Predictive value of miR-151a-3p for the onset of sepsis-induced acute kidney injury and its functional role during disease development

Abstract

Background

Sepsis-associated acute kidney injury (SA-AKI) is a significant clinical challenge due to its prevalence in intensive care units. This study evaluated the diagnostic utility of serum miR-151a-3p for SA-AKI, aiming to provide new insights into early diagnosis and mechanistic research.

Methods

Serum miR-151a-3p levels were determined via qRT-PCR and assessed for clinical correlations (Pearson correlation), risk association with SA-AKI (logistic regression), and diagnostic efficacy (ROC curve analysis). In vitro, the injury model was constructed by inducing TCMK-1 cells with LPS, and the miR-151a-3p mimic was transfected to observe its effects on inflammatory and oxidative stress. Finally, a binding relationship between miR-151a-3p and AKT3 was validated by employing both bioinformatics and dual-luciferase assay.

Results

In SA-AKI patients, miR-151a-3p was significantly decreased, and its expression level showed a significant negative correlation with disease severity and representative indicators of renal function. MiR-151a-3p is an independent protective factor for SA-AKI, with an AUC of 0.883 for predicting SA-AKI. In vitro experiments confirmed that overexpression of miR-151a-3p significantly alleviated LPS-induced inflammatory responses and oxidative stress damage. The dual-luciferase assay confirmed the binding relationship between AKT3 and miR-151a-3p.

Conclusions

MiR-151a-3p is closely related to the severity of SA-AKI. It can be used as a potential biomarker for the early prediction of the occurrence of SA-AKI. Overexpression of miR-151a-3p alleviates LPS-induced renal tubular epithelial cell injury.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12865-026-00844-6.

Background

Sepsis is a life-threatening condition caused by a dysregulated host response to infection and is characterized by organ dysfunction [1]. This syndrome often triggers a systemic inflammatory storm and can progress to multiple organ failure, such as sepsis-associated acute kidney injury (SA-AKI) [2]. Studies indicate that the mortality rate of SA-AKI patients is significantly higher than that of patients with sepsis alone [3]. SA-AKI may also lead to chronic renal injury and eventually progress to chronic renal failure, making the treatment of patients more complicated [4]. Despite continuous advances in medical standards, no specific treatments for sepsis are currently available, and the precise pathophysiological mechanisms of AKI remain incompletely understood. Meanwhile, the prevention of SA-AKI is extremely difficult. By the time patients seek medical assistance, most of them have already developed AKI [5]. Due to the lack of a sensitive and reliable predictive indicator [6], clinical responses often lag behind, thus missing the golden window for effective intervention. Therefore, whether high-risk patients can be identified early has a direct impact on the choice of subsequent treatment pathways for SA-AKI and the prognosis of patients.

Research has found that microRNA (miRNA) can participate in regulating a series of life activities such as cell growth, proliferation, differentiation and apoptosis, and serves as a key regulator of bodily immunity [7]. In the kidneys, miRNAs are believed to be involved in development, physiology, and disease pathogenesis [8]. Among them, miR-151-3p has gradually attracted attention. Multiple pieces of evidence indicate that this miRNA is associated with the regulation of renal function. For instance, in the high glucose-induced glomerular mesangial cell model, miR-151-3p effectively inhibited the inflammatory, fibrotic and oxidative stress responses of the cells by regulating downstream specific target genes [9]. Clinical tissue sample analysis also demonstrated markedly reduced levels of miR-151-3p in patients with various subtypes of nephropathy compared to healthy [10]. Although the pathophysiological mechanisms of the above studies differ from those of SA-AKI, the consistently altered expression of miR-151-3p across various kidney injury models suggests that this miRNA may be broadly associated with renal damage, warranting further investigation in the context of septic inflammation. Most directly, a recent study by Han et al. reported that miR-151a-3p was significantly downregulated in the urine of SA-AKI patients, but no functional or mechanistic investigation was performed [11]. Compared with well-studied SA-AKI-related miRNAs such as miR-21 and miR-155 [12, 13], research on miR-151a-3p remains in a relatively early stage, and systematic experimental validation of its specific functions and mechanisms is still lacking. This gap provides a novel and mechanistically grounded direction for the present study.Based on the above research background, clinical samples from patients with sepsis were collected to detect and compare the level of serum miR-151a-3p between patients with and without AKI. Furthermore, by establishing a LPS (lipopolysaccharide)-induced TCMK-1 cell injury model, the effects of miR-151a-3p on cell viability, inflammatory factors and oxidative stress levels were analyzed to clarify its role in renal injury. Finally, combined with bioinformatics analysis and experimental verification, this paper preliminarily explores whether miR-151a-3p is involved in the pathogenesis of SA-AKI through specific target genes, aiming to provide new potential targets and theoretical basis for the early diagnosis and intervention of this disease.

