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Open Access

Peer-reviewed

Research Article

Anti-inflammatory activity and metabolite profiling of myo-inositol in LPS-stimulated macrophages

  • Marwa Seif,

    Roles Investigation, Methodology, Writing – original draft

    Affiliation Department of Biological Sciences, School of Science, The University of Jordan, Amman, Jordan

    ⨯
  • Lina Dahabiyeh ,

    Roles Formal analysis, Investigation, Methodology, Project administration, Supervision, Writing – review & editing

    a.imraish@ju.edu.jo (AI); l.dahabiyeh@ju.edu.jo (LD)

    Affiliation Department of Pharmaceutical Sciences, School of Pharmacy, The University of Jordan, Amman, Jordan

    ⨯
  • Afnan Al‐Hunaiti,

    Roles Methodology, Writing – review & editing

    Affiliation Department of Chemistry, School of Science, The University of Jordan, Amman, Jordan

    ⨯
  • Mohammad Semreen,

    Roles Investigation, Methodology

    Affiliation Department of Medicinal Chemistry, College of Pharmacy, University of Sharjah, Sharjah, UAE

    ⨯
  • Malek Zihlif,

    Roles Investigation, Project administration

    Affiliation Department of Pharmacology, School of Medicine, The University of Jordan, Amman, Jordan

    ⨯
  • Hamzeh Al-Ameer,

    Roles Methodology

    Affiliation Department of Biotechnology, Faculty of Allied Medical Sciences, Al-Ahliyya Amman University, Amman, Jordan

    ⨯
  • Amer Imraish

    Roles Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing – original draft

    a.imraish@ju.edu.jo (AI); l.dahabiyeh@ju.edu.jo (LD)

    Affiliation Department of Biological Sciences, School of Science, The University of Jordan, Amman, Jordan

    ⨯

Abstract

There has been increasing interest in using dietary bioactive substances to alleviate and reduce inflammation. This study aims to assess myo-inositol’s possible anti-inflammatory effects, especially in handling conditions associated with macrophage activity. In this context, myo-inositol coated with polyethylene glycol (PEG) was created as a drug delivery system, and the macrophage cell line RAW 264.7 was used to evaluate its cytotoxicity. Additionally, their ability to suppress pro-inflammatory gene expressions induced by lipopolysaccharide (LPS) was investigated by determining the expression of pro-inflammatory genes such as interleukin (IL)-6, IL-1β, and tumor necrosis factor (TNF)-α. Furthermore, the molecular mechanisms and metabolic pathways affected by myo-inositol treatment were evaluated using a mass spectrometry-based metabolomics approach. PEGylated myo-inositol exhibited slight toxicity against RAW 264.7 cells with IC50 values 124.9 μg/ml. However, myo-inositol did not exhibit toxicity over RAW 264.7 cells. In LPS-stimulated RAW 264.7 cells, PEGylated myo-inositol at concentrations of 31.2 and 15.6 μg/ml significantly reduced the expression of pro-inflammatory cytokines IL-6, IL-1β, and TNF-α at both the mRNA and protein levels. Moreover, PEGylated myo-inositol at 31.2 μg/mL reduced nitric oxide (NO) production by approximately 11.5-fold compared to the LPS group, further supporting its anti-inflammatory and immunomodulatory potential. The Metabolomics study identified 156 metabolites and revealed that the PEGylated myo-inositol significantly altered the metabolic profile of RAW 264.7 compared to the LPS-stimulated RAW 264.7. Metabolomics showed that the treatment alters the level of metabolites involved in the essential process of pro-inflammatory macrophages including energy metabolisms (e.g., TCA cycle, fatty acid oxidation), amino acids metabolisms (e.g., arginine and tyrosine), pyrimidine and purine metabolism, and lipids metabolism (e.g., 8,11,14-eicosatrienoic acid, sphinganine). Hence, PEGylated myo-inositol reversed some of the LPS impacts. Our Findings indicate that PEGylated myo-inositol exerts a promising anti-inflammatory effect through variant pathways. This can assist in developing the use of PEGylated myo-inositol for inflammatory diseases.

Citation: Seif M, Dahabiyeh L, Al‐Hunaiti A, Semreen M, Zihlif M, Al-Ameer H, et al. (2026) Anti-inflammatory activity and metabolite profiling of myo-inositol in LPS-stimulated macrophages. PLoS One 21(2): e0341193. https://doi.org/10.1371/journal.pone.0341193

Editor: Anil Bhatia, University of California Riverside, UNITED STATES OF AMERICA

Received: March 27, 2025; Accepted: January 2, 2026; Published: February 25, 2026

Copyright: © 2026 Seif et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: This work was supported by the Deanship of Scientific Research in The University of Jordan (Grant number 2024-229/2023).

Competing interests: The authors have declared that no competing interests exist.

Introduction

Inflammation is a complicated biological response characterized by a coordination process between immune cells and biological components to eliminate harmful stimuli such as damaged tissue, pathogens, and toxic elements ally, acute inflammation represents an aspect of innate immune response that functions immediately after the injury and lasts for a few days [1]. Acute inflammation that is dysregulated or persistent may become chronic and result in several chronic inflammatory diseases, including autoimmune, cancer, cardiovascular and neurological diseases [2]. Therefore, bringing efficient treatment strategies alongside mitigating the side effects associated with anti-inflammatory drugs requires an insight into the cellular and molecular factors causing inflammation [3].

Macrophages release soluble proteins called cytokines, which mediate communication between cells and contribute to the immune response [4]. After macrophages are activated by lipopolysaccharide (LPS), tumor necrosis factor (TNF-α), and interferon-gamma (IFN-γ), macrophages polarize into M1 macrophages by secreting pro-inflammatory cytokines such as TNF-α, IL-6, and IL-1β [5]. Therefore, preventing LPS-stimulated macrophages from releasing mediators and cytokines is a necessary step in the development and evaluation of novel anti-inflammatory drugs. The use of dietary and natural bioactive compounds to reduce or suppress inflammation is becoming more popular because of their minimal side effects [6]. Myo-inositol is a polyol, or sugar alcohol, that has a role in osmoregulation and functions in responding to a range of hormones, and growth factors via controlling signal transduction [7]. Myo-inositol’s possible anti-inflammatory qualities have drawn attention. Its capacity to control inflammatory responses has been reported in human umbilical vein endothelial cells (HUVECs) and other types of cells [8].

Significant metabolic activity changes take place during activated immunological response and in sites of ongoing inflammation [9]. Metabolomics is a specific analytical approach for the identification and quantification of a set of small molecules within various types of biological samples (typically below 1,500 Da). It is frequently used to help understand the molecular mechanisms behind diseases or certain treatments, as well as to find new biomarkers and therapeutic targets [10].

The complicated interaction between inflammatory pathways and bioactive natural components is noteworthy for evaluation to explain their impact on key enzymes and mediators. However, to the best of our knowledge, no research has been conducted to investigate the potential anti-inflammatory impact of myo-inositol on macrophage cells and their metabolic profiles. To enhance cellular uptakes, stealth drug delivery systems using polyethylene glycol (PEG) polymer is preferred to maximize drug delivery to target tissues [11].

Therefore, this study introduces myo-inositol’s effect as a potential anti-inflammatory agent and introduces PEGylated myo-inositol as a new delivery system along with assessing its effect. Furthermore, the anti-inflammatory metabolic changes induced by PEGylated myo-inositol treatment on LPS-stimulated RAW 264.7 cells were investigated using LC/MS metabolomics approach.

