interactome analysis identifies novel targets of
TRANSCRIPT
Interactome Analysis Identifies Novel Targets of Phosphoinositide 3-kinase (PI3K) that Mediate
Astrocyte Neuroprotection
by
Samih Ahmad Alqawlaq
A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy
Laboratory Medicine and Pathobiology University of Toronto
© Copyright by Samih Ahmad Alqawlaq 2018
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Interactome Analysis Identifies RNA Binding Protein ZC3H14 as a Novel Interactor of Phosphoinositide 3-
kinase (PI3K) and a Mediator of Astrocyte Neuroprotection
Samih Ahmad Alqawlaq
Doctor of Philosophy
Laboratory Medicine and Pathobiology University of Toronto
2019
Abstract
In a homeostatic state, astrocytes can support neuronal survival and function through a
range of secreted signals that protect against neurotoxicity, oxidative stress, and
apoptotic cascades. Thus, the analysis of astrocyte conditioned media (ACM) may
provide valuable insight into the nature of these protective mechanisms, and how they
might be promoted. Previously, we characterized a potent neuroprotective activity
mediated by ACM in neurons and the retina in metabolic stress models. However, the
molecular entity and mechanism underlying this activity remained unclear. Here, a
chemical genetics screen revealed phosphoinositide 3-kinase (PI3K) as a central player
transducing ACM-mediated neuroprotection. To identify additional proteins contributing
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to the protective activity, endogenous neuronal PI3K was immunoprecipitated from
astrocytes exposed to ACM or control media, and MS/MS analyses were undertaken.
MS data analyses pointed toward only five additional proteins that co-
immunoprecipitated with PI3K and were regulated by the ACM signal. These hits
included expected PI3K interactors, such as the platelet-derived growth factor receptor
A (PDGFRA), and novel interactors, such as the zinc finger CCCH-type containing 14
(ZC3H14). ZC3H14 has recently emerged as an important RNA binding protein that
modifies poly-adenosine tail lengths on nascent mRNA transcripts. In downstream
validation studies we show that Platelet Derived Growth Factor-BB (PDGF-BB) strongly
activates PI3K signaling to protect neuronal cells. Finally, PDGF-BB treatment induced
recruitment of ZC3H14 to PI3K, and inhibiting this interaction eliminated ACM-mediated
neuroprotection. Thus, we identified a novel ACM- and PDGF-induced neuroprotective
signaling cascade mediated through PI3K that involves recruitment of ZC3H14.
Enhancing this pathway may present a promising strategy to promote astrocyte-
secreted neuroprotective signals.
iv
Dedication
So many of my loved ones are deserving of this dedication that I feel I am responsible
for only a minute fraction of this thesis. My grandmothers and grandfathers have
dedicated their entire being to educate my parents against all the social, economic, and
political odds. In a time where many parents sent their children to work at young age to
make a living in Palestine and Egypt, my grandparents fought with their hearts to send
my parents to university. My parents, Samia Ibrahim and Ahmad Al Qawlaq carried the
torch forward and lit my life with love, wisdom, and patience. Without them, this thesis
would not be. In all truth however, my sacrifices, and those of my parents and
grandparents were to carry our vision and identity into the future, in the hands of my
daughter Leen. Habibti Leen, on behalf of your grandparents, your mom, and whole
family, I dedicate this thesis to you. Carry it forward.
v
Acknowledgments
Throughout this five-year journey, I was inspired by so many brilliant minds and hearts.
Way before that, so many have laid down the foundation of this work, brick by brick. I
am eternally grateful for my academic team, my family, and my friends who kept their
doors open for me whenever I needed them. Without a doubt, I will fail to acknowledge
some of you, but I want you to know that I am eternally grateful for your contribution,
despite my limited memory. In the next few pages, I hope I will pay a small tribute to
those who made a difference in this work, directly and indirectly.
This work was primarily driven by the patient and kind mentoring of my project
supervisor Dr. Jeremy Sivak, who provided endless support at all stages of the project.
Dr. Sivak, there were many moments along the way where the challenges of the project
seemed overwhelming; I often shared with my colleagues that if I was supervised by
any other supervisor in the department I would not be successful. Thank you for being
so understanding and supportive, while being insightful and helpful.
I would like to acknowledge the contributions of my graduate committee advisors, Dr.
James Eubanks and Dr. Gerold Schmitt-Ulms, who provided essential feedback and
direction throughout the program. Dr. Schmitt-Ulms, you taught me excellent standards
of scientific research with your precise and thorough approach to experimental design.
Dr. Eubanks, your vast knowledge in Neuroscience was very assuring to have onboard
the committee, and it challenged me to explore the deep intricacies of my project.
Declan Williams of the Schmitt-Ulms group, I am grateful for your expert input in the
interactome study, the main pillar of this project. My friend and colleague Mohadeseh
Mehrabian was also incredibly helpful in troubleshooting the interactome study, offering
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ingenious tips and recommendations throughout the study. Dr. Anita Corbett and her
associate Sara Leung generously provided reagents and expertise on this project that
pushed us through a challenging bottleneck. Furthermore, Dr. Jeff Wrana and Dr.
Alessandro Datti of the The High-Throughput Screening Division provided the expertise
and facilities to carry out the chemical genetics screen, which kicked off this project. I
am grateful to my colleagues at the Sivak lab: Dr. Izhar Livne-Bar, Darren Chan, Cindy
Guo, Alessandra Tuccito, and Nevena Vicic who provided essential technical and
morale support throughout the years. Thank you for chats, laughs, and the venting
sessions, could not have made it without those.
My dear family, you are the foundation and the roots of this success, thank you for
teaching me to love life and to respect science. My dad, Ahmad Al Qawlaq and mom
Samia Ibrahim, thank you for always seeing me in a better way than I can see myself.
Mom, I can write forever about how a magnificent soul you are; I can only say that your
spirit and generosity can never be matched by another being. Dad, you are the best
fighter I know, you taught me that giving up is never an option. Thank you for giving me
the bows and arrows to survive the jungle of life. My dearest wife Ayat, I often came
home from the lab with multiple failed experiments and a broken heart. At times, I could
not see hope. Your love and faith in me were the only things that made me get out of
bed the next day. I always told you this is your PhD, and I say it here again. Leen, thank
you for keeping me up all night and making me forgive you the moment I see you in the
morning. Enas, Ayman, Ehab, Eyad and Yazan Alqawalq, I always sensed how much
faith you had in me- that kept me going. You supported me during my ailments and joys
and I cannot ask for better friends.
vii
My Krembil friends Yuriy Baglaenko, Nardos Tassew, Ahmed Labban, and Elena
Darmos, thank you for the many therapeutic chats, laughs, and fun outings. You have
certainly brightened my days at Krembil. Chilbert Dong, my old friend, thank you for all
of the visits, your sense of humor and kindness empowered me to carry on. Hsain Al
Shihabi and Bashar Jabbour, thank you for your love and support over the years and for
believing in me. I have learned so much from your compassion and courage. Hsain, I
will never forget that night you let me stay over at your place while Leen was at the
hospital; without you, I would not have been able to finish my PhD. Eric Lee, thank you
for all the fancy dinners and brunches; you have certainly elevated my taste in
restaurants and coffee shops.
My dear friends at the Haigue: Mark, Mel, Scott Zoltok, Scott Baker, Lexi, Melyssa, and
Jamie, thank you from the bottom of my heart. You have made a beautiful home for me
to land in in Toronto in my first year, my toughest year. You have taught me so much
about living in a community and about friendship, I am truly grateful you came into my
path. It was so hard to leave the Haigue, but I am so fortunate we stayed in touch.
And for anyone else I missed: those who smiled, those who kept the door open behind
them, and those who offered a gesture of encouragement of any sort, thank you. Your
name is on this work.
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Table of Contents
Dedication ...................................................................................................................................... iv
Acknowledgments ........................................................................................................................... v
Table of Contents ......................................................................................................................... viii
List of Tables .................................................................................................................................. x
List of Figures ................................................................................................................................ xi
Introduction ................................................................................................................................ 2
1.1 Glaucoma ............................................................................................................................ 2
1.2 Mechanisms of glaucomatous neurodegeneration .............................................................. 3
1.2.1 Astrocyte dysfunction ............................................................................................. 4
1.2.2 NF deprivation ...................................................................................................... 12
1.2.3 Metabolic and Oxidative stress ............................................................................. 14
1.3 Phosphatidylinositol 3-kinase ........................................................................................... 18
1.3.1 PI3K role in promoting cell survival .................................................................... 19
1.3.2 PI3K interaction with RNA binding proteins ....................................................... 20
1.3.3 Poly-Adenosine RNA binding RBP ...................................................................... 23
Rationale, Hypothesis and Aims .............................................................................................. 38
2.1 Rationale ........................................................................................................................... 38
2.2 Global Hypothesis ............................................................................................................. 43
2.3 Purpose and aims .............................................................................................................. 43
Interactome Analysis Identifies RNA Binding Protein ZC3H14 as a Novel Interactor of
Phosphoinositide 3-kinase (PI3K) and a Mediator of Astrocyte Neuroprotection .................. 47
3.1 Abstract ............................................................................................................................. 47
3.2 Introduction ....................................................................................................................... 48
ix
3.3 Methods ............................................................................................................................. 49
3.3.1 Cell cultures .......................................................................................................... 49
3.3.2 PI3K inhibitor and PDGF ..................................................................................... 50
3.3.3 Kinase inhibitor library screening ......................................................................... 51
3.3.4 Cell Viability Assay .............................................................................................. 52
3.3.5 Immunoprecipitations ........................................................................................... 52
3.3.6 Mass spectroscopy ................................................................................................ 53
3.3.7 Protein identification and analyses ....................................................................... 54
3.3.8 Immunoblotting ..................................................................................................... 55
3.3.9 SiRNA transfection ............................................................................................... 56
3.3.10 Statistics ................................................................................................................ 56
3.4 Results ............................................................................................................................... 57
3.4.1 ACM neuroprotection is mediated through PI3K ................................................. 57
3.4.2 PI3K immunoprecipitation .................................................................................... 60
3.4.3 ACM induced interactome identification .............................................................. 62
3.4.4 ZC3H14 complexes with PI3k .............................................................................. 68
3.4.5 PDGF induces neuroprotective PI3K recruitment of ZC3H14 ............................. 70
3.5 Discussion ......................................................................................................................... 72
3.6 ACKNOWLEDGEMENTS .............................................................................................. 75
Conclusions and future directions ............................................................................................ 88
4.1 RBPs in the context of neurodegeneration ........................................................................ 88
4.2 ZC3H14 is essential to the stability of metabolic transcripts ........................................... 89
4.3 PDGFr as a key receptor in ACM-mediated neuroprotection .......................................... 91
4.4 Considerations for future direction ................................................................................... 92
x
List of Tables
Table 1. Top 20 PI3K interactome hits, sorted by spectral counts in 6 samples ............. 63
Table 2 List of ACM-induced PI3K interactors and corresponding CFM:ACM fold
changes ....................................................................................................................................... 67
Supplementary Table 1. A list of PI3K antibodies used in immunoprecipitation screen,
along with molecular weight of target peptide, and corresponding IgG isotype
…………………………………………………………………………………………………...80
Supplementary Table 2. Full list of proteins captured in the PI3K interactome ranked by
PSM……………………………………………………………………………………………..81
xi
List of Figures
Figure 1-1. Astrocytes characterization .................................................................................... 6
Figure 1-2. ACM or CFM was injected intravitreally 1 day prior to KA challenge. ............. 8
Figure 1-3. Characterization of LXA and LXB activity in vitro ............................................. 10
Figure 1-4 A Schematic of PI3K/AKT pathway and key targets. ........................................ 20
Figure 1-5. A schematic of the mechanism of action of the RBP KSRP in response to
NF-initiated AKT phosphorylation. ........................................................................................... 22
Figure 2-1. Characterization of primary retinal astrocytes ................................................... 39
Figure 2-2. Assessing ACM neuroprotection against metabolic stress in Ht22 cells ...... 40
Figure 3-1. PI3K is necessary for ACM-mediated neuroprotection. .................................. 59
Figure 3-2. A PI3K interactome yields highly specific capture of ACM induced proteins.
....................................................................................................................................................... 61
Figure 3-3. A summary of ACM-regulated PI3K interactions. ............................................. 65
Figure 3-4. ZC3H14 is a novel PI3K interactor. .................................................................... 69
Figure 3-5. PDGF is enriched in ACM and induces PI3K recruitment of ZC3H14. ........ 71
Figure 3-6. Summary of the mechanisms of ACM neuroprotection against metabolic
stress. ........................................................................................................................................... 74
Figure 4-1. Immunostaining of ZC3H14, and AKT in retinal sections treated with vehicle and
Kianic acid (1hr)……………………………………………………………………………………… 94
1
Chapter 1: Literature Review
(Note that this chapter incorporates material published in the following articles [1, 2])
2
Introduction
Neurodegenerative diseases of the central nervous system and the retina share a range
of pathological features and disease mechanisms [3, 4]. One example that is central
among these diseases is altered astrocyte-neuron interactions [5-8]. Thus, identifying
models that enable the study of astrocyte-neuron interactions is essential to
understanding neurodegenerative processes and developing efficacious treatment
approaches. The retina is an excellent experimental model to study astrocyte-neuron
interactions for several reasons. Firstly, the retina has a simple anatomy and is highly
accessible compared to the brain. Secondly, astrocytes and neurons reside side-by-side
in the nerve fibre layer (NFL) of the inner retina, allowing direct investigation into their
communication. Thus, studying retinal diseases is an advantageous approach to
understanding astrocyte-neuron interactions in the neurodegenerative context. The
current chapter provides an overview of Glaucoma as a model neurodegenerative
model of astrocyte-neuron interactions in the context of neurodegeneration.
1.1 Glaucoma
Glaucoma is a blinding optic nerve neuropathy that is expected to affect 76 million
people worldwide by 2020 [9, 10]. In this sense, glaucoma is the most common
neurodegenerative disease, sharing pathological features throughout the central
nervous system [4]. An integrated network of pathological mechanisms has been
attributed to glaucomatous neurodegeneration, including astrocyte reactivity, loss of
neurotrophic support, and metabolic/oxidative stress. Importantly, these common
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pathological cascades can be initiated by individual, or combinations of, trigger factors.
The current framework argues that these cascades are a good target of therapeutic
intervention, since they are common to a number of triggers, as will be described in the
next part of this review. Some key cascades, investigated in detail by our laboratories’
and others, are also discussed in the following sections.
Glaucoma predispositions include age (>55 years), ethnicity, lifestyle, and family history
[11-13]. These predispositions place patients at higher risk of developing other
glaucoma risk factors including poor ocular perfusion, deleterious biomechanical
properties of the sclera and cornea, and low central corneal thickness [14-22]. Further,
glaucoma is often observed alongside a number of comorbidities, such as low mean
arterial blood pressure, diabetes, immune disease, and age-related vascular
degeneration [23-27]. While the etiology of glaucoma subtypes may vary greatly, they
share a common characteristic degeneration of retinal ganglion cells (RGCs) and their
axons, accompanied by remodeling of the lamina cribrosa of the optic nerve head
(ONH) [28-30]. This so-called cupping of the ONH is a hallmark of glaucoma, and it
occurs due to a combination of nerve fibre degeneration [31-33], along with their
support network of glial cells, and significant remodeling of extracellular matrix (ECM)
[34, 35].
