From Discovery to Application in Ag Biotech; Lessons Learned Over 14+ Years in Industry
Translating discoveries into commercial products requires balancing business drivers with scientific possibilities. This presentation will provide an overview of the path from discovery to application for biotech trait development in the Ag industry and – with a focus on complex traits – highlight some of the challenges faced along the way and opportunities provided by the emergence of new technologies.
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[00:00:00.800]The following presentation
[00:00:02.250]is part of the Agronomy and Horticulture Seminar Series
[00:00:05.830]at the University of Nebraska-Lincoln.
[00:00:08.424]So, welcome everyone, to the UNL Department of Agronomy
[00:00:11.590]and Horticulture Seminar Series.
[00:00:13.450]My name is Marc Libault.
[00:00:14.525]I am an associate professor in the department.
[00:00:16.330]I am also a member
[00:00:17.163]of the Center for Plant Science Innovation,
[00:00:20.130]and I'm the director of the Single Cell
[00:00:21.760]Genomic Core Facility at UNL.
[00:00:24.210]So, I'm very happy to introduce Jason (indistinct).
[00:00:28.873]We are working for over a year now together.
[00:00:31.970]And Jason received his PhD in biochemistry
[00:00:34.480]from Duke University in 2004,
[00:00:36.540]where he work on the molybdenum
[00:00:37.780]cofactor biosynthesis pathway.
[00:00:41.210]And then, Jason joined as a postdoctoral associate
[00:00:43.880]at the department of chemical and biomedical engineering
[00:00:46.734]at (indistinct) University,
[00:00:48.600]where he was working in the field
[00:00:49.433]of extrema field microbiology,
[00:00:51.910]so quite a diverse background already.
[00:00:54.720]In 2007, Jason joined Syngenta as an enzymologist
[00:00:59.640]in the cellulosic biofuels team.
[00:01:01.600]And at this time, Syngenta had a animal nutrition
[00:01:04.510]and biofuels group.
[00:01:06.506]In 2010, Jason changed for a different direction,
[00:01:10.830]still in Syngenta.
[00:01:12.270]He switched to focus on agronomic traits,
[00:01:14.770]growth, and other yield related traits,
[00:01:17.581]working mostly on call,
[00:01:19.300]and that's where he get expertise in plant physiology.
[00:01:23.220]And since 2016, Jason now joins,
[00:01:28.030]so is a molecular analytics group in Syngenta,
[00:01:31.400]making a shift from really pure discovery
[00:01:33.370]to applied research.
[00:01:34.407]And there, he's responsible to balance trait research
[00:01:37.950]and technology (indistinct) and evaluation.
[00:01:41.450]Most critically, Jason is especially interested
[00:01:44.768]in translating technology opportunities
[00:01:47.040]to try to research, and also to try to bridge disciplines
[00:01:51.440]in the field of corn biology.
[00:01:53.660]So, I think that I'm going to let you, Jason,
[00:01:56.390]giving your talk.
[00:01:59.203]Thanks for the introduction,
[00:02:00.036]and thanks everybody for joining.
[00:02:03.260]So, as I was putting this talk together,
[00:02:05.820]rather than doing a deep dive
[00:02:08.530]into any one particular project,
[00:02:11.160]I thought I'd give a bit of an overview
[00:02:13.400]of some of the projects that I've worked on,
[00:02:15.260]but with the perspective of sort of the evolution
[00:02:18.290]of my career and my understanding
[00:02:20.160]of shifting from basic research,
[00:02:24.720]which is really where I started, to applied research,
[00:02:27.130]and ultimately, how does a product get to market?
[00:02:30.620]That's something you're not necessarily taught in school,
[00:02:32.920]and it takes a while to really understand
[00:02:35.080]when you get into a company.
[00:02:37.715]So, I've been with Syngenta for going on 15 years, actually.
[00:02:42.230]So, even the title's slightly out of date.
[00:02:45.230]But I'm just gonna start with a bit of background
[00:02:47.760]and overview of the company.
[00:02:49.830]So, Syngenta is a global company.
[00:02:51.360]I'm sure many of you have heard of Syngenta
[00:02:54.441]as one of the major competitors in the ag space.
[00:02:58.690]So, we have over 100 sites around the world.
[00:03:01.600]I work in North Carolina, and I've been in North Carolina
[00:03:06.850]for about 22 years now.
[00:03:10.100]But we have sites in China, we have France,
[00:03:15.440]certainly all throughout the world, across the US,
[00:03:19.040]so a global company.
[00:03:20.000]And Syngenta, or, I mean, I'm sorry,
[00:03:22.500]North Carolina actually has two R&D centers.
[00:03:26.970]So, where I am located is on the left
[00:03:29.480]at Research Triangle Park, near the Raleigh-Durham area.
[00:03:34.690]And this is really a center for seeds research.
[00:03:37.440]So, GM crop development, precision breeding, genome editing,
[00:03:43.360]but also some technologies
[00:03:44.590]that get to how do we deploy traits into elite lines,
[00:03:48.540]and move those towards market?
[00:03:50.350]And what I'll call decision analytics.
[00:03:52.690]So, and that really gets to computational approaches to aid,
[00:03:57.370]and discovery, and application research.
[00:04:00.380]We have another site in Greensboro
[00:04:01.670]that is much more chemistry focused and production focused.
[00:04:09.240]So, Syngenta overall has two core businesses.
[00:04:13.693]One is labeled crop protection.
[00:04:16.000]So, think about that as chemistry.
[00:04:17.670]Whether it's spray on insecticides, herbicides,
[00:04:21.370]or seed treatments, that's a different side of the business.
[00:04:27.330]I work on the seed side, which is everything from new traits
[00:04:35.050]for corn and soy, to vegetables, flowers,
[00:04:38.320]diverse field crops.
[00:04:39.153]Syngenta works on quite a few crops.
[00:04:43.450]And as a company, we have a broad focus.
[00:04:47.610]I actually just copied this particular image
[00:04:49.710]from a 2018 business report.
[00:04:53.300]I'm not gonna go through that in detail,
[00:04:55.050]but it's just a point to illustrate that Syngenta focuses
[00:05:01.780]from the earliest stages of research
[00:05:06.040]through a development of traits and chemistry,
[00:05:10.110]but also in supporting the growers,
[00:05:13.560]supporting the supply chain,
[00:05:17.070]working with regulatory agencies and communities.
[00:05:20.880]So, we have a lot of different aspects in the business
[00:05:22.940]to really take a wholistic view of agriculture.
[00:05:29.110]But my focus has always been on research,
[00:05:31.690]and working in the industry, as Marc alluded to,
[00:05:35.420]I've had a lot of experiences.
[00:05:37.190]So, as Marc mentioned, I joined as an enzymologist.
[00:05:40.400]So, when I first joined,
[00:05:41.980]I was very much interested in biofuels.
[00:05:44.210]This was 2007.
[00:05:46.190]It was just sort of a peak of the biofuels revolution.
[00:05:51.182]I think there at the time, the department of energy
[00:05:53.320]had invested $125 million into five centers
[00:05:56.440]around the country for biofuels research,
[00:06:00.560]especially cellulosic biofuels, so it was a hot topic.
[00:06:04.270]When I was at Duke, my research was very, very fundamental.
[00:06:08.050]It was a pathway in E. coli,
[00:06:09.450]and I really wanted to move to something applied.
[00:06:16.180]and worked in the department of chemical engineering
[00:06:18.790]on extremophile biology, and that's where I started
[00:06:22.410]to touch on a little bit of the biofuels aspect.
[00:06:25.480]And then Syngenta at the time was hiring
[00:06:28.749]for just such a group, so it was a good opportunity.
[00:06:32.340]But I was not a plant biologist.
[00:06:34.670]I had worked all in microbiology, so plant biology was new.
[00:06:39.010]So, for the first couple years I worked on that.
[00:06:41.540]And over time, I switched to much more plant physiology,
[00:06:45.510]and more recently, genomics and genetics,
[00:06:48.700]but you'll notice my background
[00:06:53.107]was always in the complex traits.
