[EN] Considering cells as biofactories, we aimed to optimize its internal processes by using the same engineering principles that large industries are implementing nowadays: lean manufacturing. We have applied reverse engineering computational methods to transcriptomic, metabolomic and phenomic data obtained from a collection of tomato recombinant inbreed lines to formulate a kinetic and constraint-based model that efficiently describes the cellular metabolism from expression of a minimal core of genes. Based on predicted metabolic profiles, a close association with agronomic and organoleptic properties of the ripe fruit was revealed with high statistical confidence. Inspired in a synthetic biology approach, the model was used for exploring the landscape of all possible local transcriptional changes with the aim of engineering tomato fruits with fine-tuned biotechnological properties. The method was validated by the ability of the proposed genomes, engineered for modified desired agronomic traits, to recapitulate experimental correlations between associated metabolites. This work was supported by grant TIN2006-12860 from the Spanish Ministerio de Ciencia e Innovacion), the Structural Funds of the European Regional Development Fund (ERDF), FP7-ICT-043338 (BACTOCOM), the FP7-ICT-265505 (CADMAD), the ATIGE-Genopole, and the Fondation pour la Recherche Medicale grants to AJ, and by grant BFU2009-06993 from the Spanish Ministerio de Ciencia e Innovacion to SFE and ESPSOL Fundacion Genoma Espana and EUSOL European Commission Contract number: FOOD-CT-2006-016214, to AG. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Carrera Montesinos, J.; Fernández Del Carmen, MA.; Fernández Muñoz, R.; Rambla Nebot, JL.; Pons Puig, C.; Jaramillo Rosales, A.; Elena Fito, SF.... (2012). Fine-tuning tomato agronomic properties by computational genome redesign. PLoS Computational Biology. 8(6):1002528-1002528. https://doi.org/10.1371/journal.pcbi.1002528 Endy, D. (2005). Foundations for engineering biology. Nature, 438(7067), 449-453. doi:10.1038/nature04342 Knight, T. F. (2005). Engineering novel life. Molecular Systems Biology, 1(1). doi:10.1038/msb4100028 Andrianantoandro, E., Basu, S., Karig, D. K., & Weiss, R. (2006). Synthetic biology: new engineering rules for an emerging discipline. Molecular Systems Biology, 2(1). doi:10.1038/msb4100073 Feist, A. M., Herrgård, M. J., Thiele, I., Reed, J. L., & Palsson, B. Ø. (2008). Reconstruction of biochemical networks in microorganisms. Nature Reviews Microbiology, 7(2), 129-143. doi:10.1038/nrmicro1949 Di Bernardo, D., Thompson, M. J., Gardner, T. S., Chobot, S. E., Eastwood, E. L., Wojtovich, A. P., … Collins, J. J. (2005). Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks. Nature Biotechnology, 23(3), 377-383. doi:10.1038/nbt1075 Carrera, J., Rodrigo, G., & Jaramillo, A. (2009). Model-based redesign of global transcription regulation. Nucleic Acids Research, 37(5), e38-e38. doi:10.1093/nar/gkp022 Carrera, J., Rodrigo, G., Jaramillo, A., & Elena, S. F. (2009). Reverse-engineering the Arabidopsis thaliana transcriptional network under changing environmental conditions. Genome Biology, 10(9), R96. doi:10.1186/gb-2009-10-9-r96 Faith, J. J., Hayete, B., Thaden, J. T., Mogno, I., Wierzbowski, J., Cottarel, G., … Gardner, T. S. (2007). Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles. PLoS Biology, 5(1), e8. doi:10.1371/journal.pbio.0050008 Bonneau, R., Facciotti, M. T., Reiss, D. J., Schmid, A. K., Pan, M., Kaur, A., … Baliga, N. S. (2007). A Predictive Model for Transcriptional Control of Physiology in a Free Living Cell. Cell, 131(7), 1354-1365. doi:10.1016/j.cell.2007.10.053 Tagkopoulos, I., Liu, Y.