0:00

The eLife podcast from

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eLife, the open access journal

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for outstanding Research in

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the Life and

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Biomedical Sciences online at eLife

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Sciences.org. .org. Hello

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This is Merry Christmas, 96

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this is episode 96 of

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Chris Smith from the Chris Smith from

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The Naked In this In this

0:21

episode, the desert decomposition conundrum,

0:23

how things can break down

0:25

so quickly. so quickly in the

0:28

dry. What can reveal about a

0:30

gene linked to autism and

0:32

how mice decode the smell

0:34

of first, every year influenza Every

0:37

year influenza circulates globally,

0:39

evolving and adapting as it

0:41

goes most its arrival in

0:43

most given geographies usually

0:45

coincides seasonally with winter this steady

0:47

Now this changes the changes the

0:49

face that the virus presents to

0:51

our immune systems and it's enables it

0:53

to stay one step ahead of

0:55

us ahead keep on coming back. coming

0:57

back. So for a a vaccine strategy to

0:59

work, scientists have to try to

1:01

anticipate sufficiently far ahead far forms

1:03

of the virus are going to

1:06

emerge in any forthcoming season to

1:08

give healthcare systems enough time to

1:10

mass enough then administer an appropriate vaccine

1:12

cocktail that can protect a population.

1:14

cocktail that can get it right, a about

1:16

60 We of the time. 60% of the

1:18

time. is that number not number not 100%? Where

1:20

are the are the shortcomings in the

1:22

system and what's it going to take

1:24

to push up up, we can can? our

1:26

hit rate. This is what is what Richard

1:29

Near at the University of he where

1:31

he works on predictive models of flu,

1:33

has been wondering He's He's hoping

1:35

to shed some light on where the

1:37

weaknesses are and how how surmountable they may

1:39

or may not may not

1:41

be. influenza viruses adapt very rapidly

1:43

to human immunity, meaning they mutate

1:46

such that antibodies don't recognise the

1:48

virus as well anymore as they

1:50

used to, and these variants that...

1:53

have these immunoscape mutations, these adaptive

1:55

mutations that tend to increase in

1:57

frequency, and our primary means

1:59

of predict... future population would be that you

2:01

look at the ones that grow in

2:03

frequency and extrapolate frequency grows

2:06

further into the future. the

2:08

it turned out that many things

2:10

that grow at some point

2:12

stop growing point stop just and about.

2:14

meander about of curious curious is

2:16

what triggers this work. work. Is

2:18

that that because it grows and

2:20

turns itself into a a disaster disaster?

