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

在免疫抑制的背景下，症状性弓形虫病通常与由驻留在组织囊肿内的缓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发挥着核心作用，但人们对缓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）是第一个明确证明体内组织囊肿内的缓殖子是动态复制能力的实体。这一观点直接挑战了缓殖子的流行教条，并且容纳它们的组织包囊是代谢惰性的。这一观点被认为是为什么缓殖子对有效杀死活跃生长的速殖子的药物具有抗药性的原因。了解药物介导的清除的内在抗性的基础需要解决的机制的基础上，现在是可能的，因为我们最近确定的组织囊肿内的个体缓殖子的生理状态的可量化的标志物。这可以进一步扩展到开发一个框架，用于在体内评估潜在的新药，其依赖于可量化的生理读数，这将通过解决组织囊肿内的缓殖子群体的行为而不是目前使用的组织囊肿负荷来大大提高灵敏度。这个粗略的度量将疗效的灵敏度限制在2个数量级，因为囊肿负荷的百分比降低，并且几乎没有其他测量。我们开发了BradyCount1.0（一种基于成像的应用程序）来量化组织囊肿内的实际缓殖子水平，揭示了相对于组织囊肿大小的缓殖子数量的异质性水平，这一水平以前未被充分认识。我们利用了已知的支架蛋白TgIMC 3和核形态的动态复制的背景下，捕捉不仅复制缓殖子，但他们的相对“年龄”的信息。进一步的研究表明，缓殖子的线粒体活性和支链淀粉颗粒（AG）的积累，储存能量的措施，也是异质性的。这提供了三个不同的和可能相互关联的生理指标，可以在体内衍生的组织囊肿内的个体缓殖子的水平上进行评估。通过这项研究，我们的目标是开发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形态量化核配置文件和缓殖子支架（作为一个标记的活动和过去的复制），线粒体配置文件和AG的分布作为报告的能源和中间代谢。通过单独和集体记录组织包囊内缓殖子的多个参数，我们将建立一个框架来定义慢性感染和药物干预的影响，采用可量化的生理指标。这些输入将用于完善基于马尔可夫链的数据驱动计算模型，以了解这个神秘的阶段，缺乏有效的治疗方法。这些研究将大大提高现有和未来药物测试的灵敏度，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.