# Metabolic profiles of the human blood-brain barrier cells treated by TNF-alpha and oxysterols

Cindy Nguyen, Kenneth Cp Cheung, Xiaoli Chen, Julien Saint-Pol, Fumitaka Shimizu, Takashi Kanda, Caroline Pot, Maxime Culot, Kannie W Y Chan, Wang Lu, et al.

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To cite this version:

Cindy Nguyen, Kenneth Cp Cheung, Xiaoli Chen, Julien Saint-Pol, Fumitaka Shimizu, et al. "Metabolic profiles of the human blood-brain barrier cells treated by TNF-alpha and oxysterols." Biomedicine and Pharmacotherapy, 2025, 193, 10.1016/j.biopha.2025.118853. hal-05402277

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Distributed under a Creative Commons Attribution 4.0 International License
Metabolic profiles of the human blood-brain barrier cells treated by
TNF-alpha and oxysterols

Cindy Nguyen<sup>a</sup>, Kenneth C.P. Cheung<sup>b, c</sup>, Xiaoli Chen<sup>b</sup>, Julien Saint-Pol<sup>a, c</sup>, Fumitaka Shimizu<sup>c</sup>, Takashi Kanda<sup>d</sup>, Caroline Pot<sup>e, d</sup>, Maxime Culot<sup>a</sup>, Kannie W.Y. Chan<sup>f</sup>, Wang Lu<sup>b, g</sup>, Aiping Lyu<sup>b</sup>, Wei Jia<sup>b, g</sup>, Fabien Gosselet<sup>a, *</sup>

ARTICLE INFO

Keywords:

Blood-brain barrier

Oxysterols

Phenylpyruvic acid

TNFα

Pericytes, fatty acids

A B S T R A C T

The blood-brain barrier (BBB) is essential for central nervous system homeostasis, and its dysfunction is implicated in neuroinflammatory disorders such as Alzheimer's disease and multiple sclerosis. Oxysterols, cholesterol-derived metabolites, modulate lipid metabolism, immune responses, and BBB physiology, but their specific metabolic effects remain poorly defined. Using an in vitro human BBB model, we examined the impact of tumor necrosis factor-α (TNFα) and selected oxysterols (24S-hydroxycholesterol, 25-hydroxycholesterol, 7α,25-dihydroxycholesterol) on metabolomic profiles of brain-like endothelial cells (hBLEC) and pericytes (hBP) via targeted LC-MS/MS analysis of over 300 metabolites. TNFα markedly increased phenylpyruvic acid, indicating dysregulated phenylalanine metabolism. Oxysterols elicited minimal effects in hBLEC, but significantly altered glycolysis and fatty acid metabolism in hBP, notably reducing fructose 6-phosphate, glucose 6-phosphate, and myristoleic acid, while increasing specific fatty acids under inflammatory conditions. These results identify phenylpyruvic acid as a candidate biomarker of BBB inflammation and highlight pericyte metabolic reprogramming as a key mechanism of oxysterol action.

**1. Introduction**

The blood-brain barrier (BBB) is a specialized interface at the level of
brain microvessels that protects the central nervous system (CNS) by
restricting paracellular diffusion through tight junctions in brain endo-
thelial cells (BEC) and by tightly regulating transcellular transport [1].
In addition to providing a physical barrier, BEC ensure nutrient delivery
and waste clearance via specific transporters and efflux pumps, whose
functions are critically modulated by pericytes [2–4]. Disruption of BBB
integrity is increasingly recognized as an early and central event in
neurodegenerative and neuroinflammatory disorders such as Alz-
heimer’s disease (AD) and multiple sclerosis (MS) [5]. In AD, reduced
transporter activity contributes to impaired clearance of amyloid-β (Aβ)

peptides, accelerating deposition and neurodegeneration [6–9]. In MS, BBB dysfunction exacerbates neuroinflammation and demyelination [10]. Understanding the molecular mechanisms driving BBB impairment in these diseases is therefore crucial for therapeutic innovation.

Cholesterol metabolism represents a major focus of interest, since the
brain is the most cholesterol-rich organ and genetic polymorphisms in
lipid-regulatory genes strongly influence disease risk. Genome-wide
association studies identified more than 30 lipid-associated genes
linked to AD, including APOE, ABCA1, and ABCA7 [11–13]. Experi-
mental models demonstrate that ABCA1 and ABCA7 deficiency en-
hances amyloid deposition, whereas their overexpression delays
pathology [14–17]. Diet-induced hypercholesterolemia promotes Aβ
accumulation, while lipid-lowering drugs, such as statins, attenuate

<footnote><sup>a</sup> University of Artois, UR2465, Blood-Brain Barrier (BBB) Laboratory, Lens F-62300, France</footnote>

<footnote><sup>b</sup> Phenome Research Center, Hong Kong Baptist University School of Chinese Medicine, Hong Kong, China</footnote>

<footnote><sup>c</sup> Department of Neurology and Clinical Neuroscience, Graduate School of Medicine, Yamaguchi University, Ube, Japan</footnote>

<footnote><sup>d</sup> Department of Neurology, Neuromuscular Center Yoshimizu Hospital, Shimonoseki, Japan</footnote>

<footnote><sup>e</sup> Lausanne University Hospital (CHUV) and University of Lausanne, Laboratories of Neuroimmunology, Service of Neurology and Neuroscience Research Center, Department of Clinical Neurosciences, Lausanne, Vaud CH-1011, Switzerland</footnote>

<footnote><sup>f</sup> Department of biomedical engineering, City University of Hong Kong, Hong Kong, China</footnote>

<footnote><sup>g</sup> Department of Pharmacology and pharmacy, University of Hong Kong, Hong Kong, China</footnote>

<footnote>* Correspondence to: Blood-Brain Barrier Laboratory, Sciences Faculty Jean Perrin, Artois University, Lens, France.
E-mail address: fabien.gosselet@univ-artois.fr (F. Gosselet).</footnote>
amyloid burden and disease progression [18–21]. Similarly, in MS, lipid dysregulation contributes to both demyelination and inflammation [10]. Clinical and experimental evidence supports a link between serum lipid profiles and disease severity, with activation of the liver X receptor (LXR)/ABCA1 axis reducing demyelination and inflammatory processes [10,22–24].

Oxysterols—oxidized cholesterol derivatives—have emerged as natural LXR ligands with significant implications for BBB regulation. Among them, 24S-hydroxycholesterol (24S-HC) and 25-hydroxycholesterol (25-HC) are particularly relevant. In MS, oxysterol production is elevated in demyelinating lesions, where they regulate lipid homeostasis through APOE and ABCA1 [25,26]. Functionally, 24S-HC restores Aβ transport across the BBB, while 25-HC protects against tumor necrosis factor-α (TNFα)-induced BBB disruption [27,28]. Furthermore, 7α, 25-dihydroxycholesterol (7α,25-DHC), derived from 25-HC, acts as a ligand for the G-protein-coupled receptor EBI2 (GPR183), implicated in adaptive immune regulation and MS pathology [29]. These findings highlight oxysterols as critical mediators at the crossroads of lipid metabolism, immunity, and BBB function.

Despite these advances, the broader metabolic consequences of oxysterols on BBB cells remain poorly defined. Beyond their established roles in lipid signaling, oxysterols may trigger cell-specific metabolic reprogramming within BEC and brain pericytes, especially under inflammatory stress. Such reprogramming could unveil novel mechanistic insights into BBB pathophysiology, identify metabolite biomarkers, and provide innovative therapeutic targets for AD and MS.