2:22

is it because it grows grows? Flourishes,

2:24

but then we catch up our immune

2:27

response catches up and heads it

2:29

off. So then is kind of stuck yeah

2:31

we believe it we believe it

2:33

is so as these viruses these viruses grow

2:35

in frequency and circulate. gets Whoever

2:38

gets infected by them generates

2:40

immunity against them, so in a

2:42

a way are using up the

2:44

susceptible populations that allows them to

2:46

circulate and thereby their gross ends

2:48

up being diminished as their frequency

2:50

increases. Why do they

2:52

not though not though just stay one step ahead

2:55

of us continuously and just keep

2:57

on changing? keep on that they

2:59

can continue to grow continue always

3:01

playing catch we're always playing

3:03

some extent some do. they

3:05

do but... Sometimes you have a mutation that a

3:07

particular the virus with a particular advantage. really

3:09

go further, but But from there, it's stuck

3:11

in a place where to can't really go

3:13

further, but some other viral lineages that

3:15

also circulate, way in which they tend to

3:18

have gone a different and while they have a space

3:20

then they're offers in more future opportunities to

3:22

further adapt. then overtakes that overtakes and of a way

3:24

in which viruses can back themselves overtakes,

3:26

a corner then overtakes, then overtakes, they have a

3:28

transient advantage, then of stuck in a

3:30

place overtakes, and overtakes, and overtakes, ones that were initially

3:32

not as successful overtakes, and them. overtakes, and

3:34

overtakes, and overt There are obviously lots of different

3:36

families and types of virus. Many of

3:38

them share some characteristics with flu, but they

3:40

are different from the flu. We've just

3:43

been through a pandemic caused by a different

3:45

group of viruses, the group of but they

3:47

seem to show a very similar pattern to

3:49

the one you're describing, where we saw

3:51

jumps of the virus to adopt a new

3:53

configuration. It thrived in that form for

3:55

a bit, and then a new one came

3:57

along, came and then it got as far

3:59

as as far as the... micron variant, and it seems

4:01

to have stalled there. And we're getting

4:03

lots of offshoots of that, but we haven't

4:05

seen these dramatic shifts again. So is

4:07

COVID showing a very similar pattern to the

4:09

mechanism you think is at play with

4:11

flu? Yeah,

4:13

COVID is a very, very interesting

4:15

example of, you know, the main difference

4:17

with COVID is none of us

4:19

had seen COVID five years ago. Now

4:22

we have all gone through multiple

4:24

exposures, multiple times being vaccinated

4:26

or infected, while flu has been

4:28

around as long as we've

4:30

been living and people that have

4:32

been born in different decades

4:34

have been exposed to different flu

4:36

histories in a way. They've

4:38

seen different flu viruses throughout their

4:40

life and thereby have more

4:42

diversity in their immunological makeup. We

4:45

believe that these differences in this

4:47

immunological makeup affect the virus

4:49

host core evolution. And that for

4:51

if you have a very

4:53

heterogeneous immunological makeup, there's lots of

4:55

sort of different niches that

4:57

some viruses can exploit and others

5:00

can't. While in the more

5:02

homogeneous case with COVID, it's more

5:04

likely that you have one

5:06

variant that escapes a larger fraction

5:08

of the population and thereby

5:10

has a gross advantage that carries

5:12

it through the entire population

5:14

And it ends up wiping out all

5:16

other variants. With things like the

5:19

flu then, if you can now work

5:21

out mechanistically what is going on,

5:23

does this mean we are in a

5:25

strong position to make predictions about

5:27

the future and therefore make better judgments

5:29

about the sorts of vaccines that

5:31

we're compiling to try and head off

5:33

what the virus is going to

5:35

do as it goes through the population?

5:38

I certainly hope so. I think

5:40

the main insight from this work

5:42

is actually exploring the limits

5:44

of predictability. What aspects of a

5:46

future population are predictable, which

5:48

ones aren't, and how much phase

5:50

should be put into this

5:52

kind of predictions versus another kind

5:54

of prediction. So this -evolutionary dynamics

5:56

that we see inherently limits

5:58

the prediction horizon. horizon if you wish.

6:01

the amount of time we can

6:03

run these predictions forward is limited by

6:05

the sort of intricate interaction between

6:07

hosts and virus. How far ahead do

6:09

you think we can look then?

6:11

Because obviously people are lots of other

6:13

laboratories and rival groups to your

6:15

own, they're spending a fortune in trying

6:17

to answer this very question. They're

6:19

bringing AI in to look at past

6:21

flu behavior, future flu behavior and

6:23

so on. Are you saying then that

6:25

in fact we're going to have

6:27

to narrow our horizon? Well,

6:29

there is certainly a lot of

6:32

And the molecular features that

6:34

we've discovered over the last

6:36

decades, so mutations in particular

6:38

places, changes in the viral

6:40

protein, these are features that

6:43

often make a virus successful. And

6:46

I think these predictions are going to get better and better. This

6:48

is absolutely crucial for this

6:50

vaccine optimization vaccine selection process. And

6:53

then there is sort of

6:55

another. question as to how you know

6:57

how confidently can we predict the

6:59

relative proportions of different variants, three

7:01

months out, six months out, 12

7:03

months out. And that sort of

7:05

where the main insight from our

7:07

work here is that prediction of

7:09

these explicit measures of population makeup

7:12

of the future, these are going

7:14

to be much, much harder. Currently,

7:16

it's sort of one season that

7:18

over which these predictions can be

7:20

meaningfully made, but making now sort

7:22

of actually frequency predictions for the

7:24

next winter season the 20, 25,

7:26

26 winter that currently I don't

7:28

think is in reach and sort

7:30

of our paper here highlights some

7:32

sort of intrinsic limitations that predictions

7:35

of these kind face. If we

7:37

know what the limitations are though.

