Exploring the Genetic Landscape of Sepsis-Induced Cardiomyopathy: A Comprehensive Analysis

Exploring the Genetic Landscape of Sepsis-Induced Cardiomyopathy: A Comprehensive 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 Exploring the Genetic Landscape of Sepsis-Induced Cardiomyopathy: A Comprehensive Analysis Jinsen Weng, Xiaojing Wang, Xiuling Shang, Jun Xiao, Yong Ye, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3802999/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Sepsis poses a severe threat in critical care, often leading to septic cardiomyopathy. This study aimed to explore the genes related to mitochondrial damage in septic cardiomyopathy, observe their changes during sepsis, and analyze the possible pathogenesis of this disease. Methods: Bioinformatics methods were used to identify differentially expressed genes (DEGs) and enrichment pathways associated with mitochondrial damage in patients with septic cardiomyopathy. Subsequently, clinical specimens and cardiomyocytes were verified and compared to clarify the expression of these genes and their change trend in the pathogenesis of septic myocardial injury to explore the possible relationship between these genes and septic myocardial mitochondrial dysfunction. Results: In this study, using diverse datasets, 398 differentially expressed genes (DEGs) related to sepsis were identified, and 11 key genes (GNAS, MRPL2, TIMM17b, SLC25A3, SDHA, PRPF6, LMF2, IMMT, CS, UCP2, and CASP2) were significantly associated with these genes. Functional analysis highlighted the importance of the TIM23 complex in septic mitochondrial injury. Real-time fluorescence quantitative PCR was performed on 11 genes and TIMM23 expression in 24-48 hours in clinical specimens, and the expression of TIMM17b and TIMM23 was increased in the sepsis group, while the expression of the other 10 DEGs was decreased. Further verification via cell experiments revealed that the expression of 11 DEGs and 5 TIM23 complex member genes, TIMM23, TIMM17A, TIMM44, PAM16 and TIMM50, increased in the 6-hour group, while their expression decreased significantly in the 24-hour group; moreover, the expression of only TIMM17b was still greater than that in the normal control group. The expression of other genes was lower than or close to that of the normal control group. Conclusion: This integrative study not only provides a comprehensive overview of DEGs associated with sepsis but also emphasizes the importance of the TIM23 complex. The identified genes and pathways offer potential targets for further mechanistic studies and therapeutic interventions in the context of sepsis-related complications. Septic cardiomyopathy Mitochondrial damage DEGs TIM23 complex Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Sepsis, an intricate outcome of host response dysregulation to infection, precipitates a cascade of systemic multiorgan failures prominently exemplified by the onset of septic cardiomyopathy (SCM). SCM, which is characterized by its ability to prevent cardiomyocyte ischemia, results in a consequential decrease in the left heart ejection fraction (EF) [ 1 ] , indicating an ostensibly recoverable reduction in systolic function. The etiology of SCM has not been fully elucidated, as the initial stages of sepsis are characterized by myocardial dysfunction attributed to inflammatory stress rather than mitochondrial compromise [ 2 , 3 ] . In advanced sepsis, the pronounced depletion of cardiomyocyte ATP exacerbates mitochondrial dysfunction, the primary instigator of myocardial debilitation [ 4 ] . Cardiomyocytes, known for their profusion of mitochondria [ 5 ] , rely heavily on oxidative phosphorylation within these cellular powerhouses [ 6 ] . SCM intricately correlates with mitochondrial damage within cardiomyocytes [ 6 , 7 ] . Notably, mitochondrial morphology is markedly impaired in parallel with dysfunction in the electron transport chain (ETC) during sepsis, thereby accentuating the broader landscape of mitochondrial dysfunction [ 8 – 10 ] . Dysregulation of ETC function [ 11 – 13 ] prompts substantial alterations in mitochondrial activity, ultimately culminating in reduced adenosine triphosphate (ATPase) synthesis [ 14 ] and activation of the apoptotic pathway [ 15 ] . The preservation of mitochondrial homeostasis, pivotal for recovery from sepsis [ 16 , 17 ] , involves mitochondrial biogenesis as a cellular program regulating energy production and mediating organelle interactions through the synthesis of new organelles and components [ 18 – 20 ] . Understanding the intricacies of mitochondrial damage in septic cardiomyocytes is highly important for preserving mitochondrial homeostasis and, consequently, mitigating the impacts of SCM. This study strives to discern, through a bioinformatics lens, 11 genes featuring enriched pathways pivotal for mitochondrial injury in SCM. Concurrently, scrutiny of clinical specimens and cellular experiments endeavors to trace the expression patterns and trends of target genes during the developmental trajectory of SCM, thereby enriching our understanding of potential mechanisms and therapeutic targets relevant to septic cardiomyopathy. Methods 2.1 Bioinformatics analysis 2.1.1 Data Retrieval The DNA microarray and RNA-seq datasets utilized were sourced from the Gene Expression Omnibus (GEO) Database ( http://www.ncbi.nlm.nih.gov/geo ) and the Molecular Signatures Database (MSigDB) ( https://www.gsea-msigdb.org/gsea/msigdb/ ). The datasets included GSE5663, GSE137342, GSE185754, GSE171546, and MOOTHA MITOCHONDRIA. The dataset details are presented in Table 1 . The expression-based probes were converted to gene symbols via platform annotation files. The GSE5663 and GSE137342 datasets were processed using the limmaR package, integrating the experimental and control groups to mitigate batch effects. Similarly, the GSE185754 and GSE171546 datasets were processed for concordance. VennDiagramR facilitated intersecting gene analysis, identifying shared genes among the human sepsis, human mitochondrial, and sepsis mouse cardiomyocyte gene expression sets. Table 1 Dataset source Dataset Numbe Platform LPS Group/Sepsis Control Group/Healthy Annotation Sample Type GSE5663 GPL80 12 0 Affymetrix Human Full Length HuGeneFL Array[Hu6800] Human spleen GSE137342 GPL10558、GPL16686 27 12 Illumina HumanHT-12 V4.0 expression beadchip、Affymetrix Human Gene 2.0 ST Array[HuGene-2_0-st] Human Blood MOOTHA_MITOCHONDRIA M9577 AFFY_HG_U133 - - mitochondr_HG-U133A_probes Human muscle GSE185754 GPL24247 5 5 Illumina NovaSeq 6000 Murine myocardium GSE171546 GPL24247 10 10 Illumina NovaSeq 6000 Murine myocardium 2.1.2 Differential expression analysis The gene sets chosen for differential expression analysis were analyzed using the limmaR and reshape2R packages. Differentially expressed genes (DEGs) were identified in the shared gene dataset based on a log fold change (FC) > 1 or <-1, with adjusted p values < 0.05, < 0.01, and < 0.001. This rigorous screening categorized DEGs into up- and downregulated groups. 2.1.3 Functional and pathway analysis Pathway analysis relied on the cellular component from the Gene Ontology (GO) database. GO analysis, performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID, https://david.ncifcrf.gov/ ), was instrumental in exploring the biological processes associated with the DEGs. GSVA and pathway difference analysis were performed using the limma, reshape2, GSEABase, and GSVA packages in R to reveal the mitochondrial signaling pathways associated with the DEGs. Visualization was achieved through the pheatmap and ggpubr packages. 2.2 Clinical Data Collection The study cohort included 74 sepsis patients diagnosed within 24 hours of admission and a control group of 31 individuals. Peripheral blood specimens were collected within 48 hours after admission and stored at -80°C. Clinical case histories were gathered, encompassing demographic details, infection specificity, SOFA and APACHE II scores, prognostic indicators, and relevant biomarkers. 2.3 Experimental reagents The HL-1 cells, LPS, MEM, FBS, and penicillin/streptomycin mixture were obtained from Xiamen Taijing Biotechnology Co. Additional reagents (PBS reagent, Trizol reagent, Evo M-MLV kit, and SYBR Green Pro TaqHS premixed kit) were obtained from Accurate Biology, and the primers were designed and synthesized by Shangya Biological Company. 2.4 Cell Culture and Processing HL-1 mouse cardiomyocytes were cultured in MEM, passaged at the optimal growth density, and selected for intervention in the 5th generation (P6). The cells were divided into control and LPS-treated groups and subjected to inflammatory stimulation with 10 µg/ml LPS for 6 or 24 hours. The collected cells were lysed using Trizol reagent after washing with frozen PBS. 2.5 RNA quantification and RT‒qPCR Total RNA was extracted from patient blood specimens, reverse transcription, and quantitative PCR were conducted following established protocols, and the PCR primer sequences are shown in Table 2 . For mouse cardiomyocytes, similar procedures were followed, and the PCR primer sequences are shown in Table 3 . GAPDH served as the housekeeping gene, and relative mRNA expression was calculated using the 2-ΔΔCT method. Table 2 Primer design (human) Target gene Sequence(5'to3') base number GAPDH-F TGGAAAGCTGTGGCGTGATG 20 GAPDH-R TACTTGGCAGGTTTCTCCAGG 21 UCP2-F GGAGGTGGTCGGAGATACCAA 21 UCP2-R ACAATGGCATTACGAGCAACAT 22 TIMM17B-F ATGGAGGAGTACGCTCGGG 19 TIMM17B-R CCGATGACACCCATAGTGAAGG 22 SLC25A3-F TGGTGTTCGTGGTTTGGCTAA 21 SLC25A3-R GATGTGCGCCAGAGATAAGTATT 23 SDHA-F CAGCATGTGTTACCAAGCTGT 21 SDHA-R GGTGTCGTAGAAATGCCACCT 21 PRPF6-F CACCACGCGGTCAGACATT 19 PRPF6-R TCCCCAACGGTTCTCTTGC 19 MRPL2-F CGAATCCGGGTGCATGGTATT 21 MRPL2-R CTCAAAGGGTCCTGACTTGGT 21 LMF2-F CTCACCTACCACTACGAGACC 21 LMF2-R GCACAGCGATCTCAATTAGGAAG 23 IMMT-F CGATTCAGTCGGGTCCACTAA 21 IMMT-R AGCTGGAGTATCTCCCTTTTGT 22 GNAS-F TGCAAGGAGCAACAGCGAT 19 GNAS-R GCGGCCACAATGGTTTCAAT 20 CS-F GGTGGCATGAGAGGCATGAA 20 CS-R TAGCCTTGGGTAGCAGTTTCT 21 CASP2-F AGCTGTTGTTGAGCGAATTGT 21 CASP2-R AGCAAGTTGAGGAGTTCCACA 21 TIMM23-F GCCATTGACGCCATGAACAG 20 TIMM23-R CTGCATCCTTTGGCTGGTCT 20 Table 3 Primer design (mouse) Target gene Sequence(5'to3') base number GAPDH-F TGGAAAGCTGTGGCGTGATG 20 GAPDH-R TACTTGGCAGGTTTCTCCAGG 21 UCP2-F GGAGAGTCAAGGGCTAGTGC 20 UCP2-R TGACAGAGTCGTAGAGGCCA 20 TIMM17B-F CAGGCTATCAAGGGCTTCCG 20 TIMM17B-R ACACTGCAAAGCTTCCTCCA 20 SLC25A3-F GTGGCTTTGGTGGGGTCTTA 20 SLC25A3-R ACCACGAACGCCATCTTCTT 20 SDHA-F TCGACAGGGGAATGGTTTGG 20 SDHA-R GGACTCCTTCCGAGCTTCTG 20 PRPF6-F GTCATCTCCCGCAGTCTGTT 20 PRPF6-R CACTCTACTGCTCGGCTCAG 20 MRPL2-F AGTGCATGGTATCGGTGGAG 20 MRPL2-R TTCCGTGGCAATGATCCAGC 20 LMF2-F GCAACAGCACATCATCCTCTC 21 LMF2-R AGAACTTCCGTGACCATCCTT 21 IMMT-F TCCTATTGTATTGAGCATGGTGAC 24 IMMT-R CTTGAGTGGTTCCTATTCCTACG 23 GNAS-F2 CAGCAGCTACAACATGGTCAT 21 GNAS-R2 AGGAAGAGAATCACAGAGATGGT 23 CS-F ACAGTGAAAGCAACTTCGCC 20 CS-R GTCAATGGCTCCGATACTGC 20 CASP2-F CTGCTGAGCGAGCTGTTAGA 20 CASP2-R AGGGCTTCACAGAAGGCATC 20 TIMM44-F AGAACTCCAAAGGCGAGGTG 20 TIMM44-R CCAGGCAGCATAAGGGTTGA 20 TIMM50-F TAAGAAGCGTCCAGGCATCG 20 TIMM50-R GATGAAACCGTGAGGGTCCA 20 PAM16-F AGCCCAGCAGATTCTCAACG 20 PAM16-R CACGGACAACCTTTGACTGC 20 TIMM23-F TGAATATGGTGACTAGGCAAGGA 23 TIMM23-R GCTACTGTGTTGAGGTCATCTTC 23 TIMM17A-F CACTAGCGGTGCCTTAACAG 20 TIMM17A-R GCAAGATACCAGCTCCTTCAAT 22 2.6 Ethical approval The academic provenance of the data was determined from bioinformatics databases, namely, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), which adhered to the requisite standards. Clinical and cellular experiments transpired within the confines of the laboratory at Fujian Provincial Hospital, due to adherence to Ethics No. K2021-10-001. 