Medicine, Faculty of
DSpace
UBCV
Lin, Susan
Master of Science - MSc
University of British Columbia
In Alzheimer’s disease (AD), progressive memory loss and cognitive decline have been associated with the presence of extracellular amyloid plaques in the brain. These plaques are primarily composed of aggregated amyloid-beta, but may contain other types of amyloid species such as islet amyloid polypeptide (IAPP). Aggregated IAPP forms the islet amyloid found in 90% of type II diabetes mellitus (T2DM) patients, and IAPP can readily cross the blood brain barrier to enter the central nervous system. With substantial epidemiological evidence suggesting that T2DM can significantly increase the risk of developing AD, IAPP has been proposed to be a potential link between the two diseases. Currently, the mechanisms underlying IAPP aggregation unclear, but factors such as IAPP overexpression and impaired processing have been implicated in its toxicity. Given the sensitivity of pancreatic islet cells to endoplasmic reticulum (ER) stress, we hypothesized that ER stress in T2DM may disrupt IAPP cellular homeostasis by affecting its synthesis. We investigated the transcriptional regulation of IAPP by cloning a 1618 base pair 5’ flanking region of the human IAPP (hIAPP) promoter and analyzed its promoter activity (along with shorter deletion constructs) in beta- and non-beta-cell lines. Tunicamycin-induced ER stress significantly enhanced hIAPP promoter activity and transcript levels in a human beta-cell line via activating X-box binding protein 1 (XBP1) splicing. We further identified a binding site for spliced XBP1 on the hIAPP promoter, and abolishing this binding site by substitution or deletion mutagenesis significantly diminishes the effects of ER stress on hIAPP promoter activity. Our study uncovers a link between ER stress and IAPP at the transcriptional level, and provides novel insight into the role of ER stress in IAPP cytotoxicity during T2DM and ultimately, AD.
REGULATION OF IAPP TRANSCRIPTION BY SPLICED XBP1 by Susan Lin B.Sc. (Honours), McGill University, 2014 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Neuroscience) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) January 2019 © Susan Lin, 2019 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Regulation of IAPP Transcription by Spliced XBP1 submitted by Susan Lin in partial fulfillment of the requirements for the degree of Master of Science in Neuroscience Examining Committee: Weihong Song Supervisor Robin Hsiung Supervisory Committee Member Yu Tian Wang Additional Examiner Additional Supervisory Committee Members: William Jia Supervisory Committee Member iii Abstract In Alzheimer’s disease (AD), progressive memory loss and cognitive decline have been associated with the presence of extracellular amyloid plaques in the brain. These plaques are primarily composed of aggregated amyloid-beta, but may contain other types of amyloid species such as islet amyloid polypeptide (IAPP). Aggregated IAPP forms the islet amyloid found in 90% of type II diabetes mellitus (T2DM) patients, and IAPP can readily cross the blood brain barrier to enter the central nervous system. With substantial epidemiological evidence suggesting that T2DM can significantly increase the risk of developing AD, IAPP has been proposed to be a potential link between the two diseases. Currently, the mechanisms underlying IAPP aggregation unclear, but factors such as IAPP overexpression and impaired processing have been implicated in its toxicity. Given the sensitivity of pancreatic islet cells to endoplasmic reticulum (ER) stress, we hypothesized that ER stress in T2DM may disrupt IAPP cellular homeostasis by affecting its synthesis. We investigated the transcriptional regulation of IAPP by cloning a 1618 base pair 5’ flanking region of the human IAPP (hIAPP) promoter and analyzed its promoter activity (along with shorter deletion constructs) in beta- and non-beta-cell lines. Tunicamycin-induced ER stress significantly enhanced hIAPP promoter activity and transcript levels in a human beta-cell line via activating X-box binding protein 1 (XBP1) splicing. We further identified a binding site for spliced XBP1 on the hIAPP promoter, and abolishing this binding site by substitution or deletion mutagenesis significantly diminishes the effects of ER stress on hIAPP promoter activity. Our study uncovers a link between ER stress and IAPP at the transcriptional level, and provides novel insight into the role of ER stress in IAPP cytotoxicity during T2DM and ultimately, AD. iv Lay Summary Alzheimer’s disease (AD), one of the most prevalent diseases in the aging population, is characterized by memory loss and cognitive decline. In most cases, development of AD is sporadic, and one major risk factor is type II diabetes mellitus (T2DM). T2DM can be linked to AD by the presence of an aggregated pancreatic protein called islet amyloid polypeptide (IAPP) in the brain. In this study, we examined how IAPP gene expression is regulated under conditions of cellular stress. We found that endoplasmic reticulum stress can increase IAPP transcript levels, and this effect is attributed to a protein called X-box binding protein 1 (XBP1). XBP1 enhances IAPP expression by directly binding onto its promoter, which is a segment of DNA responsible for controlling the expression of IAPP. These findings reveal how IAPP expression can be influenced under pathological conditions, and provide insight into the mechanisms underlying T2DM and AD. v Preface This thesis describes an unpublished research project which was started and completed by the author, Susan Lin, with supervision from Dr. Weihong Song. The author designed and conducted all experiments and data analyses. All work presented in this thesis was performed in Dr. Song’s lab in Detwiller Pavilion at UBC. vi Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface ............................................................................................................................................ v Table of Contents ......................................................................................................................... vi List of Tables ................................................................................................................................ ix List of Figures ................................................................................................................................ x List of Abbreviations ................................................................................................................... xi Acknowledgements .................................................................................................................... xiv Chapter 1: Introduction: Of Memory and Metabolism ............................................................ 1 1.1 An Overview of Alzheimer’s Disease .......................................................................... 1 1.1.1 Clinical and Pathological Features of Alzheimer’s Disease ................................... 2 1.1.2 The Genetics of Alzheimer’s Disease: Familial vs. Sporadic ................................. 3 1.1.3 Amyloid β Precursor Protein Processing and Aβ Generation ................................ 4 1.1.4 The Amyloid Hypothesis ........................................................................................ 7 1.2 Bridging Type II Diabetes Mellitus and Alzheimer’s Disease ................................ 11 1.2.1 Cognitive Impairments in Type II Diabetes .......................................................... 12 1.2.2 Hyperglycemia as a Risk Factor for Alzheimer’s Disease ................................... 13 1.2.3 Impaired Insulin Signaling in Alzheimer’s Disease ............................................. 15 1.2.4 Endoplasmic Reticulum Stress ............................................................................. 16 1.2.5 X-box Binding Protein 1 ....................................................................................... 19 vii 1.3 Islet Amyloid Polypeptide in the Pancreas and the Brain ...................................... 20 1.3.1. IAPP Structure and Amyloidogenicity ................................................................. 22 1.3.2. IAPP Transcription Regulation ............................................................................. 25 1.3.3. The Role of IAPP in Alzheimer’s Disease ........................................................... 27 1.4 Research Goals ............................................................................................................ 30 Chapter 2: Materials and Methods ........................................................................................... 32 2.1 Plasmid Generation .................................................................................................... 32 2.1.1 Primer Sequences for PCR Amplification ............................................................ 32 2.1.2 Molecular Cloning ................................................................................................ 32 2.1.3 Mutagenesis .......................................................................................................... 33 2.2 Cell Culture ................................................................................................................. 34 2.3 Drugs and Reagents .................................................................................................... 34 2.4 In vitro Transient Transfection .................................................................................. 34 2.5 Dual-Luciferase Reporter Assay ............................................................................... 35 2.6 Reverse-Transcription Polymerase-Chain Reaction (RT-PCR) ............................ 36 2.6.1 RNA Extraction and cDNA Synthesis .................................................................. 36 2.6.2 RT-PCR Detection and Quantification ................................................................. 36 2.7 Western Blot Analysis................................................................................................. 36 2.8 Data Analysis ............................................................................................................... 37 Chapter 3: Results: Spliced XBP1 Enhances Human IAPP Transcription .......................... 40 3.1 Functional Analyses of the hIAPP Gene Promoter ................................................. 40 3.2 Endoplasmic Reticulum Stress Upregulates hIAPP Promoter Activity ................ 45 3.3 Spliced XBP1 Enhances hIAPP Promoter Activity and Transcript Expression .. 47 viii 3.4 Mutation Analyses of XBP1’s Putative Binding Site in the hIAPP Promoter ...... 50 3.5 XBP1 Overexpression Does Not Affect hIAPP at the Post-Translational Level ... 52 Chapter 4: Conclusion: A Fine Balance.................................................................................... 53 4.1 General Conclusions and Discussion ......................................................................... 53 4.2 Implications, Significance, and Future Directions ................................................... 56 References .................................................................................................................................... 59 ix List of Tables Table 2.1. Primers used to amplify hIAPP promoter constructs. .................................................. 38 Table 2.2. Primers used to mutate hIAPP promoter constructs. ................................................... 38 Table 2.3. Cloning primers for expression plasmids. ................................................................... 39 Table 2.4. Primers for RT-PCR. ................................................................................................... 39 x List of Figures Figure 1.1 The amyloidogenic and non-amyloidogenic pathways of APP processing. ................. 7 Figure 1.2. The revised amyloid hypothesis. ................................................................................ 10 Figure 1.3. ER stress and UPR activation. .................................................................................... 18 Figure 1.4. Mechanisms of IAPP processing. ............................................................................... 22 Figure 1.5. Amino acid sequences of IAPP in various species. .................................................... 25 Figure 3.1. Cloning and characterization of the human IAPP promoter. ..................................... 43 Figure 3.2. Characterization of the human IAPP promoter in non-β cells. .................................. 44 Figure 3.3. ER stress enhances hIAPP transcription. .................................................................... 46 Figure 3.4. XBP1s, but not XBP1u, upregulates IAPP promoter activity. ................................... 49 Figure 3.5. Mutating XBP1s’ putative binding site on the IAPP promoter decreases XBP1s’ effect on IAPP promoter activity. ................................................................................................. 51 Figure 3.6. XBP1 overexpression does not affect proIAPP at the post-translational level. ......... 