0:00

This Week in Virology, the

0:02

podcast about viruses, the kind

0:05

that make you sick. From

0:11

Microbe TV, this is Twiv,

0:13

This Week in Virology, episode

0:16

1111, recorded on May 3rd,

0:25

2024. I'm Vincent Dracken-Yellow, and you're listening

0:27

to the podcast all about viruses. Joining

0:30

me today from Fort Lee, New

0:33

Jersey, Dixon de Pomier. Hello,

0:35

Vincent and Alan. I will just include Alan,

0:37

even though he hasn't been introduced yet. I

0:41

just got back from a three-day fishing trip

0:43

in the Catskills, so that's why my face

0:45

looks a little red. And my hands, I

0:47

can't tell you how

0:50

sunburned they are. Well, I can show you.

0:53

That's from holding a fly rod in

0:55

one position and having the sun bake you

0:57

like you're some kind of a gourmet

1:00

dish for the gods. But I

1:02

had a great time. Today, the temperature

1:04

is about, I would estimate, I haven't checked

1:06

it, but it feels like in the high

1:09

40s or low 50s. It's a little bit

1:14

windy. It's typical

1:16

springtime weather, but it's delightful. It's

1:18

absolutely, all the trees are blooming.

1:21

All the leaves are unfolding. It's just my

1:23

favorite time of the year. It

1:25

is here in Chelsea, 17

1:28

Celsius and partly

1:30

cloudy. I didn't see any

1:32

sun this morning. Also joining

1:34

us from Western Massachusetts, Alan Dove. Good

1:36

to be here. And I apparently have

1:38

the warmest weather. It's 67 Fahrenheit, 19

1:40

C. She will.

1:43

Mostly cloudy. It's supposed to rain later

1:45

on this weekend on Sunday, but just

1:48

cloudy now. I guess because

1:50

you're inland, you have a higher temperature. Yeah.

1:52

We have different weather patterns than

1:54

the coast. So

1:56

it's episode 11-11. That's an

1:58

interesting number. We're just bantering. about it. If

2:00

only we could have recorded it in November. Exactly.

2:03

That's right. On

2:06

the 11th of November. There was a year, 11-11, right? There was a year

2:08

11-11, we were not recording

2:12

twiv back then. No, they weren't recording

2:14

anything then. No. This is our first

2:17

four-digit, what do you say, all

2:19

the same numbers, is there a word for that? Probably.

2:21

There might be. I wouldn't

2:23

know it. Then Dixon said,

2:26

well what about all the other twids with just

2:28

ones in them. So there's

2:30

twiv number one, which was Dixon and I.

2:32

That's West Nile story. Yeah, that's right. That's

2:34

easy. Twiv 11 was one of the ones

2:36

where Alan was here. Oh.

2:40

Actually, it was me, Alan, and

2:42

Jeremy Luban. It was

2:44

called Elite Controllers. Oh yeah.

2:46

Mosquitoes and Winter Vomiting. That's

2:50

11. Twiv 111, live at Florida

2:54

Gulf Coast University. Oh

2:56

my. That's when Rich and

2:58

I went down there and had

3:01

some guests, including. So it was

3:04

the Dengue Control, Florida Keys Mosquito

3:06

Control District. I came in as

3:09

a disembodied head on that episode.

3:11

Yeah, you did. We had the

3:13

pilot that flew the people around.

3:16

Well, and who flew the helicopters for

3:19

the pest control operations? A talking head.

3:21

Yeah, that's 111. Then 1111, well,

3:24

we don't know what the title is yet. We

3:26

do not know, but we probably can assume that

3:29

Alan had the title. Well, I don't know. We'll

3:31

see. I have a few ideas. You

3:34

like what we do on this program

3:36

this week in virology and all the

3:38

others that we produce here at Micro

3:40

TV, we would love to

3:43

have your support to continue their production.

3:45

It doesn't have to be a lot.

3:47

It could be less than a cup

3:49

of coffee a month. You know, coffee

3:51

is expensive. Well, Vincent, I

3:53

don't drink coffee. Okay. Whatever your drink

3:55

is, tea, you know, all

3:57

the stuff with sugar in it, you get at

3:59

the. fancy coffee places which are not

4:01

really coffee, but you know

4:04

I go and I order a coffee and they're like what's that?

4:08

We'd love your support. Just

4:10

think of it as giving us a cup of

4:12

coffee a month. Microb.tv slash contribute. You could set

4:14

that up at Patreon or PayPal. A

4:19

few in meeting announcements,

4:21

the pandemic preparedness meeting

4:24

is November 11th through the

4:26

13th in Trieste, Italy to

4:29

provide an overview of known and

4:31

emerging viruses and their potential epidemic

4:33

risk and we will

4:36

put a link to that in the

4:38

show notes. Also the molecular determinants of

4:40

zoonotic viruses and beyond. You're getting a

4:42

theme here about meetings. You

4:44

seem to be focusing on emerging

4:47

viruses, zoonotic viruses. Anyway

4:50

that one is in Freiberg,

4:52

Germany and that's going to be in 2025. March

4:56

1st is when it begins

4:59

at the Institute of Virology in Freiberg,

5:02

Germany. And don't forget

5:04

Dixon's book, The New City

5:07

at depomier.com. In

5:10

the news courtesy of Amy Rosenfeld,

5:12

there's a Nature

5:14

article called Chinese Virologist

5:16

Who Was First to Share

5:19

COVID-19 Genome. There's

5:21

no COVID-19 genome. Doesn't

5:24

exist folks. Sleeps on

5:26

street after lab shuts. COVID-19

5:29

does exist but the virus is

5:31

called SARS-CoV-2. Yeah, there's no genome

5:34

of COVID-19 exists. Now to be

5:36

clear, his lab has not been

5:38

shut down and he's not being,

5:41

as far as I can tell, he's

5:43

not being like punished for anything. He

5:45

just, there's a dispute about where his

5:47

lab is being moved and how and

5:50

he's left outside the lab and not

5:52

able to do his work and so

5:54

he's camping out, literally camping out on

5:57

the street. My goodness. Why does Nika

5:59

home? I think he wants

6:01

to make a point. Ah,

6:04

yeah, so that didn't take away his home. No,

6:06

no, no. He, as far as I know, he

6:08

has a home, but what's happened is that there's

6:10

some, there's some snafu

6:13

going on with a lab move

6:15

and renovations, and this is very

6:18

unfortunate. They didn't take away his Eppendorf's,

6:20

did they? I don't know

6:22

what the movers took. That would

6:25

be interesting. So if you

6:27

may remember, at the beginning of 2020, he

6:29

was among the first to sequence the genome

6:31

of SARS-CoV-2 in China, and he

6:34

is a collaborator of Eddie Holmes, and

6:37

Holmes said, you need to release this sequence. People

6:39

need to start working on it. He said, oh,

6:42

I can't. The Chinese government doesn't, they want to

6:44

do it. And apparently

6:49

Holmes was on the phone to Zhang just before

6:51

he boarded a flight. Zhang

6:53

was about to board a flight, or the

6:55

door was about to shut, and Holmes said,

6:57

you got to send it. So he emailed

6:59

it from his phone, boom, and he got

7:01

it. And then that was the sequence that

7:03

was used to make the mRNA vaccines, essentially.

7:06

And then China government got pissed at him.

7:09

And, you know, he

7:11

hasn't been, he's been persona non

7:13

grata in China, I presume. Yeah,

7:15

I don't think, so from the article,

7:17

I shouldn't say that he's

7:19

not being targeted, but he, because I

7:21

don't know what the exact situation is, but from

7:24

the article, what I get is that this is,

7:28

some lab move has been

7:30

handled poorly, and he's probably,

7:32

I think probably quite rightly upset about

7:34

it and is making his point. Hmm,

7:38

I hope he's got a blanket. Yeah,

7:41

I hope everything's okay. He's not a

7:43

young man. A sleeping bag also. Yeah.

7:46

Well, he would feel at home here in New

7:48

York, that's for sure. All

7:51

right, the next article is from

7:53

the Jerusalem Post. Health Ministry announces

7:56

measles case in Haifa, and

7:59

the case of measles... in

8:02

Haifa, the patient had returned

8:04

from abroad and left a beak

8:06

arrest on Wednesday, April 24th and was

8:12

found to have measles upon arrival

8:15

in Haifa and also visited a

8:17

urgent care clinic on

8:20

the night of Friday, April 26th. So

8:23

you can imagine that he may have, he

8:25

or she may have transmitted

8:28

the virus further at that health

8:30

clinic. Yeah. Further,

8:32

and if there are people who are

8:35

not vaccinated against measles in Israel,

8:37

this could be a problem. Indeed.

8:43

The next one is a

8:45

Reuters article. The UN

8:47

says waterborne illnesses are

8:49

spreading in Gaza due to heat and

8:52

unsafe water. And

8:55

so waterborne diseases are spreading. It's

8:58

becoming hot there. People are getting much

9:00

less water they need. They've been

9:02

waterborne diseases due to lack of safe and

9:04

clean water and the disruption of the sanitation

9:06

system. Which you can imagine

9:08

is happening because everything's getting trashed,

9:10

right? This is a depressingly

9:13

common problem in war zones and

9:16

that's what's going on. Yeah.

9:19

Since mid-October, following

9:22

the assault on Gaza, WHO

9:25

has recorded more than 345,000 cases of

9:28

diarrhea, including 100,000 and kids under five. Those

9:32

are the cases that came to the attention of the

9:35

WHO. So this is... Yeah. ...typopanis.

9:38

Yeah. Gaza's only natural source of

9:41

water is the coastal aquifer basin,

9:43

which runs along the eastern Mediterranean

9:45

coast. Its

9:47

quality over the years has deteriorated largely because

9:49

it had been pumped out to meet the

9:52

demands of Gaza's population more rapidly

9:54

than it could be replaced by rain and

9:56

water. This

9:58

is what happens in a... in

10:00

a war zone, as Alan said, it's highly

10:03

unfortunate. Yeah, there are situations too,

10:05

like when there's a flood or, you

10:07

know, anytime the water supply

10:10

is compromised. Yeah. We

10:15

have a bio-archive preprint, which

10:19

is interesting and maybe will become published

10:21

one day, and maybe we'll cover it.

10:24

Emergence and interstate spread of highly pathogenic

10:26

avian influenza A, H5N1, and dairy cattle.

10:30

You know, someone said to me recently, if

10:33

it's in other than a bird, don't

10:35

call it highly pathogenic. It's highly pathogenic

10:37

in certain birds, but like

10:39

in cows, it's not highly pathogenic. So

10:42

highly pathogenic shouldn't be applied to it everywhere.

10:44

So it's H5N1. So what

10:46

is this? Well, okay, so

10:49

it's referred to, this is

10:51

not like other viruses that have

10:53

a name controversial or

10:56

not. HPAI. Yeah,

10:58

it's a thing. H5N1 doesn't tell

11:00

you exactly which H5N1, right? Yeah,

11:03

there's HPAI and LPAI. Right.

11:05

Okay, fair enough. So this is the specific

11:07

HPAI. But yes, it is misleading to say

11:09

highly pathogenic avian influenza A and dairy cattle

11:12

because it's not highly pathogenic and dairy cattle.

11:14

But it is part of the name, you're

11:16

right. Yes.

11:18

Right. This, in North

11:20

America, so viruses related to goose, quandong,

11:22

2.3.4.4 BHA have infected wild

11:26

birds, poultry, and mammals. And

11:29

they did genomic and epidemiological investigations

11:31

showing that a reassortant event in

11:33

wild bird populations preceded a single

11:35

wild bird to cattle transmission episode.

11:39

And the movement of asymptomatic cattle has likely

11:41

played a role in the spread of a

11:43

virus within the U.S. Dearly herd. Some

11:46

molecular markers and virus populations were detected

11:49

at low frequency that may lead to

11:51

changes in transmission, efficiency in

11:53

phenotype after evolution in dairy cattle.

11:56

Continued transmission in cattle increases risk for

11:58

infection and subsequent spread. to human

12:00

populations. So

12:02

Vincent, would you call that a

12:05

lethal mutation? Oh

12:07

boy. Pretty good, Dickson.

12:10

Yes. No, it may be an attenuating mutation.

12:12

Okay. You

12:14

could be on Colbert. I used to.

