Pybus et al. Journal of Neuroinflammation(2024) 21:156
https://doi.org/10.1186/s12974-024-03128-1

Journal of Neuroinflammation

Open Access

RESEARCH

Profiling the neuroimmune cascade in 3xTgAD mice exposed to successive mild traumaticbrain injuries
Alyssa F. Pybus1, Sara Bitarafan2, Rowan O. Brothers1, Alivia Rohrer1, Arushi Khaitan1, Felix Rivera Moctezuma2,
Kareena Udeshi3, Brae Davies3, Sydney Triplett1, Martin N. Griffin3, Eric B. Dammer4, Srikant Rangaraju5,
Erin M. Buckley1,6,7*† and Levi B. Wood1,2,8*†
Abstract
Repetitive mild traumatic brain injuries (rmTBI) sustained within a window of vulnerability can result in long term
cognitive deficits, depression, and eventual neurodegeneration associated with tau pathology, amyloid beta (Aβ)
plaques, gliosis, and neuronal and functional loss. However, a comprehensive study relating acute changes in
immune signaling and glial reactivity to neuronal changes and pathological markers after single and repetitivemTBIs is currently lacking. In the current study, we addressed the question of how repeated injuries affect the brainneuroimmune response in the acute phase of injury (< 24 h) by exposing the 3xTg-AD mouse model of tau andAβ pathology to successive (1x-5x) once-daily weight drop closed-head injuries and quantifying immune markers,
pathological markers, and transcriptional profiles at 30 min, 4 h, and 24 h after each injury. We used young adult
2–4 month old 3xTg-AD mice to model the effects of rmTBI in the absence of significant tau and Aβ pathology.
We identified pronounced sexual dimorphism in this model, with females eliciting more diverse changes after
injury compared to males. Specifically, females showed: (1) a single injury caused a decrease in neuron-enrichedgenes inversely correlated with inflammatory protein expression and an increase in AD-related genes within 24 h,(2) each injury significantly increased a group of cortical cytokines (IL-1α, IL-1β, IL-2, IL-9, IL-13, IL-17, KC) and MAPK
phospho-proteins (phospho-Atf2, phospho-Mek1), several of which co-labeled with neurons and correlated withphospho-tau, and (3) repetitive injury caused increased expression of genes associated with astrocyte reactivity andmacrophage-associated immune function. Collectively our data suggest that neurons respond to a single injury
within 24 h, while other cell types, including astrocytes, transition to inflammatory phenotypes within days ofrepetitive injury.
Keywords Traumatic brain injury, Mild traumatic brain injury, Alzheimer’s pathology, Cytokines, Neuroinflammation,
MAPK, 3xTg-AD, Astrocyte reactivity†

Erin M. Buckley and Levi B. Wood contributed equally to this work.

*Correspondence:
Erin M. Buckleyerin.buckley@emory.edu
Levi B. Woodlevi.wood@me.gatech.edu
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use,sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) andthe source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in thisarticle are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not includedin the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you willneed to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The
Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available
in this article, unless otherwise stated in a credit line to the data.

Pybus et al. Journal of Neuroinflammation(2024) 21:156

Introduction
Traumatic brain injury (TBI) results in approximately
2.5 million emergency department visits every year in
the Unites States alone. Approximately 75% of TBI cases
are classified as mild (mTBI), and these mild cases carryan annual cost burden of $17 billion [1, 2]. An estimated
10–40% of mTBIs are associated with persistent functional impairment lasting longer than one month and insome cases up to a year [3–6]. Moreover, repeated mTBIs(rmTBIs) sustained within a window of vulnerability canintensify pathological and functional consequences [7, 8].
These repeated injuries, which are often observed among
athletes in high contact sports such as American footballand boxing, can result in the development of Alzheimer’s
disease-like pathology, including neurofibrillary tangles
and amyloid beta (Aβ) plaques [9]. Current treatment
paradigms for (r)mTBI are severely lacking and focus on
the alleviation of symptoms, rather than targeting theunderlying mechanisms of injury. There is thus an urgent
need to illuminate the post-injury neuro-molecularsequelae for development of targeted therapies.
Mounting evidence suggests the involvement of brain
immune signaling as a key driver of long-term outcome
following mTBI [10–21]. Brain immune signaling is
implicated in several neurodegenerative diseases associated with mTBI, including Alzheimer’s disease (AD)[22–24] and Parkinson’s disease [25–27]. Moreover, theroles of immune signaling in driving pathology following
severe TBI has been relatively well-studied [28–32], andpreliminary evidence shows similar patterns in (r)mTBI[28, 33–37]. Indeed, prior research from our own group
has identified correlations between lower cerebral bloodflow (a potential biomarker of worse cognitive outcome)and increased mitogen-activated protein kinase (MAPK)
signaling, cytokine expression, and microglial activation after repetitive closed-head injuries (CHI) in wild
type mice, supporting an essential relationship between
brain immune signaling and outcome after mTBI [38].
However, a comprehensive study relating acute changes
in immune signaling and glial activation to neuronal changes and pathological markers after single andrepetitive mTBIs is currently lacking and would provide
a much-needed perspective to guide the search for new
therapies.
The acute pathophysiology of mTBI has previously
been defined by the “neurometabolic cascade of concussion,” in which neurons undergo axonal injury anddysfunction, altered neurotransmission, ionic flux, andindiscriminate release of glutamate, while many brain
cells undergo a hyperglycolysis energy crisis to restore
homeostasis [39, 40]. When Drs. Giza and Hovda
reviewed the neurometabolic cascade in 2014 [40], relatively little was known about mTBI-induced inflammatory changes, but research over the last decade has more

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fully defined changes in immune signaling, microglial
activation, and astrocyte reactivity, as well as their effects
on secondary injury after TBI [28, 41–44]. For example,
recent studies suggest that astrocytes rapidly respondto mechanical stress after injury, potentially via openingof mechanosensitive ion channels, leading to activationof MAPK signaling, ATP release, and reactive astrogliosis [41, 45–51]. Here we propose to similarly define
the acute “neuroimmune cascade” of mTBI. These clear
changes in microglial and astrocyte responses post-mTBI
suggest a broad and complex neuroimmune response to
mTBI. Thus, a comprehensive understanding of neuroimmune signaling and its hypothesized relationship to neuronal, glial, and pathogenic changes after injury is critical
to identifying drivers of secondary injury as well as determining therapeutic targets to mitigate adverse cognitiveand pathological outcomes.
In the current study, we sought to define the neuroimmune cascade of (r)mTBI by profiling protein and transcriptional changes associated with immune signaling,pathology, and the neuronal, microglial, and astrocytic
compartments (Fig. 1). We specifically hypothesized that(r)mTBI would drive brain immune signaling in a manner correlated with markers of neurodegeneration, suchas tau, Aβ, astrocyte reactivity, and microglial activation. Because we aimed to identify relationships between
immune signaling and molecular markers associated with
AD-related pathology, and wild-type mice show inconsistent evidence of pathological changes after TBI [52–54],
we used the triple transgenic model of Alzheimer’s-like
pathology (3xTg-AD), which contains human mutant
forms of genes in the Aβ processing pathway (APP,
PSEN1) and tau (MAPT). This model is widely used to
study AD and is increasingly utilized in brain injury
research [55–62]. Here, we sought to concomitantly
define the effects of successively increasing mild traumatic brain injuries on the following molecular outcomesin the frontal cortex and hippocampus: (1) the kinetics ofbrain immune signaling and cytokine expression, (2) tau
phosphorylation and Aβ burden, (3) markers of astrocyte
reactivity and microglial activation, and (4) transcriptional profiling of the somatomotor cortex (females only)(Fig. 1). Our analyses revealed pronounced effects ofrepetitive injury on all molecular outcomes. Further, theinclusion of both male and female 3xTg-AD mice in ourstudy revealed strong effects of sexual dimorphism in thismodel across all outcomes, with males showing higher
basal levels of immune signaling but females showing
more pronounced changes in response to injury.

Materials and methods
All resources with available Research Resource Identifiers(RRIDs) are listed in Table S1.