7:39

We understand something about the mechanism of those

7:42

limitations, which means we might be in a

7:44

position to do something about it. So does

7:46

your paper also shed some light on where

7:48

we need to direct our efforts to try

7:50

to become better at doing this in the

7:52

future? Yes. So,

7:54

you know, my take home from

7:57

this work is that to

7:59

actually those predictions better we

8:01

need need a much more. a

8:04

fine of understanding of what

8:06

of systems, of what part

8:08

of the population recognize which

8:11

well. how well. would want to

8:13

be able to measure measure

8:15

of immunological of immunological makeup for

8:17

children and teenagers and adults

8:19

and all the adults older adults

8:21

elderly. and the in different parts

8:23

of the world, so that we we

8:25

we really kind of understand if there's

8:27

a new virus coming in sort

8:29

of. in, sort of is susceptible

8:31

to this new virus and

8:34

who is protected from this

8:36

virus that will allow us us,

8:38

A, to understand the future the future

8:40

frequency trajectory of the virus

8:42

for some time. time, also might

8:44

help us to identify variants that

8:47

are of particular concern because

8:49

the vulnerable are are less well

8:51

protected than they should be. be.

8:53

So, a little little way to go, but we

8:55

do at least now have a hand on

8:57

most of the gaps are are we need to

8:59

address. to address. Richard, near there. there.

9:02

Planet Earth is a giant nutrient

9:04

a giant nutrient Energy

9:06

machine. sun captured from the sun

9:08

captured by photosynthesis of a food

9:10

chain of consumers which, when

9:12

they die, army are recycled by

9:14

an army of decomposers that release

9:16

the nutrients and embodied energy

9:18

back into the environment for for reuse.

9:20

And hitherto, much of

9:22

the attention around these decomposers

9:24

has quite appropriately focused

9:26

on microbes focused on and fungi.

9:28

bacteria thrive in warm, wet

9:30

environments. So, unsurprisingly, the wetter

9:32

it is, the faster things

9:34

tend to be broken

9:36

down. But there's a a catch. in

9:39

extremely dry places where the

9:41

normal assemblage of microbial recyclers ought

9:44

to struggle. decomposition

9:46

nevertheless at the same expected rate,

9:48

rate. So something must be stepping

9:50

in to fill that gap, but

9:52

no one knew what. Now, Nevosagi,

9:55

working originally at the Hebrew at the

9:57

Hebrew in in Jerusalem and

9:59

now the... University of Texas

10:01

at Austin thinks he may

10:03

have solved what has

10:05

been dubbed the desert Decomposition

10:08

Conundrum. This is a

10:10

question that exists for about

10:12

50 years already. What

10:14

we know is that decomposition

10:17

or the breakdown of

10:19

plant litter is determined by

10:21

the activity of microorganisms

10:23

such as bacteria or fungi

10:26

and they are very

10:28

much moisture dependent. So

10:30

expect that the composition

10:32

should increase with moisture

10:34

availability. But the problem

10:36

is that in dry

10:39

lands, which is like

10:41

the dry part of

10:43

the world, it is

10:45

more than 40 %

10:47

of the global land

10:49

area, this model doesn't

10:51

work. The composition doesn't

10:53

increase with annual precipitation. So

10:56

if I drew a graph of

10:58

the rate of decomposition on the

11:00

y -axis and the amount of

11:03

moisture in the soil on the

11:05

x -axis, what you're saying is we

11:07

should see an upward sloping line

11:09

as it gets wetter. The decomposition

11:11

should go faster, but what we

11:13

actually see is almost a straight

11:16

line between the dry and the

11:18

wet. So it doesn't follow the

11:20

prediction of wetter equals faster breakdown.

11:23

Yeah, I would just comment

11:25

here that this is

11:27

the case in dry

11:29

So basically most of

11:32

the research is done

11:34

in the most wetter

11:36

ecosystems, like temperate ecosystems,

11:38

but drylands which are

11:40

less studied just show

11:42

very different trends. So

11:44

something is changing. When

11:46

it's dry, something must

11:48

be stepping in to

11:50

compensate to keep up

11:52

the rate of decomposition.

11:54

because the normal mechanism is not doing

11:56

what it would normally do. There has to

11:58

be something filling the gap. Exactly,

12:01

and this is what was

12:03

termed the dryland decomposition conundrum.

12:05

And it stimulated much research

12:07

that basically what people asked

12:09

is what is different between

12:12

drylands and these other water

12:14

ecosystems. And there are several

12:16

mechanisms that were tested, but

12:18

all of them were mostly...

