Matthew Buffington: You're listening to NASA in Silicon Valley, episode 70, and for
the intro I have Abby with me here again.
Abby Tabor (Host): Hello, hello!
Matthew Buffington: This is a slightly interesting episode just for the sake that when we planned
to recording this, I got horrifically ill, and Abby jumped in at the last minute to go
ahead and do this recording.
Host: That's right.
Matthew Buffington: So Abby, tell us about the conversation that you had.
Host: Alright, well, it turned out to be very interesting!
I met with Sylvain Costes.
He is the manager of the GeneLab project, here at Ames.
So you know how NASA does a lot of biosciences.
We do biology experiments up on the space station.
So when those experiments end, the science doesn't end because all that data goes into
a repository, which is open to the public, it's open access, for any researchers to
use.
And right now they're developing tools, and really building a system around it, where
people can come analyze this space biology data that NASA helped produce, and looking
for discoveries that they can make within it themselves.
Matthew Buffington: Oh wow!
Host: And this is for researchers, if that's your research, or for citizen scientists who
may be interested, they'll be able to explore as well, and getting more out of the data
than ever.
What Sylvain describes it as is NASA as the custodian of knowledge about how life is effected
in space.
Matthew Buffington: Sounds super exciting!
So before jump on into it, a reminder for folks listening, we have a phone number, (650)
604-1400.
Give us a call and leave a message, and we'll try to add that into future episodes.
If you want to be digital, we are on all the social media platforms, we're using the
hashtag #NASASiliconValley.
We are a NASA podcast, but we are not the only NASA podcast!
I'll give a quick little shout out to some of our friends over at headquarters, who do
Gravity Assist.
There's also another weekly podcast called This Week at NASA.
And then of course, our friends over in JSC, over at the Johnson Space Center, they have
Houston, We Have a Podcast.
But for today…
Host: … Let's listen to Sylvain Costes.
[Music]
Host: Hey, Sylvain, thanks for coming in.
Sylvain Costes: Thank you for having me.
Host: I'm excited to hear about your work a little bit.
Usually we start this off by learning about you and your background and how did you end
up at NASA.
Where do you come from originally?
Sylvain Costes: Sure.
I was born in France.
I went through physics and mathematics training in France in college.
Host: Cool.
Sylvain Costes: Eventually I transferred to -- I did an exchange at Texas A&M University.
From there, I liked the American education.
Host: Yeah?
Sylvain Costes: So I decided to go for a PhD.
After a Masters at Texas A&M, I did a PhD at UC Berkeley and spent some time at NCI
National Cancer Institute, and then became an independent investigator at Lawrence Berkeley
National Lab, which is a DOE lab.
I joined NASA only last December in 2016.
Host: So you're pretty new.
You're even newer than I am to NASA.
Interesting.
Sylvain Costes: Good.
Host: And I lived in Paris for eight years, so we have something else in common.
Sylvain Costes: Yeah.
[Foreign language].
Host: No, no, no.
[Foreign language].
Let's continue in English.
From France, what part of France?
Sylvain Costes: It's hard to tell.
I was born in Bourges, which is one of the center cities.
But I moved, I think, 20 times.
By the age of 20, I had moved 20 times in France.
Host: Oh my gosh.
Sylvain Costes: I don't have any really -- City, I would say I'm from the south.
That's really where my family is from.
So Toulouse would be --
Host: I see.
Yeah.
Sylvain Costes: The real city is called Rodez, but I don't really feel like I'm --
Host: But everyone knows Toulouse.
Sylvain Costes: Yeah, I'm just French in general.
Host: Right, okay.
Sylvain Costes: I've been everywhere in France.
Host: So you're used to moving around, it sounds like.
Sylvain Costes: That's right.
Host: From France to Texas to California, you've been all over.
Sylvain Costes: Yeah.
Host: Eventually that led you to NASA.
So you're a biologist, is that right?
