Role of mitochondrial metabolic disorder and immune infiltration in diabetic cardiomyopathy: new insights from bioinformatics analysis

Role of mitochondrial metabolic disorder and immune infiltration in diabetic cardiomyopathy: new insights from bioinformatics analysis | 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 Research Article Role of mitochondrial metabolic disorder and immune infiltration in diabetic cardiomyopathy: new insights from bioinformatics analysis Cheng Peng, Yanxiu Zhang, Xueyan Lang, Yao Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2148339/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Feb, 2023 Read the published version in Journal of Translational Medicine → Version 1 posted 4 You are reading this latest preprint version Abstract Background Diabetic cardiomyopathy (DCM) is one of the common cardiovascular complications of diabetes and a leading cause of death in diabetic patients. Mitochondrial metabolism and immune-inflammation are key for DCM pathogenesis, but their crosstalk in DCM remains an open issue. This study explored the separate roles of mitochondrial metabolism and immune microenvironment and their crosstalk in DCM with bioinformatics. Methods DCM chip data (GSE4745, GSE5606, and GSE6880) were obtained from NCBI GEO, while mitochondrial gene data were downloaded from MitoCarta3.0 database. Differentially expressed genes (DEGs) were screened by GEO2R and processed for GSEA, GO and KEGG pathway analyses. Mitochondria-related DEGs (MitoDEGs) were obtained. A PPI network was constructed, and the hub MitoDEGs closely linked to DCM or heart failure(HF) were identified with CytoHubba, MCODE and CTD scores. Transcription factors and target miRNAs of the hub MitoDEGs were predicted with Cytoscape and miRWalk database, respectively, and a regulatory network was established. The immune infiltration pattern in DCM was analyzed with ImmuCellAI, while the relationship between MitoDEGs and immune infiltration abundance was investigated using Spearman method. A rat model of DCM was established to validate the expression of hub MitoDEGs and their relationship with cardiac function. Results MitoDEGs in DCM were significantly enriched in pathways involved in mitochondrial metabolism, immunoregulation, and collagen synthesis. Nine hub MitoDEGs closely linked to DCM or HF were obtained. Immune analysis revealed significantly increased infiltration of B cells while decreased infiltration of DCs in immune microenvironment of DCM. Spearman analysis demonstrated that the hub MitoDEGs were positively associated with the infiltration of pro-inflammatory immune cells, but negatively associated with the infiltration of anti-inflammatory or regulatory immune cells. In the animal experiment, 4 hub MitoDEGs (Pdk4, Hmgcs2, Decr1, and Ivd) showed an expression trend consistent with bioinformatics analysis result. Additionally, the up-regulation of Pdk4, Hmgcs2, Decr1 and the down-regulation of Ivd were distinctly linked to reduced cardiac function. Conclusions This study unraveled the interaction between mitochondrial metabolism and immune microenvironment in DCM, providing new insights into the research on potential pathogenesis of DCM and the exploration of novel targets for medical interventions. Diabetic cardiomyopathy Mitochondria Metabolism Immune infiltration Immunometabolism Bioinformatics analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Background With the changing lifestyles, the incidence of diabetes mellitus (DM) shows a rapidly increasing trend. As estimated by the International Diabetes Federation, the number of DM patients will be increased to 0.5784 billion by 2030, resulting in a morbidity of up to 10.2%[ 1 ]. DM increases the risk of developing heart failure (HF) by 2–4 times, as compared to healthy people[ 2 ], and thus it tends to cause a highly poor prognosis. Diabetic cardiomyopathy (DCM) is one of the severe cardiovascular complications[ 3 ] of DM first reported by S Rubler in 1972[ 4 ]. It is multi-factorial in pathophysiology and has not yet been fully explored. Increasing studies have noted that mitochondrial events that lead to damage and dysfunction, including abnormal dynamics[ 5 ], mitophagy[ 6 – 8 ], calcium homeostasis imbalance[ 9 – 11 ], disturbed energy metabolism and oxidative stress[ 12 , 13 ], play an essential role in DCM. In addition, excessive accumulation of lipid intermediary metabolites is considered as directly linked to the toxic injury and dysfunction of diabetic myocardium[ 14 ]. It has been established that the immune infiltration and activation of inflammatory processes in myocardial tissues are also critical pathogeneses of DCM. For example, both type 1 and 2 DM (T1DM/T2DM) models had increased myocardial infiltration of monocytes and macrophages[ 15 , 16 ]; chronic hyperglycemia induced elevation of Th1, Th2, and Th17 cytokines by activating T cells via the RAGE-dependent pathway[ 17 ]; additionally, a high-glucose environment could also activate mast cells and induce the release of pro-inflammatory mediators, resulting in exacerbation of the pathological remodeling in DCM[ 18 ]. Interestingly, accumulating evidence has revealed that there is a potential link between immunity and mitochondrial metabolism, and the metabolic state can affect the development of inflammation through changing the immune microenvironment[ 19 ]. Typical T cell activation is accompanied by the up-regulation of insulin receptors and glycolytic enzymes[ 20 ].High levels of insulin can impair the function of regulatory T cells (Tregs) and inhibit their suppressive function towards inflammatory response via regulating the AKT/mTOR signaling pathway[ 21 ]. Both mitochondrial metabolism and immune-inflammation are key pathogeneses of DCM, but their crosstalk in DCM have not yet been reported and require further exploration. Bioinformatics allows for screening of molecules which show a difference between patients and healthy individuals from microarray data that vary at multiple levels. It is appreciated as an effective research method for exploring the potential molecular mechanism of disease. With this method, the current study analyzed how mitochondria-related genes promote the development of DCM and correlate to the immune infiltration based on associated microarray data from GEO database (GSE4745, GSE5606, and GSE6880). Additionally, the relationship between hub mitochondria-related genes and immune infiltrates in DCM was investigated to help better understand the underlying immunometabolism during disease development. 2. Methods 2.1 Microarray data retrieval GSE4745, GSE5606 and GSE6880 microarray datasets were obtained from the public repository NCBI GEO ( http://www.ncbi.nlm.nih.gov/geo ) [ 22 ] using "diabetic cardiomyopathy" as the search term and ventricular samples and modeling time as the screening criteria. The GSE4745 ([RG_U34A] Affymetrix Rat Genome U34 Array) is generated by the GPL85 platform that contains 24 left ventricular (LV) samples from rattus norvegicus. To better analyze the differential genes between DCM group and control (CON) group, 8 samples collected on day 42, including 4 DCM samples and 4 CON samples, were selected for analysis[ 23 ]. The GSE5606 ([Rat230_2] Affymetrix Rat Genome 230 2.0 Array) is generated by the GPL1355 platform and composed of 14 LV samples from DCM rats (n = 7) and CON rats (n = 7) [ 24 ]. The GSE6880 ([RAE230A] Affymetrix Rat Expression 230A Array) is generated by the GPL341 platform comprising 6 LV samples from DCM rats (n = 3) and CON rats (n = 3) [ 25 ]. 2.2 Acquisition Of Microarray Data And Identification Of Differentially Expressed Genes (Degs) Data of each microarray were accessed from GEO using R package "GEO query". DEGs from each microarray were obtained with R package "limma" as implemented by GEO2R online tool( https://www.ncbi.nlm.nih.gov/geo/geo2r/ )[ 26 ], and all identified DEGs met p < 0.05 and |log2 (FC)| ≥1. Resulting DEGs were visualized by Volcano Plot using R package "ggplot2"[ 27 ] and Heatmap using R package "ComplexHeatmap"[ 28 ]. 2.3 Functional Enrichment Analysis Gene Set Enrichment Analysis (GSEA) [ 29 ] was applied using R package "clusterProfiler"[ 30 ], with the "c2.cp.v7.2.symbols.gmt"( https://www.gsea-msigdb.org/gsea/msigdb/index.jsp ) as the reference gene set, the number of permutations as 10,000, and the threshold of significance as 10. The results were visualized with R package "ggplot2"[ 27 ]. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were accomplished in DEGs with R package "clusterProfiler"[ 30 ], and the items with P < 0.05 in Benjamini-Hochberg test were regarded has having statistical significance. The results were visualized by Chordal and Circle graphs using R packages "ggplot2"[ 27 ] and "GOplot"[ 31 ]. 2.4 Identification Of Mitochondria-related Degs (Mitodegs) The mitochondrial protein database, MitoCarta3.0 ( http://www.broadinstitute.org/mitocarta ) [ 32 ], was visited to obtain 1,140 mitochondria-localized genes. MitoDEGs were obtained via intersecting the DEGs from each microarray and the 1,140 mitochondria-localized genes using a Venn Diagram, and they were visualized as a Heatmap with R package "ggplot2"[ 27 ]. The overlapped MitoDEGs among the three microarrays were eventually obtained. 2.5 Analysis Of Protein-protein Interactions (Ppi) And Identification Of Hub Genes The overlapped MitoDEGs were processed for PPI analysis with STRING database ( https://string-db.org/ )[ 33 ], and the resulting interactions were visualized as a network using Cytoscape 3.8.2[ 34 ]. Hub MitoDEGs were screened out using the plug-ins CytoHubba and MCODE as implemented by the Cytoscape 3.8.2. 2.6 Acquisition Of Genes Potentially Key To Dcm And Hf The CTD database ( http://ctdbase.org/ )[ 35 ] assembles interaction data between chemicals, gene products, functional phenotypes, and diseases, affording great convenience to research into disease-associated environmental exposures and potential mechanisms of action of drugs. With the CTD data, the link between hub MitoDEGs and the risk of developing DCM or HF was analyzed. 2.7 Prediction Of A Hub Mitodegs-transcription Factors (Tf) -mirnas Network To explore the upstream regulators of hub MitoDEGs, TFs of hub MitoDEGs were predicted with the plug-in iRegulon of the Cytoscape 3.8.2[ 36 ], and miRNAs of hub MitoDEGs were predicted using the miRWalk database (mirwalk.umm.uni-heidelberg.de) [ 37 ]. The hub MitoDEGs, resulting TFs and miRNAs were visualized as a network by the Cytoscape 3.8.2. 2.8 Immune Infiltration Analysis The gene matrices of GSE5606 and GSE6880 original datasets were combined using the Perl script and normalized after elimination of the batch effect and the heterogeneity induced by different platforms with R package "sva"[ 38 ]. The normalized gene expression matrix was used for further immune infiltration analysis. The GSE4745 dataset was excluded from the analysis, as the number of genes in GSE4745 was significantly less than that in the other two datasets, which might result in bias results. The ImmuCellAI ( http://bioinfo.life.hust.edu.cn/web/ImmuCellAI ) estimates the infiltration abundance of 36 immune cell types based on RNA-Seq data or gene-expression profiles from microarray data[ 39 ].The normalized gene expression matrix was uploaded to the ImmuCellAI for analysis of immune infiltration, with Wilcoxon rank sum test used for between-group comparisons. Spearman correlation analysis was applied to explore the link between MitoDEGs/hub MitoDEGs and the immune cells. 2.9 Construction Of Animal Models With Dcm The animal procedure was performed in strict accordance with The Guide for Care and Use of Laboratory Animals (NIH publication No.85 − 23, revised 2011) and with the approval from the Laboratory Animal Ethics Committee. Ten SD male rats, weighing 200 ± 20 g, were housed in the laboratory animal center of our hospital. The rats were allowed to acclimate for 1 week with free access to diet and water ad libitum in an environment that provided a relative temperature of 24 ℃, a relative humidity of 50–60%, and a 12 h/12 h light/dark cycle. Following that, the rats were divided into the CON and DCM groups by random assignment. Rats in the CON group were fed normal diet, while rats in the DCM group were fed high-fat diet (HFD) containing 60 kcal% fat, 20 kcal% protein, and 20 kcal% carbohydrate. After 4 weeks, streptozotocin (STZ) (40 mg/kg, Solarbio) was intraperitoneally injected for consecutive 3 days in rats of the DCM group to induce T2DM, and citric acid buffer at the same dose was administrated in rats of the CON group. One week after injection, blood glucose was measured from the tail vein, and a random glucose level > 16.7 mmol/L was indicative of successful modeling. Tissue samples were obtained after another 12 weeks of feeding. Blood glucose and body weight were monitored during modeling, and echocardiography and measurement of tibia length were performed before sampling. 