Methods

Research subjects and information collection

Between January 2021 and December 2023, we consecutively recruited 208 sepsis patients from University-Town Hospital of Chongqing Medical University based on the Sepsis-3.0 definitions [11]. Approval was granted by the Ethics Committee of University-Town Hospital of Chongqing Medical University (Approval No. IIT 2025-209). Blood samples were collected within 6 h of ICU admission. Since the exact time of sepsis onset was difficult to determine precisely (most patients presented with established sepsis at admission), ICU admission time was used as the reference time point for standardization. For SA-AKI patients, AKI was diagnosed within 48 h of admission according to the KDIGO criteria [14]. Based on these criteria, patients were categorized into a Sepsis-non-AKI (n = 98) and a SA-AKI group (n = 110). The inclusion criteria required patients to meet both the Sepsis-3.0 and KDIGO diagnostic definitions. Specifically, sepsis was defined as an increase in SOFA score of ≥ 2 points from baseline in the presence of suspected or confirmed infection. AKI was defined according to the KDIGO criteria as an increase in serum creatinine ≥ 0.3 mg/dL within 48 h or ≥ 1.5 times baseline, or urine output < 0.5 mL/kg/h for 6 h. Exclusion criteria comprised: a pre-admission history of chronic kidney disease; prior kidney transplantation; pregnancy or lactation; comorbid autoimmune diseases, active malignancies, or long-term use of immunosuppressive agents; or an expected survival of less than 24 h. Baseline characteristics of the patients, including demographics, severity scores, inflammatory markers, renal function indicators, and clinical variables such as prior antibiotic use, fluid resuscitation volume, and vasopressor requirement, are presented in Table 1. This study was performed in accordance with the Declaration of Helsinki, and written informed consent was obtained from all participants.

Table 1.

Summary of baseline characteristics

Clinical characteristics	Sepsis-non-AKI (n = 98)	SA-AKI (n = 110)	P-value
Age (year)	53.97 ± 6.04	54.77 ± 3.12	0.223
Gender (male/female)	42/56	49/61	0.808
SOFA score	7.37 ± 1.90	8.98 ± 4.39	0.001
APACHE II score	15.20 ± 2.98	19.24 ± 6.65	< 0.001
CRP (ng/mL)	60.97 ± 25.78	77.94 ± 18.31	< 0.001
PCT (ng/mL)	2.86 ± 0.96	5.45 ± 1.40	< 0.001
Scr (µmol/L)	101.92 ± 23.07	145.64 ± 29.80	< 0.001
NGAL (ng/mL)	57.47 ± 11.87	80.66 ± 22.90	< 0.001
KIM-1 (ng/mL)	4.38 ± 0.58	22.22 ± 4.87	< 0.001
HTN (yes/no)	33/65	38/72	0.895
DM (yes/no)	40/58	46/64	0.884
Prior antibiotic use (yes/no)	38/60	40/70	0.720
Fluid resuscitation volume (mL, first 24 h)	3448.23 ± 713.79	3624.67 ± 626.77	0.059
Vasopressor requirement (yes/no)	30/68	42/68	0.252

Sepsis-Non-AKI Sepsis Without Acute Kidney Injury Group, SA-AKI Sepsis-Associated Acute Kidney Injury Group, SOFA Sepsis-related Organ Failure Assessment, APACHE II Acute Physiology and Chronic Health Evaluation II, CRP C-Reactive Protein, PCT Procalcitonin, Scr Serum Creatinine, NGAL Neutrophil Gelatinase-Associated Lipocalin, KIM-1 Kidney Injury Molecule-1, HTN Hypertension, DM Diabetes Mellitus

The date was presented as mean ± SD), or percentage (/)

qRT-PCR

This study quantified the relative expression levels of miR-151a-3p and its target gene AKT3 in both serum and cells using qRT-PCR. For serum analysis, approximately 5 mL of fasting venous blood was collected from each patient into a non-anticoagulant tube. After allowing the blood to clot completely at room temperature for 30–60 min, it was centrifuged at 3000 rpm for 15 min at 4℃. The resulting supernatant serum was then aliquoted and stored at -80℃ until further use.