Sylvain Costes: No, I'm a physicist.
Host: A physicist.
Sorry.
Sylvain Costes: My PhD is in nuclear engineering.
Host: Oh my gosh.
Sylvain Costes: I used to do -- In my Masters, I was doing nuclear reactor design, so a lot
of neutronics.
And then in nuclear engineering, there is a section called health physics, which is
understanding how radiation impacts people as life; so like how you can get cancer from
radiation, the risk of ionizing radiation.
And so I got into this.
And part of that is called medical physics, which is understanding how radiation can be
used to treat cancer
Host: That's the connection between physics and cancer reduction.
Sylvain Costes: That's right.
Host: Yeah.
Sylvain Costes: And then little by little, I left the physics world to move more and
more towards biology.
Being a physicist, I've done a lot of -- I've used a lot of the physics knowledge, so mathematics
and modeling, into understanding biological processes.
Until last year, very much focused on radiation.
And so the connection with NASA there was that there is cosmic radiation that astronauts
get exposed to.
So I've been studying their impact on humans for a long time.
Host: Right.
Radiation is a big challenge for space exploration.
Sylvain Costes: It's a big one.
Microgravity and radiation are the two big ones.
Right?
Host: Right.
Sylvain Costes: Now at GeneLab, I'm really emphasizing everything.
Radiation is just one small aspect of what we're working on.
We're really looking at the full response of the human, and life in general, in terms
of living in space.
Host: Right.
Sylvain Costes: So microgravity.
Host: The physical effects of the space environment.
Right?
Sylvain Costes: That's right.
Host: Right, okay.
So you just mentioned GeneLab.
What is that?
Sylvain Costes: I'm the Project Manager now for GeneLab.
And so let me tell you a bit about GeneLab, because it's a project that started about
four years ago, roughly.
The idea, which is I think very good, is that NASA should be the custodian of the knowledge
of how life gets impacted in space.
And so, there are a lot of studies that have been going on for 20, 30 years under the sponsorship
of NASA.
Host: Definitely.
We've got a big biosciences division here.
Sylvain Costes: Absolutely.
And so, there's a lot of scattered information here and there.
I think we're lucky to live in a time where now we have this new technology called omics.
And so the omics are -- They're trying to interpret the slight different changes in
your gene sequencing with respect to some risk to your health.
That's one omics that's exploding right now in the world.
But there's other omics that have been going on for a while, one of them is called transcriptomic,
which is 90 percent of the data in GeneLab are transcriptomic data.
There it's the idea of looking at the RNA expression in tissues or in microbes or in
anything we're looking at, any sample coming from the space station or from the space shuttle
if they have been analyzed for omics data up into our repository.
So GeneLab is that big repository of information.
Host: Okay, right.
GeneLab is a database.
Sylvain Costes: It's a database, but it's going to be more than a database.
This was the original thought for it.
Basically, let's store all this information to one local place.
And so, we've been very active in either identifying legacy dataset that should be in GeneLab from
the get go from the past.
We're also very active with any new omics being produced on the ISS, to make sure that
those data comes into our repository.
We're not only looking at one type of omics.
We're looking at many different omics.
There's something else called proteomics, which is protein profile, epigenetic, which
is how your DNA gets decorated by specific molecules that changes the expression profile
of those molecules.
All these omics techniques are coming to us, and we're working very actively in identifying
what's been already produced in the world.
The idea is to become like the custodian of knowledge and catering this information to
the public.
Host: Okay, cool.
Let's review.
Omics is this big area that it could be proteomics, genomics?
Sylvain Costes: Absolutely.
Host: Right, okay.
So any of these companies that are offering genetic analyses for the public.
Sylvain Costes: It's one type of omics.
Host: Yeah, that's one type of omics.
All of these basically are ways to study what our DNA is producing or what any organisms,
cells, are doing with their DNA.
Is it correct that that's what can be influenced by the space environment?