Pybus et al. Journal of Neuroinflammation(2024) 21:156

Study protocol

3xTg-AD on C57BL/6J mice (Jackson Laboratory, strain
033930) were aged 2–4 months (98 females, 86 males).
All protocols were approved by the U.S. Army Medical Research and Development Command Animal Careand Use Review Office (ACURO) and the Emory University Institutional Animal Care and Use Committee (IACUC) in accordance with National Institutes of
Health and ARRIVE guidelines. Mice were housed in apathogen free facility with a twelve-hour light/dark cycle.
Food and water were provided ad libitum. Mice wererandomly assigned to one of three injury groups: one
closed head injury (1xCHI), three closed-head injuries
spaced once daily (3xCHI), and five closed-head injuries
spaced once daily (5xCHI). Sham-injured controls were
exposed to the same numbers of anesthesia exposures
as each injured group but were not exposed to injury.
As noted below, sham-injured groups were combined
to reduce animal numbers. Animals were euthanized bycervical dislocation under 4.5% isoflurane (1 L/min, 100%
oxygen), and the brain was harvested for molecular andpathological assessment at the following time points foreach group: pre-injury, 30 min, 4 h, and 24 h post-injury.
Left hemispheres were micro-dissected into frontal cortex (approximately bregma + 1 mm to + 2.5 mm), hippocampus, and somatomotor cortex (approximately bregma− 1 mm to + 1 mm) sections, flash-frozen by submerging
sample tubes in liquid nitrogen, then stored at -80 °C forfollow-on molecular analysis. Right hemispheres weredrop-fixed in 4% paraformaldehyde then processed andembedded in paraffin for immunohistochemistry analysis. Sample sizes (n = 6–8 per injury time point groupin protein data, n = 2–4 in RNAseq data) are provided
in Table S2. We powered the current study with n = 12
(6 males, 6 females) per time point and injury based on
our prior cytokine data from wild-type mice exposed
to 5xCHI (α = 0.05, 80% power). Having found thatmales and females exhibited distinct baseline and injury
responses to many of our molecular markers, we separated them in the current study.
Closed head injury modelThis work was conducted using a previously characterized weight drop closed-head injury (CHI) model [63, 64].
Animals were anesthetized using 4.5% isoflurane (1 L/
min, 100% oxygen) prior to injury. The injury was delivered by dropping a 54 g bolt down a 0.96 m vertical guidetube (49035K85, McMaster-Carr, Elmhurst IL) onto thedorsal aspect of the head (skull intact), targeting between
the approximate location of the coronal and lambdoid
structures. The anesthetized animal was positioned
under the guide tube on a Kimwipes® task wipe (Kimberly-Clark, Irving, TX). The animal was grasped by thebase of the tail so that on impact the mouse penetrated

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the task wipe and underwent rapid, unrestricted rotation of the head in the anterior-posterior plane. Following injury, animals were monitored continuously until
they regain righting reflex. They were then checked every
15 min for the first hour, then daily. This diffuse injury
model is not expected to cause skull fracture or focal
injury [65] and we did not observe skull fracture in any
injured animals. Sham-injured mice were age- and sexmatched and received the same exposures to anesthesia,
matched to all injury groups and time points, but were
not subject to closed-head injury. Because principal component analysis did not identify significant differences inour protein markers between sham-injured animals of
distinct time points and numbers of exposures to anesthesia, we treated all sham-injured animals of the samesex as a single experimental group.
Quantification of immune and pathological proteins

To comprehensively profile the immune signaling kinetics after repeated CHI in 3xTg-AD male and female
mice, we quantified four classes of proteins: glial phenotypic markers (astrocyte reactivity markers GFAP and
S100B; microglial activation marker CD68 and homeostatic marker TMEM119), molecular markers of pathology (Aβ40, Aβ42, total tau, and phospho-tau T181), nine
MAPK phospho-proteins, and 30 cytokines (in total 47
measured proteins, plus ratios Aβ42/40 and pTau/tTau).
We collected frontal cortex and hippocampus samples
from the left hemisphere of the three injury groups(1xCHI, 3xCHI, and 5xCHI) across four time points each(pre-injury and 30 min, 4 h, 24 h post-injury), where thepre-injury 1xCHI group represents sham-injured controls. In total, data were collected from 98 female miceand 86 male mice. Two female and three male mice were
excluded from our analysis after Mahalanobis outlier
detection (α < 0.001) of all protein measurements, leaving
n = 96 females and n = 83 males.
Cortical and hippocampal brain tissue sections werelysed using the Bio-Plex cell lysis kit (Bio-Rad Laboratories #171,304,011) and protein concentrations were
determined using a Pierce BCA Protein Assay (Thermo
Fisher #23,225). Multiplexed cytokine quantification was
conducted using the Milliplex® MAP Mouse Cytokine/
Chemokine 32-Plex kit (Eotaxin, G-CSF, GM-CSF, IFNγ, IL-1α, IL-1β, IL-2, IL-3, IL-4, IL-5, IL-6, IL-7, IL-9,
IL-10, IL-12p40, IL-12p70, IL-13, IL-15, IL-17, IP-10,
KC, LIF, LIX, MCP-1, M-CSF, MIG, MIP-1α*, MIP-1β,
MIP-2, RANTES, TNF-α, and VEGF*) (Millipore Sigma
MCYTMAG-70 K-PX32). Cytokines marked with anasterisk did not fall within a linear range of bead fluorescent intensity vs. protein concentration and were notincluded in our analysis. Multiplexed phospho-protein
quantification was conducted for the MAPK signaling
pathway using the Bio-Plex Pro™ Cell Signaling MAPK

Pybus et al. Journal of Neuroinflammation(2024) 21:156

Panel 9-plex kit (phospho-Atf2 T71, phospho-Erk1/2
T202/Y204 T185/Y187, phospho-HSP27 S78, phosphoJNK T183/Y185, phospho-Mek1 S217/S221, phosphop38 MAPK T180/Y182, phospho-p53 S15, phospho-p90
RSK S380, phospho-Stat3 S727) (Bio-Rad Laboratories
LQ00000S6KL81S). Prior to analysis, lysates were thawedon ice and centrifuged at 4 °C for 10 min at 15,500 g.
Protein concentrations were normalized with Milliplex®
MAP Assay Buffer (EMD Millipore, Billerica, MA) to
6 µg protein per 12.5 µL for cytokine analysis and 1 µg
protein per 12.5 µL for the MAPK pathway analysis.
These protein concentrations were selected because theyfell within the linear range of bead fluorescent intensity
vs. protein concentration for detectable analytes. Quantification of pathological markers was conducted for totaltau, phospho-tau (T181), Aβ 1–40, and Aβ 1–42 using
the Milliplex® MAP Human Amyloid Beta and Tau Multiplex Assay kit (Millipore Sigma HNABTMAG-68 K). All
kits were read on a MAGPIX® system (Luminex, Austin,
TX).
We quantified astrocyte reactivity markers glial fibrillary acidic protein (GFAP) and S100 calcium binding
protein B (S100B) as well as macrophage and microglia
activation marker cluster of differentiation 68 (CD68)
and microglial homeostatic marker transmembrane protein 119 (TMEM119) via enzyme-linked immunosorbent
assay (ELISA). Protein concentrations were normalizedwith respective assay diluents from each kit to 2.5 µg/
ml in 50 µL for the GFAP ELISA kit (Abcam ab233621),
50 µg/ml in 50 µL for the S100B ELISA kit (Abcam
ab234573), and 0.1 µg/µL in 50ul for the CD68 and
TMEM119 ELISA kits (Lifespan Biosciences LS-F11095
and LS-F52734). All loaded protein concentrations
were selected because they fell within the linear rangeof absorbance vs. protein concentration for detectable
analytes. Due to the large number of samples and plateto-plate variation observed in immuno-assays [66], we
ran single technical replicates for each sample. For each
ELISA kit, we validated its ability to report protein levels by cross-validating against quantitative analysis ofWestern blot for each analyte (n = 6 samples, anti-GFAP,
anti-TMEM119, anti-CD68, SB100B), and by conducting a linear ranging analysis to identify a range of sample
loaded that leads to a linearly related output signal [64].
Adjustment for batch effect and pathology

Male and female 3xTg-AD data were each collected
simultaneously for S100B, CD68, and TMEM119 by
ELISA, but separated into different assay batches for allother protein measurements. Eight female sample replicates were included in each male batch assay (Luminex
and ELISA) and then used to transform the male data
into the female space via a linear model for each protein.