Materials and methods

Characterization and preparation of PEGylated myo-inositol

All solvents were acquired from Sigma-Aldrich and used exactly as supplied, including myo-inositol (St. Louis, MO, USA), chitosan (deacetylation degree >90%, Mw ~ 20 kDa), and polyethylene glycol (2000 Da). DLS (Zetasizer 3000 HS, Malvern Instruments, Malvern, UK) was used to measure the size and zeta potential of various NPs. And 0.45 m filter was used to filter the NPs suspensions, and three analyses were performed on each batch. A DB-5 MS capillary gas chromatographic column (30 m × 0.25 mm × 0.25 μm, Agilent, USA) was used for GC-MS analysis, with samples injected using a split ratio of 5:1 The carrier gas, helium, had a constant flow rate of 0.8 mL/min. The column oven’s heating process went as follows: 140°C for one minute, 20°C/min to 220°C, and 5°C/min to 280°C for ten minutes.

Preparation of PEGylated myo-inositol

In brief, 10 mg myo-inositol was dissolved in 10 mL deionized water; after stirring for 0.5 hr, 8 mg PEG (1.0 mmol)-chitosan solution (the chitosan solution in 1:5 molar ratio PEG: Chitosan) was added dropwise to the myo-inositol solution and stirred for 1hr. The resulting solution was then sonicated for 15 min. The resultant suspension was centrifuged for 10 minutes at 8500 rpm after being washed with water/methanol (volume ratio: 2:1). To obtain the required NPs, the precipitate was washed with deionized water and lyophilized after the previous procedure was carried out three times.

Determination of Encapsulation Efficiency (EE)

The coated myo-inositol was centrifuged at 11000 rpm for 30 min to segregate entrapped and free drugs. The supernatant, after appropriate dilution, was analyzed by GC-MS (GC-MS, Shimadzu) to determine the quantity of free drugs. The entrapped drugs in the delivery system were also determined by GC-MS after the sample was dispersed in chloroform. The procedure described by Srilatha et al. (2021) was adopted to calculate the encapsulation efficiency.

Encapsulation efficiency (EE), was calculated as follows:

EE% = (the amount of drug in the nanoparticles)/ (the amount of totally added drug) 100%

Cell culture and maintenance

Murine macrophage cell line RAW 264.7 (ATCC® TIB-71™) was cultured in Dulbecco’s modified eagle’s medium/F12 (DMEM/F12, PAN BIOTECH) with 10% fetal bovine serum (FBS) (PAN BIOTECH), 1% L-glutamine, 1% HEPES buffer, and 1% penicillin-streptomycin (Pen Strep, Gibco) added. A humidified 5% CO2 incubator (Thermo Fisher Scientific) was used to incubate the cells at 37°C.

Cytotoxicity assay

RAW 264.7 cells were seeded in 96-well plates at 5 × 103 cells/well in complete growth media and incubated for 24 hr at 37ºC. Serial dilutions were applied to seeded cells in the next day, starting from 200, 100, 50, 25, 12.5, 6.25, and 3.13 µg/ml of myo-inositol, and PEGylated myo-inositol with three repeats. Complete media was added to control wells. The colorimetric MTT assay (Cell Titer 96 ® Non-Radioactive Cell proliferation assay, Promega) was used to assess the half-maximal inhibitory concentrations (IC50s) during the 72-hour treatment period. GraphPad Prism 8 software was used to statistically determine the IC50 values after plates were read at 570 nm.

Cell treatment

In order to investigate the anti-inflammatory effects of both myo-inositol and PEGylated myo-inositol, 12-well plates were used to seed RAW 264.7 cells at density 2 × 105 cells/well and incubated for 24 hr. The next day, selected concentrations of PEGylated myo-inositol and myo-inositol were used for treatment 31.2, 15.6, 7.8, and 1.9 µg/ml which are 1/4, 1/8, 1/16, and 1/64 the value of the IC50 of PEGylated myo-inositol respectively for 1h then treated with 100 ng/mL of LPS for 6hr. Only media was added to the control wells, and 40 µg/ml of dexamethasone was employed as a positive anti-inflammatory control.

RNA extraction and gene expression analysis

Using the RNeasy Plus Micro Kit (Qiagen, Germany), total RNA was extracted by the product data sheet instructions. A Qubit 4 Fluorometer (Invitrogen) was then used to measure the quantities of total RNA at 260 nm. Next, by following the manufacturer’s instructions, the Prime Script ™RT Master Mix Kit (Takara) was used to convert the extracted RNA to cDNAs. A SYBR Green master mix in Quant Studio 1 cycler (Applied Biosystems) was used to quantify the relative expression levels of each gene. Table 1 described particular primers with sequences. Excel was then used to calculate ΔΔCT, and GraphPad Prism 8 was used to analyze the data. The experiment’s reference gene was the Rps18 gene.

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Table 1. Primers used for quantitative real-time PCR (qRT-PCR).

https://doi.org/10.1371/journal.pone.0341193.t001

Determination of Nitric Oxide (NO) Production

The production of nitric oxide (NO) by RAW 264.7 macrophage cells was quantified by measuring nitrite accumulation in the culture supernatants using the Griess reaction, as previously described by Al-Awaida, et al. (2023), with modifications [12]. After experimental treatments, 100 μL of cell culture supernatant was collected from each well and transferred into a 96-well plate, followed by the addition of 100 μL of Griess reagent. The Griess reagent was freshly prepared by mixing equal volumes of 1% sulfanilamide and 0.1% N-(1-naphthyl) ethylenediamine dihydrochloride (Sigma-Aldrich, USA) in 2.5% phosphoric acid. The mixture was incubated at room temperature for 20 minutes to allow color development. Absorbance was measured at 590 nm using a microplate reader (BioTek, USA). Nitrite concentrations were calculated from a standard curve generated using sodium nitrite ranging from 0 to 400 μM.

Data were analyzed using GraphPad Prism 8 software (GraphPad, San Diego, CA, USA). All results are expressed as the mean ± SEM from at least three independent experiments. Statistical significance was assessed using one-way ANOVA followed by Bonferroni’s multiple comparisons post hoc test, with P < 0.05 considered statistically significant.

ELISA and release of secretory pro-inflammatory cytokines

As directed by the manufacturers of the TNF-α ELISA Kit (ELK Biotechnology) and the IL-6 Quantikine ELISA Kit (R&D Systems), TNF-α and IL-6 released by RAW 264.7 cells were measured after they were treated. Excel was used to arrange the results and calculate the concentrations of released cytokines. GraphPad Prism 8 software was then used to further analyze the results statistically.

Statistical analysis

The IC50 of tested PEGylated myo-inositol was determined using the logarithmic trend line of cytotoxicity graphs (log (concentration versus inhibition)) in GraphPad Prism 8 software (GraphPad, San Diego, CA, USA). The mean ± SEM was used to display gene expression. Data were analyzed using one-way ANOVA followed by Bonferroni’s multiple comparisons post hoc test. P < 0.05 was considered to indicate a statistically significant difference.

Metabolomics analysis

Sample preparation and metabolites extraction.