52 xi List of Abbreviations Aβ Amyloid beta AD Alzheimer’s disease AICD Amyloid β precursor protein intracellular domain ANOVA Analysis of variance APOE Apolipoprotein E APP Amyloid β precursor protein APPSWE Amyloid β precursor protein, Swedish mutation ATF6 Activating transcription factor 6 BACE1 Beta-amyloid cleaving enzyme-1 BBB Blood brain barrier Bp Base pair bHLH Basic helix-loop-helix bZIP Basic-region leucine zipper cDNA Complementary DNA CAT Chloramphenicol acetyltransferase CHOP CCAAT-enhancer binding protein homologous protein CTF Carboxyterminal fragment CTR Calcitonin receptor DMSO Dimethyl sulfoxide eIF2 Eukaryotic translation initiation factor 2 E-box Enhancer box xii EOAD Early onset Alzheimer’s disease ER Endoplasmic reticulum ETS E26 transformation-specific FAD Familial Alzheimer’s disease GLP1 Glucagon-like peptide 1 hIAPP Human islet amyloid polypeptide IAPP Islet amyloid polypeptide IDE Insulin-degrading enzyme IGF Insulin-like growth factor IR Insulin receptor IRE1 Inositol-required enzyme 1 Kb Kilobase pair MCI Mild cognitive impairment MCS Multiple cloning site MMSE Mini mental state examination mRNA Messenger RNA NFT Neurofibrillary tangle Nt Nucleotide PC1/3 Prohormone convertase 1/3 PC2 Prohormone convertase 2 PCR Polymerase chain reaction PDX1 Pancreatic and duodenal homeobox 1 PERK Protein kinase RNA-like ER kinase xiii PS1 Presenilin 1 PS2 Presenilin 2 PHF Paired helical filaments RAMP Receptor modifying protein rIAPP Rodent islet amyloid polypeptide RT-PCR Reverse transcription polymerase chain reaction sAPP Secretory amyloid β precursor protein T2DM Type 2 diabetes mellitus tAβ Truncated amyloid β TSS Transcriptional start site UPR Unfolded protein response UPRE Unfolded protein response element XBP1 X-box binding protein 1 XBP1u Unspliced X-box binding protein 1 XBP1s Spliced X-box binding protein 1 xiv Acknowledgements Working in science often feels like grappling in the dark, and I have depended on the support of many incredible individuals⎯both in and out of lab⎯during the time it took to complete this thesis. It’s been an honour and a privilege to work as a graduate student with my supervisor, Dr. Weihong Song, whose supervision and guidance enabled a safe space for me to fail, learn, and grow. His commitment to scientific and academic rigour is infectious, and I am a much better scientist because of it. I’d like to thank my committee members, Dr. William Jia and Dr. Robin Hsiung, for their frank feedback and apt advice when I needed it the most. I’m lucky to have been supported by my talented and inspiring colleagues in the Song lab (past and present). They’ve taught me so much, and it’s been a joy to work with them every single day. In particular, I’d like to thank Dr. Yun Zhang for her mentorship, friendship, and generosity⎯this project would not have been possible without her support. And finally, to my friends and family: I am forever grateful that our shared language cannot be unlearned by distance or time. It is a bond that needs no explanation; it is a love that will never dilute. Thank you for everything. 1 Chapter 1: Introduction: Of Memory and Metabolism 1.1 An Overview of Alzheimer’s Disease In the fall of 1901, 51 year-old Auguste Deter was admitted into the Frankfurt mental institute for what appeared to be a textbook case of dementia. Her doctor, Alois Alzheimer, was puzzled and intrigued by the severity of her symptoms at such a young age (Maurer, Volk, & Gerbaldo, 1997). After her death in 1906, a brain autopsy revealed not only the expected neuronal atrophy, but also the presence of neuritic plaques and neurofibrillary tangles (Alzheimer, 1907). These three neuropathological features are the hallmarks of what is now known as Alzheimer’s disease (AD) (Kraepelin, 1910), the most common neurodegenerative disorder in the world. AD currently afflicts more than 45 million people worldwide, and this number is expected to double every two decades (Kawas, Gray, Brookmeyer, Fozard, & Zonderman, 2000; United Nations Population Division Publications, 2013). In addition to AD’s crippling effect on mental health and quality of life, the disease is an undeniable economic burden and the load is only getting heavier: in 2018, the Alzheimer’s Association reported that the total cost of care in the United States alone is at a staggering $277 billion, and will rocket to $1.1 trillion by 2050. A recent 2015 World Alzheimer’s Report placed global costs at over $800 billion (USD), up 35% from just five years prior. There has never been more incentive to seek out disease-modifying therapies to alleviate the global strain of AD. 2 1.1.1 Clinical and Pathological Features of Alzheimer’s Disease The most prominent and familiar feature of AD is dementia, which is characterized by a gradual loss of memory along with other cognitive functions. Because AD (and dementia in general) is a progressive disease, patients fall on a spectrum from a pre-dementia stage to a severe dementia stage (Forstl & Kurz, 1999). After disease onset, progression is gradual but inevitable: the average time course for AD is about seven to ten years, and ultimately results in death (Holtzman, Morris, & Goate, 2011). While clinical symptoms typically begin to manifest at more advanced ages (i.e. over 65), there is evidence suggesting that AD-related changes in the brain may occur between 10-20 years earlier (J. C. Morris & Price, 2001). This revelation has shifted the way we think about approaching AD therapeutics, with greater emphasis being placed on prevention strategies at earlier stages of the disease. Currently, the most reliable and definitive method of distinguishing between AD and non-AD dementias is performing a brain autopsy (Vinters, 2015). With an autopsy, it is possible to determine the presence of the three characteristic lesions in AD: brain atrophy, neurofibrillary tangles (NFTs), and neuritic plaques. Cortical atrophy is a prominent macrolesion of the AD brain, and is a reflection of the extensive neuronal loss that occurs during the progression of disease. NFTs are cytoplasmic aggregates of paired helical filaments (PHFs) (Kidd, 1963) made up of hyperphosphorylated tau protein (Brion, Couck, Passareiro, & Flament-Durand, 1985; Grundke-Iqbal et al., 1986), and may be found not only in AD but in other tauopathies as well (K. Wisniewski, Jervis, Moretz, & Wisniewski, 1979). Neuritic plaques, on the other hand, are extracellular aggregates composed primarily of amyloid β (Aβ) (Glenner & Wong, 1984a, 1984b; Masters et al., 1985) and are unique to AD (Dubois et al., 2007). Neuritic plaques also contain dystrophic neuronal processes and tau PHFs, and are usually surrounded by activated 3 microglia and astrocytes (Hyman et al., 2012). Also found in AD brains are diffuse plaques, which differ from neuritic plaques in that they are more amorphous, less compact, and do not contain dystrophic neurites (Joachim, Morris, & Selkoe, 1989; Tagliavini, Giaccone, Frangione, & Bugiani, 1988; Yamaguchi, Hirai, Morimatsu, Shoji, & Harigaya, 1988). Other lesions found in the AD brain include cerebral amyloid angiopathy (Vinters & Gilbert, 1983), granulovacuolar degeneration (Ball, 1977), and loss of synapses and synaptic proteins (Terry et al., 1991). Since AD is primarily a disease of aging, it is common for patients to have other age-related diseases (C. Fu et al., 2004) and these comorbidities may manifest itself in the AD brain as well (Vinters, 2015). 1.1.2 The Genetics of Alzheimer’s Disease: Familial vs. Sporadic The genetics involved in AD are, perhaps not surprisingly, quite complex in that AD can either be inherited (familial AD or FAD) or it can arise sporadically (sporadic AD). FAD is rare compared to sporadic AD and accounts for less than 1% of all AD cases. Typically, the onset of FAD occurs earlier in life—i.e. at age 50 or earlier—and these cases are referred to as early onset AD (EOAD). So far, mutations in three different genes have been found to be responsible for FAD/EOAD: amyloid β precursor protein (APP), presenilin 1 (PS1) (Sherrington et al., 1995; St George-Hyslop et al., 1992), and presenilin 2 (PS2) (Levy-Lahad, Wasco, et al., 1995; Levy-Lahad, Wijsman, et al., 1995; Rogaev et al., 1995). AD-causing mutations in these genes all increase the relative amount of Aβ produced, given that 1) APP is Aβ’s precursor protein and 2) PS1 and PS2 are components of γ-secretase, a key enzyme involved in APP processing (De Strooper et al., 1998; Herreman et al., 2000; Z. Zhang et al., 2000). The first FAD-associated APP mutation discovered was the London mutation in which the amino acid valine at position 4 717 was substituted with isoleucine (APPV717I) (Chartier-Harlin et al., 1991; Goate et al., 1991). Not long after, the APP Swedish (APPSWE) mutation was discovered, which features a double mutation at amino acid sites 670 and 671 (K670N/M671L) (Mullan et al., 1992). Over the years, many more APP mutations linked to AD have been discovered that all underscore its importance in the pathogenesis of the disease. Compared to FAD, our understanding of sporadic AD is murkier; its only confirmed genetic risk factor is genotype variance of apolipoprotein E (apoE). The E4 allele of apoE is a major risk factor for AD while the E2 allele is protective, so the risk of developing AD is increased in individuals with more copies of the E4 allele compared to individuals with the E3 or E2 alleles (Corder et al., 1993; Rebeck, Reiter, Strickland, & Hyman, 1993; Saunders et al., 1993; Strittmatter et al., 1993). The difficulty in pinpointing molecular mechanisms in sporadic AD that are robust enough to model the disease has led to a disproportionately large number of FAD-focused studies in the field. Due to this limitation, much of what we currently know about AD may not fully or accurately reflect the vast majority of AD cases in the population. 1.1.3 Amyloid β Precursor Protein Processing and Aβ Generation After the purification and identification of Aβ as the principal component of neuritic plaques (Glenner & Wong, 1984a, 1984b; Masters et al., 1985), it was soon discovered that Aβ is actually derived from a larger precursor protein, APP, and the full length complimentary DNA (cDNA) was cloned by multiple independent research groups (Goldgaber, Lerman, McBride, Saffiotti, & Gajdusek, 1987; Kang et al., 1987; Robakis, Ramakrishna, Wolfe, & Wisniewski, 1987; Tanzi et al., 1987). APP is a single transmembrane protein and has three predominant isoforms with isoform APP695 being the most highly expressed in the brain (Kang et al., 1987; 5 Kitaguchi, Takahashi, Tokushima, Shiojiri, & Ito, 1988; Ponte et al., 1988). Full-length APP undergoes proteolytic cleavage via two major pathways: the amyloidogenic pathway and the non-amyloidogenic pathway (Figure 1.1). Under physiological conditions, the majority of APP is processed through the non-amyloidogenic pathway, where it is first cleaved by an α-secretase at the Leu-17 site in a region of the protein known as the “Aβ domain” to release a secretory APPα (sAPPα) ectodomain and an 83-amino acid membrane-bound carboxy-terminal fragment (CTF), C83. C83 undergoes further cleavage by γ-secretase to generate a small peptide, p3, and an APP intracellular domain (AICD, also known as CTFγ). In addition to α-secretases, APP can be cleaved by a θ-secretase, BACE2, at the Phe-20 site to generate other non-amyloidogenic fragments (Sun, He, & Song, 2006). APP can also be cleaved by a β-secretase at the Aβ Glu-11 site to generate C89 (CTFβ89), and subsequently γ-secretase cleaves C89 to produce a truncated Aβ11-40 (Deng et al., 2013). When APP is processed through the amyloidogenic pathway, it is first cleaved by a β-secretase at the Asp-1 site (“β site”) of the Aβ domain to release a secretory APPβ (sAPPβ) soluble fragment. This cleavage also leaves behind a 99-amino acid membrane-bound CTF, C99, which is further cleaved by γ-secretase to generate the Aβ peptide and AICD/ CTFγ. The Aβ peptide generated can vary in length between 38-43 amino acids, with the most common isoforms being Aβ(1-40) and Aβ(1-42) (H. Mori, Takio, Ogawara, & Selkoe, 1992). Aβ peptides can aggregate in a cross-β structure to form the amyloid fibrils that are found in AD brains (Fandrich, 2007). While Aβ(1-40) is the more abundant isoform, Aβ(1-42) is thought to be the more toxic of the two species as it is more hydrophobic and fibrillogenic (Harper, Wong, Lieber, 6 & Lansbury, 1997; Jarrett, Berger, & Lansbury, 1993; Walsh, Lomakin, Benedek, Condron, & Teplow, 1997) and correlates better with AD pathogenesis (Naslund et al., 1994). The active β-secretase in vivo is beta-amyloid cleaving enzyme-1, or BACE1, and it functions as a membrane-bound aspartyl protease with its catalytic sites in the lumen of acidic intracellular compartments in neurons (Hussain et al., 1999; Sinha et al., 1999; Vassar et al., 1999; Yan et al., 1999). BACE1 activity is thought to be the rate-limiting step in Aβ generation, making it a key enzyme in amyloidogenic APP processing (Luo et al., 2001). Various in vivo studies have shown that full or partial genetic ablation of BACE1 in APP transgenic mouse models can reduce Aβ burden and rescue Aβ-induced memory deficits (Laird et al., 2005; McConlogue et al., 2007; Ohno et al., 2004), further cementing the importance of BACE1 in AD pathogenesis. 