Dokument

Summary:
The aim of this thesis was the identification and analysis of candidate genes for stress response in silver fir (Abies alba Mill.). This ecologically and economically important forest tree species is native to many mountainous regions of Europe but little is known about its ecological characteristics. Silver fir populations were heavily transformed by human activity, which results in a mismatch between past and current distribution. Recent studies suggest that silver fir can occupy warmer and dryer climates than it currently does. However, the species also suffered considerably during the 1970s and 1980s, including foliar damage, radial growth depression and local diebacks in Germany. This is attributed mainly to the peak in air pollution during this period, especially sulfur dioxide (SO2), which seems to heavily increase drought sensitivity in silver fir. The combination of both stressors, SO2 and drought events, negatively affected silver fir even in regions where drought is usually not a problem. In the context of anthropogenic global climate change that will very likely lead to an increase in temperature in Europe and to more extreme events such as severe drought periods, the question arises, how silver fir will cope with these environmental changes. Given the speed of the predicted changes and the increasing landscape fragmentation, silver fir might not be able to evade it via seed dispersal. As a sessile organism, the only other option is adaptation, which will likely draw from standing genetic variation. To successfully predict the fate of silver fir, especially in the face of global climate change, and to potentially manage populations based on such predictions, the genetic architecture of silver fir in the context of such important stressors as drought and air pollution has to be understood. There exist, however, little genomic resources for silver fir and conifers in general. This is due to their large and complex genomes and the long generational cycle, which makes conifers typical nonmodel species. As such, methods for the identification of the genetic basis of stress response are effectively limited to a candidate gene approach. The candidate gene approach includes the identification of functional candidate genes by measuring differential gene expression between a stressed and a control group. In the context of this thesis, the water content of silver fir seedlings was monitored in a laboratory using a novel terahertz spectroscopy setup. One group of seedlings was regularly irrigated while the other group was drought stressed. Continually measuring the water content allowed to harvest needles from both groups at a time when the water status was comparable between the individuals within each group. A differential expression analysis between the needles from both groups then revealed 296 genes that were significantly up- or down-regulated in response to drought stress. Of those genes, approximately 45% have not been previously described in any organism and are potentially unique to silver fir or conifers in general. However, since only needles of seedlings were analyzed at a specific level of drought stress, the results are limited in scope to the source material and stress intensity and cannot be directly applied to silver fir or drought stress in general. Also, this approach implies a cause-effect relationship between gene expression and a specific level of drought stress. Thus, it is very important that confounding factors are excluded from the experiment. Chlorophyll content in the needles, for example, might change over the course of the monitoring period due to the drought treatment. To test if the chlorophyll content could potentially influence the terahertz signal, chlorophyll was extracted from silver fir needles, in the course of this thesis, and different concentrations were measured using terahertz spectroscopy, showing that chlorophyll content does not influence terahertz monitoring. Another aspect of the candidate gene approach involves the variation within a polymorphic gene and its potential association with the variation in a phenotypic trait. Since the growth depression period of silver fir in the 1970s and 1980s was mostly influenced by the combination of air pollution and drought, in the context of this thesis, genetic variation, in the form of single nucleotide polymorphims (SNPs) in pre-selected genes, was associated with tree-ring derived phenotypes for individual trees in the Bavarian Forest National Park. These so called ’dendrophenotypes’ were measures for resistance, resilience and recovery during the depression period, as well as the drought year 1976. Using general linear models and feature selection techniques based on the machine learning algorithm random forest, 15 out of 103 polymorphic candidate genes for trait variation could be identified. Since the associated dendrophenotyes are potentially adaptively relevant, the variation in this candidate genes could influence the stress coping capability of individual trees. However, this approach is of an observational nature and thus, cause-effect relationships cannot be derived from this type of experiment. The identified SNPs might be the causal variant or physically close to the true causal variant or it might just be a spurious correlation. Further, reliance on advanced statistical techniques can be troublesome, as could be demonstrated in the course of this thesis for a random forest based feature selection technique, developed for genetic association studies in conifers. Replicating this study and evaluating the algorithm, non-uniqueness of the results could be demonstrated, which not only hinders biological interpretation but can severely negatively influence downstream analyses, such as tests for interaction between SNPs. In conclusion, this thesis presents new techniques to add to the current methodology for candidate gene selection and analysis in the stress response of the non-model organism silver fir and other conifer species. Both approaches should be combined, for example by drawing polymorphic candidate genes for trait variation from the pool of functional candidate genes to ensure the involvement of the studied genes in the variation of the trait of interest. Further, the results of this thesis add to the growing molecular resources in silver fir and thereby, hopefully, contribute to the successful prediction and management of this important forest tree species in the face of rapidly changing environmental conditions.

