TgLaforin, a glucan phosphatase, reveals the dynamic role of storage polysaccharides in Toxoplasma gondii tachyzoites and bradyzoites.

The asexual stages of Toxoplasma gondii are defined by the rapidly growing tachyzoite during the acute infection and by the slow growing bradyzoite housed within tissue cysts during the chronic infection. These stages represent unique physiological states, each with distinct glucans reflecting differing metabolic needs. A defining feature of T. gondii bradyzoites is the presence of insoluble storage glucans known as amylopectin granules (AGs) that are believed to play a role in reactivation, but their functions during the chronic infection remain largely unexplored. More recently, the presence of storage glucans has been recognized in tachyzoites where their precise function and architecture have yet to be fully defined. Importantly, the T. gondii genome encodes activities needed for glucan turnover: a glucan phosphatase (TgLaforin; TGME49_205290) and a glucan kinase (TgGWD; TGME49_214260) that catalyze a cycle of reversible glucan phosphorylation required for glucan degradation by amylases. The expression of these enzymes in tachyzoites supports the existence of a storage glucan, evidence that is corroborated by specific labeling with the anti-glycogen antibody IV58B6. Disruption of reversible glucan phosphorylation via a CRISPR/Cas9 knockout (KO) of TgLaforin revealed no growth defects under nutrient-replete conditions in tachyzoites. However, the growth of TgLaforin-KO tachyzoites was severely stunted when starved of glutamine, even under glucose replete conditions. The loss of TgLaforin also resulted in the attenuation of acute virulence in mice accompanied by a lower cyst burden. Defective cyst formation due to profound changes in AG morphology was also observed in TgLaforin-KO parasites, both in vitro and in vivo. Together, these data demonstrate the importance of glucan turnover across the T. gondii asexual cycle. These findings, alongside our previously identified class of small molecules that inhibit TgLaforin, implicate reversible glucan phosphorylation as a legitimate target for the development of new drugs against chronic T. gondii infections.

Factors Influencing Tissue Cyst Yield in a Murine Model of Chronic Toxoplasmosis.

Recent advances into the unique biology of Toxoplasma tissue cysts and the bradyzoites they house necessitate optimization of tissue cyst recovery from infected mouse brains. Here, we present data from 83 tissue cyst purifications of Type II ME49 tissue cysts in CBA/J mice performed over a period of 3 years. The effects of infection with both tissue culture tachyzoites as well as ex vivo tissue cysts were assessed. Significant mortality was restricted to tachyzoite infections with female mice being more susceptible. Infection with tissue cysts was associated with both lower overall symptomology and mortality, exhibiting no sex bias. Cumulatively, host sex did not impact overall tissue cyst yields, although tachyzoite-initiated infections generated significantly higher yields compared to tissue cyst-initiated infections. Notably, serial passage of tissue cysts was accompanied with a decreasing trend for subsequent cyst recovery. The time of tissue cyst harvest, a potential reflection of bradyzoite physiological state, had no significant impact on subsequent cyst yield at the selected time points. In aggregate, these data reveal the considerable heterogeneity associated with tissue cyst yield, making the design of adequately powered experiments critical. This is particularly the case for drug studies where overall tissue cyst burden is currently the primary and often sole metric of efficacy, as the data presented here demonstrate that cyst recovery between preparations of untreated animals can mirror and even exceed the reported effects of drug treatment.

Machine learning based classification of mitochondrial morphologies from fluorescence microscopy images of Toxoplasma gondii cysts.

The mitochondrion is intimately linked to energy and overall metabolism and therefore the morphology of mitochondrion can be very informative for inferring the metabolic state of cells. In this study we report an approach for automatic classification of mitochondrial morphologies using supervised machine learning to efficiently classify them from a large number of cells at a time. Fluorescence microscopy images of the chronic encysted form of parasite Toxoplasma gondii were used for this development. Manually classifying these morphologies from the hundreds of parasites within typical tissue cysts is tedious and error prone. In addition, because of inherent biological heterogeneity in morphologies, there can be variability and lack of reproducibility in manual classification. We used image segmentation to detect mitochondrial shapes and used features extracted from them in a multivariate logistic regression model to classify the detected shapes into five morphological classes: Blobs, Tadpoles, Lasso/Donuts, Arcs, and Other. The detected shapes from a subset of images were first used to obtain consensus classification among expert users to obtain a labeled set. The model was trained using the labeled set from five cysts and its performance was tested on the mitochondrial morphologies from ten other cysts that were not used in training. Results showed that the model had an average overall accuracy of 87%. There was high degree of confidence in the classification of Blobs and Arcs (average F scores 0.91 and 0.73) which constituted the majority of morphologies (85%). Although the current development used microscopy images from tissue cysts of Toxoplasma gondii, the approach is adaptable with minor adjustments and can be used to automatically classify morphologies of organelles from a variety of cells.

Computer Aided Image Processing to Facilitate Determination of Congruence in Manual Classification of Mitochondrial Morphologies in Toxoplasma gondii Tissue Cysts.

Toxoplasma gondii is a parasite that chronically infects about a third of the world's population. During chronic infection, the parasite resides within tissue cysts in the form of poorly understood bradyzoites which can number in the thousands. Our prior work showed that these bradyzoites are metabolically active exhibiting heterogeneous replication potential. The morphological plasticity of the mitochondrion potentially informs about parasite metabolic state. We developed an image processing based program to assist manual classification of mitochondrial morphologies by trained operators to collect data and statistics from the manual classification of shapes. We sought to determine whether certain morphologies were readily classifiable and the congruence among manual classifiers, i.e. the degree to which different operators would place the same objects within the same class. Results from three operators classifying mitochondrial morphologies from 5 tissue cyst images showed that among the four classes, one (Blobs) were the easiest to classify. There was remarkable congruence between 2 of the 3 operators in classifying the objects (96%), while the agreement among all 3 operators was somewhat modest (57%). Such information would be valuable for biologists studying these parasites as well as in development of fully automated methods of morphological classification.