In T25 flasks, RAW 264.7 cells were cultured at a density of 2 × 106 cells, and they were incubated for 24 hr in a DMEM media. Upon confluency, cells were treated with 31.2 µg/ml PEGylated myo-inositol (n = 10), and 31.2 µg/ml myo-inositol (n = 7). After an hour of treatment, 100 ng/ml of LPS was added for 6 hr. Control cells, including RAW 246.7 cells and RAW 246.7 stimulated with LPS (n = 10 each) were maintained without any treatment. The culture media was discarded after the incubation period, and the cells were washed with PBS before being centrifuged for 10 minutes at 15,000 rpm to collect the cell pellets. The cell pellet was washed with PBS and kept at −80°C for metabolite extraction, while the supernatant was discarded.

as previously described [13], metabolite extraction was performed by using a one-phase extraction protocol by utilizing 1 ml of pre-cooled extraction solvent (0.1% formic acid in methanol incubated at –80°C for 30 min) over dry ice.

Metabolite profiling using liquid chromatography-mass spectrometry (LC–MS/MS).

The quality control (QC) sample was created by pooling 10 µl of each sample. The quadrupole time-of-fight mass spectrometer (Q-TOF MS) and elute ultraperformance liquid chromatography (UHPLC) (Bruker Daltonik GmbH, Bremen, Germany) were used to separate and profile the extracted metabolites. As previously described [14,15], LC separation used Intensity Solo C18 column (Bruker Daltonik), and a binary mobile phased composed of solvent A (0.1% formic acid in LC–MS grade water (Honeywell, Germany)) and solvent B (0.1% formic acid in LC–MS grade acetonitrile) with the gradient elution mode over 30 min. Separated metabolites were analyzed under positive ESI mode, and within the 20–1300 m/z range, mass spectra were obtained in a data-dependent manner. At 20 eV, the collision energy stepping ranged between 100 and 250%. The acquisition was split into two sections: auto MS scan and auto MS/MS scan with collision induced dissociation (CID) acquisition for fragmentation. The cycle time was 0.5 s, the width of the precursor ion was ± 0.5, the number of precursors was 3, and the threshold was 400 cts.

As previously described [14,15], the analysis of the extracted samples was carried out in a single LC-MS run with the pooled QC samples injected throughout the whole analysis. To ensure the stability of the analytical system performance, pooled QC samples were interspaced throughout the sequence run (injected at the beginning, every 10 samples, and at the end), and the coefficient of variability (CV) was calculated.

Data processing and metabolites identification.

Bruker Daltonics’ MetaboScape® 4.0 software was used to process the data. Peak-picking, untargeted peak alignment, and related peak annotation were applied to the data. In the T-ReX 2D/3D workflow, molecular feature detection was carried out utilizing bucketing with a minimum intensity threshold of 1000 counts and a minimum peak length of 7 spectra for peak detection. The peak area was utilized for feature quantification. For data bucketing, the following parameters were assigned: mass range began at 50 m/z and ended at 1300 m/z, while retention time range began at 0.3 min and ended at 25 min.

The Human Metabolome Database (HMDB-4.0) spectral library was used for the annotation. The values of m/z 2.0–5.0 mDa, retention time 0.1–0.4 min, mSigma 10–20, MS/MS score 900–800, and CCS 2.0–5.0% were specified for the annotation quality (AQ) score indicator.

Statistical analysis (uni-and multivariate analysis).

Multivariate analysis was carried out using Simca P + 14 (Sartorius Stedim Data Analytics AB, Umea, Sweden) by exporting identified metabolites with their abundances. The imported datasets were centered around the mean and pareto scaled. Partial least square discriminative analysis (PLS-DA) and unsupervised principal component analysis (PCA) were used to model the differences between the groups under study. The model fitness (R2X) for PCA and (R2Y) for PLS-DA, along with the predictive ability (Q2) values, were used to assess the robustness of the developed models. When a model yields large R2X and R2Y values (values close to 1) and Q2 values more than 0.5, it is deemed acceptable. PLS-DA models were further validated using a permutation test (999 permutations). In the resulting PLS-DA scores plot, the significant metabolites responsible for the class separation between the compared groups were chosen using variable importance in the projection (VIP) of greater than 1.

The identified metabolites and their abundances were uploaded to MetaboAnalyst version 6.0 (McGill University, Montreal, QC, Canada) in order to do univariate analysis [16]. Datasets were normalized to sample total median and pareto-scaled. Significantly changed metabolites were identified using an independent t-test. A false discovery rate (FDR) less than 0.05 is defended as significant. Binary comparisons were performed by generating volcano plots and applying FDR values less than 0.05 with fold changes (FC) cutoff of 2. Biochemical pathway analysis in MetaboAnalyst 6.0 was performed to analyze significantly altered metabolites identified through univariate analysis (FDR < 0.05).

Results

Characterization of the PEGylated myo-inositol

The data from Table 2 showed the size and zeta potential of two types of nanoparticles: PEGylated myo-inositol and myo-inositol solution. The PEGylated myo-inositol nanoparticles had a zeta potential of −18.04 mV and a size of 225 ± 5 nm, indicating a moderate level of stability and a relatively small size. In contrast, the myo-inositol solution nanoparticles showed a lower magnitude of zeta potential at −8.5 mV, suggesting lower stability, and are significantly larger with a size of 790 ± 2 nm. These differences suggested that PEGylated myo-inositol indicated better stability due to increased electrostatic repulsion between particles, which affected their behavior and performance depending on the application, particularly in terms of sedimentation and diffusion within the given medium.

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Table 2. Size and ȥ-potential of the prepared particle.

https://doi.org/10.1371/journal.pone.0341193.t002

Encapsulation efficiency

The encapsulation efficiency (EE) for the PEGylated myo-inositol was determined by GC spectroscopy using inositol. The results obtained showed that the EE for the delivery system was 60.9% for PEGylated myo-inositol.

Cytotoxicity of PEGylated Myo-inositol, and myo-inositol

Data showed that PEGylated myo-inositol has slight toxicity over RAW 264.7 cells by IC50 = 124.9 ± 17 µg/ml, where myo-inositol did not show any toxicity at higher concentration reached to >800 µg/ml.

Anti-inflammatory effect of myo-inositol, and PEGylated myo-inositol on LPS-stimulated macrophage cells

To ensure at least 80% of cell viability and treatment safety and efficacy, the concentrations of 1/4, 1/8, 1/16, and 1/64 of IC50 for PEGylated myo-inositol were chosen for further investigation. Since the difficulty in determining the IC50 of myo-inositol, the chosen concentrations were the same as PEGylated myo-inositol. The findings demonstrated that compared to untreated cells, pro-inflammatory cytokine mRNA levels of TNF-α, IL-6, and IL-1β increased significantly in response to 6hr exposure of 100ng/ml LPS. In contrast, at 31.2 µg/ml, the cells treated with PEGylated myo-inositol exhibited a significant decrease in TNF-α mRNA expression, with a percentage of 80.1%, in comparison to the cells stimulated with LPS (P = 0.0021). Additionally, TNF-α mRNA expression levels were significantly reduced at concentrations of 15.6 and 7.8 µg/ml, with percentages of 49.3% and 43.4% (P = 0.0279 and P = 0.0289), respectively. However, TNF-α expression was not altered at a concentration 1.9 µg/ml (Fig 1A). Remarkably, compared to LPS-stimulated cells, the level of IL-1β expression was much lower at concentrations 31.2 and 15.6 µg/ml with a percentage of 59.5% and 44.6% (P = 0.0098 and P = 0.0175), respectively. On the other hand, both 7.8 and 1.9 µg/ml caused a non-significant decrease in IL-1β expression levels (Fig 1B). Furthermore, compared to LPS-stimulated cells, the levels of IL-6 expression were considerably lower at concentrations of 31.2, and 15.6 µg/ml with a percentage of 51.8%, and 42.2%, respectively (P = 0.0223 and 0.0139). On the other hand, at the other two concentrations, there are a non-significant reduction in IL-6 mRNA expression levels (Fig 1C). However, in contrast to the LPS-stimulated cells, treated RAW264.7 cells with myo-inositol did not exhibit any changes in TNF-α, IL-1β, and IL-6 expression levels at the selected concentrations.

