Abstract

   Morphine, a commonly used antinociceptive drug in hospitals, is known
   to cross the blood-brain barrier (BBB) by first passing through brain
   endothelial cells. Despite its pain-relieving effect, morphine also has
   detrimental effects, such as the potential induction of redox imbalance
   in the brain. However, there is still insufficient evidence of these
   effects on the brain, particularly on the brain endothelial cells and
   the extracellular vesicles that they naturally release. Indeed,
   extracellular vesicles (EVs) are nanosized bioparticles produced by
   almost all cell types and are currently thought to reflect the
   physiological state of their parent cells. These vesicles have emerged
   as a promising source of biomarkers by indicating the functional or
   dysfunctional state of their parent cells and, thus, allowing a better
   understanding of the biological processes involved in an adverse state.
   However, there is very little information on the morphine effect on
   human brain microvascular endothelial cells (HBMECs), and even less on
   their released EVs. Therefore, the current study aimed at unraveling
   the detrimental mechanisms of morphine exposure (at 1, 10, 25, 50 and
   100 µM) for 24 h on human brain microvascular endothelial cells as well
   as on their associated EVs. Isolation of EVs was carried out using an
   affinity-based method. Several orthogonal techniques (NTA, western
   blotting and proteomics analysis) were used to validate the EVs
   enrichment, quality and concentration. Data-independent mass
   spectrometry (DIA-MS)-based proteomics was applied in order to analyze
   the proteome modulations induced by morphine on HBMECs and EVs. We were
   able to quantify almost 5500 proteins in HBMECs and 1500 proteins in
   EVs, of which 256 and 148, respectively, were found to be
   differentially expressed in at least one condition. Pathway enrichment
   analysis revealed that the “cell adhesion and extracellular matrix
   remodeling” process and the “HIF1 pathway”, a pathway related to
   oxidative stress responses, were significantly modulated upon morphine
   exposure in HBMECs and EVs. Altogether, the combination of proteomics
   and bioinformatics findings highlighted shared pathways between HBMECs
   exposed to morphine and their released EVs. These results put forward
   molecular signatures of morphine-induced toxicity in HBMECs that were
   also carried by EVs. Therefore, EVs could potentially be regarded as a
   useful tool to investigate brain endothelial cells dysfunction, and to
   a different extent, the BBB dysfunction in patient circulation using
   these “signature pathways”.

   Keywords: extracellular vesicles, endothelial cells, morphine, CNS,
   BBB, proteomics, DIA-MS, oxidative stress, HIF-1, cell adhesion,
   extracellular matrix remodeling

1. Introduction

   Morphine is the most commonly used antinociceptive drug in hospitals
   for relieving moderate-to-severe pain. This opioid acts in the brain
   and the spinal cord, mainly through µ-opioid receptors [[36]1].
   Morphine treatment may also present several side effects, such as
   headache, nervousness, nausea, vomiting, weakness, dizziness, and
   hallucinations, among others. At the cellular level, numerous studies
   suggest that morphine induces oxidative stress and may contribute to
   some of these adverse effects [[37]2,[38]3,[39]4,[40]5,[41]6]. However,
   the underlying mechanisms are still not well understood. In addition,
   morphine action in the brain requires its transport to the central
   nervous system (CNS) by crossing the blood brain barrier (BBB) within
   the neurovascular unit (NVU). The BBB is a multicellular complex
   including astrocytes, pericytes and endothelial cells, which are in
   tight interaction with the NVU cells such as neurons, oligodendrocytes
   and microglia. The BBB major function is to guarantee CNS molecular
   homeostasis. Although many studies focusing on neurons and astrocytes
   have indicated that their essential properties were altered upon opioid
   exposure such as morphine [[42]7,[43]8,[44]9,[45]10], the effects of
   morphine on brain endothelial cells remain largely unknown.

   Therefore, in a previous study [[46]11], we explored morphine effects
   on human brain microvascular endothelial cells (HBMECs) using a
   proteomics approach. We showed that morphine exposure on HBMECs
   triggered changes in the biological processes associated with oxidative
   stress and highlighted a potential redox imbalance. To further explore
   the impact of morphine on these cells, we proposed investigating
   HBMECs-derived extracellular vesicles (EVs), as they may mirror
   pathways in parent cells. Indeed, EVs form a heterogeneous mixture of
   membrane-bounded organelles released by almost all cell types,
   containing key material such as nucleic acids, proteins, lipids and
   metabolites. In recent years, it has been suggested that their cargos
   may reflect the status of their parent cells. This offers the
   possibility for a deeper understanding of the modulations of cellular
   processes. Moreover, it has been shown that EVs can cross the very
   restrictive BBB [[47]12,[48]13], and thus may constitute an important
   information carrier. Furthermore, with regards to their potential role
   in cell-cell communication, EV molecular cargo could be shuttled
   between cells and might modulate biological processes such as
   proliferation, neuronal survival, energy metabolism and response to a
   stimulus [[49]14]. The investigation of EV molecular signatures is,
   therefore, crucial to obtain new insights into their biological
   functions upon primary morphine stimulation.

   In this study, we simultaneously investigated HBMECs and associated EVs
   proteome modulations after morphine exposure using mass spectrometry
   (MS)-based proteomics. HBMECs were treated with morphine at five
   different concentrations (1, 10, 25, 50 and 100 μM) for 24 h. EVs were
   isolated with a chemical affinity-based method, which was confirmed
   using orthogonal techniques such as nanotracking particle analysis
   (NTA), western blots and a proteomics approach. A bioinformatics
   analysis of the proteomics data revealed modulations of biological
   pathways involved in oxidative stress responses, as well as
   modifications of the extracellular matrix organization in both cells
   and vesicles.

