<scp>DNA</scp>–histone complexes as ligands amplify cell penetration and nuclear targeting of anti‐<scp>DNA</scp> antibodies via energy‐independent mechanisms
Bibliographic record
Abstract
We have generated three monoclonal cell-penetrating antibodies (CPAbs) from a non-immunized lupus-prone (NZB × NZW)F1 mouse that exhibited high anti-DNA serum titres. These CPAbs are polyreactive because they bind to DNA and other cellular components, and localize mainly in the nucleus of HeLa cells, albeit with a distinct nuclear labelling profile. Herein, we have examined whether DNA-histone complexes (DHC) binding to CPAbs, before cell entry, could modify the cell penetration of CPAbs or their nuclear staining properties. By applying confocal microscopy and image analysis, we found that extracellular binding of purified CPAbs to DHC significantly enhanced their subsequent cell-entry, both in terms of percentages of positively labelled cells and fluorescence intensity (internalized CPAb amount), whereas there was a variable effect on their nuclear staining profile. Internalization of CPAbs, either alone or bound to DHC, remained unaltered after the addition of endocytosis-specific inhibitors at 37° or assay performance at 4°, suggesting the involvement of energy-independent mechanisms in the internalization process. These findings assign to CPAbs a more complex pathogenetic role in systemic lupus erythematosus where both CPAbs and nuclear components are abundant.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".