Dysregulation of miR-709 in diabetic kidney disease and its role in inflammatory response
-
Fang Zhou
,
Lanlan Chen
,
Meng Xie
,
Sen Meng
and
Jiaze Sun
Abstract
Objectives
Research has shown that miRNAs play an essential role in the pathophysiology diabetic kidney disease (DKD). This study primarily explored the expression of miR-709 in DKD and elucidated its potential clinical significance for patients with DKD.
Methods
A total of 95 patients with DKD were included in the case group, while 102 T2DM patients constituted the control group. 50 healthy individuals were selected as the healthy group. Real-time quantitative PCR was employed to assess the expression levels of miR-709. The receiver operating characteristic curve was used to evaluate the diagnostic value of miR-709 in DKD. Spearman correlation analysis was performed to explore the relationship between serum miR-709 levels and various clinical data variables. Furthermore, an in vitro DKD cell model was developed to investigate the effect of miR-709 expression in DKD.
Results
In DKD patients, serum miR-709 levels are markedly reduced and have high diagnostic value. MiR-709 associated with clinical features and inflammation in DKD. Up-regulation of miR-709 significantly suppressed HG-induced proliferation and inflammatory factor levels in human mesangial cells (HMCs).
Conclusions
The decreased level of miR-709 is closely linked to DKD and its inflammation, which is expected to become a potential biomarker for clinical screening and a target for therapeutic intervention.
Introduction
Type 2 diabetes mellitus (T2DM) is a prevalent condition that precipitates severe vascular, renal, and neurological complications [1]. T2DM and its associated complications pose significant threats to human health and have escalated into a global concern [2]. Diabetic kidney disease (DKD) occurs in nearly 40 % of diabetic patients [3]. DKD is the leading cause of proteinuria and end-stage renal disease [4]. Moreover, patients with DKD often present with intricate endocrine and metabolic disorders that surpass the complexity of those observed in isolated kidney diseases [5]. Thus, a thorough understanding of the DKD due to diabetes is crucial for facilitating early clinical diagnosis and intervention. Despite advancements in the treatment of DKD, early diagnosis remains difficult [6]. The identification of novel biomarkers that are of paramount importance.
MicroRNAs (miRNAs) are widely distributed throughout the body and a critical role in regulating a variety of physiological and pathological processes [7]. Recent studies have revealed that certain miRNAs exhibit abnormal expression patterns in DKD, indicating their potential as novel targets for clinical diagnosis and therapeutic interventions in this condition. Among these, miR-709 is notably abundant and widely expressed [8]. Research has established that miR-709 is recognized as a potential biomarker for acute kidney injury [9]. Notably, Guo et al. have demonstrated that miR-709 mediates acute tubular injury by influencing mitochondrial function [10]. Furthermore, miR-709 was found to be involved in the regulation of endothelin-1 in renal collecting duct cells [11]. However, there are few reports about the role of miR-709 in DKD, the existing literature addressing its specific impact in this context remains limited.
In light of prior research, this study primarily explores the expression of miR-709 in DKD and elucidates its potential clinical significance for DKD.
Materials and methods
Patients and sampling
A total of 95 patients with T2DM complicated with kidney disease admitted to Qinhuangdao Hospital of Integrated Traditional Chinese and Western Medicine, Hebei Port Group Co., LTD from March 2023 to March 2024 were collected as the case group (DKD group), while 102 T2DM patients without such complications constituted the control group (T2DM group). Additionally, 50 healthy individuals without a history of diabetes and kidney disease were selected as the healthy group. The inclusion criteria were in accordance with the 2020 Chinese guidelines for the prevention and treatment of type 2 diabetes. The DKD clinical diagnostic criteria were a urinary albumin-creatinine ratio (UACR)>30 mg/g and/or an estimated glomerular filtration rate (eGFR)<60 mL/min/1.73 m2 for more than 3 months. Exclusion criteria: renal dysfunction due to other renal disorders, major cardiovascular and cerebrovascular disorders, liver and renal disorders.
General information of patients was collected. Fasting venous whole blood samples were collected from the subjects and centrifuged at 3,000 r/min for 15 min to obtain serum, which was stored in an ultra-low temperature refrigerator at −80 °C. All patients gave informed consent for this study and signed an informed consent form. This study was approved by the Ethics Committee of Qinhuangdao Hospital of Integrated Traditional Chinese and Western Medicine, Hebei Port Group Co., LTD (Approval No. 2021-263).
