Genomic analysis of
water use efficiency
Boyce Thompson Institute for Plant Science
Cornell University
Oklahoma State University
University of North Carolina at Chapel Hill
http://isotope.bti.cornell.edu/
Collaborators
• Cornell/Boyce Thompson: Jonathan Comstock, Susan McCouch
–
–
–
–
–
Christine Fleet
Roman Pausch
Wendy Vonhof
Shiqin Xu
Yunbi Xu
• Oklahoma State: Bjorn Martin, Chuck Tauer
– Shakuntala Fathepure
– Baige Zhao
• UNC Chapel Hill: Todd Vision
– Maria Tsompana
– Lindsey Swanson
Water use efficiency
• A fundamental trade-off for plants
– Open stomates allow photosynthesis
– But also result in water loss
• WUE is the ratio of carbon fixed to water lost
– Somewhat related to drought tolerance
– More closely to yield potential under irrigation
• Water is the most limiting resource to global
agricultural production
• In some crops, and under some conditions,
greater WUE would be desirable and in
others less
Three levels of WUE
• Whole-field (under agronomic control)
• Whole-plant (driven by respiration)
• Single-leaf (focus here)
Leaf-level WUE
sun
wind
ci
wi
ca
H2O
wa
CO2
photosynthesis
ca  ci
WUE 

transpiration
1.6wa  w i 
The challenges of working
with WUE
• WUE is a complex trait
– Rarely if ever controlled by a single gene
– Very sensitive to environment
• Breeding for WUE has not worked
– Too many deleterious side-effects
• We know almost nothing about the molecular biology
of how plants adjust their WUE
– Could we engineer WUE if we knew more?
• QTL mapping as a “foot in the door” to discover the
pathways involved in WUE
Quantitative trait loci (QTL)
P1 (+)
P2 (-)
F1 (0)
F2
+
+
0
LOD
Stable carbon isotopes
• Direct physiological measurement of WUE is
not quick and cheap enough for QTL studies a proxy is needed
• Stable isotopes are naturally occuring
– Atmospheric CO2 is 99 12C : 1 13C
• Rubisco, the key enzyme in carbon fixation,
discriminates against 13C
• Easily measured by mass spectrometry
Isotope measurements
• Isotopic ratio
R = 13C/12C
• Discrimination index
D = (Rair/Rplant) – 1
D and WUE
• Both ∆ & WUE depend on the CO2 diffusion
gradient
• In C3 plants, variation in this gradient is the
primary determinant of D and leaf-level WUE.
• D provides a high-throughput proxy for ci
– Values of D are typically negative
– Values closer to zero represent greater WUE
(more carbon fixed per unit of water)
Goals
• To dissect natural variation in WUE
• Discovery and characterization of WUE
quantitative trait loci (QTL)
– Rice (upland vs rice paddy cultivation)
– Tomato (desert versus cultivated species)
• Lay ground-work for positional cloning
– Fine mapping
– Introgression lines
Survey of variability in rice
• Assayed variation in D among
– Landraces and elite cultivars
– Related wild species
– The offspring of four wide crosses
•
•
•
•
Lamont x Teqing
Kasalath x Nipponbare
IR64 x Nipponbare
O. rufipogon x Jefferson
• Variation in the offspring of a single cross can be as
wide as the variation among all cultivated/wild
accessions!
• Upland/lowland distinction not that helpful…
Survey of variability in rice
LOD=8.60
WUE QTL
On Chromosome 1
Genetic Map
Genomic sequence
www.gramene.org
Mapping WUE QTL in tomato
• Wild desert species of tomato (e.g. Solanum
pennellii) have high WUE relative to
cultivated species (S. lycopersicon)
• On the minus side
– The genome sequence is not available yet
• On the plus side
– Zamir introgression lines for S. lycopersicon x S.
pennellii greatly facilitate mapping
QTL in pennellii population
Possible physiological basis for WUE
• Several of the candidate QTL lines have
– High nitrogen content = abundant protein
– Low specific leaf area (m2/g)
• These correlates suggest that increased
carboxylation capacity may be
responsible for greater WUE in these
QTL
Finding crossovers within IL5-4
• QTL can be located more precisely if IL5-4
introgression can be broken up
• Backcrossed IL5-4 to cultivated parent
• Genotyped F2 progeny for flanking markers
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
Principle of fine-mapping
(Mendelization)
mm
mm
qq
mm
mm
mm
mm
mm
qq
mm
mm
flanking
marker 1
QTL
qq
internal
marker 1
flanking
marker 2
Fine-mapping IL5-4 QTL
• 16 crossovers obtained from ~2000 backcross F2
plants
• These were selfed to produce backcross F3s
– D values obtained for F3 plants
• Scoring internal STS markers
– These allow us to align to the tomato physical map
– One internal STS marker done
– Several more in development
• AFLP markers are currently being mapped
– Not physically mapped, but abundant and easy to score
IL5-3
TG351
72.7
TG351
76.2
TG60, CT80
75
TG60, CT80
78.4
CP58B, CHS3
77.2
CP58B, CHS3
IL5-4
QTL
73.9
104
TG60
105
T1777
106
86.1
CD78
84.9
CD78
88.7
TG69
87.5
TG69
IL Population
F2 1992
SSR590, T1541
108
T1584
111
TG69
F2 2000
PCR length polymorphism already scored
SSR marker available
dCAPS marker available
Screening for polymorphisms (1 or more introns predicted)
Screening for polymorphisms (no intron predicted)
Primers under development
TG69 physical contig
Now what?
• Adding additional STS to IL5-4 (UNC)
– Goal is <1cM (=1 Mb) resolution
• Identifying BAC contigs containing markers in QTL
candidate region (UNC)
– BAC skimming to obtain high density markers
– Comparative mapping in Arabidopsis for candidate gene
analysis
• Generating overlapping congenic lines in IL5-4 by
marker assisted selection (OSU)