Crosstalk Between SUMO and Ubiquitin-Like Proteins: Implication for Antiviral Defense
Bibliographic record
Abstract
Interferon (IFN) is a crucial first line of defense against viral infection. This cytokine induces the expression of several IFN-Stimulated Genes (ISGs), some of which act as restriction factors. Upon IFN stimulation, cells also express ISG15 and SUMO, two key ubiquitin-like (Ubl) modifiers that play important roles in the antiviral response. IFN itself increases the global cellular SUMOylation in a PML-dependent manner. Mass spectrometry-based proteomics enables the large-scale identification of Ubl protein conjugates to determine the sites of modification and the quantitative changes in protein abundance. Importantly, a key difference amongst SUMO paralogs is the ability of SUMO2/3 to form poly-SUMO chains that recruit SUMO ubiquitin ligases such RING finger protein RNF4 and RNF111, thus resulting in the proteasomal degradation of conjugated substrates. Crosstalk between poly-SUMOylation and ISG15 has been reported recently, where increased poly-SUMOylation in response to IFN enhances IFN-induced ISGylation, stabilizes several ISG products in a TRIM25-dependent fashion, and results in enhanced IFN-induced antiviral activities. This contribution will highlight the relevance of the global SUMO proteome and the crosstalk between SUMO, ubiquitin and ISG15 in controlling both the stability and function of specific restriction factors that mediate IFN antiviral defense.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".