BioID reveals an ATG9A interaction with ATG13‐ATG101 in the degradation of p62/SQSTM1‐ubiquitin clusters
2021; Springer Nature; Volume: 22; Issue: 10 Linguagem: Inglês
10.15252/embr.202051136
ISSN1469-3178
AutoresAshari Rashmi Kannangara, Daniel M. Poole, Colten M. McEwan, Joshua C. Youngs, Vajira Weeresekara, Alexandra Thornock, Misael T Lazaro, Eranga R. Balasooriya, Laura M. Oh, Erik J. Soderblom, Jonathan J. Lee, Daniel L. Simmons, Joshua L. Andersen,
Tópico(s)RNA modifications and cancer
ResumoArticle9 August 2021free access Source DataTransparent process BioID reveals an ATG9A interaction with ATG13-ATG101 in the degradation of p62/SQSTM1-ubiquitin clusters Ashari R Kannangara Ashari R Kannangara Department of Chemistry and Biochemistry, Fritz B. Burns Cancer Research Laboratory, Brigham Young University, Provo, UT, USA These authors contributed equally to this work Search for more papers by this author Daniel M Poole Daniel M Poole Department of Chemistry and Biochemistry, Fritz B. Burns Cancer Research Laboratory, Brigham Young University, Provo, UT, USA These authors contributed equally to this work Search for more papers by this author Colten M McEwan Colten M McEwan Department of Chemistry and Biochemistry, Fritz B. Burns Cancer Research Laboratory, Brigham Young University, Provo, UT, USA Search for more papers by this author Joshua C Youngs Joshua C Youngs Department of Chemistry and Biochemistry, Fritz B. Burns Cancer Research Laboratory, Brigham Young University, Provo, UT, USA Search for more papers by this author Vajira K Weerasekara Vajira K Weerasekara orcid.org/0000-0002-9668-7091 Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA Department of Medicine, Harvard Medical School, Boston, MA, USA Search for more papers by this author Alex M Thornock Alex M Thornock orcid.org/0000-0003-0511-4376 Department of Chemistry and Biochemistry, Fritz B. Burns Cancer Research Laboratory, Brigham Young University, Provo, UT, USA Search for more papers by this author Misael T Lazaro Misael T Lazaro Department of Chemistry and Biochemistry, Fritz B. Burns Cancer Research Laboratory, Brigham Young University, Provo, UT, USA Search for more papers by this author Eranga R Balasooriya Eranga R Balasooriya orcid.org/0000-0002-5491-3089 Department of Chemistry and Biochemistry, Fritz B. Burns Cancer Research Laboratory, Brigham Young University, Provo, UT, USA Search for more papers by this author Laura M Oh Laura M Oh Department of Chemistry and Biochemistry, Fritz B. Burns Cancer Research Laboratory, Brigham Young University, Provo, UT, USA Search for more papers by this author Erik J Soderblom Erik J Soderblom Proteomics and Metabolomics Shared Resource, Duke University School of Medicine, Durham, NC, USA Search for more papers by this author Jonathan J Lee Jonathan J Lee orcid.org/0000-0002-7940-1290 Department of Chemistry and Biochemistry, Fritz B. Burns Cancer Research Laboratory, Brigham Young University, Provo, UT, USA Search for more papers by this author Daniel L Simmons Daniel L Simmons Department of Chemistry and Biochemistry, Fritz B. Burns Cancer Research Laboratory, Brigham Young University, Provo, UT, USA Search for more papers by this author Joshua L Andersen Corresponding Author Joshua L Andersen [email protected] orcid.org/0000-0003-2071-744X Department of Chemistry and Biochemistry, Fritz B. Burns Cancer Research Laboratory, Brigham Young University, Provo, UT, USA Search for more papers by this author Ashari R Kannangara Ashari R Kannangara Department of Chemistry and Biochemistry, Fritz B. Burns Cancer Research Laboratory, Brigham Young University, Provo, UT, USA These authors contributed equally to this work Search for more papers by this author Daniel M Poole Daniel M Poole Department of Chemistry and Biochemistry, Fritz B. Burns Cancer Research Laboratory, Brigham Young University, Provo, UT, USA These authors contributed equally to this work Search for more papers by this author Colten M McEwan Colten M McEwan Department of Chemistry and Biochemistry, Fritz B. Burns Cancer Research Laboratory, Brigham Young University, Provo, UT, USA Search for more papers by this author Joshua C Youngs Joshua C Youngs Department of Chemistry and Biochemistry, Fritz B. Burns Cancer Research