Proteomics reveals biomarkers for the diagnosis and treatment of septic kidney injury

Proteomics reveals biomarkers for the diagnosis and treatment of septic kidney 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 Research Article Proteomics reveals biomarkers for the diagnosis and treatment of septic kidney injury Weimin Zhu, Xiaxia He, Hanzhi Dai, Cuicui Dong, Jiatian Zhang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5466304/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Sepsis-associated acute kidney injury (SA-AKI) is a severe and life-threatening disease with high incidence and mortality rates among ICU patients. However, currently, there is still a lack of effective biomarkers for early diagnosis and treatment of kidney injury in septic patients. Methods In a multi-center prospective cohort study, 37 sepsis patients (sepsis-AKI, n = 19; sepsis-NoAKI, n = 18) and 31 healthy controls were enrolled. Peripheral blood samples were analyzed by protein mass spectrometry, and principal component analysis (PCA) was used to remove outliers. Differentially expressed proteins were identified based on p 1, then functionally enriched using DAVID. An additional validation cohort of 65 sepsis patients ((sepsis-AKI, n = 38; sepsis-NoAKI, n = 27) from three other centers was used to further validate the target proteins. ELISA and ROC curve analysis were performed to evaluate the diagnostic accuracy of the target proteins for SA-AKI and the need for continuous renal replacement therapy (CRRT), using the area under the ROC curve (AUC) as the performance metric. Results Ultimately, 7 proteins were differently expressed between the two groups, with 6 of them being significantly up-regulated and 1 being significantly down-regulated. Functional enrichment analysis showed that the selected differentially expressed proteins were mainly involved in immune responses, complement activation, coagulation cascades, and neutrophil degranulation. Further external validation showed that the AUC values of CST3, B2M, IGFBP4, CFD, and CD59 in diagnosing SA-AKI were all above 0.7, and there were significant differences between the two groups (P < 0.05). For whether or not to receive CRRT treatment, IGFBP4 was found to have good predictive value, with an AUC of 0.84. Conclusions This study suggests that CST3, B2M, IGFBP4, CFD, and CD59 may serve as potential biomarkers for the diagnosis of SA-AKI, with IGFBP4 specifically aiding in determining whether CRRT treatment is necessary. sepsis-associated acute kidney injury proteomics biomarkers IGFBP-4 continuous renal replacement therapy treatment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Sepsis-associated acute kidney injury (SA-AKI), alternatively referred to as sepsis-related acute kidney injury, is a clinical syndrome characterized by the acute reduction in glomerular filtration rate in patients with sepsis[ 1 ]. It is also one of the most common complications among critically ill patients in the intensive care unit (ICU), affecting approximately 40% of patients with sepsis who develop acute kidney injury (AKI), which can lead to higher mortality rates in sepsis patients if it is more severe[ 2 – 5 ]. The 28-day mortality rates for AKI stages 1–3 are reportedly 42.3%, 66.7%, and 84.6%, respectively[ 6 ]. Currently, the diagnosis of AKI is based on criteria such as serum creatinine and urine output, which have certain limitations. Serum creatinine only increases when the glomerular filtration rate is reduced by 50%, and urine output is easily influenced by factors such as volume status and medications, which can delay the diagnosis of AKI and the initiation of CRRT[ 7 , 8 ]. In recent years, several notable biomarkers such as kidney injury molecule-1 (KIM-1), liver-type fatty acid-binding protein (L-FABP), interleukin 18 (IL-18), neutrophil gelatinase-associated lipocalin (NGAL), tissue inhibitor of metalloproteinase-2 (TIMP-2), and insulin-like growth factor binding protein-7 (IGFBP7), which are significant in assessing renal health, have been the focus of relevant studies for their potential in early detection of AKI and predicting the optimal timing for CRRT treatment [ 9 , 10 ]. Due to the diverse etiologies of AKI, the pathophysiological mechanisms underlying its occurrence vary[ 11 ]. NGAL, often called the "troponin" of the kidney, can be used as a reference indicator for initiating CRRT when its level exceeds 150 ng/ml, although AKI's etiology is not solely ischemia and NGAL levels in SA-AKI patients may not always be higher than in patients with AKI from other causes, as they can be influenced by factors like the inflammatory response, making NGAL a highly sensitive but non-specific biomarker for SA-AKI[ 12 ]. Many novel biomarkers cannot be rapidly tested at the bedside and are expensive, making it challenging to effectively apply them in clinical practice. Hence, there is an urgent need for new methods to screen and validate highly sensitive and specific biomarkers for the early diagnosis and treatment of SA-AKI patients, aiming to reduce the mortality rate of SA-AKI and hold significant clinical significance. MATERIALS AND METHODS Patients and study design This study is a prospective, multicenter cohort study. It has been approved by the Ethics Committee of Taizhou Central Hospital, Zhejiang Province (Approval No: 2019-016, principal investigator: Yinghe Xu, date of registration: 26 February 2019). The project is registered with the Chinese Clinical Trial Registry (Registration No: ChiCTR1900022081, Registration Date: February 2019). Continuous inclusion of all patients admitted to the ICU due to sepsis from April 2019 to December 2021. Inclusion criteria: Diagnosis of sepsis upon admission to the ICU; Exclusion Criteria: Pregnant or lactating women; Advanced-stage cancer; Patients who died within 24 hours or were discharged automatically. Relevant diagnostic criteria: AKI diagnosis adopts the KDIGO-2012 AKI (Scr) criteria[ 13 ]. Based on the presence or absence of AKI in patients with sepsis, they are categorized into two groups: sepsis-AKI and sepsis-NoAKI. Further, depending on whether the patient received CRRT within one week of AKI diagnosis, they are divided into the CRRT group and the no-CRRT group. All patients who were included in the study provided informed consent before their participation. Data collection Collect and record various laboratory indicators including gender, age, sequential organ failure assessment (SOFA); acute physiology and chronic health evaluation II (APACHE II), procalcitonin(PCT), C-reactive protein(CRP), and coagulation function. Simultaneously record complications like acute kidney injury, respiratory failure, and bleeding, along with treatment measures such as mechanical ventilation, continuous renal replacement therapy, and vasoactive drug usage post-ICU admission. Sample preparation for proteomic analysis and enzyme-linked immunosorbent assay (ELISA). Collect blood samples from both patients and NC subjects and then allow them to clot untouched at room temperature for a full hour. Centrifuge the samples at 1600g for 10 minutes to separate the serum. Remove the soluble solids within 30 minutes and store the samples at -80°C for subsequent proteinomic analysis and ELISA. The ProteoPrep Blue albumin & IgG Depletion Kit (PROTBA; Sigma-Aldrich) was employed to eliminate high-abundance proteins, including albumin and IgG, from the serum, following the manufacturer's instructions. Before achieving the final sample concentration, impurities in the protein extracts were identified using a two-dimensional clean-up reagent kit (GE Healthcare). LC2MS/MS Analysis Proteins in the serum underwent digestion with sequencing-grade trypsin. The supernatant's concentration was determined using an ultramicro UV spectrophotometer, specifically the SMA1000 model. Peptides were separated employing a Waters UPLC system equipped with a BEH C18 nanoACQUITY chromatography column (75 µm × 25 cm, 1.7 µm). Proteomics analysis was performed using a nano-flow liquid chromatography system (ACQUITY UPLC; Waters Corporation) coupled with a mass spectrometer (Q Exactive; Thermo Fisher Scientific). Mass scans ranging from 300 to 140 m/z were obtained using Orbitrap profiling mode with a high resolution of 70,000 peptide segments with charges ranging from + 2 to + 6 were selected for further LC-MS/MS analysis. Fragmentation was attained through the employment of intensified collisional dissociation with a normalized collision energy of 27%, accompanied by a resolution of 35,000. A dynamic exclusion strategy was implemented, wherein the top 20 most intense peaks were selectively excluded, with a duration of 20 seconds for each exclusion. Protein Identification and Database Retrieval Proteins generated from Homo sapiens using LC-MS/MS were analyzed by searching against the SwissProt database with the MaxQuant search engine (version 1.6.1.0). A mass tolerance setting of 20 ppm was applied to both precursor and fragment ions. Variable modifications included methionine oxidation and N-terminal acetylation, whereas carbamidomethylation was set as a fixed modification. The enzymatic digestion simulation allowed for up to two missed cleavages by trypsin. Peptide and protein identifications were stringently filtered using a false discovery rate (FDR) threshold of 1%, with provisions for one missed cleavage. Label-free protein quantification was performed using MaxQuant software. Both a minimum peptide count criterion and the use of unmodified peptides are required for relative quantification. In the serum samples, a total of 879 proteins were identified. The expression matrix has not undergone normalization using the "normalizeBetweenArrays" function, which is a commonly used optimal normalization method for proteomics data analysis found in the R package Limma[ 14 ]. Protein abundance was normalized using a log2 transformation and applied to all quantitative analyses. Missing values in the proteomics data were estimated using the minimum value. Functional enrichment analysis To gain a comprehensive understanding of the altered proteins and disease-related co-expressed modules, functional enrichment analysis was performed using DAVID 6.8 [ 15 ] and ClueGO[ 16 ]. This analysis aimed to provide insights into the biological functions associated with the identified proteins. A false discovery rate threshold of 0.05 was applied to define the statistical significance of the functional enrichment analysis. Validation by ELISA To validate the proteomic analysis results and their correlation with clinical features, a subset of hub proteins was randomly chosen for validation using ELISA with external samples, following the manufacturer's instructions. The details of the ELISA kits utilized in this study, including relevant information, are available in Table S1 . Statistical analysis The measurement results were reported as either mean ± standard deviation (SD) or median with interquartile range (IQR). The sample size was not predetermined using statistical methods. Welch's t-test, assuming a two-tailed distribution, was employed to compare patients in the sepsis-AKI