Puncta intended: connecting the dots between autophagy and cell stress networks
Bibliographic record
Abstract
Proteome profiling and global protein-interaction approaches have significantly improved our knowledge of the protein interactomes of autophagy and other cellular stress-response pathways. New discoveries regarding protein complexes, interaction partners, interaction domains, and biological roles of players that are part of these pathways are emerging. The fourth Vancouver Autophagy Symposium showcased research that expands our understanding of the protein interaction networks and molecular mechanisms underlying autophagy and other cellular stress responses in the context of distinct stressors. In the keynote presentation, Dr. Wade Harper described his team’s recent discovery of a novel reticulophagy receptor for selective autophagic degradation of the endoplasmic reticulum, and discussed molecular mechanisms involved in ribophagy and non-autophagic ribosomal turnover. In other presentations, both omic and targeted approaches were used to reveal molecular players of other cellular stress responses including amyloid body and stress granule formation, anastasis, and extracellular vesicle biogenesis. Additional topics included the roles of autophagy in disease pathogenesis, autophagy regulatory mechanisms, and crosstalk between autophagy and cellular metabolism in anti-tumor immunity. The relationship between autophagy and other cell stress responses remains a relatively unexplored area in the field, with future investigations required to understand how the various processes are coordinated and connected in cells and tissues.Abbreviations: A-bodies: amyloid bodies; ACM: amyloid-converting motif; AMFR/gp78: autocrine motility factor receptor; ATG: autophagy-related; ATG4B: autophagy related 4B cysteine peptidase; CALCOCO2/NDP52: calcium binding and coiled-coil domain 2; CAR T: chimeric antigen receptor T; CASP3: caspase 3; CCPG1: cell cycle progression 1; CAR: chimeric antigen receptor; CML: chronic myeloid leukemia; CCOCs: clear cell ovarian cancers; CVB3: coxsackievirus B3; CRISPR-Cas9: clustered regularly interspaced short palindromic repeats-CRISPR associated protein 9; DDXs: DEAD-box helicases; EIF2S1/EIF-2alpha: eukaryotic translation initiation factor 2 subunit alpha; EIF2AK3: eukaryotic translation initiation factor 2 alpha kinase 3; ER: endoplasmic reticulum; EV: extracellular vesicle; FAO: fatty acid oxidation; GABARAP: GABA type A receptor-associated protein; ILK: integrin linked kinase; ISR: integrated stress response; MTOR: mechanistic target of rapamycin kinase; MPECs: memory precursory effector T cells; MAVS: mitochondrial antiviral signaling protein; NBR1: NBR1 autophagy cargo receptor; PI4KB/PI4KIIIβ: phosphatidylinositol 4-kinase beta; PLEKHM1: pleckstrin homology and RUN domain containing M1; RB1CC1: RB1 inducible coiled-coil 1; RTN3: reticulon 3; rIGSRNAs: ribosomal intergenic noncoding RNAs; RPL29: ribosomal protein L29; RPS3: ribosomal protein S3; S. cerevisiae: Saccharomyces cerevisiae; sEV: small extracellular vesicles; S. pombe: Schizosaccharomyces pombe; SQSTM1: sequestosome 1; SF3B1: splicing factor 3b subunit 1; SILAC-MS: stable isotope labeling with amino acids in cell culture-mass spectrometry; SNAP29: synaptosome associated protein 29; TEX264: testis expressed 264, ER-phagy receptor; TNBC: triple-negative breast cancer; ULK1: unc-51 like autophagy activating kinase 1; VAS: Vancouver Autophagy Symposium
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".