Plant Molecular Physiology

Dr. Harkamal Walia Author

01/07/2016 Added

96 Plays

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Plant Molecular Physiology presented by Dr. Harkamal Walia

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[00:00:00.852]Today I'm gonna talk mostly about, you know,
[00:00:04.555]improving abiotic stress tolerance in crop plants.
[00:00:09.512]Just to give you an outline of my talk,
[00:00:11.938]I will give you an overview of what my
[00:00:13.572]research program here is about, and then,
[00:00:16.510]maybe give you, at least one, maybe two, project snapshots.
[00:00:21.247]So one is on the Salinity Tolerance in Rice,
[00:00:23.378]and the other one is in Drought Tolerance in Wheat.
[00:00:28.034]So, we all know that with climate change, crop productivity,
[00:00:32.258]is going to be a challenge.
[00:00:33.737]It's going to be a challenge
[00:00:34.874]to sustain what we are producing, and, possibly,
[00:00:37.951]you know, with more people coming in on the planet,
[00:00:41.770]to be actually increasing our productivity.
[00:00:44.987]One of the reasons for--
[00:00:46.381]There's many reasons for that, and one of those is that,
[00:00:48.795]extreme events such as droughts and flooding events
[00:00:52.545]are becoming more frequent, and also, in many cases,
[00:00:56.487]more intense.
[00:00:57.723]So that limits how much farmers can produce,
[00:01:01.821]given these environmental limitations.
[00:01:05.242]So my research program's primarily focused on
[00:01:09.239]understanding the physiological and genetic basis
[00:01:12.211]of abiotic stress tolerance.
[00:01:14.992]I focus on water.
[00:01:16.770]I know that in Nebraska and in,
[00:01:18.505]almost all, every cultural settings, you know,
[00:01:20.460]water's very important.
[00:01:23.489]The particular themes that I work on are water abundance.
[00:01:27.258]You know, less water, as in drought, and then water quality,
[00:01:30.615]so, not only do you need good quality water, but--
[00:01:35.040]lots of water, for producing food, but also,
[00:01:37.370]you need good quality, so salinity, is always increasing,
[00:01:41.151]and it's particularly a challenge for irrigated systems,
[00:01:45.501]such as Nebraska.
[00:01:48.194]Because the, Nebraska being one of the--
[00:01:50.459]I think it is the most irrigated state in the country.
[00:01:55.250]So, irrigated agriculture's quite important.
[00:02:00.166]About 20% of the arable land in the world is irrigated.
[00:02:05.429]Of that, about-- you know, but it produces about 40%,
[00:02:09.035]of the total food, which sort of indicates
[00:02:12.095]how important, you know, fresh water is for food production,
[00:02:15.745]and most specifically, in cereal's,
[00:02:18.795]such as corn, wheat, rice, sorghum, you know,
[00:02:22.790]take a disproportionate amount of water,
[00:02:24.783]so about 60% of the cereal production
[00:02:28.739]is derived from irrigated agriculture.
[00:02:32.745]As the fresh water supplies diminish due to events such as
[00:02:37.249]drought events, or due to, poor quality of fresh water,
[00:02:42.503]coping with these extreme events, is going to become--
[00:02:46.735]is already difficult, but it will become more challenging.
[00:02:51.486]So the two cereal species that I focus on are
[00:02:55.250]wheat and rice.
[00:02:57.098]Wheat's-- both of them are monocots.
[00:02:59.754]Wheat's a polyploid, so it's a hexaploid,
[00:03:04.479]meaning that it has three independent genomes,
[00:03:08.387]that coexist within every cell.
[00:03:11.212]And then rice is a diploid, and there's, you know,
[00:03:15.402]especially personal reasons because I come from an area
[00:03:17.793]where wheat and rice are grown all around.
[00:03:20.385]That's where I grew up, and also,
[00:03:22.083]there's a more of a strategic reason,
[00:03:24.062]because rice is a very good model for many of the cereals.
[00:03:27.408]It was one of the--
[00:03:29.248]it was the first crop plant to be sequenced, in 2004,
[00:03:32.912]and it has a lean genome, as opposed to wheat,
[00:03:35.974]which is about 15 times as genome of humans,
[00:03:39.335]so it's really big.
[00:03:41.467]So, the economic reason for working on these two species
[00:03:45.754]is that, between rice and wheat, this shows the
[00:03:50.948]kilo-calories per capita globally.
[00:03:53.488]You know, rice and wheat provide more calories
[00:03:55.511]for human nutrition than every other, you know,
[00:03:59.173]agriculture product combined, so,
[00:04:01.424]so that's kind of the two cereals that I work on.
[00:04:04.220]So I'm gonna give you, like a overview of the
[00:04:07.081]things that I do in my lab, and then hopefully,
[00:04:09.890]a little bit more detail on a couple of those.
[00:04:13.236]So one of the projects that I work on is,
[00:04:17.752]looking at early grain development.
[00:04:20.179]The picture I've paired makes a good point.
[00:04:23.767]So this is your wheat grain after one day of formation,
[00:04:28.186]so fertilization, and then,
[00:04:30.344]if you continue to grow it in well-watered conditions,
[00:04:34.477]so this is the second day, and the third day,
[00:04:36.563]and the fourth day, you can see, that the size is increasing
[00:04:39.977]you know, quite significantly.
[00:04:42.258]But if you were to drought-stress it, for,
[00:04:45.696]after one-- 24 hours, it's size actually dramatically,
[00:04:49.968]you know, decreases.
[00:04:51.084]And what we've found, both for wheat and rice,
[00:04:53.638]is that when you only impose a short window of stress,
[00:04:58.927]drought-stress, and then you,
[00:05:00.748]relieve that stress by watering the plant,
[00:05:02.876]you still ultimately impact the final grain size.
[00:05:06.785]So even, so this stage, the reason we focus on it,
[00:05:09.950]is because it's so critical for the final output,
[00:05:14.899]in terms of yield.
