Titolo della tesi: Circadian clock disruption impairs immune oscillation in chronic endogenous hypercortisolism: a multi-level analysis from a multicenter clinical trial.
BACKGROUND: Glucocorticoids (GC) are potent entrainers of the circadian clock, however their effect on biological rhythms in human chronic exposure have been poorly studied. Endogenous hypercortisolism (Cushing’s Syndrome, CS) is a rare condition, in which circadian disruption is sustained by a tumorous source of GC excess, offering the unique opportunity to investigate the in vivo chronic effects of GC.
METHODS: In a 12-month prospective multicenter trial, the daily fluctuations in the number of circulating peripheral blood mononuclear cells (PBMCs) and the time-specific expression of clock-related genes were analyzed by RT-qPCR in a cohort of 68 subjects, 34 affected by CS and 34 age- and sex-matched controls. Rhythmicity algorithms and machine learning techniques were applied to the multi-level dataset.
FINDINGS: Multiple, 6-points, daily sampling revealed profound changes in the levels, amplitude, and rhythmicity of several PBMCs populations. More specifically, total (CD14+), intermediate (CD14+CD16+) and non-classical (CD14+CD16++) monocytes increased, while classical (CD14++CD16-) monocytes decreased, all normalizing after remission, except for a persisting dampening of the amplitude of their daily variations. On the other hand, the decrease in total (CD3+) and CD4+ lymphocytes and the increase in CD8+ lymphocytes only partially restored after CS remission, while regaining similar amplitude compared to controls. Mesor of total CD56+ NK cells and all NK cells subsets were significantly reduced in CS as well. Clock gene analyses in isolated PBMCs showed a significant flattening of circadian oscillation of PER1, PER2, PER3, PRF1 and TIMELESS expression, and a paradoxical increase in the amplitude of PER genes after remission. In the active phase, the JTK_CICLE algorithm revealed a loss of rhythmicity of all genes which were circadian in the PBMCs of controls. Most, but not all, regained physiological oscillation after remission. Machine learning revealed that while combined time-course sets of clock-genes were highly effective in separating patients from controls, immune profiling was efficient even as single time-points.
INTERPRETATION: In conclusion, the oscillation of circulating immune cells is profoundly altered in CS patients, representing a convergence point of circadian rhythm disruption, metabolic and steroid hormone imbalances. Machine learning techniques proved the superiority of immune profiling, over parameters such as cortisol, anthropometric and metabolic variables, and gene expression analysis, to identify CS activity.