Abstract
Obesity is recognized as a significant risk factor for Alzheimer’s
disease (AD). Studies have supported the notion that obesity
accelerates AD-related pathophysiology in mouse models of AD. The
majority of studies, to date, have focused on the use of early-onset AD
models. Here, we evaluate the impact of genetic risk factors on
late-onset AD (LOAD) in mice fed with a high fat/high sugar diet (HFD).
We focused on three mouse models created through the IU/JAX/PITT
MODEL-AD Center. These included a combined risk model with APOE4 and a
variant in triggering receptor expressed on myeloid cells 2
(Trem2^R47H). We have termed this model, LOAD1. Additional variants
including the M28L variant in phospholipase C Gamma 2 (Plcg2^M28L) and
the 677C > T variant in methylenetetrahydrofolate reductase (Mthfr^677C
> ^T) were engineered by CRISPR onto LOAD1 to generate LOAD1.Plcg2^M28L
and LOAD1.Mthfr^677C > ^T. At 2 months of age, animals were placed on
an HFD that induces obesity or a control diet (CD), until 12 months of
age. Throughout the study, blood was collected to assess the levels of
cholesterol and glucose. Positron emission tomography/computed
tomography (PET/CT) was completed prior to sacrifice to image for
glucose utilization and brain perfusion. After the completion of the
study, blood and brains were collected for analysis. As expected,
animals fed a HFD, showed a significant increase in body weight
compared to those fed a CD. Glucose increased as a function of HFD in
females only with cholesterol increasing in both sexes. Interestingly,
LOAD1.Plcg2^M28L demonstrated an increase in microglia density and
alterations in regional brain glucose and perfusion on HFD. These
changes were not observed in LOAD1 or LOAD1.Mthfr^677C > ^T animals fed
with HFD. Furthermore, LOAD1.Plcg2^M28L but not LOAD1.Mthfr^677C > ^T
or LOAD1 animals showed transcriptomics correlations with human AD
modules. Our results show that HFD affects the brain in a
genotype-specific manner. Further insight into this process may have
significant implications for the development of lifestyle interventions
for the treatment of AD.
Keywords: Alzheime’s disease, transcriptomics, diet, obesity, genetic
risk alleles, predisposition
Introduction
Genetic and genome-wide association studies have identified variations
in numerous genes that increase the risk for late-onset AD (LOAD). The
E4 allele of apolipoprotein E (APOE4) is the greatest genetic risk
factor but with variations in many other genes, including triggering
receptor expressed on myeloid cells 2 (TREM2), also contribute to risk.
Importantly, unlike relatively rare cases of familial AD (fAD), which
are predominantly caused by mutations in APP or one APP processing
gene, no single LOAD-associated variant is sufficient to cause AD. It
is anticipated that combinations of genetic risk factors are required
to develop LOAD. Studies also suggest that genetic factors may only
contribute between 50 and 70% of the risk for LOAD, meaning many
individuals are likely to develop LOAD due to a combination of genetic
and environmental factors ([69]Kawahara et al., 2017; [70]Sarkar et
al., 2019; [71]Rahman et al., 2020; [72]Kim et al., 2021). Studies have
shown that diet, obesity, cardiovascular diseases, hypertension,
physical activity, diabetes, educational attainment, smoking, and
traumatic brain injury increase the risk for LOAD and other dementias
([73]Ebbert et al., 2014; [74]Baumgart et al., 2015; [75]Xu et al.,
2015; [76]Wajman et al., 2018). Some of these risk factors can be
attributed to a balance between diet and exercise. For instance, a
western-style diet (e.g., a high fat/high sugar diet (HFD), and low in
vitamins) in combination with a sedentary lifestyle can contribute to a
metabolic syndrome, a cluster of conditions that includes increased
blood pressure, high blood sugar, obesity, particularly excess body fat
around the waist, and abnormal cholesterol or triglyceride levels.
Metabolic syndrome increases the risk for Type 2 diabetes and
cardiovascular disease, and AD ([77]Kivipelto et al., 2001;
[78]Luchsinger et al., 2004; [79]Cordain et al., 2005; [80]Whitmer et
al., 2008).
The Model Organism Development and Evaluation for Late-onset AD
(MODEL-AD) consortium was established to develop mouse models that more
faithfully develop LOAD-relevant phenotypes compared to previous mouse
models that were largely based on fAD. Two centers, one from the
Indiana University (IU), the Jackson Laboratory (JAX), the University
of Pittsburgh (PITT), and Sage Bionetworks, and the other from the
University of California Irvine (UCI), have focused on incorporating
genetic risk factors into C57BL/6J (B6) mice and assaying
human-relevant phenotypes. Initially, the IU/JAX/PITT group created
mice that are double homozygous for APOE4 and Trem2^R47H (termed LOAD1)
([81]Kotredes et al., 2021). The APOE4 is the greatest genetic risk
factor for LOAD which is thought to increase the risk for AD through
both gain and loss of function effects on multiple processes in the
brain including amyloid clearance, synaptic plasticity, and
cerebrovascular health ([82]Liu et al., 2013). The TREM2^R47H is a rare
coding mutation with a partial loss of function that alters the
behavior of microglia ([83]Carmona et al., 2018). LOAD1 mice and
controls were aged 24 months and data showed age but not genotype was
the major factor-driving changes in LOAD1 mice ([84]Kotredes et al.,
2021). Therefore, these mice provided a sensitized background for
assessing additional genetic and environmental risk factors
([85]Kotredes et al., 2021). To further sensitize LOAD1 mice,
additional LOAD genetic risk factors were added via CRISPR/CAS9. These
included the M28L variant in phospholipase C Gamma 2 (Plcg2^M28L)
([86]Tsai et al., 2021) and the 677C > T variant in
methylenetetrahydrofolate reductase (Mthfr^677C > ^T) ([87]Wang et al.,
2005; [88]Chan et al., 2019). The PLCG2 gene encodes an enzyme that
catalyzes the conversion of phospholipid PIP2
(1-phosphatidyl-1D-myo-inositol 4,5-bisphosphate) to IP3
(1D-myo-inositol 1,4,5-trisphosphate) and DAG (diacylglycerol). PLCG2
plays a crucial role in signal transduction between tyrosine kinases
and downstream events, protein kinase C activation, and intracellular
calcium release ([89]Hernandez et al., 1994). In the CNS, PLCG2 is
involved in TREM2/TYROBP signaling pathway and is selectively expressed
in microglia, playing important roles in inflammation, phagocytosis,
and lipid sensing. Although the precise effects of the M28L variant
have not yet been elucidated, like TREM2, PLCG2 is expressed by
microglia and plays a role in amyloid clearance ([90]Carmona et al.,
2018). MTHFR functions in the folate/methionine/homocysteine pathway
are expressed both peripherally and centrally, and the MTHFR^677C > ^T
variant causes an increase in homocysteine increasing the risk for a
variety of diseases including cardiovascular and
cerebrovascular-related disorders and AD ([91]Liu et al., 2010;
[92]Liew and Gupta, 2015; [93]Rai, 2017). Transcriptomic analyses of
the brain tissue from LOAD1.Plcg2^M28L or LOAD1.Mthfr^677C > ^T mice
revealed increased alignment to brain transcriptomes from human
patients with AD compared to B6 or LOAD1 controls ([94]Preuss et al.,
2020).
Despite carrying multiple genetic risk variants for LOAD, LOAD1,
LOAD1.Plcg2^M28L, and LOAD1.Mthfr^677C > ^T did not natively
recapitulate all phenotypes relevant to LOAD, making these strains
ideal to test the effects of environmental risk factors, such as diet.
Commonly, the diet consumed by the western world is high in fat and
refined sugar. This has led to a global obesity epidemic where, for
instance, 42% of all Americans are considered overweight ([95]The
Center For Disease Control And Prevention, 2022). Obesity can cause a
wide range of changes including increased inflammation in both the
periphery and the brain ([96]Ellulu et al., 2017; [97]Chan et al.,
2019). Inflammation is characterized, in part by increased production
of cytokines, such as IL1b and the activation of myeloid cells,
including microglia in the brain ([98]Wang et al., 2015). Chronic
inflammation has been shown to greatly increase the risk for LOAD
([99]Furman et al., 2019).
