Introduction: The early diagnosis of kidney injury in type 2 diabetes (T2DM) is important to prevent the long-term damaging effects of kidney loss and is decisive for patient outcomes. While SIRT2 is implicated in diabetes pathogenesis, its correlation with diabetic nephropathy remains unexplored. This study was designed to evaluate the association of urine SIRT2 levels with diabetic kidney injury, as well as potential underlying mechanisms. Methods: In T2DM patients, db/db mice, and high glucose plus palmitic acid treated HK2 cell models, ELISA, Immunoturbidimetry, Immunohistochemistry, Western blot, and Quantitative real-time polymerase chain reaction were used to detect SIRT2 levels and kidney damage. According to urinary albumin/creatinine ratio (UACR), 163 T2DM patients were divided into three groups. Spearman correlation analysis was used to investigate the relationship between urinary sirtuin2/creatinine ratio (USCR) and biomarkers of kidney injury. The influencing factors of albuminuria in T2DM patients were analyzed by logistic regression model. Results: In our findings, the Macro group exhibited the highest USCR levels as UACR increased. There was a positive association between USCR and UACR, α1-microglobulin/creatinine ratio (UαCR), β2-microglobulin/creatinine ratio (UβCR), and retinol-binding protein/creatinine ratio (URCR), with a negative correlation observed with eGFR. Logistic ordered multiclassification regression analysis, adjusting for confounding variables, confirmed that USCR remained a significant risk factor for the severity of albuminuria in T2DM patients. In the db/db mice kidney SIRT2 protein level increased significantly. Increased SIRT2 protein levels were also observed in renal tubular epithelial cells treated with high glucose plus palmitic acid. Moreover, SIRT2 promotes the expression of proinflammatory factors TNF-α and IL-6 by modulating the phosphorylation of p38 MAPK and p-JNK in renal tubular cells induced by high glucose and palmitic acid. Conclusion: Urinary SIRT2 is closely related to eGFR, renal tubule injury, and urinary albumin excretion in T2DM patients, which is expected to be an important indicator to comprehensively reflect renal injury.

Diabetic kidney disease (DKD) is one of the major microvascular complications of diabetes mellitus (DM) and a primary cause of chronic kidney disease. Studies have reported that 20–40% of diabetic patients are complicated with DKD [1‒3]. The Global Burden of Disease Study reported an age-standardized prevalence of DKD in 2017 at 15.48/1,000 for males and 16.50/1,000 for females, respectively [4]. In the USA, the annual average cost per DKD patient is USD 6,826 [5]. The significant prevalence and economic impact of DKD result in substantial societal and familial burdens.

The diagnosis of DKD is typically based on the urinary albumin/creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR). Nevertheless, a notable group of DKD patients (23.3%–56.6%) with an estimated eGFR below 60 mL/min/1.73 m2 may have normoalbuminuria [6]. Furthermore, studies suggest that in type 2 diabetes mellitus (T2DM) patients with renal insufficiency, the all-cause mortality was not significantly lower in the normal albuminuria group than that in albuminuria group [7]. Therefore, both of these indicators have shortcomings, and so far, there is a lack of assessment indicators for tubular damage. In DKD, proximal tubular damage not only precedes or occurs independently of glomerular lesions [8, 9] but also can further induce glomerular injury [10, 11]. While the detection of urinary α1-microglobulin (α1-MG), urinary β2-microglobulin (β2-MG), and urinary retinol-binding protein (RBP) can serve as markers for early DKD, there is a critical need to explore a more comprehensive indicator to enhance the diagnostic and prognostic capabilities for DKD.

Sirtuin 2, known as silent mating-type information regulation 2 (SIRT2), is a member of the nicotinamide adenine dinucleotide (NAD+)-dependent histone deacetylases family. SIRT2 is localized in both the cytoplasm and nucleus, with presence observed in various organs including the kidney, brain, muscle, pancreas, liver, and adipose tissue [12, 13]. Research findings indicate that SIRT2 is intricately linked to the pathogenesis of diabetes [14, 15], demonstrating associations with various complications stemming from the condition. Qu et al. [16] have shown that SIRT2 regulates oxidative stress and inflammation in diabetic osteoarthritis. Yuan et al. [17] found that SIRT2 is related to microtubule stability in diabetic cardiomyopathy. Furthermore, SIRT2 exhibits an association with renal impairment. SIRT2 modulates the expression of proinflammatory factors CXCL2 and CCL2 in renal tubules induced by lipopolysaccharide in mice [18]. SIRT2-mediated deacetylation of FOXO3a initiates FASL-induced apoptosis in the context of renal ischemia-reperfusion in rats [19]. SIRT2 exacerbates renal injury, apoptosis, necrotic apoptosis, and inflammation induced by cisplatin in mice [20]. However, the association between SIRT2 and diabetic nephropathy remains ambiguous and requires further elucidation.

Considering the research findings so far, we speculate that SIRT2 is associated with diabetic nephropathy and may serve as a comprehensive indicator for assessing renal damage in diabetes. Hence, this investigation focuses on the quantitative analysis of SIRT2 levels in both the circulation and urine of individuals diagnosed with diabetic nephropathy. The primary objective is to ascertain the correlation between SIRT2 and indicators of kidney injury. In addition, we evaluated SIRT2 expression and possible mechanisms in animal models and cellular systems that mimic diabetic nephropathy.

Subjects

T2DM patients were hospitalized in The Third Xiangya Hospital of Central South University from June 2019 to January 2020 according to the inclusion criteria. Meanwhile, healthy patients were selected as the control group. The inclusion criteria are derived from the 1999 World Health Organization (WHO) criteria for the diagnosis of diabetes [21]. Patients were excluded due to the following conditions: (1) patients with type 1 diabetes and specific types of diabetes; (2) patients with recently severe infection (within 3 months); (3) patients with acute complications of diabetes or severe heart, lung, and liver insufficiency; (4) patients with hyperthyroidism or other severe endocrine, metabolic and autoimmune diseases; (5) patients with the recently rapid increase of albuminuria, nephrotic syndrome, acute renal injury, renal transplantation, or other renal diseases (shown in Fig. 1). According to 2007 KDOQI guidelines on DKD staging criteria [22], the eligible patients were divided into three groups based on their UACR: mildly increased (Normo group, UACR <30 mg/g, N = 58), moderately increased (Micro, UACR 30–300 mg/g, N = 52), and severely increased (Macro group, UACR >300 mg/g, N = 53). This study was reviewed and approved by the Ethics Committee of The Third Xiangya Hospital, and all subjects signed informed consent.

