<i>Chlamydophila pneumoniae</i>Inhibits Differentiation of Progenitor Adipose Cells and Impairs Insulin Signaling
Bibliographic record
Abstract
BACKGROUND: Recent clinical studies have shown Chlamydophila pneumoniae seropositivity to be related to overweight status and inversely related to insulin sensitivity. The present study was performed to investigate the potential effects of C. pneumoniae infection of adipocytes. METHODS: 3T3-L1 cells and primary epididymal preadipocytes were infected with C. pneumoniae either before or after induction of differentiation, and the effects on adipogenesis and insulin signaling were determined. Tumor necrosis factor (TNF)-alpha signaling was examined by assessing the effects of C. pneumoniae infection in preadipocytes isolated from epididymal adipose tissue of both wild-type and TNF-alpha(-/-) mice. RESULTS: C. pneumoniae successfully infected both undifferentiated and differentiated 3T3-L1 cells in vitro. The bacteria were also detected in adipose tissue of infected low-density lipoprotein receptor-deficient mice. TNF-alpha protein levels were significantly increased in cells infected with either live or heat-killed C. pneumoniae or treated with lipopolysaccharide or heat-shock protein 65; this increase was associated with inhibition of adipocyte differentiation and down-regulation of insulin-stimulated tyrosine-phosphorylated insulin receptor and its substrate. In contrast, C. pneumoniae infection in TNF-alpha(-/-) adipocytes produced no apparent changes, but addition of recombinant TNF-alpha reversed this effect. CONCLUSIONS: We demonstrate for the first time that C. pneumoniae can infect murine pre- and postdifferentiated adipocytes and, through a TNF-alpha-mediated inflammatory mechanism, can impair differentiation and insulin signaling.
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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".