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, a

0:17

special episode recorded on November 1st,

0:19

2023. I'm

0:22

Vincent Raconiello, and you're listening to the

0:24

podcast all about viruses.

0:28

Today, we are coming to

0:30

you from McGill University in

0:32

Montreal, Canada. And joining

0:34

me here on this kind

0:37

of cloudy and cold day,

0:40

Angela Mingarelli. Hello,

0:43

glad to be here. You're finally in my

0:45

city. I've been to your city so many

0:47

times now. We didn't say the weather though,

0:49

Vincent. Tell me, tell me what the weather was.

0:51

There were flurries a few minutes ago, and I

0:53

think it's around zero now when we woke up

0:55

this morning, very early at 6 a.m. It was

0:57

minus 2C. I don't know what

0:59

that is in Fahrenheit. What would that be?

1:02

Minus 2C. So, you know,

1:04

zero C is freezing, which would be 32. So,

1:07

I mean, you know, it's about 30 ish, 29 ish, something like that.

1:09

A little bit colder than New York. It's freezing. Yeah, it's

1:12

much colder than New York. Yeah. And we have a, we

1:14

have a great view. Hey, Karen,

1:17

the regulator. Do

1:19

you think you could pan out the window and get

1:21

a shot? How about that?

1:23

We're asking you to do something out of

1:25

the playbook. It's

1:28

cool outside. We have a nice glass window here

1:31

on the side. Yeah, we're right next to Montréal,

1:33

which is a very

1:35

large park

1:37

and also cemetery and

1:40

mountain in the middle of Montreal, which is very

1:42

dangerous. That's what the city got its name

1:45

after, I guess. Montréal, exactly. Montréal.

1:47

Montréal. Okay. Not

1:50

Montreal. Montréal. But

1:52

if you say Montréal in New York, they think

1:54

you're putting on air. Yeah. Yeah, they think you.

1:57

Anyway, we are here again at McGill, as

1:59

I said. And we've put

2:01

together four guests for you, and

2:03

we decided to have two

2:07

professors and two of their PhD

2:10

students. And so

2:12

let's introduce them. On my right, Corinne

2:15

Maurice, welcome to Twiv. Thank

2:18

you. Hello, everyone. And

2:20

to the right of Corinne, her

2:22

PhD student, Anshul Sinha, welcome.

2:24

Thanks for having me. Yeah, pleasure.

2:28

All the way over next to Anshul

2:30

is, no,

2:32

actually, we have to rearrange

2:34

everyone. I'm

2:38

going to skip over to next to Angela.

2:41

Jesse Shapiro, welcome. Hi,

2:45

welcome. Thank you. And

2:48

next to him on his left,

2:50

his PhD student, Sana Nadary, welcome.

2:52

Thank you. Is it Nadary or

2:55

Nadiri? Oh, Nadary. Nadary

2:58

works? It doesn't really work. And Sana is correct.

3:00

Yes. I got that

3:02

right. Yes, that's right. And Anshul is correct.

3:04

Great job. All right. We're

3:06

going to find out more all about you in a moment.

3:08

And we have a small audience here. Thank you for coming.

3:11

You know, this is about science communication. We try

3:13

and teach people how science works, why

3:16

it's important for you. And

3:18

if you come to my talk later today,

3:20

I'll give you a little bit of the

3:22

history of how I went from

3:24

being a professor

3:27

to a science communicator. But

3:30

anyway, before we get going, I want to

3:32

remind everyone that we

3:36

do this because

3:38

you enjoy it. And if you do like what

3:40

we do, this kind of science communication, we'd love

3:42

your support. You can go

3:44

to microbe.tv slash contribute. The company

3:46

you can see here on the video, that's

3:49

the name of our company. That's

3:51

a plaque assay, by the way.

3:53

The greatest biological assay ever devised.

3:55

Don't argue with me. Sorry. There's nothing

3:57

better. There's nothing that tells you so

3:59

much. so much about a

4:01

virus than a plaque assay. That's

4:04

actually not a real one. It's

4:06

an artist rendering, right? But

4:09

it's beautiful. Michelle Banks

4:11

made this logo for us. She's a great

4:14

artist in the DC area. Anyway, MicropTV is

4:16

a nonprofit. In the US,

4:18

your donations would be federal US

4:20

tax deductible. All right, and the

4:22

second order

4:25

of business, Amy

4:27

Rosenfeld at the FDA is still

4:29

looking for a second technician.

4:33

The FDA is

4:36

at the Center for Biologics Evaluation and

4:38

Review, which is the part

4:40

of the FDA where you can do research. And

4:43

her laboratory works on

4:45

enteroviruses, the most

4:48

interesting viruses there. Next, the plaque assays,

4:50

right? The most interesting viruses in the

4:52

world. And she's

4:54

interested in pathogenesis.

4:57

She's made interesting discoveries about cross-reactive antibodies

5:00

among the members of that family. She

5:02

wants to make animal models. All BSL2

5:04

work, and she needs a research technician

5:06

to help her out there. So we'll

5:09

put a link in the show notes

5:11

to a description and

5:14

Amy's email. You can contact her for

5:16

further information. All right, back to

5:19

today here at McGill University.

5:22

Let's start by hearing each

5:25

of your stories. We'd

5:27

like to know where you're from,

5:30

what kind of education you had, and how

5:32

you ended up here. So Corinne, where

5:35

are you from? I'm from Mauritius Island.

5:38

I wouldn't have guessed that. Right?

5:41

I also have the same. Did you know? No,

5:43

when she said it this morning, I was surprised.

5:46

I always had heard your accent, but I

5:48

never always thought like maybe New Zealand, but then you

5:50

lived somewhere else, because then I wasn't sure. New

5:53

Zealand would have been cool. I have to admit. Sorry, so

5:55

where's Mauritius Island? Mauritius is in

5:57

the Indian Ocean, on the east of the South. Madagascar.

6:01

So okay, South Africa, keep

6:03

going east, Madagascar, keep going

6:05

east, and there's two tiny islands.

6:07

One is French, it's a Iranian island, and

6:09

the other one is Mauritius, which

6:11

is independent, and so that's where I'm from.

6:14

You were born there? I was born there, born

6:16

and raised there, yep. It's

6:18

a tiny island, right, compared to

6:21

Madagascar, right? Oh yes, Madagascar is

6:23

a continent, so Mauritius is tiny.

6:25

I think I remember a friend

6:28

mentioning that Mauritius would be the

6:30

size of Rhode Island. Do

6:33

they speak French like in New England?

6:35

Which is not an island. Which is

6:37

not an island. You are right, yeah.

6:39

They speak both English and

6:42

French because similar to parts

6:44

of Canada, we've had both

6:46

the British and French colonies.

6:49

So we started out as

6:51

a French colony and then

6:53

became a British colony and

6:55

in 68 became independent. So

6:57

what is Mauritius

6:59

famous for? Besides you, of

7:01

course. Besides me, I'd say Mauritius

7:04

is famous for the dodo bird. It's

7:06

the only island where the dodo,

7:08

like the real one, was found.

7:11

So we'd be famous for that. We'd

7:13

also be famous for the

7:15

most expensive postal,

7:19

how do you call

7:21

it, what you put on envelopes? Stamp stamp. Thank you.

7:24

We'd be, you know, gonna be

7:26

good today. Yeah, because we were

7:28

one of the first like British

7:31

stamps that was produced outside of

7:33

UK, the blue penny stamp. And

7:36

that's, these are two

7:38

claims to fame. Your last name

7:40

is Maurice, which is similar to

7:42

Mauritius. Is there any link there?

7:44

I should have had by now

7:47

a really nice backstory to that,

7:49

but I don't. Other

7:51

than my last name in French is how you

7:54

would say Mauritius in French in Maurice. It's written

7:56

the same way, it's done the same way, but

7:58

there were no, as far as as we

8:00

know, no local inhabitants in the island,

8:02

so it was an imported name from

8:04

somewhere. So

8:07

you, I presume you went to

8:09

grammar school and high school? Yes. And

8:11

then for college, did you leave? Exactly. College,

8:14

I left. I left the island at

8:16

18 years old and went to study in

8:18

France because, not because

8:20

I, well, I did have family

8:22

there, but really studying in France

8:24

was cheaper and I had gone

8:27

through the French system. So

8:29

I went there, south

8:31

of France, Montpellier is where I did my

8:33

undergrad and grad school. I kind of stayed

8:35

there. There was a little blip

8:37

in the time course where in

8:40

my master's I came to

8:42

Montréal, not at McGill, but

8:45

at Ucain, and I came to study

8:47

lakes in the eastern townships. Lakes?

8:50

Yeah, I did a year-long survey

8:52

of lakes, which actually meant drilling

8:55

holes in like over 15

8:57

centimeters of ice, which was

8:59

a really fun experience for someone who'd never

9:02

seen snow or ice for 18 years. So

9:05

that was interesting. And

9:08

then after my PhD in Montpellier,

9:10

I went to do a postdoc

9:12

in Boston for four years at

9:14

Harvard University and then got my...

9:16

Who was that with? Peter

9:19

Turnbull. So that was my change

9:21

in systems because I had done my PhD

9:24

in aquatic systems, interactions

9:26

between bacteria and phages in aquatic

9:28

systems. And then

9:30

with Peter, I discovered that,

9:32

well, we actually contain

9:35

thousands, actually millions and

9:37

billions of microorganisms. And

9:39

I was fascinated by that. So I went

9:41

to do my postdoc with him and after

9:43

four years of a very productive postdoc and

9:45

really cool research experience with him, I started

9:48

my own lab at McGill in 2015. I've

9:50

been moving steadily north. I

9:53

don't plan on going any further north,

9:56

I have to admit. The weather is

9:58

kind of... not

10:00

not not currently in

10:02

the plans. I'm

10:04

quite happy with the amount of snow we

10:06

get here. That'll be enough for me. It's

10:08

gonna get warmer and warmer up here. That's

10:11

true. Maybe it will

10:13

be in the books

10:15

actually. What originally got you

10:17

into science? Why were you interested? Biology

10:24

more than any other science

10:26

just made sense to me.

10:30

It felt logical. It felt

10:33

like I didn't see it. There was

10:35

probably a reason why it wasn't happening

10:37

or I didn't exist and I just

10:40

felt so that was just

10:42

interesting and I think all

10:44

the questions I had were all in

10:46

biology. I can't say I was very

10:49

much interested in the rest. I

10:52

think there was always a little bit of science

10:54

around the house because both my parents are

10:58

somewhere or another related to science

11:01

and I think it just made sense.

11:05

I agree. I can't disagree with that. So your parents

11:08

are not scientists

11:10

though, right? Well

11:13

a physician that is... Yeah,

11:15

not a scientist. We like them. They

11:17

think they are and the press thinks they are but they're not.

11:23

I don't want to insult physicians. You

11:25

take care of patients really well. Really

11:27

well. But you know if you

11:29

want to know about virology, do

11:32

you know who you should ask?

11:34

Not a physician. A virologist probably.

11:36

Absolutely. Both parents were physicians?

11:39

No, no, my dad. My

11:41

mom used to work in

11:43

a lab. She was a

11:46

biochemist but it didn't last

11:49

for long as she

11:51

followed my dad traveling along. So

11:53

they met in Scotland and

11:56

traveled a little bit before coming

11:58

back to Mauritius because my dad is... and

12:01

my mother Scottish. Wow

12:03

that's great story. International. More

12:05

of the accent there too. You

12:08

got a little Scottish in your accent. I do. I never

12:10

lived there so I don't have to have

12:14

that accent. It's pretty far north you know.

12:16

I know, I know. It's true.

12:19

That's not why I didn't make it. So

12:21

your mom speaking in the accent you picked

12:23

that up right? Yes. Right. Yeah. Because it's

12:25

not genetic as far as I

12:28

know. No. Speak a certain way

12:30

you can be easily influenced. Okay thank you for

12:32

that. Let's go to Anshul. Let's

12:34

get your story. You've heard now what we want

12:36

to hear. Yeah. I don't know how to call

12:38

that up. I grew up

12:41

about 30 minutes east of Toronto

12:43

and a lovely tickering. I

12:45

was gonna say Madagascar. No not quite

12:47

Madagascar. That would be great. We can

12:49

say that. Yeah.

12:53

Pickering is a kind of just

12:55

suburban landscape I guess.

12:57

It's a good place to grow up.

