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. Author manuscript; available in PMC: 2026 Oct 7.
Published before final editing as: Brain Behav Immun. 2026 Aug 21;139:106969. doi: 10.1016/j.bbi.2026.106969

The MR1/MAIT Cell Axis Has Limited Effects on the Start of Tau Pathology in a Mouse Model of Tauopathy

Season K Wyatt-Johnson 1,2,*, Sujal Joshi 1, Maggie Blair 1,2, Jay M Desai 1, SM Mansour Haeryfar 3,4,5, Cristian A Lasagna-Reeves 2,6,7, Randy R Brutkiewicz 1,2,*
PMCID: PMC13639847  NIHMSID: NIHMS2207472  PMID: 42628701

Abstract

Immunity impacts all aspects of Alzheimer’s disease (AD) pathology. Previously, we found improved pathology and cognition in the amyloid-focused 5XFAD mouse model deficient in the innate immune cell axis consisting of the innate-like mucosal-associated invariant T (MAIT) cells and the MHC class I-like antigen-presenting molecule they recognize, MR1. However, little is known about how a lack of the MR1/MAIT cell axis impacts cognition and whether an increased abundance of MAIT cells impacts pathology and cognition in the tau-focused PS19 mouse model. We crossed PS19 mice onto an MR1 KO background, resulting in a lack of both MR1 and MAIT cells, and alternatively, onto MAITCAST mice, that have ~7x more MAIT cells. Using immunofluorescent microscopy, flow cytometry, and cognitive tests including Barnes maze and Novel Object Recognition, we analyzed the impact of MAIT cell numbers on tau pathology. Although an elevation in MAIT cell frequency was found that expressed higher levels of CD69 in PS19 mice on the MAITCASTvs. wildtype background, overall MAIT cell numbers in the brain did not differ between these groups. Interestingly, increased MR1 expression was detected on microglia only in PS19 mice, whereas MR1 was higher in PS19 and PS19/MAITCAST mouse astrocytes. Increased MAIT cell numbers did not alter tau accumulation or cognitive performance, whereas the lack of the MR1/MAIT cell axis did not affect tau burden, but was associated with reduced neuronal density and impaired recognition memory. Together, these findings suggest that the MR1/MAIT cell axis does not substantially influence the early development of tau pathology.

Keywords: tau, innate immunity, Alzheimer’s disease, astrocytes, microglia

Introduction

For decades, the study of Alzheimer’s disease (AD) has been dominated by the amyloid hypothesis, resulting in a greater emphasis on amyloid-beta (Aβ) plaque pathology compared with tau-mediated neurodegeneration (Gulisano et al., 2019; Riku et al., 2025). More recently, the tau protein has been reignited as a focus for treatment, as tau buildup tends to be located in the most severe areas of pathology in AD patient brains (Bolsewig et al., 2024; Riku et al., 2025; Teunissen et al., 2025). Increasing evidence suggests that immune mechanisms play a central role in disease pathogenesis, extending beyond resident glial cells to include peripheral immune populations that influence the central nervous system (CNS) environment (Chen et al., 2023; Costa, 2024; Johnson & Lukens, 2023). Among these, innate-like T cells have emerged as possible targets, as they are capable of impacting neurodegeneration pathology (Bosch & Ulrich, 2026; Chen et al., 2023; Wyatt-Johnson, Ackley, et al., 2025; Wyatt-Johnson, Afify, et al., 2024; Wyatt-Johnson, Desai, et al., 2025; Wyatt-Johnson et al., 2023).

In a variety of tauopathies, T cells have been found to play an important role in brain pathology alongside microglia, particularly in late stages of the disease (Chen et al., 2023; Costa, 2024). Although these T cells have mainly been defined to be CD8+ T cells, many of these subsets express key markers including GZMA, GZMB, CD7, IL-17, KLRE1, IL-7R that also found on a type of innate-like T cell population, mucosal-associated invariant T (MAIT) cells (Chen et al., 2023; Costa, 2024; Godfrey et al., 2019; Hinks & Zhang, 2020). Innate-like T cells comprise specialized lymphocyte populations that exhibit features of both innate and adaptive immunity, including rapid effector responses and restricted antigen recognition. The three major types include: MAIT cells, natural killer T (NKT) cells, and γδ T cells (Afify et al., 2024). Recent studies in neurodegenerative diseases implicate MAIT cells and γδ T cells in having a major impact on pathology (Brigas et al., 2021; Fiszer et al., 1994; Wyatt-Johnson, Ackley, et al., 2025; Wyatt-Johnson et al., 2023). MAIT cells recognize microbial vitamin B metabolites presented by the MHC class I-like molecule MR1 and are capable of producing pro-inflammatory cytokines such as IFN-γ and IL-17 (Shrinivasan et al., 2024; Wyatt-Johnson, Afify, et al., 2024). γδ T cells, defined by their distinct T cell receptor chains, respond to stress-induced ligands and are another potent source of IL-17 in CNS disorders (Wyatt-Johnson, Afify, et al., 2024).