[00:05:16.209]And it's also one of the least understood,
[00:05:18.316]in context of how this development stage
[00:05:20.737]interacts with the environment.
[00:05:22.615]So we work on various aspects of wheat, and when it
[00:05:26.598]becomes too challenging to understand those,
[00:05:28.654]given the genomic complexity in wheat,
[00:05:31.439]we transition to rice, and there's always this,
[00:05:33.521]back and forth going on between
[00:05:36.217]these two species in my group.
[00:05:43.238]The second project that I work on is,
[00:05:47.262]is on Salinity Tolerance in Rice.
[00:05:49.969]The goal of this project is to understand
[00:05:53.485]you know, the physiological responses of rice,
[00:05:57.408]to increase soil salinity, and also,
[00:05:59.833]to uncover new sources of soil tolerance in rice,
[00:06:04.046]using a combination of phenomics,
[00:06:06.993]which I will detail a little bit more,
[00:06:09.245]and then combining that information with
[00:06:12.178]with genomic information for rice,
[00:06:15.763]with the idea being to come up with new genes,
[00:06:18.261]and alleles, that breeders and biotechnologists
[00:06:21.325]can use to improve soil tolerance in rice.
[00:06:26.519]The third project is to look at roots in context of drought.
[00:06:31.774]I've already described that we are interested in looking at
[00:06:34.074]early grain development under drought-stress conditions,
[00:06:36.820]where roots are also very critical for water uptake.
[00:06:41.927]They act as the syphons for taking up water from the soil,
[00:06:45.955]so we are interested in looking at root architecture,
[00:06:48.759]in context of its environment.
[00:06:50.942]How does environment change the root architecture?
[00:06:53.528]What are the genes that determine that?
[00:06:55.443]And can we actually discover new genes, from,
[00:07:00.505]in wheat and rice, to improve water uptake
[00:07:04.656]and hopefully, eventually, improve drought tolerance.
[00:07:10.762]So kinda going back to the outline.
[00:07:13.246]So I'm gonna talk for the next 10, 15 minutes about
[00:07:16.490]salinity tolerance in rice.
[00:07:17.909]These are mostly, like a very, you know,
[00:07:21.263]overview type of slides that I have,
[00:07:23.706]and if you need more information,
[00:07:25.546]if you have comments or questions,
[00:07:27.497]feel free to, you know, talk to me after the--
[00:07:30.602]during lunch, or you know, after the talks.
[00:07:33.611]So why salinity?
[00:07:34.948]So salinity is a global problem.
[00:07:37.485]This is a somewhat dated map,
[00:07:39.188]but that's the only one that's available.
[00:07:41.101]These areas in black show where the soil are--
[00:07:45.263]where soil and agriculture's affected by
[00:07:48.034]increased salinity level.
[00:07:50.121]So it's estimated that about 12%
[00:07:54.742]of the global food production
[00:07:56.777]is affected by salinity.
[00:07:59.134]Every time you irrigate,
[00:08:00.500]even if it's the best quality water, fresh water,
[00:08:03.110]you're gonna have some salts dissolved in it.
[00:08:06.209]The water would either seep down or evaporate.
[00:08:09.030]But you would still have the soils.
[00:08:11.282]Irrigation, or irrigated agriculture itself,
[00:08:14.540]tends to accumulate salt in the root zone over time.
[00:08:18.483]So there's many approaches to address issues
[00:08:21.669]of salt, and salinity in food production,
[00:08:24.848]starting with, you know, better economic practices,
[00:08:29.854]and you know, type of crops you grow.
[00:08:31.613]And genetics, and,
[00:08:34.496]Better genetics for salt tolerance is one of the solutions.
[00:08:37.484]By no means is it the only solution that would work,
[00:08:39.948]so there's a combination of different approaches
[00:08:42.516]that would be needed.
[00:08:44.229]But my interest is in trying to understand
[00:08:47.438]what's the genetic basis of salt tolerance
[00:08:49.622]and discovering new sources.
[00:08:53.515]So salt tolerance is, or, you know,
[00:08:57.735]the mechanisms for salt tolerance are varied.
[00:09:00.300]And one of the reasons why they're varied is,
[00:09:02.664]that you know, if you enter a room that's, say,
[00:09:05.179]you know, if this room's 110 degrees,
[00:09:07.759]and you enter the room, your response initially,
[00:09:10.497]may be very different from what it would be,
[00:09:13.225]20 minutes later or one day later.
[00:09:15.183]Your body and your physiology
[00:09:16.791]would be responding quite differently.
[00:09:18.656]So similarly, salt stress is, you know,
[00:09:22.679]has a similar dynamic effect on plant growth.
[00:09:27.505]So this is a map that's just, you know, it's more of a,
[00:09:31.480]thematic map, which shows that,
[00:09:33.994]when you apply salt-stress, plants typically, you know,
[00:09:37.633]the growth rate drops, somewhat dramatically,
[00:09:40.149]and then, that's primarily because of osmotic stress.
[00:09:43.162]It's not because sodium chloride,
[00:09:45.512]or sodium's kinda gone into the plant.
[00:09:47.492]It's mostly because the plant cannot take up water as easily
[00:09:51.065]so the growth rate drops and you need
[00:09:53.451]total pressure and water (mumbles)
[00:09:55.840]for the plants to continue to grow.
[00:09:57.744]And then, eventually, sodium does make its way
[00:09:59.938]through roots, into the various tissues,
[00:10:02.696]and then you start to have this, more of a,
[00:10:04.981]growth response to iron toxicity.
[00:10:07.753]And there's a suit of mechanisms that are involved in it,
[00:10:11.774]and that people have studied, and shown that are important.
[00:10:15.507]What the challenge is, that for you to generate,
[00:10:18.463]or for people to generate a graph like this, you know,
[00:10:21.046]they have to sort of take a handful of plants,
[00:10:23.635]and then sample then every day, you know,
[00:10:26.163]or every few hours, for growth rates,
[00:10:28.564]to discover any differences.
