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The role of specific gut microbiota in regulating the development of Alzheimer抯 disease

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The role of specific gut microbiota in regulating the development of Alzheimer抯 disease

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Content Copyright 2021 Pengfei Zhang

The role of specific gut microbiota in regulating the development of Alzheimer’s disease

by

Pengfei Zhang

A Thesis Presented to the
FACULTY OF THE USC Keck School of Medicine
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
Molecular Microbiology and Immunology

August 2021

ii
Acknowledgements
I’d like to thank everyone who helped me with this study during the writing process. First
and foremost, my heartfelt gratitude is to my superior, Professor Rongfu Wang, for his continuous
support and guidance on my research. He has been through all the phases of this work, from study
design to thesis writing. This study could not have achieved its present shape without his clear and
enlightening guidance.
Second, I would like to express my heartfelt gratitude to my committee members: Dr. Axel
Schonthal, Dr. Martin Kast, and Dr. Hyungjin Eoh, who have provided me great suggestions.
Third, a high tribute shall be paid to Dr. Changsheng Xing, who led me into the world of
microbiome and immunology. I am also greatly indebted to the lab colleagues in Dr. Wang’s lab at
the Keck School of Medicine: Dr. Junjun Chu, Dr. Tianhao Duan, Dr. Yang Du, Ms. Bingnan Yin,
Dr. Chen Qian, Dr. Xin Liu, and Ms. Siyao Liu, who have instructed and helped me a lot in the past
two years.
Last, my thanks would go to my beloved parents and girlfriend for their loving
considerations and great confidence in me all through these years. I also owe my sincere gratitude to
my friends and classmates who gave me their help and time listening to me and helping me work out
my problems during the challenging course of the thesis.

iii
Table of Contents
Acknowledgements ...................................................................................................................................... ii
List of Figures .............................................................................................................................................. iv
Abbreviations................................................................................................................................................ v
Abstract ....................................................................................................................................................... vii
Chapter 1: Introduction ............................................................................................................................... 1
1.1 Alzheimer’s disease ................................................................................................................... 1
1.2 Neuroinflammation in the Alzheimer’s disease ...................................................................... 2
1.3 Gut microbiota and inflammation ........................................................................................... 4
1.4 Gut microbiota and brain ......................................................................................................... 7
Chapter 2: Material and Methods .............................................................................................................. 10
2.1 Mice and histology .................................................................................................................. 10
2.2 Immunohistochemistry assay ................................................................................................. 11
2.3 Immunofluorescence assay..................................................................................................... 12
2.4 Immune cell isolation and flow cytometry ............................................................................ 12
2.5 Elisa for soluble and insoluble Aβ concentration ................................................................ 13
2.6 Elisa for cytokine concentration ............................................................................................ 13
2.7 Statistical analysis .................................................................................................................... 14
Chapter 3: The gut microbiota from Tak1
ΔM/ΔM
mice can affect the production of Aβ plaques in the
AD mouse model ....................................................................................................................................... 15
3.1 Existence of the gut microbiota from Tak1
ΔM/ΔM
mice ameliorates Aβ deposition in the
hippocampus but not the cortex .................................................................................................. 15
3.2 Tak1
ΔM/ΔM
feces-treated 5xFAD mice illustrate reduced insoluble Aβ42/40 levels and
increased soluble Aβ42/40 levels................................................................................................. 17
Chapter 4: The gut microbiota from Tak1
ΔM/ΔM
mice influences the immune system ........................ 20
4.1 Microglia are closely associated with the Aβ plaques ........................................................... 20
4.2 Several immune cell populations are altered due after the colonization of the gut bacteria
from Tak1
ΔM/ΔM
mice .................................................................................................................... 21
4.3 The Tak1
ΔM/ΔM
gut microbiota enhances the productions of serum IL-17A and IL-22 in
the AD mouse model .................................................................................................................... 23
4.4 The Tak1
ΔM/ΔM
gut microbiota influences the production of cytokines in the brains of the
AD mouse model .......................................................................................................................... 23
Chapter 5: Discussion ................................................................................................................................ 25
References ................................................................................................................................................... 28
iv
List of Figures
Figure 1. Gut microbiota from Tak1
ΔM/ΔM
mice reduced the Amyloid-β plaques in 5xFAD mouse
brains. .......................................................................................................................................................... 16
Figure 2. Gut microbiota from Tak1
ΔM/ΔM
mice reduced the Amyloid-β plaques in 5xFAD mouse
brains. .......................................................................................................................................................... 17
Figure 3. Gut microbiota from Tak1
ΔM/ΔM
mice reduced the insoluble Amyloid-β amount in 5xFAD
mouse brains. .............................................................................................................................................. 19
Figure 4. Gut microbiota from Tak1
ΔM/ΔM
mice reduced the microglia-associated Amyloid-β plaques
in both 5xFAD and APP/PS1 mouse brains. .......................................................................................... 21
Figure 5. Characterization of immune components in Tak1
ΔM/ΔM
microbiota treated 5xFAD mice. . 22
Figure 6. Tak1
ΔM/ΔM
microbiota-treated 5xFAD mice had increased Th17 cytokines. ......................... 23
Figure 7. Characterization of levels of critical cytokines in brains of Tak1
ΔM/ΔM
microbiota-treated
5xFAD mice. .............................................................................................................................................. 24

v
Abbreviations
Abbreviation Explanation
5xFAD 5x familial AD
AD Alzheimer’s disease
AMPK Adenosine 5’-monophosphate-activated
protein kinase
AOM azoxymethane
APOE Apolipoprotein E
APP amyloid precursor protein
APP/PS1 amyloid precursor protein/presenilin 1
Aβ amyloid-β
BBB blood-brain barrier
CNS central nervous system
CRC colitis and colorectal cancer
DAMPs/PAMPs damage-or pathogen-associated molecular
patterns
DCs dendric cells
HDACs histone deacetylases
HPA axis the hypothalamic–pituitary–adrenal axis
IF immunofluorescence
IHC immunohistochemistry
IL-10 interleukin-10
IL-17 interleukin-17
IL-1β interleukin-1β
IL-22 interleukin-22
IL-6 interleukin-6
LPS lipopolysaccharide
MAP3K7 mitogen-activated protein kinase kinase kinase
7
vi
MHC major histocompatibility complex
ScFAs short-chain fatty acids
SPF specific pathogen free
TAK1 Transforming growth factor-β-activated
kinase-1
TGF-β tumor growth factor-β
Th1 T helper type 1 cells
Th17 T helper type 17 cells
TLR toll-like receptors
TNF-α tumor necrosis factor-α
Treg regulatory T cells
WAT white adipose tissue

vii
Abstract
Alzheimer’s disease (AD) is a common type of neurodegenerative disease controlled by
genetic and environmental factors, including the gut microbiota. It was recently revealed that the
specific microbiota in Tak1
ΔM/ΔM
(myeloid-specific TAK1 ablation) mice could inhibit the
inflammation and thus suppress colon cancer progression. However, due to the close connection of
AD and neuroinflammation, whether the specific gut microbiota could influence the development
of AD by regulating neuroinflammation was still unknown. This study found
that Tak1
ΔM/ΔM
microbiota-treated 5x familial AD (5xFAD) mice and amyloid precursor
protein/presenilin 1 (APP/PS1) mice developed attenuated Amyloid-β pathology in the
hippocampus. Furthermore, we figured out that Tak1
ΔM/ΔM
microbiota had the ability to influence
the immune system and the microglial morphology in AD mouse models. These data suggest that
the AD progression could be controlled by manipulating the gut microbiota composition and thus
provide essential insights and therapeutic candidates for the prevention and better treatment of AD
patients.

Keywords: Alzheimer’s disease; gut microbiota; Amyloid-β; neuroinflammation.

