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Schmid, Michael F.

Publications and source records attributed to Schmid, Michael F..

Dramatic changes in mitochondrial subcellular location and morphology accompany activation of the CO 2 concentrating mechanism

Dynamic changes in intracellular ultrastructure can be critical for the ability of organisms to acclimate to environmental conditions. Microalgae, which are responsible for ~50% of global photosynthesis, compartmentalize their Ribulose 1,5 Bisphosphate Carboxylase/Oxygenase (Rubisco) into a specialized structure known as the pyrenoid when the cells experience limiting CO 2 conditions; this compartmentalization is a component of the CO 2 Concentrating Mechanism (CCM), which facilitates photosynthetic CO 2 fixation as environmental levels of inorganic carbon (Ci) decline. Changes in the spatial distribution of mitochondria in green algae have also been observed under CO 2 limitation, although a role for this reorganization in CCM function remains unclear. We used the green microalga Chlamydomonas reinhardtii to monitor changes in mitochondrial position and ultrastructure as cells transition between high CO 2 and Low/Very Low CO 2 (LC/VLC). Upon transferring cells to VLC, the mitochondria move from a central to a peripheral cell location and orient in parallel tubular arrays that extend along the cell’s apico-basal axis. We show that these ultrastructural changes correlate with CCM induction and are regulated by the CCM master regulator CIA5. The apico-basal orientation of the mitochondrial membranes, but not the movement of the mitochondrion to the cell periphery, is dependent on microtubules and the MIRO1 protein, with the latter involved in membrane–microtubule interactions. Furthermore, blocking mitochondrial respiration in VLC-acclimated cells reduces the affinity of the cells for Ci. Overall, our results suggest that mitochondrial repositioning functions in integrating cellular architecture and energetics with CCM activities and invite further exploration of how intracellular architecture can impact fitness under dynamic environmental conditions.

CO2 concentrating mechanism

Missing Wedge Completion via Unsupervised Learning with Coordinate Networks

Cryogenic electron tomography (cryoET) is a powerful tool in structural biology, enabling detailed 3D imaging of biological specimens at a resolution of nanometers. Despite its potential, cryoET faces challenges such as the missing wedge problem, which limits reconstruction quality due to incomplete data collection angles. Recently, supervised deep learning methods leveraging convolutional neural networks (CNNs) have considerably addressed this issue; however, their pretraining requirements render them susceptible to inaccuracies and artifacts, particularly when representative training data is scarce. To overcome these limitations, we introduce a proof-of-concept unsupervised learning approach using coordinate networks (CNs) that optimizes network weights directly against input projections. This eliminates the need for pretraining, reducing reconstruction runtime by 3–20× compared to supervised methods. Our in silico results show improved shape completion and reduction of missing wedge artifacts, assessed through several voxel-based image quality metrics in real space and a novel directional Fourier Shell Correlation (FSC) metric. Our study illuminates benefits and considerations of both supervised and unsupervised approaches, guiding the development of improved reconstruction strategies.

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