UNDERSTANDING PLANT RESPONSE TO GROWTH UNDER …

UNDERSTANDING PLANT RESPONSE

TO GROWTH UNDER NITROGEN LIMITATION CONDITIONS TO

IMPROVE CROP GENETICS

Steven Rothstein University of Guelph

The Future of Agriculture

Long-Term Worldwide Pressures

Growing population

Changes in diet

Increasing stress on the environment

World Population

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7

Year

Po

pu

lati

on

in

Billio

ns

World Population

1930 1940 1950 1960 1970 1980 1990 2000

Corn Grain Yield

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60

80

100

120

140

Year

Yie

ld in

bu

sh

els

per

acre Corn yield

1930 1940 1950 1960 1970 1980 1990 2000

Corn Grain Yield

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60

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100

120

140

Year

Yie

ld in

bu

sh

els

per

acre

Corn yield

1940 1950 1960 1970 1980 1990 2000

1930

The first breeders were not

trained in plant breeding

Technology was simpler

— for planting

for weed control

and for harvesting

Corn Grain Yield

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100

120

140

Year

Yie

ld in

bu

sh

els

per

acre

Corn yield

1930 1940 1950 1960 1970 1980 1990

2000

Farm Technology

Chemical Treatments

Biotechnology: Insect Resistance

High Breeding Cost Including

the use of

Millions of DNA marker points

To achieve similar yield increases today:

Cost more

Involve many complex technologies

Early Days: Crop Biotechnology First Generation Traits

5 minute history of developing first generation crop traits

Blue sky thinking but missing some important capabilities (and a little bit naïve)

Developing the Perfect Food

Ice Minus Bacteria

Producing Sticky Crop Plants

Nitrogen Use Efficiency in Corn circa 1986

Nitrate Nitrite Ammonia

Tobacco transformation

Glutamine

NR NiR

GS

1984 - Some Important Long-lasting Projects

Bt for insect resistance

Herbicide resistance

Modulating transgenic gene expression

Learn how to transform important crops

Effect of First Generation Traits on Yield

0%10%20%30%40%50%60%70%80%90%

100%

Transgenic trait yielddifference

Yield Increase geneticsplus management 1990-2010

1990 Yield

Shi et al, Nature

Biotechnology, 31; 2013

Advantages of First Generation Traits

Decreased herbicide cost: varies depending on crop

Decreased insecticide use

Decreased year to year variation

Ease of use of glyphosate: not quite as much now with herbicide resistant weeds

However: significantly more expensive seed

Lessons Learned for second generation traits:

the Tyranny of Small Differences

How much yield plot data (each data point is 3 yield plots) of wild-type and transgenic does one need to determine statistically valid differences using Shi et al data?

P Value 3.25 Bu

(1.75%) 6.5 Bu (Bt) (3.5%)

18.6 Bu (10%)

0.05 1041 of each 260 of each 32 of each

0.01 1550 of each 387 of each 48 of each

Great Dane

Yorkshire Terrier

Different

genotypes =

different

phenotypes

Different genotypes

plus different

environmental

conditions= different

phenotype

How to assess affect of genotype on phenotype

given different environmental conditions?

Nutrient Efficient Traits; A Large Market

Phosphate

40 MMt

Potassium

26MMt

Nitrogen

100 MMt

Current economic value of

N based fertilizers is between

$80-$100B annually

World N Fertilizer Consumption

World

Year

Price Cons

Value

($/Mton)

(MMton N)

($US B)

1987 425.3 75.8 $32.2

1997 610.4 81.3 $49.6

2007 795 100.6 $80.0

2012 869 103.2 $89.7

2030 1220 126.9 $154.8

Fertilizing the Nitrogen Cycle

Annual releases of fixed nitrogen caused by human activity

Source Millions of tons Fertilizer 100

Nitrogen-fixing crops 40

Fossil fuels 20

Biomass burning 40

Wetland drainage 10

Land clearing 20

Total human releases 230

Total natural fixed-nitrogen prodn* 140

*Terrestrial sources only; marine sources have not yet been reliably estimated.

Source: World Resources Institute, “Global Nitrogen Glut” www.wri.org/wri/wr-98-99/nutrient.htm.

Yearly flow Niagara Falls (maximum rate) equal to global active N in solution (optimum rate)

Soil

Available N Plant N Grain Yield

PE

Fertilizer

N

Legumes

Fixed N Leaching

Gaseous losses

N20, NO, N2

NH3

volatilization

Precipitation

RE

N uptake N uptake

N remobilization

Sources and fates of N in plants and the environment

Photorespiration

Unavailable

N

Physiological Efficiency Recovery Efficiency

Good et al. (2004) Trends Plant Science 9:597-605

Can We Improve on Randomness For Genetic Improvement for Important

Complex Traits

Breeding Selection

Selecting genes for trait improvement for transgenics

How do we utilize all of the potentially available data in a practical way

NITROGEN USE EFFICIENCY

Can we use gene knowledge to maintain yield while decreasing the rate of N fertilization or increase yield under the same N?

