Background: Tissue sensitivity to glucocorticoids is characterized by significant inter-individual variation in terms of therapeutic response and susceptibility to several stress-related disorders. Proteomics approaches, combined with appropriate bioinformatics analysis, offer a comprehensive description of molecular phenotypes with clear links to human disease pathophysiology.
Objective and Hypotheses: To investigate the usefulness of plasma proteomics in identifying a proteomic signature that could distinguish glucocorticoid resistant from glucocorticoid sensitive subjects and provide clues of the underlying physiological differences.
Methods and Results: One hundred one (n = 101) healthy volunteers were given a very low dose (0.25mg) of dexamethasone at midnight, and were polarized into the 10% most sensitive (S) and 10% most resistant (R) according to the 08: 00h serum cortisol concentrations the following morning. One month later, DNA was isolated from peripheral blood mononuclear cells, and plasma samples were collected. Sequencing analysis did not reveal any mutations or polymorphisms in the human glucocorticoid receptor (NR3C1) gene. Subsequently, we determined the proteomic profile of plasma samples using Liquid Chromatography - Mass Spectrometry (LC-MS/MS). One hundred ten up-regulated and sixty-six down-regulated proteins were identified in the S compared to the R group. Interestingly, most of the up-regulated proteins in the S group were involved in erythrocyte gas exchange and platelet activation, suggesting a state of the organism that is more capable to respond to stressful stimuli. To predict response to cortisol prior to administration, a random forest classifier was developed by using the proteomics data, in order to distinguish sensitive from resistant individuals. Among the proteins identified, apolipoprotein A4 (APOA4) and gelsolin (GSN) were the most important variables in the classification, necessitating further investigation to determine their prognostic capacity.
Conclusions: A proteomic profile indicating erythrocyte gas exchange and platelet activation was observed in the S compared to the R group. These findings indicate that a proteomics signature may differentiate the most glucocorticoid resistant from the most glucocorticoid sensitive subjects, and may be useful in clinical practice. In addition, it may provide clues of the underlying molecular mechanisms of the chronic stress-related diseases, including myocardial infarction and Alzheimers disease.
22 Sep 2021 - 26 Sep 2021