[00:10:30.870]If you're looking at genetic variation,
[00:10:32.795]you know, numbers is your best friend.
[00:10:34.707]So you need to be able to sample
[00:10:36.761]thousands and thousands of plants,
[00:10:38.495]and destroy them every day,
[00:10:40.031]to actually know how the growth factor is progressing.
[00:10:43.517]So that's something that I've become interested in,
[00:10:47.017]and using phenomics approaches to resolve that.
[00:10:51.541]So the species that we selected
[00:10:55.225]to work on for this is rice.
[00:10:58.209]Rice is, perhaps, one of the most salt-sensitive species,
[00:11:03.519]crop species, and its yield is dramatically reduced.
[00:11:08.040]So this is the, some of the life's
[00:11:10.512]main developmental stages for rice,
[00:11:12.696]and, so we decided to focus on three stages
[00:11:16.106]that were known from, based on literature,
[00:11:18.396]to be the most sensitive for salt tolerance,
[00:11:20.502]in context of yield,
[00:11:21.932]so these are stages--
[00:11:23.682]if you impose a salt-stress during those stages,
[00:11:26.057]you will end up having greater yield losses
[00:11:29.389]than in other stages.
[00:11:31.037]So today I'm gonna mostly--
[00:11:33.494]the stages of seedling stage are the tillering.
[00:11:36.069]If you make less tillers, you're gonna have less panicles,
[00:11:38.840]and less grain.
[00:11:39.971]And then, panicle initiation, that's kind of the,
[00:11:44.275]starting point or early stage of what will eventually become
[00:11:47.555]a seed-bearing panicle.
[00:11:50.689]So today, I'm gonna mostly show you some information,
[00:11:53.588]or data from the tillering stage.
[00:11:57.930]So the resource that we are using,
[00:12:00.259]to study genetic variation for salt tolerance in rice,
[00:12:04.235]is a Rice Diversity Panel.
[00:12:05.824]It's a collection about, 400 rice genotypes
[00:12:09.987]from all over the world.
[00:12:11.366]And you know, in different sub-populations,
[00:12:15.350]such as indicas and japonicas and so on.
[00:12:17.930]And it kinda shows you how this are distributed.
[00:12:20.726]So these rices range from, rices that are higher than me.
[00:12:24.774]And then rice that grows in terraces in Indonesia,
[00:12:27.698]to rice varieties or land races that grow in, you know,
[00:12:31.955](mumbles)-grown regions in Bangladesh.
[00:12:34.512]So they're very varied, and so we use this
[00:12:38.087]resource that was developed at Cornell.
[00:12:40.573]And the reason that I say that it's a resource.
[00:12:44.199]It's not just a mere collection of the worst lines.
[00:12:46.536]People have actually gone in and developed markers,
[00:12:51.269]genetic markers, which are sequenced.
[00:12:53.304]So you could think of it as, you know,
[00:12:55.728]sequenced-based position markers,
[00:12:58.385]like you would have on a highway.
[00:12:59.969]So that can tell you where a particular gene
[00:13:02.344]or a particular trait may be associated.
[00:13:04.170]So there's about 44,000 markers for
[00:13:08.017]for this Diversity Panel that we used.
[00:13:12.041]So in terms of phenomics,
[00:13:15.240]as you know that, we are increasingly capturing more
[00:13:19.237]information, in form of images.
[00:13:21.546]In fact, even have like, people instead of taking notes,
[00:13:24.261]who like, you know, take their iPad and iPhone out,
[00:13:27.624]and start capturing information,
[00:13:30.493]you know, as an image.
[00:13:31.736]And so, plant scientists are, you know,
[00:13:34.312]in tune to that idea, and so there were platforms,
[00:13:38.015]that were developed about four or five years ago,
[00:13:40.958]where you have a setup of conveyor belts,
[00:13:44.248]where parts move on these conveyor belts,
[00:13:46.459]and then they go through these series of phone booth
[00:13:49.940]type of setups, where you're sliding those and your cameras
[00:13:53.564]on the top and the side,
[00:13:55.070]and all the plants can be imaged daily,
[00:13:58.123]or even more frequently, depending on your capacity
[00:14:01.246]of, you know, automatically.
[00:14:03.780]And so this is the setup that we used.
[00:14:07.479]This work was done collaboratively with the,
[00:14:11.735]at the University of Adelaide in Australia.
[00:14:14.604]They're a fairly large facility,
[00:14:16.327]and they agreed to work with us on this.
[00:14:18.792]So this is what the setup looks like,
[00:14:21.259]and you have these booths,
[00:14:22.905]and this is the camera from the top.
[00:14:24.526]Right now the booths are open,
[00:14:25.766]so you can see through hermetical booths.
[00:14:27.962]And there's watering and irrigation system is in place.
[00:14:32.472]Another, University of Nebraska, here on Innovation Campus,
[00:14:35.994]has I would say, even a better, more sophisticated setup,
[00:14:39.991]with greater capacity to grow, you know,
[00:14:42.748]tall sorghums and long plants,
[00:14:44.460]along with tiny wheat and rice plants.
[00:14:46.657]So we are very fortunate to be able to, kind of,
[00:14:48.968]be one of the public facilities in the U--
[00:14:51.854]One of the first or second public facilities in the U.S.
[00:14:55.021]to do this.
[00:14:57.491]So what we did was, basically,
[00:15:00.144]we did a step-wide increase in salinity level,
[00:15:03.311]on the (mumbles) on a number of days,
[00:15:05.586]and then we let the plants grow.
[00:15:08.228]So we had unstressed plants and stressed plants.
[00:15:11.097]And then we imaged them every day.
[00:15:13.756]Two images from the side, you know, at 90 degree,
[00:15:16.926]and then one image from the top.
[00:15:18.220]And then we used four types of cameras.
[00:15:22.508]We had fluorescence images,
[00:15:24.420]and then we had infrared.
