Abstract

   Epidemiological studies have highlighted the existence of population
   groups exhibiting a higher sensitivity to the impact of environmental
   factors, such as exposure to air pollution. In these regards, people
   with Multiple Sclerosis (MS) or predisposed to develop MS - an
   autoimmune disorder of the Central Nervous System (CNS) - appear as a
   more vulnerable cohort to the effects of particulate matter (PM)
   exposure. Here, we aimed at disclosing the biological substrate of such
   higher vulnerability, and specifically at understanding whether
   individuals primed to develop autoimmunity (as it occurs in MS and in
   the experimental autoimmune encephalomyelitis - EAE - animal model of
   MS) respond differently to PM compared to healthy subjects. To this
   purpose, we characterized plasmatic extracellular vesicles (EVs) and
   their microRNA (miRNA) cargo in healthy and presymptomatic EAE mice
   early after exposure to PM[10], compared to unexposed healthy and EAE
   mice. Results showed that the response of EAE mice to PM[10] did not
   differ in terms of EV number or source, compared to that of healthy
   mice. Yet, remarkable differences existed in the identity of
   deregulated EV-associated miRNAs, which, in EAE mice, were predicted to
   target several MS-relevant biological processes and nervous system-,
   immune- and inflammation-related pathways, possibly contributing to
   disease worsening.

   Keywords: air pollution, particulate matter, experimental autoimmune
   encephalomyelitis, multiple sclerosis, miRNA

Introduction

   Despite increased public awareness and global improvements in air
   quality, air pollution remains a significant public health challenge
   for low-, middle-, and high-income countries. Particulate matter (PM) -
   a mix of solid particles and liquid droplets - is one of the main air
   pollutants, arising from natural sources (e.g. pollen, sea spray, and
   dust from ground erosion) and anthropic activities (e.g. combustion of
   fossil fuels, roadways and mining operations, agricultural activities;
   [39]1). Exposure to PM has been associated with an increased risk of
   cardiovascular and respiratory diseases, different types of cancer
   ([40]2), and, more recently, pathologies affecting the central nervous
   system (CNS; [41]3–[42]5).

   PM includes coarse (PM[10]), fine (PM[2.5]), and ultrafine (PM[0.1])
   particles - having an aerodynamic diameter smaller than 10, 2.5, and
   0.1 μm, respectively - that typically comprise inorganic compounds,
   aromatic hydrocarbons, metals, and microbial components ([43]6). When
   inhaled, PM penetrates the lungs and settles deep within, triggering a
   local inflammatory and defensive response that ultimately impacts all
   major organs and systems, including the CNS. ([44]3, [45]4).

   Although smaller particles - such as PM[0.1] - could directly enter the
   blood circulation and overcome the blood-brain-barrier (BBB; [46]3),
   the disruption of the homeostasis of distant organs most likely relies
   on mechanisms other than a direct effect of PM particles. Alongside the
   secretion of proinflammatory cytokines, a well-established mechanism by
   which the effects of exposure to PM can extend beyond the respiratory
   compartment and trigger a systemic response, is the release of
   extracellular vesicles (EVs; [47]7–[48]10). EVs are nano-sized (0.03–1
   μm) plasma membrane fragments actively released by cells, which can be
   distributed to all systems, including the CNS ([49]11) - via the blood
   and other biological fluids. EVs play a key role in the intercellular
   and inter-organ transfer of biological information. After reaching
   target cells through the recognition of surface-expressed ligands, EVs
   deliver their content, which can include proteins, lipids, metabolites,
   organelles, and genetic information. As regards this latter aspect, the
   most well studied molecules in EV cargo are microRNAs (miRNAs; [50]12),
   endogenous non-coding small RNAs, which play an important role in the
   post-transcriptional regulation of gene expression via either mRNA
   cleavage or translation inhibition. The power of miRNAs as regulatory
   mechanisms of cellular processes lies in their capability to tune the
   expression of multiple transcripts, exerting a broad control over
   functionally related target mRNAs. Moreover, different miRNAs can act
   together to cooperatively target mRNAs that encode proteins within the
   same functional network ([51]13). Thus, deregulation of even a
   restricted set of miRNAs can significantly impact gene expression, and
   - as a consequence - EVs are able to profoundly influence the molecular
   state and function of target cells ([52]14). Consistently, shuttling of
   miRNAs packaged in plasmatic EVs has emerged as one of the most
   powerful mechanisms linking PM exposure with the disruption of
   homeostasis in extrapulmonary compartments ([53]7, [54]15, [55]16).

   Epidemiological studies have strongly indicated that environmental
   factors play a role in shaping the geographical distribution and the
   timing of hospital admissions for a number of diseases. In individuals
   with a predisposing background, environmental factors have been
   proposed to “set the disease threshold” ([56]17). Specifically, as
   regards PM, while most individuals can maintain homeostasis even upon
   chronic or high dosage exposure, certain vulnerable groups exhibit
   heightened sensitivity to PM impact ([57]18). This increased
   susceptibility is attributed to genetic or acquired factors that make
   the biological responses to PM exposure differ from those elicited in
   “coping” individuals ([58]18).

   Lifestyle and environmental factors (e.g. smoking, limited sun
   exposure, low vitamin D, obesity, infections and exposure to
   environmental pollutants) have been associated with increased risk of
   Multiple Sclerosis (MS; [59]19–[60]21). In particular, airborne PM
   peaks have been associated with higher rates of hospital admissions for
   MS onset or relapses ([61]22–[62]30), suggesting that people with MS
   (or predisposed to develop MS) are a PM-vulnerable cohort. MS is a
   chronic disease with autoimmune components, characterized by immune
   cell infiltration in the CNS, neuroinflammation, diffuse myelin loss,
   and, over time, neurodegeneration. These processes result in functional
   impairments and, in many cases, cognitive and psychiatric symptoms
   ([63]31–[64]33). Of note, PM exposure has been reported to trigger
   MS-relevant events (e.g. inflammation, endothelial and BBB alterations,
   CNS white matter damage, and changes in brain innate immune cell
   activity) even in healthy individuals ([65]34, [66]35). This response
   seems to be more pronounced in MS patients ([67]29). Yet, the
   biological substrate of such vulnerability to PM remains uninvestigated
   so far.