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Fig 1. PEGylated myo-inositol anti-inflammatory effect on pro-inflammatory cytokine mRNA levels.

Following a 1 hr pretreatment with specific concentrations of PEGylated myo-inositol (31.2, 15.6, 7.8, and 1.9 µg/ml), RAW 264.7 cells were exposed to LPS (100 ng/ml) for 6 hr. 40 µg/ml of dexamethasone was used as a positive anti-inflammatory control. The cytokines (A) TNF-α, (B) IL-1β, and (C) IL-6 were evaluated for their expression levels. The results are shown as the relative expression (%) of each treatment group in relation to the LPS-stimulated group. Actual p-values: TNF-α: LPS vs. Ctrl = 0.0001; 31.2 µg/ml vs. LPS = 0.0021; 15.2 µg/ml vs. LPS = 0.0279; 7.8 µg/ml vs. LPS = 0.0289. IL-1β: LPS vs. Ctrl = 0.0005; Dexamethasone vs. LPS = 0.0032; 31.2 µg/ml vs. LPS = 0.0098; 15.2 µg/ml vs. LPS = 0.0175. IL-6: LPS vs. Ctrl = 0.0040; Dexamethasone vs. LPS = 0.0167; 31.2 µg/ml vs. LPS = 0.0223; 15.2 µg/ml vs. LPS = 0.0139. * p < 0.05, ** p < 0.01, *** p < 0.001, ****p < 0.0001.

https://doi.org/10.1371/journal.pone.0341193.g001

Effect of PEGylated Myo-Inositol on LPS-Induced NO Production in RAW 264.7 Cells

The production of nitric oxide (NO) was quantified by measuring nitrite levels in the culture supernatants using a standard curve generated by linear regression (Fig 2A). Stimulation with lipopolysaccharide (LPS) markedly increased NO levels in RAW 264.7 macrophages, reaching 2.3 μM. However, pretreatment with PEGylated myo-inositol significantly attenuated this effect in a dose-dependent manner. At concentrations of 31.2 and 15.6 μg/mL, NO levels were reduced to 0.2 μM and 0.5 μM, corresponding to approximately 11.5-fold and 4.6-fold decreases (P = 0.0027 and P = 0.0020), respectively. Even lower concentrations of 7.8 and 1.9 μg/mL led to notable reductions, with nitrite levels decreasing to 0.5 μM and 1.1 μM, respectively, reflecting 4.6-fold and 2.1-fold decreases (P < 0.0001 and P = 0.0368) (Fig 2B). These findings indicate a robust and concentration-dependent inhibitory effect of PEGylated myo-inositol on LPS-induced NO production, highlighting its potential anti-inflammatory activity.

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Fig 2. The effect of PEGylated myo-inositol on the production of nitric oxide (NO) in LPS-stimulated RAW 264.7 cells.

(A) Nitric oxide estimation standard curve. (B) RAW 264.7 cells were pretreated with 31.2, 15.6, 7.8, and 1.9 µg/ml of PEGylated myo-inositol for 1 hr prior to stimulation with LPS (100 ng/ml) for 6 hr. The cell supernatants were collected and assayed for NO production using the Griess reagent. Each treatment group’s results are shown as concentration (μM), and they were contrasted with the LPS-treated group. Actual p-values: LPS vs. Ctrl = 0.0124; Dexamethasone vs. LPS = 0.0040; 31.2 µg/ml vs. LPS = 0.0027; 15.2 µg/ml vs. LPS = 0.0020; 7.8 µg/ml vs. LPS > 0.0001; 1.9 µg/ml vs. LPS = 0.0368. * p < 0.05, ** p < 0.01, *** p < 0.001, ****p < 0.0001.

https://doi.org/10.1371/journal.pone.0341193.g002

ELISA and release of secretory pro-inflammatory cytokines

To confirm the gene expression findings, TNF-α and IL-6 protein levels were measured using ELISA after LPS-stimulated RAW 264.7 cells were treated with different amounts of PEGylated myo-inositol. Results showed that TNF-α levels were dramatically reduced by PEGylated myo-inositol at a concentration of 31.2 µg/ml, going from 559.6 pg/ml in the LPS-stimulated group to 178.8 pg/ml in the PEGylated treated group. This was a 3.1-fold decrease (P = 0.0011). The concentrations of 15.6 and 7.8 µg/ml dropped the level of TNF-α to 218.4 and 243.1 pg/ml approximately 2.6-fold and 2.3-fold decrease with (P < 0.0001 and P = 0.0022), respectively (Fig 3A). In addition, results showed that PEGylated myo-inositol at a concentration of 31.2 µg/ml dramatically reduced the IL-6 level from 544.8 pg/ml in the LPS-treated group to 166.5 pg/ml for the PEGylated treated group with (P = 0.0040). The concentrations of 15.6 and 7.8 µg/ml dropped the level of IL-6 to 148.1 and 521.7 pg/ml with (P = 0.0032 and P = 0.734), respectively (Fig 3B). This demonstrated the anti-inflammatory action of PEGylated myo-inositol over the tested RAW 264.7 and confirmed its influence on mRNA levels.

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Fig 3. Anti-inflammatory effect of PEGylated myo-inositol on the release of TNF-α & IL-6 proinflammatory cytokines.

Following a 1 hr pretreatment with specific concentrations of PEGylated myo-inositol (31.2, 15.6, and 7.8 µg/ml), RAW 264.7 cells were exposed to LPS (100 ng/ml) for 6 hr. 40 µg/ml of dexamethasone was used as a positive anti-inflammatory control. (A) TNF-α and (B) IL-6 release via RAW 264.7 were measured using ELISA. Each treatment group’s results are shown as concentration (pg/ml), and they were contrasted with the LPS-treated group. Actual p-values: TNF-α: LPS vs control = 0.0011; Dexamethasone vs LPS = 0.0002; 31.2 µg/ml vs LPS = 0.0011; 15.2 µg/ml vs LPS < 0.0001; and 7.8 µg/ml vs LPS = 0.0022. IL-6: LPS vs control = 0.0019; Dexamethasone vs LPS = 0.0029; 31.2 µg/ml vs LPS = 0.0040; and 15.2 µg/ml vs LPS = 0.0032. * p < 0.05, ** p < 0.01, *** p < 0.001, ****p < 0.0001.

https://doi.org/10.1371/journal.pone.0341193.g003

Metabolomics results

An overview of the four study groups (control, LPS, myo-inositol, PEGylated myo-inositol).

Multivariate analysis was performed to overview any cluster or separation pattern among the four study groups; Untreated RAW 264.7 (control group), LPS stimulated RAW 264.7 (LPS group), myo-inositol treated LPS stimulated RAW 264.7 (myo-inositol group), and PEGylated myo-inositol treated LPS stimulated RAW 264.7 (PEGylated myo-inositol group). PCA scores plot revealed clear separation and clustering of PEGylated myo-inositol and control groups, whereas there was an overlapping between samples from myo-inositol and LPS groups (Fig 4A). A heat map was used to visualize the levels of the top 40 significantly altered metabolites among the four groups (Fig 4B). As appeared from the heat map, PEGylated myo-inositol had the most pronounced effect on the intracellular metabolome of RAW 264.7 with most of the metabolites were downregulated compared to the control. One-way ANOVA statistical test revealed that 121 metabolites out of 156 identified metabolites were significantly altered between the four groups (P < 0.05).

