mechanism of pushing back against power,

02:11:30.920 | against authority, can be healthy.

02:11:33.000 | - I agree, I think it's fine to question the vaccines.

02:11:36.520 | What I have issue with is that many people

02:11:39.040 | put out incorrect information,

02:11:43.180 | and I'm not sure what their motivations are,

02:11:45.360 | and it's very hard to fight that,

02:11:47.020 | because then it's my word versus theirs,

02:11:50.080 | and I'm happy to talk with people

02:11:52.320 | about any of their concerns,

02:11:54.320 | but if you start getting into the stuff

02:11:56.720 | that just isn't true, then we have a problem.

02:11:59.240 | - The thing I struggle with is conspiracy theories,

02:12:03.320 | whatever language you wanna use,

02:12:04.680 | but sort of ideas that challenge

02:12:09.360 | the mainstream quote-unquote narrative.

02:12:12.100 | Given our current social media and internet,

02:12:16.160 | like, the way it operates,

02:12:17.840 | they can become viral much easier.

02:12:20.320 | There's something much more compelling about them.

02:12:22.720 | Like, I have a secret about the way things really work.

02:12:27.720 | That becomes viral, and that's very frustrating,

02:12:30.200 | because then you're not having a conversation

02:12:32.440 | on level ground.

02:12:33.420 | When you're trying to present scientific ideas,

02:12:38.280 | and then there's conspiracy theories,

02:12:39.840 | the conspiracy theories become much viral much faster,

02:12:42.800 | and then you're not just having a discussion

02:12:44.840 | on level ground.

02:12:47.720 | That's the frustrating part,

02:12:49.160 | that it's not an even discussion.

02:12:51.880 | - Can I just say one more thing?

02:12:53.560 | I mean, the internet is here to stay,

02:12:54.840 | so we're gonna have to figure out

02:12:56.160 | how to deal with it, right?

02:12:57.280 | But from my perspective,

02:12:59.320 | I was skeptical that any COVID vaccine

02:13:03.020 | would be ready within a year.

02:13:05.880 | That's amazing. - Me too.

02:13:07.000 | - Plus, the way I look at the mRNA vaccine as a scientist,

02:13:12.000 | it's gee whiz to me.

02:13:13.920 | It's amazing that it worked,

02:13:15.560 | and I think the data are great, so I want it.

02:13:18.660 | As a scientist, I want it.

02:13:21.240 | - One of the really sad things, again, with me too,

02:13:23.880 | as a scientist or as an admirer of science,

02:13:26.940 | I don't know if it's politics,

02:13:31.240 | but one of the sad things to me about the previous year

02:13:34.940 | is that I wasn't free to celebrate

02:13:38.440 | the incredible accomplishment of science with the vaccines.

02:13:42.360 | I was very skeptical that it's possible

02:13:44.160 | to develop a vaccine so quickly.

02:13:47.660 | So it's unfortunate that we can't celebrate

02:13:50.720 | how amazing humans are to come up with this vaccine.

02:13:54.100 | Now, this vaccine might have long-term effects.

02:13:57.280 | That doesn't mean this is not incredible.

02:13:59.400 | - Why couldn't you celebrate?

02:14:04.300 | - Because I would love to inspire the world

02:14:08.620 | with the amazing things science can do.

02:14:11.320 | And when you say something about the vaccines,

02:14:14.000 | they're not listening to the science.

02:14:15.640 | A lot of people are not listening to the science.

02:14:17.440 | What they hear is, oh, you're a Republican

02:14:22.440 | or you're a Democrat, and you're social signaling,

02:14:25.240 | doing some kind of signaling.

02:14:26.680 | - No, I think that the vaccine you're talking about

02:14:28.520 | is injecting something into you,

02:14:30.640 | and maybe you're right that the rhetoric is like,

02:14:33.720 | you better take this or you're dumb.

02:14:36.120 | It's not the right approach.

02:14:38.480 | - I've seen, actually, it's kind of interesting.

02:14:40.200 | I've seen both sides kind of imply that.

02:14:42.960 | So the people who are against the vaccine

02:14:47.800 | are dumb for not trusting science,

02:14:52.360 | and the people who are for the vaccine

02:14:55.680 | are called dumb for trusting science,

02:14:59.360 | the scientific institutions. - And nobody wins, yeah.

02:15:01.840 | - And they both kind of have a point.

02:15:03.960 | 'Cause you can always, it's like,

02:15:08.360 | is the glass half full or half empty?

02:15:10.280 | Because you can always look at science

02:15:15.280 | from a perspective of certain individuals

02:15:18.160 | that don't represent, perhaps, not greatest leaders,

02:15:23.160 | almost like political leaders.

02:15:25.360 | There's a lot of, yesterday I went on a whole rant.

02:15:29.720 | Again, I said a lot of positive things

02:15:32.920 | about Anthony Fauci before I went on a rant against him.

02:15:37.440 | 'Cause ultimately, I think he failed as a leader,

02:15:42.000 | and I know it's very difficult to be a leader,

02:15:44.280 | but I still wanted to hold him accountable for that

02:15:47.360 | as a great communicator of science and as a great leader.

02:15:50.440 | - But what do you think he didn't do right?

02:15:52.280 | I'm curious.

02:15:53.120 | - So the core of the problem is the several characteristics

02:16:00.040 | of the way he was communicating to the public.

02:16:06.540 | So one is the general inauthenticity.

02:16:09.120 | Two is a thing that, it's very hard to put into words,

02:16:14.480 | but there's certain ways of speaking to people

02:16:18.680 | that sounds like you're hiding something from them.

02:16:21.720 | That sounds like you're full of shit.

02:16:23.960 | That's the authenticity piece.

02:16:25.480 | Like it sounds like you're not really speaking

02:16:30.480 | to the full truth of what you know,

02:16:34.480 | and that you did some shady shit in your past

02:16:38.420 | that you're trying to hide.

02:16:40.860 | So that's a way of communicating

02:16:42.820 | that I think the internet and people in general

02:16:45.020 | are becoming much better at detecting.

02:16:46.740 | - Yeah, it's like you said, they're good BS detectors.

02:16:48.620 | - Yeah, good BS detectors.

02:16:50.260 | But contributing to that is speaking from authority.

02:16:54.840 | Speaking with authority and confidence

02:17:01.540 | where neither is deserved.

02:17:04.480 | So first of all, nobody's an authority on this new virus.

02:17:09.480 | We're facing a deadly pandemic,

02:17:12.200 | and especially in the early stages,

02:17:15.240 | it was unclear how deadly it would be.

02:17:17.560 | It was unclear, probably still unclear,

02:17:19.640 | fully how it's transmitted.

02:17:22.120 | The full dynamics of the virus,

02:17:24.400 | the full understanding of which solutions work and not,

02:17:28.520 | how well masks of different kinds work,

02:17:31.200 | how easy or difficult it is to create tests,

02:17:34.260 | how many months or years it's gonna take

02:17:36.480 | to create a vaccine, how well in history

02:17:40.480 | or currently do quarantine methods

02:17:43.120 | or lockdown methods work.

02:17:44.680 | What are the different data mechanisms

02:17:48.080 | that are, data collection mechanisms

02:17:50.720 | that are being implemented?

02:17:52.080 | What are the clear plans that need to happen?

02:17:55.680 | What the epidemiology that's happening?

02:17:58.380 | What is the uncertainty around that?

02:18:00.700 | Then there's the geopolitical stuff with China.

02:18:05.700 | You know, like what, I personally believe

02:18:10.820 | there should have been much more openness

02:18:12.640 | about the origins of the virus,

02:18:15.320 | whether a leak from a lab or not.

02:18:17.140 | I think communicating that you're open to these ideas

02:18:21.200 | is actually the way to get people to trust you,

02:18:25.380 | that you're legitimately open to ideas

02:18:28.220 | that are very unpleasant, that go against the mainstream.

02:18:31.280 | Showing that openness is going to get people to trust you

02:18:35.760 | when you finally decrease the variance in your uncertainty,

02:18:40.760 | like decrease uncertainty and have,

02:18:42.840 | we still have a lot of uncertainty,

02:18:44.420 | but this is the best course of action.

02:18:46.480 | Vaccines still have a lot of uncertainty around them.

02:18:49.680 | mRNA is a new technology,

02:18:51.280 | but we have increasing amounts of data

02:18:53.340 | and here's the data sources

02:18:54.980 | and like laying them out in a very clear way

02:18:58.760 | of this is the best course of action that we have now.

02:19:01.300 | We don't know if it's the perfect course of action,

02:19:04.100 | but it's by far the best course of action.

02:19:06.500 | And that would come from a leader

02:19:09.480 | that has earned the capital of trust from people.

02:19:13.100 | - I mean, I think in recent history,

02:19:15.920 | the worst pandemic is 1918 flu, right?

02:19:19.780 | But that's mainly 'cause we didn't know what to do.

02:19:22.580 | We didn't have many tools at our disposal.

02:19:24.460 | - And that was tied up with World War I.

02:19:26.500 | - That's right, that's right.

02:19:27.980 | - So the leadership there, I mean--

02:19:30.380 | - But I don't know what is a lot of deaths, right?

02:19:32.900 | And any one person is someone's family,

02:19:35.500 | so to them it's a lot, right?

02:19:37.500 | - But that logic, we don't apply that logic generally

02:19:41.820 | because there's a lot of people suffering

02:19:43.480 | and dying throughout the world

02:19:44.940 | and we turn the other way all the time.

02:19:47.860 | And that's the story of history.

02:19:49.220 | So saying you all of a sudden--

02:19:51.660 | - What bothers me though, I mean,

02:19:53.180 | personally, I don't like anyone dying anywhere,

02:19:56.300 | but especially considering what technology

02:20:00.220 | we're able to muster, yet we still kill each other.

02:20:02.460 | It's just dichotomy to me.

02:20:04.620 | - Yeah, but I mean, this is the, what is it, Paul Farmer?

02:20:07.800 | There's these great stories.

02:20:10.260 | I mean, that's the burden of being in healthcare,

02:20:15.260 | being a doctor, is you have to help.

02:20:23.100 | You can't help but help a person in front of you

02:20:25.300 | who's hurting.

02:20:26.140 | - Sure, sure.

02:20:26.980 | - But you also are burdened by the knowledge

02:20:30.100 | that you helping them, you spending money and effort

02:20:33.260 | and time on them means you're not going to help others.

02:20:36.900 | And you cannot possibly allocate

02:20:38.540 | that amount of time to everybody.

02:20:40.240 | So you're choosing which person lives

02:20:42.220 | and which person dies.

02:20:43.700 | - Sure.

02:20:44.540 | - And you're doing so, the reason you're helping

02:20:46.460 | the person in front of you

02:20:47.740 | is because they're in front of you.

02:20:49.500 | And so the reason right now we care a lot about COVID

02:20:53.260 | is because the eye of the world has turned to COVID,

02:20:56.620 | but we're not seeing all the other atrocities

02:20:59.280 | going on in the world.

02:21:00.460 | They're not necessarily related to deaths,

02:21:02.780 | they're related to suffering, human suffering,

02:21:05.260 | which you could argue is worse than death,

02:21:07.380 | prolonged suffering.

02:21:08.660 | - Of course.

02:21:09.500 | - So there's all of these questions.

02:21:11.620 | And the fundamental question here is,

02:21:15.740 | are we overreacting to COVID in our policies?

02:21:19.380 | So this is the, when we turn our eye

02:21:23.740 | and care about this particular thing and not other things,

02:21:26.940 | are we dismissing the pain that business owners

02:21:29.220 | who've lost their businesses are going to feel?

02:21:31.700 | And then the long, talking about long COVID,

02:21:35.740 | the long-term effects, economic effects

02:21:38.580 | on the millions of people that will suffer,

02:21:40.900 | that suffer financially, but also suffer from their dreams

02:21:44.900 | being completely collapsed.

02:21:46.740 | So a lot of people seek, gain meaning from work.

