The sphingolipid metabolite sphingosine protects against hypertension by targeting metabolic-inflammatory crosstalk via the NLRP3 inflammasome

Abstract

Background

Hypertension is the most common chronic non-communicable disease and one of the most significant risk factors for cardiovascular and cerebrovascular diseases. Sphingosine (SPH) is a central bioactive lipid metabolite positioned at the crucial intersection of ceramide and sphingosine-1-phosphate (S1P) synthesis is increasingly implicated in cardiometabolic health. However, its precise role in the pathophysiology of hypertension and its interplay with inflammatory pathways remain largely unknown. This study aimed to research the therapeutic effects of SPH in hypertension and to explore its underlying mechanisms, focus on a key driver of sterile inflammation that the NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome pathway.

Methods

An Angiotensin II (Ang II)-induced hypertensive mouse model and an in vitro model using human umbilical vein endothelial cells (HUVECs) were established. The effects of SPH administration on Ang II-induced hypertension, end-organ damage, and the activation status of the NLRP3 inflammasome were systematically evaluated.

Results

In vivo, Ang II infusion triggered significant hypertension, cardiac hypertrophy, and aortic fibrosis, which was accompanied by activation of the NLRP3 inflammasome in cardiovascular tissues. Therapeutic administration of SPH, in a manner comparable to the specific NLRP3 antagonist MCC950, markedly lowered blood pressure and attenuated these pathological changes. In vitro, SPH treatment effectively suppressed Ang II-induced NLRP3 inflammasome activation.

and released of pro-inflammatory cytokines in HUVECs. Furthermore, SPH exhibited direct protective effects on the endothelium by promoting HUVEC proliferation and against Ang II-induced injury. Mechanistically, SPH suppressed the expression and activation of key inflammasome components, including NLRP3, cleaved Caspase-1, and mature IL-1β and IL-18.

Conclusions

This study reveals a novel protective role for Sphingosine in hypertension, acting via the suppression of the NLRP3 inflammasome pathway to decrease inflammation and oxidative stress. These findings explore a new mechanistic link between sphingolipid metabolism and blood pressure regulation and highlight SPH as a potential therapeutic agent for targeting the critical series of metabolic dysregulation and inflammation in hypertensive cardiovascular disease.

Highlights

• •Sphingosine attenuates Angiotensin II-induced hypertension and cardiovascular remodeling.
• •The protective effect of Sphingosine is mediated by suppressing NLRP3 inflammasome activation.
• •A novel mechanistic link between sphingolipid metabolism and inflammation in hypertension is revealed.
• •Sphingosine emerges as a potential therapeutic agent for hypertensive cardiovascular disease.

1. Introduction

Hypertension remains a major global public health challenge and is one of the most significant risk factors for cardiovascular and cerebrovascular diseases [1,2]. With a global prevalence exceeding one billion individuals, a figure projected to rise to 1.56 billion by 2025—the socioeconomic burden of hypertension is immense [3]. In China, the prevalence in adults has reached 27.9 %; however, the rates of awareness (51.6 %), treatment (45.8 %), and control (16.8 %) remain alarmingly low [4,5]. This gap highlights an urgent need to deepen our understanding of hypertension's underlying pathophysiology to develop more effective therapeutic strategies.

The NLRP3 inflammasome, an essential intracellular inflammatory sensor, plays a pivotal role in the pathogenesis of hypertension [6,7]. Consequently, targeting the NLRP3 inflammasome has emerged as a promising therapeutic strategy [8]. Reactive oxygen species (ROS) are also key contributors, influencing cellular processes that lead to blood pressure elevation [9]. Various interventions have been shown to exert anti-inflammatory and anti-hypertensive effects by inhibiting NF-κB signaling, which is a critical step in NLRP3 inflammasome activation [10]. Furthermore, NLRP3-mediated inflammation drives the phenotypic switching and proliferation of vascular smooth muscle cells, a key pathological process in vascular remodeling and the progression of hypertension [10]. The activation of the NLRP3 inflammasome is particularly critical in angiotensin II (AngII)-induced hypertension models [11]. The subsequent release of inflammatory cytokines severely impairs vascular endothelial function, making the NLRP3 inflammasome an ideal therapeutic target for hypertension-associated endothelial dysfunction [12]. The inflammasome also plays a crucial role in hypertension-induced renal injury [13]. Therefore, targeted inhibition of NLRP3 represents an effective strategy for alleviating hypertension and its associated end-organ damage.

Our preliminary non-targeted metabolomics analysis revealed significant perturbations in the sphingolipid metabolism pathway in hypertensive patients. We identified sphinganine (SA), a key precursor in de novo sphingolipid synthesis, as one of the most significantly altered metabolites. Sphinganine and its downstream metabolite, sphingosine (SPH), are central players in this pathway, differing only by a single double bond [14,15]. While both molecules are bioactive, SPH is the direct precursor to the potent signaling lipid sphingosine-1-phosphate (S1P) [16] and has been controversially implicated in NLRP3 inflammasome regulation [[17], [18], [19], [20]]. Given the central role of SPH in downstream signaling, we hypothesized that the observed metabolic shift towards elevated sphinganine might ultimately impact the functional pool of SPH. Therefore, this study was designed to directly investigate the pharmacological effects of SPH on the NLRP3 inflammasome pathway in the context of AngII-induced hypertension and to clarify its potential as a therapeutic agent. Our study provides evidence that exogenous administration of SPH protects against AngII-induced hypertension and cardiovascular remodeling. This finding is intriguing because, under certain conditions, sphingosine can act as an endogenous danger-associated molecular pattern (DAMP), activating the NLRP3 inflammasome in macrophages and inducing the maturation and secretion of IL-1β [21]. Indeed, lipid metabolism, particularly that of sphingolipids, is a confirmed regulatory mechanism for NLRP3 inflammasome activity [17,20].

In the present study, we used AngII-induced models of hypertension to investigate the specific role and molecular mechanisms of SPH. We proposed a novel hypothesis: although SPH may activate the NLRP3 inflammasome at high concentrations or under specific experimental conditions, we posited that within the pathological microenvironment of hypertension, exogenous administration of SPH exerts a net inhibitory effect on the NLRP3 signaling pathway. This inhibition could occur through negative feedback loops or interactions with other signaling pathways that collectively alleviate inflammation and oxidative stress. This research aimed to explore the complex relationship between SPH and hypertension, providing a new theoretical basis for developing SPH-based therapeutic strategies.

This figure illustrates the proposed mechanism by which AngII promotes hypertension and the protective effects of sphingosine. In the context of hypertension, AngII primes and activates the NLRP3 inflammasome, leading to the activation of Caspase-1. Activated Caspase-1 subsequently cleaves Gasdermin D (GSDMD) to initiate pyroptosis and processes pro-IL-1β into its mature, active form, thereby triggering a cascade of inflammatory responses. Concurrently, increased levels of reactive oxygen species (ROS) drive further inflammation and contribute to endothelial dysfunction, which is characterized by decreased nitric oxide (NO) bioavailability and increased endothelin-1 (ET-1) production. This sequence of events culminates in the development of hypertension. Our findings indicate that SPH interrupts this pathogenic cascade by inhibiting both NLRP3 inflammasome activation and ROS production, highlighting its therapeutic potential Fig. 1.

Fig. 1.

A schematic representation illustrating how Sphingosine attenuates hypertension. The proposed mechanism involves the multiple suppression of the NLRP3 inflammasome pathway and oxidative stress.