7 Figure 1.1 The amyloidogenic and non-amyloidogenic pathways of APP processing. In the amyloidogenic pathway, APP is cleaved at the β site by β-secretase to generate sAPPβ and membrane-bound C99. C99 is further cleaved by γ-secretase to generate Aβ and AICD/CTFγ. In the non-amyloidogenic pathways, APP is cleaved by either β-secretase, α-secretase or θ-secretase at the β’, α, or θ sites to generate C89, C83, and C80 respectively along with a soluble sAPP fragment. The CTFs are further cleaved by γ-secretase to generate AICD/CTFγ and other non-amyloidogenic fragments. tAβ: truncated Aβ. Figure adapted from Y. Zhang and Song (2017). 1.1.4 The Amyloid Hypothesis Perhaps the most dominant model of AD pathogenesis is the amyloid hypothesis, which was postulated in the early 1990’s after the discovery that the main component of neuritic plaques is an amyloid protein, Aβ (Glenner & Wong, 1984a, 1984b). It was proposed that AD pathogenesis is caused by the accumulation of Aβ in the brain, and the deposition of amyloid leads to NFT formation, cell death, vascular complications, and dementia (Hardy & Higgins, 1992). There are a few key pieces of supporting evidence for this hypothesis: first, Aβ deposition is a unique feature of AD brains, whereas tau pathology and brain dystrophy are featured in other types of dementias. In AD, Aβ deposition has been shown to precede NFT formation (Bateman 8 et al., 2012; Lemere, Blusztajn, et al., 1996; Lemere, Lopera, et al., 1996) while there is no concrete evidence for the converse. Second, FAD-causing APP missense mutations within or surrounding the Aβ domain leads to increased Aβ levels or increased Aβ42/40 ratio (Citron et al., 1992; De Jonghe et al., 2001; Suzuki et al., 1994). Third, various missense mutations in PS1 or its homologue PS2—the most common cause of FAD—have been shown to either decrease Aβ(1-40) levels or increase Aβ(1-42) levels (Bentahir et al., 2006; Scheuner et al., 1996). Fourth, Down syndrome patients, who harbour an extra copy of APP, inevitably develop AD neuropathological features (Struwe, 1929; K. E. Wisniewski, Wisniewski, & Wen, 1985). Lastly, ApoE4 carriers have an increased risk of developing late onset sporadic AD (Farrer et al., 1997) most likely due to decreased Aβ clearance (Castellano et al., 2011) and increased Aβ fibrillogenesis (Evans, Berger, Cho, Weisgraber, & Lansbury, 1995; J. Ma, Yee, Brewer, Das, & Potter, 1994; T. Wisniewski, Castano, Golabek, Vogel, & Frangione, 1994). More recent discoveries have both solidified and modified the original hypothesis, but there also exists evidence that undermine the importance of Aβ in AD pathogenesis. Some of the challenges to the amyloid hypothesis include the fact that plaque burden correlates poorly with severity of cognitive impairment (Nelson et al., 2012) and that numerous clinical trials targeting amyloid have failed (Selkoe & Hardy, 2016). Alternate hypotheses that have been proposed for AD pathogenesis include cholinergic dysfunction (Bartus, Dean, Beer, & Lippa, 1982), inflammation (Eikelenboom & Veerhuis, 1999), and mitochondrial damage (Swerdlow & Khan, 2004). Nonetheless, the amyloid hypothesis has prevailed for over 25 years and is still the most oft-pursued aspect of AD pathogenesis. The toxicity of Aβ has been demonstrated many times over, but it is important to define the three major types of Aβ assemblies: Aβ monomers, soluble oligomers, and insoluble fibrils. 9 A multitude of studies over the years have shown that soluble oligomers are the most toxic type of Aβ species and can cause injury to neurons in vitro (Hartley et al., 1999), ex vivo (Lambert et al., 1998), and in vivo (Walsh et al., 2002). These findings have reshaped the original version of the amyloid hypothesis, as it may not be the plaques themselves that are deleterious, but rather the more intermediary species that are causing the bulk of the damage (Selkoe & Hardy, 2016) (Figure 1.2). It has even been suggested that insoluble fibrils may act as inert “Aβ pools” to sequester the toxic oligomers; however, whether this is actually a protective mechanism is unclear, as some studies have previously demonstrated the toxicity of Aβ fibrils (Lorenzo & Yankner, 1994; Pike, Burdick, Walencewicz, Glabe, & Cotman, 1993). 10 Figure 1.2. The revised amyloid hypothesis. Genetic mutations in AD-associated genes or failure of Aβ clearance results in the formation and accumulation of Aβ42 oligomers, which can trigger a series of pathological events leading to neuronal loss and dementia. Tangle formation (tau pathology) is downstream of oligomer formation. Figure adapted from Selkoe and Hardy (2016). 11 1.2 Bridging Type II Diabetes Mellitus and Alzheimer’s Disease Although it has been long acknowledged that metabolic disorders can negatively affect cognition, it was the Rotterdam study in 1996 that first reported a strong association between type II diabetes mellitus (T2DM) and dementia (Ott et al., 1996). Results published from the Rotterdam study in 1999 further revealed that diabetes can double the risk of dementia (Ott et al., 1999). Since then, a number of different studies have collectively confirmed that T2DM patients have an increased risk of developing AD compared with non-diabetic individuals (Biessels, Staekenborg, Brunner, Brayne, & Scheltens, 2006; Peila, Rodriguez, Launer, & Honolulu-Asia Aging, 2002). But perhaps this association is not so surprising when considering these two diseases on the whole. T2DM and AD share many common risk factors—with the most salient example being aging—and they also feature many similar pathological processes, some of which will be described in this section. Moving beyond pathology, the pancreas–brain connection is an important axis on which many key physiological processes are hinged upon. For example, the endocrine pancreas provides the brain with an important signaling molecule in insulin, while the brain regulates islet secretion via autonomic control. At the cellular and molecular level, pancreatic islet β cells and neurons both share many common features such as membrane excitability, production of neurotransmitters (e.g. γ-aminobutyric acid (GABA)) (Briel, Gylfe, Hellman, & Neuhoff, 1972), and expression of many of the same cell-specific transcription factors. Some of these similarities may be due to the fact that islet β cells do not express repressor element 1 silencing transcription factor (REST), a critical transcription factor responsible for repressing neuronal gene expression in non-neuronal cells (Atouf, Czernichow, & Scharfmann, 1997). 12 We currently do not have a clear grasp of how T2DM and AD are linked as the mechanisms involved are undisputedly multifactorial and complex. Studying these diseases in parallel may be crucial for understanding the independent and interdependent factors that contribute to T2DM and AD, and may provide us with useful tools to tackle the two diseases. 1.2.1 Cognitive Impairments in Type II Diabetes T2DM is the most prevalent type of diabetes mellitus and accounts for nearly 90% of diabetes cases worldwide. Its global prevalence not only echoes but surpasses the striking numbers described for AD, with an estimated 422 million adults living with diabetes in 2014 (although the real numbers are likely to be much higher, as up to about 60% of people living with diabetes are undiagnosed) (Roglic & World Health Organization, 2016). T2DM is characterized by hyperglycemia, peripheral insulin resistance, and abnormal insulin production and secretion due to pancreatic β cell insufficiency. Risk factors for T2DM include family history, age, diet, and excess body fat (obesity), suggesting that both genetics and lifestyle may play a role in T2DM development. Since the body relies on glucose as a primary source of energy, the disease is damaging at the systemic level and can lead to complications such as cardiovascular dysfunction and retinopathy. Previous studies have shown that T2DM is often associated with impairments in cognition and many patients eventually develop AD or other dementias (Ott et al., 1999; Peila et al., 2002). The prevalence of mild cognitive impairment (MCI) is also increased in adults with T2DM compared with non-diabetic individuals (Luchsinger et al., 2007). Although not all T2DM patients develop dementia, the disease can also affect cognition through subtler avenues. A number of clinical studies have demonstrated that T2DM patients tend to perform worse than 13 controls in cognitive tasks that test for memory, attention, and executive function (Grodstein, Chen, Wilson, Manson, & Nurses' Health, 2001; Hassing et al., 2004; Manschot et al., 2006; Reijmer, van den Berg, Ruis, Kappelle, & Biessels, 2010; Ruis et al., 2009). Neuroimaging studies using functional magnetic resonance imaging (fMRI) have also revealed altered neuronal activity and aberrant functional connectivity in T2DM patients (D. Liu et al., 2016; Xia et al., 2013). Many animal studies have shown that inducing T2DM symptoms (e.g. hyperglycemia) accelerates or exacerbates AD-like pathology in AD animals by affecting memory (Cao, Lu, Lewis, & Li, 2007), plaque burden and Aβ levels (Infante-Garcia, Ramos-Rodriguez, Galindo-Gonzalez, & Garcia-Alloza, 2016), and tau phosphorylation (Ramos-Rodriguez et al., 2016). Various diabetic animal models such as the db/db mouse model and BBZDR/Wor rats also appear to exhibit AD-like features including increased rodent APP levels (Z. G. Li, Zhang, & Sima, 2007), increased tau phosphorylation (Kim, Backus, Oh, Hayes, & Feldman, 2009), changes in synaptic machinery (Pintana et al., 2017), and deficits in memory (Biessels et al., 1998). These findings further solidify the notion that diabetes has a negative impact on cognition and memory and may even exacerbate AD neuropathology. 1.2.2 Hyperglycemia as a Risk Factor for Alzheimer’s Disease Unlike most peripheral tissues, neuronal glucose uptake is dependent on extracellular glucose concentration due to the widespread expression of glucose transporter 1 (GLUT1), a type of glucose transporter that is unresponsive to insulin (Choeiri, Staines, & Messier, 2002). Since over 80% of the energy production in the brain derives from glucose or lactate metabolism (Ebert, Haller, & Walton, 2003), the brain is highly dependent on glucose and is therefore 14 sensitive to any changes or fluctuations in its delivery. Hyperglycemia is a predominant feature of T2DM and may increase glucose concentrations in the brain by fourfold (Tomlinson & Gardiner, 2008). Chronic hyperglycemia leads to extensive neuronal damage and can result in a serious condition called diabetic neuropathy wherein the patient loses sensation in parts of the body, especially in the lower limbs. Numerous studies have been conducted to examine whether glycemic state significantly contributes to the cognitive defects observed in T2DM. Some earlier studies reported that hyperglycemia does not negatively affect cognitive function (Draelos et al., 1995; Gschwend, Ryan, Atchison, Arslanian, & Becker, 1995) while a significantly larger study later found that hyperglycemic patients with type 1 or type 2 diabetes performed poorly in tests for cognitive-motor function (D. J. Cox et al., 2005). A study conducted by Crane et al. found elevated glucose levels to be a risk factor for dementia even in non-diabetic patients (Crane, Walker, & Larson, 2013). Yet another study by Morris et al. found that hyperglycemic patients (fasting glucose >100 mg/dL) had greater overall cognitive decline compared to normoglycemic subjects (fasting glucose <100 mg/dL) over a two-year period as well as greater conversion from MCI to AD (J. K. Morris, Vidoni, Honea, Burns, & Alzheimer's Disease Neuroimaging, 2014). Despite a few inconsistent findings, it is now generally well-accepted that hyperglycemia can negatively impact cognitive function, and this notion has been supported and expanded in studies performed both in vitro and in vivo. Treating rat cortical neurons with high glucose increases tau phosphorylation and induces apoptosis (Kim, Backus, Oh, & Feldman, 2013). Enhanced phosphorylation of tau along with altered synaptic plasticity has also been observed in vivo in type I (streptozotocin injection) and type II (db/db) diabetic mouse models (Kim et al., 2009). High glucose also increases APP expression and Aβ production in vitro (Chao, Lee, Juo, 15 & Yang, 2016; Yang, Wu, Zhang, & Song, 2013) and in vivo along with increased plaque formation (Guo et al., 2016). Furthermore, older Goto-Kakizaki rats, which develop T2DM, have increased blood glucose levels along with increased tau phosphorylation and Aβ levels (Pintana et al., 2017). Taken together, these studies support the idea that hyperglycemia contributes—at least in part—to the cognitive decline observed in T2DM patients and may be a precipitating factor for AD. 1.2.3 Impaired Insulin Signaling in Alzheimer’s Disease Although neuronal insulin signaling has not been as extensively studied compared with peripheral signaling, recent studies over the years has highlighted its importance in the central nervous system. Insulin produced in the pancreas can readily cross the blood brain barrier (BBB) (Margolis & Altszuler, 1967) by receptor-mediated transport (King & Johnson, 1985) and bind to widely-distributed insulin receptors (IRs) in the brain (Havrankova, Roth, & Brownstein, 1978). Of note is that IRs are highly expressed in brain regions involved in learning and memory, such as the hippocampus and cerebral cortex; accordingly, insulin signaling has been implicated in synaptic plasticity mechanisms (Wan et al., 1997). Clinical studies have also shown that either systemic infusion or intranasal administration of insulin can increase memory and attention under euglycemic conditions (Kern, Born, Schreiber, & Fehm, 1999; Kern et al., 2001). Insulin resistance, one of the major pathological phenomena observed in T2DM, occurs when cells exhibit a diminished response to a given dose of insulin (Goldstein, 2002). Peripheral insulin resistance without T2DM increases the risk of cognitive decline and AD (Schrijvers et al., 2010; S. E. Young, Mainous, & Carnemolla, 2006), suggesting that insulin resistance may be a contributing factor to AD. It has been reported that AD brains have lower levels of insulin, 16 insulin-like growth factors (IGF), and IRs (Rivera et al., 2005; Steen et al., 2005) as well as increased expression of peripheral insulin resistance biomarkers in the hippocampus (Talbot et al., 2012). Furthermore, both intranasally-administered insulin and insulin detemir have been shown to improve cognition in patients with MCI or early-stage AD (Claxton et al., 2015; Reger et al., 2008), suggesting that insulin may have neuroprotective effects. 