2. Materials and Methods

2.1. Cell Culture

   Primary human brain microvascular endothelial cells (ACBRI 367, Cell
   Systems, Kirkland, QC, Canada) were cultured onto a rat-tail collagen
   type I-coated flasks (T75, 15 µg/mL, Merck Millipore, Darmstadt,
   Germany) and maintained in complete endothelial cell growth medium-2
   (EGM-2MV BulletKit, Lonza, Walkersville, MD, USA) at 37 °C in a 5%
   CO[2]incubator. Cells were washed three times at 80% confluence with
   phosphate-buffered saline solution containing calcium and magnesium
   (PBS, Gibco/Life technologies, Bleiswijk, Netherlands). They were
   incubated at 37 °C with 15 mL of complete endothelial cell growth
   medium-2 containing 5% of heat-inactivated exosome-depleted fetal
   bovine serum (Gibco/Life technologies, Bleiswijk, Netherlands) for 24
   h. Fifteen T75 flasks were exposed to morphine at final concentrations
   of 1, 5, 10, 25, 50 and 100 µM (morphine sulfate pentahydrate, Lipomed
   AG, Arlesheim, Switzerland) for 24 h for different omics experiments.
   The medium of each T75 flask was collected and used for extracellular
   vesicles (EV) isolation. Cells were detached with Stempro Accutase
   (Gibco/Life technologies, Bleiswijk, Netherlands) and washed three
   times with ice-cold Phosphate Buffered Saline (PBS, Gibco/Life
   technologies, Bleiswijk, Netherlands), pelleted and dry-stored at −80
   °C.

2.2. EV Isolation

   In each T75 flask, about 6 million of human brain endothelial cells
   were cultured. Ten mL of cell media were recovered. Cells and apoptotic
   bodies were removed using centrifugation (2000× g, 20 min). EVs were
   isolated in 1 mL of cell supernatant using a chemical affinity-based
   method, the EVTRAP magnetic beads provided by Tymora Analytical as a
   suspension in water. These magnetic beads are modified with amphiphilic
   groups, enabling the specific binding to the lipid bilayer of the EV
   membranes.

   The media loading buffer was added at 1:10 v/v ratio of the cell
   supernatant. Twenty-four μL of EVTRAP magnetic beads were added. The
   samples were incubated by end-over-end rotation for 30 min, according
   to updated manufacturer’s instructions. Following supernatant removal
   using a magnetic separator rack, the beads were washed with PBS and the
   EVs eluted by two 10 min incubations with fresh elution solution. Both
   eluted EVs were pooled and either resuspended in 80 μL of 0.2
   μm-filtered water for nanoparticle tracking analysis (NTA) or
   dry-stored at −80 °C.

2.3. Nanoparticle Tracking Analysis (NTA)

   NTA was carried out using a Particle Metrix ZetaView^® instrument
   (Particle Metrix GmbH, Inning, Germany). EVs were diluted at 1/400 with
   0.2 μm-filtered PBS prior to analysis. To evaluate the total particle
   count and the overall size, the samples were measured in scatter mode
   and standard instrument settings (sensitivity: 80, shutter: 100, min.
   brightness: 30; min. area: 10; max area: 1000). The samples were
   measured with ZetaView^® software version 8.05.12 SP1 (Particle Metrix
   GmbH, Inning, Germany).

2.4. Protein Extraction and Quantification

   Cell and EV pellets were resuspended in 80 μL of 0.1% Rapigest (Waters,
   Milford, MA, USA) 100 mM TEAB (Sigma-Aldrich, St. Louis, MO, USA),
   incubated for 10 min at 80 °C and then sonicated (five cycles of 20 s
   with breaks on ice). Samples were then spun down (14,000× g, 10 min, 4
   °C) and the supernatant was recovered. Protein content was measured
   using the Bradford assay (Bio-Rad, Hercules, CA, USA).

2.5. Western Blot Analysis

   The equivalent of 20 μg of proteins for cell samples and EV samples
   were separated using electrophoresis on a 10%T/2.6%C polyacrylamide gel
   and were subsequently transferred onto a PVDF membrane. Membranes were
   stained with amido black to reveal the proteins and washed with water
   to remove the excess. Immunoblot assays were performed using an
   anti-mouse antibody against PDCD6IP at a dilution of 1:500 (Biolegend,
   San Diego, CA, USA), anti-rabbit TSG101 at a dilution of 1:500 (Abcam,
   Cambridge, UK), anti-rabbit CD9 at a dilution of 1:500 (Abcam,
   Cambridge, UK) and anti-rabbit calreticulin (negative marker) at a
   dilution of 1:250 (Abcam, Cambridge, UK).

2.6. Sample Preparation for Mass Spectrometry-Based Omics

   For proteomics, 10 µg of proteins were reduced using TCEP for each
   sample of cells and EVs at morphine concentrations of 0, 1, 10, 25, 50
   and 100 μM (final concentration 5 mM, 30 min, 37 °C) (Sigma-Aldrich,
   St. Louis, MO, USA), alkylated using iodoacetamide (final concentration
   15 mM, 60 min, RT, in dark conditions) (Sigma-Aldrich, St. Louis, MO,
   USA) and digested by an overnight tryptic digestion (w/w ratio 1:50)
   (Promega, Madison, WI, USA). The RapiGest surfactant was cleaved by
   incubating samples with 0.5% trifluoacetic acid (Sigma-Aldrich, St.
   Louis, MO, USA) for 45 min at 37 °C. Samples were then desalted on a
   C18 reverse phase column (Harvard Apparatus, Holliston, MA, USA),
   peptides were dried under vacuum and subsequently resuspended in 5% ACN
   0.1% FA (peptides final concentration of 0.5 µg/µL and spiked with iRT
   peptide (Biognosys, Schlieren, Switzerland) (1:20)).

   For metabolomics, cell samples (morphine exposure: 0, 5 and 50 μM) were
   prepared based on Meister et al. [[50]15] work, as described in
   [51]Supplementary File S1.