2. Materials and methods

2.1. Analysis of clinical samples study population and inclusion criteria

The study population was derived from a cross-sectional survey on chronic diseases conducted between 2019 and 2021, in Yunnan, China. This cohort initially included 1436 participants. During the survey, comprehensive data were collected on sociodemographics, smoking and alcohol consumption history, chronic diseases, and medication use. Anthropometric measurements and physical examinations 、blood pressure assessment, were performed by trained personnel. Bblood samples were collected and centrifuged at 3500 rpm for 15 min to separate serum after an 8–12 h overnight fast. A portion of the serum samples was used for the analysis of lipid profiles and detection of other biochemical indicators, while the remaining samples were stored at −80 °C for subsequent analysis. The study was conducted in compliance with national regulations and the principles of the Declaration of Helsinki. The study protocol has been approved by the Research Ethics Committee of Yan'an Hospital Affiliated to Kunming Medical University (Approval No. 2020-096-01). All participants provided written informed consent prior to inclusion in the study.

2.2. Study design

2.2.1. Inclusion and exclusion criteria

A total of 75 participants were recruited for non-targeted metabolomics analysis.The study population was divided into two groups: a hypertension group (HTN, n = 37) and an age, sex, and geographically-matched healthy control group (CTR, n = 38).

General inclusion criteria for all participants were as follows: (1) age ≥18 years; (2) residence in Yunnan Province for more than five years; and (3) provision of written informed consent.Participants in the HTN group were diagnosed according to the latest clinical guidelines for hypertension[4,23), defined as a systolic blood pressure (SBP)≥ 140 mmHg and/or a diastolic blood pressure (DBP)≥ 90 mmHg, based on three separate measurements on different days.Patients with secondary hypertension were excluded from this group.The CTR group consisted of healthy individuals with no history of any chronic diseases.The general exclusion criteria for all participants were: a history of coronary heart disease, diabetes mellitus, cerebrovascular disease, psychiatric disorders, chronic obstructive pulmonary disease (COPD), asthma, malignant tumors, or dementia. Individuals taking hypoglycemic or lipid-lowering medications were also excluded. Participants with excessive missing data or unavailable blood samples were not included in the final analysis.

The HTN and CTR groups were matched for age (±3years), sex, and geographical location. There were no significant differences between the two groups in baseline characteristics such as smoking habits or alcohol consumption (p > 0.05 for all). All participants followed a normal, unrestricted diet. A detailed flowchart of the participant selection process is presented in Fig. 2.

Fig. 2.

Flowchart of participant enrollment.

2.2.2. Sample collection and preparation

Blood samples were collected from all participants after an overnight fast of at least 8 h. The whole blood samples were centrifuged on-site to separate the serum. All serum aliquots were immediately stored at −80 °C until non-targeted metabolomic analysis.

2.2.3. Untargeted metabolomics by liquid chromatography-mass spectrometry (LC-MS)

Metabolites were extracted from 100 μL of each serum sample. First, 400 μL of an extraction solution (acetonitrile:methanol = 1:1, v/v, containing an isotope-labeled internal standard mixture) was added to each sample. The mixture was then vortexed, sonicated in an ice-water bath, and incubated at −40 °C to precipitate proteins. After incubation, the samples were centrifuged at 12000 rpm for 15 min at 4 °C. The supernatant was collected for LC-MS analysis. Quality control (QC) samples were prepared by combining the supernatants equally divided from all samples.The analysis was performed using a Thermo Fisher Vanquish UHPLC system coupled to a Thermo Fisher Q Exactive HFX Orbitrap mass spectrometer. Chromatographic separation was achieved on a Waters ACQUITY UPLC BEH Amide column (2.1 mm × 100 mm, 1.7 μm). The mobile phase consisted of (A) an aqueous solution of 25 mmol/L ammonium acetate and 25 mmol/L ammonium hydroxide (pH 9.75), and (B) acetonitrile. A 2 μL aliquot of each sample was injected, and the autosampler was maintained at 4 °C throughout the analysis.

2.2.4. Data processing and bioinformatics analysis

The raw LC-MS data were converted to the mzXML format using ProteoWizard (v3.0.2). A custom R script utilizing the XCMS package was employed for peak detection, extraction, alignment, and integration. Metabolite annotation was performed by matching against an in-house MS2 database (BiotreeDB), with a similarity score cutoff of 0.3.To visualize the intrinsic variation in the dataset, Principal Component Analysis (PCA) was performed using the prcomp function in R [22]. Differentially abundant metabolites (DAMs) between the HTN and CTR groups were identified using Student's t-test in MetaboAnalyst 5.0, with the False Discovery Rate (FDR) controlled using the BenjaminiHochberg procedure [23]. Data visualization, including volcano plots and heatmaps, was generated using the ggplot2 (v3.4.4) and pheatmap (v1.0.12) R packages. Pathway analysis was conducted to identify enriched metabolic pathways within the Kyoto Encyclopedia of Genes and Genomes (KEGG), and the results were visualized by bubble plots.

2.3. Animal experiments

To analyze the therapeutic effects and underlying mechanisms of SPH in hypertension, a mouse model of AngII-induced hypertension was established. After the model is created, mice received intraperitoneal (i.p.) injections of SPH for 14 days. The effects of SPH were assessed by monitoring blood pressure. Pathological changes in cardiac and aortic tissues were evaluated using Hematoxylin and Eosin (H&E) and Masson's trichrome staining. The expression of key inflammatory proteins in cardiac tissue was measured by Western Blot and qPCR, and circulating inflammatory cytokines in serum were quantified by ELISA.

2.3.1. Animals and ethics statement

Four-week-old male C57BL/6 mice (body weight 15–20 g) were purchased from Tengxin Biotechnology Co., Ltd. (Chongqing, China). The animals were housed in a specific-pathogen-free (SPF) facility under controlled environmental conditions (temperature: 22 ± 2 °C; relative humidity: 40 ± 10 %) with a 12-h light/dark cycle. All mice had ad libitum access to standard chow and water. The experimental protocols were approved by the Animal Ethics Committee of Yan'an Hospital Affiliated to Kunming Medical University (Approval No. YXLL-AF-SQ-002/01).

All animal experiments were conducted in a randomized and blinded fashion. Mice were randomly assigned to treatment groups using a computer-generated randomization sequence. To minimize observer bias, the investigators responsible for osmotic pump implantation, blood pressure measurements, data collection, and all subsequent biochemical and histological analyses were blinded to the treatment allocations until the conclusion of the study.

2.3.2. Induction of hypertension model and experimental grouping

After a 2-week adaptation period, then measured baseline blood pressure, the 6-week-old mice were randomly divided into five experimental groups (n = 8 per group). All mice were anesthetized, and an osmotic mini-pump (Model 1003D; RWD Life Science, Shenzhen, China) was implanted subcutaneously in the dorsal midline, specifically within the interscapular region (between the shoulder blades).

The groups were defined as follows:(1) Sham group: received saline-filled pumps for 2 weeks.(2) HTN Model group: received AngII-filled pumps (1000 ng/kg/min) for 2 weeks. Blood pressure was measured weekly to confirm that the hypertensive mouse model was successfully modeled, and the HTN Model group were randomly grouped to i.p. injection SPH. (1) HTN Model group: subsequent daily i.p. injections of saline. (2)SPH Low-dose group: received AngII-filled pumps and subsequent daily i.p. injections of SPH (3.6 mg/kg).(3) SPH Mid-dose group: received AngII-filled pumps and subsequent daily i.p. injections of SPH (7.2 mg/kg).(4) SPH High-dose group: received AngII-filled pumps and subsequent daily i.p. injections of SPH (10.8 mg/kg). (5)Sham group (the hypertensive control group): subsequent daily i.p. injections of saline. The daily i.p. injections were administered for 14 consecutive days, starting from the day after the successful modeling of the hypertension model.

