Designing Nitrogen-Use Efficient Maize Using a Population Genomics Approach

Jinliang Yang, Associate Professor, Charles O Gardner Professorship of Agronomy; Dept of Agronomy and Horticulture, UNL Author

03/04/2025 Added

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Jinliang Yang focuses on bridging the gap between genotypes and phenotypes. At a broader scale, his group is keen to integrate various large-scale biological data such as phenomics, genomics, transcriptomics, methylomics datasets and functional annotations to boost the power of Genome-Wide Association Study (GWAS) and Genomic Selection (GS)

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[00:00:00.800]The following presentation is part of the Agronomy and Horticulture Seminar Series at
[00:00:05.920]the University of Nebraska-Lincoln.
[00:00:07.560]Good morning, folks.
[00:00:09.520]So thank you all for being here on this very cold and snowy Nebraska morning to hear from
[00:00:16.240]my colleague, all of our colleagues, Professor Jinliang Yang, the new Dr. Charles O. Gardner
[00:00:22.020]Endowed Professor of Maize Quantitative Genetics.
[00:00:27.340]Now Professor Yang comes to us having completed his bachelor's and master's at China Agricultural
[00:00:33.880]University.
[00:00:35.060]He then moved to Iowa State University where he completed his PhD working with this guy
[00:00:39.560]Patrick Schnabel developing new quantitative methods to identify which genes in corn do
[00:00:44.680]what, working particularly with yield component traits like the number of kernel rows on the
[00:00:50.000]ear.
[00:00:51.180]After that he went and moved to UC Davis and started studying the genetic basis of heterosis
[00:00:55.480]using new population genetic techniques.
[00:00:57.340]With a guy named Jeff Rossibera, also a very famous maize geneticist.
[00:01:03.840]And that brings us to 2016 when I was a brand new assistant professor and served on my very
[00:01:09.160]first faculty search committee because we needed to replace the quantitative geneticist
[00:01:13.680]role here in our department.
[00:01:15.240]We interviewed a number of candidates and we were extremely fortunate to convince Jin
[00:01:20.100]Lang to come and move here to the University of Nebraska starting in 2017.
[00:01:26.380]In a period of seven years, he has published approximately 50 papers including in Nature
[00:01:31.780]Communications, Nature Plants, and just in the last couple of months in Science.
[00:01:35.440]He's trained an outstanding number of students.
[00:01:37.780]I am not a population or quantitative geneticist, but to the extent I understand any of that,
[00:01:42.400]it is because I'm talking to my students and they're explaining to me the concepts that
[00:01:45.560]they learned in Jin Lang's classes, so I'm very grateful for that.
[00:01:53.280]His lab is currently working on a wide range of topics.
[00:01:56.380]In both corn and sorghum, from developing and testing new theoretical and conceptual
[00:02:01.220]models like genome-wide mediation analysis to applied questions and how do we develop
[00:02:07.320]and engineer more nitrogen use efficient, healthier, and more stress tolerant corn and
[00:02:12.840]sorghum.
[00:02:13.840]We're going to hear a little bit about all of those things today, so please join me in
[00:02:17.720]welcoming Professor Yang.
[00:02:21.480]Well, thank you, Dr. Shinobu.
[00:02:26.380]I'm the prior Dr. Charles O. Gardner professor, recipient.
[00:02:32.220]I'm so honored to be named after Dr. Gardner's name.
[00:02:41.360]So Dr. Gardner's 40 years of contribution to quantitative genetics is highly associated
[00:02:55.380]with the department of
[00:02:56.380]this department, and also related with the University of Nebraska-Lincoln.
[00:03:06.120]As you can see, so he started his bachelor in 1941, I don't know.
[00:03:15.000]But that unfortunately interrupted because of World War II, then get a MBA degree from
[00:03:26.380]Harvard.
[00:03:29.520]He then back to the department, this department of Guarani and
[00:03:34.060]Botany back then to get a master degree.
[00:03:39.220]After that, he pursued a PhD degree at the University of North Carolina State
[00:03:49.000]University under the supervision of Dr. Harvey.
[00:03:56.380]He started his journey on quantitative genetics.
[00:04:01.380]He was hired as an associate professor.
[00:04:05.160]So that started his career, really get started.
[00:04:08.700]That started from 1952 as an associate professor in the department back then
[00:04:16.300]called agronomy, having merged with horticulture.
[00:04:21.580]So about five years later, he promoted to the professor of agronomy
[00:04:26.380]and then became a regent professor of agronomy.
[00:04:30.820]That's 1970.
[00:04:33.320]And he retired with the regent professor title at 1989.
[00:04:40.060]And during this time, he also served as a chair of the statistical laboratory.
[00:04:48.660]That then became the Department of Statistics.
[00:04:54.680]So learn from him.
[00:04:56.380]His career and his scientific achievement.
[00:04:59.960]So I summarize his success into these three key factors.
[00:05:06.260]So that including continuous field experiments.
[00:05:12.800]And also his embracing for adapting for new technologies to his research.
[00:05:22.780]And extremely emphasizing on statistics.
[00:05:26.380]In genetic study.
[00:05:29.740]So with these three key factors he be able to like done a lot of experiments to try to
[00:05:38.620]understand what's the selection theories behind those four Sorghum improvement experiments.
[00:05:49.700]And can you hear me online?
[00:05:52.460]Okay.
[00:05:53.460]Cool.
[00:05:54.460]He also quickly adapted.
[00:05:56.380]And cutting edge technology.
[00:05:57.760]I saw them that use that as a marker to associate with quantitative traits.
[00:06:04.820]It can be considered as an early version of association study or GWAS.
[00:06:09.640]But not genome-wide though.
[00:06:11.140]There's just a number of very expensive markers.
[00:06:15.060]And one of his major achievement is try to explain the heterosis.
[00:06:23.720]He made actually a major contribution on heterosis.
[00:06:26.380]He came up with the theory, argued that dominance for gene-determined quantitative trace in
[00:06:34.820]maize is mainly resolved from linkage rather than the overdominance.
[00:06:40.700]So later on, this will become the pseudo-overdominance model.
[00:06:48.300]His achievement is beyond scientific.
[00:06:52.880]He also has a huge impact on humans.
[00:06:56.380]On people.
[00:06:57.940]So this evidence by the stack of thesis and dissertations,
[00:07:04.420]that first they show up in my office.
[00:07:06.420]That's on the bookshelf in my office.
[00:07:08.360]That I first day into that room, well,
[00:07:11.600]nothing but this whole stack of old dissertations.
[00:07:15.500]Now I realize the value of that.
[00:07:18.260]Maybe, yeah, I mean to do this type of thing,
[00:07:21.480]and mean to be associated with my research at some level
[00:07:26.380]in Dr. Gardner's research.
[00:07:29.140]And actually a lot of those names endowed this professorship.
[00:07:33.380]Further, to translate some of these key factors
[00:07:41.760]in my own research, so I reorganized this a little bit
[00:07:47.860]and integrated into this design-build-test strategy,
[00:07:52.020]focusing on nitrogen use efficiency, so that's
[00:07:56.380]the topic of my research presentation for today.