12:16

I worked on it. You

12:18

were, yeah. I know. If

12:21

it does become highly pathogenic in dairy cattle, that'll be

12:23

the dark side of the moo. That's

12:26

right. And

12:29

now finally we have. I'm sorry, I shouldn't

12:31

joke. It is a serious issue that needs to be

12:33

tracked and we will. I think we're going to have

12:35

somebody talking. Don't we have a guest coming up? We

12:37

have a guest coming up who will talk about it.

12:40

Yeah. An expert. Finally

12:43

from, and Amy writes,

12:46

because it's cool. Arangutan

12:49

plays doctor, heals himself. This

12:51

is in Science News. And

12:55

it's very interesting. They

12:57

find that an Arangutan and Sumatra used

12:59

a medicinal plant to heal a wound

13:02

on its face. That's right. It's

13:05

the first documented case of an ape

13:07

using a plant with scientifically proven medicinal

13:09

properties to treat a fresh

13:11

wound. And you

13:14

know, it's not surprising because animals

13:16

can learn what helps them and

13:18

then they remember it, right? Yeah.

13:22

So some chimps chew bitter pits

13:24

of the medicinal shrub, vernonia, and

13:26

mygdalena to treat worm infections. Did

13:28

you know that, Dixon? Oh,

13:31

yes and no. They've looked into

13:33

it, whether that mythology and some of

13:35

it has,

13:37

it has never borne fruit in terms of

13:40

coming up with a new drug to treat

13:42

worm infections with. So it's hard

13:44

to know how much truth there is in that. So

13:47

this Arangutan probably was involved in a flight,

13:49

a fight. And not in a full flight.

13:51

A fight. And they

13:54

saw him eating leaves

13:56

of fibrooria tinctoria.

13:59

They... also is applying it

14:01

to the wound itself. Yeah. Yeah, it

14:03

contains dozens of bioactive chemicals. Yes.

14:07

Yeah, so it's an medicinal plant that's

14:09

apparently used traditionally and has

14:11

been proven to have compounds in it that

14:13

are relevant for this as

14:16

an analgesic and

14:18

for other purposes. And so this ape

14:21

appears to have figured out that chewing

14:23

on this particular plant helps

14:26

kill the pain from the wounds and

14:29

then he was seen chewing some of

14:32

it and putting the paste onto the

14:34

wound. Amazing. Which is right under his eye and. Hailed

14:37

in month. Yeah. But they didn't

14:40

have a control. They did not have a

14:42

control. They did not. I

14:44

presume he was... He got the idea

14:46

from watching a Nat Geo show. Most

14:48

likely, yes. I mean, he was clearly putting

14:50

it on there, which... Yeah,

14:53

I mean... So he's self-aware, right? Yeah.

14:57

He probably didn't look... He didn't probably see it because

14:59

he has no mirror. Doesn't have a mirror. But

15:02

he probably felt that it hurt and he probably put his

15:04

fingers up there. It's got quite a wound you can see

15:06

in the picture. Yeah, yeah, he's got a real gash there.

15:09

And he's lucky he didn't lose his eye. I don't know what

15:11

the other guy looked like. You

15:13

should see the other guy. Right. What an

15:15

interesting animal though, right? Yeah. As

15:19

I would say in the trade, you should see the

15:21

other non-human primate. Right. This

15:23

looks like his lip was banged up too and that's

15:25

healed. Yeah. Yeah,

15:27

that's just very cool. I'm very happy when animals...

15:30

Well, they've been doing it longer than we have. Right?

15:34

You know, elephants seek out mineral

15:37

deposits of various sorts. Not

15:39

just salt, but other minerals as well. And

15:42

they remember where they are, of course. And... Animals

15:47

are much smarter than we give them credit for. That's

15:50

what they also know which plant to

15:52

do, right? Absolutely. They pick any plant. They

15:55

pick the one that they have had experience.

15:57

That's right. Probably many, many years of experience.

15:59

And then in which one? not to eat.

16:01

Yeah. That

16:04

they learned the years

16:06

ago, right? Just take one bite, forget about

16:08

it. Three weeks of diarrhea. I don't need that. Okay,

16:11

now we have two papers for you today.

16:13

The first is a

16:16

Nature Communications article. MyADM,

16:19

myADM is a nice word,

16:21

we'll tell you what it

16:23

is. It's myeloid association differentiated,

16:25

differentiation marker. It's a protein, but

16:29

it just has a name. Binds

16:32

human paracovirus 1 and is essential

16:34

for virus entry. This comes from

16:36

Shao, Richards, Kim, Zengel, Ding, Greenberg,

16:38

and Caret. Jan Caret is the

16:41

PI here. This comes from Stanford and

16:44

the VA. Palo Alto Health

16:47

System. And Washington University. Oh

16:49

yeah, WashU is on theirs too. So

16:51

the reason I chose this, there are two

16:54

reasons. There's always a reason. There's always a

16:56

reason. First, I

16:58

wanted you to learn about paracoviruses.

17:02

Second, it's a very clever approach

17:04

to identifying a cell receptor

17:06

for a virus. And I mean,

17:09

I was always interested in these because my

17:11

lab, these are for

17:14

hornoviruses. My laboratory, specifically

17:16

Kathy Mendelson, one of my first

17:18

graduate students, did you overlap with

17:20

Kathy? No, she had just left

17:22

when I entered the lab. She

17:24

identified the cellular receptor for poliovirus

17:27

very early in the late

17:29

80s, the time when we knew very few

17:31

virus receptors. And when I talk about how

17:34

this one was found, I'll tell you how we

17:36

did it. It's just a

17:39

totally different technology. Paracoviruses.

17:42

So the pecorinovirus is

17:45

a family that comprises like 40

17:48

genera, and one

17:50

of those genera is enterovirus,

17:53

which contains polioviruses and coxaki

17:55

viruses and echo viruses.

17:59

And the echo used to

18:02

be echoviruses.

18:05

They just were echoviruses

18:07

until Timo-Hipia in

18:09

Finland. He studied them.

18:12

He said, these are really different. And

18:15

then the genomic analysis shows, so these were discovered

18:18

long before we were able to

18:20

sequence genomes, showed that they were

18:22

distinct members of the enterovirus

18:24

genus. So now they're called paracovirus. What

18:26

are they called, echo?

18:28

What's the word? Enteric human cytopathogenic

18:32

orphan viruses. Is that right? Yeah,

18:35

say it again, because Dickson was... I'm on

18:38

the level of the issue. Sorry. Enteric

18:42

cytopathogenic human orphan viruses. Not because

18:44

they infect human orphans, but because

18:47

the viruses were discovered and were not associated

18:49

with any known disease. That's right.

18:52

So they were thought

18:54

to be completely asymptomatic.

18:57

We now know that there are echoviruses that can

19:00

cause serious problems, but a lot of

19:02

them are asymptomatic. So when Timo-Hipia

19:06

said these are different, he decided to call them paraco,

19:08

right? So not that

19:10

different, but what does the

19:12

prefix par mean? P-A-R,

19:15

is there a meaning to that?

19:18

I don't know exactly. Does it

19:20

mean around? Right.

19:24

Para is close to or... Para.

19:32

This is not ortho. Right. No.

19:34

Anyway. So

19:37

para echoes, there are 19 genotypes, and

19:40

the most prevalent are A1 and A3. They frequently

19:44

infect young children and typically

19:47

cause mild GI or respiratory symptoms.

19:49

So you can have respiratory

19:52

infections or GI infections. And

19:54

they are being increasingly realized

19:56

as to be causes of severe disease.

20:00

in young children including

20:02

neonatal sepsis, childhood

20:04

meningitis and encephalitis. So they're

20:06

an emerging pathogen

20:08

because we used to

20:11

think they didn't do anything, which was just

20:13

ignorance on our part. So they're not emerging,

20:15

but our knowledge of them is. Yeah. They've

20:17

been around for a very, very long time,

20:19

but we're figuring out now that

20:21

they can be serious. Emerging refers

20:23

to not only

20:25

a new virus to humans like

20:28

SARS-CoV-2, but a virus that we've

20:30

known about that has a new

20:32

disease like leukovirus, right? Yeah.

20:35

So this paper looks for the receptor.

20:37

This is one of the things you'd like to

20:40

know for a virus to be able to understand

20:42

how it causes disease and so forth, maybe

20:46

design some interventions, but that really doesn't

20:48

work very well. And so

20:50

they have

20:52

a very clever way of looking for the receptor

20:55

and then doing a series of experiments that

20:59

show that this is a required receptor

21:01

and where in the entry process it's

21:03

working. So a

21:05

little background on what we

21:08

know about paracoviruses. Now, the

21:10

genotype A1 binds

21:13

to cellular membrane

21:16

proteins called integrins. And

21:18

there's an arginine

21:21

glycine asp tripeptide on

21:24

the capsid of the virus

21:26

that binds to this. Argyl-Iasp is a

21:28

known ligand for

21:30

integrins and the virus capsid has it. But

21:34

they don't know if you need something else

21:36

besides the integrin. Do you need one or

21:38

do you need multiple receptors? And plus the

21:41

genotype 3A3, they don't know

21:44

what the receptor is. It doesn't bind

21:46

to integrins. So they want to identify

21:48

the receptor is. So

21:51

they do a CRISPR-Cas9

21:53

genetic screen. So they basically,

21:55

they buy a library of

21:58

lentiviruses that contain. crisper

22:01

cast nine and guide RNA is to

22:03

target every known protein

22:06

in the genome. The

22:08

the mRNA the DNA encoding it

22:10

so the idea is you infect

22:13

a population of cells. With

22:15

these viruses they're going to deliver cast

22:17

this per cast nine plus the guide

22:19

RNA to. Each cell

22:21

is gonna clip out a different

22:24

gene because you have all kinds of

22:26

guide RNA is in there now you grow

22:28

up this population of cells. The effect them

22:30

with your virus and you see what's resistant

22:32

to infection so what survives the virus is

22:35

lit it will kill cells and you ask

22:37

what's left. Right yeah and

22:39

then you take those and you can pull

22:41

out the gene that has been disrupted because

22:43

you can actually look for the guide RNA

22:46

that is present it's a great update of

22:48

a classic technique. Where yeah

22:50

so you try to inhibit infection

22:53

across the whole population in the cells

22:55

that you successfully inhibited infection in will

22:57

survive and so then you pull those

22:59

out and say hey what inhibit infection.

23:03

Now how did we do it had

23:05

a Kathy Mendelsohn do it in nineteen and

23:07

the late eighties. We did it

23:09

the opposite way we asked is

23:11

there a gene from human cells that we

23:13

put into mouse cells that can make the

23:16

mouse cells make a polio receptor. Because

23:18

we knew that mouse cells

23:20

did not bind polio virus. But

23:23

we also knew that if you took polio

23:26

RNA and put it into mouse cells it

23:28

would reproduce so there is just a lack

23:30

of a receptor inhibiting the ability of polio

23:33

virus to infect mouse so Kathy to DNA

23:35

from human cells. And sheared

23:37

it is too big and then

23:39

added it to mouse cells to

23:41

many many millions of mouse cells and culture.

23:45

And then what do you do so

23:47

this is our puzzle we said okay if we

23:49

infect them are gonna kill the cells that got

23:52

the receptor gene from the human

23:54

cell so that's not a viable approach and

23:56

then. Turned out

23:58

that a card winner had acquired. a

24:00

monoclonal antibody that blocked polio

24:03

virus attachment to cells. So I'm

24:05

a collaborator in Germany called Peter

24:07

Nobis, and he gave

24:09

it to Kathy. Kathy

24:12

coupled that antibody. Again, the antibody is

24:15

presumably against the receptor of the virus.

24:17

We didn't actually know until we did

24:19

the experiment. She coupled it to

24:21

red blood cells, which are really big, and then

24:23

she flooded the plates that she had

24:25

added human DNA to mouse cells. So the mouse

24:28

cells take up the human DNA, they make whatever

24:30

proteins encoded in it. And if there's a polio

24:32

virus receptor on the surface, the monoclonal will bind

24:34

to it, and you can look under a microscope,

24:37

and you'll see the red blood cells

24:39

sitting on the plate. Such a crude,

24:41

old-style approximation of what we can

24:43

do now. But then

24:45

she picked up those colonies with a pipette

24:48

and purified them, and indeed some of

24:50

them, now you can grow a lot of those

24:52

cells and show, yeah, they can be infected with polio

24:54

virus. And then you have to pull out the receptor

24:57

gene. We didn't have a guide RNA sequence to pull

24:59

it out. We use the

25:01

trick, which is so clever, but

25:03

it's old school. Every

25:05

5,000 bases in human DNA, there

25:08

is a repeat sequence called an

25:10

ALU element, ALU. And

25:13

this is a

25:15

repeat. It's actually

25:17

a transposon, and it's present

25:20

in many copies in the human

25:22

genome. So basically, any human gene

25:24

is likely to be next to an ALU

25:26

sequence. So she got a

25:28

clone and a piece of this ALU sequence

25:30

in a plasmid and used it

25:32

as a probe to identify. It

25:35

was very, very complicated. And to her credit, she

25:37

got it to work and identified the gene. And

25:40

there's so many places where that could go wrong, and

25:43

that's it. Now here... Yeah, so

25:45

that involved a tremendous amount of

25:47

hard work, some very, very clever

25:49

experimental design, and a healthy

25:52

dose of luck. Because it

25:54

turns out the antibody was against

25:57

the poliovirus receptor, poliovirus receptor, single...