2.10 Echocardiography Rats were anesthesized with intraperitoneal injection of 30 mg/kg pentobarbital. Echocardiography was performed with the transducer of a high-resolution imaging system. LV parameters, including left ventricular ejection fraction (LVEF), fraction shortening (FS), left ventricular internal diameters at systole (LVIDs) and diastole (LVIDd), were measured from long- /short-axis images of the LV. Cardiac function was assessed by analysis of data of 3–5 cardiac cycles. 2.11 Rna Extraction And Qrt-pcr Total RNA was extracted from cardiac tissue using Trizol and reversely transcribed into cDNA using a reverse transcription kit (Roche). qRT-PCR was fulfilled with a SYBR Green (Roche). The primers used for amplification were shown in Table S1. Target gene expression relative to GAPDH gene was shown as 2 ΔΔCt . 2.12 Correlation Between Hub Mitodegs And Cardiac Function Correlation between hub MitoDEGs and LV parameters (EF%, FS%, and LVIDs) was analyzed using the Pearson algorithm, and the results were visualized with R package "ggplot2"[ 27 ]. 2.13 Statistical Analysis Data are presented as the mean ± standard deviation (SD) of four independent experiments, and were analyzed using GraphPad Prism 8.0 (GraphPad Inc, San Diego, USA). The student`s t-test was used to measure the differences between two groups. P value < 0.05 was considered to be statistically significant. 3. Results 3.1 DEGs in DCM and functional enrichment analysis Flowchart of overall data screening strategy was shown in Fig. 1 . Three DCM-related GEO datasets, GSE4745, GSE5606, and GSE6880, were obtained for analysis. Differential analysis demonstrated 293 DEGs in the GSE4745 dataset, including 149 genes up-regulated and 144 genes down-regulated in DCM samples in comparison to normal samples; 544 DEGs in the GSE5606 dataset, including 269 up-regulated and 275 down-regulated genes; and 463 DEGs in the GSE6880 dataset, including 262 up-regulated and 201 down-regulated genes. The DEGs were visualized as Volcano Plots and Heatmaps (Fig. 2 a-f). GSEA showed that the DEGs from the three datasets were mainly involved in pathways related to lipid and fatty acid metabolism, and immunity, including Metabolism of lipids, Regulation of lipid metabolism by PPARα, Fatty acid metabolism, Biosynthesis of unsaturated Fatty acids, Antigen processing and presentation, MHC class II antigen presentation, Regulation of TLR by endogenous ligand, Complement activation (Fig. 2 g-n). In addition, it also showed enrichment of pathways involved in collagen synthesis, collagen fibril assembly, and oxidative stress. The DEGs were further processed for functional enrichment with GO and KEGG pathway analyses. The most enriched GO terms were classified to Biological Process (BP), Cellular Component (CC) and Molecular Function (MF), majoring including mitochondrial function and component, energy metabolism, inflammatory immunity, hypoxia and redox reaction, collagen synthesis, and insulin sensitivity, etc. (Fig. 3 a-f). The most enriched KEGG pathways of the DEGs were dominated by pathways involved in mitochondrial metabolism and function, hypoxia and redox reaction, substance generation, and immunity, etc. (Fig. 3 g-l). 3.2 Mitodegs In Dcm Mitochondria-related genes were retrieved from the MitoCarta3.0 database, and the genes overlapped with the DEGs from three datasets were selected as MitoDEGs. In total, there were 32 MitoDEGs (15 up-regulated and 17 down-regulated) in the GSE4745 datasets (Fig. 4 c), 34 MitoDEGs (18 up-regulated and 16 down-regulated) in the GSE5606 datasets (Fig. 4 d), and 25 MitoDEGs (14 up-regulated and 11 down-regulated) in the GSE6880 datasets (Fig. 4 e). The MitoDEGs of each dataset were combined, resulting in 67 overlapped MitoDEGs, including 35 genes up-regulated and 32 genes down-regulated in DCM samples in comparison to normal samples. 3.3 Ppi Network Analysis And Hub Mitodegs Identification PPI of the 67 MitoDEGs was analyzed using the STRING database and visualized as a network with the Cytoscape (Fig. 4 f). Significant modules (gene clusters) were identified using the plug-in MCODE as implemented by the Cytoscape with the following filter criteria: degree cut-off = 2; node score cut-off = 0.2; k-core = 2; and max depth = 100. A module that was composed of 9 nodes and 17 edges was identified as significant, and the genes involved in the module were Acsl6, Acadsb, Decr1, Ivd, Oxct1, Gpam, Pdk4, Hmgcs2, and Acot2 (Fig. 4 g). With the MCC algorithm of plug-in CytoHubba, 10 candidate hub genes were identified from the PPI network, including Cpt1a, Hsd17b4, Hmgcs2, Acadsb, Decr1, Acot2, Gpam, Oxct1, Acsl6, and Ivd (Fig. 4 h). Combining the results, 11 hub MitoDEGs, including Acadsb, Hmgcs2, Hsd17b4, Gpam, Acot2, Ivd, Decr1, Cpt1a, Acsl6, Oxct1, and Pdk4, were eventually obtained. 3.4 Relationship Between Hub Mitodegs And Dcm/hf The CTD database was applied to predict the relationship between hub MitoDEGs and DCM/HF. As analyzed, Cpt1a, Gpam, Hmgcs2, and Acadsb had the highest association with DCM (Fig. 5 a), while Cpt1a, Pdk4, Gpam, and Hmgcs2 showed the highest correlation with HF (Fig. 5 b). 3.5 Hub Mitodegs-tfs-mirnas Regulatory Network The upstream regulation of the hub MitoDEGs was explored via predicting related TFs and miRNAs. TFs of hub MitoDEGs were predicted with plug-in iRegulon of the Cytoscape, and a hub MitoDEGs-TFs regulatory network comprising 19 TFs (Rora, Maf, Ing4, Srebf2, Mafb, Zfp706, Pole3, Rreb1, Mybl2, Myb, Tcf4, Mafa, Cebpa, Thra, Pdcd11, Yy1, Runx2, Cdx1, Ubp1) was constructed (Fig. 5 c). miRNAs of hub MitoDEGs were predicted with the miRWalk 3.0, and a hub MitoDEGs-miRNAs regulatory network that involved 299 nodes and 569 edges was generated (Fig. 5 d). There were three miRNAs, including miR-298-5p that had interactions with Ivd, Acsl6, Acot2, and Hmgcs2; miR-30c-1-3p that interacted with Oxct1, Ivd, Cpt1a, and Acsl6; and miR-344b-5p that interacted with Oxct1, Cpt1a, Acsl6, and Hmgcs2. However, further validation is required. 3.6 Immune Cell Infiltration In Dcm Infiltration of 36 immune cell types was analyzed using the ImmuCellAI algorithm and compared between the DCM and CON groups in the GSE5606 and GSE6880 datasets. Significant differences were demonstrated between the DCM and CON groups in the myocardial infiltration of 9 immune cell types (P < 0.05). Specifically, B cell, Marginal Zone B and Memory B were much more abundant in the DCM group, while Granulocytes, Dendritic cells, MoDC, cDC1, pDC, and cDC2 were more abundant in the CON group (Fig. 6 a-c). Further analysis for the infiltrating immune cells in DCM showed multiple correlations between the cells (Fig. 6 d). The degree of correlation was indicated by scores. The synergistic effect was observed as the strongest between CD4 T cell and Naive CD4 T (0.99), followed by CD4 T cell and T helper cell (0.98), CD8 Tcm and CD8 Tex (0.98), Naive CD4 T and T helper cell (0.97). In contrast, the competitive effect was found as the strongest between Naive CD8 T and B cell (-0.72), followed by pDC and Marginal Zone B (-0.69), Naive CD8 T and Memory B (-0.69). 3.7 Relationship Between Mitodegs/hub Mitodegs And Immune Cells Spearman method was applied to explore the potential associations between MitoDEGs/hub MitoDEGs and immune cells. The positive/negative associations between MitoDEGs (35 up-regulated and 32 down-regulated) and immune cells were demonstrated in Fig. 7 a-b. Of the 11 hub MitoDEGs, Pdk4 was positively associated with Marginal Zone B but negatively associated with cDC2, MoDC, and pDC; Oxct1 was positively associated with pDC and CD8 Tem; Ivd was positively associated with CD8 Tem; Hsd17b4 was positively associated with Marginal Zone B and M2 macrophage but negatively associated with cDC2, MoDC, and pDC; Hmgcs2 was positively associated with Marginal Zone B, M2 macrophage but negatively associated with Granulocytes and cDC2; Gpam was negatively associated with Dendritic cells, Granulocytes, cDC1, and MoDC; Decr1 was positively associated with Marginal Zone B and M2 macrophage while negatively associated with Granulocytes, cDC2, and pDC; Cpt1a was negatively associated with Dendritic cells, cDC1, MoDC, and pDC; Acsl6 was positively associated with pDC, Eosinophil, and CD8 Tem; Acot2 was negatively associated with Dendritic cells and cDC1 (Fig. 7 c). 3.8 General Biological And Echocardiography Features Of Dcm Rats During modeling, the body weight of HFD-fed rats of the DCM group was significantly higher than that of the CON group, and it tended to decrease from 2 weeks after STZ injection and became remarkably lower than that of the CON group before tissue sampling (Fig. 8 a). After 1 week of STZ induction, the blood glucose of the DCM group began to increase, and the level was consistently higher than that of the CON group throughout the entire modelling process (Fig. 8 b). Echocardiography showed that as compared to the CON group, the DCM group witnessed significantly lower EF% and FS% (P < 0.05) but remarkably higher LVIDs (P < 0.05). Besides, the LVIDd was marginally varied between the two groups (Fig. 8 c-h). Moreover, notable increases in the heart weight normalized to body weight (HW/BW) and heart weight normalized to tibia length (HW/TL) were found in the DCM group as compared to the CON group (P < 0.05, Fig. 8 i-j). 3.9 Pcr Confirmation Of Hub Mitodegs Expression In Dcm Rats Ventricular expression of 9 hub MitoDEGs (Acadsb, Acot2, Cpt1a, Decr1, Gpam, Hmgcs2, Hsd17b4, Ivd, and Pdk4) was validated in rats with qRT-PCR. As compared to the CON group, Pdk4, Hmgcs2 and Decr1 had significantly increased expression in the DCM group (P < 0.05), while Ivd reversely exhibited remarkably decreased expression in the DCM group (P < 0.05) (Fig. 8 k). 3.10 Relationship Between Hub Mitodegs And Cardiac Function The four hub MitoDEGs (Pdk4, Hmgcs2, Decr1, and Ivd) with distinct differential expression between the DCM and CON groups were further analyzed for their associations with EF%, FS% and LVIDs. The number of PCR cycles of Pdk4 had highly significant positive correlations with EF% (R = -0.904; P = 0.002) and FS% (R = 0.934; P < 0.001), but had a highly significant negative correlation with LVIDs (R = 0.852; P = 0.007); the number of PCR cycles of Hmgcs2 exhibited highly significant positive correlations with EF% (R = 0.782; P = 0.022) and FS% (R = 0.812; P = 0.014); the number of PCR cycles of Decr1 showed highly significant positive correlations with EF% (R = 0.829; P = 0.011) and FS% (R = 0.801; P = 0.017); the number of PCR cycles of lvd showed highly significant negative correlations with EF% (R = -0.978; P < 0.001) and FS% (R = -0.943; P < 0.001), but had a highly significant positive correlation with LVIDs (R = 0.852; P = 0.007) (Fig. 8 l). Collectively, the up-regulated expression of Pdk4, Hmgcs2, and Decr1 and the down-regulated expression of Ivd in myocardial tissues of DCM were highly linked to the reduction in cardiac function. 4. Discussion The number of DM patients has grown worldwide at an alarming rate. DM commonly occurs with target organ damage that leads to a poor prognosis, and it is tightly linked to the initiation and development of HF[ 40 ]. It has been proven that the risk of developing HF in DM patients is associated with the presence of DCM[ 41 ]. However, it remains elusive about the pathogenesis of DCM, and there is a paucity of effective therapeutic strategies. In this context, strengthening our understanding on DCM pathogenesis and looking for potential therapeutic targets are in urgent need. With multiple bioinformatics methods, the present study firstly obtained DEGs from the three DCM-related microarray datasets from GEO and found that the DEGs were enriched in pathways associated with mitochondrial metabolism, immune-inflammation, and collagen synthesis. Mitochondrial dysfunction and metabolic abnormality have been proven to play a role in cardiac hypertrophy and myocardial fibrosis[ 42 ]. In addition, various activities of immune cells, such as transition from macrophages to fibroblast-like cells[ 43 ], B-cell infiltration[ 44 ], and transition between T lymphocyte subsets (Th17 to Treg) [ 45 ], are also critical for pathogenesis of myocardial fibrosis. Based on the findings, our study aimed at analyzing the regulatory roles of mitochondrial metabolism and immune dysregulation in the occurrence and development of DCM and exploring related targets. The findings of the study may help us better understand the mitochondrial metabolism, immunity, and their crosstalk in DCM. Presently, mitochondria-related genes in DCM have not yet been reported by bioinformatics studies. For the first time, our study applied the MitoCarta 3.0, an authoritative database of mitochondrial proteome, to obtain mitochondria-related genes, and then identified 9 hub MitoDEGs with had a strong correlation with DCM or HF. To validate our findings, DCM rats were modeled. Expression analysis revealed four genes, including Pdk4, Hmgcs2, Decr1, and Ivd, which showed a consistent expression trend as that detected by prior bioinformatics analysis. Additionally, we found that the up-regulation of Pdk4, Hmgcs2, Decr1 and the down-regulation of Ivd were significantly associated with the reduction in cardiac function. Mitochondrial metabolic disorder is one of the important pathogeneses of DCM[ 40 ], while Pdk4, Hmgcs2, Decr1, and Ivd are enzymes essential for mitochondrial metabolism. In DCM, the most significant metabolic disorders in myocardial tissues are decreased glucose utilization and increased fatty acid oxidation, which can lead to cardiac lipotoxicity, myocardial fibrosis, and effects on cardiac function. Pdk4 (Pyruvate dehydrogenase kinase 4) is localized to the mitochondrial matrix and participates in fatty acid oxidation as a key enzyme[ 46 ]. Studies found that Pdk4 showed increased expression in myocardial tissues of DM mice[ 47 ], and it could be used as a therapeutic target for DM[ 48 , 49 ] due to its role as a key target genes of the PPARα signaling pathway[ 50 , 51 ]. In addition, specific expression of Pdk4 could induce insulin resistance, reduction in myocardial glucose oxidation and increase in fatty acid oxidation[ 52 , 53 ]. To the contrary, suppression of Pdk4 activity could lead to reduced mitochondria-associated ER membranes (MAM) formation and improve insulin signal transduction through preventing the MAM-induced mitochondrial Ca2 + accumulation[ 54 ]. Other than the role in mediating metabolic reprogramming, Pdk4 also has implications for cell respiration by playing a role in regulation of mitochondrial dynamics[ 55 ]. Hmgcs2 (3-hydroxy-3-methylglutaryl-CoA synthase 2) is also distributed to the mitochondrial matrix and acts as a rate-limiting enzyme in ketogenesis[ 56 ]. George A.Cook et al. [ 57 ] found that Hmgcs2 was increasingly expressed in DCM rats, consistent with the present study. Another study noted significantly increased expression of Hmgcs2 enzyme in the right ventricle in cases of arrhythmogenic cardiomyopathy (AC), suggesting enhanced ketoacid metabolism, and it also reported concurrent elevation of plasm β-HB. The results indicated that up-regulation of Hmgcs2 enzyme was predictive of occurrence of major adverse cardiovascular events (MACE) and disease progression[ 58 ]. However, there was a study which demonstrated reduced cardiac content of Hmgcs2 in non-diabetic patients with end-stage HF[ 59 ]. We speculated that the discrepancy might be due to the difference in cardiac metabolic substrates between diabetic and non-diabetic cases[ 60 ]. Decr1 (2,4-dienoyl-CoA reductase 1) is a mitochondrial enzyme involved in degradation of poly-unsaturated fatty acids[ 61 ]. Most of the existing studies concentrated on its role in lipid metabolism in tumor cells[ 61 – 63 ], while only a few was performed in non-diabetic HF[ 64 , 65 ]. Therefore, further research is in demand to explore the role of Decr1 in DCM. Ivd (Isovaleryl-CoA dehydrogenase) is another mitochondrial enzyme with implications for metabolism of the branched chain amino acids leucine[ 66 ]. Previous research revealed that leucine-enriched diet was conducive to improving the cardiac injury and dysfunction caused by cancer cachexia[ 67 ] and anti-tumor drugs[ 68 ]. Furthermore, circulating levels of branched chain amino acids were proven as independently associated with the incidence of HF in diabetic patients[ 69 ]. The metabolic status and immune processes are interconnected[ 70 ]. Immune dysregulation is common in DCM and plays a role in disease progression. In the present study, we used the ImmuCellAI algorithm to analyze immune cell infiltration and found higher enrichment of multiple dendritic cells (Dendritic cells, MoDC, cDC1, pDC, and cDC2) in the CON group than the DCM group. Dendritic cells are specialized antigen-presenting cells that serve as important mediators of immune responses[ 71 ], and the number was reduced in both T1DM and T2DM patients[ 72 , 73 ]. It was reported that dendritic cells were protective immunomodulators playing a role during the healing from myocardial infarction (MI). In addition, dendritic cells tended to accumulate in infarct border zone after MI and simultaneously mediated the regulation of homeostasis by monocytes and macrophages[ 74 ]. In human infarcted myocardial tissues, the reduced number of dendritic cells was reported as associated with the recruitment of pro-inflammatory monocytes, increase in macrophages, impairment of reparative fibrosis, and the cardiac rupture after MI[ 75 ]. In all, dendritic cells protect the heart via regulating the recruitment of various types of immune cells. The current study also found that B cell, Marginal Zone B, and Memory B were highly abundant in the DCM group. B cells maintain the bridge between innate and adaptive immunity through their antigen-specific responses, and they are also conducive to sustaining the chronic inflammation in DCM[ 76 ]. Animal experiments revealed that B cells regulated the composition of the cardiac leukocyte pool, and B cell-deficient mice had a smaller fibrotic area while a higher LVEF[ 77 ]. Another study found that B cell depletion was accompanied by significant reductions in TNF-α, IL-1β, IL-18, and apoptosis in myocardial cells, and further introduction of B cells worsened inflammatory response and cardiac function[ 78 ]. Collectively, B cells are critical for the pro-inflammatory environment of the failing heart tissue and myocardial injury. There were also some studies showing that increase in neutrophil-to-lymphocyte ratio was associated with the incidence of subclinical DCM[ 79 ]; impaired Th/Treg balance and increased ventricular infiltration of T cells exacerbated the cardiac hypertrophy and fibrosis in T2DM[ 80 , 81 ]; M1 macrophages potentiated DCM progression via secreting inflammatory factors to induce insulin resistance[ 82 ]. Mitochondrial metabolism can have a huge impact on the fate and function of immune cells. Correlation analysis of the study indicated that Pdk4, Hmgcs2, and Decr1 were positively associated Marginal Zone B while negatively associated with dendritic cells. In addition, Ivd was positively associated with CD8 Tem. This is consistent with our findings that dendritic cells had a lower enrichment in the DCM group than the CON group and had significant enrichment in B cells. The findings of the study deepen our understanding about the link between mitochondrial metabolism and immune cells in DCM. 5. Conclusions This study found significant differences between DCM and healthy myocardial tissues in terms of the expression of mitochondria-related genes (especially those involved in mitochondrial metabolism) and the abundance of infiltrating immune cells using bioinformatics analysis. Four hub MitoDEGs, including Pdk4, Hmgcs2, Decr1, and Ivd, were identified as potentially important for the link between mitochondrial metabolism and immune microenvironment, highlighting the presence of mitochondrial metabolic disorder, immune dysregulation, and their crosstalk in DCM. The mitochondrial metabolism and immune-related molecules found in this study may help uncover the potential pathogenesis of DCM and look for new targets for medical interventions. Abbreviations DCM Diabetic cardiomyopathy NCBI National center for biotechnology information GEO Gene expression omnibus DEGs Diferentially expressed genes GSEA Gene set enrichment analysis GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes MitoDEGs Mitochondria-related DEGs PPI Protein–protein interaction HF Heart failure CTD Comparative toxicogenomics database TF Transcription factors miRNA MicroRNA Pdk4 Pyruvate dehydrogenase kinase 4 Hmgcs2 3-hydroxy-3-methylglutaryl-CoA synthase 2 Decr1 2,4-dienoyl-CoA reductase 1 Ivd Isovaleryl-CoA dehydrogenase DM Diabetes mellitus T1DM Type 1 diabetes mellitus T2DM Type 2 diabetes mellitus RAGE Receptor for advanced glycation end LV Left ventricular CON Control FC Fold-change SD Sprague Dawley HFD High-fat diet STZ Streptozotocin LVEF left ventricular ejection fraction FS fraction shortening LVIDs Left ventricular internal diameters at systole LVIDd Left ventricular internal diameters at diastole PPARα perixisome proliferation-activated receptor alpha MHC Major histocompatibility complex TLR Toll-like receptors BP Biological Process CC Cellular Component MF Molecular Function HW Heart weight BW Body weight TL Tibia length MAM Mitochondria-associated ER membranes AC arrhythmogenic cardiomyopathy β-HB beta-hydroxybutyrate MACE major adverse cardiovascular events MI myocardial infarction TNF Tumor necrosis factor IL Interleukin. Declarations Ethics approval and consent to participate The animal experiment of the study was approved by the Medical Ethics Committee of the 2nd Affiliated Hospital for Harbin Medical University (Ethics approval number: SYDW2021-055). Consent for publication Not applicable. Availability of data and materials The datasets analysed during the current study are available in the GEO database (https://www.ncbi.nlm.nih.gov/geo/)and MitoCarta3.0 (http://www.broadinstitute.org/mitocarta), which is an updating, openly available for free download, authoritative database of mitochondrial proteome including sub-mitochondrial localization and MitoPathway annotations. Competing interests The authors declare that they have no competing interests. Funding This study was funded by the National Natural Science Foundation of China (grant 81770255 to Y Zhang) and the Fund of Key Laboratory of Myocardial Ischemia, Ministry of Education (grant KF202103 to Y Zhang, grant KF202114 to YX Zhang, grant KF202204 to C Peng) . Authors' contributions C Peng and YX Zhang were responsible for the overall design of the study, processing of bioinformatics data, animal modeling, experimental validation, and writing of the manuscript. XY Lang was responsible for the statistical work towards experimental data. All authors read and approved the final manuscript. Acknowledgements We are grateful to the Key Laboratory of Myocardial Ischemia Department of Harbin Medical University for providing laboratory animal resource core facilities. We thank all contributors to the GEO database and developers of the MitoCarta3.0 database, Broad Institute of MIT and Harvard. We also thank developers of the ImmuCellAI tool for predicting abundance of immune cell populations, the research team of Professor Anyuan Guo from Huazhong University of Science and Technology. Authors' information C Peng and YX Zhang contributed equally Authors and Affiliations Department of Cardiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150001, China Key Laboratory of Myocardial Ischemia, Ministry of Education, Harbin Medical University, Harbin 150001, China Cheng Peng, Yanxiu Zhang, Xueyan Lang & Yao Zhang References Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, Colagiuri S, Guariguata L, Motala AA, Ogurtsova K et al . Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9(th) edition . Diabetes Res Clin Pract 2019, 157 :107843. 