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A systematic computational analysis of biosynthetic gene cluster evolution: lessons for engineering biosynthesis.

Medema MH, Cimermancic P, Sali A, Takano E, Fischbach MA - PLoS Comput. Biol. (2014)

Bottom Line: Attempts to engineer their biosynthetic gene clusters (BGCs) to produce unnatural metabolites with improved properties are often frustrated by the unpredictability and complexity of the enzymes that synthesize these molecules, suggesting that genetic changes within BGCs are limited by specific constraints.Here, by performing a systematic computational analysis of BGC evolution, we derive evidence for three findings that shed light on the ways in which, despite these constraints, nature successfully invents new molecules: 1) BGCs for complex molecules often evolve through the successive merger of smaller sub-clusters, which function as independent evolutionary entities. 2) An important subset of polyketide synthases and nonribosomal peptide synthetases evolve by concerted evolution, which generates sets of sequence-homogenized domains that may hold promise for engineering efforts since they exhibit a high degree of functional interoperability, 3) Individual BGC families evolve in distinct ways, suggesting that design strategies should take into account family-specific functional constraints.These findings suggest novel strategies for using synthetic biology to rationally engineer biosynthetic pathways.

View Article: PubMed Central - PubMed

Affiliation: Department of Microbial Physiology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands; Groningen Bioinformatics Centre, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands.

ABSTRACT
Bacterial secondary metabolites are widely used as antibiotics, anticancer drugs, insecticides and food additives. Attempts to engineer their biosynthetic gene clusters (BGCs) to produce unnatural metabolites with improved properties are often frustrated by the unpredictability and complexity of the enzymes that synthesize these molecules, suggesting that genetic changes within BGCs are limited by specific constraints. Here, by performing a systematic computational analysis of BGC evolution, we derive evidence for three findings that shed light on the ways in which, despite these constraints, nature successfully invents new molecules: 1) BGCs for complex molecules often evolve through the successive merger of smaller sub-clusters, which function as independent evolutionary entities. 2) An important subset of polyketide synthases and nonribosomal peptide synthetases evolve by concerted evolution, which generates sets of sequence-homogenized domains that may hold promise for engineering efforts since they exhibit a high degree of functional interoperability, 3) Individual BGC families evolve in distinct ways, suggesting that design strategies should take into account family-specific functional constraints. These findings suggest novel strategies for using synthetic biology to rationally engineer biosynthetic pathways.

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Complex BGC architectures evolve through new combinations of sub-clusters that are shared between multiple gene cluster types.a, Network of sub-clusters shared among 34 known BGCs. Nodes represent BGCs, and node size indicates the number of sub-clusters present in the gene cluster that are shared with other BGCs within the network. Edges represent shared sub-clusters, coded by color. The pattern of sharing indicates that many sub-clusters are regularly transferred between BGCs of different types. In the interpretation of this analysis, it should be kept in mind that in rare cases different biosynthetic routes (and hence, different sub-clusters) exist towards the same moiety. b, A sub-network from a showing the shared sub-clusters among the BGCs for rubradirin, rifamycin, simocyclinone, everninomicin, and polyketomycin, as well as the chemical moieties encoded by the sub-clusters.
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pcbi-1004016-g002: Complex BGC architectures evolve through new combinations of sub-clusters that are shared between multiple gene cluster types.a, Network of sub-clusters shared among 34 known BGCs. Nodes represent BGCs, and node size indicates the number of sub-clusters present in the gene cluster that are shared with other BGCs within the network. Edges represent shared sub-clusters, coded by color. The pattern of sharing indicates that many sub-clusters are regularly transferred between BGCs of different types. In the interpretation of this analysis, it should be kept in mind that in rare cases different biosynthetic routes (and hence, different sub-clusters) exist towards the same moiety. b, A sub-network from a showing the shared sub-clusters among the BGCs for rubradirin, rifamycin, simocyclinone, everninomicin, and polyketomycin, as well as the chemical moieties encoded by the sub-clusters.

Mentions: Earlier evidence has suggested complex mosaic patterns of sub-cluster sharing for some BGCs, such as those involved in the production of glycopeptides [18]. To further explore the role of sub-cluster sharing in the evolution of BGCs, we manually compiled a set of 35 BGCs that are rich in sub-clusters that have a known connection with a specific chemical moiety. We then used this data set to construct a network in which the nodes represent BGCs and the edges denote a sub-cluster that a pair of BGCs has in common (Fig. 2). Three observations were particularly notable (Fig. 2). First, >60% of the coding capacity of some BGCs (e.g., those encoding vancomycin and rubradirin [19]) is composed of individually conserved sub-clusters (note that this is not entirely reflected in the depiction of the rubradirin gene cluster in Fig. 2b, where only those sub-clusters are highlighted that are shared with other depicted BGCs). This supports a “bricks and mortar” model of gene cluster evolution in which gene clusters are composed of large, modular “bricks” (sub-clusters) that encode key building blocks and individual genes (the “mortar”) that encode functions such as tailoring, regulation and transport. During evolution, both bricks and mortar (scaffold and tailoring) may remain the same, only the tailoring may change or the scaffold itself may change. Second, the same sub-cluster commonly appears in otherwise unrelated BGCs, and multiple unrelated sub-clusters can be found in a single parent gene cluster, indicating that sub-clusters are independent evolutionary entities. Third, sub-clusters are not static; they are loosely organized around a core set of genes, but gene gain/loss leads to chemical changes in the corresponding part structure: for example, gene clusters encoding molecules such as everninomicin [20], simocyclinone [21] and polyketomycin [22] have different variants of deoxysugar sub-clusters, which lead to subtle variations in the final chemical structures.


