Lipophagy pathways in yeast are controlled by their distinct modes of induction

Fairman, Garrett; Ouimet, Mireille

doi:10.1002/yea.3705

Cited by 6 publications

(9 citation statements)

References 78 publications

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“…When yeast cells are kept in the same culture medium during stationary growth, they become depleted of nutrients, resulting in several starvation responses, including lipophagy, the ingestion of LDs rich in sterol and triacylglycerol esters into the vacuole [2]. We have shown previously using fluorescence microscopy, that NCR1 and NPC2 are instrumental for transport of sterols into the vacuole during stationary growth [8].…”

Section: Resultsmentioning

confidence: 99%

“…Similarly, vacuolar domains, which are thought to be essential for droplet docking during lipophagy appear to be normal in Δncr1 and Δnpc2 cells during stationary phase and glucose starvation [44, 67]. Since the mode of induction of lipophagy seems to affect the molecular machinery, which yeast cells use to execute lipophagy [2], it is possible that NPC proteins are differently involved in stationary phase compared to other forms of lipophagy. In line with this hypothesis are recent findings, that lipid imbalance by disturbed PC synthesis and ER stress affect vacuole domain formation in an NCR1- and NPC2-dependent manner during lipophagy [67].…”

Section: Discussionmentioning

confidence: 99%

“…It is a mechanism by which cells can clear excess lipids and prevent lipid accumulation, playing an important role in maintaining cellular energy homeostasis and regulating lipid metabolism. Lipophagy is gaining interest as an important cellular pathway, not only for mobilizing stored lipids and providing precursors for membrane synthesis, but also as potential mechanism for clearance of miss-folded or aggregated proteins, which have been shown to accumulate on the droplet surface [1, 2]. Defective lipid trafficking is a hallmark of many neurodegenerative diseases, including Niemann Pick type C (NPC) disease.…”

Section: Introductionmentioning

confidence: 99%

“…Yeast vacuoles retain many functions of mammalian lysosomes, and they are due to their larger size amenable to microscopic observation. This, together with the much simpler genetic manipulation compared to mammalian cells make yeast cells a widely established model for studying lipophagy and other autophagic processes [2, 9]. Also, NCR1, the yeast homolog of NPC1, and yeast NPC2 show high structural similarity to mammalian NPC proteins, and both, NPC1- and NPC2-deficient mamalian cells can be rescued by the S. cerevisiae counterpart [10, 11].…”

Section: Introductionmentioning

confidence: 99%

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Automated quantification of lipophagy in Saccharomyces cerevisiae from fluorescence and cryo-soft X-ray microscopy data using deep learning

Egebjerg

Szomek

Thaysen

et al. 2023

Preprint

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automated quantification of lipophagy in Saccharomyces cerevisiae from fluorescence and cryo-soft X-ray microscopy data using deep learning

Egebjerg

Szomek

Thaysen

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Lipophagy allows cells to clear excess lipids, which prevents lipid accumulation, playing an important role in maintaining cellular energy homeostasis and regulating lipid metabolism. Lipophagy is gaining interest as an important cellular pathway, not only for mobilizing stored lipids and providing precursors for membrane synthesis, but also as potential mechanism for clearance of misfolded or aggregated proteins, which have been shown to accumulate on the droplet surface [ 1 , 2 ]. Defective lipid trafficking is a hallmark of many neurodegenerative diseases, including Niemann Pick type C (NPC) disease.…”

Section: Introductionmentioning

confidence: 99%

Automated quantification of vacuole fusion and lipophagy in Saccharomyces cerevisiae from fluorescence and cryo-soft X-ray microscopy data using deep learning

Egebjerg,

Szomek,

Thaysen

et al. 2023

Autophagy

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During starvation in the yeast Saccharomyces cerevisiae vacuolar vesicles fuse and lipid droplets (LDs) can become internalized into the vacuole in an autophagic process named lipophagy. There is a lack of tools to quantitatively assess starvation-induced vacuole fusion and 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 these processes in yeast. We focus on yeast homologs of mammalian NPC1 (NPC intracellular cholesterol transporter 1; Ncr1 in yeast) and NPC2 proteins, whose dysfunction leads to Niemann Pick type C (NPC) disease in humans. We developed a convolutional neural network (CNN) model which classifies fully fused versus partially fused vacuoles based on fluorescence images of stained cells. This CNN, named Deep Yeast Fusion Network (DYFNet), revealed that cells lacking Ncr1 ( ncr1∆ cells) or Npc2 ( npc2∆ cells) have a reduced capacity for vacuole fusion. Using a second CNN model, we implemented a pipeline named LipoSeg to perform automated instance segmentation of LDs and vacuoles from high-resolution reconstructions of X-ray tomograms. From that, we obtained 3D renderings of LDs inside and outside of the vacuole in a fully automated manner and additionally measured droplet volume, number, and distribution. We find that ncr1∆ and npc2∆ cells could ingest LDs into vacuoles normally but showed compromised degradation of LDs and accumulation of lipid vesicles inside vacuoles. Our new method is versatile and allows for analysis of vacuole fusion, droplet size and lipophagy in intact cells. Abbreviations: BODIPY493/503: 4,4-difluoro-1,3,5,7,8-pentamethyl-4-bora-3a,4a-diaza- s -Indacene; BPS: bathophenanthrolinedisulfonic acid disodium salt hydrate; CNN: convolutional neural network; DHE; dehydroergosterol; npc2∆ , yeast deficient in Npc2; DSC, Dice similarity coefficient; EM, electron microscopy; EVs, extracellular vesicles; FIB-SEM, focused ion beam milling-scanning electron microscopy; FM 4-64, N -(3-triethylammoniumpropyl)-4-(6-[4-{diethylamino} phenyl] hexatrienyl)-pyridinium dibromide; LDs, lipid droplets; Ncr1, yeast homolog of human NPC1 protein; ncr1∆ , yeast deficient in Ncr1; NPC, Niemann Pick type C; NPC2, Niemann Pick type C homolog; OD 600 , optical density at 600 nm; ReLU, rectifier linear unit; PPV, positive predictive value; NPV, negative predictive value; MCC, Matthews correlation coefficient; SXT, soft X-ray tomography; UV, ultraviolet; YPD, yeast extract peptone dextrose

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Lipid droplets degradation mechanisms from microalgae to mammals, a comparative overview

Amari,

Carletti,

Yan

et al. 2024

Biochimie

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Lipophagy pathways in yeast are controlled by their distinct modes of induction

Cited by 6 publications

References 78 publications

Automated quantification of lipophagy in Saccharomyces cerevisiae from fluorescence and cryo-soft X-ray microscopy data using deep learning

Automated quantification of lipophagy in Saccharomyces cerevisiae from fluorescence and cryo-soft X-ray microscopy data using deep learning

Automated quantification of vacuole fusion and lipophagy in Saccharomyces cerevisiae from fluorescence and cryo-soft X-ray microscopy data using deep learning

Lipid droplets degradation mechanisms from microalgae to mammals, a comparative overview

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