2.7. MS Data Acquisition and Analysis

   For proteomics, the equivalent of 2 µg of peptides for each sample
   (cells and EVs) were analyzed using Liquid Chromatography–Electrospray
   ionization-MS/MS (LC-ESI-MS/MS) on an Orbitrap Fusion Lumos Tribrid
   mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) equipped
   with an EASY nLC1200 liquid chromatography system (Thermo Fisher
   Scientific, Waltham, MA, USA). Peptides were trapped on a 2 cm × 75 μm
   i.d. PepMap C18 precolumn packed with 3 μm particles and 100 Å pore
   size. Separation was performed using a 50 cm × 75 μm i.d. PepMap C18
   column packed with 2 μm and 100 Å particles and heated at 50 °C.
   Peptides were separated using a 160 min segmented gradient of 0.1%
   formic acid (solvent A) and 80% acetonitrile 0.1% formic acid (solvent
   B) at a flow rate of 250 nL/min. Data-Independent Acquisition (DIA) was
   performed with a MS1 full scan at a resolution of 60,000 (FWHM)
   followed by 30 DIA MS2 scan with variable windows. MS1 was performed in
   the Orbitrap with an AGC target of 1 × 10^6, a maximum injection time
   of 50 ms and a scan range from 400 to 1250 m/z. DIA MS2 was performed
   in the Orbitrap using higher-energy collisional dissociation (HCD) at
   30%. Isolation windows (30) were variables with an AGC target of 2 ×
   10^6 and a maximum injection time of 54 ms. DirectDIA performance was
   used in Spectronaut™ (Biognosys AG, Zurich, Switzerland) to match DIA
   MS raw data.

   For the analysis of cells and EVs, protein abundances were exported
   from Spectronaut™, and selected proteins were tested for significance
   using Student’s two-tailed t-test. Protein and peptide intensities were
   exported and analyzed using mapDIA. No further normalization was
   applied. The following parameters were used: min peptides = 2, max
   peptides = 10, min correl = −1 min DE = 0.01, max DE = 0.99 and
   experimental design = replicate design. Proteins were considered to
   have significantly changed in abundance with a LFDR < 0.05 and an
   absolute fold change (|FC|) > 1.2.

   For metabolomics, the profiling was based on Pezzatti et al. [[52]16]
   with slight modifications, as detailed in the [53]Supplementary File
   S1.

2.8. Enrichment Pathway Analysis

   The list of differentially expressed proteins was then analyzed with
   MetaCore™ (Clarivate Analytics, Philadelphia, PA, USA) to highlight
   significantly represented biological pathways. The top 5 biological
   pathways were selected.

2.9. MST Proliferation and LDH Cytotoxicity Assay

   HBMEC were seeded in a 96-well plate (10,000 cells per well) and
   treated for 24 h with morphine at different concentrations (1, 10, 25,
   50, 100, 200 and 400 µM). Cell proliferation was determined using the
   MTS assay (CellTiter 96^®® AQueous One Solution Cell Proliferation
   Assay, Promega, Madison, WI, USA), whereas cytotoxicity was assessed by
   measuring lactate dehydrogenase (LDH) released using a Pierce™ LDH
   cytotoxicity kit (Thermo Scientific, Rockford, IL, USA). Both the MTS
   and LDH assays were performed according to the manufacturer’s
   recommendations.

2.10. Statistical Analysis

   Data are reported as mean ± standard deviation (SD). p < 0.05 was
   considered to be statistically significant. Significance is denoted as
   * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001. The data were
   analyzed using multiple t-test comparisons or one-way analysis of
   variance (ANOVA).

3. Results

3.1. Morphine Doses Determination on Primary Human Brain Microvascular
Endothelial Cells

   Morphine-induced toxicity was evaluated on HBMECs with proliferation
   assay (MTS) and a cytotoxicity assay (LDH release). Cells were exposed
   for 24 h with various morphine concentrations (1 µM, 10 µM, 25 µM, 50
   µM, 100 µM, 200 µM and 400 µM). Results showed a significant difference
   between the untreated control and treated samples at 200 µM and 400 µM
   of morphine in both MTS proliferation assay and LDH cytotoxicity assay
   ([54]Figure 1a,b). No significant differences were found at lower
   concentrations (1 µM, 10 µM, 25 µM, 50 µM and 100 µM) ([55]Figure
   1a,b). Concentrations from 1 to 100 µM were, thus, selected for the
   analysis of morphine-induced proteome modulations.

Figure 1.

   [56]Figure 1
   [57]Open in a new tab

   MTS proliferation assay and LDH cytotoxicity assay on morphine-exposed
   human brain microvascular endothelial cells. (a) MTS assay of 24 h of
   morphine treatment to determine viability of human brain endothelial
   cells. DMSO = dimethyl sulfoxide. Mean ± SD, n = 3, **** p < 0.0001.
   (b) LDH assay of 24 h of morphine treatment to determine cytotoxicity
   of human brain endothelial cells. LB = lysis buffer, DMSO= dimethyl
   sulfoxide. Mean ± SD, n = 3, ** p < 0.01, **** p < 0.0001.

3.2. Characterization of Extracellular Vesicles from Morphine-Treated Human
Brain Microvascular Endothelial Cells

   Culture media from morphine-treated primary human brain microvascular
   endothelial cells were recovered to isolate extracellular vesicles
   (EVs). Released EVs from HBMECs were isolated using EVTRAP magnetic
   beads, an isolation method based on a chemical affinity capture
   approach [[58]17,[59]18]. As this study is not intended to distinguish
   the EV populations and due to the current disagreement around the
   definition of EV subsets, the term EV will be used in this research
   article.

   EV physical characterization was performed by NTA to determine EV sizes
   (nm) and concentrations (particles/mL). Results demonstrated that the
   median EV size for each morphine concentration was within the expected
   diameter range of small vesicles (<100 or <200 nm) according to the
   MISEV2018 ([60]Figure 2a) [[61]19]. EV concentrations were within 1.55
   × 10^10 ± 1.03 × 10^10 particles/mL to 2.07 × 10^10 ± 1.44 × 10^9, with
   no significant difference between the groups, as demonstrated in
   [62]Figure 2b. These results also suggested that EV sizes and
   concentrations are not impacted by morphine exposure.

Figure 2.