2.7 Statistical analysis The expression data, encapsulated as the mean ± standard deviation from a triad of independent experiments, were subjected to meticulous statistical scrutiny. The rubric for group comparisons delineated between those adhering to a normal distribution, appraised via the independent samples t test, and those deviating from this norm, subjected to the Mann‒Whitney U test. A criterion met at p < 0.05 was considered to indicate statistical significance, underscoring the rigor and robustness of the findings. Results 3.1 Differential gene expression screening Five distinct datasets, namely, GSE5663, GSE137342, GSE185754, GSE171546, and MOOTHA_MITOCHONDRIA, were meticulously employed in this investigation. The collective cohort included 39 sepsis patients and 12 healthy controls from the GSE5663 and GSE137342 cohorts, collectively comprising the human sepsis gene expression set. Subsequently, this dataset was amalgamated with the MOOTHA_MITOCHONDRIA dataset, resulting in the formulation of the human sepsis mitochondrial gene expression set, encompassing a total of 450 genes. In parallel, a mouse sepsis cardiomyocyte gene expression set derived from GSE185754 and GSE171546 comprising 15 septic mice and 15 healthy controls was established, totaling 54,123 genes. The integration of the human sepsis mitochondrial gene expression set and the mouse sepsis cardiomyocyte gene expression set facilitated the identification of 409 mitochondrial coexpressed genes in both human and mouse sepsis. Subsequent intersection with the healthy human gene expression set from the GSE5663 and GSE137342 datasets revealed 398 differentially expressed genes (DEGs) in humans and mice. The resulting DEGs were subjected to visualization for distribution analysis (Fig. 1A). A comprehensive examination of these DEGs revealed 11 significantly associated genes (Fig. 1B, Fig. 1C), namely, GNAS, MRPL2, TIMM17b, SLC25A3, SDHA, PRPF6, LMF2, IMMT, CS, UCP2, and CASP2. The expression patterns of these genes in the sepsis group versus the healthy control group were explored (Fig. 1D), revealing a statistically significant difference in expression, with a p value less than 0.01. 3.2 Functional and pathway enrichment analysis To gain a nuanced understanding of the functional attributes inherent to the identified DEGs, a Gene Ontology (GO) analysis was meticulously executed (Fig. 2A). In terms of molecular function (MF), the enrichment of DEGs was most significant for the term "hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds, in linear amidines" (GO:0016813), while the enrichment was greatest for the term "electron transfer activity" (GO:0009055). In terms of the cellular component (CC), the enrichment of DEGs was most significant for the entry "specific granule lumen" (GO:0035580). The highest number of DEGs was found in the entries that were concentrated in the "vesicle lumen" (GO:0031983), "cytoplasmic vesicle lumen" (GO:0060205) and "secretory granule lumen" (GO:0034774) categories. In terms of biological processes (BP), DEGs were most significantly enriched in the "innate immune response in mucosa" (GO:0002227), while the largest number of DEGs was enriched in the "defense response to bacterium" (GO:0042742). Subsequently, the 11 identified DEGs were subjected to gene set variation analysis (GSVA) for the enrichment of Gene Ontology terms. This analysis revealed that mitochondria-related pathways exhibited the most significant upregulation (Fig. 2B). These pathways encompassed mitochondrial DNA replication, the TIM22 complex, protein insertion into the mitochondrial inner membrane, the mitochondrial intermembrane space protein transport complex, and the pivotal TIM23 complex, which is renowned for its role as the mitochondrial inner membrane anterior sequence translocase complex. The strategic choice of investigating genes related to the constituents of the TIM23 complex was underpinned by its pivotal function as the primary protein precursor transport channel in the mitochondrial inner membrane. The overall protocol of this study is shown in Fig. 3. 3.3 Validation in Clinical Specimens The clinical baseline data of 74 sepsis patients are meticulously outlined in Table 4. Peripheral blood specimens from these patients, alongside those from 31 healthy individuals, were subjected to rigorous reverse transcription–quantitative polymerase chain reaction (RT–qPCR) analysis. Following stringent quality control measures and the exclusion of data deemed unsatisfactory, a 1:1 intergroup propensity score matching was performed (Fig. 4). The subsequent RT‒qPCR results revealed elevated expression levels of TIMM17b and TIMM23 in sepsis patients compared to those in healthy controls. Moreover, the remaining 10 DEGs exhibited a decrease in expression, indicating statistically significant differences between the two cohorts (Fig. 5). Table 4 The clinical baseline data of 74 sepsis patients Baseline characteristics of 74 subjects Variables Total =74 Age, year (median, range) 60（15-96） Gender Male 48（64.9%） Female 26（35.1%） Basic disease Diabetes 22（29.7%） Cardiovascular and cerebrovascular diseases 26（35.1%） Malignant tumor 20（27.0%） Immune deficiency 2（4.1%） Else 6（8.1%） Focus of infection Abdominal infection 25（33.8%） Pulmonary infection 23（31.1%） Hepatobiliary duct infection 11（14.9%） Urinary system infection 7（9.5%） Else 8（10.8%） Prognosis Survivors 48（64.9%） Poor 26（35.1%） SOFA score (median, range) 10（2-21） APACHEII score (median, range) 22（5-43） PCT (median, range) 38.18（0.25-100.00） BNP (median, range) 8695（22-35000） cTnI (median, range) 17.64（0.01-1078.00） EF value of cardiac ultrasound(median, range) 56（30-77） 3.4 Validation in Mouse Cardiomyocytes The RT‒qPCR results revealed that the expression profiles of the 11 DEGs (Fig. 6A-K) in HL-1 cells subsequent to 6 hours of lipopolysaccharide (LPS) treatment were markedly and significantly upregulated relative to those in the control group. However, a discernible reduction in DEG expression was observed at the 24-hour time point compared with that in the 6-hour LPS-treated group. Importantly, except for TIMM17b, the expression levels of DEGs at the 24-hour juncture were significantly lower than those in the control group. The RT‒qPCR results revealing the expression profiles of TIM23 complex member genes (TIMM23, TIMM17A, TIMM44, PAM16, and TIMM50) in HL-1 cells after 6 hours of LPS treatment revealed a significant increase relative to that in the control group in both temporal contexts. After 24 hours of LPS treatment, a significant reduction in the expression of TIM23 complex member genes was evident in both groups compared with that in the LPS-treated 6-hour group, indicating that the two temporal cohorts were distinct. Despite the significant reduction relative to those in the 6-hour cohort, the expression levels of TIM23 complex member genes within the 24-hour cohort were still elevated relative to those in the control group, with statistically significant differences noted in the cases of TIMM23, TIMM44, and PAM16; however, no such statistically significant differences were detected for TIMM17A or TIMM50. Discussion Mitochondrial dysfunction in mid-to-late-stage sepsis potentially exacerbates septic cardiomyopathy by disrupting energy metabolism equilibrium. Given that energy failure in septic cardiomyocytes is a primary contributor to myocardial injury, we conducted a bioinformatics study on mitochondrial damage in septic cardiomyopathy. The comprehensive bioinformatics analysis conducted in this study provided key insights into the molecular mechanisms associated with sepsis, shedding light on DEGs and their potential functional implications. The integration of diverse datasets allowed for robust exploration of the genomic landscape, providing a holistic view of gene expression changes in both human sepsis patients and a murine model of septic cardiomyopathy. The proteins encoded by several of these DEGs, such as SLC25A3 [21, 22] , CS [23, 24] , SDHA [25] , IMMT [26-29] , UCP2 [30] , and LMF2 [31] , play pivotal roles in mitochondrial electron transport chain (ETC) function and adenosine triphosphate (ATP) production. Additionally, SLC25A3 [32] and SDHA [33-35] may influence the inflammatory response of macrophages, impacting cardiomyocyte mitochondrial function. The MRPL2 and PRPF6 proteins are associated with mRNA transcription and protein translation [36] , while the GNAS [37] and CASP2 [38-40] gene products are involved in cardiomyocyte proliferation, autophagy, and apoptosis. This collective evidence suggests that these DEGs may collectively influence mitochondrial function, affecting aspects such as ETC function, ATP production, mRNA transcription, protein coding, and cardiomyocyte proliferation and apoptosis, ultimately contributing to septic myocardial injury. The subsequent functional and pathway enrichment analysis delved into the biological relevance of the identified DEGs. Gene Ontology analysis revealed significant enrichment in molecular functions related to "hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds", linear amidines" and "electron transfer activity". Notably, the pronounced enrichment in "defense against bacteria" and "innate immune response in mucosa" within the biological process domain underlines the intricate interplay between mitochondrial function and the host response during sepsis. Gene set variation analysis (GSVA) further confirmed the involvement of mitochondria-related pathways, particularly emphasizing the upregulation of critical processes such as mitochondrial DNA replication and protein insertion into the mitochondrial inner membrane. The focus on the TIM23 complex, a pivotal player in mitochondrial protein transport, suggests a potential regulatory role in septic conditions [41, 42] . Comprising a core pore (Tim17, Tim23, and Tim50) responsible for precursor protein translocation and an import motor (mtHSP70, Tim44, Tim14, Tim16, and Mge1) facilitating ATP-dependent protein translocation [43] , the TIM23 complex plays a crucial role in mitochondrial function. Among the 11 DEGs, TIMM17b, the only TIMM17b protein with elevated expression, is responsible for the expression of one of the subtypes of Tim17, and the TIM23 complex is the TIM23 complex B type, which affects mitochondrial function. For example, it is essential to maintain the normal functions of electron transport chain complex activity, organelle morphology and iron–sulfur tufting biogenesis [44] . Bioinformatics analysis revealed a unique pattern in the sepsis group, with TIMM17b being the only DEG exhibiting increased expression, while the others displayed decreased expression. Interestingly, the TIM23 complex pathway, which was enriched in DEGs, exhibited increased expression. Further examination of clinical specimens and LPS-treated 24 h cardiomyocytes validated these findings, revealing consistency across the raw signaling results, clinical specimens, and cellular experiments. Notably, in the group of cardiomyocytes treated with LPS for 6 hours, the experimental results were inconsistent with the results of bioinformatics analysis. The temporal expression patterns of the DEGs and TIM23 complex members suggested a correlation with sepsis development stage. The expression levels of these genes were elevated in the early stages, indicating temporary mitochondrial homeostasis due to compensatory mechanisms. However, as sepsis progresses, a decrease in the expression of these genes indicates escalating mitochondrial damage and ensuing energy metabolism malfunction, exacerbating myocardial dysfunction. The consistent increase in TIMM17b in the early stage of sepsis, despite a subsequent decrease, points toward its association with inflammatory stress [45, 46] and TIM23 complex function [44] . This elevation potentially enhances TIM23 complex function, facilitating efficient protein translocation into the mitochondrial inner membrane or matrix, thereby sustaining compensatory mitochondrial and cardiomyocyte functions. As sepsis progresses, the expression of TIMM17b and TIM23 complex members decreases, and the expression of the remaining 10 DEGs decreases, underscoring the inability to maintain mitochondrial homeostasis, culminating in dysfunctional energy metabolism and myocardial injury. This temporal correlation was particularly evident in cellular experiments, emphasizing the need for further targeted investigations. While this study provides valuable insights into mitochondrial damage in sepsis, the internal association mechanisms among the 11 DEGs and their interactions remain unexplored. Future investigations should explore these intricate regulatory networks to determine the underlying mechanisms of mitochondrial dysfunction in sepsis. Conclusions In conclusion, this integrative study not only provides a comprehensive