   Here, we aimed at understanding whether individuals primed to develop
   autoimmunity against CNS myelin (as it occurs in MS and in the
   MOG[35-55]-induced experimental autoimmune encephalomyelitis - EAE -
   animal model of MS) respond differently to PM compared to healthy
   subjects. Specifically, we characterized EVs (i.e. number, size and
   cellular source) and EV-associated miRNA profile in the plasma of
   healthy (Ctrl) and presymptomatic EAE mice acutely exposed to PM[10],
   compared to unexposed Ctrl and EAE mice. These analyses showed that the
   response of mice primed to develop EAE to PM exposure differed from
   that of healthy Ctrl mice and did not simply correspond to the
   amplification of differences already existing between EAE and healthy
   mice. Although the miRNA cargo of plasma EVs released following PM
   exposure had a signature relevant for MS in both healthy and EAE mice,
   deregulated miRNAs were distinct and the response of EAE mice appeared
   more “pathogenic” than that of healthy mice. In silico analyses were
   applied to identify target gene transcripts and biological processes
   possibly underlying PM effects, unveiling a significant enrichment in
   nervous system-, immune- and inflammation- related pathways. Overall,
   our data support the idea of a synergy between PM exposure and immune
   system priming toward autoimmunity, and suggest that shuttling of
   EV-associated miRNAs in response to PM exposure may participate in
   different aspects of MS pathogenesis and exacerbation.

Materials and methods

Animals and experimental design

   Mice were housed in the vivarium under standard conditions (12-hr
   light/12-hr dark cycle at 21°C) with food and water ad libitum. The
   project was designed according to the guidelines of the NIH, the
   European Communities Council (2010/63/EU) and the Italian Law for Care
   and Use of Experimental Animals (DL26/2014). It was also approved by
   the Italian Ministry of Health (authorization 510/2020-PR to EB) and
   the Bioethical Committee of the University of Turin. The study was
   conducted according to the ARRIVE guidelines.

Chronic EAE induction

   To induce chronic EAE, 8 week-old female C57BL/6J mice (Charles River,
   Calco, Italy) were immunized by 2 subcutaneous injections of 200 μg
   myelin oligodendrocyte glycoprotein 35–55 peptide (MOG[35-55]; Espikem,
   Florence, Italy) in incomplete Freund’s adjuvant (IFA; Sigma-Aldrich,
   Milan, Italy) containing 8 mg/ml Mycobacterium tuberculosis (strain
   H37Ra; Difco Laboratories Inc., Franklin Lakes, NJ, USA), followed by 2
   intravenous injections of 500 ng of Pertussis toxin (Duotech, Milan,
   Italy) on the immunization day and 48 h later ([68]36).

PM[10] administration and blood collection

   We exposed healthy (Ctrl) and EAE mice to a commercially available
   urban outdoor PM[10] (NIST Standard Reference Material SRM1648a;
   Sigma-Aldrich), which is a chemically standardized compound
   ([69]https://tsapps.nist.gov/srmext/certificates/1648a.pdf), widely
   used in toxicological studies ([70]37–[71]39). Stock suspensions of
   NIST SRM1648 PM were prepared in sterile ultrapure water and aliquots
   were thoroughly mixed under sonication for 1 hour prior to each
   experiment. Mice were randomly divided into control (saline, i.e. NaCl
   0.9% solution) and treatment (PM) groups. The treatment group was
   treated by intratracheal instillations (Intubation stand, Kent
   Scientific Corporation) of a PM[10] suspension (10 μg in 50 μl of
   saline), and the control group was treated with saline (50 μl), as in
   our previous study ([72]40). We opted for the intratracheal
   administration of PM to assure low variability of absorption and
   exclude a direct nose-to-CNS transit of PM via the olfactory mucosa.

   PM[10] dose was calculated considering the daily respiratory volume of
   mice (0.04 m^3) and the daily peaks of PM[10] concentration in East
   Europe polluted areas (>250 μg/m^3; [73]41). Therefore, in order to use
   a PM[10] dose relevant for human exposure, the dose used in this study
   was set as 10 μg (0.04 m^3/day × 250 μg/m^3). PM[10] was administered 4
   days after EAE immunization, i.e. during the presymptomatic phase of
   the EAE disease course, where debilitating symptoms are yet to be
   displayed by the animals (i.e. clinical score=0, according to the
   conventional EAE clinical score assessment ([74]36)). Peripheral blood
   of each mouse was collected in EDTA Vacutainer tubes (Becton Dickinson,
   New Jersey, USA), 6 hours after PM[10]/saline exposure, and processed
   within 3 hours from sampling. Blood was centrifuged at 1300xg for 15
   min to separate plasma.

EVs isolation and characterization

   For EV studies, we followed the MISEV2018 guidelines ([75]42;
   [76]Supplementary Table S1 , [77]Supplementary Figure S1A, B ). EVs
   have been isolated from plasma pools from six mice of each condition:
   healthy mice which received saline (Ctrl); healthy mice which received
   PM[10] (Ctrl PM); EAE mice which received saline (EAE); EAE mice which
   received PM[10] (EAE PM). The resulting pooled samples were centrifuged
   three times at increasing speeds (1000xg, 2000xg, 3000xg) for 15 min at
   4°C. After every centrifugation, the supernatant was decanted into a
   new tube and centrifuged again to remove cell debris and aggregates.
   Shortly after centrifugation, plasma samples were transferred into
   ultracentrifuge tubes (Polycarbonate Centrifuge Bottles, Beckman
   Coulter) and filled up to 10.5 ml with NaCl 0.9% solution.
   Ultracentrifugation was performed at 110,000xg for 75 min at 4°C to
   allow sedimentation of EVs. After ultracentrifugation, the supernatant
   was discarded and tubes were allowed to air dry for 2 min.

   To confirm that this isolation protocol resulted in preparations
   enriched in EVs, western blotting (WB) was used to assess the
   expression of EV markers (i.e. the tetraspanins CD63/CD9) and absence
   of contamination from other cellular components (i.e. calnexin, a
   marker for endoplasmic reticulum; [78]Supplementary Figure S1A ).
   Briefly, plasma EVs lysates were obtained in RIPA buffer (150 mM NaCl,
   1% Triton X-100, 0.5% Sodium deoxycholate, 0.1% sodium dodecyl sulfate,
   50 mM Tris Base, 5mM EDTA, 1mM EGTA) containing protease and
   phosphatase inhibitors (100mM DTT, 1M NaF, 200 mM sodium orthovanadate,
   100 mM PMSF), boiled in SDS buffer and separated using a 10% SDS-PAGE
   gel. Proteins were transferred to PVDF membranes, blocked in 5% non-fat
   milk in TBS (20 mM Tris-HCl, pH 7.5, 150 mM NaCl) + 0.1% Tween-20
   (TBS-T) for 1h and then incubated with primary antibodies (anti-rabbit
   CD63, 1:500, Immunological Sciences, Italy; anti-mouse CD9, 1:250, BD
   Biosciences; anti-rabbit calnexin 1:250, Cell signaling, USA) overnight
   at 4°C. After washes with TBS-T, membranes were incubated with
   appropriate secondary antibodies (anti-rabbit or anti-mouse, 1:3000,
   Sigma, Italy) for 1h at room temperature. Protein extract from a mouse
   cerebral cortex was used as a positive control. The chemiluminescent
   signal was visualized using Westar Antares ECL Blotting Substrates
   (Cyanagen; Italy), acquired with Bio-Rad ChemiDocTM Imagers (Bio-Rad;
   Italy).