To address these gaps, the present study was designed to assess the impact of oxysterols and inflammation on the metabolomic landscape of human BEC and pericytes. By combining an in vitro BBB model with targeted LC-MS/MS metabolomics, we aimed to characterize cell-specific responses, delineate metabolic pathways affected, and evaluate the potential of oxysterol-driven modulation as a therapeutic avenue for preserving BBB integrity in neurodegenerative and neuro-inflammatory diseases.

## 2. Material and methods

### 2.1. Reagents

Endothelial Cell Growth Medium MV2 (ECGMV2) and supplement (C22022) were purchased from PromoCell GmbH (Heidelberg, Germany), fetal bovine serum (FBS) from Thermo Fisher Scientific (Illkirch, France), and penicillin-streptomycin from ScienCell Research Laboratories Inc. (Carlsbad, CA, USA). Dulbecco's modified Eagle medium (DMEM), bovine serum albumin (A3311), TNFα (SRP3177), 25-HC (H1015–5mg), 24S-HC (700061P–1mg), 7α, 25-DHC (700080P–1mg), Lucifer Yellow (ref. L025) were all purchased from Sigma-Aldrich (Merck KGaA, Saint-Quentin-Fallavier, France). Gentamycin sulfate (L0011–100) added to the PromoCell culture medium at 0.5 % was purchased from VWR International LLC (Radnor, PA, USA).

### 2.2. Cells

Human brain pericytes (hBP) were isolated from a female patient who had suddenly died from a myocardial infarction [30]. The study protocol for human tissue was approved by the ethics committee of the Medical Faculty (IRB#: H18–033–6), University of Yamaguchi Graduate School, and was conducted in accordance with the Declaration of Hel-sinki, as amended in Somerset West in 1996. For the male CD34⁺-hematopoietic stem cells, written informed consent was obtained from the family of the participant before enrollment in the study. The protocol was approved by the French Ministry of Higher Education and Research (CODECOH, Number DC2011–1321). All experiments were carried out in accordance with the approved protocol. hBP and CD34⁺-hematopoietic stem cells were regularly checked for mycoplasma contamination using the MycoAlert™ detection kit from Lonza (Bâle, Switzerland),

and short tandem repeat (STR) analysis (Eurofins Genomics, Nantes, France) confirmed the absence of cross-contamination and the sex gender (Supplementary Figure 1). According to French legislation, human cells were handled in the laboratory under agreement number L2–1235.

CD34⁺-hematopoietic stem cells were isolated from human umbilical cord blood of a male newborn and differentiated into ECs (CD34⁺-ECs) as previously described [31]. CD34⁺-ECs were seeded in 100 mm 1 % gelatin-coated dishes containing complete ECGMV2 supplemented with 0.5 % gentamicin. hBP were grown in DMEM supplemented with 4.5 g/L D-glucose, 10 % FBS, 1 % L-glutamine, and 1 % penicillin-streptomycin, as previously described [32].

### 2.3. *In vitro* BBB model with human brain-like endothelial cells (hBLEC)

The BBB model was reproduced as previously published [32,33]. To obtain enough cells needed for this study, the BBB model is produced in 100 mm diameter culture dishes (Corning). Two days after thawing, 6.25 × 10⁵ hBP were first seeded into collagen-coated dishes (type I, BD Biosciences) and cultured in ECGMV2 medium supplemented with 0.5 % gentamicin for 3 h at 37 °C. Then, 3.1 × 10⁶ CD34⁺-derived endothelial cells were seeded onto Matrigel™-coated transwell inserts (Corning, Polycarbonate, 0.4 µm pore size) diluted 1:48 (v/v). After that, the inserts were transferred on dishes containing hBP (Fig. 1A). After 6 days of coculture, CD34⁺-ECs acquire the major BBB properties observed *in vivo* and reproduce a suitable model for investigating BBB permeability and physiology [32,34]. These cells are therefore referred to as human brain-like EC (hBLEC). hBP were also deeply characterized in our previous studies [33,35,36]. The upper compartment represents the luminal side of the model (blood), and the lower side, represents the abluminal compartment (brain). Once differentiated, hBLEC were treated, and sample collection were subsequently performed as described below.

### 2.4. Treatments

All experiments were performed in the absence of serum or other commercial growth factors. All treatments were diluted in serum-free ECGMV2 containing 0.1 % BSA. For inflammatory condition mimicking CNS inflammation, TNFα (10 ng/mL) was added to the abluminal compartment of the BBB model during 24 h (Fig. 1A). Three oxysterols were selected for this study based on previous works suggesting their high importance in BBB physiology and their key roles in neurodegenerative disorders: 24S-hydroxycholesterol (24S-HC), 25-hydroxycholesterol (25-HC) and 7α, 25 dihydroxycholesterol (7α, 25-DHC) [27,28,37–39]. Treatments and doses were selected based on previous studies [27,28,35,37]. Treatments lasted 24 h, with oxysterols, dissolved in DMSO and diluted at 10 µM in medium, added in the abluminal compartment, 15 min prior to TNFα when combined. All treated conditions were compared with DMSO-treated condition (control).

### 2.5. Reverse transcription – quantitative polymerase chain reaction (RT-qPCR)

hBLEC and hBP were treated during 8 h with 10 ng/mL of TNFα, with or without 10 µM of oxysterols (24S-HC, 25-HC or 7α,25-DHC), added in the abluminal side of the BBB model to mimic CNS inflammation. After treatment, mRNA from hBLEC and hBP were extracted using a NucleoSpin® RNA/protein kit (Macherey-Nagel, Dueren, Germany). cDNA were obtained from 250 ng of mRNA using IScript™ Reverse Transcription Supermix (Bio-Rad, Hercules, CA, USA), following the manufacturer's instructions. qPCR reactions (10 µL) were prepared using SsoFast™ EvaGreen® Supermix (Bio-Rad), primers (100 nM), deionized water, and cDNA. qPCR amplification was carried out for 40 cycles with an annealing temperature of 60 °C in a CFX96
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Schematic representation of the experimental set-up and overview of the class metabolites. (A) the human blood-brain barrier (BBB) model consists to cultivate CD34⁺-derived endothelial cells with human brain pericytes (hBP) during 6 days. At the end of this period, endothelial cells acquire BBB properties and are then named human brain-like endothelial cells (hBLEC). This model can be then used to study and decipher molecular mechanisms occurring during central nervous system inflammatory events [27,40,41]. As in previous in vitro studies, TNFα and oxysterols (24S-hydroxycholesterol (24S-HC), 25-hydroxycholesterol (25-HC) and 7α,25 dihydroxycholesterol (7α, 25-DHC)) were added at 10 µM in the abluminal compartment of the model during 24 h. hBP and hBLEC were lyzed and subsequently analyzed by LC-MS/MS-based targeted metabolomic approach. (B) Among the metabolites analyzed, 133 were present in hBP and hBLEC, and were ranked in different metabolite categories (amino acids, nucleosides, etc). Results are presented in % and are representative of the 94 analyzed samples.</figcaption></figure>

thermocycler (Bio-Rad). Ct data were obtained using Bio-Rad CFX Manager software. Gene expression levels of the targets (please see Table 1) were calculated using the 2⁻⁰ΔCT² method relative to the housekeeping gene glyceraldehyde 3-phosphate dehydrogenase (GAPDH). The specificity and efficiency of all the target genes primers were evaluated before performing the PCR.

