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[Preprint]. 2026 Feb 13:rs.3.rs-8712658. [Version 1] doi: 10.21203/rs.3.rs-8712658/v1

CD4+ T Cell Activation and Peripheral Immune Cell Influx into the Brain Following Wildfire Smoke Exposure is Modulated by a Saturated Fat Diet

Brenna Baird 1, Justin Carter 2, Ember Suh 3, Yan Jin 4, Russell Hunter 5, Jorge Moreno 6, Milad MazloumiBakhshayesh 7, Alicia Bolt 8, Marian Olewine 9, Edward Barr 10, Jessica Begay 11, Selita Lucas 12, Ximeng Liu 13, Lingjun Li 14, Shiquan Cui 15, Haiwei Gu 16, Matthew Campen 17, Shahani Noor 18
PMCID: PMC12919296  PMID: 41727585

Abstract

Over the past 40 years, wildfires across the United States have steadily increased, in terms of acreage burned. Smoke from wildfires can exacerbate several cardiovascular and respiratory diseases, with new studies highlighting potential sustained neurological outcomes. The purpose of this study was to delineate the role of peripheral immune populations in woodsmoke-induced neuroinflammation, the timeline of this response, and the impact of diet on each of these factors. Based on previous research on other pollutants and diet introduction, we included a highly saturated fatty acid (coconut oil) to represent the effects of a diet intervention that may negatively influence the severity and time course of peripheral immune infiltration. 8-week-old female C57BL/6 mice were exposed to either a sham filtered air or a woodsmoke exposure of a 0.5 mg/m3 concentration every other day for 14 days, 4 hr/day. Three time points were analyzed: 1-, 14-, and 28-days post exposure to woodsmoke to determine neuroinflammation at its peak, and when it begins to resolve. At each time point, brains were queried by high dimensional flow cytometry analysis. Markers for peripheral immune infiltration included T cells (CD3, CD4), lymphocyte trafficking (LFA-1), resident immune cells (CD11b, ACSA-2) and other inflammation-dependent markers. Endothelial cell expression (CD31) and subsequent adhesion molecules (VCAM-1, ICAM-1) were also analyzed. Results demonstrate a time and diet-dependent influx of CD4 T cells (CD3+, RORg−, FOXP3−, CD44+) potentially to resolve neuroinflammation from woodsmoke inhalation. We also see a pattern of inflammation across several trafficking and adhesion markers, including LFA-1, VCAM-1 and ICAM-1, that peaks at 1 day after the last exposure (14 days after the initial insult). Conferred with these results are metabolomic changes in the brain including upregulated taurine and glycerolipid metabolism that is mainly tied to increased susceptibility to inflammatory phenotypes in the brain. Here, we demonstrate that woodsmoke inhalation can initiate the recruitment of peripheral immune populations and activation of endothelial cell markers within the brain. The presence of these cells implicates a larger, more systemic impact on the central nervous system following woodsmoke exposure.

Keywords: Wildfire smoke, Neuroinflammation, CD4 T cells, Peripheral Immune Cells, Saturated Fat Diet

Introduction

Over the past four decades, there has been a discernible increase in the severity of wildland fires in the United States, in terms of acreage and biomass burned [1]. This trend is strongly correlated with the shift in global temperatures with greater extreme heat and drought events. Even far from the wildland fire sites, wildland fire smoke (WFS) exposure increases acute respiratory hospital admissions, along with a variety of cardiovascular hospitalizations ranging from myocardial infarction to ischemic stroke [2–5]. Large, cohort-based studies in the US have established a connection between long term exposure to air pollution and neurological impacts. Dementia incidence, in particular, was found to be elevated in areas with higher levels of residential particulate matter [6]. Additional studies have demonstrated the impacts of subacute particulate matter exposure on cognition and attention scores, where individuals exposed to higher levels of wildfire smoke had the most pronounced reduction in cognition when tested days after exposure [7].

These more general air pollution epidemiological studies implicate that outcomes from wildfire smoke are likely to extend beyond the cerebrovasculature and may confer increased susceptibility to neurodegeneration, though this relationship remains unstudied [8]. In addition to causing degenerative pathologies, smoke inhalation has been shown to activate resident immune cells within the brain with potential for peripheral immune cell recruitment [9, 10]. Peripheral immune cells including macrophages, neutrophils, and T cells have the capacity to both increase a neuroinflammatory phenotype as well as aiding in its resolution [11, 12]. T cells play an important role in the resolution of neuroinflammation and such populations may be phenotypically altered in response to WFS [13].

A critical issue surrounding WFS is that millions of people may be exposed, all with a range of risks and exposure concentrations; identifying universal interventions to mitigate risks of health impacts is challenging. Emerging studies have found that diet plays a role in modulating the immune response. Certain saturated and unsaturated fatty acids have shown to impact the response to air pollutants, with saturated fats exhibiting a broader modulatory effect on the immune response [14]. Coconut oil (CO) contains a number of fatty acids, mostly saturated, that have the ability to elevate risk of stroke and cardiovascular outcomes. This diet type was selected to evaluate the compounding effects of wildfire smoke while on a highly saturated fat diet, as this is a common scenario for many Americans [15]. The brain, being the second most lipid-dense organ, is particularly vulnerable to changes in lipid composition, which is important to consider in the context of a diet high in saturated fats [16]. Inhalation of wildfire smoke primes the vasculature for a heightened inflammatory state, leaving the brain particularly vulnerable to alterations in lipid species [17, 18]. Dietary measures, broadly applied, could help improve public health and mitigate risks of adverse health outcomes from WFS exposure, but such information is lacking.

Studies with particulate matter, specifically PM2.5, and dietary interventions illustrate the ability of diet to modulate the response to certain air pollutant exposures [19]. Saturated fats have a strong connection to metabolism and immunity modulation, mainly effecting macrophage and T cell functionality[16].