   Nanoparticle tracking analysis (NTA) and flow cytometry were used to
   characterize EV number/size ([79] Supplementary Figure S1B ) and
   cellular source, respectively (as in [80]43). To determine EV cellular
   origins, immunophenotyping was achieved with the MACSQuant Analyser
   flow cytometer (Miltenyi Biotec, Bergisch Gladbach, DE) following the
   manufacturer’s protocol. The Fluoresbrite Carboxylate Size Range Kit I
   (0.2, 0.5, 0.75, and 1 µm) was used to set the calibration gate on the
   MACSQuant Analyser system. To evaluate the integrity and to highlight
   EV subsets, 60-µL sample aliquots were stained with 0.02 µM
   5(6)-carboxyfluorescein diacetate N-succinimidyl ester (CFSE) at 37°C
   for 20 min in the dark. CFSE is a vital non-fluorescent dye that enters
   EVs, where intracellular esterase enzymes remove the acetate group and
   convert the molecule into the fluorescent ester form. To characterize
   and count EVs, the following panel of antibodies was used: monoclonal
   APC-anti-CD14 (Clone REA934; dilution 1:10) to identify EVs from
   macrophages and/or monocytes; APC-antiCD41 (Clone: REA1194; dilution
   1:10) for EVs from platelets; APC-anti-CD25 (clone: REA568; dilution
   1:10) for EVs from regulatory T (Treg) lymphocytes. All antibodies were
   purchased from Miltenyi Biotec. Quantitative multiparameter analysis of
   flow cytometry data was conducted using FlowJo software (Tree Star,
   Inc., Ashland, OR, USA) and antibody gating strategies were performed
   as previously described ([81]43).

EV-miRNAs isolation

   miRNA isolation from EV pellets in tubes was performed using the
   miRNeasy Mini Kit (Qiagen, Venlo, NL) according to the manufacturer’s
   protocol, with the addition of the “Rneasy Mini Elute Clean-up” kit
   (Qiagen). To assess the quality of miRNA purification, samples were
   analyzed by 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA,
   USA) using Agilent RNA 6000 Pico Kit ([82] Supplementary Figure S2 )
   and stored at -80°C.

miRNA profiling

   Reverse Transcriptase reaction was performed using Megaplex™ RT Primers
   Pool A and B Rodent (Life Technologies, Foster City, CA) and TaqMan ^®
   MicroRNA Reverse Transcriptase Kit (Life Technologies). Briefly, two
   distinct reactions (A and B) were performed, to cover the reverse
   transcription of 758 target miRNAs, including 0.75 µl of Megaplex RT
   Pool, 0.15 µl of dNTPs (100 mM), 0.75 µl 10X RT Buffer, 0.90 µl of
   MgCl[2] (25 mM), 0.1 µl of RNases Inhibitor (20 U/µl) and 1.5 µl of
   MultiScribe™ Reverse Transcriptase (50 U/µl). The reverse transcription
   thermal protocol consisted of 40 cycles at 16°C for 2 min, 42°C for 1
   min, and 50°C for 1 s, plus one cycle at 85°C for 5 min and final stage
   at 4°C. The cDNA samples were pre-amplified using “Low Sample Input
   Protocol (LSI) for profiling human miRNA using OpenArray^® Platform”
   (Application Note 2011- Life Technologies) with a specific Megaplex™
   Preamp Primer Mix for Pool A and Pool B (Rodent). The pre-amplified
   samples were diluted 1:20 with nuclease-free water and were analyzed by
   QuantStudio™ 12K Flex Real-Time PCR System with OpenArray^® Platform
   (Applied Biosystems) according to manufacturer’s instructions.

miRNA expression data analysis

   The Gene Expression Suite Software was used to analyze the expression
   of 754 unique miRNAs. Four small RNA species (RNU48, RNU44, U6, and
   ath-miR159a), included in the array as internal technical controls to
   assess assay performance and RNA integrity, were also present but not
   used for normalization purposes. Only miRNAs with Relative Threshold
   Cycle (C[rt]) value< 27 or AmpScore> 1.24 were considered amplified.
   The expression data were normalized using the “Global Normalization
   Factor” which calculates the average Crt of all detected miRNAs in a
   given sample. This method is recommended for EV-derived RNA, where
   validated endogenous reference genes are lacking due to the
   heterogeneity of vesicle content. Relative miRNA expression (also
   referred as Fold Change - FC) was determined using the relative
   quantification RQ= 2^(-Δcrt) formula, with -ΔC[rt] = measured C[rt]
   -medium normalization C[rt] ([83]44).

Statistical analyses

   Statistical analyses were carried out with GraphPad Prism 9 (GraphPad
   software, Inc, RRID: SCR_002798). As for EV characterization (i.e. EV
   concentration, mean EV size and percentage of marker-positive EVs;
   [84]Figure 1 ), the Shapiro-Wilk test was first applied to test for a
   normal distribution of the data. Then, as data were normally
   distributed, a Two-ways ANOVA test followed by Bonferroni’s post-hoc
   analysis was used for multiple group comparisons. P<0.05 was considered
   as statistically significant. Statistical differences were indicated
   with * P<0.05, **P<0.01, ***P<0.001, ****P<0.0001. The list of the
   applied tests and number of animals in each case are included in
   [85]Supplementary Table S2 . Differential expression analysis of 754
   unique miRNAs between groups was assessed using unpaired t-tests
   followed by the Benjamini-Hochberg FDR correction for multiple testing.
   A threshold of 0.20 was applied to the FDR p-value significance level
   to identify the set of top miRNAs.

Figure 1.

   [86]Diagram outlining an experiment and associated graphs. Panel A
   depicts the timeline: mice are immunized, treated with PM/saline after
   four days, and blood is sampled six hours later for EV isolation. Panel
   B shows EV concentrations under four conditions with statistical
   significance marked. Panel C presents mean EV sizes across the
   conditions, also noting significance. Panel D illustrates percentages
   of CD14+, CD41+, and CD25+ markers in EVs, with significance
   highlighted for CD14+ and CD25+.
   [87]Open in a new tab

   Characterization of mouse plasmatic EVs isolated after an acute PM[10]
   exposure. (A) Schematic representation of the experimental design
   (graphics created with [88]BioRender.com). (B, C) Mean EVs
   concentration (B) and size (C) in mouse plasma of CTRL and EAE mice
   exposed either to saline or PM[10]. (D) Percentage (%) of EVs positive
   for markers of monocytes/macrophages (CD14+), platelets (CD41+) and
   T-regs (CD25+). Each dot represents an individual mouse. Differences
   were assessed by Two-way ANOVA (see [89]Supplementary Table S2 for P
   and F values of each comparison). *p<0.05; **p<0.01; ***p<0.001.

Network analysis of deregulated miRNAs and KEGG pathway enrichment analysis

   The interactions between deregulated miRNAs and their target gene
   transcripts in each experimental condition were visualized using the
   web-based platform miRNet 2.0
   ([90]https://www.mirnet.ca/miRNet/home.xhtml [91]45). Significantly
   upregulated or downregulated miRNAs from each of the four groups,
   identified by their miRBase IDs, were uploaded into the “miRNA” module
   of miRNet 2.0. The target list was specified as “Genes (miRTarBase
   v9.0)” to link the input miRNAs to their predicted target gene
   transcripts, generating interaction tables and network visualizations.
   In the resulting networks, miRNAs may appear as specific strands (e.g.,
   miR-9-5p from the input miR-9) or as closely related variants (e.g.,
   miR-451a from miR-451). For the sake of clarity, only the most highly
   connected miRNAs in each network were labeled in the visualizations.
   KEGG pathway enrichment analysis was performed on the target gene lists
   from each network using the “Function Explorer” toolbox within miRNet,
   which employed a hypergeometric test to identify enriched (p-value <
   0.05) pathways by comparing the input genes against the full set of
   mouse genes annotated in the KEGG database. The resulting p-values were
   adjusted for multiple testing using the Benjamini-Hochberg procedure to
   control the false discovery rate (FDR).

Gene-to-disease association analysis

   The list of target genes identified by the miRNA network analysis was
   used as input for a gene-to-disease association analysis using the
   DisGeNET Curated dataset. Genes associated with a disease class were
   displayed using the DisGeNET application in Cytoscape ([92]46),
   operated through a DisGeNET specific R script. Edge thickness in the
   network graphical representation is proportional to the DisGeNET score
   for association robustness (S[GDA], score of Gene to Disease
   Association), which takes into consideration the number and type of
   sources and number of publications that support the association,
   following the subsequent formula:
     * S[GDA]=C+M+I+L+T
     * C = score for number of curated sources supporting the GDA (range
       from 0 to 0.7)
     * M = score for number of model sources supporting the GDA (range
       from 0 to 0.1)
     * I = score for number of inferred sources supporting the GDA (range
       from 0 to 0.05)
     * L = score for number of publications supporting the GDA (range from
       0 to 0.4)
     * T = score for number of clinical trials supporting the GDA (range
       from 0 to 0.1). Further information can be found at
       [93]https://disgenet.com/About#metrics.

Results

Acute exposure to PM[10] similarly affects circulating EV release in healthy
and presymptomatic EAE mice

   To assess whether individuals primed toward the development of
   autoimmunity against CNS myelin (i.e. immunized to develop EAE) react
   differently to PM[10] exposure, we characterized plasmatic EVs
   (number/size, source and miRNAs cargo) in healthy (Ctrl) vs.
   presymptomatic (i.e. 4 days after immunization) EAE mice exposed to
   PM[10], compared to Ctrl and presymptomatic EAE mice which received
   saline ([94] Figure 1A ). Although not resulting in an overt motor
   impairment, nor in demyelination, in the presymptomatic stage of EAE
   the biological processes leading to the pathology have already started,
   as exemplified by cytokine and chemokine alterations in the nervous
   tissue, cerebrospinal fluid (CSF), and plasma ([95]47), changes in the
   expression levels of molecules involved in neuronal-microglial
   communication ([96]48), as well as by mouse cognitive impairment
   ([97]49), thus recapitulating the early stages of MS.

   EAE mice basally showed a slightly lower concentration of plasmatic EVs
   compared to Ctrl. PM[10] exposure induced a further significant
   decrease in plasmatic EV release in both Ctrl and EAE mice, compared to
   unexposed individuals ([98] Figure 1B ). Although reduced, about 1.2
   x10^9 EVs/ml were still found in the plasma of PM[10]-exposed EAE mice
   ([99] Figure 1B ), showing a moderate increase in size, compared to
   those of unexposed EAE and PM[10]-exposed and unexposed Ctrl mice
   ([100] Figure 1C ). To characterize EV cellular source, we exploited a
   panel of markers specific for cell types reported to be at the origin
   of plasma EVs upon PM exposure in humans, i.e. monocytes/macrophages
   and platelets ([101]8, [102]15), and important players in MS
   pathophysiology, i.e. T regulatory cells (Tregs; [103]50). While in
   Ctrl mice 20-25% of EVs were produced by monocytes/macrophages and by
   Tregs, only 10% of EVs have these cellular origins in EAE mice, as
   assessed by immunopositivity for CD14 and CD25 surface antigens
   respectively ([104] Figure 1D ). Although the fraction of
   monocyte/macrophage-derived EVs appeared slightly higher in
   PM[10]-exposed EAE mice compared to unexposed EAE, overall PM[10]
   exposure did not alter the percentage of marker-positive EVs in both
   Ctrl and EAE mice ([105] Figure 1D ).

   Taken together, these analyses showed that – in terms of plasmatic EV
   number and source - mice primed to develop autoimmunity do not respond
   differently to PM[10] exposure, compared to healthy Ctrl mice.

Selective deregulation of extracellular vesicle-packaged miRNAs in EAE mice
after short-term exposure to PM[10]

   To assess whether the response to PM[10] of presymptomatic EAE mice
   differed in terms of EV miRNA cargo, miRNAs have been extracted and
   quantified from plasmatic EVs. This analysis provided a list of
   deregulated miRNAs for each of the following comparisons: PM-exposed
   Ctrl (i.e. Ctrl PM) vs. unexposed Ctrl (i.e. Ctrl); unexposed EAE (i.e.
   EAE) vs. unexposed Ctrl mice; PM-exposed EAE (i.e. EAE PM) vs.
   unexposed EAE; PM-exposed EAE vs. PM-exposed Ctrl mice ([106] Figure 2
   ). Deregulated miRNAs are listed in [107]Supplementary Table S3 . In
   Ctrl mice, PM[10] exposure induced changes in the expression of 59
   miRNAs, among which the most downregulated ones were miR-2134,
   miR-712-5p and miR-29a-5p, while the most upregulated were miR-215-5p,
   miR-199a-3p and miR-146b-5p (7 miRNA species with FDR<0.05;
   [108]Figures 2A, E ). Of note, miR-2134 was also one of the most
   downregulated miRNAs among the 45 differentially expressed miRNAs in
   EAE vs. Ctrl mice, together with miR-1839-3p, miR-200b-3p and miR-451a.
   Top upregulated miRNAs in EAE vs. Ctrl mice instead included
   miR-223-3p, miR-1195 and miR-685 (6 miRNA species with FDR<0.05;
   [109]Figures 2C, E ).

Figure 2.

