Integrated Multi-Omics Reveal PDK4-Mediated Mitochondrial Dysfunction in Renal Ischemia-Reperfusion Injury

Integrated Multi-Omics Reveal PDK4-Mediated Mitochondrial Dysfunction in Renal Ischemia-Reperfusion Injury | 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 Short Report Integrated Multi-Omics Reveal PDK4-Mediated Mitochondrial Dysfunction in Renal Ischemia-Reperfusion Injury Zhixia Song, Xiaohong Xiang, Lang Shi, Bo Peng, Yijun Pan, Yaoyiao Shu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6927061/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) Renal ischemia reperfusion injury (IRI) represents the predominant etiology of acute kidney injury, yet its molecular events and underlying mechanism remain incompletely elucidated. (Methods) To investigate the integrative and dynamic pathophysiology of renal IRI, we conducted a comprehensive multi-omics (transcriptomics, proteomics and metabolomics) analysis of kidney tissues at distinct IRI time points (2 h, 6 h, 24 h and 7 d). Given that PDK4 was identified as the most consistently upregulated kinase in carbon metabolism, we further explored its effect and mechanism by generating PDK4-knockout mice and employing pharmacological inhibitors. (Results) Our study revealed remarkable metabolic reprogramming, particularly in carbohydrate metabolism, during kidney IRI. Correspondingly, notable gene and protein regulation was observed. Integrated multi-omics analysis demonstrated that PDK4 activation plays a pivotal role in modulating carbon metabolism. Animal experiments confirmed PDK4 activation and further demonstrated that genetic ablation or pharmacological inhibition of PDK4 attenuated renal injury, reduced tubule cell death, facilitated tubular proliferation and improved renal function. Mechanistically, PDK4 contributed to mitochondrial fragmentation through mediating Drp1 activation and translocation. (Conclusion) The present study delineates the extensive molecular reprogramming in kidney IRI and establishes PDK4 as critical nexus between energy metabolism dysregulation and renal tubular injury and cell death. Our multi-omics approach provides valuable insights for identifying novel therapeutic targets and developing renal protective strategies. multi-omics ischemia reperfusion injury kidney PDK4 mitochondria fragmentation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Acute kidney injury (AKI) is a critical public health issue leading to great socioeconomic burden and posing a significant threat to patient survival( 1 ). Without full recovery, the acute injured kidney will progress to chronic kidney disease (CKD), end stage renal disease (ESRD), and even death( 2 ). Ischemia reperfusion injury (IRI) is particularly clinically relevant to AKI. Ischemia occurs when kidney irrigation is compromised due to hemodynamic instability (e.g., shock) or surgical interventions such as kidney transplantation, while reperfusion exacerbates cellular damage through the abrupt generation of excessive reactive oxygen species (ROS) upon blood flow restoration( 3 ). There is limited therapeutic strategies to mitigate IRI-induced AKI. Hence, elucidating the pathophysiological changes and mechanisms are urgently warranted. Kidney tubular epithelial cells (TECs) are the dominant affected cell population in IRI, since they are susceptible to oxygen depletion/oxidative stress and prone to inflammatory response and cell death( 4 ). Metabolic reprogramming of TECs plays a critical role in the response to kidney injury( 5 ). It is well-characterized that during ischemia, oxidative phosphorylation in mitochondrion is dampened, the energy supply mode of TECs transits from fatty acid oxidation (FAO) to anaerobic glycolysis( 6 ). During the early stage of reperfusion, glycolysis is essential for maintaining cell viability. Meanwhile, inhibition of FAO is accompanied by lipid accumulation and subsequent lipotoxicity to cells( 7 ). Phenotypic modulation is driven by genetic variation and alterations in protein expression. Although significant advances in the identification of molecular and metabolic pathways in mediating kidney IRI, there is still a paucity of integrated multi-omics data to unravel the regulatory event driving IRI. This study aimed to comprehensively analyze kidney transcriptional, protein, and metabolic profiles associated with IRI induced AKI, providing a clue for identification of potential biomarkers for early recognition and therapeutic targets. 2. Materials and Methods 2.1 Animals and bilateral IRI model All animal experiments were approved by the Ethics Committee of the Ministry of Health of the People's Republic of China and the Ethics Committee of Three Gorges University (202205010T2). Male C57BL/6 mice (8–10 weeks old) were purchased from the Experimental Animal Center of China Three Gorges University (Yichang, China). For the bilateral ischemia-reperfusion injury (IRI) model( 8 ), mice with 3 replicates in each group were anesthetized with isoflurane, and the bilateral kidneys were subjected to ischemia by clamping the renal pedicles for 28 minutes, followed by reperfusion for 2 h, 6 h, 24 h, and 7 d. The renal pedicles were exposed in the sham control group without clamping. The renal tissues were homogenized for metabolomics, proteomics, and transcriptomes analysis, immunoblot analysis, or fixed for histopathological staining or immunostaining. The mice blood was collected for renal functional measurement. Renal function analysis using plasma creatinine and blood urine nitrogen (BUN) levels were measured by the central laboratory of the Center People’s Hospital of Yichang (Roche Diagnostics GmbH, Penzberg, and Mannheim, Germany). To establish PDK4 knockout (KO) mice, guide RNA (gRNA) for the target gene was designed to guide CRISPR/Cas9 nuclease to modify the PDK4 targeted allele, thus causing the inactivation of the PDK4 gene. Tail DNA from all mice was genotyped by PCR analysis. Wild type (WT) or KO mice were subject to ischemia 28 min and reperfusion for 24h, sham operation was used as control. To evaluate the effect of PDK4 inhibitor sodium dichloroacetate (DCA) in kidney IRI, mice were administered DCA (1g/L) in drinking water for consecutive 5 days before IR treatment. Vehicle-treated animals served as controls. 2.2 Untargeted metabolomics analysis For sample collection and preparation, the frozen mice kidney samples (n = 3) dissolved in 200 µL of H2O were homogenized and mixed with 800 µL methanol/acetonitrile (1:1, v/v) for metabolite extraction. After centrifugation, the supernatant was re-dissolved in 100 µL acetonitrile/water (1:1, v/v) solvent for LC-MS analysis. LC-MS/MS analysis was performed using an UHPLC (1290 Infinity LC, Agilent Technologies) coupled to a quadrupole time-of-flight (AB Sciex TripleTOF 6600) in Shanghai Applied Protein Technology Co., Ltd. For HILIC separation, samples were analyzed using a 2.1 mm × 100 mm ACQUIY UPLC BEH 1.7 µm column (waters, Ireland). For RPLC separation, a 2.1 mm × 100 mm ACQUIY UPLC HSS T3 1.8 µm column (Waters, Ireland) was used. For data processing, the raw data were converted. Compound identification of metabolites was performed by comparing of accuracy m/z value (< 10 ppm), and MS/MS spectra with an in-house database established with available authentic standards. For statistical analysis, after sum-normalization, the processed data were subjected to multivariate data analysis. The variable importance in projection (VIP) value of each variable in the Orthogonal partial least squares-discriminant analysis (OPLS-DA) model was calculated to indicate its contribution to the classification. Student’s t test was applied to determine the significance of differences between two groups of independent samples. VIP > 1 and p value < 0.05 were used to screen significant changed metabolites. 2.3 Proteomics analysis The mass spectrometry experimental analysis process mainly includes proteins extraction (4%(w/v) SDS, 100mM Tris/HCl pH7.6, 0.1M DTT); trypsin digestion to peptides (Filter aided proteome preparation); tandem mass tag (TMT) labeling; and peptides fractionation; Then, nano-HPLC-MS/MS was performed; the samples were loaded to the loading column (Thermo Scientific Acclaim PepMap100, 100µm*2cm, nanoViper C18), and then passed through the analysis column (Thermo scientific EASY Column, 10cm, ID75µm, 3µm, C18-A2) for separation, then the separated samples were analyzed by Q-Exactive mass spectrometer. MaxQuant (Germany) was used to process the raw data. For bioinformatics analysis, proteins were annotated for differentially expressed proteins (DEPs) analysis, GO/KEGG functional analysis. After Student’s t test, proteins with P value 1.2 or < 0.83 were filtered as DEPs. 2.4 Transcriptomics analysis Total RNA extraction, quality assessment, and quantification was performed as previously reported( 9 ). High quality RNA sample was used as subsequent library construction and RNA-sequence. Paired-ended library was generated using ABclonal mRNA-seq kit (ABclonal, China). Briefly, the mRNA was purified by oligo (dT) magnetic beads. Fragmentation was performed using divalent cations under ABclonal First Strand Synthesis Buffer. Then, first strand cDNA and second strand cDNA were synthesized. The library fragment was purified with AMPure XP (Beckman Coulter, Beverly, MA, USA). Illumina Novaseq 6000 was used for sequencing. FPKM (Fragments per kilo base of transcript per million mapped fragments) value of expression of each gene in each sample was calculated. Differential expression analysis of gene was performed using Deseq2 package, genes with an adjust P value 2 were identified as significantly expressed genes. 2.5 Gene ontology enrichment analysis and Kyoto Encyclopedia of genes and genomes of DEGs and DEPs. The database for annotation, visualization, and integrated discovery (DAVID 2022; https://davidbioinformatics.nih.gov)(10, 11) , was used to determine biological functions of upregulated and downregulated DEGs and DEPs at different time points of kidney IRI, which include gene ontology analysis (biological process, BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The cutoff values for GO/KEGG analysis were set at the false discovery rate of < 0.05. 2.6 Integrative analysis of transcriptome, proteome and metabolome For integrative KEGG analysis of transcriptome and metabolome, proteome and metabolome, Fisher’s Exact Test and the different molecules of the two omics were used for enrichment analysis of the KEGG pathway based on KEGG annotations. For integrated transcriptome, proteome and metabolome analysis, we used KEGG database which includes the pathway mapping and interrelationship information of gene, protein and metabolites. By projecting the DEGs, DEPs and DEMs to the KEGG pathway at the same time, we comprehensively integrated the pathway data ( https://www.genome.jp/kegg/pathway.html ). 2.7 Histological and immunohistochemistry staining and western blot. Kidney tissues were fixed for paraffin embedding and sectioning. For histological analysis, sections were stained with hematoxylin and erosion (H&E). Tubular injury score was defined by percentage of renal tubules showing cell lysis, loss of brush border, and cast formation (0, no damage, 1, 75%)( 12 ). For immunohistochemistry (IHC) analysis, sections were deparaffinized, rehydrated, subjected to antigen retrieval and blocked. Sections were incubated with primary antibodies against PDK4 (Proteintech, 12949-1-AP), p-PDHA1(Ser232) (ThermoFisher, 81491-1-RR), Ki67 (Cell signaling, 34330) and kidney injury molecule-1 (KIM-1) (R&D system, AF1817). Sections were incubated with HRP-conjugated secondary antibodies and developed with DAB substrate. For immunoblot, kidney tissues were lysed in RIPA buffer with protease and phosphatase inhibitors followed by protein concentration analysis. Equal amount of protein was sequentially separated by SDS-PAGE, transferred to PVDF membranes and blocked by fat-free milk. The primary antibody used in the study includes PDK4 (Proteintech, 12949-1-AP), PDHA1 (Proteintech, 18068-1-AP), p-PDHA1 (Ser232) (ThermoFisher, 81491-1-RR), PCNA (Cell signaling, 13110), CyclophilinB (Cell signaling, 79652), Drp1 (BD Biosciences, 611113), GAPDH (Cell signaling, 5174), COX IV (Abcam, ab16056), p-Drp1(ser 616) (Cell signaling, 3455). Membranes were then incubated with HRP-conjugated secondary antibodies and developed using ECL detection system. Densitometric analysis was performed using ImageJ and representative images were present. 2.8 Electron microscope. Electron microscope observing mitochondria was performed as previously described( 12 ). Briefly, a fresh kidney tissue block including a portion of renal cortex and outer medulla of approximately 1 mm 3 was collected from each kidney. Then the tissues were fixed in 100 mM sodium cacodylate, 2 mM calcium chloride, 4 mM magnesium sulfate, 4% paraformaldehyde, and 2.5% glutaraldehyde. Fixed kidneys were blocked, sectioned and stained for transmission electron microscope observation. The tissue section was examined at low (x3000) and high (x15000) magnification to identify representative proximal tubules and collect electron micrographs, respectively. 2.9 Isolation of mitochondrial and cytosolic fraction. Cellular isolation of cytosolic and mitochondrial fractions from tissue was described as previously with some minor modifications from our earlier work( 12 ). Kidney tissues were minced and homogenized in a mixture of 0.1% BSA and mitochondria isolation buffer containing 225 mM mannitol, 75 mM sucrose, 1 mM ethylene glycol tetra acetic acid, 10 mM Tris-hydrochloride with protein inhibitor cocktail (pH 7.4), in the purpose of rupturing cell membrane gently. The homogenates were centrifuged at 1000 × g for 10 min at 4°C for several rounds to pellet cell debris and nuclei and the supernatants were collected. For mitochondria enrichment, the supernatants were centrifugated at 15,000 × g for 15 min to get the supernatant as cytosolic fraction and the pellet as mitochondrial fraction. 