This idea postulates promising potential for fatty acid supplementation or dietary changes to mitigate exposure-induced changes in brain lipids and metabolites. Based on this premise, we included an enriched diet with coconut oil, rich in saturated fats, in order to determine the extent of this lipid heavy diet on exposure-induced neuroinflammation. Many pro-resolving fatty acid types, such as lipoxins and resolvins, aid in the cross-talk between microglia and other immune cell subsets, whereas more saturated fatty acids can hinder these functions in the brain [20]. The combination of defining the cellular dynamics of neuroinflammation and the time course in which it resolves will help define which cell types are most important for eliciting resolution, and which diets/supplements can be implemented as actionable interventions for populations most at risk.

While much is known regarding the time course and immune cell subsets that participate in active neuroinflammation, far less is understood in terms of how the brain resets to homeostasis following a disruption of BBB [21]. Existing research on inflammation resolution has focused on traumatic brain injury, stroke models, and genetic models of neurodegenerative diseases [11, 12]. There is a substantial body of knowledge concerning the timing and immune cell subsets involved in active neuroinflammation, however, our understanding of how the brain returns to a state of equilibrium after a disruption of the blood-brain barrier (BBB) remains limited. Therefore, we included 3 time points to assess the timeline of inflammation, a diet supplementation, and a focus on the role of peripheral immunity in the context of woodsmoke exposure.

Materials and Methods

Animals

Female C57BL/6 mice (Jackson Labs) aged 8 weeks were housed in in shoebox cages in an AAALAC-approved facility, on a 12 h light: dark cycle. Mice underwent a mandatory 1-week quarantine before exposures began. Mice were weighed weekly to ensure a healthy weight gain with age. Animals received either a standard chow diet or a supplemented diet (described below) and water ad libitum. The exclusive use of female C57BL/6J mice in our study was strategically chosen due to evidence of sex-based differences in inflammatory and metabolic responses, particularly relevant to neuroinflammation [21, 22]. All procedures were conducted with approval by the University of New Mexico Institutional Animal Care and Use Committee. A total of 72 animals were involved in this study, with an n = 6 per group (Fig. 1A). Animals were divided into the following groups: filtered air, standard chow (FASC), filtered air, coconut oil (FACO), woodsmoke, standard chow (WSSC), and woodsmoke, coconut oil (WSCO). These groups remain consistent throughout the time points analyzed (1-, 14-, and 28-days post exposure).

Figure 1.

Figure 1

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Exposure characterization and study outline schematic. A: Study design and exposure/diet groups. 72 total animals were included in this study. Mice were exposed to either woodsmoke or filtered air for 14 days, as described above. Within these groups, animals were placed on a standard chow diet or a diet supplemented with 6% coconut oil, to represent a saturated fatty acid diet. Three time points were analyzed within this study design: 1 day, 14 days, and 28 days after exposure. Each time point represents an individual cohort of 24 animals, with n=6 for each condition.

B: Average PM2.5 concentrations over a 14-day exposure period. Animals are exposed every other day, 4hr/day for 14 days, with a total of 7 exposures. Daily particulate averages remained at the target concentration of 0.55mg/m3. This results in a 24-hour average of 0.046mg/m3. C: Carbon monoxide (CO) and nitric oxide species (NOx) concentration cumulative average from each exposure day. Both CO and NOx levels are below OSHA, EPA, and NIOSH permissible exposure limits.

Woodsmoke Exposure System

Animals underwent full body exposures to laboratory produced woodsmoke in a BioSpherix Medium A-Chamber over the course of 4 hours, every other day, for 14 days. The exposure system burns small pieces of cedar wood chips (30 mg units) within quartz boats, in series; each boat of woodchips takes approximately 10 minutes to burn, then the heater is moved to the next boat for a relatively stable exposure over 4 hours per day. The resulting concentration for the 4h period averages 500 μg/m3, which over a 2-week exposure period is about 50 μg/m3 as a 24h average (as a means of comparing with EPA standards on a 24h average; Fig. 1B). Concentrations were calculated by measuring the difference in Teflon filter weight before and after each 80 minutes of exposure time. Smoke concentrations were titrated manually throughout a 4-hour exposure via a series of pressurized gauges and output pumps to ensure a consistent average of smoke delivered per exposure. The 14-day exposure, every other day at this concentration was chosen based on previous studies outlining the dynamic nature of real-time wildfire smoke fluctuations at non-lethal levels [22].

WS Particulate Matter Characterization and Copollutant Gas Concentrations

During each woodsmoke exposure, particulate concentrations were measured through an adjacent tube that continuously samples the smoke in the main exposure chamber. This sampling tube is connected to a DustTrak II (TSI, Inc; Shoreview, Minnesota) where particulates of various sizes are measured. Additionally, general particulate concentrations were measured by weighing the Teflon filters used during the exposure, as described above. Particle size distribution was previously quantified before exposures began for continuity and consistency between studies [9]. Gas content produced by woodsmoke exposures was quantified with a GrayWolf™ multi-gas monitor. Levels of carbon monoxide (CO) and nitrogen species (NOx) measured during exposures were both below EPA standards [23] (Fig. 1C).

Saturated Fat Enriched Diet

Animals on the control diet received a largely unsaturated, grain-based standard chow (Teklad Global Soy Protein-Free Extruded Rodent Diet, 2020X). The experimental animals received a diet enriched with 6% coconut oil (CO) (Teklad Custom Diet TD.210292). This diet was replaced every other day to prevent oxidation. Mice began the experimental diet 3 weeks before exposures began and remained on their respective diets until their time point (1-, 14-, 28-days post exposure) came to conclusion.

