In the current study, we analyzed DEGs in ACC and PFC from patients with MDD. Correlation networks based on co-expression were constructed. Topological properties of the networks were analyzed and compared. Our results showed that the lesions of brain tissues in MDD patients were not synchronized and alterations of biological functions were not consistent either. ACC showed a greater degree of abnormality as compared to PFC suggesting a higher correlation with disease progression. We consequently analyzed the signaling pathways enriched by DEGs and further cross talk genes that bridge the multiple pathways were also identified. Through construction of the pathway-gene complex network, the genes and singling pathways with top10 degrees were extracted, which are more likely to be potential novel therapeutic targets. We also mined the drugbank database for the top10 cross talk genes to explore their drugable target potential. PTDSS2 and CD19 differentially expressed in both ACC and PFC may correlate with MDD progression, and are more likely to become the new drug targets for the treatment of MDD.
The co-expression network has the ability to mine functionally related genes with similar co-expression patterns(30), which have been widely used to identify candidate biomarkers and therapeutic targets for many complex diseases, such as Alzheimer’s disease, schizophrenia and cancer(31-33). In addition, the network perspective supports the high heterogeneity of depression and explains how different treatment methods might take effect (8, 20). Comparisons between many data sets can provide a global view of gene expression patterns across tissues (16). Therefore, we completed a comprehensive analysis of gene expressions across ACC and PFC in patients with MDD and healthy subjects. Through analyzing and comparing the four co-expression networks, we found the unconnected nodes were increased in disease condition, which may be due to loss of connections. Alteration in important nodes of the network may affect the function of the entire network, causing depression. The topology analysis of the co-expression network showed that in disease states, the number of nodes with gain of connections in PFC network higher than that in ACC network with ratio of 1.72:1 and variance of density distribution was markedly increased in ACC network, but there is no significant change in PFC. These results indicated that the PFC network status of patients with depression tended to be normal, while the ACC network presented drastic fluctuations. The stability of the PFC network and its resistance to disease signals are better than that of the ACC network, which was consistent with the results of Wilcox test. It revealed that the pathological changes of brain tissues in depression patients were not synchronized and alterations of biological functions were not consistent either. Compared with PFC, ACC showed a higher degree of abnormality and may have a strong correlation with disease progression. Therefore, ACC is more likely to be a therapeutic target for depression. ACC is located in the frontal part of the cingulate cortex inside the cerebral hemispheres and is a part of the limbic system. Substantial evidence from healthy subjects has linked the ACC to emotional behavior(10, 34). This brain area uses information about punishment to manage aversively motivated actions. Bush et al. compiled a large amount of functional imaging, electrophysiological and anatomical data , and found that the ACC is specialized for affective processes(35).
Philippi et al. used resting-state fMRI to examine the functional connectivity of the ACC subregion in 28 participants with subclinical levels of depression. The results suggested that there is a clear correlation between depression severity and functional connectivity of ACC subregions. The reduced pregenual ACC-striatum connectivity
and anterior subgenual ACC -anterior insula connectivity was related to higher depression severity(36) . Similarly, our research also found that ACC network connectivity in patients with depression has decreased, which is consistent with previous studies.
Signaling molecules commonly do not work individually but interact with other proteins or biological molecules to achieve signal transmission. for gaining further understanding of MDD. Moreover, these signaling components which may co-expressed in a dataset and correlate across samples are predicted to reconstruct multiple signaling pathways and their cross-talk maps for further biomedical research(16). Cross-talk analysis is commonly used to explore the regulation and cooperation between signaling pathways, and further reveal the pathogenesis of diseases (37). Therefore, through analyzing the pathway-gene network, we identified ten pathways and ten cross talk genes with highest degrees. Among these 10 genes, CD19, PTDSS2 and NDST2 were significantly differentially expressed in ACC and PFC of MDD patients. Moreover, CD19 and PTDSS2 have been targeted by several drugs. Therefore, these two genes may be related to the progression of MDD or other neurological diseases, and are more likely to become the new drug targets for the treatment of MDD.
Phosphatidylserine synthase 2 (PTDSS2) can convert phosphatidylethanolamine(PE)into phosphatidylserine(PS) and participate in important cell signaling processes (38, 39). Compared with other tissues, brain is enriched in PS and PE. In addition, >36% of the PS are composed of docosahexaenoic acid (DHA) which is essential for normal function of the nervous system(40, 41) . Studies have shown that the reduction of DHA is associated with the development of mild cognitive impairment to Alzheimer's disease (42). Similarly, we observed that the most significant pathway is Glycerophospholipid metabolism. Both this pathway and PTDSS2 are related to lipid metabolism. Recent studies have shown that meningeal lipids play an important role in the pathogenesis of depressive disorder and anxiety(39). The typical glycerophospholipids (GPLs) found in mammalian membranes include phosphatidylcholines (PC), PE, PS and phosphatidylinositols (PI) that are all attached through a phosphodiester linkage (39). Preclinical findings indicated that the membrane-forming n-3 polyunsaturated fatty acids, glycerolipids, GPLs, and sphingolipids (SPLs) play a crucial role in the induction of depression- and anxiety-related behaviors(43). Clinical studies suggested that compared with non-depressed non-suicide subjects, the activities of phosphatidylinositol 3-kinase (PI3K) and Akt (serine threonine kinase or protein kinase B) in MDD patients were significantly reduced (44). Another crucial gene, CD19, is a B cell-specific member of the immunoglobulin superfamily expressed by pre-B cells from the time of heavy chain rearrangement to final differentiation into plasma cells. By regulating B cell receptor signaling, CD19 guides the fate of B cells and differentiation lymphopoesis(45). In our study, CD19 is the top gene in the T cell receptor signaling pathway that participate in immune regulation and inflammatory response. Immunity dysfunction is a risk factors for depression. Large clinical cohort studies have found that autoimmune diseases or severe infections increase the risk of mood disorders (46). The activation of innate immune cells produces pro-inflammatory cytokines, which can cause major depressive disorder by inhibiting monoamine neurotransmitters, activating the HPA axis, and affecting neurogenesis and plasticity (47) . From the clinical perspective, anti-inflammatory drugs, such as minocycline, have been reported to cause improvement in patients with treatment-resistant depression (48) . Taken together, improving lipid metabolism and regulating inflammatory response can provide new directions for the prevention and treatment of MDD.
Our research has identified several critical genes and provided some interesting clues for further experiments. However, some limitations of the study should be mentioned. First, we identified several genes from microarray data analysis. But we did not perform further functional verification of these selected genes. Subsequently, a large number of clinical samples will be needed to validate our findings and clarify the underlying mechanisms of how these genes affect the pathological stage. Another limitation of the study is the AUC of the curve is low, although the ROC values of all genes are higher than the random state (0.5). Therefore, the interpretation of this result needs to be cautious. Further exploration is needed in the future.