[00:06:55.590]Never sort of the easy one gene kinda trait,
[00:06:58.430]always very complex.
[00:07:00.700]So, the traits and the specific traits and business drivers
[00:07:03.340]really have changed, but not the complexity.
[00:07:07.470]So, complexity from cellulose
[00:07:10.300]is much more challenging than starch.
[00:07:12.700]Syngenta actually has done a lot
[00:07:14.640]in the way of starch biofuels.
[00:07:16.640]Our Enogen trait is a corn expressed amylase, for instance.
[00:07:21.670]But I really focused on cellulose,
[00:07:23.450]and that is really difficult to break down.
[00:07:28.200]But over time, I switched to a focus on agronomic traits,
[00:07:31.360]and drought, yield, flowering time.
[00:07:34.470]Traits that are really a complex integration of genetics,
[00:07:38.320]environments, and just a combination of small effects
[00:07:47.060]into an ultimate phenotype.
[00:07:49.090]Really difficult to dissect.
[00:07:50.910]And as Marc mentioned more recently,
[00:07:52.730]just applying all of that experience
[00:07:54.440]to technology development, or deployment.
[00:07:58.090]Yeah, so it's a combination of now discovery
[00:08:00.070]and how do we actually move that into materials to market?
[00:08:05.600]So, this slide's a bit of an obvious thing for folks
[00:08:09.180]who have been around for a while, but it goes without saying
[00:08:11.630]that seed development is iterative.
[00:08:14.770]The approaches, so on the left
[00:08:17.680]is sort of a generic mock-up of the path from,
[00:08:20.930]I have a trait that looks promising,
[00:08:23.550]all the way through commercial development.
[00:08:28.910]What changes over time are the considerations.
[00:08:35.420]So, am I here?
[00:08:36.690]Am I just trying to show the trait works,
[00:08:38.370]or here, am I actually trying to show that it's efficacious
[00:08:42.250]in multiple genetic backgrounds, multiple environments?
[00:08:46.470]Am I focused on seed bulking,
[00:08:51.870]or really just reducing genetic variation?
[00:08:55.930]So, all the considerations change over
[00:08:57.540]as you go through that path.
[00:09:00.500]But throughout, the feedback loops are present.
[00:09:04.010]They're always important.
[00:09:05.180]Whether it's just from the earliest stages,
[00:09:08.260]my proof of concept that it can work in some corn lines,
[00:09:14.450]but I'm gonna feed that back to my discovery pipeline,
[00:09:17.270]or stuff that's already in late development,
[00:09:21.190]or just ramping up to commercial.
[00:09:23.710]Those feedback loops are relevant
[00:09:25.810]no matter what stage you're at,
[00:09:28.130]and they're relevant, they're present
[00:09:29.250]no matter if it's conventional breeding,
[00:09:30.880]which is illustrated a bit on the bottom right
[00:09:35.150]using genomic and phenotypic data
[00:09:37.810]to inform parent selection, or biotech.
[00:09:41.600]My up regulation or down regulation of a trait gene
[00:09:44.680]may or may not be efficacious
[00:09:45.990]in different genetic backgrounds or different environments,
[00:09:48.020]and that needs to be fed back
[00:09:50.340]and is fed back to inform discovery platforms.
[00:09:56.660]And over the course of trait development,
[00:09:59.400]and this is sort of my own evolution over time,
[00:10:03.440]and thinking trait development is about aligning
[00:10:08.270]the pull from the business.
[00:10:09.870]What does the business want with what are the opportunities?
[00:10:12.510]And this is a fluid and dynamic relationship.
[00:10:15.630]So, the business side is about what are the risks
[00:10:19.040]to the grower?
[00:10:19.873]How can we decrease those?
[00:10:20.706]Drought, for instance.
[00:10:21.770]How do we increase the value for the grower,
[00:10:23.490]the yield potential, or how do we increase the value
[00:10:28.140]for downstream users?
[00:10:29.180]So, seed quality or for the biofuels trade, for instance.
[00:10:33.050]And then, the technology opportunities
[00:10:34.500]that are really coming up nowadays
[00:10:36.870]are multi-scale genomics, pangenomes to single cell.
[00:10:42.520]New tools for breeding and production,
[00:10:45.520]and new analytical tools.
[00:10:47.360]That's really where those new technology opportunities
[00:10:49.126]are coming from.
[00:10:50.530]And I get down to that figure there
[00:10:54.530]that I pulled from a paper from a few years ago
[00:10:58.300]from Ed Buckler's lab.
[00:10:59.520]That sort of breeding revolution, "Breeding 4.0."
[00:11:03.680]And so, a lot of this is just how to pull
[00:11:08.060]those technologies together to get to that future state.
[00:11:14.484]Some considerations that our business colleagues
[00:11:17.950]always have in that push and pull
[00:11:21.400]between business and technology, market trends.
[00:11:25.030]That's really challenging for complex traits.
[00:11:27.820]How do we stay in front of regional needs
[00:11:31.040]and consumer demands?
[00:11:32.700]What are the delivery options?
[00:11:33.810]How do those change over time?
[00:11:35.030]So, this dynamic between can we move
[00:11:39.950]something ahead efficiently for biotech,
[00:11:42.060]or does it need to be a breeding approach?
[00:11:44.590]What is the regulatory complexity
[00:11:46.030]and the cost and time to market
[00:11:49.090]versus how long is the trait gonna stay on market?
[00:11:51.050]All of those considerations come into play.
[00:11:54.431]And of course, moving traits quickly
[00:11:56.860]once you have them is critical.
[00:11:59.640]So, staying ahead of genetic gain,
[00:12:02.330]adapting to those market shifts,
[00:12:04.110]no matter if it's conventional or biotech trait.
[00:12:06.790]And modern breeding is really using a collection of tools
[00:12:11.477]to optimize the efficiency from discovery
[00:12:16.342]to deployment of a trait into elite germplasm,
[00:12:21.370]to even seed purification and seed bulking
[00:12:25.410]to move things quicker.
[00:12:26.610]That gradual tick of genetic gain is something
[00:12:30.400]that we always have to be aware of
[00:12:33.960]and staying in front of.
[00:12:37.020]Precision deployment of traits is also essential.
[00:12:39.280]So, we want to avoid unwanted surprises down the road.
[00:12:44.520]So, whether it's genetic baggage,
[00:12:46.780]genetic linkage that we don't want,
[00:12:48.513]so some sort of deleterious effect
[00:12:50.930]of where the trait has come from.
[00:12:57.020]Being able to deploy genes where and when we want them
[00:12:59.960]in the genome, and even in the cell type.
[00:13:02.830]All of these advancements that get
[00:13:06.988]towards those capabilities are critical.
[00:13:09.560]And as I'm sure many of you are aware,
[00:13:12.400]genome editing has really changed the game
[00:13:15.000]in terms of how do we do trait discovery
[00:13:18.810]and trait development in an agile fashion?
[00:13:22.890]Realizing that this might be a diverse audience,
[00:13:26.580]I thought it'd be good to touch on the different aspects
[00:13:30.700]of approaches to crop breeding and genetic improvement
[00:13:33.720]that we utilize.
[00:13:36.550]My experience has mostly been in genetic
[00:13:39.500]or GM traits over time,
[00:13:42.970]but Syngenta looks at conventional breeding.
[00:13:45.680]So, what we call native traits,
[00:13:48.130]so found in the same species or at least related species
[00:13:52.920]where you can bring a trait over by crossing,
[00:13:54.720]or either through creating diversity by mutagenesis.
[00:14:00.280]The genetic modification or traditional GM,
[00:14:03.230]where you're introducing a foreign piece of DNA,
[00:14:07.490]that was up until I'd say recently,
[00:14:11.330]that was sort of the go-to for industry,
[00:14:15.310]for more complex traits like drought,
[00:14:20.660]or certainly has been the go-to for biostress traits
[00:14:25.780]like insect tolerance and herbicide tolerance.