Bibliographie / References

1. Krause, G. H. M., Arndt, U., Brandt, C. J., Bucher, J., Kenk, G. and Matzner, E. (1986), Forest Decline in Europe: Development and Possible Causes, in H. C. Martin, ed., 'Acidic Precipitation', Springer Netherlands, pp. 1701-1722. DOI: 10.1007/978-94-009-3385-9 171.
2. Barrett, R. D. and Schluter, D. (2008), 'Adaptation from standing genetic variation', Trends in Ecology & Evolution 23(1), 38-44.
3. Whitham, T. G., Bailey, J. K., Schweitzer, J. A., Shuster, S. M., Bangert, R. K., LeRoy, C. J., Lonsdorf, E. V., Allan, G. J., DiFazio, S. P., Potts, B. M., Fischer, D. G., Gehring, C. A., Lindroth, R. L., Marks, J. C., Hart, S. C., Wimp, G. M. and Wooley, S. C. (2006), 'A framework for community and ecosystem genetics: from genes to ecosystems', Nat Rev Genet 7(7), 510-523.
4. Allen, C. D., Macalady, A. K., Chenchouni, H., Bachelet, D., McDowell, N., Vennetier, M., Kitzberger, T., Rigling, A., Breshears, D. D., Hogg, E. T., Gonzalez, P., Fensham, R., Zhang, Z., Castro, J., Demidova, N., Lim, J.-H., Allard, G., Running, S. W., Semerci, A. and Cobb, N. (2010), 'A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests', Forest Ecology and Management 259(4), 660-684.
5. Goldstein, B. A., Hubbard, A. E., Cutler, A. and Barcellos, L. F. (2010), 'An application of Random Forests to a genome-wide association dataset: Methodological considerations & new findings', BMC Genetics 11, 49.
6. Roschanski, A. M., Fady, B., Ziegenhagen, B. and Liepelt, S. (2013), 'Annotation and Re-Sequencing of Genes from De Novo Transcriptome Assembly of Abies alba (Pinaceae)', Applications in Plant Sciences 1(1), 1200179.
7. Birol, I., Raymond, A., Jackman, S. D., Pleasance, S., Coope, R., Taylor, G. A., Yuen, M. M. S., Keeling, C. I., Brand, D., Vandervalk, B. P., Kirk, H., Pandoh, P., Moore, R. A., Zhao, Y., Mungall, A. J., Jaquish, B., Yanchuk, A., Ritland, C., Boyle, B., Bousquet, J., Ritland, K., MacKay, J., Bohlmann, J. and Jones, S. J. M. (2013), 'Assembling the 20 Gb white spruce (Picea glauca) genome from whole-genome shotgun sequencing data', Bioinformatics 29(12), 1492-1497.
8. Ayres, M. P. and Lombardero, M. J. (2000), 'Assessing the consequences of global change for forest disturbance from herbivores and pathogens', Science of The Total Environment 262(3), 263-286.
9. Gonza´lez-Mart´ınez, S. C., Huber, D., Ersoz, E., Davis, J. M. and Neale, D. B. (2008), 'Association genetics in Pinus taeda L. II. Carbon isotope discrimination', Heredity 101(1), 19-26.