group and the sepsis-NoAKI group, ensuring sensitivity. The normality assumption of the Welch t-test was assessed using the Shapiro-Wilk test when the p-value for each comparison group reached 0.05. If the data is found to deviate from normal distribution, we utilize the non-parametric Mann-Whitney U test for analysis. This approach ensures appropriate statistical assessment under different distributional assumptions. We employed the Benjamini-Hochberg correction method to control the false discovery rate and mitigate Type I errors in the context of multiple testing. Proteins with missing values below 40% were selected for further analysis. The normalization of protein abundance was conducted using the "normalize between arrays" function from the Limma package in R. After applying Log2 transformation to the normalized protein abundance data, we used the criteria of log2(FC) > 1 and an adjusted P-value < 0.05 to identify proteins that showed significant alterations. The chi-squared test was employed to compare categorical variables. All statistical analyses were conducted in R, and principal component analysis was performed using the "sva" function. The significance of hub proteins validated by ELISA was assessed using the Mann-Whitney U test, with a significance level set at p < 0.05. RESULTS Patients and clinical characteristics The study comprised 37 sepsis patients and 31 NC. Among the sepsis patients, there were 19 cases in the sepsis-AKI group and 18 cases in the sepsis-NoAKI group. The clinical characteristics of the AKI and NoAKI groups among sepsis patients are shown in Table 1 . No significant differences were observed in terms of age and gender between the two groups (both P > 0.05). The sepsis-AKI group had higher SOFA scores (7.84 ± 3.16 vs 4.89 ± 2.16), APACHE II scores (22.21 ± 7.39 vs 12.22 ± 5.69), and HR (105.36 ± 19.58 vs 90.44 ± 17.26) compared to the sepsis-NoAKI group (all P < 0.05). Laboratory examinations revealed significant group-wise differences in BUN, Scr, pH, Lac, and BE (P 0.05 for each). Table 1 Comparison of baseline data between the sepsis-AKI group and the sepsis-NoAKI group. Variables AKI (n = 19) Non-AKI (n = 18) P value Age (years) 75 (61, 79) 73 (63, 79) 0.784 Male (%) 8(42.1) 8 (44.4) 0.887 SOFA 7.84 ± 3.16 4.89 ± 2.16 0.002 APACHE II 22.21 ± 7.39 12.22 ± 5.69 < 0.001 HR (per minute) 105.36 ± 19.58 90.44 ± 17.26 0.019 RR (per minute) 22 (17, 25) 19 (15, 21) 0.189 Laboratory data at admission PLT (10^9/L) 143.16 ± 93.48 152.67 ± 68.23 0.727 WBC (10^9/L) 12.9 (9.7, 17.2) 11.5 (5.2, 17.9) 0.466 Hemoglobin (g/L) 121 (101, 151) 117.5 (108.5, 127) 0.613 Hematocrit (ratio) 35.5 (29.6, 43.2) 35.5 (30.9, 38.0) 0.420 PT (s) 14.9 (13.3, 16.9) 14.7 (13.95, 15.60) 0.872 INR 1.23 (1.11, 1.43) 1.17 (1.11, 1.26) 0.279 APTT (s) 41.1 (35.4, 48.3) 44.0 (40.7, 47.4) 0.420 PCT (ng/ml) 29.65 (9.56, 58.94) 5.7 (2.15, 36.18) 0.129 CRP (mg/L) 182.98 ± 111.61 190.89 ± 103.03 0.824 Albumin (g/dL) 29.0 ± 6.06 28.01 ± 3.86 0.594 BUN (mg/dL) 15.78 (11.54, 22.34) 11.55 (4.90, 9.85) < 0.001 Scr (umol/L) 179.0 (163.0, 289.0) 16.0 (64.5, 99.5) < 0.001 AST (u/L) 61. 0 (27.5, 181.75) 43.5 (21.75, 89.50) 0.910 Glucose (mmol/L) 6.0 (4.86, 8.44) 4.07 (4.78, 9.65) 0.117 PH 7.36 (7.31, 7.42) 7.44 (7.41, 7.46) 0.001 Lactic acid (mmol/L) 2.4 (1.2, 4.8) 1.45 (0.93, 2.25) 0.023 BE -4.4 (-7.3, -2.2) -2.2 (-2.88, -2.75) 0.006 PCO2 (mmHg) 34.2 (30.8, 38.0) 33.0 (20.0, 37.5) 0.398 PO2 (mmHg) 86.6 (73, 134) 135.5 (97.5, 150.5) 0.098 Na+ (mmol/L) 139 (133, 144) 140 (138, 142) 0.773 K+ (mmol/L) 4.22 (3.5, 4.74) 4.09 (3.78, 4.39) 0.855 Ca2+ (mmol/L) 1.86 (1.6, 2.03) 2.0 (1.92, 2.11) 0.803 Interventions Mechanical ventilation[n,(%)] 11 (57.9) 7 (38.9) 0.254 Vasopressors[n,(%)] 10 (52.6) 6 (33.3) 0.243 CRRT[n,(%)] 4 (21.1) 0 0.042 28-day mortality [n,(%)] 4 (21.1) 1 (5.6) < 0.001 SOFA, sequential organ failure assessment; APACHE, acute physiology and chronic health evaluation; PCT, procalcitonin; CRP, C-reactive protein; INR, international normalized ratio; PLT, Platelet count; APTT, activated partial thromboplastin time; AST, aspartate aminotransferase; WBC, white blood cell; HR, heart rate; RR, respiratory rate. Metabolomic analysis of patients with sepsis Based on the characteristics of the mass spectrometry proteomics data, the proteins included in the analysis are expressed in the aforementioned 50% of the samples. Therefore, a total of 404 proteins were selected for analysis. PCA analysis shows a clear distinction between sepsis-AKI (represented by red) and sepsis-NoAKI (represented by yellow) groups in the current samples, and both are different from the NC group. A total of 404 proteins were found to exhibit differential expression between the sepsis-AKI and sepsis-NoAKI groups (Fig. 1 A). When applying a significance threshold of log2(FC) > 1 and adj P < 0.05, a subset of 7 proteins showed significant differential expression between the sepsis-AKI and sepsis-NoAKI groups based on the protein analysis (Fig. 1 C). Among these, 6 proteins showed significantly upregulated expression, while 1 protein showed significantly downregulated expression (Fig. 1 B). Functional enrichment analysis was performed on the aforementioned seven significantly different proteins. The results showed that these seven proteins are mainly involved in biological functions such as immune response, complement activation, coagulation cascade, and neutrophil degranulation (Fig. 1 D). This finding suggests that the identified differentially expressed proteins (DEPs) accurately represent the pathophysiological mechanism of acute kidney injury in sepsis. Orthogonal partial least squares discriminant Analysis (OPLS-DA) clearly distinguished the sepsis patients with AKI from the NoAKI group, indicating significant differences in their serum untargeted metabolomics profiles(Fig. 2 A). The variable importance in projection (VIP) values of each metabolic product is shown in Fig. 2 B, with a total of 10 proteins having VIP values greater than 2. The above-mentioned 7 significant DEPs are all included within this set of 10 proteins, further highlighting the tremendous potential of these 7 proteins to distinguish between the sepsis-AKI and sepsis-NoAKI groups. Validation of target proteomics Introduce the NC group and identify key proteins exhibiting gradual expression changes among the NC, sepsis-AKI, and sepsis-NoAKI groups. The differential analysis identified 231 proteins that were significantly differentially expressed between the sepsis-AKI and NC groups, with 178 upregulated and 53 downregulated (Fig. 3 A). A significant difference in the expression of 167 proteins was observed when comparing the sepsis-NoAKI group to the NC group. Among these, 115 proteins were upregulated, while 52 proteins were downregulated (Fig. 3 B). Conduct an intersection analysis of the significant DEPs among the three groups. The results revealed that both IGFBP-4 and B2M proteins exhibited significant differential expression across the three groups, with their expression levels progressively increasing as the disease advanced (Fig. 3 D-E). Although the remaining five proteins did not show significant differences between the sepsis-AKI and sepsis-NoAKI groups, notable distinctions were observed when comparing the sepsis-AKI group with both the sepsis-NoAKI and NC groups (Fig. 3 F-J). These five proteins may not be directly involved in the progression of sepsis, but they may play a crucial role in the occurrence and development of AKI. Targeted Proteomics of Patient Plasma for Biomarker Validation The validation group was included to validate the identified target proteins. Receiver Operating Characteristic (ROC) curve analysis of the subjects' characteristics revealed that CST3, B2M, IGFBP4, CFD, and CD59 demonstrated AUC values above 0.7 for diagnosing SA-AKI (Fig. 5 A-G). Furthermore, significant differences were observed between the sepsis-AKI group and the sepsis-NoAKI group (P < 0.05). (Fig. 4 A-G). Comparison of CRRT treatment in the sepsis AKI group The two groups exhibited significant differences in terms of CRRT treatment (P < 0.001). The concentration of IGFBP-4 was higher in the CRRT group compared to the NoCRRT group (Fig. 6 A). Moreover, IGFBP-4 displayed a good predictive value, as indicated by an AUC of 0.84 (Fig. 6 B). DISCUSSION This study observed an incidence of AKI in septic patients at 51%, with a corresponding mortality rate of 21.1% in septic patients with AKI, significantly higher than those without AKI. It has been reported that 40–70% of AKI cases in the United States are caused by sepsis, and the mortality rate of SA-AKI is significantly higher than that of AKI or sepsis alone[ 17 , 18 ]. Additionally, through proteomic analysis of serum samples from sepsis patients and NC participants, functional enrichment analysis revealed that the differentially expressed proteins primarily participate in biological functions including immune response, complement activation, coagulation cascade, and neutrophil degranulation, which are similar to the characteristic pathogenic mechanisms of sepsis. Previous research has shown that in the context of sepsis, the activation of the complement and coagulation cascades functions as a robust innate immune defense mechanism. This mechanism functions to suppress and eliminate pathogens by triggering local inflammation and facilitating coagulation, effectively impeding the dissemination of bacteria and other pathogens throughout the body[ 19 ]. Furthermore, there is a direct or indirect connection between the coagulation cascade and the complement system, and they can mutually enhance and promote each other[ 20 ]. Our proteomic analysis results have been validated through ELISA, confirming their high reliability and quality. CST3, a low-molecular-weight non-glycosylated protein, effortlessly crosses the glomerular membrane, undergoes complete absorption and metabolism within proximal tubular epithelial cells, and is synthesized uniformly by all nucleated cells, remaining unaffected by inflammation, fever, external factors, gender, age, or body weight[ 21 , 22 ]. CST3 can serve as a biomarker that reflects the glomerular filtration rate. Clinical trial results demonstrate that CST3 exhibits superior sensitivity to creatinine in the early detection of AKI, being detectable 24–48 hours earlier, while its blood level increases in response to impaired glomerular filtration function and further rises with the severity of kidney injury[ 23 ]. Leem et al[ 24 ] conducted a study in which they observed a significant elevation in the level of CST3 in SA-AKI patients compared to NoAKI patients, which is consistent with our research findings. Moreover, our study further found that the AUC value of CST3 for diagnosing SA-AKI was 0.788 (Figure. 