2.3.3. SPH treatment protocol

Following random assignment, the hypertensive mice were divided into treatment groups and received daily i.p. injections of SPH (D-erythro-Sphingosine, MCE, Cat# HY-101047) at doses of 3.6, 7.2, or 10.8 mg/kg for 14 days.To ensure complete solubility for administration, Sphingosine was dissolved in a vehicle solution consisting of 10 % dimethyl sulfoxide (DMSO), 40 % polyethylene glycol 300 (PEG300), 5 % Tween-80, and 45 % sterile saline. The hypertensive control group received daily i.p. injections of an equivalent volume of sterile saline. Throughout the intervention period, body weight, general behavior, and blood pressure were monitored.

At the end of the treatment period, all mice were euthanized. Blood was collected via eyeball enucleation into EDTA-anticoagulant tubes. The blood was allowed to stand at room temperature for 60 min, followed by centrifugation at 3000g for 15 min at 4 °C. The supernatant (serum) was collected and stored. The heart, kidneys, and aorta were collected, washed with ice-cold PBS, and stored at −80 °C for subsequent analysis.

2.3.4. Blood pressure measurement

Systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) were measured non-invasively using a CODA tail-cuff blood pressure system (KentScientific Corporation, Torrington, CT, USA). Measurements were taken one day before pump implantation and then weekly (on days 0,7, 14, 21, and 28) thereafter. Before each measurement, mice were acclimated to a heated platform (37 °C) for 10 min to ensure adequate blood flow to the tail. Each measurement cycle consisted of several preliminary readings followed by at least four formal readings. The final blood pressure for each mouse at each time point was calculated as the average of at least three consistent measurements after excluding any outliers.

2.3.5. Western Blot analysis

Total protein was extracted from the left ventricular tissue of the heart. The tissue was washed with PBS, weighed, and homogenized in a 1:100 ratio of RIPA lysis buffer (Strong; Beyotime, Cat# P0013B) supplemented with a protease inhibitor cocktail (Proteintech, Cat# PR20032) and a phosphatase inhibitor cocktail(Proteintech, Cat# PR20015). The homogenate was centrifuged at 4 °C, and the supernatant containing the protein lysate was collected. Protein concentration was etermined using a BCA assay. Equal amounts of protein from each sample were denatured by boiling in 5 × SDS loading buffer. The proteins were then separated by SDS-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred onto polyvinylidene fluoride (PVDF) membranes. The membranes were blocked with 5 % non-fat milk or bovine serum albumin (BSA) in Tris-buffered saline with Tween 20 (TBST) for 1 h at room temperature, followed by overnight incubation at 4 °C with the respective primary antibodies.

The following primary antibodies were used: NLRP3 (Abcam, Cat# ab270449), ASC (Abcam, Cat#ab307560), Caspase-1 (Proteintech, Cat# 31020-1-AP), Cleaved Caspase-1 (CST, Cat# 89332), IL-1β(Abcam, Cat# ab254360), Cleaved IL-1β (CST, Cat#63124), IL- 18 (Proteintech, Cat# 33710-1-AP), SOD1 (Proteintech, Cat# 10269-1-AP), BCL2 (Proteintech, Cat# 26593-1-AP), BAX(Proteintech, Cat# 50599-2-Ig), and GRP78 (Proteintech, Cat# 11587-1-AP),GAPDH (Proteintech, Cat#60004-1-Ig), Phospho-PI3 Kinase p85 (CST, Cat#4228T), Phospho-AKT(Proteintech, Cat#66444-1-Ig), AKT(Proteintech, Cat#60203-2-Ig), GSDMD (N-Terminal) (Affinity,Cat# DF13758),GSDMD (Proteintech,Cat# 20770-1-AP). After washing, the membranes were incubated with the appropriate horseradish peroxidase (HRP)-conjugated secondary antibodies, including HRPGoat Anti-Rabbit IgG (Proteintech, Cat# RGAR001) and HRP-Goat Anti-Mouse IgG (Proteintech, Cat# SA00001-1), at different dilution ratios for 1 h at room temperature. The protein bands were visualized using an enhanced chemiluminescence (ECL) detection kit (Proteintech, Cat# PK10003) and imaged.

2.3.6. Real-time quantitative PCR (RT-qPCR)

To use the Eastep® Super Total RNA Extraction Kit (Promega, Shanghai, Cat# LS1040), extracted total RNA from homogenized mouse heart tissue according to the manufacturer's protocol. The concentration and purity of the extracted RNA were determined using a spectrophotometer. Total RNA was reverse-transcribed into complementary DNA (cDNA) using a reverse transcriptase kit (Abm, Cat# G592). RT-qPCR was performed using BlasTaq™ 2X qPCR MasterMix (Abm, Cat# G895) in a total reaction volume of 20 μL. Each reaction contained 10 μL of 2X MasterMix, 0.5 μL of forward primer (10 μM), 0.5 μL of reverse primer (10 μM), a specified amount of template cDNA, and nuclease-free water to complete the volume. The reactions were run in triplicate for each sample on a CFX96 Touch Real-Time PCR Detection System (Bio-Rad). The thermal cycling conditions were as follows: an initial denaturation at 95 °C for 3 min, followed by 40 cycles of denaturation at 95 °C for 15 s and annealing/extension at 60 °C for 1 min. A melt curve analysis (65 °C–95 °C, with 0.5 °C increments every 5 s) was performed to confirm product specificity. The sequences of mouse-specific primers used for RT-qPCR are listed in Supplementary Table S1.The relative expression levels of target genes were calculated using the 2^−ΔΔCt method, β-actin is served as the internal reference gene for normalization.

2.3.7. Enzyme-linked immunosorbent assay (ELISA)

Serum levels of interleukin-18 (IL-18) and interleukin-1β (IL-1β) were quantified using commercial enzyme-linked immunosorbent assay (ELISA) kits (IL-18: Lianke, Cat#EK218-96; IL-1β: Lianke, Cat# EK201BHS-96) according to the manufacturers' instructions. Serum was prepared as described in section 2.3.3 and stored at −80 °C until use.The assays were performed strictly according to the manufacturers' instructions. 100 μL of standards or serum samples were added to the pre-coated 96-well plates and incubated at 37 °C for 2 h,and after washing the plate, biotin-labeled detection antibody and HRP-labeled streptavidin were added sequentially for reaction, followed by the addition of TMB substrate for color development, and the stop solution to stop the reaction. The optical density value (OD value) was read at 450 nm using a microplate reader Varioskan LUX Multimode Microplate Reader (Thermo Fisher Scientific, Waltham, MA, USA). and the concentration of each index in the sample was calculated according to the standard curve. All samples were tested thrice, and the intra- and inter-assay coefficients of variation were less than 10 %.

2.3.8. TUNEL assay

Apoptosis in myocardial tissue was assessed using a TUNEL Apoptosis Detection Kit (Proteintech, Cat# PF00006) according to the manufacturer's protocol. Deparaffinized and rehydrated tissue sections were treated with Proteinase K for 20 min. After washing, the sections were incubated with the TUNEL reaction mixture for 1 h at 37°Cin the dark. Nuclei were counterstained with 4′,6-diamidino-2-phenylindole (DAPI). The slides were then mounted and observed under a fluorescence microscope. Apoptotic cells exhibited green fluorescence, while DAPI-stained nuclei appeared blue.

2.4. In vitro experiments

2.4.1. Cell culture and treatment

Human Umbilical Vein Endothelial Cells (HUVECs) were purchased from CELLCOOK Biosciences (Guangzhou, China). The cells were cultured in Endothelial Cell Medium (ECM; ScienCell, Cat# 1001) supplemented with 10 % Fetal Bovine Serum (FBS; ScienCell, Cat# 0025), 1 % Endothelial Cell Growth Supplement (ECGS; ScienCell, Cat# 1052), and 1 % Penicillin/Streptomycin solution (ScienCell, Cat# 0503). Cells were maintained in a humidified incubator at 37 °C with 5 % CO₂.