10. Eckert, A. J., Bower, A. D., Wegrzyn, J. L., Pande, B., Jermstad, K. D., Krutovsky, K. V., Clair, J. B. S. and Neale, D. B. (2009), 'Association Genetics of Coastal Douglas Fir (Pseudotsuga menziesii var. menziesii, Pinaceae). I. Cold-Hardiness Related Traits', Genetics 182(4), 1289-1302.
11. Neale, D. B. and Savolainen, O. (2004), 'Association genetics of complex traits in conifers', Trends in Plant Science 9(7), 325-330.
12. Balding, D. J. (2006a), 'A tutorial on statistical methods for population association studies', Nat Rev Genet 7(10), 781-791.
13. Stephens, M. and Balding, D. J. (2009), 'Bayesian statistical methods for genetic association studies', Nat Rev Genet 10(10), 681-690.
14. Tabor, H. K., Risch, N. J. and Myers, R. M. (2002), 'Candidate-gene approaches for studying complex genetic traits: practical considerations', Nat Rev Genet 3(5), 391-397.
15. Liaw, A. and Wiener, M. (2002), 'Classification and regression by randomForest',R news 2(3), 18- 22.
16. IPCC (2014), Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp., Technical report.
17. Gugerli, F., Brandl, R., Castagneyrol, B., Franc, A., Jactel, H., Koelewijn, H.-P., Martin, F., Peter, M., Pritsch, K., Schro¨der, H., Smulders, M. J. M., Kremer, A., Ziegenhagen, B. and Contributors, E. J. (2013), 'Community genetics in the time of next-generation molecular technologies', Molecular Ecology 22(12), 3198-3207.
18. Nilsson, R., Pen˜a, J. M., Bjo¨rkegren, J. and Tegn´er, J. (2007), 'Consistent feature selection for pattern recognition in polynomial time', Journal of Machine Learning Research 8(Mar), 589-612.
19. Zhao, Y., Chen, F., Zhai, R., Lin, X., Wang, Z., Su, L. and Christiani, D. C. (2012), 'Correction for population stratification in random forest analysis',Int. J. Epidemiol. 41(6), 1798-1806.
20. Puizina, J., Sviben, T., Krajaˇci´c-Sokol, I., Zoldoˇs-Pe´cnik, V., Siljak-Yakovlev, S., Papeˇs, D. and Besendorfer, V. (2008), 'Cytogenetic and molecular characterization of the Abies alba genome and its relationship with other members of the Pinaceae', Plant Biology 10(2), 256-267.
21. Neale, D. B., Wegrzyn, J. L., Stevens, K. A., Zimin, A. V., Puiu, D., Crepeau, M. W., Cardeno, C., Koriabine, M., Holtz-Morris, A. E., Liechty, J. D., Mart´ınez-Garc´ıa, P. J., Vasquez-Gross, H. A., Lin, B. Y., Zieve, J. J., Dougherty, W. M., Fuentes-Soriano, S., Wu, L.-S., Gilbert, D., Mar¸cais, G., Roberts, M., Holt, C., Yandell, M., Davis, J. M., Smith, K. E., Dean, J. F., Lorenz, W. W., Whetten, R. W., Sederoff, R., Wheeler, N., McGuire, P. E., Main, D., Loopstra, C. A., Mockaitis, K., deJong, P. J., Yorke, J. A., Salzberg, S. L. and Langley, C. H. (2014), 'Decoding the massive genome of loblolly pine using haploid DNA and novel assembly strategies', Genome Biology 15, R59.
22. Krajick, K. (2001), 'Defending Deadwood', Science 293(5535), 1579-1581.
23. Elling, W., Dittmar, C., Pfaffelmoser, K. and Ro¨tzer, T. (2009), 'Dendroecological assessment of the complex causes of decline and recovery of the growth of silver fir (Abies alba Mill.) in Southern Germany', Forest Ecology and Management 257(4), 1175-1187.
24. Gonza´lez-Mart´ınez, S. C., Ersoz, E., Brown, G. R., Wheeler, N. C. and Neale, D. B. (2006), 'DNA sequence variation and selection of tag single-nucleotide polymorphisms at candidate genes for drought-stress response in Pinus taeda L', Genetics 172(3), 1915-1926.