5G), suggesting that CST3 can serve as a novel biomarker for predicting SA-AKI. Several studies have demonstrated that B2M serves as a biomarker for renal function, and its serum levels are correlated with glomerular filtration rate (GFR)[ 25 ]. Elevations in serum B2M levels are commonly observed alongside increases in CST3 and urea nitrogen, and they are employed in the evaluation of renal injury caused by medication usage, cardiovascular risk, kidney transplantation, and other etiological factors[ 26 – 28 ]. However, there are relevant studies suggesting that when older criteria are used to define AKI, the predictive efficacy of B2M is reduced, as indicated by a low AUC of 0.59 in the test subjects[ 29 ]. With the implementation of the KDIGO-2012 AKI (Scr) criteria, a subsequent study conducted by British scholar Kevin T. Barton et al. in 2018 uncovered a correlation between B2M and AKI development, the performance was characterized by an AUC of 0.84. Moreover, it was found that higher levels of B2M in patients corresponded to more advanced stages of AKI[ 30 ]. Similar to previous research findings, our study revealed a significant upregulation of B2M levels in SA-AKI, and its expression became more pronounced with disease progression. IGFBP-7 has been established as an effective early diagnostic and prognostic biomarker for AKI based on prior research[ 31 , 32 ]. The significance of IGFBP-4, a key member of the insulin-like growth factor binding protein family, in kidney diseases has gained growing recognition. Relevant studies have found elevated expression of IGFBP-4 in the serum of patients with chronic renal insufficiency, which is associated with the severity of renal failure and decreased osteogenesis during periods of bone malnutrition[ 33 ]. Previous research has indicated a strong correlation between the serum concentration of IGFBP-4 and both the chronicity index and estimated glomerular filtration rate in lupus nephritis, suggesting its potential as a biomarker for this condition[ 34 ]. The significance of IGFBP-4, a key member of the insulin-like growth factor binding protein family, in kidney diseases has gained growing recognition. Our study revealed a notable elevation in IGFBP-4 levels within the sepsis-AKI group when compared to both the sepsis NoAKI group and the NC group. This finding implies that IGFBP-4 could have a vital role in the development and progression of SA-AKI. Furthermore, IGFBP4 was also found to have good predictive value for the need for CRRT, with an AUC of 0.84 (Fig. 6 B), which might assist in initiating CRRT in SA-AKI patients before the occurrence of related complications. CFD is a recently discovered serine protease gene predominantly expressed in adipose tissue and sciatic nerve tissue. It plays a crucial role as a rate-limiting enzyme in the activation of the complement alternative pathway[ 35 ]. Previous studies have shown that CFD can undergo filtration across the glomerular membrane, resulting in a significant increase in its circulating concentration in patients with renal impairment, reaching approximately 10 times higher levels in end-stage renal failure patients[ 36 , 37 ]. Thus, a potential association between CFD serum levels and renal dysfunction can be inferred. The definitive role of CD59 in paroxysmal nocturnal hemoglobinuria (PNH) and congenital CD59 deficiency has been well-established[ 38 ]. However, the underlying mechanisms responsible for renal dysfunction in these conditions remain unclear. Several studies suggest that CD59 is expressed in all cells of the renal tubules and glomeruli, playing a role in the pathogenesis of various diseases, and hypothesize that renal CD59 serves as a protective factor against homologous complement attack by preventing kidney vulnerability in the absence of the complement activation inhibitory system[ 39 ]. It has been found that after introducing the human CD59 gene into mice, the renal ischemia-reperfusion injury in the mice is significantly alleviated[ 40 ]. In rats, CD59 and Crry work together to mitigate complement-mediated injury and preserve the normal integrity of the kidneys. These findings suggest that the deposition of membrane attack complex (MAC) on glomerular cell membranes during complement-mediated glomerular injury has the potential to induce functional and metabolic changes and impact subsequent damage processes[ 41 ]. This study has several limitations. Firstly, the sample size of sepsis patients included in this study is relatively small. Therefore, expanding the sample size and conducting related multicenter studies are the main focus of our further research. Secondly, the specific mechanisms by which the identified DEPs contribute to septic acute kidney injury remain unclear, necessitating further experiments to investigate these mechanisms in future studies. Lastly, the staging of AKI was not investigated in our study. Given the variable severity of the disease, variations in the identified DEPs may exist, potentially influencing the experimental results and subsequent treatment strategies. CONCLUSION CST3, B2M, IGFBP4, CFD, and CD59 show promise as potential biomarkers for SA-AKI, thereby contributing to its early diagnosis. Specifically, IGFBP-4 may aid in assessing the necessity of CRRT treatment in SA-AKI patients. However, the diagnostic efficacy of these biomarkers requires further validation through large-scale prospective studies and clinical practice. Abbreviations SA-AKI sepsis associated-acute kidney injury CRRT continuous renal replacement therapy PCA principal component analysis OPLS-DA orthogonal partial least squares discriminant analysis DEPs differentially expressed proteins VIP variable importance in projection ROC receiver operating characteristic AUC area under the curve CST3 cystatin C B2M beta-2-Microglobulin IGFBP-4 insulin-like growth factor binding protein CFD complement factor D CFI complement factor I Declarations Ethics declaration Ethics approval and consent to participate All participants provided written informed consent. This study adhered to the principles of the Declaration of Helsinki for biomedical research and was approved by the Ethics Committee of Taizhou Hospital, Zhejiang Province (approval number: K20190102). Consent for publication This manuscript presents original research that has not been published or submitted for publication elsewhere. All authors have reviewed and approved the manuscript for submission to Clinical Proteomics. Data Availability Statement The data can be obtained from the corresponding author JYP ( [email protected] ) upon reasonable request. Competing interests The authors declare no competing interests. Funding This work was supported by The Science and Technology Project of Taizhou (21ywb05、23ywa47), the Medicines Health Research Fund of Zhejiang, China (2024ky1784、2022KY435), the National Key Research and Development Program of Zhejiang Province (2023C03083). Author Contributions WZ, XH, and HD contributed equally to this work. WZ, XH, HD, QC, YX, and YJ were involved in the conception and design of the study. QC, JZ, and NC were involved in the acquisition of data. CD and XH were involved in the lab experiment. SZ and YJ were involved in the analysis and interpretation of data. WZ and YJ were involved in the drafting of the manuscript. All the authors revised and approved the final manuscript. Acknowledgements None. Authors' information Corresponding Authors Yongpo Jiang - Department of Critical Care Medicine, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, No. 150, Ximen Street, Taizhou 317000, China. Phone: 86+85120120; Email: [email protected] Yinghe Xu - Department of Critical Care Medicine, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, No. 150, Ximen Street, Taizhou 317000, China. Phone: 86+13706763731; Email: [email protected] Qi Chen - Precision Medicine Center, Taizhou Central Hospital, Taizhou University Medical School, No. 999, Donghai Street, Taizhou 317000, China. Phone: 86+13757682516; Email: [email protected] Authors Weimin Zhu - Department of Emergency Medicine, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Taizhou 317000, China Xiaxia He - Department of Radiology, Taizhou Central Hospital, Taizhou University Medical School, Taizhou 317000, China Hanzhi Dai - Department of Critical Care Medicine, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Taizhou 317000, China Cuicui Dong - Department of Critical Care Medicine, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Taizhou 317000, China Jiatian Zhang - Department of Critical Care Medicine, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Taizhou 317000, China Nanjin Chen - Department of Anesthesiology, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Taizhou 317000, China Sheng Zhang - Department of Critical Care Medicine, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Taizhou 317000, China Yubin Xu - Department of Pharmacy, Taizhou Central Hospital (Taizhou University Hospital), Taizhou University, Taizhou 318000, Zhejiang, China Supporting Information The information on ELISA kits used in this study (Table S1). 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N Engl J Med. 2020;383(3):240–51. Wang K, Xie S, Xiao K, Yan P, He W, Xie L. Biomarkers of Sepsis-Induced Acute Kidney Injury. BioMed research international 2018, 2018:6937947. Klein SJ, Brandtner AK, Lehner GF, Ulmer H, Bagshaw SM, Wiedermann CJ, Joannidis M. Biomarkers for prediction of renal replacement therapy in acute kidney injury: a systematic review and meta-analysis. Intensive Care Med. 2018;44(3):323–36. Kellum JA, Prowle JR. Paradigms of acute kidney injury in the intensive care setting. Nat Rev Nephrol. 2018;14(4):217–30. Devarajan P. Review: neutrophil gelatinase-associated lipocalin: a troponin-like biomarker for human acute kidney injury. Nephrol (Carlton Vic). 2010;15(4):419–28. Stevens PE, Levin A. Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline. Ann Intern Med. 2013;158(11):825–30. Zhang B, Wang J, Wang X, Zhu J, Liu Q, Shi Z, Chambers MC, Zimmerman LJ, Shaddox KF, Kim S, et al. Proteogenomic characterization of human colon and rectal cancer. Nature. 2014;513(7518):382–7. Dennis G Jr., Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 2003;4(5):P3. Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, Fridman WH, Pagès F, Trajanoski Z, Galon J. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinf (Oxford England). 2009;25(8):1091–3. Fani F, Regolisti G, Delsante M, Cantaluppi V, Castellano G, Gesualdo L, Villa G, Fiaccadori E. Recent advances in the pathogenetic mechanisms of sepsis-associated acute kidney injury. J Nephrol. 2018;31(3):351–9. Bagshaw SM, Lapinsky S, Dial S, Arabi Y, Dodek P, Wood G, Ellis P, Guzman J, Marshall J, Parrillo JE, et al. Acute kidney injury in septic shock: clinical outcomes and impact of duration of hypotension prior to initiation of antimicrobial therapy. Intensive Care Med. 2009;35(5):871–81. Jacobi J. The pathophysiology of sepsis-2021 update: Part 1, immunology and coagulopathy leading to endothelial injury. Am J health-system pharmacy: AJHP : official J Am Soc Health-System Pharmacists. 2022;79(5):329–37. Gulla KC, Gupta K, Krarup A, Gal P, Schwaeble WJ, Sim RB, O'Connor CD, Hajela K. Activation of mannan-binding lectin-associated serine proteases leads to generation of a fibrin clot. Immunology. 