Aida Tafrishi , Varun Trivedi , Zenan Xing , Mengwan Li , Ritesh Mewalal , Sean R. Cutler , Ian Blaby , Ian Wheeldon
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Conducting fitness screens in the absence of functional non-homologous end joining (NHEJ), the dominant DNA repair mechanism in <em>K. phaffii</em>, provides a quantitative means to assess the activity of each sgRNA in the library. This approach allows for the experimental validation of each guide's targeting activity, leading to more precise screening outcomes. We used this approach to conduct growth screens with glucose as the sole carbon source and identify essential genes. Comparative analysis of the called gene sets identified a core set of <em>K. phaffii</em> essential genes, many of which relate to metabolic engineering targets, including protein production, secretion, and glycosylation. 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引用次数: 0
摘要
基于 CRISPR 的高通量全基因组功能缺失筛选是功能遗传学和菌株工程学的重要方法。Komagataella phaffii酵母是生物制药行业特别感兴趣的宿主,也是蛋白质和代谢产物的代谢工程宿主。在这里,我们为这种具有重要生物技术价值的酵母设计并验证了一个高活性的 6 倍覆盖全基因组 sgRNA 文库,其中包含 30,848 个活性 sgRNA,靶向其 99% 以上的编码序列。在没有功能性非同源末端连接(NHEJ)(K. phaffii 的主要 DNA 修复机制)的情况下进行适配性筛选,为评估文库中每个 sgRNA 的活性提供了定量方法。通过这种方法可以对每个导向基因的靶向活性进行实验验证,从而获得更精确的筛选结果。我们利用这种方法进行了以葡萄糖为唯一碳源的生长筛选,并确定了必需基因。对调用的基因组进行比较分析,确定了一组核心的 K. phaffii 必备基因,其中许多基因与代谢工程目标有关,包括蛋白质生产、分泌和糖基化。在此开发的高活性、全基因组 CRISPR 文库可对 K. phaffii 进行功能基因组筛选,应用于基因本质分类,并有望用于其他基因筛选。
Functional genomic screening in Komagataella phaffii enabled by high-activity CRISPR-Cas9 library
CRISPR-based high-throughput genome-wide loss-of-function screens are a valuable approach to functional genetics and strain engineering. The yeast Komagataella phaffii is a host of particular interest in the biopharmaceutical industry and as a metabolic engineering host for proteins and metabolites. Here, we design and validate a highly active 6-fold coverage genome-wide sgRNA library for this biotechnologically important yeast containing 30,848 active sgRNAs targeting over 99% of its coding sequences. Conducting fitness screens in the absence of functional non-homologous end joining (NHEJ), the dominant DNA repair mechanism in K. phaffii, provides a quantitative means to assess the activity of each sgRNA in the library. This approach allows for the experimental validation of each guide's targeting activity, leading to more precise screening outcomes. We used this approach to conduct growth screens with glucose as the sole carbon source and identify essential genes. Comparative analysis of the called gene sets identified a core set of K. phaffii essential genes, many of which relate to metabolic engineering targets, including protein production, secretion, and glycosylation. The high activity, genome-wide CRISPR library developed here enables functional genomic screening in K. phaffii, applied here to gene essentiality classification, and promises to enable other genetic screens.
期刊介绍:
Metabolic Engineering (MBE) is a journal that focuses on publishing original research papers on the directed modulation of metabolic pathways for metabolite overproduction or the enhancement of cellular properties. It welcomes papers that describe the engineering of native pathways and the synthesis of heterologous pathways to convert microorganisms into microbial cell factories. The journal covers experimental, computational, and modeling approaches for understanding metabolic pathways and manipulating them through genetic, media, or environmental means. Effective exploration of metabolic pathways necessitates the use of molecular biology and biochemistry methods, as well as engineering techniques for modeling and data analysis. MBE serves as a platform for interdisciplinary research in fields such as biochemistry, molecular biology, applied microbiology, cellular physiology, cellular nutrition in health and disease, and biochemical engineering. The journal publishes various types of papers, including original research papers and review papers. It is indexed and abstracted in databases such as Scopus, Embase, EMBiology, Current Contents - Life Sciences and Clinical Medicine, Science Citation Index, PubMed/Medline, CAS and Biotechnology Citation Index.