[00:14:28.430]And again, and more recently, the genome editing revolution
[00:14:33.370]has allowed us to start to do some things
[00:14:35.990]that we weren't able to do before.
[00:14:38.296]So, new sources of variation, genetic variation.
[00:14:42.450]New opportunities to produce a trait
[00:14:43.940]that doesn't have the regulatory hurdles.
[00:14:47.760]As you're probably aware that producing a GM product
[00:14:52.020]has significant regulatory costs.
[00:14:54.790]So, the numbers that always stuck in my head
[00:14:58.120]are once you have something in a greenhouse,
[00:15:01.070]you have a product ready to go,
[00:15:03.120]you have to assume it's gonna take probably seven years,
[00:15:06.610]and maybe $100 million to get through
[00:15:09.760]all of the regulatory approvals that are needed
[00:15:11.340]to bring that to market.
[00:15:13.910]Depending upon, with genome editing,
[00:15:17.740]depending upon the type of edit and the country
[00:15:20.320]where you're aiming for, you may or may not need
[00:15:22.390]that sort of regulatory scrutiny
[00:15:25.610]before we can release a product.
[00:15:26.900]So, it has a lot of value both on the technical side,
[00:15:31.280]what it can do for the biology,
[00:15:33.010]but also on the regulatory side.
[00:15:34.630]That speed to market, that agility that we go after.
[00:15:39.180]And you can see how industry as a whole
[00:15:41.637]has really jumped on the genome editing bandwagon
[00:15:46.530]over the last few years, where the different seed varieties
[00:15:52.780]that are being brought to market the last couple years,
[00:15:55.650]you're starting to see this uptick
[00:15:56.820]in genome editing products.
[00:15:59.790]And not just limited to corn and soy,
[00:16:01.490]which is really where the GM side of things
[00:16:04.270]has been largely restricted just due to cost.
[00:16:08.530]So, when you look at those different approaches,
[00:16:12.200]the early steps in a biotech trait development
[00:16:16.090]are really similar to, in a generalized sense,
[00:16:19.240]a conventional breeding pipeline.
[00:16:21.030]So, in a conventional breeding,
[00:16:22.570]you create a lot of genetic diversity.
[00:16:25.730]You're doing your crosses,
[00:16:27.130]you're doing marker assisted selection of key traits
[00:16:31.380]for your target region, and you're doing cleanup
[00:16:35.920]and purification of the genetics you want,
[00:16:39.650]or the low side that you want,
[00:16:42.240]and removing all of the background genetics
[00:16:47.780]that you don't want.
[00:16:48.613]So, stabilizing the genetics, selecting for the appropriate
[00:16:51.170]parental background, et cetera.
[00:16:53.896]You're really ramping up towards your field evaluations
[00:16:56.420]after you've got a certain degree of genetic stability.
[00:16:59.720]And then later, it's scaling up to replicated trials,
[00:17:02.600]appropriate environments, and just larger scale evaluations,
[00:17:07.260]and higher selection pressure.
[00:17:09.970]So, for a conventional or a standard pipeline
[00:17:13.440]for either GM or genome editing,
[00:17:17.330]it goes by a similar sort of concept.
[00:17:20.820]So, you start with wild pipeline, you transform it.
[00:17:25.240]Either it's GM, it's editing, or some other flavor
[00:17:29.460]of directed modification.
[00:17:34.270]Then you're creating your successive generations
[00:17:37.290]and stabilizing that genetics.
[00:17:38.580]You're selecting for homozygosity,
[00:17:40.430]you're selecting for specific minimizing the residue
[00:17:50.260]from your transformation.
[00:17:51.490]So you don't want TDNA in there,
[00:17:53.030]you don't want a lot of genetic baggage, so to speak,
[00:17:55.530]from your transformation.
[00:17:58.670]And you can kinda look at roughly in the similar staging
[00:18:02.170]as I showed at the top, where the upfront
[00:18:05.320]is a lot of molecular selection.
[00:18:07.400]You may be selecting for some single plant phenotypes,
[00:18:10.570]but largely, it's molecular selection.
[00:18:13.330]And then, later on is when you're doing
[00:18:15.980]your phenotypic selection at scale.
[00:18:19.330]Multiple trials, replicated sites,
[00:18:22.140]and really confirming that your trait
[00:18:24.660]is having the efficacy that you want.
[00:18:27.900]Now, genome editing really offers the potential
[00:18:29.800]to further mimic that breeding approach,
[00:18:34.970]and shifting to more of a forward genetics paradigm.
[00:18:39.220]I would say the industry's not quite there yet,
[00:18:41.540]but it's a huge potential.
[00:18:43.180]So, one of the challenges I'll try to leave you with
[00:18:46.210]is that I think industry as a whole
[00:18:48.090]is still trying to move into is how do we take advantage
[00:18:52.880]of that potential that genome editing offers?
[00:18:57.010]So, how do we get to that sort of scale,
[00:19:00.040]which is really what that shift requires?
[00:19:02.560]And how do we get molecular biologists
[00:19:04.170]to think like breeders,
[00:19:05.090]and breeders to think like molecular biologists?
[00:19:08.740]So, but regardless of the approach,
[00:19:12.620]we spend a lot of time evaluating
[00:19:15.780]for the same or similar criteria.
[00:19:18.760]So, we're trying to balance
[00:19:20.020]selection pressure versus relevance.
[00:19:21.770]And doesn't matter if it's conventional breeding
[00:19:23.450]or if it's biotech.
[00:19:24.870]We generate designs that are either small plot based,
[00:19:28.400]or again, sometimes even single plant based,
[00:19:30.540]but it's really about tailoring that to the stage
[00:19:33.620]and to the trait.
[00:19:36.060]Making decisions that sometimes involves cost,
[00:19:39.000]but also, what's the ability to detect a meaningful effect?
[00:19:41.970]If it's plant versus plot,
[00:19:44.210]can you get at a trait in the greenhouse versus the field?
[00:19:49.519]I know Syngenta has really pushed
[00:19:52.470]to use greenhouse environments where possible,
[00:19:55.971]but certainly some really complex traits,
[00:19:58.060]and those that are driven by an interaction
[00:20:02.510]of genetics and environment are really hard
[00:20:04.150]to assess in a greenhouse.
[00:20:05.270]So, we certainly try to rely on field, if possible.
[00:20:10.070]Deciding on the appropriate check and comparison,
[00:20:13.200]whether it's just a standard wild type
[00:20:14.870]or you're having to do a no segregate.
[00:20:18.470]And with biotech traits, the consideration
[00:20:20.990]is we're trying to make decisions based upon a construct,
[00:20:26.010]not just a single event.
[00:20:27.285]So, ultimately, we select an event
[00:20:30.340]that we're gonna progress forward,
[00:20:31.770]but we really need to understand early on,
[00:20:34.350]is the construct having an effect,
[00:20:37.171]or are you just seeing a statistical noise there
[00:20:40.070]where one thing is standing out, one event is standing out?
[00:20:43.680]No matter the stage and no matter the approach,
[00:20:46.780]trade efficacy, how heritable is the effect?
[00:20:49.880]What is the penetrance?
[00:20:51.127]So, that T by G by E,
[00:20:52.560]that trait by genetic background by environment
[00:20:55.500]are critical assessments.
[00:20:58.260]We, early stages, we're usually one genetic background,
[00:21:02.560]maybe two, one environment, maybe two.
[00:21:05.360]Later stages, trying to expand
[00:21:09.080]into multiple genetic backgrounds
[00:21:10.680]and validate you get the same effect or similar effect.
[00:21:14.110]Multiple environments, that your trait is replicable
[00:21:17.300]across those environments.
[00:21:19.080]And certainly looking for the absence of negative effects.
[00:21:24.240]Negative agronomic effects.
[00:21:26.190]So, there's no yield drag.
[00:21:27.870]There's no loss of seed germination.
[00:21:32.000]This is difficult to do in a biotech pipeline early on
[00:21:34.640]because you're usually working with inbreds if it's corn.
[00:21:38.650]And well, corn and soy certainly inbreds to begin with.