02:21:50.740 | And if you take away that work,

02:21:52.820 | there's anger that can be born, there's pain.

02:21:55.700 | And so what does that lead to?

02:21:57.180 | That can lead to the rising up of charismatic leaders

02:22:01.860 | that channel that anger towards destructive things

02:22:05.100 | that's been done throughout history.

02:22:06.700 | So you have to balance that with the policies

02:22:10.220 | that you have in COVID.

02:22:12.060 | And then, I mean, very much my main opposition

02:22:16.220 | to Fauci is not on the details, but the final result,

02:22:20.700 | which is, I just observe that there's a significant decrease

02:22:25.020 | in trust in science as a, not the institution,

02:22:30.020 | but the various sort of mechanisms of science.

02:22:32.700 | I think science is both beautiful and powerful.

02:22:35.860 | And the reason why we have so many amazing things

02:22:38.220 | and such a high quality of life and distrust in that,

02:22:42.580 | that the thing we need now to get out

02:22:44.540 | of all the troubles we're in,

02:22:46.580 | continue getting out of the troubles we're in,

02:22:48.580 | is science, the scientific process, broadly defined,

02:22:51.860 | like innovation, technological innovation,

02:22:54.380 | scientific innovation, all of that.

02:22:56.460 | Distrust in that is totally the wrong thing we need.

02:23:02.380 | And so anybody who causes a distrust in science, to me,

02:23:07.460 | carries the responsibility of that.

02:23:14.220 | And should be, because of the responsibility,

02:23:17.060 | I mean, should be fired, should be, or at least openly

02:23:21.460 | have to carry the burden of that,

02:23:24.380 | of having caused that kind of level of mistrust.

02:23:27.420 | Now, it's maybe unfair to place it on any one individual,

02:23:30.460 | but you have to, I think in your pockets,

02:23:33.940 | the buck stops at the top, like the leaders have to-

02:23:37.180 | - Sure, no, no, there's a clear leader here, yes,

02:23:40.060 | absolutely.

02:23:40.980 | - So even if it's not directly his fault,

02:23:43.700 | you know, he has to carry the price of that.

02:23:48.340 | - Do you think we should, at this point, say,

02:23:50.900 | okay, we have vaccines, you can decide

02:23:54.340 | whether you take 'em or not, let's move forward?

02:23:57.420 | - Maybe you can help me understand this,

02:23:59.180 | because it seems like, why is that not the right solution?

02:24:04.180 | Completely open society, the vaccines,

02:24:07.260 | at least in the United States, as I understand,

02:24:11.180 | are widely available, so this is the American way,

02:24:16.180 | you have the decision to make.

02:24:18.380 | If you have conditions that make you worried to get COVID

02:24:23.300 | and go to the hospital, then you should get vaccinated,

02:24:26.200 | because here's the data that shows that it's much less

02:24:29.500 | likely for you to die, right, if you get vaccinated.

02:24:34.280 | If you don't want to get vaccinated because you're worried

02:24:36.620 | about long-term effects of vaccine, then you don't have to,

02:24:40.360 | but then you suffer the consequences of that, and that's it.

02:24:44.460 | - So here's what I think is driving,

02:24:47.280 | I think it's all about kids, right,

02:24:50.260 | because they're gonna go back to school in the fall,

02:24:51.980 | and many of them can't be vaccinated, right,

02:24:54.000 | so if they get infected, they do have less frequency

02:24:59.000 | of disease, but it's not zero.

02:25:01.260 | They do get sick, and they can have long-term consequences,

02:25:04.700 | and at that age, it would be a shame, right,

02:25:09.340 | and it's not even their choice.

02:25:10.640 | They can't decide to get vaccinated or not,

02:25:13.480 | because they can't have access to it,

02:25:15.220 | so I think that's what would drive my efforts

02:25:19.380 | to try and get more people, at least in schools, vaccinated,

02:25:22.540 | but I might be wrong.

02:25:23.780 | It may not be that.

02:25:24.940 | - Can you kind of dig into that a little bit?

02:25:26.540 | So there's,

02:25:27.380 | so you're saying that there should be an effort

02:25:33.260 | for increased vaccinations of kids going to school,

02:25:37.620 | just not for societal benefit,

02:25:39.860 | but for the benefit of each individual kid, right?

02:25:42.220 | - So right now, kids under 12, right,

02:25:46.100 | are not yet vaccinated.

02:25:47.580 | Is that correct?

02:25:48.500 | - Yeah, I think so.

02:25:49.900 | - And it's not gonna be in time for school opening,

02:25:53.420 | that they get vaccinated,

02:25:54.780 | and then, I suppose the teachers

02:25:59.700 | are all gonna be vaccinated.

02:26:01.380 | Makes sense for them to do that,

02:26:02.800 | but I'm just worried the kids are gonna be transmitting it

02:26:05.360 | amongst them, and many states don't allow

02:26:07.480 | a mask mandate in school,

02:26:08.920 | so I think that's what's driving the larger narrative

02:26:13.920 | in the US to protect kids.

02:26:16.760 | It's kind of what I hear from Daniel Griffin,

02:26:19.840 | 'cause increasing numbers of kids

02:26:21.680 | are being admitted to hospitals now,

02:26:24.040 | because they're becoming the major unvaccinated population.

02:26:28.320 | They're hanging out over the summer,

02:26:29.680 | and that's just gonna get worse in the fall,

02:26:32.320 | and so you could have a lot of kids with long COVID

02:26:35.440 | and disabled their entire lives, right?

02:26:38.360 | - And of course, hearing from people

02:26:40.320 | who are vaccine-hesitant,

02:26:42.160 | I hear exactly the kids' statement,

02:26:44.900 | but they're saying they don't want the long vaccine,

02:26:49.900 | the long-term effects of the vaccine to affect the kids.

02:26:53.280 | That's of this new vaccine.

02:26:56.080 | - Which I would say is, as I said before,

02:26:58.700 | you can't say never,

02:27:00.720 | but we do know that long COVID exists.

02:27:05.720 | We don't know for how long,

02:27:06.920 | 'cause we've only looked out six or eight months.

02:27:09.360 | We know that exists, and the frequency is increasing.

02:27:12.480 | It certainly exists in young kids,

02:27:14.320 | and we have no idea about long vaccine effects,

02:27:16.640 | so I think they have to make their decision based on that.

02:27:20.700 | - But, yeah.

02:27:24.480 | - But your question is, why don't we just open up society,

02:27:28.240 | say, "Here, we have these vaccines.

02:27:29.560 | "If you want to protect yourself."

02:27:30.840 | I think it's mainly the school

02:27:32.200 | that's driving the whole narrative.

02:27:34.380 | That's my opinion.

02:27:35.360 | - In which direction, not to open up, or?

02:27:37.800 | - No, to open up, but to try and get,

02:27:40.580 | you know, their efforts at the federal level

02:27:42.340 | to get people vaccinated, right?

02:27:44.000 | - But see, how high are the risks for kids?

02:27:46.000 | I mean, my understanding was it's,

02:27:48.140 | I mean, yes, it's non-zero, but it's very low.

02:27:50.580 | - But what is the numbers?

02:27:53.680 | Now, 70,000 hospitalizations so far in kids

02:27:58.400 | as of last week, so yes, it's low,

02:28:01.120 | but polio was low.

02:28:05.480 | Polio was 20, 30,000 kids a year paralyzed,

02:28:08.840 | and many people have actually argued

02:28:11.560 | that that vaccine wasn't necessary, you know?

02:28:13.920 | That it wasn't a substantial enough health problem.

02:28:17.240 | - But paralyzed is different than hospitalized,

02:28:19.040 | so what does hospitalized mean?

02:28:20.920 | - Long COVID.

02:28:21.760 | - But this is the long COVID question.

02:28:23.080 | I mean, this is the open question.

02:28:24.560 | It was long COVID in kids.

02:28:26.440 | What is that?

02:28:27.280 | Well, a lot of the same issues,

02:28:30.680 | cognitive issues, motor issues,

02:28:34.200 | respiratory, GI dysfunction.

02:28:37.720 | How long?

02:28:39.280 | We don't know.

02:28:40.120 | I mean, it could end in a year.

02:28:43.240 | As you know, there are other post-acute

02:28:46.080 | infectious sequelae that we know about,

02:28:48.200 | you know, chronic fatigue, ME/CFS.

02:28:51.040 | It's thought to be a post-infectious sequelae,

02:28:53.240 | which has gone for many decades now

02:28:55.160 | in many millions of people.

02:28:56.560 | This could be another one of those.

02:28:58.720 | So I'm just saying it might be worth erring on the side

02:29:02.720 | of not letting the kids get infected.

02:29:05.880 | - Yeah, well, I'm trying to keep an open mind here,

02:29:09.880 | and I appreciate you doing the same.

02:29:12.080 | Of course, I lean on definitely not requiring people

02:29:17.080 | to get vaccinated, but I do think getting vaccinated

02:29:21.520 | is just the wiser choice,

02:29:24.840 | looking at all the different trajectories before us.

02:29:28.440 | Getting vaccinated is,

02:29:29.920 | seems like from the data,

02:29:33.200 | it seems like the obvious choice, frankly.

02:29:35.520 | But I'm also trying to keep an open mind.

02:29:38.120 | There's some things in the past that seemed obvious

02:29:40.120 | would turn out to be completely wrong.

02:29:42.080 | So I'm trying to keep an open mind here.

02:29:44.840 | So for example, one of the things,

02:29:48.080 | I'd love to get your thoughts on this,

02:29:50.200 | is antiviral ideas.

02:29:52.960 | So ideas outside of the vaccine.

02:29:55.000 | So ivermectin, something that Brett Weinstein

02:30:00.400 | and a few others have been talking about.

02:30:02.120 | There's been a few studies.

02:30:03.200 | Some of them have been shown not to be very good studies,

02:30:07.160 | but nevertheless, there seems to be some promise.

02:30:11.480 | And I wanted to talk to Brett about this particular topic

02:30:16.480 | for two reasons.

02:30:17.400 | One, I was really bothered by censorship of this.

02:30:19.980 | That's a whole nother topic.

02:30:22.080 | I just, I'm bothered by censorship.

02:30:25.960 | There's a gray area, of course,

02:30:27.520 | but it just feels like that should not have been censored

02:30:32.200 | from YouTube, like discussions of ivermectin.

02:30:34.920 | We can set that aside.

02:30:36.760 | The other thing, I was bothered by the lack

02:30:39.760 | of open-mindedness on exploring things like ivermectin

02:30:44.760 | in the early days, especially when,

02:30:48.040 | at least I thought the vaccine would take a long time.

02:30:51.680 | I mean, it's not just ivermectin.

02:30:53.160 | It's really seriously, at a large scale,

02:30:57.500 | rigorously exploring the effectiveness of masks.

02:31:01.360 | And the big one for me is testing.

02:31:03.700 | Like the fact that that wasn't explored aggressively

02:31:07.040 | to lead to mass manufacturing, like May 2020, is absurd.

02:31:11.500 | Anyway, so I was bothered by these solutions

02:31:14.800 | not being explored and not,

02:31:16.280 | by now having really good ivermectin studies.

02:31:19.400 | - So can I talk about ivermectin?

02:31:20.960 | - Yeah, I would love that, yeah.

02:31:21.800 | - Sure, so full disclosure,

02:31:23.040 | my wife worked on ivermectin at Merck for 20 years.

02:31:26.620 | (Lex laughing)

02:31:27.680 | Okay, so they just want people to know,

02:31:30.920 | but I didn't, don't talk to her all the time about it.

02:31:34.560 | And anyway, she hasn't been at Merck for a long time.

02:31:37.240 | As you know, ivermectin is a very safe drug

02:31:39.880 | used to treat certain parasitic infections, right?

02:31:43.600 | And it is approved, it's amazing.