Laboratory, Brigham Young University, Provo, UT, USA Search for more papers by this author Vajira K Weerasekara Vajira K Weerasekara orcid.org/0000-0002-9668-7091 Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA Department of Medicine, Harvard Medical School, Boston, MA, USA Search for more papers by this author Alex M Thornock Alex M Thornock orcid.org/0000-0003-0511-4376 Department of Chemistry and Biochemistry, Fritz B. Burns Cancer Research Laboratory, Brigham Young University, Provo, UT, USA Search for more papers by this author Misael T Lazaro Misael T Lazaro Department of Chemistry and Biochemistry, Fritz B. Burns Cancer Research Laboratory, Brigham Young University, Provo, UT, USA Search for more papers by this author Eranga R Balasooriya Eranga R Balasooriya orcid.org/0000-0002-5491-3089 Department of Chemistry and Biochemistry, Fritz B. Burns Cancer Research Laboratory, Brigham Young University, Provo, UT, USA Search for more papers by this author Laura M Oh Laura M Oh Department of Chemistry and Biochemistry, Fritz B. Burns Cancer Research Laboratory, Brigham Young University, Provo, UT, USA Search for more papers by this author Erik J Soderblom Erik J Soderblom Proteomics and Metabolomics Shared Resource, Duke University School of Medicine, Durham, NC, USA Search for more papers by this author Jonathan J Lee Jonathan J Lee orcid.org/0000-0002-7940-1290 Department of Chemistry and Biochemistry, Fritz B. Burns Cancer Research Laboratory, Brigham Young University, Provo, UT, USA Search for more papers by this author Daniel L Simmons Daniel L Simmons Department of Chemistry and Biochemistry, Fritz B. Burns Cancer Research Laboratory, Brigham Young University, Provo, UT, USA Search for more papers by this author Joshua L Andersen Corresponding Author Joshua L Andersen [email protected] orcid.org/0000-0003-2071-744X Department of Chemistry and Biochemistry, Fritz B. Burns Cancer Research Laboratory, Brigham Young University, Provo, UT, USA Search for more papers by this author Author Information Ashari R Kannangara1, Daniel M Poole1, Colten M McEwan1, Joshua C Youngs1, Vajira K Weerasekara2,3, Alex M Thornock1, Misael T Lazaro1, Eranga R Balasooriya1, Laura M Oh1, Erik J Soderblom4, Jonathan J Lee1, Daniel L Simmons1 and Joshua L Andersen *,1 1Department of Chemistry and Biochemistry, Fritz B. Burns Cancer Research Laboratory, Brigham Young University, Provo, UT, USA 2Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA 3Department of Medicine, Harvard Medical School, Boston, MA, USA 4Proteomics and Metabolomics Shared Resource, Duke University School of Medicine, Durham, NC, USA *Corresponding author. Tel: +1 801 422 7193; E-mail: [email protected] EMBO Reports (2021)22:e51136https://doi.org/10.15252/embr.202051136 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract ATG9A, the only multi-pass transmembrane protein among core ATG proteins, is an essential regulator of autophagy, yet its regulatory mechanisms and network of interactions are poorly understood. Through quantitative BioID proteomics, we identify a network of ATG9A interactions that includes members of the ULK1 complex and regulators of membrane fusion and vesicle trafficking, including the TRAPP, EARP, GARP, exocyst, AP-1, and AP-4 complexes. These interactions mark pathways of ATG9A trafficking through ER, Golgi, and endosomal systems. In exploring these data, we find that ATG9A interacts with components of the ULK1 complex, particularly ATG13 and ATG101. Using knockout/reconstitution and split-mVenus approaches to capture the ATG13-ATG101 dimer, we find that ATG9A interacts with ATG13-ATG101 independently of ULK1. Deletion of ATG13 or ATG101 causes a shift in ATG9A distribution, resulting in an aberrant accumulation of ATG9A at stalled clusters of p62/SQSTM1 and ubiquitin, which can be rescued by an ULK1 binding-deficient mutant of ATG13. Together, these data reveal ATG9A interactions in vesicle-trafficking and autophagy pathways, including a role for an ULK1-independent ATG13 complex in regulating ATG9A. Synopsis This study presents the ATG9A proximity interactome, revealing interactions with vesicle trafficking complexes, core autophagy regulators, and an ULK1-independent ATG13-ATG101 complex that promotes the basal turnover of ubiquitin-rich