Limits...
Fine-tuning tomato agronomic properties by computational genome redesign.

Carrera J, Fernández Del Carmen A, Fernández-Muñoz R, Rambla JL, Pons C, Jaramillo A, Elena SF, Granell A - PLoS Comput. Biol. (2012)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Instituto de Biologa Molecular y Celular de Plantas, Consejo Superior de Investigaciones Cientificas-UPV, Valencia, Spain. Javier.Carrera@synth-bio.org

ABSTRACT
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.

Show MeSH

Related in: MedlinePlus

Exploration and statistical significance of the landscape of multiple agronomic properties of interest for tomato fruit applying local perturbations in its effective TRN.(A) Agronomic properties improved by perturbing a single gene as function of efficiency reached by that transcriptional perturbation with respect to the wild-type scenario; only perturbations causing positive mean efficiencies are plotted. Both agronomic properties and efficiencies of a single perturbation are tested on the 169 RILs and error bars represent their minimum and maximum values in both axis. (B) Relationship between agronomic properties in the wild-type genome and the average of the agronomic properties resulting of all single perturbations in the wild-type TRN for each RIL; vertical error bars represent the best and worst optimized re-engineered TRN for a given RIL. (C) Average number of single gene perturbations that overcome a given efficiency threshold in the 169 RILs (light bars; error bars represent standard deviation for the 169 RILs) and average probability of selecting the same gene-perturbation in a set of RILs (dark bars; error bars show standard deviation for all genes of the TRN). Left and right columns represent perturbations of single gene in case of knockout or over-expression, respectively. (A, B) show fitness as related to the acceptability of tomato fruit (blue) and production vs. quality (red); (C) and fitness values associated to maximize only fruit quality (green). Agronomic properties are plotted in arbitrary units.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3369923&req=5

pcbi-1002528-g003: Exploration and statistical significance of the landscape of multiple agronomic properties of interest for tomato fruit applying local perturbations in its effective TRN.(A) Agronomic properties improved by perturbing a single gene as function of efficiency reached by that transcriptional perturbation with respect to the wild-type scenario; only perturbations causing positive mean efficiencies are plotted. Both agronomic properties and efficiencies of a single perturbation are tested on the 169 RILs and error bars represent their minimum and maximum values in both axis. (B) Relationship between agronomic properties in the wild-type genome and the average of the agronomic properties resulting of all single perturbations in the wild-type TRN for each RIL; vertical error bars represent the best and worst optimized re-engineered TRN for a given RIL. (C) Average number of single gene perturbations that overcome a given efficiency threshold in the 169 RILs (light bars; error bars represent standard deviation for the 169 RILs) and average probability of selecting the same gene-perturbation in a set of RILs (dark bars; error bars show standard deviation for all genes of the TRN). Left and right columns represent perturbations of single gene in case of knockout or over-expression, respectively. (A, B) show fitness as related to the acceptability of tomato fruit (blue) and production vs. quality (red); (C) and fitness values associated to maximize only fruit quality (green). Agronomic properties are plotted in arbitrary units.

Mentions: Hence, mimicking the optimization patterns typical from LM, the landscape of desired agronomic properties of tomato fruit was exhaustively explored perturbing its effective transcriptional regulatory network (TRN) with single-gene alterations. Figure 3A shows the improvement of two of the agronomic properties mentioned above (fruit acceptability and quality vs production) as result of single gene perturbations according to our model. The success of the approach is shown by the efficiency function obtained for each transcriptional perturbation computed and which is defined by the normalized ratio between the agronomic property obtained for the re-engineered TRN and that for the wild-type TRN. Both agronomic properties and efficiencies in the case of single-perturbations were computed for each of the 169 RILs, resulting in a high variability between the lineages for all knockouts and over-expressed gene re-engineered TRN cases. We corroborated that there is a highly significant linear correlation (, for fruit acceptability and quality vs production) between the average value of the improved agronomic properties and the efficiencies reached across the set of RILs for all transcriptional perturbations. Both gene knockout and over-expression models resulted in similar linear regression slopes when considering acceptability and quality vs production together (0.05 and 0.24, respectively, Figure 3A). In addition, we also explored the possibility of tuning a given agronomic property towards a defined value, as it is desired for some biotechnological applications (see Text S1); achieving also in this case high efficiency values (Figure S4 and Tables S1 and S2).