02:31:47.320 | You can take one dose a year

02:31:48.680 | and be protected against river blindness in Africa,

02:31:51.680 | in certain parts of Africa.

02:31:52.720 | It's remarkably effective.

02:31:54.640 | And so it's quite a safe drug

02:31:57.800 | at the doses that are approved.

02:32:01.240 | Now, early last year, a study was done,

02:32:04.480 | I believe in Australia, which showed in cells in the lab,

02:32:07.360 | if you infect with SARS-CoV-2 and then put ivermectin in,

02:32:11.120 | it would inhibit the virus production substantially.

02:32:13.840 | It was quite clear, right?

02:32:16.020 | But the concentrations they were using were rather high

02:32:19.640 | and could not be achieved by the approved dosing.

02:32:24.640 | So you would need to do a dosing study

02:32:27.840 | to make sure it's safe.

02:32:29.000 | And the reason is that ivermectin

02:32:31.000 | binds to receptors in your brain,

02:32:32.640 | and it can have high doses.

02:32:34.680 | Some people take high doses inappropriately,

02:32:37.520 | and they have neurological consequences.

02:32:39.680 | So if you needed 10 times more ivermectin,

02:32:42.680 | you'd have to make sure it would be safe.

02:32:44.920 | - So this is a question of safety too.

02:32:46.480 | - Right.

02:32:47.320 | So I think it has always been the case

02:32:51.320 | that it should have been properly studied, but it wasn't.

02:32:54.580 | There were lots of trials here and there,

02:32:56.100 | lots of improperly controlled trials

02:32:58.680 | where someone would just treat some patients and say,

02:33:00.920 | "Hey, they all did fine, but have no control arm."

02:33:03.760 | And there were some controlled trials,

02:33:05.240 | but they were very small.

02:33:06.480 | So right now, a 4,000 person trial is enrolling

02:33:12.640 | to test in a randomly controlled trial setting,

02:33:16.640 | whether it works or not.

02:33:17.800 | There's still plenty of cases that you can do that.

02:33:20.300 | So you can ask whether there are any side effects.

02:33:23.040 | I think that's completely fine.

02:33:24.960 | And if it says it works, then we should use it.

02:33:28.480 | In the meantime, I always tell people,

02:33:31.320 | if you wanna use ivermectin, you can do it off-label.

02:33:33.800 | It's FDA approved.

02:33:35.640 | And if your physician says,

02:33:36.660 | "I'm gonna give you this off-label,"

02:33:38.720 | I don't have any objection,

02:33:41.520 | but I don't know if it's gonna work.

02:33:43.800 | Now, a friend of ours last week in New Jersey got COVID.

02:33:48.800 | He went to his local hospital

02:33:50.560 | and their regimen was remdesivir, dexamethasone, ivermectin.

02:33:55.560 | It's written, that's what they do for every COVID patient.

02:33:59.180 | They just give it to them automatically.

02:34:01.200 | And so he recovered.

02:34:04.360 | So who's to say it was or was not ivermectin, right?

02:34:08.360 | So I don't have any strong ideological opposition.

02:34:12.840 | I just think it should be tested

02:34:14.640 | for what you wanna use it for.

02:34:16.720 | And that's being done, and I think that's fine.

02:34:19.280 | - Is it strange to you that ivermectin

02:34:23.520 | or other things like it weren't tested

02:34:25.640 | aggressively in the beginning?

02:34:27.680 | From a broad scientific community aspect,

02:34:32.680 | I can be a little bit conspiratorial,

02:34:36.000 | and this is what people talk about with ivermectin,

02:34:39.080 | is with the vaccines, there's quite a lot of money

02:34:41.360 | to be made.

02:34:42.680 | With ivermectin, there's not as much money to be made.

02:34:45.640 | Is that too conspiratorial?

02:34:48.400 | Like, why didn't we try more solutions in the beginning?

02:34:51.520 | - Well, all the money was put into vaccines, right?

02:34:55.560 | Very little was put into antivirals.

02:34:57.360 | Because the decision was made at a very high level,

02:34:59.520 | probably involving Dr. Fauci.

02:35:01.600 | We're gonna put 24 billion into vaccines, right?

02:35:04.720 | - Yeah.

02:35:06.280 | - And I think part of the reasoning is

02:35:08.080 | they give you years worth of protection,

02:35:10.160 | whereas an antiviral works,

02:35:11.440 | and you have to keep dosing and so forth.

02:35:13.280 | But ivermectin is not trivial in this.

02:35:16.040 | I agree, it should have been tested early on,

02:35:18.640 | but we had a really bad experience with hydroxychloroquine,

02:35:21.880 | which we can talk about too.

02:35:23.900 | Ivermectin is very hard to synthesize.

02:35:28.780 | Most drugs you synthesize chemically.

02:35:31.640 | You devise a formulation and a synthesis,

02:35:34.720 | and they do it, they scale it up, and it's fine.

02:35:36.680 | Ivermectin's really hard.

02:35:38.360 | And so what they do instead is they take the culture

02:35:41.560 | of the bacterium that makes it,

02:35:43.880 | and they grow it up, and they ferment it,

02:35:45.440 | and then they purify it.

02:35:47.240 | And Merck owns the bacteria.

02:35:50.160 | A number of years ago, two employees of Merck stole it

02:35:55.460 | and left the company and tried to market it,

02:35:58.200 | and they were arrested, and they got put in jail.

02:36:00.000 | So they protect it very carefully.

02:36:02.760 | So you can't just make it.

02:36:05.680 | If you do, it's incredibly expensive.

02:36:07.680 | And now India, it's very cheap apparently.

02:36:10.160 | They use it quite liberally there.

02:36:12.560 | And I don't know how they're making it.

02:36:14.440 | Maybe they've licensed it from Merck and so forth.

02:36:16.640 | But that's why it hasn't been tested more widely, I think.

02:36:21.640 | - There's complexities in terms of getting a lot of it

02:36:24.120 | and manufacturing a lot of it.

02:36:25.400 | - Yes. - Okay.

02:36:26.400 | So what was the other, the hydro--

02:36:28.360 | - Hydroxychloroquine was also shown early on

02:36:32.280 | to inhibit virus in cell culture.

02:36:34.400 | And that's not surprising.

02:36:37.360 | Hydroxychloroquine, of course, is used for malaria.

02:36:41.280 | And what it does, when your cell takes up things

02:36:46.280 | from the plasma membrane, including viruses,

02:36:49.640 | it goes through a pathway called the endocytic pathway,

02:36:52.120 | which involves a vesicle moving through the cell.

02:36:54.080 | And as it moves through the cell, its pH drops.

02:36:57.600 | And that lets a lot of viruses out actually.

02:37:00.200 | And hydroxychloroquine blocks that.

02:37:01.840 | So it blocks infection with a lot of viruses.

02:37:04.120 | So the problem with those early studies that were published

02:37:10.120 | is that they were done in kidney cells and culture,

02:37:14.120 | where the only way the virus can get in

02:37:16.280 | is through the endosome.

02:37:17.520 | And hydroxychloroquine inhibits that,

02:37:20.920 | and that's why it inhibits in kidney cells and culture.

02:37:24.400 | But lung cells and respiratory cells of humans

02:37:28.040 | where the virus reproduces can get in two different ways.

02:37:31.560 | It can get in from this endocytic pathway,

02:37:34.680 | which is inhibited by hydroxychloroquine,

02:37:37.880 | or it can get in at the cell surface,

02:37:40.320 | which is not inhibited by hydroxychloroquine.

02:37:43.120 | So when you treat patients, it has no effect in the lung

02:37:46.500 | because the virus can just bypass it.

02:37:50.160 | And all the usage initially were based

02:37:53.160 | on the studies done in kidney cells and culture.

02:37:57.200 | So that was just wrong, scientifically incorrect,

02:38:00.560 | yet it drove a lot of, and today,

02:38:02.400 | many people still think they should be taking it.

02:38:05.000 | - So that not panning out kind of resulted

02:38:09.240 | in a loss of optimism about other similar things panning out?

02:38:14.240 | - Well, that and many other drugs,

02:38:15.600 | repurposed drugs were tried, right?

02:38:17.880 | A lot of HIV antivirals were tried.

02:38:20.240 | I think the problem with hydroxy,

02:38:22.200 | I think hydroxychloroquine influenced

02:38:24.040 | the ivermectin narrative, right?

02:38:26.080 | People thought that data was being hidden

02:38:29.380 | about hydroxychloroquine, so they said,

02:38:30.960 | well, they must be doing the same thing with ivermectin.

02:38:33.120 | But with hydroxychloroquine,

02:38:34.840 | it just scientifically could not work as an antiviral.

02:38:39.060 | The other problem that is more broad

02:38:42.120 | that is important to point out is that

02:38:44.000 | when you have COVID and you need an antiviral,

02:38:49.600 | it's usually because you can't breathe

02:38:50.920 | when you go in a hospital.

02:38:52.960 | 'Cause if you're mildly ill,

02:38:53.980 | you're never gonna go to your doctor

02:38:55.400 | and ask for an antiviral.

02:38:56.840 | And the problem is when you can't breathe,

02:38:58.420 | it's no longer a viral issue.

02:39:00.400 | It is now an inflammatory issue,

02:39:02.280 | and no antiviral in the world is gonna help you.

02:39:05.320 | So that's why remdesivir doesn't work very well,

02:39:09.040 | 'cause it's mainly given intravenously

02:39:10.580 | to people who go in a hospital.

02:39:12.340 | If you get ivermectin in the hospital,

02:39:16.760 | it's not gonna do anything for reducing virus,

02:39:19.020 | because by that time, you have very little virus

02:39:20.880 | to begin with.

02:39:21.840 | You have an inflammatory problem

02:39:23.200 | that you need to treat in other ways.

02:39:24.880 | So this is why a lot of the antivirals failed,

02:39:28.920 | because they're used too late.

02:39:30.920 | What you need is a pill you take

02:39:33.060 | on that first positive test,

02:39:34.560 | when you have a scratchy throat.

02:39:36.880 | You get a PCR in 15 minutes, I'm positive,

02:39:39.700 | take a pill, boom, that's gonna inhibit it.

02:39:42.800 | If you wait 'til you can't breathe,

02:39:44.980 | and that's why the monoclonals even don't work

02:39:47.320 | if you're in a hospital that well,

02:39:49.220 | 'cause it's too late.

02:39:50.240 | And the approach now is, if you're in a high-risk group,

02:39:54.440 | if you're over 65, if you are obese,

02:39:57.920 | or have diabetes, or any other comorbidities,

02:40:00.760 | your first sign of a scratchy throat positive,

02:40:03.820 | you get monoclonals.

02:40:06.020 | Then they might help you.

02:40:07.360 | But if you wait 'til you go in a hospital, it's too late,

02:40:09.880 | 'cause the viral curve drops.

02:40:12.080 | After that first symptom, within three days,

02:40:15.880 | you're no longer shedding enough virus to transmit.

02:40:19.600 | It drops really quickly.

02:40:20.720 | So that's the reason a lot of these antivirals failed,

02:40:23.200 | 'cause they were tested in hospitalized patients.

02:40:25.880 | And we have nothing but remdesivir now, unfortunately.

02:40:29.500 | So it was the wrong approach.

02:40:30.940 | We should have been giving it to people

02:40:33.800 | who just tested positive from the start.

02:40:35.840 | - Or just even for preventative and see--

02:40:38.120 | - You could do that too.

02:40:39.660 | But I have to say, the other issue is,

02:40:42.040 | this monopiravir is a drug in phase three now.

02:40:45.040 | It's an oral antiviral, it looks good.

02:40:48.420 | If we go ahead with just one,

02:40:51.160 | we're gonna get resistance within a few months,

02:40:53.320 | and it will be useless.

02:40:54.360 | We need to have at least two or three drugs

02:40:56.840 | that we can give in combinations.

02:40:58.840 | And we know that, 'cause that's what took care of HIV,

02:41:01.680 | that's what took care of HCV, hepatitis C virus.