Total RNA was purified from cultured cells with TRIzol reagent (Invitrogen, USA). For serum samples, total RNA was extracted with the miRNeasy Serum/Plasma Kit (Qiagen, Germany). For cDNA synthesis, the miRCURY LNA RT Kit (Qiagen, Germany) and PrimeScript RT reagent Kit (Takara, Japan) were used for miRNA and mRNA, respectively. qPCR was conducted on a QuantStudio 5 system (Applied Biosystems, USA) with the miRCURY LNA SYBR Green PCR Kit (Qiagen, Germany) for miR-151a-3p and SYBR Premix Ex Taq II (Takara, Japan) for AKT3 mRNA. U6 snRNA and GAPDH mRNA were used as endogenous controls for normalizing miR-151a-3p and AKT3 mRNA expression, respectively. Relative expression levels were calculated using the 2^^−ΔΔCt method. U6 was used as the internal control for miRNA, and GAPDH for mRNA. The stability of both in serum has been confirmed by previous studies and our preliminary experiments [15, 16]. The primer sequences used for qRT-PCR are provided in Supplementary Table 2. The PCR conditions for miR-151a-3p and U6 were as follows: 95℃ for 10 min, followed by 40 cycles of 95℃ for 10 s and 60℃ for 1 min. The PCR conditions for AKT3 and GAPDH were: 95℃ for 30 s, followed by 40 cycles of 95℃ for 5 s and 60℃ for 30 s.

Cell culture and LPS induction

The mouse renal tubular epithelial cell line TCMK-1 was obtained from the ATCC. Cells were maintained in DMEM high-glucose medium supplemented with 10% fetal bovine serum along with penicillin (100 U/mL) and streptomycin (100 µg/mL), then kept at 37℃ within a humidified atmosphere containing 5% CO₂.

To determine the optimal concentration of LPS (Sigma, USA) for stimulation, we consulted previous studies and performed preliminary experiments [17, 18]. TCMK-1 cells were exposed to 0, 1, 2, 5, or 10 µg/ml LPS for 24 h, after which miR-151a-3p expression was measured. Based on these pre-experimental results and consideration of cell viability, a concentration of 10 µg/ml LPS with a 24-hour stimulation period was selected for subsequent formal experiments to establish the in vitro model. The titration was performed due to cell line-specific LPS sensitivity and the wide range of reported concentrations (1–100 µg/mL) [19, 20]. At 10 µg/mL, miR-151a-3p change was most pronounced with suitable cell viability, consistent with previous TCMK-1 studies [17, 18]. Although this concentration exceeds physiological LPS levels [21, 22], higher concentrations are required in vitro due to the absence of LPS-binding protein and CD14. Thus, the selection of 10 µg/mL LPS is justified and reproducible.

Cell transfection

TCMK-1 cells in good growth condition were seeded into 96-well plates at an appropriate density. Transfection was performed when cell confluence reached approximately 70–80%. The miR-151a-3p mimic (product No. miR10000757-1-5) and negative control mimic (mimic-NC, product No. miR1N0000001-1-5) were purchased from RiboBio (Guangzhou, China). According to the miRBase database (Accession No. MIMAT0000757), the mature sequence of hsa-miR-151a-3p is 5’-CUAGACUGAAGCUCCUUGAGG-3’, which serves as the sense strand of the mimic; the antisense strand is its reverse complementary sequence 5’-CCUCAAGGAGCUUCAGUCUAG-3’. Following the manufacturer’s instructions, Lipofectamine 3000 transfection reagent (Invitrogen, USA) was used to introduce either the miR-151a-3p mimic or its corresponding negative control into the cells at a final concentration of 50 nM. After 6 h of transfection, the medium was replenished with complete growth medium for continued culture. Twenty-four hours post-transfection, cells in the designated groups were subjected to stimulation with 10 µg/ml LPS.

Cell viability

After seeding in 96-well plates and subsequent treatment, cell viability was measured by adding CCK-8 solution at the indicated time points (0, 24, 48, and 72 h). The CCK-8 reagent was purchased from Dojindo (Japan, catalog No. CK04). For each well, 10 µL of CCK-8 solution was added to 100 µL of culture medium (10% v/v). Following a subsequent 2-hour incubation period, the absorbance of each well was read using a microplate reader at 450 nm.

Measurement of inflammatory cytokines

Cell culture supernatants were collected from each experimental group. For each well, 100 µL of undiluted supernatant was used for the assay. The concentrations of TNF-α, IL-6, and IL-1β in the supernatants were quantified using commercially available ELISA kits (R&D Systems, USA). All procedures were performed in strict accordance with the provided protocols.

Assessment of oxidative stress parameters

Cell lysates were collected for the analysis of oxidative stress markers. Cells were washed with pre-chilled PBS, then lysed with 200 µL of PBS. The homogenates were centrifuged at 5000 rpm for 10 min at 4℃, and 50 µL of the supernatant was used for the assays. The MDA and SOD assay kits were purchased from Nanjing Jiancheng Bioengineering Institute (China), with catalog numbers A003-1-2 for MDA and A001-3-2 for SOD. MDA content was determined using the thiobarbituric acid (TBA) method, while SOD activity was measured based on the xanthine oxidase.

Prediction of target genes

Potential target genes of miR-151a-3p were predicted using TargetScan and miRWalk databases. This yielded 35 overlapping candidates, from which AKT3 was selected for validation due to its known roles in renal injury and inflammation. TargetScan parameters for AKT3 were: cumulative weighted context + + score − 0.29, total context + + score − 0.41, predicted occupancy 0.5804, and 3P-seq tags 168. miRWalk parameters were: target rank 69 and target score 72. The complete list of the 35 overlapping genes and the full prediction results from both databases (112 from TargetScan, 220 from miRWalk) are provided in Supplementary Table 1. Dual-luciferase assay.

Based on the predicted miR-151a-3p binding site, DNA fragments containing either the wild-type (WT) sequence of the AKT3 gene 3’ untranslated region (3’UTR) or a mutant (MUT) version of this site were synthesized. These fragments were then individually cloned into the pmirGLO dual-luciferase reporter vector. TCMK-1 cells were plated in 24-well plates and transfected at approximately 70% confluence. Using Lipofectamine 3000, the reporter plasmid was co-transfected with either the miR-151a-3p mimic or the mimic negative control (mimic-NC). After 48 h of transfection, cells were harvested to measure luciferase activity with the Dual-Luciferase Assay System (Promega, USA).

Western blot analysis

To evaluate AKT3 protein expression, TCMK-1 cells were harvested and lysed in RIPA buffer containing protease inhibitors. Protein samples were separated by 10% SDS-PAGE and transferred onto PVDF membranes. The membranes were blocked with 5% non-fat milk and incubated with primary antibodies against AKT3 and GAPDH overnight at 4 °C, followed by HRP-conjugated secondary antibodies. Protein bands were visualized using an ECL chemiluminescence system and quantified using ImageJ software. The full-length, uncropped original western blot images have been provided in Supplementary File 1.

Statistical analysis

Continuous data were analyzed using SPSS 26.0 and GraphPad Prism 9.0 software and are reported as mean ± standard deviation. All in vitro experiments were performed in five independent biological replicates, with three technical replicates per experiment. The Student’s t-test and ANOVA (followed by Tukey’s test) were employed for comparing continuous variables between two or multiple groups, respectively, and the chi-square test was applied to categorical variables expressed numerically. Independent risk factors for the development of SA-AKI were identified through binary logistic regression analysis. The multivariate logistic regression model included miR-151a-3p as the main independent variable, with SOFA score, APACHE II score, CRP, Scr, NGAL, age, gender, hypertension, and diabetes mellitus as covariates. Variance inflation factor (VIF) analysis was performed to assess multicollinearity among the independent variables, with VIF < 5 indicating no significant collinearity. The early predictive value of miR-151a-3p for SA-AKI was evaluated by ROC curve analysis. Pearson correlation was used to assess the relationships between miR-151a-3p expression levels and various clinical and biochemical parameters. A P-value of less than 0.05 was considered statistically significant.

Results

Baseline characteristics of patients in the SA-AKI and sepsis-non-AKI groups

A comparison of the baseline characteristics between patients in the SA-AKI and Sepsis-non-AKI groups is presented in Table 1. No significant differences were observed between the two groups in terms of age, gender distribution, HTN, DM, prior antibiotic use, fluid resuscitation volume, or vasopressor requirement (all P > 0.05). However, patients in the SA-AKI group demonstrated significantly higher disease severity scores compared to the non-AKI group, as indicated by both the SOFA score (8.98 ± 4.39 vs. 7.37 ± 1.90, P = 0.001) and the APACHE II score (19.24 ± 6.65 vs. 15.20 ± 2.98, P < 0.001). Markers of systemic inflammation were also elevated in the SA-AKI, with higher levels of CRP (77.94 ± 18.31 vs. 60.97 ± 25.78 ng/mL, P < 0.001) and PCT (5.45 ± 1.40 vs. 2.86 ± 0.96 ng/mL, P < 0.001). Regarding renal function and injury, the SA-AKI group exhibited a pronounced increase in Scr (145.64 ± 29.80 vs. 101.92 ± 23.07 µmol/L, P < 0.001). Furthermore, levels of kidney injury-specific biomarkers were significantly higher in this group, including NGAL (80.66 ± 22.90 vs. 57.47 ± 11.87 ng/mL, P < 0.001) and KIM-1 (22.22 ± 4.87 vs. 4.38 ± 0.58 ng/mL, P < 0.001).