## 2.6. Western blotting (WB)

hBLEC and hBP were treated during 24 h with 10 ng/mL of TNFα, with or without 10 µM of oxysterols (24S-HC, 25-HC or 7α,25-DHC), added in the abluminal side of the BBB model to mimic CNS inflammation. After treatment, cells were collected with RIPA lysis buffer containing protease and phosphatase inhibitors (Sigma-Aldrich). A BCA assay (Sigma-Aldrich) was performed for protein quantification. Cell lysates (10–20 µg) were prepared, separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and then transferred to nitrocellulose membranes (GE Healthcare, Little Chalfont, UK). Nonspecific binding was blocked using Tris-buffered saline containing 0.1 % Tween 20 (TBS-T) with 5 % skim milk (1 h, RT). The membranes were incubated (4 °C, overnight) with primary antibodies (Table 2), washed extensively, and then incubated (1 h, RT) with a horseradish peroxidase-conjugated secondary antibody (Dako, Lostrup, Denmark). After rinsing, the membranes were developed with a chemiluminescence reagent (GE Healthcare), and images were acquired using the Azure c600 WB Imaging System (Azure Biosystems, Dublin, Ireland). TotalLab TL 100 1D gel analysis software was used for quantification of the relative immunoblot densities (Gosforth, UK). Whole uncropped images of the original western blots for all replicates that contributed to the quantitative analysis are presented in Supplementary Figures 2, 3 and 4.

<caption>**Table 1**</caption>

Primers sequences for qPCR. Primer pairs: F: forward primer and R: reverse primer.

<table><caption>**Table 2**</caption><thead><tr><th>Target</th><th>Sequence (F/R)</th><th>Accession Number</th></tr></thead><tbody><tr><td>ABCA1</td><td>F: CAGTGCTTCCTGATTAGCACAC<br>R: AGGCTAGCGAAGATCTTGGTG</td><td>NM_005502.4</td></tr><tr><td>HMGCR</td><td>F: TGTGTGTGGGACCGTAATGG<br>R: GCTGTCTTCTTGGTGCAAGC</td><td>NM_001130996.2</td></tr><tr><td>LDLR</td><td>F: TTCATGGCTTCATGTACTGGAC<br>R: TTTTCAGTCACCAGCGAGTAGA</td><td>NM_000527.5</td></tr><tr><td>GAPDH</td><td>F: GATGACATCAAGAAGGTGGTGA<br>R: GCTGTTGAAGTCAGAGGAGACC</td><td>NM_001357943.2</td></tr></tbody></table>

Antibodies used for the immunoblot experiments.

<table><thead><tr><th>Target</th><th>References and providers</th><th>Antibody dilution</th></tr></thead><tbody><tr><td>ABCA1</td><td>ab18180 (Abcam)</td><td>1/500</td></tr><tr><td>HMGCR</td><td>AMAB90619 (Sigma)</td><td>1/1000</td></tr><tr><td>LDLR</td><td>ab52818 (Abcam)</td><td>1/1000</td></tr><tr><td>NLRP3</td><td>ab263899 (Abcam)</td><td>1/1000</td></tr><tr><td>ACTIN</td><td>A5441 (Sigma)</td><td>1/10000</td></tr></tbody></table>

## 2.7. Permeability assay

Permeability assays were performed as previously described [42]. Ringer-HEPES (RH buffer) buffer (150 mM NaCl, 5.2 mM KCl, 2.2 mM CaCl₂, 0.2 mM MgCl₂, 6 H₂O, 6 mM NaHCO₃, 5 mM HEPES, pH 7.4) at 37°C was added to empty wells in a 12-well plate (Costar). Filter inserts containing hBLEC were subsequently transferred to a 12-well plate and filled with RH containing the fluorescent integrity marker Lucifer Yellow (LY; 10 µM; Sigma Aldrich), which poorly crosses the BBB. After 1 h, the filter inserts were withdrawn from the receiver compartment. Aliquots from the donor solution were taken at the beginning and end of the experiments, and the fluorescence was quantified using a microplate reader (Synergy H1 multiplate reader, BioTek Instruments SAS, Colmar, France) at an excitation wavelength (λ) of 425 nm and an emission wavelength of 538 nm. The permeability coefficient was then calculated as previously described [42]. Briefly, both the insert permeability (PSf, for inserts only coated with Matrigel™) and the permeability of inserts containing hBLECs (PSt, for inserts with Matrigel™ and cells) were considered according to the following formula: 1/PSe = 1/PSt - 1/PSf. The permeability value of the hBLEC monolayer was then divided by the surface area of the insert (1.12 cm²) to obtain the permeability coefficient (Pe) of each molecule (at 10⁻³ cm/min).

## 2.8. UPLC/TQMS analysis

After TNF/oxysterols treatment, 5–6 × 10⁶ cells were washed 3 times with cold Phosphate-buffered saline - calcium magnesium free (PBS-CMF buffer: 8 g/L NaCl, 0,2 g/L KCl, 0,2 g/L KH2PO4, 2,86 g/L Na2HPO4.12 H2O; pH 7,4), before being scraped in 3 mL of cold PBS buffer. Lysates were then centrifuged at 1000 x g, at 4 °C, during 2 min. Supernatants were removed and centrifugation step was repeated twice. The cellular pellets (16 treatment conditions, 94 samples (5–6 duplicates per treatment)) were photographed, logged, and immediately stored at –80 °C in a Forma 900 series freezer (Thermo Fisher Scientific, USA). Targeted metabolomics analysis on cell samples were subsequently
conducted using the Q300 Metabolite Assay Kit (Human Metabolomics Institute, Inc., Shenzhen, Guangdong, China) based on the method previously published [43,44]. Briefly, pellets were homogenized with a mixture of Millipore ultrapure water and cold methanol. After centrifugation at 13,500 g at 4 °C for 10 min, an aliquot of the supernatant was carefully transferred to a 96 well plate for derivatization. The plate was then transferred to a Biomek 4000 workstation (Biomek 4000, Beckman Coulter, Inc., Brea, California, USA). Twenty µL of freshly prepared derivative reagents (200 mM 3-NPH in 75 % aqueous methanol and 96 mM EDC-6 % pyridine solution in methanol) was added to each well. The plate was sealed, and the derivatization was carried out at 30 °C for 60 min. After derivatization, the plate was lyophilized (Labconco, Kansas City, MO, USA) to dry. Then 400 µL of ice-cold 50 % methanol solution was added to resolve the sample, followed by 4000 g centrifugation at 4 °C for 30 min. Supernatants were transferred to a new 96-well plate in each well. Finally, the plate was sealed for LC-MS analysis using a Waters ACQUITY UPLC system coupled with an XEVO TQ-S mass spectrometer (Waters, USA) equipped with an ESI source and controlled by MassLynx 4.1 software. Chromatographic separation utilized an ACQUITY BEH C18 column (1.7 µm, 100 × 2.1 mm; Waters, USA) in both positive (POS) and negative (NEG) ion modes. The workflow included sample registration, automated metabolite quantification (FIA-MS/MS), LC-MS/MS data import/export, and analysis via the Biocrates MetiQ™ software (v1.2.Ob-DB92-Lithium-1563). The raw data files generated by UPLC/TQMS were processed using the TMBQ software (v1.0, HMI, Shenzhen, Guangdong, China) to perform peak integration, calibration, and quantification for each metabolite. Supplementary Table 1 shows Raw Peak data for each analyzed metabolite on our experiments, as well as for calibration and Quality Control (QC) samples. Supplementary Table 2 displays the retention time (in minutes), the ion pairs, the ionization mode (ES positive or negative), the stable isotope-labelled internal standards used, the calibration range for each metabolite, and the calculated limits of detection (LOD) and quantification (LOQ). The QC samples were prepared along with the real samples. To limit batch-variation and other analytical artefacts, experimental samples of each condition were analyzed by 2, and QC samples were analyzed almost each 12 experimental samples. The performance of these QC samples is reflected by the calculated relative standard deviation (RSD).

## 2.9. Targeted metabolomics data processing and analysis

Metabolite type characterization and normalization was performed by the company Human Metabolomics Institute, Inc., (Shenzhen, China) and Hong Kong Traditional Chinese Medicine Phenome Research Centre. All the raw data were pre-processed (Normalization using locally weighted scatterplot smoothing (LOWESS) algorithm). Data are freely accessible to DOI 10.5281/zenodo.17176380. MetaboAnalyst6.0 website (https://www.metaboanalyst.ca/) was then used to analyze and compile results, as well as for statistical analysis of principal component analysis (PCA), volcano plot and enriched metabolite set research. Heatmaps and volcano plots were constructed using GraphPad Prism 10.4.0(621). For heatmaps, initial datasets were transposed and normalized. For each metabolite, 0 % was defined as the smallest values among the treatment groups and 100 % was defined as the highest. For overall analysis (hBLEC and hBP), the normalization was based on untreated endothelial cells. Volcano plots were generated with a conservative threshold of adjusted p-value < 0.05 (-Log₁₀ (p-value > 1.3), and fold change > 2 or < 0.5 (Log₂ > 1 or -1 < Log₂, respectively).

To reduce noise in the data and support PCA analysis, only metabolites that met significance criteria from the Kruskal–Wallis test or one-way ANOVA (p-value < 0.05 and false discovery rate (FDR) < 0.05) were selected. The overall PCA analysis (containing both hBLEC and hBP data) were based on significant metabolites (same criteria as before) that have been shared by both cell types. All PCA analysis and volcano plots were made after Log₁₀ transformations of the original data.

# 3. Results

## 3.1. Global results

On the 300 metabolites analysed in the 8 different conditions, 133 were detected in both cell types and quantified (Fig. 1B, Supplementary Table 3). Among them, fatty acids (48 on 133), amino acids (30/133), organic acids (18/133), and carbohydrates (17/133) are the most represented. Benzenoids (3/133), bile acids (8/133), carnitines (5/133), nucleosides (2/133), phenylpropanoic acids (1/133) and pyridines (1/133) are poorly represented.

## 3.2. Comparison of untreated hBLEC versus untreated hBP metabolomic profiles

To gain insight in differences between hBLEC and hBP metabolomic profiles, we first performed a principal component analysis (PCA) plot of the untreated samples. Untreated hBLEC versus the untreated hBP samples reveals a clear separation between the both samples, indicating substantial differences in their metabolomic profiles (Fig. 2A, F-value = 87.579, R-squared = 0.916, p-value = 0.009). hBP samples form a homogeneous cluster, whereas hBLEC samples exhibit greater internal variability.

These observations are confirmed by the comparison of the metabolic profiles of the two cell types with fold change (FC) threshold > 2, or FC threshold < 0.5 and false discovery rate (FDR) < 0.05. Indeed, when compared with hBP, we observe that 13 metabolites are underrepresented in hBLEC (in red), and 63 are detected in highest amounts (in green) (Fig. 2B, Supplementary Table 4). Among the poorly detected metabolites in hBLEC, the sebacic acid, a fatty acid, is 330 times less present in comparison with hBP profile. Two other fatty acids are less detected in hBLEC in comparison with hBP, i.e., myristic acid (5-fold less) and myristoleic acid (7-fold less). The 2 most detected metabolites in hBLEC, when compared with hBP, are acetic acid and azelaic acid, that also belong to the fatty acid family, and that are 510 and 35 times more present in hBLEC than in hBP, respectively. To gain insight in the different metabolic pathways differentially functioning between hBLEC and hBP, we subsequently performed quantitative enrichment pathway analysis using Kyoto Encyclopedia of Genes and Genomes database (KEGG), (Fig. 2C). Our results indicate that metabolites involved in citrate cycle (TCA cycle), butanoate metabolism and valine, leucine and isoleucine biosynthesis are enriched when comparing hBLEC versus hBP.

We next investigated the impact of an inflammatory stimulus, i.e., TNFα, on the metabolomic profiles of hBLEC and hBP of the human BBB model.

## 3.3. TNFα and oxysterols influence BBB physiology

To confirm that TNFα and oxysterols impact the cell physiology of the cells composing the human BBB model, we first performed RT-qPCR and western blot experiments. For the TNFα treatment, we observe a significant activation of the inflammasome molecule NOD-like receptor family, pyrin domain-containing 3 (NLRP3) which is a strong marker of inflammation, in both cell types (Fig. 3A). For the oxysterol treatments, we investigated the mRNA and protein expression of key players involved in cholesterol release (ATP-binding cassette family A member 1, ABCA1), cholesterol synthesis (3-hydroxy-3-methylglutaryl-coenzyme A reductase, HMGCR) and cholesterol uptake (low density lipoprotein receptor, LDLR). Whole uncropped images of the original western blots for all replicates that contributed to the quantitative analysis are presented in Supplementary Figures 2, 3 and 4. 24S-hydroxycholesterol (24S-HC) strongly induces ABCA1 expression in hBLEC and in hBP, at the mRNA and protein levels (Fig. 3B and C, respectively). ABCA1 is the main target gene regulated by the cholesterol sensing Liver X nuclear receptors (LXR) and 24S-HC is a very strong inducer of this signalling pathway. 25-hydroxycholesterol (25-HC) is principally an
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Comparison of the metabolic profiles and pathways, in untreated hBLEC versus hBP of the human in vitro BBB model. (A) PCA analysis shows a clear distinction between hBLEC and hBP profiles suggesting distinct metabolites pathways in the two cell types. (B) Volcanot plot of metabolites of hBLEC metabolites compared with hBP metabolites. Metabolites with fold change (FC) threshold > 2 (Log₂ FC > 1) are represented in green, and metabolites with FC < 0.5 are represented in red (Log₂ FC < -1). Metabolites are selected if adjusted p-value < 0.05 (so -Log₁₀ p-value > 1.3). (C) Enrichment pathway showing the top enriched metabolites pathways using Kyoto Encyclopedia of Genes and Genomes database (KEGG), with metabolites containing at least 2 entries, and after Log₁₀ normalization. Selected metabolite sets have p-value, False Discovery Rate (FDR), and Holm p-value < 0.05. Results summarize metabolite analysis of 5 independent experiments.</figcaption></figure>

<figure><img src="image_5.png" /></figure>

<table><thead><tr><td>Metabolite set</td><td>p-value</td><td>Holm p</td><td>FDR</td></tr></thead><tbody><tr><td>Citrate cycle (TCA cycle)</td><td>4.09 e<sup>-5</sup></td><td>0,00323</td><td>0,00323</td></tr><tr><td>Butanoate metabolism</td><td>2.03 e<sup>-4</sup></td><td>0,0158</td><td>0,00801</td></tr><tr><td>Valine, leucine and isoleucine biosynthesis</td><td>4.68 e<sup>-4</sup></td><td>0,036</td><td>0,0123</td></tr></tbody></table>

inhibitor of the second cholesterol sensing pathway which is the SREBP2 pathway. By inhibiting this cholesterol controlling pathway, the levels of LDLR and HMGCR are reduced, suggesting that the cells decrease their cholesterol uptake and production, as evidenced in Fig. 3B and C. 7α,25-dihydroxycholesterol (7α,25-DHC) is a metabolite of the 25-HC and is rather a ligand for the EBI2 receptor. To our knowledge, its impact on the BBB cholesterol homeostasis has never been explored before. Despite deep transcriptional decreases of HMGCR and LDLR expressions, only the protein levels of HMGCR are significantly decreased (Figs. 3B and 3C, respectively). We also calculated the permeability coefficient (Pe) of Lucifer Yellow (LY), a small paracellular marker, across the BBB. TNFα increases PeLY, as we previously published (Fig. 3D, top) [27,41]. Oxysterols alone do not modulate the BBB permeability. However, in inflammatory situation, the 3 tested oxysterols can partially restore the BBB permeability (Fig. 3D, bottom). Altogether these results demonstrate that the different oxysterol treatments significantly modify the cholesterol metabolisms of hBLEC and hBP, and can also partially alleviate the lost BBB permeability observed in inflammatory situation. We therefore investigated the impact of inflammation and of these 3 oxysterols on the metabolic profiles of these cells composing the human BBB model.