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Computations were conducted using the lm() function inR.
To adjust data for the effects of pathology, linear models were calculated using neuroimmune proteins as
dependent variables with the following combinations of
independent variables: total tau and phospho-tau T181;
Aβ40 and Aβ42; or total tau, phospho-tau T181, Aβ40,
and Aβ42. Residuals of each linear model were definedas the pathology-adjusted (tau-adjusted, Aβ-adjusted,or total pathology-adjusted) neuroimmune protein data.
Computations were conducted using the lm() function inR.
Immunohistochemistry

Immunohistochemistry was performed to investigate
GFAP, NeuN, IL-1α, IL-1β, IL-13, KC, phospho-Atf2(T71), phospho-Mek1/2 (S217/S221), and phospho-tau(S262/T263). Right brain hemispheres from each animal were fixed in 4% paraformaldehyde then processedand embedded in paraffin. Tissue slices were cut into
10 μm thick sagittal sections using a rotary microtome(Thermo Fisher) and affixed onto glass microscopy slides(Electron Microscopy Sciences, Hatfield, PA). Tissue
slices were deparafinized in xylenes and rehydrated bywashing with 100% ethanol, 95% ethanol, and deionizedwater. Antigen retrieval was performed in a microwave
by boiling slides in 10mM sodium citrate buffer at pH
6.0. Slides were rinsed in tris-buffered saline with 0.01%
tween (TBST). A hydrophobic ring was drawn aroundeach individual tissue slice using an immunohistochemistry PAP pen (Enzo Life Sciences, Farmingdale, NY), after
which samples were blocked for 2 h in blocking buffer,
5% w/v Bovine Serum Albumin (BSA) or 50% goat serum(Sigma-Aldrich) in TBST. Samples were incubated at 4 °C
overnight with primary antibodies diluted in blocking
buffer: GFAP (1:100, Diagnostic Biosystems Mob064),
NeuN (1:200, Abcam ab104224), IL-1α (1:50, Thermo
Fisher PA5-89037), IL-1β (1:500, Abcam ab283818),
IL-13 (1:100, Abcam ab106732), KC (1:50, Thermo
Fisher PA5-86508), phospho-Atf2 T71 (1:50, Thermo
Fisher PA5-97332), phospho-Mek1/2 S217/S221 (1:50,
Thermo Fisher PA5-105777), or phospho-tau S262/T263(1:100, Abcam ab92627), as appropriate. Slides wererinsed again in TBST and incubated with either Alexa
Fluor 555 and/or Alexa Fluor 488 secondary antibodies(Thermo Fisher) diluted 1:200 in blocking buffer. Slides
were counterstained with 1 µg/mL DAPI, rinsed in water,and mounted with VECTASHIELD Antifade Mounting
Medium (Vector Laboratories, Burlingame, CA). Samples
were imaged using epifluorescent microscopy on a Zeiss
Axio Observer Z.1 inverted microscope with a 20x lensand halogen bulb illumination using Zeiss filter set 49 toimage DAPI, Zeiss filter set 50 to image Alex flour 647,

Pybus et al. Journal of Neuroinflammation(2024) 21:156

and Zeiss filter set 20 to image Alexa flour 555. Images
were processed using Zen Blue V3.3.
Data analysis and visualization

Data was analyzed and figures were generated in RStudio (Boston, MA) using the R programming language.
Software packages and functions described in this section are denoted by Courier New font. Data processing
was conducted using the tidyverse collection of packages [67]. Heatmaps were generated using the R package
heatmap3 [68], bar graphs and regression plots werecreated using the packages ggplot2 [69] and ggpubr[70], and gene set variation analysis was conducted using
the gsva package [71] available on Bioconductor. For
each molecular assay, we collected background measurements using assay buffer in the absence of biological samples. We subtracted average background measurementsfrom each sample measurement and set negative values
to zero. Clustering was conducted using the hclust
function of the stats package in R using Euclidean distance in the unweighted pair group method with arithmetic mean. Outlier detection was conducted in R by
calculating Mahalanobis distance of each point from thedata’s centroid within each grouping of sex and region(female frontal cortex, female hippocampus, male frontal
cortex, and male hippocampus) using the ClassDiscovery [72] package. Two female and three male hippocampus samples were outside the cutoff threshold of
α = 0.001 and protein data from those animals was discarded in both the frontal cortex and hippocampus sets.
Bulk tissue RNA sequencing and analysis

We conducted RNA sequencing on a representative subset of 40 3xTg-AD female left somatomotor cortex samples selected from the original 96 for bulk tissue RNA
sequencing (available on the NCBI Gene Expression
Omnibus under accession number GSE226838). One
sample was removed post-sequencing after identification as an outlier animal within the protein data. RNA
was extracted using the miRNeasy Micro kit (Qiagen#217,084). Extracted RNA was sent to Admera Health,
LLC (South Plainfield, NJ) for sequencing, alignment,
and calculation of the count matrix. The count matrix
yielded 32,807 non-zero features. Features with fewer
than 10 counts in more than four samples (∼ 10%) were
filtered out of the analysis (17,844 features). The remaining 14,963 features were normalized by ratio of medianmethod then variance-stabilized in R using the DESeq2
package, available on Bioconductor [73].
Weighted gene co-expression network analysis(WGCNA) was conducted in R using the WGCNA package [74] available on the Comprehensive R Archive Network (CRAN). A WGCNA threshold power of 4 waschosen since it was the smallest threshold that resulted

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in a scale-free R2 value greater than 0.80. We constructed
our network in a single block using blockwiseModules()with the following parameters: threshold power of 4,
signed modules, a minimum module size of 20, a merge
cut height of 0.07, a reassignment threshold of 0.05, a“bicor” correlation type, and a “mean” TOM denominator. Significance of module eigengene (ME) expression
between sham-injured and injured samples was assessedby permutation test in which ME values for both populations were randomly re-assigned to either group thenpermutated mean difference was compared to the true
mean difference across 10,000 iterations. Original sample
sizes were maintained. Groups were considered significantly different if the true mean difference was greaterthan or equal to the permutated mean difference in atleast 95% of iterations.
Gene ontology was conducted for each of the 14 resulting modules using PANTHER overrepresentation test
on PANTHER 17.0, available on the Gene Ontology
Resource. We used the mm10 GO biological processcomplete annotation set with Fisher’s exact test and false
discovery rate (FDR) corrected p-values. The test wasapplied using all genes with a network module membership coefficient (kME) of at least 0.60 and a backgroundof the 14,963 features used to construct the network.
Cell type enrichment was conducted using protocol as
published in [75–77]. Briefly, a cell type marker list was
created for neurons, oligodendrocytes, endothelia, astrocytes, and microglia from cell type specific mouse brain
proteome and transcriptome studies Sharma et al. [78]and Zhang et al. [79]. Fisher exact tests with FDR correction were performed to determine enrichment for eachcell type marker list within each WGCNA network module. We used R scripts by Eric Dammer and Divya Nandakumar (Emory University School of Medicine) freely
available on GitHub at https://github.com/edammer/
CellTypeFET.
Multi-marker Analysis of GenoMic Annotation(MAGMA) [80] was conducted to identify modules withsignificant enrichment for genes related to Alzheimer’s
disease (AD) as identified in genome-wide association
studies (GWAS) [81–83]. To implement MAGMA, weused R scripts by Eric Dammer as used in Seyfried et al.
[77], freely available on GitHub at https://github.com/
edammer/MAGMA.SPA.
Gene set variation analysis (GSVA) was conducted on
the normalized and variance-stabilized count matrix in
R using the gsva package, also available on Bioconductor[71]. Gene sets used for GSVA included the C2 curated
gene sets collection from the Molecular Signatures Database [84, 85] as well as our previously published astrocyte-enriched gene sets [86].

Pybus et al. Journal of Neuroinflammation(2024) 21:156

Figure preparation

Figure 1 was prepared using BioRender.com. Other figures were prepared in Inkscape.

Results
Immune, glial, and pathological markers differed between
male and female 3xTg-AD mice

Because prior reports showed pronounced differences inpathological burden between male and female 3xTg-AD
mice [87, 88], we began the current study by evaluating
sex differences among all protein measures: cytokines,
MAPK phospho-proteins, glial markers, phosphorylated
and total tau, and Aβ40/42. We first obtained a holistic
view of variation within the data using a principal component analysis (PCA), which revealed a pronounced
effect of sex within both frontal cortex and hippocampus
brain regions (Fig. 2A).
To define the specific effects of sex versus injury andtime point, we conducted a multiple linear regression of
all quantified proteins and phospho-proteins using sex,
injury number, and time point as independent variables.

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This analysis revealed that sex, as compared to all othervariables in the model, was the most significant effect(Fig. S1). We next conducted comparisons between sex
for each measured protein in (1) sham-injured animals
only (Fig. 2B) and (2) all sham-injured and injured animals (Fig. S1A). All proteins determined to be significantly different between sexes based on the sham-injured
animals alone were also significantly different between
sexes when all samples were used, regardless of injury
condition (Fig. 2C, Fig. S2, Table S3).
Female 3xTg-AD samples showed significantly higher
levels of Aβ42 in the frontal cortex compared to males(p = 0.037, Wilcoxon rank-sum), and higher total andphosphorylated tau at a pathologically relevant residue(T181) [89] in the hippocampus compared to male samples (p < 0.0001, Wilcoxon rank-sum; Fig. 2C, Table S3).
This result matches most prior studies in 3xTg-AD mice,as reviewed in [87]. Interestingly, we found significantly
higher phospho-tau T181 in male compared to female
frontal cortices (p < 0.0001, Wilcoxon rank-sum; Fig. 2C,
Table S3).