Sylvain Costes: Absolutely.
I think if you go back about life in space, there's really two big questions that we need
to address.
One is really how microgravity confinement, ionizing radiation can affect living entities.
Here, that question is important with respect to the astronauts, because we want to make
sure they're going to be healthy in the long run.
Host: Yeah.
Sylvain Costes: So that's one big question.
We believe that in addition to the battery of tests you can do on an astronaut like blood
samples, pulse, EKG, whatever, you have other tools, molecular tools, that we can address
by using animal models.
Because the problem with omics is typically you have to sacrifice the animal to get the
information.
Host: I see.
Sylvain Costes: For a mouse, we're going to get the liver, we're going to get the brain,
we're going to get the bones, and we can then run omics on those guys.
Host: Okay.
So to see what impact microgravity or radiation is having on the body?
Sylvain Costes: On a body that is close to us.
Mammals are great, but there's also effort on drosophila, which is insect.
Host: The fruit flies.
Sylvain Costes: Fruit flies.
You also have C. elegans.
So there's a variety of animal models we can use.
There is another question that GeneLab is also helping answering in terms of the information
we're putting into the database.
It's more understanding the way an ecosystem is modified by space.
And so, here you can imagine microbes.
We talk about microbes being found on the walls of the space station.
Host: Yeah, I've seen that.
Sylvain Costes: These kinds of things would be addressable with omics, but you're more
interesting in seeing what kind of maybe new species or how a strain can deviate from its
original genomic makeup by being in space for a long period of time.
It's also helpful for the client, for instance, understanding the kind of stress you put on
an ecosystem, like plants.
Host: Yeah.
Sylvain Costes: Then you may be able to optimize how a plant grows on Mars or in the space
station.
You see, you can either look at the ecosystem side or you can look at the human health side.
Host: Yeah.
Sylvain Costes: And so, those two things are coming together in GeneLab which is interesting.
Host: That's very interesting.
That's what I had just realized as you were talking; you can look at the effect of space
on an individual, and then all the way up to the ecosystem scale.
Sylvain Costes: Exactly.
Host: That's pretty awesome.
Sylvain Costes: Right.
Host: Okay, so these studies are going on already.
And then GeneLab collects all that data that these experiments are producing, is that it?
Sylvain Costes: Yeah, so there's different ways that I can make their way in GeneLab.
One thing, as I mentioned earlier, was we talk about legacy data.
Before GeneLab existed, people were already gathering some omics.
Having said that, the omics have changed a lot over the past 10 years.
So the legacy data typically have some kind of technology for transcriptomics that we
don't use as much anymore.
Like we could microwave, which is the old way of looking at RNA labels, gene expression
labels.
Since then, now we have RNA sequencing, which is a better technique.
As we go to new omics, we have much larger datasets.
So the repository is getting bigger as the big data is coming down the pipe.
Host: Yeah, totally.
Sylvain Costes: That's one way.
But the other aspect of GeneLab is to really work actively with investigators and collaborators
to generate new data.
And so we work with PI to have their funding from NASA to fly animal models, plant, microbes,
in the space station.
And we help them maybe get more information from their samples and make sure that all
their omics go in the database at the end.
Host: This is the side that I know a little bit about from working at Ames.
Our bioscience department, they work with researchers at other institutions.
Right?
Sylvain Costes: Right.
Host: Who want to fly an experiment to space to do their science.
That's what you're talking about.
Right?
Sylvain Costes: Absolutely.
Host: We'll help them carry out that experiment, and then also we get to use the data.
Sylvain Costes: Absolutely.
There is that aspect of NASA Ames where an investigator will work with a principal investigator
that got funding to actually fly on the ISS specific mission and specific experiment.
Part of our work is also there.
I think there's something new about GeneLab that as the new project manager I'm trying
to push for is I really think that GeneLab should be serving three different communities.
