ILLUMINATING CELLULAR INDIVIDUALITY THROUGH BACTERIOPHAGE INFECTION

通过噬菌体感染阐明细胞个性

• 批准号: 10159454
• 负责人: Ido Golding
• 金额: $ 33.15万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10159454
• 关键词: Address; Awareness; Bacteria; Bacteriophage lambda; Bacteriophages; Behavior; Biological; Biology; Biophysics; Cell Death; Cell Differentiation process; Cell physiology; Cells; Complex; Cytolysis; Disease; Embryo; Environment; Escherichia coli; Event; Exhibits; Gene Expression; Gene Expression Regulation; Genetic; Goals; Health; Heterogeneity; Human; Image; Individual; Individuality; Infection; Lysogeny; Maps; Modeling; Molecular; Noise; Organism; Outcome; Phenotype; Process; Property; Proteins; Research; Role; Signal Transduction; Statistical Distributions; System; Viral; Virus; Work; cell behavior; emerging antibiotic resistance; single molecule; success; tool

PROJECT SUMMARY / ABSTRACT A key goal of biophysics is to predict the behavior of living systems. This goal is hampered by the fact that, when examined at the single-cell level, this behavior appears largely unpredictable: Genetically identical cells, within a uniform environment, exhibit heterogeneous phenotypes in terms of gene expression, signaling, and consequent fate choice. This cellular individuality is observed throughout biology, from the emergence of antibiotic resistance among bacteria, to cell differentiation in the early mammalian embryo, and numerous other examples. Studies over the last two decades have pinpointed the stochastic origins of cellular individuality, by demonstrating that the inherent randomness (“noise”) of single-molecule events can be amplified into protein number fluctuations at the cellular level. The picture that emerged from those studies is of living cells as “noisy machines”, incapable of high precision, whose fate choices are subject to significant randomness. But the widespread success of the “noise” concept in describing cellular heterogeneity also points to its weakness: It is easy to describe single-cell properties as “stochastic” and map them into statistical distributions, but doing so does not mean that we understand the underlying cellular process. On the contrary, by creating a façade of understanding, a stochastic description may impede our efforts to uncover the deterministic factors that drive single-cell behavior. Recent years have seen a growing awareness of this caveat and an increase in efforts to identify the deterministic drivers (so-called “hidden variables”) of cellular individuality, but it is fair to say that we still lack a satisfactory picture for what drives single-cell behavior even in the simplest systems, to say nothing of more complex ones. Research goal. The choice between rapid cell death (lysis) and viral dormancy (lysogeny), following infection of E. coli by bacteriophage lambda, serves as a paradigm for the way genetic networks drive cell fate decisions, and for the purported role of molecular randomness in this process. Building on our work over the last decade, we will use lambda infection to identify hidden drivers of cellular individuality in gene regulation and fate choice. By revealing how deterministic the decision process is, we aim to establish lambda as a paradigm for precise— rather than “noisy”—cell fate choice. In parallel to the work on lambda, we will continue to develop tools for the manipulation, imaging, analysis, and modeling of individual cells, and apply them in collaborative projects addressing cellular individuality across diverse biological contexts.

项目摘要 /摘要 生物物理学的一个关键目标是预测生活系统的行为。当时，当 在单细胞水平上检查，这种行为似乎在很大程度上是不可预测的：遗传相同的细胞，内部 一个均匀的环境，在基因表达，信号传导和 随之而来的命运选择。整个生物学都可以观察到这种细胞个性，从 细菌之间的抗生素耐药性，以早期哺乳动物胚胎的细胞分化和许多其他 例子。 在过去的二十年中，研究已经确定了细胞个性的随机起源 证明单分子事件的固有随机性（“噪声”）可以扩增成蛋白质 细胞水平的数量波动。从这些研究中出现的图片是活细胞“嘈杂的 机器”，无法高精度，其脂肪的选择会受到明显的随机性。但是 描述细胞异质性时“噪声”概念的宽度成功也表明了它的弱点：它是 易于描述单细胞属性为“随机”，并将其映射到统计分布中，但这样做 并不意味着我们了解潜在的细胞过程。相反，通过创建一个立面 理解，随机描述可能会阻碍我们揭示驱动因素的确定性因素的努力 单细胞行为。近年来，人们对这一警告的认识越来越大，并增加了 确定蜂窝个性的确定性驱动因素（所谓的“隐藏变量”），但是可以说我们 对于即使在最简单的系统中，什么也没说 更复杂的。 研究目标。在感染之后 大肠杆菌通过噬菌体lambda充当遗传网络驱动细胞脂肪决策的方式的范式， 以及在此过程中所谓的分子随机性的作用。在过去十年的工作中建立 我们将使用Lambda感染来鉴定基因调控和命运选择中细胞个性的隐藏驱动因素。 通过揭示决策过程的确定性，我们旨在将lambda建立为精确的范式 - 而不是“嘈杂”，而是命运的选择。与Lambda的工作并行，我们将继续为 操纵，成像，分析和建模各个单元，并将其应用于协作项目 解决潜水员生物环境中的细胞个性。