[00:15:25.672]Fluorescence would tell you the level of pigmentation,
[00:15:28.276]so if, you know, if the plant's yellowing,
[00:15:29.759]you should see chlorophyll degradation,
[00:15:31.585]and that kind of information can be picked
[00:15:34.203]from the fluorescence.
[00:15:35.502]And then you have infrared, which should act as a proxy for,
[00:15:38.672]you know, how cool or warm the plant foliage is.
[00:15:44.214]And then near-infrared should provide you information about
[00:15:47.292]the water contents.
[00:15:48.441]Remember if you have salt outside in the roots,
[00:15:51.162]it's harder for the plants to take up water.
[00:15:53.273]and the plants don't take up water,
[00:15:55.252]then they become warmer, their growth decreases,
[00:15:58.195]and they also have less water.
[00:16:01.488]So when their growth decreases, you could pick that up
[00:16:03.741]from the visible camera, which we typically call RGB,
[00:16:06.838]which is what your cellphone would capture.
[00:16:08.879]Just a regular cellphone.
[00:16:10.505]So, what we did was, we captured--
[00:16:13.082]Well, you know, we took 380 rice varieties
[00:16:15.276]under control and salt stress,
[00:16:17.217]and we captured images over 18 days,
[00:16:21.168]after applying the salt stress,
[00:16:24.703]We ended up with about five million images.
[00:16:29.229]You've all heard about big data.
[00:16:32.896]It starts popping up, you know, a few years ago,
[00:16:35.485]even when you open any newspaper.
[00:16:37.732]Your New York Times or something.
[00:16:39.048]But what really hit me, was that,
[00:16:42.018]when we tried to move this
[00:16:44.217]five million images onto, you know, an online
[00:16:49.728]plant by informatic infrastructure to analyze them--
[00:16:54.280]These images were in a fol-- zip folders.
[00:16:57.117]And it took them 23 days to unzip the folders.
[00:17:00.711](laughter)
[00:17:01.746]So all they needed to do, and I'm not talking--
[00:17:03.821]I'm talking about infrastructure.
[00:17:05.084]I don't know if many of you know, but I plant,
[00:17:07.521]We're more than $50 million dollars
[00:17:08.923]worth of investment already.
[00:17:10.769]You know, then you start thinking that
[00:17:12.129]it's not just the volume, but the number.
[00:17:15.498]So there's lots of challenges.
[00:17:17.212]So one of the things that we have done,
[00:17:19.522]over the last two years,
[00:17:21.135]is build an open-source software,
[00:17:24.341]that would analyze at least two types of images,
[00:17:27.619]the fluorescence, and the RGB images,
[00:17:30.493]so that it would give us information on the growth rate,
[00:17:33.518]and information on the rate of senescence and so on.
[00:17:36.475]So that's a open-source software that's freely available.
[00:17:39.775]And the cool thing about that software is--
[00:17:42.240]at least, I think it's very cool,
[00:17:43.480]is that it runs on a system, or a infrastructure called
[00:17:47.349]Open Science Grid.
[00:17:49.022]Some of you may have already heard about it,
[00:17:51.079]but what it is, is,
[00:17:52.779]it's a opportunistic network of clusters, or,
[00:17:57.232]clusters of CPU's, across many national labs,
[00:18:01.115]and many of the major U.S. universities.
[00:18:03.740]What it does is something similar to what
[00:18:05.878]a power grid would do,
[00:18:07.104]if you have many power grids interconnected.
[00:18:10.028]So if there's shortage of power in one area,
[00:18:13.068]surplus of power in another, you can move, you know,
[00:18:16.209]you could draw power from the other grid.
[00:18:18.163]So this grid, what it does is,
[00:18:19.887]it looks for idling CPU's in different universities
[00:18:23.846]and national labs,
[00:18:24.905]and then it, when you submit a job,
[00:18:27.762]it breaks it up into many, many pieces,
[00:18:29.979]and then sprinkles them on the grid nationally.
[00:18:32.708]And then when the jobs get done, they bring it back,
[00:18:35.264]and they integrate, and you'll never know
[00:18:37.280]what's happening in the background.
[00:18:38.875]So we have a software that's uploaded and available
[00:18:42.017]for anybody to use.
[00:18:44.092]And I think it's at least accessible in
[00:18:45.758]the public research system in the U.S.,
[00:18:48.770]for people to analyze it, and it's really fast,
[00:18:51.473]and it can, you know, do maybe 80,000 images
[00:18:53.959]in five hours or something like that.
[00:18:58.757]So what we focused on was growth rate-related
[00:19:03.028](mumbles) in early stages growth-rate and late,
[00:19:05.663]so we used the RGB image,
[00:19:07.453]for looking at the dynamic responses to salt stress,
[00:19:11.125]especially the osmotic phase, during early (mumbles).
[00:19:14.036]And then ionic phases, where, you know,
[00:19:15.729]you can start seeing that orange, instead of dark red,
[00:19:19.691]for chlorophyll, under fluorescence camera.
[00:19:22.922]So like, in effects of senescence and so on,
[00:19:27.263]with ionic stress.
[00:19:29.517]And this is what the entire population looks like.
[00:19:32.116]I've expressed it as a Salt Index, which is basically saying
[00:19:36.507]the shoot area over salt,
[00:19:39.776]by shoot area over control, and--
[00:19:42.664]so if you have a genotype that has, you know, really
[00:19:45.719]low salt index, it basically means that your,
[00:19:50.285]that genotype of that panel is very sensitive,
[00:19:53.005]in terms of growth.
[00:19:54.104]And if you have something that is close to one,
[00:19:56.900]which means that its growth rate didn't change too much
[00:19:59.493]when you applied the salt, so it's very tolerant.
[00:20:01.626]So I point out two lines.
[00:20:03.937]This is an African rice, and this is a Korean rice variety.
[00:20:09.492]And this is what the images look like.
[00:20:11.677]On the top is the sensitive line,
[00:20:13.283]so you have your pairs of control and salt.