2010;129(4):482–95. Bongiovanni C, Magrini L, Salerno G, Gori CS, Cardelli P, Hur M, Buggi M, Di Somma S. Serum Cystatin C for the Diagnosis of Acute Kidney Injury in Patients Admitted in the Emergency Department. Disease markers 2015, 2015:416059. Yang H, Lin C, Zhuang C, Chen J, Jia Y, Shi H, Zhuang C. Serum Cystatin C as a predictor of acute kidney injury in neonates: a meta-analysis. Jornal de pediatria. 2022;98(3):230–40. Maruniak-Chudek I, Owsianka-Podleśny T, Wróblewska J, Jadamus-Niebrój D. Is serum cystatin C a better marker of kidney function than serum creatinine in septic newborns? Postepy Hig Med Dosw(Online). 2012;66:175–80. Leem AY, Park MS, Park BH, Jung WJ, Chung KS, Kim SY, Kim EY, Jung JY, Kang YA, Kim YS, et al. Value of Serum Cystatin C Measurement in the Diagnosis of Sepsis-Induced Kidney Injury and Prediction of Renal Function Recovery. Yonsei Med J. 2017;58(3):604–12. Argyropoulos CP, Chen SS, Ng YH, Roumelioti ME, Shaffi K, Singh PP, Tzamaloukas AH. Rediscovering Beta-2 Microglobulin As a Biomarker across the Spectrum of Kidney Diseases. Front Med. 2017;4:73. Griffin BR, Faubel S, Edelstein CL. Biomarkers of Drug-Induced Kidney Toxicity. Ther Drug Monit. 2019;41(2):213–26. Ballew SH, Matsushita K. Cardiovascular Risk Prediction in CKD. Semin Nephrol. 2018;38(3):208–16. Johnston O, Cassidy H, O'Connell S, O'Riordan A, Gallagher W, Maguire PB, Wynne K, Cagney G, Ryan MP, Conlon PJ, et al. Identification of β2-microglobulin as a urinary biomarker for chronic allograft nephropathy using proteomic methods. Proteom Clin Appl. 2011;5(7–8):422–31. Du Y, Zappitelli M, Mian A, Bennett M, Ma Q, Devarajan P, Mehta R, Goldstein SL. Urinary biomarkers to detect acute kidney injury in the pediatric emergency center. Pediatr Nephrol. 2011;26(2):267–74. Barton KT, Kakajiwala A, Dietzen DJ, Goss CW, Gu H, Dharnidharka VR. Using the newer Kidney Disease: Improving Global Outcomes criteria, beta-2-microglobulin levels associate with severity of acute kidney injury. Clin kidney J. 2018;11(6):797–802. Kashani K, Al-Khafaji A, Ardiles T, Artigas A, Bagshaw SM, Bell M, Bihorac A, Birkhahn R, Cely CM, Chawla LS, et al. Discovery and validation of cell cycle arrest biomarkers in human acute kidney injury. Crit Care. 2013;17(1):R25. Ortega LM, Heung M. The use of cell cycle arrest biomarkers in the early detection of acute kidney injury. Is this new Ren troponin? Nefrologia. 2018;38(4):361–7. Van Doorn J, Cornelissen AJ, Van Buul-Offers SC. Plasma levels of insulin-like growth factor binding protein-4 (IGFBP-4) under normal and pathological conditions. Clin Endocrinol. 2001;54(5):655–64. Wu T, Xie C, Han J, Ye Y, Singh S, Zhou J, Li Y, Ding H, Li QZ, Zhou X, et al. Insulin-Like Growth Factor Binding Protein-4 as a Marker of Chronic Lupus Nephritis. PLoS ONE. 2016;11(3):e0151491. Lesavre PH, Müller-Eberhard HJ. Mechanism of action of factor D of the alternative complement pathway. J Exp Med. 1978;148(6):1498–509. Barnum SR, Niemann MA, Kearney JF, Volanakis JE. Quantitation of complement factor D in human serum by a solid-phase radioimmunoassay. J Immunol Methods. 1984;67(2):303–9. Volanakis JE, Barnum SR, Giddens M, Galla JH. Renal filtration and catabolism of complement protein D. N Engl J Med. 1985;312(7):395–9. Weinstock C. Association of Blood Group Antigen CD59 with Disease. Transfus Med hemotherapy: offizielles Organ der Deutschen Gesellschaft fur Transfusionsmedizin und Immunhamatologie. 2022;49(1):13–24. Matsuo S, Nishikage H, Yoshida F, Nomura A, Piddlesden SJ, Morgan BP. Role of CD59 in experimental glomerulonephritis in rats. Kidney Int. 1994;46(1):191–200. Bongoni AK, Lu B, Salvaris EJ, Roberts V, Fang D, McRae JL, Fisicaro N, Dwyer KM, Cowan PJ. Overexpression of Human CD55 and CD59 or Treatment with Human CD55 Protects against Renal Ischemia-Reperfusion Injury in Mice. Journal of immunology (Baltimore, Md : 1950) 2017, 198(12):4837–4845. Watanabe M, Morita Y, Mizuno M, Nishikawa K, Yuzawa Y, Hotta N, Morgan BP, Okada N, Okada H, Matsuo S. CD59 protects rat kidney from complement mediated injury in collaboration with crry. Kidney Int. 2000;58(4):1569–79. Additional Declarations No competing interests reported. Supplementary Files tableS1.docx The information on ELISA kits used in this study (Table S1). Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5466304","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":385547811,"identity":"a6259568-9afa-4bc5-b0e1-f45481be3c5b","order_by":0,"name":"Weimin Zhu","email":"","orcid":"","institution":"Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weimin","middleName":"","lastName":"Zhu","suffix":""},{"id":385547812,"identity":"4a566f10-e3f6-45e5-aedb-01c6e49c73e8","order_by":1,"name":"Xiaxia He","email":"","orcid":"","institution":"Taizhou Central Hospital (Taizhou University Hospital), Taizhou University","correspondingAuthor":false,"prefix":"","firstName":"Xiaxia","middleName":"","lastName":"He","suffix":""},{"id":385547813,"identity":"545abd84-2be8-4ae2-a787-3d403d60bccb","order_by":2,"name":"Hanzhi Dai","email":"","orcid":"","institution":"Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hanzhi","middleName":"","lastName":"Dai","suffix":""},{"id":385547814,"identity":"89be27be-ec31-4d21-a65f-46dc94773a21","order_by":3,"name":"Cuicui Dong","email":"","orcid":"","institution":"Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Cuicui","middleName":"","lastName":"Dong","suffix":""},{"id":385547815,"identity":"d843ac9e-0bf9-49ce-a125-bd1cd359b734","order_by":4,"name":"Jiatian Zhang","email":"","orcid":"","institution":"Taizhou Hospital of Zhejiang Province 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Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYBACxgYILcfPzHz4AUlajCXb2dIMSLItccN5HgUJopQyz0g+9vDrDhtj48M8DAYMNTbRhB02Iy3dWPZMmpzZYd4DDxiOpeU2ENaSYyYt2XbY2OwwX4IBY8Nh4rUkbm7mMZAgWovkR6CWDcxEa+l5libNeCbNWOIwMJATiPGLYXvyMcmfO2zk+PsPH37wocaGCC0TEhiYeWHKEggpBwF5/gMMjD8JmjwKRsEoGAUjGgAAHwo/Rg771fUAAAAASUVORK5CYII=","orcid":"","institution":"Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yongpo","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2024-11-16 14:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5466304/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5466304/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71800691,"identity":"8dad6f81-8763-4514-a09e-54b85912d911","added_by":"auto","created_at":"2024-12-18 16:48:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1002529,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolomic analysis of patients with sepsis. (A) Principal Component Analysis (PCA), Proteomics features in sepsis-associated AKI, sepsis-NoAKI and control (NC) groups; (B) Volcano plot illustrating differential expression of metabolites between sepsis-AKI group and sepsis-NoAKI group; (C) Heatmap of differentially expressed metabolites between sepsis-AKI group and sepsis-NoAKI group; (D) Significant protein functional enrichment analysis between sepsis-AKI group and sepsis-NoAKI group.\u003c/p\u003e","description":"","filename":"Onlinefigure.1.png","url":"https://assets-eu.researchsquare.com/files/rs-5466304/v1/d2610506196ce309fe64ed28.png"},{"id":71800693,"identity":"b6909d4d-095a-450a-97c8-4c6e1d567488","added_by":"auto","created_at":"2024-12-18 16:48:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":395031,"visible":true,"origin":"","legend":"\u003cp\u003eTargeted proteomic data analysis of sepsis-AKI group and sepsis-NoAKI group. (A) Orthogonal partial least squares discriminant analysis (OPLS-DA) of serum metabolites show significant differences between sepsis-AKI group and sepsis -NoAKI group; (B) Variable Importance in Projection (VIP) value of metabolites between sepsis-AKI group and sepsis-NoAKI group.\u003c/p\u003e","description":"","filename":"Onlinefigure.2.png","url":"https://assets-eu.researchsquare.com/files/rs-5466304/v1/62138b027fe2505603c20bc3.png"},{"id":71802073,"identity":"075c1d47-7d4d-44d0-ae99-eab3ae6b6c82","added_by":"auto","created_at":"2024-12-18 16:56:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2076721,"visible":true,"origin":"","legend":"\u003cp\u003eComprehensive analysis of serum proteomics in sepsis-AKI group, sepsis-NoAKI group, and NC group. (A) Volcano plot of differentially expressed proteins between sepsis-AKI group and NC group, with red representing upregulated genes, blue representing downregulated genes, and gray representing non-significantly different genes; (B) Volcano plot of differentially expressed proteins between sepsis-NoAKI group and NC group; (C-J) Intersection analysis of significantly differentially expressed proteins among sepsis-AKI group, sepsis-NoAKI group, and NC group.\u003c/p\u003e","description":"","filename":"Onlinefigure.3.png","url":"https://assets-eu.researchsquare.com/files/rs-5466304/v1/50742a58dd10d1041188ed4b.png"},{"id":71802074,"identity":"8a2c02e3-fcd7-4f9b-8758-634314a57244","added_by":"auto","created_at":"2024-12-18 16:56:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":526933,"visible":true,"origin":"","legend":"\u003cp\u003eTargeted proteomics of patient plasma for biomarker validation. (A-J) Comparison of target proteins among the sepsis-AKI group, sepsis-NoAKI group, and NC group. *p\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"Onlinefigure.4.png","url":"https://assets-eu.researchsquare.com/files/rs-5466304/v1/f1b52a19d8aea596ca463d64.png"},{"id":71802838,"identity":"d64f7b18-8a1d-40cc-9b6e-05bdf0b6eea1","added_by":"auto","created_at":"2024-12-18 17:04:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":477024,"visible":true,"origin":"","legend":"\u003cp\u003eArea under the curve (AUC) of the target proteins for the diagnosing SA-AKI. CFI (A) , IGFBP-4 (B) , PTGDS (C) , B2M (D) , CFD (E) , CD 59 (F) , Cystatin C (G) .\u003c/p\u003e","description":"","filename":"Onlinefigure.5.png","url":"https://assets-eu.researchsquare.com/files/rs-5466304/v1/b46109b65d6bdcb4d5a9dc06.png"},{"id":71800696,"identity":"3c9d8d3e-451e-46d8-974b-2a19761de3e0","added_by":"auto","created_at":"2024-12-18 16:48:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":166040,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analysis of differentially expressed proteins in the sepsis- AKI group with or without CRRT treatment. (A) Comparison of IGFBP-4 between the two groups; (B) Area under the curve (AUC) of IGFBP-4 between the two groups. ***p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Onlinefigure.6.png","url":"https://assets-eu.researchsquare.com/files/rs-5466304/v1/ef82e1c06c150ef40a778b4d.png"},{"id":76082362,"identity":"86fad13f-00e6-450e-b54a-5c0a4ebaaf56","added_by":"auto","created_at":"2025-02-12 06:54:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1771193,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5466304/v1/f7e4b49f-03fa-430b-a39f-f8d7e7fff07f.pdf"},{"id":71800689,"identity":"9b7784be-43e8-4e87-9acf-0c7d7c61e5c7","added_by":"auto","created_at":"2024-12-18 16:48:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13787,"visible":true,"origin":"","legend":"\u003cp\u003eThe information on ELISA kits used in this study (Table S1).\u003c/p\u003e","description":"","filename":"tableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5466304/v1/6aed7727acaf4d13fca36b76.