26:00

protein was sufficient to provide entry

26:02

to the virus. And

26:05

yeah, she managed to get that out.

26:07

Really cool piece of old school science.

26:09

I mean, it could have turned out

26:12

to be an allosteric inhibitor. Yes.

26:14

A protein next to you. Yeah, of

26:16

course. Of course. Totally. So she absolutely

26:18

just fell into a big pot

26:20

of cold. We ran. We

26:23

had a lot of faith in this experiment.

26:26

And the thing is, it wasn't going to take

26:28

her forever to get an answer. In theory, it

26:30

was a couple of months that you could do

26:32

this experiment. But

26:35

she got colonies binding red blood cells

26:37

the first time. I remember

26:39

I was in France and she called me. She goes,

26:41

oh my God, I got red blood cells. Oh

26:44

my gosh. So sometimes

26:46

you get lucky. So Vincent, is there a

26:49

way to inhibit the virus from

26:51

being taken in by the cell

26:53

once it attaches to the cell? Yes.

26:57

Well, then why couldn't you then? Well,

27:01

for poliovirus, we didn't know any way to do that.

27:04

Right. And did you know what part of the poliovirus

27:06

was binding to the... No, we didn't. We had no

27:08

idea. You didn't know that either. Okay. We had... And

27:10

you know everything now, right? You know

27:12

a lot. But boy, back then we didn't know

27:14

very much. And it is just blind faith, right?

27:17

That's the saying, blind faith, right? Blind.

27:19

Social was a good band, which I really liked.

27:23

Yeah. So they approach this with a bit

27:25

of faith too, but they're using a much

27:27

more modern technique with the CRISPR-Cas9 and then

27:29

you can do an actual

27:32

positive selection here. But you

27:34

can buy the library, the CRISPR

27:36

knockout library, right? For many cell

27:38

types, they used HT29

27:41

cells, which is some colorectal

27:44

adenocarcinoma cell line from humans,

27:46

right? And you

27:48

buy it. You infect with

27:51

the lentivirus. You let it cook for a while.

27:53

Then you infect. You see

27:55

what's left. It's a week or two experiment

27:57

at the most. And

27:59

I don't mean to... trivialize it. No, no, because the

28:02

initial, so this is one of those things

28:04

where the initial experiment, I mean, as

28:06

in Kathy's case, yeah, you get the initial experiment

28:09

to work within a month or so. And

28:11

then you got to figure out if the results

28:13

you got is correct. Yeah, so that's what they

28:16

spend most of this paper showing that what they

28:18

pulled out because they were very early

28:20

on able to get

28:23

cells resistant to

28:25

paracovirus infection. And

28:28

so they use either A1

28:30

or A2, which both bind

28:33

these integrins. They take the

28:35

resistant cell populations and they

28:38

look at the guide,

28:40

they sequence the guide RNAs. And

28:42

they identified two genes that are

28:45

the most enriched in

28:47

resistant cells, and that is

28:49

the gene encoding the integrin

28:51

subunit beta, which is

28:53

part of the known receptor.

28:56

And then, MYADM,

28:59

right? Myeloid-associated differentiation

29:04

marker. Myaloid is myadom. Just rolls

29:07

right in my head. Myadom.

29:10

By the way, is that a receptor for something

29:13

else in nature that ordinary? Oh, yes, of

29:15

course. It does not exist for

29:17

paracoviruses, for sure. No, of course not.

29:19

But so that gets me

29:21

back to my original thought about how clever, as you

29:24

are used to saying, how clever viruses are.

29:26

I guess it's like a safe cracker that

29:28

just keeps coming up with combinations

29:30

until one of them clicks. Yeah,

29:32

in order to infect the cells, the virus

29:34

had to find some protein that is on

29:36

those cells and so the selective pressure was

29:38

to... No, I understand. ...latch on to what

29:40

works and this is it. If you want

29:43

to know what the function of myadm is,

29:45

it's involved in several processes,

29:47

including negative regulation of

29:49

heterotypic cell-cell adhesion, negative

29:52

regulation of macromolecule metabolic process. Wow,

29:54

that's a broad... That's

29:56

a lot of... Yeah, I'll say. And

29:58

negative regulation of protein... It doesn't

30:00

help me understand what it does. What

30:03

does it do? Yeah, it does. It has

30:05

other functions, right? Wow.

30:07

So how do you validate it? Okay,

30:09

so first you can,

30:13

they knock the cell out in a couple

30:16

of different cell lines that are known to

30:18

be infectable with paraco viruses.

30:21

You can knock them out with CRISPR, but this

30:23

time you just have a guide RNA that matches

30:25

this particular gene. You knock it out and

30:28

you try and infect with paracos and

30:31

they can't be infected if you knock out myatum.

30:35

And if you put myatum back in, then

30:37

they can be infected. So

30:39

that's a classic experiment to

30:41

show that a gene

30:44

encoding a particular protein. And also they

30:46

look at other pecoranoviruses,

30:48

other enteros, like Echovir,

30:50

enteros A71 and

30:54

Cocksackie B3, they do not care

30:57

about myatum. They will infect those cells.

30:59

So looks like it's essential

31:01

for infection. That's all we know so far.

31:03

It's essential for the virus to infect cells.

31:06

They also do this experiment in

31:09

intestinal organoids because

31:13

these viruses are often found in

31:15

stool. They're thought to be fecally,

31:17

orally transmitted like poliovirus. So

31:20

they're probably reproducing in the gut. We don't

31:22

know for sure, but they're probably reproducing in

31:24

the gut. So you can

31:26

take stem cells and differentiate them

31:29

into organoids, which kind of approximate

31:31

the three-dimensional structure of the intestine.

31:33

They have a basolateral and

31:35

an apical domain. They have a lumen and so

31:38

forth. And so they

31:40

make these organoids

31:43

either from wild-type stem cells or stem

31:45

cells where they've knocked out myatum using

31:48

CRISPR gas, right? And

31:50

they go, without myatum, the organoids can't

31:53

be infected with myatum. They

31:55

can be infected. So in cell lines and

31:57

in organoids, which approximate the gut. They

32:01

this is a protein is important

32:04

for infection. They

32:06

also do experiments to

32:09

show what part of my atom is important. So

32:11

my atom is a multi-pass membrane

32:13

protein. So the protein

32:16

threads plasma membrane

32:18

in and out multiple times. And

32:20

so you get these loops of protein sequence

32:22

on the outside and the inside. So they

32:24

say probably these loops are

32:26

important. When they compare the loops from

32:29

different species that are known not to

32:31

be infected with paracos, they say,

32:33

oh, this seventh loop, is it

32:35

the seventh? The last extracellular loop

32:37

is divergent in amino acid sequence.

32:40

So they do some swapping of that

32:42

sequence with the human. And they show that this

32:44

is in fact the part that's really important. They

32:46

can even narrow it down to a couple of

32:48

amino acids. So if

32:50

the virus doesn't infect hamsters, it's because

32:52

of some amino acid change in that

32:54

three amino acids. I

32:58

guess you could do the same thing with mice and

33:01

make a mouse model, right? Sure. Theory. Now,

33:05

this protein is essential for infection because that's

33:08

the assay. We're putting virus in and we

33:10

see that the virus is replicating. But is

33:13

it, what part? At what part

33:15

in it, yes. Is it needed for viral

33:18

RNA replication? I would say, no,

33:21

but you have to show it because it's on the

33:23

plasma membrane. Why would it be needed for replication? You

33:25

never know. You never know. You

33:27

cannot assume things in science. No. If

33:30

you do, you're going to be led

33:32

down into a dark alley. And

33:35

who knows what's going to happen? So

33:38

they take the viral genome, they cut

33:40

out the region-encoded capsid proteins. So now

33:42

this is a replicant. It's

33:44

an RNA that you put in cells that's going

33:46

to just replicate because it encodes the polymerase and

33:48

it will copy itself. So you

33:51

can take this RNA, which you can make,

33:53

and you put it in cells and you

33:55

can measure its replication. They

33:57

put a reporter, a luciferase reporter in it.

34:00

So, they take cells with or without myatum.

34:02

It doesn't matter. It doesn't matter

34:04

if it has myatum or not. The RNA still

34:06

replicates. Okay, so you don't

34:09

need myatum for RNA replication. So

34:11

if you bypass the whole entry process and

34:13

put the RNA directly in the cell, it's

34:15

fine. It doesn't need this

34:17

receptor. So that suggests

34:19

that it might be an entry. Pretty

34:22

strongly argues for this being an entry

34:24

factor. Although there are a lot of

34:26

things between entry and RNA synthesis, right?

34:29

Okay, you know, it turns out to be the

34:31

right. So

34:34

the next thing they do, does

34:36

this bind the virus particle? That's a

34:39

great experiment, right? Because if this

34:41

is a receptor, it should bind to it. Yep. And

34:44

so, you know, we have two

34:46

proteins that seem to be involved, maybe

34:48

myatum and an integrin, right? So they

34:51

do some attachment

34:55

experiments. And they find

34:57

that, so you can

34:59

bind virus to cells at four degrees,

35:01

and then it will stay on

35:04

the surface. And then when you raise the

35:06

temperature to 37, the particles move into the

35:08

cells by endocytosis. Good times, yeah. You

35:11

remember that, Alan? Oh, yeah, yeah. Many,

35:13

many, many, many 10-minute time points. Alan

35:16

used to work on this entry problem

35:18

here for poliovirus. So

35:20

if you knock out the integrin beta

35:23

subunit, right, that's one of

35:25

the ones they identified in their screen, and

35:27

we know that the virus is binding to

35:29

integrins. If you knock out the integrin gene,

35:31

you reduce binding of the virus particles, you

35:33

reduce entry into the

35:35

cell. And then if you put

35:38

the integrin back, it restores all that. But

35:40

if you knock out myatum, it

35:43

reduces infection, but it doesn't really reduce

35:46

binding all that much. So

35:48

it's essential for entry, most likely, but

35:50

not binding. The integrin seems to be

35:52

the binder, and then somehow it's handed

35:55

off to myatum, and that pulls it

35:57

into the cell. some

36:00

other co-immuno precipitation

36:03

experiments that are basically consistent with

36:05

that. If you

36:09

can see the entry

36:11

of viral RNA into

36:13

cells, even in

36:15

cell – so if you mutate the gene

36:18

encoding myatoms in that last loop so that

36:20

the virus won't bind, you impair

36:23

the entry of viral RNA into cells,

36:26

which you don't see that, of course,

36:28

in cells with wild type receptor on

36:30

the surface. So the

36:34

data suggests that myatum is an

36:37

essential entry receptor. Yeah, they also

36:39

do this low pH experiment where

36:42

they show that myatum

36:45

does bind the particles at low

36:47

pH. That's right. So it doesn't

36:49

bind on the cell surface under neutral pH, but

36:51

if you lower the pH, you can get it

36:53

to bind to myatum. And

36:56

again, if you mutate those residues

36:58

to whatever the horse residues,

37:01

then it abolishes that binding. So

37:05

it's consistent with a model

37:07

– again,

37:09

you've got to do more to figure

37:11

out that this is really what's happening, but it fits

37:13

with the idea that the virus

37:15

probably binds the integrin at

37:18

the cell surface, gets endocytosed

37:20

into one assumes

37:22

a lysosome that

37:24

also contains myatum, pointing

37:27

in at low pH when

37:29

that acidifies, it binds myatum and uncoats, and

37:31

they present this model in one of the

37:33

figures. The paper's open access as well. Right.

37:37

So you need to do some experiments to figure

37:39

out the exact sequence. Sure. And this

37:41

is very different from poliovirus, where the

37:43

virus binds the receptor right at the cell surface.