[00:20:16.483]And you know, selected days.
[00:20:19.010]And you can see that their growth rate's quite different,
[00:20:21.775]by day 31, and you know,
[00:20:25.060]if you have the tolerant line, it's not as different.
[00:20:27.893]Now this is a difference that you and I can see,
[00:20:30.227]so really, where does the sensitivity come from,
[00:20:33.484]when you're using this technology?
[00:20:35.042]It comes from this graph, which illustrates that the
[00:20:38.401]sensitive line, which is in red, starts to drop below one,
[00:20:42.692]which is its growth rate start to dip,
[00:20:44.939]even before you reach the final concentration.
[00:20:47.378]Whereas the tolerant line seems to kinda, do quite well.
[00:20:50.988]So this is the type of sensitivity that
[00:20:53.955]you have to almost harvest a plant every few hours,
[00:20:57.696]and then harvest for control, for salt stress,
[00:21:00.416]and do replicates, and then do it for 400 varieties.
[00:21:03.213]It would just kind of be, maybe incredible
[00:21:05.767]to have such a resource, but,
[00:21:07.286]this is a good proxy for.
[00:21:08.845]It shows that you can use these type of resources.
[00:21:11.334]So now you have these images.
[00:21:12.867]You have these graphs that you can plot.
[00:21:14.986]So what next?
[00:21:16.034]How do you get from phenotype to genotype?
[00:21:21.032]And that's been a big challenge because,
[00:21:25.285]genotyping, which means trying to determine,
[00:21:28.252]or get an overview of the genetic makeup,
[00:21:30.451]of the organism or variety, has become cheaper,
[00:21:34.206]because sequencing's become cheaper.
[00:21:37.648]So to do that we,
[00:21:40.837]we used an approach, where,
[00:21:44.080]which is called genome-by-association,
[00:21:46.442]where we found these differences in growth rate,
[00:21:49.147]and then we linked them to those markers,
[00:21:51.484]or those milestones, if you want to think of choromosomes
[00:21:53.948]as highways, and you know, specific positions are markers.
[00:21:57.907]And we try to link that, and this was work that was done by
[00:22:02.499]in collaboration with Dong Wang who's a former faculty here,
[00:22:06.704]he had really great ideas on how to do this.
[00:22:09.732]And without going into the details of the models, and so on,
[00:22:12.636]what I want to tell you is that, this method works.
[00:22:17.928]That you could, indeed, go from imaging,
[00:22:20.222]and using imaging as a phenotype, to,
[00:22:23.739]linking it to the genes and genotype.
[00:22:25.636]So what I'm showing you are these, you know, dots.
[00:22:27.986]This called a Manhattan plot, which,
[00:22:30.093]on the bi-axis, the higher the dot is,
[00:22:32.455]the more significant that marker is associated is--
[00:22:36.031]more significant is the association of that marker,
[00:22:38.521]with a particular phenotype,
[00:22:40.232]in this case, growth rate under salt stress.
[00:22:43.248]And in green, are the significant ones.
[00:22:45.544]And rice has 12 chromosomes,
[00:22:47.434]which is the DNA content of the rice,
[00:22:51.472]genome is broken up into 12 pieces
[00:22:53.772]that are called chromosomes, and,
[00:22:55.827]we've found some really interesting candidates.
[00:22:58.764]Like this one, with very high--
[00:23:00.993]very low key values,
[00:23:03.080]that we are now pursuing, so we can--
[00:23:04.806]Given that the rice genome is sequenced completely,
[00:23:07.857]and you can kind of go in, in great detail,
[00:23:10.142]and find the genes.
[00:23:11.955]We're now pursuing some of the candidate genes
[00:23:13.820]to see if we can, you know, dish--
[00:23:16.782]If those are the basics of the growth response
[00:23:19.031]under salt stress.
[00:23:21.490]So now I'm gonna transition to the
[00:23:26.965]drought tolerance component in wheat.
[00:23:29.256]So this is,
[00:23:32.764]so in wheat, we're taking a slightly different approach.
[00:23:34.898]In rice, we just found this really great resource,
[00:23:37.766]and with the Diversity Panel with all the marker information
[00:23:40.651]you know, you could click it, and in less than 10 seconds,
[00:23:43.900]you'd have all the information.
[00:23:45.969]In wheat, it's a little bit challenging
[00:23:47.680]because of the genome.
[00:23:49.110]In this case, we're interested in looking at
[00:23:51.319]natural variation, for root traits.
[00:23:54.499]What I mean by that is,
[00:23:55.852]this is a picture from John Weaver, from 19--
[00:23:59.211]in a-- from a book published in 1926.
[00:24:01.879]He was a faculty at UNL.
[00:24:04.158]I don't quite know if our department existed in 1926,
[00:24:07.550]but he was a biologist of--
[00:24:09.733]and so, this is one wheat genotype,
[00:24:12.252]when you-- when grown in Lincoln environment,
[00:24:16.258]this is how the roots look.
[00:24:17.740]And when the same genotype, that same year,
[00:24:19.962]was grown in Burlington, Colorado in a very dry environment,
[00:24:23.779]this is what the root architecture looks like.
[00:24:26.068]So we're interested in, you know,
[00:24:27.804]where are the genes that provide this plasticity?
[00:24:31.006]And that, with the hope that if we can find those genes,
[00:24:33.717]we can at least know, you know,
[00:24:36.263]these are the genes and the leaves that we can use
[00:24:38.161]to get this kind of plasticity.
[00:24:40.140]It could become useful for a particular environment.
[00:24:44.699]So, the resource that I'm using for this is,
[00:24:47.534]a set of translocation lines that were provided
[00:24:51.090]by a great friend and colleague at U.C. Riverside.
[00:24:53.761]He's a psychogeneticist, so in other words, what that means
[00:24:56.596]is that, he has the scale, and the rare scale of being able
[00:25:00.465]to introduce and remove chromosomes,
[00:25:03.453]introduce smaller pieces of chromosome,
[00:25:06.484]from other species into wheat.