Fig. 1 Experimental design. Male and female 3xTg-AD mice were exposed to 1x-5xCHIs and brain tissues were collected at multiple timepoints after eachinjury. Left hemispheres were micro-dissected for quantitative protein and mRNA analysis. Right hemispheres were used for immunohistochemistry. Maintext figures associated with each experimental outcome are indicated in italic fontPybus et al. Journal of Neuroinflammation(2024) 21:156

In addition to Aβ42 and phospho-tau T181, the majority of the 46 measured immune and pathological marker
proteins in both cortical and hippocampal brain regions
were elevated on average in males compared to females(Fig. 2B, Fig S1A). Of those, 31 from the frontal cortex(24 in sham-injured only) and 21 from the hippocampus(15 in sham-injured only) reached statistical significance(p < 0.05, Wilcoxon rank-sum; Fig. 2C, Fig. S2, Table S3).

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These included, the astrocyte reactivity markers GFAPand S100B, which were significantly elevated in both thefrontal cortex hippocampus compared to females. Taken
together, these data suggest an elevated immune signaling baseline in young (2-4mo) male 3xTg-AD mice compared to their age-matched female counterparts.
To determine if differences in immune markers
between male and female 3xTg-AD mice could be

Fig. 2 Pronounced sexual dimorphism of immune signaling, glial markers, and molecular markers of pathology in 3xTg-AD mice. A) Principal component
analysis of combined protein data across only sham-injured animals (left) and all samples (injured and sham-injured, right), reveals strong separation
between male and female mice on the first two PCs. Female replicate samples measured in male batch assays are shown in purple, emphasizing that theeffects of sex were biological, rather than technical. Percent variance described by each PC is annotated in parentheses. B) Sham-injured control miceshow distinct baseline sex differences in expression of glial markers, pathological markers, MAPK signaling, and cytokines. Proteins are categorized byfunction, then clustered by Euclidean distance within each category. Red to blue indicates relatively high to low expression among all sham-injured animals (n = 13 males, n = 10 females, z-scored). Of the 46 measured immune and pathological marker proteins in sham-injured animals, 37 from the frontal
cortex (80%) and 30 from the hippocampus (65%) had higher average expression in males compared to females. C) Pie charts for each region and sample
set. Left: just sham-injured. Right: all samples, injured and sham-injured show high proportions of all analytes measured (4 glial markers, 4 pathologicalmarkers, 9 MAPK, 30 cytokines) that are significantly elevated (p < 0.05, Wilcoxon rank-sum) in males (blue) or females (pink). See summary of significant
differences in Table S3

Pybus et al. Journal of Neuroinflammation(2024) 21:156

explained by the well-documented disparities in Aβ andtau in this model [87], we adjusted our data for effects
related to Aβ (Aβ40 and Aβ42), tau (total tau and phospho-tau T181), or both (Methods, Fig. S3, Table S4).
Adjustment for all pathological markers in the frontal
cortex reduced the number of differentially expressed
proteins from 31 to 15 elevated in males and from 9 to
3 elevated in females, suggesting that approximately halfof all differentially expressed proteins can be explainedby pathology. A similar effect is seen in the hippocampus
after adjustment: from 21 to 12 proteins elevated in males
and 16 to 8 in females. Adjustment for only Aβ-related
effects (Aβ40 + Aβ42) in the hippocampus accounted
for just 2 differentially expressed proteins (Table S4).
The persistence of sexual dimorphism among immune
markers (mostly cytokines) after adjustment for pathological measures suggests inherent differences in baseline
immune signaling of young male and female 3xTg-AD
mice independent of their stage of pathogenesis. Thus,

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we analyzed male and female data separately in the following analyses.
Cytokines and MAPK phospho-proteins are elevated after
each closed-head injury

To identify patterns of co-varying cytokines and phospho-proteins with injury and time point, we next conducted hierarchical clustering within each sex and brain
region (Materials and Methods, Fig. S4-5). We found that
3xTg-AD females exhibited robust variation of signaling
proteins with both number of injuries and time point. The
clustering identified a group of injury-induced cytokines(IL-9, IL-17, KC, IL-1α, IL-2, IL-1β, IL-13) and MAPK
phospho-proteins (phospho-Atf2, phospho-Mek1) thattightly clustered together within the frontal cortex datafrom female mice (Fig. S4, “Group 1”). Proteins within
this group were significantly increased at 24 h after 1x,
2x, and 3xCHI, showed mild increases 24 h after 4xCHI,
and returned to baseline sham-injured levels 24 h after
5xCHI (Fig. 3), suggesting a dysregulated immune

Fig. 3 Cytokines and MAPK phospho-proteins in 3xTg-AD females are elevated 4 h after 5xCHI and 24 h after 1x-3x CHI. Hierarchical clustering of cytokines and MAPK phospho-proteins (subset from Fig. S4, “Group 1”) from the frontal cortex of female 2-4mo 3xTg-AD sham-injured mice and 4 h (left) or
24 h (right) after 1x-5x closed head injuries (CHI). Each row represents data from an individual mouse z-scored along each column

Pybus et al. Journal of Neuroinflammation(2024) 21:156

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response after four or more injuries. These proteins were
elevated 4 h post-5xCHI compared to sham-injured, 4 hpost-1xCHI, and 4 h post-3xCHI animals (Fig. 3), indicating an earlier response after multiple injuries consistent with a “primed” immune state [90]. Although manyof the same cytokines and phospho-proteins from Group
1 again clustered together within the 3xTg-AD female
hippocampus (Fig. S4) and male 3xTg-AD frontal cortex
and hippocampus (Fig. S5), this same trend of an injurydependent increase was only noted in the female frontal
cortex. Together, these data indicate the importance ofevaluating multiple time points after injury to fully define
the immune response and emphasize the need to evaluate males and females separately.

pathology transcriptional signature after rmTBI infemales. To do so, we conducted bulk RNA sequencing
on a subset of 40 3xTg-AD females (somatomotor cortex consisting of cortical tissue collected at a range ofapproximately bregma ± 1 mm, n = 3–4 per group, Table
S2), then conducted gene set variation analysis (GSVA)
using the Alzheimer’s disease-related gene set from the
Molecular Signatures Database (MSigDB) C2 curated
gene sets collection. This gene set was significantly
enriched after a single injury and remained enriched withsubsequent injuries (Fig. 4C, Fig. S10). Thus, our protein
and transcriptional data together support pathologicaleffects of rmTBI in this model, which are correlated withneuroimmune markers.

Protein and transcript markers of pathology are elevatedafter injury

Cytokines and MAPK phospho-proteins elevated after
injury co-label with neurons

Our motivation for conducting this study in 3xTg-AD
mice was to identify relationships between immune
signaling and molecular markers associated with ADrelated pathology. To assess the relationship between our
injury-elevated Group 1 immune signals and pathologyassociated proteins, we conducted clustering and correlation analyses between Group 1 immune signals andpathological markers (total tau, phospho-tau (T181), Aβ
1–40, and Aβ 1–42). Few significant acute changes in Aβor correlation with Group 1 proteins were observed ineither region or sex (Figs. S6-7). However, in cortex andhippocampus in both sexes, the Group 1 immune markers clustered with phospho-tau with few exceptions (Fig.
S4-5), suggesting a connection between cytokines andpathologically relevant tau phosphorylation [89]. Pearson’s correlation coefficients between phospho-tau T181
and each cortical immune marker within each injury
group (Fig. 4A) revealed that the majority of immune signals in Group 1 were correlated (|R|>0.50) across multiple time points. These data thus suggest that injury drives
acute cytokine and MAPK immune signaling associatedwith increased phosphorylated tau in this model.
Interestingly, we observed these pronounced correlations at multiple time points in the frontal cortex despite
only finding group-wise elevation of phospho-tau at 24 hpost-3xCHI in female 3xTg-AD frontal cortices (Fig. S8)
and no group-wise changes in males. However, total tau
and phospho-tau (T181) were elevated at multiple time
points after 3x and 5xCHI in the female 3xTg-AD hippocampus (Fig. 4B, Fig. S8-9). These data thus indicate that
our CHI model induces acute pathological changes in aregion-dependent manner, and that Group 1 immune
signaling correlates with phosphorylated tau even in theabsence of group changes in total or phosphorylated tau
in this model.
Having found group-wise tau differences in 3xTgAD females, we asked if we could also detect an AD

