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Gil Kanfer

Researcher at National Institutes of Health

Publications -  5
Citations -  149

Gil Kanfer is an academic researcher from National Institutes of Health. The author has contributed to research in topics: TFEB & Protein aggregation. The author has an hindex of 5, co-authored 5 publications receiving 60 citations. Previous affiliations of Gil Kanfer include Howard Hughes Medical Institute.

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Journal ArticleDOI

Loss of TAX1BP1-Directed Autophagy Results in Protein Aggregate Accumulation in the Brain

TL;DR: TAX1BP1 mediates clearance of a broad range of cytotoxic proteins indicating therapeutic potential in neurodegenerative diseases and is more specifically expressed in the brain compared to other autophagy receptors.
Journal ArticleDOI

Image-based pooled whole-genome CRISPRi screening for subcellular phenotypes.

TL;DR: The AI-photoswitchable screening (AI-PS) as discussed by the authors was proposed to classify a broad range of mammalian subcellular morphologies, an approach largely unattainable with current methodologies at genome-wide scale.
Journal ArticleDOI

VPS13D promotes peroxisome biogenesis.

TL;DR: In this paper, the VPS13D gene family was shown to regulate peroxisome biogenesis in HeLa cells and showed that VPS-13D loss leads to either partial or complete peroxideisome loss in several transformed cell lines and in fibroblasts derived from a VPS 13D mutation-carrying patient with recessive spinocerebellar ataxia.
Posted ContentDOI

Selective autophagic clearance of protein aggregates is mediated by the autophagy receptor, TAX1BP1

TL;DR: A broad role is proposed for TAX1BP1 in the clearance of cytotoxic proteins, thus identifying a new mode of clearance of protein inclusions.
Posted ContentDOI

Image-based pooled whole genome CRISPR screening for Parkin and TFEB subcellular localization

TL;DR: This approach, AI-Photoswitchable Screening (AI-PS) offers a novel screening platform capable of classifying a broad range of mammalian subcellular morphologies, an approach largely unattainable with current methodologies at genome-wide scale.