1.2.4 Endoplasmic Reticulum Stress The endoplasmic reticulum (ER) is a fundamental organelle responsible for the synthesis, folding, and maturation of most eukaryotic secreted and transmembrane proteins. These processes are highly regulated and are dependent on the activation of key signal transduction pathways, as the flux of proteins into the ER varies depending on cell state and environment (Ron & Walter, 2007). Any imbalance between the amount of unfolded proteins that enter the ER and the capacity of the ER to process these proteins leads to an phenomenon called ER stress, which is marked by the activation of an adaptive signaling pathway called the unfolded protein response (UPR). The UPR functions to either reduce protein load, increase ER capacity, or signal cell death if homeostasis cannot be achieved. These UPR functions are mediated through parallel signalling pathways initiated by activation of three different stress sensors: inositol-required enzyme 1 (IRE1) (J. S. Cox, Shamu, & Walter, 1993; K. Mori, Ma, Gething, & Sambrook, 1993), protein kinase RNA-like ER kinase (PERK) (Harding, Zhang, & Ron, 1999), and activating transcription factor 6 (ATF6) (Haze, Yoshida, Yanagi, Yura, & Mori, 1999; Yoshida, Haze, Yanagi, Yura, & Mori, 1998). The most conserved branch of UPR signaling is the activation of IRE1. After its autotransphosphorylation and homodimerization (Shamu & Walter, 1996), IRE1 acts as an 17 endoribonuclease and cleaves its only known substrate, a messenger RNA (mRNA) encoding X-box-binding protein 1 (XBP1) (J. S. Cox & Walter, 1996). This unconventional splicing event leads to the removal of a 26 nucleotide (nt) intron and produces a spliced variant of XBP1 (XBP1s), which is a transcription factor that can initiate the transcription of UPR target genes (Calfon et al., 2002; Yoshida, Matsui, Yamamoto, Okada, & Mori, 2001). Apart from IRE1 signaling, activation of the ER sensor PERK leads to the phosphorylation of eukaryotic translation initiation factor-2 (eIF2), which participates in decreasing global protein synthesis through inhibition of translation initiation (Harding et al., 1999). The third branch of the UPR involves activation and cleavage of ATF6, which results in its translocation into the nucleus to act as a transcription factor to regulate UPR gene expression (Haze et al., 1999). Perhaps not surprisingly, ER stress has been heavily implicated in both AD and T2DM (Back & Kaufman, 2012; Gerakis & Hetz, 2018). In AD, markers of dysfunctional ER proteostasis (e.g. changes in expression of UPR-associated proteins) in the brain correlates with the progression of the disease (Hoozemans et al., 2009; Hoozemans et al., 2005; Stutzbach et al., 2013). Of note is IRE1, as its phosphorylation has been shown to directly correlate with degree of neuropathology (Duran-Aniotz et al., 2017). In vitro studies also have shown that ER stress may be one mechanism through which Aβ mediates neuronal loss (Alberdi et al., 2013), and accumulation of intracellular Aβ has been shown to trigger ER stress (Kondo et al., 2013). Furthermore, targeting ER proteostasis in a mouse model of AD seems to be protective as it reduces Aβ and phosphorylated tau and improves synaptic plasticity (Peng et al., 2016). ER stress has been linked to abnormal glucose metabolism (J. Lee et al., 2011; Mao et al., 2011; Scheuner et al., 2001) and insulin resistance (Ozcan et al., 2004; Ozcan et al., 2006). There 18 is an observed upregulation of UPR-related proteins such as XBP1s in obese and insulin-resistant subjects (Boden et al., 2008), further cementing the notion that ER stress and the UPR may play a prominent role in T2DM and other metabolic disorders. A recent study by Lourenco et al. demonstrated that Aβ oligomers can induce eIF2 phosphorylation and a subsequent impairment in insulin signaling (Lourenco et al., 2013), suggesting that UPR activation may be a shared pathogenic mechanism in AD and T2DM. Figure 1.3. ER stress and UPR activation. The three branches of the UPR are activated in response to ER stress. Activation of PERK leads to the phosphorylation of eIF2, which further activates downstream pathways to inhibit protein translation. Activated IRE1 directly removes a 26-nt intron from XBP1 mRNA to generate a functionally active transcription factor, XBP1s. ATF6 activation leads to its cleavage in the Golgi; cleaved ATF6 enters the nucleus and acts as a transcription factor to influence gene transcription. 19 1.2.5 X-box Binding Protein 1 XBP1 was first identified identified as a basic-region leucine zipper (bZIP) transcription factor that bound to promoters of human major histocompatibility complex class II genes (Liou et al., 1990). It wasn’t until about a decade later when XBP1 was identified to be a stress-inducible transcription factor that is ubiquitously expressed in adult tissues (Yoshida et al., 1998). Under physiological conditions, expression of unspliced XBP1 (XBP1u) in the cytoplasm is kept quite low, given the instability and short half-life of the protein. Under periods of ER stress, activated ATF6 can stimulate an increase in XBP1 transcription, resulting in increased levels of XBP1 mRNA for IRE1 splicing (Yoshida et al., 2001). During ER stress, XBP1s regulates the transcription of genes involved in protein folding, trafficking, and secretion by preferentially binding to a core ACGT element within 200 bp of transcriptional start sites (Acosta-Alvear et al., 2007; Clauss, Chu, Zhao, & Glimcher, 1996). In addition to XBP1’s role in mediating UPR mechanisms, it also appears to have extremely diverse cell-specific roles and has been shown to be involved in secretory function, glucose homeostasis, and lipid metabolism (Acosta-Alvear et al., 2007). Given XBP1’s ubiquitous expression in the adult body, it has been implicated in a number of different diseases including AD and T2DM. XBP1s regulates APP processing enzymes α-secretase ADAM10 (Reinhardt et al., 2014) and β-secretase BACE1 (Gerakis, Dunys, Bauer, & Checler, 2016) and has been shown to play an important role in memory formation in the hippocampus (Martinez et al., 2016). In pancreatic β-cells, sustained activation of XBP1s leads to decreased insulin secretion and apoptosis (Allagnat et al., 2010) while its deletion impairs insulin processing (A. H. Lee, Heidtman, Hotamisligil, & Glimcher, 2011). It is thus evident that appropriate control of XBP1 activity is crucial for maintaining cell health. 20 1.3 Islet Amyloid Polypeptide in the Pancreas and the Brain With all the similarities and parallels between AD and T2DM, perhaps the most compelling detail is that they are both amyloid diseases. Mirroring the amyloid plaques found in AD, a common feature of T2DM is the presence of islet amyloid, first reported by two independent researchers back in 1901 (Opie, 1901; Weichselbaum A, 1901). While not a universal feature, it is present in many T2DM cases (~90%) and particularly in older individuals (Bell, 1959). For decades, the composition of islet amyloid remained elusive due to its insolubility; it wasn’t until 1986 when the major component was extracted, purified, and cloned (Westermark, Wernstedt, Wilander, & Sletten, 1986). This novel 37-amino acid peptide was eventually named islet amyloid polypeptide (IAPP) (Westermark et al., 1987), but is also often also referred to as amylin (Cooper et al., 1988). The biosynthesis of IAPP closely resembles that of insulin. It is first synthesized in the ER as an 89-amino acid preprohormone, or preproIAPP (Mosselman, Hoppener, Lips, & Jansz, 1989; Sanke, Bell, Sample, Rubenstein, & Steiner, 1988) (Figure 1.4). ProIAPP is subsequently formed after the removal of a 22-amino acid N-terminal signal sequence (Betsholtz, Svensson, et al., 1989), and it is further cleaved in the Golgi body by enzymes prohormone convertase 1/3 (PC1/3) at the C-terminal (Marzban et al., 2004) and prohormone convertase 2 (PC2) at the N-terminal (J. Wang et al., 2001). Finally, the 37-amino acid mature IAPP is formed after C-terminal amidation by carboxypeptidase E (Marzban, Soukhatcheva, & Verchere, 2005). Further post-translational modifications such as disulfide bridge formation is necessary for IAPP’s biological function (Koo & Miranker, 2005). IAPP is co-stored with insulin in secretory granules (Johnson et al., 1988), and is co-secreted from β-cells at a ~1:100 IAPP:insulin molar ratio (Kahn et al., 1990; Stridsberg, Sandler, & Wilander, 1993). 21 IAPP is predominately expressed in islet β-cells, but it can also be found in δ-cells (Mulder, Lindh, & Sundler, 1993), in the gastrointestinal tract (Miyazato et al., 1991; Toshimori et al., 1990), in sensory neurons (Gebre-Medhin, Mulder, Zhang, Sundler, & Betsholtz, 1998; Mulder et al., 1995), and in the hypothalamus (Z. Li, Kelly, Heiman, Greengard, & Friedman, 2016). While most studies have focused on IAPP’s toxic effects on islet β-cells (Lorenzo, Razzaboni, Weir, & Yankner, 1994), reported physiological roles of IAPP include inhibition of insulin secretion (Ohsawa, Kanatsuka, Yamaguchi, Makino, & Yoshida, 1989), regulation of glucagon secretion to maintain glucose homeostasis (Akesson, Panagiotidis, Westermark, & Lundquist, 2003; Gedulin, Rink, & Young, 1997), inhibition of gastric emptying (Gedulin, Jodka, Herrmann, & Young, 2006; A. A. Young, Gedulin, Vine, Percy, & Rink, 1995), and regulation of satiation and appetite (Lutz, 2005; Lutz, Geary, Szabady, Del Prete, & Scharrer, 1995; Mollet, Gilg, Riediger, & Lutz, 2004). As the circulating concentration of IAPP in the plasma is quite low (5-20 pM) (Butler et al., 1990), it has been difficult to discern the true physiological functions of the peptide in the body since most experiments were performed in the nanomolar range or higher. 22 Figure 1.4. Mechanisms of IAPP processing. PreproIAPP is an 89-amino acid preprohormone that forms the 67-amino acid proIAPP after removal of its N-terminal signal sequence. In the Golgi, proIAPP is cleaved by enzymes PC2 and PC1/3 at its N- and C-termini respectively to generate a 40-amino acid intermediate. The 37-amino acid mature IAPP is formed after C-terminal amidation by carboxypeptidase E. Figure adapted from Y. Zhang and Song (2017). 1.3.1. IAPP Structure and Amyloidogenicity Both IAPP and proIAPP have similar biophysical properties to the Aβ peptide but unlike Aβ, which is generated from cleavage of APP by BACE1 (at β site) and γ-secretase, islet amyloid is formed from the native sequence of IAPP. Rodent IAPP (rIAPP) is non-amyloidogenic (Betsholtz, Svensson, et al., 1989), and studies comparing rIAPP vs. human IAPP (hIAPP) revealed that positions 20-29 of the IAPP peptide is crucial for oligomer or fibril assembly (Betsholtz, Christmansson, et al., 1989; Glenner, Eanes, & Wiley, 1988) (Figure 1.5). Within this region, rIAPP and hIAPP differ by a mere six amino acids, but the most significant difference is the presence of three proline residues at positions 25, 28, and 29 in rIAPP (Betsholtz, Christmansson, et al., 1989). These proline residues most likely disrupt hydrogen bonding and β-sheet formation, and may provide an explanation for why rIAPP does not 23 aggregate (Raleigh, Zhang, Hastoy, & Clark, 2017). Substitution of single proline residues at positions 24 and 26 significantly reduces hIAPP aggregation and amyloid formation (Abedini, Meng, & Raleigh, 2007; Meng, Raleigh, & Abedini, 2010), further emphasizing the importance of this amyloidogenic region to islet amyloid formation. Native hIAPP is positively charged (at physiological pH) and hydrophobic, and in its monomeric state, exists as an unfolded peptide rather than adopting a rigid globular structure (Yonemoto, Kroon, Dyson, Balch, & Kelly, 2008). This property of hIAPP mirrors that of Aβ, as both molecules undergo conformational rearrangements to helical intermediates before self-assembling into β-sheet fibrils (Kirkitadze, Condron, & Teplow, 2001; Williamson & Miranker, 2007). The exact mechanisms by which amyloid fibrils form are still enigmatic, but a few factors have been implicated in hIAPP fibrillogenesis. First, research has shown that hIAPP overexpression leads to fibril formation and amyloid deposition (Janson et al., 1996), suggesting that the amount of hIAPP produced and secreted by β-cells may play a role in its amyloidogenesis. This idea