Expression, diagnostic value, and clinical correlations of serum miR-151a-3p in SA-AKI

The relative expression of serum miR-151a-3p was significantly lower in patients with SA-AKI compared to those in the Sepsis-non-AKI group (P < 0.0001, Fig. 1A). The diagnostic performance of miR-151a-3p was evaluated using ROC curve. The AUC for distinguishing SA-AKI from Sepsis-non-AKI was 0.883 (95% CI: 0.836–0.930), with a sensitivity of 0.846 and a specificity of 0.776 (P < 0.0001, Fig. 1B). Among SA-AKI patients, the level of miR-151a-3p demonstrated strong and statistically significant negative correlations with established markers of disease severity (Table 2). Specifically, miR-151a-3p levels correlated inversely with the SOFA score (r = -0.808), the APACHE II score (r = -0.754), and the inflammatory marker PCT (r = -0.771) (all P < 0.0001). Furthermore, a consistent pattern of significant negative correlation was observed between miR-151a-3p expression and key indicators of renal injury. These included NGAL (r = -0.767), KIM-1 (r = -0.694), and Scr (r = -0.666) (all P < 0.0001).

Fig. 1.

Expression and diagnostic performance of serum miR-151a-3p in patients with SA-AKI. A Relative expression levels of miR-151a-3p in the two patient groups. B ROC curve analysis evaluating the diagnostic value of miR-151a-3p for SA-AKI. ****P < 0.0001

Table 2.

Correlation analysis between serum miR-151a-3p levels and renal function-related indicators in SA-AKI patients

Variable	Correlation coefficient	P-value	Correlation
SOFA score	-0.808	p < 0.0001	Negative
APACHE II score	-0.754	p < 0.0001	Negative
PCT (ng/mL)	-0.771	p < 0.0001	Negative
NGAL (ng/mL)	-0.767	p < 0.0001	Negative
KIM-1 (ng/mL)	-0.694	p < 0.0001	Negative
Scr (µmol/L)	-0.666	p < 0.0001	Negative

SA-AKI Sepsis-Associated Acute Kidney Injury Group, SOFA Sepsis-related Organ Failure Assessment, APACHE II Acute Physiology and Chronic Health Evaluation II, PCT Procalcitonin, NGAL Neutrophil Gelatinase-Associated Lipocalin, KIM-1 Kidney Injury Molecule-1, Scr Serum Creatinine

To further identify independent determinants for the development of SA-AKI, a multivariate logistic regression analysis was performed (Table 3). The analysis revealed that SOFA score (OR = 3.427, 95% CI: 1.305–8.998, P = 0.012), APACHE II score (OR = 5.924, 95% CI: 2.166–16.197, P = 0.001), CRP (OR = 7.408, 95% CI: 2.688–20.419, P < 0.001), Scr (OR = 8.105, 95% CI: 2.735–24.024, P < 0.001), and NGAL level (OR = 6.304, 95% CI: 2.328–17.069, P < 0.001) were all independent risk factors for SA-AKI. Notably, serum miR-151a-3p expression emerged as a significant protective factor against SA-AKI (OR = 0.028, 95% CI: 0.009–0.082, P < 0.001). In contrast, age, gender, hypertension, and diabetes mellitus did not demonstrate independent predictive value in this model (all P > 0.05). To assess potential multicollinearity among the independent variables, we performed VIF analysis. As shown in Supplementary Table 3, the VIF values for all variables were below 5 (miR-151a-3p: 1.177, Age: 1.066, Gender: 1.055, SOFA score: 1.076, APACHE II score: 1.178, CRP: 1.102, Scr: 1.112, NGAL: 1.071, HTN: 1.051, DM: 1.042), indicating no significant multicollinearity.

Table 3.