37:47

It seems to make a channel in

37:49

the particle, and that lets the RNA

37:51

get in shortly after it gets

37:54

into a vesicle at the cell surface. So

37:59

that's the story. it's quite interesting

38:01

and it illustrates how

38:03

technology has changed in terms of

38:05

identifying receptors. Now this will be

38:07

used for many years because

38:09

it's quite nice. And

38:12

as long as you have some assay, you

38:14

can identify genes that are involved in it.

38:16

And virus infection is one such assay that's

38:18

easy to do. Now

38:20

you're aware of paracoviruses, which

38:23

is good. Formerly known

38:25

as echoviruses. There

38:28

you go. So I went

38:31

to Columbia in 1982 wanting to identify

38:35

the cell receptor for poliovirus.

38:39

And I remember when I was

38:41

a postdoc my last month I said to David, I

38:43

think this would be an interesting project.

38:45

He said, no, probably not. So

38:48

I said, well then I'm going to do it because he

38:50

won't work on it. And

38:52

I get to Columbia and in the

38:54

fall I present to the new graduate students what

38:56

I wanted to do. And I

38:59

left and I went to my office and Kathy Mendelson came

39:01

running after me. She said, I want to do that. I

39:03

want to do that receptor thing. It's okay. Come.

39:06

There you go. She did it. It was

39:08

a really good job. And Kathy now is a

39:10

professor at Columbia doing much

39:13

better work than I ever did. So that's

39:15

what you want. And you want people to

39:17

do great things. All

39:19

right. So that's, by the way,

39:22

now one of the next things I would do

39:24

is try and see if you can get a

39:26

structure of the virus particle

39:28

before and after this

39:32

interaction that leads to release of the RNA.

39:34

That would be interesting. And that's another area

39:36

where the tools gotten a lot better in

39:38

the past several years. So

39:41

by the way, in case you were curious, if

39:44

you infect 100 million

39:49

cells with

39:51

this lentivirus CRISPR-Cas knockout

39:53

RNA library, you

39:55

will get a thousand times coverage of all

39:57

the open reading frames. 100 million cells.

40:00

So a six, a 10 centimeter

40:02

plate, how many million

40:05

cells on it or 10 million? Because that six

40:07

centimeter had a million, I think. I think it's

40:09

10 million on it.

40:11

10 million. It's been a long time. So

40:13

10 million or 10 cent, well, you could do a maxi

40:15

plate too. Yeah. Yeah. And so this is

40:17

not a lot of plates, basically, right, that you need to

40:20

do. And

40:22

you can, so the way I would

40:24

do it, I don't know how they did it, but you

40:28

do the infection and then

40:30

you let the cells grow. And then I would

40:33

trypsinize them to disperse them into

40:36

single cells, split them into two

40:38

plates, and then keep one and infect the other

40:40

plate with virus. You keep one in case something

40:42

goes wrong, right? Because that's your library. And then

40:44

you infect the other cells and you see what

40:46

cells are left and then you amplify those. That's

40:48

the way I would do it. Memories.

40:52

So bring back memories, Dixon. No.

40:56

He's muted. Memories

40:58

of what? Just

41:01

trying to engage you. That's all. In

41:04

my field. Nobody

41:06

does receptors, right? No, we had different

41:08

things to remember by, you know, grinding

41:11

up rats. Rats. Yeah, the rat-o-matic. I

41:13

heard all about the rat-o-matic from one

41:15

of the rats. The rat-o-matic. Yeah. Oh,

41:20

we eat the control rats. Do not eat these

41:22

rats. The

41:24

next paper I picked because

41:26

it's very cool. This one was cool too,

41:28

but this one I think gives us some insight

41:31

into severe influenza,

41:33

but also maybe how to get around

41:35

it. So this is a Nature article.

41:38

Necroptosis, we'll explain that.

41:41

Necroptosis blockade prevents

41:44

lung injury in severe influenza.

41:46

Many, many authors here, the

41:48

first two co-authors are

41:51

Gautam and Boyd, and

41:53

the senior authors are Cuny,

41:57

Thomas, Degg, Terev, and

41:59

Balachandra. They come from various

42:02

places. Fox Chase Cancer Center in

42:04

Philadelphia, St. Jude Children's Research Hospital

42:06

in Memphis, you

42:09

see Santa Cruz, University of

42:11

Houston, Tufts School of Medicine

42:13

in Boston, University of Freiburg

42:15

in Germany, and then a

42:17

couple of companies, Reaction Biology

42:19

in Malvern, Pennsylvania, and VU

42:21

Therapeutics in Rancho Santa Fe,

42:23

California. So

42:25

the pitch here is that can we

42:28

find other points

42:30

where we can inhibit influenza virus

42:32

disease? Because

42:35

that's useful, right? We only have a couple of

42:37

antivirals. And they're saying that necrotic

42:41

cell death is

42:43

one potential point. So what is

42:45

that? Necroptosis. It's a great word,

42:47

right? But what

42:50

is it? We used to think that necrosis

42:52

was just kind of a default

42:54

process that wasn't really regulated,

42:57

just the cell kind of fell apart,

43:00

whereas apoptosis was regulated cell

43:02

death. And now we know

43:04

differently. These are different kinds of cell

43:06

death. There's

43:08

apoptosis, and necroptosis, and

43:11

then there's pyroptosis. They have different

43:13

features, and they're regulated. All

43:16

of them are regulated, yeah. And

43:18

the point here is that influenza

43:21

virus can kill cells, but

43:23

the cells also

43:25

initiate self-destructive programs.

43:28

These are part of normal development when you

43:30

need to get rid of cell populations, but

43:33

they also are in response to pathogen insult,

43:35

where the host recognizes that it's

43:37

infected and says, sorry, Sal, we're going to

43:39

get rid of you to try

43:42

and preserve the organism. And

43:44

so this can cause a lot of

43:46

damage. And so they tell

43:48

us that this necroptosis

43:51

accounts for most of

43:54

the program's death and

43:56

influenza lung epithelial cells. All

43:59

right? to be really a big

44:02

part of the damage that's caused in

44:04

the lung. And they say when this

44:06

damage is unchecked, you

44:09

get lung injury, you get severe

44:11

illness, even without, when the

44:13

virus is cleared by your

44:15

immune response, it can still have lung damage

44:17

because of these necrotic processes, these necropptosis going

44:20

on. So they say, let's see if we

44:22

can get an inhibitor. And

44:24

I gather, correct me if I have this wrong, but I

44:26

think apoptosis is more of

44:28

a surgical mistake. One

44:30

cell goes

44:32

through an ordered disassembly, often

44:35

by instruction from the immune system.

44:37

Necroptosis is more like the nuclear

44:39

option, and it's more likely to

44:42

cause these adjacent sequelae. Is that right?

44:45

Okay. Yeah. Yeah.

44:48

There's a good review article here I found,

44:52

which is called Necroptosis, Pyropptosis

44:54

and Apotosis, an intricate

44:57

game of cell death. Actually,

45:00

so this pronunciation has always been a bone

45:02

of contention, whether to pronounce the P or

45:04

not. Yes. And

45:07

so you heard me saying apoptosis because-

45:09

And I have been saying apoptosis. Because

45:12

I was excoriated once at a

45:14

textbook meeting for saying apoptosis. Well,

45:16

I said, isn't it helicopter? Right.

45:19

But they said, no, it's not.

45:21

It's actually- It's helicopter. Right. But

45:23

a pyroptosis, that's weird, pyroptosis, there

45:26

should be pyroptosis and necrotosis, right?

45:29

So I don't know. I'm

45:32

just a virologist. I don't make

45:34

up for it. Anyway, this review

45:36

article talks about the differences

45:38

between the three of

45:40

them. And

45:42

they're regulated. That's the point. We

45:45

used to think necrotosis, as Alan

45:47

said, was just necrosis. It was

45:49

unregulated, big knife

45:52

and so forth, but it is regulated. All

45:54

right. So how do you get

45:56

necrotosis? There's a

45:59

host sensor proton. called

46:02

ZBP1, Dickson. You're gonna have to

46:04

remember these. It's gonna be a

46:06

test. Sorry, I was gonna be absent that day. There's

46:11

a ZBP1 which detects

46:15

Z-RNA, Z-form RNA, made by

46:17

influenza virus, and then

46:19

activates a protein

46:22

kinase called RIPK3. RIPK3,

46:25

get it? Yeah,

46:27

get it? RIP, RIP. So

46:29

in fact, it actually stands for something,

46:32

but the RIP is

46:34

done on purpose. Yeah,

46:36

but exactly. What it

46:38

stands for is receptor-interacting

46:40

protein kinase 3, which is

46:43

a completely nondescriptive name. That's

46:45

a stretch. It's just for

46:47

the abbreviation, so it's RIP,

46:50

because it induces apoptosis and

46:52

necrotosis. This is from

46:54

the same scientists that brought you a hedgehog.

46:56

Yes. So our

46:59

ZBP1 activates

47:03

RIPK3, which

47:05

then phosphorylates MLK1.

47:09

What, it was okay? It's not mixed. MLK,

47:11

MLK-L. MLK-L,

47:14

sorry. It is not major lead

47:16

kinase. It's mixed lineage kinase-like protein,

47:19

L, and

47:21

that induces necrotosis, okay?

47:23

So let's review. Please.

47:25

So ZBP1, RIPK3,

47:28

MLK-L. Let's just call it MLK-L.

47:33

It phosphorylates MLK-L, and that

47:35

causes leads to

47:37

necrotosis. Then with necrotosis, you

47:39

get pulmonary tissue necrosis. You

47:41

get neutrophil recruitment. Neutrophils cause

47:44

a lot of tissue damage and

47:46

lung inflammation during severe flu, and that's

47:48

really a big part of the problem.

47:51

But you don't need it. You

47:54

don't need necrotosis for CD8 T-cell

47:56

responses or for clearance of virus.

48:00

So, RIPK3 also can

48:02

induce CasP8 protein activity and cause

48:04

apoptosis. But

48:19

that does not require phosphorylation of

48:22

CasP8. So in one

48:24

case, you've got the kinase activity

48:26

is required to lead down the

48:29

path to necrosis or necroptosis.

48:33

Without the kinase activity, you can still cause apoptosis.

48:35

And that really is the crux of what they're

48:37

going to try and do here. So

48:43

the cool thing is about what Alan just said

48:46

is that, you know, RIPK3

48:48

activates apoptosis, which

48:51

can reduce viral loads because

48:53

it's killing virus infected cells. And

48:55

you don't need necroptosis to do

48:57

that. The necroptosis,

49:00

getting rid of necroptosis, it doesn't seem to

49:03

play a role in, well, the necroptosis doesn't

49:05

play a role in clearance of virus or

49:07

apoptosis does. So they say, could we target

49:10

just necroptosis with a drug and

49:12

ameliorate the tissue damage and not worry

49:14

about screwing up viral clearance, right? Yeah,

49:16

because you have the option of having

49:18

either an effective

49:21

clearance with apoptosis only

49:23

or primarily, or you

49:26

can have bad disease involving

49:28

necroptosis. And for some

49:30

reason, in some people, in some flu

49:32

infections, you end up going

49:34

toward necroptosis and other people who are

49:36

luckier or, you know, caught a different

49:39

strain or something end up

49:41

having more apoptosis and they do

49:43

better. So

49:45

they want to do some experiments to kind

49:48

of proof of principle, right? So

49:50

first, they have mice lacking MLKL,

49:52

milkel, right? And those

49:55

mice are going to

49:57

be deficient in RIP3K-driven necroptosis.

50:00

right, because RIP3K, if you

50:02

remember Dixon, phosphorylates milkle,

50:04

which is essential for the induction of

50:07

necroptosis. So that mice lacking milkle,

50:10

which would be necroptosis-specific, right, it's not going

50:12

to mess with apoptosis. That's very cool. So

50:15

they take them and they infect them with a

50:18

lethal dose of influenza virus, a

50:20

PR8 strain, and

50:22

they say, this should kind of give

50:25

us an idea whether inhibition of necroptosis chemically

50:28

would be useful at all. So

50:30

all the wild-type mice die within 12 days. 70%

50:33

of the milkle mice survive

50:36

totally. It's

50:38

not 100%, but it's pretty good, right? So

50:40

maybe you can improve. So they

50:43

have no necroptosis, right,

50:48

and they are being protected. 70%

50:51

are protected. Now

50:54

in separate experiments that have been

50:57

published before, if you take mice

50:59

lacking ZBP1, remember

51:01

that's the sensor, or

51:05

RIPK3, they

51:08

die because they can't

51:11

induce apoptosis. So

51:14

this is a nice proof saying that

51:17

milkle, well, milkle is

51:19

hitting necroptosis specifically.