1
Chapter 1: Introduction
1.1 Alzheimer’s disease
Alzheimer’s disease (AD) is one of the most common chronic age-related progressive
neurodegenerative diseases and is the cause of approximately 70% of cases of dementia (Burns and
Iliffe 2009). During the early phase of AD, the patients would have difficulty remembering recent
events. As the disease progresses, many neurodegenerative symptoms, like difficulties in languages,
disorientation, mood swings, loss of motivation, and behavioral problems, would arise, which means
the patients would have dementia (Burns and Iliffe 2009). In the late period of AD, bodily functions
are lost gradually, and the eventual death of AD patients is inevitable.
Till now, the cause of AD is poorly understood. Being characterized by a progressive loss of
cognitive functions and impairment of specific neurons and synapses, AD is commonly considered
that has two pathological hallmarks: abnormal development of the extracellular deposition of Aβ
peptides in senile plaques and aberrant accumulation of the intracellular neurofibrillary tangles from
hyperphosphorylated Tau protein in the central nervous system (CNS), which are considered to
contribute to neuron damage and neuroinflammation (Tackenberg et al. 2020). Eighty years after Dr.
Alzheimer had discovered the disease in the 1900s, Aβ peptide was isolated from human brains by
Dr. Glenner. Abnormal phosphorylation of Tau protein had been found in AD. Thus, the Aβ and
Tau hypothesis of Alzheimer’s disease was put forward and became the most common hypothesis
of the cause of AD.
In terms of the Aβ hypothesis, extracellular Aβ plaques are the principal cause of AD. Aβ
peptide is generated from the amyloid precursor protein (APP), an integral membrane protein
expressed in many tissues and concentrated in the synapses of neurons via cleavage by β-secretase
and γ-secretase. The detailed function of APP, which may decipher the cause of AD, is still largely
2
unknown. However, it has been commonly considered as a regulator of synapse formation and
neural plasticity. Thus, in the brains of AD patients, the Aβ peptide would be produced and
accumulated as abnormal plaques. However, whether the Aβ plaques could contribute to neuron
damage and the progression of AD still need more researches and evidence.
Furthermore, the development of AD is also associated with many environmental and
genetic risk factors. For example, the increased frequency of the Apolipoprotein E (APOE) allele,
especially APOE4, leads to the loss of ability to break down Aβ peptides and the acceleration of Aβ
plaques buildup in the brain (Polvikoski et al. 1995). Therefore, although the Aβ hypothesis has not
been fully proved, Aβ plaques have been the keyword and target in many AD projects without a
doubt.
Due to the number of unknowns, the prevention and therapeutics of AD are still limited and
difficult to be developed. Currently, there is no conclusive research result that could uphold any
specific measure as being efficient to prevent the disease. Education, physical exercise, and taking a
healthy diet have become the most common ways to reduce the risk of suffering AD and delay the
appearance of the symptoms. In terms of treating AD, existing treatments only have limited ability
to alleviate the symptoms and slow the progression of the disease. Further research and clinical trials
are required to develop effective prevention and therapy of AD.
In this study, we intend to figure out a way to alleviate amyloidosis to delay the progression
of AD efficiently. Connecting neuroinflammation, gut microbiota, and amyloidosis in the brain, a
novel insight into AD would be established.

1.2 Neuroinflammation in the Alzheimer’s disease
The pathogenesis of AD is still unclear, although neuroinflammation is likely to be
implicated in the pathogenesis of AD (Heneka et al. 2015). On the one hand, chronic
3
neuroinflammation primarily driven by Th1 cells is featured by autoimmune disorders like multiple
sclerosis; on the other hand, there is a less rapidly progressive type of chronic neuroinflammation
primarily driven by innate immunity cells (Heneka et al. 2015). The latter is primarily due to age-
related deterioration of anti-inflammatory pathways, which induces subtle clinical manifestation,
such as the inflammatory response after traumatic brain injury, which may last for years before the
clinical symptoms as AD (Heneka et al. 2015).
Microglia are the resident phagocytes of the CNS and are ubiquitously distributed in the
brain. Like macrophages, microglia can differentiate between M1 microglia, inducing a pro-
inflammatory response, and M2 microglia, inducing an anti-inflammatory response (Tang and Le
2016). In normal conditions, the microglia utilize their highly motile systems to actively scan their
allocated brain regions for cleaning contaminants and cellular waste while also supplying reasons that
help tissue maintenance. However, Microglia spread their processes to the site of damage after being
triggered by pathological signals such as neuronal death or protein aggregates, later migrating to the
lesion and initiating an innate immune response (Heneka et al. 2015). As the major risk factor in
neuroinflammation, the M1 microglia, also known as neurotoxic microglia, is characterized by high
expression of major histocompatibility complex (MHC) antigens and complement receptors and
activated during aging or particularly under pathological conditions to produce pro-inflammatory
cytokines like interleukin-1β (IL-1β), interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α). Due
to the production and accumulation of these molecules, a series of processes, including amyloidosis
and neurodegeneration, would be induced during a chronic neuroinflammatory response (Heneka et
al. 2015). In AD, similar to recognizing damage-or pathogen-associated molecular patterns
(DAMPs/PAMPs), microglia have the capacity to bind to the soluble Aβ oligomers and Aβ fibrils
through various receptors, including class A scavenger receptor A1, CD36, CD14, α6β1 integrin,
CD47, and toll-like receptors (TLR2, TLR4, TLR6, and TLR9), which are considered as part of the
4
neuroinflammation in AD (Heneka et al. 2015). When Aβ binds to CD36, TLR4, and TLR6,
microglia will be activated as M1 microglia and begin to release pro-inflammatory cytokines and
chemokines. In vitro, genetic deletion of CD36, TLR4, or TLR6 inhibits Aβ-induced cytokine
synthesis and prevents amyloid aggregation and inflammasome activation (Heneka et al. 2015).
Microglia become hyper-reactive in the context of pathological aging, such as midlife
overweight and obesity, with accelerated production of pro-inflammatory cytokines and
dysfunctional phagocytosis (Krstic and Knuesel 2013). This will contribute to neuron damage in a
neurotoxic setting without the protection of neuroprotective microglia. In addition, Aβ plaques are
produced from intracellular APP aggregates as a result of neuronal degeneration. These plaques can
cause more microglia to be activated and more pro-inflammatory molecules to be released, creating a
vicious cycle of neurotoxic pro-inflammatory response (Krstic and Knuesel 2013).
Although microglial macrophages, cytokines, and microbiota components are likely to
contribute to AD in a context-dependent manner (Neurath 2014, West et al. 2015), the precise
mechanisms remain unclear.

1.3 Gut microbiota and inflammation
Gut microbiota is the collection of microorganisms, including bacteria, archaea, fungi,
viruses, bacteriophages, and protists, that live in the digestive tracts (Moszak et al. 2020). It is
inherited from the mother, environment, or other sources and has a high variability within and
between individuals. Therefore, it can be monitored and modulated easily and have a high degree of
plasticity or adaptability. Similar to AD, the compositions of gut microbiota are age-related. From 6
months old to 3 years old, the microbiotas become much more stable, and then the immunity will
also become more robust. Then from three years old to adulthood, the microbiota would change in
response to events but will shift back to the "baseline", which is the homeostasis of the gut
5
microbiome in the human body (Yatsunenko et al. 2012). When people get old, the number of
species in the microbiota will decrease, and populations become more similar among individuals. In
general, before adulthood, the α-diversity of the microbiota, which means species diversity in the gut
at a local scale, will increase, and the β-diversity of the microbiota, which means the ratio between
regional and local species diversity, will decrease gradually (Yatsunenko et al. 2012).
Bacteria make up 60% of the dry mass of feces and the majority of the flora in the colon
(Guarner and Malagelada 2003). By extracting nucleic acid from fecal specimens and generating
bacterial 16S rRNA gene sequences with bacterial primers, feces become an ideal source of gut flora
for any studies and experiments. More invasive procedures, such as biopsies, are often preferred
over this form of testing. The gut microbiota is mainly dominated by five phyla: bacteroidetes,
firmicutes, actinobacteria, proteobacteria, and verrucomicrobia, with firmicutes constituting 60~80%
and bacteroidetes constituting 20~40% of the composition (Braune and Blaut 2016). Surprisingly,
between 300 and 1000 distinct species hold their lives in the gut, estimated at 500 approximately in
most situations. However, 99 percent of the bacteria are likely to come from 30 or 40 different
species, with Faecalibacterium prausnitzii (phylum firmicutes) being the most common bacteria in
healthy adults (Miquel et al. 2013).
According to previous research, the interaction between gut flora and humans is mutualistic
and symbiotic instead of commensal (a non-harmful coexistence). Localized mainly in the gut, the
microbiota has the ability to influence the physiology and development of the individual while
maintaining the host's life as well (Angelucci et al. 2019). For example, gut microbiota provides
protection from infection by competing for resources, producing anti-microbials, and changing
abiotic factors or environmental conditions. For maintaining protective barriers, gut microbiota
could keep skin supple, promote healing and maintain tissue integrity. Furthermore, gut microbiota
could train the immune system by tolerating the friendly bacteria to avoid them attack our cells.
6
Most importantly, although it is a collection of the community of microbes populating our
intestine, gut microbiota could promote immune regulation to influence inflammation happened in
many tissues in the human body by producing molecules like short-chain fatty acids (ScFAs) and
cytokines (Rooks and Garrett 2016). In addition, various types of immune cells in the innate and
adaptive immunity, like regulatory T (Treg) cells, T helper type 1 cells (Th1), T helper type 17 cells
(Th17), effector T cells, B cells, neutrophils, macrophages, dendric cells (DCs), microglia and so on,
could be regulated by gut microbiota and several cytokines, like interleukin-10 (IL-10), interleukin-17
(IL-17), interleukin-22 (IL-22), tumor growth factor-β (TGF-β), play a significant role in gut
microbiota associated inflammatory response (Rooks and Garrett 2016). To be much more specific,
the gut microbiota can decrease inflammation and tumorigenesis by regulating immune cells in the
gut; it can inhibit HDAC9 and activate Treg cells in the lung; it can decrease lipolysis and insulin-
mediated fat accumulation in white adipose tissue(WAT); it can increase insulin secretion in the
pancreas; it can increase insulin sensitivity and Adenosine 5’-monophosphate-activated protein
kinase (AMPK) activity, decrease gluconeogenesis and lipid storage in the liver; it can increase satiety
and neurogenesis, activate microglia and also decline blood-brain barrier (BBB) permeability in the
brain (Koh et al. 2016).
Transforming growth factor-β-activated kinase-1 (TAK1), also known as mitogen-activated
protein kinase kinase kinase 7 (encoded by MAP3K7), is an essential component of innate and
adaptive immune signaling with cell-type-dependent function (Ajibade et al. 2013). Despite the fact
that TAK1 is a positive regulator in T cells (Wan et al. 2006), myeloid-specific Tak1-deficient
(Tak1
ΔM/ΔM
) mice develop significant quantities of IL-1β, IL-6, and TNF-α, and rapidly die during
lipopolysaccharide (LPS)-induced septic shock, implying a negative regulator role (Ajibade et al.
2012). The gut microbiota in Tak1
ΔM/ΔM
mice has been identified as a critical factor in resistance to
dextran sulfate sodium (DSS)-induced colitis and azoxymethane (AOM)/DSS-induced colitis and
7
colorectal cancer (CRC) by manipulating the activation of defensive Th17 cells to regulate
inflammation (Xing et al. 2021). To decipher whether the specific microbiota composition in
Tak1
ΔM/ΔM
mice may potentially regulate the AD sensitivity, the 16S and metagenomic sequencing
have been performed and compared with data from recent publications. In the aged APP/PS1
mouse model, a dramatic elevation in the abundance of Verrucomicrobia and Proteobacteria was
detected in AD mice compared with WT controls, whereas in Tak1
ΔM/ΔM
mice, both phyla were
decreased (Zhang et al. 2017). Furthermore, in human AD patients, increased Actinobacteria,
Subdoligranulum, and Ruminococcus were observed, while these taxa and their key species were
decreased in the microbiota from Tak1
ΔM/ΔM
mice (Zhuang et al. 2018). On the contrary,
Lachnospiraceae and certain members of Bacteroidetes were decreased in human AD patients, and
their abundances in Tak1
ΔM/ΔM
mice were significantly elevated compared with WT mice (Zhuang et
al. 2018).
However, the role of TAK1 deficiency in microbiota, neuroinflammation and the
development of AD remains obscure. In this study, we verified whether the gut microbiota from
Tak1
ΔM/ΔM
mice could influence the development of AD.

