Differential activation of endocrine-immune networks by arthritis challenge: Insights from colony-specific responses
Abstract Rheumatoid arthritis (RA) is a chronic inflammatory condition with variable clinical presentation and disease progression. Importantly, animal models of RA are widely used to examine disease pathophysiology/treatments. Here, we exploited known vendor colony-based differences in endocrine/im...
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| Autores principales: | , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2017
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/6ac0686352cd43b89510e17870d6db72 |
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| Sumario: | Abstract Rheumatoid arthritis (RA) is a chronic inflammatory condition with variable clinical presentation and disease progression. Importantly, animal models of RA are widely used to examine disease pathophysiology/treatments. Here, we exploited known vendor colony-based differences in endocrine/immune responses to gain insight into inflammatory modulators in arthritis, utilizing the adjuvant-induced arthritis (AA) model. Our previous study found that Sprague-Dawley (SD) rats from Harlan develop more severe AA, have lower corticosteroid binding globulin, and have different patterns of cytokine activation in the hind paw, compared to SD rats from Charles River. Here, we extend these findings, demonstrating that Harlan rats show reduced hypothalamic cytokine responses to AA, compared to Charles River rats, and identify colony-based differences in cytokine profiles in hippocampus and spleen. To go beyond individual measures, probing for networks of variables underlying differential responses, we combined datasets from this and the previous study and performed constrained principal component analysis (CPCA). CPCA revealed that with AA, Charles River rats show activation of chemokine and central cytokine networks, whereas Harlan rats activate peripheral immune/hypothalamic-pituitary-adrenal networks. These data suggest differential underlying disease mechanism(s), highlighting the power of evaluating multiple disease biomarkers, with potential implications for understanding differential disease profiles in individuals with RA. |
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