[00:21:44.160]But with corn, at least, you're not working
[00:21:45.960]with hybrids to start with.
[00:21:46.930]So, you're trying to assess this from an inbred background.
[00:21:49.123]There's always some sort of transformation drag
[00:21:51.470]that comes with those early stages.
[00:21:54.780]So, you have to, again, go back to how your designs
[00:21:57.410]are set up to account for some of those secondary effects,
[00:22:01.730]early stage, but you can still make decisions
[00:22:05.022]around as what's working and what's not.
[00:22:09.460]Trialing in relative, or trait relevant environments
[00:22:12.180]is absolutely critical.
[00:22:13.860]What I captured here was actually some diagrams that we have
[00:22:17.820]that we use for our drop trialing.
[00:22:19.870]So, Syngenta has a series of progressively
[00:22:25.990]more managed environments for traits like drought,
[00:22:29.820]where we can really isolate the stress
[00:22:32.509]or isolate the particular environmental factor
[00:22:34.660]that you're interested in,
[00:22:36.940]and say with more confidence that a trait
[00:22:40.840]is working for that particular environmental consideration.
[00:22:44.710]But the more you control an environment,
[00:22:47.410]the less potentially relevant it is,
[00:22:49.370]or you at least run the risk of that.
[00:22:50.730]So, we're always working with a mix
[00:22:53.900]of very controlled environments, either field or greenhouse,
[00:22:58.370]and some where we pick certain regions
[00:23:02.550]and assess what the environmental impact was,
[00:23:05.940]or where the environmental conditions were on the back end.
[00:23:10.970]Controlling for genetic and environmental variation is key.
[00:23:13.570]Making sure we know our fields.
[00:23:15.550]We spend a lot of time doing that.
[00:23:17.517]Any particular trials that I run,
[00:23:19.200]I collect a lot of side data
[00:23:22.420]on what the variability is across the field.
[00:23:27.150]That can impact our ability to detect
[00:23:31.960]meaningful differences in traits.
[00:23:35.240]So, Syngenta had 20-plus years of success
[00:23:39.100]with traits overall.
[00:23:41.180]And mostly when I say traits, going forward,
[00:23:43.640]I'm mostly talking about biotech traits,
[00:23:45.810]but not exclusively,
[00:23:47.530]but most of my experience is within that.
[00:23:50.870]Most of our success has been with what I generally label
[00:23:53.930]as quote, unquote, simple input traits and output traits.
[00:23:57.180]So, input being inputs that the farmer's concerned about.
[00:24:00.070]So, weeds and bugs, fertilizer, that's an input trait.
[00:24:04.980]Output traits being more what is of use
[00:24:10.550]at harvest or post-harvest.
[00:24:13.140]So, Enogen or corn amylase for fuel ethanol
[00:24:16.040]is an output trait, for instance.
[00:24:18.760]But Syngenta has a long track record of those.
[00:24:23.785]The complex traits, the yield, drought,
[00:24:26.480]those have been more limited.
[00:24:29.130]Certainly traits like drought or nitrogen use efficiency
[00:24:33.990]have been very challenging.
[00:24:36.030]Our Artesian line is actually a native trait line
[00:24:41.117]for drought tolerance, but it's one of the few materials
[00:24:45.530]that are on the market that are specific
[00:24:49.280]for such a complex trait like drought.
[00:24:53.950]The potentials are there, but the challenges
[00:24:55.770]are all over in place.
[00:24:56.800]So, of course, plant developments is dynamic.
[00:25:00.600]It's an integration of everything that that plant sees
[00:25:03.650]from the time it was planted to the time it was harvested.
[00:25:06.070]So, it's just a moving target for discovery.
[00:25:11.510]So, again, sort of taking a page from drought,
[00:25:15.120]how do you integrate some of the most sensitive points
[00:25:19.110]of kernel development and plant establishments,
[00:25:26.190]and decide where you're gonna spend your money
[00:25:28.900]to focus your discovery resources?
[00:25:34.780]The factors that are influencing your trait
[00:25:36.830]are temporally regulated, spatially regulated,
[00:25:41.640]There are multiple pathways and genes
[00:25:43.410]that you have to be aware of and consider,
[00:25:45.010]whether it's physiological pathways, biochemical pathways.
[00:25:49.300]Pleiotropy and competition with other desired traits
[00:25:52.050]is a real bugaboo for these sort of complex traits.
[00:25:57.220]And finally, an imprecise toolbox.
[00:26:00.220]So, the industry as a whole has struggled over time
[00:26:04.090]to have the right sort of precision expression tools
[00:26:08.510]with which to effect targets.
[00:26:11.900]This particular figure is from a study we published
[00:26:15.640]in I think, 2015 or '16, looking at expression
[00:26:19.950]of trehalose-6-phosphate phosphatase in maize ears.
[00:26:23.160]And one of the key findings in that paper
[00:26:26.550]was this was a trait that had been worked on
[00:26:29.020]for quite some time, to effect yield on a drought.
[00:26:34.610]But it wasn't until we put the associated genes
[00:26:41.170]under the control of the right promoter,
[00:26:43.140]which was expressing,
[00:26:44.270]I believe it was MADS6 or MADS13 promoter
[00:26:46.643]that was expressing in a very specific part
[00:26:50.250]of the developing corn cob.
[00:26:52.450]So, this was right in the developing ovules
[00:26:58.410]and at the nexus of the nodes
[00:27:03.640]from where the ear was attached.
[00:27:06.070]So, it was really just targeting to the right time,
[00:27:08.460]the right physiological tissue and developmental stage
[00:27:14.810]for this kind of gene.
[00:27:15.643]So, it's possible, but there are few tools that we have
[00:27:19.360]that could provide that sort of precision.
[00:27:22.790]So, that's still a challenge for the industry.
[00:27:26.920]But I did want to touch on a couple common themes
[00:27:29.210]that regardless of the trait that always seem to come up.
[00:27:35.250]So, it's regardless of a yield trait,
[00:27:41.890]or a input, or a biological stress trait,
[00:27:48.080]or even a drought stress trait.
[00:27:50.160]I always seem to see the same sorts of themes.
[00:27:52.610]So, there's always this tug of war between source and sink.
[00:27:59.520]I'd probably start a fight between folks
[00:28:03.150]that have been working on source as the major determinant
[00:28:07.670]of yield, or response to environment, or sink.
[00:28:13.830]Are plants, are crop plants source limited,
[00:28:16.500]or are they sink limited?
[00:28:18.740]But so, there's always that tug of war,
[00:28:21.030]and folks who really are focused on optimizing
[00:28:23.940]for the synthesis for improved yield
[00:28:25.540]versus the folks who are trying to optimize harvest index
[00:28:28.930]or seed size, the number of kernels on a row of corn.
[00:28:35.270]So, I'm not saying one's right or one's wrong,
[00:28:37.410]but it always seems to come back to that.
[00:28:39.100]And really, I think it's an integration of those two areas.
[00:28:45.120]On the left are some examples of traits
[00:28:47.630]that we actually published not too long ago
[00:28:49.650]as an example of using new editing tools.
[00:28:53.170]But affecting a source or a sink strength
[00:28:57.140]at the level of the cell number in the developing seed.
[00:29:03.160]So, sink capacity of a seed affecting that.
[00:29:08.800]And I think more importantly,
[00:29:10.760]everything always seems to come back to carbohydrates.
[00:29:12.700]And I would say over the last 14, 15 years,
[00:29:15.970]this has been the theme of everything I worked on.
[00:29:18.270]Carbohydrates, whether it was biofuels for carbohydrates,
[00:29:21.930]cellulose for carbohydrates, or droughts,
[00:29:25.150]or just even pathogen plant interactions.
[00:29:30.160]There's always this carbohydrates factor
[00:29:33.140]that seems to come to play.
[00:29:34.970]I always, when I think about carbohydrates
[00:29:38.080]and complex traits, I always think
[00:29:40.100]about this particular figure and study
[00:29:44.530]because carbohydrates, what this study looked at was,
[00:29:49.630]or showed was you could recover some degree
[00:29:53.200]of yield potential in corn that was drought-stressed
[00:29:58.300]by supplying sucrose.