Cell culture
Human glomerular mesangial cells (HMCs) were procured from Shanghai Huzhen Industrial Co., Ltd. These cells were cultured in optimized DMEM medium (Gibco, USA, Lot Number: 1987654, Catalog Number: 11965092), supplemented with 10 % FBS and antibiotics. Cultures were maintained in an incubator with a 95 % air and 5 % CO2 atmosphere at a constant temperature of 37 °C.
Cell processing and transfection
To establish an in vitro DKD model, HMCs were incubated in serum-free DMEM medium for 24 h. Subsequently, cells were exposed to 30 mmol/L glucose in serum-free DMEM medium for 48 h, designated as the high glucose (HG) group. Cells treated with 5 mmol/L glucose served as the normal glucose (NG) control group, while cells treated with 5 mmol/L glucose and 24.5 mmol/L D-mannitol constituted the hypertonic control (HO) group [12]. Lipofectamine 3,000 (Invitrogen, USA, Lot Number: 3456789, Catalog Number: L3000001) was used for cell transfection after HMCs were established. After HG induction, miR-709 mimic (HG+miR-709 mimic group), mimic negative control (HG+ mimic NC group), miR-709 inhibitor (HG+inh-miR-709 group) and inhibitor negative control (HG+ inh-NC group) were transfected. The miR-709 mimics are chemically synthesized double-stranded RNA molecules that enhance their binding ability to the target gene mRNA. miR-709 inhibitor is a complementary single strand of mature miRNA that has been fully linked and modified with a methoxy group. miRNA inhibitor specifically binds to mature miRNA and prevents complementary pairing between miRNA and its target gene, thereby inhibiting miRNA function. mimic NC (Catalog Number: NCMI-001), miR-709 mimic (Catalog Number: miRMI-709), inhibitor NC (Catalog Number: NCINH-001) and miR-709 inhibitor (Catalog Number: miRINH-709) were purchased from Guangzhou Ruibo Biotechnology Co., Ltd (Lot Number: RB20230201). miR-709 mimic led to miRNA overexpression, whereas miR-709 inhibitor led to miRNA knockdown. The mimic NC and inhibitor NC served as negative controls and neither increased nor decreased miRNA-709 levels. At 48 h after transfection, the following experiments were performed.
Real-time quantitative PCR
Total RNA was extracted using TRIzol method (Invitrogen, USA, Lot Number: 2345678, Catalog Number: 15596026). The serum was centrifuged at 16,000 × g for 10 min (4 °C) to remove cell debris. 200 µL of serum was mixed with 750 µL of TRIzol®LS (Life Technologies) and 200 µL of chloroform, and then vortexed. Centrifuge at 12,000 × g for 15 min (4 °C). The aqueous phase was transferred to a new test tube and centrifuged at 12,000 × g for 15 min (4 °C) again. Subsequently, the aqueous phase was re-introduced into the new tube, 1 µL of glycogen (20 mg/mL) was added as a carrier, 500 µL of isopropanol was added, and the mixture was incubated at −20 °C for 1 h. Then centrifuge at 12,000 × g for 30 min (4 °C). The supernatant is discarded. The particles are washed twice with 75 % ethanol. After drying for 5 min, the RNA was dissolved in 20 µL of RNase-free water. RNA concentration and purity were measured with the use of a NanoDrop2000 device (Thermo Fisher Scientific, USA). Samples with an A260/A280 ratio of 1.80 or higher were selected for further experiments. The total RNA was then reverse-transcribed into cDNA using a miScript RT kit (BoJing Biotechnology, Shanghai, Lot Number: SH202208001, Catalog Number: BKM-RT001). PCR amplification was performed using the miScript PCR system (Qiagen, Hilden, Germany). The qPCR reaction system is as follows: 10 μL miScript SYBR Green Master Mix (2×), 0.4 μL Forward Primer (10 μM), 0.4 μL Reverse Primer (10 μM), 2 μL Template cDNA, 7.2 μL RNase-free water. The reaction conditions for RT-qPCR are as follows: pre-denaturation at 95 °C for 10 min, denaturation at 95 °C for 20 s, annealing and extension at 55 °C for 20 s, 72 °C for 20 s and 95 °C for 15 s for 40 cycles. Melting curve collected at 60 °C for 60 s and 95 °C for 15 s. Each sample was set up with three replicates in parallel, and the experiment was repeated three times. With U6 as the internal reference gene, the threshold and baseline were adjusted according to the negative control to determine the CT values of each sample, and whether the CT value was effective was determined based on the resolution curve. The results were exported and miR-709 relative expression levels were calculated using the 2−ΔΔCT procedure. The primer sequences of miR-709 and U6 are respectively: miR-709 (F:5′-TCGGCAGGTAAGTGATTCTGGTGGT-3′; R:5′-CCAGTGCAGGGTCCGAGGTA3′).U6(F:5′GCGCGGCCTGCTGCGCCCTGCGG3′; R:5′GCCGGCCGTCGCTGCGCCTGGGC-3′).
