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Phenotypic and multi-omic characterization of novel C. elegans models of Alzheimer's disease
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Phenotypic and multi-omic characterization of novel C. elegans models of Alzheimer's disease
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Content
Phenotypic and multi-omic characterization of novel C. elegans models of Alzheimer's
disease
by
Angelina Holcom
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BIOLOGY OF AGING)
August 2023
Copyright 2023 Angelina Holcom
ii
Acknowledgements
I would like to express my sincere gratitude and appreciation to the following
individuals and institutions who have contributed to the successful completion of my
dissertation:
Dr. Gordon Lithgow, thank you for your unwavering support and guidance
throughout the past 6 years. Your expertise and mentorship have been invaluable to my
research journey.
Dr. Julie Andersen, I am deeply grateful for your assistance with the project, as
well as your continuous feedback and guidance over the years. I also want to thank you
for allowing me to assist Renuka with her mouse project, which provided valuable learning
experiences.
To the current and former members of the Lithgow and Andersen labs, your
feedback during lab meetings and your constant support and kindness have been
instrumental in my research. I am truly grateful for the collaborative environment that you
have fostered.
I would also like to extend my appreciation to the Schilling and Furman lab
members for their assistance in analyzing the omic data and running the insoluble
proteome protocol. Your contributions have significantly enriched my research outcomes.
Furthermore, I want to thank the former Brand lab for their invaluable assistance with the
Seahorse XFe96.
My heartfelt thanks go to the members of my Qualifying Exam Committee: Dr.
Gordon Lithgow, Dr. Julie Andersen, Dr. Sean Curran, Dr. Tara Tracy, and Dr. Lisa
iii
Ellerby. Your feedback and guidance during my qualifying exam were immensely
valuable.
To the members of my Dissertation Committee - Dr. Gordon Lithgow, Dr. Julie
Andersen, Dr. Sean Curran, and Dr. Tara Tracy - thank you for your continued support
and valuable feedback during our meetings. Your guidance and oversight have been
pivotal in ensuring the progress and completion of my dissertation.
A special thank you to Dr. Tara Tracy for allowing me to join your lab meetings and for
your continuous support and encouragement whenever we cross paths in the hallways of
the Buck Institute.
I would like to express my heartfelt appreciation to my family for their unwavering
support throughout the years, especially during my undergraduate studies when I first
ventured into the field of Biology. To my sister, thank you for being there for me and being
my constant companion during our bus rides in undergrad.
I am immensely grateful to the Buck student community for creating a vibrant and
welcoming environment. You have made my time at the Buck enjoyable and have made
me feel like I have a second family here. I would also like to extend my thanks to the
Graduate Student Society for allowing me to be a part of it as a member and part of the
committee. Lastly, I want to express my gratitude to my cohort for being an amazing group
to navigate this experience with.
Brenda Eap, your friendship has been a source of great comfort and support
throughout the years. I truly appreciate your friendship and your constant encouragement
throughout our time in this Ph.D. Program.
iv
A special appreciation goes to Carlos Galicia, who has been an important friend
during our time at the Buck. Thank you for your assistance with bioinformatics since your
rotation began at the Buck. Your support during the qualifying exam, finishing up my
publication, and the rush to defend for summer graduation has been invaluable.
I would also like to thank the faculty and administration of the Buck Institute and USC for
their assistance and support throughout my research journey.
Lastly, I acknowledge the financial support provided by This work was supported
by the Larry L. Hillblom Foundation, NIH Shared Instrumentation Grant 1S10OD010786-
01, 1S10OD016281-01 (to Buck Institute for the TripleTOF mass spectrometry system),
NIA grants RFAG057358 and R01AG029631. This work was also supported by
U54AG075932 and P01AI153559 (to Buck Bioinformatics Core). My traineeship was
supported by NIA T32 AG052374.
The following chapters 3 and 4 are based on a version of the BioRxiv manuscript
by Angelina Holcom, Matias Fuentealba Valenzuela, Renuka Sivapatham, Christina D.
King, Hadley Osman, Anna Foulger, Dipa Bhaumik, Birgit Schilling, David Furman, Julie
K. Andersen, and Gordon J. Lithgow.
v
Table of Contents
Acknowledgements ..................................................................................................................... ii
List of Figures .......................................................................................................................... viii
Abbreviations ............................................................................................................................. ix
Abstract.................................................................................................................................... xiv
Chapter 1: Introduction ............................................................................................................... 1
Aging and Alzheimer’s Disease ........................................................................................... 1
Alzheimer’s Disease ........................................................................................................... 3
Transcriptional Dysfunction in AD .................................................................................... 5
Proteomic Dysfunction in AD ........................................................................................... 7
Metabolic dysfunction in AD ............................................................................................ 8
Use of models to study AD .................................................................................................11
AD Interventions ................................................................................................................14
Chapter 2: Materials and Methods ............................................................................................17
C. elegans strains ..............................................................................................................17
Western Blot ......................................................................................................................18
Fertility and fecundity assay ...............................................................................................19
Thrashing Assay ................................................................................................................20
Mitochondrial oxygen consumption rate assay ...................................................................20
Lifespan Assay ..................................................................................................................21
3’ Tag RNA-seq .................................................................................................................22
Binarized transcriptomic aging (BiT age) clock calculation .................................................23
Metabolomics .....................................................................................................................23
Insoluble proteome ............................................................................................................24
Protein-protein interaction network .....................................................................................27
Omic Analysis ....................................................................................................................28
Enrichment Analysis: ......................................................................................................28
WGCNA (Weighted Gene Co-expression Network Analysis): .........................................28
Comparison to Human Frontal Cortex: ...........................................................................29
Heatmap and Dot Plot Figures: ......................................................................................29
vi
Data Availability .................................................................................................................29
Chapter 3: Neuronal expression of amyloid-β and Tau drives systemic changes in a
C. elegans model of Alzheimer’s Disease .................................................................................30
Abstract .............................................................................................................................30
Background ........................................................................................................................30
Results ...............................................................................................................................33
Expression of Aβ and Tau enhances insoluble protein accumulation in young
animals. ..........................................................................................................................33
Aβ expression drives transcriptomic changes in Tau;Aβ expressing strain in
young animals. ...............................................................................................................36
Synergistic effects of Aβ and Tau expression on metabolites in young animals. .............40
Systemic phenotypes observed with expression of Tau or Aβ. .......................................43
Discussion .........................................................................................................................46
Chapter 4: Insights into aging-related protein insolubility and metabolic changes in a
C. elegans model expressing Tau and amyloid-β proteins. .......................................................51
Abstract .............................................................................................................................51
Background ........................................................................................................................51
Results ...............................................................................................................................54
Increased protein insolubility induced by Tau and Aβ expression in middle-
aged animals. .................................................................................................................54
Expression of Tau and Aβ disrupts metabolism throughout life. ......................................58
Tau and Aβ expression in young adult worms resemble WT aging at insoluble
proteome and metabolic level. ........................................................................................61
Discussion .........................................................................................................................64
Chapter 5: Conclusion ...............................................................................................................71
Insoluble proteome analysis: lipid transport and protein changes .......................................71
Transcriptomic analysis: mRNA abundance and associations ............................................71
Metabolomic analysis: altered metabolite levels and energy pathways ..............................72
Phenotypic alterations: fertility, mitochondrial function, and movement ..............................72
Utility of the Tau;Aβ model and potential therapeutic targets ..............................................72
Significance, limitations, and future directions ....................................................................73
References ...............................................................................................................................76
vii
Appendices ...............................................................................................................................83
Appendix A: Upregulated Day 1 Transcriptomic Data, padj≤0.05 .......................................83
Appendix B: Downregulated Day 1 Transcriptomic Data, padj≤0.05 ................................. 108
Appendix C: Day 1 Tau;Aβ (q≤0.05) elevated metabolites compared to elevated
metabolites in human AD frontal cortex ............................................................................ 123
Appendix D: GFP image of AD strains, PCR and Western Blot of Aβ and Tau ................. 126
viii
List of Figures
Figure 1: Expression of Tau and Aβ increase insoluble proteins early in life. ............................36
Figure 2: Aβ expression drives transcriptomic changes in Tau;Aβ strain. ..................................39
Figure 3: Expression of Tau and Aβ synergistically alters metabolites. .....................................42
Figure 4: Systemic phenotypes observed with expression of Tau and Aβ. ................................45
Figure 5: Expression of Tau and Aβ increases day 8 insoluble proteome. ................................57
Figure 6: Expression of Tau and Aβ enhances metabolic changes with age. ............................60
Figure 7: Tau and Aβ expression in young adult worms resembles WT aging at insoluble
proteome and metabolomic levels. ............................................................................................64
Supplementary Figure 1: Increased insoluble proteome with Aβ expression.……………………49
Supplementary Figure 2: Altered predicted biological age of AD strains....................................50
Supplementary Figure 3: Overlap of more abundant metabolites in young Tau;Aβ and in
the frontal cortex of individuals with AD. ....................................................................................50
Supplementary Figure 4: Alterations in aged insoluble proteome with enrichment for
ribosomal proteins in Tau;Aβ strain. ..........................................................................................68
Supplementary Figure 5: Enhanced age-related metabolomic changes in Tau;Aβ, with
a focus on alpha linolenic acid and fatty acid biosynthesis. .......................................................69
ix
Abbreviations
AD: Alzheimer’s Disease
ADRD: Alzheimer's related diseases
mRNA: messenger ribonucleic acid
DNA: deoxyribonucleic acid
ANK1: ankyrin 1
HDACs: histone deacetylases
Aβ: Amyloid beta
APP: amyloid precursor protein
PS-1: presenilin-1
PS-2: presenilin-2
BACE: β-site amyloid precursor protein cleaving enzyme
APP-CTFβ: amyloid precursor protein C-terminal fragments beta
AICD: APP intracellular domain
CDK-5: cyclin dependent kinase 5
GSK-3β: glycogen synthase kinase-3 beta
NDDs: neurodegenerative diseases
DEGs: differentially expressed genes
SPCS1: Signal Peptidase Complex Subunit 1
x
NDUFS5: NADH:ubiquinone oxidoreductase subunit S5
SOD1: superoxide dismutase type 1
OGT: O-linked N-acetylglucosamine (O-GlcNAc) transferase
PURA: Pur-alpha
RERE: Arginine-Glutamic Acid Dipeptide Repeats
ZFP36L1: Butyrate response factor 1
ApoE: Apolipoprotein E
UPP: ubiquitin proteasome pathway
AbOs: amyloid beta oligomers
mTORC1: mammalian target of rapamycin complex
ATP: adenosine triphosphate
ROS: reactive oxygen species
FAD: familial AD
EOAD: early-onset AD
C. elegans: Caenorhabditis elegans
PTL-1: protein with Tau-like repeats 1
MAPT: microtubule-associated protein Tau
RNA-seq: Ribonucleic acid sequencing
xi
FTD: frontotemporal dementia
TDP-43: Tar DNA-binding protein 43
FTDP: frontotemporal dementia with parkinsonism
AChEIs: cholinesterase inhibitors
NMDA: N-methly-D-aspartate
mAbs: Monoclonal antibodies
DNMTs: DNA methyltransferases
HDAC: histone deacetylases
NSCs: neuronal stem cells
MSCs: mesenchymal stem cells
ESCs: embryonic stem cells
iPSCs: induced pluripotent stem cells
GFP: green fluorescent protein
L4: fourth larval stage
E. coli: Escherichia coli
NGM: nematode growth media
WT: wildtype
FUdR: 5'-fluorodeoxyuridine
xii
BCA: Bicinchoninic acid
NuPAGE: SDS-polyacrylamide gel electrophoresis at neutral pH
ANOVA: Analysis of Variance
OCR: oxygen consumption rate
3’ Tag RNA-seq: 3’ biased transcriptome sequencing ribonucleic acid sequencing
DNAse: Deoxyribonuclease
UPLC: ultra-performance liquid chromatography
HESI-II: heated electrospray ionization
EDTA: Ethylenediaminetetraacetic acid
SDS: sodium dodecyl-sulfate
LDS: Lithium dodecyl sulfate
HPLC: high-performance liquid chromatography
FDR: false discovery rate
WGCNA: Weighted Gene Co-expression Network Analysis
tRNA: transfer ribonucleic acid
CSF: cerebrospinal fluid
MRI/PET: Magnetic resonance imaging/ positron emission tomography
FTDP-17: frontotemporal dementia with parkinsonism linked to chromosome 17
xiii
GO: gene ontology
NADH: nicotinamide adenine dinucleotide + hydrogen
VA: ventral A neurons
DA: dorsal A neurons
KEGG: Kyoto Encyclopedia of Genes and Genomes
SEM: standard error of the mean
AMP: Adenosine monophosphate
GMP: Guanosine monophosphate
xiv
Abstract
Alzheimer's disease (AD) and related dementias present a significant global health
challenge, particularly in an aging population. This study utilized the model organism
Caenorhabditis elegans to explore the synergistic effects of amyloid-β (Aβ) and Tau
proteins, key hallmarks of AD, on various biological levels. By examining the expression
of Aβ and Tau, we observed increased accumulation of insoluble proteins and alterations
in protein-protein interactions associated with germ cells, oxidative phosphorylation, and
organism development. Transcriptomic analysis revealed changes in genes related to
reproduction, mitochondrial function, and the aging process, with Aβ playing a prominent
role. Metabolomic analysis highlighted synergistic effects on lipid and amino acid
metabolism, particularly through elevated aminoacyl tRNA biosynthesis. Furthermore, the
concurrent expression of Aβ and Tau resulted in impaired motor function, heightened
mitochondrial activity, decreased fertility, and reduced lifespan, emphasizing the systemic
consequences of their interaction. These findings shed light on AD pathogenesis and
underscore the urgent need for novel therapeutic approaches.
To further elucidate the implications of Tau and Aβ simultaneous expression in AD
pathogenesis, we examined their impact on protein insolubility and metabolism with age
using the previously mentioned Caenorhabditis elegans model. Our results revealed that
Tau and/or Aβ expression led to increased protein insolubility, exhibiting distinct patterns
during aging. Insoluble proteome analysis identified alterations in ribonucleotide binding,
aminoacyl tRNA ligase proteins, cytoskeleton structure, and ribonucleoprotein granules
in the Tau;Aβ strain. Metabolomic analysis demonstrated significant changes in
metabolites associated with glycolysis, gluconeogenesis, alpha-linolenic acid
xv
metabolism, and fatty acid biosynthesis pathways in the Tau;Aβ strain. Intriguingly, the
early-life insoluble proteomic and metabolic changes observed in the Tau;Aβ strain
resembled those typically seen in aged wildtype worms, suggesting an accelerated aging
phenotype. These findings highlight the intricate remodeling of protein insolubility and
metabolism induced by Tau and Aβ expression throughout the aging process, providing
valuable insights into the pathogenic mechanisms underlying AD. Understanding these
molecular consequences may pave the way for the development of therapeutic strategies
targeting protein aggregation and metabolic dysregulation in AD.
Overall, this study highlights the importance of investigating the systemic effects
in neurodegenerative diseases and provides a reference multi-omic datasets capturing
early-life AD changes as well as aged data. These datasets provide valuable resources
for identifying changes associated with Aβ, Tau, or their interaction, and can help identify
potential target pathways, genes or metabolites for treatment.
1
Chapter 1: Introduction
Aging and Alzheimer’s Disease
Aging is a complex biological process characterized by a gradual decline in
physiological function and is considered the driving factor behind various age-related
diseases (Li et al. 2021). It is marked by a set of distinct hallmarks, including genomic
instability, telomere attrition, epigenetic alterations, loss of proteostasis, deregulated
nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion,
and altered intercellular communication (López-Otín et al. 2023). These hallmarks
collectively contribute to the progressive deterioration of cellular and tissue integrity over
time. Based on the study of the Global Burden of Disease in 2017, it was found that 31.4%
of diseases were determined to be age-related, with neurodegenerative diseases, cancer,
cardiovascular diseases, and metabolic diseases being among the most common (Li et
al. 2021). Aging itself serves as the most prevalent risk factor for the development of
neurodegenerative diseases (Li et al. 2021).
One specific neurodegenerative condition strongly associated with aging is
Alzheimer's disease (AD). AD is characterized by the presence of extracellular amyloid
plaques, intracellular neurofibrillary tangles, and hyperphosphorylation of the Tau protein
(Xia et al. 2018). Studies have indicated that alterations in DNA methylation and histone
acetylation, crucial mechanisms involved in gene regulation, are associated with AD
pathology (Xia et al. 2018). Specifically, changes in DNA methylation patterns, including
a differentially methylated region in the ankyrin 1 (ANK1) gene, have been identified in
relation to AD neuropathology (Xia et al. 2018).
2
Histone acetylation, another epigenetic modification, has also been extensively
investigated in the context of AD. Reduced histone acetylation levels have been observed
in both human AD patients and AD mouse models (Xia et al. 2018). Enhancing histone
acetylation through the inhibition of histone deacetylases (HDACs) has demonstrated the
potential to reverse cognitive deficits in AD mouse models (Xia et al. 2018). Furthermore,
aging mice and humans exhibit a decrease in the number of neurons compared to their
younger counterparts, indicating a link between aging and neuronal loss (Li et al. 2021).
In addition to epigenetic modifications, aging and AD involve inflammatory
processes and altered glial cell function. Brain aging is characterized by the accumulation
of senescent glia, altered signaling, and pro-inflammatory factors, contributing to a
phenomenon known as "inflammaging" (Li et al. 2021; Xia et al. 2018). Microglia, the
primary phagocytic cells in the brain, play a role in the clearance of Aβ plaques, but their
effectiveness in this function diminishes with AD development (Li et al. 2021). Reactive
astrocytes induced by neuroinflammatory microglia lose normal astrocytic functions and
acquire neurotoxic properties, further contributing to the pathology of AD (Xia et al. 2018).
Vascular modifications, sleep disturbances, physical inactivity, and perturbed
energy balance are additional factors that have been associated with both aging and AD
(Xia et al. 2018). Endothelial dysfunction, arterial stiffness, and cardiovascular conditions
have been observed during aging and may potentially contribute to the development of
AD (Xia et al. 2018). Sleep disturbances, such as insomnia and sleep apnea, are more
prevalent in individuals with AD and may influence the accumulation of Aβ pathology (Xia
3
et al. 2018). Physical inactivity and disturbed energy balance also play a role in the
cognitive decline associated with aging and AD (Xia et al. 2018).
In summary, aging is a fundamental process that underlies the development of
various age-related diseases. In the case of AD, aging is a significant risk factor that
contributes to epigenetic modifications, neuronal loss, inflammatory processes, glial
dysfunction, vascular changes, and sleep disturbances.
Alzheimer’s Disease
Alzheimer's Disease (AD) is a form of dementia that can be either heritable or
sporadic, with age being one of the main risk factors for the disease. AD pathology
involves several key components, including the formation of Aβ plaques and
neurofibrillary tangles, glial cell activation, and impaired blood-brain barrier (Vegh et al.