-C., & Tavazoie, S. (2008). Predictive Behavior Within Microbial Genetic Networks. Science, 320(5881), 1313-1317. doi:10.1126/science.1154456 Covert, M. W., Knight, E. M., Reed, J. L., Herrgard, M. J., & Palsson, B. O. (2004). Integrating high-throughput and computational data elucidates bacterial networks. Nature, 429(6987), 92-96. doi:10.1038/nature02456 Endy, D., & Brent, R. (2001). Modelling cellular behaviour. Nature, 409(6818), 391-395. doi:10.1038/35053181 Joyce, A. R., & Palsson, B. Ø. (2006). The model organism as a system: integrating «omics» data sets. Nature Reviews Molecular Cell Biology, 7(3), 198-210. doi:10.1038/nrm1857 Burgard, A. P., Pharkya, P., & Maranas, C. D. (2003). Optknock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnology and Bioengineering, 84(6), 647-657. doi:10.1002/bit.10803 Segre, D., Vitkup, D., & Church, G. M. (2002). Analysis of optimality in natural and perturbed metabolic networks. Proceedings of the National Academy of Sciences, 99(23), 15112-15117. doi:10.1073/pnas.232349399 Rocha, M., Maia, P., Mendes, R., Pinto, J. P., Ferreira, E. C., Nielsen, J., … Rocha, I. (2008). Natural computation meta-heuristics for the in silico optimization of microbial strains. BMC Bioinformatics, 9(1). doi:10.1186/1471-2105-9-499 Meyer, R. C., Steinfath, M., Lisec, J., Becher, M., Witucka-Wall, H., Torjek, O., … Altmann, T. (2007). The metabolic signature related to high plant growth rate in Arabidopsis thaliana. Proceedings of the National Academy of Sciences, 104(11), 4759-4764. doi:10.1073/pnas.0609709104 Mounet, F., Moing, A., Garcia, V., Petit, J., Maucourt, M., Deborde, C., … Lemaire-Chamley, M. (2009). Gene and Metabolite Regulatory Network Analysis of Early Developing Fruit Tissues Highlights New Candidate Genes for the Control of Tomato Fruit Composition and Development. Plant Physiology, 149(3), 1505-1528. doi:10.1104/pp.108.133967 Garcia, V., Stevens, R., Gil, L., Gilbert, L., Gest, N., Petit, J., … Rothan, C. (2009). An integrative genomics approach for deciphering the complex interactions between ascorbate metabolism and fruit growth and composition in tomato. Comptes Rendus Biologies, 332(11), 1007-1021. doi:10.1016/j.crvi.2009.09.013 Schauer, N., Semel, Y., Roessner, U., Gur, A., Balbo, I., Carrari, F., … Fernie, A. R. (2006). Comprehensive metabolic profiling and phenotyping of interspecific introgression lines for tomato improvement. Nature Biotechnology, 24(4), 447-454. doi:10.1038/nbt1192 Osorio, S., Alba, R., Damasceno, C. M. B., Lopez-Casado, G., Lohse, M., Zanor, M. I., … Fernie, A. R. (2011). Systems Biology of Tomato Fruit Development: Combined Transcript, Protein, and Metabolite Analysis of Tomato Transcription Factor (nor, rin) and Ethylene Receptor (Nr) Mutants Reveals Novel Regulatory Interactions. Plant Physiology, 157(1), 405-425. doi:10.1104/pp.111.175463 Rohrmann, J., Tohge, T., Alba, R., Osorio, S., Caldana, C., McQuinn, R., … Fernie, A. R. (2011). Combined transcription factor profiling, microarray analysis and metabolite profiling reveals the transcriptional control of metabolic shifts occurring during tomato fruit development. The Plant Journal, 68(6), 999-1013. doi:10.1111/j.1365-313x.2011.04750.x Sabeti, P. C., Varilly, P., Fry, B., Lohmueller, J., Hostetter, E., … Lander, E. S. (2007). Genome-wide detection and characterization of positive selection in human populations. Nature, 449(7164), 913-918. doi:10.1038/nature06250 Daetwyler, H. D., Villanueva, B., Bijma, P., & Woolliams, J. A. (2007). Inbreeding in genome-wide selection. Journal of Animal Breeding and Genetics, 124(6), 369-376. doi:10.1111/j.1439-0388.2007.00693.x Martin-Magniette, M.-L., Aubert, J., Bar-Hen, A., Elftieh, S., Magniette, F., Renou, J.-P., & Daudin, J.-J. (2008). Normalization for triple-target microarray experiments. BMC Bioinformatics, 9(1). doi:10.1186/1471-2105-9-216 Riedelsheimer, C., Czedik-Eysenberg, A., Grieder, C., Lisec, J., Technow, F., Sulpice, R., … Melchinger, A. E. (2012). Genomic and metabolic prediction of complex heterotic traits in hybrid maize. Nature Genetics, 44(2), 217-220. doi:10.1038/ng.1033 Shah, R., & Ward, P. T. (2002). Lean manufacturing: context, practice bundles, and performance. Journal of Operations Management, 21(2), 129-149. doi:10.1016/s0272-6963(02)00108-0 Rosati, C., Diretto, G., & Giuliano, G. (2009). Biosynthesis and Engineering of Carotenoids and Apocarotenoids in Plants: State of the Art and Future Prospects. Biotechnology and Genetic Engineering Reviews, 26(1), 139-162. doi:10.5661/bger-26-139 E., F., Y., L., L., C.