BACKGROUND: Prednisolone and other glucocorticoids (GCs) are potent anti-inflammatory and immunosuppressive drugs. However, prolonged use at a medium or high dose is hampered by side effects of which the metabolic side effects are most evident. Relatively little is known about their effect on gene-expression in vivo, the effect on cell subpopulations and the relation to the efficacy and side effects of GCs.AIM: To identify and compare prednisolone-induced gene signatures in CD4⁺ T lymphocytes and CD14⁺ monocytes derived from healthy volunteers and to link these signatures to underlying biological pathways involved in metabolic adverse effects.MATERIALS & METHODS: Whole-genome expression profiling was performed on CD4⁺ T lymphocytes and CD14⁺ monocytes derived from healthy volunteers treated with prednisolone. Text-mining analyses was used to link genes to pathways involved in metabolic adverse events.RESULTS: Induction of gene-expression was much stronger in CD4⁺ T lymphocytes than in CD14⁺ monocytes with respect to fold changes, but the number of truly cell-specific genes where a strong prednisolone effect in one cell type was accompanied by a total lack of prednisolone effect in the other cell type, was relatively low. Subsequently, a large set of genes was identified with a strong link to metabolic processes, for some of which the association with GCs is novel.CONCLUSION: The identified gene signatures provide new starting points for further study into GC-induced transcriptional regulation in vivo and the mechanisms underlying GC-mediated metabolic side effects.
From PLoS website: In general, dietary antigens are tolerated by the gut associated immune system. Impairment of this so-called oral tolerance is a serious health risk. We have previously shown that activation of the ligand-dependent transcription factor aryl hydrocarbon receptor (AhR) by the environmental pollutant 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) affects both oral tolerance and food allergy. In this study, we determine whether a common plant-derived, dietary AhR-ligand modulates oral tolerance as well. We therefore fed mice with indole-3-carbinole (I3C), an AhR ligand that is abundant in cruciferous plants. We show that several I3C metabolites were detectable in the serum after feeding, including the high-affinity ligand 3,3´-diindolylmethane (DIM). I3C feeding robustly induced the AhR-target gene CYP4501A1 in the intestine; I3C feeding also induced the aldh1 gene, whose product catalyzes the formation of retinoic acid (RA), an inducer of regulatory T cells. We then measured parameters indicating oral tolerance and severity of peanut-induced food allergy. In contrast to the tolerance-breaking effect of TCDD, feeding mice with chow containing 2 g/kg I3C lowered the serum anti-ovalbumin IgG1 response in an experimental oral tolerance protocol. Moreover, I3C feeding attenuated symptoms of peanut allergy. In conclusion, the dietary compound I3C can positively influence a vital immune function of the gut.
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Psoriasis (Pso) is a chronic inflammatory skin disease, and up to 30% of Pso patients develop psoriatic arthritis (PsA), which can lead to irreversible joint damage. Early detection of PsA in Pso patients is crucial for timely treatment but difficult for dermatologists to implement. We, therefore, aimed to find disease-specific immune profiles, discriminating Pso from PsA patients, possibly facilitating the correct identification of Pso patients in need of referral to a rheumatology clinic. The phenotypes of peripheral blood immune cells of consecutive Pso and PsA patients were analyzed, and disease-specific immune profiles were identified via a machine learning approach. This approach resulted in a random forest classification model capable of distinguishing PsA from Pso (mean AUC = 0.95). Key PsA-classifying cell subsets selected included increased proportions ofdifferentiated CD4+CD196+CD183-CD194+ and CD4+CD196-CD183-CD194+ T-cells and reduced proportions of CD196+ and CD197+ monocytes, memory CD4+ and CD8+ T-cell subsets and CD4+ regulatory T-cells. Within PsA, joint scores showed an association with memory CD8+CD45RACD197- effector T-cells and CD197+ monocytes. To conclude, through the integration of in-depth flow cytometry and machine learning, we identified an immune cell profile discriminating PsA from Pso. This immune profile may aid in timely diagnosing PsA in Pso.
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