2019). In the heritable form of AD, three autosomal dominant genes-amyloid precursor
protein (APP), presenilin-1 (PS-1), and presenilin-2 (PS-2)-are commonly associated with
the disease, as they play a role in the formation of Aβ protein, which constitutes the
plaques found in the brains of AD patients (Vegh et al. 2019).
The Aβ protein is generated when APP is cleaved by β-secretase (BACE),
resulting in the production of sAPPβ and another fragment known as APP-CTFβ or C99.
Subsequently, APP-CTFβ is cleaved by the γ-secretase complex to form the APP
intracellular domain (AICD) and Aβ (Alexander, Marfil, and Li 2014). Neurofibrillary
tangles, another hallmark of AD, consist of hyperphosphorylated Tau protein, which is a
microtubule-associated protein involved in various physiological processes such as
4
neurite outgrowth and axonal transport (Johnson and Stoothoff 2004). The Tau protein
contains 16 exons, with alternative splicing of exons 2, 3, and 10 resulting in six different
isoforms of the protein (Vegh et al. 2019). When Tau becomes hyperphosphorylated, it
loses its ability to bind to microtubules, leading to microtubule disassembly and the
formation of aggregates, which give rise to intracellular neurofibrillary tangles (Vegh et al.
2019; Alexander, Marfil, and Li 2014).
Despite extensive research, current disease-modifying treatments for AD, such as
α, β, and γ secretase inhibitors, immunotherapy, and inhibitors of Aβ aggregation, have
not proven successful in halting disease progression (Vegh et al. 2019). This suggests
the need to explore novel therapeutic targets, including the interaction between Aβ and
Tau.
The interaction between Aβ and Tau contributes to a hyperactive behavior
phenotype and a decrease in the expression of genes involved in synaptic function
(Pickett et al. 2019). Aβ and vascular risk synergistically affect Tau burden in the inferior
temporal cortex, leading to increased cognitive decline (Yau et al. 2022). The interaction
of Aβ and Tau is also associated with hypometabolism in the posterior cingulate cortex,
which predicts memory decline (Busche and Hyman 2020). Aβ activates CDK-5 and GSK-
3β, leading to Tau hyperphosphorylation and the subsequent formation of neurofibrillary
tangles (Busche and Hyman 2020; Lee et al. 2022; Zhang et al. 2021). Aβ can induce
Tau oligomerization by cleaving Tau through caspase-3, resulting in a truncated form that
self-aggregates (Zhang et al. 2021). Aβ causes neuronal hyperexcitability, while Tau
suppresses neuronal activity, and their interaction favors Tau-mediated suppression
5
(Busche and Hyman 2020; Tripathi and Kalita 2019). Animal models combining Aβ and
Tau exhibit early-onset synapse loss, defects in mitochondrial function, and mitophagy
(Busche and Hyman 2020). Aβ can enhance Tau aggregation and promote the spreading
of Tau to brain regions (Busche and Hyman 2020; Lee et al. 2022). The presence of both
Aβ and Tau leads to a synergistic detrimental effect on neuronal and synaptic gene
expression, synapse loss, and cognitive decline (Busche and Hyman 2020; Pickett et al.
2019).
Alzheimer's Disease is part of a broader spectrum of neurodegenerative diseases
(NDDs) characterized by shared hallmarks, including pathological protein aggregation,
synaptic and neuronal network dysfunction, altered energy metabolism, and inflammation
(Bejanin et al. 2017). Understanding these shared hallmarks among NDDs provides
valuable insights into potential therapeutic strategies for these devastating disorders.
Transcriptional Dysfunction in AD
Transcriptomic changes in AD have been extensively studied to gain insights into
the molecular mechanisms underlying the disease. Several studies have identified
differentially expressed genes (DEGs) in various brain regions affected by AD. Patel et
al. conducted an RNA sequencing analysis and identified differentially expressed genes
specific to AD brain regions (temporal lobe, frontal lobe, parietal lobe, and cerebellum).
A total of 2495 unique genes were found to be significantly differentially expressed
across the AD brain regions, with 42 genes consistently perturbed in all four brain regions
(Patel, Dobson, and Newhouse 2019). These genes included SPCS1, NDUFS5, SOD1
6
(down-regulated), and OGT, PURA, RERE, ZFP36L1 (up-regulated), which were
considered unique to AD brains (Patel, Dobson, and Newhouse 2019). Gene set
enrichment analysis revealed that biological pathways related to protein metabolism and
viral components were significantly enriched in AD brains (Patel, Dobson, and Newhouse
2019).
Nativio et al. found that in AD, there is upregulation of genes related to transcription
and chromatin regulation, including histone acetyltransferases for H3K27ac and H3K9a.
This upregulation of histone modifications was associated with transcription, chromatin,
and disease pathways in AD (Nativio et al. 2020).
Moreover, Guennewig et al. identified genes with significant upregulation and
downregulation in AD, with enriched biological pathways related to transcription
regulation, exocytosis, and immune function. Notably, immune-related pathways were
among the top overrepresented pathways in AD (Guennewig et al. 2021).
These findings suggest that AD is associated with dysregulation of gene
expression and alterations in key biological pathways. The involvement of genes related
to transcription, chromatin regulation, protein metabolism, exocytosis, and immune
function highlights their potential roles in AD pathology. Overall, transcriptomic studies
have provided valuable insights into the molecular changes occurring in AD, helping to
unravel the complex interplay of genetic and cellular processes involved in the disease.
7
Proteomic Dysfunction in AD
Proteomic studies have provided valuable insights into the protein alterations
associated with AD, shedding light on the molecular changes underlying the pathology.
Iturria-Medina et al. conducted a study analyzing protein concentrations in different AD
subtypes and found common alterations in protein levels across all three subtypes,
including Tau phosphorylated at S262, Aβ, and nerve growth factor. Moreover, each
subtype exhibited distinctively expressed proteins associated with specific biological
processes. Subtype 1 showed alterations in proteins related to the electron transport
chain in mitochondria and response to stressful conditions (Iturria-Medina et al. 2022).
Subtypes 2 and 3 exhibited greater protein alterations in cardiovascular development and
lactate-pyruvate catalysis (Iturria-Medina et al. 2022).
Gozal et al. identified significant changes in 81 AD-specific proteins out of 512
identified proteins in their study. They observed increased levels of well-established AD-
linked proteins, such as Aβ, Tau, and apolipoprotein E (ApoE). Furthermore, they
detected the presence of serine protease 15, ankyrin B, and 14-3-3η in the detergent-
insoluble fraction, indicating their association with AD pathology (Gozal et al. 2009).
Among the identified proteins, the inflammatory complement C4 was found exclusively in
the AD sample (Gozal et al. 2009).
Kepchia et al. investigated the alteration of protein aggregation between AD and
control patients and found a stage-dependent increase in detergent-insoluble proteins,
with more pronounced changes occurring in the AD cohort. Glycolysis was identified as
the most significantly overrepresented biological process associated with the alteration of
8
protein aggregation in AD, with several low molecular weight proteins enriched in the AD
brain (Kepchia et al. 2020). Enzymes, structural proteins, and various members of the
ubiquitin proteasome pathway (UPP) were among the top proteins identified (Kepchia et
al. 2020). Furthermore, protein level alterations were generally more extreme in the AD
cohort compared to those with mild cognitive impairment (Kepchia et al. 2020).
These proteomic studies highlight the extensive protein alterations and
dysregulation of specific biological processes in AD. The identification of AD-specific
proteins, along with the association of well-established AD-linked proteins with
neuropathological lesions, supports their involvement in the disease process. Moreover,
the findings suggest that protein aggregation, alterations in energy metabolism, and
dysregulation of the ubiquitin proteasome pathway may play significant roles in AD
pathogenesis.
Overall, proteomic investigations have provided valuable insights into the complex
protein changes occurring in AD, enhancing our understanding of the molecular
mechanisms underlying the disease and potentially identifying novel targets for
therapeutic intervention.
Metabolic dysfunction in AD
AD is a neurodegenerative disorder characterized by progressive cognitive decline
and memory loss. While the exact cause of AD is still not fully understood, emerging
evidence suggests that metabolic dysfunction plays a crucial role in the pathogenesis of
the disease. This metabolic dysfunction encompasses various aspects, including
9
impaired glucose metabolism, dyslipidemia, insulin resistance, oxidative stress, and
mitochondrial dysfunction (Bloom and Norambuena 2018; Kim et al. 2021; Z. Chen and
Zhong 2013; Patro et al. 2021; Mantzavinos and Alexiou 2017; G. Wu et al. 2004; Poddar
et al. 2021).
One prominent feature of metabolic dysfunction in AD is the reduction of glucose
uptake in the brain, which is independent of systemic type 2 diabetes. This reduction is
believed to be a consequence of impaired insulin signaling and has led to the proposal of
classifying AD as a brain-specific type of diabetes, often referred to as "type 3 diabetes"
(Bloom and Norambuena 2018). This reduced ability of AD neurons to utilize extracellular
nutrients not only limits the activity of their mitochondria but also contributes to
mitochondrial dysfunction through dysregulation of the mammalian target of rapamycin
complex 1 (mTORC1) by Aβ oligomers (AbOs) and Tau proteins (Bloom and
Norambuena 2018). Dysfunctional glucose metabolism in AD is further supported by
studies showing consistent and progressive reductions in cerebral glucose metabolism,
which correlate with the severity of cognitive symptoms (Z. Chen and Zhong 2013).
Metabolic syndrome, a cluster of conditions including high triglycerides, high
glucose, abdominal obesity, low high-density lipoprotein cholesterol, and hypertension,
has also been associated with an increased risk of AD (Kim et al. 2021). The presence of
metabolic syndrome and its individual components, such as impaired fasting glucose, has
been found to be more prevalent in AD patients compared to controls (Kim et al. 2021).
This chronic state of inflammation, hyperinsulinemia, dyslipidemia, dysglycemia, vascular
10
injury, and oxidative stress associated with metabolic syndrome is thought to contribute
to the pathogenesis of AD (Kim et al. 2021).
Furthermore, disruptions in mitochondrial function have been observed in AD.
Impairments in the ATP synthase, a key enzyme involved in cellular energy production,
have been detected in the brains of AD patients, leading to decreased ATP synthesis
(Patro et al. 2021). Additionally, dysregulation of oxidative phosphorylation and enzymes
involved in glucose metabolism, such as pyruvate dehydrogenase and isocitrate
dehydrogenase, have been implicated in AD pathophysiology (Poddar et al. 2021).
Moreover, the accumulation of reactive oxygen species (ROS) and oxidative stress
in AD contributes to neurodegeneration and the formation of amyloid plaques and
neurofibrillary tangles (Mantzavinos and Alexiou 2017). Zinc, copper, and iron interactions
with Aβ proteins have been shown to promote Aβ accumulation and neurotoxicity, while
disruptions in calcium homeostasis and the opening of the mitochondrial permeability
transition pore (mPTP) contribute to mitochondrial dysfunction and apoptosis
(Mantzavinos and Alexiou 2017; Patro et al. 2021).
In conclusion, metabolic dysfunction plays a crucial role in the pathogenesis of AD.
Impaired glucose metabolism, dyslipidemia, insulin resistance, oxidative stress, and
mitochondrial dysfunction contribute to the neurodegenerative processes observed in AD.
Understanding the intricate interplay between metabolic dysregulation and AD pathology
is essential for the development of targeted therapeutic interventions aimed at mitigating
the progression of the disease.
11
Use of models to study AD
Alzheimer's disease (AD) research has greatly benefited from the use of
transgenic animal models, primarily mice, which have played a crucial role in advancing
our understanding of the disease's pathogenesis. By genetically modifying key genes like
APP, PS1, PS2, and APOE4, researchers have successfully replicated cognitive
dysfunction and overproduction of Aβ proteins in mice, mimicking AD (Qin et al. 2022).
However, despite similarities in end-stage amyloid and Tau pathologies, there are notable
differences in the biochemical and pathological characteristics of AβPP, amyloid-β, and
Tau between transgenic AD mice and sporadic AD in humans (Qin et al. 2022).
In addition to transgenic mouse models, alternative approaches have been
employed to establish animal models of AD, such as intraperitoneal injection of d-
galactose, direct injection of scopolamine, or gamma knife-mediated hippocampal
damage, which do not involve genetic modifications (Qin et al. 2022). However, the
majority of AD animal models still rely on transgenic mice that overexpress human genes
involved in the production of amyloid plaques and neurofibrillary tangles (Vitek et al.
2020). These models, though useful, have limitations. Most AD mice do not exhibit
neurodegeneration, and the models primarily focus on familial AD (FAD) with early-onset
AD (EOAD) mechanisms (Vitek et al. 2020). Furthermore, the genetic backgrounds of the
mouse strains are not standardized, and the models incompletely replicate the
neuropathology of late-onset Alzheimer's dementia (Vitek et al. 2020). Consequently,
researchers have been striving to develop new rodent models based on human data sets,
12
aiming to better replicate the progressive cognitive impairments and neuropathological
features of late-onset Alzheimer's dementia (Vitek et al. 2020).
Besides mice, other animal models have also been utilized in AD research. Aged
dogs, for example, naturally produce Aβ proteins with a similar amino acid sequence to
humans, making them advantageous over non-transgenic aged rodents (Vitek et al.
2020). Non-human primates, such as rhesus macaques, exhibit age-related cognitive
deficits resembling aspects of AD and share genetic similarities with humans (Vitek et al.
2020; Z.-Y. Chen and Zhang 2022). New World monkeys and primitive monkeys,
including squirrel monkeys, marmosets, tamarins, and mouse lemurs, have also
contributed significantly to the study of human aging due to their genetic similarity, well-
developed prefrontal cortex, and age-related cognitive deficits (Vitek et al. 2020; Z.-Y.
Chen and Zhang 2022).
In addition to the aforementioned animal models, zebrafish have gained popularity
in AD research due to their ease of breeding, rapid maturation, and high genetic homology
with humans (Z.-Y. Chen and Zhang 2022). Zebrafish models have been widely used for
drug screening to identify potential treatments for AD, allowing researchers to test the
effects of drugs like donepezil, memantine, and methylene blue (Z.-Y. Chen and Zhang
2022).
Another intriguing model organism in AD research is Drosophila melanogaster,
commonly known as the fruit fly. Despite evolutionary differences, fruit flies exhibit various
mammalian-like behaviors and offer valuable insights into attention, olfaction, feeding,
expectancy, aggression, learning and memory, orientation, sleeping, and circadian
13
rhythm (Z.-Y. Chen and Zhang 2022). Different types of D. melanogaster AD models have
been developed based on introduced transgenes, including γ-secretase-based, Tau-
based, and APP- or Aβ-based models (Z.-Y. Chen and Zhang 2022).
In addition to traditional animal models, alternative organisms such as yeast and
Caenorhabditis elegans (C. elegans) have emerged as powerful tools in AD research.
Despite lacking a nervous system, yeast exhibits highly homologous molecular signaling
pathways and proteins, along with functional conservation similar to humans (Z.-Y. Chen
and Zhang 2022). Yeast models have provided valuable insights into AD pathology,
particularly regarding the cellular pathways involved in APP processing and Aβ
oligomerization (Z.-Y. Chen and Zhang 2022).
C. elegans, a soil nematode with a well-studied neuronal system, offers a simplified
model for studying neurons and has the potential to improve modeling accuracy (Z.-Y.
Chen and Zhang 2022). Although lacking evolutionary complexity, C. elegans retains
conserved synaptic transmission functions and shares many molecular pathways and
cellular mechanisms with mammals (Z.-Y. Chen and Zhang 2022). This functional
conservation allows for comparative studies between C. elegans and humans, making it
a valuable model organism for studying AD and other genetic diseases (Z.-Y. Chen and
Zhang 2022).
C. elegans AD models have been developed by introducing human Aβ or Tau
sequences into these nematodes, enabling researchers to study AD pathology (Z.-Y.
Chen and Zhang 2022). They display phenotypes that resemble human AD, such as
impaired locomotion, neurodegeneration, reduced lifespan, and defective chemotaxis (X.
14
Chen et al. 2015; Teschendorf and Link 2009; Alexander, Marfil, and Li 2014). Continued
development of these models holds the potential to further advance our knowledge of AD
and aid in the discovery of potential therapeutic targets (X. Chen et al. 2015).
AD Interventions
The current therapeutic options for AD primarily focus on alleviating symptoms and
include cholinesterase inhibitors (AChEIs) and N-methyl D-aspartate (NMDA)
antagonists. However, these drugs do not cure or prevent the disease (Breijyeh and
Karaman 2020). To address this limitation, ongoing research is exploring various
interventions aimed at modifying or halting the progression of AD by targeting different
underlying mechanisms.
One of the classical targets in AD is the deposition of extracellular amyloid β (Aβ)
plaques and the formation of intracellular neurofibrillary tangles composed of
hyperphosphorylated Tau protein (Yiannopoulou and Papageorgiou 2020). To tackle
these pathogenic processes, several therapeutic approaches are being investigated,
including neuroprotective agents, anti-inflammatory agents, growth factor promotive
agents, metabolic efficacious agents, and stem cell therapies (Yiannopoulou and
Papageorgiou 2020).
In addition to pharmacological interventions, non-pharmacological approaches
have shown potential in mitigating AD symptoms and improving cognitive function. For
example, physical exercise has been associated with reduced neuropsychiatric
symptoms and the prevention of age-related strength loss, which can benefit cognitive
15
function in later life (Poddar et al. 2021). Furthermore, dietary interventions and certain
compounds such as antioxidants, vitamins, polyphenols, and omega-3 polyunsaturated
fatty acids have been investigated for their potential in reducing the risk of AD (Breijyeh
and Karaman 2020; Yu, Lane, and Lin 2021).
Efforts are being made to develop disease-modifying therapies (DMTs) that can
target the underlying pathological processes of AD. These include immunotherapies,
small molecules, and other approaches aimed at specific pathways involved in AD
pathology (Breijyeh and Karaman, 2020).
Despite advancements in research, numerous challenges hinder the development
and success of AD interventions. Factors such as late initiation of therapies, inappropriate
drug doses, incorrect main targets, and an incomplete understanding of AD's
pathophysiology have contributed to failures in clinical trials (Yiannopoulou and
Papageorgiou 2020). To overcome these challenges, current research aims to
incorporate new aspects of disease biology, diagnostic markers, individualized diagnosis,
and improved trial designs (Yiannopoulou and Papageorgiou 2020).
In summary, the current therapeutic agents for AD primarily focus on symptom
management, while ongoing research explores various interventions to modify or halt the
progression of the disease. These interventions target different underlying mechanisms,
including Aβ and Tau pathology, neuroprotection, inflammation, metabolism, and stem
cell therapies. Non-pharmacological approaches and dietary interventions also show
promise in improving cognitive function and reducing the risk of AD. Future therapies aim
to target the underlying pathological processes and develop disease-modifying
16
treatments that can alter the course of the disease. Overcoming challenges in clinical
trials and gaining a better understanding of AD's pathophysiology are crucial steps
towards advancing effective interventions for AD.
