-: Today I'm gonna talk mostly about, you know,
improving abiotic stress tolerance in crop plants.
Just to give you an outline of my talk,
I will give you an overview of what my
research program here is about, and then,
maybe give you, at least one, maybe two, project snapshots.
So one is on the Salinity Tolerance in Rice,
and the other one is in Drought Tolerance in Wheat.
So, we all know that with climate change, crop productivity,
is going to be a challenge.
It's going to be a challenge
to sustain what we are producing, and, possibly,
you know, with more people coming in on the planet,
to be actually increasing our productivity.
One of the reasons for--
There's many reasons for that, and one of those is that,
extreme events such as droughts and flooding events
are becoming more frequent, and also, in many cases,
more intense.
So that limits how much farmers can produce,
given these environmental limitations.
So my research program's primarily focused on
understanding the physiological and genetic basis
of abiotic stress tolerance.
I focus on water.
I know that in Nebraska and in,
almost all, every cultural settings, you know,
water's very important.
The particular themes that I work on are water abundance.
You know, less water, as in drought, and then water quality,
so, not only do you need good quality water, but--
lots of water, for producing food, but also,
you need good quality, so salinity, is always increasing,
and it's particularly a challenge for irrigated systems,
such as Nebraska.
Because the, Nebraska being one of the--
I think it is the most irrigated state in the country.
So, irrigated agriculture's quite important.
About 20% of the arable land in the world is irrigated.
Of that, about-- you know, but it produces about 40%,
of the total food, which sort of indicates
how important, you know, fresh water is for food production,
and most specifically, in cereal's,
such as corn, wheat, rice, sorghum, you know,
take a disproportionate amount of water,
so about 60% of the cereal production
is derived from irrigated agriculture.
As the fresh water supplies diminish due to events such as
drought events, or due to, poor quality of fresh water,
coping with these extreme events, is going to become--
is already difficult, but it will become more challenging.
So the two cereal species that I focus on are
wheat and rice.
Wheat's-- both of them are monocots.
Wheat's a polyploid, so it's a hexaploid,
meaning that it has three independent genomes,
that coexist within every cell.
And then rice is a diploid, and there's, you know,
especially personal reasons because I come from an area
where wheat and rice are grown all around.
That's where I grew up, and also,
there's a more of a strategic reason,
because rice is a very good model for many of the cereals.
It was one of the--
it was the first crop plant to be sequenced, in 2004,
and it has a lean genome, as opposed to wheat,
which is about 15 times as genome of humans,
so it's really big.
So, the economic reason for working on these two species
is that, between rice and wheat, this shows the
kilo-calories per capita globally.
You know, rice and wheat provide more calories
for human nutrition than every other, you know,
agriculture product combined, so,
so that's kind of the two cereals that I work on.
So I'm gonna give you, like a overview of the
things that I do in my lab, and then hopefully,
a little bit more detail on a couple of those.
So one of the projects that I work on is,
looking at early grain development.
The picture I've paired makes a good point.
So this is your wheat grain after one day of formation,
so fertilization, and then,
if you continue to grow it in well-watered conditions,
so this is the second day, and the third day,
and the fourth day, you can see, that the size is increasing
you know, quite significantly.
But if you were to drought-stress it, for,
after one-- 24 hours, it's size actually dramatically,
you know, decreases.
And what we've found, both for wheat and rice,
is that when you only impose a short window of stress,
drought-stress, and then you,
relieve that stress by watering the plant,
you still ultimately impact the final grain size.
So even, so this stage, the reason we focus on it,
is because it's so critical for the final output,
in terms of yield.
And it's also one of the least understood,
in context of how this development stage
interacts with the environment.
So we work on various aspects of wheat, and when it
becomes too challenging to understand those,
given the genomic complexity in wheat,
we transition to rice, and there's always this,
back and forth going on between
these two species in my group.
The second project that I work on is,
is on Salinity Tolerance in Rice.
