A207 RIPK2 AND AMPK AS EMERGING THERAPEUTIC TARGETS FOR INFLAMMATORY BOWEL DISEASE
Bibliographic record
Abstract
Abstract Background Persistent inflammation can trigger altered epigenetic, inflammation and bioenergetics states. Inflammatory bowel disease (IBD) is a heterogeneous disease with an abnormal inflammatory state and subsequent metabolic syndrome disorder. Current IBD therapeutics are directed to inhibit inflammatory pathways, such as inflammation of the epithelial cells in the colonic crypts. We hypothesize that in order to achieve mucosal healing and to keep patients in remission we must (i) inhibit inflammatory mediators and (ii) resolve secondary effects of inflammation such a reset of metabolic dysfunction. Aims The aims of this study are to explore correlations between inflammation/metabolic markers and the severity of the disease to uncover emerging new therapeutic players. Methods More than one hundred patients were recruited and underwent colonoscopy. In order to explore how biomarkers change with disease and time, all patients had IBD for more than 10 years or less than 5 years. Those diagnosed with cancer, celiac sprue, or diabetes were excluded. The activity of key metabolic markers (such as AMPK) was tracked using phospho-specific antibodies. Immunohistochemistry and immunoblotting were carried out, as described by Gordon et al., PLOSone 2013. All patients were consented under our IBD ethics protocol (Pro00001523 and Pro00077868). Results Using intestinal biopsies from non-IBD, UC and CD patients, we explored the expression/activation levels of markers of inflammation (such as obligate NOD2 kinase RIPK2) and metabolism (AMPK) in order to gain insight into correlations with clinical severity of the disease. We confirm that the loss in the activity of AMPK occurs with a gain of activity of RIPK2 that drives the inflammatory phenotype of the gut in patients with long-standing IBD (>10 years). (If inflammation is inhibited in a mouse model of IBD, metabolic reset occurs to regain AMPK and promote mucosal healing). However, RIPK2 remains elevated in patients that are currently on IBD therapeutics. Conclusions Therapeutics inhibiting inflammation (RIPK2) and stimulating metabolic (AMPK) drivers of the disease may be a useful combination therapy to completely eliminate inflammation, reset abnormal metabolism and achieve full remission in IBD patients with longstanding disease. Funding Agencies None
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".