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 1093, recorded

0:16

on March 1st, 2024.

0:22

I'm Vincent Draconiello, and

0:24

you're listening to the podcast all

0:26

about viruses. Joining

0:29

me today from Ann Arbor, Michigan,

0:31

Kathy Spindler. Hi, everybody.

0:33

Here it's 47 Fahrenheit,

0:36

which is six degrees Celsius. We have

0:38

a gentle breeze from the south. It's

0:41

been cloudy throughout the day. Gentle.

0:45

I was just going to say, what is gentle? Like,

0:47

is it below five miles an hour? Right

0:50

now it says nine miles per

0:52

hour. Here it's six

0:54

degrees and partly cloudy. It's

0:57

pretty warm. Yeah, same temperature. Also

1:00

joining us from Austin, Texas, Rich

1:02

Condit. Hi,

1:05

everybody. Let me

1:07

see here. Let me clean up my desktop

1:10

mess here. We

1:13

have 66 degrees

1:15

and as you can see, Tinker

1:17

Bell is kind of hanging out. I

1:21

guess there's a mild,

1:24

there's a sort of a wispy, cloudy,

1:26

just diffused today. But it's a nice

1:28

day. Diffused cloud. There you go. It's

1:31

a nice day. So joining us from Madison,

1:33

New Jersey, Brienne Barker. Hi, it's

1:35

great to be here. It is

1:37

48 Fahrenheit here. So I think we're going to

1:39

be split today. I'm on the 48 side

1:43

of temperatures and it

1:46

looks pretty sunny out there at the

1:48

moment. And

1:51

from College Station, Texas,

1:53

Jolene Ramsey. Welcome back.

1:55

Hola. Thank you for having me. It's

1:57

a very similar weather here to Austin,

2:01

64 degrees

2:03

Fahrenheit, Sunny has warmed up.

2:06

Yesterday we thought winter was back but today

2:08

it's spring again. You

2:11

know I always have to look up where

2:13

you are because you're only on like once

2:15

a semester I forget. Twice, twice a semester.

2:18

Twice. It's not

2:20

enough. You have to do things more

2:22

often than that otherwise you forget. How

2:24

about the time stamps every week on

2:26

several podcasts? I know I see

2:28

her name so I know her first name very well. I'd

2:32

like to come on more. I would. I just also

2:34

have... I'm not yeah I'm

2:36

not saying that to make

2:39

you feel guilty it's just I don't remember

2:41

College Station Texas but I

2:43

do remember your name. No, no shoulds.

2:45

No shoulds. I remember your name very well.

2:47

We'll just come visit again and then you'll

2:50

remember it better. Well

2:54

we should have coordinated. I could do it I could

2:56

do another trip to College Station that'd be fun. Yeah

2:58

we should have coordinated it for next week when I'm

3:00

gonna be there but we'll do it another time. I

3:04

understand you can't go to Texas too many times

3:06

in your lifetime right? That's right.

3:10

There's no reason to agree or disagree with that

3:13

because it's a meaningless statement. I was

3:16

gonna... that it

3:18

struck me that way. Sometimes I say stuff that

3:20

has no meaning but this does

3:23

have meaning. If you enjoy our

3:26

programs like TWIB and TWIP

3:28

and TWIM and many other science

3:30

programs we'd love your

3:32

financial support. You can go to microbe.tv

3:34

slash contribute and I

3:37

remind you that the viruses of microbes 2024

3:41

meetings is in Cairns Australia

3:44

and I think this will be the

3:46

last time I remind you but

3:48

it's a good time because Jolene will also

3:50

be there and we will do

3:52

a TWIB with

3:55

meeting participants and

3:57

there's a website which is VOM 202 or

4:01

you can go check out Cans

4:03

Australia. Are you excited

4:05

to go Jolene? You have a bid to Australia?

4:08

I am excited. I have never been.

4:10

I got my international driver's permit in the

4:12

mail last week. Oh,

4:14

you're gonna be driving. I plan to, yes. Oh,

4:17

I'm not driving anywhere. I hope they

4:19

have taxis in Cans Australia. I'm

4:22

gonna spend some extra time there. So I hope to get

4:24

a little bit around. All right. And

4:28

Kathy, any ASV announcements?

4:30

Yes, I do have one. This

4:32

is something different. For

4:35

this week, I'm gonna talk about the

4:37

plant virology lunch that's coming up at

4:39

ASV. So Anna Whitfield is

4:41

the plant virology counselor, and she's

4:43

gonna be hosting a special lunch

4:45

for anyone interested in plant virology

4:47

or learning about plant virology. So

4:50

you can meet like-minded virologists

4:52

at this lunch and discuss topics that will

4:54

be worked on for the ASV 2025 satellite.

4:59

Sorry about the phone. It never

5:01

rings. And then it rings at

5:04

unfortunate times. Okay. So

5:06

this satellite will be not

5:08

this year, but the year after, but this

5:10

will be the way to get your opinions

5:13

known about what you'd like to see covered.

5:15

And so the cost is free to trainees,

5:17

and there's a slight surcharge for the

5:19

non-trainee registrants because it's a little bit

5:22

more of a special lunch. And

5:24

so everyone that wants

5:26

to attend needs to indicate that when

5:29

they register. And if you

5:31

already registered and weren't planning to go

5:33

to this lunch, you can use the receipt

5:35

information you got when you registered to

5:38

go back and change your

5:40

choices for any of the ASV extras, for

5:43

instance, the satellites that we talked about

5:45

last week and so forth. So this

5:47

lunch will be on Wednesday of the

5:50

meeting. The meeting starts on

5:52

Monday. So on Wednesday, June 26th from

5:54

12 to 1 15 at

5:56

the normal lunchtime. So for all

5:58

the meeting information, you should just. go

6:00

to asv.org/asv2024. And

6:05

then last week I also told you about the survival guide

6:09

for first time attendees. And when you get to that

6:11

homepage at the bottom, you can click on that and

6:13

it gives you some hints about how to get the

6:15

most out of the meeting, especially if it's your first

6:18

one. So

6:20

I just realized I haven't registered yet. Yeah,

6:24

well, it's not quite the end of the

6:26

early bird registration period. I will try and

6:28

remind you all of that. I

6:30

did register. All right. I did this time.

6:32

You know, I had this problem with my

6:37

membership of ASV for years. And

6:40

so I solved that by becoming a lifetime

6:42

member. I still have to pay for it

6:44

though. I have a credit

6:46

card thing to pay. We shall write that

6:48

down here. So now can I have lifetime

6:50

meeting attendance? Yeah. That

6:53

would be good. Like, like, it'd be like

6:55

a hundred thousand dollars. Hey,

6:58

if I gave you a hundred thousand dollars, would

7:00

you let me come to the meeting? You could

7:02

have an exhibit booth. You could have free registration.

7:05

You could have a lot of things for, yeah. So

7:10

ASV meeting. What was the

7:13

other thing I just said? Oh yes. The

7:15

lifetime I have to pay. Yeah. Pay.

7:18

Okay. Oh my gosh. I'm just

7:20

so bad at certain things. I

7:23

will not plug Dixon's book, The New

7:26

City, because he's not here. And

7:30

here are some in the news from Amy. This

7:34

year's flu vaccine only moderately

7:37

effective. This is from Morbidity and Mortality

7:40

Weekly Report. Interim estimates of 2023, 24

7:42

seasonal influenza vaccine

7:45

effectiveness, US. Let's

7:49

see. Let's just read the summary, because we'll put

7:51

links to all these stories and

7:53

you can go read them yourself. These

7:56

findings indicate, so analysis of our data

7:58

from four. vaccine effectiveness

8:02

networks. And

8:04

you have to always ask, what are they using

8:06

to define VE? There are many

8:08

ways you could define it. Interim

8:10

pediatric influenza VE was 59 to

8:13

67% in

8:15

outpatient settings and 52 to 61% against

8:18

influenza associated hospitalization. So

8:21

that's the VE that

8:24

I care about hospitalization. And then interim

8:26

adult influenza VE, 33 to 49% in

8:28

outpatient, 41 to 44 against hospitalization. Not

8:33

great. A couple of

8:35

things matter to me here. First

8:38

of all, from a technical point of view, so that

8:40

people can understand this, the

8:43

vaccine effectiveness is

8:46

assessed by a

8:48

test negative tool, which

8:52

means that if

8:54

I understand it correctly, correct me

8:56

if I got this wrong. They

8:59

take people who report either as

9:01

outpatients or hospitalized and have diagnosed

9:03

influenza and then assess probably

9:06

by interview whether or not they've had

9:08

an influenza vaccination. So

9:10

100% vaccine effectiveness as I see it, and

9:14

then they do math. 100%

9:18

vaccine effectiveness would mean

9:20

to me that all

9:23

of the influenza cases were in unvaccinated

9:25

people and vaccinated people had none.

9:28

And I would say that zero

9:30

vaccine effectiveness would mean that

9:33

the influenza cases were evenly

9:35

distributed between people who

9:37

were vaccinated and people who were not vaccinated.

9:39

Mostly. And so 50% I

9:42

think that would be mostly true. I think that would

9:44

vary based on what percentage of people were

9:46

influenza vaccinated. Oh,

9:48

okay. Okay, yeah, sure. And

9:51

50% effectiveness is

9:53

somewhere in between. So

9:57

it's important to me that this is a functional test. This

9:59

is as opposed to... to some other times

10:01

when they assess whether or not a vaccine

10:03

is, an influenza vaccine is working, they measure

10:05

serum antibody and that kind of stuff, which

10:08

does, it tells you if you're getting an

10:10

immune response, but it doesn't tell you really

10:12

whether or not it's effective. It's

10:15

interesting that in this study, they

10:17

never conclude that it is only

10:19

modestly effective. They say that it

10:22

is effective. Of course. It

10:24

has some effectiveness in that it

10:26

does reduce the

10:29

influenza outpatient visits and

10:32

hospitalizations. So that's the

10:34

good news. They don't

10:37

have any sort of

10:39

qualifying assessment of what

10:43

they think is modestly or

10:45

greatly effective or whatever. And they're

10:47

concluding throughout that this says the

10:49

vaccine is working and you ought

10:51

to get the vaccine. I

10:54

mean, even though

10:56

the numbers aren't great,

10:58

they're still there and they will prevent from

11:02

getting hospitalized. Yeah. And

11:04

what's lagging from this to

11:06

me is I would like to see

11:08

a multi-year comparison because

11:11

this is, I don't think, untypical. No, it's not in

11:13

typical. For influenza vaccines. It's not in typical or

11:15

atypical, right? Yeah, you're right. This is about the numbers

11:17

you get all the time. Okay.

11:21

Right. It's a good, actually, motivation for, you

11:23

know, a lot of people working on vaccines

11:25

to try and make a universal vaccine, to

11:28

try and make something that's better, blah, blah,

11:31

blah. So then we

11:33

have from Pfizer,

11:36

full season efficacy of Abrisvo for

11:38

RSV. This is their vaccine

11:40

for respiratory syncytial virus in older

11:42

adults, which is 60

11:45

years of age and older. I

11:47

resent that older adult. I'm

11:51

looking up now to see if that's the name of the

11:53

one I got. I got

11:55

something. I don't know what it was too. Yeah, I'm not

11:57

sure. I'd have to go look at the... CVS

12:01

record. Anyway, the

12:03

name of this clinical trial, so phase three clinical

12:06

trial in the northern and

12:08

southern hemispheres, and the

12:10

trial is called Renoir, RSV efficacy

12:13

study in older adults immunized against RSV

12:15

disease. You know, they just picked letters

12:18

from that sentence to make Renoir.

12:20

That sounds awfully fancy to get for

12:23

their title too. Renoir. Oh yeah,

12:25

and there was some other trial they

12:27

had that had an artist's name to

12:30

it, as I recall. But

12:32

then, yeah, there's the Surat

12:35

plots, right, Brienne? Yeah. Got

12:37

anything in phage with artists,

12:39

Jolene? That's a good question. Nothing

12:43

comes to the top of my mind. The

12:46

name gives the impression that the vaccine

12:48

is good. I

12:51

like the impression.

12:53

The impression. I

12:55

just got it. I just got it. Thank you very much.

13:02

So basically, vaccine efficacy against

13:05

RSV-associated lower respiratory tract

13:08

disease, defined

13:10

by three or more symptoms, after surveillance

13:12

in season two was 77.8% efficacy

13:18

compared to following season one, it was

13:20

89.9%. So it's gone down a bit,

13:22

but still pretty

13:25

good. Right? That's

13:28

a great number. Yeah. Now

13:31

this is, you're vaccinated

13:33

in one year and the effectiveness

13:35

is assessed in subsequent years, right?