overview of DEGs associated with sepsis but also underscores the intricate involvement of mitochondrial pathways, particularly the importance of the TIM23 complex. The identified genes and pathways offer potential targets for further mechanistic studies and therapeutic interventions in the context of sepsis-related complications. Abbreviations DEGs Differentially expressed genes DNA Deoxyribonucleic acid TIM23 complex Translocase of inner mitochondrial membrane 23 complex SCM Septic cardiomyopathy EF Ejection fraction ATP Adenosine triphosphate ETC Electron transport chain GEO Gene expression omnibus FC Fold change GO Gene ontology DAVID Database for annotation, visualization, and integrated discovery LPS Lipopolysaccharide MEM Minimum essential medium FBS Fetal bovine serum RT‒qPCR Reverse transcription-quantitative polymerase chain reaction RNA Ribonucleic acid mRNA Messenger ribonucleic acid GAPDH Glyceraldehyde 3-phosphate dehydrogenase UCP2 Mitochondrial uncoupling protein 2 TIMM17B Translocase of inner mitochondrial membrane 17B SLC25A3 Solute carrier family 25 member 3 SDHA Succinate dehydrogenase complex flavoprotein subunit A PRPF6 pre-mRNA processing factor 6 MRPL2 Mitochondrial ribosomal protein L2 LMF2 Lipase maturation factor 2 IMMT Inner membrane mitochondrial protein GNAS Guanine nucleotide-binding protein G(s) subunit alpha isoforms Xlas CS Citrate synthase CASP2 Caspase 2 TIMM23 Translocase of inner mitochondrial membrane 23 TIMM44 Translocase of inner mitochondrial membrane 44 TIMM50 Translocase of inner mitochondrial membrane 50 PAM16 Presequence Translocase-Associated Motor 16 Homolog TIMM17A Translocase of inner mitochondrial membrane 17A MF Molecular Function CC Cellular Component BP Biological Processes GSVA Gene Set Variation Analysis Declarations Ethics approval and consent to participate The academic provenance of the data was determined from bioinformatics databases, namely, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), which adhered to the requisite standards. Clinical and cellular experiments transpired within the confines of the laboratory at Fujian Provincial Hospital, due to adherence to Ethics No. K2021-10-001. Consent for publication Not applicable. Availability of data and materials All data analysed during this study are included in the websites mentioned above. Competing interests The authors have no competing interests to declare. Funding 1.The Young-Middle-aged Backbone Talent Training Program of Fujian Provincial Health organization (2021GGA003) 2. Joint Funds for the innovation of science and Technology，Fujian province（Grant number：2021Y91020276）； Authors' contributions JSW, XJW, RGY and YY designed the study. Data analysis were performed by XJW and JX. XJW and JYS carried out the experiments. XJW and JX wrote the first draft. JSW and XLS critically revised the manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable References Hollenberg SM, Singer M. Pathophysiology of sepsis-induced cardiomyopathy[J]. Nat Rev Cardiol 2021,18(6):424–34. Smeding L, van der Laarse WJ, van Veelen TA et al. Early myocardial dysfunction is not caused by mitochondrial abnormalities in a rat model of peritonitis[J]. J Surg Res 2012,176(1):178–84. Lv X, Wang H. Pathophysiology of sepsis-induced myocardial dysfunction[J]. Mil Med Res 2016,3:30. Hotchkiss RS, Nicholson DW. Apoptosis and caspases regulate death and inflammation in sepsis[J]. Nat Rev Immunol 2006,6(11):813–22. Barth E, Stämmler G, Speiser B et al. Ultrastructural quantitation of mitochondria and myofilaments in cardiac muscle from 10 different animal species including man[J]. J Mol Cell Cardiol 1992,24(7):669–81. Preau S, Vodovar D, Jung B, et al. Energetic dysfunction in sepsis: a narrative review[J]. Ann Intensive Care. 2021;11(1):104. Cimolai MC, Alvarez S, Bode C et al. Mitochondrial Mechanisms in Septic Cardiomyopathy[J]. Int J Mol Sci 2015,16(8):17763–78. Parker MM, Suffredini AF, Natanson C et al. Responses of left ventricular function in survivors and nonsurvivors of septic shock[J]. J Crit Care 1989,4(1):19–25. Shang X, Lin K, Zhang Y, et al. Mst1 deletion reduces septic cardiomyopathy via activating Parkin-related mitophagy[J]. J Cell Physiol. 2020;235(1):317–27. Shang X, Li J, Yu R, et al. Sepsis-related myocardial injury is associated with Mst1 upregulation, mitochondrial dysfunction and the Drp1/F-actin signaling pathway[J]. J Mol Histol. 2019;50(2):91–103. Fink MP. Bench-to-bedside review: Cytopathic hypoxia[J]. Crit Care 2002,6(6):491–9. Singer M. Mitochondrial function in sepsis: acute phase versus multiple organ failure[J]. Crit Care Med 2007,35(9 Suppl):S441–8. Larsen FJ, Schiffer TA, Weitzberg E et al. Regulation of mitochondrial function and energetics by reactive nitrogen oxides[J]. Free Radic Biol Med 2012,53(10):1919–28. Brealey D, Brand M, Hargreaves I et al. Association between mitochondrial dysfunction and severity and outcome of septic shock[J]. Lancet 2002,360(9328):219–23. Zhang H, Feng YW, Yao YM. Potential therapy strategy: targeting mitochondrial dysfunction in sepsis[J]. Mil Med Res. 2018;5(1):41. Li G, Fu T, Wang W et al. Pretreatment with Kahweol Attenuates Sepsis-Induced Acute Lung Injury via Improving Mitochondrial Homeostasis in a CaMKKII/AMPK-Dependent Pathway[J]. Mol Nutr Food Res 2023,67(19):e2300083. Zhu H, Wang J, Xin T et al. DUSP1 interacts with and dephosphorylates VCP to improve mitochondrial quality control against endotoxemia-induced myocardial dysfunction[J]. Cell Mol Life Sci 2023,80(8):213. Kelly DP, Scarpulla RC. Transcriptional regulatory circuits controlling mitochondrial biogenesis and function[J]. Genes Dev 2004,18(4):357–68. Nisoli E, Carruba MO. Nitric oxide and mitochondrial biogenesis[J]. J Cell Sci 2006,119(Pt 14):2855–62. Shang X, Zhang Y, Xu J, et al. SRV2 promotes mitochondrial fission and Mst1-Drp1 signaling in LPS-induced septic cardiomyopathy[J]. Aging. 2020;12(2):1417–32. Boulet A, Vest KE, Maynard MK et al. The mammalian phosphate carrier SLC25A3 is a mitochondrial copper transporter required for cytochrome c oxidase biogenesis[J]. J Biol Chem 2018,293(6):1887–96. Vest KE, Leary SC, Winge DR et al. Copper import into the mitochondrial matrix in Saccharomyces cerevisiae is mediated by Pic2, a mitochondrial carrier family protein[J]. J Biol Chem 2013,288(33):23884–92. Macgarvey NC, Suliman HB, Bartz RR et al. Activation of mitochondrial biogenesis by heme oxygenase-1-mediated NF-E2-related factor-2 induction rescues mice from lethal Staphylococcus aureus sepsis[J]. Am J Respir Crit Care Med 2012,185(8):851–61. Protti A, Fortunato F, Artoni A, et al. Platelet mitochondrial dysfunction in critically ill patients: comparison between sepsis and cardiogenic shock[J]. Crit Care. 2015;19(1):39. Gobelli D, Serrano-Lorenzo P, Esteban-Amo MJ et al. The mitochondrial succinate dehydrogenase complex controls the STAT3-IL-10 pathway in inflammatory macrophages[J]. iScience,2023,26(8):107473. Tombo N, Imam AA, Feng Y, et al. Cardiac ischemia/reperfusion stress reduces inner mitochondrial membrane protein (mitofilin) levels during early reperfusion[J]. Free Radic Biol Med. 2020;158:181–94. Kondadi AK, Anand R, Reichert AS. Cristae Membrane Dynamics - A Paradigm Change[J]. Trends Cell Biol 2020,30(12):923–36. Gómez-Valadés AG, Pozo M, Varela L, et al. Mitochondrial cristae-remodeling protein OPA1 in POMC neurons couples Ca(2+) homeostasis with adipose tissue lipolysis[J]. Cell Metab. 2021;33(9):1820–35. Zhou J, Duan M, Wang X et al. A feedback loop engaging propionate catabolism intermediates controls mitochondrial morphology[J]. Nat Cell Biol 2022,24(4):526–37. Luo JY, Cheng CK, He L et al. Endothelial UCP2 Is a Mechanosensitive Suppressor of Atherosclerosis[J]. Circ Res 2022,131(5):424–41. Muniz-Santos R, Lucieri-Costa G, de Almeida M, et al. Lipid oxidation dysregulation: an emerging player in the pathophysiology of sepsis[J]. Front Immunol. 2023;14:1224335. Solier S, Müller S, Cañeque T et al. A druggable copper-signalling pathway that drives inflammation[J]. Nat 2023,617(7960):386–94. Yang L, Xie M, Yang M, et al. PKM2 regulates the Warburg effect and promotes HMGB1 release in sepsis[J]. Nat Commun. 2014;5:4436. Liu TF, Vachharajani V, Millet P et al. Sequential actions of SIRT1-RELB-SIRT3 coordinate nuclear-mitochondrial communication during immunometabolic adaptation to acute inflammation and sepsis[J]. J Biol Chem 2015,290(1):396–408. Wang Z, Kong L, Tan S, et al. Zhx2 Accelerates Sepsis by Promoting Macrophage Glycolysis via Pfkfb3[J]. J Immunol. 2020;204(8):2232–41. Liang Y, Tan F, Sun X et al. Aberrant Retinal Pigment Epithelial Cells Derived from Induced Pluripotent Stem Cells of a Retinitis Pigmentosa Patient with the PRPF6 Mutation[J]. Int J Mol Sci,2022,23(16). Sakabe M, Thompson M, Chen N et al. Inhibition of β1-AR/Gαs signaling promotes cardiomyocyte proliferation in juvenile mice through activation of RhoA-YAP axis[J]. Elife,2022,11. Guo Y, Srinivasula SM, Druilhe A et al. Caspase-2 induces apoptosis by releasing proapoptotic proteins from mitochondria[J]. J Biol Chem 2002,277(16):13430–7. Robertson JD, Enoksson M, Suomela M et al. Caspase-2 acts upstream of mitochondria to promote cytochrome c release during etoposide-induced apoptosis[J]. J Biol Chem 2002,277(33):29803–9. Tiwari M, Sharma LK, Vanegas D, et al. A nonapoptotic role for CASP2/caspase 2: modulation of autophagy[J]. Volume 10. Autophagy; 2014. pp. 1054–70. 6. Neupert W, Herrmann JM. Translocation of proteins into mitochondria[J]. Annu Rev Biochem. 2007;76:723–49. Schmidt O, Pfanner N, Meisinger C. Mitochondrial protein import: from proteomics to functional mechanisms[J]. Nat Rev Mol Cell Biol 2010,11(9):655–67. Waegemann Popov-eleketićD, Mapa K. Role of the import motor in insertion of transmembrane segments by the mitochondrial TIM23 complex[J]. EMBO Rep. 2011;12(6):542–8. Sinha D, Srivastava S, Krishna L et al. Unraveling the intricate organization of mammalian mitochondrial presequence translocases: existence of multiple translocases for maintenance of mitochondrial function[J]. Mol Cell Biol 2014,34(10):1757–75. Kirchmeyer M, Servais FA, Hamdorf M et al. Cytokine-mediated modulation of the hepatic miRNome: miR-146b-5p is an IL-6-inducible miRNA with multiple targets[J]. J Leukoc Biol 2018,104(5):987–1002. Mingting D, Yun R, Jiwen Z et al. High Expression of TIMM17B Is a Potential Diagnostic and Prognostic Marker of Breast Cancer[J]. Cell Mol Biol (Noisy-le-grand),2023,69(3):169–76. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3802999","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":263842076,"identity":"48386088-a0b1-42b5-80a1-7aa33c6f0b44","order_by":0,"name":"Jinsen Weng","email":"","orcid":"","institution":"Fujian Medical University Provincial Clinical Medical College: Fujian Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jinsen","middleName":"","lastName":"Weng","suffix":""},{"id":263842077,"identity":"cd3c2a70-bbfd-4664-8ee7-24802921edd1","order_by":1,"name":"Xiaojing Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBAC+2aG9A8f/9Qws7E3EKnFgJ3hGePMhmPsfDwHiNXCz/iMmbeBmV9OIoFILebMzGkPZ+5gk2aTfLzxBkONTTRBLZbNbOkGH8/IGLNJpxVbMBxLy20gqOcwT4LkDDa2ZDbpHDMJxobDxGjh/yDNw8Zc3yZ5hkgtBocZ0qR525iZ2SR4iNQi2cyQbDjjzDFmNh6gXxKI8Qs//4HEBx8qapjl2w9vvPGhxoYIvyA7kuioQdJCqo5RMApGwSgYGQAA25g6dRFfQcsAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-6952-0253","institution":"Fujian Medical University Provincial Clinical Medical College: Fujian Provincial Hospital","correspondingAuthor":true,"prefix":"","firstName":"Xiaojing","middleName":"","lastName":"Wang","suffix":""},{"id":263842078,"identity":"2ec0ffbf-7547-4fcc-89f2-971120221258","order_by":2,"name":"Xiuling Shang","email":"","orcid":"","institution":"Fujian Medical University Provincial Clinical Medical College: Fujian Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiuling","middleName":"","lastName":"Shang","suffix":""},{"id":263842079,"identity":"c311250e-28af-46f6-a160-2e8cd155ddec","order_by":3,"name":"Jun Xiao","email":"","orcid":"","institution":"Fujian Provincial Cancer Hospital Clinical Oncology School","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Xiao","suffix":""},{"id":263842080,"identity":"c755f1b1-f894-481c-8cc2-76182688cca9","order_by":4,"name":"Yong Ye","email":"","orcid":"","institution":"Fujian Provincial Cancer Hospital Clinical Oncology School","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Ye","suffix":""},{"id":263842081,"identity":"af8b3bc2-c68c-4e33-9ca9-38903954d536","order_by":5,"name":"Rongguo Yu","email":"","orcid":"","institution":"Fujian Medical University Provincial Clinical Medical College: Fujian Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rongguo","middleName":"","lastName":"Yu","suffix":""},{"id":263842082,"identity":"a65164b6-f120-4cf0-b075-4a9deed30297","order_by":6,"name":"Junya Shang","email":"","orcid":"","institution":"Fujian Medical University Provincial Clinical Medical College: Fujian Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Junya","middleName":"","lastName":"Shang","suffix":""}],"badges":[],"createdAt":"2023-12-25 05:37:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3802999/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3802999/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49089124,"identity":"c3d66f9c-4539-44ea-bee7-6f4e30b43df2","added_by":"auto","created_at":"2024-01-03 01:31:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1278265,"visible":true,"origin":"","legend":"\u003cp\u003eA Heatmap of the 398 DEGs. B. C Correlation analysis of 11 DEGs. D The expression of 11 DEGs in the sepsis group and healthy control group.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-3802999/v1/16961366fc00adbf1eb458b3.png"},{"id":49089125,"identity":"58ce0bf5-3238-4691-8714-51c6ac93aa41","added_by":"auto","created_at":"2024-01-03 01:31:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1150009,"visible":true,"origin":"","legend":"\u003cp\u003eA GO enrichment analysis of DEGs. B GO GSVA enrichment analysis of DEGs.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-3802999/v1/cbc18c7a16a2e642c34d1a00.png"},{"id":49089129,"identity":"4cb82ae1-9745-48fd-8778-e687b5d1cab7","added_by":"auto","created_at":"2024-01-03 01:31:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":87804,"visible":true,"origin":"","legend":"\u003cp\u003eThe overall protocol of this study.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-3802999/v1/9094a652ccb7178e61c3e0cf.png"},{"id":49089126,"identity":"2d4155ab-9579-438e-9df0-603497a389cb","added_by":"auto","created_at":"2024-01-03 01:31:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":66620,"visible":true,"origin":"","legend":"\u003cp\u003eThe overall protocol for validation in clinical specimens\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-3802999/v1/e83667b010c05dc6fb430ccd.png"},{"id":49089128,"identity":"7724a776-a513-4eba-90e1-56f87ea8fd88","added_by":"auto","created_at":"2024-01-03 01:31:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":119617,"visible":true,"origin":"","legend":"\u003cp\u003eThe results of RT‒qPCR analysis of clinical specimens. (A-L) The expression of DEGs. ns, p \u0026gt; 0.05; *, p \u0026lt; 0.05; **, p \u0026lt; 0.01\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-3802999/v1/38ffc6d8377a8198661b1961.png"},{"id":49089718,"identity":"c557fdf4-6199-4715-b52a-e14fd916b574","added_by":"auto","created_at":"2024-01-03 01:39:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":218273,"visible":true,"origin":"","legend":"\u003cp\u003eThe results of RT‒qPCR in mouse cardiomyocytes. (A‒K) Detection of the expression of 11 DEGs in control and LPS-treated HL-1 cells. (L-P) Detection of TIM23 complex-associated protein-encoding gene expression in control and LPS-treated HL-1 cells.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-3802999/v1/cbc6851fff17ecda6adae601.png"},{"id":51039602,"identity":"21d4231e-700f-4569-995e-6213e08fc629","added_by":"auto","created_at":"2024-02-13 06:27:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3148044,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3802999/v1/2cf1320f-1d90-4b23-92dd-19821090dbb0.pdf"}],"financialInterests":"","formattedTitle":"Exploring the Genetic Landscape of Sepsis-Induced Cardiomyopathy: A Comprehensive Analysis","fulltext":[{"header":"Background","content":"\u003cp\u003eSepsis, an intricate outcome of host response dysregulation to infection, precipitates a cascade of systemic multiorgan failures prominently exemplified by the onset of septic cardiomyopathy (SCM). SCM, which is characterized by its ability to prevent cardiomyocyte ischemia, results in a consequential decrease in the left heart ejection fraction (EF)\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e, indicating an ostensibly recoverable reduction in systolic function. The etiology of SCM has not been fully elucidated, as the initial stages of sepsis are characterized by myocardial dysfunction attributed to inflammatory stress rather than mitochondrial compromise\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. In advanced sepsis, the pronounced depletion of cardiomyocyte ATP exacerbates mitochondrial dysfunction, the primary instigator of myocardial debilitation\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCardiomyocytes, known for their profusion of mitochondria\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, rely heavily on oxidative phosphorylation within these cellular powerhouses\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. SCM intricately correlates with mitochondrial damage within cardiomyocytes\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Notably, mitochondrial morphology is markedly impaired in parallel with dysfunction in the electron transport chain (ETC) during sepsis, thereby accentuating the broader landscape of mitochondrial dysfunction\u003csup\u003e[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Dysregulation of ETC function\u003csup\u003e[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e prompts substantial alterations in mitochondrial activity, ultimately culminating in reduced adenosine triphosphate (ATPase) synthesis\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e and activation of the apoptotic pathway\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. The preservation of mitochondrial homeostasis, pivotal for recovery from sepsis\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, involves mitochondrial biogenesis as a cellular program regulating energy production and mediating organelle interactions through the synthesis of new organelles and components\u003csup\u003e[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eUnderstanding the intricacies of mitochondrial damage in septic cardiomyocytes is highly important for preserving mitochondrial homeostasis and, consequently, mitigating the impacts of SCM. This study strives to discern, through a bioinformatics lens, 11 genes featuring enriched pathways pivotal for mitochondrial injury in SCM. Concurrently, scrutiny of clinical specimens and cellular experiments endeavors to trace the expression patterns and trends of target genes during the developmental trajectory of SCM, thereby enriching our understanding of potential mechanisms and therapeutic targets relevant to septic cardiomyopathy.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Bioinformatics analysis\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Data Retrieval\u003c/h2\u003e \u003cp\u003eThe DNA microarray and RNA-seq datasets utilized were sourced from the Gene Expression Omnibus (GEO) Database (\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) and the Molecular Signatures Database (MSigDB) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb/\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The datasets included GSE5663, GSE137342, GSE185754, GSE171546, and MOOTHA MITOCHONDRIA. The dataset details are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The expression-based probes were converted to gene symbols via platform annotation files. The GSE5663 and GSE137342 datasets were processed using the limmaR package, integrating the experimental and control groups to mitigate batch effects. Similarly, the GSE185754 and GSE171546 datasets were processed for concordance. VennDiagramR facilitated intersecting gene analysis, identifying shared genes among the human sepsis, human mitochondrial, and sepsis mouse cardiomyocyte gene expression sets.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDataset source\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDataset Numbe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlatform\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLPS Group/Sepsis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl Group/Healthy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnnotation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSample Type\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE5663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPL80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAffymetrix Human Full Length HuGeneFL Array[Hu6800]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHuman spleen\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE137342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPL10558、GPL16686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIllumina HumanHT-12 V4.0 expression beadchip、Affymetrix Human Gene 2.0 ST Array[HuGene-2_0-st]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHuman Blood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMOOTHA_MITOCHONDRIA M9577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAFFY_HG_U133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003emitochondr_HG-U133A_probes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHuman muscle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE185754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPL24247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIllumina NovaSeq 6000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMurine myocardium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE171546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPL24247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIllumina NovaSeq 6000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMurine myocardium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.1.2 Differential expression analysis\u003c/h2\u003e \u003cp\u003eThe gene sets chosen for differential expression analysis were analyzed using the limmaR and reshape2R packages. Differentially expressed genes (DEGs) were identified in the shared gene dataset based on a log fold change (FC)\u0026thinsp;\u0026gt;\u0026thinsp;1 or \u0026lt;-1, with adjusted p values\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u0026lt; 0.01, and \u0026lt;\u0026thinsp;0.001. This rigorous screening categorized DEGs into up- and downregulated groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.1.3 Functional and pathway analysis\u003c/h2\u003e \u003cp\u003ePathway analysis relied on the cellular component from the Gene Ontology (GO) database. GO analysis, performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), was instrumental in exploring the biological processes associated with the DEGs. GSVA and pathway difference analysis were performed using the limma, reshape2, GSEABase, and GSVA packages in R to reveal the mitochondrial signaling pathways associated with the DEGs. Visualization was achieved through the pheatmap and ggpubr packages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Clinical Data Collection\u003c/h2\u003e \u003cp\u003eThe study cohort included 74 sepsis patients diagnosed within 24 hours of admission and a control group of 31 individuals. Peripheral blood specimens were collected within 48 hours after admission and stored at -80\u0026deg;C. Clinical case histories were gathered, encompassing demographic details, infection specificity, SOFA and APACHE II scores, prognostic indicators, and relevant biomarkers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Experimental reagents\u003c/h2\u003e \u003cp\u003e \u003cb\u003eThe\u003c/b\u003e HL-1 cells, LPS, MEM, FBS, and penicillin/streptomycin mixture were obtained from Xiamen Taijing Biotechnology Co. Additional reagents (PBS reagent, Trizol reagent, Evo M-MLV kit, and SYBR Green Pro TaqHS premixed kit) were obtained from Accurate Biology, and the primers were designed and synthesized by Shangya Biological Company.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Cell Culture and Processing\u003c/h2\u003e \u003cp\u003eHL-1 mouse cardiomyocytes were cultured in MEM, passaged at the optimal growth density, and selected for intervention in the 5th generation (P6). The cells were divided into control and LPS-treated groups and subjected to inflammatory stimulation with 10 \u0026micro;g/ml LPS for 6 or 24 hours. The collected cells were lysed using Trizol reagent after washing with frozen PBS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.5 RNA quantification and RT‒qPCR\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from patient blood specimens, reverse transcription, and quantitative PCR were conducted following established protocols, and the PCR primer sequences are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. For mouse cardiomyocytes, similar procedures were followed, and the PCR primer sequences are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. GAPDH served as the housekeeping gene, and relative mRNA expression was calculated using the 2-ΔΔCT method.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimer design (human)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget gene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSequence(5'to3')\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ebase number\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAPDH-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGGAAAGCTGTGGCGTGATG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAPDH-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTACTTGGCAGGTTTCTCCAGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUCP2-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGAGGTGGTCGGAGATACCAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUCP2-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACAATGGCATTACGAGCAACAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIMM17B-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATGGAGGAGTACGCTCGGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIMM17B-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCGATGACACCCATAGTGAAGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLC25A3-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGGTGTTCGTGGTTTGGCTAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLC25A3-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGATGTGCGCCAGAGATAAGTATT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDHA-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAGCATGTGTTACCAAGCTGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDHA-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGTGTCGTAGAAATGCCACCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRPF6-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCACCACGCGGTCAGACATT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRPF6-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCCCCAACGGTTCTCTTGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRPL2-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCGAATCCGGGTGCATGGTATT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRPL2-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTCAAAGGGTCCTGACTTGGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMF2-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTCACCTACCACTACGAGACC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMF2-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCACAGCGATCTCAATTAGGAAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIMMT-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCGATTCAGTCGGGTCCACTAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIMMT-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGCTGGAGTATCTCCCTTTTGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGNAS-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGCAAGGAGCAACAGCGAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGNAS-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCGGCCACAATGGTTTCAAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGTGGCATGAGAGGCATGAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTAGCCTTGGGTAGCAGTTTCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCASP2-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGCTGTTGTTGAGCGAATTGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCASP2-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGCAAGTTGAGGAGTTCCACA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIMM23-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCCATTGACGCCATGAACAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIMM23-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTGCATCCTTTGGCTGGTCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimer design (mouse)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget gene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSequence(5'to3')\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ebase number\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAPDH-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGGAAAGCTGTGGCGTGATG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAPDH-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTACTTGGCAGGTTTCTCCAGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUCP2-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGAGAGTCAAGGGCTAGTGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUCP2-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGACAGAGTCGTAGAGGCCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIMM17B-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAGGCTATCAAGGGCTTCCG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIMM17B-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACACTGCAAAGCTTCCTCCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLC25A3-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGTGGCTTTGGTGGGGTCTTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLC25A3-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACCACGAACGCCATCTTCTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDHA-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCGACAGGGGAATGGTTTGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDHA-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGACTCCTTCCGAGCTTCTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRPF6-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGTCATCTCCCGCAGTCTGTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRPF6-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCACTCTACTGCTCGGCTCAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRPL2-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGTGCATGGTATCGGTGGAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRPL2-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTTCCGTGGCAATGATCCAGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMF2-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCAACAGCACATCATCCTCTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMF2-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGAACTTCCGTGACCATCCTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIMMT-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCCTATTGTATTGAGCATGGTGAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIMMT-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTTGAGTGGTTCCTATTCCTACG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGNAS-F2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAGCAGCTACAACATGGTCAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGNAS-R2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGGAAGAGAATCACAGAGATGGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACAGTGAAAGCAACTTCGCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGTCAATGGCTCCGATACTGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCASP2-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTGCTGAGCGAGCTGTTAGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCASP2-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGGGCTTCACAGAAGGCATC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIMM44-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGAACTCCAAAGGCGAGGTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIMM44-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCAGGCAGCATAAGGGTTGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIMM50-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTAAGAAGCGTCCAGGCATCG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIMM50-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGATGAAACCGTGAGGGTCCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePAM16-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGCCCAGCAGATTCTCAACG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePAM16-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCACGGACAACCTTTGACTGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIMM23-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGAATATGGTGACTAGGCAAGGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIMM23-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCTACTGTGTTGAGGTCATCTTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIMM17A-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCACTAGCGGTGCCTTAACAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIMM17A-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCAAGATACCAGCTCCTTCAAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Ethical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe academic provenance of the data was determined from bioinformatics databases, namely, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), which adhered to the requisite standards. Clinical and cellular experiments transpired within the confines of the laboratory at Fujian Provincial Hospital, due to adherence to Ethics No. K2021-10-001.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe expression data, encapsulated as the mean \u0026plusmn; standard deviation from a triad of independent experiments, were subjected to meticulous statistical scrutiny. The rubric for group comparisons delineated between those adhering to a normal distribution, appraised via the independent samples t test, and those deviating from this norm, subjected to the Mann‒Whitney U test. A criterion met at p \u0026lt; 0.05 was considered to indicate statistical significance, underscoring the rigor and robustness of the findings.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Differential gene expression screening\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFive distinct datasets, namely, GSE5663, GSE137342, GSE185754, GSE171546, and MOOTHA_MITOCHONDRIA, were meticulously employed in this investigation. The collective cohort included 39 sepsis patients and 12 healthy controls from the GSE5663 and GSE137342 cohorts, collectively comprising the human sepsis gene expression set. Subsequently, this dataset was amalgamated with the MOOTHA_MITOCHONDRIA dataset, resulting in the formulation of the human sepsis mitochondrial gene expression set, encompassing a total of 450 genes. In parallel, a mouse sepsis cardiomyocyte gene expression set derived from GSE185754 and GSE171546 comprising 15 septic mice and 15 healthy controls was established, totaling 54,123 genes. The integration of the human sepsis mitochondrial gene expression set and the mouse sepsis cardiomyocyte gene expression set facilitated the identification of 409 mitochondrial coexpressed genes in both human and mouse sepsis. Subsequent intersection with the healthy human gene expression set from the GSE5663 and GSE137342 datasets revealed 398 differentially expressed genes (DEGs) in humans and mice. The resulting DEGs were subjected to visualization for distribution analysis (Fig. 1A).\u003c/p\u003e\n\u003cp\u003eA comprehensive examination of these DEGs revealed 11 significantly associated genes (Fig. 1B, Fig. 1C), namely, GNAS, MRPL2, TIMM17b, SLC25A3, SDHA, PRPF6, LMF2, IMMT, CS, UCP2, and CASP2. The expression patterns of these genes in the sepsis group versus the healthy control group were explored (Fig. 1D), revealing a statistically significant difference in expression, with a p value less than 0.01.