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Automated design of bacterial genome sequences.

Carrera J, Jaramillo A - BMC Syst Biol (2013)

Bottom Line: We found that it is theoretically possible to reorganize E. coli genome into 86% fewer regulated operons.Such refactored genomes are constituted by operons that contain sets of genes sharing around the 60% of their biological functions and, if evolved under highly variable environmental conditions, have regulatory networks, which turn out to respond more than 20% faster to multiple external perturbations.This work provides the first algorithm for producing a genome sequence encoding a rewired transcriptional regulation with wild-type behavior under alternative environments.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Life Sciences, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK. Alfonso.Jaramillo@warwick.ac.uk.

ABSTRACT

Background: Organisms have evolved ways of regulating transcription to better adapt to varying environments. Could the current functional genomics data and models support the possibility of engineering a genome with completely rearranged gene organization while the cell maintains its behavior under environmental challenges? How would we proceed to design a full nucleotide sequence for such genomes?

Results: As a first step towards answering such questions, recent work showed that it is possible to design alternative transcriptomic models showing the same behavior under environmental variations than the wild-type model. A second step would require providing evidence that it is possible to provide a nucleotide sequence for a genome encoding such transcriptional model. We used computational design techniques to design a rewired global transcriptional regulation of Escherichia coli, yet showing a similar transcriptomic response than the wild-type. Afterwards, we "compiled" the transcriptional networks into nucleotide sequences to obtain the final genome sequence. Our computational evolution procedure ensures that we can maintain the genotype-phenotype mapping during the rewiring of the regulatory network. We found that it is theoretically possible to reorganize E. coli genome into 86% fewer regulated operons. Such refactored genomes are constituted by operons that contain sets of genes sharing around the 60% of their biological functions and, if evolved under highly variable environmental conditions, have regulatory networks, which turn out to respond more than 20% faster to multiple external perturbations.

Conclusions: This work provides the first algorithm for producing a genome sequence encoding a rewired transcriptional regulation with wild-type behavior under alternative environments.

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Related in: MedlinePlus

Four types of transcriptional modifications during the optimization process that affect the gene expression of the ith gene. (A) a gene moves to other operon, (B and C) addition or deletion of tandem promoters, and (D) replace a tandem promoter (see Methods section named “Automatic genome design: rules for mutation and selection”). All genetic perturbations are represented by the regulatory scheme with their corresponding ODE before (left) and after (right) the genome modification. Color boxes represent mathematical terms added or removed from the ODEs to simulate gene expression of the ith gene after the genetic modification.
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Figure 2: Four types of transcriptional modifications during the optimization process that affect the gene expression of the ith gene. (A) a gene moves to other operon, (B and C) addition or deletion of tandem promoters, and (D) replace a tandem promoter (see Methods section named “Automatic genome design: rules for mutation and selection”). All genetic perturbations are represented by the regulatory scheme with their corresponding ODE before (left) and after (right) the genome modification. Color boxes represent mathematical terms added or removed from the ODEs to simulate gene expression of the ith gene after the genetic modification.

Mentions: We used a recent genome-wide model of E. coli gene transcription in response to selected external signals to predict changes in cell growth after genome modification [17]. Such model was inferred from experimental data and the, InferGene inference methodology [15], which is used to obtain kinetic parameters from experimental steady-state data. The model contains 4,298 non-redundant genes, 330 of which are putative TFs. As detailed in the Methods, this model is described by ordinary differential equations for the transcription level of each gene and its transcription regulation. This model allows the assignment of mathematical parameters to promoters and TF sequences, which we have assumed to be independent of genomic context (Figure 1A). In our previous work [17], we showed that we could predict experimental growth rates by assigning transcriptional parameters to genome regulatory sequences. Such assignment allows us to predict the TRN model after reshuffling genetic elements (Figure 2; see Methods).


Automated design of bacterial genome sequences.

Carrera J, Jaramillo A - BMC Syst Biol (2013)

Four types of transcriptional modifications during the optimization process that affect the gene expression of the ith gene. (A) a gene moves to other operon, (B and C) addition or deletion of tandem promoters, and (D) replace a tandem promoter (see Methods section named “Automatic genome design: rules for mutation and selection”). All genetic perturbations are represented by the regulatory scheme with their corresponding ODE before (left) and after (right) the genome modification. Color boxes represent mathematical terms added or removed from the ODEs to simulate gene expression of the ith gene after the genetic modification.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4016488&req=5

Figure 2: Four types of transcriptional modifications during the optimization process that affect the gene expression of the ith gene. (A) a gene moves to other operon, (B and C) addition or deletion of tandem promoters, and (D) replace a tandem promoter (see Methods section named “Automatic genome design: rules for mutation and selection”). All genetic perturbations are represented by the regulatory scheme with their corresponding ODE before (left) and after (right) the genome modification. Color boxes represent mathematical terms added or removed from the ODEs to simulate gene expression of the ith gene after the genetic modification.
Mentions: We used a recent genome-wide model of E. coli gene transcription in response to selected external signals to predict changes in cell growth after genome modification [17]. Such model was inferred from experimental data and the, InferGene inference methodology [15], which is used to obtain kinetic parameters from experimental steady-state data. The model contains 4,298 non-redundant genes, 330 of which are putative TFs. As detailed in the Methods, this model is described by ordinary differential equations for the transcription level of each gene and its transcription regulation. This model allows the assignment of mathematical parameters to promoters and TF sequences, which we have assumed to be independent of genomic context (Figure 1A). In our previous work [17], we showed that we could predict experimental growth rates by assigning transcriptional parameters to genome regulatory sequences. Such assignment allows us to predict the TRN model after reshuffling genetic elements (Figure 2; see Methods).

Bottom Line: We found that it is theoretically possible to reorganize E. coli genome into 86% fewer regulated operons.Such refactored genomes are constituted by operons that contain sets of genes sharing around the 60% of their biological functions and, if evolved under highly variable environmental conditions, have regulatory networks, which turn out to respond more than 20% faster to multiple external perturbations.This work provides the first algorithm for producing a genome sequence encoding a rewired transcriptional regulation with wild-type behavior under alternative environments.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Life Sciences, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK. Alfonso.Jaramillo@warwick.ac.uk.

ABSTRACT

Background: Organisms have evolved ways of regulating transcription to better adapt to varying environments. Could the current functional genomics data and models support the possibility of engineering a genome with completely rearranged gene organization while the cell maintains its behavior under environmental challenges? How would we proceed to design a full nucleotide sequence for such genomes?

Results: As a first step towards answering such questions, recent work showed that it is possible to design alternative transcriptomic models showing the same behavior under environmental variations than the wild-type model. A second step would require providing evidence that it is possible to provide a nucleotide sequence for a genome encoding such transcriptional model. We used computational design techniques to design a rewired global transcriptional regulation of Escherichia coli, yet showing a similar transcriptomic response than the wild-type. Afterwards, we "compiled" the transcriptional networks into nucleotide sequences to obtain the final genome sequence. Our computational evolution procedure ensures that we can maintain the genotype-phenotype mapping during the rewiring of the regulatory network. We found that it is theoretically possible to reorganize E. coli genome into 86% fewer regulated operons. Such refactored genomes are constituted by operons that contain sets of genes sharing around the 60% of their biological functions and, if evolved under highly variable environmental conditions, have regulatory networks, which turn out to respond more than 20% faster to multiple external perturbations.

Conclusions: This work provides the first algorithm for producing a genome sequence encoding a rewired transcriptional regulation with wild-type behavior under alternative environments.

Show MeSH
Related in: MedlinePlus