[00:25:08.262]And you can do this in wheat and very--
[00:25:10.293]it's quite difficult to do it in a different species,
[00:25:12.400]but because wheat has these abundance of chromosomes,
[00:25:17.762]it's a hexaploid, so it can tolerate losing 100, 200 genes,
[00:25:22.239]or even accommodating, or hosting, you know,
[00:25:25.247]genes from another species.
[00:25:27.036]So what I'm showing you are the 42 chromosomes of wheat.
[00:25:30.482]If you're eating bread, this is what it's coming from.
[00:25:33.562]And in red are the wheat chromosomes,
[00:25:36.384]and then you have, this piece in green is
[00:25:40.538]a wild relative of wheat, so it's a
[00:25:42.118]small piece of chromosome that was introduced in 60's,
[00:25:45.706]I think in (mumbles) for bringing in Rush resistance.
[00:25:51.534]What I-- working, you know,
[00:25:54.087]when prompted by the psychogeneticist, what I explored,
[00:25:56.629]was the idea of root
[00:26:00.989]traits for this, you know,
[00:26:02.611]special traits that might be brought in by this piece.
[00:26:04.744]So what we found was that, when we looked
[00:26:07.540]under well-watered conditions in this seedlings, of roots,
[00:26:11.269]I called the wild type is current 76,
[00:26:14.034]and the one that has the green piece as the,
[00:26:17.039]big piece of chromosome as TL, or translocation line,
[00:26:20.089]and there's another control.
[00:26:21.469]When we look under well-watered conditions,
[00:26:23.499]we don't find any differences in root.
[00:26:25.338]However, when we impose a slight water stress, what we find
[00:26:31.149]is that the translocation line continues to grow,
[00:26:35.917]and make lateral roots.
[00:26:37.890]These are side roots that come from within root.
[00:26:42.228]Whereas, the wild type, which doesn't have any green or red,
[00:26:47.487]has very little lack of roots.
[00:26:50.018]So there's a reduced rate of lateral root emergence.
[00:26:52.597]And you can see that in this three pictures here,
[00:26:54.908]on the top, or, you know, you've got
[00:26:56.594]plenty of lateral roots coming out,
[00:26:57.961]but not much in the wild type.
[00:27:02.963]We looked at this grid in bigger setups in our later stages
[00:27:07.641]and we found that there's no big differences in the limit--
[00:27:11.495]in the well-watered conditions,
[00:27:13.054]but the plants that had that alien introgression
[00:27:17.614]tends to become insensitive to water stress,
[00:27:21.637]or it's more tolerant, and it continues to grow more roots.
[00:27:24.689]And it tends to do better, in terms of,
[00:27:27.843]shoot biomass and root biomass,
[00:27:29.971]and some of that, we think, is associated with this graphs.
[00:27:33.905]I don't wanna go through all of them, but the main point is,
[00:27:37.407]that the plants that continue to make lateral roots
[00:27:40.701]under limited water,
[00:27:41.685]tends to take up more water, and then use more water.
[00:27:44.481]In the process, that takes more carbon.
[00:27:46.601]So, when you're losing water, you couldn't stress that
[00:27:49.474]as stomatal conductance.
[00:27:50.994]So these are maintained, if you look at,
[00:27:55.953]stomatal conductance, so.
[00:27:58.498]These are three different genotypes under well-watered.
[00:28:00.971]Where under limited-water, you always see a 50% drop
[00:28:05.414]in the lines that don't have that green alien introgression,
[00:28:09.544]whereas not much of a drop when you, under limited-water,
[00:28:14.925]in the translocation line.
[00:28:16.874]So we were curious about this,
[00:28:18.305]and you know, one of the same ways we can go about doing,
[00:28:21.089]learning more about what may be happening at genes,
[00:28:23.540]because we're interested in finding those genes and alleles
[00:28:25.788]that could be useful,
[00:28:26.822]is to use more of the functional genomics toolkit.
[00:28:29.554]And one of the things
[00:28:31.243]that people have been using in the past was
[00:28:33.491]a wheat microarray.
[00:28:34.881]So we used that a few years back.
[00:28:37.267]And then we also did some RNA sequencing
[00:28:40.398]with what Daniel described, you know,
[00:28:43.194]in the talk before.
[00:28:44.714]So combination of those, basically what it does is,
[00:28:49.311]it tells us these two platform, the technologies tell us
[00:28:52.244]how-- what genes are expressing,
[00:28:55.735]what genes may be different between,
[00:28:57.957]when you introduce that alien introgression,
[00:29:00.945]as opposed to the one that don't have it.
[00:29:03.022]So we did some analysis, and we found many genes
[00:29:06.028]related to hormone, growth hormone.
[00:29:08.352]Clearly there's no differences in growth rate, and so on,
[00:29:10.906]so there's the growth hormone.
[00:29:12.119]But then we also looked at candidate genes that could
[00:29:15.724]actually be responsible for this trait.
[00:29:17.792]So, the idea being, that now you have this piece
[00:29:20.346]from wild species that has replaced
[00:29:22.973]the original piece of wheat chromosome.
[00:29:26.498]So what are the genes on there,
[00:29:28.494]and could any of those genes be responsible for
[00:29:34.487]giving us more lateral roots under limited-water.
[00:29:36.980]So one of the genes that we found.
[00:29:40.228]We call it LRD, or for Lateral Root Density.
[00:29:43.492]What it did was, this is just a graph
[00:29:47.421]that shows you the level of gene expression.
[00:29:49.658]More active the gene expression,
[00:29:51.233]the higher the (mumbles) bar would be,
[00:29:53.283]and less active the gene expression is,
[00:29:55.249]the lower it would be.
[00:29:56.547]So on your left is the wild type.
[00:30:00.148]So when this gene is under well-watered--
[00:30:03.404]Let's arbitrarily put that at one expression level.
