<i>In vitro</i> Evaluation of an Enhanced Human Serum Amyloid A (<i>SAA2</i>) Promoter‐Regulated Soluble TNF Receptor Fusion Protein for Anti‐Inflammatory Gene Therapy
Bibliographic record
Abstract
Tumour necrosis factor (TNF)-alpha contributes to the pathogenesis of many inflammatory diseases. Recombinant soluble TNF receptor fusion proteins (sTNFR:Ig) are potent TNF antagonists, both in vitro and in vivo. The concentration of serum amyloid A (SAA) increases by up to 1000-fold during inflammation, largely owing to cytokine-driven transcriptional upregulation. A reporter plasmid, comprising the proximal 0.7 kb of the human SAA2 promoter fused to a luciferase gene, was used in transient transfection experiments in human HepG2 hepatoma cells to assess the quantitative and qualitative TNF antagonist properties of a construct in which sTNFR:Ig synthesis is under the control of a chimera of the SAA2 promoter and a tat/HIV element. The SAA2-tat/HIV-sTNFR:Ig construct retained the fine-tuned cytokine responsiveness of the SAA2 promoter, while exhibiting the quantitatively enhanced level of protein expression conferred by the tat/HIV element. It produced a biologically significant TNF inhibition that was at least as strong as that achieved using a CMV promoter-driven sTNFR:Ig construct. There was a dose- and time-dependent relationship between the pro-inflammatory cytokine used, and the generation of TNF antagonist activity by SAA2-tat/HIV-sTNFR:Ig. Although sTNFR:Ig protein can be induced by either TNF-alpha or interleukin (IL)-1beta, its antagonist activity is limited to the former cytokine. The SAA2-tat/HIV-sTNFR:Ig construct, and derivatives thereof, may therefore be ideally suited to gene therapy applications that require the local production of potent and specific immune modifiers only when there is active pathology. It may consequently be of particular use in the future treatment of diseases such as rheumatoid arthritis.
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 | 0.000 | 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 | 0.001 |
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".