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Proteomics reveals biomarkers for the diagnosis and treatment of septic kidney injury","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eSepsis-associated acute kidney injury (SA-AKI), alternatively referred to as sepsis-related acute kidney injury, is a clinical syndrome characterized by the acute reduction in glomerular filtration rate in patients with sepsis[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is also one of the most common complications among critically ill patients in the intensive care unit (ICU), affecting approximately 40% of patients with sepsis who develop acute kidney injury (AKI), which can lead to higher mortality rates in sepsis patients if it is more severe[\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The 28-day mortality rates for AKI stages 1\u0026ndash;3 are reportedly 42.3%, 66.7%, and 84.6%, respectively[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Currently, the diagnosis of AKI is based on criteria such as serum creatinine and urine output, which have certain limitations. Serum creatinine only increases when the glomerular filtration rate is reduced by 50%, and urine output is easily influenced by factors such as volume status and medications, which can delay the diagnosis of AKI and the initiation of CRRT[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, several notable biomarkers such as kidney injury molecule-1 (KIM-1), liver-type fatty acid-binding protein (L-FABP), interleukin 18 (IL-18), neutrophil gelatinase-associated lipocalin (NGAL), tissue inhibitor of metalloproteinase-2 (TIMP-2), and insulin-like growth factor binding protein-7 (IGFBP7), which are significant in assessing renal health, have been the focus of relevant studies for their potential in early detection of AKI and predicting the optimal timing for CRRT treatment [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Due to the diverse etiologies of AKI, the pathophysiological mechanisms underlying its occurrence vary[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. NGAL, often called the \"troponin\" of the kidney, can be used as a reference indicator for initiating CRRT when its level exceeds 150 ng/ml, although AKI's etiology is not solely ischemia and NGAL levels in SA-AKI patients may not always be higher than in patients with AKI from other causes, as they can be influenced by factors like the inflammatory response, making NGAL a highly sensitive but non-specific biomarker for SA-AKI[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Many novel biomarkers cannot be rapidly tested at the bedside and are expensive, making it challenging to effectively apply them in clinical practice. Hence, there is an urgent need for new methods to screen and validate highly sensitive and specific biomarkers for the early diagnosis and treatment of SA-AKI patients, aiming to reduce the mortality rate of SA-AKI and hold significant clinical significance.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients and study design\u003c/h2\u003e \u003cp\u003eThis study is a prospective, multicenter cohort study. It has been approved by the Ethics Committee of Taizhou Central Hospital, Zhejiang Province (Approval No: 2019-016, principal investigator: Yinghe Xu, date of registration: 26 February 2019). The project is registered with the Chinese Clinical Trial Registry (Registration No: ChiCTR1900022081, Registration Date: February 2019). Continuous inclusion of all patients admitted to the ICU due to sepsis from April 2019 to December 2021. Inclusion criteria: Diagnosis of sepsis upon admission to the ICU; Exclusion Criteria: Pregnant or lactating women; Advanced-stage cancer; Patients who died within 24 hours or were discharged automatically. Relevant diagnostic criteria: AKI diagnosis adopts the KDIGO-2012 AKI (Scr) criteria[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Based on the presence or absence of AKI in patients with sepsis, they are categorized into two groups: sepsis-AKI and sepsis-NoAKI. Further, depending on whether the patient received CRRT within one week of AKI diagnosis, they are divided into the CRRT group and the no-CRRT group. All patients who were included in the study provided informed consent before their participation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eCollect and record various laboratory indicators including gender, age, sequential organ failure assessment (SOFA); acute physiology and chronic health evaluation II (APACHE II), procalcitonin(PCT), C-reactive protein(CRP), and coagulation function. Simultaneously record complications like acute kidney injury, respiratory failure, and bleeding, along with treatment measures such as mechanical ventilation, continuous renal replacement therapy, and vasoactive drug usage post-ICU admission.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSample preparation for proteomic analysis and enzyme-linked immunosorbent assay (ELISA).\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCollect blood samples from both patients and NC subjects and then allow them to clot untouched at room temperature for a full hour. Centrifuge the samples at 1600g for 10 minutes to separate the serum. Remove the soluble solids within 30 minutes and store the samples at -80\u0026deg;C for subsequent proteinomic analysis and ELISA. The ProteoPrep Blue albumin \u0026amp; IgG Depletion Kit (PROTBA; Sigma-Aldrich) was employed to eliminate high-abundance proteins, including albumin and IgG, from the serum, following the manufacturer's instructions. Before achieving the final sample concentration, impurities in the protein extracts were identified using a two-dimensional clean-up reagent kit (GE Healthcare).\u003c/p\u003e\n\u003ch3\u003eLC2MS/MS Analysis\u003c/h3\u003e\n\u003cp\u003eProteins in the serum underwent digestion with sequencing-grade trypsin. The supernatant's concentration was determined using an ultramicro UV spectrophotometer, specifically the SMA1000 model. Peptides were separated employing a Waters UPLC system equipped with a BEH C18 nanoACQUITY chromatography column (75 \u0026micro;m \u0026times; 25 cm, 1.7 \u0026micro;m). Proteomics analysis was performed using a nano-flow liquid chromatography system (ACQUITY UPLC; Waters Corporation) coupled with a mass spectrometer (Q Exactive; Thermo Fisher Scientific). Mass scans ranging from 300 to 140 m/z were obtained using Orbitrap profiling mode with a high resolution of 70,000 peptide segments with charges ranging from +\u0026thinsp;2 to +\u0026thinsp;6 were selected for further LC-MS/MS analysis. Fragmentation was attained through the employment of intensified collisional dissociation with a normalized collision energy of 27%, accompanied by a resolution of 35,000. A dynamic exclusion strategy was implemented, wherein the top 20 most intense peaks were selectively excluded, with a duration of 20 seconds for each exclusion.\u003c/p\u003e\n\u003ch3\u003eProtein Identification and Database Retrieval\u003c/h3\u003e\n\u003cp\u003eProteins generated from Homo sapiens using LC-MS/MS were analyzed by searching against the SwissProt database with the MaxQuant search engine (version 1.6.1.0). A mass tolerance setting of 20 ppm was applied to both precursor and fragment ions. Variable modifications included methionine oxidation and N-terminal acetylation, whereas carbamidomethylation was set as a fixed modification. The enzymatic digestion simulation allowed for up to two missed cleavages by trypsin. Peptide and protein identifications were stringently filtered using a false discovery rate (FDR) threshold of 1%, with provisions for one missed cleavage. Label-free protein quantification was performed using MaxQuant software. Both a minimum peptide count criterion and the use of unmodified peptides are required for relative quantification. In the serum samples, a total of 879 proteins were identified. The expression matrix has not undergone normalization using the \"normalizeBetweenArrays\" function, which is a commonly used optimal normalization method for proteomics data analysis found in the R package Limma[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Protein abundance was normalized using a log2 transformation and applied to all quantitative analyses. Missing values in the proteomics data were estimated using the minimum value.\u003c/p\u003e\n\u003ch3\u003eFunctional enrichment analysis\u003c/h3\u003e\n\u003cp\u003eTo gain a comprehensive understanding of the altered proteins and disease-related co-expressed modules, functional enrichment analysis was performed using DAVID 6.8 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and ClueGO[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This analysis aimed to provide insights into the biological functions associated with the identified proteins. A false discovery rate threshold of 0.05 was applied to define the statistical significance of the functional enrichment analysis.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eValidation by ELISA\u003c/h2\u003e \u003cp\u003eTo validate the proteomic analysis results and their correlation with clinical features, a subset of hub proteins was randomly chosen for validation using ELISA with external samples, following the manufacturer's instructions. The details of the ELISA kits utilized in this study, including relevant information, are available in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe measurement results were reported as either mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median with interquartile range (IQR). The sample size was not predetermined using statistical methods. Welch's t-test, assuming a two-tailed distribution, was employed to compare patients in the sepsis-AKI group and the sepsis-NoAKI group, ensuring sensitivity. The normality assumption of the Welch t-test was assessed using the Shapiro-Wilk test when the p-value for each comparison group reached 0.05. If the data is found to deviate from normal distribution, we utilize the non-parametric Mann-Whitney U test for analysis. This approach ensures appropriate statistical assessment under different distributional assumptions. We employed the Benjamini-Hochberg correction method to control the false discovery rate and mitigate Type I errors in the context of multiple testing. Proteins with missing values below 40% were selected for further analysis. The normalization of protein abundance was conducted using the \"normalize between arrays\" function from the Limma package in R. After applying Log2 transformation to the normalized protein abundance data, we used the criteria of log2(FC)\u0026thinsp;\u0026gt;\u0026thinsp;1 and an adjusted P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 to identify proteins that showed significant alterations. The chi-squared test was employed to compare categorical variables. All statistical analyses were conducted in R, and principal component analysis was performed using the \"sva\" function. The significance of hub proteins validated by ELISA was assessed using the Mann-Whitney U test, with a significance level set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePatients and clinical characteristics\u003c/h2\u003e \u003cp\u003eThe study comprised 37 sepsis patients and 31 NC. Among the sepsis patients, there were 19 cases in the sepsis-AKI group and 18 cases in the sepsis-NoAKI group. The clinical characteristics of the AKI and NoAKI groups among sepsis patients are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. No significant differences were observed in terms of age and gender between the two groups (both P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The sepsis-AKI group had higher SOFA scores (7.84\u0026thinsp;\u0026plusmn;\u0026thinsp;3.16 vs 4.89\u0026thinsp;\u0026plusmn;\u0026thinsp;2.16), APACHE II scores (22.21\u0026thinsp;\u0026plusmn;\u0026thinsp;7.39 vs 12.22\u0026thinsp;\u0026plusmn;\u0026thinsp;5.69), and HR (105.36\u0026thinsp;\u0026plusmn;\u0026thinsp;19.58 vs 90.44\u0026thinsp;\u0026plusmn;\u0026thinsp;17.26) compared to the sepsis-NoAKI group (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Laboratory examinations revealed significant group-wise differences in BUN, Scr, pH, Lac, and BE (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for each). Clinical interventions like mechanical ventilation and vasoactive drug use were comparable between the groups, with no significant differences observed (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for each).