17
Chapter 2: Materials and Methods
C. elegans strains
The transgenic strains utilized in this study include non-transgenic wild-type N2,
CK10 (Paex-3::h4R1N Tau V337M; Pmyo-2::GFP)(Kraemer et al. 2003), CL2355(smg-
1(cc546);Psnb-1::Aβ1-42 + Pmtl-2::GFP)(Y. Wu et al. 2006), and the simultaneous expression
strain GL405 (Paex-3::h4R1N Tau V337M; Pmyo-2::GFP; smg-1(cc546);Psnb-1::Aβ1-42 + Pmtl-
2::GFP). CL2355 was created by the lab of Dr. Chris Link and obtained from the
Caenorhabditis Genetic Center. CK10 was obtained from the lab of Dr. Brian Kraemer.
N2, CK10, CL2355, GL405 strains are maintained at 15°C due to the smg-1(cc546) gene,
which allows for the temperature-dependent expression of Aβ. Once the worms reached
the fourth larval stage (L4), they were shifted to 25°C to induce Aβ expression.
Throughout the study, the worms were cultured on nematode growth media (NGM)
(containing: Becton Dickinson and Company Difco Agar, Bacteriological, ref:214510,
Gibco Bacto Peptone Enzymatic Digest of Protein ref: 211820, Sigma-Aldrich Sodium
Chloride S9888-1KG, Sigma-Aldrich Magnesium Sulfate M7506-500G lot#SLBR0877V,
Sigma-Aldrich Calcium Chloride C1016-500G lot#SLCC1966) plates (VWR, petri dish
polystyrene disposable sterilized size: 60 x 15mm, cat no.25384-090) containing E. coli
OP50 and passaged once a week.
18
Referenced
Name
Strain
name
Genotype Growth
condition
Experiment
condition
WT N2 wildtype 15°C 25°C &
20°C
Tau CK10 P
aex-3
::h4R1N Tau V337M; P
myo-
2
::GFP
15°C 25°C
Aβ CL2355 smg-1(cc546);P
snb-1
::Aβ
1-42
+
P
mtl-2
::GFP
15°C 25°C
Tau;Aβ,
Double
transgenic,
simultaneous
expression
GL405 smg-1(cc546);P
snb-1
::Aβ
1-42
+
P
mtl-2
::GFP x P
aex-3
::h4R1N Tau
V337M; P
myo-2
::GFP
15°C 25°C
Table 1 Strains Utilized in Experiments
Western Blot
Worms were mass cultured at 15°C and synchronized with hypochlorite solution.
Upon reaching the fourth larval stage (L4), the worms were transferred to NGM plates
(petri dish polystyrene disposable sterilized size: 100 x 15mm, cat no. 25384-342)
supplemented with 10µg/ml 5'-fluorodeoxyuridine (FUdR) (VWR, cat no. D2235) and
shifted to a temperature of 25°C. The worms were collected on adult stage day 1, 3, 5,
and 8 for subsequent analysis.
For protein extraction, the collected samples were sonicated for 10 minutes in lysis
buffer using the Biorupter®. The samples were then subjected to centrifugation at 3,000g
for 3 minutes, and the protein concentration was measured using the BCA/Bradford assay
(Thermo Scientific, ref:23225). Subsequently, a solution of 4X NUPAGE LDS Sample
Buffer (Invitrogen by Thermo Fisher Scientific, NP0007) and 4% β-mercaptoethanol
(mpbio, cat no.194834) was added to the samples to achieve a final concentration of 1X.
19
The samples were heated at 99°C on a heat block for 5-8 minutes and then centrifuged
at 3,000g for 3 minutes.
The samples were loaded onto a NuPAGE 4-12% Bis-Tris gel (Invitrogen by
Thermo Fisher Scientific, NP0321BOX) and ran at 120V for 1 hr and 30 minutes. The
gels were then transferred onto the nitrocellulose membrane using iBlot™ gel transfer
(Invitrogen by Thermo, ref: np0322BOX) device for 7 minutes. The membranes were then
blocked with 5% milk (Signature Select and NUPAGE by Thermo, ref: 28360, 20X TBS
Tween) for 1 hour. Primary antibodies against Aβ1-16 (antibody 6E10) (BioLegend,
catalog# SIG-39320) and Tau (antibody HT7) (Invitrogen by Thermo, re: MN1000) were
applied to the membranes and incubated overnight. On the subsequent day, the
membranes were washed and incubated with the appropriate secondary antibody for 2
hours. Finally, the membranes were imaged for 60 seconds to visualize the protein bands.
Fertility and fecundity assay
Egg lays were conducted at 15°C for each strain. Once worms develop to the L4
stage, 10 worms were moved to individual small NGM plates (Gen Clone, tissue culture
dishes 38x13mm, cat# 25-200) and shifted to 25°C. On day 1 of the adult stage, the
number of eggs laid was counted to determine fecundity and the number of hatched
larvae was counted to determine fertility. The worms were then transferred to fresh NGM
plates. The number of eggs laid, and hatched larvae were counted each day until the end
of their reproductive cycle. To determine if there were significant differences in fertility
and fecundity among the strains, statistical analysis was performed using ordinary one-
way ANOVA with Tukey's multiple comparisons test.
20
Thrashing Assay
Following egg lays conducted at 15°C, the worms were allowed to develop to the
L4 stage and then transferred to 10 µg/mL FUdR plates, followed by a shift to 25°C. On
day 1 of the adult stage, a droplet of S-basal solution (containing: Sigma-Aldrich
Potassium Phosphate Monobasic P5379-1KG lot#SLCG1814, Sigma-Aldrich Potassium
Phosphate Dibasic P3786-1KG lot#SLCC4706, and Sigma-Aldrich Sodium Chloride
S9888-1KG) was placed on a glass slide (Premiere, cat no.9101), and a single worm was
placed into the droplet to acclimate for 30 seconds. Subsequently, the body bends of the
worm were counted for another 30 seconds. This process was repeated for each strain,
with 10-20 worms included in each biological replicate. To determine if there were
significant differences in the number of body bends between the strains, statistical
analysis was performed using ordinary one-way ANOVA with Tukey's multiple
comparisons test.
Mitochondrial oxygen consumption rate assay
To evaluate mitochondrial function, a mitochondrial oxygen consumption rate
(OCR) assay was performed. Over 200 worms were mass cultured on 100mm NGM agar
plates (petri dish polystyrene disposable sterilized size: 100 x 15mm, cat no. 25384-342)
at 15°C. Age synchronization was achieved by isolating eggs through hypochlorite
treatment of gravid adults. Once the worms reached the L4 stage, they were transferred
to NGM plates containing 10µg/ml FUdR and shifted to a temperature of 25°C.
Approximately 200 worms were collected on day 1 of the adult stage for further analysis.
The Agilent Seahorse XFe96 Analyzer was utilized to measure the OCR. The
mitochondrial OCR assay followed a previously described protocol(Koopman et al. 2016).
21
Briefly, for each strain, approximately 20 worms were loaded into each well of a 96-well
microtiter plate, with 8 technical replicates per strain. Basal OCR was measured for 5
cycles, where each cycle consisted of 2 minutes of mixing in the well, 4 minutes of waiting,
and 2 minutes of OCR measurement in the well.
To determine if there were significant differences in OCR between the strains,
statistical analysis was performed using ordinary one-way ANOVA, followed with Tukey's
multiple comparisons test.
Lifespan Assay
To assess the lifespan of the worms, egg lays were conducted at 15°C. Once the
worms reached the L4 stage, approximately 40-60 worms per strain were transferred to
NGM plates containing 10µg/ml FUdR and maintained at a temperature of 25°C. Three
replicates were performed for each strain to ensure robustness and reproducibility of the
results. The worms were monitored every two days for touched provoked movement. This
was achieved by gently poking the worms with a platinum wire and observing their
response. The absence of movement upon stimulation was considered an endpoint,
indicating mortality.
Statistical analysis was conducted using survival representation (Kaplan-Meier) in
GraphPad Prism™ software. Survival curves were generated, and the lifespan of each
strain was compared using the Log-rank (Mantel-Cox) test. This analysis allowed for the
assessment of significant differences in survival rates between the strains.
22
3’ Tag RNA-seq
For the 3’ Tag RNA-seq analysis, worms were mass cultured at 15°C, and age-
synchronization was achieved by isolating eggs through hypochlorite treatment of gravid
adults. Once the worms reached the L4 stage, they were transferred to FUdR NGM plates
and maintained at a temperature of 25°C.
Approximately 1000 worms were collected on the first day of the adult stage and
stored in a 1.5ml Eppendorf tube (USA Scientific, cat no: 1615-5500) containing 300-
500µl of RNA lysis buffer from the Zymo Research RNA extraction kit (cat#R1055). The
samples were immediately placed at -80°C for storage. To facilitate RNA extraction, the
samples underwent three cycles of freeze-thaw, alternating between room temperature
and -80°C. Subsequently, the samples were incubated on a heat block at 55°C for 0.5-1
hour.
RNA extraction was performed using the Zymo Research RNA extraction kit
according to the provided protocol. The protocol included a DNAse treatment step to
remove any genomic DNA contamination. The elution step resulted in approximately 40µl
of purified RNA.
The prepared RNA samples were then sent out on dry ice for 3’ Tag RNA-Seq
analysis. The sequencing was conducted at the DNA Technologies and Expression
Analysis Cores at the UC Davis Genome Center, with support from NIH Shared
Instrumentation Grant 1S10OD010786-01.
23
Binarized transcriptomic aging (BiT age) clock calculation
Read counts from the RNA-seq data were transformed into counts per million using
edgeR (Robinson, McCarthy, and Smyth 2010). Using this file and the regression
coefficients in
https://github.com/Meyer-DH/AgingClock/blob/main/Data/Predictor_Genes.csv
we calculated the biological age of each sample from day 1 using the Python script
https://github.com/Meyer-DH/AgingClock/blob/main/src/biological_age_prediction.py
Metabolomics
For metabolomic analysis, worms were mass cultured at 15°C and synchronized
by isolating eggs using hypochlorite treatment. Upon reaching the L4 stage, the worms
were transferred to FUdR 100mm NGM plates at 25°C. At the adult stage day 1,3,5, and
8, approximately 250µl of worms were collected by washing off with S-basal solution and
immediately snap freezing (Murphy et al. 2019; Wang et al. 2015). These samples were
then submitted to Metabolon Inc. (Morrisville, NC, USA) for global metabolic profiling.
Sample preparation was conducted using the automated MicroLab STAR® system
from the Hamilton Company. Prior to the extraction process, several recovery standards
were added to the samples for quality control purposes. The extracted samples were
analyzed using a Waters ACQUITY ultra-performance liquid chromatography (UPLC)
system and a Thermo Scientific Q-Exactive high resolution/accurate mass spectrometer
equipped with a heated electrospray ionization (HESI-II) source and Orbitrap mass
analyzer operating at a mass resolution of 35,000.
24
Raw data obtained from the analysis was processed by Metabolon's hardware and
software. This involved extraction of the data, identification of peaks, and quality control
processing. Each metabolite was corrected by registering the medians to a value of one
(1.00) and normalizing each data point proportionately. In certain cases, metabolite data
was further normalized to total protein levels determined by Bradford assay to account
for differences in metabolite levels due to variations in the amount of biological material
present in each sample.
A total of 767 biochemicals were identified, including 695 metabolites with known
composition and 72 compounds with unknown structural identity. Statistical analysis was
performed using two-way ANOVA to identify biochemicals that exhibited significant
differences between experimental groups. Additionally, a false discovery rate (q-value)
was estimated to account for multiple comparisons.
Insoluble proteome
For the insoluble proteome analysis, which is based on (Xie et al. 2020), worms
were mass cultured at 15°C and age-synchronized by isolating eggs using hypochlorite
treatment of gravid adults. Upon reaching the L4 stage, the worms were transferred to
FUdR NGM plates at 25°C. Approximately 3,000 worms at the adult stage day 1,3,5 and
8 were collected in S-basal solution and stored at -80°C until further processing.
To extract the insoluble protein fraction, the samples were thawed and vortexed
with worm lysis buffer containing 20mM Tris base (pH 7.4), 100mM NaCl, 1mM MgCl2,
and EDTA-free protease inhibitor. The lysate was then sonicated for 10 cycles and
centrifuged at 3,000 x g for 4 minutes in a cold room. The resulting supernatant,
25
containing the aqueous-soluble protein fraction, was transferred to new 1.5mL Eppendorf
tubes, and the protein concentration was quantified using a BCA assay.
The protein lysate was further processed by centrifuging it for 15 minutes at 20,000
x g in a cold room. The resulting supernatant was saved as the SDS-soluble fraction. The
pellet obtained from this step was washed with 500µl of worm lysis buffer containing 1%
SDS and centrifuged at 20,000 x g for 15 minutes. The supernatant was removed, and
this washing step was repeated two more times to remove the SDS-soluble fraction. The
remaining pellet, known as the 1% SDS-insoluble protein fraction, was resuspended in
60µl of 7% formic acid (Thermo Scientific, ref: 85178) and vortexed to dissolve the pellet.
Subsequently, the sample was sonicated for 30 minutes and dried in a vacuum
concentrator for 1 hour to remove the formic acid solution.
The 1% SDS-insoluble protein fraction was then heated at 95°C with 40µl of 1x
LDS sample gel buffer for 10 minutes and vortexed. After centrifugation, 13µl of the
sample was loaded onto a 4-12% NuPAGE Bis-Tris gel for electrophoresis. The
remaining portion of the sample was preserved for mass spectrometry analysis using a
high-performance liquid chromatography (HPLC) system combined with a chip-based
HPLC system directly connected to a quadrupole time-of-flight mass spectrometer.
The total amount of 1% SDS-insoluble protein, per sample, was prepared for
trypsin digestion. Each sample was reduced using 20 mM dithiothreitol in 50 mM
triethylammonium bicarbonate buffer (TEAB) at 50°C for 10 min, cooled to room
temperature (RT) and held at RT for 10 min, and alkylated using 40 mM iodoacetamide
in 50 mM TEAB at RT in the dark for 30 min. Samples were acidified with 12% phosphoric
acid to obtain a final concentration of 1.2% phosphoric acid. S-Trap buffer consisting of
26
90% methanol in 100 mM TEAB at pH ~7.1, was added and samples were loaded onto
the S-Trap mini spin columns. The entire sample volume was spun through the S-Trap
mini spin columns at 4,000 × g and RT, binding the proteins to the mini spin columns.
Subsequently, S-Trap mini spin columns were washed twice with S-Trap buffer at 4,000
× g at RT and placed into clean elution tubes. Samples were incubated for one hour at
47°C with sequencing grade trypsin (Promega, San Luis Obispo, CA) dissolved in 50 mM
TEAB at a 1:25 (w/w) enzyme:protein ratio. Afterwards, trypsin solution was added again
at the same ratio, and proteins were digested overnight at 37°C.
Peptides were sequentially eluted from mini S-Trap spin columns with 50 mM
TEAB, 0.5% formic acid (FA) in water, and 50% acetonitrile (ACN) in 0.5% FA. After
centrifugal evaporation, samples were resuspended in 0.2% FA in water and desalted
with C18 Ziptips (MilliporeSigma, Burlington, MA). Desalted peptides were then subjected
to an additional round of centrifugal evaporation and re-suspended in 20 µL of 0.2% FA
in water and 1 µL of indexed Retention Time Standard (iRT, Biognosys, Schlieren,
Switzerland).
Samples were then subjected to mass spectrometric analysis using a high-
performance liquid chromatography (HPLC) system combined with a chip-based HPLC
system (Eksigent nano-LC) directly connected to a quadrupole time-of-flight mass
spectrometer (TripleTOF 5600, a QqTOF instrument) as detailed in a step-by-step
protocol by (Xie et al. 2020).
Each sample was acquired in data-dependent acquisition (DDA) mode to build
peptide spectral libraries, as described in (Xie et al. 2020). Data-Independent Acquisition
(DIA)/SWATH data was processed in Spectronaut (version 14.10.201222.47784) using
27
DIA. Data extraction parameters were set as dynamic and non-linear iRT calibration with
precision iRT was selected. DIA data was matched against an in-house Caenorhabditis
elegans spectral library that provides quantitative DIA assays for 3,651 C. elegans
peptides corresponding to 910 protein groups and supplemented with scrambled decoys
(library size fraction of 0.01), using dynamic mass tolerances and dynamic extraction
windows. DIA/SWATH data was processed for relative quantification comparing peptide
peak areas from different days. Identification was performed using 1% precursor and
protein q-value. Quantification was based on the peak areas of extracted ion
chromatograms (XICs) of 3 – 6 MS2 fragment ions, specifically b- and y-ions, with q-value
sparse data filtering and iRT profiling being applied (Supplementary Table 1). For this
sample-set, local normalization was not implemented. Differential protein expression
analyses for all comparisons were performed using a paired t-test, and p-values were
corrected for multiple testing, using the Storey method (Burger 2018). Specifically, group
wise testing corrections were applied to obtain q-values. Protein groups with at least two
unique peptides, q-value ≤ 0.01, and absolute Log2(fold-change) > 0.58 are significantly
altered.
Protein-protein interaction network
We extracted 62 proteins with increased abundance (q-value < 0.1) in the
insoluble fraction compared to N2 controls at day 1. We loaded this list of proteins into
Cytoscape (Shannon et al. 2003) and generated a protein-protein interaction network
using the stringApp plugin (Doncheva et al. 2019). Then, we performed enrichment
analysis for Biological Processes (Gene ontology), KEGG pathways, and WormBase
28
phenotypes. We colored the nodes of proteins from the top-enriched processes in the
network.
Omic Analysis
We conducted several omic analyses to gain a comprehensive understanding of
the molecular changes associated with the studied conditions. The following approaches
were employed:
Enrichment Analysis:
For the RNAseq and insoluble proteome data, we performed enrichment analysis
using g:Profiler. The custom background used for each specific comparison included all
genes or all proteins measured in that particular analysis. The enrichment analysis was
carried out with a user threshold of 0.05, and significance was determined using the
Benjamini-Hochberg false discovery rate (FDR) correction. Additionally, WormBase
Enrichment Analysis was utilized for some of the RNAseq enrichment, applying a q-value
threshold of 0.05 and the previously mentioned custom background. Metaboanalyst was
utilized for enrichment analysis of the metabolomics data, employing a custom
background consisting of all measured metabolites.
WGCNA (Weighted Gene Co-expression Network Analysis):
To identify modules of metabolites that exhibited correlated patterns across
samples, we employed WGCNA. This analysis involved using the abundance data of
metabolites and allowed us to identify groups or modules of metabolites that displayed
similar expression profiles, providing insights into potential coordinated metabolic
pathways or regulatory mechanisms(Langfelder and Horvath 2008).
