Dynamic protein expression profiles in Thai major depressive disorder with different responses to Fluoxetine
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Abstract
Introduction: Although pharmacotherapy is the primary choice for medical management of major depressive disorder (MDD), response to antidepressants is marked by inter-individual variability. Therefore, early predictors of the response are of important significant clinical value. We first report on dynamic proteomic analysis of the plasma in Thai patients with MDD who differently response to fluoxetine. Two-dimensional (2D) gel electrophoresis of plasma revealed that fast-response patients differed from non-response patients in one protein, and it was preliminary confirmed by Matrix Assisted Laser Desorption/ionization Time of Flight Mass Spectrometry (MALDI-TOF MS) analysis as α1-antitrypsin. This protein could be a peripheral biomarker candidate for early predict treatment response to the antidepressant. Material and method: Fifteen MDD patients were examined. Psychopathology was assessed and the patients were classified into fast-response (FR; n=10) and non-response (NR; n=5) groups by means of the Hamilton Depression Rating Scale (HAM-D) (see Table 1 for details). Plasma was collected before the pharmacological treatment and during the medication. Proteins in the plasma were analyzed and compared by 2D gel electrophoresis. Spot of the candidate protein was extracted from gel and verified by MALDI-TOF MS. Results: By comparing the protein pattern on gel, the protein corresponding to α1-antitrypsin was found as a difference between FR and NR. While it was present in all FR, it was absence in most NR. After received fluoxetine, the expression of this protein was not changed in FR but be inhibited in NR. Conclusion: α1-antitrypsin was. differently expressed in FR in comparison with NR. The exposure to fluoxetine was also differently induced changes in the inflammation protein in these two MDD groups, which became apparent in response to the antidepressant treatments. This inflammatory protein could be a candidate biomarker for early prediction of the response/non-response to SSRIs particularly fluoxetine.
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