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of baseline data between the sepsis-AKI group and the sepsis-NoAKI group.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAKI (n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-AKI (n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75 (61, 79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (63, 79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(42.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.84\u0026thinsp;\u0026plusmn;\u0026thinsp;3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.89\u0026thinsp;\u0026plusmn;\u0026thinsp;2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPACHE II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.21\u0026thinsp;\u0026plusmn;\u0026thinsp;7.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.22\u0026thinsp;\u0026plusmn;\u0026thinsp;5.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (per minute)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105.36\u0026thinsp;\u0026plusmn;\u0026thinsp;19.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.44\u0026thinsp;\u0026plusmn;\u0026thinsp;17.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRR (per minute)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (17, 25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (15, 21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory data at admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT (10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143.16\u0026thinsp;\u0026plusmn;\u0026thinsp;93.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152.67\u0026thinsp;\u0026plusmn;\u0026thinsp;68.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.9 (9.7, 17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.5 (5.2, 17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121 (101, 151)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117.5 (108.5, 127)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematocrit (ratio)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.5 (29.6, 43.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.5 (30.9, 38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.9 (13.3, 16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.7 (13.95, 15.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23 (1.11, 1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17 (1.11, 1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPTT (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.1 (35.4, 48.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.0 (40.7, 47.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCT (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.65 (9.56, 58.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.7 (2.15, 36.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e182.98\u0026thinsp;\u0026plusmn;\u0026thinsp;111.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190.89\u0026thinsp;\u0026plusmn;\u0026thinsp;103.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.01\u0026thinsp;\u0026plusmn;\u0026thinsp;3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.78 (11.54, 22.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.55 (4.90, 9.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScr (umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e179.0 (163.0, 289.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.0 (64.5, 99.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST (u/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61. 0 (27.5, 181.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.5 (21.75, 89.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.0 (4.86, 8.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.07 (4.78, 9.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.36 (7.31, 7.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.44 (7.41, 7.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactic acid (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.4 (1.2, 4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.45 (0.93, 2.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.4 (-7.3, -2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.2 (-2.88, -2.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCO2 (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.2 (30.8, 38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.0 (20.0, 37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePO2 (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86.6 (73, 134)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135.5 (97.5, 150.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa+ (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139 (133, 144)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140 (138, 142)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK+ (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.22 (3.5, 4.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.09 (3.78, 4.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa2+ (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.86 (1.6, 2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0 (1.92, 2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterventions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical ventilation[n,(%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (57.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (38.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVasopressors[n,(%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRRT[n,(%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28-day mortality [n,(%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eSOFA, sequential organ failure assessment; APACHE, acute physiology and chronic health evaluation; PCT, procalcitonin; CRP, C-reactive protein; INR, international normalized ratio; PLT, Platelet count; APTT, activated partial thromboplastin time; AST, aspartate aminotransferase; WBC, white blood cell; HR, heart rate; RR, respiratory rate.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMetabolomic analysis of patients with sepsis\u003c/h2\u003e \u003cp\u003eBased on the characteristics of the mass spectrometry proteomics data, the proteins included in the analysis are expressed in the aforementioned 50% of the samples. Therefore, a total of 404 proteins were selected for analysis. PCA analysis shows a clear distinction between sepsis-AKI (represented by red) and sepsis-NoAKI (represented by yellow) groups in the current samples, and both are different from the NC group. A total of 404 proteins were found to exhibit differential expression between the sepsis-AKI and sepsis-NoAKI groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). When applying a significance threshold of log2(FC)\u0026thinsp;\u0026gt;\u0026thinsp;1 and adj P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, a subset of 7 proteins showed significant differential expression between the sepsis-AKI and sepsis-NoAKI groups based on the protein analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Among these, 6 proteins showed significantly upregulated expression, while 1 protein showed significantly downregulated expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Functional enrichment analysis was performed on the aforementioned seven significantly different proteins. The results showed that these seven proteins are mainly involved in biological functions such as immune response, complement activation, coagulation cascade, and neutrophil degranulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). This finding suggests that the identified differentially expressed proteins (DEPs) accurately represent the pathophysiological mechanism of acute kidney injury in sepsis.\u003c/p\u003e \u003cp\u003eOrthogonal partial least squares discriminant Analysis (OPLS-DA) clearly distinguished the sepsis patients with AKI from the NoAKI group, indicating significant differences in their serum untargeted metabolomics profiles(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The variable importance in projection (VIP) values of each metabolic product is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, with a total of 10 proteins having VIP values greater than 2. The above-mentioned 7 significant DEPs are all included within this set of 10 proteins, further highlighting the tremendous potential of these 7 proteins to distinguish between the sepsis-AKI and sepsis-NoAKI groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eValidation of target proteomics\u003c/h2\u003e \u003cp\u003eIntroduce the NC group and identify key proteins exhibiting gradual expression changes among the NC, sepsis-AKI, and sepsis-NoAKI groups. The differential analysis identified 231 proteins that were significantly differentially expressed between the sepsis-AKI and NC groups, with 178 upregulated and 53 downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). A significant difference in the expression of 167 proteins was observed when comparing the sepsis-NoAKI group to the NC group. Among these, 115 proteins were upregulated, while 52 proteins were downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Conduct an intersection analysis of the significant DEPs among the three groups. The results revealed that both IGFBP-4 and B2M proteins exhibited significant differential expression across the three groups, with their expression levels progressively increasing as the disease advanced (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-E). Although the remaining five proteins did not show significant differences between the sepsis-AKI and sepsis-NoAKI groups, notable distinctions were observed when comparing the sepsis-AKI group with both the sepsis-NoAKI and NC groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF-J). These five proteins may not be directly involved in the progression of sepsis, but they may play a crucial role in the occurrence and development of AKI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTargeted Proteomics of Patient Plasma for Biomarker Validation\u003c/h2\u003e \u003cp\u003eThe validation group was included to validate the identified target proteins. Receiver Operating Characteristic (ROC) curve analysis of the subjects' characteristics revealed that CST3, B2M, IGFBP4, CFD, and CD59 demonstrated AUC values above 0.7 for diagnosing SA-AKI (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-G). Furthermore, significant differences were observed between the sepsis-AKI group and the sepsis-NoAKI group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-G).