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Fine-tuning tomato agronomic properties by computational genome redesign.

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.

SUBMITTER: Carrera J

PROVIDER: S-EPMC3369923 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Fine-tuning tomato agronomic properties by computational genome redesign.

Carrera Javier J Fernández Del Carmen Asun A Fernández-Muñoz Rafael R Rambla Jose Luis JL Pons Clara C Jaramillo Alfonso A Elena Santiago F SF Granell Antonio A

PLoS computational biology 20120607 6

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 gen ...[more]

PMID: 22685389

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Project description:Rationale. The microvasculature of the central nervous system includes the blood-brain barrier (BBB), which regulates the permeability to nutrients and restricts the passage of toxic agents and inflammatory cells. Canonical Wnt/b-catenin signaling is responsible for the early phases of brain vascularization and blood-brain barrier differentiation. However, this signal declines after birth and other signaling pathways able to maintain barrier integrity at postnatal stage are still unknown. Objective. Sox17 constitutes a major downstream target of Wnt/b-catenin in endothelial cells and regulates arterial differentiation. In the present paper, we asked whether Sox17 may act downstream of Wnt/b-catenin in inducing BBB differentiation and maintenance. Methods and Results. Using reporter mice and nuclear staining of Sox17 and b-catenin, we report that while b-catenin signaling declines after birth, Sox17 activation increases and remains high in the adult. Endothelial-specific inactivation of Sox17 leads to increase of permeability of the brain microcirculation. The severity of this effect depends on the degree of BBB maturation: it is strong in the embryo, and progressively declines after birth. In search of Sox17 mechanism of action, RNA-Seq analysis of gene expression of brain endothelial cells has identified members of the Wnt/b-catenin signaling pathway as downstream targets of Sox17. Consistently, we found that Sox17 is a positive inducer of Wnt/b-catenin signaling and it acts in concert with this pathway to induce and maintain BBB properties. In vivo, inhibition of the b-catenin destruction complex or expression of a degradation-resistant b-catenin mutant, prevent the increase in permeability and retina vascular malformations observed in the absence of Sox17. Conclusions. Our data highlight a novel role for Sox17 in the induction and maintenance of the BBB and they underline the strict reciprocal tuning of this transcription factor and Wnt/b-catenin pathway. Modulation of Sox17 activity may be relevant to control BBB permeability in pathological conditions.

Project description:Potato, tomato, pepper, and eggplant are worldwide important crop and vegetable species of the Solanaceae family. Molecular linkage maps of these plants have been constructed and used to map qualitative and quantitative traits of agronomic importance. This research has been undertaken with the vision to identify the molecular basis of agronomic characters on the one hand, and on the other hand, to assist the selection of improved varieties in breeding programs by providing DNA-based markers that are diagnostic for specific agronomic characters. Since 2011, whole genome sequences of tomato and potato became available in public databases. They were used to combine the results of several hundred mapping and map-based cloning studies of phenotypic characters between 1988 and 2022 in physical maps of the twelve tomato and potato chromosomes. The traits evaluated were qualitative and quantitative resistance to pathogenic oomycetes, fungi, bacteria, viruses, nematodes, and insects. Furthermore, quantitative trait loci for yield and sugar content of tomato fruits and potato tubers and maturity or earliness were physically mapped. Cloned genes for pathogen resistance, a few genes underlying quantitative trait loci for yield, sugar content, and maturity, and several hundred candidate genes for these traits were included in the physical maps. The comparison between the physical chromosome maps revealed, in addition to known intrachromosomal inversions, several additional inversions and translocations between the otherwise highly collinear tomato and potato genomes. The integration of the positional information from independent mapping studies revealed the colocalization of qualitative and quantitative loci for resistance to different types of pathogens, called resistance hotspots, suggesting a similar molecular basis. Synteny between potato and tomato with respect to genomic positions of quantitative trait loci was frequently observed, indicating eventual similarity between the underlying genes.

Project description:To create a dataset of PSMs that does not exhibit tryptic bias, we selected PSMs with a uniform distribution of amino acids at the C-terminal peptide positions from two datasets: MassIVE-KB [Wang2018] and PROSPECT [Shouman2022]. The MassIVE-KB dataset contains 30 million PSMs and consists entirely of data generated using trypsin; hence, only a small proportion of the MassIVE-KB peptides do not end in K or R, corresponding to those that appear at the C-terminus of the corresponding protein. The PROSPECT dataset is a collection of 61 million PSMs generated from synthetic peptides. To avoid overrepresentation of some peptides in this dataset, we randomly selected at most 100 PSMs per unique peptide, similar to the processing that was done during the creation of the MassIVE-KB dataset. This pre-selection step reduced the size of the PROSPECT dataset to 12.6 million PSMs. Finally, to create a non-enzymatic dataset containing 1 million peptides, we selected 50,000 PSMs for each canonical amino acid. These PSMs were selected at random from MassIVE-KB, then supplemented as necessary from PROSPECT to obtain the desired count (see Yilmaz et al. [Yilmaz2023] Supplementary Table S1). This dataset contained PSMs from 247,859 unique peptides, which were randomly split into training, validation and test sets in the ratio 80/10/10. FTP directory contains the mgf files corresponding to train, validation and test splits. [Wang2018] M. Wang, J. Wang, J. Carver, B. Pullman, S. Won Cha, N. Bandeira, "Assembling the Community-Scale Discoverable Human Proteome", Cell Systems, Volume 7, Issue 4, 2018. [Shouman2022] O. Shouman, W. Gabriel, V. Giurcoiu, V. Sternlicht, M. Wilhelm, "PROSPECT: Labeled Tandem Mass Spectrometry Dataset for Machine Learning in Proteomics", NeurIPS Datasets and Benchmarks, 2022 [Yilmaz2023] M. Yilmaz*, W. Fondrie*, W. Bittremieux, R. Nelson, V. Ananth, S. Oh, and W. Noble,"Sequence-to-sequence translation from mass spectra to peptides with a transformer model", bioRxiv, 2023

Dataset Information

Fine-tuning tomato agronomic properties by computational genome redesign.

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Fine-tuning tomato agronomic properties by computational genome redesign.

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