### 3.4. Impact of inflammation on hBLEC and hBP metabolomic profiles

TNFα, a very important inflammatory molecule involved in several neurodegenerative disorders and in BBB dysfunctions [35,41], was then

applied at 10 ng/mL for 24 h in the human BBB model. Results for the 133 analyzed metabolites are summarized in Supplementary Tables 5 and 6, for hBLEC and hBP, respectively. PCA of the two cell types show a partial but clear separation of the treated condition versus untreated condition for the hBLEC while the two conditions are clearly separated for hBP (Fig. 4A and B, respectively).

The generated volcano plots showing TNFα-treated hBLEC and hBP when compared with untreated cells (referred as CTRL condition) are shown in Fig. 4C and D, respectively. In hBLEC, phenylpyruvic acid, a benzenoic acid, and two fatty acids (azelaic acid and 3-hydroxyisovaleric acid) are the only upregulated metabolites that satisfy a FC > 2, and an adjusted p-value < 0.05. On the contrary, 3 organic acids (citric acid, cis-aconitic acid and oxoglutaric acid), 2 amino acids (glycylproline, creatine), 2 carnitines (isovalerylcaritine, acetylcaritine), and 2 fatty acids (eicosapentaenoic acid, and docosapentaenoic acid) are down-regulated by the TNFα treatment (FC < 0.5).

Regarding the hBP, the significant overexpression of 3 metabolites, phenylpyruvic acid (benzenoic acid), maltotriose (carbohydrate) and hippuric acid (benzenoic acid) have satisfied the criteria of adjusted p-value < 0.05 and FC > 2. No metabolites were downregulated (Fig. 4D).

Interestingly, phenylpyruvic acid is the only metabolite significantly upregulated in both cell types after TNFα-treatment (Fig. 4A and B). In hBLEC, enrichment analysis indicates that only the citrate cycle is significantly enriched in TNFα-treated condition (FDR < 0.05 and Holm p-value < 0.05) (Supplementary Figure 5A). Since only 3 metabolites show significant upregulation in TNFα-treated hBP (phenylpyruvic acid,
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Impact of TNFα and oxysterols on the human BBB model. hBLEC and hBP were treated with 10 ng/mL of TNFα or with 10 µM of oxysterols. (A) TNFα triggers assembly and activation of the inflammatory marker, the complex NOD-like receptor family, pyrin domain-containing 3 (NLRP3). Results were normalized on ACTIN signal. Three independent experiments were done. Results represent medians with minimum and maximum values. Unpaired *t*-tests were performed, comparing TNFα-condition with the CTRL condition (vehicle-treated condition), \*\*\*p < 0.001. Oxysterols modify transcriptional (B) and protein (C) levels of the key players of the cholesterol metabolism. ATP-binding cassette family A member 1 (ABCA1) is the major lipid transporter controlling the cholesterol release from cellular membrane. 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) is the rate-limiting enzyme of the cellular cholesterol biosynthesis. The low-density lipoprotein receptor (LDLR) is the major receptor involved in the lipoprotein uptake and endocytosis. mRNA and protein results were normalized on GAPDH and ACTIN signals, respectively. Three independent experiments were performed and analysed. Results represent medians with minimum and maximum values. Two-way ANOVA tests were performed followed by Sidak's multiple comparison tests, comparing with the CTRL condition (vehicle-treated condition), \*p < 0.05, \*\*p < 0.01. (D) Paracellular diffusion of the integrity marker Lucifer Yellow (LY) was measured in presence of TNFα w/wo oxysterols. Four independent experiments were performed and analysed. Results represent means with standard deviations (S.D.). One-way ANOVA tests were performed followed by Tukey's multiple comparison tests, comparing all the conditions together, \*\*\*\*p < 0.0001 when compared to the untreated condition (up) or TNFα condition (bottom).</figcaption></figure>

maltotriose and hippuric acid), no metabolic pathway reaches requested significance in this cell type (Supplementary Figure 5B). The consistent upregulation of phenylpyruvic acid, an abnormal metabolite in phenylalanine metabolism, in both hBLEC and hBP under inflammatory stress suggests a potential common mechanism by which TNFα disrupts BBB physiology, possibly through impairing the enzymatic activity of phenylalanine hydroxylase (PAH).

### 3.5. Effects of oxysterols on metabolomic profile of TNFα-treated hBLEC

PCA of the hBLEC (control, TNFα-treated, Oxysterols-treated and TNFα + oxysterols treated) revealed a clear separation of metabolomic profiles across the eight experimental conditions (Fig. 5A, PERMANOVA F-value: 5.3528; R-squared: 0.48999; p-value (based on 999 permutations): 0.001). The first two PC (PC1 and PC2) accounted for 26.1 % and 12.6 % of the total variance, respectively. While this indicates that additional components contribute significantly to overall variance, PC1 and PC2 still capture relevant trends in the dataset. The TNFα group (magenta) shifts towards positive PC1 values and exhibits an elongated spread along this axis, indicating that TNFα is the main driver of variance in PC1. Oxysterols alone (24S-HC, 25-HC, 7α,25-DHC) cluster more tightly around the origin and show partial overlap with the untreated condition suggesting that their isolated effects on the metabolite profile are relatively modest in comparison to TNFα. The combined treatments (TNFα + oxysterols) cluster near the TNFα group and overlap considerably, implying that TNFα dominates the metabolic signature, with no

obvious additive or synergistic effect visible in the PC1/PC2 space. When the top 9 metabolites contributing to PC1/PC2 are loaded (black arrows, Fig. 5A), we can observe that PC1 is positively associated with acetic acid (HMDB0000042) and phenylpyruvic acid (HMDB0000205), both aligned with the TNFα cluster, suggesting these metabolites are elevated under TNFα treatment as previously pointed out in Fig. 4C. Conversely, cis-aconitic acid (HMDB0000072), isocitric acid (HMDB0000193), citric acid (HMDB0000094), and creatine (HMDB0000064) point towards negative PC1 values, indicating higher levels in CTRL or oxysterol-only samples. Along PC2, metabolites such as glucose 6-phosphate (HMDB0001401) and stearic acid (HMDB0000827) contribute to negative values, whereas glycylproline (HMDB0000721) and oxoglutaric acid (HMDB0000208) contribute to positive values, allowing subtle discrimination among oxysterols, although the effect is less pronounced.

