Abrogation of RAGE signaling using semi‐synthetic glycosaminoglycan ethers (SAGEs) ameliorates inflammation in mice exposed to secondhand tobacco smoke
Bibliographic record
Abstract
Chronic obstructive pulmonary disease (COPD) is a progressive lung disease involving chronic airway inflammation and emphysema. Currently COPD is the third leading cause of death in the United States, and exposure to tobacco smoke is the leading risk factor for development of COPD. While primary smokers have the greatest risk, evidence suggests nearly half of the US population is also at risk due to being involuntarily exposed to second hand smoke (SHS). Past studies have shown increased RAGE expression in lungs and other tissues following smoke exposure. In the present study, we used control, RAGE null, and lung‐specific RAGE overexpressing transgenic (TG) mice to study the role of RAGE during short term physiologic 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. We hypothesize that SAGEs ameliorate the progression of SHS‐induced inflammation by disrupting RAGE signaling. Mice were weaned then 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. Q‐PCR and immunoblotting revealed elevated RAGE expression in SHS exposed control and TG mice and an anticipated absence of RAGE expression in RAGE null mice regardless of smoke exposure. Bronchoalveolar lavage fluid (BALF) was procured from animals in each group and data 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 multiple pro‐inflammatory modulators. Histology of lung sections from each of the groups was unremarkable. As a general theme, inflammation induced by SHS exposure was influenced by the availability of RAGE, as evidenced by RAGE nulls and SAGE treatment of animals in the other groups. These data reveal captivating information suggesting a role for RAGE signaling 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 a BYU Mentoring Environment Grant (PRR).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 teacher head, 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".