The goal of this project is to understand
you know, the physiological responses of rice,
to increase soil salinity, and also,
to uncover new sources of soil tolerance in rice,
using a combination of phenomics,
which I will detail a little bit more,
and then combining that information with
with genomic information for rice,
with the idea being to come up with new genes,
and alleles, that breeders and biotechnologists
can use to improve soil tolerance in rice.
The third project is to look at roots in context of drought.
I've already described that we are interested in looking at
early grain development under drought-stress conditions,
where roots are also very critical for water uptake.
They act as the syphons for taking up water from the soil,
so we are interested in looking at root architecture,
in context of its environment.
How does environment change the root architecture?
What are the genes that determine that?
And can we actually discover new genes, from,
in wheat and rice, to improve water uptake
and hopefully, eventually, improve drought tolerance.
So kinda going back to the outline.
So I'm gonna talk for the next 10, 15 minutes about
salinity tolerance in rice.
These are mostly, like a very, you know,
overview type of slides that I have,
and if you need more information,
if you have comments or questions,
feel free to, you know, talk to me after the--
during lunch, or you know, after the talks.
So why salinity?
So salinity is a global problem.
This is a somewhat dated map,
but that's the only one that's available.
These areas in black show where the soil are--
where soil and agriculture's affected by
increased salinity level.
So it's estimated that about 12%
of the global food production
is affected by salinity.
Every time you irrigate,
even if it's the best quality water, fresh water,
you're gonna have some salts dissolved in it.
The water would either seep down or evaporate.
But you would still have the soils.
Irrigation, or irrigated agriculture itself,
tends to accumulate salt in the root zone over time.
So there's many approaches to address issues
of salt, and salinity in food production,
starting with, you know, better economic practices,
and you know, type of crops you grow.
And genetics, and,
Better genetics for salt tolerance is one of the solutions.
By no means is it the only solution that would work,
so there's a combination of different approaches
that would be needed.
But my interest is in trying to understand
what's the genetic basis of salt tolerance
and discovering new sources.
So salt tolerance is, or, you know,
the mechanisms for salt tolerance are varied.
And one of the reasons why they're varied is,
that you know, if you enter a room that's, say,
you know, if this room's 110 degrees,
and you enter the room, your response initially,
may be very different from what it would be,
20 minutes later or one day later.
Your body and your physiology
would be responding quite differently.
So similarly, salt stress is, you know,
has a similar dynamic effect on plant growth.
So this is a map that's just, you know, it's more of a,
thematic map, which shows that,
when you apply salt-stress, plants typically, you know,
the growth rate drops, somewhat dramatically,
and then, that's primarily because of osmotic stress.
It's not because sodium chloride,
or sodium's kinda gone into the plant.
It's mostly because the plant cannot take up water as easily
so the growth rate drops and you need
total pressure and water (mumbles)
for the plants to continue to grow.
And then, eventually, sodium does make its way
through roots, into the various tissues,
and then you start to have this, more of a,
growth response to iron toxicity.
And there's a suit of mechanisms that are involved in it,
and that people have studied, and shown that are important.
What the challenge is, that for you to generate,
or for people to generate a graph like this, you know,
they have to sort of take a handful of plants,
and then sample then every day, you know,
or every few hours, for growth rates,
to discover any differences.
If you're looking at genetic variation,
you know, numbers is your best friend.
So you need to be able to sample
thousands and thousands of plants,
and destroy them every day,
to actually know how the growth factor is progressing.
So that's something that I've become interested in,
and using phenomics approaches to resolve that.
So the species that we selected
to work on for this is rice.
Rice is, perhaps, one of the most salt-sensitive species,
crop species, and its yield is dramatically reduced.
So this is the, some of the life's
main developmental stages for rice,
and, so we decided to focus on three stages
that were known from, based on literature,
to be the most sensitive for salt tolerance,
in context of yield,
so these are stages--
if you impose a salt-stress during those stages,
you will end up having greater yield losses
than in other stages.
So today I'm gonna mostly--
the stages of seedling stage are the tillering.
If you make less tillers, you're gonna have less panicles,
and less grain.
And then, panicle initiation, that's kind of the,
starting point or early stage of what will eventually become
a seed-bearing panicle.