02:41:05.280 | It really reduces the emergence of resistance.

02:41:08.120 | - Joe Rogan got quite a bit of heat recently

02:41:11.600 | about mentioning a paper and a broader idea,

02:41:16.600 | which I don't think is that controversial,

02:41:19.920 | but maybe we can expand on it.

02:41:21.740 | And the idea is that vaccines create selective pressure

02:41:27.820 | for a virus to mutate and for variants to form.

02:41:34.280 | First of all, from a biological perspective,

02:41:40.400 | can you explain this process?

02:41:42.120 | And from a societal perspective,

02:41:45.680 | what are we supposed to do about that?

02:41:47.600 | - So let's get the terminology right.

02:41:49.580 | So as we talked about earlier, viruses are always mutating.

02:41:54.080 | So no vaccine or no drug makes a virus mutate.

02:41:58.360 | - That's the wrong perspective in which to look at it.

02:42:01.240 | - What the immune response is putting pressure,

02:42:05.000 | selection pressure on the virus.

02:42:07.040 | And if there's one particle with the right mutation

02:42:11.520 | that can escape the antibody, that will emerge.

02:42:15.280 | So that's what happens with influenza virus, right?

02:42:17.720 | We vaccinate every year and there are not a lot of people

02:42:21.440 | that get infected, so they get natural immunity.

02:42:23.900 | And then the virus is incredibly varied.

02:42:28.920 | It mutates like crazy.

02:42:30.280 | And there's in some person somewhere,

02:42:32.160 | there's one variant that escapes the antibody,

02:42:34.440 | which has been induced either by infection or vaccination.

02:42:37.160 | It can be both.

02:42:38.000 | And that drives the emergence of the new variants.

02:42:41.320 | So the next year we need to change the vaccine.

02:42:43.900 | So I would say both natural infection and vaccination,

02:42:48.900 | sure, select for variants.

02:42:51.480 | Absolutely, there's no question,

02:42:53.520 | because they're inducing immunity.

02:42:55.440 | Now, what happened last year was at the beginning of 2020,

02:43:00.040 | very few people in the world were immune

02:43:02.320 | as the virus first started spreading.

02:43:04.920 | But you can see in the sequences of those isolates

02:43:09.140 | from the beginning of 2020,

02:43:11.040 | you can see all of the changes that are now present

02:43:14.660 | in the variants of concern at very, very low frequencies.

02:43:17.700 | They were already there,

02:43:18.700 | but there was no selection for them to emerge.

02:43:21.940 | Until November, when we now had many millions of people

02:43:25.380 | who had mostly been infected, but also some vaccinated,

02:43:29.780 | then we saw the alpha variant emerge in England,

02:43:33.060 | probably because of immune selection.

02:43:35.360 | Now the virus that had the change

02:43:38.300 | that evaded the antibody had an advantage,

02:43:41.980 | and that virus drove through the population.

02:43:44.060 | So that's what we're seeing.

02:43:44.900 | We're seeing all these variants

02:43:46.020 | are simply antigenic selection.

02:43:47.980 | - So the variants, the mutations that are at the core

02:43:52.740 | of these "variants," they were always there all along.

02:43:56.780 | The vaccine or the infections did not create them.

02:44:00.420 | - No, the infections don't create them, they're selected.

02:44:02.660 | - It's like the vaccine wipe out a lot of the variants,

02:44:08.220 | right, and then by making your body immune to them,

02:44:13.220 | but some of them survive.

02:44:15.540 | - Yeah, exactly.

02:44:16.820 | - And then there's another tree that's built,

02:44:19.500 | and it's unclear what that tree leads to.

02:44:22.560 | I mean, it could make things much worse or much better.

02:44:27.060 | We don't know.

02:44:28.340 | - Well, with flu, we see year after year, the virus changes.

02:44:31.220 | We change the vaccine, we deal with it,

02:44:33.580 | we change it again, there's an unending series.

02:44:35.540 | - But see, that's a very different story.

02:44:37.580 | Do you think COVID will be,

02:44:40.500 | with some likelihood, like the flu,

02:44:45.940 | where it's basically variants,

02:44:47.860 | we'll never be able to eradicate it?

02:44:52.780 | - It will never eradicate it in any case, ever.

02:44:57.380 | - Well, come up with a vaccine

02:44:59.820 | that makes you immune to enough variants

02:45:03.140 | where there's not enough evolutionary room.

02:45:07.260 | - Well, if you cut down the number of infections,

02:45:09.140 | then you reduce the diversity, sure, right?

02:45:12.220 | The problem is, let's say you're a cynic,

02:45:15.180 | and you say, "Well, vaccination is just selecting

02:45:18.060 | "for variants, so let's stop it."

02:45:20.060 | But then you're gonna have infection,

02:45:21.500 | and that's gonna select for variants,

02:45:23.300 | and you're more likely to get very sick,

02:45:26.300 | because we know the vaccines are really good

02:45:28.660 | at preventing you from dying.

02:45:30.100 | So that's why it still makes sense to use vaccines,

02:45:34.100 | because they prevent you from dying.

02:45:36.740 | That's the bottom line.

02:45:38.180 | But can we ever make a vaccine

02:45:41.060 | that deals with all variants?

02:45:44.580 | Absolutely.

02:45:45.580 | And the reason I say that is because people

02:45:49.260 | who get naturally infected with SARS-CoV-2,

02:45:53.380 | they develop COVID, they recover.

02:45:55.100 | If you give them one vaccine dose,

02:45:59.580 | they make an immune response

02:46:01.460 | that handles all the variants that are around right now.

02:46:05.740 | All of them, much better than people

02:46:07.780 | who've gotten two doses of vaccine.

02:46:10.380 | For some reason, their immune response

02:46:12.820 | has suddenly broadened after the infection vaccination,

02:46:16.860 | and they can handle all the variants

02:46:18.540 | that we know of so far.

02:46:19.380 | So that tells me we can devise a strategy

02:46:22.780 | to do the same thing with a vaccine

02:46:24.540 | that makes a really broad vaccine

02:46:26.620 | that'll handle all the variants.

02:46:27.940 | - Well, you actually, on the virology blog,

02:46:30.660 | I don't know if you're the author of that, but--

02:46:32.340 | - I am, yes.

02:46:33.900 | Oh, the blog, yes, but there's a particular post

02:46:36.820 | that was talking about reporting on a paper

02:46:39.420 | that a mix and match strategy--

02:46:40.900 | - Oh, yes, that's one of my co-writers, Trudy Ray, yeah.

02:46:44.220 | - Yeah, it's an interesting idea

02:46:46.620 | that there's some early evidence now

02:46:49.380 | that mixing and matching vaccines,

02:46:52.380 | like one shot of Pfizer and one of like Moderna or something,

02:46:56.500 | that creates a much better immunity

02:47:00.460 | than does two shots of Pfizer.

02:47:02.820 | - I think that's worth exploring, absolutely.

02:47:05.420 | And this is relevant, that what we're doing with influenza,

02:47:08.420 | instead of having to vaccinate people every year,

02:47:11.260 | why can't we devise a vaccine

02:47:12.860 | which you'd get once in your lifetime,

02:47:14.780 | or maybe once every 10 years, okay?

02:47:17.420 | So the spike of influenza, it's a long protein,

02:47:22.420 | kind of like the spike of SARS-CoV-2,

02:47:24.700 | it's stuck in the virus membrane,

02:47:26.340 | and the very tip, that's the part that changes every year.

02:47:31.340 | That's where the antibodies bind.

02:47:33.740 | But the stem doesn't change.

02:47:36.500 | And if you make antibodies to the stem,

02:47:39.900 | they can also prevent infection.

02:47:41.700 | It's just that when people are infected

02:47:44.060 | or with the current vaccines,

02:47:45.980 | they don't make many antibodies to that stem part.

02:47:48.860 | But we're trying to figure out how to make those,

02:47:51.420 | and we think they would be broadly protective,

02:47:53.660 | and you'd never be able to, or more rarely be able to,

02:47:57.880 | have a variant emerge that escaped it.

02:48:00.940 | And I think we can do the same thing

02:48:02.460 | with coronavirus too, for sure.

02:48:05.500 | - Can I ask you about testing?

02:48:08.380 | - Sure, sure.

02:48:10.140 | - You mentioned PCR, what kind of tests are there?

02:48:13.300 | The antigen test, what are your thoughts on each?

02:48:18.160 | Maybe this is a good place to also mention viral load,

02:48:22.480 | and the history of the virus

02:48:26.140 | as it passes through your body,

02:48:27.700 | in terms of what's being tested for,

02:48:31.220 | and all those kinds of things.

02:48:33.380 | - So the first tests that were developed were PCR,

02:48:38.380 | polymerase chain reaction,

02:48:40.220 | they're basically nucleic acid amplification tests.

02:48:43.740 | And the very first ones,

02:48:44.780 | they stuck the swab all the way up into your brain, almost.

02:48:47.940 | (laughing)

02:48:49.220 | I had that done a couple weeks ago.

02:48:50.820 | Oh my gosh, it's really nasty.

02:48:53.400 | But now they do an anterior nares swab.

02:48:56.260 | They get a bunch of cells and some mucus,

02:48:59.660 | which has virus and parts of virus,

02:49:02.620 | stick it in a test tube,

02:49:04.260 | and then they run a reaction,

02:49:05.940 | which by the way, involves reverse transcriptase,

02:49:09.000 | 'cause it converts the viral RNA to DNA,

02:49:11.940 | and then you amplify it.

02:49:14.200 | And you can specify what part of the viral RNA

02:49:18.900 | you want to amplify.

02:49:20.260 | And then a machine will detect it,

02:49:22.500 | and it can be done in 15 minutes.

02:49:25.060 | But you're detecting pieces of RNA, not infectious virus.

02:49:29.060 | So we're measuring viral RNA loads, right?

02:49:32.380 | And a common mistake that many people who should know better,

02:49:36.780 | you know, physicians and scientists of all kinds,

02:49:40.060 | they think that indicates how much virus you have.

02:49:42.600 | It doesn't.

02:49:44.400 | It's a diagnostic of whether you have bits of RNA in you,

02:49:48.580 | and it probably means you're infected.

02:49:51.300 | But you can't use it to shed light on what's going on.

02:49:55.460 | And I'll tell you why in a bit,

02:49:56.860 | but first we have to explain some other things.

02:49:59.460 | So until you get to about a million copies of RNA,

02:50:06.340 | so you can measure the copy number in this test,

02:50:08.520 | this PCR test.

02:50:09.660 | It's a number called CT, or cycle threshold.

02:50:13.820 | The test, the way the machine works,

02:50:15.340 | it goes through cycles.

02:50:16.380 | In every cycle, it amplifies what you put in.

02:50:19.940 | And the more cycles you need to see something,

02:50:23.900 | that means there's not a lot of RNA there.

02:50:26.660 | So if you do a test and you have a cycle threshold of 35,

02:50:31.140 | you have very little RNA in you.

02:50:33.660 | Contrary, if you have a cycle threshold of 10,

02:50:36.180 | you have a ton of RNA,

02:50:37.020 | and you only took 10 cycles to detect it.

02:50:39.540 | And you can extrapolate from that number

02:50:42.780 | the number of copies you have per sample, say per swap.

02:50:46.100 | And if you don't have a million, you're not infectious.

02:50:49.140 | You're not gonna infect anyone.

02:50:50.780 | So in the early days, no matter what PCR result you had,

02:50:54.740 | they would quarantine you.

02:50:56.060 | And that was wrong because you're not shedding.

02:50:58.340 | You don't need to be quarantined,

02:51:00.060 | but it wasn't thought through properly, right?

02:51:02.780 | - And that's where you had like 14 days

02:51:04.580 | or something like that.

02:51:05.420 | - 14 days, which is now we know is too long

02:51:07.940 | because you don't shed for that long in a normal infection.

02:51:11.620 | Now it's 10 days should be fine.