Fig. 1.

Flowchart of study participants.

Fig. 1.

Flowchart of study participants.

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Clinical Data Collection

We gathered health information from participants, covering factors like age, sex, height, weight, BMI, diabetes duration, and blood pressure. We used an automatic biochemical analyzer (Beckman Coulter, USA) to measure various biochemical markers, such as fasting plasma glucose, hemoglobin, serum creatinine, blood urea nitrogen, serum uric acid, cholesterol, triglycerides, and liver enzymes. Glycosylated hemoglobin (HbA1c) was assessed using high-performance liquid chromatography, and eGFR was calculated using the Chinese improved abbreviated Modification of Diet in Renal Disease formula [23]. Concentrations of SIRT2 in serum and urine were determined using an ELISA kit (ab227895 Human SIRT2, UK) following manufacturer instructions. For urinary biomarkers (ALB, α1-MG, β2-MG, RBP, and SIRT2), we employed the immunoturbidimetric method with the automatic biochemical analyzer. These values were normalized by urinary creatinine, resulting in ratios (UACR, UαCR, UβCR, URCR, and urinary sirtuin2/creatinine ratio [USCR]). Clinical normal ranges for urinary α1-MG, β2-MG, RBP, and creatinine were established (0–12.5 μg/mL, 0–0.3 μg/mL, 0–0.7 mg/L, and 4.4–16.8 mmol/L, respectively). Consequently, reference ranges for the normalized ratios (UαCR, UβCR, URCR) were determined as 0–25.114 mg/g, 0–0.603 mg/g, and 0–1.407 mg/g.

Animal Experimental Design

In addition, 8-week-old male C57BLKS/J db/m and db/db mice (6 mice in each group) were obtained from GemPharmatech LLC. The mice were fed regular food under specific pathogen-free conditions. After 12 weeks, the mice were killed, and blood, serum, and kidney were collected. The animal experiments were approved by the Ethics Review Committee of Central South University.

Cell Culture and Treatment

Human proximal tubular epithelial cells (HK2 cells) were provided by the Institute of Kidney Disease, Central South University. HK2 cells were cultured in MEM containing 10% fetal bovine serum, then stimulated with a final concentration of 30 mmol/L glucose and different concentrations of palmitic acid for 48 h, and then protein was extracted. The molar ratio of PA to BSA was 1:6, and the final concentration of BSA was 16.67 μmol/L. A concentration of 150 μmol/L was used for the subsequent functional experiments.

Cell Transfection

Cell transfection experiments were conducted using interfered fragments of SIRT2 (si-SIRT2) and corresponding controls, which were designed and synthesized by GenePharma (Suzhou, China). The interference sequences for SIRT2 are as follows: forward sequence: 5′-GCACGGCACCUUCUACACATT-3′; reverse sequence: 5′-AUAUCAGGCUUCACCAGGCTT-3′. Additionally, SIRT2 overexpression plasmids and their controls were designed and synthesized by Beijing Tsingke Biotech Co., Ltd. Transfection was performed using Lipofectamine 3000 reagent (Invitrogen, CA, USA) following the manufacturer's protocol.

Hematoxylin-Eosin Staining and Immunohistochemistry Staining

Immediately after collection, kidney tissue was fixed in formalin, embedded in paraffin, and then sliced into 5 μm thick sections. These sections were used for either hematoxylin-eosin staining or immunohistochemistry using antibodies targeting SIRT2 (Abcam, ab211033) or KIM-1 (Abmart, PH9995). According to previous literature, tubular injury is classified into 0 to 4 grades: 0 = normal kidney; 1 = minimal injury (involvement of <5% of cortex or outer medulla); 2 = mild injury (involvement of 5–25% of cortex or outer medulla); 3 = moderate injury (involvement of 25–75% of cortex or outer medulla); 4 = severe injury (>75% involvement of cortex or outer medulla) [24]. Image J software was employed to quantify the positive area ratio of SIRT2 in photomicrographs.

Western Blot

10% gel was prepared using SDS-PAGE, and a protein sample was placed on it. Electrophoresis separated proteins based on size. Afterward, proteins were transferred from the gel to a membrane. The membrane was blocked to prevent unwanted binding. Then, it was treated with primary antibody overnight at 4°C. The membrane underwent rinsing, followed by the addition of a secondary antibody (Abiowell, AWS0002, 1:10,000). Enhanced chemiluminescence (ECL) was used for color development, and the resulting signal was captured. Quantitative analysis was performed using image analysis software on the obtained data. Antibody information is as follows: SIRT2 (Abcam, ab211033, 1:2,000), tubulin (Abmart, M20005, 1:5,000), p-JNK (Proteintech, 80024-1-RR, 1:11,000), JNK (Proteintech, 66210-1-Ig, 1:11,000), p-p38 (Proteintech, 28796-1-AP, 1:11,000), p38 (Proteintech, 66234-1-Ig, 1:11,000).

Quantitative Real-Time Polymerase Chain Reaction

Total RNA was isolated from mouse kidney tissue and HK2 cell line using TransZol UP RNA extraction kit (TransGen Biotech). For mRNA reverse transcription, HiScript II Q RT SuperMix For qPCR (+gDNA wiper) from Vazyme was used following the manufacturer’s protocol. mRNA was amplified by quantitative real-time polymerase chain reaction (RT-qPCR) using ChamQ Universal SYBR qPCR Master Mix (Vazyme), and amplification was detected using the LightCycler 480 detection system (Roche Diagnostics, Basel, Switzerland). β-actin was used as the internal control for mRNA. Gene expression levels relative to the internal control were determined using the 2−ΔΔCt method, with each detection performed in triplicate. The sequences of all primers are provided below: h-SIRT2: forward sequence: 5′-CCT​GCG​GAA​CTT​ATT​CTC​CCA-3′, reverse sequence: 5′-GTC​ATA​GAG​GCC​GGT​GGA​TG-3′; h-TNF-α: forward sequence: 5′-ATG​AGC​ACT​GAA​AGC​ATG​ATC​C -3′, reverse sequence: 5′-GAG​GGC​TGA​TTA​GAG​AGA​GGT​C-3′; h-IL-6: forward sequence: 5′-CCT​GAA​CCT​TCC​AAA​GAT​GGC-3′, reverse sequence: 5′-TTC​ACC​AGG​CAA​GTC​TCC​TCA-3′; h-actin: forward sequence: 5′-CAT​GTA​CGT​TGC​TAT​CCA​GGC-3′, reverse sequence: 5′-CTC​CTT​AAT​GTC​ACG​CAC​GAT-3′; m-KIM-1: forward sequence: 5'′TTG​CCT​TCC​GTG​TCT​CTA​AG-3′, reverse sequence: 5′-AGA​TGT​TGT​CTT​CAG​CTC​GG-3′; m-WT1: forward sequence: 5′-ATC​CCA​GGC​AGG​AAA​GTG​TG-3′, reverse sequence: 5′-GTG​CTG​TCT​TGG​AAG​TCG​GA-3′; m-megalin: forward sequence: 5′-TGA​CGA​GCA​AGG​CTG​TGA​AT-3′, reverse sequence: 5′-TCA​GCA​CAT​CGA​AAC​TCG​CT-3′; m-nephrin: forward sequence: 5′-GCT​CAG​GGA​AGA​CAG​CAA​CA -3′, reverse sequence: 5′-GAT​AGA​GCC​CAG​AAG​CCT​CG-3′; m-actin: forward sequence: 5′-CAT​TGC​TGA​CAG​GAT​GCA​GAA​GG-3′, reverse sequence: 5′-TGC​TGG​AAG​GTG​GAC​AGT​GAG​G-3′.

