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

1121 recorded on June 7, 2024. I'm

0:23

Vincent Draconiello, and you're listening to

0:25

the podcast all about viruses. Joining

0:28

me today from Western Massachusetts,

0:30

Alan Dove. Good to be

0:32

here. And here in Western

0:35

Mass, it's muggy

0:37

and cloudy and rainy. It's

0:40

the 85 Fahrenheit to 29 C. And

0:44

it's been off and on raining for days. I'm

0:48

in Helsinki, Finland, where it is 15 C. It

0:52

is completely blue sky. I'm

0:54

looking at the hotel window here. It's 10

0:57

p.m. It is bright as anything.

1:00

Gonna get dark for another two hours. Also

1:03

joining us from Madison, New

1:05

Jersey, Brianne Barker. Hi,

1:08

great to be here. It

1:11

is 81 and sunny, 81 Fahrenheit

1:13

and sunny in Madison, New Jersey. And

1:16

quite lovely. Yesterday was pretty humid and rainy,

1:18

and today is great. From

1:21

Austin, Texas, Rich Condit.

1:24

Hi, everybody. Blue skies, 94

1:27

degrees, which is not

1:29

nearly as bad as it's. Well, it's not great,

1:31

but it was 70 this morning,

1:34

which was OK. We're

1:36

OK. It's not 100. It's

1:38

not 100. It's not 100. If

1:42

it's not 100, it's a good day. And

1:45

from College Station, Texas,

1:47

Jolene Ramsey. Ola

1:50

and Howdy here from Texas. It's

1:52

95 degrees here and 88 percent

1:54

humidity. I will

1:56

add. It's

1:59

been raining here. There's very little humidity

2:01

in Helsinki. It's very nice. It's

2:04

a bit chilly actually. 15C, remember those

2:06

days? Yeah. If

2:09

you like these programs, these science programs,

2:11

we'd love to have your support to

2:14

continue them. You

2:16

can go to microbe.tv slash

2:18

contribute. Couple

2:21

of announcements here. The

2:24

hepatitis B virus meeting, the international

2:27

HPV meeting, will

2:29

take place September 11th to 15th in

2:31

Chicago, Illinois. You can

2:34

go to hpvmeeting.org for meeting

2:36

information deadlines and how to

2:38

register. We'll be doing a twiv there with

2:40

some of the participants.

2:44

Some news about ASV 2024 coming

2:46

up soon. Special

2:49

events and workshops. This

2:51

is information that's in your program PDF, the mobile

2:54

app. If you're registered, you should have gotten an

2:56

email with the links for both on June 5th.

2:59

We previously highlighted some of these

3:02

special workshops. Check them all out

3:04

now that you can do it on your phone. Today,

3:08

Tuesday and Wednesday, Assistant Professor Bootcamp,

3:10

Tuesday at lunchtime. This is a

3:12

panel discussion with faculty at various

3:14

stages and institutions. Advice

3:17

on how to navigate the first

3:19

five years. Make connections with

3:21

them and others in the workshop that may be

3:23

useful after the meeting.

3:25

We'll describe other events in

3:28

future weeks like education, communications and

3:30

twiv. And

3:33

if you have attended

3:35

ASV before, like Athens or Madison, it's

3:37

last year or the year before, find your

3:39

name badge. ASV wants

3:41

to be eco-conscious. Your

3:44

flight attendant is holding up a name badge and

3:46

demonstrating its proper use right now. Thank you. No,

3:48

I think the proper use would be to put it on my neck. Yeah, that

3:50

would be, yes, right. And

3:53

I want you to reuse yours. You can

3:55

blow into the lanyard to inflate it. Helps

3:57

the planet. Remind

4:00

your friends who are going to ASV but may

4:02

not listen to Twiv. If you haven't

4:04

gone anywhere since last year's meeting, maybe it's still

4:07

in your suitcase. Actually, I did find- Wait a

4:09

minute. Wait a minute. Going to

4:11

ASV and not listening to Twiv? Not listening to

4:13

Twiv. Oh yes,

4:15

it happens. And if you know people

4:17

who are in that category, bring them up to speed. Yeah.

4:21

Last year I brought one of my

4:23

older badges in Warwick. It was the

4:26

only one, I think, besides Kathy. So

4:28

let's do better this year, folks. Okay.

4:30

They ought to put like a sticker on

4:33

the badge each year. I see

4:35

sometimes people have a vehicle where they've got

4:37

the beach sticker from where they go on

4:39

Cape Cod and they accumulate them over the

4:41

years to show off how many times they've

4:44

been back to wherever. So

4:47

maybe there could be- Yeah, there's space on the

4:49

top part of the badge. They could put like

4:51

little, I used this in this year,

4:53

this year, this year, and then people would hang

4:55

on to them. That's a good idea. Many of

4:57

you know, because you've written to us, that an

4:59

opinion piece was published in the

5:01

New York Times in

5:04

the past week by Alina Chan. And

5:06

the title is, Why the Pandemic Probably

5:08

Started in a Lab and Five Key

5:10

Points. We've received

5:13

many email requests to discuss this.

5:16

And so that is what

5:18

we are going to do because the

5:21

title isn't correct. And

5:24

we're going to go through each of the five key points and

5:27

tell you why. But- And

5:29

I think before we launch into that discussion specifically,

5:32

Jolene, do you want to run down the- this

5:35

is not the first time this has come up on the show? Yeah.

5:38

So we're going to go

5:40

through and discuss some of the article,

5:42

but we have done this in much

5:44

greater depth actually discussing the papers that

5:47

address these points as well as talking

5:49

to some of the scientists. And

5:52

just to remind people of where they

5:54

can hear about this discussed in greater

5:56

depth and kind of listen

5:59

to the evidence. that has been presented.

6:01

I wanted to run through a few

6:03

of the episodes that we have. We'll

6:06

put a list in the show notes

6:08

that where these things have been discussed.

6:10

So Twiv 1019, Eddie Holmes on SARS-CoV-2

6:12

origins. Twiv 940, also

6:14

Eddie Holmes in on viral

6:16

origins. We have a Twiv

6:19

861, rough draft

6:21

of Omicron origins. Twiv

6:24

762, SARS-CoV-2 origins with

6:26

Robert Gary, where

6:29

we did talk about things like the

6:31

receptor binding domain and the furin cleavage

6:33

site and the two lineages

6:35

circulating in the Wuhan wildlife markets.

6:38

Twiv 760, SARS-CoV-2 origins

6:40

with Peter Daschak, Thea Coulson,

6:43

Fisher and Marion Koopmans. And

6:46

Twiv 777, SARS-CoV-2 fitness

6:49

with Ron Foucher, fitness

6:51

and the ability

6:53

of SARS-CoV-2 to replicate

6:56

and spread in other organisms.

6:59

There is a long list. I'm not

7:01

gonna read all of them, but look

7:04

in the show notes for many more

7:06

instances of Twiv episodes where we discussed

7:08

with guests and just discussing papers. A

7:10

lot of these points are about to

7:12

go through. And if you're

7:14

on YouTube, that'll be down right below this

7:16

video. So there are five points

7:19

in this opinion, but there is an introduction. And let

7:21

me read from the introduction.

7:25

Quote, a growing volume of evidence gleaned

7:27

from public records released under the Freedom

7:29

of Information Act, digital sleuthing through online

7:31

databases, scientific papers analyzing the

7:34

virus and its spread and leaks from

7:36

within the US government suggest that

7:38

the pandemic most likely occurred because a

7:40

virus escaped from a research lab in

7:43

Wuhan, China, end quote. Ooh,

7:45

secret newly revealed knowledge, woo.

7:49

In fact, most evidence

7:51

that has been published in journals, which

7:53

we have covered in many of those

7:55

episodes that Jolene talked about, supports a

7:58

natural origin, not a lab origin. and

8:01

Dr. Chan is ignoring most

8:03

of those data because

8:05

they don't fit with her

8:07

narrative. We understand now

8:10

and we've talked about this a lot.

8:12

The pandemic began when a virus jumped

8:15

multiple times from animals on sale

8:17

in the Huanan seafood market in

8:20

Wuhan to humans. There

8:22

are several papers on this

8:25

and they basically are devastating

8:27

to the lab-origin hypothesis. They

8:29

show that the market was

8:31

the early epicenter, that two

8:33

lineages were circulating there, that

8:35

susceptible mammals like raccoon dogs

8:37

and civecats were sold in

8:39

the southwest corner of the

8:41

market and we have environmental

8:43

samples from there that showed

8:45

the presence of both animal

8:47

DNA and viral RNA

8:50

from SARS-CoV-2. So

8:52

this statement, the growing volume of

8:54

evidence suggests it occurred because a

8:56

virus escaped the lab, is simply

8:58

wrong. If

9:01

people are particularly interested

9:03

in things that Vincent

9:06

mentioned, I particularly found

9:08

the discussions in episodes

9:11

876 and 995 to really be

9:13

particularly enlightening and

9:18

the data that we saw in those two really

9:20

like was very clarifying

9:22

to me and made it very obvious

9:24

to me. Yes. Yeah,

9:27

and I just want to harp a little bit

9:29

on this two lineages thing because

9:31

when I saw that, I mean

9:34

when those data came out, it's

9:38

a subtle but really damning point if

9:40

you want to say that this came

9:43

from a lab, that there

9:45

were two genetically distinct lineages

9:47

of this virus at

9:50

the very, very earliest days of the

9:52

outbreak in Wuhan and there

9:54

is simply no way to get to

9:57

that from a leak of

9:59

blood. from a lab done on a virus somebody

10:02

was working on. Yep. All

10:05

right, point number one. Quote,

10:08

the SARS-like virus that caused the pandemic

10:10

emerged in Wuhan, the city where the

10:12

world's foremost research lab for SARS-like viruses

10:15

is located. Bats in

10:18

other parts of China have not been

10:20

found to carry viruses that are as

10:22

closely related to SARS-CoV-2.