   [110]Volcano plots and a Dot plot display microRNA data across four
   comparisons: Ctrl PM vs. Ctrl, EAE PM vs. EAE, EAE vs. Ctrl, and EAE PM
   vs. Ctrl PM. Panels A-D show log2(RQ) against -log10(FDR) for specific
   microRNAs, highlighting colored points. Panel E shows a bubble chart
   with microRNA comparisons, indicating FDR and RQ levels through bubble
   size and color gradient.
   [111]Open in a new tab

   Deregulated EV-packaged miRNAs. Volcano Plots showing upregulated and
   downregulated miRNAs (p<0.05; FDR<0.2) in (A) Ctrl PM[10] vs. Ctrl, (B)
   EAE PM[10] vs. EAE, (C) EAE vs. Ctrl and (D) EAE PM[10] vs. Ctrl PM[10]
   mice. Top deregulated miRNAs are highlighted in red (upregulated, RQ
   >1) and blue (downregulated, RQ<0.5). (E) Dot plots showing
   differentially expressed miRNAs across comparisons, with dot size
   indicating FDR and color representing RQ. See [112]Supplementary Table
   S3 for the entire list of deregulated miRNAs.

   When comparing EAE mice exposed to PM[10] to unexposed EAE mice, we
   observed 26 differentially expressed miRNAs ([113] Figures 2B, E ). In
   particular, miR-192-5p, miR-451a and miR-122-5p were the most
   downregulated, whereas miR-9-5p and miR-680 were the most up-regulated.
   Finally, 94 miRNAs were differentially expressed between EAE and
   healthy (Ctrl) mice exposed to PM[10], with miR-200c, miR-451a and
   miR-25-3p among the most downregulated, and miR-9-5p, miR-1195 and
   miR142-3p among the most upregulated miRNAs (7 miRNA species with
   FDR<0.05; [114]Figures 3D, E ).

Figure 3.

   [115]The image consists of two sections: A and B, featuring upset
   plots, and C and D, illustrating network diagrams. Sections A and B
   show bar graphs with intersection sizes for different miRNA
   comparisons: Ctrl PM vs Ctrl, EAE PM vs Ctrl PM, EAE PM vs EAE, and EAE
   vs Ctrl, with numbers indicating n. of miRNA affected. Sections C and D
   are network diagrams illustrating miRNA interactions, organized by
   colored nodes representing different comparisons, such as Ctrl PM vs
   Ctrl and EAE PM vs Ctrl. Each node has lines showing connections to
   specific miRNAs, highlighting their relationships.
   [116]Open in a new tab

   Overlap of deregulated EV-associated miRNAs among groups. (A, B) Upset
   plots showing the number of upregulated (A) and downregulated (B)
   miRNAs that are selectively or commonly expressed among comparisons.
   (C, D) Venn networks showing the identity of miRNAs upregulated (C) or
   downregulated (D) in all comparisons of interest. See
   [117]Supplementary Table S4 for the entire list of deregulated miRNAs
   that are selectively or commonly expressed among comparisons.

   Notably, a certain degree of overlap existed between the responses of
   healthy mice to immunization and to PM[10], especially in terms of
   downregulated miRNAs (58.84% of the miRNAs downregulated in Ctrl PM vs.
   Ctrl mice overlapped with those downregulated in EAE vs. Ctrl mice;
   [118]Figure 3 ; [119]Supplementary Table S4 ), suggesting a similar
   biological effect. In contrast, the responses to PM[10] of EAE and
   healthy mice were largely divergent, with only 22% of upregulated
   miRNAs and 12% of miRNAs downregulated in EAE PM vs. EAE overlapping
   with those downregulated in Ctrl PM vs. Ctrl mice ([120] Figure 3 ;
   [121]Supplementary Table S4 ).

   Overall, these analyses show that the response to PM[10] – in terms of
   deregulated EV-associated miRNAs – is qualitatively different in mice
   primed to develop autoimmunity compared to healthy Ctrl mice.

miRNA target prediction and network analysis

   To further explore the potential functional impact of miRNA
   deregulations triggered by PM[10] in healthy and EAE mice, we
   identified the target gene transcripts of miRNAs that were
   differentially expressed in the 4 comparisons ([122] Supplementary
   Table S5 ). Notably, several targets were shared among multiple
   deregulated miRNAs, suggesting potential regulatory hubs. In line with
   this idea, network-based analyses performed on upregulated ([123]
   Figure 4 ) and downregulated ([124] Figure 5 ) miRNAs for each
   experimental comparison highlighted a significant number of
   interactions between the deregulated miRNAs (yellow squares) and their
   predicted target gene transcripts (red dots for upregulated miRNAs in
   [125]Figure 4 and blue dots for downregulated miRNAs in [126]Figure 5
   ). Overall, the networks of upregulated miRNAs ([127] Figure 4 ) were
   remarkably more complex than those of the downregulated miRNAs ([128]
   Figure 5 ), which revealed relatively few target gene connections
   (especially for Ctrl PM vs Ctrl and EAE vs Ctrl, with fewer than 50
   interactions; [129]Figures 5A, C ). In contrast, the networks of
   downregulated miRNAs in EAE PM vs both EAE ([130] Figure 5B ) and Ctrl
   PM ([131] Figure 5D ) exhibited more intricate interactions, with a few
   miRNAs having over 300 connections each.

Figure 4.

   [132]Four network diagrams labeled A, B, C, and D show interactions
   between microRNAs and their targets across different conditions. Large
   yellow squares represent specific upregulated miRNAs, with smaller red
   dots connected by lines depicting complex interaction networks. Notable
   microRNAs like miR-9-5p, miR-1195, let-7b-5p, and miR-223-3p are
   highlighted. Each diagram contrasts pairs of conditions: Ctrl PM vs
   Ctrl, EAE PM vs EAE, EAE vs Ctrl, and EAE PM vs Ctrl PM, illustrating
   distinct interaction patterns across each scenario.
   [133]Open in a new tab

   Network analysis of upregulated miRNAs and their predicted target gene
   transcripts. Network diagrams represent the interactions between
   upregulated miRNAs and their predicted target gene transcripts in the
   following conditions: (A) Ctrl PM[10] vs Ctrl, (B) EAE PM[10] vs EAE,
   (C) EAE vs Ctrl, and (D) EAE PM[10] vs Ctrl PM[10]. Yellow squares
   indicate miRNAs, and red dots represent target gene transcripts. For
   clarity, only the names of miRNAs with more connections (threshold
   varies across networks) are labeled. See [134]Supplementary Table S5
   for the entire list of upregulated miRNAs and their predicted target
   gene transcripts.

Figure 5.

