INTRACELLULAR TRAFFICKING OF AMYLOID PRECURSOR

PROTEIN BY THE EXOCYST: MECHANISMS IN ALZHEIMER’S

DISEASE AND INSULIN SIGNALING

A Master’s Thesis submitted to the Graduate Division of the

University of Hawai’i at Mānoa in partial fulfillment

of the requirements for the degree of

MASTER OF SCIENCE

in

Cellular and Molecular Biology

April 2021

by

RACHEL KINSEY SACHS

Thesis Committee

Benjamin Fogelgren, Ph.D.

Robert Nichols, Ph.D.

Peter Hoffmann, Ph.D., MSPH ACKNOWLEDGEMENTS

I would like to thank my committee members (Dr. Benjamin Fogelgren, Dr. Peter

Hoffmann, and Dr. Robert Nichols), the Fogelgren Laboratory (Dr. Michael Ortega, Geetika

Patwardhan, Ross Villiger, Josh Kepler, and Malia Harrison-Chau), and Dr. Noemi Polgar for their support and guidance. I would also like to thank our collaborators from Owens Laboratory

(Dr. Jesse Owens and Dr. Brian Hew), the Nichols Laboratory (Kendra Ormsbee and Ruth

Taketa), and Dr. Nicholas James for their assistance in making this work possible.

I would also like to thank Dr. Matthew Pitts at the JABSOM Microscopy Core and Dr.

Christine Farrar at the University of Hawaii Cancer Center Microscopy, Imaging, and Flow

Cytometry Core for microscopy access and assistance. I also want to acknowledge our funding provided by the Hawaii Community Foundation (#19-ADVC-95450), and by NIH through the

IBR-COBRE (P30 GM131944; pilot award).

Lastly, thank you to my family and friends for all your support in pursuing my degree, it truly means the world to me.

1 ABSTRACT

Alzheimer’s disease is a devastating progressive neurodegenerative disease that causes memory loss, personality changes, and impaired reasoning. It currently affects approximately 6 million people in the US, and is America’s 6th leading cause of death. Research on the disease has been extensive for decades, however there are still no effective therapies and the pathogenic mechanisms are not fully understood. Two of the pathways implicated in causing the damage seen in Alzheimer’s disease focus around amyloid beta peptide aggregates, that form plaques, and hyperphosphorylated tau, which forms tangles. Studies on the Amyloid hypothesis focus on the generation and aggregation of the peptide, which is cleaved from the amyloid precursor protein (APP). How APP is cut and whether it will lead formation of the amyloid peptide has been shown to be influenced by the trafficking of APP bringing it into contact with different secretases.

Based on the intracellular trafficking patterns of APP, and hints from the literature, we hypothesized that the eight-protein exocyst complex regulates APP trafficking and amyloid beta generation in neurons. For our research, we used two cell models: the SH-SY5Y cell line, a human neuroblastoma cell line able to be differentiated into neuronal cells using retinoic acid, and mouse primary hippocampal neurons. For our experiments, we generated several transgenic

SH-SY5Y lines, including a mutant APP overexpressing line that secretes high levels of amyloid beta, as well as lines that co-express fluorescent-tagged mutant APP and exocyst proteins for use in live cell imaging.

2 We found that when several members of the exocyst family are knocked down using siRNA in our mutAPP overexpressing line, the amount of amyloid beta released in the media significantly decreases. Using the proximity ligation assay (PLA), we also discovered that Exoc5 and APP closely co-localize (within 40nm) in mouse primary hippocampal neurons.

Since the exocyst regulates the insulin-induced trafficking of Glut4 glucose transporters in adipocytes and muscle cells, we tested the effect of insulin signaling in neurons on the exocyst and its relationship to APP and Glut4. When insulin was present, the exocyst holocomplex assembly increased significantly, as did its association with Glut4, however APP association with the exocyst was largely abolished. The data suggests that insulin signaling is able to switch the exocyst complex away from APP-containing vesicles and potentially reduce amyloid beta generation. These findings point to the regulation of APP trafficking by the exocyst complex and a direct connection between the exocyst’s influence on the production of the amyloid beta peptide and insulin signaling effects. Understanding the involvement of the exocyst in

Alzheimer’s disease could provide further targets for drug development and help bridge the gap in our understanding of connections between insulin signaling in the brain and Alzheimer’s disease.

3 TABLE OF CONTENTS

Section Page

Acknowledgements 1 Abstract 2 List of Tables 5 List of Figures 6 Introduction 7 Specific Aims 21 Methods 22 Results 30 Discussion 53 Conclusion 62 References 65

4 LIST OF TABLES

Table Page Table 1. Antibodies used in immunofluorescence of PHC neurons 24 Table 2. Proximity ligation assay antibodies 25 Table 3. Real-Time qPCR primer sequences 27 Table 4. Western blot antibodies 29 Table 5. Live-cell imaging EPI-fluorescence and TIRF videos 48

5 LIST OF FIGURES

Figure Page

Figure 1. Projected number of people age 65 and older in the U.S. population with 8 Alzheimer’s Dementia Figure 2. Representative diagrams of healthy brains compared to Alzheimer’s brains 10 Figure 3. Processing pathways of the amyloid precursor protein 11 Figure 4. APP processing and Aβ production regulated by Rab GTPases 13 Figure 5. Processing of APP in amyloidogenic and non-amyloidogenic pathways 16 overlaps with insulin signaling Figure 6. The exocyst complex trafficks intracellular vesicles 18 Figure 7. Co-Immunoprecipitation of Exoc3L2 and Exoc5 in SH-SY5Y cells 19 Figure 8. Immunocytochemistry for Exoc5, Exoc7, and APP with MAP2 31 Figure 9. Immunocytochemistry for Exoc5, Exoc7, and APP with tau 32 Figure 10. PLA counterstained with phalloidin for Exoc5-APP and Exoc5-Exoc7 34 combinations Figure 11. Higher magnification PLA in primary neurons counterstained with MAP2 35 Figure 12. Quantification of PLA insulin experiments for Exoc5-Exoc7 and Exoc5- 37 APP in primary hippocampal neurons Figure 13. Quantification of PLA insulin experiments for Exoc5-Exoc7, Exoc5-Glut4, 39 and Exoc5-APP in primary hippocampal neurons Figure 14. Diagram of the siRNA experimental protocol 41 Figure 15. Total protein content of 48- and 72-hour siRNA experiments 42 Figure 16. siRNA knockdown of exocyst proteins and ELISA measurement of Aβ40, 43 sAPPɑ, and sAPPβ secretion over 72 hours Figure 17. siRNA knockdown of exocyst proteins and ELISA measurement of Aβ40, 45 sAPPɑ, and sAPPβ secretion over 48 hours Figure 18. Validation of exocyst/mutAPP overexpressing lines by western blotting 47 Figure 19. mutAPP-mScarlet travels in vesicles in a differentiated SH-SY5Y cell 49 Figure 20. Exoc1/4/7-mNeonGreen/mutAPP-mScarlet lines under epifluorescence and 49 TIRF microscopy Figure 21. Time-series TIRF microscopy of fluorescently tagged Exoc1/mutAPP 51 differentiated SH-SY5Y cell line Figure 22. Time-series TIRF microscopy of fluorescently tagged Exoc7/mutAPP 52 differentiated SH-SY5Y cell line

6 INTRODUCTION

Dementia has become a leading cause of disability and death in modern times as life expectancy has increased. The most common type of dementia, Alzheimer’s disease, is a devastating progressive neurodegenerative disease that causes memory loss, personality changes, and impaired reasoning. One of the earliest pathogenic steps leading to Alzheimer’s disease is the extracellular accumulation of the amyloid beta peptide, primarily as oligomers and later as neuritic plaques. Amyloid beta is produced from the proteolytic cleavage of the amyloid precursor protein (APP), which is regulated by a variety of factors and signaling pathways. This thesis investigates the role of a protein trafficking complex, the exocyst, in neuronal trafficking of APP and the resulting effects on the generation of amyloid beta.

Alzheimer’s Disease Mechanisms and Pathology

Alzheimer’s disease is a neurodegenerative disease characterized by neuronal cell death, inflammation, and atrophy in the brain. It manifests itself progressively over several years, with symptoms that include memory loss, difficulty with problem solving and completion of familiar tasks, confusion over time and place, declining decision making, and mood and personality changes (Alzheimer’s Association, 2021). The disease progresses slowly from preclinical AD with undetectable symptoms to severe dementia at late stages of AD, where the symptoms interfere with most aspects of life. Alzheimer’s disease is classified as the sixth leading cause of death in the United States, and its prevalence is projected to increase over the next forty years with the aging population (Figure 1). Patients become increasingly at risk for acute conditions leading to death: difficulty swallowing can result in pneumonia, immobility issues put patients at

7 risk for injuries from falls, patients become unable to maintain their own medication, nutrition, and hydration, and are more at risk of developing infection (Alzheimer’s Association, 2021).

Figure 1. Projected number of people age 65 and older in the U.S. population with Alzheimer’s Dementia. Graph of the projected number of people in the United States to develop Alzheimer’s Disease by age over the next forty years. (Rajan et al., 2016) (Alzheimer’s Association Alzheimer’s Disease Facts and Figures, 2021)

Current medical interventions for Alzheimer’s disease have so far been only able to slow the progression of symptoms, not stop or reverse them. Some current treatment options include: cholinesterase inhibitors, which block the degradation of acetylcholine (a neurotransmitter involved in the maintenance of memory) for mild to moderate Alzheimer’s as a means to correct basal forebrain cholinergic deficits in AD patients; an NMDA receptor antagonist which modulates glutamate activity involved in memory maintenance for moderate to severe

Alzheimer’s; and various antidepressants and anxiolytics to treat behavioral symptoms

8 (Alzheimer’s Association, 2021). The side effects of these medications range from mild symptoms like nausea and diarrhea to more severe symptoms like fainting and bradycardia

(slowing of heart rate). There is an urgent need for new strategies that can address the underlying biological causes of AD, however none of the drugs in this regard that have made it to clinical trials have shown efficacy in treatment outcomes, or they have resulted in toxicity or worsening of cognition (Panza et al., 2019). The lack of effective treatment options highlights the importance of better understanding the pathological mechanisms underlying Alzheimer’s in order to understand why previous targets did not work, and to find new therapeutic targets.

Two of the pathological hallmarks in the brains of patients with Alzheimer’s disease are neurofibrillary tangles, composed of hyperphosphorylated tau inside the cells forming paired- helical filaments, and plaques formed from fibrillar amyloid beta (Aβ) peptides outside the cells

(Figure 2). Amyloid beta plaques appear early in the brains of patients prior to the onset of symptoms, followed by tau tangles and neurodegenerative damage approximately a decade later as symptoms begin (Nature Outlook: Alzheimer’s Disease, 2018). Several mutations associated with early onset Alzheimer’s disease are found in Aβ’s precursor protein or the proteases that cleave it into the Aβ peptide (Lanoiselee et al., 2017). The scope of this thesis will specifically examine mechanisms surrounding the generation of Aβ plaques through the intracellular trafficking of the amyloid precursor protein (APP) in neurons.

9

Figure 2. Representative diagrams of healthy brains compared to Alzheimer’s brains. A) Comparison of a healthy brain to an atrophied brain of an Alzheimer’s patient, from the National Institute of Aging. B) Diagram of the pathology associated with Alzheimer’s Disease, consisting of intracellular tau tangles and extracellular amyloid plaques, from Silbert (2007).

Processing of the Amyloid Precursor Protein to Amyloid Beta in Neurons

The amyloid-beta peptide is a cleavage product of the amyloid precursor protein (APP), a transmembrane glycoprotein found in many tissue types throughout the body. It has large metal binding and heparin binding domains, and studies in the brain have pointed to a role for APP and its cleaved forms in neurogenesis, cellular stress response, plasticity, and synaptic function

(Polanco et al. 2017).

In the non-amyloidogenic processing pathways, APP gets cleaved by ɑ-secretase proteins

(Lys(687)-Leu(688) bond) at the plasma membrane generating a free soluble APPɑ fragment

(sAPPɑ) and a C-terminal fragment (ɑ-CTF, or C83), which remains in the membrane.

Following internalization of the ɑ-CTF through endocytosis, a ɣ-secretase complex generates two fragments: the APP intracellular domain (AICD) fragment and the easily clearable p3 fragment. However, in the amyloidogenic processing pathway, full length APP gets internalized without being cleaved by ɑ-secretase. In this case, it can interact with β-secretase in the

10 endosomal organelles, whose acidic environment is optimal for the β-secretase enzyme’s activity. Cleavage by β-secretase occurs at a different site (Met(671)-Asp(672) bond), further from the c-terminus in the sequence than ɑ-secretase, creating sAPPβ and β-CTF (C99) fragments instead. When C99 is cleaved downstream by ɣ-secretase, it generates the same AICD fragment, but instead of the p3 peptide, it generates the 40-42 amino acid Aβ peptide (Figure 3).

The Aβ peptide, which is poorly soluble, gets exocytosed and forms various low molecular weight oligomers, which as they accumulate extracellularly to relatively high levels form fibrils that assemble into neuritic plaques (Figure 4) (Polanco et al., 2017; Sathya et al. 2012; Zhang et al. 2011).

Figure 3. Processing pathways of the amyloid precursor protein. Diagram of APP non- amyloidogenic and amyloidogenic cleavage pathways. Non-amyloidogenic cleavage by ɑ-secretase and ɣ-secretase produces soluble ɑAPP, AICD, and p3 fragments. Amyloidogenic cleavage by β-secretase and ɣ-secretase produces soluble βAPP, AICD, and Aβ fragments. (Khalifa et al, 2010)

11

The trafficking of APP through the cell impacts its likelihood of being cleaved into Aβ.

Many molecules and signaling pathways play a role in the intracellular trafficking process, which is tightly controlled by various small GTPases that mark vesicles to allow cargo sorting between origins and destinations. As is currently understood, full length APP is endocytosed from the membrane by clathrin-coated vesicles and brought to the early endosome and then to the recycling endosome. APP may be endocytosed together with β-secretase and/or ɣ-secretase, but can also come into contact with them later in the endosome (Figure 4). Udayer et al. (2013) suggested that the early endosome and associated vesicles may have an optimal environment for

β-secretase cleavage of APP due to the higher acidity appropriate for β-secretase protease activity, while the recycling endosome and its vesicles may be more optimal for ɣ-secretase activity. The mechanisms that regulate the neuronal trafficking of APP are crucial to understanding the amyloidogenic pathway, and may reveal novel therapeutic targets against

Alzheimer’s Disease.

12

Figure 4. APP processing and Aβ production regulated by Rab GTPases. Diagram depicting the involvement of Rab GTPases in the trafficking of APP throughout the cell. Full length APP is initially trafficked from the Trans Golgi Network (TGN) to the plasma membrane for insertion. If APP is endocytosed, it is sent to the early endosome, where it will then either be transported to the recycling endosome to be put back in the membrane, or to the late endosome where it will end up degraded by the lysosome or exocytosed in various cleaved forms. Not depicted are the actin cytoskeleton and other aspects of pathways for vesicular transport outside of Rab GTPases. (Udayer et al., 2013)

13 Alzheimer’s Disease and Insulin Resistance

Both clinical and experimental studies have strongly established a link between insulin resistance, as seen in Type II Diabetes, and Alzheimer’s Disease (AD). A meta-analysis of longitudinal studies found that the risk for Alzheimer’s is 1.5 times higher in diabetic patients than the general population (Cheng et al., 2012). The data connecting insulin resistance and AD has been steadily growing with numerous proposed mechanisms and multiple pathways of overlap in signaling. It can be difficult to disentangle the complicated physiology of insulin resistance, which involves obesity, inflammation, and metabolic dysfunction, from Alzheimer’s biology. However, there may be a direct connection between insulin signaling in neurons and Aβ production. In APP processing, insulin signaling is suggested to directly regulate multiple points of the pathway in different tissues.

When insulin binds to the insulin receptor’s (IR) extracellular alpha domain, it leads to dimerization of the receptor and subsequent autophosphorylation of its intracellular beta subunits. The IR then phosphorylates IRS1 and IRS2 (insulin receptor substrate), leading to the activation of several downstream pathways, including the PI3K/AKT and MAPK pathways, among others. In the PI3K (phosphoinositide 3 kinase) pathway, the PI3K complex binds to the

IRS proteins, activating it and rising PIP3 levels (phosphatidylinositol 4,5-biphosphate to 3,4,5- triphosphate), leading to activation of PDK1 (phosphoinositide-dependent kinase-1) which phosphorylates AKT (protein kinase B) on the Thr308 residue and leads to downstream activation of mTORC2 (mammalian target of rapamycin complex 2) which phosphorylates AKT on the Ser473 residue. Phosphorylation of AKT activates it and leads to effects on its downstream targets, many of the pathways of which associated with metabolic functions

(Haeusler et al., 2017). In the MAPK (mitogen-activated protein kinase, also known as ERK1/2)

14 pathway, GRB2 and SOS (growth factor receptor bound protein 2 and son of sevenless respectively) are activated through IRS binding, however in this case they lead to the activation of Ras through a switch from the GDP to GTP bound form. Ras activates Raf, a kinase that phosphorylates MEK1/3 (mitogen activated kinase kinase), which phosphorylates further

MAPK. MAPK is able to translocate to the nucleus and effect downstream pathways associated with cell growth and proliferation (Boucher et al., 2014).

These pathways have been shown to have several points of overlap with APP processing.

Insulin signaling via the PI3K pathway has been shown to increase the transcription of ɑ- secretase proteins’ mRNA and decrease that of β-secretase in HEK cells, which can reduce amyloidogenic cleavage of APP (Padini et al., 2013). Conversely, insulin resistance increases β- secretase and ɣ-secretase activity in mouse brains (Son et. al., 2012). Aβ has been shown to downregulate the number of insulin receptors present on the neuronal cell surface and deactivate

IRS-1, reducing insulin signaling (De Felice et al. 2013; Wang et al. 2010). The production of

Aβ may then create a positive feedback loop, as Aβ increases insulin resistance which increases the activity of proteins that produce more Aβ (Figure 5). These studies and others like them indicate a direct connection between insulin signaling and APP processing, and may explain how insulin resistance operates to increase the risk of AD. However, an understanding of the interacting mechanisms between these key pathways is still in its early stages. Further research could give more insight into Alzheimer’s progression as influenced by insulin resistance, as well as provide preventative strategies for diabetes patients.

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Figure 5. Processing of APP in amyloidogenic and non-amyloidogenic pathways overlaps with insulin signaling. Presence or absence of insulin can affect the cleavage of APP through different downstream effectors. (Shieh et al., 2020)

16 A Possible Role for the Exocyst Complex in Alzheimer’s Disease

Research on Alzheimer’s Disease has come a long way, however there is still much that is unknown, and few therapeutic options. As discussed above, more research is needed to understand the role of neuronal protein trafficking in the generation of amyloid beta, and further, to understand mechanisms connecting insulin resistance and the regulation of amyloid beta in

Alzheimer’s. One novel candidate for regulation of APP trafficking is the exocyst complex. The exocyst complex is made up of eight proteins, known as Exoc1 through Exoc8, that participate in trafficking vesicles to the plasma membrane for the exocytosis of cell materials. The exocyst has also been implicated in endocytosis and endosomal trafficking, although these functions have been less studied. The exocyst complex mediates the tethering of vesicles prior to their fusion with the cell membrane, which is then controlled by V- & T-SNARE proteins (Polgar and

Fogelgren, 2018). Due to these trafficking activities, it has been implicated to influence many cellular processes such as cell migration, growth, ciliary genesis, and generation of the autophagosome, among others (Fogelgren et al., 2011; Martin-Urdiroz et al. (2016); Zuo et al.,

2006).

The exocyst holocomplex is highly conserved across all eukaryotes, though there is some variation in sequence (Koumandou et al., 2007). The exocyst is regulated mostly by small

GTPases in the Rab, Ral and Rho families which control its activity throughout the cell. The current model is that Exoc6 binds directly to a Rab-GTPase (Rab3, Rab8, Rab11, or Rab 29) on the intracellular vesicle, and is connected to the rest of the exocyst complex by Exoc5. Exoc1 binds directly to plasma membrane lipids at the site of exocytosis, although in some cases Exoc7 may do this as well. The other members of the complex function to bring the vesicle and membrane subcomplexes together with Exoc5 and interact with the SNARE proteins for docking

17 (Figure 6A). Additionally, with eight subunits, there are a variety of post-translational modifications that cells can use to regulate exocyst activity (Polgar and Fogelgren, 2018).

