Overexpression of Tumor Necrosis Factor-α in the Lungs Alters Immune Response, Matrix Remodeling, and Repair and Maintenance Pathways
Bibliographic record
Abstract
Increased production of tumor necrosis factor (TNF)-α and matrix metalloproteinases (MMPs) is a feature of inflammatory lung diseases, including emphysema and fibrosis, but the divergent pathological characteristics that result indicate involvement of other processes in disease pathogenesis. Transgenic mice overexpressing TNF-α in type II alveolar epithelial cells under the control of the surfactant protein (SP)-C promoter develop pulmonary inflammation and emphysema but are resistant to induction of fibrosis by administration of bleomycin or transforming growth factor-β. To study the molecular mechanisms underlying the development of this phenotype, we used a microarray approach to characterize the pulmonary transcriptome of SP-C/TNF-α mice and wild-type littermates. Four-month-old SP-C/TNF-α mice displayed pronounced pulmonary inflammation, airspace enlargement, increased MMP-2 and MMP-9 levels, and altered expression of 2332 probes. The functional assessment of genes with increased expression revealed enrichment of inflammatory/immune responses and proteases, whereas genes involved in protease inhibition, angiogenesis, cross-linking of basement membrane proteins, and myofibroblast differentiation were predominantly decreased. Comparison with multiple lung disease models identified a set of genes unique to the SP-C/TNF-α model and revealed that lack of extracellular matrix production distinguished SP-C/TNF-α mice from fibrosis models. Activation of inflammatory and proteolytic pathways and disruption of maintenance and repair processes are central features of emphysema in this TNF-overexpression model. Impairment of myofibroblast differentiation and extracellular matrix production may underlie resistance to induction of fibrosis.
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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) | 0.000 | 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".