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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

Example of a refactored genome illustrating the gene rearrangement of 5 operons of the E. coli wild-type genome. Note that blue arrows represent the rest of the wild-type (A) and refactored (B) genome.
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Figure 4: Example of a refactored genome illustrating the gene rearrangement of 5 operons of the E. coli wild-type genome. Note that blue arrows represent the rest of the wild-type (A) and refactored (B) genome.

Mentions: Instead of trying to solve the challenging problem of evolving a genome for better growth, which would require a greater degree of accuracy for our fitness function, we attempted to reorganize the genome of E. coli while maintaining its functionality wild-type. We rearranged the TRN in terms of regulatory complexity and modularity. We applied our automatic design methodology [17] to perform the genome refactoring in a way that we could later provide a corresponding nucleotide sequence. This entails the rearrangement of the operon structure while maintaining the organism’s original behavior. We modify the GTRN by modifying the placement of genes and altering the promoter regulation, but we do it without loosing the connection with a nucleotide sequence. For instance, the operator sites of many promoter sequences are known, and their mutation to neutral sequences would presumably eliminate the regulation of the promoter. This genetic modification could be implemented in such a way that, once the optimal GTRN is found, we could design the suitable nucleotide sequence of such promoters (Figure 1B). The algorithm proceeds in successive rounds of genetic modifications and selection of a collection of independent GTRNs (typically 10 “cells” or simulations), a general strategy in evolutionary computation. Genetic modifications consist of the rearrangement of the certain genome regions of the nucleotide sequence of the wild-type genome, and selection is based on two criteria that determine the cellular growth rate and the modularity of the GTRN. Alternate phases of genetic modifications and selection are performed to evolve the GTRNs and obtain networks achieving the specified function. The mutation operator is conceived such that we could later produce the corresponding nucleotide sequence. According to the genome regions to be rearranged, some rules are described in Figure 3 to determine the rearranged nucleotide sequences (Figure 4).


Automated design of bacterial genome sequences.

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

Example of a refactored genome illustrating the gene rearrangement of 5 operons of the E. coli wild-type genome. Note that blue arrows represent the rest of the wild-type (A) and refactored (B) genome.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Example of a refactored genome illustrating the gene rearrangement of 5 operons of the E. coli wild-type genome. Note that blue arrows represent the rest of the wild-type (A) and refactored (B) genome.
Mentions: Instead of trying to solve the challenging problem of evolving a genome for better growth, which would require a greater degree of accuracy for our fitness function, we attempted to reorganize the genome of E. coli while maintaining its functionality wild-type. We rearranged the TRN in terms of regulatory complexity and modularity. We applied our automatic design methodology [17] to perform the genome refactoring in a way that we could later provide a corresponding nucleotide sequence. This entails the rearrangement of the operon structure while maintaining the organism’s original behavior. We modify the GTRN by modifying the placement of genes and altering the promoter regulation, but we do it without loosing the connection with a nucleotide sequence. For instance, the operator sites of many promoter sequences are known, and their mutation to neutral sequences would presumably eliminate the regulation of the promoter. This genetic modification could be implemented in such a way that, once the optimal GTRN is found, we could design the suitable nucleotide sequence of such promoters (Figure 1B). The algorithm proceeds in successive rounds of genetic modifications and selection of a collection of independent GTRNs (typically 10 “cells” or simulations), a general strategy in evolutionary computation. Genetic modifications consist of the rearrangement of the certain genome regions of the nucleotide sequence of the wild-type genome, and selection is based on two criteria that determine the cellular growth rate and the modularity of the GTRN. Alternate phases of genetic modifications and selection are performed to evolve the GTRNs and obtain networks achieving the specified function. The mutation operator is conceived such that we could later produce the corresponding nucleotide sequence. According to the genome regions to be rearranged, some rules are described in Figure 3 to determine the rearranged nucleotide sequences (Figure 4).

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