For experiments, HUVECs were seeded into appropriate plates or dishes. Upon reaching 70–80 % confluence, cells were first challenged with Angiotensin II (AngII; 1 μM; Sigma-Aldrich, USA) for 12 h. Then the cells were treated with various concentrations of SPH for an additional 24 h before being collected for downstream analyses, including CCK-8 assays, RNA extraction, and protein isolation.

2.4.2. Cell viability assay

HUVEC viability was assessed using a Cell Counting Kit-8 (CCK-8) assay. Cells were seeded in 96-well plates at a density of 2 × 10⁴ cells/well. After the AngII and SPH treatments, the culture medium was replaced with fresh medium containing 10 μL of a Cell Counting Kit-8 (CCK-8) assay (Proteintech, Cat# PF00004). Following a 1-h incubation at 37 °C, the absorbance at 450 nm was measured using Varioskan LUX Multimode Microplate Reader (Thermo Fisher Scientific, Waltham, MA, USA).

The optimal non-toxic concentration of SPH was determined from the resulting doseresponse curve for subsequent experiments.

2.4.3. Measurement of oxidative stress markers

2.4.3.1. Preparation of cell lysates for oxidative stress assays

Following treatment, cells were collected and washed twice with ice-cold PBS. To preparelysates for MDA and SOD assays, cells (5 × 10⁶ cells) were resuspended in the appropriate extraction buffer provided by the kit manufacturer and lysed via ultrasonication (200W, 3s on, 10s off, repeated 30 times) on ice. The lysates were then centrifuged at 12000×g for 10 min at 4 °C, and the resulting supernatant was collected for analysis.

2.4.3.2. Reactive oxygen species (ROS) assay ROS

Intracellular ROS levels were measured using a Reactive Oxygen Species Assay Kit (Proteintech, Cat# PF00004), according to the manufacturer's protocol. Briefly, after treatment HUVECs were washed with PBS and then incubated with 10 μM 2′,7′-dichlorofluorescin diacetate (DCFH-DA) in serum-free medium for 30 min at 37 °C in the dark. After incubation, the cells were washed again with PBS to remove the excess probe. Fluorescence images were immediately captured using an inverted fluorescence microscope. For quantification, the mean fluorescence intensity of at least three random fields per well was measured using ImageJ software. All experiments were performed in triplicate.

2.4.3.3. Malondialdehyde (MDA) assay

The concentration of MDA in cell lysates was determined using a Thiobarbituric Acid (TBA) method-based commercial kit (Solarbio, Cat# BC0025). The absorbance of the final reaction product was measured at 532 nm and 600 nm (for background correction). The MDA value is calculated according to the manufacturer's protocol.

2.4.3.4. Superoxide dismutase (SOD) activity assay

Total SOD activity in cell lysates was measured using a WST-1 method-based commercial kit (Solarbio, Cat# BC5165) according to the manufacturer's instructions. The absorbance was measured at 450 nm.

2.4.4. Nitric oxide (NO) assay

The level of nitric oxide (NO) in the cell culture supernatant was indirectly measured by quantifying its stable metabolites (NO₂⁻and NO₃⁻) using a Nitric Oxide Assay Kit (Beyotime, Cat# S0021S). It is based on the Griess reaction was performed according to the manufacturer's protocol. The absorbance of the final azo dye product was measured at 540 nm, and NO concentration was calculated from a sodium nitrite standard curve.

2.4.5. Western Blot analysis

Total protein was extracted from HUVECs using RIPA lysis buffer (Strong; Beyotime, Cat# P0013B) supplemented with protease and phosphatase inhibitors. The subsequent steps, including protein quantification, SDS-PAGE, membrane transfer, blocking, and antibody incubation, were performed as described in s ection 1.3.5. Except Cleaved Caspase-1 (CST, Cat# 4199), Cleaved IL-1β (CST, Cat# 83186), GSDMD(N-Terminal) (Abcam, Cat#ab215203), NLRP3 (Proteintech, Cat#27458-1-AP), ASC (CST # 78000), Caspase-1 (Proteintech, Cat# 22915-1-AP), IL-18 (Proteintech, Cat# 10663-1-AP). The primary antibodies used were specific for Cleaved Caspase-1, IL-1β, Cleaved IL-1β, SOD1, and GRP78.

2.4.6. Real-time quantitative PCR (RT-qPCR)

To use the Eastep® Super Total RNA Extraction Kit (Promega, Shanghai, Cat# LS1040), extracted total RNA from homogenized HUVECs and RT-qPCR was performed as described in s ection 1.3.6. The sequences of all human-specific primers used for RT-qPCR are listed in Supplementary Table S2.The relative expression levels of target genes were calculated using the 2^−ΔΔCt method, β-actin is served as the internal reference gene for normalization.

2.4.7. Enzyme-linked immunosorbent assay (ELISA)

The concentrations of secreted proinflammatory cytokines in the cell culture supernatant were quantified using commercial ELISA kits for human IL-1β (Proteintech, Cat# KE00021) and human IL-18 (Proteintech, Cat# KE00193), following the manufacturer's instructions.

2.4.8. Immunofluorescence

Cells were cultured on glass coverslips in 6-well plates. After experimental treatments, fixed with 4% paraformaldehyde (PFA) for 15 min, permeabilized with 0.25% Triton X-100 for 10 min, and then blocked with 5% BSA for 1 h at room temperature. Probing for target proteins involved overnight incubation at 4°C with the following primary antibodies:anti-NLRP3(Proteintech, Cat#27458-1-AP), anti-ASC (CST # 78000), diluted 1:100.Visualization was achieved by incubating the cells for 1 h in the dark with conjugated goat anti-rabbit secondary antibodies. Coverslips were subsequently mounted onto slides using an antifade medium containing DAPI for nuclear counterstaining. Images were acquired on a laser scanning confocal microscope.

3. Statistical analysis

All statistical analyses were performed using IBM SPSS Statistics (Version 29.0) and GraphPad Prism (Version 9.0.2, GraphPad Software, USA). Analysis of human demographic and clinical data was conducted as follows: A two-tailed p-value of <0.05 was considered statistically significant throughout the study.

For the analysis of human clinical data, continuous variables were first assessed for normal distribution using the Shapiro-Wilk test. Normally distributed data are presented as the mean ± standard deviation (SD) and were compared between the two groups using an unpaired two-tailed Student's t-test. Data not following a normal distribution are presented as the median with interquartile range (IQR) and were compared using the Mann-Whitney U test. Categorical variables are presented as counts (n) and percentages (%) and were analyzed using the Chi-square (χ²) test.

For the analysis of experimental data from animal and in vitro studies, all experiments were independently repeated at least three times. These data are presented as the mean ± standard error of the mean (SEM). The assumption of homogeneity of variances was assessed using Levene' s test. For comparisons between two groups, an unpaired Student's t-test or Mann-Whitney U test was used as appropriate. For comparisons among three or more groups, a one-way analysis of variance (ANOVA) was performed, followed by Dunnett' s posthoc test for multiple comparisons against a single control group. For experiments involving two factors (stimulation and treatment), a two-way ANOVA followed by Tukey's post-hoc test was used to analyze interactions and main effects. For all analyses, a two-tailed p-value of<0.05 was considered statistically significant. Specific significance levels in figures are denoted as follows: ∗P < 0.05, ∗∗P < 0.01, and ∗P < 0.001.