29
Comparison to Human Frontal Cortex:
Elevated metabolites in the Day 1 Tau;Aβ mutant strain were compared to
metabolites with increased abudance in the frontal cortex of AD subjects vs non-
demented control subjects(Paglia et al. 2016).This comparison was made using Deep
Venn and this cross-species analysis aimed to identify common molecular signatures and
potential similarities between the studied model organism and human AD.
Heatmap and Dot Plot Figures:
RStudio was utilized to generate heatmaps and dot plots to provide a
comprehensive visual overview of the observed molecular changes and enriched
pathways. Code and referenced files can be found at https://github.com/Holcom-AM/AD-
worm-omics .
Data Availability
Raw data and complete MS data sets have been uploaded to the Mass
Spectrometry Interactive Virtual Environment (MassIVE) repository, developed by the
Center for Computational Mass Spectrometry at the University of California San Diego,
and can be downloaded using the following link:
https://massive.ucsd.edu/ProteoSAFe/private-
dataset.jsp?task=e2c3bbad2f214e88bc70ea487d71a3ed (MassIVE ID number:
MSV000092031; ProteomeXchange ID: PXD042484. Enter the username and
password in the upper right corner of the page: Username: MSV000092031_reviewer;
Password: winter. Metabolomics data can be found at https://github.com/Holcom-
AM/AD-worm-omics .
30
Chapter 3: Neuronal expression of amyloid-β and Tau drives systemic changes in
a C. elegans model of Alzheimer’s Disease
Abstract
Alzheimer's disease (AD) and related dementias pose a significant global health
challenge, with aging being a major risk factor. This study utilized the model organism
Caenorhabditis elegans to investigate the combined effects of amyloid-β (Aβ) and Tau
proteins, hallmarks of AD, on multiple levels. The expression of Aβ and Tau led to
increased insoluble protein accumulation and alterations in protein-protein interactions
associated with germ cells, oxidative phosphorylation, and organism development.
Transcriptomic analysis revealed changes in genes related to reproduction, mitochondrial
function, and the aging process, with Aβ playing a prominent role. Metabolomic analysis
demonstrated synergistic effects on lipid and amino acid metabolism, particularly through
elevated aminoacyl tRNA biosynthesis. Furthermore, the combined expression of Aβ and
Tau resulted in impaired motor function, heightened mitochondrial activity, decreased
fertility, and reduced lifespan, highlighting the systemic consequences of their interaction.
These findings provide insights into early AD pathogenesis and underscore the need for
early-life biomarkers.
Background
Alzheimer's disease and related forms of dementia have emerged as the most
prevalent neurodegenerative disorders, affecting millions of individuals worldwide and
imposing a substantial burden on society(Rostagno 2022). Alzheimer's disease (AD),
characterized by its heritable or sporadic nature, primarily associates with aging as a
major risk factor. Pathologically, AD is marked by the presence of Aβ plaques,
31
neurofibrillary tangles consisting of hyperphosphorylated Tau protein, glial cell activation,
and compromised blood-brain barrier function.
AD exhibits notable gender disparities, with women accounting for nearly two-
thirds of AD cases and often experiencing more pronounced cognitive decline than men.
Genetic factors, such as the ApoE4 allele, significantly heighten the risk of AD in women
compared to men (Breijyeh and Karaman 2020). Moreover, the decline of ovarian
hormones during menopause has been investigated as a potential contributor to AD risk
in women. Estrogen, a pivotal ovarian hormone, plays a crucial role in regulating various
brain functions, including neurotransmission, neural development, cell survival, oxidative
stress protection, modulation of Aβ peptide levels, and attenuation of tau
hyperphosphorylation. The reduction in estrogen levels during menopause has been
linked to an increased vulnerability to AD in women (Breijyeh and Karaman 2020).
The interplay between Aβ and Tau in AD closely correlates with hypometabolism
in the posterior cingulate cortex, which precedes memory decline (Busche and Hyman
2020). Aβ promotes the oligomerization, hyperphosphorylation, and propagation of Tau
(Busche and Hyman 2020; Lee et al. 2022; Zhang et al. 2021). Mouse models, such as
APPxTau, exhibiting concurrent Aβ and Tau pathology, display early synaptic loss and a
synergistic impact on mitochondrial dysfunction and impaired mitophagy (Busche and
Hyman 2020). Patients diagnosed with both Aβ and Tau, as identified through
cerebrospinal fluid analysis and neuroimaging techniques like MRI/PET, undergo an
accelerated rate of cognitive decline compared to those with solely Aβ, solely Tau, or
neither (Pascoal et al. 2017).
32
Despite extensive research efforts, current disease-modifying treatments for AD,
such as α, β, and γ secretase inhibitors, immunotherapy, and Aβ aggregation inhibitors,
have not succeeded in halting disease progression (Vegh et al. 2019). This highlights the
imperative to explore novel therapeutic targets, such as the Aβ-Tau interaction in the
disease process or systemic changes involving metabolic dysfunction.
This study aimed to investigate the multi-omic consequences of simultaneous,
pan-neuronal expression of human Aβ and Tau in the model organism Caenorhabditis
elegans. Leveraging the brief 20-day lifespan of C. elegans, we sought to replicate key
aspects of the human disease and uncover potential synergistic outcomes at the multi-
omic level. Previous C. elegans studies have separately expressed human Aβ in body
muscles or neurons, yielding diverse phenotypes (X. Chen et al. 2015; Teschendorf and
Link 2009). Similarly, investigations of Tau expression in C. elegans have focused on
human Tau variants associated with frontotemporal dementia (FTD), a disorder related
to AD (Alexander, Marfil, and Li 2014). These models successfully recapitulated AD-like
phenotypes, encompassing insoluble Tau accumulation, age-related uncoordinated
movement, and neurodegeneration (Alexander, Marfil, and Li 2014).
To examine the concurrent interaction of Aβ and Tau, we crossed C. elegans
strains expressing pan-neuronal Aβ and pan-neuronal Tau. This unique model enabled
us to explore the impact of these AD-related proteins on overall protein insolubility,
transcriptome dynamics, and metabolomics during early-life stages. Furthermore, this
model provided insights into the effects on reproductive functions and the development
of chronic age-related pathologies during early life.
33
Results
Expression of Aβ and Tau enhances insoluble protein accumulation in young
animals.
To investigate the impact of human Aβ and 4R1N Tau containing the V337M
mutation on protein aggregation, we generated a C. elegans strain with simultaneous
pan-neuronal expression of both proteins (Fig. 1A). This was achieved by crossing the
existing pan-neuronal Aβ strain, CL2355, with the pan-neuronal Tau strain, CK10. This
newly generated strain was used to determine whether the expression of Aβ and Tau
leads to the aggregation of other proteins as we had previously observed in C. elegans
with aging, via induction by other stressors, e.g., iron(Reis-Rodrigues et al. 2012; Klang
et al. 2014; Mark et al. 2016). We collected insoluble proteins from Day 1 adult worms of
WT, Tau, Aβ, and Tau;Aβ strains, followed by mass spectrometry analysis using data-
independent acquisition(Gillet et al. 2012; Collins et al. 2017).
Our findings revealed that Aβ expression alone significantly increased the
abundance of insoluble proteins compared to WT worms (Supplementary Fig. 1B).
Moreover, when comparing the number of insoluble proteins to the WT strain, the Tau;Aβ
strain exhibited a greater increase (Fig. 1B).
A comparison of the insoluble proteins in Tau vs WT, Aβ vs WT, and Tau;Aβ vs
WT strains demonstrated an overlap among all three AD strains, with the Tau;Aβ
expressing strain showing the most substantial alterations (Fig. 1C). Enrichment analysis
of the proteins with increased insolubility in the Tau;Aβ strain highlighted the significant
involvement of lipid transport in GO biological processes and molecular functions,
including four vitellogenin proteins (Fig. 1C).
34
To gain insights into the functional interactions of the insoluble proteins in the AD
strains, we constructed a protein-protein interaction network. This network revealed
associations with germ cells and cell physiology, oxidative phosphorylation, and organism
development (Fig. 1D).
Notably, the expression of either Tau or Aβ led to increased insolubility of lipid
transport proteins associated with reproduction, due to the essential role of vitellogenin
proteins in providing yolk to eggs within hermaphrodite worms. Perturbation in lipid
transport is also observed in human AD, suggesting that even at an early stage of
adulthood, the Tau;Aβ expressing strain exhibits specific aspects relevant to AD (Kao et
al.2020).
35
36
Figure 1: Expression of Tau and Aβ increase insoluble proteins early in life.
(A) Diagram of the cross set up for creating the simultaneous expression model with Aβ and Tau
mated to create the simultaneous expression strain, which has both pharyngeal and intestinal
GFP and expresses pan-neuronal human Aβ and 4R Tau with FTDP-17 mutation V337M
expression. (B) Graph of the number of insoluble proteins that are significantly increased in the
AD strains compared to WT on day 1 of adulthood. q ≤ 0.05. (C) Venn diagram of significantly
increased insoluble proteins in the strains and a heatmap of the proteins in GO biological process
of lipid transport for the 4 strains. (D) Protein-protein interaction network of insoluble proteins
increased in the AD mutant strains compared to WT. 3 biological replicates, q ≤ 0.1.
Aβ expression drives transcriptomic changes in Tau;Aβ expressing strain in young
animals.
To further characterize the WT, Tau, Aβ, and Tau;Aβ expressing strains, we
performed 3' Tag RNAseq analysis on day 1 adult worm samples, utilizing the DNA
Technologies and Expression Analysis Cores at the University of California Davis
Genome Center. Our analysis revealed distinct transcriptomic profiles among the different
strains. The Tau expressing strain displayed a transcript profile similar to that of the WT
strain. In contrast, the Aβ expressing strain exhibited a significant number of both
upregulated and downregulated mRNAs. Notably, the Tau;Aβ expressing strain showed
a substantial increase in upregulated mRNAs compared to the WT strain (Fig. 2A).
When comparing the significant downregulated genes in the AD model strains to
the WT strain, we observed a high number of downregulated mRNAs in the Aβ expressing
strain, while the Tau strain exhibited a relatively low number. The Tau;Aβ expressing
strain shared more downregulated mRNAs with the Aβ expressing strain (Fig. 2B).
Interestingly, we identified a single gene (coa-3) that was commonly downregulated in all
three AD strains.
Comparing the significant number of mRNAs with increased abundance in all AD
strains to the WT strain, we found that the Aβ strain had over 700 upregulated mRNAs,
37
with more than 200 genes showing similar upregulation in the Tau;Aβ expressing strain
(Fig. 2C). In contrast, the Tau expressing strain had a minimal number of significantly
upregulated genes, and only one gene (Y68A4A.13) was shared among all three AD
strains (Fig. 2C). Tissue enrichment analysis of the Tau;Aβ expressing strain's
downregulated genes revealed associations with reproduction and germ line functions
(Fig. 2D). GO enrichment analysis of the downregulated genes in the Tau;Aβ expressing
strain, compared to the WT strain, showed enrichment for GO terms associated with
ribosomes, protein localization to mitochondria, the NADH dehydrogenase complex, and
ATP synthesis coupled electron transport (Fig. 2D).
Upregulated genes were found to be expressed in a range of neurons, including
interneurons (AVA, AVK), motor neurons (VA, DA neurons), as well as tissues related to
reproduction and specific to the male gonad (Fig. 2E). GO enrichment analysis of the
upregulated genes in the Tau;Aβ strain, compared to the WT strain, revealed enrichment
for GO terms associated with nucleotide binding, modification of peptidyl-serine, and
phosphorylation processes (Fig. 2E).
In addition to the alterations in upregulated and downregulated mRNAs associated
with the expression of Tau and Aβ, we observed changes in a set of genes defined by
Meyer and Schumacher, which are used to predict biological age (Meyer and
Schumacher 2021). These genes support the involvement of specific transcription factors,
innate immunity, and neuronal signaling in the regulation of the aging process. Using this
biological clock, we predicted that the AD strains exhibited an increased transcriptomic
age compared to the average WT transcriptomic age (Supplementary Figure 2).
38
Overall, our analysis of the day 1 adult transcriptome revealed increased
upregulation and downregulation of genes associated with reproduction, the nervous
system, and RNA biosynthesis in the Tau;Aβ strain. These findings suggest that the
expression of Aβ primarily drives the observed transcriptomic changes, which are closely
associated with alterations in the insoluble proteome. Both omics analyses demonstrated
systemic changes in the Tau;Aβ strain, particularly in relation to reproduction and
mitochondrial function.
39
Figure 2: Aβ expression drives transcriptomic changes in Tau;Aβ strain.
40
(A) Volcano plots of gene expression fold change in Tau compared to WT, Aβ compared to WT
and Tau;Aβ compared to WT on day 1 of adult hood. (B) Venn diagram of significant genes that
are downregulated and (C) upregulated in Tau, Aβ and Tau;Aβ compared to WT on day 1 of
adulthood. (D) Wormbase tissue enrichment and GO enrichment of the genes downregulated in
day 1 Tau;Aβ strain compared to WT. (E) Wormbase tissue enrichment and GO enrichment of
the genes upregulated in day 1 Tau;Aβ strain compared to WT.
Synergistic effects of Aβ and Tau expression on metabolites in young animals.
To gain deeper insights into the systemic consequences of expressing human
neurotoxic proteins in C. elegans, we conducted global metabolomic analysis using day
1 adult worms from the WT, Tau, Aβ, and Tau;Aβ expressing strains. Our results revealed
that the Tau and Aβ strains exhibited relatively minor differences in metabolite fold change
compared to the WT strain. However, the Tau;Aβ expressing strain displayed extensive
alterations in metabolite profiles compared to the WT strain (Fig. 3A), resembling the
trend observed in the Aβ expressing strain (Fig. 3B). Notably, the metabolites in the
Tau;Aβ strain contrasting abundance compared to the WT strain (Fig. 3C).
Further examination of the metabolites altered in the Tau;Aβ expressing strain
revealed that they primarily belonged to the categories of lipids and amino acids (Fig. 3D),
with involvement in pathways such as long chain polyunsaturated fatty acids and
metabolism of leucine, isoleucine, and valine (Fig. 3E). Interestingly, the KEGG pathway
analysis showed that the Tau;Aβ expressing strain had elevated levels of the amino acids
essential for protein formation (Fig. 3F). Specifically, a majority of these 20 amino acids,
precursors in aminoacyl tRNA biosynthesis, were increased in the Tau;Aβ expressing
strain, while they tended to be decreased in the individual Aβ and Tau expressing strains,
as well as the WT strain. Aminoacyl tRNAs play a crucial role in protein synthesis by
providing substrates for the ribosomal translation of messenger RNA (Ibba and Söll
2001). The modulation of this pathway in the Tau;Aβ expressing strain may be connected
41
to the observed increase in protein insolubility, as well as the decreased ribonucleotide
binding and structural constituent of ribosome observed in the transcriptomic analysis.
In summary, our metabolomic analysis highlighted the synergistic effects of Aβ and
Tau expression on the metabolic profiles in young animals. The Tau;Aβ expressing strain
exhibited substantial alterations in metabolites, particularly in lipid and amino acid
metabolism. Notably, the upregulation of aminoacyl tRNA biosynthesis pathway in the
Tau;Aβ expressing strain may provide a link between increased protein insolubility and
the transcriptomic changes observed, including decreased ribonucleotide binding and
structural constituent of ribosome.
42
Figure 3: Expression of Tau and Aβ synergistically alters metabolites.
43
(A) Bar graph of metabolite fold change in Tau, Aβ and Tau;Aβ compared to WT. (B) Pearson’s
correlation of metabolite fold change between Aβ vs WT and Tau vs WT, between Tau;Aβ vs WT
and Aβ vs WT, and between Tau;Aβ vs WT and Tau vs WT. (C) Heatmap of metabolites
significantly decreased and increased in Tau;Aβ compared to WT, Tau and Aβ. (D) Graph of the
super pathways of the Tau;Aβ vs WT metabolites. (E) Graph of the subpathways of the Tau;Aβ
vs WT metabolites. (F) Heatmap of the metabolites in the KEGG pathway of aminoacyl tRNA
biosynthesis for all 4 strains.
Systemic phenotypes observed with expression of Tau or Aβ.
With these multi-omic alterations observed in the Tau;Aβ strain, we asked if these
changes have significant phenotypic consequences. In the omic data set we saw changes
in proteins, genes and metabolites associated with the mitochondria. This result led us to
test mitochondrial function through measuring the worm’s ability to move and the rate of
oxygen consumption of the mitochondria. The movement patterns of the worms were
assessed by measuring the number of body bends in a given time. The Tau;Aβ strain
exhibited a significantly decreased thrashing rate compared to the WT, Tau, and Aβ
strains, indicating impaired motor function (Fig. 4A). Mitochondrial function was quanified
by measuring the rate of oxygen consumption using the Seahorse XFe96 Analyzer. The
Tau and Tau;Aβ strains displayed increased average oxygen consumption rates
compared to the WT and Aβ strains, suggesting heightened mitochondrial activity (Fig.
4B). These findings imply that the omic-level alterations observed in the Tau;Aβ strain
have functional implications, particularly in mitochondrial activity.
Considering the increased insolubility of vitellogenin protein and the involvement
of genes associated with reproduction, the impact on fertility and fecundity was
investigated. The number of eggs laid and the number of eggs that hatched were
measured over the first 5 days of adulthood. The Tau and Tau;Aβ strains exhibited a
decreased average total number of eggs laid, while the Aβ and Tau;Aβ strains showed a
significantly decreased average total progeny (Fig. 4C). Additionally, a lifespan assay was
44
performed, revealing that the Tau;Aβ expressing strain had a decreased median and
maximum lifespan compared to the other three strains (Fig. 4D). These results indicate
that the pan-neuronal expression of Aβ and Tau exerts systemic effects on the worms,
with Aβ affecting progeny production, Tau influencing mitochondrial oxygen consumption
rate, and the combined expression of both proteins leading to reduced body movement
and lifespan. These observations underscore the impact of Tau and Aβ interactions on
the phenotypic characteristics of the worms.
45
Figure 4: Systemic phenotypes observed with expression of Tau and Aβ.
46
(A) Individual L4 worms are shifted to 25°C on individual plates and the number of eggs laid from
day 1 to day 5 of the worm’s adult stage were counted. The average total number of eggs laid in
the Tau and Tau;Aβ strain trend towards being decreased compared to WT and Aβ. Statistical
analysis was conducted using ordinary one-way ANOVA with Tukey’s multiple comparisons test.
Error bars represent mean ± SEM. (B) Using the same plates after counting the numbers of eggs
laid, the number of progeny hatched were counted from day 1 to day 5 of the worm’s adult stage.