Having found that Group 1 proteins correlated withphospho-tau, which is primarily a neuronal protein(Fig. 4A), we next asked if Group 1 signaling would be
associated with neurons or other cell types. To this end,
we conducted immunohistochemistry for various Group
1 proteins together with markers for neurons (NeuN) andastrocytes (GFAP) as a non-neuronal control at the 24 htime point in mice exposed to 1x or 3xCHI. We foundthat IL-1α, IL-1β, IL-13, phospho-Atf2, and phosphoMek1/2 co-labeled with the neuronal marker NeuN in
the cortex and hippocampus after injury, suggestingneuronal involvement in post-rmTBI immune signaling(Fig. 5, Fig. S11). Additionally, IL-1β, IL-13, KC, phospho-Atf2, and phospho-Mek1/2 did not co-labeled withthe astrocyte marker GFAP in the cortex (Fig. S12) or
hippocampus (Fig. S13). We further noted that many ofthese markers also co-labeled with NeuN in the frontal
cortex of sham-injured animals (Fig. S14), which is consistent with our prior finding that neurons abundantly
express cytokines and MAPK phospho-proteins underhomeostatic conditions [91]. Collectively, these data indicate that neurons abundantly produce many of the cytokines and phospho-proteins that our quantitative assays(Fig. 3, S4, S5) show are affected by injury.
Transcriptional and protein markers of astrocyte reactivity
are increased after repetitive closed head injury

Astrocyte reactivity is a central phenotype of brain
injury. Several studies have reported elevated expression of the astrocyte reactivity marker GFAP that begins
within hours and persists up to 1mo after TBI. GFAP isalso being evaluated as a CSF biomarker for TBI diagnosis and prognosis [92–96]. We therefore hypothesizedthat astrocyte reactivity would be increased in 3xTg-AD
mice exposed to successive mTBIs. To analyze possible
changes in astrocyte phenotypes and function following injury, we conducted a gene set variation analysis

Pybus et al. Journal of Neuroinflammation(2024) 21:156

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Fig. 4 Pathological markers in 3xTg-AD females are increased after injury and associated with cytokines in a region dependent manner. A) Heatmap ofPearson’s correlation coefficient of cortical phospho-tau T181 versus each measured cortical protein (columns) within each injury group and time point(rows); red boxes indicate FDR-adjusted p < 0.05; green and purple dots represent Group 1 and 2 proteins, respectively. Green box shows eight out ofnine Group 1 proteins cluster together and show correlation coefficients R > 0.50 with phospho-tau T181. B) Total tau is significantly upregulated in the
hippocampus at 24 h post-3xCHI and each successive time point compared to sham-injured animals (p = 0.014, p = 0.0021, p = 0.0021, p = 0.035, p = 0.022
respectively, Wilcox). Phospho-tau (T181) is significantly upregulated in the hippocampus 30 min and 24 h post-5xCHI compared to sham-injured animals (p = 0.0058, p = 0.022 respectively, Wilcox) (mean ± SEM). C) Expression of the gene set “Kegg Alzheimer’s Disease” significantly increases after 1xCHI(p = 0.006, t-test with Bonferroni correction) (mean ± SEM). See individual gene changes in Fig. S10(GSVA) [71, 84] of custom-annotated astrocyte gene setsthat define astrocyte homeostatic and reactive functions(1,740 genes spanning 17 gene sets plus the A1 and A2
gene sets [97]) from our prior work [86] (Fig. 6A). To
define the specific effects of injury and time point on
astrocyte transcriptional profiles, we conducted multiple
linear regression of the enrichment scores of all 17 astrocyte gene sets using number of injuries, time point after
injury, and a binary injury variable (injured or shaminjured) as independent variables. This analysis revealed
that the number of injuries had a greater effect on geneset enrichment than the post-injury time point (Fig. 6B).
We therefore combined post-injury time points (30 min,

4 h, 24 h) to create a single group for each number of
injuries 1-5xCHI, improving our ability to analyze differences despite our small sample sizes (n = 3–4 per time
point).
Clustering of sample enrichment scores for the 17
astrocyte gene sets revealed two distinct patterns ofexpression after repeated injury (Fig. 6A). First, 12 astrocyte gene sets formed a cluster characterized by rapid
decreases in enrichment following the first CHI, as represented by the Gliotransmission and Plasticity sets
(Fig. 6C). These sets mostly showed recovery of expression to sham-injured levels by 24 h post-4/5xCHI. Manyof these sets represent normal homeostatic astrocyticPybus et al. Journal of Neuroinflammation(2024) 21:156

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Fig. 5 Group 1 cytokines and phospho-proteins co-label with NeuN in 3xTg-AD frontal cortex 24 h post-CHI. Group 1 cytokines (IL-1α, IL-1β, IL-13, and KC)
and MAPK phospho-proteins (phospho-Atf2 and phospho-Mek1/2) (red) co-stained with NeuN (green) and DAPI (blue) show neuronal co-labeling in the
3xTg-AD frontal cortex 24 h post-1xCHI in female 3xTg-AD mice aged 2-4mo (scale bar: 500 μm, representative sections from n = 6 mice 24 h post-1xCHI)

Pybus et al. Journal of Neuroinflammation(2024) 21:156

functions, such as gliotransmission, plasticity, gene
expression, and the perisynaptic astrocyte process (PAP).
A rapid decrease in expression of these sets following
1xCHI suggests astrocytes first respond to injury in thismodel by altering their homeostatic functions. The second response pattern, represented by Stress Response
and General Metabolism (Fig. 6D), shows a gradualincrease in expression after 3-5xCHI. Coloring data
points according to Gfap revealed an association between
astrocyte reactivity and elevated Stress Response andGeneral Metabolism gene sets. Other gene sets exhibiting this trend of a gradual increase include the A1 andA2 phenotypes and Carbohydrate Metabolism, which arefunctions that are generally related to astrocyte reactivity [97, 98]. These data therefore suggest elevated astrocyte reactivity by 3-5xCHI, 72–120 h after the first daily
injury.
We next quantified astrocyte reactivity markers glial
fibrillary acidic protein (GFAP) and S100 calcium binding protein B (S100B) by ELISA in the frontal cortex andhippocampus of all female 3xTg-AD mice exposed to 1x,
3x, or 5x once daily CHIs. To assess GFAP in our injury
model, we conducted a multiple linear regression analysis
with dependent variables of time point following injury(pre-injury vs. 30 min, 4 h, or 24 h post-injury) and total
number of injuries. Across female 3xTg-AD frontal cortex GFAP levels, we found that post-injury samples weresignificantly upregulated from pre-injury samples at both
30 min and 4 h (p = 0.00061, p = 0.0082 respectively; multiple linear regression model, pre/post and number of
injuries), but not 24 h post-injury (Fig. 6E), and that the
total number of injuries was not a significant predictor of
GFAP levels. In a parallel analysis, multiple linear regression of cortical S100B in female 3xTg-AD mice similarly
revealed statistically significant upregulation 30 min
post-injury vs. pre-injury (p = 0.028) but not after 4–24 h(Fig. S15). Taken together, these data indicate a rapid,transient state of astrocyte reactivity following each
repeated CHI that begins as early as 30 min, but returns
to basal levels by 24 h. We also quantified microglial activation marker CD68 but found that changes were minimal and inconsistent between sex and region (Fig. S16).
Because astrocyte reactivity is a key component of thebrain’s response to stress and injury, we next analyzed
the relationships between GFAP protein levels, cytokine expression, and MAPK signaling activity. Among
both female cortical and hippocampal samples, a groupof MAPK phospho-proteins, termed “Group 2” (phospho-Stat3, phospho-Jnk, phospho-HSP27, phospho-p38,
phospho-p90 RSK, phospho-Erk1/2, and phospho-p53),
clustered tightly together across all injury groups andclosely with GFAP (Fig. S4-5), indicating potential
involvement of astrocyte reactivity in both brain regions.
Therefore, we next computed Pearson’s correlation

Page 12 of 21

coefficients between GFAP and each measured immune
marker within each injury and time group (Fig. 6F) andfound broad positive correlations both before and after
injury, emphasizing the involvement of astrocytes inbrain immune signaling in this model.
Co-expression network analysis reveals the effect of injury
on pathology- and cell type-associated transcriptional
modules

