Identification and Analysis of Key Genes Related to Metabolism in the Brain of Major Depressive Disorder

Identification and Analysis of Key Genes Related to Metabolism in the Brain of Major Depressive Disorder | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Identification and Analysis of Key Genes Related to Metabolism in the Brain of Major Depressive Disorder Hui Yang, Jinxi Wang, Shihui Lei, Pan Meng, Qing Du, Hongping Long, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5314827/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Major depressive disorder (MDD) is a prevalent neuropsychiatric condition and has become the second leading cause of mortality after cancer. The prefrontal cortex (PFC) is recognized as one of the brain regions most consistently affected by MDD. While both functional and structural abnormalities in the PFC have been shown to be associated with disruptions in energy metabolism, the specific genes involved in metabolic processes within this region remain poorly understood. Methods Datasets related to major depressive disorder (MDD) from the Gene Expression Omnibus (GEO) database were analyzed in this study. Initially, differentially expressed metabolism-related genes (DE-MRGs) were identified by intersecting differentially expressed genes from normal and MDD patient samples with metabolism-related genes. Subsequently, a protein-protein interaction (PPI) network was constructed based on the DE-MRGs, and hub genes were identified using the Molecular Complex Detection (MCODE) plugin. A logistic regression prediction model was then developed. To further assess the findings, Spearman correlations, Gene Set Enrichment Analysis (GSEA), and predictions of transcription factors and microRNAs targeting the hub genes were conducted. Finally, the expression of the hub genes and their potential mechanisms were validated and predicted using an animal model of depression. Results In this study, we identified 223 differentially expressed metabolism-related genes. Utilizing the MCODE plugin methods, we further identified 12 hub genes among these differentially expressed genes. Expression validation results indicated that the expression of ACLY, DLD, DLAT, FH, and SLC25A3 were consistent across various datasets for both MDD and control samples. GSEA revealed that these genes were significantly enriched in pathways associated with oxidative phosphorylation, Parkinson's disease, and the proteasome. Furthermore, animal experiments demonstrated that the expression levels of ACLY, DLD, DLAT, and FH were significantly reduced in the PFC of rats subjected to chronic unpredictable mild stress (CUMS) induction. Additionally, further investigation into the transcription factors and regulatory signals of ACLY revealed a significant decrease in the mRNA expression of SREBF1, along with marked reductions in the protein levels of PI3K, Akt, and p-ACLY. Conclusions Four key genes were identified based on metabolic characteristic genes. The PI3K/AKT/ACLY signaling pathway may play a significant role in the regulation of metabolism in major depressive disorder (MDD). These findings establish a theoretical foundation and provide valuable references for the study of central metabolism in MDD. Health sciences/Diseases/Metabolic disorders Biological sciences/Genetics Major depressive disorder Metabolism Hub gene ACLY Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background Major depressive disorder (MDD) is a complex psychiatric condition characterized by persistent low mood, loss of interest, and anhedonia. The high prevalence, recurrence, and mortality rates associated with MDD have made it a leading contributor to disease burden in Western populations[ 1 ]. In China, a prospective cohort study indicated that MDD is linked to increased mortality from all-cause and cardiovascular diseases among adults[ 2 ]. Consequently, effective management of MDD is crucial and urgent to prevent premature death. However, accurately diagnosing MDD in clinical practice remains challenging, as current diagnostic methods primarily rely on self-reporting of symptoms and clinical interviews[ 3 ]. Clarifying the biological underpinnings of MDD and developing reliable biomarkers could enhance the diagnostic criteria, facilitating more accurate and timely diagnosis[ 4 ]. The brain is a highly energy-demanding organ, constituting only 2% of total body weight yet requiring approximately 25% of the body's total glucose for its normal function[ 5 ]. MDD is increasingly recognized as a metabolic brain disease. Multiple studies have indicated that abnormalities in energy metabolism are significant contributors to the pathophysiology of MDD[ 6 – 9 ]. Enhancing energy metabolism may represent a viable therapeutic target[ 10 ]. Positron emission tomography (PET) scans have demonstrated reductions in both blood flow and metabolism in the prefrontal cortex (PFC) of patients with major depression[ 11 ]. Furthermore, preclinical studies have identified extensive metabolic abnormalities involving amino acids, glucose, and lipids in the brains of animal models of depression[ 12 – 13 ]. However, the metabolic-related biomarkers associated with MDD, particularly those present in the brain, remain inadequately defined. Thus, there is an urgent need to identify central metabolic-related genes (MRGs) linked to MDD to facilitate the development of new biomarkers and therapeutic targets. In this study, we analyzed the biological significance of metabolic-related genes (MRGs) and their association with MDD. Initially, MDD-related genes were retrieved from the Gene Expression Omnibus (GEO), while MRGs were sourced from prior literature. Subsequently, we conducted multiple functional enrichment analyses to elucidate the biological relevance of MRGs in the context of MDD. Metabolism-related biomarkers associated with MDD were identified using molecular complex detection (MCODE) and Receiver Operating Characteristic (ROC) curve analysis. Additionally, we performed Gene Set Enrichment Analysis (GSEA) on hub genes utilizing the ClusterProfilter package. We also predicted transcription factors and microRNAs (miRNAs) associated with these hub genes. Finally, we performed experimental validation of the hub genes using a chronic unpredictable mild stress (CUMS) induced rat model of depression. Additionally, we investigated the transcription factors and regulatory signals associated with the key genes identified in our study. Collectively, the findings of this study may enhance our understanding of the metabolic pathophysiology of MDD and may facilitate the identification of novel biomarkers for its treatment. Materials and methods Data Source Two datasets related to MDD, GSE54568 and GSE54570, were obtained from the GEO database ( https://www.ncbi.nlm.nih.gov/gds ). The training set consisted of fifteen dorsolateral prefrontal cortex tissue samples from MDD patients and fifteen normal control samples from GSE54568. These datasets were generated using the GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array). For the external validation set, we included thirteen dorsolateral prefrontal cortex tissue samples from MDD patients and thirteen normal control samples from GSE54570, which were derived from the GPL96 platform (Affymetrix Human Genome U133A Array). A total of 2752 MRGs were collected from previous article[ 14 ] . Acquisition of Hub genes Differential expression analysis was conducted to compare MDD samples with control samples using the limma package in the GSE54568 dataset [ 15 ], identifying differentially expressed genes (DEGs) with a significance threshold of P < 0.05. Differentially expressed metabolism-related genes (DE-MRGs) were identified by intersecting the DEGs with the MRGs. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the DE-MRGs were performed using the ClusterProfiler package. To investigate potential interactions among the DE-MRGs, a protein-protein interaction (PPI) network was constructed using the STRING database ( https://string-db.org ). Hub genes were identified through the MCODE plugin. Construction and validation of logistic regression prediction model A logistic regression prediction model was developed based on the identified hub genes, accompanied by the creation of a confusion matrix heat map. The ROC curves of the logistic regression model were plotted for both the training set and the validation set using the survival ROC package to evaluate its diagnostic efficacy [ 16 ]. Additionally, expression validation of the hub genes was performed in both the training set and the validation set. GSEA and Establishment of regulatory network To investigate potential interactions among the hub genes, Spearman correlation coefficients were calculated. GSEA of the hub genes in the training set was performed using the ClusterProfiler package [ 17 ].. Transcription factors (TFs) and miRNAs associated with the hub genes were predicted utilizing the miRNet database ( https://www.mirnet.ca ). Subsequently, a regulatory network comprising transcription factors, genes, and miRNAs was constructed using Cytoscape. Animal model All relevant institutional and national guidelines for the care and use of animals were strictly adhered to throughout the study. The animal experiments received approval from the Institutional Animal Care and Use Committee of the First Hospital of Hunan University of Chinese Medicine (Approval No. ZYFY20221111-55). All experiments were designed and reported according to the Animal Research: Reporting of In Vivo Experiments (ARRIVE) guidelines. Male Sprague-Dawley (SD) rats, aged six weeks, were obtained from the Hunan Slack Scene Laboratory Animal Company. The rats were housed in a temperature-controlled environment (20–24°C) with a 12-hour light/dark cycle, and food and water were provided ad libitum . Construction of the depression model A rat model of depression was established using the CUMS methodology. Male SD rats weighing between 180 and 220 g were randomly subjected to various stressors, including cage tilting for 24 hours, cold swimming for 3 minutes at 0°C, food deprivation for 24 hours, horizontal shaking for 15 minutes, tail nipping for 1 minute (1 cm from the tail's end), heat stress at 45°C for 5 minutes, and inversion of the light/dark cycle for 24 hours. These stressors were administered over a 28-day period, with each stressor applied four times. To prevent predictability, the stressors were randomly assigned each day, and the same stressor was not administered on consecutive days. The control group remained undisturbed by these procedures. Sucrose preference test(SPT) The Sucrose preference test (SPT) was primarily employed to assess animal preferences and anhedonic states. This method served as a valuable tool for investigating depression and related emotional conditions. Prior to the formal testing, rats were acclimated for 24 hours with access to two bottles containing a 1% sucrose solution. Subsequently, one bottle was replaced with pure water, and the rats continued to adapt for another 24 hours, with the positions of the two bottles swapped after 12 hours. The rats were then deprived of food and water for 24 hours before the formal test, during which they were provided with a pre-weighed bottle of 1% sucrose solution and a bottle of pure water for a 2-hour testing period. The remaining weights of both solution bottles were recorded. Sucrose preference was calculated using the formula: Sucrose Preference (%) = (sucrose consumption / (sucrose consumption + water consumption)) × 100%. Forced swimming test (FST) The forced swimming test (FST) was conducted to evaluate depression-like behaviors in rats. In this procedure, rats were placed in a circular fiberglass pool filled with 30 cm of water maintained at 25 ± 1°C. The duration of immobility was recorded during the final 3 minutes of the 4-minute testing period. A reduction in immobility time was utilized as an indicator of antidepressant-like efficacy. Morris water maze test (MWM) The Morris Water Maze (MWM) test was utilized to assess the cognitive functions of rats [ 18 ]. The experimental setup consisted of a circular black tank (200 cm in diameter) filled with clear water maintained at a temperature of 25 ± 1°C. The pool was divided into four equal quadrants labeled A, B, C, and D. A clear platform was submerged beneath the water surface, rendering it invisible [ 19 ]. The platform was situated in quadrant A for the first five days and was removed on the sixth day. Four days prior to the experiment, rats were placed in the black tank daily from different quadrants to enable the animals to identify the location of the submerged platform. If the rat failed to locate the submerged platform within 2 minutes, the experimenter guided it onto the platform using a wooden stick and held it there for 15 seconds. During the hidden platform test on the fifth day, the time taken by the rats to locate and climb onto the submerged platform was recorded as the escape latency, serving as an indicator of their learning abilities. On the sixth day, the platform was removed for the probe test, during which the time spent in the target quadrant and the percentage of total swimming distance in the target quadrant were recorded to evaluate the rats' memory functions. RT-PCR analysis The sodium pentobarbital was administered by intraperitoneal injection (60 mg/kg), and the rat was observed to ensure that euthanasia is effective. Total RNA was extracted from the PFC of rats, and its quality and concentration were assessed using the BioDrop Ulite. Complementary DNA (cDNA) was synthesized with the PrimeScript™ RT Reagent Kit (TaKaRa, RR037A). Quantitative reverse transcription PCR (RT-qPCR) was conducted using the TB Green Premix Ex Taq™ II (TaKaRa, RR820A). The sequences of the primers utilized are presented in Table 1 . Table 1 Primer sequences Primer name Sequence (5’−3’) ACLY-F GAAGGAAGCGGGAGTGTTT ACLY-R TGAATGAGCCAGGTTTTCG DLD-F AGGTGCTGGAGAAATGGTG DLD-R AAGCCTCTGATAAGGTCGG DLAT-F TCATAGACATCCCCATCAG DLAT-R CTCCCATATTTACATCAACAG FH-F CTTTTGTCACTGCCCCGAATA FH-R TGCTGCTCCCTGGCTCATT SLC25A3-F GCAACATACTTGGTGAGGA SLC25A3-R TATACATTTTGGGGACAGC SREBF1-F GATCAAAGAGGAGCCAGTGC SREBF1-R TAGATGGTGGCTGCTGAGTG β-actin-F GCCTCGCTGTCCACCTTCCA β-actin-R CACCTTCACCGTTCCAGTTT Determination of the levels of metabolites Adenosine triphosphate (ATP) content assay kit (Jiangsu Feiya Biological Technology Co., Ltd., FY8511-B), pyruvate content assay kit (Jiangsu Feiya Biological Technology Co., Ltd., FY21457-B), citric acid content assay kit (Jiangsu Feiya Biological Technology Co., Ltd., FY0006-RB), fumarate detection kit (Jiangsu Feiya Biological Technology Co., Ltd., FY0315-RB), lactate content assay kit (Jiangsu Feiya Biological Technology Co., Ltd., FY40175-B), acetyl CoA content assay kit (Jiangsu Feiya Biological Technology Co., Ltd., FY9063-B), and succinate assay kit (Jiangsu Feiya Biological Technology Co., Ltd., FY21286-B) were used to detect the metabolites in the PFC of rats. All the experiments were carried out according to the manufacturer’s instructions. Western blot analysis Total protein was extracted using cell lysis buffer (Bioss, Beijing, China) supplemented with protease and phosphatase inhibitors (Boster, Wuhan, China). The concentrations of the protein samples were measured with a BioDrop Ulite. Subsequently, 40 µg of protein from each sample was separated by 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) at 160 V for 25 minutes. The proteins were then transferred to polyvinylidene fluoride (PVDF) membranes at 400 mA for 20 minutes. Following transfer, the membranes were blocked with 5% non-fat dry milk (NFDM) in TBS containing 1% Tween 20 (TBST) for 1 hour at room temperature. The membranes were then incubated overnight at 4°C with primary antibody dilutions, including anti-Phosphatidylinositol 3-Kinase (PI3K, rabbit anti-rat, 1:1000, 4292, Cell Signaling), anti-Protein Kinase B (AKT, rabbit anti-rat, 1:3000, 10176-2-AP, ProteinTech), anti-p-ATP citrate lyase (p-ACLY, rabbit anti-rat, 1:1000, 4331, Cell Signaling), and anti-β-actin (rabbit anti-rat, 1:5000, bs-0061R, Bioss). After three washes with TBST, the membranes were incubated with the secondary antibody dilution (anti-rabbit IgG, HRP-linked Antibody, 1:3000, 7074, Cell Signaling) for 1 hour at room temperature. Finally, the membranes were developed using enhanced chemiluminescence reagents. Statistical methods Data analyses were conducted using R version 4.2.1. To compare two groups of continuous variables, a t-test was performed for variables exhibiting a normal distribution, while the Wilcoxon test was utilized for those with non-normal distributions. The relationship between variables was assessed using Pearson’s correlation coefficient (R version 4.2.1). Statistical significance was defined as p < 0.05. Results Identification of DE-MRGs DEGs in the dorsolateral prefrontal cortex tissue of MDD and normal control samples from GSE54568 were analyzed, resulting in a total of 1,811 DEGs with a significance threshold of p < 0.05. The heatmap of these DEGs is presented in Fig. 1 A. Among these, 778 DEGs were found to be up-regulated, while 1,033 DEGs were down-regulated in the MDD group compared to the normal control group (Fig. 1 B). Functional enrichment analysis To gain a deeper understanding of the biological functions of MRGs in MDD, we conducted a functional enrichment analysis of DE-MRGs. A total of 223 DE-MRGs were identified by intersecting DEGs and MRGs (Fig. 2 A). Subsequently, these 223 DE-MRGs underwent GO and KEGG analyses to elucidate their biological functions and associated signaling pathways. The results of the GO enrichment analysis indicated that, within biological processes (BP), DE-MRGs were significantly enriched in the sulfur compound metabolic process, generation of precursor metabolites and energy, small molecule catabolic processes, alcohol metabolic processes, and cellular amino acid metabolic processes. In terms of cellular components (CC), DE-MRGs were predominantly associated with transporter complexes, transmembrane transporter complexes, ion channel complexes, oxidoreductase complexes, and cation channel complexes. Regarding molecular functions, these genes were found to play crucial roles in several key activities, including active transmembrane transporter activity, channel activity, passive transmembrane transporter activity, ion channel activity, and gated channel activity (Fig. 2 B). Furthermore, KEGG pathway enrichment analyses demonstrated that DE-MRGs were significantly enriched in pathways related to chemical carcinogenesis-reactive oxygen species, prion disease, diabetic cardiomyopathy, carbon metabolism, and oxidative phosphorylation (Fig. 2 C-D). Acquisition of hub genes and the correlation analysis To investigate potential interactions among the 223 DE-MRGs, a PPI network was constructed using the STRING database and visualized with Cytoscape V3.8.0 (Additional file 1). The analysis identified 12 hub genes (SDHB, BCKDHB, DLD, IDH3B, ACLY, SLC25A3, FH, DLAT, UQCRFS1, IDH3A, MDH1, NDUFS1) utilizing the MCODE plugin with default parameters (degree cutoff: 2; node score cutoff: 0.2; K-core: 2; max depth: 100) (Fig. 3 A). Notably, a general positive correlation was observed among these 12 hub genes (Fig. 3 B). Among these genes, five exhibited strong predictive ability, with correlations as follows: DLD and DLAT showed a correlation of 0.87, DLD and FH had a correlation of 0.86, and FH and DLAT were correlated at 0.85, indicating a robust relationship among DLD, FH, and DLAT. Furthermore, the correlation between SLC25A3 and DLAT was 0.78, while correlations with DLD and FH were 0.63 and 0.60, respectively. These findings suggest a notable correlation between SLC25A3 and each of DLAT, DLD, and FH. Additionally, five TFs and 58 miRNAs were predicted based on the identified hub genes. A regulatory network comprising TFs, genes, and miRNAs was constructed to illustrate these relationships (Fig. 3 C). Our analysis specifically focused on TFs and miRNAs associated with ACLY, DLD, DLAT, FH, and SLC25A3. Notably, miR-16-5p was found to target all five genes to regulate transcription, while miR-107 targeted ACLY, DLAT, DLD, and FH. Furthermore, let-7a-5p, let-7b-5p, and let-7e-5p were identified as targeting ACLY, DLAT, and SLC25A3, and miR-124-3p targeted ACLY, DLAT, and DLD. Importantly, the transcription of ACLY is regulated by SREBF1. Construction and validation of diagnostic models A logistic regression prediction model was developed based on hub genes (Fig. 4 A). In the training dataset, the area under the curve (AUC) of ROC for this logistic regression model was found to be 0.889, indicating strong predictive performance (Fig. 4 B). Additionally, the diagnostic prediction model was assessed using the independent validation set GSE54570, yielding an AUC of 0.941 (Fig. 4 C). Expression validation of the hub genes was conducted in the external validation set GSE54570, revealing that ACLY, DLD, DLAT, FH, and SLC25A3 exhibited consistent expression trends in both disease and control samples across the training and validation datasets (Fig. 4 D- 4 M, Additional file 2). To further investigate the diagnostic accuracy of these five key genes, ROC curves were analyzed. In the training set, the AUCs for ACLY, DLD, DLAT, FH, and SLC25A3 were recorded as 0.747, 0.667, 0.751, 0.742, and 0.711, respectively, suggesting good diagnostic ability (Fig. 4 N). In the external validation set, the AUCs for these genes were 0.642, 0.645, 0.544, 0.680, and 0.456, respectively (Fig. 4 O). These results indicated that the five biomarkers, particularly ACLY, DLD, and FH, may serve as sensitive and specific indicators for distinguishing MDD samples from normal controls. Gene Set Enrichment Analysis (GSEA) of hub genes To explore the mechanisms underlying the role of hub genes in patients with MDD, GSEA was performed. The GSEA results indicated that Huntington's disease, oxidative phosphorylation, Parkinson's disease, ubiquitin-mediated proteolysis, and proteasome pathways exhibited the highest enrichment of hub genes (see Additional File 3). Notably, the genes ACLY, DLD, DLAT, FH, and SLC25A3 were predominantly enriched in the pathways associated with oxidative phosphorylation, Parkinson's disease, and the proteasome (Fig. 5 ). Given the close association of these five genes with energy metabolism, particularly in the context of the tricarboxylic acid cycle, we speculate that dysregulation of energy metabolism and oxidative phosphorylation in the PFC may represent a critical pathological process in the development of MDD. Furthermore, it is plausible that a shared mechanism related to energy metabolism links MDD and Parkinson's disease, with these five genes serving as pivotal factors in this connection. CUMS-induced depression-like behaviors in SD rat CUMS is a widely used method for inducing depression-like behaviors in animal models. This study employed three experimental approaches to evaluate the depression-like behaviors and cognitive function in rats subjected to CUMS. First, the SPT was conducted to evaluate anhedonia-related depression-like behaviors in the CUMS-induced model rats. The results indicated a significant reduction in sucrose preference in the model group compared to the control group (Fig. 6 A). Second, the FST was employed to assess the depressed mood of the CUMS-induced model rats. The data revealed a notable increase in immobility time in the model group when compared to the control group (Fig. 6 B). Finally, the MWM test was administered to evaluate cognitive function in the CUMS-induced model rats. In this assessment, the model rats exhibited a significantly increased escape latency in the hidden platform test relative to the control group (Fig. 6 C). Additionally, during the probe test, CUMS-induced model rats spent significantly less time in the target quadrant and demonstrated a reduced percentage of total swimming distance in the target quadrant compared to the control group (Fig. 6 D-E). Collectively, these findings confirm that CUMS effectively induces depression-like behaviors and cognitive dysfunction in SD rats. Validation of the expression of the key genes and related metabolites We further investigated the expression levels of ACLY, DLD, DLAT, FH, and SLC25A3 in the PFC of rats subjected to CUMS using RT-qPCR. Our analysis revealed that, compared to the control group, the expression of DLD, DLAT, FH, and ACLY in the model group was significantly reduced. In contrast, no significant difference was observed in the expression of SLC25A3 in the PFC of the rats (Fig. 7 A-E). Given that ACLY, DLD, DLAT, FH, and SLC25A3 are closely associated with the tricarboxylic acid cycle, we proceeded to measure the levels of various metabolite products influenced by these genes, including pyruvate, acetyl-CoA, citric acid, succinate, fumarate, lactate, and ATP. We found that, relative to the control group, the levels of pyruvate, citric acid, and lactate were significantly elevated in the PFC of CUMS-induced model rats, whereas the levels of acetyl-CoA, succinate, fumarate, and ATP were notably decreased (Fig. 7 F-L). These findings corroborate the abnormal mRNA expression of ACLY, DLD, DLAT, and FH observed in the PFC of the depression model. The down-regulation of these genes may disrupt the tricarboxylic acid cycle within the PFC of the model rats. Validation of the expression of transcription factor and regulation signals for ACLY In this study, we investigated the gene ACLY, which exhibited significant downregulation in the PFC of rats subjected to CUMS. Additionally, we identified SREBF1 as a transcription factor within the TF-gene-miRNA regulatory network associated with ACLY. Previous research has indicated that the PI3K/AKT signaling pathway plays a crucial role in the phosphorylation of ACLY at the S455 site in human (equivalent to Ser454 in rat)[ 20 ], thereby modulating its function in glucose and lipid metabolism regulation. Consequently, we assessed the expression levels of SREBF1, PI3K, AKT, as well as the phosphorylation status of ACLY in the PFC of CUMS-induced model rats. Our findings revealed that the mRNA expression of SREBF1 was significantly diminished in the model group compared to the control group (Fig. 8 A). Furthermore, protein expression levels of PI3K, AKT, and phosphorylated ACLY (p-ACLY) were also significantly reduced (Fig. 8 B-E). These results suggest that the aberrant expression of ACLY in the PFC of depression model rats may be linked to the decreased expression of its regulatory factor SREBF1. Moreover, the down-regulation of the PI3K/AKT signaling pathway further impairs the phosphorylation of ACLY at the S454 site, subsequently inhibiting its enzymatic activity. Discussion It is projected that MDD will exert profound negative effects on health, functionality, the economy, and society by the year 2030, characterized by notably high morbidity and mortality rates[ 21 ]. Despite this alarming forecast, the pathophysiological mechanisms underlying MDD remain incompletely understood. Current predominant theories regarding the pathogenesis of MDD include the monoamine neurotransmitter hypothesis, the neuroendocrine hypothesis, and the neuroinflammation hypothesis. Numerous studies have indicated that these hypotheses may influence, as well as be influenced by, energy metabolism within the central nervous system[ 22 ]. Intervention of energy metabolism among patients suffering from MDD have emerged as a promising avenue for developing novel treatments for this disease. Previous research has primarily concentrated on metabolic processes and fluctuations in metabolite levels, with relatively scant attention given to the exploration of differentially expressed genes associated with metabolism in the context of depression. This gap hampers the implementation of targeted therapeutic strategies for MDD. Accordingly, this study employs bioinformatics approaches to discern the metabolic-related genes associated with depression by analyzing datasets pertinent to MDD, aiming to elucidate the underlying mechanisms connecting MDD and energy metabolism. The findings of this study yielded the identification of four MRGs as potential key biomarkers for MDD. Furthermore, six pairs of interaction relationships and one signaling pathway were predicted. This investigation highlights novel central biomarkers of MDD from an energy metabolism perspective, reinforcing the critical role of energy metabolism in the diagnosis of MDD and contributing to the development of personalized treatment modalities. The PFC is recognized as one of the brain regions most severely affected by MDD[ 23 ]. Functional and structural abnormalities of the PFC have been documented in both individuals currently experiencing MDD and those exhibiting heightened vulnerability to developing the disorder [ 24 – 25 ]. The energy metabolism in the PFC is intrinsically linked to its overall function. In this study, a total of 223 DE-MRGs were identified through the overlap of differentially expressed genes and metabolic related genes, followed by enrichment analyses. In both GO and KEGG analyses, DE-MRGs exhibited enrichment in pathways associated with oxidative phosphorylation, the citrate cycle, the generation of precursor metabolites and energy, and the dicarboxylic acid metabolic process. Additionally, cell component of GO analyses indicated enrichment in mitochondrial respirasomes, oxidoreductase complexes, and respiratory chain complexes. These results suggest potential abnormalities in glucose metabolism within the prefrontal cortex of MDD patients. A substantial body of literature has indicated a high prevalence of concurrent glucose metabolism aberrations among individuals with MDD[ 26 ]. The classic antidepressant fluoxetine has been noted for its therapeutic efficacy in neuropsychiatric conditions characterized by disruptions in central energy metabolism, attributable to its ability to enhance glucose uptake in the brain[ 27 ]. Through utilizing MCODE and AUC methodologies, five biomarkers (ACLY, DLD, DLAT, FH, and SLC25A3) were identified. Subsequent validation revealed that these five biomarkers exhibited decreased expression levels in MDD patients compared to healthy controls. A substantial corpus of evidence suggests an association between these genes and mitochondrial function, which has been implicated in the pathogenesis of MDD[ 28 ]. Specifically, these biomarkers play pivotal roles in regulating the tricarboxylic acid (TCA) cycle within mitochondria. DLD, DLAT, PDH, and certain cofactors collectively constitute the pyruvate dehydrogenase complex (PDHC). The absence of DLD and DLAT results in decreased PDHC activity, thereby affecting the conversion of pyruvate to acetyl-CoA. Previous studies have documented that DLD and DLAT are particularly linked to anxiety susceptibility[ 29 ]. Furthermore, DLD serves as a crucial subunit of the alpha-ketoglutarate dehydrogenase complex (KGDHC), and a deficiency in DLD can hinder the conversion of α-ketoglutarate to succinyl-CoA. Fumarate hydratase (FH) predominantly catalyzes the conversion of fumaric acid to succinic acid. Its absence can lead to fumarate accumulation. Recent findings suggest that fumarate has distinct roles in macrophage activation and regulates cytokine production, including interleukin-10 and type I interferons[ 30 ]. FH has emerged as a significant regulatory point for the pro-inflammatory and anti-inflammatory effects of immune cells. Numerous studies have reported a correlation between MDD and alterations in fatty acid metabolism and oxidative phosphorylation. ACLY serves as a link between carbohydrate and lipid metabolism by generating acetyl-CoA from citrate, which is pivotal for fatty acid and cholesterol biosynthesis[ 31 ]. Moreover, ACLY functions as a key enzyme in the fatty acid biosynthesis pathway, producing cytosolic acetyl-CoA and oxaloacetate, potentially influencing the biosynthesis of acetylcholine. Evidence indicates that a deficiency in ACLY can lead to reduced overall histone acetylation levels, diminished proliferation, and altered gene expression profiles[ 32 ]. Additionally, it has been established that FH, DLD, and ACLY are principal protein-coding genes that mediate the interplay in the pathogenesis of Alzheimer and Parkinson. SLC25A3 acts as a significant transporter of inorganic phosphate into the mitochondrial matrix and constitutes an essential component of the mitochondrial ATP synthasome. The deletion of SLC25A3 in adult cardiomyocytes has demonstrated a reduction in mitochondrial ATP synthesis. MiRNAs play a crucial role in a wide array of biological processes during both normal physiological and pathological states[ 33 ]. These short, conserved RNA molecules are estimated to regulate up to 60% of all mammalian protein-coding genes[ 34 – 35 ]. Intriguingly, approximately 70% of these miRNAs have been implicated in diverse neuronal processes, including neurogenesis and neuroplasticity[ 36 ]. This study observes that miR-16-5p targets the transcription of five genes, which include ACLY, DLAT, DLD, FH, and SLC25A3, whereas miR-107 targets ACLY, DLAT, DLD, and FH. The let-7 family and miR-124-3p influence the transcription of three genes. Prior research has established a close relationship between these miRNAs and MDD. For instance, neural stem cell-derived extracellular vesicles have been demonstrated to mediate the protective effects of miR-16-5p against neuronal damage associated with depression disorder[ 37 ]. In comparison to healthy controls, miR-107 levels were found to be significantly elevated in patients diagnosed with bipolar disorder. The let-7 miRNA family, being the most abundant in the brain, has dysregulated expression linked to neuronal apoptosis, neuroinflammation, and depressive symptoms[ 38 ]. Additionally, miR-124-3p is recognized as a critical neuronal miRNA involved in regulating neuronal fate determination, development, and plasticity[ 39 ]. High expression levels of miR-124-3p have been observed in the brains of MDD patients, indicating its potential as a therapeutic target for the development of novel antidepressant pharmacotherapies[ 40 – 42 ]. Moreover, we hypothesize that the upstream transcription factor SREBP1, which modulates ACLY expression and is responsive to dietary saturated fatty acids, may play a significant role in this context. Previous studies have illuminated that mice deficient in SREBP-1c exhibit behavior akin to schizophrenia[ 43 ]. However, the relationship between the identified miRNAs and neuronal metabolism in the context of MDD has not been thoroughly investigated. Thus, the TF-gene-miRNA network established in this study can serve as a referential framework for future inquiries into gene and miRNA interactions in neuronal metabolism associated with MDD. Following this, we validated the biomarkers ACLY, DLD, DLAT, FH, and SLC25A3 through animal experiments. A depression model was devised using the CUMS methodology, noted for its ability to replicate the process of depression with high fidelity. Upon establishment of the model, we employed three behavioral evaluations to assess depression-like behavior in the CUMS-induced rats. Firstly, the assessment of anhedonia was conducted through the SPT, a reliable behavioral paradigm that evaluates the preference for sucrose solution relative to water[ 44 ]. Our findings indicated a significant reduction in sucrose preference within the model group, illustrating the anhedonic effects of CUMS. Secondly, the forced swim test (FST) was implemented to evaluate depression-like behavior, revealing increased immobility time in the model group, thereby suggesting induced despair-like behavior due to CUMS. Lastly, the MWM test was employed to assess cognitive function, where escape latency for rats to locate the submerged platform was significantly prolonged, indicating compromised learning abilities. Moreover, swimming time in the target quadrant and the percentage of total swimming distance within the target quadrant were significantly diminished in the model group, reflecting impaired spatial memory capabilities following CUMS administration. Collectively, these findings underscore the efficacy of CUMS in inducing depression-like behavior in rodent models. To ascertain the expression levels of metabolism-related biomarkers in the PFC of the model rats, we utilized RT-PCR techniques. The results demonstrated a significant decrease in the mRNA expression levels of DLD, DLAT, FH, and ACLY in the PFC of model rats, whereas the expression levels of SLC25A3 remained comparable to those observed in the control group. Furthermore, we investigated the levels of metabolites associated with these enzymes. The outcomes revealed increased concentrations of pyruvic acid, citric acid, and lactate in the PFC of model rats, while levels of succinate, fumarate, acetyl-CoA, and ATP were notably reduced. As previously mentioned, PDHC plays a critical role in converting pyruvate to acetyl-CoA. The observed downregulation of DLD and DLAT in the prefrontal cortex of the depressive rat model may directly contribute to a reduction in PDHC activity and a subsequent impediment in pyruvate conversion. Additionally, DLD serves as a vital component of KGDHC. Thus, the decreased expression of DLD could result in the downregulation of KGDHC and hinder the conversion of α-ketoglutarate to succinyl-CoA, ultimately influencing succinate production. Moreover, the reduced activity of ACLY appeared to lead to an accumulation of citric acid, while reduced FH activity inhibited the conversion of fumarate to malate, resulting in fumarate accumulation. Despite the decreased expression of FH in the PFC of model rats, fumarate levels did not exceed those observed in the control group. This could indicate an inhibitory state within the TCA cycle in the model rats, leading to a decline in the expression of associated metabolic intermediates. The increased levels of lactate coupled with decreased ATP levels suggest heightened glycolytic activity and diminished oxidative phosphorylation in the PFC of the model rats. Overall, these results indicate alterations in energy metabolism pathways and metabolic dysregulation in the PFC of model rats, with DLD, DLAT, FH, and ACLY demonstrating significant potential as biomarkers for MDD. In the established TF-gene-miRNA regulatory network, SREBP1 emerged as a pivotal upstream transcription factor influencing ACLY. Subsequent validation through in vivo experiments demonstrated a marked decrease in SREBP1 mRNA expression in the PFC of model rats compared to controls. This study provides novel evidence that the SREBP1/ACLY signaling is hindered in the PFC of CUMS-induced model rats. ACLY serves as a crucial nexus linking glucose and lipid metabolism, primarily operating through phosphorylation. Previous studies have identified the PI3K/AKT signaling pathway as a regulatory mechanism for the phosphorylation of ACLY at serine 454 (equivalent to Ser454 in rat)[ 20 ]. Notably, our findings revealed significantly reduced expression levels of PI3K, AKT, and p-ACLY in the PFC of model rats. Based on these observations, we hypothesize that the transcription factor SREBP1 and the PI3K/AKT signaling pathway in the PFC of model rats may influence both the transcriptional regulation and post-translational modifications of ACLY, respectively. Conclusions By combining bioinformatic approaches and animal experiments, we identified four metabolism-related biomarkers (DLD, DLAT, FH, and ACLY) for MDD. The changes in the above genes may lead to the glucose metabolism disorders in the PFC, manifested as a decrease in oxidative phosphorylation and an increase in glycolysis. The above research results provide a basis for the hypothesis of energy metabolism disorder and potential biomarkers in MDD. But given that this study was a retrospective study; it lacked newly collected clinical samples and information. Further research is needed to investigate the interaction between DLD, DLAT, FH, and ACLY in vitro and in vivo. By integrating bioinformatics methodologies with experimental animal models, we identified four metabolism-related biomarkers (DLD, DLAT, FH, and ACLY) associated with MDD. Alterations in these genes may precipitate glucose metabolism dysregulation in the PFC, characterized by a reduction in oxidative phosphorylation. These findings substantiate the hypothesis of energy metabolism impairments and suggest potential biomarkers for MDD. However, it is important to note that this study is retrospective in nature and does not include newly acquired clinical samples or data, indicating the need for additional clinical evidence to validate the findings. In addition, further investigations are warranted to explore the interactions among DLD, DLAT, FH, and ACLY both in vitro and in vivo. Abbreviations AUC Areas under the curve BP Biological processes CC Cell component CUMS Chronic unpredictable mild stress DE-MRGs Differentially expressed metabolism-related genes DEGs Differentially expressed genes FST Forced swimming test GEO Gene Expression Omnibus GO Gene ontology GSEA Gene set enrichment analysis KEGG Kyoto encyclopedia of genes and genomes KGDHC Alpha-ketoglutarate dehydrogenase complex MCODE Molecular complex detection MDD Major depression disorder MiRNAs MicroRNAs MRGs Metabolic related genes MWM Morris water maze test NFDM Non-fat dry milk TBS Tris buffered saline TBST TBS containing 1% tween 20 PDHC Pyruvate dehydrogenase complex PET Positron emission tomography PFC Prefrontal cortex PPI Protein-protein interaction PVDF Polyvinylidene fluoride ROC Receiver operating characteristic SD Sprague-Dawley SDS-PAGE Sodium dodecyl sulfate-polyacrylamide gel electrophoresis SPT Sucrose preference test TCA Tricarboxylic acid TF Transcription factors Declarations Ethics approval and consent to participate The animal study was reviewed and approved by the Animal Experimental Ethical Inspection in Laboratory of The First Hospital of Hunan University of Chinese Medicine. Consent for publication Not applicable. Availability of data and materials All data generated or analysed during this study are included in this published article and its supplementary information files. . Competing interests The authors declare that they have no competing interests. Funding This research was supported by funding from the National Natural Science Foundation of China (82474476, 82474067, 82305203), Natural Science Foundation of Hunan Province (2024JJ4033, 2023JJ30476, 2024JJ9427). Authors’ contribution Data collection and statistical analysis: HY, JW and SL. Experiment implementation: HY, PM and QD. Animal experiments assistance: HL and YW. Study design and supervision: WL. All authors contributed to the critical revision of the final manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Smith, K. Mental health: a world of depression. Nature 7526 , 180–181 (2014). Meng, R. et al. 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Identification of MicroRNA-124-3p as a Putative Epigenetic Signature of Major Depressive Disorder. Neuropsychopharmacology 42 (4), 864–875 (2017). Ang, M. J. et al. Transcriptome Profiling Reveals Novel Candidate Genes Related to Hippocampal Dysfunction in SREBP-1c Knockout Mice. Int. J. Mol. Sci. 21 (11), 4131 (2020). Primo, M. J. et al. Sucrose preference test: A systematic review of protocols for the assessment of anhedonia in rodents. Eur. Neuropsychopharmacol. 77 , 80–92 (2023). Additional Declarations No competing interests reported. Supplementary Files WBImage.rar Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5314827","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":384296256,"identity":"850afc7f-5e1e-4c68-8249-203dd1ce1afe","order_by":0,"name":"Hui Yang","email":"","orcid":"","institution":"The First Hospial of Hunan University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Yang","suffix":""},{"id":384296257,"identity":"080587ba-4c39-4869-a8b8-ecb2ea7db82d","order_by":1,"name":"Jinxi Wang","email":"","orcid":"","institution":"The First Hospial of Hunan University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jinxi","middleName":"","lastName":"Wang","suffix":""},{"id":384296258,"identity":"6165de1c-567b-4b0c-9b37-52f49f40fef7","order_by":2,"name":"Shihui Lei","email":"","orcid":"","institution":"The First Hospial of Hunan University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shihui","middleName":"","lastName":"Lei","suffix":""},{"id":384296259,"identity":"451ae312-7668-4d3e-850a-6f008f752e4e","order_by":3,"name":"Pan Meng","email":"","orcid":"","institution":"Hunan University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Pan","middleName":"","lastName":"Meng","suffix":""},{"id":384296260,"identity":"fe6e3d99-5766-4e95-9c2e-c81360f32822","order_by":4,"name":"Qing Du","email":"","orcid":"","institution":"Hunan Academy of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Du","suffix":""},{"id":384296261,"identity":"e3e312b0-a32e-4606-b4b1-75924cfebf0f","order_by":5,"name":"Hongping Long","email":"","orcid":"","institution":"The First Hospial of Hunan University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hongping","middleName":"","lastName":"Long","suffix":""},{"id":384296262,"identity":"25af6dee-1be1-41e5-b80b-68d43ebde4c2","order_by":6,"name":"Yuhong Wang","email":"","orcid":"","institution":"Hunan University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yuhong","middleName":"","lastName":"Wang","suffix":""},{"id":384296263,"identity":"48db5ab6-c43f-40fd-a9ab-e0254a06d337","order_by":7,"name":"Wei Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYBAC+/uPDz5IMJCo52dvIFbPgbRkgwcFFgmSPQeI1pKjJvngQ0WCwYwEInUwNpxhkwA6LM9A8vHGGww1NtEEtTAz9h62AGopNpdOK7ZgOJaW20BICxszX+INoBbGnbNzzCQYGw4T1sLDxmMAchjjhptniNQiwcNjBNKSuOEGD5FaDCTYkg2AWowle4B+SSDGLwYSzAcf/vhTJ8fPfnjjjQ81NoS1oGpPIEU5RAupOkbBKBgFo2BkAAC7LD4Noee5mAAAAABJRU5ErkJggg==","orcid":"","institution":"The First Hospial of Hunan University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-10-23 01:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5314827/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5314827/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71880481,"identity":"aedc07dc-862b-4cbf-ba48-1af0e285a2b7","added_by":"auto","created_at":"2024-12-19 11:32:01","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":228659,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of differentially expressed genes (DEGs) in MDD and normal groups. A Heatmap for DEGs in MDD and controls. B Volcano plot of DEGs in samples from MDD and controls in GSE54568.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5314827/v1/dc700805858599093519b4e9.jpeg"},{"id":71880494,"identity":"932b8e44-88af-42ba-9f5b-5d85c5dc876d","added_by":"auto","created_at":"2024-12-19 11:32:01","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":774630,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of DE-MRGs. A Venn diagram of 223 metabism related DE-MRGs in MDD. B Ten of the most significant gene ontology terms. C Enrichment of Kyoto Encyclopedia of Genes and Genomes (KEGG). D ten of the most significant KEGG pathways.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5314827/v1/48ab08d0f69bd65a7586001a.jpeg"},{"id":71881745,"identity":"e6ae50e2-1c64-4a2d-8edf-f2c26ea17beb","added_by":"auto","created_at":"2024-12-19 11:40:01","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":863381,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of hub genes and correlation analysis. A Top 12 hub genes identified using molecular complex detection (MCODE). B Matrix of correlation of hub genes. C The TF-gene-miRNA regulatory network of hub genes (Orange hexagon represent TFs, green diamonds represent miRNAs, and pink circles represent genes)\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5314827/v1/266a1ff5d420604e1a7f49b3.jpeg"},{"id":71880484,"identity":"fe77a5be-22e4-4322-aace-50c04fda72a5","added_by":"auto","created_at":"2024-12-19 11:32:01","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":566160,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic capacity assessment for hub genes. A Confusion Matrix of the training datasets. B-C Receiver operating characteristic (ROC) curves for the diagnostic power of hub genes to MDD from healthy controls in the training datasets (B) and the validation datasets (C). D-H Validation for five biomarkers in the training datasets. I-M Validation for five biomarkers in the validation datasets. N-O Areas under the curve (AUCs) of the five biomarkers in the training datasets (N) and the validation datasets (O).\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5314827/v1/cac0673e5f80c99687d5dab2.jpeg"},{"id":71881748,"identity":"992918e2-6c98-4b14-9808-c25f2081086d","added_by":"auto","created_at":"2024-12-19 11:40:02","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":528365,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of critical genes using gene set enrichment analysis (GSEA).\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5314827/v1/b7fea0c275d89ccf26b0ae45.jpeg"},{"id":71880488,"identity":"b6063254-4054-4aca-913a-99f2ab8cd7a6","added_by":"auto","created_at":"2024-12-19 11:32:01","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":141560,"visible":true,"origin":"","legend":"\u003cp\u003eDepression-like behaviors in chronic unpredictable mild stress (CUMS) - induced model rats. A Sucrose preference (%) in SPT (n=12). B Immobility time in FST (n=12). C Escape latency time in Morris water maze test (n=12). D Time in target quadrant in Morris water maze test (n=12). E Path length in target quadrant in Morris water maze test (n=12). Data are presented as the mean ± SEM. *\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5314827/v1/a5e1a59e6d04e16fcbd4cb6c.jpeg"},{"id":71880502,"identity":"42a1b8c2-9024-4504-934f-59b23b24692b","added_by":"auto","created_at":"2024-12-19 11:32:02","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":252384,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the expression of critical genes and metabolites. A-E qRT-PCR verification for critical genes (n=5). F-L ELISA vertification for metabolites. Data are presented as the mean ± SEM. *\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5314827/v1/41220dc5df6df7ad45ebc41a.jpeg"},{"id":71880500,"identity":"a75b435c-9876-4e93-a7cf-ce61a488bb38","added_by":"auto","created_at":"2024-12-19 11:32:01","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":143770,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression of transcription factor and regulation proteins of ACLY. A mRNA expression of SREBF1 in the prefrontal cortex of rats (n=5). B-E Proteins expression of PI3K (C), Akt (D), and p-ACLY (E) in the prefrontal cortex of rats (n=5). Data are presented as the mean ± SEM. *\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5314827/v1/ca22c5df120a1b670dd28243.jpeg"},{"id":90923296,"identity":"5501faa8-e163-4a66-ae5f-da7d46170f30","added_by":"auto","created_at":"2025-09-09 15:17:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4541905,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5314827/v1/74a9363d-4d42-4cf1-957a-2bce61181037.pdf"},{"id":71881750,"identity":"6881d141-6846-4035-a13d-4b2546617051","added_by":"auto","created_at":"2024-12-19 11:40:02","extension":"rar","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":7082672,"visible":true,"origin":"","legend":"","description":"","filename":"WBImage.rar","url":"https://assets-eu.researchsquare.com/files/rs-5314827/v1/72b8dab90059316be4b2cfe8.rar"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification and Analysis of Key Genes Related to Metabolism in the Brain of Major Depressive Disorder","fulltext":[{"header":"Background","content":"\u003cp\u003eMajor depressive disorder (MDD) is a complex psychiatric condition characterized by persistent low mood, loss of interest, and anhedonia. The high prevalence, recurrence, and mortality rates associated with MDD have made it a leading contributor to disease burden in Western populations[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In China, a prospective cohort study indicated that MDD is linked to increased mortality from all-cause and cardiovascular diseases among adults[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Consequently, effective management of MDD is crucial and urgent to prevent premature death. However, accurately diagnosing MDD in clinical practice remains challenging, as current diagnostic methods primarily rely on self-reporting of symptoms and clinical interviews[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Clarifying the biological underpinnings of MDD and developing reliable biomarkers could enhance the diagnostic criteria, facilitating more accurate and timely diagnosis[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe brain is a highly energy-demanding organ, constituting only 2% of total body weight yet requiring approximately 25% of the body's total glucose for its normal function[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. MDD is increasingly recognized as a metabolic brain disease. Multiple studies have indicated that abnormalities in energy metabolism are significant contributors to the pathophysiology of MDD[\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Enhancing energy metabolism may represent a viable therapeutic target[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Positron emission tomography (PET) scans have demonstrated reductions in both blood flow and metabolism in the prefrontal cortex (PFC) of patients with major depression[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Furthermore, preclinical studies have identified extensive metabolic abnormalities involving amino acids, glucose, and lipids in the brains of animal models of depression[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, the metabolic-related biomarkers associated with MDD, particularly those present in the brain, remain inadequately defined. Thus, there is an urgent need to identify central metabolic-related genes (MRGs) linked to MDD to facilitate the development of new biomarkers and therapeutic targets.\u003c/p\u003e \u003cp\u003eIn this study, we analyzed the biological significance of metabolic-related genes (MRGs) and their association with MDD. Initially, MDD-related genes were retrieved from the Gene Expression Omnibus (GEO), while MRGs were sourced from prior literature. Subsequently, we conducted multiple functional enrichment analyses to elucidate the biological relevance of MRGs in the context of MDD. Metabolism-related biomarkers associated with MDD were identified using molecular complex detection (MCODE) and Receiver Operating Characteristic (ROC) curve analysis. Additionally, we performed Gene Set Enrichment Analysis (GSEA) on hub genes utilizing the ClusterProfilter package. We also predicted transcription factors and microRNAs (miRNAs) associated with these hub genes. Finally, we performed experimental validation of the hub genes using a chronic unpredictable mild stress (CUMS) induced rat model of depression. Additionally, we investigated the transcription factors and regulatory signals associated with the key genes identified in our study. Collectively, the findings of this study may enhance our understanding of the metabolic pathophysiology of MDD and may facilitate the identification of novel biomarkers for its treatment.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Source\u003c/h2\u003e \u003cp\u003eTwo datasets related to MDD, GSE54568 and GSE54570, were obtained from the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/gds\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/gds\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The training set consisted of fifteen dorsolateral prefrontal cortex tissue samples from MDD patients and fifteen normal control samples from GSE54568. These datasets were generated using the GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array). For the external validation set, we included thirteen dorsolateral prefrontal cortex tissue samples from MDD patients and thirteen normal control samples from GSE54570, which were derived from the GPL96 platform (Affymetrix Human Genome U133A Array). A total of 2752 MRGs were collected from previous article[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] .\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAcquisition of Hub genes\u003c/h3\u003e\n\u003cp\u003eDifferential expression analysis was conducted to compare MDD samples with control samples using the limma package in the GSE54568 dataset [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], identifying differentially expressed genes (DEGs) with a significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Differentially expressed metabolism-related genes (DE-MRGs) were identified by intersecting the DEGs with the MRGs. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the DE-MRGs were performed using the ClusterProfiler package. To investigate potential interactions among the DE-MRGs, a protein-protein interaction (PPI) network was constructed using the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org\u003c/span\u003e\u003cspan address=\"https://string-db.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Hub genes were identified through the MCODE plugin.\u003c/p\u003e\n\u003ch3\u003eConstruction and validation of logistic regression prediction model\u003c/h3\u003e\n\u003cp\u003eA logistic regression prediction model was developed based on the identified hub genes, accompanied by the creation of a confusion matrix heat map. The ROC curves of the logistic regression model were plotted for both the training set and the validation set using the survival ROC package to evaluate its diagnostic efficacy [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Additionally, expression validation of the hub genes was performed in both the training set and the validation set.\u003c/p\u003e\n\u003ch3\u003eGSEA and Establishment of regulatory network\u003c/h3\u003e\n \u003cp\u003eTo investigate potential interactions among the hub genes, Spearman correlation coefficients were calculated. GSEA of the hub genes in the training set was performed using the ClusterProfiler package [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].. Transcription factors (TFs) and miRNAs associated with the hub genes were predicted utilizing the miRNet database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mirnet.ca\u003c/span\u003e\u003cspan address=\"https://www.mirnet.ca\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Subsequently, a regulatory network comprising transcription factors, genes, and miRNAs was constructed using Cytoscape.\u003c/p\u003e\n\u003ch3\u003eAnimal model\u003c/h3\u003e\n\u003cp\u003e All relevant institutional and national guidelines for the care and use of animals were strictly adhered to throughout the study. The animal experiments received approval from the Institutional Animal Care and Use Committee of the First Hospital of Hunan University of Chinese Medicine (Approval No. ZYFY20221111-55). All experiments were designed and reported according to the Animal Research: Reporting of In Vivo Experiments (ARRIVE) guidelines. Male Sprague-Dawley (SD) rats, aged six weeks, were obtained from the Hunan Slack Scene Laboratory Animal Company. The rats were housed in a temperature-controlled environment (20\u0026ndash;24\u0026deg;C) with a 12-hour light/dark cycle, and food and water were provided \u003cem\u003ead libitum\u003c/em\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of the depression model\u003c/h2\u003e \u003cp\u003eA rat model of depression was established using the CUMS methodology. Male SD rats weighing between 180 and 220 g were randomly subjected to various stressors, including cage tilting for 24 hours, cold swimming for 3 minutes at 0\u0026deg;C, food deprivation for 24 hours, horizontal shaking for 15 minutes, tail nipping for 1 minute (1 cm from the tail's end), heat stress at 45\u0026deg;C for 5 minutes, and inversion of the light/dark cycle for 24 hours. These stressors were administered over a 28-day period, with each stressor applied four times. To prevent predictability, the stressors were randomly assigned each day, and the same stressor was not administered on consecutive days. The control group remained undisturbed by these procedures.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSucrose preference test(SPT)\u003c/h3\u003e\n\u003cp\u003eThe Sucrose preference test (SPT) was primarily employed to assess animal preferences and anhedonic states. This method served as a valuable tool for investigating depression and related emotional conditions. Prior to the formal testing, rats were acclimated for 24 hours with access to two bottles containing a 1% sucrose solution. Subsequently, one bottle was replaced with pure water, and the rats continued to adapt for another 24 hours, with the positions of the two bottles swapped after 12 hours. The rats were then deprived of food and water for 24 hours before the formal test, during which they were provided with a pre-weighed bottle of 1% sucrose solution and a bottle of pure water for a 2-hour testing period. The remaining weights of both solution bottles were recorded. Sucrose preference was calculated using the formula: Sucrose Preference (%) = (sucrose consumption / (sucrose consumption\u0026thinsp;+\u0026thinsp;water consumption)) \u0026times; 100%.\u003c/p\u003e\n\u003ch3\u003eForced swimming test (FST)\u003c/h3\u003e\n\u003cp\u003eThe forced swimming test (FST) was conducted to evaluate depression-like behaviors in rats. In this procedure, rats were placed in a circular fiberglass pool filled with 30 cm of water maintained at 25\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C. The duration of immobility was recorded during the final 3 minutes of the 4-minute testing period. A reduction in immobility time was utilized as an indicator of antidepressant-like efficacy.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMorris water maze test (MWM)\u003c/h2\u003e \u003cp\u003eThe Morris Water Maze (MWM) test was utilized to assess the cognitive functions of rats [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The experimental setup consisted of a circular black tank (200 cm in diameter) filled with clear water maintained at a temperature of 25\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C. The pool was divided into four equal quadrants labeled A, B, C, and D. A clear platform was submerged beneath the water surface, rendering it invisible [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The platform was situated in quadrant A for the first five days and was removed on the sixth day. Four days prior to the experiment, rats were placed in the black tank daily from different quadrants to enable the animals to identify the location of the submerged platform. If the rat failed to locate the submerged platform within 2 minutes, the experimenter guided it onto the platform using a wooden stick and held it there for 15 seconds. During the hidden platform test on the fifth day, the time taken by the rats to locate and climb onto the submerged platform was recorded as the escape latency, serving as an indicator of their learning abilities. On the sixth day, the platform was removed for the probe test, during which the time spent in the target quadrant and the percentage of total swimming distance in the target quadrant were recorded to evaluate the rats' memory functions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRT-PCR analysis\u003c/h2\u003e \u003cp\u003eThe sodium pentobarbital was administered by intraperitoneal injection (60 mg/kg), and the rat was observed to ensure that euthanasia is effective. Total RNA was extracted from the PFC of rats, and its quality and concentration were assessed using the BioDrop Ulite. Complementary DNA (cDNA) was synthesized with the PrimeScript\u0026trade; RT Reagent Kit (TaKaRa, RR037A). Quantitative reverse transcription PCR (RT-qPCR) was conducted using the TB Green Premix Ex Taq\u0026trade; II (TaKaRa, RR820A). The sequences of the primers utilized are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimer sequences\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimer name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSequence (5\u0026rsquo;\u0026minus;3\u0026rsquo;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACLY-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGAAGGAAGCGGGAGTGTTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACLY-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGAATGAGCCAGGTTTTCG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDLD-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGGTGCTGGAGAAATGGTG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDLD-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAAGCCTCTGATAAGGTCGG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDLAT-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCATAGACATCCCCATCAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDLAT-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTCCCATATTTACATCAACAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFH-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTTTTGTCACTGCCCCGAATA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFH-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGCTGCTCCCTGGCTCATT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLC25A3-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCAACATACTTGGTGAGGA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLC25A3-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTATACATTTTGGGGACAGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSREBF1-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGATCAAAGAGGAGCCAGTGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSREBF1-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTAGATGGTGGCTGCTGAGTG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-actin-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCCTCGCTGTCCACCTTCCA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-actin-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCACCTTCACCGTTCCAGTTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDetermination of the levels of metabolites\u003c/h2\u003e \u003cp\u003eAdenosine triphosphate (ATP) content assay kit (Jiangsu Feiya Biological Technology Co., Ltd., FY8511-B), pyruvate content assay kit (Jiangsu Feiya Biological Technology Co., Ltd., FY21457-B), citric acid content assay kit (Jiangsu Feiya Biological Technology Co., Ltd., FY0006-RB), fumarate detection kit (Jiangsu Feiya Biological Technology Co., Ltd., FY0315-RB), lactate content assay kit (Jiangsu Feiya Biological Technology Co., Ltd., FY40175-B), acetyl CoA content assay kit (Jiangsu Feiya Biological Technology Co., Ltd., FY9063-B), and succinate assay kit (Jiangsu Feiya Biological Technology Co., Ltd., FY21286-B) were used to detect the metabolites in the PFC of rats. All the experiments were carried out according to the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eWestern blot analysis\u003c/h2\u003e \u003cp\u003eTotal protein was extracted using cell lysis buffer (Bioss, Beijing, China) supplemented with protease and phosphatase inhibitors (Boster, Wuhan, China). The concentrations of the protein samples were measured with a BioDrop Ulite. Subsequently, 40 \u0026micro;g of protein from each sample was separated by 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) at 160 V for 25 minutes. The proteins were then transferred to polyvinylidene fluoride (PVDF) membranes at 400 mA for 20 minutes. Following transfer, the membranes were blocked with 5% non-fat dry milk (NFDM) in TBS containing 1% Tween 20 (TBST) for 1 hour at room temperature. The membranes were then incubated overnight at 4\u0026deg;C with primary antibody dilutions, including anti-Phosphatidylinositol 3-Kinase (PI3K, rabbit anti-rat, 1:1000, 4292, Cell Signaling), anti-Protein Kinase B (AKT, rabbit anti-rat, 1:3000, 10176-2-AP, ProteinTech), anti-p-ATP citrate lyase (p-ACLY, rabbit anti-rat, 1:1000, 4331, Cell Signaling), and anti-β-actin (rabbit anti-rat, 1:5000, bs-0061R, Bioss). After three washes with TBST, the membranes were incubated with the secondary antibody dilution (anti-rabbit IgG, HRP-linked Antibody, 1:3000, 7074, Cell Signaling) for 1 hour at room temperature. Finally, the membranes were developed using enhanced chemiluminescence reagents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatistical methods\u003c/h2\u003e \u003cp\u003eData analyses were conducted using R version 4.2.1. To compare two groups of continuous variables, a t-test was performed for variables exhibiting a normal distribution, while the Wilcoxon test was utilized for those with non-normal distributions. The relationship between variables was assessed using Pearson\u0026rsquo;s correlation coefficient (R version 4.2.1). Statistical significance was defined as \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eIdentification of DE-MRGs\u003c/h2\u003e\n \u003cp\u003eDEGs in the dorsolateral prefrontal cortex tissue of MDD and normal control samples from GSE54568 were analyzed, resulting in a total of 1,811 DEGs with a significance threshold of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The heatmap of these DEGs is presented in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA. Among these, 778 DEGs were found to be up-regulated, while 1,033 DEGs were down-regulated in the MDD group compared to the normal control group (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eFunctional enrichment analysis\u003c/h2\u003e\n \u003cp\u003eTo gain a deeper understanding of the biological functions of MRGs in MDD, we conducted a functional enrichment analysis of DE-MRGs. A total of 223 DE-MRGs were identified by intersecting DEGs and MRGs (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). Subsequently, these 223 DE-MRGs underwent GO and KEGG analyses to elucidate their biological functions and associated signaling pathways. The results of the GO enrichment analysis indicated that, within biological processes (BP), DE-MRGs were significantly enriched in the sulfur compound metabolic process, generation of precursor metabolites and energy, small molecule catabolic processes, alcohol metabolic processes, and cellular amino acid metabolic processes. In terms of cellular components (CC), DE-MRGs were predominantly associated with transporter complexes, transmembrane transporter complexes, ion channel complexes, oxidoreductase complexes, and cation channel complexes. Regarding molecular functions, these genes were found to play crucial roles in several key activities, including active transmembrane transporter activity, channel activity, passive transmembrane transporter activity, ion channel activity, and gated channel activity (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). Furthermore, KEGG pathway enrichment analyses demonstrated that DE-MRGs were significantly enriched in pathways related to chemical carcinogenesis-reactive oxygen species, prion disease, diabetic cardiomyopathy, carbon metabolism, and oxidative phosphorylation (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC-D).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eAcquisition of hub genes and the correlation analysis\u003c/h2\u003e\n \u003cp\u003eTo investigate potential interactions among the 223 DE-MRGs, a PPI network was constructed using the STRING database and visualized with Cytoscape V3.8.0 (Additional file 1). The analysis identified 12 hub genes (SDHB, BCKDHB, DLD, IDH3B, ACLY, SLC25A3, FH, DLAT, UQCRFS1, IDH3A, MDH1, NDUFS1) utilizing the MCODE plugin with default parameters (degree cutoff: 2; node score cutoff: 0.2; K-core: 2; max depth: 100) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). Notably, a general positive correlation was observed among these 12 hub genes (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). Among these genes, five exhibited strong predictive ability, with correlations as follows: DLD and DLAT showed a correlation of 0.87, DLD and FH had a correlation of 0.86, and FH and DLAT were correlated at 0.85, indicating a robust relationship among DLD, FH, and DLAT. Furthermore, the correlation between SLC25A3 and DLAT was 0.78, while correlations with DLD and FH were 0.63 and 0.60, respectively. These findings suggest a notable correlation between SLC25A3 and each of DLAT, DLD, and FH. Additionally, five TFs and 58 miRNAs were predicted based on the identified hub genes. A regulatory network comprising TFs, genes, and miRNAs was constructed to illustrate these relationships (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). Our analysis specifically focused on TFs and miRNAs associated with ACLY, DLD, DLAT, FH, and SLC25A3. Notably, miR-16-5p was found to target all five genes to regulate transcription, while miR-107 targeted ACLY, DLAT, DLD, and FH. Furthermore, let-7a-5p, let-7b-5p, and let-7e-5p were identified as targeting ACLY, DLAT, and SLC25A3, and miR-124-3p targeted ACLY, DLAT, and DLD. Importantly, the transcription of ACLY is regulated by SREBF1.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003eConstruction and validation of diagnostic models\u003c/h2\u003e\n \u003cp\u003eA logistic regression prediction model was developed based on hub genes (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). In the training dataset, the area under the curve (AUC) of ROC for this logistic regression model was found to be 0.889, indicating strong predictive performance (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). Additionally, the diagnostic prediction model was assessed using the independent validation set GSE54570, yielding an AUC of 0.941 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC). Expression validation of the hub genes was conducted in the external validation set GSE54570, revealing that ACLY, DLD, DLAT, FH, and SLC25A3 exhibited consistent expression trends in both disease and control samples across the training and validation datasets (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD-\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eM, Additional file 2). To further investigate the diagnostic accuracy of these five key genes, ROC curves were analyzed. In the training set, the AUCs for ACLY, DLD, DLAT, FH, and SLC25A3 were recorded as 0.747, 0.667, 0.751, 0.742, and 0.711, respectively, suggesting good diagnostic ability (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eN). In the external validation set, the AUCs for these genes were 0.642, 0.645, 0.544, 0.680, and 0.456, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eO). These results indicated that the five biomarkers, particularly ACLY, DLD, and FH, may serve as sensitive and specific indicators for distinguishing MDD samples from normal controls.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003eGene Set Enrichment Analysis (GSEA) of hub genes\u003c/h2\u003e\n \u003cp\u003eTo explore the mechanisms underlying the role of hub genes in patients with MDD, GSEA was performed. The GSEA results indicated that Huntington\u0026apos;s disease, oxidative phosphorylation, Parkinson\u0026apos;s disease, ubiquitin-mediated proteolysis, and proteasome pathways exhibited the highest enrichment of hub genes (see Additional File 3). Notably, the genes ACLY, DLD, DLAT, FH, and SLC25A3 were predominantly enriched in the pathways associated with oxidative phosphorylation, Parkinson\u0026apos;s disease, and the proteasome (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Given the close association of these five genes with energy metabolism, particularly in the context of the tricarboxylic acid cycle, we speculate that dysregulation of energy metabolism and oxidative phosphorylation in the PFC may represent a critical pathological process in the development of MDD. Furthermore, it is plausible that a shared mechanism related to energy metabolism links MDD and Parkinson\u0026apos;s disease, with these five genes serving as pivotal factors in this connection.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003eCUMS-induced depression-like behaviors in SD rat\u003c/h2\u003e\n \u003cp\u003eCUMS is a widely used method for inducing depression-like behaviors in animal models. This study employed three experimental approaches to evaluate the depression-like behaviors and cognitive function in rats subjected to CUMS.\u003c/p\u003e\n \u003cp\u003eFirst, the SPT was conducted to evaluate anhedonia-related depression-like behaviors in the CUMS-induced model rats. The results indicated a significant reduction in sucrose preference in the model group compared to the control group (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA).\u003c/p\u003e\n \u003cp\u003eSecond, the FST was employed to assess the depressed mood of the CUMS-induced model rats. The data revealed a notable increase in immobility time in the model group when compared to the control group (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB). Finally, the MWM test was administered to evaluate cognitive function in the CUMS-induced model rats. In this assessment, the model rats exhibited a significantly increased escape latency in the hidden platform test relative to the control group (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC). Additionally, during the probe test, CUMS-induced model rats spent significantly less time in the target quadrant and demonstrated a reduced percentage of total swimming distance in the target quadrant compared to the control group (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eD-E). Collectively, these findings confirm that CUMS effectively induces depression-like behaviors and cognitive dysfunction in SD rats.\u003c/p\u003e\n \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\n \u003ch2\u003eValidation of the expression of the key genes and related metabolites\u003c/h2\u003e\n \u003cp\u003eWe further investigated the expression levels of ACLY, DLD, DLAT, FH, and SLC25A3 in the PFC of rats subjected to CUMS using RT-qPCR. Our analysis revealed that, compared to the control group, the expression of DLD, DLAT, FH, and ACLY in the model group was significantly reduced. In contrast, no significant difference was observed in the expression of SLC25A3 in the PFC of the rats (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA-E). Given that ACLY, DLD, DLAT, FH, and SLC25A3 are closely associated with the tricarboxylic acid cycle, we proceeded to measure the levels of various metabolite products influenced by these genes, including pyruvate, acetyl-CoA, citric acid, succinate, fumarate, lactate, and ATP. We found that, relative to the control group, the levels of pyruvate, citric acid, and lactate were significantly elevated in the PFC of CUMS-induced model rats, whereas the levels of acetyl-CoA, succinate, fumarate, and ATP were notably decreased (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eF-L). These findings corroborate the abnormal mRNA expression of ACLY, DLD, DLAT, and FH observed in the PFC of the depression model. The down-regulation of these genes may disrupt the tricarboxylic acid cycle within the PFC of the model rats.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n \u003ch2\u003eValidation of the expression of transcription factor and regulation signals for ACLY\u003c/h2\u003e\n \u003cp\u003eIn this study, we investigated the gene ACLY, which exhibited significant downregulation in the PFC of rats subjected to CUMS. Additionally, we identified SREBF1 as a transcription factor within the TF-gene-miRNA regulatory network associated with ACLY. Previous research has indicated that the PI3K/AKT signaling pathway plays a crucial role in the phosphorylation of ACLY at the S455 site in human (equivalent to Ser454 in rat)[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e], thereby modulating its function in glucose and lipid metabolism regulation. Consequently, we assessed the expression levels of SREBF1, PI3K, AKT, as well as the phosphorylation status of ACLY in the PFC of CUMS-induced model rats. Our findings revealed that the mRNA expression of SREBF1 was significantly diminished in the model group compared to the control group (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eA). Furthermore, protein expression levels of PI3K, AKT, and phosphorylated ACLY (p-ACLY) were also significantly reduced (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eB-E). These results suggest that the aberrant expression of ACLY in the PFC of depression model rats may be linked to the decreased expression of its regulatory factor SREBF1. Moreover, the down-regulation of the PI3K/AKT signaling pathway further impairs the phosphorylation of ACLY at the S454 site, subsequently inhibiting its enzymatic activity.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIt is projected that MDD will exert profound negative effects on health, functionality, the economy, and society by the year 2030, characterized by notably high morbidity and mortality rates[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Despite this alarming forecast, the pathophysiological mechanisms underlying MDD remain incompletely understood. Current predominant theories regarding the pathogenesis of MDD include the monoamine neurotransmitter hypothesis, the neuroendocrine hypothesis, and the neuroinflammation hypothesis. Numerous studies have indicated that these hypotheses may influence, as well as be influenced by, energy metabolism within the central nervous system[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Intervention of energy metabolism among patients suffering from MDD have emerged as a promising avenue for developing novel treatments for this disease. Previous research has primarily concentrated on metabolic processes and fluctuations in metabolite levels, with relatively scant attention given to the exploration of differentially expressed genes associated with metabolism in the context of depression. This gap hampers the implementation of targeted therapeutic strategies for MDD. Accordingly, this study employs bioinformatics approaches to discern the metabolic-related genes associated with depression by analyzing datasets pertinent to MDD, aiming to elucidate the underlying mechanisms connecting MDD and energy metabolism. The findings of this study yielded the identification of four MRGs as potential key biomarkers for MDD. Furthermore, six pairs of interaction relationships and one signaling pathway were predicted. This investigation highlights novel central biomarkers of MDD from an energy metabolism perspective, reinforcing the critical role of energy metabolism in the diagnosis of MDD and contributing to the development of personalized treatment modalities.\u003c/p\u003e \u003cp\u003eThe PFC is recognized as one of the brain regions most severely affected by MDD[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Functional and structural abnormalities of the PFC have been documented in both individuals currently experiencing MDD and those exhibiting heightened vulnerability to developing the disorder [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The energy metabolism in the PFC is intrinsically linked to its overall function. In this study, a total of 223 DE-MRGs were identified through the overlap of differentially expressed genes and metabolic related genes, followed by enrichment analyses. In both GO and KEGG analyses, DE-MRGs exhibited enrichment in pathways associated with oxidative phosphorylation, the citrate cycle, the generation of precursor metabolites and energy, and the dicarboxylic acid metabolic process. Additionally, cell component of GO analyses indicated enrichment in mitochondrial respirasomes, oxidoreductase complexes, and respiratory chain complexes. These results suggest potential abnormalities in glucose metabolism within the prefrontal cortex of MDD patients. A substantial body of literature has indicated a high prevalence of concurrent glucose metabolism aberrations among individuals with MDD[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The classic antidepressant fluoxetine has been noted for its therapeutic efficacy in neuropsychiatric conditions characterized by disruptions in central energy metabolism, attributable to its ability to enhance glucose uptake in the brain[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThrough utilizing MCODE and AUC methodologies, five biomarkers (ACLY, DLD, DLAT, FH, and SLC25A3) were identified. Subsequent validation revealed that these five biomarkers exhibited decreased expression levels in MDD patients compared to healthy controls. A substantial corpus of evidence suggests an association between these genes and mitochondrial function, which has been implicated in the pathogenesis of MDD[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Specifically, these biomarkers play pivotal roles in regulating the tricarboxylic acid (TCA) cycle within mitochondria. DLD, DLAT, PDH, and certain cofactors collectively constitute the pyruvate dehydrogenase complex (PDHC). The absence of DLD and DLAT results in decreased PDHC activity, thereby affecting the conversion of pyruvate to acetyl-CoA. Previous studies have documented that DLD and DLAT are particularly linked to anxiety susceptibility[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Furthermore, DLD serves as a crucial subunit of the alpha-ketoglutarate dehydrogenase complex (KGDHC), and a deficiency in DLD can hinder the conversion of α-ketoglutarate to succinyl-CoA. Fumarate hydratase (FH) predominantly catalyzes the conversion of fumaric acid to succinic acid. Its absence can lead to fumarate accumulation. Recent findings suggest that fumarate has distinct roles in macrophage activation and regulates cytokine production, including interleukin-10 and type I interferons[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. FH has emerged as a significant regulatory point for the pro-inflammatory and anti-inflammatory effects of immune cells.\u003c/p\u003e \u003cp\u003eNumerous studies have reported a correlation between MDD and alterations in fatty acid metabolism and oxidative phosphorylation. ACLY serves as a link between carbohydrate and lipid metabolism by generating acetyl-CoA from citrate, which is pivotal for fatty acid and cholesterol biosynthesis[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Moreover, ACLY functions as a key enzyme in the fatty acid biosynthesis pathway, producing cytosolic acetyl-CoA and oxaloacetate, potentially influencing the biosynthesis of acetylcholine. Evidence indicates that a deficiency in ACLY can lead to reduced overall histone acetylation levels, diminished proliferation, and altered gene expression profiles[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Additionally, it has been established that FH, DLD, and ACLY are principal protein-coding genes that mediate the interplay in the pathogenesis of Alzheimer and Parkinson. SLC25A3 acts as a significant transporter of inorganic phosphate into the mitochondrial matrix and constitutes an essential component of the mitochondrial ATP synthasome. The deletion of SLC25A3 in adult cardiomyocytes has demonstrated a reduction in mitochondrial ATP synthesis.\u003c/p\u003e \u003cp\u003eMiRNAs play a crucial role in a wide array of biological processes during both normal physiological and pathological states[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. These short, conserved RNA molecules are estimated to regulate up to 60% of all mammalian protein-coding genes[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Intriguingly, approximately 70% of these miRNAs have been implicated in diverse neuronal processes, including neurogenesis and neuroplasticity[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This study observes that miR-16-5p targets the transcription of five genes, which include ACLY, DLAT, DLD, FH, and SLC25A3, whereas miR-107 targets ACLY, DLAT, DLD, and FH. The let-7 family and miR-124-3p influence the transcription of three genes. Prior research has established a close relationship between these miRNAs and MDD. For instance, neural stem cell-derived extracellular vesicles have been demonstrated to mediate the protective effects of miR-16-5p against neuronal damage associated with depression disorder[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In comparison to healthy controls, miR-107 levels were found to be significantly elevated in patients diagnosed with bipolar disorder. The let-7 miRNA family, being the most abundant in the brain, has dysregulated expression linked to neuronal apoptosis, neuroinflammation, and depressive symptoms[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Additionally, miR-124-3p is recognized as a critical neuronal miRNA involved in regulating neuronal fate determination, development, and plasticity[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. High expression levels of miR-124-3p have been observed in the brains of MDD patients, indicating its potential as a therapeutic target for the development of novel antidepressant pharmacotherapies[\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Moreover, we hypothesize that the upstream transcription factor SREBP1, which modulates ACLY expression and is responsive to dietary saturated fatty acids, may play a significant role in this context. Previous studies have illuminated that mice deficient in SREBP-1c exhibit behavior akin to schizophrenia[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. However, the relationship between the identified miRNAs and neuronal metabolism in the context of MDD has not been thoroughly investigated. Thus, the TF-gene-miRNA network established in this study can serve as a referential framework for future inquiries into gene and miRNA interactions in neuronal metabolism associated with MDD.\u003c/p\u003e \u003cp\u003eFollowing this, we validated the biomarkers ACLY, DLD, DLAT, FH, and SLC25A3 through animal experiments. A depression model was devised using the CUMS methodology, noted for its ability to replicate the process of depression with high fidelity. Upon establishment of the model, we employed three behavioral evaluations to assess depression-like behavior in the CUMS-induced rats. Firstly, the assessment of anhedonia was conducted through the SPT, a reliable behavioral paradigm that evaluates the preference for sucrose solution relative to water[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Our findings indicated a significant reduction in sucrose preference within the model group, illustrating the anhedonic effects of CUMS. Secondly, the forced swim test (FST) was implemented to evaluate depression-like behavior, revealing increased immobility time in the model group, thereby suggesting induced despair-like behavior due to CUMS. Lastly, the MWM test was employed to assess cognitive function, where escape latency for rats to locate the submerged platform was significantly prolonged, indicating compromised learning abilities. Moreover, swimming time in the target quadrant and the percentage of total swimming distance within the target quadrant were significantly diminished in the model group, reflecting impaired spatial memory capabilities following CUMS administration. Collectively, these findings underscore the efficacy of CUMS in inducing depression-like behavior in rodent models.\u003c/p\u003e \u003cp\u003eTo ascertain the expression levels of metabolism-related biomarkers in the PFC of the model rats, we utilized RT-PCR techniques. The results demonstrated a significant decrease in the mRNA expression levels of DLD, DLAT, FH, and ACLY in the PFC of model rats, whereas the expression levels of SLC25A3 remained comparable to those observed in the control group. Furthermore, we investigated the levels of metabolites associated with these enzymes. The outcomes revealed increased concentrations of pyruvic acid, citric acid, and lactate in the PFC of model rats, while levels of succinate, fumarate, acetyl-CoA, and ATP were notably reduced. As previously mentioned, PDHC plays a critical role in converting pyruvate to acetyl-CoA. The observed downregulation of DLD and DLAT in the prefrontal cortex of the depressive rat model may directly contribute to a reduction in PDHC activity and a subsequent impediment in pyruvate conversion. Additionally, DLD serves as a vital component of KGDHC. Thus, the decreased expression of DLD could result in the downregulation of KGDHC and hinder the conversion of α-ketoglutarate to succinyl-CoA, ultimately influencing succinate production. Moreover, the reduced activity of ACLY appeared to lead to an accumulation of citric acid, while reduced FH activity inhibited the conversion of fumarate to malate, resulting in fumarate accumulation. Despite the decreased expression of FH in the PFC of model rats, fumarate levels did not exceed those observed in the control group. This could indicate an inhibitory state within the TCA cycle in the model rats, leading to a decline in the expression of associated metabolic intermediates. The increased levels of lactate coupled with decreased ATP levels suggest heightened glycolytic activity and diminished oxidative phosphorylation in the PFC of the model rats. Overall, these results indicate alterations in energy metabolism pathways and metabolic dysregulation in the PFC of model rats, with DLD, DLAT, FH, and ACLY demonstrating significant potential as biomarkers for MDD.\u003c/p\u003e \u003cp\u003eIn the established TF-gene-miRNA regulatory network, SREBP1 emerged as a pivotal upstream transcription factor influencing ACLY. Subsequent validation through in vivo experiments demonstrated a marked decrease in SREBP1 mRNA expression in the PFC of model rats compared to controls. This study provides novel evidence that the SREBP1/ACLY signaling is hindered in the PFC of CUMS-induced model rats. ACLY serves as a crucial nexus linking glucose and lipid metabolism, primarily operating through phosphorylation. Previous studies have identified the PI3K/AKT signaling pathway as a regulatory mechanism for the phosphorylation of ACLY at serine 454 (equivalent to Ser454 in rat)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Notably, our findings revealed significantly reduced expression levels of PI3K, AKT, and p-ACLY in the PFC of model rats. Based on these observations, we hypothesize that the transcription factor SREBP1 and the PI3K/AKT signaling pathway in the PFC of model rats may influence both the transcriptional regulation and post-translational modifications of ACLY, respectively.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBy combining bioinformatic approaches and animal experiments, we identified four metabolism-related biomarkers (DLD, DLAT, FH, and ACLY) for MDD. The changes in the above genes may lead to the glucose metabolism disorders in the PFC, manifested as a decrease in oxidative phosphorylation and an increase in glycolysis. The above research results provide a basis for the hypothesis of energy metabolism disorder and potential biomarkers in MDD. But given that this study was a retrospective study; it lacked newly collected clinical samples and information. Further research is needed to investigate the interaction between DLD, DLAT, FH, and ACLY in vitro and in vivo.\u003c/p\u003e \u003cp\u003eBy integrating bioinformatics methodologies with experimental animal models, we identified four metabolism-related biomarkers (DLD, DLAT, FH, and ACLY) associated with MDD. Alterations in these genes may precipitate glucose metabolism dysregulation in the PFC, characterized by a reduction in oxidative phosphorylation. These findings substantiate the hypothesis of energy metabolism impairments and suggest potential biomarkers for MDD. However, it is important to note that this study is retrospective in nature and does not include newly acquired clinical samples or data, indicating the need for additional clinical evidence to validate the findings. In addition, further investigations are warranted to explore the interactions among DLD, DLAT, FH, and ACLY both in vitro and in vivo.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAUC \u0026nbsp; \u0026nbsp; \u0026nbsp;Areas under the curve\u003c/p\u003e\n\u003cp\u003eBP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Biological processes\u003c/p\u003e\n\u003cp\u003eCC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Cell component\u003c/p\u003e\n\u003cp\u003eCUMS \u0026nbsp; \u0026nbsp; Chronic unpredictable mild stress\u003c/p\u003e\n\u003cp\u003eDE-MRGs \u0026nbsp;Differentially expressed metabolism-related genes\u003c/p\u003e\n\u003cp\u003eDEGs \u0026nbsp; \u0026nbsp; \u0026nbsp;Differentially expressed genes\u003c/p\u003e\n\u003cp\u003eFST \u0026nbsp; \u0026nbsp; \u0026nbsp; Forced swimming test\u003c/p\u003e\n\u003cp\u003eGEO \u0026nbsp; \u0026nbsp; \u0026nbsp;Gene Expression Omnibus\u003c/p\u003e\n\u003cp\u003eGO \u0026nbsp; \u0026nbsp; \u0026nbsp; Gene ontology\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGSEA \u0026nbsp; \u0026nbsp; Gene set enrichment analysis\u003c/p\u003e\n\u003cp\u003eKEGG \u0026nbsp; \u0026nbsp; Kyoto encyclopedia of genes and genomes\u003c/p\u003e\n\u003cp\u003eKGDHC \u0026nbsp; Alpha-ketoglutarate dehydrogenase complex\u003c/p\u003e\n\u003cp\u003eMCODE \u0026nbsp; Molecular complex detection\u003c/p\u003e\n\u003cp\u003eMDD \u0026nbsp; \u0026nbsp; \u0026nbsp;Major depression disorder\u003c/p\u003e\n\u003cp\u003eMiRNAs \u0026nbsp; MicroRNAs\u003c/p\u003e\n\u003cp\u003eMRGs \u0026nbsp; \u0026nbsp; Metabolic related genes\u003c/p\u003e\n\u003cp\u003eMWM \u0026nbsp; \u0026nbsp; Morris water maze test\u003c/p\u003e\n\u003cp\u003eNFDM \u0026nbsp; \u0026nbsp; Non-fat dry milk\u003c/p\u003e\n\u003cp\u003eTBS \u0026nbsp; \u0026nbsp; \u0026nbsp; Tris buffered saline\u003c/p\u003e\n\u003cp\u003eTBST \u0026nbsp; \u0026nbsp; \u0026nbsp;TBS containing 1% tween 20\u003c/p\u003e\n\u003cp\u003ePDHC \u0026nbsp; \u0026nbsp; \u0026nbsp;Pyruvate dehydrogenase complex\u003c/p\u003e\n\u003cp\u003ePET \u0026nbsp; \u0026nbsp; \u0026nbsp; Positron emission tomography\u003c/p\u003e\n\u003cp\u003ePFC \u0026nbsp; \u0026nbsp; \u0026nbsp; Prefrontal cortex\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePPI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Protein-protein interaction\u003c/p\u003e\n\u003cp\u003ePVDF \u0026nbsp; \u0026nbsp; \u0026nbsp;Polyvinylidene fluoride\u003c/p\u003e\n\u003cp\u003eROC \u0026nbsp; \u0026nbsp; \u0026nbsp; Receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eSD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Sprague-Dawley\u003c/p\u003e\n\u003cp\u003eSDS-PAGE \u0026nbsp;Sodium dodecyl sulfate-polyacrylamide gel electrophoresis\u003c/p\u003e\n\u003cp\u003eSPT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Sucrose preference test\u003c/p\u003e\n\u003cp\u003eTCA \u0026nbsp; \u0026nbsp; \u0026nbsp; Tricarboxylic acid\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTF \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Transcription factors\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe animal study was reviewed and approved by the Animal Experimental Ethical Inspection in Laboratory of The First Hospital of Hunan University of Chinese Medicine.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e.\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by funding from the National Natural Science Foundation of China (82474476, 82474067, 82305203), Natural Science Foundation of Hunan Province (2024JJ4033, 2023JJ30476, 2024JJ9427).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData collection and statistical analysis: HY, JW and SL. Experiment implementation: HY, PM and QD. Animal experiments assistance: HL and YW. Study design and supervision: WL. All authors contributed to the critical revision of the final manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSmith, K. Mental health: a world of depression. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e7526\u003c/b\u003e, 180\u0026ndash;181 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeng, R. et al. Association of depression with all-cause and cardiovascular disease mortality among adults in China. \u003cem\u003eJAMA Netw. Open.\u003c/em\u003e \u003cb\u003e3\u003c/b\u003e (2), e1921043 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoung, J. J. et al. Is there progress? 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Neuropsychopharmacol.\u003c/em\u003e \u003cb\u003e77\u003c/b\u003e, 80\u0026ndash;92 (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Major depressive disorder, Metabolism, Hub gene, ACLY","lastPublishedDoi":"10.21203/rs.3.rs-5314827/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5314827/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMajor depressive disorder (MDD) is a prevalent neuropsychiatric condition and has become the second leading cause of mortality after cancer. The prefrontal cortex (PFC) is recognized as one of the brain regions most consistently affected by MDD. While both functional and structural abnormalities in the PFC have been shown to be associated with disruptions in energy metabolism, the specific genes involved in metabolic processes within this region remain poorly understood.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eDatasets related to major depressive disorder (MDD) from the Gene Expression Omnibus (GEO) database were analyzed in this study. Initially, differentially expressed metabolism-related genes (DE-MRGs) were identified by intersecting differentially expressed genes from normal and MDD patient samples with metabolism-related genes. Subsequently, a protein-protein interaction (PPI) network was constructed based on the DE-MRGs, and hub genes were identified using the Molecular Complex Detection (MCODE) plugin. A logistic regression prediction model was then developed. To further assess the findings, Spearman correlations, Gene Set Enrichment Analysis (GSEA), and predictions of transcription factors and microRNAs targeting the hub genes were conducted. Finally, the expression of the hub genes and their potential mechanisms were validated and predicted using an animal model of depression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn this study, we identified 223 differentially expressed metabolism-related genes. Utilizing the MCODE plugin methods, we further identified 12 hub genes among these differentially expressed genes. Expression validation results indicated that the expression of ACLY, DLD, DLAT, FH, and SLC25A3 were consistent across various datasets for both MDD and control samples. GSEA revealed that these genes were significantly enriched in pathways associated with oxidative phosphorylation, Parkinson's disease, and the proteasome. Furthermore, animal experiments demonstrated that the expression levels of ACLY, DLD, DLAT, and FH were significantly reduced in the PFC of rats subjected to chronic unpredictable mild stress (CUMS) induction. Additionally, further investigation into the transcription factors and regulatory signals of ACLY revealed a significant decrease in the mRNA expression of SREBF1, along with marked reductions in the protein levels of PI3K, Akt, and p-ACLY.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eFour key genes were identified based on metabolic characteristic genes. The PI3K/AKT/ACLY signaling pathway may play a significant role in the regulation of metabolism in major depressive disorder (MDD). These findings establish a theoretical foundation and provide valuable references for the study of central metabolism in MDD.\u003c/p\u003e","manuscriptTitle":"Identification and Analysis of Key Genes Related to Metabolism in the Brain of Major Depressive Disorder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-19 11:31:56","doi":"10.21203/rs.3.rs-5314827/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1135d727-82a2-44ec-8b26-e1a8fd1f71af","owner":[],"postedDate":"December 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40942808,"name":"Health sciences/Diseases/Metabolic disorders"},{"id":40942809,"name":"Biological sciences/Genetics"}],"tags":[],"updatedAt":"2025-09-09T15:08:50+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-19 11:31:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5314827","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5314827","identity":"rs-5314827","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}