Department of Mathematics, University of Arizona, Tucson, Arizona, United States of America.
Department of Applied Mathematics, University of California Merced, Merced, California, United States of America.
PLoS Comput Biol. 2022 Jul 1;18(7):e1010107. doi: 10.1371/journal.pcbi.1010107. eCollection 2022 Jul.
Prion proteins cause a variety of fatal neurodegenerative diseases in mammals but are generally harmless to Baker's yeast (Saccharomyces cerevisiae). This makes yeast an ideal model organism for investigating the protein dynamics associated with these diseases. The rate of disease onset is related to both the replication and transmission kinetics of propagons, the transmissible agents of prion diseases. Determining the kinetic parameters of propagon replication in yeast is complicated because the number of propagons in an individual cell depends on the intracellular replication dynamics and the asymmetric division of yeast cells within a growing yeast cell colony. We present a structured population model describing the distribution and replication of prion propagons in an actively dividing population of yeast cells. We then develop a likelihood approach for estimating the propagon replication rate and their transmission bias during cell division. We first demonstrate our ability to correctly recover known kinetic parameters from simulated data, then we apply our likelihood approach to estimate the kinetic parameters for six yeast prion variants using propagon recovery data. We find that, under our modeling framework, all variants are best described by a model with an asymmetric transmission bias. This demonstrates the strength of our framework over previous formulations assuming equal partitioning of intracellular constituents during cell division.
朊病毒蛋白会导致哺乳动物的多种致命神经退行性疾病，但通常对贝克酵母（酿酒酵母）无害。这使得酵母成为研究与这些疾病相关的蛋白质动力学的理想模式生物。疾病的发病速度与传播子的复制和传播动力学有关，传播子是朊病毒疾病的可传播因子。确定酵母中传播子的复制动力学参数很复杂，因为单个细胞中的传播子数量取决于细胞内的复制动力学以及酵母细胞在生长的酵母细胞群体中的不对称分裂。我们提出了一个结构种群模型，描述了在酵母细胞的活跃分裂群体中朊病毒传播子的分布和复制。然后，我们开发了一种似然方法来估计细胞分裂过程中传播子的复制率及其传输偏向。我们首先证明了从模拟数据中正确恢复已知动力学参数的能力，然后使用传播子回收数据，应用似然方法来估计六种酵母朊病毒变体的动力学参数。我们发现，在我们的建模框架下，所有变体都最好用具有不对称传输偏向的模型来描述。这证明了我们的框架相对于以前的假设在细胞分裂过程中细胞内成分均等分配的公式的优势。