Multiple studies in mice have assessed the effects of a western-like
diet in the context of aging and AD. For instance, a study showed a
diet-induced myelin breakdown in aging B6 mice that were dependent on
the complement cascade ([100]Graham et al., 2020). Also, the addition
of an HFD exacerbated AD phenotypes in mouse models relevant to
early-onset AD ([101]Knight et al., 2014; [102]Jones et al., 2019;
[103]Bracko et al., 2020; [104]Robison et al., 2020; [105]Jones et al.,
2021). In one study, [106]Jones et al. (2019) found that with HFD, male
APOE4 mice were more susceptible to metabolic disturbances, including
glucose intolerance when compared to APOE3 mice. Behavioral deficits
were not observed due to HFD, suggesting that metabolic responses to
HFD are dependent on both sex and APOE genotype. A second study
concluded that early dysregulation of inflammation in APOE4 brains
could predispose to CNS damage from various insults, including diet,
and later results in the increased CNS damage normally associated with
the APOE4 genotype ([107]Jones et al., 2021). However, the effects of a
western-like diet in the context of multiple genetic risk factors for
LOAD have not been studied. To address this, a commonly used HFD (high
in fat and sugar, refer to the section, Methods) was fed to male and
female LOAD1, LOAD1.Plcg2^M28L, and LOAD1.Mthfr^677C > ^T mice from 2
to 12 months. Biometric measures were collected throughout the study,
in vivo imaging to assess glucose utilization and blood perfusion in a
region-specific manner in the brain was carried out prior to perfusion,
and brain transcriptomics, neuropathological assessments, and protein
quantification were performed postmortem. Results showed that the
effects of consumption of HFD to midlife were not uniform across all
models but dependent on specific genetic risk factors. In particular,
the effects of the HFD were most severe in the presence of Plcg2^M28L.
Materials and Methods
Animal Housing Conditions at Indiana University and the Jackson Laboratory
All animals were obtained from the JAX and are congenic to the C57BL/6J
(JAX# 000664) (B6) strain. LOAD1 is homozygous for both APOE4 and
Trem2^R47H (JAX ID:28709). B6.Plcg2^M28L/APOE4/Trem2^R47H (triple
homozygous, LOAD1.Plcg2^M28L, JAX ID:30674) was created using
CRISPR/Cas9 to introduce the M28L LOAD risk variant into LOAD1
mice.B6.Mthfr^677C > ^T/APOE4/Trem2^R47H (triple homozygous,
LOAD1.Mthfr^677C > ^T, JAX ID:30922), was also created using
CRISPR/Cas9 to introduce the 677TC > T variant into LOAD1 mice
([108]Reagan et al., 2021). All strains were validated using brain
RNA-seq to confirm no off-target effects and no alterations in target
transcript isoforms/expression levels ([109]Kotredes et al., 2021).
More details on strain creation are provided on the AD Knowledge
Portal.^[110]1
The effects of HFD on LOAD1.Plcg2^M28L mice were assessed at the
Indiana University (IU), with LOAD1.Mthfr^677C > ^T mice assessed at
JAX. LOAD1 mice acted as site-matched controls. An additional cohort of
B6 mice was assessed at IU as a strain control. At IU, for experimental
cohorts, LOAD1.Plcg2^M28L/^+ mice were intercrossed to create
LOAD1.Plcg2^M28L triple homozygous and LOAD1 litter-matched control
mice. At JAX, for experimental cohorts, LOAD1.Mthfr^677TC > T/+ mice
were intercrossed to create LOAD1.Mthfr^677TC > ^T triple homozygous
and LOAD1 litter-matched control mice. Up to five mice were housed per
cage with SaniChip bedding and were initially provided with LabDiet^®
5K52/5K67 (6% fat, control diet, CD^[111]2). Mouse rooms were kept on a
12:12 light:dark schedule with the lights on from 7:00 a.m. (6:00 a.m.
at JAX) to 7:00 p.m. daily (6:00 p.m. at JAX). Mice were initially
ear-punched for identification and then following genotype
confirmations, microchipped using a p-chip system (PharmaSeq), with the
microchips placed at the base of the tail. At 2 months of age (mos),
experimental cohorts were randomized into two groups: Group 1 continued
on CD ad libitum and Group 2 was provided with ResearchDiet^® feed
D12451i (45% of high fat; 35% of carbohydrates, HFD^[112]3) ad libitum.
All procedures were approved by the IU or JAX Institutional Animal Care
and Use Committees (IACUC). Where possible, all housing and procedures
were standardized and aligned across sites. Unless specified, n = 10
for each sex, genotype, and diet were used.
Mice were anesthetized to the surgical plane of anesthesia with
tribromoethanol at 12 months of age. Under complete anesthesia, animals
were euthanized by decapitation and perfused through the heart with
ice-cold phosphate-buffered saline (PBS). Blood and trunk blood was
centrifuged for 15–20 min at 4°C at 14,500 RPM, and the plasma was
stored at –80°C.
Blood Plasma Analysis, Perfusion, and Preparation of Tissue Samples
Blood was collected longitudinally from fasting mice at 8 months of age
via a cheek puncture and again at the terminal timepoint of 12 months
of age where mice were anesthetized, and blood was extracted through a
left ventricle cardiac puncture with a 25 g of EDTA-coated needle
before PBS perfusion. Brain tissue was collected immediately after
euthanasia. Approximately 500 mL of whole blood was transferred to a
MAP-K2 EDTA Microcontainer (BD, Franklin Lakes, NJ, United States) on
ice and centrifuged at 4°C at 4388 × g in a pre-chilled ultracentrifuge
for 15 min. Without disturbing the red blood cell fraction, serum
supernatant was pipetted into a chilled cryovial with a p200 tip and
immediately snap-frozen on dry ice for 10 min. Samples were stored at
–80°C and later thawed for the analysis of glucose, total cholesterol,
low-density lipoprotein (LDL), high-density lipoprotein (HDL),
triglyceride, and non-essential fatty acid (NEFA) levels with the
Siemens Advia 120 (Germany).
Histology and Fluorescent Immunostaining
After perfusion and dissection, the left side of each mouse brain was
fixed in 4% of paraformaldehyde. After a transfer to 10% sucrose the
following day, the brains were transferred to 30% of sucrose for
storage. Brains were sectioned, coronally (Jax) and sagittally (IU), at
10–20 μm on a freezing microtome.
NeuN Staining
Sections were washed and blocked for 1 h in 10% of host goat serum.
Sections were incubated overnight at 4°C in a solution containing the
antibody, NeuN rabbit (ab104225, 1:1,000, Abcam). After additional
washes, the sections were incubated for 1 h at room temperature in a
secondary solution containing the fluorescent markers, goat anti-rabbit
488 (A11034, 1:1,000, Invitrogen). After one additional wash, the
sections were mounted on charged slides, counterstained, and
coverslipped with Prolong Gold Antifade Mountant with DAPI.
Iba1 Staining
Sections were washed and then blocked with 10% of host goat serum for 2
h. Sections were incubated overnight at 4°C in a solution containing
the Recombinant Anti-Iba1 antibody rabbit (ab178847, 1:500, Abcam).
After additional washes, the sections were incubated for 1 h at room
temperature in a secondary solution containing the fluorescent markers,
goat anti-rabbit 488 (A11034, 1:1000, Invitrogen). After one additional
wash, the sections were mounted on charged slides, counterstained, and
coverslipped with Prolong Gold Antifade Mountant with DAPI.
Microscopy was used to view and capture images of the immunofluorescent
stains with a Leica DM6 B and DFC7000 GT camera using Leica
Microsystems’ LAS X software. Further image capture and image scanning
were performed on an Andor Zyla 5.5 sCMOS camera with an Aperio Versa
scanner and Versa software. One to three coronal (LOAD1.Mthfr^677C >
^T) or sagittal (LOAD1.Plcg2^M28L) sections were analyzed with Imaris
software to generate density measurements within the cortex and
subiculum ([113]Supplementary Figure 1A).
RNA Extraction and Nanostring Analysis
As previously described ([114]Preuss et al., 2020; [115]Oblak et al.,
2021), total RNA was extracted from frozen right brain hemispheres
using the MagMAX mirVana Total RNA Isolation Kit (Thermo Fisher
Scientific) and the KingFisher Flex purification system (Thermo Fisher
Scientific, Waltham, MA; n = 6 per sex/genotype/diet). RNA
concentration and quality were assessed using the Nanodrop 2000
spectrophotometer (Thermo Fisher Scientific) and the RNA Total RNA Nano
assay (Agilent Technologies, Santa Clara, CA, United States). The
NanoString Mouse AD gene expression panel was used for gene expression
profiling on the nCounter platform (NanoString, Seattle, WA, United
States) as described by the manufacturer. The nSolver software was used
for generating raw NanoString gene expression values. The NanoString
data were normalized by dividing raw counts within a lane by the
geometric mean of the housekeeping genes from the same lane
([116]Preuss et al., 2020). Next, normalized count values were
log-transformed for downstream analysis.