Statistical Analysis

Statistical analyses were conducted using SPSS version 25.0. Categorical data were compared using the χ2 test and presented as percentages. Continuous variables were expressed as mean ± SD for normally distributed values and median (interquartile range) for nonparametric values. The normality of data was assessed using the Kolmogorov-Smirnov test, and non-normally distributed data were transformed to meet normality assumptions. T tests or analysis of variance were used for normally distributed data, while Kruskal-Wallis and Mann-Whitney U tests were applied for non-normally distributed data. ROC curve analysis was employed to assess the diagnostic accuracy of SIRT2, and the area under the curve (AUC) was obtained. Sensitivity, specificity, positive and negative predictive values, and ideal cutoff values were determined through ROC analysis. Logistic regression was used to analyze influencing factors, and Spearman’s correlation examined the relationship between SIRT2 and kidney injury biomarkers. A significance level of p < 0.05 was considered statistically significant.

Baseline Characteristics

Baseline characteristics are summarized in Table 1. BMI, FPG, and ALT exhibited a normal distribution. No significant differences were observed in gender composition or age across the four groups (p > 0.05). Compared to the NC group, T2DM patient groups demonstrated significant increases in SBP, FPG, and TG levels, along with significant decreases in Alb, Ucr, and HDL-c levels (p < 0.05). Notably, indicators of renal function impairment, such as BUN and Scr, were higher in the Macro group than in the Micro and Normo groups, while eGFR levels were lower (p < 0.05). The proportions of renin-angiotensin-aldosterone system blockers and hypoglycemic drugs (including dipeptidyl peptidase-4 inhibitors, glucagon-like peptide-1 receptor agonists, and glucose cotransporter 2 sodium inhibitors) increased in the Normo, Micro, and Macro groups (shown in Table 1).

Table 1.