[00:30:01.760]And the experiments that were done
[00:30:03.580]were just basically putting a syringe on the stalk
[00:30:05.850]of a developing corn plant
[00:30:08.040]that was put under drought stress.
[00:30:10.300]So, the recovered cobs are ones that basically,
[00:30:14.360]had a syringe of sucrose.
[00:30:16.870]And what that showed was, well,
[00:30:19.250]when you're putting a maize under stress,
[00:30:23.150]what you're doing is starving the sugar.
[00:30:25.010]And that was causing kernel abortion,
[00:30:26.800]loss of fertilization, et cetera.
[00:30:29.980]But it's critical in plant pathogens
[00:30:33.010]and symbiotic interactions.
[00:30:34.770]So, studies that have looked at sugar transporters,
[00:30:37.120]and where they are most critical
[00:30:40.600]haven't just been at plant development.
[00:30:42.170]They've looked at sensitivity
[00:30:44.030]to particular pathogens, plant pathogens.
[00:30:49.340]Even developmental checkpoints.
[00:30:51.680]This figure on the bottom right is one from a study
[00:30:55.420]that came out a couple years ago looking at flowering time.
[00:30:58.250]And I found this fascinating
[00:30:59.570]'cause they were looking at, well,
[00:31:01.180]what is the temporal version of the circadian clock
[00:31:06.670]that you see in tropical maize?
[00:31:09.950]They were trying to make connections between maize
[00:31:15.190]where photo period sensitivity was a huge factor
[00:31:17.670]in triggering the shift from vegetative
[00:31:21.120]to floral development in the apical meristem,
[00:31:23.720]and they really looked at, well, in temperate maize,
[00:31:26.370]which is not photo period driven,
[00:31:28.300]it looks like there's sort of a sugar clock.
[00:31:31.336]And that was fascinating to me.
[00:31:32.999]That's the first time I had really seen
[00:31:35.751]that sort of connection from sugars and floral development,
[00:31:42.139]or triggering the signals that the plants
[00:31:45.910]just start developing tassels and ears, for instance.
[00:31:48.590]So, sugar is everywhere.
[00:31:51.410]As I've progressed through my career,
[00:31:54.770]especially when I was working on traits like drought,
[00:31:58.860]there's a lot of emphasis on trait components.
[00:32:00.800]So, certainly, yield is what a farmer cares about.
[00:32:05.330]How much grain am I getting in my field?
[00:32:07.220]But it's really hard to do any sort of discovery
[00:32:09.060]just on yield, right?
[00:32:10.340]It's not a very heritable trait.
[00:32:13.310]It's again, the integration of everything that plant sees
[00:32:15.810]over its lifetime.
[00:32:17.750]So, trying to do discovery on that is very challenging.
[00:32:21.103]And so, what we try to do is focus on,
[00:32:23.630]well, my discovery on some aspect of yield
[00:32:26.610]or some aspect of that plant's development,
[00:32:28.500]whether it's the development of the leaf
[00:32:30.700]or the leaf architecture.
[00:32:32.910]Maybe that the architecture
[00:32:34.380]of the developing ear, et cetera.
[00:32:38.470]And something for me that I've worked on before is,
[00:32:41.620]well, what's the impact of the developing silks
[00:32:44.750]on kernel numbers, especially as it relates to drought?
[00:32:48.410]But the utility, so it can be very valuable
[00:32:50.810]to work on component traits,
[00:32:52.800]and a lot of discoveries are really focused on that.
[00:32:55.520]But the utility really depends upon the trait,
[00:32:58.790]the genetic background that you're trying to look at,
[00:33:01.270]what's the environment?
[00:33:03.130]And so, when I say utility, it's really how easy,
[00:33:06.920]or what's the path from translating
[00:33:09.270]what you discover into that in that component
[00:33:11.850]to something that you can then deploy to market?
[00:33:14.180]So, is that gonna be a heritable connection?
[00:33:17.990]So, you're balancing relevance of that component
[00:33:20.900]versus the cost of collecting it,
[00:33:23.730]and the risks that you're oversimplifying your trait.
[00:33:27.270]So, that comes into play for any target trait
[00:33:32.790]that we're looking at.
[00:33:33.830]I want to break it down into its individual components
[00:33:36.260]because I can focus my discovery efforts,
[00:33:41.080]but I am at the risk of losing the forest for the trees.
[00:33:43.680]So, it's always that particular tug and pull.
[00:33:47.500]And so, that's another challenge that we have,
[00:33:49.990]is how do we strike that right balance?
[00:33:54.310]And for some of those component traits
[00:33:56.870]where we know they're really critically important,
[00:33:59.570]but it's just too expensive to get at them,
[00:34:01.340]how do we get at that them more readily?
[00:34:03.730]So, again, something like silk exertion.
[00:34:09.356]Maize silks are extremely sensitive to drought.
[00:34:11.920]And one of the first things you see
[00:34:13.390]when a plant is exposed to drought during flowering
[00:34:15.980]in a maize plant is the silks stop growing,
[00:34:18.230]so you get a loss of kernel yield.
[00:34:21.800]But for those who have been in a field with corn,
[00:34:24.580]and trying to really track silk development
[00:34:29.050]in a large field of corn, it's very difficult.
[00:34:32.050]So, you know, traits like that,
[00:34:33.550]how do we get at them more cost effectively?
[00:34:40.800]And there's a wide definition to trait components.
[00:34:42.700]So, I tend to think about it
[00:34:43.930]from a physiological perspective,
[00:34:45.230]but there's also molecular trait components.
[00:34:47.710]There are intermediate factors that are connected
[00:34:51.810]with the underlying developmental biology
[00:34:55.240]of tissues and the whole plant.
[00:34:57.990]And this is particularly important
[00:34:59.280]in functional genomic studies
[00:35:00.620]that try to connect the biochemistry, the RNA,
[00:35:06.710]even the factors at the DNA level,
[00:35:09.000]to what's going on at the whole plant level.
[00:35:12.460]So, really, again, goes back to that.
[00:35:14.680]The value is the relevance, the cost,
[00:35:18.070]and what's the risks of oversimplifying?
[00:35:20.530]And the further away we are from the target trait,
[00:35:22.347]the higher the risk.
[00:35:23.260]But a lot of my efforts have been on the molecular side,
[00:35:26.200]and trying to make that connection
[00:35:27.680]between molecular, physiological,
[00:35:30.240]and ultimately, the quote, unquote, endpoint phenotype
[00:35:32.723]that the farmer's interested in.
[00:35:37.150]Pangenomics is another area that's really been exploding.
[00:35:40.880]It's not just for breeding.
[00:35:43.750]We at Syngenta have had a collaboration with NRGene,
[00:35:47.700]a company out of Israel, and has been using
[00:35:50.000]their GenoMAGIC platform for four or five years now.
[00:35:55.030]Largely, to enable haplotype-based or high density breeding.
[00:35:59.710]So, moving from a standard approach
[00:36:02.950]or conventional approach of having a bunch of SNPs,
[00:36:06.000]or single nucleotide polymorphisms, where they're binary.
[00:36:11.910]So, you have an A or a T, you have an A or a C,
[00:36:14.930]so you have two states, and that's it.
[00:36:16.850]And they're sparse, so you may have a few hundred
[00:36:19.500]or a few thousand markers across each chromosome
[00:36:25.910]for a breeding pipeline.
[00:36:27.800]For discovery, you can get
[00:36:30.130]up to tens of thousands of markers, certainly.
[00:36:33.290]But it's also biased as to where those markers were found,
[00:36:36.680]so sparsity and relevance of those markers is challenging.
[00:36:41.490]Whereas in a pangenome-enabled world,
[00:36:45.350]you have multiple options, not just two.
[00:36:47.630]So, instead of an A or a T,
[00:36:49.390]you've got different haplotype states,
[00:36:52.070]and a haplotype state one, two, all the way through 100,
[00:36:56.930]and the sparsity challenge has gone out the window.