1.2 Mechanisms of glaucomatous neurodegeneration
Research into the intricate mechanisms involved in triggering the pathological chain,
and the resulting molecular cascades is laying the foundation for future effective
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treatments for glaucoma. In particular, targeting ‘cascade’ events might provide
conserved targets for therapeutic intervention of neurodegenerative mechanisms that
are not limited to specific risk factors. While these cascades progress to a common
apoptotic pathway, they provide a larger window for intervention than anti-apoptotic
approaches, which may only target those cells in the process of dying.
An integrated network of pathological mechanisms has been attributed to glaucomatous
neurodegeneration, including astrocyte reactivity, loss of neurotrophic support, and
metabolic/oxidative stress. Importantly, these common pathological cascades can be
initiated by individual, or combinations of, trigger factors. Some key cascades,
investigated in detail by our laboratories’ and others, are also discussed in the following
sections.
1.2.1 Astrocyte dysfunction
1.2.1.1 Homeostatic roles of Astrocytes
Astrocytes make up approximately 50% of the non-mylenated human ONH, and interact
closely with RGCs in the NFL (Figure 1-1A) and axonal bundles (Figure 1-1B) in the LC.
Their abundance and proximity to RGCs make their dysfunction in glaucoma of
particular importance [36]. Under physiological conditions, astrocytes and related Müller
glia support RGCs and their axons, mainly by providing metabolic and mechanical
support, vascular regulation, neurotrophic support, and ECM maintenance, in addition to
regulating ionic and neurochemical homeostasis, particularly against glutamate and
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reaction oxygen species (ROS) [37-39]. In addition, they secrete a range of
neurotrophic factors (NFs) [40, 41].
6
A B
Figure 1-1. Astrocytes characterization
(A) Astrocyte fibers (green, stained for GFAP) closely interact with neighboring RGC bodies (red, stained
for RPBMS) in the inner retina. (B) Astrocyte fibers (green, stained for GFAP) outline axonal bundles in
the ONH (red, stained for -tub). Adopted with permission from Alqawlaq,et al, 2018 [1].
Recently, our group has identified the dysregulation of astrocyte-secreted pro-resolving
lipid mediators, the lipoxins, as a contributor to inner retinal injury. Lipoxins, including
LXA and LXB, were shown to be potently neuroprotective of RGCs in vitro, and in
chronic and acute inner retinal injury and glaucoma models [2]. The protective activity
produced by transplanted retinal astrocytes could be driven by endogenous detoxifying
mechanisms or secreted signals. To distinguish these possibilities, we collected
Astrocyte conditioned media (ACM) and tested whether it was sufficient to provide
protection. Either ACM or cell-free control media incubated under identical conditions
was injected intravitreally into C57BL/6 mice 24 hours prior to Kainic Acid (KA)
challenge. RGC survival was specifically assessed by probing for the marker RBPMS,
along with complementary TUNEL staining and quantification. Significant rescue of
RGCs was observed in eyes injected with ACM compared with control media (Figure 1-
2A and B), while TUNEL staining showed that ACM injection strongly reduced GCL
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death (Figure 1-2C and D). To facilitate further study, we established an in vitro assay in
which ACM was applied to the neuronal cell line HT22. Consistent with the in vivo
results, ACM treatment produced significant HT22 cell protection from glutamate
challenge (Figure 1-2E). These data established a platform for future studies of the
ACM activity. Surprisingly, in preliminary fractionation experiments, we discovered that
a substantial portion of protective activity was contained in filtrate smaller than 3 kDa,
indicating the involvement of a small molecule (Figure 1-2F). In order to identify small
molecules in the ACM that might account for the protective activity, we performed
metabolomic screening, using liquid chromatography–tandem mass spectrometry (LC-
MS/MS). Lipid mediators were quantified to generate a lipidomic profile. Lipidomic
analyses revealed significant enrichment of the lipoxins LXA4 and LXB4 in ACM
compared with control media (Figure 1-2G).
8
Figure 1-2. ACM or CFM was injected intravitreally 1 day prior to KA challenge.
(A) ACM treatment reduced KA-induced RGC loss compared with cell-free control media (RBPMS: green,
arrows). (B) Quantification of RBPMS results showing significant RGC survival with ACM injection (n = 5).
(C) Complimentary results showing ACM-mediated reduction in TUNEL-labeled cells (green, arrows)
compared with control media. (D) Quantification of TUNEL results showing significant decrease in
ganglion cell layer apoptosis from ACM media (n = 5). (E) ACM protection was reproduced in vitro with
HT22 neuronal cells challenged with 5 mM glutamate (n = 3). (F) Substantial protective activity in ACM is
contained in a 3-kDa filtrate (n = 3). (G) High concentrations of LXA4 and LXB4 were detected in ACM
compared with control media by LC-MS/MS (n = 3). Scale bar: 50 μm. *P < 0.05; **P < 0.01; ***P < 0.005.
Bars represent SEM. Statistical analyses were performed by 1-way ANOVA with TUKEY post-hoc test.
Adopted with permission from Livne-Bar et al, 2017 [2].
9
To determine whether the protective activity we observed was direct, we treated
neuronal cells with LXA4 or LXB4 in our established in vitro assay. Increasing
concentrations of LXA4 or LXB4 produced increased viability of glutamate-challenged
HT22 cells (Figure 1-3A). Consistent with our in vivo results, LXB4 appeared more
potent than LXA4, with significant activity at 50 nM versus 500 nM, respectively (Figure
1-3A). In contrast, neither the lipoxin precursor 15-HETE nor the structurally related
trihydroxy SPM RvD2 had any protective activity up to 1 μM (Figure 1-3B). There is no
established receptor for LXB4. However, LXA4 and RvD2 signal via the ALX/FPR2 and
GPR18 receptors [42], respectively. Therefore, we assessed whether ALX/FPR2 or
GPR18 antagonists could block LXB4-mediated protection. Increasing concentrations of
WRW4 (ALX/FPR2 antagonist; IC50 = 0.23 μM) or O-1918 (GPR18 antagonist; IC50 =
5.3 μM) did not block LXB4-protective activity (Figure 1-3C). To further clarify the
LXB4 effect on cell viability, we also assessed mitochondrial membrane potential.
LXB4 treatment had no effect on membrane potential alone, but strongly protected
against glutamate-induced activity compared with vehicle (Figure 1-3D). These data
suggest that LXB4 neuroprotection is mediated through a distinct signaling mechanism
influencing mitochondrial activity independently of LXA4 or RvD2.
10
Figure 1-3. Characterization of LXA and LXB activity in vitro
(A) Treatment of HT22 neuronal cells with LXA4 or LXB4 significantly protected them from metabolic
insult in a dose-dependent manner up to 500 nM (n = 3). (B) No protective activity was observed by
treatment with up to 1 μM of the related molecules 15-HETE or RvD2 (n = 3). (C) LXB4 protective activity
at 500 nM was not blocked by treatment with increasing μM concentrations of WRW4 or the GPR18
antagonist O-1918 (n = 3). Adopted with permission from Livne-Bar et al, 2017 [2].
1.2.1.2 Characteristics of astrocyte reactivity
Following glaucomatous injury, a parainflammatory switch occurs in astrocyte function
leading to complex positive and negative non cell-autonomous effects on neighboring
RGCs and their axons. Astrocyte reactivity is characterized by a number of
morphological and functional changes that mainly include 1) an increase in type III
intermediate filament expression, namely glial fibrillary acidic protein (GFAP) and
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vimentin 2) hypertrophy, migration and invasion of axonal bundles, 3) changes in
expression of cell surface and ECM-related protein [43] 4) altered buffering capacity of
key neurochemicals, namely glutamate, as a result of reduced glutamine synthetase
(GS), 5) excess production of pro-inflammatory cytokines [44, 45], and increased
production of antioxidant enzymes [46, 47], particularly in the production of essential
precursors for the CNS peptide glutathione [48]. However, these changes do not occur
simultaneously; instead, each stage of the reactive cascade has its unique features [49].
For example, evidence suggests that early gene expression changes in the ONH mainly
affect inflammation and immune regulation, while late stage changes involve scar
formation and debris cleaning [50]. Further, astrocyte reactivity varies in duration; in
mice, it can be completely reversed 6 weeks post mild intraocular pressure (IOP)
elevation (30mmHg) for one hour, with no evidence of neurodegeneration [51].
While many examples of reactivity appear to mediate deleterious effects, emerging
evidence suggests positive consequences can also be induced [52]. These positive
responses provide another treatment opportunity, whereby astrocyte protective roles
can be harnessed and promoted to protect neurons. For example, the formation of glial
scar tissue following injury had been thought to prevent neuronal regeneration, but can
also promote regeneration given the presence of the appropriate growth factors [53].
Similarly, attenuating astrocyte hypertrophy and reactive remodeling, by knocking out
the transcription factor STAT3 led to increased RGC loss in an experimental glaucoma
and nerve crush [54]. From a more global perspective, genomic analyses of CNS
astrocyte responses by Barres et al have led to the identification of distinct induced
neurotoxic and neuroprotective astrocyte populations, respectively [8, 49]. These
12
studies also showed that activated microglia influence astrocyte reactive cascades,
indicating a complex interactive environment directs the reactive process that has only
begun to be elucidated. Thus, depending on the degree of neuronal injury, the
vulnerability of the ONH to glaucomatous damage, and the caliber of the immune
system, glial cell roles range from protective to neurodegenerative [55-57].
Taken together, these data suggest that glial reactivity is not inherently harmful, but
might be managed to promote beneficial effects. Thus, there are growing reports of so-
called ‘glioprotective’ strategies that have meaningful neuroprotective outcomes [58-60].
Also, several standard IOP lowering drugs, including Latanoprost, have been shown to
lower GFAP immunoreactivity in an IOP rat model, although the causal relationship
behind this effect remains unclear [61]. A recent study published by our group utilized
the small molecule Withaferin A to attenuate astrocyte and Müller glial reactivity in vivo,
by inhibiting GFAP and vimentin polymerization. This inhibition blocked p38 mitogen
activated protein kinase (MAPK)-dependent secretion of TNFα, resulting in dramatically
reduced neuronal apoptosis [59]. Similarly, Minocycline was shown to reduce microglial
activation and improve RGC axonal transport and survival in a chronic IOP model [62].
Additional dissection of the mechanisms regulating positive and negative aspects of the
reactive cascade may generate new therapeutic opportunities.
1.2.2 NF deprivation
Neurotrophic deprivation is a well-established mechanism of glaucomatous
degeneration [63, 64]. In experimental glaucoma models RGCs and their axons lose
13
access to key pro-survival NFs, and in contrast, show prolonged survival in response to
supplemental NFs such as brain drived neurotrophic factor (BDNF) [65-67], Nerve
growth factors (NGF) [68], neurotrophin-4/5 (NT-4/5) [69], Ciliary Neurotrophic factor
(CNTF) [70], and Glial-cell line derived neurotropic factor (GDNF) [71]. Combining NFs
generally mediates better neuroprotection outcomes in experimental models, compared
to the use of a single NF [72, 73]. However, one common finding in these studies is that
individuzal NF therapy alone has not been supported as a long-term treatment
approach for glaucoma. This is mainly due to the downregulation of tyrosine receptor
kinases (TRKB) following injury, which desensitized neurons to NFs [74]. Further, NF
therapy does not compensate for the shortage of a range of other pro-survival factors
needed for normal neuronal function.
RGCs receive NF support from two main sources: 1) axonal targets, such as the
superior colliculus, whereby NFs are transported through axons by retrograde transport,
and 2) from local glia, primarily microglia, astrocytes and Müller cells [75, 76]. In
glaucoma, the interruption of NF supply to neurons can occur from both of these
sources. First, interrupted anterograde and retrograde transport of NFs between RGCs
and their targets has been demonstrated in experimental models, by elevated IOP or
lowered CSFP [77, 78]. The pressure gradient has been shown to constrict the axons in
the LC, interrupting RGC access to key target-derived NFs. Further, studies have
shown that NFs, particularly BDNF, appear trapped in the unmyelinated ONH,
accumulating along with its TRKB receptor and motor dynein proteins, following chronic
and acute IOP elevation [79-81]. Secondly, NF productions can be altered as microglia
and astrocytic Müller cells become reactive in response to various triggers. Microglial
14
NF secretion shifts under stress, promoting pro-apoptotic or pro-survival signaling,
depending on the context [82, 83]. A recent study showed that this reduction in NF
production is reflected in lower BDNF concentrations in POAG patient aqueous humor,
lacrimal fluid and serum levels [84]. We have also demonstrated that NFL and ONH
astrocyte reactivity is associated with reduced neuroprotective lipid mediators, following
metabolic stress, leading to increased RGC vulnerability [2].
NFs promote neuronal survival through inhibition of apoptotic pathways [85]. More
specifically, NF deprivation induces the intrinsic apoptotic pathway, causing reduced
ATP production, ROS generation, cytochrome C release, and subsequent caspase
activation [86]. This occurs through the actions of MAPKs, such as c-Jun N-terminal
kinases (JNKs) and p38 [87] to mediatex mitochondrial dysfunction, and apoptosis
through the pro-apoptotic BCL-2 protein BAX [88, 89]. Thus, different trigger events,
namely elevated IOP and low CSFP, can converge to cause NF deprivation in the retina
through mechanical blockage and reduced glial secretion, resulting in increased RGC
vulnerability.
1.2.3 Metabolic and Oxidative stress
Oxidative stress, a key mechanism in glaucomatous degeneration, stems from two
major soruces: mitochondrial dysfunction, and the consequences of glial reactivity [90].
Mitochondrial dysfunction is observed following both vascular and/or mechanical injury
[91, 92], and also as part of the apoptotic cascade (both intrinsic and extrinsic) [86].
Mitochondria are abundantly present around and within the ONH, and particularly within
unmyelinated RGC axons in the retina, in order to meet the energetic demands of
15
constant neuronal communication and axoplasmic transport [93]. Their high
concentration in the ONH makes any defect in function highly amplified for the ONH’s
physiological integrity. Up to 90% of the ATP produced by mitochondria is used to
maintain action potentials and neuronal survival [94]. In addition to being energy
generating organelles, they also serve as an integration centre for the various cellular
signals, such as inflammatory cytokines, ultimately deciding the fate of RGCs.
Oxidative stress, in the form of excessive ROS production, results from impaired
mitochondrial electron chain transfer. ROS, including superoxide anions (O2-) and
hydrogen peroxide (H2O2) are highly unstable chemical species that cause DNA
damage and protein oxidation, and may also impact axonal membrane polarity [95] . In
normal physiology, ROS are produced at low levels in the cytosol and play the role of a
second messenger in several cellular transduction pathways and regulate apoptosis.
Evidence of mitochondrial stress in glaucomatous retinas is reflected by marked
upregulation of hypoxia-sensitive pathways. For example, the hypoxia-inducible factor-
1alpha (HIF-1α) is found at higher concentrations in glaucomatous retinas, compared to
normal eyes [96]. HIF-1α is a transcriptional activator that induces the expression of
several proteins whose main function is to increase O2 availability in hypoxic tissue [97].
Additionally, HIF-1α localization within the retina corresponds to positions of visual
defects in examined eyes.
Age is the number one risk factor associated with glaucoma, and this may be linked to
underlying mitochondrial dysfunction [98]. High levels of mitochondrial DNA (mtDNA)
mutations in the transgenic polymerase γ (PolG) mouse model produced decreased
16
expression of mitochondrial oxidative phosphorylation enzymes, and a higher
susceptibility to various RGC injuries [99, 100]. The coenzyme Q10 (CoQ10) was shown
to ameliorate oxidative stress–mediated RGC degeneration by preventing the
mitochondrial alterations in mice [101]. Further, Osbourne et al have suggested that
mitochondrial defects related to aging can be attenuated using non-invasive light
therapy [102, 103].
Oxidative stress occurs as a result of malfunctions in one or more of the mitochondria’s
four main functions: generation of energy in the form of ATP, regulation of ROS
production, regulation of cytosolic calcium levels, and apoptosis modulation via
mitochondrial permeability [104, 105]. An imbalance in the proportions of mitochondrial
proteins encoded by cellular and mitochondrial genes are believed to underlie these
changes [106]. Williams et al recently reported a range of differentially expressed genes
in the mitochondrial dysfunction and oxidative phosphorylation pathways in the DBA2J
glaucoma mouse model [107]. They further demonstrated that the resulting
mitochondrial vulnerability can be attenuated with Vitamin B3 supplement, providing a
candidate complementary treatment to IOP management. Oxidative stress can also
inhibit the vital mammalian target of rapamycin (mTOR) pathway, a master regulator of
nutrient sensing and mitochondrial function [108]. This inhibition acts through the
tuberous sclerosis complex (TSC1/2) and the regulated in development and DNA
damage response (REDD) proteins, leading to dendrite retraction and RGC death [109].
17
1.2.3.1 Integration of mitochondria with other cascades.
Mitochondria dysfunction can also be caused by interplay with other cascades. For
example, failure in astrocyte anti-oxidant buffering capacity plays a major role in
increasing oxidative stress and cytokine release in the retina [47]. Similarly, TNFα has
been shown to induce mitochondrial dysfunction through caspase-mediated BH3
interacting domain death agonist (BID) cleavage [110]. Our group has also
demonstrated that this loss is linked to the transcriptional co-activator peroxisome
proliferator-activated receptor γ coactivator-1α (PGC-1α), which regulates cellular
adaptive energy [46]. Rescue of this signal through agonism of the upstream AMP-
activated protein kinase (AMPK) with the AMP mimetic AICAR (5-aminoimidazole-4-
carboxamide ribonucleotide), provided therapeutic increases in astrocytic antioxidants,
particularly essential precursors for the key detoxifying CNS peptide glutathione (GSH).
This mechanism led to increased survival of RGCs and reduced glial reactivity in an
acute rat IOP-dependent ischemia model [48]. In support of this concept, a recent large-
cohort analyses showed that the AMPK-inducing drug, metformin, is associated with a
dose-dependent reduced risk of POAG in diabetic patients [111]. Further, NF withdrawal
in glaucoma also indirectly contributes to oxidative stress by activating the mitochondrial
intrinsic apoptosis pathway [85, 89].
There are three general points that emerge when discussing these cascades and
proposed interventions: 1. cascade events are not mutually exclusive, but are
overlapping and contingent on one another. For instance, while glial reactivity and
oxidative stress are often described as two distinct events, each can contribute to the
18
incidence of the other [47, 48, 90]. Thus, when targeting one glaucoma mechanism, it is
expected that other dependent mechanisms would in turn be affected. 2. Cascade
events may not be inherently deleterious, but can simply be outside of their homeostatic
range. For instance, excess nitric oxide (NO) production by reactive glia is derived from
constitutive production necessary for vascular regulation [112, 113]. This observation
may be important in determining the degree and aggressiveness of therapeutic
intervention. 3. Regardless of the induced cascades, the end result is typically RGC
apoptosis [85, 114], as described in studies utilizing animal models, such as elevated
IOP [115-118], ON crush and axotomy [117, 119], excitotoxicity [59, 120, 121], and in
human disease [122-125]. Still, some common themes emerge that are driven by
particular triggers.
1.3 Phosphatidylinositol 3-kinase
One of the key signaling pathways activated by NFs is the phosphatidylinositol 3-kinase
(PI3K) pathway. PI3ks are lipid kinases, which mediate intracellular signals regulating a
wide range of biological processes, including cell proliferation, metabolism, migration,
and protection against metabolic stress [126]. The PI3K family has three different
classes (Class I, Class II, and Class III) based on the structure, target specificity, and
cell type [127]. For the current discussion, class I will be discussed, since it is primarily
involved in transducing growth factor signaling [128]. Class IA PI3Ks, including PI3K α,
β , δ are heterodimers composed by one of the p110 catalytic subunits (p110 α , p110 β
, and p110 δ), coupled to a regulatory subunit of the p85 family, including p85α, P85β,
and p55y. (see review [129]). PI3K is activated by binding of its p85 regulatory subunit
19
directly to phosphorylated RTK, or indirectly to an adapter protein such as insulin
receptor substrate-1,2 (IRS-1,2)[130]. Following IRS activation, the signaling cascade
boosts glucose uptake through a series of phosphorylation events [131]. Alternatively,
PI3k is activated or by interaction of its p110 catalytic subunit with Ras-GTP. PI3K
activation leads to the catalyzation the second messenger polyphosphoinositide
phosphatidylinositol 3,4,5-trisphosphate (PIP3) at the cell membrane [132] and
subsequent phosphorylation of AKT [129].
1.3.1 PI3K role in promoting cell survival
Following its phosphorylation, Akt translocates in the cytosol and to the nucleus, where
it activates a number of metabolic regulators, pro-survival factors, and transcription
factors [129]. For example, phosphorylalation of AKT leads to; 1) inactivation of
apoptotic transcription factors such as the nuclear factor kappa-light-chain-enhancer of
activated B cells (NF-kB), BAD, procaspase-9 and Forkhead (FKHR) transcription
factors (Figure 1-4) [129, 133], 2) activation of metabolic regulators such as endothelial
nitric oxide synthase (eNOS) and glycogen synthase kinase (GSK)-3β, leading to
boosting of cellular bioenergetics [134, 135], and 3) activation of mammalian target of
rapamycin (mTOR), which leads to increased translation of growth proteins[135].
Furthermore, mTOR phosphorylates components of the protein synthesis machinery,
including the ribosomal protein S6 kinases (p70S6K) and 4E-binding protein (4E-BP),
resulting in anti-apoptotic activities [136]. Conversely, mTOR is inhibited in response to
energy depletion, allowing energy conservation through LKB1 (STK11)-mediated
activation of AMP-activated protein kinase (AMPK), a sensor of the cellular ATP/ADP
20
ratio. Globally, AKT regulates its targets through generating binding sites for the adaptor
protein 14-3-3 proteins, modifying their localization, activity, and stability in cytosol
[137].
Figure 1-4 A Schematic of PI3K/AKT pathway and key targets.
The PI3K/AKT pathway culminates into pro-survival, anti-apoptotic, and metabolic boosting effects.
Adopted with permission from Hennessy et al, 2005 [136].
1.3.2 PI3K interaction with RNA binding proteins
RNA binding proteins (RBP) play an important role in post-transcriptional gene
regulation. RBPs’ functions include splicing, nuclear export, and polyadenylation, which
21
are carried out through physical interactions with adenine-(or adenosine) and uridine-
rich elements of RNA [138]. A range of kinases have been shown to interact and
activate RBPs, often through adapter proteins, ultimately altering the stability or half-life
of mRNA transcripts[139-141]. Thus, RBP phosphorylation by kinases and
dephosphorylation by phosphatases functions as an on/off switch to control mRNA
decay in response to extracellular stimuli [142].
PI3K exerts part of its activity through interacting with a range of RBPs, with the adapter
protein 14-3-3 functioning as a mediator for the interaction. Among the RBP substrates
of AKT are KSRP, BRF1 (or TIS11b), NF90, CELF1, and YB1 [142]. In the case of
TIS11b, PI3K promotes the stability of Iterlukin-3 (IL-3) mRNA through AKT
phosphorylation of TIS11b, an interaction mediated by 14-3-3[143]. Following
phosphorylation of TIS11, it is sequestered in the cytoplasm, disabling it from promoting
mRNA decay[144]. The role of the PI3K pathway in controlling mRNA stability was
further illustrated by its involvement in stabilizing 20 other transcripts, through
interactions with the RBPs TIS11b and KSRP [145]. In C2C12 myoblasts, KSRP
phosphorylation at the Ser193 site by AKT detaches myogenin mRNA from KSRP in the
cytoplasm, reversing KSRP-mediated decay of myogenin mRNA (Figure 1-5) [142]. This
process occurs in conjunction with 14-3-3ζ, interaction with KSRP, translocating it from
the cytoplasm to the nucleus, and mediating the maturation of myogenin mRNA
transcript.
22
Figure 1-5. A schematic of the mechanism of action of the RBP KSRP in response to NF-initiated
AKT phosphorylation.
Following NF binding, KSRP is phosphorylated by AKT, which leads to the dissociation
of KSRP from myogenin mRNA. This dissociation leads to the stabilization of the mRNA
in the cytosol, as KSRP is inhibited from degrading it. Subsequently, KSRP associates
23
with 14-3-3 and translocates into the nucleus. Adopted with permission Thepar, R and
Denmon, A. P., 2013 [142].
1.3.3 Poly-Adenosine RNA binding RBP
Polyadenosine RNA binding proteins (Pabs) are involved in regulating several stages of
gene expression. Among the key Pabs are PABPN1 and PABPC1, as well as the
recently characterized Pab, Zinc Finger CCCH-type containing #14, or ZC3H14 [146].
Biochemical studies demonstrate that ZC3H14 utilizes five evolutionarily conserved
tandem cysteine3histidine (CCCH) zinc fingers, which binds to polyadenosine (polyA)
RNA with high affinity and specificity[147]. There are at least four known alternatively
spliced variants of ZC3H14, which give rise to four distinct protein isoforms [148].
Isoforms 1-3 have a N-terminal proline tryptophan isoleucine (PWI), which mediates
entry through the nuclear pore; thus, these isoforms are expected to localized within the
cytoplasm as well as the nucleus [149]. Isoform 4 lacks key domains needed to interact
with the nuclear pore and is therefore localized mainly within the cytoplasm [148].
Through binding with the polyA tails, ZC3H14 carries out a range of post-transcriptional
modifications on premature transcripts [147, 150]. These include control of PolyA tails
length [151], nuclear export [149], and mRNA splicing [152]. Mutation of the ZC3H14
impairs neural function in Drosophila and has been associated with cognitive deficits in
humans [153]. ZC3H14 knockout mouse models show anatomical changes in brain
ventricles as well as working memory deficits [154]. Thus far, no studies have reported
a ZC3H14 interaction with PI3K, or other kinases.
24
The THO complex 1 (Thoc1) encodes a nuclear matrix protein that also binds to PolyA
tails of mRNA [155]. As part of the THO ribonucleoprotein complex, Thoc1 protein is
recruited to premature RNA transcripts, which allows access to various RNA processing
and export machinery [156, 157]. Impaired Thoc1 function negatively impacts
transcription elongation and nuclear export, which also affects mRNA stability in the
cytoplasm [157]. A 2018 study reported that ZC3H14 interacts with THO complex to
coordinately control RNA processing, poly(A) tail length, and consequently mRNA
stability [155]. The same study showed that mRNA targets of ZC3H14 and THOC1
include the postsynaptic density protein 95 (Psd95), ATP synthase lipid-binding protein
(Atp5g1) and microtubule-associated protein tau (MAPT). Conversely, knockdown of
ZC3H14 or THO components lead to decreased levels of mature transcripts and
accumulation of Atp5g1 and Psd95 pre-mRNA in the cytoplasm. The gene encodes a
subunit of mitochondrial ATP synthase, which catalyzes ATP synthesis [158]. Thus,
ZC3H14 and THOC1’s functions may have a direct impact on cell energy synthesis and
survival.
25
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Chapter 2: Rationale, purpose, Hypothesis and Aims
38
Rationale, Hypothesis and Aims
2.1 Rationale
Until the early 1980’s, glial cells had been thought of as simply structural, binding
together neuronal tissue, including the retina, and hence their name (glia is derived from
the Greek word for glue). This concept became challenged by growing evidence
demonstrating that glial cells, and especially astrocytes, sustain neuronal functions and
homeostasis in diverse and complex ways [1]. One important facet among these
functions is the astrocyte capacity to alleviate the impact of metabolic stress in neurons
through secreted pro-survival factors. This observation is of particular importance in the
context of neurodegenerative diseases, since excessive stimulation by neurochemicals,
such as glutamate, leads to death of neurons in a number of diseases including
Alzheimer’s disease, and glaucoma. However, we currently lack an understanding of
the basic neuronal pathways involved in transducing these astrocyte signals, which
prevents us from leveraging them for future treatment or diagnostic strategies.
Studying ACM provides a window into the inner workings of astrocyte-neuron
communication and associated protective outcomes in neurons. ACM contains a
complex mixture of proteins, lipids, neurochemicals and nucleic-acid containing
exosomes [2-5]. To explore ACM components, we isolated and cultured primary retinal
astrocytes as previously described [6, 7], and subsequently collected ACM for analysis.
39
Astrocytes cultured with this protocol exhibit characteristic astrocyte morphology and a
range of specific markers, including GFAP and GS (Figure 2-1).
Figure 2-1. Characterization of primary retinal astrocytes
Astrocyte markers (I) GFAP (green), (II) GS (green) are shown in separate immunofluorescence trials in
cultured astrocytes; DAPI is used as a co-stain (blue). (III) Immuno blots of GS and GFAP in cultured
astrocytes.
We used cultured primary retinal astrocytes to collect ACM and assess its
neuroprotective effects against metabolic stress in an in vitro model, enabling high
throughput screening of involving signaling mechanisms (methods described in Chapter
40
3). ACM was shown to be highly protective against glutamate-induced metabolic stress
in transformed hippocampal neurons (Ht22), and the effect was demonstrated to have a
concentration-dependent effect (Figure 2-2).
Figure 2-2. Assessing ACM neuroprotection against metabolic stress in Ht22 cells
(I) ACM protection in Ht22 cells against 5 mM glutamate injury is demonstrated by increased metabolic
activity of neurons pre-conditioned with ACM, compared to control media. (II) ACM activity is maintained
when diluted to 25% of its volume, beyond which the neuroprotective activity is virtually lost (n=3, **
p<0.05).
However, the combined neuroprotective effect of ACM is complex, due to the wealth of
other biologics in ACM, including growth factors. Identifying the key ligands that mediate
ACM activity can be tackled through numerous approaches, including proteomic,
genomic, and pharmacological technologies. However, due to the complexity of the
ACM mixture, ‘omic’ type analyses carry with them the potential to generate large
41
biological datasets that are challenging to analyze. For example, preliminary data
generated by our group showed that boiling or trypsinizing ACM eliminates its
neuroprotective activity, strongly suggesting a protein component to the activity. This
thinking led us to initially carry out global mass spectroscopy and RNAseq analysis on
ACM and astrocytes, respectively. The analysis was unsuccessful due to two reasons:
1) the necessary presence of serum in ACM to maintain the protective activity, which
masked astrocyte-secreted proteins with its abundance, and 2) lack of a strong control
strategy for ranking sufficiently specific candidates. Eliminating serum from ACM,
through a series of optimization studies, failed to generate a suitable defined media
replacement.
An alternative approach is the use of chemical genetics screens using tool compounds
to identify specific targets induced by ACM. This approach has several advantages: 1)
in contrast with global screens, tool compounds, have defined targets that can elucidate
specific signaling pathways and ligands of interest. 2) Combining tool compound
libraries with robotic capabilities allow high throughput analyses of specific targets
simultaneously. 3) Tool compounds screens are modular in the sense that they can be
scaled up or down, based on experimental goals, to profile a wide range of tool
compound library sizes. 4) Finally, these screens can provide functional information, in
the sense that the primary endpoint is ACM induced protection. Thus, the use of tool
compound screens can identify necessary signaling pathways through which the
neuroprotection is transduced. Therefore, this strategy was ultimately chosen to identify
key neuroprotective signaling pathways activated by ACM.
42
Among the screened targets are kinases, which transduce extracellular signals
efficiently through phosphorylation of downstream targets. Kinases mediate
phosphorylation reactions by transferring the gamma phosphate of Adenosine
Triphosphate (ATP) onto hydroxyl groups of various lipids, sugars or amino acids[8].
Kinases are essential players in signaling pathways and are responsible for maintaining
cellular homeostasis, survival, and growth [9]. The current project focuses on a key pro-
survival kinase, PI3K, a key mediator of astrocyte-neuron communication. Further,
identifying the binding partners of kinases such as PI3K can provide insight into the
neuroprotective signaling pathway and its activators. Analyzing these interactions in
neurons following ACM exposure can provide valuable information into how the
neuroprotection is transduced along a specific signaling pathway, such as PI3K. Thus,
proteomic analysis was employed in this project to identify how PI3K binding patterns
change in neurons following ACM exposure.
Mass spectrometry (MS) enables high throughput identification and functional
annotation of proteins in a cell, tissue, or organisms. It is an analytical technique that
measures the mass-to-charge ratio (m/z) of an ion, generated following fragmentation of
a protein sample with high energy. It is used to identify known as well as novel
macromolecules, including proteins, and provide detailed information about their
structures. Quantitative mass spectrometry (MS) provides an effective approach to
concurrent identification and determination of concentration ratios of proteins from
different samples, eliminating inter-run variability[10]. One of these labeling methods is
featured in the current work: the isobaric tag for relative and absolute
43
quantitation (iTRAQ) technology. The technique employs isotope labeled tags that can
be covalently bonded to the N-termini and side chain amines of proteins. [11]. Due to
the isobaric design employed in iTRAQ labeling, differentially labeled peptides appear
as single peaks in MS-MS scans, which reduces the likelihood of peak
overlapping. This allows the relative quantification of proteins pooled from up to 8
different samples.
2.2 Global Hypothesis
I hypothesize that ACM mediated neuroprotection against metabolic stress is driven by
astrocyte-secreted factors. These factors activate pro-survival signaling pathways, such
as PI3K, which can be dissected and targeted to enhance neuronal survival under
stress conditions.
2.3 Purpose and aims
The purpose of the current research is to identify key signals induced by astrocyte-
secreted factors involved in protecting neurons from metabolic stress. To achieve this
goal, I will address three main aims:
1. Identify ACM-induced neuronal mechanisms, such as kinases, which can be
utilized to expand key protective signaling pathways.
a. Carry out a tool compound screen to identify key kinases and receptors
along ACM-mediated neuroprotection pathway in the glutamate sensitive
cell line Ht22.
44
b. Validate screen hits independently in Ht22 cells in primary neurons.
c. Confirm involvement of key kinases using immunoblotting.
2. Identify ACM-regulated target interactors in neurons, including downstream
effectors and upstream receptors.
a. Generate interactome data on a candidate signaling hub protein in Ht22
cells treated with ACM using mass spectroscopy.
b. Guide bioinformatic analysis to identify ACM-regulated interactors.
c. Confirm interactions using co-immunoprecipitation and immunoblotting.
3. Validate the neuroprotective activity of candidate kinases and interactors in in
vitro and in vivo metabolic stress models.
a. Assess the effect of stimulating identified protein-protein interactions on
ACM neuroprotection.
b. Assess the impact of knocking down identified interactors on ACM
neuroprotection.
Together these aims will provide new insights into how astrocytes protect neurons from
metabolic stress through secreted factors and induced neuronal pathways.
45
Chapter 2 References
1. Alqawlaq, S., J.G. Flanagan, and J.M. Sivak, All roads lead to glaucoma: Induced retinal injury cascades contribute to a common neurodegenerative outcome. Exp Eye Res, 2018.
2. Baldwin, K.T. and C. Eroglu, Molecular mechanisms of astrocyte-induced synaptogenesis. Current opinion in neurobiology, 2017. 45: p. 113-120.
3. Livne-Bar, I., et al., Astrocyte-derived lipoxins A4 and B4 promote neuroprotection from acute and chronic injury. J Clin Invest, 2017.
4. Hughes, E.G., S.B. Elmariah, and R.J. Balice-Gordon, Astrocyte secreted proteins selectively increase hippocampal GABAergic axon length, branching, and synaptogenesis. Molecular and cellular neurosciences, 2010. 43(1): p. 136-145.
5. Wang, G., et al., Astrocytes secrete exosomes enriched with proapoptotic ceramide and prostate apoptosis response 4 (PAR-4): potential mechanism of apoptosis induction in Alzheimer disease (AD). J Biol Chem, 2012. 287(25): p. 21384-95.
6. Livne-Bar, I., et al., Pharmacologic inhibition of reactive gliosis blocks TNF-α-mediated neuronal apoptosis. Cell Death Dis, 2016. 7(9): p. e2386.
7. Nahirnyj, A., et al., ROS Detoxification and Proinflammatory Cytokines Are Linked by p38 MAPK Signaling in a Model of Mature Astrocyte Activation. Plos One, 2013. 8(12).
8. Fabbro, D., S.W. Cowan-Jacob, and H. Moebitz, Ten things you should know about protein kinases: IUPHAR Review 14. Br J Pharmacol, 2015. 172(11): p. 2675-700.
9. Duncan, J.S., et al., Regulation of cell proliferation and survival: convergence of protein kinases and caspases. Biochim Biophys Acta, 2010. 1804(3): p. 505-10.
10. Wiese, S., et al., Protein labeling by iTRAQ: a new tool for quantitative mass spectrometry in proteome research. Proteomics, 2007. 7(3): p. 340-50.
11. Ross, P.L., et al., Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics, 2004. 3(12): p. 1154-69.
46
Chapter 3: Interactome Analysis Identifies RNA
Binding Protein ZC3H14 as a Novel Interactor of
Phosphoinositide 3-kinase (PI3K) and a Mediator of
Astrocyte Neuroprotection
(Please note that this chapter incorporates material from a manuscript in preparation for
publication by the authors below)
47
Interactome Analysis Identifies RNA Binding Protein ZC3H14 as a Novel Interactor of Phosphoinositide 3-kinase (PI3K) and a Mediator of Astrocyte Neuroprotection
Samih Alqawlaq, Izhar Livne-Bar, Declan Williams, Sara W Leung, Darren Chan, Anita
H Corbett, Gerold Schmitt-Ulms, Jeremy M Sivak
Candidate’s role: contributed to creating, designing and performing experiments, data
collection and analysis, as well as manuscript assembly and editing
3.1 Abstract
In a homeostatic state, astrocytes can support neuronal survival and function through a
range of secreted signals that protect against neurotoxicity, oxidative stress, and
apoptotic cascades. Thus, the analysis of astrocyte conditioned media (ACM) may
provide valuable insight into the nature of these protective mechanisms, and how they
might be promoted. Previously, we characterized a potent neuroprotective activity
mediated by ACM in neurons and the retina in metabolic stress models. However, the
molecular entity and mechanism underlying this activity remained unclear. Here, a
chemical genetics screen revealed phosphoinositide 3-kinase (PI3K) as a central player
transducing ACM-mediated neuroprotection. To identify additional proteins contributing
to the protective activity, endogenous neuronal PI3K was immunoprecipitated from
astrocytes exposed to ACM or control media, and MS/MS analyses were undertaken.
MS data analyses pointed toward only five additional proteins that co-
immunoprecipitated with PI3K and were regulated by the ACM signal. These hits
included expected PI3K interactors, such as the platelet-derived growth factor receptor
48
A (PDGFRA), and novel interactors, such as the zinc finger CCCH-type containing 14
(ZC3H14). ZC3H14 has recently emerged as an important RNA binding protein that
modifies poly-adenosine tail lengths on nascent mRNA transcripts. In downstream
validation studies we show that Platelet Derived Growth Factor-BB (PDGF-BB) strongly
activates PI3K signaling to protect neuronal cells. Finally, PDGF-BB treatment induced
recruitment of ZC3H14 to PI3K, and inhibiting this interaction eliminated ACM-mediated
neuroprotection. Thus, we identified a novel ACM- and PDGF-induced neuroprotective
signaling cascade mediated through PI3K that involves recruitment of ZC3H14.
Enhancing this pathway may present a promising strategy to promote astrocyte-
secreted neuroprotective signals.
3.2 Introduction
Astrocytes support neuronal survival through a range of homeostatic functions,
including secretions of pro-survival factors and metabolic stress mediators. Astrocyte
conditioned media (ACM) provides a window into astrocyte secretory activities and may
introduce novel biologics for the treatment of various neurodegenerative diseases.
Using a model of primary retinal astrocytes we recently demonstrated potent
neuroprotective effects of a secreted astrocyte activity in acute and chronic metabolic
injury models, both in vitro and in vivo [1]. We defined a method for collecting ACM, and
showed that it contains a complex mixture of protein and lipid neuroprotective factors.
Yet, the protective mechanism integrating these neuroprotective signals remains
unclear. Thus, the purpose of the current study is to identify and dissect the key
signaling pathways that transduce ACM-mediated protection in neurons.
49
ACM contains an array of neurotrophic factors (NF), which primarily activate the
Phosphoinositide 3-kinase (PI3K) signaling pathway [2-4]. PI3K mediates intracellular
signals regulating a wide range of neuronal processes, prominently including
bioenergetic modulation and protection against metabolic stress [5-7]. PI3K is activated
by binding of its p85 regulatory subunit directly to a phosphorylated receptor tyrosine
kinase (RTK), or indirectly to an adapter protein such as insulin receptor substrate-1,2
(IRS-1,2)[7]. However, the specific interactions, which modulate PI3K signaling in a
neurodegenerative context are still unclear.
Here we present a unique neuronal interactome that explores the shift in PI3K binding
preferences following ACM exposure. The analysis provides new insight into the
functions of PI3K through identification of a novel interaction with an RNA binding
protein (RBP).
3.3 Methods
3.3.1 Cell cultures
HT22 neuronal cells were cultured in DMEM-high glucose (Sigma, D6546)
supplemented with 10% FBS/1% penicillin/streptomycin [8]. Murine primary cortical
neurons were cultured as outlined [9]. Briefly, cortices from E16 mouse embryos were
isolated and cleaned in Hank's Balanced Salt Solution (HBSS) on ice. The tissue was
mechanically homogenized and dissociated with a Papain dissociation system
50
(Worthington, LK003150). Cortical neurons were grown in Neurobasal-A media with L-
glutamine and B27 supplement without antioxidants (Thermo, 10889038).
Primary retinal astrocytes were isolated and cultured as previously described [10, 11].
Astrocytes cultured with this protocol exhibit characteristic astrocyte morphology and a
range of specific markers, including the green fibrillary acidic protein (GFAP), vimentin,
and Glutamine Synthetase (GS), and S100A. Briefly, retinas were dissected from adult
Wistar rat eyes and placed in ice-cold EMEM (Wisent,320-005-CL) supplemented with
10% FBS/1% penicillin/streptomycin. Retinas were dissociated with a Papain
dissociation system, after which they were triturated, counted and seeded in astrocyte
growth media (Lonza, CC-4123) on the first day. Media was changed the following day
to EMEM supplemented with 5% FBS, 5% Horse serum, 1% penicillin/streptomycin. At
80% confluency, cells were placed on a rotating shaker for 6 to 8 hours to remove
microglia and then re-plated in 6-well plates at 1.5 x 105 cells/well. ACM or cell-free
control medium (CFM) was harvested after 24 hours incubation and stored at –80°C in
serum-containing media.
3.3.2 PI3K inhibitor and PDGF
To induce PI3K activity, Ht22 cells were plated in 10 cm dishes in 5% FBS DMEM.
Reduced serum conditions were used to reduce signaling baseline and enhance
detection of induced phosphorylation signals [12]. The following day, Recombinant
Murine PDGF-BB (Peprotech, 315-18) was added to cells in serum-free media at 3
51
different concentrations: 0.5, 5, 50 ng/mL. One hour later, cell lysates were collected in
NP40 lysis buffer, containing 150 mM Tris, 150 mM NaCl, in addition to
protease/phosphatase inhibitor cocktail. Various kinase inhibitors were added to cells at
over a range of concentrations, using IC50 values as guideline.
3.3.3 Kinase inhibitor library screening
In order to identify key neuronal signaling components of this protective cascade, we
used our in vitro platform to perform a chemical genetics kinase inhibitor screen
following treatment with ACM or CFM. More specifically, a library of kinase inhibitors
was used to inhibit the activity of ACM and measure the subsequent change in
neuroprotection in Ht22 cells. The study was carried out at The High-Throughput
Screening Division, University Health Network, Toronto, Ontario. A 480 tool compound
kinase inhibitor library (BIOMOL) was screened in 384 well plates at 1M, seeded with
1x103 per well, using either ACM or CFM incubated under identical conditions. Cells
received a 5mM glutamate challenge, and their survival was assessed 20 hours later
using the XTT Cell Proliferation Assay Kit (Roche, 11465015001). Selection of hit
compounds was based on two criteria: 1) the compound reduced ACM neuroprotection
by at least 10%, and 2) did not have an inherent toxic effects (less than 10% reduction
in survival) on cells.
52
3.3.4 Cell Viability Assay
Validating top hits from the screen was carried out using a similar viability assy. Briefly,
5 x 10 3 Ht22 cells, or 2 x 104 primary cortical neurons were seeded in 96-well plates;
the following day, cells were pre-treated with ACM or CFM for 3 hours, inhibitors were
then added to the cells an hour before receiving a 5mM glutamate challenge. 20 hours
later, viability was assessed using the XTT proliferation kit.
3.3.5 Immunoprecipitations
Ht22 cells were plated in 15 CM2 dishes at 70% confluency. Following a 1 hr incubation
with either ACM or CFM, cells were first fixed with 0.5% formalin solution in PBS for 10
minutes to preserve labile and transient protein-protein interactions. Formalin
neutralization was completed with 150 mM Tris base, 150 mM Glycine aolution for 10
mins. Cell lysates were collected in 0.2% NP40 lysis buffer, containing 150 mM Tris,
150 mM NaCl, in addition to protease/phosphatase inhibitor cocktail.
A library of PI3k antibodies (CST, 9655) targeting different subunits were screened to
choose the antibody with the most efficient capture efficiency (Supplementary Table 1).
25 μL of each antibody was incubated with 300 μL of fixed lysates at 4C overnight, after
which 25 μL of Protein A Sepharose slurry (GE Healthcare, CL-4B) was added to the
mixture. Following a 3-hour incubation at 4C, the IP reaction was centrifuged for 3
minutes at 1000 RPM, followed by one wash with 500 mM NaCL and two washes with
lysis buffer. Beads were eluted using 0.2% Trifluoroacetic acid (TFA) and 20%
53
Acetonitrile solution. An aliquot of lysate input, IP supernatant fraction (unbound), and
eluate was saved for confirmation by western blotting. To generate a negative control
for the affinity capture, the optimal antibody was pre-incubated with its blocking peptide
at 1:1 v/v ratio for 2 hours at room temperature, and an IP was carried out as described
above. A complimentary ZC3H14 IP was carried out using ab169061.
3.3.6 Mass spectroscopy
The preparation and analysis of immunoaffinity preparations followed previously
published methods (Wang et al. eLife 2017;6:e28401, Gunawardana et al. Mol Cell
Proteomics 2015; 14(11):3000-14). Affinity capture eluates were dried in a centrifugal
evaporator to remove the acetonitrile and trifluoroacetic acid. Next, protein samples
were denatured with 9M urea at room temperature. Subsequently, 1 M
tetraethylammonium bicarbonate (TEAB) was added to adjust the pH to 8.0, followed by
cysteine reduction for 30 min at 60°C at 5 mM tris(2-carboxyethyl) phosphine. Protein
sulfhydryl groups were then alkylated for 1 hour at room temperature in the presence of
10 mM 4-vinylpyiridine. The urea concentration was decreased below 1.5 M by dilution
with 500 mM TEAB then protein digestion was affected with porcine trypsin (Thermo
Fisher Scientific, Burlington, ON, Canada) at 37°C overnight. Isobaric labeling of the
tryptic digests with an iTRAQ 8plex reagent set (Sciex, Concord, ON, Canada) was
performed according to the manufacturer’s protocol. The iTRAQ modified digests were
pooled and the mixture purified in parallel on C18 Bond Elut OMIX columns (Agilent
Technologies, Santa Clara, CA, USA) at pH 2 and with a high pH reversed-phase
peptide fractionation kit (Thermo Fisher Scientific, Waltham, MA, USA) from which
54
fractions were collected at 12.5%, 17.5%, 22.5% and 50% acetonitrile. All purified
samples were dried in a centrifugal evaporator then reconstituted in water containing
5% acetonitrile and 0.1% formic acid then analyzed by (Thermo Fisher Scientific).
Peptide sequence information was generated from MS and MS2 scans while
quantitative iTRAQ reporter ion information was generated from MS3 scans of the ten
most abundant product ions in each MS2 spectrum. The MS, MS2 and MS3 scans
were acquired in the Orbitrap, linear ion trap and Orbitrap mass analyzers respectively.
The time allowed between MS scans was fixed to a maximum of 3 seconds, during
which the maximum number of MS2 and MS3 spectra were collected. Mass spectra
were collected during 260 minute liquid chromatography runs having acetonitrile content
increasing from 0 to 30% in 180 minutes then to 100% in 60 minutes then remaining at
100% for 20 minutes. The 25 cm long, 75 micrometer inner diameter analytical column
contained Acclaim PepMap RSLC C18 particles of 2 micrometer diameter with 100 Å
pores, and was operated at 300 nl/minute.
3.3.7 Protein identification and analyses
Protein identification and quantification was done by Mascot (Version 2.4; Matrix
Science Ltd, London, UK) and Sequest HT search engines within Proteome Discoverer
software (Version 1.4; Thermo Fisher Scientific) as well as PEAKS studio 8.5
(Bioinformatics Solutions Incorporated, Waterloo, ON, Canada) using the human
international protein index (IPI) database (Version 3.87). Protein levels were normalized
to one ACM replicate (ACM3), which was used as an internal reference.
55
To determine the extent to which PI3k interactions changed as a results of ACM
treatment, the data was filtered over three main stages: 1. Interactors with a
negative/ACM3 ratio>0.35 were filtered out to eliminate non-specific interactions. 2.
Interactors were then included on the basis of having at least a 0.8 ACM:ACM3 ratio,
which was set as an indicator of high internal reliability between ACM triplicates. 3.
Among high reliability candidates, an ACM-induced interactor was defined as a protein
with a CFM: ACM ratio of either >1.35 (downregulated) or > 0.65 (upregulated).
Identifying known interactors was done using the STRING database (http://string-
db.org), which provides an assessment and integration of protein-protein interactions,
including direct (physical) as well as indirect (functional) associations [13].
3.3.8 Immunoblotting
Cell lysates for western blots were collected in either RIPA buffer for denaturing
applications (Cell Signaling), or NP-40 (described above) for non-denaturing
applications, supplemented with protease/phosphatase inhibitor cocktail. Protein
concentrations were quantified using Bicinchoninic acid assay (BCA) (Thermo, 23225)
and equal amounts were loaded in SDS-PAGE. Proteins were transferred to
polyvinylidene fluoride (PVDF) membrane and blocked with 5% bovine serum albumin
in Tris-buffered saline with 0.1% Tween 20 (TBST), and incubated overnight at 4 °C with
the primary antibody. probed with antibodies raised PI3 Kinase p85 (CST, 4257), pan
AKT (CST,2920), Phospho-Akt (Ser473) (CST, 4060), GAPDH (Calbiochem), ZC3H14
[14], and THOC1 (Abcam, ab487). Membranes were then washed three times with
56
TBST and probed with appropriate IRDye secondary antibody (Li-Cor Biosciences,
Lincoln, NE). Blots were imaged and analyzed with an Odyssey infrared imaging system
(Li-Cor Biosciences), with each band being normalized to internal control.
3.3.9 SiRNA transfection
Stealth RNAi (Thermo) was used to deplete ZC3H14 in cultured Ht22 cells. Cells were
plated at a density of 1.5 × 105 cells per well in six-well plates in 10% FBS DMEM. Each
well received 4 µl of Lipofectamine 2000 (Thermo, 11668027), diluted in 150 µl of
Optimem media (Thermo); siRNA was also diluted in 150 µL of Optimem media.
Lipofectamine 2000-SiRNA complexes were combined with 1200 µl of serum free
DMEM, and left on for 6 hours. Next, medium containing transfection complexes was
replaced by fresh 10% FBS DMEM. ZC3H14 knockdown was evaluated by
immunoblotting hours following transfection. Ht22 cells were also transfected with
scrambled negative control – medium GC content (Thermo) or ZC3H14-1,
TGACTGACCTGAGTGTGGCACAGAA . The sequence of the ZC3H14-1 siRNA was
adopted from [15]; a predesigned siRNA was tested in parallel for optimization.
3.3.10 Statistics
For all experiments, n refers to the number of animals or biological replicates. Statistical
analyses were performed by t test or 1-way ANOVA with Tukey’s post hoc analyses.
57
3.4 Results
3.4.1 ACM neuroprotection is mediated through PI3K
In order to identify kinase signaling that is necessary to transduce ACM-mediated
neuroprotection signals, we designed a chemical genetics robotics screen (Figure 3-
1A). The kinase screen cell results show a clear separation in viability between ACM
and CFM treated samples (Figure 3-1B), demonstrating that most of the 480 kinase
inhibitors had no effect on ACM induced protection at 1M. Remarkably, out of the total
library only 10 kinase inhibitors met the selection criteria, previously defined. Six out of
the top 10 compound hits targeted the PI3k/AKT pathway, strongly implicating a role for
PI3k in transducing the protective ACM effect (Figure 3-1C). Other single-hit kinase
inhibitors targeted receptor tyrosine kinases, cyclin-dependent kinases (CDKs),
Ca2+/calmodulin-dependent protein kinase (CaMk), and the Mitosis inhibitor protein
kinase Wee1.
The effect of PI3K inhibition, and downstream targets, was verified independently in
Ht22 cells and in primary cortical neurons. PX866, (IC50: 0.1-88 nM) [16]) a potent open
ring analogue of Wortmannin [17], reduced ACM-mediated neuroprotection by more
than 80% in Ht22 cells, without causing additional cytotoxicity (Figure 3-1D). We also
reproduced the ACM neuroprotective effect in primary cortical neurons, and verified that
the PI3K inhibitor, ZSTK474 (IC50: 0.7 nM-210 nM)[18, 19], and the AKT inhibitor
GSK690693(IC50: 20 nM-890 nM) [20], similarly blocked the activity (Figure 3-1E).
Next, we assessed whether ACM treatment is sufficient to activate the PI3K pathway by
immunoblotting for AKT phosphorylation following ACM addition in Ht22 cells. ACM
58
rapidly mediated AKT phosphorylation in neurons as reflected by a significantly
increased AKT phosphorylation signal by 0.5 hr following ACM addition, and still
detectable up to 4 hours later (Figure 3-1F,G).
59
Figure 3-1. PI3K is necessary for ACM-mediated neuroprotection.
(A) Overview of chemical genetics screen. (B) Scatter blot of XTT absorbance values for 480
kinase inhibitors screened under ACM (red) and CFM (black) conditions in Ht22 cells. A clear
separation is observed between treatment conditions for most compounds, indicating general
ACM protection compared to CFM. (C) Hits were considered inhibitors that reduced ACM
protective activity, but did not affect CFM conditions. Of the top 10 hits, 7 targeted the PI3K/AKT
pathway. (D) As validation, PX866, an open ring analogue of the PI3K inhibitor Wortmannin,
was used to inhibit ACM activity in HT22 cells (n=3). (E) Similarly, the PI3K inhibitor ZSTK474,
and AKT inhibitor GSK690693 each effectively block ACM activity in murine primary cortical
neurons (n=3). (F,G) ACM treatment induces rapid phosphorylation of AKT as early as 0.5
hours, a signal that can be observed up to 4 hours after later (n=3)(**p < 0.01; bars are S.E.M).
60
3.4.2 PI3K immunoprecipitation
PI3K activation can initiate pleotropic signaling through a variety of described pathways
[21, 22]. However, identifying downstream signaling nodes provides insight into the
precise functional outcome of ACM-mediated neuroprotection. In order begin unraveling
this signaling cascade we initiated a study to immunoprecipitated PI3K for the purpose
of generating an interactome of the ACM-induced signaling complex compared to
control CFM. This was achieved by designing a strategy to immunoprecipitate
endogenous PI3K in crosslinked Ht22 cell lysates, following treatment with ACM or
CFM, and analyzing co-precipitates using mass spectroscopy (Figure 3-2A). As
endogenous PI3K interactomes have not been well described in neuronal cells, an
antibody panel was first probed for their ability to effectively deplete and elute PI3K from
crosslinked Ht22 cell lysates (Supplementary Figure 1A). Ab2, which targets the p85
subunit of PI3K, was chosen as it produced the strongest PI3K immunodepletion in the
unbound fraction, combined with the strongest elution (Supplementary Figure 1B). In
order to differentiate true PI3k interactors from non-specific binders, a negative control
was generated by carrying out a parallel immunoprecipitation by pre-incubating Ab2
with its blocking peptide. Under this control condition there was minimal depletion of
PI3k in the unbound fraction, and a reduced PI3k elution band (Figure 3-2B).
Subsequent coomassie staining of blots shows highly specific capture of PI3k, as
reflected by generally clean eluate lanes compared to the corresponding input and
unbound lanes (Figure 3-2B).
61
Figure 3-2. A PI3K interactome yields highly specific capture of ACM induced proteins.
(A) A schematic of the interactome experimental design: Cell lysates were isolated from Ht22 cells treated
with ACM (n=3), CFM (n=3). Lysates were lightly fixed and submitted to immunoprecipitation of
endogenous PI3K, along with an additional negative control with a PI3K blocking peptide. Covalent
modifications of primary amines in all 7 samples were done with iTRAQ labeling, after which samples
were pooled to be analyzed concurrently by MS/MS. (B) Following a PI3K IP screen, Ab2 generated
depletion of PI3K in the unbound fraction, and produced a strong band in the eluted fraction. An Ab2
blocking peptide inhibited the capture of PI3K as reflected by no depletion in the unbound fraction, and a
reduced PI3K band in the eluted fraction. (C) A coomassie stain of the blot indicates PI3K capture was
highly specific as shown by a relatively clear eluate lane compared to the input and unbound lanes. (D)
Box plots showing Log2 fold change of PI3K regulatory subunit count intensities in all conditions,
normalized to ACM3, which was chosen as internal control. The peptide was abundant in all ACM and
CFM conditions, but very low in the negative control, as expected, confirming specificity of affinity capture
and negative control performance.
62
3.4.3 ACM induced interactome identification
Proteome Discoverer sequencing of the LC-MS data from PIK3R1 immunoprecipitates
identified 293 proteins with 12117 peptide-to-spectrum matches (PSMs) at or exceeding
95% confidence. An inclusion criterion of at least 2 spectral counts was established to
select quantified proteins, while proteins with less than 2 assigned spectral counts were
considered unquantified. The protein list was sorted based on abundance, as
determined by PSM (Table 1). Two major observations were made that highlight the
selectivity of the affinity capture: 1) PI3k subunits, both catalytic and regulatory, were
the most abundant in the interactome with more than 2000 PSMs for each (Table 1),
indicating highly specific immunoprecipitation of PI3K, and 2) the abundance level of
PI3K subunits in the negative control was at least 6 folds lower than that of other
experimental conditions (Figure 3-2C). PIK3CA and PIK3CB were the second and third
most abundant proteins based on spectral counting respectively, with over 2000 PSMs
each, indicating that the PI3K complex was highly purified (Table 1).
63
Table 1. Top 20 PI3K interactome hits, sorted by spectral counts in 6 samples. The ratio
columns represent average iTRAQ intensity ratios observed for a given protein. Hits
were filtered to include proteins with a minimum 0.8 ratio to the internal reference
ACM3, to ensure high reliability, and 0.4 negative control/ACM ratio to eliminate non-
specific binders. Note that the most abundant hits are the regulatory (p85) and catalytic
(p110) PI3K subunits.
Accession Description PSM CFM1/ ACM3
ACM1/ ACM3
CFM2/ ACM3
ACM2/ ACM3
CFM3/ ACM3
negative ctrl/ACM3
IPI00263878.2 Phosphatidylinositol 3-kinase regulatory subunit alpha
2821 0.826 0.896 0.995 0.891 0.938 0.106
IPI00136110.4 Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit beta isoform
2108 0.83 0.885 1.078 0.804 0.926 0.114
IPI00331708.3 Isoform 1 of MKL/myocardin-like protein 1
1551 0.9 0.858 1.074 0.906 1.016 0.069
IPI00323357.3 Heat shock cognate 71 kDa protein
735 0.99 0.853 1.126 0.822 0.904 0.133
IPI00119627.1 Insulin receptor substrate 1 488 0.69 0.866 0.846 0.861 0.734 0.124
IPI00379844.5 Insulin receptor substrate 2 359 0.753 0.885 0.934 0.851 0.744 0.278
IPI00117159.2 Phosphatidylinositol 3-kinase regulatory subunit beta
171 0.681 0.84 0.925 0.98 0.926 0.059
IPI00230632.2 Isoform Cas-A of Breast cancer anti-estrogen resistance protein 1
148 1.152 0.954 1.204 0.964 1.04 0.289
IPI00406794.2 GRB2-associated-binding protein 1
106 0.718 0.847 0.785 0.892 0.603 0.097
IPI00319992.1 78 kDa glucose-regulated protein
102 0.858 0.804 1.064 0.806 0.788 0.075
IPI00554929.3 Heat shock protein HSP 90-beta
91 0.865 0.843 0.942 0.868 0.733 0.18
IPI00223902.2 Isoform 2 of Uncharacterized protein KIAA1310
66 0.84 1.052 0.965 1.139 0.874 0.013
IPI00987441.1 Uncharacterized protein (Fragment)
60 1.497 1.028 1.233 1.085 1.724 0.292
IPI00139780.1 60S ribosomal protein L23 42 0.696 0.832 0.883 0.866 0.788 0.072
IPI00323806.4 Putative uncharacterized protein
33 0.935 0.937 1.156 0.865 0.94 0.23
IPI00653962.1 Uncharacterized protein 30 1.246 0.906 1.809 0.816 1.94 0.089
IPI00762542.2 40S ribosomal protein S11 29 0.897 0.917 0.881 0.842 0.753 0.108
IPI00465880.4 40S ribosomal protein S17 27 0.706 0.922 0.963 0.949 0.558 0.164
IPI00466258.2 Isoform 1 of SH3 domain-containing kinase-binding protein 1
25 0.786 0.989 1.009 0.934 0.556 0.126
IPI00986371.1 60S ribosomal protein L27a-like
20 1.679 0.8 1.379 0.83 0.846 0.258
64
Additional bioinformatic analyses further narrowed down the list of interactors to achieve
three main criteria: 1) capturing quantifiable hits with at least 2 PSMs, 2) filtering out
non-specific binders using the negative control as a reference, and 3) including
reproducible hits across the three triplicates. A total of 122 proteins were quantified from
three or more MS3 spectra having reporter ion signals representing all six PIK3R1
immunoprecipitates (all three CFM treated replicates and all three ACM treated
replicates). Of these, 90 proteins had negative control/ACM3 iTRAQ ratios of less than
0.35, indicating that they were co-enriched with PI3KR1 specifically. Further, only 26
candidate PI3KR1 interactors fit the reproducibility criteria of having median reporter ion
signals varying less than 20% among all three ACM replicates (Figure 3-3A).
65
Figure 3-3. A summary of ACM-regulated PI3K interactions.
(A) Overview of PI3K interactors and subsequent bioinformatic analysis. (B) A total of five PI3K
protein-protein interactions were upregulated following ACM treatment. The receptor tyrosine
kinase Pdgfra, and adaptor proteins Ywhaz and Ywhae have been previously established as
PI3K interactors through a variety of sources. In addition two novel ACM induced interactors
were detected; the poly-A RBPs ZC3H14 and THOC1.
Beyond identifying specific PI3K interactors, we also investigated how PI3K binding
preferences shifted following ACM treatment. Thus, we investigated interactors that, in
addition to having high reproducibility and specificity ratios (previously defined), have a
CFM/ACM ratio between 0.65 and 1.35, indicating ACM regulated binding with PI3K.
Remarkably, a total of only 5 interactors met these criteria, all showing increased
binding with PI3K following ACM treatment (Table 2). Three ACM-induced interactors
66
are known based on established literature, including the platelet-derived growth factor
receptor alpha (PDGFRA), and two 14-3-3 adaptor proteins [23, 24]. However, two of
the identified ACM-induced binders are novel, previously unknown PI3k interactors
(Figure 3-3B). These novel interactors include the Isoform 1 of Zinc finger CCCH
domain-containing protein 14 (Zc3h14) and Isoform 1 of THO complex subunit 4
(Thoc1). Intriguingly, both of these novel interactors are involved in mRNA
polyadenylation and processing.
67
Table 2 List of ACM-induced PI3K interactors and corresponding CFM:ACM fold
changes
Accession Description PSM
Sum of Peptides
CFM1/ ACM3
CFM2/ ACM3
CFM3/ ACM3
Average fold
change Negative/ACM
IPI00461416.4 Isoform 1 of Zinc finger CCCH
domain-containing protein 14 (Zc3h14)
29 99 0.48 0.56 0.54 0.53 0.186
IPI00844650.1 Isoform 1 of Alpha-
type platelet-derived growth factor
receptor (Pdgfra)
24 70 0.62 0.52 0.45 0.53 0.392
IPI00114407.2 Isoform 1 of THO
complex subunit 4 (Thoc1)
15 60 0.31 0.44 0.38 0.38 0.03
IPI00116498.1 14-3-3 protein
zeta/delta (Ywhaz)
7 52 0.42 0.51 0.42 0.45 0.129
IPI00118384.1 14-3-3 protein epsilon (Ywhae)
6 41 0.42 0.51 0.43 0.45 0.143
68
3.4.4 ZC3H14 complexes with PI3k
Among the ACM-regulated interactors is the RBP ZC3H14, which we report here as a
PI3K interactor for the first time. ZC3H14 is an RBP that has been shown to stabilize
messenger RNA(mRNA) transcripts through modifying their poly-Adenosine (polyA) tail
length [25-27]. Thus, observing PI3K-ZC3H14 interaction fits with the role of PI3K/AKT
pathway in promoting cell survival through boosting protein synthesis [2, 28]. To confirm
the interaction between PI3k and ZC3H14 biochemically, Co-Ip were carried out by
immunoprecipitating PI3K and probing for ZC3H14 (Figure 3-4A). ZC3H14 has three
known isoforms; isoform 1, which was identified in the interactome, as well as isoform 2
and 3 all co-immunoprecipitated with PI3k. In order to confirm the identity of the eluted
band, ZC3H14 was knocked down (Figure 3-4B). A separate PI3k IP was carried out in
ZC3H14 knockdown Ht22 cell lysates compared to scrambled control, and followed by
probing for ZC3H14. Depletion of ZC3H14 led to loss of the eluted band (Figure 3-4C).
Finally, a reverse Co-Ip of the two proteins was carried out by immunoprecipitating
ZC3H14, followed by probing for PI3K p85, which further supported the interaction
(Figure 3-4D).
69
Figure 3-4. ZC3H14 is a novel PI3K interactor.
(A) Co-immunoprecipitation (Co-Ip) of PI3K was carried out in Ht22 cells and probed with an
antibody to ZC3H14 to verify interaction between the two proteins (I). Successful IP of PI3K was
confirmed by blotting for PI3K (II). (B) The efficiency of knockdown was verified by
immunoblotting for ZC3H14 in corresponding lysates, compared to control. (C) ZC3H14 was
knocked down in Ht22 cells. Eluates from the IP in knockdown lysates show a missing ZC3H14
band, compared to control. (D) As a further validation for the interaction, a reverse Co-IP was
carried out by immunoprecipitating ZC3H14 and probing for PI3K. Successful ZC3H14 IP is
demonstrated by immunoblotting for ZC3H14.
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3.4.5 PDGF induces neuroprotective PI3K recruitment of ZC3H14
The MS/MS interactome results suggested that PDGFRA binding to PI3K was
increased following ACM addition. To validate this finding, we first investigated whether
exogenous PDGF activates the PI3K pathway in HT22 cells. Addition of recombinant
PDGF-BB rapidly induced robust AKT phosphorylation at 5 and 50ng/mL, indicating
activation of the PI3k pathway (Figure 3-5A). This is consistent with the ACM-induced
activation previously shown in Figure 1E, and is consistent with PDGF-BB as a
neuroprotective component of ACM. Next, we investigated whether recombinant PDGF-
BB can account for the neuroprotection of ACM in Ht22 cells. Thus, we returned to the
glutamate injury model previously described for the chemical genetics screen. The
experiment demonstrated a robust protection effect mediated by recombinant PDGF-BB
at 50 ng/mL (Figure 3-5B).
Since PDGFRA and ZC3H14 were both components of the PI3K complex in the ACM
interactome, we investigated whether PDGF treatment affects PI3k-ZC3H14 binding. A
PI3K Co-IP was carried out, as previously described, following 20 ng/mL PDGF-BB and
vehicle treatment. For each IP, equal concentrations of cell lysate, antibody, and beads
were used. The resulting blots demonstrate a dramatic increase in ZC3H14 elution. This
observation is supported by complimentary ZC3H14 depletion in the unbound fraction of
PDGF treated cells compared to the vehicle control (Figure 3-5C). In comparison, the
PI3K depletion and elution were consistent for each condition (Figure 3-5C). As a
functional validation, knocking down ZC3H14 rendered Ht22 cells insensitive to PDGF-
71
mediated neuroprotection (Figure 3-5D). Together, these data suggest PDGF treatment
increases PI3K recruitment of ZC3H14 to mediate the neuroprotective signal.
Figure 3-5. PDGF is enriched in ACM and induces PI3K recruitment of ZC3H14.
(A) Recombinant PDGF treatment produces a strong p-akt signal in Ht22 cells at 5 ng/mL and
50 ng/mL, indicating PI3K pathway activation. (B) A robust protection effect is mediated by
recombinant PDGF-BB at 50 ng/mL against glutamate-induced metabolic stress (n=3). (C)
PDGF treatment leads to increased association between PI3K and ZC3H14 in coIP eluates.
Following PI3K IP the ZC3H14 band is increased in PDGF treated cells compared to control.
Immunoblotting of PI3K was also carried out to confirm equal amounts of captured PI3K in both
conditions. (D) ZC3H14 knockdown eliminates PDGF-mediated neuroprotection against
glutamate injury in Ht22 cells (n=3)(**p < 0.01; bars are S.E.M).
72
3.5 Discussion
Analysis of ACM is an established approach to investigating astrocyte-neuron
interactions and identifying novel astrocyte-derived neuroprotective factors against
metabolic injuries [1, 29-31]. This study used a combination of chemical genetics
screening and mass spectroscopy to identify key signaling pathways and ligands
involved in ACM-mediated neuroprotection against metabolic stress. The chemical
genetics screen identified PI3K as the main signaling hub for ACM activity; we further
validated this finding in primary cortical neurons. Following a highly specific PI3K
immunoprecipitation in crosslinked Ht22 lysates, we used mass spectroscopy to
generate a PI3K interactome with the goal of identifying upstream effectors and
downstream targets. The interactome lead to the identification of a novel PI3K-ZC3H14
interaction, which was upregulated by ACM addition in Ht22 cells. Further, the
interactome showed that PI3K-PDGFr interaction was also upregulated by ACM,
strongly implicating PDGF as a key ligand in ACM-mediated neuroprotection. Follow up
validation studies showed that PDGF protected neurons from glutamate metabolic
stress in vitro, and increased PI3K recruitment of ZC3H14. Finally, we showed that
ZC3H14 knockdown eliminated PDGF-mediated neuroprotection, which highlights the
importance of ZC3H14 in coordinating neuroprotective signals against metabolic stress.
PDGF is secreted primarily by macroglia, including astrocytes and retinal Muller cells,
as well as by neurons, mediating short-range paracraine communication between
astrocytes and neurons [32]. It has also been shown to protect neurons against
oxidative stress and NMDA-induced metabolic stress in primary neurons and retinal
73
ganglion cells [33-37]. Activation of the PI3K-AKT pathway with NFs such as PDGF is
known to promote cell survival through inactivation of apoptotic factors [6, 38], activation
of metabolic regulators [39, 40], and activation of mammalian target of rapamycin
(mTOR), which leads to increased translation of growth proteins and further anti-
apoptotic signaling [38, 41]. Less known is PI3K’s protective role through interacting
with a range of RBPs, with the adapter protein 14-3-3 functioning as a mediator,
ultimately leading to stabilizing of mRNA transcripts [42, 43].
RBPs play an important role in post-transcriptional gene regulation through physical
interactions with adenine-(or adenosine) and uridine-rich elements of RNA [44, 45]. An
example of these RBPs is ZC3H14, which binds polyadenosine (polyA) tails and carries
out a range of post-transcriptional modifications on premature transcripts [46, 47].
These include control of PolyA tail length [27], nuclear export [48], and mRNA splicing
[49]. A 2018 study reported that ZC3H14 interacts with THO complex 1 (THOC1) to
coordinately control RNA processing, poly(A) tail length, and consequently mRNA
stability [25]. THOC1 was among the ACM-regulated novel PI3K interactors in our
screen. Morris and Corbett also showed that mRNA targets of ZC3H14 and THOC1
include the postsynaptic density protein 95 (Psd95), ATP synthase lipid-binding protein
(Atp5g1) and microtubule-associated protein tau (MAPT), which are integral to neuronal
function and metabolism. Further, altering ZC3H14 expression impairs neural function in
Drosophila and has been associated with cognitive deficits in humans [50], anatomical
changes in mouse brain ventricles as well as working memory deficits [26]. Thus, our
74
findings provide a novel insight into the role of PI3K and ZC3H14 in regulating neuronal
function and survival (Figure3- 6).
Figure 3-6. Summary of the mechanisms of ACM neuroprotection against
metabolic stress.
(1) PDGF, a component of ACM, binds to PDGFr in neurons, (2) phosphorylating PI3K,
which in turn binds to the RBP ZC3H14. (3) Activated ZC3H14 then binds with target
mRNAs’ polyA tails, (4) leading to increased stability of pro-survival and metabolic
mRNAs.
Future studies will investigate whether ACM-mediated neuroprotection can be
linked to the stability of mRNA targets of ZC3H14 and THOC1. Exploring this novel
interaction further will provide a better understanding of how astrocytes communicate
with neurons to maintain their survival. Further, it may ultimately lead to the
development of novel therapeutic approaches for the treatment of neurodegenerative
diseases.
75
3.6 ACKNOWLEDGEMENTS
CIHR, NSERC, TWGH Foundation Glaucoma Research Chair (JS), VSRP Program
(SA).