Tissue Collection and Brain Digestion

Animals were placed under anesthesia with isoflurane and euthanized via cardiac exsanguination. To ensure complete removal of red blood cells from the brain, a gravity-assisted cardiac perfusion was performed using ice-cold 0.1 M PBS for a minimum of 5 minutes or until the liver turned pale. Following perfusion, the brain was removed, and the left hemisphere was immediately placed in cold DPBS for the subsequent digestion procedure, as reported in our previous study [18]. The right hemisphere was snap frozen and prefrontal cortex (PFC) was separated for metabolomic analysis. Digestion of the left hemisphere was initiated with a mechanical digestion by physically slicing and chopping the half brain in enzymes aimed at preserving surface epitopes for fragile neural tissue (Miltenyi Multi Tissue Dissociation Kit 1, #10012554). Tissues were dissociated with Miltenyi Biotec gentleMACS™ Octo Dissociator with heaters (Miltenyi Biotec #130096427) for further digestion. Further steps to remove debris and myelin were performed by filtering cells through 70μM strainers and repeated washing steps with Red Blood Cell Lysis Solution (Miltenyi Biotec, #130094183) and Debris Removal Solution (Miltenyi Biotec, #130109398). Cells were counted using a hemocytometer, and a concentration of 1.0 × 106 were added to each sample tube, diluting with the appropriate amount of PBS.

BALF Mesoscale and Cell Count Analysis

Mesoscale analysis was performed on the lung tissue of the exposed mice. Immune cell profiling in BALF was quantified as previously described [24]. Briefly, total numbers of viable white blood cells in BALF were quantified by total cells counts using trypan blue exclusion dye and counted on a Luna FX7 Automated Cell Counter (Logos Biosystems, Annandale, VA). Immunophenotyping was quantified by flow cytometry using specific cell surface markers. Cells were washed in 1x dPBS, blocked with Fc Block (rat anti-mouse CD16/32 No. 553141; BD Biosciences), and incubated with primary antibodies CD45-BV605 (No. 563053; BD Biosciences), Gr1-FITC (No. 553127; BD Biosciences), and F4/80-PE (No. 565410; BD Biosciences). Cells were washed and resuspended in staining buffer (1 x dPBS; 10 mM sodium azide, 5% FBS) before being analyzed by flow cytometry (Attune NxT V6, ThermoFisher Scientific). The total number of white blood cells was determined from total cell counts from BALF samples using trypan blue exclusion. The total number of macrophages (CD45 + F4/80 +) and neutrophils (CD45 + Gr1 +) in the BALF was quantified by determining the percentage of cells staining positive for each cell type in the flow cytometry assay, multiplied by the total number of white blood cells counted in each BALF sample. Outliers were present in the collection process which resulted in a lower sample number for some analytics. Data includes the total number of white blood cells (CD45 +), the percent macrophages, the total number of macrophages, the percent neutrophils, and the total number of neutrophils for each group.

Flow Cytometry Analysis

Following brain tissue digestion, cells are stained for viability using Live/Dead™ Fixable Olive (Invitrogen, #L34979). Standard washing and rinsing steps were completed before initiating surface staining. Surface stains underwent comprehensive titration to achieve optimal dilution of each antibody, ensuring clear spectra on the Aurora Cytek cytometer (model Y0675). Antibodies used in this study were largely obtained from ThermoFisher (CD3, CD44, CD8, Cd11b, CD45, FOXP3, LY-6G, RORγ, CD4). Others were obtained from BioLegend (ICAM, MHC II, CD25, LFA-1, VCAM) and Miltenyi Biotech (ACSA-2). A comprehensive list of antibodies is shown in Fig. S3. Surface staining was performed over the course of 30 minutes in the dark, on ice, followed by overnight fixation. The following morning, cells were permeabilized for intracellular staining with FOXP3 and RORγ transcription factors (FOXP3 transcription factor staining kit, Thermo Fisher Scientific, #00552300). Antibodies were conjugated to fluorophores based on their compatibility with each other and the Aurora Cytek cytometer. Data was acquired with Spectroflo software where appropriate compensation and spectral unmixing were applied. Gating strategy (1SA, 2SA) demonstrates the conventional initial separation of cells, single cells, live cells, the separation between immune (CD45+) and non-immune cells (CD45-), and the further sub-gating of respective markers for various cell phenotypes and activation.

Untargeted LCMS Metabolomics

LCMS-grade reagents acetonitrile (ACN), methanol (MeOH), ammonium acetate, and acetic acid were acquired from Fisher Scientific. Ammonium hydroxide was purchased from Sigma-Aldrich. DI water was provided in-house by a Water Purification System from EMD Millipore. PBS was purchased from GE Healthcare Life Sciences. Standard compounds corresponding to the specific metabolites were purchased from Sigma-Aldrich and Fisher Scientific.

For tissue preparation of the pre-frontal cortex (PFC) isolated, each sample (~ 20 mg) was homogenized in 200 μL MeOH:PBS (4:1, v:v, containing 1,810.5 μM 13C3-lactate and 142 μM 13C5-glutamic Acid) using a Bullet Blender homogenizer. 800 μL MeOH:PBS (4:1, v:v, containing 1,810.5 μM 13C3-lactate and 142 μM 13C5-glutamic Acid) was added and vortexed for 10 seconds. The samples were stored at −20 °C for 30 min. Then, the samples were sonicated in an ice bath for 30 min. Samples were centrifuged at 14,000 RPM for 10 min (4 °C), and 800 μL supernatant was transferred to a new Eppendorf tube. Samples were then dried under vacuum using a CentriVap Concentrator. The residue from this process was reconstituted in 150 μL 40% PBS/60% ACN. A quality control (QC) sample was collected from all samples in the study.

