Evolvable essentiality in the pan-genome of Streptococcus pneumoniae and its mechanistic and evolutionary consequences

总结 没有一个基因是孤立存在的，基因组中的基因组成了一个复杂的相互作用的网络。 在不同的途径和过程中聚集在一起以产生表型的组分。即使是小 遗传变化对生物体的表型有深远的影响，更不用说当生物体很大时， 基因的数量在菌株之间是不同的，这是那些具有泛基因组的物种的情况。为 例如，细菌病原体S.肺炎平均含有~2100个基因，而整个 一个物种含有> 4400个基因，这意味着两个随机菌株可能因存在和不存在以下基因而不同： 数百种不同的基因。在一个生物网络中，许多基因是重复的，在一个泛- 基因组中的大多数基因都是不存在的。相比之下，细菌中大约10 - 15%的基因 基因组是必不可少的，在任何情况下都能保持生物体的功能完整。由于其急性 重要的是，必需基因通常被认为是刚性的，在很大程度上是不可变的，因此使它们 例如，抗菌治疗的极佳靶点。然而，通过计算询问数千个 色葡萄pneumoniae菌株和17种临床菌株实验，我们已经创建了一个大型数据集，表明， 并非所有的必需基因都是"生而平等"的。具体来说，必需基因似乎并不总是存在于所有的基因组中。 根据菌株的背景，有时可以通过实验删除菌株。这就提出了一个 假设基本基因的概念比假设的要流动得多，并表明，根据权利， 环境（即，遗传背景），必需基因是可进化的，可以切换到非必需的。在这 我们的目标是了解为什么有些基因是必不可少的，而另一些则不是，我们通过实验探索 重要性如何演变，它是否是可预测的，可能的功能，表型和/或 进化的后果是什么，以及我们如何利用进化的本质。具体来说，在Aim 1.1几种基因组学工具被用于全面定位S.肺炎 通过对泛基因组中> 85%的遗传多样性进行采样。在目标1.2中，约200个基因的进化性是 探索了三个有效的策略，反映和揭示了一个必要的基因可以成为 非必要的。在目标1.3中，我们使用机器学习来确定必要基因的进化性是否是 可预测的。在Aim 2.1中，细胞壁合成和相关途径中的45个（可进化的）必需基因是 用CRISPRi-TnSeq询问以构建详细的相互作用网络。在Aim 2.2中，我们设计了配对菌株， 在一个菌株中，一个基因是必需的，而在几乎相同的菌株中，它不是必需的。在目标2.3中，我们使用配对- 菌株，并采用不同的方法来分配基因功能，并确定机制的后果， 进化的要素在目标3.1中，配对菌株用于确定是否存在适应性成本 与可进化要素相关，而在目标3.2中，我们确定适应性是否有成本， 从而潜在地产生折衷。最后，在目标3.3中，我们利用泛基因组和我们的新菌株对 试图设计一种新原理性基因靶向药物筛选的验证。

Summary No single gene exists in isolation, rather the genes in the genome make up an intricate network of interacting components that come together in different pathways and processes to generate a phenotype. Even small genetic changes can have far reaching consequences for an organism’s phenotype, let alone when large numbers of genes are different between strains, which is the case for those species with a pan-genome. For instance, a strain of the bacterial pathogen S. pneumoniae on average contains ~2100 genes, while the entire species harbors >4400 genes, which means that two random strains may differ by the presence and absence of hundreds of different genes. Within a biological network, many genes are dispensable, which within a pan- genome are mostly those genes that are variably present. In contrast, about 10-15% of genes in a bacterial genome are essential and keep an organism’s functionality intact under any circumstance. Due to their acute importance, essential genes are generally seen as rigid and largely immutable, consequently making them excellent targets for, for instance, antimicrobial therapies. However, by computationally interrogating thousands of S. pneumoniae strains, and 17 clinical strains experimentally, we have created a large dataset that shows that not all essential genes are ‘created equal’. Specifically, essential genes do not always seem to be present in all strains, and depending on a strain’s background, can sometimes be experimentally deleted. This raises the hypothesis that the essential gene concept is much more fluid than assumed and indicates that, under the right circumstances (i.e., genetic background), essential genes are evolvable and can switch to non-essential. In this proposal we aim to understand why some genes are essential, while others are not, we experimentally explore how essentiality can evolve, whether it is predictable, what the possible functional, phenotypic and/or evolutionary consequences are, and how we can take advantage of evolvable essentiality. Specifically, In Aim 1.1 several genomics tools are used to comprehensively map out evolvable essential genes in S. pneumoniae by sampling >85% of the genetic diversity in the pan-genome. In Aim 1.2 the evolvability of ~200 genes is explored with three validated strategies that reflect and uncover the ease in which an essential gene can become non-essential. And in Aim 1.3 we use machine learning to determine whether essential gene evolvability is predictable. In Aim 2.1 45 (evolvable) essential genes in cell wall synthesis and associated pathways are interrogated with CRISPRi-TnSeq to build a detailed interaction network. In Aim 2.2 we engineer paired-strains, where in one strain a gene is essential, and a near identical strain it is not. And in Aim 2.3 we use the paired- strains and employ different approaches to assign gene function and identify mechanistic consequences of evolvable essentials. In Aim 3.1 the paired-strains are used to determine whether there are fitness costs associated with evolvable essentials, while in Aim 3.2 we determine whether there is a cost to adaptability, thereby potentially creating a trade-off. Finally, in Aim 3.3 we exploit the pan-genome and our new strain-pairs in an attempt to design a novel proof-of principle gene-targeting drug screen.