is bolstered by the observation that IAPP expression is increased in pre-diabetic states (Johnson, O'Brien, Jordan, & Westermark, 1989; Z. Ma, Westermark, Johnson, O'Brien, & Westermark, 1998) and that suppression of endogenous hIAPP synthesis using small interfering RNA (siRNA) can inhibit amyloid formation and β-cell death (Marzban et al., 2008). Second, there exists ample evidence suggesting that impaired processing of proIAPP may play a role in hIAPP aggregation. proIAPP has been found in islet amyloid deposits (Paulsson, Andersson, Westermark, & Westermark, 2006; Westermark et al., 1989), and in vitro experiments have revealed that lack of PC1/3 or PC2 leads to IAPP aggregation and β-cell death (Marzban et al., 2006; Paulsson & Westermark, 2005). It has also been observed that prolonged high glucose treatment in vitro leads to increased expression of hIAPP precursors over 24 its mature form (Hou et al., 1999). And third, insufficient insulin processing due to high insulin demand in T2DM has been suggested to contribute to hIAPP amyloidogenesis. In vitro studies have revealed that insulin inhibits hIAPP fibrillogenesis by forming stabilizing heterocomplexes (Jaikaran, Nilsson, & Clark, 2004; Larson & Miranker, 2004; Sellin, Yan, Kapurniotu, & Winter, 2010), while proinsulin is significantly less efficient (Jaikaran et al., 2004; Westermark, Li, Westermark, Leckstrom, & Steiner, 1996). Impaired insulin processing in T2DM may inadvertently lead to decreased stabilization of hIAPP molecules. Another oft-debated issue in hIAPP fibrillogenesis is whether the initiation of its aggregation occurs intracellularly or extracellularly. Previous ultrastructural analyses have shown that hIAPP amyloid deposits accumulate extracellularly between β-cells and islet capillaries (Verchere et al., 1996), but there is also evidence of fibril accumulation within intracellular secretory granules in mice overexpressing hIAPP (Yagui et al., 1995). It is therefore possible that hIAPP fibrils form intracellularly and are then secreted into the extracellular space where it further aggregates (Westermark, Andersson, & Westermark, 2011), but this idea awaits experimental confirmation. 25 Figure 1.5. Amino acid sequences of IAPP in various species. Positions 20-29 of human IAPP (underlined) is largely responsible for its amyloidogenicity. Rodent IAPP contain three proline substitutions within this amyloidogenic region of the peptide and does not aggregate. Red letters indicate amino acid variations from the human IAPP peptide sequence. Asterisks (*) indicate amyloidogenic IAPP species. 1.3.2. IAPP Transcription Regulation Attempts to investigate the promoter region of hIAPP began almost as soon as the gene itself was identified. The hIAPP gene contains three exons with its translation initiation codon ATG located in exon 2 and an Alu sequence downstream of exon 3 (Mosselman et al., 1989). A transcriptional start site (TSS) occurring 25 base pairs (bp) downstream from a TATA-box was determined and confirmed by multiple groups using a combination of RNase protection assays, promoter construct analyses, and 5’ rapid amplification of cDNA ends (5’ RACE) (Hoppener, Oosterwijk, Visser-Vernooy, Lips, & Jansz, 1992; Mosselman et al., 1990; Nishi et al., 1989; van Mansfeld et al., 1990). One study reported a TSS that is supposedly farther downstream (Christmanson, Rorsman, Stenman, Westermark, & Betsholtz, 1990), but this reported start site coincided with the translational start site in exon 2 and the discrepancy is most likely a result of different numbering conventions. 26 Different promoter-reporter studies have also identified minimal control regions of varying lengths. Christmanson et al. reported a 1388 bp promoter fragment that showed significant chloramphenicol acetyltransferase (CAT) reporter activity in islet cell line HIT, while a 435 bp fragment did not (Christmanson et al., 1990). However, neither of these fragments extended past exon 1. A systematic investigation by Mosselman et al. using multiple promoter-luciferase constructs revealed that a -800/+32 construct did not have significant activity in RIN (rat islet) cells (Mosselman et al., 1990). Instead, a -800/ATG construct that extended up to the translation-initiation site in exon 2 showed much more appreciable and significant luciferase activity, suggesting that sequences within exon 1 or intron 1 may be important for hIAPP transcription. Further 5’ end deletions revealed that a -189/ATG construct was sufficient for promoter activity (De Wit, van Mansfeld, van Teeffelen, Lips, & Hoppener, 1993; Mosselman et al., 1990). Similarly, Carty et al. reported that sequences between -222 to +450 (ATG) bp is responsible for driving hIAPP gene expression (Carty, Lillquist, Peshavaria, Stein, & Soeller, 1997). Moreover, it appears that deletions or mutations in exon 1 attenuated, but did not abolish, hIAPP promoter activity whereas deleting intron 1 caused a massive 20-fold drop in promoter activity (Carty et al., 1997; Mosselman et al., 1990). While this may suggest that sequences within intron 1 are crucial for hIAPP transcription, a slightly more complex mechanism may be at work: replacing intron 1 with intronic sequences from an unrelated gene did not seem to affect promoter activity to a significant degree (Carty et al., 1997). Given the importance of 5’ introns in regulating transcription in certain genes (Brinster, Allen, Behringer, Gelinas, & Palmiter, 1988), it appears that the general presence of an intron is required for proper hIAPP promoter activity. 27 The IAPP gene is expressed predominantly in islet β-cells and this tissue- and cell-specific expression may be due to properties of the promoter region, as transfection of hIAPP promoter constructs into non-β-cell lines resulted in extremely low promoter activity (De Wit et al., 1993; Mosselman et al., 1990). The hIAPP promoter region shares many similar cis-acting elements with the β-cell-specific insulin promoter such as AT-rich regions (i.e. TAAT boxes) and enhancer boxes (Carty et al., 1997; German, Moss, Wang, & Rutter, 1992; Ohlsson, Thor, & Edlund, 1991; Park & Walker, 1992). A β-cell-specific protein complex containing pancreatic and duodenal homeobox 1 (PDX-1; also known as IUF1 and IPF1) was found to bind to TAAT motifs within the hIAPP promoter (Bretherton-Watt, Gore, & Boam, 1996). This PDX-1 complex also binds to regions within the insulin promoter (Boam & Docherty, 1989), suggesting that transcription of β-cell-specific genes may be in part regulated by similar mechanisms. Under physiological conditions, IAPP levels are drastically lower than that of insulin; however, in states of cellular stress, the ratio of IAPP:insulin expression and/or secretion is increased (Krizhanovskii, Fred, Oskarsson, Westermark, & Welsh, 2017; O'Brien, Westermark, & Johnson, 1991). This strongly indicates that IAPP and insulin can be differentially regulated at the transcriptional or post-translational level, or both. 1.3.3. The Role of IAPP in Alzheimer’s Disease In the mid-1990’s, IAPP research moved up into the nervous system with the identification of IAPP receptors in the rat brain (Beaumont, Kenney, Young, & Rink, 1993) and the discovery that IAPP is expressed in rat sensory neurons (Mulder et al., 1995). Further research revealed that hIAPP is toxic to rat hippocampal neurons (May, Boggs, & Fuson, 1993) and can induce the upregulation of oxidative stress genes in cortical neurons (Tucker, Rydel, 28 Wright, & Estus, 1998), much like Aβ. Although IAPP transcript levels have not been detected in many brain areas (except in chickens (Fan, Westermark, Chan, & Steiner, 1994)), peripheral IAPP can enter the brain by crossing the BBB (Banks, Kastin, Maness, Huang, & Jaspan, 1995). IAPP’s role in the central nervous system has been sparsely studied, but there is evidence that it has physiological functions in the brain. IAPP receptors, which are heterodimers made up of the calcitonin receptor (CTR) and receptor activity modifying protein (RAMP) (Christopoulos et al., 1999; Muff, Buhlmann, Fischer, & Born, 1999), are widely expressed in the brain (Ueda, Ugawa, Saishin, & Shimada, 2001). IAPP receptor autoradiography studies had previously revealed high-affinity IAPP binding sites in several brain areas (Sexton, Paxinos, Kenney, Wookey, & Beaumont, 1994). Activation of such receptors leads to a variety of downstream events that are involved in the regulation of energy homeostasis (Z. Zhang et al., 2011) and blood glucose (Hay & Pioszak, 2016), suggesting soluble IAPP may have neuroprotective effects. Pramlintide, an IAPP analog that’s less prone to aggregation, has been used as a clinical treatment for T2DM (Ryan, Jobe, & Martin, 2005) and has shown to have beneficial effects on AD rodent models (Zhu et al., 2015). Interestingly, Aβ can also bind to IAPP receptors to activate different signaling pathways (W. Fu et al., 2012; Jhamandas et al., 2011). Recently, in 2013, Jackson et al. reported the presence of hIAPP oligomers and fibrils in brains of cognitively impaired T2DM subjects. hIAPP was also found co-localized with Aβ in senile plaques of AD brains (Jackson et al., 2013), suggesting the two peptides may interact. Indeed, specific regions within hIAPP and Aβ-40 that have high binding affinity for each other have been identified (Andreetto et al., 2010). Proximity ligation assays performed in brains of AD subjects found hIAPP and proIAPP co-localized in cerebral and vascular Aβ deposits, whereas Aβ was not found in islet amyloid of T2DM subjects (Oskarsson et al., 2015). hIAPP 29 has been found in hippocampal pericytes of AD subjects (Schultz, Byman, Fex, & Wennstrom, 2017) and in vessels and capillaries in the dorsolateral cortex of T2DM patients with AD (Ly et al., 2017). Levels of retinal hIAPP also appear to be altered in AD patients (Schultz, Byman, Netherlands Brain, & Wennstrom, 2018). Rats overexpressing hIAPP harbour hIAPP brain depositions even at a pre-diabetic stage, and exhibit cognitive deficits along with an increased neuroinflammatory response (Srodulski et al., 2014). hIAPP and APP double transgenic mice showed increased tau phosphorylation, increased Aβ deposition, increased synapse loss, impaired insulin signaling, and hyperglycemia (Wijesekara et al., 2017), suggesting the two peptides may play a role in the overlapping pathologies of AD and T2DM. Exactly how IAPP contributes to AD pathology is currently unknown. It is possible that IAPP fibrils can interact with Aβ and induce cross-seeding (Oskarsson et al., 2015). Extracellular Aβ and hIAPP can both be degraded by insulin-degrading enzyme (IDE), and IDE hyposufficiency has been linked to T2DM (Pascoe et al., 2007) and AD (Perez, Morelli, Cresto, & Castano, 2000; Z. Zhao et al., 2007) (although this may not be true in all cases (Miners, Baig, Tayler, Kehoe, & Love, 2009)), thus sparking the possibility that IAPP deposits may arise from decreased clearance. Other possible mechanisms include induction of inflammation, damage of cellular structures, the triggering of oxidative stress, or activation of apoptosis (Y. Zhang & Song, 2017). Despite the increasing amount of evidence for a significant contribution of aggregated IAPP to AD pathogenesis, more work needs to be done to elucidate the mechanisms that underlie these observations. 30 1.4 Research Goals There is evidence pointing towards a model wherein overproduction of hIAPP itself may be an important factor its accumulation and aggregation (Howard, 1986; Johnson et al., 1989), suggesting that changes in IAPP gene transcription or protein degradation may be involved at some stage in T2DM pathogenesis. While transcriptional regulation of the IAPP gene has been studied in the past, there are still many outstanding questions that remain, with the most salient being: is IAPP gene transcription altered during pathogenic states? Previous studies have shown that IAPP promoter activity can be influenced by hypoglycemia (Alam et al., 1992) and hyperglycemia (Gasa, Gomis, Casamitjana, & Novials, 1997; Gasa, Gomis, Casamitjana, Rivera, & Novials, 1997) although a separate in vivo study did not find significant changes (Giddings, Carnaghi, & Shalwitz, 1993). Other factors such as glucagon, glucagon-like peptide-1 (GLP-1), and insulin have also been shown to increase IAPP promoter activity and transcript levels (Shepherd, Campbell, & Macfarlane, 2004). However, many of the mechanisms that are involved in the induction of IAPP gene transcription remain elusive. Several trans-acting factors that either directly or indirectly influence IAPP promoter activity have been identified, including the aforementioned PDX1 as well as hepatocyte nuclear factor 1 (HNF1) (Shepherd et al., 2004), FoxA2 (Jing et al., 2014), nuclear transcription factor Y (NFY) (Cluck, Murphy, Olson, Knezetic, & Adrian, 2003), Nkx2.2, Pax6 (Cissell, Zhao, Sussel, Henderson, & Stein, 2003), Pax4 (Campbell et al., 1999), and NeuroD1 (Itkin-Ansari et al., 2005). Whether or not these transcription factors play a role in mediating IAPP transcription during pathogenic situations is presently unclear. As detailed in section 1.2.4, ER stress is a shared pathogenic mechanism in AD and T2DM. Since islet β-cells are secretory cells, they carry a large ER load even under 31 physiological conditions. This means that β-cells are extremely sensitive to ER stress, as any ER perturbations will hinder its primary function of insulin secretion. There has been extensive work showing how overexpression of hIAPP can induce ER stress (Gurlo et al., 2016; C. J. Huang et al., 2007), but we do not know much about the effects of ER stress on IAPP itself. Therefore, we hypothesized that the transcription of IAPP can be altered in states of ER stress through increased activation of UPR-related transcription factors. To address our hypothesis, we first planned to clone the hIAPP gene promoter and to characterize its transcriptional activity in several human cell lines. Second, we would determine whether ER stress can alter IAPP gene transcription. Then, we planned to identify a candidate transcription factor(s) using a transcription factor binding site software (PROMO) and confirm whether expression of this protein is altered in our model of ER stress. We would then clone the candidate transcription factor(s) in order to determine whether its overexpression can affect IAPP transcription. Lastly, we planned to mutate the putative binding site on the IAPP promoter to determine whether this would significantly affect our transcription factor binding and effects of ER stress. 