p62/SQSTM1 clusters. BioID reveals ATG9A interactions with trafficking regulators, including an ULK1-independent ATG13-ATG101 complex. Loss of ATG13 causes a build-up of ATG9A at stalled ubiquitin-rich p62/SQSTM1 clusters. Reconstitution of ATG13-deficient cells with an ULK1-binding defective ATG13 rescues ATG9A trafficking. Introduction The recycling of misfolded proteins, dysfunctional organelles, and other molecules through macroautophagy (referred to here as autophagy) is critical for maintaining cellular homeostasis and promoting cell survival during stress. Deregulated autophagy underlies the pathophysiology of many human diseases, including a variety of degenerative disorders, cancer, autoimmunity, and infectious disease. The central event in autophagy is the formation of the autophagosome, which begins as a double-membrane cisterna that expands and captures portions of the cytosol/cell and ultimately closes to form a sealed vesicle. The autophagosome then fuses with the lysosome for degradation and recycling of the autophagosome contents. The flux of autophagy substrates through this degradative pathway increases in breadth and rate under nutrient deprivation. In contrast, under nutrient replete conditions, a more selective, lower level of autophagy (referred to here as “basal autophagy”) maintains organelle and protein homeostasis (Komatsu et al, 2005; Hara et al, 2006; Komatsu et al, 2006; Antonucci et al, 2015). Defects in basal autophagy can lead to the accumulation of defective mitochondria and toxic protein aggregates that underlie a variety of degenerative diseases (Hara et al, 2006; Komatsu et al, 2006; Dikic & Elazar, 2018). Our understanding of the upstream signaling that controls autophagy mainly derives from studies on nutrient deprivation, in which the inhibition of mTORC1 results in the activation of the ULK1 kinase complex that includes FIP200, ATG101, and ATG13 (Hosokawa et al, 2009a; Lee et al, 2010; Egan et al, 2011; Kim et al, 2011; Shang et al, 2011). Active ULK1 complex then coordinates a variety of autophagy events, such as recruitment of VPS34 lipid kinase complex, that stimulates formation of the membrane precursor to the autophagosome, referred to as the isolation membrane (IM) (Zachari & Ganley, 2017). The location of this emergent autophagosome is also called the phagophore assembly site (PAS). Additional autophagy regulatory proteins are recruited to the IM/PAS, including ATG5-ATG12-ATG16L1 conjugation systems that attach the ubiquitin-like protein LC3 to autophagosomes. In contrast to starvation-induced autophagy, basal autophagy is primarily driven by a variety of autophagy adaptors, including p62/SQSTM1, Optineurin, and TAX1BP1, that selectively deliver cargo to the autophagosome. For example, p62/SQSTM1 interacts with poly-ubiquitinated cargo via its ubiquitin association domain and then tethers these cargo to the LC3-decorated autophagosomes via its LC3-interacting region (Seibenhener et al, 2004; Pankiv et al, 2007). Transition of these p62/SQSTM1-poly-ubiquitinated protein complexes into phase-separated droplets appears to be a precursor to cargo degradation (Cloer et al, 2018; Sun et al, 2018; Jakobi et al, 2020). However, given that basal autophagy occurs under conditions in which ULK1 activity is low (and MTORC1 activity is high), the hierarchy of signaling that governs basal autophagy, including how core autophagy machinery (e.g., ATG9A) is engaged and regulated, is not yet clear. ATG9A is essential for the formation of autophagosomes (Kuma et al, 2004; Saitoh et al, 2009; Yamamoto et al, 2012), but is one of the least understood of the core ATG proteins. Studies from yeast and mammalian cells suggest that ATG9A (referred to as Atg9 in yeast) traffics on small membrane vesicles and accumulates at several sites within vesicular trafficking pathways, including the Golgi, endosomes, and ER where it colocalizes with IM/PAS markers (Young et al, 2006; Mari et al, 2010; Orsi et al, 2012; Imai et al, 2016; Takahashi et al, 2016; Kakuta et al, 2017; Nishimura et al, 2017). A few proteins have been identified as regulators of ATG9A trafficking, including the coat adaptors AP-1, AP-2, and AP-4, components of the ULK1 complex, BIF-1, and p38IP (Young et al, 2006; Takahashi et al, 2011; Tang et al, 2011; Guo et al, 2012; Orsi et al, 2012; Popovic & Dikic, 2014; Ktistakis & Tooze, 2016; Mattera et al, 2017; Davies et al, 2018). The trafficking/mobilization of ATG9A to the IM/PAS is considered an apical step in autophagy (Itakura et al, 2012; Kishi-Itakura et al, 2014; Karanasios et al, 2016). While at the IM/PAS, ATG9A is thought to supply membrane to growing autophagosomes, although the mechanism by which this may occur is still unclear (Yamamoto et al, 2012; Judith et al, 2019). Several recent studies indicate that, in addition to the role of ATG9A in starvation-induced autophagy, ATG9A is essential for basal autophagy—potentially in ways that do not easily fit within current autophagy paradigms. Although ATG9A KO MEFs still display LC3B puncta (suggesting that autophagosomes still form in the absence of ATG9A) (Saitoh et al, 2009), studies focused on the basal lysosomal turnover of autophagy adaptors demonstrate a strong requirement for ATG9A. For example, degradative flux of the autophagy adaptor NBR1 is largely independent of ULK1 and ATG factors required for LC3 lipidation, but is entirely dependent on ATG9A. Similarly, ATG9A emerged as a top hit in a genome-wide CRISPR/Cas9 screen for proteins required for basal lysosomal degradation of p62/SQSTM1, while a variety of core ATG proteins were notably not essential (Goodwin et al, 2017). In addition, the tyrosine kinase Src phosphorylates ATG9A at Tyr8 to maintain active ATG9A trafficking under basal conditions (Zhou et al, 2017). Furthermore, defective ATG9A trafficking (or genetic loss of ATG9A) is associated with impaired clearance of protein aggregates (Winslow et al, 2010; De Pace et al, 2018; Yamaguchi et al, 2018). Together, these data support a central role for ATG9A in basal autophagy. However, the general mechanisms that control basal autophagy are poorly understood, including how ATG9A may interact with autophagy machinery to promote the constitutive turnover of basal autophagy cargo/adaptors. Here, we take advantage of BioID and quantitative LC-MS/MS to identify a network of proximity-based ATG9A interactions that include a variety of vesicular trafficking complexes and autophagy regulators. In exploring these interactions further, we discover that ATG9A interacts with an ULK1-independent ATG13 “subcomplex” that is essential for proper ATG9A trafficking and basal turnover of p62/SQSTM1. Together, our data elucidate a diverse array of novel ATG9A interactions and reveal, to our knowledge, the first ULK1-independent role for ATG13 in regulating ATG9A function. Results BioID reveals proximity-based interactions between ATG9A and a network of trafficking proteins and complexes With the ultimate goal of elucidating the interactome of ATG9A, we first assessed potential protein–protein docking regions along the putative ATG9A structure. The long C terminus of ATG9A bears some hallmarks of a signaling hub, including a high degree of predicted intrinsic disorder and a concentration of phosphorylation sites that are repeatedly identified in global PTM mass spectrometry studies (Fig EV1A). These include several phosphorylations with over 20 independent mass spectrometry identifications (S735, S738, S741, S828) and an AMPK-mediated phosphorylation at S761 that we identified as a 14-3-3ζ docking site (Weerasekara et al, 2014). In addition, there is evidence from structural and molecular studies that ATG9A self-associates via its C termini, which might further expand its ability to act as a protein docking site or signaling hub (He et al, 2008; Staudt et al, 2016; Lai et al, 2020). In support of this idea, we found that ATG9A fused to split-mVenus molecules at its C termini produced robust BiFC signal in a perinuclear pattern (Fig EV1B), consistent with known localization patterns of ATG9A (Young et al, 2006; Orsi et al, 2012). In addition, we found that a C-terminally truncated ATG9A was unable to fully rescue defective LC3 processing in an ATG9A KO line (Fig EV1C). These data suggest that the ATG9A C terminus is critical for ATG9A function and likely a hub of multiple protein–protein interactions. Click here to expand this figure. Figure EV1. The C terminus of ATG9A harbors hallmarks of a signaling hub and is involved in