Logistic regression analysis predicts risk factors for SA-AKI occurrence

Clinical characteristics	Multivariate logistics analysis
	OR	95% CI	P-value
miR-151a-3p	0.028	0.009—0.082	< 0.001
Age	1.201	0.463—3.114	0.707
Gender	1.266	0.473—3.386	0.639
SOFA score	3.427	1.305—8.998	0.012
APACHE II score	5.924	2.166—16.197	0.001
CRP	7.408	2.688—20.419	< 0.001
Scr	8.105	2.735—24.024	< 0.001
NGAL	6.304	2.328—17.069	< 0.001
HTN	2.549	0.922—7.045	0.071
DM	1.283	0.485—3.395	0.616

SA-AKI Sepsis-Associated Acute Kidney Injury Group, SOFA Sepsis-related Organ Failure Assessment, APACHE II Acute Physiology and Chronic Health Evaluation II, CRP C-Reactive Protein, Scr Serum Creatinine, NGAL Neutrophil Gelatinase-Associated Lipocalin, HTN Hypertension, DM Diabetes Mellitus

Overexpression of miR-151a-3p attenuates LPS-induced injury in TCMK-1 cells

To investigate miR-151a-3p in vitro, its response to LPS was initially evaluated across a concentration gradient. As illustrated in Fig. 2A, the relative expression of miR-151a-3p in TCMK-1 cells exhibited a significant and dose-dependent decrease with increasing LPS concentrations (P < 0.0001), reaching its lowest level at 10 µg/mL. Consequently, this concentration was selected to establish the in vitro model for subsequent experiments. Transfection with a miR-151a-3p mimic successfully induced its overexpression in the cells (P < 0.0001, Fig. 2B). Functional analysis revealed that this overexpression significantly reversed the decline in cell viability caused by LPS stimulation (P < 0.0001, Fig. 2C). To determine whether miR-151a-3p overexpression exerts direct effects under basal conditions, we assessed cell viability, inflammatory cytokines (TNF-α, IL-6, IL-1β), SOD activity, and MDA content in TCMK-1 cells without LPS stimulation. As shown in Supplementary Fig. 1A-F, no significant differences were observed among the control, miR-NC, and miR-mimic groups in any of the measured parameters (P > 0.05). Regarding the inflammatory response, LPS challenge markedly elevated the concentrations of TNF-α, IL-6, and IL-1β (P < 0.0001). Notably, this pro-inflammatory effect was substantially mitigated by the overexpression of miR-151a-3p (Figs. 2D-F). Consistent with these findings, miR-151a-3p overexpression also significantly ameliorated the cellular oxidative stress status. This was evidenced by a reduction in MDA accumulation (P < 0.0001, Fig. 2G) and a partial restoration of SOD activity (P < 0.0001, Fig. 2H).

Fig. 2.

Protective effects of miR-151a-3p overexpression against LPS-induced injury in TCMK-1 cells. A The impact of LPS stimulation at different concentrations on miR-151a-3p expression in TCMK-1 cells. B Relative expression levels of miR-151a-3p in TCMK-1 cells following transfection with the miR-151a-3p mimic. Effects of miR-151a-3p overexpression on (C) cell viability, (D-F) inflammatory cytokine levels, and (G-H) oxidative stress parameters in LPS-stimulated TCMK-1 cells. Data are presented as mean ± SD from five independent experiments. ****P < 0.0001, ***p < 0.001, **p < 0.01

Involvement of miR-151a-3p in disease progression via targeting AKT3

Bioinformatic analysis predicted a conserved complementary sequence for miR-151a-3p binding within positions 214–221 of the AKT3 3’UTR (Fig. 3A). This interaction was functionally validated by a dual-luciferase reporter. Co-transfection of the miR-151a-3p mimic with the WT-AKT3 reporter construct resulted in a significant suppression of luciferase activity. Importantly, this inhibitory effect was completely abolished when the predicted binding site was MUT (P < 0.0001, Fig. 3B). This regulatory relationship was further investigated in clinical samples. Compared to the Sepsis-non-AKI group, AKT3 mRNA expression was significantly elevated in patients with SA-AKI (P < 0.0001, Fig. 3C). Consistent with the targeting mechanism, a significant negative correlation was observed between the expression of miR-151a-3p and AKT3 in SA-AKI patients (r = -0.740, P < 0.0001, Fig. 3D). In TCMK-1 cells, AKT3 mRNA and protein levels were significantly lower in the miR-151a-3p overexpression group than in the control group (P < 0.0001). Further rescue experiments showed that co-transfection with an AKT3 overexpression vector markedly reversed the reduction in AKT3 expression caused by miR-151a-3p overexpression (Fig. 3E-F).

Fig. 3.