13:37

It's not like the vaccine is

13:40

any less effective. It's that you're,

13:42

this is an assessment of the

13:44

waning of one's immunity, right? And

13:47

relative to something like COVID, I guess,

13:51

it seems like it's pretty stable. Especially

13:54

for these, these two

13:57

vaccines are this

13:59

one and the What

14:01

is it, GSK? They're

14:03

both subunit vaccines. So they're made,

14:06

they're recombinant

14:08

DNA technology manufactured in Chinese

14:11

hamster ovary cells. A

14:14

pre-stabilized protein,

14:17

kind of like the even

14:20

spike protein.

14:24

And they're, I can't believe this,

14:27

they're supplied as a lyophilized. Yeah.

14:31

Preparation that then is reconstituted on

14:34

site, you know. I always think of

14:37

freeze drying as a good way to rack a protein,

14:40

but I guess not. The problem

14:42

is it introduces another step

14:44

where something can go wrong. Like

14:46

if you remember, two

14:49

nurses reconstituted measles vaccine with

14:51

a muscle relaxer instead

14:54

of PBS. And they gave it to 12

14:58

months old and they immediately died. And

15:01

that's what... And this

15:04

vaccine has no adjuvant? The

15:07

GSK one does have an adjuvant? And

15:11

what's... Do you figure out what time you got, Kathy?

15:15

Yes, I did get a Brisbane. And

15:17

this link that Rich put in, the

15:20

understanding six types of vaccine technologies,

15:22

looks really nice. Straightforward.

15:25

Yeah, I thought that one might be people...

15:28

That looked like a pick actually, but it

15:30

ought to go into show notes somewhere. It's

15:33

a link from the Pfizer page that

15:35

uses figures and stuff to

15:38

describe the different vaccine technologies that are out

15:40

there. And I thought that was really nice.

15:42

I will bookmark this for class. Exactly. What

15:44

I just did too. Okay.

15:47

So the next one Amy put in so that

15:49

she thinks I'm going to rant about it, but

15:52

I will not... I will

15:54

not give in to the

15:56

temptation. You're

15:59

laughing. This

16:01

is from last week's

16:03

vaccine advisory meeting at

16:07

the CDC. This

16:10

is considerations, it's actually a slide presentation,

16:12

it's a PDF that was presented by

16:14

Sarah Kidd at the

16:16

ACIP meeting on

16:18

February 28th. Considerations

16:20

for the potential use of novel type 2

16:23

oral polio vaccine, NOPV2 as an outbreak control

16:25

measure in the U.S. It's a

16:27

very long presentation where she goes

16:29

through all the pluses and minuses

16:32

of reintroducing NOPV2.

16:38

And you can go through and look at it

16:40

all, but at the moment of course we use

16:42

IPV, inactivated polio vaccine in the U.S. and

16:45

it does not provide intestinal immunity

16:47

so the virus can

16:49

still circulate silently, that is

16:51

without causing disease. And

16:54

so the CDC would like to have

16:56

this approved as an emergency measure. And

16:59

that's, I don't think it's

17:01

a good idea at all and I doubt it would happen because

17:04

all you have to do is tell parents that this

17:06

could paralyze your kid and

17:08

that's the end of vaccine uptake,

17:10

right? We're past the use

17:13

of OPV in the U.S. We should be

17:15

past it in all countries because you can't

17:17

make an OPV that doesn't paralyze kids. They

17:20

tried, didn't work. So I

17:22

think this is kind of bed in the

17:24

water and it's not going

17:26

to get anywhere. I mean... Well,

17:29

that seemed to me more or

17:31

less the conclusion of the presentation

17:33

as well. She seemed to come

17:35

down and in fact I was

17:37

amused at least with

17:39

the fact that there seems to be something

17:43

resembling a risk benefit analysis as

17:45

she goes through this. I

17:48

don't know if it's the same sort

17:50

of disciplined risk benefit analysis that I

17:52

quoted a couple of episodes

17:55

ago, but it's an important

17:57

approach to looking at these things I think.

18:00

Okay, now we have

18:03

Peru and Dengue, which

18:06

the government approves declaration of a health emergency in

18:08

20 regions for a period of 90 days due

18:10

to increase in dengue. This is from the Ministry

18:13

of Health, where they're having

18:15

an increase in cases

18:17

of dengue. And I

18:19

was going to let Jolene read the headline, but I realized

18:21

I could click a button and it would translate it automatically.

18:29

Okay, that's pretty straightforward.

18:31

And more measles, yes. Just

18:36

as an anecdote, I've been corresponding recently

18:38

with Larissa.

18:42

And Brazil. Camasso in Rio. And

18:45

as a side note to

18:47

that correspondent, she said

18:52

that dengue in Brazil is

18:54

really a problem right now. Okay, and

18:56

it's a drag. And

18:58

that, you know, considering vaccine

19:01

options is difficult, blah, blah,

19:03

blah. I'm so glad you used that word.

19:05

It's a drag. I haven't heard that

19:07

in years. It's like a 60s word, right? Kind

19:11

of a drag. It's kind of a drag. It's

19:14

kind of a drag. Well, here we have in

19:16

Peru 31,364 cases of dengue and 32 deaths.

19:26

And it is a good quote from the

19:28

Minister of Health. If the population does not

19:30

collaborate, it will be difficult for us to

19:32

defeat dengue. It's

19:36

interesting. I don't know if collaborate is the

19:38

right translation. You know, this is an automatic

19:40

thing. Cooperate

19:42

would be a better translation. Okay,

19:45

thank you very much. Yeah,

19:48

I think that collaborate sounds weird, right? I

19:50

think that you could have that sentence.

19:53

Instead of saying dengue, you could have a blank at the

19:55

end of it. Yeah, of course. And I think

19:58

it would be true for an awful lot of things. Got

20:00

to collaborate. Got to

20:02

cooperate. Oh, here's one close to

20:05

home for Kathy. Michigan

20:07

experiences first case of measles since

20:09

2019. Lansing, Michigan. How

20:14

far is Lansing from Ann Arbor, roughly? An hour

20:16

away. Very

20:21

close. A child associated with

20:23

international travel. I

20:26

just pasted this into my lecture notes the other

20:28

day, too. It

20:31

doesn't say anything about the vaccination

20:33

status of the child. No,

20:36

I thought I

20:38

saw something in some other news, though. Let me

20:41

see. I

20:43

got a picture of a really cute infant

20:45

here with measles. Because

20:48

I have my lecture notes open. You're

20:51

right. I only have that it was a

20:53

child who traveled internationally. Don't know. It's

20:56

problematic because, as we have

20:58

already discussed, there is a

21:00

window between

21:02

birth. Is

21:05

it a year you're supposed to get vaccinated at

21:07

about 12 months? That's about right. And

21:10

you're relying on

21:12

maternal immunity up to then, and

21:15

it's not good to vaccinate before

21:17

that because maternal immunity will compromise

21:19

the vaccination. So it's

21:23

a tricky situation. Not

21:25

all cases

21:28

of measles in very young children are

21:30

because their parents are negligent. Some

21:33

of this is because there is a

21:36

window there where there's potentially some susceptibility.

21:38

And of course, this child, who hopefully

21:40

will fully recover, will now have

21:43

erased immune memory that

21:46

has to be restored. So

21:48

that's why vaccination is good.

21:52

Okay. Thank you, Amy, for that.

21:54

We have a snippet for you.

21:58

This is a hell of a snippet, Vincent. It's not

22:00

really a snippet, I know, I'm sorry. Yeah, okay,

22:02

thank you. I was pretty excited about it. Yeah.

22:05

I mean... Oh, it's good.

22:07

Yeah. It's good, but it's massive. Massive. It

22:09

really is massive. And as I was reading...

22:11

But it has bats. Yeah, it has bats.

22:14

Bats. Bats is cool. This

22:18

is a Nature Communications paper.

22:20

Coordinated inflammatory responses dictate Marburg

22:23

virus controlled by reservoir bats

22:27

by Guido, Kirzic, Hsu,

22:29

Amman, Sealy, Graziano, Spangler,

22:31

Harmon, Wozniak, Breskot and

22:33

Jonathan Towner, John Towner

22:36

from CDC

22:38

and also the institutes

22:41

in Germany and

22:45

et cetera. But I

22:47

met John Towner at the bat meeting. Brianne,

22:52

you were at the bat meeting. I was at the bat

22:54

meeting. The bat meeting was awesome. That was when? I don't

22:56

even remember when it was now. Oh,

22:59

it was 2022. This

23:01

was like in Fort Collins or something?

23:04

It was in Fort Collins. Yeah. It

23:06

was actually the day before my Jeopardy! episode aired. I

23:08

left. I had to leave early to come

23:10

home for my watch party. Watch

23:12

party. So I met John at

23:14

the meeting. John was a graduate

23:16

student with Bert Semler. Bert

23:19

sent me a picture of him from the olden days

23:21

today when I told him we were doing this paper.

23:25

And he said, I'd

23:27

be happy to come on to it, but

23:29

I'm going into caves next week, so I

23:31

can't do it next week. But

23:34

then I never got back to him. And then I see

23:36

this, so it jogs my memory. I'd like to have him

23:38

on to talk about this. But this is

23:40

about a particular phyllovirus,

23:44

Marburg virus, for

23:47

which we know that

23:49

the reservoir host is the

23:51

Egyptian ruzet bat, Rosettus egyptiacus.

23:55

And we don't have any such information

23:58

for Ebola virus. that

24:00

actually isolated infectious Marburg virus

24:03

over and over from these bats, which

24:05

they're abbreviated herbs, ERB, and

24:08

in Sub-Saharan Africa. And of course, there have

24:10

been a couple of outbreaks of Marburg virus

24:13

and people spill over into people from

24:16

those bats. And so, you

24:18

know, the bats, when

24:22

primates get infected, you

24:24

get all kinds of problems,

24:29

liver alterations are the big

24:31

one, big part of the disease, and they all

24:33

appear to be a consequence

24:35

of aberrant immune responses.

24:38

And so then the question is, what's

24:40

going on in the bats? It's always the question, because

24:43

the bats seem to be okay. Yeah,

24:47

and when you say aberrant immune responses, part

24:49

of that may be sort of excess immune

24:51

responses and too much inflammation. Yeah.

24:53

Right. So this is a good

24:55

system where you have

24:58

the bat, Egyptian-Wazette

25:00

bat, and then you have the virus that

25:02

naturally infects it, and you can raise

25:05

these bats in captivity. I

25:08

said, cat-tivity by mistake. I

25:10

meant captivity. What our cats would

25:12

feel about that. And

25:15

then you can do experiments, which is what this

25:17

paper is all about. But,

25:21

you know, there've been many hypotheses over the

25:23

years about why

25:25

bats are protected from such

25:27

pathology. But as they point out in this

25:29

paper, which is really a key, most

25:32

of the experiments have been observational. And

25:36

what they wanted to do here is somehow, have

25:42

an experimental system where they could do

25:44

very fine-tuned studies. And they say, their

25:46

hypothesis is that, you

25:49

see in general, minimal inflammatory regulation

25:51

at a whole tissue scale, but

25:53

they say there must be microinflammatory

25:55

responses to limit replication, because as

25:58

you will see, this virus... In

26:02

a normal bed, barely does anything on

26:04

a macro scale, but if you look closer, it

26:06

does. So

26:08

I'm looking at, I want to emphasize

26:10

that one of the, Vincent already alluded

26:13

to this, but one of the reasons

26:15

that this is of interest is that

26:17

Marburg is a filovirus related to

26:21

Ebola and other like viruses.