The NanoString gene expression data were generated for male and female
LOAD1, LOAD1.Plcg2^M28L, and LOAD1.Mthfr^677C > ^T mice fed on standard
chow diet and HFD. We assessed the effects of sex, HFD, each genetic
variant (Mthfr^677C > ^T, Plcg2^M28L), the interaction between HFD and
Mthfr^677C > ^T, and the interaction between HFD and Plcg2^M28L. To
determine the effects of each of these factors, we fit a multiple
regression model using the lm function in R as ([117]Pandey et al.,
2019):
[MATH:
Log(expr)=β0+<
munder>∑iβi+
ε :MATH]
Where the sum is over sex (male), HFD, genetic variants (Mthfr^677C >
^T, Plcg2^M28L), and interaction terms between HFD and each variant
(HFD*Mthfr^677C > ^T, HFD*Plcg2^M28L). The log(expr) represents the
log-transformed normalized count from the NanoString gene expression
panel ([118]Preuss et al., 2020). In this formulation, the standard
chow diet and LOAD1 genetic background serve as controls. The values,
β[0] and ε represent the average expression level for the reference
LOAD1 male mice on the standard chow diet and residual, respectively.
Next, we have computed Pearson’s correlation between gene expression
changes (log-fold change) in human cases with AD vs. controls in each
AMP-AD module ([119]Greenwood et al., 2020) and the effect of each
mouse perturbation (sex, HFD, Mthfr^677C > ^T, Plcg2^M28L, and
interaction terms HFD*Mthfr^677C > ^T, HFD*Plcg2^M28L) as measured
above for each gene in NanoString panel ([120]Pandey et al., 2019;
[121]Preuss et al., 2020) using cor.test function built in R as:
[MATH:
cor.test(LogF<
mn>2C(AD
/control),β) :MATH]
from which we obtained both the correlation coefficient and the
significance level (p-value) of the correlation. Log[2]FC values for
human transcripts were obtained through the AD Knowledge Portal
([122]Greenwood et al., 2020).^[123]4
Similarly, we have computed Pearson’s correlation between gene
expression changes (log-fold change) in human AD cases versus controls
in each of the LOAD subtypes ([124]Milind et al., 2020) and the effect
of each mouse perturbation (sex, HFD, Mthfr^677C > ^T, Plcg2^M28L,
interaction terms HFD*Mthfr^677C > ^T, and HFD*Plcg2^M28L) for each
gene in Nanostring panel ([125]Pandey et al., 2019; [126]Preuss et al.,
2020) using cor.test function built in R as:
[MATH:
cor.test(LogF<
mn>2C(LO
ADS<
mi>ubtype/cont
rol),β) :MATH]
from which we obtained both the correlation coefficient and the
significance level (p-value) of the correlation. Here, Log[2]FC(LOAD
Subtype/control) represents the log-fold change in gene expression in
each subtype vs. control.
We plotted the correlation results using the ggplot2 package in R.
Circles within a square correspond to significant (p < 0.05) positive
(blue) and negative (red) Pearson’s correlation coefficients. The color
intensity and size of the circles are proportional to Pearson’s
correlation coefficient.
Human Post-mortem Brain Cohorts and Gene Co-expression Modules
[127]Wan et al. (2020) discovered 30 human brain co-expression modules
based on the meta-analysis of differential gene expression from seven
distinct regions: the dorsolateral prefrontal cortex (DLPFC), superior
temporal gyrus (STG), frontal pole (FP), parahippocampal gyrus (PHG),
temporal cortex (TCX), inferior frontal gyrus (IFG), and the cerebellum
(CBE) in postmortem samples obtained from three independent LOAD
cohorts: the Religious Orders Study and the Memory and Aging Project
(ROSMAP) cohort, the Mount Sinai Brain Bank (MSBB), and the Mayo Clinic
cohort ([128]Allen et al., 2016; [129]De Jager et al., 2018; [130]Wang
et al., 2018). Briefly, [131]Wan et al. (2020) performed library
normalization and covariate adjustments for each human study separately
using fixed/mixed effects modeling to account for batch effects.
Initially, a total of 2,978 brain regions’ specific expression modules
were identified across all tissues ([132]doi: 10.7303/syn10309369.1)
using five distinct network module identification algorithms (MEGENA,
WINA, metanetwork, rWGCNA, and speakEasy). Next, 660 modules were
selected that were significantly enriched for at least one AD-specific
differentially expressed gene set from the meta-analysis. Lastly, the
graph clustering method was applied to identify 30 aggregate modules
that are not only differentially expressed but are also replicated
across multiple independent co-expression module algorithms ([133]Wan
et al., 2020). Wan et al. classified these 30 aggregate co-expression
modules into five distinct consensus clusters ([134]Wan et al., 2020).
These consensus clusters consist of a subset of modules that are
associated with similar AD-related changes across the multiple studies
and brain regions. Reactome pathway^[135]5 enrichment analysis was used
to identify specific biological themes across these five consensus
clusters. Pathways were ranked based on their Bonferroni corrected
p-values to account for multiple testing. Finally, consensus clusters
were annotated based on the highest ranked and non-overlapping term for
each functionally distinct cluster.
Human LOAD Subtypes
[136]Milind et al. (2020), integrated post-mortem brain gene
co-expression data from the frontal cortex, temporal cortex, and
hippocampus due to their relevance to LOAD neuropathology across
independent human cohorts (ROS/MAP, Mount Sinai Brain Bank, and Mayo
Clinic) and stratified patients into different molecular subtypes based
on gene co-expression profiles using iterative WGCNA ([137]Allen et
al., 2016; [138]De Jager et al., 2018; [139]Wang et al., 2018). Two
distinct LOAD subtypes were identified in the ROSMAP cohort, three LOAD
subtypes were identified in the Mayo cohort, and two distinct LOAD
subtypes were identified in the MSBB cohort. Similar subtype results
were observed in each cohort, with LOAD subtypes found to primarily
differ in their inflammatory response based on differential expression
analysis ([140]Milind et al., 2020).
Kyoto Encyclopedia of Genes and Genomes Pathway Enrichment Analysis
Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment
analysis was performed using clusterprofiler package ([141]Yu et al.,
2012) within the R software environment. Pathways were determined to be
significant after multiple testing corrections (FDR adjusted p < 0.05).
Gene set enrichment analysis (GSEA) was used based on the method
proposed by [142]Subramanian et al. (2005) as implanted in the
clusterProfiler package for the KEGG pathway library. Nanostring AD
panel genes were ranked based on regression coefficients calculated for
each factor (sex, high fat diet, Mthfr^677C > ^T, Plcg2^M28L,
interaction terms HFD*Mthfr^677C > ^T, and HFD*Plcg2^M28L). Enrichment
scores for all KEGG pathways were computed to compare relative
expression on the pathway level between each factor. Principal
component analysis (PCA) was performed using the resulting gene set
normalized enrichment scores (NES).
In vivo PET/CT Imaging
To assess regional glycolysis and tissue perfusion, mice were
non-invasively imaged via PET/CT (n = 10 mice/sex/genotype/age). To
measure the regional blood flow,
copper-pyruvaldehyde-bis(N4-methylthiosemicarbazone) (^64
Cu-PTSM)([143]Green, 1987), which has a very high first pass (>75%)
extraction ([144]Mathias et al., 1991) and glutathione reductase redox
trapping of copper ([145]Mathias et al., 1991), was administered via
tail vein in awake subjects and was given a 2 min uptake period prior
to imaging. To measure regional glycolytic metabolism,
2-fluoro-2-deoxyglucose (^18F-FDG) was administered via tail vein in
awake subjects and given a 30 min uptake period prior to imaging. Post
uptake, mice were induced with 5% of isoflurane (95% medical oxygen)
and maintained during acquisition with 1–2% of isoflurane at 37°C. To
provide both anatomical structure and function, PET/CT imaging was
performed with a Molecubes b-X-CUBE system (Molecubes NV, Gent
Belgium). For PET determination of blood flow and metabolism,
calibrated listmode PET images were acquired on the b-CUBE and
reconstructed into a single-static image using ordered subset
expectation maximization (OSEM) with 30 iterations and 3 subsets
([146]Krishnamoorthy et al., 2018). To provide anatomical reference and
attenuation maps necessary to obtain fully corrected quantitative PET
images, helical CT images were acquired with tube voltage of 50 kV, 100
mA, 100 μm slice thickness, 75 ms exposure, and 100 μm resolution. In
all cases, images were corrected for radionuclide decay, tissue
attenuation, detector dead-time loss, and photon scatter according to
the manufacturer’s methods ([147]Krishnamoorthy et al., 2018).