The untargeted LC-MS metabolomics technique used in this study was modeled after well-established methods used in a growing number of studies[25, 26]. All LC-MS experiments were performed on a Thermo Vanquish UPLC-Exploris 240 Orbitrap MS instrument. Each sample was injected twice; 10 μL for negative ionization mode and 4 μL for positive ionization mode analysis. Both chromatographic separations were performed in hydrophilic interaction chromatography (HILIC) mode on a Waters XBridge BEH Amide column (150 × 2.1 mm, 2.5 μm particle size). The flow rate was 0.3 mL/min, auto-sampler temperature was kept at 4°C, and the column compartment was set at 40°C. The mobile phase was composed of Solvents A (10 mM ammonium acetate, 10 mM ammonium hydroxide in 95% H2O/5% ACN) and B (10 mM ammonium acetate, 10 mM ammonium hydroxide in 95% ACN/5% H2O). After 1 min of isocratic elution of 90% B, the percentage of Solvent B decreased to 40% at t = 11 min. The composition of Solvent B maintained at 40% for 4 min (t = 15 min), and the percentage of B gradually went back to 90%. Untargeted data was collected using a mass spectrometer equipped with an electrospray ionization (ESI) source. MS spectra peaks were identified by using in-house chemical standards (~ 600 aqueous metabolites) and comparing the resulting MS spectra against the HMDB library and the databases used including Lipidmap, METLIN, mzCloud, Metabolika, and ChemSpider. The absolute intensity threshold for the MS data extraction was 1,000, and the mass accuracy limit was set to 5 ppm. Thermo Compound Discoverer 3.3 software was used for aqueous metabolomics data processing. The untargeted data were processed by the software for peak picking, alignment, and normalization. Only peaks with CV < 20% across quality control (QC) pools, and the signals showing up in > 80% of all the samples were included for further analysis. MetaboAnalyst (metaboanalyst.ca/home.xhtml) was used to analyze metabolites and run statistical comparisons. Volcano plots of significant downregulated and upregulated metabolites were generated using R.

Statistical Analysis

Flow cytometry markers were gated on percent parent and plotted by FASC, FACO, WSSC, WSCO treatment and exposures. Using GraphPad (version 10.6.1), a 2-way ANOVA was performed on the data sets to compare exposure and diet differences, and a 3-way ANOVA was performed when also including time points. Radar plots were generated with Visme (www.visme.co). This was accomplished by calculating fold change difference from control (FASC) for markers with the same parent plot (CD45+).

Results

Exposure Characterization

Particulate matter concentration from woodsmoke exposures was measured during each exposure period and quantified using a DustTrak II device. Daily PM2.5 averages did not exceed 550 μg/m3 (Fig. 1B). Our exposure chamber layout is outline in Figure S1. Mice were only exposed for 4 hours per day on alternating days, which resulted in a 24-hour PM2.5 average of 46 μg/m3. Two major components of wildfire smoke are carbon monoxide (CO) and nitric oxide species (NOx), which were measured and found to be well below United States Environmental Protection Agency Standards, suggesting that PM2.5 was the major contaminant of concern in this model (Fig. 1C).

Pulmonary Response Characterization: BALF Cellular Differential and Cytokine Analysis

Mesoscale analysis of cytokines within the BAL fluid demonstrates a modest impact of woodsmoke on lung inflammation at the 1-day post exposure timepoint (Fig. 2A). IL-6 expression increased with woodsmoke exposure in mice receiving the CO diet. KC/GRO was also increased from the WS exposure, but with no clear effect of the CO diet. IL-10 concentrations were not influenced by the WS exposure but significantly reduced by the CO diet. Lung lavage cytokines IL-12p70, IFN-g, and TNF-α were unaltered by either WS exposure or CO diet. Cell count differential analysis of the lung lavage fluid (Fig. 2B) revealed no evidence of inflammatory cell infiltration at 1-day post exposure. Collectively, lung lavage results show the modest inflammatory changes in the lung microenvironment 1 day after woodsmoke inhalation, with elevated cytokines IL-6 and KC/GRO, but no evidence of airway cellular activation or infiltration. The CO diet decreased the inflammation-suppressing cytokine IL-10. Pulmonary responses at 14-d and 28-d were not conducted as we have previously found that the lung inflammation is resolved by 7 days after this exposure model [18].

Figure 2.

Figure 2

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Brochoalveolar lavage fluid (BALF) cell counts and differentials with cytokine analysis 1 day post exposure. A: Cytokine analysis of BALF collected from 1 day post-exposure animals. Data shown for both filtered air (FA) and wood smoke (WS) exposed mice on standard chow (SC) or coconut oil-supplemented (CO) chow. B: Cell count differential within the BALF collected from each animal. Data analyzed by 2-way ANOVA with Tukey’s posthoc testing. Asterisks indicate significant difference between groups as indicated (*p<0.05; **p<0.01; ***p<0.001; ****p<0.0001).

Brain Endothelial Cell Phenotyping Following Woodsmoke Exposure and Effects of CO Diet

Endothelial cells are involved in immune cell trafficking, signaling and adherence, and exert neuroimmune activity [27]. CD31, which represents endothelial cells, is an important marker for cellular response to injury or inflammation. CD31 expression demonstrated increases in response to woodsmoke over all 3 time points, and with a sustained effect 28 days after exposure (Fig. 3A). Notably, the expression was significantly higher with the combination of WS and CO at 14 days post exposure, but by 28 days post exposure the CO diet condition mitigated the increase. Vascular cell adhesion molecule (VCAM-1) and intercellular adhesion molecule (ICAM-1) on CD31-expressing cells followed the same expression pattern at the 1-day post timepoint (Fig. 3B, C). At the 14-day time point, VCAM-1 demonstrated continued high expression in response to the combination of woodsmoke and diet and then decreased at the 28-day time point, where ICAM-1 remained elevated. ICAM-1 was not significantly different at 14 days post exposure but did exhibit modest increases in WS-exposed mice compared to FA control mice at 28 days post exposure. Figure 3D&E represent the median fluorescent intensity (MFI) of both ICAM-1 and VCAM-1 on brain endothelial cells. VCAM-1 MFI fluctuates in a similar pattern to the % parent population (CD31) shown in Fig. 3B with major differences subsiding 28 days post exposure. ICAM-1 MFI did not demonstrate changes between groups as this marker is expressed at a basal level on all cell types and is less sensitive to changes in fluorescence.

