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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.

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Experimental validation of the landscape of tomato agronomic properties by using genetic perturbations.Heuristic exploration (A) and statistical significance (B) of the landscape of multiple desired agronomic properties of tomato fruit perturbing its effective TRN adding multiple genetic changes and, predictive power (C–F) for optimizing the levels of volatile compounds and identifying compounds in closed metabolic pathways. (A) Median efficiencies reached by transcriptional perturbation based in gene knockouts or over-expression to improve agronomic properties. (B) Average number of single gene perturbations that overcome an efficiency threshold in the top 5 RILs scored by single perturbation (light bars; error bars represent standard deviation for the selected RILs) and average probability of selecting the same multiple-perturbation commonly in a set of RILs (dark bars; error bars show standard deviation for all genes of the TRN). Precision, recall and F-score (green, red and blue lines, respectively) compare observed experimentally volatile compound correlations vs inferred set of potential genetic perturbations (gene knockout (C, D) or over-expression (E, F)) shared to optimize each compound independently. Note that experimental metabolite correlations r<0.5 were not considered in (D, F).
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pcbi-1002528-g004: Experimental validation of the landscape of tomato agronomic properties by using genetic perturbations.Heuristic exploration (A) and statistical significance (B) of the landscape of multiple desired agronomic properties of tomato fruit perturbing its effective TRN adding multiple genetic changes and, predictive power (C–F) for optimizing the levels of volatile compounds and identifying compounds in closed metabolic pathways. (A) Median efficiencies reached by transcriptional perturbation based in gene knockouts or over-expression to improve agronomic properties. (B) Average number of single gene perturbations that overcome an efficiency threshold in the top 5 RILs scored by single perturbation (light bars; error bars represent standard deviation for the selected RILs) and average probability of selecting the same multiple-perturbation commonly in a set of RILs (dark bars; error bars show standard deviation for all genes of the TRN). Precision, recall and F-score (green, red and blue lines, respectively) compare observed experimentally volatile compound correlations vs inferred set of potential genetic perturbations (gene knockout (C, D) or over-expression (E, F)) shared to optimize each compound independently. Note that experimental metabolite correlations r<0.5 were not considered in (D, F).

Mentions: The next step in our study was to propose new genome re-designs including multiple perturbations. To do this, we sampled widely the landscape of the acceptability, quality and quality vs production of tomato fruits by introducing two-gene perturbations either by knockouts and over-expressions (Dataset S3). Figure 4A shows the median efficiencies reached by two-gene transcriptional perturbations based on knockouts and over-expression in order to improve the agronomic properties defined as multiple-objective. As expected, we corroborated that multiple perturbations, located in different pathways (Table 2), could improve the agronomic properties significantly better than single perturbations. Table 2 lists the best gene-pairs to be used in perturbations that maximize such agronomic properties of the fruit. Figure 4B shows the average number of single gene perturbations that are able to overcome a given efficiency threshold for the top 5 RILs when ranked for single perturbations as well as the average probability of selecting the same multiple-perturbation commonly in a set of RILs.


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)

Experimental validation of the landscape of tomato agronomic properties by using genetic perturbations.Heuristic exploration (A) and statistical significance (B) of the landscape of multiple desired agronomic properties of tomato fruit perturbing its effective TRN adding multiple genetic changes and, predictive power (C–F) for optimizing the levels of volatile compounds and identifying compounds in closed metabolic pathways. (A) Median efficiencies reached by transcriptional perturbation based in gene knockouts or over-expression to improve agronomic properties. (B) Average number of single gene perturbations that overcome an efficiency threshold in the top 5 RILs scored by single perturbation (light bars; error bars represent standard deviation for the selected RILs) and average probability of selecting the same multiple-perturbation commonly in a set of RILs (dark bars; error bars show standard deviation for all genes of the TRN). Precision, recall and F-score (green, red and blue lines, respectively) compare observed experimentally volatile compound correlations vs inferred set of potential genetic perturbations (gene knockout (C, D) or over-expression (E, F)) shared to optimize each compound independently. Note that experimental metabolite correlations r<0.5 were not considered in (D, F).
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1002528-g004: Experimental validation of the landscape of tomato agronomic properties by using genetic perturbations.Heuristic exploration (A) and statistical significance (B) of the landscape of multiple desired agronomic properties of tomato fruit perturbing its effective TRN adding multiple genetic changes and, predictive power (C–F) for optimizing the levels of volatile compounds and identifying compounds in closed metabolic pathways. (A) Median efficiencies reached by transcriptional perturbation based in gene knockouts or over-expression to improve agronomic properties. (B) Average number of single gene perturbations that overcome an efficiency threshold in the top 5 RILs scored by single perturbation (light bars; error bars represent standard deviation for the selected RILs) and average probability of selecting the same multiple-perturbation commonly in a set of RILs (dark bars; error bars show standard deviation for all genes of the TRN). Precision, recall and F-score (green, red and blue lines, respectively) compare observed experimentally volatile compound correlations vs inferred set of potential genetic perturbations (gene knockout (C, D) or over-expression (E, F)) shared to optimize each compound independently. Note that experimental metabolite correlations r<0.5 were not considered in (D, F).
Mentions: The next step in our study was to propose new genome re-designs including multiple perturbations. To do this, we sampled widely the landscape of the acceptability, quality and quality vs production of tomato fruits by introducing two-gene perturbations either by knockouts and over-expressions (Dataset S3). Figure 4A shows the median efficiencies reached by two-gene transcriptional perturbations based on knockouts and over-expression in order to improve the agronomic properties defined as multiple-objective. As expected, we corroborated that multiple perturbations, located in different pathways (Table 2), could improve the agronomic properties significantly better than single perturbations. Table 2 lists the best gene-pairs to be used in perturbations that maximize such agronomic properties of the fruit. Figure 4B shows the average number of single gene perturbations that are able to overcome a given efficiency threshold for the top 5 RILs when ranked for single perturbations as well as the average probability of selecting the same multiple-perturbation commonly in a set of RILs.

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