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. 2025 Sep 29;15:33624. doi: 10.1038/s41598-025-19191-4

Identification of biomarkers and mechanisms associated with palmitoylation in acute kidney injury using transcriptome and single cell data

Weinan Sun 1,#, Juntao Wang 3,#, Lina Zhang 1, Wenwen Zhang 1, Qin Xu 1, Fengmin Shao 1,2,✉, Yue Gu 1,2,✉
PMCID: PMC12480845  PMID: 41023305

Abstract

Palmitoylation is implicated in acute kidney injury (AKI) development, but its mechanisms are poorly understood. Our study aimed to identify biomarkers associated with palmitoylation-related genes (PRGs) in AKI and explore their biological mechanisms. We analyzed datasets GSE139061, GSE30718, and GSE174220, identifying intersecting genes through differential expression and WGCNA. Candidate genes were selected via PPI analysis, and biomarkers were identified using machine learning, ROC analysis, and gene expression analysis. A nomogram was constructed, and functional analysis along with Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR) and Western blotting were performed. scRNA-seq analysis was utilized to identify key cell types and their developmental trajectories in AKI. LIMD1 and MBD2 were identified as AKI biomarkers with elevated expression in AKI samples, confirmed by RT-qPCR and Western blotting. A nomogram based on these biomarkers effectively predicted AKI risk. Functional analysis showed co-enrichment in the “valine leucine and isoleucine degradation” pathway. scRNA-seq analysis identified tubular cells as key in AKI pathogenesis, with a developmental trajectory detailed through pseudo-time analysis. LIMD1 and MBD2 were confirmed as AKI biomarkers, with tubular cells identified as crucial in AKI. Our findings provide new insights into AKI treatment strategies.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-19191-4.

Keywords: Acute kidney injury, Palmitoylation, Single-cell RNA sequencing analysis, Biomarker

Subject terms: Biomarkers, Diseases, Nephrology

Introduction

Acute kidney injury (AKI) is a common clinical critical illness condition characterized by a sharp decline in renal function, accompanied by accumulation of metabolic waste products such as creatinine and urea nitrogen, electrolyte disturbance, and a reduction in urine output1. The incidence of AKI is high, which can occur in 5.0-7.5% of hospitalized patients and 50–60% of critically ill patients2. The causes of AKI are complex and diverse, such as ischemia, poisons, drugs, sepsis, etc. The pathophysiological processes involved in AKI are also very complex3–5. At present, the main therapeutic measure of AKI is still supportive treatment based on renal replacement therapy, and there is still a lack of effective interventions to improve the prognosis of AKI patients6. The sustained renal dysfunction observed in patients with AKI is associated with an elevated risk of progression to chronic kidney disease (CKD), cardiovascular events and death, which brings a heavy burden to patients’ families and the whole society7,8. Therefore, it is important to identify AKI early and give necessary targeted treatment in time. The existing diagnostic criteria of AKI based on serum creatinine and urine volume have limited value for early diagnosis. Meanwhile, some biomarkers of acute renal injury, such as KIM1 and NGAL, have been verified for risk prediction and early diagnosis of AKI2,9. Unfortunately, these biomarkers also generally have their limitations, such as regional differences and the absence of standardized reference values. Therefore, exploring new diagnostic and therapeutic methods, such as identification of new biomarkers related to AKI with clinical application value, is essential for effective prevention, timely evaluation and early intervention of AKI to reduce the incidence and severity of AKI.

Protein palmitoylation is a reversible post-translational modification that usually occurs on cysteine residues. It can be subdivided into three categories, N-palmitoylation, O-palmitoylation, and S-palmitoylation, based on the mode of ligation. Among them, S-palmitoylation is a unique and reversible modification that has the capacity to influence protein function by modulating protein sorting, secretion, trafficking, stability, and protein interaction, which is normally controlled by palmitoyl acetyltransferase, a member of the ZDHHC family10. Therefore, S-palmitoylation has been demonstrated to play a crucial role in a number of diseases in humans, including psychiatric disorders and cancer10–12. A significant number of proteins have been reported to undergo S-palmitoylation, including enzymes, viral glycoproteins, channels, receptors and transporters, and the cycle of S-palmitoylation and depalmitoylation plays a pivotal role in the occurrence, development and treatment of many diseases13. It has been found that DHHC9 palmitoacylates beta-catenin, thereby promoting its ubiquitination and degradation, leading to the inhibition of renal fibrosis, and the levels of protein palmitoylation as well as DHHC9 are reduced in the fibrotic kidneys of mice models and patients with CKD14. Protein palmitoylation serves as a novel regulator of vascular function15. In particular, Palmitoyltransferase (PAT)-DHHC21 has been observed to contribute to impaired renal perfusion and function during septic injury via the promotion of α1-adrenergic receptor (α1-AR) palmitoylation-associated vasoconstriction15. Therefore, Palmitoylation may play an significant role in AKI, but the mechanism of palmitoylation-related genes (PRGs) in AKI remains to be elucidated and necessitates further investigation. Therefore, this study aims to identify the biomarkers related to palmitoylation in AKI by bioinformatics tools, which may provide a new direction for exploring the pathogenesis and potential therapeutic targets of AKI.

Single-cell RNA sequencing (scRNA-seq) represents a pioneering technology for the exploration of the transcriptome of individual cells in sequenced samples, and it also functions as a highly efficacious tool for the study of gene expression patterns16. ScRNA-seq reveals that the macrophage-endothelial immunomodulatory axis is the basis of interleukin-6 expression and endothelial immunoregulation of macrophages can ameliorate septic acute kidney injury17. The technique also demonstrates a therapeutic strategy for mesenchymal stem cells in the treatment of acute kidney injury18. Therefore, scRNA-seq datas are of great value in further exploring the expression patterns and possible mechanisms of biomarkers related to palmitoylation in AKI.