13:01

I did my undergrad in London Ontario, not

13:04

London England, and

13:07

really started to get into

13:09

science then. I actually, my

13:11

parents signed me a first science camp when I was a kid and

13:13

I hated it. But I

13:16

think by osmosis my dad is a doctor

13:18

and my mom's a science teacher. I gradually

13:21

started to take a

13:23

shine to biology. I

13:25

took an intro microbiology course really

13:27

like that. And

13:29

then I did my honors project

13:32

about 10 years ago with David

13:34

Heinrich at Western where

13:36

we studied the production of

13:38

a pigment called Staphylosanthin in

13:40

Staph aureus. And that was really cool

13:43

to me to kind of finally

13:46

have ownership over research project that

13:48

kind of made me want to go to grad school. So

13:50

moved to Montreal to

13:54

McGill where I did my master's

13:56

with Irvé Lemois. We

13:58

studied outer membrane bicycle production

14:01

in Citrobacter Rodentium. That

14:04

was also a really cool project, but

14:06

at the time I took

14:09

one of Cred's intro to the microbiome

14:11

courses and was really kind of

14:13

sold and excited by all the cool research going

14:15

on the lab. So I

14:17

joined her lab at the PhD student in 2018

14:19

where I still am, studying

14:22

phages in the gut. So

14:24

let me impress you Citrobacter is the one

14:27

you can use in mice as a model.

14:29

Exactly. Yes. Yeah.

14:31

Okay. Sana, let's

14:34

hear your story.

14:37

So I'm from Iran. I was born

14:39

and raised there. I was there until

14:42

I was 21. I

14:46

did my undergrads there. I studied

14:49

engineering in my undergrads,

14:51

not very biology related.

14:55

But then I didn't really like it. So

14:57

I started thinking about these interdisciplinary

14:59

fields where you can

15:02

apply computational sciences to

15:04

them. And so I did

15:07

electrical engineering. But

15:09

then I took one term off and I

15:11

went to Vienna to study population genetics. And

15:14

that's when I started slowly getting

15:16

into computational biology. But

15:19

my background is almost

15:21

entirely computational. And my

15:24

project also is fully

15:26

computational. And so

15:28

you're currently a PhD student

15:30

here. Yes. So I finished

15:32

my undergrads in Iran in engineering. And

15:34

then I started as a master's student

15:36

in Jesse's lab originally. I actually

15:39

transferred to the PhD program last week. Congratulations.

15:42

Very rich.

15:45

Yeah. Transferred

15:47

to the PhD program. You get

15:49

on Twiv in the same week,

15:51

right? That's pretty darn good. A

15:53

week or two after. Yeah, it's

15:55

good. So we don't have

15:57

a good sense of what's happening in Iran.

16:00

Okay, so is it hard

16:02

to leave to do education like you have? It's

16:06

not really hard to leave most people

16:08

are leaving a lot like from my

16:10

class We were a hundred and fifty

16:12

people I think in my year in

16:14

undergrad And I think less than

16:16

30 or 40 of them are still in

16:18

Iran So everyone tries

16:20

to leave and it's it's a

16:23

plan that everyone has and we start thinking about

16:25

it very early on And

16:28

we started looking for programs and

16:30

options. It's hard. It's getting

16:32

harder in terms of Visa

16:35

restrictions for some students and also

16:37

our university Well the government in

16:39

general is making it harder for

16:41

students to leave in subtle

16:43

ways For example the most recent thing

16:46

they started doing is that they refused to give

16:48

official transcripts in English now So and

16:50

it's crazy because people will still leave

16:53

it just makes things harder So it was a whole it

16:55

was this whole drama to get my English transcripts

16:59

To make go and satisfy their requirement.

17:01

There are things like that, but generally

17:03

everyone not everyone but most people

17:06

who want to leave But

17:09

try to find a way to do that and

17:11

what once you leave Do you

17:13

have to go back in other words? You have some kind of

17:15

visa restrictions that make you go back Well, I moved

17:17

here with my family So for me,

17:19

it's easier and that my family live

17:22

in Toronto So I don't

17:24

really get I don't really go

17:27

back that often anymore for

17:29

other students, it's they try

17:32

to go back whenever they can but It's

17:35

hard. It's far the tickets are expensive. It's not

17:37

as easy for To

17:40

do it's not an easy trip. It's

17:42

usually we jump that door to door It

17:44

takes 24 hours to go back because it's

17:46

such a long flight and their layovers and

17:50

In most of our students years that

17:52

we have classes we Yeah,

17:55

and how did you find Jesse? How did

17:57

you find his lab and his research like

17:59

from electrical engineering? to. Well,

18:01

yeah, that's it. So

18:03

I slowly transitioned to what I do

18:05

through internships that I did. So I

18:08

did a few internships in Vienna. And

18:11

then I was an intern at the Genome Center

18:13

in the summer of 2019 with Ryan Hernandez. I

18:19

was doing mostly human genetics. But

18:21

then because I knew my family's moving

18:23

to Canada, I was looking for graduate

18:26

programs in Canada, and I was looking in

18:28

the Genome Center specifically because I used to

18:30

be there. And

18:32

what Jesse does is very relevant to

18:34

what I was doing, because it's population

18:37

genetics for

18:40

pathogens or microbes. So that's

18:42

how it was so cool. So

18:46

I want to ask you a question, and you don't have

18:48

to answer. But how

18:51

is it in Iran for women

18:53

to study and advance and leave the

18:56

country like you have? Is it more

18:58

difficult than for men, for example? It

19:02

is, for sure, harder to be

19:05

a woman. It's getting

19:07

better. It's getting easier, and

19:09

it's getting better. In my parents' generation, it

19:12

was much harder. Women

19:16

were considered less smart

19:18

in general. And also,

19:20

first in some families,

19:22

their restrictions, they

19:26

don't really get to go out there and experience

19:29

and study and work

19:31

and be fully independent. For

19:34

me, personally, I

19:36

was lucky in that my family was super

19:39

supportive, and they wanted me to really pursue

19:41

what I like. In my

19:43

university, it was a little bit hard, because

19:46

it's engineering school, and it was

19:48

all guys. It was 150 guys and 10 girls. And

19:53

I was often the only girl in my

19:55

classes. And I did experience...

20:00

there were times that they would come to me and

20:02

say things that were not very nice and you could

20:04

feel that a lot of people feel like you're by

20:07

default left smart just because you're a woman.

20:10

And I even got direct comments on

20:13

this sometimes, but it's getting better and

20:15

there are people who are talking about

20:17

it and are trying to make it easier.

20:20

Okay. Well look at you

20:22

now. Your PhD student

20:24

on place. We're on

20:26

publication. All right, Jesse, what's your

20:28

story? I

20:31

grew up just on the other side of the mountain. It

20:35

could be a thousand miles on the other side.

20:37

Yeah, yeah, yeah. By mountain, I mean the 300

20:40

meter, 300 meters, something like that. So

20:45

I grew up in a neighborhood called NDG. And

20:49

we moved to Vancouver. That's where you are, right? No,

20:51

that's where my partner lives. I

20:54

don't live there anymore just so that I'm

20:56

not exactly following in the precise footsteps of

20:58

my parents over in a different neighborhood. But

21:02

we moved to Vancouver when I was nine. But

21:06

then I came back to do my undergrad at McGill

21:08

when I was 17. So

21:11

I felt a really strong connection with the

21:13

city and my

21:15

center of gravity and I always end up

21:17

bouncing back here. So I did my

21:19

undergrad at McGill in biology. I

21:23

did my honors project in

21:25

yeast genetics in

21:28

Howard Bussey's lab, who's now retired, and

21:32

doing high throughput mutant

21:35

screens to make a genetic interaction

21:37

network. And

21:39

so it's a sort of high throughput science.

21:41

And he was a really traditional geneticist.

21:44

I really like this sort of like

21:47

systems biology, like study the system and

21:49

not individual components. And

21:51

I was interested in doing

21:53

for grad school computational biology, systems

21:55

biology, this kind of thing. He

21:58

said, don't do that. It's the fad. pick

22:00

up protein and study it and

22:03

this kind of thing. He was very skeptical and I did not take

22:05

that advice. I

22:09

did a master's in something called integrative

22:11

bio science which

22:14

is basically biology, but it

22:16

sounded fancy, at Oxford in the

22:18

UK where you do two

22:21

kind of rotation projects which was very fun.

22:23

One was on African trypanosomes. That

22:26

was a real molecular biology project. The

22:30

other was on great pits which

22:33

are birds which is

22:35

what they call some chickadees.

22:37

They are some terrible ornithologists.

22:39

So anyway, that got me

22:41

really interested in ecology and evolution because it was a

22:44

field project. So I was out in the woods moving

22:46

birds around and it's

22:48

really hard to work with birds. It was fun but

22:50

I really got interested in ecology and evolution. I

22:55

then went on into my PhD at

22:57

MIT in a program called computational systems

22:59

biology. So I got more quantitative background

23:03

coming from biology. And then from

23:06

there, basically never went back to the wet lab

23:08

or to the field and basically to stay in

23:10

front of a computer for five years which

23:13

for students out there, get a good chair, get up

23:15

and take breaks, people are better at this. I threw

23:17

out my back after grad

23:19

school. I went on a big trip backpacking

23:22

and completely threw out my back, had

23:24

a slip disk, had to go

23:26

back, interrupt the trip, go back and do

23:28

months of physio. Luckily didn't have to have

23:31

surgery or anything like that. I

23:33

was just sitting up straight right now. So

23:36

there are hazards of sitting at

23:38

a desk. Anyway,

23:41

yeah, so that's

23:43

it. I did a short post-doc

23:46

afterwards with Pardee

23:48

Sabeti and I guess because it's

23:50

a virology podcast that was the

23:53

first time I'd worked on viruses.

23:55

I did genomics of Lassavirus. So

23:57

samples from West Africa doing... biogenetics

24:01

and evolutionary biology based

24:03

on Lassa virus sequences. Also

24:06

worked on tuberculosis. I work on a lot

24:08

of different, I'm not a virologist, work on

24:10

lots of different things, but I mentioned the

24:12

Lassa virus. And then I came

24:15

back to Montreal. My first faculty position

24:17

was in the biology department at Université

24:19

de Maurieille, which is our sort of

24:21

sister French university. Again,

24:23

on yet another side of the

24:26

mountain, pointing over that way. And

24:30

then I was there for seven years and I moved

24:32

back to McGill three

24:35

years ago in summer 2020 and in the microbiology

24:39

department. I'm over in the

24:41

Genome Center right now. So

24:44

your body may not have done well sitting in

24:46

a chair, but your brain is really good. Well,

24:50

you know, a lot of people now have these standing desks,

24:52

walking, the treadmill desk,

25:00

all that. One of my lab mates,

25:02

she has this really fancy desk that goes

25:04

up and down with like a remote and he can

25:06

stand at it. It's very nice. He has like a

25:08

40 inch screen. Everyone needs

25:10

that. I don't have the walking, but I do

25:12

have a... I

25:14

have also, but there's too many

25:17

wires at the studio. So it's always in

25:19

a down position. Maybe we should fix that

25:21

sometimes so I could stand up, right? Okay,

25:25

so Angela, what

25:28

department are all these people in or

25:30

what division or what division? So microbiology

25:32

and immunology, Dr. Maris, the current

25:34

Maris. And you guys are in human genetics,

25:36

right? No, I mean, we're all

25:38

in the same department. We're all in microbiology, but

25:42

we have different...well, we

25:44

sit in different places. We're a diffuse

25:46

department. Yeah, they're in the

25:48

life sciences complex, which is where we

25:51

are. Well, we're in the cancer center, but it's connected

25:53

to the life sciences complex. And then the

25:55

Shapiro lab is in the genome center, which is

25:58

like a 200 meters. away,

26:00

maybe 200, across the field.

26:02

Okay. What department are you

26:04

in? I'm in Physiology, although my lab does

26:06

mainly Immunology, but we're, our department is Physiology,

26:08

but we're on the same floor as the

26:10

Marist lab. And

26:13

Jesse also has, he started on my committee and then

26:15

my project changed, so I was like only doing, I

26:17

know, it's been here doing that, so. I do get

26:19

off, I do get off, it's not before, but then.

26:22

And then we expanded more into the East House,

26:24

and then it was like, well, maybe this isn't

26:26

as relevant, but he is on other people's

26:28

committees in my lab. Okay. And

26:30

we have collaborators. So where I'm trying

26:32

to go, which we haven't reached, the

26:35

center with the funny name. L.D.M.R.CCT. Yes,

26:38

the McGill. The Center for Complex Traits.

26:41

Yes. Are you all in the center?

26:45

We are. The Marist lab is M.R.CCT. Yes.

26:48

You guys aren't M.R.CCT. No. Are

26:50

you? You're all in. We're

26:52

M.R.CCT. I'm on the committee of the... What is this

26:54

Center for Complex Traits? We were

26:56

talking about this last night at dinner. It wasn't

26:58

recorded. Exactly. And

27:01

once again, Dakota and Dinesh, people from my

27:03

lab, we were saying, what is a complex

27:06

trait? So the McGill Center for

27:08

Complex Traits is a whole bunch of researchers. There's like 50

27:10

researchers that all form part of this center.

27:14

And what is a complex trait? Well, we

27:16

can say it's a trait that is influenced

27:18

by more than one gene, because at

27:21

one point we were like, okay, two genes, but then it's not

27:23

necessarily a gene. It can be, as people

27:25

mentioned, an environmental factor, et cetera. So

27:27

we'll just say more than one gene

27:30

and numerous factors from the

27:32

trait that could be. So almost anything,

27:34

like many things could be complex

27:36

traits. Everything. Exactly. It's

27:39

a very broad name. Philip Groh, one

27:42

of the researchers on our lab

27:44

has been there for the longest. He started the M.R.CCT. It

27:46

was his doing, I think. Yes. It's

27:48

an interesting name.