Figure 3.

Figure 3

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Brain Endothelial Cell Activation. A: CD31 expression at 1, 14, and 28-day time points post-exposure. CD31 was gated off of CD45− cells based on Live Dead staining (Fig. S2). Dot plots demonstrate the increase in expression with woodsmoke exposure. B: VCAM-1 expression 1, 14 and 28 days post exposure. VCAM-1 was gated off of CD31+ cells (Fig. S5). C: ICAM-1 expression 1, 14 and 28 days post exposure. ICAM-1 was gated off of CD31+ cells (Fig. S5). D&E: Median fluorescence intensity of VCAM-1 and ICAM-1 (CD31+ parent) to represent the intensity of each marker on brain endothelial cells.

Resident Brain Glial Cell Expression Over Time

ACSA-2 is an astrocyte-specific surface marker in the brain [28, 29]. Percentage of ACSA-2 + exhibited a delayed, modest increase following WS exposure, peaking at 14 days after exposure and remaining modestly but significantly elevated 28 days post exposure (Fig. 4A). The interaction between WS and diet type was not observed for this cell type. Microglia were carefully selected and gated away from macrophages and other immune cell types from the CD45hi vs CD45 med population on CD11b+ cells (Fig. 4B, Fig. S2) while also consulting the unstained samples. Percentages of microglia cells were stable across time and experimental conditions, as anticipated. Diet did not have a significant impact on either of these resident glial cell populations. No markers of microglial activation were assessed, as previous studies provided a detailed timeline [17, 18].

Figure 4.

Figure 4

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Resident Brain Glial Cells: Astrocytes and Microglia. A: ACSA-2, a marker for astrocyte expression over time that detects presence over activation B: Microglia expression over time using CD45 and Cd11b markers to gate out macrophages and other non-microglial lymphocytes. Data analyzed by 2-way ANOVA with Tukey’s posthoc testing. Asterisks indicate significant difference between groups as indicated (*p<0.05; **p<0.01; ***p<0.001; ****p<0.0001).

CD45+ Peripheral Immune Cell Populations

Several immune populations were chosen for analysis based on their ability to mitigate neuroinflammation and migrate to sites of damage. We chose CD4 T cells, macrophage and LFA-1 expression to demonstrate the reliance on auxiliary markers to facilitate immune trafficking and infiltration into the brain. Figure 5A–C demonstrates the expression of each of these markers in response to wildfire smoke and a saturated fat diet within the brain. The representative dot plots show the differences of these markers between exposure groups. CD4 + T cells infiltrated most robustly on the first day after exposure and remained significantly elevated in the brain by day 28 (Fig. 5A). Macrophage populations also increased 1 day post exposure and remained elevated at the 14-day time point with diet playing a role in the FACO group. At the 28-day post exposure timepoint, macrophages remained elevated with woodsmoke exposure (Fig. 5B). LFA-1 expression on lymphocytes also peaked on day 1 post exposure, with highest LFA-1 + in the WSCO group. LFA-1 gradually resolved on days 14 and 28 post-exposure (Fig. 5C). Summary data demonstrates a broad overview (Fig. 5D) of the expression of these markers as well as VCAM-1 and ICAM-1 expression over time. Patterns reveal that macrophage presence in WS-exposed mice was much more conserved than other peripheral cell and activation markers. LFA-1 expression resolved faster and was only apparently elevated by WS exposure on the 1-day post timepoint. Three-way ANOVA results are reported for each marker and time point, highlighting interaction points between exposure and diet type. Macrophages, CD4 T cells, and ICAM expression remained elevated 28 days post exposure. Three-way ANOVA results report woodsmoke effects alone, woodsmoke and diet interaction, and diet effects alone (detailed in Fig. S6). Overall, we are able to present the differences in expression of neuroinflammatory markers over time and their interactions with woodsmoke exposure and diet type.

Figure 5.

Figure 5

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CD45+ Peripheral immune cells and trafficking marker expression over time. A: Gating strategy for CD45+ immune cells from Live Dead staining (Fig S2). A-C: Percentages of CD4 T cell, macrophages and LFA-1 expression over 1, 14 and 28 day time points post exposure. Dot plots represent woodsmoke vs filtered air exposed animals 1 day post exposure, where the largest separation of immune cell subsets are expressed. Data analyzed by 2-way ANOVA with Tukey’s posthoc testing. Asterisks indicate significant difference between groups as indicated (*p<0.05; **p<0.01; ***p<0.001; ****p<0.0001). D: Summary expression of peripheral markers over 3 separate time points following woodsmoke exposure. 1 day: Elevated expression of trafficking markers; ICAM, VCAM, LFA-1, followed by CD4 T cell presence and macrophage presence. 14 Days: Expression of all included markers decreased by 50–85% with VCAM and CD4 T cell expression lingering the most 28 days: All markers remain in a deficit with the exception of ICAM, VCAM (CO). ICAM expression increases slightly in all exposure and treatment groups, still remaining lowered compared to 1 day post exposure.

