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解析酿酒酵母中双向启动子调控基因共表达和代谢通量的动态机制

Unraveling the regulatory dynamics of bidirectional promoters for modulating gene co-expression and metabolic flux in Saccharomyces cerevisiae.

作者信息

Jin Zimo, Dong Yueming, Rafi Abdul Muntakim, Patwary Md Mohsin, Xu Catherine, Raadam Morten H, de Boer Carl G, Ignea Codruta

机构信息

Department of Bioengineering, Faculty of Engineering, McGill University, Montreal, QCH3A 0C3, Canada.

School of Biomedical Engineering, University of British Columbia, 6088 University Boulevard, Vancouver, BC Canada V6T 1Z3, Canada.

出版信息

Nucleic Acids Res. 2025 Jun 6;53(11). doi: 10.1093/nar/gkaf511.

Abstract

Bidirectional promoters (BDPs) hold great promise for applications in synthetic biology by enabling co-expression of multiple genes with minimized promoter size. However, the lack of well-characterized BDPs along with an incomplete understanding of their regulatory mechanisms limits broader applications. Here, we conducted genome-wide screening and characterization of 749 BDP candidates containing a single shared nucleosome-depleted region in yeast Saccharomyces cerevisiae. A pronounced asymmetry in BDP strength was observed using both transcriptomic and fluorescence reporter analyses. We demonstrated that these unbalanced BDP strengths could be utilized for fine-tuning metabolic flux in yeast, achieving yields comparable to or exceeding those of commonly used constitutive or inducible promoters for terpenoid production under the examined conditions. Using in silico mutagenesis guided by the DREAM-CNN yeast cis-regulatory AI prediction model, we identified conserved activator-binding hotspots within the central region of 63.8% of identified BDP candidates. Disruption of these hotspots in six selected BDPs significantly reduced promoter strength in both orientations, suggesting that these AI-predicted motifs are indeed critical for the functionality of BDPs. Overall, this study provides a comprehensive framework for BDP identification and engineering, leveraging AI-guided models to advance rational synthetic promoter design, thus paving the way for precise genetic control in synthetic biology.

摘要

双向启动子(BDP)通过实现多个基因的共表达且启动子尺寸最小化,在合成生物学应用中具有巨大潜力。然而,缺乏充分表征的BDP以及对其调控机制的不完全理解限制了其更广泛的应用。在此,我们对酿酒酵母中749个包含单个共享核小体缺失区域的BDP候选物进行了全基因组筛选和表征。使用转录组学和荧光报告分析均观察到BDP强度存在明显的不对称性。我们证明,这些不平衡的BDP强度可用于微调酵母中的代谢通量,在所研究条件下,萜类化合物生产的产量与常用的组成型或诱导型启动子相当或更高。利用由DREAM-CNN酵母顺式调控人工智能预测模型指导的计算机诱变,我们在63.8%的已鉴定BDP候选物的中心区域内鉴定出保守的激活剂结合热点。在六个选定的BDP中破坏这些热点显著降低了两个方向的启动子强度,表明这些人工智能预测的基序确实对BDP的功能至关重要。总体而言,本研究为BDP的鉴定和工程提供了一个全面的框架,利用人工智能指导的模型推进合理的合成启动子设计,从而为合成生物学中的精确基因控制铺平道路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae07/12159744/62bd9613a28e/gkaf511figgra1.jpg

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