02:51:14.160 | So what happens is you get infected.

02:51:16.180 | You don't know it, of course.

02:51:17.860 | The virus starts to grow very quickly.

02:51:19.780 | And within four or five days, you reach a peak of shedding.

02:51:24.700 | You're making a lot of RNA and you may be asymptomatic.

02:51:28.260 | You're shedding, you can infect others.

02:51:30.060 | And then you may or may not have your symptom onset.

02:51:32.820 | So you shed for a couple of days before symptom onset.

02:51:36.260 | And then within three days, four days,

02:51:38.660 | the viral RNA crashes and you're no longer shedding.

02:51:41.500 | You're no longer transmitting.

02:51:42.980 | So that's the one kind of test we have.

02:51:44.580 | It can tell you if you're infected at the moment,

02:51:47.700 | but it won't tell you if you're gonna be infected tomorrow.

02:51:51.500 | 'Cause if you're negative today,

02:51:52.700 | you could be positive tomorrow.

02:51:54.060 | You just might be in a different part

02:51:56.180 | of the incubation period.

02:51:58.340 | So that's one test been used the most.

02:52:01.100 | You can now get 15 minute versions of them

02:52:05.060 | in a walk-in or whatever.

02:52:06.900 | Then there are antigen tests,

02:52:08.100 | which look for the proteins that the virus is making.

02:52:11.660 | So as it's reproducing in your nose,

02:52:13.480 | it's not only making genomes, it's making proteins.

02:52:16.180 | And so these you can buy in the drug store.

02:52:18.380 | And these would have been great if they had,

02:52:22.180 | Michael Mina last year had the idea

02:52:24.340 | that if we could make a little stick,

02:52:26.500 | a little piece of paper that you would suck on

02:52:28.620 | and it would tell you if you're infected or not,

02:52:30.380 | if this could cost less than a buck,

02:52:32.580 | everybody could test themselves.

02:52:34.060 | - Which they can cost less than a buck, by the way.

02:52:37.060 | - Yeah, but they were never made, right?

02:52:39.100 | - They're never mass manufactured.

02:52:42.340 | So his idea is to do like daily tests.

02:52:44.820 | - Yeah, daily, and then the kid's going to school,

02:52:47.340 | he's positive or she's positive.

02:52:49.860 | Well, if it's cheap enough, you just take another test

02:52:51.820 | 'cause they have a certain error frequency.

02:52:53.540 | If it's positive twice, you stay home

02:52:55.300 | and the next day you try again.

02:52:57.180 | And I think this would have revolutionized

02:52:59.860 | because the PCR tests are more expensive at the time

02:53:02.460 | and they take longer to do and so forth.

02:53:05.340 | But that never happened.

02:53:07.060 | But now we do have $20 BinaxNOW and others that you can buy

02:53:10.780 | and people buy them.

02:53:12.260 | - See, but that can still happen, right?

02:53:13.980 | And this is the very frustrating thing to me

02:53:16.020 | because I'm worried about variants,

02:53:18.980 | but I'm also worried about future,

02:53:20.660 | much more deadly pandemics.

02:53:22.780 | I know we kind of said, yes, COVID, lots of deaths,

02:53:28.460 | but it could be a lot worse too.

02:53:31.020 | So I'm thinking what is going to be the right response

02:53:35.780 | for the future pandemic of its kind?

02:53:38.580 | And what's the right response

02:53:39.900 | for continued number of variants

02:53:41.620 | and some of the variants might be deadlier

02:53:44.580 | or more transmissible?

02:53:45.940 | - Well, the antigen tests will pick up the variants.

02:53:50.940 | That's not a question.

02:53:52.700 | The PCR may be influenced by changes,

02:53:55.060 | but you can quickly adapt the primers that you use.

02:53:58.460 | - But that's what I mean.

02:53:59.300 | Like to me, all these discussions about vaccines and so on,

02:54:03.260 | vaccines, we got very lucky that they took so little time.

02:54:06.820 | - Right.

02:54:07.660 | - And you have to be aware, no matter what,

02:54:10.500 | that there's hesitancy with the vaccines in this country.

02:54:13.300 | Before, I mean, yeah, that's a reality.

02:54:15.660 | You can't just be like magically saying that

02:54:18.580 | you're going to overcome that.

02:54:20.420 | And I don't think there's any hesitancy

02:54:22.180 | and cheap tests at home.

02:54:24.020 | - I agree.

02:54:24.860 | I think if someone, so the question is,

02:54:27.020 | if someone tested positive, would they stay home?

02:54:29.220 | That's the question.

02:54:30.060 | What if their job depends on them going in?

02:54:32.340 | I mean, that's-

02:54:33.540 | - Well, you have to look at sort of aggregate

02:54:36.660 | how many people would decide.

02:54:38.180 | And I think, again, a lot of that is in leadership,

02:54:43.060 | but I think a lot of them,

02:54:45.060 | I would say most people would stay home.

02:54:47.140 | - I think that Mena had the idea

02:54:49.460 | and it would have changed the whole situation for sure

02:54:53.180 | if it could have been made when we talked to him last spring,

02:54:56.220 | I think, or summer.

02:54:57.300 | We would have gotten around a lot of the issues

02:55:00.740 | that we're in today because I think people

02:55:02.380 | would have stayed home and not transmitted.

02:55:04.000 | And I think it's still valuable to this day.

02:55:06.060 | In the fall, if we don't have vaccine uptake,

02:55:09.860 | we could just test kids every day

02:55:12.420 | and keep them home when they're infected.

02:55:14.420 | And we don't have it.

02:55:17.620 | But I think, and I'm not privy to what was going on,

02:55:20.660 | but I don't think a lot of emphasis

02:55:23.020 | was put on testing early on.

02:55:25.140 | The CDC developed the first one.

02:55:27.060 | It was flawed.

02:55:27.980 | They had to recall the kits.

02:55:29.420 | I mean, that was a fiasco.

02:55:30.420 | They should have had 100 companies

02:55:32.240 | making the tests initially, right?

02:55:34.740 | So for the future, I think what we have learned

02:55:37.980 | is we need to have a rapid antigen test

02:55:40.420 | right off the bat that's doable.

02:55:43.700 | You can't do it in a day like you can for PCR

02:55:46.240 | because you need to make antibodies

02:55:49.700 | to the protein that you're looking for

02:55:51.180 | and you need to do those in animals.

02:55:52.940 | But you can do it in weeks and we should be ready for that.

02:55:57.700 | - Yeah, I mean, to me, that's obvious.

02:56:00.700 | That's obviously the best solution.

02:56:02.820 | Second to that, if we understood how well masks work.

02:56:06.900 | Like, maybe let me ask you this question.

02:56:11.340 | Let's put masks aside.

02:56:13.040 | How well do we understand how COVID is transmitted?

02:56:18.140 | There's droplets of different sizes, aerosols,

02:56:23.140 | tiny, tiny droplets.

02:56:24.860 | It seems like that's a very difficult thing

02:56:28.520 | to understand thoroughly.

02:56:30.900 | So it seems like it's transmitted both ways.

02:56:34.220 | It's unclear how exactly.

02:56:36.580 | So how much do we understand

02:56:39.460 | and why is it so difficult to understand fully?

02:56:41.460 | - I think it's clear that it's transmitted

02:56:43.980 | through the air mostly.

02:56:45.660 | It's not touching.

02:56:47.140 | We thought initially it would be a lot of touch

02:56:48.780 | but very little of that.

02:56:50.940 | It's through the air and when you talk,

02:56:53.940 | mainly when you talk, you expel a lot of droplets, right?

02:56:57.420 | Even the plosives that your foam thing here

02:57:00.220 | are meant to pee, right?

02:57:02.300 | That you send out little sprays

02:57:03.860 | and those have viruses in them.

02:57:05.420 | The big drops fall to the ground

02:57:07.980 | and the little ones can go 100 feet or more, right?

02:57:12.000 | But the little ones also have less virus in them.

02:57:14.680 | So I'm not sure, well, we certainly do not know

02:57:19.460 | how much virus you need to be infected.

02:57:22.060 | But it's probably at least several thousand particles,

02:57:25.660 | if not more.

02:57:26.900 | And it could be that for most people,

02:57:30.100 | the tiny droplets don't have enough virus

02:57:33.340 | to infect someone else.

02:57:34.860 | But there's one observation about this virus

02:57:37.740 | that's really interesting.

02:57:39.300 | And that is that 80% of transmissions

02:57:42.700 | are done by 20% of the people, of the infected people.

02:57:47.000 | Not every infected person transmits.

02:57:50.460 | That's been borne out in multiple studies.

02:57:52.540 | And in fact, there's a study at University of Colorado

02:57:56.380 | where they quantified the viral RNA loads

02:57:59.300 | in all the swabs that had been done of students

02:58:01.900 | for like a six month period.

02:58:03.460 | And most of the infectious virus,

02:58:08.420 | most of the RNA copies were found in 15 to 20% of the people.

02:58:13.420 | The rest had really low and they probably,

02:58:16.940 | that's probably why they don't transmit.

02:58:18.900 | So those are the ones that might get enough virus

02:58:22.900 | in the tiny droplets to be able to infect someone

02:58:25.780 | at a distance.

02:58:27.020 | And I think that's entirely possible.

02:58:29.340 | Why is it hard to study?

02:58:31.540 | You can't do it in real life

02:58:33.300 | because you don't know who's infected.

02:58:35.860 | And if you do this, there's not a controlled environment

02:58:38.300 | to measure droplets and so forth.

02:58:39.740 | You'd have to do it in a laboratory situation.

02:58:42.760 | If you use an animal, you just don't know

02:58:44.620 | what the relevance of that is to people.

02:58:47.000 | You'd have to use human and do challenge experiments.

02:58:50.500 | And we don't do that at this point,

02:58:52.380 | at least not for this virus.

02:58:53.800 | So that's why it's hard to know what's going on.

02:58:55.940 | So we have to make inferences

02:58:58.300 | from epidemiological associations

02:59:01.260 | where you're studying say transmission in a household

02:59:03.880 | where people are stuck in the same rooms together

02:59:06.140 | and you can get an idea

02:59:07.020 | of what kind of droplets were involved.

02:59:09.820 | - So that makes it much harder to,

02:59:11.340 | if you're leaning on epidemiological stuff

02:59:14.180 | as opposed to like biophysics or something like that.

02:59:17.260 | - Very hard.

02:59:18.380 | - So that makes it, but that makes it really hard

02:59:21.860 | to then develop solutions like masks,

02:59:24.040 | to ask the question, how well do masks work?

02:59:26.600 | Because then to answer that question,

02:59:28.820 | you can lean on epidemiological stuff again,

02:59:32.280 | like looking at populations that wear masks

02:59:34.520 | versus don't wear masks,

02:59:36.560 | as opposed to actually saying,

02:59:38.840 | like from an engineering perspective,

02:59:41.840 | like what kind of material and what kind of tightness,

02:59:46.600 | by which amount decreases the viral load

02:59:50.440 | that's received on the other end?

02:59:52.500 | - But some experiments have been done with masks

02:59:55.860 | and just droplets with no virus in them, right?

02:59:58.460 | - Yes.

02:59:59.300 | - And you can measure the efficiency

03:00:01.380 | of different mask materials at keeping those in.

03:00:04.420 | - So if I say that this mask stops 70%

03:00:09.420 | of this or larger size droplet,

03:00:12.980 | that leads to this percent decreased transmission.

03:00:19.200 | And also on both the generation

03:00:23.060 | and the receiving end and the giving end.

03:00:27.580 | - Sure.

03:00:28.420 | - So how well do masks protect you from others?

03:00:30.580 | How well do you do mask protect others from you?

03:00:34.220 | Like all of those things seem like

03:00:36.960 | they could be more rigorously studied.

03:00:40.180 | - There's no doubt about it.

03:00:41.580 | And now is the time because once this is over,

03:00:46.140 | nobody's going to do it.