1.4 Gut microbiota and brain
Not only can gut microbiota regulate the immune system and manipulate inflammatory
response, but it can also be a key regulator of brain development and aging-related
neurodegeneration through maintaining a homeostatic control on the immune system(Cryan and
Dinan 2012, Dinan and Cryan 2017).
The term "microbiome-gut-brain axis" is often used to describe paradigms that specifically
involve the gut flora, and it has been extended to include the role of the gut flora in the interplay
(Wang and Kasper 2014). A number of studies have found that the microbiota plays a crucial role in
8
brain activity and behavior and that the microbiome-gut-brain axis is anticipated to serve as a
bidirectional signaling pathway, encouraging gut microbes to connect with the brain and the brain to
interact with the gastrointestinal tract (Canfora et al. 2015). However, the various mechanisms of the
communication, including the hypothalamic–pituitary–adrenal axis (HPA axis), immune system,
sympathetic and parasympathetic arms of the autonomic nervous system that includes the enteric
nervous system, the vagus nerve, and the gastrointestinal microbiota, are not unraveled completely
(Wang and Kasper 2014).
The immune signaling pathways are included in the connection between gut microbiota and
the brain. ScFAs are up to six carbon-containing compounds that result from bacterial fermentation
of dietary fibers, especially in the colon and amino acid metabolism (Sherwin et al. 2016). By
protecting the integrity of the intestinal barrier and the blood-brain barrier, these ScFAs maintain the
homeostasis of the gastrointestinal tract and the brain (Dalile et al. 2019). The ScFAs, which contain
propionate, butyrate, and acetate, are essential metabolic products of gut microbiota and may have
central effects by contacting directly or indirectly with G-protein-coupled receptors or, in the
presence of butyrate as an epigenetic modulator, histone deacetylases (HDACs) (Paul et al. 2015,
Stilling et al. 2014). A variety of ScFA-induced pathways have been proposed as therapeutic options
for various neuropsychiatric and neuroinflammatory disorders. ScFAs could contribute to (a)
maturation and activation of microglia to eradicate unnecessary synaptic or neuronal connections,
(b) development of Treg cells and production of anti-inflammatory cytokines, (c) suppression of
neuroinflammation through impeding the maturation and differentiation of monocytes,
macrophages, and dendritic cells, thus reducing their capacity to produce pro-inflammatory
cytokines. (d) homeostatic manipulation of neurotrophic factors, neurotransmitters, and
neuropeptides in the brain, (e) preservation of relative junction protein levels to maintain the BBB
integrity, (f) stimulation of gene expression through acetylation of histones to maintain learning and
9
long-term memory activity in the brain (Goyal et al. 2021). Another pathway of communication that
has been reported is cytokine-mediated immune signaling (El Aidy et al. 2014).
The microbiota-gut-brain axis, a term recently reported to explain the biochemical signaling
that occurs between the gastrointestinal tract, the CNS, and the gut microbiota, could also be
connected with Alzheimer's disease (Goyal et al. 2021). Thus, while pursuing a therapeutic strategy
in AD, the modification of gut microbiota became one of the best choices in the view of researchers
in recent last years (Goyal et al. 2021). In this study, we report that the altered gut microbiota from
Tak1
ΔM/ΔM
mice can influence neuroinflammation and systemic immune response in the mouse
models of AD. Overall, our findings provide important insights into mechanisms by which
microbiota interact with innate immune signaling to control protective immunity against AD.
10
Chapter 2: Material and Methods
2.1 Mice and histology
The AD mouse models we used are 5x familial AD (5xFAD) mice and amyloid precursor
protein/presenilin 1 (APP/PS1) mice. The 5xFAD transgenic mice overexpress mutant human
Amyloid beta (A4) precursor protein 695 (APP) with the Swedish (K670N, M671L), Florida
(I716V), and London (V717I) Familial Alzheimer's Disease (FAD) mutations along with human
presenilin 1 (PS1) harboring two FAD mutations, M146L and L286V. The mouse Thy1 promoter
controls the overexpression of both transgenes in the brain. Thus, the 5xFAD mice gather up
significant features of Alzheimer's Disease amyloid pathology and can be a valuable model of Aβ-42-
induced neurodegeneration and amyloid plaque development. The APP/PS1 mice are double
transgenic mice expressing a chimeric mouse/human amyloid precursor protein
(Mo/HuAPP695swe) and a mutant human presenilin 1 (PS1-dE9), both directed to CNS neurons.
Both mutations are associated with early-onset Alzheimer's disease. Thus, these mice may be helpful
in studying neurological disorders of the brain, particularly Alzheimer's disease, for the amyloid
plaque formation during aging. The SPF (specific-pathogen-free) colonized mice are treated at 1-
month-old with antibiotics, including ampicillin (1 mg/ml), neomycin (1 mg/ml), metronidazole (1
mg/ml), and vancomycin (0.5 mg/ml), for one month to clean up the original gut microbiota in the
mice and then transferred the control feces and Tak1
ΔM/ΔM
mice feces orally for one month prior to
analyzing them at the age of six months. Six-month-old, different groups of mice were euthanized to
collect brain tissues, spleens, serums, bone marrow.
For fecal materials transfer, freshly weaned (3-4 weeks old) 5xFAD mice were pretreated for
four weeks with a combination of antibiotics (ampicillin, 1 mg/ml; neomycin, 1 mg/ml;
metronidazole, 1 mg/ml; vancomycin, 0.5 mg/ml) and randomly divided into different groups for
oral gavage of fecal materials. The mice received the viably preserved fecal microbial communities
11
via oral gavage once a week for four weeks. Fecal pellets were freshly collected from 5xFAD mice
and Tak1
ΔM/ΔM
mice and stored in -80°C freezer. Thawed cryopreserved fecal stocks were
resuspended in sterile PBS (1 ml per fecal pellet) and filtered through a 70 μm strainer. Each
recipient mouse received the same 0.2 mL suspension every week because fecal stocks from the
same genotype were mixed. The number of anaerobically cultured bacteria was evaluated for the
gavage of single bacteria strains based on the spectrophotometer reading at an Optical Density of
600 nm. 5x10
8
bacteria/strain were transferred to each mouse once a week in 0.2 ml PBS. Mice
given the same fecal stocks or bacteria species were housed together in the same cage.
Collected brains were quickly excised and separated along the centerline. One half was
immersed in 4% paraformaldehyde in PBS for 24 h and then 30% sucrose for 48 h for
immunohistology analysis, while the other half was cryopreserved in liquid nitrogen. For the test of
Amyloid-β (Aβ) plaque deposition by immune-staining, fixed hemispheres were placed in OCT
medium, mounted on a freezing microtome stage, and serially sectioned at 20 μm thickness in a
coronal plane through the hippocampal complex and stored at −20 °C.
2.2 Immunohistochemistry assay
For IHC staining, frozen brain sections were thawed at room temperature for 20 minutes.
Antigen retrieval was achieved by boiling the slides in a pressure cooker for 3 min in a citrated
buffer (10 mM trisodium citrate, pH 6.0). After 10 min treatment with 3% H2O2, tissue sections
were blocked with 5% normal goat serum in 1xTBS buffer for 1 hour at room temperature,
incubated with primary antibodies at 4°C overnight. The next day, the sections were incubated with
EnVision Polymer-HRP secondary antibodies (Dako) at room temperature for 30 min. Most
importantly, after each step above, the sections were washed in 1xTBS buffer three times for
5minites each. After the application of DAB chromogen (Vector), brain sections were stained with
hematoxylin and then washed in water and 1xPBS buffer. Further, the brain sections were
12
dehydrated in graded ethanol solutions and xylenes and mounted. Images were acquired using the
Olympus BX61 microscope along with a DP71 digital camera (Olympus).
2.3 Immunofluorescence assay
For IF staining, frozen brain sections were thawed at room temperature for 20 minutes.
Antigen retrieval was achieved by boiling the slides in a pressure cooker for 3 min in a citrated
buffer (10 mM trisodium citrate, pH 6.0). After 10 min treatment with 3% H2O2, tissue sections
were blocked with 5% normal goat serum in 1xTBS buffer for 1 hour at room temperature,
incubated with primary antibodies at 4°C overnight. The next day, the sections were incubated in
fluorochrome-conjugated secondary antibody diluted in antibody dilution buffer for 1-2 hours at
room temperature in the dark. Most importantly, after each step above, the sections were washed in
1xTBS buffer three times for 5minites each. Then, the sections were coverslip with Prolong Gold
Antifade Reagent (Cell Signaling, #9071) at room temperature overnight. Images were taken using
Nikon Eclipse Ti-E confocal microscope.
2.4 Immune cell isolation and flow cytometry
Bone marrow was collected from mouse leg bone in 1640 medium with 2% FBS, 2mM
EDTA, and 1% Penicillin/Streptomycin and centrifuged at 1500 rpm for 5 minutes. After removing
the supernatant, ACK buffer was added for lysing red blood cells, and the medium was added to
10X volume. Then the suspension was centrifuged at 1500 rpm for 5 minutes. After removing the
supernatant, the sediment was resuspended by the medium.
Spleens was grinded in 70 um strainers to medium in 15ml tube. The suspension was
centrifuged at 1500 rpm for 5 minutes. After removing the supernatant, ACK buffer was added for
lysing red blood cells, and the medium was added to 10X volume. Then the suspension was
centrifuged at 1500 rpm for 5 minutes. After removing the supernatant, the sediment was
resuspended by the medium.
13
Cells were stained for 30 minutes on ice with a cocktail of fluorochrome-conjugated
antibodies (eBioscience or BD Biosciences), washed twice in PBS/FBS, and resuspended in
PBS/FBS for flow cytometry. Cells were activated for 4 hours at 37°C with PMA, Ionomycin, and a
protein transport inhibitor for intracellular staining. The cells were then resuspended in Fix/Perm
buffer (BD) and treated for 20 minutes on ice before being washed with Perm/Wash buffer (BD),
and stained for 30 minutes on ice with the antibody cocktail. Cells were washed twice and
resuspended in PBS/FBS for flow cytometry. To differentiate different subsets, the Attune NxT
Flow Cytometer was used.
2.5 Elisa for soluble and insoluble Aβ concentration
To further define the soluble and insoluble Aβ level in brains, the frozen mouse hemi-brains
in liquid nitrogen were weighed and homogenized in 1 ml PBS containing protease inhibitor (Roche)
and 1 mM AEBSF (Sigma). The homogenates were sonicated and centrifuged at 100,000 g for 60
min at 4 °C. The supernatant (PBS-soluble fraction) was collected and stored at −80 °C. The pellets
were re-dissolved in 0.5 ml 70% formic acid (prepared in PBS), further sonicated and centrifuged at
100,000 g for 60 min at 4 °C, and the supernatant (formic acid-soluble fraction) were collected and
neutralized using 1 M Tris buffer, pH 8.0 at a dilution of 1:20. Protein concentration was measured
in the two fractions using the Pierce BCA Protein Assay (Thermo Fisher). After proper dilution, the
brain extracts were analyzed by sandwich ELISA for Aβ1–40, Aβ1–42, and Aβ aggregates using
commercial ELISA kits and following the manufacturer’s protocol (Aβ1–40: KHB3481; Aβ1–42:
KHB3441; Aβ aggregates: KHB3491, Thermo Fisher).
2.6 Elisa for cytokine concentration
In brief, the plates were coated with capture antibodies (eBioscience or MBL) overnight at
4°C, then blocked for an hour at room temperature with PBS/BSA. The samples and standards
were incubated in the plates at room temperature for 2~3 hours. At room temperature, the plates
14
were treated with biotinylated detection antibodies (eBioscience or MBL) for 1 hour and HRP-
Conjugated Streptavidin (Thermo) for 30 minutes. For 5-10 minutes, tetramethylbenzidine (TMB;
Sigma) was added to the plates, and the reactions were stopped with an equal volume of 2M H
2
SO
4
.
The absorbance of each well was read at 450 nm on BioTek Synergy 2 Microplate Reader.
2.7 Statistical analysis
For each group, descriptive statistics including means, standard deviations, medians, and
ranges were computed and analyzed utilizing Student's t-test or, for multiple comparisons, ANOVA.
The data are presented as the mean and standard error of the mean (SEM). The sample size for each
experiment is included in the results section, as well as the legend for the corresponding figure.
GraphPad Prism 8 was used for all analyses (GraphPad Software, La Jolla, CA). P-values of less than
0.05 were considered significant. To obtain unbiased data, all experimental mice were processed at
the same time. Quantifications were carried out independently and separately by two scientists.

15
Chapter 3: The gut microbiota from Tak1
ΔM/ΔM
mice can affect the
production of Aβ plaques in the AD mouse model
3.1 Existence of the gut microbiota from Tak1
ΔM/ΔM
mice ameliorates Aβ deposition
in the hippocampus but not the cortex
We utilized the 5xFAD and APP/PS1 mouse models, which could rapidly develop
significant features of the Aβ pathology, to examine whether gut microbiota from Tak1
ΔM/ΔM
mice is
harmful or beneficial during the neurodegeneration. In order to see if the gut microbiota could
modulate the progression of the disease in 5xFAD and APP/PS1 mice, we treated 1-month-old
5xFAD and APP/PS1 mice with a cocktail of antibiotics, including ampicillin (1 mg/ml), neomycin
(1 mg/ml), metronidazole (1 mg/ml), and vancomycin (0.5 mg/ml), for one month to clean up the
original microbiota; then transferred the fecal materials from control and Tak1
ΔM/ΔM
mice by oral
gavage, once a week for four weeks for microbiota reconstruction. The mice were analyzed at the
age of six months for disease progression.
To measure the amount of Aβ depositions and the sizes of Aβ plaques by histopathological
evaluation in hippocampi, we capitalized on immunofluorescent labeling with Aβ-specific 6E10
antibody, which targets Aβ depositions, and the Aβ burden in control and Tak1
ΔM/ΔM
feces-treated
5xFAD mice was quantified under a confocal microscope. After comparing between the control
group and Tak1
ΔM/ΔM
feces-treated group, we observed that the amount of Aβ plaques was receded
significantly in hippocampi of Tak1
ΔM/ΔM
feces-treated 5xFAD mice (p=0.0058) (Figure 1A, B).
Furthermore, after calculating the average size of Aβ plaques, we found that the average size in the
hippocampi of Tak1
ΔM/ΔM
feces-treated 5xFAD mice was significantly more extensive than that of
the control group (p<0.0001) (Figure 1A, C). Meanwhile, we also processed the
immunohistochemistry (IHC) staining of Aβ (BioLegend, Cat#: 803001), following the protocol
16
from Cell Signaling Technology for frozen slides, and quantified the plaque burden. Consistent with
the immunofluorescent staining (IF) result, Tak1
ΔM/ΔM
microbiota-transferred 5xFAD mice had
dramatically decreased Aβ plaque number and the average size of Aβ plaque in hippocampus regions
compared with control microbiota-treated mice (p<0.05) (Figure 2A-C). However, either amount or
size of Aβ plaques has no notable difference in the cortex of Tak1
ΔM/ΔM
feces-treated 5xFAD mice
compared with the control group (Figure 2A, B). These data suggest that the Tak1
ΔM/ΔM
microbiota
has the protective function to reduce the accumulation of Aβ peptides against the development of
Alzheimer’s disease.

Figure 1. Gut microbiota from Tak1
ΔM/ΔM
mice reduced the Amyloid-β plaques in 5xFAD mouse brains. (A) IF staining of
Amyloid-β on brain sections from fecal microbiota transferred 5xFAD mice after Abx-pretreatment. (B) Quantification of Amyloid-
β plaques in the hippocampus. (C) Analysis of the Aβ plaque sizes in each group. Mean ± SEM. **p<0.01; ****p<0.0001.

nucleus
Amyloid β
plaques
merged
Control feces TAK1 KO feces
Ctrl feces
TAK1 KO feces
0
50
100
150
# of A β plaque
**
Control feces
TAK1 KO feces
number of A β plaques
5xFAD feces
TAK1 KO feces
0
500
1000
1500
average A β plaque size ( µm
2
)
****
average A β plaque size ( μm
2
)
Control feces
TAK1 KO feces
A B C
17

Figure 2. Gut microbiota from Tak1
ΔM/ΔM
mice reduced the Amyloid-β plaques in 5xFAD mouse brains. (A) IHC staining
of Amyloid-β (Aβ) on brain sections from intact 5xFAD mice and fecal microbiota transferred mice after Abx-pretreatment. (B)
Quantification of Aβ plaques in cortex and hippocampus. (C) Analysis of the Aβ plaque sizes in each group. Mean + SD. *p<0.05;
**p<0.01.