   [63]Figure 2

   [64]Figure 2
   [65]Open in a new tab

   Characterization of extracellular vesicles isolated from human brain
   endothelial cells. (a) NTA measurement of EV sizes. Median ± SD, n = 3.
   (b) NTA measurement of EV concentration (particles/mL) of EVs. Mean ±
   SD, n = 3. (c) Western blots of EVs isolated by EVTRAP beads and brain
   endothelial cells lysate (cells). Alix, TSG101 and CD9 represent common
   EV markers. Calreticulin is an EV negative control, n = 3. (d) Mass
   spectrometry protein abundances of EV markers Alix and CD9. Mean ± SD,
   n = 3. (e) Gene Ontology term enrichment of EV quantified proteins
   provided by MetaCore™ software. X-axis corresponds to
   −log[10](p-value), Y-axis corresponds to the gene ontology terms and
   the dot line represents the p-value cut-off of 0.05.

   EV biochemical characterization was conducted using western blotting
   and proteomics-based mass spectrometry analysis to assess the presence
   of specific EV markers and EV proteins within the enriched EV samples.
   Three proteins were selected to confirm EV enrichment by western blots:
   two cytosolic proteins, PDCD6IP (Alix) and TSG101, as well as the
   membrane protein CD9. These three proteins were described as
   constitutively present in EVs. In addition, a protein localized in the
   lumen of the endoplasmic reticulum was also used as a negative control,
   the calreticulin (CALR) [[66]20]. As shown in [67]Figure 2c, the
   detection of PDCD6IP, TSG101 and CD9 markers was increased in EV
   samples, indicating an efficient EV isolation [[68]21]. Regarding
   calreticulin, it was only present in the whole cell lysate,
   demonstrating that EVs were properly enriched [[69]20]. A proteomic
   analysis using DIA mass spectrometry was conducted to deepen EV
   biochemical characterization. Total cell lysate was used as a control.
   Overall, 1565 proteins were quantified in EV samples ([70]Table S1).
   The quantification of EV specific markers, such as PDCD6IP and CD9, was
   also significantly higher compared to the one in the parent cells
   ([71]Figure 2d).

   Finally, to further understand these results, a bioinformatics analysis
   was performed with the list of quantified EV proteins. The enrichment
   of cellular components from gene ontology (GO) ranked terms such as
   “extracellular exosome”, “extracellular vesicle”, “extracellular
   membrane-bounded organelle”, “extracellular organelle” and
   “extracellular space” ([72]Figure 2e) with highly significant p-values,
   confirming the overall efficient EV enrichment ([73]Table S2).

   Since EV characterization yielded satisfactory results, the study was
   pursued by evaluating morphine-induced modulations on EV proteome
   through MS-based proteomics and pathway enrichment analysis.

3.3. Quantitative Proteomics Analysis to Study Morphine-Induced Protein
Modulations in HMBECs and HBMECs-Derived EVs

   After being exposed to morphine at 1, 10, 25, 50 and 100 µM for 24 h,
   DIA-based proteomics was performed to investigate morphine-induced
   alterations in HBMECs and associated EVs.

   According to the HUPO Proteomics Standards Initiative (PSI), FDR
   thresholds for protein identification were set across all DIA-MS data
   to achieve a 1% FDR at the peptide and protein level. Overall, 5348
   proteins were quantified in HBMECs and 1565 proteins in EVs in at least
   one condition ([74]Tables S1 and S3). MapDIA tool [[75]22] was used to
   compute protein ratios from precursors intensities and to perform the
   statistical analysis. Significantly modified proteins were defined with
   a local false discovery rate (LFDR) lower than 5% and an absolute fold
   change (|FC|) of 1.2, for each comparison versus the untreated control,
   based on the current trend in interpreting DIA data [[76]23].
   Interestingly, an increase in the number of differential proteins was
   noted in a dose-dependent manner in morphine-treated HBMECs and
   corresponding EVs, as presented in the volcano plots of [77]Figure S1.
   In total, 265 proteins were found to be differentially expressed in
   HBMECs and 148 proteins in EVs in at least one condition ([78]Tables S1
   and S3). Venn diagrams displaying differentially expressed proteins
   (DEPs) in HBMECs and EVs are presented in [79]Figure S2.

   DEPs (log[2] ratio of treated vs untreated condition) in
   morphine-exposed HBMECs and EVs were displayed in the heatmap of
   [80]Figure 3a,b, respectively. According to the horizontal clustering
   (representing the conditions), morphine concentrations at 1 μM, 10 μM
   and 25 μM were clustered together, while 50 μM and 100 µM of morphine
   shared another distinct cluster. This underlined similar protein
   modulation patterns at these specific concentrations. This pattern was
   also observed in both heatmaps, suggesting that morphine may act
   similarly in HBMECs and EVs. However, to investigate which biological
   pathways were impacted by these different morphine concentrations in
   HBMECs and EVs, pathway enrichment analysis was performed on DEPs.

Figure 3.

   [81]Figure 3
   [82]Open in a new tab

   Heatmaps. (a) Heatmap of differentally expressed proteins (DEPs) for
   each morphine treatment (1, 10, 25, 50 and 100 μM) in human brain
   endothelial cells and (b) in extracellular vesicles. Rows are clustered
   using euclidean distance and the clustering method used is complete.
   K-means clustering is set at 2.

3.4. Morphine Induces Specific Pathway Modulation in HBMECs and Associated
EVs

   To determine biological pathways that were affected by morphine
   exposure in HBMECs and their corresponding EVs for the different
   studied concentrations, pathway enrichment analysis was employed using
   the software MetaCore™. This manually curated database of
   protein-protein interactions and biological pathways determines which
   biological pathways are statistically enriched in a given dataset. As
   morphine treatment at 25 μM, 50 μM and 100 μM revealed a higher number
   of DEPs, the corresponding lists were loaded on MetaCore™ (|FC| > 1.2;
   p-value < 0.05) and pathway enrichment analysis was performed. The top
   five enriched pathways are represented in [83]Figure 4. This analysis
   revealed that exposure of HBMECs with morphine strongly affected the
   extracellular matrix (ECM) proteins (first, second and fifth pathways)
   ([84]Table S4) in both whole cells and EVs. In parallel, proteins
   related to hypoxia-inducible factor 1 (HIF1) targets (the fourth
   pathway) were also shown to be highly modified by morphine ([85]Table
   S4).