03:00:47.220 | - Nobody's going to care.

03:00:48.380 | - No.

03:00:49.220 | - But it seems like to me, so tests is one thing,

03:00:52.920 | but masks, like good masks, whatever the good means,

03:00:57.920 | whatever that means, like some level of a quality

03:01:01.700 | of material on your face, if it's shown to actually

03:01:05.820 | like thoroughly shown to work well,

03:01:09.020 | that seems like an obvious solution

03:01:10.980 | to reopen society with, if you have a good understanding

03:01:16.420 | of how well they work.

03:01:17.960 | Because if you don't have a good understanding,

03:01:20.100 | if there's a lot of uncertainty, that's when you get,

03:01:23.020 | and you have people speaking from authority,

03:01:25.000 | that's when you start getting the politicization

03:01:27.980 | of the solution.

03:01:28.820 | - Of course, of course.

03:01:29.660 | No, the data, there are some data,

03:01:32.220 | most, they're mostly epidemiological,

03:01:36.380 | and they show some effect in some countries, right?

03:01:39.160 | But they could be way better.

03:01:40.820 | - Yeah.

03:01:41.660 | - And, but the fact that they're not perfect,

03:01:44.940 | then people take advantage of and say,

03:01:46.780 | well, look, they don't work that well,

03:01:48.120 | so I'm not gonna wear it.

03:01:49.120 | I think, as you said, people can use it as an excuse.

03:01:53.040 | But even if it works, so Daniel always says it,

03:01:56.220 | a mask will cut down transmission by 50 to 60%,

03:02:00.640 | and then distance will do another 30%.

03:02:03.480 | - Yeah, those numbers are made up, though.

03:02:05.820 | I mean, they're not made up, but they're estimates.

03:02:08.360 | - Absolutely.

03:02:09.200 | And many of them are made based on models, right?

03:02:12.840 | - Yeah.

03:02:13.680 | - We will make this model, and let's say the mask

03:02:15.580 | cuts down this much, what will be the effect on it?

03:02:17.920 | I mean, yeah, they're models,

03:02:19.080 | and it's for the same reason.

03:02:20.840 | I don't believe the transmission of the variants,

03:02:25.040 | because it's all based on statistical models as well,

03:02:27.320 | not biological experiments done in the lab.

03:02:29.600 | - So in that sense, vaccine data's much better

03:02:32.080 | than mask data.

03:02:33.240 | - For sure, for sure.

03:02:34.520 | - So my problem with the mask data,

03:02:37.200 | which I always thought was fascinating,

03:02:38.520 | I stopped talking about it, I was in a paper about masks.

03:02:41.440 | I stopped talking about it because what started happening

03:02:45.120 | is masks created assholes on both sides.

03:02:48.060 | The people that were in Silicon Valley,

03:02:50.460 | the friends of mine that were wearing masks,

03:02:52.580 | the way they look at others who don't is like--

03:02:56.260 | - That's a whole 'nother issue, right, yeah.

03:02:58.540 | - But that's what-- - I understand.

03:02:59.940 | - That happens when you don't have solid science.

03:03:02.620 | - Understood.

03:03:03.460 | - They now start judging you like you're

03:03:05.660 | a lesser human being.

03:03:07.300 | You're not only dumb, but you're just,

03:03:10.420 | you're almost like evil.

03:03:11.580 | You're doing bad for society by not wearing a mask.

03:03:14.380 | And then the people looking in the other way

03:03:17.180 | are seeing you for the asshole that you're being

03:03:19.880 | for judging them unrightfully.

03:03:22.120 | So they almost wanna say F you by not wearing the mask.

03:03:24.560 | And there's this division that's created

03:03:26.700 | that was heartbreaking to me because masks, like testing,

03:03:30.460 | is a solution that was available early on.

03:03:33.480 | And if understood well, it could be deployed

03:03:36.000 | in a mass scale.

03:03:37.160 | And it seems like there's some historical evidence

03:03:39.220 | for other viruses where it does very well.

03:03:41.800 | - That's correct.

03:03:42.640 | - And so the fact that this was politicized,

03:03:45.800 | yeah, was a little bit heartbreaking.

03:03:48.820 | - You can find in the literature studies,

03:03:51.860 | mostly of healthcare workers and influenza,

03:03:54.660 | where you can actually, 'cause you see the people every day,

03:03:57.660 | they can sample them, you can actually see what masking does

03:04:00.600 | and some of them show an effect and others do not.

03:04:04.140 | Then that's the problem.

03:04:05.680 | Like any trial, sometimes if it's not big enough

03:04:08.360 | and then people latch onto that, see,

03:04:10.460 | it doesn't really work.

03:04:11.700 | But I think the main issue is that in January,

03:04:15.080 | both CDC and WHO said, "Masks don't work, don't use them."

03:04:20.080 | That was the kiss of death for masks.

03:04:22.320 | Because when they then changed their mind,

03:04:25.160 | they didn't say, "We screwed up."

03:04:28.140 | They just said, "Wear masks."

03:04:29.320 | If they had said, "We made a mistake, we were wrong,"

03:04:33.120 | I think more people would have worn masks, but they didn't.

03:04:36.680 | And like you said, admitting you're wrong

03:04:39.180 | is like a real big part of it.

03:04:41.580 | - I also think almost the better way

03:04:43.260 | is not just saying you're wrong,

03:04:46.640 | but in January revealing the uncertainty

03:04:51.480 | under which we operate.

03:04:52.440 | Like actually reveal what was done with the Spanish flu

03:04:57.440 | at the beginning of the previous century,

03:04:59.760 | 'cause there's a lot of mask controversy then too.

03:05:03.320 | It went back and forth and that was actually the source

03:05:05.140 | of a lot of distrust there too.

03:05:06.960 | So, and then look at influenza,

03:05:09.320 | like how is it effective with that

03:05:11.120 | and just reveal this, we don't know.

03:05:13.920 | But with some probability,

03:05:17.740 | this is the best option we got currently.

03:05:20.160 | And then in a month or two, adjust it,

03:05:23.640 | saying that, you know what,

03:05:25.320 | our uncertainty decreased a little bit,

03:05:27.880 | we have a better idea.

03:05:29.200 | That was an incorrect estimate,

03:05:32.400 | but reveal that you're struggling.

03:05:34.880 | It's not like this weird binary clock

03:05:36.860 | that goes one direction or the other.

03:05:38.480 | You're struggling with uncertainty.

03:05:41.240 | And like trusting, people maybe criticize me sometimes

03:05:44.880 | with this, but I think most people are actually intelligent.

03:05:48.040 | Like trusting the public to be intelligent

03:05:51.800 | with if you give them, if you have transparent

03:05:54.080 | and give them information in a real authentic way.

03:05:57.720 | Like don't look like you're hiding something.

03:05:59.600 | I think they're intelligent enough

03:06:01.140 | to use that data to make decisions.

03:06:03.160 | It's the same thing as with the testing,

03:06:05.120 | is if you put that power in the people's hands

03:06:07.600 | to know if they're sick or not,

03:06:08.680 | they're gonna make, en masse, the right decision, I think.

03:06:12.280 | The masks and the testing has been a bit heartbreaking.

03:06:17.120 | - I think it's a good point, though,

03:06:19.460 | that most people don't seem to have an objection to testing.

03:06:23.080 | It's a good point.

03:06:23.920 | - Yes. - Yeah.

03:06:24.740 | - And then obviously, Makumena makes that point brilliantly.

03:06:28.000 | And still, there's very little excitement around that.

03:06:31.560 | - But he said he was going to do it.

03:06:35.000 | I don't understand.

03:06:35.840 | I mean, I haven't spoken to him since then.

03:06:37.600 | So I don't know what--

03:06:38.720 | - He's pushing it.

03:06:39.560 | Well, I mean, but he can't do it alone.

03:06:41.640 | He has to get, so one of the resistances,

03:06:44.680 | FDA doesn't like cheap things.

03:06:48.160 | - Yeah.

03:06:49.120 | - They don't wanna approve it.

03:06:50.240 | So that makes the mask manufacturer,

03:06:53.000 | like with the emergency exceptions,

03:06:55.120 | all those kinds of things, very difficult.

03:06:57.440 | And then there's not much money to be made on it

03:06:59.560 | without that.

03:07:00.780 | I don't know.

03:07:01.620 | I think there's just economic pressures against it.

03:07:04.840 | And because so much investment was placed on the vaccines,

03:07:09.840 | and obviously there's an incentive mechanism there

03:07:13.360 | where the companies, lobbyists and all those,

03:07:16.120 | there's this machine that says,

03:07:18.800 | arguing for tests is difficult

03:07:22.080 | because the thing that's worked for most severe viruses

03:07:25.480 | in the past is vaccines.

03:07:27.160 | Now we have vaccines, why the hell would you need tests?

03:07:30.360 | At that time, like, why the hell do you need tests

03:07:34.480 | when we can be working on vaccines?

03:07:36.080 | It seems like the obvious thing to be working

03:07:37.720 | is the vaccines from their perspective,

03:07:40.680 | but it's not obvious at all to me.

03:07:43.160 | - I think you should have both.

03:07:44.440 | I think have vaccines and good testing,

03:07:46.600 | and that covers you really well

03:07:48.920 | because you're always gonna have people

03:07:50.080 | who don't get vaccinated.

03:07:52.080 | - I don't know if you've been paying attention to this.

03:07:54.200 | There's a guy named Brett Weinstein,

03:07:55.720 | there's a guy named Sam Harris.

03:07:57.800 | They have good representation, I would say,

03:08:01.480 | of two sides of a perspective on vaccines.

03:08:06.280 | So from Sam Harris's perspective,

03:08:09.680 | it's obvious that everybody should get vaccinated

03:08:13.240 | and it's irresponsible to not get vaccinated.

03:08:18.240 | I think he represents a lot of people's belief in that.

03:08:22.360 | And then Brett talks a lot about ivermectin,

03:08:28.520 | but also talks about a hesitancy towards the vaccine

03:08:32.680 | for people who are healthy,

03:08:35.040 | for people who are younger, that kind of thing,

03:08:37.520 | and saying we should consider long-term effects

03:08:40.840 | of the vaccine in making this calculation.

03:08:45.200 | What do you make about this conversation?

03:08:47.520 | Some of it happens on Twitter,

03:08:49.400 | some of it happens in the space of podcasts.

03:08:52.020 | Do you pay attention to this kind of thing?

03:08:56.240 | What's your role in this?

03:08:58.240 | What do you hope is the way to resolve this conversation?

03:09:02.080 | Do you think it's healthy?

03:09:04.040 | - Well, a conversation is always healthy,

03:09:05.740 | but to make definitive statements is not

03:09:09.280 | because it suggests you have information

03:09:11.340 | that you don't have.

03:09:12.180 | So we talked about long-term effects.

03:09:16.880 | I think you need to balance those

03:09:18.680 | versus long-term effects of the disease,

03:09:20.960 | and you can make your decision.

03:09:22.520 | I don't think you need to tell everybody to get vaccinated.

03:09:26.560 | I think you need to present the case.

03:09:28.760 | You say, "Here, we made good vaccines.

03:09:30.360 | "Here's the safety profile.

03:09:32.200 | "Here's the risk-benefit balance,"

03:09:34.440 | and you should decide.

03:09:35.320 | You're a smart person.

03:09:36.200 | You should decide.

03:09:37.920 | Now, companies are gonna do differently, right?

03:09:41.280 | Companies may say, "You have to be vaccinated to work here."

03:09:43.320 | My employer, Columbia, said,

03:09:45.660 | "We have to be vaccinated to work in the fall,

03:09:47.380 | "and if you wanna be a student, you have to be vaccinated."

03:09:49.480 | So you decide whether you wanna go or not.

03:09:52.280 | But the idea that you should make a decision

03:09:57.280 | based on long-term effects, there is no evidence, right?