The data repository by itself, that data really talks to the specialists, the bioinformatician
that can go in there and download the data, work with the data, and interpret the data.
Host: Specialists, yeah.
Sylvain Costes: Very, very specialized people.
Host: Yeah.
Sylvain Costes: Scientists.
But then you have another group which is the scientists in general, which they don't know
how to do the bioinformatics, but they know how to ask the right question.
Host: Yeah.
Sylvain Costes: And so, we want to provide tools for them to be able to access the information
without having to do all these very tedious and slow work.
Some of the repository data are now being used to be processed to generate a new level
of data that we would call higher order data that can be interpreted.
From there, for instance, the idea would be is there a signature of cancer in some liver
samples that I got from the space station.
A specialist on cancer, but not a specialist in bioinformatics, can ask this question by
being provided the right information.
Host: I see.
Also, that means they're not doing a brand new experiment.
They're using data that exists.
Sylvain Costes: Exactly.
Host: Cool.
Sylvain Costes: And so now you can think of this -- It's the same data, but they've already
been preprocessed by us, and then they are now -- There's a bigger emphasis on tools
to visualize this information.
And so, we're still working with this with an investigator.
The idea would be to really have, at the end, even a higher-level type of information that
would be very succinct but very simple to access.
With a few clicks, someone could go in there and ask for their favorite gene.
So is P53 modify in space.
And then you could ask to look at all the mouse data, or you could say, "Okay, I want
mouse and drosophila."
Host: Yeah.
Sylvain Costes: This now talks to not only scientists, but really even high school students
can do these kind of questions.
Host: Really?
Sylvain Costes: Yeah, we had a GeneLab for high school.
Liz Blaber was the PI who actually organized this.
It was very successful.
Host: Awesome.
Sylvain Costes: High school students can make sense out of this data with the right guidance,
so it's possible.
Host: Wow, that's impressive.
That's bioinformatics.
You're saying high school students are working on that.
Sylvain Costes: Absolutely, yeah.
It's really amazing.
The new generation is well trained.
Host: Wow, yeah.
NASA is getting the next generation ready.
That's awesome.
Sylvain Costes: Yeah.
There were like, what, 20-plus kids that came here this summer and they spent 3 weeks.
Host: Wow.
Sylvain Costes: They worked on the data that was on the database.
At the end, they did a presentation and it was really good.
Host: That's a good way to spend your summer as a high school student.
Sylvain Costes: I agree.
Host: Not just hanging out at the beach.
Right?
You said three communities that GeneLab serves.
Did we hit all three?
Sylvain Costes: Yeah.
Those visualization aspects is really -- you could have a visualization layer that would
be fairly sophisticated for still scientist type of people.
But then you could really have even a higher-level visualization that is really simple where
you can ask very simple questions.
Anyone who doesn't know science but was curious about space could say, "Is there any change
in inflammatory response in space?"
Host: Okay.
Sylvain Costes: And so at least on the omics level, looking at protein and RNA, you could
extract this kind of information, actually, and report this information back to the public.
What we're envisioning is really this multitier level where you can really, for a specialist,
you would probably much play with the data the way they are.
For the scientist community, you would have visualization tools and some processing tools
if you want to do some grinding yourself.
Host: Okay.
Crunch the numbers and that data.
Yeah.
Sylvain Costes: The system would be, by the way, on the cloud.
But then at the end, the very light level data, which doesn't take much room, is those
visualization data.
But then they're very much guided by us, because we have to make choices in what we want to
display.
Host: Right, right.
Sylvain Costes: To do this, we think that we will involve the scientific community through
something – NIH [National Institutes of Health] uses a similar model called AWG, analysis
working group.
The idea is to put together multiple principal investigator experts in one topic and put
them together to tease out what will be the best way to analyze some type of data.
For instance, we could focus on the rodent data or you could focus on the microbe data.
You could imagine different AWG, analysis working groups, for these different questions.