Clinical characteristics and biochemical indicators of participants

Subject groupsNC (N = 40)Normo (N = 58)Micro (N = 52)Macro (N = 53)
Gender (M/F) 25/15 35/23 33/19 35/18 
Age, years 58.0 (54.00–64.00) 59.00 (49.75–66.00) 59.00 (54.00–67.75) 56.00 (51.00–64.50) 
Ln (BMI), kg/m2 3.23±0.14 3.21±0.19 3.21±0.13 
Duration, years 10.00 (4.00–12.50) 10.00 (5.00–17.75) 13.00 (9.50–17.50)** 
SBP, mm Hg 116.88±16.17 128.17±16.24 141.50±19.75△△△*** 151.09±19.21△△△***# 
DBP, mm Hg 77.00 (67.50–85.00) 78.00 (70.00–86.00) 83.50 (77.00–93.25)△△88.00 (80.00–97.50)△△△** 
Hb, g/L 147.00±15.42 138.57±18.18 131.42±21.68△△△ 110.34±25.22△△△***### 
Ln (FPG), mmol/L 1.60±0.11 2.07±0.30△△△ 2.11±0.44△△△ 1.99±0.40△△△ 
HbA1C, % 8.40±1.91 8.64±2.33 8.36±2.16 
Scr, μmol/L 69.00 (60.50–77.75) 66.00 (55.50–75.25) 78.50 (58.00–108.75)* 134.00 (81.50–244.00)△△△***## 
BUN, mmol/L 4.37 (3.68–5.58) 5.67 (4.90–6.78) 6.61 (5.38–9.02)△△△8.66 (6.56–12.40)△△△***# 
sUA, μmol/L 318.10±91.60 334.26±85.25 352.37±109.50 391.38±99.17△△
Ln (ALT), U/L 3.04±0.53 3.26±0.66 2.87±0.60** 2.78±0.46*** 
AST, U/L 22.50 (19.00–30.75) 21.00 (16.00–28.00) 16.50 (15.00–22.75)△△ 18.00 (14.50–24.00)△△ 
ALB, g/L 49.25±3.31 41.38±3.84△△△ 40.33±4.76△△△ 34.89±6.42△△△***### 
TC, mmol/L 4.23 (3.67–4.83) 4.53 (3.66–5.41) 4.46 (3.82–5.60) 4.99 (4.03–5.96) 
TG, mmol/L 1.09 (0.71–1.44) 1.93 (1.38–2.51)△△△ 1.63 (1.04–3.09)△△△ 1.99 (1.28–3.29)△△△ 
LDL-c, mmol/L 1.94±0.97 2.33±0.90 2.31±0.76 2.59±1.08△△ 
HDL-c, mmol/L 1.26 (1.19–1.34) 1.09 (0.96–1.27)△△△ 1.11 (0.93–1.29)△△ 1.12 (0.94–1.25)△△ 
Ucr, mmol/L 9.98 (7.72–15.94) 7.79 (5.64–12.20) 5.16 (3.49–8.78)△△△5.05 (3.56–7.878)△△△** 
sSIRT2, pg/mL 97.55 (74.50–124.95) 116.61 (72.45–170.40) 134.82 (87.36–190.41) 133.04 (79.28–192.55) 
eGFR, mL/min/1.73 m2 125.54 (105.21–143.94) 112.91 (97.03–139.92) 94.77 (59.66–134.08)△△ 50.19 (23.99–88.22)△△△***### 
UACR, mg/g 9.16 (4.19–12.92) 14.94 (10.35–19.67) 64.12 (38.93–124.14)△△△*** 1,385.89 (593.82–3,062.74)△△△***### 
UαCR, mg/g 1.56 (1.00–2.25) 6.18 (3.70–11.88)△△△ 15.43 (6.60–34.98)△△△48.92 (19.38–92.73)△△△***## 
UβCR, mg/g 0.05 (0.02–0.07) 0.14 (0.06–0.29)△△△ 0.30 (0.11–1.16)△△△ 1.84 (0.25–15.38)△△△*** 
URCR, mg/g 0.06 (0.03–0.12) 0.18 (0.10–0.35)△△ 0.63 (0.32–2.49)△△△*** 17.17 (3.26–27.39)△△△***## 
Ln (USCR), ng/g 2.64±0.52 2.63±0.58 2.96±0.88 3.19±0.88△△** 
RAAS blocker 14/58 21/52 27/53** 
hypoglycemic agents 9/58 22/52** 30/53*** 
Subject groupsNC (N = 40)Normo (N = 58)Micro (N = 52)Macro (N = 53)
Gender (M/F) 25/15 35/23 33/19 35/18 
Age, years 58.0 (54.00–64.00) 59.00 (49.75–66.00) 59.00 (54.00–67.75) 56.00 (51.00–64.50) 
Ln (BMI), kg/m2 3.23±0.14 3.21±0.19 3.21±0.13 
Duration, years 10.00 (4.00–12.50) 10.00 (5.00–17.75) 13.00 (9.50–17.50)** 
SBP, mm Hg 116.88±16.17 128.17±16.24 141.50±19.75△△△*** 151.09±19.21△△△***# 
DBP, mm Hg 77.00 (67.50–85.00) 78.00 (70.00–86.00) 83.50 (77.00–93.25)△△88.00 (80.00–97.50)△△△** 
Hb, g/L 147.00±15.42 138.57±18.18 131.42±21.68△△△ 110.34±25.22△△△***### 
Ln (FPG), mmol/L 1.60±0.11 2.07±0.30△△△ 2.11±0.44△△△ 1.99±0.40△△△ 
HbA1C, % 8.40±1.91 8.64±2.33 8.36±2.16 
Scr, μmol/L 69.00 (60.50–77.75) 66.00 (55.50–75.25) 78.50 (58.00–108.75)* 134.00 (81.50–244.00)△△△***## 
BUN, mmol/L 4.37 (3.68–5.58) 5.67 (4.90–6.78) 6.61 (5.38–9.02)△△△8.66 (6.56–12.40)△△△***# 
sUA, μmol/L 318.10±91.60 334.26±85.25 352.37±109.50 391.38±99.17△△
Ln (ALT), U/L 3.04±0.53 3.26±0.66 2.87±0.60** 2.78±0.46*** 
AST, U/L 22.50 (19.00–30.75) 21.00 (16.00–28.00) 16.50 (15.00–22.75)△△ 18.00 (14.50–24.00)△△ 
ALB, g/L 49.25±3.31 41.38±3.84△△△ 40.33±4.76△△△ 34.89±6.42△△△***### 
TC, mmol/L 4.23 (3.67–4.83) 4.53 (3.66–5.41) 4.46 (3.82–5.60) 4.99 (4.03–5.96) 
TG, mmol/L 1.09 (0.71–1.44) 1.93 (1.38–2.51)△△△ 1.63 (1.04–3.09)△△△ 1.99 (1.28–3.29)△△△ 
LDL-c, mmol/L 1.94±0.97 2.33±0.90 2.31±0.76 2.59±1.08△△ 
HDL-c, mmol/L 1.26 (1.19–1.34) 1.09 (0.96–1.27)△△△ 1.11 (0.93–1.29)△△ 1.12 (0.94–1.25)△△ 
Ucr, mmol/L 9.98 (7.72–15.94) 7.79 (5.64–12.20) 5.16 (3.49–8.78)△△△5.05 (3.56–7.878)△△△** 
sSIRT2, pg/mL 97.55 (74.50–124.95) 116.61 (72.45–170.40) 134.82 (87.36–190.41) 133.04 (79.28–192.55) 
eGFR, mL/min/1.73 m2 125.54 (105.21–143.94) 112.91 (97.03–139.92) 94.77 (59.66–134.08)△△ 50.19 (23.99–88.22)△△△***### 
UACR, mg/g 9.16 (4.19–12.92) 14.94 (10.35–19.67) 64.12 (38.93–124.14)△△△*** 1,385.89 (593.82–3,062.74)△△△***### 
UαCR, mg/g 1.56 (1.00–2.25) 6.18 (3.70–11.88)△△△ 15.43 (6.60–34.98)△△△48.92 (19.38–92.73)△△△***## 
UβCR, mg/g 0.05 (0.02–0.07) 0.14 (0.06–0.29)△△△ 0.30 (0.11–1.16)△△△ 1.84 (0.25–15.38)△△△*** 
URCR, mg/g 0.06 (0.03–0.12) 0.18 (0.10–0.35)△△ 0.63 (0.32–2.49)△△△*** 17.17 (3.26–27.39)△△△***## 
Ln (USCR), ng/g 2.64±0.52 2.63±0.58 2.96±0.88 3.19±0.88△△** 
RAAS blocker 14/58 21/52 27/53** 
hypoglycemic agents 9/58 22/52** 30/53*** 

Results are expressed as mean ± SD, medians, and interquartile range. “Ln” represents the natural logarithm.

NC, Normal control group; Normo, UACR mildly increased group; Micro, UACR moderately increased group; Macro, UACR moderately increased group. RAAS, renin-angiotensin-aldosterone system; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; Hb, hemoglobin; FPG, fasting plasma glucose; HbA1c, hemoglobin A1c; Scr, serum creatinine; BUN, blood urea nitrogen; sUA, serum uric acid; ALT, alanine transaminase; AST, aspartate aminotransferase; ALB, serum albumin; TC, total cholesterol; TG, triglyceride; LDL-c, low-density lipoprotein cholesterol; HDL-c, high-density lipoprotein cholesterol; Ucr, urinary creatinine; sSIRT2, serum sirtuin 2; eGFR, estimated glomerular filtration rate; UACR, urinary albumin/creatinine; UαCR, urinary α1-MG/creatinine; UβCR, urinary β2-MG/creatinine; URCR, urinary RBP/creatinine; USCR, urinary SIRT2/creatinine.

p < 0.05 (compared with NC group).