To holistically define the effects of repetitive mTBI in
our model, we concluded the current study by conducting weighted gene co-expression network analysis(WGCNA) on the same subset of 40 female 3xTg-AD
cortical samples used in Figs. 4C and 6 sampled from all
injury groups and time points (n = 3–4 per group). One
sample was removed from the analysis due to its outlier
status and exclusion within the protein data (Mahalanobis multivariate outlier detector with α < 0.001, Methods).
WGCNA identified 14 distinct modules of co-varying
genes (Fig. 7A). A module eigengene (ME) was computed for each module as its first principal component,
and expression scores of each ME were displayed for eachsample across all injury and time point groups (Fig. 7B).
Due to small sample sizes and relatively low variation
within time points of each injury group (Fig. 7B), we combined post-injury time points (30 min, 4 h, 24 h) into one
group per injury number and compared module expression across number of injuries. The two modules comprised of the largest number of genes, ME1 (turquoise)
and ME2 (blue), rapidly changed after 1xCHI, with ME1
showing a significant increase (p = 0.047, permutation
test compared to cham-injured; see Methods) and ME2
showing trending decreases (p = 0.054) post-injury. In
contrast, ME9 (magenta) progressively increased withsuccessive injuries (p = 0.0015 after 3xCHI, p = 0.023 after
5xCHI; permutation tests compared to sham-injured)(Fig. 7C) and tracked closely with ME7 and ME8 (black,pink) after 2-5xCHI. Moreover, ME3 (brown) and ME6(red) were increased after 1xCHI (p = 0.030 brown,
p = 0.043 red) then decreased compared to sham-injured
after 5xCHI (p = 0.037 brown, p = 0.072 red), while ME10
(purple) and ME11 (green-yellow) peaked after 2xCHI(p = 0.029 purple, p = 0.11 green-yellow), then returned
to sham-injured levels. These modules therefore definechanges associated with rapid (ME1, ME2), transient(ME3, ME6, ME10, ME11) and gradual time scales (ME7,
ME8, ME9).
To understand the biological relevance of each module,
we used gene ontology (GO, via PANTHER) to identify
top GO terms enriched in each module (Fig. 7D). The
rapidly changing ME2 (blue) was enriched for genes
related to nervous system development, suggesting a
rapid downregulation of homeostatic neuronal functions.
The gradually changing modules, ME7, ME8, and ME9

Pybus et al. Journal of Neuroinflammation(2024) 21:156

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Fig. 6 Cortical astrocytes become reactive following mild TBI. A) Gene set variation of custom-curated astrocyte gene sets (columns) across injury andtime point groups (rows) reveals two distinct expression patterns: gradual increase by 3-5xCHI (left cluster) and rapid decrease after 1xCHI (right cluster).
Gene sets are z-scored and clustered by Euclidean distance. B) Multiple linear regression models were calculated for each gene set using fixed effects oftime point after most recent injury, binary injury status (injured, sham-injured), and number of injuries. The model shows that the effects of injury (bothnumeric and binary) outweigh time-dependent changes for most gene sets. Red boxes indicate statistical significance (p < 0.05). C) Astrocyte gene sets
Gliotransmission and Plasticity are representative of right cluster, displaying a decrease after 1xCHI followed by return to sham-injured levels by 4-5xCHI(p = 0.013, p = 0.032, t-test with Bonferroni adjustment) (mean ± SEM). Data points are colored by the z-score of the log of astrocyte reactivity marker geneGfap. D) Astrocyte gene sets Stress Response and General Metabolism are representative of left cluster, displaying a gradual increase after 3-5xCHI (StressResponse 3xCHI p = 0.038, 4xCHI p < 0.0001, 5xCHI p = 0.015; General Metabolism 4xCHI p = 0.00012, 5xCHI p = 0.020; t-test with Bonferroni adjustment)(mean ± SEM). Data points are colored by the z-score of the log of astrocyte reactivity marker gene Gfap, which is associated with high expression in either
set. E) Glial fibrillary acidic protein (GFAP) is significantly upregulated 30 min and 4 h post-injury vs. pre-injury (p = 0.00061, p = 0.0082 respectively; multiple
linear regression model, pre/post and number of injuries) (mean ± SEM). Note that sham-injured is equivalent to pre-1xCHI, 2xCHI at 24 h is equivalent
to pre-3xCHI, and 4xCHI at 24 h is equivalent to pre-5xCHI in this representation. F) Heatmap of Pearson’s correlation coefficient of cortical GFAP versus
each measured cortical protein (columns) within each injury group and time point (rows); red boxes indicate FDR-corrected p < 0.05. Panels A-D present
transcriptional data from female somatomotor cortex. Panels E-F present protein data from female frontal cortex

Pybus et al. Journal of Neuroinflammation(2024) 21:156

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Fig. 7 (See legend on next page.)(black, pink, magenta), were enriched for GO terms associated with wound healing, cytokine response, gliogenesis, and regulation of the immune response, suggestinga multi-day response that involves immune processes andhealing. Importantly, those modules that increase withinjury were all correlated with immune proteins (inflammatory cytokines, MAPK activation, and GFAP) quantified in the frontal cortex of the same animals (ME1, ME7,

Pybus et al. Journal of Neuroinflammation(2024) 21:156

Page 15 of 21(See figure on previous page.)
Fig. 7 WGCNA of 3xTg-AD somatomotor cortex transcriptomes identifies modules of co-expressed genes associated with injury, GO biological processes, inflammatory protein expression, brain cell types, and AD genetic risk factors. A) WGCNA identified 14 modules of co-expressed genes. B) Expression scores for the 14 module eigengenes (MEs, the first principal component of each module) for each sample. MEs are clustered by Euclidean distance(columns) and expression scores are displayed by increasing injury number and time point from bottom to top of the heatmap (rows). C) Line plots showchanging ME expression scores over increasing numbers of injuries. Time points for 30 min, 4 h, and 24 h after 1x, 3x, and 5xCHI are combined due to low
sample sizes (n = 2–4). ME1 (turquoise) and ME2 (blue) show opposite behavior after 1xCHI, with ME1 significantly increasing (p = 0.047, permutation test)
alongside trending decreases (p = 0.054) in ME2 after a single injury. ME 9 (magenta) shows significant increases after successive injuries that track closelywith ME7 and 8 (black and pink) (p = 0.0015 after 3xCHI, p = 0.023 after 5xCHI; permutation tests compared to sham-injured; see Methods) (mean ± SEM).
D) Gene ontology enrichment of biological processes was conducted for each module (Fisher’s exact test with FDR-corrected p-value < 0.05). ME3 andME6 had fewer than two significant GO sets. Sets marked with asterisks had FDR-corrected p > 0.05 but uncorrected p < 0.0001. E) Protein expression datafrom the frontal cortex of the same 3xTg-AD animals were correlated against each ME. Selected proteins showed a correlation p-value of at least 0.01
against at least one ME. Red indicates a positive Pearson’s correlation coefficient (R) while blue indicates negative correlation. F) Cell type enrichment
analysis showed significant enrichment of neuronal genes in ME2 (blue), oligodendrocyte genes in ME4 (yellow), endothelia genes in ME7 (black), astrocyte genes in ME8 (pink), and microglial genes in ME9 (magenta). G) MAGMA enrichment for GWAS AD genes showed significant over-representation
within ME1 (turquoise), ME9 (magenta), and ME14 (cyan). The dotted red line indicates an enrichment z-score of 1.96, above which a module is consideredsignificantly enriched (normal distribution, p < 0.05)

ME9; “Trait Correlations” Fig. 7E), whereas ME2, whichshows decreasing trends after injury and is associatedwith neuronal function, was inversely correlated withimmune proteins (Fig. 7E).
We next conducted a cell-type enrichment analysis
for genes with high specificity in neurons, oligodendrocytes, endothelia, astrocytes, and microglia derived fromcell type specific mouse brain proteome and transcriptome studies [78, 79](Fig. 7F). We found that ME2 (blue),
which decreases after a single injury and is inversely correlated with immune protein markers, was significantly
enriched for neuronal genes. In contrast, ME7, ME8, andME9 (black, pink, magenta), which show gradual increasing trends with successive injuries and (with the exception of ME8) correlate with immune protein markers,
were enriched for endothelial, astrocyte, and microglial
genes, respectively.
We concluded this unbiased analysis by asking if specific modules were associated with AD-related genes. To
test this, we used a Multi-marker Analysis of GenoMic
Annotation (MAGMA) analysis to determine if AD genes
were significantly over-represented within specific gene
modules. Indeed, AD genes were enriched in ME1 (i.e.
Pfdn5, Rps11, Rpl13a, Lamtor4, Rpl14), which increases
with a single injury and correlates significantly to several
inflammatory proteins. AD genes were also enriched in
ME9 (i.e. Hepacam, Vav1, Hexb, Grn, Laptm5), which
increases gradually, correlates with inflammatory proteins, and is enriched for microglial genes (Fig. 7G).
Taken together, these data suggest an early inflammatory response associated with AD genes (ME1) and aconcomitant neuronal decrease in homeostatic functions(ME2) after a single injury in 3xTg-AD mice. Moreover,multiple injuries progressively increased gene expression
profiles associated with wound healing and regulation of
inflammation within endothelia, astrocytes, and microglia, emphasizing that repetitive injuries affect all cellularcompartments.