Host: Okay, so different groups of scientists will decide this particular kind of data is
probably most useful for the community, so we're going to create some tools to process
it?
Sylvain Costes: Some tools or some way of displaying them.
Host: Okay.
Sylvain Costes: How can you make it very, very easy for anyone to understand what's
going on.
What is the right processing pipeline?
We call them pipelines.
It's like a bunch of different scripts that you put together that will take the raw data,
which are very big, and turn them into a very small amount of data that is small but very
meaningful to us.
You can imagine, Google does the same thing with their data.
They have all these very large databases that they work with.
But at the end, when you type a keyword for a specific question you're asking, the system
is able to point immediately to a webpage with an actual answer to your question, which
is remarkable.
In the background there's a lot of things happening for this, and there's that huge
database working for you.
Host: Are you creating the search engine that will browse this huge database of biological
information?
Sylvain Costes: We're thinking of that.
It's a bit more difficult for us because when you think of Google, they really have what
they call big data, which is a lot of data.
GeneLab doesn't have big data.
We have complex data.
Host: Okay.
Sylvain Costes: We don't have that many experiments from space mission.
If you go on our website right now, you will find 130-plus studies, and less than half
of them are actually space missions, and the other ones are ground studies that mimic what's
happening in space.
Host: Okay.
Simulations?
Sylvain Costes: Simulation.
This is the caveat is that we have lots of data, but they are complex data and they're
not big data.
We have very sparse metrics of information.
And so, there's still some question about how you're going to go about those data.
And so, that's really where working with the scientific community will help us figure out
what are the best pipelines with these specific constraints in mind, which is an additional
challenge.
But I think the technology and I think machine learning may be helpful there.
Host: Really?
Machine learning is part of this?
Sylvain Costes: We're thinking of that, too.
Host: Interesting.
Sylvain Costes: Because there might be some clever way of interpreting those sparse metrics
that we're dealing with.
There are a lot of things still that are undefined in the scientific community.
I think GeneLab is really at the cutting edge of this information.
It's super exciting, but it's a challenge.
I think it's a visionary approach to have created GeneLab.
But any visionary approach also brings a lot of challenges that needs to be dealt with.
Host: Yeah, but NASA is all about challenges and taking them on.
Sylvain Costes: Absolutely.
That's why we're here. Right?
Host: Right.
Exactly.
I wanted to ask you.
You've spoken about how GeneLab will be accessible to different levels of expertise.
Is it also open to anyone to go browse and look at?
Sylvain Costes: Absolutely.
The intent is to have these different tier levels.
Currently the current version we have is 1.0, and we're moving to 2.0.
1.0 is very much a repository where you can just download the data.
The version 2.0 would have -- And it's public, sorry.
Anyone can go in there.
There is no restriction.
Host: Amazing.
Sylvain Costes: A high school student can download the data on his or her hard drive
and play with them if they want to.
There are a lot of free tools out there that you can do that, really.
But 2.0 is going to have more interesting things coming down the pipe.
We have now a workspace so people can log in and actually see all your data that you
want to add to the current GeneLab data.
You can bring your own data.
If you want to do a comparison, for instance, with your favorite experiment and some space
samples, you can do it inside the system.
The other thing is 2.0, as we move on, we'll be having move and more tools that you can
use to process some samples and do some analysis.
Host: Does that mean like a cancer researcher could take their own data from their own lab
and compare, I don't know, genetic changes to what we see in space?
Sylvain Costes: Absolutely.
That's exactly the idea.
You could have someone who's a specialist in breast cancer.
We know for instance breast is a very sensitive tissue for radiation.
It's a classic model.
It would not be a bad idea to look at the -- A lot of the animals that were flown on
the ISS and in the space shuttle are female mice.
For many reasons, it's easier to work with female than male.
Typically males tend to fight in the same cage, for a start.
So we can't put as many males as we can put female in a cage.
Host: Yeah.