2.10 Cell culture and treatment The rat proximal tubular cells (RPTCs) were originally obtained from Sciencell Research laboratories and cultured as previously described( 12 ). Hypoxia-reperfusion model in vitro was achieved by carbonyl cyanide 3-chlorophenylhydrazone (CCCP) treatment at 10 µM for 3 h and followed by full-medium replacement for 2 h for reperfusion. The cells were pretreated with 5mM DCA for 1 h before CCCP treatment. Cells were stained with TdT-mediated dUTP Nick-End Labeling (TUNEL) and Dihydroethidium (DHE) to observe cell apoptosis and ROS production, respectively. 2.11 Statistics For plots, the heatmap plots and KEGG plots were drawn by https://www.bioinformatics.com.cn , an online platform for data analysis and visualization. The GO-BP plots and integrative KEGG plots were plotted by Excel. For statistical analysis, the Student’s t test was used for the significant difference between 2 groups. ANOVA was used for multi groups comparison. Data was shown as mean ± SD, and P < 0.05 was set as significant difference. GraphPad Prism version 9 was used for statistical analysis. 3. Result 3.1 Metabolomics reveals metabolic perturbation in kidney IRI. Adult C57BL/6 mice were subjected to bilateral kidney ischemic injury for 28 min followed by reperfusion at designated time points (2 h, 6 h, 24 h, and 7 d). The sham operated or injured kidneys were dissected for metabolomics to clarify temporal changes in metabolites. 2066 metabolites in total were identified after combining positive (1265) and negative (801) ion modes. From the chemical taxonomy (Fig. 1 A), we can figure out that lipids and lipid-like molecules (green) along with organic acids and derivatives (dark blue) account for the most proportion, corroborating significant carbohydrate, lipid and amino acid metabolic reprogramming in kidney IRI. Multivariate statistical analysis demonstrated clear temporal metabolic shifts. Principal component analysis (PCA) model showed distinct clustering of experimental groups, with principal component 2 (PC2) effectively separating sham controls from IRI 24 h and 6 h samples, suggesting maximal metabolic perturbations at these time points (Fig. 1 B). Different expressed metabolites (DEMs) were screened by cutoff values of OPLS-DA VIP > 1 and P value < 0.05. As shown in Fig. 1 C, in combination of positive and negative ion modes, there are 95 upregulated metabolites and 39 downregulated at 2 h; 72 and 40 at 6 h; 132 and 60 at 24 h; 111 and 53 at 7 d; respectively. For carbohydrate metabolism (Fig. 1 D), some intermediate products of tricarboxylic acid (TCA) cycle (2-hydroxyglutarate, fumarate, malate, cis-aconitate) showed significant elevation but paradoxical absence of acetyl-CoA accumulation, the central ingredient of TCA cycle. Notably, pyruvate, the linkage between glycolysis and TCA cycle, exhibits sustained increase (peaking at 24 h, red box), suggesting enhanced glycolytic flux (Fig. 1 D). For amino acid metabolism ( Supplementary Fig. 1 ), creatine and creatinine increase indicated deteriorated renal function upon IRI (red box). In contrast, specific reduction of amino acids that promote oxidative metabolism via the TCA cycle was observed, including glutamine, glutamic acid, leucine, asparagine, etc (blue box), suggesting compensatory depletion of amino acid when carbohydrate metabolism is compromised. From lipid metabolic analysis ( Supplementary Fig. 2 ),there is global increase in most lipid species upon IRI, except for some specific prenol lipids (blue box), which is consistent with prior reports of lipid deposition in IRI-induced AKI( 13 ). 3.2 Transcriptomic profiling and integration of transcriptomic and metabolomic analysis in renal IRI. PCA analysis of RNA-seq data demonstrated clustering of replicates intra-group and clear separation inter-groups (Fig. 2 A). The most pronounced transcriptional changes occurred at 24 h post-IRI, as evidenced by maximal segregation along PC1 between IRI 24 h and sham groups. When setting the adjusted P value of 2, generous differentiated expressed genes (DEGs) were identified as shown in Fig. 2 B. For example, 2407 genes elevated while 2563 dropped post-IRI 24 h. Biological significance of DEGs was explored by gene ontology (GO) term enrichment analysis of biological process (BP). As expected, increased genes enriched in response to stimulus/stress or immune/inflammatory process at 2 h ( Supplementary Fig. 3A ), 6 h( Supplementary Fig. 3B ), 24 h(Fig. 2 C), and 7 d( Supplementary Fig. 3C ), in keeping with our and others’ previous reports( 9 , 14 ). In contrast, genes enriched in organic acid metabolic process, mitochondrial TCA cycle and oxidation-reduction reactions decreased, which is consistent across all time points. KEGG analysis corroborated the GO-BP findings: activated pathways included inflammatory signaling pathways (NF-κB, TNF signaling) and apoptosis pathways, while decreased DEGs mainly enriched in TCA cycle, glyoxylate and dicarboxylate metabolism, and amino acid metabolism (Fig. 2 D, Supplementary Fig. 3D-F ). Integrative KEGG analysis of transcriptomic and metabolomic indicated central carbon metabolism dysregulation post-IRI. For example, transcriptional suppression of TCA cycle enzymes (IDH2, SDHB, MDH2) and corresponding metabolite accumulation (malate, fumarate) suggested impairment of TCA cycle. Besides, glutathione metabolism impairment or amino acid utilization shifts indicate their involvement in pathophysiology of IRI (Fig. 2 E ). 3.3 Proteomic profiling and integration of proteomic with metabolomic/transcriptomic analysis in renal IRI. PCA analysis of the global proteome revealed distinct temporal patterns, with decent isolation of sham controls from IRI 24 h IRI samples in PC2 and from IRI 7 d in PC1, which highlights the dominant protein-level alterations in these 2 time points (Fig. 3 A). The numbers of different expressed proteins (DEPs) across all time points were displayed in Fig. 3 B, with the fold change > 1.2 or < 0.83 and adjust P value < 0.05. To determine the critical proteins in acute phase of kidney IRI, we used Venn diagram to show 64 proteins persistently upregulated from 2 h to 24 h, while 11 proteins kept going down ( Supplementary Fig. 4A ). The heatmap of these acute-phase DEPs (Fig. 3 C ) revealed PDK4 as top-ranked candidate, showing progressive upregulation peaking at 24 h. This expression pattern suggests its pivotal role in metabolic disturbance during IRI. We then took advantage of KEGG analysis to show proteins enriched in inflammatory pathway, ferroptosis were activated, while proteins enriched in pyruvate metabolism, amino acid metabolism were suppressed generally (Fig. 3 D, Supplementary Fig. 4B-D ). Integrated proteomic-metabolomic KEGG mapping manifested the important role of carbohydrate, amino acid metabolism and ferroptosis pathway upon IRI especially in acute phase (Fig. 3 E). In addition, transcript-protein correlation analysis at different time points were performed and their correlation coefficients were calculated. The scatter plots were shown in Supplementary Fig. 5A-D . Strong concordance of mRNA and proteins variation was revealed at 24 h (Spearman’s R = 0.7012, Supplementary Fig. 5C ). Otherwise, both the mRNA and protein levels of PDK4 upregulated consistently from 2 h to 6 h until 24 h ( Supplementary Fig. 5A-C ). We can also figure out that some new stress marker like calcium-binding protein complex s100a8/a9( 15 ), krt20( 16 ), and some classic stress marker like Lcn2, Havcr1 upregulated both in mRNA and protein level at early time and sustained through 24 h, indicating their sensitivity and stability as early stress markers ( Supplementary Fig. 5A-C ). 3.4 Integrated analysis of transcriptome, proteome, and metabolome in renal IRI. We finally took advantage of integrative analysis of muti-omics to clarify the pathophysiologic events in kidney IRI. From Fig. 4 A, hexokinase, the first key rate-limiting enzyme of glycolysis was upregulated at transcriptional and protein level. Meanwhile, the end-product of glycolysis, pyruvate, was markedly increased. Another mode of oxidative breakdown of glucose is pentose phosphate pathway (PPP). We can see that the 2 key enzymes of PPP, glucose-6-phosphate dehydrogenase (G6PD) and 6-phosphogluconate dehydrogenase (PGD), were transcriptionally increased, indicating the activation of PPP. In contrast, the most effective glucose-oxidizing pathway, TCA cycle, was dampened. Firstly, the activation of PDK4 and inhibition of pyruvate dehydrogenase complex (PDC) limits acetyl-CoA production. Secondly, the enzymes in TCA cycle were downregulated to impair cycle turnover. For example, the rate limiting enzymes, isocitrate dehydrogenase (IDH) and oxoglutarate dehydrogenase (OGDC), were diminished. The blockade of TCA cycle decreases the production of hydrion, leading to collapse of oxidative phosphorylation (OXPHOS) and surge of reactive oxygen (ROS). Another ingredient of acetyl-CoA is fatty acid oxidation (FAO), which is the major energy source of renal tubule epithelial cells. The multi-omics analysis revealed that FAO was hindered due to the suppression of key enzymes (ACOX/ACADM). Glucogenic amino acids (Asp, Asn, Glu, Gln, etc.) were also decreased because of consumption when shortage of carbohydrate. Except for the disorder of central carbon metabolism, cell death including apoptosis, ferroptosis was dramatically triggered in kidney IRI. 3 pillars of ferroptosis (iron, PUFA and glutamate metabolism) were all disturbed, in favor of ferroptosis aberrantly activation ( Supplementary Fig. 5E) . Glutathione crisis emerged despite SLC7A11 upregulation, as glutamate scarcity and γ-glutamylcysteine (γ-GC) deficiency constrained glutathione (GSH) synthesis. On the other hand, lipid peroxidation was driven by polyunsaturated fatty acid (PUFA: adrenic acid, linoleic acid) accumulation and induction of Acyl-CoA synthetase long-chain family member 4 (ACSL4), the first essential enzyme for ferroptosis execution. Transferrin (TF) to transport extracellular ferric iron and HO-1 to catalyze heme together resulted in deposition of iron divalent and triggered ferroptosis. From the integrative analysis of multi-omics, we can conclude that ferroptosis is definitely activated and plays a critical role in kidney IRI. 3.5 Validating of PDK4 upregulation in renal IRI in vivo . Our integrated multi-omics analysis identified PDK4 as a central regulator in kidney IRI pathogenesis. To experimentally validate these findings, we firstly exploring its regulation in kidney biopsies from mice with bilateral IRI by 28 minutes of renal ischemia followed by 24 h of reperfusion. Notable histological changes and kidney tubules injury were observed in the kidneys of IRI 24 h when compared with control ones by H&E staining (Supplementary Fig. 6A) and KIM-1 staining (Supplementary Fig. 6B) . Immunohistochemical analysis confirmed dramatic PDK4 upregulation in injured tubules (Fig. 4 B), with quantitative scoring showing a 4.0-fold increase versus sham controls (p < 0.05, Fig. 4 C). Meanwhile, phosphorylated PDHE1α/ PDHA1at Ser 232 (E1 α subunit of pyruvate dehydrogenase, a target protein of PDK4) was induced in the tubules of renal IRI, with 4.5-fold increase in immunostaining intensity ( Fig. 4 D, E ) . Immunoblot further demonstrated PDK4 and p-PDHA1/PDHA1 ratio elevation in renal IRI ( Fig. 4 F ). The above data collectively demonstrated PDK4 expression is significantly induced and its enzymatic activity is functionally increased during kidney IRI. The concordance between multi-omics predictions and experimental validation solidifies PDK4's role as a key metabolic regulator in IRI pathophysiology. 3.6 PDK4 inhibition attenuates kidney IRI. To demonstrate the role of PDK4 in IR-induced kidney injury, we established PDK4 knockout mice by Cas9/gRNA. The process of gene modification and verification were illustrated in Fig. 5 A and 7 B, respectively. Immunoblot analysis confirmed complete PDK4 ablation and concomitant reduction in PDHA1 phosphorylation (Fig. 5 C). The BUN and serum creatinine concentrations showed remarkable increase in IR-induced WT mice than in sham operated mice, while the upregulation was attenuated in PDK4-deficient mice significantly (Fig. 5 D). From histological analysis, no abnormality was observed in sham-operated knockout mice in comparison to WT mice, but substantial protection against IR-induced kidney injury was found in PDK4-KO mice when compared to WT-injured mice. As shown in Fig. 5 E, F, there were plenty of tubule’s vacuum (red star), even lysed (yellow arrow) due to brush border shedding or tubular cells death in IR-treated WT mice, while the injury was alleviated by PDK4 knockout. Meanwhile, KIM-1, the indicator of injured kidney proximal tubules, demonstrated remarkable protection in IR-induced KO mice when compared with WT mice (Fig. 5 G, H). In addition, PDHE1α phosphorylation by IR-treatment was lowered by PDK4 knockout (Fig. 5 I). Pharmacological inhibition of PDK4 mimics genetic protection. Classic PDK4 inhibitor dichloroactate (DCA) was administered to C57BL/6 mice for 5 consecutive days prior to renal IRI execution ( Supplementary Fig. 7A ). DCA effectively inhibited PDK4 upregulation in renal IRI, evidenced by PDK4 reduction and p-PDHA1/PDHA1 ratio decrease ( Supplementary Fig. 7B ). When compared with the vehicle feeding mice, the DCA-treated mice were more tolerated to IRI, as evidenced by lower serum BUN and creatinine concentrations ( Supplementary Fig. 7C, D ), improved kidney morphology ( Supplementary Fig. 7E, F ) and less tubular damage by KIM-1 detection ( Supplementary Fig. 7G, H ). Reduced P-PDHE1α immunostaining again demonstrated the efficiency of inhibitory effect of DCA on PDK4 ( Supplementary Fig. 7I ). In addition, we mimicked kidney IRI in vitro by CCCP treatment followed by reperfusion in RPTCs. Phase contrast showed that cell death and detachment caused by CCCP-R was significantly suppressed by DCA pre-treatment (50%-35% statistically, Supplementary Fig. 8A, B ). Meanwhile, TUNEL staining showed that DCA pre-treatment markedly alleviated CCCP-R induced RPTCs apoptosis ( Supplementary Fig. 8C, D ). The superoxide indicator DHE exhibited a mass of fluorescent red upon CCCP-R, which was notably inhibited by DCA ( Supplementary Fig. 8E ). 