The exocyst complex is still being investigated in the brain and in neurons. It has been shown that the exocyst is abundant in developing neurons at the axon growth cones and dendritic branches, but can still also be found at mature synapses, though it does not play a role in neurotransmitter release (Murthy et al., 2003). It has been implicated in neurite outgrowth, which becomes impaired when exocyst subunits are absent (Das et al. 2014, Peng et al. 2015) (Figure

6B). The exocyst is thus involved in the development of neurons in this capacity, but its role in the mature neuron is still largely unclear.

Figure 6. The exocyst complex traffics intracellular vesicles. A) Diagram of exocyst complex proteins trafficking a vesicle, modified from Lipschutz and Mostov (2002). B) Diagram from Martin- Urdiroz et al. (2016) depicting loss of exocyst function causing decreased development of neuronal projections from studies like Das et al. (2014) and Peng et al. (2015). Red dots indicate the localization of the exocyst.

18 There are two studies that initially suggested to us the involvement of the exocyst in

Alzheimer’s disease. The first study, a genome-wide association study (GWAS) by Belbin et. al

(2011), identified variants in the exocyst-like protein Exoc3L2 as a potential risk-affecting gene for Late Onset Alzheimer’s Disease (LOAD). Exoc3L2, also known as exocyst complex component 3 like 2, is an uncharacterized protein hypothesized to have a connection with the exocyst based on sequence homology. Barkefors et al. (2011) were able to show that it co- immunoprecipitated with Exoc4 from endothelial cell lysates, suggesting a functional association with the classic exocyst subunits. Similarly, in our own preliminary experiments, we were able to show that Exoc3L2 co-immunoprecipitated with Exoc5 in SH-SY5Y cells (Figure 7). If

Exoc3L2 is involved in LOAD risk, we hypothesize that it may be due to its effect on exocyst trafficking of APP.

Figure 7. Co-Immunoprecipitation of Exoc3L2 and Exoc5 in SH-SY5Y cells. Co-immunoprecipitation of myc-tagged Exoc3L2 with Exoc5 from SH-SY5Y cells indicates the Exoc3L2 binds to the exocyst in human neuron-like cells.

19 The second study that served as the foundation of our hypothesis, Udayer et al. (2013), identified Rab11 as a major regulator of Aβ production from a large-scale screen of Rab-

GTPases. Rab11 regulates the trafficking of vesicles specifically from the trans-Golgi network and recycling endosomes, in concert with several other Rabs (Wu and Guo, 2015) (Figure 4), and is suggested in their screen to be involved in the recycling of β-secretase back to the membrane and thus potentially back to the early endosome. In many cell types, from yeast to humans, Rab11 marks vesicles trafficked by the exocyst. This second connection led us to question whether the exocyst may be involved in the trafficking of APP-containing vesicles bound by Rab11, and as a result, potentially influence amyloid beta generation.

There is one additional point of interest with the exocyst and Alzheimer’s disease, though it is less direct. The exocyst has been shown to be centrally involved in insulin-dependent trafficking of the glucose transporter Glut4 to the plasma membrane in adipocytes and skeletal muscle (Inoue et al. 2003, Fujimoto et al. 2019). Fujimoto et al. (2019) was able to show that exocyst activity increases following insulin signaling, as does the exocyst involvement in Glut4 trafficking to the plasma membrane, an important step to allow glucose uptake. Further, inhibition of the exocyst impairs Glut4 membrane delivery and glucose uptake. This impairment impacts cellular metabolism even when insulin is present, indicating the exocyst is required for insulin-induced insertion of Glut4 into the plasma membrane. If the exocyst is similarly insulin- signaling responsive in neurons, and the exocyst is connected to Alzheimer’s disease, it may serve as a direct connection between insulin signaling and APP trafficking.

20 SPECIFIC AIMS

To determine the role of the exocyst in the regulation of APP, this thesis will:

Specific Aim 1: Determine the role of exocyst proteins in APP trafficking and resulting production of β amyloid

Hypothesis: The exocyst protein complex regulates neuronal APP trafficking and Aβ generation.

Approach: In this aim, we will determine whether the exocyst colocalizes with APP vesicles in mouse primary hippocampal neurons. We will also assess whether RNAi knockdown of exocyst genes affects the release of amyloid beta and the soluble cleavage products into the media.

Lastly, we will test cell lines developed for live-cell imaging of fluorescent tagged exocyst proteins Exoc1, Exoc4, and Exoc7, along with APP, in order to determine real-time interactions of these proteins in neurons.

Specific Aim 2: Evaluate whether insulin availability alters the exocyst’s activity in neurons and its influence on APP trafficking

Hypothesis: Insulin switches the exocyst activity away from APP, linking insulin signaling to

APP trafficking

Approach: In this aim, we will determine whether insulin signaling affects the association between the exocyst and APP, the exocyst and Glut4, and the overall assembly of the exocyst holocomplex in primary hippocampal neurons.

21 METHODS

Mouse Primary Hippocampal Neurons

Mouse primary neurons were isolated from the hippocampus removed from neonatal (P1)

C57Bl6/J or B6SJL wild type (WT) mice and cultured to 8 days in vitro (DIV) by Ms. Kendra

Ormsbee in the laboratory of our collaborator, Dr. Robert Nichols, according to the protocol from Cheng and Yakel (2015) as modified by Forest et al. (2018). Briefly, mice were sacrificed by decapitation and brains were moved to ice-cold Neurobasal media. The hippocampi were dissected and placed in papain solution containing 10mM L-cysteine and cell dissociation buffer, and then incubated at 37°C for 15 min. After recovery by centrifugation, the tissue was suspended in fetal bovine serum (FBS)-containing Neurobasal A media and cells were dissociated by sequential trituration, followed by pre-plating to remove adherent non-neuronal cells (primarily glia). The neuron-enriched suspensions were plated onto poly-D-lysine-coated coverslips in Neurobasal A medium containing 5% FBS, B-27 supplement, and Gentamicin.

Subsequently, media changes were done using Neurobasal A media containing B-27 and

Glutamax.

Culture and Differentiation of SH-SY5Y Human Neuroblastoma Cells

The SH-SY5Y human neuroblastoma cell line was purchased from Sigma. Cells were cultured using 50/50 DMEM/F12 media containing 10% FBS, 1X Antibiotic/Antimycotic

(Thermofisher), and kept at 37°C in 5% CO2, with regular media changes. Cells were detached for passaging with 0.05% trypsin, and cell lines were frozen in complete medium with 10%

DMSO and kept in liquid nitrogen. Differentiation of SH-SY5Y cells was done following an 18- day protocol adapted from Shipley et al. (2016). Briefly, undifferentiated cells were plated at

22 20% confluency on day 0, and then on day 1 were put into Medium 1 (phenol-red free/Glutamine-free DMEM, Thermofisher; 2.5% heat inactivated FBS; Glutamax; penicillin/streptomycin) until day 7, changing the media every other day. On day 8, cells were changed to Medium 2 (Medium 1 but with only 1% hiFBS). On day 10, cells were changed to

Neurobasal Medium 3 (phenol-red free/Glutamine-free Neurobasal; B-27; N-2; Glutamax; penicillin/streptomycin; BDNF) and media was changed every other day. All media was sterile filtered for use, and for each media change fresh retinoic acid was added in the dark to a final concentration of 10μM.

Plasmid Transfections

Plasmids were designed and cloned by Dr. Fogelgren in collaboration with the Institute for

Biogenesis Research Transgenic Core (University of Hawaii at Manoa) utilizing the piggyBac transposon expression plasmid, pmGENIE-3 (Urschitz et al., 2010). Plasmids were transformed into DH5ɑ competent E. coli cells and purified using the GenElute HP Endotoxin-Free Plasmid

Maxiprep Kit (Sigma), stored in water at -20°C. Transfections were performed on SH-SY5Y cells using Lipofectamine 3000 (Thermofisher) for a final plasmid concentration of 2500ng with

4μL of reagent in 250μL of Opti-MEM per manufacturer instructions. Cells were supplemented with an additional 750μL of media, and the cells were incubated for 48 hours prior to selection with the antibiotics hygromycin for the Exoc1/4/7-mNeonGreen/mutAPP-mScarlet lines, and

G418 for the mutAPP only line. Hygromycin concentration was determined via a kill curve to be optimal at 200μg/mL, and G418 similarly at 400μg/mL.

23 Immunofluorescent Staining of Cells

Immunocytochemistry was performed on primary hippocampal neurons fixed in 4% paraformaldehyde (PFA) and permeabilized in 0.1% Triton X-100 phosphate-buffered saline

(PBS) for 10 min, each at room temperature. Cells were washed with PBS, blocked in 0.1% bovine serum albumin (BSA) in PBS and stored at 4°C in blocking prior to staining. Primary antibodies were used at a concentration of 1:100 in 1% BSA in PBS, unless otherwise specified, and incubated for 1 hour at room temperature, or overnight at 4°C. Washes were done in 0.1%

BSA in PBS, and fluorescence-labeled secondary antibodies were added at a concentration of

1:1000 diluted in 1% BSA for 1 hour at room temperature. Final washes with PBS contained 1X

DAPI for 5 minutes, after which slides were mounted in anti-fade mounting media. Protocol was adapted from Lee et al. (2016). Confocal microscopy was performed using the Zeiss LSM 5

Pascal Confocal Microscope through the JABSOM Microscopy and Imaging Core.

Table 1. Antibodies used in immunofluorescence of PHC neurons

Antibody Species Clonality Manufacturer/Catalog # Dilution

APP (Y188) Rabbit mAb Abcam, ab32136 1:100

Exoc7/EXO70 Rabbit pAb Protein Tech, 12014-1-AP 1:100

Exoc5/Sec10 (C-4) Mouse mAb Santacruz, sc-514802 1:100

Map 2 Chicken pAb Abcam, ab5392 1:5000

Tau Chicken pAb Abcam, ab75714 1:500

Proximity Ligation Assay (PLA)

Primary hippocampal neurons were fixed for 10 min in 4% PFA and permeabilized in 0.1%

Triton-X 100 in PBS for 10 min, before performing the PLA, as described in Fujimoto et al.

