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

肺炎链球菌全基因组的进化本质及其机制和进化后果

• 批准号: 10503286
• 负责人: Jason W. Rosch
• 金额: $ 61.74万
• 依托单位: ST. JUDE CHILDREN'S RESEARCH HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10503286
• 关键词: Acute; Affect; Anabolism; Bacterial Genome; Behavior; Biological; Buffers; CRISPR interference; Cell Wall; Cell division; Cellular Morphology; Clinical; Comparative Genomic Analysis; Data; Drug Screening; Engineering; Environment; Essential Genes; Evolution; Foundations; Gene Targeting; Gene Transfer; Genes; Genetic; Genetic Variation; Genome; Genomics; Knock-out; Knowledge; Liquid substance; Machine Learning; Maps; Microscopy; Mutation; Organism; Pathway Analysis; Pathway interactions; Phenotype; Process; Rest; Role; Sampling; Streptococcus pneumoniae; Stress; Testing; Virulence; Work; antimicrobial; comparative genomics; cost; design; fitness; fluidity; gene function; gene interaction; genome sequencing; genome-wide; genomic data; genomic tools; large datasets; machine learning model; network architecture; novel; pan-genome; pathogenic bacteria; tool; transposon sequencing; whole genome

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.

概括 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网络 通过不同的途径和过程聚集在一起产生表型的成分，甚至很小。 基因变化可能对生物体的表型产生深远的影响，更不用说当基因变化很大时 菌株之间的基因数量不同，具有泛基因组的物种就是这种情况。 例如，细菌病原体肺炎链球菌的一个菌株平均包含约 2100 个基因，而整个 物种拥有超过 4400 个基因，这意味着两个随机菌株可能因存在和不存在基因而有所不同 在一个生物网络中，有数百个不同的基因，许多基因都是可有可无的。 基因组主要是那些存在差异的基因，相比之下，细菌中大约有 10-15% 的基因。 基因组是必不可少的，并且由于其敏锐性而在任何情况下都能保持生物体的功能完整。 重要的是，必需基因通常被认为是僵化的并且在很大程度上是不可改变的，因此使得它们 然而，通过计算询问数千个，例如抗菌疗法。 通过对肺炎链球菌菌株和 17 种临床菌株进行实验，我们创建了一个大型数据集，表明 并非所有必需基因都是“生来平等”的。具体来说，必需基因似乎并不总是存在于所有基因中。 菌株，并且根据菌株的背景，有时可以通过实验删除。 假设基本基因概念比假设的要流动得多，并表明，在正确的条件下 在这种情况下（即遗传背景），必需基因是可以进化的并且可以转变为非必需基因。 我们的目标是了解为什么有些基因是必需的，而另一些则不是，我们通过实验探索 重要性如何演变，是否可预测，可能的功能、表型和/或 进化的后果是，以及我们如何利用可进化的必要性，具体来说，是 In Aim。 1.1 使用多种基因组学工具全面绘制肺炎链球菌进化必需基因 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 个基因的进化能力为： 通过三种经过验证的策略进行了探索，这些策略反映并揭示了重要基因可以变得容易的程度 在目标 1.3 中，我们使用机器学习来确定基因的可进化性是否是必需的。 在目标 2.1 中，细胞壁合成和相关途径中的 45 个（可进化）必需基因是可预测的。 使用 CRISPRi-TnSeq 进行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， 其中，在一个菌株中，一个基因是必需的，而在目标 2.3 中，我们使用配对的基因，而在几乎相同的菌株中则不是。 菌株并采用不同的方法来分配基因功能并识别其机械后果 在目标 3.1 中，配对菌株用于确定是否存在适应度成本。 与可进化的要素相关，而在目标 3.2 中，我们确定适应性是否有成本， 最后，在目标 3.3 中，我们利用泛基因组和我们的新菌株对。 试图设计一种新颖的原理验证基因靶向药物筛选。