Based on the GEO database, we first downloaded two original datasets to obtain the gene data of AKI and normal kidney tissues. Then we screened and identified the biomarkers and key cells related to palmitoylation in AKI by bioinformatics methods. For the selected biomarkers, functional enrichment analysis, and molecular regulatory network analysis were used to further revealed the potential relationship between AKI and palmitoylation. Importantly, we combined scRNA-seq data to perform the identification of key cell clusters, cell communication analysis and pseudo-timing analysis to explore the mechanism of biomarkers in AKI at the cellular level. It is anticipated that these studies will furnish a robust theoretical framework to underpin the effective diagnosis and treatment of AKI.

Results

The DEGs and key module genes were identified

Through differential expression analysis, a total of 694 DEGs were identified in this study. Among these, 605 genes exhibited up-regulation in AKI samples, whereas 89 genes showed down-regulation (Fig. 1a,b). Additionally, AKI samples demonstrated a notably higher PRGs score compared to control samples (Supplementary Fig. S1a). There were no outlier samples in the GSE139061 dataset (Supplementary Fig. S1b). The optimal power value was determined to be 8, supported by an R2 value of 0.909 and a mean connectivity close to 0 (Fig. 1c). Utilizing the co-expression matrix, similar modules were merged, resulting in the identification of 22 gene modules, excluding the grey module (Supplementary Fig. S1c). Notably, 2 key modules, MEblack and MElightgreen, exhibited significant positive (cor = 0.79, P = 2 × 10− 11) and negative (cor = -0.54, P = 7 × 10− 5) correlations with the PRGs score, respectively (Fig. 1d). Consequently, the 829 genes within the MEblack and MElightgreen were identified as key module genes.

Fig. 1.

Fig. 1

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Verification of differentially expressed genes (DEGs) in AKI. (a) Volcano map of DEGs; the vertical coordinate denotes − log10 (adj.pvalue) and the horizontal coordinate denotes the difference multiplier log2FC. (b) Heat map of DEGs, the top 10 up-regulated genes and the top 10 down-regulated genes in the log2FC sequence are shown. (c) Screening of scale-free soft thresholds. (d) Heatmap of correlations between modules and ssGSEA, the leftmost colour blocks represent modules and the rightmost colour bars represent correlation ranges. In the heatmap in the middle section, red indicates a positive correlation and blue indicates a negative correlation; the numbers in each cell indicate correlation and significance.

The candidate genes were ascertained

By overlapping the 694 DEGs and 829 key module genes, a total of 366 intersection genes were identified (Fig. 2a). Functional enrichment analysis of these 366 intersection genes revealed significant associations with 124 GO terms [74 biological processes (BPs), 20 cellular components (CCs), and 30 molecular functions (MFs)] and 8 KEGG pathways (P < 0.05). The enriched GO terms encompassed diverse functions such as “ribonucleoprotein complex biogenesis” (BP), “small nuclear ribonucleoprotein complex” (CC), and “histone binding” (CC), among others (Fig. 2b). In terms of KEGG pathways, the 336 intersection genes were notably enriched in pathways such as “spliceosome”, “ribosome biogenesis in eukaryotes”, “nucleocytoplasmic transport”, “glutamatergic synapse”, “ATP-dependent chromatin remodeling”, “hippo signaling pathway - multiple species”, “long-term potentiation”, and “p53 signaling pathway” (Fig. 2c).

Fig. 2.

Fig. 2

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Identification of candidate genes. (a) Candidate genes related to PRGs in AKI, record intersecting genes as candidate genes. (b) GO enrichment analysis. (c) KEGG enrichment analysis. (d) PPI Network Construction.

Furthermore, these 366 genes were utilized to construct a PPI network. This PPI network (an average node degree of 0.18, a local clustering coefficient of 0.105, and a statistical significance of P = 0.0172) comprised 27 nodes and 18 edges after excluding 339 outlier genes (Fig. 2d). Notably, genes like MTRNR2L2 were found to interact closely with MTRNR2L1 and MTRNR2L8 within this network. Therefore, these 27 genes identified within the PPI network represented promising candidate genes warranting further detailed analysis.

LIMD1 and MBD2 were identified as biomarkers for AKI

In machine learning, the Random Forest (RF) model became the best model due to its lowest root mean square of residuals and an excellent AUC value of 1 (Fig. 3a,b). Subsequently, RF was utilized to identify feature genes from a pool of 27 candidate genes using the “randomForest” package (v 4.7-1.1)19. From this analysis, the top 5 genes ranked by Gini index were selected as feature genes: MTRNR2L8, LIMD1, UBTF, MBD2, and AEBP2 (Fig. 3c). Following their selection, ROC analysis demonstrated that LIMD1 and MBD2 exhibited AUC values greater than 0.7 in both the GSE139061 and GSE30718 datasets, indicating their robust ability to differentiate between disease and control samples (Fig. 3d,e). Importantly, both LIMD1 and MBD2 showed significantly elevated expression levels in AKI samples from both datasets (P < 0.05), further supporting their potential as biomarkers for AKI (Fig. 3f,g). We induced a LPS-induced AKI mice model. The levels of serum creatinine (Scr) in AKI mice were markedly higher than those in the control group (Fig. 3h). Additionally, Using quantitative RT-PCR, our results revealed that the expression of LIMD1 (P = 0.0003) and MBD2 (P = 0.0002) were significantly higher in AKI samples (Fig. 3I). Moreover, we detected the protein expressions of LIMD1 and MBD2. Western blot analysis demonstrated that the protein levels of LIMD1 (P = 0.0019) and MBD2 (P = 0.0035) were increased in the kidneys of LPS-induced AKI mice (Fig. 3j). Overall, these findings underscored the utility of LIMD1 and MBD2 as promising biomarkers for AKI diagnosis.

Fig. 3.

Fig. 3

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LIMD1 and MBD2 were identified as biomarkers for AKI. (a) Machine learning residual boxplots. (b) Machine learning ROC diagram. (c) Genetic importance ranking diagram, candidate biomarkers on vertical axis, Gini index on horizontal axis. (d) ROC curves for biomarkers in training set GSE139061. (e) ROC curves for biomarkers in validation set GSE30718. (f) Expression levels of LIMD1, MBD2 in training set GSE139061. (g) Expression levels of LIMD1, MBD2 in validation set GSE30718. (h) The levels of serum creatinine (Scr) in the normal and AKI groups(n = 5). (l) Box plots of relative LIMD1 and MBD2 levels in the normal and AKI groups (n = 5). (j) Representative western blot and quantitative data of LIMD1 and MBD2 protein expressions in the normal and AKI groups (n = 3).

The ability of nomogram was excellent

Based on the identified biomarkers, a nomogram was developed to assess the risk of AKI (Fig. 4a). Higher total points on the nomogram correlated with an increased likelihood of AKI. The DCA further demonstrated that the model provided a higher net benefit compared to using individual factors alone, emphasizing its diagnostic utility (Fig. 4b). Additionally, the nomogram achieved an impressive AUC value of 0.949 (Fig. 4c), indicating strong predictive performance. These results collectively highlight the robust predictive efficacy of the nomogram. The slope of the calibration curve of the nomogram was close to 1 (Fig. 4d), and the P-value of the HL test was 0.56, which was greater than 0.05. This indicated that the nomogram model constructed in this study had good prediction accuracy.

Fig. 4.

Fig. 4

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Construction and testing of nomogram. (a) Nomogram of LIMD1, MBD2. (b) DCA decision curve. (c) ROC curve and AUC, the closer the AUC is to 1, the more accurate the diagnosis. (d) Calibration curve of the nomogram. The closer the slope is to 1, the higher the predictive accuracy of the nomogram.

Functions and immune correlations of biomarkers were explored

The GSEA of the biomarkers revealed significant co-enrichment of LIMD1 and MBD2 in the “valine leucine and isoleucine degradation” pathway (Fig. 5a,b). Additionally, LIMD1 showed enrichment in pathways including “alzheimers disease”, “huntingtons disease”, “oxidative phosphorylation”, and “parkinsons disease” (Fig. 5a). For MBD2, the pathways included “arginine and proline metabolism”, “butyrate metabolism”, “drug metabolism cytochrome P450”, and “metabolism of xenobiotics by cytochrome P450” (Fig. 5b).

Fig. 5.

Fig. 5

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Functions and immune correlations of biomarkers. (a) LIMD1 was significantly enriched in 40 pathways. (b) MBD2 was significantly enriched in 10 pathways.

These findings underscored the potential roles of LIMD1 and MBD2 in metabolic pathways, highlighting their broader implications in disease mechanisms.

Regulated network was helpful for exploring the potential mechanism for AKI

Through our investigation, we identified 31 key miRNAs by overlapping 106 predicted miRNAs from miRDB and 99 predicted miRNAs from the TargetScan database. Subsequently, using the StarBase database, we predicted 55 lncRNAs that target these 31 key miRNAs. These findings enabled us to construct an lncRNA–miRNA–mRNA network involving two biomarkers, LIMD1 and MBD2, along with 31 key miRNAs and 55 lncRNAs (Fig. 6a). Examples of relationships within this network included H19-‘hsa-miR-454-3p’-MBD2 and AC005394.2-‘hsa-miR-24-3p’-LIMD1 (Fig. 6a).

Fig. 6.

Fig. 6

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The potential mechanism for AKI. (a) lncRNA–mRNA–miRNA regulatory networks, pink indicates biomarkers, green indicates target miRNAs, and purple indicates lncRNAs. (b) LIMDI, MBD2-drug network diagrams.

Furthermore, our screening identified 8 drugs targeting LIMD1 and 17 drugs targeting MBD2. Notably, tetradioxin and valproic acid were found to co-target these 2 biomarkers (Fig. 6b).

Tubular cell was chosen as the key cell

Initially, ineligible cells were filtered out, leaving 7,126 cells and 24,171 genes for further scRNA-seq analysis (Supplementary Fig. S2). A set of 2,000 HVGs was identified (Supplementary Fig. S3). Subsequently, PCA was performed, demonstrating no noticeable batch effects (Supplementary Fig. S4). The top 30 PCs were selected for subsequent analysis (Supplementary Fig. S5). Using the UMAP method, the cells passing QC were classified into 11 distinct clusters (Supplementary Fig. S6). Subsequently, these clusters were annotated into 7 cell types, including epithelial cell, monocyte, endothelial cell, proximal tubular cell, tubular cell, myofibroblast, and glomerular mesangial cell (Supplementary Fig. S6). Moreover, in the AKI samples from the GSE174220 dataset, tubular cell was predominant, accounting for 63.42% of the annotated cells (Supplementary Fig. S6). As a result, tubular cell was chosen as the key cell for subsequent analyses.

Function and interactions of annotated cells were explored

The function enrichment analysis indicated that annotated cells were primarily involved in pathways such as “Regulation of thyroid hormone activity,” “Hydroxycarboxylic acid-binding receptors,” and “Alanine metabolism” (Fig. 7a).

Fig. 7.