12:20

focused on the role of

12:23

microbes or the other things

12:25

that facilitate the activity of

12:27

microbes. And what we offer

12:29

here is a different hypothesis.

12:31

Basically, some other organisms, larger

12:34

organisms, are more adapted or

12:36

activity under very dry conditions.

12:38

And maybe they... kind of

12:40

compensate for the lack of

12:43

microbial decompositions and this is

12:45

why we see same decomposition

12:47

rate across different precipitation levels.

12:49

How did you test that

12:51

hypothesis then? What we did

12:54

is a multi-site decomposition experiment.

12:56

We went to seven different

12:58

sites across a grandiant of

13:00

precipitation. We did this litter

13:02

box experiment. We have this

13:05

box or cage where we

13:07

keep plant litter and we

13:09

allow different sizes of organism

13:11

to access this litter. So

13:13

we have three different treatments.

13:16

One was just allowing microorganisms

13:18

to access the litter, the

13:20

other allowed also misofauna, and

13:22

the third treatment allowed the

13:24

whole decomposer community, which also

13:27

includes the macrofauna, which we

13:29

believe has the more important...

13:31

in drier

13:33

sites. Right so

13:36

basically what

13:38

what you're able

13:40

to say

13:42

is well if

13:44

we allow

13:47

different categories of

13:49

decomposer in

13:51

and they vary

13:53

by size

13:55

if they are

13:58

filling the

14:00

gap in the

14:02

dry place

14:04

and we exclude

14:06

them we

14:09

should see that

14:11

desert decomposition

14:13

gap come back

14:15

if we

14:17

exclude the big

14:20

guys and

14:22

they're responsible. exactly

14:24

exactly. you just right

14:27

to look if

14:29

the mechanism we suggest

14:31

is right. We

14:33

also use default just

14:35

to capture ground -active

14:37

insects, and we

14:39

use this to really

14:41

characterize the macro

14:43

decomposer community or assemblage

14:45

in these different

14:47

sites. And we repeated

14:49

this experiment in

14:51

the winter, which is

14:53

cool and wet, then in

14:55

the summer which is a and

14:57

very dry. What did

15:00

it show? Have you got to

15:02

the bottom of the desert decomposition

15:04

conundrum? Do we think what you

15:06

have done shows that it is

15:08

the bigger consumers that move in

15:10

and fill the gap yes so what

15:12

we have found is very beautiful

15:14

because in the winter, we saw

15:16

that microbes dominated

15:18

decomposition in general

15:20

and that decomposition

15:22

increased with precipitation

15:24

but in the

15:26

summer what we saw

15:28

is that macro decomposers

15:30

were the dominant decomposers

15:32

and they decomposition in

15:34

general picked in the

15:36

we call it the

15:38

arid sites and when

15:40

you look at the

15:42

two seasons combined and

15:44

the total decomposition you

15:46

really see comparable decomposition

15:48

rates across the gradient. one

15:51

of the arid sites

15:54

decomposition was higher than

15:56

the wetter sites Well,

15:59

congrats Congratulations on solving

16:01

a 50-year-old mystery and apart

16:04

from being extremely satisfying academically,

16:06

why does this matter? Drylands

16:08

cover more than 40% of

16:10

the global land area. So

16:12

this is kind of a

16:15

big deal. It means that

16:17

we should look at different

16:19

sizes of decomposers and treat

16:21

them separately when we do,

16:23

for example, carbon cycle models,

16:25

when we want to understand

16:28

how trajectories of carbon cycling

16:30

and then... of ecosystem in

16:32

the world in general are

16:34

going to change in the

16:36

future because we do see

16:39

the certification. We see this

16:41

trend and therefore I think

16:43

this is very crucial. Novosagi

16:45

there. This is the e-life

16:47

podcast from the Naked Scientists.

16:50

I'm Chris Smith. In a

16:52

moment how mice detect and

16:54

respond to the smell of

16:56

cats. But first, a gene

16:58

called P10 has been linked

17:01

to a number of neurodevelopmental

17:03

and behavioral disorders, including autism.

17:05

In some cases, it seems

17:07

to affect the relative proportion

17:09

of inhibitory and excitatory nerve

17:12

cell populations. How exactly changes

17:14

to P10 cause these presentations

17:16

and whether we can use

17:18

this knowledge to ameliorate them,

17:20

we don't know. But now,

17:22

working with C. elegans worms,

17:25

which also use this same

17:27

gene in their nervous systems,

17:29

Argentina's UNS researcher Diego Rashes

17:31

has made an intriguing observation.