(2019). PLA was performed according to manufacturer instructions using the Duolink™ In Situ

24 Red Starter Kit (Mouse/Rabbit) (Millipore). Briefly, cells were blocked in the provided blocking solution for 1 hour at 37°C in a humidity chamber, and then incubated overnight at 4°C with the antibody combinations Exoc5-Exoc7, Exoc5-Glut4, or Exoc5-APP, as well as single antibody controls. Cells were washed and incubated for 1 hour at 37°C in the humidity chamber with the

PLUS and MINUS antibody probes. Cells were washed again, and then incubated with ligase similarly for 30 min. Lastly, after washing cells were incubated for 100 min similarly with the amplification buffer and polymerase. Additional antibody counterstaining was done using anti-

MAP2 (Chicken, 1:5000, Abcam) or anti-tau (Chicken, 1:200, Abcam) with Alexa-488 secondary antibodies (1:1000, Thermofisher), or using Alexa Fluor 488 Phalloidin

(Thermofisher) to stain F-actin, based on methods by Tai et al. (2020). Antibody combinations were used at 1:100 dilutions for the antibodies listed in the table below.

Table 2. Proximity ligation assay antibodies

Antibody Species Clonality Manufacturer/Catalog #

APP (Y188) Rabbit mAb Abcam, ab32136

Exoc7/EXO70 Rabbit pAb Protein Tech, 12014-1-AP

Glut4 Rabbit pAb Thermofisher, PA5-23052

Exoc5/Sec10 (C-4) Mouse mAb Santacruz, sc-514802

Cells were imaged on the fluorescent Olympus BX41 microscope. Quantification was done by taking 10 to 40 images of each condition, with each frame containing between 5 and 15 cells.

Images were then analyzed with ImageJ software, using the detect maxima function to count

PLA signals across the entire image. The signal number was then divided by the number of nuclei in the image to yield the signal per cell ratio for each image.

25 Insulin Starvation and Treatment

Primary hippocampal neurons were incubated in insulin-free neurobasal media for 1hr, at 37°C in 5% CO2. For some samples, insulin-free medium was aspirated off and replaced with

Neurobasal media containing insulin at a final concentration of 100nM, and cells were incubated at 37°C for another for 15 min before being lysed or fixed for experiments. Parallel “no insulin”

(untreated) controls were included, along with an “insulin plus inhibitor” condition containing

100nM insulin with three pooled inhibitors against MEK (PD98059), IGF-1R/IR (GSK-

1838705A), and PI3K (LY294002) at concentrations of 10μM, 1μM, and 50μM respectively, or with the PI3K inhibitor individually.

siRNA Transfection and Medium Collection

SH-SY5Y mutAPP cells were transfected with Dharmacon SMARTpool siRNAs targeting exocyst proteins 1-8 and Exoc3L2. Twenty-five pmol of siRNAs were transfected using

RNAiMAX (Thermofisher) in 250μL Opti-MEM by manufacturer instruction, and supplemented with 750μL of 50/50 media. After 72 hours, cells were trypsinized (0.05% Trypsin) and replated with fresh media in a 12-well plate at 150,000 cells/mL, with some cells saved for analysis of knockdown by qPCR. At 24hr and 48hr time points, 100μL of media was collected, with the remaining media were collected at 72hrs (or at 48 hours for the shorter time point) for ELISAs.

Cells were lysed in RIPA buffer containing protease and phosphatase inhibitors for analysis of protein content by Bicinchoninic Acid (BCA) Protein Assay (Thermofisher).

26 Quantitative Real-Time PCR

The qPCR protocol was obtained from Lee et al. (2016). RNA was extracted from SH-SY5Y cells using the Qiagen RNeasy Micro Kit. The concentration of RNA was measured using the

Nanodrop 2000c (Thermofisher), and cDNA was generated from 1μg of RNA using iScript

Reverse Transcriptase (Bio-Rad). RT-PCR was done using SYBR green (Bio-Rad) with 5μL of cDNA in a CFX96 Real Time System (Bio-Rad) based on the manufacturer's instructions. The reaction was as follows: 3 min denaturation at 94°C, followed by cycles of 10 s at 95°C

(denaturation) and 10 s at 58°C (annealing, primer-based temperature) cycled 40 times, and a plate read by melting curve between 65°C and 95°C for 5 s increasing by 0.5°C. The 2(−훥훥퐶푡) method of analysis was used to calculate fold changes of expression (Lee et al., 2016). Primer sequences used are listed in Table 3.

Table 3. Real-Time qPCR primer sequences

Gene Forward Sequence Reverse Sequence Size (bp)

B-actin ACCGAGCGTGGCTACAGCTTCACC AGCACCCGTGGCCATCTCTTTCTCG 113

Exoc1 TCAGATCTCTGAAAGCAACCACC CCTCTGGAAGAAGCAAGATCTCC 150

Exoc2 ACCTCAACAGTCTCTTTCAAGC TTAGCAGGACGTAAGGACAAGG 146

Exoc3 CTCCACACGTGGTCTCTGAGC TCTGGCTCTGTCTCTTTGACCC 124

Exoc4 ATCAAGCAAGTGCCTCAAAAGC CTAGTCGAAGGTCACTCAGTCC 139

Exoc5 AGTAATCCAGAAACAGTCCTGGC TTTGAGATATTGCTCTGCATCGG 120

Exoc6 GAGAACAGCGAGAGTCTGGG TAAACTTCTTGTGCGCATTTGG 136

Exoc7 TCATCAGCTACTACCATGTGGC TGTCCTGGAAATACTCCACTGC 126

Exoc8 TTTTCGAACTCTCCCCAGATCG ACGAATTGCAGTATGAACAGCG 149

Exoc3L2 CAACTGCAGAGGCTGTTCCG CAACTGCAGAGGCTGTTCCG 129

27 ELISA

ELISAs were performed according to manufacturer instructions for each respective kit: Human sAPPɑ Assay Kit (Immuno-Biological Laboratories), Human sAPPβ-w Assay Kit (Immuno-

Biological Laboratories), and Human Amyloid β40 Brain Kit (Millipore). Briefly, media samples were added to the ELISA plates and incubated overnight at 4°C. Wells were washed five times with the provided 1X wash solution and then incubated for 30 min at 4°C with the labeled antibody or enzyme conjugate. Wells were washed again five times, and then the detection solution was added. After 30 min, at room temperature, the stop solution was added and plates were read on a plate reader at 450nm. Sample concentrations were calculated based on a standard curve from provided protein standards. Concentrations were then normalized to the non-targeting siRNA control samples concentrations, and then further normalized for cell number by using total protein content measured by the BCA assay for each respective knockdown.

Western Immunoblot

Proteins were extracted from cells using RIPA buffer containing protease and phosphatase inhibitors, and then electrophoresed on SDS-PAGE as described in Fujimoto et al. (2019).

Measurements of protein content of samples was done using the Bradford Assay relative to a

BSA standard curve. Antibodies were diluted 1:1000 in 5% BSA in 0.1% PBST for primary antibodies and 1:20,000 in 5% milk in 0.1% PBST for secondary antibodies. Blocking was performed in 5% milk/PBST. Blots were scanned with a LI-COR Odyssey Clx scanner, and visualized using LI-COR Image Studio. Antibodies used are listed in Table 4.

28 Table 4. Western blot antibodies

Antibody Species Clonality mW Manufacturer/Catalog # (kDa)

APP (Y188) Rabbit mAb 4-100 Abcam, ab32136

Exoc1/Sec3 Rabbit pAb 102 Protein Tech, 11690-1-AP

Exoc4/Sec8 Rat mAb 110 Enzo, ADI-VAM-SV016

Exoc5/Sec10 (C-4) Mouse mAb 77 Santacruz, sc-514802

Exoc7/EXO70 Rabbit pAb 74 Protein Tech, 12014-1-AP

Glut4 Rabbit pAb 55 Thermofisher, PA5-23052 mNeonGreen (32F6) Mouse mAb 26 Chromotek, AB_2827566

RFP (6G6) Mouse mAb 27 Chromotek, AB_2631395

Live-Cell Imaging and TIRF Microscopy

Live-cell imaging and TIRF microscopy was done on differentiated SH-SY5Y cell lines using the Leica Thunder 3D Live Cell Imaging System at the University of Hawaii Cancer Center

Microscopy, Imaging, and Flow Cytometry Core (funded by NIH 1S10ODO28515-01). Cells were grown and differentiated on #1.5 glass- bottom plates, and kept at 37°C and 5% CO2 for the duration of imaging.

Data Analysis and Statistics

Data analysis was done using Microsoft Excel, Image Studio, ImageJ, and GraphPad Prism software. All statistics were performed using Prism, with statistical significance represented by

*(<0.05), **(<0.01), ***(<0.001), nd (not detected), and ns (no significance). One-way ANOVA was followed by either the Dunnett or Tukey post hoc tests.

29 RESULTS

Immuno-Localization of APP, Exoc5, and Exoc7 in Mouse Primary Hippocampal Neurons

Immunofluorescent staining was performed on cultures of mouse primary hippocampal neurons to assess the expression and subcellular localization of exocyst proteins compared to

APP. Neurons were also co-stained with MAP2 and tau in order to label dendrites and axons, respectively. Primary neurons were isolated and cultured from the hippocampi of neonatal wild- type mice (in the laboratory of our collaborator Dr. Robert Nichols) until 8 days in vitro (DIV), after which they were fixed and probed (see methods).

When imaged with confocal microscopy, APP, Exoc5, and Exoc7 proteins were each observed throughout the cell body, and in both dendritic and axon projections of neurons, coinciding with both MAP2 and tau (Figures 8,9). Immunostaining with tau was focused primarily in axons, however also stained some dendrites as can be seen in Figure 9 as thicker processes compared to axons. All three proteins can be found in the tips of projections, however

Exoc5 and Exoc7 seemed to extend further than the MAP2 and tau, likely indicative of their role in growth of neuronal projections (Martin-Urdiroz et al., 2016). Secondary-only controls (not shown) did not show significantly identifiable signal compared to antibody conditions. These results indicate that all three proteins can be found in and throughout mouse hippocampal neurons, and therefore may colocalize and have the potential to functionally interact.