32 Chapter 2: Materials and Methods 2.1 Plasmid Generation 2.1.1 Primer Sequences for PCR Amplification Cloning primers for the IAPP gene promoter were designed with restriction sites for insertion into the multiple cloning site (MCS) of the pGL3-basic vector (Promega). Promoter fragments from -1618 base pairs (bp) upstream to +450 bp downstream of the TSS were amplified with appropriate primers (Table 2.1). pIAPP-luc -1007/+435 was cloned by restriction enzyme digest of the pIAPP-luc -1618/+435 plasmid at a unique SmaI site. Mutant constructs were generated with the pIAPP-luc -240/+435 (Table 2.2). Cloning primers for expression plasmids were designed with restriction sites for insertion into the MCS of the pcDNA4-mycHisA vector (Promega) (Table 2.3). 2.1.2 Molecular Cloning IAPP promoter plasmids were generated with genomic DNA from HEK293 cells. HEK293 cells were collected with ice-cold phosphate-buffered saline (PBS) and spun down. The pellets were resuspended with a lysis buffer (10 mM Tris-HCl pH 8.0, 10 mM EDTA pH 8.0, 150 mM NaCl, 0.5% SDS) containing proteinase K and incubated overnight at 55°C. Samples were then spun down and DNA was precipitated with isopropanol and washed with 70% ethanol before resuspension in deionized water. DNA concentration was determined with a BioPhotometer (Eppendorf). Expression plasmids were generated with complementary DNA (cDNA) from HEK293 or SH-SY5Y cells (see section 2.6). 33 Plasmid inserts were amplified with polymerase chain reaction (PCR) from either genomic DNA or cDNA and PCR products were isolated with agarose gels (1-2%). Gel images were captured using the GelDoc-It Gel Imaging System. The desired bands were cut from the gel and melted in a gel extraction buffer (5.5M guanidine thiocyanate, 20 mM Tris-HCl pH 6.6) at 42°C. The melted gel products were spun through a spin column, washed with 70% ethanol, and eluted in deionized water. The inserts and vectors were then digested with the appropriate restriction enzymes and isolated by gel electrophoresis. The digested inserts and vectors were ligated with T4 ligase (Invitrogen) at room temperature for 1 hour. The ligation products were transformed into Dh5α E.Coli competent cells (Invitrogen) and plated onto agar plates containing the appropriate antibiotic for overnight incubation at 37°C. Colonies were picked the next day and inoculated overnight (37°C, 220 rpm) in 1.5 mL Luria-Bertani (LB) medium supplemented with antibiotic for plasmid minipreparation (Qiagen). Minipreps were checked with restriction digest and positive clones were confirmed by Sanger sequencing. 2.1.3 Mutagenesis Plasmid mutations were generated using overlap extension PCR. Completely complementary internal primers containing the mutation site were used in separate PCR reactions with either 5’ or 3’ flanking primers to generate overlapping gene segments. These gene segments were used as template DNA in a second round of PCR with the flanking primers to generate the full-length product. The resulting PCR product was gel extracted, digested, and ligated into the appropriate vector. The ligation products were transformed into competent cells and positive colonies identified after minipreparation were confirmed by sequencing. 34 2.2 Cell Culture HEK293 and SH-SY5Y cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) containing 25 mM glucose and supplemented with 10% (v/v) fetal bovine serum (FBS), 1 mM sodium pyruvate, 2 mM L-glutamine, 100 units/mL penicillin, and 100 µg/mL streptomycin (Invitrogen). 1.1B4 cells were purchased from Sigma-Aldrich and cultured in Roswell Park Memorial Institute (RPMI 1640) medium containing 11.1 mM glucose supplemented with 10% FBS, 2 mM L-glutamine, 100 units/mL penicillin, and 100 µg/mL streptomycin (Invitrogen). All cell lines were maintained in a 37°C incubator containing 5% CO2 and cell medium was changed when necessary. Cells were routinely subcultured using trypsin-EDTA (Gibco) and were not kept beyond ~50 passages. 2.3 Drugs and Reagents Tunicamycin (Sigma) was dissolved in dimethyl sulfoxide (DMSO) at a stock concentration of 10 mg/mL, filter sterilized through a 0.22 µm membrane, and stored at -20°C. Tunicamycin was added at a dilution of 1:1000 (10 µg/ml) to cell culture medium for 24 hours to induce ER stress. Polyethylenimine, linear molecular weight 25,000 Da (PEI) (Sigma), was prepared as a stock solution at a concentration of 1 mg/ml in deionized water. The pH was adjusted to 7.0 and the solution was filter sterilized through a 0.22 µm membrane and stored at -20°C. 2.4 In vitro Transient Transfection Cells were seeded to 50-70% confluency one day before transfection. For the Lipofectamine method, plasmid DNA and Lipofectamine 2000 reagent (Invitrogen) were diluted 35 in Opti-MEM reduced serum medium (Gibco). The diluted DNA was added to the Lipofectamine reagent and the DNA-lipid complex was incubated at room temperature for 5 minutes before addition to cells cushioned with Opti-MEM. For the PEI method, plasmid DNA and PEI (1 µg/µL) were diluted in serum-free DMEM. The volume of PEI used is based on a 3:1 ratio of PEI (µg) : DNA (µg). The diluted DNA was added to the diluted PEI and the mixture was incubated at room temperature before addition to cells cushioned with serum-free DMEM. The cell medium was changed back to FBS-supplemented DMEM after 4-6 hours. 2.5 Dual-Luciferase Reporter Assay Cells were seeded onto 24 well plates and co-transfected with 500 ng luciferase plasmid and 1 ng Renilla luciferase plasmid (pRL-CMV) to normalize for transfection efficiency. For triple co-transfections, 250 ng of each plasmid and 1 ng pRL-CMV were transfected. For drug treatments, the drug was added 16 hours post-transfection. After 24 hours, cells were washed with PBS and incubated with passive lysis buffer (Promega). The culture plates were then placed on a platform shaker and gently rocked for 15-30 minutes. Firefly and Renilla luciferase activity were sequentially assayed using the Dual-Luciferase reporter system with a luminometer (Turner BioSystems TD-20/20). Firefly luciferase activity was normalized to Renilla luciferase activity, and expressed as relative luciferase units (RLU). 36 2.6 Reverse-Transcription Polymerase-Chain Reaction (RT-PCR) 2.6.1 RNA Extraction and cDNA Synthesis Cells were harvested in PBS and resuspended in TRI reagent (Sigma). Chloroform was added to the samples and after vigorous shaking, samples were left to stand at room temperature for 15 minutes before centrifugation at 12,000 × g at 4°C for 15 minutes. The top phase was immediately separated and transferred into a new RNase-free tube and mixed with isopropanol. Samples were incubated at room temperature for 10 minutes and centrifuged at 12,000 × g at 4°C for 10 minutes. The supernatant was discarded and pellets were washed with 75% ethanol and centrifuged again at 7,500 × g at 4°C. After drying, pellets were dissolved in diethyl pyrocarbonate (DEPC)-treated water and stored at -20°C. cDNA was synthesized from 1 µg RNA using the ThermoScript RT kit. 2.6.2 RT-PCR Detection and Quantification The RT reaction was amplified by PCR with primers for human IAPP, XBP1, GAPDH, and ACTB (Table 2.4). The PCR products were detected by 1.5% agarose gel electrophoresis and imaged with the GelDoc-It Gel Imaging System. Band intensities were quantified with ImageJ software and normalized to loading controls. 2.7 Western Blot Analysis Cells were lysed in a triton lysis buffer containing 50 mM Tris-HCl (pH 7.2), 150 mM sodium chloride, 1.0% Triton X-100, 1% SDS, 0.5% sodium deoxycholate, and protease inhibitor cocktail (Roche) on ice for 30 minutes, followed by brief sonication and centrifugation (15,000 × g for 10 minutes at 4°C). Protein concentrations were determined using the Bradford protein 37 assay (BioRad) and samples were diluted with 4x sodium dodecyl sulfate (SDS) loading buffer (200 mM Tris-Cl pH 6.8, 8% SDS, 0.04% bromophenol blue, 40% glycerol, and 5% β-mercaptoethanol). Samples (10-30 µg protein) were resolved with 10% Tris–glycine SDS–polyacrylamide gel electrophoresis (PAGE). Samples were then transferred onto nitrocellulose membranes (0.45 µm, GVS) and blocked in 5% non-fat milk at room temperature for 1 hour. Membranes were then incubated with primary antibodies diluted in the blocking medium overnight at 4°C. The primary antibodies used were the following: mouse monoclonal anti-C-myc 9E10 antibody (1:100, Santa Cruz Biotechnology), rabbit monoclonal GFP antibody (1:1000, Abcam), and mouse monoclonal anti-β-actin AC-15 antibody (1:5000, Sigma). After washing with PBS with 1% (v/v) Tween 20 (PBST), membranes were incubated with secondary IRDye 800CW-labelled goat anti-mouse antibody (1:5000) and IRDye 680RD-labelled goat anti-rabbit antibody (1:5000) at room temperature for 1 hour. The membranes were scanned and visualized with the Odyssey Infrared Imaging System (LI-COR Biosciences). 2.8 Data Analysis All statistical analyses were performed using GraphPad Prism 8 software. All results and error bars are presented as means ± SEM. For two-group comparisons, results were analyzed by two-tailed Student’s t-test. For multiple-group comparisons, results were analyzed by one-way analysis of variance (ANOVA) or two-way ANOVA. Statistical significance was accepted when p<0.05 (*p<0.05, **p<0.01, ***p<0.001, ****p<0.0001). All experiments were repeated at least three times (detailed in each figure legend). 38 Table 2.1. Primers used to amplify hIAPP promoter constructs. Position (bp) Restriction Enzyme Primer Sequence (5’ – 3’) Forward (sense) -1618 MluI AATTTGGTCTGGTGTTCAAGCC -553 MluI TACCTAGAATAATCCCTACCACAG -240 MluI GGCAAATTCAAACTTCTGCTGTG -240 XhoI GGCAAATTCAAACTTCTGCTGTG -163 MluI ACTGATGAGTTAATGTAATAATGACCC -65 MluI TGCCTGATGTCAGAGCTGAG Reverse (antisense) +435 XhoI CTGCAAAGAATAATCCAGCAC +435 MluI CTGCAAAGAATAATCCAGCAC +450 XhoI TGCTTCTCAAATTTTCTGCAAAGA Table 2.2. Primers used to mutate hIAPP promoter constructs. Position (bp) Template Primer Sequence (5’ – 3’) -175 Substitution pIAPP-luc -240/+435 CACTGTGTATTTGCTGTCTTAATATTTACTG CAGTAAATATTAAGACAGCAAATACACAGTG -175 Deletion pIAPP-luc -240/+435 CACTGTGTATTTGCTTAATATTTACTGATG CATCAGTAAATATTAAGCAAATACACAGTG 39 Table 2.3. Cloning primers for expression plasmids. Gene Restriction Enzyme Primer Sequence (5’ – 3’) XBP1u HindIII Sense ATGGTGGTGGTGGCAGCC XhoI Antisense GTTCATTAATGGCTTCCAGCTTGGCT XBP1s HindIII Sense ATGGTGGTGGTGGCAGCC XhoI Antisense GACACTAATCAGCTGGGGAAAGA Table 2.4. Primers for RT-PCR. Gene Primer Sequence (5’ – 3’) IAPP Sense CCCATTGAAAGTCATCAGGTGG Antisense TGGATCCCACGTTGGTAGAT XBP1 Sense CCTTGTAGTTGAGAACCAGG Antisense TGGATCCCACGTTGGTAGAT GAPDH Sense GGATTTGGTCGTATTGGG Antisense GGAAGATGGTGATGGGATT ACTB Sense GGACTTCGAGCAAGAGATGG Antisense GAAGCATTTGCGGTGGAG 40 Chapter 3: Results: Spliced XBP1 Enhances Human IAPP Transcription 3.1 Functional Analyses of the hIAPP Gene Promoter The hIAPP TSS is a guanine mapped to 450 bp upstream of the translation start site (ATG) in exon 2 (Hoppener et al., 1992; Mosselman et al., 1990; Nishi et al., 1989; van Mansfeld et al., 1990) and will be designated as +1. In order to carry out functional analyses of the hIAPP gene promoter, a 5’ flanking region of the hIAPP gene spanning 2053 bp upstream of exon 2 was cloned from HEK293 cells (Figure 3.1A). This 2053 bp promoter fragment spans 1618 bp upstream of the TSS and also covers +435 bp downstream of the TSS, up to the beginning of exon 2. The promoter fragment was cloned into a luciferase reporter vector, pGL3-basic, which lacks an eukaryotic promoter and enhancer; expression of the firefly luciferase protein will be driven by any functional promoter correctly inserted upstream of the gene. Therefore, bioluminescent detection of the firefly luciferase protein allows for quantitative measurement of the activity of the inserted promoter fragment. In order to study the activity of the hIAPP gene promoter, deletion constructs covering varying lengths of the 5’ flanking region of the gene were generated. After cloning the desired promoters into the pGL3-basic vector, the constructs were verified by restriction enzyme digest and visualized on a 1% agarose gel (Figure 3.1B). Each construct was further verified by