ERVK Polyprotein Processing and Reverse Transcriptase Expression in Human Cell Line Models of Neurological Disease
Bibliographic record
Abstract
Enhanced expression of the reverse transcriptase (RT) protein encoded by human endogenous retrovirus-K (ERVK) is a promising biomarker for several inflammatory and neurological diseases. However, unlike RT enzymes encoded by exogenous retroviruses, little work has been done to identify ERVK RT isoforms, their expression patterns, and cellular localization. Using Western blot, we showcase the ERVK gag-pro-pol polyprotein processing leading to the production of several ERVK RT isoforms in human neuronal (ReNcell CX) and astrocytic (SVGA) models of neuroinflammatory disease. Since the pro-inflammatory cytokine IFNγ plays a key role in the pathology of several ERVK-associated neurological diseases, we sought to determine if IFNγ can drive ERVK RT expression. IFNγ signalling markedly enhanced ERVK polyprotein and RT expression in both human astrocytes and neurons. RT isoforms were expressed in a cell-type specific pattern and the RT-RNase H form was significantly increased with IFNγ treatment. Fluorescent imaging revealed distinct cytoplasmic, perinuclear and nuclear ERVK RT staining patterns upon IFNγ stimulation of astrocytes and neurons. These findings indicate that ERVK expression is inducible under inflammatory conditions such as IFNγ exposure-and thus, these newly established in vitro models may be useful in exploring ERVK biology in the context of neuroinflammatory disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".