25. Kursa, M. B. and Rudnicki, W. R. (2010), 'Feature selection with the Boruta package', Journal of Statistical Software 36(11).
26. Neale, D. B. and Kremer, A. (2011), 'Forest tree genomics: growing resources and applications', Nat Rev Genet 12(2), 111-122.
27. Hornoy, B., Pavy, N., G´erardi, S., Beaulieu, J. and Bousquet, J. (2015), 'Genetic Adaptation to Climate in White Spruce Involves Small to Moderate Allele Frequency Shifts in Functionally Diverse Genes', Genome Biol Evol 7(12), 3269-3285.
28. Hess, J. E., Zendt, J. S., Matala, A. R. and Narum, S. R. (2016), 'Genetic basis of adult migration timing in anadromous steelhead discovered through multivariate association testing', Proc. R. Soc. B 283(1830), 20153064.
29. Lander, E. S. and Schork, N. J. (1994), 'Genetic dissection of complex traits', Science 265(5181), 2037-2048.
30. Rockman, M. V. and Kruglyak, L. (2006), 'Genetics of global gene expression', Nat Rev Genet 7(11), 862-872.
31. Davila Olivas, N. H., Kruijer, W., Gort, G., Wijnen, C. L., van Loon, J. J. A. and Dicke, M. (2017), 'Genome-wide association analysis reveals distinct genetic architectures for single and combined stress responses in Arabidopsis thaliana', New Phytol 213(2), 838-851.
32. Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., Chapin, F. S., Coe, M. T., Daily, G. C., Gibbs, H. K., Helkowski, J. H., Holloway, T., Howard, E. A., Kucharik, C. J., Monfreda, C., Patz, J. A., Prentice, I. C., Ramankutty, N. and Snyder, P. K. (2005), 'Global Consequences of Land Use', Science 309(5734), 570-574.
33. Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O. and Townshend, J. R. G. (2013), 'High-Resolution Global Maps of 21st-Century Forest Cover Change', Science 342(6160), 850-853.
34. Bureau, A., Dupuis, J., Falls, K., Lunetta, K. L., Hayward, B., Keith, T. P. and Van Eerdewegh, P. (2005), 'Identifying SNPs predictive of phenotype using random forests', Genet. Epidemiol. 28(2), 171-182.
35. Kovats, R., Valentini, R., Bouwer, L., Georgopoulou, E., Jacob, D., Martin, E., Rounsevell, M. and Soussana, J.-F. (2014), Europe. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]., Technical report, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1267-1326.
36. Torre, A. R. D. L., Birol, I., Bousquet, J., Ingvarsson, P. K., Jansson, S., Jones, S. J. M., Keeling, C. I., MacKay, J., Nilsson, O., Ritland, K., Street, N., Yanchuk, A., Zerbe, P. and Bohlmann, J. (2014), 'Insights into Conifer Giga-Genomes', Plant Physiol. 166(4), 1724-1732.
37. Guo, Y., Sheng, Q., Li, J., Ye, F., Samuels, D. C. and Shyr, Y. (2013), 'Large Scale Comparison of Gene Expression Levels by Microarrays and RNAseq Using TCGA Data', PLOS ONE 8(8), e71462.
38. Doleˇzel, J., Bartoˇs, J., Voglmayr, H. and Greilhuber, J. (2003), 'Letter to the editor', Cytometry 51A(2), 127-128.
39. Libbrecht, M. W. and Noble, W. S. (2015), 'Machine learning applications in genetics and genomics', Nat Rev Genet 16(6), 321-332.
40. Mauricio, R. (2001), 'Mapping quantitative trait loci in plants: uses and caveats for evolutionary biology', Nat Rev Genet 2(5), 370-381.
41. Rabbani, M. A., Maruyama, K., Abe, H., Khan, M. A., Katsura, K., Ito, Y., Yoshiwara, K., Seki, M., Shinozaki, K. and Yamaguchi-Shinozaki, K. (2003), 'Monitoring Expression Profiles of Rice Genes under Cold, Drought, and High-Salinity Stresses and Abscisic Acid Application Using cDNA Microarray and RNA Gel-Blot Analyses', Plant Physiol. 133(4), 1755-1767.
