<scp>TMEM</scp>
            41B is a novel regulator of autophagy and lipid mobilization

Moretti, Francesca; Bergman, Philip S.; Dodgson, Stacie E.; Marcellin, David; Claerr, Isabelle; Goodwin, Jonathan M.; DeJesus, Rowena; Zhao, Kang; Antczak, Christophe; Bégué, Damien; Bonenfant, Débora; Graff, Alexandra; Genoud, Christel; Reece‐Hoyes, John S.; Russ, Carsten; Yang, Zinger; Hoffman, Gregory R.; Müeller, Matthias; Murphy, Leon O.; Xavier, Ramnik J.; Nyfeler, Beat

doi:10.15252/embr.201845889

Cited by 136 publications

(153 citation statements)

References 51 publications

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“…As in our study, these screens were performed using cells expressing fluorescently-tagged LC3 or autophagy receptors such as SQSTM1, NPD52, TAX1BP1 or NBR1. All of these screens resulted in the identification of novel candidates, most notably the ER protein TMEM41B, which was shown to participate in autophagosome biogenesis [68] [69] [70]. Our screen differed from previous ones in that we used cells expressing endogenously-tagged LC3B, thus avoiding artifacts of overexpression.…”

Section: Comparison To Previous Crispr/cas9 Ko Screensmentioning

confidence: 99%

Negative Regulation of Autophagy by UBA6-BIRC6–Mediated Ubiquitination of LC3

Jia

Bonifacino

2019

Preprint

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Although the process of autophagy has been extensively studied, the mechanisms that regulate it remain insufficiently understood. The ability to manipulate autophagy is important not only for addressing fundamental biological questions, but also for its possible application to the treatment of various human diseases. To identify novel regulators of autophagy, we performed a whole-genome CRISPR/Cas9 knockout screen in H4 human neuroblastoma cells gene-edited to express the endogenous autophagy effector LC3B fused to a tandem of GFP and mCherry.Using this methodology, we identified the ubiquitin-activating (E1) enzyme UBA6 and the hybrid ubiquitin-conjugating (E2)/ubiquitin-ligase (E3) enzyme BIRC6 as important autophagy regulators. We found that these two enzymes cooperate to monoubiquitinate LC3B on lysine-51, targeting it for degradation by the proteasome. Knockout of UBA6 or BIRC6 increased the levels of LC3B as well as autophagic flux under conditions of nutrient deprivation or protein synthesis inhibition. Moreover, depletion of UBA6 or BIRC6 KO decreased the formation of aggresomelike induced structures in H4 cells, and aggregates of an a-synuclein mutant in the axon of rat hippocampal neurons. These findings demonstrate that UBA6 and BIRC6 negatively regulate autophagy by limiting the availability of LC3B, possibly to prevent the deleterious effects of excessive autophagy. Inhibition of UBA6 or BIRC6, on the other hand, could be used to enhance autophagic clearance of protein aggregates in neurodegenerative disorders.

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Section: Comparison To Previous Crispr/cas9 Ko Screensmentioning

confidence: 99%

Negative Regulation of Autophagy by UBA6-BIRC6–Mediated Ubiquitination of LC3

Jia

Bonifacino

2019

Preprint

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“…However, recent studies have increasingly emphasized the existence of systems controlling and executing autophagy in mammalian cells that are quite different from those in yeast [3]. Among wellaccepted examples, are several autophagy factors absent in yeast that have been identified in organisms from C. elegans to H. sapiens, including ATG101 [4], FIP200 [5], VMP1 [6], EPG5 [7], Stx17 [8,9], and TMEM41B [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Autophagy dark genes: Can we find them with machine learning?

Ti¹,

Yang²,

Dr³

et al. 2019

Preprint

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Identifying novel genes associated with autophagy (ATG) in man remains an important task for gaining complete understanding on this fundamental physiological process. A machine-learning guided approach can highlight potentially "missing pieces" linking core autophagy genes with understudied, "dark" genes that can help us gain deeper insight into these processes. In this study, we used a set of 103 (out of 288 genes from the Autophagy Database, ATGdb), based on the presence of ATG-associated terms annotated from 3 secondary sources: GO (gene ontology), KEGG pathway and UniProt keywords, respectively. We regarded these as additional confirmation for their importance in ATG. As negative labels, we used the OMIM list of genes associated with monogenic diseases (after excluding the 288 ATG-associated genes). Data associated with these genes from 17 different public sources were compiled and used to derive a Meta Path/XGBoost (MPxgb) machine learning model trained to distinguish ATG and non-ATG genes (10-fold cross-validated, 100-times randomized models, median AUC = 0.994 +/-0.0084). Sixteen ATG-relevant variables explain 64% of the total model gain, and 23% of the top 251 predicted genes are annotated in ATGdb. Another 15 genes have potential ATG associations, whereas 193 do not. We suggest that some of these 193 genes may represent "autophagy dark genes", and argue that machine learning can be used to guide autophagy research in order to gain a more complete functional and pathway annotation of this complex process.

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“…As such, it provides an opportunity to uncover new information about the regulation of protein secretion. Pooled CRISPR screening has been used for a wide range of applications, including the investigation of drug-resistance mechanisms in cancer cells 9 , the genetics of pluripotency 10 , autophagy regulators 11,12 and host factors required for viral infection 13 . We previously demonstrated that unbiased pooled genome-wide CRISPR screening could effectively reveal key players required for glycoprotein secretion, using galectin-3 retention at the cell surface to assess glycoprotein secretion 14 .…”

Section: Introductionmentioning

confidence: 99%

Genome-wide CRISPR screening identifies new regulators of glycoprotein secretion

Popa

Villeneuve

Stewart

et al. 2019

Preprint

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The fundamental process of protein secretion from eukaryotic cells has been well described for many years, yet gaps in our understanding of how this process is regulated remain. With the aim of identifying novel genes involved in the secretion of glycoproteins, we used a screening pipeline consisting of a pooled genome-wide CRISPR screen followed by secondary siRNA screening of the hits to identify and validate several novel regulators of protein secretion. We present approximately 50 novel genes not previously associated with protein secretion, many of which also had an effect on the structure of the Golgi apparatus. We further studied a small selection of hits to investigate their subcellular localisation. One of these, GPR161, is a novel Golgi-resident protein that we propose maintains Golgi structure via an interaction with golgin A5.

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TMEM 41B is a novel regulator of autophagy and lipid mobilization

Cited by 136 publications

References 51 publications

Negative Regulation of Autophagy by UBA6-BIRC6–Mediated Ubiquitination of LC3

Negative Regulation of Autophagy by UBA6-BIRC6–Mediated Ubiquitination of LC3

Autophagy dark genes: Can we find them with machine learning?

Genome-wide CRISPR screening identifies new regulators of glycoprotein secretion

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