30

Figure 8. Immunocytochemistry for Exoc5, Exoc7, and APP with MAP2. Immunocytochemistry and confocal microscopy on mouse primary hippocampal neurons for APP (top), Exoc5 (middle), and Exoc7 (bottom) in red counterstained with MAP2 in green. DAPI is shown in blue. All three proteins localize throughout the cell body and dendritic projections.

31

Figure 9. Immunocytochemistry for Exoc5, Exoc7, and APP with tau. Immunocytochemistry and confocal microscopy on mouse primary hippocampal neurons for APP (top), Exoc5 (middle), and Exoc7 (bottom) in red counterstained with tau in green. DAPI is shown in blue. All three proteins localize throughout the axons, though there was some cross-staining for the tau present in dendrites.

32 Close proximity of the exocyst and APP along neuronal projections

After examining protein localization in neurons for Exoc5, Exoc7, and APP, we wanted to determine whether they were within close proximity of each other beyond sharing similar space in the cell. This would provide preliminary support for our hypothesis that the exocyst may shuttle APP-containing vesicles. Coincidence of the proteins in the cell was assessed through use of the proximity ligation assay (PLA) on mouse primary hippocampal neurons. A signal from the

PLA indicates that the two target proteins are within 40nM of each other, as <40nM proximity is required for the hybridization of the fluorescently tagged DNA probes present on the secondary antibodies. For reference, the length of an antibody is 15nm and intracellular transport vesicles are typically 30-100nm. Cells were counterstained with either MAP2 or phalloidin (F-actin) prior to the completion of the protocol in order to map localization within the cell (phalloidin) and in the soma and dendrites (MAP2). Results were compared to the PLA run with single antibody negative controls to identify the level of background signal. We tested two combinations of PLA target proteins: Exoc5 and Exoc7, and Exoc5 and APP, with the APP antibody (Y188, Abcam) recognizing the cytoplasmic C-terminal domain. Exoc5 and Exoc7 PLA signal was used to indicate exocyst holocomplex assembly, as they are closely localized only when the holocomplex is formed. PLA signal from Exoc5 and APP would show close proximity of full- length APP or its ɑ-CTF and β-CTF cleaved forms to Exoc5, which would be likely due to trafficking of APP-positive vesicles by the exocyst.

We found that signals from both combinations were present throughout the cell body of primary hippocampal neurons (Figure 10). In differentiated neurons counterstained with MAP2,

PLA signal could be localized to the dendrites and soma, with signal also observed at the tips of projections (Figure 11). From these data, we can conclude that APP and Exoc5 can be found

33 closely located in hippocampal neurons, suggesting that vesicles trafficked by the exocyst may contain APP. Some signal was also located outside the cell, potentially indicative of its presence in differentiating neurons not expressing MAP2 or possibly in glial cells. It also confirms exocyst holocomplex assembly in neurons through robust PLA signal from the Exoc5 and Exoc7 antibody combination.

Figure 10. PLA counterstained with phalloidin for Exoc5-APP and Exoc5-Exoc7 combinations. PLA in mouse primary hippocampal neurons counterstained with Phalloidin (green) to label actin and DAPI (blue) to mark nuclei. A) Exoc5-APP signal (red) indicates the proteins are within 40nm. B) Exoc5-Exoc7 signal (red) indicates exocyst holocomplex assembly in these neurons.

34

Figure 11. Higher magnification PLA in primary neurons counterstained with MAP2. A-C) Exoc5-Exoc7 PLA signal (red) counterstained with MAP2 (green) indicates the exocyst assembles within dendrites and at the tips. D-F) Close up of localization of Exoc5 and APP by PLA (red) in mouse primary hippocampal neurons. Signals can be found throughout the soma, projections (MAP2, green), and at the tips. DAPI is shown in blue.

35 Insulin signaling switches the exocyst away from APP trafficking

After establishing that Exoc5 and APP can be found in close proximity to each other in primary neurons in the previously described PLA experiments, we tested whether insulin would have an effect on the quantity of exocyst-APP association as insulin regulates exocyst activity in other tissues. Mouse primary hippocampal neurons were starved of insulin for 1 hour, after which they underwent a 15-min incubation with either no insulin, 100nM insulin, or 100nM insulin plus small molecule inhibitors added. In the first experiment, only the PI3K inhibitor

(LY294002) was used. A follow up experiment included a pool of three antagonists: an insulin receptor (IR)/insulin-like growth factor 1 receptor (IGF-1R) inhibitor (GSK-1838705A), a MEK inhibitor (PD98059), and the PI3K inhibitor (LY294002). PLA signal was quantified as present throughout the culture, and signal per cell was calculated from the total signal divided by the number of nuclei in the image.

In the first experiment, the same PLA combinations were tested as in the earlier section:

Exoc5 and Exoc7, and Exoc5 and APP. As hypothesized based on PLAs performed in myoblasts

(Fujimoto et al., 2019), insulin significantly increased the PLA signal from the Exoc5 and Exoc7 combination in these neurons, indicative of increased exocyst holocomplex assembly (Figure

12A). However, PI3K inhibition did not seem to counteract the effect of insulin signaling.

Despite the overall increase in exocyst assembly, the addition of insulin largely abolished the

PLA signal from Exoc5-APP, suggesting they were no longer associating (Figure 12B). PI3K inhibition also did not change this effect, suggesting that insulin is perhaps regulating the exocyst through a PI3K independent pathway.

36

Figure 12. Quantification of PLA insulin experiment of Exoc5-Exoc7 and Exoc5-APP in primary hippocampal neurons. A) Insulin increased exocyst holocomplex assembly significantly, and inhibition of PI3K did not reverse this effect. B) Exoc5 decreased its interaction with APP when insulin was added, and inhibition of PI3K did not reverse this effect. PLA single antibody controls showed minimal signal compared to PLA combinations for all antibodies used. Each dot represents PLA signal per nuclei from one field of 5-15 cells calculated with ImageJ, with the line indicating the mean. Statistical significance was calculated with one-way ANOVA followed by the Tukey post hoc test, and is represented by * (p<0.05), ** (p<0.01), and *** (p<0.001). No significance is marked by ns.

For the follow-up experiments, we examined whether additional insulin-signaling pathway inhibitors would be able to reverse insulin-induced PLA changes. One additional PLA antibody combination was included, Glut4 and Exoc5, based on the study by Fujimoto et al.

(2019), which showed that insulin increased Exoc5-Glut4 PLA signal in skeletal muscle cells.

This combination in parallel with Exoc5-APP could help determine some aspects of where exocyst activity is being targeted in primary hippocampal neurons. When neurons were insulin- starved and insulin was applied in the same way as the previous experiment (100nM for 15 min),

Exoc5-Exoc7 PLA signal was significantly increased as before while signal from Exoc5-APP decreased (Figure 13BD). Conversely, Exoc5-Glut4 PLA signal significantly increased in response to insulin, indicating that the increased exocyst activity may be for insulin-specific

37 trafficking functions rather than just a general increase in exocyst activity (Figure 13C). This may be insulin inducing a “vesicle switch” for the Rab-GTPases that use the exocyst from APP- positive vesicles to Glut4-positive vesicles. For all antibody combinations, the pool of three inhibitors successfully reversed the effects of added insulin. These results indicate that insulin causes exocyst holocomplex assembly overall to increase, but it is being directed away from

APP trafficking and towards insulin-related energetic pathways. The ability of the pooled inhibitors to reverse that effect suggests that the cause of the switch is upstream of PI3K but downstream of IR/IGF-1R and possibly MEK, although more experiments will be required to identify the specific regulatory pathways, beginning with use of the IR and MEK inhibitors each individually.

38

Figure 13. Quantification of PLA insulin experiment of Exoc5-Exoc7, Exoc5-Glut4, and Exoc5- APP in primary hippocampal neurons. A) Representative PLA images of all antibody combinations: Exoc5-Exoc7 (B), Exoc5-Glut4 (C), and Exoc5-APP (D) in red counterstained for MAP2 (green) and DAPI (blue). Insulin increased exocyst holocomplex assembly and interaction with Glut4, but decreased its interaction with APP. Pooled inhibitors of MEK, IR/IGF-1R, and PI3K were able to block the change caused by insulin. PLA single antibody controls showed minimal signal compared to PLA combinations for all antibodies used. Each dot represents PLA signal per nuclei from one field of 5-15 cells calculated with ImageJ, with the line indicating the mean. Statistical significance was calculated with one-way ANOVA followed by the Tukey post hoc test, and is represented by * (p<0.05), ** (p<0.01), and *** (p<0.001).

39 Knockdown of exocyst by siRNA in SH-SY5Y cells reduces the secretion of Aβ into cell media, as well as that of sAPPɑ and sAPPβ

If APP is trafficked by the exocyst, which would influence its proteolytic cleavage events, then targeted loss of the exocyst proteins may decrease the processing of APP and/or exocytosis of its cleaved forms. To test this hypothesis, we transfected a panel of exocyst- targeting siRNAs (Dharmacon pool) into a mutant APP overexpressing SH-SY5Y line to knockdown individual exocyst mRNAs, and then measured the release of sAPPɑ, sAPPβ, and

Aβ40 into the media over 48 and 72 hours (Figure 14). The APP sequence in the mutant APP

(mutAPP) construct was designed to contain three mutations found in human Swedish and

Indiana families as causative genes for early-onset Alzheimer’s: the KM670/671NL (Swedish) double mutation and the V717F (Indiana) mutation. All three mutations occur around the Aβ sequence and increase the rate of proteolytic cleavage of APP, increasing the production and release of Aβ in our assays well over endogenous levels (Mullan et al., 1992; Murrell et al.,

2000). All eight core proteins of the exocyst holocomplex were targeted individually, along with the potential alternative subunit, Exoc3L2. We measured percentage mRNA knockdown with real-time quantitative PCR (Figures 16A, 17A). In the 48-hour experiment, three additional combinations of exocyst siRNAs were assessed to see if it created a compounded effect: Exoc5 and Exoc6, Exoc2 and Exoc4, and Exoc2 and Exoc5. These combinations were chosen based on their role in the exocyst: Exoc5 and Exoc6 travel with the vesicle, Exoc2 and Exoc4 are located on the plasma membrane, and Exoc2 and Exoc5 represent one of each. An additional positive control was added for the 48-hour experiment to measure the reduction of secreted Aβ and fragments from treatment with a BACE1 antagonist.