A systematic computational analysis of biosynthetic gene cluster evolution: lessons for engineering biosynthesis.

Medema MH, Cimermancic P, Sali A, Takano E, Fischbach MA - PLoS Comput. Biol. (2014)

Complex BGC architectures evolve through new combinations of sub-clusters that are shared between multiple gene cluster types.a, Network of sub-clusters shared among 34 known BGCs. Nodes represent BGCs, and node size indicates the number of sub-clusters present in the gene cluster that are shared with other BGCs within the network. Edges represent shared sub-clusters, coded by color. The pattern of sharing indicates that many sub-clusters are regularly transferred between BGCs of different types. In the interpretation of this analysis, it should be kept in mind that in rare cases different biosynthetic routes (and hence, different sub-clusters) exist towards the same moiety. b, A sub-network from a showing the shared sub-clusters among the BGCs for rubradirin, rifamycin, simocyclinone, everninomicin, and polyketomycin, as well as the chemical moieties encoded by the sub-clusters.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4256081&req=5

pcbi-1004016-g002: Complex BGC architectures evolve through new combinations of sub-clusters that are shared between multiple gene cluster types.a, Network of sub-clusters shared among 34 known BGCs. Nodes represent BGCs, and node size indicates the number of sub-clusters present in the gene cluster that are shared with other BGCs within the network. Edges represent shared sub-clusters, coded by color. The pattern of sharing indicates that many sub-clusters are regularly transferred between BGCs of different types. In the interpretation of this analysis, it should be kept in mind that in rare cases different biosynthetic routes (and hence, different sub-clusters) exist towards the same moiety. b, A sub-network from a showing the shared sub-clusters among the BGCs for rubradirin, rifamycin, simocyclinone, everninomicin, and polyketomycin, as well as the chemical moieties encoded by the sub-clusters.
Mentions: Earlier evidence has suggested complex mosaic patterns of sub-cluster sharing for some BGCs, such as those involved in the production of glycopeptides [18]. To further explore the role of sub-cluster sharing in the evolution of BGCs, we manually compiled a set of 35 BGCs that are rich in sub-clusters that have a known connection with a specific chemical moiety. We then used this data set to construct a network in which the nodes represent BGCs and the edges denote a sub-cluster that a pair of BGCs has in common (Fig. 2). Three observations were particularly notable (Fig. 2). First, >60% of the coding capacity of some BGCs (e.g., those encoding vancomycin and rubradirin [19]) is composed of individually conserved sub-clusters (note that this is not entirely reflected in the depiction of the rubradirin gene cluster in Fig. 2b, where only those sub-clusters are highlighted that are shared with other depicted BGCs). This supports a “bricks and mortar” model of gene cluster evolution in which gene clusters are composed of large, modular “bricks” (sub-clusters) that encode key building blocks and individual genes (the “mortar”) that encode functions such as tailoring, regulation and transport. During evolution, both bricks and mortar (scaffold and tailoring) may remain the same, only the tailoring may change or the scaffold itself may change. Second, the same sub-cluster commonly appears in otherwise unrelated BGCs, and multiple unrelated sub-clusters can be found in a single parent gene cluster, indicating that sub-clusters are independent evolutionary entities. Third, sub-clusters are not static; they are loosely organized around a core set of genes, but gene gain/loss leads to chemical changes in the corresponding part structure: for example, gene clusters encoding molecules such as everninomicin [20], simocyclinone [21] and polyketomycin [22] have different variants of deoxysugar sub-clusters, which lead to subtle variations in the final chemical structures.

Bottom Line: Attempts to engineer their biosynthetic gene clusters (BGCs) to produce unnatural metabolites with improved properties are often frustrated by the unpredictability and complexity of the enzymes that synthesize these molecules, suggesting that genetic changes within BGCs are limited by specific constraints.Here, by performing a systematic computational analysis of BGC evolution, we derive evidence for three findings that shed light on the ways in which, despite these constraints, nature successfully invents new molecules: 1) BGCs for complex molecules often evolve through the successive merger of smaller sub-clusters, which function as independent evolutionary entities. 2) An important subset of polyketide synthases and nonribosomal peptide synthetases evolve by concerted evolution, which generates sets of sequence-homogenized domains that may hold promise for engineering efforts since they exhibit a high degree of functional interoperability, 3) Individual BGC families evolve in distinct ways, suggesting that design strategies should take into account family-specific functional constraints.These findings suggest novel strategies for using synthetic biology to rationally engineer biosynthetic pathways.

View Article: PubMed Central - PubMed

Affiliation: Department of Microbial Physiology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands; Groningen Bioinformatics Centre, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands.

ABSTRACT
Bacterial secondary metabolites are widely used as antibiotics, anticancer drugs, insecticides and food additives. Attempts to engineer their biosynthetic gene clusters (BGCs) to produce unnatural metabolites with improved properties are often frustrated by the unpredictability and complexity of the enzymes that synthesize these molecules, suggesting that genetic changes within BGCs are limited by specific constraints. Here, by performing a systematic computational analysis of BGC evolution, we derive evidence for three findings that shed light on the ways in which, despite these constraints, nature successfully invents new molecules: 1) BGCs for complex molecules often evolve through the successive merger of smaller sub-clusters, which function as independent evolutionary entities. 2) An important subset of polyketide synthases and nonribosomal peptide synthetases evolve by concerted evolution, which generates sets of sequence-homogenized domains that may hold promise for engineering efforts since they exhibit a high degree of functional interoperability, 3) Individual BGC families evolve in distinct ways, suggesting that design strategies should take into account family-specific functional constraints. These findings suggest novel strategies for using synthetic biology to rationally engineer biosynthetic pathways.

Show MeSH