Ca2+ entry at the plasma membrane and uptake by acidic stores is regulated by the activity of the V-H+ -ATPase in Toxoplasma gondii.

Ca2+ is a universal intracellular signal that regulates many cellular functions. In Toxoplasma gondii, the controlled influx of extracellular and intracellular Ca2+ into the cytosol initiates a signaling cascade that promotes pathogenic processes like tissue destruction and dissemination. In this work, we studied the role of proton transport in cytosolic Ca2+ homeostasis and the initiation of Ca2+ signaling. We used a T. gondii mutant of the V-H+ -ATPase, a pump previously shown to transport protons to the extracellular medium, and to control intracellular pH and membrane potential and we show that proton gradients are important for maintaining resting cytosolic Ca2+ at physiological levels and for Ca2+ influx. Proton transport was also important for Ca2+ storage by acidic stores and, unexpectedly, the endoplasmic reticulum. Proton transport impacted the amount of polyphosphate (polyP), a phosphate polymer that binds Ca2+ and concentrates in acidocalcisomes. This was supported by the co-localization of the vacuolar transporter chaperone 4 (VTC4), the catalytic subunit of the VTC complex that synthesizes polyP, with the V-ATPase in acidocalcisomes. Our work shows that proton transport regulates plasma membrane Ca2+ transport and control acidocalcisome polyP and Ca2+ content, impacting Ca2+ signaling and downstream stimulation of motility and egress in T. gondii.

Artifact-Tolerant Clustering-Guided Contrastive Embedding Learning for Ophthalmic Images in Glaucoma.

Ophthalmic images, along with their derivatives like retinal nerve fiber layer (RNFL) thickness maps, play a crucial role in detecting and monitoring eye diseases such as glaucoma. For computer-aided diagnosis of eye diseases, the key technique is to automatically extract meaningful features from ophthalmic images that can reveal the biomarkers (e.g., RNFL thinning patterns) associated with functional vision loss. However, representation learning from ophthalmic images that links structural retinal damage with human vision loss is non-trivial mostly due to large anatomical variations between patients. This challenge is further amplified by the presence of image artifacts, commonly resulting from image acquisition and automated segmentation issues. In this paper, we present an artifact-tolerant unsupervised learning framework called EyeLearn for learning ophthalmic image representations in glaucoma cases. EyeLearn includes an artifact correction module to learn representations that optimally predict artifact-free images. In addition, EyeLearn adopts a clustering-guided contrastive learning strategy to explicitly capture the affinities within and between images. During training, images are dynamically organized into clusters to form contrastive samples, which encourage learning similar or dissimilar representations for images in the same or different clusters, respectively. To evaluate EyeLearn, we use the learned representations for visual field prediction and glaucoma detection with a real-world dataset of glaucoma patient ophthalmic images. Extensive experiments and comparisons with state-of-the-art methods confirm the effectiveness of EyeLearn in learning optimal feature representations from ophthalmic images.