3.2 Tak1
ΔM/ΔM
feces-treated 5xFAD mice illustrate reduced insoluble Aβ42/40 levels
and increased soluble Aβ42/40 levels
To investigate the effect of the gut microbiota from Tak1
ΔM/ΔM
mice on Aβ deposition from
another perspective, we homogenized one hemisphere of the brains from control and Tak1
ΔM/ΔM

feces-treated 5xFAD and APP/PS1 mice and prepared PBS-soluble and 70 % formic acid-insoluble
fractions for detecting the levels of Aβ1-40 and Aβ1-42 species using the commercial ELISA kits,
following the manufacturer’s protocol (Aβ1–40: KHB3481; Aβ1–42: KHB3441; Aβ aggregates:
KHB3491, Thermo Fisher). Protein concentration was measured in the two fractions using the
Pierce BCA Protein Assay (Thermo Fisher). We observed the reductions in both insoluble Aβ1-40
and Aβ1-42 levels in Tak1
ΔM/ΔM
feces-treated 5xFAD mice compared with controls. Primarily,
insoluble Aβ1-42 levels decreased significantly in Tak1
ΔM/ΔM
feces-treated 5xFAD mice compared
with controls (Figure 3A). Furthermore, both soluble Aβ1-40 and Aβ1-42 level in Tak1
ΔM/ΔM
feces-
treated 5xFAD mice tended to increase, compared with controls (Figure 3A). We quantified the
Aβ1-42: Aβ1-40 ratio to verify the composition of these two Αβ peptide subtypes and observed that
both soluble and insoluble Aβ1-42: Aβ1-40 ratio in Tak1
ΔM/ΔM
feces-treated 5xFAD mice tended to
decrease compared with controls (Figure 3B). According to the levels of Aβ aggregates, both soluble
A C
5xFAD feces
TAK1 KO feces
0
200
400
600
# of A β plaque
ns
5xFAD feces
TAK1 KO feces
0
50
100
150
200
# of A β plaque
*
Control feces
TAK1 KO feces
number of A β plaques
Control feces
TAK1 KO feces
number of A β plaques
Cortex Hippocampus
B
18
and insoluble Aβ aggregates tended to decrease after Tak1
ΔM/ΔM
feces treatment, although the
differences are not significant due to the short in the animal numbers (Figure 3C). Normalized by
the concentration of Aβ aggregates, significant reductions in insoluble Aβ1-40 and Aβ1-42 levels
were observed in Tak1
ΔM/ΔM
feces-treated 5xFAD mice compared with controls (Figure 3D).
Meanwhile, the soluble Aβ1-40 and Aβ1-42 levels in Tak1
ΔM/ΔM
feces-treated 5xFAD mice were
higher than vehicle controls (Figure 3D), consistent with the data normalized by whole protein
concentration in Figure 3C. Together with the immunohistochemistry and immunofluorescent
staining results showed above, our results suggest an inhibitory role of the gut microbiota from
Tak1
ΔM/ΔM
mice in coordinating the development of Aβ plaques in the AD animal model.
19

Figure 3. Gut microbiota from Tak1
ΔM/ΔM
mice reduced the insoluble Amyloid-β amount in 5xFAD mouse brains.
ELISA assay to determine the Aβ1–40, Aβ1–42 in brain samples from fecal microbiota transferred 5xFAD mice after Abx-
pretreatment. Mean ± SEM. *p<0.05.

A β1:40 A β1:42
0
200
400
600
800
1000
soluble Ab
5xFAD Feces
Tak1
ΔM/ ΔM
Feces
5xFAD feces Tak1
ΔM/ ΔM
Feces
0
2
4
6
8
soluble Ab 42/Ab 40
5xFAD feces Tak1
ΔM/ ΔM
Feces
0.0
0.5
1.0
1.5
2.0
Insoluble Ab 42/Ab 40
A β1:40 A β1:42
0
1000
2000
3000
4000
insoluble Ab
5xFAD Feces
Tak1
ΔM/ ΔM
Feces
*
Control Feces
Tak1
ΔM/ ΔM
Feces
Control Feces
Tak1
ΔM/ ΔM
Feces
Soluble A β levels Insoluble A β levels
PBS A β (pg/mg protein)
TFA A β (pg/mg protein)
A β 1-40 A β 1-42 A β 1-40 A β 1-42
Control feces
Tak1
ΔM/ ΔM
feces
Tak1
ΔM/ ΔM
feces
Control feces
Soluble A β 42:40 Insoluble A β 42:40
A β 1-42/A β 1-40
A β 1-42/A β 1-40
A
B
P=0.0607
5xFAD feces
Ta k1
ΔM/ ΔM
F e c e s
0
20000
40000
60000
80000
Soluble Ab total
A β1:40 A β1:42
0
100
200
300
400
500
insoluble Ab/agg
5xFAD Feces
Tak1
ΔM/ ΔM
Feces
* *
5xFAD feces
Ta k1
ΔM/ ΔM
F e c e s
0
10000
20000
30000
40000
Insoluble Ab total
Control Feces
Tak1
ΔM/ ΔM
Feces
Control Feces
Tak1
ΔM/ ΔM
Feces
Soluble A β levels Insoluble A β levels
PBS A β (pg/mg Total A β)
TFA A β (pg/mg Total A β)
A β 1-40 A β 1-42 A β 1-40 A β 1-42 A β1:40 A β1:42
0
5
10
15
20
soluble Ab/agg
5xFAD Feces
Tak1
ΔM/ ΔM
Feces
Control feces
Tak1
ΔM/ ΔM
feces
Tak1
ΔM/ ΔM
feces
Control feces
Soluble total A β levels
Insoluble total A β levels
TFA A β (pg/ng protein)
PBS A β (pg/mg protein)
C
D
20
Chapter 4: The gut microbiota from Tak1
ΔM/ΔM
mice influences the
immune system
4.1 Microglia are closely associated with the Aβ plaques
To examine the plaque-localized glial reactivity and microglial morphology, we performed
the co-IF immunostaining of Aβ-specific 6E10 antibody and IBA-1 antibody (targeting the
microglia, Cell Signaling Technology, Cat#: 17198). Besides the decrease of Aβ plaque number in
Tak1
ΔM/ΔM
microbiota-transferred 5xFAD mice, we also observed the strong association of Aβ
plaques with the microglia (Figure 4A). However, the number of microglia did not change between
the two groups. Therefore, the ratio of microglia-associated Aβ plaques was consistently decreased
in Tak1
ΔM/ΔM
microbiota-treated mice (Figure 4A). Consistent results were further observed in
control or Tak1
ΔM/ΔM
microbiota-treated APP/PS1 mice (Figure 4B). These data indicate that the
Tak1
ΔM/ΔM
microbiota could ameliorate the development of AD in both 5xFAD and APP/PS1
mice. Although Aβ plaques are highly associated with the microglia in brains, Tak1
ΔM/ΔM
microbiota
did not alter the development of microglia in these models but only decreased the microglia-
associated Aβ plaques.

21

Figure 4. Gut microbiota from Tak1
ΔM/ΔM
mice reduced the microglia-associated Amyloid-β plaques in both 5xFAD and
APP/PS1 mouse brains. Co-IF staining of Amyloid-β and microglia on brain sections from fecal microbiota transferred 5xFAD
mice (A) or APP/PS1 mice (C) after Abx-pretreatment, and the analysis of plaque-localized glial reactivity in 5xFAD mice (B) or
APP/PS1 mice (D). Mean ± SEM. *p<0.05.