[00:07:59.580]So I'm gonna focus on just utilizing the factors,
[00:08:03.740]the successful factors that Dr. O'Connor used before
[00:08:10.320]to tackle one of the most challenging problems
[00:08:13.900]in Nebraska, in the U.S., and all around the world,
[00:08:18.660]the nitrogen use efficiency problem,
[00:08:21.580]or the overuse of inorganic nitrogen fertilizer
[00:08:25.200]in agricultural production.
[00:08:26.380]First of all, start from the second year
[00:08:31.380]I'm in this position, I started to conduct
[00:08:35.580]a replicated field experiment,
[00:08:38.700]focusing on nitrogen use efficiency.
[00:08:41.560]Well, I'm fortunate enough to work with Dr. James Straubel.
[00:08:46.560]The first year, or first day, I was hired.
[00:08:51.800]I'm fortunate enough to
[00:08:56.380]be brought on this huge project called CIRRIE.
[00:09:01.380]This project empowered me to work
[00:09:06.520]with some brilliant scientists in this university
[00:09:12.600]to conduct this collaborative field trial, field experiments.
[00:09:17.600]So, otherwise, I cannot manage
[00:09:20.880]such a big scale experiment myself.
[00:09:23.100]So, we've been continuing doing that
[00:09:26.380]since then, so until last year.
[00:09:29.760]So, have accumulated tons of population-wide
[00:09:34.560]phenotypic data and also other omics data.
[00:09:39.560]So, every year is under high and low-end conditions,
[00:09:43.000]each with two replications.
[00:09:44.740]So, this continuous field experiment
[00:09:48.540]enabled us to do a lot of population genetic analysis.
[00:09:51.900]The technology I'm using is including
[00:09:56.380]some molecular CRISPR/Cas9-based technology,
[00:10:01.280]but also the cutting-edge greenhouse phenotyping
[00:10:05.160]and a field of phenotyping facilities
[00:10:07.560]that are developed here at this university.
[00:10:10.720]So, like I said, with this data and technologies,
[00:10:17.600]we are emphasizing on understanding
[00:10:21.180]the molecular regulatory mechanisms for nitrogen,
[00:10:26.380]uptake, reallocate, and usage.
[00:10:31.380]So, nitrogen is critical.
[00:10:37.060]It's not only because it provides,
[00:10:40.320]basically provides a food for plants to develop,
[00:10:43.460]but also for agricultural production.
[00:10:47.160]It's one of the most expensive agricultural input.
[00:10:50.600]It's highly relevant to the farming profitability
[00:10:56.380]that you can imagine.
[00:10:58.040]So, if this fertilizer price goes high,
[00:11:02.880]the farming, given the constant farming output,
[00:11:07.880]the benefit of doing this agriculture,
[00:11:12.320]they're gonna, less valuable as compared to less input.
[00:11:17.320]Well, maize productivity is high because it's a safer crop
[00:11:26.380]and it's inorganic nitrogen utilization rate
[00:11:31.320]as compared to the other C3 crops is high
[00:11:35.960]in terms of translate this absorbed nitrogen fertilizers
[00:11:40.960]into photosynthesis and for yield.
[00:11:44.860]However, so this inorganic nitrogen fixation process
[00:11:49.860]is environmental unfriendly, so a lot of this,
[00:11:56.380]the fertilizers applied into the soil
[00:12:00.060]could not be completely utilized by the plant.
[00:12:03.620]Only 40% can be used by maize,
[00:12:08.060]and a large proportion actually leach to the soil,
[00:12:12.660]to the groundwater, eventually lead to a series
[00:12:16.140]of environmental concerns, ecological burdens.
[00:12:20.340]So it's very urgent to make this nitrogen cycle
[00:12:24.800]more sustainable.
[00:12:26.380]That's actually one of Dr. Gardiner's lifetime goal.
[00:12:31.380]He's a big advocate for agricultural sustainability.
[00:12:36.460]So to design experiment for this,
[00:12:42.000]for improving nitrogen efficiency,
[00:12:44.340]so the first pillar here is field experiment design
[00:12:50.760]on population genetics, because that's what,
[00:12:53.680]I will trend, that's where,
[00:12:56.380]that's where I came from.
[00:12:57.580]So maize to me is not only a crop species,
[00:13:00.420]but it also provides an ideal system
[00:13:03.560]to study plant adaptation,
[00:13:05.960]because this is the whole history of evolution,
[00:13:10.400]domestication, and the recent improvement
[00:13:13.240]on these different time scales.
[00:13:15.560]So maize, well, ancestor is a sub,
[00:13:18.800]Z-maize subspecies called a public gloomis.
[00:13:22.580]So this also considered as a two-scented,
[00:13:26.380]general name, so this is the direct ancestor of maize.
[00:13:30.140]Modern maize is a wild species,
[00:13:32.820]Z-maize subspecies public gloomis.
[00:13:35.920]So it's a so-called public gloomis two-scented.
[00:13:38.960]So that carries millions of years evolution.
[00:13:42.520]So about 10,000 years ago, to be exact,
[00:13:46.060]it's around 9,000 years ago in central Mexico.
[00:13:49.800]So there's some farmers, just Asian farmers,
[00:13:53.300]our ancestors, just random selected
[00:13:56.380]until 70, and eventually bring this to 70
[00:14:01.380]into this modern agricultural world,
[00:14:06.360]become the major staple theory crop
[00:14:11.360]for the world population.
[00:14:14.000]So that happened around 9,000 years ago.
[00:14:17.300]So a lot of things happened during this time
[00:14:21.500]from the 9,000 years ago until recent.
[00:14:24.480]But a lot of things haven't been completed
[00:14:26.380]until we figure out.
[00:14:27.720]It's not a one-time event for this domestication.
[00:14:31.780]But more recently, about 100 years ago,
[00:14:35.460]modern breeding, empowered by better experimental design,
[00:14:39.620]mechanics, fertilizer, and statistics for
[00:14:44.700]this flower design, that kind of thing,
[00:14:49.660]so eventually make the yield, well, sorry,
[00:14:56.380]this was supposed to be nicer 'cause also show
[00:15:00.360]the yield trend, and so this line actually show
[00:15:03.820]the yield improvement over the different time scales,
[00:15:07.200]and also show rate of the nitrogen fertilizer usage
[00:15:12.200]based on the USA data, but somehow it cannot show up.
[00:15:17.560]So with this system, first of all,
[00:15:23.140]I want to emphasize that maize is very,
[00:15:26.380]very, very adaptive.
[00:15:27.680]So if you look at this map globally,
[00:15:30.380]so there's a lot of regions,
[00:15:33.760]non-agriculture, not heavily industrialized
[00:15:38.760]geographic regions actually experience
[00:15:43.460]the different levels and limitation.
[00:15:46.100]So nitrogen is not uniformly distributed around the world.
[00:15:51.100]So you can imagine, so the nitrification and all mineral
[00:15:56.380]transition processes through microbiome actually
[00:15:59.520]that help fix some of the atmosphere nitrogen.
[00:16:04.520]So that eventually be used by plants.
[00:16:09.660]But that mineralization process,
[00:16:13.360]it happened unevenly around the world.
[00:16:18.640]So 20% of the natural soils experience
[00:16:22.940]the different levels of nitrogen limitation
[00:16:26.380]and this nitrogen limitation show a clear
[00:16:29.980]latitudinal from north to south
[00:16:33.120]and also elevation pattern.
