Identification and integration of systems level microbial synthetic lethality interactions to enhance genome design

合成生物学是一门新兴学科，它有可能给从化学工业到医学的许多领域带来革命性的变化。其目标是重新设计生物有机体，如酵母、酿酒酵母或肠道微生物大肠杆菌，以生产感兴趣的化学物质。这些生物有机体可以产生的分子范围几乎是任何生物分子(从生物燃料到芳香，再到用于癌症治疗的抗体)，具有更高的生物可持续产量。DNA体外合成和组装方法的最新进展支持了合成生物学即将蓬勃发展的观点，从而使新的基因组设计得以实现。然而，这些技术能力现在将破译生物学复杂性作为理性基因组设计的下一个重大挑战。因此，这项研究计划的长期目标是通过增加我们目前对分子相互作用和限制的知识来加强基因组设计，从而使有机体茁壮成长。 删除一个基因是通过研究目标基因所涉及的细胞过程来破译基因功能的经典方法。在细菌模式生物大肠杆菌中，有一种名为Keio集合的有用资源，其中约3800个非必需基因都被单独删除。虽然单基因缺失提供了有关基因本身功能的信息，但大约三分之一的大肠杆菌基因仍然缺乏功能的实验证据。考虑到基因在复杂的网络中工作，在全基因组范围内研究双基因缺失有可能在基因之间建立新的功能关系，并增加我们对这种网络的理解。然而，双重缺失的组合数量是令人望而生畏的(在大肠杆菌中为900万)，探测它需要高通量的方法。随机转座子突变是这样一种方法，在这种方法中，一种称为转座子的DNA元件可以在基因组的几乎任何位置整合自己，并通过高通量测序进行鉴定。基因中没有插入片段被用来推断重要性，其基础是杀死种群中有插入重要基因片段的细胞。 在目前的研究计划中，我们建议将我们最近优化的转座子突变方法应用于完整的KEIO收集的单基因突变株，从而鉴定在大肠杆菌中可能存在的所有可行和无效的双基因缺失。为了进一步理解这些相互作用，我们将把我们产生的数据与计算模型结合起来，并产生几个关于新基因功能的假设。目前基因组设计的局限性是缺乏对基因功能的完整知识，我们的研究计划将为社区提供关键资源，并改进基因组缩减设计，对合成生物学的发展做出重要贡献。

Synthetic biology is an emerging discipline that has the potential to revolutionize many areas from the chemical industry to medicine. The goal is to redesign biological organisms such as the yeast Saccharomyces cerevisiae or the gut microbe Escherichia coli to produce chemicals of interest. The range of molecules that can be produced by these biological organisms is virtually any biological molecules (from biofuels, to aromas, to antibodies for cancer treatment), with a more biosustainable production. The recent advances of in vitro DNA synthesis and assembly methods support the idea that synthetic biology is on the verge of blooming, allowing the materialization of new genome designs. However, these technical abilities now pose the deciphering of biological complexity as the next grand challenge for rational genome designs. The long-term goal of this research program is thus to enhance genome design by increasing our current knowledge of molecular interactions and constraints allowing organisms to thrive. Deleting a gene is a classical way of deciphering gene functions by studying the cellular processes in which the target gene is involved. In the bacterial model organism E. coli, a useful resource named the Keio collection is available where each of the ~3800 non-essential genes were individually deleted. While single-gene deletion provides information on the function of the gene itself, about a third of E. coli genes are still lacking experimental evidence of function. Considering that genes are working in complex networks, studying double-gene deletions on a genome-wide scale has the potential to establish new functional relationships between genes and increase our understanding of such networks. The number of combinations for double deletions is nevertheless daunting (> 9 million in E. coli) and probing it requires a high-throughput method. Random transposon mutagenesis is such method where a DNA element called transposon can integrate itself at virtually any location in the genome and be identified using high-throughput sequencing. The absence of insertions in a gene is used to infer essentiality, based on killing that depletes from the population the cells having an insertion in an important gene. In the current research program, we propose to apply a transposon mutagenesis method that we recently optimized, to the complete Keio collection single-gene mutant strains, hereby identifying all viable and non-viable double-gene deletions possible in E. coli. To further our understanding of these interactions, we will integrate the data we generated with computational models and generate several hypotheses of novel gene functions. The current limitation on genome design being the lack of complete knowledge of gene functions, our research program will provide a key resource for the community and improve genome reduction design, importantly contributing to the development of synthetic biology.