Fig. 7

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Function and interactions of annotated cells. (a) Enrichment analysis heat map, the horizontal axis is the different cell types and the vertical axis is the enrichment pathways. Each row is a pathway and each column is a cell type. (b) Key gene expression map. (c) Cellular communication interactions map, the size of the various coloured circles around the periphery indicates the number of cells; the larger the circle, the greater the number of cells. Cells that emit arrows express ligands, and cells with arrows pointing to express receptors. The more ligand–receptor pairs there are, the thicker the line is. (d) Probability intensity values of interactions (e) Receptor–ligand pair interaction correlation diagram.

Additionally, biomarker expression analysis revealed that both LIMD1 and MBD2 were expressed in all annotated cells, with MBD2 showing higher expression (Fig. 7b). Moreover, the expression levels of MBD2 were relatively higher in Endothelial_cell and Tubular_cell (Supplementary Fig. S7). Furthermore, cell-cell communication analysis revealed significant interactions among annotated cells. Specifically, the key cell type, tubular cell, exhibited interactions with monocyte, epithelial cell, endothelial cell, and glomerular mesangial cell, while showing no interactions with proximal tubular cell and myofibroblast (Fig. 7c,d). Notably, the interaction between tubular cells and epithelial cells was mediated by SPP1-(ITGAV + ITGB1) (Fig. 7e).

Exploring tubular cell was helpful in better Understanding the pathogenesis of AKI

Based on tubular cell, the top 15 PCs were utilized for secondary clustering (Supplementary Fig. S8). The analysis identified 5 distinct subtypes (0–4) within tubular cell (Fig. 8a). Subsequent pseudo-time analysis illustrated the developmental trajectory of tubular cells from left to right, represented by a transition from dark blue to lighter blue (Fig. 8b). What’s more, tubular cell was further classified into 7 states (state 1–7), with stage 1 and 2 indicative of early differentiation phases (Fig. 8c). Among the 5 subtypes of tubular cells, subtypes 1 and 2 were specifically associated with early differentiation stages (Fig. 8d). Besides, a heatmap of biomarkers depicted their expression dynamics throughout the differentiation process of tubular cell, demonstrating an initial increase followed by a decrease (Fig. 8e). These findings revealed the dynamic expression patterns of LIMD1 and MBD2 in the key cells, tubular cells, during the progression of AKI, thus providing a basis for exploring the potential regulatory mechanisms of LIMD1 and MBD2 in AKI.

Fig. 8.

Fig. 8

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Tubular cell was helpful in better understanding the pathogenesis of AKI. (a) Analysis of cellular heterogeneity. (b) Proposed time-series trajectory diagram. (c) Trajectory plots of different cell subpopulations. (d) Different stages of differentiation trajectories. (e) Dynamic heat map of key genes.

Discussion

AKI is a complex group of clinical syndromes associated with a variety of etiologies. AKI often leads to severe kidney damage with poor prognosis and a lack of effective treatment. Unfortunately, the current technology cannot timely and accurately predict the occurrence of AKI and assess the severity of renal injury20. Studies have demonstrated that the identification of biomarkers can facilitate the acquisition of precise and current biological data in real time21. As an important protein modification, palmitoylation may play a pivotal role in AKI. Research has shown that palmitoylation can aggravate renal perfusion and function impairment during septic injury15. However, the specific mechanism of PRGs in AKI remains to be elucidated and further studies are needed. In the present study, we used bioinformatics technology to screen and identify the biomarkers associated with palmitoylation in AKI, and further explored their mechanism of action at the cellular level combined with single-cell sequencing data.

As an important protein modification, palmitoylation may play an important role in AKI. Therefore, AKI biomarkers LIMD1 and MBD2 associated with palmitoylation were screened in this study, which has certain reference for exploring new diagnosis and treatment of AKI. LIM domains containing 1 (LIMD1) is a member of the Zyxin family proteins which plays significant roles in a variety of cellular processes, including cell–cell adhesion, gene transcription, cell growth, and cell cycle regulation22. LIMD1 has been identified as a key regulator of mitotic progression, and its dysfunction has been demonstrated to contribute to tumourigenesis23. Sur et al. reported that the reduced expression of LIMD1 and VHL in renal cell carcinoma(RCC) might have a synergistic effect on the induction of HIF1α, resulting in increased cellular proliferation and disease progression24. However, whether LIMD1 plays a role in other kidney diseases has not been reported. LIMD1 has been mainly studied as a tumor suppressor gene, but its role in cell signaling pathways may be related to AKI. For example, p53 has been shown to play a role as an autophagy regulator in many forms of AKI, and LIMD1’s interaction with pRB may affect p53 activity25. In addition, LIMD1 may influence the pathophysiological processes of AKI through cell adhesion and cell migration26. Our study showed for the first time that there is a significant increase in LIMD1 expression in renal tissues of AKI patients. This finding suggests that LIMD1 expression has the potential to enhance the diagnostic efficiency of AKI. It is well established that epigenetic regulation plays an important role in the development of diseases and tumorigenesis. DNA methylation, in particular, represents a major research area within the field of epigenetics. Methyl-CpG-binding domain protein 2 (MBD2) is a protein reader of methylation that has been demonstrated to play a critical role in the modulation of gene transcriptional activity and the development of various diseases27–30. Some studies have shown that MBD2 promoted the occurrence of renal fibrosis, at least in part, by means of the upregulation of EGR1 expression, and inhibition of MBD2 expression could be capable of attenuating renal fibrosis induced by TGF-ß1, unilateral ureteral obstruction (UUO) and ischemia/reperfusion (I/R)31. As was demonstrated in previous studies, the global knockout of MBD2 attenuated AKI caused by sepsis, rhabdomyolysis and vancomycin32–34. MBD2 could activate PKCη/p38MAPK and ERK1/2 signaling pathways, thereby exacerbating the inflammatory response in sepsis induced AKI33. Meanwhile, the activation of MBD2 is closely related to renal cell apoptosis. In rhabdomyolysis-induced AKI, MBD2 promoted renal cell apoptosis by activating Tox434. MBD2 could also induce renal cell apoptosis by up-regulating the expression of miR-301a-5p in vancomycin-induced AKI32. In conclusion, the expression of MBD2 is increased during the occurrence of AKI and CKD caused by various etiologies, which is a pathogenic factor and can promote disease progression through a series of mechanisms. Our results showed that the trend of MBD2 expression in AKI was consistent with previous studies, and further reveal the potential of MBD2 as a biomarker for the prediction of AKI. It is worth discussing that no studies have clearly demonstrated that the functions of LIMD1 and MBD2 were regulated by palmitoylation. However, considering the important role of LIMD1 in tumors35,36 and the regulatory effects of palmitoylation on a variety of tumor-related proteins37,38, it can be speculated that the activity of LIMD1 may be indirectly regulated by palmitoylation, thus affecting the process of AKI. Moreover, MBD2 is involved in immune regulation, such as inhibiting the macrophage M2 program and thereby inhibiting pulmonary fibrosis39. Palmitoylation is also involved in immune regulation. For example, ZDHHC28 could negatively regulate CGAS-mediated innate immunity through palmitoylation40. Therefore, MBD2 and palmitoylation maybe co-regulate the immune response. In addition, the nomogram was constructed on the basis of LIMD1 and MBD2 in order to evaluate the risk of AKI. The results indicated that the area under the curve (AUC) value was 0.949, suggesting that the combined detection of LIMD1 and MBD2 had strong predictive and diagnostic performance. The expressions of LIMD1 and MBD2 in AKI mice were significantly increased through RT-qPCR and Western blot analysis. The above results are expected to provide a good theoretical basis for subsequent clinical application.