self-association, and ATG9A trafficking Graph showing disorder tendency of ATG9A across its amino acid sequence (predicted by ANCHOR web server at http://anchor.elte.hu/) and PTM distribution across the protein quantified by High Throughput Paper (HTP) observations (generated by PhosphoSitePlus® at https://www.phosphosite.org/psrSearchAction) (top). Schematic of ATG9A domain structure (bottom). Schematic representation showing N- or C-terminal halves of mVenus fused to C terminus of ATG9A (top left). HCT-116 cells expressing ATG9A C-terminally labeled N mVenus or N + C mVenus constructs were grown in full DMEM media, fixed, and imaged (Scale bar = 10 µm) (right). Fluorescence intensity of cells expressing ATG9A C-terminally labeled mVenus constructs was measured via flow cytometry. Mean ± SEM, n = 1(bottom left). Hela ATG9A WT, ATG9A KO, or ATG9A KO cells reconstituted with ATG9A WT and ATG9A ΔC mutant stably expressing GFP-LC3 were grown in full media and whole-cell lysates followed by immunoblotting with indicated antibodies (left). Quantification of normalized GFP and normalized LCII/LC3I ratio. Mean ± SEM, n = 3 (biological replicates). Significance measured using RM one-way ANOVA followed by Fisher’s LSD tests. nsP > 0.05, *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001. Download figure Download PowerPoint Our efforts to probe ATG9A protein–protein interactions by co-IP proteomics had limited success (unpublished). The multi-pass transmembrane nature of ATG9A presents challenges to co-IP proteomics, most notably the difficulty of extracting ATG9A from intracellular membranes while maintaining protein–protein interactions. On the contrary, these same qualities make ATG9A a good candidate for BioID (Roux et al, 2012), in which promiscuous interactions are relatively limited by ATG9A being fixed in membrane. Thus, we fused the modified bacterial biotin ligase BirA (R118G—denoted with an asterisk) to the C terminus of hemagglutinin (HA)-tagged ATG9A (HA-ATG9A-BirA*) (Roux et al, 2012; Rees et al, 2015). We verified that fusion of BirA* to the ATG9A C terminus did not impair the function of ATG9A, as the HA-ATG9A-BirA* construct was able to fully rescue the defects in p62/SQSTM1 degradation and LC3B lipidation in ATG9A KO cells and showed the same cellular localization patterns as endogenous ATG9A (Figs 1A and EV2A–C). We also verified that biotin signal overlaps with HA-ATG9A-BirA* (Fig EV2D). Figure 1. BioID reveals a network of ATG9A interactors, including multiple trafficking regulators and members of the ULK1 complex HEK293T ATG9A WT, ATG9A KO, or ATG9A KO cells reconstituted with overexpressed HA-ATG9A and HA-ATG9A-BirA* were grown in full DMEM media. Endogenous p62/SQSTM1 level was measured by immunoblot (top). Graph below shows quantification of p62 infrared signal normalized to Actin. Mean ± SEM, n = 3 (biological replicates). Significance measured using RM one-way ANOVA followed by Fisher’s LSD tests. nsP > 0.05, *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001. HEK293T parental cells or HEK293T cells stably expressing HA-BirA* or HA-ATG9A-BirA* (stably integrated with lentivirus) were grown in full DMEM media, treated with 50 µM biotin for 12 h, followed by detergent lysis and incubation with streptavidin resin. Streptavidin pulldown samples were resolved in a 4–15% gradient gel and Coomassie stained. An experimental schematic of Bio-ID workflow. Quantitative proteomics data from three independent experiments were analyzed by volcano plot. Significant interactors were selected based on a cutoff of P ≤ 0.05 (two-tailed heteroscedastic t-test) and ≥ 2-fold increase in interaction comparing the AUC signal for each peptide from HA-ATG9A-BirA* versus HA-BirA* samples. A log 2-fold change = 1 and -log P -value 0.05 = 1.3 were marked by dash lines on the volcano plot. Significant interactors were colored in salmon, and interactors of particular interest were color coded (See heat map S2B). A subset of interactors were validated by immunoblotting with indicated antibodies after the streptavidin pulldown step in panel C. The subcellular localization of significantly scored interactors from three independent proteomics experiments was assigned by using panther GO enrichment analysis (http://www.geneontology.org/page/go-enrichment-analysis). A schematic representation of proteins identified by proteomics analysis grouped into protein complexes and associated trafficking pathways. Protein complexes were assembled from the GO enrichment and analyzed using STRING (https://string-db.org/). Download figure Download PowerPoint Click here to expand this figure. Figure EV2. Functional validation of HA-ATG9A-BirA* constructs HEK293T WT, ATG9A KO, and ATG9A KO cells reconstituted with HA-ATG9A and HA-ATG9A-BirA* were grown in full DMEM media, lysed, and immunoblotted with indicated proteins (left). Quantification of normalized LC3B infrared signal. Mean ± SEM, n = 3 (biological replicates). Significance measured using RM one-way ANOVA followed by Fisher’s LSD tests (right). HEK293T WT, ATG9A KO, and ATG9A KO cells reconstituted with HA-ATG9A and HA-ATG9A-BirA* were grown in full DMEM media with or without 100nM Bafilomycin for 24 h, lysed, and immunoblotted with indicated proteins (left). Quantification of normalized p62 infrared signal. Mean ± SEM, n = 3 (biological replicates). Significance measured using RM one-way ANOVA followed by Fisher’s LSD tests (right). Confocal images of p62/SQSTM1 colocalization with HA-ATG9A-BirA* in HEK293T ATG13 WT and ATG13 KO cells stably expressing HA-ATG9A-BirA*. Cells were grown in full DMEM media, fixed, and labeled with antibodies for p62/SQSTM1 and HA (Scale bar = 10 µm). Confocal images of biotinylated proteins colocalized with HA-ATG9A-BirA*. HA-ATG9A-BirA*-expressing cells were grown in full DMEM media with or without biotin incubation (12 h), fixed, and labeled with Alexa Fluor 488-conjugated streptavidin and HA antibody (Scale bar=10 µm). nsP > 0.05, *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001. Download figure Download PowerPoint To pursue BioID proteomics, we generated cell lines stably expressing either HA-ATG9A-BirA* or, as a control, HA-BirA* alone. These cells were supplemented with biotin, followed by detergent lysis and capture of biotinylated proteins on streptavidin resin. An initial evaluation of captured proteins by Coomassie staining suggested an overall lower level of biotinylation by HA-ATG9A-BirA* compared with HA-BirA* alone, as perhaps expected given the anchored, transmembrane nature of ATG9A (Fig 1B). Therefore, we proceeded with BioID proteomics following the experimental schematic outlined in Fig 1C. LC-MS/MS data from these experiments are available in Appendix Table S1. Quantitative LC-MS/MS of biological triplicates of the experiment in Fig 1C revealed 283 proteins that were significantly enriched (≥ 2-fold increase, ≤ 0.05 P-value; Dataset EV1) in the HA-ATG9A-BirA* samples versus HA-BirA* alone (see volcano plot, Fig 1D, Appendix Fig S1A and B). These spanned an array of autophagy and trafficking regulators, including multiple components of EARP/GARP, AP-1, AP-3, AP-4, Retromer, TRAPP, and SNARE complexes and all components of the canonical ULK1 complex (Fig 1D), a subset of which were validated by immunoblot (Fig 1E). Several of these HA-ATG9A-BirA*-biotinylated proteins are already known to interact with ATG9A, including STX16, Arfaptin-1, TBC1D5, AP-1, AP-2, and AP-4, which increased our confidence in the BioID data (Orsi et al, 2012; Popovic & Dikic, 2014; Imai et al, 2016; Lamb et al, 2016; Mattera et al, 2017; Zhou et al, 2017; Aoyagi et al, 2018; Davies et al, 2018; Soreng et al, 2018; Judith et al, 2019). Furthermore, this proximity-based ATG9A interactome was highly enriched for proteins associated with the organelles where ATG9A is known to reside, including the ER, TGN, ERGIC, and endosomal systems (Fig 1F and G). ATG9A interacts with an ULK1-independent ATG13 subcomplex that includes ATG101 Among the BioID proteomics data (Dataset EV1), our attention was drawn to members of the ULK1 complex, which emerged as top hits (Fig 1D). Of the ULK1 complex proteins, ATG13 showed the highest fold-change increase in signal across all of the HA-ATG9A-BirA* replicates and we had previously observed interaction between ATG9A with ATG13 by co-IP (Kannangara and Andersen, unpublished). To investigate the interaction between ATG9A and ATG13 further, we generated ATG13 KO cells and then stably reconstituted them with WT ATG13 or one of two