Dataset Information

Relationship between SIRT1 gene and adolescent depressive disorder with nonsuicidal self-injury behavior: Based on gene methylation and mRNA expression.

ABSTRACT:

Objective

The incidence of non-suicidal self-injury (NSSI) behavior in adolescents is increasing year by year. Patients with a history of both depression and NSSI behavior tend to be at greater risk for suicide. At present, the mechanism of adolescent depressive disorder with NSSI behavior is not clear and still in research and exploration. The expression of the Silent Information Regulator 2 Related Enzyme 1 (SIRT1) gene is closely related to the level of serotonin in molecular mechanisms, and may be closely related to the occurrence and development of depressive disorder. This study aimed to explore the relationship between the SIRT1 gene and NSSI behaviors in adolescents with depressive disorder.

Methods

A total of 15 adolescent depressed patients with NSSI behavior and 15 healthy controls were enrolled in the study. Bisulfite Sequencing PCR (BSP) was used to test the methylation level of SIRT1 gene promoter region of the participants. The real-time fluorescent quantitative PCR was conducted to measure the mRNA expression level of SIRT1 gene.

Results

Our study found that the methylation level of SIRT1 gene promoter region at cytosine-guanine dinucleotide 5 (CpG5) site in depression group was higher than that of control group. Compared with that of control group, the plasma concentration of Sirt1 protein significantly decreased in depression group.

Conclusion

Our study investigated the methylation level and the mRNA expression of SIRT1 gene in adolescent depressive patients with NSSI behavior. The study points towards finding an in vivo molecular marker for those adolescent patients.

SUBMITTER: Wang L

PROVIDER: S-EPMC8341318 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: Major Depressive Disorder (MDD) is a moderately heritable disorder with a high lifetime prevalence. At present, laboratory blood tests to support MDD diagnosis are not available. Methods: We used a classifier approach on blood gene expression profiles of a unique set of non-medicated subjects (MDD patients and controls) to select genes of which expression is predictive for disease status. To reveal blood gene expression changes related to MDD disease, we applied a powerful ex vivo stimulus to the blood, i.e. incubation with lipopolysaccharide (LPS; 10 ng/ml blood). Results: Based on LPS-stimulated blood gene expression using whole-genome microarrays in 42 subjects (primary cohort; 21 MDD patients (mean age 42.3 years), 21 healthy controls (mean age 41.9 years)), we identified a set of genes (CAPRIN1, CLEC4A, KRT23, MLC1, PLSCR1, PROK2, ZBTB16) that serves as a molecular signature of MDD. These findings were validated for the primary cohort using an independent quantitative PCR method (P = 0.007). The difference between depressive patients and controls was confirmed (P = 0.019) in a replication cohort of 13 patients with MDD (mean age 42.8 years) and 14 controls (mean age 45.6 years). The MDD-signature score comprised of expression levels of 7 genes could discriminate depressive patients from controls with sensitivity of 76.9% and specificity of 71.8%. Conclusions: We show for the first time that molecular analysis of stimulated blood cells can be used as an endophenotype for MDD diagnosis, which is a milestone in establishing biomarkers for neuropsychiatric disorders with moderate heritability in general. Our results may provide a new entry point for following and predicting treatment outcome, as well as prediction of severity and recurrence of MDD. In total, 33 MDD patients and 34 healthy controls were analyzed using basal gene expression in whole blood, and gene expression from whole blood that was stimulated with LPS for 5-6 h, using microarrays. Patients were arbitrarily selected from all patients to serve as primary cohort (nMDD = 21 (MDD01-MDD21); nControls = 21 (Con01-Con21)), or replication cohort (nMDD = 12 (MDD22-MDD35); nControls = 13 (Con22-Con37)) using microarrays. This submission does not include Samples CON21_LPS or CON30_LPS.