△△p < 0.01 (compared with NC group).

△△△p < 0.001 (compared with NC group).

*p < 0.05 (compared with Normo group).

**p < 0.01 (compared with Normo group).

***p < 0.001 (compared with Normo group).

#p < 0.05 (compared with Micro group).

##p < 0.01 (compared with Micro group).

###p < 0.001 (compared with Micro group).

USCR and Renal Injury Indexes Were Increased in Diabetes Groups

After urinary creatinine correction, USCR conformed to normal distribution. The USCR levels of NC, Normo, Micro, and Macro groups were 2.64 ± 0.52 ng/g, 2.63 ± 0.58 ng/g, 2.96 ± 0.88 ng/g, and 3.19 ± 0.88 ng/g, respectively. The comparison between groups demonstrated that the USCR level of the Macro group was highest than that of the NC and Normo groups, and the difference was statistically significant (p < 0.01). The concentrations of renal injury indexes UαCR, UβCR, and URCR were the lowest in NC group and gradually increased in Normo, Micro, and Macro groups. Moreover, UαCR and URCR concentrations were varied significantly different between diabetes groups (shown in Fig. 2). Meanwhile, the serum SIRT2 is also examined. The serum SIRT2 levels in NC, Normo, Micro, and Macro groups were 97.55 (74.50–124.95) pg/mL, 116.61 (72.45–170.40) pg/mL, 134.82 (87.36–190.41) pg/mL, and 133.04 (79.28–192.55) pg/mL, respectively. The comparative analysis among groups revealed that there was no significant difference in subgroups (p > 0.05) (shown in Table 1).

Fig. 2.

The levels of USCR, UαCR, UβCR, and URCR in control and T2DM patients stratified according to albuminuria status. a USCR. b UαCR. c UβCR. d URCR. Compared with NC group: △△p < 0.01, △△△p < 0.001; compared with Normo group: *p < 0.05, **p < 0.01, ***p < 0.001; compared with Micro group: ##p < 0.01. “Ln” represents the natural logarithm.

Fig. 2.

The levels of USCR, UαCR, UβCR, and URCR in control and T2DM patients stratified according to albuminuria status. a USCR. b UαCR. c UβCR. d URCR. Compared with NC group: △△p < 0.01, △△△p < 0.001; compared with Normo group: *p < 0.05, **p < 0.01, ***p < 0.001; compared with Micro group: ##p < 0.01. “Ln” represents the natural logarithm.

Close modal

USCR Is Related to UACR and Is an Independent Risk Factor for Proteinuria

T2DM patients were divided into two groups according to their USCR median level, and their clinical data and renal function indicators were compared. FPG, HbA1C, TG and UαCR level were normally distributed after the data conversion. The results indicated that UACR, UαCR, UβCR, and URCR levels were all higher in the group with USCR levels above the median. While the levels of Hb were declined in higher USCR group (p < 0.05) (shown in Table 2). We then used Spearman’s correlation analysis to investigate the association between USCR and UACR. As shown in Figure 3, USCR was positively correlated with UACR (r = 0.35, p < 0.001). Then, with the severity of albuminuria as the dependent variables (albuminuria normal to mildly increased, moderately increased, and severely increased), four models were established after adjusting for various confounding factors such as diabetes duration, blood pressure, renin-angiotensin-aldosterone system blocker, hypoglycemic agents, blood glucose, blood lipids, and renal function, and logistic ordered multiclassification regression model was performed. The results revealed that USCR was a risk factor for severity of albuminuria in T2DM patients (OR = 1.034, p < 0.001). After controlling confounding factors, USCR remained a risk factor for severity of albuminuria in T2DM patients (OR = 1.035, p = 0.001) (shown in Table 3).

Table 2.

Clinical data and kidney injury biomarkers divided by USCR median

Subject groupsLower than the USCR median array (N = 81)Higher than the USCR median array (N = 81)p value
Gender (M/F) 59/22 44/37 
Age, years 57.75±10.61 59.40±10.15 0.316 
BMI, kg/m2 25.72±3.80 24.50±3.88 0.044 
Duration, years 10.00 (4.00–15.00) 12.00 (7.50–19.00) 0.008 
SBP, mm Hg 137.56±19.88 142.15±21.33 0.158 
DBP, mm Hg 82.73±10.82 84.51±13.60 0.359 
Hb, g/L 135.00 (120.00–152.50) 125.00 (101.50–139.00) <0.001 
Ln (FPG), mmol/L 2.09±0.36 2.02±0.41 0.229 
Ln (HbA1C), % 2.14±0.24 2.07±0.26 0.119 
Scr, μmol/L 74.00 (59.00–108.50) 80.00 (59.50–143.50) 0.273 
BUN, mmol/L 7.33±2.91 8.64±5.19 0.051 
sUA, μmol/L 370.00 (309.00–412.00) 361.00 (286.00–450.00) 0.538 
ALT, U/L 21.00 (15.00–28.50) 17.00 (12.00–24.00) 0.012 
AST, U/L 19.00 (15.00–26.50) 18.00 (15.00–25.00) 0.286 
ALB, g/L 40.10 (37.40–42.80) 38.95 (34.60–42.68) 0.167 
TC, mmol/L 4.75 (3.76–5.48) 4.71 (3.94–5.79) 0.437 
Ln (TG), mmol/L 0.67±0.71 0.62±0.70 0.683 
LDL-c, mmol/L 2.38±0.85 2.45±0.99 0.661 
HDL-c, mmol/L 1.09 (0.94–1.23) 1.12 (0.95–1.28) 0.501 
eGFR, mL/min/1.73 m2 86.50±35.93 75.11±42.18 0.066 
Ln (UαCR), mg/g 2.33±1.10 3.20±1.24 <0.001 
UβCR, mg/g 0.16 (0.08–0.37) 0.56 (0.20–8.38) <0.001 
URCR, mg/g 0.35 (0.16–3.04) 0.97 (0.36–18.05) <0.001 
UACR, mg/g 34.97 (13.37–356.39) 137.32 (28.26–652.12) 0.003 
RAAS blocker 32/81 29/81 0.627 
hypoglycemic agents 30/81 31/81 0.871 
Subject groupsLower than the USCR median array (N = 81)Higher than the USCR median array (N = 81)p value
Gender (M/F) 59/22 44/37 
Age, years 57.75±10.61 59.40±10.15 0.316 
BMI, kg/m2 25.72±3.80 24.50±3.88 0.044 
Duration, years 10.00 (4.00–15.00) 12.00 (7.50–19.00) 0.008 
SBP, mm Hg 137.56±19.88 142.15±21.33 0.158 
DBP, mm Hg 82.73±10.82 84.51±13.60 0.359 
Hb, g/L 135.00 (120.00–152.50) 125.00 (101.50–139.00) <0.001 
Ln (FPG), mmol/L 2.09±0.36 2.02±0.41 0.229 
Ln (HbA1C), % 2.14±0.24 2.07±0.26 0.119 
Scr, μmol/L 74.00 (59.00–108.50) 80.00 (59.50–143.50) 0.273 
BUN, mmol/L 7.33±2.91 8.64±5.19 0.051 
sUA, μmol/L 370.00 (309.00–412.00) 361.00 (286.00–450.00) 0.538 
ALT, U/L 21.00 (15.00–28.50) 17.00 (12.00–24.00) 0.012 
AST, U/L 19.00 (15.00–26.50) 18.00 (15.00–25.00) 0.286 
ALB, g/L 40.10 (37.40–42.80) 38.95 (34.60–42.68) 0.167 
TC, mmol/L 4.75 (3.76–5.48) 4.71 (3.94–5.79) 0.437 
Ln (TG), mmol/L 0.67±0.71 0.62±0.70 0.683 
LDL-c, mmol/L 2.38±0.85 2.45±0.99 0.661 
HDL-c, mmol/L 1.09 (0.94–1.23) 1.12 (0.95–1.28) 0.501 
eGFR, mL/min/1.73 m2 86.50±35.93 75.11±42.18 0.066 
Ln (UαCR), mg/g 2.33±1.10 3.20±1.24 <0.001 
UβCR, mg/g 0.16 (0.08–0.37) 0.56 (0.20–8.38) <0.001 
URCR, mg/g 0.35 (0.16–3.04) 0.97 (0.36–18.05) <0.001 
UACR, mg/g 34.97 (13.37–356.39) 137.32 (28.26–652.12) 0.003 
RAAS blocker 32/81 29/81 0.627 
hypoglycemic agents 30/81 31/81 0.871 