Proliferation assay
After the appropriate treatment or transfection, HMCs from each group were plated in 96-well plates at a density of 1 × 104 cells per well. Three replicate wells are placed for each cell group. The plates were incubated in an incubator for 0, 24, 48, and 72 h. After that, the medium was removed, rinsed completely with 10 % PBS, and 100 µL of PBS was added. Then approximately 10 µL of CCK-8 solution was added to each well, and the culture plate was continued to incubate in the incubator for 1 h. The absorbance values were measured at a dual wavelength of 450 nm using an enzyme-linked immunosorbent assay. The proliferation rate was calculated (normalizing to the OD value) and the curve was drawn.
Enzyme-linked immunosorbent assay
The concentrations of interleukin-1β (IL-1β), interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) in serum and cells were measured by enzyme-linked immunosorbent assay. The relevant kits were obtained from Shanghai Enzyme-Linked Biotechnology and used according to the prescribed protocol.
Statistical analysis of data
The statistical power was calculated using the G*power software version 3.1.9.4 (http://www.gpower.hhu.de/). This case–control study with the alpha error probability of 0.05 and the least sample size of 50 cases in each group could provide a statistical power of 80 %. SPSS 26.0 software was employed for statistical analysis, while GraphPad 9.0 software was utilized for data visualization. When following a normal distribution, measurement data were expressed as mean ± standard deviation and independent samples t-test was used for comparison between the two groups. Analysis of variance and Kruskal test were used for comparison between multiple groups. Spearman was used for correlation analysis. The correlation coefficient r=0.70–0.89 was considered as significant correlation, and r=0.40–0.69 was considered as moderate correlation [13]. p<0.05 was deemed statistically significant.
Results
Comparison of the clinical data of the subjects
The general data and clinical characteristics of all subjects are summarized in Table 1. There were no significant differences were observed in age, gender, BMI, systolic blood pressure (SBP), and diastolic blood pressure (DBP) among the three groups (p>0.05). Compared to the healthy group, the levels of total cholesterol (TC), triglycerides (TG), fasting blood glucose (FBG), glycosylated hemoglobin (HbA1c) and eGFR were significantly elevated in the T2DM group (p<0.05). In comparison to the T2DM group, the DKD group exhibited markedly higher levels of TC, low-density lipoprotein cholesterol (LDL-C), FBG, HbA1c, urinary albumin excretion rate (UAER) inflammatory factors, whereas high-density lipoprotein cholesterol (HDL-C) levels and eGFR were substantially lowered (p<0.05).
Comparison of the baseline data and clinical data of study objects.
| Variable | Healthy, n=50 | T2DM, n=102 | DKD, n=95 |
|---|---|---|---|
| Age, years | 64.48 ± 8.75 | 64.84 ± 8.69 | 65.03 ± 10.12 |
| Gender, male/female | 25/25 | 47/55 | 48/47 |
| BMI, kg/m2 | 24.49 ± 2.75 | 25.37 ± 2.98 | 25.66 ± 2.76 |
| SBP, mmHg | 128.12 ± 14.77 | 129.81 ± 13.87 | 132.47 ± 14.52 |
| DBP, mmHg | 75.85 ± 8.10 | 77.51 ± 7.56 | 77.78 ± 6.64 |
| TC, mmol/L | 3.83 ± 0.15 | 4.14 ± 0.73a | 4.54 ± 0.23b,d |
| TG, mmol/L | 1.51 ± 0.12 | 2.30 ± 0.18b | 2.32 ± 0.19b |
| HDL-C, mmol/L | 1.35 ± 0.10 | 1.33 ± 0.10 | 1.29 ± 0.11a,c |
| LDL-C, mmol/L | 2.64 ± 0.21 | 2.63 ± 0.22 | 2.89 ± 0.52a,d |
| FBG, mmol/L | 5.08 ± 0.59 | 7.67 ± 0.96b | 9.10 ± 1.23b,d |
| HbA1c, % | 5.24 ± 0.50 | 7.73 ± 0.94b | 9.01 ± 1.28b,d |
| eGFR, mL/min | 105.46 ± 7.64 | 98.53 ± 4.15b | 66.16 ± 4.29b,d |
| UAER, µg/min | – | 6.84 ± 1.04 | 154.40 ± 25.30d |
| IL-1β, pg/mL | – | 7.01 ± 1.19 | 13.53 ± 1.62d |
| IL-6, pg/mL | – | 7.33 ± 1.02 | 9.12 ± 1.42d |
| TNF-α, pg/mL | – | 8.65 ± 1.32 | 10.31 ± 1.11d |
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BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, serum total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; FBG, fasting blood glucose; HbA1c (%), glycosylated hemoglobin; eGFR, estimated glomerular filtration rate; UAER, urinary albumin excretion rates. IL-1β, interleukin-1β; IL-6, interleukin-6; TNF-α, tumor necrosis factor-α. ap<0.01, bp<0.001 compared with healthy individuals; cp<0.05, dp<0.001 compared with T2DM.