Nitrogen Use Efficiency

Can we use gene knowledge to maintain yield while decreasing the rate of N fertilization or increase yield under the same N?

Decreased cost to farmer and decreased environmental cost

Need to develop highly predictive tools and low cost screening methods to be successful with this type of trait

Gene Discovery

Genetics Genomics Transcriptome: Proteomics Metabolomics Phenotyping Model plants

Our Syngenta collaboration- knowledge from:

1. Model plants: GNC and CGA1 transcription factors

2. Transcriptome: OsENOD93 and another transcription

factor

Approach Using Arabidopsis

>Develop defined N growth conditions >Screen the mutants using the growth system >Identify N-responsive genes

Dr. Yong-Mei Bi, Tara

Signorelli, Rong Zhao

Reduced chlorophyll level in gnc mutants

0

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40

chlo

rophyll

(SPA

D u

nit

s)

A

5’

exon 1 exon 2 exon 3

T-DNA 3’ C

UBI

GNC

wt gnc

D

B

wt gnc

wt gnc

An Arabidopsis GATA transcription factor gene GNC important for chlorophyll synthesis and

regulating carbon metabolism

Bi et al 2005 Plant J

Transgenic Rice with Altered OsGNC Expression

OX Control RNAi

Delayed Senescence in Transgenic Corn Overexpressing OsGNC

Control

OsGNC

NH4+

2-OG

Glu

2-OG

GGAT

Aminolevulinic Acid

Protoporphyrin IX

Chlorophyll

Heme

Phytochrome

TCA cycle

2-OG

Mitochondrion Peroxisome

NH4+ and NO3

-

uptake by roots and transport to the shoot

Cytoplasm Chloroplast

Critrate

ICDH

NO3-

NH4+

NO3-

NO2-

NRT

NR

NiR

Glu

NO2-

2-OG

GOGAT AMT

GS1

Gln

Gln Glu

NH4+

Critrate

FeCH CHLM

Nucle

us

CGA

1

P

M

Overexpression of OsGNC- change in

transcript level of:

NR: Nitrate Reductase; NiR: Nitrite Reductase; GS1: Glutamine Synthetase; ICDH: Isocitrate

Dehydrogenase; GGAT: Glutamate:Glyoxylate Aminotransferase; CHLM: Magnesium-Protoporphyrin IX

Methyltransferase; FeCH: Fe Chelatase

Approach 2-Identify Genes – Profiling experiments

1. Identify candidate genes using: transcript profiling experiments

2. Defined 50 rice genes to test

3. Syngenta researchers made transgenic lines over-expressing each of these

N growth conditions

10mM N 1mM N 3mM N

Microarray design

H M L

Stable: High, medium, low

Transient: induction and reduction 2 hr

Tissue: shoot and root

HM HL H-HL L-LH

root 0 59 401 295

125 170 231

induction reduction

Significant Genes Identified

An Early Noduline gene; OsENOD93-1 responded to both N Reduction and Induction

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200

400

600

800

1000

N treatment

10mM 1mM Reduction Induction 1 10 10 1

Yong-Mei Bi, Surya Kant, Lixin Hao, Rong Zhao,

Satinder Gidda, Robert Mullen, Amanda Rochon,

Barry Shelp, Joseph Clarke, Tong Zhu

Over-expression of OsENOD93 – Increase

Seed Yield and Dry Shoot Biomass

Shoot DW (g)

Seed Yield (g)

3 mM N

OX-1 5.1 0.34 3.8 0.23

OX-2 4.9 0.28 3.7 0.17

Wild Type 4.4 0.23* 3.1 0.21*

10 mM N

OX-1 10 0.51 8.7 0.52

OX-2 9.8 0.56 8.8 0.46

Wild Type 8.9 0.61 7.7 0.46*

Bi et al PCE,2009

OsENOD93 Is Localized to Mitochondrial Membrane

NH4+

Amt GOGAT

2-OG

Glu

2-OG GGAT

NH4+

GS2

NH4+

TCA cycle 2-OG

Glu

GDH

NH4+

Mitochondrion Peroxisome

NH4+ and

NO3- uptake

by roots and transport to the shoot

Cytoplasm Chloroplast

Pyr

Gln

NO3-

NH4+

NO3-

NO2-

NH4+

Nrt

Nar

Nir Glu

GS1

NO2-

Amino Acids

Gln Glu

Pyr Glu + 2-OG

ENOD

+ Alanine

Over-expression of OsENOD93 changes transcript level

of:

Over-expression of OsENOD93 increased rice growth and yield, more specifically under low N conditions

OsENOD93 might play important role in transport of amino acids from roots to shoots

OsENOD93 in Rice

Position 95 344 348 383 387 662 687 980 982 1110 1718

OsMYB55

MeJA MeJA MeJA MeJA MeJA HSE HSE ABRE HSE ABRE ABRE

ABA responsive elements (ARE)