26:24

And then I looked up, because I

26:26

wasn't sure what the case fatality rate

26:28

is for this. There's a nice Wikipedia

26:30

site, of course, that

26:32

summarizes various Marburg outbreaks. And

26:34

if I look at some of the bigger ones,

26:37

like with 300 or 150 or 300 some odd cases, the

26:41

case fatality rate is between 83 and 90%. So

26:46

this is a nasty creature. Burns

26:49

through humans in a really nasty way and

26:51

has already been pointed out. Doesn't

26:55

seem to bother the bats and so why. Yeah,

26:58

and I think that this

27:01

is really nice in that it's

27:03

letting them not look just at

27:06

sort of systemic immune responses, but

27:08

actually drill down into individual organs

27:11

and tissues. And

27:14

they also spend a bunch of time talking

27:16

about just how hard it

27:18

is to do immunology in

27:20

any non-model organism, particularly in

27:23

these bats. And

27:25

so they talk a lot about, you know, and

27:27

I have done some work on some

27:30

non-model organisms and you don't

27:32

necessarily even have an antibody

27:34

to do really basic flow cytometry and

27:36

identify T cells or other

27:38

things. And so one

27:41

of the things that makes us really nice

27:43

is that they're looking not just

27:45

systemically at sort of, you know, blood

27:47

antibody levels or blood cytokine levels, but

27:49

in different organs. And

27:52

they also have actually made sure to have

27:54

some reagents so that they can start to

27:56

tease apart some of those things. Yeah,

27:59

there are a lot of words. in this paper. Yeah,

28:01

there are two terms that maybe you are going

28:03

to get to this instance, but I'm going to

28:05

steal your thunder on it, which

28:07

is that they talk about the fact

28:09

that there's sort of a lack of

28:11

immunopathology is thought

28:14

to be explained by something

28:17

called disease tolerance, which is that

28:20

there's a defense strategy that's dependent

28:22

on controlling

28:24

the inflammatory response activation. But

28:27

the other way that you could do this, control

28:30

the infection is to just actively, in

28:33

a way, I was thinking of this, like

28:35

sort of directly control the virus burden. So

28:37

disease tolerance is focusing

28:40

on controlling the inflammatory response

28:42

activation. And the

28:44

second one where you're controlling

28:46

the burden is this host

28:48

resistance term. And so

28:51

they, in this paper, they're

28:53

going to do some things where they

28:55

specifically alter the balance

28:58

of things by

29:00

giving dexamethasone and

29:02

sort of changing this inflammatory

29:05

response. And then I

29:07

think that's what's really cool about this is I think in

29:10

manipulate it. Yeah, the

29:12

hypotheses about disease tolerance

29:15

versus sort of the resistance aspect are

29:20

things that people are talking about in a bunch

29:22

of different areas of immunology. But I think that

29:25

one of the things I really liked the way they

29:27

talked about here was that,

29:29

yes, disease tolerance is fabulous.

29:32

You want to limit immunopathology.

29:35

And so one strategy might

29:37

be to sort of turn down the

29:39

immune response so you don't have excess

29:41

responses. And so you have this disease

29:43

tolerance. But turning

29:46

down an immune response also

29:48

has a risk in that you're

29:50

not going to clear the microbe. And

29:53

so the idea of what we just don't have

29:55

an immune response isn't really something that's going to

29:57

work. This has to be a very, very important

29:59

thing. very targeted situation, you

30:01

need to have a targeted

30:03

immune response and a

30:06

disease tolerance that is

30:08

still allowing control

30:10

of the microbe. And so how you kind

30:12

of think of those not, again, as sort

30:14

of two global processes, but ways that you

30:16

can have them be specific in different situations

30:19

is a nice thing that they talk about

30:21

here. And

30:24

this all involves infecting

30:27

bats from a colony,

30:31

you know, a maintained colony.

30:34

They call it a captive bat colony, which

30:36

means, I think, that they were originally captive

30:38

bats that have been raised

30:40

for generations, at least several

30:42

generations in a colony. And since

30:45

we're talking about Marburg, which

30:48

has a high lethality, it's all done

30:51

at BSL4, which I presume is in

30:53

CDC's high containment lab. And

30:56

I try to imagine infecting

31:01

and keeping bats and

31:04

processing and sampling them and doing all

31:06

this at BSL4, they describe some of

31:08

the details, major

31:11

hassle, okay? Yeah,

31:13

maybe not as

31:15

bad as crawling into a cave where it's 100

31:17

degrees. So

31:20

the bat colony might be 100 degrees

31:22

in the PPE, though. Maybe.

31:25

Well, this is a bit of a digression, but I

31:27

learned of it because of the paper. But they talk

31:29

about these other two viruses at the end. And I

31:31

went off on a tangent with the one that's carried

31:34

by ticks as

31:36

a vector. And

31:39

these investigators got

31:41

it walking into the cave, and there were

31:43

the ticks, and they got it. It

31:46

was bad. Yeah, the Pecilovirus was right. No,

31:49

one, I have

31:51

it here, one's a Paramixo, but

31:54

the tick one is an Orthonirovirus.

31:57

Okay. So,

32:00

like a bunch of- The tetrachotomy has

32:02

changed so much that I'm often baffled.

32:07

Yeah, so those are the Sosuga and

32:09

Kasakira viruses. Well,

32:14

when we were in Fort Collins, Tony

32:16

Showns, who ran the meeting, he's

32:19

a professor there, he took me, so

32:21

they have a colony of fruit bats, Egyptian fruit

32:23

bats, and he took

32:25

me to see them. They're nice to,

32:27

apparently they breed well. Not

32:30

all animals do, right? So,

32:34

even if it may not

32:36

be the reservoir host of your

32:38

particular virus, they

32:41

can breed. So, and he brought me in

32:43

and they were all hanging from the roof

32:45

and the walls of this room, and they

32:47

had just fed them, and there were big

32:49

slices of mango and watermelon and other fruits

32:51

lying, well, yeah, they're fruit bats. And

32:54

there was fruit everywhere, and

32:56

he said, in the morning, it'll all be gone.

33:00

And he said, watch out, they'll pee on you as you walk in. And

33:05

he gave me big gloves to wear and some eye

33:07

protection and so forth. It's pretty cool to

33:09

see them. I mean, they're very small, right?

33:11

They're folded

33:13

up, they're less than six inches,

33:16

and they tend to cluster and hang on the

33:19

walls and the ceiling, and right now, and then

33:21

one will peel off and fly around in

33:23

this little room. It's very cool. Bats are just very

33:25

cool. I don't know what about them is

33:28

cool, but. All

33:30

right, so everything is cool about them, that

33:32

they fly, first of all. So

33:35

they say that the virus in

33:38

a regular fruit bat

33:40

is the liver and the spleen and

33:42

the lymph nodes are the primary targets

33:44

of reproduction, and

33:48

they look at this by infecting animals.

33:52

And you know, they said previously, people

33:54

have just done staining of the tissue

33:56

because, and maybe some histochemistry of antibodies

33:58

to viral proteins. because we don't have

34:01

any reagents here. But

34:03

they tried to take that a

34:05

little bit further by inoculating the

34:08

bats and looking in the

34:10

liver, spleen, lymph nodes for immuno,

34:12

for pathology. And they see inflammatory

34:16

foci in the livers. They have two

34:19

time points that they mainly sample six

34:21

and 12 days post-infection. Nothing

34:25

in any other tissue. Just

34:28

in this case, the liver. And

34:33

there's small inflammatory foci. It's not like

34:35

the entire liver is full of

34:38

inflammation. There's a few foci here and there,

34:40

right? And they can see what

34:42

kind of cells are there. They have monocytes

34:45

and not many T cells and very

34:48

rare B cells, they say. But

34:50

all of that's cool because nobody has

34:52

ever actually had the antibodies to distinguish

34:54

what the cells were before. And

34:58

they can see that T cells

35:00

are probably proliferating in response to

35:02

Marburg virus antigen. They're probably virus

35:04

specific T cells. And then

35:06

they look into spleens. You can

35:08

see viral RNA in the spleens,

35:11

in the capillaries, within

35:13

the sinuses around small blood vessels, in

35:15

the capsule, the follicles. And

35:18

they're consistent with mononuclear cells.

35:22

But no pathological alterations like

35:24

they saw in the liver.

35:27

In this spleen, they don't see any of that stuff.

35:29

So nothing, no other tissue. They sample all

35:31

the other tissue, nothing else. Which

35:34

is interesting because as you see,

35:36

that changes when you immunosuppress. And

35:39

in people, when people get Marburg, they

35:42

get many, many organs infected. So

35:47

they then, the main part

35:49

of this paper is to immunosuppress these

35:51

bats with dexamethasone. Dex.

35:57

Dex, this is something that's popping into my head about

35:59

dex, but I can't. I can't remember it. So

36:03

they wanna know if- You can't

36:05

be president. No. I'll

36:10

think of it later. Sorry, it just came to mind. So

36:13

they wanna know if inflammatory responses, the

36:16

likes of which we have seen in the liver, right?

36:18

Little foci of inflammation. Those

36:20

needed for controlling infection. So

36:22

they use DEX to,

36:25

they treated the bats. And this

36:27

blocks TNF, NF-kappa B responses. It

36:29

depletes lymphocytes. It

36:32

disrupts pro-inflammatory activities. It's

36:35

a real sledgehammer, right? I'll tell you.

36:39

And they give people DEX, you know,

36:41

just to suppress

36:44

immuno-inflammation. Yeah, inflammation.

36:46

Cause we don't have, we can't necessarily do it in a

36:48

more specific way. Yeah, or we don't

36:50

know what to do, like- Right. What

36:53

exactly to do. So

36:55

they show that this DEX treatment does what we

36:57

think it does. It depletes immune cells. And then

36:59

they- What

37:01

is DEX's target? I

37:04

think they say it here. I

37:09

think of it as an NF-kappa B target, but

37:12

I probably hit other things. Yeah,

37:15

so it blocks TNF and NF-kappa B responses. Okay.

37:19

So way high up in the chain. Yeah.

37:22

So I found interesting is that the spleens and

37:24

lymph nodes of these bats

37:26

treated with DEX were

37:28

40 to 50% smaller. Oh

37:31

my gosh. That's what this, it's getting rid

37:34

of all those cells, right? Right. It's

37:36

like your immune system is doing something most

37:38

of the time. Especially after such a short period

37:41

of time. Yeah,

37:43

it's not a long time. It's right. Hey,

37:46

Breanne, what did Cindy say? How

37:48

many neutrophils do you make a day per kilogram?

37:50

A billion, right? I think so. Some

37:53

insane number. I learned so much in that

37:55

episode. A billion per day per kilo of body

37:57

weight, right? Yeah. So this

37:59

is neutrophil. That's how many we make

38:02

per day per kilo of body

38:04

weight, one billion, oh my gosh. It's

38:07

just incredible what's going on in there.

38:09

So then they do experiments with DEX

38:12

treated bats and

38:14

infected. And

38:17

they don't see

38:20

any DEX toxicity.

38:22

So they have a different protocol

38:25

for doing

38:27

this. They infect and treat with

38:29

DEX at the same time and

38:33

then they sample at six

38:35

and 12 days. And then there's another cohort where they infect

38:39

and then they wait till peak viremia, which I think is day six

38:41

and then they treat with dexamazosone, okay. And

38:48

they do a lot of research on this. The,

38:54

so I am his immunomodulated

38:58

bats. I

39:00

guess that's, yeah, that's the abbreviation for that. So

39:03

the bats don't have any clinical

39:06

signs. Some

39:10

of them, when you infect them and treat

39:12

them with DEX, say a few

39:15

of them had modestly reduced appetents. Yeah,

39:19

I went off on a tangent about that and tried

39:21

to figure out what's the difference between

39:24

appetite and this word.

39:26

And I basically figured that there's

39:28

no difference. Although they said, appetents

39:32

is more for non-food

39:34

things. Okay. For whatever

39:36

that means. At least one thing I found.

39:40

Like drinking water, they don't want to.

39:43

Well, I didn't even apply that. Yeah,

39:46

I don't know what that means. Yeah,

39:49

it was, yeah,

39:52

maybe I can find it again. The point is that's

39:54

the only symptom. No infected bats that

39:56

were immunosuppressed had any clinical signs. They

39:59

had no weight loss. temperature deviations except a

40:01

few had appetites. And one animal found dead

40:03

in its cage, animal

40:05

number one. But

40:08

they said that it was fine the day before and

40:10

then boom, they came in and it was dead. And

40:18

the real key here is that the viral

40:20

loads are much bigger, much higher in the

40:22

dexamethasone treated animals, they're exponentially

40:25

higher. And they get different,

40:28

they have some bats that make a lot of

40:30

virus and some bats that make small amounts of

40:32

virus. And those are experimentally useful in

40:35

a bit. But

40:37

then they also have

40:39

higher shedding of viral RNA

40:41

from the oral and rectal mucosa and

40:45

more prolonged compared to

40:47

untreated animals. And

40:51

we're gonna see in a moment the tropism,

40:53

how it's expanded as well. And then they

40:55

also look at the pathology

40:58

in the liver and spleen to

41:00

see how immunosuppression changes

41:02

it. And so they

41:05

see many discrete inflammatory

41:07

foci. Remember

41:11

in the untreated animals, discrete.

41:14

And in this case, the entire liver

41:17

is inflamed in the

41:19

case of immunosuppression. So it's no longer restricted

41:21

to foci. So this is

41:23

the big picture thing that Brianne was talking

41:25

about later there. Boy, these day 12 immunosuppress

41:27

guys, I can look at those livers and

41:29

know that there's a problem. And

41:32

I came away with the impression because they're

41:34

talking about these animals not showing any clinical

41:36

symptoms. I know, and the liver is a

41:38

mess. And their liver is a mess. So

41:41

I get the impression,

41:43

well, I'm not enough.