27:51

But even if you took one protein, like

27:53

you were told to do, right, and just

27:55

knock it, you're still going to have

27:57

interactions with other things. So you could say that's a complex trait.

28:00

environment, other proteins and so

28:02

forth. Exactly. But we're the

28:04

MRCCT. Okay. I think that

28:06

was the point of having,

28:08

I mean that's why you

28:11

have so many researchers in

28:13

that center with so many

28:15

different expertise. So microbiology, physiology,

28:17

human genetics, we have clinical

28:21

researchers at the MUHC, so

28:23

the hospital that's estimated to

28:25

the university. So yes,

28:27

it's many different conditions, many different diseases

28:30

and I think that

28:32

kind of showcases this idea

28:34

of like multiple traits and multiple

28:36

possible interactions. And it's nice because

28:38

we have, well I'm on the

28:41

graduate student committee and we have work

28:43

in progress seminars which because it's so

28:45

diverse and heterogeneous, all of the researchers

28:47

that form part of this, it's nice to see other students

28:49

work and we actually do it in this room where we're

28:51

recording is where we have a lot of these with seminars.

28:55

And now also the genome centers included

28:57

in that. Now they're called Haplotox. Haplotox?

29:00

Haplotox, yeah. Why? We

29:03

changed the name because now there's the genome

29:05

center people and the MRCCT people.

29:07

Before it was ATGC, ATGC

29:09

talks with MRCCT and now

29:12

we're Haplotox. So for

29:14

listeners who don't have any idea what

29:16

we're talking about, right? True. So

29:19

in academic institutions we often have departments

29:22

or divisions which

29:24

I think, and you tell me if this is

29:26

wrong, the idea is to bring people together who

29:28

would then interact in ways

29:31

and collaborate that they wouldn't if they were

29:33

just an isolated lab in a building. Exactly.

29:36

Right? And so one approach to

29:38

that is to have a diverse collection of people

29:40

like this complex trait center and

29:43

you have some meetings and you

29:45

hope that spurs interactions. Or the

29:47

other approach is to say microbiology

29:49

and have all microbiologists which has

29:51

gone out of favor

29:53

I think. Interdisciplinary is more interesting, more

29:56

collaborative. Mix people up. So throw in

29:58

some computational biology. who

30:01

knows, maybe they'll be useful. An electrical engineer is sprinkled

30:03

in. Because

30:05

you want innovation in the

30:08

end, you want new ideas, and sometimes your

30:10

view of something is too myopic, and

30:12

someone else says, hey, have you thought of this? Definitely.

30:16

So that's why we have these kinds

30:19

of departments. But there are

30:21

other reasons, too. There are financial

30:23

reasons, there are organizational reasons. Shared

30:25

equipment. Shared equipment is a good one,

30:27

yeah. I mean, the departments, at least in

30:29

the U.S., they get a chunk of money from the dean

30:32

usually to do run things, and so that's

30:34

why it's good to have a department as

30:37

opposed to individual people. So that's what we're talking

30:39

about, folks, okay? Any questions? Good.

30:42

I'm glad. Because I wouldn't be able to answer

30:44

them. Email them. So

30:47

I was in a department, when I

30:49

first went to Columbia, I was in the Department of

30:51

Microbiology, which in

30:54

the 50s was made as a department

30:56

of bacteriology. Because bacteria were

30:58

the first things to be studied intensely

31:00

because they were small and accessible. So

31:02

bacteriology. And then they realized there's more

31:04

than just bacteria, there are other microbes.

31:07

And then a few years ago, it

31:09

became microbiology and immunology, as yours now,

31:12

as your micro-immunology? Yeah, micro-immunology. Because

31:14

you can't take the two apart

31:17

from each other. Anyway. And

31:20

speaking of that, because the Marist

31:22

Lab studies phages, is

31:24

there anything else you wanted to say before I go into this? No, that's good. You

31:26

can take it. What is, for all

31:28

the listeners and other people in this room that might

31:30

not know, what is a phage? I guess Corinne will let

31:33

you answer this, and we can ask some more

31:35

of the other questions to answer, but what

31:38

is a phage? Well, it's a sort

31:40

of virus. Okay. It's as

31:42

simple as that. Basically, a

31:44

phage is a virus that is

31:46

only going to be able to

31:49

infect bacteria. It

31:51

can be recognized by our immune

31:53

system or by our eukaryotic cells,

31:55

but it usually won't infect them.

31:57

So it doesn't have the machinery.

32:00

have the way to recognize and infect

32:02

these cells whereas bacteria they

32:04

can. So we

32:06

study the viruses that don't make

32:08

you sick or that indirectly

32:12

can make you sick. I was

32:14

going to say, we'll talk about

32:16

it, but there's this IBD linked

32:19

to certain phases. Yeah, exactly. So

32:23

as I was mentioning earlier, what

32:26

interests me in studying phages is really

32:29

their initial interactions with bacteria. Like I've

32:31

always always been interested in how these

32:33

two kind of like predator and prey

32:37

tend to interact with each other. And I used to do it

32:39

in aquatic systems, but at the end of the day, you

32:42

find these microorganisms everywhere. And

32:45

in the human body, they can have really

32:47

strong health or

32:49

impacts on our health. And

32:51

in some cases, you would want these phages

32:53

to stick around because they would

32:56

be in charge of like killing your bad bacteria,

32:58

for example, the ones that can make you sick.

33:00

So these would be the phages that you'd want to keep. But

33:04

because these phages can also be recognized

33:06

by your immune system or some of

33:08

them would actually kill your

33:10

good bacteria, those are the ones that

33:13

could potentially make you sick, but it

33:15

would be their indirect effect. And

33:19

we've had several projects

33:21

with different conditions, including

33:23

IBD. And that was a project that

33:27

started a while back, but that Anshul

33:29

took the lead on. And so I'll

33:31

defer to him to actually talk about

33:34

that because he did incredible work. Yeah,

33:36

I want to just highlight the paper itself. There's Anshul

33:38

has the first author on a paper from 2022 in

33:40

Microbiome titled, Transplantation of

33:45

Bacteriophages from All Sort of Colitis

33:47

Patients Shifts the Gut Micro... Sorry,

33:49

the Gut Bacterium and Exacerbates the

33:51

Severity of DSS Colitis. So

33:54

Bacteriophage, it's anonymous with phage for

33:56

all the listeners. And

33:59

explain... what this means, what was an overview

34:01

of this publication and your research. Before you do

34:04

that, I have a very basic question. So all

34:06

of us have bacteriophages in

34:08

us. Yes. Free living,

34:10

free floating bacteriophages as well as

34:12

integrated into the bacteria. Absolutely, we have

34:14

the two. So on my skin I

34:16

would have some phages too? Yep, everywhere. Everywhere.

34:18

How many? About

34:20

similar to how much bacteria we have. Which

34:23

is how many? 10

34:26

to the 13 per gram of

34:28

stool. That's our reference. 20

34:30

pounds of stool. We

34:32

have a pound of bacteria. How many pounds?

34:34

I heard one, this crazy number

34:37

of microorganisms in our body was...

34:39

And I do have it on my slide

34:41

to the first year undergrads and I right

34:44

now cannot remember the weight of it. But

34:46

is it over a pound? It's

34:48

approximately a pound of microorganism.

34:52

And it's about one to one bacterial

34:54

cell per human cell. And

34:57

then count one phage per

34:59

bacteria in all? Yes, if you bring it

35:01

to the human body, for each human

35:03

cell you have one bacterial cell. And on

35:05

average for each bacterial cell you're going to

35:07

have one phage. However,

35:09

if you get out of the human body

35:12

and you look at the Earth in general,

35:14

for each bacterial cell that you're going to

35:16

found on Earth, there's

35:19

going to be approximately 10 phages.

35:22

They can be the same or they

35:25

can be different phages. So it's not only

35:27

abundant or their numbers, but it's

35:29

also their diversity and the types of phages

35:31

that are there. So there's something very interesting

35:34

about the human body. Like why

35:36

isn't this 10 to 1 ratio

35:39

there in the human body? That may be

35:41

due to the immune system. It may be

35:43

due to the whole system itself. But

35:45

yes, the phages, the chirifages

35:48

and phages are for the moment the

35:50

most abundant... Entities.

35:53

Entities. There you go. It's crazy to

35:55

think that we are just as much phage

35:57

as much as we are humans. So

36:00

like so so these are these in our

36:02

blood as well these pages I Think

36:05

that's a bit controversial I

36:09

think there have been some studies that have sequenced

36:11

small amounts of pages, but mostly they remain

36:13

got got How about my brain do

36:16

I have pages in my brain? Yeah,

36:20

I don't know about I have on my brain

36:22

yeah Probably

36:24

not What about other barrier

36:27

surfaces like you said the skin yet, but in

36:29

the lungs there must be tons of

36:31

phages right like a whole other lung

36:34

my Viral a

36:36

phageo I guess in the lungs Not

36:39

only been studied too in depth. I

36:41

think the guts been the first logical

36:43

place to look for phages We're

36:46

still kind of in the early stages of phage discovery

36:48

in human body. It's a field. That's only 10

36:51

to 15 years old So

36:53

I don't I'm not sure about So

36:55

the the papers that would be looking

36:58

at phages in the lung have typically

37:00

done so in for example

37:02

cystic fibrosis So you have the disease

37:04

that's there and you're trying to understand

37:06

like is it because

37:08

of some microorganisms that are there? Overgrowth

37:11

of some of them so typically

37:13

there yes, we're finding phages not

37:15

very Abundant

37:17

not very diversified, but it could be

37:20

this is a challenging body

37:23

location to sample And

37:26

contamination is a big deal there so yes, we

37:28

have usually started with Organs

37:30

or locations where you can easily access

37:32

the community so get through the stool

37:34

samples the skin because it's really widely

37:37

accessible oral for

37:39

the mouth There

37:41

has been a limited number of studies,

37:43

but they still exist for everything. That's

37:45

your genital tract But

37:47

they're also like accessing the samples isn't

37:50

always easy for different reasons. I'm going

37:52

to say And

37:55

then in the mouth what's

37:57

interesting to is that you've got like

37:59

like the answer mouth versus the case,

38:02

there does seem to be quite a strong difference

38:04

there. And then lung

38:07

is one of the low ones. I'm

38:10

not aware inside the body. I don't

38:12

think phages, well, they haven't been shown

38:15

to do it yet. I'm

38:17

open to the idea that we're going to find them, but

38:19

we haven't seen them yet. My colleague,

38:21

Elio Schechter, on Twim, used

38:23

to always say, you know,

38:25

it's the fecal microbiome, not

38:27

necessarily the mucosal microbiome, right?

38:29

Absolutely. It could be different. It's hard

38:32

to sample that, but feces are easy

38:34

to sample. That's where we do it, right? Absolutely, yeah.

38:36

So the reason I'm asking all this is, if

38:38

you take serum from people, do you find antibodies

38:41

to any of these resident

38:43

phages? I'm going

38:45

to defer the immunology questions that way,

38:47

because this is, as I keep saying

38:50

to students, I'm not an immunologist. There

38:54

have been phage therapy papers that have looked at,

38:56

you know, you give someone a dose of phages,

38:58

and then you look at antibody titers, and you

39:00

do see, at least

39:03

transiently, the production of antibodies. And

39:05

it's a moral dose of phages?

39:08

Yeah. Like a drink

39:10

of phage? A drink of phage. A phage flurry,

39:12

we can call it. Also, they give it IV,

39:14

depending on... No, we could do it. Yeah.

39:17

You can do IV therapy of phages. But I'm

39:19

just curious, if you make antibodies to your resident

39:21

phages, or since you grew up with them, I

39:24

assume you get them at birth and curly hair.

39:26

Maybe they're viewed as self, right? That's

39:28

the question. That's the question, and there are

39:31

really... There are several teams trying to look into

39:33

that, because we're starting to get a good idea

39:37

of where we get the bacteria from

39:39

at birth, and how we get these

39:41

bacteria, how we acquire them, what makes

39:43

some of these bacteria stay in the

39:46

gut versus some of them just like

39:48

they're lost of the system. And

39:51

now these same questions are coming with the

39:53

phages. And for the

39:55

moment, we are assuming that we're getting them at

39:57

birth through the bacteria. earlier

40:00

about free phages and the ones that

40:02

are integrated within their bacterial host, that's

40:05

a way for phages to replicate.

40:08

They basically can multiply in different

40:10

ways, either directly infecting and killing

40:12

or integrating and staying protected by

40:14

the bacterial cell. We think

40:17

that's what's happening at birth, that we first get

40:19

the bacteria. Once they're settling

40:21

in the gut, plenty of signals,

40:23

which we don't know what they are, these

40:27

signals, environmental, maybe body,

40:29

maybe immune linked, unclear,

40:32

stress the bacteria cause the production of

40:34

these new phages who start establishing the

40:37

system. And then for anyone who's seen

40:39

an infant, it's very clear

40:41

that the rest is going to be

40:43

environmental and direct contact with

40:46

the parents, the environment, the floor, whatever

40:49

goes in their mouth, really. But

40:52

yeah, that's a super interesting...

40:55

Yeah, okay. All right,

40:57

so now back

40:59

to you in the paper,

41:01

this paper, how did this come about, as

41:03

Angela said? Right, so I guess

41:06

it's been known for a couple decades

41:08

now that the bacterial communities in the

41:11

gut and inflammatory bowel diseases are very

41:13

important. The

41:15

changes that we see in patients in

41:18

certain types of bacteria are really important in

41:21

causing access to inflammation. And

41:24

what's been found in the last five

41:26

or 10 years is that the phage

41:28

communities are also altered in these patients.

41:31

But at the time, we didn't really know

41:33

what the consequence of those changes were. So

41:36

we wanted to see how the phage

41:38

communities from IBD patients were interacting with

41:41

bacterial communities and how that might be

41:43

different from phage communities from people who

41:45

don't have IBD. So IBD

41:47

inflammatory bowel disease, so what kinds of

41:49

diseases are included there? Bone

41:51

disease and ulcerative colitis are the two

41:53

main diseases under the

41:55

umbrella of IBDs. So in

41:57

this study, we were specifically looking at ulcerative colitis. colitis

42:00

patients. So what

42:02

we did is we took germ-free mice, so

42:04

sterile mice, we colonized them

42:06

with fecal bacteria and

42:09

then we gave those mice phages

42:11

either from ulcerative colitis patients or

42:14

from sort of non IBD controls. Are

42:16

these human? So you took it from...

42:18

Okay, yeah, we took human stool samples,

42:21

colonized these in mice. The mice were kind of our vessel

42:23

to do these experiments. These are

42:25

people without IBD. So

42:28

the bacterial communities were from patients

42:30

who had IBD and then we

42:32

took the phages from either IBD

42:34

or non IBD and

42:37

then we did sequencing to look at

42:39

how the bacterial and phage communities were changing over

42:41

time in the mice and then we also in

42:44

collaboration with an immunologist at

42:46

McGill, Ira King, we

42:49

looked at the immune responses in the mice too. So

42:52

basically what we saw was that the

42:55

sort of healthy and IBD

42:57

phage communities were shifting the bacterial

42:59

communities in different ways. For

43:03

the UC phage communities we saw

43:05

increases in certain sort of pro-inflammatory

43:07

bacteria like E. coli and

43:10

a depletion of certain bacterial taxa like

43:12

we saw U. bacterium decrease which is

43:14

sort of protective in the context of

43:16

inflammation and sort of

43:19

in addition to those changes in the bacterial

43:21

community, the mice that received

43:23

the dose of phages from the UC

43:26

patients also had higher disease severity.

43:29

So we gave them a

43:31

compound called dextran sodium sulfate

43:33

which induces colitis in

43:35

mice and we saw that the mice

43:37

that received those sort of UC phages

43:39

had higher severity which

43:42

suggests that these phage communities might

43:44

be sort of playing a role

43:47

in disease progression possibly through altering

43:49

the bacterial community to a somewhat

43:52

pro-inflammatory state. So how do

43:54

you prepare these phage preparations

43:56

from people? What do you

43:58

do exactly? So we

44:00

get our fecal sample. We

44:02

take it to our anaerobic chamber here,

44:05

two floors below. And then

44:07

we separate the phage and the

44:10

bacterial communities using centrifugation. So we

44:12

pel the bacteria, and then we're

44:14

left with a phage supername that we filter, which

44:18

should contain the majority

44:20

of the phages in the fecal flurry.

44:23

Do you look at the distribution

44:25

of that phage population at all to

44:27

see what it

44:29

is, for example? Yeah, so we

44:31

sequenced the phage inoculum.

44:34

So

44:36

understanding what's in a phage community is a

44:38

little bit of a challenge, because there's so

44:40

much diversity worldwide of phages. But we can

44:43

get some signals of what the

44:45

phage community looks like. And it was

44:47

pretty consistent with what's been seen in

44:50

other studies in terms of the IBD phage

44:52

community. So we see a lot of temperate

44:54

phages that can undergo both the

44:56

lysogenic cycle, meaning integrating into the

44:59

bacterial chromosome, and the canonical lytic

45:01

replication. So that's a hallmark of

45:04

the phage community in

45:06

IBD patients. And we saw that in our own fecal

45:10

flurries as well. So when you

45:12

take phages from UC patients and

45:15

put them into mice and then treat them with this

45:17

chemical, phages

45:20

from the UC patients exacerbate the

45:22

disease caused by that? Exactly. Yeah.

45:25

It's DSS colitis. So it's not a perfect model.

45:29

Clinicians won't say that

45:31

it's a great model to study IBD.

45:34

But it's useful for our

45:36

approach where we want to sort of induce colitis. We

45:38

want to see how the phages are changing the bacteria,

45:40

and then induce the colitis and see how that affects

45:43

the mice. So when

45:45

you say it exacerbates, like what are we talking?

45:47

Forget about statistics. Just is it really good?

45:50

The mice don't want it. They

45:53

lose a lot of weight. So the mice

45:55

that got those phages lost more

45:58

weight. And we also saw. Pro-inflammatory

46:02

cytokine production and those mice

46:05

Now and in the mice receiving phages from

46:07

healthy people you don't see this. Yeah, well

46:09

that they do exhibit

46:12

disease symptoms but at a much lower

46:14

level and the pro-inflammatory Responses

46:16

lowers as well. Exactly. Yeah, and

46:19

there's a shift in the microbiome It comes

46:21

as a consequence of the conference and we

46:23

see during inflammation compared to before inflammation when

46:26

they have the phages and bacteria Yeah during

46:28

inflammation those changes are even greater So in

46:30

the context of an inflamed gut it seems

46:32

like these phage bacteria interactions are really sort

46:35

of expedited Did you characterize

46:38

again the the bacterium after

46:40

the the challenge? Yes, we

46:42

did it kind of at all stages like

46:44

baseline after we add stages,

46:46

but before The chemical

46:48

induction and then also after and you

46:51

saw an increase in pathogenic Bacteria,

46:53

yeah sort of throughout and then it kind

46:55

of Went up sharply

46:58

with the onset of colitis in

47:00

certain cases So maybe

47:02

you were asked this in when you had the

47:04

paper reviewed, but how do you know? There's something

47:07

else there isn't something else in the phage preparation

47:09

that's mediating this effect. That's a good question We

47:12

did a heat-killed control as well. So we

47:15

heat kill these pages. So what we're left

47:17

with is kind of just the supernome of

47:19

everything Except okay

47:21

the phages and that was similar to

47:23

our PBS control That's

47:26

what I was a good fan of the

47:28

heat killing could kill a toxin it can

47:30

kill other things so it's not yeah It's

47:32

not perfect. We did check endotoxin level. I'm

47:34

reviewer number three So

47:41

if you take so you give let's say

47:44

you give mice Phages from

47:46

UC patients, right? What

47:48

if you followed with phages from healthy patients

47:50

with that reverse? The

47:53

sensitivity to DSS a great follow-up study

47:55

that I'd love to do because

47:58

yeah, I think the idea

48:01

in some people's minds is that

48:03

phages from healthy donors can be used as

48:06

sort of a therapy because

48:08

they might, like Corinne was saying, kind

48:10

of infect and reduce the abundance

48:12

of some of those more pro-inflammatory bacteria in

48:14

that context. So I think that'd be really

48:16

cool. I guess you could also give microbiome

48:19

fecal transplants as well. Exactly.

48:22

Along with that, you would give the lysogenic phages

48:24

and that would accomplish the same thing. Right.

48:28

And it might be better. Right. People have done

48:30

these kinds of studies, right? Yeah,

48:33

it's been done. I

48:36

mean, in C. diff, for example,

48:38

there's been fecal-virome transplants

48:40

that have been shown to be quite

48:43

effective. I think the problem with IBDs

48:45

is that fecal microbiota

48:47

transplants in and of themselves

48:49

are not that effective. Okay. So it's hard

48:51

to imagine that for patients giving

48:54

them a dose of phages from a healthy donor

48:56

would really, for everything, it has to be, I

48:58

think, a multifaceted approach with something

49:00

targeting the immune system and then

49:03

maybe a microbiota-based therapy kind of

49:05

supplementing that. Sorry,

49:08

go ahead. You had a

49:10

really good point earlier about our

49:14

immune system actually recognizing or

49:16

not the phages. So

49:19

basically, our immune system recognizes

49:21

or should recognize our phages

49:23

as like self. And

49:26

in IBD and all these other chronic diseases

49:28

where you do have an immunology

49:33

defect or an

49:35

immunological issue or challenge, your immune

49:37

system is not responding the way

49:40

it should. And so you're

49:42

not having the microorganisms that should be there, nor are

49:44

the phages. So phage therapy

49:46

or just proceeding with an FMT

49:48

in that case will

49:50

not be the magic solution because your immune

49:52

response is not going also in the direction

49:55

that it should be. So

49:57

it's definitely more challenging in the context of

49:59

these diseases. is, but an infectious

50:01

disease where you have a fully

50:04

functional normal immune system, every

50:06

SMT that has been tried

50:08

seems to be working much

50:10

more effectively and much better.

50:13

But really the poster child

50:15

for SMT, so fecal microbiota

50:17

transplant, or even going a

50:19

step further of SVT, fecal

50:21

viral transplant, is custodian

50:24

difficile infections. Those

50:26

are the ones where it works really well. Yeah,

50:29

that's the ones where we're trying, well,

50:32

other teams are trying to develop therapies,

50:34

and then it's just a case of can

50:36

this be applied to other infectious diseases.

50:40

And I think they're, well, we

50:42

should try. So this has been done,

50:44

or is it

50:46

ongoing now, fecal viral transplants?

50:49

So you called it? SVTs.

50:52

And how is it, I guess it's a challenge

50:54

of the stomach, then how do you, is

50:56

there some sort of encapsulated, are these encapsulated

50:58

in pill form? Like practically how does

51:00

this happen, if it is happening in humans?

51:04

All of the above. We're

51:06

still at the stage really where we're testing

51:09

things out to try and identify the best

51:11

way to approach this problem, if ever we

51:13

were to give it out as a therapy.

51:16

So right now, SMTs, so

51:18

the pole microbiota transplant, are

51:21

done, but it's really the last resort

51:23

treatment. So you'd have to consider a

51:25

variety of parameters, but it is done.

51:29

The FVT, so this time just

51:31

focusing on the viral part of things,

51:34

I don't know to what extent it's

51:37

actually done, other

51:40

than the research papers that show the

51:42

really good success and the promise of

51:44

it. Then there are

51:46

several labs that are looking either trying

51:50

to find the perfect donor, so the

51:52

human volunteer, where this time you would

51:54

transfer the whole community. Other

51:58

labs and companies are actually doing it. considering

52:01

making synthetic communities. So this

52:04

time it's lab-based complex

52:06

bacterial and viral communities. So you

52:08

know what's going in and that

52:11

can be provided as a pill

52:13

form, can be provided in some

52:16

specific foods in some cases, but then

52:18

it goes like which which

52:20

bacteria should you be adding in and

52:22

then like only bacteria or which viruses

52:24

or should yeast also be part of

52:27

this. So that's it

52:29

gets complexified and other teams are just

52:31

like totally going the other way or

52:33

instead of using instead

52:35

of giving you microorganisms it's like

52:37

okay can we actually modify what's

52:40

already there and maybe trying to

52:42

use phages this time as a

52:44

delivery agent like we know

52:46

that phages are going to reach

52:48

that stage can we just give

52:50

the medicine or the compound with

52:52

these phages. So all of those

52:54

are actively being pursued by research

52:56

labs, by companies. In our

52:59

lab we're

53:02

trying to just

53:05

I guess put some of the

53:08

pieces of the puzzle together or just

53:10

adding more puzzle pieces. We're

53:13

really early on trying

53:15

to understand that at

53:17

the start of Anshul's project we didn't know

53:19

if these phages were even going to have

53:21

an effect. It could be that they wouldn't

53:23

do anything because it's all

53:26

about the bacteria. His work showed

53:28

that no phages are actually actively

53:30

playing a role not

53:32

the main one because it seems to

53:34

be dependent on the bacteria but

53:38

without them the the symptoms are

53:40

less strong so they're clearly driving

53:42

something there but that's in

53:44

mice. How does it translate

53:46

to humans is right

53:49

now an open question that

53:51

we're going to be

53:53

looking into maybe one day one

53:55

day. And apart from IBD because you said

53:57

that your lab also has other models

54:00

other disease models that are possibly indirectly

54:03

or directly affected by phage

54:06

populations and which are those apart

54:08

from just why the

54:10

use of which includes

54:13

Crohn's disease and ulcerative

54:15

colitis is there are there any other models that you

54:17

work with? Right now we've taken

54:19

a shift towards early life development

54:23

and childhood stunting. So

54:26

childhood stunting is a condition

54:28

that actually

54:33

it's a worldwide problem man-made

54:36

a man-made problem it's linked

54:38

to malnutrition and

54:41

really one-third of children under the

54:43

age of five worldwide experience malnutrition

54:46

and malnutrition in the sense of

54:48

not getting enough food or not

54:50

getting good quality food. So there's

54:52

a variety of reasons why that's

54:55

happening but really one of the

54:57

consequences of child malnutrition is stunted

55:01

growth so children who don't grow

55:03

to their full potential and

55:05

it's not only physical but

55:07

it's also mental and cognition

55:09

development that's taking place and

55:12

it is a condition that is

55:14

multi-generational child

55:16

who is stunted and develops into a

55:18

stunted adult will most

55:20

likely have stunted children too and

55:24

the issue with that is that

55:26

while stunting is just one of

55:28

the conditions of malnutrition you have

55:30

more severe forms of malnutrition but

55:32

basically this condition puts you at

55:34

higher risk of developing chronic diseases

55:36

it puts you at higher risk

55:38

of catching infectious diseases

55:40

and so forth and

55:43

once again we have been showing

55:45

that phages are an

55:47

important member of your gut microbial

55:49

community in the sense that

55:51

if you don't get the right phages

55:54

during early childhood

55:56

you are setting yourself

55:59

off in a not

56:01

wrong trajectory, but not

56:05

being able to fully develop the way

56:07

you should be. And that

56:09

is you're not going to have

56:11

the correct immune responses. Once again,

56:13

you're not going to meet the

56:15

correct developmental milestones at

56:17

several levels. And we

56:19

currently have a project looking

56:21

into this early life system.

56:24

And for me, in terms of

56:27

like microbial interactions, it's fascinating because

56:29

we're looking at early in life.

56:31

So the whole system is setting

56:33

up. Microbially

56:36

speaking, the bacteria, the immune system,

56:38

the phages, the viruses, they're all

56:40

kind of like fighting with each

56:42

other and recognizing each other and

56:44

kind of setting the stage for what's going to

56:47

be happening earlier on. So... So, importantly, maybe this

56:49

would be breastfeeding versus bottle feeding

56:51

and cesarean birth versus vaginal. And just

56:53

for the listeners and people, just

56:56

in general, that's very important in

56:59

colonizing your gut and then

57:01

when it's permeable when you're born. And

57:04

is this what you're referring to in your life? And

57:06

your diet can influence this as well, right?

57:08

And your diet can influence this as well. So if you

57:11

have a poor diet, as you were saying, leading to stunting,

57:13

that can influence the phages. So

57:15

potentially phages could help fix some of that.

57:17

Potentially, yes. Do you have a mouse model

57:19

for that? We have just developed

57:21

a mouse model for that. That's

57:23

the work of another graduate student

57:25

in the lab because the current

57:28

mouse models of malnutrition typically use

57:30

adult mice, which is great

57:33

if you want to know what's happening

57:35

with mature communities or adults. But

57:38

this whole aspect of if you're a stunted

57:40

adult, you're going to give birth to what

57:42

is a high chance

57:44

of being a stunted child and so

57:47

forth. And this whole

57:49

zero to three year window, that's

57:51

what was missing with our animal models. And

57:54

so right now, we have actually developed –

57:58

we're still waiting on the sequencing data. But

58:00

we do have, once

58:02

again, germ-free mice, so the same mice

58:04

that Antoine has been using. So they've

58:07

never seen microorganisms in their

58:09

life. So these are not animals that

58:11

you would find outside. They don't exist

58:13

outside of a lab. And

58:16

they allow us to ask us

58:18

these cool questions of, what happens

58:20

when you give some specific microorganisms

58:23

to these animals? And so in our case,

58:25

we've been giving infant

58:29

stool samples to these adult mice. So

58:31

we know that there's not a match

58:33

there, but we have these adult

58:35

mice with these infant microbial

58:37

communities. We breed the mice, so

58:39

we let them reproduce. And then

58:41

we're actually following the pups from

58:44

birth to weaning, so

58:47

when they actually stop breastfeeding from

58:51

their dams, from their mothers.

58:53

And that's where we're looking

58:55

what's happening how

58:58

are the bacteria establishing, how are the

59:00

phage establishing? Does it change if we

59:02

give a different diet to the mothers?

59:04

Because obviously these pups don't eat the

59:07

solid food yet, and so forth. And

59:09

so we're just starting there, but

59:11

it's super exciting, and there's lots to

59:13

come there. I'm excited about it. Very

59:15

cool work. So Anshul, we're listening here

59:18

about maybe one day having phage therapy,

59:20

right? But how much is your phage

59:22

different from my phage, right? Is it

59:25

gonna be the same for everyone, or

59:27

is it gonna have to be tailored

59:29

to a certain extent? I

59:31

think it'll have to be tailored to a certain extent. I think, I

59:34

mean, between like in the room, for example,

59:36

there's not a phage probably that is shared

59:38

between all of us. There's a high

59:41

level of inter-individuality in

59:44

phages. With that said, a lot

59:47

of phages between different people

59:49

can infect potentially the

59:51

same bacterial host. But I

59:53

think if we're thinking in a disease like

59:55

IBD, I think

59:58

understanding what's wrong in the phage. bacterial

1:00:00

community. So maybe if I

1:00:02

had IBD and I had a lot of clubs yellow, which is

1:00:04

known to cause higher levels of inflammation,

1:00:06

then maybe we develop a phage

1:00:09

cocktail that's specifically targeting clubs yellow.

1:00:12

So I think that's kind of the direction where things

1:00:14

are going. There's a few clinical

1:00:16

trials going on now with phages

1:00:18

targeting certain bacterial sort

1:00:20

of pathobions in IBDs.

1:00:23

But yeah, I think it's going to be more of a sort

1:00:25

of personalized approach. Well, so isn't

1:00:29

it part of it knowing what the bacteria

1:00:31

are contributing in terms of metabolites and so

1:00:33

forth? And maybe that's the end goal that

1:00:35

you want to modify, right? Right. Yeah, exactly.

1:00:37

So I think that that

1:00:39

is a bit more difficult. And we have ways in

1:00:42

the lab of kind of understanding what

1:00:44

the bacteria are doing at a metabolic level and activity

1:00:47

level. But I think the first

1:00:49

thing would be to sort of target the ones that we kind

1:00:51

of know are causing disease. So

1:00:54

what are you doing now? I

1:00:57

am trying to wrap up my PhD. You'll

1:00:59

get there. I'll get there. Yeah. A

1:01:02

couple months ago. And then,

1:01:04

yeah, I guess the world is my oyster will see

1:01:06

what's with the next step. Sorry. But wrapping up my

1:01:08

PhD and sort of the next, the

1:01:11

other part of my project is

1:01:13

understanding the switch of replication

1:01:15

cycles in phages during

1:01:18

inflammation. So I talked a

1:01:20

little bit about sort of what happens with

1:01:22

the phages after the community has been altered.

1:01:24

But how do they get there in the

1:01:26

first place? So

1:01:28

we think that a prophage induction

1:01:31

is possibly driving the switch. So

1:01:33

when lysogenic phages are integrated as

1:01:36

a prophage into the bacterium, prophage

1:01:38

induction is the process by which

1:01:40

they excise and replicate. And

1:01:43

that happens in the context of bacterial

1:01:45

stress. And

1:01:47

during inflammation, there's a lot of stressors

1:01:49

in the gut. There's increased

1:01:52

oxygen levels in the gut. They're

1:01:54

exposed to bacteria, exposed to a number of

1:01:56

metabolites that they wouldn't otherwise be exposed to.

1:02:00

that sort of make you

1:02:02

driving prophage induction and sort of

1:02:04

causing a shift in the community.

1:02:08

What do you want to do next in your career? That

1:02:10

is a fantastic question. I'm

1:02:13

doing soul searching as we speak to try

1:02:15

to find that out. So I love science.

1:02:18

I love working on

1:02:20

this project. So I want to continue

1:02:22

to work ideally in the field

1:02:24

of the microbiome. I think it's a really cool

1:02:26

system to study. My

1:02:28

project I touch on bacteriology,

1:02:31

virology, immunology, computational biology. So

1:02:33

it's a really cool system

1:02:35

to really try

1:02:38

different things. So I really

1:02:40

like that aspect of it. So ideally

1:02:42

stay kind of in the microbiome field. But do you

1:02:44

want to do postdoc next? Is that? Yeah,

1:02:47

that's probably on the horizon. Okay.

1:02:49

Yeah. All right. Angela,

1:02:51

should we move on to Jesse and Sana? Yes.

1:02:54

So Jesse and Sana have

1:02:56

Sana is first author of a

1:02:58

publication in ELICE that was published in

1:03:01

April of 2023, titled

1:03:03

Do Anthroponautic Transmission of SARS-CoV-2

1:03:05

and host specific viral

1:03:08

mutations revealed by genome-wide

1:03:10

phylogenetic analysis. So what

1:03:13

does this mean? Because for

1:03:15

those, for the listeners out there, there's a lot of

1:03:17

big words in this title and we need to

1:03:20

dissect these. So Sana, can

1:03:22

you please explain a little

1:03:24

bit about your research? Very

1:03:26

simply put, what we were

1:03:28

looking at was how frequently

1:03:30

is SARS-CoV-2 being transmitted between

1:03:33

humans and animals. So

1:03:35

that's the transmission analysis

1:03:38

by the first part of the title. That's what it's referring

1:03:40

to. Another analysis that we

1:03:42

did was that we then looked

1:03:44

at genomes of

1:03:47

SARS-CoV-2 that were taken from animals

1:03:49

and then we were trying to

1:03:51

find mutations that are specifically adapted

1:03:54

to different animal hosts. Okay.

1:03:57

So which animals did you guys do

1:03:59

analysis? So there are a lot of animals. of animals that

1:04:01

have been infected by SARS-CoV-2,

1:04:03

I think a year

1:04:05

into the pandemic, I don't remember the

1:04:08

exact number, but there was a report

1:04:10

saying more than 50 non-human mammals are

1:04:13

able to be infected by SARS-CoV-2. But

1:04:15

then at the time when I started

1:04:17

this project, I looked

1:04:19

through the data, the data sets that

1:04:21

were available, and the only species

1:04:24

that we had enough data of were cats,

1:04:26

dogs, minks, and deer. The

1:04:28

white-tailed deer farmed minks and

1:04:30

cats and dogs. That's the four

1:04:32

species that we looked at. So

1:04:35

you didn't do any sampling yourself,

1:04:37

right? No. You got sequences from

1:04:39

databases, correct? Yeah, so there are

1:04:42

public databases of SARS-CoV-2

1:04:44

genomes, and they were

1:04:48

being updated regularly, and it was a large

1:04:50

data set already. We

1:04:52

didn't do any sequencing or sampling

1:04:54

ourselves. Would you

1:04:56

like to? Okay,

1:04:59

this is not a... No, I think I

1:05:01

would say no. We had a

1:05:03

lab retreat in the woods last

1:05:06

weekend. We saw a couple of deer in

1:05:08

this animal. Mine had a swab. You

1:05:12

can't catch a deer anywhere. You run fast. They'll

1:05:14

run away from you. You also thought, stay away

1:05:16

from it. Yeah, you know what? Probably have to

1:05:18

go. This is a good point because there are

1:05:22

so much data available that you can

1:05:25

mine it, right? Right, exactly. Yeah,

1:05:27

I think when I was doing

1:05:29

my project, there were around 1,000

1:05:32

in 500 animal host

1:05:35

sequences, and there were around 8 million

1:05:39

human-derived SARS-CoV-2 sequences

1:05:42

on the public database available, which

1:05:44

is a very large number. So

1:05:46

before we talk about the data, Jesse,

1:05:50

you did other things before SARS-CoV-2,

1:05:53

right? Yeah. So why did

1:05:55

you decide to do this, and what did

1:05:57

you think you would contribute? Yeah, great question.

1:06:00

I swore after my work on Lassa

1:06:03

virus, I said, that's it. I'm

1:06:05

never going to work on viruses again. I'm going back to

1:06:08

bacteria and I work on many

1:06:10

different bacterial species and some pretty

1:06:13

basic questions in ecology and evolution

1:06:15

using microbes. I'm never going to

1:06:17

do this again. But

1:06:19

the pandemic started and

1:06:22

I just said,

1:06:25

you know, around April 2020, oh, you know, certainly

1:06:30

someone is sequencing the viruses

1:06:33

in Quebec and building trees and

1:06:35

things like that. I

1:06:37

happened to know someone at the Quebec Public

1:06:39

Health Lab, Stauntrin Moreira,

1:06:41

who I'd met at a conference

1:06:43

the year before. I said, hey, are you guys

1:06:45

doing this? And then she was like,

1:06:47

we were doing it. Like, yeah, you know, sure.

1:06:50

Do you want to help? Like, we're trying to

1:06:52

do it. And I was like, all right. So

1:06:54

I sort of got involved a little bit with

1:06:57

the provincial effort that then was coordinated

1:07:00

into a more federal effort across Canada

1:07:02

to sequence and build

1:07:04

phylogenetic trees and just sort of do

1:07:06

the surveillance. So

1:07:09

yes, despite my best effort, I sort of did

1:07:11

get involved. You have skills

1:07:13

in computational analysis, right? That's the

1:07:16

point. Yeah. Yeah. And

1:07:20

of this kind of workflow, right, where,

1:07:22

you know, people are getting swab, things

1:07:25

are getting sequenced, you know, going through this whole workflow

1:07:27

and then we were kind of analyzing the data, building

1:07:30

these phylogenies. I

1:07:32

have two questions for the listeners that I think

1:07:34

are very important considering the title. What

1:07:37

is a phylogeny and

1:07:39

what is zoonthroponosis? They

1:07:43

can each answer one. Exactly. You guys

1:07:45

can choose. But I'm sure there

1:07:47

are many people wondering what do these things mean?

1:07:50

Phylogenetic analysis, what does that mean? I can do

1:07:52

phylogeny. So

1:07:56

phylogeny means, so a phylogeny

1:07:58

is basically a... tree,

1:08:00

a family tree that

1:08:03

describes the genealogical relationship

1:08:05

between things. You can

1:08:07

make phylogenies of different species or

1:08:09

you can make phylogenies of different

1:08:12

individuals within the same species or

1:08:14

you can make phylogenies of the

1:08:16

same gene across different

1:08:20

individuals. So it's basically a tree. So

1:08:23

you use sequences to build phylogenies.

1:08:25

But in the old days, before

1:08:27

sequences, we could measure parts

1:08:30

of animals and use that, for example,

1:08:32

to make a phylogeny, right? Basically.

1:08:36

Yeah. But

1:08:38

we use genomes, a

1:08:41

whole genome of SARS-CoV-2. And you

1:08:43

have programs that have been written that you use for

1:08:45

that, right? Yeah. So

1:08:47

there are so many pipelines that construct

1:08:49

them based on what you're inputting into

1:08:51

them. The one we

1:08:53

use is a maximum likelihood algorithm that

1:08:55

just tells you what is the most

1:08:58

likely tree given the data that you're

1:09:00

giving. So if you have 100 sequences,

1:09:03

the program will do what? It will put the

1:09:05

closest ones next to each other? Pretty much. Is

1:09:08

that it? Yeah. So the

1:09:10

ones that are closer will end up closer to one another

1:09:12

on the tree. The

1:09:14

way it's done, it can be

1:09:17

the algorithm or the process through

1:09:19

which the tree is generated can

1:09:21

be different. But

1:09:23

yeah. And

1:09:26

you also try and infer an ancestor,

1:09:28

right? Yeah. Yeah. Even

1:09:31

though you might not have the ancestor. You say, this is the ancestor

1:09:33

of these and so forth. So in

1:09:35

our case, we were lucky that we

1:09:37

knew the ancestors, the Wuhan strain that

1:09:40

was sequenced in December 2019, that we

1:09:42

knew that that's the root of the

1:09:44

tree or the ancestor. But

1:09:47

yeah. Okay. So that's

1:09:49

phylogeny. Yes. And

1:09:52

then if I can, well, so I think this is linked

1:09:54

as well. And

1:09:56

so right, so Sanneh is using phylogeny methods

1:09:58

and using existing methods. methods. But one

1:10:00

thing that she did was actually to write

1:10:05

some computer code to essentially parse

1:10:08

this information, right? And then on the

1:10:10

tree you have information about is

1:10:12

it a human or is it let's

1:10:16

say a mink, right? So we'll do sort of one

1:10:18

animal at a time, right? So you got this. So

1:10:20

each leaf on the tree is a virus that was

1:10:22

sampled either from a human or from a deer. And

1:10:25

then there's a method

1:10:27

called ancestral state reconstruction, which is

1:10:30

basically gonna walk down the tree

1:10:32

and say what was the

1:10:34

ancestors given these leaves of the tree,

1:10:36

was ancestor more likely was

1:10:38

it in a deer or was it in

1:10:41

a human if you trace back? So Fauna

1:10:43

wrote some code to basically trace that back

1:10:45

and label those ancestors, those internal nodes, saying

1:10:47

was it probably in a deer or probably

1:10:49

in a human to be able to then

1:10:52

say what's the

1:10:54

most likely scenario of did

1:10:56

it then go from

1:10:58

a human to a deer and then

1:11:00

from a deer to a human tracing

1:11:03

along the tree where the tree sort

1:11:05

of actually measures these transmission events that

1:11:07

we didn't observe but we're trying to

1:11:09

infer, right? How

1:11:11

those transmissions happened along this this

1:11:13

tree. Yeah because when

1:11:15

you have a phylogeny you only

1:11:17

have the data or the information

1:11:19

for the tips or the leaves of the

1:11:21

tree but the internal nodes we

1:11:24

don't have them.

1:11:27

In a way you can say they're their

1:11:29

ancestors of those individuals that we didn't

1:11:31

sample. So we don't know if

1:11:34

they were taken from an animal or from

1:11:36

a human. We will have to estimate that based

1:11:38

on the information that we have from the

1:11:40

tips of the tree and the topology of

1:11:42

the tree you have to estimate what

1:11:44

is the most likely state for

1:11:47

each of the internal nodes and then

1:11:49

you just you have to browse the

1:11:51

tree to look for transitions from one

1:11:53

state to another and that's when

1:11:56

it's an

1:11:58

actual transmission from a human to an

1:12:00

animal or from an animal to human probably

1:12:03

has happened. So for example, if you have a

1:12:06

human sequence and you

1:12:08

find the, it's unlikely, but just to illustrate, you find

1:12:10

the exact same thing in a deer, you can infer

1:12:12

that it came from a human, right?

1:12:15

And then in the deer, you don't agree

1:12:17

with it, that's okay. And then in the

1:12:19

deer, you can, in the

1:12:21

deer, then you have some changes that

1:12:23

are never found in humans, so

1:12:25

you would assume that those are deer changes.

1:12:27

And then if you find any of those in people, it

1:12:30

suggests that that virus went back into people.

1:12:33

Is that how it works? So it's a bit

1:12:35

more general than that. So

1:12:38

we took whatever deer sequence

1:12:40

we could find, and

1:12:42

then I tried to match those

1:12:44

deer sequences. When

1:12:46

I say deer sequences, I mean SARS-12-2

1:12:49

sequences that were taken from deer. And

1:12:53

then I matched those with all the human SARS-2

1:12:56

genomes that existed at

1:12:58

the time to find the closest

1:13:00

relatives or the sequences that are most

1:13:02

closely or most similar to the

1:13:04

deer ones. And then I

1:13:07

built a tree of those sequences

1:13:10

pulled together. And

1:13:12

then when we have the phylogeny,

1:13:14

then the sequences that are closer, genetically

1:13:17

closer tend to cluster closer to each

1:13:19

other on the tree as well. And

1:13:22

then when we do the ancestral state

1:13:24

reconstruction and then we look at the

1:13:26

– we identify the transmission – the

1:13:28

transitions from one state to another, that's

1:13:30

where we infer that a

1:13:34

transmission has happened. We don't

1:13:37

explicitly look at mutations

1:13:40

directly to infer transmission, but that's

1:13:42

implicitly included in the phylogeny because

1:13:44

when sequences are genetically more similar,

1:13:46

then they cluster together more closely

1:13:48

and they are more likely

1:13:50

to – those are the

1:13:53

branches along which the transmission has happened. So if

1:13:55

you have several

1:13:57

lineages here, tips. They're

1:14:00

all human then there's a deer one in there.

1:14:02

Yeah, that means they had a common ancestor most

1:14:04

like yes, right Yeah, so we have to estimate

1:14:07

is that common ancestor more

1:14:09

likely to be a human ancestor or

1:14:11

an animal ancestors based on the

1:14:13

topology of the tree and that information we have

1:14:16

from the tips and then wherever an

1:14:18

Animal node has a human

1:14:21

child node. That's

1:14:24

the tree terminology On

1:14:27

the tree there's their nodes have children or

1:14:29

those are branches that come out of them,

1:14:31

right? So if an animal node has a

1:14:33

human child node or tip

1:14:35

then we would we would guess

1:14:38

that That's probably a transmission from

1:14:40

an animal to a human. All right.

1:14:43

So you had about a hundred deer

1:14:45

SARS-CoV-2 sequences Yeah, they download all of

1:14:47

those right? Yeah And then how

1:14:49

do you how do you find which human sequence

1:14:51

you find the closest human sequences to those for your

1:14:53

analysis? Is that it so far? So

1:14:55

let's say we have 150 deer sequences.

1:14:58

I created a distance matrix Between

1:15:01

those 150 deer sequences

1:15:03

and all of the 8 million humans. So

1:15:06

you did 8 million. Yeah, that's like a

1:15:08

that's a matrix That's 150 by 8 million.

1:15:11

It's a very big matrix. Does that take a long

1:15:13

time? Yeah I think it took me a week

1:15:15

to calculate that make me to the computers just

1:15:18

so yeah, I was running it Yeah, you're not sitting there for

1:15:20

a week I

1:15:22

didn't do it manually.

1:15:25

I ran it

1:15:27

on compute Canada clusters. Okay,

1:15:29

so very powerful computers, right? Yeah,

1:15:31

but even on those it took Multiple

1:15:35

days because this is not something you can

1:15:37

parallelize a lot So because

1:15:39

you will have to go through every single

1:15:42

sequence and you can't do this on your

1:15:44

iPhone No, not even

1:15:46

on my laptop No

1:15:48

years ago, I asked Michael Rossman a

1:15:50

crystallographer Yeah, it had you you

1:15:53

know used to not even have the computing power. What

1:15:55

do you do now? And he pulled his iPhone now

1:15:57

we can do a lot of the structural analysis on

1:15:59

my I wish

1:16:01

I could run that on my iPhone. My life would have been easier.

1:16:03

You can log in to the cluster. I

1:16:06

love hearing your answers. An

1:16:08

app, yeah. You can get a terminal app. So

1:16:11

because you need to block a chunk

1:16:13

of time on the cluster for multiple

1:16:15

days, usually you might stay in the

1:16:17

queue for a long time. And I remember

1:16:19

during that week when I was submitting these

1:16:21

jobs, I would wake up because, like, in

1:16:23

the middle of the night, like, no one would submit

1:16:26

the job. So sometimes I would, like,

1:16:28

set an alarm and wake up and, like, do my own

1:16:30

way out. And

1:16:32

then I would check if it's been, like, queued.

1:16:35

And sometimes when you are – so when you

1:16:37

submit a job, there might be, like, a small

1:16:40

typo or a tiny error

1:16:42

that you don't go in a module and then it will

1:16:44

fail. So you wait five

1:16:47

hours in the queue and then it won't

1:16:49

run and that's so annoying. You know, it

1:16:51

hadn't changed. When I was in

1:16:53

college, we used punch cards. You

1:16:55

submit jobs and if you had an error, you'd

1:16:57

come in and get your print out and it

1:16:59

failed and you'd have to – the same thing,

1:17:02

nothing there. Yeah.

1:17:05

So while this is

1:17:07

happening, you're doing other things, you're not sleeping the whole

1:17:10

time. Well, I was not doing much during those

1:17:12

five days because that was, like, the bottleneck that

1:17:14

I had to – like, that was the main

1:17:16

thing I was waiting for. But,

1:17:20

yeah, usually if I'm running something that

1:17:22

takes long, I try to keep myself

1:17:24

busy the other way. And

1:17:26

what were some of the interesting findings? We

1:17:29

haven't – we haven't gotten there yet. So

1:17:32

what are some of the – The

1:17:34

results for the – yeah. So of

1:17:37

the four species that we looked

1:17:39

at, we found that transmission rates

1:17:41

are really high in

1:17:43

the mink-to-human direction. So mink

1:17:46

tend to infect humans very

1:17:48

frequently. Whereas – Because they're

1:17:50

farmed, right? Exactly. Because they're farmed

1:17:52

and they interact with workers on

1:17:54

the farm. Whereas

1:17:57

for deer, we inferred very low transmission

1:17:59

from deer. deer to humans and

1:18:01

that's because they're wild animals and

1:18:03

probably they don't get to

1:18:05

come in contact with humans that

1:18:07

often. Unless they're killed, right?

1:18:10

Yeah. For humans to deer, like we did

1:18:12

a paper recently on Twiv, there's so many

1:18:14

deer now, almost all the white-tailed deer that

1:18:17

they've collected samples from are infected, starts to

1:18:19

re-tow or have. So there's definitely

1:18:21

one way of transmission happening.

1:18:23

Yeah. So in the

1:18:25

human to animal direction, we

1:18:28

found that the transmission rates are

1:18:30

somewhat similar and they're kind of

1:18:32

high. We

1:18:34

have a scatter plot in our, so the

1:18:37

whole point of this is

1:18:39

comparing different species. So

1:18:41

we found that in the human to animal

1:18:43

direction, it's fairly consistent. We found around 50

1:18:46

events more or less, it's

1:18:48

like centered around 50 or in

1:18:50

bird count in the human to animal

1:18:52

direction, but in the animal to human

1:18:54

direction, the transmission is very variable. So

1:18:56

minks have high transmission rates, deer have

1:18:58

low transmission rates. For cats

1:19:01

and dogs, it's trickier because almost all

1:19:03

the data that's available for cats and

1:19:05

dogs are pets. So

1:19:07

pets that got sick or

1:19:09

their owners got sick and they

1:19:11

had them sequenced. So

1:19:14

the data is very sparse in

1:19:17

random in a way. How

1:19:20

many cats and dogs were

1:19:22

sequenced? In the end of, is it like five

1:19:24

cats? Is it 500 cats? No, it was around 80

1:19:26

cats in 50, 60, less than 100 cats and

1:19:28

dogs. We

1:19:32

originally downloaded more, but we had to do

1:19:34

quality

1:19:37

filtering of our data and a lot of the sequences

1:19:39

are just randomly deposited onto GSAID, which is

1:19:42

where we got our data from and some

1:19:44

of them very low quality. Remember, I had

1:19:46

to throw out so many cat sequences because

1:19:48

the genomes were not complete. When

1:19:54

you say the transmission from deer to

1:19:56

humans is low, but it's not zero?

1:19:58

It's not zero. zero. We inferred

1:20:01

it. So the median, I

1:20:03

think, because we had different replicate

1:20:06

runs to count the transmissions,

1:20:08

I think we found between zero and

1:20:10

one or one and a half was the median

1:20:12

more or less. Very

1:20:15

low but not zero. So

1:20:18

does that prove it, do you think, or

1:20:20

just imply it? I

1:20:23

know of several studies that

1:20:26

have reported with high

1:20:28

confidence that deer transmitted

1:20:32

COVID to humans. I think there's

1:20:34

the Ontario deer paper that

1:20:37

with the high level of certainty.

1:20:40

It's all based on phylogenies, right? Exactly.

1:20:42

Yeah, I don't think there's any real, you

1:20:44

know, traditional epidemiology, linking, right?

1:20:47

So it's true that it's all

1:20:50

inferred but I think it's,

1:20:53

even if it's rare, right, I think there's lots and

1:20:55

lots of deer, as you said. So it's

1:20:59

rare but probably not zero, right?

1:21:02

So it is something to think

1:21:05

about as this virus

1:21:07

goes and evolves off in different directions

1:21:09

in different animals, including deer, that

1:21:12

there's a non-zero chance that it'll then come

1:21:14

back into humans. And this is, I guess,

1:21:16

sort of motivating this whole study. Why do

1:21:18

we care about this, right? Is that as

1:21:21

soon as a virus infects a different animal,

1:21:23

it can go off in a whole new

1:21:25

evolutionary trajectory that may or may not have

1:21:27

anything to do with how it will

1:21:31

infect humans but it

1:21:33

will sort of start sampling a

1:21:35

new set of mutations, a new

1:21:37

evolutionary space that can have unexpected

1:21:39

effects when it comes back into

1:21:41

humans. So that's why we need

1:21:43

to continue surveillance in non-human animals,

1:21:45

right? Exactly. And I think, you

1:21:47

know, Sana said that, well, since

1:21:51

our study there has been a lot more deer sequencing,

1:21:53

for example, which is great, other animals,

1:21:55

but, you know, at the time there is

1:21:57

something like 8 million human sequences.

1:22:00

or viruses from humans now it's

1:22:02

over 15 million I think so it's been pretty

1:22:05

good at sampling humans which

1:22:07

is great and that should continue maybe

1:22:09

not at the same super high level

1:22:11

we had during the peak of the

1:22:14

pandemic but I think

1:22:16

that there's a lot of potentially very interesting

1:22:18

stuff happening in animals and those are under

1:22:20

samples so we could stand

1:22:23

to sample more. As more data

1:22:25

comes available do you look at it and add it to your

1:22:28

analysis? So we have we're

1:22:30

not looking at animal

1:22:34

sequences of SARS-CoV-2 anymore. The

1:22:37

project I think pretty

1:22:39

much finalized. We

1:22:41

did think it so we are doing

1:22:44

some other analyses of within host dynamics

1:22:46

of SARS-CoV-2 in animals and that's almost

1:22:49

finalized. We did think about

1:22:51

adding the new deer data because there's a

1:22:54

significant amount of deer data available now that

1:22:56

we could add. We're not sure

1:22:58

yet maybe. So

1:23:00

one of the things you show in the paper

1:23:04

you have single nucleotide variant SNPs

1:23:06

that associate with deer right so

1:23:09

that presumably these are changes that are

1:23:11

fixed in deer. How do

1:23:13

you know what they

1:23:15

mean and how do you know

1:23:17

if there are any consequence for people? Is

1:23:19

there any way to look at that? I think

1:23:23

the only so the clinical

1:23:25

consequences of these mutations in

1:23:27

humans we don't know unless

1:23:31

there's like experimental follow-up

1:23:33

of these mutations. I

1:23:35

don't think we can

1:23:38

make a judgment unless we could

1:23:40

guess based on where on the

1:23:42

genome they are and what protein

1:23:44

they affect. We could maybe make

1:23:47

a guess but I don't think

1:23:49

without experimental follow-up we could.

1:23:51

You could make I mean you could

1:23:53

do experiments in cells and culture and see if

1:23:55

they make any. Yeah. That's not great but it's

1:23:58

all you can't really infect people that's yeah problem,

1:24:00

right? Yeah, that's true. But in deer

1:24:02

you could, right? People do

1:24:04

infect deer experimentally. So if you had some

1:24:07

sniffs that you thought might be important, you could test

1:24:09

that. Not you, but in

1:24:11

general. In deer. Yeah, in deer. Right.

1:24:14

Yeah. We need that very expensive setup somehow. Well,

1:24:17

some people do. There have been challenges. Sure. And

1:24:19

that's how we know that in deer, infection is relatively

1:24:21

mild, right? Because

1:24:24

they do challenge in deer. Yeah, it's very expensive

1:24:27

and it's hard to do, but it can't be

1:24:29

done. So if you had a single

1:24:31

nucleotide change that you found in

1:24:33

all deer that caused an amino acid

1:24:35

change, you might want to say, make

1:24:38

the virus and see what it does. It would be interesting,

1:24:40

right? Yep. Because there can be neutral changes

1:24:42

as well, right? Yep. Yep,

1:24:44

for sure. Did you know, by the way, and I love to mention

1:24:47

this every time this comes up, the cat

1:24:49

story. When they first found out in

1:24:51

the UK that the

1:24:53

virus was going into cats, Angela knows where I'm

1:24:55

going. Boris

1:24:58

Johnson wanted to kill all the cats in the

1:25:00

UK. Of course he did. Of course

1:25:02

he did, right? Of course he did. Well,

1:25:04

thankfully he did not. Can you imagine

1:25:07

they come to your house and take your cats? Many

1:25:09

people love their cats. I mean,

1:25:12

they did it with mink farms, right?

1:25:14

Yeah, they did. Yes. There's

1:25:16

no more mink farming in the Netherlands now. Yeah. They

1:25:19

called all the mink. Yeah. There

1:25:21

was a huge industry. Yep. I

1:25:23

mean, I guess it's because it's a

1:25:26

non-essential industry, right? Well, it's

1:25:28

also not domestic. It's not someone's property.

1:25:30

It's not someone's animal because even

1:25:32

though, like, our pets are considered our property, right?

1:25:35

This is an industrial farming. But you

1:25:37

know, viruses come from pigs, but

1:25:39

we're not thinking of not doing pig farm because

1:25:41

we need it for protein, right? No, of course.

1:25:43

Of course. So that's the difference between mink.

1:25:46

Mink is just for clothing and for... And pigs. Yeah.

1:25:49

Anyway. So I found, talking

1:25:51

about these sniffs, these FNDs, there

1:25:53

was 26 in deer, multiple in

1:25:55

minks, but you didn't find any in

1:25:57

cats or dogs. Can

1:26:00

you comment on this? What do you think

1:26:02

is happening there in the 60s?

1:26:04

I think so mainly I think the main thing

1:26:06

is a caveat, which is it's a smaller sample

1:26:08

size, right? So again with more sampling, it doesn't

1:26:11

need don't exist. We could find them so it

1:26:13

could be that we're just Statistically

1:26:15

underpowered. So I think that's the first thing to

1:26:17

say it's a strong possibility other

1:26:20

possibility if you want to put

1:26:22

make some interpretation of the data is Is

1:26:25

that these are mostly dead-end? Infections

1:26:28

and so there's not there's not a lot

1:26:30

of time For

1:26:32

for adaptation. There's not multiple cycles

1:26:34

of cat to cat to cat

1:26:37

to cat transmission Which is

1:26:39

sort of how we interpret the deer results, which is

1:26:41

there seem to be a lot of cycles of

1:26:43

deer to deer They live in herds a

1:26:45

lot of transmission a lot of time lots of transmission

1:26:47

cycles in deer. So there's sort of time to Adapt

1:26:52

and and that might be less the case

1:26:54

in a cat where a person infects their

1:26:56

own house capithen That just stops there. There's

1:26:58

no other cat. Yeah, yeah. Yeah, so that's

1:27:01

one that's one interpretation We've

1:27:04

only sampled Small numbers of

1:27:06

non-human you think lots of other mammals

1:27:08

are infected out there that we don't

1:27:10

know about You ever thought about

1:27:12

that squirrels of Montreal

1:27:15

probably I think

1:27:17

you know This is a very

1:27:20

You know broadly a generalist virus already doesn't care

1:27:22

that it's maybe a little bit, you know It

1:27:24

makes deer less sick and it's what you like

1:27:27

that. There's some variation viral loads But

1:27:29

broadly if this is a virus that kind of thinks

1:27:31

off mammal are it, you know some

1:27:34

kind of two receptor

1:27:37

Yeah, I think rodents are most likely

1:27:39

widely and they're not so hard to

1:27:41

sample right? Yeah, it would be really

1:27:44

interesting I think we should look yeah,

1:27:46

there been any sampling in brass. I think there was

1:27:48

a New York right in rats There

1:27:50

is a jank. No, and

1:27:52

I think in New York the sewage Had

1:27:54

some cryptic lineages that they thought might be rodent because

1:27:57

we have so many rats in New York So now

1:27:59

when I see rats on the subway. Hey,

1:28:02

you have some SARS-CoV-2? And the rats

1:28:04

like, so do you. Exactly

1:28:07

right. But I think it's way more widespread

1:28:09

than we know, but we should find

1:28:11

out. And there's not a lot of

1:28:14

sampling initiatives going on. People

1:28:16

don't seem to care until

1:28:18

something happens. And then, you know, in

1:28:21

retrospect, in hindsight, whatever it is.

1:28:23

But I think we need to do that. So,

1:28:26

Sana, what are you doing now? Anything you can

1:28:28

talk about? Yeah. So,

1:28:30

right now, we are

1:28:32

doing wastewater stuff. Very

1:28:34

exciting wastewater thing. So, I'm

1:28:37

working on a project with

1:28:39

two of the postdocs in our lab where,

1:28:42

so there has been extensive

1:28:44

sequencing of wastewater in

1:28:47

Quebec. So, we have SARS-CoV-2 wastewater

1:28:49

sequences over the

1:28:52

course of, I think, a year, almost like 370

1:28:54

days in different cities. And so, what we're

1:29:01

trying to do is to see if we

1:29:03

can forecast changes

1:29:06

in case counts using

1:29:08

the information that we can get from

1:29:10

wastewater sequences of SARS-CoV-2. Is

1:29:13

this from 2022, the data you have? 2021? Yes.

1:29:16

I think it starts mid-2021 until late

1:29:18

2022. For some cities, I do

1:29:21

have sequences as

1:29:30

recent as early 2023. But I think

1:29:32

the consensus is around 370 days starting

1:29:34

from 2021. So, we're

1:29:42

using the sequences that were taken from

1:29:44

sewage in Quebec and

1:29:46

then the case counts that we have for these cities and

1:29:49

doing some time series, cool time

1:29:51

series stuff and see if we

1:29:54

can find a way to predict

1:29:57

or improve existing predictions. Do

1:30:00

you see any of these cryptic lineages when you do

1:30:02

this? We're not. So

1:30:05

we did do some

1:30:07

exploratory analyses of the

1:30:09

lineages that exist in these sequences, but

1:30:11

we're not really focusing on lineages that

1:30:14

much right now. We're mostly looking at

1:30:16

the snips and the

1:30:18

diversity that

1:30:20

we find in the samples.