The generated volcano plots indicated that oxysterols do no significantly impact metabolites in hBLEC, with or without TNFα treatment (data not shown). It was therefore not possible to demonstrate any effect of oxysterols on hBLEC. For this reason, we generated a heatmap comprising the 8 conditions together to allow a global analysis of the 8 conditions, with the exclusive selection of metabolites displaying FDR < 0.05 and p-value < 0.05. A clear modulation of several amino acids, benzenoids and fatty acid levels in TNFα and oxysterol-treated conditions are observed (Fig. 5B). Full Heat maps showing all metabolites before statistical filtering in order to fully appreciate the data structure behind the significant ones are shown in Supplementary Figure 6 A.
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Impact of TNFα on metabolomic profiles of hBLEC and hBP. PCA reveal a partial but clear separation of the TNFα-treated condition versus untreated condition for the hBLEC (A, (PERMANOVA F-value: 4.3304; R-squared: 0.32485; p-value (based on 999 permutations): 0.016)) while the two conditions are clearly separated for hBP (B, (PERMANOVA F-value: 11.683; R-squared: 0.56485; p-value (based on 999 permutations): 0.001)). Volcano plots of metabolites affected by a TNFα treatment in hBLEC (C) and in hBP (D). Cells were treated 24 h with 10 ng/mL of TNFα. Results summarize metabolites analysis of 5–6 independent experiments. Results show upregulated (FC > 2) or downregulated (FC < 0.5) metabolites, with adjusted p-value < 0.05.</figcaption></figure>

Univariate statistical analysis (ANOVA or Kruskal-Wallis test, depending on the normality of data and homogeneity of variance), was then made and allowed to identify the top 9 of differential metabolites among the 8 different hBLEC conditions (Fig. 5C). Phenylpyruvic acid and alpha linoleic acid are the metabolites the most significantly upregulated after TNFα treatment, but oxysterol treatments did not alleviate this upregulation. Oxysterols do not modify levels of histidine, leucine, tyrosine and isovalerylcarnitine that are all downregulated by the TNFα-treatment. However, phenylalanine and tyrosine levels seem to be decreased by 7α,25-DHC in particular in presence of TNFα. This strengthens the fact that all oxysterols finally show a very limited effects on metabolite levels in hBLEC in our experimental conditions.

### 3.6. Effects of oxysterols on metabolomic profile of TNFα-treated hBP

Focusing then on hBP, PCA demonstrates distinct clustering patterns between the 8 conditions, with overlapping confidence ellipses indicating partial similarity among some conditions (Fig. 6A). Untreated condition is centrally located, suggesting a baseline metabolic state. 24S-HC and 25-HC conditions are clearly separated along PC1, indicating specific metabolic shifts associated with these oxysterols, but are almost superposed with the untreated condition, suggesting a very limited effect of these treatments on the metabolomic profiles. TNFα treatment alone forms a distinct cluster along negative PC2 values, while TNFα in combination with oxysterols tends to shift towards positive PC1,

<figure><img src="image_8.png" /></figure>

suggesting a synergistic metabolic effect. This data clearly suggests that both oxysterol treatments and TNFα stimulation induce distinct metabolic alterations, with TNFα and oxysterol co-treatments exhibiting unique clustering patterns compared to single treatments or controls. The partial overlap between certain conditions (e.g., CTRL and 7α,25-DHC) suggests some shared metabolic profiles, while the divergence along PC1 underscores the strong discriminative effect of oxysterols, particularly in the presence of TNFα. The biplot vectors, representing discriminating metabolites highlight the features driving separation between treatments. For instance, phenylpyruvic acid (HMDB0000205) strongly influences PC1, distinguishing TNFα + 7α,25-DHC (orange) from other groups, while metabolites like lithocholic acid (HMDB0000761) and taurodeoxycholic acid (HMDB0000896) contribute to the separation of 25-HC and 24S-HC conditions.

These findings imply that oxysterols modulate TNFα-induced metabolic responses in a compound-specific manner, which may reflect differential activation of lipid metabolic pathways. Additional statistical analysis was made, using univariate statistical analysis, ANOVA or Kruskal-Wallis test, depending on the normality of the data and homogeneity of variance.

Heatmap of the 8 conditions, shown in Fig. 6B, recapitulates variations among the metabolites displaying FDR < 0.05 and a p-value < 0.05. Full Heat maps showing all metabolites are shown in Supplementary Figure 6B. The top 9 differential metabolites are shown in Fig. 6C. As suggested above by the PCA and the table 3, and contrary to
<figure><img src="image_9.png" /><figcaption>**Fig. 5.** Effects of oxysterols on TNFα-treated hBLEC. (A) PCA analysis of the 8 conditions (CTRL, TNFα, 24S-HC, 25-HC, 7α,25-DHC, TNFα + 24S-HC, TNFα + 25-HC, TNFα + 7α,25-DHC) show spatial distribution of the metabolomic profiles, in particular when cells are treated with TNFα. (B) Heat map showing effects of TNFα and oxysterols on amino acids, benzenoids, carbohydrates, carnitines, fatty acids, nucleosides, organic acids, phenylpropanoids and pyridines levels. This heat map only includes metabolites that have both p-value < 0.05 and FDR < 0.05. (C) Univariate analysis of the data allowed us to select the top 9 differential metabolites in hBLEC after TNFα treatment, with or without oxysterol co-treatment for 24 h. ANOVA or Kruskal-Wallis test were made depending on the normality of data and homogeneity of the variance. \*p < 0.05, \*\*p < 0.01, \*\*\*p < 0.001.</figcaption></figure>

the observations made previously on hBLEC, oxysterols significantly change the levels of several metabolites, even in absence of TNFα. Thus, 24S-HC and 25-HC significantly decrease fructose 6-phosphate and glucose 6-phosphate levels. Treatments with 7α-25-DHC, 24S-HC and 25-HC decrease levels of myristoleic acid and 9E-tetradecenoic acid. Galactonic level is decreased by 25-HC and 7α-25-DHC, while 24S-HC and 7α-25-DHC decrease linoleic acid level. 7α-25-DHC increase pro-pionylcarnitine and acetylcarnitine levels. As for the hBLEC, none of these oxysterols affect phenylpyruvic acid level, that is highly upregulated in inflammatory state.

These trends are confirmed by comparing the different untreated or TNFα-treated conditions, with the oxysterol conditions, by calculating FC ratio (FC > 2 and FC < 0.5), and by selecting metabolites with adjusted p-value < 0.05 (Table 3). In these conditions, myristoleic acid is significantly downregulated in all hBP oxysterol-treated samples. In addition, 25-HC and its metabolite, 7α,25-DHC also decrease glucose 6-phosphate and fructose 6-phosphate levels, in particular in TNFα-treated conditions, and increase citric acid levels in untreated conditions. Interestingly, 7α,25-DHC increases butyric acid, heptanoic acid and pentadecanoic acid in presence of TNFα.

## 4. Discussion

Using an in vitro model mimicking the human BBB and an LC-MS/ MS-based targeted metabolomic profiling approach, we investigated how TNFα-mediated inflammation and oxysterols regulate the levels of 133 metabolites. Comparison of the metabolomic profiles of hBLEC and hBP highlights clear different metabolomic profiles. We indeed observed that 63 metabolites are more produced by hBLEC, and 13 by hBP. Several of these metabolites belong to the fatty acid family like acetic acid, azelaic acid, sebacic acid, myristic acid, and myristoleic acid, thus strongly suggesting that these 2 cell types display differences

particularly in lipid metabolism as previously suggested [27,45]. In CNS, fatty acids are key biomolecules serving as key components of membrane lipids, substrates for mitochondrial β-oxidation, and to produce bioactive lipids regulating cellular signalling and inflammation [46]. In BBB endothelial cells, functions of fatty acids may be different since these cells perform aerobic glycolysis and display an important phosphorylation activity compared to astrocytes [47,48]. These data are consistent with the modification of levels of several fatty acids that we report in hBLEC when compared with hBP or under TNFα-treated condition and highlight the importance of fatty acids in BBB integrity and functions, in particular in inflammatory conditions.