[00:16:34.980]So the higher the elevation,
[00:16:36.620]you're at low levels of nitrogen.
[00:16:39.920]So that's reasonable because you're higher up,
[00:16:44.660]so less mechanism, microorganisms activity,
[00:16:49.660]so less mineralization, less nitrogen.
[00:16:56.380]But maize adapted to this various different
[00:17:00.560]environmental conditions, even high elevation.
[00:17:03.080]So maize domestication origin is in lowland Mexico.
[00:17:07.760]It's in this highlighted region,
[00:17:11.660]plus that's the river valley area.
[00:17:15.400]So that's lowland, Mexico lowland.
[00:17:17.560]So recent study by Rasibara and colleagues recently
[00:17:26.380]suggests that it's not only domesticated
[00:17:30.380]from the subspecies of pelvic gloomis teosinte,
[00:17:35.380]but also possibly there's an administrator
[00:17:39.000]about 6,000 years ago with another highland teosinte.
[00:17:44.000]This is another wild ancestor of modern maize.
[00:17:49.740]That's a so-called sea maize subspecies of pelvic gloomis,
[00:17:54.140]Mexican in this case.
[00:17:56.380]So that administrator happened around 6,000 years ago.
[00:18:01.160]So Mexicana actually is a highland teosinte.
[00:18:05.860]So adapted to possibly low nitrogen,
[00:18:10.000]highland cold condition.
[00:18:12.000]So that administrator make maize more broadly adapted.
[00:18:17.000]So after that administrator event,
[00:18:21.280]it started to spread to other parts of the Americas.
[00:18:26.380]So to north and to south.
[00:18:28.720]So indeed, the ecological evidence suggests that
[00:18:32.920]maize first adapted to Mexico highland
[00:18:37.920]and then about 4,000 years ago adapted to Andes highland.
[00:18:43.820]And about 2,000 years ago,
[00:18:46.740]adapted to Colorado plateau highland.
[00:18:50.640]So especially there's a very interesting
[00:18:53.380]science paper published about
[00:18:56.380]five, 10 years ago.
[00:18:58.880]I don't remember exactly,
[00:19:01.520]but suggests that using a modern genomic selection approach
[00:19:05.020]predicts from the ancient corn cobs.
[00:19:09.160]So using the DNA to predicts well,
[00:19:12.300]so it's been about 1,000 year to eventually adapt
[00:19:17.200]the environment to be able to flower
[00:19:19.600]by using flowering time prediction.
[00:19:22.500]So that's pretty cool.
[00:19:24.600]So all those evidence suggests
[00:19:26.380]that maize adapted to not only lowland,
[00:19:30.180]but possibly unlimited highland.
[00:19:32.960]And so just a little bit summary of this introduction part.
[00:19:39.920]So maize is actually very diverse, as you can see,
[00:19:45.460]and it's widely adapted
[00:19:49.500]to different environmental conditions,
[00:19:51.900]different temperature from tropical to temperate
[00:19:56.380]all the way to Canada.
[00:20:00.460]So, and also a lot of those different natural soils
[00:20:05.220]experience a different nitrogen conditions.
[00:20:07.220]So they possibly adapted to nitrogen sufficient soil
[00:20:11.200]and nitrogen limited soil.
[00:20:13.700]And I'm from a quantitative genetics background.
[00:20:18.700]So we always focus on the number of trees determined
[00:20:24.240]or number of genes controlling
[00:20:26.380]or the characteristics actually is controlled
[00:20:31.380]by a large number of genes.
[00:20:35.460]This so-called quantitative trait, only a few.
[00:20:38.260]For example, the color controlled by small number of genes.
[00:20:42.500]This you really call a quality trait.
[00:20:45.000]But most of the traits, the yields,
[00:20:49.100]the nitrogen use efficiency, those complex traits
[00:20:51.980]are determined by tens, hundreds, or even thousands of genes
[00:20:56.380]in the gene.
[00:20:57.220]So although maize has a huge diversity,
[00:21:01.780]but the U.S. corn belt only focus on this type of corn.
[00:21:06.620]So there's a huge genetic diversity.
[00:21:09.260]So this huge bottleneck possibly lots of some historical
[00:21:17.360]or value-adapted beneficial alleles.
[00:21:21.480]So have this plant to combat for disease,
[00:21:26.380]resistance, combat for climate change,
[00:21:30.180]combat for low nitrogen conditions.
[00:21:35.060]So realize this limitation.
[00:21:38.600]So a long time ago, so USDA has this germplasm enhancement
[00:21:43.140]of maize project, or GEM.
[00:21:46.400]So the overarching goal of this GEM project
[00:21:50.200]is to utilize this diverse maize genetic resource
[00:21:56.380]from all around the world to improve the U.S. genetic
[00:22:02.140]materials.
[00:22:03.960]So fortunately, I work with one of the coordinators
[00:22:11.480]of this GEM project, Adam Van Ness,
[00:22:15.260]to help them better understand what's going on with it.
[00:22:20.000]For example, so this represents the tropical ED lands, Adam
[00:22:26.380]collected.
[00:22:27.420]Well, part of this--
[00:22:29.560]well, this is just a subset of the genetic resources
[00:22:32.060]they use for the overall GEM project.
[00:22:35.240]The GEM project has been 30, 40 years already.
[00:22:39.440]So this is one of the largest long-term public
[00:22:43.040]and private collaborative projects.
[00:22:46.560]Tom Hogmeyer used to be a chair of this GEM.
[00:22:50.600]And he recommended me to be a university representative.
[00:22:55.040]So there's a ongoing--
[00:22:56.380]ongoing collaboration with them.
[00:22:57.840]So as you can see, after we sequence,
[00:23:03.420]we obtain this pressure C. So a lot of this
[00:23:06.200]Cs, we actually have very limited number available.
[00:23:11.340]So this historical elite materials
[00:23:14.840]used to improve US maids.
[00:23:18.300]Adam shared some of this with me, and we sequenced them.
[00:23:21.260]So we call this Tropical Thunderlands,
[00:23:23.980]or BGEM Tropical Thunderlands.
[00:23:26.380]Introduce BGEM later.
[00:23:27.620]So BGEM is a small project of this overall GEM project.
[00:23:31.880]As you can see, this is the diversity
[00:23:38.140]of the US genetic materials, so including
[00:23:44.660]stiff stock, non-stiff stock, some sweet corn, popcorn.
[00:23:49.620]So materials developed 60 years ago or more recent.
[00:23:56.380]So those BGEM foundry lines are genetically quite different
[00:24:01.880]from the genetic pool, the US genetic pool.
[00:24:07.580]So with the idea that these are geographically
[00:24:12.580]widely adapted materials, so we aim
[00:24:16.160]to identify regions possibly adapted
[00:24:20.720]to different natural conditions.
[00:24:24.060]So these regions--
[00:24:25.940]in population genetics, they'd be
[00:24:27.700]considered as regions under balancing selection.
[00:24:31.340]So idea is that there's a locus that have multiple alleles.
[00:24:35.320]Alleles might adapt to high conditions
[00:24:37.940]to utilize the sufficient N2 growth factor, high yielding,
[00:24:42.080]or under low conditions, they survive, get some seeds,
[00:24:46.520]and quickly go to next generation.
