左背外側前頭前野の活動は血漿キヌレニン濃度と関連し、うつ病におけるエスシタロプラムの治療反応を予測する

広島大学(Hiroshima University)博士(医学)Doctor of Philosophy in Medical Sciencedoctora

DLPFC and KYN in MDD treatment response

Aim: To establish treatment response biomarkers that reflect the pathophysiology of depression, it is important to use an integrated set of features. This study aimed to determine the relationship between regional brain activity at rest and blood metabolites related to treatment response to escitalopram to identify the characteristics of depression that respond to treatment. Methods: Blood metabolite levels and resting-state brain activity were measured in patients with moderate to severe depression (n = 65) before and after 6–8 weeks of treatment with escitalopram, and these were compared between Responders and Nonresponders to treatment. We then examined the relationship between blood metabolites and brain activity related to treatment responsiveness in patients and healthy controls (n = 36). Results: Thirty-two patients (49.2%) showed a clinical response (>50% reduction in the Hamilton Rating Scale for Depression score) and were classified as Responders, and the remaining 33 patients were classified as Nonresponders. The pretreatment fractional amplitude of low-frequency fluctuation (fALFF) value of the left dorsolateral prefrontal cortex (DLPFC) and plasma kynurenine levels were lower in Responders, and the rate of increase of both after treatment was correlated with an improvement in symptoms. Moreover, the fALFF value of the left DLPFC was significantly correlated with plasma kynurenine levels in pretreatment patients with depression and healthy controls. Conclusion: Decreased resting-state regional activity of the left DLPFC and decreased plasma kynurenine levels may predict treatment response to escitalopram, suggesting that it may be involved in the pathophysiology of major depressive disorder in response to escitalopram treatment

Optimized protocol for the extraction of RNA and DNA from frozen whole blood sample stored in a single EDTA tube

Cryopreservation of whole blood is useful for DNA collection, and clinical and basic research. Blood samples in ethylenediaminetetraacetic acid disodium salt (EDTA) tubes stored at − 80 °C are suitable for DNA extraction, but not for high-quality RNA extraction. Herein, a new methodology for high-quality RNA extraction from human blood samples is described. Quickly thawing frozen whole blood on aluminum blocks at room temperature could minimize RNA degradation, and improve RNA yield and quality compared with thawing the samples in a 37 °C water bath. Furthermore, the use of the NucleoSpin RNA kit increased RNA yield by fivefold compared with the PAXgene Blood RNA Kit. Thawing blood samples on aluminum blocks significantly increased the DNA yield by ~ 20% compared with thawing in a 37 °C water bath or on ice. Moreover, by thawing on aluminum blocks and using the NucleoSpin RNA and QIAamp DNA Blood kits, the extraction of RNA and DNA of sufficient quality and quantity was achieved from frozen EDTA whole blood samples that were stored for up to 8.5 years. Thus, extracting RNA from frozen whole blood in EDTA tubes after long-term storage is feasible. These findings may help advance gene expression analysis, as well as biomarker research for various diseases

Interferon signaling and hypercytokinemia-related gene expression in the blood of antidepressant non-responders

Only 50% of patients with depression respond to the first antidepressant drug administered. Thus, biomarkers for prediction of antidepressant responses are needed, as predicting which patients will not respond to antidepressants can optimize selection of alternative therapies. We aimed to identify biomarkers that could predict antidepressant responsiveness using a novel data-driven approach based on statistical pattern recognition. We retrospectively divided patients with major depressive disorder into antidepressant responder and non-responder groups. Comprehensive gene expression analysis was performed using peripheral blood without narrowing the genes. We designed a classifier according to our own discrete Bayes decision rule that can handle categorical data. Nineteen genes showed differential expression in the antidepressant non-responder group (n = 15) compared to the antidepressant responder group (n = 15). In the training sample of 30 individuals, eight candidate genes had significantly altered expression according to quantitative real-time polymerase chain reaction. The expression of these genes was examined in an independent test sample of antidepressant responders (n = 22) and non-responders (n = 12). Using the discrete Bayes classifier with the HERC5, IFI6, and IFI44 genes identified in the training set yielded 85% discrimination accuracy for antidepressant responsiveness in the 34 test samples. Pathway analysis of the RNA sequencing data for antidepressant responsiveness identified that hypercytokinemia- and interferon-related genes were increased in non-responders. Disease and biofunction analysis identified changes in genes related to inflammatory and infectious diseases, including coronavirus disease. These results strongly suggest an association between antidepressant responsiveness and inflammation, which may be useful for future treatment strategies for depression