4.2 Several immune cell populations are altered due after the colonization of the gut
bacteria from Tak1
ΔM/ΔM
mice
To further determine the key immune cell components that were regulated by Tak1
ΔM/ΔM

microbiota in 5xFAD mice, we purified immune cells from the spleen and bone marrow, and
evaluated the key components through flow cytometry, including neutrophils (CD11b+/Ly-6G+),
macrophages (CD11b+/F4/80+), dendritic cells (CD11b+/CD11c+), B cells (CD3-/B220+), T
cells (CD3+/B220-), NK cells (CD3-/Nk1-1+), NKT cells (CD3+/Nk1-1+), CD8+ T cells
(CD3+/CD8+), CD4+ T cells (CD3+/CD4+), Th1 cells (CD3+/CD4+/IFN-γ+/Foxp3-), Th2
cells (CD3+/CD4+/IFN-γ-/IL-4+), Th17 cells (CD3+/CD4+/IFN-γ-/IL-17A+), and Treg cells
(CD3+/CD4+/IFN-γ-/Foxp3+).
While most of the immune cell populations remained unchanged in Tak1
ΔM/ΔM
microbiota-
treated 5xFAD mice, we observed a significant increase of neutrophils in both spleen and bone
marrow samples, compared with control microbiota-treated mice (Figure 5). We further found
decreased macrophages and dendritic cells in the bone marrow samples and increased NKT cells in
A
C
B
D
Control feces
TAK1 KO feces
5xFAD feces
TAK1 KO feces
0
50
100
150
# of combined A β plaque
APPPS1 feces
TAK1 KO feces
0
10
20
30
# of combined A β plaque
5xFAD feces
TAK1 KO feces
0.0
0.5
1.0
1.5
ratio of
microglia combined A β plaques
APPPS1 feces
TAK1 KO feces
0.92
0.94
0.96
0.98
1.00
1.02
ratio of
microglia combined A β plaques
5xFAD feces
TAK1 KO feces
0
200
400
600
800
# of microglia
APPPS1 feces
TAK1 KO feces
0
50
100
150
200
# of microglia
5xFAD feces
TAK1 KO feces
0
100
200
300
# of combined microglia
APPPS1 feces
TAK1 KO feces
0
20
40
60
80
# of combined microglia
5xFAD feces
TAK1 KO feces
0.0
0.2
0.4
0.6
ratio of
A β plaques combined microglia APPPS1 feces
TAK1 KO feces
0.0
0.1
0.2
0.3
0.4
0.5
ratio of
A β plaques combined microglia
5xFAD feces
TAK1 KO feces
0
50
100
150
# of combined A β plaque
APPPS1 feces
TAK1 KO feces
0
10
20
30
# of combined A β plaque
5xFAD feces
TAK1 KO feces
0.0
0.5
1.0
1.5
ratio of
microglia combined A β plaques
APPPS1 feces
TAK1 KO feces
0.92
0.94
0.96
0.98
1.00
1.02
ratio of
microglia combined A β plaques
5xFAD feces
TAK1 KO feces
0
200
400
600
800
# of microglia
APPPS1 feces
TAK1 KO feces
0
50
100
150
200
# of microglia
5xFAD feces
TAK1 KO feces
0
100
200
300
# of combined microglia
APPPS1 feces
TAK1 KO feces
0
20
40
60
80
# of combined microglia
5xFAD feces
TAK1 KO feces
0.0
0.2
0.4
0.6
ratio of
A β plaques combined microglia APPPS1 feces
TAK1 KO feces
0.0
0.1
0.2
0.3
0.4
0.5
ratio of
A β plaques combined microglia
Control feces
TAK1 KO feces
Control feces
TAK1 KO feces
Control feces
TAK1 KO feces
Control feces
TAK1 KO feces
Control feces
TAK1 KO feces
Control feces
TAK1 KO feces
Control feces
TAK1 KO feces
Control feces
TAK1 KO feces
Control feces
TAK1 KO feces
number of combined A β plaques
ratio of
microglia combined A β plaques
number of combined microglia
Number of microglia
ratio of
A β plaques combined microglia
number of combined A β plaques
ratio of
microglia combined A β plaques
number of combined microglia
Number of microglia
ratio of
A β plaques combined microglia
5xFAD fe ces
TAK1 KO fe ces
0
50
100
150
# o f c o m b i n e d A β p l a q u e
APPPS1 fe ces
TAK1 KO fe ces
0
10
20
30
# o f c o m b i n e d A β p l a q u e
5xFAD fe ces
TAK1 KO fe ces
0.0
0.5
1.0
1.5
r a t i o o f
m i c r o g l i a c o m b i n e d A β p l a q u e s
APPPS1 fe ces
TAK1 KO fe ces
0.92
0.94
0.96
0.98
1.00
1.02
r a t i o o f
m i c r o g l i a c o m b i n e d A β p l a q u e s
5xFAD fe ces
TAK1 KO fe ces
0
200
400
600
800
# o f m i c r o g l i a
APPPS1 fe ces
TAK1 KO fe ces
0
50
100
150
200
# o f m i c r o g l i a
5xFAD fe ces
TAK1 KO fe ces
0
100
200
300
# o f c o m b i n e d m i c r o g l i a
APPPS1 fe ces
TAK1 KO fe ces
0
20
40
60
80
# o f c o m b i n e d m i c r o g l i a
5xFAD fe ces
TAK1 KO fe ces
0.0
0.2
0.4
0.6
r a t i o o f
A β p l a q u e s c o m b i n e d m i c r o g l i a APPPS1 fe ces
TAK1 KO fe ces
0.0
0.1
0.2
0.3
0.4
0.5
r a t i o o f
A β p l a q u e s c o m b i n e d m i c r o g l i a
*
22
Tak1
ΔM/ΔM
microbiota-treated 5xFAD mice. Previous data illustrated the increase of gut Th17 cells
in Tak1
ΔM/ΔM
microbiota-treated WT mice (Xing et al. 2021). Consistently, we observed the increase
of Th17 cells in the spleen from Tak1
ΔM/ΔM
microbiota-treated 5xFAD mice (Figure 5), although the
difference is not significant due to the short in animal number in this experiment. These altered cell
populations may play essential roles in mediating AD development, and their functions will be
determined in future studies.

Figure 5. Characterization of immune components in Tak1
ΔM/ΔM
microbiota treated 5xFAD mice. Flow cytometry to
determine the key immune cell populations in spleen and bone marrow from fecal microbiota transferred 5xFAD mice after Abx-
pretreatment. Mean ± SEM. *p<0.05; **p<0.01.
5xFAD fe ces
TAK1 KO fe ces
0
50
100
150
# o f c o m b i n e d A β p l a q u e
APPPS1 fe ces
TAK1 KO fe ces
0
10
20
30
# o f c o m b i n e d A β p l a q u e
5xFAD fe ces
TAK1 KO fe ces
0.0
0.5
1.0
1.5
r a t i o o f
m i c r o g l i a c o m b i n e d A β p l a q u e s
APPPS1 fe ces
TAK1 KO fe ces
0.92
0.94
0.96
0.98
1.00
1.02
r a t i o o f
m i c r o g l i a c o m b i n e d A β p l a q u e s
5xFAD fe ces
TAK1 KO fe ces
0
200
400
600
800
# o f m i c r o g l i a
APPPS1 fe ces
TAK1 KO fe ces
0
50
100
150
200
# o f m i c r o g l i a
5xFAD fe ces
TAK1 KO fe ces
0
100
200
300
# o f c o m b i n e d m i c r o g l i a
APPPS1 fe ces
TAK1 KO fe ces
0
20
40
60
80
# o f c o m b i n e d m i c r o g l i a
5xFAD fe ces
TAK1 KO fe ces
0.0
0.2
0.4
0.6
r a t i o o f
A β p l a q u e s c o m b i n e d m i c r o g l i a APPPS1 fe ces
TAK1 KO fe ces
0.0
0.1
0.2
0.3
0.4
0.5
r a t i o o f
A β p l a q u e s c o m b i n e d m i c r o g l i a
*
5xFAD fe ces
TAK1 KO fe ces
0
50
100
150
# o f c o m b i n e d A β p l a q u e
APPPS1 fe ces
TAK1 KO fe ces
0
10
20
30
# o f c o m b i n e d A β p l a q u e
5xFAD fe ces
TAK1 KO fe ces
0.0
0.5
1.0
1.5
r a t i o o f
m i c r o g l i a c o m b i n e d A β p l a q u e s
APPPS1 fe ces
TAK1 KO fe ces
0.92
0.94
0.96
0.98
1.00
1.02
r a t i o o f
m i c r o g l i a c o m b i n e d A β p l a q u e s
5xFAD fe ces
TAK1 KO fe ces
0
200
400
600
800
# o f m i c r o g l i a
APPPS1 fe ces
TAK1 KO fe ces
0
50
100
150
200
# o f m i c r o g l i a
5xFAD fe ces
TAK1 KO fe ces
0
100
200
300
# o f c o m b i n e d m i c r o g l i a
APPPS1 fe ces
TAK1 KO fe ces
0
20
40
60
80
# o f c o m b i n e d m i c r o g l i a
5xFAD fe ces
TAK1 KO fe ces
0.0
0.2
0.4
0.6
r a t i o o f
A β p l a q u e s c o m b i n e d m i c r o g l i a APPPS1 fe ces
TAK1 KO fe ces
0.0
0.1
0.2
0.3
0.4
0.5
r a t i o o f
A β p l a q u e s c o m b i n e d m i c r o g l i a
*
5xFAD fe ces
TAK1 KO fe ces
0
50
100
150
# o f c o m b i n e d A β p l a q u e
APPPS1 fe ces
TAK1 KO fe ces
0
10
20
30
# o f c o m b i n e d A β p l a q u e
5xFAD fe ces
TAK1 KO fe ces
0.0
0.5
1.0
1.5
r a t i o o f
m i c r o g l i a c o m b i n e d A β p l a q u e s
APPPS1 fe ces
TAK1 KO fe ces
0.92
0.94
0.96
0.98
1.00
1.02
r a t i o o f
m i c r o g l i a c o m b i n e d A β p l a q u e s
5xFAD fe ces
TAK1 KO fe ces
0
200
400
600
800
# o f m i c r o g l i a
APPPS1 fe ces
TAK1 KO fe ces
0
50
100
150
200
# o f m i c r o g l i a
5xFAD fe ces
TAK1 KO fe ces
0
100
200
300
# o f c o m b i n e d m i c r o g l i a
APPPS1 fe ces
TAK1 KO fe ces
0
20
40
60
80
# o f c o m b i n e d m i c r o g l i a
5xFAD fe ces
TAK1 KO fe ces
0.0
0.2
0.4
0.6
r a t i o o f
A β p l a q u e s c o m b i n e d m i c r o g l i a
APPPS1 fe ces
TAK1 KO fe ces
0.0
0.1
0.2
0.3
0.4
0.5
r a t i o o f
A β p l a q u e s c o m b i n e d m i c r o g l i a
*
5xFAD fe ces
TAK1 KO fe ces
0
50
100
150
# o f c o m b i n e d A β p l a q u e
APPPS1 fe ces
TAK1 KO fe ces
0
10
20
30
# o f c o m b i n e d A β p l a q u e
5xFAD fe ces
TAK1 KO fe ces
0.0
0.5
1.0
1.5
r a t i o o f
m i c r o g l i a c o m b i n e d A β p l a q u e s
APPPS1 fe ces
TAK1 KO fe ces
0.92
0.94
0.96
0.98
1.00
1.02
r a t i o o f
m i c r o g l i a c o m b i n e d A β p l a q u e s
5xFAD fe ces
TAK1 KO fe ces
0
200
400
600
800
# o f m i c r o g l i a
APPPS1 fe ces
TAK1 KO fe ces
0
50
100
150
200
# o f m i c r o g l i a
5xFAD fe ces
TAK1 KO fe ces
0
100
200
300
# o f c o m b i n e d m i c r o g l i a
APPPS1 fe ces
TAK1 KO fe ces
0
20
40
60
80
# o f c o m b i n e d m i c r o g l i a
5xFAD fe ces
TAK1 KO fe ces
0.0
0.2
0.4
0.6
r a t i o o f
A β p l a q u e s c o m b i n e d m i c r o g l i a APPPS1 fe ces
TAK1 KO fe ces
0.0
0.1
0.2
0.3
0.4
0.5
r a t i o o f
A β p l a q u e s c o m b i n e d m i c r o g l i a
**
5xFAD fe ces
TAK1 KO fe ces
0
50
100
150
# o f c o m b i n e d A β p l a q u e
APPPS1 fe ces
TAK1 KO fe ces
0
10
20
30
# o f c o m b i n e d A β p l a q u e
5xFAD fe ces
TAK1 KO fe ces
0.0
0.5
1.0
1.5
r a t i o o f
m i c r o g l i a c o m b i n e d A β p l a q u e s
APPPS1 fe ces
TAK1 KO fe ces
0.92
0.94
0.96
0.98
1.00
1.02
r a t i o o f
m i c r o g l i a c o m b i n e d A β p l a q u e s
5xFAD fe ces
TAK1 KO fe ces
0
200
400
600
800
# o f m i c r o g l i a
APPPS1 fe ces
TAK1 KO fe ces
0
50
100
150
200
# o f m i c r o g l i a
5xFAD fe ces
TAK1 KO fe ces
0
100
200
300
# o f c o m b i n e d m i c r o g l i a
APPPS1 fe ces
TAK1 KO fe ces
0
20
40
60
80
# o f c o m b i n e d m i c r o g l i a
5xFAD fe ces
TAK1 KO fe ces
0.0
0.2
0.4
0.6
r a t i o o f
A β p l a q u e s c o m b i n e d m i c r o g l i a APPPS1 fe ces
TAK1 KO fe ces
0.0
0.1
0.2
0.3
0.4
0.5
r a t i o o f
A β p l a q u e s c o m b i n e d m i c r o g l i a
*
23