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eComparison of CRRT treatment in the sepsis AKI group\u003c/h2\u003e \u003cp\u003eThe two groups exhibited significant differences in terms of CRRT treatment (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The concentration of IGFBP-4 was higher in the CRRT group compared to the NoCRRT group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Moreover, IGFBP-4 displayed a good predictive value, as indicated by an AUC of 0.84 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study observed an incidence of AKI in septic patients at 51%, with a corresponding mortality rate of 21.1% in septic patients with AKI, significantly higher than those without AKI. It has been reported that 40\u0026ndash;70% of AKI cases in the United States are caused by sepsis, and the mortality rate of SA-AKI is significantly higher than that of AKI or sepsis alone[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Additionally, through proteomic analysis of serum samples from sepsis patients and NC participants, functional enrichment analysis revealed that the differentially expressed proteins primarily participate in biological functions including immune response, complement activation, coagulation cascade, and neutrophil degranulation, which are similar to the characteristic pathogenic mechanisms of sepsis. Previous research has shown that in the context of sepsis, the activation of the complement and coagulation cascades functions as a robust innate immune defense mechanism. This mechanism functions to suppress and eliminate pathogens by triggering local inflammation and facilitating coagulation, effectively impeding the dissemination of bacteria and other pathogens throughout the body[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Furthermore, there is a direct or indirect connection between the coagulation cascade and the complement system, and they can mutually enhance and promote each other[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Our proteomic analysis results have been validated through ELISA, confirming their high reliability and quality.\u003c/p\u003e \u003cp\u003eCST3, a low-molecular-weight non-glycosylated protein, effortlessly crosses the glomerular membrane, undergoes complete absorption and metabolism within proximal tubular epithelial cells, and is synthesized uniformly by all nucleated cells, remaining unaffected by inflammation, fever, external factors, gender, age, or body weight[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. CST3 can serve as a biomarker that reflects the glomerular filtration rate. Clinical trial results demonstrate that CST3 exhibits superior sensitivity to creatinine in the early detection of AKI, being detectable 24\u0026ndash;48 hours earlier, while its blood level increases in response to impaired glomerular filtration function and further rises with the severity of kidney injury[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Leem et al[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] conducted a study in which they observed a significant elevation in the level of CST3 in SA-AKI patients compared to NoAKI patients, which is consistent with our research findings. Moreover, our study further found that the AUC value of CST3 for diagnosing SA-AKI was 0.788 (Figure. 5G), suggesting that CST3 can serve as a novel biomarker for predicting SA-AKI.\u003c/p\u003e \u003cp\u003eSeveral studies have demonstrated that B2M serves as a biomarker for renal function, and its serum levels are correlated with glomerular filtration rate (GFR)[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Elevations in serum B2M levels are commonly observed alongside increases in CST3 and urea nitrogen, and they are employed in the evaluation of renal injury caused by medication usage, cardiovascular risk, kidney transplantation, and other etiological factors[\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, there are relevant studies suggesting that when older criteria are used to define AKI, the predictive efficacy of B2M is reduced, as indicated by a low AUC of 0.59 in the test subjects[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. With the implementation of the KDIGO-2012 AKI (Scr) criteria, a subsequent study conducted by British scholar Kevin T. Barton et al. in 2018 uncovered a correlation between B2M and AKI development, the performance was characterized by an AUC of 0.84. Moreover, it was found that higher levels of B2M in patients corresponded to more advanced stages of AKI[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Similar to previous research findings, our study revealed a significant upregulation of B2M levels in SA-AKI, and its expression became more pronounced with disease progression.\u003c/p\u003e \u003cp\u003eIGFBP-7 has been established as an effective early diagnostic and prognostic biomarker for AKI based on prior research[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The significance of IGFBP-4, a key member of the insulin-like growth factor binding protein family, in kidney diseases has gained growing recognition. Relevant studies have found elevated expression of IGFBP-4 in the serum of patients with chronic renal insufficiency, which is associated with the severity of renal failure and decreased osteogenesis during periods of bone malnutrition[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Previous research has indicated a strong correlation between the serum concentration of IGFBP-4 and both the chronicity index and estimated glomerular filtration rate in lupus nephritis, suggesting its potential as a biomarker for this condition[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The significance of IGFBP-4, a key member of the insulin-like growth factor binding protein family, in kidney diseases has gained growing recognition. Our study revealed a notable elevation in IGFBP-4 levels within the sepsis-AKI group when compared to both the sepsis NoAKI group and the NC group. This finding implies that IGFBP-4 could have a vital role in the development and progression of SA-AKI. Furthermore, IGFBP4 was also found to have good predictive value for the need for CRRT, with an AUC of 0.84 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB), which might assist in initiating CRRT in SA-AKI patients before the occurrence of related complications.\u003c/p\u003e \u003cp\u003eCFD is a recently discovered serine protease gene predominantly expressed in adipose tissue and sciatic nerve tissue. It plays a crucial role as a rate-limiting enzyme in the activation of the complement alternative pathway[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Previous studies have shown that CFD can undergo filtration across the glomerular membrane, resulting in a significant increase in its circulating concentration in patients with renal impairment, reaching approximately 10 times higher levels in end-stage renal failure patients[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Thus, a potential association between CFD serum levels and renal dysfunction can be inferred.\u003c/p\u003e \u003cp\u003eThe definitive role of CD59 in paroxysmal nocturnal hemoglobinuria (PNH) and congenital CD59 deficiency has been well-established[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. However, the underlying mechanisms responsible for renal dysfunction in these conditions remain unclear. Several studies suggest that CD59 is expressed in all cells of the renal tubules and glomeruli, playing a role in the pathogenesis of various diseases, and hypothesize that renal CD59 serves as a protective factor against homologous complement attack by preventing kidney vulnerability in the absence of the complement activation inhibitory system[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. It has been found that after introducing the human CD59 gene into mice, the renal ischemia-reperfusion injury in the mice is significantly alleviated[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In rats, CD59 and Crry work together to mitigate complement-mediated injury and preserve the normal integrity of the kidneys. These findings suggest that the deposition of membrane attack complex (MAC) on glomerular cell membranes during complement-mediated glomerular injury has the potential to induce functional and metabolic changes and impact subsequent damage processes[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study has several limitations. Firstly, the sample size of sepsis patients included in this study is relatively small. Therefore, expanding the sample size and conducting related multicenter studies are the main focus of our further research. Secondly, the specific mechanisms by which the identified DEPs contribute to septic acute kidney injury remain unclear, necessitating further experiments to investigate these mechanisms in future studies. Lastly, the staging of AKI was not investigated in our study. Given the variable severity of the disease, variations in the identified DEPs may exist, potentially influencing the experimental results and subsequent treatment strategies.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eCST3, B2M, IGFBP4, CFD, and CD59 show promise as potential biomarkers for SA-AKI, thereby contributing to its early diagnosis. Specifically, IGFBP-4 may aid in assessing the necessity of CRRT treatment in SA-AKI patients. However, the diagnostic efficacy of these biomarkers requires further validation through large-scale prospective studies and clinical practice.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSA-AKI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esepsis associated-acute kidney injury\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRRT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econtinuous renal replacement therapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprincipal component analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOPLS-DA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eorthogonal partial least squares discriminant analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEPs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edifferentially expressed proteins\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVIP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003evariable importance in projection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCST3\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecystatin C\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eB2M\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebeta-2-Microglobulin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIGFBP-4\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einsulin-like growth factor binding protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCFD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecomplement factor D\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecomplement factor I\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided written informed consent. This study adhered to the principles of the Declaration of Helsinki for biomedical research and was approved by the Ethics Committee of Taizhou Hospital, Zhejiang Province (approval number: K20190102).