[00:37:01.170]It's very high density data.
[00:37:04.732]But of course, really the big push is for breeding.
[00:37:07.300]I look at it for discovery.
[00:37:09.920]And we're really seeing its value
[00:37:13.230]in terms of understanding my traits of interest,
[00:37:19.350]how they interact with my target genetics
[00:37:23.090]at the locus level, at the genetic population level.
[00:37:29.840]What are the right traits
[00:37:31.020]that go with the right genetic backgrounds
[00:37:33.570]with the right maturity groups, for instance?
[00:37:37.400]So, it's enabling a level of predictability
[00:37:41.580]that we haven't had with conventional data.
[00:37:45.840]And it just allows us to go to connect
[00:37:48.800]that sort of global genetic pool to specific genes
[00:37:53.630]and specific potential mutations
[00:37:58.060]that are related to our traits and potentially (indistinct).
[00:38:04.330]The cost of genome sequencing is something I've seen.
[00:38:06.440]Since I've been at Syngenta, I've seen that just plummet.
[00:38:09.738]It was not impossible, but challenging
[00:38:13.100]to push a genome through cost-wise and time-wise
[00:38:16.630]when I first joined, and now it's just,
[00:38:20.802]the cost and the throughput potential,
[00:38:23.570]and the quality is just mind-numbing.
[00:38:27.140]This figure on the bottom right
[00:38:28.770]is a couple years old now, I think,
[00:38:30.810]but it just illustrates the steep drop
[00:38:34.460]in the cost per gigabase for sequencing
[00:38:37.920]over the last 15 years or so.
[00:38:41.703]And really, with the advent of Illumina sequencing,
[00:38:46.010]it's just, it's very cheap.
[00:38:47.090]It's very accessible now.
[00:38:50.350]And more recently, what's really been even more valuable
[00:38:54.320]for genome quality and for reference genomes
[00:38:58.010]is this advancement in what we call
[00:39:00.840]third generation sequencing.
[00:39:02.290]So, Oxford Nanopore, PacBio,
[00:39:06.890]and then optical mapping technologies as well,
[00:39:09.440]to help stitch all of that information together
[00:39:12.920]to high quality genomes.
[00:39:16.420]It's enabled what I would call just genomes on demand
[00:39:19.360]for industry, as well as even academia.
[00:39:22.100]It's just more accessible in a way that it wasn't before.
[00:39:24.920]And you're starting to see sequences
[00:39:26.440]of not just elite lines, but also wild species,
[00:39:29.100]land races, which are highly complex,
[00:39:32.500]mixed-ploidy genomes, and wheat,
[00:39:34.690]and other genomes like that.
[00:39:37.400]And it's just enabling discovery at a level
[00:39:40.180]that we haven't had before.
[00:39:43.330]This figure was a paper that came out last year,
[00:39:46.350]and it just shows the explosions
[00:39:47.620]in available genomes over time.
[00:39:51.120]And almost 800 land plant species
[00:39:54.380]with available genomes as of last year.
[00:39:58.450]Gene networks and modeling
[00:39:59.540]is something I'm a huge supporter of.
[00:40:02.380]I think it's extremely valuable.
[00:40:05.410]And the goal there is just, is integrating components,
[00:40:08.790]layers of regulation in a way that we can query.
[00:40:13.340]And it's especially important for multigenic traits,
[00:40:16.690]mixed pathways connecting across
[00:40:19.930]different stages of development.
[00:40:22.320]Doing that in a way where you sort of take
[00:40:24.390]the hunt and peck out of it,
[00:40:27.580]is where this is most critical and has the most value.
[00:40:30.960]The challenge is we don't have enough data,
[00:40:31.957]and we don't have enough data of the right kind,
[00:40:34.870]especially functional data.
[00:40:37.480]Biochemical data, functional data are really challenging,
[00:40:40.220]especially protein data.
[00:40:42.290]So, that has been a sticking point,
[00:40:44.300]and it's still a bit of a sticking point
[00:40:45.520]with being able to fully leverage
[00:40:48.350]the potential of these tools.
[00:40:51.040]Machine learning does offer some new avenues,
[00:40:53.470]and Syngenta is moving into that direction as well.
[00:40:58.760]Successes to-date demonstrates that we need precision tools
[00:41:03.810]for expression and modulating function.
[00:41:09.960]As I mentioned before, most of our expression tools to-date
[00:41:13.200]are crude, constitutive, or tissue-preferred,
[00:41:17.080]so just we express it everywhere at all times,
[00:41:20.000]or maybe it's roughly in the leaf,
[00:41:22.730]but it's still not very controlled and not very precise.
[00:41:26.040]And I mention this particular study
[00:41:28.450]that really illustrated the value
[00:41:29.850]of having a much tighter and well-annotated control.
[00:41:37.332]And the tools to really introduce gain-of-function mutations
[00:41:41.450]have been really lacking.
[00:41:44.750]Even with genome editing revolution,
[00:41:46.700]we're really just limited to largely empirical introduction
[00:41:51.310]of gain-of-function so far.
[00:41:53.250]So, another challenge.
[00:41:55.870]We're limited by the understanding of gene regulation.
[00:41:59.600]Gene regulation's complex.
[00:42:02.360]This cartoon doesn't do it justice.
[00:42:05.110]We're still understanding that.
[00:42:06.170]And that's just even whether, not just for transcription,
[00:42:09.330]but also translational regulation.
[00:42:11.330]So, that's still a significant hurdle.
[00:42:16.840]The nice part is there's just a rapid explosion of tools
[00:42:19.860]and resources to provide this level of precision.
[00:42:26.470]And I'm excited to see where this goes over time.
[00:42:28.790]As Marc said, we have a collaboration ongoing with his lab
[00:42:34.700]that gets at that need for the data and resources
[00:42:39.330]for that precision.
[00:42:41.910]And just as a nice segue into that,
[00:42:43.350]that single cell genomics is really another game changer.
[00:42:48.820]For those who aren't familiar with that, I like this figure.
[00:42:52.910]If you look at what sort of standard genomic studies
[00:42:57.360]that they provide in the past,
[00:42:59.320]I like this sort of tomato soup version.
[00:43:02.350]All of your population of cells,
[00:43:05.130]all of your information sort of in one giant pool.
[00:43:08.770]Useful, but you really don't know what goes into that.
[00:43:11.500]So, what you really want to see is,
[00:43:12.720]well, what's the contribution from the different ingredients
[00:43:14.770]that I have in there?
[00:43:16.656]What you're trying to avoid
[00:43:17.745]is the dilution of your informativeness,
[00:43:20.600]and what was called Simpson's paradox,
[00:43:23.180]where like in the top left here,
[00:43:26.610]where if you just look at the bulk information
[00:43:29.660]and you see a trend, what you may actually be observing
[00:43:33.050]are two, or more than two trends within that data
[00:43:37.400]that are competing and sort of washing each other out.
[00:43:41.240]So, we're trying to avoid that.
[00:43:43.041]What we're trying to see is how individual types of cells
[00:43:47.980]differ in their regulations.
[00:43:49.270]So, I don't want to just see a leaf.
[00:43:51.060]I want to see, well, what's the difference
[00:43:52.260]between how a mesophyll cell develops and regulates
[00:43:55.430]with a particular trait in mind
[00:43:56.680]versus a bundle sheath cell or a companion cell,
[00:43:59.400]and how that changes over the course
[00:44:02.840]of each cell's differentiation and aging?
[00:44:06.740]And single cell techniques are really allowing us
[00:44:09.850]to get at that kind of revolution or resolution.
[00:44:15.371]And the pace of those plant studies,
[00:44:17.070]plants, unfortunately, are always
[00:44:18.430]sort of lagging behind human studies.
[00:44:21.520]We're a little bit ignored sometimes in the plant space,
[00:44:24.070]but we're really catching up in the plant world.