Validation of the relationship between miR-151a-3p and AKT3. A The predicted binding site for miR-151a-3p within the 3’UTR of the AKT3 gene, as indicated by bioinformatic analysis. B Dual-luciferase reporter assay verifying the regulatory effect of miR-151a-3p on luciferase activity from WT and MUT-AKT3 reporter constructs. C Relative mRNA expression of AKT3 in patients with Sepsis-non-AKI and SA-AKI. D Analysis showing the negative correlation between miR-151a-3p expression and AKT3 mRNA levels in SA-AKI patients. E Relative AKT3 mRNA expression levels in TCMK-1 cells under different treatments. F Representative Western blot bands and quantitative analysis of AKT3 protein expression. Data are presented as mean ± SD from five independent experiments. ****P < 0.0001, **P < 0.01

In this study, cell viability was significantly lower in the oe-AKT3 group than in the oe-NC group (P < 0.0001), indicating that AKT3 overexpression reversed the protective effect of miR-151a-3p on cell viability. In terms of inflammatory response, the levels of TNF-α, IL-6, and IL-1β were significantly higher in the oe-AKT3 group than in the oe-NC group (Fig. 4B-D, P < 0.0001). Moreover, compared with the oe-NC group, the oe-AKT3 group showed significantly lower SOD activity and higher MDA content (Fig. 4E-F, P < 0.0001).

Fig. 4.

AKT3 overexpression reverses the protective effects of miR-151a-3p against LPS-induced injury in TCMK-1 cells. A Cell viability measured by CCK-8 assay at 0, 24, 48, and 72 h. B-D Concentrations of TNF-α, IL-6, and IL-1β measured by ELISA. E SOD activity. F MDA content. Data are presented as mean ± SD from five independent experiments. ****P < 0.0001, ***P < 0.001

Discussion

Because AKI significantly increases mortality in septic patients [23], there is an urgent clinical need for biomarkers that enable early, sensitive, and specific diagnosis of SA-AKI. Although Scr and urine output remain the most commonly used indicators for AKI diagnosis, they are influenced by various non-renal factors, and their changes often lag behind the actual injury [24]. In recent years, miRNAs in serum have become emerging markers of great concern in the diagnosis of kidney diseases due to their stability and high sensitivity and specificity when detected by PCR methods [25, 26]. Although miR-151a-3p has been reported to be related to changes in renal function, its clinical diagnostic value, role, biological function and molecular mechanism in the development of SA-AKI have not yet been clarified. For this purpose, this study aims to systematically reveal the role of miR-151a-3p in SA-AKI. This study found that miR-151a-3p in patients with SA-AKI was significantly decreased. SOFA and APACHE II scores were used to systematically assess the severity of SA-AKI [27, 28]. Elevated levels of CRP and PCT indicate that the core inflammatory response driving kidney injury has been activated [29], while the specific increases in NGAL and KIM-1 suggest tubular injury [30]. The rise in Scr can further confirm the impairment of renal function [31]. Importantly, miR-151a-3p levels exhibited a strong negative correlation with all the aforementioned indicators, suggesting that this molecule may play a role in regulating inflammation, injury, and functional modulation in SA-AKI. Notably, the expression trend of miR-151a-3p observed in this study is also consistent with the results recently reported by Han et al. [11]. However, the study by Han et al. was limited to descriptive analysis without functional or mechanistic investigation. Further analysis revealed that serum miR-151a-3p had a good differential diagnostic value for SA-AKI, and multivariate regression analysis confirmed that it was an independent protective factor for the occurrence of SA-AKI. Beyond these clinical associations, the above-mentioned studies have confirmed the extensive association between miR-151a-3p and the pathological process of SA-AKI from multiple aspects. These findings provide a preliminary clinical basis for its potential as a candidate biomarker for SA-AKI.