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Functional and pathway enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo gain a nuanced understanding of the functional attributes inherent to the identified DEGs, a Gene Ontology (GO) analysis was meticulously executed (Fig. 2A). In terms of molecular function (MF), the enrichment of DEGs was most significant for the term \u0026quot;hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds, in linear amidines\u0026quot; (GO:0016813), while the enrichment was greatest for the term \u0026quot;electron transfer activity\u0026quot; (GO:0009055). In terms of the cellular component (CC), the enrichment of DEGs was most significant for the entry \u0026quot;specific granule lumen\u0026quot; (GO:0035580). The highest number of DEGs was found in the entries that were concentrated in the \u0026quot;vesicle lumen\u0026quot; (GO:0031983), \u0026quot;cytoplasmic vesicle lumen\u0026quot; (GO:0060205) and \u0026quot;secretory granule lumen\u0026quot; (GO:0034774) categories. In terms of biological processes (BP), DEGs were most significantly enriched in the \u0026quot;innate immune response in mucosa\u0026quot; (GO:0002227), while the largest number of DEGs was enriched in the \u0026quot;defense response to bacterium\u0026quot; (GO:0042742).\u003c/p\u003e\n\u003cp\u003eSubsequently, the 11 identified DEGs were subjected to gene set variation analysis (GSVA) for the enrichment of Gene Ontology terms. This analysis revealed that mitochondria-related pathways exhibited the most significant upregulation (Fig. 2B). These pathways encompassed mitochondrial DNA replication, the TIM22 complex, protein insertion into the mitochondrial inner membrane, the mitochondrial intermembrane space protein transport complex, and the pivotal TIM23 complex, which is renowned for its role as the mitochondrial inner membrane anterior sequence translocase complex. The strategic choice of investigating genes related to the constituents of the TIM23 complex was underpinned by its pivotal function as the primary protein precursor transport channel in the mitochondrial inner membrane. The overall protocol of this study is shown in Fig. 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Validation in Clinical Specimens\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe clinical baseline data of 74 sepsis patients are meticulously outlined in Table 4. Peripheral blood specimens from these patients, alongside those from 31 healthy individuals, were subjected to rigorous reverse transcription\u0026ndash;quantitative polymerase chain reaction (RT\u0026ndash;qPCR) analysis. Following stringent quality control measures and the exclusion of data deemed unsatisfactory, a 1:1 intergroup propensity score matching was performed (Fig. 4). The subsequent RT‒qPCR results revealed elevated expression levels of TIMM17b and TIMM23 in sepsis patients compared to those in healthy controls. Moreover, the remaining 10 DEGs exhibited a decrease in expression, indicating statistically significant differences between the two cohorts (Fig. 5).\u003c/p\u003e\n\u003cp\u003eTable 4 The clinical baseline data of 74 sepsis patients\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"427\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eBaseline characteristics of 74 subjects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.71896955503513%\" valign=\"bottom\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28103044496487%\" valign=\"bottom\"\u003e\n \u003cp\u003eTotal =74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.71896955503513%\" valign=\"bottom\"\u003e\n \u003cp\u003eAge, year (median, range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28103044496487%\" valign=\"bottom\"\u003e\n \u003cp\u003e60（15-96）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.71896955503513%\" valign=\"bottom\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28103044496487%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.71896955503513%\" valign=\"bottom\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28103044496487%\" valign=\"bottom\"\u003e\n \u003cp\u003e48（64.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.71896955503513%\" valign=\"bottom\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28103044496487%\" valign=\"bottom\"\u003e\n \u003cp\u003e26（35.1%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.71896955503513%\" valign=\"bottom\"\u003e\n \u003cp\u003eBasic disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28103044496487%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.71896955503513%\" valign=\"bottom\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28103044496487%\" valign=\"bottom\"\u003e\n \u003cp\u003e22（29.7%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.71896955503513%\" valign=\"bottom\"\u003e\n \u003cp\u003eCardiovascular and cerebrovascular diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28103044496487%\" valign=\"bottom\"\u003e\n \u003cp\u003e26（35.1%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.71896955503513%\" valign=\"bottom\"\u003e\n \u003cp\u003eMalignant tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28103044496487%\" valign=\"bottom\"\u003e\n \u003cp\u003e20（27.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.71896955503513%\" valign=\"bottom\"\u003e\n \u003cp\u003eImmune deficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28103044496487%\" valign=\"bottom\"\u003e\n \u003cp\u003e2（4.1%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.71896955503513%\" valign=\"bottom\"\u003e\n \u003cp\u003eElse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28103044496487%\" valign=\"bottom\"\u003e\n \u003cp\u003e6（8.1%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.71896955503513%\" valign=\"bottom\"\u003e\n \u003cp\u003eFocus of infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28103044496487%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.71896955503513%\" valign=\"bottom\"\u003e\n \u003cp\u003eAbdominal infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28103044496487%\" valign=\"bottom\"\u003e\n \u003cp\u003e25（33.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.71896955503513%\" valign=\"bottom\"\u003e\n \u003cp\u003ePulmonary infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28103044496487%\" valign=\"bottom\"\u003e\n \u003cp\u003e23（31.1%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.71896955503513%\" valign=\"bottom\"\u003e\n \u003cp\u003eHepatobiliary duct infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28103044496487%\" valign=\"bottom\"\u003e\n \u003cp\u003e11（14.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.71896955503513%\" valign=\"bottom\"\u003e\n \u003cp\u003eUrinary system infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28103044496487%\" valign=\"bottom\"\u003e\n \u003cp\u003e7（9.5%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.71896955503513%\" valign=\"bottom\"\u003e\n \u003cp\u003eElse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28103044496487%\" valign=\"bottom\"\u003e\n \u003cp\u003e8（10.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.71896955503513%\" valign=\"bottom\"\u003e\n \u003cp\u003ePrognosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28103044496487%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.71896955503513%\" valign=\"bottom\"\u003e\n \u003cp\u003eSurvivors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28103044496487%\" valign=\"bottom\"\u003e\n \u003cp\u003e48（64.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.71896955503513%\" valign=\"bottom\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28103044496487%\" valign=\"bottom\"\u003e\n \u003cp\u003e26（35.1%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.71896955503513%\" valign=\"bottom\"\u003e\n \u003cp\u003eSOFA score (median, range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28103044496487%\" valign=\"bottom\"\u003e\n \u003cp\u003e10（2-21）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.71896955503513%\" valign=\"bottom\"\u003e\n \u003cp\u003eAPACHEII score (median, range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28103044496487%\" valign=\"bottom\"\u003e\n \u003cp\u003e22（5-43）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.71896955503513%\" valign=\"bottom\"\u003e\n \u003cp\u003ePCT (median, range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28103044496487%\" valign=\"bottom\"\u003e\n \u003cp\u003e38.18（0.25-100.00）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.71896955503513%\" valign=\"bottom\"\u003e\n \u003cp\u003eBNP (median, range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28103044496487%\" valign=\"bottom\"\u003e\n \u003cp\u003e8695（22-35000）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.71896955503513%\" valign=\"bottom\"\u003e\n \u003cp\u003ecTnI (median, range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28103044496487%\" valign=\"bottom\"\u003e\n \u003cp\u003e17.64（0.01-1078.00）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.71896955503513%\" valign=\"bottom\"\u003e\n \u003cp\u003eEF value of cardiac ultrasound(median, range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28103044496487%\" valign=\"bottom\"\u003e\n \u003cp\u003e56（30-77）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.71896955503513%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28103044496487%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Validation in Mouse Cardiomyocytes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RT‒qPCR results revealed that the expression profiles of the 11 DEGs (Fig. 6A-K) in HL-1 cells subsequent to 6 hours of lipopolysaccharide (LPS) treatment were markedly and significantly upregulated relative to those in the control group. However, a discernible reduction in DEG expression was observed at the 24-hour time point compared with that in the 6-hour LPS-treated group. Importantly, except for TIMM17b, the expression levels of DEGs at the 24-hour juncture were significantly lower than those in the control group. The RT‒qPCR results revealing the expression profiles of TIM23 complex member genes (TIMM23, TIMM17A, TIMM44, PAM16, and TIMM50) in HL-1 cells after 6 hours of LPS treatment revealed a significant increase relative to that in the control group in both temporal contexts. After 24 hours of LPS treatment, a significant reduction in the expression of TIM23 complex member genes was evident in both groups compared with that in the LPS-treated 6-hour group, indicating that the two temporal cohorts were distinct. Despite the significant reduction relative to those in the 6-hour cohort, the expression levels of TIM23 complex member genes within the 24-hour cohort were still elevated relative to those in the control group, with statistically significant differences noted in the cases of TIMM23, TIMM44, and PAM16; however, no such statistically significant differences were detected for TIMM17A or TIMM50.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMitochondrial dysfunction in mid-to-late-stage sepsis potentially exacerbates septic cardiomyopathy by disrupting energy metabolism equilibrium. Given that energy failure in septic cardiomyocytes is a primary contributor to myocardial injury, we conducted a bioinformatics study on mitochondrial damage in septic cardiomyopathy. The comprehensive bioinformatics analysis conducted in this study provided key insights into the molecular mechanisms associated with sepsis, shedding light on DEGs and their potential functional implications. The integration of diverse datasets allowed for robust exploration of the genomic landscape, providing a holistic view of gene expression changes in both human sepsis patients and a murine model of septic cardiomyopathy.\u003c/p\u003e\n\u003cp\u003eThe proteins encoded by several of these DEGs, such as SLC25A3\u003csup\u003e[21, 22]\u003c/sup\u003e, CS\u003csup\u003e[23, 24]\u003c/sup\u003e, SDHA\u003csup\u003e[25]\u003c/sup\u003e, IMMT\u003csup\u003e[26-29]\u003c/sup\u003e, UCP2\u003csup\u003e[30]\u003c/sup\u003e, and LMF2\u003csup\u003e[31]\u003c/sup\u003e, play pivotal roles in mitochondrial electron transport chain (ETC) function and adenosine triphosphate (ATP) production. Additionally, SLC25A3\u003csup\u003e[32]\u003c/sup\u003e and SDHA\u003csup\u003e[33-35]\u003c/sup\u003e may influence the inflammatory response of macrophages, impacting cardiomyocyte mitochondrial function. The MRPL2 and PRPF6 proteins are associated with mRNA transcription and protein translation\u003csup\u003e[36]\u003c/sup\u003e, while the GNAS\u003csup\u003e[37]\u003c/sup\u003e and CASP2\u003csup\u003e[38-40]\u003c/sup\u003e gene products are involved in cardiomyocyte proliferation, autophagy, and apoptosis. This collective evidence suggests that these DEGs may collectively influence mitochondrial function, affecting aspects such as ETC function, ATP production, mRNA transcription, protein coding, and cardiomyocyte proliferation and apoptosis, ultimately contributing to septic myocardial injury.