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Fig 4. (A) Principal component analysis (PCA) scores plot of RAW 264.7 samples treated with (PEGylated myo-inositol, myo-inositol) for 1hr followed 6 hr exposure to LPS.

The PEGylated myo-inositol (red circles) and myo-inositol (purple circles) control samples (green circles) and LPS sample (light blue circles). PCA Scores plot (R2X=0.74, Q2 = 0.49, n = 37) (B) Heatmap of the top 40 altered metabolites. The lines represent the metabolites found in each sample, and the columns represent the samples. The relative expression levels of the metabolites across all samples are shown by the color scale (right); greater values (red) indicate up-regulated metabolites, while lower values (blue) indicate down-regulated metabolites.

https://doi.org/10.1371/journal.pone.0341193.g004

The effect of LPS on the metabolic profile of RAW 264.7 cells.

To investigate the impact of LPS on RAW 264.7 macrophage cells metabolic profile a binary comparison between control group and LPS group was performed. PCA and PLS-DA models showed evident separation between the two groups as observed in Figs 5A and 5B, respectively indicating that LPS stimulation induced metabolic changes in RAW 264.7 cells. The PLS-DA model yielded satisfactory fitness (R2Y=0.94), and prediction (Q2 = 0.84) values and passed the permutation test. The top metabolites with VIP score >1 responsible for class separation are shown in Fig 5C. A total of 31 metabolites (out of 156) were significantly changed in the LPS compared to controls with 23 metabolites were downregulated (e.g., niacinamide and citric acid) and 8 upregulated (e.g., sphinganine and L-acetylcarnitine).

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Fig 5. PCA (R2X=0.70, Q2 = 0.18, n = 20) (A) and PLS-DA (R2Y=0.94, Q2 = 0.84, n = 20).

(B) scores plot of RAW 264.7 cells stimulated with LPS (light blue circle) and untreated RAW 264.7 (green circle). (C) Frequency plot for the top metabolites with VIP scores >1 between LPS vs control. (D) Heatmap of the top 30 altered metabolites in LPS-stimulated RAW 264 cells. The lines represent the metabolites found in each sample, and the columns represent the samples. The relative expression levels of the metabolites across all samples are shown by the color scale (right); greater values (red) indicate up-regulated metabolites, while lower values (blue) indicate down-regulated metabolites.

https://doi.org/10.1371/journal.pone.0341193.g005

The complete list of the significantly altered metabolites with their fold change is provided in S1 Table. Compared to RAW 264.7, LPS stimulation induced significant changes in the levels of dihomo-γ-linolenic acid (DGLA) chemically named 8,11,14-eicosatrienoic acid, and niacinamide or nicotinamide (NAM) where both metabolites were downregulated. At the same time, sphinganine and acetylcarnitine were upregulated under LPS stimulation. The heatmap displayed an illustration of the top 30 changed metabolites, emphasizing the variations in the distribution and intensity of various metabolites between the samples and the two groups (Fig 5D).

The effect of PEGylated myo-inositol on the metabolic profile of RAW 264.7 cells.

The PEGylated myo-inositol group and LPS group displayed evident separation in the PCA and PLS-DA scores plots, as shown in Figs 6A and 6B, respectively. The PLS-DA model passed the cross-validation and the permutation test. The top 20 metabolites with VIP > 1 used to extract the differentially expressed metabolites in the PLS-DA plot (Fig 6C) included amino acids (e.g., tyrosine, tryptophan) and metabolites involved in energy metabolisms (e.g., citric acid), Lipid metabolism (e.g., sphinganine), purine metabolism pathway (e.g., xanthine, hypoxanthine), and fatty acid oxidation pathway (L-carnitine).

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Fig 6. PCA (R2X=0.70, Q2 = 0.52, n = 20) (A) and PLS-DA (R2Y=0.98, Q2 = 0.93, n = 20) (B) scores plot of RAW 264.7 cells treated with PEGylated myoinositol (red circle) and LPS (light blue circle), (C) Frequency plot for the top 20 metabolites with VIP scores >1 between PEGylated myo-inositol vs LPS.

https://doi.org/10.1371/journal.pone.0341193.g006

A total of 106 metabolites were significantly changed (FDR < 0.05). The complete list of the significantly altered metabolites with their fold change is provided in S2 Table. Volcano plot applying (FDR < 0.05) and FC values (≥2) or (≤0.5) showed that treating RAW 264.7 cells with the PEGylated myo-inositol increased and decreased the level of 6 and 19 metabolites, respectively (Fig 7A). Among the increased metabolites were 8,11,14-eicosatrienoic acid, and citric acid, while norepinephrine, sphinganine, and nicotinamide adenine dinucleotide (NAD) were among the decreased ones. The heatmap displayed a visual representation of the top 40 changed metabolites, emphasizing the variations in the distribution and intensity of various metabolites between the samples and the two groups (Fig 7B). Significantly altered metabolites (FDR < 0.05) due to PEGylated myo-inositol treatment were subjected to pathway analysis (Fig 7C). The significantly altered metabolites were involved in various biochemical pathways, including purine and pyrimidine metabolism, alanine, aspartate, glutamate metabolism, arginine biosynthesis, and the citric acid cycle.

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Fig 7. Significantly altered metabolites upon treating LPS-stimulated RAW 264.7 cells with PEGylated myo-inositol.

(A) Volcano plot highlights statistically significant different metabolites between PEGylated myo-inositol treated RAW 264.7 and LPS applying FDR < 0.05, and FC (>2) or (<0.5). Red circles indicate metabolites that are significantly up-regulated, while blue circles indicate down-regulated metabolites. (B) Heatmap of the top 40 altered metabolites in PEGylated myo-inositol treated LPS-stimulated RAW 264 cells. The lines represent the metabolites found in each sample, and the columns represent the samples. The relative expression levels of the metabolites across all samples are shown by the color scale (right); greater values (red) indicate up-regulated metabolites, while lower values (blue) indicate down-regulated metabolites. (C) Pathway analysis for significantly altered metabolites between PEGylated myo-inositol-treated RAW 264.7 cells and LPS. Based on the p-value and impact value of the altered pathway, the node size and color are determined.

https://doi.org/10.1371/journal.pone.0341193.g007

Discussion

The current work introduces a novel delivery system of myo-inositol coating with PEG as a nanoparticle. By preventing the generation of inflammatory factors and mediators such as IL-1β, IL-6, and TNF-α in LPS-stimulated RAW 264.7 cells, PEGylated myo-inositol demonstrates a significant anti-inflammatory effect. The level of the metabolites significantly changes in RAW 264.7 cells due to stimulation by LPS (e.g., increased levels of sphinganine, l-acetylcarnitine, and decreased levels of niacinamide, 8,11,14-eicosatrienoic acid), which is reversed after PEGylated myo-inositol treatment, suggesting a well-balanced effect of PEGylated myo-inositol against the metabolic abnormalities caused by LPS in RAW 264.7 cells. Additionally, the alterations in several endogenous metabolites associated with the purine and pyrimidine pathway, energy metabolism, amino acid metabolism, and lipid metabolism provides a preliminary explanation of the possible metabolic mechanism of the anti-inflammatory activity of PEGylated myo-inositol.