[00:24:49.100]So both of this can be beneficial.
[00:24:51.220]Now this alleles are universally beneficial than the others.
[00:24:55.500]So it's not a direct selection for one or another.
[00:24:58.860]So therefore, so this kind of locus
[00:25:01.600]might maintain the population longer than expected.
[00:25:05.040]So therefore, we use the genetic signatures
[00:25:09.600]associated with this long-term maintenance
[00:25:12.180]of this balanced loci to detect signals.
[00:25:16.080]So this on the X-axis, that's the 10-mes chromosome.
[00:25:20.100]On the Y-axis, that's the balancing selection statistics.
[00:25:23.640]So in this case, we call this
[00:25:25.060]B2 statistics.
[00:25:27.700]So examining the signals, we detected
[00:25:30.960]there's some nitrogen-related gene being identified.
[00:25:35.500]For example, this glutamate ammonia legacies.
[00:25:42.280]But we also identify a number of signaling-related genes.
[00:25:46.060]For example, at this locus on chromosome 7,
[00:25:48.720]there's a cluster of glutamate-like receptor gene.
[00:25:51.940]So you know glutamate is a major amino
[00:25:54.880]acid, is one of the most abundant amino acids,
[00:25:58.120]is a direct product after nitrate being taken up by plant.
[00:26:04.200]It directly translates into this amino acid as a storage.
[00:26:10.000]So a lot of these nitrogen-related signals
[00:26:13.340]have something to do with glutamate.
[00:26:14.960]For example, this GS gene is a glutamate ammonia legacies.
[00:26:20.240]And this gene seems to have something to do with--
[00:26:24.700]nitrogen remobilization.
[00:26:27.340]And others seem to be related with potassium signaling
[00:26:32.700]or ion signaling in general.
[00:26:35.580]But as you can imagine, those plants
[00:26:37.880]are adapted widely in these different geographic conditions
[00:26:42.480]and not only adapt to different nitrogen conditions.
[00:26:46.020]There's also elevation differences,
[00:26:48.800]temperature differences, the other soil nutrient
[00:26:53.560]differences.
[00:26:54.520]So in addition to nitrogen-related genes,
[00:26:59.420]well, indeed, we identify some other developmental climate
[00:27:03.560]adaptive genes.
[00:27:05.300]For example, it's the hay shock protein.
[00:27:08.140]So likely have something to do with the temperature tolerance.
[00:27:14.140]And also fluorine time genes.
[00:27:16.240]So this early fluorine FT, so photosynthesis sensitive gene.
[00:27:23.560]So this fluorine is highly related to plant fitness.
[00:27:33.580]If you cannot fluorine, so you cannot reproduce, so therefore it certainly cannot adapt to
[00:27:39.500]the environment.
[00:27:40.820]So some of this makes sense, but I cannot rule out that there's a lot of false discoveries
[00:27:48.640]through this type of analysis.
[00:27:50.740]Well, one more thing.
[00:27:52.400]So some of the...
[00:27:53.400]Interesting genes we identified is the kernel size.
[00:27:57.260]So the kernel size certainly...
[00:27:59.760]So it's a kernel, big or small kernel, certainly have something to do with quick adaptation
[00:28:04.980]or more case to be more productive, therefore more adaptive.
[00:28:10.940]So things like that.
[00:28:11.940]Like I said, so there's a possible false discovery on this.
[00:28:16.180]So to further reduce the false discovery and only focusing on regions
[00:28:23.240]that are highly related with nitrogen use efficiency.
[00:28:26.380]So that's a major research topic.
[00:28:28.440]So we are working on.
[00:28:29.780]So in addition to just look at this topical funder lines.
[00:28:35.300]So we leverage the global panel on Dr. Schnabel lab developed to put the topical funder lines
[00:28:45.280]on this principal component analysis figure, but also bringing in the back cross
[00:28:53.080]of the gem lines that Adam developed, Adam Van Ness developed.
[00:28:58.440]So those are in total 300 lines that using two elite temperate material is a BHB 47 and
[00:29:11.220]a PHC 51 back cross with this 50/60 topical funder lines, back cross actually twice to
[00:29:21.720]in progress.
[00:29:22.920]Those topical materials into temperate genetic backgrounds to create stiff stock B-GEM lines
[00:29:30.220]and non-stiff stock B-GEM lines.
[00:29:33.540]So after obtain those 300 B-GEM lines in two heterotic groups, so we also back cross while
[00:29:42.080]we also make a cross with two typical stiff stock, non-stiff stock maize lines.
[00:29:49.680]So for those of you who are working on maize.
[00:29:52.760]We understand.
[00:29:53.760]So there's a two major heterotic group.
[00:29:56.540]So in the breeding.
[00:29:57.540]So there's a stiff stock, non-stiff stock.
[00:30:00.120]So only crossing those will make the hybrids more vigorous and more productive.
[00:30:05.800]So we begin academia.
[00:30:07.640]So we understand B-SEM3 and MOT17 is not the most elite in brands, but for research purposes,
[00:30:16.080]that's okay.
[00:30:17.080]So we went ahead to cross with B-SEM3, this can be considered as a test cross.
[00:30:22.600]So those are B-SEM3 and MOT17 can be considered as testers in this case.
[00:30:28.660]So we have two groups of high brass, and they're inbred parents.
[00:30:33.320]So this enable us to test one of the topics of Dr. Gunner's interest, the heterosis.
[00:30:40.880]So heterosis is also one of my research interests.
[00:30:48.360]So we put this population, about 500 individuals.
[00:30:52.440]We have a research farm under high and low-end conditions using this incomplete block design,
[00:30:59.560]blocking by plant height, farming time, things like that.
[00:31:03.760]In total, we have 2,400 rows, or 2,400 plots with some checks.
[00:31:15.280]So we collected the wild ground, ear trees after harvesting, and also dug up the roots
[00:31:22.280]to collect a lot of root trees.
[00:31:24.860]Because we have both earbrows and eyebrows, we will be able to calculate the level of
[00:31:30.720]the heterosis using this parameter, mid-parental heterosis.
[00:31:34.560]It's a value-user parameter for numerically estimating the levels of heterosis.
[00:31:42.040]Basically, you subtract the eyebrow value with the mean of the two earbrow parents'
[00:31:47.560]value to get the level
[00:31:52.120]of overperformance.
[00:31:53.120]That value means the higher levels of heterosis.
[00:31:57.900]As expected, for this above-ground trace, after introgression with the tropical materials,
[00:32:04.960]it shows higher levels of heterosis under high nitrogen conditions compared to low nitrogen
[00:32:11.740]conditions.
[00:32:12.740]For below-ground trace, as you can see, especially for this dry root weight, the trend is opposite.
[00:32:21.960]In introgression, the roots actually are showing a larger heterosis under nitrogen-limited
[00:32:28.880]conditions.
[00:32:29.880]That's kind of surprising.
[00:32:34.240]And we also conducted whole genome sequencing for this 300 individuals of embryos, the BGEM
[00:32:41.180]embryos.
[00:32:43.460]Because this 300, so back-crossed, separated into two groups, back-crossed to this recurrent
[00:32:51.800]parent twice, it's supposed to return 25% of the topical integration.
