Calsequestrin depolymerizes when calcium is depleted in the sarcoplasmic reticulum of working muscle.

Calsequestrin, the only known protein with cyclical storage and supply of calcium as main role, is proposed to have other functions, which remain unproven. Voluntary movement and the heart beat require this calcium flow to be massive and fast. How does calsequestrin do it? To bind large amounts of calcium in vitro, calsequestrin must polymerize and then depolymerize to release it. Does this rule apply inside the sarcoplasmic reticulum (SR) of a working cell? We answered using fluorescently tagged calsequestrin expressed in muscles of mice. By FRAP and imaging we monitored mobility of calsequestrin as [Ca2+] in the SR--measured with a calsequestrin-fused biosensor--was lowered. We found that calsequestrin is polymerized within the SR at rest and that it depolymerized as [Ca2+] went down: fully when calcium depletion was maximal (a condition achieved with an SR calcium channel opening drug) and partially when depletion was limited (a condition imposed by fatiguing stimulation, long-lasting depolarization, or low drug concentrations). With fluorescence and electron microscopic imaging we demonstrated massive movements of calsequestrin accompanied by drastic morphological SR changes in fully depleted cells. When cells were partially depleted no remodeling was found. The present results support the proposed role of calsequestrin in termination of calcium release by conformationally inducing closure of SR channels. A channel closing switch operated by calsequestrin depolymerization will limit depletion, thereby preventing full disassembly of the polymeric calsequestrin network and catastrophic structural changes in the SR.

Artificial intelligence approaches to the volumetric quantification of glycogen granules in EM images of human tissue.

Skeletal muscle, the major processor of dietary glucose, stores it in myriad glycogen granules. Their numbers vary with cellular location and physiological and pathophysiological states. AI models were developed to derive granular glycogen content from electron-microscopic images of human muscle. Two UNet-type semantic segmentation models were built: "Locations" classified pixels as belonging to different regions in the cell; "Granules" identified pixels within granules. From their joint output, a pixel fraction pf was calculated for images from patients positive (MHS) or negative (MHN) to a test for malignant hyperthermia susceptibility. pf was used to derive vf, the volume fraction occupied by granules. The relationship vf (pf) was derived from a simulation of volumes ("baskets") containing virtual granules at realistic concentrations. The simulated granules had diameters matching the real ones, which were measured by adapting a utility devised for calcium sparks. Applying this relationship to the pf measured in images, vf was calculated for every region and patient, and from them a glycogen concentration. The intermyofibrillar spaces and the sarcomeric I band had the highest granular content. The measured glycogen concentration was low enough to allow for a substantial presence of non-granular glycogen. The MHS samples had an approximately threefold lower concentration (significant in a hierarchical test), consistent with earlier evidence of diminished glucose processing in MHS. The AI models and the approach to infer three-dimensional magnitudes from two-dimensional images should be adaptable to other tasks on a variety of images from patients and animal models and different disease conditions.