mutants of ATG13: ATG13 Δ2AA, which lacks a C-terminal 2-amino acid segment required for ULK1 binding (Alers et al, 2011; Hieke et al, 2015); or ATG13 ΔHORMA, which lacks the HORMA domain required for interaction with ATG101 and reported in yeast to be essential for recruiting Atg9 vesicles to the PAS (Fig 2A) (Jao et al, 2013; Qi et al, 2015; Suzuki et al, 2015). We verified that the ATG13 Δ2AA indeed fails to interact with ULK1 (Fig EV3A). Likewise, we found that the ATG13 ΔHORMA mutant is defective in interacting with endogenous ATG9A in mammalian cells (Fig EV3B). Figure 2. ATG9A interacts with an ULK1-independent ATG13 complex that includes ATG101 Schematic representation of ATG13 mutations used in the study. HA-ATG9A-BirA* was expressed in HCT-116 ATG13 WT, ATG13 KO, or ATG13 KO cells reconstituted with WT ATG13 or ATG13 ΔHORMA. Cells were grown in full DMEM media, treated with 50 µM biotin for 12 h, followed by detergent lysis and incubation with streptavidin resin. The graph on right shows quantification of normalized ATG101 infrared signal. Mean ± SEM, n = 3 (biological replicates). Significance measured using RM one-way ANOVA test followed by Fisher’s LSD tests. Cells were treated as in panel B but included reconstitution with ATG13 Δ2AA mutant. The graph on right shows quantification of normalized ATG101 infrared signal. Mean ± SEM, n = 3 (biological replicates). Significance measured using RM one-way ANOVA test followed by Fisher’s LSD tests (right). HA-ATG9A-BirA* was overexpressed in WT and ULK1/2 Double KO MEFs. Cells were subjected to streptavidin pulldown and immunoblotting with indicated antibodies. The graph on right shows quantification of normalized ATG13 infrared signal. Mean ± SEM. n = 3 (biological replicates). Significance measured using one-sample t-test compared with hypothetical mean of 1 (right). nsP > 0.05, *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001. Download figure Download PowerPoint Click here to expand this figure. Figure EV3. ATG9A interacts with an ULK1-independent ATG13-ATG101 subcomplex via the ATG13 HORMA domain The ULK1 binding-deficient mutant of ATG13 (ATG13 ∆2AA) was validated by immunoprecipitating HA-ATG13 or HA-ATG13 ∆2AA and immunoblotting for Myc-ULK1 in HCT-116 ATG13 KO cells. HCT-116 parental or HCT-116 ATG9A-HA KI ATG13 KO cells reconstituted with 3X FLAG-ATG13 WT or 3X FLAG-ATG13 ΔHORMA were grown in full DMEM media and subjected to size exclusion chromatography. Fractions with high levels of ATG9A-HA were pooled to facilitate immunoprecipitation. ATG9A-HA was immunoprecipitated from pooled fractions followed by immunoblotting with indicated antibodies. Co-IP of overexpressed HA-ATG9A from HCT-116 ATG13 KO cells followed by immunoblotting with indicated antibodies. Co-IP of HA-ULK1 from HCT-116 ATG13 WT and KO cells followed by immunoblotting with indicated antibodies. Co-IP of endogenous ATG9A from HCT-116 ATG9A-HA KI ATG101 KO cells followed by immunoblotting with indicated antibodies. Co-IP of endogenous ATG9A from HCT-116 ATG9A-HA KI FIP200 KO cells followed by immunoblotting with indicated antibodies. Co-IP of overexpressed HA-ATG13 from HEK293T ATG9A KO cells followed by immunoblotting with indicated antibodies. Download figure Download PowerPoint Importantly, in HA-ATG9A-BirA*-expressing cells, the loss of ATG13 had no effect on streptavidin capture of ULK1, but completely abrogated the capture of ATG101 (Fig 2B), suggesting that ATG13 is required for the interaction of ATG9A with ATG101, but not ULK1. Also, the streptavidin capture of ATG101 was rescued by reconstituting the ATG13 KO cells with WT ATG13, while reconstitution with ATG13 ΔHORMA did not recover ATG101 binding (Fig 2B), which is consistent with a model wherein ATG9A interacts with ATG101 via ATG13. In contrast to ATG13 ΔHORMA, the reconstitution of ATG13 KO cells with the ULK1 binding-defective ATG13 Δ2AA completely restored HA-ATG9A-BirA*-mediated biotinylation of ATG101 (Fig 2C). Furthermore, in mouse embryonic fibroblasts, HA-ATG9A-BirA* biotinylates ATG13 regardless of the presence or absence of ULK1/2 (Fig 2D). Reciprocal co-IP experiments also demonstrated that ATG9A-ULK1 binding was not affected by loss of ATG13 (Fig
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