41:45

I'm not a physiologist, so I don't know. But

41:48

there's something about these bats where

41:50

they can go right up to the brink with

41:53

all sorts of nasty stuff going on and

41:55

then just keel over. Yeah, I wonder

41:57

if there's a difference in... how

42:00

they would be in the wild where

42:02

they might be under somewhat more stressful

42:04

conditions than in the lab. Yeah,

42:06

maybe very different, yeah. I

42:10

was gonna say back to their modest reduced

42:14

appetites. What I had found

42:17

was that it's a synonym

42:19

for appetite, but

42:21

here's the caveat. You can

42:23

use appetites instead of a noun

42:26

appetite if it concerns topics such

42:29

as worldly goods. So

42:31

I said it was things other than food. That

42:34

doesn't really apply to bats, I think. No,

42:37

no, not in this sense. But

42:39

then other things, it

42:41

just made it sound like it's a synonym. This

42:44

is like an extra, because I'd never

42:46

heard of appetites, so it's like why are there two

42:48

words for the same thing? This

42:51

one bat here had a

42:54

shrunken green liver. Yeah,

42:56

it's just really gross. So

42:59

massive, massive liver damage here.

43:02

And- Yeah, for some of the listeners,

43:04

typically in something like this, there are,

43:09

animal folks know more about this than I

43:11

do, but typically there are guidelines

43:14

for dealing with animals where if they've

43:17

lost a certain amount of weight or

43:19

their temperature passes a

43:22

certain threshold, then

43:24

you relieve their suffering

43:26

by sacrificing them. And

43:29

so it's not common that

43:31

an animal under these circumstances will

43:33

die during the experiment, because there

43:35

are limits to how much they

43:39

can be put through. But

43:41

this is the situation here where they're

43:43

going right up to the brink without

43:46

showing any symptoms. Yeah, they said none

43:48

of the merited premature euthanasia. Yeah,

43:50

and Rich mentioned that he could

43:52

sort of see the difference by

43:55

eye. I am not a

43:57

particularly good histologist.

44:00

or anatomist. So there are definitely times

44:02

in papers where I look at things

44:04

like this and I say, okay, authors,

44:06

I believe you. Yeah. I

44:09

had never seen a bat liver before

44:12

today. However, I

44:15

could tell that these livers were

44:17

not okay. Right. So

44:21

then they also look for viral

44:23

RNA and other tissues, because remember

44:25

in the untreated bats, they

44:28

only find it in liver and spleen and

44:30

lymph nodes, but they find

44:32

it in these immunosuppressed bats everywhere,

44:36

stomach, intestines, colon,

44:39

kidney, bladder, adrenal, heart, salivary,

44:41

it goes on everywhere.

44:46

So this, the

44:48

infection is no longer contained to the

44:50

liver, spleen and lymph nodes. It's

44:53

really quite interesting. They

44:56

also, they go on to look at some of

44:58

the infiltration

45:01

experiments in these animals. And

45:04

I don't want to go into that

45:06

too much, but there are a lot, there are

45:08

big differences in the immunosuppressed animals.

45:12

They also look at cytokine responses

45:14

in untreated and immunosuppressed

45:18

bats to see if they could make some

45:20

correlations between what they're

45:23

observing. And they

45:26

look at specific cytokines. They

45:28

look at tumor necrosis factor,

45:31

IL-6 pro-inflammatory ones,

45:33

and then an anti-inflammatory cytokine

45:36

IL-10 to just to see

45:38

what's going on. And in the spleens

45:41

of immunosuppressed bats,

45:48

the TNF went up and

45:51

so did IL-10. And

45:56

they have different, those

45:58

different animals with treatments at different. different

46:00

times, but basically the

46:02

key that they pull away from this is

46:05

TNF looks like it's a key cytokine

46:08

for regulating and for restricting

46:11

the infection to discrete

46:13

foci. And once you interfere

46:15

with that, it looks like everything

46:17

goes awry. Now,

46:21

so that's the basic overview of

46:23

what they find. So

46:27

basically, they believe that this

46:29

early response, which probably

46:31

involves TNF, is

46:33

really important for restricting the

46:35

inflammation to discrete foci because

46:38

if you get rid of that with dexamethasone,

46:40

then the virus goes in many, many different

46:42

places. So they

46:44

should just superb control

46:46

of, by

46:49

these bats, to

46:51

prohibit the virus from hijacking responses

46:53

that lead to inflammatory disease are

46:57

happening here. Go ahead, Brienne, sorry. Yeah,

46:59

so I think that one sort of very

47:01

simple way to think about it is that,

47:03

you know, you might have thought before this,

47:05

well, bats just have a dampened immune response

47:07

in general to this virus. But in fact,

47:10

if they had a dampened response in general,

47:12

which they do experimentally here, bad

47:15

news happens. And so it's

47:17

not about just a broadly dampened

47:19

response. It's about a very tightly

47:22

controlled response. And that

47:24

tightly controlled response is, you know, happening

47:26

via TNF, not maybe by interferons, which

47:28

are typically the first things people might think about

47:30

when they're thinking about viruses. And

47:33

I also say that, so they had this

47:35

one treatment protocol where they treat the, they

47:38

infect the bats and they wait six days

47:40

before the immunosuppression. And

47:42

dexamethasone doesn't have any effect there.

47:44

So they conclude that you

47:47

need an early control of an

47:49

infection, you know, before day six, because

47:53

if you suppress it day six, it doesn't

47:55

matter. Those bats still restrict replication to the

47:57

liver and spleen and so forth. So somehow.

48:00

Now an early response in the first

48:02

few days of infection is established that

48:04

restricts the infection. I mean,

48:06

that's an evolutionary thing, right? The bats have

48:08

been living with this virus and

48:10

the ones that couldn't do this are gone long ago

48:12

and they evolved to be

48:14

able to do these really specific controls.

48:18

The amiensis and the mingle deluxe. Now

48:20

what's cool here is they

48:23

speculate that, when

48:25

bats are stressed, they can

48:28

get immunosuppressed, right,

48:30

habitat changes and food

48:34

shortages and migration or

48:36

even birthing, breeding and

48:38

birthing cycles. You can

48:40

get increased production of

48:42

glucocorticoids like, which is what

48:44

Dex is, right? So they speculate that

48:46

maybe that's what leads to a spillover

48:49

that these bats have these viruses,

48:52

but then periodically they get stressed

48:54

and then that makes the

48:56

virus reproduce out of control.

48:59

And then if there's a human around, the human

49:01

gets it. If

49:03

you remember, we did a paper on Nipah

49:06

in Bangladesh bats and they

49:08

conclude the same thing. They're these cycles of virus

49:11

production and one of

49:13

the speculations was that it corresponds with

49:16

birthing and breeding cycles and that may

49:18

be when you get the spillover. So

49:21

I think that's pretty neat. So

49:24

the last sentence, our findings suggest

49:26

a compelling scenario in which environmental

49:29

stress could weaken aspects of wild

49:31

bat immunity, like

49:33

inflammatory responses akin to Dex, fomenting

49:36

recurrent Marburg virus super shedding

49:38

events that enhance juvenile bat

49:41

transmission and ultimately risk of

49:43

spillover to unprotected hosts, including

49:45

humans. So,

49:47

you know, this is a speculation, but they should actually

49:49

be able to test that with their captive

49:52

bat colonies. Disruption

49:55

of habitat, starvation and so

49:57

forth. They

50:00

were just breeding and birthing cycles, right? That would probably

50:02

be an easy one, yeah. Yeah,

50:05

they could test it. I think it's a very nice

50:07

paper. It's an interesting approach. And you learn quite a

50:09

bit there about one bat

50:11

species, of course. We don't know if all bats

50:14

are created equal, who knows? Maybe

50:16

different things that happen in different bats with

50:18

different viruses. They did mention Hendra

50:22

seroprevalence increasing and shedding

50:24

increasing in toropus bats.

50:28

Yeah. I looked those bats

50:30

up and they're enormous. They're

50:32

the huge ones. They're like the size of

50:34

a person. Yeah, flying foxes,

50:36

yeah, they're big. So Jonathan Epstein

50:39

from the EcoHealth Alliance, he did

50:41

that study in Bangladesh

50:43

on NEPA. He says

50:46

they're very, they trap them with nets.

50:48

And he says, you get these huge bats and

50:51

you want to take some blood. And he said,

50:53

they have claws

50:55

at the tips of their wings. And

50:58

he said, they always try and get the claws around to

51:00

your eye and poke your eye out when you're holding them.

51:03

So you have to really be careful of this wing

51:05

coming around the side and your blind side and going

51:08

into your eye. That wouldn't

51:10

be good, would it? Uh-uh. Field

51:13

work. Okay, that

51:16

was a snippet. It's not bad. We didn't

51:18

take too long, right? Half hour. And

51:21

now we go something completely different, right,

51:23

Jolene? That's right. Yes,

51:26

so now we're gonna talk

51:28

about a paper that

51:31

is called,

51:34

Degradation of Host Translational

51:36

Machinery Drives TRNA Acquisition

51:38

in Viruses. And

51:41

this is specifically bacteriophages.

51:44

This paper was published in Cell

51:46

Systems in 2021. There

51:50

are co-first authors, Joy Yang and

51:52

Wenwen Feng, and

51:54

middle authors Fabiola Miranda

51:56

Sanchez and

51:59

Julia Brown. Catherine Kaufman, Chantel

52:01

Acevero, David Bartle, and it comes

52:03

out of the labs of Martin

52:05

Pohl's and Libouche Kelly. These

52:08

are from Massachusetts Institute of

52:11

Technology, MIT, the Whitehead, Albert

52:13

Einstein College of Medicine, and

52:16

University of Buffalo and the University of Vienna.

52:20

So in this

52:22

paper, we're talking about

52:24

how phage, like all viruses, when

52:27

they come into the cell, they

52:29

take over these critical systems

52:31

that the cell has to be able to

52:33

produce what they need to build more viruses.

52:36

And one of those systems is translation. Since

52:40

phage, like many viruses, are very

52:42

small, the thought is that they

52:44

don't have space often for some

52:46

of the pieces of essential systems

52:48

like ribosomes and all of the

52:50

tRNAs and the things

52:53

that are used to make proteins. And

52:55

it turns out that many viruses

52:57

do carry pieces of these

53:00

complex machines that the cell uses. And

53:02

the question is why, what are they

53:04

doing, what's the advantage that is provided

53:06

to the viruses. So

53:08

we know that the

53:10

host will react to an infection

53:13

and try to abort

53:15

an infection, you could say. They

53:20

can shut down their own transcription machinery. They

53:22

can shut down their own translation. One of

53:24

the ways that they do that is by

53:27

cleaving tRNAs, which are necessary

53:29

for translating mRNAs

53:31

into proteins. And

53:34

the phages themselves

53:36

are known to often

53:39

encode tRNAs. It

53:41

turns out that about 40% of

53:43

phage encode tRNAs of the ones

53:45

that have been analyzed. They're usually

53:47

between one and five if they

53:50

have any at all, but they

53:52

can encode up to mid-30 numbers

53:54

of tRNAs, which is quite a lot

53:56

actually. These are just DNA phages, right?

53:59

Not RNA phages. So, yeah,

54:02

they've done most of the analysis

54:04

to see these in DNA phages.

54:06

RNA phages have much smaller genomes,

54:08

so there are correlations, both between

54:10

size of phage, as

54:14

in the size of the genome. So smaller genomes are

54:16

less likely to have a tRNA, and

54:18

larger ones are more likely to have them and

54:20

more likely to have more of them, the bigger they get. I

54:23

think very similar in viruses that

54:26

infect eukaryotes. Because there's

54:28

also a correlation between whether or not

54:30

the phage can establish

54:33

a lysogen or can become

54:36

part of the host and remain

54:38

there in a dormant state until

54:41

it reactivates later to replicate. So,

54:43

temperate phages are less likely to have

54:45

these tRNAs in their genomes, but virulent

54:47

phages that come in and immediately wreck

54:50

the cell to make new phage, those

54:52

are more likely to have tRNAs. I

54:55

find this really interesting because, as

54:57

you know, giant viruses have encode

55:00

tRNAs and people are always speculating about what

55:02

they're doing. Here's some experimental evidence. I like

55:04

this very much. Yeah. So,

55:07

there are, people have wondered for a

55:09

long time what is the purpose of

55:11

these tRNAs. There are a few systems

55:13

where they've been able to delete the

55:15

tRNA genes, and they do notice a

55:18

decrease in the birth size, which is

55:21

the yield of number of phage that

55:23

can be built from an infected cell.

55:26

But it's not under all conditions,

55:28

and so not thought to be the

55:30

only explanation for the presence of the tRNAs

55:33

in the genome. Now,

55:35

another thought for why the

55:38

tRNAs might be there is because

55:40

maybe the phage is able to

55:44

infect more than one kind of

55:46

host, and not every host has

55:48

the same tRNA complement encoded in

55:50

its genome, because every organism has

55:52

a slightly different codon usage. So,

55:55

the codons are what the tRNAs recognize,

55:57

so the host will have the tRNA...

56:00

that are needed to translate its own genes.