Discussion
In this study, we comprehensively profiled immune andpathological responses to rmTBI in the brains of 3xTgAD mice using a combination of targeted protein panels
and RNA sequencing. We hypothesized that repeatedmTBI drives progressive changes in immune signaling
together with markers of astrocyte reactivity, microglial
activation, and changes in tau and Aβ. Because wild typemice do not develop plaques and tangles associated withAD or brain injury-induced pathology, we employed thetriple transgenic model of AD (3xTg-AD) in the present
study to evaluate relationships between injury, pathological hallmarks, and markers of immune response acrossmultiple acute time points. To our knowledge, this study
is the first to define the temporal evolution of immune
and pathological responses to rmTBI at both the protein
and transcriptional levels in a mouse model capable ofdisplaying both Aβ plaques and neurofibrillary tangles.
While sex differences in pathology and cognitive outcomes after injury in 3xTg-AD mice have been extensively studied [87], few studies have evaluated sex
differences in immune signaling. Indeed, to our knowledge, all published brain injury studies that make use of
the 3xTg-AD model report only male mice or combinedsex experimental groups. Sexual dimorphism of brain
immune signaling in both humans and other mousemodels is well-documented, generally showing a stronger immune response in females [99]. Female 3x-Tg AD
mice are reported to have increased Aβ pathology, possibly due to increased β-secretase processing [88, 100, 101]
while male 3xTg-AD mice and have been reported tohave increased systemic autoimmunity [102], emphasizing the need to evaluate the role of sex. Indeed, we foundthat 40 out of 46 measured proteins were significantly
different between males and females in the frontal cortex, as well as 37 out of 46 in the hippocampus. Further,
we determined that total tau, phospho-tau T181, Aβ40,
and Aβ42 accounted for only half of the differentiallyexpressed proteins. In particular, pathology-adjusted dataPybus et al. Journal of Neuroinflammation(2024) 21:156

left 18 significantly different proteins in the frontal cortex and 20 in the hippocampus. These findings emphasize the importance of evaluating the role of sex. Indeed,dichotomizing our protein data into male and female
datasets revealed that females exhibited a much stronger
response to injury (Fig. S4, Fig S5). For this reason, we
focused our analysis on females.
A key finding of our work is that many of the quantified cytokines and phospho-protein signaling molecules
co-labeled with NeuN. Neurons are known to express aplethora of cytokine receptors and undergo phosphoprotein signaling cascades (i.e., MAPK and NF-κB,
among others) in response to inflammation [103–108],
but an increasing body of work suggests that neurons also
participate in cytokine secretion in both homeostatic andpathological conditions [109–120]. Indeed, prior workfrom our own group has found increased MAPK and
NF-κB signaling and expression of cytokines that co-labelwith neurons following rmTBI in male wild type mice[38]. Moreover, multiple studies have reported evidenceof co-localization of immune signaling proteins and/or
their transcripts with neurons in most of our “Group 1”
proteins, which are elevated in the cortex in females following each CHI: IL-1α [109, 110], IL-1β [111, 112], IL-2
[113, 114], IL-9 [115], IL-13 [116–118], and KC. Our IHC
in the current study revealed prominent NeuN co-labeling of four cytokines (IL-1α, IL-1β, IL-13, and KC) andboth MAPK phospho-proteins (phospho-Atf2 and phospho-Mek1) within “Group 1,” which is consistent withprior literature. One caveat of our NeuN labeling in thesedrop-fixed brains is that there was reduced NeuN labeling in the nuclei of many cells. This may be an artifact ofour drop fixation protocol [121], which we adopted toenable microdissection of the left hemisphere for protein
and mRNA analysis. Although future IHC studies are
warranted using a perfusion-fixation approach, our data
together with prior literature therefore support a role forneurons in orchestrating an immune signaling response
post-rmTBI. Our current findings are further supported
by our prior work showing that numerous cytokines andphospho-proteins were neuron-derived in resting state
brain via native-state proteomics [91], which will provide
a quantitative approach to interrogate neuronal signaling
in future rmTBI studies.
To our knowledge, our work is the first to conduct transcriptional profiling after successive mTBIs in 3xTg-AD
mice. We used WGCNA to provide an unbiased summary of gene expression trends in the data, then assessed
each identified gene module for enhanced expression of
specific biological processes (gene ontology), cell typespecific markers, and GWAS-identified markers of AD, as
well as correlation with paired protein expression (Fig. 7).
Indeed, we identified an early inflammatory response
associated with AD-GWAS gene expression and protein

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translation after a single injury, paired with a concomitant decrease in neuronal-enriched functions associatedwith neuronal development and synaptic signaling. Thesechanges after a single injury seemingly parallel our findings of increased 24 h cytokines after 1x or 3xCHI thatare no longer elevated after 5xCHI at 24 h. Within daysof repeated injury, there was a gradual increase of endothelia-, astrocyte-, and microglia-enriched gene modules
associated with wound healing and regulation of inflammation, suggesting a possible “cumulative” response. We
also found a significant enrichment of AD-GWAS risk
genes within the microglia-enriched gene modules, suggesting microglial involvement in acute pathogenesis
after injury. We note, however, that our CD68 ELISA data
did not change with injury, and thus we did not focus ourprotein and histological analyses on microglia. Collectively, these data suggest that even a single mTBI elicits
a change in genes associated with lost neuronal homeostasis, whereas repeated daily mTBIs yield changes that
propagate into astrocytes, microglia, and endothelial
cells. The appearance of both transient and accumulating
aspects of repetitive closed head injuries may be essential to identifying the mechanisms that cause cumulative
functional deficits.
Interestingly, our protein data showed that the astrocyte reactivity markers GFAP and S100B are transiently
elevated at 30 min after each repetitive mTBI in thefemale 3xTg-AD frontal cortex. Although few studies
have characterized such rapid changes in reactivity markers following closed-head injury, elevated GFAP and
GFAP breakdown products are detectable in serum lessthan one hour after mild to severe TBI in mice [92–94].
This parallels findings in human patients, where serum
GFAP levels rapidly increase within an hour then decline
within 24–72 h following mild-to-severe injury [94–96,
122–124] as well as some mouse studies [18, 125], but isin contrast to several other studies in rodents showing
sustained GFAP elevation up to two weeks post-injury[126–129]. Differences in rodent and injury models, sample type (serum, CSF, tissue lysate), and sex compositionof experimental groups may account for some of theseconflicting results. On the transcriptional level, we foundthat astrocyte-specific gene sets for stress response,
general metabolism, and immune-related phenotypes
A1 and A2 [97] showed gradual increases compared
to sham-injured after 3-5xCHI, while homeostatic sets
associated with gliotransmission and astrocyte plasticity
showed decreased expression compared to sham-injured
after just 1xCHI. These findings mirror previous studies
which have identified elevated immune response genes
in astrocytes 7 days post-severe TBI [130] and the lack ofastrocyte-specific inflammatory gene changes 24 h after
mild TBI [131]. Combined, our protein and transcriptional data indicate that astrocytes are responsive to (r)Pybus et al. Journal of Neuroinflammation(2024) 21:156