[00:44:26.510]And you're seeing this rapid increase
[00:44:29.580]in the types of studies,
[00:44:32.746]the relevance of studies to even crop plants
[00:44:35.060]like corn and soy.
[00:44:36.840]So, it's an exciting time to be in this field.
[00:44:42.270]Genome editing is another area where there's...
[00:44:48.580]The potential is huge,
[00:44:49.790]what we get to do with genome editing.
[00:44:51.750]So, as we overcome the challenge
[00:44:54.210]of understanding how genes are regulated,
[00:44:57.940]the tools are there to be able to affect that regulation
[00:45:02.920]in multiple ways.
[00:45:04.320]So, just creating just random variations
[00:45:07.040]through small deletions or large deletions,
[00:45:10.700]but also, targeted editing and replacements
[00:45:13.480]of particular amino acids of transcriptional
[00:45:17.000]or translational regulatory elements,
[00:45:19.220]regulating the epigenome, or just fully swapping out genes
[00:45:24.340]from one cultivar with the ortho log from another cultivar
[00:45:28.270]that may be having a more optimal characteristic.
[00:45:34.150]Syngenta is heavily invested in this,
[00:45:35.980]as well as certainly our competitor industries.
[00:45:39.740]So, it's really changing the game
[00:45:43.700]as far as how we think about our trait development.
[00:45:50.030]An area I'm very excited about
[00:45:51.490]or interested to see expand further
[00:45:56.090]is the potential of genome editing
[00:45:58.400]as a forward-genetic tool.
[00:45:59.920]It's sort of a next generation breeding tool.
[00:46:04.170]I would say to-date, it's being used
[00:46:07.300]in a similar fashion as our GM tools.
[00:46:11.360]It's sort of one gene at a time.
[00:46:12.920]It's we're gonna make a subset of modifications
[00:46:16.667]and then see how this functioned in our test environment.
[00:46:20.910]But it really lends to this idea
[00:46:22.660]that you can create a ton of (indistinct) diversity.
[00:46:25.220]So, I think of it as sort of tilling on steroids,
[00:46:29.670]but in a targeted fashion.
[00:46:30.780]So, instead of creating a chemical mutogenesis library
[00:46:33.740]and having to clean up all the background mutations
[00:46:36.650]before you can assess your trait effect,
[00:46:39.013]your target gene effect, we can really target that mutation,
[00:46:42.460]that allelic variation, that genetic diversity
[00:46:45.220]to a gene-specific region of the genome
[00:46:48.210]and screen for the phenotypes we're interested in.
[00:46:51.610]It requires a commitment to scale,
[00:46:54.070]and the tools to be able to go through
[00:46:57.760]all of that diversity in a cost-effective
[00:47:01.510]and throughput-effective manner.
[00:47:05.481]And yeah, trait development and complex traits getting,
[00:47:11.040]I think, as we have access to these tools,
[00:47:14.150]we're getting past this idea that I can always predict
[00:47:18.630]a modification to my gene for a complex trait,
[00:47:21.050]and I'm gonna see this particular phenotype.
[00:47:24.000]And so, the chance to supercharge diversity screening,
[00:47:26.760]and move towards more, again, that breeding mentality,
[00:47:29.650]getting molecular biologists to think more like breeders
[00:47:32.120]is there with that.
[00:47:35.650]Screening all that diversity can be definitely a challenge,
[00:47:37.730]but molecular, there have been certainly some reports
[00:47:40.445]of using molecular signatures as phenotypic proxies.
[00:47:45.240]So, we do that at points for certain traits,
[00:47:49.830]but it certainly depends on the trait.
[00:47:53.120]So, technological advancements
[00:47:55.960]accelerating trait deployment.
[00:47:58.250]This is the last really detailed slide I have,
[00:48:02.120]but this one is probably the most important,
[00:48:04.410]and the one that it's taken me
[00:48:06.360]to the most recent part of my career to really appreciate.
[00:48:10.010]Everything I showed you up to-date,
[00:48:12.260]or everything I worked on up until recently
[00:48:14.540]was very much on discovery on finding traits,
[00:48:18.250]finding genes that are gonna affect a trait,
[00:48:21.130]trying to manipulate those, trying to see
[00:48:22.820]that there's sort of some physiological impact,
[00:48:26.187]and show that it works in one line or one environment.
[00:48:29.500]But more recently, I've gotten a lot more exposure
[00:48:32.660]and had more experience at the other side of that,
[00:48:37.330]and how do you move those quickly and efficiently
[00:48:40.700]into other genetics that are more elite
[00:48:45.477]or specific to certain regions or certain markets?
[00:48:50.400]And how do you do that in a way that doesn't break the bank,
[00:48:52.810]and doesn't take 20 years to get a trait to market?
[00:48:59.830]So, some of the current development or current efforts
[00:49:03.590]are directed to moving away from the need
[00:49:06.530]to move a trait gene or a locus
[00:49:11.260]into new genetic backgrounds by traditional crossing
[00:49:15.750]and selecting for crossing and selection,
[00:49:19.190]which takes time, to direct deployment.
[00:49:23.860]And one of the technologies
[00:49:25.660]that Syngenta came out with not too long ago,
[00:49:28.644]it was driven by my colleague, Tim Kelliher and Qiudeng Que.
[00:49:33.110]It was called HI-Edit.
[00:49:35.330]And so, this technology uses genome editing technology,
[00:49:39.840]combined with haploid induction technology,
[00:49:42.380]to basically allow us to edit any line directly
[00:49:46.880]without needing to go through transformation of those lines.
[00:49:49.660]So, we have one line that's transformed.
[00:49:52.100]We cross it with our target line, and are able to edit
[00:49:55.330]through the germ line in those target genetics.
[00:49:58.630]So, basically, one generation editing of target genetics
[00:50:04.820]across our germplasm pool.
[00:50:08.130]So, this can save a huge, huge amount of time.
[00:50:10.250]It can be deployed in corn and other crops.
[00:50:14.390]Wheat, rice, dicot as well.
[00:50:19.020]The other part that I spend a decent amount of time
[00:50:23.050]thinking about is how do we de-bottleneck
[00:50:25.830]when we're conventional trait introgression,
[00:50:28.730]which does require crossing?
[00:50:30.680]And certain traits are still gonna go that way
[00:50:33.320]for GM especially.
[00:50:36.500]How do we de-bottleneck that, where it's still critical?
[00:50:39.210]So, instead of taking six generations,
[00:50:41.274]can it take five generation to four generations
[00:50:43.990]to get to the right type of genetic stability and purity?
[00:50:50.140]So, that's understanding the crossover landscape,
[00:50:54.980]or the sort of recombination genomic landscape
[00:50:57.670]that we have to deal with,
[00:50:59.100]and even potentially developing tools
[00:51:02.290]to control that landscape.
[00:51:06.510]So, I'll leave you with this slide as a summary,
[00:51:09.260]realizing I'm a bit at time.
[00:51:12.350]So, the overall theme that I hope I left you with
[00:51:16.150]was that throughout the advancement of the industry
[00:51:21.800]over the time I've been at Syngenta,
[00:51:24.090]and certainly my colleagues who've been there before,
[00:51:28.830]we've seen over the last, I'd say, 10 years,
[00:51:31.630]the advancements in sequencing
[00:51:33.850]and genetic analysis annotation,
[00:51:35.988]those precision biotechnology tools,
[00:51:38.310]all of that is really exploding and enabling discovery
[00:51:42.480]and deployment of traits and complex traits.
[00:51:45.960]It's allowing (indistinct) sort of put on the shelf
[00:51:49.540]as too complex or not ready for prime time,
[00:51:55.600]because it was gonna be too costly
[00:51:57.020]and take too much time to get to market
[00:51:59.910]by traditional means.
[00:52:02.460]But a lot of challenges remain.
[00:52:04.910]So, our success rates, we still want to improve those.
[00:52:08.250]So, how do we improve our precision of,
[00:52:12.830]and applicability of combining genetics and our traits
[00:52:17.310]with a particular phenotype of interest
[00:52:19.040]and environment, target environment?