76
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80
Supplementary Tables
Supplementary Table 3. A list of PI3K antibodies used in immunoprecipitation
screen, along with molecular weight of target peptide, and corresponding IgG
isotype
Ab # Antibody description and catalogue number Size (kD)
IgG isotype
Ab 1 Phospho-PI3 Kinase p85 (Tyr458)/p55 (Tyr199) Antibody 4228
60 and 85
Rabbit IgG
Ab 2 PI3 Kinase p85 (19H8) Rabbit mAb 4257 85 Rabbit IgG
Ab 3 PI3 Kinase p110α (C73F8) Rabbit mAb 4249 110 Rabbit IgG
Ab 4 PI3 Kinase p110β (C33D4) Rabbit mAb 3011 110 Rabbit IgG
Ab 5 PI3 Kinase Class III (D4E2) Rabbit mAb 3358 100 Rabbit IgG
Ab 6 PI3 Kinase p110γ (D55D5) Rabbit mAb 5405 110 Rabbit IgG
Ab 7 PI3 Kinase p85 Antibody #4292 85 Rabbit IgG
81
81
Supplementary Table 4. Full list of proteins captured in the PI3K interactome ranked by PSM
Accession Description
Sum(Protein
Sequence Coverage)
Sum(#
Proteins)
Sum of
Unique Peptides
Sum of
Peptides
Sum of Peptide-
to-
spectrum matches (# PSMs)
Cf1/ACM3
(113/118)
Cf1/ACM3
Count
ACM1/ACM3
(114/118)
ACM1/ACM3
Count
Cf2/ACM3
(115/118)
Cf2/ACM3
Count
ACM2/ACM3
(116/118)
ACM2/ACM3
Count
Cf3/ACM3
(117/118)
Cf3/ACM3
Count
negative
ctrl/ACM3 (119/118)
negative
ctrl/ACM3 Count
IPI00263878.2 Phosphatidylinositol 3-kinase regulatory subunit alpha 88.54% 9 40 63 2821 0.826 899 0.896 905 0.995 907 0.891 898 0.938 901 0.106 344
IPI00309224.5
Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha isoform 77.62% 8 59 81 2267 0.669 763 0.85 772 0.914 772 0.792 767 0.777 766 0.084 360
IPI00136110.4
Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit beta isoform 66.64% 3 52 77 2108 0.83 676 0.885 674 1.078 681 0.804 667 0.926 677 0.114 308
IPI00331708.3 Isoform 1 of MKL/myocardin-like protein 1 54.56% 12 29 41 1551 0.9 382 0.858 389 1.074 388 0.906 392 1.016 390 0.069 152
IPI00117352.1 Tubulin beta-5 chain 65.77% 2 4 25 866 0.747 295 0.814 294 0.915 294 0.78 294 0.757 291 0.058 196
IPI00169463.1 Tubulin beta-2C chain 57.08% 3 1 21 790 0.802 246 0.823 245 0.951 246 0.767 245 0.781 242 0.061 170
IPI00323357.3 Heat shock cognate 71 kDa protein 74.30% 8 30 42 735 0.99 265 0.853 264 1.126 271 0.822 265 0.904 264 0.133 173
IPI00109073.5 Tubulin beta-4 chain 45.95% 2 1 16 717 0.803 215 0.821 214 0.958 215 0.771 214 0.79 211 0.061 148
IPI00338039.1 Tubulin beta-2A chain 62.02% 4 1 21 701 0.797 216 0.795 215 0.957 214 0.743 214 0.783 213 0.056 145
IPI00117348.4 Tubulin alpha-1B chain 69.18% 4 1 22 538 0.666 242 0.758 245 0.893 246 0.803 247 0.708 244 0.065 150
IPI00110753.1 Tubulin alpha-1A chain 69.18% 3 1 22 528 0.655 241 0.753 244 0.878 245 0.791 245 0.688 242 0.064 154
IPI00403810.2 Tubulin alpha-1C chain 63.70% 2 1 20 517 0.655 234 0.759 237 0.89 238 0.796 238 0.683 235 0.062 151
IPI00119627.1 Insulin receptor substrate 1 51.42% 2 24 51 488 0.69 155 0.866 157 0.846 156 0.861 157 0.734 156 0.124 95
IPI00885697.2
phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit delta isoform isoform c 46.46% 8 25 40 467 0.824 143 0.898 143 1.037 146 0.783 137 0.875 141 0.126 74
IPI00112251.1 Tubulin beta-3 chain 44.44% 4 1 18 458 0.817 152 0.814 150 0.955 152 0.768 152 0.79 151 0.054 115
IPI00379844.5 Insulin receptor substrate 2 37.55% 1 19 36 359 0.753 94 0.885 96 0.934 99 0.851 97 0.744 95 0.278 54
IPI00130391.1 protease, serine, 1 19.92% 4 2 5 278 0.746 102 0.741 108 1.155 109 0.739 107 1.122 109 1.433 111
IPI00122928.1 Tubulin beta-6 chain 46.09% 2 1 17 235 0.878 74 0.841 73 0.962 75 0.745 74 0.785 74 0.062 57
IPI00331628.5 Peroxisomal multifunctional enzyme type 2 43.95% 1 15 28 182 1.121 58 0.594 49 0.875 58 0.743 55 0.676 55 0.659 57
IPI00117159.2 Phosphatidylinositol 3-kinase regulatory subunit beta 35.32% 1 2 22 171 0.681 60 0.84 61 0.925 60 0.98 61 0.926 60 0.059 30
IPI00338309.5 Cardiac Ca2+ release channel 21.40% 2 3 69 161 0.293 5 0.578 4 1.654 6 0.272 3 0.121 4 0.357 3
IPI00755181.1 keratin, type I cytoskeletal 10 37.79% 4 6 19 153 0.373 38 0.206 38 0.43 41 0.305 37 0.519 42 0.209 38
82
IPI00230632.2
Isoform Cas-A of Breast cancer anti-estrogen resistance protein 1 39.59% 3 10 26 148 1.152 43 0.954 43 1.204 43 0.964 43 1.04 42 0.289 24
IPI00970652.1
protein-L-isoaspartate(D-aspartate) O-methyltransferase 76.84% 5 8 16 143 1.139 47 0.673 45 1.209 45 0.697 45 0.852 46 0.936 47
IPI00850337.2 glyceraldehyde-3-phosphate dehydrogenase-like isoform 1 48.90% 29 6 12 126 0.499 36 0.782 36 0.735 36 0.644 36 0.573 36 0.096 30
IPI00759953.1 trypsin 5 19.92% 7 2 5 123 1.322 13 0.747 13 1.308 12 0.807 12 1.335 12 1.462 13
IPI00139795.2 60S acidic ribosomal protein P2 78.26% 2 5 7 118 0.85 56 0.579 56 0.648 56 0.719 56 0.417 54 0.287 53
IPI00649335.3 Uncharacterized protein 32.56% 2 1 8 116 0.07 11 0.104 12 2.667 12 0.052 8 0.064 11 0.052 5
IPI00988790.1 60S ribosomal protein L13-like 49.29% 7 5 12 116 1.072 39 0.87 37 1.42 40 0.788 37 1.425 37 0.334 36
IPI00553798.2 AHNAK nucleoprotein isoform 1 18.58% 2 2 39 111 0.391 14 0.51 14 0.608 14 0.545 14 0.3 14 0.049 12
IPI00406794.2 GRB2-associated-binding protein 1 36.69% 1 9 17 106 0.718 37 0.847 36 0.785 39 0.892 37 0.603 35 0.097 28
IPI00348328.4 keratin Kb40 39.33% 3 2 25 105 0.477 34 0.192 34 0.521 34 0.315 34 0.808 34 0.229 33
IPI00319992.1 78 kDa glucose-regulated protein 36.18% 1 3 20 102 0.858 30 0.804 30 1.064 30 0.806 30 0.788 30 0.075 19
IPI00461416.4 Isoform 1 of Zinc finger CCCH domain-containing protein 14 46.12% 4 11 29 99 0.48 21 0.88 20 0.559 22 0.707 20 0.536 21 0.186 12
IPI00221797.1 Keratin, type II cytoskeletal 75 31.40% 8 2 14 98 0.417 35 0.174 32 0.558 35 0.396 35 1.006 35 0.231 29
IPI00222228.5 hypothetical protein LOC239673 25.84% 5 1 13 93 0.596 12 0.173 12 0.76 12 0.288 12 0.657 12 0.192 10
IPI00554929.3 Heat shock protein HSP 90-beta 37.43% 4 4 19 91 0.865 24 0.843 24 0.942 24 0.868 24 0.733 24 0.18 16
IPI00889253.1
Isoform 4 of GTPase-activating protein and VPS9 domain-containing protein 1 37.43% 6 1 31 90 0.051 3 0.188 3 2.722 3 0.024 3 0.168 3 0.038 2
IPI00622240.4 Keratin, type II cytoskeletal 2 epidermal 33.66% 2 3 17 87 0.84 16 0.307 13 0.929 17 0.349 15 1.343 16 0.353 16
IPI00230365.5 Keratin, type I cytoskeletal 17 42.26% 4 2 18 86 0.419 21 0.178 19 0.475 22 0.337 19 0.7 21 0.243 20
IPI00307837.6 Elongation factor 1-alpha 1 47.40% 4 10 15 85 0.623 34 0.797 34 1.082 34 0.749 34 0.694 34 0.137 27
IPI00354151.3 RIMS-binding protein 3 11.52% 2 1 16 82 0.117 5 0.252 7 3.035 7 0.043 3 0.237 6 0.093 3
IPI01027267.1 disco-interacting protein 2 homolog C 17.03% 5 1 17 80 0.258 7 0.856 8 1.187 8 0.3 6 0.39 7 0.772 8
IPI00625729.2 Keratin, type II cytoskeletal 1 37.52% 3 2 22 73 0.612 25 0.294 22 0.693 25 0.405 25 0.908 25 0.431 22
IPI00473320.2 Putative uncharacterized protein 42.25% 27 5 15 72 0.415 25 0.574 26 0.65 26 0.569 26 0.385 26 0.074 21
IPI00346834.1 Keratin, type II cytoskeletal 2 oral 30.64% 2 1 16 71 0.861 17 0.308 16 1.458 18 0.597 16 1.293 18 0.243 12
IPI00124287.2 Polyadenylate-binding protein 1 46.23% 10 5 25 71 0.75 16 0.81 16 0.963 17 0.782 16 0.657 17 0.326 13
IPI00844650.1
Isoform 1 of Alpha-type platelet-derived growth factor receptor 28.47% 3 9 24 70 0.616 15 0.676 16 0.524 15 0.823 18 0.455 16 0.392 6
IPI00131138.11 Isoform 1 of Filamin-A 24.86% 8 7 42 68 0.76 9 0.814 10 0.791 10 0.654 10 0.509 9 0.477 9
IPI00223902.2 Isoform 2 of Uncharacterized protein KIAA1310 49.83% 3 3 25 66 0.84 3 1.052 3 0.965 3 1.139 3 0.874 3 0.013 1
IPI00114407.2 Isoform 1 of THO complex subunit 4 58.82% 5 1 15 60 0.308 11 0.661 12 0.442 12 0.855 12 0.384 11 0.03 6
IPI00987441.1 Uncharacterized protein (Fragment) 24.68% 2 1 15 60 1.497 4 1.028 4 1.233 4 1.085 4 1.724 4 0.292 4
83
IPI00313222.5 60S ribosomal protein L6 37.84% 5 3 13 58 0.756 12 0.684 12 1.28 13 0.646 13 0.805 13 0.302 13
IPI00123181.4 Myosin-9 21.22% 4 1 32 56 0.699 4 0.724 4 0.645 4 0.71 4 0.356 3 0.079 3
IPI00111412.3 60S ribosomal protein L4 42.96% 1 5 20 56 0.609 12 0.737 13 1.212 13 0.693 13 0.597 11 0.074 8
IPI00122577.8 Isoform 2 of Cytospin-B 22.28% 5 1 15 56 0.121 8 0.24 8 2.844 9 0.187 6 0.211 7 0.157 6
IPI00468696.3 Keratin, type I cytoskeletal 42 46.68% 2 2 16 55 0.357 12 0.138 9 0.731 13 0.178 10 0.42 12 0.132 11
IPI00988271.1 Uncharacterized protein 50.86% 16 2 6 54 0.486 16 0.573 16 0.622 17 0.717 16 0.489 17 0.176 16
IPI00462140.1 Keratin, type II cytoskeletal 1b 32.87% 3 1 16 53 0.772 17 0.193 15 0.704 17 0.365 17 1.206 17 0.302 15
IPI00116498.1 14-3-3 protein zeta/delta 35.51% 14 4 7 52 0.42 21 0.704 22 0.509 22 0.741 19 0.423 18 0.129 12
IPI00756198.2 Kinetochore-associated protein 1 26.64% 1 4 33 52 1.239 6 0.855 7 1.058 6 0.973 7 0.819 8 1.676 3
IPI00331092.7 40S ribosomal protein S4, X isoform 38.78% 3 5 10 51 0.667 14 0.813 14 0.983 15 0.758 15 0.766 15 0.208 14
IPI00458765.2
KH domain-containing, RNA-binding, signal transduction-associated protein 1 39.05% 1 3 20 48 0.841 7 0.642 7 1.182 7 0.724 7 1.043 7 0.198 2
IPI00604967.4 Uncharacterized protein 63.35% 18 6 13 48 0.561 17 0.706 16 0.923 17 0.775 17 0.68 17 0.081 15
IPI00124499.2 Keratin, type II cytoskeletal 79 30.13% 1 1 11 44 0.76 23 0.393 22 0.855 23 0.932 23 1.789 23 0.344 21
IPI00113377.1 60S acidic ribosomal protein P1 57.02% 1 2 3 44 0.738 29 0.546 29 0.637 29 0.609 29 0.479 29 0.169 29
IPI00775915.1 23 kDa protein 33.83% 6 2 8 44 0.796 19 0.891 19 1.01 19 0.712 19 0.614 19 0.068 9
IPI00139780.1 60S ribosomal protein L23 71.43% 3 5 8 42 0.696 20 0.832 20 0.883 20 0.866 20 0.788 20 0.072 20
IPI00118384.1 14-3-3 protein epsilon 21.57% 13 2 6 41 0.418 16 0.698 17 0.509 16 0.76 14 0.429 13 0.143 10
IPI00320267.1 Microspherule protein 1 41.99% 3 3 15 41 0.368 4 0.518 4 0.897 4 0.499 4 0.837 3
IPI00227522.2 Uncharacterized protein 10.19% 2 1 17 41 0.841 3 0.229 3 0.777 3 0.807 3 1.542 3 0.363 3
IPI00461946.2 Isoform 1 of Probable histone acetyltransferase MYST1 41.92% 1 1 13 40 1.78 8 1.303 9 2.079 8 0.975 6 1.757 6 1.488 4
IPI00111957.3 Histone H2B type 1-A 51.18% 15 1 8 36 0.529 4 0.438 4 0.626 4 0.649 4 0.481 4 0.529 4
IPI00131674.3 trypsinogen 7 23.08% 1 1 5 36 0.743 14 0.628 14 1.416 14 0.814 14 1.173 14 1.801 14
IPI00895079.1 Uncharacterized protein 16.17% 3 1 11 36 0.114 3 0.199 3 2.797 3 0.09 3 0.168 3 0.051 3
IPI00330363.8 60S ribosomal protein L7a 39.85% 14 4 9 34 0.749 8 0.823 8 1.3 8 0.729 8 0.655 8 0.117 8
IPI00457585.1 Isoform 2 of Probable histone acetyltransferase MYST1 65.46% 1 1 9 34 1.77 8 1.313 8 2.079 8 0.986 5 1.397 6 1.667 3
IPI00971244.1 40S ribosomal protein S8-like 26.09% 9 2 6 34 0.599 15 0.771 15 1.134 15 0.591 15 0.601 15 0.138 13
IPI00323806.4 Putative uncharacterized protein 44.38% 4 3 11 33 0.935 12 0.937 12 1.156 12 0.865 12 0.94 11 0.23 11
IPI00137787.3 60S ribosomal protein L8 29.96% 1 2 10 32 1.063 4 1.823 4 1.33 4 0.685 4 0.652 4 0.765 4
IPI00410858.2 Isoform 2 of Polyadenylate-binding protein 2 33.90% 6 1 8 32 0.82 6 0.746 6 0.933 6 0.703 6 0.544 6 0.174 2
IPI00775948.1 Uncharacterized protein 28.32% 4 3 8 31 0.705 8 0.892 8 1.737 9 0.525 9 0.685 8 0.179 7
IPI00307907.1 HAUS augmin-like complex subunit 4 15.70% 1 1 7 30 0.106 7 0.202 8 2.687 8 0.043 5 0.168 8 0.039 6
84
IPI00653962.1 Uncharacterized protein 30.65% 3 1 4 30 1.246 8 0.906 6 1.809 7 0.816 6 1.94 8 0.089 1
IPI00762542.2 40S ribosomal protein S11 37.97% 3 4 6 29 0.897 11 0.917 11 0.881 11 0.842 11 0.753 11 0.108 11
IPI00222547.6 60S ribosomal protein L28 39.42% 2 1 6 28 0.804 3 0.86 3 1.231 3 0.678 3 1.202 3 0.207 3
IPI00465880.4 40S ribosomal protein S17 54.81% 5 3 5 27 0.706 14 0.922 15 0.963 14 0.949 15 0.558 14 0.164 14
IPI00229542.1 Histone H2A 51.94% 15 2 8 27 0.581 6 0.674 6 0.737 6 0.819 6 0.577 6 0.67 6
IPI00955744.1 ribonuclease inhibitor isoform b 23.58% 2 2 9 27 0.732 4 0.808 4 1.14 4 0.663 4 0.668 4 0.524 1
IPI00555113.2 60S ribosomal protein L18 41.49% 2 1 11 25 0.409 7 0.564 6 1.692 7 0.487 7 0.554 7 0.118 7
IPI00322562.5 40S ribosomal protein S14 46.36% 3 3 7 25 0.869 6 0.73 6 1.12 6 0.702 6 0.498 6 0.116 6
IPI00466258.2
Isoform 1 of SH3 domain-containing kinase-binding protein 1 30.04% 9 2 13 25 0.786 3 0.989 3 1.009 3 0.934 3 0.556 3 0.126 2
IPI00626366.3 Uncharacterized protein 62.78% 3 4 11 25 0.517 7 0.596 7 1.257 7 0.597 7 0.646 6 0.403 4
IPI00115992.1 Uncharacterized protein 42.40% 3 3 7 24 1.7 5 0.818 5 1.265 5 0.783 5 0.805 5 0.196 4
IPI00136251.1 DnaJ homolog subfamily A member 2 31.80% 1 1 6 23 0.883 7 0.738 8 0.836 8 0.939 8 0.786 8 0.438 4
IPI00128904.1 Poly(rC)-binding protein 1 51.69% 1 2 9 23 0.714 3 0.756 3 0.851 3 0.666 3 0.574 3 0.117 3
IPI00331597.6 Histone H1.3 27.60% 4 1 8 22 0.796 8 0.621 8 1.033 8 0.834 8 0.72 8 0.237 8
IPI00122421.5 60S ribosomal protein L27 22.06% 4 2 4 21 1.455 7 0.664 6 1.254 7 0.969 6 0.77 6 0.262 7
IPI00990114.1 Uncharacterized protein 25.99% 2 2 6 21 0.594 11 0.681 11 1.231 11 0.589 10 0.546 11 0.066 5
IPI00986371.1 60S ribosomal protein L27a-like 30.67% 8 2 4 20 1.679 11 0.8 11 1.379 11 0.83 10 0.846 11 0.258 6
IPI00116281.3 T-complex protein 1 subunit zeta 28.06% 1 3 10 19 0.599 8 0.794 8 0.775 8 0.848 8 0.609 8 0.107 7
IPI00116279.3 T-complex protein 1 subunit epsilon 27.36% 2 2 11 18 0.696 3 0.618 3 0.876 3 0.659 3 1.028 3 0.348 1
IPI00469327.5 hypothetical protein LOC277089 30.36% 2 1 6 18 0.897 7 0.775 6 0.976 7 0.338 6 0.342 6 0.25 2
IPI00605025.2 Uncharacterized protein 30.77% 4 1 5 17 0.55 5 0.734 5 1.153 5 0.731 5 0.758 5 0.106 5
IPI00989404.1 60S ribosomal protein L12-like 60.61% 7 3 8 17 0.749 4 0.64 4 1.212 4 0.537 4 0.633 4 0.177 4
IPI00121311.1 DbpA murine homologue 29.22% 9 1 7 16 0.511 4 0.631 4 0.767 5 0.784 5 0.623 4 0.084 3
IPI00621625.1 Uncharacterized protein 41.03% 5 3 6 15 1.516 8 0.813 8 1.237 8 0.653 8 0.875 8 0.178 7
IPI00676904.3 19 kDa protein 28.40% 1 1 3 15 1.021 3 0.953 3 1.633 3 1.06 3 0.35 3 1.523 3