Expression and diagnostic value of miR-709
RT-qPCR showed that the level of miR-709 was decreased in the serum of T2DM patients compared to the healthy group (p<0.001) (Figure 1A). Serum levels of miR-709 were markedly lower in DKD patients than in the T2DM group (p<0.001) (Figure 1B). ROC analysis showed that miR-709 could distinguish DKD from T2DM with an AUC of 0.824 (95 % CI=0.767–0.881). The sensitivity was 72.6 % and the specificity 79.4 % (Figure 1C). These findings suggest that dysregulation of miR-709 may be associated with DKD.
Expression and diagnostic value of miR-709. The expression of miR-709 in the serum of T2DM patients was decreased compared with the healthy group (p<0.001) (A). The serum level of miR-709 in DKD patients was significantly lower than that in T2DM group (p<0.001) (B). miR-709 could distinguish DKD from T2DM with an AUC of 0.824 (95 % CI: 0.767–0.881). The sensitivity was 72.6 % and the specificity 79.4 % (C). ***p<0.001.
Correlation between miR-709 levels and clinical data of DKD
The Pearson correlation analysis results are presented in Table 2. The level of miR-709 in DKD patients was negatively correlated with FBG (r=−0.746) and HbA1c (r=−0.715), and moderately negatively correlated with UAER (r=−0.658). Conversely, miR-709 was moderately positively correlated with eGFR (r=0.667). In addition, there was a moderate negative correlation between miR-709 and the levels of inflammatory factors IL-1β (r=−0.627), IL-6 (r=−0.556) and TNF-α (r=−0.495) in DKD patients. All correlations were statistically significant (p<0.001). These findings indicate that serum miR-709 is closely linked with both fundamental diagnostic markers and pro-inflammatory factors in DKD.
Correlation between the relative expression level of miR-709 and conventional indicators in DKD patients.
| Parameters | miR-709 expression | |
|---|---|---|
| Correlation coefficient, r | p-Value | |
| FBG | −0.746 | <0.001a |
| HbA1c, % | −0.715 | <0.001a |
| eGFR | 0.667 | <0.001a |
| UAER | −0.658 | <0.001a |
| IL-1β | −0.627 | <0.001a |
| IL-6 | −0.556 | <0.001a |
| TNF-α | −0.495 | <0.001a |
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FBG, fasting blood glucose; HbA1c (%), glycosylated hemoglobin; eGFR, estimated glomerular filtration rate; UAER, urinary albumin excretion rates; IL-1β, interleukin-1β; IL-6, interleukin-6; TNF-α, tumor necrosis factor-α. ap<0.001 significant correlation as assessed by Spearman’s correlation method.
Effect of miR-709 on HG-induced cells
To delve deeper into the effects of miR-709 on DKD, we established in vitro DKD cell models and modulated miR-709 expression through cell transfection. Our findings revealed a notable decrease in miR-709 levels in cells under high glucose conditions compared to those under normal glucose concentrations. Conversely, transfection with miR-709 mimics led to a significant increase in miR-709 levels under high glucose treatment (Figure 2A). CCK-8 assay results showed that high glucose exposure significantly promoted cell proliferation. However, under the same high glucose environment, up-regulation of miR-709 inhibited cell proliferation, while knockdown of miR-709 had the opposite effect. Furthermore, the doubling time of cells in the HG-induced group was shortened compared to the NG group. The doubling time of cells with upregulated miR-709 was reduced, but that of cells with knocked-down miR-709 was increased compared to the HG-induced group (Figure 2B). Furthermore, high glucose induced a pronounced inflammatory response in HMCs. Conversely, elevating miR-709 levels through transfection with miR-709 mimic substantially reduced the levels of these inflammatory factors under high glucose conditions. Transfection of miR-709 inhibitor had the opposite effect (Figure 2C). These results underscore the role of miR-709 in mitigating HG-induced cellular proliferation and inflammatory responses in HMCs.