CD3 T cell Phenotypes and CD4 T cell Activation and Differentiation

Based on the unexpected, prominent expression of CD4 T cells in the brain following woodsmoke exposure, CD4 T cells underwent further phenotyping by both activation markers and transcription factors. This population was used as the ‘parent population’ for the subsequent plots and further gating techniques (Fig. S2). CD44 was used as a marker for general T cell activation and demonstrated elevated levels in response to exposure and representative plots are shown from both exposure and CO diet group (Fig. 6A). We revealed a presence of CD44 + LFA-1 + effector T cells that is larger than the other, more inflammatory CD4 T cell populations with WSSC, which remained elevated in WSCO group (Fig. 6B). FOXP3, a transcription factor for T regulatory T cells, was elevated with the combination of woodsmoke exposure and a saturated fat diet (Fig. 6C). Other populations of CD4 phenotypes include TH17+ cells, represented by the ROR γ transcription factor, and are largely inflammatory in nature and do not have a significant presence in this exposure paradigm (Fig. 6D). Figure 6E provides a summary of these findings over time through representation of fold change to control (FASC).

Figure 6.

Figure 6

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CD3 T cell Phenotypes and CD4 T cell Differentiation and Activation Markers Over Time. A: CD44 T cell expression 1, 14, and 28-days post exposure with dot plots to show increased expression with both smoke exposure and a CO diet. CD44 expression was gated on a CD4 parent to represent general CD4 T cell activation. B: T Effector T cells 1, 14, and 28-days post exposure. Dot plots are based on a CD4 T cell parent population further separated by CD44 and LFA-1 expression to reveal the T Effector phenotype that closely resembles overall CD4 T cell activation, as seen in Fig 6A. C: T Regulatory T cells (T regs) 1, 14, and 28-days post exposure. Tregs are identified by FOXP3xCD4 expression. Dot plots demonstrate increased expression of the transcription factor FOXP3 in response to woodsmoke. D: TH-17 T cell expression 1, 14, and 28-days post exposure. TH-17 T cells are represented through ROR xCD4 expression. Dot plots demonstrate little to no increased expression of the transcription factor ROR in response to woodsmoke. E: Fold change of T cell phenotypes compared to control (filtered air, standard chow). Black circles around data points indicate a significance difference (p >0.05) compared to control. Data analyzed by 2-way ANOVA with Tukey’s posthoc testing. Asterisks indicate significant difference between groups as indicated (*p<0.05; **p<0.01; ***p<0.001; ****p<0.0001).

Metabolic Changes in Prefrontal Cortex from Coconut Oil Diet and Woodsmoke Exposure

Untargeted metabolomics were performed on the prefrontal cortex (PFC) to assess the metabolic changes from woodsmoke exposure and CO diet. The PFC was chosen due to its role in higher cognition and our previous findings of woodsmoke-induced metabolomic alterations in this region [17]. The number of significant metabolites identified between the standard chow (SC) and CO diets and between the filtered air (FA) and WS exposures from T tests (FDR < 0.1) were limited. However, the general pattern of upregulated and downregulated metabolites in the PFC from the CO diet (Fig. 7A) and from WS exposure (Fig. 7B) were observed. Due to the limited upregulated metabolites found at each timepoint from diet and from exposure, upregulated metabolites (fold change > 1.5) from each timepoint were pooled together for KEGG pathway analysis on the metabolites. Phenylalanine, tyrosine, and tryptophan biosynthesis and tyrosine metabolism were significantly upregulated in the PFCs of CO-fed mice compared to SC-fed mice (Fig. 7C). On the other hand, taurine and hypotaurine metabolism and glycerolipid metabolism were significantly upregulated in the PFCs of WS-exposed mice compared to FA-exposed mice (Fig. 7D). The number of significantly downregulated metabolites with KEGG IDs was too low in both CO diet and WS exposure; therefore, downregulated pathway analyses from CO diet and WS exposure were not possible and not performed. To assess specific downregulated and upregulated metabolites from CO diet and from WS exposure, volcano plots for each timepoint (1-day, 14-days, and 28-days post-exposure) were overlapped for CO diet (Fig. 7E) and WS exposure (Fig. 7F). At 1-day post-exposure, S-(5-deoxy-beta-D-ribos-5-yl)-L-homocysteine was upregulated in the PFCs of CO-fed mice (Fig. 7E). At 14-days post-exposure, thiamine, or vitamin B1, was downregulated in the PFCs of CO-fed mice while at 28-days post-exposure, 1-methylnicotinamide, a metabolite of nicotinamide (a form of vitamin B3 or niacin), was upregulated. In WS-exposed mice, 3-tert-butyladipic acid and L-valine were downregulated at 1-day and 28-days post-exposure, respectively (Fig. 7F). At 28-days post-exposure, (2R,3S)-2-methylcitric acid was upregulated in WS-exposed mice.

Figure 7.

Figure 7

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Metabolomic changes in the prefrontal cortex (PFC). A: Heatmap of top 50 metabolites that were upregulated and downregulated in the PFC from the coconut oil diet. B: Heatmap of top 50 metabolites that were upregulated and downregulated in the PFC from the woodsmoke exposure. C: Pathway analysis scatterplot of significantly upregulated metabolites in the PFC of CO-fed mice. D: Pathway analysis scatterplot of significantly upregulated metabolites in the PFC of WS-exposed mice. E: Overlaying volcano plot of metabolites that were significantly upregulated and downregulated in the PFC of CO-fed mice compared to the SC-fed mice 1-day post-exposure (black squares), 14-days post-exposure (blue triangles), and 28-days post-exposure (red diamonds) with the top 2 metabolites per comparison group labelled. F: Overlaying volcano plot of metabolites that were significantly upregulated and downregulated in the PFC of WS-exposed mice compared to the FA-exposed mice 1-day post-exposure (black squares), 14-days post-exposure (blue triangles), and 28-days post-exposure (red diamonds) with the top 2 metabolites per comparison group labelled. Significant metabolites in the volcano plots were defined based on the FC threshold = 1.5 and p = 0.05.

