RAGE and SAGE: Ameliorating COPD Pathogenesis via RAGE Abrogation
Bibliographic record
Abstract
Chronic obstructive pulmonary disease (COPD) is a progressive lung disease involving chronic airway inflammation and emphysema. While primary smoke poses the greatest risk, evidence suggests nearly half of the US population is also at risk due to exposure to second hand smoke (SHS). In the present study, we used control, RAGE null, and lung‐specific RAGE overexpressing transgenic (TG) mice to study the role of RAGE during responses to SHS. We evaluated inflammatory effects of SHS in these mice with and without the administration of semi‐synthetic glycosaminoglycan ethers (SAGEs), a family of anionic, partially lipophilic sulfated polysaccharide derivatives known to inhibit RAGE signaling. Mice were weaned and fed doxycycline to induce RAGE in TG mice at PN30. Select mice were exposed to SHS from three Kentucky 3R4F research cigarettes via a nose‐only delivery system (Sireq Scientific, Montreal, Canada) five days a week and ip injections of PBS or SAGE (a 30mg/kg body weight) occurred three times per week from PN40‐70 before all mice were sacrificed on PN70. RAGE mRNA and protein expression was elevated following SHS exposure of control and TG mice and no expression was detected in RAGE null mice. Bronchoalveolar lavage fluid (BALF) analysis revealed RAGE‐mediated influence of inflammatory cell diapedesis and BALF protein abundance. A multiplex cytokine array was performed in order to precisely determine the secretion of pro‐inflammatory modulators into BALF. Lung histological assessment revealed indistinguishable morphological changes following exposure, yet apoptosis was increased in lung parenchyma. Thematically, inflammatory signaling intermediates and downstream responses induced by SHS exposure were influenced by the availability of RAGE, as evidenced by RAGE nulls and SAGE treatment. These data reveal fascinating data suggesting therapeutic potential for the use of RAGE inhibitors in lungs exposed to SHS smoke. Support or Funding Information This work was supported by a grant from the Flight Attendant's Medical Research Institute (FAMRI, PRR and JAA) and a BYU Mentoring Environment Grant (JAA and PRR). This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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".