Furthermore, using KEGG database, we identified the citrate cycle as the most enriched metabolic pathway since citric acid, cis-aconitic acid and isocitric acid are more abundant in hBLEC. This suggests that hBLEC display high energetic and metabolic activity in comparison with hBP, probably because they are enriched in mitochondria and extensively use ATP to select and restrict the passage of molecules and cells to the CNS [49]. In relation with this, creatine is overrepresented in hBLEC in comparison with hBP, that are yet derived from smooth muscle cells [50]. Butanoate metabolism is the second enriched pathway observed in this analysis since the neurotransmitter gamma-aminobutyric acid (GABA), oxoglutaric acid, succinic acid and butyric acid are down-regulated in hBLEC. Butanoate metabolism degrades L-glutamate into the signal molecule GABA followed by subsequent reactions to generate various downstream metabolites. That suggests that hBP are more prone to hBLEC to regulate neurotransmitter levels, in particular GABA levels, that is consistent with their close vicinity to neurons in comparison with hBLEC. While role of BBB endothelial cells in GABA clearance and regulation has been previously studied [51], the contribution of hBP in this process remains uncharacterized.

Despite no metabolic pathways identified in hBP under inflammatory conditions, it is noteworthy that phenylpyruvic acid is significantly
<figure><img src="image_10.png" /><figcaption>**Fig. 6.** Effects of oxysterols on TNFα-treated hBP. (A) PCA analysis of the 8 conditions (CTRL, TNFα, 24S-HC, 25-HC, 7α,25-DHC, TNFα + 24S-HC, TNFα + 25-HC, TNFα + 7α,25-DHC) show spatial distribution of the metabolomic profiles. (B) Heat map showing effects of TNFα and oxysterols on amino acids, benzenoids, carbohydrates, carnitines, fatty acids, nucleosides, and organic acids levels. This heat map only includes metabolites that have both p-value < 0.05 and FDR < 0.05. (C) Univariate analysis of the data allowed us to select the top 9 differential metabolites in hBLEC after TNFα treatment, with or without oxysterol co-treatment for 24 h. ANOVA or Kruskal-Wallis test were made depending on the normality of data and homogeneity of the variance. \*p < 0.05, \*\*p < 0.01, \*\*\*p < 0.001.</figcaption></figure>

overproduced by both cell types. This is highly provocative, as it mirrors the biochemical hallmark of phenylketonuria (PKU), a neurological disorder caused by deficient phenylalanine hydroxylase (PAH) activity [52]. We posit that pro-inflammatory signaling, notably TNFα, may suppress PAH function or expression, creating a localized, acquired PKU-like microenvironment at the BBB. This could lead to the accumulation of neurotoxic phenylalanine and its derivatives, potentially contributing to the cognitive impairment and neuronal excitotoxicity observed in neuroinflammatory and neurodegenerative diseases. To our knowledge, this is the first time that such results are reported and it indicates that this metabolite may serve as potential biomarker of BBB inflammatory state. It is well known that disturbances in phenylalanine metabolism, as reported in PKU disrupts the neurotransmitter synthesis, thus contributing to hyperactivity, mental retardation, and epileptic seizures [52]. Interestingly, it has also been previously suggested that phenylpyruvic acid may be used to predict the rate of periodontitis progression [53], that is closely linked to several diseases onset and evolution, including Alzheimer's disease [54]. With all these considerations in mind, it appears that links between inflammation, BBB physiology and phenylpyruvic acid merits further investigation.

Using the KEGG database, we observed that citrate cycle is the only metabolic pathway significantly enriched in hBLEC treated with TNFα, but noteworthy, levels of several metabolites of the fatty acid, organic acid, carnitine and benzenoic acid families are also affected. These results are in line with recent studies investigating the effect of ischemic stroke, traumatic brain injury (TBI) or large brain vessel occlusion, that all promote inflammation, on metabolomic profiles of brain endothelial cells in vitro, in mouse and in patients. Among the most upregulated metabolites by TBI, authors identified phosphatidylcholines,

sphingomyelin, ethylmalonic acid, deoxyadenosine, inosine, and methylistidine. On the contrary, butyric acid and isobutyric acid were the most downregulated [55]. In addition, levels of several fatty acids were decreased in case of large vessel occlusion [56]. All together, these results strongly suggests that inflammation states deeply alter BEC energy supply, and highlight the key role of the fatty acids in this process.

Oxysterols, like 24S-HC, 25-HC and 7α,25-DHC regulates several cellular processes including lipid metabolism, cell survival and death, or cell division and migration, and are therefore considered as promising molecules to prevent disease onset and progression. Previous studies reported effects of the 3 oxysterols on BBB lipid metabolism, physiology and permeability [27–29,57]. In our culture conditions, we indeed observed that 24S-HC strongly induces ABCA1 expression in hBLEC and hBP, while 25-HC and 7α,25-DHC both reduce HMGCR expression without affecting LDLR levels. Surprisingly, none of these oxysterols significantly modify the levels of the studied metabolites in hBLEC, both in absence and presence of TNFα. We cannot exclude, however, that such modifications would occur in different times of the cell culture, i.e., 6, 12 or 48 h, for example, or for different oxysterols’ concentrations. We indeed selected 24 h and 10 µM in our experimental set-up based on previous effects of such oxysterols observed on BBB cells in vitro [27,28, 35]. Further experiments at low/medium/high doses of oxysterols (1, 5, 10 µM) and multiple time points (6, 12, 24, 48 h) would give additional and more dynamic information. Interestingly, we also observed that the 3 tested oxysterols partially alleviate the BBB opening triggered by TNFα. We previously reported this effect for 25-HC in our study [27], but this is the first time that such effects are observed at the human BBB for 24S-HC and 7α,25-DHC.

In contrast, some significant metabolites changes are observed in
<figure><img src="image_11.png" /><figcaption>**Fig. 7.** Summarizing picture of the effects of TNFα (in purple) and oxysterols (blue) on hBLEC and hBP. TNFα increases phenylpyruvic acid levels in both cell types. Exclusively in hBLEC, TNFα modifies the levels of several organic acids, fatty acids and carnitine metabolites, thus altering the citrate cycle and energy production. Interestingly, tested oxysterol do no show any significant effects on metabolic profiles of hBLEC. However, these cholesterol metabolites modify the carnitine, carbohydrate and fatty acid metabolisms in hBP. Since the astrocytes strongly influence endothelial-pericytes crosstalk and are plausible oxysterol source, we included their potential contribution in the above molecular processes.</figcaption></figure>

hBP. In particular for myristoleic acid which present in high amount in hBP, with its precursor, the myristic acid, and which is surprisingly downregulated by the 3 oxysterols, independently of ABCA1 or HMGCR expression, in absence or in presence of TNFα. Myristoleic acid is a rare omega-5 monounsaturated fatty acid produced from myristic acid by stearoyl-Coa desaturase-1 [58]. Its consistent downregulation by all three oxysterols, independent of the inflammatory state, suggests this is a fundamental and specific response to oxysterol signaling in pericytes, rather than a general consequence of cellular stress. This raises a critical question: what is the functional consequence of depleting this specific lipid in oxysterol conditions? Given that myristoleic acid can be incorporated into phospholipids and influence membrane fluidity, its downregulation may represent a mechanism by which oxysterols modulate pericyte membrane dynamics and function. Alternatively, as myristoleic acid has been implicated as a signaling molecule in lipid metabolism, its reduction could be either a cause or an effect of the broader metabolic reprogramming we observed, such as the changes in glycolysis (glucose-6-phosphate, fructose-6-phosphate) and other fatty acids (pentadecanoic, butyric, heptanoic acid). Interestingly, this effect occurred alongside oxysterol-induced changes in the expression of key cholesterol regulators (ABCA1, HMGCR), yet it is unclear if the shift in myristoleic acid is directly linked to these classical pathways or represents a distinct, novel signaling axis.