Effect of PEGylated myo-inositol, and myo-inositol on the viability of RAW 264.7

The current work shows that PEGylated myo-inositol has slight toxicity on RAW 264.7 cells in a concentration-dependent manner. In contrast, myo-inositol did not show any toxicity on the cells even in high concentrations reaching 800 µg/ml, which may be explained by the lack of myo-inositol transporters in mouse macrophages [17]. Myo-inositol is known to have specific transporters SMIT1/2 and HMIT [18]. Moreover, Warskulat et al (1997) demonstrated undetectable myo-inositol transporter SMIT mRNA when studying myo-inositol uptake by RAW 264.7 cells [17]. This indicates that myo-inositol is not taken up by RAW 264.6 cells. PCA analysis supports this assumption when myo-inositol overlaps with LPS which implies that myo-inositol has the same effect as LPS, which means that myo-inositol entry inside the cell does not occur. All of these observations support the fact that RAW 264.7 does not have any myo-inositol transporter to facilitate its uptake. Accordingly, synthesizing a new delivery system using PEG coating myo-inositol as an NPs is necessary. PEG was selected in this study to coat the myo-inositol due to its characteristics which enhance drug uptake, indicating its uptake by RAW 264.7 cells through showing a slight toxicity reaching IC50 = 124.9 µg/ml. PEG is characterized by electrical neutrality which minimizes electrostatic interaction with charged molecules such as cell membranes and proteins, enhancing the redaction of nonspecific binding [19]. Moreover, the significant spatial repulsion prevents close contact with other particles or biological components, and high hydrophilicity which increases NP-water solubility and avoids aggregation [20,21].

Anti-inflammatory effect of PEGylated myo-inositol on LPS-stimulated RAW 264.7

PEGylated myo-inositol affected the mRNA expression of LPS-stimulated inflammatory cytokine genes.

Based on our current information, this study is the first to examine the anti-inflammatory properties of PEGylated myo-inositol on LPS-stimulated RAW 264.7. In previous studies myo-inositol was demonstrated as the active compound in Actinidia arguta extract, 40 μM of myo-inositol showed its ability to inhibit TNF-α induced monocyte adhesion in HUVECs during in vitro experiments [22]. In another study, myo-inositol at concentrations 0.1, 0.5, and 1μM reduced TNF-α induced monocyte adhesion in HUVECs in both the control and gestational diabetes [8]. This study and other investigations have employed different myo-inositol concentrations, which are justified by variations in cell type and the duration of exposure to an inflammatory insult. Based on these findings, PEGylated myo-inositol inhibits the production of TNF-α, IL-1β, and IL-6 by interfering with the expression of inflammatory cytokine mRNAs and their release. The highest concentration of PEGylated myo-inositol suppresses TNF-α, IL-1β, and IL-6 by about 80.07%,59.55%, and 51.77%, respectively. Interestingly, as compared to other pro-inflammatory cytokines that have been tested, PEGylated myo-inositol had the least effect on the expression of IL-6. This is to be expected given this cytokine’s participation in various immunological processes [23]. It functions as a crucial modulator of the immune response to exogenous pathogens, including bacterial and viral toxins, when paired with other cytokines like TNF-α and IL-1β. Also, when NF-κB and the signal transducer and activator of transcription 3 (STAT3) cooperate to upregulate NF-κB, inducing elevated levels of IL-6 expression and initiating numerous immunological pathways [24]. TNF-α is known as a “master regulator” because once released, TNF-α orchestrates the production of other pro-inflammatory cytokines, including IL-1β and IL-6, enhancing the inflammatory response and starting a downstream cytokine cascade [25]. Within 30 minutes of the triggering event, TNF-α is produced from macrophages, establishing it as an early regulator of the immune response [26,27]. In contrast to the majority of cytokines promoters, the regulatory region of IL-1β is dispersed throughout thousands of base pairs upstream from the transcriptional start point. This complexity allows IL-1β to be regulated in response to several stimuli such as the cAMP response element, NF-κB, and stress signal [28]. Therefore, anti-inflammatory agents might need to inhibit several pathways simultaneously to suppress IL-1β fully, whereas TNF-α is more directly controlled and, thus, more readily suppressed. This could explain the better suppression of TNF-α than other cytokines. This regulatory complexity of IL-1β underscores its critical and resilient role in inflammatory responses.

Suppression of LPS-Induced Nitric Oxide Production by PEGylated Myo-Inositol in RAW 264.7 Macrophages.

The present study demonstrates that PEGylated myo-inositol exhibits a strong inhibitory effect on nitric oxide (NO) production in LPS-stimulated RAW 264.7 macrophages, highlighting its potential as an anti-inflammatory agent. The most significant suppression was observed at higher concentrations of PEGylated myo-inositol, accompanied by a clear dose-dependent trend, indicating a pharmacologically relevant response. These findings suggest that PEGylated myo-inositol modulates macrophage activation, likely through downregulation of inducible nitric oxide synthase (iNOS) and upstream inflammatory signaling cascades.

Our data are consistent with emerging research on PEGylated natural compounds that show enhanced anti-inflammatory efficacy compared to their non-PEGylated counterparts. For instance, Thapa et al. (2022) demonstrated that PEGylated quercetin significantly inhibited NO and iNOS expression in LPS-stimulated macrophages, with improved solubility and cellular delivery efficiency [29]. Similarly, Kim et al. (2024) found that PEG-based antioxidants effectively blocked NF-κB and MAPK signaling pathways, both of which are key transcriptional drivers of iNOS expression [30]. These mechanisms may also underlie the action of PEGylated myo-inositol.

While previous studies have highlighted the anti-inflammatory potential of native inositol derivatives such as chiro-inositol and inositol hexaphosphate (IP6) [31,32], their activity often requires higher doses or longer exposure times. In contrast, PEGylated myo-inositol in this study demonstrated potent NO suppression at lower concentrations, supporting the advantage of PEGylation in enhancing bioavailability, pharmacokinetics, and cellular uptake [33]. Recent studies by Kang et al. (2022) and Armengol et al. (2021) reinforce this strategy, showing that PEG-modified natural products offer superior stability and targeted delivery in inflammatory models [29,34].

Furthermore, recent advances in nanocarrier and PEG-based systems support the use of PEGylation to amplify the therapeutic potential of anti-inflammatory agents. Kang et al. (2022) found that quercetin nanosuspensions conjugated with polyethylene glycol provided superior control over oxidative stress and proinflammatory markers in macrophage models [29]. Similarly, Armengol et al. (2021) emphasized the growing role of PEG-modified natural products in improving therapeutic delivery, targeting, and immune modulation [34]. The parallel between these findings and our current results further underscores the utility of PEGylation in inflammation-targeted strategies. Taken together, our findings provide compelling evidence that PEGylation enhances the anti-inflammatory efficacy of myo-inositol, making it a promising candidate for therapeutic intervention in inflammation-related conditions. Its ability to significantly inhibit NO production in macrophages, coupled with the advantages conferred by PEGylation, positions PEGylated myo-inositol as a viable modulator of innate immune responses.

PEGylated myo-inositol alters cytokine protein levels.

The plasticity of macrophages exhibits the ability to polarize in response to stimulation with various cytokines, either toward proinflammatory M1 or anti-inflammatory M2 phenotypes [35]. After PEGylated pretreatment, we next examined the proinflammatory cytokines that were produced by RAW 264.7 cells stimulated by LPS. LPS-stimulated macrophages polarize toward the M1 phenotype, as evidenced by the considerable increase in proinflammatory factor production by macrophages in the LPS group relative to the control group [36]. Comparing PEGylated myo-inositol treatment to LPS treatment alone, TNF-α and IL-6 protein levels were considerably lower at all three concentrations. Therefore, the results of this investigation show that PEGylated myo-inositol has an anti-inflammatory effect, as seen by the decreased release of TNF-α and IL-6 cytokines in RAW 264.7 macrophages activated by LPS. Due to their biocompatibility and safety, anti-inflammatory agents in the form of nanoparticles have been introduced [37].