3.7 PDK4 deficiency increase tubular proliferation post-IRI. Tubular proliferation represents a critical mechanism for renal regeneration following kidney IRI. To investigate whether PDK4 deficiency enhances the proliferative response, we assessed Ki67 by immunohistochemistry staining. As shown in Fig. 6 A, there was minimal Ki67 staining in sham-operated kidneys. Following IRI, Ki67-positive cells generally increased. Notably, while Ki67 staining in WT mice was predominantly spotted in the renal interstitium, Ki67 positive cells in PDK4-KO mice primarily localized within tubular epithelia. The statistical analysis confirmed the significant shift (Fig. 6 B, C). Consistent with these findings, immunoblot analysis of PCNA, a key regulator of cell proliferation, was significantly upregulated following IRI in comparison to sham treatment. However, the upregulation was more pronounced in PDK4-KO kidneys than WT ones subjected to IRI (Fig. 6 D, E). These finding indicates PDK4 ablation improves the recovery and regeneration of tubular cells. 3.8 PDK4-knockout inhibited mitochondrial fragmentation via Drp1 phosphorylation and mitochondria translocation in kidney IRI. To elucidate the potential mechanism by which PDK4 contributes to renal tubular injury, we investigated mitochondrial dynamics in kidney IRI using genetic modification mice. As shown in Fig. 7 A, electron microscope observing proximal tubular cells from IRI subjected WT kidneys revealed a loss of filamentous mitochondrial morphology, with mitochondria predominantly adopting a punctate appearance, indicative of mitochondrial fragmentation. In contrast, mitochondrial fragmentation was significantly attenuated in PDK4 KO mice. In quantification, nearly 45% tubules in WT mice were observed with fragmented mitochondria, whereas the number reduced to approximately 25% in PDK4-KO mice (Fig. 7 B). Given the established role of dynamin-related protein 1(Drp1) in regulating mitochondria fission and fusion ( 17 , 18 ), particularly through its activation via phosphorylation at serine 616 and subsequent translocation to mitochondria to mediate fission ( 19 ), we assessed Drp1 activation status. Immunoblot analysis demonstrated obvious Drp1 phosphorylation at Ser616 (activation) in IR-injured WT kidneys, which was significantly suppressed in PDK4-KO kidneys (Fig. 7 C, D). Meanwhile, subcellular fractionation analysis revealed IR-induced translocation of Drp1 to the mitochondria compartment in WT kidneys. This translocation was markedly inhibited by PDK4 deficiency (Fig. 7 E, F). Taken together, these findings collectively indicate that PDK4 promotes mitochondria fragmentation during kidney IRI through facilitating the phosphorylation and mitochondria translocation of key fission protein Drp1. 3. Discussion Through systematic multi-omics analysis, this study comprehensively characterizes the metabolic, transcriptional, and proteomic alterations in renal ischemia-reperfusion injury (IRI), with PDK4 emerging as a central regulatory hub. Our experimental validation demonstrates significant PDK4 upregulation in a murine IRI model, where it orchestrates key pathological processes. Both genetic knockout and pharmacological inhibition of PDK4 conferred remarkable renal protection, as evidenced by improvement in renal function, attenuation of tubular injury, and enhancement of tubular proliferative capacity post-IRI. Mechanistically, PDK4 contributed to Drp1 phosphorylation and mitochondrial translocation, resulting mitochondrial fragmentation. This study establishes PDK4 as a critical node in IRI pathogenesis, bridging metabolic dysfunction (particularly TCA cycle impairment), mitochondrial damage, and cell death, while providing a foundation for mechanism-based AKI therapies. Reprogramming of central carbon metabolism (CCM, including glycolysis, PPP and TCA cycle) represents a critical adaptive response in renal IRI. Our multi-omics revealed coordinated activation of glycolytic and PPP, evidenced by upregulation of key enzymes (HK, G6PD, PGD) and significant pyruvate accumulation. However, aerobic energy metabolism is substantially impaired through PDK4-mediated inhibition of PDC, which limits acetyl-CoA production and consequently suppresses TCA cycle activity and OXPHOS. The underlying mechanism of PDK4 in the pathology of IRI is not clear. Since it is acknowledged that succinate accumulation during ischemia is responsible to mitochondrial ROS production during reperfusion and lead to tubule injury( 20 ).Oh et al firstly reported PDK4 involvement in cisplatin AKI ( 21 ) and IRI-induced AKI ( 22 ). They implicated that PDK4 aggravates succinate accumulation during ischemia and mitochondrial ROS generation during reperfusion ( 22 ). Our multi-omics study provides novel mechanistic insights by demonstrating PDK4 drives mitochondrial fragmentation through Drp1-dependent mechanisms. What’s more, the coordinated suppression of FAO and specific glucogenic amino acid (proline, glutamate, glutamine, phenylalanine) depletion suggest PDK4 may serve as a central regulator of multiple metabolic pathways in renal IRI. These findings significantly expand our understanding of PDK4's pathophysiological role and highlight the need for further investigation into its multi-faceted regulation of renal metabolic homeostasis during ischemic injury. Fatty acid metabolism dysregulation and ferroptotic cell death play pivotal roles in renal IRI ( 23 ). As the primary energy source for proximal tubular cells, impaired FAO-evidenced by marked downregulation of key enzymes (Acox1/2, Acadm, Acsm1/3) - contributes significantly to tubular injury, consistent with previous findings by ours( 9 ) and others( 13 ). Notably, polyunsaturated fatty acids (PUFAs) as a special fatty acid, particularly arachidonic acid derivatives, drive ferroptosis through three established mechanisms: ( 1 ) ACSL4-mediated PUFA activation (confirmed at mRNA/protein levels) promotes lipid peroxidation; ( 2 ) iron dysregulation via increased transferrin/ferritin and HO-1 induction leads to labile Fe²⁺ accumulation and Fenton reactions( 24 ); and ( 3 ) compromised glutathione metabolism occurs despite SLC7A11 upregulation, as glutamate scarcity limits γ-glutamylcysteine and subsequent GSH synthesis ( 25 ).Our integrated multi-omics analysis reveals a coordinated shift toward pro-ferroptotic conditions (elevated PGs/eicosanoids, ACSL4 activation, unstable divalent iron accumulation to trigger Fenton activity) while anti-ferroptotic pathways (depleted GPX4) are suppressed. These findings not only confirm ferroptosis as a dominant cell death pathway in renal IRI but also suggest PDK4 may modulate this process Future studies should identify potential role of PDK4 in regulating lipid peroxidation and iron metabolism, to develop targeted strategies against ferroptosis in acute kidney injury. In addition, emerging evidence highlights the critical role of amino acid metabolic dysregulation in kidney disease pathogenesis, particularly AKI( 26 ). In the present study, we observed significant accumulation of arginine catabolism byproducts alongside upregulation of arginase 2 (Arg2), a potential mediator of renal IRI pathology and promising therapeutic target( 27 ). In contrast, a substantial reduction in branched-chain amino acid transferase 1 (BCAT1) expression was identified with unknown role in kidney, representing a novel finding in renal pathophysiology. As BCAT1 catalyzes the conversion of branched-chain amino acids to glutamate while maintaining α-ketoglutarate homeostasis, its downregulation may directly contribute to the observed glutamate deficiency and consequently impair cellular redox balance( 28 ). These findings not only expand our understanding of metabolic reprogramming in AKI but also, beyond PDK4, identify additional potential therapeutic targets such as Arg2 and BCAT1-related pathways that warrant further investigation for renal protection strategies. There are some limitations and prospects to be acknowledged in the present study. First, the cellular heterogeneity induced by IRI may affect the interpretation of bulk RNA-Seq and proteomic data, suggesting that single-cell resolution approaches could yield more precise cell type-specific information in future investigations. Second, although our untargeted metabolomics identified significant alterations in specific metabolite classes, targeted metabolomic validation would provide more quantitative and detailed characterization of these metabolic changes. Importantly, our integrated analysis not only confirms PDK4 as a central metabolic regulator but also identifies several other potential diagnostic markers and therapeutic targets. These findings establish a robust foundation for: ( 1 ) developing PDK4-targeted therapies, ( 2 ) exploring combination strategies with other identified targets, and ( 3 ) validating candidate biomarkers in clinical AKI settings. Abbreviations Acute kidney injury (AKI), Acyl-CoA synthetase long-chain family member 4 (ACSL4), arachidic acid (AA) ,arginase 2 (Arg2) ,blood urine nitrogen (BUN) ,biological process (BP) , 3-chlorophenylhydrazone (CCCP) ,central carbon metabolism (CCM) ,chronic kidney disease (CKD) ,sodium dichloroacetate (DCA) ,differentially expressed proteins (DEPs) ,Different expressed metabolites (DEMs) ,differentiated expressed genes (DGEs), The database for annotation, visualization, and integrated discovery (DAVID), Dihydroethidium (DHE), dynamin-related protein 1(Drp1), end stage renal disease (ESRD) , glyceraldehyde-3-phosphate dehydrogenase (GAPDH) fatty acid oxidation (FAO), guide RNA (gRNA) , gene ontology (GO) , glucose-6-phosphate dehydrogenase (G6PD),glutathione (GSH) , γ-glutamylcysteine (γ-GC) , heme oxygenase (HO-1), hematoxylin and erosion (H&E) , immunohistochemistry (IHC) , isocitrate dehydrogenase (IDH) , ischemia reperfusion injury (IRI) , knock out (KO) , Kyoto Encyclopedia of Genes and Genomes (KEGG) , kidney injury molecule-1 (KIM-1) , Orthogonal partial least squares-discriminant analysis (OPLS-DA) , oxoglutarate dehydrogenase (OGDC) , oxidative phosphorylation (OXPHOS) , E1 α subunit of pyruvate dehydrogenase (PDHE1α) ,prostaglandins (PGs) , pentose phosphate pathway (PPP) , 6-phosphogluconate dehydrogenase (PGD) , pyruvate dehydrogenase complex (PDC) , polyunsaturated fatty acid (PUFA) , principal component analysis (PCA) , Principal component 1 (PC1) , rat proximal tubular cells (RPTCs) , reactive oxygen species (ROS) , tubular epithelial cells (TECs) , tandem mass tag (TMT) , TdT-mediated dUTP Nick-End Labeling (TUNEL) , tricarboxylic acid (TCA) , Transferrin (TF) , variable importance in projection (VIP), Wild type (WT). Declarations Ethics approval and consent to participate All animal experiments were approved by the Ethics Committee of the Ministry of Health of the People's Republic of China and the Ethics Committee of Three Gorges University (202205010T2). Consent for publication Not applicable. Availability of data and materials. The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare no competing interest. Funding This work is supported by National Youth Science Foundation of China (82402552), Knowledge Innovation Program of Wuhan-Shuguang Project (2023020201020505), The Medical Research Special Talent Project of Longhua District Medical Association, Shenzhen (2024LHMA01), and Undergraduate Training Programs for Innovation of Wuhan University. Author contributions Conceptualization: Zhu J, Xiang X; Methodology: Song Z, Shi L; Validation: Peng B, Pan Y; Formal analysis: Peng B, Shu Y; Investigation: Shu Y, Pan Y; Draft writing: Xiang X, Review and Editing: Song Z, Zhu J, Figures: Zhu J, Xiang X; Visualization: Song Z, Zhu J; Supervision: Zhu J, Xiang X. Acknowledgements Not applicable. Clinical trial Not applicable. References Hoste EAJ, Kellum JA, Selby NM, Zarbock A, Palevsky PM, Bagshaw SM, et al. Global epidemiology and outcomes of acute kidney injury. Nat Rev Nephrol. 2018;14(10):607–25. Peerapornratana S, Manrique-Caballero CL, Gómez H, Kellum JA. Acute kidney injury from sepsis: current concepts, epidemiology, pathophysiology, prevention and treatment. Kidney Int. 2019;96(5):1083–99. Li C, Yu Y, Zhu S, Hu Y, Ling X, Xu L, et al. The emerging role of regulated cell death in ischemia and reperfusion-induced acute kidney injury: current evidence and future perspectives. Cell Death Discov. 