Post-acquisition, all PET and CT images were co-registered using a
mutual information-based normalized entropy algorithm ([148]Studholme
et al., 1998) with 12 degrees of freedom and mapped to stereotactic
mouse brain coordinates ([149]Franklin and Paxinos, 2013). Finally, to
quantify regional changes, voxels of interest (VOIs) for 27 brain (54
bilateral) regions were extracted and analyzed for standardized uptake
value ratios (SUVRs) according to published methods ([150]Dandekar et
al., 2007).
Autoradiography
To provide secondary confirmation of the in vivo PET images, and to
quantify tracer uptake regionally, the brains were extracted post rapid
decapitation, gross sectioned along the midline, slowly frozen on dry
ice, then embedded in cryomolds with optimal cutting temperature (OCT)
compound (Tissue-Tek). Thin frozen sections (20 μm) were obtained via
cryotomy at prescribed bregma targets (n = 6 bregma/mouse, 6
replicates/bregma) according to stereotactic mouse brain coordinates
([151]Franklin and Paxinos, 2013). Sections were mounted on glass
slides, air dried, and exposed on BAS Storage Phosphor Screens (SR 2040
E, Cytiva Inc.) for up to 12 h. The post-exposure, screens were imaged
via Typhoon FL 7000IP (GE Medical Systems) phosphor-imager at 25 μm
resolution along with custom 18F or ^64Cu standards described
previously ([152]Territo et al., 2017).
Image Analysis
All PET and MRI images were co-registered using a ridged-body mutual
information-based normalized entropy algorithm ([153]Studholme et al.,
1997) with 12 degrees of freedom and mapped to stereotactic mouse brain
coordinates ([154]Franklin and Paxinos, 2013) using MIM 7.0.5 (MIM
Software Inc., Beachwood, OH, United States). Post-registration, 56
bilateral regions were extracted via brain atlas and averaged to yield
27 unique volumes of interest that map to key cognitive and motor
centers that include the agranular insular cortex, auditory cortex,
caudate putamen, cerebellum, cingulate cortex, corpus callosum,
dorsolateral orbital cortex, dorsintermed entorhinal cortex,
dysgranular insular cortex, ectorhinal cortex, fornix, frontal
association cortex, hippocampus, lateral orbital cortex, medial orbital
cortex, parietal cortex, parietal association cortex, perirhinal
cortex, prelimbic cortex, primary motor cortex, primary somatosensory
cortex, retrosplenial dysgranular cortex, secondary motor cortex,
secondary somatosensory cortex, temporal association cortex, thalamus,
ventral orbital cortex, and visual cortex. For autoradiographic
analysis, tracer uptake was quantified on hemi-coronal sections by
manually drawing regions of interest for 17 regions of interest (i.e.,
the auditory cortex, caudate putamen, cerebellum, cingulate cortex,
corpus callosum, dorso-intermed entorhinal cortex, dysgranular insular
cortex, ectorhinal cortex, hippocampus, hypothalamus, medial septum,
primary motor cortex, primary somatosensory cortex, retrosplenial
dysgranular cortex, temporal association cortex, thalamus, and visual
cortex) on calibrated phosphor screen at bregma 0.38, -1.94, and -3.8
mm using MCID (InterFocus Ltd.). To permit dose and brain uptake
normalization, SUVRs relative to the cerebellum were computed for PET
and autoradiograms for each subject, genotype, and age as follows:
[MATH:
SUVR(s,R,g,a
)=R(s,g,a)C(s,g,a) :MATH]
where, s, g,a, R, and C are the subject, genotype, age, region/volume
of interest, and cerebellum region/volume of interest. In all cases,
the region/volumes of interest were subjected to PCA analysis, and
regions that explain the top 80% of the variance were analyzed for
differences with time and genotype using a two-way ANOVA (Prism,
GraphPad Inc.), where significance was taken at p < 0.05.
Homogenization and Protein Extraction
Each hemibrain was weighed prior to homogenizing in tissue protein
extraction reagent (T-PER Thermo Fisher Scientific; 1 mL per 100 mg of
tissue weight) supplemented with protease and phosphatase inhibitors
cocktail (Sigma-Aldrich). Total protein concentration was measured
using bicinchoninic acid (BCA; Pierce). Hemibrain lysates were then
aliquoted and kept in the –80°C freezer for long-term storage. The
primary supernatant was utilized to analyze the content of
proinflammatory cytokines.
Cytokine Panel Assay
Mouse hemibrain samples were assayed in duplicate using the MSD
Proinflammatory Panel I (K15048D; MesoScale Discovery, Gaithersburg,
MD, United States), a highly sensitive multiplex enzyme-linked
immunosorbent assay (ELISA). This panel quantifies the following 10
proinflammatory cytokines in a single small sample volume (25 μL) of
supernatant using an electrochemiluminescent detection method (MSD):
interferon γ (IFN-γ), interleukin (IL)-1β, IL-2, IL-4, IL-6, IL-8,
IL-10, IL-12p70, IL-13, and tumor necrosis factor α (TNFα). The mean
intra-assay coefficient for each cytokine was less than 8.5%, based on
the cytokine standards. Any value below the lower limit of detection
(LLOD) for the cytokine assay was replaced with ½ LLOD of the assay for
statistical analysis.
Data Analysis and Study Design
For biometric studies where multiple samples were collected at multiple
time points for a mouse, a mixed ANOVA for repeated measures was done.
For IHC and cytokine analyses, a two-way ANOVA was completed, followed
by Tukey’s post hoc testing. For these studies, p < 0.05 was considered
significant. Multiple cohorts of animals were utilized in this study
for different outcome measures, a summary of mouse numbers can be found
in [155]Supplementary Figure 1B.
Results
Chronic Consumption of HFD-Induced Features of Metabolic Syndrome
This study consisted of three mouse strains, LOAD1, LOAD1.Mthfr^677C^>
^T, and LOAD1.Plcg2^M28L on either a normal control diet (6%, CD) or an
HFD (45%). LOAD1.Plcg2^M28L and LOAD1 mice were assessed at IU and
LOAD1.Mthfr^677C > ^T and LOAD1 mice were assessed at JAX. LOAD1 acted
as site-matched controls. For both studies, HFD was introduced at 2
months of age in half of the animals, while the other half was
maintained on CD ([156]Figure 1A, refer to Methods for breeding
strategies). Bodyweight was monitored once a month for the
LOAD1.Mthfr^677C > ^T study ([157]Figure 1B) and once every two months
for the LOAD1.Plcg2^M28L study ([158]Figure 1C). Mice fed on HFD gained
more weight compared to those fed on CD, irrespective of genotype
([159]Figures 1B,C). These data are consistent with the induction of
diet-induced obesity (DIO) that has been previously reported for B6
mice ([160]Wanrooy et al., 2018). Blood samples were collected from
fasting, non-anesthetized mice at 8 (mid stage) and 12 (end stage)
months of age to determine glucose and total cholesterol levels
([161]Figures 1D–G). In general, blood glucose levels were slightly
lower in all mice housed at JAX ([162]Figure 1D) compared to IU
([163]Figure 1E) which may be due to differences in environment,
despite efforts to standardize housing conditions. In both studies,
there was a significant effect of HFD in female, but not male mice fed
on HFD. The effect was more striking in female mice from IU (p <
0.0001) compared to female mice from JAX (p = 0.165). Fasting blood
glucose levels only exceeded 240 mg/dL (a level that previous studies
consider B6 mice to be diabetic ([164]Xiang et al., 2021) in mice fed
with LOAD1 HFD and LOAD1.Plcg2^M28L HFD. Fasting serum cholesterol
levels were consistently elevated at both 8 and 12 months of age in all
strains fed with an HFD compared to those fed with CD independent of
genotype and sex (p > 0.0001 at JAX, p > 0.0001 at IU; [165]Figures
1F–G). Collectively, these data suggest HFD-induced features of some
metabolic syndrome, potentially in a sex-specific manner, but
independent of genotype.