56:02

Maybe the phage will infect a host

56:05

that doesn't have the best complement of

56:07

tRNAs, so maybe it brings them along,

56:09

and that way it doesn't have any

56:11

roadblocks as it's trying to translate its

56:13

genes. Some

56:15

other potential explanations are

56:17

that some phage come

56:20

into the host, and not only does

56:22

the host try to respond and shut down,

56:25

the phage, in particular T4-like phages,

56:27

T4 itself, will come in and

56:29

degrade the entire host chromosome down

56:31

into bits that it can recycle

56:34

and use for its own production

56:36

of phage DNA, and that shuts

56:38

down all the host processes.

56:40

And so if the phage

56:43

needs something that isn't already there and

56:45

isn't being made, then maybe it needs

56:47

to provide those tRNAs to be able

56:49

to sustain a long enough infection to

56:51

produce more phage. And

56:54

one of the other ideas is

56:56

that tRNAs will bias translation. So

56:59

not all phage shut down the

57:01

host cell. Maybe it'll

57:03

bias translation towards the phage transcripts

57:05

over the host transcripts. And

57:09

one other thing about this

57:11

is that phage tRNAs are

57:13

sometimes different from the

57:15

host tRNAs, even though they can recognize

57:17

the same codon. There'll

57:21

be variations of them, and that will

57:23

be either in the recognition loops or

57:25

in the modifications that they have that

57:27

allow them to be able to expand

57:30

their range of translation. And

57:32

although the host will often

57:35

encode something that cleaves tRNAs,

57:37

phage tRNAs are sometimes resistant

57:39

to this cleavage. So

57:42

in this study, they were

57:44

asking, what are the advantages

57:46

that tRNAs can confer to

57:48

phage? And they systematically test

57:50

some of these hypotheses to

57:52

get at why phage have

57:54

them. What

57:57

they used as their model system in this

57:59

study was a bacteria

58:02

phage, a double-stranded DNA tailed

58:05

bacteria phage, a myophage. So

58:08

this has a contractile tail and it's

58:10

T4-like. It's very large actually.

58:12

The head size itself is quite large

58:14

and the genome is about 350 kilobases

58:16

in size. The

58:21

host for this phage, well the name of

58:23

the phage is 2.275.0. So

58:27

we'll just call it the phage from here on

58:29

out. Why? It's such a creative name. Well,

58:33

the reason it's so creative is because it

58:35

comes from the Nahont collection which

58:37

is something that

58:40

these investigators took 27, I

58:42

think, 2200 samples from

58:46

a particular location in a bay

58:49

in Massachusetts from a marine environment.

58:52

So they enriched for many different

58:54

kinds of Vibrios and the

58:56

organisms that were living there and they also have a

58:58

bunch of phages. So maybe once

59:01

you get up to hundreds or thousands

59:03

you stop getting so creative with your

59:05

names. So I

59:07

have a question at this point because I

59:09

wondered more about this Nahont collection and

59:13

tried to follow up on the Kauffman 2014

59:15

reference which

59:18

seems to be truncated in the reference

59:20

section and maybe it's a

59:22

dissertation or something. It is a

59:24

dissertation. Anyway, something led me to

59:26

think that most of this collection

59:28

is of nontailed phages. That's right.

59:32

So I found

59:34

a little confusing. Yeah,

59:36

it turns out they're

59:39

called the autolikeviridae

59:42

or at least that's their taxonomic classification. They're

59:45

very strange, well very unusual.

59:47

They're underrepresented, I should say,

59:49

in the samples that we

59:51

have. They are

59:53

nontailed and they don't behave

59:56

like other phage. They're small

59:58

genome, very small genome. of

1:00:00

only about 10 KB in length. The

1:00:03

majority of what they got out of

1:00:05

this collection was those, but

1:00:07

there were also these tailed phage. Actually,

1:00:09

I knew about this because there was

1:00:12

a protein that I study that

1:00:14

had a hit to one of the phages

1:00:16

in the Nihant collection that wasn't one of the

1:00:18

autolikey viridae. Right. Okay.

1:00:23

So, the phage that

1:00:25

they were using, they present

1:00:28

the genome sequence. This

1:00:30

is where they show that the

1:00:32

tRNA genes are clustered in

1:00:34

about a 50 KB

1:00:37

region. So, I say clustered,

1:00:39

but a 50 KB region could be the size

1:00:41

of an entire phage. And

1:00:43

there are 18 of these tRNA genes

1:00:45

that are complete. There are several more

1:00:48

that seem to be interrupted by introns,

1:00:50

but the 18 that are complete seem

1:00:52

to be able to recognize about 15

1:00:54

different amino acids, which is a pretty

1:00:56

high representation of recognition.

1:01:00

And they show... This is sort of off

1:01:02

topic, but what are these introns?

1:01:05

I was going to ask the same

1:01:07

question. Who knows? So,

1:01:10

I put a question on my concept

1:01:13

test, which is before class. It's just

1:01:15

for extra credit. And it's a trick

1:01:17

question because it's about splicing. And

1:01:20

one of the choices is something about...

1:01:22

Anyway, what they have to do is

1:01:25

Google or something and figure out that

1:01:27

some tRNAs do have splicing. That's right.

1:01:30

So... In... It's in

1:01:33

Eukaryotes. In prokaryotes? Well,

1:01:35

this is what I know is from work

1:01:37

in John Abelson's lab. But...

1:01:40

Yeah. In prokaryotes? Yeah.

1:01:43

There are self-splicing... I don't know a

1:01:45

lot about it, but there are several

1:01:48

kinds of examples of

1:01:50

interrupted genes where

1:01:52

those regions are excised to form

1:01:54

the complete gene. Okay.

1:01:58

All right. Yes. Good

1:02:00

question there. So

1:02:04

they show that they've annotated the genome,

1:02:06

and they first look at the transcriptional

1:02:09

profile over time of the phage

1:02:12

genome. And I think that they

1:02:14

display it really nicely. It's very

1:02:16

common for these kinds of bacteriophages

1:02:19

to have a timed

1:02:22

transcriptional program. They have early genes

1:02:24

that come on relatively immediately, and

1:02:27

then they have middle genes

1:02:29

and late genes. And the early

1:02:31

genes tend to encode things that

1:02:33

are necessary for transcription

1:02:36

and for some

1:02:38

translation, taking over the host,

1:02:40

defense proteins, things like that.

1:02:43

Middle genes are much more about replication,

1:02:45

and then the late genes tend to be

1:02:48

the structural genes and the exit genes. And

1:02:50

so you can see that very well. These T and

1:02:53

RNA genes are mostly in the middle genes

1:02:55

where you'd be ramping up translation. Then

1:03:00

what they do is they look at

1:03:03

the codon usage bias

1:03:05

in both the host system

1:03:07

as well as the phage

1:03:09

system. Now they tell us

1:03:12

that through this particular phage, they know they

1:03:14

have at least two different hosts in the

1:03:16

lab that it can infect. But an important

1:03:18

point is that even though it can infect

1:03:20

those hosts in the lab, we don't know

1:03:22

if that's its preferred host in the environment.

1:03:26

And they see, they

1:03:28

have several different ways of plotting

1:03:31

the codon usage bias between

1:03:34

the phage genome and the host genome.

1:03:37

And they show that

1:03:39

although the tRNA pool

1:03:41

that the phage encodes

1:03:43

is somewhat overlapping with

1:03:45

the host tRNA pool, it's

1:03:48

not a perfect overlap. There is

1:03:50

some preference one way or the

1:03:52

other. And in both

1:03:54

cases, the genome

1:03:57

is sort of tailored to the tRNA pool.

1:04:00

that's available. And

1:04:02

they also look at the gene

1:04:06

expression over time and

1:04:09

plot that next to early

1:04:12

versus late genes. And they

1:04:14

see that there is a

1:04:17

slight increase in the likelihood

1:04:20

that the phage genes are

1:04:22

biased towards phage tRNAs that

1:04:24

are available the later in

1:04:26

infection that it is. So

1:04:30

when we're talking about phage genes

1:04:32

being biased towards phage tRNAs or

1:04:34

host versus host, we're talking about

1:04:36

a codon usage bias, is that

1:04:38

correct? Correct. Okay.

1:04:42

Yes. This is actually something that comes up a lot

1:04:44

when people are doing

1:04:46

something like protein expression. Most

1:04:49

people who express proteins over

1:04:51

express them in E. coli. And if you're

1:04:53

trying to take a gene from, say, a

1:04:56

bat and you want E. coli

1:04:58

to express it, you have to make sure

1:05:00

that the codons that are being used to

1:05:02

produce that protein in the mRNA are going

1:05:04

to be recognized and efficiently translated in E.

1:05:07

coli. So this is something that is done

1:05:09

quite a bit outside of phage biology

1:05:11

and just biology in general for us

1:05:13

to be able to make things in

1:05:15

the lab. So

1:05:17

was the spike recoded to

1:05:20

work in whatever

1:05:22

cells in humans? Yeah, the mRNA, because

1:05:24

it's a bat virus, right? I

1:05:27

don't know. We have

1:05:29

to find out. Let's see. I

1:05:32

would... We're saying with... When you say with

1:05:34

the spike, I mean with SARS-CoV-2? Yeah,

1:05:36

the mRNA vaccine. Right. No,

1:05:38

I think that they probably used sequence

1:05:41

from a human... Isolate. Isolate.

1:05:44

So if it had

1:05:47

to be codon-optimized, nature may have

1:05:49

started the codon optimization process. Or

1:05:52

may not, or maybe not. I don't know how different it is,

1:05:54

because we don't have the original bat, right? So we don't know.

1:05:56

I know. Jolene, I already

1:05:58

talking about this, made me want to... know about bat

1:06:00

codon usage, but I'll have

1:06:02

to look at it later. I

1:06:05

want to do a really simple thing to

1:06:08

maybe bring in some of the people

1:06:10

who have less background, which

1:06:12

is that the genetic code

1:06:15

is redundant. Oh, yes. So

1:06:17

you can have several different triplet codons,

1:06:19

several different ways of coding for a

1:06:22

given amino acid. There's usually at least

1:06:24

two, except in one case, nuzmothionine. In

1:06:28

some cases, there are four, and in

1:06:30

some cases, there are six different ways

1:06:32

of coding for one amino acid. So

1:06:36

what we're saying is that

1:06:39

some organisms may use a different

1:06:42

subset of the redundant

1:06:44

codons to encode their proteins, and

1:06:46

the question becomes, is

1:06:48

the tRNA pool matched to that

1:06:51

codon bias or

1:06:53

not? And what you're saying

1:06:55

here is that it appears to be at

1:06:57

least to a limited extent in this case.

1:07:00

Is that right, Dolly? Yeah, that's right. And

1:07:03

actually, it's important to mention here as

1:07:05

well that not only is there some

1:07:07

flexibility in the genetic code, there's also

1:07:09

a significant amount of

1:07:11

tRNA modification that allows

1:07:13

a tRNA to recognize more than

1:07:16

one codon. By

1:07:18

the way, the mRNA

1:07:21

vaccines were extensively codon

1:07:24

optimized. Is that right? Yeah.

1:07:27

I found a nice article which details it, so. Cool.

1:07:29

I think that would make sense

1:07:32

from a biotechnological cost perspective. Yeah.

1:07:36

So, Jolene, I have a question. I

1:07:39

don't know if you know the answer to this

1:07:41

or not, but maybe somebody does. As

1:07:45

we'll probably get into, and you've already alluded

1:07:47

to, these tRNAs are heavily post-transcriptionally

1:07:50

modified. So, you

1:07:52

make a transcript, and then there's all

1:07:55

sorts of modifications that are made, and

1:07:57

there's enzymes involved in all those. So,

1:08:04

those enzymes, are

1:08:07

they quite specific for a

1:08:10

given tRNA? In

1:08:12

other words, for each different

1:08:14

tRNA that might be specific for a

1:08:16

codon, is there a

1:08:19

whole battery of enzymes specific for that tRNA

1:08:21

or are there modification enzymes that work on

1:08:23

a number of different tRNAs? I

1:08:26

actually don't know. I

1:08:29

don't know. Okay.

1:08:32

Some aficionado out there needs to send

1:08:35

us some email. I do remember seeing

1:08:37

this part where they talk about the

1:08:39

CCA modification that the host

1:08:41

carries an enzyme that can do that

1:08:44

and the phage carries its own CCA

1:08:46

modification. Each time

1:08:48

they call it a protein and I just was like,

1:08:50

don't you mean enzymes? But yeah. So,

1:08:53

that's where I was going with this, okay? If

1:08:56

you're a phage and you're going to

1:08:58

make a tRNA that matches your codon

1:09:01

bias, okay, and it's different than the

1:09:03

host, does that mean that you need

1:09:05

to have your own modification – you

1:09:07

can't carry all the modification enzymes, right?