Sanger sequencing. The -1618/+435 construct had surprisingly low promoter activity in a human β-cell line, 1.1B4, with mean activity of 2.629 ± 0.5635 relative light units (RLU) (Figure 3.1C). There was no significant difference (p=0.9120) compared with the empty pGL3-basic vector which had mean basal activity of 1.029 ± 0.2171 RLU. Slightly extending the 3’ end to span up to the ATG start site (-1618/+450) did not significantly increase promoter activity (3.836 ± 0.1986 RLU, p= 41 0.8028). Deleting 611 bp from the 5’ flanking region generated a -1007/+435 construct, and this fragment also did not have significant promoter activity (4.948 ± 0.2988 RLU, p=0.4140). However, an even shorter construct, spanning from -553 to +435, had approximately tenfold higher promoter activity compared with the empty vector (11.15 ± 1.354 RLU, p<0.0001). Deleting 313 bp from the 5’ end of the -553/+435 construct generated a -240/+435 fragment, and this construct also had significant luciferase activity compared with pGL3-basic (16.57 ± 3.059 RLU, p<0.0001). The -240/+435 promoter construct also had significantly higher luciferase activity than the -553/+435 construct (p=0.0461). Further deletion to create a -163/+435 construct seemed to abolish luciferase activity, with a mean RLU of 4.994 ± 0.3463 (p=0.1492) – a fourfold drop in activity with deletion of only 77 nucleotides from the 5’ flanking region. A short -65/+435 construct also had no significant luciferase activity compared with pGL3-basic (2.427 ± 0.5158 RLU, p=0.9322). Lastly, a +435 to -240 bp spanning fragment was cloned into the pGL3-basic vector in reverse orientation; this construct had, as expected, absolutely no promoter activity (0.05319 ± 0.005577 RLU, p=0.9786). It was previously reported that the hIAPP gene promoter has little activity in non-β-cell lines (De Wit et al., 1993). Our hIAPP promoter constructs were transfected into two different human cell lines, HEK293 cells (Figure 3.2A) and neuronal SH-SY5Y cells (Figure 3.2B). Similar to 1.1B4 cells, the longest cloned fragment -1618/+435 showed no significant luciferase activity compared with pGL3-basic in HEK293 (8.553 ± 2.041 RLU, p=0.1347) and SH-SY5Y (11.62 ± 4.388 RLU p=0.7849). The -1618/+450 construct also did not have significant promoter activity in HEK293 cells (1.345 ± 0.1111 RLU, p=0.0723) or SH-SY5Y cells (10.92 ± 0.2718 RLU, p=0.8901). The -1007/+435 promoter fragment did not exhibit significant activity in HEK293 cells (3.409 ± 0.123 RLU, p= 0.9824) but had appreciable activity in SH-SY5Y cells 42 (32.8 ± 0.9523 RLU, p=0.0260). We found that the shorter -553/+435 construct had significant activity in both HEK293 (35.3 ± 4.668 RLU, p<0.0001) and SH-SY5Y (50.58 ± 11.15 RLU, p<0.0001) cells. The -240/+435 promoter construct also displayed significant luciferase activity in HEK293 (43.23 ± 2.821 RLU, p<0.0001) and SH-SY5Y (67.42 ± 10.73 RLU, p<0.0001) cells, which is consistent with what was observed in the 1.1B4 cells. The shorter -163/+435 and -65/+435 constructs did not have significant activity in HEK293 cells (6.228 ± 1.015 RLU, p=0.8106 and 1.434 ± 0.196 RLU, p>0.9999 respectively) but were active in SH-SY5Y cells (52.33 ± 1.751 RLU, p<0.0001 and 69.86 ± 3.453 RLU, p<0.0001 respectively). Taken together, it appears that 240 bp upstream of the hIAPP promoter TSS is sufficient to drive luciferase firefly protein expression, while a longer 1.6 kilobase (kb) 5’ fragment seems to have little promoter activity, suggesting the presence of negative regulatory cis-acting elements upstream of -240 bp. All promoter constructs were active and behaved similarly across human cell lines 1.1B4, HEK293, and SH-SY5Y. 43 Figure 3.1. Cloning and characterization of the human IAPP promoter. (A) Schematic diagrams of cloned hIAPP promoter constructs, which consist of 5’ flanking regions with serial deletions inserted into the pGL3-basic vector. Arrows represent the TSS and direction of transcription, and numbers represent the 5’ start and 3’ end points of each construct relative to the TSS. (B) hIAPP promoter constructs were verified by restriction enzyme digest. Digested products were resolved on a 1% agarose gel and were further confirmed by sequencing. (C) Promoter deletion plasmids were co-transfected with pRL-CMV into 1.1B4 cells. Firefly luciferase activity was measured after 24 hours and are expressed in relative light units (RLU). Renilla luciferase activity was used to normalize for transfection efficiency. pGL3-basic was used as the empty vector control. Values represent means ± SEM. n=4 independent experiments, p*<0.05 by one-way ANOVA with post-hoc Tukey’s multiple comparisons test. 44 Figure 3.2. Characterization of the human IAPP promoter in non-β cells. hIAPP promoter constructs were co-transfected with pRL-CMV into (A) HEK293 cells or (B) SH-SY5Y cells and assayed for luciferase activity 24 hours later. Firefly luciferase activity is expressed in relative light units (RLU) and values are normalized to Renilla luciferase activity to control for transfection efficiency. pGL3-basic was used as the empty vector control. Numbers represent 5’-start/3’-end points of the promoter construct relative to the TSS. Values represent means ± SEM. n=4 independent experiments, p*<0.05 by one-way ANOVA followed by post-hoc Tukey’s multiple comparisons test. 45 3.2 Endoplasmic Reticulum Stress Upregulates hIAPP Promoter Activity To determine whether ER stress can influence hIAPP promoter activity, 1.1B4 cells were transfected with hIAPP promoter construct -553/+435 and were treated with tunicamycin (an N-linked glycosylation inhibitor) for 24 hours to induce ER stress. DMSO treatment was used as a control. After harvesting cells and assaying for luciferase reporter activity, tunicamycin treatment was found to significantly increase hIAPP promoter activity (9.113 ± 1.115 RLU) compared with DMSO treatment (6.168 ± 0.2617 RLU, p=0.0331) (Figure 3.3A). In order to demonstrate that ER stress influences hIAPP transcription endogenously, 1.1B4 cells were treated with either DMSO or tunicamycin for 24 hours before performing reverse-transcription polymerase-chain reaction (RT-PCR) to detect mRNA levels. After normalizing to β-actin, a conservative but significant increase in hIAPP mRNA expression was observed in tunicamycin-treated cells compared with DMSO controls (1.364 ± 0.08679 fold change, p=0.0161) (Figure 3.3C-D). ER stress activates the UPR, which consists of three parallel signaling pathways mediated by IRE1, PERK, and ATF6. The most conserved branch of UPR is IRE1 activation and autophosphorylation leading to the unconventional splicing of XBP1 mRNA to generate XBP1s. To confirm whether tunicamycin treatment in the relatively new human β-cell line 1.1B4 (McCluskey et al., 2011) activates XBP1 mRNA splicing, DMSO- and tunicamycin-treated cells were subjected to RT-PCR. Although β-cells normally have a basal level of XBP1 splicing due to its high secretory demands, XPB1 splicing was not detected in DMSO-treated cells. However, tunicamycin treatment for 24 hours successfully induced XBP1 mRNA splicing and XBP1s mRNA expression (Figure 3.3B), allowing for the possibility that IRE1 signaling could be a mediating factor for tunicamycin’s effects on the hIAPP promoter in 1.1B4 cells. 46 Figure 3.3. ER stress enhances hIAPP transcription. (A) 1.1B4 cells were co-transfected with pIAPP-luc -553/+435 promoter fragment and pRL-CMV and treated with either 10 µg/ml tunicamycin or DMSO for 24 hours. Values are represented as means ± SEM of three independent experiments (n=3). p*<0.05 by Student’s t-test. (B) 1.1B4 cells were treated with either 10 µg/ml tunicamycin or DMSO for 24 hours and RT-PCR was performed to detect IRE1 activation via XBP1 splicing. (C) 1.1B4 cells were treated with either 10 µg/ml tunicamycin or DMSO for 24 hours and RT-PCR was performed to determine hIAPP transcript levels normalized to β-actin. (D) Quantification of C. Data are represented as % of DMSO control. Band intensities were quantified by ImageJ software. n=3, *p<0.05 by Student’s t-test. 47 3.3 Spliced XBP1 Enhances hIAPP Promoter Activity and Transcript Expression Since ER stress can enhance luciferase activity of the short hIAPP -553/+435 promoter construct, we employed a computational transcription factor search (PROMO) for this 5’ flanking region in an attempt to determine the molecular mechanisms responsible for our observations. The PROMO results revealed a plethora of putative binding sites for different transcription factors such as Sp1, NFκB, HIF-1, and more. There are putative XBP1s binding sites at position -226 to -221 bp (ATGACA) and at position -171 to -177 bp (GCTACGT) (Figure 3.4A); thus, the unspliced and spliced isoforms of XBP1 (XBP1u and XBP1s, respectively) were co-transfected with hIAPP promoter constructs in 1.1B4 cells to elucidate their effects on hIAPP transcription. The shortest promoter construct, -65/+435, did not have a significant response to either XBP1u or XPB1s overexpression compared with the empty vector control (XBP1u: 1.071 ± 0.034 fold change, p=0.9994; XBP1s: 3.123 ± 0.214 fold change, p=0.3695) (Figure 3.4B). Similarly, the -163/+435 construct co-transfected with either XBP1u or XBP1s did not have significantly different promoter activity from the empty vector control (XBP1u: 1.07 ± 0.042 fold change, p=0.9997; XBP1s: 4.076 ± 0.09 fold change, p=0.5616). However, the -240/+435 promoter fragment displayed significantly increased luciferase activity when co-transfected with XBP1s (60.303 ± 12.526 fold change, p<0.0001) but not with XBP1u (5.093 ± 0.382 fold change, p=0.8030). The longer -1618/+435 construct also had a significant increase in promoter activity when co-transfected with XBP1s (31.778 ± 7.825 fold change, p=0.0003) but not with XBP1u (2.344 ± 0.524 fold change, p=0.9573). XBP1u-myc, XBP1s-myc, or an empty vector were transfected separately into 1.1B4 cells for RT-PCR (Figure 3.4C). XBP1s overexpression significantly increased hIAPP mRNA levels compared with the empty vector control after normalization to GAPDH (2.151 ± 0.1697 48 fold change, p=0.0036), but XBP1u overexpression did not (1.623 ± 0.2249 fold change, p=0.0753) (Figure 3.4d). Taken together, these results suggest that XBP1s can significantly increase hIAPP promoter activity and transcript levels, while XBP1u seems to only have a minimal effect. 49 Figure 3.4. XBP1s, but not XBP1u, upregulates IAPP promoter activity. (A) The -240 to -1 region of the hIAPP promoter contains a putative XBP1s binding site. (B) hIAPP promoter constructs were co-transfected with pRL-CMV and either pcDNA4-mycHisA, XBP1u-myc, or XBP1s-myc into 1.1B4 cells and assayed for luciferase activity 24 hours later. Firefly luciferase activity is expressed in relative light units (RLU) and values are normalized to Renilla luciferase activity to control for transfection efficiency. Values represent means ± SEM. n=3 independent experiments, p*<0.05 by one-way ANOVA with Tukey’s multiple comparisons test. (C) 1.1B4 cells were transfected with either pcDNA4-mycHisA, XBP1u-myc, or XPB1s-myc plasmids and RT-PCR was performed 24 hours later to detect hIAPP mRNA expression normalized to GAPDH. (D) Quantification of C. Data are represented as % of empty vector control. Band intensities were quantified by ImageJ software. Values represent means ± SEM. n=3, *p<0.05 by one-way ANOVA with post-hoc Tukey’s multiple comparisons test. 50 3.4 Mutation Analyses of XBP1’s Putative Binding Site in the hIAPP Promoter In order to determine whether XBP1’s putative binding sites on the hIAPP promoter are responsible for the increase in promoter activity, mutation and deletion constructs of the hIAPP -240/+435 promoter fragment were generated (Figure 3.5A). Since the GCTACGT binding site has an intact ACGT core and a greater number of matched base pairs to the consensus sequence, this site was chosen as a potential functional binding site over the ATGACA site. Mutating this putative binding site by just three nucleotides (ACG to GTC) decreased promoter activity in XBP1s-transfected 1.1B4 cells from 138.534 ± 12.834 RLU to 107.342 ± 6.322 RLU (p=0.0032) (Figure 3.5b). Deleting these nucleotides further decreased promoter activity to 65.910 ± 4.7 RLU (p<0.0001). In XBP1u-transfected 1.1B4 cells, neither the substitution (8.125 ± 0.423 RLU, p>0.9999) or deletion promoter constructs (7.736 ± 0.420 RLU, p>0.9999) significantly changed promoter activity compared with the -240/+435 construct (7.874 ± 0.462 RLU). These hIAPP promoter mutations also appear to diminish tunicamycin’s effect on hIAPP promoter activity. The -240/+435 promoter construct displayed a significant increase in promoter activity in response to 24 hours of tunicamycin treatment compared with DMSO (p=0.0221) (Figure 3.5C). The substitution construct also showed a significant response to tunicamycin treatment vs. control (p=0.0428); however, the deletion construct did not (p=0.2985), suggesting that the deletion of the putative XBP1 binding site can abolish tunicamycin’s effects on IAPP transcription. 51 Figure 3.5. Mutating XBP1s’ putative binding site on the IAPP promoter decreases XBP1s’ effect on IAPP promoter activity. (A) DNA sequences of the original promoter fragment (top), substitution construct (“sub”, middle), and deletion construct (“del”, bottom). (B) hIAPP promoter fragments -240/+435, -240 mutant/+435 and -240 deletion/+435 were co-transfected with pRL-CMV and either pcDNA4-mycHisA, XBP1u-myc, or XPB1s-myc plasmids into 1.1B4 cells. 