-G., A., G., M., S., T., P., … D., Z. (2002). Two tightly linked QTLs modify tomato sugar content via different physiological pathways. Molecular Genetics and Genomics, 266(5), 821-826. doi:10.1007/s00438-001-0599-4 Cong, B., Barrero, L. S., & Tanksley, S. D. (2008). Regulatory change in YABBY-like transcription factor led to evolution of extreme fruit size during tomato domestication. Nature Genetics, 40(6), 800-804. doi:10.1038/ng.144 Wang, H., Schauer, N., Usadel, B., Frasse, P., Zouine, M., Hernould, M., … Bouzayen, M. (2009). Regulatory Features Underlying Pollination-Dependent and -Independent Tomato Fruit Set Revealed by Transcript and Primary Metabolite Profiling. The Plant Cell, 21(5), 1428-1452. doi:10.1105/tpc.108.060830 Klee, H. J. (2010). Improving the flavor of fresh fruits: genomics, biochemistry, and biotechnology. New Phytologist, 187(1), 44-56. doi:10.1111/j.1469-8137.2010.03281.x Minoia, S., Petrozza, A., D’Onofrio, O., Piron, F., Mosca, G., Sozio, G., … Carriero, F. (2010). A new mutant genetic resource for tomato crop improvement by TILLING technology. BMC Research Notes, 3(1). doi:10.1186/1756-0500-3-69 Bogdanove, A. J., & Voytas, D. F. (2011). TAL Effectors: Customizable Proteins for DNA Targeting. Science, 333(6051), 1843-1846. doi:10.1126/science.1204094 Hetherington, S. E., Smillie, R. M., & Davies, W. J. (1998). Photosynthetic activities of vegetative and fruiting tissues of tomato. Journal of Experimental Botany, 49(324), 1173-1181. doi:10.1093/jxb/49.324.1173 Fridman, E. (2004). Zooming In on a Quantitative Trait for Tomato Yield Using Interspecific Introgressions. Science, 305(5691), 1786-1789. doi:10.1126/science.1101666 Agius, F., González-Lamothe, R., Caballero, J. L., Muñoz-Blanco, J., Botella, M. A., & Valpuesta, V. (2003). Engineering increased vitamin C levels in plants by overexpression of a D-galacturonic acid reductase. Nature Biotechnology, 21(2), 177-181. doi:10.1038/nbt777 Cahoon, E. B., Hall, S. E., Ripp, K. G., Ganzke, T. S., Hitz, W. D., & Coughlan, S. J. (2003). Metabolic redesign of vitamin E biosynthesis in plants for tocotrienol production and increased antioxidant content. Nature Biotechnology, 21(9), 1082-1087. doi:10.1038/nbt853 Ye, X. (2000). Engineering the Provitamin A (-Carotene) Biosynthetic Pathway into (Carotenoid-Free) Rice Endosperm. Science, 287(5451), 303-305. doi:10.1126/science.287.5451.303 Aharoni, A., & Galili, G. (2011). Metabolic engineering of the plant primary–secondary metabolism interface. Current Opinion in Biotechnology, 22(2), 239-244. doi:10.1016/j.copbio.2010.11.004 Alba, J. M., Montserrat, M., & Fernández-Muñoz, R. (2008). Resistance to the two-spotted spider mite (Tetranychus urticae) by acylsucroses of wild tomato (Solanum pimpinellifolium) trichomes studied in a recombinant inbred line population. Experimental and Applied Acarology, 47(1), 35-47. doi:10.1007/s10493-008-9192-4 Zanor, M. I., Rambla, J.-L., Chaïb, J., Steppa, A., Medina, A., Granell, A., … Causse, M. (2009). Metabolic characterization of loci affecting sensory attributes in tomato allows an assessment of the influence of the levels of primary metabolites and volatile organic contents. Journal of Experimental Botany, 60(7), 2139-2154. doi:10.1093/jxb/erp086 Lytovchenko, A., Eickmeier, I., Pons, C., Osorio, S., Szecowka, M., Lehmberg, K., … Fernie, A. R. (2011). Tomato Fruit Photosynthesis Is Seemingly Unimportant in Primary Metabolism and Ripening But Plays a Considerable Role in Seed Development. Plant Physiology, 157(4), 1650-1663. doi:10.1104/pp.111.186874

Fine-tuning tomato agronomic properties by computational genome redesign

Abstract