42. Frankham, R. and Weber, K. (2000), Nature of quantitative genetic variation, in R. Singh and C. Krimbas, eds, 'Evolutionary Genetics: From Molecules to Morphology', Cambridge University Press, Cambridge, UK, pp. 351-368.
43. Hobbs, R. J., Arico, S., Aronson, J., Baron, J. S., Bridgewater, P., Cramer, V. A., Epstein, P. R., Ewel, J. J., Klink, C. A., Lugo, A. E., Norton, D., Ojima, D., Richardson, D. M., Sanderson, E. W., Valladares, F., Vila`, M., Zamora, R. and Zobel, M. (2006), 'Novel ecosystems: theoretical and management aspects of the new ecological world order', Global Ecology and Biogeography 15(1), 1-7.
44. Van Nuland, M. E., Wooliver, R. C., Pfennigwerth, A. A., Read, Q. D., Ware, I. M., Mueller, L., Fordyce, J. A., Schweitzer, J. A. and Bailey, J. K. (2016), 'Plant-soil feedbacks: connecting ecosystem ecology and evolution', Funct Ecol 30(7), 1032-1042.
45. Liepelt, S., Cheddadi, R., de Beaulieu, J.-L., Fady, B., Go¨mo¨ry, D., Hussendo¨rfer, E., Konnert, M., Litt, T., Longauer, R., Terhu¨rne-Berson, R. and Ziegenhagen, B. (2009), 'Postglacial range expansion and its genetic imprints in Abies alba (Mill.) - A synthesis from palaeobotanic and genetic data', Review of Palaeobotany and Palynology 153(1-2), 139-149.
46. Holliday, J. A., Wang, T. and Aitken, S. (2012), 'Predicting Adaptive Phenotypes From Multilocus Genotypes in Sitka Spruce (Picea sitchensis) Using Random Forest', G3 2(9), 1085-1093.
47. Sork, V. L., Aitken, S. N., Dyer, R. J., Eckert, A. J., Legendre, P. and Neale, D. B. (2013), 'Putting the landscape into the genomics of trees: approaches for understanding local adaptation and population responses to changing climate', Tree Genetics & Genomes 9(4), 901-911.
48. R Core Team (2016), 'R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna Austria. URL https://www.R-project.org/'.
49. C´ali´c, I., Bussotti, F., Mart´ınez-Garc´ıa, P. J. and Neale, D. B. (2015), 'Recent landscape genomics studies in forest trees-what can we believe?', Tree Genetics & Genomes 12(1), 1-7.
50. Barber, V. A., Juday, G. P. and Finney, B. P. (2000), 'Reduced growth of Alaskan white spruce in the twentieth century from temperature-induced drought stress', Nature 405(6787), 668-673.
51. Kahl, G., Molina, C., Rotter, B., Ju¨ngling, R., Frank, A., Krezdorn, N., Hoffmeier, K. and Winter, P. (2012), 'Reduced representation sequencing of plant stress transcriptomes', J. Plant Biochem. Biotechnol. 21(1), 119-127.
52. Levitt, J. (1980), Responses of Plants to Environmental Stresses, Vol. 1, Academic Press, New York, NY.
53. Wang, Z., Gerstein, M. and Snyder, M. (2009), 'RNA-Seq: a revolutionary tool for transcriptomics', Nature reviews genetics 10(1), 57-63.
54. Karnosky, D. F., Pregitzer, K. S., Zak, D. R., Kubiske, M. E., Hendrey, G. R., Weinstein, D., Nosal, M. and Percy, K. E. (2005), 'Scaling ozone responses of forest trees to the ecosystem level in a changing climate', Plant, Cell & Environment 28(8), 965-981.