While our targeted approach provided valuable insights, several limitations must be acknowledged. First, our study captures a static

snapshot of metabolite abundance; stable isotope-resolved flux analysis is required to determine if the observed changes reflect alterations in metabolic pathway flux, which might be more dramatic than steady-state pool sizes suggest. Furthermore, additional experiments performed at low/medium/high doses of oxysterols and multiple time points would be able to give more dynamic information on metabolic flux. Second, while our co-culture model recapitulates key BBB features, incorporating other neural cell types, like astrocytes, would provide a more physiologically complete system to study the role of neuronal-derived oxysterol and astrocyte-pericyte crosstalk in these metabolic pathways.

In conclusion, our study moves beyond a descriptive catalog of metabolic changes to propose novel mechanistic frameworks for BBB dysfunction. We identify a shared inflammatory metabolic signature (phenylpyruvic acid accumulation) and reveal a previously unappreciated cellular specificity in the response to oxysterols, centering on pericytes. These findings reframe our understanding of neuroinflammatory metabolic dysregulation and open new avenues for therapeutic strategies aimed at modulating pericyte metabolism to preserve BBB integrity in neurological disorders.

### CRediT authorship contribution statement

**Julien Saint-Pol:** Writing – review & editing, Visualization, Formal analysis.
**Xiaoli Chen:** Writing – review & editing, Methodology,
<caption>**Table 3**</caption>

Metabolites modulated by oxysterols and inflammation in hBP. Only those with FC ratio (FC > 2 and FC < 0.5), and adjusted p-value < 0.05 were selected.

<table><thead><tr><th></th><th>ID</th><th>FC</th><th>Log2 FC</th><th>Ajusted p value</th><th>Direction</th></tr></thead><tbody><tr><td rowspan="2">CTRL vs 24S-HC</td><td>Myristoleic acid</td><td>0.29089</td><td>-1.7815</td><td>8.9088E-6</td><td>down</td></tr><tr><td>9E-tetradecenoic acid</td><td>0.29089</td><td>-1.7815</td><td>8.9088E-6</td><td>down</td></tr><tr><td rowspan="6">CTRL vs 25-HC</td><td>Fructose 6-phosphate</td><td>0.12805</td><td>-2.9652</td><td>0.0022506</td><td>down</td></tr><tr><td>Glucose 6-phosphate</td><td>0.16834</td><td>-2.5705</td><td>0.0024181</td><td>down</td></tr><tr><td>Myristoleic acid</td><td>0.24594</td><td>-2.0236</td><td>2.1731E-5</td><td>down</td></tr><tr><td>9E-tetradecenoic acid</td><td>0.24594</td><td>-2.0236</td><td>2.1731E-5</td><td>down</td></tr><tr><td>cis-Aconitic acid</td><td>2.3465</td><td>1.2305</td><td>0.0095174</td><td>up</td></tr><tr><td>Citric acid</td><td>2.3696</td><td>1.2446</td><td>7.5465E-5</td><td>up</td></tr><tr><td rowspan="4">CTRL vs 7α,25-DHC</td><td>Myristoleic acid</td><td>0.31335</td><td>-1.6742</td><td>1.0214E-4</td><td>down</td></tr><tr><td>9E-tetradecenoic acid</td><td>0.31335</td><td>-1.6742</td><td>1.0214E-4</td><td>down</td></tr><tr><td>Citric acid</td><td>2.3267</td><td>1.2183</td><td>0.0054358</td><td>up</td></tr><tr><td>Propionylcarnitine</td><td>2.5921</td><td>1.3741</td><td>0.0047148</td><td>up</td></tr><tr><td rowspan="4">TNFα vs TNFα + 24S-HC</td><td>Fructose 6-phosphate</td><td>0.20073</td><td>-2.3167</td><td>0.0057463</td><td>down</td></tr><tr><td>Glucose 6-phosphate</td><td>0.23638</td><td>-2.0808</td><td>0.0041596</td><td>down</td></tr><tr><td>Myristoleic acid</td><td>0.44594</td><td>-1.1651</td><td>1.3917E-5</td><td>down</td></tr><tr><td>9E-tetradecenoic acid</td><td>0.44594</td><td>-1.1651</td><td>1.3917E-5</td><td>down</td></tr><tr><td rowspan="3">TNFα vs TNFα + 25-HC</td><td>Pentadecanoic acid</td><td>2.2478</td><td>1.1685</td><td>0.033894</td><td>up</td></tr><tr><td>Glucose 6-phosphate</td><td>0.2688</td><td>-1.8954</td><td>0.028305</td><td>down</td></tr><tr><td>Myristoleic acid</td><td>0.43214</td><td>-1.2104</td><td>6.1793E-4</td><td>down</td></tr><tr><td rowspan="6">TNFα vs TNFα + 7α,25-DHC</td><td>Fructose 6-phosphate</td><td>0.18521</td><td>-2.4328</td><td>0.0045502</td><td>down</td></tr><tr><td>Glucose 6-phosphate</td><td>0.23169</td><td>-2.1097</td><td>0.011925</td><td>down</td></tr><tr><td>Myristoleic acid</td><td>0.47986</td><td>-1.0593</td><td>2.2408E-5</td><td>down</td></tr><tr><td>9E-tetradecenoic acid</td><td>0.47986</td><td>-1.0593</td><td>2.2408E-5</td><td>down</td></tr><tr><td>Butyric acid</td><td>2.1087</td><td>1.0763</td><td>0.04531</td><td>up</td></tr><tr><td>Heptanoic acid</td><td>2.6123</td><td>1.3853</td><td>0.017242</td><td>up</td></tr><tr><td></td><td>Pentadecanoic acid</td><td>3.6221</td><td>1.8568</td><td>0.048853</td><td>up</td></tr></tbody></table>

Investigation, Formal analysis. **Gosselet Fabien P.:** Writing – original draft, Visualization, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization.
**Maxime Culot:** Writing – review & editing, Resources. **Caroline Pot:** Writing – review & editing, Resources. **Takashi Kanda:** Writing – review & editing, Resources. **Fumitaka Shimizu:** Writing – review & editing, Resources. **Cindy Nguyen:** Investigation, Conceptualization.
**Wei Jia:** Writing – review & editing, Resources, Data curation. **Aiping Lyu:** Writing – review & editing, Resources, Data curation. **Wang Lu:** Writing – review & editing, Resources, Data curation. **Chan Kannie W. Y.:** Writing – review & editing, Resources, Data curation. **Kenneth CP Cheung:** Writing – review & editing, Methodology, Funding acquisition.

Declaration of Competing Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Fabien Gosselet, PhD reports was provided by ARTOIS UNIVERSITY. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

Cindy Nguyen received a fellowship from ‘Artois University’. Fabien Gosselet received further funding from the French State and Region Hauts-de-France as part of the CPER 2021–2027 MOSOPS project. Fabien Gosselet and Kenneth CP Cheung received funds from the France/Hong Kong Joint Research Scheme (F-HKBU201/22) and PRO-CORE Program (49347RA); General Research Fund (GRF, #12101023). The authors used OpenAI ChatGPT to shorten the text and to check grammar, spelling, and improve readability and language. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.biopha.2025.118853.

Data Availability

Dataset are available on Zenodo. This is indicated in the manuscript.
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