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An inflammatory cascade leading to hyperresistinemia in humans . PLoS Med 2004, 1 (2):e45. Supplementary Files FigureS1.pdf Additional file 1: Figure S1. Box-plot of GSE4745, GSE5606 and GSE6880. TableS1.xlsx Additional file 2: Table S1. Data cohort characteristics. renamede441b.xlsx Additional file 3: Table S2. Results of GSEA analysis. renamedb6ad2.xlsx Additional file 4: Table S3. GO enrichment analyses of DEGs from GSE4745, GSE5606 and GSE6880. renamedfc507.xlsx Additional file 5: Table S4. KEGG pathway enrichment analyses of DEGs from GSE4745, GSE5606 and GSE6880. renamedc3c72.xlsx Additional file 6: Table S5. MitoDEGs in GSE4745, GSE5606 and GSE6880. renamed148f3.xlsx Additional file 7: Table S6. Hub MitoDEGs explored by MCODE and CytoHubba. renamedcfeb0.xlsx Additional file 8: Table S7. Hub MitoDEGs regulated by TF. renamed55659.xlsx Additional file 9: Table S8. Hub MitoDEGs regulated by miRNAs. renamed15859.xlsx Additional file 10: Table S9. Infiltration of immune cell types compared between the DCM and CON. renamed1ee21.xlsx Additional file 11: Table S10. Relationship between hub MitoDEGs and immune cells. renamed5c48a.xlsx Additional file 12: Table S11. Primers sequence of hub MitoDEGs. Cite Share Download PDF Status: Published Journal Publication published 01 Feb, 2023 Read the published version in Journal of Translational Medicine → Version 1 posted Reviewers agreed at journal 22 Oct, 2022 Reviewers invited by journal 22 Oct, 2022 Editor assigned by journal 15 Oct, 2022 First submitted to journal 12 Oct, 2022 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-2148339","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":146215667,"identity":"66e0961c-0c22-4dd6-bd3e-e4b4bf032074","order_by":0,"name":"Cheng Peng","email":"","orcid":"","institution":"Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Peng","suffix":""},{"id":146215668,"identity":"aac051ce-eafb-4724-a6b4-629013486592","order_by":1,"name":"Yanxiu Zhang","email":"","orcid":"","institution":"Second Affiliated Hospital of Harbin Medical University Department of Radiology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yanxiu","middleName":"","lastName":"Zhang","suffix":""},{"id":146215669,"identity":"81c0d8ac-b663-465a-ae1d-7c19708be704","order_by":2,"name":"Xueyan Lang","email":"","orcid":"","institution":"Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xueyan","middleName":"","lastName":"Lang","suffix":""},{"id":146215670,"identity":"d4c5c3e4-8143-4c1a-8b51-4701b430061b","order_by":3,"name":"Yao Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYJACZgiVwPiYgeEAmClBrBZmY5K1sEkTpcXg+OHDnwtq7tg1sOc+qy7ccyfa4ADzwds8DHZ5OLWcSUswnnHsWXIDz3Oz2zOePcvdcIAt2ZqHIbkYp5YDOQbJPGyHkxkk0thu8xw4DNTCYybNw3AgsQGXlvNvDA7z/INoKYZo4f+GX8uNHMNm3rbDdiAtzFBb2PBqkbzxLJmZt+9wAgPPM2ZpngPPcmceZjO2nGOQjFML3/nkw595vh22Z2BPY/zMc+BObt/x5oc33lTY4dSicABCJ+4/ABMCR5MBDvVAIA81yx63klEwCkbBKBjxAACWnVxqTh7rQwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-3853-3550","institution":"Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yao","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2022-10-09 16:57:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2148339/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2148339/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12967-023-03928-8","type":"published","date":"2023-02-01T18:39:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":28332419,"identity":"b13f127f-bedc-4d21-bce3-b5a7f29e5db3","added_by":"auto","created_at":"2022-10-27 14:41:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1065626,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the multistep screening strategy on bioinformatics data.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-2148339/v1/56645a2c61888b63ab11f695.png"},{"id":28333691,"identity":"01aaecc0-abea-4dcc-a927-19d7e3c053dc","added_by":"auto","created_at":"2022-10-27 14:46:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":712837,"visible":true,"origin":"","legend":"\u003cp\u003eDEGs in DCM and results of GSEA analysis. (a-c) Volcano plot of DEGs in GSE4745, GSE5606, GSE6880; (d-f) Clustered heatmap of DEGs in GSE4745, GSE5606, GSE6880; (g-i) GSEA profiles depicting the 7 significant GSEA sets in lipid metabolism; (j) 3 significant GSEA sets in oxidative stress; (k. l) 4 significant GSEA sets in immunity; (m. n) 10 significant GSEA sets in collagen biosynthesis.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-2148339/v1/69a4cf44306df114bc14b5d1.png"},{"id":28332417,"identity":"22b6bd83-1d9c-4a51-8625-32dce604c966","added_by":"auto","created_at":"2022-10-27 14:41:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1187595,"visible":true,"origin":"","legend":"\u003cp\u003eGO and KEGG enrichment analyses of DEGs from GSE4745, GSE5606 and GSE6880. (a. b) The enriched GO terms of DEGs in GSE4745; (c. d) The enriched GO terms of DEGs in GSE5606; (e. f) The enriched GO terms of DEGs in GSE6880; (g. h) KEGG pathway enrichment results in GSE4745; (i. j) KEGG pathway enrichment results in GSE5606; (k. l) KEGG pathway enrichment results in GSE6880. BP, biological process; CC, cellular component; MF, molecular function.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-2148339/v1/9b339ba4107cb4817c808dbf.png"},{"id":28332432,"identity":"29964ae3-52fb-467c-bb5b-410a55725708","added_by":"auto","created_at":"2022-10-27 14:41:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1109590,"visible":true,"origin":"","legend":"\u003cp\u003eMitoDEGs in DCM; PPI network analysis and hub MitoDEGs identification. (a. b) Venn diagrams showed the number of upregulated (a) and downregulated(b) DEGs that overlap between GSE4745, GSE5606, GSE6880, MitoCarta3.0; (c) Clustered heatmap of DEGs both in GSE4745 and MitoCarta3.0; (d) Clustered heatmap of DEGs both in GSE5606 and MitoCarta3.0; (e) Clustered heatmap of DEGs both in GSE6880 and MitoCarta3.0; (f) PPI network of MitoDEGs; (g) A key cluster with 9 genes was further chosen as hub genes by MCODE; (h) Top 10 hub genes explored by CytoHubba.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-2148339/v1/48cee7db1057b1c3a29b427f.png"},{"id":28332436,"identity":"5eba90a2-9807-4cd1-86f9-79db0973a712","added_by":"auto","created_at":"2022-10-27 14:41:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1305017,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between hub MitoDEGs and DCM/HF; Hub MitoDEGs-TFs-miRNAs regulatory network. (a. b) Hub MitoDEGs related to DCM and HF diseases based on the CTD database; (c) TF–hub MitoDEGs regulatory network: the red squares represent hub MitoDEGs, and the yellow dots represent transcription factors; (d) miRNA–hub MitoDEGs regulatory network: the red squares represent hub MitoDEGs, and the purple dots represent miRNA.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-2148339/v1/85fdb7cb579dd812aa0b7ea7.png"},{"id":28333696,"identity":"276feffa-581d-4cf5-9aeb-21f5ba91cbe7","added_by":"auto","created_at":"2022-10-27 14:46:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":528308,"visible":true,"origin":"","legend":"\u003cp\u003eInfiltration of immune cell types compared between the DCM and CON. (a)The violin plot of the immune cell proportions; (b) Stacked bar chart of the immune cell; (c) Heatmap of the proportions of 36 immune cell types; (d) The correlation matrix of immune cell proportions.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-2148339/v1/5846b21ec5265fb709538f23.png"},{"id":28332427,"identity":"79bec9e2-afbf-4a25-bc41-e2771b60d4fa","added_by":"auto","created_at":"2022-10-27 14:41:19","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":238445,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between MitoDEGs/hub MitoDEGs and immune cells. (a. b) The correlation between upregulated(a)and downregulated(b) DEGs and immune cells; (c) The correlation between hub MitoDEGs and immune cells.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-2148339/v1/a088d58b6a3bef498f720e2f.png"},{"id":28332425,"identity":"78ee48aa-3ab2-4827-8229-f1a89712a46e","added_by":"auto","created_at":"2022-10-27 14:41:19","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":655994,"visible":true,"origin":"","legend":"\u003cp\u003eConfirmation of hub MitoDEGs expression and association with cardiac function in DCM rats. (a-j) General biological and echocardiography features of DCM rats; (k) Hub MitoDEGs mRNA expression of CON and DCM rats; (l) Correlations between Pdk4, Hmgcs2, Decr1, Ivd mRNA levels and cardiac functional parameters in CON and DCM rats, including: EF%, FS%, LVIDs(mm). Mean± SD, n=4 rats per group. *p\u0026lt;0.05**, p\u0026lt;0.01***, p\u0026lt;0.001**** and p\u0026lt;0.0001 vs. control group. ns, no significance.\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-2148339/v1/01398d02f042bc78f1990ff1.png"},{"id":44718946,"identity":"ee39afe2-cae0-472b-850a-4cb8e422eb9e","added_by":"auto","created_at":"2023-10-16 18:52:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6794514,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2148339/v1/ae6fe034-3708-4a59-8c25-b18584e88199.pdf"},{"id":28333693,"identity":"c10a4501-3878-4bb9-b140-9256721e5c39","added_by":"auto","created_at":"2022-10-27 14:46:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":408332,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 1: Figure S1. \u003c/strong\u003eBox-plot of GSE4745, GSE5606 and GSE6880.\u003c/p\u003e","description":"","filename":"FigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2148339/v1/3878b3857b30b36238bb2078.pdf"},{"id":28332418,"identity":"81590f74-0b67-449f-86c2-0d18179a3396","added_by":"auto","created_at":"2022-10-27 14:41:18","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10043,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 2: Table S1. \u003c/strong\u003eData cohort characteristics.\u003c/p\u003e","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2148339/v1/25083fbdf8edf4f188ccb9ae.xlsx"},{"id":28332422,"identity":"bc0945fe-3994-4986-94b1-5c6f92de3da6","added_by":"auto","created_at":"2022-10-27 14:41:19","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":13099,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 3: Table S2.\u003c/strong\u003e Results of GSEA analysis.\u003c/p\u003e","description":"","filename":"renamede441b.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2148339/v1/5022f7a046424573344d7c96.xlsx"},{"id":28333694,"identity":"2c126ba2-d47b-46f0-9c43-e3c9b6589d8e","added_by":"auto","created_at":"2022-10-27 14:46:19","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":18295,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 4: Table S3. \u003c/strong\u003eGO enrichment analyses of DEGs from GSE4745, GSE5606 and GSE6880.\u003c/p\u003e","description":"","filename":"renamedb6ad2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2148339/v1/14ed8419c5e64b01fe52a609.xlsx"},{"id":28332430,"identity":"e68c9e11-931d-4601-8214-c9fe354fdfaa","added_by":"auto","created_at":"2022-10-27 14:41:19","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":12477,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 5: Table S4.\u003c/strong\u003e KEGG pathway enrichment analyses of DEGs from GSE4745, GSE5606 and GSE6880.\u003c/p\u003e","description":"","filename":"renamedfc507.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2148339/v1/01b258448538d0837152754d.xlsx"},{"id":28332423,"identity":"234e959f-1ca4-4cf4-b75d-5b3e4b3e6c94","added_by":"auto","created_at":"2022-10-27 14:41:19","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":16480,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 6: Table S5.\u003c/strong\u003e MitoDEGs in GSE4745, GSE5606 and GSE6880.\u003c/p\u003e","description":"","filename":"renamedc3c72.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2148339/v1/4620eec945abaee13f4065f9.xlsx"},{"id":28332434,"identity":"1cec9b47-04a9-4fe2-8051-a6a826f274b2","added_by":"auto","created_at":"2022-10-27 14:41:19","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":10020,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 7: Table S6. \u003c/strong\u003eHub MitoDEGs explored by MCODE and CytoHubba.\u003c/p\u003e","description":"","filename":"renamed148f3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2148339/v1/6353034e4da928acc44068cf.xlsx"},{"id":28333695,"identity":"c4a0767f-f799-48ed-b28f-c0d5b15225c3","added_by":"auto","created_at":"2022-10-27 14:46:19","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":10973,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 8: Table S7. \u003c/strong\u003eHub MitoDEGs regulated by TF.\u003c/p\u003e","description":"","filename":"renamedcfeb0.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2148339/v1/28863af7945df19f07561c9a.xlsx"},{"id":28332428,"identity":"bbaac947-a8b7-4422-a1a6-be6cbb1669d9","added_by":"auto","created_at":"2022-10-27 14:41:19","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":92437,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 9: Table S8. \u003c/strong\u003eHub MitoDEGs regulated by miRNAs.\u003c/p\u003e","description":"","filename":"renamed55659.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2148339/v1/2b5e92139699ccb37bf08c76.xlsx"},{"id":28333697,"identity":"728da965-caf0-410d-9f43-4909c6e82e86","added_by":"auto","created_at":"2022-10-27 14:46:19","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":10298,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 10: Table S9.\u003c/strong\u003e Infiltration of immune cell types compared between the DCM and CON.\u003c/p\u003e","description":"","filename":"renamed15859.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2148339/v1/6a29b5055f6b0329323912a9.xlsx"},{"id":28332437,"identity":"90528d76-3731-48f0-ac18-ef9a817e0821","added_by":"auto","created_at":"2022-10-27 14:41:19","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":19623,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 11: Table S10.\u003c/strong\u003e Relationship between hub MitoDEGs and immune cells.\u003c/p\u003e","description":"","filename":"renamed1ee21.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2148339/v1/4b7e2a9fb6a8f354689f5d3c.xlsx"},{"id":28332438,"identity":"4cc0da3a-a5e4-483a-b56d-ef3e8167afa6","added_by":"auto","created_at":"2022-10-27 14:41:19","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":9531,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 12: Table S11.\u003c/strong\u003e Primers sequence of hub MitoDEGs.\u003c/p\u003e","description":"","filename":"renamed5c48a.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2148339/v1/b77f213345bbc4b7fe786400.xlsx"}],"financialInterests":"","formattedTitle":"Role of mitochondrial metabolic disorder and immune infiltration in diabetic cardiomyopathy: new insights from bioinformatics analysis","fulltext":[{"header":"1. Background","content":"\u003cp\u003eWith the changing lifestyles, the incidence of diabetes mellitus (DM) shows a rapidly increasing trend. As estimated by the International Diabetes Federation, the number of DM patients will be increased to 0.5784\u0026nbsp;billion by 2030, resulting in a morbidity of up to 10.2%[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. DM increases the risk of developing heart failure (HF) by 2\u0026ndash;4 times, as compared to healthy people[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and thus it tends to cause a highly poor prognosis. Diabetic cardiomyopathy (DCM) is one of the severe cardiovascular complications[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] of DM first reported by S Rubler in 1972[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It is multi-factorial in pathophysiology and has not yet been fully explored.\u003c/p\u003e \u003cp\u003eIncreasing studies have noted that mitochondrial events that lead to damage and dysfunction, including abnormal dynamics[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], mitophagy[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], calcium homeostasis imbalance[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], disturbed energy metabolism and oxidative stress[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], play an essential role in DCM. In addition, excessive accumulation of lipid intermediary metabolites is considered as directly linked to the toxic injury and dysfunction of diabetic myocardium[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. It has been established that the immune infiltration and activation of inflammatory processes in myocardial tissues are also critical pathogeneses of DCM. For example, both type 1 and 2 DM (T1DM/T2DM) models had increased myocardial infiltration of monocytes and macrophages[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]; chronic hyperglycemia induced elevation of Th1, Th2, and Th17 cytokines by activating T cells via the RAGE-dependent pathway[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]; additionally, a high-glucose environment could also activate mast cells and induce the release of pro-inflammatory mediators, resulting in exacerbation of the pathological remodeling in DCM[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInterestingly, accumulating evidence has revealed that there is a potential link between immunity and mitochondrial metabolism, and the metabolic state can affect the development of inflammation through changing the immune microenvironment[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Typical T cell activation is accompanied by the up-regulation of insulin receptors and glycolytic enzymes[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].High levels of insulin can impair the function of regulatory T cells (Tregs) and inhibit their suppressive function towards inflammatory response via regulating the AKT/mTOR signaling pathway[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Both mitochondrial metabolism and immune-inflammation are key pathogeneses of DCM, but their crosstalk in DCM have not yet been reported and require further exploration.\u003c/p\u003e \u003cp\u003eBioinformatics allows for screening of molecules which show a difference between patients and healthy individuals from microarray data that vary at multiple levels. It is appreciated as an effective research method for exploring the potential molecular mechanism of disease. With this method, the current study analyzed how mitochondria-related genes promote the development of DCM and correlate to the immune infiltration based on associated microarray data from GEO database (GSE4745, GSE5606, and GSE6880). Additionally, the relationship between hub mitochondria-related genes and immune infiltrates in DCM was investigated to help better understand the underlying immunometabolism during disease development.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Microarray data retrieval\u003c/h2\u003e \u003cp\u003eGSE4745, GSE5606 and GSE6880 microarray datasets were obtained from the public repository NCBI GEO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] using \"diabetic cardiomyopathy\" as the search term and ventricular samples and modeling time as the screening criteria. The GSE4745 ([RG_U34A] Affymetrix Rat Genome U34 Array) is generated by the GPL85 platform that contains 24 left ventricular (LV) samples from rattus norvegicus. To better analyze the differential genes between DCM group and control (CON) group, 8 samples collected on day 42, including 4 DCM samples and 4 CON samples, were selected for analysis[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The GSE5606 ([Rat230_2] Affymetrix Rat Genome 230 2.0 Array) is generated by the GPL1355 platform and composed of 14 LV samples from DCM rats (n\u0026thinsp;=\u0026thinsp;7) and CON rats (n\u0026thinsp;=\u0026thinsp;7) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The GSE6880 ([RAE230A] Affymetrix Rat Expression 230A Array) is generated by the GPL341 platform comprising 6 LV samples from DCM rats (n\u0026thinsp;=\u0026thinsp;3) and CON rats (n\u0026thinsp;=\u0026thinsp;3) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.2 Acquisition Of Microarray Data And Identification Of Differentially Expressed Genes (Degs)\u003c/h3\u003e\n\u003cp\u003eData of each microarray were accessed from GEO using R package \"GEO query\". DEGs from each microarray were obtained with R package \"limma\" as implemented by GEO2R online tool(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/geo2r/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/geo2r/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and all identified DEGs met p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2 (FC)| \u0026ge;1. Resulting DEGs were visualized by Volcano Plot using R package \"ggplot2\"[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and Heatmap using R package \"ComplexHeatmap\"[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003e2.3 Functional Enrichment Analysis\u003c/h3\u003e\n\u003cp\u003eGene Set Enrichment Analysis (GSEA) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] was applied using R package \"clusterProfiler\"[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], with the \"c2.cp.v7.2.symbols.gmt\"(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb/index.jsp\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) as the reference gene set, the number of permutations as 10,000, and the threshold of significance as 10. The results were visualized with R package \"ggplot2\"[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were accomplished in DEGs with R package \"clusterProfiler\"[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and the items with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in Benjamini-Hochberg test were regarded has having statistical significance. The results were visualized by Chordal and Circle graphs using R packages \"ggplot2\"[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and \"GOplot\"[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003e2.4 Identification Of Mitochondria-related Degs (Mitodegs)\u003c/h3\u003e\n\u003cp\u003eThe mitochondrial protein database, MitoCarta3.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.broadinstitute.org/mitocarta\u003c/span\u003e\u003cspan address=\"http://www.broadinstitute.org/mitocarta\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], was visited to obtain 1,140 mitochondria-localized genes. MitoDEGs were obtained via intersecting the DEGs from each microarray and the 1,140 mitochondria-localized genes using a Venn Diagram, and they were visualized as a Heatmap with R package \"ggplot2\"[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The overlapped MitoDEGs among the three microarrays were eventually obtained.\u003c/p\u003e\n\u003ch3\u003e2.5 Analysis Of Protein-protein Interactions (Ppi) And Identification Of Hub Genes\u003c/h3\u003e\n\u003cp\u003eThe overlapped MitoDEGs were processed for PPI analysis with 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)[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], and the resulting interactions were visualized as a network using Cytoscape 3.8.2[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Hub MitoDEGs were screened out using the plug-ins CytoHubba and MCODE as implemented by the Cytoscape 3.8.2.\u003c/p\u003e\n\u003ch3\u003e2.6 Acquisition Of Genes Potentially Key To Dcm And Hf\u003c/h3\u003e\n\u003cp\u003eThe CTD database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ctdbase.org/\u003c/span\u003e\u003cspan address=\"http://ctdbase.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] assembles interaction data between chemicals, gene products, functional phenotypes, and diseases, affording great convenience to research into disease-associated environmental exposures and potential mechanisms of action of drugs. With the CTD data, the link between hub MitoDEGs and the risk of developing DCM or HF was analyzed.\u003c/p\u003e\n\u003ch3\u003e2.7 Prediction Of A Hub Mitodegs-transcription Factors (Tf) -mirnas Network\u003c/h3\u003e\n\u003cp\u003eTo explore the upstream regulators of hub MitoDEGs, TFs of hub MitoDEGs were predicted with the plug-in iRegulon of the Cytoscape 3.8.2[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and miRNAs of hub MitoDEGs were predicted using the miRWalk database (mirwalk.umm.uni-heidelberg.de) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The hub MitoDEGs, resulting TFs and miRNAs were visualized as a network by the Cytoscape 3.8.2.\u003c/p\u003e\n\u003ch3\u003e2.8 Immune Infiltration Analysis\u003c/h3\u003e\n\u003cp\u003eThe gene matrices of GSE5606 and GSE6880 original datasets were combined using the Perl script and normalized after elimination of the batch effect and the heterogeneity induced by different platforms with R package \"sva\"[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The normalized gene expression matrix was used for further immune infiltration analysis. The GSE4745 dataset was excluded from the analysis, as the number of genes in GSE4745 was significantly less than that in the other two datasets, which might result in bias results.\u003c/p\u003e \u003cp\u003eThe ImmuCellAI (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinfo.life.hust.edu.cn/web/ImmuCellAI\u003c/span\u003e\u003cspan address=\"http://bioinfo.life.hust.edu.cn/web/ImmuCellAI\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) estimates the infiltration abundance of 36 immune cell types based on RNA-Seq data or gene-expression profiles from microarray data[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].The normalized gene expression matrix was uploaded to the ImmuCellAI for analysis of immune infiltration, with Wilcoxon rank sum test used for between-group comparisons. Spearman correlation analysis was applied to explore the link between MitoDEGs/hub MitoDEGs and the immune cells.\u003c/p\u003e\n\u003ch3\u003e2.9 Construction Of Animal Models With Dcm\u003c/h3\u003e\n\u003cp\u003eThe animal procedure was performed in strict accordance with \u003cem\u003eThe Guide for Care and Use of Laboratory Animals (NIH publication No.85\u0026thinsp;\u0026minus;\u0026thinsp;23, revised 2011)\u003c/em\u003e and with the approval from the Laboratory Animal Ethics Committee. Ten SD male rats, weighing 200\u0026thinsp;\u0026plusmn;\u0026thinsp;20 g, were housed in the laboratory animal center of our hospital. The rats were allowed to acclimate for 1 week with free access to diet and water ad libitum in an environment that provided a relative temperature of 24 ℃, a relative humidity of 50\u0026ndash;60%, and a 12 h/12 h light/dark cycle. Following that, the rats were divided into the CON and DCM groups by random assignment. Rats in the CON group were fed normal diet, while rats in the DCM group were fed high-fat diet (HFD) containing 60 kcal% fat, 20 kcal% protein, and 20 kcal% carbohydrate. After 4 weeks, streptozotocin (STZ) (40 mg/kg, Solarbio) was intraperitoneally injected for consecutive 3 days in rats of the DCM group to induce T2DM, and citric acid buffer at the same dose was administrated in rats of the CON group. One week after injection, blood glucose was measured from the tail vein, and a random glucose level\u0026thinsp;\u0026gt;\u0026thinsp;16.7 mmol/L was indicative of successful modeling. Tissue samples were obtained after another 12 weeks of feeding. Blood glucose and body weight were monitored during modeling, and echocardiography and measurement of tibia length were performed before sampling.\u003c/p\u003e\n\u003ch3\u003e2.10 Echocardiography\u003c/h3\u003e\n\u003cp\u003eRats were anesthesized with intraperitoneal injection of 30 mg/kg pentobarbital. Echocardiography was performed with the transducer of a high-resolution imaging system. LV parameters, including left ventricular ejection fraction (LVEF), fraction shortening (FS), left ventricular internal diameters at systole (LVIDs) and diastole (LVIDd), were measured from long- /short-axis images of the LV. Cardiac function was assessed by analysis of data of 3\u0026ndash;5 cardiac cycles.\u003c/p\u003e\n\u003ch3\u003e2.11 Rna Extraction And Qrt-pcr\u003c/h3\u003e\n\u003cp\u003eTotal RNA was extracted from cardiac tissue using Trizol and reversely transcribed into cDNA using a reverse transcription kit (Roche). qRT-PCR was fulfilled with a SYBR Green (Roche). The primers used for amplification were shown in Table S1. Target gene expression relative to GAPDH gene was shown as 2\u003csup\u003eΔΔCt\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003e2.12 Correlation Between Hub Mitodegs And Cardiac Function\u003c/h3\u003e\n\u003cp\u003eCorrelation between hub MitoDEGs and LV parameters (EF%, FS%, and LVIDs) was analyzed using the Pearson algorithm, and the results were visualized with R package \"ggplot2\"[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003e2.13 Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eData are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) of four independent experiments, and were analyzed using GraphPad Prism 8.0 (GraphPad Inc, San Diego, USA). The student`s t-test was used to measure the differences between two groups. P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to be statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.1 DEGs in DCM and functional enrichment analysis\u003c/h2\u003e \u003cp\u003eFlowchart of overall data screening strategy was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Three DCM-related GEO datasets, GSE4745, GSE5606, and GSE6880, were obtained for analysis. Differential analysis demonstrated 293 DEGs in the GSE4745 dataset, including 149 genes up-regulated and 144 genes down-regulated in DCM samples in comparison to normal samples; 544 DEGs in the GSE5606 dataset, including 269 up-regulated and 275 down-regulated genes; and 463 DEGs in the GSE6880 dataset, including 262 up-regulated and 201 down-regulated genes. The DEGs were visualized as Volcano Plots and Heatmaps (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-f).\u003c/p\u003e \u003cp\u003eGSEA showed that the DEGs from the three datasets were mainly involved in pathways related to lipid and fatty acid metabolism, and immunity, including Metabolism of lipids, Regulation of lipid metabolism by PPARα, Fatty acid metabolism, Biosynthesis of unsaturated Fatty acids, Antigen processing and presentation, MHC class II antigen presentation, Regulation of TLR by endogenous ligand, Complement activation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg-n). In addition, it also showed enrichment of pathways involved in collagen synthesis, collagen fibril assembly, and oxidative stress.\u003c/p\u003e \u003cp\u003eThe DEGs were further processed for functional enrichment with GO and KEGG pathway analyses. The most enriched GO terms were classified to Biological Process (BP), Cellular Component (CC) and Molecular Function (MF), majoring including mitochondrial function and component, energy metabolism, inflammatory immunity, hypoxia and redox reaction, collagen synthesis, and insulin sensitivity, etc. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-f). The most enriched KEGG pathways of the DEGs were dominated by pathways involved in mitochondrial metabolism and function, hypoxia and redox reaction, substance generation, and immunity, etc. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg-l).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e3.2 Mitodegs In Dcm\u003c/h3\u003e\n\u003cp\u003eMitochondria-related genes were retrieved from the MitoCarta3.0 database, and the genes overlapped with the DEGs from three datasets were selected as MitoDEGs. In total, there were 32 MitoDEGs (15 up-regulated and 17 down-regulated) in the GSE4745 datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec), 34 MitoDEGs (18 up-regulated and 16 down-regulated) in the GSE5606 datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed), and 25 MitoDEGs (14 up-regulated and 11 down-regulated) in the GSE6880 datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). The MitoDEGs of each dataset were combined, resulting in 67 overlapped MitoDEGs, including 35 genes up-regulated and 32 genes down-regulated in DCM samples in comparison to normal samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e3.3 Ppi Network Analysis And Hub Mitodegs Identification\u003c/h3\u003e\n\u003cp\u003ePPI of the 67 MitoDEGs was analyzed using the STRING database and visualized as a network with the Cytoscape (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef). Significant modules (gene clusters) were identified using the plug-in MCODE as implemented by the Cytoscape with the following filter criteria: degree cut-off =\u0026thinsp;2; node score cut-off =\u0026thinsp;0.2; k-core\u0026thinsp;=\u0026thinsp;2; and max depth\u0026thinsp;=\u0026thinsp;100. A module that was composed of 9 nodes and 17 edges was identified as significant, and the genes involved in the module were Acsl6, Acadsb, Decr1, Ivd, Oxct1, Gpam, Pdk4, Hmgcs2, and Acot2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg). With the MCC algorithm of plug-in CytoHubba, 10 candidate hub genes were identified from the PPI network, including Cpt1a, Hsd17b4, Hmgcs2, Acadsb, Decr1, Acot2, Gpam, Oxct1, Acsl6, and Ivd (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eh). Combining the results, 11 hub MitoDEGs, including Acadsb, Hmgcs2, Hsd17b4, Gpam, Acot2, Ivd, Decr1, Cpt1a, Acsl6, Oxct1, and Pdk4, were eventually obtained.\u003c/p\u003e\n\u003ch3\u003e3.4 Relationship Between Hub Mitodegs And Dcm/hf\u003c/h3\u003e\n\u003cp\u003eThe CTD database was applied to predict the relationship between hub MitoDEGs and DCM/HF. As analyzed, Cpt1a, Gpam, Hmgcs2, and Acadsb had the highest association with DCM (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), while Cpt1a, Pdk4, Gpam, and Hmgcs2 showed the highest correlation with HF (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e3.5 Hub Mitodegs-tfs-mirnas Regulatory Network\u003c/h3\u003e\n\u003cp\u003eThe upstream regulation of the hub MitoDEGs was explored via predicting related TFs and miRNAs. TFs of hub MitoDEGs were predicted with plug-in iRegulon of the Cytoscape, and a hub MitoDEGs-TFs regulatory network comprising 19 TFs (Rora, Maf, Ing4, Srebf2, Mafb, Zfp706, Pole3, Rreb1, Mybl2, Myb, Tcf4, Mafa, Cebpa, Thra, Pdcd11, Yy1, Runx2, Cdx1, Ubp1) was constructed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). miRNAs of hub MitoDEGs were predicted with the miRWalk 3.0, and a hub MitoDEGs-miRNAs regulatory network that involved 299 nodes and 569 edges was generated (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). There were three miRNAs, including miR-298-5p that had interactions with Ivd, Acsl6, Acot2, and Hmgcs2; miR-30c-1-3p that interacted with Oxct1, Ivd, Cpt1a, and Acsl6; and miR-344b-5p that interacted with Oxct1, Cpt1a, Acsl6, and Hmgcs2. However, further validation is required.\u003c/p\u003e\n\u003ch3\u003e3.6 Immune Cell Infiltration In Dcm\u003c/h3\u003e\n\u003cp\u003eInfiltration of 36 immune cell types was analyzed using the ImmuCellAI algorithm and compared between the DCM and CON groups in the GSE5606 and GSE6880 datasets. Significant differences were demonstrated between the DCM and CON groups in the myocardial infiltration of 9 immune cell types (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Specifically, B cell, Marginal Zone B and Memory B were much more abundant in the DCM group, while Granulocytes, Dendritic cells, MoDC, cDC1, pDC, and cDC2 were more abundant in the CON group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea-c). Further analysis for the infiltrating immune cells in DCM showed multiple correlations between the cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed). The degree of correlation was indicated by scores. The synergistic effect was observed as the strongest between CD4 T cell and Naive CD4 T (0.99), followed by CD4 T cell and T helper cell (0.98), CD8 Tcm and CD8 Tex (0.98), Naive CD4 T and T helper cell (0.97). In contrast, the competitive effect was found as the strongest between Naive CD8 T and B cell (-0.72), followed by pDC and Marginal Zone B (-0.69), Naive CD8 T and Memory B (-0.69).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e3.7 Relationship Between Mitodegs/hub Mitodegs And Immune Cells\u003c/h3\u003e\n\u003cp\u003eSpearman method was applied to explore the potential associations between MitoDEGs/hub MitoDEGs and immune cells. The positive/negative associations between MitoDEGs (35 up-regulated and 32 down-regulated) and immune cells were demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea-b. Of the 11 hub MitoDEGs, Pdk4 was positively associated with Marginal Zone B but negatively associated with cDC2, MoDC, and pDC; Oxct1 was positively associated with pDC and CD8 Tem; Ivd was positively associated with CD8 Tem; Hsd17b4 was positively associated with Marginal Zone B and M2 macrophage but negatively associated with cDC2, MoDC, and pDC; Hmgcs2 was positively associated with Marginal Zone B, M2 macrophage but negatively associated with Granulocytes and cDC2; Gpam was negatively associated with Dendritic cells, Granulocytes, cDC1, and MoDC; Decr1 was positively associated with Marginal Zone B and M2 macrophage while negatively associated with Granulocytes, cDC2, and pDC; Cpt1a was negatively associated with Dendritic cells, cDC1, MoDC, and pDC; Acsl6 was positively associated with pDC, Eosinophil, and CD8 Tem; Acot2 was negatively associated with Dendritic cells and cDC1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e3.8 General Biological And Echocardiography Features Of Dcm Rats\u003c/h3\u003e\n\u003cp\u003eDuring modeling, the body weight of HFD-fed rats of the DCM group was significantly higher than that of the CON group, and it tended to decrease from 2 weeks after STZ injection and became remarkably lower than that of the CON group before tissue sampling (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea). After 1 week of STZ induction, the blood glucose of the DCM group began to increase, and the level was consistently higher than that of the CON group throughout the entire modelling process (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb). Echocardiography showed that as compared to the CON group, the DCM group witnessed significantly lower EF% and FS% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but remarkably higher LVIDs (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Besides, the LVIDd was marginally varied between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec-h). Moreover, notable increases in the heart weight normalized to body weight (HW/BW) and heart weight normalized to tibia length (HW/TL) were found in the DCM group as compared to the CON group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ei-j).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e3.9 Pcr Confirmation Of Hub Mitodegs Expression In Dcm Rats\u003c/h3\u003e\n\u003cp\u003eVentricular expression of 9 hub MitoDEGs (Acadsb, Acot2, Cpt1a, Decr1, Gpam, Hmgcs2, Hsd17b4, Ivd, and Pdk4) was validated in rats with qRT-PCR. As compared to the CON group, Pdk4, Hmgcs2 and Decr1 had significantly increased expression in the DCM group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while Ivd reversely exhibited remarkably decreased expression in the DCM group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ek).\u003c/p\u003e\n\u003ch3\u003e3.10 Relationship Between Hub Mitodegs And Cardiac Function\u003c/h3\u003e\n\u003cp\u003eThe four hub MitoDEGs (Pdk4, Hmgcs2, Decr1, and Ivd) with distinct differential expression between the DCM and CON groups were further analyzed for their associations with EF%, FS% and LVIDs. The number of PCR cycles of Pdk4 had highly significant positive correlations with EF% (R = -0.904; P\u0026thinsp;=\u0026thinsp;0.002) and FS% (R\u0026thinsp;=\u0026thinsp;0.934; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but had a highly significant negative correlation with LVIDs (R\u0026thinsp;=\u0026thinsp;0.852; P\u0026thinsp;=\u0026thinsp;0.007); the number of PCR cycles of Hmgcs2 exhibited highly significant positive correlations with EF% (R\u0026thinsp;=\u0026thinsp;0.782; P\u0026thinsp;=\u0026thinsp;0.022) and FS% (R\u0026thinsp;=\u0026thinsp;0.812; P\u0026thinsp;=\u0026thinsp;0.014); the number of PCR cycles of Decr1 showed highly significant positive correlations with EF% (R\u0026thinsp;=\u0026thinsp;0.829; P\u0026thinsp;=\u0026thinsp;0.011) and FS% (R\u0026thinsp;=\u0026thinsp;0.801; P\u0026thinsp;=\u0026thinsp;0.017); the number of PCR cycles of lvd showed highly significant negative correlations with EF% (R = -0.978; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and FS% (R = -0.943; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but had a highly significant positive correlation with LVIDs (R\u0026thinsp;=\u0026thinsp;0.852; P\u0026thinsp;=\u0026thinsp;0.007) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003el). Collectively, the up-regulated expression of Pdk4, Hmgcs2, and Decr1 and the down-regulated expression of Ivd in myocardial tissues of DCM were highly linked to the reduction in cardiac function.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe number of DM patients has grown worldwide at an alarming rate. DM commonly occurs with target organ damage that leads to a poor prognosis, and it is tightly linked to the initiation and development of HF[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. It has been proven that the risk of developing HF in DM patients is associated with the presence of DCM[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. However, it remains elusive about the pathogenesis of DCM, and there is a paucity of effective therapeutic strategies. In this context, strengthening our understanding on DCM pathogenesis and looking for potential therapeutic targets are in urgent need. With multiple bioinformatics methods, the present study firstly obtained DEGs from the three DCM-related microarray datasets from GEO and found that the DEGs were enriched in pathways associated with mitochondrial metabolism, immune-inflammation, and collagen synthesis. Mitochondrial dysfunction and metabolic abnormality have been proven to play a role in cardiac hypertrophy and myocardial fibrosis[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In addition, various activities of immune cells, such as transition from macrophages to fibroblast-like cells[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], B-cell infiltration[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], and transition between T lymphocyte subsets (Th17 to Treg) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], are also critical for pathogenesis of myocardial fibrosis. Based on the findings, our study aimed at analyzing the regulatory roles of mitochondrial metabolism and immune dysregulation in the occurrence and development of DCM and exploring related targets. The findings of the study may help us better understand the mitochondrial metabolism, immunity, and their crosstalk in DCM.\u003c/p\u003e \u003cp\u003ePresently, mitochondria-related genes in DCM have not yet been reported by bioinformatics studies. For the first time, our study applied the MitoCarta 3.0, an authoritative database of mitochondrial proteome, to obtain mitochondria-related genes, and then identified 9 hub MitoDEGs with had a strong correlation with DCM or HF. To validate our findings, DCM rats were modeled. Expression analysis revealed four genes, including Pdk4, Hmgcs2, Decr1, and Ivd, which showed a consistent expression trend as that detected by prior bioinformatics analysis. Additionally, we found that the up-regulation of Pdk4, Hmgcs2, Decr1 and the down-regulation of Ivd were significantly associated with the reduction in cardiac function.\u003c/p\u003e \u003cp\u003eMitochondrial metabolic disorder is one of the important pathogeneses of DCM[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], while Pdk4, Hmgcs2, Decr1, and Ivd are enzymes essential for mitochondrial metabolism. In DCM, the most significant metabolic disorders in myocardial tissues are decreased glucose utilization and increased fatty acid oxidation, which can lead to cardiac lipotoxicity, myocardial fibrosis, and effects on cardiac function. Pdk4 (Pyruvate dehydrogenase kinase 4) is localized to the mitochondrial matrix and participates in fatty acid oxidation as a key enzyme[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Studies found that Pdk4 showed increased expression in myocardial tissues of DM mice[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], and it could be used as a therapeutic target for DM[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] due to its role as a key target genes of the PPARα signaling pathway[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. In addition, specific expression of Pdk4 could induce insulin resistance, reduction in myocardial glucose oxidation and increase in fatty acid oxidation[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. To the contrary, suppression of Pdk4 activity could lead to reduced mitochondria-associated ER membranes (MAM) formation and improve insulin signal transduction through preventing the MAM-induced mitochondrial Ca2\u0026thinsp;+\u0026thinsp;accumulation[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Other than the role in mediating metabolic reprogramming, Pdk4 also has implications for cell respiration by playing a role in regulation of mitochondrial dynamics[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Hmgcs2 (3-hydroxy-3-methylglutaryl-CoA synthase 2) is also distributed to the mitochondrial matrix and acts as a rate-limiting enzyme in ketogenesis[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. George A.Cook et al. [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] found that Hmgcs2 was increasingly expressed in DCM rats, consistent with the present study. Another study noted significantly increased expression of Hmgcs2 enzyme in the right ventricle in cases of arrhythmogenic cardiomyopathy (AC), suggesting enhanced ketoacid metabolism, and it also reported concurrent elevation of plasm β-HB. The results indicated that up-regulation of Hmgcs2 enzyme was predictive of occurrence of major adverse cardiovascular events (MACE) and disease progression[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. However, there was a study which demonstrated reduced cardiac content of Hmgcs2 in non-diabetic patients with end-stage HF[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. We speculated that the discrepancy might be due to the difference in cardiac metabolic substrates between diabetic and non-diabetic cases[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Decr1 (2,4-dienoyl-CoA reductase 1) is a mitochondrial enzyme involved in degradation of poly-unsaturated fatty acids[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Most of the existing studies concentrated on its role in lipid metabolism in tumor cells[\u003cspan additionalcitationids=\"CR62\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], while only a few was performed in non-diabetic HF[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Therefore, further research is in demand to explore the role of Decr1 in DCM. Ivd (Isovaleryl-CoA dehydrogenase) is another mitochondrial enzyme with implications for metabolism of the branched chain amino acids leucine[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Previous research revealed that leucine-enriched diet was conducive to improving the cardiac injury and dysfunction caused by cancer cachexia[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] and anti-tumor drugs[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Furthermore, circulating levels of branched chain amino acids were proven as independently associated with the incidence of HF in diabetic patients[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe metabolic status and immune processes are interconnected[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Immune dysregulation is common in DCM and plays a role in disease progression. In the present study, we used the ImmuCellAI algorithm to analyze immune cell infiltration and found higher enrichment of multiple dendritic cells (Dendritic cells, MoDC, cDC1, pDC, and cDC2) in the CON group than the DCM group. Dendritic cells are specialized antigen-presenting cells that serve as important mediators of immune responses[\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e], and the number was reduced in both T1DM and T2DM patients[\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. It was reported that dendritic cells were protective immunomodulators playing a role during the healing from myocardial infarction (MI). In addition, dendritic cells tended to accumulate in infarct border zone after MI and simultaneously mediated the regulation of homeostasis by monocytes and macrophages[\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. In human infarcted myocardial tissues, the reduced number of dendritic cells was reported as associated with the recruitment of pro-inflammatory monocytes, increase in macrophages, impairment of reparative fibrosis, and the cardiac rupture after MI[\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. In all, dendritic cells protect the heart via regulating the recruitment of various types of immune cells. The current study also found that B cell, Marginal Zone B, and Memory B were highly abundant in the DCM group. B cells maintain the bridge between innate and adaptive immunity through their antigen-specific responses, and they are also conducive to sustaining the chronic inflammation in DCM[\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Animal experiments revealed that B cells regulated the composition of the cardiac leukocyte pool, and B cell-deficient mice had a smaller fibrotic area while a higher LVEF[\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Another study found that B cell depletion was accompanied by significant reductions in TNF-α, IL-1β, IL-18, and apoptosis in myocardial cells, and further introduction of B cells worsened inflammatory response and cardiac function[\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Collectively, B cells are critical for the pro-inflammatory environment of the failing heart tissue and myocardial injury. There were also some studies showing that increase in neutrophil-to-lymphocyte ratio was associated with the incidence of subclinical DCM[\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]; impaired Th/Treg balance and increased ventricular infiltration of T cells exacerbated the cardiac hypertrophy and fibrosis in T2DM[\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]; M1 macrophages potentiated DCM progression via secreting inflammatory factors to induce insulin resistance[\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMitochondrial metabolism can have a huge impact on the fate and function of immune cells. Correlation analysis of the study indicated that Pdk4, Hmgcs2, and Decr1 were positively associated Marginal Zone B while negatively associated with dendritic cells. In addition, Ivd was positively associated with CD8 Tem. This is consistent with our findings that dendritic cells had a lower enrichment in the DCM group than the CON group and had significant enrichment in B cells. The findings of the study deepen our understanding about the link between mitochondrial metabolism and immune cells in DCM.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study found significant differences between DCM and healthy myocardial tissues in terms of the expression of mitochondria-related genes (especially those involved in mitochondrial metabolism) and the abundance of infiltrating immune cells using bioinformatics analysis. Four hub MitoDEGs, including Pdk4, Hmgcs2, Decr1, and Ivd, were identified as potentially important for the link between mitochondrial metabolism and immune microenvironment, highlighting the presence of mitochondrial metabolic disorder, immune dysregulation, and their crosstalk in DCM. The mitochondrial metabolism and immune-related molecules found in this study may help uncover the potential pathogenesis of DCM and look for new targets for medical interventions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDCM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiabetic cardiomyopathy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCBI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational center for biotechnology information\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGEO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene expression omnibus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiferentially expressed genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGSEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene set enrichment analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Ontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMitoDEGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMitochondria-related DEGs\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProtein\u0026ndash;protein interaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHeart failure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCTD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComparative toxicogenomics database\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTranscription factors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emiRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMicroRNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePdk4\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePyruvate dehydrogenase kinase 4\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHmgcs2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e3-hydroxy-3-methylglutaryl-CoA synthase 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDecr1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e2,4-dienoyl-CoA reductase 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIvd\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIsovaleryl-CoA dehydrogenase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT1DM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eType 1 diabetes mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT2DM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eType 2 diabetes mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRAGE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceptor for advanced glycation end\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLeft ventricular\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCON\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFold-change\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSprague Dawley\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHFD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-fat diet\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTZ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStreptozotocin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLVEF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eleft ventricular ejection fraction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efraction shortening\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLVIDs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLeft ventricular internal diameters at systole\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLVIDd\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLeft ventricular internal diameters at diastole\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPARα\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eperixisome proliferation-activated receptor alpha\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMajor histocompatibility complex\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eToll-like receptors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBiological Process\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCellular Component\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMolecular Function\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHeart weight\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody weight\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTibia length\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMAM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMitochondria-associated ER membranes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earrhythmogenic cardiomyopathy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eβ-HB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebeta-hydroxybutyrate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMACE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emajor adverse cardiovascular events\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emyocardial infarction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTNF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor necrosis factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterleukin.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe animal experiment of the study was approved by the Medical Ethics Committee of the 2nd Affiliated Hospital for Harbin Medical University (Ethics approval number: SYDW2021-055).\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\u003eThe datasets analysed during the current study are available in the GEO database (https://www.ncbi.nlm.nih.gov/geo/)and MitoCarta3.0 (http://www.broadinstitute.org/mitocarta), which is an updating, openly\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eavailable for free download, authoritative database of mitochondrial proteome including sub-mitochondrial localization and MitoPathway annotations.\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 study was funded by the National Natural Science Foundation of China (grant 81770255 to Y Zhang) and the Fund of Key Laboratory of Myocardial Ischemia, Ministry of Education (grant KF202103 to\u0026nbsp;Y Zhang, grant KF202114 to YX Zhang, grant KF202204 to C Peng) .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC Peng and YX Zhang were responsible for the overall design of the study, processing of bioinformatics data, animal modeling, experimental validation, and writing of the manuscript. XY Lang was responsible for the statistical work towards experimental data. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the Key Laboratory of Myocardial Ischemia Department of Harbin Medical University for providing laboratory animal resource core facilities. We thank all contributors to the GEO database and developers of the MitoCarta3.0 database, Broad Institute of MIT and Harvard. We also thank developers of the ImmuCellAI tool for predicting abundance of immune cell populations, the research team of Professor Anyuan Guo from Huazhong University of Science and Technology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC Peng and YX Zhang contributed equally\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Cardiology, the Second Affiliated Hospital of Harbin Medical University,\u0026nbsp;Harbin 150001,\u0026nbsp;China\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKey Laboratory of Myocardial Ischemia, Ministry of Education, Harbin Medical University,\u0026nbsp;Harbin 150001,\u0026nbsp;China\u003c/p\u003e\n\u003cp\u003eCheng Peng, Yanxiu Zhang, Xueyan Lang \u0026amp; Yao Zhang\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSaeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, 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\u003cstrong\u003e1\u003c/strong\u003e(2):e45.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Diabetic cardiomyopathy, Mitochondria, Metabolism, Immune infiltration, Immunometabolism, Bioinformatics analysis","lastPublishedDoi":"10.21203/rs.3.rs-2148339/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2148339/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDiabetic cardiomyopathy (DCM) is one of the common cardiovascular complications of diabetes and a leading cause of death in diabetic patients. Mitochondrial metabolism and immune-inflammation are key for DCM pathogenesis, but their crosstalk in DCM remains an open issue. This study explored the separate roles of mitochondrial metabolism and immune microenvironment and their crosstalk in DCM with bioinformatics.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eDCM chip data (GSE4745, GSE5606, and GSE6880) were obtained from NCBI GEO, while mitochondrial gene data were downloaded from MitoCarta3.0 database. Differentially expressed genes (DEGs) were screened by GEO2R and processed for GSEA, GO and KEGG pathway analyses. Mitochondria-related DEGs (MitoDEGs) were obtained. A PPI network was constructed, and the hub MitoDEGs closely linked to DCM or heart failure(HF) were identified with CytoHubba, MCODE and CTD scores. Transcription factors and target miRNAs of the hub MitoDEGs were predicted with Cytoscape and miRWalk database, respectively, and a regulatory network was established. The immune infiltration pattern in DCM was analyzed with ImmuCellAI, while the relationship between MitoDEGs and immune infiltration abundance was investigated using Spearman method. A rat model of DCM was established to validate the expression of hub MitoDEGs and their relationship with cardiac function.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMitoDEGs in DCM were significantly enriched in pathways involved in mitochondrial metabolism, immunoregulation, and collagen synthesis. Nine hub MitoDEGs closely linked to DCM or HF were obtained. Immune analysis revealed significantly increased infiltration of B cells while decreased infiltration of DCs in immune microenvironment of DCM. Spearman analysis demonstrated that the hub MitoDEGs were positively associated with the infiltration of pro-inflammatory immune cells, but negatively associated with the infiltration of anti-inflammatory or regulatory immune cells. In the animal experiment, 4 hub MitoDEGs (Pdk4, Hmgcs2, Decr1, and Ivd) showed an expression trend consistent with bioinformatics analysis result. Additionally, the up-regulation of Pdk4, Hmgcs2, Decr1 and the down-regulation of Ivd were distinctly linked to reduced cardiac function.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study unraveled the interaction between mitochondrial metabolism and immune microenvironment in DCM, providing new insights into the research on potential pathogenesis of DCM and the exploration of novel targets for medical interventions.\u003c/p\u003e","manuscriptTitle":"Role of mitochondrial metabolic disorder and immune infiltration in diabetic cardiomyopathy: new insights from bioinformatics analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-10-27 14:41:15","doi":"10.21203/rs.3.rs-2148339/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2022-10-22T06:42:30+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-10-22T06:02:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-10-15T11:53:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Translational Medicine","date":"2022-10-12T12:29:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"163421cb-20a2-4b95-bd21-277c46d64366","owner":[],"postedDate":"October 27th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T18:49:10+00:00","versionOfRecord":{"articleIdentity":"rs-2148339","link":"https://doi.org/10.1186/s12967-023-03928-8","journal":{"identity":"journal-of-translational-medicine","isVorOnly":false,"title":"Journal of Translational Medicine"},"publishedOn":"2023-02-01 18:39:16","publishedOnDateReadable":"February 1st, 2023"},"versionCreatedAt":"2022-10-27 14:41:15","video":"","vorDoi":"10.1186/s12967-023-03928-8","vorDoiUrl":"https://doi.org/10.1186/s12967-023-03928-8","workflowStages":[]},"version":"v1","identity":"rs-2148339","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2148339","identity":"rs-2148339","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}