Niemann-Pick type C (NPC) proteins are essential for sterol homeostasis, believed to drive sterol integration into the vacuolar/lysosomal membrane before redistribution to other cellular membranes. Here, using a combination of crystallography, cryo-electron microscopy, biochemical and in vivo studies on the Saccharomyces cerevisiae NPC system, NCR1/NPC2, we present a framework for sterol membrane integration. Sterols are transferred between hydrophobic pockets of vacuolar NPC2 and membrane-protein NCR1. NCR1 has its N terminal domain (NTD) positioned to deliver a sterol to a tunnel connecting NTD to the luminal membrane leaflet 50 Å away. A sterol is caught inside this tunnel during transport, and a proton-relay network of charged residues in the transmembrane region is linked to this tunnel supporting a proton-driven transport mechanism. We propose a model for sterol integration which clarifies the role of NPC proteins in this essential eukaryotic pathway and which rationalizes mutations in patients with Niemann-Pick disease Type C.

Lipophagy is a form of autophagy by which lipid droplets (LDs) become digested to provide nutrients as a cellular response to starvation. Lipophagy is often studied in yeast, Saccharomyces cerevisiae, in which LDs become internalized into the vacuole. There is a lack of tools to quantitatively assess lipophagy in intact cells with high resolution and throughput. Here, we combine soft X-ray tomography (SXT) with fluorescence microscopy and use a deep learning computational approach to visualize and quantify lipophagy in yeast. We focus on yeast homologs of mammalian Niemann Pick type C proteins, whose dysfunction leads to Niemann Pick type C disease in humans, i.e., NPC1 (named NCR1 in yeast) and NPC2. We developed a convolutional neural network (CNN) model which classifies ring-shaped versus lipid-filled or fragmented vacuoles containing ingested LDs in fluorescence images from wild-type yeast and from cells lacking NCR1 (delta ncr1 cells) or NPC2 (Δnpc2 cells). Using a second CNN model, which performs automated segmentation of LDs and vacuoles from high-resolution reconstructions of X-ray tomograms, we can obtain 3D renderings of LDs inside and outside of the vacuole in a fully automated manner and additionally measure droplet volume, number, and distribution. We find that cells lacking functional NPC proteins can ingest LDs into vacuoles normally but show compromised degradation of LDs and accumulation of lipid vesicles inside vacuoles. This phenotype is most severe in delta npc2 cells. Our new method is versatile and allows for automated high-throughput 3D visualization and quantification of lipophagy in intact cells.