In Vivo Multicellular Feedback Control in Synthetic Microbial Consortia.

IF 3.7 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

ACS Synthetic Biology Pub Date : 2025-06-11 DOI:10.1021/acssynbio.4c00862

Davide Salzano, Barbara Shannon, Claire Grierson, Lucia Marucci, Nigel J Savery, Mario di Bernardo

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引用次数: 0

Abstract

In this paper, we present a biomolecular control architecture able to guarantee stable and precise regulation of gene expression. Specifically, we engineer a microbial consortium comprising a cellular population, named controllers, that is tasked to regulate the expression of a gene in a second population, termed targets. Traditional biomolecular control strategies, while effective, are predominantly confined to single-cell applications, limiting their complexity and adaptability due to factors such as competition for limited cell resources and incompatible chemical reactions. Our approach overcomes these limitations by employing a distributed multicellular feedback loop between two strains of Escherichia coli, allowing for division of labor across the consortium. In vivo experiments demonstrate that this control system maintains precise and robust gene expression in the target population, even amid variations in consortium composition. Our study fills a critical gap in synthetic biology and paves the way for more complex and reliable applications in the field.

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合成微生物群落的体内多细胞反馈控制。

在本文中，我们提出了一种生物分子控制体系结构，能够保证基因表达的稳定和精确调控。具体来说，我们设计了一个微生物联合体，包括一个细胞群体，被称为控制器，其任务是调节基因在第二个群体，被称为目标的表达。传统的生物分子控制策略虽然有效，但主要局限于单细胞应用，由于对有限细胞资源的竞争和不相容的化学反应等因素，限制了它们的复杂性和适应性。我们的方法通过在两株大肠杆菌之间采用分布式多细胞反馈回路来克服这些限制，从而允许整个联合体的劳动分工。体内实验表明，即使在财团组成的变化中，该控制系统也能在目标群体中保持精确和稳健的基因表达。我们的研究填补了合成生物学的一个关键空白，为该领域更复杂、更可靠的应用铺平了道路。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACS Synthetic Biology 生物-

CiteScore

8.00

自引率

10.60%

发文量

380

审稿时长

6-12 weeks

期刊介绍： The journal is particularly interested in studies on the design and synthesis of new genetic circuits and gene products; computational methods in the design of systems; and integrative applied approaches to understanding disease and metabolism. Topics may include, but are not limited to: Design and optimization of genetic systems Genetic circuit design and their principles for their organization into programs Computational methods to aid the design of genetic systems Experimental methods to quantify genetic parts, circuits, and metabolic fluxes Genetic parts libraries: their creation, analysis, and ontological representation Protein engineering including computational design Metabolic engineering and cellular manufacturing, including biomass conversion Natural product access, engineering, and production Creative and innovative applications of cellular programming Medical applications, tissue engineering, and the programming of therapeutic cells Minimal cell design and construction Genomics and genome replacement strategies Viral engineering Automated and robotic assembly platforms for synthetic biology DNA synthesis methodologies Metagenomics and synthetic metagenomic analysis Bioinformatics applied to gene discovery, chemoinformatics, and pathway construction Gene optimization Methods for genome-scale measurements of transcription and metabolomics Systems biology and methods to integrate multiple data sources in vitro and cell-free synthetic biology and molecular programming Nucleic acid engineering.