“Ln” represents the natural logarithm.

RAAS, renin-angiotensin-aldosterone system.

Fig. 3.

USCR has significant correlation with renal function indicators.

Fig. 3.

USCR has significant correlation with renal function indicators.

Close modal
Table 3.

Logistic regression analysis of the influencing factors for albuminuria in T2DM patients

ModelOR (95% confidence interval)p value
Model 1 1.034 (1.017∼1.050) <0.001 
Model 2 1.037 (1.018∼1.057) <0.001 
Model 3 1.040 (1.020∼1.061) <0.001 
Model 4 1.035 (1.014∼1.056) 0.001 
ModelOR (95% confidence interval)p value
Model 1 1.034 (1.017∼1.050) <0.001 
Model 2 1.037 (1.018∼1.057) <0.001 
Model 3 1.040 (1.020∼1.061) <0.001 
Model 4 1.035 (1.014∼1.056) 0.001 

Model 1: before adjustment; model 2: after adjustment for duration of diabetes, blood pressure (SBP, DBP), RAAS blocker, hypoglycemic agents; model 3: after adjustment for duration of diabetes, blood pressure (SBP, DBP), RAAS blocker, hypoglycemic agents, blood glucose (FPG, HbA1c), blood lipid (TC, TG, LDL-c, HDL-c); model 4: after adjustment for duration of diabetes, blood pressure (SBP, DBP), RAAS blocker, hypoglycemic agents, blood glucose (FPG, HbA1c), blood lipids (TC, TG, LDL-c, HDL-c), and renal function (Scr, BUN, sUA).

RAAS, renin-angiotensin-aldosterone system.

Association of UACR with eGFR and Kidney Injury Markers

Spearman correlation analysis was employed to examine the relationship between USCR and eGFR along with tubular injury indicators. USCR demonstrated a negative correlation with eGFR (r = −0.21, p = 0.008). Additionally, USCR exhibited positive associations with renal tubular biomarkers, including UαCR (r = 0.40, p < 0.001), UβCR (r = 0.45, p < 0.001), and URCR (r = 0.32, p < 0.001). USCR was also positively correlated with Scr (r = 0.13, p < 0.01) and BUN (r = 0.11, p < 0.01) (shown in Fig. 3).

USCR Has High Sensitivity and Specificity in Differentiating Renal Damage

ROC curve analysis was conducted to evaluate the effectiveness of the urine biomarkers in distinguishing glomerular and tubular damage. ROC curve of USCR in T2DM patients was performed with UACR >30 mg/g as the cutoff point. The AUC was 0.675 with a 95% confidence interval of 0.579–0.746. The optimal critical value of USCR was 26.451 ng/g, with a sensitivity and specificity of 47.62% and 87.93%, respectively. Similarly, when eGFR <60 mL/min/1.73 m2 was taken as the cutoff point, the AUC was 0.597. The optimal critical value was 13.367 ng/g, and the sensitivity and specificity were 77.55% and 42.98%, respectively. The ROC curves of USCR in T2DM patients were also used to compare the areas beneath them with UαCR >25.114 mg/g, UβCR >0.603 mg/g, and URCR >1.407 mg/g taken as the cutoff points. The AUC was 0.700, 0.730, and 0.597, respectively. The results confirmed that USCR in the distinguishment of renal damage had high sensitivity or specificity (shown in Table 4; Fig. 4).

Table 4.

ROC analysis of USCR with kidney injury biomarkers

BiomarkerCutoffSensitivity, %Specificity, %AUC (95% confidence interval)Valuep value
eGFR <60 mL/min/1.73 m2 77.55 42.98 0.597 (0.517, 0.673) 13.367 0.0489 
UACR >30 mg/g 47.62 87.93 0.675 (0.597, 0.746) 26.451 <0.0001 
UαCR >25.114 mg/g 65.00 70.87 0.700 (0.623, 0.769) 21.100 <0.0001 
UβCR >0.603 mg/g 83.93 57.94 0.730 (0.655, 0.797) 15.741 <0.0001 
URCR >1.407 mg/g 57.14 66.00 0.597 (0.518, 0.673) 21.092 0.0449 
BiomarkerCutoffSensitivity, %Specificity, %AUC (95% confidence interval)Valuep value
eGFR <60 mL/min/1.73 m2 77.55 42.98 0.597 (0.517, 0.673) 13.367 0.0489 
UACR >30 mg/g 47.62 87.93 0.675 (0.597, 0.746) 26.451 <0.0001 
UαCR >25.114 mg/g 65.00 70.87 0.700 (0.623, 0.769) 21.100 <0.0001 
UβCR >0.603 mg/g 83.93 57.94 0.730 (0.655, 0.797) 15.741 <0.0001 
URCR >1.407 mg/g 57.14 66.00 0.597 (0.518, 0.673) 21.092 0.0449 
Fig. 4.

