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: 10657786
• 负责人: Jason W. Rosch
• 金额: $ 60.16万
• 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/10657786
• 关键词: Acute; Affect; Anabolism; Bacterial Genome; Behavior; Biological; Buffers; CRISPR interference; Categories; 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; 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 没有单个基因孤立地存在，而是基因组中的基因构成了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生成表型的组件。甚至很小 遗传变化可能会对有机体的表型产生远大影响，更不用说大了 菌株之间的基因数量不同，对于那些具有泛基因组的物种是这种情况。为了 实例，细菌病原体链球菌的菌株平均含有约2100个基因，而整个基因 物种港口> 4400个基因，这意味着两个随机菌株可能因存在和不存在而有所不同 数百个不同的基因。在一个生物网络中，许多基因都是可分配的，在泛泛 基因组主要是那些可变的基因。相反，细菌中约有10-15％的基因 基因组是必不可少的，在任何情况下都使生物体的功能保持完整。由于他们的敏锐 重要的是，基本基因通常被视为刚性且在很大程度上不变，因此使它们成为 例如，抗菌疗法的良好靶标。但是，通过计算询问数千 肺炎链球菌菌株和17种临床菌株在实验上，我们创建了一个大数据集，表明 并非所有基本基因都是“相等的”。具体来说，基本基因似乎并不总是存在 菌株，取决于菌株的背景，有时可以通过实验删除。这提高了 假设基本基因概念比假设要多得多，并表明在右下 情况（即遗传背景），必需基因是可以发展的，并且可以切换到非必需基因。在这个 建议我们旨在了解为什么有些基因是必不可少的，而另一些基因不是，我们在实验中探索 重要性如何发展，无论是可预测的，可能的功能，表型和/或 进化后果是，以及我们如何利用可发展的本质。具体来说，目的 1.1几种基因组学工具用于全面绘制肺炎链球菌中可转化的必需基因 通过对泛基因组中遗传多样性的85％采样。在目标1.2中，约200个基因的可发展性是 通过三种经过验证的策略探索，这些策略反映和揭示了基本基因的容易性 非必要的。在AIM 1.3中，我们使用机器学习来确定基本基因的可转化性是否为 可预测。在AIM 2.1 45中（可演化）细胞壁合成和相关途径的基因是 对CRISPRI-TNSEQ进行审问，以建立一个详细的交互网络。在AIM 2.2中，我们设计了配对， 在一个菌株中，基因是必不可少的，而几乎相同的菌株不是。在AIM 2.3中，我们使用配对 - 菌株和员工不同的方法分配基因功能并确定机械后果 可进化的必需品。在AIM 3.1中，配对量用于确定是否有健身成本 与可转化的必需品相关，而在AIM 3.2中，我们确定适应性是否存在成本 从而有可能创造权衡。最后，在AIM 3.3中，我们利用了泛基因组和新的应变对 试图设计一种新颖的原则证明基因靶向药物筛查。