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Engineered bacterial swarm patterns as spatial records of environmental inputs.
Doshi, Anjali; Shaw, Marian; Tonea, Ruxandra; Moon, Soonhee; Minyety, Rosalía; Doshi, Anish; Laine, Andrew; Guo, Jia; Danino, Tal.
Afiliação
  • Doshi A; Department of Biomedical Engineering, Columbia University, New York City, NY, USA.
  • Shaw M; Department of Biomedical Engineering, Columbia University, New York City, NY, USA.
  • Tonea R; Department of Biomedical Engineering, Columbia University, New York City, NY, USA.
  • Moon S; Department of Biomedical Engineering, Columbia University, New York City, NY, USA.
  • Minyety R; Department of Biomedical Engineering, Columbia University, New York City, NY, USA.
  • Doshi A; Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.
  • Laine A; Department of Biomedical Engineering, Columbia University, New York City, NY, USA.
  • Guo J; Department of Psychiatry, Columbia University, New York City, NY, USA.
  • Danino T; Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York City, NY, USA.
Nat Chem Biol ; 19(7): 878-886, 2023 07.
Article em En | MEDLINE | ID: mdl-37142806
ABSTRACT
A diverse array of bacteria species naturally self-organize into durable macroscale patterns on solid surfaces via swarming motility-a highly coordinated and rapid movement of bacteria powered by flagella. Engineering swarming is an untapped opportunity to increase the scale and robustness of coordinated synthetic microbial systems. Here we engineer Proteus mirabilis, which natively forms centimeter-scale bullseye swarm patterns, to 'write' external inputs into visible spatial records. Specifically, we engineer tunable expression of swarming-related genes that modify pattern features, and we develop quantitative approaches to decoding. Next, we develop a dual-input system that modulates two swarming-related genes simultaneously, and we separately show that growing colonies can record dynamic environmental changes. We decode the resulting multicondition patterns with deep classification and segmentation models. Finally, we engineer a strain that records the presence of aqueous copper. This work creates an approach for building macroscale bacterial recorders, expanding the framework for engineering emergent microbial behaviors.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Bactérias / Flagelos Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Bactérias / Flagelos Idioma: En Ano de publicação: 2023 Tipo de documento: Article