Figure 4.

   [86]Figure 4
   [87]Open in a new tab

   Pathway enrichment analysis of modulated proteins in morphine-treated
   human brain endothelial cells and associated extracellular vesicles.
   Top five enriched pathways in both total cells and EVs upon 25, 50 and
   100 μM of morphine for 24 h (|FC|> 1.2, p-value ≤ 0.05, n = 3). X-axis
   corresponds to −log[10](p-value), Y-axis corresponds to the biological
   pathways and the dot line represents the p-value cut-off of 0.05.
   HBMECs = human brain microvascular endothelial cells, EVs =
   extracellular vesicles.

   “HIF1 targets” is a pathway linked to oxidative stress, in which
   multiple protein levels were modulated by morphine exposure. In line
   with the major finding of our previous study [[88]11], morphine
   exposure induces oxidative stress response in HBMECs, but more
   interestingly, also in EVs (transcription HIF1 targets pathway of
   [89]Figure 4). DEPs shared in HBMECs and EVs (in bold in [90]Figure 5
   and [91]Table S4) are linked to different physiological processes
   regulated by HIF1, including glucose metabolism (GAPDH, HXK1, HXK2),
   ECM remodeling (PLAUR, MMP2), vascular biology (TSP1, PAI1, VEGF) and
   iron/erythropoiesis (TFR), among others. These findings suggest that
   EVs have the potential to carry modulated pathway signatures
   originating from parent cells [[92]14], mainly at 50 and 100 µM
   morphine exposures. For example, MMP2 and TSP1 were found to be
   downregulated, after morphine exposure at 25, 50 and 100 µM, in cells
   and in EVs, unlike PAI1 and TRF, which were upregulated. The regulation
   of these proteins has already been reported in other studies on
   oxidative stress [[93]24,[94]25,[95]26,[96]27], suggesting the
   possibility of considering morphine as an oxidative stress inducer.

Figure 5.

   [97]Figure 5
   [98]Open in a new tab

   HIF-1 targets adapted from [[99]28] and created with BioRender.com.
   Text in bold represents DEPs found in HBMECs and EVs upon various
   morphine concentrations.

   The most highly affected biological pathway shared between HBMECs and
   EVs after morphine exposure was the “cell adhesion and extracellular
   matrix remodeling” ([100]Figure 4). Almost all the DEPs present in this
   pathway are associated with the extracellular matrix (ECM) remodeling
   ([101]Figure 6a), which is essential in normal physiological processes
   such as proliferation, cell motility and adhesion, angiogenesis and
   diseases progression, among others. DEPs directely linked to the ECM
   remodeling as nidogen (NID1), osteonectin (SPARC), laminin subunit
   alpha-4 (LAMA4) and fibronectin (FN1) ([102]Figure 6a) are all
   downregulated upon morphine treatment (essentially at 50 and 100 µM) in
   HBMECs and EVs, suggesting a strong perturbation of the ECM. The only
   exception is the SPARC protein, which is upregulated in EV samples
   ([103]Figure 6b).

Figure 6.

   [104]Figure 6

   [105]Figure 6
   [106]Open in a new tab

   Cell adhesion and extracellular matrix remodeling pathway map with
   associated heatmap representation of morphine-treated human brain
   endothelial cells and extracellular vesicles. (a) Pathway map of cell
   adhesion and extracellular matrix remodeling. Modulated proteins upon
   morphine treatment of HBMECs and associated EVs are presented in grey
   circles. Red lines represent inhibition/negative interactions, green
   lines represent activation/positive interactions and grey lines
   represent unspecified interactions. (b) Heatmap displaying DEPs
   associated to cell adhesion and extracellular matrix remodeling
   pathway. Color scale corresponds to log ratio of proteins. White color
   corresponds to unquantified proteins. Numbers in squares are log ratio
   of DEPs. HBMECs = human brain microvascular endothelial cells, EVs =
   extracellular vesicles.

   In additon, two matrix metalloproteinases (MMPs), the MMP1
   (collagenase) and MMP2 (gelatinase), are indirectly related to the ECM
   remodeling ([107]Figure 6a), via the degradation of ECM components.
   MMP1 presented an increased protein level upon morphine exposure, while
   MMP2 was decreased ([108]Figure 6b) at 50 and 100 µM of morphine. Such
   a discrepancy in matrix metalloproteinases levels was already described
   upon ECM homeostasis perturbation [[109]29].

   In addition, a serpin peptidase inhibitor (PAI1) at 50 and 100 µM of
   morphine, and plasminogen activator tissue (PLAT) only at the highest
   morphine condition, were upregulated in HBMECs and EVs ([110]Figure
   6b). These proteins play a pivotal role in the regulation of cell
   adhesion and migration during tissue remodeling, suggesting a strong
   morphine-induced alteration of cell adhesion and remodeling.

3.5. Confirmatory Experiments of Morphine-Induced Biological Pathways Related
to HIF1 Target and ECM Remodeling by Metabolomics

   Metabolomics was used to verify the biological pathways evidenced by
   enrichment analysis of altered proteins in HBMECs upon morphine
   exposure. HBMECs exposed for 24 h to morphine at 5 and 50 μM in
   triplicates were used. Treated samples were compared to their untreated
   samples. Metabolomic analysis revealed a significant change in
   metabolite levels related to glucose metabolism (glucose and
   UDP-glucose) and ECM remodeling (4-hydroxyproline and glutamic acid),
   both associated to the HIF-1 target pathway. Glucose showed a
   significant increase upon morphine exposure (5 and 50 µM), whereas
   UDP-glucose, 4-hydroxyproline and glutamic acid were only significantly
   increased upon 50 μM of morphine exposure ([111]Figure 7).

Figure 7.

   [112]Figure 7
   [113]Open in a new tab

   Relative levels of metabolites involved in HIF1 target pathway (glucose
   and UDP-glucose) and ECM remodeling process (4-hydroxyproline and
   glutamic acid) after 24 h of exposure to morphine at 5 and 50 μM on
   HBMECs. Geometric shapes represents individual values. Statistical
   analysis for the morphine effects was evaluated with an unpaired
   t-test. Significant p-values: * p-value ≤ 0.05; ** p-value ≤ 0.01 and
   *** p-value ≤ 0.001.