MS-based metabolomics study of PEG-myoinositol on LPS-stimulated RAW 264.7 cells.

Metabolomics study investigation revealed that PEGylated myo-inositol induced significant changes in the level of various intracellular metabolites of RAW 264.7 cells compared with untreated LPS-stimulated cells. The metabolic profiles of the myo-inositol and LPS groups overlapped in the multivariate PCA analysis, indicating that myo-inositol by itself did not significantly alter the metabolic levels of RAW 264.7 cells. This confirms our hypothesis that the lack of a myo-inositol transporter like SMIT prevents myo-inositol from entering RAW 264.7 cells.

As shown in (Fig 8), several metabolic pathways provide energy for cellular processes including glycolysis, TCA cycle, and β-oxidation. Glycolytic activity increases under inflammation in LPS-stimulated RAW 264.7 due to disruption in mitochondrial function [38]. Herein, the level of sugars (e.g., trehalose) and amino glucose (e.g., N-Acetyl-D-glucosamine) involved in the glycolysis pathway was reduced after PEGylated myo-inositol treatment.

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Fig 8. An illustration of the several pathways that are impacted by intermediates in LPS-stimulated RAW 264.7 cells after PEGylated myo-inositol treatment.

α-KG: α-Ketoglutarate, NAD+: Nicotinamide adenine dinucleotide, NMN: nicotinamide mononucleotide, NAM: nicotinamide, NMNT: nicotinamide nucleotide adenylyl transferase and NAMPT: nicotinamide phosphoribosyl transferase, PGE1: 1-series prostaglandins, 15-HETrE: 15-(S)-hydroxy-8,11,13-eicosatrienoic acid, LOX: 15- lipoxygenase, COX-1 and COX-2: cyclooxygenase, PAH: phenylalanine hydroxylase, TH: tyrosine hydroxylase, DBH: dopamine β-hydroxylase, CDP: cytidine diphosphate, UMP: uridine monophosphate, UDP: uridine diphosphate, CMP: cytidine monophosphate, UCK: uridine/cytidine kinase, CMPK: cytidine monophosphate kinase, dCMP: deoxycytidine monophosphate, AMP: adenine monophosphate, ADP: adenine diphosphate ASA: acetyl succinic acid, IMP: inosine monophosphate, GMP: guanine monophosphate, GDP: guanine diphosphate APT: adenosine-phosphoribosyl transferase, PRPP: phosphoribosyl-pyrophosphate.Blue arrows show downregulation, and red arrows show upregulation.

https://doi.org/10.1371/journal.pone.0341193.g008

TCA remodeling in classical macrophage activation coupled with a rapid accumulation of itaconate and succinate, these metabolites affect macrophages’ inflammatory condition [39]. Here, perturbations in the level of TCA cycle metabolites, such as α-ketoglutarate, were observed in PEGylated myo-inositol treated LPS-stimulated macrophage (Fig 8). α-ketoglutarate can regulate the polarization of macrophages in the TCA cycle [40]. In lung tissues, α-ketoglutarate enhanced the anti-inflammatory production of the M2 marker genes while decreasing the expression of IL-1β, IL-6, and TNF-α [41]. Moreover, NF-κB is suppressed by α-ketoglutarate via the prolyl hydroxylase domain (PHD), which has been shown to prevent inhibitory kappa B kinase beta (IKKβ) activation by hydroxylating IKKβ on proline, which reduces proinflammatory responses in M1 macrophages [42]. Additionally, a reduction in arginine, lysine, and aspartate was observed (Fig 8). These amino acids are degradation intermediates from the glutaminolysis pathway in the TCA cycle. The glutaminolysis pathway is responsible for replenishing the energy requirement of the TCA cycle which is achieved through the formation of α-ketoglutarate [43]. In our study remodeling in TCA intermediates was detected including the upregulation of α-ketoglutarate, citrate, and oxaloacetate indicating restoring mitochondrial function and oxidative phosphorylation, that is considered the hallmarks of anti-inflammatory macrophages. The role of oxaloacetate in immunity remains obscure, but reports have shown physiological effects including reducing neuroinflammation in mammals [44].

During the β-oxidation process, long-chain fatty acids are transferred to mitochondria by the action of L-carnitine to produce acetyl-coenzyme [45]. In the current work, L-acetylcarnitine was downregulated by the PEGylated myo-inositol. This metabolite appears to be increasing in both the M1 and M2 phenotypes [46,47]. In the context of macrophage metabolism, more research will be required to determine if these metabolites have a role in a pro-, anti-, or both types of response.

Another key player in energy production pathways is nicotinamide adenine dinucleotide (NAD+). Since the salvage pathway effectively recycles NAD+ precursors and uses fewer resources, it is the main source of NAD+ in mammalian cells [48]. Nicotinamide phosphoribosyl transferase (NAMPT) metabolizes NAM to nicotinamide mononucleotide (NMN). Then nicotinamide nucleotide adenylyl transferase (NMNAT) reconstructs NAD+ from NMN [49]. According to our findings, stimulating macrophages with LPS decreased the level of NAM relative to the control group, which may have been caused by enhanced NAMPT enzyme activity (Fig 8). Numerous investigations have documented that LPS-stimulated macrophages exhibit elevated expression of NAMPT in the NAD+ salvage pathway [50–53]. On the other hand, the treatment enhances the upregulation of NAM and the downregulation of NAD+ in LPS-stimulated RAW 264.7. These findings suggest that PEGylated myo-inositol may impair the NAD⁺ salvage pathway, leading to an accumulation of NAM. Nevertheless, this interpretation is based on metabolite level changes, and additional studies are required to verify the underlying mechanisms. Additionally, NAM has been reported due to its capacity to elevate the expression of anti-inflammatory mediators like IL-10 and mitigate the expression of pro-inflammatory cytokines like TNF-α and IL-6 [54]. In addition, a reduction of the activity of the salvage pathway results in mitigated glycolysis since NAD+-dependent GAPDH function is limited, which causes the failure of the production of inflammatory mediators [55].

According to current findings, LPS diminished the level of DGLA and elevated the level of sphinganine, while PEGylated myo-inositol significantly affected lipid metabolism and caused a disturbance of several lipids including upregulation of linolenic acid (LA) and DGLA, and down-regulation of sphinganine (Fig 8). LA can be metabolized to gamma-linolenic acid (GLA), which elongates into DGLA that can be desaturated to form arachidonic acid (AA) [56]. DGLA reveals anti-inflammatory and antiproliferative properties [57]. Cyclooxygenase (COX-1 and COX-2) converts DGLA to 1-series prostaglandins (PGE1), and 15-lipoxygenase (LOX) changes it into 15-(S)-hydroxy-8,11,13-eicosatrienoic acid (15-HETrE) [58]. It has been demonstrated that 15-HETrE and PGE1 decrease inflammation and prevent the synthesis of lipid mediators made from AA, such as leukotrienes that may increase inflammation [59]. Furthermore, pro-inflammatory cytokines (IL-6), nitric oxide, and ROS are all reduced by DGLA [60]. Our findings suggest that PEGylated myo-inositol treatment may support an anti-inflammatory response, potentially through modulation of DGLA levels.