GSEA is a kind of high-performance, gene-set enrichment analysis, which can further reveal the biological function and potential molecular mechanism of biomarkers. In the subsequent study, we used GSEA to explore the main functions of the above biomarkers. This study found that LIMD1 was significantly enriched in a total of 40 pathways, including oxidative phosphorylation, valine-leucine and isoleucine degradation, Alzheimer’s disease, etc. MBD2 was significantly enriched in 10 pathways, including cytochrome P450 mediated drug metabolism, cytochrome P450 mediated exogenous substance metabolism, valine-leucine and isoleucine degradation pathways, etc. In conclusion, the enriched pathways of MBD2 and LIMD1 were mostly metabolism-related pathways, and the common enriched pathway was valine leucine and isoleucine degradation pathway. Relevant research data have shown that metabolic pathways such as valine leucine and isoleucine degradation pathways are almost universally altered in all cases with genomic diversity. These alterations have the potential to contribute significantly to tumour progression and survival41. In addition, metabolism-related pathways are also involved in the transformation of AKI to CKD. Zhu et al. demonstrated that renal tubular epithelial cells (TECs) are more susceptible to metabolic reprogramming during AKI due to pathological mechanisms such as hypoxia and mitochondrial dysfunction, such as fatty acid β-oxidation (FAO) conversion to glycolysis, and the enhancement of glycolysis can promote inflammatory response and fibrosis in renal tissue42. Studies have shown that cytochrome p450 mediated drug metabolism is also involved in AKI. In AKI caused by sepsis, liver/intestinal cytochrome P450 and intestinal drug transporters were significantly impaired43.

Gene regulatory network research can help to dig deeper into the function of target genes and the regulatory mechanism in specific diseases. In order to explore the molecular regulation mechanism of biomarkers (LIMD1 and MBD2), we constructed an lncrNA-mirNA-biomarker regulatory network, including LIMD1 and MBD2, 31 key miRNAs and 55 lncRNAs. Many previous studies have reported the role of some mirnas and upstream lncRNAs in AKI, such as miR-21, miR-210, miR-375, etc44–48. The above selected miRNAs and lncrnas are rarely reported, which are worthy of further study.

As we know, there is still a lack of effective drugs to intervene and improve the prognosis of AKI6. In order to find possible drug targets for the treatment of AKI, biomarkers (LIMD1, MBD2) were imported into the CTD database to search for potential drugs. MBD2 predicted a total of 17 interacting drugs including estradiol, genistein, and resveratrol. Eight drugs such as norgestrel and Bortezomib were predicted by LIMD1. Among them, tetradioxin and valproic acid were found to co-target these 2 biomarkers. Cheng Yan’s research results showed that tetradioxin is also one of the predictive drugs for COVID-19 treatment, but the possible mechanism is not clear49. In pancreatic cancer, tetradioxin may exert anti-tumor effects by targeting Major Vault Protein (MVP). The lack of MVP has a significant effect on inhibiting pancreatic cancer cell proliferation, inhibiting cell migration and promoting apoptosis50. Dioxins are actually a general term for chlorinated polynuclear aromatic compounds, which are divided into polychlorinated dibenzo-p-dioxins (pCDDs) and polychlorinated dibenzofurans (PCDFs). However, studies have pointed out that exposure to long-term pCDDs can lead to insulin resistance and the risk of metabolic disorders, which may place a burden on the kidneys51. Valproic acid is a histone deacetylase inhibitor, which is prescribed for epilepsy and as prophylaxis for bipolar disorder and migraine headaches. Valproic acid can increase survival in preclinical animal models of hemorrhage and trauma and has a protective effect on AKI caused by hemorrhage and trauma52,53. In addition, Valproic acid may be capable of inhibiting cisplatin-induced kidney injury by suppressing proximal tubular cell damage54. It should be noted that valproic acid also has side effects. Previous datas suggest that valproic acid may induce significant tubular injury, which is associated with proximal tubular mitochondrial toxicity55. However, the above results are mainly derived from animal model research data, and the biomarkers and predictive drugs explored in this study rely on bioinformatics analysis. Further research is required to ascertain the precise value of associated biomarkers and pharmaceuticals in the context of AKI.