So today, I'm gonna mostly show you some information,
or data from the tillering stage.
So the resource that we are using,
to study genetic variation for salt tolerance in rice,
is a Rice Diversity Panel.
It's a collection about, 400 rice genotypes
from all over the world.
And you know, in different sub-populations,
such as indicas and japonicas and so on.
And it kinda shows you how this are distributed.
So these rices range from, rices that are higher than me.
And then rice that grows in terraces in Indonesia,
to rice varieties or land races that grow in, you know,
(mumbles)-grown regions in Bangladesh.
So they're very varied, and so we use this
resource that was developed at Cornell.
And the reason that I say that it's a resource.
It's not just a mere collection of the worst lines.
People have actually gone in and developed markers,
genetic markers, which are sequenced.
So you could think of it as, you know,
sequenced-based position markers,
like you would have on a highway.
So that can tell you where a particular gene
or a particular trait may be associated.
So there's about 44,000 markers for
for this Diversity Panel that we used.
So in terms of phenomics,
as you know that, we are increasingly capturing more
information, in form of images.
In fact, even have like, people instead of taking notes,
who like, you know, take their iPad and iPhone out,
and start capturing information,
you know, as an image.
And so, plant scientists are, you know,
in tune to that idea, and so there were platforms,
that were developed about four or five years ago,
where you have a setup of conveyor belts,
where parts move on these conveyor belts,
and then they go through these series of phone booth
type of setups, where you're sliding those and your cameras
on the top and the side,
and all the plants can be imaged daily,
or even more frequently, depending on your capacity
of, you know, automatically.
And so this is the setup that we used.
This work was done collaboratively with the,
at the University of Adelaide in Australia.
They're a fairly large facility,
and they agreed to work with us on this.
So this is what the setup looks like,
and you have these booths,
and this is the camera from the top.
Right now the booths are open,
so you can see through hermetical booths.
And there's watering and irrigation system is in place.
Another, University of Nebraska, here on Innovation Campus,
has I would say, even a better, more sophisticated setup,
with greater capacity to grow, you know,
tall sorghums and long plants,
along with tiny wheat and rice plants.
So we are very fortunate to be able to, kind of,
be one of the public facilities in the U--
One of the first or second public facilities in the U.S.
to do this.
So what we did was, basically,
we did a step-wide increase in salinity level,
on the (mumbles) on a number of days,
and then we let the plants grow.
So we had unstressed plants and stressed plants.
And then we imaged them every day.
Two images from the side, you know, at 90 degree,
and then one image from the top.
And then we used four types of cameras.
We had fluorescence images,
and then we had infrared.
Fluorescence would tell you the level of pigmentation,
so if, you know, if the plant's yellowing,
you should see chlorophyll degradation,
and that kind of information can be picked
from the fluorescence.
And then you have infrared, which should act as a proxy for,
you know, how cool or warm the plant foliage is.
And then near-infrared should provide you information about
the water contents.
Remember if you have salt outside in the roots,
it's harder for the plants to take up water.
and the plants don't take up water,
then they become warmer, their growth decreases,
and they also have less water.
So when their growth decreases, you could pick that up
from the visible camera, which we typically call RGB,
which is what your cellphone would capture.
Just a regular cellphone.
So, what we did was, we captured--
Well, you know, we took 380 rice varieties
under control and salt stress,
and we captured images over 18 days,
after applying the salt stress,
We ended up with about five million images.
You've all heard about big data.
It starts popping up, you know, a few years ago,
even when you open any newspaper.
Your New York Times or something.
But what really hit me, was that,
when we tried to move this
five million images onto, you know, an online
plant by informatic infrastructure to analyze them--
These images were in a fol-- zip folders.
And it took them 23 days to unzip the folders.
(laughter)
So all they needed to do, and I'm not talking--
I'm talking about infrastructure.
I don't know if many of you know, but I plant,
We're more than $50 million dollars
worth of investment already.
You know, then you start thinking that
it's not just the volume, but the number.
So there's lots of challenges.
