庞基因组景观决定了合成基因回路在不同Stutzerimonas物种中的表现。

IF 5 2区 生物学 Q1 MICROBIOLOGY
mSystems Pub Date : 2024-09-17 Epub Date: 2024-08-21 DOI:10.1128/msystems.00849-24
Dennis Tin Chat Chan, Hans C Bernstein
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引用次数: 0

摘要

由于每个宿主独特的细胞环境背景,将相同的基因回路植入不同物种通常会导致性能上的巨大差异,这种现象被称为 "底盘效应 "或 "环境依赖性"。更好地了解基因组和生理环境是如何支撑底盘效应的,将有助于改进不同微生物的生物设计策略。在这里,我们将基于泛基因组学的基因表达分析与工程基因变频装置性能的定量测量相结合,揭示了基因组结构和功能与六种密切相关的Stutzerimonas宿主所观察到的底盘效应之间的关系。我们的研究结果表明,基因组结构是我们选择的非模式细菌宿主对工程化装置产生不同反应的基础。具体来说,我们发现核心基因组(所有宿主共有的基因簇)的差异表达是导致所观察到的基因装置性能差异的主要原因,而来自各自附属基因组的特异基因并不显著。一项数据驱动的调查显示,参与反硝化的基因和跨膜转运蛋白的成分是宿主之间对基因装置反应差异最大的基因簇。我们的研究结果表明,底盘效应可以沿着最保守的基因组编码功能之间的差异进行追踪,这些差异在密切相关的物种之间创造了独特的生物设计空间。大肠杆菌等模式生物因其可操作性而成为具有吸引力的宿主,但并不一定能提供优化性能的理想环境。随着越来越多的新型微生物被驯化用作生物技术平台,合成生物学家必须探索底盘设计空间,以优化他们的系统,实现合成生物学的承诺。因此,随着生物设计领域的发展,底盘效应的后果只会变得更加重要。在我们的工作中,我们证明了基因装置的性能高度依赖于其运行的宿主环境,从而推广了底盘可被视为调整电路功能的设计变量这一概念。重要的是,我们的研究结果揭示了底盘效应可以沿着基因组结构的相似性进行追踪,特别是共享核心基因组。我们的研究倡导探索底盘设计空间,并为合成生物学家提供了更有效探索底盘设计空间的知识,使下一代广泛的合成生物学向前迈进了一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pangenomic landscapes shape performances of a synthetic genetic circuit across Stutzerimonas species.

Engineering identical genetic circuits into different species typically results in large differences in performance due to the unique cellular environmental context of each host, a phenomenon known as the "chassis-effect" or "context-dependency". A better understanding of how genomic and physiological contexts underpin the chassis-effect will improve biodesign strategies across diverse microorganisms. Here, we combined a pangenomic-based gene expression analysis with quantitative measurements of performance from an engineered genetic inverter device to uncover how genome structure and function relate to the observed chassis-effect across six closely related Stutzerimonas hosts. Our results reveal that genome architecture underpins divergent responses between our chosen non-model bacterial hosts to the engineered device. Specifically, differential expression of the core genome, gene clusters shared between all hosts, was found to be the main source of significant concordance to the observed differential genetic device performance, whereas specialty genes from respective accessory genomes were not significant. A data-driven investigation revealed that genes involved in denitrification and components of trans-membrane transporter proteins were among the most differentially expressed gene clusters between hosts in response to the genetic device. Our results show that the chassis-effect can be traced along differences among the most conserved genome-encoded functions and that these differences create a unique biodesign space among closely related species.IMPORTANCEContemporary synthetic biology endeavors often default to a handful of model organisms to host their engineered systems. Model organisms such as Escherichia coli serve as attractive hosts due to their tractability but do not necessarily provide the ideal environment to optimize performance. As more novel microbes are domesticated for use as biotechnology platforms, synthetic biologists are urged to explore the chassis-design space to optimize their systems and deliver on the promises of synthetic biology. The consequences of the chassis-effect will therefore only become more relevant as the field of biodesign grows. In our work, we demonstrate that the performance of a genetic device is highly dependent on the host environment it operates within, promoting the notion that the chassis can be considered a design variable to tune circuit function. Importantly, our results unveil that the chassis-effect can be traced along similarities in genome architecture, specifically the shared core genome. Our study advocates for the exploration of the chassis-design space and is a step forward to empowering synthetic biologists with knowledge for more efficient exploration of the chassis-design space to enable the next generation of broad-host-range synthetic biology.

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来源期刊
mSystems
mSystems Biochemistry, Genetics and Molecular Biology-Biochemistry
CiteScore
10.50
自引率
3.10%
发文量
308
审稿时长
13 weeks
期刊介绍: mSystems™ will publish preeminent work that stems from applying technologies for high-throughput analyses to achieve insights into the metabolic and regulatory systems at the scale of both the single cell and microbial communities. The scope of mSystems™ encompasses all important biological and biochemical findings drawn from analyses of large data sets, as well as new computational approaches for deriving these insights. mSystems™ will welcome submissions from researchers who focus on the microbiome, genomics, metagenomics, transcriptomics, metabolomics, proteomics, glycomics, bioinformatics, and computational microbiology. mSystems™ will provide streamlined decisions, while carrying on ASM''s tradition of rigorous peer review.
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