4. Results

4.1. Baseline characteristics of the study participants

A total of 75 participants were enrolled in this study, consisting of 37 patients with hypertension (HTN group) and 38 healthy individuals (CTR group). The two groups were well-matched, with no significant differences observed in age (HTN: 59.31 ± 6.51 years vs. CTR: 58.00 ± 5.78 years; P > 0.05) or sex distribution (P > 0.05).As expected, patients in the HTN group had higher systolic (P < 0.001) and diastolic blood pressure (P < 0.001) compared to the healthy controls. Furthermore, the HTN group exhibited a clear decreased levels of red blood cell count (RBC), hematocrit (HCT), hemoglobin (HGB), total protein (TP), and blood glucose (P < 0.05 for all).Conversely, the level of alanine aminotransferase (ALT) was lower in the HTN group compared to the CTR group (P < 0.05). A comprehensive summary of the demographic, anthropometric, and biochemical characteristics of all participants is presented in Supplementary Table S1. No other statistically significant differences were observed in the baseline characteristics between the two groups.

4.2. Non-targeted metabolomics analysis reveals metabolic dysregulation in hypertension

4.2.1. Hypertension induces systemic metabolic reprogramming centered on sphingolipid metabolism

To map the metabolic landscape of hypertension, we first applied a non-targeted LC-MS/MS approach to serum samples from our patient and control cohorts. An initial, unsupervised look at the data using Principal Component Analysis (PCA) was revealing: it showed a clear trend of separation between the HTN and CTR groups, suggesting a distinct metabolic fingerprint for hypertension (Fig. 3A and B).To sharpen this initial observation and pinpoint the molecules driving this difference, we built supervised OPLS-DA models. These models successfully captured the variation between the groups, resulting in a complete and robust discrimination between the hypertensive and control profiles (Fig. 3C and D). We rigorously validated these models to ensure they were not simply an artifact of overfitting. A series of permutation tests confirmed the statistical integrity of our positive ion mode model (R²Y = 0.97, Q² = 0.30, P < 0.05), as demonstrated by a negative Q² intercept in its validation plot (Fig. 3E–G). Interestingly, while the negative ion mode model also appeared robust against overfitting (Fig. 3F), its overall predictive power did not reach statistical significance (P > 0.05) (Fig. 3H). This finding led us to focus our subsequent biomarker discovery primarily on the more statistically powerful positive ion mode data. Hierarchical clustering analysis visualized the specific patterns of these metabolic changes. A large cluster of lipid species was upregulated in the HTN group, while certain amino acids and their derivatives showed a downward trend (Fig. 4A and B).

Fig. 3.

Multivariate statistical analysis reveals a distinct metabolic signature in hypertensive patients. (A, B) Principal Component Analysis (PCA) score plots of serum metabolomic data in positive (A) and negative (B) ion modes. (C, D) Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) score plots in positive (C) and negative (D) ion modes. Red circles represent controls (C), and blue squares represent hypertensive patients (HTN). (E–H) Permutation tests validating the OPLS-DA models. The negative Q² intercepts (E, F) and significant p-value for the positive ion mode. (G, H) Histograms of the permutation test results (G) For the negative ion mode (H).In all score plots (A-D), red circles represent the control group (C), and blue squares represent the hypertension group (HTN). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 4.

Heatmap and pathway analysis of differentially abundant metabolites. (A, B) Hierarchical clustering heatmaps of the top 50 differentially abundant metabolites identified in positive (A) and negative (B) ion modes. (C, D) Bubble plots of the KEGG pathway enrichment analysis for metabolites identified in positive (C) and negative (D) ion modes. (E, F) Treemap visualization of the enriched metabolic pathways in positive (E) and negative (F) ion modes.

When we looked at the specific pathways perturbed in hypertension, our analysis pointed strongly toward a single conclusion. In the positive ion mode, Sphingolipid metabolism emerged as the most significantly impacted pathway (P < 0.05), clearly highlighting it as a central hub of dysregulation in our patient cohort (Fig. 4C–E). While we also observed disturbances in core energy pathways like the TCA cycle and Purine metabolism in the negative ion mode (Fig. 4D–F), the signal from the sphingolipid axis was unmistakable.

Drilling down into the individual metabolites, we identified the sphingolipid precursor sphinganine (SA) as one of the most significantly elevated molecules in the HTN group (Supplementary Table S2). This was a critical clue. It pointed not necessarily to sphingosine itself, but to a potential "bottleneck" in its production pathway. We reasoned that if SA was accumulating, its downstream product, sphingosine (SPH) might be the more functionally relevant molecule. To test this, we ran a preliminary screen and found that while SA had a minimal protective effect on AngII-injured endothelial cells, SPH was potently active (data not shown).This series of findings—from the global metabolic profile down to a single enzymatic step,it provided a clear and compelling rationale for our study. We therefore proceeded with a comprehensive investigation focused specifically on the functional role of SPH in the context of hypertension Fig. 5.

Fig. 5.

Schematic of the overall study design. The study began with the collection of serum samples from healthy controls and patients with hypertension for a non-targeted metabolomics analysis (LC-MS) to screen for differentially abundant metabolites. Based on these findings, the function and underlying mechanism of a key identified metabolite, Sphingosine (SPH), were subsequently validated using both in vitro (AngII-induced endothelial cell injury) and in vivo (AngII-induced hypertensive mouse) models.

4.2.2. Identification of differentially abundant metabolites (DAMs)

Metabolites responsible for the group separation were identified based on a dual-criterion selection: a Variable Importance in Projection (VIP) score>1.0 from the OPLS-DA, model and a p-value<0.05 from a Student's t-test. This screening process yielded a total of 414 differential metabolic features (119 in positive ion mode and 295 in negative ion mode). 22 of these features were confidently identified as distinct metabolites, after annotation against in MS2 database.These 22 DAMs belong to diverse chemical classes, including organic acids, alkaloids, amino acids, and complex lipids such as sphingolipids (sphingosine, lactosylceramide (d18:1/16:0)) and glycerophospholipids (PC(16:0/16:0)). Sphingosine was identified as one of the key DAMs discriminating between the HTN and CTR groups. A comprehensive list of these 22 identified DAMs, along with their statistical significance, is provided in Supplementary Table S2.

4.2.3. Metabolic pathway enrichment analysis

To understand the biological functions of the DAMs, we performed a pathway enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The analysis revealed that the 22 identified DAMs were enriched in 14 metabolic pathways, suggesting their potential involvement in the pathogenesis of hypertension.These enriched pathways included key biological processes such as Sphingolipid metabolism, Nicotinate and nicotinamide metabolism, Ascorbate and aldarate metabolism, the Citrate cycle (TCA cycle), Glycolysis/Gluconeogenesis, and Purine metabolism. (Fig. 4C–F. Supplementary Table S3).

Sphingolipid metabolism was one of the most noticeably affected pathways among these, based on pathway impact scores and the number of associated DAMs, including sphingosine itself. Given its importance in pathway analysis and the known role of sphingolipids in cell signaling, we selected the sphingolipid metabolism pathway and its key metabolite, sphingosine, to further explore its mechanisms.

4.3. In vivo administration of SPH ameliorates angiotensin II-induced hypertension and end-organ damage

4.3.1. SPH lowers blood pressure and prevents cardiac hypertrophy in hypertensive mice

To evaluate the physiological effects of SPH in vivo, we established a mouse model of hypertension via continuous AngII infusion (Fig. 6A). AngII infusion led to a significant and sustained increase in SBP, DBP, and MAP compared to control (NC) group (Fig. 6B–G).Treatment with SPH at all doses (3.6, 7.2, and 10.8 mg/kg/day) significantly attenuated this pressor response, effectively lowering the blood pressure in hypertensive mice (P < 0.05 for all doses vs. HBP group). AngII infusion induced significant cardiac hypertrophy, as evidenced by the increased heart size in the HBP group. SPH treatment prevented this change, with the hearts of SPH-treated mice appearing smaller and comparable to those of the NC group (Fig. 6I). No significant differences in body weight were observed among the all groups(Fig. 6H), indicating that the observed effects were not due to general toxicity.