So one of the things that we have done,
over the last two years,
is build an open-source software,
that would analyze at least two types of images,
the fluorescence, and the RGB images,
so that it would give us information on the growth rate,
and information on the rate of senescence and so on.
So that's a open-source software that's freely available.
And the cool thing about that software is--
at least, I think it's very cool,
is that it runs on a system, or a infrastructure called
Open Science Grid.
Some of you may have already heard about it,
but what it is, is,
it's a opportunistic network of clusters, or,
clusters of CPU's, across many national labs,
and many of the major U.S. universities.
What it does is something similar to what
a power grid would do,
if you have many power grids interconnected.
So if there's shortage of power in one area,
surplus of power in another, you can move, you know,
you could draw power from the other grid.
So this grid, what it does is,
it looks for idling CPU's in different universities
and national labs,
and then it, when you submit a job,
it breaks it up into many, many pieces,
and then sprinkles them on the grid nationally.
And then when the jobs get done, they bring it back,
and they integrate, and you'll never know
what's happening in the background.
So we have a software that's uploaded and available
for anybody to use.
And I think it's at least accessible in
the public research system in the U.S.,
for people to analyze it, and it's really fast,
and it can, you know, do maybe 80,000 images
in five hours or something like that.
So what we focused on was growth rate-related
(mumbles) in early stages growth-rate and late,
so we used the RGB image,
for looking at the dynamic responses to salt stress,
especially the osmotic phase, during early (mumbles).
And then ionic phases, where, you know,
you can start seeing that orange, instead of dark red,
for chlorophyll, under fluorescence camera.
So like, in effects of senescence and so on,
with ionic stress.
And this is what the entire population looks like.
I've expressed it as a Salt Index, which is basically saying
the shoot area over salt,
by shoot area over control, and--
so if you have a genotype that has, you know, really
low salt index, it basically means that your,
that genotype of that panel is very sensitive,
in terms of growth.
And if you have something that is close to one,
which means that its growth rate didn't change too much
when you applied the salt, so it's very tolerant.
So I point out two lines.
This is an African rice, and this is a Korean rice variety.
And this is what the images look like.
On the top is the sensitive line,
so you have your pairs of control and salt.
And you know, selected days.
And you can see that their growth rate's quite different,
by day 31, and you know,
if you have the tolerant line, it's not as different.
Now this is a difference that you and I can see,
so really, where does the sensitivity come from,
when you're using this technology?
It comes from this graph, which illustrates that the
sensitive line, which is in red, starts to drop below one,
which is its growth rate start to dip,
even before you reach the final concentration.
Whereas the tolerant line seems to kinda, do quite well.
So this is the type of sensitivity that
you have to almost harvest a plant every few hours,
and then harvest for control, for salt stress,
and do replicates, and then do it for 400 varieties.
It would just kind of be, maybe incredible
to have such a resource, but,
this is a good proxy for.
It shows that you can use these type of resources.
So now you have these images.
You have these graphs that you can plot.
So what next?
How do you get from phenotype to genotype?
And that's been a big challenge because,
genotyping, which means trying to determine,
or get an overview of the genetic makeup,
of the organism or variety, has become cheaper,
because sequencing's become cheaper.
So to do that we,
we used an approach, where,
which is called genome-by-association,
where we found these differences in growth rate,
and then we linked them to those markers,
or those milestones, if you want to think of choromosomes
as highways, and you know, specific positions are markers.
And we try to link that, and this was work that was done by
in collaboration with Dong Wang who's a former faculty here,
he had really great ideas on how to do this.
And without going into the details of the models, and so on,
what I want to tell you is that, this method works.
That you could, indeed, go from imaging,
and using imaging as a phenotype, to,
linking it to the genes and genotype.
So what I'm showing you are these, you know, dots.
This called a Manhattan plot, which,
on the bi-axis, the higher the dot is,
the more significant that marker is associated is--
more significant is the association of that marker,
with a particular phenotype,
in this case, growth rate under salt stress.
And in green, are the significant ones.
And rice has 12 chromosomes,
which is the DNA content of the rice,
genome is broken up into 12 pieces
that are called chromosomes, and,
we've found some really interesting candidates.