Sylvain Costes: And so, the female have the mammary gland, which is another very interesting
tissue because they're very sensitive to radiation.
You could look at cancer incidents through radiation.
It would be an interesting question to look at specific early onset of cancer, a signature
at the genomic level, and then compare it to the space station data that's on GeneLab,
for instance.
I don't think anyone has done that yet.
Host: Interesting.
All right, and GeneLab would make that comparison possible.
Sylvain Costes: Should be, if we have -- Having said that, we need to first have some mammary
gland data in GeneLab for ISS or space shuttle.
They might be out there somewhere.
A lot of the data are being generated by the PI as we're talking.
So there's more that are going to come along.
Hopefully some of this information will be there as we -- The longer we wait, the more
information that will be there.
Host: Right, that's going to grow with time.
Sylvain Costes: Exactly.
Host: Yeah, cool.
So do you guys just receive data, or do you ever work with the samples that come back
from the space station?
Because there are biological experiments happening up there.
Right?
Sylvain Costes: It's a great question.
Actually we do both.
The majority of the work is obviously on taking other people's data.
But NASA has recognized that some samples may not be taken by any PI, and so it would
be a bit of a waste.
Host: They may not be used by -- ?
Sylvain Costes: Right.
And so GeneLab has come up with a prioritization of samples that we think are very important.
One of the strategies would be that if we can really focus our attention to specific
tissue on a regular basis, then we'll have a very clear characterization of this tissue.
As time goes by, we'll have multiple time points in space.
So a long duration versus a short duration, looking always at a same tissue in the same
type of animals, then we'll be able to see how the time dependencies are showing up.
To do this, we have what we call the sample processing lab, which is a small group in
GeneLab that either work with other principal investigators when they need help to process
samples.
But also there's something called tissue sharing agreement where we can get some tissue from
the ISS that are not clamed by anyone else.
There's a list of tissue that we'd rather see coming in through this prioritization.
Host: What would be an example?
What would be tissues you're interested in?
Sylvain Costes: The one we've been looking at a lot is liver.
The reason no one wanted to look at liver is because it's not a tissue that's been showing
very much response.
Having said that, we actually now have a publication being prepared on that topic showing that
actually there is some real changes in the liver in space.
Host: Really?
Sylvain Costes: Which is surprising.
There was one study before that had suggested there was a change in a longer duration from
space shuttle samples.
And now the study we're preparing actually is showing that on the ISS as well, the same
strain of mice called C57 are showing some kind of a change in the liver over a 30-day
course in space.
Host: That could be important for human astronauts.
Sylvain Costes: Absolutely.
The big question is -- The problem with animal system is that you have to remember that we
work with one strain, which means that all the data is coming from one single strain,
which is the equivalent of -- When you work on one strain of mice, you're looking at identical
twins, if you want.
Host: Yeah.
Right.
Sylvain Costes: So you have no idea of how genetic variance is affecting this response.
Host: Okay.
Sylvain Costes: What you see in one strand may not be seen in another strand.
Host: Yeah.
Sylvain Costes: And so that's one of the big challenges with the animal work.
Host: I see.
Sylvain Costes: That's a caveat, and that's why insects are pretty cool, because with
insects you can actually have a bunch of different genetic backgrounds in one experiment.
Host: More easily, more of them.
They're smaller.
Sylvain Costes: That's right.
So you see, this is the art of science.
It's like how do you use each model to their best -- Are you optimizing the usage of these
animals?
Host: Yeah, put them to their best use.
Yeah.
Sylvain Costes: Right.
Rodents are great because they're very close to us genetically, but that's the limitation.
Insects are great because like Drosophila, you can have a huge spectrum of genetic differences
and you can have many of them, but then they're much further away from us than a mammal.
Host: Yeah.
Right.
Sylvain Costes: And then we put all this information together.