Fig. 6.

Sphingosine (SPH) ameliorates Angiotensin II-induced hypertension and cardiac hypertrophy in mice.

(A) Schematic of the in vivo experimental design and timeline.(B–D) Time-course analysis of systolic blood pressure (SBP) (B), diastolic blood pressure (DBP) (C), and mean arterial pressure (MAP) (D) measured weekly from baseline (W1) to the experimental endpoint (W5).(E–G) Bar graphs comparing the final SBP (E), DBP (F), and MAP (G) of mice in each group at the end of the study. (H) Comparison of final body weights across all experimental groups. (I) Representative macroscopic images of whole hearts harvested from each group at the experimental endpoint, showing differences in cardiac size. ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001, ∗∗∗P < 0.0001 vs. the HBP group. Abbreviations: NC, normotensive control; HBP, hypertension (AngII-induced) model; SPH3.6, SPH low dose (3.6 mg/kg); SPH7.2, SPH medium dose (7.2 mg/kg); SPH-10.8, SPH high dose (10.8 mg/kg); MCC, MCC950 treatment group.

4.3.2. SPH attenuates pathological cardiac remodeling and fibrosis

We next assessed the protective effects of SPH on cardiac structure using histopathology. H&E staining of heart sections revealed that AngII infusion caused significant myocardial damage, characterized by myocyte disarray, interstitial edema, and inflammatory cell infiltration. These pathological changes were markedly ameliorated by SPH administration (Fig. 7C). To assess fibrosis, Masson's trichrome staining was performed. The HBP model group exhibited extensive collagen deposition (stained blue) in the myocardial interstitium, indicative of significant cardiac fibrosis. SPH treatment, particularly at higher doses, substantially reduced the fibrotic area, preserving the structural integrity of the myocardium (Fig. 7A).

Fig. 7.

SPH attenuates pathological changes and suppresses NLRP3 inflammasome

activation in the hearts of hypertensive mice.

(A, B) Masson's trichrome staining of heart (A) and aorta (B) sections, showing reduced collagen deposition (blue) with SPH treatment. Scale bars: 20 μm for heart, 50 μm (main) and 20 μm (inset) for aorta. (C) H&E staining of heart sections showing amelioration of myocardial disarray and inflammation. Scale bars: 500 μm (main) and 20 μm (inset).(D) ELISA quantification of serum IL-18 and IL-1β levels. Data are mean ± SEM. ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001, ∗∗∗∗P < 0.0001 vs. HBP group. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

4.3.3. SPH protects against aortic remodeling and fibrosis

We examined the impact of SPH on the aorta. H&E staining showed that AngII induced significant aortic remodeling, including endothelial disarray and medial thickening. SPH treatment mitigated these structural changes (Fig. 7A). Furthermore, Masson's staining revealed substantial collagen deposition within the aortic wall of HBP mice, confirming vascular fibrosis. This AngII-induced fibrosis was also significantly reduced in mice treated with SPH (Fig. 7B).

4.3.4. The cardioprotective effects of SPH are similar with NLRP3 inflammasome inhibitor

Importantly, the protective effects observed with SPH treatment were phenocopied by the administration of MCC950 which is a specific inhibitor of the NLRP3 inflammasome. The MCC950-treated group showed a similar reduction in blood pressure and a marked attenuation of AngII-induced cardiac and aortic remodeling, including reduced inflammation and fibrosis (Fig. 7A,B,C).This striking similarity in outcomes strongly suggests that SPH exerts its cardioprotective and antihypertensive effects primarily by suppressing the NLRP3 inflammasome pathway.

4.4. SPH ameliorates hypertension-induced myocardial injury by inhibiting the NLRP3

Inflammasome pathway to research the molecular mechanisms underlying the protective effects of SPH, we investigated its impact on key pathological pathways in the heart tissue of AngII-infused mice.

4.4.1. SPH suppresses NLRP3 inflammasome activation in the heart

Given the central role of sterile inflammation in hypertension, we first examined the NLRP3 inflammasome pathway. Western blot analysis revealed that the protein levels of key inflammasome components—including NLRP3, its adaptor protein ASC, and the active form of Caspase-1 (cleaved-Caspase-1)—were upregulated in the hearts of the HBP group compared to the NC group. SPH administration markedly and dose-dependently suppressed the expression of these proteins (Fig. 8A and B). Consistent with the protein level data, qRT-PCR analysis demonstrated that the mRNA expression of Nlrp3, Asc, and Caspase 1 was raised in the HBP group and wassubstantially downregulated by SPH treatment (Fig. 8C–H). These findings indicate that SPH inhibits the activation of the NLRP3 inflammasome at both the transcriptional and post-translational levels.

Fig. 8.

SPH regulates NLRP3 inflammasome, ER stress, oxidative stress, and

apoptosis/survival pathways in the hearts of hypertensive mice.

(A, B) Representative Western blots and densitometric analysis for ER stress (GRP78), NLRP3 inflammasome components (NLRP3, ASC, C-Caspase-1, C-IL-1β, C-GSDMD), and oxidative stress (SOD1). (C–H) Relative mRNA expression of Nlrp3, Asc, Casp1, Gsdmd, IL1b, and IL18 by qRT-PCR. (I, J) Representative Western blots and densitometric analysis for apoptosis markers (BAX, BCL2, C-Caspase-3) and PI3K/AKT pathway proteins (p-PI3K, PI3K, p-AKT, AKT). GAPDH or total proteins were used for normalization. Data are mean ± SEM (n = 3 per group). Statistical analysis was by one-way ANOVA with Tukey's post-hoc test.

4.4.2. SPH reduces systemic and local pro-inflammatory cytokine production

Activation of the NLRP3 inflammasome leads to the maturation and release of pro-inflammatory cytokines. We found that the protein expression of pro-IL-1β and cleaved-IL-1β, as well as the mRNA levels of IL1b and IL18, were significantly increased in the hearts of HBP mice. SPH treatment effectively reversed these changes (Fig. 7A, B, G, H). To assess whether this local antiinflammatory effect translated to a systemic response, we measured cytokine levels in the serum. As shown by ELISA, the circulating concentrations of IL-1β and IL-18 were raised in the HBP group. SPH administration, in a manner comparable to the specific NLRP3 inhibitor MCC950, robustly reduced the serum levels of both cytokines (Fig. 7E).

4.4.3. SPH mitigates myocardial oxidative and endoplasmic reticulum (ER) stress

We explored the effect of SPH on cellular stress pathways often linked to NLRP3 activation. Western blot analysis showed that the expression of the ER stress marker GRP78 and the oxidative stress marker SOD1 were significantly altered in the hearts of HBP mice. SPH treatment effectively normalized the expression of both GRP78 and SOD1, suggesting that SPH alleviates both ER and oxidative stress in the hypertensive heart (Fig. 8A and B).

4.4.4. SPH inhibits apoptosis and activates the pro-survival PI3K/AKT pathway

Finally, we investigated whether SPH could protect cardiomyocytes from apoptosis. In the HBP group, we observed a significant increase in the expression of the pro-apoptotic proteins BAX and cleaved-Caspase-3, alongside a decrease in the anti-apoptotic protein BCL2. SPH treatment reversed this apoptotic phenotype, decreasing BAX and cleaved-Caspase-3 levels while restoring BCL2 expression (Fig. 8I and J). Furthermore, SPH treatment increased the phosphorylation of key pro-survival kinases PI3K and AKT, indicating the activation of this critical cell survival pathway (Fig. 8I and J). These results demonstrate that SPH exerts its cardioprotective effects through a multi-faceted mechanism involving the potent suppression of NLRP3 inflammasome-mediated inflammation, alleviation of cellular stress, and promotion of cardiomyocyte survival via the PI3K/AKT pathway.

4.5. SPH directly protects endothelial cells by inhibiting AngII-Induced NLRP3 inflammasome activation

To validate our in vivo findings and explore the direct effects of SPH on endothelial cells, we established an in vitro model of hypertensive injury using AngII-treated Human Umbilical Vein Endothelial Cells (HUVECs).

4.5.1. SPH protects HUVECs from AngII-Induced cell injury

First, we determined the optimal working concentration of SPH. HUVECs were treated with various concentrations of SPH for 24 h. A CCK-8 assay revealed that SPH exhibited no significant cytotoxicity at concentrations up to 1.5 μM (Fig. 9B). Next, to mimic hypertensive injury, HUVECs were challenged with AngII (1 μM for 12 h), which resulted in a significant decrease in cell viability. Subsequent treatment with SPH (0.5–1.5 μM) for 24 h dosedependently rescued the cells from AngII-induced death (Fig. 9A). Based on these results, a concentration of 0.5 μM SPH was selected for subsequent mechanistic studies.

Fig. 9.

SPH protects human umbilical vein endothelial cells (HUVECs) from Angiotensin IIinduced injury, pyroptosis, and oxidative stress by inhibiting the NLRP3 inflammasome pathway.

(A) Cell viability of HUVECs treated with AngII (1 μM) for 12 h followed by various concentrations of SPH (0.5–6 μM) for 24 h. (B) Cell viability of HUVECs treated with SPH alone for 24 h. (C, D) Representative Western blots and densitometric analysis for NLRP3 inflammasome components (NLRP3, ASC, c-Caspase-1, c-IL-1β), the pyroptosis effector N-GSDMD, ER stress marker GRP78, and oxidative stress marker SOD1. (E–J) Relative mRNA expression of NLRP3, ASC, CASP1, IL1B, IL18, and GSDMD by RT-qPCR. Data are the mean ± SEM of three independent experiments. ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001, ∗∗∗∗P < 0.0001 vs. the AngII-only group, unless otherwise indicated.

4.5.2. SPH suppresses AngII-Induced NLRP3 inflammasome activation and pyroptosis in HUVECs

Consistent with our in vivo data, AngII stimulation triggered a robust activation of the NLRP3 inflammasome in HUVECs. Western blot analysis showed a significant upregulation of NLRP3, ASC, and cleaved-Caspase-1, along with the maturation of IL-1β (c-IL-1β). Importantly, these effects were reversed by SPH treatment (0.5 μM) (Fig. 9C and D). The efficacy of SPH was comparable to that of the specific NLRP3 inhibitor, MCC950. Activation of the inflammasome can lead to pyroptosis, a form of inflammatory cell death executed by Gasdermin D (GSDMD). We observed that AngII led to a marked increase in the active N-terminal fragment of GSDMD (N-GSDMD), indicating the induction of pyroptosis. SPH suppressed the cleavage of GSDMD (Fig. 9C and D). These protein-level findings were supported by RT-qPCR analysis, which demonstrated that SPH and MCC950 both significantly inhibited the AngII-induced transcriptional upregulation of NLRP3, ASC, CASP1, IL1B, IL18, and GSDMD (Fig. 9E–J). These results confirm that SPH directly protects endothelial cells by suppressing the NLRP3 inflammasome-pyroptosis axis at both the transcriptional and post-translational levels.

4.5.3. SPH alleviates AngII-Induced endothelial oxidative and ER stress

We then investigated the effect of SPH on cellular stress pathways in HUVECs. AngII treatment led to significant oxidative stress, as evidenced by the downregulation of the antioxidant enzyme SOD1. SPH treatment restored SOD1 protein levels, indicating a potent antioxidant effect (Fig. 9C and D). AngII induced endoplasmic reticulum (ER) stress, shown by the upregulation of the ER stress marker GRP78. This was alleviated by SPH (Fig. 9C and D).

4.5.4. SPH attenuates endothelial cell poptosis and oxidative stress

To further characterize the protective mechanisms of SPH at the cell level, we performed additional assays for inflammasome assembly, apoptosis, and oxidative stress. Immunofluorescence staining confirmed that AngII timulation increased the expression and potential co-localization of the inflammasome components NLRP3 and ASC, an effect that was visibly attenuated by SPH treatment (Fig. 10A and B). AngII induced significant endothelial cell apoptosis, as demonstrated by a marked increase in TUNEL-positive cells. SPH treatment provided substantial protection, significantly reducing the number of apoptotic cells (Fig. 10C). We then assessed markers of oxidative stress. AngII treatment significantly increased the production of intracellular reactive oxygen species (ROS) and the lipid peroxidation marker malondialdehyde (MDA), while decreasing the activity of the antioxidant enzyme superoxide dismutase 1 (SOD1)and levels of nitric oxide (NO) (Fig. 10D–H). The effect of SPH administration was similar to that of NLRP3 inhibitor MCC950, effectively reversing all these changes and restoring the redox balance of the cells. Electron microscopy imaging revealed that AngII caused significant cell surface damage characteristic of pyroptosis, including punch holes in the cell membrane and its integrity lossed. SPH treatment preserved a much healthier and more intact cell morphology, comparable to the MCC950 group(Fig. 10K).

Fig. 10.

SPH mitigates Angiotensin II-induced inflammasome activation, apoptosis, and

oxidative stress in HUVECs.

(A, B) Representative immunofluorescence images showing increased expression of NLRP3 (red, A) and ASC (green, B) in AngII-treated cells, which is reduced by subsequent SPH treatment. Scale bar = 100 μm. (C) TUNEL assay (green) demonstrating increased apoptosis in AngII-treated cells, which is attenuated by SPH. Scale bar = 100 μm. (D, E) Biochemical assays showing that SPH or MCC950 treatment reverses the AngII-induced decrease in SOD1 activity (G).NO (H) and increase in MDA levels (D). (F, G) DCFH-DA staining showing increased intracellular ROS (green) after AngII treatment, which is suppressed by SPH or MCC950. Representative images (F) and quantification (E) are shown. (I, J) ELISA results showing that SPH or MCC950 treatment inhibits the AngII-induced secretion of IL-1β (I) and IL-18 (J). (K) Scanning electron microscopy (SEM) images showing that SPH or MCC950 treatment improves the cell surface morphology and reduces features of pyroptotic damage induced by AngII. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

5. Discussion

The principal finding of this study is the previously unrecognized protective role of the sphingolipid metabolite, sphingosine (SPH), in the pathophysiology of hypertension. This suggests that beyond traditional risk factors, alterations in bioactive lipid signaling are a core component of hypertension's pathology, and that correcting dysregulated sphingolipid metabolism by restoring a functional SPH pool could represent a novel therapeutic strategy. Our data show that exogenous SPH administration markedly ameliorated AngII-induced hypertension, cardiovascular remodeling, and endothelial cell injury, indicating a potent and multifaceted protective effect.

A key mechanistic insight from our findings relates to the dual role of SPH in suppressing both inflammation and oxidative stress. We propose a "metabolic bottleneck" hypothesis, which originated from our initial metabolomics screen showing an accumulation of sphinganine (SA), not SPH, in hypertensive patients. Because the enzyme DES1 catalyzes the conversion of SA to SPH, this finding strongly suggests that hypertension may be associated with impaired DES1 activity, thereby limiting the endogenous production of functionally critical SPH [15,24]. Our results also help to clarify the complex role of SPH in inflammation. While some in vitro studies suggest SPH can activate the NLRP3 inflammasome [21], our data demonstrate that in the specific context of AngII-induced stress, SPH acts as a potent inhibitor of this pathway. We found that SPH downregulated the expression of NLRP3, ASC, and active Caspase-1, leading to reduced IL-1β and IL-18 release. This anti-inflammatory effect is intimately linked to SPH's antioxidant properties. Given that reactive oxygen species (ROS) are a known trigger for NLRP3 inflammasome activation [9,[25], [26], [27]], we propose that SPH's ability to reduce ROS production is a primary mechanism for its inflammasome suppression.

From a clinical perspective, our findings have two major implications. First, the altered SA/SPH ratio might can serve as a novel biomarker to identify hypertensive individuals at higher risk for target organ damage. Second, the SPH-NLRP3 axis itself represents a promising new therapeutic target for antihypertensive drug development. Hypertension is increasingly viewed as a disease of metabolic inflexibility, and the NLRP3 inflammasome is a shared driver in many related cardiometabolic diseases, including atherosclerosis and type 2 diabetes [[28], [29], [30], [31]]. Our study positions SPH as a key node connecting sphingolipid metabolism to the inflammatory and oxidative stress responses that drive hypertensive pathology.

Furthermore, our molecular findings provide a potential mechanistic explanation for the well-established benefits of lifestyle interventions in hypertension management. The link between this metabolic-inflammatory axis and clinical outcomes is underscored by the importance of such interventions. For instance, increased physical activity, measured by daily step count, is proven to lower the risk of atherosclerotic cardiovascular disease (ASCVD) in high-risk patients [32]. We propose that a potential underlying mechanism for this benefit could be the modulation of the very sphingolipid pathways identified in our study, thereby dampening the associated pro-inflammatory state. Similarly, interventions using smart devices that improve cardiorespiratory fitness (peak VO₂) have been successful [33]. Given that higher peak VO₂ is strongly associated with lower systemic inflammation, it is plausible that the benefits of such digital health interventions are, at least in part, mediated through the normalization of the metabolic signatures we have reported. Additionally, the ability of mobile health technology to improve heart rate variability (HRV), a marker of autonomic function, in high-risk patients is particularly relevant [34]. Since autonomic dysfunction is intertwined with systemic inflammation, future research should explore whether the positive effects of lifestyle modifications on HRV also translate to a beneficial regulation of this novel metabolic-inflammatory axis.

We acknowledge several limitations in our study. A key strength is the integration of clinical metabolomics with preclinical mechanistic validation; however, our findings are based on an AngII-infusion model, and validation in other models (spontaneously hypertensive rats) is needed to assess generalizability. Second, our "metabolic bottleneck" hypothesis, while strongly supported by our data, remains an inference. Future studies should aim to directly measure DES1 activity in hypertensive tissues. Third, our mechanistic work has not yet pinpointed the direct molecular interactor of SPH. Finally, the long-term efficacy and safety of SPH supplementation require more extended evaluation before clinical translation can be considered.

In conclusion, this study demonstrates that sphingosine alleviates AngII-induced hypertension and cardiovascular damage by suppressing NLRP3 inflammasome activation and oxidative stress. We propose a "metabolic bottleneck" hypothesis wherein hypertension is associated with impaired endogenous SPH production, which can be therapeutically overcome by exogenous SPH supplementation. These findings establish the SPH-NLRP3 axis as a novel and promising therapeutic target. Future prospective studies should investigate whether plasma sphingolipid profiles can predict patient responses to lifestyle interventions and whether targeting the DES1 enzyme or SPH signaling offers a new strategy for managing hypertension and its associated cardiometabolic complications.

6. Conclusion

This study demonstrates that SPH exerts important protective effects against Angiotensin II-induced hypertensive injury both in vivo and in vitro. Our findings reveal that SPH modifies hypertension and adverse cardiovascular remodeling in mice by suppressing the NLRP3 inflammasome pathway. SPH inhibits the activation of the NLRP3 inflammasome and subsequent release of pro-inflammatory cytokines, attenuates oxidative stress, and prevents endothelial cell apoptosis and pyroptosis. These multifaceted protective actions highlight SPH is a critical regulator of endothelial homeostasis and suggest that targeting the SPH-NLRP3 axis represents a novel and promising therapeutic strategy for the treatment of hypertension and its associated cardiovascular complications.

CRediT authorship contribution statement

Wenjun Li: Writing – review & editing, Writing – original draft, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation. Dan Zhou: Visualization, Software, Resources, Project administration. Yanmei Ji: Project administration, Methodology, Investigation. Meirong Yang: Writing – review & editing, Visualization, Software, Resources, Investigation, Data curation. Yunhong Yang: Validation, Supervision, Software, Resources. Mengyao Dao: Validation, Project administration, Methodology. Xianyu He: Visualization, Validation, Project administration, Data curation. Xingfang Jin: Visualization, Methodology, Funding acquisition, Conceptualization.

Ethics approval and consent to participate

The study was approved by the Medical Ethics Committee of Yan'an Hospital in Kunming, Yunnan Province (2020-096-01)., and written informed consent was obtained from all subjects. All animal experiments were performed in compliance with institutional guidelines and were approved by the Institutional Animal Care and Use Committee of Yan'an Hospital in Kunming, (2023046).

Consent for publication

Not applicable.

Availability of data and materials

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

Funding

This work was supported by the Joint Program of Yunnan Provincial Department of Science and Technology and Kunming Medical University (Grant No. 202401AY070001-320); the Central Government Fund for Guiding Local Science and Technology Development of Yunnan Province (Grant Nos. 202307AB110005 and 202107AA110003); the Health Research Project of Kunming Municipal Health Commission (Grant No. 2023-03-01-003); the Open Project Fund of the Yunnan Provincial Key Laboratory for Cardiovascular Diseases (Grant No. 2024SPR-07); the Clinical Medical Research Center for Cardiovascular Diseases of Yunnan Province (Grant No. 202102AA310003); the Yunnan Provincial Key Laboratory for Tumor Immunobiology and Prevention (Grant No. 2017DG004-01); and the National Natural Science Foundation of China (NSFC) (Grant No. 81460209).Key Laboratory of Cardiovascular Disease of Yunnan Province, China (2018DG008). Kunming Health Science and Technology Talent Program: Reserve Talent in Medical Science and Technology (2023-SW (Reserve)-10).Clinical Medical Center for Cardiovascular Disease of Yunnan Province (ZX-2019-08-01)

Declaration of competing interest

The authors declare no competing interests.

Abbreviations

The following abbreviations are used in this manuscript.

SBP

Systolic Blood Pressure

DBP

Diastolic Blood Pressure

GGT

Gamma-Glutamyl Transferase

ALB

Albumin

WBC

White Blood Cell

TSH

Thyroid-Stimulating Hormone

CHOL:

Total Cholesterol

LDL-CH

Low-Density Lipoprotein Cholesterol

TG

Triglycerides

HDL-CH

High-Density Lipoprotein Cholesterol

ALT

Alanine Aminotransferase

AST

Aspartate Aminotransferase

S/L:

AST/ALT Ratio

RBC

Red Blood Cell

HCT

Hematocrit

Cr

Creatinine

IBIL:

Indirect Bilirubin

UREA

Urea

UA

Uric Acid

GLOB

Globulin

LDH

Lactate Dehydrogenase

HGB

Hemoglobin

PLT

Platelet

FT4

Free Thyroxine

FT3

Free Triiodothyronine

DBIL:

Direct Bilirubin

NEU

Neutrophil

TBIL:

Total Bilirubin

TP

Total Protein

GLU

Glucose

Appendix A. Supplementary data

The following is the Supplementary data to this article: