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

摘要 没有孤立的单一基因存在，相反，基因组中的基因组成了一个错综复杂的相互作用网络。 在不同的途径和过程中聚集在一起以产生表型的成分。即使是小的 基因变化可能会对生物体的表型产生深远的影响，更不用说当 不同菌株的基因数量不同，对于那些拥有泛基因组的物种来说就是如此。为 例如，一株肺炎链球菌平均含有约2100个基因，而整个 物种含有&gt；4400基因，这意味着两个随机菌株可能因存在和不存在而有所不同。 数百种不同的基因。在生物网络中，许多基因是可有可无的，而在一个生物网络中，这些基因是可有可无的 基因组主要是那些以不同方式存在的基因。相比之下，细菌中约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不变。由于他们的急性 重要的是，基本基因通常被认为是僵化的，在很大程度上是不变的，因此使它们 例如，抗菌疗法的极佳靶点。然而，通过计算审问数千人 肺炎链球菌菌株和17个临床菌株的实验，我们已经创建了一个大型数据集，表明 并不是所有的基本基因都是“生而平等”的。具体地说，必需基因似乎并不总是存在于所有人身上。 菌株有时可以被实验性地删除，这取决于菌株的背景。这引发了 基本基因概念比假设的流动性更大的假说，并表明在正确的情况下 在环境(即遗传背景)下，必需基因是可以进化的，可以切换到非必需基因。在这 我们的目标是理解为什么某些基因是必需的，而另一些不是，我们通过实验探索 本质如何演变，它是否可预测，可能的功能、表型和/或 进化的后果是，以及我们如何利用进化的重要性。具体地说，在AIM 1.1利用多种基因组学工具对肺炎链球菌的进化必需基因进行了全面的定位 通过对泛基因组中85%的遗传多样性进行抽样。在Aim 1.2中，~200个基因的进化性是 探索了三种有效的策略，反映并揭示了必要基因可以变得多么容易 非必要的。在目标1.3中，我们使用机器学习来确定关键基因的进化性是否 这是可预见的。在Aim 2.1 45(可进化)中，细胞壁合成和相关途径中的必需基因是 与CRISPRi-TnSeq一起询问，以建立详细的互动网络。在Aim 2.2中，我们设计了成对的菌株， 在一个菌株中，基因是必不可少的，而在一个几乎相同的菌株中，基因却不是。在AIM 2.3中，我们使用配对的- 菌株，并使用不同的方法来分配基因功能和确定 可进化的要素。在目标3.1中，使用配对菌株来确定是否存在健康成本 与进化要素相关联，而在目标3.2中，我们确定适应性是否有代价， 从而潜在地造成了一种权衡。最后，在Aim 3.3中，我们开发了泛基因组和我们的新菌株对 试图设计一种新颖的原理证明的基因靶向药物筛选。

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.