Single-cell sequencing technology represents a significant advancement in scientific research, offering researchers a unique opportunity to deepen the understanding of transcriptomics, genomics, proteomics, epigenomic and metabolomic information in individual cells56,57. Finally, a series of single-cell analyses were performed to explore the gene expression profile of AKI at the single-cell level, to confirm that the analyzed cells belong to different cell populations, and to identify cell types in AKI patient samples. A total of 7 types of cells were annotated in our study and visualized according to their proportion in the AKI group. Among them, renal tubular cells accounted for the largest proportion and were recorded as the key cells for heterogeneity analysis and quasitemporal analysis. LIMD1 and MBD2 were expressed in all the annotated cells, suggesting their broad expression characteristics in renal tissues, and the expression of MBD2 was relatively higher. The expression of LIMD1 and MBD2 also changed dynamically during the whole differentiation process of renal tubular cells, with increased expression at the initial stage but decreased at the later stage.

In our research, two biomarkers (LIMD1 and MBD2) associated with AKI were identified by difference analysis, WGCNA analysis, machine learning, ROC analysis, gene expression analysis, and single cell screening. In addition, GSEA enrichment analysis, drug prediction analysis, molecular regulatory network analysis and quasi-time series analysis were carried out respectively based on biomarkers. These results may provide new ideas for the clinical diagnosis and treatment of AKI. Unfortunately, our study was mainly based on bioinformatics analysis, and the selected biomarkers were only validated by RT-qPCR and Western blotting. Therefore, the direct role of LIMD1 and MBD2 in AKI-related palmitoylation and their specific mechanism need to be verificated by further experiments. In follow-up studies, we plan to identify whether these genes are directly covalently bound to palmitic acid by mass spectrometry, such as LC-MS/MS. Simultaneous biochemical experiments, such as immunoprecipitation assay, will be performed to confirm whether LIMD1 and MBD2 interact with known palmitoacylase or other related factors. We can also study the palmitoylation status of LIMD1 and MBD2 in kidney cells and how this status affects their function by palmitoylation inhibitors such as 2-Bromopalmitate or by overexpression/knockout of LIMD1 and MBD2. In addition, the role of LIMD1 and MBD2 in AKI related signaling pathways can be verified by cultured HK-2 or NRK-52E cells in vitro.

Materials and methods

Data extraction

In this study, AKI-related transcriptome datasets (GSE139061 and GSE30718) were downloaded from the Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo/). Specifically, the GSE139061 dataset (GPL20301 platform) included 39 AKI kidney tissue samples and 9 control kidney tissue samples. Additionally, the GSE30718 dataset (GPL570 platform) comprised 28 AKI and 19 control kidney tissue samples. Furthermore, the scRNA-seq dataset related to AKI, GSE174220 (GPL20795 platform), was also obtained from the GEO database. The GSE174220 dataset contained kidney tissue samples from 2 AKI patients and kidney tissue samples from 2 control subjects. Moreover, 30 Palmitoylation-related genes (PRGs) were searched from previous literature research (Supplementary Table S1)12.

Differential expression analysis

The differentially expressed genes (DEGs) between AKI and control samples in the GSE139061 dataset were identified using the “DEseq2” package (v 1.38.0)58, with thresholds set at |log2 fold-change (FC)| > 1 and adj. P < 0.05. The DEGs were then visualized using a volcano plot and heat map (only displayed the top 10 up-regulated and down-regulated genes), generated through the “ggplot2” package (v 3.4.4)59 and the “pheatmap” package (v 1.0.12)60, respectively.

Weighted gene co-expression network analysis (WGCNA)

In the GSE139061 dataset, the PRGs scores of the samples were calculated using the single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm from the “GSVA” package (v 1.46.0)61. The difference in PRGs scores between AKI and control samples was subsequently analyzed using the Wilcoxon test (P < 0.05). Additionally, WGCNA was employed to identify key modules associated with PRGs scores using the “WGCNA” package (v 1.71)62. Initially, all samples from GSE139061 were clustered to remove outliers. The optimal soft threshold (power) was selected to achieve a scale-free R2 value greater than 0.9 and a mean connectivity close to 0. A co-expression matrix was then constructed, with the minimum number of genes for each gene module set to 100, the cut-off tree parameter set to 4, and the module merge parameter set to 0.2. Subsequently, gene modules were obtained. To identify modules most correlated with the PRGs scores, the Pearson correlation coefficient was calculated between the modules and the PRGs scores. Finally, the key modules exhibiting the highest positive and negative correlation with the PRGs score were separately identified (P < 0.05), and the genes within these key modules were considered as key module genes.

Function analyses of intersection genes and identification of candidate genes

Intersection genes were identified by overlapping the DEGs with the key module genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were subsequently conducted using the “clusterProfiler” package (v 4.7.1.003)63 to elucidate the functions of these intersection genes, with a significance threshold of P < 0.05. Besides, to explore protein interactions among the intersection genes, a protein-protein interaction (PPI) network was constructed using the STRING database (https://string-db.org/), with a confidence score > 0.9. Outlier genes were excluded from the network, and the resulting PPI network was visualized using Cytoscape (v 3.9.1)64. Notably, the remaining genes in PPI network were considered as candidate genes for subsequent analysis.

