Commentary: Analysis of SUMO1-conjugation at synapses
Bibliographic record
Abstract
A commentary on \nAnalysis of SUMO1-conjugation at synapses \n \nby Daniel, J. A., Cooper, B. H., Palvimo, J. J., Zhang, F. P., Brose, N., and Tirard, M. (2017). eLife 6:e26338. doi: 10.7554/eLife.26338 \n \nThere is a large and growing literature on protein SUMOylation in neurons and other cell \ntypes. While there is a consensus that most protein SUMOylation occurs within the nucleus, \nSUMOylation of many classes of extranuclear proteins has been identified and, importantly, \nfunctionally validated. Notably, in neurons these include neurotransmitter receptors, transporters, \nsodium and potassium channels, mitochondrial proteins, and numerous key pre- and post-synaptic \nproteins (for reviews see Martin et al., 2007b; Scheschonka et al., 2007; Craig and Henley, 2012; \nLuo et al., 2013; Guo and Henley, 2014; Henley et al., 2014; Wasik and Filipek, 2014; Peng \net al., 2016; Schorova and Martin, 2016; Wu et al., 2016). Furthermore, several groups have \nreported SUMO1-ylated proteins in synaptic fractions using biochemical subcellular fractionation \napproaches, using a range of different validated anti-SUMO1 antibodies (Martin et al., 2007a; \nFeligioni et al., 2009; Loriol et al., 2012; Luo et al., 2013; Marcelli et al., 2017) and many studies \nhave independently observed colocalization of SUMO1 immunoreactivity with synaptic markers \n(Martin et al., 2007a; Konopacki et al., 2011; Gwizdek et al., 2013; Jaafari et al., 2013; Hasegawa \net al., 2014; Ghosh et al., 2016). Tirard and co-workers (Daniel et al., 2017) directly challenge \nthis wealth of compelling evidence. Primarily using a His6-HA-SUMO1 knock-in (KI) mouse, the \nauthors contest any significant involvement of post-translational modification by SUMO1 in the \nfunction of synaptic proteins. \n
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.060 | 0.051 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".