[00:52:22.240]How do we reduce risks and attrition?
[00:52:24.470]We don't want surprises, and we don't want a wasted effort.
[00:52:29.380]How do we get access to some of those
[00:52:31.050]really dynamic component phenotypes that I mentioned,
[00:52:33.680]physiological and molecular, in a more cost-effective way?
[00:52:37.300]We can certainly capture them, but they're very expensive,
[00:52:40.560]and it's hard to do it large scale.
[00:52:44.490]And it's hard to do in a field environment,
[00:52:46.800]it's hard to do in broad scale genetics.
[00:52:54.770]That is one, I would say, one of our grand challenges,
[00:52:57.407]to get at those.
[00:52:59.900]Data and analytics for precision annotation,
[00:53:02.820]expression tool development,
[00:53:04.160]and predictive functional modification.
[00:53:09.150]So, just the next generation tools and more data.
[00:53:15.300]So, it's always this push and pull
[00:53:17.330]between we need more data, now we need the analytics
[00:53:19.140]to process the data, but we need both.
[00:53:22.190]As I mentioned that there's a huge diversity,
[00:53:25.410]or potential with genome editing,
[00:53:27.130]so how do we take advantage of it?
[00:53:28.730]How do we scale it in a way that's cost-effective?
[00:53:33.560]And tools for analytics and visualization
[00:53:37.810]to really pull all these datasets together.
[00:53:41.470]That is one thing that I think
[00:53:44.360]has significantly lagged behind is the ability.
[00:53:47.620]We can generate data faster than we can analyze it,
[00:53:50.110]and a lot faster than we can visualize it
[00:53:52.530]in an easily digestible way.
[00:53:54.260]In a way that's queryable, that you can make sense of,
[00:53:58.565]and that challenge is growing, I would say, a bit.
[00:54:02.940]So, I would just want to leave you
[00:54:05.140]with there are other challenges,
[00:54:07.000]but for those who are at the start of their career,
[00:54:11.050]or still in school, I put these challenges up here,
[00:54:14.200]and it's certainly not all-inclusive, but come join us,
[00:54:18.720]and we're always hiring.
[00:54:21.300]We've been hiring a lot recently,
[00:54:22.710]so we're always looking for new talent
[00:54:25.180]and folks to help us address some of these challenges.
[00:54:29.240]So, I'll end there and just say thanks,
[00:54:31.302]and take any questions.
[00:54:33.490]Thank you very much, Jason.
[00:54:34.446]I would like to ask maybe the first one,
[00:54:36.250]waiting for the first questions to come in.
[00:54:39.630]So, you mentioned different apertures for crop breeding.
[00:54:43.858]Traditional breeding, GMOs, and upcoming gene editing.
[00:54:48.410]I would like to know if you believe
[00:54:50.580]that one of those approaches will decrease in interest.
[00:54:55.800]Obviously, there's an increase for gene editing,
[00:54:57.990]based on the model you presented.
[00:55:00.180]And I was also wondering what could be the best (indistinct)
[00:55:03.328]as you observe consumers regarding the emergence
[00:55:06.270]of genome editing.
[00:55:09.320]Yeah, I don't think, so GM will not decrease, right?
[00:55:12.850]Because GM is still gonna be very relevant
[00:55:16.110]for herbicide traits, for insect traits.
[00:55:17.947]So, really sort of those simple,
[00:55:20.750]I call it simple, bio stress type traits,
[00:55:25.280]where you can really affect something
[00:55:27.110]with one gene at a time.
[00:55:28.170]And also, inherently, the genes are coming from
[00:55:33.770]bacterial species or similar, so foreign species.
[00:55:37.010]So, there will always be a place in my mind for GM.
[00:55:40.940]So, I don't think it would be displaced.
[00:55:42.030]I would look at it as complementary to genome editing.
[00:55:47.320]It's a really good question as far as acceptance, right?
[00:55:52.170]And I'm not in regulatory affairs,
[00:55:54.040]but I talk to folks there a lot.
[00:55:57.170]I think the industry is, I think the industry could do more
[00:56:02.170]to educate folks on that.
[00:56:04.780]I do think there's potential for much more acceptance.
[00:56:09.110]I think industry has learned a lot over time
[00:56:12.500]of how to talk about technology
[00:56:15.390]and its relationship to producing the food that we eat,
[00:56:20.330]from the early GM years to now.
[00:56:22.630]So, we're more savvy as scientists,
[00:56:28.560]in both academia and industry,
[00:56:29.760]of how to talk about these things.
[00:56:32.460]So, I think certainly in the US,
[00:56:35.360]I think there'll be more acceptance to it,
[00:56:37.300]'cause it can also be viewed,
[00:56:38.250]especially for more simple edits where it's just,
[00:56:40.170]you're just creating a small deletion.
[00:56:42.680]Since you can't really distinguish it from natural variation
[00:56:46.630]at a technical level, I certainly think
[00:56:49.090]it'll be more widely accepted.
[00:56:51.330]But there will always be people who feel that...
[00:56:56.250]Who sort of kind of combine that concern
[00:56:58.880]with the concern about there's too much concentration
[00:57:02.890]of agriculture in a few companies or whatnot.
[00:57:05.600]So, sort of that we're messing too much with agriculture.
[00:57:09.040]So, yeah, I think more acceptance,
[00:57:12.070]but we'll still have some challenges there.
[00:57:14.190]Will CRISPR technology and other things you mentioned
[00:57:17.140]help produce regulatory delay in potential trait release?
[00:57:21.970]I think so.
[00:57:23.260]So, I think GM is regulated.
[00:57:26.280]There's no escaping that.
[00:57:28.380]So, we have to go through that, and it's country-specific.
[00:57:30.820]So, each country decides how and when
[00:57:34.580]it's going to deregulate,
[00:57:36.360]or if it's gonna deregulate something.
[00:57:37.820]And so, for a seed company, we're always concerned,
[00:57:40.790]not just about where the current (indistinct) the US,
[00:57:45.370]but also where do we plan to export seed to,
[00:57:49.450]and making sure we have appropriate approvals there.
[00:57:52.440]With CRISPR, it really depends on the type of edit,
[00:57:55.690]and what countries we're talking about.
[00:57:58.640]The US, if it's simple edits, if it's just simple,
[00:58:01.270]what is traditionally labeled as SDN1
[00:58:04.130]or just nuclease1, where it's just you're using the enzyme
[00:58:07.580]to cut the DNA, and using its natural repair processes
[00:58:11.670]to create small mutations,
[00:58:13.450]but you're not leaving any foreign DNA behind.
[00:58:16.908]The US generally looks at that, and a lot of countries
[00:58:19.730]are looking at that as not needing to be regulated.
[00:58:23.260]But other countries like Europe, the European Union
[00:58:26.360]is still looking at that as potentially something
[00:58:28.860]to be regulated.
[00:58:29.710]So, Syngenta still treats that
[00:58:34.510]as a kind of regulated material in the way we handle it,
[00:58:36.860]because we don't want to just run into problems later,
[00:58:39.600]and potentially contaminating a string of grain
[00:58:44.810]with something that a particular country decides,
[00:58:47.277]"No, we're not gonna accept that."
[00:58:50.520]But as far as the potential for release, yeah,
[00:58:54.240]I think it could smooth the regulatory environment
[00:58:56.690]in certain regions, and certainly in certain countries,
[00:58:58.770]depending upon the technology.
[00:59:01.610]Yeah, and I think that direct deployments, as well,
[00:59:04.070]the technologies that allow us to directly edit
[00:59:08.300]in elite lines, whereas before, we'd have to go through
[00:59:11.250]more of a conventional introgression,
[00:59:13.930]which takes multiple generations and multiple years
[00:59:16.480]before we're able to release a new genetic line
[00:59:20.400]with that trait, that's hugely valuable as well.
[00:59:23.920]Thank you very much, and for your time.
[00:59:26.120]Thank you everyone for attending this meeting.
[00:59:28.620]Yeah, thanks, guys.
[00:59:29.700]Thank you, Jason.
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