A Residual N-Terminal Peptide Enhances Signaling of Depalmitoylated Hedgehog to the Patched Receptor.

During their biosynthesis, Sonic hedgehog (Shh) morphogens are covalently modified by cholesterol at the C-terminus and palmitate at the N-terminus. Although both lipids initially anchor Shh to the plasma membrane of producing cells, it later translocates to the extracellular compartment to direct developmental fates in cells expressing the Patched (Ptch) receptor. Possible release mechanisms for dually lipidated Hh/Shh into the extracellular compartment are currently under intense debate. In this paper, we describe the serum-dependent conversion of the dually lipidated cellular precursor into a soluble cholesteroylated variant (ShhC) during its release. Although ShhC is formed in a Dispatched- and Scube2-dependent manner, suggesting the physiological relevance of the protein, the depalmitoylation of ShhC during release is inconsistent with the previously postulated function of N-palmitate in Ptch receptor binding and signaling. Therefore, we analyzed the potency of ShhC to induce Ptch-controlled target cell transcription and differentiation in Hh-sensitive reporter cells and in the Drosophila eye. In both experimental systems, we found that ShhC was highly bioactive despite the absence of the N-palmitate. We also found that the artificial removal of N-terminal peptides longer than eight amino acids inactivated the depalmitoylated soluble proteins in vitro and in the developing Drosophila eye. These results demonstrate that N-depalmitoylated ShhC requires an N-peptide of a defined minimum length for its signaling function to Ptch.

U-Net-Based Segmentation of Current Imaging Biomarkers in OCT-Scans of Patients with Age Related Macular Degeneration.

Age-related macular degeneration (AMD) is the leading cause of blindness in the Western world. In this work, the non-invasive imaging technique spectral domain optical coherence tomography (SD-OCT) is used to acquire retinal images, which are then analyzed using deep learning techniques. The authors trained a convolutional neural network (CNN) using 1300 SD-OCT scans annotated by trained experts for the presence of different biomarkers associated with AMD. The CNN was able to accurately segment these biomarkers and the performance was further enhanced through transfer learning with weights from a separate classifier, trained on a large external public OCT dataset to distinguish between different types of AMD. Our model is able to accurately detect and segment AMD biomarkers in OCT scans, which suggests that it could be useful for prioritizing patients and reducing ophthalmologists' workloads.