In amyloid-focused mouse models of AD, γδ T cells have also been observed to accumulate in the meninges and brain during neurodegenerative processes, where their production of IL-17 has been linked to synaptic dysfunction and cognitive impairment (Brigas et al., 2021). In our previous work, we found that MAIT cells were increased in the brain and liver in the amyloid-focused 5XFAD mouse model of AD (Wyatt-Johnson, Kersey, et al., 2024; Wyatt-Johnson et al., 2023). Moreover, we observed an increase in MR1 expression in microglia near amyloid plaques in brain sections from both the 5XFAD mouse model and human AD patients (Wyatt-Johnson et al., 2023). Furthermore, we found that MR1 KO 5XFAD mice had reduced plaque formation over time and better cognitive performance than MR1+ 5XFAD mice (Wyatt-Johnson, Ackley, et al., 2025; Wyatt-Johnson, Desai, et al., 2025; Wyatt-Johnson et al., 2023). Despite growing interest in studying T cells in the CNS, the contribution of innate-like T cells in tau-mediated neurodegeneration remains poorly understood. The PS19 transgenic mouse model, which expresses a human gene encoding a mutant tau protein (P301S), recapitulates key features of tauopathy, including neurofibrillary tangle formation, gliosis, and progressive neuronal loss (Martinez et al., 2025; Yoshiyama et al., 2007). While this model has provided significant insight into glial responses, the role of the MR1/MAIT cell axis in shaping tau-associated pathology and cognition has not been fully explored.

In this study, we examined how the abundance of MAIT cells might influence disease start in the tau-focused PS19 mouse model. To do so, we crossed PS19 mice with MR1 knockout mice and MAITCAST mice, the latter derived from the CAST/EiJ strain and bred onto a B6 background for over 10 generations and selectively enriched for increased MAIT cell numbers (Cui et al., 2015). By characterizing the PS19 model under three distinct MAIT cell conditions (i.e., normal abundance, higher abundance and none at all), we aimed to better define the role of MAIT cells in modulating early tau pathology and cognitive outcomes. Identifying contributions of MAIT cells in tauopathies would have the potential to reveal potential targets for therapeutic intervention.

1. Materials and Methods

1.1. Animals and ethics statement

The PS19 (Jax Strain #: 008169) mouse model was bred onto the C57BL/6 (B6) background and used throughout the study. MR1-deficient (MR1 KO) mice, also on the B6 background (Priya & Brutkiewicz, 2020), were kindly provided by Dr. Daniel Hoft (St. Louis University, St. Louis, MO). MAITCAST mice were generated by crossing CAST/EiJ mice with B6 mice over multiple generations. They have more MAIT cells because they possess a specific genetic locus from the CAST/EiJ strain on chromosome 14, which promotes the increased generation of Vα19-Jα33 TCRα chain rearrangements, and are crucial for MAIT cell development (Cui et al., 2015). There were no differences observed between males and females from the different genotypes; therefore, males and females were combined equally. All mice were euthanized at 7 months of age, after cognitive tests, due to the high mortality after that age. All mice were bred and maintained at the Indiana University School of Medicine Laboratory Animal Resource Center under standard conditions and housed in groups of 3–5 per cage. All experimental procedures were approved by the Institutional Animal Care and Use Committee of the Indiana University School of Medicine. For cognitive assessments, mice were transferred to individual training cages and moved to a nearby testing room. Animals were allowed to acclimate in the dark for 30 minutes prior to the start of behavioral testing. Cognitive testing alternated either starting with recognition memory tests or spatial memory tests first between cohorts.

2.2. Flow cytometry

Mice were anesthetized by CO2 asphyxiation and perfused with ice cold 1X PBS. Brains, livers, and spleens were removed. Brains and livers were passed through a 70-μm nylon cell strainer and centrifuged. Cells were washed in 1X PBS, placed upon a 40% Percoll gradient and then centrifuged at 2000 rpm for 20 minutes at room temperature (RT). Spleens were homogenized and centrifuged. All cells from brain, liver, and spleen followed the rest of the protocol. The cells were incubated in mouse red blood cell lysis buffer for 5 minutes, then centrifuged at 1200 rpm for 5 minutes. The cells were resuspended in buffer containing the Fcγ receptor-specific blocker, anti-CD16/32 mAb, 2.4G2 (the hybridoma was a kind gift from Dr. J. Yewdell, NIH), then the primary antibodies: anti-TCR-β (PE-labeled; Clone H57–597, 553172, BD Pharmingen), anti-CD44 (Pacific Blue-labeled, Clone IM7, 103020, Biolegend), 5-OP-RU-loaded MR1 Tetramer (APC-conjugated, NIH Tetramer Core), anti-CD69 (PerCP-Cy5.5-labeled, Clone H1.2F3, 561931, BD Pharmingen), anti-CD4 (PE-labeled, Clone GK1.5, 557308, BD Pharmingen), anti-CD8 (Fitc-labeled, Clone 53-6.7, 553031, BD Pharmingen), anti-B220 (PerCP-Cy5.5-labeled; Clone: RA3-6B2, 552771, BD Pharmingen), and MR1 (APC-labeled; Clone: 26.5, 361108, Biolegend) were added depending upon the experiment and kept on ice for 30 minutes. Next, the cells were washed and fixed in 1% paraformaldehyde (PFA), followed by washes in 1X PBS and FACS buffer (1% BSA, 0.1% Sodium Azide, and 1XPBS). All data were acquired using a BD LSRFortessa™ cell analyzer (BD Bioscience, Franklin Lakes, NJ) and analyzed using FlowJo v10 software (Becton Dickinson, San Jose, CA). Cells stained with an isotype control mAb and 6-FP-loaded MR1 tetramers served as our negative controls.

2.3. Immunofluorescence (IF)

Mice were euthanized and the brains were harvested, post-fixed in 4% PFA, cryoprotected in 30% sucrose, and subsequently frozen and stored at −80°C. Immunofluorescence (IF) staining was performed on free-floating sections as previously described (Wyatt-Johnson et al., 2023). All washes in 1X PBS+0.1% Triton (0.1% PBST; X-100, Sigma) were performed at RT. Sections were quickly washed in 0.1% PBST followed by a 30 minute incubation in 3% PBST. Tissues were then incubated in mouse Fc Blocker (5553242, BD Pharmingen, Plainfield, IN) for 1-2 hours before incubation in goat immuno buffer [5% goat serum (0060-01, SouthernBiotech, Birmingham, AL), 0.3% BSA, .3% Sodium Azide, 1XPBS+0.3%Triton] for 24 hours. Primary antibodies: mouse anti-phospho-Tau (monoclonal, 1:50; AT8; MN1020; ThermoFisher Scientific), rabbit anti-phospho-Tau (polyclonal, 1:100; S214; ab170892; Abcam, Waltham, MA), mouse anti-MR1 (monoclonal, 1:50, 361102, Biolegend, San Diego, CA), rabbit anti-IBA1 (polyclonal, 1:50; 019-19741, Wako Chemicals, Richmond, VA), and rabbit anti-S100β (polyclonal, 1:100; ab52642; Abcam) were added as above and incubated for 2 days on a rocking platform at 4°C. Following primary incubation, the sections were washed in 0.1% PBST and then incubated with the following secondary antibodies as appropriate: goat anti-mouse IgG1 Alexa fluor 488, goat anti-mouse IgG2a Alexa fluor 488/647, goat anti-rabbit Alexa fluor 568 (1:5000; A21121; A21131/A21241, and A11011, respectively, ThermoFisher Scientific) and DAPI (62248, ThermoFisher Scientific) for 2 hours. Tissue sections were washed again in 0.1% PBST and then mounted and sealed on glass slides using Vectashield Antifade (H-1000, Vector Labs) and CoverGrip Coverslip Sealant (23005, Biotium, Fremont, CA).

2.4. Immunohistochemistry (IHC)

IHC in free-floating tissue sections was performed as previously described (Wyatt-Johnson, Ackley, et al., 2025). Sections were washed in 0.1% PBST followed by 30 minutes in hydrogen peroxide and 20 minutes in 3% PBST at RT. Tissues were incubated with goat immuno buffer for 24 hours. Next, tissues were incubated in the primary antibody [rabbit anti-NeuN (1:100; PA5-78499, ThermoFisher Scientific)] for 24 hours on a rocking platform at 4°C. Next, sections were washed in 0.1% PBST and then incubated with the secondary antibody [goat anti-rabbit Ig (1:2000; BA-1000, ThermoFisher Scientific)] for 1 hour. Again, sections were washed in 0.1% PBST and then incubated in the ABC Peroxidase Kit (PK-4000, Vector Laboratories). The sections were then washed and stained with the DAB Peroxidase Substrate Kit (SK-4100, Vector Laboratories). The sections were mounted on gelatin-coated slides, dehydrated through ethanol washes (50%, 70%, 95%, and 100%), and then treated with xylene before being coverslipped with Permount mounting medium (SP15-100, ThermoFisher Scientific).

2.5. Microscopy and semi-quantitative IF/IHC densitometry analysis

IF signals were visualized using a Nikon Eclipse Ti inverted microscope equipped with a Plan Fluor 20×/1.30; 60x objective. Images were acquired and processed using NIS-Elements Software (version 4.5; Nikon Instruments). IHC signals were visualized using an Echo inverted microscope with a 10× objective. Quantitative analysis, including relative mean pixel intensity and integrated density, was performed using ImageJ Fiji (version 1.53; NIH) as previously described (Wyatt-Johnson, Ackley, et al., 2025; Wyatt-Johnson et al., 2023). Background intensity was measured for each image and subtracted from the corresponding signal.

2.6. Open Field (OF)

Mice were placed in an open-field arena (40 × 40 × 40 cm) and allowed to explore freely for 10 minutes under low-light conditions (Seibenhener & Wooten, 2015). The arena was cleaned with 70% ethanol between trials to eliminate olfactory cues. Behavior was recorded using an overhead camera and analyzed with ANY-maze Software (version 7.44; Wood Dale, IL). The parameters assessed included total distance traveled, average speed, and time spent in the inner versus outer zones of the arena. The open-field session also served as the habituation phase for subsequent novel object recognition (NOR) testing.

2.7. Novel Object Recognition (NOR)

Twenty-four hours after OF, the NOR testing began (Wyatt-Johnson et al., 2021). Mice were placed into the same box as OF with two identical objects and given 10 minutes to explore. Twenty-four hours later, mice were placed back into the box for 10 minutes with a novel object (NO); the other object remained the “familiar” object (FO). The box and objects were cleaned with 70% ethanol between mice. The test was video recorded from above and tracked with ANY-maze. Video recordings were analyzed by multiple investigators blinded to the experimental conditions, who quantified the time spent exploring each object. Measurements were averaged across investigators and AnyMaze results. The relative discrimination index (DI) was calculated as previously described (Akkerman et al., 2012). DI was calculated by [exploration time of NO – exploration time of familiar location (FO)] / [exploration time of NO + exploration time of FO].

2.8. Barnes Maze (BM)

Barnes maze (BM) training was performed on a apparatus consisted of a circular platform (91 cm diameter, 100 cm elevation) with multiple holes positioned around the perimeter, one of which led to an escape box. Distinct visual cues were placed on the surrounding walls to facilitate spatial navigation. Trials were recorded from above and analyzed using ANY-maze. The task was performed as previously described (Attar et al., 2013; Wyatt-Johnson, Desai, et al., 2025; Wyatt-Johnson et al., 2021).

2.8.1. BM: Habituation

On day 1, mice underwent habituation to the escape box. Mice were placed in the center of the platform and guided using a large clear beaker to the escape hole. Mice were given up to 2 minutes to enter the escape box; if unsuccessful, they were gently nudged into the opening. Once inside, mice remained in the escape box for 1 minute before being returned to their home cages. For training and testing the escape box location was rotated 180° from its original position. The platform was cleaned with 70% ethanol between individual mice and all trials to minimize olfactory cues.

2.8.2. BM: Training

Training consisted of five trials across two days: three trials on day 1 (T1–T3) and two trials on day 2 (T4–T5), with inter-trial intervals of 20–30 minutes. Each trial began with the mouse placed in the center of the platform beneath a solid black start box in darkness. After a short delay, lights were turned on and the start box was removed. Mice were given 2 minutes to locate and enter the escape box. If a mouse failed to find or enter the escape box within this period, it was gently guided to the correct hole and gently nudged to enter if the mouse still had not done so. Mice remained in the escape box for 1 minute before being returned to their home cages. Latency to reach the target location, time to enter the hole, number of incorrect holes investigated, and time spent in the target quadrant were recorded using ANY-maze.

2.8.2. BM: Probe Trial

Forty-eight hours after the final training trial (T5), a probe trial was conducted with the escape box removed. Mice were placed in the center of the platform beneath a solid black start box, which was removed after a short delay. Mice were allowed to explore the platform again for 2 minutes. For this trial, latency to the target location, number of incorrect holes investigated, and time spent in the target quadrant were recorded using ANY-maze. Heat maps were generated and averaged across groups using ANY-maze software.

2.9. Statistical Analysis

Graphpad Prism software v11.0 was used for statistical analyses. All samples were blinded to the researcher before the start of each analysis. A one-way ANOVA with Tukey’s multiple comparisons test was used to analyze the multiple comparisons for graphs containing only PS19 groups. A two-way ANOVA with Tukey’s multiple comparisons test was used to analyze the multiple comparisons with all five genotypes. Student’s t-test was used to measure the percentage of time spent exploring objects. A mixed effects ANOVA with Tukey’s multiple comparisons test was used to analyze the Barnes maze training across trials. All statistical tests were two-tailed with an alpha of 0.05. All data are shown as the mean ± standard error of the mean (SEM). All figures were made in GIMP v2.10.38.

2. RESULTS

3.1. PS19/MAITCAST Mice Exhibit Elevated Liver MAIT Cell Frequencies and Activation, but Not in the Brain

To determine MAIT cell abundance and activation status in PS19 mice, flow cytometry was performed on liver and brain tissues (Fig. 1A). MAIT cell activation status was assessed using the canonical T cell activation marker CD69. In the liver, the percentage of MAIT cells was significantly increased in MAITCAST genotypes [Fig. 1B; main effect: F(2,70) = 24.52, p < 0.0001], with the greatest overall expansion observed in PS19/MAITCAST background mice. There was also a significant difference between MAIT cells in the PS19 mice compared to non-PS19 mice [main effect: F(1,70) = 9.693, p = 0.0027], where the PS19 and PS19/MAITCAST mice had significantly more MAIT cells than their respective controls, WT and MAITCAST (PS19 vs WT: p = 0.0218; PS19/MAITCAST vs MAITCAST: p = 0.0218). Notably, MAIT cells from PS19/MAITCAST mice also exhibited enhanced CD69 expression [Fig. 1C; main effect: F(1,47) = 11.07, p = 0.0017], indicating increased cellular activation. A similar pattern was observed in the brain, where MAITCAST mice displayed greater MAIT cell numbers regardless of PS19 status [Fig. 1D; main effect: F(2,59) = 11.52, p < 0.0001], with no MAIT cell frequency differences between WT and PS19 mice (p = 0.7289). Notably, in the brain, CD69 expression did not differ across genotypes [Fig. 1E; main effect: F(1,43) = 0.7863, p = 0.3802]. Overall, these findings indicate that WT MAITCAST mice exhibit increased MAIT cell abundance, whereas those on the PS19 background show increased MAIT cells in the liver, but no change in the brain.

Fig. 1.

Fig. 1

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MAITCAST mice have ~7x more MAIT cells than WT mice. (A) Flow cytometry gating schematic from one representative experiment is shown for identifying MAIT cells. (B) Percentage of TCRβ+/CD44+/MR1 Tetramer+ cells in the liver. (C) Mean fluorescent intensity (MFI) of CD69 on MAIT cells in the liver. (D) Number of TCRβ+/CD44+/MR1 Tetramer+ cells in the brain. (E) MFI of CD69 on brain MAIT cells. The data are presented as the mean ± standard error of the mean. Statistical analysis was performed using a two-way ANOVA with a Tukey post hoc multiple comparison test. *p < 0.05, **p < 0.01, *** p < 0.001, **** p < 0.0001.

3.2. MR1 Expression is Elevated in the Hippocampus of PS19 Mice

Previously, we observed increases in MR1 expression in 5XFAD mice (Wyatt-Johnson et al., 2023); therefore, we sought to assess MR1 expression in the PS19 brain (Fig. 2A–B). In the cortex, there were no significant differences detected in the levels of MR1 across genotypes [Fig. 2C; main effect: F(2,24) = 2.883, p = 0.0755]. In the hippocampus, there was a significant effect of MR1 expression which was elevated in PS19 mice, specifically in the CA1 region and dentate gyrus (DG) [CA1: Fig. 2D; main effect: F(2,24) = 4.983, p = 0.0155; DG: Fig. 2E; main effect: F(2,24) = 9.247, p = 0.0011]. Interestingly, while the overall IBA1 signal did not differ between groups [Fig. 2F; main effect: F(2,24) = 0.2604, p = 0.7729], S100β intensity was enhanced in PS19 mice [Fig. 2G; main effect: F(2,24) = 4.269, p = 0.0498].This increase was largely observed within IBA1+ microglia, with PS19 mice showing the highest levels of MR1 intensity in these cells [Fig. 2H; main effect: F(1,18) = 4.778, p = 0.0423]. Interestingly, MR1 expression in S100β+ astrocytes was significantly elevated in both PS19 and PS19/MAITCAST mice [Fig. 2I; main effect: F(1,18) = 19.03, p = 0.0004]. Of note, a major antigen presenting cell for MAIT cells are B cells, which are found in the highest abundance in the spleen. Although there were no changes in the number of B220+ B cells in the spleen [Fig. S1A–B; main effect: F(2,65) = 1.388, p = 0.2568], there was an increase in MR1 expression on the B220+ B cells of only PS19 mice [Fig. S1C; main effect: F(1,52) = 8.865, p = 0.0044]. Together, these results demonstrate that there is increased MR1 expression on splenic B cells and hippocampal microglia and astrocytes in PS19 mice, with PS19/MAITCAST mice displaying high MR1 levels on astrocytes but not microglia.

Fig. 2.

Fig. 2

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MR1 expression is increased in the hippocampus of PS19 mice. (A) Representative images showing merged and individual channels for MR1 (white), IBA1-labeled microglia/macrophages (red), Tau-AT8 (green), and Dapi (blue). Scale bar = 50 μm. (B) Representative images showing merged and individual channels for MR1 (white), S100β+ astrocytes (red), Tau (AT8; green), and Dapi (blue). (C-E) Densitometry analysis is presented as the relative mean pixel intensity for MR1 in the cortex (C), hippocampal CA1 region (D), and dentate gyrus (DG; E). (F) Densitometry analysis of the IBA1 signal within the boundaries of IBA1+ cells. (G) Densitometry analysis of the S100β signal within the boundaries of S100β + cells. (H) Densitometry analysis of the MR1 signal within the boundaries of IBA1+ cells. (I) Densitometry analysis of the MR1 signal within the boundaries of S100β + cells. Data are presented as the mean ± standard error of the mean. Statistical analysis was performed using a two-way ANOVA with a Tukey post hoc multiple comparison test. *p < 0.05, **p < 0.01, *** p < 0.001.

3.3. MAIT Cell Frequency Does Not Alter Tau Pathology at 7 Months

To ascertain whether MAIT cell frequency (i.e., none, normal numbers of MAIT cells or ~7x more MAIT cells) influenced tau pathology, tau accumulation was assessed by IF staining using tau-specific antibodies including AT8 (pSer202/pThr205), which is found on more established neurofibrillary tangles (NFTs), and anti-pTau-Ser214, which is an early marker of seeding generally focused at synaptic compartments (Martinez et al., 2025) (Fig. 3A). No contrast in AT8 intensity was observed among PS19 genotypes in the CA1, DG, or cortex [Fig. 3B; CA1: F(2,19) = 0.0779, p = 0.9253; DG: F(2,19) = 1.252, p = 0.3083; CTX: F(2,19) = 1.270, p = 0.3035]. Similarly, no significant differences were detected in pTau-Ser214 levels [Fig. 3C; CA1: F(2,14) = 0.1774, p = 0.8393; DG: F(2,14) = 1.275, p = 0.3101; CTX: F(2,14) = 0.1724, p = 0.8434]. Given the apparent localization of tau to neuronal layers (Uemura et al., 2020), we further analyzed the pyramidal (in the CA1) and granular (in the DG) cell layers (Fig. 3D). No differences were observed in either AT8 [Fig. 3E; CA1: F(2,14) = 1.049, p = 0.3762; DG: F(2,14) = 1.618, p = 0.2332] or pTau-Ser214 staining [Fig. 3F; CA1: F(2,14) = 0.7440, p = 0.4931; DG: F(2,14) = 1.913, p = 0.1843]. These findings indicate that changes in MAIT cell frequency do not impact tau pathology at 7 months of age.

Fig. 3.

Fig. 3

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Tau pathology remains unchanged regardless of MAIT cell numbers. (A) Representative images showing merged and individual channels for Dapi (blue), AT8 (green), and pTau-Ser214 (red), from hippocampal CA1, dentate gyrus (DG), and cortex. Scale bar = 100 μm. (B-C) Densitometry analysis is presented as the relative mean pixel intensity of CA1, DG, and Cortex stained with AT8 (B) and anti-pTau-Ser214 (C). (D) Representative images showing merged and individual channels for Dapi (blue), AT8 (green), and pTau-Ser214 (red), from hippocampal CA1 pyramidal cell layer and DG granular cell layer. Scale bar = 50 μm. (E-F) Densitometry analysis presented as the relative mean pixel intensity of the CA1 pyramidal cell layer and the DG granular cell layer in the AT8 (E) and pTau-Ser214 channels (F). The data are presented as the mean ± standard error of the mean. Statistical analysis was performed using a one-way ANOVA with a Tukey post hoc multiple comparison test. *p < 0.05.

3.4. An MR1 Deficiency Reduces Neuronal Density Without Affecting Pathology Progression

Next, we evaluated whether the observed similarities in tau distribution across genotypes were attributable to potential changes in neuronal density by analyzing NeuN levels via IHC (Fig. 4A). We have previously reported that NeuN expression in wildtype MR1 KO mice was lower than that found in MR1+ mice (Wyatt-Johnson, Ackley, et al., 2025). Also in the current study, a significant reduction in NeuN signal was detected in the pyramidal layer of the CA1 region in PS19/MR1 KO mice [Fig. 4B; main effect: F(2,24) = 29.65, p < 0.0001]. A similar reduction was observed in the granular layer of the DG, again specific to PS19/MR1 KO mice [Fig. 4C; main effect: F(2,24) = 12.85, p = 0.0002]. Thus, these results suggest that although an MR1 deficiency impacts neuronal density, this does not appear to influence overall disease pathology.

Fig. 4.

Fig. 4

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Hippocampal expression of NeuN is decreased in PS19/MR1 KO mice. (A) Representative images showing NeuN staining in the CA1 region and dentate gyrus (DG). (B) Densitometry analysis is presented as the relative mean pixel intensity of the CA1 pyramidal cell layer (top) and the DG granular cell layer (bottom). The data are presented as the mean ± standard error of the mean. Statistical analysis was performed using a two-way ANOVA with a Tukey post hoc multiple comparison test. *p < 0.05, **p < 0.01, *** p < 0.001, **** p < 0.0001.

3.5. Cognitive Performance Is Largely Unaffected by MAIT Cell Frequency

After finding significant differences in MR1 expression, yet no changes in tau pathology, we sought to determine if cognition was impacted by various MAIT cell frequencies. To address this question, mice were subjected to a battery of cognitive tests. An Open Field test was performed first to determine if there were any underlying baseline issues. Overall, no differences were observed in total distance traveled, average speed, or time spent in the inner zone [Fig. S2A–C; main effect: distance: F(1,64)=0.5409, p = 0.4649; speed: F(1,64)=0.2688, p = 0.6059; inner zone: F(1,64)=0.2931, p = 0.5901]. In the NOR test (Fig. 5A), WT mice displayed their expected recognition memory by spending more time exploring the novel object [t(10)=2.365, p = 0.0396]. In contrast, the MAITCAST mice spent equal time with both objects, demonstrating no recognition memory [t(13)=0.4958, p = 0.6283]. PS19 mice also spent equal time with both objects [t(15)=1.874, p = 0.0806]. Intriguingly, PS19/MR1 KO mice spent more time with the familiar object, suggesting a memory deficit and certainly, an altered behavioral response [t(11)=3.866, p = 0.0026]. PS19/MAITCAST mice also failed to recall the familiar object, similar to the PS19/MR1 KO mice, although the difference did not reach statistical significance [t(14)=1.835, p = 0.0879]. Next, we sought to understand if the average novelty preferences were different between groups using the discrimination index (DI). The DI only showed a major change in that PS19/MR1 KO mice had significantly worse discrimination [Fig. 5B; main effect: F(2,64) = 5.701; p = 0.0053]]. These findings indicate that while the magnitude of novelty discrimination was not significantly impaired in PS19 mice relative to WT, PS19 mice--regardless of MAIT cell frequency--failed to demonstrate a significant preference for the novel object, suggesting reduced recognition memory at this early disease stage. Spatial learning and memory were further assessed using the Barnes maze. Across training trials, all genotypes improved performance in locating and entering the target hole [Finding: Fig. 5C; main effect: F(3.810,240) = 7.803, p < 0.0001; Entering: Fig. 5D; main effect: F(3.122,196.7) = 17.06, p < 0.0001]. Correspondingly, during the probe trial, no differences were observed in errors, latency to target, or time spent in the target quadrant [Fig. 5E; main effect: F(1,64) = 1.827, p = 0.1812], time to find the target [Fig. 5F; main effect: F(1,64) = 2.113, p = 0.1510], or time spent in the target hole quadrant [Fig. 5G–H; main effect: F(1,64) = 2.060, p = 0.1561]. Overall, whereas NOR deficits were observed in several genotypes, no impairments in spatial learning or memory were detected in the Barnes maze.

Fig. 5.

Fig. 5

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Cognitive changes in recognition memory remain unchanged regardless of MAIT cell abundance. (A) Percentage of time mice spent exploring the novel object (NO) compared to familiar object (FO) in the novel object recognition (NOR) test. (B) Analysis of the discrimination index (DI) from the time spent with each object in NOR Trial 2. (C) The time to find the escape box across all 5 trials in the Barnes maze. (D) The elapsed time for mice to enter the escape box across all 5 trials. (E) The number of incorrect holes explored before finding the target location in the Barnes maze probe trial. (F) The time to find the target hole location in the probe trial. (G) Percentage of time spent in the target hole quadrants in the probe trial. (H) The averaged heat maps across each group for the probe trial display the time spent across the platform. The data are presented as the mean ± standard error of the mean. Statistical analysis was performed using a paired t-test (A), two-way ANOVA (B, E, F, G), or mixed-effects ANOVA (C, D) with Tukey post hoc multiple comparisons test. *p < 0.05, **p < 0.01.