24 hours after transfection, cells were harvested for luciferase assay. (C) hIAPP promoter fragments -240/+435, -240 substitution/+435 and -240 deletion/+435 were co-transfected with pRL-CMV into 1.1B4 cells for 12 hours before addition of either DMSO or 10 µg/ml tunicamycin for another 24 hours before cells were harvested for luciferase assay. Firefly luciferase activity is expressed in relative light units (RLU) and values are normalized to Renilla luciferase activity to control for transfection efficiency. Values represent means ± SEM. n=3, p*<0.05 by two-way ANOVA with Sidak's multiple comparisons test. 52 3.5 XBP1 Overexpression Does Not Affect hIAPP at the Post-Translational Level Recent studies have revealed that XBP1u has biological functions outside of being a mere precursor for XBP1s. In particular, XBP1u has been implicated in the degradation of various proteins such as FoxO1 (Y. Zhao et al., 2013) and MDM2 (C. Huang et al., 2017). To determine whether XBP1 can influence hIAPP expression at the post-translational level, 1.1B4 cells were co-transfected with proIAPP-GFP and either empty vector, XBP1u, or XBP1s for 24 hours. ProIAPP protein levels were determined with Western blot (Figure 3.6A). Neither XBP1u or XBP1s overexpression significantly altered proIAPP expression levels compared with the empty vector control (XBP1u p=0.4265; XBP1s p=0.3997) (Figure 3.6B), indicating that XBP1 does not affect hIAPP degradation. Figure 3.6. XBP1 overexpression does not affect proIAPP at the post-translational level. (A) 1.1B4 cells were transfected with proIAPP-GFP and re-plated before transfection with either pcDNA-mycHisA, XBP1u-myc, or XBP1s-myc. Cells were harvested 24 hours after the second transfection and cell lysates were separated on a 10% Tris-glycine gel. The membrane was subsequently probed with EGFP, 9E10, and beta-actin primary antibodies. (B) Quantification of A. Protein levels are normalized to β-actin and represented as % of control (empty vector). Band intensities were measured with ImageJ software. Values represent means ± SEM. n=3, p*<0.05 by one-way ANOVA with post-hoc Tukey’s multiple comparisons test. 53 Chapter 4: Conclusion: A Fine Balance 4.1 General Conclusions and Discussion In patients suffering from T2DM, the reduction of β-cell area only occurs in islets with IAPP accumulation (Jurgens et al., 2011), suggesting a significant role for IAPP aggregation in the development of T2DM. IAPP is mainly produced in human pancreatic islet β-cells, and its promoter has previously been reported to have little to no activity in non-β-cells (Carty et al., 1997; De Wit et al., 1993; Mosselman et al., 1990). IAPP promoter activity can be regulated by several factors implicated in T2DM, such as hypoglycemia (Alam et al., 1992), hyperglycemia (Gasa, Gomis, Casamitjana, & Novials, 1997; Gasa, Gomis, Casamitjana, Rivera, et al., 1997), and fatty acids (Qi et al., 2010). In this study we have cloned hIAPP promoter fragments of varying sizes, spanning from 1618 bp upstream of the TSS up to the translation start site (+450 bp). The longest promoter fragments had lower promoter activity compared with fragments -553/+435 and -240/+435 in a human β-cell line, suggesting the existence of negative regulatory elements upstream of -553 bp. The shorter -240/+435 fragment also had significantly higher promoter activity compared the -163/+435 fragment, indicating the existence of positive regulatory elements within the 77 nucleotides between -240 bp and -163 bp. These results are largely congruent with previous hIAPP promoter studies, despite differences in vector backbones, methods for quantifying promoter activity, and cell lines used. We have also found that inducing ER stress through tunicamycin treatment can upregulate hIAPP promoter activity and mRNA expression to a modest degree. Overexpression of UPR transcription factor XBP1s, and not XBP1u, can significantly increase hIAPP promoter activity and endogenous transcript levels, and mutation of 54 its putative binding site in the promoter region can diminish, but not completely abolish, this effect. One conspicuous difference between our current study and previous promoter analyses is the observation that our hIAPP promoter constructs display significant activity in human non-β-cell lines, HEK293 and SH-SY5Y. To our knowledge, this is the first study examining hIAPP promoter activity in neuronal cell lines, and we found that all promoter fragments spanning between -1007 bp and +435 bp had significantly higher luciferase activity than the empty vector control in SH-SY5Y neuroblastoma cells. However, it was previously reported that the hIAPP promoter had little activity in HEK293 (Mosselman et al., 1990), HEP3B, BRL3A (De Wit et al., 1993), and HeLa S3 cells (Carty et al., 1997) and the general consensus was that the hIAPP promoter is only active in β-cells. While we did not examine hIAPP promoter activity in liver or HeLa cell lines, our HEK293 data diverges from Mosselman et al.’s study. One possible explanation may be differences in quantifying promoter activity, for Mosselman et al. normalized luciferase readings with total protein content rather than an internal control such as Renilla luciferase. It is possible that transfection of their promoter into HEK293 cells was less efficient than that of the other cell lines used in their study, resulting in lower normalized luciferase readings. The finding that the hIAPP promoter is active in both human β-cell non-β-cell lines adds another layer of complexity to our current understanding of how hIAPP transcription is regulated. While hIAPP is known to be expressed in certain non-β-cells, the bulk of its production occurs in pancreatic β-cells. It is worth noting that the longest promoter fragments spanning from -1618 bp to +450 bp had significantly lower activity compared to the shorter fragments across all cell lines. It is likely that cell type differences are regulated further 5’ 55 upstream and perhaps if even longer fragments were cloned and characterized, differences in promoter activity between β-cells and non-β-cells may become more apparent. However, based on our current study, hIAPP promoter fragments ~2kb upstream of the translation start site behave similarly between cell types. Activation of the UPR in response to ER stress is primarily an adaptive mechanism used to restore ER homeostasis through decreasing unfolded protein load. As mentioned in Chapter 1.2.4, there are three canonical ER stress transducers that are responsible for activating and engaging downstream cytoplasmic pathways: IRE1, PERK, and ATF6. In our study, we focused on the most evolutionally conserved branch of the UPR, IRE1 activation (Welihinda & Kaufman, 1996). IRE1 has two isoforms: IRE1α, which is expressed in majority of cells including the pancreas, and IRE1β, which is expressed mostly in intestinal epithelial cells (Tirasophon, Welihinda, & Kaufman, 1998; X. Z. Wang et al., 1998). When IRE1 detects elevated unfolded protein levels in the lumen of the ER, its luminal domain dimerizes to allow for the trans-autophosphorylation of its activation sites. This action permits nucleotide binding and allows for further dimerization of IRE1’s cytoplasmic domain to generate a catalytic surface for ribonuclease activity (K. P. Lee et al., 2008). IRE1 cleaves mRNA encoding XBP1 in a spliceosome-independent manner to remove a 26-nt intron (Calfon et al., 2002; K. Lee et al., 2002; Sidrauski & Walter, 1997; Yoshida et al., 2001), while a RNA ligase joins the adjacent exons to form the mature transcript (Gonzalez, Sidrauski, Dorfler, & Walter, 1999; Lu, Liang, & Wang, 2014; Sidrauski, Cox, & Walter, 1996). Removing this intron creates a translational frame shift to produce the active UPR transcription factor, XBP1s (Yoshida et al., 2001). The biological functions of the unspliced form, XBP1u, have not been extensively studied due to its instability and short half life. It has been reported to play a role in mediating protein degradation 56 (Yoshida, Oku, Suzuki, & Mori, 2006; Y. Zhao et al., 2013), but XBP1u does not appear to regulate either the transcription or degradation of hIAPP. As a transcription factor, XBP1s recognizes a few different consensus sequences: a CCACG-box (GCCACG), unfolded protein response element (UPRE) sequences (CGACGTGG and GTGACGTG), an ACGT core (G(C/T)(C/G)ACGT), an E26 transformation-specific (ETS) domain (CGGAAG), and a CAAT-box (CCAATC) (Acosta-Alvear et al., 2007). There are multiple predicted binding sites for XBP1s on the hIAPP promoter, but given that our -163 bp construct did not have a significant response to XBP1s overexpression, the putative functional sites are most likely between -240 and -163 bp. Within this region, there are two potential binding sites, ATGACAC and GCTACGT; both sites have at least one mismatched base pair, but the latter has the important ACGT-core intact (Clauss et al., 1996). Mutating part of the GCTACGT site significantly decreases, but does not completely abolish, the dramatic increase in hIAPP promoter activity in response to XBP1s overexpression. Deleting part of this site also diminishes the modest increase in promoter activity induced by ER stress. These results strongly indicate that XBP1s can directly regulate hIAPP promoter activity through the GCTACGT binding site, but does not rule out the possibility that it might bind elsewhere on the promoter. It is also possible XBP1s can regulate the expression of another protein that can in turn influence hIAPP promoter activity. 4.2 Implications, Significance, and Future Directions XBP1s primarily regulates genes involved in protein folding, trafficking, and secretion. However, recent studies have revealed that its targets are much more diverse, falling under categories such as ion channels, lipid metabolism, and glucose homeostasis (Acosta-Alvear et al., 57 2007). IAPP is known to regulate glucose homeostasis by influencing insulin secretion from β cells (Ohsawa et al., 1989) and glucagon secretion from α cells (Akesson et al., 2003; Gedulin et al., 2006). However, overexpression of hIAPP leads to its fibrillogenesis and islet amyloid deposition (Bailey, Potter, Verchere, Edelstein-Keshet, & Coombs, 2011; Janson et al., 1996), which induces islet β-cell death. One of the major mechanisms of hIAPP toxicity is ER stress (Cnop, Foufelle, & Velloso, 2012). hIAPP transgenic rats fed with high fat diets have increased ER-stress-induced β-cell apoptosis compared with wild type rats (Matveyenko, Gurlo, Daval, Butler, & Butler, 2009), and overexpression of hIAPP in β-cells induces apoptosis through the upregulation of ER-stress transcription factor CCAAT-enhancer binding protein homologous protein (CHOP) (C. J. Huang et al., 2007) and other unknown mechanisms (Gurlo et al., 2016). The ways in which hIAPP expression can be affected during pathogenic states remain unclear, and there are also many aspects of the relationship between ER stress and hIAPP that still have not been fully explored. In Chapter 3 we showed how ER stress itself can alter hIAPP transcription regulation through the XBP1 pathway. With hIAPP’s physiological role in mediating insulin secretion, this increase in hIAPP transcription could be initially a protective mechanism. It is well known that prolonged ER stress leads to apoptosis, and a study has shown that sustained XBP1s overexpression in islet cells can increase cell death (Allagnat et al., 2010). Further studies will be required to determine whether this change in hIAPP gene regulation in response to ER stress also contributes to its cytotoxic activity. While we have primarily investigated the effect of ER stress on IAPP transcription in pancreatic islet β cells, these effects may ultimately play a role in IAPP toxicity in AD. IAPP transcript levels have been difficult to detect in the brain, suggesting that the majority of IAPP 58 produced in the body is derived from the pancreas. Hyperamylinemia is often observed in obese and insulin-resistant patients (Despa et al., 2012; Hou et al., 2011), and IAPP expression is generally the greatest at earlier stages of T2DM (Enoki et al., 1992; Johnson et al., 1989). Increases in IAPP in the peripheral circulation may lead to greater IAPP levels in the brain, where it may interact with Aβ to exert toxic effects on neurons. Additionally, given the recent observations that hIAPP and Aβ are capable of cross-seeding (Oskarsson et al., 2015), increases in IAPP may contribute to amyloid formation in the brain. The relationship between ER stress, XBP1, and neuronal function has also been explored within the context of AD and dementia. Changes in XBP1s have been discovered in AD brains (Reinhardt et al., 2014) and recently, a polymorphism in XBP1’s promoter region has been found to be associated with an increased risk of sporadic AD in a Chinese population (S. Y. Liu et al., 2013). XBP1s also regulates key AD factors such as ADAM10 (Reinhardt et al., 2014) and BACE1 (Gerakis et al., 2016). XBP1s seems to play a neuroprotective role in AD brains (Cisse et al., 2017), but other evidence suggests it could also be deleterious. 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Regulation of IAPP transcription by spliced XBP1
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