\u003c/p\u003e\n\u003cp\u003eThe subsequent functional and pathway enrichment analysis delved into the biological relevance of the identified DEGs. Gene Ontology analysis revealed significant enrichment in molecular functions related to \u0026quot;hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds\u0026quot;, linear amidines\u0026quot; and \u0026quot;electron transfer activity\u0026quot;. Notably, the pronounced enrichment in \u0026quot;defense against bacteria\u0026quot; and \u0026quot;innate immune response in mucosa\u0026quot; within the biological process domain underlines the intricate interplay between mitochondrial function and the host response during sepsis.\u003c/p\u003e\n\u003cp\u003eGene set variation analysis (GSVA) further confirmed the involvement of mitochondria-related pathways, particularly emphasizing the upregulation of critical processes such as mitochondrial DNA replication and protein insertion into the mitochondrial inner membrane. The focus on the TIM23 complex, a pivotal player in mitochondrial protein transport, suggests a potential regulatory role in septic conditions\u003csup\u003e[41, 42]\u003c/sup\u003e. Comprising a core pore (Tim17, Tim23, and Tim50) responsible for precursor protein translocation and an import motor (mtHSP70, Tim44, Tim14, Tim16, and Mge1) facilitating ATP-dependent protein translocation\u003csup\u003e[43]\u003c/sup\u003e, the TIM23 complex plays a crucial role in mitochondrial function. Among the 11 DEGs, TIMM17b, the only TIMM17b protein with elevated expression, is responsible for the expression of one of the subtypes of Tim17, and the TIM23 complex is the TIM23 complex B type, which affects mitochondrial function. For example, it is essential to maintain the normal functions of electron transport chain complex activity, organelle morphology and iron\u0026ndash;sulfur tufting biogenesis\u003csup\u003e[44]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eBioinformatics analysis revealed a unique pattern in the sepsis group, with TIMM17b being the only DEG exhibiting increased expression, while the others displayed decreased expression. Interestingly, the TIM23 complex pathway, which was enriched in DEGs, exhibited increased expression. Further examination of clinical specimens and LPS-treated 24 h cardiomyocytes validated these findings, revealing consistency across the raw signaling results, clinical specimens, and cellular experiments.\u003c/p\u003e\n\u003cp\u003eNotably, in the group of cardiomyocytes treated with LPS for 6 hours, the experimental results were inconsistent with the results of bioinformatics analysis. The temporal expression patterns of the DEGs and TIM23 complex members suggested a correlation with sepsis development stage. The expression levels of these genes were elevated in the early stages, indicating temporary mitochondrial homeostasis due to compensatory mechanisms. However, as sepsis progresses, a decrease in the expression of these genes indicates escalating mitochondrial damage and ensuing energy metabolism malfunction, exacerbating myocardial dysfunction. The consistent increase in TIMM17b in the early stage of sepsis, despite a subsequent decrease, points toward its association with inflammatory stress\u003csup\u003e[45, 46]\u003c/sup\u003e and TIM23 complex function\u003csup\u003e[44]\u003c/sup\u003e. This elevation potentially enhances TIM23 complex function, facilitating efficient protein translocation into the mitochondrial inner membrane or matrix, thereby sustaining compensatory mitochondrial and cardiomyocyte functions. As sepsis progresses, the expression of TIMM17b and TIM23 complex members decreases, and the expression of the remaining 10 DEGs decreases, underscoring the inability to maintain mitochondrial homeostasis, culminating in dysfunctional energy metabolism and myocardial injury. This temporal correlation was particularly evident in cellular experiments, emphasizing the need for further targeted investigations.\u003c/p\u003e\n\u003cp\u003eWhile this study provides valuable insights into mitochondrial damage in sepsis, the internal association mechanisms among the 11 DEGs and their interactions remain unexplored. Future investigations should explore these intricate regulatory networks to determine the underlying mechanisms of mitochondrial dysfunction in sepsis.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this integrative study not only provides a comprehensive overview of DEGs associated with sepsis but also underscores the intricate involvement of mitochondrial pathways, particularly the importance of the TIM23 complex. The identified genes and pathways offer potential targets for further mechanistic studies and therapeutic interventions in the context of sepsis-related complications.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"665\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eDEGs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eDifferentially expressed genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eDNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eDeoxyribonucleic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eTIM23 complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eTranslocase of inner mitochondrial membrane 23 complex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eSCM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eSeptic cardiomyopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eEjection fraction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eATP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eAdenosine triphosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eETC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eElectron transport chain\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eGEO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eGene expression omnibus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eFold change\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eGene ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eDAVID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eDatabase for annotation, visualization, and integrated discovery\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eLPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eLipopolysaccharide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eMEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eMinimum essential medium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eFBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eFetal bovine serum\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eRT‒qPCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eReverse transcription-quantitative polymerase chain reaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eRibonucleic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003emRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eMessenger ribonucleic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eGAPDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eGlyceraldehyde 3-phosphate dehydrogenase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eUCP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eMitochondrial uncoupling protein 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eTIMM17B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eTranslocase of inner mitochondrial membrane 17B\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eSLC25A3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eSolute carrier family 25 member 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eSDHA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eSuccinate dehydrogenase complex flavoprotein subunit A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003ePRPF6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003epre-mRNA processing factor 6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eMRPL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eMitochondrial ribosomal protein L2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eLMF2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eLipase maturation factor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eIMMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eInner membrane mitochondrial protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eGNAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eGuanine nucleotide-binding protein G(s) subunit alpha isoforms Xlas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eCitrate synthase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eCASP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eCaspase 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eTIMM23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eTranslocase of inner mitochondrial membrane 23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eTIMM44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eTranslocase of inner mitochondrial membrane 44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eTIMM50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eTranslocase of inner mitochondrial membrane 50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003ePAM16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003ePresequence Translocase-Associated Motor 16 Homolog\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eTIMM17A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eTranslocase of inner mitochondrial membrane 17A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eMolecular Function\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eCellular Component\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eBiological Processes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.26726726726727%\"\u003e\n \u003cp\u003eGSVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.73273273273273%\"\u003e\n \u003cp\u003eGene Set Variation Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe academic provenance of the data was determined from bioinformatics databases, namely, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), which adhered to the requisite standards. Clinical and cellular experiments transpired within the confines of the laboratory at Fujian Provincial Hospital, due to adherence to Ethics No. K2021-10-001.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data analysed during this study are included in the websites mentioned above.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1.The Young-Middle-aged Backbone Talent Training Program of Fujian Provincial Health organization (2021GGA003)\u003c/p\u003e\n\u003cp\u003e2. Joint Funds for the innovation of science and Technology，Fujian province（Grant number：2021Y91020276）；\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJSW, XJW, RGY and YY designed the study. Data analysis were performed by XJW and JX. XJW and JYS carried out the experiments. XJW and JX wrote the first draft. JSW and XLS critically revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHollenberg SM, Singer M. Pathophysiology of sepsis-induced cardiomyopathy[J]. Nat Rev Cardiol 2021,18(6):424\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmeding L, van der Laarse WJ, van Veelen TA et al. Early myocardial dysfunction is not caused by mitochondrial abnormalities in a rat model of peritonitis[J]. J Surg Res 2012,176(1):178\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLv X, Wang H. Pathophysiology of sepsis-induced myocardial dysfunction[J]. Mil Med Res 2016,3:30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHotchkiss RS, Nicholson DW. Apoptosis and caspases regulate death and inflammation in sepsis[J]. Nat Rev Immunol 2006,6(11):813\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarth E, St\u0026auml;mmler G, Speiser B et al. Ultrastructural quantitation of mitochondria and myofilaments in cardiac muscle from 10 different animal species including man[J]. J Mol Cell Cardiol 1992,24(7):669\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePreau S, Vodovar D, Jung B, et al. Energetic dysfunction in sepsis: a narrative review[J]. Ann Intensive Care. 2021;11(1):104.