03:10:02.520 | So how can you make a decision when we don't have evidence,

03:10:05.240 | whereas we do have evidence

03:10:06.320 | that there are long-term effects of getting COVID?

03:10:08.480 | So I don't think that's a fair argument,

03:10:10.240 | and it just makes people scared to say that.

03:10:13.680 | But on the other hand, for someone to say it's a no-brainer

03:10:16.240 | and to denigrate people for not being vaccinated,

03:10:19.320 | that's not the approach either

03:10:20.440 | because they're gonna dig in and say,

03:10:23.280 | "I'm not doing this 'cause you tell me to," right?

03:10:25.720 | I think the middle ground is to say,

03:10:28.640 | take a bit of both and say,

03:10:31.640 | "Here are the potential issues, and here are the benefits,

03:10:34.980 | "and this is what I would do,"

03:10:37.320 | and you have to just decide on your own.

03:10:38.720 | I'd leave it to them.

03:10:39.560 | I say, "You decide, and if you don't want to,

03:10:40.900 | "you know, it's up to you.

03:10:42.240 | "You don't have to get vaccinated.

03:10:44.440 | "And you'll probably get infected at some point,

03:10:46.300 | "and maybe you'll be okay."

03:10:47.880 | (Luke laughs)

03:10:50.080 | But here's the best available data,

03:10:51.640 | and it looks like the vaccines are a pretty damn

03:10:55.800 | smart solution.

03:10:56.780 | They seem to work.

03:10:57.620 | - I think you tell people what you did

03:11:00.040 | and present both sides calmly,

03:11:01.920 | and I think digging in, you know, like in a debate,

03:11:04.740 | I don't think that's terribly useful.

03:11:06.960 | So that's my view.

03:11:08.940 | I mean, people come to me all the time and ask me,

03:11:11.840 | "I'm worried.

03:11:14.160 | "What should I do?"

03:11:14.980 | And I say, "What are you worried about?

03:11:16.120 | "Let's talk about it and go through it calmly."

03:11:19.020 | And if they want to still take ivermectin,

03:11:20.760 | I say, "It's fine, it's your choice.

03:11:22.600 | "I don't have a problem with that."

03:11:23.440 | - I love that.

03:11:25.000 | I love that's the way you think.

03:11:26.720 | People should definitely listen to "This Week in Virology"

03:11:30.420 | and follow your work.

03:11:33.000 | It's brilliant.

03:11:33.960 | I've been really enjoying it lately.

03:11:35.800 | It's like, it's my favorite way to stay in touch

03:11:38.360 | with the happenings of COVID.

03:11:42.200 | Obviously, you put in a lot of other stuff in there, but.

03:11:45.560 | - We used to do other viruses before COVID.

03:11:47.800 | It was quite interesting.

03:11:49.160 | And I'm trying to slip other viruses in

03:11:52.560 | because I think they're informative in many ways,

03:11:56.160 | and we're gonna do more and more of that.

03:11:57.320 | But I have to say, I canceled,

03:11:58.640 | usually I record on Tuesday and Friday,

03:12:00.880 | and I canceled today so I could be with you.

03:12:02.960 | - It's a huge honor.

03:12:03.920 | I appreciate that.

03:12:05.080 | - No, no, it's fine.

03:12:06.700 | I think a couple of other people

03:12:07.920 | were gonna be away anyway, so.

03:12:10.260 | So I do a lot of different pods.

03:12:12.060 | They're all on YouTube.

03:12:13.320 | I also do a live stream on Wednesday nights on YouTube,

03:12:17.640 | which you can find,

03:12:18.520 | and that's where people can come and ask questions.

03:12:21.400 | We don't have an agenda.

03:12:22.460 | We just start, and by 30 minutes in,

03:12:25.320 | there's 700 people with questions

03:12:27.480 | that I can't even get through

03:12:28.600 | 'cause there's so many of them.

03:12:29.600 | And I'm actually astounded that so many people

03:12:33.800 | have really good questions.

03:12:35.680 | Most of them are reasonable,

03:12:37.480 | and they come back every week.

03:12:39.000 | So it's a great, it's turning into a great forum

03:12:41.900 | to have a nice discussion.

03:12:43.280 | - And the YouTube channel's called what?

03:12:46.960 | - So you could search for my name,

03:12:49.000 | which is Vincent Draconello.

03:12:50.400 | It'll turn up.

03:12:51.320 | Or my handle on YouTube is profvrr,

03:12:56.400 | P-R-O-F-V-R-R.

03:12:58.060 | - Have you read The Plague by Camus by any chance?

03:13:03.080 | - Years ago, years ago.

03:13:05.800 | I have to read it again.

03:13:07.180 | That's really relevant, yeah.

03:13:08.720 | - Let me sort of ask you a question about it.

03:13:10.840 | It describes a town that's overtaken by a plague,

03:13:14.000 | and it's blocked off from the rest of the world.

03:13:16.500 | And it kind of reveals the best and worst of human nature.

03:13:19.480 | That's like how people respond to that,

03:13:21.240 | sort of the encroaching, their own mortality,

03:13:25.120 | their own death on the horizon.

03:13:27.240 | I think one of the messages in the book

03:13:28.960 | that ultimately, like love for others.

03:13:33.960 | So it's like a lot of people wanna become isolated,

03:13:36.840 | and they hide from each other.

03:13:38.440 | But ultimately the thing that saves you is love,

03:13:42.040 | which is one of the things I've,

03:13:44.000 | just watching this pandemic,

03:13:46.360 | you know, with the distance, with the masks,

03:13:48.940 | that's all fine.

03:13:50.560 | But there's a distancing from people,

03:13:53.880 | of that tension, the breaking of the common humanity

03:13:58.300 | between people.

03:13:59.260 | That's one of the reasons I,

03:14:00.660 | when I came to Austin earlier this year, just to visit,

03:14:05.180 | I fell in love with the city,

03:14:06.420 | because even with the masks and the distance,

03:14:09.020 | there was still a camaraderie,

03:14:12.340 | like, I don't know, just a love for each other,

03:14:15.780 | just a kindness towards each other.

03:14:18.040 | And that's what I took away from the plague.

03:14:20.840 | Mostly it's told the story of the doctor,

03:14:23.420 | who basically gives in,

03:14:27.020 | and just gives himself as a service to others.

03:14:30.500 | And that love is the thing that liberates him

03:14:33.000 | from his own conception of mortality.

03:14:34.700 | The fact that he's here, he's going to die.

03:14:37.940 | What do you think about this, the effect of the virus?

03:14:41.780 | We talked a lot about biology,

03:14:43.100 | but the effect of the virus on the fabric

03:14:48.100 | of the common humanity that connects us.

03:14:50.620 | - That's what a pandemic does.

03:14:54.220 | It really cuts that, right?

03:14:56.340 | 'Cause small outbreaks are local,

03:14:58.500 | they don't have global effects.

03:15:00.940 | But when you have something this big,

03:15:03.120 | where pretty much nobody escapes,

03:15:07.480 | and not just making people sick,

03:15:10.260 | it changes your life, right?

03:15:12.500 | People lose jobs, they change jobs,

03:15:14.620 | they move somewhere else.

03:15:16.140 | They have all kinds of disruptions.

03:15:20.300 | Kids can't go to school.

03:15:22.100 | Really shows you, I mean, I always like to say,

03:15:25.540 | a tiny virus can bring earth to its knees.

03:15:30.300 | A tiny virus that you can't even see,

03:15:31.860 | and that most people don't even think about

03:15:33.460 | most of the time.

03:15:34.380 | And the real effect is not just sickness,

03:15:37.780 | it's what it does to people.

03:15:39.260 | Because in the end, we are animals,

03:15:42.860 | and most animals like each other.

03:15:45.340 | And they interact, they have great social structures,

03:15:47.640 | and that makes them do well.

03:15:49.820 | I guess the exception is people in AI, right?

03:15:54.060 | They can be on their own.

03:15:56.620 | - Well, that's why you build robots

03:15:57.900 | that you fall in love with.

03:15:59.020 | - That's right.

03:15:59.860 | And so I think when a,

03:16:02.020 | the real story is what it does to society, for sure.

03:16:06.100 | Which has ramifications way beyond

03:16:08.680 | the number of people dying,

03:16:10.580 | and the vaccines, and the tests, and all of that.

03:16:12.760 | And this one has really made a big rupture.

03:16:15.660 | And you could tell, not now so much,

03:16:17.400 | I think being out and about now,

03:16:18.700 | things look pretty normal,

03:16:21.140 | except for some people wearing masks.

03:16:23.820 | You would never know.

03:16:25.060 | I mean, the airport this morning was completely jammed.

03:16:28.260 | People going, and they're all on vacation,

03:16:29.740 | they're all wearing shorts, right?

03:16:31.540 | So they're back to normal, it's August.

03:16:34.380 | But last year is really different.

03:16:36.840 | In New York, where you're used to lots of people

03:16:39.660 | on the street, it was eerie.

03:16:41.220 | It was just quiet.

03:16:43.080 | But under it all, people are still,

03:16:46.060 | most people help each other when they have to, right?

03:16:49.060 | Most people are willing to,

03:16:52.360 | if something happens to someone,

03:16:53.660 | to reach out and help them.

03:16:55.360 | There are always exceptions where people are meaned,

03:16:58.880 | and that's just the way animals are.

03:17:01.940 | We're not the only ones that can be meaned

03:17:03.880 | to our own species.

03:17:05.780 | But I think most of the motivation

03:17:08.740 | for everything that was done is to help other people.

03:17:12.300 | I mean, I do think that the vaccine manufacturers,

03:17:15.660 | maybe not the leaders, but the people working in the labs,

03:17:18.180 | really wanted to get this out quickly and help people.

03:17:21.660 | I think at every level, people who are contributing

03:17:26.420 | really wanted to help other people,

03:17:28.020 | and feel proud that they're able to do that.

03:17:30.260 | So I view it as, we're never gonna be 100% good

03:17:35.740 | because animals are not.

03:17:37.740 | Evolution made us, I mean, we're lucky.

03:17:40.520 | We somehow rose above by having

03:17:42.700 | incredible brain and so forth.

03:17:44.200 | But a lot of our base instincts are animals.

03:17:47.020 | They chase each other and have alpha males

03:17:50.460 | and all that stuff.

03:17:51.300 | And we always have a little bit of that in us.

03:17:53.520 | But we do have some humanity that this really ripped up.

03:17:58.520 | It really did.

03:18:00.020 | And I think, for me, someone who studied viruses

03:18:05.240 | for over 40 years, it's just amazing

03:18:07.860 | that an invisible thing can do that, right?

03:18:11.340 | - It goes back to the thing you found fascinating,

03:18:13.420 | which is a virus affecting human behavior.

03:18:15.300 | - Yes. - Or behavior of the organism.

03:18:17.660 | - Yes, so humans can make weapons and do harm,

03:18:22.960 | and you can see that, but this you can't even see.

03:18:25.460 | You can't, and look what it has done.

03:18:27.700 | And it'll do it again.

03:18:28.620 | There'll be more.

03:18:29.780 | I just, I wish we would be more prepared,

03:18:32.840 | because we know what to do.

03:18:34.840 | We know we should be making antivirals, vaccines, masks,

03:18:38.900 | testing masks, making test modalities

03:18:42.140 | that we can really quickly redesign.

03:18:46.140 | But after SARS-1, all that went out the door.

03:18:49.100 | People didn't do anything,

03:18:50.200 | and that's why we're in this situation.

03:18:51.900 | So people ask me this all the time.

03:18:54.860 | Are we gonna be ready for the next one?

03:18:57.840 | And I always say, we should be.

03:18:59.540 | We have all the information we need to know what to do.

03:19:03.040 | But somehow, I think people forget.

03:19:05.880 | - That said, sometimes we really step up

03:19:12.780 | when the tragedy is right in front of us.

03:19:14.860 | - We do. - On the catastrophe.

