Transcription engineering for biosensor-based screening of metagenomic libraries

描述（由申请人提供）：有效筛选宏基因组文库将使探索自然界中微生物的巨大多样性成为可能，这些微生物对人类生活有许多有趣和有价值的必要功能。例如，在人体肠道中，细菌群落可以解毒有害化学物质，合成维生素，并帮助消化人体自身无法消化的营养物质，以及其他重要功能。不幸的是，绝大多数细菌不能在实验室中培养，因此生物催化活性的遗传基础往往未被发现。微生物学的主力生物大肠杆菌可以容纳这些细菌群落的基因组DNA的遗传集合（称为宏基因组文库）。由于两个主要的限制，筛选宏基因组文库效率低下。首先，大肠杆菌通常无法识别元基因组文库中的异源启动子。其次，寻找生物催化功能通常涉及筛选小分子的存在，这可能是时间和资源密集的。提出的解决方案是开发一种与培养无关的方法，用于高通量筛选宏基因组文库。在第一个目标中，我们提出通过表达来自系统发育上远离宿主的原核生物的转录机制（sigma因子），宿主的RNA聚合酶可以更好地识别异源启动子，从而改善宏基因组文库中遗传元件的转录。为了提高元基因组文库中异源基因的表达，sigma因子将在一系列细菌（包括枯草芽孢杆菌和植物乳杆菌）的宿主菌株中表达。将绿色荧光蛋白基因融合到小文库插入片段上，用流式细胞术测量荧光，定量分析异种文库的转录。通过对启动子和核糖体结合位点（RBS）进行工程化改造，优化异源sigma因子的表达水平，以获得良好的表达菌株。利用该系统，构建土壤细菌宏基因组文库，筛选增强丁醇耐受性的基因。在第二个目标，一个转录激活生物传感器系统将纳入大肠杆菌感兴趣的生化。该生物传感器可以感知细胞内生化浓度，诱导gfp基因转录。该生物传感器将通过启动子和RBS工程进行调整，以优化荧光激活细胞分选，这是一种高通量方法。最后，在第三个目标中，将生物传感器系统整合到异种表达sigma因子的菌株中，并用大插入体、基于fosmid的合成宏基因组文库进行转化。一种前体化学品将被提供给培养物；如果前体转化为生物传感器感测到的生化物质，细胞就会发出荧光，可以被分离出来。这些分离物将被表征，以确定负责酶活性的遗传因素。一旦开发出来，这种方法有可能快速准确地从难以培养的生物体中鉴定酶基因，从而为探索更大的宏基因组空间提供一种新的手段。

DESCRIPTION (provided by applicant): Effective screening of metagenomic libraries will enable the exploration of the great diversity of microbes in nature that are responsible for many interesting and valuable functions necessary for human life. For example, in the human gut, the bacterial community detoxifies harmful chemicals, synthesizes vitamins, and helps digest nutrients that the human body cannot digest by itself, among other vital functions. Unfortunately, the vast majority of bacteria cannot be cultured in a laboratory, so the genetic basis of the biocatalytic activity often goes undiscovered. The microbiology workhorse organism E. coli can house genetic collections of these bacterial communities' genomic DNA (known as metagenomic libraries). Screening of metagenomic libraries is inefficient due to two main limitations. First, E. coli is often unable to recognize heterologous promoters in the metagenomic library. Second, searching for biocatalytic functions often involves screening for the presence of small molecules, which can be time and resource intensive. The proposed solution is to develop a culture-independent method for high-throughput screening of metagenomic libraries. In the first aim, it is proposed that by expressing transcription machinery (sigma factors) from prokaryotes phylogenetically distant from the host, the host's RNA polymerase will better recognize heterologous promoters, improving transcription of the genetic elements in metagenomic libraries. To improve heterologous gene expression from metagenomic libraries, sigma factors will be expressed in host strains from a range of bacterial species (including Bacillus subtilis and Lactobacillus plantarum). Transcription of heterologous libraries will be quantified by fusing the green fluorescent protein gene to small library inserts and measuring fluorescence with flow cytometry. The expression level of heterologous sigma factors will be optimized by engineering the promoters and ribosomal binding sites (RBS) in order to obtain a good expression strain. Using this system, a metagenomic library will be constructed from soil bacteria and screened for genes imparting enhanced butanol tolerance. In the second aim, a transcriptional activator biosensor system will be incorporated in E. coli to sense a biochemical of interest. The biosensor can sense the intracellular biochemical concentration and induce transcription of the gfp gene. The biosensor will be tuned by promoter and RBS engineering for optimized fluorescence-activated cell sorting, a high-throughput method. Finally, in the third aim, the biosensor system will be incorporated into the heterologous sigma factor expressing strain and transformed with a large-insert, fosmid-based synthetic metagenomic library. A precursor chemical will be supplied to the culture; if the precursor is converted to the biochemical sensed by the biosensor, the cell will fluoresce and can be isolated. These isolates will be characterized to determine the genetic element responsible for the enzymatic activity. Once developed, this method has the potential to quickly and accurately identify enzymatic genes from difficult-to-culture organisms, thereby providing a new means by which to explore a larger part of the large metagenomic space.