Discussion

These results confirm findings of central nervous system (CNS) infiltration of peripheral immune cells and newly document the complex dynamics of T cell subpopulation recruitment following woodsmoke inhalation. Of note, a simple modification to the diet – supplementation with elevated saturated fat content – exacerbated the influx and activation of peripheral immune markers even at 28 days. Dietary influences on the neurological effects of air pollution exposure, which may contribute to both short-term mental health and long-term neurodegenerative disease, may have important implications for public health. Although air pollution has been demonstrated to impact neurological health on a national scale [6], modifying factors that augment or mitigate such effects for public health are not well understood.

Neuroinflammation caused by inhaled environmental toxins appears largely driven by the production of secondary factors, such as cytokines and fragmented peptides, that are shed from the lung. These circulating factors, which can act as damage-associated molecular patterns, lead to the activation of neurovascular endothelial receptors and innate immune populations, causing a cascade of neuroinflammatory responses [30–32]. Formation of pulmonary matrix metalloproteinase-derived peptide fragments as a mechanistic underpinning for this inhaled toxicant “spillover” effect was specifically detailed for multi-walled carbon nanotubes; however, it is likely these mechanisms apply to a broader array of air pollutants, including ozone and wood smoke. It has specifically been shown that systemic vascular effects of inhaled pollutants can be mediated by endothelial receptors, such as CD36 [31, 33, 34] and LOX-1 [35, 36] which are responsive to oxidized lipids.

WS exposure elicited a modest impact on lung inflammatory cytokines, with little further modulation by diet type. IL-6, which is a cytokine typically elevated in association with asthma and acute allergy, was elevated with exposure and the saturated fat diet compounded this increase in expression [37]. KC/GRO, also known as CXCL1, had a similar trend of expression. CXCL1 is known for facilitating trafficking of neutrophils into the lung in response to injury. The anti-inflammatory cytokine IL-10, interestingly, was unaffected by WS exposure but exhibited a significant decrease due to the saturated fat diet. IL-10 dampens the immune response and therefore lower expression may contribute to the inflammatory response in this context [38]. This may indicate the ability of a diet with excess saturated fats to prime the lung microenvironment towards a pro-inflammatory state. Previous studies have shown that a saturated fat diet exacerbates COPD symptoms and lowers lung function, demonstrating that unhealthy fats can impact the lung microenvironment and begin the inflammatory cascade to the brain [39]. Alterations of lipids in the diet can also modify the lipid content in the lung surfactant, which in turn alters the initial chemical reactions to and downstream inflammatory effects of ozone [14], which we postulate may play a similar role in mediating systemic outcomes of WS exposure.

The persistent neuroimmune response to WS and related inhaled toxicants has not been studied thoroughly. Establishing a clear timeline of neuroinflammation is essential before diving deeper into mechanisms, cellular phenotypes, and potential interventions. Resident immune cells within the brain were examined first in order to establish continuity with previous studies showing differences in astrocyte expression [17]. Astrocyte marker (ACSA-2) demonstrated sensitivity to woodsmoke exposure, indicating an inflammatory response [29] which implies astrogliosis, although essential markers to show gliosis were not analyzed (GFAP, GLAST). Astrogliosis is a common response to interactions with inflammatory cells and previous work on other inhaled toxicants suggest that this effect is downstream of neurovascular insult from lung-derived factors [30–32]. Astrocytes typically interact with microglia to coordinate responses to defend against such vascular barrier deficits, but they may also communicate with CD4 effector T cells and promote their reactive (TH17) and productive (TH1/TH2) phenotypes [40]. This falls in line with the elevated CD4 effector T cell population that we see in the brain (Fig. 5C). Cell markers CD45 and CD11b are not sole indicators of microglial activation, as more specific activation and transcription factors (TMEM119, Iba-1) need to be included to determine this outcome. Therefore, based on the markers used in this study, we did not expect a change in expression as microglia change shape and phenotype, and not typically number, in response to inflammation [41].

The three time points for the peripheral immune cells were chosen to define the natural progression of innate and adaptive immunity activation. The innate immune system activates within hours after injury or infection, whereas the adaptive immune system can be found in its most activated state 9–14 days after initial insult [42]. Notably, endothelial cell activation was observed early with adhesion molecule expression elevated immediately after the 14-day exposure period, which was sustained but lowered out to 28-days post exposure. Activated T cells and other lymphocytes were also elevated 1 day following the 14 days of woodsmoke exposure. The 14-day post and 28-day post exposure timepoints reveal the dynamics of these immune cells and the influence of the saturated fat diet, which enhanced the numbers of regulatory T cells at 1- and 14-days post exposure. Overall, we were able to determine that the coconut oil diet prolonged the inflammatory effects of WS out to the 14-day time point in peripheral immune cells, endothelial cells and plausibly beyond.

Saturated fats are inherently difficult to break down and process by the body [39]. The saturated fat diet imbued a moderately higher neuroinflammatory response, but there are many factors at play that lead to a saturated fat diet causing basal level inflammation and dysregulation. The brain is particularly susceptible to these types of changes especially when considering fatty acids within this lipid heavy organ. In the filtered air group, the coconut oil-enriched diet upregulated phenylalanine, tyrosine, and tryptophan biosynthesis and tyrosine metabolism in the PFC (Figure S7). Meanwhile, the woodsmoke exposure upregulated taurine and hypotaurine metabolism and glycerolipid metabolism in the PFC. These metabolic changes coincide with a greater susceptibility to inflammatory phenotypes. The impacts we see on the brain – at the intersection of WS and saturated fats – may also be due to indirect pathways stemming from the initial insult in the lung, as described above. We postulate that this inflammatory cascade reaches the brain through ligand-receptor interactions at the endothelial level, particularly at the BBB. This step then initiates resident glial cells of the brain to launch a response to recruit additional help to shore up this critical defense [16]. This additional help includes the activation of other brain cells, but in more unique cases, the recruitment of peripheral immune cells into the brain.