1:09:09

No, no, they definitely do not carry

1:09:11

all the modification enzymes. They might carry

1:09:14

one or a few, but for

1:09:16

the most part they're relying on the host enzyme complement.

1:09:19

Right. Okay. So,

1:09:24

they do see some codon

1:09:27

usage bias by looking

1:09:29

at the tRNA pool here. And

1:09:33

I should mention that they actually

1:09:35

did do some tRNA sequencing. So

1:09:37

that's not something

1:09:40

that's always covered really well and they

1:09:42

were able to get data on the

1:09:45

tRNAs themselves. And

1:09:47

that's a really useful piece of information that they

1:09:49

have here. And

1:09:52

the next thing that they look at is

1:09:54

the tRNA –

1:09:57

sorry, the codon usage. The

1:09:59

tRNA – a code on usage

1:10:01

availability across both the phage and

1:10:03

the host. And

1:10:06

they have plotted them across

1:10:09

essentially all the possibilities. And

1:10:11

the nice thing that you see is that the phage, tRNAs

1:10:14

in this case, even though there are only 18 of

1:10:17

them, they are spread out pretty

1:10:19

widely. And that is

1:10:21

somewhat similar to what you see for the

1:10:23

host. And I guess I was a little

1:10:25

surprised to see this because there's

1:10:27

not representation of all of them in the

1:10:30

host. And there's just one

1:10:32

or two of each tRNA in

1:10:35

the host. And I

1:10:37

think that's reflective of the fact that

1:10:39

with both modification and wobble, you can

1:10:41

still recognize other codons with

1:10:43

an individual tRNA. So

1:10:46

they see that the tRNAs in the

1:10:48

phage, there are three of them that are

1:10:50

not present in the host. And

1:10:52

what's in the host, there are nine of them that

1:10:55

are not present in the phage. And

1:10:57

then they look at the odds

1:10:59

ratio of the codons

1:11:02

in the phage compared to the

1:11:05

host. And what they

1:11:07

show that there are some that

1:11:10

the codons cannot be recognized

1:11:12

by the phage tRNA using

1:11:15

even consideration of wobble rules.

1:11:18

But there is a large

1:11:20

variation in the codon odds

1:11:22

ratio across the

1:11:24

different tRNAs that are

1:11:26

found in both the phage and the host.

1:11:32

The next thing that they look at is a

1:11:34

time course of the phage

1:11:36

infection over time. And so

1:11:38

they actually did plaque assays.

1:11:42

And they see, as you would

1:11:44

expect, they see a period at the beginning

1:11:46

of the infection when there's no change

1:11:49

in the infectious titer.

1:11:51

They see it go up. And

1:11:54

then they see a precipitous drop at a

1:11:56

time when there is secondary infection of hosts

1:11:58

in the culture. have not been

1:12:01

infected initially. So

1:12:03

I had some, we'll get it further

1:12:05

into this, but they're talking about, so

1:12:07

that means there's some uninfected cells in

1:12:10

the country. Right. I

1:12:12

could not find a multiplicity of infection. I did. It's

1:12:15

in the supplemental. You did. No, it's okay.

1:12:17

No, it's okay. Go ahead. So

1:12:20

at least they talk about using an

1:12:23

MOI of one to eight for the

1:12:25

supplemental figure 12 and supplemental figure 14.

1:12:28

But that is quite

1:12:30

a wide range of uninfected

1:12:32

cells. With an MOI of one, you'd have 37%.

1:12:36

And for an MOI of eight, you'd have 0.03% uninfected.

1:12:41

So it's weird to me that they

1:12:44

did not specify what percentage,

1:12:46

but for this figure four is what

1:12:48

I think you're talking about. Before it

1:12:51

even gets to A, it says approximately

1:12:53

a tenth of the hosts remain uninfected

1:12:55

and begin to grow again. A

1:12:58

second round of infection starts after 90 minutes.

1:13:00

But it's like, why didn't

1:13:02

they just tell us the MOI? And

1:13:06

why did they use a range of MOI from one

1:13:08

to eight? But

1:13:11

these are questions that we don't know yet. If

1:13:14

I'm setting up an experiment like this, the first

1:13:16

and most important thing I think about is MOI.

1:13:19

Because I don't want any uninfected cells left

1:13:22

around unless I'm doing something special. And it

1:13:24

makes me wonder if there aren't some maybe

1:13:26

issues with either growing or quantifying a phage.

1:13:28

I mean, usually in a

1:13:32

situation, sometimes you can't grow

1:13:34

enough phage or quantify

1:13:37

it accurately enough to

1:13:39

set it up any better than this.

1:13:42

Or at least with some animal viruses, it's that way.

1:13:44

But man, okay.

1:13:47

Yeah, with

1:13:49

what we would call this a jumbo phage,

1:13:51

and with jumbo phages, they can be a

1:13:53

little bit tricky to grow. They

1:13:56

don't have huge birth sizes and they don't make as

1:13:58

nice of plaques. if

1:14:00

you don't adjust the settings, no, not

1:14:03

the setting, the parameters that you use in your experiment.

1:14:06

I like setting, I like setting. In

1:14:08

particular, the concentration of top auger to

1:14:10

allow the jumbo fish to diffuse throughout

1:14:12

the plate. But they

1:14:15

did have some tiring, so

1:14:17

it's possible that they just weren't able to

1:14:20

get a high titer. It's also possible that

1:14:22

this particular Vibrio, which is Vibrio

1:14:25

cyclotrophicus, does some

1:14:28

things that make it difficult to

1:14:30

actually get a complete adsorption. Because

1:14:33

it also matters what conditions

1:14:35

you use to mix the

1:14:37

phage and the bacteria to get

1:14:39

them to be infected. Reaching

1:14:43

way back, I can recall even

1:14:45

doing an experiment where I quantified

1:14:47

the survivors. Okay.

1:14:52

And work backwards to figure

1:14:54

out what your effective multiplicity actually

1:14:57

was. Okay.

1:15:00

If you're having these sorts

1:15:02

of difficulties, you might have to

1:15:04

resort to that. Yeah. That's

1:15:06

true. That's

1:15:10

only reaching back 55 years. That's

1:15:16

impressive, I just have to say. Sometimes,

1:15:18

I'm like, what did I do this morning? I might be making

1:15:20

it up. I might be making

1:15:23

it up, okay? I don't think so. Which part,

1:15:25

the 55 years is it? I

1:15:27

don't know, the 55 years is real. So

1:15:31

with this infection profile that they

1:15:33

give us, so that we

1:15:35

can see generally that the phage are infecting

1:15:39

the host cells from about zero to

1:15:42

at least 90 minutes. They do QPCR

1:15:44

over time, so they told us that

1:15:47

this is a T4-like, so they're not going to be

1:15:49

able to do that, so they told us that this

1:15:51

is a T4-like phage, and T4

1:15:53

is the one that I mentioned will come into the

1:15:55

cell and degrade everything and shut it all down. And

1:15:58

they wanted to know if this is occurring. with the

1:16:00

particular phase that they're studying. And

1:16:03

they use qPCR to measure host,

1:16:07

GROEL, and CTP synthase

1:16:09

over time. And they

1:16:11

also measure a phage GROEL and the

1:16:13

major capsid protein. So you

1:16:15

definitely see for the phage gene

1:16:18

expression, it goes up when

1:16:20

you would expect, and then it goes down

1:16:22

a little bit before

1:16:25

the time that you see

1:16:27

the viral particles being released.

1:16:30

And with the host gene expression, you

1:16:32

see it goes down and then might

1:16:35

go up a little bit and

1:16:37

then go down again. To

1:16:42

me, did not look dramatic until I realized it was

1:16:44

a log scale. Correct. Yes.

1:16:49

They then also look at

1:16:51

the tRNA expression, normalized

1:16:54

over time. And

1:16:56

when you look at the phage

1:16:58

infection, I mean in the phage

1:17:00

tRNA pool, you see it goes up

1:17:03

and it is peaking much

1:17:05

earlier than you see virions released, which

1:17:07

makes sense because you would need to be

1:17:09

using them when they're

1:17:11

making their structural genes to produce virions and all

1:17:14

of the other things that it's making in that

1:17:16

350 KB genome. And

1:17:19

they see a little bit of reduction

1:17:22

in tRNA levels, but

1:17:25

then it goes up. And this is the part

1:17:27

where they were talking about uninfected cells

1:17:30

growing. I'm

1:17:32

not entirely sure that that's the

1:17:34

most satisfactory explanation, but it's

1:17:37

one potential explanation. It's

1:17:40

also possible that maybe

1:17:43

the tRNA pool is recovered. But

1:17:48

they also say that the reason you see a

1:17:51

second reduction after time is

1:17:53

because there is a second round

1:17:55

of infection. And

1:17:57

that would indicate that those uninfected cells

1:18:00

cells were susceptible, they just didn't

1:18:02

have phage absorbed for whatever reason

1:18:04

at the beginning of the experiment.

1:18:09

And the idea too, at least to

1:18:11

me, was that then having

1:18:14

some uninfected cells meant

1:18:17

something about the tRNAs that were around,

1:18:19

although I guess they would still all

1:18:21

be within a cell. So maybe it

1:18:23

doesn't matter. Except

1:18:26

that when you do these sequencing experiments,

1:18:29

right, or when you're assessing tRNA, you're

1:18:31

looking at both tRNAs, so you're looking

1:18:34

at the average of the infected and the

1:18:36

uninfected cells, you can't discriminate against them between

1:18:38

them. Yes,

1:18:40

that's right. It's

1:18:42

interesting that the phage tRNAs themselves

1:18:45

are significantly different enough that you

1:18:47

can tell them apart between the host and

1:18:49

the phage. And that's partly

1:18:51

due to the fact that some of

1:18:54

the tRNAs appear to

1:18:56

be host-derived, but many of

1:18:58

them actually have very little similarity

1:19:00

to host tRNA genes. So

1:19:05

then the next thing that they

1:19:07

look at is whether

1:19:09

or not, I think

1:19:11

I kind of neglected to really

1:19:14

emphasize in one of the earlier figures, but

1:19:16

they see that there is more codon usage

1:19:20

bias in the late genes compared

1:19:22

to the early genes. And

1:19:25

that makes the most sense because that's

1:19:27

when there would be the least amount

1:19:29

of host tRNAs available. So

1:19:31

what they looked at were, if the

1:19:34

phage has all this number of

1:19:36

tRNAs, how likely is it that

1:19:39

these were acquired randomly or that

1:19:41

they were acquired kind of as

1:19:43

a unit together from

1:19:45

a different source? And

1:19:49

they quantify anticodon

1:19:52

entropy, and

1:19:54

they use that, which

1:19:56

I'm not entirely sure how to

1:19:58

explain, but they use that as

1:20:00

a measure of anticodon diversity.

1:20:03

So as we've mentioned, the

1:20:05

tRNA has the anticodon, which

1:20:08

will recognize the codon in the

1:20:10

mRNA. And when they

1:20:12

plot this, they see that the

1:20:14

tRNAs in the phage have a

1:20:16

much closer pattern to what would

1:20:19

be randomly acquired than what you

1:20:21

see in the host where the

1:20:23

tRNAs appear to be more

1:20:25

similar to each other next to each other in

1:20:28

the genome. If you have one tRNA gene

1:20:30

and you have a duplication, then you'd expect the tRNAs

1:20:32

next to each other to be similar. And that's

1:20:35

what they see for the host. Or

1:20:38

that's what they would see for host. And when

1:20:40

they look at host tRNAs, they're

1:20:43

also looking at

1:20:45

the likelihood that there is an – no.

1:20:47

So when they – we look at tRNA

1:20:51

expression levels, they saw that they are not

1:20:53

all at the same level in the host.

1:20:55

Some of them are higher expressed than others.

1:20:58

And they thought if all the tRNAs in

1:21:00

the host cell are being degraded kind of

1:21:02

at the same rate, then the ones that

1:21:04

were higher initially might still have some stuck

1:21:07

around later in infection than

1:21:09

the ones that were at lower levels

1:21:11

to begin with. So when

1:21:13

they look at the tRNA expression

1:21:16

of the host tRNAs that

1:21:18

don't have any analog

1:21:21

in the phage genome, those do seem to

1:21:24

be at higher expression levels

1:21:26

compared to the ones that have

1:21:28

an analog in the phage genome.

1:21:31

They say that this suggests that

1:21:33

the host tRNAs that are higher

1:21:35

expression are better able to support

1:21:38

phage translation. Okay.

