Monocyte activation and differentiation augment human endogenous retrovirus expression: Implications for inflammatory brain diseases
Bibliographic record
Abstract
Human endogenous retroviruses (HERVs) have been implicated as causative agents in diseases characterized by inflammation and macrophage activation, such as multiple sclerosis. Because monocyte activation and differentiation influence retroviral transcription and replication, we investigated the contribution of these processes to the expression of four HERV families (HERV-W, HERV-K, HERV-E, and HERV-H) in human monocytes, and autopsied brain tissue from patients with brain diseases associated with increased macrophage activity. Reverse transcriptase-polymerase chain reaction analysis of primary macrophages and U937 monocytoid cells stimulated with phorbol-12-myristate-13-acetate or lipopolysaccharide revealed three- to ninefold increases in HERV-W, HERV-K, and HERV-H RNA levels. In addition, elevated reverse transcriptase activity and HERV RNA were detectable in supernatants from PMA-stimulated U937 cultures, properties that could be attenuated with the inhibitor of monocyte differentiation threonine-lysine-proline. In contrast, stimulation of monocytes decreased or had no effect on HERV-E expression. Compared with controls, HERV-W and HERV-K expression was increased in brain tissue from patients with multiple sclerosis or human immunodeficiency virus infection or AIDS, with concomitant elevated tumor necrosis factor-alpha levels. Similarly, elevated HERV-W levels were detected in patients with Alzheimer's dementia only when tumor necrosis factor-alpha expression was also evident (2 of 6 cases). The detection of several HERVs in inflammatory brain diseases and the capacity to augment HERV expression in monocytes with compounds that influence cellular activity suggest that increased expression of these viruses is a consequence of increased immune activity rather than causative of distinct diseases.
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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 | 0.000 |
| 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".