4. Discussion

   Although morphine is an efficient and powerful analgesic, it also
   causes side effects such as dizziness, nausea, vomiting or
   hallucinations through different mechanisms. In our previous research
   on the morphine-induced effects on human brain microvascular
   endothelial cells [[114]11], we proposed that the main affected
   biological pathway due to morphine exposure was related to oxidative
   stress. We assumed that morphine effect was mediated by the mu receptor
   in HBMECs, as demonstrated elsewhere [[115]30,[116]31,[117]32].
   Nervertheless, the expression of other opioid receptors in HBMECs and
   their activation was not excluded and would require further
   investigation.

   Furthermore, our investigation revealed the induction of oxidative
   stress by morphine via the NRF2 pathway in human brain microvascular
   endothelial cells (HBMECs). The NRF2 pathway was previously reported to
   increase morphine-induced analgesia while reducing its hyperanalgesic
   effects [[118]33]. Even though EVs molecular mechanisms are
   increasingly studied, the current knowledge in brain endothelial cells
   is still poor. Therefore, the present study investigated the morphine
   effect on the proteome of HBMECs as well as the released EVs using the
   proteomics-based strategy to determine biological pathways that may be
   altered.

   In our previous study [[119]11], morphine induced mitochondrial
   dysfunctions by decreasing mitochondria respiration in HBMECs at 50 and
   100 µM for 12 h, 24 h, 48 h and 72 h, indicating that morphine already
   affects HBMECs at these doses. Vigilant interpretation is required as
   selected concentrations in this study (1, 10, 25, 50 and 100 µM) are
   outside the therapeutic range of morphine, as suggested in other
   studies focused on BBB permeability that generally used morphine
   concentration at 10 µM [[120]34,[121]35,[122]36]. However, chosen
   concentrations have been verified to withstand a physiological change
   without inducing a cytotoxic effect in the cell system used in this
   study ([123]Figure 1). Furthermore, although these concentrations are
   higher than those reported in plasma, numerous studies have used them
   in in vitro models [[124]37,[125]38,[126]39,[127]40,[128]41],
   suggesting that they remain relevant to be investigated from a
   biological point of view.

   After EV affinity-based enrichment, EV sizes and concentrations were
   measured to verify the enrichment procedure. EVs enriched from HBMECs
   met MISEV2018 physical characteristics standards [[129]19]. Common EV
   markers including Alix, TSG101 and CD9 were detected by western blot,
   without cellular contamination (no detection of calreticulin). To
   further check EV enrichment quality, mass spectrometry-based proteomics
   analysis was used to verify the presence of several proteins known as
   EV markers. These markers displayed a higher protein abundance in EVs
   compared to total cell samples. GO term enrichment analysis revealed
   the increase of cellular component category terms linked to
   “extracellular exosome” and “extracellular vesicle”, among others, with
   highly significant p-values. Therefore, our results appear to comply
   with the expectations of the MSIEV2018 guidelines. Moreover, we used
   orthogonal techniques such as electron microscopy (EVs morphology) to
   strengthen our EV characterization. In addition, the current trend in
   the EV scientific community is to combine enrichment methods for EVs
   [[130]34]. They provide evidence that characterization was
   significantly enhanced with this type of strategy
   [[131]35,[132]36,[133]37,[134]38], therefore it would be interesting to
   explore this combined isolation.

   The HBMECs and EVs proteomes were also established to investigate
   morphine effects. Data provided evidence that morphine induced a
   dose-dependent alteration as shown in the volcano plots ([135]Figure
   S1). The higher the dose, the higher the number of DEPs was in
   morphine-treated HBMECs and EVs. Some in vitro studies comparing
   different concentrations of a compound reported more differentially
   expressed proteins or genes after high exposure than low-dose exposure
   [[136]39,[137]40,[138]41,[139]42,[140]43,[141]44,[142]45,[143]46,[144]4
   7], supporting our findings. Regarding DEPs induced by morphine in
   HBMECs and EVs, heatmaps established with mass spectrometry data
   present two distinct clusters according to low-medium morphine
   concentrations (1 µM, 10 µM and 25 µM) and the high morphine
   concentrations (50 µM and 100 µM), suggesting different modulations for
   these concentrations.

4.1. Oxidative Stress Related Pathway

   The pathway enrichment analysis identified an oxidative stress-related
   pathway as being modulated by morphine exposure, in agreement with
   previous studies [[145]11,[146]48,[147]49,[148]50]. Oxidative stress is
   an abnormal physiological process and is defined as “an imbalance
   between antioxidants and oxidants, resulting in the disruption of redox
   signaling and the corresponding molecular damage” [[149]51]. Under
   normal physiological conditions, ROS production and elimination are
   balanced, whereas this balance is disrupted during oxidative stress.
   Production of ROS is induced by multiple stimuli and, therefore, is
   responsible for the modulation of various biological processes such as
   apoptosis [[150]52,[151]53], inflammation [[152]54,[153]55,[154]56],
   angiogenesis [[155]57,[156]58] or neurodegenerative diseases
   [[157]59,[158]60,[159]61,[160]62]. Moreover, ROS are known to promote a
   redox cascade by oxidizing and activating several transcription factors
   such as nuclear factor erythroid 2-related factor 2 (NRF2), heat shock
   factor 1 (HSF1), sterol regulatory element-binding protein 1 (SREBP1)
   or hypoxia inducible factor 1-alpha (HIF1A). The present study mainly
   highlighted HIF1A-target proteins modulation by morphine exposure on
   cells and EVs. HIF1 regulates cellular and systemic O[2] homeostasis in
   animals, and, thus, controls several physiological processes, including
   glucose metabolism, proliferation/survival, iron/erythropoiesis,
   extracellular matrix remodeling and vascular biology, among others
   [[161]28].

