Identification of key genes and crucial pathways for major depressive disorder using peripheral blood samples and chronic unpredictable mild stress rat models

Abstract

Background: Accurate diagnosis of major depressive disorder (MDD) remains difficult, and one of the key challenges in diagnosing MDD is the lack of reliable diagnostic biomarkers. The objective of this study was to explore gene networks and identify potential biomarkers for MDD.

Methods: In the present study, we performed a comprehensive analysis of the mRNA expression profiles using blood samples of four patients with MDD and four controls by RNA sequencing. Differentially expressed genes (DEGs) were screened, and functional and pathway enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery. All DEGs were inputted to the STRING database to build a PPI network, and the top 10 hub genes were screened using the cytoHubba plugin of the Cytoscape software. The relative expression of 10 key genes was identified by quantitative real-time polymerase chain reaction (qRT-PCR) of blood samples from 50 MDD patients and 50 controls. Plasma levels of SQSTM1 and TNFα were measured using an enzyme-linked immunosorbent assay in blood samples of 44 MDD patients and 44 controls. A sucrose preference test was used to evaluate depression-like behavior in chronic unpredictable mild stress (CUMS) model rats. Immunofluorescence assay and western blotting were performed to study the expression of proteins in the brain samples of CUMS model rats.

Results: We identified 247 DEGs that were closely associated with MDD. Gene ontology analyses suggested that the DEGs were mainly enriched in negative regulation of transcription by RNA polymerase II promoter, cytoplasm, and protein binding. Moreover, Kyoto Encyclopedia of Genes and Genomes pathway analysis suggested that the DEGs were significantly enriched in the MAPK signaling pathway. Ten hub genes were screened through the PPI network, and qRT-PCR assay revealed that one and six genes were downregulated and upregulated, respectively; however, SMARCA2, PPP3CB, and RAB5C were not detected. Pathway enrichment analysis for the 10 genes showed that the mTOR signaling pathway was also enriched. A strong positive correlation was observed between SQSTM1 and TNFα protein levels in patients with MDD. LC3 II and SQSTM1 protein levels were increased in the CUMS rat model; however, p-mTOR protein levels were decreased. The sucrose preference values decreased in the CUMS rat model.

Conclusions: We identified 247 DEGs and constructed an MDD-specific network; thereafter, 10 hub genes were selected for further analysis. Our results provide novel insights into the pathogenesis of MDD. Moreover, SQSTM1, which is related to autophagy and inflammatory reactions, may play a key role in MDD. SQSTM1 may be used as a promising therapeutic target in MDD; additionally, more molecular mechanisms have been suggested that should be focused on in future in vivo and in vitro studies.

Keywords: Autophagy; CUMS; Inflammation; Major depressive disorder; PPI network analysis; SQSTM1.

Figure 1. Screening of differentially expressed genes.

(A) Heatmap showing the differential expression of mRNA between four MDD patients and four healthy controls. Highly expressed mRNAs are showed in red and lowly expressed mRNAs in green. (B) Differentially expressed genes are represented as a Volcano plot (P-value < 0.001).

Figure 2. GO and KEGG enrichment analyses of differentially expressed genes (DEGs) between the control blood samples and the MDD blood samples.

(A) GO Functional Enrichment Map of DEGs; blue represented biological process; brown represented cellular component; green represented molecular function (Count; p-value < 0.05). (B) The first nine significantly enriched pathways of 247 DEGs were shown by their scores (Count; p-value < 0.05).

Figure 3. Protein–protein interaction (PPI) network construction and analysis of the most significant module of differentially expressed genes (DEGs).

(A) The PPI network of DEGs was constructed in STRING database (https://string-db.org/) and visualized in Cytoscape. Highly expressed mRNAs are showed in red and lowly expressed mRNAs in green, the unchanged mRNAs in blue. (B) The most significant module was obtained from the PPI network by using cytoHubba plugin. (C) The first 10 significantly enriched pathways of 10 hub genes are shown by their scores (Count; P-value < 0.05).

Figure 4. Quantitative real-time PCR analysis of the differences in expression of mRNA of 10 hub genes between controls and patients with major depressive disorder.

A total of one downregulated gene (ITGB2) and six upregulated genes (YWHAZ, SQSTM1, RAB1A, MYSM1, AKT2, TSC2) were detected; however, SMARCA2, PPP3CB and RAB5C were not detected. ** P < 0.01.

Figure 5. Quantification of SQSTM1 and TNFα levels in plasma.

(A) The protein level of SQSTM1 was increased in the plasma (ng/mL). (B) The protein level of TNFα was upregulated in the plasma (pg/mL). (C) A strong positive correlation was showed between SQSTM1 and TNFα protein (r = 0.659, P = 0.00000115). Data are presented as mean ± standard deviation; n =44/group. ** P < 0.01.

Figure 6. Autophagy related proteins were detected in CUMS rat models.

Immunofluorescence double-labeling showed the upregulation of SQSTM1 (A) and LC3 (B) in CUMS rat models. (C, D) LC3 II and SQSTM1 protein levels were increased in CUMS rat models according to the western blot assay. (E) p-mTOR protein level was decreased in CUMS rat model as per the western blot assay. (F) TNFα protein levels were increased in CUMS rat models according to the western blot assay. (G) The sucrose preference values were decreased in CUMS rat models. Scale bar = 50 µm. Data are presented as mean ±standard deviation; n =6/group. * P < 0.05, ** P < 0.01.