Like this one, with very high--
very low key values,
that we are now pursuing, so we can--
Given that the rice genome is sequenced completely,
and you can kind of go in, in great detail,
and find the genes.
We're now pursuing some of the candidate genes
to see if we can, you know, dish--
If those are the basics of the growth response
under salt stress.
So now I'm gonna transition to the
drought tolerance component in wheat.
So this is,
so in wheat, we're taking a slightly different approach.
In rice, we just found this really great resource,
and with the Diversity Panel with all the marker information
you know, you could click it, and in less than 10 seconds,
you'd have all the information.
In wheat, it's a little bit challenging
because of the genome.
In this case, we're interested in looking at
natural variation, for root traits.
What I mean by that is,
this is a picture from John Weaver, from 19--
in a-- from a book published in 1926.
He was a faculty at UNL.
I don't quite know if our department existed in 1926,
but he was a biologist of--
and so, this is one wheat genotype,
when you-- when grown in Lincoln environment,
this is how the roots look.
And when the same genotype, that same year,
was grown in Burlington, Colorado in a very dry environment,
this is what the root architecture looks like.
So we're interested in, you know,
where are the genes that provide this plasticity?
And that, with the hope that if we can find those genes,
we can at least know, you know,
these are the genes and the leaves that we can use
to get this kind of plasticity.
It could become useful for a particular environment.
So, the resource that I'm using for this is,
a set of translocation lines that were provided
by a great friend and colleague at U.C. Riverside.
He's a psychogeneticist, so in other words, what that means
is that, he has the scale, and the rare scale of being able
to introduce and remove chromosomes,
introduce smaller pieces of chromosome,
from other species into wheat.
And you can do this in wheat and very--
it's quite difficult to do it in a different species,
but because wheat has these abundance of chromosomes,
it's a hexaploid, so it can tolerate losing 100, 200 genes,
or even accommodating, or hosting, you know,
genes from another species.
So what I'm showing you are the 42 chromosomes of wheat.
If you're eating bread, this is what it's coming from.
And in red are the wheat chromosomes,
and then you have, this piece in green is
a wild relative of wheat, so it's a
small piece of chromosome that was introduced in 60's,
I think in (mumbles) for bringing in Rush resistance.
What I-- working, you know,
when prompted by the psychogeneticist, what I explored,
was the idea of root
traits for this, you know,
special traits that might be brought in by this piece.
So what we found was that, when we looked
under well-watered conditions in this seedlings, of roots,
I called the wild type is current 76,
and the one that has the green piece as the,
big piece of chromosome as TL, or translocation line,
and there's another control.
When we look under well-watered conditions,
we don't find any differences in root.
However, when we impose a slight water stress, what we find
is that the translocation line continues to grow,
and make lateral roots.
These are side roots that come from within root.
Whereas, the wild type, which doesn't have any green or red,
has very little lack of roots.
So there's a reduced rate of lateral root emergence.
And you can see that in this three pictures here,
on the top, or, you know, you've got
plenty of lateral roots coming out,
but not much in the wild type.
We looked at this grid in bigger setups in our later stages
and we found that there's no big differences in the limit--
in the well-watered conditions,
but the plants that had that alien introgression
tends to become insensitive to water stress,
or it's more tolerant, and it continues to grow more roots.
And it tends to do better, in terms of,
shoot biomass and root biomass,
and some of that, we think, is associated with this graphs.
I don't wanna go through all of them, but the main point is,
that the plants that continue to make lateral roots
under limited water,
tends to take up more water, and then use more water.
In the process, that takes more carbon.
So, when you're losing water, you couldn't stress that
as stomatal conductance.
So these are maintained, if you look at,
stomatal conductance, so.
These are three different genotypes under well-watered.
Where under limited-water, you always see a 50% drop
in the lines that don't have that green alien introgression,
whereas not much of a drop when you, under limited-water,
in the translocation line.
So we were curious about this,
and you know, one of the same ways we can go about doing,
learning more about what may be happening at genes,
because we're interested in finding those genes and alleles
that could be useful,
is to use more of the functional genomics toolkit.