51:22

If you can somehow inhibit necroptosis

51:24

specifically, maybe we can make inroads

51:26

here, right? We definitely don't want

51:28

to inhibit apoptosis because if you

51:30

do that and stop necroptosis, then

51:33

you die too. Now there

51:36

are other RIPK molecules,

51:38

right? There's one,

51:40

two, and three, and they show that if

51:42

you inactivate one, the kinase

51:44

activity of one, it doesn't protect the mice.

51:46

So they have to go after three. So

51:49

we need a pharmacological agent that targets RIPK3

51:53

without interfering with RIPK3-induced

51:57

apoptosis. And then that would be

51:59

a... to theory, in theory, protect the

52:01

mice like these milk

52:04

ill knockout mice were protected, okay?

52:07

Now they're not the first ones to think

52:09

of this. No, they are not. So in

52:11

fact they are going to test this against

52:13

two compounds that I assume were developed by

52:15

Glaxo Smith Klein, GSK843, GSK872, I think there

52:17

actually might be a

52:20

third, but these GSK compounds

52:22

are the comparators because

52:24

I guess Glaxo tried

52:26

to do this and their drugs

52:29

ended up not working out so well.

52:32

They had some on-target toxicities

52:35

and other issues. It worked.

52:37

It's an inhibitor of RIPK3. It

52:39

is an inhibitor of RIPK3. But in

52:42

vivo or in cells it didn't really have

52:44

much activity for whatever reason. So they say

52:46

let's see if we can do better. And

52:49

that's brave folks because

52:52

this time it's not trivial to do better.

52:56

It's not like the previous paper. Here

52:58

you're gonna have to make an investment and

53:01

they focus on a molecule that exists already.

53:03

It's called PD180970. You don't have to memorize

53:07

that Dixon because in a moment we're gonna

53:09

simplify it. It's an

53:11

inhibitor. It doesn't matter. I just wrote

53:13

it down. It's a known inhibitor of

53:15

RIPK2. That's not the one we want

53:18

but RIPK2 is related to

53:20

RIPK3. How many RIPs are

53:22

there? At least three. Well

53:24

at least three that's good. Don't

53:27

forget, Riptide. Yeah that's right. Or

53:30

RIPTorn. So they find that

53:32

this PD molecule inhibits RIPK3.

53:35

Pretty good half-maximal

53:37

concentration, micromolar concentration. It was

53:40

pretty moderately potent at

53:42

blocking necroptosis in cells.

53:44

So moderately potent. Not good enough, right? So

53:47

what do you do? You call the chemists and

53:50

you say make it better. So

53:53

they generated 40 analogs and

53:56

one of them showed potent anti

53:59

necroptosis. and you know, that's a supplemental

54:01

figure. Oh my gosh. Yes. Oh

54:04

my gosh. So this is a

54:06

modern nature paper, which means that there's like an entire

54:08

additional paper worth

54:11

of supplemental data. By the way, it's also paywalled, so unfortunately.

54:13

Yeah, that's right. Well, the listeners are not going to be

54:15

able to look at this. But there's just an enormous amount

54:17

of work in this paper, and that's, it becomes pretty obvious

54:23

why, because they're aiming to produce a drug, which is, you

54:25

know, a very, very, which

54:29

is a tall order. So

54:32

this new compound, Dixon, they name UH15-38. Ask

54:37

your doctor about UH15-38. I

54:39

would have named it that. And

54:41

this blocks is necroptosis and primary

54:43

mouse fibroblasts that

54:47

were, concentrations that are

54:49

lower than the GSK compounds that

54:52

Alan mentioned, okay. It

54:55

prevents phosphorylation of milco following

54:57

stimulation. So you can artificially

55:00

stimulate necroptosis in the absence

55:02

of influenza virus by adding

55:04

tumor necrosis factor, okay.

55:07

And they say it blocks, this

55:09

blocks phosphorylation of milco in the

55:11

presence of tumor necrosis factor. And

55:14

also- But tumor necrosis factor,

55:16

despite its name, and this has been

55:18

a hard thing to get across to

55:20

MDs especially, it is not

55:22

a good thing in general. It's a-

55:25

Correct. It's a kind of kind

55:27

that is just bad news when it shows up. Right,

55:30

right, right. So when I

55:32

think about apoptosis, I think about the

55:34

death of single cells. And

55:36

when I think of necroptosis, do I think

55:38

of death of

55:41

a tissue rather than a single cell? And

55:44

necroptosis happens at the single cell level,

55:46

but I gather it tends to have

55:48

more sequelae in the tissue at large.

55:51

That's why I was asking Vincent

55:53

if that's- Does it occur in the spread of

55:55

the virus in the tissue? Necroptosis?

56:01

It doesn't inhibit it. It doesn't have

56:03

any effect on virus clearance. When

56:05

a cell lysis though, it's filled with virus.

56:08

Yeah, you would think so. The whole

56:10

animal studies show that if

56:13

you knock out necroptosis, you don't affect virus

56:15

clearance. It's apoptosis that plays a role in

56:17

that. And I'm not sure why that would

56:19

be. It's a good question. I'm

56:21

not enough of an aficionado of cell death.

56:25

There's supposed to be a funny there, but it's

56:28

not really funny. All right, so they're thinking, all

56:30

right, you know, UH15-38 must somehow inhibit RIPK3

56:36

differently from the GSK compounds. So

56:38

what is it? They do some

56:40

molecular modeling, and they find

56:42

some substantial differences basically between

56:45

the GSK and

56:47

the UH, I've

56:50

already forgotten, the UH15. And

56:52

in particular, it interacts with

56:54

more parts of RIPK3 than

56:56

GSK does. And they say maybe that's

56:59

responsible for the increased potency.

57:01

They look to

57:05

see if it binds RIPK1 in

57:07

vitro. It doesn't have any activity.

57:10

And RIPK1 is involved in apoptosis, right?

57:12

So that's good. There's no activity. It's

57:15

not going to inhibit apoptosis according to

57:17

that. It's selective for RIPK3-driven necropotic

57:20

cell death. Okay, so

57:22

let's now go to

57:24

the mouse. They dose

57:26

mice 30 milligrams

57:29

per kilogram per day for

57:31

four days intraperitoneal.

57:35

And lucky them, it

57:37

accumulates in lung tissues. Oh

57:40

my gosh, you know, that's

57:42

exactly what you want. It's

57:45

eight times higher in lung

57:48

than in plasma. So that's good, right?

57:50

It does accumulate in some other organs as well, but they're

57:52

going to look at that later. You know, the lung is where

57:54

the influence is. The lung is where you want it. So

57:56

then they give it to mice. Caspase

58:00

knockout mice where

58:02

the milkel has been tagged with an epitope

58:04

so that you can track it with

58:08

using antibodies. And

58:11

so it knows my select

58:13

caspase and the reason that that's

58:15

there is because it triggers multi

58:17

organ rip K3 activation and

58:19

phosphorylation of milkel but.

58:22

The end terminal epitope added

58:25

to milk will prevent it

58:27

from executing necker optosis so. What

58:31

they can do is identify

58:33

all the cells that have a

58:35

s because

58:42

they want to see where this is working

58:44

but they can't if it dies from it

58:46

dies yes from necker optosis so just by

58:49

tagging milk prevents it from

58:51

inducing. And that crop toes is

58:53

so clever this and I'm sure that was kind

58:55

of accidental discovered at one point

58:58

we flag tag milk it doesn't work. And

59:01

it's actually that's good that it

59:03

doesn't work good so

59:06

they find that so

59:08

that what does it mean it

59:10

means you can ask whether you age

59:12

15 prevents phosphorylation

59:14

of milkel. You

59:17

have to worry about the cells dying

59:19

from necker optosis and they find that

59:21

it prevents phosphorylation in every organ they

59:23

test lung liver heart and

59:25

kidney so. It probably

59:28

could be used for treating diseases of other

59:30

organs as well besides mom. I

59:33

think it's a cool experiment that they use

59:36

that clever clever trick how

59:38

about safety. So

59:40

they give a ton of

59:42

that actually first screen

59:46

a bunch of protein targets 50 critical protein

59:48

targets is a typical thing that you do

59:50

now in drug development. And

59:53

these 50 targets are known to be

59:55

associated with side effects in humans right

59:57

we know this and so they ask.

1:00:00

Will this interact with those 50 proteins? It doesn't

1:00:02

seem to inhibit it. You have to run a

1:00:04

lot of assays, activity assays, for

1:00:06

all these proteins. This

1:00:08

is what contract research organizations are

1:00:10

for, right? You give it

1:00:12

to a CRO and you pay them a lot of money

1:00:14

and they do the experiment for you. You

1:00:17

don't have to burn through postdocs or graduates.

1:00:21

And that's a thing, right? So

1:00:23

it doesn't inhibit any of these targets, so looks

1:00:26

like it should be safe, but you never know until you

1:00:28

test it. Then they ask, they give a lot to mice.

1:00:32

There's no induction of

1:00:35

apoptosis in any tissue. There's no general

1:00:37

toxicity, so no liver toxicity, which

1:00:39

you could measure by having liver

1:00:41

enzymes enter the blood if you're

1:00:43

killing liver cells, none of that.

1:00:46

And they say it has a pharmacological

1:00:48

profile that would be

1:00:50

safe for deployment, in vivo deployment.

1:00:53

So you could go into a clinical trial at some point.

1:00:56

All right, then finally, what

1:01:00

is the actual cell that's undergoing RIPK3-dependent

1:01:03

necropptosis in lungs of mice infected

1:01:05

with influenza virus? And they do

1:01:08

single cell analysis, sequencing

1:01:11

analysis, and they say the type

1:01:13

I alveolar epithelial cells are the

1:01:15

prime replicative niche for

1:01:17

influenza A virus in mouse lungs.

1:01:20

And so they say, we're

1:01:23

gonna focus on these cells and ask whether

1:01:25

our drug, UH15, can

1:01:28

prevent necropptosis in those type

1:01:30

I alveolar

1:01:32

epithelial cells. So

1:01:36

they isolate these cells from mouse

1:01:39

lungs. This is

1:01:41

another thing. You have to chop up

1:01:43

the mouse lungs. It makes single cells. And

1:01:46

then you use an antibody

1:01:48

that will bind to protein specific for

1:01:50

those alveolar epithelial cells. And then

1:01:52

they couple those to magnets and you can

1:01:55

pull those cells out with magnets. It's

1:01:57

called immunomagnetic selection. I

1:02:00

just love it. And then

1:02:03

they show that

1:02:05

they're pure, they show that they

1:02:07

undergo ZBP1 dependent cell death when

1:02:09

you infect them with influenza A

1:02:11

virus. If you take

1:02:14

the same cells from ZBP null

1:02:17

mice, they do not

1:02:20

succumb to influenza

1:02:22

virus death. They're

1:02:25

mostly viable because they don't have the necroptosis

1:02:29

inhibitor, stimulator, right?

1:02:33

And so they say, okay,

1:02:35

what about UH15? Can it prevent

1:02:38

influenza virus-induced necroptosis

1:02:40

without blocking

1:02:43

apoptosis? And

1:02:46

the answer is yes. This drug

1:02:48

blocks necroptosis and these, and again,

1:02:50

this is type 1 alveolar epithelial

1:02:52

cells cultured in vitro. And apoptosis

1:02:55

still goes on, but

1:02:58

no necroptosis. And so

1:03:01

this drug which blocks virus-induced

1:03:03

phosphorylation of milkle is

1:03:06

blocking necroptosis. And

1:03:10

they also look at other influenza viruses and

1:03:12

show that it works against a large panel of

1:03:15

cells. So clearly,

1:03:18

RIPK3 is important because that's what the

1:03:20

target is of this inhibitor.