10:25

All right. So we have to talk a little

10:27

bit about what is closely

10:30

related to SARS-CoV-2. The

10:32

closest relative at the

10:34

onset of the pandemic is

10:37

a virus is a genome sequence called

10:39

RATG13, which

10:42

was taken from a bat in a cave

10:44

called Tonguan Cave in southern Yunnan, which is

10:46

about 1,800 kilometers from Wuhan.

10:49

RATG13 is 1,200 bases different from SARS-CoV-2. That's

10:55

96.2% similarity. So

10:59

RATG13 could not have

11:01

been the progenitor of

11:04

SARS-CoV-2. And 96.2% genome

11:07

identity, we're talking a little bit about this before the

11:09

show. You know, if you get 96.2% on a test,

11:11

you aced it. But

11:14

if your genome is 96.2% identical to something else, that's

11:19

what? What is that, humans and sooty mangabays

11:21

or something? I mean, that's? Yeah, something

11:24

like that. And this is

11:26

particularly important because coronaviruses for

11:29

RNA viruses are quite large. So

11:32

the genome size of

11:34

the virus is 30,000 base pairs. And

11:40

so 1% difference

11:43

would be 300 base pairs. And

11:47

so you somehow have to

11:49

account for if it was 1% difference,

11:52

and of course, this is 4% difference, 1,200 unexpected

11:58

changes. between these

12:00

viruses. 1200 and scattered

12:02

ones, not ones that are all

12:04

next to each other. So I think

12:07

that I often find that thinking

12:09

about that number, it's one thing to be

12:11

like, oh, so that means four

12:13

base pairs happen to change. No, we're not

12:15

talking about, it's not 100 base

12:17

pairs and only four changed. You

12:19

have to be able to account for how

12:21

some there are random mutations at 1200 sites

12:25

compared to these other viruses that are known. And

12:28

to your point of 1200 sites, this is

12:30

not one segment of the genome that's been

12:32

changed. This is all over the place. Yeah,

12:36

exactly. So basically, and we're going

12:38

to hammer on this more. No

12:42

one had something close enough to source COVID

12:46

to be able to engineer it to become

12:49

a pandemic virus. This RATG13 is too

12:51

distant. This

12:54

1200 bases, by the way, it would take

12:56

20 to 30 years

12:58

at minimum for a

13:00

virus circulating in nature to

13:02

accumulate those number of mutations given the

13:05

mutation rate of the virus. And

13:08

if you were going to engineer something in the lab

13:10

to study it, for example, you

13:12

would not change 1200 bases

13:14

scattered throughout the genome. That's just,

13:17

nobody would even embark on such a

13:19

project. Who would even know what's the

13:22

change? Exactly. These are not changes that

13:24

would not have any effect on proteins.

13:26

And therefore, you would not choose to

13:28

make that kind of a decision as

13:31

a scientist who's designing something because

13:34

you don't know, you would imagine that the

13:36

majority of them would have impacts that you

13:38

couldn't predict. And

13:40

I think that people have

13:42

tried to argue at some point, oh, well, those

13:45

changes came because of, you know, you got

13:47

the virus and passaged it in the lab

13:49

for a while after you did all that

13:51

engineering. But how do you get 1200 changes

13:54

around the virus but none in

13:56

the spots that you engineered? It

14:00

just doesn't make any sense. All

14:02

right. One more quote from point one

14:05

quote, bat coronavirus spillover into humans

14:08

is rare, end quote. I

14:11

would say that pandemics are rare, but

14:13

spillovers are not right before

14:15

SARS COVID two emerged for

14:18

coronaviruses made the jump and

14:20

these are the common cold

14:22

coronaviruses that continue to cause

14:24

seasonal respiratory disease to this

14:26

day. SARS-CoV-1 in

14:28

2002 was a spillover, SARS-CoV-2

14:30

in 2012. And

14:33

during SARS-CoV-2 pandemic, we

14:35

talked about on Twiv spillovers

14:38

of pig and canine coronaviruses

14:40

into people. Research

14:43

from wildlife markets in Southeast Asia

14:45

has been done to quantify zoonotic

14:47

risk in these kinds of settings

14:49

where you have animals being

14:52

sowed. And one

14:54

study found that in a sampling of

14:56

lotion markets, civets, which

15:00

are one of the animals sold in these

15:02

markets, averaged seven human contacts per hour so

15:04

that a single infected civet might get over

15:06

50 opportunities to infect the person in an

15:08

eight hour day. And furthermore, studies

15:10

in rural China have shown that about

15:13

3% of the population of

15:15

antibodies to SARS-related coronaviruses, bats indicating

15:17

that spillovers are not rare at

15:19

all. So this is not a

15:21

correct statement. Absolutely.

15:24

Yeah. I

15:27

think that we

15:30

can also think about just general

15:32

how frequently are virus

15:34

spillovers from animals into humans

15:37

happening. Is the

15:39

general idea of spillover rare? And it

15:41

is very much not. It's

15:44

sort of the way we think this

15:46

happens in almost every other situation. So

15:48

again, the idea

15:50

that this doesn't happen is weird. It

15:54

happens with so many viruses in so

15:56

many ways. This is a relatively recent

15:59

change. in our perception

16:01

of how often spillovers occur, because

16:03

previously we didn't have the tools

16:05

to detect these unless they became

16:08

pandemics. So we said, oh,

16:10

well, coronaviruses have only spilled over four

16:12

times, five times, you know, because

16:14

that's the only evidence

16:17

we had. Now,

16:20

in part due to not just the

16:22

advanced molecular biology, but all the attention

16:24

on SARS-CoV-2, now

16:27

that we're looking and that we can look, we

16:29

see, oh, in fact, this

16:31

is a day-to-day occurrence

16:33

for all kinds of viruses, coronaviruses and

16:35

several others. How would we know? If

16:38

you want some, if you want,

16:40

I'm sorry, Jolene. I was gonna

16:42

say, how would we know that there was SARS-CoV-2

16:44

in spreading in deer if we didn't

16:46

have the tools we use today? And we've been talking

16:48

about H5N1 in cows, and

16:52

we talk about previous

16:54

influenza spillovers and mixing

16:57

that happens in pigs. And these

16:59

are maybe things that we

17:01

don't see them in our everyday

17:03

life, but at the population level,

17:06

they happen more than we used to think. And

17:10

since we've found out how

17:12

easily SARS-CoV-2 hops species, this

17:14

is an incredibly promiscuous virus.

17:18

You know, the spill back to deer,

17:20

the spill back to cats, the spill

17:23

back to pretty much everything with fur.

17:25

I mean, it's the, we've talked about

17:27

this on earlier episodes. This is a

17:30

virus that can go anywhere. So it's prone

17:32

to this kind of thing. For

17:35

anybody interested in a layman's read

17:37

of this larger topic, I would

17:39

refer them to David Quammen's book,

17:41

Spillover. Yes. It's a great read.

17:44

Yes, it's got lots of different examples. I think it

17:46

came out in 2013. So

17:50

it's not, you know, not as if this idea

17:52

was new and just something's

17:54

new. No dog in this fight then.

17:56

That's right. It's actually subtitled, Animal Infections

17:58

and the Next Human. pandemic.

18:01

This was inspired among other things by

18:04

the Ebola outbreak, the

18:06

big one in Africa. Yeah.

18:08

And I think there's one other piece that's

18:10

sort of related to something we've been saying

18:13

here about this point one

18:15

that Dr. Chan makes. Because

18:18

when I read this point one, I

18:20

feel like at least lay

18:22

people are looking at it and saying, well,

18:24

that can't be a coincidence that this pandemic

18:27

emerged in the same

18:30

city as the virology

18:32

Institute. And we've mentioned

18:36

some details about how we don't

18:38

always detect all

18:41

virus spillovers. We in the past have largely

18:43

detected the ones that have become

18:46

pandemics. We

18:49

have some pretty good evidence that we detected

18:51

very early cases, but we should note that

18:54

we are talking about the first cases that

18:56

were detected here. And one

18:58

of the things that is important

19:00

in detection is sort of a

19:02

good medical care infrastructure, a place

19:06

where you could do that. In

19:08

fact, you needed to have

19:10

physicians who could say, hey, we have

19:12

these patients who have pneumonia

19:14

that is atypical and we are

19:17

going to actually do sequencing and

19:19

find out what they have and not just

19:21

say, huh, you got weird pneumonia, that sucks

19:23

for you. If

19:25

we think about a bunch of other infectious

19:28

diseases we know about, we

19:31

can think about what we know about their origins.