And one of the things
that people have been using in the past was
a wheat microarray.
So we used that a few years back.
And then we also did some RNA sequencing
with what Daniel described, you know,
in the talk before.
So combination of those, basically what it does is,
it tells us these two platform, the technologies tell us
how-- what genes are expressing,
what genes may be different between,
when you introduce that alien introgression,
as opposed to the one that don't have it.
So we did some analysis, and we found many genes
related to hormone, growth hormone.
Clearly there's no differences in growth rate, and so on,
so there's the growth hormone.
But then we also looked at candidate genes that could
actually be responsible for this trait.
So, the idea being, that now you have this piece
from wild species that has replaced
the original piece of wheat chromosome.
So what are the genes on there,
and could any of those genes be responsible for
giving us more lateral roots under limited-water.
So one of the genes that we found.
We call it LRD, or for Lateral Root Density.
What it did was, this is just a graph
that shows you the level of gene expression.
More active the gene expression,
the higher the (mumbles) bar would be,
and less active the gene expression is,
the lower it would be.
So on your left is the wild type.
So when this gene is under well-watered--
Let's arbitrarily put that at one expression level.
When you have low water, this gene's expression increases.
And similarly, for the translocation line,
if you put the well-watered expression at one,
when you water stress it, its expression drops to half,
or even more, depending on what technology you use
to measure gene expression.
So we thought that was quite interesting,
that you have this one gene that maps to that piece,
and it has opposite response, you know.
It's going up in response to drought stress in one,
but going down in the other.
So we then directly tested this.
Typically, my lab would immediately jump to rice,
because we can make genetically-modified rice
with changing gene expression fairly easily in the lab.
But because, you know, Tom has this facility,
Tom Clemente, it's close the--
So he generated some wheat plants for us,
which is sort of unique capacity
that we have in Nebraska,
and not many universities can call for.
So he generated wheat lines, where he,
he took the gene and either he either
suppressed that expression permanently,
or ramped its expression up a lot.
This is what the lateral root density,
or the number of side roots per unit length of root
looked like, so if you have--
So he uses a wild type called CBO37,
which is easy to transform.
I mean easy, relatively speaking.
Wheat's quite difficult.
So in this case, when you have a limited-water,
which is in the white bar, as opposed to grey,
which is well-watered,
you see a drop in lateral root density.
However if you suppress this gene--
it's permanently suppressed,
you don't see as much of a drop.
But if you have over expressed this gene,
you still see a very significant, almost half,
of what the original, so,
what it indicated to us was that,
this could potentially be the gene that is rec--
is on that piece that's introduced,
from the alien, well, wild relative of wheat,
into the wheat genome.
That could be involved in, you know,
regulating lateral root density.
So this is what the plants look like,
when we grow them in tall pipes, and, with sand,
and we wash them out.
So the first three plants are the wild type.
The next three are the ones with suppressed expression.
And the last three are the ones with over express-- so,
you don't see much of a difference when your well-watered.
But if you limit the water--
we don't stop watering it.
We just reduce the amount of water.
This is how the roots look like.
So it seems that what we were seeing is
smaller plants, you know.
We've continued to see that back then,
and I don't have the slides up, but stomatal conductance
and everything seems to be consistent
with what we originally thought,
with that alien translocation line.
What was surprising was that,
when we grew these plants in greenhouse,
under well-watered conditions,
mostly for seed increase,
we observed that there was a
distinct difference in seed size.
So on the top are the wild type which is non-modified wheat.
Ten of those seeds lined up.
And then if you suppress the expression
of that particular gene,
you see that you get bigger seed.
And if you overexpress that gene,
you actually get smaller seed.
So this is--
Typically, you know, seed size is a very plastic trait
in context of overall yield.
Bigger seeds sometimes can mean less seed,
or smaller seed can mean more seed.
So it's no guarantee that your bigger seed has more yield.
But in this case what was surprising was that the--
You know, this is a wild type, again.
These three are independent events
that Tom transformed to RNAI in the full expression--
that the number of seeds per plant also increased.
