Deficiency of C5L2 Increases Macrophage Infiltration and Alters Adipose Tissue Function in Mice
Bibliographic record
Abstract
BACKGROUND: Obesity is considered as a systemic chronic low grade inflammation characterized by increased serum pro-inflammatory proteins and accumulation of macrophages within white adipose tissue (WAT) of obese patients. C5L2, a 7-transmembrane receptor, serves a dual function, binding the lipogenic hormone acylation stimulating protein (ASP), and C5a, involved in innate immunity. AIM: We evaluated the impact of C5L2 on macrophage infiltration in WAT of wildtype (Ctl) and C5L2 knock-out (C5L2(-/-)) mice over 6, 12 and 24 weeks on a chow diet and moderate diet-induced obesity (DIO) conditions. RESULTS: In Ctl mice, WAT C5L2 and C5a receptor mRNA increased (up to 10-fold) both over time and with DIO. By contrast, in C5L2(-/-), there was no change in C5aR in WAT. C5L2(-/-) mice displayed higher macrophage content in WAT, varying by time, fat depot and diet, associated with altered systemic and WAT cytokine patterns compared to Ctl mice. However, in all cases, the M1 (pro-) vs M2 (anti-inflammatory) macrophage proportion was unchanged but C5L2(-/-) adipose tissue secretome appeared to be more chemoattractant. Moreover, C5L2(-/-) mice have increased food intake, increased WAT, and altered WAT lipid gene expression, which is reflected systemically. Furthermore, C5L2(-/-) mice have altered glucose/insulin metabolism, adiponectin and insulin signalling gene expression in WAT, which could contribute to development of insulin resistance. CONCLUSION: Disruption of C5L2 increases macrophage presence in WAT, contributing to obesity-associated pathologies, and further supports a dual role of complement in WAT. Understanding this effect of the complement system pathway could contribute to targeting treatment of obesity and its comorbidities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".