2024;10(1):216. Eltzschig HK, Eckle T. Ischemia and reperfusion–from mechanism to translation. Nat Med. 2011;17(11):1391–401. van der Rijt S, Leemans JC, Florquin S, Houtkooper RH, Tammaro A. Immunometabolic rewiring of tubular epithelial cells in kidney disease. Nat Rev Nephrol. 2022;18(9):588–603. Lan R, Geng H, Singha PK, Saikumar P, Bottinger EP, Weinberg JM, et al. Mitochondrial Pathology and Glycolytic Shift during Proximal Tubule Atrophy after Ischemic AKI. J Am Soc Nephrology: JASN. 2016;27(11):3356–67. Todorović Z, Đurašević S, Stojković M, Grigorov I, Pavlović S, Jasnić N et al. Lipidomics Provides New Insight into Pathogenesis and Therapeutic Targets of the Ischemia-Reperfusion Injury. Int J Mol Sci. 2021;22(6). Shi L, Song Z, Li Y, Huang J, Zhao F, Luo Y, et al. MiR-20a-5p alleviates kidney ischemia/reperfusion injury by targeting ACSL4-dependent ferroptosis. Am J Transpl. 2023;23(1):11–25. Zhu J, Xiang X, Shi L, Song Z, Dong Z. Identification of Differentially Expressed Genes in Cold Storage-associated Kidney Transplantation. Transplantation. 2024;108(10):2057–71. Sherman BT, Hao M, Qiu J, Jiao X, Baseler MW, Lane HC, et al. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 2022;50(W1):W216–21. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44–57. Song Z, Xia Y, Shi L, Zha H, Huang J, Xiang X, et al. Inhibition of Drp1- Fis1 interaction alleviates aberrant mitochondrial fragmentation and acute kidney injury. Cell Mol Biol Lett. 2024;29(1):31. Lee LE, Doke T, Mukhi D, Susztak K. The key role of altered tubule cell lipid metabolism in kidney disease development. Kidney Int. 2024;106(1):24–34. Liu J, Kumar S, Dolzhenko E, Alvarado GF, Guo J, Lu C, et al. Molecular characterization of the transition from acute to chronic kidney injury following ischemia/reperfusion. JCI Insight. 2017;2:18. Dessing MC, Tammaro A, Pulskens WP, Teske GJ, Butter LM, Claessen N, et al. The calcium-binding protein complex S100A8/A9 has a crucial role in controlling macrophage-mediated renal repair following ischemia/reperfusion. Kidney Int. 2015;87(1):85–94. Gerhardt LMS, Liu J, Koppitch K, Cippà PE, McMahon AP. Single-nuclear transcriptomics reveals diversity of proximal tubule cell states in a dynamic response to acute kidney injury. Proc Natl Acad Sci USA. 2021;118(27). Zhu J, Zhang G, Song Z, Xiang X, Shu S, Liu Z, et al. Protein Kinase C-δ Mediates Kidney Tubular Injury in Cold Storage-Associated Kidney Transplantation. J Am Soc Nephrology: JASN. 2020;31(5):1050–65. Brooks C, Wei Q, Cho S-G, Dong Z. Regulation of mitochondrial dynamics in acute kidney injury in cell culture and rodent models. J Clin Investig. 2009;119(5):1275–85. Koval OM, Nguyen EK, Santhana V, Fidler TP, Sebag SC, Rasmussen TP et al. Loss of MCU prevents mitochondrial fusion in G1-S phase and blocks cell cycle progression and proliferation. Sci Signal. 2019;12(579). Chouchani ET, Pell VR, Gaude E, Aksentijević D, Sundier SY, Robb EL, et al. Ischaemic accumulation of succinate controls reperfusion injury through mitochondrial ROS. Nature. 2014;515(7527):431–5. Oh CJ, Ha C-M, Choi Y-K, Park S, Choe MS, Jeoung NH, et al. Pyruvate dehydrogenase kinase 4 deficiency attenuates cisplatin-induced acute kidney injury. Kidney Int. 2017;91(4):880–95. Oh CJ, Kim M-J, Lee J-M, Kim DH, Kim I-Y, Park S, et al. Inhibition of pyruvate dehydrogenase kinase 4 ameliorates kidney ischemia-reperfusion injury by reducing succinate accumulation during ischemia and preserving mitochondrial function during reperfusion. Kidney Int. 2023;104(4):724–39. Kang HM, Ahn SH, Choi P, Ko Y-A, Han SH, Chinga F, et al. Defective fatty acid oxidation in renal tubular epithelial cells has a key role in kidney fibrosis development. Nat Med. 2015;21(1):37–46. Jiang X, Stockwell BR, Conrad M. Ferroptosis: mechanisms, biology and role in disease. Nat Rev Mol Cell Biol. 2021;22(4):266–82. Pefanis A, Ierino FL, Murphy JM, Cowan PJ. Regulated necrosis in kidney ischemia-reperfusion injury. Kidney Int. 2019;96(2):291–301. Knol MGE, Wulfmeyer VC, Müller R-U, Rinschen MM. Amino acid metabolism in kidney health and disease. Nat Rev Nephrol. 2024;20(12):771–88. Hara M, Torisu K, Tomita K, Kawai Y, Tsuruya K, Nakano T, et al. Arginase 2 is a mediator of ischemia-reperfusion injury in the kidney through regulation of nitrosative stress. Kidney Int. 2020;98(3):673–85. Peng H, Wang Y, Luo W. Multifaceted role of branched-chain amino acid metabolism in cancer. Oncogene. 2020;39(44):6747–56. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6927061","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":481912049,"identity":"5257ca6a-7b4c-4c46-95e7-f9f0d51eb940","order_by":0,"name":"Zhixia Song","email":"","orcid":"","institution":"The Longhua District People's Hospital of Shenzhen","correspondingAuthor":false,"prefix":"","firstName":"Zhixia","middleName":"","lastName":"Song","suffix":""},{"id":481912050,"identity":"213e0a13-c6e2-4938-ad3a-6e78b641b086","order_by":1,"name":"Xiaohong Xiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYDACZjApAUYMDBUMMC7RWs4QowUOQMoY24jQYnCc+dnDr20W8vzSzccefp1nZ29wgPngbR4GuzxcWiSb2cyNZc5IGM6ccyzdWHZbcuKGA2zJ1jwMycW4tPAzM5hJS1RIMG64kWMmLbntQILBAR4zaR6GA4kNOLSwMbN/k5YwkLCHaJlzAOgw/m94tfAz85hJfqiQSARpkfzYcIBxwwEeNrxaJJt5yqQZzkgkz5yRlibNcCw5ceZhNmPLOQbJOLUYnD++TfJnW51tv0TyMckfNXb2fMebH954U2GHUwsIMPOgMMCRa4BHPRAw/kBnjIJRMApGwShABgAt3E5sm5QcMQAAAABJRU5ErkJggg==","orcid":"","institution":"The Second Xiangya Hospital of Central South University","correspondingAuthor":true,"prefix":"","firstName":"Xiaohong","middleName":"","lastName":"Xiang","suffix":""},{"id":481912051,"identity":"b56b7885-d5b7-43b5-bb70-28ed25e9cbc4","order_by":2,"name":"Lang Shi","email":"","orcid":"","institution":"The First Hospital of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Lang","middleName":"","lastName":"Shi","suffix":""},{"id":481912052,"identity":"faeb9fb9-d3dd-4b64-843e-a859d7358bfc","order_by":3,"name":"Bo Peng","email":"","orcid":"","institution":"The Third Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Peng","suffix":""},{"id":481912053,"identity":"b98603f1-d7ae-4dde-8552-5613286396cf","order_by":4,"name":"Yijun Pan","email":"","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Yijun","middleName":"","lastName":"Pan","suffix":""},{"id":481912054,"identity":"a9d28c2d-8a44-457a-a4ec-b03b3818ad44","order_by":5,"name":"Yaoyiao Shu","email":"","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Yaoyiao","middleName":"","lastName":"Shu","suffix":""},{"id":481912055,"identity":"f2c035d7-7e7c-46e2-877d-d7bd485f0336","order_by":6,"name":"Jiefu Zhu","email":"","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Jiefu","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2025-06-19 03:23:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6927061/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6927061/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86354492,"identity":"f0c8c625-9f97-42b9-b35b-8e39bf031e40","added_by":"auto","created_at":"2025-07-09 16:41:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":321677,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetabolomics reveals metabolic perturbation in kidney IRI.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Chemical classification of identified metabolites, with colors representing distinct metabolite classes. (B) Principal component analysis (PCA) of metabolic profiles across sham and IRI time points (2h, 6h, 24h, 7d). (C) Quantification of differentially expressed metabolites (DEMs, VIP\u0026gt;1, p\u0026lt;0.05) in positive (pink) and negative (blue) ion modes, showing upregulated (upper) and downregulated (lower) metabolites. (D) Heatmap of carbohydrate-class DEMs across IRI time points.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6927061/v1/6b072f0f85196f9999793b19.png"},{"id":86354486,"identity":"c6e9436f-fee1-4bd6-99c7-ee292d595a4e","added_by":"auto","created_at":"2025-07-09 16:41:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":359893,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptomic profiling and integration of transcriptomic and metabolomic analysis in renal IRI.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) PCA of transcriptomic profiles. (B) Temporal changes in differentially expressed genes (DEGs, red=upregulated, green=downregulated). (C) Gene Ontology (GO) biological process analysis of 24h DEGs (red=upregulated, green=downregulated). (D) KEGG pathway analysis of 24h DEGs (triangles=upregulated, circles=downregulated). (E) Integrated transcriptome-metabolome pathway analysis showing top 5 enriched pathways at each time point.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6927061/v1/bb6941ac5b1e0ece8b13348d.png"},{"id":86354817,"identity":"03591a08-5fb7-49af-a642-b030561cd97b","added_by":"auto","created_at":"2025-07-09 16:49:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":486853,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProteomic profiling and integration of proteomic with metabolomic/transcriptomic analysis in renal IRI.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) PCA of proteomic profiles. (B) Temporal changes in differentially expressed proteins (DEPs, fold change \u0026gt;1.2, p\u0026lt;0.05). (C) Heatmap of 75 DEPs consistently altered during acute phase (64 upregulated, 11 downregulated). (D) KEGG pathway analysis of 24h DEPs. (E) Integrated proteome-metabolome pathway analysis. Top 5 enriched pathways are represented at 2h and 6h, top 10 enriched pathways are represented at 24h.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6927061/v1/c7caca0ff34fc3f92c9457d7.png"},{"id":86354818,"identity":"3a617b30-0327-47fe-aad3-02d99d99d882","added_by":"auto","created_at":"2025-07-09 16:49:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1005279,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMulti-omics integration and PDK4 validation in renal IRI.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Integrated pathway analysis of central carbon metabolism and fatty acid oxidation at 24h. (B-F)\u003cstrong\u003e \u003c/strong\u003eC57BL/6 mice underwent bilateral ischemia for 28 min followed by reperfusion for 24h, control mice were subjected to sham operation. Red filled box (upregulated enzymes), green filled box (downregulated enzymes), red font (increased metabolites), green font (decreased metabolites), red border box (upregulated pathways), green border box (downregulated pathways), (B) Immunohistochemistry staining of PDK4. (C) Quantification of PDK4 positive staining. (D) Immunohistochemistry staining of p-PDHA1(Ser232). (E) Quantification of p-PDHA1(Ser232) positive staining. (F) Western blot of PDK4, p-PDHA1(Ser232), PDHA1, Cyclophilin B as loading control. Scale bar: 50 µm, the boxed areas were enlarged and presented at bottom panels. *P\u0026lt;0.05 vs sham.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6927061/v1/1aaa737a6c7bdc4d8918f736.png"},{"id":86354820,"identity":"1659d3d5-3899-4d09-a9d0-b729e95a4282","added_by":"auto","created_at":"2025-07-09 16:49:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1044512,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePDK4 deficiency attenuates kidney IRI.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePDK4 knockout mice were established by cas9/gRNA，KO/WT mice\u003cstrong\u003e \u003c/strong\u003eunderwent bilateral ischemia for 28 min followed by reperfusion for 24h or sham operation.(A) Graphic of gene modification mice establishment. (B) Genotyping of mice. (C) Western blot of PDK4, p-PDHA1(Ser232), PDHA1, Cyclophilin B as loading control. (D) Serum creatinine and BUN measurement. (E) H\u0026amp;E staining of renal histology. (F) Tubular injury score. (G) Immunohistochemistry staining of KIM-1. (H) The percentage of KIM-1 positive tubules. (I) Immunohistochemistry staining of p-PDHA1(Ser232). Scale bar: 50 µm, the boxed areas were enlarged and presented at bottom panels. \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs sham. \u003csup\u003e#\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs WT IRI.\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6927061/v1/7a76e1cf33eb8ba252745678.png"},{"id":86354501,"identity":"3ce282fb-6c7a-4316-8d55-e997542df4fe","added_by":"auto","created_at":"2025-07-09 16:41:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":703457,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePDK4 deficiency increase tubular proliferation post-IRI.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePDK4 knockout or wildtype mice were subjected\u003cstrong\u003e \u003c/strong\u003ebilateral ischemia for 28 min followed by reperfusion for 24h or sham operation. (A) Representative image of immunohistochemistry staining of Ki67. (B) Tubular Ki67\u003csup\u003e+ \u003c/sup\u003ecells statistical analysis. (C) Interstitial Ki67\u003csup\u003e+\u003c/sup\u003e cells statistical analysis. (D) Western blot of PCNA, Cyclophilin B as loading control. (E) Quantitative analysis of PCNA density. Scale bar: 50 µm, the boxed areas were enlarged and presented at bottom panels. \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs sham. \u003csup\u003e#\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs WT IRI.\u003c/p\u003e","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6927061/v1/569f9c043e9f1f55e997950d.png"},{"id":86355544,"identity":"b0699179-3ca3-49b6-83b5-d7641ed4f5e8","added_by":"auto","created_at":"2025-07-09 16:57:50","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":560477,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePDK4-knockout inhibited mitochondrial fragmentation via Drp1 phosphorylation and mitochondria translocation in kidney IRI.