mTBI as early as 30 min after injury, while transcriptionalchanges in stress response pathways persist on the orderof days.
The MAPK intracellular signaling pathway is a known
mediator of astrocyte reactivity [132–134], including after a traumatic brain injury [48]. The clusteringof female 3xTg-AD cortical GFAP and multiple MAPK
phospho-proteins suggests that MAPK signaling may be
responsible for the phenotypic transition to a reactive
state in our model. Further, GFAP also correlates with ahost of cytokines such as LIX, IL-7, IL-3, IL-12p40, RANTES, IL-15, M-CSF, G-CSF. These relationships generally
exist independently of injury and suggest involvement
within reactive astrocyte signaling, which peaks on atime scale of minutes-to-hours after each injury. Further
studies should investigate the effect of MAPK signaling inastrocyte response to brain injury.
The current study has several limitations requiring
future work. First, while the 3xTg-AD mouse model isbecoming relatively popular for brain injury research[55–62], its predisposition towards spontaneous development of pathology due to the expression of three causal
AD transgenes is not genetically representative of mosthuman TBI patients. The use of human transgenes is necessary due to the lack of pathogenicity of native mouse
tau and Aβ, while the use of mutations specifically associated with dementia is justified by the short life spanof mice. Here, we mitigated the potential effects of preexisting pathology on injury outcomes by using mice aged
to 2–4 months, well before the reported build-up of Aβ
and phosphorylated tau at 6 and 12 months, respectively[135, 136]. Second, while bulk tissue processing enabledappropriate sample sizes and comparison between 12
injury and time point groups, it also limited our ability to draw conclusions at the level of a single cell. Ourbulk analysis highlights injury groups and time points ofinterest to be interrogated in future work for single cell
analysis. Single cell profiling would identify how subtypes of neurons, glia, and other cells respond to successive mTBIs at a higher resolution than can be inferred by
network analysis of bulk transcriptomic data. Specifically,
single cell analysis may help clarify the extent to which
neurons may be involved in immune signaling throughcytokine expression and MAPK activation as seen in the
injury-driven “Group 1”. Additionally, bulk and single cell
RNAseq profiling are needed in both sexes in future work
to determine which transcriptional changes are shared
between or distinct to each sex. Third, our findings are
purely correlative. While we have established evidenceof a relationship between elevated neuroimmune signaling and outcome, future studies are required to establish
whether this signaling indeed drives pathogenesis after
rmTBI, or is merely correlated.

Page 17 of 21

Conclusions
In total, our data define an acute neuroimmune cascade of mild traumatic brain injury in 3xTg-AD mice,
consisting of (i) an immediate decrease in neuronal
homeostatic gene expression and an elevation of ADassociated genes after a single injury, (ii) elevation of asubset of cortical cytokines that correlated with phosphorylated tau and co-labeled with neurons in injured
mice, and (iii) increased expression of non-neuronal
genes suggesting glial reactivity within days of repeatedinjury. We provide resolution of these acute protein andtranscriptional changes at a previously uncharacterized
minutes-to-days time scale alongside the key experimental variables of sex, brain region, and number of
injuries. Pronounced changes in neuronal gene expression that correlate with inflammatory protein expression
and co-labeling of injury-elevated cytokines and MAPK
phospho-proteins with neuronal marker NeuN suggest a
key role for neurons in the modulation of neuroimmune
activity following brain injury. Further, the association ofphospho-Mek1 and phospho-Atf2, cytokines, and phospho-tau T181 after injury may represent a therapeutic
avenue for regulating inflammation and acute pathological mechanisms. Future work should explore the causal
functions of these key molecular signals.
Data availability

The gene expression FASTQ files and count matrix that
support the findings of this study have been deposited in
the Gene Expression Omnibus (GEO) repository underseries record GSE226838. Protein expression data andmouse metadata has been deposited in the Open Data
Commons for Traumatic Brain Injury repository under
DOI: https://doi.org/10.34945/F5ZK51. The ODC-TBI
is a secure, cloud-based repository platform designed toshare research data [137].
Abbreviations
3xTg-AD	Triple transgenic Alzheimer’s disease mouse modelAβ	Amyloid beta
ACURO	Animal Care and Use Review Office (U.S. Army MRDC)
AD	Alzheimer’s disease
APP	Amyloid precursor protein
ARRIVE	Animal Research: Reporting of In Vivo ExperimentsATP	Adenosine triphosphate
BSA	Bovine serum albumin
CD68	Cluster of differentiation 68
CHI	Closed-head injury
CRAN	Comprehensive R Archive Network
DAPI	4′,6-diamidino-2-phenylindole
ELISA	Enzyme-linked immunosorbent assay
FDR	False discovery rate
G-CSF	Granulocyte colony-stimulating factor
GEO	Gene expression omnibus
GFAP	Glial fibrillary acidic protein
GM-CSF	Granulocyte macrophage colony-stimulating factor
GSVA	Gene set variation analysis
GWAS	Genome-wide association study
IACUC	Institutional Animal Care and Use Committee
Iba1	Ionized calcium binding adaptor molecule 1

Pybus et al. Journal of Neuroinflammation(2024) 21:156

IFN-γ	Interferon gamma
IHC	ImmunohistochemistryIL	Interleukin
IP-10	Interferon gamma-induced protein 10
KC	Keratinocyte chemoattractant
LIF	Leukemia inhibitory factor
LIX	Lipopolysaccharide-induced CXC chemokine
MAGMA	Multi-marker Analysis of GenoMic Annotation
MAPK	Mitogen-activated protein kinase
MAPT	Microtubule-associated protein tau
MCP-1	Monocyte chemoattractant protein 1
M-CSF	Macrophage colony-stimulating factor
MIG	Monokine induced by interferon gamma
MIP	Macrophage inflammatory protein
MRDC	Medical Research and Development Command (U.S. Army)
NCBI	National Center for Biotechnology Information
mTBI	Mild traumatic brain injury
PAP	Perisynaptic astrocyte processes
PCA	Principal component analysis
PD	Parkinson’s disease
PSEN1	Presenilin 1
pTau	Phospho-tau
RANTES	Regulated upon activation, normal T cell expressed andpresumably secreted
rmTBI	Repetitive traumatic brain injuryRNA	Ribonucleic acid
S100B	S100 calcium binding protein B
TBI	Traumatic brain injury
TBST	Tris-buffered saline with tween
TMEM119	Transmembrane protein 119
TNF-α	Tumor necrosis factorαtTau	Total tau
WGCNA	Weighted gene co-expression network analysis

Supplementary Information

The online version contains supplementary material available at https://doi.
org/10.1186/s12974-024-03128-1.
Supplementary Material 1
Acknowledgements
We wish to acknowledge fruitful discussions with Michelle LaPlaca andManu Platt. We also thank the core facilities at the Parker H. Petit Institute for
Bioengineering and Bioscience at the Georgia Institute of Technology for useof their shared equipment, services, and expertise.
Author contributions
AFP conducted data analysis, prepared figures, and wrote the manuscript.
ROB, LR, and ST conducted animal experiments, curated animal data, andconducted cerebral blood flow measurements. AFP, SB, MNG, AK, FRM, KU, andBD conducted molecular assays and experimentation. ED and SR contributed
to transcriptional profiling design, computational analysis, and biologicalinterpretation. LBW and EMB conceived the study, supervised the research,and revised the manuscript. All authors reviewed the manuscript.
Funding
This work was supported by the U.S. Department of Defense through theCongressionally Directed Medical Research Programs (CDMRP) Peer Reviewed
Alzheimer’s Research Program (PRARP) under Award No. W81XWH-18-1-0669(LBW/EMB). Opinions, interpretations, conclusions, and recommendations
are those of the author and are not necessarily endorsed by the Departmentof Defense. It was also supported by the National Institutes of Healthunder Award Nos. 1 R01 NS115994 (LBW/EB), 1R01AG075820 (SR/LBW),R01NS114130 (SR), 1RF1AG071587 (SR). AFP was supported in part by the
National Institutes of Health Cell and Tissue Engineering Biotechnology
Training Grant (T32-GM008433).

Page 18 of 21

Data availability
The gene expression FASTQ files and count matrix that support the findingsof this study have been deposited in the Gene Expression Omnibus (GEO)
repository under series record GSE226838. Protein expression data and mouse
metadata has been deposited in the Open Data Commons for Traumatic Brain
Injury repository under DOI: 10.34945/F5ZK51. The ODC-TBI is a secure, cloudbased repository platform designed to share research data (123).

Declarations
Ethics approval and consent to participate
All protocols were approved by the U.S. Army Medical Research andDevelopment Command (MRDC) Animal Care and Use Review Office (ACURO)
and the Emory University Institutional Animal Care and Use Committee(IACUC) in accordance with National Institutes of Health guidelines.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Author details
1
Wallace H. Coulter Department of Biomedical Engineering, Georgia
Institute of Technology and Emory University, Atlanta, GA, USA
2
George W. Woodruff School of Mechanical Engineering, Georgia Instituteof Technology, Atlanta, GA, USA
3
School of Biological Sciences, Georgia Institute of Technology, Atlanta,
GA, USA
4
Center for Neurodegenerative Diseases, School of Medicine, Emory
University, Atlanta, GA, USA
5
Department of Neurology, School of Medicine, Yale University, New
Haven, CT, USA
6
Department of Pediatrics, School of Medicine, Emory University, Atlanta,
GA, USA
7Children’s Healthcare of Atlanta, Atlanta, GA, USA
8
Parker H. Petit Institute for Bioengineering and Bioscience, Georgia
Institute of Technology, Atlanta, GA, USA
Received: 16 February 2024 / Accepted: 12 May 2024

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