The light chain but not the heavy chain of botulinum A toxin inhibits exocytosis from permeabilized adrenal chromaffin cells

The heavy and light chains of botulinum A toxin were separated by anion exchange chromatography. Their intracellular actions were studied using bovine adrenal chromaffin cells permeabilized with streptolysin O. Purified light chain inhibited the Ca2+-stimulated [3H]noradrenaline release with a half-maximal effect at about 1.8 nM. The inhibition was incomplete. Heavy chain up to 28 nM was neither effective by itself nor did it enhance the inhibitory effect of light chain. It is concluded that the light chain of botulinum A toxin contains the functional domain responsible for the inhibition of exocytosis

Vergleich dreier Ringversuche zur radioimmunologischen Thyrotropin-Bestimmung nach dem "Münchner Modell"

Peer Reviewe

Reductive chain separation of botulinum A toxin — a prerequisite to its inhibitory action on exocytosis in chromaffin cells

Cleavage of the disulfide bond linking the heavy and the light chains of tetanus toxin is necessary for its inhibitory action on exocytotic release ofcatecholamines from permeabi1ized chromaffin cells [(1989) FEBS Lett. 242, 245-248; (1989) J. Neurochern., in press]. The related botulinum A toxin also consists of a heavy and a light chain linked by a disulfide bond. The actions ofboth neurotoxins on exocytosis were presently compared using streptolysin O-permeabilized bovine adrenal chromaffin cells. Botulinum A toxin inhibited Ca2 +-stimulated catecholamine release from these cells. Addition of dithiothreitollowered the effective doses to values below 5 nM. Under the same conditions, the effective doses of tetanus toxin were decreased by a factor of five. This indicates that the interchain S-S bond of botulinum A toxin must also be split before the neurotoxin can exert its effect on exocytosis

A Deeper Look into DeepCap

Human performance capture is a highly important computer vision problem with many applications in movie production and virtual/augmented reality. Many previous performance capture approaches either required expensive multi-view setups or did not recover dense space-time coherent geometry with frame-to-frame correspondences. We propose a novel deep learning approach for monocular dense human performance capture. Our method is trained in a weakly supervised manner based on multi-view supervision completely removing the need for training data with 3D ground truth annotations. The network architecture is based on two separate networks that disentangle the task into a pose estimation and a non-rigid surface deformation step. Extensive qualitative and quantitative evaluations show that our approach outperforms the state of the art in terms of quality and robustness. This work is an extended version of DeepCap where we provide more detailed explanations, comparisons and results as well as applications