   [135]Four network diagrams labeled A, B, C, and D illustrate
   downregulated miRNA interactions with their respective targets in
   different experimental comparisons. Each diagram has nodes and edges,
   with yellow squares highlighting key miRNAs like miR-203-3p,
   miR-712-5p, miR-15a-5p, and miR-451a. Blue dots represent connected
   targets, varying in complexity across diagrams A (Ctrl PM vs Ctrl), B
   (EAE PM vs EAE), C (EAE vs Ctrl), and D (EAE PM vs Ctrl PM).
   [136]Open in a new tab

   Network analysis of downregulated miRNAs and their predicted target
   gene transcripts. Network diagrams represent the interactions between
   downregulated miRNAs and their predicted target gene transcripts in the
   following conditions: (A) Ctrl PM[10] vs Ctrl, (B) EAE PM[10] vs EAE,
   (C) EAE vs Ctrl, and (D) EAE PM[10] vs Ctrl PM[10]. Yellow squares
   indicate miRNAs, and blue dots represent target gene transcripts. For
   clarity, only the names of miRNAs with more connections (threshold
   varies across networks) are labeled. See [137]Supplementary Table S5
   for the entire list of downregulated miRNAs and their predicted target
   gene transcripts.

   Examining the shared miRNAs within these networks allowed us to provide
   more insightful information about the actual biological consequences of
   miRNA deregulation, accounting for both the miRNA-target gene
   transcript interactions and their collective regulatory impact, rather
   than simply considering the shared miRNAs across conditions.
   Interestingly, among the upregulated miRNAs displaying the highest
   number of connections, a partial overlap appeared again between the
   response of healthy mice to immunization (EAE vs Ctrl) and to PM[10]
   (Ctrl PM vs Ctrl; e.g. miR-24-3p, and miR-223-3p among the upregulated
   miRNAs and miR-221-3p among the downregulated miRNAs; [138]Figures 4A,
   C , [139]5A, C ), suggesting that these miRNAs may lead to similar
   cascades in response to both environmental and autoimmune triggers.
   Except for miR-1195, miR223-3p and miR-451a which were shared across
   EAE vs Ctrl and EAE PM vs Ctrl PM ([140] Figures 4B–D ), “hub” miRNAs
   deregulated in EAE mice responding to PM[10] were exclusive. Among
   them, miR-9-5p ([141] Figures 4B, D ) and miR-15a-5p ([142] Figures 5B,
   D ) were the most connected upregulated and downregulated miRNAs,
   respectively.

   To further investigate the biological pathways potentially influenced
   by the detected miRNA deregulation, KEGG pathway enrichment analysis
   was performed on the predicted target genes from the upregulated and
   downregulated miRNA networks across the different comparisons. Of
   relevance in the context of an autoimmune CNS disease like MS, this
   analysis revealed a significant enrichment of terms related to synapse
   function, nervous system, and immune response, for both up- and
   downregulated miRNAs ([143] Figures 6A, B , respectively,
   [144]Supplementary Table S6 ). Terms such as Glutamatergic synapse,
   GABAergic synapse, Dopaminergic synapse, Cholinergic synapse suggest
   that miRNA deregulation may play a role in modulating synaptic activity
   and plasticity, as further supported by the enrichment of Long-term
   potentiation and Long-term depression pathways. Moreover, several terms
   associated with addiction - such as Morphine addiction, Cocaine
   addiction, Amphetamine addiction, and Nicotine addiction - emerged
   across the comparisons, suggesting a possible influence of deregulated
   miRNAs on neurotransmitter systems. Besides nervous system-related
   pathways, immune- and inflammation-related pathways were also
   significantly enriched. These pathways were significantly enriched in
   the networks of upregulated miRNAs across the different comparisons
   ([145] Figure 6A ; [146]Supplementary Table S5 ). In contrast, for
   downregulated miRNAs, nervous system- and immune-related pathways
   emerge almost exclusively in the comparisons involving EAE mice (i.e.
   EAE vs Ctrl, EAE PM vs Ctrl PM and EAE PM vs EAE comparisons). Although
   this might be due only to the very small size of the network of
   downregulated miRNAs in Ctrl PM vs Ctrl comparison ([147] Figure 5A ;
   [148]Supplementary Table S5 ), this pointed again to the special
   character of the response of EAE mice to PM[10]. In line with this
   idea, while the pathways described so far were largely shared across
   the four comparisons, several terms associated with fatty acid (FA)
   metabolism (Biosynthesis of unsaturated fatty acids, Fatty acid
   metabolism, Fatty acid elongation) were instead specifically enriched
   for the upregulated miRNA networks of EAE mice responding to PM (i.e.
   PM vs Ctrl PM and EAE PM vs EAE comparisons; [149]Figure 6A ;
   [150]Supplementary Table S6 ).

Figure 6.

   [151]Bar charts titled “Pathway enrichment for upregulated miRNA
   targets” (A) and “Pathway enrichment for downregulated miRNA targets”
   (B). Each chart displays pathway categories including immune response,
   nervous system, fatty acids & inositol metabolism. Bars represent
   comparisons: Ctrl PM vs Ctrl, EAE PM vs EAE, EAE vs Ctrl, and EAE PM vs
   Ctrl PM, using orange, green, blue and yellow colors, respectively.
   Presence of the Epstein-Barr virus infection pathway is highlighted.
   Data values are on the y-axis as -log10(adjusted p-value).
   [152]Open in a new tab

   KEGG Pathway enrichment analysis of miRNA target networks. Bar plots
   showing selected significantly enriched pathways (adjusted p < 0.05)
   for the target genes of upregulated (A) and downregulated (B) miRNAs
   across the distinct comparisons. The y-axis indicates the –log[10] of
   the adjusted p-values (adj. p-value). The displayed pathways represent
   those most relevant to nervous system function, immune response, and
   fatty acid/inositol phosphate metabolism. See [153]Supplementary Table
   S6 for the entire list of enriched pathways.

Gene-to-disease association of miRNA targets

   To link EV-packaged miRNA dysregulation with possible pathological
   alterations elicited by PM exposure, we used the list of predicted
   target gene transcripts to perform a “gene-to-disease association”
   analysis. Again of relevance in the context of MS, and consistent with
   the KEGG pathway analysis, many of the targets of the miRNAs
   selectively deregulated in PM-exposed EAE vs PM-exposed healthy mice
   (i.e. EAE PM vs Ctrl PM; see [154]Supplementary Table S5 ) are involved
   in nervous systems diseases and immune system diseases, and
   particularly in autoimmune diseases including MS ([155] Figures 7A–D ;
   [156]Supplementary Table S7 ). Consistently, when assessing the
   fraction of gene transcripts targeted by miRNAs selectively deregulated
   in PM-exposed EAE mice over the entire number of genes listed in each
   of the disease classes included in the DisGeNET database, “Nervous
   System Diseases” was the most represented class, followed by Mental
   Disorders, Hemic and Lymphatic Diseases, Behavior and Behavior
   Mechanisms and Immune System Diseases ([157] Figures 7E, F ).

Figure 7.