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCimolai MC, Alvarez S, Bode C et al. Mitochondrial Mechanisms in Septic Cardiomyopathy[J]. Int J Mol Sci 2015,16(8):17763\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParker MM, Suffredini AF, Natanson C et al. Responses of left ventricular function in survivors and nonsurvivors of septic shock[J]. J Crit Care 1989,4(1):19\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShang X, Lin K, Zhang Y, et al. Mst1 deletion reduces septic cardiomyopathy via activating Parkin-related mitophagy[J]. J Cell Physiol. 2020;235(1):317\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShang X, Li J, Yu R, et al. Sepsis-related myocardial injury is associated with Mst1 upregulation, mitochondrial dysfunction and the Drp1/F-actin signaling pathway[J]. J Mol Histol. 2019;50(2):91\u0026ndash;103.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFink MP. Bench-to-bedside review: Cytopathic hypoxia[J]. Crit Care 2002,6(6):491\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinger M. Mitochondrial function in sepsis: acute phase versus multiple organ failure[J]. Crit Care Med 2007,35(9 Suppl):S441\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLarsen FJ, Schiffer TA, Weitzberg E et al. Regulation of mitochondrial function and energetics by reactive nitrogen oxides[J]. Free Radic Biol Med 2012,53(10):1919\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrealey D, Brand M, Hargreaves I et al. Association between mitochondrial dysfunction and severity and outcome of septic shock[J]. Lancet 2002,360(9328):219\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang H, Feng YW, Yao YM. Potential therapy strategy: targeting mitochondrial dysfunction in sepsis[J]. Mil Med Res. 2018;5(1):41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi G, Fu T, Wang W et al. Pretreatment with Kahweol Attenuates Sepsis-Induced Acute Lung Injury via Improving Mitochondrial Homeostasis in a CaMKKII/AMPK-Dependent Pathway[J]. Mol Nutr Food Res 2023,67(19):e2300083.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu H, Wang J, Xin T et al. DUSP1 interacts with and dephosphorylates VCP to improve mitochondrial quality control against endotoxemia-induced myocardial dysfunction[J]. Cell Mol Life Sci 2023,80(8):213.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKelly DP, Scarpulla RC. Transcriptional regulatory circuits controlling mitochondrial biogenesis and function[J]. Genes Dev 2004,18(4):357\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNisoli E, Carruba MO. Nitric oxide and mitochondrial biogenesis[J]. J Cell Sci 2006,119(Pt 14):2855\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShang X, Zhang Y, Xu J, et al. SRV2 promotes mitochondrial fission and Mst1-Drp1 signaling in LPS-induced septic cardiomyopathy[J]. Aging. 2020;12(2):1417\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoulet A, Vest KE, Maynard MK et al. The mammalian phosphate carrier SLC25A3 is a mitochondrial copper transporter required for cytochrome c oxidase biogenesis[J]. J Biol Chem 2018,293(6):1887\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVest KE, Leary SC, Winge DR et al. Copper import into the mitochondrial matrix in Saccharomyces cerevisiae is mediated by Pic2, a mitochondrial carrier family protein[J]. J Biol Chem 2013,288(33):23884\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacgarvey NC, Suliman HB, Bartz RR et al. Activation of mitochondrial biogenesis by heme oxygenase-1-mediated NF-E2-related factor-2 induction rescues mice from lethal Staphylococcus aureus sepsis[J]. Am J Respir Crit Care Med 2012,185(8):851\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eProtti A, Fortunato F, Artoni A, et al. Platelet mitochondrial dysfunction in critically ill patients: comparison between sepsis and cardiogenic shock[J]. Crit Care. 2015;19(1):39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGobelli D, Serrano-Lorenzo P, Esteban-Amo MJ et al. The mitochondrial succinate dehydrogenase complex controls the STAT3-IL-10 pathway in inflammatory macrophages[J]. iScience,2023,26(8):107473.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTombo N, Imam AA, Feng Y, et al. Cardiac ischemia/reperfusion stress reduces inner mitochondrial membrane protein (mitofilin) levels during early reperfusion[J]. Free Radic Biol Med. 2020;158:181\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKondadi AK, Anand R, Reichert AS. Cristae Membrane Dynamics - A Paradigm Change[J]. Trends Cell Biol 2020,30(12):923\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG\u0026oacute;mez-Valad\u0026eacute;s AG, Pozo M, Varela L, et al. Mitochondrial cristae-remodeling protein OPA1 in POMC neurons couples Ca(2+) homeostasis with adipose tissue lipolysis[J]. Cell Metab. 2021;33(9):1820\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou J, Duan M, Wang X et al. A feedback loop engaging propionate catabolism intermediates controls mitochondrial morphology[J]. Nat Cell Biol 2022,24(4):526\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo JY, Cheng CK, He L et al. Endothelial UCP2 Is a Mechanosensitive Suppressor of Atherosclerosis[J]. Circ Res 2022,131(5):424\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuniz-Santos R, Lucieri-Costa G, de Almeida M, et al. Lipid oxidation dysregulation: an emerging player in the pathophysiology of sepsis[J]. Front Immunol. 2023;14:1224335.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSolier S, M\u0026uuml;ller S, Ca\u0026ntilde;eque T et al. A druggable copper-signalling pathway that drives inflammation[J]. Nat 2023,617(7960):386\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang L, Xie M, Yang M, et al. PKM2 regulates the Warburg effect and promotes HMGB1 release in sepsis[J]. Nat Commun. 2014;5:4436.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu TF, Vachharajani V, Millet P et al. Sequential actions of SIRT1-RELB-SIRT3 coordinate nuclear-mitochondrial communication during immunometabolic adaptation to acute inflammation and sepsis[J]. J Biol Chem 2015,290(1):396\u0026ndash;408.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z, Kong L, Tan S, et al. Zhx2 Accelerates Sepsis by Promoting Macrophage Glycolysis via Pfkfb3[J]. J Immunol. 2020;204(8):2232\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang Y, Tan F, Sun X et al. Aberrant Retinal Pigment Epithelial Cells Derived from Induced Pluripotent Stem Cells of a Retinitis Pigmentosa Patient with the PRPF6 Mutation[J]. Int J Mol Sci,2022,23(16).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSakabe M, Thompson M, Chen N et al. Inhibition of β1-AR/Gαs signaling promotes cardiomyocyte proliferation in juvenile mice through activation of RhoA-YAP axis[J]. Elife,2022,11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo Y, Srinivasula SM, Druilhe A et al. Caspase-2 induces apoptosis by releasing proapoptotic proteins from mitochondria[J]. J Biol Chem 2002,277(16):13430\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobertson JD, Enoksson M, Suomela M et al. Caspase-2 acts upstream of mitochondria to promote cytochrome c release during etoposide-induced apoptosis[J]. J Biol Chem 2002,277(33):29803\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTiwari M, Sharma LK, Vanegas D, et al. A nonapoptotic role for CASP2/caspase 2: modulation of autophagy[J]. Volume 10. Autophagy; 2014. pp. 1054\u0026ndash;70. 6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeupert W, Herrmann JM. Translocation of proteins into mitochondria[J]. Annu Rev Biochem. 2007;76:723\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmidt O, Pfanner N, Meisinger C. Mitochondrial protein import: from proteomics to functional mechanisms[J]. Nat Rev Mol Cell Biol 2010,11(9):655\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaegemann Popov-eleketićD, Mapa K. Role of the import motor in insertion of transmembrane segments by the mitochondrial TIM23 complex[J]. EMBO Rep. 2011;12(6):542\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinha D, Srivastava S, Krishna L et al. Unraveling the intricate organization of mammalian mitochondrial presequence translocases: existence of multiple translocases for maintenance of mitochondrial function[J]. Mol Cell Biol 2014,34(10):1757\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKirchmeyer M, Servais FA, Hamdorf M et al. Cytokine-mediated modulation of the hepatic miRNome: miR-146b-5p is an IL-6-inducible miRNA with multiple targets[J]. J Leukoc Biol 2018,104(5):987\u0026ndash;1002.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMingting D, Yun R, Jiwen Z et al. High Expression of TIMM17B Is a Potential Diagnostic and Prognostic Marker of Breast Cancer[J]. Cell Mol Biol (Noisy-le-grand),2023,69(3):169\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Septic cardiomyopathy, Mitochondrial damage, DEGs, TIM23 complex","lastPublishedDoi":"10.21203/rs.3.rs-3802999/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3802999/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eSepsis poses a severe threat in critical care, often leading to septic cardiomyopathy. This study aimed to explore the genes related to mitochondrial damage in septic cardiomyopathy, observe their changes during sepsis, and analyze the possible pathogenesis of this disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eBioinformatics methods were used to identify differentially expressed genes (DEGs) and enrichment pathways associated with mitochondrial damage in patients with septic cardiomyopathy. Subsequently, clinical specimens and cardiomyocytes were verified and compared to clarify the expression of these genes and their change trend in the pathogenesis of septic myocardial injury to explore the possible relationship between these genes and septic myocardial mitochondrial dysfunction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eIn this study, using diverse datasets, 398 differentially expressed genes (DEGs) related to sepsis were identified, and 11 key genes (GNAS, MRPL2, TIMM17b, SLC25A3, SDHA, PRPF6, LMF2, IMMT, CS, UCP2, and CASP2) were significantly associated with these genes. Functional analysis highlighted the importance of the TIM23 complex in septic mitochondrial injury. Real-time fluorescence quantitative PCR was performed on 11 genes and TIMM23 expression in 24-48 hours in clinical specimens, and the expression of TIMM17b and TIMM23 was increased in the sepsis group, while the expression of the other 10 DEGs was decreased. Further verification via cell experiments revealed that the expression of 11 DEGs and 5 TIM23 complex member genes, TIMM23, TIMM17A, TIMM44, PAM16 and TIMM50, increased in the 6-hour group, while their expression decreased significantly in the 24-hour group; moreover, the expression of only TIMM17b was still greater than that in the normal control group. The expression of other genes was lower than or close to that of the normal control group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e This integrative study not only provides a comprehensive overview of DEGs associated with sepsis but also emphasizes the importance of the TIM23 complex. The identified genes and pathways offer potential targets for further mechanistic studies and therapeutic interventions in the context of sepsis-related complications.\u003c/p\u003e","manuscriptTitle":"Exploring the Genetic Landscape of Sepsis-Induced Cardiomyopathy: A Comprehensive Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-03 01:31:17","doi":"10.21203/rs.3.rs-3802999/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"111fa6b9-7f5c-42aa-8e52-a0b2974ba53b","owner":[],"postedDate":"January 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-13T06:18:53+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-03 01:31:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3802999","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3802999","identity":"rs-3802999","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}