1:03:22

All right, so then they do experiments

1:03:24

in human cells to see

1:03:27

if it works. They have to jigger human

1:03:29

cells because most of them don't have

1:03:33

one or more of the components of

1:03:35

the necroptosis machinery. These are common cell

1:03:37

lines, right, like HeLa cells. And

1:03:40

so they jigger them to work

1:03:42

and then they can show that this

1:03:45

drug at nanomolar concentrations protects

1:03:48

them from influenza virus-induced

1:03:51

necroptosis. So both

1:03:53

in mouse and human cells, it works. And

1:03:55

they also use human lung

1:03:59

sections. So

1:04:01

one of the experiments, they took

1:04:04

lung sections that had been surgically removed and

1:04:07

looked at those and did a

1:04:10

similar experiment and see that

1:04:12

the UH15 also appears

1:04:14

to inhibit necroptosis

1:04:16

in those. They

1:04:19

say, this is a key

1:04:22

point, they say, the drug

1:04:24

inhibits wide separation of

1:04:27

necroptosis inhibitory activity from the

1:04:29

capacity to trigger on target

1:04:31

apoptosis. That's what you

1:04:33

want. So you have to, you can

1:04:36

get it to inhibit apoptosis,

1:04:38

but you've got to go to much,

1:04:40

much, much higher doses than you use

1:04:42

to inhibit necroptosis. So

1:04:46

listeners, what do you think the next experiment

1:04:48

would be? Dixon, what's the next

1:04:50

experiment? You want to put this right in people now? No

1:04:53

idea? I'm an

1:04:56

innocent here. I'm trying

1:04:58

to follow this as best I

1:05:00

can. So we've done this

1:05:02

in cells, the next thing I think. Well, you got to

1:05:05

do it in vivo next. So now we're going to look

1:05:07

in mice. So we ask, can

1:05:09

the drug prevent lethality

1:05:12

driven by influenza virus? Okay, and they'll be 50. So

1:05:16

they infect wild-type mice with a

1:05:18

lethal dose of influenza virus and

1:05:21

then they start treating them with

1:05:23

the drug a day later, intraperitoneal.

1:05:27

They give a range of doses from

1:05:29

7.5 migs per kilogram per day up to

1:05:31

50, and they

1:05:34

look at survival. Weight loss,

1:05:37

and they compare them to mice with just

1:05:39

the vehicle, no drug. As

1:05:43

low as 7.5 migs per gig per day

1:05:45

prevents lethality in 40% of mice. And

1:05:49

this is at a lethal, like

1:05:52

way more than enough virus to kill all mice.

1:05:57

And you can delay administration. You can do

1:05:59

a show. shorter time course, you

1:06:02

still prevent lethality in

1:06:04

a significant number of mice, and you

1:06:07

reduce the weight loss. And

1:06:09

if you do the experiment at a, I think

1:06:12

it's an LD60 actually, but they

1:06:15

knock the dosage down so that it's a

1:06:17

little more realistic than the way

1:06:20

over the lethal dose. And

1:06:22

at that point, anything

1:06:26

above actually any dose,

1:06:28

all the doses, one

1:06:30

meg per gig, you don't even have to go up

1:06:32

to three, all the mice

1:06:35

survive. And the mice

1:06:37

that got just the vehicle,

1:06:39

just the solvent, 60% of them

1:06:42

die. That's

1:06:49

right. So they call

1:06:51

it a patient-relevant inoculum of the ones

1:06:53

that have virus. So

1:06:56

it's fully 100% protective in mice when you

1:06:58

give them that level. And

1:07:01

this thing with the delayed dosage was really

1:07:03

interesting because they waited, they went

1:07:07

anywhere from day two to

1:07:09

day five before starting the

1:07:12

drug regimen, and you can still

1:07:15

significantly reduce lethality

1:07:18

when you give it at day five, which is

1:07:21

a big deal because a lot of people are not

1:07:23

going to know they have the flu the day after

1:07:25

they got infected. Now,

1:07:29

they do the same experiment with one of

1:07:31

the GSK drugs. No

1:07:34

protection. It doesn't go as well. It's

1:07:37

really, they have improved. Oh my gosh, it's

1:07:39

just a great story, right? Yeah. They

1:07:42

also check the H1N1 2009 pandemic influenza virus.

1:07:46

It protects them. So both

1:07:48

pandemic and seasonal strains it seems to work

1:07:51

on. And

1:07:53

it works even if you give it five

1:07:55

days after infection, as Alan said. And

1:07:58

they say this is an advantage. over Tamiflu,

1:08:02

which you have to give within 48 hours.

1:08:05

They're still in mice and they're already trying to

1:08:07

compete with marketed drugs. Yeah, love it. Right,

1:08:11

but I mean, this is

1:08:14

not going to reduce viral loads, right,

1:08:16

but it's going to reduce the

1:08:18

lower lung symptoms, which is

1:08:20

lethality that's going to say, you're not going

1:08:22

to feel better. Right. Well,

1:08:26

you will probably feel better than

1:08:28

you would if you were getting

1:08:30

necropetosis and being

1:08:32

put on a respirator. Oh, that's true, but I

1:08:34

meant like upper tract symptoms. No, you're not going

1:08:36

to suddenly, it's not going to be the Tamiflu

1:08:39

effect of, you know, while the symptoms

1:08:41

resolved in a hurry. Or

1:08:43

the Pax Lovid effect, Sorescovito. So,

1:08:47

the next thing they do, which

1:08:49

is the last set of experiments,

1:08:53

is to look

1:08:55

at the lung itself to

1:08:58

see, because they've been looking at big

1:09:01

reporters now, you know, survival and weight loss. But

1:09:03

if we look at the lungs, do we see

1:09:05

an effect? So,

1:09:09

they do this same experiment.

1:09:11

In fact, treat with UH15,

1:09:13

then they remove the lungs,

1:09:16

and of course they have a vehicle

1:09:19

control. And the lungs from the

1:09:21

vehicle treated mice, a lot of

1:09:24

evidence of necropetosis at day three,

1:09:27

post-infection by day six, you see

1:09:29

necropetosis in a big substantial fraction of

1:09:31

type one, alveolar

1:09:34

epithelial cells. The

1:09:36

mice that get UH15,

1:09:38

reduced numbers of necropetotic

1:09:40

cells at both time points,

1:09:43

and these are cells that are infected with

1:09:45

influenza virus, right? So, the

1:09:47

virus is driving the necropetosis. It

1:09:51

didn't affect the number of cells undergoing

1:09:53

apoptosis. And you may say, how do

1:09:55

you know this? Well, there are markers

1:09:57

for necropetosis and apoptosis.

1:10:00

You can stain the sections with antibodies against those

1:10:02

and you can tell. They

1:10:04

also found that, so Dixon,

1:10:07

you asked earlier about cytokine storm, right?

1:10:12

So that is a thing. Inflammatory

1:10:14

cytokines are released during

1:10:17

infection of the lung like IL-1

1:10:19

alpha, IL-33, IL-6, TNF,

1:10:22

CXCL-1. This

1:10:25

drug, UH15, prevents the

1:10:28

release of these from cells

1:10:30

in culture and they found

1:10:32

the same thing in vivo.

1:10:34

They can do broncoalveolar lavage

1:10:36

of mice and they show

1:10:38

that you get reduced levels of these

1:10:41

cytokines when you treat with this

1:10:43

drug. So this is the

1:10:45

potential to dampening the cytokine

1:10:47

storm as well. And

1:10:51

finally, we have to look at the

1:10:53

cells. So they

1:10:55

take sections and look at it. A pathologist

1:10:58

examines them. So the

1:11:00

lungs from infected mice that were

1:11:02

treated with a UH15

1:11:05

show a decrease in alveolar

1:11:07

damage and in

1:11:09

the formation of hyaline membranes have

1:11:13

reduced bronchiolar denudation

1:11:16

and fibrosis. The

1:11:20

drug also reduces fibrotic lung damage,

1:11:23

all of which are much higher in mice that

1:11:25

get the vehicle. They

1:11:28

have relatively normal lung function that

1:11:30

they can measure. You

1:11:32

can measure oxygen saturation

1:11:35

and airway resistance and those

1:11:37

are relatively normal. And

1:11:40

then they can measure virus reproduction. They

1:11:42

can do plaque assays to look for

1:11:44

virus in the lung. The drug doesn't

1:11:47

alter virus replication. It doesn't alter spread

1:11:50

of the virus in the lungs. It

1:11:52

is not an antiviral. It's

1:11:54

not an antiviral. It does not, if

1:11:57

negatively affect virus specific CD8, cells,

1:12:01

which are important for restricting

1:12:05

virus infection, but

1:12:07

it does dampen the influx of neutrophils.

1:12:09

Remember, I told you at the beginning

1:12:12

that neutrophil influx is a characteristic

1:12:14

of necroptosis. And

1:12:16

so those are reduced, and

1:12:18

that reduces lung

1:12:20

injury. And

1:12:23

it does not affect the virus clearance,

1:12:25

which is carried out by CD8s and

1:12:27

apoptosis. You

1:12:30

couldn't ask for better

1:12:32

results, really. It's a beautiful paper

1:12:34

about a very, very promising drug. So

1:12:41

what would you do with this? I mean, I

1:12:43

guess you have people who are at risk for

1:12:45

severe influenza. When they first start to

1:12:48

show symptoms, you treat them, or do

1:12:50

you preempt it and say, you're 85

1:12:52

years old, you have all these comorbidities,

1:12:54

we're going to give this to you

1:12:57

ahead of time if you test

1:12:59

positive. I'm not sure what the

1:13:01

clinical application is, but in

1:13:03

theory, it can prevent pneumonia deaths influencing pneumonia.

1:13:05

Yeah, give this when you give Tamiflu. And

1:13:08

I don't know what the

1:13:11

pharmacokinetics of this are. Is it orally bioavailable?

1:13:13

Is this something that's going to have to

1:13:15

be injected? So

1:13:17

that could factor into it as well. I'm

1:13:20

assuming they'd like to make it a

1:13:23

pill, but I'm not a

1:13:26

pharmacological chemist, so I can't,

1:13:29

I don't know what the deal is here.

1:13:32

I'm sure that they're looking

1:13:34

into all this. I just searched

1:13:36

the article for oral, and there's no mention

1:13:38

of the word oracle. So, I

1:13:41

mean, the mice, they don't do that. No,

1:13:43

they don't give pills to mice. So that

1:13:45

will have to be determined in studies

1:13:48

in humans, I suppose, or

1:13:50

something else. Alan, that's because when you give it

1:13:52

to them, they tell you that they took it,

1:13:55

but they actually lied. They actually lied. They might

1:13:57

lie, right? So that's what it means when you

1:13:59

say mice. That's right. They

1:14:01

hit it underneath the straw. So

1:14:04

I hope we explained that to you

1:14:06

clearly, folks. I want to make

1:14:09

sure you understand this at a good level. And

1:14:11

I know there are a lot of complicated

1:14:13

abbreviations and technologies, but

1:14:19

to really, both papers teach

1:14:21

you a lot. Yes. But

1:14:23

the science of virology. And

1:14:26

now we will read some emails to see, in

1:14:28

fact, how well you have learned other things that

1:14:30

you have learned. Dixon,

1:14:33

can you take the first one,

1:14:35

please? I would be happy to. John

1:14:38

writes, Vincent and

1:14:40

Alan and

1:14:43

Dixon, too.

1:14:46

I think particularly both of you

1:14:48

were discussing claimed religious objections to

1:14:51

vaccination in either 1107 or

1:14:54

1109. That brought to mind

1:14:56

this one that crossed my window recently.

1:15:00

Willpower dissolves in alcohol.

1:15:03

Integrity dissolves in money.

1:15:06

And reality dissolves in

1:15:08

ideology. That

1:15:11

might have relevance to the current

1:15:13

campus protests that you commented on in

1:15:15

1109, too. I

1:15:19

would like to make a lighter discussion

1:15:21

about the mutability of viruses in the

1:15:23

context of sloppy polymerases. It

1:15:26

should often, probably be mentioned

1:15:28

more often than the nucleotide

1:15:30

called at any position in the

1:15:32

genome in a sequencing effort is

1:15:35

the one that gives the strongest signal. But

1:15:37

that doesn't mean that there weren't some strands in

1:15:40

the mixture with one or the

1:15:42

other three nucleotides at that position. Otherwise,

1:15:45

unseasonably warm here in greater

1:15:47

Braddock today at nearly

1:15:49

30 degrees C,

1:15:51

as I imagine it was in New

1:15:53

York and New Jersey as well. Cheers, John.

1:15:57

So the sequencing is an interesting point. It depends on the

1:15:59

number of people. what method you use, right?