4.3 The Tak1
ΔM/ΔM
gut microbiota enhances the productions of serum IL-17A and IL-
22 in the AD mouse model
Previous data illustrated the increase of Th17 cytokines (IL-17A and IL-22) in both serum and
gut levels in Tak1
ΔM/ΔM
microbiota-treated WT mice (Xing et al. 2021). Here we collected the serum
samples from control or Tak1
ΔM/ΔM
microbiota-treated 5xFAD mice and performed the ELISA assay.
Consistently, we observed the increases of IL-17A and IL-22 after the transfer of Tak1
ΔM/ΔM

microbiota, with a significant increase of IL-17A (p<0.05) (Figure 6). These data suggest that the Th17
cells and cytokines are dramatically altered in Tak1
ΔM/ΔM
microbiota-treated 5xFAD mice and may
mediate the protection against AD development. Detailed mechanisms will be determined in future
studies.

Figure 6. Tak1
ΔM/ΔM
microbiota-treated 5xFAD mice
had increased Th17 cytokines. ELISA assay to determine
the serum level IL-17A and IL-22 in fecal microbiota
transferred 5xFAD mice after Abx-pretreatment. Mean ±
SEM. *p<0.05.

4.4 The Tak1
ΔM/ΔM
gut microbiota influences the production of cytokines in the
brains of the AD mouse model
The production of cytokines in the brains could indicate the level of neuroinflammatory
response. To investigate the connection between the Tak1
ΔM/ΔM
gut microbiota and
neuroinflammation, we collected the brains from 5xFAD mice and homogenized in PBS, then
performed an ELISA assay to detect a series of pro-inflammatory cytokines, including IL-1β, IL-6,
TNF-α, and Interferon-γ (IFN-γ). The level of IL-6 tended to be decreased in the brains of
24
Tak1
ΔM/ΔM
feces-treated 5xFAD mice compared with control mice, although we did not observe a
significant difference (Figure 7). Meanwhile, the level of IFN-γ tended to be increased in the brains
of Tak1
ΔM/ΔM
feces-treated 5xFAD mice compared with controls (Figure 7). These data show that
Tak1
ΔM/ΔM
gut microbiota could influence the level of pro-inflammatory cytokines generated from
neurotoxic microglia in the brain and might play a role in regulating neuroinflammation. Detailed
mechanisms will be investigated in future studies.

Figure 7. Characterization of levels of critical
cytokines in brains of Tak1
ΔM/ΔM
microbiota-
treated 5xFAD mice. ELISA assay to determine the
brain level IL-1β, IL-6, TNF-α and IFN-γ in fecal
microbiota transferred 5xFAD mice after Abx-
pretreatment. Mean ± SEM.

5xF A D F e c e s
Ta k1
ΔM/ ΔM
F e c e s
0
100
200
300
400
500
IL1-beta
5xF A D F e c e s
Ta k1
ΔM/ ΔM
F e c e s
0
200
400
600
800
1000
IL6
5xF A D F e c e s
Ta k1
ΔM/ ΔM
F e c e s
0
20
40
60
TNF-alpha
5xF A D F e c e s
Ta k1
ΔM/ ΔM
F e c e s
0
500
1000
1500
IFN-gamma
IL-1 β IL-6
Τ ΝF- α IFN- γ
Control feces
Tak1
ΔM/ ΔM
feces
Control feces
Tak1
ΔM/ ΔM
feces
Control feces
Tak1
ΔM/ ΔM
feces
Control feces
Tak1
ΔM/ ΔM
feces
Expression level (pg/mg protein)
Expression level (pg/mg protein)
Expression level (pg/mg protein)
Expression level (pg/mg protein)
25
Chapter 5: Discussion
Since AD is a stubborn disease that has not been explored thoroughly, we urgently need to
develop effective therapeutics and drugs for its prevention and better treatment. Recent studies have
unraveled that the gut microbiota plays an essential role in regulating innate immunity and
neuroinflammation response, thus influencing neurodegenerative diseases, such as AD. Therefore,
figuring out the specific composition of the gut microbiome that could inhibit the progression of
AD or even eliminate AD pathology is a novel insight to conquer AD. In previous research, the gut
microbiota of Tak1
ΔM/ΔM
mice has the ability to regulate inflammation against DSS-induced colitis
and AOM/DSS-induced CRC by activating defensive Th17 cells (Xing et al. 2021). Therefore, we
aim to identify whether the gut microbiota of Tak1
ΔM/ΔM
mice could influence the development of
AD via regulating microglia-related neuroinflammation. First, we indicated that Tak1
ΔM/ΔM
gut
microbiota could alleviate the Aβ depositions in the hippocampus in AD mouse models. Second, we
observed a remarkable decreased level of insoluble Aβ species and escalated level of soluble Aβ
species in 5xFAD and APP/PS1 mouse brains under the actions of Tak1
ΔM/ΔM
gut microbiota.
Finally, this specific composition of gut microbiota also altered the peripherally circulating immune
cells and cytokine productions, which may, in turn, adjust the gliosis of microglia.
Although we have demonstrated that the Tak1
ΔM/ΔM
microbiota can manipulate Aβ
pathology and affect the immune system, more profound research is required for the application of
the specific gut microbiota composition in AD therapeutics.
First, although Tak1
ΔM/ΔM
gut microbiota could influence the immune cell composition in
the peripheral blood, as well as the lymphoid organs and tissues, the effect of Tak1
ΔM/ΔM
gut
microbiota on the neuroimmune system is still not apparent. Considering microglia are intimately
associated with neuroinflammation, whether Tak1
ΔM/ΔM
gut microbiota could regulate microglia in
the brain should be investigated. Our observation of the decreased ratio of microglia-associated Aβ
26
plaques is not enough to state that the specific microbiota could regulate neuroinflammation in
Tak1
ΔM/ΔM
microbiota-treated mice. Further studies will be performed to determine the detailed
regulatory processes, for example, whether M1/M2 subtype microglia composition in the brain be
altered upon Tak1
ΔM/ΔM
microbiota treatment.
Second, we have detected the altered amounts of pro-inflammatory cytokines in the brains,
such as IL-6 and TNF-α, after the treatment of Tak1
ΔM/ΔM
microbiota, but whether these cytokines
are critical in mediating the microbiota-induced disease amelioration is still unclear. Therefore,
functional studies should be performed to deplete the upregulated cytokines or provide the
downregulated cytokines in our model and observe the alteration of the disease progression. Besides,
considering Tak1
ΔM/ΔM
microbiota have more effect on the AD pathology in the hippocampus, the
local level of these cytokines in the hippocampi area may be examined by tissue staining.
Third, we should continue to examine the main strains of bacteria in the Tak1
ΔM/ΔM

