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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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Predictive power and statistical significance of the effective global model of tomato fruit.(A) Prediction of the agronomic properties experimentally measured over the 169 RILs. The straight line represents the exact prediction. (B) Distance between distributions of Pearson correlations for the fruit agronomic properties, metabolites and genes (green, red and blue points, respectively) over training sets and in random permutations of them with different noise levels. (C, D) Histogram of Pearson correlations between the measured and predicted metabolite and gene levels over their training sets (blue bars) and over sets with a 10- and 5-fold cross validation tests (red bars), respectively.
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pcbi-1002528-g002: Predictive power and statistical significance of the effective global model of tomato fruit.(A) Prediction of the agronomic properties experimentally measured over the 169 RILs. The straight line represents the exact prediction. (B) Distance between distributions of Pearson correlations for the fruit agronomic properties, metabolites and genes (green, red and blue points, respectively) over training sets and in random permutations of them with different noise levels. (C, D) Histogram of Pearson correlations between the measured and predicted metabolite and gene levels over their training sets (blue bars) and over sets with a 10- and 5-fold cross validation tests (red bars), respectively.

Mentions: Transcriptomic and metabolomic data from these 50 RILs were normalized by the LOWESS method [26] and used to construct a model that predicts components of the fruit quality metabolome from transcriptome data; i.e., level of a given metabolite is effectively determined by the expression of a minimal set of genes. The size of the space of possible gene-predictors was reduced in one order of magnitude by using a CLR method (Dataset S1). After that, LASSO method was used to find a minimal set of potential predictor genes for each metabolite; subsequently, multiple regressions were obtained to estimate the effective kinetic parameters of a linear model based on ODEs that integrates transcription and metabolism processes in steady state (Figure 2) [7]. Values were used as optimal threshold in order to limit the number of possible gene-metabolite interactions and minimize the distance between the predicted and measured metabolic profiles over the training set in terms of average Pearson correlations (blue bars in Figure 2C; r = 0.85, 167 d.f., ). Hence, on average, each metabolite required 18 genes for explaining its behavior, thus a total of 959 genes was required to describe our tomato fruit metabolome. This subset of genes constitutes the effective transcription network. We performed a 5-fold cross-validation test to rule out dependence of the testing set, this reducing the metabolite average prediction (red bars in Figure 2C; r = 0.42, 167 d.f., p = 0.067 with a mean false positive rate (FPR) of 14% and a 56% mean positive predictive value (PPV) of predictors (bootstrap test, and , respectively).


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)

Predictive power and statistical significance of the effective global model of tomato fruit.(A) Prediction of the agronomic properties experimentally measured over the 169 RILs. The straight line represents the exact prediction. (B) Distance between distributions of Pearson correlations for the fruit agronomic properties, metabolites and genes (green, red and blue points, respectively) over training sets and in random permutations of them with different noise levels. (C, D) Histogram of Pearson correlations between the measured and predicted metabolite and gene levels over their training sets (blue bars) and over sets with a 10- and 5-fold cross validation tests (red bars), respectively.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3369923&req=5

pcbi-1002528-g002: Predictive power and statistical significance of the effective global model of tomato fruit.(A) Prediction of the agronomic properties experimentally measured over the 169 RILs. The straight line represents the exact prediction. (B) Distance between distributions of Pearson correlations for the fruit agronomic properties, metabolites and genes (green, red and blue points, respectively) over training sets and in random permutations of them with different noise levels. (C, D) Histogram of Pearson correlations between the measured and predicted metabolite and gene levels over their training sets (blue bars) and over sets with a 10- and 5-fold cross validation tests (red bars), respectively.
Mentions: Transcriptomic and metabolomic data from these 50 RILs were normalized by the LOWESS method [26] and used to construct a model that predicts components of the fruit quality metabolome from transcriptome data; i.e., level of a given metabolite is effectively determined by the expression of a minimal set of genes. The size of the space of possible gene-predictors was reduced in one order of magnitude by using a CLR method (Dataset S1). After that, LASSO method was used to find a minimal set of potential predictor genes for each metabolite; subsequently, multiple regressions were obtained to estimate the effective kinetic parameters of a linear model based on ODEs that integrates transcription and metabolism processes in steady state (Figure 2) [7]. Values were used as optimal threshold in order to limit the number of possible gene-metabolite interactions and minimize the distance between the predicted and measured metabolic profiles over the training set in terms of average Pearson correlations (blue bars in Figure 2C; r = 0.85, 167 d.f., ). Hence, on average, each metabolite required 18 genes for explaining its behavior, thus a total of 959 genes was required to describe our tomato fruit metabolome. This subset of genes constitutes the effective transcription network. We performed a 5-fold cross-validation test to rule out dependence of the testing set, this reducing the metabolite average prediction (red bars in Figure 2C; r = 0.42, 167 d.f., p = 0.067 with a mean false positive rate (FPR) of 14% and a 56% mean positive predictive value (PPV) of predictors (bootstrap test, and , respectively).

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