1:16:02

If you use an old-school method, yeah,

1:16:04

you get an average of each base,

1:16:07

and you cannot detect anything less than 15% of the total. But

1:16:09

if you use modern

1:16:12

high-throughput technologies, right,

1:16:15

the sensitivity is much greater, and you

1:16:17

can see mixtures. You're always up against

1:16:19

the error rate of the sequencing reaction,

1:16:22

right? That has a certain error rate,

1:16:24

so it's sometimes hard to distinguish that

1:16:26

from the actual variation at

1:16:29

any particular base. But

1:16:31

when I sequenced polio back in 1980, it was a method

1:16:33

that gave you

1:16:37

one sequence only, even though we know now

1:16:39

that that's not the case, right, because it's

1:16:41

an average, yeah. Well, how many

1:16:43

reads do you have to make to get it to be 100%

1:16:45

accurate? The

1:16:47

more the better. An infinite number. The more

1:16:49

the better. To get 100% accuracy. I

1:16:54

did two reads. Two. To

1:16:57

both strands, yeah, one strand, one one strand

1:16:59

and one the other, two reads, two X

1:17:01

coverage, and now you can

1:17:03

do hundreds, yeah. But

1:17:05

there is an error in the sequencing, so

1:17:08

that's why if you do many reads... To

1:17:11

make sure that you've found the right error. You

1:17:13

can sometimes tell if an error

1:17:15

is, if it's only in one read, for

1:17:17

example, then it's probably a sequencing error. But

1:17:19

there are more sophisticated ways to do that. We should

1:17:21

get people out who can talk about it. Alan,

1:17:24

can you take the next one? Sure. Hunter

1:17:27

writes, Twiv Personalities. I was pleasantly shocked

1:17:29

when I read this article about animal

1:17:31

sampling in the USA. Someone

1:17:33

must have been listening to Twiv to come up with

1:17:35

such a bold plan. It might

1:17:37

seem foreign to many of the lab leakers,

1:17:40

but it is refreshing to see that scientists

1:17:42

are doing what scientists should do. Try to

1:17:44

answer questions. Hopefully, the project

1:17:46

will give us some idea about the prevalence of

1:17:48

SARS-CoV-2 in other animals

1:17:51

besides humans, and

1:17:53

provide the link that I have not

1:17:56

read yet. project.

1:18:00

Oh yeah. This

1:18:04

is sampling 50 different animal species

1:18:07

to look at how SARS-CoV-2 moves

1:18:10

between people and wildlife. They're

1:18:14

catching wild animals, bighorn sheep

1:18:16

they've got photos of and

1:18:19

bunch of other things. I mean, as we've talked

1:18:21

about, SARS-CoV-2 can infect many,

1:18:24

many, many different species of mammals. So

1:18:27

this is a very cool project to look at a

1:18:30

whole bunch of different ones and see how it

1:18:32

transmits. Yeah, this is good.

1:18:35

Not how like mechanistically, but how,

1:18:38

you know, like are the deer giving it

1:18:40

to the raccoons, are the skunks getting it

1:18:42

from the mink, are the, you know, what,

1:18:45

where, how is it passing through

1:18:47

the population? What are the interactions

1:18:50

there? It's part

1:18:52

of a program to monitor big horn

1:18:54

sheep health and it was expanded. Yeah.

1:18:56

That's very good. I like this. Favorite

1:19:00

quote from the article, she and Bowman

1:19:02

quote, feel really quite stupid at meetings

1:19:05

when colleagues ask why the virus persists

1:19:07

in deer, she says. Real

1:19:09

scientists say things like this because it is

1:19:12

true and they know when to say, we

1:19:14

don't know. Take care

1:19:16

and thanks for all you do. Hunter, who's

1:19:18

a veterinarian, retired food

1:19:20

animal veteran. Cool.

1:19:24

Yeah. You have to say we don't know a

1:19:26

lot and then the non-scientists

1:19:28

start to think that you're

1:19:31

waffling or something. Vocal

1:19:34

rights. Thank you for

1:19:36

reading my email regarding the worldwide uses

1:19:38

of IPV versus NOPV on epitope 1107.

1:19:42

The two of you agreed to disagree, yet I

1:19:44

kindly ask if you could reconsider. There

1:19:48

are questions of taste, cheesecake

1:19:50

versus strawberry cake, and of moral. What

1:19:53

is more important, equality or freedom where

1:19:56

there is no clear right or wrong and

1:19:58

differing opinions are Warning,

1:20:01

rabbit hole alert. Then

1:20:04

there are questions with definitive answers, laws

1:20:06

of physics, Pax Loved versus ivermectin for

1:20:08

COVID. These are

1:20:10

amenable to the scientific method, and here

1:20:14

disagreements should be resolvable. Here

1:20:17

there are facts, maybe uncertainties, but

1:20:19

after a thorough analysis there is

1:20:21

no room for opinions or alternative

1:20:23

facts. Although

1:20:25

we cannot perform large-scale repeated

1:20:28

experiments on the IPV versus

1:20:30

OPV question, I believe,

1:20:32

for example, based on simulations, it falls

1:20:34

into the second category. How

1:20:37

about bringing a public health expert on the show

1:20:39

to delve into the details? What would it take

1:20:41

to use only IPV? What

1:20:44

would this mean if we had to operate

1:20:46

within the same financial resources? Please

1:20:49

make sure both of you are available for this session.

1:20:51

We are here. Thanks and

1:20:53

best regards, Volker. P.S. The price of Pax

1:20:56

Loved in Germany is 60 euros rather

1:20:58

than 600 US dollars, and

1:21:01

even this small price is almost always covered by

1:21:03

your insurance. It's tough to live in

1:21:05

a country where the interest of big pharma is

1:21:07

so little protected. All

1:21:10

right. So Alan's argument was that

1:21:14

we can't use IPV globally. We

1:21:16

have to just accept the side

1:21:18

effects of OPV, and

1:21:20

I'm more idealistic. We

1:21:23

should not have side effects and

1:21:26

give everyone IPV. Admittedly,

1:21:30

I do not know the logistics of

1:21:33

injecting everyone with IPV. However, we did

1:21:35

try and inject a lot of people

1:21:37

with SARS-CoV-2 vaccines, right? Yep. We

1:21:41

didn't get everyone for sure, but it seems like

1:21:45

it's feasible. But

1:21:49

I would still say I just

1:21:51

don't like paralyzing kids. No,

1:21:54

I don't either. Well, I know

1:21:56

you don't. Yeah, and that's why I— But that's driving me. That's what's

1:21:58

driving me. see your

1:22:00

point and I could very easily take that

1:22:03

side if I weighed

1:22:05

things just a little bit differently. But

1:22:09

as a pragmatist, you

1:22:12

know, the question of what would it mean

1:22:14

if we had to operate within the same financial

1:22:16

resources, well, that's the current situation. So

1:22:18

we cannot, with the current budgets

1:22:20

given to public health, and that

1:22:23

is an important distinction. We

1:22:25

cannot deliver IPV to every kid

1:22:27

in the world. With

1:22:30

the current budgets given to the

1:22:32

Defense Department, I'm pretty sure we

1:22:34

could. Now, the question that

1:22:36

would be interesting to have a public health

1:22:39

policy person on about is where

1:22:41

in between those, you

1:22:43

know, between the cost of an F-35

1:22:46

and the, you know, the cost of the

1:22:48

public health actually gets, would

1:22:51

we have to be to actually get IPV out

1:22:53

there? And I don't know what that number is.

1:22:55

I do know that it's a lot more than

1:22:57

what we have now. And so the

1:23:00

choice is not IPV or OPV.

1:23:03

It's OPV or nothing in

1:23:05

poor countries. And I don't think

1:23:07

nothing is the right choice. I

1:23:10

am not a pragmatist for sure. I know.

1:23:12

You're an idealist. I'm an idealist and I...

1:23:16

And I like that about you. I'm

1:23:18

happy to be an idealist because sometimes

1:23:20

idealists can influence pragmatists. Yes, absolutely. I

1:23:22

would like to get someone on who

1:23:25

could speak to this. Yeah.

1:23:28

I don't know who would be. They

1:23:31

would have to be somebody in not

1:23:33

just public health research, but public health connected

1:23:36

with policy. There's

1:23:39

got to be somebody at Columbia. So,

1:23:42

while Steve Morse could certainly address it... Oh, often would

1:23:45

have an opinion, I'm sure he would. He would have

1:23:47

an opinion. We

1:23:49

want someone who can say, no, this

1:23:51

is why it can or cannot happen. Someone

1:23:54

who's actually sat at those tables of where

1:23:56

people are budgeting for public health campaigns. If

1:23:58

you have... thoughts folks let

1:24:00

us know. All right we have two more left let's get

1:24:02

to it. Dickson can you do the next one? I can

1:24:05

do the next one. Daniel

1:24:07

writes, Dear Twiv Team, the

1:24:09

discussion on Twiv 1105 about the future of SARS-CoV-2

1:24:15

and whether it will end up like HCoV-OC43

1:24:18

from the 1889 pandemic,

1:24:20

allegedly, reminded

1:24:26

me of a study I discovered earlier in

1:24:28

the pandemic originating originally from 2006

1:24:30

about OC43 outbreak in 2003. That's a lot of

1:24:32

numbers. Many

1:24:36

gives a reference. An outbreak

1:24:39

of human coronavirus OC43

1:24:41

infection and serological cross-reactivity

1:24:43

with SARS coronavirus. In

1:24:46

the summer of 2003 a respiratory outbreak was

1:24:49

investigated in British Columbia during

1:24:52

which nucleic acid tests

1:24:54

and serology unexpectedly indicated

1:24:56

reactivity of severe acute respiratory

1:24:58

syndrome coronavirus SARS-CoV

1:25:00

cases. Cases

1:25:03

at a care facility

1:25:05

were epidemiologically, he's quoting the

1:25:07

article now so I don't have a further

1:25:10

wording on that. The study

1:25:12

claims that OC43 can

1:25:15

have a case fatality of 8% in long-term case settings,

1:25:18

care settings. Is

1:25:21

it possible that OC43 never

1:25:23

fully lost its virulence and

1:25:26

just faded into the background of ILIs

1:25:29

especially compared to influenza

1:25:31

and RSV, especially without

1:25:34

explicit testing? Would

1:25:36

adapting the mRNA vaccines for

1:25:38

OC43 spike make sense that

1:25:40

could be given as a

1:25:43

yearly booster for the elderly elderly?

1:25:45

Like that's why you had the me read that

1:25:47

I presume. Would Pex-Lo would

1:25:49

work? Thank you. So

1:25:53

Daniel, the virus is not

1:25:55

the thing that's losing its virulence. It's

1:25:57

population immunity that is resulting in... a

1:26:00

different pathogenicity

1:26:02

pattern. And so in these

1:26:05

long-term care settings, I'm not surprised

1:26:07

that any virus might be more

1:26:10

pathogenic than in the general population because these

1:26:12

are people with poor

1:26:14

immune systems and other diseases. So

1:26:17

yeah, I think it's possible that OC43

1:26:19

could cause some severe illness. But overall, in

1:26:21

the overall population, it's a very mild infection.

1:26:25

And I think that's

1:26:27

because it has gone through and

1:26:30

in fact, most of the people have been infected at

1:26:32

a young age where they can make good

1:26:35

memory B and T cells that last

1:26:37

their lifetimes. It also sounds to me

1:26:39

like 8% is not

1:26:41

a very high number for that age group. And

1:26:44

especially in long-term care settings, there

1:26:47

must be some underlying comorbidities that

1:26:49

contributed to their fatality. Exactly.

1:26:52

It was one more that you

1:26:54

skipped. Can you do that, Alan?

1:26:56

Sure. Robert writes, thank you for

1:26:58

your informative podcasts that are great

1:27:00

continuing education in virology and immunology.

1:27:03

Interesting story of H5N1 in our

1:27:05

milk supply at least since February.

1:27:08

That is a cool article, yeah. A

1:27:11

doctor in Sitka, Alaska who graduated from

1:27:13

Columbia PNS in 1972. Look

1:27:15

at that. This

1:27:19

is from successful farming. Yeah.

1:27:24

Yeah, so this is an interesting story of how they

1:27:26

figured out what was going on with the cows in

1:27:29

Texas. Back in February, they were

1:27:32

producing less milk. And

1:27:34

so this story tells you how

1:27:37

they found it was H5N1. In

1:27:41

any case, you're wondering where Sitka, Alaska is

1:27:43

and what the word Sitka

1:27:45

might mean. It's actually the name of a

1:27:47

very remarkable gymnosperm

1:27:50

or a pine tree.