1:21:42

So thinking about what this

1:21:46

means overall, the degradation of host tRNAs seems to be causing the phage

1:21:48

to need to have a way to maintain its

1:21:50

translation over time during the phase. the

1:22:00

infection. The phage has enough diversity in

1:22:02

this case of tRNAs to be able

1:22:04

to do that and that seems to

1:22:07

be giving it a higher advantage than

1:22:09

it being able to code

1:22:11

on, bias its

1:22:14

genes one way or another or be

1:22:16

able to sequester most of the translation

1:22:19

machinery. And this is

1:22:21

probably an effect of the fact that

1:22:23

this particular phage is shutting down

1:22:25

the host translation. There's no bacterial mRNA

1:22:27

left by the time you're 30, 60

1:22:31

minutes into an infection. There's not really

1:22:34

a competition for translational machinery at that

1:22:36

time, but the more translation that can

1:22:38

occur of the phage mRNAs that are

1:22:40

present, the better it will be for

1:22:42

phage production. And

1:22:46

this is therefore kind of

1:22:48

one possible, maybe more

1:22:50

likely reason that phage would

1:22:52

carry tRNAs in

1:22:54

addition to the possibility of

1:22:56

expanding host range and allowing

1:22:59

it to have better translation of the late

1:23:01

genes when needed. Are

1:23:05

host tRNAs synthesis decreased?

1:23:09

Are host tRNA synthesis decreased

1:23:11

later in infection? In

1:23:14

this case, yes, because the chromosome is

1:23:16

degraded and there's – they won't

1:23:18

be making any more. But

1:23:21

the phage doesn't have all the tRNAs,

1:23:24

so how does it deal with that? So

1:23:26

the ones that the phage doesn't encode

1:23:29

seem to be higher expression in

1:23:31

this particular cell. And

1:23:34

they persist? They persist long enough to be useful.

1:23:37

And so the phage basically hasn't had pressure

1:23:39

to pick up versions of the – Right.

1:23:42

Exactly. So for phage that encode fewer

1:23:45

– or there are

1:23:47

phage that encode fewer tRNAs, right? So

1:23:50

would you say that

1:23:52

– What's the advantage for that? Yeah,

1:23:55

what's that? I think

1:23:57

it's going to depend on its mode of

1:23:59

replication. If it comes in

1:24:01

and trashes the

1:24:03

cell, then it's probably going

1:24:05

to need more tRNAs. If it comes

1:24:07

in and doesn't trash the cell as

1:24:09

bad, then it won't need as many. Yeah,

1:24:16

but I don't understand. So if you

1:24:18

encoded two tRNAs, maybe that's

1:24:20

more for making

1:24:22

better, more efficient translation then, because

1:24:25

if you don't trash the host, then it's not

1:24:27

an option, I guess. Could it also be related

1:24:29

to what your host's basal

1:24:32

tRNA level is? If

1:24:36

you are infecting a host that has

1:24:38

so much tRNA, then you have

1:24:40

a lot of room to trash the cells before

1:24:42

you are out of tRNAs. Yes.

1:24:46

And it turns out that

1:24:48

in some other phage groups

1:24:50

where these have been looked

1:24:52

at, it does seem that

1:24:54

the tRNA profile is more

1:24:57

closely correlated to host

1:24:59

range expansion than

1:25:01

to allowing

1:25:04

it to replicate for longer. I'm

1:25:08

thinking about T7. I

1:25:10

don't think T7 has any tRNAs. It

1:25:13

trashes the host cell, I mean,

1:25:16

like completely, instantaneously.

1:25:18

It's also really

1:25:20

quick. I think it's

1:25:22

done with its business before the host

1:25:24

even knows it's dead. Yeah. How

1:25:28

does it do that? I

1:25:32

couldn't tell you. I don't know. It

1:25:34

doesn't shut everything down because it

1:25:37

doesn't need to. It's

1:25:40

so quick that

1:25:44

it also has a smaller genome. Yeah,

1:25:47

it's like what 40kD, something like that.

1:25:51

So there's one

1:25:53

experiment that I was thinking about throughout this.

1:25:56

It's not a small experiment. It would probably be a

1:25:58

whole other paper. Throughout this,

1:26:00

I was thinking, okay, when

1:26:03

they're talking about tRNAs that are present, they're

1:26:05

talking about, first of all,

1:26:07

both infected and uninfected cells as

1:26:09

we discover, which is

1:26:11

one thing, but you

1:26:14

don't know what tRNAs are actually

1:26:16

in use. So

1:26:18

I'm thinking ribosome profiling,

1:26:21

and I'm wondering if you could do that.

1:26:23

If you could isolate, you should be able

1:26:26

to, it seems to me, isolate polyzomes throughout

1:26:29

infection, okay, and

1:26:32

look at what tRNAs are present on

1:26:34

the polyzomes and see

1:26:36

if that's really a

1:26:38

reflection of what the pool of tRNAs is in or

1:26:41

whether there are certain ones that are selected

1:26:43

out for host versus

1:26:46

phage RNAs. So

1:26:49

they tried to address this

1:26:51

with their tRNA sequencing because

1:26:55

they said that if they're expressed

1:26:57

and modified, then they're likely being

1:26:59

involved in translation. Now that's not

1:27:01

the same as catching them in

1:27:04

the act, but since

1:27:06

the mRNAs are so long,

1:27:08

you'd probably have to have really high

1:27:10

coverage of ribosomes to get an accurate

1:27:13

picture. Yeah,

1:27:16

I wasn't even aware that you could

1:27:20

use ribosome profiling to look at

1:27:22

the tRNAs. I don't

1:27:24

know. I just think that I don't even know if that's true.

1:27:28

You're just basically looking to see

1:27:30

what the mRNAs are there. I

1:27:34

mean, you can isolate the polyzomes and

1:27:36

sequence everything, right? Right. Are

1:27:41

there any DNA, you

1:27:44

carry out a DNA viruses that trash

1:27:46

the host genome, Kathy or Rich?

1:27:50

I think the idea is that plax viruses do,

1:27:58

but that literature is aimed. When

1:28:01

years are. Easy

1:28:03

even or even any. Okay

1:28:05

so that when when the

1:28:07

literature suggests this or is

1:28:09

this, I. Like. Disappearance of

1:28:11

the chromosome or dissolving of the

1:28:14

nuclear arms. Hello sir, I

1:28:18

don't even know what the evidence was. To.

1:28:21

I have it in my head that

1:28:23

part's ours is trashed the genome day,

1:28:25

but I can't I can't remember what

1:28:27

the evidence was and one of my

1:28:29

old if there's any old park service

1:28:32

buddies listening out there. Go

1:28:34

ahead and. That

1:28:37

says, they reared for a year. A

1:28:41

non giant fires to encode tyrannies.

1:28:43

I. Heard

1:28:47

him for many years her what

1:28:49

it was in a situation where

1:28:51

the universe scampi to the trash

1:28:53

those now not. And not.

1:28:56

By. Doing things to the hosts dna that

1:28:58

I'm aware as. See.

1:29:02

Now. They

1:29:04

do things with messenger on a transport.

1:29:06

Things like saw. So.

1:29:10

There are some. Are

1:29:14

some viruses that. Tyrannies

1:29:17

internet not Sages is they're

1:29:19

super. They're pretty rare. And

1:29:22

wonder if this is you know, for the

1:29:25

giant viruses that encodes. Some.

1:29:27

As some of them include all the t

1:29:29

are in a straight am like to Penn

1:29:31

virus. I wonder is

1:29:34

it is they are trashing the.

1:29:37

Zoo sailor Dna or for if it's

1:29:39

or some other reason because obviously the

1:29:41

could be multiple reasons for including tyrannies

1:29:43

my son's mates. And indeed first

1:29:45

needs to be to do the

1:29:47

experiment they did where he deleted

1:29:49

them out to see and how

1:29:52

much that was delirious to those

1:29:54

viruses These has. I

1:29:56

think some of the idea as the some of those die of

1:29:58

viruses is also that they're sitting up. Lot of.

1:30:01

Whose dna in other reason?

1:30:03

So how different are there

1:30:06

tyrannies from hosts? tr days

1:30:08

and how necessary. Are they

1:30:10

for the virus replication? Another

1:30:13

thing that's different between the you carry out

1:30:15

a close in the pro carry out a

1:30:17

coast is that the the and marinades and

1:30:19

bacteria are very. Short lived there,

1:30:21

not cops. They're not. Tailed.

1:30:25

So once the once the

1:30:27

Dna is no longer being

1:30:29

transcribed. It's. Gonna just depend

1:30:31

on the t are in a lifetime and. Whether or not

1:30:33

the t are in his of the hosts are being

1:30:35

pleased are. So

1:30:39

Marine Herpes Virus Sixty

1:30:41

Eight. Codes

1:30:44

Eight. P Earnings.

1:30:48

That's the only non giant virus is

1:30:51

at least in this review papers and

1:30:53

it would have the abbreviation Mhp Sixty

1:30:55

Eight which is. Also. Like

1:30:57

the via he busy like.

1:31:00

Mouse. Be as

1:31:03

successful. As it

1:31:05

into one of the be other people use as

1:31:07

a model and myself as as only one on

1:31:09

this table everything else is a giant virus or

1:31:11

sage and. Herpes viruses have some

1:31:13

similarities. A Sage. Braids,

1:31:19

Didn't we do a paper on that? I

1:31:21

feel like I'm remembering a paper on

1:31:23

that. Has existed. As

1:31:25

said, during a long time ago, I

1:31:27

can remember five years of roots. You

1:31:30

remember? Fifty Five. The

1:31:32

saw him or her Murder Twenty Twenty

1:31:34

Review on. Or

1:31:37

for you shut off of. Her.

1:31:39

Shut off of endorses. Of

1:31:42

by Parker since from somewhere

1:31:44

during for when he was

1:31:46

believes your store or so

1:31:48

in terms of, and I

1:31:50

talks about shutting off. Messenger.

1:31:53

On a degrading pro. Talks

1:31:56

about shutting off or

1:31:58

host dna synthesis. But

1:32:01

doesn't shut off. Doesn't talk about

1:32:03

degradation of the hooking Romeo. so.

1:32:09

I wonder if as also really

1:32:11

into the idea that you probably

1:32:13

are trying to hide from dna

1:32:15

damage responses and stings like if

1:32:17

it chose this. right?

1:32:23

That's interesting. The

1:32:27

multicellular organisms priestesses.

1:32:34

Heard them school. I like Sensory for an

1:32:36

awesome to join my love doing. I love

1:32:38

doing this thread stuff very very a lot

1:32:40

of fun May. I say like I learned

1:32:42

so much. I'm. Definitely.

1:32:45

Excited for our are some of this to

1:32:47

come up and Microliter and I think I

1:32:49

in the future. I'm hoping to start doing

1:32:51

some of the papers that demonstrate. How a

1:32:53

lot of the pro carry Arctic?

1:32:55

What? We might call any immunity

1:32:58

type. proteins are similar to you

1:33:00

carry out acts. Ah proteins and

1:33:02

that's is one of the things

1:33:04

that I think this is. This

1:33:06

is reminding me of how many

1:33:09

similarities there are. I

1:33:12

have. I would love to have

1:33:14

a whole long conversation getting out

1:33:17

with you about sea bass be

1:33:19

says I think says there are

1:33:21

some. I. Have

1:33:23

a lot of questions in thinking about

1:33:25

see gas of and mammalian cells are

1:33:27

Sometimes I read that some of the sea

1:33:30

bass papers at I'm like wait we

1:33:32

react vacancies south style of yes I

1:33:34

do into geek out about I say

1:33:36

there are three prince every week about

1:33:38

the next anti save his defense system

1:33:40

said it's. Tyranny clear hid sars

1:33:42

recognition or him so forth. So

1:33:44

the summer to com so Julie when

1:33:46

was the north as you mentioned

1:33:48

this briefly but. Where.

1:33:51

Did these. Tyrannies in the siege

1:33:53

two know come from to enough. Wealth.

1:33:56

As. If we

1:33:58

If we if. That I've done

1:34:00

some analyses. There's one really good system

1:34:03

for doing this and that is where

1:34:05

we have high representation of Sages the

1:34:07

Michael Bacteriophage Because there have been so

1:34:10

much, there has been so much sampling

1:34:12

by students that it fades discovery and

1:34:14

they were able to analyze many many

1:34:17

like over here. If it's genome, send

1:34:19

me so he can look at the

1:34:21

distribution of tyrannies there and compare them

1:34:24

to the host. And there are some

1:34:26

that are very similar to the hosts.

1:34:29

Are some that are somewhat similar to the

1:34:31

house, and are some that. Are.

1:34:33

Not similar to the host and all, but

1:34:35

they're still recognizable as tyranny. And

1:34:38

I don't actually have a good handle on

1:34:40

the numbers with the distribution. Among those three

1:34:42

categories. But I think that. They.

1:34:45

Come from where wherever tyrannies

1:34:47

come from. Because

1:34:49

the sage doesn't need them, the face

1:34:51

doesn't require them since the hosts provides

1:34:53

them. So. They think they're

1:34:55

probably mostly stolen. Unless

1:34:58

it's side and example of Total genome.

