1. 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/

2. 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

3. 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

4. Three levels of WUE Whole-field (under agronomic control) Whole-plant (driven by respiration) Single-leaf (focus here)

5. Leaf-level WUE cici wiwi caca wawa CO 2 H2OH2O wind sun

6. 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

7. Quantitative trait loci (QTL) P1 (+) P2 (-) F1 (0) F2 0 - + + LOD

8. 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 CO 2 is 99 12 C : 1 13 C Rubisco, the key enzyme in carbon fixation, discriminates against 13 C Easily measured by mass spectrometry

9. Isotope measurements Isotopic ratio R = 13 C/ 12 C Discrimination index  = (R air /R plant ) – 1

10.  and WUE Both ∆ & WUE depend on the CO 2 diffusion gradient In C3 plants, variation in this gradient is the primary determinant of  and leaf-level WUE.  provides a high-throughput proxy for c i –Values of  are typically negative –Values closer to zero represent greater WUE (more carbon fixed per unit of water)

11. 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

12. Environmental controls Air temperature Relative humidity Wind velocity Soil moisture Soil volume Photosynthetically active radiation Carbon dioxide concentration Isotopic ratio of carbon dioxide

13. Survey of variability in rice Assayed variation in  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…

14. Survey of variability in rice

15. www.gramene.org Genomic sequence Genetic Map LOD=8.60 WUE QTL On Chromosome 1

16. 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

18. QTL in pennellii population

20. Possible physiological basis for WUE Several of the candidate QTL lines have –High nitrogen content = abundant protein –Low specific leaf area (m 2 /g) These correlates suggest that increased carboxylation capacity may be responsible for greater WUE in these QTL

21. 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

22. Principle of fine-mapping (Mendelization) flanking marker 1 flanking marker 2 internal marker 1 QTL q q qqmm m mmm m m m

23. Fine-mapping IL5-4 QTL 16 crossovers obtained from ~2000 backcross F2 plants These were selfed to produce backcross F3s –  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

24. TG35172.7 TG60, CT8075 CP58B, CHS377.2 CD7884.9 TG6987.5 SSR590, T1541 TG60 104 T1777 105 106 T1584 108 TG69 111 F2 1992 F2 2000 IL5-4 IL5-5 TG35173.9 TG60, CT8076.2 CP58B, CHS378.4 CD7886.1 TG6988.7 IL Population IL5-3 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 QTL

25. TG69 physical contig

26. 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)