FIGURE 1.
[166]FIGURE 1
[167]Open in a new tab
Bodyweight, glucose, and cholesterol increased on a high fat/high sugar
(HFD) diet, regardless of genotype. LOAD1, LOAD1.Mthfr^677C > ^T, and
LOAD1.Plcg2^M28L mice were fed with a control diet (CD) or HFD from 2
to 12 months of age (A). At multiple timepoints throughout the study,
blood was collected. At the terminal time point, transcriptomics,
limited neuropathology, and biochemical studies were completed. Over
the course of the study, regardless of genotype or sex, mice fed with
HFD gained more weight than animals fed with a control diet (B).
Glucose (D,E) and Cholesterol (F,G) increased over time, which was
related to diet, but not to genotype (C–F). A mixed ANOVA with repeated
measures was completed; p < 0.05 is considered significant. *p < 0.05,
**p < 0.01, ****p < 0.0001.
Neuron Number Was Unchanged but Reactive Microglia Increased in
LOAD1.Plcg2^M28L Animals
A hallmark of LOAD and other dementias is the loss of neurons,
particularly in the cortex and hippocampus. However, the cortical and
hippocampal neuronal cell loss is largely absent in the most widely
used mouse models of fAD and LOAD. Neuronal cell loss was also not
observed in LOAD1 mice, even at 24 months of age ([168]Kotredes et al.,
2021). To assess the neuronal cell density in this study, we examined
both the cortex and the hippocampus of CD and HFD animals from both
LOAD1.Mthfr^677C > ^T and LOAD1.Plcg2^M28L animals and controls
utilizing fluorescent immunostaining ([169]Figures 2A–D and
[170]Supplementary Figure 1). In both LOAD1.Mthfr^C677T and
LOAD1.Plcg2^M28L animals, there was no quantitative difference in the
density of neurons in either genotype when comparing the CD to the HFD
([171]Figure 2). The second hallmark of LOAD is neuroinflammation,
particularly microglia activation. In our previous study, LOAD1 mice
did not show microglia activation. When analyzing the number of IBA1+
cells in the brain, compared to LOAD1 mice either on the CD or HFD, we
found increased IBA1+ cells in the cortex of female LOAD1.Plcg2^M28L
mice fed on HFD compared to LOAD1.Plcg2^M28L on CD ([172]Figure 3 and
[173]Supplementary Figure 1). These data are not collected based on
stereological analyses and are therefore suggestive of a reduced
density of IBA1+ cells. Interestingly, we did not see a difference in
LOAD1.Mthfr^677C > ^T mice ([174]Figure 3), suggesting an HFD-induced
neuroinflammatory reaction that is specific to the LOAD1.Plcg2^M28L
genotype.
FIGURE 2.
[175]FIGURE 2
[176]Open in a new tab
Neuron density is unaffected by HFD in LOAD1.Plcg2^M28L and
LOAD1.Mthfr^677C > ^T mice. Immunohistochemistry was completed using
NeuN to visualize the density of neurons (A–D) in the LOAD1.Plcg2^M28L
mice. The HFD has no significant effect on the density of neurons in
either the cortex (E,G) or the hippocampus (F,H) of either
LOAD1.Plcg2^M28L or LOAD1.Mthfr^677C > ^T mice. Statistical analysis
was completed using an ANOVA followed by Tukey’s post hoc tests. P <
0.05 is considered significant.
FIGURE 3.
[177]FIGURE 3
[178]Open in a new tab
Microglial density is increased in LOAD1.Plcg2^M28L high fat diet mice.
Immunohistochemistry was completed using Iba1 to visualize the density
of microglia (A–D) in the LOAD1.Plcg2^M28L mice. LOAD1.Mthfr^677C > ^T
mice did not show any significant changes in microglia density in the
cortex (E) or hippocampus (F), regardless of diet. However, female
LOAD1.Plcg2^M28L was found to have an increase in the microglial
density in the cortex of the mice fed with HFD (G). No changes were
observed in LOAD1.Plcg2^M28L mice fed with the control diet (CD) (G,H).
Data suggest a gene by diet interaction that may be sex-specific.
Statistical analysis was completed using an ANOVA followed by Tukey’s
post hoc tests. P < 0.05 is considered significant.
Plasma Cytokines and Brain Cytokines Are Altered in HFD Animals
Based on the specific interaction between the LOAD1.Plcg2^M28L mice and
the HFD in microglia density, we sought to identify cytokines in the
brain or periphery that may be driving this interaction. Analysis of
cytokines in the brain revealed increases of IL-1β in HFD fed-LOAD1
animals ([179]Figure 4A); however, the same changes were not observed
in the LOAD1.Mthfr^677C > ^T animals. In addition, a significant
increase in TNF-a was observed in LOAD1 males but not in females
([180]Figure 4C). No changes in IFN-γ were observed in the LOAD1.
Mthfr^677C > ^T study.
FIGURE 4.
[181]FIGURE 4
[182]Open in a new tab
Cytokine production is altered in mice fed with HFD. In order to
identify peripheral factors that may be driving the genetic by diet
interaction, we examined the brain (A–F) and plasma (G–I) cytokines in
both LOAD1.Plcg2^M28L or LOAD1.Mthfr^677C > ^T mice. In LOAD1 mice,
significant increases in brain IL-1β (A) and TNF-a (C) were observed in
mice fed with HFD; however, LOAD1.Mthfr^677C > ^T mice did not have any
significant increases. In the LOAD1.Plcg2^M28L mice, a significant
decrease in brain IL-1b (D) and IFN-g (E) was observed in the females,
regardless of diet. The HFD did not show the same increase in the brain
cytokine levels as the control (LOAD1) animals, suggesting a deficiency
in cytokines due to the variant. TNF-a was reduced in females as well
(F), but only in the HFD group. In the plasma of LOAD1.Plcg2^M28L mice,
we observed a reduction in IL-1b (G) and IFN-g (H) in male and female,
respectively, LOAD1.Plcg2^M28L mice. The TNF-a was elevated in the HFD
LOAD1.Plcg2^M28L males only (I). Statistical analysis was completed
using an ANOVA followed by Tukey’s post hoc tests. P < 0.05 is
considered significant.
In LOAD1.Plcg2^M28L, we found significant reductions in several
proinflammatory cytokines (IL-1β, IFN-γ) in the brain regardless of
diet which was inversely correlated with plasma ([183]Figures 4D–F).
Female LOAD1 animals had an increase in plasma IL-1β in response to an
HFD that was not observed in males. In contrast, in the plasma of
LOAD1.Plcg2^M28L mice, we found a significant increase in TNF-a
([184]Figure 4I) in mice fed on HFD. However, there was a reduction in
IL-1β (LOAD1.Plcg2^M28L males) and IFN-γ (LOAD1.Plcg2^M28L females),
suggesting that there is a difference in the peripheral and central
immune responses to an HFD.
Interaction Between Risk Variant Plcg2^M28L and HDF Correlates With AMP-AD
Modules Enriched With Inflammatory- and Neuronal-System Associated Pathways
Hallmark pathologies, such as amyloid and tau accumulation and
neurodegeneration have been used widely to align mouse models to human
AD. However, more recently, molecular approaches have been developed by
MODEL-AD ([185]Preuss et al., 2020) and others that allow for a more
precise assessment of the relevance of mouse models to the molecular
changes observed in human LOAD ([186]Allen et al., 2016; [187]Mostafavi
et al., 2018; [188]Wang et al., 2018; [189]Wan et al., 2020). Here, we
correlated the effect of each mouse perturbation (sex, HFD, genetic
variants, and interaction between variants and HFD) with 30 human
AMP-AD co-expression modules ([190]Wan et al., 2020). These 30 modules,
derived from different brain regions and study cohorts, were grouped
into five “consensus clusters” based on similar gene content and
repeated signals in the multiple regions. Both risk variants,
Mthfr^677C > ^T and Plcg2^M28L exhibited significant positive
correlations (p < 0.05) with cell cycle and myelination-associated
modules in Consensus Cluster D and cellular stress-response associated
modules in Consensus Cluster E ([191]Figure 4A). Moreover, Plcg2^M28L
displayed significant positive correlation (p < 0.05) with neuronal
system-associated modules in Consensus Cluster C ([192]Figure 5A).