[00:30:06.563]When you have low water, this gene's expression increases.
[00:30:09.771]And similarly, for the translocation line,
[00:30:12.445]if you put the well-watered expression at one,
[00:30:15.702]when you water stress it, its expression drops to half,
[00:30:20.823]or even more, depending on what technology you use
[00:30:23.620]to measure gene expression.
[00:30:25.114]So we thought that was quite interesting,
[00:30:26.914]that you have this one gene that maps to that piece,
[00:30:29.429]and it has opposite response, you know.
[00:30:31.384]It's going up in response to drought stress in one,
[00:30:34.180]but going down in the other.
[00:30:36.291]So we then directly tested this.
[00:30:38.511]Typically, my lab would immediately jump to rice,
[00:30:42.494]because we can make genetically-modified rice
[00:30:45.699]with changing gene expression fairly easily in the lab.
[00:30:49.112]But because, you know, Tom has this facility,
[00:30:53.220]Tom Clemente, it's close the--
[00:30:55.122]So he generated some wheat plants for us,
[00:30:58.724]which is sort of unique capacity
[00:31:00.575]that we have in Nebraska,
[00:31:01.865]and not many universities can call for.
[00:31:06.514]So he generated wheat lines, where he,
[00:31:11.011]he took the gene and either he either
[00:31:13.026]suppressed that expression permanently,
[00:31:15.095]or ramped its expression up a lot.
[00:31:18.238]This is what the lateral root density,
[00:31:22.742]or the number of side roots per unit length of root
[00:31:25.806]looked like, so if you have--
[00:31:27.508]So he uses a wild type called CBO37,
[00:31:31.045]which is easy to transform.
[00:31:32.999]I mean easy, relatively speaking.
[00:31:35.003]Wheat's quite difficult.
[00:31:36.536]So in this case, when you have a limited-water,
[00:31:38.969]which is in the white bar, as opposed to grey,
[00:31:41.758]which is well-watered,
[00:31:42.742]you see a drop in lateral root density.
[00:31:44.658]However if you suppress this gene--
[00:31:48.220]it's permanently suppressed,
[00:31:50.507]you don't see as much of a drop.
[00:31:52.535]But if you have over expressed this gene,
[00:31:54.514]you still see a very significant, almost half,
[00:31:57.724]of what the original, so,
[00:31:59.360]what it indicated to us was that,
[00:32:01.509]this could potentially be the gene that is rec--
[00:32:04.314]is on that piece that's introduced,
[00:32:08.766]from the alien, well, wild relative of wheat,
[00:32:11.422]into the wheat genome.
[00:32:12.482]That could be involved in, you know,
[00:32:14.311]regulating lateral root density.
[00:32:17.023]So this is what the plants look like,
[00:32:20.246]when we grow them in tall pipes, and, with sand,
[00:32:23.374]and we wash them out.
[00:32:24.536]So the first three plants are the wild type.
[00:32:27.640]The next three are the ones with suppressed expression.
[00:32:30.501]And the last three are the ones with over express-- so,
[00:32:32.501]you don't see much of a difference when your well-watered.
[00:32:34.918]But if you limit the water--
[00:32:36.489]we don't stop watering it.
[00:32:37.587]We just reduce the amount of water.
[00:32:39.388]This is how the roots look like.
[00:32:41.150]So it seems that what we were seeing is
[00:32:43.308]smaller plants, you know.
[00:32:45.313]We've continued to see that back then,
[00:32:46.807]and I don't have the slides up, but stomatal conductance
[00:32:49.502]and everything seems to be consistent
[00:32:50.880]with what we originally thought,
[00:32:52.486]with that alien translocation line.
[00:32:55.457]What was surprising was that,
[00:32:57.521]when we grew these plants in greenhouse,
[00:32:59.685]under well-watered conditions,
[00:33:00.969]mostly for seed increase,
[00:33:03.485]we observed that there was a
[00:33:05.766]distinct difference in seed size.
[00:33:08.251]So on the top are the wild type which is non-modified wheat.
[00:33:13.260]Ten of those seeds lined up.
[00:33:15.046]And then if you suppress the expression
[00:33:16.721]of that particular gene,
[00:33:17.973]you see that you get bigger seed.
[00:33:20.922]And if you overexpress that gene,
[00:33:22.966]you actually get smaller seed.
[00:33:25.458]So this is--
[00:33:26.722]Typically, you know, seed size is a very plastic trait
[00:33:29.762]in context of overall yield.
[00:33:31.666]Bigger seeds sometimes can mean less seed,
[00:33:34.270]or smaller seed can mean more seed.
[00:33:36.153]So it's no guarantee that your bigger seed has more yield.
[00:33:38.733]But in this case what was surprising was that the--
[00:33:41.778]You know, this is a wild type, again.
[00:33:43.291]These three are independent events
[00:33:44.938]that Tom transformed to RNAI in the full expression--
[00:33:48.269]that the number of seeds per plant also increased.
[00:33:51.120]So we were excited enough, and you know,
[00:33:54.172]since wheat transformation takes a long time.
[00:33:57.289]We also found a relative of wheat and rice,
[00:34:01.093]because they have common ancestors,
[00:34:02.652]so many times with gene order,
[00:34:05.492]More often the gene order among cereals is maintained.
[00:34:09.489]But also in many cases, the gene function is maintained,
[00:34:12.339]so we transformed this same rela--
[00:34:15.505]the nearest relative of this LRD, we found in rice,
[00:34:19.400]and we transformed and made RNAi and overexpression lines.
[00:34:23.230]And this is some of the results that we're seeing.
[00:34:26.279]So this is the wild type, the three plants here.
[00:34:28.769]And this is the, when you suppress the expression,
[00:34:31.323]these are panicles that show that the--
[00:34:33.538]there's increased panicle-branching.
[00:34:35.536]And if you overexpress, then you actually have
[00:34:37.909]reduced panicle-branching.
[00:34:39.506]And these are the seed sizes.