   [158]Network diagrams labeled A to D showcase gene-disease associations
   with nodes and connections in blue, yellow, and red, indicating various
   relationships and diseases. Diagrams E and F display bar graphs with
   overlapping percentages of different disease classes genes with the
   observed target genes, colored in gradients from yellow to red in E and
   light blue to dark blue in F.
   [159]Open in a new tab

   Pathological relevance of the selective response of EAE mice to PM[10].
   Gene-to-disease association performed with DisGeNET performed for the
   target genes of miRNAs significantly upregulated (A, C) and
   downregulated (B, D) specifically in EAE PM vs Ctrl PM. (A-D) Networks
   of target genes (in yellow dots) associated with pathologies (in blue
   dots) in the DisGeNET disease class “Nervous Systems Diseases” (A, B)
   and “Immune System Diseases” (C, D). Multiple Sclerosis (MS) node is
   highlighted with a red border in (A, B). In the “Immune System
   Diseases” Network, Autoimmune diseases are represented by red dots.
   Thickness of links is proportional to the DisGeNET score, as a measure
   of quality/number of evidence supporting the gene-to-disease
   association. (E, F) Gene enrichment in disease classes found in the
   DisGeNET database. Bars represent the fraction of target genes over the
   entire number of genes listed in each disease class. Source data are
   provided in [160]Supplementary Table S7 .

   Overall, this analysis showed that gene transcripts targeted by miRNAs
   selectively altered in EAE mice exposed to PM[10] were associated with
   nervous system disorders and with pathologies closely related to MS,
   suggesting that the plasmatic EV cargo may mediate the transfer of
   PM-induced damage to the CNS and synergize with the immune setting of
   the individual, possibly contributing to immune dysregulation in the
   CNS via yet unknown mechanisms.

Discussion

   With the aim of disclosing the biological bases of the higher
   vulnerability of immunologically-primed subjects to PM exposure, here
   we characterized plasmatic EVs and their miRNA cargo in healthy and
   presymptomatic EAE mice following a single exposure to high level
   PM[10]. We focused on PM[10] because it has been consistently
   associated with MS-related outcomes in epidemiological studies (see
   above), and it includes finer fractions such as PM[2.5]. Results showed
   that the response of EAE mice to PM did not differ in terms of EV
   number and source, compared to that of healthy mice. Yet, remarkable
   differences existed in the identity of deregulated EV-associated
   miRNAs, which were predicted to target MS-relevant pathways and might
   contribute to disease worsening.

   As regards EV abundance and features, PM[10] exposure induced a
   significant decrease in plasmatic EV release in both healthy and EAE
   mice - accompanied by a moderate increase in size in PM[10]-exposed EAE
   mice - compared to unexposed individuals. These findings clash with
   what observed in humans - where short-term PM exposure has been
   associated with increases in plasmatic EVs ([161]8, [162]15, [163]51),
   and may suggest a species-specific response, with possible change in EV
   release mechanisms in PM[10]-exposed EAE mice ([164]42). Yet, EV
   numbers and size remained in the range of those reported in humans
   following PM exposure ([165]8, [166]15) and EV size did not reach that
   of apoptotic bodies ([167]42), thus not indicating higher toxicity of
   PM[10] to murine cells.

   As regards EV cargo, here we focused on EV-associated miRNAs as
   informative post-transcriptional markers, given their regulatory role
   and the interpretability of their dysregulation through known mRNA
   targets. In both healthy and EAE mice, the miRNA cargo of EVs released
   following PM exposure had a signature relevant for MS. However,
   deregulated miRNAs were distinct and the response of EAE mice appeared
   remarkably more “pathogenic” than that of healthy mice. Specifically,
   in Ctrl mice, exposure to PM[10] led to a complex and “ambivalent”
   miRNA signature, including decreased “protective” miRNAs (e.g.
   miR-29a-5p; [168]52–[169]55; miR-2134; [170]56), but also decreased
   proinflammatory miRNAs (e.g. miR-712-5p, [171]57) and increased
   anti-inflammatory miRNAs (e.g. miR-215-5p; [172]58, [173]59;
   miR146b-5p; [174]60, [175]61; miR-199a-3p; [176]62, [177]63). Of note,
   among miRNAs upregulated in healthy mice exposed to PM[10], miR-146b-5p
   was formerly found upregulated in the blood and white matter tissue of
   MS patients and MS animal models ([178]64), presenting a peak of
   expression in peripheral blood mononuclear cells (PBMCs) of
   treatment-naïve relapsing and remitting MS (RRMS) patients during the
   remission phase ([179]65), in line with miR-146b-5p role as a molecular
   brake for T-cell B-cell crosstalk restraining autoimmunity ([180]66).
   Similarly, miR-199a-3p expression in PBMCs was proposed to mediate the
   effects of disease-modifying therapies (DMTs) in RRMS patients
   ([181]67), in line with its role in promoting immune tolerance
   ([182]68).

   Among these deregulated miRNAs, miR-215-5p and miR-199a-3p have been
   already reported as EV-packaged miRNAs in human serum ([183]69) and
   mouse bronchoalveolar lavage fluid ([184]70), respectively. Moreover,
   increased expression of miR-215-5p, miR-146b-5p and miR-199a-3p was
   detected in human and mouse lung epithelial cells upon exposure to
   PM[2.5] and other pollutants ([185]70–[186]72).

   All in all, these findings point to a prevalent compensatory/protective
   response elicited in healthy mice by short term exposure to PM[10].

   In contrast, in EAE mice, PM exposure resulted in a strong enrichment
   in miR-9-5p, whose level in the serum was correlated with clinical
   activity, brain atrophy and disability progression in MS patients
   ([187]73–[188]75). Such an association appears to rely on miR-9-5p role
   in microglia activation and neuroinflammation ([189]76, [190]77),
   dendritic cell activation ([191]78), Th17/Treg differentiation
   ([192]79, [193]80), and oligodendroglia maturation ([194]81). Notably,
   miR-9-5p was also one of the “hub” upregulated miRNAs highlighted by
   the network analysis in EAE mice exposed to PM[10] (vs unexposed EAE
   mice and vs PM[10] exposed healthy mice), corroborating the idea of its
   critical involvement in the response of EAE mice to PM[10]. In
   addition, EAE mice showed a strong decrease of anti-inflammatory
   miRNAs, such as miR-345-5p ([195]82), miR-192-5p ([196]83, [197]84) and
   miR-451a ([198]85), compared to unexposed EAE mice. Of note, miR451a
   was formerly shown to play a wide array of immunoregulatory actions, by
   attenuating macrophage and dendritic cell responses ([199]86),
   inhibiting T cell activation ([200]87), suppressing NK cell activation
   and cytotoxicity ([201]88) and regulating myeloid-derived suppressor
   cells differentiation in the context of autoimmune disease ([202]85).
   In line with this, miR-451a was proposed to contribute to the remission
   phase of a number of autoimmune diseases, such as familial
   Mediterranean fever ([203]89), rheumatoid arthritis (RA, [204]90) and
   systemic lupus erythematosus (SLE, [205]85, [206]91). Similar to
   miR-9-5p, miR-451a was also one of the “hub” downregulated miRNAs
   highlighted by the network analysis in EAE mice exposed to PM[10] (vs
   unexposed EAE mice and vs PM[10] exposed healthy mice), in line with a
   key role in EAE mouse response to PM[10].