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePDK4 knockout or wildtype mice were subjected\u003cstrong\u003e \u003c/strong\u003ebilateral ischemia for 28 min followed by reperfusion for 24h or sham operation. (A) Representative electron micrographs of mitochondrial morphology in proximal tubule cells. (B) Statistical analysis of percentage of tubule with fragmented mitochondria. \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs WT IRI. (C) Representative Immunoblot and p-Drp1(Ser616), Drp1, PDK4 in kidney tissue. Cyclophilin B was used as a loading control. (D)Quantitative analysis of p-Drp1(Ser616)/Drp1. \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs sham. \u003csup\u003e#\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs WT IRI. (E) Representative Immunoblot of Drp1 in mitochondria and cytosol fractions. COX IV and GAPDH were used as loading controls of mitochondrial and cytosolic fractions, respectively. (F)Densitometry analysis of Drp1 in renal mitochondrial fractions. \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs WT IRI. Scale bar: 1 µm, the boxed areas were enlarged and presented at bottom panels.\u003c/p\u003e","description":"","filename":"figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-6927061/v1/da128204620a3cd1c25a2deb.png"},{"id":98436956,"identity":"0bebfb01-389f-41d3-82a4-c61ca382574f","added_by":"auto","created_at":"2025-12-17 16:56:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5997633,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6927061/v1/75d9b2a1-4439-4919-aa32-abfee70f9ad6.pdf"},{"id":86355543,"identity":"0b7dccc2-a07d-4f6f-88b1-dde1e3c2752c","added_by":"auto","created_at":"2025-07-09 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16:57:51","extension":"tif","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":15647008,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfigure7.tif","url":"https://assets-eu.researchsquare.com/files/rs-6927061/v1/9e10164c93bed0f4ff306132.tif"},{"id":86354828,"identity":"c7607437-5f38-4085-9dfe-ecc99b150bb1","added_by":"auto","created_at":"2025-07-09 16:49:51","extension":"tif","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":5548704,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfigure8.tif","url":"https://assets-eu.researchsquare.com/files/rs-6927061/v1/d43699d7da4ba70e384c609f.tif"},{"id":86354822,"identity":"ea137e31-bc96-46c6-9115-1aef36ddce0d","added_by":"auto","created_at":"2025-07-09 16:49:50","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":267996,"visible":true,"origin":"","legend":"","description":"","filename":"Supplentaryfilefulluncroppedgelsandblots.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6927061/v1/b5e39f5f051ed3f1aa95da3d.pdf"},{"id":86354507,"identity":"5c71c94f-b1c9-4a39-ba8c-4b9b3b9ce954","added_by":"auto","created_at":"2025-07-09 16:41:51","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":20764,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFiguresLegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-6927061/v1/c5c108502c8e3fe033ea4bb6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated Multi-Omics Reveal PDK4-Mediated Mitochondrial Dysfunction in Renal Ischemia-Reperfusion Injury","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAcute kidney injury (AKI) is a critical public health issue leading to great socioeconomic burden and posing a significant threat to patient survival(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Without full recovery, the acute injured kidney will progress to chronic kidney disease (CKD), end stage renal disease (ESRD), and even death(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Ischemia reperfusion injury (IRI) is particularly clinically relevant to AKI. Ischemia occurs when kidney irrigation is compromised due to hemodynamic instability (e.g., shock) or surgical interventions such as kidney transplantation, while reperfusion exacerbates cellular damage through the abrupt generation of excessive reactive oxygen species (ROS) upon blood flow restoration(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). There is limited therapeutic strategies to mitigate IRI-induced AKI. Hence, elucidating the pathophysiological changes and mechanisms are urgently warranted.\u003c/p\u003e\u003cp\u003eKidney tubular epithelial cells (TECs) are the dominant affected cell population in IRI, since they are susceptible to oxygen depletion/oxidative stress and prone to inflammatory response and cell death(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Metabolic reprogramming of TECs plays a critical role in the response to kidney injury(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). It is well-characterized that during ischemia, oxidative phosphorylation in mitochondrion is dampened, the energy supply mode of TECs transits from fatty acid oxidation (FAO) to anaerobic glycolysis(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). During the early stage of reperfusion, glycolysis is essential for maintaining cell viability. Meanwhile, inhibition of FAO is accompanied by lipid accumulation and subsequent lipotoxicity to cells(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Phenotypic modulation is driven by genetic variation and alterations in protein expression. Although significant advances in the identification of molecular and metabolic pathways in mediating kidney IRI, there is still a paucity of integrated multi-omics data to unravel the regulatory event driving IRI. This study aimed to comprehensively analyze kidney transcriptional, protein, and metabolic profiles associated with IRI induced AKI, providing a clue for identification of potential biomarkers for early recognition and therapeutic targets.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Animals and bilateral IRI model\u003c/h2\u003e\u003cp\u003e All animal experiments were approved by the Ethics Committee of the Ministry of Health of the People's Republic of China and the Ethics Committee of Three Gorges University (202205010T2). Male C57BL/6 mice (8\u0026ndash;10 weeks old) were purchased from the Experimental Animal Center of China Three Gorges University (Yichang, China). For the bilateral ischemia-reperfusion injury (IRI) model(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), mice with 3 replicates in each group were anesthetized with isoflurane, and the bilateral kidneys were subjected to ischemia by clamping the renal pedicles for 28 minutes, followed by reperfusion for 2 h, 6 h, 24 h, and 7 d. The renal pedicles were exposed in the sham control group without clamping. The renal tissues were homogenized for metabolomics, proteomics, and transcriptomes analysis, immunoblot analysis, or fixed for histopathological staining or immunostaining. The mice blood was collected for renal functional measurement. Renal function analysis using plasma creatinine and blood urine nitrogen (BUN) levels were measured by the central laboratory of the Center People\u0026rsquo;s Hospital of Yichang (Roche Diagnostics GmbH, Penzberg, and Mannheim, Germany).\u003c/p\u003e\u003cp\u003eTo establish PDK4 knockout (KO) mice, guide RNA (gRNA) for the target gene was designed to guide CRISPR/Cas9 nuclease to modify the PDK4 targeted allele, thus causing the inactivation of the PDK4 gene. Tail DNA from all mice was genotyped by PCR analysis. Wild type (WT) or KO mice were subject to ischemia 28 min and reperfusion for 24h, sham operation was used as control.\u003c/p\u003e\u003cp\u003eTo evaluate the effect of PDK4 inhibitor sodium dichloroacetate (DCA) in kidney IRI, mice were administered DCA (1g/L) in drinking water for consecutive 5 days before IR treatment. Vehicle-treated animals served as controls.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Untargeted metabolomics analysis\u003c/h2\u003e\u003cp\u003eFor sample collection and preparation, the frozen mice kidney samples (n\u0026thinsp;=\u0026thinsp;3) dissolved in 200 \u0026micro;L of H2O were homogenized and mixed with 800 \u0026micro;L methanol/acetonitrile (1:1, v/v) for metabolite extraction. After centrifugation, the supernatant was re-dissolved in 100 \u0026micro;L acetonitrile/water (1:1, v/v) solvent for LC-MS analysis.\u003c/p\u003e\u003cp\u003eLC-MS/MS analysis was performed using an UHPLC (1290 Infinity LC, Agilent Technologies) coupled to a quadrupole time-of-flight (AB Sciex TripleTOF 6600) in Shanghai Applied Protein Technology Co., Ltd. For HILIC separation, samples were analyzed using a 2.1 mm \u0026times; 100 mm ACQUIY UPLC BEH 1.7 \u0026micro;m column (waters, Ireland). For RPLC separation, a 2.1 mm \u0026times; 100 mm ACQUIY UPLC HSS T3 1.8 \u0026micro;m column (Waters, Ireland) was used. For data processing, the raw data were converted. Compound identification of metabolites was performed by comparing of accuracy m/z value (\u0026lt;\u0026thinsp;10 ppm), and MS/MS spectra with an in-house database established with available authentic standards.\u003c/p\u003e\u003cp\u003eFor statistical analysis, after sum-normalization, the processed data were subjected to multivariate data analysis. The variable importance in projection (VIP) value of each variable in the Orthogonal partial least squares-discriminant analysis (OPLS-DA) model was calculated to indicate its contribution to the classification. Student\u0026rsquo;s t test was applied to determine the significance of differences between two groups of independent samples. VIP\u0026thinsp;\u0026gt;\u0026thinsp;1 and p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were used to screen significant changed metabolites.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Proteomics analysis\u003c/h2\u003e\u003cp\u003eThe mass spectrometry experimental analysis process mainly includes proteins extraction (4%(w/v) SDS, 100mM Tris/HCl pH7.6, 0.1M DTT); trypsin digestion to peptides (Filter aided proteome preparation); tandem mass tag (TMT) labeling; and peptides fractionation; Then, nano-HPLC-MS/MS was performed; the samples were loaded to the loading column (Thermo Scientific Acclaim PepMap100, 100\u0026micro;m*2cm, nanoViper C18), and then passed through the analysis column (Thermo scientific EASY Column, 10cm, ID75\u0026micro;m, 3\u0026micro;m, C18-A2) for separation, then the separated samples were analyzed by Q-Exactive mass spectrometer. MaxQuant (Germany) was used to process the raw data.\u003c/p\u003e\u003cp\u003eFor bioinformatics analysis, proteins were annotated for differentially expressed proteins (DEPs) analysis, GO/KEGG functional analysis. After Student\u0026rsquo;s t test, proteins with P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and fold change\u0026thinsp;\u0026gt;\u0026thinsp;1.2 or \u0026lt;\u0026thinsp;0.83 were filtered as DEPs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Transcriptomics analysis\u003c/h2\u003e\u003cp\u003eTotal RNA extraction, quality assessment, and quantification was performed as previously reported(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). High quality RNA sample was used as subsequent library construction and RNA-sequence. Paired-ended library was generated using ABclonal mRNA-seq kit (ABclonal, China). Briefly, the mRNA was purified by oligo (dT) magnetic beads. Fragmentation was performed using divalent cations under ABclonal First Strand Synthesis Buffer. Then, first strand cDNA and second strand cDNA were synthesized. The library fragment was purified with AMPure XP (Beckman Coulter, Beverly, MA, USA). Illumina Novaseq 6000 was used for sequencing. FPKM (Fragments per kilo base of transcript per million mapped fragments) value of expression of each gene in each sample was calculated. Differential expression analysis of gene was performed using Deseq2 package, genes with an adjust P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and absolute fold change\u0026thinsp;\u0026gt;\u0026thinsp;2 were identified as significantly expressed genes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Gene ontology enrichment analysis and Kyoto Encyclopedia of genes and genomes of DEGs and DEPs.\u003c/h2\u003e\u003cp\u003eThe database for annotation, visualization, and integrated discovery (DAVID 2022; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://davidbioinformatics.nih.gov)(10, 11)\u003c/span\u003e\u003cspan address=\"https://davidbioinformatics.nih.gov)(10, 11)\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, was used to determine biological functions of upregulated and downregulated DEGs and DEPs at different time points of kidney IRI, which include gene ontology analysis (biological process, BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The cutoff values for GO/KEGG analysis were set at the false discovery rate of \u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Integrative analysis of transcriptome, proteome and metabolome\u003c/h2\u003e\u003cp\u003eFor integrative KEGG analysis of transcriptome and metabolome, proteome and metabolome, Fisher\u0026rsquo;s Exact Test and the different molecules of the two omics were used for enrichment analysis of the KEGG pathway based on KEGG annotations.\u003c/p\u003e\u003cp\u003eFor integrated transcriptome, proteome and metabolome analysis, we used KEGG database which includes the pathway mapping and interrelationship information of gene, protein and metabolites. By projecting the DEGs, DEPs and DEMs to the KEGG pathway at the same time, we comprehensively integrated the pathway data (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genome.jp/kegg/pathway.html\u003c/span\u003e\u003cspan address=\"https://www.genome.jp/kegg/pathway.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Histological and immunohistochemistry staining and western blot.