[00:34:41.140]On the top, again, is the unmodified rice.
[00:34:43.930]And then this is the RNAi,
[00:34:46.715]and these are the overexpression.
[00:34:48.545]And so the rice--
[00:34:50.533]wheat that I showed you was grown in greenhouse conditions,
[00:34:53.309]but the rice was grown--
[00:34:54.664]since we have this really great facility,
[00:34:57.515]you know, maintained by RD,
[00:34:59.107]you know, with lots of assistance from Tom,
[00:35:01.529]we were able to grow these in (mumbles) plot.
[00:35:06.758]So all the rice was grown in paddy field conditions,
[00:35:10.556]you know, with the (mumbles) irrigation almost sitting
[00:35:13.609]on top of the small rice plot.
[00:35:16.405]You know, you could see that this--
[00:35:18.078]it does regulate the seed size.
[00:35:19.636]And one final slide.
[00:35:21.599]These are the-- both data from, I won't say--
[00:35:25.491]I would say, non-irrigated, but not,
[00:35:27.941]you know, drought stressed, wheat field,
[00:35:30.082]and paddy field.
[00:35:31.167]So these are 1,000 grain weight, most from field, which,
[00:35:34.181]altered the impact of this gene's expression.
[00:35:36.837]Loss of expression in rice is greater,
[00:35:39.545]but we do see consistent phenotype,
[00:35:43.107]in terms of seed weight and seed size,
[00:35:45.444]across the species.
[00:35:47.763]So, just to summarize,
[00:35:50.732]we are sort of barely scratching the surface
[00:35:53.953]of phenomics.
[00:35:56.022]But we, as a nation, we have a tremendous
[00:35:59.532]intellectual and infrastructure platform to spring from.
[00:36:04.502]As a public university, it's a great asset.
[00:36:07.478]And so we can use that asset to, you know,
[00:36:10.530]map dynamic responses.
[00:36:12.139]Not just wait for three weeks and say,
[00:36:14.753]"How my plant looks like?"
[00:36:16.212]Because you could have two plants that look
[00:36:17.595]exactly the same after three weeks.
[00:36:19.447]But their trajectory, in terms of growth,
[00:36:22.192]or any other phenotype that you can image,
[00:36:24.498]or extract from images,
[00:36:26.775]could have completely different routes, so,
[00:36:29.498]This really does help with that.
[00:36:31.987]And of course, you know, genomics
[00:36:33.330]is already ahead of phenomics.
[00:36:34.799]It's just a matter of coming up with better and better,
[00:36:37.477]improved models, so.
[00:36:39.745]The second thing is that,
[00:36:42.269]the viable relatives can help improve today's wheat.
[00:36:47.273]It's no news.
[00:36:49.252]As I said, the line that I looked at was developed for
[00:36:52.500]biotic resistance in 1960.
[00:36:55.997]But you know, there's possibilities
[00:36:57.840]of using new genomics approaches,
[00:36:59.615]which we could not--
[00:37:00.573]which we didn't have, you know, in a toolkit,
[00:37:03.459]even 10 years ago, that can be used to improve
[00:37:07.031]wheat and maybe even other species.
[00:37:10.951]I wanna point out three key people.
[00:37:14.744]For the salinity and phenomics work,
[00:37:16.731]Matt Campbell, my phD student's been instrumental.
[00:37:19.765]He's done most of the experiments.
[00:37:21.392]Spent about eight months in Australia,
[00:37:23.065]although he didn't complain because
[00:37:24.342]the beach was only three miles.
[00:37:26.245]And then Avi Knecht's a undergraduate
[00:37:29.092]Maths and Computer Major,
[00:37:32.212]who started working on this project when he--
[00:37:34.964]a couple of months before he started at UNL.
[00:37:37.473]And so he's kind of been the driver of
[00:37:40.231]developing that open-source software.
[00:37:42.568]Putting it on the grid.
[00:37:43.893]And then you know, he's been fantastic.
[00:37:45.771]And he's been mentored from Holland Computing Center,
[00:37:48.797]so that's been very responsive, been a big resource there.
[00:37:53.966]Aaron Lorenz, is (mumbles),
[00:37:56.462]who I didn't show lots of work
[00:37:58.595]that he's done on this project, but.
[00:38:00.509]And then Chi Zhang's done--
[00:38:02.515]He generated about 800 (mumbles) seed samples
[00:38:05.277]for half the panel,
[00:38:07.546]so he's been trying to figure out what we can do
[00:38:11.398]with the gene expression data
[00:38:12.675]and how we can link that to the
[00:38:15.951](mumbles), with phenomics and then
[00:38:18.328]the association analysis.
[00:38:20.757]And then Tom's done the transformations,
[00:38:23.792]and then Don Wang, he's done all the modeling.
[00:38:27.048]Without him, the critical, you know,
[00:38:30.444]bridge between genotype and phenotype,
[00:38:32.744]we would definitely not have them there.
[00:38:36.257]Then these are people in my labs.
[00:38:38.597]The (mumbles) kinda entirely developed
[00:38:41.489]by my former graduate student Dante Placido.
[00:38:45.453]My colleague's, Bettina's, kinda been,
[00:38:48.314]a great collaborator at Australian Phenomics Facility,
[00:38:50.884]and Guntur's been the guy who's solv--
[00:38:53.811]well, partially solved some of the
[00:38:57.009]cumbersome aspects of data transfer
[00:39:00.237]and management for us.
[00:39:02.226]I've been fortunate to be funded
[00:39:05.753]from the Wheat Board, the Sorghum Board.
[00:39:08.375]I've been working with George on that.
[00:39:09.781]I didn't have a chance to tell you.
[00:39:10.943]The facility work is supported by NSF,
[00:39:15.013]and the wheat work's supported by (mumbles) food
[00:39:17.979]and also by INR.
[00:39:20.206]With that, if you have any questions.
[00:39:22.478](mumbles)
[00:39:24.509](applause)

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Plant Molecular Physiology

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