17:33

Mutating the worm equivalent to

17:36

P10 produces an imbalance of

17:38

inhibitory and excitatory neural numbers.

17:40

But giving the animals the

17:42

substance beta-hydroxy butyrate, which is

17:44

a chemical known as a

17:47

ketone body and produced naturally

17:49

by our own metabolisms when

17:51

we burn fat, can reverse

17:53

the effect of the mutation.

17:55

In recent years there is

17:58

several reports that link mutations

18:00

in a specific she... called

18:02

peten with neurodevelopmental defects,

18:04

instance instance autism. This gene is

18:06

is present in only in

18:08

humans but throughout the animal

18:11

even in very simple multicellular

18:14

animals like worms. In

18:16

autism there is

18:18

an imbalance between excitation

18:20

and inhibition. So we wanted

18:22

to to see what is

18:25

the relationship between this this

18:27

gene. and the imbalance between

18:29

excitatory and and in theory. So given that

18:31

given that you've said that this

18:33

gene is present throughout the

18:35

animal kingdom, even in worms. in Does

18:38

that mean then you could study a worm? study

18:40

a look at some of the same

18:42

changes of the that are in the

18:44

genes in humans with things like autism.

18:46

Put those changes into a worm. into

18:49

a worm and the worms nervous

18:51

system change as well? well?

18:53

Yes, that was the idea. Given

18:55

that the activity of is is

18:58

very well and it has it has more

19:00

or less the same function. throughout

19:02

the animal kingdom. I mean, what

19:04

we can manipulate very

19:07

easily in the laboratory,

19:09

we we wanted to

19:11

study these relationships between P10 and and

19:13

excitatory and inhibitory

19:16

neurons. tried to to understand what happened.

19:18

And when you do you do this,

19:20

if you look at the manipulations

19:22

of that gene, it is

19:24

it reflected in changes in the

19:26

balance of excitatory and inhibitory neurons

19:28

in the the worm it is in

19:30

the human brain? human what

19:32

we observe is that is

19:34

that animals that not working. working

19:36

has specific impairment in the

19:39

development and in the and

19:41

in the morphology of the GABA -L6

19:43

The the GABA -L6 units are the

19:45

inhibitory ones. ones. while the the

19:47

excitatory neurons are quite normal,

19:49

they are not affected. So So

19:51

is too much excitation in in this.

19:53

And is there any way to is there any

19:55

way to revert that? Is there any

19:57

intervention we've got got to have an an

19:59

on if we we to to

20:01

apply this knowledge.

20:03

neurodevelopmental disorders, is there anything

20:05

we could potentially do to

20:08

rebalance things? Yes, the first the

20:10

first achievement of this work is

20:12

that have now a kind of kind of where

20:14

to study the study. between

20:16

between this imbalance between existential

20:19

innovation and can

20:21

try. now different interracial to

20:23

try to ameliorate that. We

20:25

try one of these interventions when

20:27

we expose the animal to our ketone

20:30

body. Ketone body is a

20:32

metabolite that we produce when

20:34

we are And we found

20:36

And we found that the of this ketone

20:38

body. are majorized, the specific effects

20:40

on the bary signals are majorizing this this

20:43

imbalance the animals are more normal,

20:45

even though they have mutation in potent.

20:47

Just to to clarify then, are are

20:49

you saying that when you

20:51

feed or expose the animals

20:53

to this ketone body that

20:56

we naturally make when we're

20:58

starving? when we're starving? it resets

21:00

the balance of activation and

21:02

inhibition in the nervous system?

21:04

Is that because it changes

21:06

the it populations or does it

21:09

just change just change nerve And does

21:11

it only do that when the ketone

21:13

body is there, or does it change

21:15

it is there? Or does it change it we

21:17

found that there is

21:20

a specific time window when the

21:22

the body is efficient. is

21:24

important It is important that the

21:26

is is present when the neurons

21:29

are being developed. If we just

21:31

expose the animal, when they are

21:33

adults, who keto body, the

21:35

nothing. body do what we

21:37

found is found body acts

21:39

when the neurons are

21:41

developing developing and somehow ketone body

21:43

acts very early in development.