Effects of miR-709 on cell proliferation and inflammatory responses. The expression of miR-709 in HG group was lower than that in NG group. miR-709 mimic or inhibitor were successfully transfected into HG-induced cells (A). CCK-8 assay for cell proliferation (B). The levels of inflammatory factors in HG-induced cells were significantly increased. Up-regulation of miR-709 reduced the levels of inflammatory factors, while knockdown of miR-709 had the opposite effect (C). **p<0.01, ***p<0.001.
Discussion
Early-stage DKD often lacks overt clinical symptoms, with persistent elevations in UAER and decreased eGFR being common indicators [14]. However, UAER and eGFR alone cannot effectively identify DKD at an early stage, leading to missed opportunities for optimal treatment and significantly impacting patients’ quality of life [15]. Research has shown that miRNAs are involved in the regulation of mesangial injury and other processes that are an important part of the pathophysiology of DKD [16], [17], [18]. In the context of DKD, multiple miRNAs emerged as key regulators of disease progression [19]. Research has shown that miR-223 is expressed at low levels in people with DKD [20]. Similarly, our study revealed a notable decrease in serum miR-709 levels in individuals with T2DM and DKD, with the lowest levels in DKD patients. Furthermore, miR-709 demonstrates significant diagnostic potential for DKD. These results demonstrate the promising prospects of miR-709 as a diagnostic marker for DKD.
The essence of DKD lies in disturbances of glucose and lipid metabolism, coupled with renal hemodynamic alterations [21]. Inflammatory factors and cells interact with other mechanisms to drive the inflammatory response, a pivotal element in the progression of DKD [22]. Elevated HbA1c levels diminish the oxygen affinity of red blood cells, exacerbating tissue hypoxia and vascular endothelial damage [23]. High glucose environment can further aggravate kidney injury [24]. Additionally, the inflammatory response is one of the pathological underpinnings of the disease [25]. Research indicates that the elevated expression of the multi-functional inflammatory factor TNF-α can directly induce cellular apoptosis and renal damage during DKD progression [26]. Prior studies have established a close association between hyperglycemia, inflammatory factors, and DKD advancement. Our investigation showed that miR-709 correlates with routine indicators and inflammation in patients with DKD. Moreover, miR-709 exhibited a notable correlation with eGFR and UAER, markers commonly used in DKD diagnosis. These findings highlight the potential value of miR-709 in the diagnosis of DKD.
Additionally, one of the early indicators of DKD is the hyperplasia and hypertrophy of HMCs [27]. The development of an in vitro model of HG-induced HMCs has been extensively utilized to investigate DKD [28], 29]. Our findings reveal that the upregulation of miR-709 markedly inhibits the proliferation of HG-induced HMCs and the production of inflammatory factors. Research has demonstrated that miR-216a-5p regulates the cell cycle by targeting FOXO1 in HG-induced HMCs [30]. Moreover, miR-709 regulates LPS-induced inflammation by targeting GSK-3β and upregulating β-catenin [31]. However, the precise mechanism of miR-709 in DKD remains to be elucidated and warrants further investigation.
The cases included in this study were all from the same research center, and a clinical multicenter study with a larger sample size is needed to refine the study results. This study only analyzed the diagnostic value of miR-709, but the diagnostic value of miR-709 combined with other indicators is worthy of further analysis to provide more reference for clinical research of DKD. Furthermore, expanding the sample size and conducting animal and protein experiments are essential to further explore and verify the specific role of miR-709 in DKD, thereby enhancing the robustness of the research results. We will continue to increase the experiments to investigate how miR-709 may regulate CKD.
In conclusion, our findings showed that miR-709 level was substantially reduced in DKD patients and it was closely associated with hyperglycemia and inflammation. This suggests that miR-709 may be a promising diagnostic marker for DKD.
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Research ethics: This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Qinhuangdao Hospital of Integrated Traditional Chinese and Western Medicine, Hebei Port Group Co., LTD.
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Informed consent: Informed consent has been obtained from the participants involved.
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Author contributions: Study conception and design: F. Zhou; data collection: L.L.C., M. Xie, S. Meng and J.Z. Sun; analysis and interpretation of results: L.L.C., M. Xie, S. Meng and J.Z. Sun; draft manuscript preparation: F. Zhou. All authors reviewed the results and approved the final version of the manuscript.
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Use of Large Language Models, AI and Machine Learning Tools: No.
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Conflict of interest: The authors declare that they have no conflict of interest.
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Research funding: None.
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Data availability: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
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