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慢性形式的感染重新激活有关，这种感染是由驻留在组织包囊内的缓殖子介导的。尽管它们在发病机制中起着核心作用，但人们对缓体的基本生物学知之甚少。事实上，我们的工作(Watts等人MBio 2015)首次明确证明体内组织包囊内的缓殖子是具有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，而容纳它们的组织囊在新陈代谢方面是惰性的。这一观点被认为是缓殖子对有效杀死活跃生长的速殖子的药物产生抗药性的原因。了解药物介导清除的内在抵抗的潜在基础需要在机制的基础上解决，这现在是可能的，因为我们最近识别了组织囊内单个缓殖体的生理状态的可量化标记。这可以进一步扩展到开发一个评估体内潜在新药的框架，该框架依赖于可量化的生理读数，该读数将通过解决组织囊内缓殖子种群的行为来极大地提高敏感性，而不是目前使用的组织囊负担。这一粗略的指标将疗效的敏感度限制在2个数量级，因为囊性负担的百分比减少，几乎没有其他指标被测量。我们开发了BradyCount1.0，这是一款基于成像的应用程序，用于量化组织囊内的缓殖体实际水平，揭示了缓体数量相对于组织囊大小的异质性之前被低估的水平。我们利用已知的关于支架蛋白TgIMC3的动力学和复制背景下的核形态，不仅捕捉到复制中的缓虫体，还捕捉到了它们的相对“年龄”的信息。其他研究表明，缓殖体线粒体的活动和积累的支链淀粉颗粒(AG)(衡量储存能量的指标)也是不同的。这提供了三个不同的和可能相互关联的生理指标，可以在体内来源的组织囊内单个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，并将这种新的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来从形态上量化细胞核轮廓和缓殖体支架(作为活跃和过去复制的标记)、线粒体轮廓以及作为能量和中间代谢报告的AG的分布。通过单独和集体记录组织囊内缓殖子的多个参数，我们将建立一个框架，使用可量化的生理指标来定义慢性感染和药物干预的影响。这些输入将被用来改进一个基于马尔可夫链的数据驱动的计算模型，以理解这个缺乏有效治疗的神秘阶段。这些研究将极大地提高现有和未来药物测试的敏感性，同时提供关键的机械洞察力。

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.