[00:33:00.980]But the observed value is very, very lower than that, it's about 70% or 16%, 70% for
[00:33:08.000]this non-slip stock, and 16% for this slip stock population.
[00:33:16.800]So meaning that a lot of topical materials cannot.
[00:33:21.640]That's possible because a lot of this temperate alleles have already adapted to temperate
[00:33:34.820]environments.
[00:33:37.000]So you bring new topical alleles, they make it comparatively less competitive.
[00:33:47.200]So using that Florentine as another example.
[00:33:51.480]This region on chromosome 10 is highly lowly introgressed because we found while there's
[00:33:58.920]photoperiod-sensitive genes, CMCCT there, so that are related with the Florentine.
[00:34:07.800]So if you cannot reach the Florentine, certainly those topical materials could not be utilized
[00:34:14.980]or can make the selection criteria, so therefore it's very likely those regions cannot be
[00:34:21.320]intergressed as expected.
[00:34:28.680]So with the rate of intergression, the proportion of tropical regions being intergressed to
[00:34:35.920]the temperate background, so we did a correlation analysis with the phenotype observed for yin
[00:34:43.300]blood, high blood, and hydrolysis, so under high-end condition and low-end condition.
[00:34:51.160]For above-ground trees, in the yin blood population, more intergression actually reduces the value
[00:34:59.060]of the phenotypical value, meaning that more tropical intergression for this above-ground
[00:35:05.940]yield-related trees, so the yield actually gets worse, it won't get better, so with more
[00:35:14.280]tropical intergression, that's expected, that's why breeding is not that easy, so you cannot
[00:35:21.000]directly integrate some exotic materials into your breeding program because they have a huge
[00:35:28.320]yield drag. So the interesting thing is that for the below-ground trees that we don't directly
[00:35:38.880]select it for, so bringing those tropical materials actually makes the root performance better. So all
[00:35:50.840]the root getting bigger, so bigger root not necessarily be beneficial if it's a nitrogen
[00:35:57.940]sufficient condition. But if there's a nitrogen limited,
[00:36:02.100]bigger roots, certainly going to help plants to absorb more nutrients. But what's the yield
[00:36:10.180]benefits? Well, the yield probably won't directly benefit from a bigger root because there's always
[00:36:22.900]limited resources. If you invest more on roots, probably you'll have less yield. So there's a
[00:36:28.020]trade-off. So what's the optimal root architecture is something that we don't know yet. But certainly,
[00:36:34.740]so these tropical materials have something to do with a better yield, better root, better
[00:36:43.220]nutrient uptake. Well, so we use this data also conducted association analysis to associate
[00:36:52.260]regions with the tree variations and plotted
[00:36:57.860]it on the balancing selection plots we previously generated. Each blue dot here means that there's
[00:37:05.140]an association signal overlap with the previous balancing selection signal.
[00:37:09.620]So some previous balancing selection signal associated with phenotypes like kernel size
[00:37:20.900]or tonic kernel weight, this yield component tree. So
[00:37:27.540]in this case, the tropical alleles actually make the hybrids perform better under lower conditions
[00:37:38.660]than the homozygous temperate alleles. So suggesting we'll bring some of the tropical alleles
[00:37:47.620]indeed that certain genetic loci can positively contribute to some yield component trees.
[00:37:57.220]So that's the first design of this design-build-test cycle. But that's not the
[00:38:08.500]end. So we further design the second experiment. So investigate what's going on for the
[00:38:15.540]recent breeding practice. So especially after, so remember, so early days when breeders start to
[00:38:26.900]breeding maize, it's usually under natural soil, unlimited soil conditions. And after 1960s,
[00:38:35.220]when inorganic nitrogen fertilizer can be produced relatively cheaply and through this
[00:38:43.540]Haber-Bosch process, through this industrialization, so that we can get
[00:38:49.700]a nitrogen fertilizer much cheaper in large quantity. So after that, so this is really after
[00:38:56.580]the green revolution, so modern breeding is usually conducted under a nitrogen sufficient condition.
[00:39:03.460]So this can be considered as a major environmental shift. So what's going on during these different
[00:39:10.660]errors, the old errors before 1960s, new error after 1960s, so when fertilizer start to be
[00:39:18.900]broadly used. So we obtained some old error in Burland and the new error in Burland. Plant is the
[00:39:26.260]amino fields and observe their phenotype. So once again, so we observed the old error,
[00:39:33.860]and both the old error and the new error under high nitrogen conditions performs better
[00:39:41.060]than low nitrogen conditions. But the difference is under
[00:39:44.260]low nitrogen conditions between the new error and the old error are different.
[00:39:48.660]So if you calculate the ratio, so you can see the new error performs better if there's
[00:39:55.940]sufficient nitrogen. They overperformed than the old error, but the old error didn't change much
[00:40:02.900]after you apply more nitrogen. In other words, if you reduce nitrogen for the old error lines,
[00:40:11.140]it doesn't reduce the yield that much, so indicating they're more resilient to nitrogen
[00:40:17.540]limited conditions. That actually fit our initial hypothesis pretty well. So
[00:40:25.780]we went ahead to using another population genetic approach called a positive selection scan to
[00:40:32.580]identify regions under recent positive selection. Those regions the under selection during this
[00:40:39.860]shift nitrogen conditions by utilizing these two groups of the imbalance after sequencing
[00:40:47.140]their whole genome. Once again, so we identify this GS gene
[00:40:55.460]and also this ATG gene we mentioned earlier. And remember previously we identified a cluster of
[00:41:02.980]glutamate-like receptor gene. So one of the most striking signals we identified
[00:41:11.060]through this positive selection scan is a cluster, another cluster of glutamate-like
[00:41:19.460]receptor gene. So those are three genes nearby each other.
[00:41:25.300]So zooming in this region, so we found that there's six LD linkage disequilibrium blocks.
[00:41:31.860]So we named them B1, B2, B3, B4, B5, B6. So B4 is where those three genes were really located.
[00:41:40.820]And it seems like this pattern is not population structure related.
[00:41:48.980]So using different population structure, we thought this
[00:41:54.660]IOD pattern clearly as well.
[00:41:58.500]So we leverage the phenotyping facility to collect more trees using a larger diversity panel
[00:42:10.580]composed of 300 individuals. And so conducted a targeted association analysis
[00:42:19.220]focusing on leaf nitrogen content, leaf angle,
[00:42:24.340]and leaf area, and drive it, and found this region contain that three genes
[00:42:31.940]all associated with this different morphological trees or physiological trees, but also associated
[00:42:39.540]with the secondary metabolic trees, so that my colleague Toshi Obata helped me to collect.
[00:42:45.460]So you can see, so this new arrow allele haplotype that highly associated
[00:42:54.020]with secondary metabolites have N elements in their formula on the opposite. So this old
[00:43:03.620]arrow lines are more enriched for this sugar-carbon secondary metabolites without any N in it.
[00:43:14.420]Well, with this different set of evidence, now we'll start to think and move forward for
[00:43:23.700]the next step. So to build and test, is this the GLR genes that really is a causal gene
[00:43:33.460]during this evolution or breeding process? Does that really contribute to nitrogen utilization
[00:43:42.100]or nitrogen resiliency? So there are several things to consider before we start to build.