   HIF1-regulated genes include glycolytic enzymes [[162]63,[163]64]. In
   this study, hexokinase 1 and 2 (HXK1 and 2) and
   glyceraldehyde-3-phosphate dehydrogenase (GAPDH) were modulated upon
   morphine treatment, and thereby may influence glucose metabolism.
   Indeed, a label-free proteomics analysis on rat hippocampus exposed to
   morphine reported similar GAPDH downregulation [[164]65]. Since GAPDH
   has already been documented to undergo post-translational modifications
   [[165]66,[166]67,[167]68], they speculated that these
   post-translational modifications of GAPDH may have a role in oxidative
   stress generation [[168]65]. In addition, under oxidative stress, ATP
   levels are reduced and glycolysis is blocked, mainly due to the
   inactivation of GAPDH [[169]69], which may be applicable to
   morphine-induced effects in our study. Moreover, although the
   underlying mechanisms of GAPDH are not yet fully understood, it has
   been proposed that GAPDH may be an intracellular sensor of oxidative
   stress, emphasizing its role in this process. In addition, two
   metabolites related to the glucose metabolism were found altered after
   exposure to morphine: glucose and UDP-glucose. Under normal cellular
   conditions, glucose is phosphorylated to glucose-6-phosphate by
   hexokinases and converted to UDP-glucose [[170]70,[171]71]. Therefore,
   glucose is a crucial metabolite mostly implicated in the production of
   ATP but is also the precursor of neuro-transmitters and
   neuro-modulators [[172]71]. In turn, UDP-glucose is a metabolite
   involved in many cellular processes such as the synthesis of glycogen,
   glycolipids, glycoproteins or proteoglycans, among many others
   [[173]70]. In the present study, morphine treatment induced significant
   changes in glucose level by a dose-dependent increase, while
   UDP-glucose level significantly increases at 50 μM only. Modification
   of metabolites related to glucose metabolism was already denoted in
   mammalian cells, especially under hypoxic condition [[174]70].

   Other HIF1-targets, such as the plasminogen activator, urokinase
   receptor (PLAUR) and metalloproteinase 2 (MMP2), were modified by
   morphine according to our findings. PLAUR was only impacted upon one
   morphine exposed HBMECs condition, whereas MMP2 was modified by all the
   conditions in both HBMECs and EVs. MMP2, also known as gelatinase A, is
   a proteolytic enzyme, involved in the ECM organization [[175]72]. Our
   results showed a decrease in the expression of this protein, proposing
   a probable disturbance at the cellular and extracellular level due to
   morphine exposure. Numerous results have proven that oxidative stress
   conditions alter the expression of MMPs, leading to cellular
   alterations [[176]72,[177]73,[178]74,[179]75,[180]76]. In the brain,
   endothelial basal lamina may be disturbed leading to the BBB opening
   [[181]77,[182]78,[183]79] and, finally, to the leukocytes
   transmigration [[184]80,[185]81]. In line with these findings, Gach et
   al. indicated that MMP decreases after morphine exposure was due to
   oxidative stress rather than classical opioid receptor mediation
   [[186]24]. Moreover, metabolomics experiments confirmed proteomics
   analysis as metabolites related to the ECM remodeling (4-hydroxyproline
   and glutamic acid) were found to be affected by morphine treatment.
   Both metabolite levels were shown to be significantly increased after
   morphine exposure at 50 μM. 4-hydroxyproline is a collagen-related
   metabolite, playing a crucial role in collagen stability to protect
   tissues from damage. Under stress condition, it has been reported that
   4-hydroxyproline urinary levels are increased due to accelerated
   collagen degradation [[187]82]. Glutamic acid is an essential
   metabolite in cellular metabolism. This metabolite is indirectly linked
   to the ECM by being a precursor in the synthesis of the amino acid
   proline [[188]83], another prominent amino acid in collagen. Therefore,
   collagen modification may be involved in the observed altered
   protective function in ECM [[189]84].

   Acting in tissue remodeling as a component of ECM, the plasminogen
   activator inhibitor 1 (PAI1) was upregulated in our study. PAI1 is a
   crucial element in fibrinolysis control. It has been evidenced that,
   upon generation of ROS, HIF1 is activated, which, in turn, induces the
   expression of PAI1 [[190]85]. In the in vitro study of Görlach et al.,
   they highlighted the decreased expression of PAI1 after ROS elevation
   [[191]26], thereby suggesting a similar effect of morphine exposure on
   HBMECs and EVs by acting as a stress inducer [[192]26].

   In addition, we demonstrated in this study that altered proteins
   contained in EVs predominantly mirrored those of their parent cells.
   Thus, since the oxidative stress response is also a piece of
   information held by EVs, this could suggest their potential role in
   modulating distant physiological processes. Some studies hypothesized
   that EVs were mirroring parent cells status and were involved in
   biological processes, therefore, the redox status of the parent cells
   may determine the oxidative stress-related EV molecular cargo
   [[193]14]. Indeed, EVs composition may be strongly influenced by an
   external stimulus or a pathological condition originating from their
   parent cells. As reported in the study of Biasutto et al., oxidatively
   stressed cells and EVs presented decreased protein expressions
   associated to pro-survival pathways and increased ones of pro-apoptotic
   pathways [[194]86]. These results indicated that cells were
   transferring, through EVs, modulated molecules related to pathways
   regulating cell death. Conversely, under oxidative stress, cells can
   also release EVs with positive effects. Eldh et al. demonstrated that,
   under oxidative stress, EVs carried antioxidant molecules (mRNAs)
   contributing to the oxidative stress resistance of recipient cells
   [[195]87]. In addition, the study of Liao et al. illustrated another
   EVs functionality, which is to activate distant brain cell types
   [[196]88]. Indeed, they highlighted that EVs from morphine-treated
   astrocytes can be transported into microglia and induce microglia
   activation with subsequent morphological alterations.