Jasmonate responsive element (MeJA-R)

Heat stress responsiveness (HSE)

Case 3: Nitrogen responsive gene with cis-

acting regulatory elements in the promoter

OsMYB55

OsMYB55

• Its expression is up-regulated by high temperature in rice

29°C 45°C

leaf

med

vein

leaf

blade

OsMYB55 HSE MeJA HSE ABRE MeJA ABRE HSE MeJA ABRE MeJA

-100 -1115

El-kereamy et al., PLoS One 2013, 7: e52030

(4-week treatment)

WT WT

L4 L11

29°C

35°C

Growth of OsMYB55 OX lines under high temperature

El-kereamy et al., PLoS One 2013, 7: e52030

WT P1

P5 Control

(29/23°C)

Heat

(42/35°C)

WT P1

P5

Re

du

cti

on

(%

of

co

ntr

ol)

Dry

biomass Plant

height

Stem

diameter

Chlorophyll

WT P1 P5

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50

Maize OsMYB55 OX lines also display heat

tolerance

WT P1 WT P1 P4

P5 Control Heat (5 d) +

recovery (7 d)

WT P1 WT P1 P4

P5 Contr

ol

Drought (5 d)

+ recovery (7

d)

Phenotype of OsMYB55 OX maize plants after recovery

from stress treatments

In summary…

• Under heat stress, OsMYB55 OX rice and maize show

significant less reduction of biomass, height, leaf damage and

chlorophyll than WT.

• Under water deficit conditions, OsMYB55 OX maize show

differences in dry biomass, water potential and leaf-level

parameters (greeness, temperature) compared to WT.

• Transcriptome of OsMYB55 OX maize revealed up-regulation of

genes encoding proteins involved in the general stress

responses including HS and drought.

Example 4: Functional study of

OsGATA12

Guangwen Lu

Expression profile of OsNUE62 in Rice

OsNUE62 OE lines and RNAi lines

Overexpression of OsNUE62 results in lower yield on a per plant basis

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25

WT 13A 16A

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WT 13A 16A

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WT 13A 16A0

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WT 13A 16A

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WT 13A 16A

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WT 13A 16A

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1.5

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2.5

3

WT 13A 16A

Panic

le length

No. of

gra

ins

per

panic

le

Yie

ld per

pla

nt

Tota

l se

eds

per

pla

nt

Tota

l dry

weig

ht

1000 g

rain

weig

ht

Yie

ld per

panic

le

Overexpression of OsGATA12 improve yield performance under high density.

Yie

ld/a

rea

(g)

0

300

600

900

1200

1500

HD LD

WT

OE

Summary Overexpression of OsNUE62 resulted in

a pleiotropic phenotype.

RNAi lines showed early senescence and less chlorophyll.

At low densities: yield much lower; under high density yield higher than wild type

Summary of rice results

5/50 showed some positive change in phenotype; 3/50 interesting for basic knowledge

In some cases need to try different promoters to optimize phenotypic effect and avoid negative pleiotropy

Field testing being done by Syngenta

What One Needs: Methods to Test Large Number of Field Plots

Low cost

Accurate

Predictive

Aerial imaging

Spectral reflectance: visible (blue, green, red); infrared

Thermal imaging

LIDAR: light plus radar for plant height

Compare to our ground measurements

Syngenta: color calibration panels and vehicle - from plane

0102030405060708090

100

Ground imaging:

Low stress

Controlled

growth:

mild stress

NDVI at V12 stage: Myb55 Field Test Minnesota and Greenhouse Test

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100

Wild-type GNC Wild-type GNC

Chlorophyll level Senescence

score

Chlorophyll and Senescence:

GNC Field Test Minnesota R4 stage

Conclusions and Issues

1. Transgenics of interest: replicated control growth

results

2. Need much more work to see if truly of commercial

interest: reliant on company interest being sustained

3. Needs sustained long-term interest which is difficult for

difficult traits

What One Needs: Time and

Organization

20 years later …

Gene Discovery

Gene Function Validation by a Transgenic Approach

Trait Enhanced Trait Partially Enhanced Negative Pleiotropic Effect

No Change

Further Field Test Design New Construct Use Alternative Promoters

Where is Guelph?

University of Guelph

Toronto

Niagara Fall

Key Collaborators

Guelph: Yong-Mei Bi

Darryl Hudson

Surya Kant

Kosala Ranathunge

Jose Casaretto

Viktoriya Coneva

Zhenhua Xu

Syngenta: Nic Bate

Tong Zhu

Chris Batie

Martin Allen

John Salmeron

Joe Clarke

Xi Chen

Rothstein lab

Yong-Mei Bi, Surya Kant, Darryl Hudson, Ashraf El-Kereamy, Dave Guevera,

Sabrina Humbert, Kashif Mahmood, Max Misyura, Xiaolou Zou, Sophie

Zhong, Guangwen Lu, Lixin Hao, Bin Zeng