1:35:01

Reduction the other way. Or.

1:35:05

Slept. That the safety

1:35:07

of this is as good word as

1:35:09

to some pics. Of

1:35:12

the week. And. Six and

1:35:14

is not here. We don't have him for so

1:35:16

a steak brands first with you have for us

1:35:18

arm. I have a story actually

1:35:20

learn about yesterday from my parents.

1:35:23

Who said they spend part of their

1:35:26

year and season and they just are

1:35:28

taking our search trip to Death Valley.

1:35:30

and and it turns out that right

1:35:33

now in Death Valley there is a

1:35:35

leak. Because of

1:35:37

the levels of rain arms than it

1:35:39

actually happened in sort of October November

1:35:41

and wasn't it dried up at it

1:35:43

has actually just come back on the

1:35:45

used to be a lake in this

1:35:48

particular spot in the place to seen.

1:35:52

That there is now out up on

1:35:54

late here as called late manly and

1:35:56

people are tie acting up arms and

1:35:59

people are walking. I got a picture of

1:36:01

my dad waiting in this lake yesterday. And

1:36:04

so I looked into more of this.

1:36:06

I didn't realize until I started reading

1:36:08

about it. You know, at first I just

1:36:10

saw some pictures of my dad waiting and waiting

1:36:12

at Death Valley and that seems nice. And then

1:36:15

I actually sort of started reading a little more

1:36:17

and found out that it had, it has been

1:36:19

dried up since the place to see and is

1:36:21

now back because of all the

1:36:23

rain. And lots of

1:36:25

people are flocking to Death Valley right now to see

1:36:27

this rare event. And I thought that was pretty cool.

1:36:30

That's very cool. I like that. I

1:36:34

wonder if it will rehydrate

1:36:36

and reactivate some Pleistocene critters.

1:36:40

It's a good question. It

1:36:42

sounds like it's a very saline area or

1:36:44

there sounds like there's a lot of salt

1:36:46

deposits in that area that I was talking

1:36:48

a little bit about. It's not

1:36:51

really clear. And I can't say that,

1:36:53

you know, the idea that lakes that

1:36:55

have been gone since the Pleistocene coming

1:36:57

back is necessarily a great thing to

1:36:59

hear about, but it is kind of cool.

1:37:03

Hmm. Neat. Kathy, what do you have

1:37:05

for us? I have something

1:37:07

that comes from Johns Hopkins that's

1:37:09

called the Practical Playbook for Addressing

1:37:11

Health Misinformation. And it's

1:37:14

free. You can download this PDF. It's

1:37:16

multi pages. And I

1:37:19

found out about it because there's a, I think

1:37:22

he's a business writer originally for the

1:37:24

LA Times and two different people will

1:37:26

occasionally send me his, I

1:37:29

guess their op-eds. His name is Michael

1:37:31

Hiltzik. And he referred to

1:37:33

this document

1:37:36

within his article. And

1:37:39

so it's about how you

1:37:41

can set yourself up for

1:37:43

success in combating

1:37:46

misinformation in science. And

1:37:49

so it talks about the fact that you need to

1:37:51

have a, probably have a team that

1:37:53

you can call on when you need to address a

1:37:55

rumor, how you can Connect

1:37:58

with communities, how it's important. To

1:38:00

know your audience set up a way

1:38:02

to figure out. What? Is actually

1:38:04

miss information mom and and so

1:38:07

forth and and then decision about

1:38:09

whether or not to address the

1:38:11

rumor. Anyway it just seemed like

1:38:14

at a really interesting thing and

1:38:16

and it also caught my eye

1:38:18

because sir I see no address

1:38:21

at a a C is going

1:38:23

to be a speaker about and

1:38:25

misinformation in science and so. It

1:38:29

just seemed like it. It went along well

1:38:31

with sat in front of a second be

1:38:33

Rfk Junior. Know Sundance a

1:38:35

faculty member from M I

1:38:37

T is A in Political

1:38:40

Science so it's gonna be

1:38:42

very different to hear that

1:38:44

School I know Roy is

1:38:46

this is great You know?

1:38:48

last week. I

1:38:51

ran across. A

1:38:54

bit of misinformation, which

1:38:57

was somebody spob circulating

1:38:59

a link to an

1:39:01

article. That is

1:39:04

listed in Pubmed. A

1:39:06

day that for all the world

1:39:09

looks like a perfectly legitimate. Article.

1:39:12

From a. It

1:39:14

would appear to be more well

1:39:16

known, but. Possibly

1:39:19

legitimate journal since it's and

1:39:21

pubmed. From

1:39:24

people who have credentials from

1:39:26

ah you know. Of.

1:39:29

Places. Like Stanford and etc.

1:39:31

And yet it basically.

1:39:34

The article basically said that

1:39:37

the are covered. Vaccines are.

1:39:40

A. Terrible. And

1:39:42

there's. Term side effects and

1:39:44

they don't work and always kind of

1:39:46

stuff the and so I went through

1:39:48

and I thought what is this Okay

1:39:50

and I looked up the authors. I.

1:39:53

Looked up the journal. I

1:39:55

did a little probing. It took me about

1:39:57

ten or fifteen minutes to figure out that.

1:40:00

You know this is nonsense. Okay,

1:40:04

And are nicer person I thought.

1:40:07

Look. At what I just did. Okay,

1:40:09

why can't everybody do that? Okay,

1:40:11

we ought to be able to teach people.

1:40:14

To. I'm. To.

1:40:17

To properly investigate things, to ask

1:40:20

questions and to figure out how

1:40:22

to figure out for themselves whether

1:40:24

or not is nonsense. Course, the

1:40:26

people who buy into this stuff

1:40:28

there is still in that time

1:40:31

they're doing it. They're looking for

1:40:33

evidence to support their conclusion rather

1:40:35

than looking to find out in

1:40:37

a what the evidence actually does

1:40:39

mean. Snitch

1:40:42

Barsky Junior says no vaccine

1:40:44

has ever been placebo controlled.

1:40:46

I mean, it's a blatant lie.

1:40:50

To send it's and people publish

1:40:52

these tasks and they believe I

1:40:54

just don't. Know. You can

1:40:56

make you can have all these articles

1:40:59

on addressing health and then you get

1:41:01

a very prominent person who just wrecks

1:41:03

it all by saying stuff like that

1:41:05

here in Aus. Easier Daffy Great! This

1:41:07

is so This is a really nice

1:41:09

resource and I'm really happy about the

1:41:11

keynote speaker. Who's who's who's? the President?

1:41:14

Oh the and of com and miss

1:41:16

gonna. Have any so was

1:41:18

at them as mint her idea right up. Good

1:41:21

for citizen. Playbook says that

1:41:23

it's designed to help public and

1:41:26

medical professionals and other health communicators

1:41:28

such as yourself. Lenders

1:41:31

and it also says

1:41:33

the actions assume the

1:41:35

following year. Institutional leadership

1:41:37

supports misinformation, management activities.

1:41:40

Staff. Members are available and have the

1:41:42

time and resources to carry out the

1:41:44

suggested accents. And you can

1:41:46

identify. Misinformation. It's circulating. Think.

1:41:49

The last one is the other

1:41:51

one's not as much like which

1:41:53

would he have for us. On

1:41:55

a lighter note. this

1:41:59

is in the Sci-fi, ARC

1:42:03

subcategory Star Trek. It

1:42:07

is a memoir called

1:42:09

Making It So by Patrick

1:42:11

Stewart, famous

1:42:13

as Jean-Luc Picard. And

1:42:16

my wife just stumbled on this in the

1:42:18

library and brought it home for me, okay?

1:42:20

And it sat around on my desk for

1:42:22

a while because I was busy reading other

1:42:24

stuff. Like what was

1:42:27

Alan's pick? Strange Bedfellows, right?

1:42:29

And so I

1:42:31

picked this up and I devoured it in about

1:42:34

two days. Because

1:42:37

many, I mean, if

1:42:39

you're a Trekkie, you don't care at all, okay? I

1:42:42

mean, if you're not a Trekkie, you don't care at all

1:42:44

and that's fine, okay? But the

1:42:47

Trekkies out there may

1:42:50

understand that Patrick Stewart was trained

1:42:53

as a, he was a Shakespearean

1:42:55

stage actor, okay? And

1:42:58

this traces his whole history

1:43:00

and it's fascinating. He was

1:43:03

raised in poverty, essentially, in

1:43:08

a mining town in Yorkshire, okay?

1:43:11

And but had an early

1:43:14

interest when he was in the

1:43:16

equivalent, you know, pre-high school years, an

1:43:19

early interest in acting. And he had some mentors

1:43:23

and teachers who recognized his interest

1:43:26

and encouraged him and

1:43:29

gave him some opportunities and he wound up getting into

1:43:31

an acting school in Bristol and

1:43:34

went on to do Shakespeare

1:43:37

and other plays in

1:43:41

regional theaters and was ultimately a staff

1:43:44

member, you know, of the Royal

1:43:47

Shakespeare Company for

1:43:49

at least a decade, I think. And

1:43:52

then got

1:43:54

an opportunity to do movies, do

1:43:57

a TV series and things took off.

1:44:00

I always thought that was an

1:44:02

odd transition, but he

1:44:04

makes it make sense. So

1:44:07

it's very good. I also didn't realize that

1:44:10

he did six X-Men films.

1:44:14

I need to watch those, I guess. I

1:44:16

would sort of blew them off, but never mind.

1:44:19

Oh no, he's very good. He's

1:44:21

X, right? As Professor X, yeah. I

1:44:24

once saw him in a hotel in Berlin in

1:44:26

the lobby. Cool. That

1:44:29

was pretty cool. I'll recognize

1:44:31

him right away. Jolene, what do you have for

1:44:33

us? I have a book. It's called

1:44:35

Thinking Like a Phage, which

1:44:38

is written by Mary

1:44:40

Ewell and illustrated by Leah Pantea.

1:44:42

I have it right here. And

1:44:45

this was a gift to me

1:44:47

by my PhD supervisor, Tuli Makupadai.

1:44:51

She gave it to me when I

1:44:53

defended and was leaving to go do

1:44:55

a postdoc on phage. And the thing

1:44:57

I really like about this book, it's

1:44:59

not just about bacteriophages, it's also about

1:45:01

archaeal viruses. It takes

1:45:04

the perspective of a

1:45:07

phage and what the

1:45:09

phage is encountering, what are the different

1:45:11

strategies that phages use. And it's kind

1:45:13

of like a survey book. At

1:45:16

the beginning, it has profiles

1:45:18

of various different phages

1:45:20

and viruses, and it gives

1:45:22

either an image, a micrograph

1:45:24

of it, or a drawing,

1:45:28

and it gives some facts.

1:45:30

And this kind of gives you a broad overview of the

1:45:33

different kinds of phages and infection cycles and

1:45:35

so forth that they have. And then it

1:45:37

goes through and uses lay

1:45:39

language and kind of takes some

1:45:41

artistic license about how to describe

1:45:43

a lot of personification, what's going

1:45:45

on inside the cell when there's an

1:45:48

infection going on. And I think it's

1:45:50

really nice because of all the pictures. So

1:45:53

if I flip through,

1:45:56

you can see there's lots of color

1:45:58

pictures. And it's... It's

1:46:00

a nice introductory book to things.

1:46:03

And I bookmarked here my favorite

1:46:05

one. It's about lysis, which kind

1:46:08

of demonstrates what's happening. The

1:46:10

phage are exploding open the

1:46:12

cells to release the

1:46:15

virions that are inside. Very

1:46:17

nice. All

1:46:20

right, by the way, before I do my pick, decks.

1:46:23

I was trying to remember where I heard

1:46:27

decks in a movie. It was a Star Wars movie.

1:46:31

OK, there's a scene where Obi-Wan

1:46:34

is talking to this

1:46:36

chef in a diner. Oh, yes.

1:46:40

And he says. Go to the article about the

1:46:42

poison darts. Yes. And

1:46:45

there's a line where Obi-Wan says, depends

1:46:47

on what deck? And

1:46:50

that's what I was coming into my head with,

1:46:52

deck the methazone. OK. I

1:46:55

figured it out. I actually had to Google

1:46:57

it to figure it out. But

1:47:00

I did. OK, my pick is the

1:47:02

science of leap year, which we

1:47:05

encountered this year. So

1:47:08

maybe everyone knows why there's a leap year.

1:47:10

But if you don't, this is a national

1:47:13

air and space museum, not NASA,

1:47:15

national air and space museum Smithsonian

1:47:17

article on why

1:47:20

we have leap year and the

1:47:22

leap day, the 29th of February. Because

1:47:26

it takes a little less time for Earth to

1:47:28

orbit the sun. So we have to add it

1:47:30

up. Every four years, we have an

1:47:33