[00:43:47.780]First of all, there's a different genetic background. There's an old error, there's a new error, and
[00:43:53.380]there are three genes we need to consider. So while you're just focusing on that,
[00:43:59.620]chromosome 5 region that is a cluster of three. And there's different metagenic approaches. So
[00:44:07.940]uniform mu is directly available, but you need to purify the genetic background by backcrossing
[00:44:14.980]several times. And there's an emerging promising new technology, but at the time it's not available to us
[00:44:23.060]and fulfill testing. So this traditional metagenesis approach can be directly tested,
[00:44:30.260]it's not regulated, but CRISPR considered as a gene editing approach is regulated. So we have to
[00:44:37.380]do this step by step. So only observing promising results, then move forward. So that's our build and
[00:44:43.860]test strategy. So we actually get this uniform mu mutant from the database five years ago,
[00:44:52.740]let's start from 2020. And we keep backcrossing, keep salving, and eventually in 2003, we get this
[00:45:01.220]BC2S3. So backcross twice and solve three times. So to clean the background. So because there's
[00:45:09.140]some other uniform mu insertion, possibly around the other parts of the chromosome.
[00:45:13.780]So therefore people will argue, well, the phenotype you observe might be caused by
[00:45:18.340]some other genes. That's why. So we need to do a lot of backcrossing and salving
[00:45:22.420]to clean the genetic background. But it's not enough. Backcross twice and
[00:45:28.980]salve three times is usually not enough. That's why, so we consider this as a
[00:45:33.140]preliminary results. But in this preliminary results, we observe that this
[00:45:37.960]mutant, that's a yellow valine plot, as compared to the light
[00:45:46.460]blue valine plot, it performs better. So under both nitrogen conditions in both
[00:45:52.100]wraps, that kind of a promising. And also the kernel size is getting bigger of the
[00:45:58.700]mutant, as compared to the wild type. Especially in this case under hand
[00:46:04.860]condition, under low end, it seems that doesn't change that much. So this is a
[00:46:09.860]preliminary results, that's promising then. So we took another year to keep
[00:46:14.760]solving, keep back-processing, to get this BC3F3. So this has been considered as
[00:46:21.780]a much purified genetic materials for further testing. So this
[00:46:31.160]time we use this BC3F3 mutant, mutagging, so on this JLR3.C gene.
[00:46:40.100]So we named this cluster of three as 3.4a, b, and c. So this one is just
[00:46:45.960]have a mutant on the third gene. So we found,
[00:46:51.460]under high-end conditions, so for the seedling traits, there's not much in the
[00:46:56.200]greenhouse, there's not much change, but for the mutant, under low-end
[00:47:03.400]conditions, it performs better. So you can see this green, so it performs
[00:47:08.980]better than the white-tailed green. So this seedling dry weight shows a very
[00:47:13.600]similar pattern, as well as the seedling height. So that's good. So that's suggesting
[00:47:19.000]the mutant accumulates mobile mass.
[00:47:21.140]And so we use different parameters that basically show the same story, so that
[00:47:27.260]mutant performs better than the white-tailed, especially under long conditions.
[00:47:32.120]And we plant those in the fields, because this is now regulated, and so you
[00:47:39.680]can see, so mutant grow taller and flowering earlier, accumulate more chlorophyll,
[00:47:45.980]and yeah, it's consistent with the
[00:47:50.820]greenhouse observation. So this is very encouraging, so we therefore decided to
[00:47:57.660]let's go CRISPR to edit all those three genes, because there are three genes we
[00:48:02.940]don't know what other genes affect, so just knock out that one
[00:48:09.420]gene probably is not enough. So using this approach, we'll be able to knock out
[00:48:15.540]all those three genes simultaneously to get four different
[00:48:20.500]events for the triple mutant, but we also get one double mutant. In the
[00:48:26.720]meanwhile, my students and Dr. Jin worked together to develop a high
[00:48:33.100]efficient in-house transformation system, so we'll be able to reduce the
[00:48:39.140]tissue culture from six months to two months, and also transformation
[00:48:44.120]efficiency increase from about zero to seven percent to about 20 percent, but
[00:48:50.180]that's not completely on us because there's a technology development,
[00:48:54.620]there's a baby boom, osho genes, those vectors really make these changes.
[00:49:01.520]We just adapted this new technology for our own research in the lab. So with this
[00:49:09.980]mutant, we conducted a greenhouse experiment for seedling trees and for
[00:49:15.440]adult trees. So I'll summarize these results in these two slides. First of all,
[00:49:19.860]so just keep this short, this mutant performs better again as compared to the
[00:49:28.500]wild type. So just use this as an example. Actually it's progressively better.
[00:49:33.360]So from old error double mutant to the new error triple mutant. So well, I
[00:49:39.780]forgot to mention, so W22, that's an old error genetic material, genetic
[00:49:45.140]background, is B7-3, be considered as a new error genetic background.
[00:49:49.540]So for seedling trait, you can see, so this is a wild type, so Vx is a mutant
[00:49:55.540]divided by wild type, that's a ratio. So this value is very, very above 1, meaning that
[00:50:01.160]mutant is very, very better outperforming the wild type. So you can see, so this seedling
[00:50:08.300]trait for seedling dry weight performs very, very better under low-end conditions, so is
[00:50:17.360]the seedling height.
[00:50:19.220]And not so much for the chlorophyll content, though, but for adult trait, the chlorophyll
[00:50:25.700]content is certainly increased under low-end condition for the mutant as compared to the
[00:50:33.260]matched wild type. So we use W22 wild type for W22 mutant, Bsens3, and wild type for
[00:50:42.400]Bsens3 mutant. So interestingly, we found the mutant also
[00:50:48.900]flowering earlier, so two to three days earlier under different genetic
[00:50:53.580]backgrounds. So triple mutant is even more earlier, so on average four to five
[00:50:58.900]days, yeah, about. Alright, so with this observation, now let's summarize what we
[00:51:05.640]have learned. First of all, through some bioinformatic analysis, so we found this
[00:51:12.060]this is a cluster of glutamate-like receptor genes kind of placed into
[00:51:18.580]two gene clusters. One is nitrogen assimilation, the other is N signaling.
[00:51:28.300]So this glutamate receptor is not new, it's conserved in even mammalian
[00:51:38.200]species. So after, well it's a transmemory big
[00:51:43.660]protein, so have two domains, transmemory domain and actual memory
[00:51:48.260]domain. So actually this domain, after bound with a small amino acid
[00:51:54.320]molecule glutamate, will change the configuration of this cross-memory part
[00:52:00.100]to open the gate. So this is an ion gate. So once that gate opens, so this trigger
[00:52:05.300]calcium or some other ion to pass through. So especially calcium is being very
[00:52:11.720]considered or well known as a signaling messenger. So once this signaling
[00:52:17.940]pathway is being triggered, it will probably involve a bunch of
[00:52:22.980]downstream reactions. So implants, so there's some studies before
[00:52:29.360]suggesting this is not glutamine specific. It's a broad spectrum
[00:52:36.360]amino acid receptor. So similarly, it opens the gate to possibly trigger some
[00:52:43.560]nitrogen pathway. But this nitrogen path is really something we found
[00:52:47.620]so this is pretty new. So this took us seven years. So we found, well, the
[00:52:57.340]mutant have more root hair as compared to the wild type. And the greener, so
[00:53:04.620]because of the root hair help them to uptake the nutrients. So therefore, they have
[00:53:11.980]more energy to produce chlorophyll to make the above-ground greener accumulate more
[00:53:17.300]biomass. That's kind of cool. And based on the literature study, we know that once
[00:53:24.740]this calcium signaling has been triggered, it possibly enhances nitrogen assimilation
[00:53:32.320]pathway. So that's indeed what we observed through RNA-seq analysis. So once again,
[00:53:38.040]so we compared different genetic backgrounds with their matched wild types for the gene
[00:53:44.540]expression pattern. So you can see.