Machine learning and gene expression analyses

In the GSE139061 dataset, machine learning algorithms were employed to identify feature genes. Initially, the best model was selected from Generalized Linear Model (GLM), Random Forest (RF), and Support Vector Machine (SVM) models using the “DALEX” package (v 2.4.3)65. Model performance was assessed using boxplots of residuals and receiver operating characteristic (ROC) curve. Subsequently, based on the selected model, feature genes were identified. Their ability to distinguish between AKI patient samples and controls was evaluated using ROC curves generated with the “pROC” package (v 1.18.0)66 in both GSE139061 and GSE30718 datasets, with the area under the curve (AUC) calculated. Candidate biomarkers with an AUC > 0.7 were identified.

Further gene expression analyses of these feature genes were conducted in both GSE139061 and GSE30718 datasets. Genes showing significant differential expression between AKI and control samples (P < 0.05), with consistent expression trends across both datasets, were defined as biomarkers for subsequent analysis.

Construction and validation of nomogram

Based on the identified biomarkers, a nomogram was constructed with the “rms” package (v 6.5-0)67. The accuracy of the nomogram was subsequently evaluated using both decision curve analysis (DCA) and ROC curve analysis. Notably, DCA was performed utilizing the “ggDCA” package (version 1.2) (https://cran.r-project.org/web/packages/ggDCA/index.html). Together, these analyses provided a robust evaluation of the nomogram’s accuracy and clinical utility in predicting AKI based on the identified biomarkers. In order to evaluate the prediction accuracy of the nomogram, the calibration curve was plotted using the “rms” package (v 6.5.0) (https://CRAN.R-project.org/package=rms), and the Hosmer-Lemeshow (HL) test was conducted (P > 0.05).

Function analysis of biomarkers

To elucidate the biological functions and signaling pathways associated with the biomarkers, we conducted function enrichment analysis. Initially, samples from the GSE139061 dataset were stratified into high and low expression groups based on the median expression of the biomarkers. Differential expression analysis was then performed between these groups, with genes ranked by log2FC (from high to low). To identify enriched biological pathways, we utilized the ‘c2.cp.kegg.v7.4.symbols.gmt’ data set downloaded from the molecular signatures database (MSigDB, http://www.broadinstitute.org/gsea/msigdb/index.jsp) as the background gene set. Gene Set Enrichment Analysis (GSEA) was employed to assess the enrichment of ranked genes within this background set, using a significance threshold of P < 0.05.

Regulation network analysis

To explore the regulatory mechanisms of the biomarkers, we employed the TargetScan database (http://www.targetscan.org/) and miRDB database (http://mirdb.org) accessed through the “multiMiR” package (v 1.20.0)68 to predict upstream miRNAs associated with the biomarkers. Key miRNAs were identified by overlapping predictions from both databases. Subsequently, StarBase (http://starbase.sysu.edu.cn) was utilized to predict lncRNAs targeting these key miRNAs. Following the organization of these relationships, a comprehensive lncRNA-miRNA-mRNA network was constructed and visualized using Cytoscape.

Additionally, the comparative toxicogenomics database (CTD, https://ctdbase.org/) was utilized to predict drugs targeting the identified biomarkers. A biomarkers-drugs network was then constructed and visualized, providing insights into potential therapeutic interventions.

The scRNA-seq analysis

To investigate the underlying mechanisms of AKI at the single-cell level, the GSE174220 dataset underwent scRNA-seq analysis using the “Seurat” package (v 5.0.1)69. Initially, stringent quality control (QC) criteria were applied to filter out low-quality cells: cells with 200 < nFeature-RNA (number of genes detected per cell) < 6000 and percent.mt (proportion of mitochondrial genes) < 20% were excluded. Subsequently, data were normalized using the ‘LogNormalize’ function, followed by identification of the top 2000 highly variable genes (HVGs) using the ‘FindVariableFeatures’ function. Next, Principal Component Analysis (PCA) was conducted, and the optimal dimension value was determined. Following PCA, cells were clustered using the Uniform Manifold Approximation and Projection (UMAP) facilitated by the ‘RunUMAP’ function. Later, cell clusters were annotated to specific cell types using marker genes sourced from the CellMarker database (http://biocc.hrbmu.edu.cn/CellMarker/). The search for marker genes involved using the ‘FindAllMarkers’ function. Subsequently, the ‘DotPlot’ function was utilized to analyze the expression of biomarkers in the annotated cell types.

Additionally, the proportion of each annotated cell type in AKI samples was analyzed. The cell type with the highest proportion in AKI samples was selected as key cell for further analysis.

Enrichment analysis and biomarkers expression analysis

To understand the biological functions of the annotated cells, “ReactomeGSA” package (v 1.12.0)70 was utilized. The top 15 pathways (ranked by enrichment score) were visualized in a heatmap. Additionally, the expression of biomarkers in the annotated cells was analyzed by plotting them on a UMAP map.

The a series of analyses of key cell

In GSE174220, to investigate interactions between different cell types, a cell-cell communication analysis was conducted using “Cellchat” (v 1.6.1)71, with ‘CellChatDB.human’ as a reference obtained from the CellPhoneDB database (https://www.cellphonedb.org/).

To explore the heterogeneity of the key cell, the expression profile of the key cell was initially extracted. Subsequently, clustering analysis was conducted on key cell, and the principal components (PC) was chosen. The clustering results were then visualized using UMAP, providing a visual representation of the cellular heterogeneity.

Additionally, to further investigate the differentiation states and developmental trajectory of the key cell, pseudo-time analysis was conducted using “Monocle” (v 2.24.0)72.