3. DISCUSSION

In this study, we investigated how various MAIT cell numbers (from no MAIT cells to ~7x more MAIT cells) influence MR1 expression, tau pathology, and cognition in the tau-focused PS19 mouse model. Our findings demonstrate that while MAITCAST mice exhibit robust increases in MAIT cell frequency—particularly in the liver—as well as region-specific changes in MR1 expression in the brain, these immune alterations do not translate into measurable changes in tau pathology or cognition at 7 months of age; there were no discernible sex differences.

Compared to WT and PS19 mice, approximately 7x more MAIT cells were found in the brains and livers of MAITCAST and PS19/MAITCAST mice (Fig. 1). Interestingly, we did find an increase in MAIT cell numbers in the PS19 liver, similar to our previous findings in the 5XFAD mouse model; however, there were no changes in the number of MAIT cells in the brain, unlike in 5XFAD mice (Wyatt-Johnson, Kersey, et al., 2024; Wyatt-Johnson et al., 2023). Moreover, we observed an elevation in MAIT cell surface expression of CD69 in PS19/MAITCAST mice in the liver, but not the brain (Fig. 1). CD69 as an activation marker provides a sensitive measure of recent MAIT cell activation, which was particularly advantageous at this early stage of disease in PS19 mice, when immune changes are expected to be subtle. Furthermore, CD69 can also be displayed on tissue resident MAIT cells (Hinks & Zhang, 2020). This is also unlike what we found in the 5XFAD amyloid model, where there was increased CD69 expression of MAIT cells in both the brain and liver (Wyatt-Johnson, Kersey, et al., 2024; Wyatt-Johnson et al., 2023). These data therefore suggest that, despite various MAIT cell numbers in individual groups of mice, the transgenic expression of tau in the PS19 model is limited in the consequent in vivo effects observed.

In contrast to our observations regarding MAIT cells, the expression of MR1 showed clear region- and cell-specific alterations (Fig. 2). We observed increased MR1 levels in the hippocampus of PS19 mice, particularly within microglia and astrocytes. Although this result is comparable to what we observed in the 5XFAD mouse model with increased MR1 expression in microglia (Wyatt-Johnson et al., 2023), in PS19 background mice, the elevated MR1 expression did not correspond to increased MAIT cell numbers or their activation state in the brain. This disconnect may indicate that MR1 upregulation alone is not sufficient to drive MAIT cell activation or increase MAIT cell numbers in PS19 mice, suggesting another signal may be important in increasing MAIT cell numbers in the brain. Notably, there was no increase in MR1 levels in PS19/MAITCAST microglia, in contrast to the significant elevation of MR1 expression in astrocytes, implying that the MAITCAST background may have a heretofore undiscovered differential expression of MR1 in glia. This is notable, because CAST/EiJ mice [from which MAITCAST mice were originally derived (Cui et al., 2015)] have been shown, upon injection of a tau-expressing AAV, to have increased tau seeding activity and a unique transcriptional profile as compared to AAV-tau-injected B6 mice (Acri et al., 2023). Although MAITCAST mice have been bred onto the B6 background for more than 10 generations (Cui et al., 2015), residual CAST/EiJ genetic variation may still influence aspects of the immune response or glial biology. Such background effects could contribute to the increased CD69 expression observed specifically in PS19/MAITCAST MAIT cells. However, because MAITCAST mice differ from B6 mice primarily by the introduction of the CAST-derived TCR-α locus that increases MAIT cell frequencies, it is unlikely that all observed phenotypes can be attributed solely to residual background variation. Nevertheless, this remains a limitation of the model, and future studies using complementary approaches to manipulate MAIT cells or additional congenic controls will be valuable to further distinguish MAIT cell-dependent effects from strain-specific genetic influences. Despite changes in MAIT cell abundance and MR1 expression, we found no evidence that MAIT cells influence tau accumulation at this early disease stage (Fig. 3). Moreover, neither AT8 nor pTau-Ser214 levels were altered across genotypes, including in hippocampal subregions that are highly vulnerable in the PS19 model (Wang et al., 2026). Even when focusing on neuronal cell layers, where tau pathology can be very prominent (Wu et al., 2016), no genotype-dependent differences emerged. These findings suggest that increasing MAIT cell frequency alone is insufficient to modulate tau pathology in PS19 mice, at least at 7 months of age. It remains possible, however, that MAIT cells may exert effects at later stages of disease progression, but due to the high mortality rate of our PS19 mice beyond 7 months, we were unable to analyze any later time points.

Another key observation was that an MR1 deficiency selectively reduced neuronal density in PS19/MR1 KO mice (Fig. 4), as indicated by decreased NeuN staining in both CA1 and DG cell layers, similar to what we previously found in WT MR1 KO and 5XFAD/MR1 KO mice (Wyatt-Johnson, Ackley, et al., 2025). This suggests that the lack of MR1 may influence neuronal maintenance across multiple disease contexts. Importantly, the reduction in neuronal density was not accompanied by either increased or decreased tau accumulation, indicating that an MR1 deficiency alters neuronal vulnerability and/or homeostasis independently of early tau pathology. The mechanism(s) underlying reduced NeuN expression in MR1-deficient mice remains unclear but may involve altered glial–neuronal communication, changes in immune-mediated support of neuronal function, or developmental effects associated with the absence of MR1.