\u003c/h2\u003e\u003cp\u003eKidney tissues were fixed for paraffin embedding and sectioning. For histological analysis, sections were stained with hematoxylin and erosion (H\u0026amp;E). Tubular injury score was defined by percentage of renal tubules showing cell lysis, loss of brush border, and cast formation (0, no damage, 1, \u0026lt;\u0026thinsp;25%; 2, 25\u0026ndash;50%; 3, 50\u0026ndash;75%; 4\u0026thinsp;\u0026gt;\u0026thinsp;75%)(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor immunohistochemistry (IHC) analysis, sections were deparaffinized, rehydrated, subjected to antigen retrieval and blocked. Sections were incubated with primary antibodies against PDK4 (Proteintech, 12949-1-AP), p-PDHA1(Ser232) (ThermoFisher, 81491-1-RR), Ki67 (Cell signaling, 34330) and kidney injury molecule-1 (KIM-1) (R\u0026amp;D system, AF1817). Sections were incubated with HRP-conjugated secondary antibodies and developed with DAB substrate.\u003c/p\u003e\u003cp\u003eFor immunoblot, kidney tissues were lysed in RIPA buffer with protease and phosphatase inhibitors followed by protein concentration analysis. Equal amount of protein was sequentially separated by SDS-PAGE, transferred to PVDF membranes and blocked by fat-free milk. The primary antibody used in the study includes PDK4 (Proteintech, 12949-1-AP), PDHA1 (Proteintech, 18068-1-AP), p-PDHA1 (Ser232) (ThermoFisher, 81491-1-RR), PCNA (Cell signaling, 13110), CyclophilinB (Cell signaling, 79652), Drp1 (BD Biosciences, 611113), GAPDH (Cell signaling, 5174), COX IV (Abcam, ab16056), p-Drp1(ser 616) (Cell signaling, 3455). Membranes were then incubated with HRP-conjugated secondary antibodies and developed using ECL detection system. Densitometric analysis was performed using ImageJ and representative images were present.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Electron microscope.\u003c/h2\u003e\u003cp\u003eElectron microscope observing mitochondria was performed as previously described(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Briefly, a fresh kidney tissue block including a portion of renal cortex and outer medulla of approximately 1 mm\u003csup\u003e3\u003c/sup\u003e was collected from each kidney. Then the tissues were fixed in 100 mM sodium cacodylate, 2 mM calcium chloride, 4 mM magnesium sulfate, 4% paraformaldehyde, and 2.5% glutaraldehyde. Fixed kidneys were blocked, sectioned and stained for transmission electron microscope observation. The tissue section was examined at low (x3000) and high (x15000) magnification to identify representative proximal tubules and collect electron micrographs, respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Isolation of mitochondrial and cytosolic fraction.\u003c/h2\u003e\u003cp\u003eCellular isolation of cytosolic and mitochondrial fractions from tissue was described as previously with some minor modifications from our earlier work(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Kidney tissues were minced and homogenized in a mixture of 0.1% BSA and mitochondria isolation buffer containing 225 mM mannitol, 75 mM sucrose, 1 mM ethylene glycol tetra acetic acid, 10 mM Tris-hydrochloride with protein inhibitor cocktail (pH 7.4), in the purpose of rupturing cell membrane gently. The homogenates were centrifuged at 1000 \u0026times; g for 10 min at 4\u0026deg;C for several rounds to pellet cell debris and nuclei and the supernatants were collected. For mitochondria enrichment, the supernatants were centrifugated at 15,000 \u0026times; g for 15 min to get the supernatant as cytosolic fraction and the pellet as mitochondrial fraction.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10 Cell culture and treatment\u003c/h2\u003e\u003cp\u003eThe rat proximal tubular cells (RPTCs) were originally obtained from Sciencell Research laboratories and cultured as previously described(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Hypoxia-reperfusion model \u003cem\u003ein vitro\u003c/em\u003e was achieved by carbonyl cyanide 3-chlorophenylhydrazone (CCCP) treatment at 10 \u0026micro;M for 3 h and followed by full-medium replacement for 2 h for reperfusion. The cells were pretreated with 5mM DCA for 1 h before CCCP treatment. Cells were stained with TdT-mediated dUTP Nick-End Labeling (TUNEL) and Dihydroethidium (DHE) to observe cell apoptosis and ROS production, respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.11 Statistics\u003c/h2\u003e\u003cp\u003eFor plots, the heatmap plots and KEGG plots were drawn by \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioinformatics.com.cn\u003c/span\u003e\u003cspan address=\"https://www.bioinformatics.com.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, an online platform for data analysis and visualization. The GO-BP plots and integrative KEGG plots were plotted by Excel.\u003c/p\u003e\u003cp\u003eFor statistical analysis, the Student\u0026rsquo;s t test was used for the significant difference between 2 groups. ANOVA was used for multi groups comparison. Data was shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was set as significant difference. GraphPad Prism version 9 was used for statistical analysis.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Metabolomics reveals metabolic perturbation in kidney IRI.\u003c/h2\u003e\u003cp\u003eAdult C57BL/6 mice were subjected to bilateral kidney ischemic injury for 28 min followed by reperfusion at designated time points (2 h, 6 h, 24 h, and 7 d). The sham operated or injured kidneys were dissected for metabolomics to clarify temporal changes in metabolites. 2066 metabolites in total were identified after combining positive (1265) and negative (801) ion modes. From the chemical taxonomy (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), we can figure out that lipids and lipid-like molecules (green) along with organic acids and derivatives (dark blue) account for the most proportion, corroborating significant carbohydrate, lipid and amino acid metabolic reprogramming in kidney IRI. Multivariate statistical analysis demonstrated clear temporal metabolic shifts. Principal component analysis (PCA) model showed distinct clustering of experimental groups, with principal component 2 (PC2) effectively separating sham controls from IRI 24 h and 6 h samples, suggesting maximal metabolic perturbations at these time points (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Different expressed metabolites (DEMs) were screened by cutoff values of OPLS-DA VIP\u0026thinsp;\u0026gt;\u0026thinsp;1 and P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, in combination of positive and negative ion modes, there are 95 upregulated metabolites and 39 downregulated at 2 h; 72 and 40 at 6 h; 132 and 60 at 24 h; 111 and 53 at 7 d; respectively. For carbohydrate metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), some intermediate products of tricarboxylic acid (TCA) cycle (2-hydroxyglutarate, fumarate, malate, cis-aconitate) showed significant elevation but paradoxical absence of acetyl-CoA accumulation, the central ingredient of TCA cycle. Notably, pyruvate, the linkage between glycolysis and TCA cycle, exhibits sustained increase (peaking at 24 h, red box), suggesting enhanced glycolytic flux (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). For amino acid metabolism (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e), creatine and creatinine increase indicated deteriorated renal function upon IRI (red box). In contrast, specific reduction of amino acids that promote oxidative metabolism via the TCA cycle was observed, including glutamine, glutamic acid, leucine, asparagine, etc (blue box), suggesting compensatory depletion of amino acid when carbohydrate metabolism is compromised. From lipid metabolic analysis (\u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e),there is global increase in most lipid species upon IRI, except for some specific prenol lipids (blue box), which is consistent with prior reports of lipid deposition in IRI-induced AKI(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Transcriptomic profiling and integration of transcriptomic and metabolomic analysis in renal IRI.\u003c/h2\u003e\u003cp\u003ePCA analysis of RNA-seq data demonstrated clustering of replicates intra-group and clear separation inter-groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The most pronounced transcriptional changes occurred at 24 h post-IRI, as evidenced by maximal segregation along PC1 between IRI 24 h and sham groups. When setting the adjusted P value of \u0026lt;\u0026thinsp;0.05 and fold change\u0026thinsp;\u0026gt;\u0026thinsp;2, generous differentiated expressed genes (DEGs) were identified as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB. For example, 2407 genes elevated while 2563 dropped post-IRI 24 h. Biological significance of DEGs was explored by gene ontology (GO) term enrichment analysis of biological process (BP). As expected, increased genes enriched in response to stimulus/stress or immune/inflammatory process at 2 h (\u003cb\u003eSupplementary Fig.\u0026nbsp;3A\u003c/b\u003e), 6 h(\u003cb\u003eSupplementary Fig.\u0026nbsp;3B\u003c/b\u003e), 24 h(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), and 7 d(\u003cb\u003eSupplementary Fig.\u0026nbsp;3C\u003c/b\u003e), in keeping with our and others\u0026rsquo; previous reports(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). In contrast, genes enriched in organic acid metabolic process, mitochondrial TCA cycle and oxidation-reduction reactions decreased, which is consistent across all time points. KEGG analysis corroborated the GO-BP findings: activated pathways included inflammatory signaling pathways (NF-κB, TNF signaling) and apoptosis pathways, while decreased DEGs mainly enriched in TCA cycle, glyoxylate and dicarboxylate metabolism, and amino acid metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, \u003cb\u003eSupplementary Fig.\u0026nbsp;3D-F\u003c/b\u003e). Integrative KEGG analysis of transcriptomic and metabolomic indicated central carbon metabolism dysregulation post-IRI. For example, transcriptional suppression of TCA cycle enzymes (IDH2, SDHB, MDH2) and corresponding metabolite accumulation (malate, fumarate) suggested impairment of TCA cycle. Besides, glutathione metabolism impairment or amino acid utilization shifts indicate their involvement in pathophysiology of IRI (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Proteomic profiling and integration of proteomic with metabolomic/transcriptomic analysis in renal IRI.\u003c/h2\u003e\u003cp\u003ePCA analysis of the global proteome revealed distinct temporal patterns, with decent isolation of sham controls from IRI 24 h IRI samples in PC2 and from IRI 7 d in PC1, which highlights the dominant protein-level alterations in these 2 time points (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The numbers of different expressed proteins (DEPs) across all time points were displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, with the fold change\u0026thinsp;\u0026gt;\u0026thinsp;1.2 or \u0026lt;\u0026thinsp;0.83 and adjust P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. To determine the critical proteins in acute phase of kidney IRI, we used Venn diagram to show 64 proteins persistently upregulated from 2 h to 24 h, while 11 proteins kept going down (\u003cb\u003eSupplementary Fig.\u0026nbsp;4A\u003c/b\u003e). The heatmap of these acute-phase DEPs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e revealed PDK4 as top-ranked candidate, showing progressive upregulation peaking at 24 h. This expression pattern suggests its pivotal role in metabolic disturbance during IRI. We then took advantage of KEGG analysis to show proteins enriched in inflammatory pathway, ferroptosis were activated, while proteins enriched in pyruvate metabolism, amino acid metabolism were suppressed generally (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, \u003cb\u003eSupplementary Fig.\u0026nbsp;4B-D\u003c/b\u003e). Integrated proteomic-metabolomic KEGG mapping manifested the important role of carbohydrate, amino acid metabolism and ferroptosis pathway upon IRI especially in acute phase (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn addition, transcript-protein correlation analysis at different time points were performed and their correlation coefficients were calculated. The scatter plots were shown in \u003cb\u003eSupplementary Fig.\u0026nbsp;5A-D\u003c/b\u003e. Strong concordance of mRNA and proteins variation was revealed at 24 h (Spearman\u0026rsquo;s R\u0026thinsp;=\u0026thinsp;0.7012, \u003cb\u003eSupplementary Fig.\u0026nbsp;5C\u003c/b\u003e). Otherwise, both the mRNA and protein levels of PDK4 upregulated consistently from 2 h to 6 h until 24 h (\u003cb\u003eSupplementary Fig.\u0026nbsp;5A-C\u003c/b\u003e). We can also figure out that some new stress marker like calcium-binding protein complex s100a8/a9(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), krt20(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), and some classic stress marker like Lcn2, Havcr1 upregulated both in mRNA and protein level at early time and sustained through 24 h, indicating their sensitivity and stability as early stress markers (\u003cb\u003eSupplementary Fig.\u0026nbsp;5A-C\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Integrated analysis of transcriptome, proteome, and metabolome in renal IRI.\u003c/h2\u003e\u003cp\u003eWe finally took advantage of integrative analysis of muti-omics to clarify the pathophysiologic events in kidney IRI. From Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, hexokinase, the first key rate-limiting enzyme of glycolysis was upregulated at transcriptional and protein level. Meanwhile, the end-product of glycolysis, pyruvate, was markedly increased. Another mode of oxidative breakdown of glucose is pentose phosphate pathway (PPP). We can see that the 2 key enzymes of PPP, glucose-6-phosphate dehydrogenase (G6PD) and 6-phosphogluconate dehydrogenase (PGD), were transcriptionally increased, indicating the activation of PPP. In contrast, the most effective glucose-oxidizing pathway, TCA cycle, was dampened. Firstly, the activation of PDK4 and inhibition of pyruvate dehydrogenase complex (PDC) limits acetyl-CoA production. Secondly, the enzymes in TCA cycle were downregulated to impair cycle turnover. For example, the rate limiting enzymes, isocitrate dehydrogenase (IDH) and oxoglutarate dehydrogenase (OGDC), were diminished. The blockade of TCA cycle decreases the production of hydrion, leading to collapse of oxidative phosphorylation (OXPHOS) and surge of reactive oxygen (ROS). Another ingredient of acetyl-CoA is fatty acid oxidation (FAO), which is the major energy source of renal tubule epithelial cells. The multi-omics analysis revealed that FAO was hindered due to the suppression of key enzymes (ACOX/ACADM). Glucogenic amino acids (Asp, Asn, Glu, Gln, etc.) were also decreased because of consumption when shortage of carbohydrate.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eExcept for the disorder of central carbon metabolism, cell death including apoptosis, ferroptosis was dramatically triggered in kidney IRI. 3 pillars of ferroptosis (iron, PUFA and glutamate metabolism) were all disturbed, in favor of ferroptosis aberrantly activation (\u003cb\u003eSupplementary Fig.\u0026nbsp;5E)\u003c/b\u003e. Glutathione crisis emerged despite SLC7A11 upregulation, as glutamate scarcity and γ-glutamylcysteine (γ-GC) deficiency constrained glutathione (GSH) synthesis. On the other hand, lipid peroxidation was driven by polyunsaturated fatty acid (PUFA: adrenic acid, linoleic acid) accumulation and induction of Acyl-CoA synthetase long-chain family member 4 (ACSL4), the first essential enzyme for ferroptosis execution. Transferrin (TF) to transport extracellular ferric iron and HO-1 to catalyze heme together resulted in deposition of iron divalent and triggered ferroptosis. From the integrative analysis of multi-omics, we can conclude that ferroptosis is definitely activated and plays a critical role in kidney IRI.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Validating of PDK4 upregulation in renal IRI \u003cem\u003ein vivo\u003c/em\u003e.\u003c/h2\u003e\u003cp\u003eOur integrated multi-omics analysis identified PDK4 as a central regulator in kidney IRI pathogenesis. To experimentally validate these findings, we firstly exploring its regulation in kidney biopsies from mice with bilateral IRI by 28 minutes of renal ischemia followed by 24 h of reperfusion. Notable histological changes and kidney tubules injury were observed in the kidneys of IRI 24 h when compared with control ones by H\u0026amp;E staining \u003cb\u003e(Supplementary Fig.\u0026nbsp;6A)\u003c/b\u003e and KIM-1 staining \u003cb\u003e(Supplementary Fig.\u0026nbsp;6B)\u003c/b\u003e. Immunohistochemical analysis confirmed dramatic PDK4 upregulation in injured tubules (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), with quantitative scoring showing a 4.0-fold increase versus sham controls (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Meanwhile, phosphorylated PDHE1α/ PDHA1at Ser 232 (E1 α subunit of pyruvate dehydrogenase, a target protein of PDK4) was induced in the tubules of renal IRI, with 4.5-fold increase in immunostaining intensity \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, E\u003cb\u003e)\u003c/b\u003e. Immunoblot further demonstrated PDK4 and p-PDHA1/PDHA1 ratio elevation in renal IRI \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF\u003cb\u003e).\u003c/b\u003e The above data collectively demonstrated PDK4 expression is significantly induced and its enzymatic activity is functionally increased during kidney IRI. The concordance between multi-omics predictions and experimental validation solidifies PDK4's role as a key metabolic regulator in IRI pathophysiology.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.6 PDK4 inhibition attenuates kidney IRI.\u003c/h2\u003e\u003cp\u003eTo demonstrate the role of PDK4 in IR-induced kidney injury, we established PDK4 knockout mice by Cas9/gRNA. The process of gene modification and verification were illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eB, respectively. Immunoblot analysis confirmed complete PDK4 ablation and concomitant reduction in PDHA1 phosphorylation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The BUN and serum creatinine concentrations showed remarkable increase in IR-induced WT mice than in sham operated mice, while the upregulation was attenuated in PDK4-deficient mice significantly (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). From histological analysis, no abnormality was observed in sham-operated knockout mice in comparison to WT mice, but substantial protection against IR-induced kidney injury was found in PDK4-KO mice when compared to WT-injured mice. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, F, there were plenty of tubule\u0026rsquo;s vacuum (red star), even lysed (yellow arrow) due to brush border shedding or tubular cells death in IR-treated WT mice, while the injury was alleviated by PDK4 knockout. Meanwhile, KIM-1, the indicator of injured kidney proximal tubules, demonstrated remarkable protection in IR-induced KO mice when compared with WT mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG, H). In addition, PDHE1α phosphorylation by IR-treatment was lowered by PDK4 knockout (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePharmacological inhibition of PDK4 mimics genetic protection. Classic PDK4 inhibitor dichloroactate (DCA) was administered to C57BL/6 mice for 5 consecutive days prior to renal IRI execution (\u003cb\u003eSupplementary Fig.\u0026nbsp;7A\u003c/b\u003e). DCA effectively inhibited PDK4 upregulation in renal IRI, evidenced by PDK4 reduction and p-PDHA1/PDHA1 ratio decrease (\u003cb\u003eSupplementary Fig.\u0026nbsp;7B\u003c/b\u003e). When compared with the vehicle feeding mice, the DCA-treated mice were more tolerated to IRI, as evidenced by lower serum BUN and creatinine concentrations (\u003cb\u003eSupplementary Fig.\u0026nbsp;7C, D\u003c/b\u003e), improved kidney morphology (\u003cb\u003eSupplementary Fig.\u0026nbsp;7E, F\u003c/b\u003e) and less tubular damage by KIM-1 detection (\u003cb\u003eSupplementary Fig.\u0026nbsp;7G, H\u003c/b\u003e). Reduced P-PDHE1α immunostaining again demonstrated the efficiency of inhibitory effect of DCA on PDK4 (\u003cb\u003eSupplementary Fig.\u0026nbsp;7I\u003c/b\u003e). In addition, we mimicked kidney IRI \u003cem\u003ein vitro\u003c/em\u003e by CCCP treatment followed by reperfusion in RPTCs. Phase contrast showed that cell death and detachment caused by CCCP-R was significantly suppressed by DCA pre-treatment (50%-35% statistically, \u003cb\u003eSupplementary Fig.\u0026nbsp;8A, B\u003c/b\u003e). Meanwhile, TUNEL staining showed that DCA pre-treatment markedly alleviated CCCP-R induced RPTCs apoptosis (\u003cb\u003eSupplementary Fig.\u0026nbsp;8C, D\u003c/b\u003e). The superoxide indicator DHE exhibited a mass of fluorescent red upon CCCP-R, which was notably inhibited by DCA (\u003cb\u003eSupplementary Fig.\u0026nbsp;8E\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.7 PDK4 deficiency increase tubular proliferation post-IRI.\u003c/h2\u003e\u003cp\u003eTubular proliferation represents a critical mechanism for renal regeneration following kidney IRI. To investigate whether PDK4 deficiency enhances the proliferative response, we assessed Ki67 by immunohistochemistry staining. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, there was minimal Ki67 staining in sham-operated kidneys. Following IRI, Ki67-positive cells generally increased. Notably, while Ki67 staining in WT mice was predominantly spotted in the renal interstitium, Ki67 positive cells in PDK4-KO mice primarily localized within tubular epithelia. The statistical analysis confirmed the significant shift (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, C). Consistent with these findings, immunoblot analysis of PCNA, a key regulator of cell proliferation, was significantly upregulated following IRI in comparison to sham treatment. However, the upregulation was more pronounced in PDK4-KO kidneys than WT ones subjected to IRI (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eD, E). These finding indicates PDK4 ablation improves the recovery and regeneration of tubular cells.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.8 PDK4-knockout inhibited mitochondrial fragmentation via Drp1 phosphorylation and mitochondria translocation in kidney IRI.\u003c/h2\u003e\u003cp\u003eTo elucidate the potential mechanism by which PDK4 contributes to renal tubular injury, we investigated mitochondrial dynamics in kidney IRI using genetic modification mice. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, electron microscope observing proximal tubular cells from IRI subjected WT kidneys revealed a loss of filamentous mitochondrial morphology, with mitochondria predominantly adopting a punctate appearance, indicative of mitochondrial fragmentation. In contrast, mitochondrial fragmentation was significantly attenuated in PDK4 KO mice. In quantification, nearly 45% tubules in WT mice were observed with fragmented mitochondria, whereas the number reduced to approximately 25% in PDK4-KO mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Given the established role of dynamin-related protein 1(Drp1) in regulating mitochondria fission and fusion (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), particularly through its activation via phosphorylation at serine 616 and subsequent translocation to mitochondria to mediate fission (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), we assessed Drp1 activation status. Immunoblot analysis demonstrated obvious Drp1 phosphorylation at Ser616 (activation) in IR-injured WT kidneys, which was significantly suppressed in PDK4-KO kidneys (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eC, D). Meanwhile, subcellular fractionation analysis revealed IR-induced translocation of Drp1 to the mitochondria compartment in WT kidneys. This translocation was markedly inhibited by PDK4 deficiency (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eE, F). Taken together, these findings collectively indicate that PDK4 promotes mitochondria fragmentation during kidney IRI through facilitating the phosphorylation and mitochondria translocation of key fission protein Drp1.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eThrough systematic multi-omics analysis, this study comprehensively characterizes the metabolic, transcriptional, and proteomic alterations in renal ischemia-reperfusion injury (IRI), with PDK4 emerging as a central regulatory hub. Our experimental validation demonstrates significant PDK4 upregulation in a murine IRI model, where it orchestrates key pathological processes. Both genetic knockout and pharmacological inhibition of PDK4 conferred remarkable renal protection, as evidenced by improvement in renal function, attenuation of tubular injury, and enhancement of tubular proliferative capacity post-IRI. Mechanistically, PDK4 contributed to Drp1 phosphorylation and mitochondrial translocation, resulting mitochondrial fragmentation. This study establishes PDK4 as a critical node in IRI pathogenesis, bridging metabolic dysfunction (particularly TCA cycle impairment), mitochondrial damage, and cell death, while providing a foundation for mechanism-based AKI therapies.\u003c/p\u003e\u003cp\u003eReprogramming of central carbon metabolism (CCM, including glycolysis, PPP and TCA cycle) represents a critical adaptive response in renal IRI. Our multi-omics revealed coordinated activation of glycolytic and PPP, evidenced by upregulation of key enzymes (HK, G6PD, PGD) and significant pyruvate accumulation. However, aerobic energy metabolism is substantially impaired through PDK4-mediated inhibition of PDC, which limits acetyl-CoA production and consequently suppresses TCA cycle activity and OXPHOS. The underlying mechanism of PDK4 in the pathology of IRI is not clear. Since it is acknowledged that succinate accumulation during ischemia is responsible to mitochondrial ROS production during reperfusion and lead to tubule injury(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).Oh \u003cem\u003eet al\u003c/em\u003e firstly reported PDK4 involvement in cisplatin AKI (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) and IRI-induced AKI (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). They implicated that PDK4 aggravates succinate accumulation during ischemia and mitochondrial ROS generation during reperfusion (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Our multi-omics study provides novel mechanistic insights by demonstrating PDK4 drives mitochondrial fragmentation through Drp1-dependent mechanisms. What\u0026rsquo;s more, the coordinated suppression of FAO and specific glucogenic amino acid (proline, glutamate, glutamine, phenylalanine) depletion suggest PDK4 may serve as a central regulator of multiple metabolic pathways in renal IRI. These findings significantly expand our understanding of PDK4's pathophysiological role and highlight the need for further investigation into its multi-faceted regulation of renal metabolic homeostasis during ischemic injury.