Furthermore, Plcg2^M28L displayed significant negative correlation (p <
0.05) with immune-related modules in Consensus Cluster B, while
interaction between Plcg2^M28L and HFD displayed significant positive
correlation (p < 0.05), suggesting interaction with HFD increased
neuroinflammation in mice carrying the Plcg2^M28L risk variant
([193]Figure 5A). In addition, an interaction between Plcg2^M28L and
HFD also exhibited significant positive correlation (p < 0.05) with
extracellular matrix organization- related module in Consensus Cluster
A and neuronal system-associated modules in Consensus Cluster C
([194]Figure 5A). Overall, we observed AD relevant phenotypes in mice
for an interaction between HFD and Plcg2^M28L risk variant.
FIGURE 5.
[195]FIGURE 5
[196]Open in a new tab
Interaction between HFD and Plcg2^M28L in mice exhibits transcriptional
changes in immune function similar to human LOAD. (A) Correlation
between the effect of each mouse perturbation and 30 human
co-expression modules. Each column represents one of the 30 human
co-expression modules identified in seven different brain regions: the
dorsolateral prefrontal cortex (DLPFC), superior temporal gyrus (STG),
frontal pole (FP), parahippocampal gyrus (PHG), temporal cortex (TCX),
inferior frontal gyrus (IFG), and cerebellum (CBE). These modules are
grouped into five consensus clusters with similar gene content across
the multiple studies and brain regions. Note that HFD*Plcg2.M28L and
HFD*Mthfr.C677T results denote interaction effects separated from the
individual effects of diet and variants. Controls for corresponding
rows were therefore labeled N/A as the control is not strictly defined.
Circles within a square correspond to significant (p < 0.05) positive
(blue) and negative (red) Pearson’s correlation coefficients. The color
intensity and size of the circles are proportional to Pearson’s
correlation coefficient. (B) Correlation between the effect of each
mouse perturbation and molecular subtypes of LOAD. The columns
represent the two molecular subtypes associated with LOAD in the
Religious Orders Study and the Memory and Aging Project (ROSMAP)
cohort, three molecular subtypes associated with LOAD in the Mayo
cohort, and two molecular subtypes associated with LOAD in the Mount
Sinai Brain Bank (MSBB) cohort ([197]Milind et al., 2020). The effects
of interaction between HFD and Plcg2^M28L in mice significantly
correlate with the inflammatory subtypes of LOAD (Subtypes A) in each
of the cohorts. Circles within a square correspond to significant (p <
0.05) positive (blue) and negative (red) Pearson’s correlation
coefficients. (C) Kyoto Encyclopedia of Genes and Genomes (KEGG)
Pathway enrichment analysis (FDR adjusted p < 0.05) of genes exhibiting
directional coherence between the effects of interaction between HFD
and Plcg2^M28L in mice and ECM organization related AMP-AD modules in
Consensus Cluster A. (D) KEGG Pathway enrichment analysis (FDR adjusted
p < 0.05) of genes exhibiting directional coherence between the effects
of interaction between HFD and Plcg2^M28L in mice and immune-related
AMP-AD modules in Consensus Cluster B. (E) Identification of genes
exhibiting directional coherence for the interaction between HFD and
Plcg2^M28L in mice and change in the expression of immune-associated
AMP-AD modules in Consensus Cluster B, including microglia-related
genes listed.
We also correlated the effect of perturbation of each mouse (HFD,
genetic variants, and interaction between variants and HFD) with 30
human AMP-AD co-expression modules ([198]Wan et al., 2020) for both
sexes, separately. For both male and female mice, we observed a
significant positive correlation between some of the immune-related
modules in Consensus Cluster B and interaction between Plcg2^M28L and
HFD ([199]Supplementary Figures 2A,B). However, the effects were
stronger in females as we observed a significant positive correlation
(p < 0.05) with immune modules associated with the PHG brain region as
well in females, which was not significant (p > 0.05) in male mice.
Genes exhibiting directional coherence between the effects of
interaction between HFD and Plcg2^M28L in mice and change in expression
in AMP-AD modules in Consensus Clusters A and B, respectively, were
extracted. A total of 99 genes were extracted that exhibited
directional coherence for interaction between HFD and Plcg2^M28L in
mice and change in expression in AMP-AD modules in Consensus Cluster A,
such as Aldh2, Aldh6a1, Aldh7a1, lamb2, Rab31, and Sox9
([200]Supplementary File 3). We identified a total of 192 genes that
exhibit a directional coherence between interaction with HFD and
Plcg2^M28L in mice and a change in the expression in immune-associated
AMP-AD modules in Consensus Cluster B, including microglia-related
genes, such as Trem2, Tyrobp, Cldn5, and C1qc ([201]Figure 5E and
[202]Supplementary File S3). To elucidate the role of these
disease-related genes, we performed a KEGG pathway enrichment analysis.
Genes that showed directional coherence for the interaction between HFD
and Plcg2^M28L in mice and human co-expression modules in Consensus
Cluster A were enriched for “Regulation of actin cytoskeleton,” “fatty
acid degradation,” and multiple “metabolism” associated pathways
([203]Figure 5C). Genes that showed directional coherence for
interaction between HFD and Plcg2^M28L in mice and human co-expression
modules in Consensus Cluster B were enriched for multiple KEGG
pathways, such as “Tight junction,” “Hippo signaling pathway,”
“Lysosome,” and “phagocytosis” pathways ([204]Figure 5D).
Interaction Between Risk Variant Plcg2^M28L and HFD Significantly Correlates
With Inflammatory LOAD Subtypes
Next, to identify variants that resemble the inflammatory and
non-inflammatory subtypes in human patients, we correlated the effect
of each variant (sex, HFD, genetic variants, and interaction between
variants and HFD) with inflammatory and non-inflammatory subtypes
associated with LOAD in the ROSMAP, MSBB, and Mayo cohorts ([205]Allen
et al., 2016; [206]De Jager et al., 2018, [207]Wang et al., 2018). The
effect of the Mthfr^677C > ^T variant showed a significant positive
correlation (p < 0.05) with the inflammatory subtypes across all three
cohorts, while the effect of Plcg2^M28L and HFD showed a significant
positive correlation (p < 0.05) with the non-inflammatory subtype B in
the ROSMAP and MSBB cohorts ([208]Figure 4B). Notably, the interaction
between HFD and Plcg2^M28L risk variant showed a strong significant
positive correlation (p < 0.05) with the inflammatory subtypes across
all three cohorts, while no significant correlation was observed for
interaction between HFD and Mthfr^677C > ^T risk variant ([209]Figure
5B).
GESA Identified Upregulation of Multiple Pathways of LOAD1.Plcg2^M28L HFD
Animals but Not Mthfr^677C > ^T
Further, Nanostring AD panel genes were ranked based on regression
coefficients calculated for each factor, and GSEA ([210]Subramanian et
al., 2005) was performed (refer to section “Materials and Methods”).
The GSEA identified upregulation of Alzheimer’s disease, focal adhesion
pathways in presence of each perturbation except Mthfr^677C > ^T
([211]Supplementary Figure 2). Multiple immune-related pathways were
upregulated and synaptic associated pathways, such as GABAergic
synapse, and axon guidance were downregulated in the presence of HFD
and interaction between HFD and Plcg2^M28L ([212]Supplementary Figure
1). Moreover, PCA using gene set enrichment score (NES) revealed HFD as
the main effect in mice. We observed clear discrimination between
genetic variants with and without interaction with HFD along the first
principal component (accounting for around 59% of total variation)
([213]Supplementary Figure 2D). The second principal component accounts
for 25% of the total variance and separated Mthfr^677C > ^T and
interaction between HFD and Plcg2^M28L factors from other factors
([214]Supplementary Figure 2).
Increased Brain Glycolysis and Perfusion in Plcg2^M28L HFD Animals
Due to the interaction between HFD and Plcg2^M28L leading to a
transcriptomic inflammatory signature as well as an alteration in tight
junctions, it was important to determine if there were any
corresponding functional deficits. To visualize alterations in regional
glycolysis and perfusion, we performed in vivo PET/CT imaging using
^18F-FDG ([215]Figures 6A–C) and ^64Cu-PTSM ([216]Figures 6D,E),
respectively. Findings were confirmed using autoradiography. PCA
comparing sex, genotype, and age determined 15 of the 27 brain regions
that explained 80 percent of the variance in brain glycolysis in all
mice and all conditions ([217]Figures 6B,C). Interestingly, an HFD mice
show overall increased glucose uptake compared to CD mice. Utilizing
PCA analysis again for blood flow analysis, 13 of the 27 brain regions
explained 80 percent of the variance in blood flow in all mice and all
conditions ([218]Figures 6D,E). Both tracers were utilized in the same
animals; therefore, autoradiographic studies were only performed at the
terminal scan (^64Cu-PTSM; [219]Figures 6F,G). Interestingly, an HFD
mice showed overall increased perfusion compared to CD mice. Several
regions that showed increased glucose metabolism and increased blood
perfusion in a genotype-dependent manner are involved in memory and
behavior. These data support a functional consequence of the observed
interaction between HFD and Plcg2^M28L by IHC and transcriptomics.
