Back

SR9009 Regulates Macrophage Polarization via the NR1D1/NF-κB Axis to Ameliorate Obesity Associated Ulcerative Colitis

Zheng, Z.; Cai, X.; Liu, Y. Q.; Bi, J. T.

2025-06-01 molecular biology
10.1101/2025.06.01.657286 bioRxiv
Show abstract

ObjectiveThe incidence of ulcerative colitis (UC) and obesity has risen in recent years, potentially linked through metabolic dysregulation and chronic inflammation. The nuclear receptor NR1D1 is pivotal in regulating circadian rhythms and plays a significant role in inflammation and metabolism. This study investigates the therapeutic effects and mechanisms of the NR1D1 agonist SR9009 on obesity-related UC. MethodsWe established a mouse model of obesity-induced UC utilizing a high-fat diet alongside dextran sulfate sodium (DSS). 32 male C57BL/6 mice were divided into four groups: control (DZ), high-fat diet (GZ), obesity associated UC model group (UC), and SR9009 intervention group (JD), with eight mice each. We evaluated body weight, blood lipids, colonic tissue alterations, IL-1{beta}, IL-18, macrophage polarization, and NR1D1 expression levels. ResultsMice in the UC group demonstrated significantly elevated body weight, spleen index, TG, CHOL and inflammatory markers (P<0.01). Pathological scores of colonic tissues increased markedly (P=0.000), with a rise in M1 macrophages (CD68) and a decline in M2 macrophages (CD206) (P<0.001). NR1D1 expression was notably downregulated (P<0.01). Post-SR9009 intervention, the JD group showed significantly reduced serum TG and CHOL levels (P=0.001, 0.011), IL-1{beta} and IL-18 (P<0.001), improved colonic pathology (P=0.000), a decrease in M1 macrophages, an increase in M2 macrophages, and an enhanced M1/M2 ratio (P<0.001). ConclusionSR9009 mitigates intestinal inflammation in obesity associated UC by activating NR1D1, inhibiting the NF-{kappa}B pathway, and modulating macrophage polarization (suppressing M1 and enhancing M2). These findings propose a novel strategy for targeting NR1D1 in the treatment of obesity-related ulcerative colitis.

Published in Frontiers in Gastroenterology · not in our set (fewer than 10 published preprints to learn from) · training set

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.