21:47

preventing the developmental defects that

21:49

we do see in

21:51

pit music. Do you have any Do

21:53

you have any insights into how

21:55

it might be doing that? it's quite a

21:57

quite a bizarre observation that this

21:59

naturally present... chemical, could shift brain development

22:01

so dramatically? Is it signalling in

22:03

some way? it triggering some other

22:05

genes to get switched on to

22:08

compensate in these animals? Do you

22:10

know how it might be doing

22:12

this? In mutations

22:14

in p10 There is

22:16

an inhibition of a transcription

22:18

factor. So there are a lot

22:20

of genes that are not

22:22

being expressed. and this probably arise for

22:25

the defects that we do see

22:27

in p10 mutants. the exposure

22:29

of this animal to the

22:31

ketone body enhance the activity. of

22:34

these transcription factors. Therefore,

22:36

we think that rescue the

22:38

expression of some important

22:40

scenes for the

22:42

inhibitory neuron development. I

22:46

know this is a very

22:48

speculative question. and you haven't

22:50

looked at this per se, and

22:52

certainly not in anything bigger

22:54

than a worm. But there will

22:56

be circumstances where a pregnant

22:58

animal, including a pregnant human, may

23:01

well be exposed to chemicals

23:03

like ketone bodies. Women will be

23:05

starving at certain points. They

23:07

may have really serious morning sickness,

23:09

so they just can't eat,

23:11

for example, and that could do

23:13

this. Do you think, then,

23:15

that this is happening during development

23:17

in, say, mammals and could

23:19

be affecting the development of the

23:22

nervous system? for

23:24

sure, during pregnancy, there is

23:26

an increased levels of ketone body,

23:28

even in mothers that are

23:30

eating normally, right? So maybe endogenous

23:33

ketone bodies are playing a

23:35

role in neurodevelopment, and maybe enhancing

23:37

this effect of the ketone

23:39

body in neurodevelopment, let's put it

23:41

this way, it would be

23:43

good to evaluate. in mammals is

23:45

increasing the levels of ketone body

23:47

to see if there

23:49

is an improvement in the

23:52

neurodevelopment of p10 mutants in mice,

23:54

for instance. It's an

23:56

intriguing finding, isn't it? Diego Rashes there.

24:00

Mice are are genuinely fearful

24:02

of cats encounter they encounter

24:04

the aroma of a feline

24:06

they freeze scarper. This happens

24:08

thanks to an happens thanks to

24:10

an accessory smell system called

24:12

the or VMO. organ roles is to detect

24:15

One of its roles its

24:17

to detect pheromones and its

24:19

wiring into the limbic system

24:21

enables it to coordinate mating behaviour,

24:23

but but it can also,

24:25

as it turns out, detect

24:27

predator smells, including cats, and

24:29

projections to the brain's hypothalamus

24:32

can then initiate defensive responses. responses.

24:34

Interestingly, the the extent

24:36

of these responses isn't binary.

24:38

They're they're proportional to the intensity of

24:40

the stimulus. Suchiko Haga-Yamanaka from

24:42

the University of California California

24:44

used used cat tears, which

24:47

contain odourance the the mouse is

24:49

sensitive to to, to unpick

24:51

the neurological pathways responsible.

24:54

What we are

24:56

looking at at is

24:58

interspecies communications. The organ or

25:01

VNO can detect B&O

25:03

from predator species in

25:05

prey animals. predator species in

25:07

looking at So what we

25:09

are looking at is can

25:12

control can behavior. the

25:14

behavior of the prey animals.

25:17

is the vomeronase logon located? located?

25:20

the marrow nasal organ is

25:22

situated within the nasal

25:24

cavity. between the nasal

25:26

cavity and oral cavity. Is it

25:29

it, therefore, anatomically distinct from

25:31

the bits of the... olfactory

25:34

system smell other smells? For

25:36

smells. if I For instance, if nose my nose

25:38

in a bunch of flowers, I were

25:40

a mouse and I were sniffing flowers,

25:42

I would I would smell the with with

25:44

one part of my system, but I

25:46

would smell other things with this foam

25:48

or a nasal organ. It's a separate

25:50

entity from the rest of the olfactory

25:52

system. old factory exactly. exactly. The

25:54

organ is organ organ

25:57

of the of the accessory

25:59

old factory. system And then

26:01

the main smell is detected

26:03

through the main olfactory

26:05

system. And how is it wired

26:07

up? What's it connected to? and

26:10

what supplies the innovation to carry

26:12

signals from it and to where.