FIGURE 6.
[220]FIGURE 6
[221]Open in a new tab
In vivo PET/CT imaging of LOAD1.Plcg2^M28L fed with HFD. Representative
images for ^64Cu-PTSM PET/CT and autoradiography of randomly selected
12-months old female LOAD1.Plcg2^M28L mice following 10-month HFD
treatment (A). In all cases, images are presented as standardized
uptake value ratios (SUVRs) to the cerebellum. Representative bregma
image panel presented as average CT (left), PET (center-left), Fused
(center-right), and Autoradiography (right). Data presented are the
brain regions that explain 80% of the variance determined using
Principal Component Analysis (PCA) in brain glycolysis (B,C) and brain
perfusion (D,E) in both females (B,D) and males (C,E). Following the
terminal ^64Cu-PTSM scans, the brains were subjected to
autoradiographic analysis (F–G). Data presented are the brain regions
that explain 80% of the variance.
Discussion
Late-onset AD is a complex disorder that is caused by a combination of
genetic and environmental risk factors. However, the specific
combinations of genetic and environmental factors that increase the
risk for LOAD are not well understood. In fact, it is likely that these
vary between individuals, making predictions of risk for LOAD based on
their genetics and exposures imprecise. Previous studies support this,
with E4FAD (APOE4+5xFAD) mice showing increased amyloid deposition,
specifically more compact plaques, compared to E3FAD (APOE3+5xFAD) mice
([222]Youmans et al., 2012). To further explore gene by diet effects,
in our study, three new mouse models carrying different genetic risk
factors for LOAD were exposed to HFD for10 months (from 2 to 12 months)
to test the hypothesis that the reported effects of an HFD will vary
depending on the genetic context. The results supported our hypothesis,
with mice homozygous for the Plcg2^M28L risk variant on a LOAD1
background showing more extreme AD phenotypes compared to either LOAD1
mice or LOAD1 mice homozygous for Mthfr^677C > ^T. To our knowledge,
this is the first study to show that specific LOAD risk variants
interact with HFD to exacerbate LOAD phenotypes whereas others do not.
Diet was provided from 2 to 12 months to model long-term exposure, from
young adult to midlife, in the human population. However, our study was
not designed to determine whether the changes are reversible. It is
common that during an individual’s lifetime, diet is varied,
particularly as a part of a weight loss program. People may go through
periods of healthy eating or short-term extreme “fad” diets, such as
the South Beach diet ([223]Agatston, 2003) or the anti-inflammatory
Whole 30 diet ([224]Hartwig, 2015). However, although the benefits of
changes in diet are observed for overt measures of health, such as
weight, cholesterol, and blood pressure, the effects of changes in diet
on the brain are not commonly tracked. Given the similarity between the
effects of an HFD in humans and mice, studies where diet is varied
during a lifetime, considering factors, such as time on the diet and
age, from which HFD exposure began, should be performed. Here, the
widely used 45% HFD (ResearchDiet feed D12451i) was used to mimic DIO.
This allowed for the effects of DIO to be determined within the 12
months of this study and further studies would be needed to assess
long-term effects in old age. In addition, B6 mice do not develop overt
type 2 diabetes when fed with an HFD, but they are a model for early
stages of the disease with phenotypes that include obesity, mildly
elevated non-fasting blood glucose, increased serum glucose, glucose
intolerance with advancing age and elevated triglycerides, glucose,
HDL, LDL, insulin, and leptin ([225]Collins et al., 2004)^[226]6. Some
of those changes were observed in this study ([227]Figure 1). However,
a detailed assessment of Type 2 diabetic phenotypes was not performed
here. Therefore, although we suggest not likely, we cannot rule out the
interaction between HFD and Plcg2^M28L observed in the brain is due to
increased susceptibility to Type 2 diabetes in LOAD1.Plcg2^M28L
compared to the other strains tested. Furthermore, the HFD used here is
somewhat extreme, not many individuals consume 45% fat daily.
Therefore, to further align mouse studies to the human situation,
alternative diets should be tested, such as a recently developed
western diet that incorporates 16% of fat that was designed to model
the more common diet consumed in the western world ([228]Graham et al.,
2016; [229]Yang et al., 2019).
In the present study, we found alterations in several peripheral
cytokines including IL-1β, IFN-γ, and TNF-α in only the
LOAD1.Plcg2^M28L animals. In the LOAD1 and LOAD1. Mthfr^677C > ^T
animals, TNF-a and IL-1b were upregulated in LOAD1 animals fed HFD, but
not the LOAD1.Plcg2^M28L animals. However, TNF-α, IL-1β, and IFN-γ were
downregulated in LOAD1.Plcg2^M28L female brains, regardless of the
diet. Taken together, these data suggest the mechanisms driving the
interaction between HFD and Plcg2^M28L may be due to a combination of
peripheral and central factors (likely involving microglia responses)
that conspire to increase the alignment of brain transcriptomes to
human LOAD.
The interaction between Plcg2^M28L and HFD was determined using the
Nanostring Mouse AD panel. These analyses indicated Plcg2^M28L:HFD
(interaction) resulted in changes in the expression of genes enriched
for inflammatory pathways. The direction of the changes correlated with
gene expression changes observed in human AD based on the AMP-AD
modules ([230]Preuss et al., 2020). Many of these “inflammation” genes
are expressed by microglia including Csf1, Tgfbr2, Fcer1g, Trem2, and
Tyrobp. PLCG2 is a critical signaling element for various immune
receptors and is a key regulatory hub gene for immune signaling. The
PLCG2 is expected to be important in AD due to the previous findings
that suggest that a hypermorphic variant in PLCG2, rs72824905, is
protective against AD risk. However, the role of PLCG2 has not yet been
comprehensively explored ([231]Tsai et al., 2021; [232]Maguire et al.,
2021). A previous study has reported that reduced PLCG2 gene expression
alters microglial phenotypes in 5XFAD mice, affects plaque pathology,
and drives distinct transcriptional phenotypes of microglia in the
presence of amyloid pathology ([233]Tsai et al., 2021). The HFD used
here is reported to create a more inflammatory environment in the
brain, causing microglia activation. Previous HFD studies have been
associated with increased microglial activity in wild-type mice
([234]Rivera et al., 2013; [235]Wanrooy et al., 2018). In the present
study, transcriptomic changes were observed in the neuroinflammation
module, indicative of alterations in the microglial function.
Consistent with the transcriptomic changes, this study revealed an
increase in microgliosis in the hippocampus of LOAD1.Plcg2^M28L mice,
but not in LOAD1.Mthfr^677C > ^T. As with all studies involving mouse
models, these findings can be further supported using alternative
approaches in humans and/or cell-based models (e.g., human iPSC-derived
microglia), given some reports that indicate differences in microglia
responses between humans and mice ([236]Streit et al., 2009;
[237]Navarro et al., 2018; [238]Streit et al., 2018; [239]Olah et al.,
2020).
Recent in vivo imaging studies have suggested that microglia displayed
higher glucose uptake than neurons and astrocytes ([240]Xiang et al.,
2021). In patients with AD, glucose uptake was measured using FDG-PET,
and increases in glucose uptake were observed with an increase in the
microglial activity (utilizing TSPO-PET) ([241]Xiang et al., 2021). In
the present study, we observed alterations in FDG activity in multiple
brain areas in LOAD1.Plcg2^M28L mice. It is possible that the predicted
loss of function of this variant alters the microglial state, leading
to a dampened FDG signal and reduced function, which would agree with
the reduced cytokine release that was observed in the brain tissue.
Although we observed increases in the microglial number in female
LOAD1.Plcg2^M28L mice, this was only in the hippocampus of HFD animals,
suggesting a potential compensatory effect. The present study suggests
that FDG-PET may provide evidence for the microglial state in vivo.