03:19:16.140 | So I don't know.

03:19:17.260 | Somehow humans have still survived.

03:19:18.780 | The fact that we had nuclear weapons for so many decades,

03:19:21.100 | and we still have not blown each other up,

03:19:23.140 | whether by terrorists or by nation,

03:19:24.980 | is quite surprising. - It's amazing.

03:19:27.540 | That's always, after reading the Pentagon Papers,

03:19:30.660 | it's even more amazing, right?

03:19:32.960 | So I don't know how we do it.

03:19:34.420 | I tend to believe there's that,

03:19:38.560 | at the surface, you notice the greed, the corruption,

03:19:42.300 | the evil, but the core of human nature,

03:19:45.580 | of the human spirit is,

03:19:46.980 | one, in the scientific realm, is curiosity,

03:19:50.840 | and more deeply is kindness, compassion,

03:19:54.340 | and wanting to do good for the world.

03:19:56.820 | I believe that desire to do good

03:19:59.380 | outpowers all the other stuff

03:20:02.160 | by a large amount, and that's why we don't,

03:20:04.560 | we have not yet destroyed ourselves.

03:20:06.960 | There's a lot of bickering.

03:20:08.320 | There's a lot of wars on the surface,

03:20:10.360 | but underneath it all, there's this ocean

03:20:13.140 | of love for each other.

03:20:16.240 | I mean, I think there's an evolutionary advantage to that,

03:20:20.020 | and it would be a good explanation

03:20:23.640 | why we still haven't destroyed ourselves.

03:20:25.440 | - God, we had so many opportunities.

03:20:27.440 | If you look at all the wars in history, so many.

03:20:30.540 | I was just, my son was telling me

03:20:33.360 | about the Ottoman Empire, right?

03:20:35.880 | I mean, it's just, you know, war after war,

03:20:39.520 | and then other countries splitting up countries

03:20:42.120 | with no regard to who's living where, right?

03:20:45.300 | It's just, how can these people do this?

03:20:49.440 | - Yeah, it's fascinating.

03:20:50.840 | Human history's fascinating,

03:20:52.040 | and we're still young as a species.

03:20:54.600 | We have a lot-- - Very young, yeah.

03:20:56.320 | - More time to go, and a lot more ways

03:20:59.120 | to destroy ourselves.

03:21:00.540 | Do you have advice, like you said,

03:21:02.240 | you have many decades of research

03:21:03.880 | and an incredible career and life.

03:21:06.400 | Do you have advice for young people

03:21:08.060 | about career, about life, people in high school,

03:21:11.720 | people in college, of how to live a life

03:21:15.360 | they can be proud of?

03:21:16.460 | - So, what I like to do is tell people,

03:21:20.000 | don't plan it, because I didn't plan anything.

03:21:22.360 | Everything I did was one step at a time.

03:21:25.000 | You don't have to plan.

03:21:26.660 | I just found things that were interesting to me.

03:21:31.140 | So, my father was a doctor,

03:21:33.500 | and he wanted me to be a doctor,

03:21:35.820 | but I was not interested in taking care of people.

03:21:39.100 | I learned that, but I couldn't say no to him.

03:21:43.220 | So, I was a biology major in college,

03:21:46.340 | and I graduated, and I didn't have anything to do.

03:21:51.340 | So, I liked science, so I got a job in a lab.

03:21:56.180 | It was very exciting, and that led to everything else

03:22:00.700 | that I've done, one step at a time.

03:22:02.900 | And I think the most important thing you can do,

03:22:05.860 | well, there are two important things.

03:22:06.980 | You can be really curious all the time.

03:22:08.700 | You mentioned curiosity.

03:22:10.080 | I think curiosity is essential.

03:22:12.060 | You have to be curious about everything,

03:22:15.620 | and if you are, you're never gonna be bored.

03:22:18.180 | And so, people who say they're bored,

03:22:20.980 | I say, you are not curious.

03:22:22.540 | You should just think about things

03:22:24.220 | and say, look at something and say, how does that work?

03:22:27.460 | Or what is it doing, and how do they get there?

03:22:29.340 | And you'll never be bored.

03:22:30.500 | And the other thing is when you find something,

03:22:32.900 | which may take time, it's fine.

03:22:35.360 | You have to be passionate about it.

03:22:38.580 | You have to put everything into it,

03:22:40.900 | and that's what I did with viruses.

03:22:42.980 | So, I think they're amazing, and I tell my classes,

03:22:47.980 | I love viruses.

03:22:51.220 | They're amazing, and people think I'm morbid

03:22:53.140 | because obviously they kill people,

03:22:55.340 | and I shouldn't love something.

03:22:56.580 | But that's not the point.

03:22:57.500 | That's not what I mean.

03:22:58.340 | I love them in the way they have emerged

03:23:00.540 | and how they work and so forth

03:23:03.120 | and all that we don't know about them.

03:23:04.300 | So, you need to be curious and passionate

03:23:06.300 | and don't plan too much.

03:23:07.760 | And just find something that you don't call a job.

03:23:11.060 | As someone said on the livestream last week,

03:23:14.580 | I wish I had a job I liked as much as you.

03:23:17.380 | I said, it's not a job.

03:23:18.460 | I never looked at it as a job.

03:23:20.420 | It's my vocation, it's my passion.

03:23:23.740 | If it's a job, then you're not gonna like it.

03:23:25.940 | - Yeah, something that doesn't feel like a job.

03:23:28.420 | So, you said viruses are kind of passive,

03:23:33.020 | non-living, you could say, or even cells are passive.

03:23:38.540 | And humans are kind of active.

03:23:39.900 | We seem to be making our own decisions.

03:23:42.340 | So, let me ask you the why question.

03:23:44.780 | What do you think is the meaning of this life of ours?

03:23:48.820 | - Oh, there's no meaning, it just happened.

03:23:51.380 | It's an accident.

03:23:52.320 | I think there's no life elsewhere

03:23:55.100 | because this is just a rare accident that happened

03:23:57.380 | in the right conditions.

03:23:58.740 | I mean, people all think I'm wrong

03:24:00.360 | because there are billions and billions

03:24:01.780 | of stars out there, right?

03:24:02.940 | So, there's a lot of opportunity.

03:24:05.220 | There's no meaning.

03:24:06.340 | It's just, what do they call it?

03:24:10.580 | A perfect storm of events that led

03:24:13.340 | to molecules being formed and eventually,

03:24:15.840 | I mean, it took a long time for life to evolve, right?

03:24:19.960 | But it's just driven by conditions.

03:24:24.080 | If something emerged that worked,

03:24:25.600 | it would then go on to the next step.

03:24:27.040 | There's no meaning other than that.

03:24:29.200 | The only difference is that we,

03:24:31.160 | and I think many other animals can probably,

03:24:34.120 | we have the ability, we're sentient, right?

03:24:36.080 | We can influence what happens to us.

03:24:38.280 | We can take medicines, right?

03:24:41.480 | We can alter what would normally happen to us

03:24:44.480 | so we can remove some of the selection pressure.

03:24:47.880 | But I think everything else on the planet just goes,

03:24:52.720 | looks for food and give a lot of offspring

03:24:55.160 | so you can perpetuate.

03:24:56.240 | It's just a natural biological function.

03:24:58.720 | - Yeah, they're much more directly concerned with survival.

03:25:01.960 | I think humans are able to contemplate their mortality.

03:25:04.640 | We can see that even if we're okay today,

03:25:08.500 | we're eventually going to die

03:25:10.320 | and we really don't like that.

03:25:13.040 | So we try to come up with ways

03:25:14.400 | to push that deadline farther and farther away.

03:25:17.040 | - Well, we have really,

03:25:18.440 | I mean, we used to die in our 30s, right?

03:25:20.440 | Now it's 70s, 80s.

03:25:22.040 | - Well, most of us used to die in the first few weeks.

03:25:26.120 | - That's true.

03:25:27.260 | Yeah, infant death.

03:25:29.120 | I always tell people the only thing

03:25:31.980 | that's 100% is death.

03:25:34.280 | It's the only thing in the world that's 100.

03:25:36.280 | - Do you think about your own mortality?

03:25:37.720 | - No, I never think about it.

03:25:39.040 | I'm just enjoying day to day and I don't think about it.

03:25:42.200 | - Really?

03:25:43.040 | You work on viruses,

03:25:44.200 | you don't contemplate your own mortality

03:25:47.600 | given the deadliness of the viruses around us?

03:25:51.760 | - I never thought COVID would kill me.

03:25:53.600 | No, I never was afraid of that, not at all.

03:25:56.280 | I mostly feared for other people getting sick,

03:26:00.400 | especially people who could die.

03:26:02.160 | I didn't want that to happen to them.

03:26:03.640 | But I always thought that,

03:26:04.920 | it's obviously not a realistic viewpoint not to be worried

03:26:11.800 | because many people are.

03:26:13.920 | But I've been relatively healthy.

03:26:16.120 | They should sequence my genome

03:26:18.520 | because it works really well and I have a good immune system.

03:26:21.360 | - Maybe you'd be the first immortal person.

03:26:24.160 | - I don't think so. - There's gotta be a first.

03:26:25.920 | - I don't think so.

03:26:27.200 | I think that biologically you just can't,

03:26:31.280 | the ends of our chromosomes keep getting shorter

03:26:33.420 | and shorter and that's eventually what kills us.

03:26:36.200 | So you just can't keep going on.

03:26:39.680 | But that's fine, I don't need to.

03:26:42.280 | I understand from the vampires

03:26:43.760 | that it's not good to live forever.

03:26:45.560 | - I guess make the most of the time you got.

03:26:50.440 | That's the, bacteria live a much shorter time

03:26:53.240 | so we got that on bacteria.

03:26:55.160 | - Bacteria are just little bags of chemicals that split.

03:27:00.160 | So they have no stake in the matter at all.

03:27:05.160 | I think you have to go a long ways

03:27:08.240 | before you get into some kind of consciousness.

03:27:13.000 | - Yeah, it's weird that this bag of chemicals

03:27:15.480 | has a stake in the matter.

03:27:17.080 | Like our human body is, consciousness is a weird thing.

03:27:21.400 | - Not just in us, but they make half of the oxygen

03:27:23.560 | on the planet, 20% of the oxygen comes from bacteria.

03:27:26.400 | And they made, in the beginning of Earth,

03:27:29.940 | they made enough oxygen to start oxygenation going,

03:27:34.120 | life going, I mean, they have an incredible role.

03:27:36.560 | It's all an accident, just happened.

03:27:38.400 | - Well, Vincent, like I told you, I'm a huge fan.

03:27:44.680 | It's a big honor that you were talking with me today.

03:27:47.080 | Thank you so much for coming down.

03:27:48.440 | Thank you for spending so much time with me.

03:27:50.640 | And thank you for everything you do

03:27:53.040 | in terms of educating about viruses,

03:27:54.920 | about biology, microbiology and everything else.

03:27:57.320 | I can't wait, everybody should check out

03:27:59.160 | Vincent's YouTube, watch his lectures,

03:28:01.920 | listen to the podcast, it's truly incredible.

03:28:05.080 | Thank you so much for talking to me, Vincent.

03:28:06.800 | - My pleasure.

03:28:08.260 | - Thanks for listening to this conversation

03:28:10.240 | with Vincent Recaniello.

03:28:12.280 | To support this podcast, please check out

03:28:13.920 | our sponsors in the description.

03:28:16.580 | And now, let me leave you with some words

03:28:18.760 | from Isaac Asimov.

03:28:21.000 | The saddest aspect of life right now

03:28:23.160 | is that science gathers knowledge

03:28:25.420 | faster than society gathers wisdom.

03:28:27.760 | Thank you for listening and hope to see you next time.

03:28:31.520 | (upbeat music)

03:28:34.100 | (upbeat music)

03:28:36.680 | [BLANK_AUDIO]

Vincent Racaniello: Viruses and Vaccines | Lex Fridman Podcast #216

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