microbiota that play essential roles in regulating the development of AD. To conduct further
analysis of microbiota composition at the species level, we have performed the whole shotgun
metagenomic sequencing and figured out the species which composition is altered in the Tak1
ΔM/ΔM

feces samples. The top 10 increased species in Tak1
ΔM/ΔM
mice that had the most abundances have
been selected for further functional studies (single bacteria strain treatment of AD animal models).
Finally, the existence of the metabolites formed by various microbial communities in our
mouse model, as well as the possible effect of these metabolites on amyloid deposition, remain
unknown. Therefore, future efforts will also focus on this vital topic.
Taken together, although this project is only a preliminary study with limited information,
our data have demonstrated that specific microbiota composition can ameliorate the disease
progression of AD. With the hope that the upcoming experiments could provide more insights into
27
the detailed mechanisms, our work will significantly expand the current knowledge and lead to the
advancement of novel therapeutic modalities for AD.

28
References
Ajibade AA, Wang HY, Wang RF. 2013. Cell type-specific function of TAK1 in innate immune
signaling. Trends Immunol 34:307-316.
Ajibade AA, et al. 2012. TAK1 negatively regulates NF-kappaB and p38 MAP kinase activation in
Gr-1+CD11b+ neutrophils. Immunity 36:43-54.
Angelucci F, Cechova K, Amlerova J, Hort J. 2019. Antibiotics, gut microbiota, and Alzheimer's
disease. J Neuroinflammation 16:108.
Braune A, Blaut M. 2016. Bacterial species involved in the conversion of dietary flavonoids in the
human gut. Gut Microbes 7:216-234.
Burns A, Iliffe S. 2009. Alzheimer’s disease. BMJ 338:b158.
Canfora EE, Jocken JW, Blaak EE. 2015. Short-chain fatty acids in control of body weight and
insulin sensitivity. Nat Rev Endocrinol 11:577-591.
Cryan JF, Dinan TG. 2012. Mind-altering microorganisms: the impact of the gut microbiota on
brain and behaviour. Nat Rev Neurosci 13:701-712.
Dalile B, Van Oudenhove L, Vervliet B, Verbeke K. 2019. The role of short-chain fatty acids in
microbiota–gut–brain communication. Nature Reviews Gastroenterology & Hepatology 16:461-478.
Dinan TG, Cryan JF. 2017. Gut instincts: microbiota as a key regulator of brain development, ageing
and neurodegeneration. The Journal of physiology 595:489-503.
El Aidy S, Dinan T, Cryan J. 2014. Immune modulation of the brain-gut-microbe axis. Frontiers in
Microbiology 5:146.
Goyal D, Ali SA, Singh RK. 2021. Emerging role of gut microbiota in modulation of
neuroinflammation and neurodegeneration with emphasis on Alzheimer's disease. Progress in
Neuro-Psychopharmacology and Biological Psychiatry 106:110112.
Guarner F, Malagelada JR. 2003. Gut flora in health and disease. Lancet 361:512-519.
29
Heneka MT, et al. 2015. Neuroinflammation in Alzheimer's disease. Lancet Neurol 14:388-405.
Koh A, De Vadder F, Kovatcheva-Datchary P, Backhed F. 2016. From Dietary Fiber to Host
Physiology: Short-Chain Fatty Acids as Key Bacterial Metabolites. Cell 165:1332-1345.
Krstic D, Knuesel I. 2013. Deciphering the mechanism underlying late-onset Alzheimer disease.
Nature Reviews Neurology 9:25-34.
Miquel S, Martín R, Rossi O, Bermúdez-Humarán LG, Chatel JM, Sokol H, Thomas M, Wells JM,
Langella P. 2013. Faecalibacterium prausnitzii and human intestinal health. Current Opinion in
Microbiology 16:255-261.
Moszak M, Szulińska M, Bogdański P. 2020. You Are What You Eat—The Relationship between
Diet, Microbiota, and Metabolic Disorders—A Review. Nutrients 12.
Neurath MF. 2014. Cytokines in inflammatory bowel disease. Nat Rev Immunol 14:329-342.
Paul B, Barnes S, Demark-Wahnefried W, Morrow C, Salvador C, Skibola C, Tollefsbol TO. 2015.
Influences of diet and the gut microbiome on epigenetic modulation in cancer and other diseases.
Clinical Epigenetics 7:112.
Polvikoski T, Sulkava R, Haltia M, Kainulainen K, Vuorio A, Verkkoniemi A, Niinisto L, Halonen
P, Kontula K. 1995. Apolipoprotein E, dementia, and cortical deposition of beta-amyloid protein. N
Engl J Med 333:1242-1247.
Rooks MG, Garrett WS. 2016. Gut microbiota, metabolites and host immunity. Nat Rev Immunol
16:341-352.
Sherwin E, Rea K, Dinan TG, Cryan JF. 2016. A gut (microbiome) feeling about the brain. Current
Opinion in Gastroenterology 32.
Stilling RM, Dinan TG, Cryan JF. 2014. Microbial genes, brain & behaviour – epigenetic regulation
of the gut–brain axis. Genes, Brain and Behavior 13:69-86.
30
Tackenberg C, Kulic L, Nitsch RM. 2020. Familial Alzheimer’s disease mutations at position 22 of
the amyloid β-peptide sequence differentially affect synaptic loss, tau phosphorylation and neuronal
cell death in an ex vivo system. PLOS ONE 15:e0239584.
Tang Y, Le W. 2016. Differential Roles of M1 and M2 Microglia in Neurodegenerative Diseases.
Molecular Neurobiology 53:1181-1194.
Wan YY, Chi H, Xie M, Schneider MD, Flavell RA. 2006. The kinase TAK1 integrates antigen and
cytokine receptor signaling for T cell development, survival and function. Nat Immunol 7:851-858.
Wang Y, Kasper LH. 2014. The role of microbiome in central nervous system disorders. Brain
Behav Immun 38:1-12.
West NR, McCuaig S, Franchini F, Powrie F. 2015. Emerging cytokine networks in colorectal
cancer. Nat Rev Immunol 15:615-629.
Xing C, et al. 2021. Microbiota regulate innate immune signaling and protective immunity against
cancer. Cell Host & Microbe.
Yatsunenko T, et al. 2012. Human gut microbiome viewed across age and geography. Nature
486:222-227.
Zhang L, et al. 2017. Altered Gut Microbiota in a Mouse Model of Alzheimer’s Disease. Journal of
Alzheimer's Disease 60:1241-1257.
Zhuang Z-Q, et al. 2018. Gut Microbiota is Altered in Patients with Alzheimer’s Disease. Journal of
Alzheimer's Disease 63:1337-1346.

Abstract (if available)

Abstract Alzheimer抯 disease (AD) is a common type of neurodegenerative disease controlled by genetic and environmental factors, including the gut microbiota. It was recently revealed that the specific microbiota in Tak1?M/?M (myeloid-specific TAK1 ablation) mice could inhibit the inflammation and thus suppress colon cancer progression. However, due to the close connection of AD and neuroinflammation, whether the specific gut microbiota could influence the development of AD by regulating neuroinflammation was still unknown. This study found that Tak1?M/?M microbiota-treated 5x familial AD (5xFAD) mice and amyloid precursor protein/presenilin 1 (APP/PS1) mice developed attenuated Amyloid-? pathology in the hippocampus. Furthermore, we figured out that Tak1?M/?M microbiota had the ability to influence the immune system and the microglial morphology in AD mouse models. These data suggest that the AD progression could be controlled by manipulating the gut microbiota composition and thus provide essential insights and therapeutic candidates for the prevention and better treatment of AD patients.

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Asset Metadata

Creator Zhang, Pengfei (author)

Core Title The role of specific gut microbiota in regulating the development of Alzheimer抯 disease

School Keck School of Medicine

Degree Master of Science

Degree Program Molecular Microbiology and Immunology

Degree Conferral Date 2021-08

Publication Date 07/24/2021

Defense Date 06/03/2021

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag Alzheimer抯 disease,amyloid-?,gut microbiota,neuroinflammation,OAI-PMH Harvest

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Schonthal, Axel (committee chair), Eoh, Hyungjin (committee member), Kast, Martin (committee member), Wang, Rongfu (committee member), Xing, Changsheng (committee member)

Creator Email alfaphrederick@gmail.com

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