Interestingly, the interaction between Mthfr^677C > ^T and HFD did not
result in gene expression changes relating to inflammation and
therefore PET/CT was not performed in LOAD1.Mthfr^677C > ^T mice. MTHFR
is a key enzyme in the folate/methionine/homocysteine pathway.
Variations in MTHFR, particularly the Mthfr^677C > ^T variant, are
associated with cardiovascular diseases, AD, and vascular dementia
([242]Liu et al., 2010; [243]Liew and Gupta, 2015; [244]Rai, 2017). In
humans, MTHFR^677C > ^T reduces liver enzyme activity resulting in a
decrease in enzyme function. Common effects of this include increases
in homocysteine which is reported to increase the risk for vascular
inflammation and dysregulation. The MTHFR is also expressed in multiple
cells in the brain, particularly in vascular-related cells, such as
endothelial and vascular smooth muscle cells. Supporting a role for
MTHFR in the cerebrovascular function, B6 mice homozygous or
heterozygous for Mthfr^677C > ^T show reduced enzyme activity in both
the liver and brain, elevated levels of homocysteine, cerebral blood
flow deficits, reduced collagen 4 in the brain, and neurovascular
damage by electron microscopy ([245]Reagan et al., 2021). However,
unlike Plcg2^M28L, a function for MTHFR in microglia has not been
reported, further suggesting the interaction between Plcg2^M28L and HFD
is driven by changes in microglial function.
Despite the increased alignment at the gene expression of
LOAD1.Plcg2^M28L on an HFD to human LOAD compared to strain-matched
mice fed on CD, LOAD1.Plcg2^M28L mice fed on HFD still lack
amyloid-beta plaques. This is not surprising as mice require mutations
or engineering of the human sequence to drive plaque deposition,
neither of which are present in these mice. Notably, this allows us to
study the effect of LOAD genetic risk x aging in the absence of amyloid
pathology. Also, neuronal loss was not detected in LOAD1.Plcg2^M28L
mice on an HFD, at least in the brain regions assessed up to 12 months
of age, suggesting additional pathways/processes need to be perturbed
likely in combination with extended aging to further align these models
to human LOAD. The presence of age-dependent amyloid accumulation would
be expected to further modify LOAD phenotypes and possibly result in
neuronal cell loss. Therefore, a humanized amyloid-beta (hAb) allele
has been created by MODEL-AD and added to LOAD1 (to create LOAD2;
B6.APOE4.Trem2^R47H.hAb) and to LOAD1.Plcg2^M28L and LOAD1.Mthfr^677C >
^T to create LOAD2.Plcg2^M28L and LOAD2.Mthfr^C677T, respectively. Male
and female test and control mice are provided with HFD and aged from 18
to 24 months to determine the long-term effects of combining APOE4,
Trem2^R47H, Plcg2^M28L, or Mthfr^677C > ^T risk variants in combination
with aging and the more amyloidogenic humanized hAb.
Despite the fact that all mice carried the APOE4 and Trem2^R47H
variants, linear modeling identified the specific effects of the
Plcg2^M28L and Mthfr^677C > ^T variants and the Plcg2^M28L:HFD and
Mthfr^677C > ^T:HFD interactions. However, data now suggest that
although APOE4 and TREM2^R47H are strong genetic risk factors for LOAD
in humans, TREM2^R47H may be reducing the effect of the APOE4 variant
when present together ([246]Kotredes et al., 2021). This may be due in
part to the reduced expression levels of Trem2 in Trem2^R47H mice
caused by a cryptic splice site that results in an aberrant splice form
(JAX#27918). MODEL-AD has created a new Trem2^R47H allele (Trem2*R47H
^HSS) that incorporates a human splice site (HSS) and restores Trem2
expression to normal levels, at least in young wild type B6 mice
(JAX#33781). Future studies will incorporate APOE4 or Trem2*R47H^HSS in
combination with the hAb allele and a recently created humanized,
MAPT-GR (JAX # 35398 and 33668) to study the interaction between HFD
and Tau pathology in the context of LOAD. These mice express the human
MAPT (H1 or H2 haplotype, respectively) and MAPT-AS1 transcripts and
the typical MAPT protein isoforms.
In summary, combining genetic and environmental risk factors (e.g.,
HFD) leads to better translational and preclinical models of LOAD. The
interactions we have observed here that correlate with LOAD in the
human population suggest that the effects of an HFD are
genotype-specific and further investigation is needed to resolve the
mechanistic interactions between genetics and diet.
Data Availability Statement
The datasets presented in this study can be found in online
repositories. The names of the repository/repositories and accession
number(s) can be found in the article/[247]Supplementary Material.
Ethics Statement
The animal study was reviewed and approved by Indiana University IACUC;
The Jackson Laboratory AUC.
Author Contributions
AO, KK, and GH contributed to the design of the study and wrote the
manuscript. RP, GC, and MS contributed to data analysis and
interpretation of data. AR, CI, BP, CL, DB, PL, DS, AT, SP, AB, KE, RS,
JM, JP, and LF ran assays and completed data analysis for these
studies. GH, PT, SR, and BL oversaw the study. All authors contributed
to the article and approved the submitted version.
Conflict of Interest
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
Publisher’s Note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or claim
that may be made by its manufacturer, is not guaranteed or endorsed by
the publisher.
Footnotes
^1
[248]https://adknowledgeportal.synapse.org/Explore/Studies/DetailsPage?
Study=syn21595255
^2
[249]https://www.labsupplytx.com/wp-content/uploads/2012/10/5K52.pdf
^3
[250]https://researchdiets.com/formulas/d12451
^4
[251]https://www.synapse.org/#!Synapse:syn14237651
^5
[252]https://reactome.org/
^6
[253]https://www.jax.org/jax-mice-and-services/strain-data-sheet-pages/
phenotypeinformation-380050
Funding
The MODEL-AD Center was supported through funding by NIA grant
U54AG054345. AO was supported by K01AG054753. GH was supported by the
Diana Davis Spencer Endowed Chair Research and GC was supported by the
Bernard and Lusia Milch Endowed Chair. The results published here are
in whole or in part based on data obtained from the AD Knowledge Portal
([254]https://adknowledgeportal.org). Study data were provided by the
Rush Alzheimer’s Disease Center, Rush University Medical Center,
Chicago. Data collection was supported through funding by NIA grants
P30AG10161 (ROS), R01AG15819 (ROSMAP; genomics and RNAseq), R01AG17917
(MAP), R01AG36836 (RNAseq), the Illinois Department of Public Health
(ROSMAP), and the Translational Genomics Research Institute (genomic).
Additional phenotypic data can be requested at [255]www.radc.rush.edu.
Mount Sinai Brain Bank data were generated from postmortem brain tissue
collected through the Mount Sinai VA Medical Center Brain Bank and were
provided by Dr. Eric Schadt from Mount Sinai School of Medicine. The
Mayo RNAseq study data was led by Nilüfer Ertekin-Taner, Mayo Clinic,
Jacksonville, FL as part of the multi-PI U01 AG046139 (MPIs Golde,
Ertekin-Taner, Younkin, Price). Samples were provided from the
following sources: The Mayo Clinic Brain Bank. Data collection was
supported through funding by NIA grants P50 AG016574, R01 AG032990, U01
AG046139, R01 AG018023, U01 AG006576, U01 AG006786, R01 AG025711, R01
AG017216, R01 AG003949, NINDS grant R01 NS080820, CurePSP Foundation,
and support from Mayo Foundation. Study data includes samples collected
through the Sun Health Research Institute Brain and Body Donation
Program of Sun City, Arizona. The Brain and Body Donation Program was
supported by the National Institute of Neurological Disorders and
Stroke (U24 NS072026 National Brain and Tissue Resource for Parkinson’s
Disease and Related Disorders), the National Institute on Aging (P30
AG19610 Arizona Alzheimer’s Disease Core Center), the Arizona
Department of Health Services (contract 211002, Arizona Alzheimer’s
Research Center), the Arizona Biomedical Research Commission (contracts
4001, 0011, 05-901 and 1001 to the Arizona Parkinson’s Disease
Consortium), and the Michael J. Fox Foundation for Parkinson’s
Research.
Supplementary Material
The Supplementary Material for this article can be found online at:
[256]https://www.frontiersin.org/articles/10.3389/fnagi.2022.886575/ful
l#supplementary-material
[257]Click here for additional data file.^ (218.8KB, PDF)
[258]Click here for additional data file.^ (301.8KB, PDF)
[259]Click here for additional data file.^ (42.7KB, XLSX)
References