   In healthy mice miRNAs deregulated after immunization and after PM[10]
   exposure partly overlapped, suggesting a similar outcome in response to
   autoimmune and environmental triggers. In contrast, the response of EAE
   mice to PM[10] was largely segregated from the response of healthy
   mice. Notably, while comparing the response of EAE and healthy mice to
   PM[10] (i.e. EAE PM vs Ctrl PM), one of the top enriched miRNAs was
   miR-142-3p, which was shown to impact MS progression, severity, and
   therapeutic outcomes by orchestrating neuronal toxicity and excitotoxic
   synaptic alterations, ([207]92–[208]95), and possibly by regulating
   also Treg cell differentiation ([209]96) and myelin repair ([210]97).
   On the other hand, in addition to miR-451a, one of the top
   downregulated miRNAs in EAE PM vs Ctrl PM was the anti-inflammatory
   miR-25-3p ([211]98), formerly found down-regulated in Treg cells of MS
   patients and proposed to alter Treg cell activity in MS by targeting
   TGF-β biological functions ([212]99).

   Overall, these findings indicate that the response to PM[10] – in terms
   of deregulated plasma EV-associated miRNAs – is qualitatively different
   in mice primed to develop autoimmunity compared to healthy mice, and
   includes a repertoire of miRNAs that, if also deregulated in
   PM[10]-exposed MS patients, might contribute to key pathological
   aspects, including autoimmunity, CNS infiltration, neuroinflammation,
   neurodegeneration and myelin repair.

   The special character of the response of EAE mice to PM[10] and its
   relevance for MS were also highlighted by miRNA target prediction and
   network analysis, as well as by the analysis of the biological pathways
   potentially influenced by the detected miRNA deregulation. In general,
   the networks of upregulated miRNAs were remarkably complex, in line
   with the idea that upregulated miRNAs might have a broad biological
   impact, influencing a wide range of cellular processes. Pathways
   related to synapse function, neurotransmitter systems, nervous system,
   immune system and inflammation were significantly enriched for the
   networks of upregulated miRNAs in both healthy and EAE mice, whereas
   pathways related to immune system and inflammation emerged almost
   exclusively for the networks of downregulated miRNAs in EAE mice
   responding to PM[10]. Interestingly, among immune-related pathways,
   Epstein Barr virus (EBV) infection emerged as one of the most enriched
   pathways for downregulated miRNAs in PM-exposed EAE mice, suggesting
   that exposure to PM[10] might result in the upregulation of target
   genes belonging to this category and might mimic the anti-EBV response
   in EAE mice. This finding is particularly intriguing as EBV infection
   is epidemiologically linked with the development of autoimmune diseases
   - including SLE, RA and MS - and multiple mechanisms associated with
   EBV infections (e.g. molecular mimicry, B cell reprogramming, crosstalk
   between EBV-infected B cells and T cells, etc.) are now considered
   causally implicated in autoimmunity and MS development ([213]100,
   [214]101). Moreover, as an additional element of uniqueness of EAE
   mouse response to PM[10], terms associated with FA metabolism
   (Biosynthesis of unsaturated fatty acids, Fatty acid metabolism, Fatty
   acid elongation) were specifically enriched for the upregulated miRNA
   networks of EAE PM vs Ctrl PM and EAE PM vs EAE comparisons. Of note,
   altered lipid profiles were reported in MS patients, with FA
   serological concentration reflecting disease activity and disability
   score, and a number of FA metabolism-related enzyme single nucleotide
   polymorphisms (SNPs) associated with MS incidence ([215]102). Even
   before onset, MS patients display a unique FA serological profile and
   FA intake and metabolism are thought to contribute to MS susceptibility
   ([216]102). Availability and profiles of FA species might alter the
   disease course by influencing immune cell function and specifically by
   modulating the polarization, differentiation, and cytokine production
   of T cells, which largely contribute to MS pathogenesis ([217]103).
   Thus, a clear MS-relevant signature of the response of presymptomatic
   EAE mice to PM[10] emerged, as also corroborated by “gene-to-disease
   association” analysis.

Limitations of the study and future directions

   Overall, our findings support the view that, in immunologically primed
   individuals - such as people with MS - exposure to PM[10] can trigger a
   unique response through the delivery of EV-associated miRNAs, which can
   contribute to a worse manifestation or an unfavorable evolution of the
   pathology. Yet, our study has limitations that must be disclosed to
   correctly interpret our findings and to identify persisting open
   issues. Here, we combined exposure to PM[10] with EAE, which is the
   closest animal model that approximates human MS pathogenesis by
   reflecting its autoimmune component ([218]104). Yet, as our results
   point to a likely participation of exposure to PM[10] in immune
   dysregulation, it is relevant to acknowledge that some differences
   exist in the innate and adaptive immune system of mice and humans
   ([219]105), potentially limiting the translatability of our findings
   and calling for future investigations in MS patients. Moreover, besides
   the relatively small size of individuals used in this kind of in vivo
   experiments, here we used only female mice, as in most studies focusing
   on EAE. This choice relies on the well-known sexual dimorphism existing
   in MS, with women showing a higher susceptibility to the disease
   ([220]106). Yet, disease progression appears worse in men ([221]106)
   and mechanisms leading to EAE partially differ in female and male mice
   ([222]107). Inclusion of male mice in future studies will validate the
   generalizability of our findings or, alternatively, unveil sex-specific
   aspects in the response to PM[10] of immunologically primed
   individuals. Moreover, as regards the analysis of EV-packaged miRNAs,
   this study did not include EV subtype-specific miRNA profiling due to
   limited EV yield, and sample pooling - while necessary due to sample
   constraints and ethical (i.e. 3Rs) considerations - limited the
   assessment of inter-individual variability. Finally, our study
   investigated an acute (i.e. occurring within 6 hours) biological
   response triggered by a single exposure to PM[10]. While this was
   instrumental to investigate the short-term effect of the peaks of
   airborne PM[10] which were associated with higher rates of hospital
   admissions for MS and MS relapses ([223]22, [224]29), results obtained
   in this experimental setting might not reflect the biological responses
   elicited upon long-term or chronic exposure to PM[10]. Similarly, in
   our study PM[10] dose was calculated to mimic daily peaks of PM[10]
   concentration in the most polluted European areas (>250 μg/m^3;
   [225]41). Whether exposure to higher PM[10] concentrations - as
   detected in other geographical regions ([226]108) - or to other risk
   factors epidemiologically associated with MS risk may converge on the
   same biological mechanisms unveiled by our study deserves further
   investigation.

Acknowledgments