IPI01007744.1 Uncharacterized protein (Fragment) 34.20% 13 1 7 15 1.73 5 0.626 5 1.27 5 0.618 5 0.829 5 0.167 5
IPI00606550.3 Isoform 1 of Ig gamma-2B chain C region 20.05% 5 1 6 14 1.028 6 3.592 6 5.078 6 0.552 5 0.431 6 0.961 4
IPI00226993.5 Thioredoxin 47.62% 1 1 4 14 0.627 11 0.623 11 2.132 11 0.504 11 0.531 11 0.411 10
IPI00228616.5 Histone H1.1 27.70% 1 1 6 12 0.537 3 0.489 3 0.934 3 0.62 3 0.613 3 0.205 2
IPI00118775.2 Uncharacterized protein 31.30% 6 2 3 12 0.482 7 0.493 7 1.183 7 0.51 7 0.635 7 0.129 6
IPI00377441.3 40S ribosomal protein S26 29.57% 2 2 3 11 0.777 3 0.842 3 1.094 3 0.703 3 0.746 3 0.078 3
85
IPI00648105.1 Uncharacterized protein 16.47% 6 3 3 11 0.656 9 0.604 9 0.875 9 0.586 9 0.427 9 0.206 9
IPI00137736.1 40S ribosomal protein S28 43.48% 2 1 4 10 0.992 4 0.894 3 1.919 5 1.026 3 0.978 3 0.868 3
IPI00849847.1 60S ribosomal protein L23a-like 17.31% 1 1 3 9 0.587 4 0.495 4 1.524 4 0.737 4 0.842 4 0.146 4
IPI00473170.1 Proliferating cell nuclear antigen 31.68% 1 1 5 8 0.432 3 0.886 3 0.769 3 0.845 3 0.379 3 0.039 2
IPI00626239.4 Uncharacterized protein 21.51% 3 1 3 8 1.102 4 0.714 4 0.939 4 0.758 4 0.667 4 0.086 4
IPI00111959.2 CTP synthase 1 10.32% 1 1 5 7 1.04 3 0.547 3 0.703 3 0.579 3 0.595 3 0.277 3
IPI00117288.3 Heterogeneous nuclear ribonucleoprotein A/B 20.00% 3 2 5 7 0.239 3 0.652 3 0.707 3 0.595 3 0.709 3 0.17 1
IPI00798511.1 Isoform 2 of Heterogeneous nuclear ribonucleoprotein F 15.19% 4 2 4 6 0.502 3 0.649 3 0.589 3 0.579 3 0.448 3 0.165 3
86
86
Supplementary Figures
Supplementary Figure 1. PI3K Immunoprecipitation antibody screen. A seven-monoclonal PI3K
antibody panel was screened to identify an optimal reagent antibody for affinity capture. (A) Ab2 depleted
most of the target protein as shown by the minimal reduced PI3K band in the unbound fraction. (B) Four
antibodies captured PI3K effectively (A2, Ab3, Ab4, and Ab7) with Ab2 demonstrating mediating the
strongest best elution capture band.
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Chapter 4: Conclusions and future directions
88
Conclusions and future directions
The current project identified the PI3K pathway as a key player for ACM mediated
neuroprotection in a metabolic stress model. PI3K directs a range of key cellular
processes relating to survival, growth, and oxidative stress modulation [1-3]. Aside from
phosphorylating pro-survival kinases and suppressing apoptotic signals, PI3K and AKT
also interact with a range of RBPs, through the adapter protein 14-3-3, which serves as
a mediator [4]. PI3K’s interaction with RBPs ultimately promotes the stability of a range
of target mRNAs such as IL-3, myogenin, and 20 other identified transcripts[5-7] In the
current study, a highly specific interactome identified novel PI3K binders , including
ZC3H14 and THOC1, through 14-3-3 adaptors. This is the first study to identify these
PI3K binders, shedding new light on the kinase’s in activating Poly-A binding RBPs in
the context of neuroprotection.
4.1 RBPs in the context of neurodegeneration
Neurodegenerative diseases are increasingly recognized as having an RNA regulatory
dysfunction component, through abnormal RBP activity[8-10]. Mutations in RBPs have
been associated with several neurodegenerative diseases, including Amyotrophic
Lateral Sclerosis (ALS) [11], epilepsy [12], and Parkinson’s disease [13]. In order to
orchestrate the complex neuronal functions, nascent mRNAs must be modified
extensively through dynamic processes carried out by specialized RBPs. A single RBP
is typically involved in the processing of hundreds of different mRNAs. [14] Thus,
mutations in a single RBP can lead to widespread errors in post-transcriptional
modification of target transcripts in neurons. Furthermore, RBPs’ processes are precise
89
and elaborate, involving multiple steps, and requiring a constant supply of energy in the
energetically taxed neuronal environment [15]. Thus, changes in metabolic rates,
typically seen with aging, leads to a reduction in mature mRNA turnover rates and the
accuracy with which transcripts are processed [16, 17]. Given the involvement of aging
in neurodegenerative diseases, it is reasonable to expect that cumulative mutations in
RBPs, combined with age-related metabolic decline? make RBPs’ functions highly
relevant to neuronal degeneration. Thus, RBPs can be a highly relevant area of focus
when considering novel treatments for neurodegenerative diseases,
4.2 ZC3H14 is essential to the stability of metabolic transcripts
Through binding with mRNA transcripts’ polyA tails, ZC3H14 carries out a range of post-
transcriptional modifications including modifying PolyA tails length [18], nuclear export
[19], and mRNA splicing [20]. Several lines of evidence affirm ZC3H14’s role in
regulating neuronal functional and survival. Firstly, mutation of the ZC3H14 impairs
neural function in Drosophila and has been associated with cognitive deficits in humans
[21]. An increase in bulk RNA poly(A) tail length of target mRNAs is seen in these
models; however, it remains unclear how hyperadenylated mRNAs lead to neuronal
dysfunction. Further work is needed to investigate how hyperadenylation in this context
affects mRNA stability and subsequent expression patterns. Alternatively, one can
explore a possible correlation between hyperadenylation and prevalence in
neurodegenerative disease populations. Secondly, ZC3H14 knockout mouse models
show anatomical changes in brain ventricles as well as working memory deficits [22].
This evidence further emphasizes the role of ZC3H14 in maintaining normal neuronal
functioning, as seen in its impact on anatomy of neuronal tissue, not just their functions.
90
Thirdly, ZC3H14 has been shown to control mRNA transcripts critical to metabolism,
such as the ATP5g1, which is essential to mitochondrial output [23, 24]. Altered stability
of ATP5g1 may therefore hinder mitochondrial functions, which has negative
implications on neuronal function and survival. Mitochondria are abundantly present in
neuronal tissue; in the eye, mitochondria are particularly dense within unmyelinated
RGC axons in the retina, in order to sustain energetic needs of neurons [25]. To provide
context, up to 90% of the ATP produced by mitochondria is used to maintain action
potentials and neuronal survival [26]. In addition to being the cell’s powerhouse,
mitochodnria also serve as an integration centre for inflammatory and apoptotic signals
in neurons. Thus, one future direction would be to investigate a possible link between
altered ZC3H14 expression and mitochondrial health. An example of this direction
would be to correlate the rate of ZC3H14 mutations with mitochondrial dysfunction in
neurodgenerative diseases, such as glaucoma and Alzheimer’s disease. Another
direction would be to explore the effect of upregulating ZC3H14 expression on neuronal
rescue in the context of metabolic stress.
Another PI3K binder that emerged from the interactome is the RBP THOC1. THOC1
encodes a nuclear matrix protein and is recruited to premature RNA transcripts to
perform various post-transcriptional processing functions [27, 28]. THOC1 also forms a
complex with ZC3H14 coordinately control RNA processing, poly(A) tail length, and
consequently mRNA stability, including ATP5g1 [23]. The scope of the current project
did not include the characterization of THOC1-PI3K interaction; however, a proposed
future direction would be to explore this interaction. Because it is known that THOC1
91
forms a complex with ZC3H14, it would be insightful to demonstrate whether PI3K
interacts directly with THOC1.
4.3 PDGFr as a key receptor in ACM-mediated neuroprotection
The current study also shows that PI3K-ZC3H14 association was upregulated through
activation of PDGFr with PDGF-BB in Ht22 cells. Exogenous PDGF-BB was also
sufficient to reproduce both ACM-induced AKT phosphorylation as well as ACM-
mediated neuroprotection in Ht22 cells. A number of other studies showed that
astrocyte-mediated neuroprotection relieson growth factors, including vascular
endothelial growth factors (VEGF) and fibroblast growth factor (FGF), and the insulin-
like growth factor-1 (IGF-1) neurons [29-32]. Neurons of the central nervous system
(CNS) and sensory tissues like the retina, rely on NF support from neighboring glia and
target sites to modulate their function from developmental stages through maturity [33,
34]. NFs maintain neuronal survival through inhibition of apoptotic pathways, promoting
anti-oxidant activities, and boosting cellular bioenergetics [35]. Conversely, NF
deprivation induces the intrinsic apoptotic pathway, causing reduced ATP production,
reactive oxygen species generation, cytochrome C release, and subsequent caspase
activation [36]. However, to our knowledge this is the first study describing a
neuroprotective role of astrocyte-induced PDGFr activation. PDGF is a dimer of A- and
B-chains (AA, BB, or AB) which signals through tyrosine kinase receptors (RTKs)
(PDGFRα and PDGFRβ [37, 38].
92
In a neuronal context PDGF is secreted primarily by macroglia, including astrocytes and
retinal Muller cells, as well as by neurons, mediating short-range paracrine
communication between astrocytes and neurons [39]. It has also been shown to protect
neurons against oxidative stress and NMDA-induced metabolic stress in primary
neurons and retinal ganglion cells [40-44]. In human, PDGF is highly expressed in white
matter in humans following stroke, suggesting that PDGF may be involved in either
regeneration or protection of damaged neurons [45]. Thus, PDGF involvement in
astrocyte-mediated neuroprotection in the current study adds further evidence to the
role PDGF in neuroprotection against metabolic stress. However, there are some
considerations for the use of PDGF as a neuroprotective biologic in aerogeneration.
One of PDGF’s roles is to promote new blood vessel maturation; thus, the impact of
PDGF on retinal vasculature needs to be considered carefully, given the risk of aberrant
vascularization[46] . Furthermore, unlike the homogenous cell line used to asses PDGF
neuroprotective potential, neuronal tissue is highly complex and heterogenous. As a
result, administering PDGF will affect surrounding glia and connective tissue, initiating
signaling cascades in a number of cell populations. The collective outcome of this
signaling wave needs to be addressed carefully in future studies to assess PDGF’s
safety in neuronal tissue.
4.4 Considerations for future direction
Taken together, this study provides a case for the use of interactomes in
exploring signalling pathways involved in biological activities of interest, such as
neuroprotection or regeneration. This approach can be especially powerful if a specific
93
protein is known to be critical to an activity; in this case, the protein can serve as a bait
with which the rest of the pathway is captured. However, a few points are important to
point out with respect to the interactome data. While the interaction between ZC3H14
and PI3K has been implied using evidence mass spectroscopy and IP studies, it is
unclear whether the interaction occurs directly or as part of a larger complex. PI3K
interacts with a number of targets along the AKT-mTOR pathway (see Chapter 1).
Therefore, it is important to investigate the involvement of other targets downstream of
PI3K in transducing ACM signalling in neurons through ZC3H14 binding. A likely
downstream target is mTOR, which has been shown to be involved in a range of
neuroprotection and regeneration models. Secondly, while using ACM derived from
primary astrocytes, for consistency the PI3K interactome performed in this work was
carried out using the transformed hippocampal cell line Ht22, which is a well-established
model in neurodegenerative research [47-49]. While useful and convenient,
immortalized cell lines are genetically altered, and are prone to cumulative mutations
with passaging, which may introduce functional and phenotypic differences compared to
their primary counterparts. Therefore, one future direction of this project is to confirm the
PI3K-ZC3H14 interaction in vivo using brain or retinal models. We have generated
immunofluorescence images of retina sections showing localization of ZC3H14 in the
GCL layer; we also demonstrate co-localization with PI3K following Kainic acid injury
(Figure 4-1). Further, it is essential to confirm whether PDGF administration increases
the PI3K association with ZC3H14, as observed in Ht22 cells. Similarly, it would be
useful to assess the neuroprotective effect of PDGF administration in ZC3H14 knockout
models under metabolic stress conditions.
94
Figure 4- 1 Immunostaining of ZC3H14, and AKT in retinal sections treated with
vehicle and Kianic acid (1hr).
In conclusion, neurodegeneration and neuroprotection are extremely complex
processes that involve hundreds of biological factors and signalling pathways in delicate
orchestration. Thus, it is more than likely that ZC3H14’s role in protecting neurons is
complemented with a range of other factors and pathways. Future work can expand on
the current work using proteomic analyses that can identify other binding partners of
ZC3H14, to generate a snapshot of this RBP in its natural complex. Using this
approach, one can then compare global functional patterns of this ZC3H14 and its
partners in the normal and disease settings. This ‘bird eye’ view takes into account the
complexity of neurodegenerative disease, and may ultimately bring us closer to
incorporating ZC3H14 targeting in a neuroprotective therapy.
95
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