40

Figure 14. Diagram of the siRNA experimental protocol. Stable mutAPP-expressing SH-SY5Y cells were transfected with Dharmacon pools for each individual exocyst subunit siRNA and then plated in triplicate, while remaining cells were taken for qPCR analysis. After 48 or 72 hours, media was collected and cells were lysed. Media was then used for three ELISAs and total protein content was measured from lysate samples by BCA.

ELISA measurements were interpreted against a standard curve with purified protein provided by the manufacturer and normalized against the non-targeting siRNA control (siCtrl) to determine any changes. The exocyst is a widespread trafficking complex that could potentially affect numerous pathways, so total protein measured from each cell lysate sample was used as a surrogate for total cell population for the ELISA tests. All results from ELISAs were then normalized further against the total protein concentration of that sample (Figure 15). No significant changes in total protein content were observed from knockdown of the exocyst proteins individually by 48 hours, but there were some significant changes measured by 72 hours for that experiment, pointing to possible deficits accumulating over time. Combination knockdown of two exocyst members together did cause a reduction in cell growth by 48 hours in two out of three of the combinations tested. It is also possible that the variations may be due to unequal plating of samples, though all cell counts were measured with a hemocytometer to seed the same number of cells.

41

Figure 15. Total protein content measured in 48- and 72-hour siRNA experiments. Total protein content quantified by BCA for the 48-hour (A) and 72-hour (B) experiments. All subsequent ELISA results were normalized by protein content to control for differences in cell number. Data are expressed as means +/- standard error. Statistical significance was calculated with one-way ANOVA followed by the Dunnett post hoc test and is represented by * (p<0.05), ** (p<0.01), and *** (p<0.001).

The 72-hour experiment was the first conducted to assess the effect of knockdown on

APP processing. It included siRNA knockdowns of the eight exocyst proteins and Exoc3L2, compared against a non-targeting siRNA control. Knockdown efficiency varied with most subunits reduced to less than 25% of the control, but with less robust knockdown of Exoc3 and

Exoc7 (Figure 16A). Across the tested exocyst members, most knockdowns showed statistically significant decreases in Aβ40 released into the media by 72 hours, with the exceptions of Exoc1

Exoc6, Exoc7, and Exoc8. With similar exceptions of Exoc1 and Exoc7-8, the other exocyst samples also showed a reduction in the release of sAPPɑ into the media. Exoc1 knockdown seemed to unexpectedly showed a higher release of sAPPβ into the media, however it did not reach statistical significance. Most other samples showed no significant change, however, Exoc3 and Exoc5 knockdown did show a significant reduction in sAPPβ (Figure 16).

42

Figure 16. siRNA knockdown of exocyst proteins and ELISA measurement of Aβ40, sAPPɑ, and sAPPβ secretion over 72 hours. A) Assessment of siRNA knockdown efficiency by real-time qPCR for the 72-hour experiment, compared to a non-targeting control. Exoc3L2 knockdown efficiency could not be confirmed in the 72-hour experiment. B-D) Results from ELISAs on the 72-hour media for Aβ40, sAPPɑ, and sAPPβ show some variability, but overall indicates significant reduction in APP trafficking by inactivating exocyst subunits. Data are expressed as means +/- standard error. Statistical significance was calculated with Student’s t-test of each siCtrl gene compared to its siRNA counterpart (A) and one-way ANOVA followed by the Dunnett’s post hoc test (B-D) and is represented by * (p<0.05), ** (p<0.01), and *** (p<0.001).

43 Following the first experiment, we used a shorter time scale to see if the same changes could still be detected at 48 hours while aiming to minimize knockdown impairment of cell growth. Combinations of exocyst siRNA knockdowns were added to evaluate any potential for compounding effects, along with a BACE1 inhibitor control. The results of the 48-hour experiment showed more variability than the previous 72-hour time point. In general, the siRNAs were less effective at knocking down the target mRNAs, although most were significantly decreased. Most exocyst knockdowns did not show a significant change in the release of Aβ40, sAPPɑ, or sAPPβ at 48 hours (Figure 17). Unexpectedly, the release of APP cleaved forms increased relative to the control for certain knockdowns, as was the case for siExoc3L2’s effect on sAPPɑ, and siExoc1 and siExoc6’s effect on sAPPβ. When normalized for the reduced protein content in those samples, the siRNA combinations did not show significant differences from the control for sAPPɑ and Aβ40. However, two of the three combinations did show a significant reduction in the release of sAPPβ. siExoc8 also showed a significant reduction in sAPPβ, in contrast to the increases from siExoc1 and siExoc6. The BACE1 inhibitor control results were as expected, with strong decreases in Aβ40 and sAPPβ (to the point of being undetectable by the assay). Surprisingly, BACE1 inhibition significantly increased sAPPɑ, which could explain why knockdown of the exocyst proteins and Exoc3L2 increased sAPPɑ as well.

These experiments indicate that exocyst members may have varied effects on exocytosis of APP cleaved forms. The influence of the exocyst, however, seems to be more detectable over longer periods (72 hours compared to 48 hours), and also the degree of mRNA knockdown may need to be more efficient to prevent compensation by the other exocyst members.

44

Figure 17. siRNA knockdown of exocyst proteins and ELISA measurement of Aβ40, sAPPɑ, and sAPPβ secretion over 48 hours. A) Assessment of knockdown efficiency by real-time qPCR for the 48-hour experiment, compared to a non-targeting control. Additional combinations of knockdowns were also measured. B-D) Results from ELISAs on the 48-hour media for Aβ40, sAPPɑ, and sAPPβ. Data are expressed as means +/- standard error. Statistical significance was calculated with Student’s t- test (A) and one-way ANOVA followed by the Dunnett post hoc test (B-D) and is represented by * (p<0.05), ** (p<0.01), and *** (p<0.001).

45 Generation and live-cell imaging of fluorescently-tagged Exoc1/4/7-mutAPP overexpressing

SH-SY5Y cells

Given the complicated multi-step trafficking of APP in neuronal cells, we wanted to be able to view the interactions between the exocyst proteins and APP in living cells. This approach also later may allow us to investigate the effects of overexpression of exocyst proteins. SH-

SY5Y cells were transfected with pGenie3 plasmids designed to co-express the mutAPP gene fused on the C-terminal end with mScarlet and one of three exocyst proteins fused on the C- terminal with mNeonGreen (Figure 18A). Expression of each transgene was driven by the CAG promoter, with an IRES cassette between the two open reading frames. The three particular exocyst members, Exoc1, Exoc4, and Exoc7, were chosen based on a study by Ahmed et al.

(2018), which determined that only some of the exocyst proteins can be tagged with fluorescent proteins and still retain their function. Each plasmid was individually transfected to generate three stable overexpressing lines selected for with hygromycin. Expression of the tagged proteins in the stable lines was then validated by western blotting, using specific antibodies against mNeonGreen, RFP, APP, and exocyst subunits (Figure 18B, C). Expressed proteins were successfully detected at the expected larger size than the endogenous proteins due to their fluorescent tag. The RFP tag is located on the C-terminus of APP, and thus can still be detected on some of ɑ-CTF, β-CTF, and AICD forms (Figure 18C).

46

Figure 18. Validation of exocyst/mutAPP overexpressing SH-SY5Y cell lines by western blot. A) Graphic of designed transgenes, with mScarlet and mNeonGreen cloned in frame on the 3’ end of APP and exocyst cDNAs, respectively. B) Exoc1, Exoc4, and Exoc7 over expressed in relevant lines at a higher size due to the mNeonGreen tag, alongside their endogenous counterparts at the expected size. C) RFP and APP antibodies were able to detect the mutAPP-mScarlet fragments.

47 Once the three cell lines were established and validated, preliminary live cell imaging was performed. For this, cells were grown on glass-bottomed 12-well plates, and followed our

18-day protocol for differentiating SH-SY5Y cells into neurons. Under epifluorescence, the mNeonGreen-tagged exocyst members appeared to be evenly distributed throughout the cytoplasm and individual puncti were not identifiable (Figure 20A-C). However, the mScarlet- tagged mutAPP were only seen in larger circular structures, suggesting through time-lapse that the transmembrane APP is being intracellularly trafficked in transport vesicles, which can be seen moving through the cell (Figure 19, 20). It is also possible that some of the APP-mScarlet signal was coming from endosomes or other organelles, as β-CTF would also be labeled at the C- terminal with mScarlet. Videos of the movement of the APP-mScarlet vesicles through differentiated SH-SY5Y cells can be accessed at the Google Drive link in Table 5, corresponding to figure images.

Table 5. Live-cell imaging EPI-fluorescence and TIRF videos

Video Type Figure

Exoc1-mNeonGreen/mutAPP-mScarlet - Exoc1 colocalized with APP vesicles TIRF 21 prior to exocytosis Exoc4-mNeonGreen/mutAPP-mScarlet (Red Channel Only) - APP vesicles Epi 19 moving through a differentiated SH-SY5Y cell Exoc7-mNeonGreen/mutAPP-mScarlet - Exoc7 colocalized with APP vesicles TIRF 22 moving through the differentiated SH-SY5Y cell Exoc7-mNeonGreen/mutAPP-mScarlet - APP moving through a differentiated Epi 20C SH-SY5Y cell and projection outlined by Exoc7 fluorescence

Link: Trafficking of APP by the Exocyst - Masters Thesis Videos RKS

48

Figure 19. mutAPP-mScarlet travels in vesicles in a differentiated SH-SY5Y cell. APP-positive vesicles traveling through a projection in a differentiated Exoc4-mNeonGreen/ mutAPP- mScarlet SH-SY5Y cell.