In this study, TCMK-1 mouse renal tubular epithelial cells were selected to construct the in vitro model of SA-AKI. Although previous studies have suggested that miR-151a-3p is downregulated in HK-2 cells treated with high glucose [32], there are significant differences in the septic inflammatory environment and metabolic damage specific to SA-AKI. TCMK-1 cells are a classic model for studying renal tubular injury and can well simulate the direct attack of inflammatory factors on renal tubules in the state of sepsis [33]. To simulate this process, we stimulated the cells with LPS. Concentration gradient experiments indicated that the expression of miR-151a-3p in cells significantly decreased with the increase of LPS concentration (0–10 µg/mL), and the expression level was the lowest and tended to be stable at 10 µg/mL. Therefore, this concentration was selected for modeling in the subsequent experiments, and this concentration choice is also consistent with the previous literature reports. In SA-AKI, excessive inflammation and oxidative stress triggered by LPS are the core mediators leading to damage of renal tubular epithelial cells [34–36]. In this study, overexpression of miR-151a-3p effectively suppressed LPS-induced inflammatory responses and oxidative stress and improved cell viability, whereas under normal culture conditions, its overexpression did not change these indicators. Similar to previous findings, miR-151-3p expression was downregulated under stress conditions such as high glucose or LPS stimulation, and its overexpression alleviated high glucose-induced proliferation, inflammation, and oxidative stress [9] or relieved Stat3 suppression and promoted IL-6 production upon LPS stimulation [37]. Moreover, it has been reported that many microRNAs are only activated under stress conditions [38]. These observations, together with our finding that miR-151a-3p overexpression caused no significant changes under normal culture conditions, support the idea that miR-151a-3p functions in a stress-dependent manner. Although miR-151a-3p has intrinsic anti-inflammatory and anti-oxidative potential, cells in a normal physiological state are already in homeostasis; therefore, its overexpression does not lead to dramatic fluctuations in basal indicators, further supporting its safety profile.

AKT3 is a key kinase that regulates cell survival, proliferation and inflammatory response [39]. AKT3 plays a core role in various renal pathologies. It is predicted to be a key signaling molecule downstream of STAT3 mediating renal inflammation [40], and is significantly upregulated as a carcinogenic driver in nephroblastoma [41]. Meanwhile, AKT3 drives fibrosis, inflammation and oxidative stress in diabetic nephropathy through the SP1-AKT3-AKT/mTOR axis [42]. This study preliminarily explored the relationship between miR-151a-3p and AKT3 in the context of SA-AKI for the first time. It was found that the mRNA expression of AKT3 in the serum of SA-AKI patients was significantly increased. The dual-luciferase assay confirmed that miR-151a-3p could directly target the 3’UTR of AKT3 and inhibit its expression. In addition, the expression of miR-151a-3p in clinical samples was significantly negatively correlated with that of AKT3. AKT3 overexpression significantly reversed the protective effect of miR-151a-3p against LPS-induced cell injury, suggesting that miR-151a-3p exerts its anti-inflammatory and antioxidant functions at least in part through targeting and inhibiting AKT3.

This study provides potential new evidence for the diagnostic value and potential protective role of miR-151a-3p in SA-AKI, but there are still several limitations. First, this is a single-center retrospective study with a limited sample size, limiting causal inference. A multi-center prospective cohort is needed to validate the long-term predictive value of miR-151a-3p for SA-AKI. Second, the in vitro model used mouse TCMK-1 cells and LPS alone, lacking immune cells and multi-organ interactions. Further validation using human cell lines, co-culture systems, or animal models is required. Third, serum AKT3 mRNA may originate from circulating immune cells or platelets, an inherent limitation of serum-based studies. Kidney tissue or urine-specific markers could verify its kidney-specific expression. Fourth, although AKT3 was selected from 35 overlapping candidates, the other 34 were not functionally screened. Functional screening of the remaining candidates could more fully reveal the regulatory network of miR-151a-3p. Fifth, miR-151a-3p was measured only once within 6 h of admission, and variability in collection timing may affect the results. Dynamic measurements at multiple time points are needed to evaluate its value in sepsis diagnosis and AKI staging. Sixth, because NGAL and KIM-1 showed large between-group differences, we did not compare miR-151a-3p with these markers in the same cohort. Better balanced cohorts are needed for direct comparison. Finally, although in vitro experiments support a protective role for miR-151a-3p, the current data are insufficient to conclude that it directly regulates inflammation and injury. In addition, the clinical part is observational and cannot establish causality. Future studies combining multi-omics, histology, animal models, and prospective studies are needed to further clarify the protective mechanism.

Conclusions

This study suggests the potential application value of serum miR-151a-3p in SA-AKI. The research results show that miR-151a-3p concentrations in the serum of patients with SA-AKI are significantly decreased. This molecule is inversely related to both the severity of the disease and renal injury, and shows good early diagnostic value. In vitro experiments further validated the protective effect of miR-151a-3p overexpression against LPS-induced TCMK-1 cell injury, which was significantly attenuated by AKT3 overexpression. Mechanism studies have shown that miR-151a-3p can target AKT3, suggesting a potential miR-151a-3p/AKT3 regulatory axis.

Authors’ contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yongyang Tian, Haiyuan Zhu, Jinhui Wang, Lilin Wang and Li Liu. The first draft of the manuscript was written by Xiao Gui and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of University-Town Hospital of Chongqing Medical University. Informed consent was obtained from all individual participants included in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.