[00:53:46.980]For the new era B-sensory triple mutant, its nitrogen pathway has been activated and enhanced,
[00:53:58.540]so meaning that they can, with this more root hair, they have more nitrogen assimilation
[00:54:06.740]capacity, evidenced by the higher gene activities as compared to the wild type. So especially
[00:54:15.460]for the new era B-sensory triple mutant.
[00:54:16.660]For the old era W22 mutant, the levels of activation is not so much. But there is also
[00:54:28.980]enhanced chlorophyll synthesis pathway, but some parts is decreased, other parts increased.
[00:54:35.860]So this suggesting, so there's a change, possibly change the chlorophyll biosynthesis. So glutamine
[00:54:46.340]is one of the precursor, but plants, especially maize, can also use some other amino acids
[00:54:52.900]for produce chlorophyll, but that part we don't know yet. So this actually raises more
[00:55:01.220]questions for further study. I'll skip this because I don't have a lot of time to explain
[00:55:07.540]all the details in this regulatory mechanism. But I would like to use a
[00:55:16.100]the remaining several minutes to talk about how we go forward. So after changing this gene,
[00:55:24.740]so we found well it's not only changing this gene itself. This gene has a huge gene family. Oh it's
[00:55:30.500]not that huge, it's 18 gene members, 18 members of a gene family. It's a relatively small gene family.
[00:55:38.580]Change of this gene also change other gene. And remember back then in the balancing selection we also identified
[00:55:45.700]another cluster of glutamate 2.7 gene. So that also after we mutated this 3.4,
[00:55:55.300]that 2.7 also being changed as well as some other not well-known members of this
[00:56:04.900]gene family. So now with this in-house CRISPR gene editing system we'll be able to edit about
[00:56:15.380]10 others, maybe not 10 yet. Anyway, so we add that scale. So we don't need to repeat this seven
[00:56:22.660]year cycle to build from scratch. Now we can build quick because we have a lot of knowledge about
[00:56:29.220]this. But that's still, so for this transformation still goes through this culture, regeneration,
[00:56:37.540]solving, genotyping, and testing cycle. That still take years and tedious process.
[00:56:45.060]And labor intensive. So it's more interesting is how to scale this up to build faster and
[00:56:56.740]test the most promising ones. So I came up with this build on the predictor strategy.
[00:57:05.000]So the idea is that we're not necessary to test every edit. So, but instead we're using
[00:57:14.740]the Crotoplus system to edit using the Crotoplus with more recent editing tools, so-called
[00:57:26.260]CRISPR-A. So this using to target the region of interest, but not cut, but activate
[00:57:34.300]the gene expression. So my colleague, Dr. Liu at the Kansas City University already
[00:57:41.060]proved this method works perfectly for meson's organs.
[00:57:44.420]They can activate, while it's attached to a transcriptional activator, they can activate
[00:57:53.280]gene of their interest more than 100 fold or 500 fold in some cases.
[00:57:59.740]And that's the transient activation is highly correlated with the conventional transgene
[00:58:07.300]approach. And you can see this, the GLASIS-3 gene of their target have been highly activated.
[00:58:14.100]This is a suppressor. So this gene being activated can suppress a lot of other genes' expression.
[00:58:23.160]So both of these cases show that this works okay. But how to use this activation information?
[00:58:34.780]So one thing we can use is recover the activation information from sequencing. So this technology
[00:58:43.780]is called CRISPR-ASIC. So using this transcriptome information being perturbed by this CRISPR-A,
[00:58:53.120]so we will be able to understand what the transcriptome status looks like for this gene
[00:59:02.420]after it's mutated. So you can imagine, so gene interaction network is a complex system.
[00:59:09.420]You knock out this gene, it not only affects this gene's expression, it affects all the
[00:59:13.460]gene within that cell, or within different cells, or even the whole plant.
[00:59:20.460]So this product path is just to provide a first look at this perturbed transcriptome.
[00:59:28.980]So with this information, we can build in our previously developed prediction model.
[00:59:35.260]So with the genotype and the perturbed transcriptome, we will be able to predict
[00:59:43.140]the phenotype of performance if we train the model properly.
[00:59:48.960]So that actually largely shortened this cycle from build to test.
[00:59:54.760]We actually start with the build and the predict, only select the most promising ones for stable
[01:00:01.640]transformation and do the real testing.
[01:00:05.520]So our preliminary results, our pilot study suggests that while indeed our model by incorporating
[01:00:12.820]the Trastomic information can enhance those, because this seedling Trastomic information,
[01:00:19.080]they can increase the seedling trait performance in terms of prediction accuracy for up to
[01:00:25.940]40%.
[01:00:27.580]So using even seedling trait to predict adult traits that can improve up to 20% for certain
[01:00:34.120]traits.
[01:00:35.120]In this case, D2 pollen is a farm-time gene, farm-time trait.
[01:00:42.500]So all mutant data, so we found about this can relatively accurately predict the thin
[01:00:50.220]type performance.
[01:00:51.220]So this is the mutant, this is the wild type.
[01:00:56.260]So using traditional approach, we can only get this information after several years of
[01:01:02.980]testing.
[01:01:03.980]But with this prediction approach, we can get it and then build a stable transformation
[01:01:10.100]and go ahead with the testing.
[01:01:12.180]So hopefully it works and help us to further scale up this build, predict, and test cycle
[01:01:24.540]for our next experiments.
[01:01:26.040]So our ultimate goal is a lower nitrogen input for tomorrow's agriculture production.
[01:01:34.420]So this is the summary.
[01:01:36.120]So at the end, I would like to thank my team members.
[01:01:41.860]So who are in the audience.
[01:01:45.080]And I want to especially thank Dr. Massa and George Graf and Elkhorn Hoon for the guidance
[01:01:52.420]over the years, and also a true friend and a great collaborator, James Schnabel, nominating
[01:01:59.720]me for this professorship position and also all those years of collaboration and work
[01:02:08.120]together.
[01:02:10.000]Thank you very much.
[01:02:11.540]Thank you, folks.
[01:02:20.420]So we are right at noon, but we have this room for another hour, I believe, for the
[01:02:24.100]lunch to celebrate.
[01:02:25.320]So if people have to jump up and run out, that's completely okay.
[01:02:29.060]If you've got questions for Jinlang, though, we have him for quite some time.
[01:02:39.660]Thank you for the excellent seminar.
[01:02:41.220]Can you translate your finding to the other grass species?
[01:02:46.400]Yes, I think so.
[01:02:49.060]For example, sorghum and wheat.
[01:02:53.140]So while we try the Arabidopsis, it's not a crop, but the model species, it's a different
[01:03:04.600]story.
[01:03:05.600]We also have mutants for sorghum.
[01:03:10.900]So this cluster of GLR3.4 also show up as a cluster of three.
[01:03:20.540]So in the exactly syntactic region, and it's also responsive to nitrogen in terms of trastormic
[01:03:27.240]response.
[01:03:28.240]Gene expression level are responsive, but after the knockout, so it's lethal.
[01:03:34.900]It's not lethal.
[01:03:36.700]We can generate the seedling, but it won't accumulate.
[01:03:40.580]It won't accumulate any chlorophyll at all.
[01:03:42.180]It's yellowish to white.
[01:03:45.180]So we don't know what's going on there.
[01:03:46.740]Are these spontaneous mutations or?
[01:03:49.240]No, it's also CRISPR.
[01:03:50.680]Okay.
[01:03:51.680]Mm-hmm.
[01:03:52.680]Yeah, Tom Clemente help us to generate those sorghum mutants.
[01:03:57.480]All right.
[01:04:00.040]Other questions?
[01:04:02.440]That was very interesting.
[01:04:07.780]I'm a soils guy, so I'll ask you.
[01:04:10.260]A soils type question, but there are a number of companies out there selling biological
[01:04:16.020]products to enhance nitrogen uptake or whatever, and you talked a fair bit about root phenology,
[01:04:26.960]so some of their findings are positive and some are negative or zero.
[01:04:34.700]Does your phenology work relate to that, and some of their results?
[01:04:39.940]Is it because of what you've studied or learned about phenology?
[01:04:44.940]So yeah, I did a little bit of microbiome type of work, so this probably has something
[01:04:56.840]to do with some of those companies that sell biologicals for enhancing nitrogen uptake.
[01:05:04.760]So the plants, you can imagine during this long-term evolution.
[01:05:09.620]So plants, to deal with this nitrogen limited budget in a natural environment, they have
[01:05:17.980]to work with somebody if they cannot take up enough food for them to survive.
[01:05:24.540]So there's a well-established symbiosis relationship of the plant root and its root-associated
[01:05:35.460]microorganisms.
[01:05:39.300]There's a lot, there's hundreds or even thousands of different microorganisms.
[01:05:47.000]Some of them help them to resist environmental stresses, droughts, others help them to uptake
[01:05:58.600]nutrients.
[01:06:00.300]So there's a group of specific microbes.
[01:06:08.980]I forgot the group of names.
[01:06:16.220]But anyway, so there's a bunch of beneficial, we call this beneficial microbes, can help
[01:06:22.460]plants to gather nitrogen nutrients for them.
[01:06:27.320]So yeah, I believe it may work.
[01:06:30.060]But again, so microbiome also is a very complex system.
[01:06:33.000]So they compete with each other as well.
[01:06:35.520]So those companies...
[01:06:38.660]They have something that work, but not necessarily work consistently in different conditions.
[01:06:45.520]So there's other effects.
[01:06:47.440]This microbe come first, so they'll occupy that environment or occupy that position.
[01:06:55.820]So others, prevent others to come.
[01:06:58.400]So there's a study called synthetic microbial communities, SYNCOM, so try to make a bunch
[01:07:07.760]of different microbes.
[01:07:08.340]And microbromes work together to better work with the plants.
[01:07:16.840]Thanks, Jingliang.
[01:07:23.280]Are there your discovery platform, your population genetics discovery platform, do you think
[01:07:28.680]there are other traits that the data lends to as well for looking at other traits that
[01:07:34.240]might have been under balancing selection that you can find other genes that would be
[01:07:38.020]useful in today's and if we were to lower inputs for other traits?
[01:07:43.240]Yeah, I think that's a good question.
[01:07:49.100]So even JLR itself is not only for nitrogen, it has something to do with code.
[01:08:00.600]Because I didn't show this one, but this is what we observed last year.
[01:08:07.700]In the greenhouse, when it's very cold, the mutant will show this type of tip burning
[01:08:15.580]phenotype.
[01:08:16.580]And we talked with a lot of experts, and eventually determined this is likely to be a calcium deficiency
[01:08:27.120]symptom.
[01:08:28.120]And with the help of a governor, so we add some calcium fertilizer and get that trait
[01:08:37.380]better.
[01:08:40.060]So that suggesting that mutant itself has something to do with code.
[01:08:45.980]But yeah, you're right.
[01:08:46.980]So in the back to the balancing selection pipeline, there's a lot of genes related with
[01:08:52.200]the flowering and related with heat resistance, heat shock genes.
[01:09:01.620]And this possibly help them to adapt to high temperature.
[01:09:07.060]During the summertime.
[01:09:10.940]So that's not my expertise.
[01:09:13.220]I cannot comment much about that.
[01:09:15.760]But yeah, so there's potentially some other genes.
[01:09:18.840]All right, and this will be our last formal question.
[01:09:23.280]But Jinling really will be around for the next hour.
[01:09:25.800]So stick around and ask more if you have more things to ask him.
[01:09:33.520]It seems to me there might be several traits that
[01:09:36.740]are involved in this whole nitrogen uptake and utilization thing.
[01:09:42.560]My old friend Jim Shepard has always told me that corn wasn't very efficient because
[01:09:48.200]we only took up about half of the nitrate that we put on.
[01:09:52.560]And secondly, old time corn breeders in the 50s and 60s, when we first started putting
[01:10:00.140]on high rates of nitrogen, soon found out there were some genotypes that utilize nitrogen
[01:10:06.420]and really showed higher rates of growth and higher yields.
[01:10:10.920]And some just didn't respond very well.
[01:10:13.840]In the corn that we use, even what we used in the 50s and 60s, we only represented two
[01:10:21.680]or three of the races of about 300 that are known.
[01:10:25.960]And it always occurred to me, there are some of these races that developed under high rainfall
[01:10:31.980]sorts of conditions.
[01:10:33.920]And I wonder if there aren't.
[01:10:36.100]You know, traits and genes for those traits that we haven't even thought about using in
[01:10:42.240]nitrates uptake.
[01:10:44.500]And maybe we had to go back and look at some of that old stuff.
[01:10:47.400]Yeah, so you're completely right, Tom.
[01:10:53.160]Yeah, that's that's the basis of this study.
[01:10:55.620]So there's some variations.
[01:10:57.340]Some genotypes respond to nitrogen better than others.
[01:11:01.200]Some genotypes are more resilient to nitrogen than others.
[01:11:04.740]So, yeah.
[01:11:05.780]That's the basis of this study.
[01:11:07.440]So we compare those different genotypes, show different responses to different nitrogen
[01:11:13.500]conditions and be able to identify that gene.
[01:11:16.080]And our test suggests that, yeah.
[01:11:18.540]So there's possibly certain nitrogen pathway, signaling pathway involved.
[01:11:24.300]And this gene, so the hypothesis is either as a cellular level nitrogen sensor, but it's
[01:11:35.460]a calcium dependent sensor.
[01:11:38.880]So it trigger a calcium signaling pathway.
[01:11:44.460]But what's going on downstream, that's something that we need to figure out in the future.
[01:11:51.340]All right.
[01:11:52.340]With that, please join me in thanking our speaker one more time.
[01:12:01.700]you
[01:12:02.320]Thank you.

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Designing Nitrogen-Use Efficient Maize Using a Population Genomics Approach

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