Abstract

Synthetic microbial consortia represent a new frontier for synthetic biology given that they can solve more complex problems than monocultures. However, most attempts to co-cultivate these artificial communities fail because of the winner-takes-all in nutrients competition. In soil, multiple species can coexist with a spatial organization. Inspired by nature, here we show that an engineered spatial segregation method can assemble stable consortia with both flexibility and precision. We create microbial swarmbot consortia (MSBC) by encapsulating subpopulations with polymeric microcapsules. The crosslinked structure of microcapsules fences microbes, but allows the transport of small molecules and proteins. MSBC method enables the assembly of various synthetic communities and the precise control over the subpopulations. These capabilities can readily modulate the division of labor and communication. Our work integrates the synthetic biology and material science to offer insights into consortia assembly and serve as foundation to diverse applications from biomanufacturing to engineered photosynthesis.

Most attempts to co-cultivate the artificial microbial communities fail mostly due to the mismatched rates of consumption and production of nutrients among subpopulations. Here, the authors develop a microbial swarmbot mediated spatial segregation method to assemble stably coexisting consortia with both flexibility and precision.

Details

Title
Engineering consortia by polymeric microbial swarmbots
Author
Wang, Lin 1 ; Zhang, Xi 1 ; Tang, Chenwang 1 ; Li, Pengcheng 1 ; Zhu, Runtao 1 ; Sun, Jing 2 ; Zhang, Yunfeng 1 ; Cui, Hua 1 ; Ma, Jiajia 3 ; Song, Xinyu 3 ; Zhang, Weiwen 3 ; Gao, Xiang 1 ; Luo, Xiaozhou 1 ; You, Lingchong 4   VIAFID ORCID Logo  ; Chen, Ye 1   VIAFID ORCID Logo  ; Dai, Zhuojun 1   VIAFID ORCID Logo 

 Chinese Academy of Sciences, CAS Key Laboratory of Quantitative Engineering Biology, Center for Materials Synthetic Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, China (GRID:grid.9227.e) (ISNI:0000000119573309) 
 Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Soft Bio-interface Electronics Lab, Center of Neural Engineering, CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China (GRID:grid.458489.c) (ISNI:0000 0001 0483 7922) 
 Tianjin University, Laboratory of Synthetic Microbiology, School of Chemical Engineering & Technology, Tianjin, China (GRID:grid.33763.32) (ISNI:0000 0004 1761 2484) 
 Duke University, Department of Biomedical Engineering, Durham, USA (GRID:grid.26009.3d) (ISNI:0000 0004 1936 7961) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2684779821
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.