\u003c/p\u003e\u003cp\u003eFatty acid metabolism dysregulation and ferroptotic cell death play pivotal roles in renal IRI (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). As the primary energy source for proximal tubular cells, impaired FAO-evidenced by marked downregulation of key enzymes (Acox1/2, Acadm, Acsm1/3) - contributes significantly to tubular injury, consistent with previous findings by ours(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) and others(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Notably, polyunsaturated fatty acids (PUFAs) as a special fatty acid, particularly arachidonic acid derivatives, drive ferroptosis through three established mechanisms: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) ACSL4-mediated PUFA activation (confirmed at mRNA/protein levels) promotes lipid peroxidation; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) iron dysregulation via increased transferrin/ferritin and HO-1 induction leads to labile Fe\u0026sup2;⁺ accumulation and Fenton reactions(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e); and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) compromised glutathione metabolism occurs despite SLC7A11 upregulation, as glutamate scarcity limits γ-glutamylcysteine and subsequent GSH synthesis (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).Our integrated multi-omics analysis reveals a coordinated shift toward pro-ferroptotic conditions (elevated PGs/eicosanoids, ACSL4 activation, unstable divalent iron accumulation to trigger Fenton activity) while anti-ferroptotic pathways (depleted GPX4) are suppressed. These findings not only confirm ferroptosis as a dominant cell death pathway in renal IRI but also suggest PDK4 may modulate this process Future studies should identify potential role of PDK4 in regulating lipid peroxidation and iron metabolism, to develop targeted strategies against ferroptosis in acute kidney injury.\u003c/p\u003e\u003cp\u003eIn addition, emerging evidence highlights the critical role of amino acid metabolic dysregulation in kidney disease pathogenesis, particularly AKI(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). In the present study, we observed significant accumulation of arginine catabolism byproducts alongside upregulation of arginase 2 (Arg2), a potential mediator of renal IRI pathology and promising therapeutic target(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). In contrast, a substantial reduction in branched-chain amino acid transferase 1 (BCAT1) expression was identified with unknown role in kidney, representing a novel finding in renal pathophysiology. As BCAT1 catalyzes the conversion of branched-chain amino acids to glutamate while maintaining α-ketoglutarate homeostasis, its downregulation may directly contribute to the observed glutamate deficiency and consequently impair cellular redox balance(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). These findings not only expand our understanding of metabolic reprogramming in AKI but also, beyond PDK4, identify additional potential therapeutic targets such as Arg2 and BCAT1-related pathways that warrant further investigation for renal protection strategies.\u003c/p\u003e\u003cp\u003eThere are some limitations and prospects to be acknowledged in the present study. First, the cellular heterogeneity induced by IRI may affect the interpretation of bulk RNA-Seq and proteomic data, suggesting that single-cell resolution approaches could yield more precise cell type-specific information in future investigations. Second, although our untargeted metabolomics identified significant alterations in specific metabolite classes, targeted metabolomic validation would provide more quantitative and detailed characterization of these metabolic changes. Importantly, our integrated analysis not only confirms PDK4 as a central metabolic regulator but also identifies several other potential diagnostic markers and therapeutic targets. These findings establish a robust foundation for: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) developing PDK4-targeted therapies, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) exploring combination strategies with other identified targets, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) validating candidate biomarkers in clinical AKI settings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAcute kidney injury (AKI), Acyl-CoA synthetase long-chain family member 4 (ACSL4), arachidic acid (AA) ,arginase 2 (Arg2) ,blood urine nitrogen (BUN) ,biological process (BP) , 3-chlorophenylhydrazone (CCCP) ,central carbon metabolism (CCM) ,chronic kidney disease (CKD) ,sodium dichloroacetate (DCA) ,differentially expressed proteins (DEPs) ,Different expressed metabolites (DEMs) ,differentiated expressed genes (DGEs), The database for annotation, visualization, and integrated discovery (DAVID), Dihydroethidium (DHE), dynamin-related protein 1(Drp1), end stage renal disease (ESRD) , glyceraldehyde-3-phosphate dehydrogenase (GAPDH) fatty acid oxidation (FAO), guide RNA (gRNA) , gene ontology (GO) , glucose-6-phosphate dehydrogenase (G6PD),glutathione (GSH) , \u0026gamma;-glutamylcysteine (\u0026gamma;-GC) , heme oxygenase (HO-1), hematoxylin and erosion (H\u0026amp;E) , immunohistochemistry (IHC) , isocitrate dehydrogenase (IDH) , ischemia reperfusion injury (IRI) , knock out (KO) , Kyoto Encyclopedia of Genes and Genomes (KEGG) , kidney injury molecule-1 (KIM-1) , Orthogonal partial least squares-discriminant analysis (OPLS-DA) , oxoglutarate dehydrogenase (OGDC) , oxidative phosphorylation (OXPHOS) , E1 \u0026alpha; subunit of pyruvate dehydrogenase (PDHE1\u0026alpha;) ,prostaglandins (PGs) , pentose phosphate pathway (PPP) , 6-phosphogluconate dehydrogenase (PGD) , pyruvate dehydrogenase complex (PDC) , polyunsaturated fatty acid (PUFA) , principal component analysis (PCA) , Principal component 1 (PC1) , rat proximal tubular cells (RPTCs) , reactive oxygen species (ROS) , tubular epithelial cells (TECs) , tandem mass tag (TMT) , TdT-mediated dUTP Nick-End Labeling (TUNEL) , tricarboxylic acid (TCA) , Transferrin (TF) , variable importance in projection (VIP), Wild type (WT).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll animal experiments were approved by the Ethics Committee of the Ministry of Health of the People\u0026apos;s Republic of China and the Ethics Committee of Three Gorges University (202205010T2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is supported by National Youth Science Foundation of China (82402552), Knowledge Innovation Program of Wuhan-Shuguang Project (2023020201020505), The Medical Research Special Talent Project of Longhua District Medical Association, Shenzhen (2024LHMA01), and Undergraduate Training Programs for Innovation of Wuhan University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Zhu J, Xiang X; Methodology: Song Z, Shi L; Validation: Peng B, Pan Y; Formal analysis: Peng B, Shu Y; Investigation: Shu Y, Pan Y; Draft writing: Xiang X, Review and Editing: Song Z, Zhu J, Figures: Zhu J, Xiang X; Visualization: Song Z, Zhu J; Supervision: Zhu J, Xiang X.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHoste EAJ, Kellum JA, Selby NM, Zarbock A, Palevsky PM, Bagshaw SM, et al. 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Am J Transpl. 2023;23(1):11\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu J, Xiang X, Shi L, Song Z, Dong Z. Identification of Differentially Expressed Genes in Cold Storage-associated Kidney Transplantation. Transplantation. 2024;108(10):2057\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSherman BT, Hao M, Qiu J, Jiao X, Baseler MW, Lane HC, et al. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 2022;50(W1):W216\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSong Z, Xia Y, Shi L, Zha H, Huang J, Xiang X, et al. Inhibition of Drp1- Fis1 interaction alleviates aberrant mitochondrial fragmentation and acute kidney injury. Cell Mol Biol Lett. 2024;29(1):31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee LE, Doke T, Mukhi D, Susztak K. The key role of altered tubule cell lipid metabolism in kidney disease development. Kidney Int. 2024;106(1):24\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu J, Kumar S, Dolzhenko E, Alvarado GF, Guo J, Lu C, et al. Molecular characterization of the transition from acute to chronic kidney injury following ischemia/reperfusion. JCI Insight. 2017;2:18.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDessing MC, Tammaro A, Pulskens WP, Teske GJ, Butter LM, Claessen N, et al. The calcium-binding protein complex S100A8/A9 has a crucial role in controlling macrophage-mediated renal repair following ischemia/reperfusion. Kidney Int. 2015;87(1):85\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGerhardt LMS, Liu J, Koppitch K, Cipp\u0026agrave; PE, McMahon AP. Single-nuclear transcriptomics reveals diversity of proximal tubule cell states in a dynamic response to acute kidney injury. Proc Natl Acad Sci USA. 2021;118(27).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu J, Zhang G, Song Z, Xiang X, Shu S, Liu Z, et al. Protein Kinase C-δ Mediates Kidney Tubular Injury in Cold Storage-Associated Kidney Transplantation. J Am Soc Nephrology: JASN. 2020;31(5):1050\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrooks C, Wei Q, Cho S-G, Dong Z. Regulation of mitochondrial dynamics in acute kidney injury in cell culture and rodent models. J Clin Investig. 2009;119(5):1275\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKoval OM, Nguyen EK, Santhana V, Fidler TP, Sebag SC, Rasmussen TP et al. 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Kidney Int. 2023;104(4):724\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKang HM, Ahn SH, Choi P, Ko Y-A, Han SH, Chinga F, et al. Defective fatty acid oxidation in renal tubular epithelial cells has a key role in kidney fibrosis development. Nat Med. 2015;21(1):37\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang X, Stockwell BR, Conrad M. Ferroptosis: mechanisms, biology and role in disease. Nat Rev Mol Cell Biol. 2021;22(4):266\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePefanis A, Ierino FL, Murphy JM, Cowan PJ. Regulated necrosis in kidney ischemia-reperfusion injury. Kidney Int. 2019;96(2):291\u0026ndash;301.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKnol MGE, Wulfmeyer VC, M\u0026uuml;ller R-U, Rinschen MM. Amino acid metabolism in kidney health and disease. 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Oncogene. 2020;39(44):6747\u0026ndash;56.\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":"multi-omics, ischemia reperfusion injury, kidney, PDK4, mitochondria fragmentation","lastPublishedDoi":"10.21203/rs.3.rs-6927061/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6927061/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003e(Background) \u003c/strong\u003eRenal ischemia reperfusion injury (IRI) represents the predominant etiology of acute kidney injury, yet its molecular events and underlying mechanism remain incompletely elucidated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Methods)\u003c/strong\u003eTo investigate the integrative and dynamic pathophysiology of renal IRI, we conducted a comprehensive multi-omics (transcriptomics, proteomics and metabolomics) analysis of kidney tissues at distinct IRI time points (2 h, 6 h, 24 h and 7 d). Given that PDK4 was identified as the most consistently upregulated kinase in carbon metabolism, we further explored its effect and mechanism by generating PDK4-knockout mice and employing pharmacological inhibitors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Results)\u003c/strong\u003e Our study revealed remarkable metabolic reprogramming, particularly in carbohydrate metabolism, during kidney IRI. Correspondingly, notable gene and protein regulation was observed. Integrated multi-omics analysis demonstrated that PDK4 activation plays a pivotal role in modulating carbon metabolism. Animal experiments confirmed PDK4 activation and further demonstrated that genetic ablation or pharmacological inhibition of PDK4 attenuated renal injury, reduced tubule cell death, facilitated tubular proliferation and improved renal function. Mechanistically, PDK4 contributed to mitochondrial fragmentation through mediating Drp1 activation and translocation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Conclusion) \u003c/strong\u003eThe present study delineates the extensive molecular reprogramming in kidney IRI and establishes PDK4 as critical nexus between energy metabolism dysregulation and renal tubular injury and cell death. Our multi-omics approach provides valuable insights for identifying novel therapeutic targets and developing renal protective strategies.\u003c/p\u003e","manuscriptTitle":"Integrated Multi-Omics Reveal PDK4-Mediated Mitochondrial Dysfunction in Renal Ischemia-Reperfusion Injury","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-09 16:41:45","doi":"10.21203/rs.3.rs-6927061/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":"7d0a0751-cd3a-44ed-8eab-6ff242ea58f7","owner":[],"postedDate":"July 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-16T11:24:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-09 16:41:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6927061","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6927061","identity":"rs-6927061","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}