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Advances in combinatorial CRISPRi screening and applications: Decoding higher-order interactions for next-generation
Xiaofei Zhu1, Weiwen Zhang2, Tao Sun2
1School of Synthetic Biology and Biomanufacturing, Tianjin University, Tianjin, China; State Key Laboratory of Synthetic Biology, Tianjin University, Tianjin, 300072, PR China; Frontier Science Center for Synthetic Biology & Key Laboratory of Systems Bioengineering, Ministry of Education of China, Tianjin 300072, PR China.
Abstract:
Biological systems possess high robustness and intricate genetic redundancy, rendering traditional single-gene perturbations largely inadequate for comprehensively elucidating the true regulatory mechanisms underlying complex phenotypes. To address this, combinatorial CRISPR interference (CRISPRi) has emerged as an essential tool in systems and synthetic biology, offering reversible epigenetic control, multi-target regulation, and an absence of DNA toxicity. Unlike pooled single-gene screens, combinatorial CRISPRi facilitates the systematic dissection of buffering, synergy, and metabolic trade-offs by targeting multiple loci simultaneously. This review explores the latest advancements in pairwise and higher-order combinatorial CRISPRi screening, beginning with design strategies for multiplex guide RNA (gRNA) arrays and orthogonal systems. Computational techniques utilized for analyzing high-dimensional screening data and visualizing complex genetic networks are subsequently examined. Building upon these methodological foundations, crucial applications within microbial engineering are highlighted. Specifically, the review details the optimization of carbon flux in microbial cell factories to circumvent production bottlenecks, alongside the elucidation of protective multigenic networks against severe environmental stress. Furthermore, it addresses current obstacles, such as system noise and library construction challenges, and outlines future research directions. Ultimately, this review provides a comprehensive guide for decoding complex traits and driving rational designs of next-generation cell factories.
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