The correlation between USCR and renal injury markers. a USCR versus UαCR. b USCR versus UβCR. c USCR versus URCR. d USCR versus UACR. e USCR versus eGFR. *p < 0.05, **p < 0.01, ***p < 0.001.

Fig. 4.

The correlation between USCR and renal injury markers. a USCR versus UαCR. b USCR versus UβCR. c USCR versus URCR. d USCR versus UACR. e USCR versus eGFR. *p < 0.05, **p < 0.01, ***p < 0.001.

Close modal

SIRT2 Protein Levels Were Increased in Renal Tissues under Diabetic Conditions

Subsequently, we verified the expression of SIRT2 in animal models. Immunohistochemical staining showed that KIM-1 and SIRT2 expression in db/db mice were significantly higher than those in db/m mice (shown in Fig. 5a–f). Western blot and RT-qPCR analysis further confirmed that SIRT2 expression increased concurrently with renal injury. The results showed elevated levels of KIM-1 mRNA and SIRT2 protein, while the mRNA levels of megalin, WT1, and nephrin decreased (shown in Fig. 5g–k). Furthermore, a concentration-dependent elevation in SIRT2 expression was noted in proximal renal tubules treated with high glucose and palmitic acid (abbreviated as HG + PA) (shown in Fig. 5l). These results indicate that during the progression of diabetes, renal tissue injury correlates with an increase in SIRT2 levels, suggesting its potential as a novel injury index for diabetic nephropathy.

Fig. 5.

SIRT2 expression in renal tissue and HK2 cells. a, b Hematoxylin-eosin staining and statistical analysis of mouse kidney tissue. The arrows represent the injured renal tubules. c–f Immunohistochemistry staining and statistical analysis of KIM-1 and SIRT2 in mouse kidney tissue. g KIM-1 mRNA expression in renal tissue; h–j mRNA expression of WT1, nephrin, and megalin in renal tissue. k, l SIRT2 expression in renal tissue and HK2 cells. The data are presented as the mean ± SD; *p < 0.05, **p < 0.01.

Fig. 5.

SIRT2 expression in renal tissue and HK2 cells. a, b Hematoxylin-eosin staining and statistical analysis of mouse kidney tissue. The arrows represent the injured renal tubules. c–f Immunohistochemistry staining and statistical analysis of KIM-1 and SIRT2 in mouse kidney tissue. g KIM-1 mRNA expression in renal tissue; h–j mRNA expression of WT1, nephrin, and megalin in renal tissue. k, l SIRT2 expression in renal tissue and HK2 cells. The data are presented as the mean ± SD; *p < 0.05, **p < 0.01.

Close modal

SIRT2 Promotes the Expression of ProInflammatory Cytokines in Renal Tubular Epithelial Cells Induced by HG + PA

Diabetic nephropathy is considered to be a state of microinflammation. Persistent inflammation can lead to damage to the glomeruli and renal tubules, promoting increased proteinuria and further decline in renal function [25]. Therefore, we further analyzed the regulatory role of SIRT2 in the inflammatory response induced by HG + PA. We constructed SIRT2 overexpression plasmids and interference fragments and assessed the expression of proinflammatory factors TNF-a and IL-6 in these respective models. Figure 6a, d represent interference and transfection efficiency detection, respectively. In Figure 6b, c, we observed a significant increase in TNF-a and IL-6 mRNA levels in the HG + PA groups compared to the control group, and interference with SIRT2 alleviated this increase, while overexpression of SIRT2 further enhanced the expression of TNF-a and IL-6 mRNA induced by HG + PA (shown in Fig. 6e, f). These results suggest that SIRT2 exacerbates the expression of proinflammatory cytokines induced by HG + PA.

Fig. 6.

SIRT2 promotes the expression of proinflammatory cytokines. a, d SIRT2 expression in HK2 cells. b, e TNF-α expression in HK2 cells. c, f IL-6 expression in HK2 cells. The data are presented as the mean ± SD; *p < 0.05, **p < 0.01.

Fig. 6.

SIRT2 promotes the expression of proinflammatory cytokines. a, d SIRT2 expression in HK2 cells. b, e TNF-α expression in HK2 cells. c, f IL-6 expression in HK2 cells. The data are presented as the mean ± SD; *p < 0.05, **p < 0.01.

Close modal

SIRT2 Regulates MAPK Signaling in Renal Tubular Epithelial Cells under HG + PA Conditions

It has been demonstrated in cisplatin and LPS-induced AKI that SIRT2 regulates the expression of inflammatory factors through the MAPK pathway [18, 20]. Therefore, we also assessed the impact of SIRT2 on the MAPK signaling pathway induced by HG + PA. The results showed that compared to the control group, the phosphorylation levels of p38 MAPK and JNK increased in the HG + PA groups. Meanwhile, transfection of SIRT2-siRNA could inhibit the increase of phosphorylation of p38 MAPK and JNK induced by HG + PA (shown in Fig. 7a, c, d), while the overexpression of SIRT2 further increased the phosphorylation of p38 MAPK and JNK induced by HG + PA (shown in Fig. 7b, f, g). These results suggest that SIRT2 can promote inflammation by regulating the MAPK pathway in renal tubular epithelial cells under HG + PA conditions.

Fig. 7.

SIRT2 regulates phosphorylation of JNK and P38 in the MAPK pathway. a Expression of p-JNK, p-p38, and SIRT2 in SIRT2 interference model. b Expression of p-JNK, p-p38, and SIRT2 in SIRT2 overexpression model. c, f Semiquantitative statistical analysis of p-p38. d, g Semiquantitative statistical analysis of p-JNK. e, h Semiquantitative statistical analysis of SIRT2. The data are presented as the mean ± SD; *p < 0.05, **p < 0.01.

Fig. 7.