Figure 20. Exoc1/4/7-mNeonGreen/mutAPP-mScarlet lines viewed with epifluorescence and TIRF microscopy. A-C) Images of the three engineered SH-SY5Y lines taken under epifluorescence. mutAPP-mScarlet travels in what appear to be distinct vesicles or cell structures (red) while the exocyst members (green) are more evenly distributed throughout the cytoplasm. D-F) Images of the three SH- SY5Y lines taken using TIRF microscopy, which allowed us to see specific areas of high intensity for the exocyst proteins.

49 Since our hypothesis is that the exocyst regulates APP trafficking and exocytosis, utilizing a more specific and high-resolution microscopic approach may help detect coordinated exocyst-APP movement. We used TIRF (Total Internal Reflection Fluorescence) microscopy, which uses lasers at a wide angle that only excites evanescent fluorescence very close to the coverslip (100nm) due to the differences in refractive densities (glass coverslip versus water of the cell). Using TIRF allowed us to better view the mNeonGreen tagged exocyst members

(Figure 20D-F). In preliminary experiments, several high-resolution videos of the lines were obtained (Table 5). First, in contrast to epifluorescence, TIRF microscopy revealed clear high- intensity areas for each of the exocyst-mNeonGreen proteins. This confirmed subcellular locations of high protein concentrations. Furthermore, many points of exocyst overlap in each line with mutAPP-mScarlet were observed, confirming close-proximity colocalization. In the

Exoc1-mutAPP line, we found several instances where APP-positive vesicles disappeared from view upon contact with Exoc1, suggesting Exoc1-mediated exocytosis (Figure 21B, C). Video from the Exoc7-mutAPP line showed APP-positive vesicles travelling together with Exoc7, further supporting a role for the exocyst in APP trafficking (Figure 22C). The information gathered from these videos supports that exocyst members have a functional association with

APP in neurons, because APP is likely being trafficked in vesicles guided by the exocyst.

50

Figure 21. Time-series TIRF microscopy of fluorescently tagged Exoc1/mutAPP differentiated SH-SY5Y cell line. A) SH-SY5Y cell corresponding to the image sequences in B-C. B-C) Likely exocytosis events of APP-mScarlet at the plasma membrane sites marked with Exoc1-mNeonGreen. The signal from APP (red) disappears and Exoc1 (green) remains. `

51

Figure 22. Time-series TIRF microscopy of fluorescently tagged Exoc7/mutAPP differentiated SH-SY5Y cell line. A) SH-SY5Y cell corresponding to the image sequences in B-C. B) Likely exocytosis event of APP-mScarlet at the plasma membrane site marked with Exoc7-mNeonGreen. C) Tracking of Exoc7 (green) colocalized with APP (red) vesicle. The pair move together through the cytoplasm.

52 DISCUSSION

Alzheimer’s disease and its biology can be linked to numerous pathological pathways that are still poorly understood. The amyloid beta hypothesis is one of the most well studied, however, major gaps in knowledge still exist. The neuronal trafficking of APP and how that trafficking is regulated by the cell remain poorly researched areas, as is the intersection between

APP processing and insulin resistance, a strong risk factor for Alzheimer’s disease. This thesis describes, for the first time, a connection between the exocyst trafficking complex and APP in neurons, and how that relationship may be influenced by insulin signaling.

Through use of techniques like immunocytochemistry and the proximity ligation assay

(PLA), we showed that APP closely colocalizes with exocyst proteins in primary hippocampal neurons. APP, Exoc5, and Exoc7 localize in the same areas of mouse neurons, including the soma, throughout the dendrites, and at the tips of neuritic processes. Exoc5 and Exoc7 seem to extend further in the dendritic tips past where APP is located, possibly indicative of other roles that the exocyst plays in growth of neuronal projections. Signals from the PLA indicate that

Exoc5 and Exoc7 do assemble in mouse neurons, illustrating the activity of the holocomplex within these cells. Further, PLA results from Exoc5 and APP show that Exoc5 comes into close proximity (<40nm) with APP. This proximity is consistent with Exoc5 attached to an APP- positive transport vesicle. Some PLA signal was also observed outside of MAP2 staining, which indicates a possibility of the protein associations occurring in either differentiating neurons not expressing MAP2 or in glial cells. Further investigation will be needed to examine the extent to which signal is present in neurons specifically compared to other cell types, and how this may affect observed trends in signal localization and quantification under different treatment conditions like describe in the insulin experiments.

53 From PLAs in insulin-starved or insulin-treated neurons, the results suggest that Exoc5-

APP’s interaction is highly regulated by insulin signaling. Addition of insulin significantly increased the PLA signal from the Exoc5-Exoc7 combination, suggesting that insulin was able to stimulate exocyst holocomplex assembly. These results have been observed in other tissue types, such as skeletal muscle in Fujimoto et al. (2019), but never before in neurons. Despite an overall increase in exocyst assembly, PLA signal from Exoc5-APP strongly decreased in response to insulin, almost to the point of being undetectable. So, insulin signaling increased exocyst assembly, but also seemed to direct it away from trafficking APP-containing vesicles. In adipocytes and muscle, it is known that the exocyst traffics the Glut4 glucose transporter in response to insulin, but for the first time we were able to demonstrate that it does so in neurons as well. Insulin strongly increased the PLA signal from Exoc5-Glut4 in our primary mouse neurons. This result, compared with the other two PLA combinations, indicates that the overall exocyst activity is not only being increased, but also switched away from other forms of trafficking for insulin-specific functions.

Two different inhibitor experiments were also conducted to try to identify insulin signaling pathways that may be the source of this “vesicle switch”. PI3K inhibition alone was unable to reverse the effect observed; however, a combination of three antagonists blocking several points along the insulin signaling pathways did. This indicates that there may be PI3K independent signals from insulin that induce this “vesicle switch”. Future experiments will aim to clarify the signaling pathways through individual use of each of the other two inhibitors, MEK and IGF-R1/IR, plus others. IGF-R1/IR inhibition can be expected to block insulin signaling since it inhibits the receptors, so MEK antagonists and others in comparison will likely be more informative to identify more specific downstream pathways.

54 There are aspects of the PLA technique that raise some additional questions, and limitations to be considered. The PLA is a relatively new technique that has been gaining popularity over the past few years for its ability to detect close localization of proteins in cells and tissues. Previous research on the exocyst and our own immunofluorescent staining indicate the scale of how many exocyst proteins exist within the cells. It would likely be expected that exocyst activity would be much higher in cells than was observed in our PLA, with a maximum in our assays at around 80 signals per cell for our combination of Exoc5 and Exoc7. The sensitivity of the assay seems to be highly dependent on the quality of the antibodies used, the efficiency of the PLA reaction, and likely the kinetics of the interactions. So, the actual number of detected interactions between proteins, and between different antibody combinations, cannot be considered exact to the number of biological events occurring in the cell. However, we were able to reliably quantify the PLA signal in our insulin experiments using ImageJ software to count signals from images. Within antibody combinations under different conditions (like insulin-starved or insulin-added) statistically significant trends could be measured since one antibody combination can be assumed to have similar efficiency at detecting protein proximity between conditions. However, between different antibody combinations, it cannot necessarily be assumed that one pair of proteins interacts more than another pair of proteins because equal antibody efficiency cannot be guaranteed. One example of this is Exoc5-Glut4 signals compared to Exoc5-Exoc7. In the insulin treated samples, the average PLA signal for Exoc5-Exoc7 was between 30 and 40 signals per cell in the two experiments. For Exoc5-Glut4 it was 23 signals per cell. It cannot necessarily be assumed that Exoc5-Glut4 trafficking makes up two-thirds of the trafficking by the exocyst because it is possible that the Glut4 antibody may be more or less efficient than the Exoc7 antibody in the PLA. Additionally, the PLA provides a snapshot of the

55 moment of fixation, and since these interactions occur on a short time-scale, some degree of association may not be able to be captured as they dissociate quickly. These limitations mean that we can reliably identify overall trends within combinations to understand proteins coming into contact, but we cannot take the quantity of signal overall to be exactly representative of total protein contact within the cells.

In the siRNA knockdown experiment, the release of sAPPɑ, sAPPβ, and Aβ40 into the media was measured by ELISA and compared to a non-targeting siRNA control. Total protein content was used to normalize samples for possible differences in cell number that may be due to effects on the growth rate from exocyst knockdown. All samples were plated initially at the same cell number (150,000 cells per milliliter). However, by the time of media collection (48-72 hours later), there were a few differences in measured protein content, indicative of different cell numbers. This may have been due to errors in cell counts when plating, or potentially due to impairment of cell growth or attachment by exocyst knockdown. Of particular interest was that the protein content of two of the three combinations of exocyst knockdown, siExoc5/Exoc6 and siExoc2/Exoc4, was significantly decreased compared to the control at 48 hours while no other knockdowns were significantly affected by that time point. This could suggest that exocyst knockdowns have a compounded effect, and that individual knockdown of exocyst genes do not completely impair the rest of the complex alone. The different functions of exocyst members may translate to variation in how essential each protein is for certain activities. The apparent effects on cell growth may also contribute another limitation, as the signaling activities in unhealthy cells will be different from those of healthy cells. The degree to which exocyst mRNA knockdown leads to abnormal cell functioning outside of slowing down growth is, however, so far undetermined.

56 In the 72-hour experiment, most exocyst mRNAs were knocked down by 50% or more, and up to 85% for the most efficient siRNA. There were some significant changes in the media release of APP cleaved forms, depending on the exocyst member. siExoc2, siExoc3, siExoc4, and siExoc5 samples showed a strong reduction in Aβ40 and sAPPɑ release, but curiously only siExoc3 and siExoc5 were significantly decreased for sAPPβ, despite the Aβ40 decrease. siExoc1, with the lowest knockdown of all the samples, appeared to show an increase in sAPPβ, though it was not statistically significant, and siExoc1 did not affect either Aβ40 or sAPPɑ release. siExoc3L2, targeting the uncharacterized exocyst-like protein, also decreased Aβ40 release and increased sAPPɑ release, the only one to do so at this time point, but did not affect sAPPβ release. Unfortunately though, its degree of knockdown was unable to be confirmed for this experiment, so the results cannot be interpreted with certainty.

In the 48-hour experiment, we saw less efficiency in the exocyst siRNA knockdowns.