Direct interactions between sphinganine and TLR4 adaptor proteins facilitate the recruitment of MyD88 to the membrane. This enhances M1 macrophage activation and inflammatory cytokine production [61]. The same study reported that LPS triggers de novo sphingolipid synthesis, which is essential for proper TLR4 signaling. Blocking this pathway disrupts TLR4 adaptor protein recruitment and suppresses inflammatory signaling [61]. This is parallel to what we found, the level of sphinganine significantly increased upon stimulating RAW 264.7 with LPS, and when treated with PEGylated myo-inositol it significantly decreased. This suggests to that PEGylated myo-inositol may control macrophage-driven inflammation and alleviate the inflammation by targeting sphingolipid metabolism.

Pyrimidine and purines contribute to multiple biological activities because it are the basic key in DNA and RNA [62]. It is well known that macrophage in innate immune response require a large amount of energy to cover their demand for DNA and RNA synthesis and processing to produce pro-inflammatory cytokines [63]. The upregulation in the levels of pyrimidines metabolite is influenced by the pentose phosphate pathway (PPP) which is induced in M1 macrophages [64]. Also, a study revealed a notable rise in cytidine levels, and this pyrimidine was thought to be a possible novel essential metabolite for M1 macrophages [65]. The de novo pyrimidine biosynthesis starts with the amino acid glutamate which undergoes under several reactions to form uridine monophosphate (UMP) [66]. In the salvage pathway, pyrimidine can be regenerated from the recycling of uridine and cytidine by utilizing different enzymes [67]. The finding herein demonstrated that PEGylated myo-inositol treatment caused disturbances in RAW 264.7 RNA synthesis by inhibiting pyrimidine biosynthesis as reflected by a decreased level of pro-inflammatory cytokines mRNA and protein. Our results showed that the suppression of the pyrimidine de novo and salvage metabolism in RAW 264.7 was achieved by PEGylated myo-inositol which downregulates UMP, cytidine monophosphate (CMP), cytidine, deoxycytidine monophosphate (dCMP), and thymine (Fig 8).

Purines also contribute to the regulation of the immune response and enhance the complex relationships involving the host and the pathogen [68]. The two primary mechanisms for purine metabolism in mammalian cells are de novo synthesis and the salvage pathway, which recycles broken bases to better meet the needs of the cell [69]. Rattigan et al. 2018 revealed that when LPS was used to stimulate macrophages, inosine monophosphate (IMP) is upregulated [46]. This is in line with our result where IMP was increased upon stimulating RAW 264.7 with LPS. Additionally, by lowering the levels of adenosine monophosphate (AMP), xanthine, uric acid, and urea and raising the levels of IMP, LPS may disrupt the purine metabolism’s de novo production and breakdown pathways in macrophages [70]. Our finding herein demonstrated that our treatment caused disturbances in RAW 264.7 energy requirement by inhibiting the purine de-novo pathway due to the downregulation of aspartic acid (ASA), guanine monophosphate (GMP) by PEGylated myo-inositol suggesting that the cells no longer need to enhance the metabolic demand that consumes a lot of energy during the immune response thus shifting toward salvage pathway. PEGylated myo-inositol led to an upregulation of AMP and adenine levels, which may contribute to their anti-inflammatory effects. Adenine has been shown to decrease the generation of inflammatory lipid mediators, prostaglandin E2, leukotriene B, and pro-inflammatory cytokines TNF-α and IL-6 [71]. Furthermore, numerous investigations have demonstrated that adenine is an AMP-activated protein kinase (AMPK) activator with anti-inflammatory properties [72,73]. Therefore, AMPK has become a prospective therapeutic target due to its anti-inflammatory effects [74].

Inflammatory areas had greater levels of hypoxanthine, xanthine, and guanine than healthy regions because there is a substantial oxidative load on cells as a result of the excessive purine degradation process and ROS production [75]. Here, xanthine and hypoxanthine were downregulated which indicates that purine catabolism is suppressed, ROS production is reduced and IMP and AMP turnover to these metabolites is reduced. These findings indicate a state of energy conservation rather than energy consumption that occurs in the M1 phenotype, and suggesting preparation for protection against inflammation (Fig 8).

Amino acids are the precursors of protein and contribute directly to the metabolism of organisms alongside their role in the progression of inflammation [70]. Our bodies can release inducible nitric oxide synthase (iNOS) in response to inflammation, which breaks down arginine and generates a significant amount of NO gas linked to inflammatory reactions [76]. The downregulation of arginine after treating the cells with PEGylated myo-inositol could indicate a low NO gas production. The level of phenylalanine noticeably dropped in the PEGylated myo-inositol which led to decreased conversion of phenylalanine to tyrosine (Fig 8). RAW 264.7 cells synthesize and release catecholamine and LPS-stimulated macrophages had higher intracellular dopamine and norepinephrine levels [77]. Interestingly in this study with reduced inflammation by PEGylated myo-inositol, there is less demand for catecholamine synthesis, leading to lower utilization of tyrosine and its precursor, phenylalanine. Additionally, the lower level of norepinephrine detected herein might suggest a decreased activity of the catecholamine synthesis pathway. However, the function of norepinephrine produced by macrophages in enhancing or decreasing inflammation requires more research.

In conclusion, this research is the first of its type to look into the anti-inflammatory effect of PEGylated myo-inositol against RAW 264.7 cells using an untargeted MS-based metabolomics approach. The use of nanotechnology can lead to better medicine delivery. The current study emphasizes the value of nanoparticles in boosting medication effectiveness, and this technique was found to be worthy of delivering myo-inositol. The anti-inflammatory effect of myo-inositol, and PEGylated myo-inositol, was investigated initially on RAW 264.7 macrophage cells. Our findings showed that myo-inositol had an inefficient anti-inflammatory effect on the cells which aligns with the results from the metabolomics study. PEGylated myo-inositol showed sufficient anti-inflammatory effect against RAW 264.7 cells by inhibiting the release of TNF-α, IL-1β, and IL-6 cytokines on mRNA levels and protein levels, with mild toxicity. Metabolomics demonstrated that PEGylated myo-inositol caused a notable change in the level of several metabolites and biochemical pathways compared to the negative control. Cellular disturbances included several important metabolism pathways required for optimum inflammation development and progression such as energy metabolism pathways including, glycolysis, and fatty acids oxidation, which jointly led to scarcity in energy production. In addition, a dysfunction in the role of different amino acids and nucleotide biosynthesis was also noted. Furthermore, a pronounced impairment in various lipids biosynthesis and metabolism, as well as activation of the TCA cycle. These results suggest that PEGylated myo-inositol as a delivery system may have therapeutic promise in inflammatory disorder by downregulating inflammatory biomarkers and cytokines mediated by macrophages.

Supporting information

S1 Table. Summary for significantly altered metabolites (in multivariate and/or univariate analysis), in LPS-stimulated RAW 264.7 cells compared to control RAW 264.7 cells with their fold change (FC).

https://doi.org/10.1371/journal.pone.0341193.s001

(DOCX)

S2 Table. Summary for significantly altered metabolites (in multivariate and/or univariate analysis), in PEGylated treated LPS-stimulated RAW 264.7 cells compared to LPS-stimulated RAW 264.7 with their fold change (FC).

https://doi.org/10.1371/journal.pone.0341193.s002

(DOCX)

Acknowledgments

We would like to thank Mrs. Tuqa Abu Thiab and Mrs. Maha Rashed for technical support.

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