26:14

The vomeronasal organ contains

26:16

sensory neurons and

26:18

then the axons of

26:20

the sensory neurons.

26:22

travel to the frontal part

26:24

of the brain called the

26:26

accessory olfactory bulb and then

26:28

from there send the

26:31

signal to the amygdala

26:33

or the hyposalamus

26:35

that controls innate behaviors

26:37

as mating or defensive

26:39

behavior. How did

26:41

you... use that understanding

26:44

then to test whether or

26:46

not it could detect for

26:48

instance the and presence of

26:50

a potential predator. First

26:52

of all, we looked at the

26:54

behavior to the source of

26:56

the creator queue. In our

26:58

case, cat was used as

27:01

a creator queue source and

27:03

then we looked at the

27:05

behavior changes. At the

27:07

same time, we also looked at

27:09

whether the sensory neurons in the

27:11

bone marrow are organ was

27:13

activated or not using a

27:15

specific neural activity marker called

27:18

the C -Pos. Right.

27:20

So in other words, you

27:22

can present that the cat saliva,

27:24

which you, which you hypothesize

27:26

the mouse can smell and be

27:28

alarmed by and you're looking

27:30

to see if when that is

27:32

present, the nerves connect

27:34

between the vomeronasal organ and relevant

27:36

brain target structures whether they

27:38

change their activity, which would argue

27:40

they are sensing the smell

27:42

of the cat saliva. Yes.

27:46

So that tells you they're sensitive to

27:48

it, but how do you probe what

27:50

it does to their behavior? What

27:52

we looked at was freezing

27:54

behavior. The freezing is one type

27:56

of the defensive behavior response. So

27:59

if If I present some of

28:01

the the particular the to

28:03

the mouse, it will it. when it

28:05

smells it. That's what you're saying, isn't

28:07

it? It suddenly lock up. lock up.

28:10

Lock up, yes. Once you establish that

28:12

that scent is detected it's going

28:14

to these different brain areas, how

28:16

did you then try to

28:18

establish how the response is programmed?

28:20

response is at the specific At the

28:23

of the neurons in the

28:25

neurons in the are activated

28:27

by by cat saliva

28:29

exposure. and then the

28:32

activation controls the freezing behaviour

28:34

output. And is it a

28:36

proportional response? So if I had

28:38

a big dollop of dollop of would

28:40

I get a bigger response a is

28:42

it a binary thing? It's all

28:44

or nothing. thing? It's all we Yeah,

28:47

we tested that amount of

28:49

of cat saliva the diluted

28:51

cat survivor. cat saliva.

28:53

The non-diluted cat saliva

28:55

in this robust but

28:58

but... So it does mean that the

29:00

animals can make a proportionate it

29:02

does mean that the animals

29:04

can make a proportionate response

29:07

to to a concentration of the

29:09

potential threat stimulus from the

29:11

environment. from the

29:13

environment? Exactly. What If you

29:15

could learned then? up if you were

29:17

to sort of give us a summary

29:19

of what this study adds or

29:21

how this changes our understanding, what

29:23

do you think you've done to move

29:25

the field forward with this? with this?

29:28

What What we

29:30

found was that to

29:32

contain the time-sensitive component

29:34

that influenced the intensity

29:36

of the defensive behavior

29:39

output, and then the VNO can

29:41

can detect. such

29:43

time-sensitive chemical cues, and

29:45

then the and then

29:47

the intensity of the behavior

29:50

through the VNO mediated neurocircuitry.

29:52

The circuitry. medial

29:54

hyposalamus is involved

29:56

in in this circuitry and

29:59

a regular it's defensive behaviour

30:01

out of it. Such a go hagga yamanaka there.

30:03

Well that's where we're that's to gonna have

30:05

leave it for this episode, and in

30:07

fact, this year. listening been listening

30:09

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be back in 2025 with another look

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inside with Until then, from me Chris

30:39

Smith, thank you for listening, have a

30:41

wonderful Christmas Smith, a very you new year. Have

30:44

see you on the other side. Goodbye. and a very

30:46

happy new year and I'll see you on the

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