1:30:22

Yeah. Yeah, wastewater is a great predictor

1:30:24

of... There's this

1:30:26

website, I forgot the name of it,

1:30:29

that Daniel Griffin always refers to. We look

1:30:31

at the through the US, you can

1:30:33

see it's divided into four regions, and the Northeast

1:30:35

has just kept going up and up and up

1:30:37

and finally... He blamed

1:30:40

it on the Italians, you know. He

1:30:42

said Columbus Day, all the Italians got

1:30:44

together. But I

1:30:47

think it's a really interesting predictor of what's

1:30:49

going on, right? So

1:30:52

it's your relatively new PhD student, but I'll

1:30:54

still ask you, what do you want to

1:30:56

do next? Have you thought about

1:30:58

it? Well, first of all, finish my PhD.

1:31:02

Yeah, that would be good. Yeah,

1:31:05

yeah. Only been a week. A

1:31:08

week if you're starting, I mean in the

1:31:10

PhD program. Technically now, I'm a

1:31:12

second year PhD student. Yeah, you

1:31:14

started PhD too. Yeah. Oh, well, as

1:31:16

of January, I'll be third year PhD student.

1:31:20

Yeah, we'll see. But

1:31:22

you have skills now that can be applied

1:31:24

to many fields. You have computational skills. They

1:31:27

don't have to be viruses or bacteria. They

1:31:29

could be anything, human genomes, right? I

1:31:31

like viruses. Good. Right

1:31:34

after. I

1:31:37

really like... No, I do like what

1:31:39

I do right now very much, and

1:31:41

I would like to continue doing the

1:31:43

same thing. And what I

1:31:46

always... We were actually talking

1:31:48

about this with Jesse this week in our

1:31:50

meeting. What I was saying is that there's

1:31:52

so many computational tools that

1:31:55

have been developed for other

1:31:57

fields, such as finance or...

1:32:00

or econometrics or neuroscience or

1:32:02

engineering and all these very

1:32:04

powerful tools that can be

1:32:06

applied to so many things,

1:32:09

so many problems that we look at in,

1:32:12

in, in, in biology.

1:32:15

If you, if you Google, genomics.

1:32:17

time series forecasting, right? Yeah. It's

1:32:20

all stock market, right? Yeah.

1:32:23

That's what all people wanna do. And no one's

1:32:25

using them in, in

1:32:28

the genomics or biology world. Why not?

1:32:30

Why not? And I think

1:32:32

it's because, I

1:32:35

don't, people are scared

1:32:37

of the math and the. Agreed.

1:32:39

Yeah. In my case, I agree. A

1:32:42

lot of the time, a lot of us don't really

1:32:44

like math. Don't, we're in the stock market. My father's

1:32:46

a mathematician, like, you can do that,

1:32:48

but it's true. I think there is, it can be

1:32:50

intimidating as well, like a lot of times. Yeah.

1:32:53

And I often see this reluctance to go

1:32:56

for really complicated, not

1:32:58

complicated, but really math heavy or

1:33:00

computationally heavy. But that's just that

1:33:03

they're very useful. And I really

1:33:05

like going to the other side

1:33:07

and finding something that's useful on this side and then

1:33:09

like bringing it over. I

1:33:11

went to biology because 40 years ago,

1:33:13

there was no math. That's

1:33:15

not true. That's not true. There was a lot of

1:33:17

age. I don't know. There was differential equations. There's

1:33:20

no math in the biology I did. Now

1:33:22

it's different. But I

1:33:24

don't do any research anymore. So,

1:33:27

you know, there's, you're in academics,

1:33:29

obviously. Do you think you'd like to

1:33:31

stay in academics or maybe do industry?

1:33:33

I don't know. No, yeah, you

1:33:35

could ask you the same question. You don't

1:33:37

know. Yeah. I think there

1:33:40

is obviously pros and cons to both. I think

1:33:42

being a grad student, you're a little bit underpaid

1:33:44

over the course of- A little bit.

1:33:47

A little bit. A little bit. Nicely

1:33:49

put. Go ahead. You can definitely say

1:33:51

it. It's what the state says. Thank

1:33:53

you. It is safe. I am also a graduate student.

1:33:56

Yeah, so I think, I mean, there's pros and cons.

1:33:58

I like, I love being in academia. just concerned

1:34:00

by other academics and so many cool

1:34:02

research questions all over the place. So

1:34:06

pros and cons and personally I'm still

1:34:08

trying to figure that out myself. What

1:34:10

about you, Sana? I

1:34:13

think I'm, I would say

1:34:15

I'm a little bit impatient

1:34:17

and I prefer more immediate

1:34:19

results to like long

1:34:22

term results maybe. That's

1:34:25

why I like the nature of

1:34:27

industry work maybe better because I

1:34:29

do something and I see the

1:34:31

result as I contribute to something

1:34:33

more quickly whereas in academia you

1:34:35

have to like maybe

1:34:37

wait years or decades. The

1:34:39

long game. Yeah.

1:34:43

I might change my mind in two

1:34:45

years. No, we're

1:34:47

going to hold you too. This is recording. I've

1:34:49

written in so many. You can't change. You can't

1:34:52

change. So you have any more questions? Yeah, well

1:34:54

I wanted to ask both, well we

1:34:56

have a question for Karan and Jesse after but

1:34:58

I'll ask Anshul and Sana first.

1:35:01

What advice, let's say Anshul first, what advice

1:35:03

would you have for future

1:35:05

graduate students? For undergrads,

1:35:08

high schoolers, even you know

1:35:10

anyone that's looking to go into graduate school do

1:35:12

you have any advice

1:35:14

or insights? I

1:35:17

think one thing is just number

1:35:21

one, surround yourself, make sure

1:35:23

you're getting into a situation where you

1:35:25

know you're surrounded by good people because

1:35:27

I think the environment in

1:35:29

grad school sometimes isn't thought about

1:35:31

enough. Obviously

1:35:33

you're going to do it because you like the

1:35:36

science but I think also just being in an

1:35:38

environment where you are

1:35:40

supported and are supported

1:35:42

for the things that you do and appreciate it.

1:35:44

I think that's really important as

1:35:46

a grad student. Agreed, I

1:35:48

think that's a very good answer. Your supervisor,

1:35:51

the students that are in the

1:35:53

lab, meeting them before, starting in the lab

1:35:55

multiple times. I know a lot

1:35:57

of people, at least in my lab, I met with... four

1:36:00

different people, three times before I

1:36:02

even agreed or signed anything. Yeah,

1:36:05

you're there for five years. At least. At

1:36:08

least. You signed something? No,

1:36:10

I signed something. I need my gun. You

1:36:14

said yes. I couldn't believe any of that.

1:36:17

It is verbal agreement. It

1:36:19

is verbal agreement. You're

1:36:21

a big paycheck. You

1:36:23

have to sign the stipend form.

1:36:25

Yeah. Really? Yeah. They

1:36:28

do need their approval to give them the little money

1:36:30

they get. But yes,

1:36:32

I think that's really good advice. Mitzana,

1:36:35

what's your advice? I think

1:36:38

my main advice would be

1:36:40

to explore and don't

1:36:42

be afraid of trying out different

1:36:45

things to find what you like.

1:36:48

I think as someone who did this

1:36:50

huge shift from electrical

1:36:53

engineering and system dynamics to

1:36:55

viral genomics, I

1:36:58

can say that there's

1:37:00

so many different things out

1:37:02

there that you can try. And

1:37:06

right now, the world is a crazy place because

1:37:09

everything is so interdisciplinary and

1:37:11

you can do everything in

1:37:14

so many different ways. For

1:37:18

me, my undergrad experience was

1:37:20

difficult in that I didn't like what I

1:37:22

was doing. So in the

1:37:24

process of trying to find something that matches

1:37:27

my interests but also I'm skilled

1:37:31

in was a

1:37:33

journey. And

1:37:36

I was often intimidated by

1:37:38

being in the Faculty of Medicine

1:37:40

in the Department of Microbiology and

1:37:42

Immunology. I haven't had a single

1:37:44

biology course since seventh

1:37:47

grade. But

1:37:49

I learned through osmosis, right?

1:37:52

So I think my main advice

1:37:54

is that don't be

1:37:56

afraid to explore and find something that

1:37:58

you like. because

1:38:02

there are so many things out there

1:38:04

to try. So don't be intimidated,

1:38:06

right? Yeah, yeah. Exploring is fun.

1:38:10

All right, that brings us to our last questions.

1:38:13

Angela, you want to start? Yeah.

1:38:16

So Corinne, what

1:38:19

would you have done if

1:38:21

you weren't a scientist? What

1:38:23

would you have done? Something else? Most

1:38:28

probably. Exactly.

1:38:31

I don't quite know. I

1:38:33

need to think about that

1:38:35

one there. Have

1:38:40

you ever thought about a parallel life? Or Jesse,

1:38:42

if you have something that you already maybe have

1:38:44

a whole story in your head. For sure, for

1:38:46

sure. You're like, I dream about this every day.

1:38:48

Yeah, exactly. I

1:38:51

think this is, can I jump in

1:38:53

while you think? If you already have it. Yeah,

1:38:56

yeah. I

1:38:58

think it's a bit of a cliche for scientists,

1:39:00

but I'll go with it anyway, just to open

1:39:02

a coffee shop. Oh, yeah. We

1:39:05

love coffee. And I think it's sort of a

1:39:07

grass is always greener. I love coffee, right? I

1:39:09

like, well, I'm not that good at cooking, but

1:39:11

I like preparing stuff. And I like making coffee.

1:39:14

And I like coffee. And I like

1:39:16

working in coffee shops too. Or just

1:39:18

a different, yeah, I like coffee shops.

1:39:22

Would this be like a $9 latte coffee shop? Definitely.

1:39:24

Yeah, definitely. This is just like pure

1:39:26

profit. Not like a

1:39:28

$2 full of coffee. It's nice, like

1:39:31

oat milk lattes. What's

1:39:33

with the oat milk? So

1:39:35

at least make Americanos, because the coffee

1:39:37

shop, we went through this and said,

1:39:39

no Americanos. It's an Italian coffee shop.

1:39:42

And they're very strict about being. They

1:39:44

won't make Americanos. Only like short of

1:39:46

breath though. They wouldn't even put ice

1:39:48

in your coffee. You'd think they're pure.

1:39:50

They're very pure Italian. They're also

1:39:52

testa luda. They're hard headed Italian.

1:39:55

I can say that. I learned

1:39:57

in Italy that also

1:39:59

if you order. a cappuccino in the afternoon. It's

1:40:01

like are you crazy? It's not breakfast. A

1:40:03

cappuccino is for breakfast. Exactly. Afternoon you have

1:40:06

a little espresso that like where you eat

1:40:08

your crazy. This is

1:40:10

the first coffee shop answer in all

1:40:12

the years we've been asking this. Okay.

1:40:14

I've heard someone say that though before

1:40:16

as well when you, but not. I've

1:40:18

heard other. I've definitely, definitely heard it

1:40:20

before. I could see you maybe in

1:40:22

like 30 years we'll find

1:40:25

you in like little, little

1:40:27

somewhere with a coffee shop. Have

1:40:29

you come up with something? Well, I

1:40:32

think mostly there's

1:40:35

the cooking part that I like doing so I

1:40:37

would have probably ended up in

1:40:39

a restaurant somewhere, but also I love

1:40:42

explaining things and I can just like

1:40:44

ramble for hours. So probably I would

1:40:46

have ended up as an educator. I

1:40:49

just I'm not

1:40:52

sure I like the sound of my own

1:40:54

voice, but definitely like talking is not a

1:40:56

problem and like trying to make a point

1:40:58

also. So yes, I probably would have been

1:41:00

a teacher. I'm not sure

1:41:04

would have had the patience with like

1:41:07

for young children. I'm learning

1:41:10

that I am actually more patient

1:41:12

than I thought, but I

1:41:14

think yes, education somewhere to always be

1:41:16

able to just like talk all the

1:41:19

time. You spoke

1:41:21

very well today on Twitter.

1:41:23

You like talking. I do.

1:41:25

I have a proposal for

1:41:27

you. I'll get back to

1:41:29

you. I have a question for

1:41:31

Jesse because you've had both the Lassa

1:41:34

virus and Alsars-CoV-2. I want to know

1:41:36

like would you go back to study

1:41:38

more viruses or like that's it? I

1:41:41

don't know. I think that there is and to

1:41:43

be dead serious, no, no, but there is and

1:41:45

to Sana's

1:41:54

point as well sort of like how

1:41:57

bra and it's part like a fun

1:41:59

part of research and being an

1:42:01

academic is you don't

1:42:03

really have a boss like if you get funding to do something

1:42:05

you have a club like you do it no one so like

1:42:08

you can work on as many different things like it

1:42:10

no one's gonna tell you not to so I love

1:42:12

that that you can get you can try different things

1:42:14

right but so for

1:42:16

me anyway there's always this tension of sort

1:42:18

of spreading yourself too thin and then there

1:42:21

is a nice tension of sort of not

1:42:23

being the expert you're always learning something right

1:42:25