So we were excited enough, and you know,
since wheat transformation takes a long time.
We also found a relative of wheat and rice,
because they have common ancestors,
so many times with gene order,
More often the gene order among cereals is maintained.
But also in many cases, the gene function is maintained,
so we transformed this same rela--
the nearest relative of this LRD, we found in rice,
and we transformed and made RNAi and overexpression lines.
And this is some of the results that we're seeing.
So this is the wild type, the three plants here.
And this is the, when you suppress the expression,
these are panicles that show that the--
there's increased panicle-branching.
And if you overexpress, then you actually have
reduced panicle-branching.
And these are the seed sizes.
On the top, again, is the unmodified rice.
And then this is the RNAi,
and these are the overexpression.
And so the rice--
wheat that I showed you was grown in greenhouse conditions,
but the rice was grown--
since we have this really great facility,
you know, maintained by RD,
you know, with lots of assistance from Tom,
we were able to grow these in (mumbles) plot.
So all the rice was grown in paddy field conditions,
you know, with the (mumbles) irrigation almost sitting
on top of the small rice plot.
You know, you could see that this--
it does regulate the seed size.
And one final slide.
These are the-- both data from, I won't say--
I would say, non-irrigated, but not,
you know, drought stressed, wheat field,
and paddy field.
So these are 1,000 grain weight, most from field, which,
altered the impact of this gene's expression.
Loss of expression in rice is greater,
but we do see consistent phenotype,
in terms of seed weight and seed size,
across the species.
So, just to summarize,
we are sort of barely scratching the surface
of phenomics.
But we, as a nation, we have a tremendous
intellectual and infrastructure platform to spring from.
As a public university, it's a great asset.
And so we can use that asset to, you know,
map dynamic responses.
Not just wait for three weeks and say,
"How my plant looks like?"
Because you could have two plants that look
exactly the same after three weeks.
But their trajectory, in terms of growth,
or any other phenotype that you can image,
or extract from images,
could have completely different routes, so,
This really does help with that.
And of course, you know, genomics
is already ahead of phenomics.
It's just a matter of coming up with better and better,
improved models, so.
The second thing is that,
the viable relatives can help improve today's wheat.
It's no news.
As I said, the line that I looked at was developed for
biotic resistance in 1960.
But you know, there's possibilities
of using new genomics approaches,
which we could not--
which we didn't have, you know, in a toolkit,
even 10 years ago, that can be used to improve
wheat and maybe even other species.
I wanna point out three key people.
For the salinity and phenomics work,
Matt Campbell, my phD student's been instrumental.
He's done most of the experiments.
Spent about eight months in Australia,
although he didn't complain because
the beach was only three miles.
And then Avi Knecht's a undergraduate
Maths and Computer Major,
who started working on this project when he--
a couple of months before he started at UNL.
And so he's kind of been the driver of
developing that open-source software.
Putting it on the grid.
And then you know, he's been fantastic.
And he's been mentored from Holland Computing Center,
so that's been very responsive, been a big resource there.
Aaron Lorenz, is (mumbles),
who I didn't show lots of work
that he's done on this project, but.
And then Chi Zhang's done--
He generated about 800 (mumbles) seed samples
for half the panel,
so he's been trying to figure out what we can do
with the gene expression data
and how we can link that to the
(mumbles), with phenomics and then
the association analysis.
And then Tom's done the transformations,
and then Don Wang, he's done all the modeling.
Without him, the critical, you know,
bridge between genotype and phenotype,
we would definitely not have them there.
Then these are people in my labs.
The (mumbles) kinda entirely developed
by my former graduate student Dante Placido.
My colleague's, Bettina's, kinda been,
a great collaborator at Australian Phenomics Facility,
and Guntur's been the guy who's solv--
well, partially solved some of the
cumbersome aspects of data transfer
and management for us.
I've been fortunate to be funded
from the Wheat Board, the Sorghum Board.
I've been working with George on that.
I didn't have a chance to tell you.
The facility work is supported by NSF,
and the wheat work's supported by (mumbles) food
and also by INR.
With that, if you have any questions.
(mumbles)
(applause)