신종오 (University of California San Diego)

한빛사논문

조회171

University of California San Diego

Nucleic Acids Res., Aug 28 2024, gkae742 | https://doi.org/10.1093/nar/gkae742 Modulating bacterial function utilizing A knowledge base of transcriptional regulatory modules

Jongoh Shin¹, Daniel C. Zielinski¹ and Bernhard O. Palsson^1,2,3,*

¹Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA

²Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby 2800, Denmark

³Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA

^*To whom correspondence should be addressed.: Bernhard O. Palsson

Abstract

Synthetic biology enables the reprogramming of cellular functions for various applications. However, challenges in scalability and predictability persist due to context-dependent performance and complex circuit-host interactions. This study introduces an iModulon-based engineering approach, utilizing machine learning-defined co-regulated gene groups (iModulons) as design parts containing essential genes for specific functions. This approach identifies the necessary components for genetic circuits across different contexts, enhancing genome engineering by improving target selection and predicting module behavior. We demonstrate several distinct uses of iModulons: (i) discovery of unknown iModulons to increase protein productivity, heat tolerance and fructose utilization; (ii) an iModulon boosting approach, which amplifies the activity of specific iModulons, improved cell growth under osmotic stress with minimal host regulation disruption; (iii) an iModulon rebalancing strategy, which adjusts the activity levels of iModulons to balance cellular functions, significantly increased oxidative stress tolerance while minimizing trade-offs and (iv) iModulon-based gene annotation enabled natural competence activation by predictably rewiring iModulons. Comparative experiments with traditional methods showed our approach offers advantages in efficiency and predictability of strain engineering. This study demonstrates the potential of iModulon-based strategies to systematically and predictably reprogram cellular functions, offering refined and adaptable control over complex regulatory networks.

논문정보

형식Research article
게재일2024년 08월 (BRIC 등록일 2024-08-29)
연구진국외 연구진
분야 생명과학 > 미생물학

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