SIRT2 regulates phosphorylation of JNK and P38 in the MAPK pathway. a Expression of p-JNK, p-p38, and SIRT2 in SIRT2 interference model. b Expression of p-JNK, p-p38, and SIRT2 in SIRT2 overexpression model. c, f Semiquantitative statistical analysis of p-p38. d, g Semiquantitative statistical analysis of p-JNK. e, h Semiquantitative statistical analysis of SIRT2. The data are presented as the mean ± SD; *p < 0.05, **p < 0.01.

Close modal

In this research, the focus was on evaluating the potential of SIRT2 as biomarkers indicative of renal damage in individuals with T2DM. USCR demonstrated strong correlations with biomarkers associated with glomerular and tubular injuries in T2DM patients. Additionally, baseline experiments confirm that under diabetic conditions, SIRT2 can promote inflammation and contribute to kidney damage by regulating the MAPK pathway. Therefore, SIRT2 holds promise as a valuable biomarker for assessing renal damage in individuals with T2DM.

In current research, glomerular injury is recognized as a prominent factor in DKD, evident by reduced GFR or elevated UACR. Microalbuminuria, an early clinical stage of DKD, also serves as a primary indicator of glomerular injury [26]. This study also uniquely establishes the association of USCR with glomerular damage. SIRT2-mRNA encodes two protein isotypes, and when the glomerular filtration membrane is compromised, more SIRT2 protein, being of low molecular weight, can pass through [27]. This research, the first to detect SIRT2 expression in serum and urine of T2DM patients, observed higher USCR levels in severely increased albuminuria compared to normal to mildly increased albuminuria and control groups. USCR in T2DM patients correlated positively with UACR and negatively with estimated GFR (eGFR), indicating its association with glomerular injury. ROC curve analysis using eGFR and UACR cutoffs demonstrated that USCR exhibits high specificity in distinguishing glomerular injury.

The concept of “diabetic renal tubular disease” suggests that tubular injury is more severe in T2DM than glomerular injury [28]. Early urinary markers of tubular injury, such as α1-MG, β2-MG, and RBP, serve as sensitive indicators occurring before microalbuminuria [29, 30]. Our investigation revealed a positive correlation between USCR and tubular injury biomarkers (UαCR, UβCR, URCR), with above-median USCR levels associated with higher expressions of these biomarkers. ROC curve analysis using UαCR, UβCR, and URCR cutoffs indicated that USCR effectively discriminates tubular injury with high sensitivity. This suggests that USCR may serve as a predictor of renal tubule injury, possibly linked to SIRT2 involvement in various renal processes.

Our study found upregulated SIRT2 expression in kidney tissues of type 2 diabetic patients and diabetic mice, accompanied by an increase in KIM-1, an indicator of kidney damage. Moreover, in human renal tubular epithelial cells exposed to high glucose and palmitic acid, there is an elevation in SIRT2 expression. He and his team also observed increased SIRT2 levels in the renal tissue of patients with renal tubulointerstitial fibrosis and in a mouse model of unilateral ureteral obstruction [31]. It has been reported that DNA sequence variants may increase SIRT2 gene promoter activity and SIRT2 levels, contributing to T2D development as a risk factor [32]. Additionally, in a mouse model of acute renal injury, the downregulation of SIRT2 not only reduced neutrophil and macrophage infiltration, lipopolysaccharide-induced acute renal tubular injury, and renal function decline but also ameliorated cisplatin-induced tubular apoptosis, necrosis, and inflammation [18, 20]. Selective SIRT1/2 inhibitor improves renal fibrosis in diabetic nephropathy mice induced by high fat diet through antioxidant and anti-inflammatory pathways [33]. Our results also suggest that SIRT2 promotes HG + PA-induced inflammation by regulating the phosphorylation of JNK and p38 MAPK in renal tubular epithelial cells. Therefore, our research findings, along with previous literature, collectively indicate a complex association between SIRT2 expression and renal health.

The activation of the MAPK pathway is associated with various cellular processes, including apoptosis, inflammation, and oxidative stress [34‒36], all of which are critical factors in renal dysfunction in conditions such as diabetic nephropathy [37]. Under diabetic conditions, SIRT2 may influence not only the inflammatory response in this pathway but also apoptosis and oxidative stress reactions, which may be pivotal in the progression of kidney diseases. Further elucidating the precise molecular mechanisms of SIRT2-mediated regulation of the MAPK pathway could provide new therapeutic targets for alleviating metabolic disorder-related kidney damage. Additionally, research has shown that in lipopolysaccharide and cisplatin-induced acute kidney injury, SIRT2 can affect MAPK signaling through deacetylation of MPK-1 [18, 20]. We speculate that in the diabetic state, SIRT2 may also influence MAPK signal activation through deacetylation of MPK-1. This will be our focus for future research.

In conclusion, urinary SIRT2 is not only an effective indicator of glomerular and tubular injury in T2DM patients but also an important risk factor for severity of albuminuria in T2DM patients. However, our research has certain limitations. The present study was a single-center cross-sectional clinical study, which provided evidence of phenotype but could not prove causality. The sample size was relatively small due to regional and ethnic limitations. Due to poor follow-up compliance, we did not evaluate for changes in serum and urine SIRT2 levels over time. Therefore, further large-sample cohort studies will provide accurate evidence to clarify SIRT2 role in the renal injury of T2DM, and further basic research will offer a sufficient basis for exploring relevant mechanisms.

The study design was approved by the Ethics Committee of Third Xiangya Hospital, Central South University (Ethics code: 2017-S167). All participants obtained written informed consent. The animal experiments were approved by the Ethics Review Committee of Central South University (Ethics code: CSU-2022-0706).

The authors have no conflict of interest to declare.

This work was supported by the National Natural Science Foundation of China (82070759) and the Natural Science Foundation of Hunan Province (2021JJ31032).

Y.D., D.L., and B.Y. designed the study. Y.D., L.X., and Y.L. collected and analyzed the data. D.L. completed the basic experimental. J.P., X.L., and S.W. collected the data. Y.D. and D.L. drafted the manuscript. Y. L and L.X. modified and verified the manuscript. B.Y. completed critical review of the article.

Additional Information

Yali Dai and Dan Li contributed equally to this work.

All data generated or analyzed during this study are included in this article. Further inquiries can be directed to the corresponding author. A preprint version of this article is available on Research Square [38].

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. 04 April 2023, PREPRINT (Version 1) Available from: Research Square.