55. Adams, H. D., Guardiola-Claramonte, M., Barron-Gafford, G. A., Villegas, J. C., Breshears, D. D., Zou, C. B., Troch, P. A. and Huxman, T. E. (2009), 'Temperature sensitivity of drought-induced tree mortality portends increased regional die-off under global-change-type drought', PNAS 106(17), 7063-7066.
56. Korte, A. and Farlow, A. (2013), 'The advantages and limitations of trait analysis with GWAS: a review', Plant Methods 9, 29.
57. Tautz, D. and Domazet-Loˇso, T. (2011), 'The evolutionary origin of orphan genes', Nat Rev Genet 12(10), 692-702.
58. Dawkins, R. (1982), The Extended Phenotype: The Gene as the Unit of Selection, Freeman, Oxford.
59. Ingram, J. and Bartels, D. (1996), 'The molecular basis of dehydration tolerance in plants', Annual review of plant biology 47(1), 377-403.
60. Nystedt, B., Street, N. R., Wetterbom, A., Zuccolo, A., Lin, Y.-C., Scofield, D. G., Vezzi, F., Delhomme, N., Giacomello, S., Alexeyenko, A., Vicedomini, R., Sahlin, K., Sherwood, E., Elfstrand, M., Gramzow, L., Holmberg, K., Ha¨llman, J., Keech, O., Klasson, L., Koriabine, M., Kucukoglu, M., Ka¨ller, M., Luthman, J., Lysholm, F., Niittyla¨, T., Olson, A˚., Rilakovic, N., Ritland, C., Rossello´, J. A., Sena, J., Svensson, T., Talavera-Lo´pez, C., Theißen, G., Tuominen, H., Vanneste, K., Wu, Z.-Q., Zhang, B., Zerbe, P., Arvestad, L., Bhalerao, R., Bohlmann, J., Bousquet, J., Garcia Gil, R., Hvidsten, T. R., de Jong, P., MacKay, J., Morgante, M., Ritland, K., Sundberg, B., Lee Thompson, S., Van de Peer, Y., Andersson, B., Nilsson, O., Ingvarsson, P. K., Lundeberg, J. and Jansson, S. (2013), 'The Norway spruce genome sequence and conifer genome evolution', Nature 497(7451), 579-584.
61. Tinner, W., Colombaroli, D., Heiri, O., Henne, P. D., Steinacher, M., Untenecker, J., Vescovi, E., Allen, J. R. M., Carraro, G., Conedera, M., Joos, F., Lotter, A. F., Luterbacher, J., Samartin, S. and Valsecchi, V. (2013), 'The past ecology of Abies alba provides new perspectives on future responses of silver fir forests to global warming', Ecological Monographs 83(4), 419-439.
62. Lichtenthaler, H. K. (1998), 'The Stress Concept in Plants: An Introduction', Annals of the New York Academy of Sciences 851(1), 187-198.
63. Loarie, S. R., Duffy, P. B., Hamilton, H., Asner, G. P., Field, C. B. and Ackerly, D. D. (2009), 'The velocity of climate change', Nature 462(7276), 1052-1055.
64. Kreps, J. A., Wu, Y., Chang, H.-S., Zhu, T., Wang, X. and Harper, J. F. (2002), 'Transcriptome Changes for Arabidopsis in Response to Salt, Osmotic, and Cold Stress', Plant Physiol. 130(4), 2129-2141.
65. Roth, R., Ebert, I. and Schmidt, J. (1997), 'Trisomy associated with loss of maturation capacity in a long-term embryogenic culture of Abies alba', Theoretical and applied genetics 95(3), 353-358.
66. Armenise, L., Simeone, M. C., Piredda, R. and Schirone, B. (2012), 'Validation of DNA barcoding as an efficient tool for taxon identification and detection of species diversity in Italian conifers', Eur J Forest Res 131(5), 1337-1353.
67. Genuer, R., Poggi, J.-M. and Tuleau-Malot, C. (2015), 'VSURF: An R Package for Variable Selection Using Random Forests', The R Journal 7(2), 19-33.

Das Dokument ist im Internet frei zugänglich - Hinweise zu den Nutzungsrechten