Fine-tuning tomato agronomic properties by computational genome redesign.

Carrera J, Fernández Del Carmen A, Fernández-Muñoz R, Rambla JL, Pons C, Jaramillo A, Elena SF, Granell A - PLoS Comput. Biol. (2012)

Exploration and statistical significance of the landscape of multiple agronomic properties of interest for tomato fruit applying local perturbations in its effective TRN.(A) Agronomic properties improved by perturbing a single gene as function of efficiency reached by that transcriptional perturbation with respect to the wild-type scenario; only perturbations causing positive mean efficiencies are plotted. Both agronomic properties and efficiencies of a single perturbation are tested on the 169 RILs and error bars represent their minimum and maximum values in both axis. (B) Relationship between agronomic properties in the wild-type genome and the average of the agronomic properties resulting of all single perturbations in the wild-type TRN for each RIL; vertical error bars represent the best and worst optimized re-engineered TRN for a given RIL. (C) Average number of single gene perturbations that overcome a given efficiency threshold in the 169 RILs (light bars; error bars represent standard deviation for the 169 RILs) and average probability of selecting the same gene-perturbation in a set of RILs (dark bars; error bars show standard deviation for all genes of the TRN). Left and right columns represent perturbations of single gene in case of knockout or over-expression, respectively. (A, B) show fitness as related to the acceptability of tomato fruit (blue) and production vs. quality (red); (C) and fitness values associated to maximize only fruit quality (green). Agronomic properties are plotted in arbitrary units.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3369923&req=5

pcbi-1002528-g003: Exploration and statistical significance of the landscape of multiple agronomic properties of interest for tomato fruit applying local perturbations in its effective TRN.(A) Agronomic properties improved by perturbing a single gene as function of efficiency reached by that transcriptional perturbation with respect to the wild-type scenario; only perturbations causing positive mean efficiencies are plotted. Both agronomic properties and efficiencies of a single perturbation are tested on the 169 RILs and error bars represent their minimum and maximum values in both axis. (B) Relationship between agronomic properties in the wild-type genome and the average of the agronomic properties resulting of all single perturbations in the wild-type TRN for each RIL; vertical error bars represent the best and worst optimized re-engineered TRN for a given RIL. (C) Average number of single gene perturbations that overcome a given efficiency threshold in the 169 RILs (light bars; error bars represent standard deviation for the 169 RILs) and average probability of selecting the same gene-perturbation in a set of RILs (dark bars; error bars show standard deviation for all genes of the TRN). Left and right columns represent perturbations of single gene in case of knockout or over-expression, respectively. (A, B) show fitness as related to the acceptability of tomato fruit (blue) and production vs. quality (red); (C) and fitness values associated to maximize only fruit quality (green). Agronomic properties are plotted in arbitrary units.
Mentions: Hence, mimicking the optimization patterns typical from LM, the landscape of desired agronomic properties of tomato fruit was exhaustively explored perturbing its effective transcriptional regulatory network (TRN) with single-gene alterations. Figure 3A shows the improvement of two of the agronomic properties mentioned above (fruit acceptability and quality vs production) as result of single gene perturbations according to our model. The success of the approach is shown by the efficiency function obtained for each transcriptional perturbation computed and which is defined by the normalized ratio between the agronomic property obtained for the re-engineered TRN and that for the wild-type TRN. Both agronomic properties and efficiencies in the case of single-perturbations were computed for each of the 169 RILs, resulting in a high variability between the lineages for all knockouts and over-expressed gene re-engineered TRN cases. We corroborated that there is a highly significant linear correlation (, for fruit acceptability and quality vs production) between the average value of the improved agronomic properties and the efficiencies reached across the set of RILs for all transcriptional perturbations. Both gene knockout and over-expression models resulted in similar linear regression slopes when considering acceptability and quality vs production together (0.05 and 0.24, respectively, Figure 3A). In addition, we also explored the possibility of tuning a given agronomic property towards a defined value, as it is desired for some biotechnological applications (see Text S1); achieving also in this case high efficiency values (Figure S4 and Tables S1 and S2).

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Instituto de Biologa Molecular y Celular de Plantas, Consejo Superior de Investigaciones Cientificas-UPV, Valencia, Spain. Javier.Carrera@synth-bio.org

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus