Quantitative Approaches for Drug Testing in Chronic Toxoplasmosis: Leveraging New Insights Into Bradyzoite Biology Within Tissue Cysts In Vivo

在免疫抑制的情况下，症状性弓形虫病通常与驻留在组织囊肿内的慢殖子介导的慢性感染形式的再激活有关。尽管它们在发病机制中起着核心作用，但对慢殖子的基本生物学知之甚少。事实上，我们的工作（Watts et al. mBio 2015）首次明确证明，体内组织囊肿内的慢殖子是动态复制能力实体。这一观点直接挑战了慢速子的主流教条，以及容纳慢速子的组织囊肿是代谢惰性的。这一观点被认为是为什么慢殖子对有效杀死活跃生长的快殖子的药物具有耐药性的原因。了解对药物介导清除的内在抗性的潜在基础需要在机制基础上加以解决，鉴于我们最近确定了组织囊肿内单个慢殖子生理状态的可量化标记物，这一点现在是可能的。这可以进一步扩展到开发一个框架，用于评估潜在的体内新药，该框架依赖于可量化的生理读数，通过解决组织囊肿内慢殖子群体的行为，而不是目前使用的组织囊肿负担，将大大提高敏感性。这一粗糙的指标将疗效的敏感性限制在2个数量级，即囊肿负荷的百分比减少，其他几乎没有测量。我们开发的BradyCount1.0是一种基于成像的应用程序，用于量化组织囊肿内实际的慢殖子水平，揭示了以前被低估的慢殖子数量相对于组织囊肿大小的异质性。我们利用已知的支架蛋白TgIMC3的动力学和复制背景下的核形态，不仅捕获复制的慢殖子，还捕获它们的相对“年龄”信息。进一步的研究表明，慢殖子线粒体活性和支链淀粉颗粒（储存能量的量度）的积累也是不均匀的。这提供了三个不同的和可能相互关联的生理指标，可以在体内组织囊肿内的单个慢殖子水平上进行评估。通过本研究，我们旨在建立一种新的标准来评估慢性弓形虫感染的进展，并将这种新方法转化为评估现有和未来药物的有效性。为此，我们建议开发BradyCount 2.0，从形态学上量化核谱和慢殖子支架（作为活跃和过去复制的标记）、线粒体谱和AG分布（作为能量和中间代谢的报告者）。通过记录组织囊肿内慢殖子的多个参数，我们将建立一个框架，用可量化的生理指标来定义慢性感染和药物干预的影响。这些输入将用于改进基于马尔可夫链的数据驱动计算模型，以了解缺乏有效治疗的这一神秘阶段。这些研究将大大提高现有和未来药物测试的敏感性，同时提供关键的机制见解。

Symptomatic toxoplasmosis in the context of immune suppression is typically associated with the reactivation of the chronic form of the infection mediated by bradyzoites that reside within tissue cysts. Despite their central role in the pathogenesis, very little is known about the basic biology of bradyzoites. In fact, our work (Watts et al. mBio 2015) was the first to definitively demonstrate that bradyzoites within tissue cysts in vivo are dynamic replication competent entities. This view directly challenged the prevailing dogma of bradyzoites and the tissue cysts that house them were metabolically inert. This view has been proffered as the reason for why bradyzoites are resistant to drugs that effectively kill the actively growing tachyzoites. Understanding the underlying basis for the inherent resistance to drug-mediated clearance needs to be addressed on a mechanistic basis which is now possible given our recent identification of quantifiable markers for the physiological status of individual bradyzoites within tissue cysts. This can further be extended to developing a framework for the evaluation of potential new drugs in vivo that rely on quantifiable physiological readouts that will greatly enhance sensitivity by addressing the behavior of populations of bradyzoites within tissue cysts as opposed to the currently used tissue cyst burden. This crude metric limits the sensitivity of efficacy to 2 orders of magnitude as percentage decreases in cyst burden and little else are measured. Our development of BradyCount1.0, an imaging based application, to quantify the actual bradyzoite levels within tissue cysts, revealed a previously underappreciated level of heterogeneity in bradyzoite numbers relative to tissue cyst size. We exploited what was known about the dynamics of the scaffold protein TgIMC3 and nuclear morphology in the context of replication, to capture not only replicating bradyzoites but also information of their relative “age”. Additional studies revealed that bradyzoite mitochondrial activity and the accumulation amylopectin granules (AG), a measure of stored energy, were also heterogeneous. This provides three distinct and likely interrelated physiological metrics that can be assessed at the level of individual bradyzoites within in vivo derived tissue cysts. With this study, we aim to develop a new standard to assess the progression of the chronic Toxoplasma infections, and to translate this novel approach to assess the effectiveness of existing and future drugs. Toward this end we propose develop BradyCount 2.0 to morphologically quantify nuclear profiles and bradyzoite scaffolds (as a markers of active and past replication), mitochondrial profiles and the distribution of AG as reporters of energy and intermediary metabolism. By recording multiple parameters individually and collectively on bradyzoites within tissue cysts, we will establish a framework to define the chronic infection and the impact of drug intervention employing quantifiable physiological metric. These inputs will be used to refine a Markov Chain-based data-driven computational model to understand this enigmatic phase, for which effective treatments are lacking. These studies will greatly improve the sensitivity of testing of both existing and future drugs while providing crucial mechanistic insights.