This, coupled with less time for APP fragments to accumulate in the media, may have limited our ability to detect significant effects. The results for the 48-hour time point were compared to a

β-secretase inhibitor treated sample as well, which did show a large decrease in both sAPPβ and

Aβ40, and a significant increase in sAPPɑ. Secreted levels of Aβ40 were not affected by any of the siRNA knockdowns to statistical significance, nor did it affect sAPPɑ release in any samples except for siExoc3L2. The increase in sAPPɑ release upon knockdown of Exoc3L2 at both time points was interesting, and may be related to similar increase of sAPPɑ seen with the BACE1 inhibitor. sAPPβ release at 48 hours was affected differently depending on the targeted exocyst protein. Similar to 72 hours, siExoc1 again increased sAPPβ release, this time statistically significant, without significantly changing sAPPɑ or Aβ40 release. siExoc6 was similar, though only at 48 and not 72 hours. siExoc8 however strongly decreased sAPPβ release at 48 hours, as

57 did two of the three combinations of exocyst knockdowns, one of which included knockdown of

Exoc6.

The data from these two experiments was varied by time point and targeted exocyst protein. There could be a number of explanations for this, but all of them warrant further experiments before any large conclusions can be made. The large variability in the efficiency of mRNA knockdown may be the source of some of these differences. Other exocyst proteins may be able to compensate for partial loss of one protein, minimizing any effects of individual knockdown. We also did not examine exocyst protein expression in our knockdown cells to be sure of the degree of difference in the availability of active protein, which could vary depending on the mRNA dynamics. Certain exocyst proteins may also be more crucial for neuronal trafficking of APP than others, or play stronger or weaker roles at different points of the trafficking process. Variability between the ELISAs may also affect the consistency between changes in Aβ40 and sAPPβ, as they are produced by two different companies. Possibly, the exocyst has a different degree of influence on exocytosis of one fragment compared to the other.

Time is also a factor, as it seems to take more time for detectable amounts of APP fragments to accumulate in the media based on the differences between the 48- and 72-hour time points. There is also the additional limitation of knockdown experiments in that it decreases exocyst function overall, preventing its activity from the very first step in the APP trafficking process. If the exocyst and its individual proteins are involved in different stages of APP transport, like full length APP delivery from the trans-Golgi network (TGN) to the plasma membrane, or exocytosis of APP cleaved forms, or trafficking to/from the early endosome, then inhibiting exocyst proteins may make it difficult to see the effects on all parts of its involvement.

58 By 72 hours, it seems that knockdown of most members of the exocyst decreased release of Aβ40 into the media, but otherwise these experiments need more data to be able to draw conclusions. Two specific points worth noting are the results from siExoc1 and siExoc3L2. siExoc1 is interesting because it seemed to increase sAPPβ release, while targeting other exocyst proteins did not show an effect or showed a decreased sAPPβ level. Exoc1 is known to have a unique function of binding directly to modified lipids at the plasma membrane target site of exocytosis. It may be possible the deletion of Exoc1 does not impair exocytosis of APP, but rather partially de-regulates it, leading to more exocytosis. siExoc3L2 results are similarly consistent across both times, showing an increase in sAPPɑ release instead when inhibited. As with the BACE1 inhibitor, the increase in sAPPɑ may suggest that Exoc3L2 is important for

BACE1 cleavage of APP, and when inhibited, more APP is available for cleavage by ɑ- secretase.

Live cell fluorescent microscopy, combined with Total Internal Reflection Fluorescence

(TIRF), of tagged APP and exocyst proteins in differentiated SH-SY5Y cells was used to examine the activity of these proteins in real time. The mNeonGreen-tagged exocyst proteins were only be seen as ubiquitous green fluorescence under live cell epifluorescent microscopy. which makes sense since they are cytoplasmic proteins, not travelling in intracellular vesicles like the transmembrane APP. In order to see individual protein interactions and look for possible exocytosis events at the plasma membrane, we used TIRF microscopy on a Leica Thunder 3D

Live Cell Imaging System. TIRF allows for higher resolution over background compared to normal microscopy by only exciting fluorescence very close to the coverslip (approximately

100nm) rather than through the whole cell. This can occur because of the use of glass as the coverslip, as the laser can be completely reflected between the interface of the glass and the

59 small amount of aqueous solution between the glass and the cells. As a result, only fluorophores close to the coverslip are excited (Fish, 2009). For our engineered cell lines, TIRF allowed us to see fluorescently-tagged exocyst proteins more clearly to measure their interaction with APP in living cells.

Microscopy on these transfected SH-SY5Y cell lines is still in its early stages. Our initial results support the hypothesis that the exocyst comes into contact with APP in transport vesicles, as fluorescent APP can be visualized moving in vesicles throughout the cells, with several points of colocalization with the tagged exocyst proteins. However, future experiments may be needed to fully validate that APP is moving in vesicles rather than as aggregates using a vesicle marker to solidify this result. Real-time TIRF recordings of these cells show some differences between exocyst members’ activity. In the Exoc1-mutAPP tagged line, possible exocytosis events can be observed via TIRF, as Exoc1 in green overlaps with APP in red, after which APP disappears and

Exoc1 remains. From decades of exocyst studies, we know that Exoc1 is bound to the plasma membrane at the vesicle delivery site, so these results are consistent with exocytosis of tagged

APP following delivery to the membrane. Exoc7 has also been implicated in binding to the vesicle delivery site, but in our cells, Exoc7 seemed to move more with APP vesicles throughout the cell, suggesting it may be traveling instead with the transport vesicle in this context. It’s also possible that the vesicle may be becoming untethered and moving out of the plane of focus. This observation still requires future examination and repetition, but could represent a novel function of Exoc7 in neurons. The cell line with Exoc4-mNeongreen still remains to be thoroughly imaged, and further experiments may additionally require a marker of exocytosis or comparison to negative control proteins (that are not trafficked by the exocyst) to confirm our observations.

60 Overall, these findings provide further support that vesicular trafficking of APP or APP cleaved forms is regulated by the exocyst in neuronal cells.

Work with these three cell lines is set to continue in order to further identify exocyst-APP trafficking events. Difficulties in the early stages in getting clear images resulted from the fluorescence of mNeonGreen on the exocyst members being of a lower intensity compared to mScarlet on APP, while background on the green channel was too high. This is likely due to

APP signal being concentrated in transport vesicles, and exocyst proteins being distributed throughout the cytoplasm. Our differentiated cells also were not firmly attached to the coverslip, which meant that projections were often either out of view of TIRF or moving around on the coverslip during imaging. Future microscopy on these cells will need some refinements on signal-to-background ratio as well. Despite these challenges, live cell fluorescent imaging allowed us to gather exciting real-time data on how these proteins interact in human neurons.

61 CONCLUSION

In this project, we tested the hypotheses that the exocyst is a regulator of APP trafficking,

APP trafficking influences amyloid beta generation, and APP trafficking is influenced by insulin signaling in neurons. Through a combination of immunofluorescence and the proximity ligation assay, we were able to demonstrate that the exocyst is present and colocalizes within 40nm with

APP in mouse primary hippocampal neurons. We successfully generated three fluorescent protein-tagged overexpressing cell lines, and were able to view exocyst protein activity in differentiated SH-SY5Y cells in real time. We showed direct exocyst interaction with APP (or its cleaved forms) in these cells, confirming additionally that they are being trafficked in vesicles.

Through TIRF microscopy, Exoc1-mNeonGreen could be seen interacting with APP-mScarlet vesicles prior to their disappearance, indicating exocyst involvement in APP exocytosis. Exoc7 seemed to move with APP vesicles throughout the cell, possibly indicating a departure from the currently understood framework for the role of Exoc7. siRNA knockdown experiments to investigate the role of the exocyst in exocytosis of cleaved forms were so far inconclusive. The experiments provided some indication that different exocyst subunits may influence Aβ release, though further investigation is needed. Lastly, use of the PLA with different insulin conditions in primary neurons provided evidence of a direct connection between trafficking of APP by the exocyst and insulin signaling. Despite increases in overall exocyst assembly through Exoc5 and

Exoc7, insulin was able to significantly diminish PLA signals from Exoc5 and APP. Insulin also increased exocyst association with Glut4, suggesting a switch away from APP trafficking to perhaps energetic mechanisms when insulin signaling pathways are activated. We were able to narrow the possible pathways involved in this change of activity by determining PI3K inhibition

62 alone is not able to reverse these results, though more complete inhibition with MEK and IGF-

1R/IR inhibitors added could block these insulin effects.

There is still need for further research to better understand the exact role of the exocyst in

APP trafficking. Future experiments will need to address the limitations of previous siRNA experiments to understand how exocyst involvement affects cleavage by ɑ-secretase compared to

β-secretase and the generation of amyloid beta. There is also a need to study which exact APP transport steps the exocyst participates in. There are many events to investigate: if it traffics full length APP to the plasma membrane from the Golgi affecting overall availability of APP in the membrane, or if it traffics to or from the early endosome affecting cleavage, and what role it plays in the recycling and exocytosis of APP and amyloid beta.

Overall, our data supports our hypotheses that the exocyst plays an important role in intracellular trafficking of APP, and that this trafficking of APP is regulated by insulin. These results are significant for the field as they indicate a previously unknown role for the exocyst in neurons, and expand our understanding of how APP is trafficked in multiple pathways. This project identified a novel mechanistic connection between APP and insulin signaling, which can add context to other studies to explain how insulin influences production of the amyloid beta peptide, and why there is high comorbidity for diabetes and AD. Further, the data provide interesting revelations about the novel protein Exoc3L2, whose functions are largely unknown.

Exoc3L2, implicated in human genetic studies of late onset Alzheimer’s disease (LOAD), may have a unique connection to exocyst-specific APP trafficking, and could contribute to our understanding of Alzheimer’s disease risk. The exocyst’s connection to APP intracellular trafficking in vesicles also opens up new potential drug targets due to its specific regulation by small GTPases, GAPs, and GEFs, some of which are neuron and brain specific. There is still a

63 large amount of research on this topic that needs to be done to fully understand the exocyst’s involvement in Alzheimer’s, but in this thesis, we establish first and foremost that it is indeed involved.

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