The total progeny in the AD strains were significantly decreased compared to WT. The Tau;Aβ
and Aβ strain were significantly decreased compared to Tau. Statistical analysis was conducted
using ordinary one-way ANOVA with Tukey’s multiple comparisons test. Error bars represent
mean ± SEM. (C) The percent of progeny viable was determined by the total number of progeny
hatched and total eggs laid. The Tau;Aβ and Aβ strain were significantly decreased compared to
WT and Tau. Ordinary one-way ANOVA with Tukey’s multiple comparisons test. Error bars
represent mean ± SEM. (D) L4 worms are shifted to 25°C and were placed in a droplet of S-Basal
for 30 seconds and then for another 30 seconds the body bends were counted. In day 1 adult
worms the Tau;Aβ strain had decreased body bends compared to WT, Tau and Aβ strains.
Statistical analysis was conducted using ordinary one-way ANOVA with Tukey’s multiple
comparisons test. Error bars represent mean ± SEM. (E) Average basal mitochondrial oxygen
consumption rate (OCR) of day 1 adult WT, Tau, Aβ and Tau;Aβ strains were measured in a 96
well plate with a Seahorse XFe96 Analyzer and normalized to the number of worms per well.
There was a significant increase in OCR for the Tau;Aβ strain when compared to WT, Tau and
Aβ strains. Statistical analysis was conducted using ordinary one-way ANOVA with Tukey’s
multiple comparisons test. Error bars represent mean ± SEM. (F) Average lifespan for WT, Tau,
Aβ and Tau;Aβ strains. The average max lifespan of the Tau;Aβ strain was decreased compared
to WT, Tau and Aβ strains. Statistical analysis was conducted using survival curve comparison
with log-rank (mantel-cox) test. Error bars represent mean ± SEM.
Discussion
The present study investigated the multi-omic consequences of pan-neuronal
expression of human Aβ and Tau proteins in the C. elegans model. By examining protein
insolubility, transcriptomic dynamics, metabolomics, and phenotypic changes, we gained
valuable insights into the interaction between Aβ and Tau and their impact on various
biological processes.
One of the key findings of this study was the enhanced insoluble protein
accumulation observed in worms expressing both Aβ and Tau compared to the individual
strains. This suggests a synergistic effect between these proteins, potentially contributing
to the pathogenesis of AD. The identification of lipid transport processes, particularly
47
vitellogenin proteins, as a significant component of the insoluble proteome in the Tau;Aβ
strain further supports the connection between lipid dysregulation and AD.
Transcriptomic analysis revealed distinct gene expression profiles in the Tau;Aβ
strain, with Aβ being the primary driver of the observed changes. The upregulation of
genes associated with reproduction, nervous system function, and RNA biosynthesis
suggests a complex interplay between Aβ and Tau and their effects on various
physiological processes. Notably, the downregulation of specific genes related to
reproduction and germ line functions suggests a potential link between Aβ and impaired
reproductive capacity.
Metabolomic analysis highlighted the significant impact of Aβ and Tau expression
on lipid and amino acid metabolism. The observed upregulation of aminoacyl tRNA
biosynthesis in the Tau;Aβ strain suggests a potential compensatory mechanism in
response to increased protein insolubility. This finding strengthens the link between
protein aggregation, altered ribosomal mRNAs, and the observed metabolic changes.
The phenotypic consequences of Aβ and Tau expression were also explored in
this study. The impaired motor function observed in the Tau;Aβ strain, as indicated by
decreased thrashing rates, suggests functional deficits in the nervous system and
mitochondria. Furthermore, increased mitochondrial activity in the Tau and Tau;Aβ strains
implicates mitochondrial dysfunction as a potential contributor to AD-related pathology.
Reproductive abnormalities were evident in the Tau;Aβ strain, characterized by
decreased egg production and progeny numbers. These findings suggest that the
combined expression of Aβ and Tau affects fertility and fecundity, likely through
48
disruptions in reproductive processes associated with lipid transport and vitellogenin
proteins.
Additionally, the Tau;Aβ strain exhibited a decreased median and maximum
lifespan compared to other strains, indicating a shortened lifespan associated with the
simultaneous expression of Aβ and Tau. This observation underscores the detrimental
effects of Aβ and Tau interactions on organismal health and lifespan.
Overall, this study provides a comprehensive understanding of the multi-omic
consequences and systemic effects of Aβ and Tau expression in the early life of C.
elegans. The findings contribute to our knowledge of AD pathogenesis, highlighting the
intricate relationships between protein aggregation, transcriptomic alterations, metabolic
dysregulation, and phenotypic changes. The identified pathways and processes may
serve as potential targets for the development of novel therapeutic interventions in AD
and related dementias. Further studies are warranted to elucidate the underlying
molecular mechanisms and validate these findings in more complex models and human
systems.
49
Supplemental Figures
Supplementary Figure 1: Increased insoluble proteome with Aβ expression.
(A) List of insoluble proteins that are increased in Tau vs WT, Aβ vs WT and Tau;Aβ vs WT,
significance was based on q ≤0.05. (B) Graph of the sum of total insoluble protein abundance
measured for each strain on Day 1 adulthood.
50
Supplementary Figure 2: Altered predicted biological age of AD strains.
(A) Based on transcriptomic clock from (Meyer and Schumacher 2021), the predicted biological
age of Tau, Aβ and Tau;Aβ are increased compared to WT.
Supplementary Figure 3: Overlap of more abundant metabolites in young Tau;Aβ and in
the frontal cortex of individuals with AD.
(A) Compared the elevated metabolites in the Day 1 Tau;Aβ vs WT to metabolites elevated in
frontal cortex of AD subjects vs non-demented control subjects (Paglia et al. 2016). The overlap
consists of AMP, GMP, S-adenosylmethioninamine, methionine, serine, and tryptophan.
51
Chapter 4: Insights into aging-related protein insolubility and metabolic changes
in a C. elegans model expressing Tau and amyloid-β proteins.
Abstract
The co-expression of Tau and Aβ proteins has been implicated in the pathogenesis
of AD. In this study, we investigated the consequences of Tau and Aβ expression on
protein insolubility and metabolism with age using a Caenorhabditis elegans model. We
found that the expression of Tau and/or Aβ led to increased insolubility of proteins, with
distinct patterns during aging. The insoluble proteome analysis revealed alterations in
ribonucleotide binding, aminoacyl tRNA ligase proteins, cytoskeleton structure, and
ribonucleoprotein granules in the Tau;Aβ strain. Metabolomic analysis demonstrated
significant changes in metabolites associated with glycolysis, gluconeogenesis, alpha-
linolenic acid metabolism, and fatty acid biosynthesis pathways in the Tau;Aβ strain.
Interestingly, the early life insoluble proteome and metabolic changes in the Tau;Aβ strain
resembled those observed in aged wildtype worms, indicating an accelerated aging
phenotype. These findings highlight the complex remodeling of protein insolubility and
metabolism induced by Tau and Aβ expression with age, providing insights into the
pathogenic mechanisms underlying AD. Understanding these molecular consequences
may pave the way for the development of therapeutic strategies targeting protein
aggregation and metabolic dysregulation in AD.
Background
Alzheimer's disease and Alzheimer's related diseases represent the most
prevalent forms of dementia, affecting millions of individuals worldwide, either directly or
indirectly (Rostagno 2022). AD, characterized by its heritable or sporadic nature, is
52
primarily associated with aging as a major risk factor. The pathological hallmarks of AD
include the formation of Aβ plaques, neurofibrillary tangles composed of
hyperphosphorylated Tau protein, glial cell activation, and impairment of the blood-brain
barrier.
Proteomic studies have revealed important protein alterations in AD. Common
changes in protein levels were found across different AD subtypes, including
phosphorylated Tau, Aβ, and nerve growth factor (Iturria-Medina et al. 2022). Each
subtype also exhibited distinct protein alterations related to specific biological processes.
In addition, an increase in detergent-insoluble proteins was observed in AD patients,
particularly in later stages, with alterations in glycolysis and proteins involved in the
ubiquitin proteasome pathway (Kepchia et al. 2020).
These proteomic studies highlight the extensive protein alterations and
dysregulation of specific biological processes in AD, suggesting their involvement in the
disease process. Moreover, they indicate that protein aggregation, alterations in energy
metabolism, and dysregulation of the ubiquitin proteasome pathway may play significant
roles in AD pathogenesis (Kepchia et al. 2020).
Alzheimer's disease is also associated with metabolic dysfunction, which
contributes to the pathogenesis of the disease. Impaired glucose metabolism,
dyslipidemia, insulin resistance, oxidative stress, and mitochondrial dysfunction are
among the key features of metabolic dysfunction in AD (Bloom and Norambuena 2018;
Kim et al. 2021; Z. Chen and Zhong 2013; Patro et al. 2021; Mantzavinos and Alexiou
2017; G. Wu et al. 2004; Poddar et al. 2021).
53
Impaired glucose uptake in the brain, independent of systemic diabetes, is a key
feature of AD and is linked to impaired insulin signaling, leading to the classification of AD
as a brain-specific type of diabetes, often referred to as "type 3 diabetes"(Bloom and
Norambuena 2018). Studies have consistently shown reduced cerebral glucose
metabolism in AD, which correlates with cognitive decline (Z. Chen and Zhong 2013).
Metabolic syndrome, characterized by conditions like high triglycerides, high glucose,
obesity, low high-density lipoprotein cholesterol, and hypertension, is associated with an
increased risk of AD (Kim et al.). The chronic inflammation, hyperinsulinemia,
dyslipidemia, vascular injury, and oxidative stress seen in metabolic syndrome contribute
to AD pathogenesis (Kim et al. 2021).
In conclusion, proteomic studies have revealed extensive protein alterations and
dysregulation of specific biological processes in AD, suggesting their involvement in the
disease process. Moreover, metabolic dysfunction, including impaired glucose
metabolism, dyslipidemia, insulin resistance, oxidative stress, and mitochondrial
dysfunction, plays a crucial role in the pathogenesis of AD. Understanding the intricate
interplay between protein changes and metabolic dysregulation in AD is essential for the
development of targeted therapeutic interventions aimed at mitigating the progression of
the disease.
In this study, our aim was to investigate the insoluble proteomic and metabolomic
consequences of pan-neuronal, simultaneous expression of human Aβ and Tau in the
model organism Caenorhabditis elegans. By leveraging the short lifespan of C. elegans,
we created a model that mimics the human disease and uncovers potential synergistic
outcomes at the multi-omic level. In the previous chapter, we demonstrated early-life
54
changes in the insoluble proteomic, transcriptomic and metabolomic profiles, along with
systemic phenotypic changes. Therefore, we sought to delve further into the changes that
occur with age, considering that age is the primary risk factor for AD. This unique model
allowed us to explore the impact of AD-related proteins on whole-organism protein
insolubility and metabolomics throughout the aging process. Additionally, this model
provided valuable insights into the intricate relationship between normal aging and the
development of chronic age-related pathologies.
Results
Increased protein insolubility induced by Tau and Aβ expression in middle-aged
animals.
Building upon our observations of increased protein insolubility in the Tau;Aβ
expressing strain in young animals, we sought to investigate this molecular phenotype
during the aging process. We collected insoluble proteins from Day 3, 5, and 8 adult
worms of the WT, Tau, Aβ, and Tau;Aβ expressing strains, followed by mass
spectrometry analysis. Our findings revealed that the total abundance of insoluble
proteins in the WT strain increased with age, consistent with previous reports (Xie et al.
2020; Reis-Rodrigues et al. 2012; Huang et al. 2019). In contrast, the Aβ and Tau;Aβ
strains exhibited lower total insoluble protein abundance on day 3 and 5, but displayed
an increase on day 8 (Supplementary Fig. 4A). The Tau expressing strain consistently
exhibited lower insoluble protein abundance across all ages.
Comparing the insoluble proteins significantly elevated on day 3, 5, and 8 in the
WT, Tau, Aβ, and Tau;Aβ strains to Day 1, we observed overlaps on day 3, 5 and 8 with
day 1 for all four strains (Fig. 5A). On day 5, the Tau strain showed an increase in insoluble
55
proteins compared to day 1. On day 8, the Aβ and Tau;Aβ strains exhibited an increase
in insoluble proteins compared to day 1. Enrichment analysis revealed an enrichment of
ribonucleotide binding and aminoacyl tRNA ligase proteins in the insoluble proteome of
the Tau;Aβ expressing strain on day 8 compared to day 1 (Fig. 5B). Similarly, enrichment
analysis of the Tau strain on day 5 compared to day 1 highlighted an increase in proteins
associated with ribonucleotide binding (Fig. 5C). Enrichment analysis of the Aβ strain on
day 8 compared to day 1 revealed an increase in proteins associated with cytoskeleton
structure and ribonucleoprotein granules (Fig. 5D). Furthermore, enrichment analysis of
the WT strain on day 8 compared to day 1 showed an increase in proteins involved in ion
and small molecule binding (Fig. 5E).
Supplementary Figure 4B demonstrates that the insoluble proteome of the Tau;Aβ
strain was increased compared to the WT strain on day 3, 5, and 8, in comparison to Tau
or Aβ alone. Enrichment analysis of the insoluble proteome on day 3 and day 5 of the
Tau;Aβ strain compared to WT highlighted increased insolubility of ribosomal proteins
(Supplementary Fig. 4C). When comparing day 3, 5, and 8 of the Tau;Aβ strain to day 1,
the insoluble proteins observed on day 3 and 5 were also present on day 8
(Supplementary Fig. 4D). There was an overlap of insoluble proteins on day 1, 3, 5, and
8 of the Tau;Aβ strain compared to WT, with each day also featuring proteins not
observed on the other days (Supplementary Fig. 4E).
Overall, the aging insoluble proteome analysis revealed that the Tau;Aβ
expressing strain exhibited an early increase in insoluble proteins on day 1 (Fig. 1), which
decreased on day 5 and then increased again on day 8. The aging insoluble proteome of
the WT, Tau, and Tau;Aβ strains consisted of proteins involved in binding a range of small
56
molecules. In summary, the expression of Tau and/or Aβ leads to a complex remodeling
of protein insolubility throughout the proteome. The non-linear aging response observed
may reflect compensatory mechanisms aimed at preventing or removing insoluble
proteins.
57
Figure 5: Expression of Tau and Aβ increases day 8 insoluble proteome.
58
(A) Venn diagram of significantly increased insoluble proteins in the strains on day 3 compared
to day 1, day 5 compared to day 1, and day 8 compared to day 1 for all 4 strains. (B) GO molecular
function of proteins elevated in day 8 vs day 1 in Tau;Aβ. (C) GO molecular function of proteins
elevated in day 5 vs day 1 Tau. (D) Wormbase Gene ontology enrichment of proteins elevated in
day 8 vs day 1 in Aβ. (E) GO molecular function of proteins elevated in day 8 vs day 1 in WT.
Expression of Tau and Aβ disrupts metabolism throughout life.
To gain insights into the metabolic changes associated with aging in the WT, Tau,
Aβ, and Tau;Aβ strains, we performed global metabolomic analysis. We observed that
significant changes in metabolites were primarily evident on day 8 for the WT and Tau
expressing strains (Fig. 6A). The Aβ expressing strain exhibited a slight increase in
significantly altered metabolites on day 5, which became more prominent on day 8. In
contrast, the Tau;Aβ expressing strain showed the most significant changes on all three
days, indicating a potential additive or synergistic relationship resulting from the
simultaneous expression of both proteins in neurons.
Examining the decreased and increased metabolites on day 8 compared to day 1
for each strain, we identified an overlap in metabolites altered on day 8 across all four
strains. However, the Tau;Aβ strain had a greater number of unique decreased
metabolites that did not overlap with the other strains (Supplementary Fig. 5A&B).
Furthermore, when comparing the AD strains to the WT on day 3, 5, and 8, the Tau;Aβ
strain exhibited the most significant alterations in metabolites on day 5 (Supplementary
Fig. 5C).
We performed a weighted correlation network analysis (WGCNA) to cluster highly
correlated metabolites into different modules. One of the modules, labeled "salmon,"
displayed relatively consistent levels of these clustered metabolites with age in the WT
and Tau expressing strains (Fig. 6B). The Aβ strain showed a slight increase on day 8,
59
while the Tau;Aβ strain exhibited higher levels on day 1 that decreased with age, reaching
WT levels on day 8. Metabolite set enrichment analysis of this module revealed that it
primarily consisted of metabolites involved in glycolysis and gluconeogenesis pathways
(Fig. 6C). Examining the levels of metabolites within the module, we observed mostly
downregulation from day 1 to 8 in the WT and Tau strains, and from day 1 to 5 in the Aβ
strain. However, in the Tau;Aβ strain, the metabolites were initially elevated on day 1 to
day 5, followed by a decrease on day 8, reaching levels similar to those in the WT and
Tau strains.
In addition to the "salmon" WGCNA module, we also identified the "purple" module,
which represents a cluster of metabolites that were elevated on day 1 but decreased with
age in the Tau;Aβ strain, in contrast to WT where the metabolites increased with age
(Supplementary Fig.5D&E). These metabolites are associated with alpha-linolenic acid
metabolism and fatty acid biosynthesis pathways (Supplementary Fig.5F).
The modulated pathways in the Tau;Aβ strain, including energy, fat, and
mitochondrial function, are consistent with the alterations observed in genes, proteins,
and metabolites associated with the mitochondria and lipids in the day 1 transcriptomics,
insoluble proteome, and metabolomic analyses. These findings suggest that the
expression of Tau and Aβ leads to disruptions in energy metabolism, fat metabolism, and
mitochondrial function, which may contribute to the observed changes in the proteome
and metabolome associated with the Tau;Aβ strain.
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Figure 6: Expression of Tau and Aβ enhances metabolic changes with age.
61
(A) Graph of number of metabolites significantly changing in day 3, day 5 and day 8 vs day 1 in
all 4 strains. (B) WGCNA plot of salmon module with all 4 strains from day 1 to day 8. (C)
Metabolite set enrichment analysis of metabolites in salmon module. (D) Heatmap of metabolites
in salmon module for all strains on day 1, 3, 5 and 8.
Tau and Aβ expression in young adult worms resemble WT aging at insoluble
proteome and metabolic level.
Comparing the insoluble proteome and metabolomic changes in the day 1 Tau;Aβ
expressing strain to the aged WT strain revealed similarities between the two conditions.
The proteins that were more abundant in the insoluble protome of day 8 WT vs day 1 WT
overlapped with the majority of insoluble proteins in day 1 Tau;Aβ vs WT. These
overlapping proteins were enriched for lipid transport and localization processes (Fig. 7A).
Similarly, some of the metabolites that were altered in day 8 WT vs day 1 WT also
overlapped with metabolites altered in day 1 Tau;Aβ vs WT (Fig. 7B). In the Tau;Aβ strain,
these metabolites were already elevated on day 1, resembling the levels observed in day
8 WT.
Using WGCNA, we identified a cluster of metabolites that exhibited an age-
dependent increase in WT but were already elevated in the Tau;Aβ expressing strain on
day 1 (Fig. 7C). These metabolites are associated with the plasmalogen synthesis
pathway, which showed increased abundance in day 8 WT and day 1-5 Tau;Aβ.
Plasmalogens are important for the structure of lipoproteins, synaptic membranes, and
ion transport.
These findings suggest that the expression of Tau and Aβ in young adult worms
resembles the aging-associated changes observed in the insoluble proteome and
metabolic pathways of WT worms. The simultaneous expression of Tau and Aβ in young
adult worms appears to accelerate and enhance aging processes, particularly affecting
62
lipid pathways. The observed similarities between the insoluble proteome and metabolic
changes in the Tau;Aβ strain on day 1 and the aged WT strain suggest that the presence
of Tau and Aβ proteins leads to an accelerated aging phenotype, resembling the changes
typically seen in older organisms. These alterations in lipid pathways, including lipid
transport, localization, and plasmalogen synthesis, may contribute to the dysregulation of
cellular processes and potentially exacerbate the detrimental effects associated with
aging.
63
64
Figure 7: Tau and Aβ expression in young adult worms resembles WT aging at insoluble
proteome and metabolomic levels.
(A) Venn diagram of increased insoluble proteins in day 1 Tau;Aβ vs day 1 WT compared to day
8 WT vs day 1 WT and GO biological process of the insoluble proteins shared between the day
8 WT and day 1 Tau;Aβ. (B) Venn diagram of the metabolites in day 1 Tau;Aβ vs day 1 WT
compared to day 8 WT vs day 1 WT and a heatmap of the shared metabolites. (C) WGCNA plot
of midnight blue module with all 4 strains from day 1 to day 8 and heatmap of the metabolites in
the midnight blue module for all strains on day 1, 3, 5 and 8.
Discussion
The present study aimed to investigate the impact of Tau and Aβ expression on
protein insolubility and metabolism in an aged C. elegans AD model. Our findings reveal
novel insights into the complex remodeling of these molecular phenotypes and shed light
on the potential mechanisms underlying the pathogenesis of AD.
One of the key observations in our study was the distinct patterns of insoluble
protein abundance during aging, suggesting dynamic changes in protein aggregation and
solubility over time. Interestingly, the Tau;Aβ strain exhibited an early increase in insoluble
proteins on day 1, followed by a decrease on day 5 and a subsequent increase on day 8.
This non-linear aging response may reflect compensatory mechanisms aimed at
preventing or removing insoluble proteins. The consistent lower insoluble protein
abundance in the Tau strain across all ages suggests a specific role of Tau in modulating
protein solubility.
Furthermore, our analysis of the insoluble proteome identified specific proteins
enriched in the day 8 Tau;Aβ strain compared to day 1. Enrichment analysis revealed an
enrichment of ribonucleotide binding and aminoacyl tRNA ligase proteins, suggesting
potential dysregulation of RNA metabolism and protein synthesis in the Tau;Aβ strain.
Similarly, the Tau strain exhibited an increase in ribonucleotide binding proteins, while the
Aβ strain showed an enrichment of proteins associated with cytoskeleton structure and
65
ribonucleoprotein granules. These findings highlight the diverse molecular pathways
affected by Tau and Aβ expression, further emphasizing their role in disrupting cellular
processes.
Metabolomic analysis provided additional insights into the metabolic changes
associated with Tau and Aβ expression. We observed significant alterations in
metabolites primarily on day 8, with the Tau;Aβ strain showing the most pronounced
changes across all three time points. This suggests a potential additive or synergistic
effect resulting from the simultaneous expression of Tau and Aβ in neurons. The elevated
metabolite pathways in the Tau;Aβ strain, including glycolysis, gluconeogenesis, energy,
fat, and mitochondrial function, are consistent with the alterations observed in our
previous study and to changes observed in human AD. These findings suggest that the
expression of Tau and Aβ disrupts metabolism throughout life, leading to dysregulation
of energy and fat metabolism as well as mitochondrial dysfunction.
Importantly, our study also revealed intriguing parallels between the early-life
insoluble proteomic and metabolic changes in the Tau;Aβ strain and the aged WT strain.
The insoluble proteins in the aged WT strain overlapped with the majority of the insoluble
proteins in the day 1 Tau;Aβ strain, indicating a resemblance between the aging-
associated changes and the early effects of Tau and Aβ expression. Similarly, metabolites
altered in the Tau;Aβ strain on day 1 resembled those altered in the aged WT strain.
These findings suggest that the expression of Tau and Aβ in young adult worms
accelerates and enhances aging processes, particularly affecting lipid pathways. The
dysregulation of lipid transport, localization, and plasmalogen synthesis may contribute
66
to the observed changes in cellular processes and potentially exacerbate the detrimental
effects associated with aging.
Overall, our study provides valuable insights into the molecular consequences of
Tau and Aβ expression on protein insolubility and metabolism. The dynamic changes in
insolubility and the dysregulation of metabolic pathways highlight the intricate interplay
between Tau, Aβ, and cellular processes in AD pathogenesis. Understanding these
mechanisms may open new avenues for therapeutic interventions targeting protein
aggregation and metabolic dysfunction in AD. Future studies could further elucidate the
specific molecular pathways involved and explore potential interventions to mitigate the
detrimental effects induced by Tau and Aβ.
67
Supplemental Figures
68
Supplementary Figure 4: Alterations in aged insoluble proteome with enrichment for
ribosomal proteins in Tau;Aβ strain.
(A) Graph of the sum of total insoluble protein abundance measured for each strain on day 3, 5,
8 adulthood. (B) Venn diagrams of increased insoluble proteins on day 3, 5 and 8 of adulthood in
Tau, Aβ, and Tau;Aβ compared to WT, significance was based on q ≤0.05. (C) Table of g:Profiler
enrichment of day 3 Tau;Aβ vs WT and day 5 Tau;Aβ vs WT. (D) Venn diagram of increased
insoluble proteins on day 3, 5 and 8 of adulthood in Tau;Aβ compared to day 1 Tau;Aβ,
significance was based on q ≤0.05. (E) Venn diagram of increased insoluble proteins on day 1,
3, 5 and 8 of adulthood in Tau;Aβ compared to WT, significance was based on q ≤0.05.
69
Supplementary Figure 5: Enhanced age-related metabolomic changes in Tau;Aβ, with a
focus on alpha linolenic acid and fatty acid biosynthesis.
70
(A) Venn diagram of decreased metabolites on day 8 of adulthood in Tau, Aβ, and Tau;Aβ
compared to day 1, significance was based on q ≤0.05. (B) Venn diagram of metabolites with
increased abundance on day 8 of adulthood in Tau, Aβ, and Tau;Aβ compared to day 1,
significance was based on q ≤0.05. (C) Table of number of significantly up and down regulated
metabolites on day 3, 5 and 8 of adulthood in Tau, Aβ, and Tau;Aβ compared to WT,
significance was based on q ≤0.05. (D) WGCNA plot of “purple” module with all 4 strains from
day 1 to day 8. (E) Heatmap of metabolites in “purple” module for all strains on day 1, 3, 5 and
8. (F) Metabolite set enrichment analysis of metabolites in “purple” module.
71
Chapter 5: Conclusion
In this study, we developed a C. elegans model of Alzheimer's disease
proteotoxicity by expressing human Aβ and human 4R1N Tau with a V337 mutation in
neurons. The aim was to investigate the combined effect of Aβ and Tau expression on
disease progression. We performed multi-omic analysis and validated the results
phenotypically.
Insoluble proteome analysis: lipid transport and protein changes
The insoluble proteome analysis revealed an increase in insoluble proteins
associated with lipid transport in the Tau;Aβ strain compared to the WT strain.
Furthermore, these vitellogenin lipid transport proteins are associated with reproduction.
As the Tau;Aβ strain ages, they also exhibit an increase in insoluble proteins associated
with nucleotide and small molecule binding.
Transcriptomic analysis: mRNA abundance and associations
Examining mRNA abundance, significant changes in reproductive-related mRNAs
were observed in the Tau;Aβ strain on day 1, consistent with the findings from the
insoluble proteome analysis. Changes in mRNAs associated with mitochondria were also
detected, correlating with the findings in the insoluble proteome and metabolic profiles of
the aged Tau;Aβ strain. Similarities were found with the transcriptomic data published by
Wang et al., including shared upregulated mRNAs and genes associated with protein
phosphorylation and UDP-Glucuronosyl Transferase (C. Wang et al., 2018). However,
differences were observed in downregulated genes, including those involved in ribosome
structure, peptide biosynthesis, protein localization to the mitochondrion, and NADH
dehydrogenase complex.
72
Metabolomic analysis: altered metabolite levels and energy pathways
Metabolomic analysis showed increased changes in metabolite levels linked to
aminoacyl tRNA biosynthesis in the Tau;Aβ strain on day 1. As the worms aged, there
was a further increase in metabolite alterations associated with energy pathways in the
Tau;Aβ strain. The early changes in aminoacyl tRNA biosynthesis may be linked with the
increased aggregation of ribonucleotide-binding proteins.
Phenotypic alterations: fertility, mitochondrial function, and movement
The observed omic changes were reflected in the phenotypic alterations, including
decreased fertility and altered mitochondrial function in the Tau;Aβ strain. Decreased
progeny viability was also reported in the double mutant strain studied by Wang et al.
However, differences in temperature and measurement methods may explain the
discrepancies between the two studies. The decrease in body movement observed in the
Tau;Aβ strain aligns with the findings of another double mutant strain expressing Aβ and
4R1N Tau without the V337M mutation (Benbow et al., 2020).
Utility of the Tau;Aβ model and potential therapeutic targets
The Tau;Aβ strain, along with findings from previous studies by Wang et al. and
Benbow et al., collectively demonstrates that the expression of both Aβ and Tau leads to
neuronal and systemic phenotypes. This integrated model enhances our understanding
of the individual contributions of Tau and Aβ while shedding light on their combined
effects, providing a comprehensive view of their impact on AD progression. Furthermore,
it enables the examination of early systemic changes in AD and the identification of
potential therapeutic targets.
73
Importantly, the Tau;Aβ strain contributes novel insights into AD by revealing changes at
the metabolite and protein levels, complementing the transcriptomic knowledge provided
by Wang et al. The observed overlap between the metabolomic data of the Tau;Aβ strain
and those from the frontal cortex of humans with AD confirms that this model accurately
replicates the omic changes seen in the disease. This strengthens its relevance and utility
as a tool for studying AD pathogenesis.
Significance, limitations, and future directions
Our study highlights the significance of investigating the systemic effects of
neurodegenerative diseases, as evidenced by our phenotypic data. Furthermore, it
emphasizes the importance of studying the individual effects of the toxic proteins
associated with AD, as well as their interactions. Through our comprehensive analyses,
we have elucidated early systemic changes, including distinct phenotypes in AD worm
models driven by Aβ, Tau, or the combined toxic proteins. We have also raised the
question of whether fecundity/fertility is an understudied aspect in AD models.
Additionally, our project has introduced reference multi-omic datasets capturing early-life
AD changes as well as aged data. These datasets provide valuable resources for
identifying changes associated with Aβ, Tau, or the interaction between the two toxic
proteins. They can aid in determining potential target pathways influenced by Aβ or Tau
and unveil additional pathways exclusively affected by their interaction. Furthermore,
these datasets can facilitate the identification of potential genes or metabolites as
avenues for treatment targets.
However, our study does have some limitations. The analysis of the insoluble
proteome solely represents a subset of the total proteome, lacking information on the
74
soluble proteome's protein levels. The transcriptomic data only captures changes at day
1 and does not account for aging-related changes. Moreover, there is inherent variability
observed among the three biological replicates, underscoring the need for additional
replicates to strengthen our findings. Additionally, the phenotypic characterization could
be more comprehensive, incorporating diverse measurements and exploring neuronal-
specific phenotypes. Furthermore, it is unclear if there is inconsistency in the expression
of AD proteins among biological replicates or within each replicate, potentially influencing
the observed variability in the data.
Future avenues of study should focus on unraveling the underlying mechanisms
driving these phenotypes. One approach could involve supplementing the strains with
metabolites that are decreased in AD, such as glutamate and lyxonate. Additionally,
feeding the strains with RNAi bacteria targeting upregulated genes in the Tau;Aβ strain,
like btb-12, C06C3.7, or Y68A4A.13, could provide insights into the germ line, muscle, or
other associated pathways. By examining the impact of metabolite supplementation or
RNAi knockdown on fertility/fecundity and thrashing phenotypes in the Tau;Aβ strain, or
even in the individual Tau or Aβ strains, we can assess their potential for restoring
phenotypes to wild-type levels. Alternatively, feeding the WT strain with metabolites that
are elevated in the Tau;Aβ strain or utilizing RNAi bacteria to downregulate genes found
to be decreased in the Tau;Aβ strain would allow us to confirm if the WT strain exhibits
phenotypes similar to those observed in the Tau;Aβ strain. An additional route of study
would be to target specific pathways found to be altered in the Tau;Aβ strain, such as
tryptophan metabolism or glycolysis. One way would be to conduct RNAi knockdown of
genes encoding enzymes involved in a metabolic pathway. For example, knocking down
75
the gene haao-1 will affect the kynurenine pathway, and upstream of kynurenine is
serotonin which is involved in locomotion and egg laying. This would allow us to determine
if the alteration in the kynurenine pathway is linked to the locomotion and fecundity
phenotypes that were observed in the Tau;Aβ strain.
In conclusion, our study sheds light on the importance of investigating the systemic
effects of neurodegenerative diseases, highlighting distinct phenotypes in AD worm
models driven by Aβ, Tau, or their combined expression. The introduction of reference
multi-omic datasets capturing early-life AD changes and aged data provides valuable
resources for identifying potential treatment targets and unraveling the underlying
mechanisms driving these phenotypes. Although our study has limitations, such as
incomplete proteome representation and variability among biological replicates, future
avenues of research can explore metabolite supplementation, RNAi knockdown, and
targeted pathway investigations to further elucidate the intricate connections between
toxic proteins, phenotypes, and potential therapeutic interventions.
76
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83
Appendices
Appendix A: Upregulated Day 1 Transcriptomic Data, padj≤0.05
Day 1 Tau vs WT Day 1 Aβ vs WT Day 1 Tau;Aβ vs WT
M04C3.1 erfa-1 T24B1.1
F38E9.1 coq-2 Y20F4.4
lido-12 E01G4.3 immt-1
C32F10.4 hpo-12 K07B1.7
Y22D7AL.15 K07B1.7 bpl-1
F43C11.7 T27A3.7 T27A3.7
T26H5.9 C15F1.8 Y54G2A.21
F53A9.8 M04C3.1 M02B7.2
Y71F9AR.2 hpo-10 tos-1
Y68A4A.13 F42G4.7 F53H2.3
scl-2 C04E7.3 lsy-2
mtl-1 trak-1 smg-1
T26H5.8 nmrk-1 pqn-21
H28G03.2 lin-59
vps-41 parp-1
F41C3.11 pvf-1
K02A6.3 vps-41
AC8.1 C36A4.12
atgp-1 W09D6.1
T05A12.4 E02D9.3
DH11.2 F47B7.2
smg-1 R08E3.3
T24B1.1 daf-19
dpy-23 ubh-1
chil-13 C15F1.8
farl-11 Y55F3BR.11
F23B12.4 Y48B6A.16
Y48G1BL.8 Y116F11B.10
snx-1 T05A12.4
C36A4.12 F22D6.9
spat-3 wht-6
lsy-2 mib-1
F22F4.4 H28G03.2
cpx-1 rpl-25.1
asb-2 pqn-65
F35G12.5 C32E12.4
ccg-1 F35F11.2
Y43C5A.3 T23G7.2
84
ife-5 C50D2.10
let-23 mtm-5
W01B11.6 ife-5
rpl-25.1 K04C2.8
tig-2 hst-6
D1054.1 AC8.1
nab-1 ugt-31
Y48B6A.16 Y57A10A.3
F25E5.8 C35D10.8
F35A5.8 Y34B4A.2
T19D12.6 R12C12.8
F39C12.1 B0035.18
Y37H9A.5 R06C7.2
C02F5.10 F35A5.8
Y32B12C.1 rga-8
arc-1 R07H5.11
hpo-28 npr-21
T19D12.2 F46F11.7
M04F3.2 B0511.2
unc-36 T06A10.2
K11D12.7 Y71F9AL.4
R12C12.8 E01G4.5
clec-146 spe-15
Y71F9AL.4 W01A8.2
rpl-11.2 let-858
ZC581.3 C30F12.3
ddo-2 Y37E11AL.2
ugt-26 C08F11.13
K07A1.13 col-111
C34F11.8 grsp-1
C35D10.8 elf-1
daf-19 W03D2.6
Y34B4A.2 Y48G1A.7
pgp-5 lst-2
F10G2.1 Y37H9A.5
ubh-1 C55C3.6
F53C11.9 T20F5.5
C32E12.4 zip-11
ZK856.7 T16H12.1
ttr-5 Y102A5C.2
Y80D3A.11 Y53H1B.2
T28H10.2 Y18H1A.11
F23C8.8 R10F2.6
85
glna-3 Y81G3A.1
C50D2.10 M04F3.2
F36F2.2 M03C11.1
F14F9.5 Y80D3A.11
egl-19 spat-3
ttr-59 R05D3.3
R07C12.2 cnnm-4
ZK1193.2 T28A8.8
F35E12.9 ttn-1
T23G7.2 ZK1193.2
pvf-1 F35H8.4
best-15 fbxc-12
Y73B3A.3 fbxc-11
T08D2.2 C17H12.12
unc-105 frpr-13
R07H5.11 K09F6.3
heh-1 fbxc-10
fbxa-156 fbxc-9
lmp-2 cyn-2
twk-12 fbxc-8
hst-6 F39C12.1
C49C8.5 W04A4.2
Y45F10D.6 C15C6.2
R11H6.4 elo-8
nlp-33 F25F2.1
T05A8.8 F53G12.8
sdz-27 scbp-2
F56C4.4 C31C9.9
B0035.18 C09G9.8
C24F3.2 C33E10.1
cest-35.1 F35E2.5
T08B2.4 ZK354.3
col-71 cpb-2
F53A9.1 W03D8.2
rga-8 C01G12.12
F22D6.9 T16G1.5
Y42H9AR.2 K09H9.7
clp-6 K05C4.4
let-858 fbxa-217
R10E4.3 F42G4.6
F55G1.15 K09H9.9
Y55F3BR.11 ZK418.13
M03C11.1 T08B2.4
86
faah-1 F14D2.5
C30F12.3 ZC15.3
C14C11.1 T08D2.5
ZC412.10 F48F5.2
Y70G10A.3 btb-16
T16G12.3 F21H7.2
R10F2.6 T04A8.13
Y76B12C.4 W07G1.25
lst-2 C43D7.11
Y18H1A.11 ins-37
C04E12.2 C01F6.2
mib-1 spe-6
try-10 Y54F10BM.3
nlp-17 ZK550.5
glb-1 C04G2.14
ZK384.7 F59C6.10
B0511.2 unc-54
F47G3.1 Y43B11AL.5
fbxa-55 C15F1.5
F43D2.7 F20H11.4
Y43C5A.2 F47B3.5
R06C7.2 Y50D4A.5
K08D8.7 C27D8.2
M02D8.7 set-28
kin-14 nspa-7
flp-20 clec-12
W04E12.7 T16G12.3
cup-16 clec-47
F48F5.2 nspa-6
atg-16.1 C06C3.7
dur-1 Y106G6H.10
hpo-15 avr-14
F08B12.4 wht-5
ift-74 F23B2.7
ZK930.2 Y37H2A.18
W03D8.2 D2045.5
Y22D7AL.11 fipr-24
nlp-27 F53B2.8
pbo-5 F47B3.4
C06A1.3 C15A11.2
tsp-8 ZK512.10
Y34B4A.10 Y37E11B.10
ZK418.13 Y51H7C.8
87
K02F6.1 ZK938.1
math-4 F09C12.8
acs-6 Y110A7A.7
Y38H6C.23 C08F11.7
nhr-101 ZC15.4
cnc-4 W09D6.4
frpr-13 Y51H4A.13
fbxa-106 Y75B12B.3
zipt-17 nlp-18
F26E4.5 F22D6.8
R05D3.3 C03E10.3
C03C10.2 F36A2.14
nlp-30 Y39G8C.2
Y57A10A.3 C32E12.1
scbp-2 C02F5.5
Y48G1A.7 Y37H9A.4
D1086.18 fbxa-124
mlt-9 C08F8.6
drd-5 lips-15
F46C5.2 F45C12.17
Y51H4A.25 F58D2.2
nlp-12 F58D5.7
R11E3.1 B0244.9
Y75B8A.23 ZK354.2
clec-222 K01D12.3
Y102A5C.2 F47B3.7
Y53H1B.2 Y106G6G.1
T01H3.5 Y57G11C.5
F14D2.5 cylc-2
T26H5.14 C16A11.7
Y105E8A.27 F47B3.2
B0511.11 D1022.17
F42G4.2 AC8.2
msra-1 ZK418.2
Y54F10BM.3 K04H4.5
far-3 moa-1
spe-46 C55C3.3
F57B9.8 F46A9.1
F46F11.7 Y106G6G.4
Y42A5A.3 C46G7.5
K08F8.5 T02H6.7
T26H5.9 F41G4.7
R13D11.4 R105.1
88
C05D9.9 B0041.1
T08D2.5 C39H7.1
K08B4.7 T13F2.9
T14A8.2 msp-78
Y4C6A.4 Y71G12B.27
C09D4.3 Y38F2AL.12
T26H5.10 Y57G11B.8
spe-15 exc-13
F13D12.9 F52C6.4
B0393.9 Y22D7AR.12
C01G12.12 fbxa-218
T20F5.5 K06A5.2
F35F11.2 Y24D9B.1
C08G9.1 H27M09.5
R12B2.3 BE0003N10.6
F09B9.4 C04G2.12
F26G1.5 mpst-6
grsp-1 C43E11.5
ugt-31 F56D6.19
grl-7 T04B2.7
spe-6 C35E7.9
T16G1.5 K04G2.4
F35H10.2 C34F11.5
clec-12 M04C9.2
Y71A12B.3 gsp-4
F25H5.7 K09C6.2
Y53F4B.19 F17C8.7
nep-4 C50F2.5
ttr-10 msrp-2
C46G7.5 nmur-3
wht-6 fbxa-122
W05F2.7 btb-12
Y110A7A.7 T05D4.5
Y37H9A.4 Y23H5B.2
W01A8.2 glit-1
M88.3 irld-53
catp-3 F26A1.4
C28A5.6 R10E9.2
F22B5.5 C44B12.3
B0361.11 ubxn-5
fbxa-124 ZC395.4
ttbk-3 Y54G2A.15
nas-30 F32H2.7
89
Y50D4A.5 F28A10.12
csp-2 C10G11.8
C18H2.5 F34D10.8
F35E2.9 Y32F6A.6
F26A3.5 gln-2
C15C6.2 C33C12.7
Y71A12B.2 W02B12.12
nas-11 C37A5.5
C31C9.9 sss-2
C03B8.3 T16A9.5
Y51H4A.935 mps-3
irld-8 spe-11
col-107 Y37D8A.5
C08F11.13 F20D6.6
Y37E11AL.2 F56B3.6
Y37D8A.8 C32E8.1
T28A8.8 F36A2.12
F56F3.4 ZK546.3
W07G1.25 Y54G2A.13
clec-225 C17F3.3
cpb-2 msp-79
clec-48 C34D4.3
F41G4.7 Y71A12B.3
F56D6.22 F56D6.9
cnnm-4 C53D6.5
fbxb-36 Y71A12B.2
madf-4 srbc-15
Y71F9AL.2 ZK484.6
B0035.13 Y68A4A.13
ZK686.5 F36A4.3
fbxa-217 W09B7.1
F42H11.1 F07B7.1
K09H9.9 ZK1010.5
Y53F4B.45 K07F5.8
F34D10.9 Y38H8A.3
M05D6.1 C50E10.1
smd-1 Y59H11AM.4
W04A4.2 wht-9
Y66D12A.11 htas-1
C02F5.2 C45G9.4
C15F1.5 D1014.13
F47D12.7 C33F10.12
Y46G5A.14 ZK354.7
90
T21D12.14 C36H8.1
F28E10.5 F58E6.5
oac-9 C33F10.26
spp-13 F56A4.3
K08D8.3 K06A4.8
C46A5.1 B0025.5
clec-169 W03D8.10
F07C6.2 F47B7.6
C15A11.7 C08F11.6
clp-8 set-33
C49A9.5 AC8.11
acdh-5 F54G2.2
Y46H3A.4 AC8.10
H06A10.1 bath-8
Y39G10AR.15 F01D4.3
M70.3 Y113G7A.12
F26B1.1 T13A10.1
T09B4.3 T11G6.11
col-3 F15D4.6
nlp-10 F16G10.5
F54H12.7 T28A11.19
T23B12.8 F20D6.1
cyn-2 C07G1.6
F53B2.8 AC8.3
pph-2 C38D9.2
ZK512.8 basl-2
W03F11.4 F54G2.4
ZK546.4 msp-71
nhr-110 Y59E9AL.8
F55A11.11 C54F6.6
C17C3.5 C17C3.11
grl-21 Y4C6A.1
F13B6.3 Y59E9AL.5
nspa-8 Y39B6A.31
C45B2.2 C47A4.4
abf-1 Y62E10A.3
F52F12.5 msp-42
C38C3.10 C54F6.15
Y39G10AR.16 R02C2.8
B0524.5 F55B11.6
col-38 C07G1.7
col-109 C38D9.13
B0285.11 F28A10.11
91
F55C5.2 F28A10.3
F25F2.1 T10H9.10
D2045.5 Y37E11AL.4
nspd-9 T08G5.1
F40F12.8
K07A3.3
nspa-1
nspa-3
F02E9.3
F40F9.3
dpy-13
T04A8.13
spin-3
T21C12.4
T13F3.8
Y59E9AL.2
C44B12.3
nas-9
AC8.2
C25G4.7
ZK354.3
C09G9.8
wht-8
Y41E3.6
F58H1.6
K10C9.7
sst-20
lys-5
Y69F12A.1
K09F6.3
C47E12.12
C06G4.4
ptp-5.1
ZK1098.12
ZK970.8
C48D1.9
acl-13
catp-4
C28F5.1
nep-18
F35C11.2
grl-5
F53G12.8
92
C06A5.2
C55C3.6
C40H1.2
Y47D3A.13
ZK930.5
F54D1.1
Y116A8C.38
C03E10.3
C09F9.1
C55C3.4
grd-10
T05F1.5
F21H7.2
C50B6.7
Y119C1B.1
Y43F8C.9
K05C4.4
Y43F8A.2
C15H7.4
C26C6.6
avr-14
fbxa-57
Y37H2A.18
F42G8.8
clec-190
T22C1.8
irld-18
F57H12.6
C17G10.3
F33D11.2
F37A8.2
F35H8.1
F21D9.2
D1086.17
ZK105.3
C43F9.4
F53B6.7
tag-164
C17H12.12
T08G11.2
F52C6.4
ptp-5.2
W03B1.9
93
T28B8.4
C24H11.1
C08F8.6
W03C9.8
elo-8
col-117
F14D2.19
W03D8.3
ZK930.7
unc-54
W09C3.7
nlp-18
dhhc-12
F29B9.7
T09B4.7
T16A9.3
nspa-4
ZK550.5
M28.9
C10H11.7
nspa-2
R06B10.1
Y37E11B.10
F11G11.14
F22D6.8
F26A1.3
F56D6.20
mpst-5
bli-1
Y50E8A.12
lido-16
Y34F4.2
F56F4.4
ztf-31
col-34
F30A10.12
C43G2.3
F47B7.6
gska-3
kin-5
Y80D3A.8
Y81G3A.1
T27C10.8
94
fip-6
ZK596.2
sss-1
F36H1.3
Y39B6A.30
Y106G6A.4
nspa-5
F42G4.6
set-28
W03G1.2
nlp-20
K01D12.3
M176.9
R01H2.2
C36F7.5
Y49F6B.8
moa-1
dao-4
F36D3.16
Y37F4.5
mec-17
R02D5.10
Y38F1A.1
nep-20
C08F11.7
Y45F10B.8
D1081.12
C27D8.2
F23F12.3
decr-1.2
R08A2.1
F58A6.5
ZK849.6
M7.7
H27M09.5
F55F8.7
F58D2.2
T23B7.2
Y51H7C.8
F36A2.14
C24A11.1
F14F7.4
F37C12.18
95
F47B3.5
T22C1.9
F52F12.8
clec-79
ZK1128.3
K07A1.5
C04G6.2
tsp-19
ttr-56
fbxa-218
ZK593.9
alg-3
spe-10
C28D4.7
C04G2.14
C54G4.2
F20H11.4
Y53C10A.15
F47B3.1
T08H10.3
R10E4.7
F27C1.1
F35H8.4
smz-2
BE0003N10.6
Y49E10.10
F47B3.6
C01G6.9
C15A11.2
F59C6.3
msp-76
T16G1.13
wht-5
K11C4.1
C36C5.12
col-168
Y39B6A.18
rol-8
F46A9.2
rnh-1.1
glit-1
T02H6.7
C32E12.1
96
F23B2.7
ttbk-5
dpy-5
F44G3.7
F13A7.1
W03D8.5
F28A10.12
F57F4.1
F48C1.9
msp-56
Y73F8A.14
C35E7.12
H06I04.5
C04F12.7
Y57G11C.5
B0252.5
C54D10.4
kin-21
ZC581.7
K04A8.20
Y54G2A.15
T23F11.2
dod-3
F47B3.4
Y54G2A.13
nlp-25
F38E1.3
Y51H4A.13
btb-16
B0207.1
snb-6
C06A6.7
T21G5.1
K08C9.1
rmd-4
Y71G12B.18
F26B1.8
F36H12.9
B0261.6
acdh-4
fbxc-49
icmt-1
T04B2.7
97
F43G9.8
F36D4.1
ZK354.2
Y75B12B.3
Y39G8C.2
ssq-2
ZK938.1
srj-35
F59C6.18
W03B1.5
Y39F10C.3
msp-78
C27D8.1
F32B6.4
C30G7.3
F33D4.6
C34F11.2
C28D4.5
fbxa-122
C55C3.3
ZC155.2
sqt-1
mar-1
C02F5.5
Y59E9AL.3
C54G4.3
C28C12.11
Y38E10A.17
C04F1.1
F17E9.5
C39H7.1
T08B6.9
fbxa-224
ZC477.7
Y38F2AL.12
nspd-7
Y69A2AR.19
decr-1.3
F47B3.7
F08H9.15
smz-1
C05C12.5
R13H9.6
98
Y24D9B.1
Y57G11A.2
F36A2.10
ZK353.4
W03D2.6
T10E9.4
F31E8.5
T04F3.3
msrp-1
Y71G12B.3
F56F4.3
ZK858.2
K01H12.4
Y106G6G.1
gsp-3
F14D7.7
F44D12.6
C18D11.10
AC8.3
exc-13
set-33
H05L14.1
ZK688.12
col-39
msrp-4
R09E10.1
T23G11.1
F26F12.2
C34F11.5
C40C9.3
C04G2.3
F53F4.18
F14D7.10
col-167
C04G2.12
bli-2
gipc-2
F56A11.6
spch-1
R07H5.9
C32E8.1
R07E5.15
ZC395.4
99
W09C3.2
mpst-6
ZK1053.2
msrp-3
R01H2.4
F35E2.10
C38C10.3
F47B3.2
lon-3
K10D6.3
C24D10.2
msp-81
C01F6.2
ssp-9
K04H4.5
F40G12.10
F44D12.8
snf-10
F36D3.4
F07C6.6
linc-36
spe-27
msrp-6
C14C10.1
C16A11.7
C49C3.20
Y106G6G.4
B0218.7
T23F6.1
cest-1.1
nspa-7
snf-2
ZK892.5
F58B4.7
F09C12.8
msrp-2
R13A1.3
F42A9.3
ZK622.1
Y67D8B.5
C02G6.2
nspa-6
msd-4
100
C33F10.1
K03H1.12
C04C11.25
F58G1.3
ZK512.10
ZK856.18
C17H12.3
Y50D4B.10
Y45F10B.3
C16D2.1
F58E6.13
C14A6.16
W09D6.4
ZK666.15
AC8.10
T05C12.1
F58D5.7
C34D4.2
T22B3.3
F56D6.18
B0218.5
T05D4.5
C34F11.1
C31H1.5
ttbk-2
ZK105.13
AH6.3
C27D6.11
rmd-6
F36H12.3
irld-3
C14A4.13
Y106G6H.13
C32E8.4
D1014.13
F28H1.5
T28H11.7
C25D7.1
B0244.9
T03F6.6
E03H12.7
Y43F8C.5
ssp-11
101
C27B7.6
scl-23
F32H2.7
Y71G12B.27
K05F1.8
T23B3.5
F17C8.7
F10D11.4
Y57G11B.8
C35E7.9
B0379.2
ZK945.6
F40H6.1
K06A5.2
comp-1
Y38H8A.4
F14D7.14
nep-9
F59H6.15
B0280.11
W01B6.6
H08M01.1
F26D2.10
B0041.1
T13F2.9
C10G11.8
F59E12.3
C34G6.3
W02B12.12
acs-23
Y68A4A.13
Y22D7AR.12
F56D6.14
msp-38
ZK484.6
R105.1
D1022.9
Y113G7C.1
col-175
msp-77
bli-6
mpst-4
K04G2.4
102
K07A1.21
Y73F8A.15
gsp-4
col-146
ZK418.2
T20D4.10
Y4C6B.2
D2024.1
C06A8.6
R05D7.2
H38K22.7
C50F2.5
C02B10.6
M70.1
msp-79
Y47G6A.26
F10C1.23
R10E9.2
ZK354.6
F41E6.1
F53B6.4
B0379.7
K12C11.5
fipr-24
B0205.10
Y73F8A.12
C34B2.3
C27D6.3
K09C6.2
acs-10
F49D11.7
msrp-5
C18A3.7
col-63
K08F4.5
C55C3.7
col-13
C01G10.14
T10B5.2
cylc-2
C34D4.3
fis-1
F07A5.2
103
Y69E1A.3
Y41C4A.7
nkb-2
M05B5.1
ent-3
T16A9.5
F54D11.4
F44D12.16
C38D9.2
C17H12.5
F54G2.2
C05B5.2
C53D6.5
C55C2.3
ZC581.10
tag-344
F59A6.4
clec-47
C04F12.6
F36H12.4
F13A7.7
C33F10.12
E03H12.5
F54G2.4
F58A4.12
Y32F6A.6
col-149
R102.1
col-139
B0025.5
E01G4.6
mps-3
D1081.5
F21C10.11
acs-18
ZK1010.5
C43E11.5
col-129
K07H8.5
C14A6.13
gln-2
ZC21.8
F34D10.8
104
snf-4
F36A2.12
C01G5.3
dlhd-1
Y47D7A.6
ZK546.3
ZC581.2
T08G3.7
Y37E11AL.12
col-81
F36D3.5
C45G9.4
Y47D3A.31
C32D5.4
ptp-5.3
ZC449.2
C17F3.3
AC8.11
T20D4.3
rol-1
R02D5.7
F36A4.3
msp-33
ZK858.8
F20D6.6
chil-14
ZK84.5
C04G2.5
F46A9.1
F56B3.6
R193.3
clec-99
nspd-1
C14A6.6
ZK637.12
F56D6.13
Y57G11B.3
C47E8.10
AC8.12
sss-2
spe-11
col-49
AC8.7
105
F36D1.4
F15D4.6
ZK354.7
F26A1.4
C36H8.1
Y38H8A.3
htas-1
Y47D9A.3
ilys-2
C33F10.26
K06A4.8
C37A5.5
Y65B4A.9
Y55B1AR.4
clec-60
K07F5.8
F56D6.19
ubxn-5
F46F5.1
Y113G7A.12
C54F6.15
F21C10.17
nspd-5
Y23H5B.2
Y6E2A.10
EGAP1.1
wht-9
ZK637.15
spe-8
R07E5.6
C48D1.7
C49C3.2
C42D4.19
F10G8.2
R08A2.2
Y59H11AM.4
C08F11.6
T22F3.7
fbxa-125
T08G3.4
R09E10.2
clec-155
col-126
106
C50E10.1
F58E6.5
K05F1.1
C33C12.7
F01D4.3
nep-7
col-127
ZK616.61
M03E7.1
F46F5.2
msp-74
Y37D8A.5
nspd-4
K03D3.2
srp-10
F45B8.5
F35E2.3
T01H10.4
Y54G2A.24
F20D6.1
col-12
T28A11.19
C07G1.6
srp-9
T11G6.11
fbxa-178
W01B11.1
ZC262.1
F16G10.5
T13A10.1
R05D3.5
W03D8.10
D1081.10
B0545.4
ZK354.9
M195.4
sfxn-1.1
C14A6.5
Y53F4B.36
R160.11
R09E10.9
Y4C6A.1
basl-2
107
Y59E9AL.5
Y65B4BR.1
clpr-2
T08G3.15
msp-71
Y59E9AL.8
Y39B6A.31
srbc-15
F36G9.13
C38D9.13
irld-35
C17C3.11
msp-42
F46A8.9
K06H6.2
T15H9.4
R02C2.8
F56A4.3
C07G1.7
Y62E10A.3
irld-53
F28A10.3
T10H9.10
C47A4.4
F28A10.11
Y37E11AL.4
T08G5.1
108
Appendix B: Downregulated Day 1 Transcriptomic Data, padj≤0.05
Day 1 Tau vs
WT
Day 1 Aβ vs
WT
Day 1 Tau;Aβ vs
WT
H10E21.5 col-121 F28B4.3
nhr-80 ZK813.6 spp-23
fat-5 sri-61 fat-7
F31D4.8 nhr-201 T27A8.9
coa-3 Y17D7C.9 T27A8.8
ndfl-4 linc-88 cnc-7
tin-10 nhr-127 rml-3
fbxc-15 T25D10.4
F32H2.6 plpp-1.3
F35E2.2 R08E5.3
Y17D7C.4 nhr-114
C47E12.13 F33D11.10
nape-1 K07C11.7
pals-28 pelo-1
pes-1 mrps-22
F54B11.10 F54F2.7
W04H10.5 T10E9.14
F26F2.1 ZK512.4
math-5 M04C9.3
F28B4.3 W09G10.9
cut-3 tbck-1
srg-65 sptl-1
linc-54 mrpl-49
B0507.8 F44E2.9
ceh-45 dnj-16
srw-60 ndfl-4
C32B5.18 nfs-1
fbxb-88 dpm-3
C41G6.13 elpc-4
ZK6.8 mrpl-15
Y105C5A.1271 coa-3
sri-39 mrpl-32
cpg-24 mrps-26
pals-29 ssup-72
F44A6.3 prp-4
T20D4.19 mrps-2
K08E4.8 mrps-7
fbxb-101 mrpl-53
B0250.18 mrpl-13
M163.11 R04F11.5
109
cyp-34A1 cuc-1
C08F1.11 C17E4.11
M03D4.4 mrpl-50
cpg-20 C29E4.12
linc-32 C33A12.1
fbxc-40 fkb-2
R12H7.4 let-60
srw-85 mag-1
T28A11.22 rbm-22
hch-1 mrpl-1
C06E2.9 tag-124
nhr-203 nbet-1
cutl-2 dnj-1
dpy-14 eif-3.L
F53C3.7 tin-10
eol-1 tgt-1
Y41E3.465 spcs-1
T16G1.18 Y48E1C.4
ttr-52 yif-1
K12H6.7 rpc-25
cutl-25 Y54H5A.1
K02E7.7 mrpl-11
W04H10.2
Y8A9A.3
C40A11.6
C04F6.9
Y75B8A.39
C54F6.17
ceh-27
B0281.6
fbxc-34
M117.7
fbxc-22
clec-266
ins-2
C45B11.6
sre-54
F40G9.6
fbxc-14
clec-196
M163.8
nspe-7
C08F1.10
110
Y46G5A.7
Y54G2A.10
F36H5.4
C10C6.13
fbxb-66
fbxb-107
F31D5.6
W04H10.6
atz-1
eya-1
ZK1053.4
Y71F9AL.25
cutl-8
Y110A2AL.4
C32B5.15
del-4
fbxb-77
tab-1
T17A3.12
Y45G5AM.5
C03D6.9
cht-1
ceh-37
T20G5.15
dsl-3
Y17G9B.2
fbxa-135
F48E3.6
F37D6.3
zig-4
fbxc-24
dsl-6
gcy-21
F07H5.13
fbxb-98
zip-8
fbxb-35
Y77E11A.23
T05H10.3
C03C10.5
C01F6.16
ugt-57
his-20
111
C06E1.1
vab-15
his-54
bath-10
lin-32
Y6D1A.2
R11G1.7
fbxb-46
C07D10.5
olrn-1
F40G9.12
sdz-10
fkh-8
C46C11.4
txt-2
C25F9.16
EEED8.2
fbxb-56
W04A8.2
fbxa-158
T28C6.10
K09A9.12
C40A11.4
Y47D3A.1
ZK512.1
cfz-2
C10A4.3
sdz-25
M116.1
Y87G2A.25
Y39B6A.69
gadr-5
F56F3.7
fbxb-3
fbxb-18
fbxb-26
fbxc-29
C39B5.2
F56D2.3
sre-36
T02E9.5
nhr-25
Y57A10C.1
112
sdz-33
unc-39
C26B2.8
hsp-16.2
W08F4.13
fbxb-75
cnd-1
F14B4.1
his-52
W04A8.4
fbxb-50
ncam-1
hmg-11
T24C4.2
hil-6
str-31
T24E12.13
F54C8.6
Y71F9AL.7
ttr-50
T26C12.3
F53E10.8
wrt-10
fbxb-43
his-22
ins-35
F49B2.7
rml-3
fbxc-26
Y82E9BR.17
arrd-4
F40H3.6
fbxb-102
ref-2
EEED8.15
Y71F9AL.6
cav-1
dpy-17
fbxb-24
fbxb-1
zig-10
Y71A12B.12
mls-2
113
cutl-16
dhs-8
sdz-28
nhr-145
R03H10.11
C08F8.15
ceh-86
epg-2
fbn-1
sdz-5
F57G12.1
C27A7.8
hsp-16.11
cec-8
cyd-1
cyp-35D1
T05E11.8
fbxb-40
fbxb-45
bus-12
F22E5.20
fbxb-15
K11H12.5
fbxc-21
fbxb-91
noah-1
Y27F2A.8
R09A8.9
sdz-4
Y65A5A.1
sex-1
fbxc-28
W04A8.3
hsp-16.1
C08F1.6
fbxc-18
F20C5.6
F19C7.3
C35E7.5
Y53F4B.62
F14B6.3
fbxb-37
fbxc-33
114
sym-1
nhr-94
hlh-14
F46E10.2
mig-13
crn-6
F33E2.5
F23A7.1
F21A3.2
nit-1
swt-6
fbxb-62
F23D12.2
C01G6.3
sprp-4
ztf-11
R11A5.3
C05A9.2
igcm-3
T22C8.4
zip-7
fbxb-115
C39B5.16
F53B3.5
fbxb-2
C35E7.3
jmjd-3.2
C33G3.4
sysm-1
efl-3
C09B8.5
F19B10.4
C23H4.6
glb-14
K08H2.4
Y15E3A.5
Y38H6C.16
C35E7.4
fbxb-95
kin-34
ces-2
blos-8
H23N18.6
115
fbxb-118
vab-19
C06E7.4
jmjd-3.1
fbxa-83
ham-1
mnp-1
Y41D4A.3
lam-3
tir-1
sepa-1
C31C9.2
pha-4
C18A3.9
K02B12.2
fbxb-20
sto-1
bra-1
sdz-30
T09B4.5
inx-3
lact-5
inx-2
fkb-7
fbxb-57
F35D2.3
ceh-43
C47D12.4
B0281.5
ceh-40
T02C1.1
F17A2.13
fbxc-36
C17E4.20
C06E2.5
F15B10.3
T25D10.4
his-24
chd-3
hlh-1
T26E3.8
skr-20
elc-1
116
ZC449.5
Y47G6A.31
T18D3.1
hid-1
fbxb-72
wdr-5.2
dex-1
fbxb-104
K05F6.4
F15A4.2
fbxb-70
ugt-53
C04A11.2
dct-1
hnd-1
skr-12
wht-1
T22B7.4
kin-33
dlg-1
rnr-2
nac-2
K07E3.2
ceh-99
C05D9.7
die-1
F16B12.4
btb-10
cst-1
T22C8.3
Y71A12B.11
cdc-25.2
lin-25
taf-7.1
rgs-11
K04G2.10
aex-3
fbxc-52
hil-3
nhr-161
skr-15
K10B3.5
C31H5.4
117
unc-84
T24E12.11
nhr-2
skr-7
smcl-1
C02B8.6
K07E3.1
F28C10.3
glo-1
R11.4
nrde-3
F58H1.5
sdz-9
AH9.3
C29F7.2
rbg-1
scd-1
tag-294
D2089.8
skr-14
hlh-2
F46H5.4
K03A11.1
Y6D1A.3
M04F3.7
rgs-8.1
tth-1
rgs-8.2
div-1
skr-16
ztf-29
vet-6
linc-4
C17H11.2
T04D3.5
par-2
T05H10.4
C33G3.6
tbx-11
myrf-2
C01G10.9
cdh-4
F40B5.2
118
ZK1307.1
ipla-2
athp-3
bet-2
F53B1.2
ZK154.5
uggt-1
pes-5
Y59C2A.3
F28H6.6
inso-1
Y9D1A.2
T10E9.14
cest-2.1
mboa-3
vwa-8
K09E9.3
plpp-1.3
pkn-1
C36B7.6
mec-8
F38B7.3
R08E5.3
Y69A2AR.28
nfya-1
K07C11.7
cya-1
tag-52
B0416.4
C16E9.2
tyms-1
set-15
rnr-1
cam-1
ten-1
D2005.1
B0416.5
T12G3.2
R05H11.1
R04B3.2
eif-2Bepsilon
psf-2
mrp-5
119
lin-15B
sptl-3
W09G10.9
cec-5
R07E4.5
rpl-41.1
adr-1
K09C4.10
lin-2
aco-1
gpr-1
dph-1
homt-1
swan-2
R02F2.9
F42A6.6
cyn-8
alg-2
F20D12.12
B0205.8
exos-7
tam-1
tipn-1
cpg-8
dpm-3
unc-37
nkat-3
ssup-72
ama-1
Y6D1A.1
mag-1
plk-1
ZK185.5
hcp-3
hrpa-1
vrk-1
swsn-6
gex-2
dnj-23
T10F2.5
apc-11
mvb-12
rbm-22
120
M02B7.7
Y45G5AM.7
mep-1
elof-1
dli-1
csn-5
bmy-1
toe-1
znfx-1
T07A9.15
ptps-1
pri-1
coq-4
snr-5
mcm-5
T24H10.1
smu-1
dcn-1
chdp-1
bud-31
ZK484.3
coa-3
attf-2
htz-1
tba-1
mek-1
Y38A10A.7
F02E9.10
skp-1
C38D4.10
adm-4
B0303.3
T25B9.8
lin-9
unc-116
nsf-1
uaf-2
hda-1
hcp-4
kle-2
rpb-12
prp-4
hpk-1
121
C17E4.11
morc-1
mrps-23
M03F8.3
moa-2
Y54G2A.50
snr-3
vps-37
C29E4.12
snpc-4
tag-124
cope-1
spr-4
fkb-2
swsn-9
copd-1
smo-1
F36D4.5
mes-4
pbrm-1
ubl-5
vps-2
npp-2
dnj-1
imp-2
icln-1
sec-31
T09A5.5
W09C3.4
cdc-14
mrpl-53
atg-18
mtm-1
F26H9.5
fkb-6
K08F9.4
mrpl-49
ZK1236.5
R07E5.7
C07D8.6
ubc-14
nra-4
pafo-1
122
rpb-5
B0019.2
ZK418.5
mrpl-1
exos-8
ntl-2
knl-1
ubh-4
crn-5
F31D4.2
adh-5
npl-4.1
lin-53
srpa-68
pacs-1
npp-20
nmt-1
C23G10.8
lin-5
cap-1
swsn-4
ncbp-1
npp-10
mrpl-14
chp-1
smc-6
hrpl-1
F37A4.1
let-502
capg-1
rfp-1
ccr-4
tmem-208
dnj-19
let-92
lmn-1
Y54H5A.1
123
Appendix C: Day 1 Tau;Aβ (q≤0.05) elevated metabolites compared to elevated
metabolites in human AD frontal cortex
Day 1 Tau;Aβ vs WT Human AD vs CN frontal cortex Overlap
serine acetylaspartic acid AMP
S-1-pyrroline-5-carboxylate aspartic acid GMP
lysine alanine S-adenosylmethioninamine
fructosyllysine asparagine methionine
2-aminoadipate pyruvate serine
tyrosine glutamic acid tryptophan
tryptophan glutamine alanine
kynurenine succinic acid glutamic acid
anthranilate arginine
leucine proline
alpha-hydroxyisocaproate S-adenosylmethioninamine
beta-hydroxyisovaleroylcarnitine hydroxyproline
isoleucine pyruvate
3-methyl-2-oxovalerate acetylglutamic acid
aline serine
3-hydroxyisobutyrate cystine
methionine methionine
2-hydroxy-4-(methylthio)butanoic acid S-Adenosyl-L-homocysteine
cysteine threonine
cyano-alanine choline
ornithine tryptophan
agmatine pentose 5-phosphate
acetylagmatine ADP-ribose
spermidine ADP
N('1)-acetylspermidine AMP
diacetylspermidine* GMP
spermine guanosine
N(1)-acetylspermine IMP
N1-N12-diacetylspermine uric acid
S-adenosylmethioninamine xanthine
5-oxoproline xanthosine
2-hydroxybutyrate/2-hydroxyisobutyrate hypoxanthine
S-(1-2-dicarboxyethyl)glutathione inosine
histidylalanine valine
isoleucylglycine pantothenic acid
lysylleucine
phenylalanylalanine
phenylalanylglycine
124
tyrosylglycine
valylglutamine
valylglycine
glucose
6-phosphogluconate
maltotriose
maltose
mannose
UDP
N-acetylglucosaminylasparagine
heptadecatrienoate (17:3)*
stearidonate (18:4n3)
eicosapentaenoate (EPA; 20:5n3)
docosapentaenoate (n3 DPA; 22:5n3)
hexadecadienoate (16:2n6)
linoleate (18:2n6)
linolenate [alpha or gamma; (18:3n3 or 6)]
dihomo-linolenate (20:3n3 or n6)
arachidonate (20:4n6)
GPC
glycerophosphoethanolamine
1-palmitoyl-2-stearoyl-GPC (16:0/18:0)
1-palmitoleoyl-2-oleoyl-GPC (16:1/18:1)*
1-2-distearoyl-GPC (18:0/18:0)
1-stearoyl-2-linoleoyl-GPC (18:0/18:2)*
1-stearoyl-2-arachidonoyl-GPC
(18:0/20:4)
1-palmitoyl-2-arachidonoyl-GPE
(16:0/20:4)*
1-stearoyl-2-arachidonoyl-GPE
(18:0/20:4)
1-stearoyl-2-arachidonoyl-GPS
(18:0/20:4)
1-stearoyl-2-linoleoyl-GPI (18:0/18:2)
1-stearoyl-GPC (18:0)
1-oleoyl-GPC (18:1)
1-arachidonoyl-GPC (20:4n6)*
1-palmitoyl-GPE (16:0)
2-stearoyl-GPE (18:0)*
1-oleoyl-GPE (18:1)
1-stearoyl-GPS (18:0)*
1-stearoyl-GPG (18:0)
1-stearoyl-GPI (18:0)
1-oleoyl-GPI (18:1)
1-(1-enyl-palmitoyl)-2-oleoyl-GPE (P-
16:0/18:1)*
125
1-(1-enyl-stearoyl)-2-arachidonoyl-GPE
(P-18:0/20:4)*
1-palmitoleoylglycerol (16:1)*
1-linolenoylglycerol (18:3)
1-dihomo-linolenylglycerol (20:3)
2-palmitoleoylglycerol (16:1)*
AMP
adenylosuccinate
adenine
GMP
UMP
CMP
riboflavin (vitamin B2)
3'-dephosphocoenzyme A
threonate
5-methyltetrahydrofolate (5MeTHF)
biliverdin
pyridoxal
2-4-di-tert-butylphenol
X-25026
X-25551
X-25563
X-25009
X-12100
X-24456
126
Appendix D: GFP image of AD strains, PCR and Western Blot of Aβ and Tau
Abstract (if available)
Abstract
Alzheimer's disease (AD) and related dementias present a significant global health challenge, particularly in an aging population. This study utilized the model organism Caenorhabditis elegans to explore the synergistic effects of amyloid-β (Aβ) and Tau proteins, key hallmarks of AD, on various biological levels. By examining the expression of Aβ and Tau, we observed increased accumulation of insoluble proteins and alterations in protein-protein interactions associated with germ cells, oxidative phosphorylation, and organism development. Transcriptomic analysis revealed changes in genes related to reproduction, mitochondrial function, and the aging process, with Aβ playing a prominent role. Metabolomic analysis highlighted synergistic effects on lipid and amino acid metabolism, particularly through elevated aminoacyl tRNA biosynthesis. Furthermore, the concurrent expression of Aβ and Tau resulted in impaired motor function, heightened mitochondrial activity, decreased fertility, and reduced lifespan, emphasizing the systemic consequences of their interaction. These findings shed light on AD pathogenesis and underscore the urgent need for novel therapeutic approaches.
To further elucidate the implications of Tau and Aβ simultaneous expression in AD pathogenesis, we examined their impact on protein insolubility and metabolism with age using the previously mentioned Caenorhabditis elegans model. Our results revealed that Tau and/or Aβ expression led to increased protein insolubility, exhibiting distinct patterns during aging. Insoluble proteome analysis identified alterations in ribonucleotide binding, aminoacyl tRNA ligase proteins, cytoskeleton structure, and ribonucleoprotein granules in the Tau;Aβ strain. Metabolomic analysis demonstrated significant changes in metabolites associated with glycolysis, gluconeogenesis, alpha-linolenic acid metabolism, and fatty acid biosynthesis pathways in the Tau;Aβ strain. Intriguingly, the early-life insoluble proteomic and metabolic changes observed in the Tau;Aβ strain resembled those typically seen in aged wildtype worms, suggesting an accelerated aging phenotype. These findings highlight the intricate remodeling of protein insolubility and metabolism induced by Tau and Aβ expression throughout the aging process, providing valuable insights into the pathogenic mechanisms underlying AD. Understanding these molecular consequences may pave the way for the development of therapeutic strategies targeting protein aggregation and metabolic dysregulation in AD.
Overall, this study highlights the importance of investigating the systemic effects in neurodegenerative diseases and provides a reference multi-omic datasets capturing early-life AD changes as well as aged data. These datasets provide valuable resources for identifying changes associated with Aβ, Tau, or their interaction, and can help identify potential target pathways, genes or metabolites for treatment.
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Asset Metadata
Creator
Holcom, Angelina
(author)
Core Title
Phenotypic and multi-omic characterization of novel C. elegans models of Alzheimer's disease
School
Leonard Davis School of Gerontology
Degree
Doctor of Philosophy
Degree Program
Biology of Aging
Degree Conferral Date
2023-08
Publication Date
07/10/2023
Defense Date
06/14/2023
Publisher
University of Southern California
(original),
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(digital)
Tag
Alzheimer's disease,Caenorhabditis elegans,insoluble proteome,metabolomics,multi-omic datasets,OAI-PMH Harvest,systemic analysis,transcriptomics
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theses
(aat)
Language
English
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Advisor
Lithgow, Gordon (
committee chair
), Andersen, Julie (
committee member
), Curran, Sean (
committee member
), Tracy, Tara (
committee member
)
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angelina.holcom@gmail.com,holcom@usc.edu
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Holcom, Angelina
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Tags
Alzheimer's disease
Caenorhabditis elegans
insoluble proteome
metabolomics
multi-omic datasets
systemic analysis
transcriptomics