The reverse transcription quantitative PCR (RT-qPCR)

To verify the expression of biomarkers in kidney tissue samples, we conducted RT-qPCR validation. This study was conducted in accordance with the Declaration of Helsinki. The protocol was approved by the Ethics Committee for Laboratory Animals of the Henan Institute of Ophthalmology on December 11, 2020 with the ethical approval number [HNEECA-2020-11]. In addition, the ARRIVE guidelines were strictly adhered to. All experiments were conducted using male Wild-type (WT) mice (weight 20–25 g, 6–8 weeks old, each group n = 5), which were obtained from the Center of Experimental Animals of Zhengzhou University. The murine model of sepsis-induced acute kidney injury (AKI) was established by intraperitoneal injection of 15 mg/kg of lipopolysaccharide (LPS) from Escherichia coli 055:B5 (Sigma-Aldrich) as previously described73. Saline solution was administered into the control mice. The mice were sacrificed with pentobarbital sodium 24 h after the injection of LPS or saline solution, and their blood and tissues were preserved for subsequent research. The total RNA from the ten samples was extracted using the TRIzol reagent (Ambion, USA), in accordance with the manufacturer’s protocol. Then the RNA concentration was tasted using NanoPhotometer N50. The cDNA was synthetised by reverse-transcribed using the SureScript-First-strand-cDNA-synthesis-kit, and the reverse-transcribed was performed with S1000™ Thermal Cycler (Bio-Rad, USA). The sequences of all primers can be found in Supplementary Table S2. The qPCR assay was performed with CFX Connect Real-time Quantitative Fluorescence PCR Instrument (Bio-Rad, USA) (pre-denaturation at 95 ℃ for 1 min, denaturation at 95℃ for 20s, annealing at 55 ℃ for 20 s, extension at 72 ℃ for 30 s, a total of 40 cycles). The relative quantification of mRNAs was calculated using the 2−ΔΔCT method.

Western blotting

Total protein was extracted from kidney tissues of mice by cell lysis using RIPA Lysis Buffer (Meilunbio, China). The lysate was centrifuged at 12,000 rpm at 4 °C for 5 min, and the supernatant was transferred to a new tube to quantify total protein using the BCA assay. The homogenate was separated by sodium dodecyl sulfate polyacrylamide gel electrophoresis through a 12% gel, and the proteins were transferred to PVDF membranes (Genscript, China). Then, the membranes were incubated in TBST (blocking solution) containing 5% skim milk for 2 h at room temperature. Anti-LIMD1 (1:2000, Aibotek, Wuhan, China), anti-MBD2 (1:1000, Sanying, Wuhan, China) and anti-proliferating cell nuclear antigen (PCNA) (1:10000, Sanying, Wuhan, China) were incubated at 4 °C. After five thoroughly washes with TBST, the membranes were incubated at room temperature for two hours with an HRP-labeled secondary antibody (1:10000). Protein bands were observed using enhanced chemiluminescence (ECL) reagent (affinity, USA).

Statistical analysis

The R (v 4.2.2) was utilized to conduct statistical analysis. Differential analyses between cohorts were executed via the Wilcoxon test (P < 0.05).

Conclusion

In summary, LIMD1 and MBD2 were obtained as biomarkers associated with palmitoylation with predictive value for AKI. The study is expected to provide new targets and theoretical basis for the clinical diagnosis and treatment of AKI.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (11.2KB, docx)
Supplementary Material 2 (11.9KB, docx)
Supplementary Material 3 (17.9KB, docx)
Supplementary Material 4 (4.3MB, tif)
Supplementary Material 5 (2.7MB, tif)
Supplementary Material 6 (2.7MB, tif)
Supplementary Material 7 (2.6MB, tif)
Supplementary Material 8 (3MB, tif)
Supplementary Material 9 (4.2MB, tif)
Supplementary Material 10 (7.3MB, tif)
Supplementary Material 11 (2.3MB, tif)
Supplementary Material 12 (9.3MB, pdf)

Acknowledgements

We sincerely thank all colleagues and collaborators who participated in this study, the Laboratory Animal Ethics Committee of Henan Eye Institute for approving and guiding our study. In addition, this work was supported by the Funding of Zhongyuan Scholars of Henan Provincial Health Commission (224000510005), Zhongyuan Scholar Workstation (234400510024), Technology Attack Plan Project of Henan Province (242102311062), and Medical Science and Technology Attack Plan Project of Henan Province (SBGJ202302002).

Author contributions

W.S. and Y.G. conceived the idea of this study and designed experiments. W.S. and J.W. performed experiments and were responsible for writing the main manuscript text, while L.Z. was involved in preparing the methodology. W.Z. conducted the validation process. Q.X. has contributed to data acquisition and analysis. F.S. and Y.G. provided resources and acquired funding for the study. The manuscript underwent review and editing by F.S. and Y.G. All authors have reviewed and approved the final version of the manuscript for publication.

Data availability

The datasets analysed in this study are available in the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/, GSE139061, GSE30718 and GSE174220).

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Weinan Sun and Juntao Wang contributed equally to this work.

Contributor Information

Fengmin Shao, Email: fengminshao@126.com.

Yue Gu, Email: guyuesunny@zzu.edu.cn.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (11.2KB, docx)
Supplementary Material 2 (11.9KB, docx)
Supplementary Material 3 (17.9KB, docx)
Supplementary Material 4 (4.3MB, tif)
Supplementary Material 5 (2.7MB, tif)
Supplementary Material 6 (2.7MB, tif)
Supplementary Material 7 (2.6MB, tif)
Supplementary Material 8 (3MB, tif)
Supplementary Material 9 (4.2MB, tif)
Supplementary Material 10 (7.3MB, tif)
Supplementary Material 11 (2.3MB, tif)
Supplementary Material 12 (9.3MB, pdf)

Data Availability Statement

The datasets analysed in this study are available in the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/, GSE139061, GSE30718 and GSE174220).

Articles from Scientific Reports are provided here courtesy of Nature Publishing Group

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