Cognitively, we observed within-group deficits in NOR across the PS19 genotypes regardless of MAIT cell presence or frequency (Fig. 5). The deficiencies in recognition memory were neither rescued nor worsened by increased MAIT cell frequencies or by having no MAIT cells at all. Although DI analysis did not detect a significant difference between WT and PS19 mice, it did find PS19/MR1 KO mice were worse in the cognitive assessments. The differences in the within-group paired analysis and DI analysis are due to the paired analysis comparing novel and familiar object exploration within the same animal, which is more sensitive to detecting recognition memory by reducing the influence of inter-animal variability in exploratory behavior. In contrast, the discrimination index compares summary scores between animals and therefore tests whether the magnitude of novelty preference differs between genotypes. This increased sensitivity is particularly relevant, as cognitive testing was performed at 7 months of age, an early stage of disease progression in the PS19 model when cognitive deficits are often subtle. We selected this earlier point due to a high mortality rate. Thus, the absence of a significant difference in discrimination index between WT and PS19 likely reflects the combination of modest effect size and biological variability at this early disease stage rather than preserved recognition memory. Moreover, spatial learning and memory assessed by the Barnes maze were largely intact across all groups. The lack of differences between PS19 and WT in the Barnes maze is not surprising, as other studies have reached similar conclusions, with only slightly poorer performance by PS19 mice despite varying numbers of training trials (Kron et al., 2026; Lyons et al., 2024). Overall, these findings suggest that the modulation of MAIT cell frequency does not substantially influence cognitive outcomes in PS19 mice.

Overall, these findings stand in contrast to our previous work in the 5XFAD model, where the absence of MAIT cells led to a significant reduction in amyloid pathology along with improved cognitive performance in both NOR and Barnes maze tasks (Wyatt-Johnson, Ackley, et al., 2025; Wyatt-Johnson, Desai, et al., 2025; Wyatt-Johnson et al., 2023). Together, these results suggest that MAIT cells may play fundamentally different roles depending on the underlying proteinopathy. In amyloid-driven disease, MAIT cells appear to contribute to pathology and cognitive decline, whereas in tau-driven pathology, their role may be limited or at least context-dependent. This divergence may reflect differences in how innate-like T cells interact with amyloid versus tau pathology, or how these pathologies differentially shape the neuroimmune environment.

Notably, our findings differ from a previous study in the PS19 model reporting that a MAIT cell deficiency exacerbated tau pathology (Zhang et al., 2025). One important distinction is that the PS19 mice used in that study were maintained on a mixed genetic background, whereas our study utilized PS19 mice fully backcrossed onto a C57BL/6 (B6) background. The genetic background is known to strongly influence MAIT cell numbers and phenotype (Rahimpour et al., 2015), as well as disease progression in neurodegenerative models (Acri et al., 2023). Thus, it is plausible that strain-dependent differences in immune and innate-like T cell responses or glial reactivity may underlie the discrepancy between studies. In this context, MAIT cells may exert protective or pathogenic effects depending on the broader genetic and immunological landscape.

Taken together, our results indicate that while MAIT cells and MR1 are dynamically regulated in both peripheral organs and the brain, their impact on tau pathology and cognition is limited. When considered alongside prior findings in amyloid models and other PS19 studies, our data support the idea that the MAIT cell response is highly context-dependent, shaped by disease type, genetic background, and local immune environment. Future studies should ideally examine later disease stages in the tau models as well as determine why amyloid models drive a stronger MAIT cell response than do tau models.

In conclusion, this study highlights a complex and context-dependent role for MAIT cells and MR1 in neurodegenerative disease, emphasizing that their contributions may differ substantially between amyloid and tau pathologies.

Supplementary Material

1

Highlights:

  • PS19/MAITCAST mice have an increased number and activation state of MAIT cells

  • An elevation in MAIT cell numbers alone does not alter tau pathology in PS19 mice

  • MR1 expression is increased in PS19 microglia and astrocytes

  • An MR1 deficiency reduces neuronal density without affecting tau pathology progression

  • MAIT cell numbers do not affect the extent of cognitive deficits in PS19 mice

Acknowledgements

We thank Dr. J. Yewdell for the TW2.3 and 2.4G2 hybridomas, as well as the NIH Tetramer Core Facility for providing 6-FP- and 5-OP-RU-loaded mouse MR1 tetramers.

Funding

This work was supported by Alzheimer’s Association Research Grant AARGD-NTF-22-928436 (to RRB) and National Institutes of Health grant R01 AG092565-01A1 (to RRB).

Footnotes

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CRediT authorship contribution statement

Season K. Wyatt-Johnson: Writing – original draft, Writing – review & editing, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Sujal Joshi: Investigation, Formal Analysis. Maggie Blair: Investigation, Formal Analysis. Jay M. Desai: Investigation, Formal Analysis. S.M. Mansour Haeryfar: Resources. Cristian A Lasagna-Reeves: Methodology. Randy R. Brutkiewicz: Writing – review & editing, Methodology, Conceptualization, Funding acquisition, Supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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