The idea, again, as we move forward with technology, we expect to see some new algorithm that will
be able to make these bridges between the different species and come up with some real
response from space and understand better how space affects us.
Host: Yeah. Right. Okay.
So take the results from those studies happening in space, look at the data in a broad way
and draw conclusions?
Sylvain Costes: Absolutely.
Host: Okay.
That's super interesting.
Sylvain Costes: It is. Right?
Host: Yeah, cool.
Sylvain Costes: 10 years from now, we can go back and see what we discover.
But I think there's going to be a lot of discovery by the scientific community through this database.
Host: Yeah, no doubt.
The other thing I love about the space station biology experiments is it's not just for space
applications.
But everything we learn about human health from that can be applied down here potentially.
Sylvain Costes: Absolutely.
That's a great point, thanks for raising it.
Because we're discovering this as we -- One of the things we're doing right now at GeneLab
is as we are generating those preprocess file for opening the door to a bigger community
that don't need to do all this processing that we can provide to them, we're discovering
some confronting factors in the sample.
For instance, if you modify the carbon dioxide level in the cage of an animal -- I don't
know if you know that, but carbon dioxide levels are different in space because it tends
to be higher.
Host: Really?
Sylvain Costes: For the longest time, we thought that there was no impact because they were
still pretty low level.
Now with the GeneLab data, what we're discovering is that when you do a ground control and you
increase the carbon dioxide to the level that you have in space on the space station, we
do see some [unintelligible] natures in the gene.
Host: It has an impact then.
Sylvain Costes: It has an impact.
Now again, you have to be careful.
RNA level is just one very small piece of the puzzle.
You may have a change at the RNA level but not at the protein level, which is what's
more relevant, I would say, physiologically.
It's like the final signal is turning to an actual protein.
There are caveats in everything we do.
But it's really telling us that, yeah, those carbon dioxide have an impact.
It's not maybe picked up -- Physiological changes are maybe not picked up by it.
Host: Okay, yeah.
Sylvain Costes: But those very sensitive molecular tools can pick up those features.
Host: Right.
So that's an example where GeneLab is allowing you to discover that it's very complex, the
interactions between environment and DNA and proteins produced.
Sylvain Costes: Exactly.
Host: And you're teasing that apart.
Right?
Sylvain Costes: Right.
Because back to the carbon dioxide example.
You could imagine a situation on earth where we are exposed to a high level of carbon dioxide.
No one would ever study this stuff because no one would ever think of that.
But it turns out that [this] is clearly putting their fingers on one thing that maybe suggesting
more and more studies even by other investigators.
What are those signatures?
What are those changes in the RNA will do on the long term?
Is there a situation on earth where you get a low carbon dioxide level and they should
be concerned about it?
It is really going much more beyond space.
People being bedridden for like a month is the equivalent of being in microgravity [as]
one of the classic models.
Microgravity can tell us about bone loss and things like this.
Host: That's right.
Sylvain Costes: There are a lot of parallels between what's happening in space.
You can think of space as an accelerator of aging, in a way.
That's the way I look at it often.
And so I think everything we're discovering on those data will be relevant for humans
on earth as well.
Host: Fascinating.
I like the way earlier you described NASA as the custodian of data about biology and
physiology and health in space.
It sounds like you're making that easier to use and accessible to more people.
Sylvain Costes: That's what we're trying to do.
Host: Wonderful.
Excellent.
This was super fascinating.
I think for a lot of people it's surprising, first of all, that NASA does biology, and
then that they can take a look at this data and maybe use it themselves in their labs
or at home.
So thank you for sharing that with us.
Sylvain Costes: No, thank you for highlighting GeneLab.
Anyone who is listening, feel free to come to genelab.nasa.gov.
Host: Awesome.
Also online, we are @NASAAmes.
We can take any questions for Sylvain about GeneLab with the hashtag #NASASiliconValley.
Thanks again for being here.
Sylvain Costes: Thank you very much.
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