The recruitment of T cells into the brain is typically tied to diseases involved in autoimmune dysfunction or neurodegenerative conditions, such as multiple sclerosis, Parkinson’s, Alzheimer’s, traumatic brain injury, or stroke [11]. Following WS exposure, CD44 + and CD4 T effector/helper cells. Our gating strategy demonstrates the ability to exclude the more inflammatory TH17 and T regulatory (FOXP3) T cells out from the CD3 + population to leave only a few populations left, which are the T effector t cells. This population is the one largely involved in the resolution of neuroinflammation, as they are T helper or T effector CD4 T cells [12]. Their main goal is to aid in the resolution of inflammation to return to homeostasis. More of these T cell types are hypothesized to be associated with the reduction of inflammatory cytokine expression and resident glial activation [42, 43].

Interestingly, the coconut oil diet elevated levels of T regulatory CD4 T cells in the woodsmoke exposed mice. T regs keep the immune response in check, and in the brain, can dampen astrogliosis [44]. In some cases, T regs are elevated as a protective mechanism to mediate the inflammatory response, which opens up the possibility of coconut oil to promote a healthy immune response [45]. The coconut oil diet used here is made up of many fatty acids, including monounsaturated, polyunsaturated and saturated fats. T regs typically favor polyunsaturated fatty acids for the metabolism of phospholipids, and the response to saturated fats is more complex [46]. In many cases, the coconut oil diet increased markers and cells associated with inflammation, but the underlying mechanism may lean more towards generalized imbalance and an elevated immune response, including T regs.

The WS exposure model is based off our experience with a real-world wildland fire, which contributed to intermittent and varying PM2.5 concentrations from day to day based on meteorologic patterns [18]. Those exposures were from fires in California, approximately 1,000 km away. But communities are going to be exposed to a myriad variety of conditions depending on how close they live to the source and prevailing weather patterns, with those living closer being exposed to far higher concentrations [47]. The concentration we chose to use is much lower than an actual wildfire, which range from < 0.5 mg/m3 for those not in direct proximity to the fire, to 1.2mg/m3 for front-line firefighters [48]. Many studies touch on the higher end of this concentration with more pronounced effects[10].

Despite the strengths of this study, there are some limitations present to consider. Due to the low number of significantly downregulated metabolites with assigned KEGG IDs from the coconut oil diet and the woodsmoke exposure, we were unable to perform pathway analyses on the downregulated metabolites. However, multiple metabolites without KEGG IDs were found to be downregulated from the diet and exposure (Fig. 6E–F, Fig. S8, S10, S11). Additionally, T effector cell gating is based off ruling out the other CD4 T cell phenotypes. We did not include T effector cell cytokines in the panel to prioritize extracellular markers.

Conclusions

Results demonstrate a time- and diet-dependent infiltration of CD4 T cells (CD3+, RORg−, FOXP3−, CD44+) as a previously unappreciated component of neuroinflammation following WS inhalation. We also see a pattern of inflammation across several immune markers, including LFA-1, ICAM and VCAM that peak at 1 day after the last exposure (14 days after the initial insult) increased with both exposure and a saturated fat diet. CD4 T cells that are not regulatory (FOXP3−) or inflammatory Th17 cells (RORγ−), leave a T effector phenotype, that seem to be the primary T cell population present in the brain in response to woodsmoke. This phenotype is a common T helper cell and is likely aiding in the resolution of neuroinflammation through communications with resident glial cells.

Coconut also increased T regulatory T cells, which may play a significant role in mitigating diet-induced inflammation and their presence in the brain represent a compelling avenue for further study.

Understanding how diet can influence this process is essential for developing strategies to reduce the public health burden of air pollution-related diseases. Moreover, peripheral immune cells play an important role in the brain’s inflammatory response and are a key component to neural health in the context of exposure to wildfire smoke.

Supplementary Material

Supplementary Files

This is a list of supplementary files associated with this preprint. Click to download.

Acknowledgments

The authors acknowledge UNM shared facilities at the UNM cancer center, UNM HSC department of neurology, and UNM college of pharmacy.

Funding

This work was supported by the National Institutes of Health (R01 ES026673 to SN and MJC)

This research was partially supported by UNM Comprehensive Cancer Center Support Grant NCI P30CA118100 and by the Flow Cytometry Shared Resource.

Funding Statement

This work was supported by the National Institutes of Health (R01 ES026673 to SN and MJC)

This research was partially supported by UNM Comprehensive Cancer Center Support Grant NCI P30CA118100 and by the Flow Cytometry Shared Resource.

Footnotes

Declaration of competing interest

The authors declare no competing interests.

Contributor Information

Brenna Baird, University of New Mexico.

Justin Carter, University of New Mexico.

Ember Suh, University of New Mexico.

Yan Jin, Arizona State University.

Russell Hunter, West Virginia University.

Jorge Moreno, University of New Mexico.

Milad MazloumiBakhshayesh, University of New Mexico.

Alicia Bolt, University of New Mexico.

Marian Olewine, University of California, Los Angeles.

Edward Barr, University of New Mexico.

Jessica Begay, University of New Mexico.

Selita Lucas, University of New Mexico.

Ximeng Liu, Arizona State University.

Lingjun Li, Arizona State University.

Shiquan Cui, Arizona State University.

Haiwei Gu, Arizona State University.

Matthew Campen, University of New Mexico.

Shahani Noor, University of New Mexico.

Availability of Data and Materials

Additional datasets analyzed during the current study are available from the corresponding author on reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Additional datasets analyzed during the current study are available from the corresponding author on reasonable request.

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