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript presents original research that has not been published or submitted for publication elsewhere. All authors have reviewed and approved the manuscript for submission to Clinical Proteomics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data can be obtained from the corresponding author JYP ([email protected]) upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by The Science and Technology Project of Taizhou (21ywb05、23ywa47), the Medicines Health Research Fund of Zhejiang, China (2024ky1784、2022KY435), the National Key Research and Development Program of Zhejiang Province (2023C03083).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWZ, XH, and HD contributed equally to this work. WZ, XH, HD, QC, YX, and YJ were involved in the conception and design of the study. QC, JZ, and NC were involved in the acquisition of data. CD and XH were involved in the lab experiment. SZ and YJ were involved in the analysis and interpretation of data. WZ and YJ were involved in the drafting of the manuscript. All the authors revised and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding Authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYongpo Jiang - Department of Critical Care Medicine, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, No. 150, Ximen Street, Taizhou 317000, China. Phone: 86+85120120; Email: [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYinghe Xu\u0026nbsp;\u003c/strong\u003e- Department of Critical Care Medicine, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, No. 150, Ximen Street, Taizhou 317000, China. Phone: 86+13706763731; Email: [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQi Chen\u0026nbsp;\u003c/strong\u003e- Precision Medicine Center, Taizhou Central Hospital, Taizhou University Medical School, No. 999, Donghai Street, Taizhou 317000, China. Phone: 86+13757682516; Email:\u003c/p\u003e\n\u003cp\[email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWeimin Zhu\u0026nbsp;\u003c/strong\u003e- Department of Emergency Medicine, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Taizhou 317000, China\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXiaxia He\u0026nbsp;\u003c/strong\u003e- Department of Radiology, Taizhou Central Hospital, Taizhou University Medical School, Taizhou 317000, China\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHanzhi Dai -\u0026nbsp;\u003c/strong\u003eDepartment of Critical Care Medicine, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Taizhou 317000, China\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCuicui Dong -\u0026nbsp;\u003c/strong\u003eDepartment of Critical Care Medicine, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Taizhou 317000, China\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJiatian Zhang -\u0026nbsp;\u003c/strong\u003eDepartment of Critical Care Medicine, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Taizhou 317000, China\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNanjin Chen\u0026nbsp;\u003c/strong\u003e- Department of Anesthesiology, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Taizhou 317000, China\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSheng Zhang\u0026nbsp;\u003c/strong\u003e- Department of Critical Care Medicine, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Taizhou 317000, China\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYubin Xu\u0026nbsp;\u003c/strong\u003e- Department of Pharmacy, Taizhou Central Hospital (Taizhou University Hospital), Taizhou University, Taizhou 318000, Zhejiang, China\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupporting Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe information on ELISA kits used in this study (Table S1).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZarbock A, Nadim MK, Pickkers P, Gomez H, Bell S, Joannidis M, Kashani K, Koyner JL, Pannu N, Meersch M, et al. 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Pediatr Nephrol. 2011;26(2):267\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarton KT, Kakajiwala A, Dietzen DJ, Goss CW, Gu H, Dharnidharka VR. Using the newer Kidney Disease: Improving Global Outcomes criteria, beta-2-microglobulin levels associate with severity of acute kidney injury. Clin kidney J. 2018;11(6):797\u0026ndash;802.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKashani K, Al-Khafaji A, Ardiles T, Artigas A, Bagshaw SM, Bell M, Bihorac A, Birkhahn R, Cely CM, Chawla LS, et al. Discovery and validation of cell cycle arrest biomarkers in human acute kidney injury. Crit Care. 2013;17(1):R25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrtega LM, Heung M. The use of cell cycle arrest biomarkers in the early detection of acute kidney injury. Is this new Ren troponin? Nefrologia. 2018;38(4):361\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Doorn J, Cornelissen AJ, Van Buul-Offers SC. Plasma levels of insulin-like growth factor binding protein-4 (IGFBP-4) under normal and pathological conditions. Clin Endocrinol. 2001;54(5):655\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu T, Xie C, Han J, Ye Y, Singh S, Zhou J, Li Y, Ding H, Li QZ, Zhou X, et al. Insulin-Like Growth Factor Binding Protein-4 as a Marker of Chronic Lupus Nephritis. PLoS ONE. 2016;11(3):e0151491.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLesavre PH, M\u0026uuml;ller-Eberhard HJ. Mechanism of action of factor D of the alternative complement pathway. J Exp Med. 1978;148(6):1498\u0026ndash;509.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarnum SR, Niemann MA, Kearney JF, Volanakis JE. Quantitation of complement factor D in human serum by a solid-phase radioimmunoassay. J Immunol Methods. 1984;67(2):303\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVolanakis JE, Barnum SR, Giddens M, Galla JH. Renal filtration and catabolism of complement protein D. N Engl J Med. 1985;312(7):395\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeinstock C. Association of Blood Group Antigen CD59 with Disease. Transfus Med hemotherapy: offizielles Organ der Deutschen Gesellschaft fur Transfusionsmedizin und Immunhamatologie. 2022;49(1):13\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsuo S, Nishikage H, Yoshida F, Nomura A, Piddlesden SJ, Morgan BP. Role of CD59 in experimental glomerulonephritis in rats. Kidney Int. 1994;46(1):191\u0026ndash;200.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBongoni AK, Lu B, Salvaris EJ, Roberts V, Fang D, McRae JL, Fisicaro N, Dwyer KM, Cowan PJ. Overexpression of Human CD55 and CD59 or Treatment with Human CD55 Protects against Renal Ischemia-Reperfusion Injury in Mice. \u003cem\u003eJournal of immunology (Baltimore, Md\u003c/em\u003e: 1950) 2017, 198(12):4837\u0026ndash;4845.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatanabe M, Morita Y, Mizuno M, Nishikawa K, Yuzawa Y, Hotta N, Morgan BP, Okada N, Okada H, Matsuo S. CD59 protects rat kidney from complement mediated injury in collaboration with crry. Kidney Int. 2000;58(4):1569\u0026ndash;79.\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":"sepsis-associated acute kidney injury, proteomics, biomarkers, IGFBP-4, continuous renal replacement therapy treatment","lastPublishedDoi":"10.21203/rs.3.rs-5466304/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5466304/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSepsis-associated acute kidney injury (SA-AKI) is a severe and life-threatening disease with high incidence and mortality rates among ICU patients. However, currently, there is still a lack of effective biomarkers for early diagnosis and treatment of kidney injury in septic patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn a multi-center prospective cohort study, 37 sepsis patients (sepsis-AKI, n\u0026thinsp;=\u0026thinsp;19; sepsis-NoAKI, n\u0026thinsp;=\u0026thinsp;18) and 31 healthy controls were enrolled. Peripheral blood samples were analyzed by protein mass spectrometry, and principal component analysis (PCA) was used to remove outliers. Differentially expressed proteins were identified based on p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2 fold change|\u0026gt;1, then functionally enriched using DAVID. An additional validation cohort of 65 sepsis patients ((sepsis-AKI, n\u0026thinsp;=\u0026thinsp;38; sepsis-NoAKI, n\u0026thinsp;=\u0026thinsp;27) from three other centers was used to further validate the target proteins. ELISA and ROC curve analysis were performed to evaluate the diagnostic accuracy of the target proteins for SA-AKI and the need for continuous renal replacement therapy (CRRT), using the area under the ROC curve (AUC) as the performance metric.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eUltimately, 7 proteins were differently expressed between the two groups, with 6 of them being significantly up-regulated and 1 being significantly down-regulated. Functional enrichment analysis showed that the selected differentially expressed proteins were mainly involved in immune responses, complement activation, coagulation cascades, and neutrophil degranulation. Further external validation showed that the AUC values of CST3, B2M, IGFBP4, CFD, and CD59 in diagnosing SA-AKI were all above 0.7, and there were significant differences between the two groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For whether or not to receive CRRT treatment, IGFBP4 was found to have good predictive value, with an AUC of 0.84.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study suggests that CST3, B2M, IGFBP4, CFD, and CD59 may serve as potential biomarkers for the diagnosis of SA-AKI, with IGFBP4 specifically aiding in determining whether CRRT treatment is necessary.\u003c/p\u003e","manuscriptTitle":"Proteomics reveals biomarkers for the diagnosis and treatment of septic kidney injury","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-18 16:48:54","doi":"10.21203/rs.3.rs-5466304/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":"09459c1b-f1e4-4066-8de3-c00d500f803f","owner":[],"postedDate":"December 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-12T06:53:49+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-18 16:48:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5466304","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5466304","identity":"rs-5466304","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}