Advancing the scale of synthetic biology via cross-species transfer of cellular functions enabled by iModulon engraftment.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
15 Mar 2024
15 Mar 2024
Historique:
received:
30
12
2023
accepted:
29
02
2024
medline:
18
3
2024
pubmed:
16
3
2024
entrez:
16
3
2024
Statut:
epublish
Résumé
Machine learning applied to large compendia of transcriptomic data has enabled the decomposition of bacterial transcriptomes to identify independently modulated sets of genes, such iModulons represent specific cellular functions. The identification of iModulons enables accurate identification of genes necessary and sufficient for cross-species transfer of cellular functions. We demonstrate cross-species transfer of: 1) the biotransformation of vanillate to protocatechuate, 2) a malonate catabolic pathway, 3) a catabolic pathway for 2,3-butanediol, and 4) an antimicrobial resistance to ampicillin found in multiple Pseudomonas species to Escherichia coli. iModulon-based engineering is a transformative strategy as it includes all genes comprising the transferred cellular function, including genes without functional annotation. Adaptive laboratory evolution was deployed to optimize the cellular function transferred, revealing mutations in the host. Combining big data analytics and laboratory evolution thus enhances the level of understanding of systems biology, and synthetic biology for strain design and development.
Identifiants
pubmed: 38490991
doi: 10.1038/s41467-024-46486-3
pii: 10.1038/s41467-024-46486-3
pmc: PMC10943186
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2356Subventions
Organisme : Novo Nordisk Fonden (Novo Nordisk Foundation)
ID : NNF20CC0035580
Informations de copyright
© 2024. The Author(s).
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