   According to our pathway enrichment analysis, EVs released by
   morphine-exposed HBMECs have the potential to exert negative effects,
   as their altered proteins (those of the cell of origin) are related to
   the transcription HIF1 target pathway. This biological pathway may be
   an EV signature pathway induced by morphine, reflecting the same
   changes in cells of origin. Some studies have already speculated on EVs
   functions in redox signaling and oxidative stress-related diseases such
   as neurologic, oncologic or cardiovascular pathologies
   [[197]89,[198]90,[199]91,[200]92,[201]93,[202]94,[203]95]. However,
   more studies are needed to confirm this hypothesis.

   As observed here, several major HIF1 targets have been significantly
   affected after morphine treatment in HBMECs and EVs, especially those
   from ECM, suggesting a strong ECM modulation by morphine. Indeed,
   pathway enrichment analysis highlighted that “cell adhesion and
   extracellular matrix remodeling” was the major pathway altered in this
   study. Morphine-related oxidative stress might be at the origin of this
   change. Indeed, a variety of cellular and extracellular changes occurs
   upon oxidative stress, including extracellular matrix remodeling
   [[204]96].

4.2. Cell Adhesion and ECM Remodeling Related Pathway

   Extracellular matrix (ECM) remodeling is a known and controlled
   mechanism permitting the readjustment of tissues in reaction to a
   stimulus. As mentioned above, MMPs are involved in the degradation of
   various components of the ECM and, therefore, have a pivotal role in
   these mechanisms. In our study, MMP1 expression was increased, while
   MMP2 was decreased. Such phenomena have already been observed in the
   study of Alge-Priglinger et al. [[205]29]. Their study on cultured
   cells exposed to oxidative stress showed an increased MMP-1 but reduced
   MMP-2 [[206]29], contributing to a perturbed ECM homeostasis, and,
   thus, a possible BBB alteration. However, it has been reported that
   excessive proteolytic ECM degradation due to MMPs may be balanced by
   tissue inhibitors of metalloproteinase (TIMPs) such as TIMP-1, -2 and
   -3, controlling the extent of ECM remodeling [[207]97,[208]98].
   According to our results, TIMP2 is the only protein that was
   significantly decreased in the EVs at 100 µM of morphine, suggesting
   that TIMPs potentially struggled to act as MMP scavengers upon morphine
   exposure.

   Furthermore, some proteins associated with the ECM degradation via
   MMPs, such as the tissue-type plasminogen activator (PLAT), urokinase
   plasminogen activator surface receptor (PLAUR), plasminogen activator
   inhibitor 1 (PAI1), plasmin and plasminogen (PLG), could be cited.
   These proteins are all included in the plasminogen activator (PA)
   system, which has two classical roles: regulation of ECM degradation in
   several tissues and regulation of fibrinolysis in the bloodstream
   [[209]99]. In the ECM, plasmin activates MMPs, allowing them to degrade
   ECM elements, which is an essential function to prevent fibrosis, cell
   migration and cell growth. In the blood, plasmin cleaves fibrin into
   fibrin degradation molecules. This is crucial for the prevention of
   fibrin clot formation, which may induce vascular occlusions. In
   addition, it is interesting to note that intravascular injection of
   recombinant PLAT is used in clinics to induce thrombolysis in ischemic
   stroke [[210]100]. Moreover, PAI1 is the primary and irreversible
   inhibitor of PLAT and PLAU in the brain [[211]101,[212]102,[213]103].
   It is mainly synthetized in the vascular endothelial cells but also in
   astrocytes [[214]104]. In our study, PAI1 was upregulated after
   morphine exposure in HBMECs as well as in EVs, while plasminogen was
   only upregulated in HBMECs. In the study of Wilhelm et al., they
   reported the same modulation of PAI1 after ethanol treatment on
   astrocytes [[215]105], suggesting functional impairments in astrocytes.
   In addition, Wang et al., also working with HBMECs, demonstrated that
   PAI1 expression was upregulated under hypoxic condition [[216]106].
   These results suggest that PAI1 is strongly influenced by agents such
   as those used in the cited studies (including ours)
   [[217]107,[218]108,[219]109,[220]110,[221]111,[222]112,[223]113,[224]11
   4]. The common feature of these agents is their ability to generate ROS
   and, thus, to alter ECM elements. Moreover, proteins that have been
   shown to be directly involved in the remodeling of ECM (LAMA4, ND1, FN1
   and SPARC) were all decreased upon almost every morphine concentration,
   suggesting that these proteins were not able to ensure their structural
   and functional properties in the ECM [[225]115,[226]116,[227]117].
   Similar to MMPs protein group, their altered expressions lead to the
   degradation of ECM, and, ultimately, to an impaired cell integrity,
   whose consequences may be irreversible.

5. Conclusions

   In conclusion, physical and biochemical EV characterization supported
   an appropriate vesicle enrichment from HBMECs. We were, then, able to
   build upon our previous study on morphine by introducing two
   intermediate concentrations (25 µM and 50 µM) to cover a wider exposure
   range. Proteomics data demonstrated morphine-induced modulations in the
   HBMECs and EVs proteome. Moreover, enrichment pathway analysis
   indicated that the intermediate morphine concentrations (25 µM and
   particularly 50 µM) impacted HBMECs and EVs in a similar manner than
   the highest concentration (100 µM). We confirmed here that morphine
   negatively affects HBMECs since oxidative stress-related proteins were
   significantly modulated. We also demonstrated these changes in
   HBMECs-derived EVs, highlighting the ability of EVs to mirror the
   content of their parent cells after a stimulus. EVs enriched after
   morphine exposure give useful information on “signature pathways” that
   could potentially be delivered to neighboring cells. Furthermore, both
   results of morphine-exposed HBMECs and EVs provided striking evidence
   that proteins from ECM were strongly modified by morphine, indicating
   important cellular changes. Therefore, it appears that morphine
   compromised the two main functions of brain endothelial cells: the
   preservation of the integrity of the brain and its homeostasis.
   However, further studies are needed to better understand the impact of
   “stimulated EVs” on neighboring cells and their underlying biological
   mechanisms. Indeed, with the current trend of using conditioned media
   in the EV field, it would be of great interest to study the influence
   of media containing EVs from HBMECs treated with morphine on other cell
   types, especially those from the brain.

Acknowledgments