19:33

But in many cases, the

19:36

first ever case that was

19:38

seen and published was

19:41

seen in a place that had

19:43

particularly good hospitals and

19:45

medical care to

19:48

diagnose those patients and probably

19:50

was not the first situation

19:52

of an infection. I

19:55

often use the first published account

19:57

of HIV is more of a viruses

22:00

with mutations that allowed them to thrive

22:02

emerged as victors. This

22:05

is science fiction. This

22:08

description completely overstates and misrepresents what

22:10

was actually being done at the

22:12

Institute. So let's, we need to

22:14

go into that so you understand

22:16

that. Yang Li-she's

22:19

laboratory was sampling SARS-related

22:21

coronavirus from bats. This is

22:24

SARS-1, not SARS-CoV-2.

22:29

And the research described was

22:31

based on a bat

22:34

SARS-related coronavirus called WIV-1.

22:37

And then another version of

22:40

WIV-1 where its spike gene

22:42

was replaced with spikes from

22:44

bat coronaviruses called SCH14,

22:46

WIV-16, and RS-4231. All

22:52

four of these viruses are

22:55

in the SARS-CoV-1 branch of

22:57

the SARS-related virus family. And

22:59

they're very distinct from SARS-CoV-2.

23:02

Nowhere in this report is

23:05

there evidence of research

23:07

on SARS-CoV-2-related viruses. And so

23:10

really, these experiments are relevant to

23:12

the origin of SARS-CoV-2. The idea

23:14

was to see if the spike

23:17

proteins from these bat-related, SARS-related coronaviruses

23:19

could let the viruses reproduce in

23:21

human cells and if they caused

23:23

a different disease in ACE2

23:26

transgenic mice. There was no

23:28

forced adaptation to new species,

23:31

right? Now one

23:33

of the points made in this piece

23:35

is that they

23:38

made more dangerous viruses, right?

23:40

So one chimeric virus had

23:42

enhanced viral replication relative to WIV-1

23:44

in the lungs of ACE2 transgenic

23:46

mice that caused greater weight loss,

23:48

which is a common proxy for

23:50

disease severity in mouse

23:52

models. The other two chimeric viruses

23:55

had similarly enhanced virus replication in

23:57

mice, but one caused the same

23:59

weight loss. of

44:00

this paper Michael Waraby and 12 876

44:04

so you can get the

44:06

story from the horse's mouth in that episode

44:08

that was that was following on this

44:11

publication right yeah yeah yeah

44:14

and Chan also in number four

44:16

says writes quote furthermore

44:18

the existing genetic and early case data

44:21

show that all known COVID-19 cases probably

44:23

stem from a single introduction of

44:26

SARS-CoV-2 into people and the outbreak

44:28

at the Wuhan market probably happened

44:30

after the virus had already been

44:32

circulating in humans this is 180

44:34

degrees wrong this is completely wrong

44:37

she shows a phylogenetic tree in

44:39

the in the piece which is

44:41

just a crappy phylogenetic tree as

44:43

we have said there were two

44:46

lineages circulating at least two

44:48

separate introductions they're called A

44:50

and B they were both

44:52

found in the market

44:54

in December and we know this

44:56

from from this

44:59

environmental surveillance that the

45:01

Chinese authority done did there's

45:04

no question that there were there were more than

45:06

one in fact the paper another

45:09

paper in this collection estimates

45:12

that there were probably at least

45:15

eight separate spillovers from eight infected

45:17

animals to people and only two

45:19

of them seem to have proceeded

45:22

Vincent one of the things that she

45:24

talks about that you you've quoted is

45:26

a couple of

45:28

papers that are supposed supposedly

45:36

question at least question the statistical analysis

45:38

in the Warby paper that caught my

45:40

attention could you address that place yeah

45:43

so one of the issues that was

45:45

claimed was that there was this ascertainment

45:47

bias in other words we were looking

45:49

at the market because we knew we

45:51

knew it was an early epicenter and

45:53

they used really advanced statistical methods to

45:55

show that there is no ascertainment bias

45:57

that it was they were in fact

46:00

Many of the early physicians were picking up

46:02

the cases before they knew anything about the

46:04

market. They were really good at saying,

46:06

oh, this is an unusual respiratory disease. So that

46:08

was one point. The other point, there was a

46:10

mistake in the computer program

46:13

that was used to calculate the probability that

46:15

there was more than one spillover. So they

46:17

corrected it. Someone wrote and said, you have

46:19

this mistake. They corrected it. The

46:21

error gave them 99% probability

46:24

that there was more than one spillover. And

46:27

after the correction, it was 97% probable. So

46:30

it made no material effect on the

46:32

conclusions of the paper. Another

46:36

point from number four, quote, not a

46:38

single infected animal has ever been confirmed

46:40

at the market or in its supply

46:42

chain without good evidence that the pandemic

46:44

started at the seafood market. The fact

46:46

that the virus emerged in Wuhan points

46:48

squarely at its unique SARS-like virus laboratory.

46:50

End quote. Oh, my

46:53

God. No one tested animals

46:55

in the market. They were all taken away on

46:57

January 1 and moved

46:59

into the countryside or slaughtered. This is

47:02

a completely dishonest statement. They shut down

47:04

the market. They killed everything. They disinfected

47:06

the place. So I want to make a

47:08

statement that's sort of parallel that

47:10

might help people if they're not on top of this.

47:13

So the statement that

47:15

Vincent quoted is, not a single infected

47:17

animal has ever been confirmed at the

47:19

market or in its supply chain. And

47:22

I just wanted to let you know that

47:24

also it has never been

47:26

confirmed that my

47:28

right hand was not

47:31

radioactive for one day

47:34

during the year where I was 17 years old. And

47:38

the reason and I say that and

47:40

the reason is because we never looked.

47:43

Maybe it was randomly. And

47:45

so it's important to say it has

47:49

never been confirmed because you didn't look. But

47:52

also note that

47:54

this is something that we can't go

47:56

back and check. We don't have the

47:58

samples from those animals. ago,

50:00

end quote. There has

50:02

not been an intense search. The

50:05

Chinese authorities are not doing it. In

50:07

SARS-1, the markets were not

50:09

closed. Animals were found

50:12

to be infected seven months

50:14

later, not days within days,

50:16

as Dr. Chan states. MERS

50:19

was identified in 2012, found in camels in

50:22

2013. So the claim of days is fabrication.

50:25

And as we've already discussed, war or be at

50:27

all in a subsequent paper.

50:30

So there were environmental samplings by

50:32

the Chinese authorities of the market

50:34

twice in early 2020. Those

50:37

samples were deep sequenced and

50:39

the results were posted. And

50:43

the analysis of the samples show that

50:45

stalls in the southwest part of the

50:47

market, which is where the mammals were

50:49

sold, have environmental

50:52

samples that not only have

50:54

SARS-CoV-2 RNA in them, but

50:56

also mammal DNA from raccoon

50:59

dogs and civet cats. And

51:01

there's a particular stall, 6.29,

51:03

which had lots of mammals,

51:05

it had lots of SARS-CoV-2 positivity,

51:08

and had been photographed previously

51:10

to have sold these animals

51:12

as far back as 2019.

51:14

And so we

51:18

know there were... So even though we

51:20

haven't tested animals directly, environmental samples show

51:24

contamination with animal DNA

51:27

and viral

51:29

DNA. And another part

51:32

of this analysis is

51:35

one of the positive swabs, SARS-CoV-2

51:37

positive swabs, was from stalls with

51:39

documented human cases. And

51:43

one of them was a shrimp vendor

51:45

who we'll get to in a moment.

51:47

Stall 6.29, which had a lot of

51:49

raccoon dog and civet cat DNA and

51:51

SARS-CoV-2 RNA is not associated with a

51:54

known human case. So

51:57

we don't find human DNA in those

51:59

samples. End

56:00

quote except for the other lineage. Yeah

56:03

exactly I mean this is gibberish it's

56:06

only to detected jumps into humans. So

56:09

ours was not detected until first jumped

56:11

into people the first detection of murs

56:13

was in a human with pneumonia I

56:15

mean that's how we find them when

56:18

they jump into people so. Just we don't

56:20

know what happened before that we have no

56:22

idea this could have been going on for

56:24

years. Yeah

56:26

that one guy with a weird

56:29

respiratory symptom who did who

56:31

lived in a place that didn't have

56:33

the ability to do really fan fantastic

56:36

medical care and

56:38

all of this virus sequencing and so he

56:41

just died of a weird respiratory infection.

56:45

I would just want to point out that. You

56:48

know you may say all this virus was

56:50

ready to cause a pandemic but as the

56:52

virus spread. It

56:55

changed right and variants emerge that

56:57

were more fit so it wasn't

56:59

really ready that mean that I

57:01

was in during the pandemic is

57:03

evidence that it was becoming better

57:05

and better adapted to people. No

57:09

and her the last sentence that I want to address

57:12

she writes quote a laboratory accident

57:14

is the most parsimonious explanation of

57:16

how the pandemic began and quote.

57:20

This seems to be the

57:22

least parsimonious explanation especially even

57:24

all the data showing the

57:26

market origin and the fact

57:28

that no one had something close enough

57:31

to stars kovie to to be able

57:33

to engineer it parsimonious depends on having

57:35

some information there is none. There are

57:38

no data in this piece that support

57:40

a lab origin is just in you

57:42

and i hear say in conspiracy it's

57:45

only parsimonious if you believe

57:47

that a whole lot of lies and

57:49

cover ups also happened. I

57:52

have to start with the assumption that

57:54

the hypothesis is true and

57:56

then pick the evidence that fits it which

57:58

is exactly how this was put. together. And

58:01

you, like I said, you have to keep saying,

58:03

well, but that's a lie to, to hit

58:05

the to combat other evidence. And

58:08

that is not parsimonious. No. So

58:11

if you've made it this far, folks, this

58:15

opinion piece is

58:17

all wrong. Just

58:19

doesn't agree with the data that we've been talking about. And

58:21

you know, we have many people on Twitter who've talked about

58:24

all of these data over the years. We

58:26

don't, we

58:28

just look at the data. We don't do any conspiracy

58:30

stuff. We don't lie about it.

58:32

We do the science and it tells us that

58:35

it originated in an animal. There's no question

58:37

about it. Anything

58:42

else? No, it's just all

58:45

too bad. Yeah, it is all

58:47

too bad. But you know, the truth floats. Unfortunately,

58:50

it takes a long time to get

58:52

to the surface. I'm thinking about

58:54

climate change and all the

58:56

acquaintances I have who've been climate deniers

58:59

for so long. And now, you know,

59:03

it's so blatantly obvious.

59:05

I like that statement. The truth floats. I really

59:07

like that. All

59:11

right. Now we now we move to

59:13

something that is scientifically

59:15

sound. Now we can talk

59:17

about some really cool science

59:19

that Jolene brought up. Okay,

59:23

I am very excited to be able

59:25

to bring forward this

59:27

paper. It's a nature communications paper

59:29

from January of this year. It's

59:32

called phage pareidae can

59:35

kill dormant antibiotic tolerant

59:37

cells of pseudomonas pseudomonas

59:39

aruginosa by direct lytic

59:42

replication. The

59:44

first author is Ania Mafeyi.

59:46

And the last author is

59:49

Alexander Harms, who runs the

59:51

molecular phage biology lab. These

59:53

authors, there's quite a few authors in

59:55

the middle as well. They're all they

59:57

all were at the University of Michigan.

1:00:00

of Basel or its hospital and

1:00:02

now the primary authors are at

1:00:04

ETH Zurich in Switzerland. So

1:00:07

it's going to be a change of

1:00:09

topic for sure. We're thinking not only

1:00:11

about data but also

1:00:13

about the viruses that infect bacteria.

1:00:16

So we're talking about bacteriophages here.

1:00:19

The hosts are a little different than the

1:00:21

hosts we often talk about, but I want

1:00:23

to give some context for why this is

1:00:25

still an interesting type of infection to think

1:00:28

about. One reason is

1:00:30

that viruses need

1:00:33

to use not

1:00:35

just cellular resources when they get

1:00:37

into a cell but also cellular

1:00:39

machinery. The cell has

1:00:41

to be actively able to do the things

1:00:44

that a virus doesn't bring with it for

1:00:46

it to be successful at its infection. It

1:00:49

turns out that some

1:00:51

viruses can mess with the ability of

1:00:53

a cell to do things. So if

1:00:55

a virus infects a cell that's dormant

1:00:57

or quiescent, it can reactivate that cell.

1:00:59

That's one thing that we're thinking about

1:01:01

here. There are human cancer-causing

1:01:04

viruses in particular that are

1:01:06

known to either arrest or

1:01:08

accelerate the transitions between the

1:01:10

phases of the cell cycle

1:01:12

to make them either replicate

1:01:14

or stop replicating whatever is

1:01:17

more advantageous for the virus.

1:01:20

In this case, for the bacteria, in

1:01:23

microbiology in general, what we do in

1:01:26

the lab is we pamper our

1:01:29

bacteria. We put them

1:01:31

in a nice, warm environment,

1:01:33

no temperature fluctuations, and we give them

1:01:36

lots of things to eat, and then

1:01:38

we ask them to jump through hoops.

1:01:40

They do it happily. But

1:01:43

most bacteria are out in the

1:01:45

environment where there are many changes

1:01:48

in their environment. It is

1:01:50

a cold, cruel world for bacteria. They're

1:01:54

actually not usually growing at an exponential

1:01:56

rate, as we would like them to

1:01:58

be doing for our reproduction. They tend to

1:02:00

be in states of maybe what you might call

1:02:02

famine or feast. This is something we think about

1:02:05

a lot in the context of the gut, your

1:02:07

gut microbes. They get fed when you eat and

1:02:09

then they run out of food and then you

1:02:11

eat again and they're fed. And

1:02:14

if you think about outside, right now it's

1:02:16

hot and wet, but pretty soon it'll be hot and

1:02:18

dry. And

1:02:21

the microbes have to adapt to that. So

1:02:24

most of the bacteria in the environment aren't growing very

1:02:26

quickly. They can often be in a dormant state. This

1:02:32

is also something really relevant to the

1:02:34

authors in terms of antibiotic resistance. So

1:02:37

the bacteria that

1:02:40

we treat with antibiotics, some of them do something called

1:02:42

persisting. That is, they don't die in the presence of

1:02:44

an antibiotic, even if they might be susceptible, because

1:02:48

they are not actively replicating. So

1:02:51

the antibiotics typically target processes that are essential

1:02:53

for a bacterium, but

1:02:56

it's not essential if you're not doing it at the

1:02:58

time you're exposed to the thing that tries to stop

1:03:00

it. So as a quick overview of the

1:03:03

– I also

1:03:06

– I just wanted to quote

1:03:09

the sentence that I love from

1:03:11

the introduction. The antibiotic drugs administered

1:03:13

in clinics constitute just another unpredictable

1:03:15

existential threat that bacteria can evade

1:03:17

through dormancy. Exactly.

1:03:19

Something bad is happening. I'm just

1:03:21

not going to do anything for a while. We're going to take

1:03:23

a nap for now. So

1:03:26

the quick overview is that these

1:03:28

authors, they fished for and found

1:03:31

a phage that kills bacterial cells

1:03:33

that are not growing. And

1:03:35

it even makes antibiotics kill the bacteria

1:03:37

more than they would otherwise in the

1:03:39

lab and in a mouse infection model.

1:03:43

Most phage can't kill non-growing cells, so

1:03:45

this presents a great opportunity to study

1:03:47

how they can do this. And

1:03:49

this is important because non-growing bacteria

1:03:51

are harder to kill with antibiotics

1:03:53

when they're causing human infections. Jolene?

1:03:56

Yeah. Was this really surprising

1:03:58

to you? Well, I

1:04:01

think because most of

1:04:03

the phage that we study in the lab don't

1:04:05

do this, we've always wanted to find phage that

1:04:07

do this. And there are a couple that have

1:04:09

a property like that, but we want to find

1:04:11

more. And it was surprising to me that

1:04:14

it was as hard to find as it was for them.

1:04:17

Okay. Yeah. Yeah, the virus

1:04:19

we're talking about in this paper emphatically did not

1:04:21

come from a lab. Sure

1:04:23

didn't. Sorry, it's COVID too emphatically.

1:04:26

No, it didn't either. So

1:04:29

basically this episode is about two viruses that came

1:04:32

from nature. Yes. That's

1:04:34

right. This one came from rotting plant

1:04:36

material in a cemetery actually. So

1:04:41

the authors here were motivated to find

1:04:43

ways to kill dormant cells, dormant bacterial

1:04:46

cells in the ways that they exist

1:04:48

in nature and to resolve

1:04:50

difficult to treat antibiotic bacterial

1:04:52

infections in humans. And

1:04:57

the thing that they did was

1:04:59

they first looked for these phage.

1:05:02

And initially they said, okay, let's

1:05:05

just look at how phage

1:05:07

infect in normal growing cells

1:05:09

compared to not growing cells. And they took

1:05:11

lab phage and they had some nice methods

1:05:14

to be able to distinguish between the phage

1:05:16

and the cells. And here

1:05:19

they were looking at E. coli

1:05:21

and Pseudomonas. Both are gram-negative bacteria.

1:05:24

Both cause a lot of problematic infections

1:05:26

in humans. And they took

1:05:28

a collection of bacteria phages that

1:05:31

they had in the lab and

1:05:33

they infected first growing bacteria. And

1:05:36

they looked over time to see

1:05:39

how many viable host cells were

1:05:41

left and how many virions

1:05:43

or free phage were in the

1:05:45

experiment. And they also looked at

1:05:47

how many cells are infected, not

1:05:50

yet dead, not

1:05:52

yet releasing virions. This

1:05:54

is with phages that they already knew

1:05:57

about and had identified and had in the lab.

1:06:00

And so this is a straightforward lab experiment

1:06:02

you pull a bunch of pages out of the

1:06:04

freezer and you. Do

1:06:06

this infection they haven't found the new one

1:06:08

yet correct correct they pulled a

1:06:10

collection of well well well known

1:06:12

or related to well known pages

1:06:15

are they actually have this nice

1:06:17

collection where they characterized a large

1:06:19

number of page called the bottle

1:06:21

collection and they show how they're

1:06:23

related to the commonly known pages

1:06:25

t four which is the classic

1:06:28

myofage. T T.

1:06:32

One classic syphophage T

1:06:34

five another classic syphophage

1:06:37

and T seven classic podophage

1:06:39

and then V five and

1:06:42

so E. Coli was killed by all of

1:06:44

these page when they were

1:06:46

growing and you saw increases in free

1:06:48

variance same thing for pseudomonas

1:06:51

they had a collection of known page

1:06:53

or some new ones that they had

1:06:55

isolated and all of them. Except

1:06:57

one killed this host

1:07:00

very well and was able to replicate.

1:07:04

Then what they did was they made

1:07:06

the cell they they took away all

1:07:08

that nice food from the cell so

1:07:10

they made them a dormant culture. Here

1:07:13

they they feed the bacteria and then they

1:07:15

let them grow until they run out of

1:07:17

nutrients and then they become what is dormant

1:07:20

and the E. Coli. That you are

1:07:23

no longer killed by all of these page

1:07:26

and the number of infected cells,

1:07:28

however, was high so the bacteria

1:07:30

page attached to the cell they

1:07:32

get in. But they are

1:07:34

not in an environment where they can

1:07:36

replicate so they just wait until

1:07:39

the cell reactivates and

1:07:41

the number of infected cells says cells

1:07:43

stays constant over time. They

1:07:46

saw a similar thing with the pseudomonas

1:07:48

dormant bacteria when they infected with this

1:07:50

panel of bacteria page. They

1:07:53

were able to show that the dormant

1:07:55

cultures were also resistant to antibiotics. Okay,

1:07:57

so this is setting the stage. Growing

1:08:00

bacteria, easy to kill with these

1:08:02

phage, dormant bacteria, not so easy

1:08:04

to kill. But as I

1:08:06

said, there was a phage that's well known

1:08:08

called T7 that is known to be able

1:08:11

to... The cool thing that happens

1:08:13

in the lab is we do these plaque assays

1:08:15

and you put the phage on the plate and

1:08:17

you pull your plates out in the morning and

1:08:19

you have plaques and they're large. And then you

1:08:22

put the plates in the fridge and they keep

1:08:24

growing. Or you put the plates on the bench

1:08:26

and they just keep growing. And so T7 was

1:08:28

known to be able to continue to infect cells

1:08:31

that weren't growing anymore. So

1:08:33

they looked at this phenomenon for

1:08:35

T7 with E. coli and they

1:08:37

saw that early stationary phase cells

1:08:39

were easily killed by T7, as

1:08:42

expected. And

1:08:47

then they said, okay,

1:08:49

so can T7 infect

1:08:51

and kill cells

1:08:53

that have been starved for a

1:08:55

long time? And actually

1:08:57

T7 can kill cells that are

1:08:59

in early stationary phase, as we

1:09:02

call it. So to define that

1:09:04

in the lab, when we're looking at what

1:09:06

we look at the growth phases of bacteria,

1:09:09

we see that initially they take a time

1:09:11

to adjust to their new environment, which is

1:09:13

when we feed them. Then they start growing

1:09:15

very quickly and doubling exponentially, we call it

1:09:17

exponential phase. And then when they start to

1:09:19

run out of nutrients, if you're plotting this

1:09:21

on a graph, then the curve starts

1:09:23

to flatten out. That's when they

1:09:26

are what we call entering early

1:09:28

stationary phase until they're in later

1:09:30

stationary phase. So they

1:09:32

saw that T7 could kill early

1:09:34

stationary E. coli, but not

1:09:36

later stationary E. coli. And

1:09:39

this is an important thing that they've

1:09:41

gone to some effort to set up

1:09:43

in their previous work, I gather, is

1:09:45

this setting

1:09:48

very, very careful conditions on

1:09:50

what constitutes dormant bacteria,

1:09:52

because there has been, I gather,

1:09:55

a controversy in the field that

1:09:57

phage can or can't infect dormant

1:09:59

bacteria. bacteria and it turns

1:10:01

out that if you just take

1:10:04

them right at stationary phase,

1:10:06

that's not really dormant and

1:10:08

so this group goes to

1:10:10

a much longer time period.

1:10:13

That's right because as cells are running

1:10:15

out of nutrients and entering stationary phase,

1:10:17

they turn on a general stress response

1:10:20

pathway. So they're sensing that a time

1:10:22

of phantom is coming and

1:10:24

it gives them resistance to many

1:10:26

different adverse conditions that would otherwise

1:10:28

kill the cells like pH, oxidative

1:10:31

– Unanticipated existential threats. Yeah,

1:10:33

exactly. Things like antibiotics

1:10:36

are going into survival mode and the

1:10:38

longer that they've been in survival mode,

1:10:40

the less likely they are to easily

1:10:42

awake from that or

1:10:44

to be jolted out by something like

1:10:47

a phage infection. So they have very

1:10:49

carefully defined their growth conditions, the temperature,

1:10:51

the media to make sure that these

1:10:53

are reproducibly what they call deep dormant

1:10:56

cells and that means they're not growing.

1:10:58

They've been under severe nutrient limitation for

1:11:00

40 hours after starvation began. There's

1:11:03

no loss of cell viability though so they

1:11:05

can take these cells out of starvation and

1:11:07

then grow them up again and

1:11:10

they're resistant to antibiotics. So

1:11:13

using these deep dormant host cells, they did

1:11:15

something that we love to do in our

1:11:17

lab which is they went on a phage

1:11:19

hunt. They got that plant material as well

1:11:21

as a bunch of other samples from all

1:11:23

over the place and they looked for phage

1:11:26

that could infect these deep

1:11:28

dormant bacteria. It turns out – I love it.

1:11:31

Rodding plant material isn't enough. It's

1:11:33

got to be rotting plant material

1:11:35

from a cemetery. From a cemetery.

1:11:37

Right. You just

1:11:39

got to be creative if you really want to find

1:11:41

these interesting phages. So

1:11:45

they were able to find

1:11:47

phage parede that is able

1:11:49

to infect and kill dormant

1:11:51

bacteria. They see that the

1:11:54

CFUs or the viable cells goes down

1:11:56

when it's infected with phage and the

1:11:58

number of phage – go up. Do

1:12:02

we have, I wasn't able to find

1:12:04

this, do we have any idea why

1:12:06

they chose to call this phage paridé

1:12:08

or paridé? The only thing I

1:12:10

could, you know, is that

1:12:13

a novel word? Well,

1:12:15

no, it's one of the, so

1:12:17

I looked this up, I was like, that

1:12:19

sounds like it's probably somebody,

1:12:21

some character, and then later on they're

1:12:24

gonna find a couple of other phages

1:12:26

that they named Cassandra and Di phobo

1:12:28

and Etorre. And so

1:12:30

these are children of Priam who

1:12:32

is a legendary king

1:12:34

of Troy in Greek mythology. That's

1:12:38

as far as I got, like why this- The

1:12:41

only other thing I noticed is

1:12:43

that the first three letters are

1:12:46

P-A-R, Pseudomonas Origenosa. Pseudomonas Origenosa, but

1:12:48

other than that- I don't know if that helps. Yeah.

1:12:52

I don't actually know why they called it

1:12:55

this name, but we

1:12:57

get a lot of creative names. Paridé is probably

1:12:59

correct, yeah. Yeah, I mean,

1:13:01

you're talking about a discipline, the

1:13:03

name's phages stuff like corn dog,

1:13:05

okay? So- That's

1:13:08

right. That's

1:13:10

right. If you aren't bound by any

1:13:12

rules, you can get all kinds of creative things. Now,

1:13:18

what they- Well, they tried to find more.

1:13:20

They tried to find more of these, and

1:13:22

like Alan said, they did find a few,

1:13:24

but it turns out they were all relatives

1:13:26

of this one. They were not really, really

1:13:29

unique, but this one is actually pretty unique.

1:13:31

It's not really related to a lot of

1:13:33

the known phages. So

1:13:35

looking at the phage itself

1:13:38

and kind of its basic

1:13:40

characteristics, they first wanted to

1:13:42

know, does this only infect

1:13:44

the lab strain of Pseudomonas aeruginosa that

1:13:46

we have? And they got a

1:13:49

collection of clinical isolates, and then they looked to

1:13:51

see if it could infect them, and it could.

1:13:54

And they also looked to see if it

1:13:56

could infect dormant versions of those as well.

1:14:00

And it could. It released

1:14:02

virions, made the cells lice just

1:14:04

like it did in the original strain. They

1:14:08

looked, and this was an interesting question. So

1:14:11

one thought would be that this had

1:14:14

evolved to fit the niche of growing

1:14:16

on dormant cells. But it

1:14:18

turns out that pareidae can also

1:14:20

replicate in growing cells. So that

1:14:22

was definitely interesting. They checked the

1:14:24

burst size. So they're looking at

1:14:26

the average number of phage that

1:14:28

come out of each infected cell.

1:14:31

And they saw that that was a burst size

1:14:34

of 60 for

1:14:36

hosts that were growing and a burst size of 9

1:14:39

for host cells that were not growing, which

1:14:41

is definitely reduced. And

1:14:44

then they decided to see if this

1:14:46

property was something that they could improve

1:14:48

upon. So they passaged

1:14:51

pareidae in a pretty

1:14:53

extensive passaging process on

1:14:55

deep dormant cells. They took out the phage. They

1:14:57

put it on deep dormant cells. And they did

1:15:00

that for hundreds of passages.

1:15:03

And then they characterized the burst size again. And

1:15:06

they saw that they were able to increase the

1:15:08

burst size on growing cells to either 118

1:15:12

or 90 for the two different lineages that

1:15:14

they took out. So this was,

1:15:17

again, a function experiment. They

1:15:19

had to say it. They

1:15:21

certainly could have not had a gain

1:15:24

of function. That's right. They

1:15:26

could not have anticipated that it would be gain of

1:15:28

function. It could have gotten worse and not

1:15:31

propagated. Yes. But

1:15:34

it got better in terms of

1:15:36

burst size anyway in these conditions.

1:15:39

And the dormant host burst size went up for

1:15:41

the first lineage up to 24, but was about

1:15:44

the same for the second lineage. They

1:15:48

looked at another thing, the latency period,

1:15:50

which is how long it takes after

1:15:52

infection to start having fully

1:15:54

formed virions to be able to release.

1:15:58

And that didn't really change significantly. hurt

1:18:01

bacteria but not us or

1:18:03

to inhibit bacterial growth but not hurt our cells

1:18:05

at the same time. They

1:18:07

tried a couple of other antibiotics.

1:18:10

They tried some Aflora quinolone which

1:18:12

targets an enzyme that the bacteria

1:18:14

uses to unwind DNA and they

1:18:17

also tried an amino glycoside

1:18:19

called Tobramycin which targets the

1:18:21

bacterial ribosome which is where

1:18:23

they translate proteins. It turns

1:18:25

out that they only saw

1:18:27

effects for carbapenem. So

1:18:29

what they did was they had deep

1:18:31

dormant bacteria. They treated with phage,

1:18:35

just phage, just antibiotic or with

1:18:37

both and with just

1:18:39

phage they saw their typical paraday killing

1:18:41

and increase in virions but when they

1:18:43

treated with both phage and meropenem they

1:18:45

saw a much greater decrease

1:18:48

in the number of viable bacteria

1:18:50

over time. And

1:18:52

here their time after the 40 hours of deep dormancy

1:18:54

is 168 hours. So within 120 hours after the double

1:19:01

treatment they see that the CFUs are

1:19:03

dramatically reduced almost to their limit of

1:19:06

detection. And the free virions,

1:19:08

the presence of the antibiotic did not

1:19:11

interfere with the replication of paraday. And

1:19:14

this kind of, so it seems

1:19:16

to make sense that the meropenem worked

1:19:18

in this and the other antibiotics didn't

1:19:20

because if you muck up

1:19:22

the ribosome the virus can't complete

1:19:25

its life cycle. If you muck up the

1:19:27

cell wall, no problem. Yes,

1:19:30

right, right. So the virus

1:19:33

wouldn't be able to replicate. Exactly. They

1:19:38

had an interesting experiment here to look

1:19:40

at maybe what might be the cause

1:19:42

of some of the effects that they

1:19:44

were seeing.

1:19:48

And so they were wondering

1:19:50

if they added some extra

1:19:53

paraday resistant cells, would they

1:19:55

still see this dramatic enhancement

1:19:57

of meropenem activity? I

1:20:00

think I didn't say this, but when

1:20:02

they treated the deep dormant bacteria with

1:20:04

meropenem, they didn't see any decrease in

1:20:06

viable cells. So then

1:20:09

what they thought is, okay, can

1:20:11

the meropenem plus

1:20:13

pareidae together work

1:20:16

only in the cells where pareidae is

1:20:18

infecting or can it work on neighbor

1:20:20

cells too? And what

1:20:23

they did was they spiked in

1:20:25

these cultures that were infected with

1:20:28

some pareidae resistant cells or with

1:20:30

meropenem resistant cells. So

1:20:32

the meropenem resistant cells, you wouldn't expect

1:20:34

those to be killed by the antibiotic

1:20:37

and they were not. They didn't

1:20:39

see as a dramatic of decrease

1:20:41

in the viable cells, but when

1:20:44

they had pareidae resistant cells, they

1:20:46

still had killing by meropenem to

1:20:48

similar levels that they had when

1:20:50

there weren't pareidae resistant cells in

1:20:52

the infection. So

1:20:55

this suggests that maybe the

1:20:58

signaling, what they hypothesize is that

1:21:01

some cells get infected by pareidae

1:21:03

and those get

1:21:05

lysed and they release nutrients and

1:21:07

other signals that the neighboring

1:21:10

cells that weren't infected that are

1:21:12

resistant to pareidae detect as dangerous

1:21:14

signals and it can reactivate them

1:21:16

to grow because they have a little bit of

1:21:19

nutrients and so then those are susceptible to meropenem

1:21:21

and the meropenem kills them. And

1:21:24

then they did an experiment

1:21:26

in the, in a mouse

1:21:28

model of infection. So

1:21:30

this particular mouse model

1:21:33

is one where they put a,

1:21:35

they do a small surgery on the

1:21:38

mouse and they put this little cage

1:21:40

on the back of the mouse and then

1:21:42

they put the pseudomonas in there and the

1:21:45

pseudomonas will cause an infection and

1:21:47

after that infection is established they

1:21:49

will either treat with phage,

1:21:52

meropenem or the combination of

1:21:54

the two and then after a

1:21:57

bit they'll take out the cage and

1:21:59

count the bacteria. to see if

1:22:01

the combination of phage and antibiotic

1:22:03

in vivo has had the same

1:22:05

effect that they saw in vitro

1:22:08

in the culture tubes. Excuse me. One

1:22:11

thing to say about the

1:22:13

cage model is

1:22:16

that the reason why that's important is

1:22:18

because it's sort of trying to mimic

1:22:20

a chronic infection, an infection of a

1:22:22

medical device that's been implanted, which is

1:22:25

a place where we commonly see a

1:22:27

lot of issues with these types of

1:22:30

resistant infections in humans. There

1:22:32

are not a lot of mice, maybe

1:22:35

I don't know about them, that have prosthetics

1:22:38

in the same way that certain humans do. This

1:22:41

is sort of a way to mimic that

1:22:44

chronic infection on an implanted medical device.

1:22:47

Is the cage structured so that it actually

1:22:50

contains the bacteria but

1:22:53

admits phage and drug or

1:22:56

is it just like a

1:22:58

piece of stuff in the mouse? If

1:23:02

I understand correctly, it's like a metal tube and

1:23:04

then they drill, I think they set 130 holes

1:23:06

into it and then that gets put onto the

1:23:08

mouse. Then

1:23:12

the bacteria can come in contact with the skin

1:23:14

of the mouse and then

1:23:16

they take the cage off and they're actually

1:23:18

only measuring planktonic bacteria when they take the

1:23:20

cage off. Define planktonic?

1:23:25

Bacteria when they grow in liquid and

1:23:28

they're motile by flagella, that's planktonic. They

1:23:30

can move around, they're growing as single

1:23:32

cells. That's as

1:23:35

opposed to a biofilm where they're growing

1:23:37

in a community, sessile. They

1:23:39

are not moving around and they have

1:23:42

secreted an extracellular matrix that tends to

1:23:44

protect them from many

1:23:46

of those existential threats. The

1:23:50

planktonic ones or the swimmy guys? That's right. Yeah.

1:23:54

The wigglers as I like to call them. Okay,

1:23:58

so when they took the... The

1:24:00

tissue cage off and counted

1:24:02

the CFUs with

1:24:05

just Parete. Actually, it didn't

1:24:07

really have a significant decrease

1:24:09

in the viable cells. So

1:24:12

Parete by itself in this model didn't

1:24:14

really kill the bacteria that

1:24:16

were infecting. And

1:24:19

the meropenem, it did

1:24:21

have a small effect of

1:24:23

reduced reduction in viable cells,

1:24:26

but the combination of the two

1:24:28

had a much bigger effect. So

1:24:30

we're talking something like

1:24:33

a few, almost a few logs decrease

1:24:35

in viable cells, which is a lot.

1:24:37

That would definitely be something that if

1:24:39

this were a clinical outcome

1:24:42

that we were looking for, you would see an effect

1:24:44

for them. Okay. So

1:24:46

the last experiment that they did that

1:24:48

is in the main part of the

1:24:50

paper, I should say, because they did

1:24:53

many more experiments. There's a lot. It

1:24:56

is looking at whether or not there

1:24:58

is something specific about the stress response

1:25:00

in the bacterium that is necessary for

1:25:03

infection of dormant hosts. So the question

1:25:05

is why can they actually infect these

1:25:07

dormant hosts and otherwise

1:25:10

things can't. So

1:25:12

for this, I have to explain a little

1:25:14

bit about the general stress response. So this

1:25:16

has been characterized really well in E. coli

1:25:18

and it's mediated, I guess

1:25:21

it's induced by many different stresses.

1:25:23

So an individual stress

1:25:25

or multiple stresses, environmental conditions

1:25:28

that change in the bacterium's

1:25:30

environment can cause the

1:25:33

cell to respond to these stresses

1:25:35

in a way that allows them

1:25:37

to resist other stresses that are

1:25:39

not the one that caused the

1:25:41

induction. This is mediated

1:25:43

in E. coli primarily by a

1:25:45

global alternative sigma

1:25:48

factor, something that's involved

1:25:50

in controlling transcription called

1:25:52

RPOS. It

1:25:54

is very conserved in gamma

1:25:57

proteobacteria and

1:25:59

it's something that is made,

1:26:03

so it's regulated at many different

1:26:05

levels. It's regulated at the level

1:26:07

of transcription, at the level of

1:26:09

translation, post-translationally, and when

1:26:12

it's active, then it turns

1:26:14

on transcription of lots of lots of

1:26:16

different E. coli genes. So

1:26:19

when this happens, then you're going to have

1:26:21

different proteins present. And if

1:26:23

you get rid of RPOS, the

1:26:25

master regulator of the general stress

1:26:27

response, then you're no longer going

1:26:29

to be protected from those stresses

1:26:31

when you go into nutrient starvation.

1:26:34

So they wanted to get rid of RPOS, and

1:26:37

they did this by deleting

1:26:39

a couple of genes called

1:26:41

Rele and Spoti, which are

1:26:44

important for the production of

1:26:46

a signaling molecule called PPGPP

1:26:48

or PPGPP. Magic

1:26:50

Spot. Magic Spot is another name

1:26:52

for it. This is a secondary

1:26:56

messenger. It's a signaling molecule

1:26:58

that bacteria use to turn

1:27:01

on and off transcription of certain

1:27:03

genes. And RPOS

1:27:06

needs PPGPP around to

1:27:08

work well. And

1:27:10

so they took these

1:27:12

cells that couldn't make RPOS, and they

1:27:14

compared their behavior to the wild-type cells

1:27:17

that could. And they

1:27:19

looked at non-growing cells after

1:27:22

the time of deep dormancy and

1:27:24

infected with Paree Day. So the

1:27:26

wild-type, as a reminder,

1:27:29

does decrease the viable cell count.

1:27:31

But the PPGPP- and

1:27:33

RPOS- cells no

1:27:37

longer have Paree Day

1:27:41

dependent viable cell reduction.

1:27:44

But those cells are susceptible

1:27:46

to Ciprofloxacin and Tobramycin, a

1:27:48

couple of antibiotics that they

1:27:50

were testing. Suggesting

1:27:53

that Paree Day needs the

1:27:55

general stress response to be active for it

1:27:57

to be able to replicate in deep dormancy.

1:28:00

cells. And

1:28:02

they didn't see a significant

1:28:04

increase in the Paree

1:28:06

Day PFU or number

1:28:08

of V-aryons that are produced as you

1:28:10

would expect because it couldn't

1:28:13

replicate. They

1:28:15

also induced this general

1:28:18

stress response or at least they induced a nutrient

1:28:21

starvation response another way. So

1:28:24

one thing would be they took

1:28:26

away the master regulator RPOS. The

1:28:28

other thing they did was they

1:28:30

treated with serine hydroximate. So

1:28:32

serine hydroximate is something that

1:28:34

chemically stimulates the stringent response

1:28:36

because it's an amino acid

1:28:38

analog but it's bacteria static.

1:28:40

The bacteria can't really use

1:28:43

it so they try to

1:28:45

use it but it doesn't allow them

1:28:47

to grow and it

1:28:49

inhibits protein synthesis. It's

1:28:52

actually a competitive inhibitor

1:28:54

of seral transfer ribonucleic

1:28:56

acid synthetase. So

1:28:58

this is basically a quick signal for them

1:29:01

to add to tell the bacteria that they're

1:29:03

starving. And when they did this

1:29:05

they had the number

1:29:08

of CFU for Paree Day

1:29:10

went down a little bit but not

1:29:12

as much and not as early as

1:29:15

it had in the other experiments. And

1:29:17

they did see later some

1:29:19

more increase in the Paree Day

1:29:21

PFU, number

1:29:24

of V-aryons that were released. And they

1:29:26

didn't see this effect when

1:29:28

they had their infections with a

1:29:30

couple of other pseudomonasphages that they

1:29:32

were using. So the

1:29:34

two others which were

1:29:37

called Aergia and DMS3-Vir

1:29:40

they did not significantly

1:29:43

decrease the number of CFUs. They

1:29:46

didn't have a huge increase in the

1:29:48

number of PFUs so they didn't produce

1:29:50

viable phage. And they

1:29:53

didn't change a whole lot in

1:29:55

the serine hydroximate treatment either. So

1:29:58

this suggests that Paree Day requires stress

1:30:00

responses for infection of dormant

1:30:03

hosts. Now how it requires

1:30:05

it, that's a really

1:30:07

interesting question that I hope that the

1:30:09

authors will follow up on because knowing

1:30:11

that would give us a really good

1:30:13

handle on how to be able to

1:30:15

maybe induce the use of this in

1:30:19

the future. Yeah,

1:30:22

to me, I get the

1:30:25

sense that cells bacteria in

1:30:28

that metabolic, it's

1:30:31

a fundamentally different metabolic state. The protein

1:30:33

profile, the proteome is gonna be different.

1:30:36

That's right. Okay. And this is

1:30:38

a big fate, right? It's

1:30:40

250 KB, something like that. Right,

1:30:43

I did not mention that. It has

1:30:46

a lot of potential for

1:30:48

tweaking, adapting

1:30:51

to a different environment.

1:30:54

So this is gonna take significant

1:30:57

sorting out to figure it out. However,

1:30:59

it's gonna be fascinating and as you

1:31:02

say, potentially useful. Right.

1:31:05

Somebody's got a very useful project to pursue. Oh yeah,

1:31:07

they're super useful. I'm sitting here

1:31:09

trying to kind of imagine what's

1:31:13

going on inside the dormant cell and

1:31:16

what types of things the phage

1:31:18

is sort of doing to

1:31:21

deal with the missing stuff. What

1:31:26

is actually, what is sort of normally missing

1:31:29

in a dormant cell that blocks

1:31:31

all of these other phages from

1:31:34

replicating and what thing,

1:31:36

how is Pareto actually getting

1:31:38

over those constraints? I'm

1:31:41

sitting here realizing sort of how many questions there

1:31:43

are and how many interesting things I would love

1:31:45

to know about this. Yeah, this can go

1:31:47

on forever. So somebody could make a career

1:31:49

out of this. Yeah, somebody will maybe. So

1:31:53

Pareto, does it do

1:31:55

okay on log phase cells? Yes.

1:32:00

Okay, so it doesn't, it

1:32:02

can go either way. Mm-hmm. It

1:32:04

can replicate. You

1:32:07

must be able to, I mean, you

1:32:09

must be able to isolate

1:32:12

mutants of the phage that

1:32:15

can't grow on stationary cells and can

1:32:17

on log phase cells or something like

1:32:19

that to try and get a handle

1:32:21

on at least from the phages point

1:32:23

of view what genes are involved.

1:32:25

That sounds like a great next experiment to me. Yeah.

1:32:29

Yeah. Very interesting. Right,

1:32:34

so I wanted to give a

1:32:37

little recap just for

1:32:39

those who, who this is not very

1:32:41

familiar. The

1:32:44

authors hypothesize that since nature is

1:32:46

full of cells that are not

1:32:48

growing all the time and

1:32:51

phage are abundant and successful in nature,

1:32:53

there should be phage that can infect

1:32:55

non-growing cells. They

1:32:57

found Pareide that infects

1:32:59

Pseudomonas aeruginosa and seems

1:33:01

to take advantage of

1:33:03

conserved pathways that are

1:33:05

present during non-growth. And

1:33:08

this phage is potentiating or

1:33:11

making an antibiotic that doesn't always

1:33:14

work on non-growing cells work to

1:33:16

kill them. And

1:33:19

this is important because it helps us understand

1:33:21

how phage are infecting in nature and

1:33:23

it could be useful for us as

1:33:25

we try to kill cells that aren't

1:33:27

growing in or that have

1:33:29

been able to resist antibiotics in infections

1:33:32

of humans. So and

1:33:34

just, you know, to

1:33:36

try and enhance the

1:33:38

relevance here, Pseudomonas aeruginosa is

1:33:41

everywhere, including rotting

1:33:44

vegetation in cemeteries and

1:33:47

probably on your skin and around everywhere

1:33:49

that you are. It's

1:33:54

in humans, the problems

1:33:57

arise from what are called opportunistic

1:33:59

infections. That is, the bacterium

1:34:01

is always around, but it will take

1:34:03

the opportunity, if presented,

1:34:06

to proliferate in some

1:34:09

location and that can cause

1:34:11

a disease. So burn

1:34:14

victims have a problem

1:34:16

with this. The thing I'm thinking about

1:34:19

all the time is cystic fibrosis because

1:34:22

the pneumonia associated with cystic fibrosis

1:34:24

is... Deadly. ...

1:34:26

is Pseudomonas aeruginosa

1:34:29

and it's deadly and

1:34:31

this potentially is a novel treatment for

1:34:33

that. Okay. And Pseudomonas

1:34:35

is, it's a tough

1:34:38

bug. It has antibiotic

1:34:40

resistances, it picks up

1:34:42

more, it has

1:34:45

this, when it's dormant on

1:34:47

a medical implant or forms a biofilm,

1:34:49

I mean it has multiple mechanisms of

1:34:51

dealing with the things that we throw at

1:34:53

it to get rid of it where we don't want it and

1:34:56

so having a new tactic is a great,

1:34:59

great idea. Jolene,

1:35:01

in our sheet

1:35:05

here you put in an

1:35:08

interview with the first author. That's right, yes.

1:35:10

Which I hope that that should go in

1:35:12

the show notes. I listen to it and

1:35:15

it's very good and

1:35:17

in particular, it's 20 minutes long, in the

1:35:19

second half of this for

1:35:23

10 minutes, the interviewers ask

1:35:26

the first author for

1:35:29

sort of more a personal reflection

1:35:34

on his experiments and

1:35:36

his work and what

1:35:38

being a PhD student and studying

1:35:40

this did to him and

1:35:43

it's fabulous. He's just

1:35:45

so thoughtful and he

1:35:47

went through so much that all

1:35:50

of us are familiar with, failure

1:35:52

among other things and am I

1:35:54

gonna quit? And

1:35:57

no, I'm not gonna quit and what...

1:36:00

and what that does to you, okay? It's

1:36:03

great. It's a wonderful, very

1:36:06

human expose on

1:36:08

what it's like to become

1:36:11

a scientist, okay? It's really

1:36:13

good. Yeah, so Phagecast

1:36:15

is a podcast by a

1:36:17

bunch of, well, a few

1:36:19

trainees in a lab that

1:36:21

studies phage and they interview well-known

1:36:24

characters in the phage world as well

1:36:26

as some early career individuals.

1:36:28

And so I love

1:36:30

the quote from Aenea was, distance

1:36:33

yourself from failure is what he

1:36:35

learned. And that kind of

1:36:37

encapsulates one of the rules in my

1:36:39

lab, which is you can't let the outcome

1:36:41

of your experiments dictate your emotions. Okay,

1:36:44

good luck with that. No

1:36:48

crying in this lab. Thank

1:36:51

you, Jolene. That's

1:36:53

great. And so one other

1:36:55

thing. So

1:36:59

Pseudomonas aeruginosa is not necessarily

1:37:01

the natural host of this phage. That's

1:37:05

possible. They

1:37:07

didn't look at a whole bunch of different bacteria,

1:37:10

did they? They did not

1:37:12

report looking at a whole bunch of

1:37:14

bacteria. Okay. Yeah. So there may

1:37:16

be other bacteria out there. They

1:37:19

looked pretty hard for other phage

1:37:21

that did this relative

1:37:23

to Pseudomonas, okay? But

1:37:26

there's gotta be other, there's gotta be

1:37:28

other examples of this. And that may

1:37:30

be another way to approach what the

1:37:32

mechanisms are. There's some

1:37:34

sort of comparative molecular biology

1:37:37

of different hosts and phages that do this

1:37:39

trick and whether or not they're changeable and

1:37:41

all that kind of stuff. And

1:37:44

I think that's a big reason why the

1:37:46

receptor part is in here. So the

1:37:49

part of the receptor that

1:37:51

Paree Day is binding to is

1:37:53

something that is common among bacteria.

1:37:56

And certainly it can be something that's

1:37:59

a little modified. archiving

1:42:00

any of this. Right, and

1:42:02

I don't think I'd realize any of

1:42:04

those things until I first heard of

1:42:06

this whole field and this whole problem.

1:42:08

Yeah, very interesting. Yeah,

1:42:11

so but then again, you know,

1:42:13

there's the standard trope in fantasy

1:42:15

stories, adventurers exploring an ancient ruin

1:42:17

unearth the horror. So

1:42:19

we're setting people up for that exact same

1:42:22

adventure. I mean, I think

1:42:24

it's need something needed now, because, you

1:42:26

know, there's that story that they tell

1:42:28

about the hunters that found the warm

1:42:30

barrel in the woods, and then they

1:42:32

had major radiation burns. Yeah,

1:42:35

one of the things they actually talked

1:42:38

about in here is that, you know, the

1:42:40

first idea was, well, we have the radiation

1:42:42

symbol. So we just put the radiation symbol

1:42:44

all over that, and that will be perfect.

1:42:46

And they polled people and they found that

1:42:48

3% of people knew with that symbol

1:42:50

meant. Rich, what do you

1:42:52

have for us? I have something very

1:42:54

non science for you, but we've done

1:42:56

music and stuff before. I just, I

1:42:59

ran across this at

1:43:01

random, which I have to confess probably

1:43:03

means that I was mindlessly

1:43:05

scrolling through Facebook late one night.

1:43:07

Okay, and I ran across

1:43:11

a music video that

1:43:14

I blinked here, Dr. My Eyes, Jackson Brown,

1:43:16

songs around the world playing for change.

1:43:19

This is one of these music videos

1:43:22

where they have a

1:43:26

compilation, not really

1:43:28

a compilation, but a condensation of

1:43:31

contributions from people all over the

1:43:33

globe, okay, into

1:43:35

a single music piece. And

1:43:37

I first listened to it and I thought, oh, yeah, I know

1:43:39

that song. And I couldn't

1:43:41

remember who did it. So I looked

1:43:43

up and I said, oh, it's Jackson

1:43:46

Brown. Yeah, but who's this old guy

1:43:48

playing it on this video? Oh,

1:43:50

that's Jackson Brown. Why happened to him?

1:43:52

So it's the same thing that happened

1:43:55

to you, dude. Okay. And

1:43:57

then I listened to it and I said, I never paid any

1:43:59

attention. the lyrics before. The lyrics

1:44:01

are great. Okay. And

1:44:04

I, you

1:44:06

know, looked at a bunch of different resources and

1:44:08

came up with this quote

1:44:10

from Jackson Brown himself about the song.

1:44:13

He says, my eye trouble was the

1:44:15

initial inspiration for the song's lyrics. But

1:44:17

as I wrote them, the eye issue

1:44:19

became a metaphor for lost innocence and

1:44:22

for having seen too much. Quote, Doctor,

1:44:25

my eyes have seen the years

1:44:27

and the slow parade of fears

1:44:29

without crying. Now I want to

1:44:31

understand. So I, I put

1:44:33

a link to the lyrics independently here. I encourage

1:44:35

you to have a look, but I've watched this

1:44:38

thing about a thousand times because all

1:44:40

of that aside, it's a terrific musical

1:44:42

piece. They got a guy in here from

1:44:45

Chennai, India. Okay. Playing

1:44:47

a rock sitar. Okay.

1:44:50

They got terrific percussion. They

1:44:53

got a Japanese dude who plays an

1:44:55

amazing guitar rift, rift

1:44:57

in it. And it's just a delightful

1:44:59

piece. So have fun. The Indian guy

1:45:02

with the, I think the instrument is

1:45:04

called a Vina. Okay.

1:45:07

He and the woman from California who does

1:45:09

one of the verses who just has an

1:45:11

amazing voice. Amazing voice. They were the big

1:45:13

stars, but everybody in this video is great.

1:45:15

Yeah. It's just really well done. I

1:45:18

really needed this because I spent too

1:45:20

much time on the news, you

1:45:22

know, and I forget, you

1:45:25

tend to forget

1:45:27

that there are beautiful things. Yeah.

1:45:30

But you want to leave now since it's 10 of.

1:45:32

Thank you. I will. Okay. And

1:45:34

I left a little comment in the

1:45:37

chat. Okay. Rich Condit, emeritus

1:45:39

professor, University of Florida Gainesville. Thank

1:45:41

you, Rich. Thanks. Always

1:45:43

a good time. Alan, what do you have for us?

1:45:46

I have, I think I

1:45:49

may have picked this last year, but

1:45:51

it's been another year and the World

1:45:53

Health Organization has concluded its Health For

1:45:55

All Film Festival again. And I've

1:45:58

linked to the page where they basically

1:48:00

is looking at new ways to

1:48:03

portray data. First is a population

1:48:05

dot map of New York City.

1:48:08

Basically, we know

1:48:10

how many people live in blocks

1:48:12

in New York. So he converted it into a dot

1:48:15

map and he has a figure here where you can

1:48:17

just see the whole of New York City, same million

1:48:19

people. You see where most people live, where

1:48:21

it's densest, where it's lightest, and

1:48:24

Staten Island is the least populated

1:48:26

apparently. Just a really cool

1:48:28

way to look at

1:48:30

it and you can blow it up. There's

1:48:32

a high resolution version that you can blow

1:48:34

up and look at. He

1:48:37

also has race and

1:48:39

ethnicity in New

1:48:41

York. Although I think you should

1:48:43

take the race part out because there's

1:48:47

no race, right? It's not a meaningful

1:48:49

measure. Not a meaningful measure. It's just

1:48:51

ethnicity in New York City. Again, different

1:48:53

ethnicities looking

1:48:59

in New York City and he made a dot map at

1:49:01

it. I think it's really cool to see where

1:49:04

people are living. These are neat.

1:49:06

I'm not familiar with New York City. I

1:49:09

can't tell parts of town, but

1:49:11

it's really striking because the colors

1:49:14

are quite separated in most of

1:49:16

the areas. We

1:49:18

have two listener picks. One is from

1:49:20

David. Blows a humble offering that might

1:49:22

temporarily take your minds off the dysfunctional

1:49:24

state of the US government. Sent

1:49:27

to me by a friend, this

1:49:29

video covers a unique map clock

1:49:31

configuration I'd never seen before and

1:49:33

reveals the fascinating inner mechanical workings

1:49:35

involved. Even if you

1:49:37

do not choose to use this in an

1:49:39

upcoming episode, I would encourage you to take

1:49:42

a look. Really an amazing piece of

1:49:44

science and engineering. The greatest

1:49:46

clock and map ever

1:49:49

made. Oh, this is

1:49:51

a geochron. It's a

1:49:53

teardown of a geochron. Oh, that's awesome.

1:49:58

Peter writes,