1:27:53

And they're remarkably beautiful and

1:27:56

becoming more rare. Okay,

1:27:59

now it's time for some picks of the week and

1:28:01

we will start with you, Dixon. Well,

1:28:03

okie dokie. Everybody

1:28:06

wonders whether there's life in other

1:28:09

universes or other parts of our own

1:28:11

galaxy. And you

1:28:13

know, you tend to poo-poo the idea

1:28:15

because, you know, it takes a

1:28:17

lot of stuff to come together to create

1:28:21

the right conditions for life,

1:28:23

period. And we have never been able to

1:28:26

do that, but we think

1:28:28

we were the mixing vessel where all those

1:28:30

things came together in a

1:28:32

remarkable fashion. But here's a

1:28:35

new look because now we have the James

1:28:38

Webb Space Telescope and

1:28:40

with a much higher refinement for

1:28:43

the spectroscopy that you can

1:28:45

use to say what's out there in terms

1:28:47

of molecules. And

1:28:49

they actually looked at a nucleus

1:28:51

of star formation where

1:28:53

the dust clouds are coming

1:28:56

together and condensing and forming stars.

1:29:00

And in that cloud that they

1:29:02

looked at, they

1:29:04

also looked at the

1:29:06

complex carbon compounds that

1:29:09

you could identify by mass

1:29:11

spectroscopy. And it is

1:29:14

quite remarkable to see how

1:29:17

many different familiar molecules,

1:29:20

but more than three or

1:29:22

four carbon lengths in length

1:29:25

were identified by the space

1:29:27

telescope. Now, carbons,

1:29:30

they come together but they also come apart because

1:29:32

there's a lot of radiation out there, especially

1:29:34

when a star ignites and

1:29:37

forms its first radiation

1:29:40

release, so to speak. But

1:29:43

in addition to that, of course, in these

1:29:45

star clouds, you

1:29:47

can get planets forming. And

1:29:50

so the way

1:29:52

you get a planet with

1:29:55

the chances for life is

1:29:58

to include those molecules right away. so

1:30:00

that they have a head start. And this, I

1:30:02

was just blown away by this. I must say

1:30:04

that when I saw this, first I passed over

1:30:06

and then I came back to it and I said, wow, you

1:30:09

know, now we have a way of looking to see whether

1:30:12

or not we can identify concentrations

1:30:15

of carbon-based molecules

1:30:18

that would go

1:30:20

in further than that. We found amino acids when

1:30:22

we found, I don't know if

1:30:24

we found nucleic acids or not

1:30:26

yet, but we're certainly looking intensely

1:30:28

for them. But now it's

1:30:30

really an amazing thing

1:30:32

to know that we can use something

1:30:36

as remarkable as a space telescope to

1:30:39

serve the universe and see what we find. This

1:30:42

is really cool. Yeah, I think it

1:30:44

is. Yeah. And

1:30:46

they found formic acids, so there are ants in

1:30:48

that. There are ants in that, Nipsey. Exactly, exactly.

1:30:52

Or am I leaping to conclusions? Maybe a little bit.

1:30:54

All right, ants. And

1:30:58

then there's ethanol somewhere. There

1:31:00

are bars, right? They're bars. Yeah.

1:31:05

Very cool. All right, thank you, Dixon. Alan,

1:31:08

what do you have for us? I have a

1:31:12

calculator that I found a little while ago.

1:31:15

So I was planning a trip.

1:31:17

I'm actually gonna be at ASV.

1:31:19

Briefly, I won't be at the Twiv, but

1:31:22

I will be giving a presentation in one of

1:31:24

the workshops. And

1:31:27

I was planning my trip and going out to

1:31:29

Ohio, and gee, how do I get there? Well,

1:31:31

you can't take the train because the Amtrak doesn't

1:31:33

go to Columbus. So I'm

1:31:35

gonna fly, or does it make more sense

1:31:37

to drive? And gee, how would

1:31:40

I even figure that out? And

1:31:42

it turns out there are a bunch of

1:31:44

these climate calculators online. This one, as far

1:31:46

as I can tell, is, well,

1:31:49

it's the most reliable-looking one that I found.

1:31:51

And I cross-checked it against a few other sources.

1:31:54

What it allows you to do is put in, it's

1:31:57

got a bunch of different tools on it, but what I used is...

1:32:00

the flight and the car tool and

1:32:03

you can put in your trip where you're

1:32:05

going from and to the one drawback

1:32:07

is it's all it's all in Canadian

1:32:09

units so you have to convert to

1:32:11

kilometers and you know liters of

1:32:14

fuel per 200 kilometers instead of miles per

1:32:16

gallon there's so there's a little bit of

1:32:19

additional work you need to do but

1:32:21

the cool thing with the flight calculator is

1:32:23

you can tell it your exact route so

1:32:26

if I'm taking I know I'm I would

1:32:29

be taking Southwest from Hartford to BWI

1:32:32

to Columbus and they

1:32:34

always fly 737s you put in the type of

1:32:36

plane and it

1:32:38

takes all of that into account

1:32:40

and it's drawing its numbers directly

1:32:43

from the from the International Aviation

1:32:45

Organization's database on

1:32:48

fuel consumption for aircraft and the effects

1:32:50

of that fuel expenditure at altitude which

1:32:52

is a significant thing with aviation I

1:32:55

calculated that it's actually a wash I

1:32:58

could fly or drive for that particular trip

1:33:01

and my carbon footprint will be equivalent

1:33:05

it would be different if I were going to be sharing the car

1:33:07

with somebody then it would make more sense

1:33:09

to drive. So the answer is rent a Tesla. That

1:33:14

just shifts the carbon footprint. That's right, well

1:33:16

you know. The shoe

1:33:18

is on the other foot, that is exactly right. I

1:33:21

was just it just flew to Columbus just

1:33:23

an hour each way but

1:33:26

yeah driving is longer so I can see. Driving

1:33:28

is it's a 10-hour drive from where I am

1:33:30

and that was not looking forward to it but

1:33:32

I thought you know there's a

1:33:34

big difference in the footprint I might I

1:33:36

might try and plan a multi-day trip and

1:33:38

you know what are you gonna do fly?

1:33:40

Just out of curiosity Alan what would it

1:33:42

cost to rent an airplane a little

1:33:44

Piper Cobb or Comanche or something like

1:33:47

that. Yeah so I don't do that

1:33:49

anymore but that would be well

1:33:52

it's more weather dependent because you're flying down in

1:33:55

the weather but

1:33:58

it would it would be a about

1:34:00

$150 an hour for the planes I used to fly,

1:34:02

and hours to Columbus, it would probably be

1:34:11

about a four hour, reasonably

1:34:13

a four hour trip each way. So,

1:34:17

yeah, we're over a grand.

1:34:20

Right. Now you can fly for less?

1:34:22

Yeah, you can. Yeah, I mean, it's, it's for

1:34:25

since airline deregulation, it has not been

1:34:27

cost effective to fly your own airplane.

1:34:30

And it is definitely not carbon effective.

1:34:32

The carbon footprint for that trip would

1:34:35

be far worse than either the

1:34:37

car or the airline. All

1:34:41

right, thank you. My pick,

1:34:43

well, my pick is, I think I made

1:34:45

in January, but I want to do it

1:34:48

again because it's my lectures

1:34:50

in virology, which my virology course at

1:34:52

Columbia University, which just ended. And

1:34:55

now all 25 lectures are on YouTube. So

1:34:59

you can go watch them. And I'm sure some people don't know

1:35:01

this. So I wanted to pick

1:35:03

it again. So again,

1:35:05

from the beginning, it's all

1:35:08

about how viruses reproduce in cells and then

1:35:10

at the end, how they

1:35:12

cause disease, how we attempt to

1:35:14

prevent disease that we vaccinate, how

1:35:16

viruses emerge, evolve, etc.

1:35:19

And then the last lecture is

1:35:21

about therapeutic viruses, trying

1:35:24

these viruses so they can help us. So it's

1:35:26

a complete introductory virology course.

1:35:28

It is free. You can

1:35:30

watch the lectures and

1:35:32

learn something about viruses. Nice.

1:35:35

So check that out. We have a

1:35:37

listener pick from Alan, who is not

1:35:39

Alan. Not me. Greetings

1:35:44

from the editorial offices of

1:35:46

the Journal of Clinical Ambivalence.

1:35:49

Here's an NPR story, an

1:35:52

interactive quiz on

1:35:54

pandemic respiratory

1:35:57

disease transmission based on the new

1:35:59

WHO. guidelines. The staff

1:36:02

here got 100%.

1:36:04

Mostly thanks to Careful, Twiv,

1:36:07

Listening. Now wait a minute, is

1:36:10

there a journal called Clinical

1:36:12

Ambivalence? No. It should be

1:36:14

if there isn't. No, that's a joke. The

1:36:17

Journal of Irreproducible is also happening. When

1:36:19

I saw this in my news feed,

1:36:21

I immediately went and took it

1:36:23

and I also got 100% on it. Yeah,

1:36:26

100%. Yeah, I wish I was happy to see

1:36:28

that. There's no link here. I

1:36:30

just included it. Did you put it in? Thank you. Yeah.

1:36:33

Because

1:36:36

when I copied it, it didn't go over. All right,

1:36:38

very good. Thank you very much. And

1:36:41

then from AZ, a short

1:36:43

video by Dr. Rob Swan-Dye

1:36:45

explains why has H5N1 bird

1:36:47

flu infected other mammals more

1:36:50

readily? Humans lack one type of

1:36:52

cell receptor in the upper tract

1:36:54

that H5N1 viruses can use to

1:36:57

establish an infection. So let's

1:37:00

see. H5N1, who

1:37:03

is Rob Swan-Dye? I

1:37:06

don't know. But I didn't look at this,

1:37:08

but we will... Let's see,

1:37:11

1.6 thousand views.

1:37:13

That's a good sign. Because

1:37:16

if it was fake, we'd have 1.6 million

1:37:18

views. What

1:37:20

do you think of that metric, huh? It

1:37:23

is a... It says

1:37:25

he's a PhD, Dr. Rob Swan-Dye,

1:37:27

making science accessible. This

1:37:31

sounds good. We

1:37:34

probably ought to meet this guy. He has

1:37:36

a website. Medicine for thyroid disease. He's

1:37:38

a science... Very into thyroid disease, it seems.

1:37:42

But he's got a bunch of other stuff. Anyway,

1:37:47

I don't know what he is saying

1:37:49

is why bird flu has infected

1:37:51

other mammals more readily if

1:37:54

there has been a receptor change or what.

1:37:56

We should know this, right? know

1:38:00

offhand, Alan, if there's

1:38:02

a HA change to include

1:38:04

alpha-2,6-sialic acid? I don't know.

1:38:06

I'm still

1:38:10

trying to figure out who Rob Swanda is. Yeah,

1:38:14

he's mostly about thyroid

1:38:16

dysfunction. Yeah,

1:38:19

so they combine alpha-2,6-sialic acids

1:38:21

and that may in part

1:38:24

explain why they're infecting many more mammals.

1:38:26

But I will watch this. Thank you, AZ.

1:38:32

That'll do it for TWIV 1111. You can find

1:38:35

the show notes at microbe.tv

1:38:40

slash TWIV. You can send us questions,

1:38:42

comments, picks of the week to TWIV

1:38:45

at microbe.tv and we'd love

1:38:47

your financial support so we can continue

1:38:49

to do these programs. You

1:38:51

can go to micro.tv slash contribute for

1:38:53

various ways where you can do that.

1:38:56

Dixon de Pommier can be found

1:38:58

at trichinella.org and the living river.org. Thank

1:39:02

you, Dixon. You're quite welcome. What a joy

1:39:04

it is to be a part of this group. Alan

1:39:07

Dove is at alandove.com and turbidplac.com.

1:39:10

Thank you, Alan. Thank you. It's

1:39:12

always a pleasure. I'm

1:39:14

Vincent Racaniello. You can find me at

1:39:16

virology... no, not anymore. Old

1:39:20

columns are at virology.ws but I

1:39:23

haven't written in a long time. You

1:39:25

can find me at microbe.tv or here at

1:39:27

the incubator. Come visit. Send us an

1:39:29

email. We'd love to have you. Some

1:39:31

students are going to visit next week from my class.

1:39:34

Cool. I'd like

1:39:36

to thank the American Society for

1:39:39

Virology and the American Society for

1:39:41

Microbiology for their support of TWIV.

1:39:43

Ronald Jenkes for the music and

1:39:45

Jolene for the timestamps.

1:39:47

You've been listening to This Week

1:39:50

in Virology. Thanks for

1:39:52

joining us. We'll be back next week.

1:39:54

Another TWIV. Viro.

1:40:00

Thank you.